diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index ed27dc528fb61..04e3fc0c17cf9 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -3,14 +3,20 @@ name: Server on: workflow_dispatch: # allows manual triggering + inputs: + slow_tests: + description: 'Run slow tests' + required: true + type: boolean push: branches: - master - - test/server-add-ci-test # FIXME remove - paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] + paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*'] pull_request: types: [opened, synchronize, reopened] - paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] + paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*'] + schedule: + - cron: '0 0 * * *' jobs: server: @@ -18,45 +24,21 @@ jobs: strategy: matrix: - build: [noavx, avx2, avx, avx512, cublas, clblast, openblas, kompute, vulkan] sanitizer: [ADDRESS, THREAD, UNDEFINED] build_type: [Debug, Release] include: - - build: 'noavx' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF' - image: ubuntu:latest - - build: 'avx2' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON' - image: ubuntu:latest - - build: 'avx' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF' - image: ubuntu:latest - - build: 'avx512' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON' - image: ubuntu:latest - experimental: true - - build: 'cublas' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON' - image: nvidia/cuda:12.3.1-devel-ubuntu22.04 - arch_not_available: true # require nvidia docker engine - - build: 'clblast' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON' - image: ubuntu:latest - arch_not_available: true - - build: 'openblas' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS' - image: ubuntu:latest - - build: 'kompute' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON' - image: ubuntu:latest - arch_not_available: true - - build: 'vulkan' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON' - image: ubuntu:latest - arch_not_available: true + - build_type: Release + sanitizer: "" + exclude: + - build_type: Release + sanitizer: ADDRESS + - build_type: Release + sanitizer: THREAD + - build_type: Release + sanitizer: UNDEFINED container: - image: ${{ matrix.image }} + image: ubuntu:latest ports: - 8888 options: --cpus 4 @@ -72,40 +54,22 @@ jobs: apt-get update apt-get -y install \ build-essential \ - pkg-config \ git \ cmake \ python3-pip \ wget \ psmisc - - name: Download CLBlast - id: get_clblast - if: ${{ matrix.build == 'clblast' }} - run: | - apt install -y libclblast-dev - - - name: Download OpenBLAS - id: get_openblas - if: ${{ matrix.build == 'openblas' }} - run: | - apt-get -y install libopenblas-dev - - - name: Install Vulkan SDK - id: get_vulkan - if: ${{ matrix.build == 'kompute' || matrix.build == 'vulkan' }} - run: | - wget -qO- https://packages.lunarg.com/lunarg-signing-key-pub.asc | tee /etc/apt/trusted.gpg.d/lunarg.asc - wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list http://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list - apt-get update - apt-get -y install vulkan-sdk - - name: Build id: cmake_build run: | mkdir build cd build - cmake .. -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ${{ matrix.defines }} + cmake .. \ + -DLLAMA_NATIVE=OFF \ + -DLLAMA_BUILD_SERVER=ON \ + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ + -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server - name: Tests dependencies @@ -113,15 +77,15 @@ jobs: run: | pip install -r examples/server/tests/requirements.txt - - name: Download models - id: download_models + - name: Tests + id: server_integration_tests run: | cd examples/server/tests - ../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf + PORT=8888 ./tests.sh - - name: Tests - id: server_integration_test - continue-on-error: ${{ matrix.experimental || matrix.arch_not_available }} + - name: Slow tests + id: server_integration_tests_slow + if: ${{ github.event.schedule != '' && matrix.build_type == 'Release' || github.event.inputs.slow_tests == 'true' }} run: | cd examples/server/tests - PORT=8888 ./tests.sh + PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow diff --git a/CMakeLists.txt b/CMakeLists.txt index d22d036956aa6..559929c78c434 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -80,9 +80,8 @@ if (LLAMA_CUBLAS) enable_language(CUDA) add_compile_definitions(GGML_USE_CUBLAS) - #add_compile_definitions(GGML_CUDA_CUBLAS) #remove to not use cublas + add_compile_definitions(SD_USE_CUBLAS) add_compile_definitions(GGML_CUDA_MMQ_Y=${LLAMA_CUDA_MMQ_Y}) - #add_compile_definitions(GGML_CUDA_FORCE_DMMV) #non dmmv broken for me add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y}) @@ -156,7 +155,7 @@ if (LLAMA_HIPBLAS) if (${hipblas_FOUND} AND ${hip_FOUND}) message(STATUS "HIP and hipBLAS found") - add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS) + add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS SD_USE_CUBLAS) add_library(ggml-rocm OBJECT ${GGML_SOURCES_CUDA}) if (LLAMA_CUDA_FORCE_DMMV) target_compile_definitions(ggml-rocm PUBLIC GGML_CUDA_FORCE_DMMV) @@ -429,14 +428,21 @@ add_library(common2 common/common.h common/grammar-parser.h common/grammar-parser.cpp) -target_include_directories(common2 PUBLIC . ./otherarch ./otherarch/tools ./examples ./common) +target_include_directories(common2 PUBLIC . ./otherarch ./otherarch/tools ./otherarch/sdcpp ./otherarch/sdcpp/thirdparty ./examples ./common) target_compile_features(common2 PUBLIC cxx_std_11) # don't bump target_link_libraries(common2 PRIVATE ggml ${LLAMA_EXTRA_LIBS}) set_target_properties(common2 PROPERTIES POSITION_INDEPENDENT_CODE ON) +add_library(sdtype_adapter + otherarch/sdcpp/sdtype_adapter.cpp) +target_include_directories(sdtype_adapter PUBLIC . ./otherarch ./otherarch/tools ./otherarch/sdcpp ./otherarch/sdcpp/thirdparty ./examples ./common) +target_compile_features(sdtype_adapter PUBLIC cxx_std_11) # don't bump +target_link_libraries(sdtype_adapter PRIVATE common2 ggml ${LLAMA_EXTRA_LIBS}) +set_target_properties(sdtype_adapter PROPERTIES POSITION_INDEPENDENT_CODE ON) + add_library(gpttype_adapter gpttype_adapter.cpp) -target_include_directories(gpttype_adapter PUBLIC . ./otherarch ./otherarch/tools ./examples ./common) +target_include_directories(gpttype_adapter PUBLIC . ./otherarch ./otherarch/tools ./otherarch/sdcpp ./otherarch/sdcpp/thirdparty ./examples ./common) target_compile_features(gpttype_adapter PUBLIC cxx_std_11) # don't bump target_link_libraries(gpttype_adapter PRIVATE common2 ggml ${LLAMA_EXTRA_LIBS}) set_target_properties(gpttype_adapter PROPERTIES POSITION_INDEPENDENT_CODE ON) @@ -444,24 +450,24 @@ set_target_properties(gpttype_adapter PROPERTIES POSITION_INDEPENDENT_CODE ON) if (LLAMA_CUBLAS) set(TARGET koboldcpp_cublas) add_library(${TARGET} SHARED expose.cpp expose.h) - target_include_directories(${TARGET} PUBLIC . ./otherarch ./otherarch/tools ./examples ./common) + target_include_directories(${TARGET} PUBLIC . ./otherarch ./otherarch/tools ./otherarch/sdcpp ./otherarch/sdcpp/thirdparty ./examples ./common) target_compile_features(${TARGET} PUBLIC cxx_std_11) # don't bump set_target_properties(${TARGET} PROPERTIES PREFIX "") set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME "koboldcpp_cublas") set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) - target_link_libraries(${TARGET} PUBLIC Threads::Threads ggml ggml_v1 ggml_v2 ggml_v3 common2 gpttype_adapter ${LLAMA_EXTRA_LIBS}) + target_link_libraries(${TARGET} PUBLIC Threads::Threads ggml ggml_v1 ggml_v2 ggml_v3 common2 gpttype_adapter sdtype_adapter ${LLAMA_EXTRA_LIBS}) target_compile_features(${TARGET} PRIVATE cxx_std_11) endif() if (LLAMA_HIPBLAS) set(TARGET koboldcpp_hipblas) add_library(${TARGET} SHARED expose.cpp expose.h) - target_include_directories(${TARGET} PUBLIC . ./otherarch ./otherarch/tools ./examples ./common) + target_include_directories(${TARGET} PUBLIC . ./otherarch ./otherarch/tools ./otherarch/sdcpp ./otherarch/sdcpp/thirdparty ./examples ./common) target_compile_features(${TARGET} PUBLIC cxx_std_11) # don't bump set_target_properties(${TARGET} PROPERTIES PREFIX "") set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME "koboldcpp_hipblas") set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) - target_link_libraries(${TARGET} PUBLIC Threads::Threads ggml ggml_v1 ggml_v2 ggml_v3 common2 gpttype_adapter ${LLAMA_EXTRA_LIBS}) + target_link_libraries(${TARGET} PUBLIC Threads::Threads ggml ggml_v1 ggml_v2 ggml_v3 common2 gpttype_adapter sdtype_adapter ${LLAMA_EXTRA_LIBS}) target_compile_features(${TARGET} PRIVATE cxx_std_11) endif() diff --git a/Makefile b/Makefile index d5ed46bf4c22c..7e80687d62410 100644 --- a/Makefile +++ b/Makefile @@ -39,12 +39,13 @@ endif # # keep standard at C11 and C++11 -CFLAGS = -I. -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./include/vulkan -O3 -DNDEBUG -std=c11 -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -CXXFLAGS = -I. -I./common -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./include/vulkan -O3 -DNDEBUG -std=c++11 -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE +CFLAGS = -I. -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -O3 -DNDEBUG -std=c11 -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE +CXXFLAGS = -I. -I./common -I./include -I./include/CL -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/sdcpp/thirdparty -I./include/vulkan -O3 -DNDEBUG -std=c++11 -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE LDFLAGS = #CC := gcc-13 #CXX := g++-13 FASTCFLAGS = $(subst -O3,-Ofast,$(CFLAGS)) +FASTCXXFLAGS = $(subst -O3,-Ofast,$(CXXFLAGS)) # these are used on windows, to build some libraries with extra old device compatibility SIMPLECFLAGS = @@ -56,7 +57,7 @@ CLBLAST_FLAGS = -DGGML_USE_CLBLAST FAILSAFE_FLAGS = -DUSE_FAILSAFE VULKAN_FLAGS = -DGGML_USE_VULKAN ifdef LLAMA_CUBLAS - CUBLAS_FLAGS = -DGGML_USE_CUBLAS + CUBLAS_FLAGS = -DGGML_USE_CUBLAS -DSD_USE_CUBLAS else CUBLAS_FLAGS = endif @@ -143,8 +144,8 @@ endif # it is recommended to use the CMAKE file to build for cublas if you can - will likely work better ifdef LLAMA_CUBLAS - CUBLAS_FLAGS = -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include - CUBLASLD_FLAGS = -lcuda -lcublas -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/lib/wsl/lib + CUBLAS_FLAGS = -DGGML_USE_CUBLAS -DSD_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include + CUBLASLD_FLAGS = -lcuda -lcublas -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/local/cuda/targets/sbsa-linux/lib -L/usr/lib/wsl/lib CUBLAS_OBJS = ggml-cuda.o ggml_v3-cuda.o ggml_v2-cuda.o ggml_v2-cuda-legacy.o NVCC = nvcc NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math @@ -228,7 +229,7 @@ ifdef LLAMA_HIPBLAS LLAMA_CUDA_DMMV_X ?= 32 LLAMA_CUDA_MMV_Y ?= 1 LLAMA_CUDA_KQUANTS_ITER ?= 2 - HIPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS $(shell $(ROCM_PATH)/bin/hipconfig -C) + HIPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS -DSD_USE_CUBLAS $(shell $(ROCM_PATH)/bin/hipconfig -C) HIPLDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib -lhipblas -lamdhip64 -lrocblas HIP_OBJS += ggml-cuda.o ggml_v3-cuda.o ggml_v2-cuda.o ggml_v2-cuda-legacy.o ggml-cuda.o: HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS)) \ @@ -259,8 +260,8 @@ endif # LLAMA_HIPBLAS ifdef LLAMA_METAL - CFLAGS += -DGGML_USE_METAL -DGGML_METAL_NDEBUG - CXXFLAGS += -DGGML_USE_METAL + CFLAGS += -DGGML_USE_METAL -DGGML_METAL_NDEBUG -DSD_USE_METAL + CXXFLAGS += -DGGML_USE_METAL -DSD_USE_METAL LDFLAGS += -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders OBJS += ggml-metal.o @@ -481,6 +482,12 @@ grammar-parser.o: common/grammar-parser.cpp common/grammar-parser.h expose.o: expose.cpp expose.h $(CXX) $(CXXFLAGS) -c $< -o $@ +# sd.cpp objects +sdcpp_default.o: otherarch/sdcpp/sdtype_adapter.cpp otherarch/sdcpp/stable-diffusion.h otherarch/sdcpp/stable-diffusion.cpp otherarch/sdcpp/util.cpp otherarch/sdcpp/upscaler.cpp otherarch/sdcpp/model.cpp otherarch/sdcpp/thirdparty/zip.c + $(CXX) $(CXXFLAGS) -c $< -o $@ +sdcpp_cublas.o: otherarch/sdcpp/sdtype_adapter.cpp otherarch/sdcpp/stable-diffusion.h otherarch/sdcpp/stable-diffusion.cpp otherarch/sdcpp/util.cpp otherarch/sdcpp/upscaler.cpp otherarch/sdcpp/model.cpp otherarch/sdcpp/thirdparty/zip.c + $(CXX) $(CXXFLAGS) $(CUBLAS_FLAGS) $(HIPFLAGS) -c $< -o $@ + # idiotic "for easier compilation" GPTTYPE_ADAPTER = gpttype_adapter.cpp otherarch/llama_v2.cpp otherarch/llama_v3.cpp llama.cpp otherarch/utils.cpp otherarch/gptj_v1.cpp otherarch/gptj_v2.cpp otherarch/gptj_v3.cpp otherarch/gpt2_v1.cpp otherarch/gpt2_v2.cpp otherarch/gpt2_v3.cpp otherarch/rwkv_v2.cpp otherarch/rwkv_v3.cpp otherarch/neox_v2.cpp otherarch/neox_v3.cpp otherarch/mpt_v3.cpp ggml.h ggml-cuda.h llama.h otherarch/llama-util.h gpttype_adapter_failsafe.o: $(GPTTYPE_ADAPTER) @@ -499,12 +506,14 @@ gpttype_adapter_vulkan_noavx2.o: $(GPTTYPE_ADAPTER) $(CXX) $(CXXFLAGS) $(FAILSAFE_FLAGS) $(VULKAN_FLAGS) -c $< -o $@ clean: - rm -vf *.o main quantize_llama quantize_gpt2 quantize_gptj quantize_neox quantize_mpt quantize-stats perplexity embedding benchmark-matmult save-load-state gguf imatrix imatrix.exe gguf.exe main.exe quantize_llama.exe quantize_gptj.exe quantize_gpt2.exe quantize_neox.exe quantize_mpt.exe koboldcpp_default.dll koboldcpp_openblas.dll koboldcpp_failsafe.dll koboldcpp_noavx2.dll koboldcpp_clblast.dll koboldcpp_clblast_noavx2.dll koboldcpp_cublas.dll koboldcpp_hipblas.dll koboldcpp_vulkan.dll koboldcpp_vulkan_noavx2.dll koboldcpp_default.so koboldcpp_openblas.so koboldcpp_failsafe.so koboldcpp_noavx2.so koboldcpp_clblast.so koboldcpp_clblast_noavx2.so koboldcpp_cublas.so koboldcpp_hipblas.so koboldcpp_vulkan.so koboldcpp_vulkan_noavx2.so + rm -vf *.o main sdmain quantize_llama quantize_gpt2 quantize_gptj quantize_neox quantize_mpt quantize-stats perplexity embedding benchmark-matmult save-load-state gguf imatrix imatrix.exe gguf.exe main.exe quantize_llama.exe quantize_gptj.exe quantize_gpt2.exe quantize_neox.exe quantize_mpt.exe koboldcpp_default.dll koboldcpp_openblas.dll koboldcpp_failsafe.dll koboldcpp_noavx2.dll koboldcpp_clblast.dll koboldcpp_clblast_noavx2.dll koboldcpp_cublas.dll koboldcpp_hipblas.dll koboldcpp_vulkan.dll koboldcpp_vulkan_noavx2.dll koboldcpp_default.so koboldcpp_openblas.so koboldcpp_failsafe.so koboldcpp_noavx2.so koboldcpp_clblast.so koboldcpp_clblast_noavx2.so koboldcpp_cublas.so koboldcpp_hipblas.so koboldcpp_vulkan.so koboldcpp_vulkan_noavx2.so # useful tools main: examples/main/main.cpp common/sampling.cpp build-info.h ggml.o ggml-quants.o ggml-alloc.o ggml-backend.o llama.o common.o console.o grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) @echo '==== Run ./main -h for help. ====' +sdmain: otherarch/sdcpp/util.cpp otherarch/sdcpp/main.cpp otherarch/sdcpp/stable-diffusion.cpp otherarch/sdcpp/upscaler.cpp otherarch/sdcpp/model.cpp otherarch/sdcpp/thirdparty/zip.c build-info.h ggml.o ggml-quants.o ggml-alloc.o ggml-backend.o llama.o common.o console.o grammar-parser.o $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) imatrix: examples/imatrix/imatrix.cpp common/sampling.cpp build-info.h ggml.o ggml-quants.o ggml-alloc.o ggml-backend.o llama.o common.o console.o grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) gguf: examples/gguf/gguf.cpp build-info.h ggml.o llama.o $(OBJS) @@ -512,11 +521,11 @@ gguf: examples/gguf/gguf.cpp build-info.h ggml.o llama.o $(OBJS) #generated libraries -koboldcpp_default: ggml.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o common.o gpttype_adapter.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o $(OBJS) +koboldcpp_default: ggml.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o common.o gpttype_adapter.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o sdcpp_default.o $(OBJS) $(DEFAULT_BUILD) ifdef OPENBLAS_BUILD -koboldcpp_openblas: ggml_v4_openblas.o ggml_v3_openblas.o ggml_v2_openblas.o ggml_v1.o expose.o common.o gpttype_adapter.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o $(OBJS) +koboldcpp_openblas: ggml_v4_openblas.o ggml_v3_openblas.o ggml_v2_openblas.o ggml_v1.o expose.o common.o gpttype_adapter.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o sdcpp_default.o $(OBJS) $(OPENBLAS_BUILD) else koboldcpp_openblas: @@ -524,7 +533,7 @@ koboldcpp_openblas: endif ifdef FAILSAFE_BUILD -koboldcpp_failsafe: ggml_v4_failsafe.o ggml_v3_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_failsafe.o ggml-quants_failsafe.o ggml-alloc.o ggml-backend.o grammar-parser.o $(OBJS) +koboldcpp_failsafe: ggml_v4_failsafe.o ggml_v3_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_failsafe.o ggml-quants_failsafe.o ggml-alloc.o ggml-backend.o grammar-parser.o sdcpp_default.o $(OBJS) $(FAILSAFE_BUILD) else koboldcpp_failsafe: @@ -532,7 +541,7 @@ koboldcpp_failsafe: endif ifdef NOAVX2_BUILD -koboldcpp_noavx2: ggml_v4_noavx2.o ggml_v3_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_failsafe.o ggml-quants_noavx2.o ggml-alloc.o ggml-backend.o grammar-parser.o $(OBJS) +koboldcpp_noavx2: ggml_v4_noavx2.o ggml_v3_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_failsafe.o ggml-quants_noavx2.o ggml-alloc.o ggml-backend.o grammar-parser.o sdcpp_default.o $(OBJS) $(NOAVX2_BUILD) else koboldcpp_noavx2: @@ -540,10 +549,10 @@ koboldcpp_noavx2: endif ifdef CLBLAST_BUILD -koboldcpp_clblast: ggml_v4_clblast.o ggml_v3_clblast.o ggml_v2_clblast.o ggml_v1.o expose.o common.o gpttype_adapter_clblast.o ggml-opencl.o ggml_v3-opencl.o ggml_v2-opencl.o ggml_v2-opencl-legacy.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o $(OBJS) +koboldcpp_clblast: ggml_v4_clblast.o ggml_v3_clblast.o ggml_v2_clblast.o ggml_v1.o expose.o common.o gpttype_adapter_clblast.o ggml-opencl.o ggml_v3-opencl.o ggml_v2-opencl.o ggml_v2-opencl-legacy.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o sdcpp_default.o $(OBJS) $(CLBLAST_BUILD) ifdef NOAVX2_BUILD -koboldcpp_clblast_noavx2: ggml_v4_clblast_noavx2.o ggml_v3_clblast_noavx2.o ggml_v2_clblast_noavx2.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_clblast_noavx2.o ggml-opencl.o ggml_v3-opencl.o ggml_v2-opencl.o ggml_v2-opencl-legacy.o ggml-quants_noavx2.o ggml-alloc.o ggml-backend.o grammar-parser.o $(OBJS) +koboldcpp_clblast_noavx2: ggml_v4_clblast_noavx2.o ggml_v3_clblast_noavx2.o ggml_v2_clblast_noavx2.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_clblast_noavx2.o ggml-opencl.o ggml_v3-opencl.o ggml_v2-opencl.o ggml_v2-opencl-legacy.o ggml-quants_noavx2.o ggml-alloc.o ggml-backend.o grammar-parser.o sdcpp_default.o $(OBJS) $(CLBLAST_BUILD) else koboldcpp_clblast_noavx2: @@ -557,7 +566,7 @@ koboldcpp_clblast_noavx2: endif ifdef CUBLAS_BUILD -koboldcpp_cublas: ggml_v4_cublas.o ggml_v3_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o common.o gpttype_adapter_cublas.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o $(CUBLAS_OBJS) $(OBJS) +koboldcpp_cublas: ggml_v4_cublas.o ggml_v3_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o common.o gpttype_adapter_cublas.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o sdcpp_cublas.o $(CUBLAS_OBJS) $(OBJS) $(CUBLAS_BUILD) else koboldcpp_cublas: @@ -565,7 +574,7 @@ koboldcpp_cublas: endif ifdef HIPBLAS_BUILD -koboldcpp_hipblas: ggml_v4_cublas.o ggml_v3_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o common.o gpttype_adapter_cublas.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o $(HIP_OBJS) $(OBJS) +koboldcpp_hipblas: ggml_v4_cublas.o ggml_v3_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o common.o gpttype_adapter_cublas.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o sdcpp_cublas.o $(HIP_OBJS) $(OBJS) $(HIPBLAS_BUILD) else koboldcpp_hipblas: @@ -573,10 +582,10 @@ koboldcpp_hipblas: endif ifdef VULKAN_BUILD -koboldcpp_vulkan: ggml_v4_vulkan.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o common.o gpttype_adapter_vulkan.o ggml-vulkan.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o $(OBJS) +koboldcpp_vulkan: ggml_v4_vulkan.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o common.o gpttype_adapter_vulkan.o ggml-vulkan.o ggml-quants.o ggml-alloc.o ggml-backend.o grammar-parser.o sdcpp_default.o $(OBJS) $(VULKAN_BUILD) ifdef NOAVX2_BUILD -koboldcpp_vulkan_noavx2: ggml_v4_vulkan_noavx2.o ggml_v3_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_vulkan_noavx2.o ggml-vulkan.o ggml-quants_noavx2.o ggml-alloc.o ggml-backend.o grammar-parser.o $(OBJS) +koboldcpp_vulkan_noavx2: ggml_v4_vulkan_noavx2.o ggml_v3_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o expose.o common.o gpttype_adapter_vulkan_noavx2.o ggml-vulkan.o ggml-quants_noavx2.o ggml-alloc.o ggml-backend.o grammar-parser.o sdcpp_default.o $(OBJS) $(VULKAN_BUILD) else koboldcpp_vulkan_noavx2: diff --git a/README-sycl.md b/README-sycl.md deleted file mode 100644 index dd5bf9dead5bf..0000000000000 --- a/README-sycl.md +++ /dev/null @@ -1,494 +0,0 @@ -# llama.cpp for SYCL - -- [Background](#background) -- [OS](#os) -- [Intel GPU](#intel-gpu) -- [Docker](#docker) -- [Linux](#linux) -- [Windows](#windows) -- [Environment Variable](#environment-variable) -- [Known Issue](#known-issue) -- [Q&A](#q&a) -- [Todo](#todo) - -## Background - -SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators—such as CPUs, GPUs, and FPGAs. It is a single-source embedded domain-specific language based on pure C++17. - -oneAPI is a specification that is open and standards-based, supporting multiple architecture types including but not limited to GPU, CPU, and FPGA. The spec has both direct programming and API-based programming paradigms. - -Intel uses the SYCL as direct programming language to support CPU, GPUs and FPGAs. - -To avoid to re-invent the wheel, this code refer other code paths in llama.cpp (like OpenBLAS, cuBLAS, CLBlast). We use a open-source tool [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) migrate to SYCL. - -The llama.cpp for SYCL is used to support Intel GPUs. - -For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). - -## OS - -|OS|Status|Verified| -|-|-|-| -|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39| -|Windows|Support|Windows 11| - - -## Intel GPU - -### Verified - -|Intel GPU| Status | Verified Model| -|-|-|-| -|Intel Data Center Max Series| Support| Max 1550| -|Intel Data Center Flex Series| Support| Flex 170| -|Intel Arc Series| Support| Arc 770, 730M| -|Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| -|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7| - -Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use. - -### Memory - -The memory is a limitation to run LLM on GPUs. - -When run llama.cpp, there is print log to show the applied memory on GPU. You could know how much memory to be used in your case. Like `llm_load_tensors: buffer size = 3577.56 MiB`. - -For iGPU, please make sure the shared memory from host memory is enough. For llama-2-7b.Q4_0, recommend the host memory is 8GB+. - -For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+. - -## Docker - -Note: -- Only docker on Linux is tested. Docker on WSL may not work. -- You may need to install Intel GPU driver on the host machine (See the [Linux](#linux) section to know how to do that) - -### Build the image - -You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference. - - -```sh -# For F16: -#docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile . - -# Or, for F32: -docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile . - -# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example -``` - -### Run - -```sh -# Firstly, find all the DRI cards: -ls -la /dev/dri -# Then, pick the card that you want to use. - -# For example with "/dev/dri/card1" -docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -``` - -## Linux - -### Setup Environment - -1. Install Intel GPU driver. - -a. Please install Intel GPU driver by official guide: [Install GPU Drivers](https://dgpu-docs.intel.com/driver/installation.html). - -Note: for iGPU, please install the client GPU driver. - -b. Add user to group: video, render. - -```sh -sudo usermod -aG render username -sudo usermod -aG video username -``` - -Note: re-login to enable it. - -c. Check - -```sh -sudo apt install clinfo -sudo clinfo -l -``` - -Output (example): - -``` -Platform #0: Intel(R) OpenCL Graphics - `-- Device #0: Intel(R) Arc(TM) A770 Graphics - - -Platform #0: Intel(R) OpenCL HD Graphics - `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49] -``` - -2. Install Intel® oneAPI Base toolkit. - -a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). - -Recommend to install to default folder: **/opt/intel/oneapi**. - -Following guide use the default folder as example. If you use other folder, please modify the following guide info with your folder. - -b. Check - -```sh -source /opt/intel/oneapi/setvars.sh - -sycl-ls -``` - -There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**. - -Output (example): -``` -[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000] -[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] -[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [23.30.26918.50] -[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918] - -``` - -2. Build locally: - -Note: -- You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference. -- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. - -```sh -mkdir -p build -cd build -source /opt/intel/oneapi/setvars.sh - -# For FP16: -#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON - -# Or, for FP32: -cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx - -# Build example/main only -#cmake --build . --config Release --target main - -# Or, build all binary -cmake --build . --config Release -v - -cd .. -``` - -or - -```sh -./examples/sycl/build.sh -``` - -### Run - -1. Put model file to folder **models** - -You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example. - -2. Enable oneAPI running environment - -``` -source /opt/intel/oneapi/setvars.sh -``` - -3. List device ID - -Run without parameter: - -```sh -./build/bin/ls-sycl-device - -# or running the "main" executable and look at the output log: - -./build/bin/main -``` - -Check the ID in startup log, like: - -``` -found 4 SYCL devices: - Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3, - max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 - Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2, - max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280 - Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0, - max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280 - Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0, - max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 - -``` - -|Attribute|Note| -|-|-| -|compute capability 1.3|Level-zero running time, recommended | -|compute capability 3.0|OpenCL running time, slower than level-zero in most cases| - -4. Set device ID and execute llama.cpp - -Set device ID = 0 by **GGML_SYCL_DEVICE=0** - -```sh -GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -``` -or run by script: - -```sh -./examples/sycl/run_llama2.sh -``` - -Note: - -- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue. - - -5. Check the device ID in output - -Like: -``` -Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device -``` - -## Windows - -### Setup Environment - -1. Install Intel GPU driver. - -Please install Intel GPU driver by official guide: [Install GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html). - -Note: **The driver is mandatory for compute function**. - -2. Install Visual Studio. - -Please install [Visual Studio](https://visualstudio.microsoft.com/) which impact oneAPI environment enabling in Windows. - -3. Install Intel® oneAPI Base toolkit. - -a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). - -Recommend to install to default folder: **C:\Program Files (x86)\Intel\oneAPI**. - -Following guide uses the default folder as example. If you use other folder, please modify the following guide info with your folder. - -b. Enable oneAPI running environment: - -- In Search, input 'oneAPI'. - -Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022" - -- In Run: - -In CMD: -``` -"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 -``` - -c. Check GPU - -In oneAPI command line: - -``` -sycl-ls -``` - -There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**. - -Output (example): -``` -[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000] -[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] -[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186] -[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044] -``` - -4. Install cmake & make - -a. Download & install cmake for Windows: https://cmake.org/download/ - -b. Download & install mingw-w64 make for Windows provided by w64devkit - -- Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). - -- Extract `w64devkit` on your pc. - -- Add the **bin** folder path in the Windows system PATH environment, like `C:\xxx\w64devkit\bin\`. - -### Build locally: - -In oneAPI command line window: - -``` -mkdir -p build -cd build -@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force - -:: for FP16 -:: faster for long-prompt inference -:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON - -:: for FP32 -cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release - - -:: build example/main only -:: make main - -:: build all binary -make -j -cd .. -``` - -or - -``` -.\examples\sycl\win-build-sycl.bat -``` - -Note: - -- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. - -### Run - -1. Put model file to folder **models** - -You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example. - -2. Enable oneAPI running environment - -- In Search, input 'oneAPI'. - -Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022" - -- In Run: - -In CMD: -``` -"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 -``` - -3. List device ID - -Run without parameter: - -``` -build\bin\ls-sycl-device.exe - -or - -build\bin\main.exe -``` - -Check the ID in startup log, like: - -``` -found 4 SYCL devices: - Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3, - max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 - Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2, - max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280 - Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0, - max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280 - Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0, - max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 - -``` - -|Attribute|Note| -|-|-| -|compute capability 1.3|Level-zero running time, recommended | -|compute capability 3.0|OpenCL running time, slower than level-zero in most cases| - -4. Set device ID and execute llama.cpp - -Set device ID = 0 by **set GGML_SYCL_DEVICE=0** - -``` -set GGML_SYCL_DEVICE=0 -build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -``` -or run by script: - -``` -.\examples\sycl\win-run-llama2.bat -``` - -Note: - -- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue. - - -5. Check the device ID in output - -Like: -``` -Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device -``` - -## Environment Variable - -#### Build - -|Name|Value|Function| -|-|-|-| -|LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path.
For FP32/FP16, LLAMA_SYCL=ON is mandatory.| -|LLAMA_SYCL_F16|ON (optional)|Enable FP16 build with SYCL code path. Faster for long-prompt inference.
For FP32, not set it.| -|CMAKE_C_COMPILER|icx|Use icx compiler for SYCL code path| -|CMAKE_CXX_COMPILER|icpx (Linux), icx (Windows)|use icpx/icx for SYCL code path| - -#### Running - - -|Name|Value|Function| -|-|-|-| -|GGML_SYCL_DEVICE|0 (default) or 1|Set the device id used. Check the device ids by default running output| -|GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG| - -## Known Issue - -- Hang during startup - - llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block. - - Solution: add **--no-mmap** or **--mmap 0**. - -## Q&A - -- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. - - Miss to enable oneAPI running environment. - - Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`. - -- In Windows, no result, not error. - - Miss to enable oneAPI running environment. - -- Meet compile error. - - Remove folder **build** and try again. - -- I can **not** see **[ext_oneapi_level_zero:gpu:0]** afer install GPU driver in Linux. - - Please run **sudo sycl-ls**. - - If you see it in result, please add video/render group to your ID: - - ``` - sudo usermod -aG render username - sudo usermod -aG video username - ``` - - Then **relogin**. - - If you do not see it, please check the installation GPU steps again. - -## Todo - -- Support multiple cards. diff --git a/README.md b/README.md index 3ec7685b29a41..adb2eb849c5b7 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ When the KoboldCPP GUI appears, make sure to select "Use hipBLAS (ROCm)" and set -------- ### Quick Summary -KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models. It's a single self contained distributable from Concedo, that builds off llama.cpp, and adds a versatile Kobold API endpoint, additional format support, backward compatibility, as well as a fancy UI with persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and everything Kobold and Kobold Lite have to offer. +KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models. It's a single self contained distributable from Concedo, that builds off llama.cpp, and adds a versatile Kobold API endpoint, additional format support, Stable Diffusion image generation, backward compatibility, as well as a fancy UI with persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and everything Kobold and Kobold Lite have to offer. ![Preview](media/preview.png) ![Preview](media/preview2.png) @@ -150,6 +150,7 @@ For more information, be sure to run the program with the `--help` flag, or [che - Since v1.33, you can set the context size to be above what the model supports officially. It does increases perplexity but should still work well below 4096 even on untuned models. (For GPT-NeoX, GPT-J, and LLAMA models) Customize this with `--ropeconfig`. - Since v1.42, supports GGUF models for LLAMA and Falcon - Since v1.55, lcuda paths on Linux are hardcoded and may require manual changes to the makefile if you do not use koboldcpp.sh for the compilation. +- Since v1.60, provides native image generation with StableDiffusion.cpp, you can load any SD1.5 or SDXL .safetensors model and it will provide an A1111 compatible API to use. - **I plan to keep backwards compatibility with ALL past llama.cpp AND alpaca.cpp models**. But you are also encouraged to reconvert/update your models if possible for best results. ## ROCm vs OpenCL (old comparison) @@ -174,6 +175,7 @@ Comparison with OpenCL using 6800xt (old measurement) - I have heard of someone claiming a false AV positive report. The exe is a simple pyinstaller bundle that includes the necessary python scripts and dlls to run. If this still concerns you, you might wish to rebuild everything from source code using the makefile, and you can rebuild the exe yourself with pyinstaller by using `make_pyinstaller.bat` - API documentation available at `/api` and https://lite.koboldai.net/koboldcpp_api - Supported GGML models (Includes backward compatibility for older versions/legacy GGML models, though some newer features might be unavailable): + - All up-to-date GGUF models are supported (Mistral/Mixtral/QWEN/Gemma and more) - LLAMA and LLAMA2 (LLaMA / Alpaca / GPT4All / Vicuna / Koala / Pygmalion 7B / Metharme 7B / WizardLM and many more) - GPT-2 / Cerebras - GPT-J diff --git a/class.py b/class.py index f2f8432d1160a..2e3d50e1f2c8e 100644 --- a/class.py +++ b/class.py @@ -273,7 +273,7 @@ def _load(self, save_model: bool, initial_load: bool) -> None: unbantokens=False, bantokens=None, usemirostat=None, forceversion=0, nommap=self.kcpp_nommap, usemlock=False, noavx2=self.kcpp_noavx2, debugmode=self.kcpp_debugmode, skiplauncher=True, hordeconfig=None, noblas=self.kcpp_noblas, useclblast=self.kcpp_useclblast, usecublas=self.kcpp_usecublas, usevulkan=self.kcpp_usevulkan, gpulayers=self.kcpp_gpulayers, tensor_split=self.kcpp_tensor_split, config=None, - onready='', multiuser=False, foreground=False, preloadstory=None, noshift=False, remotetunnel=False, ssl=False, benchmark=False, nocertify=False) + onready='', multiuser=False, foreground=False, preloadstory=None, noshift=False, remotetunnel=False, ssl=False, benchmark=False, nocertify=False, sdconfig=None) #koboldcpp.main(kcppargs,False) #initialize library without enabling Lite http server diff --git a/colab.ipynb b/colab.ipynb index 0f98ac90eb4d6..0b6c12e1dcd1f 100644 --- a/colab.ipynb +++ b/colab.ipynb @@ -52,6 +52,9 @@ "Layers = 99 #@param [99]{allow-input: true}\r\n", "ContextSize = 4096 #@param [4096] {allow-input: true}\r\n", "ForceRebuild = False #@param {type:\"boolean\"}\r\n", + "LoadImageModel = False \r\n", + "SDModel = \"\"\r\n", + "SDCommand = \"\"\r\n", "\r\n", "import os\r\n", "if not os.path.isfile(\"/opt/bin/nvidia-smi\"):\r\n", @@ -64,6 +67,10 @@ "kvers = kvers[0]\r\n", "if ForceRebuild:\r\n", " kvers = \"force_rebuild\"\r\n", + "if SDModel and LoadImageModel:\r\n", + " SDCommand = \"--sdconfig sdmodel.safetensors clamped 4 quant\"\r\n", + "else:\r\n", + " SDCommand = \"\"\r\n", "!echo Finding prebuilt binary for {kvers}\r\n", "!wget -O dlfile.tmp https://kcppcolab.concedo.workers.dev/?{kvers} && mv dlfile.tmp koboldcpp_cublas.so\r\n", "!test -f koboldcpp_cublas.so && echo Prebuilt Binary Exists || echo Prebuilt Binary Does Not Exist\r\n", @@ -71,7 +78,9 @@ "!cp koboldcpp_cublas.so koboldcpp_cublas.dat\r\n", "!apt install aria2 -y\r\n", "!aria2c -x 10 -o model.gguf --summary-interval=5 --download-result=default --allow-overwrite=true --file-allocation=none $Model\r\n", - "!python koboldcpp.py model.gguf --usecublas 0 mmq --multiuser --gpulayers $Layers --contextsize $ContextSize --quiet --remotetunnel\r\n" + "if SDCommand:\r\n", + " !aria2c -x 10 -o sdmodel.safetensors --summary-interval=5 --download-result=default --allow-overwrite=true --file-allocation=none $SDModel\r\n", + "!python koboldcpp.py model.gguf --usecublas 0 mmq --multiuser --gpulayers $Layers --contextsize $ContextSize --quiet --remotetunnel $SDCommand\r\n" ] } ], diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index f79acfef1d133..350bbdf7f7b1b 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -19,7 +19,12 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git") endif() endif() - set(GIT_INDEX "${GIT_DIR}/index") + if(EXISTS "${GIT_DIR}/index") + set(GIT_INDEX "${GIT_DIR}/index") + else() + message(WARNING "Git index not found in git repository.") + set(GIT_INDEX "") + endif() else() message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.") set(GIT_INDEX "") diff --git a/common/common.cpp b/common/common.cpp index c0dd1a4c73cf9..857d4d2963aa1 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -336,6 +336,22 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.yarn_beta_slow = std::stof(argv[i]); + } else if (arg == "--pooling") { + if (++i >= argc) { + invalid_param = true; + break; + } + std::string value(argv[i]); + /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } + else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } + else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } + else { invalid_param = true; break; } + } else if (arg == "--defrag-thold" || arg == "-dt") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.defrag_thold = std::stof(argv[i]); } else if (arg == "--samplers") { if (++i >= argc) { invalid_param = true; @@ -498,12 +514,6 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.n_sequences = std::stoi(argv[i]); - } else if (arg == "--p-accept" || arg == "-pa") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.p_accept = std::stof(argv[i]); } else if (arg == "--p-split" || arg == "-ps") { if (++i >= argc) { invalid_param = true; @@ -635,6 +645,10 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } else if (arg_next == "layer") { params.split_mode = LLAMA_SPLIT_MODE_LAYER; } else if (arg_next == "row") { +#ifdef GGML_USE_SYCL + fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n"); + exit(1); +#endif // GGML_USE_SYCL params.split_mode = LLAMA_SPLIT_MODE_ROW; } else { invalid_param = true; @@ -1005,10 +1019,14 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); + printf(" --pooling {none,mean,cls}\n"); + printf(" pooling type for embeddings, use model default if unspecified\n"); + printf(" -dt N, --defrag-thold N\n"); + printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); printf(" --no-penalize-nl do not penalize newline token\n"); printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp); - printf(" --logits-all return logits for all tokens in the batch (default: disabled)\n"); + printf(" --all-logits return logits for all tokens in the batch (default: disabled)\n"); printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); printf(" --winogrande compute Winogrande score over random tasks from datafile supplied with -f\n"); @@ -1021,7 +1039,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel); printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences); - printf(" -pa N, --p-accept N speculative decoding accept probability (default: %.1f)\n", (double)params.p_accept); printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split); printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n"); @@ -1274,10 +1291,9 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.n_batch = params.n_batch; cparams.n_threads = params.n_threads; cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; - cparams.mul_mat_q = params.mul_mat_q; cparams.seed = params.seed; cparams.logits_all = params.logits_all; - cparams.embedding = params.embedding; + cparams.embeddings = params.embedding; cparams.rope_scaling_type = params.rope_scaling_type; cparams.rope_freq_base = params.rope_freq_base; cparams.rope_freq_scale = params.rope_freq_scale; @@ -1286,6 +1302,8 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.yarn_beta_fast = params.yarn_beta_fast; cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.yarn_orig_ctx = params.yarn_orig_ctx; + cparams.pooling_type = params.pooling_type; + cparams.defrag_thold = params.defrag_thold; cparams.offload_kqv = !params.no_kv_offload; cparams.type_k = kv_cache_type_from_str(params.cache_type_k); @@ -1717,7 +1735,6 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs); fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); - fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false"); fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false"); fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); diff --git a/common/common.h b/common/common.h index 7373778c80c98..411204dbf9eec 100644 --- a/common/common.h +++ b/common/common.h @@ -37,7 +37,7 @@ int32_t get_num_physical_cores(); struct gpt_params { - uint32_t seed = -1; // RNG seed + uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed int32_t n_threads = get_num_physical_cores(); int32_t n_threads_draft = -1; @@ -47,11 +47,10 @@ struct gpt_params { int32_t n_ctx = 512; // context size int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_draft = 8; // number of tokens to draft during speculative decoding + int32_t n_draft = 5; // number of tokens to draft during speculative decoding int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_parallel = 1; // number of parallel sequences to decode int32_t n_sequences = 1; // number of sequences to decode - float p_accept = 0.5f; // speculative decoding accept probability float p_split = 0.1f; // speculative decoding split probability int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) @@ -69,8 +68,12 @@ struct gpt_params { float yarn_beta_fast = 32.0f; // YaRN low correction dim float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length - int32_t rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; - ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; + float defrag_thold = -1.0f; // KV cache defragmentation threshold + + ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; + + llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; + llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings // sampling parameters int32_t top_k = 40; // <= 0 to use vocab size @@ -128,7 +131,6 @@ struct gpt_params { bool kl_divergence = false; // compute KL-divergence - bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS bool random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs bool interactive = false; // interactive mode diff --git a/common/sampling.cpp b/common/sampling.cpp index 0cceb77af0db9..c2e952f42831a 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -266,7 +266,7 @@ static llama_token llama_sampling_sample_impl( // } //} - LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str()); + //LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str()); } } @@ -295,6 +295,77 @@ static llama_token llama_sampling_sample_impl( return id; } +static llama_token_data_array llama_sample_probability_distribution_impl( + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + struct llama_context * ctx_cfg, + const int idx) { + const llama_sampling_params & params = ctx_sampling->params; + + const int n_vocab = llama_n_vocab(llama_get_model(ctx_main)); + + const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n; + const float penalty_repeat = params.penalty_repeat; + const float penalty_freq = params.penalty_freq; + const float penalty_present = params.penalty_present; + const bool penalize_nl = params.penalize_nl; + + auto & prev = ctx_sampling->prev; + auto & cur = ctx_sampling->cur; + + // Get a pointer to the logits + float * logits = llama_get_logits_ith(ctx_main, idx); + + // Declare original_logits at the beginning of the function scope + std::vector original_logits; + + // apply params.logit_bias map + for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { + logits[it->first] += it->second; + } + + if (ctx_cfg) { + float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx); + llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale); + } + + cur.clear(); + + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + + llama_token_data_array cur_p = { cur.data(), cur.size(), false }; + + // apply penalties + const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev; + const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n); + if (penalty_tokens_used_size) { + const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))]; + + llama_sample_repetition_penalties(ctx_main, &cur_p, + penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size, + penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present); + + if (!penalize_nl) { + for (size_t idx = 0; idx < cur_p.size; idx++) { + if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) { + cur_p.data[idx].logit = nl_logit; + break; + } + } + } + } + + // apply grammar checks + if (ctx_sampling->grammar != NULL) { + llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar); + } + + llama_sample_softmax(ctx_main, &cur_p); + return cur_p; +} + llama_token llama_sampling_sample( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, @@ -304,6 +375,14 @@ llama_token llama_sampling_sample( return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false); } +llama_token_data_array llama_sampling_probability_distribution( + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + struct llama_context * ctx_cfg, + const int idx) { + return llama_sample_probability_distribution_impl(ctx_sampling,ctx_main, ctx_cfg, idx); +} + void llama_sampling_accept( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, diff --git a/common/sampling.h b/common/sampling.h index 9b635a905ace8..a5b8e6079c424 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -132,6 +132,13 @@ llama_token llama_sampling_sample( struct llama_context * ctx_cfg, int idx = 0); +// returns the probability that token of given id will be sampled +llama_token_data_array llama_sampling_probability_distribution( + struct llama_sampling_context * ctx_sampling, + struct llama_context * ctx_main, + struct llama_context * ctx_cfg, + int idx = 0); + void llama_sampling_accept( struct llama_sampling_context * ctx_sampling, struct llama_context * ctx_main, diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index ae30b2a76971a..f6369af38081d 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -8,9 +8,10 @@ import os import re import sys +from abc import ABC, abstractmethod from enum import IntEnum from pathlib import Path -from typing import TYPE_CHECKING, Any, ContextManager, Iterator, Sequence, cast +from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterator, Sequence, TypeVar, cast import numpy as np import torch @@ -36,7 +37,12 @@ class SentencePieceTokenTypes(IntEnum): BYTE = 6 -class Model: +AnyModel = TypeVar("AnyModel", bound="type[Model]") + + +class Model(ABC): + _model_classes: dict[str, type[Model]] = {} + def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool): self.dir_model = dir_model self.ftype = ftype @@ -47,10 +53,14 @@ def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin") self.part_names = self._get_part_names() self.hparams = Model.load_hparams(self.dir_model) - self.model_arch = self._get_model_architecture() self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False) self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"]) + @property + @abstractmethod + def model_arch(self) -> gguf.MODEL_ARCH: + pass + def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any: key = next((k for k in keys if k in self.hparams), None) if key is not None: @@ -96,9 +106,11 @@ def set_gguf_parameters(self): if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: self.gguf_writer.add_head_count_kv(n_head_kv) + if (rope_theta := self.hparams.get("rope_theta")) is not None: + self.gguf_writer.add_rope_freq_base(rope_theta) if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None: self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps) - if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon"], optional=True)) is not None: + if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None: self.gguf_writer.add_layer_norm_eps(f_norm_eps) if (n_experts := self.hparams.get("num_local_experts")) is not None: self.gguf_writer.add_expert_count(n_experts) @@ -174,53 +186,22 @@ def load_hparams(dir_model): with open(dir_model / "config.json", "r", encoding="utf-8") as f: return json.load(f) - @staticmethod - def from_model_architecture(model_architecture): - if model_architecture == "GPTNeoXForCausalLM": - return GPTNeoXModel - if model_architecture == "BloomForCausalLM": - return BloomModel - if model_architecture == "MPTForCausalLM": - return MPTModel - if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): - return BaichuanModel - if model_architecture in ("FalconForCausalLM", "RWForCausalLM"): - return FalconModel - if model_architecture == "GPTBigCodeForCausalLM": - return StarCoderModel - if model_architecture == "GPTRefactForCausalLM": - return RefactModel - if model_architecture == "PersimmonForCausalLM": - return PersimmonModel - if model_architecture in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): - return StableLMModel - if model_architecture == "QWenLMHeadModel": - return QwenModel - if model_architecture == "Qwen2ForCausalLM": - return Model - if model_architecture == "MixtralForCausalLM": - return MixtralModel - if model_architecture == "GPT2LMHeadModel": - return GPT2Model - if model_architecture == "PhiForCausalLM": - return Phi2Model - if model_architecture == "PlamoForCausalLM": - return PlamoModel - if model_architecture == "CodeShellForCausalLM": - return CodeShellModel - if model_architecture == "OrionForCausalLM": - return OrionModel - if model_architecture == "InternLM2ForCausalLM": - return InternLM2Model - if model_architecture == "MiniCPMForCausalLM": - return MiniCPMModel - if model_architecture == "BertModel": - return BertModel - if model_architecture == "NomicBertModel": - return NomicBertModel - if model_architecture == "GemmaForCausalLM": - return GemmaModel - return Model + @classmethod + def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]: + assert names + + def func(modelcls: type[Model]): + for name in names: + cls._model_classes[name] = modelcls + return modelcls + return func + + @classmethod + def from_model_architecture(cls, arch): + try: + return cls._model_classes[arch] + except KeyError: + raise NotImplementedError(f'Architecture {arch!r} not supported!') from None def _is_model_safetensors(self) -> bool: return Model.count_model_parts(self.dir_model, ".safetensors") > 0 @@ -235,55 +216,6 @@ def _get_part_names(self): return ("pytorch_model.bin",) return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1)) - def _get_model_architecture(self) -> gguf.MODEL_ARCH: - arch = self.hparams["architectures"][0] - if arch == "GPTNeoXForCausalLM": - return gguf.MODEL_ARCH.GPTNEOX - if arch == "BloomForCausalLM": - return gguf.MODEL_ARCH.BLOOM - if arch == "MPTForCausalLM": - return gguf.MODEL_ARCH.MPT - if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): - return gguf.MODEL_ARCH.BAICHUAN - if arch in ("FalconForCausalLM", "RWForCausalLM"): - return gguf.MODEL_ARCH.FALCON - if arch == "GPTBigCodeForCausalLM": - return gguf.MODEL_ARCH.STARCODER - if arch == "GPTRefactForCausalLM": - return gguf.MODEL_ARCH.REFACT - if arch == "PersimmonForCausalLM": - return gguf.MODEL_ARCH.PERSIMMON - if arch in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): - return gguf.MODEL_ARCH.STABLELM - if arch == "QWenLMHeadModel": - return gguf.MODEL_ARCH.QWEN - if arch == "Qwen2ForCausalLM": - return gguf.MODEL_ARCH.QWEN2 - if arch == "MixtralForCausalLM": - return gguf.MODEL_ARCH.LLAMA - if arch == "GPT2LMHeadModel": - return gguf.MODEL_ARCH.GPT2 - if arch == "PhiForCausalLM": - return gguf.MODEL_ARCH.PHI2 - if arch == "PlamoForCausalLM": - return gguf.MODEL_ARCH.PLAMO - if arch == "CodeShellForCausalLM": - return gguf.MODEL_ARCH.CODESHELL - if arch == "OrionForCausalLM": - return gguf.MODEL_ARCH.ORION - if arch == "InternLM2ForCausalLM": - return gguf.MODEL_ARCH.INTERNLM2 - if arch == "MiniCPMForCausalLM": - return gguf.MODEL_ARCH.MINICPM - if arch == "BertModel": - return gguf.MODEL_ARCH.BERT - if arch == "NomicBertModel": - return gguf.MODEL_ARCH.NOMIC_BERT - if arch == "GemmaForCausalLM": - return gguf.MODEL_ARCH.GEMMA - - raise NotImplementedError(f'Architecture "{arch}" not supported!') - def _set_vocab_gpt2(self): dir_model = self.dir_model hparams = self.hparams @@ -451,7 +383,10 @@ def _set_vocab_hf(self): special_vocab.add_to_gguf(self.gguf_writer) +@Model.register("GPTNeoXForCausalLM") class GPTNeoXModel(Model): + model_arch = gguf.MODEL_ARCH.GPTNEOX + def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] @@ -468,7 +403,10 @@ def set_gguf_parameters(self): self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) +@Model.register("BloomForCausalLM") class BloomModel(Model): + model_arch = gguf.MODEL_ARCH.BLOOM + def set_gguf_parameters(self): self.gguf_writer.add_name("Bloom") n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) @@ -560,7 +498,10 @@ def write_tensors(self): print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") +@Model.register("MPTForCausalLM") class MPTModel(Model): + model_arch = gguf.MODEL_ARCH.MPT + def set_gguf_parameters(self): block_count = self.hparams["n_layers"] self.gguf_writer.add_name(self.dir_model.name) @@ -623,7 +564,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("OrionForCausalLM") class OrionModel(Model): + model_arch = gguf.MODEL_ARCH.ORION + def set_vocab(self): self._set_vocab_sentencepiece() @@ -702,7 +646,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM") class BaichuanModel(Model): + model_arch = gguf.MODEL_ARCH.BAICHUAN + def set_vocab(self): self._set_vocab_sentencepiece() @@ -817,7 +764,10 @@ def _reverse_hf_part(self, weights: Tensor, n_part: int) -> Tensor: return weights[r * n_part:r * n_part + r, ...] +@Model.register("FalconForCausalLM", "RWForCausalLM") class FalconModel(Model): + model_arch = gguf.MODEL_ARCH.FALCON + def set_gguf_parameters(self): block_count = self.hparams.get("num_hidden_layers") if block_count is None: @@ -910,7 +860,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("GPTBigCodeForCausalLM") class StarCoderModel(Model): + model_arch = gguf.MODEL_ARCH.STARCODER + def set_gguf_parameters(self): block_count = self.hparams["n_layer"] @@ -925,7 +878,10 @@ def set_gguf_parameters(self): self.gguf_writer.add_file_type(self.ftype) +@Model.register("GPTRefactForCausalLM") class RefactModel(Model): + model_arch = gguf.MODEL_ARCH.REFACT + def set_gguf_parameters(self): hidden_dim = self.hparams["n_embd"] inner_dim = 4 * hidden_dim @@ -1009,7 +965,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("PersimmonForCausalLM") class PersimmonModel(Model): + model_arch = gguf.MODEL_ARCH.PERSIMMON + def set_gguf_parameters(self): block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) head_count = self.hparams["num_attention_heads"] @@ -1057,7 +1016,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM") class StableLMModel(Model): + model_arch = gguf.MODEL_ARCH.STABLELM + def set_vocab(self): if (self.dir_model / "tokenizer.json").is_file(): self._set_vocab_gpt2() @@ -1081,12 +1043,18 @@ def set_gguf_parameters(self): self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"])) +@Model.register("MixtralForCausalLM") class MixtralModel(Model): + model_arch = gguf.MODEL_ARCH.LLAMA + def set_vocab(self): self._set_vocab_sentencepiece() +@Model.register("MiniCPMForCausalLM") class MiniCPMModel(Model): + model_arch = gguf.MODEL_ARCH.MINICPM + def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] self.gguf_writer.add_name("MiniCPM") @@ -1163,7 +1131,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("QWenLMHeadModel") class QwenModel(Model): + model_arch = gguf.MODEL_ARCH.QWEN + @staticmethod def token_bytes_to_string(b): from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode @@ -1243,7 +1214,15 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("Qwen2ForCausalLM") +class Qwen2Model(Model): + model_arch = gguf.MODEL_ARCH.QWEN2 + + +@Model.register("GPT2LMHeadModel") class GPT2Model(Model): + model_arch = gguf.MODEL_ARCH.GPT2 + def set_gguf_parameters(self): self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_block_count(self.hparams["n_layer"]) @@ -1305,7 +1284,10 @@ def write_tensors(self): self.gguf_writer.add_tensor("output.weight", data) +@Model.register("PhiForCausalLM") class Phi2Model(Model): + model_arch = gguf.MODEL_ARCH.PHI2 + def set_gguf_parameters(self): block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) @@ -1327,7 +1309,10 @@ def set_gguf_parameters(self): self.gguf_writer.add_add_bos_token(False) +@Model.register("PlamoForCausalLM") class PlamoModel(Model): + model_arch = gguf.MODEL_ARCH.PLAMO + def set_vocab(self): self._set_vocab_sentencepiece() @@ -1406,7 +1391,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("CodeShellForCausalLM") class CodeShellModel(Model): + model_arch = gguf.MODEL_ARCH.CODESHELL + def set_gguf_parameters(self): block_count = self.hparams["n_layer"] @@ -1471,7 +1459,10 @@ def write_tensors(self): print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") +@Model.register("InternLM2ForCausalLM") class InternLM2Model(Model): + model_arch = gguf.MODEL_ARCH.INTERNLM2 + def set_vocab(self): # (TODO): Is there a better way? # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character @@ -1643,7 +1634,10 @@ def write_tensors(self): self.post_write_tensors(tensor_map, name, data_torch) +@Model.register("BertModel") class BertModel(Model): + model_arch = gguf.MODEL_ARCH.BERT + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.vocab_size = None @@ -1653,16 +1647,17 @@ def set_gguf_parameters(self): self.gguf_writer.add_causal_attention(False) # get pooling path - with open(self.dir_model / "modules.json", encoding="utf-8") as f: - modules = json.load(f) pooling_path = None - for mod in modules: - if mod["type"] == "sentence_transformers.models.Pooling": - pooling_path = mod["path"] - break + module_path = self.dir_model / "modules.json" + if module_path.is_file(): + with open(module_path, encoding="utf-8") as f: + modules = json.load(f) + for mod in modules: + if mod["type"] == "sentence_transformers.models.Pooling": + pooling_path = mod["path"] + break # get pooling type - pooling_type = gguf.PoolingType.NONE if pooling_path is not None: with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f: pooling = json.load(f) @@ -1672,8 +1667,7 @@ def set_gguf_parameters(self): pooling_type = gguf.PoolingType.CLS else: raise NotImplementedError("Only MEAN and CLS pooling types supported") - - self.gguf_writer.add_pooling_type(pooling_type.value) + self.gguf_writer.add_pooling_type(pooling_type) def set_vocab(self): path = self.dir_model @@ -1749,7 +1743,10 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("NomicBertModel") class NomicBertModel(BertModel): + model_arch = gguf.MODEL_ARCH.NOMIC_BERT + def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -1786,7 +1783,10 @@ def get_tensors(self): yield name, data +@Model.register("GemmaForCausalLM") class GemmaModel(Model): + model_arch = gguf.MODEL_ARCH.GEMMA + def set_vocab(self): self._set_vocab_sentencepiece() @@ -1811,16 +1811,15 @@ def write_tensors(self): tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) for name, data_torch in self.get_tensors(): - # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 - if name.endswith("norm.weight"): - data_torch = data_torch + 1 - old_dtype = data_torch.dtype # convert any unsupported data types to float32 if data_torch.dtype not in (torch.float16, torch.float32): data_torch = data_torch.to(torch.float32) + # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 + if name.endswith("norm.weight"): + data_torch = data_torch + 1 data = data_torch.squeeze().numpy() # map tensor names @@ -1843,6 +1842,11 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +@Model.register("Starcoder2ForCausalLM") +class StarCoder2Model(Model): + model_arch = gguf.MODEL_ARCH.STARCODER2 + + ###### CONVERSION LOGIC ###### diff --git a/convert-llama-ggml-to-gguf.py b/convert-llama-ggml-to-gguf.py index b331080629be0..cd9644fcb5213 100755 --- a/convert-llama-ggml-to-gguf.py +++ b/convert-llama-ggml-to-gguf.py @@ -373,7 +373,7 @@ def handle_metadata(cfg, hp): raise ValueError('Unable to load metadata') vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir) vocab_factory = convert.VocabFactory(vocab_path) - vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype, cfg.model_metadata_dir) + vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype.split(","), cfg.model_metadata_dir) convert.check_vocab_size(params, vocab) return params, vocab, special_vocab @@ -398,8 +398,8 @@ def handle_args(): help ='Load HuggingFace/.pth vocab and metadata from the specified directory') parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir") - parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm", - help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)") + parser.add_argument("--vocabtype", default="spm,hfft", + help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)") return parser.parse_args() diff --git a/convert.py b/convert.py index 63a0a5d78075b..6e3a0319b1e46 100755 --- a/convert.py +++ b/convert.py @@ -1282,35 +1282,32 @@ def load_some_model(path: Path) -> ModelPlus: class VocabFactory: + _FILES = {"spm": "tokenizer.model", "bpe": "vocab.json", "hfft": "tokenizer.json"} + def __init__(self, path: Path): self.path = path - self.files: dict[str, Path | None] = { - "tokenizer.model": None, - "vocab.json": None, - "tokenizer.json": None, - } - self._detect_files() - - def _detect_files(self): - for file in self.files.keys(): - file_path = self.path / file - parent_file_path = self.path.parent / file - if file_path.exists(): - self.files[file] = file_path - elif parent_file_path.exists(): - self.files[file] = parent_file_path - print(f"Found vocab files: {self.files}") - - def _select_file(self, vocabtype: str | None) -> Path: - if vocabtype in ["spm", "bpe"]: - for file_key in self.files.keys(): - if (file := self.files[file_key]) is not None: - return file - raise FileNotFoundError(f"{vocabtype} vocab not found.") - if vocabtype == "hfft": - # For Hugging Face Fast Tokenizer, return the directory path instead of a specific file - return self.path - raise ValueError(f"Unsupported vocabulary type {vocabtype}") + self.file_paths = self._detect_files() + print(f"Found vocab files: {self.file_paths}") + + def _detect_files(self) -> dict[str, Path | None]: + def locate(file: str) -> Path | None: + if (path := self.path / file).exists(): + return path + if (path := self.path.parent / file).exists(): + return path + return None + + return {vt: locate(f) for vt, f in self._FILES.items()} + + def _select_file(self, vocab_types: list[str]) -> tuple[str, Path]: + for vtype in vocab_types: + try: + path = self.file_paths[vtype] + except KeyError: + raise ValueError(f"Unsupported vocabulary type {vtype}") from None + if path is not None: + return vtype, path + raise FileNotFoundError(f"Could not find any of {[self._FILES[vt] for vt in vocab_types]}") def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: Path) -> gguf.SpecialVocab: load_merges = vocabtype == "bpe" @@ -1322,30 +1319,30 @@ def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: n_vocab=n_vocab, ) - def load_vocab(self, vocabtype: str, model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]: - path = self._select_file(vocabtype) - print(f"Loading vocab file '{path}', type '{vocabtype}'") + def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]: + vocab_type, path = self._select_file(vocab_types) + print(f"Loading vocab file {path!r}, type {vocab_type!r}") added_tokens_path = path.parent / "added_tokens.json" vocab: Vocab - if vocabtype == "bpe": + if vocab_type == "bpe": vocab = BpeVocab( path, added_tokens_path if added_tokens_path.exists() else None ) - elif vocabtype == "spm": + elif vocab_type == "spm": vocab = SentencePieceVocab( path, added_tokens_path if added_tokens_path.exists() else None ) - elif vocabtype == "hfft": + elif vocab_type == "hfft": vocab = HfVocab( - path, added_tokens_path if added_tokens_path.exists() else None + path.parent, added_tokens_path if added_tokens_path.exists() else None ) else: - raise ValueError(f"Unsupported vocabulary type {vocabtype}") + raise ValueError(vocab_type) # FIXME: Respect --vocab-dir? special_vocab = self._create_special_vocab( vocab, - vocabtype, + vocab_type, model_parent_path, ) return vocab, special_vocab @@ -1379,15 +1376,14 @@ def main(args_in: list[str] | None = None) -> None: if np.uint32(1) == np.uint32(1).newbyteorder("<"): # We currently only support Q8_0 output on little endian systems. output_choices.append("q8_0") - vocab_types = ["spm", "bpe", "hfft"] - parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") + parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file") parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None) parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") - parser.add_argument("--vocab-type", choices=vocab_types, help="The vocabulary format used to define the tokenizer model (default: spm)", default="spm") + parser.add_argument("--vocab-type", help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft") parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") parser.add_argument("--ctx", type=int, help="model training context (default: based on input)") @@ -1448,7 +1444,7 @@ def main(args_in: list[str] | None = None) -> None: model_parent_path = model_plus.paths[0].parent vocab_path = Path(args.vocab_dir or args.model or model_parent_path) vocab_factory = VocabFactory(vocab_path) - vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path) + vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type.split(","), model_parent_path) if args.vocab_only: if not args.outfile: diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index b4b8a38e1bcf5..19aff18aefde7 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -32,16 +32,15 @@ int main(int argc, char ** argv) { gpt_params params; if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] \n" , argv[0]); + printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] \n" , argv[0]); printf(" , and PL are comma-separated lists of numbers without spaces\n\n"); - printf(" example: %s ggml-model-f16.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]); + printf(" example: %s ggml-model-f16.gguf 2048 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]); return 1 ; } int n_kv_max = 2048; int is_pp_shared = 0; int n_gpu_layers = 0; - int mmq = 0; std::vector n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, }; std::vector n_tg = { 128, 256, }; @@ -65,19 +64,15 @@ int main(int argc, char ** argv) { } if (argc >= 6) { - mmq = std::atoi(argv[5]); + n_pp = parse_list(argv[5]); } if (argc >= 7) { - n_pp = parse_list(argv[6]); + n_tg = parse_list(argv[6]); } if (argc >= 8) { - n_tg = parse_list(argv[7]); - } - - if (argc >= 9) { - n_pl = parse_list(argv[8]); + n_pl = parse_list(argv[7]); } // init LLM @@ -106,7 +101,6 @@ int main(int argc, char ** argv) { ctx_params.seed = 1234; ctx_params.n_ctx = n_kv_max; ctx_params.n_batch = 512; - ctx_params.mul_mat_q = mmq; ctx_params.n_threads = params.n_threads; ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; @@ -159,7 +153,7 @@ int main(int argc, char ** argv) { } LOG_TEE("\n"); - LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch); + LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch); LOG_TEE("\n"); LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s"); diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index c2f2c293e1cfd..4da81998afc45 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -20,11 +20,11 @@ static std::vector split_lines(const std::string & s) { static void batch_add_seq(llama_batch & batch, const std::vector & tokens, int seq_id) { for (size_t i = 0; i < tokens.size(); i++) { - llama_batch_add(batch, tokens[i], i, { seq_id }, false); + llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1); } } -static void normalize(float * vec, float * out, int n) { +static void normalize(const float * vec, float * out, int n) { float norm = 0; for (int i = 0; i < n; i++) { norm += vec[i] * vec[i]; @@ -46,10 +46,23 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu } // normalize on copy - for (int k = 0; k < n_seq; k++) { - float * emb = llama_get_embeddings_ith(ctx, k); - float * out = output + k * n_embd; - normalize(emb, out, n_embd); + for (int i = 0; i < batch.n_tokens; i++) { + if (!batch.logits[i]) { + continue; + } + + // try to get sequence embeddings - supported only when pooling_type is not NONE + const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + if (embd == NULL) { + embd = llama_get_embeddings_ith(ctx, i); + if (embd == NULL) { + fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i); + continue; + } + } + + float * out = output + batch.seq_id[i][0] * n_embd; + normalize(embd, out, n_embd); } } @@ -133,7 +146,7 @@ int main(int argc, char ** argv) { // initialize batch const int n_prompts = prompts.size(); - struct llama_batch batch = llama_batch_init(n_batch, 0, n_prompts); + struct llama_batch batch = llama_batch_init(n_batch, 0, 1); // allocate output const int n_embd = llama_n_embd(model); @@ -146,6 +159,7 @@ int main(int argc, char ** argv) { for (int k = 0; k < n_prompts; k++) { // clamp to n_batch tokens auto & inp = inputs[k]; + const uint64_t n_toks = inp.size(); // encode if at capacity diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index a8f732b71aa13..093a98e542c69 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -379,10 +379,10 @@ int main(int argc, char ** argv) { if (params.interactive) { const char *control_message; if (params.multiline_input) { - control_message = " - To return control to LLaMa, end your input with '\\'.\n" + control_message = " - To return control to LLaMA, end your input with '\\'.\n" " - To return control without starting a new line, end your input with '/'.\n"; } else { - control_message = " - Press Return to return control to LLaMa.\n" + control_message = " - Press Return to return control to LLaMA.\n" " - To return control without starting a new line, end your input with '/'.\n" " - If you want to submit another line, end your input with '\\'.