diff --git a/.github/workflows/bench.yml b/.github/workflows/bench.yml index 758796632..6dc91d27b 100644 --- a/.github/workflows/bench.yml +++ b/.github/workflows/bench.yml @@ -32,7 +32,7 @@ on: - cron: '04 2 * * *' concurrency: - group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}-${{ github.event.inputs.sha }} + group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}-${{ github.event.inputs.sha }} cancel-in-progress: true jobs: diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index ff7238aba..2d747e688 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -32,6 +32,8 @@ jobs: - name: Clone id: checkout uses: actions/checkout@v4 + with: + fetch-depth: 0 - name: Dependencies id: depends @@ -52,7 +54,7 @@ jobs: id: cmake_test run: | cd build - ctest -L main --verbose --timeout 900 + ctest -L 'main|curl' --verbose --timeout 900 - name: Determine tag name id: tag @@ -88,6 +90,8 @@ jobs: - name: Clone id: checkout uses: actions/checkout@v4 + with: + fetch-depth: 0 - name: Dependencies id: depends @@ -101,7 +105,9 @@ jobs: sysctl -a mkdir build cd build - cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON .. + # Metal is disabled due to intermittent failures with Github runners not having a GPU: + # https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313 + cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF -DLLAMA_CURL=ON .. cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) - name: Test @@ -204,26 +210,28 @@ jobs: - name: Clone id: checkout uses: actions/checkout@v4 + with: + fetch-depth: 0 - name: Dependencies id: depends run: | sudo apt-get update - sudo apt-get install build-essential + sudo apt-get install build-essential libcurl4-openssl-dev - name: Build id: cmake_build run: | mkdir build cd build - cmake .. -DLLAMA_FATAL_WARNINGS=ON + cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON cmake --build . --config Release -j $(nproc) - name: Test id: cmake_test run: | cd build - ctest -L main --verbose --timeout 900 + ctest -L 'main|curl' --verbose --timeout 900 - name: Test llama2c conversion id: llama2c_test @@ -236,6 +244,33 @@ jobs: ./bin/convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf ./bin/main -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 + - name: Determine tag name + id: tag + shell: bash + run: | + BUILD_NUMBER="$(git rev-list --count HEAD)" + SHORT_HASH="$(git rev-parse --short=7 HEAD)" + if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then + echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT + else + SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-') + echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT + fi + + - name: Pack artifacts + id: pack_artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + run: | + cp LICENSE ./build/bin/ + zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip ./build/bin/* + + - name: Upload artifacts + if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip + name: llama-bin-ubuntu-x64.zip + # ubuntu-latest-cmake-sanitizer: # runs-on: ubuntu-latest # @@ -558,6 +593,63 @@ jobs: run: | make swift + windows-msys2: + runs-on: windows-latest + + strategy: + fail-fast: false + matrix: + include: + - { sys: UCRT64, env: ucrt-x86_64, build: Release } + - { sys: CLANG64, env: clang-x86_64, build: Release } + + steps: + - name: Clone + uses: actions/checkout@v4 + + - name: Setup ${{ matrix.sys }} + uses: msys2/setup-msys2@v2 + with: + update: true + msystem: ${{matrix.sys}} + install: >- + base-devel + mingw-w64-${{matrix.env}}-toolchain + mingw-w64-${{matrix.env}}-cmake + mingw-w64-${{matrix.env}}-openblas + + - name: Build using make + shell: msys2 {0} + run: | + make -j $(nproc) + + - name: Clean after building using make + shell: msys2 {0} + run: | + make clean + + - name: Build using make w/ OpenBLAS + shell: msys2 {0} + run: | + make LLAMA_OPENBLAS=1 -j $(nproc) + + - name: Build using CMake + shell: msys2 {0} + run: | + cmake -B build + cmake --build build --config ${{ matrix.build }} -j $(nproc) + + - name: Clean after building using CMake + shell: msys2 {0} + run: | + rm -rf build + + - name: Build using CMake w/ OpenBLAS + shell: msys2 {0} + run: | + cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS + cmake --build build --config ${{ matrix.build }} -j $(nproc) + windows-latest-cmake: runs-on: windows-latest @@ -938,6 +1030,12 @@ jobs: - name: Download artifacts id: download-artifact uses: actions/download-artifact@v4 + with: + path: ./artifact + + - name: Move artifacts + id: move_artifacts + run: mkdir -p ./artifact/release && mv ./artifact/*/*.zip ./artifact/release - name: Create release id: create_release @@ -956,7 +1054,7 @@ jobs: const path = require('path'); const fs = require('fs'); const release_id = '${{ steps.create_release.outputs.id }}'; - for (let file of await fs.readdirSync('./artifact')) { + for (let file of await fs.readdirSync('./artifact/release')) { if (path.extname(file) === '.zip') { console.log('uploadReleaseAsset', file); await github.repos.uploadReleaseAsset({ @@ -964,7 +1062,7 @@ jobs: repo: context.repo.repo, release_id: release_id, name: file, - data: await fs.readFileSync(`./artifact/${file}`) + data: await fs.readFileSync(`./artifact/release/${file}`) }); } } diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index eefd87878..9b03d19bc 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -91,6 +91,12 @@ jobs: echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT fi + - name: Downcase github.repository_owner + run: | + echo "repository_owner_lowercase=${GITHUB_REPOSITORY_OWNER@L}" >> $GITHUB_ENV + env: + GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}' + - name: Build and push Docker image (versioned) if: github.event_name == 'push' uses: docker/build-push-action@v4 @@ -98,7 +104,7 @@ jobs: context: . push: true platforms: ${{ matrix.config.platforms }} - tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}" + tags: "ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}" file: ${{ matrix.config.dockerfile }} - name: Build and push Docker image (tagged) @@ -107,5 +113,5 @@ jobs: context: . push: ${{ github.event_name == 'push' }} platforms: ${{ matrix.config.platforms }} - tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}" + tags: "ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}" file: ${{ matrix.config.dockerfile }} diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml index f4ae65495..5be17f157 100644 --- a/.github/workflows/python-lint.yml +++ b/.github/workflows/python-lint.yml @@ -21,4 +21,4 @@ jobs: uses: py-actions/flake8@v2 with: ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503" - exclude: "examples/*,examples/*/**,*/**/__init__.py" + exclude: "examples/*,examples/*/**,*/**/__init__.py,convert-hf-to-gguf-update.py" diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 521cc29ae..79cd7d643 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -23,7 +23,7 @@ on: - cron: '2 4 * * *' concurrency: - group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} + group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }} cancel-in-progress: true jobs: @@ -41,23 +41,16 @@ jobs: sanitizer: "" fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken - container: - image: ubuntu:latest - ports: - - 8888 - options: --cpus 4 - steps: - name: Dependencies id: depends run: | - apt-get update - apt-get -y install \ + sudo apt-get update + sudo apt-get -y install \ build-essential \ xxd \ git \ cmake \ - python3-pip \ curl \ wget \ language-pack-en \ @@ -70,6 +63,17 @@ jobs: fetch-depth: 0 ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }} + - name: Python setup + id: setup_python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Tests dependencies + id: test_dependencies + run: | + pip install -r examples/server/tests/requirements.txt + - name: Verify server deps id: verify_server_deps run: | @@ -100,10 +104,6 @@ jobs: -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server - - name: Tests dependencies - id: test_dependencies - run: | - pip install -r examples/server/tests/requirements.txt - name: Tests id: server_integration_tests @@ -129,6 +129,7 @@ jobs: uses: actions/checkout@v4 with: fetch-depth: 0 + ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }} - name: libCURL id: get_libcurl diff --git a/.gitignore b/.gitignore index 9fb5b80c3..60f9d1f8d 100644 --- a/.gitignore +++ b/.gitignore @@ -34,6 +34,7 @@ lcov-report/ gcovr-report/ build* +!build.zig cmake-build-* out/ tmp/ @@ -48,6 +49,7 @@ models-mnt /convert-llama2c-to-ggml /embd-input-test /embedding +/eval-callback /gguf /gguf-llama-simple /gguf-split @@ -99,7 +101,25 @@ qnt-*.txt perf-*.txt examples/jeopardy/results.txt +examples/server/*.html.hpp +examples/server/*.js.hpp +examples/server/*.mjs.hpp poetry.lock poetry.toml nppBackup + +# Test binaries +/tests/test-grammar-parser +/tests/test-llama-grammar +/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 +/tests/test-tokenizer-1-spm +/tests/test-tokenizer-1-bpe +/tests/test-rope +/tests/test-backend-ops diff --git a/CMakeLists.txt b/CMakeLists.txt index 19fdfa46c..477c5b57c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -43,6 +43,8 @@ else() set(LLAMA_METAL_DEFAULT OFF) endif() +set(LLAMA_LLAMAFILE_DEFAULT ON) + # general option(BUILD_SHARED_LIBS "build shared libraries" OFF) option(LLAMA_STATIC "llama: static link libraries" OFF) @@ -88,6 +90,7 @@ endif() # 3rd party libs option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON) option(LLAMA_BLAS "llama: use BLAS" OFF) +option(LLAMA_LLAMAFILE "llama: use llamafile SGEMM" ${LLAMA_LLAMAFILE_DEFAULT}) set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor") option(LLAMA_CUDA "llama: use CUDA" OFF) option(LLAMA_CUBLAS "llama: use CUDA (deprecated, use LLAMA_CUDA)" OFF) @@ -286,6 +289,7 @@ if (LLAMA_METAL) ${METALKIT_FRAMEWORK} ) endif() + if (LLAMA_BLAS) if (LLAMA_STATIC) set(BLA_STATIC ON) @@ -368,6 +372,13 @@ if (LLAMA_BLAS) endif() endif() +if (LLAMA_LLAMAFILE) + add_compile_definitions(GGML_USE_LLAMAFILE) + + set(GGML_HEADERS_LLAMAFILE sgemm.h) + set(GGML_SOURCES_LLAMAFILE sgemm.cpp) +endif() + if (LLAMA_QKK_64) add_compile_definitions(GGML_QKK_64) endif() @@ -1151,15 +1162,16 @@ add_library(ggml OBJECT ggml-backend.h ggml-quants.c ggml-quants.h - ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} - ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} - ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} - ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} - ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} - ${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL} - ${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE} - ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} - ${GGML_SOURCES_ROCM} ${GGML_HEADERS_ROCM} + ${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} + ${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL} + ${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE} + ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} + ${GGML_SOURCES_ROCM} ${GGML_HEADERS_ROCM} + ${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE} ) target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES}) diff --git a/Makefile b/Makefile index 11b31c5c8..0a73f2a58 100644 --- a/Makefile +++ b/Makefile @@ -1,16 +1,28 @@ # Define the default target now so that it is always the first target BUILD_TARGETS = \ main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ - simple batched batched-bench save-load-state server gguf gguf-split llama-bench libllava.a llava-cli baby-llama beam-search \ + simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama beam-search \ retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm 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-llama tests/test-tokenizer-1-bpe tests/test-rope \ - tests/test-backend-ops tests/test-model-load-cancel tests/test-autorelease \ - tests/test-json-schema-to-grammar tests/test-grammar-integration + tests/test-autorelease \ + tests/test-backend-ops \ + tests/test-double-float \ + tests/test-grad0 \ + tests/test-grammar-integration \ + tests/test-grammar-parser \ + tests/test-json-schema-to-grammar \ + tests/test-llama-grammar \ + tests/test-model-load-cancel \ + tests/test-opt \ + tests/test-quantize-fns \ + tests/test-quantize-perf \ + tests/test-rope \ + tests/test-sampling \ + tests/test-tokenizer-0 \ + tests/test-tokenizer-1-bpe \ + tests/test-tokenizer-1-spm # Code coverage output files COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report @@ -27,6 +39,17 @@ ifndef UNAME_M UNAME_M := $(shell uname -m) endif +# In GNU make default CXX is g++ instead of c++. Let's fix that so that users +# of non-gcc compilers don't have to provide g++ alias or wrapper. +DEFCC := cc +DEFCXX := c++ +ifeq ($(origin CC),default) +CC := $(DEFCC) +endif +ifeq ($(origin CXX),default) +CXX := $(DEFCXX) +endif + # Mac OS + Arm can report x86_64 # ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789 ifeq ($(UNAME_S),Darwin) @@ -49,11 +72,17 @@ default: $(BUILD_TARGETS) 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 [ "$$test_target" = "tests/test-tokenizer-0-falcon" ]; then \ + if [ "$$test_target" = "tests/test-tokenizer-0" ]; then \ + ./$$test_target $(CURDIR)/models/ggml-vocab-llama-spm.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-llama-bpe.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-phi-3.gguf; \ ./$$test_target $(CURDIR)/models/ggml-vocab-falcon.gguf; \ - elif [ "$$test_target" = "tests/test-tokenizer-1-llama" ]; then \ + ./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-coder.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-llm.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-bert-bge.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-starcoder.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-gpt-2.gguf; \ + elif [ "$$test_target" = "tests/test-tokenizer-1-spm" ]; then \ continue; \ elif [ "$$test_target" = "tests/test-tokenizer-1-bpe" ]; then \ continue; \ @@ -384,6 +413,11 @@ ifdef LLAMA_OPENBLAS MK_LDFLAGS += $(shell pkg-config --libs openblas) endif # LLAMA_OPENBLAS +ifndef LLAMA_NO_LLAMAFILE + MK_CPPFLAGS += -DGGML_USE_LLAMAFILE + OBJS += sgemm.o +endif + ifdef LLAMA_BLIS MK_CPPFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis MK_LDFLAGS += -lblis -L/usr/local/lib @@ -480,11 +514,9 @@ ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/com ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh) $(NVCC_COMPILE) - endif # LLAMA_CUDA ifdef LLAMA_CLBLAST - MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL) MK_CFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) @@ -603,6 +635,11 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h $(CC) $(CFLAGS) -c $< -o $@ endif # LLAMA_MPI +ifndef LLAMA_NO_LLAMAFILE +sgemm.o: sgemm.cpp sgemm.h ggml.h + $(CXX) $(CXXFLAGS) -c $< -o $@ +endif + GF_CC := $(CC) include scripts/get-flags.mk @@ -646,7 +683,7 @@ CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])' ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1) ifndef CUDA_DOCKER_ARCH ifndef CUDA_POWER_ARCH -$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via CUDA_DOCKER_ARCH) +$(error I ERROR: For CUDA versions < 11.7 a target CUDA architecture must be explicitly provided via environment variable CUDA_DOCKER_ARCH, e.g. by running "export CUDA_DOCKER_ARCH=compute_XX" on Unix-like systems, where XX is the minimum compute capability that the code needs to run on. A list with compute capabilities can be found here: https://developer.nvidia.com/cuda-gpus ) endif # CUDA_POWER_ARCH endif # CUDA_DOCKER_ARCH endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1) @@ -687,8 +724,8 @@ OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h $(CXX) $(CXXFLAGS) -c $< -o $@ -COMMON_H_DEPS = common/common.h common/sampling.h common/log.h -COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o +COMMON_H_DEPS = common/common.h common/sampling.h common/log.h llama.h +COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o common.o: common/common.cpp $(COMMON_H_DEPS) $(CXX) $(CXXFLAGS) -c $< -o $@ @@ -756,11 +793,11 @@ batched: examples/batched/batched.cpp ggml.o llama.o $(C $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o common.o $(OBJS) +batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -quantize: examples/quantize/quantize.cpp build-info.o ggml.o llama.o $(OBJS) +quantize: examples/quantize/quantize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -788,10 +825,19 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp json-schema-to-grammar.o common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) +# Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`: +examples/server/%.hpp: examples/server/public/% Makefile + @( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \ + echo "unsigned char $${NAME}[] = {" && \ + cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \ + echo "};" && \ + echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \ + ) > $@ + gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -800,6 +846,10 @@ gguf-split: examples/gguf-split/gguf-split.cpp ggml.o llama.o $(COMMON_DEPS) $(O $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +eval-callback: examples/eval-callback/eval-callback.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -950,11 +1000,7 @@ tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - -tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) +tests/test-tokenizer-0: tests/test-tokenizer-0.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -962,7 +1008,7 @@ tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMM $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) +tests/test-tokenizer-1-spm: tests/test-tokenizer-1-spm.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) diff --git a/Package.swift b/Package.swift index 8b7195869..183e64757 100644 --- a/Package.swift +++ b/Package.swift @@ -2,6 +2,45 @@ import PackageDescription +var sources = [ + "ggml.c", + "sgemm.cpp", + "llama.cpp", + "unicode.cpp", + "unicode-data.cpp", + "ggml-alloc.c", + "ggml-backend.c", + "ggml-quants.c", +] + +var resources: [Resource] = [] +var linkerSettings: [LinkerSetting] = [] +var cSettings: [CSetting] = [ + .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]), + .unsafeFlags(["-fno-objc-arc"]), + // NOTE: NEW_LAPACK will required iOS version 16.4+ + // We should consider add this in the future when we drop support for iOS 14 + // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc) + // .define("ACCELERATE_NEW_LAPACK"), + // .define("ACCELERATE_LAPACK_ILP64") +] + +#if canImport(Darwin) +sources.append("ggml-metal.m") +resources.append(.process("ggml-metal.metal")) +linkerSettings.append(.linkedFramework("Accelerate")) +cSettings.append( + contentsOf: [ + .define("GGML_USE_ACCELERATE"), + .define("GGML_USE_METAL") + ] +) +#endif + +#if os(Linux) + cSettings.append(.define("_GNU_SOURCE")) +#endif + let package = Package( name: "llama", platforms: [ @@ -28,34 +67,11 @@ let package = Package( "ggml-cuda.h", "Makefile" ], - sources: [ - "ggml.c", - "llama.cpp", - "unicode.cpp", - "unicode-data.cpp", - "ggml-alloc.c", - "ggml-backend.c", - "ggml-quants.c", - "ggml-metal.m", - ], - resources: [ - .process("ggml-metal.metal") - ], + sources: sources, + resources: resources, publicHeadersPath: "spm-headers", - cSettings: [ - .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]), - .define("GGML_USE_ACCELERATE"), - .unsafeFlags(["-fno-objc-arc"]), - .define("GGML_USE_METAL"), - // NOTE: NEW_LAPACK will required iOS version 16.4+ - // We should consider add this in the future when we drop support for iOS 14 - // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc) - // .define("ACCELERATE_NEW_LAPACK"), - // .define("ACCELERATE_LAPACK_ILP64") - ], - linkerSettings: [ - .linkedFramework("Accelerate") - ] + cSettings: cSettings, + linkerSettings: linkerSettings ) ], cxxLanguageStandard: .cxx11 diff --git a/README-sycl.md b/README-sycl.md index 4372a32e3..dc98c7b3e 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -8,9 +8,9 @@ - [Linux](#linux) - [Windows](#windows) - [Environment Variable](#environment-variable) -- [Known Issue](#known-issue) -- [Q&A](#q&a) -- [Todo](#todo) +- [Known Issue](#known-issues) +- [Q&A](#qa) +- [TODO](#todo) ## Background @@ -54,10 +54,10 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, ## OS -|OS|Status|Verified| -|-|-|-| -|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39| -|Windows|Support|Windows 11| +| OS | Status | Verified | +|---------|---------|------------------------------------| +| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39 | +| Windows | Support | Windows 11 | ## Hardware @@ -66,13 +66,13 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, **Verified devices** -|Intel GPU| Status | Verified Model| -|-|-|-| -|Intel Data Center Max Series| Support| Max 1550| -|Intel Data Center Flex Series| Support| Flex 170| -|Intel Arc Series| Support| Arc 770, 730M| -|Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| -|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7| +| Intel GPU | Status | Verified Model | +|-------------------------------|---------|---------------------------------------| +| Intel Data Center Max Series | Support | Max 1550, 1100 | +| Intel Data Center Flex Series | Support | Flex 170 | +| Intel Arc Series | Support | Arc 770, 730M | +| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake | +| Intel iGPU | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 | *Notes:* @@ -84,24 +84,18 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, - **Execution Unit (EU)** - If the iGPU has less than 80 EUs, the inference speed will likely be too slow for practical use. -### Nvidia GPU -The BLAS acceleration on Nvidia GPU through oneAPI can be obtained using the Nvidia plugins for oneAPI and the cuBLAS backend of the upstream oneMKL library. Details and instructions on how to setup the runtime and library can be found in [this section](#i-setup-environment) +### Other Vendor GPU **Verified devices** -|Nvidia GPU| Status | Verified Model| -|-|-|-| -|Ampere Series| Support| A100, A4000| -|Ampere Series *(Mobile)*| Support| RTX 40 Series| - -*Notes:* - - Support for Nvidia targets through oneAPI is currently limited to Linux platforms. - - - Please make sure the native oneAPI MKL *(dedicated to intel CPUs and GPUs)* is not "visible" at this stage to properly setup and use the built-from-source oneMKL with cuBLAS backend in llama.cpp for Nvidia GPUs. - +| Nvidia GPU | Status | Verified Model | +|--------------------------|---------|----------------| +| Ampere Series | Support | A100, A4000 | +| Ampere Series *(Mobile)* | Support | RTX 40 Series | ## Docker The docker build option is currently limited to *intel GPU* targets. + ### Build image ```sh # Using FP16 @@ -167,30 +161,11 @@ Platform #0: Intel(R) OpenCL HD Graphics - **Nvidia GPU** -In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cublas)-* are installed. -Installation can be verified by running the following: -```sh -nvidia-smi -``` -Please make sure at least one CUDA device is available, which can be displayed like this *(here an A100-40GB Nvidia GPU)*: -``` -+---------------------------------------------------------------------------------------+ -| NVIDIA-SMI 535.54.03 Driver Version: 535.54.03 CUDA Version: 12.2 | -|-----------------------------------------+----------------------+----------------------+ -| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | -| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | -| | | MIG M. | -|=========================================+======================+======================| -| 0 NVIDIA A100-PCIE-40GB On | 00000000:8D:00.0 Off | 0 | -| N/A 36C P0 57W / 250W | 4MiB / 40960MiB | 0% Default | -| | | Disabled | -+-----------------------------------------+----------------------+----------------------+ -``` - +In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed. 2. **Install Intel® oneAPI Base toolkit** -- **Base installation** +- **For Intel GPU** The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page. @@ -202,10 +177,10 @@ Upon a successful installation, SYCL is enabled for the available intel devices, - **Adding support to Nvidia GPUs** -**oneAPI**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup. +**oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup. -**oneMKL**: The current oneMKL releases *(shipped with the oneAPI base-toolkit)* do not contain the cuBLAS backend. A build from source of the upstream [oneMKL](https://github.com/oneapi-src/oneMKL) with the *cuBLAS* backend enabled is thus required to run it on Nvidia GPUs. +**oneMKL for cuBlas**: The current oneMKL releases *(shipped with the oneAPI base-toolkit)* do not contain the cuBLAS backend. A build from source of the upstream [oneMKL](https://github.com/oneapi-src/oneMKL) with the *cuBLAS* backend enabled is thus required to run it on Nvidia GPUs. ```sh git clone https://github.com/oneapi-src/oneMKL @@ -237,7 +212,7 @@ When targeting an intel GPU, the user should expect one or more level-zero devic - **Nvidia GPU** -Similarly, user targetting Nvidia GPUs should expect at least one SYCL-CUDA device [`ext_oneapi_cuda:gpu`] as bellow: +Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`ext_oneapi_cuda:gpu`] as bellow: ``` [opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.12.0.12_195853.xmain-hotfix] [opencl:cpu:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix] @@ -254,12 +229,14 @@ source /opt/intel/oneapi/setvars.sh # Build LLAMA with MKL BLAS acceleration for intel GPU mkdir -p build && cd build -# Option 1: Use FP16 for better performance in long-prompt inference -cmake --build .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON -# Or without "--build", run "make" next +# Option 1: Use FP32 (recommended for better performance in most cases) +cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -# Option 2: Use FP32 by default -cmake --build .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx +# Option 2: Use FP16 +cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON + +#build all binary +cmake --build . --config Release -j -v ``` #### Nvidia GPU @@ -273,11 +250,15 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR # Build LLAMA with Nvidia BLAS acceleration through SYCL mkdir -p build && cd build -# Option 1: Use FP16 for better performance in long-prompt inference -cmake --build .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON +# Option 1: Use FP32 (recommended for better performance in most cases) +cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx + +# Option 2: Use FP16 +cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON + +#build all binary +cmake --build . --config Release -j -v -# Option 2: Use FP32 by default -cmake --build .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ``` ### III. Run the inference @@ -313,10 +294,10 @@ found 6 SYCL devices: | 5| [opencl:acc:0]| Intel(R) FPGA Emulation Device| 1.2| 24|67108864| 64| 67064815616| ``` -|Attribute|Note| -|-|-| -|compute capability 1.3|Level-zero driver/runtime, recommended | -|compute capability 3.0|OpenCL driver/runtime, slower than level-zero in most cases| +| Attribute | Note | +|------------------------|-------------------------------------------------------------| +| compute capability 1.3 | Level-zero driver/runtime, recommended | +| compute capability 3.0 | OpenCL driver/runtime, slower than level-zero in most cases | 4. Launch inference @@ -325,10 +306,10 @@ There are two device selection modes: - Single device: Use one device target specified by the user. - Multiple devices: Automatically select the devices with the same largest Max compute-units. -|Device selection|Parameter| -|-|-| -|Single device|--split-mode none --main-gpu DEVICE_ID | -|Multiple devices|--split-mode layer (default)| +| Device selection | Parameter | +|------------------|----------------------------------------| +| Single device | --split-mode none --main-gpu DEVICE_ID | +| Multiple devices | --split-mode layer (default) | Examples: @@ -357,7 +338,6 @@ Otherwise, you can run the script: *Notes:* -- By default, `mmap` is used to read the model file. In some cases, it causes runtime hang issues. Please disable it by passing `--no-mmap` to the `/bin/main` if faced with the issue. - Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow: ```sh @@ -436,9 +416,13 @@ mkdir -p build cd build @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force +# Option 1: Use FP32 (recommended for better performance in most cases) +cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release + +# Option 2: Or FP16 cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON -make +make -j ``` Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions: @@ -486,10 +470,10 @@ found 6 SYCL devices: ``` -|Attribute|Note| -|-|-| -|compute capability 1.3|Level-zero running time, recommended | -|compute capability 3.0|OpenCL running time, slower than level-zero in most cases| +| Attribute | Note | +|------------------------|-----------------------------------------------------------| +| compute capability 1.3 | Level-zero running time, recommended | +| compute capability 3.0 | OpenCL running time, slower than level-zero in most cases | 4. Launch inference @@ -499,10 +483,10 @@ There are two device selection modes: - Single device: Use one device assigned by user. - Multiple devices: Automatically choose the devices with the same biggest Max compute units. -|Device selection|Parameter| -|-|-| -|Single device|--split-mode none --main-gpu DEVICE_ID | -|Multiple devices|--split-mode layer (default)| +| Device selection | Parameter | +|------------------|----------------------------------------| +| Single device | --split-mode none --main-gpu DEVICE_ID | +| Multiple devices | --split-mode layer (default) | Examples: @@ -525,7 +509,6 @@ Otherwise, run the following wrapper script: Note: -- By default, `mmap` is used to read the model file. In some cases, it causes runtime hang issues. Please disable it by passing `--no-mmap` to the `main.exe` if faced with the issue. - Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow: ```sh @@ -540,29 +523,23 @@ use 1 SYCL GPUs: [0] with Max compute units:512 #### Build -|Name|Value|Function| -|-|-|-| -|LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path.| -|LLAMA_SYCL_TARGET | INTEL *(default)* \| NVIDIA|Set the SYCL target device type.| -|LLAMA_SYCL_F16|OFF *(default)* \|ON *(optional)*|Enable FP16 build with SYCL code path.| -|CMAKE_C_COMPILER|icx|Set *icx* compiler for SYCL code path.| -|CMAKE_CXX_COMPILER|icpx *(Linux)*, icx *(Windows)*|Set `icpx/icx` compiler for SYCL code path.| +| Name | Value | Function | +|--------------------|-----------------------------------|---------------------------------------------| +| LLAMA_SYCL | ON (mandatory) | Enable build with SYCL code path. | +| LLAMA_SYCL_TARGET | INTEL *(default)* \| NVIDIA | Set the SYCL target device type. | +| LLAMA_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. | +| CMAKE_C_COMPILER | icx | Set *icx* compiler for SYCL code path. | +| CMAKE_CXX_COMPILER | icpx *(Linux)*, icx *(Windows)* | Set `icpx/icx` compiler for SYCL code path. | #### Runtime -|Name|Value|Function| -|-|-|-| -|GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG| -|ZES_ENABLE_SYSMAN| 0 (default) or 1|Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.
Recommended to use when --split-mode = layer| +| Name | Value | Function | +|-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------| +| GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG | +| ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.
