diff --git a/.clang-tidy b/.clang-tidy index 1a42b9abc..3078beacc 100644 --- a/.clang-tidy +++ b/.clang-tidy @@ -3,6 +3,7 @@ Checks: > bugprone-*, -bugprone-easily-swappable-parameters, -bugprone-implicit-widening-of-multiplication-result, + -bugprone-misplaced-widening-cast, -bugprone-narrowing-conversions, readability-*, -readability-avoid-unconditional-preprocessor-if, @@ -15,4 +16,8 @@ Checks: > -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling, performance-*, portability-*, + misc-*, + -misc-const-correctness, + -misc-non-private-member-variables-in-classes, + -misc-no-recursion, FormatStyle: none diff --git a/.devops/cloud-v-pipeline b/.devops/cloud-v-pipeline new file mode 100644 index 000000000..f3a4944f8 --- /dev/null +++ b/.devops/cloud-v-pipeline @@ -0,0 +1,22 @@ +node('x86_runner1'){ // Running on x86 runner containing latest vector qemu, latest vector gcc and all the necessary libraries + stage('Cleanup'){ + cleanWs() // Cleaning previous CI build in workspace + } + stage('checkout repo'){ + retry(5){ // Retry if the cloning fails due to some reason + checkout scm // Clone the repo on Runner + } + } + stage('Compiling llama.cpp'){ + sh'''#!/bin/bash + make RISCV=1 RISCV_CROSS_COMPILE=1 # Compiling llama for RISC-V + ''' + } + stage('Running llama.cpp'){ + sh'''#!/bin/bash + module load gnu-bin2/0.1 # loading latest versions of vector qemu and vector gcc + qemu-riscv64 -L /softwares/gnu-bin2/sysroot -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./main -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt # Running llama.cpp on vector qemu-riscv64 + cat llama_log.txt # Printing results + ''' + } +} diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile index e5fcb37d6..360602d65 100644 --- a/.devops/full-cuda.Dockerfile +++ b/.devops/full-cuda.Dockerfile @@ -12,7 +12,7 @@ FROM ${BASE_CUDA_DEV_CONTAINER} as build ARG CUDA_DOCKER_ARCH=all RUN apt-get update && \ - apt-get install -y build-essential python3 python3-pip + apt-get install -y build-essential python3 python3-pip git COPY requirements.txt requirements.txt diff --git a/.devops/main-cuda.Dockerfile b/.devops/main-cuda.Dockerfile index 30c01196a..2b7faf7c1 100644 --- a/.devops/main-cuda.Dockerfile +++ b/.devops/main-cuda.Dockerfile @@ -12,7 +12,7 @@ FROM ${BASE_CUDA_DEV_CONTAINER} as build ARG CUDA_DOCKER_ARCH=all RUN apt-get update && \ - apt-get install -y build-essential + apt-get install -y build-essential git WORKDIR /app diff --git a/.editorconfig b/.editorconfig index 135a7e4bc..f8245b85c 100644 --- a/.editorconfig +++ b/.editorconfig @@ -17,3 +17,6 @@ indent_style = tab [prompts/*.txt] insert_final_newline = unset + +[examples/server/public/*] +indent_size = 2 diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 9d0a6c222..8b869f688 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -27,7 +27,7 @@ jobs: steps: - name: Clone id: checkout - uses: actions/checkout@v1 + uses: actions/checkout@v3 - name: Dependencies id: depends @@ -52,7 +52,7 @@ jobs: steps: - name: Clone id: checkout - uses: actions/checkout@v1 + uses: actions/checkout@v3 - name: Dependencies id: depends @@ -87,7 +87,7 @@ jobs: steps: - name: Clone id: checkout - uses: actions/checkout@v1 + uses: actions/checkout@v3 - name: Dependencies id: depends @@ -121,7 +121,7 @@ jobs: steps: - name: Clone id: checkout - uses: actions/checkout@v1 + uses: actions/checkout@v3 - name: Dependencies id: depends @@ -149,7 +149,7 @@ jobs: steps: - name: Clone id: checkout - uses: actions/checkout@v1 + uses: actions/checkout@v3 - name: Dependencies id: depends @@ -174,7 +174,7 @@ jobs: steps: - name: Clone id: checkout - uses: actions/checkout@v1 + uses: actions/checkout@v3 - name: Dependencies id: depends @@ -197,6 +197,62 @@ jobs: cd build ctest --verbose --timeout 900 + macOS-latest-cmake-ios: + runs-on: macos-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v1 + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + + - name: Build + id: cmake_build + run: | + sysctl -a + mkdir build + cd build + cmake -G Xcode .. \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=iOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 + cmake --build . --config Release + + macOS-latest-cmake-tvos: + runs-on: macos-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v1 + + - name: Dependencies + id: depends + continue-on-error: true + run: | + brew update + + - name: Build + id: cmake_build + run: | + sysctl -a + mkdir build + cd build + cmake -G Xcode .. \ + -DLLAMA_BUILD_EXAMPLES=OFF \ + -DLLAMA_BUILD_TESTS=OFF \ + -DLLAMA_BUILD_SERVER=OFF \ + -DCMAKE_SYSTEM_NAME=tvOS \ + -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 + cmake --build . --config Release + windows-latest-cmake: runs-on: windows-latest @@ -224,7 +280,9 @@ jobs: steps: - name: Clone id: checkout - uses: actions/checkout@v1 + uses: actions/checkout@v3 + with: + fetch-depth: 0 - name: Download OpenCL SDK id: get_opencl @@ -334,20 +392,21 @@ jobs: strategy: matrix: - cuda: ['12.1.0', '11.7.1'] + cuda: ['12.2.0', '11.7.1'] build: ['cublas'] steps: - name: Clone id: checkout - uses: actions/checkout@v1 + uses: actions/checkout@v3 + with: + fetch-depth: 0 - - uses: Jimver/cuda-toolkit@v0.2.10 + - uses: Jimver/cuda-toolkit@v0.2.11 id: cuda-toolkit with: cuda: ${{ matrix.cuda }} - # TODO(green-sky): _dev seems to fail, and non dev are not enought - #sub-packages: '["nvcc", "cudart", "cublas", "cudart_dev", "cublas_dev"]' + sub-packages: '["nvcc", "cudart", "cublas", "cublas_dev", "thrust", "visual_studio_integration"]' - name: Build id: cmake_build @@ -384,27 +443,11 @@ jobs: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip - name: Copy and pack Cuda runtime - if: ${{ matrix.cuda == '12.1.0' }} - # TODO(green-sky): paths are cuda 12 specific run: | echo "Cuda install location: ${{steps.cuda-toolkit.outputs.CUDA_PATH}}" - mkdir '.\build\bin\cudart\' - cp "${{steps.cuda-toolkit.outputs.CUDA_PATH}}\bin\cudart64_12.dll" '.\build\bin\cudart\' - cp "${{steps.cuda-toolkit.outputs.CUDA_PATH}}\bin\cublas64_12.dll" '.\build\bin\cudart\' - cp "${{steps.cuda-toolkit.outputs.CUDA_PATH}}\bin\cublasLt64_12.dll" '.\build\bin\cudart\' - 7z a cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip .\build\bin\cudart\* - - - name: Copy and pack Cuda runtime - if: ${{ matrix.cuda == '11.7.1' }} - # TODO(green-sky): paths are cuda 11 specific - run: | - echo "Cuda install location: ${{steps.cuda-toolkit.outputs.CUDA_PATH}}" - mkdir '.\build\bin\cudart\' - ls "${{steps.cuda-toolkit.outputs.CUDA_PATH}}\bin" - cp "${{steps.cuda-toolkit.outputs.CUDA_PATH}}\bin\cudart64_110.dll" '.\build\bin\cudart\' - cp "${{steps.cuda-toolkit.outputs.CUDA_PATH}}\bin\cublas64_11.dll" '.\build\bin\cudart\' - cp "${{steps.cuda-toolkit.outputs.CUDA_PATH}}\bin\cublasLt64_11.dll" '.\build\bin\cudart\' - 7z a cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip .\build\bin\cudart\* + $dst='.\build\bin\cudart\' + robocopy "${{steps.cuda-toolkit.outputs.CUDA_PATH}}\bin" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll + 7z a cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip $dst\* - name: Upload Cuda runtime if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} @@ -413,6 +456,22 @@ jobs: path: | cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip + freeBSD-latest: + runs-on: macos-12 + steps: + - name: Clone + uses: actions/checkout@v3 + + - name: Build + uses: cross-platform-actions/action@v0.19.0 + with: + operating_system: freebsd + version: '13.2' + run: | + sudo pkg update + sudo pkg install -y gmake automake autoconf pkgconf llvm15 clinfo clover opencl clblast openblas + gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 + release: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} @@ -429,7 +488,9 @@ jobs: steps: - name: Clone id: checkout - uses: actions/checkout@v1 + uses: actions/checkout@v3 + with: + fetch-depth: 0 - name: Determine tag name id: tag @@ -487,7 +548,7 @@ jobs: # # steps: # - name: Clone -# uses: actions/checkout@v1 +# uses: actions/checkout@v3 # # - name: Dependencies # run: | @@ -511,7 +572,7 @@ jobs: # # steps: # - name: Clone -# uses: actions/checkout@v1 +# uses: actions/checkout@v3 # # - name: Dependencies # run: | @@ -535,7 +596,7 @@ jobs: # # steps: # - name: Clone -# uses: actions/checkout@v1 +# uses: actions/checkout@v3 # # - name: Dependencies # run: | @@ -565,7 +626,7 @@ jobs: # # steps: # - name: Clone -# uses: actions/checkout@v1 +# uses: actions/checkout@v3 # # - name: Add msbuild to PATH # uses: microsoft/setup-msbuild@v1 @@ -604,7 +665,7 @@ jobs: # # steps: # - name: Clone -# uses: actions/checkout@v1 +# uses: actions/checkout@v3 # # - name: Add msbuild to PATH # uses: microsoft/setup-msbuild@v1 @@ -650,7 +711,7 @@ jobs: # # steps: # - name: Clone -# uses: actions/checkout@v1 +# uses: actions/checkout@v3 # # - name: Dependencies # run: | diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 379fbd7ad..9c90c77ac 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -26,8 +26,15 @@ jobs: strategy: matrix: config: - - { tag: "light", dockerfile: ".devops/main.Dockerfile" } - - { tag: "full", dockerfile: ".devops/full.Dockerfile" } + - { tag: "light", dockerfile: ".devops/main.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" } + # NOTE(canardletter): The CUDA builds on arm64 are very slow, so I + # have disabled them for now until the reason why + # is understood. + - { tag: "light-cuda", dockerfile: ".devops/main-cuda.Dockerfile", platforms: "linux/amd64" } + - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" } + - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } steps: - name: Check out the repo uses: actions/checkout@v3 @@ -51,7 +58,7 @@ jobs: with: context: . push: true - platforms: linux/amd64,linux/arm64 + platforms: ${{ matrix.config.platforms }} tags: "ghcr.io/ggerganov/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}" file: ${{ matrix.config.dockerfile }} @@ -60,6 +67,6 @@ jobs: with: context: . push: ${{ github.event_name == 'push' }} - platforms: linux/amd64,linux/arm64 + platforms: ${{ matrix.config.platforms }} tags: "ghcr.io/ggerganov/llama.cpp:${{ matrix.config.tag }}" file: ${{ matrix.config.dockerfile }} diff --git a/.github/workflows/gguf-publish.yml b/.github/workflows/gguf-publish.yml index a6289e335..e61bfc6c3 100644 --- a/.github/workflows/gguf-publish.yml +++ b/.github/workflows/gguf-publish.yml @@ -24,7 +24,7 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python uses: actions/setup-python@v2 with: diff --git a/.gitignore b/.gitignore index f9244fadc..b862a0415 100644 --- a/.gitignore +++ b/.gitignore @@ -31,27 +31,29 @@ tmp/ models/* models-mnt -/main -/quantize -/quantize-stats -/result -/perplexity -/embedding -/train-text-from-scratch -/convert-llama2c-to-ggml -/simple -/benchmark-matmult -/vdot -/server /Pipfile +/baby-llama +/beam-search +/benchmark-matmult +/convert-llama2c-to-ggml /embd-input-test +/embedding /gguf /gguf-llama-simple /libllama.so /llama-bench -/baby-llama -/beam-search +/main +/metal +/perplexity +/quantize +/quantize-stats +/result /save-load-state +/server +/simple +/speculative +/train-text-from-scratch +/vdot build-info.h arm_neon.h compile_commands.json diff --git a/CMakeLists.txt b/CMakeLists.txt index 1b7cce9f1..3283f7b65 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -36,6 +36,12 @@ endif() # Option list # +if (APPLE) + set(LLAMA_METAL_DEFAULT ON) +else() + set(LLAMA_METAL_DEFAULT OFF) +endif() + # general option(LLAMA_STATIC "llama: static link libraries" OFF) option(LLAMA_NATIVE "llama: enable -march=native flag" OFF) @@ -76,7 +82,8 @@ option(LLAMA_CUDA_F16 "llama: use 16 bit floats for some set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K") option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF) option(LLAMA_CLBLAST "llama: use CLBlast" OFF) -option(LLAMA_METAL "llama: use Metal" OFF) +option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) +option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF) option(LLAMA_MPI "llama: use MPI" OFF) option(LLAMA_K_QUANTS "llama: use k-quants" ON) option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) @@ -128,6 +135,7 @@ set(CMAKE_C_STANDARD 11) set(CMAKE_C_STANDARD_REQUIRED true) set(THREADS_PREFER_PTHREAD_FLAG ON) find_package(Threads REQUIRED) +include(CheckCXXCompilerFlag) if (NOT MSVC) if (LLAMA_SANITIZE_THREAD) @@ -158,6 +166,32 @@ if (APPLE AND LLAMA_ACCELERATE) endif() endif() +if (LLAMA_METAL) + find_library(FOUNDATION_LIBRARY Foundation REQUIRED) + find_library(METAL_FRAMEWORK Metal REQUIRED) + find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) + + message(STATUS "Metal framework found") + set(GGML_HEADERS_METAL ggml-metal.h) + set(GGML_SOURCES_METAL ggml-metal.m) + + add_compile_definitions(GGML_USE_METAL) + if (LLAMA_METAL_NDEBUG) + add_compile_definitions(GGML_METAL_NDEBUG) + endif() + + # get full path to the file + #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/") + + # copy ggml-metal.metal to bin directory + configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY) + + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} + ${FOUNDATION_LIBRARY} + ${METAL_FRAMEWORK} + ${METALKIT_FRAMEWORK} + ) +endif() if (LLAMA_BLAS) if (LLAMA_STATIC) set(BLA_STATIC ON) @@ -234,7 +268,8 @@ if (LLAMA_BLAS) endif() if (LLAMA_K_QUANTS) - set(GGML_SOURCES_EXTRA ${GGML_SOURCES_EXTRA} k_quants.c k_quants.h) + set(GGML_HEADERS_EXTRA k_quants.h) + set(GGML_SOURCES_EXTRA k_quants.c) add_compile_definitions(GGML_USE_K_QUANTS) if (LLAMA_QKK_64) add_compile_definitions(GGML_QKK_64) @@ -250,7 +285,8 @@ if (LLAMA_CUBLAS) enable_language(CUDA) - set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h) + set(GGML_HEADERS_CUDA ggml-cuda.h) + set(GGML_SOURCES_CUDA ggml-cuda.cu) add_compile_definitions(GGML_USE_CUBLAS) # if (LLAMA_CUDA_CUBLAS) @@ -293,34 +329,12 @@ if (LLAMA_CUBLAS) endif() endif() -if (LLAMA_METAL) - find_library(FOUNDATION_LIBRARY Foundation REQUIRED) - find_library(METAL_FRAMEWORK Metal REQUIRED) - find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) - - set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h) - - add_compile_definitions(GGML_USE_METAL) - #add_compile_definitions(GGML_METAL_NDEBUG) - - # get full path to the file - #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/") - - # copy ggml-metal.metal to bin directory - configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY) - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} - ${FOUNDATION_LIBRARY} - ${METAL_FRAMEWORK} - ${METALKIT_FRAMEWORK} - ) -endif() - if (LLAMA_MPI) cmake_minimum_required(VERSION 3.10) find_package(MPI) if (MPI_C_FOUND) message(STATUS "MPI found") + set(GGML_HEADERS_MPI ggml-mpi.h) set(GGML_SOURCES_MPI ggml-mpi.c ggml-mpi.h) add_compile_definitions(GGML_USE_MPI) add_compile_definitions(${MPI_C_COMPILE_DEFINITIONS}) @@ -343,7 +357,8 @@ if (LLAMA_CLBLAST) if (CLBlast_FOUND) message(STATUS "CLBlast found") - set(GGML_SOURCES_OPENCL ggml-opencl.cpp ggml-opencl.h) + set(GGML_HEADERS_OPENCL ggml-opencl.h) + set(GGML_SOURCES_OPENCL ggml-opencl.cpp) add_compile_definitions(GGML_USE_CLBLAST) @@ -371,13 +386,15 @@ if (LLAMA_HIPBLAS) message(STATUS "HIP and hipBLAS found") add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS) add_library(ggml-rocm OBJECT ggml-cuda.cu ggml-cuda.h) + if (BUILD_SHARED_LIBS) + set_target_properties(ggml-rocm PROPERTIES POSITION_INDEPENDENT_CODE ON) + endif() if (LLAMA_CUDA_FORCE_DMMV) target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_DMMV) endif() target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) target_compile_definitions(ggml-rocm PRIVATE K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) - target_compile_definitions(ggml-rocm PRIVATE CC_TURING=1000000000) set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX) target_link_libraries(ggml-rocm PRIVATE hip::device PUBLIC hip::host roc::rocblas roc::hipblas) @@ -415,7 +432,7 @@ if (LLAMA_ALL_WARNINGS) ) if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU") # g++ only - set(cxx_flags ${cxx_flags} -Wno-format-truncation) + set(cxx_flags ${cxx_flags} -Wno-format-truncation -Wno-array-bounds) endif() else() # todo : msvc @@ -428,7 +445,7 @@ if (LLAMA_ALL_WARNINGS) endif() -if (MSVC) +if (WIN32) add_compile_definitions(_CRT_SECURE_NO_WARNINGS) if (BUILD_SHARED_LIBS) @@ -450,6 +467,13 @@ endif() # TODO: probably these flags need to be tweaked on some architectures # feel free to update the Makefile for your architecture and send a pull request or issue message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}") +if (MSVC) + string(TOLOWER "${CMAKE_GENERATOR_PLATFORM}" CMAKE_GENERATOR_PLATFORM_LWR) + message(STATUS "CMAKE_GENERATOR_PLATFORM: ${CMAKE_GENERATOR_PLATFORM}") +else () + set(CMAKE_GENERATOR_PLATFORM_LWR "") +endif () + if (NOT MSVC) if (LLAMA_STATIC) add_link_options(-static) @@ -465,25 +489,33 @@ if (NOT MSVC) endif() endif() -if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") +if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") OR ("${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "arm64")) message(STATUS "ARM detected") if (MSVC) - # TODO: arm msvc? + add_compile_definitions(__ARM_NEON) + add_compile_definitions(__ARM_FEATURE_FMA) + add_compile_definitions(__ARM_FEATURE_DOTPROD) + # add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) # MSVC doesn't support vdupq_n_f16, vld1q_f16, vst1q_f16 + add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead else() + check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E) + if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") + add_compile_options(-mfp16-format=ieee) + endif() if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6") # Raspberry Pi 1, Zero - add_compile_options(-mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access) + add_compile_options(-mfpu=neon-fp-armv8 -mno-unaligned-access) endif() if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7") # Raspberry Pi 2 - add_compile_options(-mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations) + add_compile_options(-mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations) endif() if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8") # Raspberry Pi 3, 4, Zero 2 (32-bit) - add_compile_options(-mfp16-format=ieee -mno-unaligned-access) + add_compile_options(-mno-unaligned-access) endif() endif() -elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$") +elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "^(x86_64|i686|amd64|x64)$" ) message(STATUS "x86 detected") if (MSVC) if (LLAMA_AVX512) @@ -540,27 +572,84 @@ else() message(STATUS "Unknown architecture") endif() +# +# POSIX conformance +# + +# clock_gettime came in POSIX.1b (1993) +# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional +# posix_memalign came in POSIX.1-2001 / SUSv3 +# M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985) +add_compile_definitions(_XOPEN_SOURCE=600) + +# Somehow in OpenBSD whenever POSIX conformance is specified +# some string functions rely on locale_t availability, +# which was introduced in POSIX.1-2008, forcing us to go higher +if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD") + remove_definitions(-D_XOPEN_SOURCE=600) + add_compile_definitions(_XOPEN_SOURCE=700) +endif() + +# Data types, macros and functions related to controlling CPU affinity and +# some memory allocation are available on Linux through GNU extensions in libc +if (CMAKE_SYSTEM_NAME MATCHES "Linux") + add_compile_definitions(_GNU_SOURCE) +endif() + +# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1, +# and on macOS its availability depends on enabling Darwin extensions +# similarly on DragonFly, enabling BSD extensions is necessary +if ( + CMAKE_SYSTEM_NAME MATCHES "Darwin" OR + CMAKE_SYSTEM_NAME MATCHES "iOS" OR + CMAKE_SYSTEM_NAME MATCHES "tvOS" OR + CMAKE_SYSTEM_NAME MATCHES "DragonFly" +) + add_compile_definitions(_DARWIN_C_SOURCE) +endif() + +# alloca is a non-standard interface that is not visible on BSDs when +# POSIX conformance is specified, but not all of them provide a clean way +# to enable it in such cases +if (CMAKE_SYSTEM_NAME MATCHES "FreeBSD") + add_compile_definitions(__BSD_VISIBLE) +endif() +if (CMAKE_SYSTEM_NAME MATCHES "NetBSD") + add_compile_definitions(_NETBSD_SOURCE) +endif() +if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD") + add_compile_definitions(_BSD_SOURCE) +endif() + # # libraries # # ggml +if (GGML_USE_CPU_HBM) + add_definitions(-DGGML_USE_CPU_HBM) + find_library(memkind memkind REQUIRED) +endif() + add_library(ggml OBJECT ggml.c ggml.h ggml-alloc.c ggml-alloc.h - ${GGML_SOURCES_CUDA} - ${GGML_SOURCES_OPENCL} - ${GGML_SOURCES_METAL} - ${GGML_SOURCES_MPI} - ${GGML_SOURCES_EXTRA} + ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} + ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} + ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} + ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} + ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} ) target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES}) target_compile_features(ggml PUBLIC c_std_11) # don't bump target_link_libraries(ggml PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS}) +if (GGML_USE_CPU_HBM) + target_link_libraries(ggml PUBLIC memkind) +endif() add_library(ggml_static STATIC $) if (BUILD_SHARED_LIBS) @@ -590,14 +679,53 @@ if (BUILD_SHARED_LIBS) if (LLAMA_METAL) set_target_properties(llama PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal") endif() - install(TARGETS llama LIBRARY) endif() + # # install # include(GNUInstallDirs) +include(CMakePackageConfigHelpers) + +set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} + CACHE PATH "Location of header files") +set(LLAMA_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR} + CACHE PATH "Location of library files") +set(LLAMA_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR} + CACHE PATH "Location of binary files") +set(LLAMA_BUILD_NUMBER ${BUILD_NUMBER}) +set(LLAMA_BUILD_COMMIT ${BUILD_COMMIT}) +set(LLAMA_INSTALL_VERSION 0.0.${BUILD_NUMBER}) + +configure_package_config_file( + ${CMAKE_CURRENT_SOURCE_DIR}/scripts/LlamaConfig.cmake.in + ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake + INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama + PATH_VARS LLAMA_INCLUDE_INSTALL_DIR + LLAMA_LIB_INSTALL_DIR + LLAMA_BIN_INSTALL_DIR ) + +write_basic_package_version_file( + ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfigVersion.cmake + VERSION ${LLAMA_INSTALL_VERSION} + COMPATIBILITY SameMajorVersion) + +install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake + ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfigVersion.cmake + DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama) + +set(GGML_PUBLIC_HEADERS "ggml.h" + "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" + "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}") + +set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") +install(TARGETS ggml PUBLIC_HEADER) + +set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}/llama.h) +install(TARGETS llama LIBRARY PUBLIC_HEADER) + install( FILES convert.py PERMISSIONS diff --git a/Makefile b/Makefile index a6de78ac7..a4d4ca872 100644 --- a/Makefile +++ b/Makefile @@ -1,28 +1,72 @@ # Define the default target now so that it is always the first target -BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam-search tests/test-c.o +BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam-search speculative tests/test-c.o # Binaries only useful for tests -TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1 +TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama # Code coverage output files COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report +ifndef UNAME_S +UNAME_S := $(shell uname -s) +endif + +ifndef UNAME_P +UNAME_P := $(shell uname -p) +endif + +ifndef UNAME_M +UNAME_M := $(shell uname -m) +endif + +# Mac OS + Arm can report x86_64 +# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789 +ifeq ($(UNAME_S),Darwin) + ifndef LLAMA_NO_METAL + LLAMA_METAL := 1 + endif + + ifneq ($(UNAME_P),arm) + SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null) + ifeq ($(SYSCTL_M),1) + # UNAME_P := arm + # UNAME_M := arm64 + warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789) + endif + endif +endif + +ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))' +BUILD_TARGETS += metal +endif + default: $(BUILD_TARGETS) -test: - @echo "Running tests..." - @for test_target in $(TEST_TARGETS); do \ +test: $(TEST_TARGETS) + @failures=0; \ + for test_target in $(TEST_TARGETS); do \ if [ "$$test_target" = "tests/test-tokenizer-0-llama" ]; then \ ./$$test_target $(CURDIR)/models/ggml-vocab-llama.gguf; \ elif [ "$$test_target" = "tests/test-tokenizer-0-falcon" ]; then \ continue; \ - elif [ "$$test_target" = "tests/test-tokenizer-1" ]; then \ + elif [ "$$test_target" = "tests/test-tokenizer-1-llama" ]; then \ continue; \ else \ + echo "Running test $$test_target..."; \ ./$$test_target; \ fi; \ - done - @echo "All tests have been run." + if [ $$? -ne 0 ]; then \ + printf 'Test $$test_target FAILED!\n\n' $$test_target; \ + failures=$$(( failures + 1 )); \ + else \ + printf 'Test %s passed.\n\n' $$test_target; \ + fi; \ + done; \ + if [ $$failures -gt 0 ]; then \ + printf '\n%s tests failed.\n' $$failures; \ + exit 1; \ + fi + @echo 'All tests passed.' all: $(BUILD_TARGETS) $(TEST_TARGETS) @@ -38,18 +82,6 @@ gcovr-report: coverage ## Generate gcovr report mkdir -p gcovr-report gcovr --root . --html --html-details --output gcovr-report/coverage.html -ifndef UNAME_S -UNAME_S := $(shell uname -s) -endif - -ifndef UNAME_P -UNAME_P := $(shell uname -p) -endif - -ifndef UNAME_M -UNAME_M := $(shell uname -m) -endif - ifdef RISCV_CROSS_COMPILE CC := riscv64-unknown-linux-gnu-gcc CXX := riscv64-unknown-linux-gnu-g++ @@ -58,19 +90,6 @@ endif CCV := $(shell $(CC) --version | head -n 1) CXXV := $(shell $(CXX) --version | head -n 1) -# Mac OS + Arm can report x86_64 -# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789 -ifeq ($(UNAME_S),Darwin) - ifneq ($(UNAME_P),arm) - SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null) - ifeq ($(SYSCTL_M),1) - # UNAME_P := arm - # UNAME_M := arm64 - warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789) - endif - endif -endif - # # Compile flags # @@ -83,10 +102,52 @@ else OPT = -O3 endif MK_CPPFLAGS = -I. -Icommon -MK_CFLAGS = $(CPPFLAGS) $(OPT) -std=c11 -fPIC -MK_CXXFLAGS = $(CPPFLAGS) $(OPT) -std=c++11 -fPIC +MK_CFLAGS = $(OPT) -std=c11 -fPIC +MK_CXXFLAGS = $(OPT) -std=c++11 -fPIC MK_LDFLAGS = +# clock_gettime came in POSIX.1b (1993) +# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional +# posix_memalign came in POSIX.1-2001 / SUSv3 +# M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985) +MK_CPPFLAGS += -D_XOPEN_SOURCE=600 + +# Somehow in OpenBSD whenever POSIX conformance is specified +# some string functions rely on locale_t availability, +# which was introduced in POSIX.1-2008, forcing us to go higher +ifeq ($(UNAME_S),OpenBSD) + MK_CPPFLAGS += -U_XOPEN_SOURCE -D_XOPEN_SOURCE=700 +endif + +# Data types, macros and functions related to controlling CPU affinity and +# some memory allocation are available on Linux through GNU extensions in libc +ifeq ($(UNAME_S),Linux) + MK_CPPFLAGS += -D_GNU_SOURCE +endif + +# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1, +# and on macOS its availability depends on enabling Darwin extensions +# similarly on DragonFly, enabling BSD extensions is necessary +ifeq ($(UNAME_S),Darwin) + MK_CPPFLAGS += -D_DARWIN_C_SOURCE +endif +ifeq ($(UNAME_S),DragonFly) + MK_CPPFLAGS += -D__BSD_VISIBLE +endif + +# alloca is a non-standard interface that is not visible on BSDs when +# POSIX conformance is specified, but not all of them provide a clean way +# to enable it in such cases +ifeq ($(UNAME_S),FreeBSD) + MK_CPPFLAGS += -D__BSD_VISIBLE +endif +ifeq ($(UNAME_S),NetBSD) + MK_CPPFLAGS += -D_NETBSD_SOURCE +endif +ifeq ($(UNAME_S),OpenBSD) + MK_CPPFLAGS += -D_BSD_SOURCE +endif + ifdef LLAMA_DEBUG MK_CFLAGS += -O0 -g MK_CXXFLAGS += -O0 -g @@ -101,12 +162,11 @@ endif ifdef LLAMA_CODE_COVERAGE - CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase '' + MK_CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase '' endif ifdef LLAMA_DISABLE_LOGS - CFLAGS += -DLOG_DISABLE_LOGS - CXXFLAGS += -DLOG_DISABLE_LOGS + MK_CPPFLAGS += -DLOG_DISABLE_LOGS endif # LLAMA_DISABLE_LOGS # warnings @@ -114,9 +174,9 @@ MK_CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wmissing-prototypes -Werror=implicit-int -Wno-unused-function MK_CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar -ifeq '' '$(findstring clang++,$(CXX))' +ifeq '' '$(findstring clang,$(shell $(CXX) --version))' # g++ only - CXXFLAGS += -Wno-format-truncation + MK_CXXFLAGS += -Wno-format-truncation -Wno-array-bounds endif # OS specific @@ -180,8 +240,8 @@ endif # https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412 # https://github.com/ggerganov/llama.cpp/issues/2922 ifneq '' '$(findstring mingw,$(shell $(CC) -dumpmachine))' - CFLAGS += -Xassembler -muse-unaligned-vector-move - CXXFLAGS += -Xassembler -muse-unaligned-vector-move + MK_CFLAGS += -Xassembler -muse-unaligned-vector-move + MK_CXXFLAGS += -Xassembler -muse-unaligned-vector-move endif ifneq ($(filter aarch64%,$(UNAME_M)),) @@ -218,8 +278,8 @@ ifneq ($(filter ppc64%,$(UNAME_M)),) endif else - CFLAGS += -march=rv64gcv -mabi=lp64d - CXXFLAGS += -march=rv64gcv -mabi=lp64d + MK_CFLAGS += -march=rv64gcv -mabi=lp64d + MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d endif ifndef LLAMA_NO_K_QUANTS @@ -231,8 +291,8 @@ endif endif ifndef LLAMA_NO_ACCELERATE - # Mac M1 - include Accelerate framework. - # `-framework Accelerate` works on Mac Intel as well, with negliable performance boost (as of the predict time). + # Mac OS - include Accelerate framework. + # `-framework Accelerate` works both with Apple Silicon and Mac Intel ifeq ($(UNAME_S),Darwin) MK_CPPFLAGS += -DGGML_USE_ACCELERATE MK_LDFLAGS += -framework Accelerate @@ -340,7 +400,6 @@ ifdef LLAMA_HIPBLAS HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) HIPFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) HIPFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER) - HIPFLAGS += -DCC_TURING=1000000000 ifdef LLAMA_CUDA_FORCE_DMMV HIPFLAGS += -DGGML_CUDA_FORCE_DMMV endif # LLAMA_CUDA_FORCE_DMMV @@ -350,9 +409,12 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h endif # LLAMA_HIPBLAS ifdef LLAMA_METAL - MK_CPPFLAGS += -DGGML_USE_METAL #-DGGML_METAL_NDEBUG + MK_CPPFLAGS += -DGGML_USE_METAL MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit OBJS += ggml-metal.o +ifdef LLAMA_METAL_NDEBUG + MK_CPPFLAGS += -DGGML_METAL_NDEBUG +endif endif # LLAMA_METAL ifdef LLAMA_METAL @@ -371,9 +433,8 @@ k_quants.o: k_quants.c k_quants.h endif # LLAMA_NO_K_QUANTS # combine build flags with cmdline overrides -override CPPFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) -override