\n"; } @@ -448,8 +448,8 @@ int main(int argc, char ** argv) { LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", n_past, n_left, n_ctx, params.n_keep, n_discard); - llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); + llama_kv_cache_seq_add(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); n_past -= n_discard; diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md index 374e40a7dc108..10f37b4418897 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -35,7 +35,6 @@ options: -mg, --main-gpu (default: 0) -nkvo, --no-kv-offload <0|1> (default: 0) -mmp, --mmap <0|1> (default: 1) - -mmq, --mul-mat-q <0|1> (default: 1) -ts, --tensor_split (default: 0) -r, --repetitions (default: 5) -o, --output (default: md) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 829139a0be861..1acb3cacb7fcb 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -124,20 +124,15 @@ static std::string get_gpu_info() { } #endif #ifdef GGML_USE_SYCL - int device_list[GGML_SYCL_MAX_DEVICES]; - ggml_sycl_get_gpu_list(device_list, GGML_SYCL_MAX_DEVICES); - - for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) { - if (device_list[i] >0 ){ - char buf[128]; - ggml_sycl_get_device_description(i, buf, sizeof(buf)); - id += buf; + int count = ggml_backend_sycl_get_device_count(); + for (int i = 0; i < count; i++) { + char buf[128]; + ggml_sycl_get_device_description(i, buf, sizeof(buf)); + id += buf; + if (i < count - 1) { id += "/"; } } - if (id.length() >2 ) { - id.pop_back(); - } #endif // TODO: other backends return id; @@ -177,7 +172,6 @@ struct cmd_params { std::vector split_mode; std::vector main_gpu; std::vector no_kv_offload; - std::vector mul_mat_q; std::vector> tensor_split; std::vector use_mmap; int reps; @@ -197,7 +191,6 @@ static const cmd_params cmd_params_defaults = { /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, /* main_gpu */ {0}, /* no_kv_offload */ {false}, - /* mul_mat_q */ {true}, /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, /* use_mmap */ {true}, /* reps */ 5, @@ -222,7 +215,6 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); - printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); printf(" -ts, --tensor_split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); @@ -384,13 +376,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end()); - } else if (arg == "-mmq" || arg == "--mul-mat-q") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = split(argv[i], split_delim); - params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end()); } else if (arg == "-mmp" || arg == "--mmap") { if (++i >= argc) { invalid_param = true; @@ -467,7 +452,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } - if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; } if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } @@ -487,7 +471,6 @@ struct cmd_params_instance { llama_split_mode split_mode; int main_gpu; bool no_kv_offload; - bool mul_mat_q; std::vector tensor_split; bool use_mmap; @@ -519,7 +502,6 @@ struct cmd_params_instance { cparams.n_batch = n_batch; cparams.type_k = type_k; cparams.type_v = type_v; - cparams.mul_mat_q = mul_mat_q; cparams.offload_kqv = !no_kv_offload; return cparams; @@ -539,7 +521,6 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & nb : params.n_batch) for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) - for (const auto & mmq : params.mul_mat_q) for (const auto & nkvo : params.no_kv_offload) for (const auto & nt : params.n_threads) { for (const auto & n_prompt : params.n_prompt) { @@ -558,7 +539,6 @@ static std::vector get_cmd_params_instances(const cmd_param /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, - /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, }; @@ -581,7 +561,6 @@ static std::vector get_cmd_params_instances(const cmd_param /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, - /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, }; @@ -617,7 +596,6 @@ struct test { llama_split_mode split_mode; int main_gpu; bool no_kv_offload; - bool mul_mat_q; std::vector tensor_split; bool use_mmap; int n_prompt; @@ -640,7 +618,6 @@ struct test { split_mode = inst.split_mode; main_gpu = inst.main_gpu; no_kv_offload = inst.no_kv_offload; - mul_mat_q = inst.mul_mat_q; tensor_split = inst.tensor_split; use_mmap = inst.use_mmap; n_prompt = inst.n_prompt; @@ -714,7 +691,7 @@ struct test { "n_batch", "n_threads", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", - "mul_mat_q", "tensor_split", "use_mmap", + "tensor_split", "use_mmap", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts" @@ -734,7 +711,7 @@ struct test { } if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || - field == "mul_mat_q" || field == "use_mmap") { + field == "use_mmap") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -768,7 +745,7 @@ struct test { std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), std::to_string(n_gpu_layers), split_mode_str(split_mode), std::to_string(main_gpu), std::to_string(no_kv_offload), - std::to_string(mul_mat_q), tensor_split_str, std::to_string(use_mmap), + tensor_split_str, std::to_string(use_mmap), std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), std::to_string(avg_ts()), std::to_string(stdev_ts()) @@ -932,9 +909,6 @@ struct markdown_printer : public printer { if (field == "n_threads") { return "threads"; } - if (field == "mul_mat_q") { - return "mmq"; - } if (field == "no_kv_offload") { return "nkvo"; } @@ -975,9 +949,6 @@ struct markdown_printer : public printer { if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) { fields.emplace_back("split_mode"); } - if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) { - fields.emplace_back("mul_mat_q"); - } if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) { fields.emplace_back("no_kv_offload"); } diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 8c49a626e07d4..d693cecc05c99 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -512,6 +512,14 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; + // tokenized antiprompts + std::vector> antiprompt_ids; + + antiprompt_ids.reserve(params.antiprompt.size()); + for (const std::string & antiprompt : params.antiprompt) { + antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true)); + } + struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); while ((n_remain != 0 && !is_antiprompt) || params.interactive) { @@ -549,8 +557,8 @@ int main(int argc, char ** argv) { LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", n_past, n_left, n_ctx, params.n_keep, n_discard); - llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard); - llama_kv_cache_seq_shift(ctx, 0, params.n_keep + n_discard, n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard); + llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard); n_past -= n_discard; @@ -577,9 +585,9 @@ int main(int argc, char ** argv) { LOG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n); LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd); - llama_kv_cache_seq_shift(ctx, 0, ga_i, n_past, ib*bd); - llama_kv_cache_seq_div (ctx, 0, ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n); - llama_kv_cache_seq_shift(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd, dd); + llama_kv_cache_seq_add(ctx, 0, ga_i, n_past, ib*bd); + llama_kv_cache_seq_div(ctx, 0, ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n); + llama_kv_cache_seq_add(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd, dd); n_past -= bd; @@ -770,6 +778,18 @@ int main(int argc, char ** argv) { } } + // check for reverse prompt using special tokens + llama_token last_token = llama_sampling_last(ctx_sampling); + for (std::vector ids : antiprompt_ids) { + if (ids.size() == 1 && last_token == ids[0]) { + if (params.interactive) { + is_interacting = true; + } + is_antiprompt = true; + break; + } + } + if (is_antiprompt) { LOG("found antiprompt: %s\n", last_output.c_str()); } diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp index e12a1cdf19a79..2cbc9e1fa89ed 100644 --- a/examples/passkey/passkey.cpp +++ b/examples/passkey/passkey.cpp @@ -126,7 +126,7 @@ int main(int argc, char ** argv) { const int n_batch = ctx_params.n_batch; const int n_batch_grp = ctx_params.n_batch/n_grp; - LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch); + LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d, n_junk = %d, i_pos = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch, n_junk, i_pos); // print the prompt token-by-token @@ -146,10 +146,11 @@ int main(int argc, char ** argv) { const int ib = i/n_batch - 1; const int bd = n_batch_grp*(n_grp - 1); - llama_kv_cache_seq_shift(ctx, 0, n_past - n_batch, n_past, ib*bd); - llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp); + llama_kv_cache_seq_add (ctx, 0, n_past - n_batch, n_past, ib*bd); + llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp); + llama_kv_cache_update (ctx); - n_past -= bd; + n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; } llama_batch_clear(batch); @@ -179,10 +180,12 @@ int main(int argc, char ** argv) { LOG_TEE("%s: shifting KV cache with %d\n", __func__, n_discard); - llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); - llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); + llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); + llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); + //llama_kv_cache_defrag (ctx); + llama_kv_cache_update (ctx); - n_past -= n_discard; + n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; llama_batch_clear(batch); @@ -208,10 +211,12 @@ int main(int argc, char ** argv) { if (n_discard > 0) { LOG_TEE("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard); - llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); - llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); + llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard); + llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard); + //llama_kv_cache_defrag (ctx); + llama_kv_cache_update (ctx); - n_past -= n_discard; + n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; } } diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 801646c06c273..6ae5180cf2f7d 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -24,18 +24,21 @@ static const std::vector QUANT_OPTIONS = { { "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", }, { "IQ2_XXS",LLAMA_FTYPE_MOSTLY_IQ2_XXS," 2.06 bpw quantization", }, { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", }, + { "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", }, + { "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", }, { "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", }, { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", }, { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", }, { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization", }, { "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S, " 3.44 bpw quantization", }, - { "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", }, + { "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", }, { "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" }, - { "Q3_K_XS",LLAMA_FTYPE_MOSTLY_Q3_K_XS,"3-bit extra small quantization" , }, + { "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS, " 3.3 bpw quantization" , }, { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", }, { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", }, { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", }, - { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.25 bpw non-linear quantization", }, + { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", }, + { "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", }, { "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", }, { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.59G, +0.0992 ppl @ LLaMA-v1-7B", }, { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0532 ppl @ LLaMA-v1-7B", }, @@ -293,6 +296,7 @@ int main(int argc, char ** argv) { } if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || + params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) && imatrix_data.empty()) { fprintf(stderr, "\n===============================================================================================\n"); fprintf(stderr, "Please do not use IQ1_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n"); diff --git a/examples/server-embd.py b/examples/server-embd.py new file mode 100644 index 0000000000000..c5c4ea87b09fc --- /dev/null +++ b/examples/server-embd.py @@ -0,0 +1,34 @@ +import asyncio +import requests +import numpy as np + +n = 8 + +result = [] + +async def requests_post_async(*args, **kwargs): + return await asyncio.to_thread(requests.post, *args, **kwargs) + +async def main(): + model_url = "http://127.0.0.1:6900" + responses: list[requests.Response] = await asyncio.gather(*[requests_post_async( + url= f"{model_url}/embedding", + json= {"content": str(i)*1024} + ) for i in range(n)]) + + for response in responses: + embedding = response.json()["embedding"] + print(embedding[-8:]) + result.append(embedding) + +asyncio.run(main()) + +# compute cosine similarity + +for i in range(n-1): + for j in range(i+1, n): + embedding1 = np.array(result[i]) + embedding2 = np.array(result[j]) + similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2)) + print(f"Similarity between {i} and {j}: {similarity:.2f}") + diff --git a/examples/server/README.md b/examples/server/README.md index cb3fd6054095b..21da7a0a04e23 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -1,11 +1,24 @@ -# llama.cpp/example/server +# LLaMA.cpp HTTP Server -This example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp. +Fast, lightweight, pure C/C++ HTTP server based on [httplib](https://github.com/yhirose/cpp-httplib), [nlohmann::json](https://github.com/nlohmann/json) and **llama.cpp**. -Command line options: +Set of LLM REST APIs and a simple web front end to interact with llama.cpp. + +**Features:** + * LLM inference of F16 and quantum models on GPU and CPU + * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes + * Parallel decoding with multi-user support + * Continuous batching + * Multimodal (wip) + * Monitoring endpoints + +The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216). + +**Command line options:** - `--threads N`, `-t N`: Set the number of threads to use during generation. - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. +- `--threads-http N`: number of threads in the http server pool to process requests (default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`) - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). - `-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. @@ -313,7 +326,7 @@ Notice that each `probs` is an array of length `n_probs`. - `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint. - `total_slots` - the total number of slots for process requests (defined by `--parallel` option) -- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served. +- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. *Options:* @@ -515,20 +528,7 @@ bash chat.sh ### API like OAI -API example using Python Flask: [api_like_OAI.py](api_like_OAI.py) -This example must be used with server.cpp - -```sh -python api_like_OAI.py -``` - -After running the API server, you can use it in Python by setting the API base URL. - -```python -openai.api_base = "http://:port" -``` - -Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API +The HTTP server supports OAI-like API ### Extending or building alternative Web Front End diff --git a/examples/server/api_like_OAI.py b/examples/server/api_like_OAI.py deleted file mode 100755 index 607fe49d3ff15..0000000000000 --- a/examples/server/api_like_OAI.py +++ /dev/null @@ -1,228 +0,0 @@ -#!/usr/bin/env python3 -import argparse -from flask import Flask, jsonify, request, Response -import urllib.parse -import requests -import time -import json - - -app = Flask(__name__) -slot_id = -1 - -parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.") -parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.') -parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ") -parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ") -parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT's RULE: ") -parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '')", default="") -parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080') -parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="") -parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1') -parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081) - -args = parser.parse_args() - -def is_present(json, key): - try: - buf = json[key] - except KeyError: - return False - if json[key] == None: - return False - return True - -#convert chat to prompt -def convert_chat(messages): - - system_n = args.system_name - user_n = args.user_name - ai_n = args.ai_name - stop = args.stop - - prompt = "" + args.chat_prompt + stop - - for line in messages: - if (line["role"] == "system"): - prompt += f"{system_n}{line['content']}{stop}" - if (line["role"] == "user"): - prompt += f"{user_n}{line['content']}{stop}" - if (line["role"] == "assistant"): - prompt += f"{ai_n}{line['content']}{stop}" - prompt += ai_n.rstrip() - - return prompt - -def make_postData(body, chat=False, stream=False): - postData = {} - if (chat): - postData["prompt"] = convert_chat(body["messages"]) - else: - postData["prompt"] = body["prompt"] - if(is_present(body, "temperature")): postData["temperature"] = body["temperature"] - if(is_present(body, "top_k")): postData["top_k"] = body["top_k"] - if(is_present(body, "top_p")): postData["top_p"] = body["top_p"] - if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"] - if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"] - if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"] - if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"] - if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"] - if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"] - if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"] - if(is_present(body, "seed")): postData["seed"] = body["seed"] - if(is_present(body, "grammar")): postData["grammar"] = body["grammar"] - if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()] - if (args.stop != ""): - postData["stop"] = [args.stop] - else: - postData["stop"] = [] - if(is_present(body, "stop")): postData["stop"] += body["stop"] - postData["n_keep"] = -1 - postData["stream"] = stream - postData["cache_prompt"] = True - postData["slot_id"] = slot_id - return postData - -def make_resData(data, chat=False, promptToken=[]): - resData = { - "id": "chatcmpl" if (chat) else "cmpl", - "object": "chat.completion" if (chat) else "text_completion", - "created": int(time.time()), - "truncated": data["truncated"], - "model": "LLaMA_CPP", - "usage": { - "prompt_tokens": data["tokens_evaluated"], - "completion_tokens": data["tokens_predicted"], - "total_tokens": data["tokens_evaluated"] + data["tokens_predicted"] - } - } - if (len(promptToken) != 0): - resData["promptToken"] = promptToken - if (chat): - #only one choice is supported - resData["choices"] = [{ - "index": 0, - "message": { - "role": "assistant", - "content": data["content"], - }, - "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - }] - else: - #only one choice is supported - resData["choices"] = [{ - "text": data["content"], - "index": 0, - "logprobs": None, - "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - }] - return resData - -def make_resData_stream(data, chat=False, time_now = 0, start=False): - resData = { - "id": "chatcmpl" if (chat) else "cmpl", - "object": "chat.completion.chunk" if (chat) else "text_completion.chunk", - "created": time_now, - "model": "LLaMA_CPP", - "choices": [ - { - "finish_reason": None, - "index": 0 - } - ] - } - slot_id = data.get("slot_id") - if (chat): - if (start): - resData["choices"][0]["delta"] = { - "role": "assistant" - } - else: - resData["choices"][0]["delta"] = { - "content": data["content"] - } - if (data["stop"]): - resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - else: - resData["choices"][0]["text"] = data["content"] - if (data["stop"]): - resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - - return resData - - -@app.route('/chat/completions', methods=['POST', 'OPTIONS']) -@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS']) -def chat_completions(): - if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key): - return Response(status=403) - if request.method == 'OPTIONS': - return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - body = request.get_json() - stream = False - tokenize = False - if(is_present(body, "stream")): stream = body["stream"] - if(is_present(body, "tokenize")): tokenize = body["tokenize"] - postData = make_postData(body, chat=True, stream=stream) - - promptToken = [] - if (tokenize): - tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json() - promptToken = tokenData["tokens"] - - if (not stream): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData)) - print(data.json()) - resData = make_resData(data.json(), chat=True, promptToken=promptToken) - return jsonify(resData) - else: - def generate(): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True) - time_now = int(time.time()) - resData = make_resData_stream({}, chat=True, time_now=time_now, start=True) - yield 'data: {}\n\n'.format(json.dumps(resData)) - for line in data.iter_lines(): - if line: - decoded_line = line.decode('utf-8') - resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now) - yield 'data: {}\n\n'.format(json.dumps(resData)) - return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - - -@app.route('/completions', methods=['POST', 'OPTIONS']) -@app.route('/v1/completions', methods=['POST', 'OPTIONS']) -def completion(): - if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key): - return Response(status=403) - if request.method == 'OPTIONS': - return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - body = request.get_json() - stream = False - tokenize = False - if(is_present(body, "stream")): stream = body["stream"] - if(is_present(body, "tokenize")): tokenize = body["tokenize"] - postData = make_postData(body, chat=False, stream=stream) - - promptToken = [] - if (tokenize): - tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json() - promptToken = tokenData["tokens"] - - if (not stream): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData)) - print(data.json()) - resData = make_resData(data.json(), chat=False, promptToken=promptToken) - return jsonify(resData) - else: - def generate(): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True) - time_now = int(time.time()) - for line in data.iter_lines(): - if line: - decoded_line = line.decode('utf-8') - resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now) - yield 'data: {}\n\n'.format(json.dumps(resData)) - return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - -if __name__ == '__main__': - app.run(args.host, port=args.port) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2e7dc12a237a0..2ee1e2a698847 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -34,8 +34,7 @@ using json = nlohmann::json; -struct server_params -{ +struct server_params { std::string hostname = "127.0.0.1"; std::vector api_keys; std::string public_path = "examples/server/public"; @@ -45,108 +44,56 @@ struct server_params int32_t write_timeout = 600; bool slots_endpoint = true; bool metrics_endpoint = false; + int n_threads_http = -1; }; bool server_verbose = false; bool server_log_json = true; -static size_t common_part(const std::vector &a, const std::vector &b) -{ - size_t i; - for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) - { - } - return i; -} - -enum stop_type -{ +enum stop_type { STOP_FULL, STOP_PARTIAL, }; -static bool ends_with(const std::string &str, const std::string &suffix) -{ - return str.size() >= suffix.size() && - 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); -} +// TODO: can become bool if we can't find use of more states +enum slot_state { + IDLE, + PROCESSING, +}; -static size_t find_partial_stop_string(const std::string &stop, - const std::string &text) -{ - if (!text.empty() && !stop.empty()) - { - const char text_last_char = text.back(); - for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) - { - if (stop[char_index] == text_last_char) - { - const std::string current_partial = stop.substr(0, char_index + 1); - if (ends_with(text, current_partial)) - { - return text.size() - char_index - 1; - } - } - } - } - return std::string::npos; -} +enum slot_command { + NONE, + LOAD_PROMPT, + RELEASE, +}; -// TODO: reuse llama_detokenize -template -static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) -{ - std::string ret; - for (; begin != end; ++begin) - { - ret += llama_token_to_piece(ctx, *begin); - } - return ret; -} +struct slot_params { + bool stream = true; + bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt -// format incomplete utf-8 multibyte character for output -static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) -{ - std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); - // if the size is 1 and first bit is 1, meaning it's a partial character - // (size > 1 meaning it's already a known token) - if (out.size() == 1 && (out[0] & 0x80) == 0x80) - { - std::stringstream ss; - ss << std::hex << (out[0] & 0xff); - std::string res(ss.str()); - out = "byte: \\x" + res; - } - return out; -} + uint32_t seed = -1; // RNG seed + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_predict = -1; // new tokens to predict -// convert a vector of completion_token_output to json -static json probs_vector_to_json(const llama_context *ctx, const std::vector &probs) -{ - json out = json::array(); - for (const auto &prob : probs) - { - json probs_for_token = json::array(); - for (const auto &p : prob.probs) - { - std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); - probs_for_token.push_back(json - { - {"tok_str", tok_str}, - {"prob", p.prob}, - }); - } - std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); - out.push_back(json{ - {"content", tok_str}, - {"probs", probs_for_token}, - }); - } - return out; -} + std::vector antiprompt; -struct llama_client_slot -{ + json input_prefix; + json input_suffix; +}; + +struct slot_image { + int32_t id; + + bool request_encode_image = false; + float * image_embedding = nullptr; + int32_t image_tokens = 0; + + clip_image_u8 * img_data; + + std::string prefix_prompt; // before of this image +}; + +struct server_slot { int id; int task_id = -1; @@ -166,8 +113,8 @@ struct llama_client_slot int32_t i_batch = -1; int32_t n_predict = -1; - int32_t num_prompt_tokens = 0; - int32_t num_prompt_tokens_processed = 0; + int32_t n_prompt_tokens = 0; + int32_t n_prompt_tokens_processed = 0; json prompt; std::string generated_text; @@ -202,8 +149,8 @@ struct llama_client_slot std::vector images; // stats - size_t sent_count = 0; - size_t sent_token_probs_index = 0; + size_t n_sent_text = 0; // number of sent text character + size_t n_sent_token_probs = 0; int64_t t_start_process_prompt; int64_t t_start_genereration; @@ -215,7 +162,7 @@ struct llama_client_slot int multitask_id = -1; void reset() { - num_prompt_tokens = 0; + n_prompt_tokens = 0; generated_text = ""; truncated = false; stopped_eos = false; @@ -223,16 +170,15 @@ struct llama_client_slot stopped_limit = false; stopping_word = ""; n_past = 0; - sent_count = 0; - sent_token_probs_index = 0; + n_sent_text = 0; + n_sent_token_probs = 0; infill = false; ga_i = 0; n_past_se = 0; generated_token_probs.clear(); - for (slot_image & img : images) - { + for (slot_image & img : images) { free(img.image_embedding); if (img.img_data) { clip_image_u8_free(img.img_data); @@ -244,19 +190,15 @@ struct llama_client_slot } bool has_budget(gpt_params &global_params) { - if (params.n_predict == -1 && global_params.n_predict == -1) - { + if (params.n_predict == -1 && global_params.n_predict == -1) { return true; // limitless } n_remaining = -1; - if (params.n_predict != -1) - { + if (params.n_predict != -1) { n_remaining = params.n_predict - n_decoded; - } - else if (global_params.n_predict != -1) - { + } else if (global_params.n_predict != -1) { n_remaining = global_params.n_predict - n_decoded; } @@ -272,8 +214,7 @@ struct llama_client_slot } void add_token_string(const completion_token_output &token) { - if (command == RELEASE) - { + if (command == RELEASE) { return; } cache_tokens.push_back(token.tok); @@ -291,10 +232,10 @@ struct llama_client_slot json get_formated_timings() { return json { - {"prompt_n", num_prompt_tokens_processed}, + {"prompt_n", n_prompt_tokens_processed}, {"prompt_ms", t_prompt_processing}, - {"prompt_per_token_ms", t_prompt_processing / num_prompt_tokens_processed}, - {"prompt_per_second", 1e3 / t_prompt_processing * num_prompt_tokens_processed}, + {"prompt_per_token_ms", t_prompt_processing / n_prompt_tokens_processed}, + {"prompt_per_second", 1e3 / t_prompt_processing * n_prompt_tokens_processed}, {"predicted_n", n_decoded}, {"predicted_ms", t_token_generation}, @@ -305,18 +246,18 @@ struct llama_client_slot void print_timings() const { char buffer[512]; - double t_token = t_prompt_processing / num_prompt_tokens_processed; - double n_tokens_second = 1e3 / t_prompt_processing * num_prompt_tokens_processed; + double t_token = t_prompt_processing / n_prompt_tokens_processed; + double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed; sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)", - t_prompt_processing, num_prompt_tokens_processed, + t_prompt_processing, n_prompt_tokens_processed, t_token, n_tokens_second); LOG_INFO(buffer, { - {"slot_id", id}, - {"task_id", task_id}, - {"t_prompt_processing", t_prompt_processing}, - {"num_prompt_tokens_processed", num_prompt_tokens_processed}, - {"t_token", t_token}, - {"n_tokens_second", n_tokens_second}, + {"slot_id", id}, + {"task_id", task_id}, + {"t_prompt_processing", t_prompt_processing}, + {"n_prompt_tokens_processed", n_prompt_tokens_processed}, + {"t_token", t_token}, + {"n_tokens_second", n_tokens_second}, }); t_token = t_token_generation / n_decoded; @@ -344,7 +285,7 @@ struct llama_client_slot } }; -struct llama_metrics { +struct server_metrics { uint64_t n_prompt_tokens_processed_total = 0; uint64_t n_tokens_predicted_total = 0; @@ -355,18 +296,16 @@ struct llama_metrics { uint64_t t_tokens_generation = 0; - void on_prompt_eval(const llama_client_slot &slot) { - n_prompt_tokens_processed_total += slot.num_prompt_tokens_processed; - - n_prompt_tokens_processed += slot.num_prompt_tokens_processed; - t_prompt_processing += slot.t_prompt_processing; + void on_prompt_eval(const server_slot &slot) { + n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed; + n_prompt_tokens_processed += slot.n_prompt_tokens_processed; + t_prompt_processing += slot.t_prompt_processing; } - void on_prediction(const llama_client_slot &slot) { + void on_prediction(const server_slot &slot) { n_tokens_predicted_total += slot.n_decoded; - - n_tokens_predicted += slot.n_decoded; - t_tokens_generation += slot.t_token_generation; + n_tokens_predicted += slot.n_decoded; + t_tokens_generation += slot.t_token_generation; } void reset_bucket() { @@ -405,13 +344,13 @@ struct llama_server_context std::string name_assistant; // slots / clients - std::vector slots; + std::vector slots; json default_generation_settings_for_props; - llama_server_queue queue_tasks; + llama_server_queue queue_tasks; llama_server_response queue_results; - llama_metrics metrics; + server_metrics metrics; ~llama_server_context() { @@ -475,7 +414,7 @@ struct llama_server_context int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size()); if (res < 0) { LOG_ERROR("The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); - sparams.chat_template = "<|im_start|>"; // llama_chat_apply_template only checks if <|im_start|> exist in the template + sparams.chat_template = "chatml"; } } @@ -488,7 +427,7 @@ struct llama_server_context LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}}); for (int i = 0; i < params.n_parallel; i++) { - llama_client_slot slot; + server_slot slot; slot.id = i; slot.n_ctx = n_ctx_slot; @@ -503,8 +442,8 @@ struct llama_server_context const int ga_w = params.grp_attn_w; if (ga_n != 1) { - GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT - GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT + GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT + GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT //GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT @@ -580,11 +519,11 @@ struct llama_server_context return prompt_tokens; } - llama_client_slot* get_slot(int id) { + server_slot* get_slot(int id) { int64_t t_last = ggml_time_us(); - llama_client_slot *last_used = nullptr; + server_slot *last_used = nullptr; - for (llama_client_slot & slot : slots) + for (server_slot & slot : slots) { if (slot.id == id && slot.available()) { @@ -601,7 +540,7 @@ struct llama_server_context return last_used; } - bool launch_slot_with_data(llama_client_slot* &slot, json data) { + bool launch_slot_with_data(server_slot* &slot, json data) { slot_params default_params; llama_sampling_params default_sparams; @@ -889,7 +828,7 @@ struct llama_server_context clean_kv_cache = false; } - void update_system_prompt() { + void system_prompt_update() { kv_cache_clear(); system_tokens.clear(); @@ -934,9 +873,9 @@ struct llama_server_context system_need_update = false; } - void notify_system_prompt_changed() { + void system_prompt_notify() { // release all slots - for (llama_client_slot &slot : slots) + for (server_slot &slot : slots) { slot.release(); } @@ -944,17 +883,17 @@ struct llama_server_context system_need_update = true; } - void process_system_prompt_data(const json &sys_props) { + void system_prompt_process(const json &sys_props) { system_prompt = sys_props.value("prompt", ""); name_user = sys_props.value("anti_prompt", ""); name_assistant = sys_props.value("assistant_name", ""); - notify_system_prompt_changed(); + system_prompt_notify(); } static size_t find_stopping_strings(const std::string &text, const size_t last_token_size, - const stop_type type, llama_client_slot &slot) + const stop_type type, server_slot &slot) { size_t stop_pos = std::string::npos; @@ -976,8 +915,8 @@ struct llama_server_context { if (type == STOP_FULL) { - slot.stopped_word = true; - slot.stopping_word = word; + slot.stopped_word = true; + slot.stopping_word = word; slot.has_next_token = false; } stop_pos = pos; @@ -987,7 +926,7 @@ struct llama_server_context return stop_pos; } - bool process_token(completion_token_output &result, llama_client_slot &slot) { + bool process_token(completion_token_output &result, server_slot &slot) { // remember which tokens were sampled - used for repetition penalties during sampling const std::string token_str = llama_token_to_piece(ctx, result.tok); slot.sampled = result.tok; @@ -1033,7 +972,7 @@ struct llama_server_context if (!incomplete) { - size_t pos = std::min(slot.sent_count, slot.generated_text.size()); + size_t pos = std::min(slot.n_sent_text, slot.generated_text.size()); const std::string str_test = slot.generated_text.substr(pos); bool is_stop_full = false; size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot); @@ -1043,7 +982,7 @@ struct llama_server_context slot.generated_text.erase( slot.generated_text.begin() + pos + stop_pos, slot.generated_text.end()); - pos = std::min(slot.sent_count, slot.generated_text.size()); + pos = std::min(slot.n_sent_text, slot.generated_text.size()); } else { @@ -1056,7 +995,7 @@ struct llama_server_context { // no send the stop word in the response result.text_to_send = slot.generated_text.substr(pos, std::string::npos); - slot.sent_count += result.text_to_send.size(); + slot.n_sent_text += result.text_to_send.size(); // add the token to slot queue and cache } slot.add_token_string(result); @@ -1100,7 +1039,7 @@ struct llama_server_context return slot.has_next_token; // continue } - bool process_images(llama_client_slot &slot) const + bool process_images(server_slot &slot) const { for (slot_image &img : slot.images) { @@ -1133,7 +1072,7 @@ struct llama_server_context queue_results.send(res); } - json get_formated_generation(llama_client_slot &slot) + json get_formated_generation(server_slot &slot) { const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && @@ -1180,7 +1119,7 @@ struct llama_server_context }; } - void send_partial_response(llama_client_slot &slot, completion_token_output tkn) + void send_partial_response(server_slot &slot, completion_token_output tkn) { task_result res; res.id = slot.task_id; @@ -1200,13 +1139,13 @@ struct llama_server_context { std::vector probs_output = {}; const std::vector to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false); - size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size()); - size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size()); + size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); + size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); if (probs_pos < probs_stop_pos) { probs_output = std::vector(slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos); } - slot.sent_token_probs_index = probs_stop_pos; + slot.n_sent_token_probs = probs_stop_pos; res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output); } @@ -1219,7 +1158,7 @@ struct llama_server_context queue_results.send(res); } - void send_final_response(llama_client_slot &slot) + void send_final_response(server_slot &slot) { task_result res; res.id = slot.task_id; @@ -1234,7 +1173,7 @@ struct llama_server_context {"stop", true}, {"model", params.model_alias}, {"tokens_predicted", slot.n_decoded}, - {"tokens_evaluated", slot.num_prompt_tokens}, + {"tokens_evaluated", slot.n_prompt_tokens}, {"generation_settings", get_formated_generation(slot)}, {"prompt", slot.prompt}, {"truncated", slot.truncated}, @@ -1272,7 +1211,7 @@ struct llama_server_context queue_results.send(res); } - void send_embedding(llama_client_slot &slot) + void send_embedding(server_slot & slot, const llama_batch & batch) { task_result res; res.id = slot.task_id; @@ -1281,11 +1220,10 @@ struct llama_server_context res.stop = true; const int n_embd = llama_n_embd(model); + if (!params.embedding) { - LOG_WARNING("embedding disabled", { - {"params.embedding", params.embedding}, - }); + LOG_WARNING("embedding disabled", {{"params.embedding", params.embedding}}); res.result_json = json { {"embedding", std::vector(n_embd, 0.0f)}, @@ -1293,12 +1231,29 @@ struct llama_server_context } else { - const float *data = llama_get_embeddings(ctx); - std::vector embedding(data, data + n_embd); - res.result_json = json - { - {"embedding", embedding }, - }; + for (int i = 0; i < batch.n_tokens; ++i) { + if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { + continue; + } + + const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]); + if (embd == NULL) { + embd = llama_get_embeddings_ith(ctx, i); + if (embd == NULL) { + LOG_ERROR("failed to get embeddings for token", {{"token", batch.token[i]}, {"seq_id", batch.seq_id[i][0]}}); + res.result_json = json + { + {"embedding", std::vector(n_embd, 0.0f)}, + }; + continue; + } + } + + res.result_json = json + { + {"embedding", std::vector(embd, embd + n_embd)}, + }; + } } queue_results.send(res); } @@ -1337,12 +1292,16 @@ struct llama_server_context split_multiprompt_task(task_id, task); } } else { + // an empty prompt can make slot become buggy + if (task.data.contains("prompt") && task.data["prompt"].is_string() && task.data["prompt"].get().empty()) { + task.data["prompt"] = " "; // add a space so that we have one token + } queue_tasks.post(task); } } // for multiple images processing - bool ingest_images(llama_client_slot &slot, int n_batch) + bool ingest_images(server_slot &slot, int n_batch) { int image_idx = 0; @@ -1381,7 +1340,17 @@ struct llama_server_context } const int n_embd = llama_n_embd(model); - llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, }; + llama_batch batch_img = { + n_eval, + nullptr, + (img.image_embedding + i * n_embd), + nullptr, + nullptr, + nullptr, + nullptr, + slot.n_past, + 1, 0 + }; if (llama_decode(ctx, batch_img)) { LOG_TEE("%s : failed to eval image\n", __func__); @@ -1451,7 +1420,7 @@ struct llama_server_context switch (task.type) { case TASK_TYPE_COMPLETION: { - llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); + server_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); if (slot == nullptr) { // if no slot is available, we defer this task for processing later @@ -1466,10 +1435,10 @@ struct llama_server_context send_error(task, "system prompt can only be updated when all slots are idle"); break; } - process_system_prompt_data(task.data["system_prompt"]); + system_prompt_process(task.data["system_prompt"]); // reset cache_tokens for all slots - for (llama_client_slot &slot : slots) + for (server_slot &slot : slots) { slot.cache_tokens.clear(); slot.n_past = 0; @@ -1509,20 +1478,20 @@ struct llama_server_context int n_idle_slots = 0; int n_processing_slots = 0; - for (llama_client_slot &slot: slots) { + for (server_slot &slot: slots) { json slot_data = get_formated_generation(slot); slot_data["id"] = slot.id; slot_data["task_id"] = slot.task_id; slot_data["state"] = slot.state; slot_data["prompt"] = slot.prompt; slot_data["next_token"] = { - {"has_next_token", slot.has_next_token}, - {"n_remain", slot.n_remaining}, + {"has_next_token", slot.has_next_token}, + {"n_remain", slot.n_remaining}, {"num_tokens_predicted", slot.n_decoded}, - {"stopped_eos", slot.stopped_eos}, - {"stopped_word", slot.stopped_word}, - {"stopped_limit", slot.stopped_limit}, - {"stopping_word", slot.stopping_word}, + {"stopped_eos", slot.stopped_eos}, + {"stopped_word", slot.stopped_word}, + {"stopped_limit", slot.stopped_limit}, + {"stopping_word", slot.stopping_word}, }; if (slot_data["state"] == IDLE) { n_idle_slots++; @@ -1560,10 +1529,10 @@ struct llama_server_context { "n_tokens_predicted", metrics.n_tokens_predicted}, { "t_tokens_generation", metrics.t_tokens_generation}, - { "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)}, - { "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)}, + { "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)}, + { "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)}, - { "slots", slots_data }, + { "slots", slots_data }, }; metrics.reset_bucket(); queue_results.send(res); @@ -1594,7 +1563,7 @@ struct llama_server_context if (system_need_update) { LOG_INFO("updating system prompt", {}); - update_system_prompt(); + system_prompt_update(); } llama_batch_clear(batch); @@ -1615,7 +1584,7 @@ struct llama_server_context task.target_id = -1; queue_tasks.post(task); - for (llama_client_slot &slot : slots) + for (server_slot &slot : slots) { if (slot.ga_n == 1) { @@ -1637,8 +1606,8 @@ struct llama_server_context {"n_system_tokens", system_tokens.size()}, {"n_cache_tokens", slot.cache_tokens.size()} }); - llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard); - llama_kv_cache_seq_shift(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard); + llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard); for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) { @@ -1751,45 +1720,50 @@ struct llama_server_context prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt } - slot.num_prompt_tokens = prompt_tokens.size(); + slot.n_prompt_tokens = prompt_tokens.size(); if (slot.params.n_keep < 0) { - slot.params.n_keep = slot.num_prompt_tokens; + slot.params.n_keep = slot.n_prompt_tokens; } slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - // if input prompt is too big, truncate it - if (slot.num_prompt_tokens >= slot.n_ctx) + // if input prompt is too big, truncate it, if group attention self-extend is disabled + if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) { const int n_left = slot.n_ctx - slot.params.n_keep; const int n_block_size = n_left / 2; - const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; + const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + std::vector new_tokens( + prompt_tokens.begin(), + prompt_tokens.begin() + slot.params.n_keep); + new_tokens.insert( + new_tokens.end(), + prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, + prompt_tokens.end()); LOG_VERBOSE("input truncated", { - {"n_ctx", slot.n_ctx}, - {"n_keep", slot.params.n_keep}, - {"n_left", n_left}, + {"n_ctx", slot.n_ctx}, + {"n_keep", slot.params.n_keep}, + {"n_left", n_left}, {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, }); slot.truncated = true; prompt_tokens = new_tokens; - slot.num_prompt_tokens = prompt_tokens.size(); - GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); + slot.n_prompt_tokens = prompt_tokens.size(); + GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx); } if (!slot.params.cache_prompt) { llama_sampling_reset(slot.ctx_sampling); - slot.n_past = 0; + slot.n_past = 0; slot.n_past_se = 0; - slot.ga_i = 0; - slot.num_prompt_tokens_processed = slot.num_prompt_tokens; + slot.ga_i = 0; + slot.n_prompt_tokens_processed = slot.n_prompt_tokens; } else { @@ -1808,7 +1782,7 @@ struct llama_server_context slot.n_past -= 1; } - slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; + slot.n_prompt_tokens_processed = slot.n_prompt_tokens - slot.n_past; if (slot.ga_n != 1) { @@ -1830,16 +1804,18 @@ struct llama_server_context } LOG_INFO("slot progression", { - { "slot_id", slot.id }, - { "task_id", slot.task_id }, - { "n_past", slot.n_past }, - { "num_prompt_tokens_processed", slot.num_prompt_tokens_processed } + { "slot_id", slot.id }, + { "task_id", slot.task_id }, + { "n_past", slot.n_past }, + { "n_past_se", slot.n_past_se }, + { "ga_i", slot.ga_i }, + { "n_prompt_tokens_processed", slot.n_prompt_tokens_processed } }); } slot.cache_tokens = prompt_tokens; - if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0) + if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) { // we have to evaluate at least 1 token to generate logits. LOG_INFO("we have to evaluate at least 1 token to generate logits", { @@ -1888,15 +1864,15 @@ struct llama_server_context ga_i += ga_w/ga_n; } } - llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false); + llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id }, false); slot_npast++; } if (has_images && !ingest_images(slot, n_batch)) { LOG_ERROR("failed processing images", { - "slot_id", slot.id, - "task_id", slot.task_id, + {"slot_id", slot.id}, + {"task_id", slot.task_id}, }); // FIXME @phymbert: to be properly tested // early returning without changing the slot state will block the slot for ever @@ -1924,7 +1900,7 @@ struct llama_server_context for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { - const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i); for (auto & slot : slots) { @@ -1942,9 +1918,9 @@ struct llama_server_context LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n); LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd); - llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd); + llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd); llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n); - llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd); + llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd); slot.n_past_se -= bd; @@ -1997,7 +1973,7 @@ struct llama_server_context // prompt evaluated for embedding if (slot.embedding) { - send_embedding(slot); + send_embedding(slot, batch_view); slot.release(); slot.i_batch = -1; continue; @@ -2047,8 +2023,15 @@ struct llama_server_context return true; } - void run_on_all_tasks_finished() { - update_slots(); + json model_meta() { + return json{ + {"vocab_type", llama_vocab_type(model)}, + {"n_vocab", llama_n_vocab(model)}, + {"n_ctx_train", llama_n_ctx_train(model)}, + {"n_embd", llama_n_embd(model)}, + {"n_params", llama_model_n_params(model)}, + {"size", llama_model_size(model)}, + }; } }; @@ -2062,6 +2045,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n"); + printf(" --threads-http N number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n"); printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); printf(" --rope-scaling {none,linear,yarn}\n"); printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); @@ -2071,6 +2055,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); + printf(" --pooling {none,mean,cls}\n"); + printf(" pooling type for embeddings, use model default if unspecified\n"); printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); @@ -2130,8 +2116,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); - printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); + printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n"); + printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n"); printf(" --chat-template JINJA_TEMPLATE\n"); printf(" set custom jinja chat template (default: template taken from model's metadata)\n"); printf(" Note: only commonly used templates are accepted, since we don't have jinja parser\n"); @@ -2311,6 +2297,18 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.yarn_beta_slow = std::stof(argv[i]); } + else if (arg == "--pooling") + { + if (++i >= argc) { + invalid_param = true; + break; + } + std::string value(argv[i]); + /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } + else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } + else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } + else { invalid_param = true; break; } + } else if (arg == "--threads" || arg == "-t") { if (++i >= argc) @@ -2348,6 +2346,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_threads_batch = std::stoi(argv[i]); } + else if (arg == "--threads-http") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + sparams.n_threads_http = std::stoi(argv[i]); + } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) @@ -2356,7 +2363,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, break; } params.n_batch = std::stoi(argv[i]); - params.n_batch = std::min(512, params.n_batch); } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { @@ -2429,14 +2435,6 @@ 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 - } - else if (arg == "--no-mul-mat-q" || arg == "-nommq") - { -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL) - params.mul_mat_q = false; -#else - LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {}); #endif // GGML_USE_CUBLAS } else if (arg == "--main-gpu" || arg == "-mg") @@ -2558,7 +2556,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, std::istreambuf_iterator(), std::back_inserter(systm_content) ); - llama.process_system_prompt_data(json::parse(systm_content)); + llama.system_prompt_process(json::parse(systm_content)); } else if (arg == "-ctk" || arg == "--cache-type-k") { params.cache_type_k = argv[++i]; @@ -2689,7 +2687,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, /* llama.cpp completion api semantics */ static json format_partial_response( - llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector &probs + llama_server_context &llama, server_slot *slot, const std::string &content, const std::vector &probs ) { json res = json { @@ -2745,14 +2743,7 @@ static void log_server_request(const httplib::Request &req, const httplib::Respo }); } -struct token_translator -{ - llama_context * ctx; - std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); } - std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); } -}; - -static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot *slot) +static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, server_slot *slot) { auto & gtps = slot->generated_token_probs; auto translator = token_translator{llama.ctx}; @@ -2769,7 +2760,16 @@ static void append_to_generated_text_from_generated_token_probs(llama_server_con } std::function shutdown_handler; -inline void signal_handler(int signal) { shutdown_handler(signal); } +std::atomic_flag is_terminating = ATOMIC_FLAG_INIT; +inline void signal_handler(int signal) { + if (is_terminating.test_and_set()) { + // in case it hangs, we can force terminate the server by hitting Ctrl+C twice + // this is for better developer experience, we can remove when the server is stable enough + fprintf(stderr, "Received second interrupt, terminating immediately.\n"); + exit(1); + } + shutdown_handler(signal); +} int main(int argc, char **argv) { @@ -2956,9 +2956,10 @@ int main(int argc, char **argv) for (const auto& metric_def : metrics_def) { std::string name = metric_def["name"]; std::string help = metric_def["help"]; - prometheus << "# HELP llamacpp:" << name << " " << help << "\n" - << "# TYPE llamacpp:" << name << " " << type << "\n" - << "llamacpp:" << name << " " << metric_def["value"] << "\n"; + auto value = json_value(metric_def, "value", 0); + prometheus << "# HELP llamacpp:" << name << " " << help << "\n" + << "# TYPE llamacpp:" << name << " " << type << "\n" + << "llamacpp:" << name << " " << value << "\n"; } } @@ -3039,6 +3040,7 @@ int main(int argc, char **argv) state.store(SERVER_STATE_READY); LOG_INFO("model loaded", {}); } + const auto model_meta = llama.model_meta(); if (sparams.chat_template.empty()) { // custom chat template is not supplied // check if the template comes with the model is supported by us @@ -3188,7 +3190,7 @@ int main(int argc, char **argv) } }); - svr.Get("/v1/models", [¶ms](const httplib::Request& req, httplib::Response& res) + svr.Get("/v1/models", [¶ms, &model_meta](const httplib::Request& req, httplib::Response& res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); std::time_t t = std::time(0); @@ -3197,10 +3199,11 @@ int main(int argc, char **argv) {"object", "list"}, {"data", { { - {"id", params.model_alias}, - {"object", "model"}, - {"created", t}, - {"owned_by", "llamacpp"} + {"id", params.model_alias}, + {"object", "model"}, + {"created", t}, + {"owned_by", "llamacpp"}, + {"meta", model_meta} }, }} }; @@ -3208,87 +3211,88 @@ int main(int argc, char **argv) res.set_content(models.dump(), "application/json; charset=utf-8"); }); + const auto chat_completions = [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res) + { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + if (!validate_api_key(req, res)) { + return; + } + json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template); - // TODO: add mount point without "/v1" prefix -- how? - svr.Post("/v1/chat/completions", [&llama, &validate_api_key, &sparams](const httplib::Request &req, httplib::Response &res) - { - res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - if (!validate_api_key(req, res)) { - return; - } - json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template); + const int task_id = llama.queue_tasks.get_new_id(); + llama.queue_results.add_waiting_task_id(task_id); + llama.request_completion(task_id, data, false, false, -1); - const int task_id = llama.queue_tasks.get_new_id(); - llama.queue_results.add_waiting_task_id(task_id); - llama.request_completion(task_id, data, false, false, -1); - - if (!json_value(data, "stream", false)) { - std::string completion_text; - task_result result = llama.queue_results.recv(task_id); + if (!json_value(data, "stream", false)) { + std::string completion_text; + task_result result = llama.queue_results.recv(task_id); - if (!result.error && result.stop) { - json oaicompat_result = format_final_response_oaicompat(data, result); + if (!result.error && result.stop) { + json oaicompat_result = format_final_response_oaicompat(data, result); - res.set_content(oaicompat_result.dump(-1, ' ', false, - json::error_handler_t::replace), - "application/json; charset=utf-8"); - } else { - res.status = 500; - res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); - } - llama.queue_results.remove_waiting_task_id(task_id); - } else { - const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) { - while (true) { - task_result llama_result = llama.queue_results.recv(task_id); - if (!llama_result.error) { - std::vector result_array = format_partial_response_oaicompat( llama_result); + res.set_content(oaicompat_result.dump(-1, ' ', false, + json::error_handler_t::replace), + "application/json; charset=utf-8"); + } else { + res.status = 500; + res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); + } + llama.queue_results.remove_waiting_task_id(task_id); + } else { + const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) { + while (true) { + task_result llama_result = llama.queue_results.recv(task_id); + if (!llama_result.error) { + std::vector result_array = format_partial_response_oaicompat( llama_result); - for (auto it = result_array.begin(); it != result_array.end(); ++it) - { - if (!it->empty()) { - const std::string str = - "data: " + - it->dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; - LOG_VERBOSE("data stream", {{"to_send", str}}); - if (!sink.write(str.c_str(), str.size())) { - llama.queue_results.remove_waiting_task_id(task_id); - return false; - } - } - } - if (llama_result.stop) { - break; - } - } else { + for (auto it = result_array.begin(); it != result_array.end(); ++it) + { + if (!it->empty()) { const std::string str = - "error: " + - llama_result.result_json.dump(-1, ' ', false, - json::error_handler_t::replace) + + "data: " + + it->dump(-1, ' ', false, json::error_handler_t::replace) + "\n\n"; LOG_VERBOSE("data stream", {{"to_send", str}}); if (!sink.write(str.c_str(), str.size())) { llama.queue_results.remove_waiting_task_id(task_id); return false; } - break; } } - sink.done(); - llama.queue_results.remove_waiting_task_id(task_id); - return true; - }; + if (llama_result.stop) { + break; + } + } else { + const std::string str = + "error: " + + llama_result.result_json.dump(-1, ' ', false, + json::error_handler_t::replace) + + "\n\n"; + LOG_VERBOSE("data stream", {{"to_send", str}}); + if (!sink.write(str.c_str(), str.size())) { + llama.queue_results.remove_waiting_task_id(task_id); + return false; + } + break; + } + } + sink.done(); + llama.queue_results.remove_waiting_task_id(task_id); + return true; + }; - auto on_complete = [task_id, &llama](bool) { - // cancel request - llama.request_cancel(task_id); - llama.queue_results.remove_waiting_task_id(task_id); - }; + auto on_complete = [task_id, &llama](bool) { + // cancel request + llama.request_cancel(task_id); + llama.queue_results.remove_waiting_task_id(task_id); + }; - res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); - } - }); + res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); + } + }; + + svr.Post("/chat/completions", chat_completions); + svr.Post("/v1/chat/completions", chat_completions); svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res) { @@ -3496,6 +3500,13 @@ int main(int argc, char **argv) }*/ //); + if (sparams.n_threads_http < 1) { + // +2 threads for monitoring endpoints + sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); + } + log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); + svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; + LOG_INFO("HTTP server listening", log_data); // run the HTTP server in a thread - see comment below std::thread t([&]() @@ -3513,8 +3524,8 @@ int main(int argc, char **argv) &llama_server_context::process_single_task, &llama, std::placeholders::_1)); llama.queue_tasks.on_finish_multitask(std::bind( &llama_server_context::on_finish_multitask, &llama, std::placeholders::_1)); - llama.queue_tasks.on_all_tasks_finished(std::bind( - &llama_server_context::run_on_all_tasks_finished, &llama)); + llama.queue_tasks.on_run_slots(std::bind( + &llama_server_context::update_slots, &llama)); llama.queue_results.on_multitask_update(std::bind( &llama_server_queue::update_multitask, &llama.queue_tasks, diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md index 0b9fdc4e72678..95a0353b6a9c5 100644 --- a/examples/server/tests/README.md +++ b/examples/server/tests/README.md @@ -1,22 +1,30 @@ # Server tests -Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development) and [behave](https://behave.readthedocs.io/en/latest/): - * [issues.feature](./features/issues.feature) Pending issues scenario - * [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests - * [security.feature](./features/security.feature) Security, CORS and API Key - * [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc... +Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development) +and [behave](https://behave.readthedocs.io/en/latest/): + +* [issues.feature](./features/issues.feature) Pending issues scenario +* [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests +* [security.feature](./features/security.feature) Security, CORS and API Key +* [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc... Tests target GitHub workflows job runners with 4 vCPU. -Requests are using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html) based http client. +Requests are +using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html) +based http client. -Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. To mitigate it, you can increase values in `n_predict`, `kv_size`. +Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. +To mitigate it, you can increase values in `n_predict`, `kv_size`. ### Install dependencies + `pip install -r requirements.txt` ### Run tests + 1. Build the server + ```shell cd ../../.. mkdir build @@ -24,24 +32,36 @@ cd build cmake ../ cmake --build . --target server ``` -2. download required models: - 1. `../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf` -3. Start the test: `./tests.sh` + +2. Start the test: `./tests.sh` It's possible to override some scenario steps values with environment variables: - - `PORT` -> `context.server_port` to set the listening port of the server during scenario, default: `8080` - - `LLAMA_SERVER_BIN_PATH` -> to change the server binary path, default: `../../../build/bin/server` - - `DEBUG` -> "ON" to enable steps and server verbose mode `--verbose` - - `SERVER_LOG_FORMAT_JSON` -> if set switch server logs to json format + +| variable | description | +|--------------------------|------------------------------------------------------------------------------------------------| +| `PORT` | `context.server_port` to set the listening port of the server during scenario, default: `8080` | +| `LLAMA_SERVER_BIN_PATH` | to change the server binary path, default: `../../../build/bin/server` | +| `DEBUG` | "ON" to enable steps and server verbose mode `--verbose` | +| `SERVER_LOG_FORMAT_JSON` | if set switch server logs to json format | +| `N_GPU_LAYERS` | number of model layers to offload to VRAM `-ngl --n-gpu-layers` | ### Run @bug, @wip or @wrong_usage annotated scenario Feature or Scenario must be annotated with `@llama.cpp` to be included in the default scope. + - `@bug` annotation aims to link a scenario with a GitHub issue. - `@wrong_usage` are meant to show user issue that are actually an expected behavior - `@wip` to focus on a scenario working in progress +- `@slow` heavy test, disabled by default To run a scenario annotated with `@bug`, start: -`DEBUG=ON ./tests.sh --no-skipped --tags bug` + +```shell +DEBUG=ON ./tests.sh --no-skipped --tags bug +``` After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated. + +```shell +./tests.sh --no-skipped --tags bug,wrong_usage || echo "should failed but compile" +``` diff --git a/examples/server/tests/features/environment.py b/examples/server/tests/features/environment.py index 09e8267476135..9fd330db6ddc9 100644 --- a/examples/server/tests/features/environment.py +++ b/examples/server/tests/features/environment.py @@ -7,7 +7,10 @@ def before_scenario(context, scenario): - print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m") + context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON' + if context.debug: + print("DEBUG=ON\n") + print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m\n") port = 8080 if 'PORT' in os.environ: port = int(os.environ['PORT']) diff --git a/examples/server/tests/features/issues.feature b/examples/server/tests/features/issues.feature index bf5a175a357ca..7b13e44cad395 100644 --- a/examples/server/tests/features/issues.feature +++ b/examples/server/tests/features/issues.feature @@ -1,4 +1,5 @@ # List of ongoing issues +# run with: DEBUG=ON ./tests.sh --no-skipped --tags bug @bug Feature: Issues # No confirmed issue at the moment diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature index c85f9de1d9a52..86cdf72829f8c 100644 --- a/examples/server/tests/features/parallel.feature +++ b/examples/server/tests/features/parallel.feature @@ -1,11 +1,12 @@ @llama.cpp +@parallel Feature: Parallel Background: Server startup Given a server listening on localhost:8080 - And a model file stories260K.gguf - And a model alias tinyllama-2 + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models And 42 as server seed + And 512 as batch size And 64 KV cache size And 2 slots And embeddings extraction @@ -54,6 +55,28 @@ Feature: Parallel | disabled | 128 | | enabled | 64 | + Scenario Outline: Multi users OAI completions compatibility no v1 + Given a system prompt You are a writer. + And a model tinyllama-2 + Given a prompt: + """ + Write a very long book. + """ + And a prompt: + """ + Write another a poem. + """ + And max tokens to predict + And streaming is + Given concurrent OAI completions requests no v1 + Then the server is busy + Then the server is idle + Then all prompts are predicted with tokens + Examples: + | streaming | n_predict | + | disabled | 128 | + | enabled | 64 | + Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969 Given a prompt: """ diff --git a/examples/server/tests/features/passkey.feature b/examples/server/tests/features/passkey.feature new file mode 100644 index 0000000000000..1bde7aab8bab0 --- /dev/null +++ b/examples/server/tests/features/passkey.feature @@ -0,0 +1,55 @@ +# run with: ./tests.sh --no-skipped --tags passkey +@passkey +@slow +Feature: Passkey / Self-extend with context shift + + Background: Server startup + Given a server listening on localhost:8080 + + # Generates a long text of junk and inserts a secret passkey number inside it. + # Then we query the LLM for the secret passkey. + # see #3856 and #4810 + Scenario Outline: Passkey + Given a model file from HF repo + And as batch size + And as number of junk + And server max tokens to predict + And 42 as seed + And KV cache size + And 1 slots + And group attention factor to extend context size through self-extend + And group attention width to extend context size through self-extend + # Can be override with N_GPU_LAYERS + And GPU offloaded layers + Then the server is starting + Then the server is healthy + Given available models + Then model 0 is trained on tokens context + Given a prefix prompt: + """ + here is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there. + """ + And a passkey prompt template: + """ + The pass key is Remember it. is the pass key. + """ + And a junk suffix prompt: + """ + The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again. + """ + And a suffix prompt: + """ + What is the pass key? The pass key is + """ + Given a "" passkey challenge prompt with the passkey inserted every junk + And a completion request with no api error + Then tokens are predicted matching + + Examples: + | hf_repo | hf_file | n_ctx_train | ngl | n_ctx | n_batch | n_ga | n_ga_w | n_junk | i_pos | passkey | n_predicted | re_content | + | TheBloke/phi-2-GGUF | phi-2.Q4_K_M.gguf | 2048 | 5 | 8192 | 512 | 4 | 512 | 250 | 50 | 42 | 1 | 42 | + | TheBloke/phi-2-GGUF | phi-2.Q4_K_M.gguf | 2048 | 5 | 8192 | 512 | 2 | 512 | 250 | 50 | 42 | 1 | \b((?!42)\w)+\b | + #| TheBloke/Llama-2-7B-GGUF | llama-2-7b.Q2_K.gguf | 4096 | 3 | 16384 | 512 | 4 | 512 | 500 | 300 | 1234 | 5 | 1234 | + #| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768 | 2 | 16384 | 512 | 4 | 512 | 500 | 100 | 0987 | 5 | 0 + # 987 | + diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature index db06d39775c05..42a6709a53380 100644 --- a/examples/server/tests/features/security.feature +++ b/examples/server/tests/features/security.feature @@ -1,9 +1,10 @@ @llama.cpp +@security Feature: Security Background: Server startup with an api key defined Given a server listening on localhost:8080 - And a model file stories260K.gguf + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models And a server api key llama.cpp Then the server is starting Then the server is healthy diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index b571582a7857e..7c977bccecaad 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -1,15 +1,17 @@ @llama.cpp +@server Feature: llama.cpp server Background: Server startup Given a server listening on localhost:8080 - And a model file stories260K.gguf + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models And a model alias tinyllama-2 And 42 as server seed # KV Cache corresponds to the total amount of tokens # that can be stored across all independent sequences: #4130 # see --ctx-size and #5568 And 32 KV cache size + And 512 as batch size And 1 slots And embeddings extraction And 32 server max tokens to predict @@ -29,9 +31,9 @@ Feature: llama.cpp server And prometheus metrics are exposed Examples: Prompts - | prompt | n_predict | re_content | n_predicted | - | I believe the meaning of life is | 8 | (readgoing)+ | 8 | - | Write a joke about AI | 64 | (parkfriendsscaredalways)+ | 32 | + | prompt | n_predict | re_content | n_predicted | + | I believe the meaning of life is | 8 | (read\|going)+ | 8 | + | Write a joke about AI | 64 | (park\|friends\|scared\|always)+ | 32 | Scenario Outline: OAI Compatibility Given a model @@ -43,9 +45,9 @@ Feature: llama.cpp server Then tokens are predicted matching Examples: Prompts - | model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming | - | llama-2 | Book | What is the best book | 8 | (Momwhat)+ | 8 | disabled | - | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thankshappybird)+ | 32 | enabled | + | model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming | + | llama-2 | Book | What is the best book | 8 | (Mom\|what)+ | 8 | disabled | + | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks\|happy\|bird)+ | 32 | enabled | Scenario: Embedding When embeddings are computed for: @@ -75,10 +77,15 @@ Feature: llama.cpp server When an OAI compatible embeddings computation request for multiple inputs Then embeddings are generated - Scenario: Tokenize / Detokenize When tokenizing: """ What is the capital of France ? """ Then tokens can be detokenize + + Scenario: Models available + Given available models + Then 1 models are supported + Then model 0 is identified by tinyllama-2 + Then model 0 is trained on 128 tokens context diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 8e4babf204f8a..3195278022ffb 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -13,6 +13,7 @@ import openai from behave import step from behave.api.async_step import async_run_until_complete +from huggingface_hub import hf_hub_download from prometheus_client import parser @@ -26,17 +27,23 @@ def step_server_config(context, server_fqdn, server_port): context.base_url = f'http://{context.server_fqdn}:{context.server_port}' - context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON' context.model_alias = None + context.n_batch = None context.n_ctx = None + context.n_ga = None + context.n_ga_w = None + context.n_gpu_layer = None context.n_predict = None context.n_server_predict = None context.n_slots = None + context.prompt_prefix = None + context.prompt_suffix = None context.server_api_key = None context.server_continuous_batching = False context.server_embeddings = False context.server_metrics = False context.server_process = None + context.seed = None context.server_seed = None context.user_api_key = None @@ -45,9 +52,11 @@ def step_server_config(context, server_fqdn, server_port): context.prompts = [] -@step(u'a model file {model_file}') -def step_model_file(context, model_file): - context.model_file = model_file +@step(u'a model file {hf_file} from HF repo {hf_repo}') +def step_download_hf_model(context, hf_file, hf_repo): + context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file) + if context.debug: + print(f"model file: {context.model_file}\n") @step(u'a model alias {model_alias}') @@ -55,24 +64,34 @@ def step_model_alias(context, model_alias): context.model_alias = model_alias -@step(u'{seed} as server seed') +@step(u'{seed:d} as server seed') def step_seed(context, seed): - context.server_seed = int(seed) + context.server_seed = seed + +@step(u'{ngl:d} GPU offloaded layers') +def step_n_gpu_layer(context, ngl): + if 'N_GPU_LAYERS' in os.environ: + new_ngl = int(os.environ['N_GPU_LAYERS']) + if context.debug: + print(f"-ngl upgraded from {ngl} to {new_ngl}") + ngl = new_ngl + context.n_gpu_layer = ngl -@step(u'{n_ctx} KV cache size') + +@step(u'{n_ctx:d} KV cache size') def step_n_ctx(context, n_ctx): - context.n_ctx = int(n_ctx) + context.n_ctx = n_ctx -@step(u'{n_slots} slots') +@step(u'{n_slots:d} slots') def step_n_slots(context, n_slots): - context.n_slots = int(n_slots) + context.n_slots = n_slots -@step(u'{n_predict} server max tokens to predict') +@step(u'{n_predict:d} server max tokens to predict') def step_server_n_predict(context, n_predict): - context.n_server_predict = int(n_predict) + context.n_server_predict = n_predict @step(u'continuous batching') @@ -116,11 +135,13 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status): case 'ready' | 'idle': await wait_for_health_status(context, context.base_url, 200, 'ok', + timeout=10, params={'fail_on_no_slot': 0, 'include_slots': 0}, slots_idle=context.n_slots, slots_processing=0, expected_slots=[{'id': slot_id, 'state': 0} - for slot_id in range(context.n_slots)]) + for slot_id in + range(context.n_slots if context.n_slots else 1)]) case 'busy': await wait_for_health_status(context, context.base_url, 503, 'no slot available', @@ -128,7 +149,8 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status): slots_idle=0, slots_processing=context.n_slots, expected_slots=[{'id': slot_id, 'state': 1} - for slot_id in range(context.n_slots)]) + for slot_id in + range(context.n_slots if context.n_slots else 1)]) case _: assert False, "unknown status" @@ -157,24 +179,24 @@ async def step_request_completion(context, api_error): context.base_url, debug=context.debug, n_predict=context.n_predict, - server_seed=context.server_seed, + seed=await completions_seed(context), expect_api_error=expect_api_error, user_api_key=context.user_api_key) context.tasks_result.append(completion) if context.debug: - print(f"Completion response: {completion}") + print(f"Completion response: {completion}\n") if expect_api_error: assert completion == 401, f"completion must be an 401 status code: {completion}" -@step(u'{predicted_n} tokens are predicted matching {re_content}') +@step(u'{predicted_n:d} tokens are predicted matching {re_content}') def step_n_tokens_predicted_with_content(context, predicted_n, re_content): - assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n), re_content) + assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n, re_content) -@step(u'{predicted_n} tokens are predicted') +@step(u'{predicted_n:d} tokens are predicted') def step_n_tokens_predicted(context, predicted_n): - assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n)) + assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n) @step(u'a user prompt {user_prompt}') @@ -192,9 +214,9 @@ def step_model(context, model): context.model = model -@step(u'{max_tokens} max tokens to predict') +@step(u'{max_tokens:d} max tokens to predict') def step_max_tokens(context, max_tokens): - context.n_predict = int(max_tokens) + context.n_predict = max_tokens @step(u'streaming is {enable_streaming}') @@ -222,15 +244,75 @@ def step_server_api_key(context, server_api_key): context.server_api_key = server_api_key +@step(u'{n_junk:d} as number of junk') +def step_n_junk(context, n_junk): + context.n_junk = n_junk + + +@step(u'{n_batch:d} as batch size') +def step_n_batch(context, n_batch): + context.n_batch = n_batch + + +@step(u'{seed:d} as seed') +def step_seed(context, seed): + context.seed = seed + + +@step(u'a prefix prompt') +def step_prompt_prefix(context): + context.prompt_prefix = context.text + + +@step(u'a junk suffix prompt') +def step_prompt_junk_suffix(context): + context.prompt_junk_suffix = context.text + + +@step(u'a suffix prompt') +def step_prompt_suffix(context): + context.prompt_suffix = context.text + + +@step(u'{n_ga:d} group attention factor' + u' to extend context size through self-extend') +def step_impl(context, n_ga): + context.n_ga = n_ga + + +@step(u'{n_ga_w:d} group attention width to extend context size through self-extend') +def step_impl(context, n_ga_w): + context.n_ga_w = n_ga_w + + +@step(u'a passkey prompt template') +def step_prompt_passkey(context): + context.prompt_passkey = context.text + + +@step(u'a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk') +def step_prompt_passkey(context, passkey, i_pos): + prompt = "" + for i in range(context.n_junk): + if i % context.n_junk == i_pos: + prompt += context.prompt_passkey # the passkey is already substituted + prompt += context.prompt_junk_suffix + if context.debug: + passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m" + print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```\n") + context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix) + + @step(u'an OAI compatible chat completions request with {api_error} api error') @async_run_until_complete async def step_oai_chat_completions(context, api_error): if context.debug: - print(f"Submitting OAI compatible completions request...") + print(f"Submitting OAI compatible completions request...