Recommended to use when --split-mode = layer | ## Known Issues -- Hanging during startup - - llama.cpp uses *mmap* as the default mode for reading the model file and copying it to the GPU. In some systems, `memcpy` might behave abnormally and therefore hang. - - - **Solution**: add `--no-mmap` or `--mmap 0` flag to the `main` executable. - - `Split-mode:[row]` is not supported. ## Q&A @@ -574,7 +551,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512 - General compiler error: - - Remove build folder or try a clean-build. + - Remove **build** folder or try a clean-build. - I can **not** see `[ext_oneapi_level_zero:gpu]` afer installing the GPU driver on Linux. @@ -591,6 +568,6 @@ use 1 SYCL GPUs: [0] with Max compute units:512 ### **GitHub contribution**: Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay. -## Todo +## TODO - Support row layer split for multiple card runs. diff --git a/README.md b/README.md index b60662387..a2aa9214f 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Recent API changes +- [2024 Apr 21] `llama_token_to_piece` can now optionally render special tokens https://github.com/ggerganov/llama.cpp/pull/6807 - [2024 Apr 4] State and session file functions reorganized under `llama_state_*` https://github.com/ggerganov/llama.cpp/pull/6341 - [2024 Mar 26] Logits and embeddings API updated for compactness https://github.com/ggerganov/llama.cpp/pull/6122 - [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017 @@ -19,7 +20,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics -- **MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387** +- **BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920** +- MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387 - Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404 - Fix major bug in Metal batched inference https://github.com/ggerganov/llama.cpp/pull/6225 - Multi-GPU pipeline parallelism support https://github.com/ggerganov/llama.cpp/pull/6017 @@ -92,9 +94,11 @@ Typically finetunes of the base models below are supported as well. - [X] LLaMA 🦙 - [x] LLaMA 2 🦙🦙 +- [x] LLaMA 3 🦙🦙🦙 - [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) -- [X] Falcon +- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct) +- [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon) - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne) - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/) @@ -117,10 +121,14 @@ Typically finetunes of the base models below are supported as well. - [x] [CodeShell](https://github.com/WisdomShell/codeshell) - [x] [Gemma](https://ai.google.dev/gemma) - [x] [Mamba](https://github.com/state-spaces/mamba) +- [x] [Grok-1](https://huggingface.co/keyfan/grok-1-hf) - [x] [Xverse](https://huggingface.co/models?search=xverse) -- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01) +- [x] [Command-R models](https://huggingface.co/models?search=CohereForAI/c4ai-command-r) - [x] [SEA-LION](https://huggingface.co/models?search=sea-lion) - [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B) +- [x] [OLMo](https://allenai.org/olmo) + +(instructions for supporting more models: [HOWTO-add-model.md](./docs/HOWTO-add-model.md)) **Multimodal models:** @@ -130,6 +138,8 @@ Typically finetunes of the base models below are supported as well. - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V) - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) - [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL) +- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM) +- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2) **HTTP server** @@ -185,6 +195,8 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [Dot](https://github.com/alexpinel/Dot) (GPL) - [MindMac](https://mindmac.app) (proprietary) - [KodiBot](https://github.com/firatkiral/kodibot) (GPL) +- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) +- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* @@ -483,20 +495,20 @@ Building the program with BLAS support may lead to some performance improvements The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance: - | Option | Legal values | Default | Description | - |--------------------------------|------------------------|---------|-------------| - | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | - | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | - | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | - | LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | - | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | - | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | + | Option | Legal values | Default | Description | + |--------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | + | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | + | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | + | LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | + | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | + | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | - #### hipBLAS 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). + You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick). - Using `make`: ```bash @@ -532,18 +544,18 @@ Building the program with BLAS support may lead to some performance improvements If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above): - | Option | Legal values | Default | Description | - |-------------------------|------------------------|---------|-------------| - | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | - | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | - | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | + | Option | Legal values | Default | Description | + |-------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | + | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | + | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | - #### CLBlast OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU. You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK). - - For Ubuntu or Debian, the packages `opencl-headers`, `ocl-icd` may be needed. + - For Ubuntu, Debian, and Fedora the packages `opencl-headers`, `ocl-icd` may be needed. - For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page. @@ -568,6 +580,12 @@ Building the program with BLAS support may lead to some performance improvements Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages. + Linux packaging: + Fedora Linux: + ```bash + sudo dnf install clblast + ``` + Alternatively, they may be built from source. -
@@ -744,11 +762,11 @@ From the unzipped folder, open a terminal/cmd window here and place a pre-conver As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same. | Model | Original size | Quantized size (Q4_0) | -|------:|--------------:|-----------------------:| -| 7B | 13 GB | 3.9 GB | -| 13B | 24 GB | 7.8 GB | -| 30B | 60 GB | 19.5 GB | -| 65B | 120 GB | 38.5 GB | +|------:|--------------:|----------------------:| +| 7B | 13 GB | 3.9 GB | +| 13B | 24 GB | 7.8 GB | +| 30B | 60 GB | 19.5 GB | +| 65B | 120 GB | 38.5 GB | ### Quantization @@ -756,7 +774,7 @@ Several quantization methods are supported. They differ in the resulting model d *(outdated)* -| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 | +| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 | |------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:| | 7B | perplexity | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 | | 7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G | @@ -1104,7 +1122,9 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m - Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a` - See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions - Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices -- Matrix multiplication is unconventional: [`z = ggml_mul_mat(ctx, x, y)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means `zT = x @ yT` +- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$ + +![matmul](media/matmul.png) ### Docs diff --git a/SECURITY.md b/SECURITY.md index 14504b1bf..f4322c6ee 100644 --- a/SECURITY.md +++ b/SECURITY.md @@ -49,11 +49,11 @@ If you intend to run multiple models in parallel with shared memory, it is your 1. Tenant Isolation: Models should run separately with strong isolation methods to prevent unwanted data access. Separating networks is crucial for isolation, as it prevents unauthorized access to data or models and malicious users from sending graphs to execute under another tenant's identity. -1. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring. +2. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring. -1. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk. +3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk. -1. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time. +4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time. ## Reporting a vulnerability diff --git a/build.zig b/build.zig index 7f36e5968..96783574f 100644 --- a/build.zig +++ b/build.zig @@ -112,6 +112,7 @@ pub fn build(b: *std.build.Builder) !void { make.enable_lto = b.option(bool, "lto", "Enable LTO optimization, (default: false)") orelse false; const ggml = make.obj("ggml", "ggml.c"); + const sgemm = make.obj("sgemm", "sgemm.cpp"); const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c"); const ggml_backend = make.obj("ggml-backend", "ggml-backend.c"); const ggml_quants = make.obj("ggml-quants", "ggml-quants.c"); @@ -128,15 +129,44 @@ pub fn build(b: *std.build.Builder) !void { const clip = make.obj("clip", "examples/llava/clip.cpp"); const llava = make.obj("llava", "examples/llava/llava.cpp"); - _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, sampling, console, grammar_parser }); - _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo }); - _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo }); - _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo }); - _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, train }); - _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, train }); + _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, console, grammar_parser }); + _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); + _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); + _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); + _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train }); + _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, sampling, grammar_parser, json_schema_to_grammar, clip, llava }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, grammar_parser, clip, llava }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } + + const server_assets = [_][]const u8{ "index.html", "index.js", "completion.js", "json-schema-to-grammar.mjs" }; + for (server_assets) |asset| { + const input_path = b.fmt("examples/server/public/{s}", .{asset}); + const output_path = b.fmt("examples/server/{s}.hpp", .{asset}); + + // Portable equivalent of `b.addSystemCommand(&.{ "xxd", "-n", asset, "-i", input_path, output_path }) })`: + + const input = try std.fs.cwd().readFileAlloc(b.allocator, input_path, std.math.maxInt(usize)); + defer b.allocator.free(input); + + var buf = std.ArrayList(u8).init(b.allocator); + defer buf.deinit(); + + for (input) |byte| { + try std.fmt.format(buf.writer(), "0x{X:0>2}, ", .{byte}); + } + + var name = try std.mem.replaceOwned(u8, b.allocator, asset, "-", "_"); + defer b.allocator.free(name); + std.mem.replaceScalar(u8, name, '.', '_'); + + try std.fs.cwd().writeFile(output_path, b.fmt( + "unsigned char {s}[] = {{{s}}};\nunsigned int {s}_len = {d};\n", + .{ name, buf.items, name, input.len }, + )); + + std.debug.print("Dumped hex of \"{s}\" ({s}) to {s}\n", .{ input_path, name, output_path }); + } } diff --git a/ci/run.sh b/ci/run.sh index 19776b5f7..da05f0d48 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -153,6 +153,55 @@ function gg_sum_ctest_release { gg_printf '```\n' } +# test_scripts_debug + +function gg_run_test_scripts_debug { + cd ${SRC} + + set -e + + # TODO: too slow, run on dedicated node + (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + #(cd ./examples/quantize && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + + set +e +} + +function gg_sum_test_scripts_debug { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs test scripts in debug mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)" + gg_printf '```\n' + gg_printf '\n' +} + +# test_scripts_release + +function gg_run_test_scripts_release { + cd ${SRC} + + set -e + + (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + (cd ./examples/quantize && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + + set +e +} + +function gg_sum_test_scripts_release { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs test scripts in release mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)" + gg_printf '```\n' + gg_printf '\n' +} + function gg_get_model { local gguf_3b="$MNT/models/open-llama/3B-v2/ggml-model-f16.gguf" local gguf_7b="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf" @@ -642,6 +691,9 @@ test $ret -eq 0 && gg_run ctest_release if [ -z ${GG_BUILD_LOW_PERF} ]; then test $ret -eq 0 && gg_run embd_bge_small + test $ret -eq 0 && gg_run test_scripts_debug + test $ret -eq 0 && gg_run test_scripts_release + if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then if [ -z ${GG_BUILD_CUDA} ]; then test $ret -eq 0 && gg_run open_llama_3b_v2 diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 1d840e5f7..0ec8d6d8d 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -47,9 +47,6 @@ if (BUILD_SHARED_LIBS) set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) endif() -set(TARGET json-schema-to-grammar) -add_library(${TARGET} OBJECT json-schema-to-grammar.cpp json-schema-to-grammar.h) - set(TARGET common) add_library(${TARGET} STATIC @@ -63,6 +60,7 @@ add_library(${TARGET} STATIC grammar-parser.h grammar-parser.cpp json.hpp + json-schema-to-grammar.cpp train.h train.cpp ngram-cache.h diff --git a/common/common.cpp b/common/common.cpp index 98fc8388c..aa494291d 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1,4 +1,6 @@ #include "common.h" +#include "json.hpp" +#include "json-schema-to-grammar.h" #include "llama.h" #include @@ -68,6 +70,8 @@ #define LLAMA_CURL_MAX_HEADER_LENGTH 256 #endif // LLAMA_USE_CURL +using json = nlohmann::ordered_json; + int32_t get_num_physical_cores() { #ifdef __linux__ // enumerate the set of thread siblings, num entries is num cores @@ -104,6 +108,79 @@ int32_t get_num_physical_cores() { return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4; } +#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__) +#include + +static void cpuid(unsigned leaf, unsigned subleaf, + unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) { + __asm__("movq\t%%rbx,%%rsi\n\t" + "cpuid\n\t" + "xchgq\t%%rbx,%%rsi" + : "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx) + : "0"(leaf), "2"(subleaf)); +} + +static int pin_cpu(int cpu) { + cpu_set_t mask; + CPU_ZERO(&mask); + CPU_SET(cpu, &mask); + return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask); +} + +static bool is_hybrid_cpu(void) { + unsigned eax, ebx, ecx, edx; + cpuid(7, 0, &eax, &ebx, &ecx, &edx); + return !!(edx & (1u << 15)); +} + +static bool is_running_on_efficiency_core(void) { + unsigned eax, ebx, ecx, edx; + cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx); + int intel_atom = 0x20; + int core_type = (eax & 0xff000000u) >> 24; + return core_type == intel_atom; +} + +static int count_math_cpus(int cpu_count) { + int result = 0; + for (int cpu = 0; cpu < cpu_count; ++cpu) { + if (pin_cpu(cpu)) { + return -1; + } + if (is_running_on_efficiency_core()) { + continue; // efficiency cores harm lockstep threading + } + ++cpu; // hyperthreading isn't useful for linear algebra + ++result; + } + return result; +} + +#endif // __x86_64__ && __linux__ + +/** + * Returns number of CPUs on system that are useful for math. + */ +int get_math_cpu_count() { +#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__) + int cpu_count = sysconf(_SC_NPROCESSORS_ONLN); + if (cpu_count < 1) { + return get_num_physical_cores(); + } + if (is_hybrid_cpu()) { + cpu_set_t affinity; + if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) { + int result = count_math_cpus(cpu_count); + pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity); + if (result > 0) { + return result; + } + } + } +#endif + return get_num_physical_cores(); +} + void process_escapes(std::string & input) { std::size_t input_len = input.length(); std::size_t output_idx = 0; @@ -157,15 +234,63 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { return result; } +bool parse_kv_override(const char * data, std::vector & overrides) { + const char * sep = strchr(data, '='); + if (sep == nullptr || sep - data >= 128) { + fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data); + return false; + } + llama_model_kv_override kvo; + std::strncpy(kvo.key, data, sep - data); + kvo.key[sep - data] = 0; + sep++; + if (strncmp(sep, "int:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.val_i64 = std::atol(sep); + } else if (strncmp(sep, "float:", 6) == 0) { + sep += 6; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; + kvo.val_f64 = std::atof(sep); + } else if (strncmp(sep, "bool:", 5) == 0) { + sep += 5; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; + if (std::strcmp(sep, "true") == 0) { + kvo.val_bool = true; + } else if (std::strcmp(sep, "false") == 0) { + kvo.val_bool = false; + } else { + fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data); + return false; + } + } else if (strncmp(sep, "str:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + if (strlen(sep) > 127) { + fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data); + return false; + } + strncpy(kvo.val_str, sep, 127); + kvo.val_str[127] = '\0'; + } else { + fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data); + return false; + } + overrides.emplace_back(std::move(kvo)); + return true; +} + bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) { - llama_sampling_params& sparams = params.sparams; + llama_sampling_params & sparams = params.sparams; if (arg == "-s" || arg == "--seed") { if (++i >= argc) { invalid_param = true; return true; } + // This is temporary, in the future the samplign state will be moved fully to llama_sampling_context. params.seed = std::stoul(argv[i]); + sparams.seed = std::stoul(argv[i]); return true; } if (arg == "-t" || arg == "--threads") { @@ -1010,6 +1135,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.n_print = std::stoi(argv[i]); return true; } + if (arg == "--check-tensors") { + params.check_tensors = true; + return true; + } if (arg == "--ppl-output-type") { if (++i >= argc) { invalid_param = true; @@ -1148,52 +1277,24 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa ); return true; } + if (arg == "-j" || arg == "--json-schema") { + if (++i >= argc) { + invalid_param = true; + return true; + } + sparams.grammar = json_schema_to_grammar(json::parse(argv[i])); + return true; + } if (arg == "--override-kv") { if (++i >= argc) { invalid_param = true; return true; } - char* sep = strchr(argv[i], '='); - if (sep == nullptr || sep - argv[i] >= 128) { - fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]); - invalid_param = true; - return true; - } - struct llama_model_kv_override kvo; - std::strncpy(kvo.key, argv[i], sep - argv[i]); - kvo.key[sep - argv[i]] = 0; - sep++; - if (strncmp(sep, "int:", 4) == 0) { - sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; - kvo.int_value = std::atol(sep); - } - else if (strncmp(sep, "float:", 6) == 0) { - sep += 6; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; - kvo.float_value = std::atof(sep); - } - else if (strncmp(sep, "bool:", 5) == 0) { - sep += 5; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; - if (std::strcmp(sep, "true") == 0) { - kvo.bool_value = true; - } - else if (std::strcmp(sep, "false") == 0) { - kvo.bool_value = false; - } - else { - fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]); - invalid_param = true; - return true; - } - } - else { + if (!parse_kv_override(argv[i], params.kv_overrides)) { fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); invalid_param = true; return true; } - params.kv_overrides.push_back(kvo); return true; } #ifndef LOG_DISABLE_LOGS @@ -1353,6 +1454,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { 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(" -j SCHEMA, --json-schema SCHEMA\n"); + printf(" JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object.\n"); + printf(" For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead\n"); printf(" --cfg-negative-prompt PROMPT\n"); printf(" negative prompt to use for guidance. (default: empty)\n"); printf(" --cfg-negative-prompt-file FNAME\n"); @@ -1461,9 +1565,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" path to dynamic lookup cache to use for lookup decoding (updated by generation)\n"); printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); - printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); + printf(" types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); printf(" -ptc N, --print-token-count N\n"); printf(" print token count every N tokens (default: %d)\n", params.n_print); + printf(" --check-tensors check model tensor data for invalid values\n"); printf("\n"); #ifndef LOG_DISABLE_LOGS log_print_usage(); @@ -1588,6 +1693,18 @@ std::vector string_split(std::string input, char separator) { return parts; } +std::string string_strip(const std::string & str) { + size_t start = 0; + size_t end = str.size(); + while (start < end && std::isspace(str[start])) { + start++; + } + while (end > start && std::isspace(str[end - 1])) { + end--; + } + return str.substr(start, end - start); +} + std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names) { std::unordered_map sampler_canonical_name_map { {"top_k", llama_sampler_type::TOP_K}, @@ -1684,6 +1801,7 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & mparams.tensor_split = params.tensor_split; mparams.use_mmap = params.use_mmap; mparams.use_mlock = params.use_mlock; + mparams.check_tensors = params.check_tensors; if (params.kv_overrides.empty()) { mparams.kv_overrides = NULL; } else { @@ -1745,6 +1863,8 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.yarn_orig_ctx = params.yarn_orig_ctx; cparams.pooling_type = params.pooling_type; cparams.defrag_thold = params.defrag_thold; + cparams.cb_eval = params.cb_eval; + cparams.cb_eval_user_data = params.cb_eval_user_data; cparams.offload_kqv = !params.no_kv_offload; cparams.type_k = kv_cache_type_from_str(params.cache_type_k); @@ -2192,7 +2312,7 @@ std::tuple llama_init_from_gpt_par params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; } - { + if (params.warmup) { LOG("warming up the model with an empty run\n"); std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; @@ -2236,12 +2356,12 @@ std::vector llama_tokenize( return result; } -std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) { +std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) { std::vector result(8, 0); - const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size()); + const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special); if (n_tokens < 0) { result.resize(-n_tokens); - int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size()); + int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special); GGML_ASSERT(check == -n_tokens); } else { result.resize(n_tokens); diff --git a/common/common.h b/common/common.h index a7f476c1b..eea63a114 100644 --- a/common/common.h +++ b/common/common.h @@ -39,6 +39,7 @@ extern char const *LLAMA_BUILD_TARGET; struct llama_control_vector_load_info; +int get_math_cpu_count(); int32_t get_num_physical_cores(); // @@ -48,7 +49,7 @@ int32_t get_num_physical_cores(); struct gpt_params { uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed - int32_t n_threads = get_num_physical_cores(); + int32_t n_threads = get_math_cpu_count(); int32_t n_threads_draft = -1; int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) int32_t n_threads_batch_draft = -1; @@ -80,10 +81,13 @@ struct gpt_params { int32_t yarn_orig_ctx = 0; // YaRN original context length float defrag_thold = -1.0f; // KV cache defragmentation threshold + ggml_backend_sched_eval_callback cb_eval = nullptr; + void * cb_eval_user_data = nullptr; + ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; - llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; - llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings + enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; + enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings // // sampling parameters struct llama_sampling_params sparams; @@ -156,6 +160,8 @@ struct gpt_params { bool infill = false; // use infill mode bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes bool no_kv_offload = false; // disable KV offloading + bool warmup = true; // warmup run + bool check_tensors = false; // validate tensor data std::string cache_type_k = "f16"; // KV cache data type for the K std::string cache_type_v = "f16"; // KV cache data type for the V @@ -165,6 +171,8 @@ struct gpt_params { std::string image = ""; // path to an image file }; +bool parse_kv_override(const char * data, std::vector & overrides); + bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params); bool gpt_params_parse(int argc, char ** argv, gpt_params & params); @@ -188,6 +196,7 @@ bool validate_file_name(const std::string & filename); std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names); std::vector sampler_types_from_chars(const std::string & names_string); std::vector string_split(std::string input, char separator); +std::string string_strip(const std::string & str); std::string sampler_type_to_name_string(llama_sampler_type sampler_type); // @@ -232,11 +241,12 @@ std::vector llama_tokenize( bool add_special, bool parse_special = false); -// tokenizes a token into a piece +// tokenizes a token into a piece, optionally renders special/control tokens // should work similar to Python's `tokenizer.id_to_piece` std::string llama_token_to_piece( const struct llama_context * ctx, - llama_token token); + llama_token token, + bool special = true); // TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function // that takes into account the tokenizer type and decides how to handle the leading space diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index 0e4680346..0f8f1b1d4 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -11,35 +11,101 @@ using json = nlohmann::ordered_json; +template +static std::string join(Iterator begin, Iterator end, const std::string & separator); + +static std::string repeat(const std::string & str, size_t n); + +static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "", bool item_rule_is_literal = false) { + if (separator_rule.empty()) { + if (min_items == 0 && max_items == 1) { + return item_rule + "?"; + } else if (min_items == 1 && max_items == std::numeric_limits::max()) { + return item_rule + "+"; + } + } + + std::string result; + if (min_items > 0) { + if (item_rule_is_literal && separator_rule.empty()) { + result = "\"" + repeat(std::string(item_rule.begin() + 1, item_rule.end() - 1), min_items) + "\""; + } else { + std::vector items(min_items, item_rule); + result = join(items.begin(), items.end(), separator_rule.empty() ? " " : " " + separator_rule + " "); + } + } + + std::function opt_repetitions = [&](int up_to_n, bool prefix_with_sep) -> std::string { + auto content = prefix_with_sep && !separator_rule.empty() ? separator_rule + " " + item_rule : item_rule; + + if (up_to_n == 0) { + return ""; + } else if (up_to_n == 1) { + return "(" + content + ")?"; + } else if (!separator_rule.empty() && !prefix_with_sep) { + return "(" + content + " " + opt_repetitions(up_to_n - 1, true) + ")?"; + } else { + std::string res = repeat("(" + content + " ", up_to_n); + // strip trailing space + res = res.substr(0, res.length() - 1); + res += repeat(")?", up_to_n); + return res; + } + }; + + if (min_items > 0 && max_items != min_items) { + result += " "; + } + + if (max_items != std::numeric_limits::max()) { + result += opt_repetitions(max_items - min_items, min_items > 0); + } else { + std::string item_operator = "(" + (separator_rule.empty() ? "" : separator_rule + " ") + item_rule + ")"; + if (min_items == 0 && !separator_rule.empty()) { + result = "(" + item_rule + " " + item_operator + "*)?"; + } else { + result += item_operator + "*"; + } + } + + return result; +} + const std::string SPACE_RULE = "\" \"?"; -std::unordered_map PRIMITIVE_RULES = { - {"boolean", "(\"true\" | \"false\") space"}, - {"number", "(\"-\"? ([0-9] | [1-9] [0-9]*)) (\".\" [0-9]+)? ([eE] [-+]? [0-9]+)? space"}, - {"integer", "(\"-\"? ([0-9] | [1-9] [0-9]*)) space"}, - {"value", "object | array | string | number | boolean"}, - {"object", "\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space"}, - {"array", "\"[\" space ( value (\",\" space value)* )? \"]\" space"}, - {"uuid", "\"\\\"\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " - "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " - "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " - "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " - "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] \"\\\"\" space"}, - {"string", " \"\\\"\" (\n" - " [^\"\\\\] |\n" - " \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])\n" - " )* \"\\\"\" space"}, - {"null", "\"null\" space"} +struct BuiltinRule { + std::string content; + std::vector deps; }; -std::vector OBJECT_RULE_NAMES = {"object", "array", "string", "number", "boolean", "null", "value"}; -std::unordered_map DATE_RULES = { - {"date", "[0-9] [0-9] [0-9] [0-9] \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )"}, - {"time", "([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9] [0-9] [0-9] )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )"}, - {"date-time", "date \"T\" time"}, - {"date-string", "\"\\\"\" date \"\\\"\" space"}, - {"time-string", "\"\\\"\" time \"\\\"\" space"}, - {"date-time-string", "\"\\\"\" date-time \"\\\"\" space"} +const std::string _up_to_15_digits = build_repetition("[0-9]", 0, 15); + +std::unordered_map PRIMITIVE_RULES = { + {"boolean", {"(\"true\" | \"false\") space", {}}}, + {"decimal-part", {"[0-9] " + _up_to_15_digits, {}}}, + {"integral-part", {"[0-9] | [1-9] " + _up_to_15_digits, {}}}, + {"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}}, + {"integer", {"(\"-\"? integral-part) space", {"integral-part"}}}, + {"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}}, + {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}}, + {"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}}, + {"uuid", {"\"\\\"\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " + "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " + "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " + "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " + "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] \"\\\"\" space", {}}}, + {"char", {"[^\"\\\\] | \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])", {}}}, + {"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}}, + {"null", {"\"null\" space", {}}}, +}; + +std::unordered_map STRING_FORMAT_RULES = { + {"date", {"[0-9] [0-9] [0-9] [0-9] \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}}, + {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9] [0-9] [0-9] )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}}, + {"date-time", {"date \"T\" time", {"date", "time"}}}, + {"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}}, + {"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}}, + {"date-time-string", {"\"\\\"\" date-time \"\\\"\" space", {"date-time"}}} }; static bool is_reserved_name(const std::string & name) { @@ -47,7 +113,7 @@ static bool is_reserved_name(const std::string & name) { if (RESERVED_NAMES.empty()) { RESERVED_NAMES.insert("root"); for (const auto &p : PRIMITIVE_RULES) RESERVED_NAMES.insert(p.first); - for (const auto &p : DATE_RULES) RESERVED_NAMES.insert(p.first); + for (const auto &p : STRING_FORMAT_RULES) RESERVED_NAMES.insert(p.first); } return RESERVED_NAMES.find(name) != RESERVED_NAMES.end(); } @@ -192,7 +258,7 @@ private: if (_dotall) { rule = "[\\U00000000-\\U0010FFFF]"; } else { - rule = "[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]"; + rule = "[^\\x0A\\x0D]"; } return _add_rule("dot", rule); }; @@ -308,47 +374,21 @@ private: auto &sub = last.first; auto sub_is_literal = last.second; - if (min_times == 0 && max_times == std::numeric_limits::max()) { - sub += "*"; - } else if (min_times == 0 && max_times == 1) { - sub += "?"; - } else if (min_times == 1 && max_times == std::numeric_limits::max()) { - sub += "+"; - } else { - if (!sub_is_literal) { - std::string & sub_id = sub_rule_ids[sub]; - if (sub_id.empty()) { - sub_id = _add_rule(name + "-" + std::to_string(sub_rule_ids.size()), sub); - } - sub = sub_id; + if (!sub_is_literal) { + std::string & sub_id = sub_rule_ids[sub]; + if (sub_id.empty()) { + sub_id = _add_rule(name + "-" + std::to_string(sub_rule_ids.size()), sub); } - std::string result; - if (sub_is_literal && min_times > 0) { - result = "\"" + repeat(sub.substr(1, sub.length() - 2), min_times) + "\""; - } else { - for (int j = 0; j < min_times; j++) { - if (j > 0) { - result += " "; - } - result += sub; - } - } - if (min_times > 0 && min_times < max_times) { - result += " "; - } - if (max_times == std::numeric_limits::max()) { - result += sub + "*"; - } else { - for (int j = min_times; j < max_times; j++) { - if (j > min_times) { - result += " "; - } - result += sub + "?"; - } - } - seq.back().first = result; - seq.back().second = false; + sub = sub_id; } + seq.back().first = build_repetition( + sub_is_literal ? "\"" + sub + "\"" : sub, + min_times, + max_times, + "", + sub_is_literal + ); + seq.back().second = false; } else { std::string literal; auto is_non_literal = [&](char c) { @@ -424,7 +464,7 @@ private: if (additional_properties.is_object() || (additional_properties.is_boolean() && additional_properties.get())) { std::string sub_name = name + (name.empty() ? "" : "-") + "additional"; std::string value_rule = visit(additional_properties.is_object() ? additional_properties : json::object(), sub_name + "-value"); - std::string kv_rule = _add_rule(sub_name + "-kv", _add_rule("string", PRIMITIVE_RULES.at("string")) + " \":\" space " + value_rule); + std::string kv_rule = _add_rule(sub_name + "-kv", _add_primitive("string", PRIMITIVE_RULES.at("string")) + " \":\" space " + value_rule); prop_kv_rule_names["*"] = kv_rule; optional_props.push_back("*"); } @@ -486,6 +526,25 @@ private: return rule; } + std::string _add_primitive(const std::string & name, const BuiltinRule & rule) { + auto n = _add_rule(name, rule.content); + for (const auto & dep : rule.deps) { + BuiltinRule dep_rule; + auto it = PRIMITIVE_RULES.find(dep); + if (it == PRIMITIVE_RULES.end()) { + it = STRING_FORMAT_RULES.find(dep); + if (it == STRING_FORMAT_RULES.end()) { + _errors.push_back("Rule " + dep + " not known"); + continue; + } + } + if (_rules.find(dep) == _rules.end()) { + _add_primitive(dep, it->second); + } + } + return n; + } + public: SchemaConverter( const std::function & fetch_json, @@ -647,49 +706,33 @@ public: return _add_rule(rule_name, rule); } else { std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item"); - std::string list_item_operator = "( \",\" space " + item_rule_name + " )"; - std::string successive_items; int min_items = schema.contains("minItems") ? schema["minItems"].get() : 0; json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json(); - int max_items = max_items_json.is_number_integer() ? max_items_json.get() : -1; - if (min_items > 0) { - successive_items += repeat(list_item_operator, min_items - 1); - min_items--; - } - if (max_items >= 0 && max_items > min_items) { - successive_items += repeat(list_item_operator + "?", max_items - min_items - 1); - } else { - successive_items += list_item_operator + "*"; - } - std::string rule; - if (min_items == 0) { - rule = "\"[\" space ( " + item_rule_name + " " + successive_items + " )? \"]\" space"; - } else { - rule = "\"[\" space " + item_rule_name + " " + successive_items + " \"]\" space"; - } - return _add_rule(rule_name, rule); + int max_items = max_items_json.is_number_integer() ? max_items_json.get() : std::numeric_limits::max(); + + return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " \"]\" space"); } } else if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) { return _visit_pattern(schema["pattern"], rule_name); } else if ((schema_type.is_null() || schema_type == "string") && std::regex_match(schema_format, std::regex("^uuid[1-5]?