CFLAGS := $(MK_CFLAGS) $(CFLAGS) -override CXXFLAGS := $(MK_CXXFLAGS) $(CXXFLAGS) +override CFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(CFLAGS) +override CXXFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS) override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS) # @@ -477,9 +538,8 @@ baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o common.o $(OBJS) beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))' -BUILD_TARGETS += metal -endif +speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) ifdef LLAMA_METAL metal: examples/metal/metal.cpp ggml.o $(OBJS) @@ -537,7 +597,7 @@ tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h gg tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-tokenizer-1: tests/test-tokenizer-1.cpp build-info.h ggml.o llama.o common.o $(OBJS) +tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-c.o: tests/test-c.c llama.h diff --git a/Package.swift b/Package.swift index 96f52c4f0..fb95ef7eb 100644 --- a/Package.swift +++ b/Package.swift @@ -2,8 +2,30 @@ import PackageDescription +#if arch(arm) || arch(arm64) +let platforms: [SupportedPlatform]? = [ + .macOS(.v11), + .iOS(.v14), + .watchOS(.v4), + .tvOS(.v14) +] +let exclude: [String] = [] +let additionalSources: [String] = ["ggml-metal.m"] +let additionalSettings: [CSetting] = [ + .unsafeFlags(["-fno-objc-arc"]), + .define("GGML_SWIFT"), + .define("GGML_USE_METAL") +] +#else +let platforms: [SupportedPlatform]? = nil +let exclude: [String] = ["ggml-metal.metal"] +let additionalSources: [String] = [] +let additionalSettings: [CSetting] = [] +#endif + let package = Package( name: "llama", + platforms: platforms, products: [ .library(name: "llama", targets: ["llama"]), ], @@ -11,23 +33,23 @@ let package = Package( .target( name: "llama", path: ".", - exclude: ["ggml-metal.metal"], + exclude: exclude, sources: [ "ggml.c", "llama.cpp", "ggml-alloc.c", - "k_quants.c" - ], + "k_quants.c", + ] + additionalSources, publicHeadersPath: "spm-headers", cSettings: [ .unsafeFlags(["-Wno-shorten-64-to-32"]), .define("GGML_USE_K_QUANTS"), .define("GGML_USE_ACCELERATE") - ], + ] + additionalSettings, linkerSettings: [ .linkedFramework("Accelerate") ] - ), + ) ], cxxLanguageStandard: .cxx11 ) diff --git a/README.md b/README.md index 0cfd94db4..b3845afd7 100644 --- a/README.md +++ b/README.md @@ -11,21 +11,9 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics -- #### IMPORTANT: Tokenizer fixes and API change (developers and projects using `llama.cpp` built-in tokenization must read): https://github.com/ggerganov/llama.cpp/pull/2810 +- Local Falcon 180B inference on Mac Studio -- GGUFv2 adds support for 64-bit sizes + backwards compatible: https://github.com/ggerganov/llama.cpp/pull/2821 - -- Added support for Falcon models: https://github.com/ggerganov/llama.cpp/pull/2717 - -- A new file format has been introduced: [GGUF](https://github.com/ggerganov/llama.cpp/pull/2398) - - Last revision compatible with the old format: [dadbed9](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa) - - ### Current `master` should be considered in Beta - expect some issues for a few days! - - ### Be prepared to re-convert and / or re-quantize your GGUF models while this notice is up! - - ### Issues with non-GGUF models will be considered with low priority! + https://github.com/ggerganov/llama.cpp/assets/1991296/98abd4e8-7077-464c-ae89-aebabca7757e ---- @@ -280,29 +268,11 @@ In order to build llama.cpp you have three different options. ### Metal Build -Using Metal allows the computation to be executed on the GPU for Apple devices: +On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU. +To disable the Metal build at compile time use the `LLAMA_NO_METAL=1` flag or the `LLAMA_METAL=OFF` cmake option. -- Using `make`: - - ```bash - LLAMA_METAL=1 make - ``` - -- Using `CMake`: - - ```bash - mkdir build-metal - cd build-metal - cmake -DLLAMA_METAL=ON .. - cmake --build . --config Release - ``` - -When built with Metal support, you can enable GPU inference with the `--gpu-layers|-ngl` command-line argument. -Any value larger than 0 will offload the computation to the GPU. For example: - -```bash -./main -m ./models/7B/ggml-model-q4_0.gguf -n 128 -ngl 1 -``` +When built with Metal support, you can explicitly disable GPU inference with the `--gpu-layers|-ngl 0` command-line +argument. ### MPI Build @@ -431,7 +401,7 @@ Building the program with BLAS support may lead to some performance improvements - #### hipBLAS - This provide BLAS acceleation on HIP supported GPU like AMD GPU. + This provides BLAS acceleration on HIP-supported AMD GPUs. Make sure to have ROCm installed. You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html). Windows support is coming soon... @@ -755,12 +725,12 @@ python3 convert.py pygmalion-7b/ --outtype q4_1 - Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data. - Alternatively, if you want to save time and space, you can download already converted and quantized models from [TheBloke](https://huggingface.co/TheBloke), including: - - [LLaMA 2 7B base](https://huggingface.co/TheBloke/Llama-2-7B-GGML) - - [LLaMA 2 13B base](https://huggingface.co/TheBloke/Llama-2-13B-GGML) - - [LLaMA 2 70B base](https://huggingface.co/TheBloke/Llama-2-70B-GGML) - - [LLaMA 2 7B chat](https://huggingface.co/TheBloke/Llama-2-7B-chat-GGML) - - [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML) - - [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGML) + - [LLaMA 2 7B base](https://huggingface.co/TheBloke/Llama-2-7B-GGUF) + - [LLaMA 2 13B base](https://huggingface.co/TheBloke/Llama-2-13B-GGUF) + - [LLaMA 2 70B base](https://huggingface.co/TheBloke/Llama-2-70B-GGUF) + - [LLaMA 2 7B chat](https://huggingface.co/TheBloke/Llama-2-7B-chat-GGUF) + - [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF) + - [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF) ### Verifying the model files @@ -874,8 +844,17 @@ Place your desired model into the `~/llama.cpp/models/` directory and execute th #### Images We have two Docker images available for this project: -1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. -2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. +1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`) +2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`) + +Additionally, there the following images, similar to the above: + +- `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) + +The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the Gitlab Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now). #### Usage diff --git a/common/common.cpp b/common/common.cpp index 313821375..9969cb97d 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -57,7 +57,7 @@ int32_t get_num_physical_cores() { siblings.insert(line); } } - if (siblings.size() > 0) { + if (!siblings.empty()) { return static_cast(siblings.size()); } #elif defined(__APPLE__) && defined(__MACH__) @@ -374,6 +374,17 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { #else fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); +#endif + } else if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") { + if (++i >= argc) { + invalid_param = true; + break; + } +#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD + params.n_gpu_layers_draft = std::stoi(argv[i]); +#else + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); #endif } else if (arg == "--main-gpu" || arg == "-mg") { if (++i >= argc) { @@ -423,8 +434,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { #endif // GGML_USE_CUBLAS } else if (arg == "--no-mmap") { params.use_mmap = false; - } else if (arg == "--mtest") { - params.mem_test = true; } else if (arg == "--numa") { params.numa = true; } else if (arg == "--export") { @@ -584,109 +593,110 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { } void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { - fprintf(stdout, "usage: %s [options]\n", argv[0]); - fprintf(stdout, "\n"); - fprintf(stdout, "options:\n"); - fprintf(stdout, " -h, --help show this help message and exit\n"); - fprintf(stdout, " -i, --interactive run in interactive mode\n"); - fprintf(stdout, " --interactive-first run in interactive mode and wait for input right away\n"); - fprintf(stdout, " -ins, --instruct run in instruction mode (use with Alpaca models)\n"); - fprintf(stdout, " --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n"); - fprintf(stdout, " -r PROMPT, --reverse-prompt PROMPT\n"); - fprintf(stdout, " halt generation at PROMPT, return control in interactive mode\n"); - fprintf(stdout, " (can be specified more than once for multiple prompts).\n"); - fprintf(stdout, " --color colorise output to distinguish prompt and user input from generations\n"); - fprintf(stdout, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n"); - fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - fprintf(stdout, " -p PROMPT, --prompt PROMPT\n"); - fprintf(stdout, " prompt to start generation with (default: empty)\n"); - fprintf(stdout, " -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); - fprintf(stdout, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); - fprintf(stdout, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); - fprintf(stdout, " not supported with --interactive or other interactive options\n"); - fprintf(stdout, " --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n"); - fprintf(stdout, " --random-prompt start with a randomized prompt.\n"); - fprintf(stdout, " --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n"); - fprintf(stdout, " --in-prefix STRING string to prefix user inputs with (default: empty)\n"); - fprintf(stdout, " --in-suffix STRING string to suffix after user inputs with (default: empty)\n"); - fprintf(stdout, " -f FNAME, --file FNAME\n"); - fprintf(stdout, " prompt file to start generation.\n"); - fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict); - fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); - fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); - fprintf(stdout, " --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k); - fprintf(stdout, " --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p); - fprintf(stdout, " --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z); - fprintf(stdout, " --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p); - fprintf(stdout, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n); - fprintf(stdout, " --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty); - fprintf(stdout, " --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty); - fprintf(stdout, " --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty); - fprintf(stdout, " --mirostat N use Mirostat sampling.\n"); - fprintf(stdout, " Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); - fprintf(stdout, " (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat); - fprintf(stdout, " --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta); - fprintf(stdout, " --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau); - fprintf(stdout, " -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n"); - fprintf(stdout, " modifies the likelihood of token appearing in the completion,\n"); - fprintf(stdout, " i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"); - fprintf(stdout, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n"); - fprintf(stdout, " --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n"); - fprintf(stdout, " --grammar-file FNAME file to read grammar from\n"); - fprintf(stdout, " --cfg-negative-prompt PROMPT\n"); - fprintf(stdout, " negative prompt to use for guidance. (default: empty)\n"); - fprintf(stdout, " --cfg-negative-prompt-file FNAME\n"); - fprintf(stdout, " negative prompt file to use for guidance. (default: empty)\n"); - fprintf(stdout, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale); - fprintf(stdout, " --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale); - fprintf(stdout, " --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base); - fprintf(stdout, " --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale); - fprintf(stdout, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); - fprintf(stdout, " --no-penalize-nl do not penalize newline token\n"); - fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); - fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n"); - fprintf(stdout, " --temp N temperature (default: %.1f)\n", (double)params.temp); - fprintf(stdout, " --perplexity compute perplexity over each ctx window of the prompt\n"); - fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); - fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); - fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); - fprintf(stdout, " --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft); - fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); + printf("usage: %s [options]\n", argv[0]); + printf("\n"); + printf("options:\n"); + printf(" -h, --help show this help message and exit\n"); + printf(" -i, --interactive run in interactive mode\n"); + printf(" --interactive-first run in interactive mode and wait for input right away\n"); + printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n"); + printf(" --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n"); + printf(" -r PROMPT, --reverse-prompt PROMPT\n"); + printf(" halt generation at PROMPT, return control in interactive mode\n"); + printf(" (can be specified more than once for multiple prompts).\n"); + printf(" --color colorise output to distinguish prompt and user input from generations\n"); + printf(" -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n"); + printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); + printf(" -p PROMPT, --prompt PROMPT\n"); + printf(" prompt to start generation with (default: empty)\n"); + printf(" -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); + printf(" --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); + printf(" --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); + printf(" not supported with --interactive or other interactive options\n"); + printf(" --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n"); + printf(" --random-prompt start with a randomized prompt.\n"); + printf(" --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n"); + printf(" --in-prefix STRING string to prefix user inputs with (default: empty)\n"); + printf(" --in-suffix STRING string to suffix after user inputs with (default: empty)\n"); + printf(" -f FNAME, --file FNAME\n"); + printf(" prompt file to start generation.\n"); + printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict); + printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); + printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k); + printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p); + printf(" --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z); + printf(" --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p); + printf(" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n); + printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty); + printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty); + printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty); + printf(" --mirostat N use Mirostat sampling.