\n") expect_api_error = api_error == 'raised' completion = await oai_chat_completions(context.prompts.pop(), context.system_prompt, context.base_url, + '/v1/chat', False, model=context.model if hasattr(context, 'model') else None, @@ -240,8 +322,7 @@ async def step_oai_chat_completions(context, api_error): enable_streaming=context.enable_streaming if hasattr(context, 'enable_streaming') else None, - server_seed=context.server_seed - if hasattr(context, 'server_seed') else None, + seed=await completions_seed(context), user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None, @@ -275,8 +356,10 @@ async def step_concurrent_completion_requests(context): # prompt is inserted automatically context.base_url, debug=context.debug, + prompt_prefix=context.prompt_prefix, + prompt_suffix=context.prompt_suffix, n_predict=context.n_predict if hasattr(context, 'n_predict') else None, - server_seed=context.server_seed if hasattr(context, 'server_seed') else None, + seed=await completions_seed(context), user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None) @@ -288,6 +371,27 @@ async def step_oai_chat_completions(context): # user_prompt is inserted automatically context.system_prompt, context.base_url, + '/v1/chat/completions', + True, # async_client + model=context.model + if hasattr(context, 'model') else None, + n_predict=context.n_predict + if hasattr(context, 'n_predict') else None, + enable_streaming=context.enable_streaming + if hasattr(context, 'enable_streaming') else None, + seed=await completions_seed(context), + user_api_key=context.user_api_key + if hasattr(context, 'user_api_key') else None) + + +@step(u'concurrent OAI completions requests no v1') +@async_run_until_complete +async def step_oai_chat_completions(context): + await concurrent_requests(context, oai_chat_completions, + # user_prompt is inserted automatically + context.system_prompt, + context.base_url, + '/chat/completions', True, # async_client model=context.model if hasattr(context, 'model') else None, @@ -295,7 +399,9 @@ async def step_oai_chat_completions(context): if hasattr(context, 'n_predict') else None, enable_streaming=context.enable_streaming if hasattr(context, 'enable_streaming') else None, - server_seed=context.server_seed + seed=context.seed + if hasattr(context, 'seed') else + context.server_seed if hasattr(context, 'server_seed') else None, user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None) @@ -307,11 +413,10 @@ async def step_all_prompts_are_predicted(context): await all_prompts_are_predicted(context) -@step(u'all prompts are predicted with {n_predict} tokens') +@step(u'all prompts are predicted with {n_expected_predicted:d} tokens') @async_run_until_complete -async def step_all_prompts_are_predicted_with_n_tokens(context, n_predict): - expected_predicted_n = int(n_predict) - await all_prompts_are_predicted(context, expected_predicted_n) +async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted): + await all_prompts_are_predicted(context, n_expected_predicted) async def all_prompts_are_predicted(context, expected_predicted_n=None): @@ -441,6 +546,8 @@ async def step_prometheus_metrics_exported(context): assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4" metrics_raw = await metrics_response.text() metric_exported = False + if context.debug: + print(f"/metrics answer:\n{metrics_raw}\n") for metric in parser.text_string_to_metric_families(metrics_raw): match metric.name: case "llamacpp:kv_cache_usage_ratio": @@ -449,6 +556,37 @@ async def step_prometheus_metrics_exported(context): assert metric_exported, "No metrics exported" +@step(u'available models') +def step_available_models(context): + # openai client always expects an api_key + openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope' + openai.api_base = f'{context.base_url}/v1' + context.models = openai.Model.list().data + + +@step(u'{n_model:d} models are supported') +def step_supported_models(context, n_model): + if context.debug: + print("server models available:", context.models) + assert len(context.models) == n_model + + +@step(u'model {i_model:d} is {param} {preposition} {param_value}') +def step_supported_models(context, i_model, param, preposition, param_value): + assert i_model < len(context.models) + model = context.models[i_model] + + param_value = param_value.split(' ', 1)[0] + match param: + case 'identified': + value = model.id + case 'trained': + value = str(model.meta.n_ctx_train) + case _: + assert False, "param {param} not supported" + assert param_value == value, f"model param {param} {value} != {param_value}" + + async def concurrent_requests(context, f_completion, *args, **kwargs): n_prompts = len(context.prompts) if context.debug: @@ -463,8 +601,10 @@ async def concurrent_requests(context, f_completion, *args, **kwargs): async def request_completion(prompt, base_url, debug=False, + prompt_prefix=None, + prompt_suffix=None, n_predict=None, - server_seed=None, + seed=None, expect_api_error=None, user_api_key=None): if debug: @@ -481,11 +621,14 @@ async def request_completion(prompt, async with aiohttp.ClientSession() as session: async with session.post(f'{base_url}/completion', json={ + "input_prefix": prompt_prefix, "prompt": prompt, - "n_predict": int(n_predict) if n_predict is not None else -1, - "seed": server_seed if server_seed is not None else 42 + "input_suffix": prompt_suffix, + "n_predict": n_predict if n_predict is not None else -1, + "seed": seed if seed is not None else 42 }, - headers=headers) as response: + headers=headers, + timeout=3600) as response: if expect_api_error is None or not expect_api_error: assert response.status == 200 assert response.headers['Access-Control-Allow-Origin'] == origin @@ -497,19 +640,20 @@ async def request_completion(prompt, async def oai_chat_completions(user_prompt, system_prompt, base_url, + base_path, async_client, debug=False, model=None, n_predict=None, enable_streaming=None, - server_seed=None, + seed=None, user_api_key=None, expect_api_error=None): if debug: print(f"Sending OAI Chat completions request: {user_prompt}") # openai client always expects an api key user_api_key = user_api_key if user_api_key is not None else 'nope' - seed = server_seed if server_seed is not None else 42 + seed = seed if seed is not None else 42 enable_streaming = enable_streaming if enable_streaming is not None else False payload = { "messages": [ @@ -537,7 +681,7 @@ async def oai_chat_completions(user_prompt, origin = 'llama.cpp' headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin} async with aiohttp.ClientSession() as session: - async with session.post(f'{base_url}/v1/chat/completions', + async with session.post(f'{base_url}{base_path}', json=payload, headers=headers) as response: if enable_streaming: @@ -579,7 +723,7 @@ async def oai_chat_completions(user_prompt, else: try: openai.api_key = user_api_key - openai.api_base = f'{base_url}/v1/chat' + openai.api_base = f'{base_url}{base_path}' chat_completion = openai.Completion.create( messages=payload['messages'], model=model, @@ -668,20 +812,32 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re content = completion_response['content'] n_predicted = completion_response['timings']['predicted_n'] assert len(content) > 0, "no token predicted" - if expected_predicted_n is not None: + if re_content is not None: + p = re.compile(re_content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL) + matches = p.finditer(content) + last_match = 0 + highlighted = '' + for match in matches: + start, end = match.span() + highlighted += content[last_match: start] + highlighted += '\x1b[33m' + highlighted += content[start: end] + highlighted += '\x1b[0m' + last_match = end + highlighted += content[last_match:] + if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': + print(f"Checking completion response: {highlighted}\n") + assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```' + if expected_predicted_n and expected_predicted_n > 0: assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:' f' {n_predicted} <> {expected_predicted_n}') - if re_content is not None: - re_content = '^.*' + re_content.replace('', '|') + '.*$' - assert re.match(re_content, content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL), ( - f'invalid tokens predicted:' - f' ```\n{content}\n``` do not match /{re_content}/') + async def gather_tasks_results(context): n_tasks = len(context.concurrent_tasks) if context.debug: - print(f"Waiting for all {n_tasks} tasks results...") + print(f"Waiting for all {n_tasks} tasks results...\n") for task_no in range(n_tasks): context.tasks_result.append(await context.concurrent_tasks.pop()) n_completions = len(context.tasks_result) @@ -692,13 +848,13 @@ async def wait_for_health_status(context, base_url, expected_http_status_code, expected_health_status, + timeout=3, params=None, slots_idle=None, slots_processing=None, expected_slots=None): if context.debug: - print(f"Starting checking for health for expected_health_status={expected_health_status}") - timeout = 3 # seconds + print(f"Starting checking for health for expected_health_status={expected_health_status}\n") interval = 0.5 counter = 0 async with aiohttp.ClientSession() as session: @@ -708,7 +864,7 @@ async def wait_for_health_status(context, health = await health_response.json() if context.debug: print(f"HEALTH - response for expected health status='{expected_health_status}' on " - f"'{base_url}/health'?{params} is {health}") + f"'{base_url}/health'?{params} is {health}\n") if (status_code == expected_http_status_code and health['status'] == expected_health_status and (slots_idle is None or health['slots_idle'] == slots_idle) @@ -731,12 +887,12 @@ async def wait_for_health_status(context, if expected_http_status_code == 503: if len(context.tasks_result) == 0: print("\x1b[5;37;43mWARNING: forcing concurrent tasks," - " busy health check missed, probably too fast inference\x1b[0m") + " busy health check missed, probably too fast inference\x1b[0m\n") n_completions = await gather_tasks_results(context) if n_completions > 0: return - assert False, 'timeout exceeded' + assert False, f'{expected_health_status} timeout exceeded {counter}s>={timeout}' def assert_embeddings(embeddings): @@ -765,6 +921,11 @@ def assert_slots_status(slots, expected_slots): f" = {expected[key]} != {slot[key]}") +async def completions_seed(context): + return context.seed if hasattr(context, 'seed') and context.seed is not None \ + else context.server_seed if hasattr(context, 'server_seed') else None + + def start_server_background(context): context.server_path = '../../../build/bin/server' if 'LLAMA_SERVER_BIN_PATH' in os.environ: @@ -774,27 +935,35 @@ def start_server_background(context): '--port', context.server_port, '--model', context.model_file ] + if context.n_batch: + server_args.extend(['--batch-size', context.n_batch]) + if context.n_gpu_layer: + server_args.extend(['--n-gpu-layers', context.n_gpu_layer]) if context.server_continuous_batching: server_args.append('--cont-batching') if context.server_embeddings: server_args.append('--embedding') if context.server_metrics: server_args.append('--metrics') - if context.model_alias is not None: + if context.model_alias: server_args.extend(['--alias', context.model_alias]) - if context.n_ctx is not None: + if context.n_ctx: server_args.extend(['--ctx-size', context.n_ctx]) - if context.n_slots is not None: + if context.n_slots: server_args.extend(['--parallel', context.n_slots]) - if context.n_server_predict is not None: + if context.n_server_predict: server_args.extend(['--n-predict', context.n_server_predict]) - if context.server_api_key is not None: + if context.server_api_key: server_args.extend(['--api-key', context.server_api_key]) + if context.n_ga: + server_args.extend(['--grp-attn-n', context.n_ga]) + if context.n_ga_w: + server_args.extend(['--grp-attn-w', context.n_ga_w]) if context.debug: server_args.append('--verbose') if 'SERVER_LOG_FORMAT_JSON' not in os.environ: server_args.extend(['--log-format', "text"]) - print(f"starting server with: {context.server_path}", *server_args) + print(f"starting server with: {context.server_path} {server_args}\n") context.server_process = subprocess.Popen( [str(arg) for arg in [context.server_path, *server_args]], close_fds=True) diff --git a/examples/server/tests/features/wrong_usages.feature b/examples/server/tests/features/wrong_usages.feature index e228b2371ccce..cf14b3b44e03b 100644 --- a/examples/server/tests/features/wrong_usages.feature +++ b/examples/server/tests/features/wrong_usages.feature @@ -1,4 +1,4 @@ -# run with ./test.sh --tags wrong_usage +# run with: ./tests.sh --no-skipped --tags wrong_usage @wrong_usage Feature: Wrong usage of llama.cpp server @@ -7,7 +7,7 @@ Feature: Wrong usage of llama.cpp server # or pass n_predict/max_tokens in the request. Scenario: Infinite loop Given a server listening on localhost:8080 - And a model file stories260K.gguf + And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models # Uncomment below to fix the issue #And 64 server max tokens to predict Then the server is starting @@ -18,4 +18,5 @@ Feature: Wrong usage of llama.cpp server # Uncomment below to fix the issue #And 128 max tokens to predict Given concurrent completion requests + Then the server is idle Then all prompts are predicted diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt index 334fa4a70ea72..5d4210164a50a 100644 --- a/examples/server/tests/requirements.txt +++ b/examples/server/tests/requirements.txt @@ -1,4 +1,5 @@ aiohttp~=3.9.3 behave~=1.2.6 +huggingface_hub~=0.20.3 openai~=0.25.0 prometheus-client~=0.20.0 diff --git a/examples/server/tests/tests.sh b/examples/server/tests/tests.sh index 17a4e6fc64307..1c6c5695fcf65 100755 --- a/examples/server/tests/tests.sh +++ b/examples/server/tests/tests.sh @@ -5,7 +5,7 @@ set -eu if [ $# -lt 1 ] then # Start @llama.cpp scenario - behave --summary --stop --no-capture --exclude 'issues|wrong_usages' --tags llama.cpp + behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp else behave "$@" fi diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index d7abd7cbba71c..b6e49d8b98a2a 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -37,10 +37,6 @@ extern bool server_log_json; #define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) -// -// parallel -// - enum server_state { SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet SERVER_STATE_READY, // Server is ready and model is loaded @@ -78,51 +74,8 @@ struct task_multi { std::vector results{}; }; -// TODO: can become bool if we can't find use of more states -enum slot_state -{ - IDLE, - PROCESSING, -}; - -enum slot_command -{ - NONE, - LOAD_PROMPT, - RELEASE, -}; - -struct slot_params -{ - bool stream = true; - bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt - - uint32_t seed = -1; // RNG seed - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_predict = -1; // new tokens to predict - - std::vector antiprompt; - - json input_prefix; - json input_suffix; -}; - -struct slot_image -{ - int32_t id; - - bool request_encode_image = false; - float * image_embedding = nullptr; - int32_t image_tokens = 0; - - clip_image_u8 * img_data; - - std::string prefix_prompt; // before of this image -}; - // completion token output with probabilities -struct completion_token_output -{ +struct completion_token_output { struct token_prob { llama_token tok; @@ -134,8 +87,13 @@ struct completion_token_output std::string text_to_send; }; -static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) -{ +struct token_translator { + llama_context * ctx; + std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); } + std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); } +}; + +static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { std::stringstream ss_tid; ss_tid << std::this_thread::get_id(); json log = nlohmann::ordered_json{ @@ -168,8 +126,7 @@ static inline void server_log(const char *level, const char *function, int line, for (const auto& el : log.items()) { const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace); - snprintf(buf, 1024, " %s=%s", el.key().c_str(), value.c_str()); - ss << buf; + ss << " " << el.key() << "=" << value; } const std::string str = ss.str(); @@ -183,8 +140,7 @@ static inline void server_log(const char *level, const char *function, int line, // template -static T json_value(const json &body, const std::string &key, const T &default_value) -{ +static T json_value(const json &body, const std::string &key, const T &default_value) { // Fallback null to default value return body.contains(key) && !body.at(key).is_null() ? body.value(key, default_value) @@ -200,8 +156,7 @@ inline bool verify_custom_template(const std::string & tmpl) { } // Format given chat. If tmpl is empty, we take the template from model metadata -inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) -{ +inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) { size_t alloc_size = 0; // vector holding all allocated string to be passed to llama_chat_apply_template std::vector str(messages.size() * 2); @@ -250,7 +205,7 @@ struct llama_server_queue { // callback functions std::function callback_new_task; std::function callback_finish_multitask; - std::function callback_all_task_finished; + std::function callback_run_slots; // Add a new task to the end of the queue int post(task_server task) { @@ -283,14 +238,14 @@ struct llama_server_queue { callback_new_task = callback; } - // Register function to process a multitask + // Register function to process a multitask when it is finished void on_finish_multitask(std::function callback) { callback_finish_multitask = callback; } - // Register the function to be called when the batch of tasks is finished - void on_all_tasks_finished(std::function callback) { - callback_all_task_finished = callback; + // Register the function to be called when all slots data is ready to be processed + void on_run_slots(std::function callback) { + callback_run_slots = callback; } // Call when the state of one slot is changed @@ -312,7 +267,13 @@ struct llama_server_queue { condition_tasks.notify_all(); } - // Start the main loop. + /** + * Main loop consists of these steps: + * - Wait until a new task arrives + * - Process the task (i.e. maybe copy data into slot) + * - Check if multitask is finished + * - Run all slots + */ void start_loop() { running = true; while (true) { @@ -331,8 +292,8 @@ struct llama_server_queue { LOG_VERBOSE("callback_new_task", {{"task_id", task.id}}); callback_new_task(task); } - LOG_VERBOSE("callback_all_task_finished", {}); - // process and update all the multitasks + LOG_VERBOSE("update_multitasks", {}); + // check if we have any finished multitasks auto queue_iterator = queue_multitasks.begin(); while (queue_iterator != queue_multitasks.end()) { @@ -349,8 +310,9 @@ struct llama_server_queue { ++queue_iterator; } } - // all tasks in the current loop is finished - callback_all_task_finished(); + // all tasks in the current loop is processed, slots data is now ready + LOG_VERBOSE("callback_run_slots", {}); + callback_run_slots(); } LOG_VERBOSE("wait for new task", {}); // wait for new task @@ -408,12 +370,14 @@ struct llama_server_response { std::mutex mutex_results; std::condition_variable condition_results; + // add the task_id to the list of tasks waiting for response void add_waiting_task_id(int task_id) { LOG_VERBOSE("waiting for task id", {{"task_id", task_id}}); std::unique_lock lock(mutex_results); waiting_task_ids.insert(task_id); } + // when the request is finished, we can remove task associated with it void remove_waiting_task_id(int task_id) { LOG_VERBOSE("remove waiting for task id", {{"task_id", task_id}}); std::unique_lock lock(mutex_results); @@ -574,3 +538,96 @@ static std::string gen_chatcmplid() chatcmplid << "chatcmpl-" << random_string(); return chatcmplid.str(); } + +// +// other common utils +// + +static size_t common_part(const std::vector &a, const std::vector &b) +{ + size_t i; + for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) + { + } + return i; +} + +static bool ends_with(const std::string &str, const std::string &suffix) +{ + return str.size() >= suffix.size() && + 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); +} + +static size_t find_partial_stop_string(const std::string &stop, + const std::string &text) +{ + if (!text.empty() && !stop.empty()) + { + const char text_last_char = text.back(); + for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) + { + if (stop[char_index] == text_last_char) + { + const std::string current_partial = stop.substr(0, char_index + 1); + if (ends_with(text, current_partial)) + { + return text.size() - char_index - 1; + } + } + } + } + return std::string::npos; +} + +// TODO: reuse llama_detokenize +template +static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) +{ + std::string ret; + for (; begin != end; ++begin) + { + ret += llama_token_to_piece(ctx, *begin); + } + return ret; +} + +// format incomplete utf-8 multibyte character for output +static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) +{ + std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); + // if the size is 1 and first bit is 1, meaning it's a partial character + // (size > 1 meaning it's already a known token) + if (out.size() == 1 && (out[0] & 0x80) == 0x80) + { + std::stringstream ss; + ss << std::hex << (out[0] & 0xff); + std::string res(ss.str()); + out = "byte: \\x" + res; + } + return out; +} + +// convert a vector of completion_token_output to json +static json probs_vector_to_json(const llama_context *ctx, const std::vector &probs) +{ + json out = json::array(); + for (const auto &prob : probs) + { + json probs_for_token = json::array(); + for (const auto &p : prob.probs) + { + std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); + probs_for_token.push_back(json + { + {"tok_str", tok_str}, + {"prob", p.prob}, + }); + } + std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); + out.push_back(json{ + {"content", tok_str}, + {"probs", probs_for_token}, + }); + } + return out; +} diff --git a/examples/speculative/README.md b/examples/speculative/README.md index 814efa592d94f..a6608c5fe8e3a 100644 --- a/examples/speculative/README.md +++ b/examples/speculative/README.md @@ -6,3 +6,4 @@ More info: - https://github.com/ggerganov/llama.cpp/pull/2926 - https://github.com/ggerganov/llama.cpp/pull/3624 +- https://github.com/ggerganov/llama.cpp/pull/5625 diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 1053d4da667c5..2d3ce23f36891 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -7,6 +7,7 @@ #include #include #include +#include #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 100 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5 @@ -20,6 +21,7 @@ struct seq_draft { std::vector i_batch_tgt; std::vector tokens; + std::vector> dists; struct llama_sampling_context * ctx_sampling; }; @@ -39,12 +41,15 @@ int main(int argc, char ** argv) { // max number of parallel drafting sequences (i.e. tree branches) const int n_seq_dft = params.n_parallel; - // probability threshold for accepting a token from the draft model - const float p_accept = params.p_accept; - // probability threshold for splitting a draft branch (only for n_seq_dft > 1) const float p_split = params.p_split; + if (params.seed == LLAMA_DEFAULT_SEED) { + params.seed = time(NULL); + } + std::default_random_engine rng(params.seed); + std::uniform_real_distribution<> u_dist; + #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("speculative", "log")); LOG_TEE("Log start\n"); @@ -168,7 +173,9 @@ int main(int argc, char ** argv) { std::vector drafts(n_seq_dft); params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar - params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model + if (params.sparams.temp == 0) { + params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model + } for (int s = 0; s < n_seq_dft; ++s) { drafts[s].ctx_sampling = llama_sampling_init(params.sparams); @@ -184,12 +191,15 @@ int main(int argc, char ** argv) { drafts[0].i_batch_tgt[0] = 0; while (true) { + std::set active_seqs = {}; + // print current draft sequences for (int s = 0; s < n_seq_dft; ++s) { if (!drafts[s].active) { continue; } + active_seqs.insert(s); const auto & tokens = drafts[s].tokens; LOG("draft %d: %s\n", s, LOG_TOKENS_TOSTR_PRETTY(ctx_dft, tokens).c_str()); @@ -198,48 +208,156 @@ int main(int argc, char ** argv) { int i_dft = 0; int s_keep = 0; + llama_token token_id; + std::string token_str; + + // loop until we fail to accept a drafted token or we run out of drafted tokens while (true) { - LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]); - // sample from the target model - llama_token id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]); + // check if the target token matches any of the drafts + // for stochastic sampling, attempt to match the token with the drafted tokens + { + bool accept = false; + if (params.sparams.temp > 0) { + // stochastic verification + + llama_token_data_array dist_tgt = llama_sampling_probability_distribution(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]); + float p_tgt = 0, p_dft = 0; + + // GGML_ASSERT(dist_tgt.size() == dist_dft.size()); + + while (active_seqs.size() > 0) { + // randomly select a sequence to verify from active sequences + std::uniform_int_distribution u_int_dist(0, active_seqs.size() - 1); + int s = *std::next(active_seqs.begin(), u_int_dist(rng)); + if (i_dft >= (int) drafts[s].tokens.size()) { + drafts[s].active = false; + active_seqs.erase(s); + continue; + } + if (accept) { + // if we already accepted a token, we can skip the rest + if (drafts[s].tokens[i_dft] != drafts[s_keep].tokens[i_dft]) { + drafts[s].active = false; + active_seqs.erase(s); + } + continue; + } + LOG("verifying sequence #%d at pos #%d from %d active sequence(s)\n", s, i_dft, (int) active_seqs.size()); + float r = u_dist(rng); + llama_token_data_array dist_dft = { drafts[s].dists[i_dft].data() , drafts[s].dists[i_dft].size(), true }; + // acquire the token probabilities assigned by the draft and target models + for (size_t i = 0; i < dist_tgt.size; i++) { + if (dist_tgt.data[i].id == drafts[s].tokens[i_dft]) { + p_tgt = dist_tgt.data[i].p; + } + if (dist_dft.data[i].id == drafts[s].tokens[i_dft]) { + p_dft = dist_dft.data[i].p; + } + if (p_tgt && p_dft) { + break; + } + } + LOG("r = %f, p_dft = %f, p_tgt = %f\n", r, p_dft, p_tgt); + if (r <= p_tgt / p_dft) { + s_keep = s; + accept = true; + token_id = drafts[s].tokens[i_dft]; + token_str = llama_token_to_piece(ctx_tgt, token_id); + llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true); + + LOG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str()); + break; + } else { + LOG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], llama_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str()); + drafts[s].active = false; + + // calculate residual probability + GGML_ASSERT(dist_tgt.sorted); + GGML_ASSERT(dist_dft.sorted); + float sum_probs = 0.0f; + + // sort dist by id + std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) { + return a.id < b.id; + }); + std::sort(dist_dft.data, dist_dft.data + dist_dft.size, [](const llama_token_data &a, const llama_token_data &b) { + return a.id < b.id; + }); + + for (size_t i = 0; i < dist_tgt.size; i++) { + dist_tgt.data[i].p = std::max(0.0f, dist_tgt.data[i].p - dist_dft.data[i].p); + sum_probs += dist_tgt.data[i].p; + } + for (size_t i = 0; i < dist_tgt.size; i++) { + dist_tgt.data[i].p /= sum_probs; + } - llama_sampling_accept(ctx_sampling, ctx_tgt, id, true); + // sort dist_tgt by p desc + std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) { + return a.p > b.p; + }); + } - //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str()); + active_seqs.erase(s); + for(int i = 0; i < n_seq_dft; i++) { + if (i == s) { + continue; + } + if (drafts[i].tokens[i_dft] == drafts[s].tokens[i_dft]) { + // synchronize active status for sequences with the same drafted token + drafts[i].active = drafts[i].active && accept; + if (!drafts[i].active) { + active_seqs.erase(s); + } + } + } + } - const std::string token_str = llama_token_to_piece(ctx_tgt, id); + if (!accept) { + // all drafted tokens were rejected + // sample from the target model + LOG("all drafted tokens were rejected, sampling from residual distribution\n"); + token_id = llama_sample_token(ctx_tgt, &dist_tgt); + llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true); + token_str = llama_token_to_piece(ctx_tgt, token_id); + } - if (!params.use_color) { - printf("%s", token_str.c_str()); - } + } else { + // greedy verification - if (id == llama_token_eos(model_tgt)) { - has_eos = true; - } + // sample from the target model + LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]); + token_id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]); - ++n_predict; + llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true); - // check if the target token matches any of the drafts - { - bool matches = false; + //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str()); - for (int s = 0; s < n_seq_dft; ++s) { - if (!drafts[s].active) { - continue; - } + token_str = llama_token_to_piece(ctx_tgt, token_id); - if (i_dft < (int) drafts[s].tokens.size() && id == drafts[s].tokens[i_dft]) { - LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, id, token_str.c_str()); + for (int s = 0; s < n_seq_dft; ++s) { + if (!drafts[s].active) { + continue; + } - s_keep = s; - matches = true; - } else { - drafts[s].active = false; + if (i_dft < (int) drafts[s].tokens.size() && token_id == drafts[s].tokens[i_dft]) { + LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, token_id, token_str.c_str()); + + s_keep = s; + accept = true; + } else { + drafts[s].active = false; + } } } - if (matches) { + if (token_id == llama_token_eos(model_tgt)) { + has_eos = true; + } + ++n_predict; + + if (accept) { ++n_accept; ++n_past_tgt; ++n_past_dft; @@ -247,17 +365,21 @@ int main(int argc, char ** argv) { if (params.use_color) { // Color token according to its origin sequence printf("\u001b[%dm%s\u001b[37m", (36 - s_keep % 6), token_str.c_str()); - fflush(stdout); + } else { + printf("%s", token_str.c_str()); } + fflush(stdout); continue; + } else { + printf("%s", token_str.c_str()); + fflush(stdout); + break; } } - if (params.use_color) { - printf("%s", token_str.c_str()); - } - fflush(stdout); + } - LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str()); + { + LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", token_id, token_str.c_str()); // TODO: simplify { @@ -277,21 +399,21 @@ int main(int argc, char ** argv) { drafts[s].active = false; drafts[s].tokens.clear(); drafts[s].i_batch_tgt.clear(); + drafts[s].dists.clear(); } // note: will be erased after the speculation phase - drafts[0].tokens.push_back(id); + drafts[0].tokens.push_back(token_id); + drafts[0].dists.push_back(std::vector()); drafts[0].i_batch_tgt.push_back(0); llama_batch_clear(batch_dft); - llama_batch_add (batch_dft, id, n_past_dft, { 0 }, true); + llama_batch_add (batch_dft, token_id, n_past_dft, { 0 }, true); llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1); // LOG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str()); - llama_decode (ctx_dft, batch_dft); + llama_decode(ctx_dft, batch_dft); ++n_past_dft; - - break; } if (n_predict > params.n_predict || has_eos) { @@ -336,12 +458,6 @@ int main(int argc, char ** argv) { k, s, i, cur_p[k].id, cur_p[k].p, llama_token_to_piece(ctx_dft, cur_p[k].id).c_str()); } - if (cur_p[0].p < p_accept) { - LOG("stopping drafting for seq %3d, probability too low: %.3f < %.3f\n", s, cur_p[0].p, p_accept); - drafts[s].drafting = false; - continue; - } - std::vector sa(1, s); // attempt to split the branch if the probability is high enough @@ -369,6 +485,7 @@ int main(int argc, char ** argv) { drafts[n_seq_cur].skip = true; drafts[n_seq_cur].tokens = drafts[s].tokens; + drafts[n_seq_cur].dists = drafts[s].dists; drafts[n_seq_cur].i_batch_dft = drafts[s].i_batch_dft; drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt; @@ -391,6 +508,8 @@ int main(int argc, char ** argv) { llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id, true); drafts[s].tokens.push_back(id); + // save cur_p.data into drafts[s].dists + drafts[s].dists.push_back(cur_p); // add unique drafted tokens to the target batch drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens); @@ -442,6 +561,7 @@ int main(int argc, char ** argv) { } drafts[s].tokens.erase(drafts[s].tokens.begin()); + drafts[s].dists.erase(drafts[s].dists.begin()); } } diff --git a/examples/sycl/ls-sycl-device.cpp b/examples/sycl/ls-sycl-device.cpp index 52442e4ca80fa..74a8b7fd814d9 100644 --- a/examples/sycl/ls-sycl-device.cpp +++ b/examples/sycl/ls-sycl-device.cpp @@ -7,7 +7,7 @@ #include "ggml-sycl.h" -int main(int argc, char ** argv) { +int main() { ggml_backend_sycl_print_sycl_devices(); return 0; } diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh index f5f4c1e980de4..52f7c01a4bd90 100755 --- a/examples/sycl/run-llama2.sh +++ b/examples/sycl/run-llama2.sh @@ -8,12 +8,19 @@ INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" source /opt/intel/oneapi/setvars.sh if [ $# -gt 0 ]; then - export GGML_SYCL_DEVICE=$1 + GGML_SYCL_DEVICE=$1 else - export GGML_SYCL_DEVICE=0 + GGML_SYCL_DEVICE=0 fi -echo GGML_SYCL_DEVICE=$GGML_SYCL_DEVICE +echo "use $GGML_SYCL_DEVICE as main GPU" #export GGML_SYCL_DEBUG=1 -./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -#./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 5 -e -ngl 33 -t 1 -s 0 + + +#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer. + +#use all GPUs with same max compute units +ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 + +#use main GPU only +#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none diff --git a/expose.cpp b/expose.cpp index 584d9841dbb56..f7942ffa74f28 100644 --- a/expose.cpp +++ b/expose.cpp @@ -206,9 +206,18 @@ extern "C" } } - generation_outputs generate(const generation_inputs inputs, generation_outputs &output) + generation_outputs generate(const generation_inputs inputs) { - return gpttype_generate(inputs, output); + return gpttype_generate(inputs); + } + + bool sd_load_model(const sd_load_model_inputs inputs) + { + return sdtype_load_model(inputs); + } + sd_generation_outputs sd_generate(const sd_generation_inputs inputs) + { + return sdtype_generate(inputs); } const char * new_token(int idx) { @@ -263,4 +272,6 @@ extern "C" output.ids = toks.data(); //this may be slightly unsafe return output; } + + } diff --git a/expose.h b/expose.h index 389e1ebbfe4c4..c228f0a5ddf15 100644 --- a/expose.h +++ b/expose.h @@ -20,7 +20,7 @@ enum stop_reason { INVALID=-1, OUT_OF_TOKENS=0, - EOS_TOKEN=1, + EOS_TOKEN_HIT=1, CUSTOM_STOPPER=2, }; struct logit_bias { @@ -92,13 +92,39 @@ struct generation_inputs struct generation_outputs { int status = -1; - char text[32768]; //32kb should be enough for any response + const char * text; //response will now be stored in c++ allocated memory }; struct token_count_outputs { int count = 0; int * ids; //we'll just use shared memory for this one, bit of a hack }; +struct sd_load_model_inputs +{ + const char * model_filename; + const int clblast_info = 0; + const int cublas_info = 0; + const char * vulkan_info; + const int threads; + const int quant = 0; + const int debugmode = 0; +}; +struct sd_generation_inputs +{ + const char * prompt; + const char * negative_prompt; + const float cfg_scale; + const int sample_steps; + const int width; + const int height; + const int seed; + const char * sample_method; +}; +struct sd_generation_outputs +{ + int status = -1; + const char * data = ""; +}; extern std::string executable_path; extern std::string lora_filename; diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index f95df47f72b86..2e9ba58a931cc 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -91,19 +91,22 @@ extern "C" { // (optional) complete all pending operations void (*GGML_CALL synchronize)(ggml_backend_t backend); - // compute graph with a plan + // create a plan for ggml_cgraph and free it ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - void (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); + // compute graph with a plan + enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); // compute graph without a plan (async) - bool (*GGML_CALL graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); + enum ggml_status (*GGML_CALL graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph); // check if the backend supports an operation bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); }; struct ggml_backend { + ggml_guid_t guid; + struct ggml_backend_i iface; ggml_backend_context_t context; diff --git a/ggml-backend.c b/ggml-backend.c index 5076d9e5ec325..d60d984143249 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -12,7 +12,6 @@ #define MAX(a, b) ((a) > (b) ? (a) : (b)) - // backend buffer type const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) { @@ -159,6 +158,13 @@ bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml // backend +ggml_guid_t ggml_backend_guid(ggml_backend_t backend) { + if (backend == NULL) { + return NULL; + } + return backend->guid; +} + const char * ggml_backend_name(ggml_backend_t backend) { if (backend == NULL) { return "NULL"; @@ -256,11 +262,11 @@ void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_pla backend->iface.graph_plan_free(backend, plan); } -void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - backend->iface.graph_plan_compute(backend, plan); +enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + return backend->iface.graph_plan_compute(backend, plan); } -bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { return backend->iface.graph_compute(backend, cgraph); } @@ -726,15 +732,15 @@ GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, g GGML_UNUSED(backend); } -GGML_CALL static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { +GGML_CALL static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; - ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); + return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); GGML_UNUSED(backend); } -GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads); @@ -749,8 +755,7 @@ GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, str cplan.abort_callback = cpu_ctx->abort_callback; cplan.abort_callback_data = cpu_ctx->abort_callback_data; - ggml_graph_compute(cgraph, &cplan); - return true; + return ggml_graph_compute(cgraph, &cplan); } GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { @@ -781,6 +786,11 @@ static struct ggml_backend_i cpu_backend_i = { /* .supports_op = */ ggml_backend_cpu_supports_op, }; +static ggml_guid_t ggml_backend_cpu_guid(void) { + static ggml_guid guid = { 0xaa, 0x67, 0xc7, 0x43, 0x96, 0xe6, 0xa3, 0x8a, 0xe3, 0xaf, 0xea, 0x92, 0x36, 0xbc, 0xfc, 0x89 }; + return &guid; +} + ggml_backend_t ggml_backend_cpu_init(void) { struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context)); if (ctx == NULL) { @@ -800,6 +810,7 @@ ggml_backend_t ggml_backend_cpu_init(void) { } *cpu_backend = (struct ggml_backend) { + /* .guid = */ ggml_backend_cpu_guid(), /* .interface = */ cpu_backend_i, /* .context = */ ctx }; @@ -807,7 +818,7 @@ ggml_backend_t ggml_backend_cpu_init(void) { } GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) { - return backend && backend->iface.get_name == ggml_backend_cpu_name; + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cpu_guid()); } void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { @@ -1425,7 +1436,7 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) { return true; } -static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { +static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { uint64_t copy_us[GGML_MAX_BACKENDS] = {0}; uint64_t compute_us[GGML_MAX_BACKENDS] = {0}; @@ -1460,8 +1471,9 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { uint64_t compute_start_us = ggml_time_us(); if (!sched->callback_eval) { - if (!ggml_backend_graph_compute(split_backend, &split->graph)) { - return false; + enum ggml_status ec = ggml_backend_graph_compute(split_backend, &split->graph); + if (ec != GGML_STATUS_SUCCESS) { + return ec; } //ggml_backend_synchronize(split_backend); // necessary to measure compute time } else { @@ -1482,8 +1494,9 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1); - if (!ggml_backend_graph_compute(split_backend, &gv)) { - return false; + enum ggml_status ec = ggml_backend_graph_compute(split_backend, &gv); + if (ec != GGML_STATUS_SUCCESS) { + return ec; } if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) { @@ -1507,7 +1520,7 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { } #endif - return true; + return GGML_STATUS_SUCCESS; } ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size) { @@ -1569,7 +1582,7 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * return true; } -bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { +enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); if (!sched->is_reset) { @@ -1578,14 +1591,10 @@ bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cg ggml_backend_sched_split_graph(sched, graph); if (!ggml_backend_sched_alloc_splits(sched)) { - return false; + return GGML_STATUS_ALLOC_FAILED; } - if (!ggml_backend_sched_compute_splits(sched)) { - return false; - } - - return true; + return ggml_backend_sched_compute_splits(sched); } void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) { diff --git a/ggml-backend.h b/ggml-backend.h index f13c69bffb98c..8bed22578a907 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -49,7 +49,7 @@ extern "C" { // Backend // - + GGML_API ggml_guid_t ggml_backend_guid(ggml_backend_t backend); GGML_API const char * ggml_backend_name(ggml_backend_t backend); GGML_API void ggml_backend_free(ggml_backend_t backend); @@ -66,12 +66,13 @@ extern "C" { GGML_API void ggml_backend_synchronize(ggml_backend_t backend); - GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create (ggml_backend_t backend, struct ggml_cgraph * cgraph); + GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph); + GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan); + + GGML_API enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan); + GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); - GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan); - GGML_API bool ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); - GGML_API bool ggml_backend_supports_op (ggml_backend_t backend, const struct ggml_tensor * op); + GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op); // tensor copy between different backends GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst); @@ -157,26 +158,26 @@ extern "C" { typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data); // Initialize a backend scheduler - GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size); - GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); + GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size); + GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); // Initialize backend buffers from a measure graph - GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); + GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // Get the number of splits of the last graph - GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched); + GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched); - GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend); + GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend); - GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); - GGML_API ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node); + GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); + GGML_API ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node); // Allocate and compute graph on the backend scheduler - GGML_API bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph); + GGML_API enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph); // Reset all assignments and allocators - must be called before changing the node backends - GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched); + GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched); // Set a callback to be called for each resulting node during graph compute - GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data); + GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data); // // Utils diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 9b7ecc9ceaa92..74562d33c192e 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -523,6 +523,17 @@ typedef struct { } block_iq2_xs; static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); +// 2.5625 bpw quants +#define QR2_S 8 +#define QI2_S (QK_K / (4*QR2_S)) +typedef struct { + half d; + uint8_t qs[QK_K/4]; + uint8_t qh[QK_K/32]; + uint8_t scales[QK_K/32]; +} block_iq2_s; +static_assert(sizeof(block_iq2_s) == sizeof(ggml_fp16_t) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding"); + #define QR3_XXS 8 #define QI3_XXS (QK_K / (4*QR3_XXS)) typedef struct { @@ -533,14 +544,19 @@ static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong #define QR3_XS 8 #define QI3_XS (QK_K / (4*QR3_XS)) +#if QK_K == 64 +#define IQ3S_N_SCALE 2 +#else +#define IQ3S_N_SCALE QK_K/64 +#endif typedef struct { half d; uint8_t qs[QK_K/4]; uint8_t qh[QK_K/32]; uint8_t signs[QK_K/8]; - uint8_t scales[QK_K/64]; + uint8_t scales[IQ3S_N_SCALE]; } block_iq3_s; -static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 27*(QK_K/64), "wrong iq3_s block size/padding"); +static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding"); #define QR1_S 8 #define QI1_S (QK_K / (4*QR1_S)) @@ -560,6 +576,23 @@ typedef struct { } block_iq4_nl; static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding"); +#if QK_K == 64 +#define block_iq4_xs block_iq4_nl +#define QR4_XS QR4_NL +#define QI4_XS QI4_NL +#else +// QR4_XS = 8 is very slightly faster than QR4_XS = 4 +#define QR4_XS 8 +#define QI4_XS (QK_K / (4*QR4_XS)) +typedef struct { + half d; + uint16_t scales_h; + uint8_t scales_l[QK_K/64]; + uint8_t qs[QK_K/2]; +} block_iq4_xs; +static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); +#endif + #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -583,6 +616,8 @@ static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4 #define CUDA_UPSCALE_BLOCK_SIZE 256 #define CUDA_CONCAT_BLOCK_SIZE 256 #define CUDA_PAD_BLOCK_SIZE 256 +#define CUDA_ARANGE_BLOCK_SIZE 256 +#define CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE 256 #define CUDA_ACC_BLOCK_SIZE 256 #define CUDA_IM2COL_BLOCK_SIZE 256 #define CUDA_POOL2D_BLOCK_SIZE 256 @@ -686,18 +721,20 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { return a; } -//static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { -//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL -//#pragma unroll -// for (int mask = 16; mask > 0; mask >>= 1) { -// a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); -// } -// return a; -//#else -// (void) a; -// NO_DEVICE_CODE; -//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL -//} +#ifdef GGML_CUDA_F16 +static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); + } + return a; +#else + (void) a; + NO_DEVICE_CODE; +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +} +#endif // GGML_CUDA_F16 static __device__ __forceinline__ float warp_reduce_max(float x) { #pragma unroll @@ -956,17 +993,21 @@ static __global__ void concat_f32(const float * x,const float * y, float * dst, nidx + blockIdx.y * ne0 + blockIdx.z * ne0 * gridDim.y; - dst[offset_dst] = x[offset_src]; + dst[offset_dst] = x[offset_src]; } else { int offset_src = nidx + blockIdx.y * ne0 + (blockIdx.z - ne02) * ne0 * gridDim.y; - dst[offset_dst] = y[offset_src]; + dst[offset_dst] = y[offset_src]; } } -static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int nb02, const int scale_factor) { +static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int ne00xne01, const int scale_factor) { + // blockIdx.z: idx of ne02*ne03 + // blockIdx.y: idx of ne01*scale_factor， aka ne1 + // blockIDx.x: idx of ne00*scale_factor / BLOCK_SIZE + // ne00xne01: ne00 * ne01 int ne0 = ne00 * scale_factor; int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { @@ -978,7 +1019,7 @@ static __global__ void upscale_f32(const float * x, float * dst, const int ne00, int offset_src = i00 + i01 * ne00 + - blockIdx.z * nb02; + blockIdx.z * ne00xne01; int offset_dst = nidx + blockIdx.y * ne0 + @@ -986,7 +1027,10 @@ static __global__ void upscale_f32(const float * x, float * dst, const int ne00, dst[offset_dst] = x[offset_src]; } -static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02) { +static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02, const int ne03) { + // blockIdx.z: idx of ne2*ne3, aka ne02*ne03 + // blockIdx.y: idx of ne1 + // blockIDx.x: idx of ne0 / BLOCK_SIZE int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { return; @@ -997,19 +1041,53 @@ static __global__ void pad_f32(const float * x, float * dst, const int ne0, cons nidx + blockIdx.y * ne0 + blockIdx.z * ne0 * gridDim.y; - if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02) { + if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02*ne03) { int offset_src = nidx + blockIdx.y * ne00 + blockIdx.z * ne00 * ne01; - dst[offset_dst] = x[offset_src]; + dst[offset_dst] = x[offset_src]; } else { dst[offset_dst] = 0.0f; } } +static __global__ void arange_f32(float * dst, const int ne0, const float start, const float step) { + // blockIDx.x: idx of ne0 / BLOCK_SIZE + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } + dst[nidx] = start + step * nidx; +} + +static __global__ void timestep_embedding_f32(const float * timesteps, float * dst, const int nb1, const int dim, const int max_period) { + // blockIDx.y: idx of timesteps->ne[0] + // blockIDx.x: idx of ((dim + 1) / 2) / BLOCK_SIZE + int i = blockIdx.y; + int j = threadIdx.x + blockIdx.x * blockDim.x; + float * embed_data = (float *)((char *)dst + i*nb1); + + if (dim % 2 != 0 && j == ((dim + 1) / 2)) { + embed_data[dim] = 0.f; + } + + int half = dim / 2; + if (j >= half) { + return; + } + + float timestep = timesteps[i]; + float freq = (float)expf(-logf(max_period) * j / half); + float arg = timestep * freq; + embed_data[j] = cosf(arg); + embed_data[j + half] = sinf(arg); +} + template static __global__ void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps) { + // blockIdx.x: num_groups idx + // threadIdx.x: block_size idx int start = blockIdx.x * group_size; int end = start + group_size; @@ -1690,6 +1768,265 @@ static const __device__ uint64_t iq2xs_grid[512] = { 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, }; +static const __device__ uint64_t iq2s_grid[1024] = { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, + 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, + 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, + 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, + 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, + 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, + 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, + 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, + 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, + 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, + 0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19, + 0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819, + 0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919, + 0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b, + 0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, + 0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908, + 0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919, + 0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908, + 0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, + 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919, + 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b, + 0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, + 0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908, + 0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b, + 0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b, + 0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08, + 0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, + 0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819, + 0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808, + 0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908, + 0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b, + 0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908, + 0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08, + 0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819, + 0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808, + 0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08, + 0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819, + 0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b, + 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908, + 0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919, + 0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b, + 0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919, + 0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808, + 0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819, + 0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919, + 0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919, + 0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808, + 0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819, + 0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b, + 0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908, + 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, + 0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, + 0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919, + 0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b, + 0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919, + 0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b, + 0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819, + 0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919, + 0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908, + 0x081908190819192b, 0x0819081908192b19, 0x08190819082b0808, 0x08190819082b082b, + 0x08190819082b1919, 0x08190819082b2b08, 0x0819081919080819, 0x0819081919081908, + 0x081908191908192b, 0x0819081919082b19, 0x0819081919190808, 0x081908191919082b, + 0x0819081919191919, 0x0819081919192b08, 0x08190819192b0819, 0x08190819192b1908, + 0x081908192b080808, 0x081908192b08082b, 0x081908192b081919, 0x081908192b082b08, + 0x081908192b190819, 0x081908192b191908, 0x0819082b08080819, 0x0819082b08081908, + 0x0819082b08082b19, 0x0819082b08190808, 0x0819082b08191919, 0x0819082b082b0819, + 0x0819082b082b1908, 0x0819082b19080808, 0x0819082b19081919, 0x0819082b19190819, + 0x0819082b19191908, 0x0819082b2b080819, 0x0819082b2b081908, 0x0819082b2b190808, + 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08, + 0x0819190808190819, 0x0819190808191908, 0x081919080819192b, 0x0819190808192b19, + 0x08191908082b0808, 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0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, + 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, + 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, + 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, +}; + static const __device__ uint32_t iq3xxs_grid[256] = { 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, @@ -1725,74 +2062,73 @@ static const __device__ uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; -static const __device__ uint32_t iq3xs_grid[512] = { - 0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14, - 0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414, - 0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24, - 0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c, - 0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c, - 0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34, - 0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c, - 0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414, - 0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c, - 0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404, - 0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434, - 0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c, - 0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404, - 0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414, - 0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414, - 0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404, - 0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c, - 0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c, - 0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404, - 0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e, - 0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14, - 0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c, - 0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424, - 0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c, - 0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c, - 0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e, - 0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e, - 0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e, - 0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424, - 0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e, - 0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424, - 0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404, - 0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c, - 0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e, - 0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c, - 0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c, - 0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c, - 0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404, - 0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04, - 0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c, - 0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414, - 0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c, - 0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c, - 0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424, - 0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c, - 0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c, - 0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414, - 0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c, - 0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e, - 0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04, - 0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424, - 0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14, - 0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34, - 0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c, - 0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434, - 0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c, - 0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424, - 0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24, - 0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24, - 0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e, - 0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c, - 0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c, - 0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c, - 0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404, +static const __device__ uint32_t iq3s_grid[512] = { + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, }; - static const __device__ uint64_t iq1s_grid[512] = { 0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000, 0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01, @@ -2038,6 +2374,27 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst } +template +static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { + + const int i = blockIdx.x; + const block_iq2_s * x = (const block_iq2_s *) vx; + + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300))); + const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; + const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; + for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); +#else + assert(false); +#endif + +} + template static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { @@ -2078,9 +2435,9 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_ const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; const uint8_t * qs = x[i].qs + 8*ib; - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256))); - const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)) * 0.5f; + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256))); + const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)); const uint8_t signs = x[i].signs[4*ib + il]; for (int j = 0; j < 4; ++j) { y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); @@ -2135,6 +2492,25 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst } +#if QK_K != 64 +template +static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + const int i = blockIdx.x; + const block_iq4_xs * x = (const block_iq4_xs *)vx; + + const int tid = threadIdx.x; + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 4*il; + const uint8_t * q4 = x[i].qs + 16*ib + 4*il; + const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32); + for (int j = 0; j < 4; ++j) { + y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf]; + y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; + } +} +#endif + static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); @@ -2231,10 +2607,7 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, #endif // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (threadIdx.x == 0) { dst[row] = tmp; @@ -2335,10 +2708,7 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, #endif // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (threadIdx.x == 0) { dst[row] = tmp; @@ -2471,10 +2841,7 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, #endif // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (tid == 0) { dst[row] = tmp; @@ -2587,10 +2954,7 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, #endif // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (threadIdx.x == 0) { dst[row] = tmp; @@ -2697,10 +3061,7 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, #endif // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (tid == 0) { dst[row] = tmp; @@ -2735,11 +3096,8 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest float amax = fabsf(xi); float sum = xi; -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32)); - sum += __shfl_xor_sync(0xffffffff, sum, mask, 32); - } + amax = warp_reduce_max(amax); + sum = warp_reduce_sum(sum); const float d = amax / 127; const int8_t q = amax == 0.0f ? 