$"))) { - return _add_rule(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid")); - } else if ((schema_type.is_null() || schema_type == "string") && DATE_RULES.find(schema_format) != DATE_RULES.end()) { - for (const auto & kv : DATE_RULES) { - _add_rule(kv.first, kv.second); - } - return schema_format + "-string"; + return _add_primitive(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid")); + } else if ((schema_type.is_null() || schema_type == "string") && STRING_FORMAT_RULES.find(schema_format + "-string") != STRING_FORMAT_RULES.end()) { + auto prim_name = schema_format + "-string"; + return _add_rule(rule_name, _add_primitive(prim_name, STRING_FORMAT_RULES.at(prim_name))); + } else if (schema_type == "string" && (schema.contains("minLength") || schema.contains("maxLength"))) { + std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char")); + int min_len = schema.contains("minLength") ? schema["minLength"].get() : 0; + int max_len = schema.contains("maxLength") ? schema["maxLength"].get() : std::numeric_limits::max(); + return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space"); } else if (schema.empty() || schema_type == "object") { - for (const auto & n : OBJECT_RULE_NAMES) { - _add_rule(n, PRIMITIVE_RULES.at(n)); - } - return _add_rule(rule_name, "object"); + return _add_rule(rule_name, _add_primitive("object", PRIMITIVE_RULES.at("object"))); } else { if (!schema_type.is_string() || PRIMITIVE_RULES.find(schema_type.get()) == PRIMITIVE_RULES.end()) { _errors.push_back("Unrecognized schema: " + schema.dump()); return ""; } // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero - return _add_rule(rule_name == "root" ? "root" : schema_type.get(), PRIMITIVE_RULES.at(schema_type.get())); + return _add_primitive(rule_name == "root" ? "root" : schema_type.get(), PRIMITIVE_RULES.at(schema_type.get())); } } diff --git a/common/log.h b/common/log.h index e4edcac7d..2b2f0e455 100644 --- a/common/log.h +++ b/common/log.h @@ -234,7 +234,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: // INTERNAL, DO NOT USE // USE LOG() INSTEAD // -#if !defined(_MSC_VER) or defined(__INTEL_LLVM_COMPILER) +#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) #define LOG_IMPL(str, ...) \ do { \ if (LOG_TARGET != nullptr) \ @@ -257,7 +257,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: // INTERNAL, DO NOT USE // USE LOG_TEE() INSTEAD // -#if !defined(_MSC_VER) or defined(__INTEL_LLVM_COMPILER) +#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) #define LOG_TEE_IMPL(str, ...) \ do { \ if (LOG_TARGET != nullptr) \ diff --git a/common/sampling.cpp b/common/sampling.cpp index 45d68b26c..cc83600d9 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -1,4 +1,6 @@ +#define LLAMA_API_INTERNAL #include "sampling.h" +#include struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) { struct llama_sampling_context * result = new llama_sampling_context(); @@ -33,6 +35,8 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_ result->prev.resize(params.n_prev); + llama_sampling_set_rng_seed(result, params.seed); + return result; } @@ -62,6 +66,13 @@ void llama_sampling_reset(llama_sampling_context * ctx) { ctx->cur.clear(); } +void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed) { + if (seed == LLAMA_DEFAULT_SEED) { + seed = std::random_device{}(); + } + ctx->rng.seed(seed); +} + void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) { if (dst->grammar) { llama_grammar_free(dst->grammar); @@ -203,7 +214,7 @@ static llama_token llama_sampling_sample_impl( sampler_queue(ctx_main, params, cur_p, min_keep); - id = llama_sample_token(ctx_main, &cur_p); + id = llama_sample_token_with_rng(ctx_main, &cur_p, ctx_sampling->rng); //{ // const int n_top = 10; diff --git a/common/sampling.h b/common/sampling.h index 639b819ab..cf7081e36 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -4,9 +4,10 @@ #include "grammar-parser.h" +#include #include -#include #include +#include // sampler types enum class llama_sampler_type : char { @@ -20,25 +21,26 @@ enum class llama_sampler_type : char { // sampling parameters typedef struct llama_sampling_params { - int32_t n_prev = 64; // number of previous tokens to remember - int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. - int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens - int32_t top_k = 40; // <= 0 to use vocab size - float top_p = 0.95f; // 1.0 = disabled - float min_p = 0.05f; // 0.0 = disabled - float tfs_z = 1.00f; // 1.0 = disabled - float typical_p = 1.00f; // 1.0 = disabled - float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities - float dynatemp_range = 0.00f; // 0.0 = disabled - float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler - int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) - float penalty_repeat = 1.00f; // 1.0 = disabled - float penalty_freq = 0.00f; // 0.0 = disabled - float penalty_present = 0.00f; // 0.0 = disabled - int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 - float mirostat_tau = 5.00f; // target entropy - float mirostat_eta = 0.10f; // learning rate - bool penalize_nl = false; // consider newlines as a repeatable token + int32_t n_prev = 64; // number of previous tokens to remember + int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens + int32_t top_k = 40; // <= 0 to use vocab size + float top_p = 0.95f; // 1.0 = disabled + float min_p = 0.05f; // 0.0 = disabled + float tfs_z = 1.00f; // 1.0 = disabled + float typical_p = 1.00f; // 1.0 = disabled + float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities + float dynatemp_range = 0.00f; // 0.0 = disabled + float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler + int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) + float penalty_repeat = 1.00f; // 1.0 = disabled + float penalty_freq = 0.00f; // 0.0 = disabled + float penalty_present = 0.00f; // 0.0 = disabled + int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 + float mirostat_tau = 5.00f; // target entropy + float mirostat_eta = 0.10f; // learning rate + bool penalize_nl = false; // consider newlines as a repeatable token + uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampling_context std::vector samplers_sequence = { llama_sampler_type::TOP_K, @@ -79,6 +81,8 @@ struct llama_sampling_context { // TODO: replace with ring-buffer std::vector prev; std::vector cur; + + std::mt19937 rng; }; #include "common.h" @@ -93,6 +97,9 @@ void llama_sampling_free(struct llama_sampling_context * ctx); // - reset grammar void llama_sampling_reset(llama_sampling_context * ctx); +// Set the sampler seed +void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed); + // Copy the sampler context void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst); diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py new file mode 100644 index 000000000..1c559c3f6 --- /dev/null +++ b/convert-hf-to-gguf-update.py @@ -0,0 +1,275 @@ +# This script downloads the tokenizer models of the specified models from Huggingface and +# generates the get_vocab_base_pre() function for convert-hf-to-gguf.py +# +# This is necessary in order to analyze the type of pre-tokenizer used by the model and +# provide the necessary information to llama.cpp via the GGUF header in order to implement +# the same pre-tokenizer. +# +# ref: https://github.com/ggerganov/llama.cpp/pull/6920 +# +# Instructions: +# +# - Add a new model to the "models" list +# - Run the script with your huggingface token: +# +# python3 convert-hf-to-gguf-update.py +# +# - Copy-paste the generated get_vocab_base_pre() function into convert-hf-to-gguf.py +# - Update llama.cpp with the new pre-tokenizer if necessary +# +# TODO: generate tokenizer tests for llama.cpp +# TODO: automate the update of convert-hf-to-gguf.py +# + +import os +import requests +import sys +import json + +from hashlib import sha256 +from enum import IntEnum, auto + +class TOKENIZER_TYPE(IntEnum): + SPM = auto() + BPE = auto() + WPM = auto() + +# TODO: this string has to exercise as much pre-tokenizer functionality as possible +# will be updated with time - contributions welcome +chktxt = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL' + +if len(sys.argv) == 2: + token = sys.argv[1] +else: + print("Usage: python convert-hf-to-gguf-update.py ") + sys.exit(1) + +# TODO: add models here, base models preferred +models = [ + { "name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", }, + { "name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", }, + { "name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", }, + { "name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", }, + { "name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", }, + { "name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", }, + { "name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", }, + { "name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, + { "name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, + { "name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, + ] + +# make directory "models/tokenizers" if it doesn't exist +if not os.path.exists("models/tokenizers"): + os.makedirs("models/tokenizers") + +def download_file_with_auth(url, token, save_path): + headers = {"Authorization": f"Bearer {token}"} + response = requests.get(url, headers=headers) + if response.status_code == 200: + with open(save_path, 'wb') as f: + f.write(response.content) + print(f"File {save_path} downloaded successfully") + else: + print(f"Failed to download file. Status code: {response.status_code}") + +# download the tokenizer models +for model in models: + name = model["name"] + repo = model["repo"] + tokt = model["tokt"] + + if not os.path.exists(f"models/tokenizers/{name}"): + os.makedirs(f"models/tokenizers/{name}") + else: + print(f"Directory models/tokenizers/{name} already exists - skipping") + continue + + print(f"Downloading {name} to models/tokenizers/{name}") + + url = f"{repo}/raw/main/config.json" + save_path = f"models/tokenizers/{name}/config.json" + download_file_with_auth(url, token, save_path) + + url = f"{repo}/raw/main/tokenizer.json" + save_path = f"models/tokenizers/{name}/tokenizer.json" + download_file_with_auth(url, token, save_path) + + if tokt == TOKENIZER_TYPE.SPM: + url = f"{repo}/resolve/main/tokenizer.model" + save_path = f"models/tokenizers/{name}/tokenizer.model" + download_file_with_auth(url, token, save_path) + + url = f"{repo}/raw/main/tokenizer_config.json" + save_path = f"models/tokenizers/{name}/tokenizer_config.json" + download_file_with_auth(url, token, save_path) + +# generate the source code for the convert-hf-to-gguf.py:get_vocab_base_pre() function: +# TODO: auto-update convert-hf-to-gguf.py with the generated function + +src_ifs = "" +for model in models: + name = model["name"] + tokt = model["tokt"] + + if tokt == TOKENIZER_TYPE.SPM: + continue + + # create the tokenizer + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + + chktok = tokenizer.encode(chktxt) + chkhsh = sha256(str(chktok).encode()).hexdigest() + + print(f"model: {name}") + print(f"tokt: {tokt}") + print(f"repo: {model['repo']}") + print(f"chktok: {chktok}") + print(f"chkhsh: {chkhsh}") + + # print the "pre_tokenizer" content from the tokenizer.json + with open(f"models/tokenizers/{name}/tokenizer.json", "r") as f: + cfg = json.load(f) + pre_tokenizer = cfg["pre_tokenizer"] + print("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4)) + + print(f"\n") + + src_ifs += f" if chkhsh == \"{chkhsh}\":\n" + src_ifs += f" # ref: {model['repo']}\n" + src_ifs += f" res = \"{name}\"\n" + +src_func = "" +src_func += " def get_vocab_base_pre(self, tokenizer) -> str:\n" +src_func += " # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that\n" +src_func += " # is specific for the BPE pre-tokenizer used by the model\n" +src_func += " # we will use this unique identifier to write a \"tokenizer.ggml.pre\" entry in the GGUF file which we can\n" +src_func += " # use in llama.cpp to implement the same pre-tokenizer\n" +src_func += "\n" +src_func += f" chktxt = {repr(chktxt)}\n" +src_func += "\n" +src_func += " chktok = tokenizer.encode(chktxt)\n" +src_func += " chkhsh = sha256(str(chktok).encode()).hexdigest()\n" +src_func += "\n" +src_func += " print(f\"chktok: {chktok}\")\n" +src_func += " print(f\"chkhsh: {chkhsh}\")\n" +src_func += "\n" +src_func += " res = None\n" +src_func += "\n" +src_func += " # NOTE: if you get an error here, you need to add the model to the if-elif chain below\n" +src_func += " # don't do this manually - use the convert-hf-to-gguf-update.py script!\n" +src_func += f"{src_ifs}\n" +src_func += " if res is None:\n" +src_func += " print(\"\\n\")\n" +src_func += " print(\"**************************************************************************************\")\n" +src_func += " print(\"** WARNING: The BPE pre-tokenizer was not recognized!\")\n" +src_func += " print(\"** This means that it was not added yet or you are using an older version.\")\n" +src_func += " print(\"** Check convert-hf-to-gguf-update.py and update it accordingly.\")\n" +src_func += " print(\"**\")\n" +src_func += " print(f\"** chkhsh: {chkhsh}\")\n" +src_func += " print(\"**************************************************************************************\")\n" +src_func += " print(\"\\n\")\n" +src_func += " raise NotImplementedError(\"BPE pre-tokenizer was not recognized - update get_vocab_base_pre()\")\n" +src_func += "\n" +src_func += " print(f\"tokenizer.ggml.pre: {res}\")\n" +src_func += " print(f\"chkhsh: {chkhsh}\")\n" +src_func += "\n" +src_func += " return res\n" + +print(src_func) + +print("\n") +print("!!! Copy-paste the function above into convert-hf-to-gguf.py !!!") +print("\n") + +# generate tests for each tokenizer model + +tests = [ + "", + " ", + " ", + " ", + "\t", + "\n", + "\n\n", + "\n\n\n", + "\t\n", + "Hello world", + " Hello world", + "Hello World", + " Hello World", + " Hello World!", + "Hello, world!", + " Hello, world!", + " this is 🦙.cpp", + "w048 7tuijk dsdfhu", + "нещо на Български", + "កាន់តែពិសេសអាចខលចេញ", + "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", + "Hello", + " Hello", + " Hello", + " Hello", + " Hello", + " Hello\n Hello", + " (", + "\n =", + "' era", + "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～", + "3", + "33", + "333", + "3333", + "33333", + "333333", + "3333333", + "33333333", + "333333333", + chktxt, +] + +# write the tests to ./models/ggml-vocab-{name}.gguf.inp +# the format is: +# +# test0 +# __ggml_vocab_test__ +# test1 +# __ggml_vocab_test__ +# ... +# + +# with each model, encode all tests and write the results in ./models/ggml-vocab-{name}.gguf.out +# for each test, write the resulting tokens on a separate line + +for model in models: + name = model["name"] + tokt = model["tokt"] + + # create the tokenizer + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + + with open(f"models/ggml-vocab-{name}.gguf.inp", "w") as f: + for text in tests: + f.write(f"{text}") + f.write("\n__ggml_vocab_test__\n") + + with open(f"models/ggml-vocab-{name}.gguf.out", "w") as f: + for text in tests: + res = tokenizer.encode(text, add_special_tokens=False) + for r in res: + f.write(f" {r}") + f.write("\n") + + print(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*") + +# generate commands for creating vocab files + +print("\nRun the following commands to generate the vocab files for testing:\n") + +for model in models: + name = model["name"] + + print(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") + +print("\n") diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 63710676b..d1b8cef11 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -11,6 +11,7 @@ import sys from abc import ABC, abstractmethod from enum import IntEnum from pathlib import Path +from hashlib import sha256 from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterator, Sequence, TypeVar, cast import numpy as np @@ -43,17 +44,18 @@ AnyModel = TypeVar("AnyModel", bound="type[Model]") class Model(ABC): _model_classes: dict[str, type[Model]] = {} - def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool): + def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool, use_temp_file: bool): self.dir_model = dir_model self.ftype = ftype self.fname_out = fname_out self.is_big_endian = is_big_endian self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE + self.use_temp_file = use_temp_file self.is_safetensors = self._is_model_safetensors() self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin") self.part_names = self._get_part_names() self.hparams = Model.load_hparams(self.dir_model) - self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False) + self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file) self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"]) @property @@ -228,7 +230,7 @@ class Model(ABC): return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1)) # used for GPT-2 BPE and WordPiece vocabs - def get_basic_vocab(self) -> tuple[list[str], list[int]]: + def get_vocab_base(self) -> tuple[list[str], list[int], str]: tokens: list[str] = [] toktypes: list[int] = [] @@ -237,6 +239,8 @@ class Model(ABC): vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab)) assert max(tokenizer.vocab.values()) < vocab_size + tokpre = self.get_vocab_base_pre(tokenizer) + reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} added_vocab = tokenizer.get_added_vocab() @@ -254,11 +258,75 @@ class Model(ABC): tokens.append(reverse_vocab[i]) toktypes.append(gguf.TokenType.NORMAL) - return tokens, toktypes + return tokens, toktypes, tokpre + + # NOTE: this function is generated by convert-hf-to-gguf-update.py + # do not modify it manually! + # ref: https://github.com/ggerganov/llama.cpp/pull/6920 + def get_vocab_base_pre(self, tokenizer) -> str: + # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that + # is specific for the BPE pre-tokenizer used by the model + # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can + # use in llama.cpp to implement the same pre-tokenizer + + chktxt = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶\u200d🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````""""......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL' + + chktok = tokenizer.encode(chktxt) + chkhsh = sha256(str(chktok).encode()).hexdigest() + + print(f"chktok: {chktok}") + print(f"chkhsh: {chkhsh}") + + res = None + + # NOTE: if you get an error here, you need to add the model to the if-elif chain below + # don't do this manually - use the convert-hf-to-gguf-update.py script! + if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5": + # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B + res = "llama-bpe" + if chkhsh == "049ecf7629871e3041641907f3de7c733e4dbfdc736f57d882ba0b0845599754": + # ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-base + res = "deepseek-llm" + if chkhsh == "347715f544604f9118bb75ed199f68779f423cabb20db6de6f31b908d04d7821": + # ref: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base + res = "deepseek-coder" + if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed": + # ref: https://huggingface.co/tiiuae/falcon-7b + res = "falcon" + if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": + # ref: https://huggingface.co/BAAI/bge-small-en-v1.5 + res = "bert-bge" + if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166": + # ref: https://huggingface.co/mosaicml/mpt-7b + res = "mpt" + if chkhsh == "35d91631860c815f952d711435f48d356ebac988362536bed955d43bfa436e34": + # ref: https://huggingface.co/bigcode/starcoder2-3b + res = "starcoder" + if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454": + # ref: https://huggingface.co/openai-community/gpt2 + res = "gpt-2" + + if res is None: + print("\n") + print("**************************************************************************************") + print("** WARNING: The BPE pre-tokenizer was not recognized!") + print("** This means that it was not added yet or you are using an older version.") + print("** Check convert-hf-to-gguf-update.py and update it accordingly.") + print("**") + print(f"** chkhsh: {chkhsh}") + print("**************************************************************************************") + print("\n") + raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()") + + print(f"tokenizer.ggml.pre: {res}") + print(f"chkhsh: {chkhsh}") + + return res def _set_vocab_gpt2(self) -> None: - tokens, toktypes = self.get_basic_vocab() + tokens, toktypes, tokpre = self.get_vocab_base() self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) @@ -276,6 +344,8 @@ class Model(ABC): vocab_size = hparams["vocab_size"] assert max(tokenizer.get_vocab().values()) < vocab_size + tokpre = self.get_vocab_base_pre(tokenizer) + merges = [] vocab = {} mergeable_ranks = tokenizer.mergeable_ranks @@ -303,6 +373,7 @@ class Model(ABC): toktypes.append(gguf.TokenType.NORMAL) self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) @@ -362,9 +433,20 @@ class Model(ABC): scores.append(-1000.0) toktypes.append(SentencePieceTokenTypes.USER_DEFINED) + if vocab_size > len(tokens): + pad_count = vocab_size - len(tokens) + print( + f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]" + ) + for i in range(1, pad_count + 1): + tokens.append(f"[PAD{i}]") + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.UNUSED) + assert len(tokens) == vocab_size self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_scores(scores) self.gguf_writer.add_token_types(toktypes) @@ -386,6 +468,7 @@ class Model(ABC): assert len(tokens) == vocab.vocab_size self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_scores(scores) self.gguf_writer.add_token_types(toktypes) @@ -829,6 +912,7 @@ class XverseModel(Model): toktypes.append(toktype) self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) @@ -1206,9 +1290,91 @@ class StableLMModel(Model): rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"]) self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"]))) self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"]) self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"])) + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + n_head = self.hparams.get("num_attention_heads") + n_kv_head = self.hparams.get("num_key_value_heads") + q_norms = dict() + k_norms = dict() + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + n_dims = len(data.shape) + if name.find("q_layernorm.norms") != -1: + q_norms[name] = data + if len(q_norms) >= (block_count * n_head): + self._stack_qk_norm(block_count, name, tensor_map, n_head, q_norms, n_dims, layer_name="q_layernorm") + continue + if name.find("k_layernorm.norms") != -1: + k_norms[name] = data + if len(k_norms) >= (block_count * n_kv_head): + self._stack_qk_norm(block_count, name, tensor_map, n_kv_head, k_norms, n_dims, layer_name="k_layernorm") + continue + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.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 self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")): + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + def _stack_qk_norm(self, block_count, name, tensor_map, n_head, norms, n_dims, layer_name="q_layernorm"): + for bid in range(block_count): + datas = [] + for xid in range(n_head): + ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight" + datas.append(norms[ename]) + del norms[ename] + data = np.stack(datas, axis=0) + data_dtype = data.dtype + merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight" + new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")): + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + @Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM") class LlamaModel(Model): @@ -1218,7 +1384,23 @@ class LlamaModel(Model): try: self. _set_vocab_sentencepiece() except FileNotFoundError: - self._set_vocab_llama_hf() + try: + self._set_vocab_llama_hf() + except (FileNotFoundError, TypeError): + # Llama 3 + self._set_vocab_gpt2() + + # Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256) + if self.hparams.get("vocab_size", 32000) == 32016: + special_vocab = gguf.SpecialVocab( + self.dir_model, load_merges=False, + special_token_types = ['prefix', 'suffix', 'middle', 'eot'] + ) + special_vocab._set_special_token("prefix", 32007) + special_vocab._set_special_token("suffix", 32008) + special_vocab._set_special_token("middle", 32009) + special_vocab._set_special_token("eot", 32010) + special_vocab.add_to_gguf(self.gguf_writer) def set_gguf_parameters(self): super().set_gguf_parameters() @@ -1226,6 +1408,11 @@ class LlamaModel(Model): self.gguf_writer.add_vocab_size(hparams["vocab_size"]) self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "linear": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + # Same as super class, but permuting q_proj, k_proj def write_tensors(self): block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) @@ -1427,6 +1614,102 @@ class GrokModel(Model): self.gguf_writer.add_tensor(new_name, data) +@Model.register("DbrxForCausalLM") +class DbrxModel(Model): + model_arch = gguf.MODEL_ARCH.DBRX + + def set_gguf_parameters(self): + ffn_config = self.hparams["ffn_config"] + attn_config = self.hparams["attn_config"] + self.gguf_writer.add_name(self.hparams["model_type"]) + self.gguf_writer.add_block_count(self.hparams["n_layers"]) + + self.gguf_writer.add_context_length(self.hparams["max_seq_len"]) + self.gguf_writer.add_embedding_length(self.hparams["d_model"]) + self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"]) + + self.gguf_writer.add_head_count(self.hparams["n_heads"]) + self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"]) + + self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"]) + + self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"]) + self.gguf_writer.add_file_type(self.ftype) + + self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"]) + self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"]) + + self.gguf_writer.add_layer_norm_eps(1e-5) + + self.gguf_writer.add_file_type(self.ftype) + print(f"gguf: file type = {self.ftype}") + + def write_tensors(self): + block_count = self.hparams.get("n_layers") + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + for name, data_torch in self.get_tensors(): + n_expert = self.hparams["ffn_config"]["moe_num_experts"] + n_ff = self.hparams["ffn_config"]["ffn_hidden_size"] + n_embd = self.hparams["d_model"] + + # Specific behavior for experts tensors: suffix .weight, view as 3D and transpose + # original implementation expects (n_expert, n_ff, n_embd) for all experts weights + # But llama.cpp moe graph works differently + # AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions + # so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor + exp_tensor_names = {"ffn.experts.mlp.w1": None, # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} + "ffn.experts.mlp.w2": (0, 2, 1), # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff, n_embd, n_expert} + "ffn.experts.mlp.v1": None} # LLM_TENSOR_FFN_UP_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} + experts = False + for exp_tensor_name in exp_tensor_names.keys(): + if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1: + experts = True + data_torch = data_torch.view(n_expert, n_ff, n_embd) + if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None: + data_torch = data_torch.permute(*permute_tensor) + break + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + # In MoE models the ffn tensors are typically most of the model weights, + # and need to be quantizable. Quantize expects tensor names to be suffixed by .weight. + # Every other model has the weight names ending in .weight, + # let's assume that is the convention which is not the case for dbrx: + # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15 + new_name = tensor_map.get_name(name if not experts else name + ".weight", try_suffixes=(".weight",)) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # Most of the codebase that takes in 1D tensors only handles F32 tensors + # and most of the outputs tensors are F32. + if data_dtype != np.float32 and n_dims == 1: + print(f"Can not map tensor {name!r}: all 1D tensors must be F32") + sys.exit() + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and n_dims > 1: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + @Model.register("MiniCPMForCausalLM") class MiniCPMModel(Model): model_arch = gguf.MODEL_ARCH.MINICPM @@ -1594,6 +1877,111 @@ class QwenModel(Model): class Qwen2Model(Model): model_arch = gguf.MODEL_ARCH.QWEN2 + def set_vocab(self): + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + +@Model.register("Qwen2MoeForCausalLM") +class Qwen2MoeModel(Model): + model_arch = gguf.MODEL_ARCH.QWEN2MOE + + def set_gguf_parameters(self): + super().set_gguf_parameters() + if (n_experts := self.hparams.get("num_experts")) is not None: + self.gguf_writer.add_expert_count(n_experts) + + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + n_experts = self.hparams.get("num_experts") + experts = dict() + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # process the experts separately + if name.find("experts") != -1: + experts[name] = data + if len(experts) >= n_experts * 3: + # merge the experts into a single 3d tensor + for bid in range(block_count): + for w_name in ["down_proj", "gate_proj", "up_proj"]: + full = True + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + if ename not in experts: + full = False + break + if not full: + continue + + datas = [] + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(experts[ename]) + del experts[ename] + + data = np.stack(datas, axis=0) + data_dtype = data.dtype + + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + if self.ftype == 1 and data_dtype == np.float32: + data = data.astype(np.float16) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + continue + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.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 self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")): + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts.keys()}") + @Model.register("GPT2LMHeadModel") class GPT2Model(Model): @@ -1685,6 +2073,92 @@ class Phi2Model(Model): self.gguf_writer.add_add_bos_token(False) +@Model.register("Phi3ForCausalLM") +class Phi3MiniModel(Model): + model_arch = gguf.MODEL_ARCH.PHI3 + + def set_vocab(self): + from sentencepiece import SentencePieceProcessor + + tokenizer_path = self.dir_model / 'tokenizer.model' + + if not tokenizer_path.is_file(): + print(f'Error: Missing {tokenizer_path}', file=sys.stderr) + sys.exit(1) + + tokenizer = SentencePieceProcessor(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + + piece = tokenizer.id_to_piece(token_id) + text = piece.encode("utf-8") + score = tokenizer.get_score(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.is_unknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.is_control(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.is_unused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.is_byte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + + for key in added_tokens_json: + token_id = added_tokens_json[key] + if (token_id >= vocab_size): + print(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) + + rot_pct = 1.0 + n_embd = self.find_hparam(["hidden_size", "n_embd"]) + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + rms_eps = self.find_hparam(["rms_norm_eps"]) + + self.gguf_writer.add_name("Phi3") + self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"])) + + self.gguf_writer.add_embedding_length(n_embd) + self.gguf_writer.add_feed_forward_length(8192) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(n_head) + self.gguf_writer.add_head_count_kv(n_head) + self.gguf_writer.add_layer_norm_rms_eps(rms_eps) + self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) + self.gguf_writer.add_file_type(self.ftype) + + @Model.register("PlamoForCausalLM") class PlamoModel(Model): model_arch = gguf.MODEL_ARCH.PLAMO @@ -1899,6 +2373,7 @@ class InternLM2Model(Model): toktypes.append(SentencePieceTokenTypes.USER_DEFINED) self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_scores(scores) self.gguf_writer.add_token_types(toktypes) @@ -1908,6 +2383,8 @@ class InternLM2Model(Model): old_eos = special_vocab.special_token_ids["eos"] if "chat" in os.path.basename(self.dir_model.absolute()): # For the chat model, we replace the eos with '<|im_end|>'. + # TODO: this is a hack, should be fixed + # https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048 special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer) print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \ in chat mode so that the conversation can end normally.") @@ -2046,7 +2523,7 @@ class BertModel(Model): self.gguf_writer.add_pooling_type(pooling_type) def set_vocab(self): - tokens, toktypes = self.get_basic_vocab() + tokens, toktypes, tokpre = self.get_vocab_base() self.vocab_size = len(tokens) # we need this to validate the size of the token_type embeddings @@ -2064,6 +2541,7 @@ class BertModel(Model): # add vocab to gguf self.gguf_writer.add_tokenizer_model("bert") + self.gguf_writer.add_tokenizer_pre(tokpre) self.gguf_writer.add_token_list(tokens) self.gguf_writer.add_token_types(toktypes) @@ -2085,6 +2563,10 @@ class BertModel(Model): print(f"Can not map tensor {name!r}") sys.exit() + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + data = data_torch.squeeze().numpy() n_dims = len(data.shape) new_dtype: type[np.floating[Any]] @@ -2144,6 +2626,16 @@ class GemmaModel(Model): def set_vocab(self): self._set_vocab_sentencepiece() + # TODO: these special tokens should be exported only for the CodeGemma family + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False, + special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot']) + special_vocab._set_special_token("prefix", 67) + special_vocab._set_special_token("suffix", 69) + special_vocab._set_special_token("middle", 68) + special_vocab._set_special_token("fsep", 70) + special_vocab._set_special_token("eot", 107) + special_vocab.add_to_gguf(self.gguf_writer) + def set_gguf_parameters(self): hparams = self.hparams block_count = hparams["num_hidden_layers"] @@ -2165,6 +2657,12 @@ class GemmaModel(Model): tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) for name, data_torch in self.get_tensors(): + # lm_head is not used in llama.cpp, while autoawq will include this tensor in model + # To prevent errors, skip loading lm_head.weight. + if name == "lm_head.weight": + print(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.") + continue + old_dtype = data_torch.dtype # convert any unsupported data types to float32 @@ -2224,28 +2722,37 @@ class MambaModel(Model): field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL) self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1])) + + field = neox_reader.get_field(gguf.Keys.Tokenizer.PRE) + self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1])) + field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST) self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE) self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES) self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID) self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID) self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0]) + field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID) self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0]) def set_gguf_parameters(self): - d_model = self.find_hparam(["hidden_size", "d_model"]) - d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4 + d_model = self.find_hparam(["hidden_size", "d_model"]) + d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4 d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model - d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16 + d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16 # ceiling division # ref: https://stackoverflow.com/a/17511341/22827863 # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58 - dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16) + dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16) rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5 # Fail early for models which don't have a block expansion factor of 2 @@ -2337,6 +2844,66 @@ class CommandR2Model(Model): self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) +@Model.register("OlmoForCausalLM") +@Model.register("OLMoForCausalLM") +class OlmoModel(Model): + model_arch = gguf.MODEL_ARCH.OLMO + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_layer_norm_eps(1e-5) + if "clip_qkv" in self.hparams is not None: + self.gguf_writer.add_clamp_kqv(self.hparams["clip_qkv"]) + + # Same as super class, but permuting q_proj, k_proj + # Copied from: LlamaModel + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + n_head = self.hparams.get("num_attention_heads") + n_kv_head = self.hparams.get("num_key_value_heads") + for name, data_torch in self.get_tensors(): + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.numpy() + + if name.endswith("q_proj.weight"): + data = permute(data, n_head, n_head) + if name.endswith("k_proj.weight"): + data = permute(data, n_head, n_kv_head) + + data = data.squeeze() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # 1d tensors need to be converted to float32 + if self.