\n"); + printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); + printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat); + printf(" --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta); + printf(" --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau); + printf(" -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n"); + printf(" modifies the likelihood of token appearing in the completion,\n"); + printf(" i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"); + printf(" or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n"); + printf(" --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n"); + printf(" --grammar-file FNAME file to read grammar from\n"); + printf(" --cfg-negative-prompt PROMPT\n"); + printf(" negative prompt to use for guidance. (default: empty)\n"); + printf(" --cfg-negative-prompt-file FNAME\n"); + printf(" negative prompt file to use for guidance. (default: empty)\n"); + printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale); + printf(" --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale); + printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base); + printf(" --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale); + 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(" --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"); + printf(" --temp N temperature (default: %.1f)\n", (double)params.temp); + printf(" --perplexity compute perplexity over each ctx window of the prompt\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(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); + printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft); + printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); if (llama_mlock_supported()) { - fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); + printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } if (llama_mmap_supported()) { - fprintf(stdout, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); + printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } - fprintf(stdout, " --numa attempt optimizations that help on some NUMA systems\n"); - fprintf(stdout, " if run without this previously, it is recommended to drop the system page cache before using this\n"); - fprintf(stdout, " see https://github.com/ggerganov/llama.cpp/issues/1437\n"); + printf(" --numa attempt optimizations that help on some NUMA systems\n"); + printf(" if run without this previously, it is recommended to drop the system page cache before using this\n"); + printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n"); #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - fprintf(stdout, " -ngl N, --n-gpu-layers N\n"); - fprintf(stdout, " number of layers to store in VRAM\n"); - fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n"); - fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); - fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); - fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n"); + printf(" -ngl N, --n-gpu-layers N\n"); + printf(" number of layers to store in VRAM\n"); + printf(" -ngld N, --n-gpu-layers-draft N\n"); + printf(" number of layers to store in VRAM for the draft model\n"); + printf(" -ts SPLIT --tensor-split SPLIT\n"); + printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); + printf(" -lv, --low-vram don't allocate VRAM scratch buffer\n"); #ifdef GGML_USE_CUBLAS - fprintf(stdout, " -nommq, --no-mul-mat-q\n"); - fprintf(stdout, " use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n"); - fprintf(stdout, " Not recommended since this is both slower and uses more VRAM.\n"); + printf(" -nommq, --no-mul-mat-q\n"); + printf(" use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n"); + printf(" Not recommended since this is both slower and uses more VRAM.\n"); #endif // GGML_USE_CUBLAS #endif - fprintf(stdout, " --mtest compute maximum memory usage\n"); - fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n"); - fprintf(stdout, " --verbose-prompt print prompt before generation\n"); + printf(" --export export the computation graph to 'llama.ggml'\n"); + printf(" --verbose-prompt print prompt before generation\n"); fprintf(stderr, " --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n"); - fprintf(stdout, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); - fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - fprintf(stdout, " -m FNAME, --model FNAME\n"); - fprintf(stdout, " model path (default: %s)\n", params.model.c_str()); - fprintf(stdout, " -md FNAME, --model-draft FNAME\n"); - fprintf(stdout, " draft model for speculative decoding (default: %s)\n", params.model.c_str()); - fprintf(stdout, " -ld LOGDIR, --logdir LOGDIR\n"); - fprintf(stdout, " path under which to save YAML logs (no logging if unset)\n"); - fprintf(stdout, "\n"); + printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); + printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); + printf(" -m FNAME, --model FNAME\n"); + printf(" model path (default: %s)\n", params.model.c_str()); + printf(" -md FNAME, --model-draft FNAME\n"); + printf(" draft model for speculative decoding (default: %s)\n", params.model.c_str()); + printf(" -ld LOGDIR, --logdir LOGDIR\n"); + printf(" path under which to save YAML logs (no logging if unset)\n"); + printf("\n"); } std::string gpt_random_prompt(std::mt19937 & rng) { @@ -717,7 +727,9 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param lparams.n_ctx = params.n_ctx; lparams.n_batch = params.n_batch; - lparams.n_gpu_layers = params.n_gpu_layers; + if (params.n_gpu_layers != -1) { + lparams.n_gpu_layers = params.n_gpu_layers; + } lparams.main_gpu = params.main_gpu; lparams.tensor_split = params.tensor_split; lparams.low_vram = params.low_vram; @@ -770,8 +782,8 @@ std::tuple llama_init_from_gpt_par { LOG("warming up the model with an empty run\n"); - const std::vector tmp = { llama_token_bos(lctx), }; - llama_eval(lctx, tmp.data(), tmp.size(), 0, params.n_threads); + const std::vector tmp = { llama_token_bos(lctx), llama_token_eos(lctx), }; + llama_eval(lctx, tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, params.n_threads); llama_reset_timings(lctx); } @@ -1210,9 +1222,8 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str()); fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); - fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false"); fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); - fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers); + fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers); 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", params.n_probs); fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); diff --git a/common/common.h b/common/common.h index 33f12bf7f..f9dfd4a2c 100644 --- a/common/common.h +++ b/common/common.h @@ -21,6 +21,9 @@ #define DIRECTORY_SEPARATOR '/' #endif // _WIN32 +#define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0) +#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0) + #define print_build_info() do { \ fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); \ fprintf(stderr, "%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET); \ @@ -40,7 +43,8 @@ struct gpt_params { int32_t n_keep = 0; // number of tokens to keep from initial prompt int32_t n_draft = 16; // number of tokens to draft during speculative decoding int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) - int32_t n_gpu_layers = 0; // number of layers to store in VRAM + 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) int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. @@ -112,7 +116,6 @@ struct gpt_params { bool perplexity = false; // compute perplexity over the prompt bool use_mmap = true; // use mmap for faster loads bool use_mlock = false; // use mlock to keep model in memory - bool mem_test = false; // compute maximum memory usage bool numa = false; // attempt optimizations that help on some NUMA systems bool export_cgraph = false; // export the computation graph bool verbose_prompt = false; // print prompt tokens before generation diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp index e76bd11c3..177d1e3a8 100644 --- a/common/grammar-parser.cpp +++ b/common/grammar-parser.cpp @@ -415,6 +415,7 @@ namespace grammar_parser { std::vector parse_state::c_rules() { std::vector ret; + ret.reserve(rules.size()); for (const auto & rule : rules) { ret.push_back(rule.data()); } diff --git a/common/log.h b/common/log.h index 0b9b01052..18f3b9761 100644 --- a/common/log.h +++ b/common/log.h @@ -513,16 +513,16 @@ inline bool log_param_pair_parse(bool check_but_dont_parse, const std::string & inline void log_print_usage() { - fprintf(stdout, "log options:\n"); + printf("log options:\n"); /* format - fprintf(stdout, " -h, --help show this help message and exit\n");*/ + printf(" -h, --help show this help message and exit\n");*/ /* spacing - fprintf(stdout, "__-param----------------Description\n");*/ - fprintf(stdout, " --log-test Run simple logging test\n"); - fprintf(stdout, " --log-disable Disable trace logs\n"); - fprintf(stdout, " --log-enable Enable trace logs\n"); - fprintf(stdout, " --log-file Specify a log filename (without extension)\n"); - fprintf(stdout, " Log file will be tagged with unique ID and written as \"..log\"\n"); /* */ + printf("__-param----------------Description\n");*/ + printf(" --log-test Run simple logging test\n"); + printf(" --log-disable Disable trace logs\n"); + printf(" --log-enable Enable trace logs\n"); + printf(" --log-file Specify a log filename (without extension)\n"); + printf(" Log file will be tagged with unique ID and written as \"..log\"\n"); /* */ } #define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv) diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py new file mode 100755 index 000000000..8bd34dc44 --- /dev/null +++ b/convert-baichuan-hf-to-gguf.py @@ -0,0 +1,304 @@ +#!/usr/bin/env python3 +# HF baichuan --> gguf conversion + +from __future__ import annotations + +import argparse +import json +import os +import struct +import sys +from pathlib import Path +from typing import TYPE_CHECKING, Any +import itertools +import gguf +import numpy as np +import torch +from sentencepiece import SentencePieceProcessor # type: ignore[import] + + +if TYPE_CHECKING: + from typing import TypeAlias + +NDArray: TypeAlias = 'np.ndarray[Any, Any]' + +# reverse HF permute back to original pth layout + + +def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray: + if n_kv_head is not None and n_head != n_kv_head: + n_head //= n_kv_head + + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + +def reverse_hf_permute_part(weights: NDArray, n_part: int, n_head: int, n_head_kv: int| None = None) -> NDArray: + r = weights.shape[0] // 3 + return (reverse_hf_permute(weights[r * n_part : r * n_part + r, ...], n_head, n_head_kv)) + +def reverse_hf_part(weights: NDArray, n_part: int) -> NDArray: + r = weights.shape[0] // 3 + return weights[r * n_part : r * n_part + r, ...] + +def count_model_parts(dir_model: str) -> int: + num_parts = 0 + + for filename in os.listdir(dir_model): + if filename.startswith("pytorch_model-"): + num_parts += 1 + + if num_parts > 0: + print("gguf: found " + str(num_parts) + " model parts") + + return num_parts + + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert a HuggingFace LLaMA model to a GGML compatible file") + parser.add_argument( + "--vocab-only", action="store_true", + help="extract only the vocab", + ) + 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 (*.bin)", + ) + parser.add_argument( + "ftype", type=int, choices=[0, 1], default=1, nargs='?', + help="output format - use 0 for float32, 1 for float16", + ) + return parser.parse_args() + +args = parse_args() + +dir_model = args.model +ftype = args.ftype +if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file = sys.stderr) + sys.exit(1) + +# possible tensor data types +# ftype == 0 -> float32 +# ftype == 1 -> float16 + +# map from ftype to string +ftype_str = ["f32", "f16"] + +if args.outfile is not None: + fname_out = args.outfile +else: + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' + +print("gguf: loading model "+dir_model.name) + +with open(dir_model / "config.json", "r", encoding="utf-8") as f: + hparams = json.load(f) +print("hello print: ",hparams["architectures"][0]) +if hparams["architectures"][0] != "BaichuanForCausalLM": + print("Model architecture not supported: " + hparams["architectures"][0]) + + sys.exit() + +# get number of model parts +num_parts = count_model_parts(dir_model) +print(f"num_parts:{num_parts}\n") +ARCH=gguf.MODEL_ARCH.BAICHUAN +gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) + +print("gguf: get model metadata") + +block_count = hparams["num_hidden_layers"] +head_count = hparams["num_attention_heads"] + +if "num_key_value_heads" in hparams: + head_count_kv = hparams["num_key_value_heads"] +else: + head_count_kv = head_count + +if "_name_or_path" in hparams: + hf_repo = hparams["_name_or_path"] +else: + hf_repo = "" + +if "max_sequence_length" in hparams: + ctx_length = hparams["max_sequence_length"] +elif "max_position_embeddings" in hparams: + ctx_length = hparams["max_position_embeddings"] +elif "model_max_length" in hparams: + ctx_length = hparams["model_max_length"] +else: + print("gguf: can not find ctx length parameter.") + + sys.exit() + + +gguf_writer.add_name(dir_model.name) +gguf_writer.add_source_hf_repo(hf_repo) +gguf_writer.add_tensor_data_layout("Meta AI original pth") +gguf_writer.add_context_length(ctx_length) +gguf_writer.add_embedding_length(hparams["hidden_size"]) +gguf_writer.add_block_count(block_count) +gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) +gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) +gguf_writer.add_head_count(head_count) +gguf_writer.add_head_count_kv(head_count_kv) +gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) + +if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]: + if "type" in hparams["rope_scaling"]: + if hparams["rope_scaling"]["type"] == "linear": + gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"]) + + +# TOKENIZATION + +print("gguf: get tokenizer metadata") + +tokens: list[bytes] = [] +scores: list[float] = [] +toktypes: list[int] = [] + +tokenizer_model_file = dir_model / 'tokenizer.model' +if not tokenizer_model_file.is_file(): + print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr) + sys.exit(1) + +# vocab type sentencepiece +print("gguf: get sentencepiece tokenizer vocab, scores and token types") + +tokenizer = SentencePieceProcessor(str(tokenizer_model_file)) + +for i in range(tokenizer.vocab_size()): + text: bytes + score: float + + piece = tokenizer.id_to_piece(i) + text = piece.encode("utf-8") + score = tokenizer.get_score(i) + + toktype = 1 # defualt to normal token type + if tokenizer.is_unknown(i): + toktype = 2 + if tokenizer.is_control(i): + toktype = 3 + + # toktype = 4 is user-defined = tokens from added_tokens.json + + if tokenizer.is_unused(i): + toktype = 5 + if tokenizer.is_byte(i): + toktype = 6 + + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + +added_tokens_file = dir_model / 'added_tokens.json' +if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + addtokens_json = json.load(f) + + print("gguf: get added tokens") + + for key in addtokens_json: + tokens.append( key.encode("utf-8") ) + scores.append(-1000.0) + toktypes.append(4) # user-defined token type + + +gguf_writer.add_tokenizer_model("llama") +gguf_writer.add_token_list(tokens) +gguf_writer.add_token_scores(scores) +gguf_writer.add_token_types(toktypes) + +special_vocab = gguf.SpecialVocab(dir_model) +special_vocab.add_to_gguf(gguf_writer) + +# TENSORS + +tensor_map = gguf.get_tensor_name_map(ARCH,block_count) + +# tensor info +print("gguf: get tensor metadata") + +if num_parts == 0: + part_names = iter(("pytorch_model.bin",)) +else: + part_names = ( + f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) + ) + + +for part_name in part_names: + if args.vocab_only: + break + print("gguf: loading model part '" + part_name + "'") + model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") + + tmp=model_part + for i in range(block_count): + if f"model.layers.