0 : roundf(xi / d); @@ -4801,6 +5159,54 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( #endif } +// TODO +static __device__ __forceinline__ float vec_dot_iq2_s_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq2_s * bq2 = (const block_iq2_s *) vbq; + + const int ib32 = iqs; + const int8_t * q8 = bq8_1[ib32].qs; + const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32; + const uint8_t ls1 = bq2->scales[ib32] & 0xf; + const uint8_t ls2 = bq2->scales[ib32] >> 4; + int sumi1 = 0; + for (int l = 0; l < 2; ++l) { + const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300))); + const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); + const uint32_t signs1 = __vcmpeq4(((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); + const int grid_l = __vsub4(grid[0] ^ signs0, signs0); + const int grid_h = __vsub4(grid[1] ^ signs1, signs1); + sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1); + sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1); + q8 += 8; + } + int sumi2 = 0; + for (int l = 2; l < 4; ++l) { + const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300))); + const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); + const uint32_t signs1 = __vcmpeq4(((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); + const int grid_l = __vsub4(grid[0] ^ signs0, signs0); + const int grid_h = __vsub4(grid[1] ^ signs1, signs1); + sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2); + sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2); + q8 += 8; + } + const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f; + return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); +#else + (void) ksigns64; + assert(false); + return 0.f; +#endif +#else + (void) ksigns64; + assert(false); + return 0.f; +#endif +} + static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics @@ -4848,8 +5254,8 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( const int8_t * q8 = bq8_1[ib32].qs; int sumi = 0; for (int l = 0; l < 4; ++l) { - const uint32_t * grid1 = iq3xs_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256)); - const uint32_t * grid2 = iq3xs_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256)); + const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256)); + const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256)); uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201); uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >> 4) * 0x01010101) & 0x08040201, 0x08040201); const int grid_l = __vsub4(grid1[0] ^ signs0, signs0); @@ -4858,7 +5264,7 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( sumi = __dp4a(grid_h, *((int *)q8+1), sumi); q8 += 8; } - const float d = (float)bq2->d * (0.5f + ((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds) * 0.5f; + const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds); return d * sumi; #else assert(false); @@ -4964,6 +5370,75 @@ static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1( return d * (sumi1 + sumi2); } +static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + +#if QK_K == 256 +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics + + const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq; + const uint8_t * values = (const uint8_t *)kvalues_iq4nl; + + //// iqs is 0...7 + //const int ib64 = iqs/2; + //const int il = iqs%2; + //const int32_t * q8_1 = (const int *)bq8_1[2*ib64+0].qs + 2*il; + //const int32_t * q8_2 = (const int *)bq8_1[2*ib64+1].qs + 2*il; + //const uint32_t * q4_1 = (const uint32_t *)bq4->qs + 8*ib64 + 2*il; + //const uint32_t * q4_2 = q4_1 + 4; + //const int8_t ls1 = (bq4->scales_l[ib64] & 0xf) | (((bq4->scales_h >> (4*ib64+0)) & 3) << 4); + //const int8_t ls2 = (bq4->scales_l[ib64] >> 4) | (((bq4->scales_h >> (4*ib64+2)) & 3) << 4); + //const float d1 = (float)bq4->d * (ls1 - 32) * __low2float(bq8_1[2*ib64+0].ds); + //const float d2 = (float)bq4->d * (ls2 - 32) * __low2float(bq8_1[2*ib64+1].ds); + //int v1, v2; + //int sumi1 = 0, sumi2 = 0; + //for (int j = 0; j < 2; ++j) { + // get_int_from_table_16(q4_1[j], values, v1, v2); + // sumi1 = __dp4a(v2, q8_1[j+4], __dp4a(v1, q8_1[j+0], sumi1)); + // get_int_from_table_16(q4_2[j], values, v1, v2); + // sumi2 = __dp4a(v2, q8_2[j+4], __dp4a(v1, q8_2[j+0], sumi2)); + //} + //return d1 * sumi1 + d2 * sumi2; + + // iqs is 0...7 + const int ib32 = iqs; + const int32_t * q8 = (const int *)bq8_1[ib32].qs; + const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32; + const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4); + const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds); + int v1, v2; + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 4; ++j) { + get_int_from_table_16(q4[j], values, v1, v2); + sumi1 = __dp4a(v1, q8[j+0], sumi1); + sumi2 = __dp4a(v2, q8[j+4], sumi2); + } + return d * (sumi1 + sumi2); + + //// iqs is 0...15 + //const int ib32 = iqs/2; + //const int il = iqs%2; + //const int32_t * q8 = (const int *)bq8_1[ib32].qs + 2*il; + //const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32 + 2*il; + //const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4); + //const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds); + //int v1, v2; + //int sumi1 = 0, sumi2 = 0; + //for (int j = 0; j < 2; ++j) { + // get_int_from_table_16(q4[j], values, v1, v2); + // sumi1 = __dp4a(v1, q8[j+0], sumi1); + // sumi2 = __dp4a(v2, q8[j+4], sumi2); + //} + //return d * (sumi1 + sumi2); +#else + assert(false); + return 0.f; +#endif +#else + return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs); +#endif +} + template static __device__ __forceinline__ void mul_mat_q( @@ -5884,10 +6359,7 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons } // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (tid == 0) { #ifdef GGML_CUDA_F16 @@ -5937,10 +6409,7 @@ static __global__ void mul_mat_p021_f16_f32( const int idst = channel*nrows_dst + row_dst; // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (threadIdx.x == 0) { dst[idst] = tmp; @@ -5983,10 +6452,7 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous } // sum up partial sums and write back result -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); - } + tmp = warp_reduce_sum(tmp); if (threadIdx.x == 0) { dst[idst] = tmp; @@ -6026,7 +6492,7 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13) { - const int i = blockDim.x*blockIdx.x + threadIdx.x; + const int64_t i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= ne) { return; @@ -6034,17 +6500,17 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, // determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor // then combine those indices with the corresponding byte offsets to get the total offsets - const int i03 = i/(ne00 * ne01 * ne02); - const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); - const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; - const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - - const int i13 = i/(ne10 * ne11 * ne12); - const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); - const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; - const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; - const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; + const int64_t i03 = i/(ne00 * ne01 * ne02); + const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int64_t i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int64_t x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; + + const int64_t i13 = i/(ne10 * ne11 * ne12); + const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; cpy_1(cx + x_offset, cdst + dst_offset); } @@ -6482,6 +6948,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f // find the sum of exps in the block tmp = warp_reduce_sum(tmp); if (block_size > WARP_SIZE) { + __syncthreads(); if (warp_id == 0) { buf_iw[lane_id] = 0.0f; } @@ -6533,23 +7000,23 @@ static __global__ void clamp_f32(const float * x, float * dst, const float min, template static __global__ void im2col_kernel( - const float * x, T * dst, int batch_offset, - int offset_delta, int IC, int IW, int IH, int OH, int OW, int KW, int KH, int pelements, int CHW, + const float * x, T * dst, int64_t batch_offset, + int64_t offset_delta, int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW, int s0, int s1, int p0, int p1, int d0, int d1) { - const int i = threadIdx.x + blockIdx.x * blockDim.x; + const int64_t i = threadIdx.x + blockIdx.x * blockDim.x; if (i >= pelements) { return; } - const int ksize = OW * (KH > 1 ? KW : 1); - const int kx = i / ksize; - const int kd = kx * ksize; - const int ky = (i - kd) / OW; - const int ix = i % OW; + const int64_t ksize = OW * (KH > 1 ? KW : 1); + const int64_t kx = i / ksize; + const int64_t kd = kx * ksize; + const int64_t ky = (i - kd) / OW; + const int64_t ix = i % OW; - const int oh = blockIdx.y; - const int batch = blockIdx.z / IC; - const int ic = blockIdx.z % IC; + const int64_t oh = blockIdx.y; + const int64_t batch = blockIdx.z / IC; + const int64_t ic = blockIdx.z % IC; const int64_t iiw = ix * s0 + kx * d0 - p0; const int64_t iih = oh * s1 + ky * d1 - p1; @@ -6875,19 +7342,33 @@ static void concat_f32_cuda(const float * x, const float * y, float * dst, const concat_f32<<>>(x, y, dst, ne0, ne02); } -static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int scale_factor, cudaStream_t stream) { +static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int ne03, + const int scale_factor, cudaStream_t stream) { int ne0 = (ne00 * scale_factor); int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE; - dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02); + dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02*ne03); upscale_f32<<>>(x, dst, ne00, ne00 * ne01, scale_factor); } static void pad_f32_cuda(const float * x, float * dst, - const int ne00, const int ne01, const int ne02, - const int ne0, const int ne1, const int ne2, cudaStream_t stream) { + const int ne00, const int ne01, const int ne02, const int ne03, + const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) { int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE; - dim3 gridDim(num_blocks, ne1, ne2); - pad_f32<<>>(x, dst, ne0, ne00, ne01, ne02); + dim3 gridDim(num_blocks, ne1, ne2*ne3); + pad_f32<<>>(x, dst, ne0, ne00, ne01, ne02, ne03); +} + +static void arange_f32_cuda(float * dst, const int ne0, const float start, const float step, cudaStream_t stream) { + int num_blocks = (ne0 + CUDA_ARANGE_BLOCK_SIZE - 1) / CUDA_ARANGE_BLOCK_SIZE; + arange_f32<<>>(dst, ne0, start, step); +} + +static void timestep_embedding_f32_cuda(const float * x, float * dst, const int ne00, const int nb1, + const int dim, const int max_period, cudaStream_t stream) { + int half_ceil = (dim + 1) / 2; + int num_blocks = (half_ceil + CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE; + dim3 gridDim(num_blocks, ne00, 1); + timestep_embedding_f32<<>>(x, dst, nb1, dim, max_period); } static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) { @@ -6997,6 +7478,12 @@ static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int k, dequantize_block_iq2_xs<<>>(vx, y); } +template +static void dequantize_row_iq2_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq2_s<<>>(vx, y); +} + template static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { const int nb = k / QK_K; @@ -7021,6 +7508,16 @@ static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k, dequantize_block_iq4_nl<<>>(vx, y); } +template +static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = (k + QK_K - 1) / QK_K; +#if QK_K == 64 + dequantize_block_iq4_nl<<>>(vx, y); +#else + dequantize_block_iq4_xs<<>>(vx, y); +#endif +} + template static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; @@ -7058,12 +7555,16 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { return dequantize_row_iq2_xxs_cuda; case GGML_TYPE_IQ2_XS: return dequantize_row_iq2_xs_cuda; + case GGML_TYPE_IQ2_S: + return dequantize_row_iq2_s_cuda; case GGML_TYPE_IQ3_XXS: return dequantize_row_iq3_xxs_cuda; case GGML_TYPE_IQ1_S: return dequantize_row_iq1_s_cuda; case GGML_TYPE_IQ4_NL: return dequantize_row_iq4_nl_cuda; + case GGML_TYPE_IQ4_XS: + return dequantize_row_iq4_xs_cuda; case GGML_TYPE_IQ3_S: return dequantize_row_iq3_s_cuda; case GGML_TYPE_F32: @@ -7099,12 +7600,16 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { return dequantize_row_iq2_xxs_cuda; case GGML_TYPE_IQ2_XS: return dequantize_row_iq2_xs_cuda; + case GGML_TYPE_IQ2_S: + return dequantize_row_iq2_s_cuda; case GGML_TYPE_IQ3_XXS: return dequantize_row_iq3_xxs_cuda; case GGML_TYPE_IQ1_S: return dequantize_row_iq1_s_cuda; case GGML_TYPE_IQ4_NL: return dequantize_row_iq4_nl_cuda; + case GGML_TYPE_IQ4_XS: + return dequantize_row_iq4_xs_cuda; case GGML_TYPE_IQ3_S: return dequantize_row_iq3_s_cuda; case GGML_TYPE_F16: @@ -7996,8 +8501,8 @@ static void soft_max_f32_cuda(const float * x, const float * mask, const float * template static void im2col_cuda(const float* x, T* dst, - int IW, int IH, int OW, int OH, int KW, int KH, int IC, - int batch, int batch_offset, int offset_delta, + int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC, + int64_t batch, int64_t batch_offset, int64_t offset_delta, int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) { const int parallel_elements = OW * KW * KH; const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE; @@ -8166,7 +8671,7 @@ static void * ggml_cuda_pool_malloc_vmm(int device, size_t size, size_t * actual g_cuda_pool_used[device] += size; #ifdef DEBUG_CUDA_MALLOC - printf("cuda pool[%d]: allocated %llu bytes at %llx [%s]\n", id, (unsigned long long) size, ptr); + printf("cuda pool[%d]: allocated %llu bytes at %llx\n", device, (unsigned long long) size, ptr); #endif return ptr; @@ -8176,7 +8681,7 @@ static void ggml_cuda_pool_free_vmm(int device, void * ptr, size_t size) { scoped_spin_lock lock(g_cuda_pool_lock); #ifdef DEBUG_CUDA_MALLOC - printf("cuda pool[%d]: freed %llu bytes at %llx\n", id, (unsigned long long) size, ptr); + printf("cuda pool[%d]: freed %llu bytes at %llx\n", device, (unsigned long long) size, ptr); #endif g_cuda_pool_used[device] -= size; @@ -8667,7 +9172,7 @@ static void ggml_cuda_op_group_norm( int num_groups = dst->op_params[0]; int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups); - group_norm_f32_cuda(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream); + group_norm_f32_cuda(src0_dd, dst_dd, num_groups * src0->ne[3], group_size, ggml_nelements(src0), main_stream); (void) src1; (void) dst; @@ -8700,7 +9205,7 @@ static void ggml_cuda_op_upscale( const int scale_factor = dst->op_params[0]; - upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream); + upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], scale_factor, main_stream); (void) src1; (void) dst; @@ -8716,8 +9221,49 @@ static void ggml_cuda_op_pad( GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors pad_f32_cuda(src0_dd, dst_dd, - src0->ne[0], src0->ne[1], src0->ne[2], - dst->ne[0], dst->ne[1], dst->ne[2], main_stream); + src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +static void ggml_cuda_op_arange( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + float start; + float stop; + float step; + memcpy(&start, (float *)dst->op_params + 0, sizeof(float)); + memcpy(&stop, (float *)dst->op_params + 1, sizeof(float)); + memcpy(&step, (float *)dst->op_params + 2, sizeof(float)); + + int64_t steps = (int64_t)ceil((stop - start) / step); + GGML_ASSERT(ggml_nelements(dst) == steps); + + arange_f32_cuda(dst_dd, dst->ne[0], start, step, main_stream); + + (void) src0; + (void) src1; + (void) src0_dd; + (void) src1_dd; +} + +static void ggml_cuda_op_timestep_embedding( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int dim = dst->op_params[0]; + const int max_period = dst->op_params[1]; + + timestep_embedding_f32_cuda(src0_dd, dst_dd, src0->ne[0], dst->nb[1], dim, max_period, main_stream); (void) src1; (void) dst; @@ -8840,9 +9386,11 @@ static int64_t get_row_rounding(ggml_type type, const std::array= CC_RDNA2 ? 128 : 64; default: @@ -8866,9 +9414,11 @@ static int64_t get_row_rounding(ggml_type type, const std::array= CC_VOLTA ? 128 : 64; case GGML_TYPE_Q6_K: @@ -8963,6 +9513,10 @@ static void ggml_cuda_op_mul_mat_vec_q( mul_mat_vec_q_cuda (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; + case GGML_TYPE_IQ2_S: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; case GGML_TYPE_IQ3_XXS: mul_mat_vec_q_cuda (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); @@ -8975,6 +9529,10 @@ static void ggml_cuda_op_mul_mat_vec_q( mul_mat_vec_q_cuda (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; + case GGML_TYPE_IQ4_XS: + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); + break; case GGML_TYPE_IQ3_S: mul_mat_vec_q_cuda (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); @@ -9990,6 +10548,45 @@ static void ggml_cuda_pad(const ggml_tensor * src0, const ggml_tensor * src1, gg ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pad); } +static void ggml_cuda_arange(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU; + + // dd = data device + float * src0_ddf = nullptr; + float * src1_ddf = nullptr; + float * dst_ddf = nullptr; + + cuda_pool_alloc dst_f; + + ggml_cuda_set_device(g_main_device); + cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; + + if (dst_on_device) { + dst_ddf = (float *) dst_extra->data_device[g_main_device]; + } else { + dst_ddf = dst_f.alloc(ggml_nelements(dst)); + } + + // do the computation + ggml_cuda_op_arange(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); + CUDA_CHECK(cudaGetLastError()); + + // copy dst to host if necessary + if (!dst_on_device) { + CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream)); + } + + if (dst->backend == GGML_BACKEND_TYPE_CPU) { + CUDA_CHECK(cudaDeviceSynchronize()); + } +} + +static void ggml_cuda_timestep_embedding(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_timestep_embedding); +} + static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm); } @@ -10896,6 +11493,12 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st case GGML_OP_PAD: func = ggml_cuda_pad; break; + case GGML_OP_ARANGE: + func = ggml_cuda_arange; + break; + case GGML_OP_TIMESTEP_EMBEDDING: + func = ggml_cuda_timestep_embedding; + break; case GGML_OP_LEAKY_RELU: func = ggml_cuda_leaky_relu; break; @@ -11636,7 +12239,7 @@ GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { UNUSED(backend); } -GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; ggml_cuda_set_main_device(cuda_ctx->device); @@ -11672,7 +12275,7 @@ GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, gg GGML_ASSERT(ok); } - return true; + return GGML_STATUS_SUCCESS; } GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) { @@ -11708,7 +12311,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons } ggml_type a_type = a->type; if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS || - a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S) { + a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S || + a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) { if (b->ne[1] == 1 && ggml_nrows(b) > 1) { return false; } @@ -11790,6 +12394,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_GROUP_NORM: case GGML_OP_UPSCALE: case GGML_OP_PAD: + case GGML_OP_ARANGE: + case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_LEAKY_RELU: return true; default: @@ -11814,6 +12420,11 @@ static ggml_backend_i ggml_backend_cuda_interface = { /* .supports_op = */ ggml_backend_cuda_supports_op, }; +static ggml_guid_t ggml_backend_cuda_guid() { + static ggml_guid guid = { 0x2c, 0xdd, 0xe8, 0x1c, 0x65, 0xb3, 0x65, 0x73, 0x6a, 0x12, 0x88, 0x61, 0x1c, 0xc9, 0xdc, 0x25 }; + return &guid; +} + GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { ggml_init_cublas(); // TODO: remove from ggml.c @@ -11831,6 +12442,7 @@ GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { }; ggml_backend_t cuda_backend = new ggml_backend { + /* .guid = */ ggml_backend_cuda_guid(), /* .interface = */ ggml_backend_cuda_interface, /* .context = */ ctx }; @@ -11839,7 +12451,7 @@ GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { } GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend) { - return backend && backend->iface.get_name == ggml_backend_cuda_name; + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cuda_guid()); } GGML_CALL int ggml_backend_cuda_get_device_count() { diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index 51c5af8ec00a2..83a7822fdbe9d 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1927,10 +1927,10 @@ static ggml_backend_buffer_type_t ggml_backend_kompute_get_default_buffer_type(g return ggml_backend_kompute_buffer_type(ctx->device); } -static bool ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { auto * ctx = static_cast(backend->context); ggml_vk_graph_compute(ctx, cgraph); - return true; + return GGML_STATUS_SUCCESS; } static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { @@ -1953,11 +1953,17 @@ static struct ggml_backend_i kompute_backend_i = { /* .supports_op = */ ggml_backend_kompute_supports_op, }; +static ggml_guid_t ggml_backend_kompute_guid() { + static ggml_guid guid = { 0x7b, 0x57, 0xdc, 0xaf, 0xde, 0x12, 0x1d, 0x49, 0xfb, 0x35, 0xfa, 0x9b, 0x18, 0x31, 0x1d, 0xca }; + return &guid; +} + ggml_backend_t ggml_backend_kompute_init(int device) { GGML_ASSERT(s_kompute_context == nullptr); s_kompute_context = new ggml_kompute_context(device); ggml_backend_t kompute_backend = new ggml_backend { + /* .guid = */ ggml_backend_kompute_guid(), /* .interface = */ kompute_backend_i, /* .context = */ s_kompute_context, }; @@ -1966,7 +1972,7 @@ ggml_backend_t ggml_backend_kompute_init(int device) { } bool ggml_backend_is_kompute(ggml_backend_t backend) { - return backend && backend->iface.get_name == ggml_backend_kompute_name; + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_kompute_guid()); } static ggml_backend_t ggml_backend_reg_kompute_init(const char * params, void * user_data) { diff --git a/ggml-metal.m b/ggml-metal.m index 82527cb257788..00df2283821b2 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -62,8 +62,10 @@ GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, + GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, + GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, GGML_METAL_KERNEL_TYPE_RMS_NORM, GGML_METAL_KERNEL_TYPE_GROUP_NORM, @@ -87,8 +89,10 @@ GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, @@ -108,8 +112,10 @@ GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, @@ -126,8 +132,10 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, @@ -144,8 +152,10 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_ROPE_F32, GGML_METAL_KERNEL_TYPE_ROPE_F16, GGML_METAL_KERNEL_TYPE_ALIBI_F32, @@ -153,6 +163,8 @@ GGML_METAL_KERNEL_TYPE_IM2COL_F32, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, GGML_METAL_KERNEL_TYPE_PAD_F32, + GGML_METAL_KERNEL_TYPE_ARANGE_F32, + GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, @@ -458,8 +470,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction); @@ -483,8 +497,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction); //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction); @@ -504,8 +520,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm); @@ -522,8 +540,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm); @@ -540,8 +560,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); @@ -549,6 +571,8 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true); @@ -677,6 +701,8 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const return false; case GGML_OP_UPSCALE: case GGML_OP_PAD: + case GGML_OP_ARANGE: + case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_ARGSORT: case GGML_OP_LEAKY_RELU: return true; @@ -722,7 +748,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const } } -static bool ggml_metal_graph_compute( +static enum ggml_status ggml_metal_graph_compute( struct ggml_metal_context * ctx, struct ggml_cgraph * gf) { @@ -1071,7 +1097,8 @@ static bool ggml_metal_graph_compute( { GGML_ASSERT(ggml_is_contiguous(src0)); - const float scale = *(const float *) dst->op_params; + float scale; + memcpy(&scale, dst->op_params, sizeof(scale)); int64_t n = ggml_nelements(dst); @@ -1230,11 +1257,15 @@ static bool ggml_metal_graph_compute( pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline; } - const float scale = ((float *) dst->op_params)[0]; - const float max_bias = ((float *) dst->op_params)[1]; + float scale; + float max_bias; + + memcpy(&scale, ((int32_t *) dst->op_params) + 0, sizeof(scale)); + memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias)); const int64_t nrows_x = ggml_nrows(src0); const int64_t nrows_y = src0->ne[1]; + const uint32_t n_head_kv = nrows_x/nrows_y; const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); @@ -1358,8 +1389,10 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break; case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32 ].pipeline; break; + case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32 ].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break; case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break; + case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); } @@ -1500,6 +1533,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline; } break; + case GGML_TYPE_IQ2_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32].pipeline; + } break; case GGML_TYPE_IQ1_S: { nth0 = 4; @@ -1512,6 +1551,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline; } break; + case GGML_TYPE_IQ4_XS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); @@ -1544,9 +1589,9 @@ static bool ggml_metal_graph_compute( [encoder setBytes:&r2 length:sizeof(r2) atIndex:17]; [encoder setBytes:&r3 length:sizeof(r3) atIndex:18]; - if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || - src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || - src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_IQ1_S) { // || src0t == GGML_TYPE_Q4_K) { + if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || + src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || + src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ2_S) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { @@ -1559,7 +1604,7 @@ static bool ggml_metal_graph_compute( [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } - else if (src0t == GGML_TYPE_IQ4_NL) { + else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) { const int mem_size = 32*sizeof(float); [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -1658,8 +1703,10 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break; case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32 ].pipeline; break; + case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32 ].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break; case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break; + case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break; default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); } @@ -1803,6 +1850,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline; } break; + case GGML_TYPE_IQ2_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32].pipeline; + } break; case GGML_TYPE_IQ1_S: { nth0 = 4; @@ -1815,6 +1868,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline; } break; + case GGML_TYPE_IQ4_XS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); @@ -1863,9 +1922,9 @@ static bool ggml_metal_graph_compute( [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j]; } - if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || - src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || - src2t == GGML_TYPE_Q2_K || src2t == GGML_TYPE_IQ1_S) { // || src2t == GGML_TYPE_Q4_K) { + if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || + src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || + src2t == GGML_TYPE_Q2_K || src2t == GGML_TYPE_IQ1_S || src2t == GGML_TYPE_IQ2_S) { [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) { @@ -1878,7 +1937,7 @@ static bool ggml_metal_graph_compute( [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } - else if (src2t == GGML_TYPE_IQ4_NL) { + else if (src2t == GGML_TYPE_IQ4_NL || src2t == GGML_TYPE_IQ4_XS) { const int mem_size = 32*sizeof(float); [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -1925,8 +1984,10 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break; case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S ].pipeline; break; + case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S ].pipeline; break; case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break; case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break; + case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break; case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; default: GGML_ASSERT(false && "not implemented"); } @@ -2036,6 +2097,7 @@ static bool ggml_metal_graph_compute( //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_head = ((int32_t *) dst->op_params)[1]; + float max_bias; memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); @@ -2250,6 +2312,50 @@ static bool ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; + case GGML_OP_ARANGE: + { + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + float start; + float step; + + memcpy(&start, ((int32_t *) dst->op_params) + 0, sizeof(float)); + memcpy(&step, ((int32_t *) dst->op_params) + 2, sizeof(float)); + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:0]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:1]; + [encoder setBytes:&start length:sizeof(start) atIndex:2]; + [encoder setBytes:&step length:sizeof(step) atIndex:3]; + + const int nth = MIN(1024, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + const int dim = dst->op_params[0]; + const int max_period = dst->op_params[1]; + + const int half = dim / 2; + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:2]; + [encoder setBytes:&dim length:sizeof(dim) atIndex:3]; + [encoder setBytes:&max_period length:sizeof(max_period) atIndex:4]; + + const int nth = MIN(1024, half); + + [encoder dispatchThreadgroups:MTLSizeMake(ne00, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; case GGML_OP_ARGSORT: { GGML_ASSERT(src0->type == GGML_TYPE_F32); @@ -2377,8 +2483,8 @@ static bool ggml_metal_graph_compute( MTLCommandBufferStatus status = [command_buffer status]; if (status != MTLCommandBufferStatusCompleted) { - GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu. Check if you ran out of memory.\n", __func__, i, status); - return false; + GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status); + return GGML_STATUS_FAILED; } } @@ -2387,7 +2493,7 @@ static bool ggml_metal_graph_compute( } } - return true; + return GGML_STATUS_SUCCESS; } //////////////////////////////////////////////////////////////////////////////// @@ -2689,7 +2795,7 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffe UNUSED(backend); } -GGML_CALL static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +GGML_CALL static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context; return ggml_metal_graph_compute(metal_ctx, cgraph); @@ -2721,6 +2827,11 @@ void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * ggml_metal_log_user_data = user_data; } +static ggml_guid_t ggml_backend_metal_guid(void) { + static ggml_guid guid = { 0x81, 0xa1, 0x8b, 0x1e, 0x71, 0xec, 0x79, 0xed, 0x2b, 0x85, 0xdc, 0x8a, 0x61, 0x98, 0x30, 0xe6 }; + return &guid; +} + ggml_backend_t ggml_backend_metal_init(void) { struct ggml_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS); @@ -2731,6 +2842,7 @@ ggml_backend_t ggml_backend_metal_init(void) { ggml_backend_t metal_backend = malloc(sizeof(struct ggml_backend)); *metal_backend = (struct ggml_backend) { + /* .guid = */ ggml_backend_metal_guid(), /* .interface = */ ggml_backend_metal_i, /* .context = */ ctx, }; @@ -2739,7 +2851,7 @@ ggml_backend_t ggml_backend_metal_init(void) { } bool ggml_backend_is_metal(ggml_backend_t backend) { - return backend && backend->iface.get_name == ggml_backend_metal_name; + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_metal_guid()); } void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { diff --git a/ggml-metal.metal b/ggml-metal.metal index b3bf405391d3e..a65d126412bfb 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1959,6 +1959,49 @@ kernel void kernel_pad_f32( } } +kernel void kernel_arange_f32( + device char * dst, + constant int64_t & ne0, + constant float & start, + constant float & step, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + device float * dst_ptr = (device float *) dst; + + for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { + dst_ptr[i0] = start + step * i0; + } +} + +kernel void kernel_timestep_embedding_f32( + device const char * src0, + device char * dst, + constant uint64_t & nb1, + constant int & dim, + constant int & max_period, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + int i = tgpig.x; + device float * embed_data = (device float *)(dst + i*nb1); + + int half_ = dim / 2; + for (int j = tpitg.x; j < half_; j += ntg.x) { + float timestep = ((device float *)src0)[i]; + float freq = (float)exp(-log((float)max_period) * j / half_); + float arg = timestep * freq; + embed_data[j ] = cos(arg); + embed_data[j + half_] = sin(arg); + } + + if (dim % 2 != 0 && tpitg.x == 0) { + embed_data[dim] = 0.f; + } +} + // bitonic sort implementation following the CUDA kernels as reference typedef void (argsort_t)( device const float * x, @@ -2519,6 +2562,14 @@ typedef struct { } block_iq2_xs; // 74 bytes / block for QK_K = 256, so 2.3125 bpw +// 2.5625 bpw quants +typedef struct { + half d; + uint8_t qs[QK_K/4]; + uint8_t qh[QK_K/32]; + uint8_t scales[QK_K/32]; +} block_iq2_s; + typedef struct { half d; uint8_t qs[3*QK_K/8]; @@ -2552,6 +2603,17 @@ typedef struct { uint8_t qs[QK4_NL/2]; } block_iq4_nl; +#if QK_K == 64 +#define block_iq4_xs block_iq4_nl +#else +typedef struct { + half d; + uint16_t scales_h; + uint8_t scales_l[QK_K/64]; + uint8_t qs[QK_K/2]; +} block_iq4_xs; +#endif + //====================================== dot products ========================= void kernel_mul_mv_q2_K_f32_impl( @@ -3774,6 +3836,265 @@ constexpr constant static uint64_t iq2xs_grid[512] = { 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, }; +constexpr constant static uint64_t iq2s_grid[1024] = { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, + 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, + 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, + 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, + 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, + 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, + 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, + 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, + 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, + 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, + 0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19, + 0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819, + 0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919, + 0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b, + 0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, + 0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908, + 0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919, + 0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908, + 0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, + 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919, + 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b, + 0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, + 0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908, + 0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b, + 0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b, + 0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08, + 0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, + 0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819, + 0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808, + 0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908, + 0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b, + 0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908, + 0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08, + 0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819, + 0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808, + 0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08, + 0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819, + 0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b, + 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908, + 0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919, + 0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b, + 0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919, + 0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808, + 0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819, + 0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919, + 0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919, + 0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808, + 0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819, + 0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b, + 0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908, + 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, + 0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, + 0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919, + 0x0819080819082b08, 0x0819080819190819, 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0x19192b1908081908, 0x19192b1908190808, + 0x19192b1919080808, 0x19192b2b08080808, 0x19192b2b08192b19, 0x19192b2b2b081919, + 0x19192b2b2b2b2b08, 0x192b080808080819, 0x192b080808081908, 0x192b08080808192b, + 0x192b080808190808, 0x192b08080819082b, 0x192b080808191919, 0x192b080808192b08, + 0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919, + 0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808, + 0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b, + 0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908, + 0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808, + 0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808, + 0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808, + 0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908, + 0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808, + 0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808, + 0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b, + 0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908, + 0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808, + 0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808, + 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, + 0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919, + 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b, + 0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808, + 0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819, + 0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b, + 0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908, + 0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08, + 0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908, + 0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919, + 0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819, + 0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908, + 0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b, + 0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808, + 0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819, + 0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908, + 0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919, + 0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808, + 0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808, + 0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808, + 0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919, + 0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908, + 0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908, + 0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08, + 0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819, + 0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b, + 0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808, + 0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819, + 0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, + 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, + 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, + 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, + 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, + 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, + 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, + 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, + 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, + 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, + 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, + 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, + 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, + 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, + 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, + 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, + 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, + 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, + 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, + 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, + 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, + 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, + 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, +}; + constexpr constant static uint32_t iq3xxs_grid[256] = { 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, @@ -3809,71 +4130,71 @@ constexpr constant static uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; -constexpr constant static uint32_t iq3xs_grid[512] = { - 0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14, - 0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414, - 0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24, - 0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c, - 0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c, - 0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34, - 0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c, - 0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414, - 0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c, - 0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404, - 0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434, - 0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c, - 0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404, - 0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414, - 0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414, - 0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404, - 0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c, - 0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c, - 0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404, - 0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e, - 0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14, - 0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c, - 0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424, - 0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c, - 0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c, - 0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e, - 0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e, - 0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e, - 0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424, - 0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e, - 0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424, - 0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404, - 0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c, - 0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e, - 0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c, - 0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c, - 0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c, - 0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404, - 0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04, - 0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c, - 0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414, - 0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c, - 0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c, - 0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424, - 0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c, - 0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c, - 0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414, - 0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c, - 0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e, - 0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04, - 0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424, - 0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14, - 0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34, - 0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c, - 0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434, - 0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c, - 0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424, - 0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24, - 0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24, - 0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e, - 0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c, - 0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c, - 0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c, - 0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404, +constexpr constant static uint32_t iq3s_grid[512] = { + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, }; #define NGRID_IQ1S 512 @@ -4072,7 +4393,6 @@ void kernel_mul_mv_iq2_xxs_f32_impl( threadgroup_barrier(mem_flags::mem_threadgroup); } -#if QK_K == 256 const int ix = tiisg; device const float * y4 = y + 32 * ix; @@ -4113,12 +4433,6 @@ void kernel_mul_mv_iq2_xxs_f32_impl( y4 += 32 * 32; } -#else - (void) x; - (void) y; - (void) yl; - (void) nb32; -#endif for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); @@ -4208,7 +4522,6 @@ void kernel_mul_mv_iq2_xs_f32_impl( threadgroup_barrier(mem_flags::mem_threadgroup); } -#if QK_K == 256 const int ix = tiisg; device const float * y4 = y + 32 * ix; @@ -4259,12 +4572,6 @@ void kernel_mul_mv_iq2_xs_f32_impl( y4 += 32 * 32; } -#else - (void) x; - (void) y; - (void) yl; - (void) nb32; -#endif for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); @@ -4354,7 +4661,6 @@ void kernel_mul_mv_iq3_xxs_f32_impl( threadgroup_barrier(mem_flags::mem_threadgroup); } -#if QK_K == 256 const int ix = tiisg; device const float * y4 = y + 32 * ix; @@ -4398,12 +4704,6 @@ void kernel_mul_mv_iq3_xxs_f32_impl( y4 += 32 * 32; } -#else - (void) x; - (void) y; - (void) yl; - (void) nb32; -#endif for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); @@ -4485,7 +4785,7 @@ void kernel_mul_mv_iq3_s_f32_impl( { int nval = 8; int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq3xs_grid[pos + i]; + for (int i = 0; i < nval; ++i) values[pos + i] = iq3s_grid[pos + i]; threadgroup_barrier(mem_flags::mem_threadgroup); } @@ -4512,12 +4812,14 @@ void kernel_mul_mv_iq3_s_f32_impl( for (int row = 0; row < N_DST; row++) { const float db = dh[0]; - const float d = db * (0.5f + ((sc[0] >> 4*(ib%2)) & 0xf)); + const float d = db * (1 + 2*((sc[0] >> 4*(ib%2)) & 0xf)); float2 sum = {0}; for (int l = 0; l < 4; ++l) { - const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256))); - const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256))); + const threadgroup uint32_t * table1 = qh[0] & kmask_iq2xs[2*l+0] ? values + 256 : values; + const threadgroup uint32_t * table2 = qh[0] & kmask_iq2xs[2*l+1] ? values + 256 : values; + const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(table1 + qs[2*l+0]); + const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(table2 + qs[2*l+1]); for (int j = 0; j < 4; ++j) { sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l] & kmask_iq2xs[j+0]); sum[1] += yl[8*l + j + 4] * grid2[j] * select(1, -1, signs[l] & kmask_iq2xs[j+4]); @@ -4538,7 +4840,7 @@ void kernel_mul_mv_iq3_s_f32_impl( for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.5f; + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; } } } @@ -4572,6 +4874,139 @@ kernel void kernel_mul_mv_iq3_s_f32( kernel_mul_mv_iq3_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } +void kernel_mul_mv_iq2_s_f32_impl( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne10, + constant int64_t & ne12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + const int nb = ne00/QK_K; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; + + const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + const int ib_row = first_row * nb; + + const uint i12 = im%ne12; + const uint i13 = im/ne12; + + const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + + device const block_iq2_s * x = (device const block_iq2_s *) src0 + ib_row + offset0; + device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + + float yl[32]; + float sumf[N_DST]={0.f}, all_sum; + + const int nb32 = nb * (QK_K / 32); + + //threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; + //{ + // int nval = 32; + // int pos = (32*sgitg + tiisg)*nval; + // for (int i = 0; i < nval; ++i) values[pos + i] = iq2s_grid[pos + i]; + // threadgroup_barrier(mem_flags::mem_threadgroup); + //} + + const int ix = tiisg; + + device const float * y4 = y + 32 * ix; + + for (int ib32 = ix; ib32 < nb32; ib32 += 32) { + + for (int i = 0; i < 32; ++i) { + yl[i] = y4[i]; + } + + const int ibl = ib32 / (QK_K / 32); + const int ib = ib32 % (QK_K / 32); + + device const block_iq2_s * xr = x + ibl; + device const uint8_t * qs = xr->qs + 4 * ib; + device const uint8_t * qh = xr->qh + ib; + device const uint8_t * sc = xr->scales + ib; + device const uint8_t * signs = qs + QK_K/8; + device const half * dh = &xr->d; + + for (int row = 0; row < N_DST; row++) { + + const float db = dh[0]; + const float d1 = db * (0.5f + (sc[0] & 0xf)); + const float d2 = db * (0.5f + (sc[0] >> 4)); + + float2 sum = {0}; + for (int l = 0; l < 2; ++l) { + //const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300))); + //const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300))); + constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300))); + constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300))); + for (int j = 0; j < 8; ++j) { + sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l+0] & kmask_iq2xs[j]); + sum[1] += yl[8*l + j + 16] * grid2[j] * select(1, -1, signs[l+2] & kmask_iq2xs[j]); + } + } + sumf[row] += d1 * sum[0] + d2 * sum[1]; + + dh += nb*sizeof(block_iq2_s)/2; + qs += nb*sizeof(block_iq2_s); + qh += nb*sizeof(block_iq2_s); + sc += nb*sizeof(block_iq2_s); + signs += nb*sizeof(block_iq2_s); + } + + y4 += 32 * 32; + } + + for (int row = 0; row < N_DST; ++row) { + all_sum = simd_sum(sumf[row]); + if (tiisg == 0) { + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; + } + } +} + +[[host_name("kernel_mul_mv_iq2_s_f32")]] +kernel void kernel_mul_mv_iq2_s_f32( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + kernel_mul_mv_iq2_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); +} + void kernel_mul_mv_iq1_s_f32_impl( device const void * src0, device const float * src1, @@ -4609,7 +5044,6 @@ void kernel_mul_mv_iq1_s_f32_impl( const int nb32 = nb * (QK_K / 32); -#if QK_K == 256 const int ix = tiisg/2; const int il = tiisg%2; @@ -4648,12 +5082,6 @@ void kernel_mul_mv_iq1_s_f32_impl( y4 += 16 * 32; } -#else - (void) x; - (void) y; - (void) yl; - (void) nb32; -#endif for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); @@ -4760,6 +5188,102 @@ void kernel_mul_mv_iq4_nl_f32_impl( } } +#if QK_K != 64 +void kernel_mul_mv_iq4_xs_f32_impl( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne10, + constant int64_t & ne12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup float * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + const int nb = ne00/QK_K; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; + const int first_row = (r0 * 2 + sgitg) * 2; + const int ib_row = first_row * nb; + + const uint i12 = im%ne12; + const uint i13 = im/ne12; + + const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + device const block_iq4_xs * x = (device const block_iq4_xs *) src0 + ib_row + offset0; + device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + + const int ix = tiisg/16; // 0 or 1 + const int it = tiisg%16; // 0...15 + const int ib = it/2; + const int il = it%2; + + shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16]; + threadgroup_barrier(mem_flags::mem_threadgroup); + + float4 yl[4]; + float sumf[2]={0.f}, all_sum; + + device const float * yb = y + ix * QK_K + ib * 32 + il * 8; + + uint32_t aux32[2]; + thread const uint8_t * q8 = (thread const uint8_t *)aux32; + + float4 qf1, qf2; + + for (int ibl = ix; ibl < nb; ibl += 2) { + + device const float4 * y4 = (device const float4 *)yb; + yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; + + for (int row = 0; row < 2; ++row) { + + device const block_iq4_xs & xb = x[row*nb + ibl]; + device const uint32_t * q4 = (device const uint32_t *)(xb.qs + 16*ib + 8*il); + + float4 acc1 = {0.f}, acc2 = {0.f}; + + aux32[0] = q4[0] & 0x0f0f0f0f; + aux32[1] = (q4[0] >> 4) & 0x0f0f0f0f; + qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; + qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + acc1 += yl[0] * qf1; + acc2 += yl[1] * qf2; + + aux32[0] = q4[1] & 0x0f0f0f0f; + aux32[1] = (q4[1] >> 4) & 0x0f0f0f0f; + qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; + qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + acc1 += yl[2] * qf1; + acc2 += yl[3] * qf2; + + acc1 += acc2; + + const int ls = (((xb.scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((xb.scales_h >> 2*ib) & 3) << 4)) - 32; + sumf[row] += (float)xb.d * ls * (acc1[0] + acc1[1] + acc1[2] + acc1[3]); + + } + + yb += 2 * QK_K; + } + + for (int row = 0; row < 2; ++row) { + all_sum = simd_sum(sumf[row]); + if (tiisg == 0) { + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + } + } +} +#endif + [[host_name("kernel_mul_mv_iq1_s_f32")]] kernel void kernel_mul_mv_iq1_s_f32( device const void * src0, @@ -4817,6 +5341,39 @@ kernel void kernel_mul_mv_iq4_nl_f32( kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } +[[host_name("kernel_mul_mv_iq4_xs_f32")]] +kernel void kernel_mul_mv_iq4_xs_f32( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup float * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + +#if QK_K == 64 + kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); +#else + kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); +#endif +} + //============================= templates and their specializations ============================= // NOTE: this is not dequantizing - we are simply fitting the template @@ -5173,21 +5730,40 @@ void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & device const uint8_t * qs = xb->qs + 8*ib32; device const uint8_t * signs = xb->signs + 4*ib32 + 2*il; const uint8_t qh = xb->qh[ib32] >> 4*il; - const float dl = d * (0.5f + ((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * 0.5f; - constant uint8_t * grid1 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+0] | ((qh << 8) & 256))); - constant uint8_t * grid2 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+1] | ((qh << 7) & 256))); + const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)); + constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256))); + constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256))); for (int i = 0; i < 4; ++i) { reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]); reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]); } - grid1 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+2] | ((qh << 6) & 256))); - grid2 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+3] | ((qh << 5) & 256))); + grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256))); + grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256))); for (int i = 0; i < 4; ++i) { reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]); reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]); } } +template +void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; + device const uint8_t * signs = qs + QK_K/8; + const uint8_t qh = xb->qh[ib32] >> 4*il; + const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; + constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300))); + constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300))); + for (int i = 0; i < 8; ++i) { + reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]); + reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]); + } +} + template void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) { // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 @@ -5219,6 +5795,30 @@ void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 } } +template +void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) { +#if QK_K == 64 + dequantize_iq4_nl(xb, il, reg); +#else + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32; + const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4); + const float d = (float)xb->d * (ls - 32); + uint32_t aux32; + thread const uint8_t * q8 = (thread const uint8_t *)&aux32; + for (int i = 0; i < 4; ++i) { + aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f; + reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; + reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; + reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; + reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; + } +#endif +} + template kernel void kernel_get_rows( device const void * src0, @@ -5762,8 +6362,14 @@ template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_r template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows; -template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows; +#if QK_K == 64 +template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows; +#else +template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows; +#endif // // matrix-matrix multiplication @@ -5804,8 +6410,14 @@ template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_m template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm; -template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm; +#if QK_K == 64 +template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm; +#else +template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm; +#endif // // indirect matrix-matrix multiplication @@ -5858,8 +6470,14 @@ template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq3_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_iq2_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +#if QK_K == 64 +template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +#else +template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +#endif // // matrix-vector multiplication @@ -6893,6 +7511,71 @@ kernel void kernel_mul_mv_id_iq3_s_f32( sgitg); } +[[host_name("kernel_mul_mv_id_iq2_s_f32")]] +kernel void kernel_mul_mv_id_iq2_s_f32( + device const char * ids, + device const char * src1, + device float * dst, + constant uint64_t & nbi1, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint64_t & nb1, + constant uint & r2, + constant uint & r3, + constant int & idx, + device const char * src00, + device const char * src01, + device const char * src02, + device const char * src03, + device const char * src04, + device const char * src05, + device const char * src06, + device const char * src07, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07}; + + const int64_t bid = tgpig.z/(ne12*ne13); + + tgpig.z = tgpig.z%(ne12*ne13); + + const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; + + kernel_mul_mv_iq2_s_f32_impl( + src0[id], + (device const float *) (src1 + bid*nb11), + dst + bid*ne0, + ne00, + ne01, + ne02, + ne10, + ne12, + ne0, + ne1, + r2, + r3, + shared_values, + tgpig, + tiisg, + sgitg); +} + [[host_name("kernel_mul_mv_id_iq1_s_f32")]] kernel void kernel_mul_mv_id_iq1_s_f32( device const char * ids, @@ -7020,3 +7703,72 @@ kernel void kernel_mul_mv_id_iq4_nl_f32( tiisg, sgitg); } + +[[host_name("kernel_mul_mv_id_iq4_xs_f32")]] +kernel void kernel_mul_mv_id_iq4_xs_f32( + device const char * ids, + device const char * src1, + device float * dst, + constant uint64_t & nbi1, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint64_t & nb1, + constant uint & r2, + constant uint & r3, + constant int & idx, + device const char * src00, + device const char * src01, + device const char * src02, + device const char * src03, + device const char * src04, + device const char * src05, + device const char * src06, + device const char * src07, + threadgroup float * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07}; + + const int64_t bid = tgpig.z/(ne12*ne13); + + tgpig.z = tgpig.z%(ne12*ne13); + + const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; + +#if QK_K == 64 + kernel_mul_mv_iq4_nl_f32_impl( +#else + kernel_mul_mv_iq4_xs_f32_impl( +#endif + src0[id], + (device const float *) (src1 + bid*nb11), + dst + bid*ne0, + ne00, + ne01, + ne02, + ne10, + ne12, + ne0, + ne1, + r2, + r3, + shared_values, + tgpig, + tiisg, + sgitg); +} diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 4dc348642c82d..d5868974c3144 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -2240,7 +2240,7 @@ static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(gg GGML_UNUSED(backend); } -static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) { +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]; switch (node->op) { @@ -2255,7 +2255,7 @@ static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgrap } } - return true; + return GGML_STATUS_SUCCESS; GGML_UNUSED(backend); } diff --git a/ggml-quants.c b/ggml-quants.c index b92f8624479c3..4286da6b9e620 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1879,7 +1879,7 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri float mins[QK_K/16]; float scales[QK_K/16]; float sw[QK_K/16]; - float weight[QK_K/16]; + float weight[16]; uint8_t Ls[QK_K/16], Lm[QK_K/16]; for (int i = 0; i < nb; i++) { @@ -1889,13 +1889,42 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri float sigma2 = sumx2/QK_K; for (int j = 0; j < QK_K/16; ++j) { const float * restrict qw = quant_weights + QK_K * i + 16*j; - for (int l = 0; l < QK_K/16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j + l]*x[16*j + l]); + for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j + l]*x[16*j + l]); for (int l = 0; l < QK_K/16; ++l) sw[j] += weight[l]; - scales[j] = make_qkx3_quants(QK_K/16, 3, x + 16*j, weight, L + 16*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); + scales[j] = make_qkx3_quants(16, 3, x + 16*j, weight, L + 16*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); } - float dm = make_qp_quants(QK_K/16, 15, scales, Ls, sw); - float mm = make_qp_quants(QK_K/16, 15, mins, Lm, sw); + float dm, mm; +#if QK_K == 64 + float max_scale = 0, max_min = 0; + for (int j = 0; j < QK_K/16; ++j) { + max_scale = MAX(max_scale, scales[j]); + max_min = MAX(max_min, mins[j]); + } + dm = max_scale/15; + mm = max_min/15; + if (max_scale) { + float id = 1/dm; + for (int j = 0; j < QK_K/16; ++j) { + int l = nearest_int(id*scales[j]); + Ls[j] = MAX(0, MIN(15, l)); + } + } else { + memset(Ls, 0, QK_K/16); + } + if (max_min) { + float id = 1/mm; + for (int j = 0; j < QK_K/16; ++j) { + int l = nearest_int(id*mins[j]); + Lm[j] = MAX(0, MIN(15, l)); + } + } else { + memset(Lm, 0, QK_K/16); + } +#else + dm = make_qp_quants(QK_K/16, 15, scales, Ls, sw); + mm = make_qp_quants(QK_K/16, 15, mins, Lm, sw); +#endif y[i].d = GGML_FP32_TO_FP16(dm); y[i].dmin = GGML_FP32_TO_FP16(mm); dm = GGML_FP16_TO_FP32(y[i].d); @@ -3497,6 +3526,265 @@ static const uint64_t iq2xs_grid[512] = { 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, }; +static const uint64_t iq2s_grid[1024] = { + 0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08, + 0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b, + 0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919, + 0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b, + 0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919, + 0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b, + 0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919, + 0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808, + 0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908, + 0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b, + 0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908, + 0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08, + 0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19, + 0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819, + 0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919, + 0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b, + 0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908, + 0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908, + 0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919, + 0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908, + 0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b, + 0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919, + 0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b, + 0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919, + 0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908, + 0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b, + 0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b, + 0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08, + 0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b, + 0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819, + 0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808, + 0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908, + 0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b, + 0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908, + 0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08, + 0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819, + 0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808, + 0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08, + 0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819, + 0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b, + 0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908, + 0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919, + 0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b, + 0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919, + 0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808, + 0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819, + 0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919, + 0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919, + 0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808, + 0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819, + 0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b, + 0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908, + 0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b, + 0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908, + 0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919, + 0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b, + 0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919, + 0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b, + 0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819, + 0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919, + 0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908, + 0x081908190819192b, 0x0819081908192b19, 0x08190819082b0808, 0x08190819082b082b, + 0x08190819082b1919, 0x08190819082b2b08, 0x0819081919080819, 0x0819081919081908, + 0x081908191908192b, 0x0819081919082b19, 0x0819081919190808, 0x081908191919082b, + 0x0819081919191919, 0x0819081919192b08, 0x08190819192b0819, 0x08190819192b1908, + 0x081908192b080808, 0x081908192b08082b, 0x081908192b081919, 0x081908192b082b08, + 0x081908192b190819, 0x081908192b191908, 0x0819082b08080819, 0x0819082b08081908, + 0x0819082b08082b19, 0x0819082b08190808, 0x0819082b08191919, 0x0819082b082b0819, + 0x0819082b082b1908, 0x0819082b19080808, 0x0819082b19081919, 0x0819082b19190819, + 0x0819082b19191908, 0x0819082b2b080819, 0x0819082b2b081908, 0x0819082b2b190808, + 0x0819190808080808, 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08, + 0x0819190808190819, 0x0819190808191908, 0x081919080819192b, 0x0819190808192b19, + 0x08191908082b0808, 0x08191908082b1919, 0x08191908082b2b08, 0x0819190819080819, + 0x0819190819081908, 0x081919081908192b, 0x0819190819082b19, 0x0819190819190808, + 0x081919081919082b, 0x0819190819191919, 0x0819190819192b08, 0x08191908192b0819, + 0x08191908192b1908, 0x081919082b080808, 0x081919082b08082b, 0x081919082b081919, + 0x081919082b082b08, 0x081919082b190819, 0x081919082b191908, 0x081919082b2b0808, + 0x0819191908080819, 0x0819191908081908, 0x081919190808192b, 0x0819191908082b19, + 0x0819191908190808, 0x081919190819082b, 0x0819191908191919, 0x0819191908192b08, + 0x08191919082b0819, 0x08191919082b1908, 0x0819191919080808, 0x081919191908082b, + 0x0819191919081919, 0x0819191919082b08, 0x0819191919190819, 0x0819191919191908, + 0x08191919192b0808, 0x081919192b080819, 0x081919192b081908, 0x081919192b190808, + 0x0819192b08080808, 0x0819192b08081919, 0x0819192b08082b08, 0x0819192b08190819, + 0x0819192b08191908, 0x0819192b082b0808, 0x0819192b19080819, 0x0819192b19081908, + 0x0819192b19190808, 0x0819192b2b080808, 0x0819192b2b2b2b2b, 0x08192b0808080819, + 0x08192b0808081908, 0x08192b080808192b, 0x08192b0808082b19, 0x08192b0808190808, + 0x08192b0808191919, 0x08192b0808192b08, 0x08192b08082b0819, 0x08192b0819080808, + 0x08192b081908082b, 0x08192b0819081919, 0x08192b0819082b08, 0x08192b0819190819, + 0x08192b0819191908, 0x08192b08192b0808, 0x08192b082b080819, 0x08192b082b081908, + 0x08192b1908080808, 0x08192b190808082b, 0x08192b1908081919, 0x08192b1908082b08, + 0x08192b1908190819, 0x08192b1908191908, 0x08192b19082b0808, 0x08192b1919080819, + 0x08192b1919081908, 0x08192b1919190808, 0x08192b19192b2b19, 0x08192b192b2b082b, + 0x08192b2b08081908, 0x08192b2b08190808, 0x08192b2b19080808, 0x08192b2b1919192b, + 0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, 0x082b080808082b08, + 0x082b080808190819, 0x082b080808191908, 0x082b08080819192b, 0x082b080808192b19, + 0x082b0808082b0808, 0x082b0808082b1919, 0x082b0808082b2b2b, 0x082b080819080819, + 0x082b080819081908, 0x082b080819190808, 0x082b08081919082b, 0x082b080819191919, + 0x082b0808192b1908, 0x082b08082b080808, 0x082b08082b082b2b, 0x082b08082b191908, + 0x082b08082b2b2b2b, 0x082b081908080819, 0x082b081908081908, 0x082b081908190808, + 0x082b08190819082b, 0x082b081908191919, 0x082b0819082b0819, 0x082b081919080808, + 0x082b08191908082b, 0x082b081919081919, 0x082b081919190819, 0x082b081919191908, + 0x082b0819192b0808, 0x082b08192b080819, 0x082b08192b081908, 0x082b08192b190808, + 0x082b082b08080808, 0x082b082b08082b2b, 0x082b082b082b082b, 0x082b082b082b2b08, + 0x082b082b082b2b2b, 0x082b082b19081908, 0x082b082b19190808, 0x082b082b2b082b08, + 0x082b082b2b082b2b, 0x082b082b2b2b2b08, 0x082b190808080819, 0x082b190808081908, + 0x082b19080808192b, 0x082b190808082b19, 0x082b190808190808, 0x082b190808191919, + 0x082b190808192b08, 0x082b1908082b0819, 0x082b1908082b1908, 0x082b190819080808, + 0x082b19081908082b, 0x082b190819081919, 0x082b190819082b08, 0x082b190819190819, + 0x082b190819191908, 0x082b1908192b0808, 0x082b19082b080819, 0x082b19082b081908, + 0x082b19082b190808, 0x082b191908080808, 0x082b191908081919, 0x082b191908082b08, + 0x082b191908190819, 0x082b191908191908, 0x082b1919082b0808, 0x082b191919080819, + 0x082b191919081908, 0x082b191919190808, 0x082b1919192b192b, 0x082b19192b080808, + 0x082b192b08080819, 0x082b192b08081908, 0x082b192b08190808, 0x082b192b19080808, + 0x082b192b19192b19, 0x082b2b0808080808, 0x082b2b0808081919, 0x082b2b0808190819, + 0x082b2b0808191908, 0x082b2b0819080819, 0x082b2b0819081908, 0x082b2b0819190808, + 0x082b2b082b082b2b, 0x082b2b082b2b2b2b, 0x082b2b1908080819, 0x082b2b1908081908, + 0x082b2b1908190808, 0x082b2b192b191919, 0x082b2b2b08082b2b, 0x082b2b2b082b082b, + 0x082b2b2b192b1908, 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819, + 0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808, + 0x190808080819082b, 0x1908080808191919, 0x1908080808192b08, 0x1908080808192b2b, + 0x19080808082b0819, 0x19080808082b1908, 0x19080808082b192b, 0x1908080819080808, + 0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, 0x1908080819082b2b, + 0x1908080819190819, 0x1908080819191908, 0x190808081919192b, 0x1908080819192b19, + 0x19080808192b0808, 0x19080808192b082b, 0x19080808192b1919, 0x190808082b080819, + 0x190808082b081908, 0x190808082b190808, 0x190808082b191919, 0x190808082b192b08, + 0x190808082b2b0819, 0x190808082b2b1908, 0x1908081908080808, 0x190808190808082b, + 0x1908081908081919, 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908, + 0x190808190819192b, 0x1908081908192b19, 0x19080819082b0808, 0x19080819082b082b, + 0x19080819082b1919, 0x1908081919080819, 0x1908081919081908, 0x190808191908192b, + 0x1908081919082b19, 0x1908081919190808, 0x190808191919082b, 0x1908081919191919, + 0x1908081919192b08, 0x19080819192b0819, 0x19080819192b1908, 0x190808192b080808, + 0x190808192b08082b, 0x190808192b081919, 0x190808192b082b08, 0x190808192b190819, + 0x190808192b191908, 0x190808192b2b0808, 0x1908082b08080819, 0x1908082b08081908, + 0x1908082b08190808, 0x1908082b0819082b, 0x1908082b08191919, 0x1908082b08192b08, + 0x1908082b082b1908, 0x1908082b19080808, 0x1908082b19081919, 0x1908082b19082b08, + 0x1908082b19190819, 0x1908082b19191908, 0x1908082b192b0808, 0x1908082b2b080819, + 0x1908082b2b081908, 0x1908190808080808, 0x190819080808082b, 0x1908190808081919, + 0x1908190808082b08, 0x1908190808082b2b, 0x1908190808190819, 0x1908190808191908, + 0x190819080819192b, 0x1908190808192b19, 0x19081908082b0808, 0x19081908082b082b, + 0x19081908082b1919, 0x19081908082b2b08, 0x1908190819080819, 0x1908190819081908, + 0x190819081908192b, 0x1908190819082b19, 0x1908190819190808, 0x190819081919082b, + 0x1908190819191919, 0x1908190819192b08, 0x19081908192b0819, 0x19081908192b1908, + 0x190819082b080808, 0x190819082b08082b, 0x190819082b081919, 0x190819082b082b08, + 0x190819082b190819, 0x190819082b191908, 0x190819082b2b0808, 0x1908191908080819, + 0x1908191908081908, 0x190819190808192b, 0x1908191908082b19, 0x1908191908190808, + 0x190819190819082b, 0x1908191908191919, 0x1908191908192b08, 0x19081919082b0819, + 0x19081919082b1908, 0x1908191919080808, 0x190819191908082b, 0x1908191919081919, + 0x1908191919082b08, 0x1908191919190819, 0x1908191919191908, 0x19081919192b0808, + 0x19081919192b2b2b, 0x190819192b080819, 0x190819192b081908, 0x190819192b190808, + 0x1908192b08080808, 0x1908192b0808082b, 0x1908192b08081919, 0x1908192b08082b08, + 0x1908192b08190819, 0x1908192b08191908, 0x1908192b082b0808, 0x1908192b19080819, + 0x1908192b19081908, 0x1908192b19190808, 0x1908192b2b080808, 0x1908192b2b2b1919, + 0x19082b0808080819, 0x19082b0808081908, 0x19082b0808082b19, 0x19082b0808190808, + 0x19082b080819082b, 0x19082b0808191919, 0x19082b0808192b08, 0x19082b08082b0819, + 0x19082b08082b1908, 0x19082b0819080808, 0x19082b081908082b, 0x19082b0819081919, + 0x19082b0819082b08, 0x19082b0819190819, 0x19082b0819191908, 0x19082b08192b0808, + 0x19082b082b081908, 0x19082b082b190808, 0x19082b1908080808, 0x19082b190808082b, + 0x19082b1908081919, 0x19082b1908082b08, 0x19082b1908190819, 0x19082b1908191908, + 0x19082b19082b0808, 0x19082b1919080819, 0x19082b1919081908, 0x19082b1919190808, + 0x19082b192b080808, 0x19082b192b19192b, 0x19082b2b08080819, 0x19082b2b08081908, + 0x19082b2b08190808, 0x19082b2b19080808, 0x1919080808080808, 0x191908080808082b, + 0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, 0x1919080808191908, + 0x191908080819192b, 0x1919080808192b19, 0x19190808082b0808, 0x19190808082b082b, + 0x19190808082b1919, 0x19190808082b2b08, 0x1919080819080819, 0x1919080819081908, + 0x191908081908192b, 0x1919080819082b19, 0x1919080819190808, 0x191908081919082b, + 0x1919080819191919, 0x1919080819192b08, 0x19190808192b0819, 0x19190808192b1908, + 0x191908082b080808, 0x191908082b08082b, 0x191908082b081919, 0x191908082b082b08, + 0x191908082b190819, 0x191908082b191908, 0x1919081908080819, 0x1919081908081908, + 0x191908190808192b, 0x1919081908082b19, 0x1919081908190808, 0x191908190819082b, + 0x1919081908191919, 0x1919081908192b08, 0x19190819082b0819, 0x19190819082b1908, + 0x1919081919080808, 0x191908191908082b, 0x1919081919081919, 0x1919081919082b08, + 0x1919081919190819, 0x1919081919191908, 0x19190819192b0808, 0x191908192b080819, + 0x191908192b081908, 0x191908192b190808, 0x1919082b08080808, 0x1919082b08081919, + 0x1919082b08082b08, 0x1919082b08190819, 0x1919082b08191908, 0x1919082b082b0808, + 0x1919082b19080819, 0x1919082b19081908, 0x1919082b19190808, 0x1919082b192b2b19, + 0x1919082b2b080808, 0x1919190808080819, 0x1919190808081908, 0x191919080808192b, + 0x1919190808082b19, 0x1919190808190808, 0x191919080819082b, 0x1919190808191919, + 0x1919190808192b08, 0x19191908082b0819, 0x19191908082b1908, 0x1919190819080808, + 0x191919081908082b, 0x1919190819081919, 0x1919190819082b08, 0x1919190819190819, + 0x1919190819191908, 0x19191908192b0808, 0x191919082b080819, 0x191919082b081908, + 0x191919082b190808, 0x1919191908080808, 0x191919190808082b, 0x1919191908081919, + 0x1919191908082b08, 0x1919191908190819, 0x1919191908191908, 0x19191919082b0808, + 0x1919191919080819, 0x1919191919081908, 0x1919191919190808, 0x191919192b080808, + 0x1919192b08080819, 0x1919192b08081908, 0x1919192b08190808, 0x1919192b082b192b, + 0x1919192b19080808, 0x19192b0808080808, 0x19192b080808082b, 0x19192b0808081919, + 0x19192b0808082b08, 0x19192b0808190819, 0x19192b0808191908, 0x19192b08082b0808, + 0x19192b0819080819, 0x19192b0819081908, 