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 self.ftype == 1 and data_dtype == np.float32 and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + ###### CONVERSION LOGIC ###### @@ -2363,6 +2930,8 @@ def parse_args() -> argparse.Namespace: "model", type=Path, help="directory containing model file", ) + parser.add_argument("--use-temp-file", action="store_true", help="use the tempfile library while processing (helpful when running out of memory, process killed)") + parser.add_argument("--model-name", type=str, default=None, help="name of the model") return parser.parse_args() @@ -2406,7 +2975,7 @@ def main() -> None: with torch.inference_mode(): model_class = Model.from_model_architecture(hparams["architectures"][0]) - model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian) + model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file) print("Set model parameters") model_instance.set_gguf_parameters() diff --git a/convert-llama-ggml-to-gguf.py b/convert-llama-ggml-to-gguf.py index cd9644fcb..5354b748b 100755 --- a/convert-llama-ggml-to-gguf.py +++ b/convert-llama-ggml-to-gguf.py @@ -281,6 +281,7 @@ class GGMLToGGUF: def add_vocab(self, gguf_writer): hp = self.model.hyperparameters gguf_writer.add_tokenizer_model('llama') + gguf_writer.add_tokenizer_pre('default') tokens = [] scores = [] toktypes = [] diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py index 69be17f94..aba575426 100755 --- a/convert-persimmon-to-gguf.py +++ b/convert-persimmon-to-gguf.py @@ -99,6 +99,7 @@ def main(): tokens, scores, toktypes = _get_sentencepiece_tokenizer_info(args.model_dir) gguf_writer.add_tokenizer_model('llama') + gguf_writer.add_tokenizer_pre('default') gguf_writer.add_token_list(tokens) gguf_writer.add_token_scores(scores) gguf_writer.add_token_types(toktypes) diff --git a/convert.py b/convert.py index e860ac89f..1c700cf6a 100755 --- a/convert.py +++ b/convert.py @@ -525,7 +525,14 @@ class LlamaHfVocab(Vocab): # pre-check so we know if we need transformers tokenizer_model: dict[str, Any] = tokenizer_json['model'] - if ( + is_llama3 = ( + tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False) + and not tokenizer_model.get('byte_fallback', True) + ) + if is_llama3: + raise TypeError('Llama 3 must be converted with BpeVocab') + + if not is_llama3 and ( tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False) or tokenizer_json['decoder']['type'] != 'Sequence' ): @@ -1350,7 +1357,7 @@ def load_some_model(path: Path) -> ModelPlus: # Be extra-friendly and accept either a file or a directory: if path.is_dir(): # Check if it's a set of safetensors files first - globs = ["model-00001-of-*.safetensors", "model.safetensors"] + globs = ["model-00001-of-*.safetensors", "model.safetensors", "consolidated.safetensors"] files = [file for glob in globs for file in path.glob(glob)] if not files: # Try the PyTorch patterns too, with lower priority diff --git a/docs/HOWTO-add-model.md b/docs/HOWTO-add-model.md new file mode 100644 index 000000000..a56b78344 --- /dev/null +++ b/docs/HOWTO-add-model.md @@ -0,0 +1,119 @@ +## Add a new model architecture to `llama.cpp` + +Adding a model requires few steps: + +1. Convert the model to GGUF +2. Define the model architecture in `llama.cpp` +3. Build the GGML graph implementation + +After following these steps, you can open PR. + +Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially: +- [main](../examples/main) +- [imatrix](../examples/imatrix) +- [quantize](../examples/quantize) +- [server](../examples/server) + +### 1. Convert the model to GGUF + +This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library. +Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py). + +The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors. + +The required steps to implement for an HF model are: + +1. Define the model `Model.register` annotation in a new `Model` subclass, example: + +```python +@Model.register("MyModelForCausalLM") +class MyModel(Model): + model_arch = gguf.MODEL_ARCH.GROK +``` + +2. Define the layout of the GGUF tensors in [constants.py](../gguf-py/gguf/constants.py) + +Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`. + +Example for `falcon` model: +```python + MODEL_ARCH.FALCON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ] +``` + +3. Map the original tensor names to the standardize equivalent in GGUF + +As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist. + +Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](../gguf-py/gguf/tensor_mapping.py) file. + +If the tensor name is part of a repetitive layer/block, the key word `bid` substitutes it. + +Example for the normalization tensor in attention layers: + +```python +block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { + # Attention norm + MODEL_TENSOR.ATTN_NORM: ( + "gpt_neox.layers.{bid}.input_layernorm", # gptneox + "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen + "transformer.blocks.{bid}.norm_1", # mpt + ... + ) +} +``` + +`transformer.blocks.{bid}.norm_1` will be mapped to `blk.{bid}.attn_norm` in GGUF. + +Depending on the model configuration, tokenizer, code and tensors layout, you will have to override: +- `Model#set_gguf_parameters` +- `Model#set_vocab` +- `Model#write_tensors` + +NOTE: Tensor names must end with `.weight` suffix, that is the convention and several tools like `quantize` expect this to proceed the weights. + +### 2. Define the model architecture in `llama.cpp` + +The model params and tensors layout must be defined in `llama.cpp`: +1. Define a new `llm_arch` +2. Define the tensors layout in `LLM_TENSOR_NAMES` +3. Add any non standard metadata in `llm_load_hparams` +4. Create the tensors for inference in `llm_load_tensors` +5. If the model has a RoPE operation, add the rope type in `llama_rope_type` + +NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions. + +### 3. Build the GGML graph implementation + +This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`. + +Have a look to existing implementation like `build_llama`, `build_dbrx` or `build_bert`. + +When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support of missing backend operations can be added in another PR. + +Note: to debug the inference graph: you can use [eval-callback](../examples/eval-callback). + +## GGUF specification + +https://github.com/ggerganov/ggml/blob/master/docs/gguf.md + +## Resources + +- YaRN RoPE scaling https://github.com/ggerganov/llama.cpp/pull/2268 +- support Baichuan serial models https://github.com/ggerganov/llama.cpp/pull/3009 +- support attention bias https://github.com/ggerganov/llama.cpp/pull/4283 +- Mixtral support https://github.com/ggerganov/llama.cpp/pull/4406 +- BERT embeddings https://github.com/ggerganov/llama.cpp/pull/5423 +- Grok-1 support https://github.com/ggerganov/llama.cpp/pull/6204 +- Command R Plus support https://github.com/ggerganov/llama.cpp/pull/6491 +- support arch DBRX https://github.com/ggerganov/llama.cpp/pull/6515 +- How to convert HuggingFace model to GGUF format https://github.com/ggerganov/llama.cpp/discussions/2948 diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 76496bf06..f421769cc 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -19,6 +19,7 @@ else() add_subdirectory(benchmark) add_subdirectory(convert-llama2c-to-ggml) add_subdirectory(embedding) + add_subdirectory(eval-callback) add_subdirectory(finetune) add_subdirectory(gritlm) add_subdirectory(gguf-split) diff --git a/examples/batched.swift/Sources/main.swift b/examples/batched.swift/Sources/main.swift index d75c503d5..dbbd06da5 100644 --- a/examples/batched.swift/Sources/main.swift +++ b/examples/batched.swift/Sources/main.swift @@ -153,7 +153,7 @@ while n_cur <= n_len { // const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); // is it an end of stream? -> mark the stream as finished - if new_token_id == llama_token_eos(model) || n_cur == n_len { + if llama_token_is_eog(model, new_token_id) || n_cur == n_len { i_batch[i] = -1 // print("") if n_parallel > 1 { @@ -229,7 +229,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] { private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? { var result = [CChar](repeating: 0, count: 8) - let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count)) + let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), false) if nTokens < 0 { let actualTokensCount = -Int(nTokens) result = .init(repeating: 0, count: actualTokensCount) @@ -237,7 +237,8 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String model, token, &result, - Int32(result.count) + Int32(result.count), + false ) assert(check == actualTokensCount) } else { diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 7aaf63ceb..be30d20bf 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -191,8 +191,8 @@ int main(int argc, char ** argv) { //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); - // is it an end of stream? -> mark the stream as finished - if (new_token_id == llama_token_eos(model) || n_cur == n_len) { + // is it an end of generation? -> mark the stream as finished + if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) { i_batch[i] = -1; LOG_TEE("\n"); if (n_parallel > 1) { diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp index 866c6d7a6..3d34378a5 100644 --- a/examples/beam-search/beam-search.cpp +++ b/examples/beam-search/beam-search.cpp @@ -47,7 +47,7 @@ struct beam_search_callback_data { // In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same. // For example, eob can be flagged due to maximum token length, stop words, etc. static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) { - return n_tokens && tokens[n_tokens-1] == llama_token_eos(llama_get_model(callback_data.ctx)); + return n_tokens && llama_token_is_eog(llama_get_model(callback_data.ctx), tokens[n_tokens-1]); } // Function matching type llama_beam_search_callback_fn_t. diff --git a/examples/eval-callback/CMakeLists.txt b/examples/eval-callback/CMakeLists.txt new file mode 100644 index 000000000..c56ba780b --- /dev/null +++ b/examples/eval-callback/CMakeLists.txt @@ -0,0 +1,9 @@ +set(TARGET eval-callback) +add_executable(${TARGET} eval-callback.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) + +set(TEST_TARGET test-eval-callback) +add_test(NAME ${TEST_TARGET} COMMAND eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0) +set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl) diff --git a/examples/eval-callback/README.md b/examples/eval-callback/README.md new file mode 100644 index 000000000..66a37e878 --- /dev/null +++ b/examples/eval-callback/README.md @@ -0,0 +1,95 @@ +# llama.cpp/examples/eval-callback + +A simple example which demonstrates how to use callback during the inference. +It simply prints to the console all operations and tensor data. + +Usage: + +```shell +eval-callback \ + --hf-repo ggml-org/models \ + --hf-file phi-2/ggml-model-q4_0.gguf \ + --model phi-2-q4_0.gguf \ + --prompt hello \ + --seed 42 \ + -ngl 33 +``` + +Will print: + +```shell +llm_load_tensors: offloaded 33/33 layers to GPU +... +llama_new_context_with_model: n_ctx = 512 +... +llama_new_context_with_model: CUDA0 compute buffer size = 105.00 MiB +llama_new_context_with_model: CUDA_Host compute buffer size = 6.01 MiB +llama_new_context_with_model: graph nodes = 1225 +llama_new_context_with_model: graph splits = 2 +ggml_debug: inp_embd = (f32) GET_ROWS(token_embd.weight{2560, 51200, 1, 1}, inp_tokens{1, 1, 1, 1}}) = {2560, 1, 1, 1} + [ + [ + [ -0.0181, 0.0272, 0.0272, ...], + ], + ] +ggml_debug: norm-0 = (f32) NORM(CUDA0#inp_embd#0{2560, 1, 1, 1}, }) = {2560, 1, 1, 1} + [ + [ + [ -0.6989, 1.0636, 1.0636, ...], + ], + ] +ggml_debug: norm_w-0 = (f32) MUL(norm-0{2560, 1, 1, 1}, blk.0.attn_norm.weight{2560, 1, 1, 1}}) = {2560, 1, 1, 1} + [ + [ + [ -0.1800, 0.2817, 0.2632, ...], + ], + ] +ggml_debug: attn_norm-0 = (f32) ADD(norm_w-0{2560, 1, 1, 1}, blk.0.attn_norm.bias{2560, 1, 1, 1}}) = {2560, 1, 1, 1} + [ + [ + [ -0.1863, 0.2970, 0.2604, ...], + ], + ] +ggml_debug: wqkv-0 = (f32) MUL_MAT(blk.0.attn_qkv.weight{2560, 7680, 1, 1}, attn_norm-0{2560, 1, 1, 1}}) = {7680, 1, 1, 1} + [ + [ + [ -1.1238, 1.2876, -1.8086, ...], + ], + ] +ggml_debug: bqkv-0 = (f32) ADD(wqkv-0{7680, 1, 1, 1}, blk.0.attn_qkv.bias{7680, 1, 1, 1}}) = {7680, 1, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + ], + ] +ggml_debug: bqkv-0 (view) = (f32) VIEW(bqkv-0{7680, 1, 1, 1}, }) = {2560, 1, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + ], + ] +ggml_debug: Qcur-0 = (f32) CONT(bqkv-0 (view){2560, 1, 1, 1}, }) = {2560, 1, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + ], + ] +ggml_debug: Qcur-0 (reshaped) = (f32) RESHAPE(Qcur-0{2560, 1, 1, 1}, }) = {80, 32, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + [ -0.3608, 0.5076, -1.8866, ...], + [ 1.7643, 0.0273, -2.1065, ...], + ... + ], + ] +ggml_debug: Qcur-0 = (f32) ROPE(Qcur-0 (reshaped){80, 32, 1, 1}, CUDA0#inp_pos#0{1, 1, 1, 1}}) = {80, 32, 1, 1} + [ + [ + [ -1.1135, 1.4604, -1.9226, ...], + [ -0.3608, 0.5076, -1.8866, ...], + [ 1.7643, 0.0273, -2.1065, ...], + ... + ], + ] +``` diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp new file mode 100644 index 000000000..29b5f3b3c --- /dev/null +++ b/examples/eval-callback/eval-callback.cpp @@ -0,0 +1,195 @@ +#include "common.h" +#include "llama.h" +#include "ggml.h" + +#include +#include +#include +#include +#include + +/** + * This the arbitrary data which will be passed to each callback. + * Later on we can for example add operation or tensor name filter from the CLI arg, or a file descriptor to dump the tensor. + */ +struct callback_data { + std::vector data; +}; + +static std::string ggml_ne_string(const ggml_tensor * t) { + std::string str; + for (int i = 0; i < GGML_MAX_DIMS; ++i) { + str += std::to_string(t->ne[i]); + if (i + 1 < GGML_MAX_DIMS) { + str += ", "; + } + } + return str; +} + +static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) { + GGML_ASSERT(n > 0); + float sum = 0; + for (int64_t i3 = 0; i3 < ne[3]; i3++) { + printf(" [\n"); + for (int64_t i2 = 0; i2 < ne[2]; i2++) { + if (i2 == n && ne[2] > 2*n) { + printf(" ..., \n"); + i2 = ne[2] - n; + } + printf(" [\n"); + for (int64_t i1 = 0; i1 < ne[1]; i1++) { + if (i1 == n && ne[1] > 2*n) { + printf(" ..., \n"); + i1 = ne[1] - n; + } + printf(" ["); + for (int64_t i0 = 0; i0 < ne[0]; i0++) { + if (i0 == n && ne[0] > 2*n) { + printf("..., "); + i0 = ne[0] - n; + } + size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0]; + float v; + if (type == GGML_TYPE_F16) { + v = ggml_fp16_to_fp32(*(ggml_fp16_t *) data + i); + } else if (type == GGML_TYPE_F32) { + v = *(float *) data + i; + } else if (type == GGML_TYPE_I32) { + v = (float) *(int32_t *) data + i; + } else if (type == GGML_TYPE_I16) { + v = (float) *(int16_t *) data + i; + } else if (type == GGML_TYPE_I8) { + v = (float) *(int8_t *) data + i; + } else { + GGML_ASSERT(false); + } + printf("%12.4f", v); + sum += v; + if (i0 < ne[0] - 1) printf(", "); + } + printf("],\n"); + } + printf(" ],\n"); + } + printf(" ]\n"); + printf(" sum = %f\n", sum); + } +} + +/** + * GGML operations callback during the graph execution. + * + * @param t current tensor + * @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor + * if we return true, a follow-up call will be made with ask=false in which we can do the actual collection. + * see ggml_backend_sched_eval_callback + * @param user_data user data to pass at each call back + * @return true to receive data or continue the graph, false otherwise + */ +static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) { + auto * cb_data = (callback_data *) user_data; + + const struct ggml_tensor * src0 = t->src[0]; + const struct ggml_tensor * src1 = t->src[1]; + + if (ask) { + return true; // Always retrieve data + } + + char src1_str[128] = {0}; + if (src1) { + sprintf(src1_str, "%s{%s}", src1->name, ggml_ne_string(src1).c_str()); + } + + printf("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, + t->name, ggml_type_name(t->type), ggml_op_desc(t), + src0->name, ggml_ne_string(src0).c_str(), + src1 ? src1_str : "", + ggml_ne_string(t).c_str()); + + + // copy the data from the GPU memory if needed + const bool is_host = ggml_backend_buffer_is_host(t->buffer); + + if (!is_host) { + auto n_bytes = ggml_nbytes(t); + cb_data->data.resize(n_bytes); + ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes); + } + + if (!ggml_is_quantized(t->type)) { + uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data(); + ggml_print_tensor(data, t->type, t->ne, t->nb, 3); + } + + return true; +} + +static bool run(llama_context * ctx, const gpt_params & params) { + const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + + std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + + if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return false; + } + + return true; +} + +int main(int argc, char ** argv) { + + callback_data cb_data; + + gpt_params params; + if (!gpt_params_parse(argc, argv, params)) { + return 1; + } + + print_build_info(); + + std::mt19937 rng(params.seed); + if (params.random_prompt) { + params.prompt = gpt_random_prompt(rng); + } + + llama_backend_init(); + llama_numa_init(params.numa); + + // pass the callback to the backend scheduler + // it will be executed for each node during the graph computation + params.cb_eval = ggml_debug; + params.cb_eval_user_data = &cb_data; + params.warmup = false; + + // init + llama_model * model; + llama_context * ctx; + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == nullptr || ctx == nullptr) { + fprintf(stderr, "%s : failed to init\n", __func__); + return 1; + } + + // print system information + { + fprintf(stderr, "\n"); + fprintf(stderr, "%s\n", get_system_info(params).c_str()); + } + + bool OK = run(ctx, params); + if (!OK) { + return 1; + } + + llama_print_timings(ctx); + + llama_free(ctx); + llama_free_model(model); + + llama_backend_free(); + + return 0; +} diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp index e4c0c1689..091069ffa 100644 --- a/examples/gbnf-validator/gbnf-validator.cpp +++ b/examples/gbnf-validator/gbnf-validator.cpp @@ -17,7 +17,7 @@ static bool llama_sample_grammar_string(struct llama_grammar * grammar, const st size_t pos = 0; for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { auto prev_stacks = grammar->stacks; - grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it); + llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks); if (grammar->stacks.empty()) { error_pos = pos; error_msg = "Unexpected character '" + unicode_cpt_to_utf8(*it) + "'"; diff --git a/examples/gguf-split/README.md b/examples/gguf-split/README.md index ddb1f7649..ad1d86651 100644 --- a/examples/gguf-split/README.md +++ b/examples/gguf-split/README.md @@ -5,5 +5,6 @@ CLI to split / merge GGUF files. **Command line options:** - `--split`: split GGUF to multiple GGUF, default operation. +- `--split-max-size`: max size per split in `M` or `G`, f.ex. `500M` or `2G`. - `--split-max-tensors`: maximum tensors in each split: default(128) - `--merge`: merge multiple GGUF to a single GGUF. diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp index 24acbf02a..39c75e0a7 100644 --- a/examples/gguf-split/gguf-split.cpp +++ b/examples/gguf-split/gguf-split.cpp @@ -59,10 +59,10 @@ static size_t split_str_to_n_bytes(std::string str) { int n; if (str.back() == 'M') { sscanf(str.c_str(), "%d", &n); - n_bytes = n * 1024 * 1024; // megabytes + n_bytes = (size_t)n * 1024 * 1024; // megabytes } else if (str.back() == 'G') { sscanf(str.c_str(), "%d", &n); - n_bytes = n * 1024 * 1024 * 1024; // gigabytes + n_bytes = (size_t)n * 1024 * 1024 * 1024; // gigabytes } else { throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back())); } diff --git a/examples/gguf-split/tests.sh b/examples/gguf-split/tests.sh new file mode 100755 index 000000000..57588204d --- /dev/null +++ b/examples/gguf-split/tests.sh @@ -0,0 +1,89 @@ +#!/bin/bash + +set -eu + +if [ $# -lt 1 ] +then + echo "usage: $0 path_to_build_binary [path_to_temp_folder]" + echo "example: $0 ../../build/bin ../../tmp" + exit 1 +fi + +if [ $# -gt 1 ] +then + TMP_DIR=$2 +else + TMP_DIR=/tmp +fi + +set -x + +SPLIT=$1/gguf-split +MAIN=$1/main +WORK_PATH=$TMP_DIR/gguf-split +ROOT_DIR=$(realpath $(dirname $0)/../../) + +mkdir -p "$WORK_PATH" + +# Clean up in case of previously failed test +rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf + +# 1. Get a model +( +cd $WORK_PATH +"$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf +) +echo PASS + +# 2. Split with max tensors strategy +$SPLIT --split-max-tensors 28 $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split +echo PASS +echo + +# 2b. Test the sharded model is loading properly +$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --random-prompt --n-predict 32 +echo PASS +echo + +# 3. Merge +$SPLIT --merge $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-merge.gguf +echo PASS +echo + +# 3b. Test the merged model is loading properly +$MAIN --model $WORK_PATH/ggml-model-merge.gguf --random-prompt --n-predict 32 +echo PASS +echo + +# 4. Split with no tensor in metadata +#$SPLIT --split-max-tensors 32 --no-tensor-in-metadata $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors +#echo PASS +#echo + +# 4b. Test the sharded model is loading properly +#$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf --random-prompt --n-predict 32 +#echo PASS +#echo + +# 5. Merge +#$SPLIT --merge $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf $WORK_PATH/ggml-model-merge-2.gguf +#echo PASS +#echo + +# 5b. Test the merged model is loading properly +#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --random-prompt --n-predict 32 +#echo PASS +#echo + +# 6. Split with size strategy +$SPLIT --split-max-size 2G $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-2G +echo PASS +echo + +# 6b. Test the sharded model is loading properly +$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --random-prompt --n-predict 32 +echo PASS +echo + +# Clean up +rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp index 5444503a5..575143771 100644 --- a/examples/gguf/gguf.cpp +++ b/examples/gguf/gguf.cpp @@ -142,7 +142,7 @@ static bool gguf_ex_read_0(const std::string & fname) { } // read and create ggml_context containing the tensors and their data -static bool gguf_ex_read_1(const std::string & fname) { +static bool gguf_ex_read_1(const std::string & fname, bool check_data) { struct ggml_context * ctx_data = NULL; struct gguf_init_params params = { @@ -206,7 +206,7 @@ static bool gguf_ex_read_1(const std::string & fname) { printf("\n\n"); // check data - { + if (check_data) { const float * data = (const float *) cur->data; for (int j = 0; j < ggml_nelements(cur); ++j) { if (data[j] != 100 + i) { @@ -229,9 +229,16 @@ static bool gguf_ex_read_1(const std::string & fname) { int main(int argc, char ** argv) { if (argc < 3) { - printf("usage: %s data.gguf r|w\n", argv[0]); + printf("usage: %s data.gguf r|w [n]\n", argv[0]); + printf("r: read data.gguf file\n"); + printf("w: write data.gguf file\n"); + printf("n: no check of tensor data\n"); return -1; } + bool check_data = true; + if (argc == 4) { + check_data = false; + } const std::string fname(argv[1]); const std::string mode (argv[2]); @@ -242,7 +249,7 @@ int main(int argc, char ** argv) { GGML_ASSERT(gguf_ex_write(fname) && "failed to write gguf file"); } else if (mode == "r") { GGML_ASSERT(gguf_ex_read_0(fname) && "failed to read gguf file"); - GGML_ASSERT(gguf_ex_read_1(fname) && "failed to read gguf file"); + GGML_ASSERT(gguf_ex_read_1(fname, check_data) && "failed to read gguf file"); } return 0; diff --git a/examples/gritlm/README.md b/examples/gritlm/README.md index 64cc19204..a3a3c1389 100644 --- a/examples/gritlm/README.md +++ b/examples/gritlm/README.md @@ -21,12 +21,12 @@ not have to be performed at all. ### Running the example Download a Grit model: ```console -$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf +$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf --outdir models ``` Run the example using the downloaded model: ```console -$ ./gritlm -m gritlm-7b_q4_1.gguf +$ ./gritlm -m models/gritlm-7b_q4_1.gguf Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605 Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103 diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 1bf55f90c..71e7a727f 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -23,6 +23,7 @@ struct Stats { }; struct StatParams { + std::string dataset; std::string ofile = "imatrix.dat"; int n_output_frequency = 10; int verbosity = 1; @@ -44,9 +45,9 @@ private: std::mutex m_mutex; int m_last_call = 0; std::vector m_src1_data; - std::vector m_ids; // the expert ids from ggml_mul_mat_id + std::vector m_ids; // the expert ids from ggml_mul_mat_id // - void save_imatrix(const char * file_name) const; + void save_imatrix(const char * file_name, const char * dataset) const; void keep_imatrix(int ncall) const; }; @@ -81,6 +82,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * if (ask) { if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications if (t->op != GGML_OP_MUL_MAT) return false; + // why are small batches ignored (<16 tokens)? if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false; if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && wname == "output.weight"))) return false; return true; @@ -101,16 +103,19 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * // this has been adapted to the new format of storing merged experts in a single 3d tensor // ref: https://github.com/ggerganov/llama.cpp/pull/6387 if (t->op == GGML_OP_MUL_MAT_ID) { - const int idx = ((int32_t *) t->op_params)[0]; + // ids -> [n_experts_used, n_tokens] + // src1 -> [cols, n_expert_used, n_tokens] const ggml_tensor * ids = t->src[2]; const int n_as = src0->ne[2]; + const int n_ids = ids->ne[0]; // the top-k selected expert ids are stored in the ids tensor // for simplicity, always copy ids to host, because it is small // take into account that ids is not contiguous! - GGML_ASSERT(ids->ne[1] == src1->ne[1]); - GGML_ASSERT(n_as*ggml_nrows(ids)*sizeof(int) == GGML_PAD(ggml_nbytes(ids), n_as*sizeof(int))); - m_ids.resize(ggml_nbytes(ids)/sizeof(int)); + + GGML_ASSERT(ids->ne[1] == src1->ne[2]); + + m_ids.resize(ggml_nbytes(ids)); ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids)); auto & e = m_stats[wname]; @@ -120,26 +125,35 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * // using the following line, we can correct for that if needed by replacing the line above with: //if (idx == t->src[0]->ne[0] - 1) ++e.ncall; + if (e.values.empty()) { + e.values.resize(src1->ne[0]*n_as, 0); + } + else if (e.values.size() != (size_t)src1->ne[0]*n_as) { + fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as); + exit(1); //GGML_ASSERT(false); + } + if (m_params.verbosity > 1) { + printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type); + } // loop over all possible experts, regardless if they are used or not in the batch for (int ex = 0; ex < n_as; ++ex) { size_t e_start = ex*src1->ne[0]; - if (e.values.empty()) { - e.values.resize(src1->ne[0]*n_as, 0); - } - else if (e.values.size() != (size_t)src1->ne[0]*n_as) { - fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as); - exit(1); //GGML_ASSERT(false); - } - if (m_params.verbosity > 1) { - printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type); - } - for (int row = 0; row < (int)src1->ne[1]; ++row) { - const int excur = m_ids[row*n_as + idx]; - GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check - if (excur != ex) continue; - const float * x = data + row * src1->ne[0]; - for (int j = 0; j < (int)src1->ne[0]; ++j) { - e.values[e_start + j] += x[j]*x[j]; + + for (int idx = 0; idx < n_ids; ++idx) { + for (int row = 0; row < (int)src1->ne[2]; ++row) { + const int excur = *(const int32_t *) (m_ids.data() + row*ids->nb[1] + idx*ids->nb[0]); + + GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check + + if (excur != ex) continue; + + const int64_t i11 = idx % src1->ne[1]; + const int64_t i12 = row; + const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]); + + for (int j = 0; j < (int)src1->ne[0]; ++j) { + e.values[e_start + j] += x[j]*x[j]; + } } } if (e.ncall > m_last_call) { @@ -186,7 +200,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * } void IMatrixCollector::save_imatrix() const { - save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str()); + save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str(), m_params.dataset.c_str()); } void IMatrixCollector::keep_imatrix(int ncall) const { @@ -194,24 +208,33 @@ void IMatrixCollector::keep_imatrix(int ncall) const { if (file_name.empty()) file_name = "imatrix.dat"; file_name += ".at_"; file_name += std::to_string(ncall); - save_imatrix(file_name.c_str()); + save_imatrix(file_name.c_str(), m_params.dataset.c_str()); } -void IMatrixCollector::save_imatrix(const char * fname) const { +void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) const { std::ofstream out(fname, std::ios::binary); int n_entries = m_stats.size(); - out.write((const char*)&n_entries, sizeof(n_entries)); - for (auto& p : m_stats) { + out.write((const char *) &n_entries, sizeof(n_entries)); + for (const auto & p : m_stats) { int len = p.first.size(); - out.write((const char*)&len, sizeof(len)); + out.write((const char *) &len, sizeof(len)); out.write(p.first.c_str(), len); - out.write((const char*)&p.second.ncall, sizeof(p.second.ncall)); + out.write((const char *) &p.second.ncall, sizeof(p.second.ncall)); int nval = p.second.values.size(); - out.write((const char*)&nval, sizeof(nval)); - if (nval > 0) out.write((const char*)p.second.values.data(), nval*sizeof(float)); + out.write((const char *) &nval, sizeof(nval)); + if (nval > 0) out.write((const char *) p.second.values.data(), nval * sizeof(float)); } + + // Write the number of call the matrix was computed with + out.write((const char *) &m_last_call, sizeof(m_last_call)); + + // Write the dataset name at the end of the file to later on specify it in quantize + int n_dataset = strlen(dataset); + out.write((const char *) &n_dataset, sizeof(n_dataset)); + out.write(dataset, n_dataset); + if (m_params.verbosity > 0) { - fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n",__func__,m_last_call,fname); + fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname); } } @@ -534,6 +557,29 @@ int main(int argc, char ** argv) { } } + gpt_params params; + params.n_batch = 512; + if (!gpt_params_parse(args.size(), args.data(), params)) { + return 1; + } + + params.logits_all = true; + params.n_batch = std::min(params.n_batch, params.n_ctx); + + print_build_info(); + + if (params.seed == LLAMA_DEFAULT_SEED) { + params.seed = time(NULL); + } + + fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); + + std::mt19937 rng(params.seed); + if (params.random_prompt) { + params.prompt = gpt_random_prompt(rng); + } + + sparams.dataset = params.prompt_file; g_collector.set_parameters(std::move(sparams)); if (!combine_files.empty()) { @@ -572,49 +618,21 @@ int main(int argc, char ** argv) { } } - gpt_params params; - params.n_batch = 512; - if (!gpt_params_parse(args.size(), args.data(), params)) { - return 1; - } - - params.logits_all = true; - params.n_batch = std::min(params.n_batch, params.n_ctx); - - print_build_info(); - - if (params.seed == LLAMA_DEFAULT_SEED) { - params.seed = time(NULL); - } - - fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); - - std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); - } - llama_backend_init(); llama_numa_init(params.numa); - llama_model_params mparams = llama_model_params_from_gpt_params(params); - - llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams); - if (model == NULL) { - fprintf(stderr, "%s: error: unable to load model\n", __func__); - return 1; - } - - llama_context_params cparams = llama_context_params_from_gpt_params(params); - // pass the callback to the backend scheduler // it will be executed for each node during the graph computation - cparams.cb_eval = ik_collect_imatrix; - cparams.cb_eval_user_data = NULL; + params.cb_eval = ik_collect_imatrix; + params.cb_eval_user_data = NULL; + params.warmup = false; - llama_context * ctx = llama_new_context_with_model(model, cparams); - if (ctx == NULL) { - fprintf(stderr, "%s: error: unable to create context\n", __func__); + // init + llama_model * model; + llama_context * ctx; + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == nullptr || ctx == nullptr) { + fprintf(stderr, "%s : failed to init\n", __func__); return 1; } diff --git a/examples/infill/README.md b/examples/infill/README.md index 8c97f719b..6b076c839 100644 --- a/examples/infill/README.md +++ b/examples/infill/README.md @@ -36,6 +36,11 @@ The `infill` program offers a seamless way to interact with LLaMA models, allowi ### Example +Download a model that supports infill, for example CodeLlama: +```console +scripts/hf.sh --repo TheBloke/CodeLlama-13B-GGUF --file codellama-13b.Q5_K_S.gguf --outdir models +``` + ```bash ./infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n print(\"hell" --in-suffix "\n print(\"goodbye world\")\n " ``` diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index c69dcd06e..afac145f6 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -586,7 +586,7 @@ int main(int argc, char ** argv) { // deal with eot token in infill mode if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){ - if(is_interacting && !params.interactive_first) { + if (is_interacting && !params.interactive_first) { // print an eot token printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str()); } @@ -651,8 +651,8 @@ int main(int argc, char ** argv) { // LOG_TEE("took new input\n"); is_interacting = false; } - // deal with end of text token in interactive mode - else if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) { + // deal with end of generation tokens in interactive mode + else if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) { LOG("found EOS token\n"); if (params.interactive) { @@ -731,8 +731,8 @@ int main(int argc, char ** argv) { } } - // end of text token - if (!embd.empty() && embd.back() == llama_token_eos(model) && !params.interactive) { + // end of generation + if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !params.interactive) { break; } diff --git a/examples/json-schema-to-grammar.py b/examples/json_schema_to_grammar.py similarity index 73% rename from examples/json-schema-to-grammar.py rename to examples/json_schema_to_grammar.py index 91dd734cc..826cd3f72 100755 --- a/examples/json-schema-to-grammar.py +++ b/examples/json_schema_to_grammar.py @@ -6,37 +6,94 @@ import re import sys from typing import Any, Dict, List, Set, Tuple, Union +def _build_repetition(item_rule, min_items, max_items, separator_rule=None, item_rule_is_literal=False): + if not separator_rule: + if min_items == 0 and max_items == 1: + return f'{item_rule}?' + elif min_items == 1 and max_items is None: + return f'{item_rule}+' + + result = '' + + if min_items > 0: + if item_rule_is_literal and separator_rule is None: + result = '"' + (item_rule[1:-1] * min_items) + '"' + else: + result = (f' {separator_rule} ' if separator_rule else ' ').join([item_rule] * min_items) + + def opt_repetitions(up_to_n, prefix_with_sep=False): + ''' + - n=4, no sep: '(a (a (a (a)?)