{i}.self_attn.W_pack.weight" in model_part: + print(f"Unpacking and permuting layer {i}") + tmp[f"model.layers.{i}.self_attn.q_proj.weight"]=reverse_hf_permute_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],0,head_count,head_count) + tmp[f"model.layers.{i}.self_attn.k_proj.weight"]=reverse_hf_permute_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],1,head_count,head_count_kv) + tmp[f"model.layers.{i}.self_attn.v_proj.weight"]=reverse_hf_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],2) + del tmp[f"model.layers.{i}.self_attn.W_pack.weight"] + + for name in model_part.keys(): + data = model_part[name] + # we don't need these + if name.endswith(".rotary_emb.inv_freq"): + continue + + old_dtype = data.dtype + + # convert any unsupported data types to float32 + if data.dtype != torch.float16 and data.dtype != torch.float32: + data = data.to(torch.float32) + + data = data.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) + if new_name is None: + print("Can not map tensor '" + name + "'") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(name + " -> " + new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) + gguf_writer.add_tensor(new_name, data) + + +print("gguf: write header") +gguf_writer.write_header_to_file() +print("gguf: write metadata") +gguf_writer.write_kv_data_to_file() +if not args.vocab_only: + print("gguf: write tensors") + gguf_writer.write_tensors_to_file() + +gguf_writer.close() + +print(f"gguf: model successfully exported to '{fname_out}'") +print("") diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index 271e58972..88338d823 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-hf-to-gguf.py @@ -55,10 +55,22 @@ def count_model_parts(dir_model: Path) -> int: def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Convert a Falcon model to a GGML compatible file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - 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 (*.bin)") - parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) + parser.add_argument( + "--vocab-only", action="store_true", + help="extract only the vocab", + ) + 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 (*.bin)", + ) + parser.add_argument( + "ftype", type=int, choices=[0, 1], default=1, nargs='?', + help="output format - use 0 for float32, 1 for float16", + ) return parser.parse_args() args = parse_args() @@ -137,7 +149,9 @@ with open(tokenizer_json_file, "r", encoding="utf-8") as f: print("gguf: get gpt2 tokenizer vocab") -vocab_size = len(tokenizer_json["model"]["vocab"]) +# The number of tokens in tokenizer.json can differ from the expected vocab size. +# This causes downstream issues with mismatched tensor sizes when running the inference +vocab_size = hparams["vocab_size"] if "vocab_size" in hparams else len(tokenizer_json["model"]["vocab"]) # ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py tokenizer = AutoTokenizer.from_pretrained(dir_model) diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py index b9c8b4607..782410e44 100755 --- a/convert-gptneox-hf-to-gguf.py +++ b/convert-gptneox-hf-to-gguf.py @@ -56,10 +56,22 @@ def count_model_parts(dir_model: Path) -> int: def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Convert a GPT-NeoX model to a GGML compatible file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - 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 (*.bin)") - parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) + parser.add_argument( + "--vocab-only", action="store_true", + help="extract only the vocab", + ) + 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 (*.bin)", + ) + parser.add_argument( + "ftype", type=int, choices=[0, 1], default=1, nargs='?', + help="output format - use 0 for float32, 1 for float16", + ) return parser.parse_args() args = parse_args() diff --git a/convert-llama-ggmlv3-to-gguf.py b/convert-llama-ggml-to-gguf.py similarity index 68% rename from convert-llama-ggmlv3-to-gguf.py rename to convert-llama-ggml-to-gguf.py index 08ba0c490..b5d3e0b3c 100755 --- a/convert-llama-ggmlv3-to-gguf.py +++ b/convert-llama-ggml-to-gguf.py @@ -5,6 +5,7 @@ import argparse import math import struct import sys +from enum import IntEnum from pathlib import Path import numpy as np @@ -34,10 +35,35 @@ GGML_QUANT_SIZES = { gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8), } +class GGMLFormat(IntEnum): + GGML = 0 + GGMF = 1 + GGJT = 2 + +class GGMLFType(IntEnum): + ALL_F32 = 0 + MOSTLY_F16 = 1 + MOSTLY_Q4_0 = 2 + MOSTLY_Q4_1 = 3 + MOSTLY_Q4_1_SOME_F16 = 4 + MOSTLY_Q8_0 = 7 + MOSTLY_Q5_0 = 8 + MOSTLY_Q5_1 = 9 + MOSTLY_Q2_K = 10 + MOSTLY_Q3_K_S = 11 + MOSTLY_Q3_K_M = 12 + MOSTLY_Q3_K_L = 13 + MOSTLY_Q4_K_S = 14 + MOSTLY_Q4_K_M = 15 + MOSTLY_Q5_K_S = 16 + MOSTLY_Q5_K_M = 17 + MOSTLY_Q6_K = 18 + class Hyperparameters: def __init__(self): - self.n_vocab = self.n_embd = self.n_mult = self.n_head = self.n_layer = self.n_rot = self.ftype = 0 - self.n_ff = 0 + self.n_vocab = self.n_embd = self.n_mult = self.n_head = 0 + self.n_layer = self.n_rot = self.n_ff = 0 + self.ftype = GGMLFType.ALL_F32 def set_n_ff(self, model): ff_tensor_idx = model.tensor_map.get(b'layers.0.feed_forward.w1.weight') @@ -53,16 +79,21 @@ class Hyperparameters: self.n_head, self.n_layer, self.n_rot, - self.ftype, + ftype, ) = struct.unpack('<7I', data[offset:offset + (4 * 7)]) + try: + self.ftype = GGMLFType(ftype) + except ValueError: + raise ValueError(f'Invalid ftype {ftype}') return 4 * 7 def __str__(self): - return f'' + return f'' class Vocab: - def __init__(self): + def __init__(self, load_scores = True): self.items = [] + self.load_scores = load_scores def load(self, data, offset, n_vocab): orig_offset = offset @@ -70,20 +101,24 @@ class Vocab: itemlen = struct.unpack(' 3: + raise ValueError(f'Cannot handle unexpected GGJT file version {version}') + self.file_format = GGMLFormat.GGJT + self.format_version = version + return 8 + raise ValueError(f"Unexpected file magic {magic!r}! This doesn't look like a GGML format file.") + + def validate_conversion(self, ftype): + err = '' + if (self.file_format < GGMLFormat.GGJT or self.format_version < 2): + if ftype not in (GGMLFType.ALL_F32, GGMLFType.MOSTLY_F16): + err = 'Quantizations changed in GGJTv2. Can only convert unquantized GGML files older than GGJTv2.' + elif (self.file_format == GGMLFormat.GGJT and self.format_version == 2): + if ftype in ( GGMLFType.MOSTLY_Q4_0, GGMLFType.MOSTLY_Q4_1, + GGMLFType.MOSTLY_Q4_1_SOME_F16, GGMLFType.MOSTLY_Q8_0): + err = 'Q4 and Q8 quantizations changed in GGJTv3.' + if len(err) > 0: + raise ValueError(f'{err} Sorry, your {self.file_format.name}v{self.format_version} file of type {ftype.name} is not eligible for conversion.') def load(self, data, offset): offset += self.validate_header(data, offset) hp = Hyperparameters() offset += hp.load(data, offset) - vocab = Vocab() + print(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}') + self.validate_conversion(hp.ftype) + vocab = Vocab(load_scores = self.file_format > GGMLFormat.GGML) offset += vocab.load(data, offset, hp.n_vocab) tensors: list[Tensor] = [] tensor_map = {} while offset < len(data): - tensor = Tensor() + tensor = Tensor(use_padding = self.file_format > GGMLFormat.GGMF) offset += tensor.load(data, offset) tensor_map[tensor.name] = len(tensors) tensors.append(tensor) @@ -168,7 +235,10 @@ class GGMLToGGUF: def save(self): print('* Preparing to save GGUF file') - gguf_writer = gguf.GGUFWriter(self.cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False) + gguf_writer = gguf.GGUFWriter( + self.cfg.output, + gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], + use_temp_file = False ) self.add_params(gguf_writer) self.add_vocab(gguf_writer) if self.special_vocab is not None: @@ -185,7 +255,10 @@ class GGMLToGGUF: def add_params(self, gguf_writer): hp = self.model.hyperparameters cfg = self.cfg - desc = cfg.desc if cfg.desc is not None else 'converted from legacy GGJTv3 format' + if cfg.desc is not None: + desc = cfg.desc + else: + desc = f'converted from legacy {self.model.file_format.name}v{self.model.format_version} {hp.ftype.name} format' try: # Filenames aren't necessarily valid UTF8. name = cfg.name if cfg.name is not None else cfg.input.name @@ -195,6 +268,7 @@ class GGMLToGGUF: if name is not None: gguf_writer.add_name(name) gguf_writer.add_description(desc) + gguf_writer.add_file_type(int(hp.ftype)) if self.params_override is not None: po = self.params_override assert po.n_embd == hp.n_embd, 'Model hyperparams mismatch' @@ -231,7 +305,8 @@ class GGMLToGGUF: tokens.append(vbytes) scores.append(score) toktypes.append(ttype) - assert len(tokens) == hp.n_vocab, f'Override vocab has a different number of items than hyperparameters - override = {len(tokens)} but n_vocab={hp.n_vocab}' + assert len(tokens) == hp.n_vocab, \ + f'Override vocab has a different number of items than hyperparameters - override = {len(tokens)} but n_vocab={hp.n_vocab}' gguf_writer.add_token_list(tokens) gguf_writer.add_token_scores(scores) if len(toktypes) > 0: @@ -283,7 +358,11 @@ class GGMLToGGUF: tempdims[1] = tempdims[0] tempdims[0] = temp # print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}') - gguf_writer.add_tensor(mapped_name, data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], raw_shape = tempdims, raw_dtype = tensor.dtype) + gguf_writer.add_tensor( + mapped_name, + data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], + raw_shape = tempdims, + raw_dtype = tensor.dtype ) def handle_metadata(cfg, hp): import convert @@ -305,32 +384,46 @@ def handle_metadata(cfg, hp): params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path) else: raise ValueError('Unable to load metadata') - vocab = convert.load_vocab(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, cfg.vocabtype) + vocab = convert.load_vocab( + cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, + cfg.vocabtype ) # FIXME: Respect cfg.vocab_dir? svocab = gguf.SpecialVocab(cfg.model_metadata_dir) convert.check_vocab_size(params, vocab) return (params, vocab, svocab) def handle_args(): - parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF') - parser.add_argument('--input', '-i', type = Path, required = True, help = 'Input GGMLv3 filename') - parser.add_argument('--output', '-o', type = Path, required = True, help ='Output GGUF filename') - parser.add_argument('--name', help = 'Set model name') - parser.add_argument('--desc', help = 'Set model description') - parser.add_argument('--gqa', type = int, default = 1, help = 'grouped-query attention factor (use 8 for LLaMA2 70B)') - parser.add_argument('--eps', default = '5.0e-06', help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2') - parser.add_argument('--context-length', '-c', type=int, default = 2048, help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096') - parser.add_argument('--model-metadata-dir', '-m', type = Path, 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"], help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)", default="spm") + parser = argparse.ArgumentParser(description = 'Convert GGML models to GGUF') + parser.add_argument('--input', '-i', type = Path, required = True, + help = 'Input GGMLv3 filename') + parser.add_argument('--output', '-o', type = Path, required = True, + help ='Output GGUF filename') + parser.add_argument('--name', + help = 'Set model name') + parser.add_argument('--desc', + help = 'Set model description') + parser.add_argument('--gqa', type = int, default = 1, + help = 'grouped-query attention factor (use 8 for LLaMA2 70B)') + parser.add_argument('--eps', default = '5.0e-06', + help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2') + parser.add_argument('--context-length', '-c', type=int, default = 2048, + help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096') + parser.add_argument('--model-metadata-dir', '-m', type = Path, + 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)") return parser.parse_args() def main(): cfg = handle_args() print(f'* Using config: {cfg}') print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n') + if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'): + print('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".') data = np.memmap(cfg.input, mode = 'r') - model = GGMLV3Model() + model = GGMLModel() print('* Scanning GGML input file') offset = model.load(data, 0) print(f'* GGML model hyperparameters: {model.hyperparameters}') @@ -345,7 +438,12 @@ def main(): print(f'* Special vocab: {special_vocab}') else: print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n') - converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override, special_vocab = special_vocab) + if model.file_format == GGMLFormat.GGML: + print('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!') + converter = GGMLToGGUF(model, data, cfg, + params_override = params_override, + vocab_override = vocab_override, + special_vocab = special_vocab ) converter.save() print(f'* Successful completion. Output saved to: {cfg.output}') diff --git a/convert-starcoder-hf-to-gguf.py b/convert-starcoder-hf-to-gguf.py new file mode 100755 index 000000000..331e84e98 --- /dev/null +++ b/convert-starcoder-hf-to-gguf.py @@ -0,0 +1,248 @@ +#!