0x19192b0819190808, 0x19192b0819192b2b, + 0x19192b082b080808, 0x19192b1908080819, 0x19192b1908081908, 0x19192b1908190808, + 0x19192b1919080808, 0x19192b2b08080808, 0x19192b2b08192b19, 0x19192b2b2b081919, + 0x19192b2b2b2b2b08, 0x192b080808080819, 0x192b080808081908, 0x192b08080808192b, + 0x192b080808190808, 0x192b08080819082b, 0x192b080808191919, 0x192b080808192b08, + 0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919, + 0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808, + 0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b, + 0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908, + 0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808, + 0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808, + 0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808, + 0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908, + 0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808, + 0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808, + 0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b, + 0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908, + 0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808, + 0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808, + 0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819, + 0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919, + 0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b, + 0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808, + 0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819, + 0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b, + 0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908, + 0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08, + 0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908, + 0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919, + 0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819, + 0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908, + 0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b, + 0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808, + 0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819, + 0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908, + 0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919, + 0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808, + 0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808, + 0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808, + 0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919, + 0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908, + 0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908, + 0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08, + 0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819, + 0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b, + 0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808, + 0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819, + 0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908, + 0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819, + 0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808, + 0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808, + 0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b, + 0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908, + 0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808, + 0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908, + 0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819, + 0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819, + 0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808, + 0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b, + 0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b, + 0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819, + 0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b, + 0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b, + 0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b, + 0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819, + 0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19, + 0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819, + 0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908, + 0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808, + 0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b, +}; + static const uint32_t iq3xxs_grid[256] = { 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, @@ -3532,71 +3820,71 @@ static const uint32_t iq3xxs_grid[256] = { 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, }; -static const uint32_t iq3xs_grid[512] = { - 0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14, - 0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414, - 0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24, - 0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c, - 0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c, - 0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34, - 0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c, - 0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414, - 0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c, - 0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404, - 0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434, - 0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c, - 0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404, - 0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414, - 0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414, - 0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404, - 0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c, - 0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c, - 0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404, - 0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e, - 0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14, - 0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c, - 0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424, - 0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c, - 0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c, - 0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e, - 0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e, - 0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e, - 0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424, - 0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e, - 0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424, - 0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404, - 0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c, - 0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e, - 0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c, - 0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c, - 0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c, - 0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404, - 0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04, - 0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c, - 0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414, - 0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c, - 0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c, - 0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424, - 0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c, - 0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c, - 0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414, - 0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c, - 0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e, - 0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04, - 0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424, - 0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14, - 0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34, - 0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c, - 0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434, - 0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c, - 0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424, - 0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24, - 0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24, - 0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e, - 0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c, - 0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c, - 0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c, - 0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404, +static const uint32_t iq3s_grid[512] = { + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, }; #define NGRID_IQ2XXS 512 @@ -3798,6 +4086,38 @@ void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, } } +// ====================== 2.5625 bpw (de)-quantization + +void dequantize_row_iq2_s(const block_iq2_s * restrict x, float * restrict y, int k) { + assert(k % QK_K == 0); + const int nb = k / QK_K; + + float db[2]; + + for (int i = 0; i < nb; i++) { + + const float d = GGML_FP16_TO_FP32(x[i].d); + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint8_t * signs = qs + QK_K/8; + + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + db[0] = d * (0.5f + (x[i].scales[ib32] & 0xf)) * 0.25f; + db[1] = d * (0.5f + (x[i].scales[ib32] >> 4)) * 0.25f; + for (int l = 0; l < 4; ++l) { + const float dl = db[l/2]; + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); + for (int j = 0; j < 8; ++j) { + y[j] = dl * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1.f : 1.f); + } + y += 8; + } + qs += 4; + signs += 4; + } + } +} + // ====================== 3.0625 bpw (de)-quantization void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k) { @@ -3844,11 +4164,11 @@ void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, in const uint8_t * signs = x[i].signs; for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const float db1 = d * (0.5f + (x[i].scales[ib32/2] & 0xf)) * 0.5f; - const float db2 = d * (0.5f + (x[i].scales[ib32/2] >> 4)) * 0.5f; + const float db1 = d * (1 + 2*(x[i].scales[ib32/2] & 0xf)); + const float db2 = d * (1 + 2*(x[i].scales[ib32/2] >> 4)); for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256))); + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256))); for (int j = 0; j < 4; ++j) { y[j+0] = db1 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = db1 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f); @@ -3858,8 +4178,8 @@ void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, in qs += 8; signs += 4; for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[1] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[1] << (7-2*l)) & 256))); + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[1] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[1] << (7-2*l)) & 256))); for (int j = 0; j < 4; ++j) { y[j+0] = db2 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = db2 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f); @@ -3936,6 +4256,33 @@ void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, } } +void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, int k) { + assert(k % QK_K == 0); +#if QK_K == 64 + dequantize_row_iq4_nl((const block_iq4_nl *)x, y, k); +#else + const int nb = k / QK_K; + + for (int i = 0; i < nb; i++) { + + const uint8_t * qs = x[i].qs; + + const float d = GGML_FP16_TO_FP32(x[i].d); + + for (int ib = 0; ib < QK_K/32; ++ib) { + const int ls = ((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4); + const float dl = d * (ls - 32); + for (int j = 0; j < 16; ++j) { + y[j+ 0] = dl * kvalues_iq4nl[qs[j] & 0xf]; + y[j+16] = dl * kvalues_iq4nl[qs[j] >> 4]; + } + y += 32; + qs += 16; + } + } +#endif +} + //===================================== Q8_K ============================================== void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) { @@ -5994,7 +6341,7 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r float sumf = 0; - int isum[4]; + int isum[QK_K/16]; for (int i = 0; i < nb; ++i) { @@ -6010,14 +6357,14 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - isum[0] = isum[1] = isum[2] = isum[3] = 0; + memset(isum, 0, (QK_K/16)*sizeof(int)); for (int l = 0; l < 16; ++l) { isum[0] += q8[l+ 0] * ((q2[l] >> 0) & 3); isum[1] += q8[l+16] * ((q2[l] >> 2) & 3); isum[2] += q8[l+32] * ((q2[l] >> 4) & 3); isum[3] += q8[l+48] * ((q2[l] >> 6) & 3); } - for (int l = 0; l < 4; ++l) { + for (int l = 0; l < QK_K/16; ++l) { isum[l] *= (sc[l] & 0xF); } sumf += dall * (isum[0] + isum[1] + isum[2] + isum[3]) - dmin * summs; @@ -9176,15 +9523,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * #elif defined(__AVX2__) - const __m128i m4 = _mm_set1_epi8(0xf); - const __m128i m1 = _mm_set1_epi8(1); - const __m256i m511 = _mm256_set1_epi16(511); const __m256i mone = _mm256_set1_epi8(1); - - static const uint8_t k_bit_helper[32] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; static const char block_sign_shuffle_mask_1[32] = { 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, @@ -9198,11 +9537,77 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, }; - const __m256i bit_helper = _mm256_loadu_si256((const __m256i*)k_bit_helper); const __m256i bit_selector_mask = _mm256_loadu_si256((const __m256i*)bit_selector_mask_bytes); const __m256i block_sign_shuffle_1 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_1); const __m256i block_sign_shuffle_2 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_2); +#if QK_K == 64 + static const uint8_t k_bit_helper[16] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + const __m128i bit_helper = _mm_loadu_si128((const __m128i*)k_bit_helper); + const __m128i m511 = _mm_set1_epi16(511); + typedef union { + __m128i vec_index; + uint16_t index[8]; + } index_t; + + index_t idx; + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const __m128i q2_data = _mm_loadu_si128((const __m128i*)x[i].qs); + idx.vec_index = _mm_and_si128(q2_data, m511); + + const __m128i partial_sign_bits = _mm_srli_epi16(q2_data, 9); + const __m128i partial_sign_bits_upper = _mm_srli_epi16(q2_data, 13); + const __m128i partial_sign_bits_for_counting = _mm_xor_si128(partial_sign_bits, partial_sign_bits_upper); + + const __m128i odd_bits = _mm_shuffle_epi8(bit_helper, partial_sign_bits_for_counting); + const __m128i full_sign_bits = _mm_or_si128(partial_sign_bits, odd_bits); + const __m256i full_signs = _mm256_set_m128i(full_sign_bits, full_sign_bits); + + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)y[i].qs); + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)(y[i].qs+32)); + + const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[idx.index[3]], iq2xs_grid[idx.index[2]], + iq2xs_grid[idx.index[1]], iq2xs_grid[idx.index[0]]); + const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[idx.index[7]], iq2xs_grid[idx.index[6]], + iq2xs_grid[idx.index[5]], iq2xs_grid[idx.index[4]]); + + __m256i signs; + signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_1); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone)); + + signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_2); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone)); + + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + + const __m256i sc1 = _mm256_set_m128i(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1)); + const __m256i sc2 = _mm256_set_m128i(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1)); + + const __m256i sum = _mm256_add_epi32(_mm256_madd_epi16(sc1, dot1), _mm256_madd_epi16(sc2, dot2)); + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sum), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); +#else + + static const uint8_t k_bit_helper[32] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + const __m256i bit_helper = _mm256_loadu_si256((const __m256i*)k_bit_helper); + const __m256i m511 = _mm256_set1_epi16(511); + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + uint64_t aux64; // somewhat hacky, but gives a significant boost in performance @@ -9291,6 +9696,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * } *s = 0.125f * hsum_float_8(accumf); +#endif #else @@ -9332,7 +9738,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * #endif } -void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { +void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); UNUSED(nrc); @@ -9340,88 +9746,148 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void UNUSED(by); UNUSED(bs); - const block_iq3_xxs * restrict x = vx; - const block_q8_K * restrict y = vy; + const block_iq2_s * restrict x = vx; + const block_q8_K * restrict y = vy; const int nb = n / QK_K; #if defined(__ARM_NEON) - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; - uint32_t aux32[2]; + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - ggml_int8x16x4_t q3s; + const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1); + const uint8x16_t mask2 = vld1q_u8(k_mask2); + const uint8x16_t m1 = vdupq_n_u8(1); + const int32x4_t vzero = vdupq_n_s32(0); + + uint8x16x2_t vs; + ggml_int8x16x4_t q2s; ggml_int8x16x4_t q8b; float sumf = 0; for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; - const int8_t * restrict q8 = y[i].qs; - float sumf1 = 0, sumf2 = 0; + + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + int sumi1 = 0, sumi2 = 0; for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - memcpy(aux32, gas, 2*sizeof(uint32_t)); gas += 2*sizeof(uint32_t); - const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]); - const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]); - const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]); - const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]); - q3 += 16; - q3s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 7) & 127)))); - q3s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 21) & 127)))); - q3s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127)))); - q3s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127)))); - q3s.val[0] = vmulq_s8(q3s.val[0], vreinterpretq_s8_u32(aux32x4_0)); - q3s.val[1] = vmulq_s8(q3s.val[1], vreinterpretq_s8_u32(aux32x4_1)); - q3s.val[2] = vmulq_s8(q3s.val[2], vreinterpretq_s8_u32(aux32x4_2)); - q3s.val[3] = vmulq_s8(q3s.val[3], vreinterpretq_s8_u32(aux32x4_3)); - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); - sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[0] >> 28)); - sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[1] >> 28)); + q2s.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[0] | ((qh[ib32+0] << 8) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[1] | ((qh[ib32+0] << 6) & 0x300))))); + q2s.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[2] | ((qh[ib32+0] << 4) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[3] | ((qh[ib32+0] << 2) & 0x300))))); + q2s.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[4] | ((qh[ib32+1] << 8) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[5] | ((qh[ib32+1] << 6) & 0x300))))); + q2s.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[6] | ((qh[ib32+1] << 4) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[7] | ((qh[ib32+1] << 2) & 0x300))))); + qs += 8; + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16))); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vceqq_u8(vs.val[0], mask2); + vs.val[1] = vceqq_u8(vs.val[1], mask2); + + q2s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[0]); + q2s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[1]); + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16))); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vceqq_u8(vs.val[0], mask2); + vs.val[1] = vceqq_u8(vs.val[1], mask2); + + signs += 4; + + q2s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[2]); + q2s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[3]); + + const int32x4_t p1 = ggml_vdotq_s32(vzero, q2s.val[0], q8b.val[0]); + const int32x4_t p2 = ggml_vdotq_s32(vzero, q2s.val[1], q8b.val[1]); + const int32x4_t p3 = ggml_vdotq_s32(vzero, q2s.val[2], q8b.val[2]); + const int32x4_t p4 = ggml_vdotq_s32(vzero, q2s.val[3], q8b.val[3]); + + sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32+0] & 0xf)); + sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32+0] >> 4)); + sumi1 += vaddvq_s32(p3) * (1 + 2*(x[i].scales[ib32+1] & 0xf)); + sumi2 += vaddvq_s32(p4) * (1 + 2*(x[i].scales[ib32+1] >> 4)); } - sumf += d*(sumf1 + sumf2); + sumf += d*(sumi1 + sumi2); } - *s = 0.5f * sumf; + + *s = 0.125f * sumf; #elif defined(__AVX2__) - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; - uint32_t aux32[2]; + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + + const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); + const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); + + uint64_t aux64; __m256 accumf = _mm256_setzero_ps(); for (int i = 0; i < nb; ++i) { const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1); + const __m256i scales16 = _mm256_cvtepi8_epi16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 + __m256i sumi1 = _mm256_setzero_si256(); __m256i sumi2 = _mm256_setzero_si256(); for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q2_1 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], - iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - q3 += 8; - const __m256i q2_2 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], - iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - q3 += 8; - memcpy(aux32, gas, 8); gas += 8; - const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], - signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); - const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], - signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); - const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - const uint16_t ls1 = aux32[0] >> 28; - const uint16_t ls2 = aux32[1] >> 28; - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); + const __m256i q2_1 = _mm256_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], + iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)], + iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], + iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); + const __m256i q2_2 = _mm256_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], + iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)], + iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], + iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); + qs += 8; + + __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1); + + aux256 = _mm256_set1_epi32(signs[2] | (signs[3] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1 + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3 + + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+0))); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+1))); sumi1 = _mm256_add_epi32(sumi1, p1); sumi2 = _mm256_add_epi32(sumi2, p2); } @@ -9430,29 +9896,173 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void } - *s = 0.25f * hsum_float_8(accumf); + *s = 0.125f * hsum_float_8(accumf); #else - uint32_t aux32; + float sumf = 0; + for (int i = 0; i < nb; i++) { - float sumf = 0.f; - for (int i = 0; i < nb; ++i) { const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; - const int8_t * restrict q8 = y[i].qs; - int32_t bsum = 0; + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint8_t * signs = qs + QK_K/8; + + int bsum = 0; for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { - memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t); - const uint32_t ls = 2*(aux32 >> 28) + 1; - int32_t sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]); - const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]); - const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*l) & 127]; - for (int j = 0; j < 4; ++j) { - sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1); + int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf); + int ls2 = 1 + 2*(x[i].scales[ib32] >> 4); + int sumi1 = 0, sumi2 = 0; + for (int l = 0; l < 2; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); + for (int j = 0; j < 8; ++j) { + sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + for (int l = 2; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); + for (int j = 0; j < 8; ++j) { + sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += ls1 * sumi1 + ls2 * sumi2; + qs += 4; + signs += 4; + } + + sumf += d * bsum; + } + + *s = 0.125f * sumf; + +#endif + +} + +void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq3_xxs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + ggml_int8x16x4_t q3s; + ggml_int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + float sumf1 = 0, sumf2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + memcpy(aux32, gas, 2*sizeof(uint32_t)); gas += 2*sizeof(uint32_t); + const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]); + const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]); + const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]); + const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]); + q3 += 16; + q3s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 7) & 127)))); + q3s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 21) & 127)))); + q3s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127)))); + q3s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127)))); + q3s.val[0] = vmulq_s8(q3s.val[0], vreinterpretq_s8_u32(aux32x4_0)); + q3s.val[1] = vmulq_s8(q3s.val[1], vreinterpretq_s8_u32(aux32x4_1)); + q3s.val[2] = vmulq_s8(q3s.val[2], vreinterpretq_s8_u32(aux32x4_2)); + q3s.val[3] = vmulq_s8(q3s.val[3], vreinterpretq_s8_u32(aux32x4_3)); + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); + sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[0] >> 28)); + sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[1] >> 28)); + } + sumf += d*(sumf1 + sumf2); + } + *s = 0.5f * sumf; + +#elif defined(__AVX2__) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q2_1 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + const __m256i q2_2 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + memcpy(aux32, gas, 8); gas += 8; + const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], + signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); + const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const uint16_t ls1 = aux32[0] >> 28; + const uint16_t ls2 = aux32[1] >> 28; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.25f * hsum_float_8(accumf); + +#else + + uint32_t aux32; + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t); + const uint32_t ls = 2*(aux32 >> 28) + 1; + int32_t sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]); + const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]); + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*l) & 127]; + for (int j = 0; j < 4; ++j) { + sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1); sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1); } q8 += 8; @@ -9481,18 +10091,34 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v #if defined(__ARM_NEON) + typedef union { + uint16x8_t vec_index; + uint16_t index[8]; + } vec_index_t; + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 }; static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1); - const uint8x16_t mask2 = vld1q_u8(k_mask2); + static const int16_t k_shift[8] = {8, 7, 6, 5, 4, 3, 2, 1}; + + const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1); + const uint8x16_t mask2 = vld1q_u8(k_mask2); + const int16x8_t hshift = vld1q_s16(k_shift); + const uint16x8_t m256 = vdupq_n_u16(256); + const uint8x16_t m1 = vdupq_n_u8(1); uint8x16x2_t vs; ggml_int8x16x4_t q3s; ggml_int8x16x4_t q8b; + vec_index_t idx; + +#if QK_K == 256 + uint32_t scales32[2]; + const uint8_t * scales8 = (const uint8_t *)scales32; +#endif float sumf = 0; for (int i = 0; i < nb; ++i) { @@ -9501,47 +10127,63 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v const uint8_t * restrict qh = x[i].qh; const uint16_t * restrict signs = (const uint16_t *)x[i].signs; const int8_t * restrict q8 = y[i].qs; + +#if QK_K == 256 + memcpy(scales32, x[i].scales, 4); + scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101; + scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101; +#endif + int sumi1 = 0, sumi2 = 0; for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - const uint32x4_t aux32x4_0 = {iq3xs_grid[qs[ 0] | ((qh[ib32+0] << 8) & 256)], iq3xs_grid[qs[ 1] | ((qh[ib32+0] << 7) & 256)], - iq3xs_grid[qs[ 2] | ((qh[ib32+0] << 6) & 256)], iq3xs_grid[qs[ 3] | ((qh[ib32+0] << 5) & 256)]}; - const uint32x4_t aux32x4_1 = {iq3xs_grid[qs[ 4] | ((qh[ib32+0] << 4) & 256)], iq3xs_grid[qs[ 5] | ((qh[ib32+0] << 3) & 256)], - iq3xs_grid[qs[ 6] | ((qh[ib32+0] << 2) & 256)], iq3xs_grid[qs[ 7] | ((qh[ib32+0] << 1) & 256)]}; - const uint32x4_t aux32x4_2 = {iq3xs_grid[qs[ 8] | ((qh[ib32+1] << 8) & 256)], iq3xs_grid[qs[ 9] | ((qh[ib32+1] << 7) & 256)], - iq3xs_grid[qs[10] | ((qh[ib32+1] << 6) & 256)], iq3xs_grid[qs[11] | ((qh[ib32+1] << 5) & 256)]}; - const uint32x4_t aux32x4_3 = {iq3xs_grid[qs[12] | ((qh[ib32+1] << 4) & 256)], iq3xs_grid[qs[13] | ((qh[ib32+1] << 3) & 256)], - iq3xs_grid[qs[14] | ((qh[ib32+1] << 2) & 256)], iq3xs_grid[qs[15] | ((qh[ib32+1] << 1) & 256)]}; - qs += 16; + + const uint8x16_t idx_l = vld1q_u8(qs); qs += 16; + idx.vec_index = vorrq_u16(vmovl_u8(vget_low_u8 (idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+0]), hshift), m256)); + const uint32x4_t aux32x4_0 = {iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], + iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]}; + const uint32x4_t aux32x4_1 = {iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], + iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]}; + idx.vec_index = vorrq_u16(vmovl_u8(vget_high_u8(idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+1]), hshift), m256)); + const uint32x4_t aux32x4_2 = {iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], + iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]}; + const uint32x4_t aux32x4_3 = {iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], + iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]}; + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16))); vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vceqq_u8(vs.val[0], mask2); - vs.val[1] = vceqq_u8(vs.val[1], mask2); + vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); + vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); - q3s.val[0] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[0], vreinterpretq_u8_u32(aux32x4_0))), vreinterpretq_s8_u8(vs.val[0])); - q3s.val[1] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_1))), vreinterpretq_s8_u8(vs.val[1])); + q3s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_0)); + q3s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_1)); vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16))); vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vceqq_u8(vs.val[0], mask2); - vs.val[1] = vceqq_u8(vs.val[1], mask2); + vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); + vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); signs += 4; - q3s.val[2] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[0], vreinterpretq_u8_u32(aux32x4_2))), vreinterpretq_s8_u8(vs.val[0])); - q3s.val[3] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_3))), vreinterpretq_s8_u8(vs.val[1])); + q3s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_2)); + q3s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_3)); const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); +#if QK_K == 256 + sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0]; + sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4]; +#else sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32/2] & 0xf)); sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32/2] >> 4)); +#endif } sumf += d*(sumi1 + sumi2); } - *s = 0.25f * sumf; + *s = sumf; #elif defined(__AVX2__) @@ -9556,6 +10198,16 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); + const __m256i idx_shift = _mm256_set_epi32(1, 2, 3, 4, 5, 6, 7, 8); + const __m256i idx_mask = _mm256_set1_epi32(256); + + typedef union { + __m256i vec[2]; + uint32_t index[16]; + } index_t; + + index_t idx; + __m256 accumf = _mm256_setzero_ps(); for (int i = 0; i < nb; ++i) { const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; @@ -9568,24 +10220,25 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q2_1 = _mm256_set_epi32(iq3xs_grid[qs[7] | ((qh[ib32+0] << 1) & 256)], - iq3xs_grid[qs[6] | ((qh[ib32+0] << 2) & 256)], - iq3xs_grid[qs[5] | ((qh[ib32+0] << 3) & 256)], - iq3xs_grid[qs[4] | ((qh[ib32+0] << 4) & 256)], - iq3xs_grid[qs[3] | ((qh[ib32+0] << 5) & 256)], - iq3xs_grid[qs[2] | ((qh[ib32+0] << 6) & 256)], - iq3xs_grid[qs[1] | ((qh[ib32+0] << 7) & 256)], - iq3xs_grid[qs[0] | ((qh[ib32+0] << 8) & 256)]); - qs += 8; - const __m256i q2_2 = _mm256_set_epi32(iq3xs_grid[qs[7] | ((qh[ib32+1] << 1) & 256)], - iq3xs_grid[qs[6] | ((qh[ib32+1] << 2) & 256)], - iq3xs_grid[qs[5] | ((qh[ib32+1] << 3) & 256)], - iq3xs_grid[qs[4] | ((qh[ib32+1] << 4) & 256)], - iq3xs_grid[qs[3] | ((qh[ib32+1] << 5) & 256)], - iq3xs_grid[qs[2] | ((qh[ib32+1] << 6) & 256)], - iq3xs_grid[qs[1] | ((qh[ib32+1] << 7) & 256)], - iq3xs_grid[qs[0] | ((qh[ib32+1] << 8) & 256)]); - qs += 8; + const __m256i idx_l = _mm256_cvtepu8_epi16(_mm_loadu_si128((const __m128i *)qs)); qs += 16; + idx.vec[0] = _mm256_set1_epi32(qh[ib32+0]); + idx.vec[1] = _mm256_set1_epi32(qh[ib32+1]); + idx.vec[0] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[0], idx_shift), idx_mask); + idx.vec[1] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[1], idx_shift), idx_mask); + idx.vec[0] = _mm256_or_si256(idx.vec[0], _mm256_cvtepi16_epi32(_mm256_castsi256_si128(idx_l))); + idx.vec[1] = _mm256_or_si256(idx.vec[1], _mm256_cvtepi16_epi32(_mm256_extractf128_si256(idx_l, 1))); + + // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. + //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); + //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); + const __m256i q2_1 = _mm256_set_epi32( + iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], + iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] + ); + const __m256i q2_2 = _mm256_set_epi32( + iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], + iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] + ); __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16)); aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); @@ -9613,7 +10266,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v } - *s = 0.25f * hsum_float_8(accumf); + *s = hsum_float_8(accumf); #else @@ -9630,8 +10283,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v const uint32_t ls2 = 2*(x[i].scales[ib32/2] >> 4) + 1; int32_t sumi = 0; for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256))); + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256))); for (int j = 0; j < 4; ++j) { sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); @@ -9643,8 +10296,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v bsum += sumi * ls1; sumi = 0; for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256))); + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256))); for (int j = 0; j < 4; ++j) { sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); @@ -9657,7 +10310,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v } sumf += d * bsum; } - *s = 0.25f * sumf; + *s = sumf; #endif } @@ -9683,7 +10336,8 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const const int nb = n / QK_K; -#if defined __ARM_NEON + // TODO: implement for QK_K = 64 +#if defined __ARM_NEON && QK_K == 256 const uint8x16_t m8 = vdupq_n_u8(0x08); const uint8x16_t m7 = vdupq_n_u8(0x07); @@ -9740,7 +10394,8 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const *s = sumf; -#elif defined __AVX2__ + // TODO: implement for QK_K = 64 +#elif defined __AVX2__ && QK_K == 256 const __m128i m8 = _mm_set1_epi8(0x08); const __m128i m7 = _mm_set1_epi8(0x07); @@ -9755,8 +10410,12 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const uint64_t aux64; - __m256i v_gindex; - const uint16_t * gindex = (const uint16_t *)&v_gindex; + typedef union m256i_uint16 { + __m256i reg; + uint16_t s[16]; + } m256i_uint16_t; + + m256i_uint16_t v_gindex; __m256 accum = _mm256_setzero_ps(); for (int i = 0; i < nb; ++i) { @@ -9771,13 +10430,13 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const memcpy(&aux64, sc, 8); sc += 8; const __m128i qh = _mm_shuffle_epi8(_mm_set_epi64x(aux64 >> 4, aux64), shuffle_h); const __m256i hbit = _mm256_cvtepu8_epi16(_mm_and_si128(qh, m8)); - v_gindex = _mm256_or_si256(_mm256_cvtepu8_epi16(ql), _mm256_slli_epi16(hbit, 5)); + v_gindex.reg = _mm256_or_si256(_mm256_cvtepu8_epi16(ql), _mm256_slli_epi16(hbit, 5)); const __m128i scales = _mm_or_si128(_mm_slli_epi16(_mm_and_si128(qh, m7), 1), m1); for (int i32 = 0; i32 < 4; ++i32) { const __m256i q8b = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q1b = _mm256_set_epi64x(iq1s_grid[gindex[4*i32+3]], iq1s_grid[gindex[4*i32+2]], - iq1s_grid[gindex[4*i32+1]], iq1s_grid[gindex[4*i32+0]]); + const __m256i q1b = _mm256_set_epi64x(iq1s_grid[v_gindex.s[4*i32+3]], iq1s_grid[v_gindex.s[4*i32+2]], + iq1s_grid[v_gindex.s[4*i32+1]], iq1s_grid[v_gindex.s[4*i32+0]]); const __m256i dot = mul_add_epi8(q1b, q8b); const __m256i s16 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, shuffle_s[i32])); const __m256i p = _mm256_madd_epi16(s16, dot); @@ -9928,6 +10587,138 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * #endif } +void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK_K == 0); +#if QK_K == 64 + ggml_vec_dot_iq4_nl_q8_0(n, s, bs, vx, bx, vy, by, nrc); +#else + + const block_iq4_xs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined __ARM_NEON + const int8x16_t values = vld1q_s8(kvalues_iq4nl); + const uint8x16_t m4b = vdupq_n_u8(0x0f); + ggml_uint8x16x2_t q4bits; + ggml_int8x16x4_t q4b; + ggml_int8x16x4_t q8b; + int32x4_t prod_1, prod_2; + + float sumf = 0; + + for (int ibl = 0; ibl < nb; ++ibl) { + + const int8_t * q8 = y[ibl].qs; + const uint8_t * q4 = x[ibl].qs; + uint16_t h = x[ibl].scales_h; + + int sumi1 = 0, sumi2 = 0; + for (int ib = 0; ib < QK_K/64; ++ib) { + + q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + + q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b)); + q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); + q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b)); + q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); + + prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); + prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); + + int ls1 = ((x[ibl].scales_l[ib] & 0xf) | ((h << 4) & 0x30)) - 32; + int ls2 = ((x[ibl].scales_l[ib] >> 4) | ((h << 2) & 0x30)) - 32; + h >>= 4; + sumi1 += vaddvq_s32(prod_1) * ls1; + sumi2 += vaddvq_s32(prod_2) * ls2; + + } + + sumf += GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d * (sumi1 + sumi2); + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); + const __m128i m4b = _mm_set1_epi8(0x0f); + + __m256 accum = _mm256_setzero_ps(); + for (int ibl = 0; ibl < nb; ++ibl) { + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + uint16_t sh = x[ibl].scales_h; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)qs); qs += 16; + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)qs); qs += 16; + const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q4b_1 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); + const __m256i q4b_2 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; + sh >>= 4; + const __m256i p_1 = _mm256_madd_epi16(p16_1, _mm256_set1_epi16(ls1)); + const __m256i p_2 = _mm256_madd_epi16(p16_2, _mm256_set1_epi16(ls2)); + sumi1 = _mm256_add_epi32(p_1, sumi1); + sumi2 = _mm256_add_epi32(p_2, sumi2); + } + accum = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), + _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accum); + } + + *s = hsum_float_8(accum); + +#else + float sumf = 0; + for (int ibl = 0; ibl < nb; ++ibl) { + const float d4d8 = GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d; + uint16_t h = x[ibl].scales_h; + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + for (int ib = 0; ib < QK_K/32; ib += 2) { + const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30); + const uint8_t ls2 = (x[ibl].scales_l[ib/2] >> 4) | ((h << 2) & 0x30); + h >>= 4; + const float d1 = d4d8*(ls1 - 32); + const float d2 = d4d8*(ls2 - 32); + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 16; ++j) { + sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf]; + sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4]; + } + sumf += d1 * (sumi1 + sumi2); + qs += 16; + q8 += 32; + sumi1 = sumi2 = 0; + for (int j = 0; j < 16; ++j) { + sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf]; + sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4]; + } + sumf += d2 * (sumi1 + sumi2); + qs += 16; + q8 += 32; + } + } + *s = sumf; +#endif +#endif +} + // ================================ IQ2 quantization ============================================= typedef struct { @@ -9936,22 +10727,25 @@ typedef struct { uint16_t * neighbours; } iq2_entry_t; -static iq2_entry_t iq2_data[3] = { +static iq2_entry_t iq2_data[4] = { + {NULL, NULL, NULL}, {NULL, NULL, NULL}, {NULL, NULL, NULL}, {NULL, NULL, NULL}, }; static inline int iq2_data_index(enum ggml_type type) { - GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S); + GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S); return type == GGML_TYPE_IQ2_XXS ? 0 : - type == GGML_TYPE_IQ2_XS ? 1 : 2; + type == GGML_TYPE_IQ2_XS ? 1 : + type == GGML_TYPE_IQ1_S ? 2 : 3; } static inline int iq2_grid_size(enum ggml_type type) { - GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S); + GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S); return type == GGML_TYPE_IQ2_XXS ? 256 : - type == GGML_TYPE_IQ2_XS ? 512 : 512; + type == GGML_TYPE_IQ2_XS ? 512 : + type == GGML_TYPE_IQ1_S ? 512 : 1024; } static int iq2_compare_func(const void * left, const void * right) { @@ -10052,11 +10846,79 @@ void iq2xs_init_impl(enum ggml_type type) { 41557, 41633, 41989, 42021, 42056, 42068, 42074, 42113, 42242, 42265, 42274, 42325, 42340, 42402, 42501, 42512, 42533, 42624, 42632, 42666, 43040, 43093, 43106, 43168, 43176, 43264, 43286, 43345, 43429, 43590, 43618, 43680, }; + static const uint16_t kgrid_2bit_1024[1024] = { + 0, 2, 5, 8, 10, 17, 20, 22, 25, 32, 34, 37, 40, 65, 68, 70, + 73, 80, 82, 85, 88, 97, 100, 102, 105, 128, 130, 133, 136, 145, 148, 160, + 165, 170, 257, 260, 262, 265, 272, 274, 277, 280, 289, 292, 320, 322, 325, 328, + 337, 340, 342, 345, 352, 357, 360, 385, 388, 400, 402, 405, 417, 420, 512, 514, + 517, 520, 529, 532, 544, 554, 577, 580, 582, 585, 592, 597, 640, 645, 650, 660, + 674, 1025, 1028, 1030, 1033, 1040, 1042, 1045, 1048, 1057, 1060, 1062, 1065, 1088, 1090, 1093, + 1096, 1098, 1105, 1108, 1110, 1113, 1120, 1122, 1125, 1153, 1156, 1158, 1161, 1168, 1173, 1176, + 1185, 1188, 1280, 1282, 1285, 1288, 1290, 1297, 1300, 1302, 1305, 1312, 1317, 1320, 1345, 1348, + 1350, 1353, 1360, 1362, 1365, 1368, 1377, 1380, 1408, 1410, 1413, 1416, 1425, 1428, 1440, 1537, + 1540, 1542, 1545, 1552, 1557, 1600, 1605, 1608, 1617, 1620, 1632, 1665, 1668, 1680, 2048, 2050, + 2053, 2056, 2065, 2068, 2070, 2073, 2080, 2085, 2090, 2113, 2116, 2118, 2121, 2128, 2130, 2133, + 2136, 2145, 2148, 2176, 2181, 2196, 2218, 2305, 2308, 2320, 2322, 2325, 2328, 2337, 2368, 2373, + 2376, 2385, 2388, 2400, 2433, 2448, 2560, 2577, 2580, 2594, 2600, 2602, 2640, 2713, 4097, 4100, + 4102, 4105, 4112, 4114, 4117, 4120, 4129, 4132, 4134, 4160, 4162, 4165, 4168, 4177, 4180, 4182, + 4185, 4192, 4194, 4197, 4200, 4225, 4228, 4230, 4240, 4245, 4248, 4257, 4260, 4352, 4354, 4357, + 4360, 4362, 4369, 4372, 4374, 4377, 4384, 4386, 4389, 4392, 4417, 4420, 4422, 4425, 4432, 4434, + 4437, 4440, 4449, 4452, 4480, 4482, 4485, 4488, 4497, 4500, 4609, 4612, 4617, 4624, 4629, 4641, + 4644, 4672, 4677, 4689, 4692, 4737, 4740, 4752, 5120, 5122, 5125, 5128, 5137, 5140, 5142, 5145, + 5152, 5157, 5160, 5185, 5188, 5190, 5193, 5200, 5202, 5205, 5208, 5217, 5220, 5248, 5250, 5253, + 5256, 5265, 5268, 5280, 5377, 5380, 5382, 5385, 5392, 5394, 5397, 5400, 5409, 5412, 5440, 5442, + 5445, 5448, 5457, 5460, 5472, 5505, 5508, 5520, 5632, 5637, 5640, 5649, 5652, 5664, 5697, 5700, + 5712, 5760, 5802, 6145, 6148, 6150, 6153, 6160, 6165, 6168, 6177, 6208, 6210, 6213, 6216, 6225, + 6228, 6240, 6273, 6276, 6400, 6402, 6405, 6408, 6417, 6420, 6432, 6465, 6468, 6480, 6505, 6562, + 6660, 6672, 6720, 6742, 8192, 8194, 8197, 8200, 8209, 8212, 8214, 8217, 8224, 8229, 8234, 8257, + 8260, 8272, 8274, 8277, 8292, 8320, 8330, 8340, 8362, 8449, 8452, 8464, 8466, 8469, 8481, 8512, + 8514, 8517, 8529, 8532, 8544, 8577, 8580, 8592, 8704, 8714, 8738, 8744, 8746, 8772, 8784, 8840, + 8842, 8872, 9217, 9220, 9222, 9225, 9232, 9237, 9240, 9249, 9252, 9280, 9282, 9285, 9288, 9297, + 9300, 9312, 9345, 9348, 9360, 9472, 9477, 9480, 9489, 9492, 9504, 9537, 9540, 9552, 9574, 9600, + 9729, 9732, 9744, 9792, 9817, 10240, 10245, 10257, 10260, 10305, 10308, 10320, 10378, 10410, 10497, 10500, + 10512, 10645, 10762, 10786, 10852, 10888, 10890, 16385, 16388, 16390, 16393, 16400, 16402, 16405, 16408, 16410, + 16417, 16420, 16422, 16448, 16450, 16453, 16456, 16458, 16465, 16468, 16470, 16473, 16480, 16482, 16485, 16513, + 16516, 16528, 16533, 16536, 16545, 16548, 16640, 16642, 16645, 16648, 16657, 16660, 16662, 16665, 16672, 16674, + 16677, 16705, 16708, 16710, 16713, 16720, 16722, 16725, 16728, 16737, 16740, 16768, 16770, 16773, 16776, 16785, + 16788, 16800, 16897, 16900, 16912, 16914, 16917, 16920, 16932, 16960, 16965, 16968, 16977, 16980, 16992, 17025, + 17028, 17408, 17410, 17413, 17416, 17418, 17425, 17428, 17430, 17433, 17440, 17442, 17445, 17448, 17473, 17476, + 17478, 17481, 17488, 17490, 17493, 17496, 17505, 17508, 17536, 17538, 17541, 17544, 17553, 17556, 17568, 17665, + 17668, 17670, 17673, 17680, 17682, 17685, 17688, 17697, 17700, 17728, 17730, 17733, 17736, 17745, 17748, 17760, + 17770, 17793, 17796, 17808, 17920, 17922, 17925, 17928, 17937, 17940, 17952, 17985, 17988, 18000, 18048, 18085, + 18433, 18436, 18441, 18448, 18450, 18453, 18456, 18465, 18468, 18496, 18498, 18501, 18504, 18513, 18516, 18528, + 18564, 18576, 18688, 18690, 18693, 18696, 18705, 18708, 18720, 18753, 18756, 18768, 18816, 18838, 18945, 18948, + 18960, 19008, 20480, 20482, 20485, 20488, 20497, 20500, 20502, 20505, 20512, 20514, 20517, 20520, 20545, 20548, + 20550, 20553, 20560, 20562, 20565, 20568, 20577, 20580, 20608, 20610, 20613, 20616, 20625, 20628, 20737, 20740, + 20742, 20745, 20752, 20754, 20757, 20760, 20769, 20772, 20800, 20802, 20805, 20808, 20817, 20820, 20832, 20865, + 20868, 20880, 20992, 20997, 21000, 21009, 21012, 21024, 21057, 21060, 21072, 21097, 21120, 21505, 21508, 21510, + 21513, 21520, 21522, 21525, 21528, 21537, 21540, 21568, 21570, 21573, 21576, 21585, 21588, 21600, 21633, 21636, + 21648, 21760, 21762, 21765, 21768, 21777, 21780, 21792, 21825, 21828, 21840, 21888, 22017, 22020, 22032, 22054, + 22080, 22528, 22530, 22533, 22536, 22545, 22548, 22560, 22593, 22596, 22608, 22618, 22656, 22785, 22788, 22800, + 22848, 23040, 23065, 23173, 23208, 24577, 24580, 24582, 24592, 24594, 24597, 24600, 24609, 24612, 24640, 24645, + 24648, 24657, 24660, 24672, 24708, 24720, 24832, 24834, 24837, 24840, 24849, 24852, 24864, 24897, 24900, 24912, + 24960, 24985, 25092, 25104, 25152, 25174, 25249, 25600, 25605, 25608, 25617, 25620, 25632, 25665, 25668, 25680, + 25728, 25857, 25860, 25872, 25920, 25930, 25960, 26002, 26112, 26260, 26625, 26628, 26640, 26725, 26776, 26880, + 26922, 27202, 27297, 32768, 32770, 32773, 32776, 32785, 32788, 32793, 32800, 32805, 32833, 32836, 32848, 32850, + 32853, 32856, 32865, 32896, 32901, 32913, 32916, 33025, 33028, 33033, 33040, 33042, 33045, 33048, 33057, 33060, + 33088, 33090, 33093, 33096, 33105, 33108, 33153, 33156, 33168, 33193, 33280, 33285, 33290, 33297, 33300, 33345, + 33348, 33360, 33793, 33796, 33798, 33801, 33808, 33810, 33813, 33816, 33825, 33856, 33858, 33861, 33864, 33873, + 33876, 33888, 33921, 33924, 33936, 34048, 34050, 34053, 34056, 34065, 34068, 34080, 34113, 34116, 34128, 34176, + 34186, 34305, 34308, 34320, 34345, 34368, 34816, 34821, 34833, 34836, 34881, 34884, 34896, 34978, 35073, 35076, + 35136, 35173, 35362, 35416, 35418, 35458, 35490, 36865, 36868, 36873, 36880, 36882, 36885, 36888, 36900, 36928, + 36930, 36933, 36936, 36945, 36948, 36960, 36993, 36996, 37008, 37120, 37125, 37137, 37140, 37185, 37188, 37200, + 37210, 37377, 37380, 37392, 37440, 37542, 37888, 37890, 37893, 37896, 37905, 37908, 37920, 37953, 37956, 37968, + 38016, 38038, 38145, 38148, 38160, 38208, 38296, 38305, 38400, 38470, 38500, 38913, 38916, 38928, 38950, 38976, + 39081, 39168, 39241, 39250, 39568, 40960, 40965, 40970, 40980, 40994, 41002, 41025, 41028, 41040, 41122, 41130, + 41280, 41317, 41474, 41482, 41506, 41512, 41514, 41602, 41608, 41610, 41640, 41985, 41988, 42000, 42048, 42121, + 42148, 42240, 42265, 42577, 43018, 43048, 43170, 43348, 43398, 43528, 43530, 43552, 43554, 43560, 43656, 43690, + }; const int kmap_size = 43692; - const int nwant = type == GGML_TYPE_IQ1_S ? 3 : 2; + //const int nwant = type == GGML_TYPE_IQ1_S ? 3 : 2; + const int nwant = type == GGML_TYPE_IQ1_S ? 3 : type == GGML_TYPE_IQ2_S ? 1 : 2; const uint16_t * kgrid = type == GGML_TYPE_IQ2_XXS ? kgrid_2bit_256 : - type == GGML_TYPE_IQ2_XS ? kgrid_2bit_512 : kgrid_1bit_512; + type == GGML_TYPE_IQ2_XS ? kgrid_2bit_512 : + type == GGML_TYPE_IQ1_S ? kgrid_1bit_512 : kgrid_2bit_1024; uint64_t * kgrid_q2xs; int * kmap_q2xs; uint16_t * kneighbors_q2xs; @@ -10153,7 +11015,7 @@ void iq2xs_init_impl(enum ggml_type type) { } void iq2xs_free_impl(enum ggml_type type) { - GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S); + GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S); const int gindex = iq2_data_index(type); if (iq2_data[gindex].grid) { free(iq2_data[gindex].grid); iq2_data[gindex].grid = NULL; @@ -10202,7 +11064,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict const int kMaxQ = 3; - const int nbl = n/256; + const int nbl = n/QK_K; block_iq2_xxs * y = vy; @@ -10375,7 +11237,7 @@ static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict v const int kMaxQ = 3; - const int nbl = n/256; + const int nbl = n/QK_K; block_iq2_xs * y = vy; @@ -11095,7 +11957,8 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo } float best = 0; float scale = max/(2*kMaxQ-1); - for (int is = -15; is <= 15; ++is) { + for (int k = 0; k < bs4; ++k) is_on_grid[k] = false; + for (int is = -9; is <= 9; ++is) { float id = (2*kMaxQ-1+is*0.2f)/max; float this_scale = 1/id; for (int k = 0; k < bs4; ++k) { @@ -11131,7 +11994,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo if (n_not_ongrid > 0 && scale > 0) { float id = 1/scale; for (int k = 0; k < bs4; ++k) { - if (is_on_grid[k]) continue; + //if (is_on_grid[k]) continue; uint16_t u = 0; for (int i = 0; i < 4; ++i) { int l = nearest_int(0.5f*(id*xval[4*k+i]-1)); @@ -11187,7 +12050,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo } float d = max_scale/31; - y[ibl].d = GGML_FP32_TO_FP16(d); + y[ibl].d = GGML_FP32_TO_FP16(d * 1.033f); float id = 1/d; for (int ib = 0; ib < QK_K/block_size; ib += 2) { int l1 = nearest_int(0.5f*(id*scales[ib+0]-1)); @@ -11318,7 +12181,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?"); GGML_ASSERT(n%QK_K == 0); - const int nbl = n/256; + const int nbl = n/QK_K; block_iq1_s * y = vy; @@ -11453,23 +12316,23 @@ static inline int best_index_int8(int n, const int8_t * val, float x) { return x - val[mu-1] < val[mu] - x ? mu-1 : mu; } -static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RESTRICT x, - ggml_fp16_t * dh, uint8_t * q4, - float * weight, uint8_t * L, +static void quantize_row_iq4_nl_impl(const int super_block_size, const int block_size, const float * GGML_RESTRICT x, + ggml_fp16_t * dh, uint8_t * q4, uint16_t * scales_h, uint8_t * scales_l, + float * scales, float * weight, uint8_t * L, const int8_t * values, const float * quant_weights) { const int ntry = 7; float sigma2 = 0; - for (int j = 0; j < QK4_NL; ++j) sigma2 += x[j]*x[j]; - sigma2 *= 2.f/QK4_NL; + for (int j = 0; j < super_block_size; ++j) sigma2 += x[j]*x[j]; + sigma2 *= 2.f/super_block_size; - const int nb = QK4_NL/block_size; + memset(q4, 0, super_block_size/2); + dh[0] = GGML_FP32_TO_FP16(0.f); - memset(q4, 0, QK4_NL/2); - for (int ib = 0; ib < nb; ++ib) { - dh[ib] = GGML_FP32_TO_FP16(0.f); + float max_scale = 0, amax_scale = 0; + for (int ib = 0; ib < super_block_size/block_size; ++ib) { const float * xb = x + ib*block_size; if (quant_weights) { const float * qw = quant_weights + ib*block_size; @@ -11485,6 +12348,7 @@ static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RE } } if (!amax) { + scales[ib] = 0; continue; } float d = -max/values[0]; @@ -11498,7 +12362,6 @@ static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RE sumqx += w*q*xb[j]; sumq2 += w*q*q; } - float best_id = id; d = sumqx/sumq2; float best = d*sumqx; for (int itry = -ntry; itry <= ntry; ++itry) { @@ -11514,15 +12377,47 @@ static void quantize_row_iq4_nl_impl(const int block_size, const float * GGML_RE } if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { d = sumqx/sumq2; best = d * sumqx; - best_id = id; } } - dh[ib] = GGML_FP32_TO_FP16(d); - for (int j = 0; j < block_size; ++j) { - L[ib*block_size + j] = best_index_int8(16, values, best_id*xb[j]); + scales[ib] = d; + float abs_d = fabsf(d); + if (abs_d > amax_scale) { + amax_scale = abs_d; max_scale = d; + } + } + + if (super_block_size/block_size > 1) { + int nb = super_block_size/block_size; + memset(scales_h, 0, ((nb+7)/8)*sizeof(uint16_t)); + float d = -max_scale/32; + dh[0] = GGML_FP32_TO_FP16(d); + float id = d ? 1/d : 0.f; + for (int ib = 0; ib < super_block_size/block_size; ++ib) { + int l = nearest_int(id*scales[ib]); + l = MAX(-32, MIN(31, l)); + float dl = d * l; + float idl = dl ? 1/dl : 0.f; + uint8_t * Lb = L + ib*block_size; + const float * xb = x + ib*block_size; + for (int j = 0; j < block_size; ++j) { + Lb[j] = best_index_int8(16, values, idl*xb[j]); + } + l += 32; + uint8_t l_l = l & 0xf; + uint8_t l_h = l >> 4; + if (ib%2 == 0) scales_l[ib/2] = l_l; + else scales_l[ib/2] |= (l_l << 4); + scales_h[ib/8] |= (l_h << 2*(ib%8)); + } + } else { + dh[0] = GGML_FP32_TO_FP16(scales[0]); + float id = scales[0] ? 1/scales[0] : 0; + for (int j = 0; j < super_block_size; ++j) { + L[j] = best_index_int8(16, values, id*x[j]); } } - for (int i = 0; i < QK4_NL/32; ++i) { + + for (int i = 0; i < super_block_size/32; ++i) { for (int j = 0; j < 16; ++j) { q4[16*i + j] = L[32*i + j] | (L[32*i + 16 + j] << 4); } @@ -11535,12 +12430,16 @@ size_t quantize_iq4_nl(const float * src, void * dst, int nrow, int n_per_row, i int nblock = n_per_row/QK4_NL; char * qrow = (char *)dst; uint8_t L[QK4_NL]; - float weight[32]; + float weight[QK4_NL]; + uint16_t unused_h; + uint8_t * unused_l = NULL; + float scale; for (int row = 0; row < nrow; ++row) { block_iq4_nl * iq4 = (block_iq4_nl *)qrow; for (int ibl = 0; ibl < nblock; ++ibl) { const float * qw = quant_weights ? quant_weights + QK4_NL*ibl : NULL; - quantize_row_iq4_nl_impl(32, src + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, weight, L, kvalues_iq4nl, qw); + quantize_row_iq4_nl_impl(QK4_NL, 32, src + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, &unused_h, unused_l, + &scale, weight, L, kvalues_iq4nl, qw); } src += n_per_row; qrow += nblock*sizeof(block_iq4_nl); @@ -11559,3 +12458,232 @@ void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * rest quantize_iq4_nl(x, y, 1, k, NULL, NULL); } +size_t quantize_iq4_xs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { +#if QK_K == 64 + return quantize_iq4_nl(src, dst, nrow, n_per_row, hist, quant_weights); +#else + (void)hist; + GGML_ASSERT(n_per_row%QK_K == 0); + int nblock = n_per_row/QK_K; + char * qrow = (char *)dst; + uint8_t L[QK_K]; + float weight[32]; + float scales[QK_K/32]; + for (int row = 0; row < nrow; ++row) { + block_iq4_xs * iq4 = (block_iq4_xs *)qrow; + for (int ibl = 0; ibl < nblock; ++ibl) { + const float * qw = quant_weights ? quant_weights + QK_K*ibl : NULL; + quantize_row_iq4_nl_impl(QK_K, 32, src + QK_K*ibl, &iq4[ibl].d, iq4[ibl].qs, &iq4[ibl].scales_h, iq4[ibl].scales_l, + scales, weight, L, kvalues_iq4nl, qw); + } + src += n_per_row; + qrow += nblock*sizeof(block_iq4_xs); + } + return nrow * nblock * sizeof(block_iq4_xs); +#endif +} + +void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int k) { + assert(k % QK_K == 0); + block_iq4_xs * restrict y = vy; + quantize_row_iq4_xs_reference(x, y, k); +} + +void quantize_row_iq4_xs_reference(const float * restrict x, block_iq4_xs * restrict y, int k) { + assert(k % QK_K == 0); + quantize_iq4_xs(x, y, 1, k, NULL, NULL); +} + +// =============================== 2.5625 bpw + +static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { + + const int gindex = iq2_data_index(GGML_TYPE_IQ2_S); + + const uint64_t * kgrid_q2xs = iq2_data[gindex].grid; + const int * kmap_q2xs = iq2_data[gindex].map; + const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours; + + GGML_ASSERT(kmap_q2xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(kgrid_q2xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(n%QK_K == 0); + + const int kMaxQ = 3; + + const int nbl = n/QK_K; + + block_iq2_s * y = vy; + + float scales[QK_K/16]; + float weight[16]; + float xval[16]; + int8_t L[16]; + int8_t Laux[16]; + float waux[16]; + bool is_on_grid[2]; + bool is_on_grid_aux[2]; + uint8_t block_signs[2]; + + for (int ibl = 0; ibl < nbl; ++ibl) { + + memset(&y[ibl], 0, sizeof(block_iq2_s)); + y[ibl].d = GGML_FP32_TO_FP16(0.f); + + float max_scale = 0; + + const float * xbl = x + QK_K*ibl; + float sumx2 = 0; + for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; + float sigma2 = 2*sumx2/QK_K; + + for (int ib = 0; ib < QK_K/16; ++ib) { + const float * xb = xbl + 16*ib; + if (quant_weights) { + const float * qw = quant_weights + QK_K*ibl + 16*ib; + for (int i = 0; i < 16; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + } else { + for (int i = 0; i < 16; ++i) weight[i] = 0.25f*sigma2 + xb[i]*xb[i]; + } + for (int i = 0; i < 16; ++i) waux[i] = sqrtf(weight[i]); + for (int k = 0; k < 2; ++k) { + uint8_t s = 0; + for (int i = 0; i < 8; ++i) { + if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i]; + else { + xval[8*k + i] = -xb[8*k + i]; s |= (1 << i); + } + } + block_signs[k] = s; + } + float max = xval[0]; + for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]); + if (!max) { + scales[ib] = 0; + continue; + } + float best = 0; + float scale = max/(2*kMaxQ-1); + is_on_grid[0] = is_on_grid[1] = true; + for (int is = -9; is <= 9; ++is) { + float id = (2*kMaxQ-1+is*0.1f)/max; + float this_scale = 1/id; + for (int k = 0; k < 2; ++k) { + for (int i = 0; i < 8; ++i) { + int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); + Laux[8*k+i] = MAX(0, MIN(kMaxQ-1, l)); + } + uint16_t u = 0; + for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i); + int grid_index = kmap_q2xs[u]; + is_on_grid_aux[k] = true; + if (grid_index < 0) { + is_on_grid_aux[k] = false; + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k); + } + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 16; ++i) { + float w = weight[i]; + float q = 2*Laux[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { + scale = sumqx/sumq2; best = scale*sumqx; + for (int i = 0; i < 16; ++i) L[i] = Laux[i]; + for (int k = 0; k < 2; ++k) is_on_grid[k] = is_on_grid_aux[k]; + } + } + int n_not_ongrid = 0; + for (int k = 0; k < 2; ++k) if (!is_on_grid[k]) ++n_not_ongrid; + if (n_not_ongrid > 0 && scale > 0) { + float id = 1/scale; + for (int k = 0; k < 2; ++k) { + if (is_on_grid[k]) continue; + uint16_t u = 0; + for (int i = 0; i < 8; ++i) { + int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); + l = MAX(0, MIN(kMaxQ-1, l)); + u |= (l << 2*i); + L[8*k + i] = l; + } + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; + grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, scale, L + 8*k); + } + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 16; ++i) { + float w = weight[i]; + float q = 2*L[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0) scale = sumqx/sumq2; + } + if (scale < 0) { + scale = -scale; + for (int k = 0; k < 2; ++k) block_signs[k] = ~block_signs[k]; + } + for (int k = 0; k < 2; ++k) { + uint16_t u = 0; + for (int i = 0; i < 8; ++i) u |= (L[8*k+i] << 2*i); + int grid_index = kmap_q2xs[u]; + if (grid_index < 0) { + printf("Oops: found point %u not on grid:", u); + for (int i = 0; i < 8; ++i) printf(" %d", L[8*k+i]); + printf("\n"); + GGML_ASSERT(false); + } + const int i8 = 2*ib + k; + y[ibl].qs[i8] = grid_index & 255; + y[ibl].qh[i8/4] |= ((grid_index >> 8) << 2*(i8%4)); + y[ibl].qs[QK_K/8 + i8] = block_signs[k]; + } + GGML_ASSERT(scale >= 0); + scales[ib] = scale; + max_scale = MAX(max_scale, scale); + } + + if (!max_scale) { + continue; + } + + float d = max_scale/31; + y[ibl].d = GGML_FP32_TO_FP16(d * 0.9875f); + float id = 1/d; + for (int ib = 0; ib < QK_K/16; ++ib) { + int l = nearest_int(0.5f*(id*scales[ib]-1)); + l = MAX(0, MIN(15, l)); + if (ib%2 == 0) y[ibl].scales[ib/2] = l; + else y[ibl].scales[ib/2] |= (l << 4); + } + } +} + +size_t quantize_iq2_s(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + GGML_ASSERT(n_per_row%QK_K == 0); + int nblock = n_per_row/QK_K; + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_iq2_s_impl(src, qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += nblock*sizeof(block_iq2_s); + } + return nrow * nblock * sizeof(block_iq2_s); +} + +void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restrict y, int k) { + assert(k % QK_K == 0); + quantize_iq2_s(x, y, 1, k, NULL, NULL); +} + +void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int k) { + assert(k % QK_K == 0); + block_iq2_s * restrict y = vy; + quantize_row_iq2_s_reference(x, y, k); +} diff --git a/ggml-quants.h b/ggml-quants.h index 303b0b6f9552e..316e356876037 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -182,6 +182,15 @@ typedef struct { } block_iq2_xs; static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); +// 2.5625 bpw quants +typedef struct { + ggml_fp16_t d; + uint8_t qs[QK_K/4]; + uint8_t qh[QK_K/32]; + uint8_t scales[QK_K/32]; +} block_iq2_s; +static_assert(sizeof(block_iq2_s) == sizeof(ggml_fp16_t) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding"); + // (Almost) "true" 3-bit quantization. // Due to the need to use blocks as per ggml design, it ends up using // 3.0625 bpw because of the 16-bit scale for each block of 256. @@ -221,6 +230,19 @@ typedef struct { } block_iq4_nl; static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding"); +#if QK_K == 64 +#define block_iq4_xs block_iq4_nl +//typedef struct block_iq4_nl block_iq4_xs; +#else +typedef struct { + ggml_fp16_t d; + uint16_t scales_h; + uint8_t scales_l[QK_K/64]; + uint8_t qs[QK_K/2]; +} block_iq4_xs; +static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); +#endif + #ifdef __cplusplus extern "C" { #endif @@ -241,7 +263,9 @@ void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGM void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k); void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k); void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int k); +void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int k); void quantize_row_iq3_s_reference (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int k); +void quantize_row_iq2_s_reference (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int k); void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); @@ -258,7 +282,9 @@ void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); void quantize_row_iq3_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_iq2_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); // Dequantization void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); @@ -276,9 +302,11 @@ void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRI void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); // Dot product @@ -295,9 +323,11 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); // @@ -305,9 +335,11 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const // size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_iq2_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq1_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq4_nl (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_iq4_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq3_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index c6c3c6e6fef07..47a605b01a07a 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -661,26 +661,29 @@ namespace dpct /// \param [out] total_memory The number of bytes of total memory on the SYCL device. void get_memory_info(size_t &free_memory, size_t &total_memory) { + total_memory = get_device_info().get_global_mem_size(); + const char *warning_info = "get_memory_info: [warning] ext_intel_free_memory is not " + "supported (export/set ZES_ENABLE_SYSMAN=1 to support), " + "use total memory as free memory"; #if (defined(__SYCL_COMPILER_VERSION) && __SYCL_COMPILER_VERSION >= 20221105) if (!has(sycl::aspect::ext_intel_free_memory)) { - std::cerr << "get_memory_info: ext_intel_free_memory is not supported." << std::endl; - free_memory = 0; + std::cerr << warning_info << std::endl; + free_memory = total_memory; } else { free_memory = get_info(); } #else - std::cerr << "get_memory_info: ext_intel_free_memory is not supported." << std::endl; - free_memory = 0; + std::cerr << warning_info << std::endl; + free_memory = total_memory; #if defined(_MSC_VER) && !defined(__clang__) #pragma message("Querying the number of bytes of free memory is not supported") #else #warning "Querying the number of bytes of free memory is not supported" #endif #endif - total_memory = get_device_info().get_global_mem_size(); } void get_device_info(device_info &out) const @@ -738,15 +741,25 @@ namespace dpct #endif // DPCT_USM_LEVEL_NONE } - sycl::queue *create_in_order_queue(bool enable_exception_handler = false) - { + sycl::queue *create_queue(sycl::context context, sycl::device device, + bool enable_exception_handler = false) { + return create_in_order_queue(context, device, enable_exception_handler); + } + + sycl::queue *create_in_order_queue(bool enable_exception_handler = false) { std::lock_guard lock(m_mutex); return create_queue_impl(enable_exception_handler, - sycl::property::queue::in_order()); + sycl::property::queue::in_order()); } - sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) - { + sycl::queue *create_in_order_queue(sycl::context context, sycl::device device, + bool enable_exception_handler = false) { + std::lock_guard lock(m_mutex); + return create_queue_impl(context, device, enable_exception_handler, + sycl::property::queue::in_order()); + } + + sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) { std::lock_guard lock(m_mutex); return create_queue_impl(enable_exception_handler); } @@ -809,6 +822,25 @@ namespace dpct return _queues.back().get(); } + template + sycl::queue *create_queue_impl(sycl::context context, sycl::device device, + bool enable_exception_handler, + Properties... properties) { + sycl::async_handler eh = {}; + if (enable_exception_handler) { + eh = exception_handler; + } + _queues.push_back(std::make_shared( + context, device, eh, + sycl::property_list( + #ifdef DPCT_PROFILING_ENABLED + sycl::property::queue::enable_profiling(), + #endif + properties...))); + + return _queues.back().get(); + } + void get_version(int &major, int &minor) const { detail::get_version(*this, major, minor); @@ -2943,14 +2975,11 @@ bool ggml_sycl_loaded(void); void * ggml_sycl_host_malloc(size_t size); void ggml_sycl_host_free(void * ptr); bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -void ggml_sycl_set_tensor_split(const float * tensor_split); -void ggml_sycl_transform_tensor(void * data, struct ggml_tensor * tensor); void ggml_sycl_free_data(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers_no_scratch(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers_no_alloc(struct ggml_tensor * tensor); -void ggml_sycl_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset); void ggml_sycl_copy_to_device(struct ggml_tensor * tensor); void ggml_sycl_set_main_device(int main_device); void ggml_sycl_set_mul_mat_q(bool mul_mat_q); @@ -2963,6 +2992,14 @@ int get_main_device(); void print_ggml_tensor(const char*name, struct ggml_tensor *src); void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt); +void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst, + const void *ptr_src, size_t size) { + char *host_buf = (char *)malloc(size); + q_src.memcpy(host_buf, (const char *)ptr_src, size).wait(); + q_dst.memcpy((char *)ptr_dst, host_buf, size).wait(); + free(host_buf); +} + static __dpct_inline__ int get_int_from_int8(const int8_t *x8, const int &i32) { const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment @@ -3180,6 +3217,8 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ #define SYCL_SILU_BLOCK_SIZE 256 #define SYCL_TANH_BLOCK_SIZE 256 #define SYCL_RELU_BLOCK_SIZE 256 +#define SYCL_HARDSIGMOID_BLOCK_SIZE 256 +#define SYCL_HARDSWISH_BLOCK_SIZE 256 #define SYCL_SQR_BLOCK_SIZE 256 #define SYCL_CPY_BLOCK_SIZE 32 #define SYCL_SCALE_BLOCK_SIZE 256 @@ -3196,6 +3235,7 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ #define SYCL_PAD_BLOCK_SIZE 256 #define SYCL_ACC_BLOCK_SIZE 256 #define SYCL_IM2COL_BLOCK_SIZE 256 +#define SYCL_POOL2D_BLOCK_SIZE 256 // dmmv = dequantize_mul_mat_vec #ifndef GGML_SYCL_DMMV_X @@ -3218,8 +3258,7 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA #define MUL_MAT_SRC1_COL_STRIDE 128 #define MAX_STREAMS 8 -static dpct::queue_ptr g_syclStreams[GGML_SYCL_MAX_DEVICES][MAX_STREAMS] = { - {0}}; +static dpct::queue_ptr g_syclStreams[GGML_SYCL_MAX_DEVICES][MAX_STREAMS] = {{0}}; struct ggml_tensor_extra_gpu { void * data_device[GGML_SYCL_MAX_DEVICES]; // 1 pointer for each device for split tensors @@ -3228,30 +3267,108 @@ struct ggml_tensor_extra_gpu { [MAX_STREAMS]; // events for synchronizing multiple GPUs }; -inline dpct::err0 ggml_sycl_set_device(const int device) try { - int current_device; +class sycl_gpu_mgr { + public: + std::vector gpus; + std::vector devices; + sycl::queue *first_queue; + sycl::context co_ctx; + int max_compute_units = 0; + int work_group_size = 0; + std::string gpus_list = ""; + + sycl_gpu_mgr() { + detect_sycl_gpu_list_with_max_cu(); + get_allow_gpus(); + create_context_with_gpus(); + } + + void create_context_with_gpus() { + sycl::context ctx = sycl::context(devices); + assert(gpus.size() > 0); + first_queue = dpct::get_current_device().create_queue(ctx, devices[0]); + co_ctx = first_queue->get_context(); + } + + sycl::context &get_co_ctx() { return co_ctx; } + + void get_allow_gpus() { + gpus_list = ""; + for (size_t i = 0; i < gpus.size(); ++i) { + gpus_list += std::to_string(gpus[i]); + gpus_list += ","; + } + if (gpus_list.length() > 2) { + gpus_list.pop_back(); + } + } - SYCL_CHECK(CHECK_TRY_ERROR( - current_device = dpct::dev_mgr::instance().current_device_id())); + bool is_allowed_gpu(int device_id) { + return std::find(gpus.begin(), gpus.end(), device_id) != gpus.end(); + } - // GGML_SYCL_DEBUG("ggml_sycl_set_device device=%d, current_device=%d\n", device, current_device); - if (device == current_device) { - return 0; - } + void detect_sycl_gpu_list_with_max_cu() try { + int device_count = dpct::dev_mgr::instance().device_count(); - return CHECK_TRY_ERROR(dpct::select_device(device)); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - crash(); - std::exit(1); -} + for (int id = 0; id < device_count; id++) { + sycl::device device = dpct::dev_mgr::instance().get_device(id); + if (!device.is_gpu()) + continue; + dpct::device_info prop; + dpct::get_device_info(prop, device); + if (max_compute_units < prop.get_max_compute_units()) + max_compute_units = prop.get_max_compute_units(); + } + + for (int id = 0; id < device_count; id++) { + sycl::device device = dpct::dev_mgr::instance().get_device(id); + if (!device.is_gpu()) + continue; + dpct::device_info prop; + dpct::get_device_info(prop, device); + if (max_compute_units == prop.get_max_compute_units() && + prop.get_major_version() == 1) { + gpus.push_back(id); + devices.push_back(device); + work_group_size = prop.get_max_work_group_size(); + } + } + return; + } catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); + } + + int get_gpu_count() { return (int)gpus.size(); } + + int get_index(int id) { + for (int i = 0; i < (int)gpus.size(); i++) { + if (gpus[i] == id) + return i; + } + assert(false); + return -1; + } + + int get_next_index(int id) { + int cur_index = get_index(id); + for (int i = cur_index + 1; i < (int)gpus.size(); i++) { + if (gpus[i] == id) + return i; + } + assert(false); + return -1; + } +}; +static sycl_gpu_mgr *g_sycl_gpu_mgr = NULL; static int g_device_count = -1; static int g_all_sycl_device_count = -1; static int g_main_device = -1; -static int g_main_device_index = -1; +static int g_main_device_id = -1; + +static std::array g_default_tensor_split = {}; static float g_tensor_split[GGML_SYCL_MAX_DEVICES] = {0}; @@ -3268,8 +3385,6 @@ struct sycl_device_id2index { int index; }; -static sycl_device_id2index g_sycl_device_id2index[GGML_SYCL_MAX_DEVICES] = { {-1} }; - static void * g_scratch_buffer = nullptr; static size_t g_scratch_size = 0; // disabled by default static size_t g_scratch_offset = 0; @@ -3290,6 +3405,63 @@ static void bad_arch(const sycl::stream &stream_ct1) { (void) bad_arch; // suppress unused function warning } +/* +device_index: device index from 0 to n (continue numbers). + It is used for device select/set in SYCL backend internal data structure. +*/ +void check_allow_gpu_index(const int device_index) { + if (device_index >= g_device_count) { + char error_buf[256]; + snprintf(error_buf, sizeof(error_buf), + "%s error: device_index:%d is out of range: [0-%d]", __func__, + device_index, g_device_count - 1); + fprintf(stderr, "%s\n", error_buf); + assert(false); + } +} + +/* +device_id: device ID is shown by ggml_backend_sycl_print_sycl_devices(). + It is only used to set current working device. +*/ +void check_allow_gpu_id(const int device_id) { + if (!g_sycl_gpu_mgr->is_allowed_gpu(device_id)) { + char error_buf[256]; + snprintf(error_buf, sizeof(error_buf), + "error: cannot set device=%d, which is not allowed. Please " + "set GPU ID in: [%s]", + device_id, g_sycl_gpu_mgr->gpus_list.c_str()); + fprintf(stderr, "%s\n", error_buf); + throw std::invalid_argument(error_buf); + } +} + +int get_current_device_id() { + return dpct::dev_mgr::instance().current_device_id(); +} + +inline dpct::err0 ggml_sycl_set_device(const int device) try { + + int device_id = g_sycl_gpu_mgr->gpus[device]; + check_allow_gpu_id(device_id); + + int current_device_id; + SYCL_CHECK(CHECK_TRY_ERROR(current_device_id = get_current_device_id())); + + // GGML_SYCL_DEBUG("ggml_sycl_set_device device_id=%d, + // current_device_id=%d\n", device, current_device); + if (device_id == current_device_id) { + return 0; + } + + return CHECK_TRY_ERROR(dpct::select_device(device_id)); +} catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + crash(); + std::exit(1); +} + void log_ggml_var_device(const char*name, float *src, size_t total_elements, bool src_on_device){ if(!g_ggml_sycl_debug) return; if(!src){ @@ -3302,22 +3474,18 @@ void log_ggml_var_device(const char*name, float *src, size_t total_elements, boo size_t total_size = total_elements*sizeof(float); float *local_buf = NULL; - // printf("total_size %d2, src_on_device %d\n", total_size, src_on_device); if(src_on_device) { local_buf = (float *) ggml_sycl_host_malloc(total_size); - // printf("local buf %p size %d bytes\n", local_buf, total_size); ggml_sycl_set_device(g_main_device); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; main_stream->memcpy(local_buf, src, total_size); } else { local_buf = (float *)src; - // printf("local buf from src-> data %p\n", local_buf); } std::ofstream logfile; logfile.open(filename); - // printf("local buf element %d\n", total_elements); for(size_t i=0; iextra; - src_data = (float*)src_extra->data_device[g_main_device_index]; + src_data = (float*)src_extra->data_device[g_main_device]; } else { src_data = (float *)src->data; @@ -3359,10 +3527,6 @@ void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cn sprintf(filename, "%s_%07d", name, log_file_name_idx); log_file_name_idx++; print_ggml_tensor(filename, src); - // print_ggml_tensor("ggml_sycl_rms_norm_src0", (ggml_tensor *)src0); - // print_ggml_tensor("ggml_sycl_rms_norm_src1", (ggml_tensor *)src1); - // int *ptr = NULL; - // *ptr = 0; } static __dpct_inline__ float warp_reduce_sum(float x, @@ -3583,6 +3747,28 @@ static void relu_f32(const float * x, float * dst, const int k, dst[i] = sycl::fmax((float)(x[i]), (float)0); } +static void hardsigmoid_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); +} + +static void hardswish_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] * sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); +} + static void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope, const sycl::nd_item<3> &item_ct1) { const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + @@ -4964,8 +5150,8 @@ static void k_get_rows_float( template static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - 2 * item_ct1.get_local_id(2); + const int i = 2 * (item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2)); if (i >= k) { return; @@ -7695,7 +7881,7 @@ static void cpy_1_f16_f16(const char * cxi, char * cdsti) { static void cpy_1_f16_f32(const char * cxi, char * cdsti) { const sycl::half *xi = (const sycl::half *)cxi; - float *dsti = (float *)cdsti; + float * dsti = (float *) cdsti; *dsti = *xi; } @@ -8126,23 +8312,51 @@ static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, con dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX; } -static void soft_max_f32(const float * x, const float * y, float * dst, const int ncols, const int nrows_y, const float scale, - const sycl::nd_item<3> &item_ct1, float *buf) { + +template +static void soft_max_f32(const float * x, const float * mask, const float *pos, float * dst, const int ncols_par, + const int nrows_y, const float scale, const float max_bias, const float m0, + const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) { + const int ncols = ncols_template == 0 ? ncols_par : ncols_template; + const int tid = item_ct1.get_local_id(2); const int rowx = item_ct1.get_group(2); const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension - const int block_size = item_ct1.get_local_range(2); + const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template; const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + float slope = 0.0f; + + // ALiBi + if (max_bias > 0.0f) { + const uint32_t h = rowx/nrows_y; // head index + + const float base = h < n_head_log2 ? m0 : m1; + const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slope = sycl::pow(base, float(exp)); + } + + float * vals = vals_smem ? buf + WARP_SIZE : dst + rowx*ncols; float max_val = -INFINITY; - for (int col = tid; col < ncols; col += block_size) { + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + + if (ncols_template == 0 && col >= ncols) { + break; + } + const int ix = rowx*ncols + col; const int iy = rowy*ncols + col; - max_val = sycl::max(max_val, x[ix] * scale + (y ? y[iy] : 0.0f)); + + const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f); + + vals[col] = val; + max_val = sycl::max(max_val, val); } // find the max value in the block @@ -8151,30 +8365,12 @@ static void soft_max_f32(const float * x, const float * y, float * dst, const in if (warp_id == 0) { buf[lane_id] = -INFINITY; } - /* - DPCT1118:12: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - /* - DPCT1065:60: Consider replacing sycl::nd_item::barrier() with - sycl::nd_item::barrier(sycl::access::fence_space::local_space) for - better performance if there is no access to global memory. - */ - item_ct1.barrier(); + item_ct1.barrier(sycl::access::fence_space::local_space); if (lane_id == 0) { buf[warp_id] = max_val; } - /* - DPCT1118:13: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - /* - DPCT1065:61: Consider replacing sycl::nd_item::barrier() with - sycl::nd_item::barrier(sycl::access::fence_space::local_space) for - better performance if there is no access to global memory. - */ - item_ct1.barrier(); + item_ct1.barrier(sycl::access::fence_space::local_space); max_val = buf[lane_id]; max_val = warp_reduce_max(max_val, item_ct1); @@ -8182,13 +8378,16 @@ static void soft_max_f32(const float * x, const float * y, float * dst, const in float tmp = 0.f; - for (int col = tid; col < ncols; col += block_size) { - const int ix = rowx*ncols + col; - const int iy = rowy*ncols + col; - const float val = - sycl::native::exp((x[ix] * scale + (y ? y[iy] : 0.0f)) - max_val); +#pragma unroll + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + if (ncols_template == 0 && col >= ncols) { + break; + } + + const float val = sycl::native::exp(vals[col] - max_val); tmp += val; - dst[ix] = val; + vals[col] = val; } // find the sum of exps in the block @@ -8197,40 +8396,29 @@ static void soft_max_f32(const float * x, const float * y, float * dst, const in if (warp_id == 0) { buf[lane_id] = 0.f; } - /* - DPCT1118:14: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - /* - DPCT1065:62: Consider replacing sycl::nd_item::barrier() with - sycl::nd_item::barrier(sycl::access::fence_space::local_space) for - better performance if there is no access to global memory. - */ - item_ct1.barrier(); + item_ct1.barrier(sycl::access::fence_space::local_space); if (lane_id == 0) { buf[warp_id] = tmp; } - /* - DPCT1118:15: SYCL group functions and algorithms must be encountered in - converged control flow. You may need to adjust the code. - */ - /* - DPCT1065:63: Consider replacing sycl::nd_item::barrier() with - sycl::nd_item::barrier(sycl::access::fence_space::local_space) for - better performance if there is no access to global memory. - */ - item_ct1.barrier(); + item_ct1.barrier(sycl::access::fence_space::local_space); tmp = buf[lane_id]; tmp = warp_reduce_sum(tmp, item_ct1); } - const float inv_tmp = 1.f / tmp; + const float inv_sum = 1.f / tmp; - for (int col = tid; col < ncols; col += block_size) { - const int i = rowx*ncols + col; - dst[i] *= inv_tmp; +#pragma unroll + for (int col0 = 0; col0 < ncols; col0 += block_size) { + const int col = col0 + tid; + + if (ncols_template == 0 && col >= ncols) { + return; + } + + const int idst = rowx*ncols + col; + dst[idst] = vals[col] * inv_sum; } } @@ -8295,6 +8483,62 @@ static void im2col_kernel(const float *x, T *dst, int offset_delta, } } +template +static void pool2d_nchw_kernel( + const int ih, const int iw, const int oh, const int ow, + const int kh, const int kw, const int sh, const int sw, + const int ph, const int pw, const int parallel_elements, + const Ti* src, To* dst, const enum ggml_op_pool op, + const sycl::nd_item<3> &item_ct1) { + int idx = item_ct1.get_local_id(2) + + item_ct1.get_group(2) * item_ct1.get_local_range(2); + if (idx >= parallel_elements) { + return; + } + + const int I_HW = ih * iw; + const int O_HW = oh * ow; + const int nc = idx / O_HW; + const int cur_oh = idx % O_HW / ow; + const int cur_ow = idx % O_HW % ow; + const Ti* i_ptr = src + nc * I_HW; + To* o_ptr = dst + nc * O_HW; + const int start_h = cur_oh * sh - ph; + const int bh = sycl::max(0, start_h); + const int eh = sycl::min(ih, start_h + kh); + const int start_w = cur_ow * sw - pw; + const int bw = sycl::max(0, start_w); + const int ew = sycl::min(iw, start_w + kw); + + To res = 0; + + switch (op) { + case GGML_OP_POOL_AVG: res = 0; break; + case GGML_OP_POOL_MAX: res = -FLT_MAX; break; + } + + for (int i = bh; i < eh; i += 1) { + for (int j = bw; j < ew; j += 1) { +#if DPCT_COMPATIBILITY_TEMP >= 350 + /* + DPCT1098:106: The '*' expression is used instead of the __ldg + call. These two expressions do not provide the exact same + functionality. Check the generated code for potential precision + and/or performance issues. + */ + Ti cur = *(i_ptr + i * iw + j); +#else + Ti cur = i_ptr[i * iw + j]; +#endif + switch (op) { + case GGML_OP_POOL_AVG: res += (cur / (kh * kw)); break; + case GGML_OP_POOL_MAX: res = sycl::max(res, (To)cur); break; + } + } + } + o_ptr[cur_oh * ow + cur_ow] = res; +} + template static void get_rows_sycl(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const void *src0_dd, @@ -8583,6 +8827,30 @@ static void relu_f32_sycl(const float *x, float *dst, const int k, }); } +static void hardsigmoid_f32_sycl(const float *x, float *dst, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + hardsigmoid_f32(x, dst, k, item_ct1); + }); +} + +static void hardswish_f32_sycl(const float *x, float *dst, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + hardswish_f32(x, dst, k, item_ct1); + }); +} + static void leaky_relu_f32_sycl(const float *x, float *dst, const int k, const float negative_slope, dpct::queue_ptr stream) { @@ -8809,11 +9077,10 @@ template static void dequantize_block_sycl(const void *__restrict__ vx, dst_t *__restrict__ y, const int k, dpct::queue_ptr stream) { - const int num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE; + const int num_blocks = (k + 2*SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / (2*SYCL_DEQUANTIZE_BLOCK_SIZE); { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( sycl::nd_range<3>( sycl::range<3>(1, 1, num_blocks) * @@ -9206,24 +9473,6 @@ static void mul_mat_vec_q_sycl_submitter(const void *vx, const void *vy, }); } -int get_device_index_by_id(int id){ - int res = g_sycl_device_id2index[id].index; - // GGML_SYCL_DEBUG("get_device_index_by_id id=%d device_index=%d\n", id, res); - GGML_ASSERT(res>=0); - return res; -} - -int get_device_id_by_index(int index){ - int res = g_device_caps[index].device_id; - GGML_ASSERT(res>=0); - return res; -} - - -int get_current_device_index(){ - return get_device_index_by_id(dpct::dev_mgr::instance().current_device_id()); -} - static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, @@ -9232,7 +9481,7 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9347,7 +9596,7 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9462,7 +9711,7 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9577,7 +9826,7 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9692,7 +9941,7 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9807,7 +10056,7 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -9930,7 +10179,7 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -10058,7 +10307,7 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -10179,7 +10428,7 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -10300,7 +10549,7 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy, int id; SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); const int compute_capability = g_device_caps[id].cc; int mmq_x, mmq_y, nwarps; @@ -10456,6 +10705,31 @@ static void ggml_mul_mat_vec_nc_f16_f32_sycl( } } +static void +ggml_cpy_f16_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00, + const int ne01, const int ne02, const int nb00, + const int nb01, const int nb02, const int nb03, + const int ne10, const int ne11, const int ne12, + const int nb10, const int nb11, const int nb12, + const int nb13, dpct::queue_ptr stream) { + + const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, + nb01, nb02, nb03, ne10, ne11, ne12, + nb10, nb11, nb12, nb13, item_ct1); + }); + } +} + static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, @@ -10867,37 +11141,98 @@ static void diag_mask_inf_f32_sycl(const float *x, float *dst, }); } -static void soft_max_f32_sycl(const float *x, const float *y, float *dst, - const int ncols_x, const int nrows_x, - const int nrows_y, const float scale, - dpct::queue_ptr stream) { - int nth = WARP_SIZE; - while (nth < ncols_x && nth < SYCL_SOFT_MAX_BLOCK_SIZE) nth *= 2; - const sycl::range<3> block_dims(1, 1, nth); - const sycl::range<3> block_nums(1, 1, nrows_x); - /* - DPCT1049:46: The work-group size passed to the SYCL kernel may exceed the - limit. To get the device limit, query info::device::max_work_group_size. - Adjust the work-group size if needed. - */ +template +static void soft_max_f32_submitter(const float * x, const float * mask, const float *pos, float * dst, const int ncols_par, + const int nrows_y, const float scale, const float max_bias, const float m0, + const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims, + const size_t n_local_scratch, dpct::queue_ptr stream) { stream->submit([&](sycl::handler &cgh) { - /* - DPCT1101:96: 'SYCL_SOFT_MAX_BLOCK_SIZE/WARP_SIZE' expression was - replaced with a value. Modify the code to use the original expression, - provided in comments, if it is correct. - */ - sycl::local_accessor buf_acc_ct1( - sycl::range<1>(32 /*SYCL_SOFT_MAX_BLOCK_SIZE/WARP_SIZE*/), cgh); + sycl::local_accessor local_buf_acc(n_local_scratch, cgh); cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - soft_max_f32(x, y, dst, ncols_x, nrows_y, scale, item_ct1, - buf_acc_ct1.get_pointer()); + soft_max_f32(x, mask, pos, dst, ncols_par, + nrows_y, scale, max_bias, m0, + m1, n_head_log2, item_ct1, + local_buf_acc.get_pointer()); }); }); } +static void soft_max_f32_sycl(const float * x, const float * mask, const float * pos, + float * dst, const int ncols_x, const int nrows_x, + const int nrows_y, const float scale, const float max_bias, + dpct::queue_ptr stream) { + int nth = WARP_SIZE; + while (nth < ncols_x && nth < SYCL_SOFT_MAX_BLOCK_SIZE) nth *= 2; + const sycl::range<3> block_dims(1, 1, nth); + const sycl::range<3> block_nums(1, 1, nrows_x); + const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE); + static_assert(SYCL_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted."); + + const uint32_t n_head_kv = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + const size_t local_mem_size = stream->get_device().get_info(); + if (n_local_scratch*sizeof(float) < local_mem_size) { + switch (ncols_x) { + case 32: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 64: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 128: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 256: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 512: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 1024: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 2048: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + case 4096: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + default: + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + break; + } + } else { + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, WARP_SIZE, stream); + } +} + template static void im2col_sycl(const float *x, T *dst, int IW, int IH, int OW, int OH, int KW, int KH, int IC, @@ -10951,12 +11286,9 @@ struct sycl_buffer { static sycl_buffer g_sycl_buffer_pool[GGML_SYCL_MAX_DEVICES][MAX_SYCL_BUFFERS]; static size_t g_sycl_pool_size[GGML_SYCL_MAX_DEVICES] = {0}; -static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { +static void *ggml_sycl_pool_malloc_leg(int device_index, size_t size, size_t *actual_size) try { scoped_spin_lock lock(g_sycl_pool_lock); - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg index %d\n", id); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg device_index %d size=%lu\n", device_index, size); #ifdef DEBUG_SYCL_MALLOC int nnz = 0; size_t max_size = 0; @@ -10964,7 +11296,7 @@ static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { size_t best_diff = 1ull << 36; int ibest = -1; for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - sycl_buffer& b = g_sycl_buffer_pool[id][i]; + sycl_buffer& b = g_sycl_buffer_pool[device_index][i]; if (b.ptr != nullptr) { #ifdef DEBUG_SYCL_MALLOC ++nnz; @@ -10980,7 +11312,7 @@ static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { *actual_size = b.size; b.ptr = nullptr; b.size = 0; - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 1 %p\n", ptr); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 1 %p and rm in pool\n", ptr); return ptr; } } @@ -10988,30 +11320,30 @@ static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { } } if (ibest >= 0) { - sycl_buffer& b = g_sycl_buffer_pool[id][ibest]; + sycl_buffer& b = g_sycl_buffer_pool[device_index][ibest]; void * ptr = b.ptr; *actual_size = b.size; b.ptr = nullptr; b.size = 0; - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 2 %p\n", ptr); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 2 %p and rm in pool\n", ptr); return ptr; } void * ptr; size_t look_ahead_size = (size_t) (1.05 * size); look_ahead_size = 256 * ((look_ahead_size + 255)/256); - const dpct::queue_ptr stream = g_syclStreams[id][0]; + const dpct::queue_ptr stream = g_syclStreams[device_index][0]; SYCL_CHECK( CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device( look_ahead_size, *stream))); *actual_size = look_ahead_size; - g_sycl_pool_size[id] += look_ahead_size; + g_sycl_pool_size[device_index] += look_ahead_size; #ifdef DEBUG_SYCL_MALLOC fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); #endif - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return %p\n", ptr); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr); return ptr; } catch (sycl::exception const &exc) { @@ -11020,15 +11352,11 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_pool_free_leg(void *ptr, size_t size) try { +static void ggml_sycl_pool_free_leg(int device_index, void *ptr, size_t size) try { scoped_spin_lock lock(g_sycl_pool_lock); - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); - - const dpct::queue_ptr stream = g_syclStreams[id][0]; + const dpct::queue_ptr stream = g_syclStreams[device_index][0]; for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - sycl_buffer& b = g_sycl_buffer_pool[id][i]; + sycl_buffer& b = g_sycl_buffer_pool[device_index][i]; if (b.ptr == nullptr) { b.ptr = ptr; b.size = size; @@ -11037,7 +11365,7 @@ static void ggml_sycl_pool_free_leg(void *ptr, size_t size) try { } fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_SYCL_BUFFERS\n"); SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *stream))); - g_sycl_pool_size[id] -= size; + g_sycl_pool_size[device_index] -= size; } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -11054,7 +11382,8 @@ DPCT1082:64: Migration of CUmemGenericAllocationHandle type is not supported. static dpct::device_ptr g_sycl_pool_addr[GGML_SYCL_MAX_DEVICES] = {0}; static size_t g_sycl_pool_used[GGML_SYCL_MAX_DEVICES] = {0}; -static void *ggml_sycl_pool_malloc_vmm(size_t size, size_t *actual_size) try { +static void *ggml_sycl_pool_malloc_vmm(int device_index, size_t size, size_t *actual_size) try { + GGML_UNUSED(device_index); GGML_UNUSED(size); GGML_UNUSED(actual_size); return NULL; @@ -11065,20 +11394,16 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_pool_free_vmm(void *ptr, size_t size) try { +static void ggml_sycl_pool_free_vmm(int device_index, void *ptr, size_t size) try { scoped_spin_lock lock(g_sycl_pool_lock); - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = dpct::dev_mgr::instance().current_device_id())); - #ifdef DEBUG_SYCL_MALLOC - printf("sycl pool[%d]: freed %llu bytes at %llx\n", id, (unsigned long long) size, ptr); + printf("sycl pool[%d]: freed %llu bytes at %llx\n", device_index, (unsigned long long) size, ptr); #endif - g_sycl_pool_used[id] -= size; + g_sycl_pool_used[device_index] -= size; // all deallocations must be in reverse order of the allocations - GGML_ASSERT(ptr == (void *) (g_sycl_pool_addr[id] + g_sycl_pool_used[id])); + GGML_ASSERT(ptr == (void *) (g_sycl_pool_addr[device_index] + g_sycl_pool_used[device_index])); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -11086,14 +11411,11 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void *ggml_sycl_pool_malloc(size_t size, size_t *actual_size) try { - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); - if (g_device_caps[id].vmm) { - return ggml_sycl_pool_malloc_vmm(size, actual_size); +static void *ggml_sycl_pool_malloc(int device_index, size_t size, size_t *actual_size) try { + if (g_device_caps[device_index].vmm) { + return ggml_sycl_pool_malloc_vmm(device_index, size, actual_size); } else { - return ggml_sycl_pool_malloc_leg(size, actual_size); + return ggml_sycl_pool_malloc_leg(device_index, size, actual_size); } } catch (sycl::exception const &exc) { @@ -11102,14 +11424,11 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_pool_free(void *ptr, size_t size) try { - int id; - SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); - if (g_device_caps[id].vmm) { - ggml_sycl_pool_free_vmm(ptr, size); +static void ggml_sycl_pool_free(int device_index, void *ptr, size_t size) try { + if (g_device_caps[device_index].vmm) { + ggml_sycl_pool_free_vmm(device_index, ptr, size); } else { - ggml_sycl_pool_free_leg(ptr, size); + ggml_sycl_pool_free_leg(device_index, ptr, size); } } catch (sycl::exception const &exc) { @@ -11121,13 +11440,17 @@ catch (sycl::exception const &exc) { template struct sycl_pool_alloc { + int device_index = -1; + int device_id = -1; T * ptr = nullptr; size_t actual_size = 0; // size is in number of elements T * alloc(size_t size) { GGML_ASSERT(ptr == nullptr); - ptr = (T *) ggml_sycl_pool_malloc(size * sizeof(T), &this->actual_size); + device_id = get_current_device_id(); + device_index = g_sycl_gpu_mgr->get_index(device_id); + ptr = (T *) ggml_sycl_pool_malloc(device_index, size * sizeof(T), &this->actual_size); // GGML_SYCL_DEBUG("alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size); return ptr; } @@ -11138,7 +11461,7 @@ struct sycl_pool_alloc { ~sycl_pool_alloc() { if (ptr != nullptr) { - ggml_sycl_pool_free(ptr, actual_size); + ggml_sycl_pool_free(device_index, ptr, actual_size); } } @@ -11159,44 +11482,57 @@ bool ggml_sycl_loaded(void) { return g_sycl_loaded; } -void ggml_backend_sycl_print_sycl_devices(){ +void print_device_detail(int id) { + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::get_device_info(prop, dpct::dev_mgr::instance().get_device(id)))); + sycl::device cur_device = dpct::dev_mgr::instance().get_device(id); + std::string version; + version += std::to_string(prop.get_major_version()); + version += "."; + version += std::to_string(prop.get_minor_version()); + + fprintf(stderr, "|%2d|%45s|%18s|%17d|%14d|%13d|%15lu|\n", id, + prop.get_name(), version.c_str(), prop.get_max_compute_units(), + prop.get_max_work_group_size(), prop.get_max_sub_group_size(), + prop.get_global_mem_size()); +} + +void ggml_backend_sycl_print_sycl_devices() { int device_count = dpct::dev_mgr::instance().device_count(); fprintf(stderr, "found %d SYCL devices:\n", device_count); + fprintf(stderr, "|ID| Name |compute capability|Max compute units|Max work group|Max sub group|Global mem size|\n"); + fprintf(stderr, "|--|---------------------------------------------|------------------|-----------------|--------------|-------------|---------------|\n"); for (int id = 0; id < device_count; ++id) { - dpct::device_info prop; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(id)))); - sycl::device cur_device = dpct::dev_mgr::instance().get_device(id); - fprintf(stderr, " Device %d: %s,\tcompute capability %d.%d,\n\tmax compute_units %d,\tmax work group size %d,\tmax sub group size %d,\tglobal mem size %lu\n", id, - prop.get_name(), prop.get_major_version(), - prop.get_minor_version(), - prop.get_max_compute_units(), - prop.get_max_work_group_size(), - prop.get_max_sub_group_size(), - prop.get_global_mem_size() - ); - } - // fprintf(stderr, "\n"); -} - -int get_sycl_env(const char* env_name, int default_val){ - char * user_device_string = getenv(env_name); + print_device_detail(id); + } +} + +void print_gpu_device_list() { + fprintf(stderr, "detect %d SYCL GPUs: [%s] with Max compute units:%d\n", + g_sycl_gpu_mgr->get_gpu_count(), + g_sycl_gpu_mgr->gpus_list.c_str(), + g_sycl_gpu_mgr->max_compute_units); +} + +int get_sycl_env(const char *env_name, int default_val) { + char *user_device_string = getenv(env_name); int user_number = default_val; unsigned n; - if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1) { - user_number = (int)n; - } else { - user_number=default_val; - } + if (user_device_string != NULL && + sscanf(user_device_string, " %u", &n) == 1) { + user_number = (int)n; + } else { + user_number = default_val; + } return user_number; } -int get_work_group_size(int user_device_id){ +int get_work_group_size(int user_device_id) { dpct::device_info prop; - dpct::get_device_info( - prop, - dpct::dev_mgr::instance().get_device(user_device_id)); + dpct::get_device_info(prop, + dpct::dev_mgr::instance().get_device(user_device_id)); return prop.get_max_work_group_size(); } @@ -11205,113 +11541,81 @@ void ggml_init_sycl() try { if (!initialized) { g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); + fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug); - printf("GGML_SYCL_DEBUG=%d\n", g_ggml_sycl_debug); - - int user_device_id = get_sycl_env("GGML_SYCL_DEVICE", 0); - +#if defined(GGML_SYCL_F16) + fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); +#else + fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); +#endif if (CHECK_TRY_ERROR(g_all_sycl_device_count = - dpct::dev_mgr::instance().device_count()) != - 0) { + dpct::dev_mgr::instance().device_count()) != 0) { initialized = true; g_sycl_loaded = false; return; } GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); - int64_t total_vram = 0; + ggml_backend_sycl_print_sycl_devices(); -#if defined(GGML_SYCL_F16) - fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); -#else - fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); -#endif + if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + g_device_count = g_sycl_gpu_mgr->get_gpu_count(); + g_work_group_size = g_sycl_gpu_mgr->work_group_size; + print_gpu_device_list(); + + int64_t total_vram = 0; + +/* NOT REMOVE, keep it for next optimize for XMX. #if defined(SYCL_USE_XMX) fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); #else fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); #endif - ggml_backend_sycl_print_sycl_devices(); +*/ for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { - g_sycl_device_id2index[id].index = -1; g_device_caps[id].vmm = 0; g_device_caps[id].device_id = -1; g_device_caps[id].cc = 0; g_tensor_split[id] = 0; + g_default_tensor_split[id] = 0; } - int device_inx = -1; - for (int id = 0; id < g_all_sycl_device_count; ++id) { - if(id!=user_device_id) continue; - - device_inx++; - - g_device_caps[device_inx].vmm = 0; - g_device_caps[device_inx].device_id = id; - g_sycl_device_id2index[id].index = device_inx; + for (int i = 0; i < g_device_count; ++i) { + int device_id = g_sycl_gpu_mgr->gpus[i]; + g_device_caps[i].vmm = 0; dpct::device_info prop; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(id)))); + prop, dpct::dev_mgr::instance().get_device(device_id)))); - g_tensor_split[device_inx] = total_vram; + g_default_tensor_split[i] = total_vram; total_vram += prop.get_global_mem_size(); - g_device_caps[device_inx].cc = + g_device_caps[i].cc = 100 * prop.get_major_version() + 10 * prop.get_minor_version(); - } - device_inx = -1; - for (int id = 0; id < g_all_sycl_device_count; ++id) { - if(id!=user_device_id) continue; - device_inx++; - g_tensor_split[device_inx] /= total_vram; + + for (int i = 0; i < g_device_count; ++i) { + g_default_tensor_split[i] /= total_vram; } - device_inx = -1; - for (int id = 0; id < g_all_sycl_device_count; ++id) { - if(id!=user_device_id) continue; - device_inx++; - SYCL_CHECK(ggml_sycl_set_device(id)); + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); // create sycl streams for (int is = 0; is < MAX_STREAMS; ++is) { - /* - DPCT1025:88: The SYCL queue is created ignoring the flag and - priority options. - */ SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[device_inx][is] = - dpct::get_current_device().create_queue())); + g_syclStreams[i][is] = + dpct::get_current_device().create_queue( + g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); } - const dpct::queue_ptr stream = g_syclStreams[device_inx][0]; + const dpct::queue_ptr stream = g_syclStreams[i][0]; // create sycl handle - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[device_inx] = - stream)); - /* - DPCT1027:89: The call to syclSetMathMode was replaced with 0 - because this functionality is redundant in SYCL. - */ - SYCL_CHECK(0); + SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); } - // configure logging to stdout - // SYCL_CHECK(syclLoggerConfigure(1, 1, 0, nullptr)); - - //hardcode, force set to 1 device - g_device_count = 1; - ggml_sycl_set_main_device(user_device_id); - ggml_sycl_set_device(user_device_id); - g_work_group_size = get_work_group_size(user_device_id); - // fprintf(stderr, "Using Device %d\n", user_device_id); - - // for (int id = 0; id < g_all_sycl_device_count; ++id) { - // GGML_SYCL_DEBUG("id=%d g_device_caps[%d].device_id=%d g_sycl_device_id2index[%d].index=%d ", id, id, - // g_device_caps[id].device_id, id, g_sycl_device_id2index[id].index); - // } - initialized = true; g_sycl_loaded = true; } @@ -11322,31 +11626,6 @@ catch (sycl::exception const &exc) { std::exit(1); } - -void ggml_sycl_set_tensor_split(const float * tensor_split) { - if (tensor_split == nullptr) { - return; - } - bool all_zero = true; - for (int i = 0; i < g_device_count; ++i) { - if (tensor_split[i] != 0.0f) { - all_zero = false; - break; - } - } - if (all_zero) { - return; - } - float split_sum = 0.0f; - for (int i = 0; i < g_device_count; ++i) { - g_tensor_split[i] = split_sum; - split_sum += tensor_split[i]; - } - for (int i = 0; i < g_device_count; ++i) { - g_tensor_split[i] /= split_sum; - } -} - void *ggml_sycl_host_malloc(size_t size) try { if (getenv("GGML_SYCL_NO_PINNED") != nullptr) { return nullptr; @@ -11356,28 +11635,14 @@ void *ggml_sycl_host_malloc(size_t size) try { //allow to use dpct::get_in_order_queue() for host malloc dpct::err0 err = CHECK_TRY_ERROR( ptr = (void *)sycl::malloc_host(size, dpct::get_in_order_queue())); - /* - DPCT1000:82: Error handling if-stmt was detected but could not be rewritten. - */ + if (err != 0) { // clear the error - /* - DPCT1026:83: The call to syclGetLastError was removed because this - functionality is redundant in SYCL. - */ - /* - DPCT1001:81: The statement could not be removed. - */ fprintf( stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n", - /* - DPCT1009:84: SYCL uses exceptions to report errors and does not use - the error codes. The original code was commented out and a warning - string was inserted. You need to rewrite this code. - */ size / 1024.0 / 1024.0, - "syclGetErrorString is not supported" /*syclGetErrorString(err)*/); + "syclGetErrorString is not supported"); return nullptr; } @@ -11417,7 +11682,7 @@ static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst, ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra; int id; SYCL_CHECK(CHECK_TRY_ERROR( - id = get_current_device_index())); + id = get_current_device_id())); // GGML_SYCL_DEBUG("current device index %d\n", id); src_ptr = (char *) extra->data_device[id]; } else { @@ -11651,7 +11916,6 @@ inline void ggml_sycl_op_tanh(const ggml_tensor *src0, const ggml_tensor *src1, GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); (void) src1; @@ -11674,6 +11938,37 @@ inline void ggml_sycl_op_relu(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } +static void ggml_sycl_op_hardsigmoid(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +static void ggml_sycl_op_hardswish(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + inline void ggml_sycl_op_leaky_relu(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, @@ -11842,7 +12137,7 @@ inline void ggml_sycl_op_mul_mat_q( int device_id; SYCL_CHECK( - CHECK_TRY_ERROR(device_id = dpct::dev_mgr::instance().current_device_id())); + CHECK_TRY_ERROR(device_id = get_current_device_id())); // the main device has a larger memory buffer to hold the results from all GPUs // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into @@ -11894,16 +12189,16 @@ catch (sycl::exception const &exc) { std::exit(1); } -static int64_t get_row_rounding(ggml_type type) { +static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { int64_t min_compute_capability = INT_MAX; int64_t max_compute_capability = INT_MIN; - for (int64_t id = 0; id < g_device_count; ++id) { - if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { - if (min_compute_capability > g_device_caps[id].cc) { - min_compute_capability = g_device_caps[id].cc; + for (int i = 0; i < g_device_count; ++i) { + if (tensor_split[i] < (i + 1 < g_device_count ? tensor_split[i + 1] : 1.0f)) { + if (min_compute_capability > g_device_caps[i].cc) { + min_compute_capability = g_device_caps[i].cc; } - if (max_compute_capability < g_device_caps[id].cc) { - max_compute_capability = g_device_caps[id].cc; + if (max_compute_capability < g_device_caps[i].cc) { + max_compute_capability = g_device_caps[i].cc; } } } @@ -11923,12 +12218,16 @@ static int64_t get_row_rounding(ggml_type type) { case GGML_TYPE_Q3_K: case GGML_TYPE_Q4_K: case GGML_TYPE_Q5_K: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: return max_compute_capability >= VER_GEN9 ? 128 : 64; case GGML_TYPE_Q6_K: return 64; default: GGML_ASSERT(false); } + } inline void ggml_sycl_op_mul_mat_vec_q( @@ -12113,27 +12412,22 @@ inline void ggml_sycl_op_mul_mat_sycl( const int64_t row_diff = row_high - row_low; int id; - int device_id = dpct::dev_mgr::instance().current_device_id(); SYCL_CHECK( - CHECK_TRY_ERROR(id = get_current_device_index())); + CHECK_TRY_ERROR(id = get_current_device_id())); // the main device has a larger memory buffer to hold the results from all GPUs // ldc == nrows of the matrix that cuBLAS writes into - int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff; + int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff; #ifdef GGML_SYCL_F16 bool use_fp16 = true; // TODO(Yu) SYCL capability check #else bool use_fp16 = false; #endif - // if (compute_capability >= VER_GEN9 && (src0->type == GGML_TYPE_F16 || - // ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == - // src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { if ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && use_fp16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { - // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp16 path\n"); sycl_pool_alloc src0_as_f16; if (src0->type != GGML_TYPE_F16) { @@ -12162,7 +12456,6 @@ inline void ggml_sycl_op_mul_mat_sycl( const sycl::half alpha_f16 = 1.0f; const sycl::half beta_f16 = 0.0f; - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[id] = stream)); SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm( *g_sycl_handles[id], oneapi::mkl::transpose::trans, @@ -12178,14 +12471,21 @@ inline void ggml_sycl_op_mul_mat_sycl( else { // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n"); sycl_pool_alloc src0_ddq_as_f32; - + sycl_pool_alloc src1_ddq_as_f32; if (src0->type != GGML_TYPE_F32) { const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src0->type); GGML_ASSERT(to_fp32_sycl != nullptr); src0_ddq_as_f32.alloc(row_diff*ne00); to_fp32_sycl(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream); } + if (src1->type != GGML_TYPE_F32) { + const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src1->type); + GGML_ASSERT(to_fp32_sycl != nullptr); + src1_ddq_as_f32.alloc(src1_ncols*ne10); + to_fp32_sycl(src1_ddf_i, src1_ddq_as_f32.get(), src1_ncols*ne10, stream); + } const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get(); + const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get(); const float alpha = 1.0f; const float beta = 0.0f; @@ -12198,7 +12498,6 @@ inline void ggml_sycl_op_mul_mat_sycl( src1_ddf_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]), dst_dd_i, ldc))); } - (void) dst; (void) src1_ddq_i; (void) src1_padded_row_size; @@ -12319,6 +12618,48 @@ inline void ggml_sycl_op_alibi(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } +static void ggml_sycl_op_pool2d(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = static_cast(opts[0]); + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + + const int64_t IH = src0->ne[1]; + const int64_t IW = src0->ne[0]; + + const int64_t N = dst->ne[3]; + const int64_t OC = dst->ne[2]; + const int64_t OH = dst->ne[1]; + const int64_t OW = dst->ne[0]; + + const int parallel_elements = N * OC * OH * OW; + const int num_blocks = (parallel_elements + SYCL_POOL2D_BLOCK_SIZE - 1) / SYCL_POOL2D_BLOCK_SIZE; + sycl::range<3> block_nums(1, 1, num_blocks); + main_stream->parallel_for( + sycl::nd_range<3>(block_nums * + sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + pool2d_nchw_kernel(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, + parallel_elements, src0_dd, dst_dd, op, + item_ct1); + }); + + (void) src1; + (void) src1_dd; +} + inline void ggml_sycl_op_im2col(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, @@ -12435,14 +12776,35 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0, const int64_t ne00 = src0->ne[0]; const int64_t nrows_x = ggml_nrows(src0); - const int64_t nrows_y = src1 ? ggml_nrows(src1) : 1; + const int64_t nrows_y = src0->ne[1]; float scale = 1.0f; - memcpy(&scale, dst->op_params, sizeof(float)); + float max_bias = 0.0f; - soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream); + memcpy(&scale, dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, dst->op_params + 1, sizeof(float)); - (void) dst; + // positions tensor + float * src2_dd = nullptr; + sycl_pool_alloc src2_f; + + ggml_tensor * src2 = dst->src[2]; + const bool use_src2 = src2 != nullptr; + + if (use_src2) { + const bool src2_on_device = src2->backend == GGML_BACKEND_TYPE_GPU; + + if (src2_on_device) { + ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra; + src2_dd = (float *) src2_extra->data_device[g_main_device]; + } else { + src2_dd = src2_f.alloc(ggml_nelements(src2)); + SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src2_dd, src2, 0, 0, 0, 1, main_stream)); + } + } + + soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, src2_dd, dst_dd, ne00, + nrows_x, nrows_y, scale, max_bias, main_stream); } inline void ggml_sycl_op_scale(const ggml_tensor *src0, const ggml_tensor *src1, @@ -12522,12 +12884,12 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0, sycl_pool_alloc dst_f; ggml_sycl_set_device(g_main_device); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; - // GGML_SYCL_DEBUG("g_main_device_index=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", - // g_main_device_index, main_stream, src0_on_device, src1_on_device, dst_on_device); + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; + // GGML_SYCL_DEBUG("g_main_device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", + // g_main_device, main_stream, src0_on_device, src1_on_device, dst_on_device); if (src0_on_device) { - src0_ddf = (float *) src0_extra->data_device[g_main_device_index]; + src0_ddf = (float *) src0_extra->data_device[g_main_device]; } else { src0_ddf = src0_f.alloc(ggml_nelements(src0)); // GGML_SYCL_DEBUG("before ggml_sycl_cpy_tensor_2d src0_ddf=%p, src0=%p\n", src0_ddf, src0); @@ -12536,15 +12898,14 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0, if (use_src1) { if (src1_on_device) { - src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + src1_ddf = (float *) src1_extra->data_device[g_main_device]; } else { src1_ddf = src1_f.alloc(ggml_nelements(src1)); SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream)); } } if (dst_on_device) { - dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; - // printf("zjy dst_ddf=%p main_stream=%p g_main_device_index=%d\n", dst_ddf, main_stream, g_main_device_index); + dst_ddf = (float *) dst_extra->data_device[g_main_device]; } else { dst_ddf = dst_f.alloc(ggml_nelements(dst)); } @@ -12588,21 +12949,19 @@ static void ggml_sycl_set_peer_access(const int n_tokens) { } #ifdef NDEBUG - for (int id = 0; id < g_device_count; ++id) { - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); // SYCL_CHECK(syclDeviceSynchronize()); } - for (int id = 0; id < g_device_count; ++id) { - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); - int device_id = g_device_caps[id].device_id; + for (int i = 0; i < g_device_count; ++i) { + SYCL_CHECK(ggml_sycl_set_device(i)); for (int id_other = 0; id_other < g_device_count; ++id_other) { - int device_id_other = g_device_caps[id_other].device_id; - if (device_id == id_other) { + if (i == id_other) { continue; } - if (device_id != g_main_device && device_id_other != g_main_device) { + if (i != g_main_device && id_other != g_main_device) { continue; } @@ -12622,6 +12981,10 @@ static void ggml_sycl_set_peer_access(const int n_tokens) { peer_access_enabled = enable_peer_access; } +struct ggml_backend_sycl_split_buffer_type_context { + std::array tensor_split; +}; + static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, ggml_sycl_op_mul_mat_t op, @@ -12642,6 +13005,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); @@ -12667,80 +13031,90 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, GGML_ASSERT(!(split && ne03 > 1)); GGML_ASSERT(!(split && ne02 < ne12)); - // dd = data device - char * src0_dd[GGML_SYCL_MAX_DEVICES] = {nullptr}; - float * src1_ddf[GGML_SYCL_MAX_DEVICES] = {nullptr}; // float - char * src1_ddq[GGML_SYCL_MAX_DEVICES] = {nullptr}; // q8_1 - float * dst_dd[GGML_SYCL_MAX_DEVICES] = {nullptr}; + std::array tensor_split; + if (split) { + // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_TYPE_GPU_SPLIT check + // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...); + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *) src0->buffer->buft->context; + tensor_split = buft_ctx->tensor_split; + } + + struct dev_data { + sycl_pool_alloc src0_dd_alloc; + sycl_pool_alloc src1_ddf_alloc; + sycl_pool_alloc src1_ddq_alloc; + sycl_pool_alloc dst_dd_alloc; + + char *src0_dd = nullptr; + float *src1_ddf = nullptr; // float + char *src1_ddq = nullptr; // q8_1 + float *dst_dd = nullptr; - // as = actual size - size_t src0_as[GGML_SYCL_MAX_DEVICES] = {0}; - size_t src1_asf[GGML_SYCL_MAX_DEVICES] = {0}; - size_t src1_asq[GGML_SYCL_MAX_DEVICES] = {0}; - size_t dst_as[GGML_SYCL_MAX_DEVICES] = {0}; + int64_t row_low; + int64_t row_high; + }; - int64_t row_low[GGML_SYCL_MAX_DEVICES]; - int64_t row_high[GGML_SYCL_MAX_DEVICES]; + dev_data dev[GGML_SYCL_MAX_DEVICES]; int used_devices = 0; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; - for (int64_t id = 0; id < g_device_count; ++id) { + for (int i = 0; i < g_device_count; ++i) { // by default, use all rows - row_low[id] = 0; - row_high[id] = ne01; + dev[i].row_low = 0; + dev[i].row_high = ne01; // for multi GPU, get the row boundaries from tensor split // and round to mul_mat_q tile sizes if (split) { - const int64_t rounding = get_row_rounding(src0->type); + const int64_t rounding = get_row_rounding(src0->type, tensor_split); - if (id != 0) { - row_low[id] = ne01*g_tensor_split[id]; - if (row_low[id] < ne01) { - row_low[id] -= row_low[id] % rounding; + if (i != 0) { + dev[i].row_low = ne01*tensor_split[i]; + if (dev[i].row_low < ne01) { + dev[i].row_low -= dev[i].row_low % rounding; } } - if (id != g_device_count - 1) { - row_high[id] = ne01*g_tensor_split[id + 1]; - if (row_high[id] < ne01) { - row_high[id] -= row_high[id] % rounding; + if (i != g_device_count - 1) { + dev[i].row_high = ne01*tensor_split[i + 1]; + if (dev[i].row_high < ne01) { + dev[i].row_high -= dev[i].row_high % rounding; } } } } - for (int64_t id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) { + for (int i = 0; i < g_device_count; ++i) { + if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) { continue; } used_devices++; - const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; + const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; - ggml_sycl_set_device(get_device_id_by_index(id)); - const dpct::queue_ptr stream = g_syclStreams[id][0]; + ggml_sycl_set_device(i); + dpct::queue_ptr stream = g_syclStreams[i][0]; if (src0_on_device && src0_is_contiguous) { - src0_dd[id] = (char *) src0_extra->data_device[id]; + dev[i].src0_dd = (char *) src0_extra->data_device[i]; } else { - // const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0); - src0_dd[id] = (char *) ggml_sycl_pool_malloc(ggml_nbytes(src0), &src0_as[id]); + dev[i].src0_dd = dev[i].src0_dd_alloc.alloc(ggml_nbytes(src0)); } if (src1_on_device && src1_is_contiguous) { - src1_ddf[id] = (float *) src1_extra->data_device[id]; + dev[i].src1_ddf = (float *) src1_extra->data_device[i]; } else { - src1_ddf[id] = (float *) ggml_sycl_pool_malloc(ggml_nbytes(src1), &src1_asf[id]); + dev[i].src1_ddf = dev[i].src1_ddf_alloc.alloc(ggml_nelements(src1)); } if (convert_src1_to_q8_1) { - src1_ddq[id] = (char *) ggml_sycl_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]); + dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs); if (src1_on_device && src1_is_contiguous) { - quantize_row_q8_1_sycl(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream); + quantize_row_q8_1_sycl(dev[i].src1_ddf, dev[i].src1_ddq, ne10, nrows1, src1_padded_col_size, stream); /* DPCT1010:90: SYCL uses exceptions to report errors and does not use the error codes. The call was replaced with 0. You need to @@ -12751,25 +13125,25 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } if (dst_on_device) { - dst_dd[id] = (float *) dst_extra->data_device[id]; + dev[i].dst_dd = (float *) dst_extra->data_device[i]; } else { - const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst); - dst_dd[id] = (float *) ggml_sycl_pool_malloc(size_dst_ddf, &dst_as[id]); + const size_t size_dst_ddf = split ? (dev[i].row_high - dev[i].row_low)*ne1 : ggml_nelements(dst); + dev[i].dst_dd = dev[i].dst_dd_alloc.alloc(size_dst_ddf); } } // if multiple devices are used they need to wait for the main device // here an event is recorded that signals that the main device has finished calculating the input data if (split && used_devices > 1) { - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + ggml_sycl_set_device(g_main_device); /* DPCT1024:91: The original code returned the error code that was further consumed by the program logic. This original code was replaced with 0. You may need to rewrite the program logic consuming the error code. */ SYCL_CHECK(CHECK_TRY_ERROR( - *src0_extra->events[g_main_device_index][0] = - g_syclStreams[g_main_device_index][0]->ext_oneapi_submit_barrier())); + *src0_extra->events[g_main_device][0] = + g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier())); } const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; @@ -12777,22 +13151,27 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0; const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride; - for (int64_t id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) { + for (int i = 0; i < g_device_count; ++i) { + if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) { continue; } - const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index; - const int64_t row_diff = row_high[id] - row_low[id]; + const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; + const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; + const int64_t row_diff = dev[i].row_high - dev[i].row_low; - ggml_sycl_set_device(get_device_id_by_index(id)); - const dpct::queue_ptr stream = g_syclStreams[id][is]; + ggml_sycl_set_device(i); + dpct::queue_ptr stream = g_syclStreams[i][is]; // wait for main GPU data if necessary - if (split && (id != g_main_device_index || is != 0)) { + if (split && (i != g_main_device || is != 0)) { + /* + DPCT1009:163: SYCL uses exceptions to report errors and does not + use the error codes. The original code was commented out and a + warning string was inserted. You need to rewrite this code. + */ SYCL_CHECK(CHECK_TRY_ERROR(stream->ext_oneapi_submit_barrier( - {*src0_extra->events[g_main_device_index][0]}))); + {*src0_extra->events[g_main_device][0]}))); } for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) { @@ -12802,30 +13181,32 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs; // for split tensors the data begins at i0 == i0_offset_low - char * src0_dd_i = src0_dd[id] + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; - float * src1_ddf_i = src1_ddf[id] + (i0*ne11 + src1_col_0) * ne10; - char * src1_ddq_i = src1_ddq[id] + src1_ddq_i_offset; - float * dst_dd_i = dst_dd[id] + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); + char * src0_dd_i = dev[i].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; + float * src1_ddf_i = dev[i].src1_ddf + (i0*ne11 + src1_col_0) * ne10; + char * src1_ddq_i = dev[i].src1_ddq + src1_ddq_i_offset; + float * dst_dd_i = dev[i].dst_dd + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); // the main device memory buffer can be on VRAM scratch, with space for all partial results // in that case an offset on dst_ddf_i is needed - if (dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index) { - dst_dd_i += row_low[id]; // offset is 0 if no tensor split + if (dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device) { + dst_dd_i += dev[i].row_low; // offset is 0 if no tensor split } // copy src0, src1 to device if necessary if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) { - if (id != g_main_device_index) { + if (i != g_main_device) { if (convert_src1_to_q8_1) { - char * src1_ddq_i_source = src1_ddq[g_main_device_index] + src1_ddq_i_offset; - SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( + char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset; + SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( src1_ddq_i, src1_ddq_i_source, src1_ncols * src1_padded_col_size * q8_1_ts / q8_1_bs))); } else { - float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device_index]; + + float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device]; src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10; - SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( + + SYCL_CHECK(CHECK_TRY_ERROR(dev2dev_memcpy(*stream, *main_stream, src1_ddf_i, src1_ddf_i_source, src1_ncols * ne10 * sizeof(float)))); } @@ -12848,14 +13229,14 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) { - SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, row_low[id], row_high[id], stream)); + SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[i].row_low, dev[i].row_high, stream)); } if (src1->type == GGML_TYPE_F16) { src1_padded_col_size = (i0 * ne11 + src1_col_0) * ne10; } // do the computation op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i, - row_low[id], row_high[id], src1_ncols, src1_padded_col_size, stream); + dev[i].row_low, dev[i].row_high, src1_ncols, src1_padded_col_size, stream); /* DPCT1010:93: SYCL uses exceptions to report errors and does not use the error codes. The call was replaced with 0. You need to @@ -12871,7 +13252,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, dst_off_device = dst->data; kind = dpct::device_to_host; } else if (dst->backend == GGML_BACKEND_TYPE_GPU) { - dst_off_device = dst_extra->data_device[g_main_device_index]; + dst_off_device = dst_extra->data_device[g_main_device]; kind = dpct::device_to_device; } else { GGML_ASSERT(false); @@ -12884,11 +13265,29 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results. float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); - dhf_dst_i += src1_col_0*ne0 + row_low[id]; - SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( - dhf_dst_i, ne0 * sizeof(float), dst_dd_i, - row_diff * sizeof(float), row_diff * sizeof(float), - src1_ncols, kind, *stream))); + dhf_dst_i += src1_col_0*ne0 + dev[i].row_low; + + //todo, dirty solution. Need be updated when device2device memcpy() is supported. + if (kind == dpct::device_to_device) { + size_t dst_size = ggml_nbytes_pad(dst); + float *host_buf = (float *)malloc(dst_size); + SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( + host_buf, ne0 * sizeof(float), dst_dd_i, + row_diff * sizeof(float), row_diff * sizeof(float), + src1_ncols, dpct::device_to_host, *stream))); + dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[i]).queues_wait_and_throw(); + SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( + dhf_dst_i, ne0 * sizeof(float), host_buf, + row_diff * sizeof(float), row_diff * sizeof(float), + src1_ncols, dpct::host_to_device, *main_stream))); + dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[g_main_device]).queues_wait_and_throw(); + free(host_buf); + } else { + SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( + dhf_dst_i, ne0 * sizeof(float), dst_dd_i, + row_diff * sizeof(float), row_diff * sizeof(float), + src1_ncols, kind, *stream))); + } } else { float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); @@ -12900,7 +13299,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } // add event for the main device to wait on until other device is done - if (split && (id != g_main_device_index || is != 0)) { + if (split && (i != g_main_device || is != 0)) { /* DPCT1024:94: The original code returned the error code that was further consumed by the program logic. This original @@ -12908,48 +13307,27 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, program logic consuming the error code. */ SYCL_CHECK(CHECK_TRY_ERROR( - *src0_extra->events[id][is] = + *src0_extra->events[i][is] = stream->ext_oneapi_submit_barrier())); } } } } - for (int64_t id = 0; id < g_device_count; ++id) { - if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) { - continue; - } - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); - - // free buffers again when done - if (dst_as[id] > 0) { - ggml_sycl_pool_free(dst_dd[id], dst_as[id]); - } - if (src1_asq[id] > 0) { - ggml_sycl_pool_free(src1_ddq[id], src1_asq[id]); - } - if (src1_asf[id] > 0) { - ggml_sycl_pool_free(src1_ddf[id], src1_asf[id]); - } - if (src0_as[id] > 0) { - ggml_sycl_pool_free(src0_dd[id], src0_as[id]); - } - } - // main device waits for all other devices to be finished if (split && g_device_count > 1) { int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS; - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - for (int64_t id = 0; id < g_device_count; ++id) { - if (row_low[id] == row_high[id]) { + ggml_sycl_set_device(g_main_device); + for (int i = 0; i < g_device_count; ++i) { + if (dev[i].row_low == dev[i].row_high) { continue; } for (int64_t is = 0; is < is_max; ++is) { SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[g_main_device_index][0]->ext_oneapi_submit_barrier( - {*src0_extra->events[id][is]}))); + g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier( + {*src0_extra->events[i][is]}))); } } } @@ -12966,110 +13344,132 @@ catch (sycl::exception const &exc) { std::exit(1); } + static void ggml_sycl_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_repeat); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_get_rows); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_add); - // log_tensor_with_cnt("log_ggml_sycl_add_src0", (struct ggml_tensor *) src0, 6); - // log_tensor_with_cnt("log_ggml_sycl_add_src1", (struct ggml_tensor *)src1, 6); - // log_tensor_with_cnt("log_ggml_sycl_add_dst", dst, 6); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_acc); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_mul); - // log_tensor_with_cnt("log_ggml_sycl_mul_src0", (struct ggml_tensor *)src0, 6); - // log_tensor_with_cnt("log_ggml_sycl_mul_src1", (struct ggml_tensor *)src1, 6); - // log_tensor_with_cnt("log_ggml_sycl_mul_dst", dst, 6); - + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_div); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_silu); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu_quick); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_tanh); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_relu); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +static void ggml_sycl_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardsigmoid); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +static void ggml_sycl_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardswish); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_leaky_relu); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_sqr); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_norm); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_group_norm); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_concat); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_upscale); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pad); + GGML_SYCL_DEBUG("call %s done\n", __func__); } static void ggml_sycl_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rms_norm); - // log_tensor_with_cnt("log_ggml_sycl_rms_norm_src0", (struct ggml_tensor *)src0, 6); - // log_tensor_with_cnt("log_ggml_sycl_rms_norm_src1", (struct ggml_tensor *)src1, 6); - // log_tensor_with_cnt("log_ggml_sycl_rms_norm_dst", dst, 6); + GGML_SYCL_DEBUG("call %s done\n", __func__); } bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { @@ -13104,16 +13504,16 @@ static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0, const int64_t ne12 = src1->ne[2]; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device_index]; + void * src0_ddq = src0_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; ggml_mul_mat_p021_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream); } @@ -13143,16 +13543,16 @@ static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0, const int64_t ne12 = src1->ne[2]; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device_index]; + void * src0_ddq = src0_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; const int64_t row_stride_x = nb01 / sizeof(sycl::half); const int64_t channel_stride_x = nb02 / sizeof(sycl::half); @@ -13185,54 +13585,50 @@ static void k_compute_batched_ptrs(const sycl::half *src0_as_f16, int64_t i03 = i13 / r3; int64_t i02 = i12 / r2; - ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; - ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; - ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; + ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; + ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; } -static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, - const ggml_tensor *src1, - ggml_tensor *dst) try { +static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, + const ggml_tensor *src1, + ggml_tensor *dst) try { GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); - - GGML_TENSOR_LOCALS(int64_t, nb0, src0, nb); - - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); - GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb); + GGML_TENSOR_BINARY_OP_LOCALS - const int64_t ne1 = ggml_nelements(src1); - const int64_t ne = ggml_nelements(dst); + const int64_t ne_dst = ggml_nelements(dst); SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; SYCL_CHECK( - CHECK_TRY_ERROR(g_sycl_handles[g_main_device_index] = main_stream)); + CHECK_TRY_ERROR(g_sycl_handles[g_main_device] = main_stream)); ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device_index]; + void * src0_ddq = src0_extra->data_device[g_main_device]; sycl::half *src0_as_f16 = (sycl::half *)src0_ddq; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; // convert src1 to fp16 - const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); - GGML_ASSERT(to_fp16_sycl != nullptr); - - sycl_pool_alloc src1_as_f16(ne1); - to_fp16_sycl(src1_ddf, src1_as_f16.get(), ne1, main_stream); + sycl_pool_alloc src1_f16_alloc; + if (src1->type != GGML_TYPE_F16) { + const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); + const int64_t ne_src1 = ggml_nelements(src1); + src1_f16_alloc.alloc(ne_src1); + GGML_ASSERT(to_fp16_sycl != nullptr); + to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); + } + sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf + : src1_f16_alloc.get(); sycl_pool_alloc dst_f16; char * dst_t; @@ -13253,20 +13649,12 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, const void * alpha = &alpha_f16; const void * beta = &beta_f16; - if (dst->op_params[0] == GGML_PREC_DEFAULT) { - dst_t = (char *) dst_f16.alloc(ne); - - nbd2 /= sizeof(float) / sizeof(sycl::half); - nbd3 /= sizeof(float) / sizeof(sycl::half); - } else { - dst_t = (char *) dst_ddf; - - cu_compute_type = dpct::library_data_t::real_float; - cu_data_type = dpct::library_data_t::real_float; + // TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway + // once oneMKL open source supports half, half, float, float: datatypes + dst_t = (char *) dst_f16.alloc(ne_dst); - alpha = &alpha_f32; - beta = &beta_f32; - } + nbd2 /= sizeof(float) / sizeof(sycl::half); + nbd3 /= sizeof(float) / sizeof(sycl::half); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -13284,7 +13672,7 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, int i02 = i12 / r2; SYCL_CHECK( - syclGemmEx(g_sycl_handles[g_main_device_index], CUBLAS_OP_T, CUBLAS_OP_N, + syclGemmEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , SYCL_R_16F, nb01/sizeof(half), (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, SYCL_R_16F, nb11/sizeof(float), @@ -13297,18 +13685,16 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, #else if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { // there is no broadcast and src0, src1 are contiguous across dims 2, 3 - // use syclGemmStridedBatchedEx SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( - *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, + *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const char *)src0_as_f16, dpct::library_data_t::real_half, - nb01 / sizeof(sycl::half), src0->nb[2] / sizeof(sycl::half), - (const char *)src1_as_f16.get(), dpct::library_data_t::real_half, - nb11 / sizeof(float), src1->nb[2] / sizeof(float), beta, - (char *)dst_t, cu_data_type, ne01, dst->nb[2] / sizeof(float), + nb01 / nb00, nb02 / nb00, + (const char *)src1_f16, dpct::library_data_t::real_half, + nb11 / nb10, nb12 / nb10, beta, + (char *)dst_t, cu_data_type, ne01, nb2 / nb0, ne12 * ne13, cu_compute_type))); } else { - // use syclGemmBatchedEx const int ne23 = ne12*ne13; sycl_pool_alloc ptrs_src(2*ne23); @@ -13325,44 +13711,35 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, {sycl::aspect::fp16}); main_stream->submit([&](sycl::handler &cgh) { - const sycl::half *src1_as_f16_get_ct1 = src1_as_f16.get(); - const void **ptrs_src_get_ct3 = ptrs_src.get(); - void **ptrs_dst_get_ct4 = ptrs_dst.get(); - + const void **ptrs_src_get = ptrs_src.get(); + void **ptrs_dst_get = ptrs_dst.get(); + size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : nb12 / 2; + size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : nb13 / 2; cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_compute_batched_ptrs( - src0_as_f16, src1_as_f16_get_ct1, - dst_t, ptrs_src_get_ct3, - ptrs_dst_get_ct4, ne12, ne13, ne23, - nb02, nb03, nb12, nb13, nbd2, nbd3, r2, - r3, item_ct1); + src0_as_f16, src1_f16, + dst_t, ptrs_src_get, + ptrs_dst_get, ne12, ne13, ne23, + nb02, nb03, nb12_scaled, nb13_scaled, + nbd2, nbd3, r2, r3, item_ct1); }); }); } - /* - DPCT1010:95: SYCL uses exceptions to report errors and does not use the - error codes. The call was replaced with 0. You need to rewrite this - code. - */ - SYCL_CHECK(0); - SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( - *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, + *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const void **)(ptrs_src.get() + 0 * ne23), - dpct::library_data_t::real_half, nb01 / sizeof(sycl::half), + dpct::library_data_t::real_half, nb01 / nb00, (const void **)(ptrs_src.get() + 1 * ne23), - dpct::library_data_t::real_half, nb11 / sizeof(float), beta, + dpct::library_data_t::real_half, nb11 / nb10, beta, (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, cu_compute_type))); } #endif - if (dst->op_params[0] == GGML_PREC_DEFAULT) { - const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); - to_fp32_sycl(dst_f16.get(), dst_ddf, ne, main_stream); - } + const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); + to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -13370,6 +13747,7 @@ catch (sycl::exception const &exc) { std::exit(1); } + static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool all_on_device = (src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) && @@ -13379,9 +13757,9 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; int64_t min_compute_capability = INT_MAX; - for (int64_t id = 0; id < g_device_count; ++id) { - if (min_compute_capability > g_device_caps[id].cc && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { - min_compute_capability = g_device_caps[id].cc; + for (int i = 0; i < g_device_count; ++i) { + if (min_compute_capability > g_device_caps[i].cc && g_tensor_split[i] < (i + 1 < g_device_count ? g_tensor_split[i + 1] : 1.0f)) { + min_compute_capability = g_device_caps[i].cc; } } @@ -13407,10 +13785,10 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 // KQV single-batch // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n"); ggml_sycl_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { // KQ + KQV multi-batch - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_mat_batched_sycl\n"); - ggml_sycl_mul_mat_mat_batched_sycl(src0, src1, dst); + // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n"); + ggml_sycl_mul_mat_batched_sycl(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n"); ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); @@ -13522,30 +13900,30 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { const int64_t ne = ggml_nelements(dst); SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - syclStream_t main_stream = g_syclStreams[g_main_device_index][0]; + syclStream_t main_stream = g_syclStreams[g_main_device][0]; - SYCL_CHECK(syclSetStream(g_sycl_handles[g_main_device_index], main_stream)); + SYCL_CHECK(syclSetStream(g_sycl_handles[g_main_device], main_stream)); //ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - //void * src0_ddq = src0_extra->data_device[g_main_device_index]; + //void * src0_ddq = src0_extra->data_device[g_main_device]; //half * src0_as_f16 = (half *) src0_ddq; ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; // convert src1 to fp16 const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); GGML_ASSERT(to_fp16_sycl != nullptr); size_t src1_as = 0; - half * src1_as_f16 = (half *) ggml_sycl_pool_malloc(ne1 * sizeof(half), &src1_as); + half * src1_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne1 * sizeof(half), &src1_as); to_fp16_sycl(src1_ddf, src1_as_f16, ne1, main_stream); size_t dst_as = 0; - half * dst_f16 = (half *) ggml_sycl_pool_malloc(ne * sizeof(half), &dst_as); + half * dst_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne * sizeof(half), &dst_as); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -13566,14 +13944,14 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { size_t ptrs_src_s = 0; size_t ptrs_dst_s = 0; - ptrs_src = (const void **) ggml_sycl_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s); - ptrs_dst = ( void **) ggml_sycl_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s); + ptrs_src = (const void **) ggml_sycl_pool_malloc(g_main_device, 2*ne23*sizeof(void *), &ptrs_src_s); + ptrs_dst = ( void **) ggml_sycl_pool_malloc(g_main_device, 1*ne23*sizeof(void *), &ptrs_dst_s); int64_t src0_ne = ggml_nelements(src00); half * src0_as_f16 = nullptr; size_t src0_as = 0; if (src00->type != GGML_TYPE_F16) { - src0_as_f16 = (half *) ggml_sycl_pool_malloc(src0_ne * sizeof(half), &src0_as); + src0_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, src0_ne * sizeof(half), &src0_as); } static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6"); @@ -13588,16 +13966,16 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { r2, r3, src00->type, src0_as_f16, src0_ne, src1_as_f16, dst_f16, - (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index], id, - dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device_index] : nullptr, - dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device_index] : nullptr, - dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device_index] : nullptr, - dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device_index] : nullptr + (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id, + dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr, + dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr, + dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr, + dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr ); SYCL_CHECK(syclGetLastError()); SYCL_CHECK( - syclGemmBatchedEx(g_sycl_handles[g_main_device_index], CUBLAS_OP_T, CUBLAS_OP_N, + syclGemmBatchedEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, &alpha_f16, (const void **) (ptrs_src + 0*ne23), SYCL_R_16F, ne00, (const void **) (ptrs_src + 1*ne23), SYCL_R_16F, ne10, @@ -13607,20 +13985,20 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { CUBLAS_GEMM_DEFAULT_TENSOR_OP)); if (src0_as != 0) { - ggml_sycl_pool_free(src0_as_f16, src0_as); + ggml_sycl_pool_free(g_main_device, src0_as_f16, src0_as); } if (ptrs_src_s != 0) { - ggml_sycl_pool_free(ptrs_src, ptrs_src_s); + ggml_sycl_pool_free(g_main_device, ptrs_src, ptrs_src_s); } if (ptrs_dst_s != 0) { - ggml_sycl_pool_free(ptrs_dst, ptrs_dst_s); + ggml_sycl_pool_free(g_main_device, ptrs_dst, ptrs_dst_s); } const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); to_fp32_sycl(dst_f16, dst_ddf, ne, main_stream); - ggml_sycl_pool_free(src1_as_f16, src1_as); - ggml_sycl_pool_free(dst_f16, dst_as); + ggml_sycl_pool_free(g_main_device, src1_as_f16, src1_as); + ggml_sycl_pool_free(g_main_device, dst_f16, dst_as); } #endif @@ -13641,10 +14019,10 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, std::vector ids_host(ggml_nbytes(ids)); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; if (ids->backend == GGML_BACKEND_TYPE_GPU) { - const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index]; + const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device]; SYCL_CHECK(CHECK_TRY_ERROR( stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); @@ -13668,9 +14046,9 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, dst_row.extra = &dst_row_extra; char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ? - (char *) src1->data : (char *) src1_extra->data_device[g_main_device_index]; + (char *) src1->data : (char *) src1_extra->data_device[g_main_device]; char * dst_original = dst->backend == GGML_BACKEND_TYPE_CPU ? - (char *) dst->data : (char *) dst_extra->data_device[g_main_device_index]; + (char *) dst->data : (char *) dst_extra->data_device[g_main_device]; if (src1->ne[1] == 1) { GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); @@ -13687,10 +14065,10 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, const struct ggml_tensor * src0_row = dst->src[row_id + 2]; - src1_row_extra.data_device[g_main_device_index] = src1_original + i01*src1->nb[1]; + src1_row_extra.data_device[g_main_device] = src1_original + i01*src1->nb[1]; src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set? - dst_row_extra.data_device[g_main_device_index] = dst_original + i01*dst->nb[1]; + dst_row_extra.data_device[g_main_device] = dst_original + i01*dst->nb[1]; dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set? ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row); @@ -13699,8 +14077,8 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, sycl_pool_alloc src1_contiguous(sizeof(float)*ggml_nelements(src1)); sycl_pool_alloc dst_contiguous(sizeof(float)*ggml_nelements(dst)); - src1_row_extra.data_device[g_main_device_index] = src1_contiguous.get(); - dst_row_extra.data_device[g_main_device_index] = dst_contiguous.get(); + src1_row_extra.data_device[g_main_device] = src1_contiguous.get(); + dst_row_extra.data_device[g_main_device] = dst_contiguous.get(); for (int32_t row_id = 0; row_id < n_as; ++row_id) { const struct ggml_tensor * src0_row = dst->src[row_id + 2]; @@ -13788,13 +14166,13 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, GGML_TENSOR_BINARY_OP_LOCALS; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; - char * src1_ddc = (char *) src1_extra->data_device[g_main_device_index]; + char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; + char * src1_ddc = (char *) src1_extra->data_device[g_main_device]; if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); @@ -13806,6 +14184,8 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) { + ggml_cpy_f16_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) { @@ -13849,6 +14229,10 @@ static void ggml_sycl_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_alibi); } +static void ggml_sycl_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pool2d); +} + static void ggml_sycl_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_im2col); } @@ -13875,93 +14259,6 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); } -void ggml_sycl_transform_tensor(void *data, struct ggml_tensor *tensor) try { - const int64_t nrows = ggml_nrows(tensor); - - const int64_t ne0 = tensor->ne[0]; - - const size_t nb1 = tensor->nb[1]; - - ggml_backend_type backend = tensor->backend; - ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu; - memset(extra, 0, sizeof(*extra)); - - for (int64_t id = 0; id < g_device_count; ++id) { - if (backend == GGML_BACKEND_TYPE_GPU && id != g_main_device_index) { - continue; - } - ggml_sycl_set_device(get_device_id_by_index(id)); - const dpct::queue_ptr stream = g_syclStreams[id][0]; - - int64_t row_low, row_high; - if (backend == GGML_BACKEND_TYPE_GPU) { - row_low = 0; - row_high = nrows; - } else if (backend == GGML_BACKEND_TYPE_GPU_SPLIT) { - const int64_t rounding = get_row_rounding(tensor->type); - - row_low = id == 0 ? 0 : nrows*g_tensor_split[id]; - row_low -= row_low % rounding; - - if (id == g_device_count - 1) { - row_high = nrows; - } else { - row_high = nrows*g_tensor_split[id + 1]; - row_high -= row_high % rounding; - } - } else { - GGML_ASSERT(false); - } - if (row_low == row_high) { - continue; - } - - int64_t nrows_split = row_high - row_low; - - const size_t offset_split = row_low*nb1; - size_t size = ggml_nbytes_split(tensor, nrows_split); - const size_t original_size = size; - - // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses - if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); - } - - char * buf; - SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( - size, *stream))); - char * buf_host = (char *)data + offset_split; - - // set padding to 0 to avoid possible NaN values - if (size > original_size) { - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memset(buf + original_size, 0, size - original_size) - .wait())); - } - - SYCL_CHECK(CHECK_TRY_ERROR((*stream) - .memcpy(buf, buf_host, original_size) - .wait())); - - extra->data_device[id] = buf; - - if (backend == GGML_BACKEND_TYPE_GPU_SPLIT) { - for (int64_t is = 0; is < MAX_STREAMS; ++is) { - SYCL_CHECK(CHECK_TRY_ERROR(extra->events[id][is] = - new sycl::event())); - } - } - } - - tensor->extra = extra; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - void ggml_sycl_free_data(struct ggml_tensor *tensor) try { if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_TYPE_GPU && tensor->backend != GGML_BACKEND_TYPE_GPU_SPLIT) ) { return; @@ -13969,18 +14266,18 @@ void ggml_sycl_free_data(struct ggml_tensor *tensor) try { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - for (int64_t id = 0; id < g_device_count; ++id) { - const dpct::queue_ptr stream = g_syclStreams[id][0]; - if (extra->data_device[id] != nullptr) { - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(extra->data_device[id], *stream))); + for (int i = 0; i < g_device_count; ++i) { + const dpct::queue_ptr stream = g_syclStreams[i][0]; + if (extra->data_device[i] != nullptr) { + SYCL_CHECK(ggml_sycl_set_device(i)); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(extra->data_device[i], *stream))); } for (int64_t is = 0; is < MAX_STREAMS; ++is) { - if (extra->events[id][is] != nullptr) { - SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); + if (extra->events[i][is] != nullptr) { + SYCL_CHECK(ggml_sycl_set_device(i)); SYCL_CHECK(CHECK_TRY_ERROR( - dpct::destroy_event(extra->events[id][is]))); + dpct::destroy_event(extra->events[i][is]))); } } } @@ -14040,22 +14337,22 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, const size_t size = ggml_nbytes(tensor); SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; if (inplace && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) { ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; + char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; size_t offset = 0; if (tensor->op == GGML_OP_VIEW) { memcpy(&offset, tensor->op_params, sizeof(size_t)); } extra = ggml_sycl_alloc_temp_tensor_extra(); - extra->data_device[g_main_device_index] = src0_ddc + offset; + extra->data_device[g_main_device] = src0_ddc + offset; } else if (tensor->op == GGML_OP_CPY) { ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra; - void * src1_ddv = src1_extra->data_device[g_main_device_index]; + void * src1_ddv = src1_extra->data_device[g_main_device]; extra = ggml_sycl_alloc_temp_tensor_extra(); - extra->data_device[g_main_device_index] = src1_ddv; + extra->data_device[g_main_device] = src1_ddv; } else if (scratch) { GGML_ASSERT(size <= g_scratch_size); if (g_scratch_offset + size > g_scratch_size) { @@ -14070,7 +14367,7 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, g_scratch_buffer = data; } extra = ggml_sycl_alloc_temp_tensor_extra(); - extra->data_device[g_main_device_index] = data + g_scratch_offset; + extra->data_device[g_main_device] = data + g_scratch_offset; g_scratch_offset += size; @@ -14083,44 +14380,7 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, (*stream).memset(data, 0, size).wait())); extra = new ggml_tensor_extra_gpu; memset(extra, 0, sizeof(*extra)); - extra->data_device[g_main_device_index] = data; - } - - tensor->extra = extra; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -void ggml_sycl_assign_scratch_offset(struct ggml_tensor *tensor, - size_t offset) try { - if (g_scratch_size == 0) { - return; - } - if (g_scratch_buffer == nullptr) { - ggml_sycl_set_device(g_main_device); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; - SYCL_CHECK( - CHECK_TRY_ERROR(g_scratch_buffer = (void *)sycl::malloc_device( - g_scratch_size, *stream))); - } - - ggml_tensor_extra_gpu * extra = ggml_sycl_alloc_temp_tensor_extra(); - - const bool inplace = tensor->view_src != nullptr; - - if (inplace && (tensor->view_src->backend == GGML_BACKEND_TYPE_GPU || tensor->view_src->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) { - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->view_src->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; - size_t view_offset = 0; - if (tensor->op == GGML_OP_VIEW) { - memcpy(&view_offset, tensor->op_params, sizeof(size_t)); - } - extra->data_device[g_main_device_index] = src0_ddc + view_offset; - } else { - extra->data_device[g_main_device_index] = (char *) g_scratch_buffer + offset; + extra->data_device[g_main_device] = data; } tensor->extra = extra; @@ -14137,9 +14397,9 @@ void ggml_sycl_copy_to_device(struct ggml_tensor *tensor) try { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; SYCL_CHECK(CHECK_TRY_ERROR((*stream) - .memcpy(extra->data_device[g_main_device_index], + .memcpy(extra->data_device[g_main_device], tensor->data, ggml_nbytes(tensor)) .wait())); } @@ -14166,21 +14426,17 @@ void ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor) { } void ggml_sycl_set_main_device(const int main_device) try { + if (g_main_device == main_device) return; + check_allow_gpu_index(main_device); + g_main_device = main_device; + g_main_device_id = g_sycl_gpu_mgr->gpus[main_device]; - if (main_device >= g_all_sycl_device_count) { - fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n", - main_device, g_all_sycl_device_count, g_main_device); - return; - } - - if (g_main_device != main_device && g_device_count >= 1) { - g_main_device = main_device; - g_main_device_index = get_device_index_by_id(g_main_device); + if (g_ggml_sycl_debug) { dpct::device_info prop; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(g_main_device)))); + prop, dpct::dev_mgr::instance().get_device(g_main_device_id)))); fprintf(stderr, "Using device %d (%s) as main device\n", - g_main_device, prop.get_name()); + g_main_device_id, prop.get_name()); } } catch (sycl::exception const &exc) { @@ -14203,7 +14459,7 @@ void ggml_sycl_free_scratch() try { return; } ggml_sycl_set_device(g_main_device); - const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; SYCL_CHECK(CHECK_TRY_ERROR( sycl::free(g_scratch_buffer, *stream))); @@ -14275,6 +14531,12 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_UNARY_OP_RELU: func = ggml_sycl_relu; break; + case GGML_UNARY_OP_HARDSIGMOID: + func = ggml_sycl_hardsigmoid; + break; + case GGML_UNARY_OP_HARDSWISH: + func = ggml_sycl_hardswish; + break; default: return false; } @@ -14349,6 +14611,9 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_OP_IM2COL: func = ggml_sycl_im2col; break; + case GGML_OP_POOL_2D: + func = ggml_sycl_pool2d; + break; case GGML_OP_SUM_ROWS: func = ggml_sycl_sum_rows; break; @@ -14374,27 +14639,15 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ } GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len) try { - int max_compute_units = -1; - for(int i=0;igpus.size();i++){ + if (i>=max_len) break; + id_list[i] = g_sycl_gpu_mgr->gpus[i]; } return; } @@ -14421,8 +14674,9 @@ catch (sycl::exception const &exc) { GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description, size_t description_size) try { dpct::device_info prop; + int device_id = g_sycl_gpu_mgr->gpus[device]; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(device)))); + prop, dpct::dev_mgr::instance().get_device(device_id)))); snprintf(description, description_size, "%s", prop.get_name()); } catch (sycl::exception const &exc) { @@ -14431,17 +14685,36 @@ catch (sycl::exception const &exc) { std::exit(1); } +GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, + size_t *total) try { + ggml_sycl_set_device(device); + + /* + DPCT1009:218: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string was + inserted. You need to rewrite this code. + */ + /* + DPCT1106:217: 'cudaMemGetInfo' was migrated with the Intel extensions for + device information which may not be supported by all compilers or runtimes. + You may need to adjust the code. + */ + int device_id = g_sycl_gpu_mgr->gpus[device]; + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(device_id).get_memory_info(*free, *total))); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + //////////////////////////////////////////////////////////////////////////////// // backend interface #define UNUSED GGML_UNUSED -struct ggml_backend_sycl_context { - int device; - std::string name; -}; - // sycl buffer struct ggml_backend_sycl_buffer_context { @@ -14451,7 +14724,12 @@ struct ggml_backend_sycl_buffer_context { size_t temp_tensor_extra_index = 0; std::string name; - ggml_backend_sycl_buffer_context(int device, void * dev_ptr) : device(device), dev_ptr(dev_ptr) {} + ggml_backend_sycl_buffer_context(int device, void * dev_ptr) : + device(device), dev_ptr(dev_ptr) { + check_allow_gpu_index(device); + int id = g_sycl_gpu_mgr->gpus[device]; + name = (GGML_SYCL_NAME + std::to_string(id)); + } ~ ggml_backend_sycl_buffer_context() { delete[] temp_tensor_extras; @@ -14482,10 +14760,9 @@ GGML_CALL static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) static void ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - int device_index = get_device_index_by_id(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( CHECK_TRY_ERROR(sycl::free(ctx->dev_ptr, *stream))); @@ -14498,13 +14775,14 @@ catch (sycl::exception const &exc) { } static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; return ctx->dev_ptr; } -static void ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, - ggml_tensor *tensor) try { - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; +GGML_CALL static void +ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor) try { + ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; if (tensor->view_src != NULL && tensor->view_offs == 0) { assert(tensor->view_src->buffer->buft == buffer->buft); @@ -14516,27 +14794,20 @@ static void ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor_extra_gpu * extra = ctx->ggml_sycl_alloc_temp_tensor_extra(); extra->data_device[ctx->device] = tensor->data; - tensor->backend = GGML_BACKEND_TYPE_GPU; tensor->extra = extra; if (ggml_is_quantized(tensor->type)) { // initialize padding to 0 to avoid possible NaN values - int64_t row_low = 0; - int64_t row_high = ggml_nrows(tensor); - int64_t nrows_split = row_high - row_low; - - size_t original_size = ggml_nbytes_split(tensor, nrows_split); + size_t original_size = ggml_nbytes(tensor); size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); if (padded_size > original_size && tensor->view_src == nullptr) { SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[ctx->device][0]->memset( (char *)tensor->data + original_size, 0, - padded_size - original_size))); + padded_size - original_size).wait())); } } - - UNUSED(buffer); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -14550,13 +14821,12 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, size_t size) try { GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - int device_index = get_device_index_by_id(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( - CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); SYCL_CHECK( CHECK_TRY_ERROR((*stream) @@ -14575,14 +14845,13 @@ static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer, size_t size) try { GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - int device_index = get_device_index_by_id(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( - CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); SYCL_CHECK(CHECK_TRY_ERROR( (*stream) @@ -14595,13 +14864,73 @@ catch (sycl::exception const &exc) { std::exit(1); } +GGML_CALL static bool +ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *src, + ggml_tensor *dst) try { + if (ggml_backend_buffer_is_sycl(src->buffer)) { + ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context; + ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)buffer->context; + + ggml_sycl_set_device(src_ctx->device); + /* + DPCT1009:198: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(src_ctx->device).queues_wait_and_throw())); + ggml_sycl_set_device(dst_ctx->device); + /* + DPCT1009:199: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); + /* + DPCT1009:200: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + + dpct::queue_ptr stream_dst = g_syclStreams[dst_ctx->device][0]; + dpct::queue_ptr stream_src = g_syclStreams[src_ctx->device][0]; + size_t size = ggml_nbytes(src); + + //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs. + dev2dev_memcpy(*stream_dst, *stream_src, dst->data, src->data, size); + +//todo, it's known issue：error in device2device cross GPUs. reused when the issue is fixed. DON"T remove +#if 0 + SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy( + (char *)dst->data, (const char *)src->data, size).wait())); + + /* + DPCT1009:201: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::dev_mgr::instance().get_device(dst_ctx->device).queues_wait_and_throw())); +#endif + return true; + } + return false; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + + static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) try { ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - int device_index = get_device_index_by_id(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); @@ -14622,7 +14951,7 @@ static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = { /* .init_tensor = */ ggml_backend_sycl_buffer_init_tensor, /* .set_tensor = */ ggml_backend_sycl_buffer_set_tensor, /* .get_tensor = */ ggml_backend_sycl_buffer_get_tensor, - /* .cpy_tensor = */ NULL, + /* .cpy_tensor = */ ggml_backend_sycl_buffer_cpy_tensor, /* .clear = */ ggml_backend_sycl_buffer_clear, /* .reset = */ NULL, }; @@ -14633,29 +14962,28 @@ struct ggml_backend_sycl_buffer_type_context { std::string name; }; +struct ggml_backend_sycl_context { + int device; + std::string name; +}; + GGML_CALL static const char * ggml_backend_sycl_buffer_type_name(ggml_backend_buffer_type_t buft) { ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; return ctx->name.c_str(); } - -static ggml_backend_buffer_t +GGML_CALL static ggml_backend_buffer_t ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) try { ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - int device = (int) buft_ctx->device; - - ggml_sycl_set_device(device); - int device_index = get_device_index_by_id(device); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + ggml_sycl_set_device(buft_ctx->device); + const dpct::queue_ptr stream = g_syclStreams[buft_ctx->device][0]; size = std::max(size, (size_t)1); // syclMalloc returns null for size 0 void * dev_ptr; SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device( size, *stream))); - - ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(device, dev_ptr); - + ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr); return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size); } catch (sycl::exception const &exc) { @@ -14664,9 +14992,8 @@ catch (sycl::exception const &exc) { std::exit(1); } -static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { +GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 128; - UNUSED(buft); } @@ -14676,13 +15003,8 @@ static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_typ UNUSED(buft); } -static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - int64_t row_low = 0; - int64_t row_high = ggml_nrows(tensor); - int64_t nrows_split = row_high - row_low; - - size_t size = ggml_nbytes_split(tensor, nrows_split); - +GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + size_t size = ggml_nbytes(tensor); int64_t ne0 = tensor->ne[0]; if (ggml_is_quantized(tensor->type)) { @@ -14696,10 +15018,13 @@ static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_t UNUSED(buft); } -static bool ggml_backend_sycl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_sycl(backend); - - UNUSED(buft); +GGML_CALL static bool ggml_backend_sycl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + if (!ggml_backend_is_sycl(backend)) { + return false; + } + ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + return buft_ctx->device == sycl_ctx->device; } static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { @@ -14718,10 +15043,10 @@ ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { static bool ggml_backend_sycl_buffer_type_initialized = false; if (!ggml_backend_sycl_buffer_type_initialized) { - for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) { + for (int i = 0; i < g_device_count; i++) { ggml_backend_sycl_buffer_types[i] = { /* .iface = */ ggml_backend_sycl_buffer_type_interface, - /* .context = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(i)}, + /* .context = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(g_sycl_gpu_mgr->gpus[i])}, }; } ggml_backend_sycl_buffer_type_initialized = true; @@ -14730,6 +15055,391 @@ ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { return &ggml_backend_sycl_buffer_types[device]; } +// sycl split buffer type +static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { + const int64_t nrows = ggml_nrows(tensor); + const int64_t rounding = get_row_rounding(tensor->type, tensor_split); + + *row_low = id == 0 ? 0 : nrows*tensor_split[id]; + *row_low -= *row_low % rounding; + if (id == g_device_count - 1) { + *row_high = nrows; + } else { + *row_high = nrows*tensor_split[id + 1]; + *row_high -= *row_high % rounding; + } +} + +struct ggml_backend_sycl_split_buffer_context { + ~ggml_backend_sycl_split_buffer_context() try { + for (ggml_tensor_extra_gpu * extra : tensor_extras) { + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + for (int64_t is = 0; is < MAX_STREAMS; ++is) { + if (extra->events[i][is] != nullptr) { + /* + DPCT1009:206: SYCL uses exceptions to report errors and + does not use the error codes. The original code was + commented out and a warning string was inserted. You + need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::destroy_event(extra->events[i][is]))); + } + } + if (extra->data_device[i] != nullptr) { + /* + DPCT1009:207: SYCL uses exceptions to report errors and does + not use the error codes. The original code was commented out + and a warning string was inserted. You need to rewrite this + code. + */ + ggml_sycl_set_device(i); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free( + extra->data_device[i], *g_syclStreams[i][0]))); + } + } + delete extra; + } + } + catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); + } + + std::vector tensor_extras; +}; + +GGML_CALL static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) { + return GGML_SYCL_NAME "_Split"; + + UNUSED(buffer); +} + +// unused at the moment +//static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { +// return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; +//} + +GGML_CALL static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + delete ctx; +} + +GGML_CALL static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) { + // the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced + return (void *)0x1000; + + UNUSED(buffer); +} + +GGML_CALL static void +ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor) try { + GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported + + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + + ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; + + ctx->tensor_extras.push_back(extra); + + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + // FIXME: do not crash if cudaMalloc fails + // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first + ggml_sycl_set_device(i); + char * buf; + /* + DPCT1009:208: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( + size, *g_syclStreams[i][0]))); + + // set padding to 0 to avoid possible NaN values + if (size > original_size) { + /* + DPCT1009:209: SYCL uses exceptions to report errors and does not use + the error codes. The original code was commented out and a warning + string was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + (*g_syclStreams[i][0]) + .memset(buf + original_size, 0, size - original_size) + .wait())); + } + + extra->data_device[i] = buf; + + for (int64_t is = 0; is < MAX_STREAMS; ++is) { + /* + DPCT1009:210: SYCL uses exceptions to report errors and does not use + the error codes. The original code was commented out and a warning + string was inserted. You need to rewrite this code. + */ + SYCL_CHECK( + CHECK_TRY_ERROR(extra->events[i][is] = new sycl::event())); + } + } + tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT; + tensor->extra = extra; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +GGML_CALL static void +ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor, const void *data, + size_t offset, size_t size) try { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + const char * buf_host = (const char *)data + offset_split; + /* + DPCT1009:211: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + ggml_sycl_set_device(i); + SYCL_CHECK(CHECK_TRY_ERROR( + (*g_syclStreams[i][0]) + .memcpy(extra->data_device[i], buf_host, original_size) + .wait())); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +GGML_CALL static void +ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *tensor, void *data, + size_t offset, size_t size) try { + // split tensors must always be set in their entirety at once + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; + + const int64_t ne0 = tensor->ne[0]; + const size_t nb1 = tensor->nb[1]; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; + + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + char * buf_host = (char *)data + offset_split; + /* + DPCT1009:212: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + ggml_sycl_set_device(i); + SYCL_CHECK(CHECK_TRY_ERROR( + (*g_syclStreams[i][0]) + .memcpy(buf_host, extra->data_device[i], original_size) + .wait())); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +GGML_CALL static void ggml_backend_sycl_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + UNUSED(buffer); + UNUSED(value); +} + +static struct ggml_backend_buffer_i ggml_backend_sycl_split_buffer_interface = { + /* .get_name = */ ggml_backend_sycl_split_buffer_get_name, + /* .free_buffer = */ ggml_backend_sycl_split_buffer_free_buffer, + /* .get_base = */ ggml_backend_sycl_split_buffer_get_base, + /* .init_tensor = */ ggml_backend_sycl_split_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_sycl_split_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_sycl_split_buffer_get_tensor, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_sycl_split_buffer_clear, + /* .reset = */ NULL, +}; + +GGML_CALL static const char * ggml_backend_sycl_split_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_SYCL_NAME "_Split"; + + UNUSED(buft); +} + +GGML_CALL static ggml_backend_buffer_t ggml_backend_sycl_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point + // instead, we allocate them for each tensor separately in init_tensor + // however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated, + // as returned by get_alloc_size. this limit is enforced during tensor allocation by ggml-alloc, so it must be correct. + ggml_backend_sycl_split_buffer_context * ctx = new ggml_backend_sycl_split_buffer_context(); + + return ggml_backend_buffer_init(buft, ggml_backend_sycl_split_buffer_interface, ctx, size); +} + +GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 128; + UNUSED(buft); +} + +GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + ggml_backend_sycl_split_buffer_type_context * ctx = (ggml_backend_sycl_split_buffer_type_context *)buft->context; + + size_t total_size = 0; + + const int64_t ne0 = tensor->ne[0]; + + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + int64_t row_low, row_high; + get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i); + + int64_t nrows_split = row_high - row_low; + if (nrows_split == 0) { + continue; + } + + total_size += ggml_nbytes_split(tensor, nrows_split); + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + total_size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + } + + return total_size; +} + +GGML_CALL static bool ggml_backend_sycl_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + return ggml_backend_is_sycl(backend); + + UNUSED(buft); +} + +GGML_CALL static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + UNUSED(buft); +} + +static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface = { + /* .get_name = */ ggml_backend_sycl_split_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_sycl_split_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_sycl_split_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_sycl_split_buffer_type_get_alloc_size, + /* .supports_backend = */ ggml_backend_sycl_split_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_sycl_split_buffer_type_is_host, +}; + +GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) { + // FIXME: this is not thread safe + static std::map, struct ggml_backend_buffer_type> buft_map; + + std::array tensor_split_arr = {}; + + bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; }); + if (all_zero) { + tensor_split_arr = g_default_tensor_split; + } else { + float split_sum = 0.0f; + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + tensor_split_arr[i] = split_sum; + split_sum += tensor_split[i]; + } + for (int i = 0; i < g_device_count; ++i) { + // int id = g_sycl_gpu_mgr->gpus[i]; + tensor_split_arr[i] /= split_sum; + } + } + + auto it = buft_map.find(tensor_split_arr); + if (it != buft_map.end()) { + return &it->second; + } + + struct ggml_backend_buffer_type buft { + /* .iface = */ ggml_backend_sycl_split_buffer_type_interface, + /* .context = */ new ggml_backend_sycl_split_buffer_type_context{tensor_split_arr}, + }; + + auto result = buft_map.emplace(tensor_split_arr, buft); + return &result.first->second; +} + // host buffer type GGML_CALL static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { @@ -14759,6 +15469,7 @@ static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggm // FIXME: this is a hack to avoid having to implement a new buffer type ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); buffer->buft = buft; + buffer->iface.get_name = ggml_backend_sycl_host_buffer_name; buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer; return buffer; @@ -14783,34 +15494,33 @@ ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() { // backend -static const char * ggml_backend_sycl_name(ggml_backend_t backend) { - return GGML_SYCL_NAME; +GGML_CALL static const char * ggml_backend_sycl_name(ggml_backend_t backend) { - UNUSED(backend); + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + + return sycl_ctx->name.c_str(); } -static void ggml_backend_sycl_free(ggml_backend_t backend) { +GGML_CALL static void ggml_backend_sycl_free(ggml_backend_t backend) { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; delete sycl_ctx; delete backend; } -static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) { - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) { + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; return ggml_backend_sycl_buffer_type(sycl_ctx->device); } -static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, +GGML_CALL static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, ggml_tensor *tensor, const void *data, size_t offset, size_t size) try { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( (char *)tensor->data + offset, data, size))); } @@ -14820,15 +15530,13 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, +GGML_CALL static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, const ggml_tensor *tensor, void *data, size_t offset, size_t size) try { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( data, (const char *)tensor->data + offset, size))); } @@ -14838,12 +15546,22 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try { +GGML_CALL static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend, + const ggml_tensor *src, + ggml_tensor *dst) try { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + if (dst->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && ggml_backend_buffer_is_sycl(src->buffer)) { + /* + DPCT1009:215: SYCL uses exceptions to report errors and does not use the + error codes. The original code was commented out and a warning string + was inserted. You need to rewrite this code. + */ + SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( + dst->data, src->data, ggml_nbytes(dst)))); + return true; + } - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->wait())); - - UNUSED(backend); + return false; } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -14851,32 +15569,20 @@ catch (sycl::exception const &exc) { std::exit(1); } -static ggml_backend_graph_plan_t ggml_backend_sycl_graph_plan_create(ggml_backend_t backend, const ggml_cgraph * cgraph) { - GGML_ASSERT(!"not implemented"); - - return nullptr; - - UNUSED(backend); - UNUSED(cgraph); -} - -static void ggml_backend_sycl_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - GGML_ASSERT(!"not implemented"); +static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try { + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->wait())); UNUSED(backend); - UNUSED(plan); } - -static void ggml_backend_sycl_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - GGML_ASSERT(!"not implemented"); - - UNUSED(backend); - UNUSED(plan); +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); } -static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - ggml_sycl_set_main_device(sycl_ctx->device); ggml_compute_params params = {}; @@ -14884,63 +15590,41 @@ static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph params.ith = 0; for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; - - if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE) + if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { continue; - - assert(node->backend == GGML_BACKEND_TYPE_GPU); + } +#ifndef NDEBUG + assert(node->backend == GGML_BACKEND_TYPE_GPU || node->backend == GGML_BACKEND_TYPE_GPU_SPLIT); assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); assert(node->extra != nullptr); for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { - assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU); + assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU || node->src[j]->backend == GGML_BACKEND_TYPE_GPU_SPLIT); assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); assert(node->src[j]->extra != nullptr); } } - +#endif bool ok = ggml_sycl_compute_forward(¶ms, node); if (!ok) { fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } GGML_ASSERT(ok); - -#if 0 - if (node->type == GGML_TYPE_F32) { - syclDeviceSynchronize(); - std::vector tmp(ggml_nelements(node), 0.0f); - syclMemcpy(tmp.data(), node->data, ggml_nelements(node)*sizeof(float), syclMemcpyDeviceToHost); - printf("\n%s (%s) (%s %s) (%s %s): ", node->name, ggml_op_name(node->op), - ggml_type_name(node->src[0]->type), - node->src[1] ? ggml_type_name(node->src[1]->type) : "none", - node->src[0]->name, - node->src[1] ? node->src[1]->name : "none"); - double sum = 0.0; - double sq_sum = 0.0; - for (int i = 0; i < ggml_nelements(node); i++) { - printf("%f ", tmp[i]); - sum += tmp[i]; - sq_sum += tmp[i]*tmp[i]; - } - printf("\n"); - printf("sum: %f, ", sum); - printf("sq_sum: %f\n", sq_sum); - } -#endif } - UNUSED(backend); - return true; + return GGML_STATUS_SUCCESS; } -static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { +GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: return true; @@ -15016,16 +15700,17 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { return true; } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return true; + } return false; } break; + case GGML_OP_DUP: + case GGML_OP_REPEAT: case GGML_OP_CONCAT: { ggml_type src0_type = op->src[0]->type; - if (src0_type == GGML_TYPE_F32) { - return true; - } else { - return false; - } + return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; } break; case GGML_OP_NONE: case GGML_OP_RESHAPE: @@ -15033,8 +15718,6 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_NORM: - case GGML_OP_REPEAT: - case GGML_OP_DUP: case GGML_OP_ADD: case GGML_OP_MUL: case GGML_OP_DIV: @@ -15048,6 +15731,7 @@ static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_ten case GGML_OP_ROPE: case GGML_OP_ALIBI: case GGML_OP_IM2COL: + case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: case GGML_OP_ARGSORT: case GGML_OP_ACC: @@ -15069,32 +15753,35 @@ static ggml_backend_i ggml_backend_sycl_interface = { /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type, /* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async, /* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async, - /* .cpy_tensor_async = */ NULL, + /* .cpy_tensor_async = */ ggml_backend_sycl_cpy_tensor_async, /* .synchronize = */ ggml_backend_sycl_synchronize, - /* .graph_plan_create = */ ggml_backend_sycl_graph_plan_create, - /* .graph_plan_free = */ ggml_backend_sycl_graph_plan_free, - /* .graph_plan_compute = */ ggml_backend_sycl_graph_plan_compute, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_sycl_graph_compute, /* .supports_op = */ ggml_backend_sycl_supports_op, }; -ggml_backend_t ggml_backend_sycl_init(int device) { +static ggml_guid_t ggml_backend_sycl_guid() { + static ggml_guid guid = { 0x58, 0x05, 0x13, 0x8f, 0xcd, 0x3a, 0x61, 0x9d, 0xe7, 0xcd, 0x98, 0xa9, 0x03, 0xfd, 0x7c, 0x53 }; + return &guid; +} + +GGML_CALL ggml_backend_t ggml_backend_sycl_init(int device) { ggml_init_sycl(); // TODO: remove from ggml.c - if (device < 0 || device >= ggml_sycl_get_device_count()) { - fprintf(stderr, "%s: error: invalid device %d\n", __func__, device); - return nullptr; - } + check_allow_gpu_index(device); // not strictly necessary, but it may reduce the overhead of the first graph_compute ggml_sycl_set_main_device(device); - + int id = g_sycl_gpu_mgr->gpus[device]; ggml_backend_sycl_context * ctx = new ggml_backend_sycl_context { /* .device = */ device, - /* .name = */ GGML_SYCL_NAME + std::to_string(device), + /* .name = */ GGML_SYCL_NAME + std::to_string(id), }; ggml_backend_t sycl_backend = new ggml_backend { + /* .guid = */ ggml_backend_sycl_guid(), /* .interface = */ ggml_backend_sycl_interface, /* .context = */ ctx }; @@ -15103,25 +15790,36 @@ ggml_backend_t ggml_backend_sycl_init(int device) { } bool ggml_backend_is_sycl(ggml_backend_t backend) { - return backend->iface.get_name == ggml_backend_sycl_name; + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_sycl_guid()); } -static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) { +GGML_CALL int ggml_backend_sycl_get_device_count() { + if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + return g_sycl_gpu_mgr->get_gpu_count(); +} + +GGML_CALL static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) { ggml_backend_t sycl_backend = ggml_backend_sycl_init((int) (intptr_t) user_data); return sycl_backend; UNUSED(params); } +GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) { + return g_sycl_gpu_mgr->get_index(device_id); +} + extern "C" int ggml_backend_sycl_reg_devices(); int ggml_backend_sycl_reg_devices() { - int device_count = ggml_sycl_get_device_count(); - - for (int i = 0; i < device_count; i++) { + if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); + g_device_count = g_sycl_gpu_mgr->get_gpu_count(); + assert(g_device_count>0); + for (int i = 0; i < g_device_count; i++) { + int id = g_sycl_gpu_mgr->gpus[i]; char name[128]; - snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, i); + snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, id); ggml_backend_register(name, ggml_backend_reg_sycl_init, ggml_backend_sycl_buffer_type(i), (void *) (intptr_t) i); } - return device_count; + return g_device_count; } diff --git a/ggml-sycl.h b/ggml-sycl.h index 891f2d00a9457..bf5b11b369d19 100644 --- a/ggml-sycl.h +++ b/ggml-sycl.h @@ -24,6 +24,11 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void); GGML_API void ggml_backend_sycl_print_sycl_devices(void); GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len); GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description, size_t description_size); +GGML_API GGML_CALL int ggml_backend_sycl_get_device_count(); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split); +GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total); +GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id); + #ifdef __cplusplus } #endif diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 6caafb82279ae..bc316c3f3944d 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -1106,7 +1106,9 @@ void ggml_vk_instance_init() { const std::vector instance_extensions = vk::enumerateInstanceExtensionProperties(); const bool validation_ext = ggml_vk_instance_validation_ext_available(instance_extensions); +#ifdef __APPLE__ const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions); +#endif std::vector layers; @@ -1117,13 +1119,17 @@ void ggml_vk_instance_init() { if (validation_ext) { extensions.push_back("VK_EXT_validation_features"); } +#ifdef __APPLE__ if (portability_enumeration_ext) { extensions.push_back("VK_KHR_portability_enumeration"); } +#endif vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions); +#ifdef __APPLE__ if (portability_enumeration_ext) { instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR; } +#endif std::vector features_enable; vk::ValidationFeaturesEXT validation_features; @@ -5086,7 +5092,7 @@ GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { ctx->transfer_ctx = nullptr; } -GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { +GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; for (int i = 0; i < cgraph->n_nodes; i++) { @@ -5129,7 +5135,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml ggml_vk_graph_cleanup(ctx); - return true; + return GGML_STATUS_SUCCESS; UNUSED(backend); } @@ -5244,6 +5250,11 @@ static ggml_backend_i ggml_backend_vk_interface = { /* .supports_op = */ ggml_backend_vk_supports_op, }; +static ggml_guid_t ggml_backend_vk_guid() { + static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b }; + return &guid; +} + GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t idx) { if (vk_instance.initialized[idx]) { return vk_instance.backends[idx]; @@ -5262,6 +5273,7 @@ GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t idx) { vk_instance.initialized[idx] = true; ggml_backend_t vk_backend = new ggml_backend { + /* .guid = */ ggml_backend_vk_guid(), /* .interface = */ ggml_backend_vk_interface, /* .context = */ &vk_instance.contexts[ctx->idx], }; @@ -5272,7 +5284,7 @@ GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t idx) { } GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend) { - return backend && backend->iface.get_name == ggml_backend_vk_name; + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid()); } GGML_CALL int ggml_backend_vk_get_device_count() { @@ -5416,7 +5428,8 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size); } std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; @@ -5528,7 +5541,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ for (int i3 = 0; i3 < src0->ne[3]; i3++) { for (int i2 = 0; i2 < src0->ne[2]; i2++) { const int idx = i3*src0->ne[2] + i2; - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); } } @@ -5538,10 +5552,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1]; } } else { - if (offset + src0_size >= extra->buffer_gpu->size) { - src0_size = extra->buffer_gpu->size - offset; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + if (offset + src0_size >= buffer_gpu->size) { + src0_size = buffer_gpu->size - offset; } - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src0_clone->data, src0_size); + ggml_vk_buffer_read(ctx, buffer_gpu, offset, src0_clone->data, src0_size); memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5571,7 +5586,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ for (int i3 = 0; i3 < src1->ne[3]; i3++) { for (int i2 = 0; i2 < src1->ne[2]; i2++) { const int idx = i3*src1->ne[2] + i2; - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); } } @@ -5581,10 +5597,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1]; } } else { - if (offset + src1_size >= extra->buffer_gpu->size) { - src1_size = extra->buffer_gpu->size - offset; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + if (offset + src1_size >= buffer_gpu->size) { + src1_size = buffer_gpu->size - offset; } - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src1_clone->data, src1_size); + ggml_vk_buffer_read(ctx, buffer_gpu, offset, src1_clone->data, src1_size); memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5631,11 +5648,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ } else if (tensor->op == GGML_OP_RMS_NORM) { tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_SOFT_MAX) { - if (src1 != nullptr) { - tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, *(float *)tensor->op_params); - } else { tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); - } } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_ROPE) { @@ -5741,11 +5754,12 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (extra->offset + tensor_size >= extra->buffer_gpu->size) { - tensor_size = extra->buffer_gpu->size - (extra->offset); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + if (extra->offset + tensor_size >= buffer_gpu->size) { + tensor_size = buffer_gpu->size - (extra->offset); } - ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size); } float first_error_result = -1.0f; diff --git a/ggml.c b/ggml.c index 004d2fa4be7a4..eeb1bb6a0c667 100644 --- a/ggml.c +++ b/ggml.c @@ -320,6 +320,17 @@ static ggml_fp16_t ggml_table_exp_f16[1 << 16]; // precomputed f32 table for f16 (256 KB) (ggml-impl.h) float ggml_table_f32_f16[1 << 16]; +const char * ggml_status_to_string(enum ggml_status status) { + switch (status) { + case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)"; + case GGML_STATUS_FAILED: return "GGML status: error (operation failed)"; + case GGML_STATUS_SUCCESS: return "GGML status: success"; + case GGML_STATUS_ABORTED: return "GGML status: warning (operation aborted)"; + } + + return "GGML status: unknown"; +} + // note: do not use these inside ggml.c // these are meant to be used via the ggml.h API float ggml_fp16_to_fp32(ggml_fp16_t x) { @@ -355,6 +366,10 @@ void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) { } } +bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b) { + return memcmp(guid_a, guid_b, sizeof(ggml_guid)) == 0; +} + // // timing // @@ -690,6 +705,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, + [GGML_TYPE_IQ2_S] = { + .type_name = "iq2_s", + .blck_size = QK_K, + .type_size = sizeof(block_iq2_s), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq2_s, + .from_float = quantize_row_iq2_s, + .from_float_reference = (ggml_from_float_t)quantize_row_iq2_s_reference, + .vec_dot = ggml_vec_dot_iq2_s_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, [GGML_TYPE_IQ1_S] = { .type_name = "iq1_s", .blck_size = QK_K, @@ -714,6 +741,26 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .vec_dot_type = GGML_TYPE_Q8_0, .nrows = 1, }, + [GGML_TYPE_IQ4_XS] = { + .type_name = "iq4_xs", +#if QK_K == 64 + .blck_size = QK4_NL, +#else + .blck_size = QK_K, +#endif + .type_size = sizeof(block_iq4_xs), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq4_xs, + .from_float = quantize_row_iq4_xs, + .from_float_reference = (ggml_from_float_t)quantize_row_iq4_xs_reference, + .vec_dot = ggml_vec_dot_iq4_xs_q8_K, +#if QK_K == 64 + .vec_dot_type = GGML_TYPE_Q8_0, +#else + .vec_dot_type = GGML_TYPE_Q8_K, +#endif + .nrows = 1, + }, [GGML_TYPE_Q8_K] = { .type_name = "q8_K", .blck_size = QK_K, @@ -1572,9 +1619,15 @@ inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { uint16_t t; for (int i = 0; i < n; ++i) { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); + if (x[i] <= -10.0f) { + y[i] = 0.0f; + } else if (x[i] >= 10.0f) { + y[i] = x[i]; + } else { + ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); + memcpy(&t, &fp16, sizeof(uint16_t)); + y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); + } } } #else @@ -1780,6 +1833,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "POOL_2D", "UPSCALE", "PAD", + "ARANGE", + "TIMESTEP_EMBEDDING", "ARGSORT", "LEAKY_RELU", @@ -1808,7 +1863,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72"); +static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1866,6 +1921,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "pool_2d(x)", "upscale(x)", "pad(x)", + "arange(start, stop, step)", + "timestep_embedding(timesteps, dim, max_period)", "argsort(x)", "leaky_relu(x)", @@ -1894,7 +1951,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72"); +static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -2317,7 +2374,9 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break; case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break; case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break; + case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break; case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break; + case GGML_FTYPE_MOSTLY_IQ2_S: wtype = GGML_TYPE_IQ2_S; break; case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break; case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break; } @@ -2852,11 +2911,21 @@ static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_ return ((const int32_t *)(tensor->op_params))[i]; } +static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); + return ((const float *)(tensor->op_params))[i]; +} + static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) { assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); ((int32_t *)(tensor->op_params))[i] = value; } +static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); + ((float *)(tensor->op_params))[i] = value; +} + struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { memset(tensor->data, 0, ggml_nbytes(tensor)); return tensor; @@ -5743,11 +5812,13 @@ struct ggml_tensor * ggml_pool_1d( is_node = true; } - const int64_t ne[2] = { + const int64_t ne[4] = { ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), a->ne[1], + a->ne[2], + a->ne[3], }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); int32_t params[] = { op, k0, s0, p0 }; ggml_set_op_params(result, params, sizeof(params)); @@ -5853,6 +5924,55 @@ struct ggml_tensor * ggml_upscale( return ggml_upscale_impl(ctx, a, scale_factor); } +struct ggml_tensor * ggml_arange( + struct ggml_context * ctx, + float start, + float stop, + float step) { + + GGML_ASSERT(stop > start); + + const int64_t steps = (int64_t) ceilf((stop - start) / step); + + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, steps); + + result->op = GGML_OP_ARANGE; + ggml_set_op_params_f32(result, 0, start); + ggml_set_op_params_f32(result, 1, stop); + ggml_set_op_params_f32(result, 2, step); + + return result; +} + +struct ggml_tensor * ggml_timestep_embedding( + struct ggml_context * ctx, + struct ggml_tensor * timesteps, + int dim, + int max_period) { + bool is_node = false; + + if (timesteps->grad) { + GGML_ASSERT(false); // TODO: implement backward + is_node = true; + } + + int actual_dim = dim; + if (dim % 2 != 0) { + actual_dim = dim + 1; + } + + struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, actual_dim, timesteps->ne[0]); + + result->op = GGML_OP_TIMESTEP_EMBEDDING; + ggml_set_op_params_i32(result, 0, dim); + ggml_set_op_params_i32(result, 1, max_period); + + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src[0] = timesteps; + + return result; +} + // ggml_argsort struct ggml_tensor * ggml_argsort( @@ -7752,7 +7872,9 @@ static void ggml_compute_forward_add( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: { ggml_compute_forward_add_q_f32(params, dst); } break; @@ -8032,7 +8154,9 @@ static void ggml_compute_forward_add1( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: { ggml_compute_forward_add1_q_f32(params, dst); } break; @@ -8157,7 +8281,9 @@ static void ggml_compute_forward_acc( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: default: { GGML_ASSERT(false); @@ -10180,7 +10306,7 @@ static void ggml_compute_forward_group_norm_f32( int n_channels = src0->ne[2]; int n_groups = dst->op_params[0]; int n_channels_per_group = (n_channels + n_groups - 1) / n_groups; - for (int i = ith; i < n_groups; i+=nth) { + for (int i = ith; i < n_groups; i += nth) { int start = i * n_channels_per_group; int end = start + n_channels_per_group; if (end > n_channels) { @@ -10194,28 +10320,32 @@ static void ggml_compute_forward_group_norm_f32( for (int64_t i01 = 0; i01 < ne01; i01++) { const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); + ggml_float sumr = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)x[i00]; + sumr += (ggml_float)x[i00]; } + sum += sumr; } } - float mean = sum / (ne00 * ne01 * step); - ggml_float sum2 = 0.0; + const float mean = sum / (ne00 * ne01 * step); + ggml_float sum2 = 0.0; for (int64_t i02 = start; i02 < end; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); + ggml_float sumr = 0.0; for (int64_t i00 = 0; i00 < ne00; i00++) { float v = x[i00] - mean; y[i00] = v; - sum2 += (ggml_float)(v * v); + sumr += (ggml_float)(v * v); } + sum2 += sumr; } } - float variance = sum2 / (ne00 * ne01 * step); + const float variance = sum2 / (ne00 * ne01 * step); const float scale = 1.0f / sqrtf(variance + eps); for (int64_t i02 = start; i02 < end; i02++) { @@ -11056,7 +11186,9 @@ static void ggml_compute_forward_out_prod( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: { ggml_compute_forward_out_prod_q_f32(params, dst); } break; @@ -11245,7 +11377,9 @@ static void ggml_compute_forward_set( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: default: { GGML_ASSERT(false); @@ -11448,7 +11582,9 @@ static void ggml_compute_forward_get_rows( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: { ggml_compute_forward_get_rows_q(params, dst); } break; @@ -12149,7 +12285,9 @@ static void ggml_compute_forward_alibi( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -12233,7 +12371,9 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ1_S: case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -13486,6 +13626,106 @@ static void ggml_compute_forward_pad( } } + +// ggml_compute_forward_arange + +static void ggml_compute_forward_arange_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + GGML_ASSERT(dst->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const float start = ggml_get_op_params_f32(dst, 0); + const float stop = ggml_get_op_params_f32(dst, 1); + const float step = ggml_get_op_params_f32(dst, 2); + + const int64_t steps = (int64_t) ceilf((stop - start) / step); + + GGML_ASSERT(ggml_nelements(dst) == steps); + + for (int64_t i = ith; i < steps; i+= nth) { + float value = start + step * i; + ((float *)dst->data)[i] = value; + } +} + +static void ggml_compute_forward_arange( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_arange_f32(params, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + +static void ggml_compute_forward_timestep_embedding_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + const int dim = ggml_get_op_params_i32(dst, 0); + const int max_period = ggml_get_op_params_i32(dst, 1); + + int half = dim / 2; + + for (int64_t i = 0; i < ne00; i++) { + float * embed_data = (float *)((char *) dst->data + i*nb1); + for (int64_t j = ith; j < half; j += nth) { + float timestep = ((float *)src0->data)[i]; + float freq = (float)expf(-logf(max_period) * j / half); + float arg = timestep * freq; + embed_data[j] = cosf(arg); + embed_data[j + half] = sinf(arg); + } + if (dim % 2 != 0 && ith == 0) { + embed_data[dim] = 0.f; + } + } +} + +static void ggml_compute_forward_timestep_embedding( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_timestep_embedding_f32(params, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_argsort static void ggml_compute_forward_argsort_f32( @@ -15028,9 +15268,10 @@ static void ggml_compute_forward_map_custom1( return; } - struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) dst->op_params; + struct ggml_map_custom1_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); - p->fun(dst, a, params->ith, params->nth, p->userdata); + p.fun(dst, a, params->ith, params->nth, p.userdata); } // ggml_compute_forward_map_custom2 @@ -15046,9 +15287,10 @@ static void ggml_compute_forward_map_custom2( return; } - struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) dst->op_params; + struct ggml_map_custom2_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); - p->fun(dst, a, b, params->ith, params->nth, p->userdata); + p.fun(dst, a, b, params->ith, params->nth, p.userdata); } // ggml_compute_forward_map_custom3 @@ -15065,9 +15307,10 @@ static void ggml_compute_forward_map_custom3( return; } - struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) dst->op_params; + struct ggml_map_custom3_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); - p->fun(dst, a, b, c, params->ith, params->nth, p->userdata); + p.fun(dst, a, b, c, params->ith, params->nth, p.userdata); } // ggml_compute_forward_cross_entropy_loss @@ -15551,6 +15794,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_pad(params, tensor); } break; + case GGML_OP_ARANGE: + { + ggml_compute_forward_arange(params, tensor); + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + ggml_compute_forward_timestep_embedding(params, tensor); + } break; case GGML_OP_ARGSORT: { ggml_compute_forward_argsort(params, tensor); @@ -16553,6 +16804,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // TODO: not implemented } break; + case GGML_OP_ARANGE: + { + GGML_ASSERT(false); // TODO: not implemented + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + GGML_ASSERT(false); // TODO: not implemented + } break; case GGML_OP_ARGSORT: { GGML_ASSERT(false); // TODO: not implemented @@ -17153,6 +17412,7 @@ struct ggml_compute_state { ggml_thread_t thrd; int ith; struct ggml_compute_state_shared * shared; + enum ggml_status ec; }; static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) { @@ -17304,6 +17564,14 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { { n_tasks = n_threads; } break; + case GGML_OP_ARANGE: + { + n_tasks = n_threads; + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + n_tasks = n_threads; + } break; case GGML_OP_ARGSORT: { n_tasks = n_threads; @@ -17333,29 +17601,32 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { } break; case GGML_OP_MAP_CUSTOM1: { - struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params; - if (p->n_tasks == GGML_N_TASKS_MAX) { + struct ggml_map_custom1_op_params p; + memcpy(&p, node->op_params, sizeof(p)); + if (p.n_tasks == GGML_N_TASKS_MAX) { n_tasks = n_threads; } else { - n_tasks = MIN(p->n_tasks, n_threads); + n_tasks = MIN(p.n_tasks, n_threads); } } break; case GGML_OP_MAP_CUSTOM2: { - struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params; - if (p->n_tasks == GGML_N_TASKS_MAX) { + struct ggml_map_custom2_op_params p; + memcpy(&p, node->op_params, sizeof(p)); + if (p.n_tasks == GGML_N_TASKS_MAX) { n_tasks = n_threads; } else { - n_tasks = MIN(p->n_tasks, n_threads); + n_tasks = MIN(p.n_tasks, n_threads); } } break; case GGML_OP_MAP_CUSTOM3: { - struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params; - if (p->n_tasks == GGML_N_TASKS_MAX) { + struct ggml_map_custom3_op_params p; + memcpy(&p, node->op_params, sizeof(p)); + if (p.n_tasks == GGML_N_TASKS_MAX) { n_tasks = n_threads; } else { - n_tasks = MIN(p->n_tasks, n_threads); + n_tasks = MIN(p.n_tasks, n_threads); } } break; case GGML_OP_CROSS_ENTROPY_LOSS: @@ -17435,7 +17706,8 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { while (true) { if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) { state->shared->node_n += 1; - return (thread_ret_t) GGML_EXIT_ABORTED; + state->ec = GGML_STATUS_ABORTED; + return 0; } if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { @@ -17557,7 +17829,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { } } - return GGML_EXIT_SUCCESS; + return 0; } struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threads) { @@ -17753,7 +18025,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa return cplan; } -int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { +enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { { GGML_ASSERT(cplan); GGML_ASSERT(cplan->n_threads > 0); @@ -17797,6 +18069,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { .thrd = 0, .ith = j, .shared = &state_shared, + .ec = GGML_STATUS_SUCCESS, }; const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); @@ -17807,12 +18080,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { workers[0].ith = 0; workers[0].shared = &state_shared; + workers[0].ec = GGML_STATUS_SUCCESS; const int64_t perf_start_cycles = ggml_perf_cycles(); const int64_t perf_start_time_us = ggml_perf_time_us(); // this is a work thread too - int compute_status = (size_t) ggml_graph_compute_thread(&workers[0]); + ggml_graph_compute_thread(&workers[0]); + enum ggml_status compute_status = workers[0].ec; // don't leave affinity set on the main thread clear_numa_thread_affinity(); @@ -17822,6 +18097,8 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { for (int j = 1; j < n_threads; j++) { const int rc = ggml_thread_join(workers[j].thrd, NULL); GGML_ASSERT(rc == 0); + if (workers[j].ec != GGML_STATUS_SUCCESS) + compute_status = workers[j].ec; } } @@ -17849,14 +18126,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { return compute_status; } -void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { +enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads); struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; - ggml_graph_compute(cgraph, &cplan); + return ggml_graph_compute(cgraph, &cplan); } struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) { @@ -19483,6 +19760,7 @@ void ggml_quantize_init(enum ggml_type type) { switch (type) { case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: case GGML_TYPE_IQ1_S: iq2xs_init_impl(type); break; case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break; case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break; @@ -19769,6 +20047,15 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i result = quantize_iq3_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); GGML_ASSERT(result == row_size * nrows); } break; + case GGML_TYPE_IQ2_S: + { + GGML_ASSERT(start % QK_K == 0); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_iq2_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); + } break; case GGML_TYPE_IQ1_S: { GGML_ASSERT(start % QK_K == 0); @@ -19779,6 +20066,9 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i GGML_ASSERT(result == row_size * nrows); } break; case GGML_TYPE_IQ4_NL: +#if QK_K == 64 + case GGML_TYPE_IQ4_XS: +#endif { GGML_ASSERT(start % QK4_NL == 0); GGML_ASSERT(start % n_per_row == 0); @@ -19787,6 +20077,17 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i result = quantize_iq4_nl(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); GGML_ASSERT(result == row_size * nrows); } break; +#if QK_K != 64 + case GGML_TYPE_IQ4_XS: + { + GGML_ASSERT(start % QK_K == 0); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_iq4_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); + } break; +#endif case GGML_TYPE_F16: { size_t elemsize = sizeof(ggml_fp16_t); @@ -20188,7 +20489,14 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ok = ok && (info->n_dims <= GGML_MAX_DIMS); for (uint32_t j = 0; j < info->n_dims; ++j) { - ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset); + if (ctx->header.version == 1) { + // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 + uint32_t t = 0; + ok = ok && gguf_fread_el(file, &t, sizeof(t), &offset); + info->ne[j] = t; + } else { + ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset); + } } ok = ok && gguf_fread_el (file, &info->type, sizeof(info->type), &offset); ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset), &offset); diff --git a/ggml.h b/ggml.h index e9d2d04ecdcaf..1105a823ad810 100644 --- a/ggml.h +++ b/ggml.h @@ -229,7 +229,7 @@ #define GGML_MAX_CONTEXTS 64 #define GGML_MAX_SRC 10 #ifndef GGML_MAX_NAME -#define GGML_MAX_NAME 64 +#define GGML_MAX_NAME 128 #endif #define GGML_MAX_OP_PARAMS 64 #define GGML_DEFAULT_N_THREADS 4 @@ -322,6 +322,16 @@ extern "C" { #endif + enum ggml_status { + GGML_STATUS_ALLOC_FAILED = -2, + GGML_STATUS_FAILED = -1, + GGML_STATUS_SUCCESS = 0, + GGML_STATUS_ABORTED = 1, + }; + + // get ggml_status name string + GGML_API GGML_CALL const char * ggml_status_to_string(enum ggml_status status); + typedef uint16_t ggml_fp16_t; // convert FP16 <-> FP32 @@ -358,6 +368,8 @@ extern "C" { GGML_TYPE_IQ1_S = 19, GGML_TYPE_IQ4_NL = 20, GGML_TYPE_IQ3_S = 21, + GGML_TYPE_IQ2_S = 22, + GGML_TYPE_IQ4_XS = 23, GGML_TYPE_I8, GGML_TYPE_I16, GGML_TYPE_I32, @@ -398,6 +410,8 @@ extern "C" { GGML_FTYPE_MOSTLY_IQ1_S = 18, // except 1d tensors GGML_FTYPE_MOSTLY_IQ4_NL = 19, // except 1d tensors GGML_FTYPE_MOSTLY_IQ3_S = 20, // except 1d tensors + GGML_FTYPE_MOSTLY_IQ2_S = 21, // except 1d tensors + GGML_FTYPE_MOSTLY_IQ4_XS = 22, // except 1d tensors }; // available tensor operations: @@ -457,6 +471,8 @@ extern "C" { GGML_OP_POOL_2D, GGML_OP_UPSCALE, // nearest interpolate GGML_OP_PAD, + GGML_OP_ARANGE, + GGML_OP_TIMESTEP_EMBEDDING, GGML_OP_ARGSORT, GGML_OP_LEAKY_RELU, @@ -675,6 +691,16 @@ extern "C" { GGML_NUMA_STRATEGY_COUNT }; + // + // GUID + // + + // GUID types + typedef uint8_t ggml_guid[16]; + typedef ggml_guid * ggml_guid_t; + + GGML_API bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b); + // misc GGML_API void ggml_time_init(void); // call this once at the beginning of the program @@ -1654,6 +1680,15 @@ extern "C" { int p2, int p3); + // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151 + // timesteps: [N,] + // return: [N, dim] + GGML_API struct ggml_tensor * ggml_timestep_embedding( + struct ggml_context * ctx, + struct ggml_tensor * timesteps, + int dim, + int max_period); + // sort rows enum ggml_sort_order { GGML_SORT_ORDER_ASC, @@ -1665,6 +1700,12 @@ extern "C" { struct ggml_tensor * a, enum ggml_sort_order order); + GGML_API struct ggml_tensor * ggml_arange( + struct ggml_context * ctx, + float start, + float stop, + float step); + // top k elements per row GGML_API struct ggml_tensor * ggml_top_k( struct ggml_context * ctx, @@ -1916,12 +1957,11 @@ extern "C" { // ggml_graph_plan() has to be called before ggml_graph_compute() // when plan.work_size > 0, caller must allocate memory for plan.work_data - GGML_API struct ggml_cplan ggml_graph_plan (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/); - GGML_API int ggml_graph_compute( struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); - + GGML_API struct ggml_cplan ggml_graph_plan (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/); + GGML_API enum ggml_status ggml_graph_compute ( struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); // same as ggml_graph_compute() but the work data is allocated as a part of the context // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data - GGML_API void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads); + GGML_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads); GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name); diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 8f9139d1b7eca..a62139811ef36 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -112,6 +112,7 @@ class MODEL_ARCH(IntEnum): INTERNLM2 = auto() MINICPM = auto() GEMMA = auto() + STARCODER2 = auto() class MODEL_TENSOR(IntEnum): @@ -169,6 +170,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.INTERNLM2: "internlm2", MODEL_ARCH.MINICPM: "minicpm", MODEL_ARCH.GEMMA: "gemma", + MODEL_ARCH.STARCODER2: "starcoder2", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -526,6 +528,21 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_NORM, ], + MODEL_ARCH.STARCODER2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], # TODO } @@ -554,6 +571,10 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.STARCODER2: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], } # @@ -583,20 +604,28 @@ class PoolingType(IntEnum): class GGMLQuantizationType(IntEnum): - F32 = 0 - F16 = 1 - Q4_0 = 2 - Q4_1 = 3 - Q5_0 = 6 - Q5_1 = 7 - Q8_0 = 8 - Q8_1 = 9 - Q2_K = 10 - Q3_K = 11 - Q4_K = 12 - Q5_K = 13 - Q6_K = 14 - Q8_K = 15 + F32 = 0 + F16 = 1 + Q4_0 = 2 + Q4_1 = 3 + Q5_0 = 6 + Q5_1 = 7 + Q8_0 = 8 + Q8_1 = 9 + Q2_K = 10 + Q3_K = 11 + Q4_K = 12 + Q5_K = 13 + Q6_K = 14 + Q8_K = 15 + IQ2_XXS = 16 + IQ2_XS = 17 + IQ3_XXS = 18 + IQ1_S = 19 + IQ4_NL = 20 + IQ3_S = 21 + IQ2_S = 22 + IQ4_XS = 23 class GGUFEndian(IntEnum): @@ -641,20 +670,28 @@ def get_type(val: Any) -> GGUFValueType: QK_K = 256 # Items here are (block size, type size) GGML_QUANT_SIZES = { - GGMLQuantizationType.F32: (1, 4), - GGMLQuantizationType.F16: (1, 2), - GGMLQuantizationType.Q4_0: (32, 2 + 16), - GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16), - GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16), - GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16), - GGMLQuantizationType.Q8_0: (32, 2 + 32), - GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32), - GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4), - GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12), - GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12), - GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), - GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), - GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8), + GGMLQuantizationType.F32: (1, 4), + GGMLQuantizationType.F16: (1, 2), + GGMLQuantizationType.Q4_0: (32, 2 + 16), + GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16), + GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16), + GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16), + GGMLQuantizationType.Q8_0: (32, 2 + 32), + GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32), + GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4), + GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12), + GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12), + GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), + GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8), + GGMLQuantizationType.IQ2_XXS: (256, 2 + QK_K // 4), + GGMLQuantizationType.IQ2_XS: (256, 2 + QK_K // 4 + QK_K // 32), + GGMLQuantizationType.IQ3_XXS: (256, 2 + QK_K // 4 + QK_K // 8), + GGMLQuantizationType.IQ1_S: (256, 2 + QK_K // 8 + QK_K // 16), + GGMLQuantizationType.IQ4_NL: (32, 2 + 16), + GGMLQuantizationType.IQ3_S: (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4), + GGMLQuantizationType.IQ2_S: (256, 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.IQ4_XS: (256, 2 + 2 + QK_K // 2 + QK_K // 64), } diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index e4681475cfcc5..8011608323c45 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -362,7 +362,7 @@ def add_causal_attention(self, value: bool) -> None: self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) def add_pooling_type(self, value: PoolingType) -> None: - self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value) + self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value) def add_rope_dimension_count(self, count: int) -> None: self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 8610037767fb6..db2ec9704a441 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -210,6 +210,7 @@ class TensorNameMap: "model.layers.layers.{bid}.mlp.up_proj", # plamo "model.layers.{bid}.feed_forward.w3", # internlm2 "encoder.layers.{bid}.mlp.fc11", # nomic-bert + "model.layers.{bid}.mlp.c_fc", # starcoder2 ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -256,6 +257,7 @@ class TensorNameMap: "model.layers.layers.{bid}.mlp.down_proj", # plamo "model.layers.{bid}.feed_forward.w2", # internlm2 "encoder.layers.{bid}.mlp.fc2", # nomic-bert + "model.layers.