?)?)?' + - n=4, sep=',', prefix: '("," a ("," a ("," a ("," a)?)?)?)?' + - n=4, sep=',', no prefix: '(a ("," a ("," a ("," a)?)?)?)?' + ''' + + content = f'{separator_rule} {item_rule}' if prefix_with_sep and separator_rule else item_rule + if up_to_n == 0: + return '' + elif up_to_n == 1: + return f'({content})?' + elif separator_rule and not prefix_with_sep: + return f'({content} {opt_repetitions(up_to_n - 1, prefix_with_sep=True)})?' + else: + return (f'({content} ' * up_to_n).rstrip() + (')?' * up_to_n) + + if min_items > 0 and max_items != min_items: + result += ' ' + + if max_items is not None: + result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0) + else: + item_operator = f'({separator_rule + " " if separator_rule else ""}{item_rule})' + + if min_items == 0 and separator_rule: + result = f'({item_rule} {item_operator}*)?' + else: + result += f'{item_operator}*' + + return result + + +class BuiltinRule: + def __init__(self, content: str, deps: list = None): + self.content = content + self.deps = deps or [] + +_up_to_15_digits = _build_repetition('[0-9]', 0, 15) + # whitespace is constrained to a single space char to prevent model "running away" in # whitespace. Also maybe improves generation quality? SPACE_RULE = '" "?' PRIMITIVE_RULES = { - 'boolean': '("true" | "false") space', - 'number': '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', - 'integer': '("-"? ([0-9] | [1-9] [0-9]*)) space', - 'value' : 'object | array | string | number | boolean', - 'object' : '"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', - 'array' : '"[" space ( value ("," space value)* )? "]" space', - 'uuid' : '"\\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + ' "\\"" space', - 'string': r''' "\"" ( - [^"\\] | - "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) - )* "\"" space''', - 'null': '"null" space', + 'boolean' : BuiltinRule('("true" | "false") space', []), + 'decimal-part' : BuiltinRule('[0-9] ' + _up_to_15_digits, []), + 'integral-part': BuiltinRule('[0-9] | [1-9] ' + _up_to_15_digits, []), + 'number' : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']), + 'integer' : BuiltinRule('("-"? integral-part) space', ['integral-part']), + 'value' : BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']), + 'object' : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']), + 'array' : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']), + 'uuid' : BuiltinRule(r'"\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + r' "\"" space', []), + 'char' : BuiltinRule(r'[^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])', []), + 'string' : BuiltinRule(r'"\"" char* "\"" space', ['char']), + 'null' : BuiltinRule('"null" space', []), } -OBJECT_RULE_NAMES = ['object', 'array', 'string', 'number', 'boolean', 'null', 'value'] # TODO: support "uri", "email" string formats -DATE_RULES = { - 'date' : '[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', - 'time' : '([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', - 'date-time': 'date "T" time', - 'date-string': '"\\"" date "\\"" space', - 'time-string': '"\\"" time "\\"" space', - 'date-time-string': '"\\"" date-time "\\"" space', +STRING_FORMAT_RULES = { + 'date' : BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []), + 'time' : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []), + 'date-time' : BuiltinRule('date "T" time', ['date', 'time']), + 'date-string' : BuiltinRule('"\\"" date "\\"" space', ['date']), + 'time-string' : BuiltinRule('"\\"" time "\\"" space', ['time']), + 'date-time-string': BuiltinRule('"\\"" date-time "\\"" space', ['date-time']), } -RESERVED_NAMES = set(["root", *PRIMITIVE_RULES.keys(), *DATE_RULES.keys()]) +DOTALL = '[\\U00000000-\\U0010FFFF]' +DOT = '[^\\x0A\\x0D]' + +RESERVED_NAMES = set(["root", "dot", *PRIMITIVE_RULES.keys(), *STRING_FORMAT_RULES.keys()]) INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+') GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]') @@ -46,8 +103,6 @@ GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']' NON_LITERAL_SET = set('|.()[]{}*+?') ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('[]()|{}*+?') -DATE_PATTERN = '[0-9]{4}-(0[1-9]|1[0-2])-([0-2][0-9]|3[0-1])' -TIME_PATTERN = '([01][0-9]|2[0-3])(:[0-5][0-9]){2}(\\.[0-9]{1,3})?(Z|[+-](([01][0-9]|2[0-3]):[0-5][0-9]))' # Cap millisecond precision w/ 3 digits class SchemaConverter: def __init__(self, *, prop_order, allow_fetch, dotall, raw_pattern): @@ -55,7 +110,9 @@ class SchemaConverter: self._allow_fetch = allow_fetch self._dotall = dotall self._raw_pattern = raw_pattern - self._rules = {'space': SPACE_RULE} + self._rules = { + 'space': SPACE_RULE, + } self._refs = {} self._refs_being_resolved = set() @@ -65,6 +122,29 @@ class SchemaConverter: ) return f'"{escaped}"' + def not_literal(self, literal: str, dotall: bool = True, maybe_escaped_underscores = False) -> str: + ''' + not_literal('a') -> '[^a]' + not_literal('abc') -> '([^a] | "a" ([^b] | "b" ([^c])?)?)?' + ''' + assert len(literal) > 0, 'Empty literal not supported' + def recurse(i: int): + c = literal[i] + if maybe_escaped_underscores and c == '_': + yield f'[^{c}\\\\]' + yield ' | ' + yield f'"\\\\"? "{c}"' + else: + yield f'[^{c}]' + if i < len(literal) - 1: + yield ' | ' + yield self._format_literal(c) + yield ' (' + yield from recurse(i + 1) + yield ')?' + + return ''.join(('(', *recurse(0), ')')) + def _add_rule(self, name, rule): esc_name = INVALID_RULE_CHARS_RE.sub('-', name) if esc_name not in self._rules or self._rules[esc_name] == rule: @@ -169,10 +249,10 @@ class SchemaConverter: def get_dot(): if self._dotall: - rule = '[\\U00000000-\\U0010FFFF]' + rule = DOTALL else: # Accept any character... except \n and \r line break chars (\x0A and \xOD) - rule = '[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]' + rule = DOT return self._add_rule(f'dot', rule) def join_seq(): @@ -246,26 +326,14 @@ class SchemaConverter: (sub, sub_is_literal) = seq[-1] - if min_times == 0 and max_times is None: - seq[-1] = (f'{sub}*', False) - elif min_times == 0 and max_times == 1: - seq[-1] = (f'{sub}?', False) - elif min_times == 1 and max_times is None: - seq[-1] = (f'{sub}+', False) - else: - if not sub_is_literal: - id = sub_rule_ids.get(sub) - if id is None: - id = self._add_rule(f'{name}-{len(sub_rule_ids) + 1}', sub) - sub_rule_ids[sub] = id - sub = id + if not sub_is_literal: + id = sub_rule_ids.get(sub) + if id is None: + id = self._add_rule(f'{name}-{len(sub_rule_ids) + 1}', sub) + sub_rule_ids[sub] = id + sub = id - seq[-1] = ( - ' '.join( - ([f'"{sub[1:-1] * min_times}"'] if sub_is_literal else [sub] * min_times) + - ([f'{sub}?'] * (max_times - min_times) if max_times is not None else [f'{sub}*'])), - False - ) + seq[-1] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times, item_rule_is_literal=sub_is_literal), False) else: literal = '' while i < length: @@ -373,49 +441,47 @@ class SchemaConverter: ' "]" space') else: item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item') - list_item_operator = f'( "," space {item_rule_name} )' - successive_items = "" min_items = schema.get("minItems", 0) max_items = schema.get("maxItems") - if min_items > 0: - successive_items = list_item_operator * (min_items - 1) - min_items -= 1 - if max_items is not None and max_items > min_items: - successive_items += (list_item_operator + "?") * (max_items - min_items - 1) - else: - successive_items += list_item_operator + "*" - if min_items == 0: - rule = f'"[" space ( {item_rule_name} {successive_items} )? "]" space' - else: - rule = f'"[" space {item_rule_name} {successive_items} "]" space' - return self._add_rule(rule_name, rule) + return self._add_rule(rule_name, '"[" space ' + _build_repetition(item_rule_name, min_items, max_items, separator_rule='"," space') + ' "]" space') elif schema_type in (None, 'string') and 'pattern' in schema: return self._visit_pattern(schema['pattern'], rule_name) elif schema_type in (None, 'string') and re.match(r'^uuid[1-5]?$', schema_format or ''): - return self._add_rule( + return self._add_primitive( 'root' if rule_name == 'root' else schema_format, PRIMITIVE_RULES['uuid'] ) - elif schema_type in (None, 'string') and schema_format in DATE_RULES: - for t, r in DATE_RULES.items(): - self._add_rule(t, r) - return schema_format + '-string' + elif schema_type in (None, 'string') and f'{schema_format}-string' in STRING_FORMAT_RULES: + prim_name = f'{schema_format}-string' + return self._add_rule(rule_name, self._add_primitive(prim_name, STRING_FORMAT_RULES[prim_name])) + + elif schema_type == 'string' and ('minLength' in schema or 'maxLength' in schema): + char_rule = self._add_primitive('char', PRIMITIVE_RULES['char']) + min_len = schema.get('minLength', 0) + max_len = schema.get('maxLength') + + return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\"" space') elif (schema_type == 'object') or (len(schema) == 0): - for n in OBJECT_RULE_NAMES: - self._add_rule(n, PRIMITIVE_RULES[n]) - return self._add_rule(rule_name, 'object') + return self._add_rule(rule_name, self._add_primitive('object', PRIMITIVE_RULES['object'])) else: assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}' # TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero - return self._add_rule( - 'root' if rule_name == 'root' else schema_type, - PRIMITIVE_RULES[schema_type] - ) + return self._add_primitive('root' if rule_name == 'root' else schema_type, PRIMITIVE_RULES[schema_type]) + + def _add_primitive(self, name: str, rule: BuiltinRule): + n = self._add_rule(name, rule.content) + + for dep in rule.deps: + dep_rule = PRIMITIVE_RULES.get(dep) or STRING_FORMAT_RULES.get(dep) + assert dep_rule, f'Rule {dep} not known' + if dep not in self._rules: + self._add_primitive(dep, dep_rule) + return n def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[str], name: str, additional_properties: Union[bool, Any]): prop_order = self._prop_order @@ -437,7 +503,7 @@ class SchemaConverter: value_rule = self.visit({} if additional_properties == True else additional_properties, f'{sub_name}-value') prop_kv_rule_names["*"] = self._add_rule( f'{sub_name}-kv', - self._add_rule('string', PRIMITIVE_RULES['string']) + f' ":" space {value_rule}' + self._add_primitive('string', PRIMITIVE_RULES['string']) + f' ":" space {value_rule}' ) optional_props.append("*") diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 27e113203..8b532c8b6 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -190,7 +190,7 @@ static const cmd_params cmd_params_defaults = { /* n_ubatch */ {512}, /* type_k */ {GGML_TYPE_F16}, /* type_v */ {GGML_TYPE_F16}, - /* n_threads */ {get_num_physical_cores()}, + /* n_threads */ {get_math_cpu_count()}, /* n_gpu_layers */ {99}, /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, /* main_gpu */ {0}, diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/app/src/main/cpp/llama-android.cpp index ce8ab3b70..4af9de303 100644 --- a/examples/llama.android/app/src/main/cpp/llama-android.cpp +++ b/examples/llama.android/app/src/main/cpp/llama-android.cpp @@ -408,7 +408,7 @@ Java_com_example_llama_Llm_completion_1loop( const auto new_token_id = llama_sample_token_greedy(context, &candidates_p); const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value); - if (new_token_id == llama_token_eos(model) || n_cur == n_len) { + if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) { return env->NewStringUTF(""); } diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift index c249291ae..737f882fb 100644 --- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift +++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift @@ -158,7 +158,7 @@ actor LlamaContext { new_token_id = llama_sample_token_greedy(context, &candidates_p) } - if new_token_id == llama_token_eos(model) || n_cur == n_len { + if llama_token_is_eog(model, new_token_id) || n_cur == n_len { print("\n") let new_token_str = String(cString: temporary_invalid_cchars + [0]) temporary_invalid_cchars.removeAll() @@ -322,7 +322,7 @@ actor LlamaContext { defer { result.deallocate() } - let nTokens = llama_token_to_piece(model, token, result, 8) + let nTokens = llama_token_to_piece(model, token, result, 8, false) if nTokens < 0 { let newResult = UnsafeMutablePointer.allocate(capacity: Int(-nTokens)) @@ -330,7 +330,7 @@ actor LlamaContext { defer { newResult.deallocate() } - let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens) + let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, false) let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens)) return Array(bufferPointer) } else { diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index 96b048525..413e433dd 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -22,7 +22,7 @@ After building, run: `./llava-cli` to see the usage. For example: ## Model conversion -- Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally: +1. Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally: ```sh git clone https://huggingface.co/mtgv/MobileVLM-1.7B diff --git a/examples/llava/README.md b/examples/llava/README.md index 67cb0f22b..d4810d42e 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -24,7 +24,7 @@ After building, run: `./llava-cli` to see the usage. For example: ## LLaVA 1.5 -- Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example: +1. Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example: ```sh git clone https://huggingface.co/liuhaotian/llava-v1.5-7b diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 5954bf6cd..e3c9bcd43 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -3,6 +3,7 @@ // I'll gradually clean and extend it // Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch #include "clip.h" +#include "log.h" #include "ggml.h" #include "ggml-alloc.h" #include "ggml-backend.h" @@ -23,7 +24,6 @@ #include #include #include -#include #include #include #include @@ -104,6 +104,7 @@ static std::string format(const char * fmt, ...) { #define TN_POS_EMBD "%s.position_embd.weight" #define TN_CLASS_EMBD "v.class_embd" #define TN_PATCH_EMBD "v.patch_embd.weight" +#define TN_PATCH_BIAS "v.patch_embd.bias" #define TN_ATTN_K "%s.blk.%d.attn_k.%s" #define TN_ATTN_Q "%s.blk.%d.attn_q.%s" #define TN_ATTN_V "%s.blk.%d.attn_v.%s" @@ -145,7 +146,7 @@ static std::map PROJECTOR_TYPE_NAMES = { static int get_key_idx(const gguf_context * ctx, const char * key) { int i = gguf_find_key(ctx, key); if (i == -1) { - fprintf(stderr, "key %s not found in file\n", key); + LOG_TEE("key %s not found in file\n", key); throw std::runtime_error(format("Missing required key: %s", key)); } @@ -247,7 +248,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") { size_t tensor_size = ggml_nbytes(tensor); - printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n", + LOG_TEE("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n", prefix, ggml_n_dims(tensor), tensor->name, tensor_size, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type)); } @@ -265,7 +266,7 @@ static projector_type clip_projector_type_from_string(const std::string & name) static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) { std::ofstream file(filename, std::ios::binary); if (!file.is_open()) { - std::cerr << "Failed to open file for writing: " << filename << std::endl; + LOG_TEE("Failed to open file for writing: %s\n", filename.c_str()); return; } @@ -284,7 +285,7 @@ static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::s static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) { std::ofstream file(filename, std::ios::binary); if (!file.is_open()) { - std::cerr << "Failed to open file for writing: " << filename << std::endl; + LOG_TEE("Failed to open file for writing: %s\n", filename.c_str()); return; } @@ -425,6 +426,7 @@ struct clip_vision_model { // embeddings struct ggml_tensor * class_embedding; struct ggml_tensor * patch_embeddings; + struct ggml_tensor * patch_bias; struct ggml_tensor * position_embeddings; struct ggml_tensor * pre_ln_w; @@ -501,6 +503,11 @@ struct clip_ctx { bool use_gelu = false; int32_t ftype = 1; + bool has_class_embedding = true; + bool has_pre_norm = true; + bool has_post_norm = false; + bool has_patch_bias = false; + struct gguf_context * ctx_gguf; struct ggml_context * ctx_data; @@ -515,7 +522,7 @@ struct clip_ctx { static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) { if (!ctx->has_vision_encoder) { - printf("This gguf file seems to have no vision encoder\n"); + LOG_TEE("This gguf file seems to have no vision encoder\n"); return nullptr; } @@ -526,7 +533,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 const int patch_size = hparams.patch_size; const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); const int num_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side); - const int num_positions = num_patches + 1; + const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0); const int hidden_size = hparams.hidden_size; const int n_head = hparams.n_head; const int d_head = hidden_size / n_head; @@ -557,16 +564,23 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size); inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3)); + if (ctx->has_patch_bias) { + // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp)); + inp = ggml_add(ctx0, inp, model.patch_bias); + } + // concat class_embeddings and patch_embeddings - struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); + struct ggml_tensor * embeddings = inp; + if (ctx->has_class_embedding) { + embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); + embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); + embeddings = ggml_acc(ctx0, embeddings, inp, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); + } ggml_set_name(embeddings, "embeddings"); ggml_set_input(embeddings); - embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); - - embeddings = ggml_acc(ctx0, embeddings, inp, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions); ggml_set_name(positions, "positions"); @@ -576,7 +590,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); // pre-layernorm - { + if (ctx->has_pre_norm) { embeddings = ggml_norm(ctx0, embeddings, eps); ggml_set_name(embeddings, "pre_ln"); @@ -664,6 +678,14 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 embeddings = cur; } + // post-layernorm + if (ctx->has_post_norm) { + embeddings = ggml_norm(ctx0, embeddings, eps); + ggml_set_name(embeddings, "post_ln"); + + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b); + } + // llava projector { embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); @@ -879,21 +901,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { const int idx_name = gguf_find_key(ctx, KEY_NAME); if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug const std::string name = gguf_get_val_str(ctx, idx_name); - printf("%s: model name: %s\n", __func__, name.c_str()); + LOG_TEE("%s: model name: %s\n", __func__, name.c_str()); } - printf("%s: description: %s\n", __func__, description.c_str()); - printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx)); - printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); - printf("%s: n_tensors: %d\n", __func__, n_tensors); - printf("%s: n_kv: %d\n", __func__, n_kv); - printf("%s: ftype: %s\n", __func__, ftype_str.c_str()); - printf("\n"); + LOG_TEE("%s: description: %s\n", __func__, description.c_str()); + LOG_TEE("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx)); + LOG_TEE("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); + LOG_TEE("%s: n_tensors: %d\n", __func__, n_tensors); + LOG_TEE("%s: n_kv: %d\n", __func__, n_kv); + LOG_TEE("%s: ftype: %s\n", __func__, ftype_str.c_str()); + LOG_TEE("\n"); } const int n_tensors = gguf_get_n_tensors(ctx); // kv const int n_kv = gguf_get_n_kv(ctx); - printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n", + LOG_TEE("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n", __func__, n_kv, n_tensors, fname); { std::map n_type; @@ -904,7 +926,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { n_type[type]++; } - printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__); + LOG_TEE("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__); for (int i = 0; i < n_kv; i++) { const char * name = gguf_get_key(ctx, i); const enum gguf_type type = gguf_get_kv_type(ctx, i); @@ -920,7 +942,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } replace_all(value, "\n", "\\n"); - printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str()); + LOG_TEE("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str()); } // print type counts @@ -929,7 +951,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { continue; } - printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second); + LOG_TEE("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second); } } @@ -944,7 +966,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { size_t tensor_size = ggml_nbytes(cur); model_size += tensor_size; if (verbosity >= 3) { - printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n", + LOG_TEE("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n", __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type)); } } @@ -971,18 +993,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { #ifdef GGML_USE_CUDA new_clip->backend = ggml_backend_cuda_init(0); - printf("%s: CLIP using CUDA backend\n", __func__); + LOG_TEE("%s: CLIP using CUDA backend\n", __func__); #endif #ifdef GGML_USE_METAL new_clip->backend = ggml_backend_metal_init(); - printf("%s: CLIP using Metal backend\n", __func__); + LOG_TEE("%s: CLIP using Metal backend\n", __func__); #endif if (!new_clip->backend) { new_clip->backend = ggml_backend_cpu_init(); - printf("%s: CLIP using CPU backend\n", __func__); + LOG_TEE("%s: CLIP using CPU backend\n", __func__); } // model size and capabilities @@ -1006,15 +1028,15 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { new_clip->use_gelu = gguf_get_val_bool(ctx, idx); if (verbosity >= 1) { - printf("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder); - printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder); - printf("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector); - printf("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0); - printf("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0); + LOG_TEE("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder); + LOG_TEE("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder); + LOG_TEE("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector); + LOG_TEE("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0); + LOG_TEE("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0); } } - printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors); + LOG_TEE("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors); // load tensors { @@ -1027,7 +1049,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { new_clip->ctx_data = ggml_init(params); if (!new_clip->ctx_data) { - fprintf(stderr, "%s: ggml_init() failed\n", __func__); + LOG_TEE("%s: ggml_init() failed\n", __func__); clip_free(new_clip); gguf_free(ctx); return nullptr; @@ -1035,7 +1057,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { auto fin = std::ifstream(fname, std::ios::binary); if (!fin) { - printf("cannot open model file for loading tensors\n"); + LOG_TEE("cannot open model file for loading tensors\n"); clip_free(new_clip); gguf_free(ctx); return nullptr; @@ -1057,7 +1079,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i); fin.seekg(offset, std::ios::beg); if (!fin) { - printf("%s: failed to seek for tensor %s\n", __func__, name); + LOG_TEE("%s: failed to seek for tensor %s\n", __func__, name); clip_free(new_clip); gguf_free(ctx); return nullptr; @@ -1128,34 +1150,61 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } if (verbosity >= 2) { - printf("\n%s: vision model hparams\n", __func__); - printf("image_size %d\n", hparams.image_size); - printf("patch_size %d\n", hparams.patch_size); - printf("v_hidden_size %d\n", hparams.hidden_size); - printf("v_n_intermediate %d\n", hparams.n_intermediate); - printf("v_projection_dim %d\n", hparams.projection_dim); - printf("v_n_head %d\n", hparams.n_head); - printf("v_n_layer %d\n", hparams.n_layer); - printf("v_eps %f\n", hparams.eps); - printf("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]); - printf("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]); - printf("v_image_grid_pinpoints: "); + LOG_TEE("\n%s: vision model hparams\n", __func__); + LOG_TEE("image_size %d\n", hparams.image_size); + LOG_TEE("patch_size %d\n", hparams.patch_size); + LOG_TEE("v_hidden_size %d\n", hparams.hidden_size); + LOG_TEE("v_n_intermediate %d\n", hparams.n_intermediate); + LOG_TEE("v_projection_dim %d\n", hparams.projection_dim); + LOG_TEE("v_n_head %d\n", hparams.n_head); + LOG_TEE("v_n_layer %d\n", hparams.n_layer); + LOG_TEE("v_eps %f\n", hparams.eps); + LOG_TEE("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]); + LOG_TEE("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]); + LOG_TEE("v_image_grid_pinpoints: "); for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) { - printf("%d ", hparams.image_grid_pinpoints[i]); + LOG_TEE("%d ", hparams.image_grid_pinpoints[i]); } - printf("\n"); - printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type); + LOG_TEE("\n"); + LOG_TEE("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type); } + try { + vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); + new_clip->has_class_embedding = true; + } catch (const std::exception& e) { + new_clip->has_class_embedding = false; + } + + try { + vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); + vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); + new_clip->has_pre_norm = true; + } catch (std::exception & e) { + new_clip->has_pre_norm = false; + } + + try { + vision_model.post_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight")); + vision_model.post_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias")); + new_clip->has_post_norm = true; + } catch (std::exception & e) { + new_clip->has_post_norm = false; + } + + try { + vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS); + new_clip->has_patch_bias = true; + } catch (std::exception & e) { + new_clip->has_patch_bias = false; + } + try { vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); - vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v")); - vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); - vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); } catch(const std::exception& e) { - fprintf(stderr, "%s: failed to load vision model tensors\n", __func__); + LOG_TEE("%s: failed to load vision model tensors\n", __func__); } // LLaVA projection @@ -1184,7 +1233,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } catch (std::runtime_error & e) { } try { vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE); - // fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__); + // LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__); } catch (std::runtime_error & e) { } } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) { // MobileVLM projection @@ -1264,7 +1313,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch); ggml_gallocr_reserve(new_clip->compute_alloc, gf); size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0); - printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0); + LOG_TEE("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0); } return new_clip; @@ -1304,7 +1353,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { int nx, ny, nc; auto * data = stbi_load(fname, &nx, &ny, &nc, 3); if (!data) { - fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname); + LOG_TEE("%s: failed to load image '%s'\n", __func__, fname); return false; } build_clip_img_from_data(data, nx, ny, img); @@ -1316,7 +1365,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length int nx, ny, nc; auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3); if (!data) { - fprintf(stderr, "%s: failed to decode image bytes\n", __func__); + LOG_TEE("%s: failed to decode image bytes\n", __func__); return false; } build_clip_img_from_data(data, nx, ny, img); @@ -1325,7 +1374,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length } // Linear interpolation between two points -inline float lerp(float s, float e, float t) { +inline float clip_lerp(float s, float e, float t) { return s + (e - s) * t; } // Bilinear resize function @@ -1347,17 +1396,17 @@ static void bilinear_resize(const clip_image_u8& src, clip_image_u8& dst, int ta float y_lerp = py - y_floor; for (int c = 0; c < 3; c++) { - float top = lerp( + float top = clip_lerp( static_cast(src.buf[3 * (y_floor * src.nx + x_floor) + c]), static_cast(src.buf[3 * (y_floor * src.nx + (x_floor + 1)) + c]), x_lerp ); - float bottom = lerp( + float bottom = clip_lerp( static_cast(src.buf[3 * ((y_floor + 1) * src.nx + x_floor) + c]), static_cast(src.buf[3 * ((y_floor + 1) * src.nx + (x_floor + 1)) + c]), x_lerp ); - dst.buf[3 * (y * target_width + x) + c] = static_cast(lerp(top, bottom, y_lerp)); + dst.buf[3 * (y * target_width + x) + c] = static_cast(clip_lerp(top, bottom, y_lerp)); } } } @@ -1506,7 +1555,7 @@ static std::pair select_best_resolution(const std::pair & or int downscaled_height = static_cast(original_height * scale); int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height); int wasted_resolution = (width * height) - effective_resolution; - // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); + // LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) { max_effective_resolution = effective_resolution; min_wasted_resolution = wasted_resolution; @@ -1545,7 +1594,7 @@ static std::vector divide_to_patches_u8(const clip_image_u8 & im bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) { bool pad_to_square = true; if (!ctx->has_vision_encoder) { - printf("This gguf file seems to have no vision encoder\n"); + LOG_TEE("This gguf file seems to have no vision encoder\n"); return false; } auto & params = ctx->vision_model.hparams; @@ -1622,7 +1671,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli } for (size_t i = 0; i < patches.size(); i++) { - // printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny); + // LOG_TEE("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny); clip_image_u8_free(patches[i]); } @@ -1765,7 +1814,7 @@ int clip_n_patches(const struct clip_ctx * ctx) { bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) { if (!ctx->has_vision_encoder) { - printf("This gguf file seems to have no vision encoder\n"); + LOG_TEE("This gguf file seems to have no vision encoder\n"); return false; } @@ -1777,7 +1826,7 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3 bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) { if (!ctx->has_vision_encoder) { - printf("This gguf file seems to have no vision encoder\n"); + LOG_TEE("This gguf file seems to have no vision encoder\n"); return false; } @@ -1939,7 +1988,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i new_type = type; if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) { new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type - // fprintf(stderr, "%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type)); + // LOG_TEE("%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type)); } const size_t n_elms = ggml_nelements(cur); float * f32_data; @@ -1958,7 +2007,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i f32_data = (float *)conv_buf.data(); break; default: - printf("Please use an input file in f32 or f16\n"); + LOG_TEE("Please use an input file in f32 or f16\n"); gguf_free(ctx_out); return false; } @@ -1985,7 +2034,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i fout.put(0); } - printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize, + LOG_TEE("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize, orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); } @@ -2001,8 +2050,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i gguf_free(ctx_out); { - printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); - printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); + LOG_TEE("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); + LOG_TEE("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); } return true; diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 75948806e..a44c6cd76 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -1,4 +1,5 @@ #include "ggml.h" +#include "log.h" #include "common.h" #include "clip.h" #include "llava.h" @@ -18,7 +19,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END); + LOG_TEE("%s: invalid base64 image tag. must be %s%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END); return NULL; } @@ -87,7 +88,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size()); if (!embed) { - fprintf(stderr, "%s: could not load image from base64 string.\n", __func__); + LOG_TEE("%s: could not load image from base64 string.\n", __func__); return NULL; } @@ -112,8 +113,8 @@ struct llava_context { }; static void show_additional_info(int /*argc*/, char ** argv) { - fprintf(stderr, "\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); - fprintf(stderr, " note: a lower temperature value like 0.1 is recommended for better quality.\n"); + LOG_TEE("\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + LOG_TEE(" note: a lower temperature value like 0.1 is recommended for better quality.\n"); } static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) { @@ -123,18 +124,18 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para auto prompt = params->prompt; if (prompt_contains_image(prompt)) { if (!params->image.empty()) { - fprintf(stderr, "using base64 encoded image instead of command line image path\n"); + LOG_TEE("using base64 encoded image instead of command line image path\n"); } embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt); if (!embed) { - fprintf(stderr, "%s: can't load image from prompt\n", __func__); + LOG_TEE("%s: can't load image from prompt\n", __func__); return NULL; } params->prompt = remove_image_from_prompt(prompt); } else { embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str()); if (!embed) { - fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str()); + LOG_TEE("%s: is %s really an image file?