/usr/bin/env python3 +# HF starcoder --> gguf conversion + +from __future__ import annotations + +import argparse +import json +import os +import struct +import sys +from pathlib import Path +from typing import Any + +import numpy as np +import torch +from transformers import AutoTokenizer # type: ignore[import] + +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) +import gguf + + +def bytes_to_unicode(): + # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a significant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + return dict(zip(bs, (chr(n) for n in cs))) + + +def count_model_parts(dir_model: Path) -> int: + num_parts = 0 + for filename in os.listdir(dir_model): + if filename.startswith("pytorch_model-"): + num_parts += 1 + + if num_parts > 0: + print("gguf: found " + str(num_parts) + " model parts") + return num_parts + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert a StarCoder model to a GGML compatible file") + parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + 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 (*.bin)") + parser.add_argument("ftype", type=int, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1) + return parser.parse_args() + +args = parse_args() + +dir_model = args.model +ftype = args.ftype +if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file = sys.stderr) + sys.exit(1) + +# possible tensor data types +# ftype == 0 -> float32 +# ftype == 1 -> float16 + +# map from ftype to string +ftype_str = ["f32", "f16"] + +if args.outfile is not None: + fname_out = args.outfile +else: + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' + +print("gguf: loading model "+dir_model.name) + +with open(dir_model / "config.json", "r", encoding="utf-8") as f: + hparams = json.load(f) + +if hparams["architectures"][0] != "GPTBigCodeForCausalLM": + print("Model architecture not supported: " + hparams["architectures"][0]) + + sys.exit(1) + +# get number of model parts +num_parts = count_model_parts(dir_model) + +ARCH=gguf.MODEL_ARCH.STARCODER +gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) + +print("gguf: get model metadata") + +block_count = hparams["n_layer"] + +gguf_writer.add_name("StarCoder") +gguf_writer.add_context_length(hparams["n_positions"]) +gguf_writer.add_embedding_length(hparams["n_embd"]) +gguf_writer.add_feed_forward_length(4 * hparams["n_embd"]) +gguf_writer.add_block_count(block_count) +gguf_writer.add_head_count(hparams["n_head"]) +gguf_writer.add_head_count_kv(1) +gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"]) +gguf_writer.add_file_type(ftype) + +# TOKENIZATION + +print("gguf: get tokenizer metadata") + +tokens: list[bytearray] = [] +scores: list[float] = [] +toktypes: list[int] = [] + +tokenizer_json_file = dir_model / 'tokenizer.json' +if not tokenizer_json_file.is_file(): + print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr) + sys.exit(1) + +# gpt2 tokenizer +gguf_writer.add_tokenizer_model("gpt2") + +with open(tokenizer_json_file, "r", encoding="utf-8") as f: + tokenizer_json = json.load(f) + +print("gguf: get gpt2 tokenizer vocab") + +# The number of tokens in tokenizer.json can differ from the expected vocab size. +# This causes downstream issues with mismatched tensor sizes when running the inference +vocab_size = hparams["vocab_size"] if "vocab_size" in hparams else len(tokenizer_json["model"]["vocab"]) + +# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py +tokenizer = AutoTokenizer.from_pretrained(dir_model) + +reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} +byte_encoder = bytes_to_unicode() +byte_decoder = {v: k for k, v in byte_encoder.items()} + +for i in range(vocab_size): + if i in reverse_vocab: + try: + text = bytearray([byte_decoder[c] for c in reverse_vocab[i]]) + except KeyError: + text = bytearray() + for c in reverse_vocab[i]: + if ord(c) < 256: # single byte character + text.append(byte_decoder[ord(c)]) + else: # multibyte special token character + text.extend(c.encode('utf-8')) + else: + print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.") + pad_token = f"[PAD{i}]".encode("utf8") + text = bytearray(pad_token) + + tokens.append(text) + scores.append(0.0) # dymmy + toktypes.append(gguf.TokenType.NORMAL) # dummy + +gguf_writer.add_token_list(tokens) +gguf_writer.add_token_scores(scores) +gguf_writer.add_token_types(toktypes) + +special_vocab = gguf.SpecialVocab(dir_model, load_merges = True) +special_vocab.add_to_gguf(gguf_writer) + +# TENSORS + +tensor_map = gguf.get_tensor_name_map(ARCH,block_count) + +# params for qkv transform +n_head = hparams["n_head"] +n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1 + +head_dim = hparams["n_embd"] // n_head + +# tensor info +print("gguf: get tensor metadata") + +if num_parts == 0: + part_names = iter(("pytorch_model.bin",)) +else: + part_names = ( + f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) + ) + +for part_name in part_names: + if args.vocab_only: + break + print("gguf: loading model part '" + part_name + "'") + model_part = torch.load(dir_model / part_name, map_location="cpu") + + for name in model_part.keys(): + data = model_part[name] + + old_dtype = data.dtype + + # convert any unsupported data types to float32 + if data.dtype != torch.float16 and data.dtype != torch.float32: + data = data.to(torch.float32) + + data = data.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) + if new_name is None: + print("Can not map tensor '" + name + "'") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 + if ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype)) + + gguf_writer.add_tensor(new_name, data) + + +print("gguf: write header") +gguf_writer.write_header_to_file() +print("gguf: write metadata") +gguf_writer.write_kv_data_to_file() +if not args.vocab_only: + print("gguf: write tensors") + gguf_writer.write_tensors_to_file() + +gguf_writer.close() + +print(f"gguf: model successfully exported to '{fname_out}'") +print("") diff --git a/convert.py b/convert.py index 5a7483b43..4ac5030db 100755 --- a/convert.py +++ b/convert.py @@ -145,7 +145,6 @@ GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = { class Params: n_vocab: int n_embd: int - n_mult: int n_layer: int n_ctx: int n_ff: int @@ -161,15 +160,6 @@ class Params: # path to the directory containing the model files path_model: Path | None = None - @staticmethod - def find_n_mult(n_ff: int, n_embd: int) -> int: - # hardcoded magic range - for n_mult in range(8192, 1, -1): - calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult - if calc_ff == n_ff: - return n_mult - raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).") - @staticmethod def guessed(model: LazyModel) -> Params: # try transformer naming first @@ -197,7 +187,6 @@ class Params: return Params( n_vocab = n_vocab, n_embd = n_embd, - n_mult = n_mult, n_layer = n_layer, n_ctx = -1, n_ff = n_ff, @@ -225,8 +214,6 @@ class Params: else: f_rope_scale = None - n_mult = Params.find_n_mult(n_ff, n_embd) - if "max_sequence_length" in config: n_ctx = config["max_sequence_length"] elif "max_position_embeddings" in config: @@ -238,7 +225,6 @@ class Params: return Params( n_vocab = n_vocab, n_embd = n_embd, - n_mult = n_mult, n_layer = n_layer, n_ctx = n_ctx, n_ff = n_ff, @@ -250,7 +236,7 @@ class Params: ) # LLaMA v2 70B params.json - # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1 + # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1} @staticmethod def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params: config = json.load(open(config_path)) @@ -258,7 +244,6 @@ class Params: n_vocab = config["vocab_size"] if "vocab_size" in config else -1 n_embd = config["dim"] n_layer = config["n_layers"] - n_mult = config["multiple_of"] n_ff = -1 n_head = config["n_heads"] n_head_kv = config["n_kv_heads"] if "n_kv_heads" in config else n_head @@ -266,7 +251,7 @@ class Params: f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None # hack to determine LLaMA v1 vs v2 vs CodeLlama - if f_rope_freq_base and f_rope_freq_base == 1000000: + if f_rope_freq_base == 1000000: # CodeLlama n_ctx = 16384 elif config["norm_eps"] == 1e-05: @@ -285,7 +270,6 @@ class Params: return Params( n_vocab = n_vocab, n_embd = n_embd, - n_mult = n_mult, n_layer = n_layer, n_ctx = n_ctx, n_ff = n_ff, @@ -673,7 +657,7 @@ class LazyUnpickler(pickle.Unpickler): assert isinstance(pid[1], LazyStorageKind) data_type = pid[1].data_type filename_stem = pid[2] - filename = self.data_base_path + '/' + filename_stem + filename = f'{self.data_base_path}/{filename_stem}' info = self.zip_file.getinfo(filename) def load(offset: int, elm_count: int) -> NDArray: @@ -689,7 +673,6 @@ class LazyUnpickler(pickle.Unpickler): @staticmethod def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any, - # pyright: ignore[reportSelfClsParameterName] requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor: assert isinstance(storage, LazyStorage) @@ -842,9 +825,9 @@ class OutputFile: name = "LLaMA" # TODO: better logic to determine model name - if (params.n_ctx == 4096): + if params.n_ctx == 4096: name = "LLaMA v2" - elif params.path_model: + elif params.path_model is not None: name = str(params.path_model.parent).split('/')[-1] self.gguf.add_name (name) @@ -857,13 +840,13 @@ class OutputFile: self.gguf.add_head_count_kv (params.n_head_kv) self.gguf.add_layer_norm_rms_eps (params.f_norm_eps) - if params.f_rope_freq_base: + if params.f_rope_freq_base is not None: self.gguf.add_rope_freq_base(params.f_rope_freq_base) - if params.f_rope_scale: + if params.f_rope_scale is not None: self.gguf.add_rope_scale_linear(params.f_rope_scale) - if params.ftype: + if params.ftype is not None: self.gguf.add_file_type(params.ftype) def add_meta_vocab(self, vocab: Vocab) -> None: diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp index 76448d4d4..76c4a3e1e 100644 --- a/examples/beam-search/beam-search.cpp +++ b/examples/beam-search/beam-search.cpp @@ -1,7 +1,3 @@ -#ifndef _GNU_SOURCE -#define _GNU_SOURCE -#endif - #include "common.h" #include "llama.h" diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 9e856c21a..293b455d0 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -1,5 +1,6 @@ #include "ggml.h" #include "llama.h" +#include "common.h" #include #include @@ -499,10 +500,10 @@ struct llama_file { errno = 0; std::size_t ret = std::fread(ptr, size, 1, fp); if (ferror(fp)) { - throw std::runtime_error(format("read error: %s", strerror(errno))); + die_fmt("fread failed: %s", strerror(errno)); } if (ret != 1) { - throw std::runtime_error(std::string("unexpectedly reached end of file")); + die("unexpectedly reached end of file"); } } @@ -597,8 +598,7 @@ void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename); llama_file file(filename, "rb"); if (!file.fp) { - fprintf(stderr, "error: %s: %s\n", strerror(errno), filename); - exit(1); + die_fmt("%s: %s", strerror(errno), filename); } const int n_vocab = config->vocab_size; /* uint32_t max_token_length = */ file.read_u32(); // unused diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp index 1b520fab6..fc6e44eb2 100644 --- a/examples/embd-input/embd-input-lib.cpp +++ b/examples/embd-input/embd-input-lib.cpp @@ -1,8 +1,3 @@ -// Defines sigaction on msys: -#ifndef _GNU_SOURCE -#define _GNU_SOURCE -#endif - #include "common.h" #include "embd-input.h" @@ -24,7 +19,7 @@ extern "C" { struct MyModel* create_mymodel(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return nullptr; } diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 990e3a579..0788f362c 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -10,17 +10,12 @@ int main(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return 1; } params.embedding = true; - if (params.n_ctx > 2048) { - fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);" - "expect poor results\n", __func__, params.n_ctx); - } - print_build_info(); if (params.seed == LLAMA_DEFAULT_SEED) { @@ -46,6 +41,12 @@ int main(int argc, char ** argv) { return 1; } + const int n_ctx_train = llama_n_ctx_train(ctx); + if (params.n_ctx > n_ctx_train) { + fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n", + __func__, n_ctx_train, params.n_ctx); + } + // print system information { fprintf(stderr, "\n"); diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp index cda517bde..a34010f10 100644 --- a/examples/gguf/gguf.cpp +++ b/examples/gguf/gguf.cpp @@ -76,7 +76,7 @@ bool gguf_ex_write(const std::string & fname) { gguf_write_to_file(ctx, fname.c_str(), false); - fprintf(stdout, "%s: wrote file '%s;\n", __func__, fname.c_str()); + printf("%s: wrote file '%s;\n", __func__, fname.c_str()); ggml_free(ctx_data); gguf_free(ctx); @@ -93,20 +93,20 @@ bool gguf_ex_read_0(const std::string & fname) { struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params); - fprintf(stdout, "%s: version: %d\n", __func__, gguf_get_version(ctx)); - fprintf(stdout, "%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); - fprintf(stdout, "%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx)); + printf("%s: version: %d\n", __func__, gguf_get_version(ctx)); + printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); + printf("%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx)); // kv { const int n_kv = gguf_get_n_kv(ctx); - fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv); + printf("%s: n_kv: %d\n", __func__, n_kv); for (int i = 0; i < n_kv; ++i) { const char * key = gguf_get_key(ctx, i); - fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key); + printf("%s: kv[%d]: key = %s\n", __func__, i, key); } } @@ -116,10 +116,10 @@ bool gguf_ex_read_0(const std::string & fname) { const int keyidx = gguf_find_key(ctx, findkey); if (keyidx == -1) { - fprintf(stdout, "%s: find key: %s not found.\n", __func__, findkey); + printf("%s: find key: %s not found.\n", __func__, findkey); } else { const char * key_value = gguf_get_val_str(ctx, keyidx); - fprintf(stdout, "%s: find key: %s found, kv[%d] value = %s\n", __func__, findkey, keyidx, key_value); + printf("%s: find key: %s found, kv[%d] value = %s\n", __func__, findkey, keyidx, key_value); } } @@ -127,13 +127,13 @@ bool gguf_ex_read_0(const std::string & fname) { { const int n_tensors = gguf_get_n_tensors(ctx); - fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors); + printf("%s: n_tensors: %d\n", __func__, n_tensors); for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name (ctx, i); const size_t offset = gguf_get_tensor_offset(ctx, i); - fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); + printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); } } @@ -153,20 +153,20 @@ bool gguf_ex_read_1(const std::string & fname) { struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params); - fprintf(stdout, "%s: version: %d\n", __func__, gguf_get_version(ctx)); - fprintf(stdout, "%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); - fprintf(stdout, "%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx)); + printf("%s: version: %d\n", __func__, gguf_get_version(ctx)); + printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); + printf("%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx)); // kv { const int n_kv = gguf_get_n_kv(ctx); - fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv); + printf("%s: n_kv: %d\n", __func__, n_kv); for (int i = 0; i < n_kv; ++i) { const char * key = gguf_get_key(ctx, i); - fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key); + printf("%s: kv[%d]: key = %s\n", __func__, i, key); } } @@ -174,13 +174,13 @@ bool gguf_ex_read_1(const std::string & fname) { { const int n_tensors = gguf_get_n_tensors(ctx); - fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors); + printf("%s: n_tensors: %d\n", __func__, n_tensors); for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name (ctx, i); const size_t offset = gguf_get_tensor_offset(ctx, i); - fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); + printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); } } @@ -189,13 +189,13 @@ bool gguf_ex_read_1(const std::string & fname) { const int n_tensors = gguf_get_n_tensors(ctx); for (int i = 0; i < n_tensors; ++i) { - fprintf(stdout, "%s: reading tensor %d data\n", __func__, i); + printf("%s: reading tensor %d data\n", __func__, i); const char * name = gguf_get_tensor_name(ctx, i); struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name); - fprintf(stdout, "%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, cur->n_dims, cur->name, cur->data); + printf("%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, cur->n_dims, cur->name, cur->data); // print first 10 elements const float * data = (const float *) cur->data; @@ -219,7 +219,7 @@ bool gguf_ex_read_1(const std::string & fname) { } } - fprintf(stdout, "%s: ctx_data size: %zu\n", __func__, ggml_get_mem_size(ctx_data)); + printf("%s: ctx_data size: %zu\n", __func__, ggml_get_mem_size(ctx_data)); ggml_free(ctx_data); gguf_free(ctx); @@ -229,7 +229,7 @@ bool gguf_ex_read_1(const std::string & fname) { int main(int argc, char ** argv) { if (argc < 3) { - fprintf(stdout, "usage: %s data.gguf r|w\n", argv[0]); + printf("usage: %s data.gguf r|w\n", argv[0]); return -1; } diff --git a/examples/gptneox-wip/falcon-main.cpp b/examples/gptneox-wip/falcon-main.cpp index 43b6a29f3..7f9a1620b 100644 --- a/examples/gptneox-wip/falcon-main.cpp +++ b/examples/gptneox-wip/falcon-main.cpp @@ -305,9 +305,9 @@ struct ggml_tensor * get_tensor_ex( struct ggml_context * ctx, std::string name) struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str()); if( cur == NULL ) { - fprintf(stdout, "%s: tensor '%s' not found!