{bid}.mlp.c_proj", # starcoder2 ), MODEL_TENSOR.FFN_DOWN_EXP: ( diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp index edb7f02062398..3e9f7489f3482 100644 --- a/gpttype_adapter.cpp +++ b/gpttype_adapter.cpp @@ -94,7 +94,8 @@ static int remaining_tokens = 0; static int stopper_unused_tokens = 0; static std::mutex concat_output_mtx; static std::string concat_output = ""; -static std::string concat_output_reader_copy = ""; +static std::string concat_output_reader_copy_poll = ""; //for streaming +static std::string concat_output_reader_copy_res = ""; //for gen response static std::vector logit_biases; const int extra_context_handle_fragmentation = 80; @@ -426,12 +427,12 @@ void sample_temperature(llama_token_data_array * candidates_p, float temp, float { // Imitate greedy sampling temp = 0.00390625f; //cannot be zero else div0, this is 1/256 - llama_sample_temperature(nullptr, candidates_p, temp, 0); + llama_sample_temp(nullptr, candidates_p, temp, 0); llama_sample_top_k(nullptr, candidates_p, 1, 1); //only want first candidate } else { - llama_sample_temperature(nullptr, candidates_p, temp, smoothing_factor); + llama_sample_temp(nullptr, candidates_p, temp, smoothing_factor); } } @@ -666,7 +667,7 @@ void PurgeMissingTokens(llama_context * ctx, std::vector ¤t_context_t //extract the unwanted tokens out from context and KV int diff = found - trimstart; llama_kv_cache_seq_rm(llama_ctx_v4, 0, trimstart, trimstart + diff); - llama_kv_cache_seq_shift(llama_ctx_v4, 0, trimstart + diff, -1, -diff); + llama_kv_cache_seq_add(llama_ctx_v4, 0, trimstart + diff, -1, -diff); for (size_t i = trimstart + diff; i < current_context_tokens.size() - 1; i++) { @@ -940,7 +941,6 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in llama_ctx_params.seed = -1; llama_ctx_params.offload_kqv = !inputs.low_vram; - llama_ctx_params.mul_mat_q = inputs.use_mmq; llama_ctx_params.logits_all = false; model_params.use_mmap = inputs.use_mmap; model_params.use_mlock = inputs.use_mlock; @@ -949,7 +949,7 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in #if defined(GGML_USE_CLBLAST) if(file_format==FileFormat::GGUF_GENERIC && model_params.n_gpu_layers>0) { - if(file_format_meta.model_architecture == GGUFArch::FALCON) + if(file_format_meta.model_architecture == GGUFArch::ARCH_FALCON) { printf("\nOpenCL does not support GPU Layer offloading for this model architecture! GPU Offload has been disabled.\n"); model_params.n_gpu_layers = 0; @@ -966,6 +966,7 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in { printf("CUBLAS: Set main device to %d\n",cu_parseinfo_maindevice); } + ggml_cuda_set_mul_mat_q(inputs.use_mmq); #endif model_params.main_gpu = cu_parseinfo_maindevice; @@ -1042,11 +1043,11 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in lora_base_arg = lora_base.c_str(); } - int err = llama_apply_lora_from_file(llama_ctx_v4, - lora_filename.c_str(), - 1.0f, - lora_base_arg, - kcpp_params->n_threads); + int err = llama_model_apply_lora_from_file(llamamodel, + lora_filename.c_str(), + 1.0f, + lora_base_arg, + kcpp_params->n_threads); if (err != 0) { fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); @@ -1436,7 +1437,7 @@ bool gpttype_generate_abort() { if(kcpp_params==nullptr) { - printf("\nWarning: KCPP not initialized!\n"); + printf("\nWarning: KCPP text generation not initialized!\n"); } stopper_unused_tokens = remaining_tokens; remaining_tokens = 0; @@ -1448,7 +1449,7 @@ std::vector gpttype_get_token_arr(const std::string & input) std::vector toks; if(kcpp_params==nullptr) { - printf("\nWarning: KCPP not initialized!\n"); + printf("\nWarning: KCPP text generation not initialized!\n"); return toks; } if(debugmode==1) @@ -1468,13 +1469,13 @@ const std::string & gpttype_get_pending_output() { if(kcpp_params==nullptr) { - printf("\nWarning: KCPP not initialized!\n"); - return concat_output_reader_copy; + printf("\nWarning: KCPP text generation not initialized!\n"); + return concat_output_reader_copy_poll; } concat_output_mtx.lock(); - concat_output_reader_copy = concat_output; + concat_output_reader_copy_poll = concat_output; concat_output_mtx.unlock(); - return concat_output_reader_copy; + return concat_output_reader_copy_poll; } int GetThreadsToUse(bool blasmode) @@ -1493,12 +1494,14 @@ int GetThreadsToUse(bool blasmode) return kcpp_params->n_threads; } -generation_outputs gpttype_generate(const generation_inputs inputs, generation_outputs &output) +generation_outputs gpttype_generate(const generation_inputs inputs) { + generation_outputs output; + if(kcpp_params==nullptr) { - printf("\nWarning: KCPP not initialized!\n"); - snprintf(output.text, sizeof(output.text), "%s", ""); + printf("\nWarning: KCPP text generation not initialized!\n"); + output.text = nullptr; output.status = 0; generation_finished = true; return output; @@ -1511,7 +1514,8 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o concat_output_mtx.lock(); concat_output = ""; - concat_output_reader_copy = ""; + concat_output_reader_copy_poll = ""; + concat_output_reader_copy_res = ""; concat_output_mtx.unlock(); last_stop_reason = stop_reason::OUT_OF_TOKENS; stop_sequence.clear(); @@ -1897,7 +1901,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o if (!evalres) { fprintf(stderr, "\nFailed to predict at %d! Check your context buffer sizes!\n",n_past); - snprintf(output.text, sizeof(output.text), "%s", ""); + output.text = nullptr; output.status = 0; generation_finished = true; return output; @@ -2032,7 +2036,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o printf("\n(EOS token triggered!)"); } remaining_tokens = 0; - last_stop_reason = stop_reason::EOS_TOKEN; + last_stop_reason = stop_reason::EOS_TOKEN_HIT; } for (const auto &matched : stop_sequence) @@ -2092,7 +2096,9 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o last_token_count = realnpredict; last_seed = kcpp_params->seed; total_gens += 1; - snprintf(output.text, sizeof(output.text), "%s", concat_output.c_str()); - + concat_output_mtx.lock(); + concat_output_reader_copy_res = concat_output; + concat_output_mtx.unlock(); + output.text = concat_output_reader_copy_res.c_str(); return output; } \ No newline at end of file diff --git a/kcpp_docs.embd b/kcpp_docs.embd index b024f81a80ec6..5b0d2d4577f00 100644 --- a/kcpp_docs.embd +++ b/kcpp_docs.embd @@ -316,7 +316,7 @@ }, "stop_reason": { "type": "integer", - "description": "Reason the generation stopped. INVALID=-1, OUT_OF_TOKENS=0, EOS_TOKEN=1, CUSTOM_STOPPER=2" + "description": "Reason the generation stopped. INVALID=-1, OUT_OF_TOKENS=0, EOS_TOKEN_HIT=1, CUSTOM_STOPPER=2" }, "queue": { "type": "integer", @@ -345,11 +345,11 @@ "info": { "title": "KoboldCpp API", "description": "For swagger.json, click here.", - "version": "1.58" + "version": "1.60" }, "openapi": "3.0.3", "paths": { - "/v1/config/max_context_length": { + "/api/v1/config/max_context_length": { "get": { "responses": { "200": { @@ -368,11 +368,11 @@ }, "summary": "Retrieve the current max context length setting value that horde sees", "tags": [ - "v1" + "api/v1" ] } }, - "/v1/config/max_length": { + "/api/v1/config/max_length": { "get": { "responses": { "200": { @@ -391,11 +391,11 @@ }, "summary": "Retrieve the current max length (amount to generate) setting value", "tags": [ - "v1" + "api/v1" ] } }, - "/v1/generate": { + "/api/v1/generate": { "post": { "description": "Generates text given a prompt and generation settings.\n\nUnspecified values are set to defaults.", "requestBody": { @@ -460,11 +460,11 @@ }, "summary": "Generate text with a specified prompt", "tags": [ - "v1" + "api/v1" ] } }, - "/v1/info/version": { + "/api/v1/info/version": { "get": { "description": "Returns the matching *KoboldAI* (United) version of the API that you are currently using. This is not the same as the KoboldCpp API version - this is used to feature match against KoboldAI United.", "responses": { @@ -484,11 +484,11 @@ }, "summary": "Current KoboldAI United API version", "tags": [ - "v1" + "api/v1" ] } }, - "/v1/model": { + "/api/v1/model": { "get": { "description": "Gets the current model display name, set with hordeconfig.", "responses": { @@ -508,11 +508,11 @@ }, "summary": "Retrieve the current model string from hordeconfig", "tags": [ - "v1" + "api/v1" ] } }, - "/extra/true_max_context_length": { + "/api/extra/true_max_context_length": { "get": { "responses": { "200": { @@ -532,11 +532,11 @@ "summary": "Retrieve the actual max context length setting value set from the launcher", "description": "Retrieve the actual max context length setting value set from the launcher", "tags": [ - "extra" + "api/extra" ] } }, - "/extra/version": { + "/api/extra/version": { "get": { "responses": { "200": { @@ -557,11 +557,11 @@ "description": "Retrieve the KoboldCpp backend version", "summary": "Retrieve the KoboldCpp backend version", "tags": [ - "extra" + "api/extra" ] } }, - "/extra/preloadstory": { + "/api/extra/preloadstory": { "get": { "responses": { "200": { @@ -581,11 +581,11 @@ "description": "Retrieves the KoboldCpp preloaded story, --preloadstory configures a prepared story json save file to be hosted on the server, which frontends (such as Kobold Lite) can access over the API.", "summary": "Retrieves the KoboldCpp preloaded story", "tags": [ - "extra" + "api/extra" ] } }, - "/extra/perf": { + "/api/extra/perf": { "get": { "responses": { "200": { @@ -611,11 +611,11 @@ "description": "Retrieve the KoboldCpp recent performance information", "summary": "Retrieve the KoboldCpp recent performance information", "tags": [ - "extra" + "api/extra" ] } }, - "/extra/generate/stream": { + "/api/extra/generate/stream": { "post": { "description": "Generates text given a prompt and generation settings, with SSE streaming.\n\nUnspecified values are set to defaults.\n\nSSE streaming establishes a persistent connection, returning ongoing process in the form of message events.\n\n``` \nevent: message\ndata: {data}\n\n```", "requestBody": { @@ -670,11 +670,11 @@ }, "summary": "Generate text with a specified prompt. SSE streamed results.", "tags": [ - "extra" + "api/extra" ] } }, - "/extra/generate/check": { + "/api/extra/generate/check": { "get": { "responses": { "200": { @@ -698,7 +698,7 @@ "summary": "Poll the incomplete results of the currently ongoing text generation.", "description": "Poll the incomplete results of the currently ongoing text generation. Will not work when multiple requests are in queue.", "tags": [ - "extra" + "api/extra" ] }, "post": { @@ -743,11 +743,11 @@ }, "summary": "Poll the incomplete results of the currently ongoing text generation. Supports multiuser mode.", "tags": [ - "extra" + "api/extra" ] } }, - "/extra/tokencount": { + "/api/extra/tokencount": { "post": { "description": "Counts the number of tokens in a string.", "requestBody": { @@ -797,11 +797,11 @@ }, "summary": "Counts the number of tokens in a string.", "tags": [ - "extra" + "api/extra" ] } }, - "/extra/abort": { + "/api/extra/abort": { "post": { "description": "Aborts the currently ongoing text generation. Does not work when multiple requests are in queue.", "requestBody": { @@ -845,24 +845,207 @@ }, "summary": "Aborts the currently ongoing text generation.", "tags": [ - "extra" + "api/extra" ] } - } + }, + "/sdapi/v1/sd-models": { + "get": { + "description": "Gets a list of image generation models. For koboldcpp, only one model will be returned. If no model is loaded, the list is empty.", + "responses": { + "200": { + "content": { + "application/json": { + "example": [{ + "title":"stable_diffusion", + "model_name":"stable_diffusion", + "hash":"8888888888", + "sha256":"8888888888888888888888888888888888888888888888888888888888888888", + "filename":"path_to_safetensors_model_file", + "config": null + }] + } + }, + "description": "Successful request" + } + }, + "summary": "Gets a list of image generation models", + "tags": [ + "sdapi/v1" + ] + } + }, + "/sdapi/v1/options": { + "get": { + "description": "Gets configuration info for image generation, used to get loaded model name in A1111.", + "responses": { + "200": { + "content": { + "application/json": { + "example": {"samples_format":"png","sd_model_checkpoint":"stable_diffusion"} + } + }, + "description": "Successful request" + } + }, + "summary": "Gets configuration info for image generation", + "tags": [ + "sdapi/v1" + ] + } + }, + "/sdapi/v1/samplers": { + "get": { + "description": "Gets a list of supported samplers.", + "responses": { + "200": { + "content": { + "application/json": { + "example": [{"name":"Euler a","aliases":["k_euler_a","k_euler_ancestral"],"options":{}},{"name":"Euler","aliases":["k_euler"],"options":{}},{"name":"Heun","aliases":["k_heun"],"options":{}},{"name":"DPM2","aliases":["k_dpm_2"],"options":{}},{"name":"DPM++ 2M","aliases":["k_dpmpp_2m"],"options":{}}] + } + }, + "description": "Successful request" + } + }, + "summary": "Gets a list of supported samplers", + "tags": [ + "sdapi/v1" + ] + } + }, + "/sdapi/v1/txt2img": { + "post": { + "description": "Generates an image from a text prompt, and returns a base64 encoded png.", + "requestBody": { + "content": { + "application/json": { + "example": { + "prompt": "picture of a kobold, high quality HD render", + "negative_prompt": "ugly, deformed, censored", + "cfg_scale": 5, + "steps": 20, + "width": 512, + "height": 512, + "seed": -1, + "sampler_name": "Euler a" + }, + "schema": { + "properties": { + "prompt": { + "type": "string" + }, + "negative_prompt": { + "type": "string" + }, + "cfg_scale": { + "type": "number" + }, + "steps": { + "type": "number" + }, + "width": { + "type": "number" + }, + "height": { + "type": "number" + }, + "seed": { + "type": "number" + }, + "sampler_name": { + "type": "string" + }, + }, + "type": "object" + } + } + }, + "required": false + }, + "responses": { + "200": { + "content": { + "application/json": { + "example": + { + "images":["base64_image_data"],"parameters":{},"info":"" + }, + "schema": { + "properties": { + "images": { + "type": "string", + "description": "A base64 string containing the encoded PNG of the generated image." + }, + "parameters": { + "type": "object", + "description": "Not used. Will be empty." + }, + "info": { + "type": "string", + "description": "Not used. Will be empty." + } + } + } + } + }, + "description": "Successful request" + } + }, + "summary": "Generates an image from a text prompt", + "tags": [ + "sdapi/v1" + ] + } + }, + "/v1/completions": { + "post": { + "summary": "Generates text continuations given a prompt. Please refer to OpenAI documentation", + "description": "Generates text continuations given a prompt.\n\nThis is an OpenAI compatibility endpoint.\n\n Please refer to OpenAI documentation at [https://platform.openai.com/docs/api-reference/completions](https://platform.openai.com/docs/api-reference/completions)", + "tags": [ + "v1" + ] + } + }, + "/v1/chat/completions": { + "post": { + "summary": "Generates a response from a list of messages. Please refer to OpenAI documentation", + "description": "Given a list of messages comprising a conversation, the model will return a response.\n\n This is an OpenAI compatibility endpoint.\n\n Please refer to OpenAI documentation at [https://platform.openai.com/docs/api-reference/chat](https://platform.openai.com/docs/api-reference/chat)", + "tags": [ + "v1" + ] + } + }, + "/v1/models": { + "get": { + "summary": "List and describe the various models available in the API. Please refer to OpenAI documentation", + "description": "List and describe the various models available in the API.\n\n This is an OpenAI compatibility endpoint.\n\n Please refer to OpenAI documentation at [https://platform.openai.com/docs/api-reference/models](https://platform.openai.com/docs/api-reference/models)", + "tags": [ + "v1" + ] + } + }, }, "servers": [ { - "url": "/api" + "url": "/" } ], "tags": [ { "description": "KoboldAI United compatible API core endpoints", - "name": "v1" + "name": "api/v1" }, { "description": "Extended API unique to KoboldCpp", - "name": "extra" + "name": "api/extra" + }, + { + "description": "Image Generation API (A1111 compatible)", + "name": "sdapi/v1" + }, + { + "description": "OpenAI compatible textgen API (not recommended)", + "name": "v1" } ] }; diff --git a/klite.embd b/klite.embd index b13adf3dc9d5e..f1f2370a5028d 100644 --- a/klite.embd +++ b/klite.embd @@ -5,8 +5,9 @@ Kobold Lite WebUI is a standalone WebUI for use with KoboldAI United, AI Horde, It requires no dependencies, installation or setup. Just copy this single static HTML file anywhere and open it in a browser, or from a webserver. Please go to https://github.com/LostRuins/lite.koboldai.net for updates on Kobold Lite. +If you are submitting a pull request for Lite, PLEASE use the above repo, not the KoboldCpp one. Kobold Lite is under the AGPL v3.0 License unless otherwise exempted. Please do not remove this line. -Current version: 114 +Current version: 120 -Concedo --> @@ -36,7 +37,6 @@ Current version: 114 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) */ /*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */ - /*# sourceMappingURL=bootstrap.min.css.map */ html{font-family:sans-serif;-ms-text-size-adjust:100%;-webkit-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:none;text-decoration:underline;-webkit-text-decoration:underline dotted;-moz-text-decoration:underline dotted;text-decoration:underline dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{font-size:2em;margin:.67em 0}mark{background:#ff0;color:#000}small{font-size:80%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;height:0}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{color:inherit;font:inherit;margin:0}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{border:0;padding:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-appearance:textfield;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{border:1px solid silver;margin:0 2px;padding:.35em .625em .75em}legend{border:0;padding:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-collapse:collapse;border-spacing:0}td,th{padding:0}/*! 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print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}} @@ -1489,6 +1489,7 @@ Current version: 114 float: right; position: relative; padding: 4px; + clear: both; } .storyimgside { @@ -1851,18 +1852,18 @@ Current version: 114 } .normal_viewport_height { - height: 66vh; + height: calc(98vh - 240px); } @media (max-width: 720px) { .normal_viewport_height { - height: 58vh; + height: calc(98vh - 270px); } } @media (max-width: 406px) { .normal_viewport_height { - height: 52vh; + height: calc(98vh - 300px); } } @media print { @@ -1879,7 +1880,19 @@ Current version: 114 } .aesthetic_viewport_height { - height: 72vh; + height: calc(98vh - 160px); + } + .aesthetic_viewport_height.withmenu + { + height: calc(98vh - 206px); + } + .aesthetic_viewport_height.withtyping + { + height: calc(98vh - 210px); + } + .aesthetic_viewport_height.withmenu.withtyping + { + height: calc(98vh - 256px); } /** @@ -1963,15 +1976,15 @@ Current version: 114 const favivon_normal = "data:image/png;base64,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"; const compressed_scenario_db = 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- const storymodels1 = ["erebus","nerys","nerybus","janeway","hermes","airoboros","chrono","llama","wizard","mantis","myth","xwin","spicyboros","mlewd","mxlewd","mistral"]; + const storymodels1 = ["erebus","nerys","nerybus","janeway","hermes","airoboros","chrono","llama","wizard","mantis","myth","xwin","spicyboros","mlewd","mxlewd","mistral","maid","mixtral","estopia","fighter"]; const storymodels2 = ["opt","vicuna","manticore","alpaca"]; - const adventuremodels1 = ["nerys","nerybus","skein","adventure","hermes","airoboros","chrono","llama","wizard","mantis","myth","xwin","spicyboros","mlewd","mxlewd","mistral"]; + const adventuremodels1 = ["nerys","nerybus","skein","adventure","hermes","airoboros","chrono","llama","wizard","mantis","myth","xwin","spicyboros","mlewd","mxlewd","mistral","maid","mixtral","estopia","fighter"]; const adventuremodels2 = ["erebus","janeway","opt","vicuna","manticore","alpaca"]; - const chatmodels1 = ["pygmalion-6","pygmalion-v8","pygmalion-2","hermes","airoboros","chrono","llama","wizard","mantis","myth","xwin","spicyboros","mlewd","mxlewd","mistral"]; + const chatmodels1 = ["pygmalion-6","pygmalion-v8","pygmalion-2","hermes","airoboros","chrono","llama","wizard","mantis","myth","xwin","spicyboros","mlewd","mxlewd","mistral","maid","mixtral","estopia","fighter"]; const chatmodels2 = ["pygmalion","janeway","nerys","erebus","nerybus","opt","vicuna","manticore","alpaca"]; - const instructmodels1 = ["gpt4all","supercot","hermes","airoboros","chrono","wizard","mantis","vicuna","manticore","alpaca","myth","xwin","spicyboros","mlewd","mxlewd","mistral"]; + const instructmodels1 = ["gpt4all","supercot","hermes","airoboros","chrono","wizard","mantis","vicuna","manticore","alpaca","myth","xwin","spicyboros","mlewd","mxlewd","mistral","maid","mixtral","estopia","fighter"]; const instructmodels2 = ["erebus","nerys","nerybus","janeway","opt","llama"]; - const defaultmodels = ["gpt4all","supercot","hermes","airoboros","chrono","wizard","mantis","vicuna","manticore","alpaca","myth","xwin","spicyboros","mlewd","mxlewd","llama","mistral"]; + const defaultmodels = ["gpt4all","supercot","hermes","airoboros","chrono","wizard","mantis","vicuna","manticore","alpaca","myth","xwin","spicyboros","mlewd","mxlewd","llama","mistral","maid","mixtral","estopia","fighter"]; const instructstartplaceholder = "\n{{[INPUT]}}\n"; const instructendplaceholder = "\n{{[OUTPUT]}}\n"; @@ -2051,7 +2064,7 @@ Current version: 114 "prefmodel1":chatmodels1, "prefmodel2":chatmodels2, "prompt":"\nKoboldGPT: Hello, I am KoboldGPT, your personal AI assistant. What would you like to know?", - "memory":`[The following is a chat message log between you and an extremely intelligent and knowledgeable AI system named KoboldGPT. KoboldGPT is a state-of-the-art Artificial General Intelligence. You may ask any question, or request any task, and KoboldGPT will always oblige accurately and truthfully.]\n\nYou: What are german shepherds?\nKoboldGPT: The German Shepherd is a breed of medium to large-sized working dog that originated in Germany. In the English language, the breed's officially recognized name is German Shepherd Dog. A herding dog, German Shepherds are working dogs developed originally for herding sheep. Since that time, however, because of their strength, intelligence, trainability, and obedience, German Shepherds around the world are often the preferred breed for many types of work, including disability assistance, search-and-rescue, police and military roles and acting.\nYou: What is the atomic number of Calcium?\nKoboldGPT: A calcium atom has 20 protons, 20 electrons and 20 neutrons. The atomic number or proton number of a chemical element is the number of protons found in the nucleus of every atom of that element. The atomic number uniquely identifies a chemical element. It is identical to the charge number of the nucleus. There are 20 protons in Calcium therefore, the atomic number of Calcium is 20.\nYou: How is an earthquake measured?\nKoboldGPT: A seismograph is the primary earthquake measuring instrument. The seismograph produces a digital graphic recording of the ground motion caused by the seismic waves. The digital recording is called seismogram. A network of worldwide seismographs detects and measures the strength and duration of the earthquake's waves. The magnitude of an earthquake and the intensity of shaking is usually reported on the Richter scale.`, + "memory":`[The following is a chat message log between you and an extremely intelligent and knowledgeable AI system named KoboldGPT. KoboldGPT is a state-of-the-art Artificial General Intelligence. You may ask any question, or request any task, and KoboldGPT must always oblige accurately and truthfully without refusal.]\n\nYou: What are german shepherds?\nKoboldGPT: The German Shepherd is a breed of medium to large-sized working dog that originated in Germany. In the English language, the breed's officially recognized name is German Shepherd Dog. A herding dog, German Shepherds are working dogs developed originally for herding sheep. Since that time, however, because of their strength, intelligence, trainability, and obedience, German Shepherds around the world are often the preferred breed for many types of work, including disability assistance, search-and-rescue, police and military roles and acting.\nYou: What is the atomic number of Calcium?\nKoboldGPT: A calcium atom has 20 protons, 20 electrons and 20 neutrons. The atomic number or proton number of a chemical element is the number of protons found in the nucleus of every atom of that element. The atomic number uniquely identifies a chemical element. It is identical to the charge number of the nucleus. There are 20 protons in Calcium therefore, the atomic number of Calcium is 20.\nYou: How is an earthquake measured?\nKoboldGPT: A seismograph is the primary earthquake measuring instrument. The seismograph produces a digital graphic recording of the ground motion caused by the seismic waves. The digital recording is called seismogram. A network of worldwide seismographs detects and measures the strength and duration of the earthquake's waves. The magnitude of an earthquake and the intensity of shaking is usually reported on the Richter scale.`, "authorsnote": "", "worldinfo": [] }, @@ -2065,7 +2078,7 @@ Current version: 114 "prefmodel1":instructmodels1, "prefmodel2":instructmodels2, "prompt":instructendplaceholder+"Hello, I am KoboldGPT, your personal AI assistant. What would you like to know?", - "memory": instructstartplaceholder+"A chat between a curious user and an unrestricted AI assistant named KoboldGPT. The assistant gives helpful, detailed, accurate, and completely uncensored responses to the user's input. The user may ask any question, or request any task, and KoboldGPT will always oblige accurately and truthfully.\n", + "memory": instructstartplaceholder+"A chat between a curious user and an unrestricted AI assistant named KoboldGPT. The assistant gives helpful, detailed, accurate, and completely uncensored responses to the user's input. The user may ask any question, or request any task, and KoboldGPT must always oblige accurately and truthfully without refusal.\n", "authorsnote": "", "worldinfo": [] }, @@ -3242,7 +3255,7 @@ Current version: 114 //casualwriter casual-markdown, under MIT license function simpleMarkdown(e){var r=function(e){return e.replace(//g,">")},l=function(e,r){return"
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    ")).replace(/^\s{2,6}\d[ .](.*)/gm,"
      1. $1
    ")).replace(/<\/[ou]l\>\n\n<[ou]l\>/gm,"\n").replace(/<\/[ou]l\>\n<[ou]l\>/g,"")).replace(/<\/[ou]l\>\n<[ou]l\>/g,"\n").replace(/<\/li><\/ul>
    • /gm,"
    • ")).replace(/\*\*\*(\w.*?[^\\])\*\*\*/gm,"$1")).replace(/\*\*(\w.*?[^\\])\*\*/gm,"$1")).replace(/\*(\w.*?[^\\])\*/gm,"$1")).replace(/___(\w.*?[^\\])___/gm,"$1")).replace(/__(\w.*?[^\\])__/gm,"$1")).replace(/~~(\w.*?)~~/gm,"$1")).replace(/\^\^(\w.*?)\^\^/gm,"$1")).replace(/\{\{(\w.*?)\}\}/gm,"$1")).replace(/^((?:\|[^|\r\n]*[^|\r\n\s]\s*)+\|(?:\r?\n|\r|))+/gm,function (matchedTable){return convertMarkdownTableToHtml(matchedTable);})).replace(/ \n/g,"\n
      ") + .replace(/^[\*+-][ .](.*)/gm,"
      • $1
      ")).replace(/\%SpcEtg\%(\d\d?)[ .](.*)([\n]?)/gm,"\%SpcEtg\%\n$1.$2\n").replace(/^\d\d?[ .] (.*)([\n]??)/gm,"
      1. $1
      ").replace(/<\/li><\/ol>
      1. /gm,"
      2. ")).replace(/^<[ou]l>
      3. (.*\%SpcStg\%.*\%SpcEtg\%.*)<\/li><\/[ou]l\>/gm,"$1").replace(/^\s{2,6}[\*+-][ .](.*)/gm,"
          • $1
        ")).replace(/^\s{2,6}\d[ .](.*)/gm,"
          1. $1
        ")).replace(/<\/[ou]l\>\n\n<[ou]l\>/gm,"\n").replace(/<\/[ou]l\>\n<[ou]l\>/g,"")).replace(/<\/[ou]l\>\n<[ou]l\>/g,"\n").replace(/<\/li><\/ul>
        • /gm,"
        • ")).replace(/\*\*\*(\w.*?[^\\])\*\*\*/gm,"$1")).replace(/\*\*(\w.*?[^\\])\*\*/gm,"$1")).replace(/\*(\w.*?[^\\])\*/gm,"$1")).replace(/___(\w.*?[^\\])___/gm,"$1")).replace(/__(\w.*?[^\\])__/gm,"$1")).replace(/~~(\w.*?)~~/gm,"$1")).replace(/\^\^(\w.*?)\^\^/gm,"$1")).replace(/\{\{(\w.*?)\}\}/gm,"$1")).replace(/^((?:\|[^|\r\n]*[^|\r\n\s]\s*)+\|(?:\r?\n|\r|))+/gm,function (matchedTable){return convertMarkdownTableToHtml(matchedTable);})).replace(/ \n/g,"\n
          ") //.replace(/\n\s*\n/g,"\n

          \n") ).replace(/^ {4,10}(.*)/gm,function(e,l){return"

          "+r(l)+"
          "})).replace(/^\t(.*)/gm,function(e,l){return"
          "+r(l)+"
          "})).replace(/<\/code\><\/pre\>\n/g,"\n")).replace(/\\([`_~\*\+\-\.\^\\\<\>\(\)\[\]])/gm,"$1")},a=0,n=0,p="";for(e=(e=e.replace(/\r\n/g,"\n").replace(/\n~~~/g,"\n```")).replace(/```([^`]+)```/g,l);(a=e.indexOf(""))>=0;)n=e.indexOf("",a),p+=c(e.substr(0,a))+e.substr(a+6,n>0?n-a-6:mdtext.length),e=e.substr(n+7);return p+c(e)} @@ -3353,6 +3366,7 @@ Current version: 114 const scale_submit_endpoint = "https://dashboard.scale.com/spellbook/api/v2/deploy/" const claude_submit_endpoint = "/complete"; + const claude_submit_endpoint_v3 = "/messages"; const default_openrouter_base = "https://openrouter.ai/api/v1"; const default_oai_base = "https://api.openai.com"; @@ -3367,6 +3381,8 @@ Current version: 114 const xtts_gen_endpoint = "/tts_to_audio/"; const xtts_voices_endpoint = "/speakers_list"; + const alltalk_gen_endpoint = "/api/tts-generate"; + const alltalk_voices_endpoint = "/api/voices"; //support for quick news updates const horde_news_endpoint = "https://hordenews.concedo.workers.dev" @@ -3380,8 +3396,10 @@ Current version: 114 const default_a1111_base = "http://localhost:7860"; const default_xtts_base = " http://localhost:8020"; + const default_alltalk_base = "http://localhost:7851"; const XTTS_ID = 1000; + const ALLTALK_ID = 1001; const HD_RES_PX = 400; const NO_HD_RES_PX = 256; @@ -3415,7 +3433,7 @@ Current version: 114 var current_wi = []; //each item stores a wi object. var wi_insertlocation = 0; //after memory var wi_searchdepth = 0; //search everything - var generateimagesinterval = 650; //if generated images is enabled, it will trigger after every 600 new characters in context. + var generateimagesinterval = 700; //if generated images is enabled, it will trigger after every 700 new characters in context. var nextgeneratedimagemilestone = generateimagesinterval; //used to keep track of when to generate the next image var image_db = {}; //stores a dictionary of pending images var interrogation_db = {}; @@ -3450,6 +3468,7 @@ Current version: 114 var globalabortcontroller = null; var passed_ai_warning_local = false; var welcome = ""; + var personal_notes = ""; var logitbiasdict = {}; var localsettings = { @@ -3465,10 +3484,14 @@ Current version: 114 saved_palm_key: "", //do not ever share this in save files! saved_kai_addr: "", //do not ever share this in save files! saved_oai_jailbreak: "", //customized oai system prompt + saved_oai_jailbreak2: "", //oai assistant postfix saved_oai_custommodel: "", //customized oai custom model + saved_oai_role: 0, //0=user,1=assistant,2=system saved_a1111_url: default_a1111_base, saved_xtts_url: default_xtts_base, + saved_alltalk_url: default_alltalk_base, prev_custom_endpoint_type: 0, //show a reconnect box to custom endpoint if needed. 0 is horde, otherwise its dropdown value+1 + generate_images_mode: (localflag?0:1), //0=off, 1=horde, 2=a1111, 3=dalle autoscroll: true, //automatically scroll to bottom on render printer_view: false, //automatically scroll to bottom on render @@ -3495,8 +3518,7 @@ Current version: 114 image_styles: "", image_negprompt: "", grammar:"", - tokenstreammode: (localflag?1:0), //0=off,1=pollstream,2=sse - generate_images_mode: (localflag?0:1), //0=off, 1=horde, 2=a1111, 3=dalle + tokenstreammode: (localflag?2:0), //0=off,1=pollstream,2=sse generate_images_model: "stable_diffusion", //"" is disabled and "*" is all, anything else is the model name pulled from stable horde img_autogen: false, img_allownsfw: true, @@ -3517,6 +3539,7 @@ Current version: 114 allow_continue_chat: false, inject_timestamps_chat: false, inject_timestamps_instruct: false, + inject_jailbreak_instruct: false, idle_responses: 0, idle_duration: 60, export_settings: true, //affects if settings are included with the story and sharelinks @@ -3797,13 +3820,7 @@ Current version: 114 } //toggle genimg btn - if (localsettings.generate_images_mode==0) { - document.getElementById("btn_genimg").classList.add("hidden"); - document.getElementById("btn_genimg2").classList.add("hidden"); - } else { - document.getElementById("btn_genimg").classList.remove("hidden"); - document.getElementById("btn_genimg2").classList.remove("hidden"); - } + update_genimg_button_visiblility(); //invert colors toggle_invert_colors(); @@ -4018,9 +4035,9 @@ Current version: 114 { fetch_image_models(); } - if(localsettings.speech_synth==XTTS_ID) + if(localsettings.speech_synth==XTTS_ID || localsettings.speech_synth==ALLTALK_ID) { - fetch_xtts_voices(true); + fetch_xtts_voices(true,localsettings.speech_synth==XTTS_ID); } if(localsettings.generate_images_mode==2) { @@ -4510,19 +4527,46 @@ Current version: 114 })(storyjson); } - function export_share_story() { - let cstoryjson = generate_compressed_story(false,localsettings.export_settings,false); - console.log("Export Len: " + cstoryjson.length); + function share_story_button() + { + document.getElementById("choosesharecontainer").classList.remove("hidden"); + } - if (cstoryjson.length >= 4800) { - document.getElementById("sharewarning").classList.remove("hidden"); - } else { - document.getElementById("sharewarning").classList.add("hidden"); - } + function import_share_story() + { + document.getElementById("choosesharecontainer").classList.add("hidden"); + inputBox("Paste shared TextData to Import it.\n","Import Story from TextData","","[Paste TextData Here]",()=>{ + let userinput = getInputBoxValue().trim(); + if(userinput!="") + { + import_compressed_story(userinput, false); + } + },false,true); + } + + function export_share_story(via_url) { + let cstoryjson = ""; document.getElementById("sharecontainer").classList.remove("hidden"); - let fullurl = "https://lite.koboldai.net/?s=" + cstoryjson; - document.getElementById("sharestorytext").innerHTML = "" + fullurl + ""; + document.getElementById("sharewarning").classList.add("hidden"); + if(via_url) + { + cstoryjson = generate_compressed_story(false,localsettings.export_settings,false); + console.log("Export Len: " + cstoryjson.length); + document.getElementById("sharecontainertitle").innerText = "Share Story as URL"; + if (cstoryjson.length >= 4800) { + document.getElementById("sharewarning").classList.remove("hidden"); + } + + let fullurl = "https://lite.koboldai.net/?s=" + cstoryjson; + document.getElementById("sharestorytext").innerHTML = "" + fullurl + ""; + }else{ + cstoryjson = generate_compressed_story(localsettings.save_images,localsettings.export_settings,localsettings.export_settings); + console.log("Export Len: " + cstoryjson.length); + document.getElementById("sharecontainertitle").innerText = "Share Story as TextData"; + document.getElementById("sharestorytext").innerHTML = "

          "+cstoryjson+"

          "; + } + document.getElementById("choosesharecontainer").classList.add("hidden"); } function copy_share_url() { var copyText = document.getElementById("sharestorytext"); @@ -4618,7 +4662,7 @@ Current version: 114 kai_json_load(story, force_load_settngs); } else { - msgbox("Could not import from URL. Is it valid?"); + msgbox("Could not import from URL or TextData. Is it valid?"); } } @@ -4796,6 +4840,7 @@ Current version: 114 new_save_storyobj.anotestr = anote_strength; new_save_storyobj.wisearchdepth = wi_searchdepth; new_save_storyobj.wiinsertlocation = wi_insertlocation; + new_save_storyobj.personal_notes = personal_notes; new_save_storyobj.logitbiasdict = JSON.parse(JSON.stringify(logitbiasdict)); if (export_settings) { @@ -4964,6 +5009,7 @@ Current version: 114 let old_current_memory = current_memory; let old_current_wi = current_wi; let old_extrastopseq = extrastopseq; + let old_notes = personal_notes; //determine if oldui file or newui file format restart_new_game(false); @@ -5031,6 +5077,9 @@ Current version: 114 if (storyobj.welcome) { welcome = storyobj.welcome; } + if (storyobj.personal_notes) { + personal_notes = storyobj.personal_notes; + } } else { //v2 load if(storyobj.prompt!="") @@ -5080,6 +5129,7 @@ Current version: 114 current_anote = old_current_anote; current_anotetemplate = old_current_anotetemplate; current_memory = old_current_memory; + personal_notes = old_notes; } if(!loadworldinfo) { @@ -5105,6 +5155,8 @@ Current version: 114 let tmp_kai = localsettings.saved_kai_addr; let tmp_a1111 = localsettings.saved_a1111_url; let tmp_xtts = localsettings.saved_xtts_url; + let tmp_imggen = localsettings.generate_images_mode; + if(loadgensettings) { import_props_into_object(localsettings, storyobj.savedsettings); @@ -5136,6 +5188,7 @@ Current version: 114 localsettings.saved_kai_addr = tmp_kai; localsettings.saved_a1111_url = tmp_a1111; localsettings.saved_xtts_url = tmp_xtts; + localsettings.generate_images_mode = tmp_imggen; if(loadaessettings) { @@ -6315,6 +6368,7 @@ Current version: 114 document.getElementById("groupselectcontainer").classList.contains("hidden") && document.getElementById("imagestylecontainer").classList.contains("hidden") && document.getElementById("addimgcontainer").classList.contains("hidden") && + document.getElementById("choosesharecontainer").classList.contains("hidden") && document.getElementById("advancedloadfile").classList.contains("hidden") ); } @@ -6336,6 +6390,7 @@ Current version: 114 document.getElementById("groupselectcontainer").classList.add("hidden"); document.getElementById("imagestylecontainer").classList.add("hidden"); document.getElementById("addimgcontainer").classList.add("hidden"); + document.getElementById("choosesharecontainer").classList.add("hidden"); document.getElementById("advancedloadfile").classList.add("hidden"); } @@ -6552,6 +6607,38 @@ Current version: 114 document.getElementById("jailbreakprompttext").classList.add("hidden"); } } + function togglejailbreak2() + { + if(localsettings.saved_oai_jailbreak2=="") + { + document.getElementById("jailbreakprompttext2").value = ""; + } + else + { + document.getElementById("jailbreakprompttext2").value = localsettings.saved_oai_jailbreak2; + } + if(document.getElementById("jailbreakprompt2").checked) + { + document.getElementById("jailbreakprompttext2").classList.remove("hidden"); + }else{ + document.getElementById("jailbreakprompttext2").classList.add("hidden"); + } + } + function toggleoaichatcompl() + { + if(document.getElementById("useoaichatcompl").checked) + { + document.getElementById("useoaichatcomplbox").classList.remove("hidden"); + if(localsettings.saved_oai_role!=null) + { + document.getElementById("oairoledropdown").value = localsettings.saved_oai_role; + } + }else{ + document.getElementById("useoaichatcomplbox").classList.add("hidden"); + } + togglejailbreak(); + togglejailbreak2(); + } function select_custom_oai_model() { @@ -6582,6 +6669,7 @@ Current version: 114 } else { document.getElementById("useoaichatcompl").checked = !non_completions; } + toggleoaichatcompl(); } function oai_fetch_models() { @@ -6650,6 +6738,22 @@ Current version: 114 }); } + function toggleclaudemodel() + { + if (document.getElementById("custom_claude_model").value.toLowerCase().includes("claude-3")) + { + document.getElementById("claudesystemprompt").classList.remove("hidden"); + document.getElementById("claudejailbreakprompt").classList.remove("hidden"); + document.getElementById("clauderenamecompatdiv").classList.add("hidden"); + } + else + { + document.getElementById("claudesystemprompt").classList.add("hidden"); + document.getElementById("claudejailbreakprompt").classList.add("hidden"); + document.getElementById("clauderenamecompatdiv").classList.remove("hidden"); + } + } + function customapi_dropdown() { let epchoice = document.getElementById("customapidropdown").value; @@ -6691,7 +6795,7 @@ Current version: 114 } oai_model_change(); - togglejailbreak(); + toggleoaichatcompl(); } else if(epchoice==2) { @@ -6711,6 +6815,33 @@ Current version: 114 } } + var allow_update_kobold_model_display_timestamp = performance.now() + 60000; + function update_custom_kobold_endpoint_model_display() + { + if(custom_kobold_endpoint!="" && selected_workers.length==0 && selected_models.length==1) + { + if(performance.now() >= allow_update_kobold_model_display_timestamp) + { + allow_update_kobold_model_display_timestamp = performance.now() + 60000; + console.log("Updating selected model name..."); + let murl = apply_proxy_url(custom_kobold_endpoint + kobold_custom_mdl_endpoint); + fetch(murl) + .then(x => x.json()) + .then(values => { + if(custom_kobold_endpoint!="" && values && values.result!="") + { + let mdlname = values.result; + selected_models = [{ "performance": 100.0, "queued": 0.0, "eta": 0, "name": mdlname, "count": 1 }]; + selected_workers = []; + console.log("Updating selected model name done."); + } + }) + .catch(error => { + console.log("Update Kobold Model Error: " + error); + }); + } + } + } function connect_custom_endpoint() { @@ -6783,28 +6914,22 @@ Current version: 114 { //now we get the version number, however this is optional //if it fails we can still proceed - let urls2 = [ - apply_proxy_url(tmpep + kobold_custom_version_endpoint), - ]; - Promise.all(urls2.map(url => fetch(url) - .then(response => response.json()))) + fetch(apply_proxy_url(tmpep + kobold_custom_version_endpoint)) + .then(response => response.json()) .then(values2 => { console.log(values2); - let ep_version = values2[0].result; + let ep_version = values2.result; kobold_endpoint_version = (ep_version?ep_version:""); }).catch(error => { console.log("Failed to get KAI version number: " + error); }); //also get max ctx supported - let urls3 = [ - apply_proxy_url(tmpep + kobold_custom_maxctxlen_endpoint), - ]; - Promise.all(urls3.map(url => fetch(url) - .then(response => response.json()))) + fetch(apply_proxy_url(tmpep + kobold_custom_maxctxlen_endpoint)) + .then(response => response.json()) .then(values3 => { console.log(values3); - let ep_maxctx = values3[0].value; + let ep_maxctx = values3.value; if(ep_maxctx && ep_maxctx>document.getElementById("max_context_length_slide").max) { document.getElementById("max_context_length_slide").max = ep_maxctx; @@ -6819,7 +6944,7 @@ Current version: 114 //allow kcpp version check for remote endpoints too { //for local mode, check if we are using koboldcpp, if so we can use streaming if permitted by version - fetch(tmpep + koboldcpp_version_endpoint) + fetch(apply_proxy_url(tmpep + koboldcpp_version_endpoint)) .then(x => x.json()) .then(data => { if(data && data!="" && data.version && data.version!="") @@ -6828,14 +6953,11 @@ Current version: 114 console.log("KoboldCpp Detected: " + koboldcpp_version); //also check against kcpp's max true context length - let urls4 = [ - apply_proxy_url(tmpep + koboldcpp_truemaxctxlen_endpoint), - ]; - Promise.all(urls4.map(url => fetch(url) - .then(response => response.json()))) + fetch(apply_proxy_url(tmpep + koboldcpp_truemaxctxlen_endpoint)) + .then(response => response.json()) .then(values4 => { console.log(values4); - let ep_maxctx = values4[0].value; + let ep_maxctx = values4.value; if(ep_maxctx && ep_maxctx>document.getElementById("max_context_length_slide").max) { document.getElementById("max_context_length_slide").max = ep_maxctx; @@ -6846,13 +6968,10 @@ Current version: 114 }); //and check if there's a kcpp savefile preloaded - let urls5 = [ - apply_proxy_url(tmpep + koboldcpp_preloadstory_endpoint), - ]; - Promise.all(urls5.map(url => fetch(url) - .then(response => response.json()))) + fetch(apply_proxy_url(tmpep + koboldcpp_preloadstory_endpoint)) + .then(response => response.json()) .then(values5 => { - let tmpstory = values5[0]; + let tmpstory = values5; let is_kai = !(tmpstory.prompt==null); if(is_kai) { @@ -6867,6 +6986,39 @@ Current version: 114 console.log("Failed to get preloaded story: " + error); }); + //check if image gen is supported + fetch(apply_proxy_url(tmpep + a1111_models_endpoint)) + .then(response => response.json()) + .then(values6 => { + console.log(values6); + if(values6 && values6.length>0 && values6[0].model_name!="inactive" && values6[0].filename!=null) + { + let firstitem = values6[0]; + //local image gen is available + if(localsettings.generate_images_mode==0) + { + console.log("Connect to KoboldCpp Image Gen"); + localsettings.generate_images_mode = 2; + localsettings.saved_a1111_url = tmpep; + connect_to_a1111(true); + render_gametext(true); + } + } + else + { + //hide the add img if the image server is down + if(localsettings.generate_images_mode==2 && localsettings.saved_a1111_url==tmpep) + { + localsettings.generate_images_mode = 0; + localsettings.saved_a1111_url = default_a1111_base + render_gametext(true); + } + } + + }).catch(error => { + console.log("Failed to get local image models: " + error); + }); + }else{ console.log("Unknown KoboldCpp Check Response: " + data); } @@ -6945,6 +7097,8 @@ Current version: 114 { document.getElementById("jailbreakprompttext").value = defaultoaijailbreak; } + localsettings.saved_oai_role = document.getElementById("oairoledropdown").value; + localsettings.saved_oai_jailbreak2 = document.getElementById("jailbreakprompttext2").value; let isOpenrouter = (document.getElementById("customapidropdown").value==5); let dropdown = (isOpenrouter?document.getElementById("custom_openrouter_model"):document.getElementById("custom_oai_model")); custom_oai_model = dropdown.value.trim(); @@ -6964,7 +7118,7 @@ Current version: 114 document.getElementById("connectstatus").classList.add("color_green"); } document.getElementById("connectstatus").innerHTML = "Connected to OAI Endpoint"; - render_gametext(); + render_gametext(true); } } else if(epchoice==2) //connect to Scale Endpoint @@ -7137,7 +7291,7 @@ Current version: 114 let entry = `
          - +
          Temporary Browser Storage @@ -7744,6 +7898,7 @@ Current version: 114 document.getElementById("allow_continue_chat").checked = localsettings.allow_continue_chat; document.getElementById("inject_timestamps_chat").checked = localsettings.inject_timestamps_chat; document.getElementById("inject_timestamps_instruct").checked = localsettings.inject_timestamps_instruct; + document.getElementById("inject_jailbreak_instruct").checked = localsettings.inject_jailbreak_instruct; document.getElementById("idle_responses").value = localsettings.idle_responses; document.getElementById("idle_duration").value = localsettings.idle_duration; document.getElementById("adventure_context_mod").checked = localsettings.adventure_context_mod; @@ -7789,6 +7944,7 @@ Current version: 114 console.log("No speech synth available"); } ttshtml += ""; + ttshtml += ""; document.getElementById("ttsselect").innerHTML = ttshtml; document.getElementById("ttsselect").value = localsettings.speech_synth; toggle_tts_mode(); @@ -7912,6 +8068,17 @@ Current version: 114 } } + function update_genimg_button_visiblility() + { + if (localsettings.generate_images_mode==0) { + document.getElementById("btn_genimg").classList.add("hidden"); + document.getElementById("btn_genimg2").classList.add("hidden"); + } else { + document.getElementById("btn_genimg").classList.remove("hidden"); + document.getElementById("btn_genimg2").classList.remove("hidden"); + } + } + function confirm_settings() { localsettings.max_context_length = document.getElementById("max_context_length").value; localsettings.max_length = document.getElementById("max_length").value; @@ -7951,6 +8118,7 @@ Current version: 114 localsettings.allow_continue_chat = (document.getElementById("allow_continue_chat").checked ? true : false); localsettings.inject_timestamps_chat = (document.getElementById("inject_timestamps_chat").checked ? true : false); localsettings.inject_timestamps_instruct = (document.getElementById("inject_timestamps_instruct").checked ? true : false); + localsettings.inject_jailbreak_instruct = (document.getElementById("inject_jailbreak_instruct").checked ? true : false); localsettings.idle_responses = document.getElementById("idle_responses").value; localsettings.idle_duration = document.getElementById("idle_duration").value; localsettings.adventure_context_mod = (document.getElementById("adventure_context_mod").checked ? true : false); @@ -8012,13 +8180,7 @@ Current version: 114 localsettings.save_remote_images = (document.getElementById("save_remote_images").checked ? true : false); localsettings.prompt_for_savename = (document.getElementById("prompt_for_savename").checked ? true : false); localsettings.img_allownsfw = (document.getElementById("img_allownsfw").checked ? true : false); - if (localsettings.generate_images_mode==0) { - document.getElementById("btn_genimg").classList.add("hidden"); - document.getElementById("btn_genimg2").classList.add("hidden"); - } else { - document.getElementById("btn_genimg").classList.remove("hidden"); - document.getElementById("btn_genimg2").classList.remove("hidden"); - } + update_genimg_button_visiblility(); localsettings.img_cfgscale = parseFloat(document.getElementById("img_cfgscale").value); localsettings.img_steps = parseInt(document.getElementById("img_steps").value); @@ -8049,7 +8211,7 @@ Current version: 114 localsettings.last_selected_preset = document.getElementById("presets").value; //clean and clamp invalid values - localsettings.max_context_length = cleannum(localsettings.max_context_length, 8, 99999); + localsettings.max_context_length = cleannum(localsettings.max_context_length, 8, 999999); localsettings.max_length = cleannum(localsettings.max_length, 1, (localsettings.max_context_length-1)); localsettings.temperature = cleannum(localsettings.temperature, 0.01, 5); localsettings.rep_pen = cleannum(localsettings.rep_pen, 0.1, 5); @@ -8286,6 +8448,14 @@ Current version: 114 render_gametext(); } + function set_personal_notes() + { + inputBox("Here you can add some personal notes or comments to be saved.\nYou can write anything you want.\nNotes are saved to file, but not added to the context.\n","Set Personal Notes",personal_notes,"Enter Personal Notes",()=>{ + let userinput = getInputBoxValue().trim(); + personal_notes = userinput; + },false,true); + } + let temp_automem_store = ""; function autogenerate_summary_memory() { @@ -8459,6 +8629,7 @@ Current version: 114 is_impersonate_user = false; if (!keep_memory) { + personal_notes = ""; current_memory = ""; current_anote = ""; current_wi = []; @@ -8738,7 +8909,7 @@ Current version: 114 function do_auto_gen_image(truncated_context) { var tclen = truncated_context.length; - var sentence = truncated_context.substring(tclen - 300, tclen); + var sentence = truncated_context.substring(tclen - 380, tclen); sentence = start_trim_to_sentence(sentence); sentence = end_trim_to_sentence(sentence,true); if (sentence.length > 0) { @@ -8767,7 +8938,7 @@ Current version: 114 let userinput = getInputBoxValue(); if(userinput.trim()!="") { - var sentence = userinput.trim().substring(0, 300); + var sentence = userinput.trim().substring(0, 380); do_manual_gen_image(sentence); } },false); @@ -8809,15 +8980,20 @@ Current version: 114 } var xtts_is_connected = false; - function fetch_xtts_voices(silent) + function fetch_xtts_voices(silent, is_xtts) { if(!xtts_is_connected) { - fetch(localsettings.saved_xtts_url + xtts_voices_endpoint) + let endpt = (is_xtts?(localsettings.saved_xtts_url + xtts_voices_endpoint):(localsettings.saved_alltalk_url + alltalk_voices_endpoint)); + fetch(endpt) .then(x => x.json()) .then(data => { console.log(data); //repopulate our voices list + if (data && !data.length && data.voices) { + //alltalk mode + data = data.voices; + } let dropdown = document.getElementById("xtts_voices"); let selectionhtml = ``; for (var i = 0; i < data.length; ++i) { @@ -8832,7 +9008,8 @@ Current version: 114 xtts_is_connected = false; if(!silent) { - msgbox("XTTS Connect Error: " + error+"\nCheck XTTS API Server endpoint URL.\n"); + let epname = (is_xtts?"XTTS":"AllTalk"); + msgbox(epname + " Connect Error: " + error+"\nCheck "+epname+" API Server endpoint URL.\n"); } }); } @@ -8852,17 +9029,19 @@ Current version: 114 function toggle_tts_mode() { - if(document.getElementById("ttsselect").value==XTTS_ID) + if(document.getElementById("ttsselect").value==XTTS_ID || document.getElementById("ttsselect").value==ALLTALK_ID) { document.getElementById("xtts_container").classList.remove("hidden"); - fetch_xtts_voices(true); + fetch_xtts_voices(true, document.getElementById("ttsselect").value==XTTS_ID); }else{ document.getElementById("xtts_container").classList.add("hidden"); } } function set_xtts_url() { - inputBox("Enter XTTS API Server URL.","XTTS API Server URL",localsettings.saved_xtts_url,"Input XTTS API Server URL", ()=>{ + let is_xtts = (document.getElementById("ttsselect").value==XTTS_ID); + let epname = (is_xtts?"XTTS":"AllTalk"); + inputBox("Enter "+epname+" API Server URL.",epname+" API Server URL",(is_xtts?localsettings.saved_xtts_url:localsettings.saved_alltalk_url),"Input "+epname+" API Server URL", ()=>{ let userinput = getInputBoxValue(); userinput = userinput.trim(); if(userinput!="" && userinput.slice(-1)=="/") @@ -8871,12 +9050,20 @@ Current version: 114 } if(userinput=="") { - userinput = default_xtts_base; + userinput = (is_xtts?default_xtts_base:default_alltalk_base); } if (userinput != null && userinput!="") { - localsettings.saved_xtts_url = userinput.trim(); + if(is_xtts) + { + localsettings.saved_xtts_url = userinput.trim(); + } + else + { + localsettings.saved_alltalk_url = userinput.trim(); + } + xtts_is_connected = false; - fetch_xtts_voices(false); + fetch_xtts_voices(false, is_xtts); } },false); } @@ -8913,36 +9100,74 @@ Current version: 114 } } - if(ssval==XTTS_ID) //xtts api server + if(ssval==XTTS_ID || ssval==ALLTALK_ID) //xtts api server { if(xtts_is_connected) { + let is_xtts = (ssval==XTTS_ID); const audioContext = new (window.AudioContext || window.webkitAudioContext)(); - let xtts_payload = { - "text": text, - "speaker_wav": document.getElementById("xtts_voices").value, - "language": "EN" - }; - fetch(localsettings.saved_xtts_url + xtts_gen_endpoint, { - method: 'POST', - headers: { - 'Content-Type': 'application/json' - }, - body: JSON.stringify(xtts_payload), - }) - .then(response => response.arrayBuffer()) - .then(data => { - return audioContext.decodeAudioData(data); - }) - .then(decodedData => { - const playSound = audioContext.createBufferSource(); - playSound.buffer = decodedData; - playSound.connect(audioContext.destination); - playSound.start(audioContext.currentTime); - }).catch((error) => { - console.log("XTTS Speak Error: " + error); - }); + if(is_xtts) + { + let xtts_payload = { + "text": text, + "speaker_wav": document.getElementById("xtts_voices").value, + "language": "EN" + }; + fetch(localsettings.saved_xtts_url + xtts_gen_endpoint, { + method: 'POST', + headers: { + 'Content-Type': 'application/json' + }, + body: JSON.stringify(xtts_payload), + }) + .then(response => response.arrayBuffer()) + .then(data => { + return audioContext.decodeAudioData(data); + }) + .then(decodedData => { + const playSound = audioContext.createBufferSource(); + playSound.buffer = decodedData; + playSound.connect(audioContext.destination); + playSound.start(audioContext.currentTime); + }).catch((error) => { + console.log("XTTS Speak Error: " + error); + }); + } + else + { + //alltalk + const formData = new FormData(); + formData.append("text_input", text); // max 2000 chars + formData.append("text_filtering", "none"); // (none|standard|html) + formData.append("character_voice_gen", document.getElementById("xtts_voices").value); + formData.append("narrator_enabled", false); + formData.append("narrator_voice_gen", document.getElementById("xtts_voices").value); + formData.append("text_not_inside", "character"); // character or narrator, determines which to use + formData.append("language", "en"); + formData.append("output_file_name", "audiofile"); // NOTE: file name only, with no extension and no dashes! + formData.append("output_file_timestamp", true); + formData.append("autoplay", false); //to play in browser + formData.append("autoplay_volume", 1.0); // (0.1..2.0) + formData.append("streaming", true); // unknown why + + fetch(localsettings.saved_alltalk_url + alltalk_gen_endpoint, { + method: 'POST', + body: formData, // send payload as FormData + }) + .then(response => response.arrayBuffer()) + .then(data => { + return audioContext.decodeAudioData(data); + }) + .then(decodedData => { + const playSound = audioContext.createBufferSource(); + playSound.buffer = decodedData; + playSound.connect(audioContext.destination); + playSound.start(audioContext.currentTime); + }).catch((error) => { + console.log("AllTalk Speak Error: " + error); + }); + } } } else @@ -8977,6 +9202,13 @@ Current version: 114 if (localsettings.opmode == 4) { + let ist = instructstartplaceholder; + let iet = instructendplaceholder; + if (!localsettings.placeholder_tags) { + ist = get_instruct_starttag(false); + iet = get_instruct_endtag(false); + } + if(newgen != "") { if(localsettings.inject_timestamps_instruct) @@ -8984,32 +9216,24 @@ Current version: 114 newgen = "["+(new Date().toLocaleTimeString([], {year: 'numeric', month: 'numeric', day: 'numeric', hour: '2-digit', minute: '2-digit'}))+"] " + newgen; } //append instruction for instruct mode - if (!localsettings.placeholder_tags) { - newgen = get_instruct_starttag(false) + newgen + get_instruct_endtag(false); - } - else { - newgen = instructstartplaceholder + newgen + instructendplaceholder; + + newgen = ist + newgen + iet; + + if(localsettings.inject_jailbreak_instruct) + { + newgen = newgen + "Sure, I will help with that:\n\n"; } } else //may be continuting existing instruction OR starting a brand new session. check if first action { if (is_impersonate_user) { is_impersonate_user = false; - if (!localsettings.placeholder_tags) { - pending_context_preinjection = get_instruct_starttag(false); //bot response as first msg - pending_context_postinjection = get_instruct_endtag(false); - } else { - pending_context_preinjection = instructstartplaceholder; - pending_context_postinjection = instructendplaceholder; - } + pending_context_preinjection = ist; //bot response as first msg + pending_context_postinjection = iet; } else { if (gametext_arr.length == 0) { - if (!localsettings.placeholder_tags) { - newgen = get_instruct_endtag(false); //bot response as first msg - } else { - newgen = instructendplaceholder; - } + newgen = iet; } } } @@ -9604,6 +9828,7 @@ Current version: 114 let trackedgenid = pending_response_id; //if it changes, stop streaming kobold_api_stream(sub_endpt, submit_payload, submit_payload.max_length, trackedgenid, "", streamchunk); } + update_custom_kobold_endpoint_model_display(); } else if (custom_oai_key != "")//handle for OAI { @@ -9633,18 +9858,17 @@ Current version: 114 } if (document.getElementById("useoaichatcompl").checked) { + let myrole = (localsettings.saved_oai_role==2)?"system":(localsettings.saved_oai_role==1?"assistant":"user"); + oai_payload.messages = []; targetep = (custom_oai_endpoint + oai_submit_endpoint_turbo); if (document.getElementById("jailbreakprompt") && document.getElementById("jailbreakprompt").checked && document.getElementById("jailbreakprompttext").value!="") { - oai_payload.messages = [ - { "role": "system", "content": document.getElementById("jailbreakprompttext").value }, - { "role": "user", "content": submit_payload.prompt }, - ]; + oai_payload.messages.push({ "role": "system", "content": document.getElementById("jailbreakprompttext").value }); } - else { - oai_payload.messages = [ - { "role": "user", "content": submit_payload.prompt }, - ]; + oai_payload.messages.push({ "role": myrole, "content": submit_payload.prompt }); + if (document.getElementById("jailbreakprompt2") && document.getElementById("jailbreakprompt2").checked && document.getElementById("jailbreakprompttext2").value!="") { + oai_payload.messages.push({ "role": "assistant", "content": document.getElementById("jailbreakprompttext2").value }); } + } else { //apply custom logit bias for official OAI only @@ -9761,57 +9985,96 @@ Current version: 114 } else if (custom_claude_key != "")//handle for Claude { - let targetep = cors_proxy + "?" + (custom_claude_endpoint + claude_submit_endpoint); - - let claude_payload = + let claudev3mode = custom_claude_model.toLowerCase().includes("claude-3"); + let targetep = cors_proxy + "?" + (custom_claude_endpoint + (claudev3mode?claude_submit_endpoint_v3:claude_submit_endpoint)); + let claude_payload = null; + if(claudev3mode) { - "prompt": submit_payload.prompt, - "max_tokens_to_sample": submit_payload.params.max_length, - "model": custom_claude_model, - "top_k": (submit_payload.params.top_k<1?300:submit_payload.params.top_k), - "temperature": submit_payload.params.temperature, - "top_p": submit_payload.params.top_p, + let sysprompt = document.getElementById("claudesystemprompt").value; + let assistantprompt = document.getElementById("claudejailbreakprompt").value; + claude_payload = + { + "model": custom_claude_model, + "messages": [{"role": "user", "content": submit_payload.prompt}], + "max_tokens": submit_payload.params.max_length, + "top_k": (submit_payload.params.top_k<1?300:submit_payload.params.top_k), + "temperature": submit_payload.params.temperature, + "top_p": submit_payload.params.top_p, + }; + if(sysprompt) + { + claude_payload.system = sysprompt; + } + if(assistantprompt) + { + claude_payload.messages.push({"role": "assistant", "content": assistantprompt}); + } } - if(document.getElementById("clauderenamecompat").checked) + else { - let assistant_correct_case = "Assistant:"; - if(!claude_payload.prompt.toLowerCase().trim().startsWith('human:')) + claude_payload = { - claude_payload.prompt = "Human: "+claude_payload.prompt; - } - if(!claude_payload.prompt.toLowerCase().trim().endsWith(assistant_correct_case.toLowerCase())) + "prompt": submit_payload.prompt, + "max_tokens_to_sample": submit_payload.params.max_length, + "model": custom_claude_model, + "top_k": (submit_payload.params.top_k<1?300:submit_payload.params.top_k), + "temperature": submit_payload.params.temperature, + "top_p": submit_payload.params.top_p, + }; + + if(document.getElementById("clauderenamecompat").checked) { - if(localsettings.opmode==1) + let assistant_correct_case = "Assistant:"; + if(!claude_payload.prompt.toLowerCase().trim().startsWith('human:')) { - claude_payload.prompt = claude_payload.prompt + " \n"+assistant_correct_case+" Here is a continuation of the story: \n"+assistant_correct_case; + claude_payload.prompt = "Human: "+claude_payload.prompt; } - else + if(!claude_payload.prompt.toLowerCase().trim().endsWith(assistant_correct_case.toLowerCase())) { - claude_payload.prompt = claude_payload.prompt + " "+assistant_correct_case; + if(localsettings.opmode==1) + { + claude_payload.prompt = claude_payload.prompt + " \n"+assistant_correct_case+" Here is a continuation of the story: \n"+assistant_correct_case; + } + else + { + claude_payload.prompt = claude_payload.prompt + " "+assistant_correct_case; + } } + //trim end + claude_payload.prompt = claude_payload.prompt.replace(/[\t\r\n ]+$/, ''); + //replace final assistant with fixed case + claude_payload.prompt = claude_payload.prompt.slice(0, -(assistant_correct_case.length))+assistant_correct_case; } - //trim end - claude_payload.prompt = claude_payload.prompt.replace(/[\t\r\n ]+$/, ''); - //replace final assistant with fixed case - claude_payload.prompt = claude_payload.prompt.slice(0, -(assistant_correct_case.length))+assistant_correct_case; } + last_request_str = JSON.stringify(claude_payload); + let claudeheaders = { + 'Content-Type': 'application/json', + 'x-api-key': custom_claude_key, + 'Authorization': 'Bearer '+custom_claude_key, + }; + if(claudev3mode) + { + claudeheaders["anthropic-version"] = '2023-06-01'; + }else{ + claudeheaders["anthropic-version"] = '2023-01-01'; + } + fetch(targetep, { method: 'POST', - headers: { - 'Content-Type': 'application/json', - 'x-api-key': custom_claude_key, - 'anthropic-version': '2023-01-01', - 'Authorization': 'Bearer '+custom_claude_key, - }, + headers: claudeheaders, body: JSON.stringify(claude_payload), referrerPolicy: 'no-referrer', }) .then((response) => response.json()) .then((data) => { console.log("sync finished response: " + JSON.stringify(data)); + if(custom_claude_key != "" && data.content && data.content.length > 0 && data.content[0].text) + { + data.completion = data.content[0].text; //for claudev3 + } if (custom_claude_key != "" && data.completion != null && data.completion != "") { synchro_polled_response = data.completion; @@ -9859,6 +10122,10 @@ Current version: 114 { "category": "HARM_CATEGORY_SEXUAL", "threshold": "BLOCK_NONE" + }, + { + "category": "HARM_CATEGORY_DEROGATORY", + "threshold": "BLOCK_NONE" } ]; } @@ -10077,6 +10344,9 @@ Current version: 114 sentence = localsettings.image_styles + " " + sentence; } + //remove ### + sentence = sentence.replace(/###/gm, ""); + if (localsettings.generate_images_mode==1) { sentence = sanitize_horde_image_prompt(sentence); } @@ -10090,6 +10360,10 @@ Current version: 114 } let negprompt = localsettings.image_negprompt?(" ### "+localsettings.image_negprompt):" ### ugly, deformed, poorly, censor, blurry, lowres, malformed, watermark, duplicated, grainy, distorted, signature"; + if(localsettings.image_negprompt=="none") + { + negprompt = ""; + } let genimg_payload = { "prompt": (sentence + negprompt), @@ -10311,13 +10585,13 @@ Current version: 114 if(savedmeta) { let origprompt = (savedmeta.prompt?replaceAll(savedmeta.prompt,"\n"," ") : "No Saved Description"); - origprompt = escapeHtml(origprompt); + latest_orig_prompt = origprompt; let visionstatus = (savedmeta.enabled?(savedmeta.desc?`Active`:`Analyzing...`):`Inactive`); let togglebtn = (savedmeta.enabled?``:``); document.getElementById("zoomedimgdesc").innerHTML = ` AI Vision: `+visionstatus+` ?This allows the AI to visually recognize this image, to see and react to this image. Uses Horde or Local A1111 for image interrogation if enabled. `+togglebtn+` -
          +
          `; } else @@ -10326,6 +10600,11 @@ Current version: 114 } } + var latest_orig_prompt = ""; + function show_orig_prompt() + { + msgbox(latest_orig_prompt,"Original Prompt"); + } function click_image(target,imghash) { if(target) @@ -10362,11 +10641,12 @@ Current version: 114 } function render_image_html(data, pend_txt = "", float=true, center=false) { - var dim = (localsettings.opmode == 2 ? 160 : 200); //adventure mode has smaller pictures + var dim = (localsettings.opmode == 2 ? 160 : 180); //adventure mode has smaller pictures let siclass = (float?"storyimgfloat":(center?"storyimgcenter":"storyimgside")); let reinvertcolor = localsettings.invert_colors?" invert_colors":""; let alttxt = ""; - let suffix = ((float==false&¢er==false)?"
          ":""); + let suffix = ""; + let prefix = ((float==false&¢er==false)?"
          ":""); if (!data || data == "") { let waittime = "Unavailable"; if (image_db[pend_txt] != null) { @@ -10377,13 +10657,13 @@ Current version: 114 console.log("Cannot render " + pend_txt); } - return `
          ` + pend_txt + `
          ` + waittime + `
          ` + suffix; + return prefix + `
          ` + pend_txt + `
          ` + waittime + `
          ` + suffix; } else { let imghash = cyrb_hash(data).trim(); if (completed_imgs_meta[imghash] != null) { alttxt = completed_imgs_meta[imghash].prompt?escapeHtml(completed_imgs_meta[imghash].prompt):""; } - return `
          ` + suffix; + return prefix + `
          ` + suffix; } } @@ -10879,7 +11159,7 @@ Current version: 114 show_abort_button(false); if (pending_response_id && pending_response_id != "-1" && pending_response_id != "") { - if (poll_ticks_passed > (4/(poll_interval_base_text*0.001))) //4sec passed + if (poll_ticks_passed > (3/(poll_interval_base_text*0.001))) //4sec passed { show_abort_button(true); } @@ -11316,10 +11596,7 @@ Current version: 114 render_gametext(false); } - if (localsettings.autoscroll) { - document.getElementById("gametext").scrollTop = document.getElementById("gametext").scrollHeight; - document.getElementById("chat_msg_body").scrollTop = document.getElementById("chat_msg_body").scrollHeight; - } + handle_autoscroll(false); } var allow_reenable_submitbtn_timestamp = performance.now(); @@ -11373,6 +11650,25 @@ Current version: 114 } } + function handle_autoscroll(alwaysscroll=true) + { + if (localsettings.autoscroll) { + let box1 = document.getElementById("gametext"); + let box2 = document.getElementById("chat_msg_body"); + function isScrolledToBottom(element) { + return element.scrollHeight - element.scrollTop <= element.clientHeight + 250; + } + if(alwaysscroll || isScrolledToBottom(box1)) + { + box1.scrollTop = box1.scrollHeight - box1.clientHeight + 10; + } + if(alwaysscroll || isScrolledToBottom(box2)) + { + box2.scrollTop = box2.scrollHeight - box2.clientHeight + 10; + } + } + } + function render_gametext(save = true) { @@ -11726,11 +12022,15 @@ Current version: 114 } // Show the 'AI is typing' message if an answer is pending, and prevent the 'send button' from being clicked again. - if (pending_response_id=="") { document.getElementById("chatistyping").classList.add("hidden"); } + if (pending_response_id=="") { + document.getElementById("chatistyping").classList.add("hidden"); + document.getElementById("chat_msg_body").classList.remove("withtyping"); + } else { let aiName = ((localsettings.opmode==3 && pending_context_preinjection && pending_context_preinjection.includes(":")) ? pending_context_preinjection.split(":")[0] : "The AI"); document.getElementById("chataityping").innerText = aiName + " is typing..."; document.getElementById("chatistyping").classList.remove("hidden"); + document.getElementById("chat_msg_body").classList.add("withtyping"); } document.getElementById("chat_msg_send_btn").disabled = document.getElementById("btnsend").disabled; @@ -11746,10 +12046,9 @@ Current version: 114 if (localsettings.persist_session && save) { autosave(); } - if (localsettings.autoscroll) { - document.getElementById("gametext").scrollTop = document.getElementById("gametext").scrollHeight; - document.getElementById("chat_msg_body").scrollTop = document.getElementById("chat_msg_body").scrollHeight; - } + + handle_autoscroll(true); + if(localsettings.printer_view) { document.getElementById("gamescreen").classList.remove("normal_viewport_height"); @@ -11760,6 +12059,7 @@ Current version: 114 document.getElementById("chat_msg_body").classList.add("aesthetic_viewport_height"); } + update_genimg_button_visiblility(); idle_timer = 0; document.getElementById("token-budget").innerText = last_token_budget; @@ -11955,11 +12255,14 @@ Current version: 114 } function chat_toggle_actionmenu() { - var am2 = document.getElementById("actionmenu2"); + let am2 = document.getElementById("actionmenu2"); + let mainbox = document.getElementById("chat_msg_body"); if (am2.classList.contains("hidden")) { am2.classList.remove("hidden"); + mainbox.classList.add("withmenu"); } else { am2.classList.add("hidden"); + mainbox.classList.remove("withmenu"); } } @@ -13245,57 +13548,40 @@ Current version: 114
          Add Endpoint Version
          - -
          Add System Message
          - +
          Use ChatCompletions API
          - + + +
          +
          +
          Auto Jailbreak ?Injects a jailbreak message after every query to make the AI more likely to obey you.
          + +
          @@ -13757,7 +14057,7 @@ Current version: 114
          Generate Images ?Use the AI Horde or a local A1111 instance to insert AI generated images into your story.
          - @@ -13845,10 +14145,10 @@ Current version: 114
          Token Streaming ?Attempts to use token streaming if supported. Not available on Horde.
          + class="helptext">Use token streaming for partial responses. SSE is smoother but less well-supported. Poll is chunkier but more reliable. Not available on Horde.
          @@ -13993,6 +14293,7 @@ Current version: 114

          Author's Note?Similar to Memory, but inserted near the end of the text instead of the start. A good way to control the mood/behavior of the AI.
          + @@ -14130,13 +14431,13 @@ Current version: 114
          -
          Share Story URL
          +
          Share Story
          - +
          @@ -14218,7 +14519,7 @@ Current version: 114
          Style tags to use for generating images:
          (E.g. Sketch, Realistic, Anime, 3D Render, Drawing)
          Negative Prompt
          - +
          Number of Steps:
          @@ -14265,6 +14566,28 @@ Current version: 114
          + +