\n", __func__, params->image.c_str()); return NULL; } } @@ -153,18 +154,18 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ // new templating mode: Provide the full prompt including system message and use as a placeholder for the image system_prompt = prompt.substr(0, image_pos); user_prompt = prompt.substr(image_pos + std::string("").length()); - printf("system_prompt: %s\n", system_prompt.c_str()); + LOG_TEE("system_prompt: %s\n", system_prompt.c_str()); if (params->verbose_prompt) { auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true); for (int i = 0; i < (int) tmp.size(); i++) { - printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); } } - printf("user_prompt: %s\n", user_prompt.c_str()); + LOG_TEE("user_prompt: %s\n", user_prompt.c_str()); if (params->verbose_prompt) { auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); for (int i = 0; i < (int) tmp.size(); i++) { - printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); } } } else { @@ -174,7 +175,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ if (params->verbose_prompt) { auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); for (int i = 0; i < (int) tmp.size(); i++) { - printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); } } } @@ -185,7 +186,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ // generate the response - fprintf(stderr, "\n"); + LOG_TEE("\n"); struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams); std::string response = ""; @@ -224,7 +225,7 @@ static struct llava_context * llava_init(gpt_params * params) { llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params); if (model == NULL) { - fprintf(stderr , "%s: error: unable to load model\n" , __func__); + LOG_TEE("%s: error: unable to load model\n" , __func__); return NULL; } @@ -234,7 +235,7 @@ static struct llava_context * llava_init(gpt_params * params) { llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params); if (ctx_llama == NULL) { - fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + LOG_TEE("%s: error: failed to create the llama_context\n" , __func__); return NULL; } @@ -257,6 +258,12 @@ static void llava_free(struct llava_context * ctx_llava) { llama_backend_free(); } +static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) { + (void) level; + (void) user_data; + LOG_TEE("%s", text); +} + int main(int argc, char ** argv) { ggml_time_init(); @@ -266,6 +273,14 @@ int main(int argc, char ** argv) { show_additional_info(argc, argv); return 1; } + +#ifndef LOG_DISABLE_LOGS + log_set_target(log_filename_generator("llava", "log")); + LOG_TEE("Log start\n"); + log_dump_cmdline(argc, argv); + llama_log_set(llama_log_callback_logTee, nullptr); +#endif // LOG_DISABLE_LOGS + if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { gpt_print_usage(argc, argv, params); show_additional_info(argc, argv); @@ -274,7 +289,7 @@ int main(int argc, char ** argv) { auto ctx_llava = llava_init(¶ms); if (ctx_llava == NULL) { - fprintf(stderr, "%s: error: failed to init llava\n", __func__); + LOG_TEE("%s: error: failed to init llava\n", __func__); return 1; } diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 29764757a..9a990bb18 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -54,7 +54,7 @@ static std::pair select_best_resolution(const std::pair& ori int downscaled_height = static_cast(original_height * scale); int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height); int wasted_resolution = (width * height) - effective_resolution; - // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); + // LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) { max_effective_resolution = effective_resolution; min_wasted_resolution = wasted_resolution; @@ -154,13 +154,13 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]); if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) { if (newline_tmp->buffer == NULL) { - printf("newline_tmp tensor buffer is NULL\n"); + LOG_TEE("newline_tmp tensor buffer is NULL\n"); } ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp)); } else { model.newline->data = newline_tmp->data; if (model.newline->data == NULL) { - printf("newline_tmp tensor data is NULL\n"); + LOG_TEE("newline_tmp tensor data is NULL\n"); } } @@ -224,7 +224,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli img_res_v.size = 0; img_res_v.data = nullptr; if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) { - fprintf(stderr, "%s: unable to preprocess image\n", __func__); + LOG_TEE("%s: unable to preprocess image\n", __func__); delete[] img_res_v.data; return false; } @@ -239,7 +239,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096 delete[] img_res_v.data; if (!encoded) { - fprintf(stderr, "Unable to encode image\n"); + LOG_TEE("Unable to encode image\n"); return false; } @@ -252,12 +252,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184 const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside if (!encoded) { - fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size); + LOG_TEE("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size); return false; } } const int64_t t_img_enc_batch_us = ggml_time_us(); - printf("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0); + LOG_TEE("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0); const int32_t * image_grid = clip_image_grid(ctx_clip); @@ -290,12 +290,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli // clip_image_save_to_bmp(*tmp, "image_feature.bmp"); } - printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos); + LOG_TEE("%s: image embedding created: %d tokens\n", __func__, *n_img_pos); const int64_t t_img_enc_end_us = ggml_time_us(); float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0; - printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos); + LOG_TEE("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos); return true; } @@ -305,7 +305,7 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama)); auto n_image_embd = clip_n_mmproj_embd(ctx_clip); if (n_image_embd != n_llama_embd) { - printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd); + LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd); return false; } return true; @@ -314,13 +314,13 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) { float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model if (!image_embd) { - fprintf(stderr, "Unable to allocate memory for image embeddings\n"); + LOG_TEE("Unable to allocate memory for image embeddings\n"); return false; } int n_img_pos; if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) { - fprintf(stderr, "%s: cannot encode image, aborting\n", __func__); + LOG_TEE("%s: cannot encode image, aborting\n", __func__); free(image_embd); return false; } @@ -340,7 +340,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_ } llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, }; if (llama_decode(ctx_llama, batch)) { - fprintf(stderr, "%s : failed to eval\n", __func__); + LOG_TEE("%s : failed to eval\n", __func__); return false; } *n_past += n_eval; @@ -352,7 +352,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c clip_image_u8 * img = clip_image_u8_init(); if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) { clip_image_u8_free(img); - fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__); + LOG_TEE("%s: can't load image from bytes, is it a valid image?", __func__); return NULL; } @@ -361,7 +361,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos); if (!image_embed_result) { clip_image_u8_free(img); - fprintf(stderr, "%s: coulnd't embed the image\n", __func__); + LOG_TEE("%s: coulnd't embed the image\n", __func__); return NULL; } @@ -375,7 +375,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) { auto file = fopen(path, "rb"); if (file == NULL) { - fprintf(stderr, "%s: can't read file %s\n", __func__, path); + LOG_TEE("%s: can't read file %s\n", __func__, path); return false; } @@ -385,7 +385,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data if (buffer == NULL) { - fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path); + LOG_TEE("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path); perror("Memory allocation error"); fclose(file); return false; @@ -410,7 +410,7 @@ struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx long image_bytes_length; auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length); if (!loaded) { - fprintf(stderr, "%s: failed to load %s\n", __func__, image_path); + LOG_TEE("%s: failed to load %s\n", __func__, image_path); return NULL; } diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index 5af6a8ab6..9c3540b20 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -299,7 +299,7 @@ int main(int argc, char ** argv) { } fflush(stdout); - if (id == llama_token_eos(model)) { + if (llama_token_is_eog(model, id)) { has_eos = true; } diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp index 41b62c2fe..87ecc0a4f 100644 --- a/examples/lookup/lookup-stats.cpp +++ b/examples/lookup/lookup-stats.cpp @@ -30,7 +30,6 @@ int main(int argc, char ** argv){ // load the model std::tie(model, ctx) = llama_init_from_gpt_params(params); - llama_set_rng_seed(ctx, params.seed); GGML_ASSERT(llama_n_vocab(model) < (1 << 16)); // tokenize the prompt diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index 65ed408a2..eebbd00a5 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -38,7 +38,6 @@ int main(int argc, char ** argv){ // load the model std::tie(model, ctx) = llama_init_from_gpt_params(params); - llama_set_rng_seed(ctx, params.seed); GGML_ASSERT(llama_n_vocab(model) < (1 << 16)); // tokenize the prompt @@ -141,7 +140,7 @@ int main(int argc, char ** argv){ printf("%s", token_str.c_str()); } - if (id == llama_token_eos(model)) { + if (llama_token_is_eog(model, id)) { has_eos = true; } diff --git a/examples/main/README.md b/examples/main/README.md index bb696b562..649f4e0f3 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -304,13 +304,15 @@ These options help improve the performance and memory usage of the LLaMA models. - `--prompt-cache FNAME`: Specify a file to cache the model state after the initial prompt. This can significantly speed up the startup time when you're using longer prompts. The file is created during the first run and is reused and updated in subsequent runs. **Note**: Restoring a cached prompt does not imply restoring the exact state of the session at the point it was saved. So even when specifying a specific seed, you are not guaranteed to get the same sequence of tokens as the original generation. -### Grammars +### Grammars & JSON schemas - `--grammar GRAMMAR`, `--grammar-file FILE`: Specify a grammar (defined inline or in a file) to constrain model output to a specific format. For example, you could force the model to output JSON or to speak only in emojis. See the [GBNF guide](../../grammars/README.md) for details on the syntax. +- `--json-schema SCHEMA`: Specify a [JSON schema](https://json-schema.org/) to constrain model output to (e.g. `{}` for any JSON object, or `{"items": {"type": "string", "minLength": 10, "maxLength": 100}, "minItems": 10}` for a JSON array of strings with size constraints). If a schema uses external `$ref`s, you should use `--grammar "$( python examples/json_schema_to_grammar.py myschema.json )"` instead. + ### Quantization -For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-data--run). +For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-and-quantize). ## Additional Options diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 249fc2bb6..a74d4d9c7 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -240,7 +240,6 @@ int main(int argc, char ** argv) { return 1; } session_tokens.resize(n_token_count_out); - llama_set_rng_seed(ctx, params.seed); LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size()); } } @@ -795,8 +794,8 @@ int main(int argc, char ** argv) { } } - // deal with end of text token in interactive mode - if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) { + // deal with end of generation tokens in interactive mode + if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) { LOG("found EOS token\n"); if (params.interactive) { @@ -920,8 +919,8 @@ int main(int argc, char ** argv) { } } - // end of text token - if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive || params.chatml)) { + // end of generation + if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.instruct || params.interactive || params.chatml)) { LOG_TEE(" [end of text]\n"); break; } diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index f66c91013..7c5595d6e 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -359,7 +359,7 @@ int main(int argc, char ** argv) { // client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str()); if (client.n_decoded > 2 && - (id == llama_token_eos(model) || + (llama_token_is_eog(model, id) || (params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) || client.response.find("User:") != std::string::npos || client.response.find('\n') != std::string::npos)) { diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp index 2cbc9e1fa..f2ef9ca10 100644 --- a/examples/passkey/passkey.cpp +++ b/examples/passkey/passkey.cpp @@ -252,8 +252,8 @@ int main(int argc, char ** argv) { // sample the most likely token const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); - // is it an end of stream? - if (new_token_id == llama_token_eos(model) || n_cur == n_len) { + // is it an end of generation? + if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) { LOG_TEE("\n"); break; diff --git a/examples/perplexity/README.md b/examples/perplexity/README.md index 50e1af011..1a8c0dd64 100644 --- a/examples/perplexity/README.md +++ b/examples/perplexity/README.md @@ -3,19 +3,18 @@ TODO ## Llama 2 70B Scorechart -Quantization | Model size (GiB) | Perplexity | Delta to fp16 --- | -- | -- | -- -Q4_0 | 36.20 | 3.5550 | 3.61% -Q4_1 | 40.20 | 3.5125 | 2.37% -Q5_0 | 44.20 | 3.4744 | 1.26% -Q2_K | 27.27 | 3.7339 | 8.82% -Q3_K_S | 27.86 | 3.7019 | 7.89% -Q3_K_M | 30.83 | 3.5932 | 4.72% -Q3_K_L | 33.67 | 3.5617 | 3.80% -Q4_K_S | 36.39 | 3.4852 | 1.57% -Q4_K_M | 38.54 | 3.4725 | 1.20% -Q5_K_S | 44.20 | 3.4483 | 0.50% -Q5_K_M | 45.41 | 3.4451 | 0.40% -Q6_K | 52.70 | 3.4367 | 0.16% -fp16 | 128.5 | 3.4313 | - - +| Quantization | Model size (GiB) | Perplexity | Delta to fp16 | +|--------------|------------------|------------|---------------| +| Q4_0 | 36.20 | 3.5550 | 3.61% | +| Q4_1 | 40.20 | 3.5125 | 2.37% | +| Q5_0 | 44.20 | 3.4744 | 1.26% | +| Q2_K | 27.27 | 3.7339 | 8.82% | +| Q3_K_S | 27.86 | 3.7019 | 7.89% | +| Q3_K_M | 30.83 | 3.5932 | 4.72% | +| Q3_K_L | 33.67 | 3.5617 | 3.80% | +| Q4_K_S | 36.39 | 3.4852 | 1.57% | +| Q4_K_M | 38.54 | 3.4725 | 1.20% | +| Q5_K_S | 44.20 | 3.4483 | 0.50% | +| Q5_K_M | 45.41 | 3.4451 | 0.40% | +| Q6_K | 52.70 | 3.4367 | 0.16% | +| fp16 | 128.5 | 3.4313 | - | diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index bab79aaea..9a3374fdc 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -1852,12 +1852,20 @@ int main(int argc, char ** argv) { const int32_t n_ctx = params.n_ctx; + if (n_ctx <= 0) { + fprintf(stderr, "%s: perplexity tool requires '--ctx-size' > 0\n", __func__); + return 1; + } + const bool ppl = !params.hellaswag && !params.winogrande && !params.multiple_choice && !params.kl_divergence; + if (ppl) { - int n_seq = std::max(1, params.n_batch / n_ctx); - int32_t n_kv = n_seq * n_ctx; + const int32_t n_seq = std::max(1, params.n_batch / n_ctx); + const int32_t n_kv = n_seq * n_ctx; + params.n_parallel = n_seq; - params.n_ctx = n_kv; + params.n_ctx = n_kv; + params.n_batch = std::min(params.n_batch, n_kv); } else { params.n_batch = std::min(params.n_batch, params.n_ctx); diff --git a/examples/quantize/CMakeLists.txt b/examples/quantize/CMakeLists.txt index 6f374a2bd..6b977fde8 100644 --- a/examples/quantize/CMakeLists.txt +++ b/examples/quantize/CMakeLists.txt @@ -1,6 +1,6 @@ set(TARGET quantize) add_executable(${TARGET} quantize.cpp) install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/quantize/README.md b/examples/quantize/README.md index c8b9a27a0..8a10365c0 100644 --- a/examples/quantize/README.md +++ b/examples/quantize/README.md @@ -4,17 +4,17 @@ TODO ## Llama 2 7B -Quantization | Bits per Weight (BPW) --- | -- -Q2_K | 3.35 -Q3_K_S | 3.50 -Q3_K_M | 3.91 -Q3_K_L | 4.27 -Q4_K_S | 4.58 -Q4_K_M | 4.84 -Q5_K_S | 5.52 -Q5_K_M | 5.68 -Q6_K | 6.56 +| Quantization | Bits per Weight (BPW) | +|--------------|-----------------------| +| Q2_K | 3.35 | +| Q3_K_S | 3.50 | +| Q3_K_M | 3.91 | +| Q3_K_L | 4.27 | +| Q4_K_S | 4.58 | +| Q4_K_M | 4.84 | +| Q5_K_S | 5.52 | +| Q5_K_M | 5.68 | +| Q6_K | 6.56 | ## Llama 2 13B Quantization | Bits per Weight (BPW) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 64cb6db19..432cc2b4f 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -8,7 +8,6 @@ #include #include #include -#include struct quant_option { std::string name; @@ -53,6 +52,10 @@ static const std::vector QUANT_OPTIONS = { { "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", }, }; +static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE = "quantize.imatrix.file"; +static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET = "quantize.imatrix.dataset"; +static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES = "quantize.imatrix.entries_count"; +static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS = "quantize.imatrix.chunks_count"; static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) { std::string ftype_str; @@ -97,6 +100,7 @@ static void usage(const char * executable) { printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n"); printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n"); printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n"); + printf(" --keep-split: will generate quatized model in the same shards as input"); printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n"); printf("Note: --include-weights and --exclude-weights cannot be used together\n"); @@ -112,7 +116,7 @@ static void usage(const char * executable) { exit(1); } -static void load_imatrix(const std::string & imatrix_file, std::unordered_map> & imatrix_data) { +static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map> & imatrix_data) { std::ifstream in(imatrix_file.c_str(), std::ios::binary); if (!in) { printf("%s: failed to open %s\n",__func__, imatrix_file.c_str()); @@ -159,18 +163,33 @@ static void load_imatrix(const std::string & imatrix_file, std::unordered_map dataset_as_vec(dataset_len); + in.read(dataset_as_vec.data(), dataset_len); + imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end()); + printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str()); + } + printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call); + return m_last_call; } -static void prepare_imatrix(const std::string & imatrix_file, +static int prepare_imatrix(const std::string & imatrix_file, + std::string & imatrix_dataset, const std::vector & included_weights, const std::vector & excluded_weights, std::unordered_map> & imatrix_data) { + int m_last_call = -1; if (!imatrix_file.empty()) { - load_imatrix(imatrix_file, imatrix_data); + m_last_call = load_imatrix(imatrix_file, imatrix_dataset, imatrix_data); } if (imatrix_data.empty()) { - return; + return m_last_call; } if (!excluded_weights.empty()) { for (auto& name : excluded_weights) { @@ -196,6 +215,7 @@ static void prepare_imatrix(const std::string & imatrix_file, if (!imatrix_data.empty()) { printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size())); } + return m_last_call; } static ggml_type parse_ggml_type(const char * arg) { @@ -210,43 +230,6 @@ static ggml_type parse_ggml_type(const char * arg) { return result; } -static bool parse_kv_override(const char * data, std::vector & overrides) { - const char* sep = strchr(data, '='); - if (sep == nullptr || sep - data >= 128) { - fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data); - return false; - } - llama_model_kv_override kvo; - std::strncpy(kvo.key, data, sep - data); - kvo.key[sep - data] = 0; - sep++; - if (strncmp(sep, "int:", 4) == 0) { - sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; - kvo.int_value = std::atol(sep); - } else if (strncmp(sep, "float:", 6) == 0) { - sep += 6; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; - kvo.float_value = std::atof(sep); - } else if (strncmp(sep, "bool:", 5) == 0) { - sep += 5; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; - if (std::strcmp(sep, "true") == 0) { - kvo.bool_value = true; - } else if (std::strcmp(sep, "false") == 0) { - kvo.bool_value = false; - } else { - fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data); - return false; - } - } else { - fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data); - return false; - } - overrides.emplace_back(std::move(kvo)); - return true; -} - int main(int argc, char ** argv) { if (argc < 3) { usage(argv[0]); @@ -300,6 +283,8 @@ int main(int argc, char ** argv) { } else { usage(argv[0]); } + } else if (strcmp(argv[arg_idx], "--keep-split")) { + params.keep_split = true; } else { usage(argv[0]); } @@ -313,10 +298,43 @@ int main(int argc, char ** argv) { usage(argv[0]); } + std::string imatrix_dataset; std::unordered_map> imatrix_data; - prepare_imatrix(imatrix_file, included_weights, excluded_weights, imatrix_data); + int m_last_call = prepare_imatrix(imatrix_file, imatrix_dataset, included_weights, excluded_weights, imatrix_data); if (!imatrix_data.empty()) { params.imatrix = &imatrix_data; + { + llama_model_kv_override kvo; + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_FILE); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + strncpy(kvo.val_str, imatrix_file.c_str(), 127); + kvo.val_str[127] = '\0'; + kv_overrides.emplace_back(std::move(kvo)); + } + if (!imatrix_dataset.empty()) { + llama_model_kv_override kvo; + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + strncpy(kvo.val_str, imatrix_dataset.c_str(), 127); + kvo.val_str[127] = '\0'; + kv_overrides.emplace_back(std::move(kvo)); + } + + { + llama_model_kv_override kvo; + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.val_i64 = imatrix_data.size(); + kv_overrides.emplace_back(std::move(kvo)); + } + + if (m_last_call > 0) { + llama_model_kv_override kvo; + std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS); + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.val_i64 = m_last_call; + kv_overrides.emplace_back(std::move(kvo)); + } } if (!kv_overrides.empty()) { kv_overrides.emplace_back(); @@ -332,20 +350,28 @@ int main(int argc, char ** argv) { std::string fname_out; std::string ftype_str; + std::string suffix = ".gguf"; if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) { std::string fpath; const size_t pos = fname_inp.find_last_of("/\\"); if (pos != std::string::npos) { fpath = fname_inp.substr(0, pos + 1); } - // export as [inp path]/ggml-model-[ftype].gguf - fname_out = fpath + "ggml-model-" + ftype_str + ".gguf"; + + // export as [inp path]/ggml-model-[ftype]. Only add extension if there is no splitting + fname_out = fpath + "ggml-model-" + ftype_str; + if (!params.keep_split) { + fname_out += suffix; + } arg_idx++; if (ftype_str == "COPY") { params.only_copy = true; } } else { fname_out = argv[arg_idx]; + if (params.keep_split && fname_out.find(suffix) != std::string::npos) { + fname_out = fname_out.substr(0, fname_out.length() - suffix.length()); + } arg_idx++; if (argc <= arg_idx) { diff --git a/examples/quantize/tests.sh b/examples/quantize/tests.sh new file mode 100644 index 000000000..160c12bee --- /dev/null +++ b/examples/quantize/tests.sh @@ -0,0 +1,65 @@ +#!/bin/bash + +set -eu + +if [ $# -lt 1 ] +then + echo "usage: $0 path_to_build_binary [path_to_temp_folder]" + echo "example: $0 ../../build/bin ../../tmp" + exit 1 +fi + +if [ $# -gt 1 ] +then + TMP_DIR=$2 +else + TMP_DIR=/tmp +fi + +set -x + +SPLIT=$1/gguf-split +QUANTIZE=$1/quantize +MAIN=$1/main +WORK_PATH=$TMP_DIR/quantize +ROOT_DIR=$(realpath $(dirname $0)/../../) + +mkdir -p "$WORK_PATH" + +# Clean up in case of previously failed test +rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf + +# 1. Get a model +( +cd $WORK_PATH +"$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf +) +echo PASS + +# 2. Split model +$SPLIT --split-max-tensors 28 $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split +echo PASS +echo + +# 3. Requant model with '--keep_split' +$QUANTIZE --allow-requantize --keep_split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K +echo PASS +echo + +# 3a. Test the requanted model is loading properly +$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --random-prompt --n-predict 32 +echo PASS +echo + +# 4. Requant mode without '--keep_split' +$QUANTIZE --allow-requantize $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant-merge.gguf Q4_K +echo PASS +echo + +# 4b. Test the requanted model is loading properly +$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --random-prompt --n-predict 32 +echo PASS +echo + +# Clean up +rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf diff --git a/examples/regex-to-grammar.py b/examples/regex-to-grammar.py index c98118e95..5cd9210a4 100644 --- a/examples/regex-to-grammar.py +++ b/examples/regex-to-grammar.py @@ -8,7 +8,7 @@ print(subprocess.check_output( "python", os.path.join( os.path.dirname(os.path.realpath(__file__)), - "json-schema-to-grammar.py"), + "json_schema_to_grammar.py"), *rest, "-", "--raw-pattern", diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index d2ee47d01..4b89c5302 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -1,17 +1,34 @@ set(TARGET server) option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON) option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF) -include_directories(${CMAKE_CURRENT_SOURCE_DIR}) -add_executable(${TARGET} +include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR}) +set(TARGET_SRCS server.cpp utils.hpp httplib.h ) +set(PUBLIC_ASSETS + index.html + index.js + completion.js + json-schema-to-grammar.mjs +) +foreach(asset ${PUBLIC_ASSETS}) + set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}") + set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp") + list(APPEND TARGET_SRCS ${output}) + add_custom_command( + DEPENDS "${input}" + OUTPUT "${output}" + COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake" + ) +endforeach() +add_executable(${TARGET} ${TARGET_SRCS}) install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ ) -target_link_libraries(${TARGET} PRIVATE common json-schema-to-grammar ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT}) if (LLAMA_SERVER_SSL) find_package(OpenSSL REQUIRED) target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto) diff --git a/examples/server/README.md b/examples/server/README.md index a6fc92eaa..918ac1295 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -11,6 +11,7 @@ Set of LLM REST APIs and a simple web front end to interact with llama.cpp. * Continuous batching * Multimodal (wip) * Monitoring endpoints + * Schema-constrained JSON response format The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216). @@ -250,6 +251,8 @@ node index.js `grammar`: Set grammar for grammar-based sampling. Default: no grammar + `json_schema`: Set a JSON schema for grammar-based sampling (e.g. `{"items": {"type": "string"}, "minItems": 10, "maxItems": 100}` of a list of strings, or `{}` for any JSON). See [tests](../../tests/test-json-schema-to-grammar.cpp) for supported features. Default: no JSON schema. + `seed`: Set the random number generator (RNG) seed. Default: `-1`, which is a random seed. `ignore_eos`: Ignore end of stream token and continue generating. Default: `false` @@ -365,6 +368,8 @@ Notice that each `probs` is an array of length `n_probs`. See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported. + The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}`), similar to other OpenAI-inspired API providers. + *Examples:* You can use either Python `openai` library with appropriate checkpoints: diff --git a/examples/server/bench/script.js b/examples/server/bench/script.js index c4c486cdf..bdf4f5abc 100644 --- a/examples/server/bench/script.js +++ b/examples/server/bench/script.js @@ -90,7 +90,8 @@ export default function () { "model": model, "stream": true, "seed": 42, - "max_tokens": max_tokens + "max_tokens": max_tokens, + "stop": ["<|im_end|>"] // This is temporary for phi-2 base (i.e. not instructed) since the server expects that the model always to emit BOS } const params = {method: 'POST', body: JSON.stringify(payload)}; diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp deleted file mode 100644 index 9a7f3986d..000000000 --- a/examples/server/completion.js.hpp +++ /dev/null @@ -1,496 +0,0 @@ -unsigned char completion_js[] = { - 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$1

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$2

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a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public/json-schema-to-grammar.mjs index 17bd60fde..8e0be1b40 100644 --- a/examples/server/public/json-schema-to-grammar.mjs +++ b/examples/server/public/json-schema-to-grammar.mjs @@ -1,33 +1,95 @@ -// WARNING: This file was ported from json-schema-to-grammar.py, please fix bugs / add features there first. +// WARNING: This file was ported from json_schema_to_grammar.py, please fix bugs / add features there first. const SPACE_RULE = '" "?'; +function _buildRepetition(itemRule, minItems, maxItems, opts={}) { + const separatorRule = opts.separatorRule ?? ''; + const itemRuleIsLiteral = opts.itemRuleIsLiteral ?? false + + if (separatorRule === '') { + if (minItems === 0 && maxItems === 1) { + return `${itemRule}?`; + } else if (minItems === 1 && maxItems === undefined) { + return `${itemRule}+`; + } + } + + let result = ''; + if (minItems > 0) { + if (itemRuleIsLiteral && separatorRule === '') { + result = `"${itemRule.slice(1, -1).repeat(minItems)}"`; + } else { + result = Array.from({ length: minItems }, () => itemRule) + .join(separatorRule !== '' ? ` ${separatorRule} ` : ' '); + } + } + + const optRepetitions = (upToN, prefixWithSep=false) => { + const content = separatorRule !== '' && prefixWithSep ? `${separatorRule} ${itemRule}` : itemRule; + if (upToN === 0) { + return ''; + } else if (upToN === 1) { + return `(${content})?`; + } else if (separatorRule !== '' && !prefixWithSep) { + return `(${content} ${optRepetitions(upToN - 1, true)})?`; + } else { + return Array.from({ length: upToN }, () => `(${content}`).join(' ').trim() + Array.from({ length: upToN }, () => ')?').join(''); + } + }; + + if (minItems > 0 && maxItems !== minItems) { + result += ' '; + } + + if (maxItems !== undefined) { + result += optRepetitions(maxItems - minItems, minItems > 0); + } else { + const itemOperator = `(${separatorRule !== '' ? separatorRule + ' ' : ''}${itemRule})`; + + if (minItems === 0 && separatorRule !== '') { + result = `(${itemRule} ${itemOperator}*)?`; + } else { + result += `${itemOperator}*`; + } + } + + return result; +} + +class BuiltinRule { + constructor(content, deps) { + this.content = content; + this.deps = deps || []; + } +} + +const UP_TO_15_DIGITS = _buildRepetition('[0-9]', 0, 15); + const PRIMITIVE_RULES = { - boolean: '("true" | "false") space', - number: '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', - integer: '("-"? ([0-9] | [1-9] [0-9]*)) space', - value: 'object | array | string | number | boolean', - object: '"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', - array: '"[" space ( value ("," space value)* )? "]" space', - uuid: '"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space', - string: ` "\\"" ( - [^"\\\\] | - "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) - )* "\\"" space`, - null: '"null" space', + boolean : new BuiltinRule('("true" | "false") space', []), + 'decimal-part' : new BuiltinRule('[0-9] ' + UP_TO_15_DIGITS, []), + 'integral-part': new BuiltinRule('[0-9] | [1-9] ' + UP_TO_15_DIGITS, []), + number : new BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']), + integer : new BuiltinRule('("-"? integral-part) space', ['integral-part']), + value : new BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']), + object : new BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']), + array : new BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']), + uuid : new BuiltinRule('"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space', []), + char : new BuiltinRule(`[^"\\\\] | "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])`, []), + string : new BuiltinRule(`"\\"" char* "\\"" space`, ['char']), + null : new BuiltinRule('"null" space', []), }; -const OBJECT_RULE_NAMES = ['object', 'array', 'string', 'number', 'boolean', 'null', 'value']; // TODO: support "uri", "email" string formats -const DATE_RULES = { - 'date' : '[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', - 'time' : '([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', - 'date-time': 'date "T" time', - 'date-string': '"\\"" date "\\"" space', - 'time-string': '"\\"" time "\\"" space', - 'date-time-string': '"\\"" date-time "\\"" space', -}; +const STRING_FORMAT_RULES = { + 'date' : new BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []), + 'time' : new BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []), + 'date-time' : new BuiltinRule('date "T" time', ['date', 'time']), + 'date-string' : new BuiltinRule('"\\"" date "\\"" space', ['date']), + 'time-string' : new BuiltinRule('"\\"" time "\\"" space', ['time']), + 'date-time-string': new BuiltinRule('"\\"" date-time "\\"" space', ['date-time']), +} -const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...DATE_RULES}; +const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...STRING_FORMAT_RULES}; const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g; const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g; @@ -158,7 +220,7 @@ export class SchemaConverter { rule = '[\\U00000000-\\U0010FFFF]'; } else { // Accept any character... except \n and \r line break chars (\x0A and \xOD) - rule = '[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]'; + rule = '[^\\x0A\\x0D]'; } return this._addRule('dot', rule); }; @@ -259,26 +321,19 @@ export class SchemaConverter { let [sub, subIsLiteral] = seq[seq.length - 1]; - if (minTimes === 0 && maxTimes === Infinity) { - seq[seq.length - 1] = [`${sub}*`, false]; - } else if (minTimes === 0 && maxTimes === 1) { - seq[seq.length - 1] = [`${sub}?`, false]; - } else if (minTimes === 1 && maxTimes === Infinity) { - seq[seq.length - 1] = [`${sub}+`, false]; - } else { - if (!subIsLiteral) { - let id = subRuleIds[sub]; - if (id === undefined) { - id = this._addRule(`${name}-${Object.keys(subRuleIds).length + 1}`, sub); - subRuleIds[sub] = id; - } - sub = id; + if (!subIsLiteral) { + let id = subRuleIds[sub]; + if (id === undefined) { + id = this._addRule(`${name}-${Object.keys(subRuleIds).length + 1}`, sub); + subRuleIds[sub] = id; } - - const repeatedSub = Array.from({ length: minTimes }, () => subIsLiteral ? `"${sub.slice(1, -1).repeat(minTimes)}"` : sub); - const optionalSub = maxTimes !== undefined ? Array.from({ length: maxTimes - minTimes }, () => `${sub}?`) : [`${sub}*`]; - seq[seq.length - 1] = [repeatedSub.concat(optionalSub).join(' '), false]; + sub = id; } + + seq[seq.length - 1] = [ + _buildRepetition(subIsLiteral ? `"${sub}"` : sub, minTimes, maxTimes, {itemRuleIsLiteral: subIsLiteral}), + false + ]; } else { let literal = ''; while (i < length) { @@ -394,49 +449,50 @@ export class SchemaConverter { ); } else { const itemRuleName = this.visit(items, `${name ?? ''}${name ? '-' : ''}item`); - const listItemOperator = `( "," space ${itemRuleName} )`; - let successiveItems = ''; - let minItems = schema.minItems || 0; + const minItems = schema.minItems || 0; const maxItems = schema.maxItems; - if (minItems > 0) { - successiveItems = listItemOperator.repeat(minItems - 1); - minItems--; - } - if (maxItems !