\n", __func__, name.c_str()); + printf("%s: tensor '%s' not found!\n", __func__, name.c_str()); } else { -// fprintf(stdout, "%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name); +// printf("%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name); } return cur; @@ -333,21 +333,21 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ return false; } - fprintf(stdout, "%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx)); - fprintf(stdout, "%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx)); - fprintf(stdout, "%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx)); + printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx)); + printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx)); + printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx)); // print all kv #if 0 { const int n_kv = gguf_get_n_kv(ggufctx); - fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv); + printf("%s: n_kv: %d\n", __func__, n_kv); for (int i = 0; i < n_kv; ++i) { const char * key = gguf_get_key(ggufctx, i); - fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key); + printf("%s: kv[%d]: key = %s\n", __func__, i, key); } } #endif @@ -357,21 +357,21 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ int keyidx; keyidx = gguf_find_key(ggufctx, "general.name"); - if (keyidx != -1) { fprintf(stdout, "%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.description"); - if (keyidx != -1) { fprintf(stdout, "%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.author"); - if (keyidx != -1) { fprintf(stdout, "%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.license"); - if (keyidx != -1) { fprintf(stdout, "%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.architecture"); - if (keyidx != -1) { fprintf(stdout, "%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.file_type"); - if (keyidx != -1) { fprintf(stdout, "%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout"); - if (keyidx != -1) { fprintf(stdout, "%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.source.hugginface.repository"); - if (keyidx != -1) { fprintf(stdout, "%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } } // check required metadata @@ -382,11 +382,11 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ keyidx = gguf_find_key(ggufctx, "general.architecture"); if (keyidx != -1) { if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "falcon") != 0) { - fprintf(stdout, "%s: model architecture not supported!\n", __func__); + printf("%s: model architecture not supported!\n", __func__); return false; } } else { - fprintf(stdout, "%s: gguf model architecture not found!\n", __func__); + printf("%s: gguf model architecture not found!\n", __func__); return false; } @@ -394,11 +394,11 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ keyidx = gguf_find_key(ggufctx, "falcon.tensor_data_layout"); if (keyidx != -1) { if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "jploski") != 0) { - fprintf(stdout, "%s: model tensor data layout not supported!\n", __func__); + printf("%s: model tensor data layout not supported!\n", __func__); return false; } } else { - fprintf(stdout, "%s: gguf model tensor data layout not found!\n", __func__); + printf("%s: gguf model tensor data layout not found!\n", __func__); return false; } @@ -455,11 +455,11 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ if (keyidx != -1) { if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gpt2") != 0) { - fprintf(stdout, "%s: tokenizer model not supported!\n", __func__); + printf("%s: tokenizer model not supported!\n", __func__); return false; } } else { - fprintf(stdout, "%s: tokenizer model not found!\n", __func__); + printf("%s: tokenizer model not found!\n", __func__); return false; } @@ -467,22 +467,22 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens"); if (tokens_keyidx == -1) { - fprintf(stdout, "%s: gpt2 tokenizer vocab not found!\n", __func__); + printf("%s: gpt2 tokenizer vocab not found!\n", __func__); return false; } int merges_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.merges"); if (merges_keyidx == -1) { - fprintf(stdout, "%s: gpt2 tokenizer merges not found!\n", __func__); + printf("%s: gpt2 tokenizer merges not found!\n", __func__); return false; } hparams.n_vocab = gguf_get_arr_n(ggufctx,tokens_keyidx); hparams.n_merges = gguf_get_arr_n(ggufctx,merges_keyidx); - fprintf(stdout, "%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab); - fprintf(stdout, "%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges); + printf("%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab); + printf("%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges); for (size_t i = 0; i < hparams.n_vocab; i++) { std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i); @@ -523,12 +523,12 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.separator_token_id"); if( keyidx != -1 ) { vocab.special_sep_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.padding_token_id"); if( keyidx != -1 ) { vocab.special_pad_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - if( vocab.special_bos_id != -1 ) { fprintf(stdout, "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); } - if( vocab.special_eos_id != -1 ) { fprintf(stdout, "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); } - if( vocab.special_unk_id != -1 ) { fprintf(stdout, "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); } - if( vocab.special_sep_id != -1 ) { fprintf(stdout, "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); } - if( vocab.special_pad_id != -1 ) { fprintf(stdout, "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); } - if( vocab.linefeed_id != -1 ) { fprintf(stdout, "%s: LF token = %d\n", __func__, vocab.linefeed_id ); } + if( vocab.special_bos_id != -1 ) { printf("%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); } + if( vocab.special_eos_id != -1 ) { printf("%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); } + if( vocab.special_unk_id != -1 ) { printf("%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); } + if( vocab.special_sep_id != -1 ) { printf("%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); } + if( vocab.special_pad_id != -1 ) { printf("%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); } + if( vocab.linefeed_id != -1 ) { printf("%s: LF token = %d\n", __func__, vocab.linefeed_id ); } } @@ -543,13 +543,13 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ { const int n_tensors = gguf_get_n_tensors(ggufctx); - fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors); + printf("%s: n_tensors: %d\n", __func__, n_tensors); for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name (ggufctx, i); const size_t offset = gguf_get_tensor_offset(ggufctx, i); - fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); + printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); } } #endif @@ -953,7 +953,7 @@ int main(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return 1; } diff --git a/examples/gptneox-wip/gptneox-main.cpp b/examples/gptneox-wip/gptneox-main.cpp index 6291523f2..55eba0cdc 100644 --- a/examples/gptneox-wip/gptneox-main.cpp +++ b/examples/gptneox-wip/gptneox-main.cpp @@ -318,9 +318,9 @@ struct ggml_tensor * get_tensor_ex( struct ggml_context * ctx, std::string name) struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str()); if( cur == NULL ) { - fprintf(stdout, "%s: tensor '%s' not found!\n", __func__, name.c_str()); + printf("%s: tensor '%s' not found!\n", __func__, name.c_str()); } else { -// fprintf(stdout, "%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name); +// printf("%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name); } return cur; @@ -346,21 +346,21 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 return false; } - fprintf(stdout, "%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx)); - fprintf(stdout, "%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx)); - fprintf(stdout, "%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx)); + printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx)); + printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx)); + printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx)); // print all kv #if 0 { const int n_kv = gguf_get_n_kv(ggufctx); - fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv); + printf("%s: n_kv: %d\n", __func__, n_kv); for (int i = 0; i < n_kv; ++i) { const char * key = gguf_get_key(ggufctx, i); - fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key); + printf("%s: kv[%d]: key = %s\n", __func__, i, key); } } #endif @@ -370,21 +370,21 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 int keyidx; keyidx = gguf_find_key(ggufctx, "general.name"); - if (keyidx != -1) { fprintf(stdout, "%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.description"); - if (keyidx != -1) { fprintf(stdout, "%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.author"); - if (keyidx != -1) { fprintf(stdout, "%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.license"); - if (keyidx != -1) { fprintf(stdout, "%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.architecture"); - if (keyidx != -1) { fprintf(stdout, "%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.file_type"); - if (keyidx != -1) { fprintf(stdout, "%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout"); - if (keyidx != -1) { fprintf(stdout, "%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.source.hugginface.repository"); - if (keyidx != -1) { fprintf(stdout, "%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } } // check required metadata @@ -395,11 +395,11 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 keyidx = gguf_find_key(ggufctx, "general.architecture"); if (keyidx != -1) { if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gptneox") != 0) { - fprintf(stdout, "%s: model architecture not supported!\n", __func__); + printf("%s: model architecture not supported!\n", __func__); return false; } } else { - fprintf(stdout, "%s: gguf model architecture not found!\n", __func__); + printf("%s: gguf model architecture not found!\n", __func__); return false; } @@ -456,11 +456,11 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 if (keyidx != -1) { if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gpt2") != 0) { - fprintf(stdout, "%s: tokenizer model not supported!\n", __func__); + printf("%s: tokenizer model not supported!\n", __func__); return false; } } else { - fprintf(stdout, "%s: tokenizer model not found!\n", __func__); + printf("%s: tokenizer model not found!\n", __func__); return false; } @@ -468,22 +468,22 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens"); if (tokens_keyidx == -1) { - fprintf(stdout, "%s: gpt2 tokenizer vocab not found!\n", __func__); + printf("%s: gpt2 tokenizer vocab not found!\n", __func__); return false; } int merges_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.merges"); if (merges_keyidx == -1) { - fprintf(stdout, "%s: gpt2 tokenizer merges not found!\n", __func__); + printf("%s: gpt2 tokenizer merges not found!\n", __func__); return false; } hparams.n_vocab = gguf_get_arr_n(ggufctx,tokens_keyidx); hparams.n_merges = gguf_get_arr_n(ggufctx,merges_keyidx); - fprintf(stdout, "%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab); - fprintf(stdout, "%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges); + printf("%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab); + printf("%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges); for (size_t i = 0; i < hparams.n_vocab; i++) { std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i); @@ -524,12 +524,12 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.separator_token_id"); if( keyidx != -1 ) { vocab.special_sep_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.padding_token_id"); if( keyidx != -1 ) { vocab.special_pad_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - if( vocab.special_bos_id != -1 ) { fprintf(stdout, "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); } - if( vocab.special_eos_id != -1 ) { fprintf(stdout, "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); } - if( vocab.special_unk_id != -1 ) { fprintf(stdout, "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); } - if( vocab.special_sep_id != -1 ) { fprintf(stdout, "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); } - if( vocab.special_pad_id != -1 ) { fprintf(stdout, "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); } - if( vocab.linefeed_id != -1 ) { fprintf(stdout, "%s: LF token = %d\n", __func__, vocab.linefeed_id ); } + if( vocab.special_bos_id != -1 ) { printf("%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); } + if( vocab.special_eos_id != -1 ) { printf("%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); } + if( vocab.special_unk_id != -1 ) { printf("%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); } + if( vocab.special_sep_id != -1 ) { printf("%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); } + if( vocab.special_pad_id != -1 ) { printf("%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); } + if( vocab.linefeed_id != -1 ) { printf("%s: LF token = %d\n", __func__, vocab.linefeed_id ); } } @@ -543,13 +543,13 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 { const int n_tensors = gguf_get_n_tensors(ggufctx); - fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors); + printf("%s: n_tensors: %d\n", __func__, n_tensors); for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name (ggufctx, i); const size_t offset = gguf_get_tensor_offset(ggufctx, i); - fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); + printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); } } #endif @@ -925,7 +925,7 @@ int main(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return 1; } diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp old mode 100755 new mode 100644 index bf3a487ab..34ddfde39 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -74,14 +74,6 @@ static T stdev(const std::vector & v) { return stdev; } -static bool ggml_cpu_has_metal() { -#if defined(GGML_USE_METAL) - return true; -#else - return false; -#endif -} - static std::string get_cpu_info() { std::string id; #ifdef __linux__ @@ -165,26 +157,26 @@ static const cmd_params cmd_params_defaults = { }; static void print_usage(int /* argc */, char ** argv) { - fprintf(stdout, "usage: %s [options]\n", argv[0]); - fprintf(stdout, "\n"); - fprintf(stdout, "options:\n"); - fprintf(stdout, " -h, --help\n"); - fprintf(stdout, " -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); - fprintf(stdout, " -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); - fprintf(stdout, " -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); - fprintf(stdout, " -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); - fprintf(stdout, " --memory-f32 <0|1> (default: %s)\n", join(cmd_params_defaults.f32_kv, ",").c_str()); - fprintf(stdout, " -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); - fprintf(stdout, " -ngl N, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); - fprintf(stdout, " -mg i, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); - fprintf(stdout, " -lv, --low-vram <0|1> (default: %s)\n", join(cmd_params_defaults.low_vram, ",").c_str()); - fprintf(stdout, " -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); - fprintf(stdout, " -ts, --tensor_split \n"); - fprintf(stdout, " -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); - fprintf(stdout, " -o, --output (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : cmd_params_defaults.output_format == MARKDOWN ? "md" : "sql"); - fprintf(stdout, " -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); - fprintf(stdout, "\n"); - fprintf(stdout, "Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n"); + printf("usage: %s [options]\n", argv[0]); + printf("\n"); + printf("options:\n"); + printf(" -h, --help\n"); + printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); + printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); + printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); + printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); + printf(" --memory-f32 <0|1> (default: %s)\n", join(cmd_params_defaults.f32_kv, ",").c_str()); + printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); + printf(" -ngl N, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -mg i, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); + printf(" -lv, --low-vram <0|1> (default: %s)\n", join(cmd_params_defaults.low_vram, ",").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 \n"); + printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); + printf(" -o, --output (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : cmd_params_defaults.output_format == MARKDOWN ? "md" : "sql"); + printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); + printf("\n"); + printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n"); } @@ -986,7 +978,12 @@ int main(int argc, char ** argv) { test t(inst, lmodel, ctx); // warmup run - test_gen(ctx, 1, 0, t.n_threads); + if (t.n_prompt > 0) { + test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads); + } + if (t.n_gen > 0) { + test_gen(ctx, 1, 0, t.n_threads); + } for (int i = 0; i < params.reps; i++) { uint64_t t_start = get_time_ns(); diff --git a/examples/main-cmake-pkg/.gitignore b/examples/main-cmake-pkg/.gitignore new file mode 100644 index 000000000..e32c11c7f --- /dev/null +++ b/examples/main-cmake-pkg/.gitignore @@ -0,0 +1,51 @@ +# Prerequisites +*.d + +# Compiled Object files +*.slo +*.lo +*.o +*.obj + +# Precompiled Headers +*.gch +*.pch + +# Compiled Dynamic libraries +*.so +*.dylib +*.dll + +# Fortran module files +*.mod +*.smod + +# Compiled Static libraries +*.lai +*.la +*.a +*.lib + +# Executables +*.exe +*.out +*.app + +*.gguf + +*.log +.DS_Store +.build/ +.cache/ +.direnv/ +.envrc +.swiftpm +.venv +.clang-tidy +.vs/ +.vscode/ + +build*/ +out/ +tmp/ + diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt new file mode 100644 index 000000000..473738719 --- /dev/null +++ b/examples/main-cmake-pkg/CMakeLists.txt @@ -0,0 +1,36 @@ +cmake_minimum_required(VERSION 3.12) +project("main-cmake-pkg" C CXX) +set(TARGET main-cmake-pkg) + +find_package(Llama 0.0.1 REQUIRED) + +# Bake common functionality in with target. Because applications +# using the relocatable Llama package should be outside of the +# source tree, main-cmake-pkg pretends the dependencies are built-in. + +set(_common_path "${CMAKE_CURRENT_LIST_DIR}/../../common") +add_library(common OBJECT + ${_common_path}/common.h + ${_common_path}/common.cpp + ${_common_path}/console.h + ${_common_path}/console.cpp + ${_common_path}/grammar-parser.h + ${_common_path}/grammar-parser.cpp + ) + +# WARNING: because build-info.h is auto-generated, it will only +# be available after the user has built the llama.cpp sources. +# +configure_file(${_common_path}/../build-info.h + ${CMAKE_CURRENT_BINARY_DIR}/build-info.h + COPYONLY) + +target_include_directories(common PUBLIC ${LLAMA_INCLUDE_DIR} + ${CMAKE_CURRENT_BINARY_DIR}) + +add_executable(${TARGET} ${CMAKE_CURRENT_LIST_DIR}/../main/main.cpp) +target_include_directories(${TARGET} PRIVATE ${_common_path}) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) + diff --git a/examples/main-cmake-pkg/README.md b/examples/main-cmake-pkg/README.md new file mode 100644 index 000000000..6d665f28f --- /dev/null +++ b/examples/main-cmake-pkg/README.md @@ -0,0 +1,37 @@ +# llama.cpp/example/main-cmake-pkg + +This program builds the [main](../main) application using a relocatable CMake package. It serves as an example of using the `find_package()` CMake command to conveniently include [llama.cpp](https://github.com/ggerganov/llama.cpp) in projects which live outside of the source tree. + +## Building + +Because this example is "outside of the source tree", it is important to first build/install llama.cpp using CMake. An example is provided here, but please see the [llama.cpp build instructions](../..) for more detailed build instructions. + +### Considerations + +When hardware acceleration libraries are used (e.g. CUBlas, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_. + +### Build llama.cpp and install to C:\LlamaCPP directory + +In this case, CLBlast was already installed so the CMake package is referenced in `CMAKE_PREFIX_PATH`. + +```cmd +git clone https://github.com/ggerganov/llama.cpp +cd llama.cpp +mkdir build +cd build +cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64 +cmake --build . --config Release +cmake --install . --prefix C:/LlamaCPP +``` + +### Build main-cmake-pkg + + +```cmd +cd ..\examples\main-cmake-pkg +mkdir build +cd build +cmake .. -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64 +cmake --build . --config Release +cmake --install . --prefix C:/MyLlamaApp +``` diff --git a/examples/main/README.md b/examples/main/README.md index 2773fe976..26e1e28dd 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -144,7 +144,7 @@ The `--ctx-size` option allows you to set the size of the prompt context used by Some fine-tuned models have extened the context length by scaling RoPE. For example, if the original pretrained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8. -- `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model. +- `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model. ### Keep Prompt @@ -274,7 +274,7 @@ These options help improve the performance and memory usage of the LLaMA models. ### NUMA support -- `--numa`: Attempt optimizations that help on some systems with non-uniform memory access. This currently consists of pinning an equal proportion of the threads to the cores on each NUMA node, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop\_caches' as root. +- `--numa`: Attempt optimizations that help on some systems with non-uniform memory access. This currently consists of pinning an equal proportion of the threads to the cores on each NUMA node, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root. ### Memory Float 32 @@ -302,7 +302,6 @@ These options provide extra functionality and customization when running the LLa - `-h, --help`: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated. - `--verbose-prompt`: Print the prompt before generating text. -- `--mtest`: Test the model's functionality by running a series of tests to ensure it's working properly. - `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance. - `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS. - `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS. diff --git a/examples/main/main.cpp b/examples/main/main.cpp index b7734930a..4883a2f52 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -1,8 +1,3 @@ -// Defines sigaction on msys: -#ifndef _GNU_SOURCE -#define _GNU_SOURCE -#endif - #include "common.h" #include "console.h" @@ -48,8 +43,9 @@ static bool is_interacting = false; void write_logfile( const llama_context * ctx, const gpt_params & params, const llama_model * model, - const std::vector input_tokens, const std::string output, const std::vector output_tokens) { - + const std::vector & input_tokens, const std::string & output, + const std::vector & output_tokens +) { if (params.logdir.empty()) { return; } @@ -109,7 +105,7 @@ int main(int argc, char ** argv) { gpt_params params; g_params = ¶ms; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return 1; } @@ -151,14 +147,6 @@ int main(int argc, char ** argv) { LOG_TEE("%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale); } - if (params.n_ctx > 2048) { - // TODO: determine the actual max context of the model (e.g. 4096 for LLaMA v2) and use that instead of 2048 - LOG_TEE("%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx); - } else if (params.n_ctx < 8) { - LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__); - params.n_ctx = 8; - } - LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); LOG_TEE("%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET); @@ -195,6 +183,15 @@ int main(int argc, char ** argv) { return 1; } + const int n_ctx_train = llama_n_ctx_train(ctx); + if (params.n_ctx > n_ctx_train) { + LOG_TEE("%s: warning: model was trained on only %d context tokens (%d specified)\n", + __func__, n_ctx_train, params.n_ctx); + } else if (params.n_ctx < 8) { + LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__); + params.n_ctx = 8; + } + // print system information { LOG_TEE("\n"); @@ -202,23 +199,6 @@ int main(int argc, char ** argv) { params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); } - // determine the maximum memory usage needed to do inference for the given n_batch and n_ctx parameters - // uncomment the "used_mem" line in llama.cpp to see the results - if (params.mem_test) { - { - LOG_TEE("%s: testing memory usage for n_batch = %d, n_ctx = %d\n", __func__, params.n_batch, params.n_ctx); - - const std::vector tmp(params.n_batch, llama_token_bos(ctx)); - llama_eval(ctx, tmp.data(), tmp.size(), params.n_ctx, params.n_threads); - } - - llama_print_timings(ctx); - llama_free(ctx); - llama_free_model(model); - - return 0; - } - // export the cgraph and exit if (params.export_cgraph) { llama_eval_export(ctx, "llama.ggml"); @@ -305,7 +285,7 @@ int main(int argc, char ** argv) { // debug message about similarity of saved session, if applicable size_t n_matching_session_tokens = 0; - if (session_tokens.size() > 0) { + if (!session_tokens.empty()) { for (llama_token id : session_tokens) { if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) { break; @@ -403,7 +383,7 @@ int main(int argc, char ** argv) { LOG_TEE("%s: interactive mode on.\n", __func__); - if (params.antiprompt.size()) { + if (!params.antiprompt.empty()) { for (const auto & antiprompt : params.antiprompt) { LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str()); } @@ -501,7 +481,7 @@ int main(int argc, char ** argv) { while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict - if (embd.size() > 0) { + if (!embd.empty()) { // Note: n_ctx - 4 here is to match the logic for commandline prompt handling via // --prompt or --file which uses the same value. int max_embd_size = n_ctx - 4; @@ -626,7 +606,7 @@ int main(int argc, char ** argv) { LOG("n_past = %d\n", n_past); } - if (embd.size() > 0 && !path_session.empty()) { + if (!embd.empty() && !path_session.empty()) { session_tokens.insert(session_tokens.end(), embd.begin(), embd.end()); n_session_consumed = session_tokens.size(); } @@ -697,7 +677,7 @@ int main(int argc, char ** argv) { // if not currently processing queued inputs; if ((int) embd_inp.size() <= n_consumed) { // check for reverse prompt - if (params.antiprompt.size()) { + if (!params.antiprompt.empty()) { std::string last_output; for (auto id : last_tokens) { last_output += llama_token_to_piece(ctx, id); @@ -734,7 +714,7 @@ int main(int argc, char ** argv) { LOG("found EOS token\n"); if (params.interactive) { - if (params.antiprompt.size() != 0) { + if (!params.antiprompt.empty()) { // tokenize and inject first reverse prompt const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false); embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end()); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index b28e91db4..790703df0 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -367,7 +367,7 @@ results_perplexity perplexity(llama_context * ctx, const gpt_params & params) { // Example, we have a context window of 512, we will compute perplexity for each of the // last 256 tokens. Then, we split the input up into context window size chunks to // process the entire prompt. - const int first = std::min(512, params.n_ctx/2); + const int first = params.n_ctx/2; process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first, workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first); count += params.n_ctx - first - 1; @@ -654,7 +654,7 @@ int main(int argc, char ** argv) { gpt_params params; params.n_batch = 512; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return 1; } @@ -667,11 +667,6 @@ int main(int argc, char ** argv) { params.n_ctx += params.ppl_stride/2; } - if (params.n_ctx > 2048) { - fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);" - "expect poor results\n", __func__, params.n_ctx); - } - print_build_info(); if (params.seed == LLAMA_DEFAULT_SEED) { @@ -697,6 +692,12 @@ int main(int argc, char ** argv) { return 1; } + const int n_ctx_train = llama_n_ctx_train(ctx); + if (params.n_ctx > n_ctx_train) { + fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n", + __func__, n_ctx_train, params.n_ctx); + } + // print system information { fprintf(stderr, "\n"); diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index 9977526ea..b06a5cbfe 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -70,7 +70,7 @@ void quantize_stats_print_usage(int /*argc*/, char ** argv) { } // Check if a layer is included/excluded by command line -bool layer_included(const quantize_stats_params params, const std::string & layer) { +bool layer_included(const quantize_stats_params & params, const std::string & layer) { for (const auto& excluded : params.exclude_layers) { if (std::regex_search(layer, std::regex(excluded))) { return false; diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 2a34117d0..ae2afa998 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -142,10 +142,9 @@ int main(int argc, char ** argv) { if (!try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) { fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]); return 1; - } else { - if (ftype_str == "COPY") { - params.only_copy = true; - } + } + if (ftype_str == "COPY") { + params.only_copy = true; } arg_idx++; } diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 8f55b1fb0..eac307904 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -12,7 +12,7 @@ int main(int argc, char ** argv) { params.repeat_last_n = 64; params.prompt = "The quick brown fox"; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return 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background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1)); + animation: loading-bg-wipe 2s linear infinite; + } + @media (prefers-color-scheme: dark) { + .loading { + --loading-color-1: #22222200; + --loading-color-2: #222222ff; + } .popover-content { background-color: black; } @@ -321,7 +343,10 @@ const llamaStats = signal(null) const controller = signal(null) - const generating = computed(() => controller.value == null ) + // currently generating a completion? + const generating = computed(() => controller.value != null) + + // has the user started a chat? const chatStarted = computed(() => session.value.transcript.length > 0) const transcriptUpdate = (transcript) => { @@ -430,11 +455,19 @@ return html`
-