== undefined && maxItems > minItems) { - successiveItems += `${listItemOperator}?`.repeat(maxItems - minItems - 1); - } else { - successiveItems += `${listItemOperator}*`; - } - const rule = minItems === 0 - ? `"[" space ( ${itemRuleName} ${successiveItems} )? "]" space` - : `"[" space ${itemRuleName} ${successiveItems} "]" space`; - return this._addRule(ruleName, rule); + return this._addRule(ruleName, '"[" space ' + _buildRepetition(itemRuleName, minItems, maxItems, {separatorRule: '"," space'}) + ' "]" space'); } } else if ((schemaType === undefined || schemaType === 'string') && 'pattern' in schema) { return this._visitPattern(schema.pattern, ruleName); } else if ((schemaType === undefined || schemaType === 'string') && /^uuid[1-5]?$/.test(schema.format || '')) { - return this._addRule( - ruleName === 'root' ? 'root' : schemaFormat, - PRIMITIVE_RULES['uuid']) - } else if ((schemaType === undefined || schemaType === 'string') && schema.format in DATE_RULES) { - for (const [t, r] of Object.entries(DATE_RULES)) { - this._addRule(t, r); - } - return schemaFormat + '-string'; + return this._addPrimitive( + ruleName === 'root' ? 'root' : schemaFormat, + PRIMITIVE_RULES['uuid'] + ); + } else if ((schemaType === undefined || schemaType === 'string') && `${schema.format}-string` in STRING_FORMAT_RULES) { + const primName = `${schema.format}-string` + return this._addRule(ruleName, this._addPrimitive(primName, STRING_FORMAT_RULES[primName])); + } else if (schemaType === 'string' && ('minLength' in schema || 'maxLength' in schema)) { + const charRuleName = this._addPrimitive('char', PRIMITIVE_RULES['char']); + const minLen = schema.minLength || 0; + const maxLen = schema.maxLength; + return this._addRule(ruleName, '"\\\"" ' + _buildRepetition(charRuleName, minLen, maxLen) + ' "\\\"" space'); } else if ((schemaType === 'object') || (Object.keys(schema).length === 0)) { - for (const n of OBJECT_RULE_NAMES) { - this._addRule(n, PRIMITIVE_RULES[n]); - } - return this._addRule(ruleName, 'object'); + return this._addRule(ruleName, this._addPrimitive('object', PRIMITIVE_RULES['object'])); } else { if (!(schemaType in PRIMITIVE_RULES)) { throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`); } // TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero - return this._addRule(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]); + return this._addPrimitive(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]); } } + _addPrimitive(name, rule) { + let n = this._addRule(name, rule.content); + for (const dep of rule.deps) { + const depRule = PRIMITIVE_RULES[dep] || STRING_FORMAT_RULES[dep]; + if (!depRule) { + throw new Error(`Rule ${dep} not known`); + } + if (!(dep in this._rules)) { + this._addPrimitive(dep, depRule); + } + } + return n; + } + _buildObjectRule(properties, required, name, additionalProperties) { const propOrder = this._propOrder; // sort by position in prop_order (if specified) then by original order @@ -462,7 +518,7 @@ export class SchemaConverter { const valueRule = this.visit(additionalProperties === true ? {} : additionalProperties, `${subName}-value`); propKvRuleNames['*'] = this._addRule( `${subName}-kv`, - `${this._addRule('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`); + `${this._addPrimitive('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`); optionalProps.push('*'); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2e791190b..2760aea8f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -854,12 +854,12 @@ struct server_context { slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep); slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard); - slot.params.seed = json_value(data, "seed", default_params.seed); + slot.sparams.seed = json_value(data, "seed", default_sparams.seed); slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); // process "json_schema" and "grammar" - if (data.contains("json_schema") && data.contains("grammar")) { + if (data.contains("json_schema") && !data["json_schema"].is_null() && data.contains("grammar") && !data["grammar"].is_null()) { send_error(task, "Either \"json_schema\" or \"grammar\" can be specified, but not both", ERROR_TYPE_INVALID_REQUEST); return false; } else if (data.contains("json_schema") && !data.contains("grammar")) { @@ -1028,7 +1028,6 @@ struct server_context { send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST); return false; } - llama_set_rng_seed(ctx, slot.params.seed); } slot.command = SLOT_COMMAND_LOAD_PROMPT; @@ -1083,7 +1082,7 @@ struct server_context { }; if (llama_decode(ctx, batch_view) != 0) { - LOG_TEE("%s: llama_decode() failed\n", __func__); + LOG_ERROR("llama_decode() failed", {}); return; } } @@ -1118,7 +1117,7 @@ struct server_context { bool process_token(completion_token_output & result, server_slot & slot) { // remember which tokens were sampled - used for repetition penalties during sampling - const std::string token_str = llama_token_to_piece(ctx, result.tok); + const std::string token_str = llama_token_to_piece(ctx, result.tok, false); slot.sampled = result.tok; // search stop word and delete it @@ -1201,13 +1200,34 @@ struct server_context { }); } - if (result.tok == llama_token_eos(model)) { + if (llama_token_is_eog(model, result.tok)) { slot.stopped_eos = true; slot.has_next_token = false; LOG_VERBOSE("eos token found", {}); } + auto n_ctx_train = llama_n_ctx_train(model); + if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.ga_n == 1 + && slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) { + LOG_WARNING("n_predict is not set and self-context extend is disabled." + " Limiting generated tokens to n_ctx_train to avoid EOS-less generation infinite loop", { + { "id_slot", slot.id }, + { "params.n_predict", slot.params.n_predict }, + { "slot.n_prompt_tokens", slot.n_prompt_tokens }, + { "slot.n_decoded", slot.n_decoded }, + { "slot.n_predict", slot.n_predict }, + { "n_slots", params.n_parallel }, + { "slot.n_ctx", slot.n_ctx }, + { "n_ctx", n_ctx }, + { "n_ctx_train", n_ctx_train }, + { "ga_n", slot.ga_n }, + }); + slot.truncated = true; + slot.stopped_limit = true; + slot.has_next_token = false; // stop prediction + } + LOG_VERBOSE("next token", { {"id_slot", slot.id}, {"id_task", slot.id_task}, @@ -1281,7 +1301,11 @@ struct server_context { } void send_error(const int id_task, const int id_multi, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) { - LOG_TEE("task %i - error: %s\n", id_task, error.c_str()); + LOG_ERROR("task error", { + {"id_multi", id_multi}, + {"id_task", id_task}, + {"error", error}, + }); server_task_result res; res.id = id_task; @@ -2138,7 +2162,7 @@ struct server_context { }); // process the created batch of tokens - for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { + for (int32_t i = 0; i < batch.n_tokens; i += n_batch) { const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i); for (auto & slot : slots) { @@ -2186,7 +2210,11 @@ struct server_context { if (ret != 0) { if (n_batch == 1 || ret < 0) { // if you get here, it means the KV cache is full - try increasing it via the context size - LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret); + LOG_ERROR("failed to decode the batch: KV cache is full - try increasing it via the context size", { + {"i", i}, + {"n_batch", ret}, + {"ret", ret}, + }); for (auto & slot : slots) { slot.state = SLOT_STATE_PROCESSING; slot.command = SLOT_COMMAND_NONE; @@ -2196,12 +2224,16 @@ struct server_context { break; // break loop of n_batch } - LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2); - // retry with half the batch size to try to find a free slot in the KV cache n_batch /= 2; i -= n_batch; + LOG_WARNING("failed to find free space in the KV cache, retrying with smaller batch size - try increasing it via the context size or enable defragmentation", { + {"i", i}, + {"n_batch", n_batch}, + {"ret", ret}, + }); + continue; // continue loop of n_batch } @@ -2360,7 +2392,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict); printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); - printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); + printf(" types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n"); printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n"); printf(" --chat-template JINJA_TEMPLATE\n"); @@ -2791,43 +2823,11 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, invalid_param = true; break; } - char * sep = strchr(argv[i], '='); - if (sep == nullptr || sep - argv[i] >= 128) { - fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - - struct llama_model_kv_override kvo; - std::strncpy(kvo.key, argv[i], sep - argv[i]); - kvo.key[sep - argv[i]] = 0; - sep++; - if (strncmp(sep, "int:", 4) == 0) { - sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; - kvo.int_value = std::atol(sep); - } else if (strncmp(sep, "float:", 6) == 0) { - sep += 6; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; - kvo.float_value = std::atof(sep); - } else if (strncmp(sep, "bool:", 5) == 0) { - sep += 5; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; - if (std::strcmp(sep, "true") == 0) { - kvo.bool_value = true; - } else if (std::strcmp(sep, "false") == 0) { - kvo.bool_value = false; - } else { - fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - } else { + if (!parse_kv_override(argv[i], params.kv_overrides)) { fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); invalid_param = true; break; } - params.kv_overrides.push_back(kvo); } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md index feb2b1d6c..83c0208f3 100644 --- a/examples/server/tests/README.md +++ b/examples/server/tests/README.md @@ -29,7 +29,7 @@ To mitigate it, you can increase values in `n_predict`, `kv_size`. cd ../../.. mkdir build cd build -cmake ../ +cmake -DLLAMA_CURL=ON ../ cmake --build . --target server ``` diff --git a/examples/server/tests/features/results.feature b/examples/server/tests/features/results.feature new file mode 100644 index 000000000..f17120f7b --- /dev/null +++ b/examples/server/tests/features/results.feature @@ -0,0 +1,57 @@ +@llama.cpp +@results +Feature: Results + + Background: Server startup + Given a server listening on localhost:8080 + And a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models + And a model file test-model-00001-of-00003.gguf + And 128 as batch size + And 256 KV cache size + And 128 max tokens to predict + + Scenario Outline: Multi users completion + Given slots + And continuous batching + Then the server is starting + Then the server is healthy + + Given 42 as seed + And a prompt: + """ + Write a very long story about AI. + """ + + Given 42 as seed + And a prompt: + """ + Write a very long story about AI. + """ + + Given 42 as seed + And a prompt: + """ + Write a very long story about AI. + """ + + Given 42 as seed + And a prompt: + """ + Write a very long story about AI. + """ + + Given 42 as seed + And a prompt: + """ + Write a very long story about AI. + """ + + Given concurrent completion requests + Then the server is busy + Then the server is idle + And all slots are idle + Then all predictions are equal + Examples: + | n_slots | + | 1 | + | 2 | diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index ca400efa4..f71e0d706 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -61,6 +61,7 @@ def step_server_config(context, server_fqdn, server_port): context.server_metrics = False context.server_process = None context.seed = None + context.draft = None context.server_seed = None context.user_api_key = None context.response_format = None @@ -107,6 +108,11 @@ def step_n_gpu_layer(context, ngl): context.n_gpu_layer = ngl +@step('{draft:d} as draft') +def step_draft(context, draft): + context.draft = draft + + @step('{n_ctx:d} KV cache size') def step_n_ctx(context, n_ctx): context.n_ctx = n_ctx @@ -254,6 +260,15 @@ def step_n_tokens_predicted(context, predicted_n): assert_n_tokens_predicted(context.completion, predicted_n) +@step('all predictions are equal') +@async_run_until_complete +async def step_predictions_equal(context): + n_completions = await gather_tasks_results(context) + assert n_completions >= 2, "need at least 2 completions" + assert_all_predictions_equal(context.tasks_result) + context.tasks_result = [] + + @step('the completion is truncated') def step_assert_completion_truncated(context): step_assert_completion_truncated(context, '') @@ -1020,6 +1035,23 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:' f' {n_predicted} <> {expected_predicted_n}') +def assert_all_predictions_equal(completion_responses): + content_0 = completion_responses[0]['content'] + + if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': + print(f"content 0: {content_0}") + + i = 1 + for response in completion_responses[1:]: + content = response['content'] + + if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': + print(f"content {i}: {content}") + + assert content == content_0, "contents not equal" + + i += 1 + async def gather_tasks_results(context): n_tasks = len(context.concurrent_tasks) @@ -1148,6 +1180,8 @@ def start_server_background(context): server_args.extend(['--ubatch-size', context.n_ubatch]) if context.n_gpu_layer: server_args.extend(['--n-gpu-layers', context.n_gpu_layer]) + if context.draft is not None: + server_args.extend(['--draft', context.draft]) if context.server_continuous_batching: server_args.append('--cont-batching') if context.server_embeddings: diff --git a/examples/server/tests/tests.sh b/examples/server/tests/tests.sh index 1c6c5695f..72a0fbad8 100755 --- a/examples/server/tests/tests.sh +++ b/examples/server/tests/tests.sh @@ -4,9 +4,8 @@ set -eu if [ $# -lt 1 ] then - # Start @llama.cpp scenario - behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp + # Start @llama.cpp scenario + behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp else - behave "$@" + behave "$@" fi - diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index a8d43ac63..1a2212502 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -381,10 +381,6 @@ static json oaicompat_completion_params_parse( } else { llama_params["stop"] = json_value(body, "stop", json::array()); } - // Some chat templates don't use EOS token to stop generation - // We must add their end sequences to list of stop words - llama_params["stop"].push_back("<|im_end|>"); // chatml - llama_params["stop"].push_back(""); // gemma // Handle "response_format" field if (body.contains("response_format")) { diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index 39e2d8ea4..b0f8e0fdc 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -133,8 +133,8 @@ int main(int argc, char ** argv) { // sample the most likely token const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); - // is it an end of stream? - if (new_token_id == llama_token_eos(model) || n_cur == n_len) { + // is it an end of generation? + if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) { LOG_TEE("\n"); break; diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 6a7367b0c..12e46fbc9 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -360,7 +360,7 @@ int main(int argc, char ** argv) { } } - if (token_id == llama_token_eos(model_tgt)) { + if (llama_token_is_eog(model_tgt, token_id)) { has_eos = true; } ++n_predict; diff --git a/examples/sycl/build.sh b/examples/sycl/build.sh index f20391d7a..db46d57ca 100755 --- a/examples/sycl/build.sh +++ b/examples/sycl/build.sh @@ -20,4 +20,4 @@ cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx #cmake --build . --config Release --target llama-bench #build all binary -cmake --build . --config Release -v +cmake --build . --config Release -j -v diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh index c979a52f6..7b39a18c0 100755 --- a/examples/sycl/run-llama2.sh +++ b/examples/sycl/run-llama2.sh @@ -12,6 +12,7 @@ if [ $# -gt 0 ]; then GGML_SYCL_SINGLE_GPU=1 else GGML_SYCL_DEVICE=0 + GGML_SYCL_SINGLE_GPU=0 fi #export GGML_SYCL_DEBUG=1 diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 7d06e401b..587418cc7 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -73,6 +73,7 @@ struct my_llama_model { static const char * LLM_KV_TRAINING_TYPE_TRAIN_MODEL = "train_model"; static const char * LLM_KV_TRAINING_TYPE = "training.type"; +static const char * LLM_KV_GENERAL_NAME = "general.name"; static const char * LLM_KV_GENERAL_ARCHITECTURE = "general.architecture"; static const char * LLM_KV_GENERAL_FILE_TYPE = "general.file_type"; @@ -529,6 +530,7 @@ static void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_contex static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model) { const char * arch = "llama"; + enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32; std::vector keybuf; @@ -540,6 +542,7 @@ static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vo // set arch gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch); + gguf_set_val_str(fctx, LLM_KV_GENERAL_NAME, arch); gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype); // set hparams diff --git a/examples/ts-type-to-grammar.sh b/examples/ts-type-to-grammar.sh index a7ad00173..9abba2a3d 100755 --- a/examples/ts-type-to-grammar.sh +++ b/examples/ts-type-to-grammar.sh @@ -1,7 +1,7 @@ #!/bin/bash # # ./examples/ts-type-to-grammar.sh "{a:string,b:string,c?:string}" -# python examples/json-schema-to-grammar.py https://json.schemastore.org/tsconfig.json +# python examples/json_schema_to_grammar.py https://json.schemastore.org/tsconfig.json # set -euo pipefail @@ -25,4 +25,4 @@ npx ts-json-schema-generator --unstable --no-top-ref --path "$DTS_FILE" --type M # https://github.com/YousefED/typescript-json-schema # npx typescript-json-schema --defaultProps --required "$DTS_FILE" MyType | tee "$SCHEMA_FILE" >&2 -./examples/json-schema-to-grammar.py "$SCHEMA_FILE" +./examples/json_schema_to_grammar.py "$SCHEMA_FILE" diff --git a/flake.lock b/flake.lock index ed48dd8da..b738da7c6 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1712163089, - "narHash": "sha256-Um+8kTIrC19vD4/lUCN9/cU9kcOsD1O1m+axJqQPyMM=", + "lastModified": 1714076141, + "narHash": "sha256-Drmja/f5MRHZCskS6mvzFqxEaZMeciScCTFxWVLqWEY=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "fd281bd6b7d3e32ddfa399853946f782553163b5", + "rev": "7bb2ccd8cdc44c91edba16c48d2c8f331fb3d856", "type": "github" }, "original": { diff --git a/ggml-alloc.c b/ggml-alloc.c index 7ceafec30..1fbd376ed 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -371,16 +371,16 @@ struct ggml_gallocr { }; ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs) { - ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(sizeof(struct ggml_gallocr), 1); + ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(1, sizeof(struct ggml_gallocr)); GGML_ASSERT(galloc != NULL); - galloc->bufts = calloc(sizeof(ggml_backend_buffer_type_t) * n_bufs, 1); + galloc->bufts = calloc(n_bufs, sizeof(ggml_backend_buffer_type_t)); GGML_ASSERT(galloc->bufts != NULL); - galloc->buffers = calloc(sizeof(ggml_backend_buffer_t) * n_bufs, 1); + galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t) * n_bufs); GGML_ASSERT(galloc->buffers != NULL); - galloc->buf_tallocs = calloc(sizeof(struct ggml_dyn_tallocr *) * n_bufs, 1); + galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *)); GGML_ASSERT(galloc->buf_tallocs != NULL); for (int i = 0; i < n_bufs; i++) { @@ -646,8 +646,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c free(galloc->hash_set.keys); free(galloc->hash_values); galloc->hash_set.size = hash_size; - galloc->hash_set.keys = calloc(sizeof(struct ggml_tensor *), hash_size); - galloc->hash_values = calloc(sizeof(struct hash_node), hash_size); + galloc->hash_set.keys = calloc(hash_size, sizeof(struct ggml_tensor *)); + galloc->hash_values = calloc(hash_size, sizeof(struct hash_node)); GGML_ASSERT(galloc->hash_set.keys != NULL); GGML_ASSERT(galloc->hash_values != NULL); } else { @@ -667,7 +667,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c // set the node_allocs from the hash table if (galloc->n_nodes < graph->n_nodes) { free(galloc->node_allocs); - galloc->node_allocs = calloc(sizeof(struct node_alloc), graph->n_nodes); + galloc->node_allocs = calloc(graph->n_nodes, sizeof(struct node_alloc)); GGML_ASSERT(galloc->node_allocs != NULL); } galloc->n_nodes = graph->n_nodes; @@ -697,7 +697,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c } if (galloc->n_leafs < graph->n_leafs) { free(galloc->leaf_allocs); - galloc->leaf_allocs = calloc(sizeof(galloc->leaf_allocs[0]), graph->n_leafs); + galloc->leaf_allocs = calloc(graph->n_leafs, sizeof(galloc->leaf_allocs[0])); GGML_ASSERT(galloc->leaf_allocs != NULL); } galloc->n_leafs = graph->n_leafs; diff --git a/ggml-backend.c b/ggml-backend.c index 402d86ef3..f5bdcf078 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -822,7 +822,11 @@ GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { switch (op->op) { case GGML_OP_CPY: - return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS && op->type != GGML_TYPE_IQ1_S; // missing type_traits.from_float + return + op->type != GGML_TYPE_IQ2_XXS && + op->type != GGML_TYPE_IQ2_XS && + op->type != GGML_TYPE_IQ1_S && + op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float case GGML_OP_MUL_MAT: return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type; default: @@ -1721,23 +1725,23 @@ ggml_backend_sched_t ggml_backend_sched_new( GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS); GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU - struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1); + struct ggml_backend_sched * sched = calloc(1, sizeof(struct ggml_backend_sched)); // initialize hash table sched->hash_set = ggml_hash_set_new(graph_size); - sched->tensor_backend_id = calloc(sizeof(sched->tensor_backend_id[0]), sched->hash_set.size); - sched->tensor_copies = calloc(sizeof(sched->tensor_copies[0]), sched->hash_set.size); + sched->tensor_backend_id = calloc(sched->hash_set.size, sizeof(sched->tensor_backend_id[0])); + sched->tensor_copies = calloc(sched->hash_set.size, sizeof(sched->tensor_copies[0])); const size_t nodes_size = graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2; - sched->node_backend_ids = calloc(sizeof(sched->node_backend_ids[0]), nodes_size); - sched->leaf_backend_ids = calloc(sizeof(sched->leaf_backend_ids[0]), nodes_size); + sched->node_backend_ids = calloc(nodes_size, sizeof(sched->node_backend_ids[0])); + sched->leaf_backend_ids = calloc(nodes_size, sizeof(sched->leaf_backend_ids[0])); sched->n_backends = n_backends; sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1; const int initial_splits_capacity = 16; - sched->splits = calloc(sizeof(sched->splits[0]), initial_splits_capacity); + sched->splits = calloc(initial_splits_capacity, sizeof(sched->splits[0])); sched->splits_capacity = initial_splits_capacity; for (int b = 0; b < n_backends; b++) { @@ -1780,12 +1784,14 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) { void ggml_backend_sched_reset(ggml_backend_sched_t sched) { // reset state for the next run - size_t hash_size = sched->hash_set.size; - memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); // NOLINT - memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size); - memset(sched->tensor_copies, 0, sizeof(sched->tensor_copies[0]) * hash_size); + if (!sched->is_reset) { + size_t hash_size = sched->hash_set.size; + memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); // NOLINT + memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size); + memset(sched->tensor_copies, 0, sizeof(sched->tensor_copies[0]) * hash_size); - sched->is_reset = true; + sched->is_reset = true; + } sched->is_alloc = false; } @@ -1968,10 +1974,10 @@ static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_te struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) { struct ggml_hash_set hash_set = { /* .size = */ graph->visited_hash_table.size, - /* .keys = */ calloc(sizeof(hash_set.keys[0]), graph->visited_hash_table.size) // NOLINT + /* .keys = */ calloc(graph->visited_hash_table.size, sizeof(hash_set.keys[0])) // NOLINT }; - struct ggml_tensor ** node_copies = calloc(sizeof(node_copies[0]), hash_set.size); // NOLINT - bool * node_init = calloc(sizeof(node_init[0]), hash_set.size); + struct ggml_tensor ** node_copies = calloc(hash_set.size, sizeof(node_copies[0])); // NOLINT + bool * node_init = calloc(hash_set.size, sizeof(node_init[0])); struct ggml_init_params params = { /* .mem_size = */ ggml_tensor_overhead()*hash_set.size + ggml_graph_overhead_custom(graph->size, false), diff --git a/ggml-cuda.cu b/ggml-cuda.cu index bff8ad9d9..d277104d1 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1231,7 +1231,7 @@ static void ggml_cuda_op_mul_mat_cublas( if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 - ggml_cuda_pool_alloc src0_as_f16(ctx.pool()); + ggml_cuda_pool_alloc src0_as_f16(ctx.pool(id)); if (src0->type != GGML_TYPE_F16) { const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type); GGML_ASSERT(to_fp16_cuda != nullptr); @@ -1241,7 +1241,7 @@ static void ggml_cuda_op_mul_mat_cublas( } const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16.get(); - ggml_cuda_pool_alloc src1_as_f16(ctx.pool()); + ggml_cuda_pool_alloc src1_as_f16(ctx.pool(id)); if (src1->type != GGML_TYPE_F16) { const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); GGML_ASSERT(to_fp16_cuda != nullptr); @@ -1250,7 +1250,7 @@ static void ggml_cuda_op_mul_mat_cublas( to_fp16_cuda(src1_ddf_i, src1_as_f16.get(), ne, stream); } const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16.get(); - ggml_cuda_pool_alloc dst_f16(ctx.pool(), row_diff*src1_ncols); + ggml_cuda_pool_alloc dst_f16(ctx.pool(id), row_diff*src1_ncols); const half alpha_f16 = 1.0f; const half beta_f16 = 0.0f; @@ -1946,7 +1946,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor } else if (!split && !fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_cuda_mul_mat_vec_nc(ctx, src0, src1, dst); - } else if (!split && fp16_performance_good && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { + } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || fp16_performance_good) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { // KQ + KQV multi-batch ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst); } else if (use_dequantize_mul_mat_vec) { @@ -1960,20 +1960,73 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor } } +struct mmid_row_mapping { + int32_t i1; + int32_t i2; +}; + +static __global__ void k_copy_src1_to_contiguous(const char * __restrict__ src1_original, char * __restrict__ src1_contiguous, + int * __restrict__ cur_src1_row, mmid_row_mapping * __restrict__ row_mapping, + const char * __restrict ids, int64_t i02, size_t ids_nb1, size_t ids_nb0, + int64_t ne11, int64_t ne10, + size_t nb11, size_t nb12) { + int32_t iid1 = blockIdx.x; + int32_t id = blockIdx.y; + + const int32_t row_id_i = *(const int32_t *) (ids + iid1*ids_nb1 + id*ids_nb0); + + if (row_id_i != i02) { + return; + } + + const int64_t i11 = id % ne11; + const int64_t i12 = iid1; + + __shared__ int src1_row; + if (threadIdx.x == 0) { + src1_row = atomicAdd(cur_src1_row, 1); + row_mapping[src1_row] = {id, iid1}; + } + __syncthreads(); + + const float * src1_row_original = (const float *)(src1_original + i11*nb11 + i12*nb12); + float * src1_row_contiguous = (float *)(src1_contiguous + src1_row*nb11); + + for (int i = threadIdx.x; i < ne10; i += blockDim.x) { + src1_row_contiguous[i] = src1_row_original[i]; + } +} + +static __global__ void k_copy_dst_from_contiguous(char * __restrict__ dst_original, const char * __restrict__ dst_contiguous, + const mmid_row_mapping * __restrict__ row_mapping, + int64_t ne0, + size_t nb1, size_t nb2) { + int32_t i = blockIdx.x; + + const int32_t i1 = row_mapping[i].i1; + const int32_t i2 = row_mapping[i].i2; + + const float * dst_row_contiguous = (const float *)(dst_contiguous + i*nb1); + float * dst_row_original = (float *)(dst_original + i1*nb1 + i2*nb2); + + for (int j = threadIdx.x; j < ne0; j += blockDim.x) { + dst_row_original[j] = dst_row_contiguous[j]; + } +} + static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; const ggml_tensor * ids = dst->src[2]; + GGML_TENSOR_BINARY_OP_LOCALS + GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0->buffer) && "mul_mat_id does not support split buffers"); cudaStream_t stream = ctx.stream(); - const size_t nb11 = src1->nb[1]; - const size_t nb1 = dst->nb[1]; - - const int32_t id = ((int32_t *) dst->op_params)[0]; - const int32_t n_as = src0->ne[2]; + const int64_t n_as = ne02; + const int64_t n_ids = ids->ne[0]; std::vector ids_host(ggml_nbytes(ids)); const char * ids_dev = (const char *) ids->data; @@ -1982,7 +2035,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * ggml_tensor src0_row = *src0; ggml_tensor src1_row = *src1; - ggml_tensor dst_row = *dst; + ggml_tensor dst_row = *dst; char * src0_original = (char *) src0->data; char * src1_original = (char *) src1->data; @@ -1990,19 +2043,39 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * src0_row.ne[2] = 1; src0_row.ne[3] = 1; - src0_row.nb[3] = src0->nb[2]; + src0_row.nb[3] = nb02; - if (src1->ne[1] == 1) { - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + src1_row.ne[1] = 1; + src1_row.ne[2] = 1; + src1_row.ne[3] = 1; + src1_row.nb[2] = nb11; + src1_row.nb[3] = nb11; - GGML_ASSERT(row_id >= 0 && row_id < n_as); + dst_row.ne[1] = 1; + dst_row.ne[2] = 1; + dst_row.ne[3] = 1; + dst_row.nb[2] = nb1; + dst_row.nb[3] = nb1; - src0_row.data = src0_original + row_id*src0->nb[2]; - src1_row.data = src1_original + i01*src1->nb[1]; - dst_row.data = dst_original + i01*dst->nb[1]; + if (ne12 == 1) { + for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { + for (int64_t id = 0; id < n_ids; id++) { + const int32_t i02 = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); - ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row); + GGML_ASSERT(i02 >= 0 && i02 < n_as); + + const int64_t i11 = id % ne11; + const int64_t i12 = iid1; + + const int64_t i1 = id; + const int64_t i2 = i12; + + src0_row.data = src0_original + i02*nb02; + src1_row.data = src1_original + i11*nb11 + i12*nb12; + dst_row.data = dst_original + i1*nb1 + i2*nb2; + + ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row); + } } } else { ggml_cuda_pool_alloc src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1)); @@ -2011,54 +2084,69 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * src1_row.data = src1_contiguous.get(); dst_row.data = dst_contiguous.get(); - for (int32_t row_id = 0; row_id < n_as; ++row_id) { + for (int64_t i02 = 0; i02 < n_as; i02++) { int64_t num_src1_rows = 0; - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); - if (row_id_i != row_id) { - continue; + for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { + for (int64_t id = 0; id < n_ids; id++) { + const int32_t row_id_i = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); + + GGML_ASSERT(row_id_i >= 0 && row_id_i < n_as); + + if (row_id_i != i02) { + continue; + } + + num_src1_rows++; } - - GGML_ASSERT(row_id >= 0 && row_id < n_as); - - CUDA_CHECK(cudaMemcpyAsync(src1_contiguous.get() + num_src1_rows*nb11, src1_original + i01*nb11, - nb11, cudaMemcpyDeviceToDevice, stream)); - num_src1_rows++; } if (num_src1_rows == 0) { continue; } - src0_row.data = src0_original + row_id*src0->nb[2]; + ggml_cuda_pool_alloc dev_cur_src1_row(ctx.pool(), 1); + ggml_cuda_pool_alloc dev_row_mapping(ctx.pool(), num_src1_rows); + CUDA_CHECK(cudaMemsetAsync(dev_cur_src1_row.get(), 0, sizeof(int), stream)); + + { + dim3 block_dims(std::min((unsigned int)ne10, 768u)); + dim3 grid_dims(ids->ne[1], n_ids); + k_copy_src1_to_contiguous<<>>( + src1_original, src1_contiguous.get(), + dev_cur_src1_row.get(), dev_row_mapping.get(), + ids_dev, i02, ids->nb[1], ids->nb[0], + ne11, ne10, + nb11, nb12); + CUDA_CHECK(cudaGetLastError()); + } + + src0_row.data = src0_original + i02*nb02; + + GGML_ASSERT(nb11 == sizeof(float)*ne10); + GGML_ASSERT(nb1 == sizeof(float)*ne0); src1_row.ne[1] = num_src1_rows; - dst_row.ne[1] = num_src1_rows; - src1_row.nb[1] = nb11; src1_row.nb[2] = num_src1_rows*nb11; src1_row.nb[3] = num_src1_rows*nb11; + dst_row.ne[1] = num_src1_rows; dst_row.nb[1] = nb1; dst_row.nb[2] = num_src1_rows*nb1; dst_row.nb[3] = num_src1_rows*nb1; ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row); - num_src1_rows = 0; - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); - - if (row_id_i != row_id) { - continue; - } - - GGML_ASSERT(row_id >= 0 && row_id < n_as); - - CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous.get() + num_src1_rows*nb1, - nb1, cudaMemcpyDeviceToDevice, stream)); - num_src1_rows++; + { + dim3 block_dims(std::min((unsigned int)ne0, 768u)); + dim3 grid_dims(num_src1_rows); + k_copy_dst_from_contiguous<<>>( + dst_original, dst_contiguous.get(), + dev_row_mapping.get(), + ne0, + nb1, nb2); + CUDA_CHECK(cudaGetLastError()); } } } @@ -2487,7 +2575,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons GGML_CALL static bool ggml_backend_cuda_offload_op(ggml_backend_t backend, const ggml_tensor * op) { const int min_batch_size = 32; - return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS; + return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) || + (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID); GGML_UNUSED(backend); } diff --git a/ggml-cuda/binbcast.cu b/ggml-cuda/binbcast.cu index 959eaed95..19b08b74f 100644 --- a/ggml-cuda/binbcast.cu +++ b/ggml-cuda/binbcast.cu @@ -22,6 +22,7 @@ static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst int ne0, int ne1, int ne2, int ne3, int ne10, int ne11, int ne12, int ne13, /*int s0, */ int s1, int s2, int s3, + /*int s00,*/ int s01, int s02, int s03, /*int s10,*/ int s11, int s12, int s13) { const int i0s = blockDim.x*blockIdx.x + threadIdx.x; const int i1 = (blockDim.y*blockIdx.y + threadIdx.y); @@ -36,9 +37,9 @@ static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst const int i12 = i2 % ne12; const int i13 = i3 % ne13; - const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; + const size_t i_src0 = i3*s03 + i2*s02 + i1*s01; const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; - const size_t i_dst = i_src0; + const size_t i_dst = i3*s3 + i2*s2 + i1*s1; const src0_t * src0_row = src0 + i_src0; const src1_t * src1_row = src1 + i_src1; @@ -55,6 +56,7 @@ static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * s int ne0, int ne1, int ne2, int ne3, int ne10, int ne11, int ne12, int ne13, /*int s0, */ int s1, int s2, int s3, + /*int s00,*/ int s01, int s02, int s03, /*int s10,*/ int s11, int s12, int s13) { const int i = blockDim.x*blockIdx.x + threadIdx.x; @@ -72,9 +74,9 @@ static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * s const int i12 = i2 % ne12; const int i13 = i3 % ne13; - const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; + const size_t i_src0 = i3*s03 + i2*s02 + i1*s01; const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; - const size_t i_dst = i_src0; + const size_t i_dst = i3*s3 + i2*s2 + i1*s1; const src0_t * src0_row = src0 + i_src0; const src1_t * src1_row = src1 + i_src1; @@ -101,10 +103,14 @@ struct bin_bcast_cuda { int nr[4] = { nr0, nr1, nr2, nr3 }; // collapse dimensions until first broadcast dimension - int64_t cne0[] = {ne0, ne1, ne2, ne3}; + int64_t cne[] = {ne0, ne1, ne2, ne3}; + int64_t cne0[] = {ne00, ne01, ne02, ne03}; int64_t cne1[] = {ne10, ne11, ne12, ne13}; - size_t cnb0[] = {nb0, nb1, nb2, nb3}; + + size_t cnb[] = {nb0, nb1, nb2, nb3}; + size_t cnb0[] = {nb00, nb01, nb02, nb03}; size_t cnb1[] = {nb10, nb11, nb12, nb13}; + auto collapse = [](int64_t cne[]) { cne[0] *= cne[1]; cne[1] = cne[2]; @@ -118,32 +124,47 @@ struct bin_bcast_cuda { cnb[3] *= cne[3]; }; - for (int i = 0; i < 4; i++) { - if (nr[i] != 1) { - break; - } - if (i > 0) { - collapse_nb(cnb0, cne0); - collapse_nb(cnb1, cne1); - collapse(cne0); - collapse(cne1); + if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) { + for (int i = 0; i < 4; i++) { + if (nr[i] != 1) { + break; + } + if (i > 0) { + collapse_nb(cnb, cne); + collapse_nb(cnb0, cne0); + collapse_nb(cnb1, cne1); + collapse(cne); + collapse(cne0); + collapse(cne1); + } } } + { - int64_t ne0 = cne0[0]; - int64_t ne1 = cne0[1]; - int64_t ne2 = cne0[2]; - int64_t ne3 = cne0[3]; + int64_t ne0 = cne[0]; + int64_t ne1 = cne[1]; + int64_t ne2 = cne[2]; + int64_t ne3 = cne[3]; + + //int64_t ne00 = cne0[0]; GGML_UNUSED(ne00); + //int64_t ne01 = cne0[1]; GGML_UNUSED(ne01); + //int64_t ne02 = cne0[2]; GGML_UNUSED(ne02); + //int64_t ne03 = cne0[3]; GGML_UNUSED(ne03); int64_t ne10 = cne1[0]; int64_t ne11 = cne1[1]; int64_t ne12 = cne1[2]; int64_t ne13 = cne1[3]; - size_t nb0 = cnb0[0]; - size_t nb1 = cnb0[1]; - size_t nb2 = cnb0[2]; - size_t nb3 = cnb0[3]; + size_t nb0 = cnb[0]; + size_t nb1 = cnb[1]; + size_t nb2 = cnb[2]; + size_t nb3 = cnb[3]; + + size_t nb00 = cnb0[0]; + size_t nb01 = cnb0[1]; + size_t nb02 = cnb0[2]; + size_t nb03 = cnb0[3]; size_t nb10 = cnb1[0]; size_t nb11 = cnb1[1]; @@ -160,7 +181,28 @@ struct bin_bcast_cuda { size_t s12 = nb12 / sizeof(src1_t); size_t s13 = nb13 / sizeof(src1_t); + size_t s00 = nb00 / sizeof(src0_t); + size_t s01 = nb01 / sizeof(src0_t); + size_t s02 = nb02 / sizeof(src0_t); + size_t s03 = nb03 / sizeof(src0_t); + + GGML_ASSERT(nb0 % sizeof(dst_t) == 0); + GGML_ASSERT(nb1 % sizeof(dst_t) == 0); + GGML_ASSERT(nb2 % sizeof(dst_t) == 0); + GGML_ASSERT(nb3 % sizeof(dst_t) == 0); + + GGML_ASSERT(nb00 % sizeof(src0_t) == 0); + GGML_ASSERT(nb01 % sizeof(src0_t) == 0); + GGML_ASSERT(nb02 % sizeof(src0_t) == 0); + GGML_ASSERT(nb03 % sizeof(src0_t) == 0); + + GGML_ASSERT(nb10 % sizeof(src1_t) == 0); + GGML_ASSERT(nb11 % sizeof(src1_t) == 0); + GGML_ASSERT(nb12 % sizeof(src1_t) == 0); + GGML_ASSERT(nb13 % sizeof(src1_t) == 0); + GGML_ASSERT(s0 == 1); + GGML_ASSERT(s00 == 1); GGML_ASSERT(s10 == 1); const int block_size = 128; @@ -179,13 +221,14 @@ struct bin_bcast_cuda { ); if (block_nums.z > 65535) { - // this is the maximum number of blocks in z direction, fallback to 1D grid kernel + // this is the maximum number of blocks in z dimension, fallback to 1D grid kernel int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size; k_bin_bcast_unravel<<>>( src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3, ne10, ne11, ne12, ne13, /* s0, */ s1, s2, s3, + /* s00, */ s01, s02, s03, /* s10, */ s11, s12, s13); } else { k_bin_bcast<<>>( @@ -193,6 +236,7 @@ struct bin_bcast_cuda { ne0, ne1, ne2, ne3, ne10, ne11, ne12, ne13, /* s0, */ s1, s2, s3, + /* s00, */ s01, s02, s03, /* s10, */ s11, s12, s13); } } diff --git a/ggml-cuda/convert.cu b/ggml-cuda/convert.cu index ed4fa2748..75e50c985 100644 --- a/ggml-cuda/convert.cu +++ b/ggml-cuda/convert.cu @@ -5,16 +5,16 @@ template static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k) { - const int64_t i = 2*(blockDim.x*blockIdx.x + threadIdx.x); + const int64_t i = (int64_t)2*(blockDim.x*blockIdx.x + threadIdx.x); if (i >= k) { return; } const int64_t ib = i/qk; // block index - const int iqs = (i%qk)/qr; // quant index - const int iybs = i - i%qk; // y block start index - const int y_offset = qr == 1 ? 1 : qk/2; + const int64_t iqs = (i%qk)/qr; // quant index + const int64_t iybs = i - i%qk; // y block start index + const int64_t y_offset = qr == 1 ? 1 : qk/2; // dequantize dfloat2 v; @@ -29,7 +29,7 @@ static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, h #if __CUDA_ARCH__ >= CC_PASCAL constexpr int nint = CUDA_Q8_0_NE_ALIGN/sizeof(int) + WARP_SIZE; - const int i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x; + const int64_t i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x; const int * x0 = ((int *) vx) + blockIdx.x * nint; half2 * y2 = (half2 *) (y + i0); @@ -45,6 +45,8 @@ static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, h vals[ix] = x0[ix]; } + __syncthreads(); + #pragma unroll for (int iy = 0; iy < CUDA_Q8_0_NE_ALIGN; iy += 2*WARP_SIZE) { if (need_check && i0 + iy + 2*threadIdx.x >= k) { @@ -71,9 +73,9 @@ static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t const int64_t i = blockIdx.x; // assume 32 threads - const int tid = threadIdx.x; - const int il = tid/8; - const int ir = tid%8; + const int64_t tid = threadIdx.x; + const int64_t il = tid/8; + const int64_t ir = tid%8; const int64_t ib = 8*i + ir; if (ib >= nb32) { return; @@ -99,9 +101,9 @@ static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t const int64_t i = blockIdx.x; // assume 32 threads - const int tid = threadIdx.x; - const int il = tid/8; - const int ir = tid%8; + const int64_t tid = threadIdx.x; + const int64_t il = tid/8; + const int64_t ir = tid%8; const int64_t ib = 8*i + ir; if (ib >= nb32) { return; @@ -125,14 +127,14 @@ static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t template static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; const block_q2_K * x = (const block_q2_K *) vx; - const int tid = threadIdx.x; + const int64_t tid = threadIdx.x; #if QK_K == 256 - const int n = tid/32; - const int l = tid - 32*n; - const int is = 8*n + l/16; + const int64_t n = tid/32; + const int64_t l = tid - 32*n; + const int64_t is = 8*n + l/16; const uint8_t q = x[i].qs[32*n + l]; dst_t * y = yy + i*QK_K + 128*n; @@ -144,8 +146,8 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4); y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4); #else - const int is = tid/16; // 0 or 1 - const int il = tid%16; // 0...15 + const int64_t is = tid/16; // 0 or 1 + const int64_t il = tid%16; // 0...15 const uint8_t q = x[i].qs[il] >> (2*is); dst_t * y = yy + i*QK_K + 16*is + il; float dall = __low2half(x[i].dm); @@ -159,19 +161,19 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t template static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; const block_q3_K * x = (const block_q3_K *) vx; #if QK_K == 256 - const int r = threadIdx.x/4; - const int tid = r/2; - const int is0 = r%2; - const int l0 = 16*is0 + 4*(threadIdx.x%4); - const int n = tid / 4; - const int j = tid - 4*n; + const int64_t r = threadIdx.x/4; + const int64_t tid = r/2; + const int64_t is0 = r%2; + const int64_t l0 = 16*is0 + 4*(threadIdx.x%4); + const int64_t n = tid / 4; + const int64_t j = tid - 4*n; uint8_t m = 1 << (4*n + j); - int is = 8*n + 2*j + is0; + int64_t is = 8*n + 2*j + is0; int shift = 2*j; int8_t us = is < 4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) : @@ -187,11 +189,11 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)); #else - const int tid = threadIdx.x; - const int is = tid/16; // 0 or 1 - const int il = tid%16; // 0...15 - const int im = il/8; // 0...1 - const int in = il%8; // 0...7 + const int64_t tid = threadIdx.x; + const int64_t is = tid/16; // 0 or 1 + const int64_t il = tid%16; // 0...15 + const int64_t im = il/8; // 0...1 + const int64_t in = il%8; // 0...7 dst_t * y = yy + i*QK_K + 16*is + il; @@ -225,15 +227,15 @@ template static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { const block_q4_K * x = (const block_q4_K *) vx; - const int i = blockIdx.x; + const int64_t i = blockIdx.x; #if QK_K == 256 // assume 32 threads - const int tid = threadIdx.x; - const int il = tid/8; - const int ir = tid%8; - const int is = 2*il; - const int n = 4; + const int64_t tid = threadIdx.x; + const int64_t il = tid/8; + const int64_t ir = tid%8; + const int64_t is = 2*il; + const int64_t n = 4; dst_t * y = yy + i*QK_K + 64*il + n*ir; @@ -252,7 +254,7 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t y[l +32] = d2 * (q[l] >> 4) - m2; } #else - const int tid = threadIdx.x; + const int64_t tid = threadIdx.x; const uint8_t * q = x[i].qs; dst_t * y = yy + i*QK_K; const float d = (float)x[i].dm[0]; @@ -266,14 +268,14 @@ template static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { const block_q5_K * x = (const block_q5_K *) vx; - const int i = blockIdx.x; + const int64_t i = blockIdx.x; #if QK_K == 256 // assume 64 threads - this is very slightly better than the one below - const int tid = threadIdx.x; - const int il = tid/16; // il is in 0...3 - const int ir = tid%16; // ir is in 0...15 - const int is = 2*il; // is is in 0...6 + const int64_t tid = threadIdx.x; + const int64_t il = tid/16; // il is in 0...3 + const int64_t ir = tid%16; // ir is in 0...15 + const int64_t is = 2*il; // is is in 0...6 dst_t * y = yy + i*QK_K + 64*il + 2*ir; @@ -296,11 +298,11 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2; y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2; #else - const int tid = threadIdx.x; + const int64_t tid = threadIdx.x; const uint8_t q = x[i].qs[tid]; - const int im = tid/8; // 0...3 - const int in = tid%8; // 0...7 - const int is = tid/16; // 0 or 1 + const int64_t im = tid/8; // 0...3 + const int64_t in = tid%8; // 0...7 + const int64_t is = tid/16; // 0 or 1 const uint8_t h = x[i].qh[in] >> im; const float d = x[i].d; dst_t * y = yy + i*QK_K + tid; @@ -357,13 +359,13 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t template static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; const block_iq2_xxs * x = (const block_iq2_xxs *) vx; - const int tid = threadIdx.x; + const int64_t tid = threadIdx.x; #if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; const uint16_t * q2 = x[i].qs + 4*ib; const uint8_t * aux8 = (const uint8_t *)q2; @@ -381,13 +383,13 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds template static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; const block_iq2_xs * x = (const block_iq2_xs *) vx; - const int tid = threadIdx.x; + const int64_t tid = threadIdx.x; #if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; const uint16_t * q2 = x[i].qs + 4*ib; const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511)); @@ -403,13 +405,13 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst template static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; const block_iq2_s * x = (const block_iq2_s *) vx; - const int tid = threadIdx.x; + const int64_t tid = threadIdx.x; #if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300))); const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; @@ -424,13 +426,13 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_ template static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; const block_iq3_xxs * x = (const block_iq3_xxs *) vx; - const int tid = threadIdx.x; + const int64_t tid = threadIdx.x; #if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; const uint8_t * q3 = x[i].qs + 8*ib; const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib; @@ -452,13 +454,13 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds template static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; const block_iq3_s * x = (const block_iq3_s *) vx; - const int tid = threadIdx.x; + const int64_t tid = threadIdx.x; #if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; const uint8_t * qs = x[i].qs + 8*ib; const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256))); @@ -478,13 +480,13 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_ template static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; const block_iq1_s * x = (const block_iq1_s *) vx; - const int tid = threadIdx.x; + const int64_t tid = threadIdx.x; #if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA; const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1); @@ -504,18 +506,18 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_ template static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; const block_iq1_m * x = (const block_iq1_m *) vx; - const int tid = threadIdx.x; + const int64_t tid = threadIdx.x; #if QK_K == 256 - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; const uint16_t * sc = (const uint16_t *)x[i].scales; iq1m_scale_t scale; scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - const int ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4); + const int64_t ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4); const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1); const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA; uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32; @@ -535,12 +537,12 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_ template static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL); - const int tid = threadIdx.x; - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 + const int64_t tid = threadIdx.x; + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 4*il; const uint8_t * q4 = x[ib].qs + 4*il; const float d = (float)x[ib].d; @@ -554,12 +556,12 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst #if QK_K != 64 template static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { - const int i = blockIdx.x; + const int64_t i = blockIdx.x; const block_iq4_xs * x = (const block_iq4_xs *)vx; - const int tid = threadIdx.x; - const int il = tid/8; // 0...3 - const int ib = tid%8; // 0...7 + const int64_t tid = threadIdx.x; + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 4*il; const uint8_t * q4 = x[i].qs + 16*ib + 4*il; const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32); diff --git a/ggml-cuda/softmax.cu b/ggml-cuda/softmax.cu index 9bda18e58..fa8f987cf 100644 --- a/ggml-cuda/softmax.cu +++ b/ggml-cuda/softmax.cu @@ -28,7 +28,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f extern __shared__ float data_soft_max_f32[]; float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication // shared memory buffer to cache values between iterations: - float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + rowx*ncols; + float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + (int64_t)rowx*ncols; float max_val = -INFINITY; @@ -40,8 +40,8 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f break; } - const int ix = rowx*ncols + col; - const int iy = rowy*ncols + col; + const int64_t ix = (int64_t)rowx*ncols + col; + const int64_t iy = (int64_t)rowy*ncols + col; const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f); @@ -109,7 +109,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f return; } - const int idst = rowx*ncols + col; + const int64_t idst = (int64_t)rowx*ncols + col; dst[idst] = vals[col] * inv_sum; } } diff --git a/ggml-impl.h b/ggml-impl.h index e68b72877..2087f7ded 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -11,6 +11,12 @@ #include // memcpy #include // fabsf +#undef MIN +#undef MAX + +#define MIN(a, b) ((a) < (b) ? (a) : (b)) +#define MAX(a, b) ((a) > (b) ? (a) : (b)) + #ifdef __cplusplus extern "C" { #endif @@ -45,7 +51,7 @@ extern "C" { // 16-bit float // on Arm, we use __fp16 // on x86, we use uint16_t -#if defined(__ARM_NEON) && !defined(_MSC_VER) +#if defined(__ARM_NEON) // if YCM cannot find , make a symbolic link to it, for example: // @@ -53,8 +59,262 @@ extern "C" { // #include +#ifdef _MSC_VER + +typedef uint16_t ggml_fp16_internal_t; + +#define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) } + +#else + typedef __fp16 ggml_fp16_internal_t; +#define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) } + +#endif // _MSC_VER + +#if !defined(__aarch64__) + +// 32-bit ARM compatibility + +// vaddvq_s16 +// vpaddq_s16 +// vpaddq_s32 +// vaddvq_s32 +// vaddvq_f32 +// vmaxvq_f32 +// vcvtnq_s32_f32 +// vzip1_u8 +// vzip2_u8 + +inline static int32_t vaddvq_s16(int16x8_t v) { + return + (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) + + (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) + + (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) + + (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7); +} + +inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) { + int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a)); + int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b)); + return vcombine_s16(a0, b0); +} + +inline static int32x4_t vpaddq_s32(int32x4_t a, int32x4_t b) { + int32x2_t a0 = vpadd_s32(vget_low_s32(a), vget_high_s32(a)); + int32x2_t b0 = vpadd_s32(vget_low_s32(b), vget_high_s32(b)); + return vcombine_s32(a0, b0); +} + +inline static int32_t vaddvq_s32(int32x4_t v) { + return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); +} + +inline static float vaddvq_f32(float32x4_t v) { + return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3); +} + +inline static float vmaxvq_f32(float32x4_t v) { + return + MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), + MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3))); +} + +inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) { + int32x4_t res; + + res[0] = roundf(vgetq_lane_f32(v, 0)); + res[1] = roundf(vgetq_lane_f32(v, 1)); + res[2] = roundf(vgetq_lane_f32(v, 2)); + res[3] = roundf(vgetq_lane_f32(v, 3)); + + return res; +} + +inline static uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) { + uint8x8_t res; + + res[0] = a[0]; res[1] = b[0]; + res[2] = a[1]; res[3] = b[1]; + res[4] = a[2]; res[5] = b[2]; + res[6] = a[3]; res[7] = b[3]; + + return res; +} + +inline static uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) { + uint8x8_t res; + + res[0] = a[4]; res[1] = b[4]; + res[2] = a[5]; res[3] = b[5]; + res[4] = a[6]; res[5] = b[6]; + res[6] = a[7]; res[7] = b[7]; + + return res; +} + +// vld1q_s16_x2 +// vld1q_u8_x2 +// vld1q_u8_x4 +// vld1q_s8_x2 +// vld1q_s8_x4 +// TODO: double-check these work correctly + +typedef struct ggml_int16x8x2_t { + int16x8_t val[2]; +} ggml_int16x8x2_t; + +inline static ggml_int16x8x2_t ggml_vld1q_s16_x2(const int16_t * ptr) { + ggml_int16x8x2_t res; + + res.val[0] = vld1q_s16(ptr + 0); + res.val[1] = vld1q_s16(ptr + 8); + + return res; +} + +typedef struct ggml_uint8x16x2_t { + uint8x16_t val[2]; +} ggml_uint8x16x2_t; + +inline static ggml_uint8x16x2_t ggml_vld1q_u8_x2(const uint8_t * ptr) { + ggml_uint8x16x2_t res; + + res.val[0] = vld1q_u8(ptr + 0); + res.val[1] = vld1q_u8(ptr + 16); + + return res; +} + +typedef struct ggml_uint8x16x4_t { + uint8x16_t val[4]; +} ggml_uint8x16x4_t; + +inline static ggml_uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) { + ggml_uint8x16x4_t res; + + res.val[0] = vld1q_u8(ptr + 0); + res.val[1] = vld1q_u8(ptr + 16); + res.val[2] = vld1q_u8(ptr + 32); + res.val[3] = vld1q_u8(ptr + 48); + + return res; +} + +typedef struct ggml_int8x16x2_t { + int8x16_t val[2]; +} ggml_int8x16x2_t; + +inline static ggml_int8x16x2_t ggml_vld1q_s8_x2(const int8_t * ptr) { + ggml_int8x16x2_t res; + + res.val[0] = vld1q_s8(ptr + 0); + res.val[1] = vld1q_s8(ptr + 16); + + return res; +} + +typedef struct ggml_int8x16x4_t { + int8x16_t val[4]; +} ggml_int8x16x4_t; + +inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) { + ggml_int8x16x4_t res; + + res.val[0] = vld1q_s8(ptr + 0); + res.val[1] = vld1q_s8(ptr + 16); + res.val[2] = vld1q_s8(ptr + 32); + res.val[3] = vld1q_s8(ptr + 48); + + return res; +} + +// NOTE: not tested +inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) { + int8x16_t res; + + res[ 0] = a[b[ 0]]; + res[ 1] = a[b[ 1]]; + res[ 2] = a[b[ 2]]; + res[ 3] = a[b[ 3]]; + res[ 4] = a[b[ 4]]; + res[ 5] = a[b[ 5]]; + res[ 6] = a[b[ 6]]; + res[ 7] = a[b[ 7]]; + res[ 8] = a[b[ 8]]; + res[ 9] = a[b[ 9]]; + res[10] = a[b[10]]; + res[11] = a[b[11]]; + res[12] = a[b[12]]; + res[13] = a[b[13]]; + res[14] = a[b[14]]; + res[15] = a[b[15]]; + + return res; +} + +// NOTE: not tested +inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) { + uint8x16_t res; + + res[ 0] = a[b[ 0]]; + res[ 1] = a[b[ 1]]; + res[ 2] = a[b[ 2]]; + res[ 3] = a[b[ 3]]; + res[ 4] = a[b[ 4]]; + res[ 5] = a[b[ 5]]; + res[ 6] = a[b[ 6]]; + res[ 7] = a[b[ 7]]; + res[ 8] = a[b[ 8]]; + res[ 9] = a[b[ 9]]; + res[10] = a[b[10]]; + res[11] = a[b[11]]; + res[12] = a[b[12]]; + res[13] = a[b[13]]; + res[14] = a[b[14]]; + res[15] = a[b[15]]; + + return res; +} + +#else + +#define ggml_int16x8x2_t int16x8x2_t +#define ggml_uint8x16x2_t uint8x16x2_t +#define ggml_uint8x16x4_t uint8x16x4_t +#define ggml_int8x16x2_t int8x16x2_t +#define ggml_int8x16x4_t int8x16x4_t + +#define ggml_vld1q_s16_x2 vld1q_s16_x2 +#define ggml_vld1q_u8_x2 vld1q_u8_x2 +#define ggml_vld1q_u8_x4 vld1q_u8_x4 +#define ggml_vld1q_s8_x2 vld1q_s8_x2 +#define ggml_vld1q_s8_x4 vld1q_s8_x4 +#define ggml_vqtbl1q_s8 vqtbl1q_s8 +#define ggml_vqtbl1q_u8 vqtbl1q_u8 + +#endif // !defined(__aarch64__) + +#if !defined(__ARM_FEATURE_DOTPROD) + +inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) { + const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b)); + const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b)); + + return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1))); +} + +#else + +#define ggml_vdotq_s32(a, b, c) vdotq_s32(a, b, c) + +#endif // !defined(__ARM_FEATURE_DOTPROD) + +#endif // defined(__ARM_NEON) + +#if defined(__ARM_NEON) && !defined(__MSC_VER) + #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) @@ -75,8 +335,6 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { #else -typedef uint16_t ggml_fp16_internal_t; - #ifdef __wasm_simd128__ #include #else @@ -88,7 +346,7 @@ typedef uint16_t ggml_fp16_internal_t; #if defined(_MSC_VER) || defined(__MINGW32__) #include #else -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) || defined(__SSE__) #if !defined(__riscv) #include #endif @@ -221,7 +479,7 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { #endif // __F16C__ -#endif // __ARM_NEON +#endif // defined(__ARM_NEON) && (!defined(__MSC_VER) // precomputed f32 table for f16 (256 KB) // defined in ggml.c, initialized in ggml_init() diff --git a/ggml-metal.m b/ggml-metal.m index 419d8b9e5..9cb421988 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -37,11 +37,15 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_DIV_ROW, GGML_METAL_KERNEL_TYPE_SCALE, GGML_METAL_KERNEL_TYPE_SCALE_4, + GGML_METAL_KERNEL_TYPE_CLAMP, GGML_METAL_KERNEL_TYPE_TANH, GGML_METAL_KERNEL_TYPE_RELU, GGML_METAL_KERNEL_TYPE_GELU, + GGML_METAL_KERNEL_TYPE_GELU_4, GGML_METAL_KERNEL_TYPE_GELU_QUICK, + GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, GGML_METAL_KERNEL_TYPE_SILU, + GGML_METAL_KERNEL_TYPE_SILU_4, GGML_METAL_KERNEL_TYPE_SOFT_MAX, GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, @@ -468,11 +472,15 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX, soft_max, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, soft_max_4, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true); @@ -713,6 +721,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_MUL: case GGML_OP_DIV: case GGML_OP_SCALE: + case GGML_OP_CLAMP: case GGML_OP_SQR: case GGML_OP_SUM_ROWS: return true; @@ -1154,8 +1163,30 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; + case GGML_OP_CLAMP: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline; + + float min; + float max; + memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float)); + memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float)); + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&min length:sizeof(min) atIndex:2]; + [encoder setBytes:&max length:sizeof(max) atIndex:3]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; case GGML_OP_UNARY: switch (ggml_get_unary_op(gf->nodes[i])) { + // we are not taking into account the strides, so for now require contiguous tensors + GGML_ASSERT(ggml_is_contiguous(src0)); + case GGML_UNARY_OP_TANH: { id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline; @@ -1182,42 +1213,60 @@ static enum ggml_status ggml_metal_graph_compute( } break; case GGML_UNARY_OP_GELU: { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; + int64_t n = ggml_nelements(dst); + + id pipeline = nil; + + if (n % 4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_4].pipeline; + n /= 4; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; + } [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_UNARY_OP_GELU_QUICK: { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; + int64_t n = ggml_nelements(dst); + + id pipeline = nil; + + if (n % 4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK_4].pipeline; + n /= 4; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; + } [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_UNARY_OP_SILU: { - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; + int64_t n = ggml_nelements(dst); + + id pipeline = nil; + + if (n % 4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU_4].pipeline; + n /= 4; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; + } [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - const int64_t n = ggml_nelements(dst); - GGML_ASSERT(n % 4 == 0); - - [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; default: { @@ -1683,15 +1732,10 @@ static enum ggml_status ggml_metal_graph_compute( } break; case GGML_OP_MUL_MAT_ID: { - //GGML_ASSERT(ne00 == ne10); - //GGML_ASSERT(ne03 == ne13); const int n_as = src0->ne[2]; - // max size of the src1ids array in the kernel shared buffer - GGML_ASSERT(ne11 <= 4096); - // src2 = ids - const int64_t ne20 = src2->ne[0]; GGML_UNUSED(ne20); + const int64_t ne20 = src2->ne[0]; const int64_t ne21 = src2->ne[1]; const int64_t ne22 = src2->ne[2]; GGML_UNUSED(ne22); const int64_t ne23 = src2->ne[3]; GGML_UNUSED(ne23); @@ -1712,15 +1756,13 @@ static enum ggml_status ggml_metal_graph_compute( // find the break-even point where the matrix-matrix kernel becomes more efficient compared // to the matrix-vector kernel - int ne11_mm_min = n_as; + // ne20 = n_used_experts + // ne21 = n_rows + const int dst_rows = ne20*ne21; + const int dst_rows_min = n_as; - const int idx = ((int32_t *) dst->op_params)[0]; - - // batch size - GGML_ASSERT(ne21 == ne11); // ? - GGML_ASSERT(ne12 == 1 && ne13 == 1); // no broadcasting - const uint r2 = 1; - const uint r3 = 1; + // max size of the rowids array in the kernel shared buffer + GGML_ASSERT(dst_rows <= 2048); // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel @@ -1730,7 +1772,7 @@ static enum ggml_status ggml_metal_graph_compute( // !!! if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && ne00 % 32 == 0 && ne00 >= 64 && - ne11 > ne11_mm_min) { + dst_rows > dst_rows_min) { // some Metal matrix data types require aligned pointers // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) @@ -1772,26 +1814,26 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:4]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:5]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:9]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:10]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:11]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:12]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:13]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:14]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:15]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:16]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:17]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:18]; - [encoder setBytes:&idx length:sizeof(idx) atIndex:19]; + [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; + [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; + [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:8]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:9]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:10]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:18]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:19]; - [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + 2*ne11, 16) atIndex:0]; + [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + dst_rows*4/*sizeof(ushort2)*/, 16) atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne01 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 31)/32, (ne01 + 63)/64, n_as) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; } else { int nth0 = 32; int nth1 = 1; @@ -1926,7 +1968,12 @@ static enum ggml_status ggml_metal_graph_compute( { nth0 = 4; nth1 = 16; + #if QK_K == 64 + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline; + #else pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline; + #endif + } break; default: { @@ -1939,72 +1986,72 @@ static enum ggml_status ggml_metal_graph_compute( GGML_ASSERT(ne00 >= nth0*nth1); } - const int64_t _ne1 = 1; // kernels needs a reference in constant memory - [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:4]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:5]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:6]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:7]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:8]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:9]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:18]; - [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:19]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:20]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:21]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:22]; - [encoder setBytes:&idx length:sizeof(idx) atIndex:23]; + [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; + [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; + [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:8]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:9]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:10]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:11]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:12]; + [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:13]; + [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:14]; + [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:15]; + [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:16]; + [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:17]; + [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:18]; + [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:19]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:20]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:21]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:22]; + + const int64_t _ne1 = 1; + const int tgz = dst_rows; if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) { const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4; [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) { const int mem_size = 32*sizeof(float); [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_Q3_K) { #ifdef GGML_QKK_64 - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; #else - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; #endif } else if (src0t == GGML_TYPE_Q5_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_Q6_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else { - const int64_t ny = (_ne1 + nrows - 1)/nrows; - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + const int64_t ny = (_ne1 + nrows - 1)/nrows; // = _ne1 + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } } } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index 9a29f57a3..191880af1 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -213,6 +213,15 @@ kernel void kernel_scale_4( dst[tpig] = src0[tpig] * scale; } +kernel void kernel_clamp( + device const float * src0, + device float * dst, + constant float & min, + constant float & max, + uint tpig[[thread_position_in_grid]]) { + dst[tpig] = src0[tpig] < min ? min : (src0[tpig] > max ? max : src0[tpig]); +} + kernel void kernel_relu( device const float * src0, device float * dst, @@ -233,6 +242,15 @@ constant float GELU_QUICK_COEF = -1.702f; constant float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; kernel void kernel_gelu( + device const float * src0, + device float * dst, + uint tpig[[thread_position_in_grid]]) { + device const float & x = src0[tpig]; + + dst[tpig] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); +} + +kernel void kernel_gelu_4( device const float4 * src0, device float4 * dst, uint tpig[[thread_position_in_grid]]) { @@ -246,6 +264,15 @@ kernel void kernel_gelu( } kernel void kernel_gelu_quick( + device const float * src0, + device float * dst, + uint tpig[[thread_position_in_grid]]) { + device const float & x = src0[tpig]; + + dst[tpig] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x))); +} + +kernel void kernel_gelu_quick_4( device const float4 * src0, device float4 * dst, uint tpig[[thread_position_in_grid]]) { @@ -255,6 +282,14 @@ kernel void kernel_gelu_quick( } kernel void kernel_silu( + device const float * src0, + device float * dst, + uint tpig[[thread_position_in_grid]]) { + device const float & x = src0[tpig]; + dst[tpig] = x / (1.0f + exp(-x)); +} + +kernel void kernel_silu_4( device const float4 * src0, device float4 * dst, uint tpig[[thread_position_in_grid]]) { @@ -866,6 +901,7 @@ void mul_vec_q_n_f32_impl( int64_t ne1, uint r2, uint r3, + threadgroup int8_t * shared_values, uint3 tgpig, uint tiisg, uint sgitg) { const int nb = ne00/QK4_0; @@ -942,7 +978,7 @@ kernel void kernel_mul_mv_q4_0_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg); + mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } kernel void kernel_mul_mv_q4_1_f32( @@ -968,7 +1004,7 @@ kernel void kernel_mul_mv_q4_1_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg); + mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } kernel void kernel_mul_mv_q5_0_f32( @@ -994,7 +1030,7 @@ kernel void kernel_mul_mv_q5_0_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg); + mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } kernel void kernel_mul_mv_q5_1_f32( @@ -1020,7 +1056,7 @@ kernel void kernel_mul_mv_q5_1_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg); + mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } @@ -1030,18 +1066,19 @@ void kernel_mul_mv_q8_0_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiisg, + uint sgitg) { const int nr = N_DST; const int nsg = N_SIMDGROUP; const int nw = N_SIMDWIDTH; @@ -1119,7 +1156,7 @@ kernel void kernel_mul_mv_q8_0_f32( uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,tgpig,tiisg,sgitg); + kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); } #define N_F32_F32 4 @@ -1128,24 +1165,24 @@ void kernel_mul_mv_f32_f32_impl( device const char * src0, device const char * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + uint64_t nb00, + uint64_t nb01, + uint64_t nb02, + int64_t ne10, + int64_t ne11, + int64_t ne12, + uint64_t nb10, + uint64_t nb11, + uint64_t nb12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + uint3 tgpig, + uint tiisg) { const int64_t r0 = tgpig.x; const int64_t rb = tgpig.y*N_F32_F32; @@ -1398,24 +1435,24 @@ void kernel_mul_mv_f16_f32_impl( device const char * src0, device const char * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + uint64_t nb00, + uint64_t nb01, + uint64_t nb02, + int64_t ne10, + int64_t ne11, + int64_t ne12, + uint64_t nb10, + uint64_t nb11, + uint64_t nb12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + uint3 tgpig, + uint tiisg) { const int64_t r0 = tgpig.x; const int64_t rb = tgpig.y*N_F16_F32; @@ -2700,18 +2737,19 @@ void kernel_mul_mv_q2_K_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiisg, + uint sgitg) { const int nb = ne00/QK_K; const int r0 = tgpig.x; @@ -2871,7 +2909,7 @@ kernel void kernel_mul_mv_q2_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } #if QK_K == 256 @@ -2879,18 +2917,19 @@ void kernel_mul_mv_q3_K_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiisg, + uint sgitg) { const int nb = ne00/QK_K; @@ -3046,6 +3085,7 @@ void kernel_mul_mv_q3_K_f32_impl( constant int64_t & ne1, constant uint & r2, constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -3135,7 +3175,7 @@ kernel void kernel_mul_mv_q3_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } #if QK_K == 256 @@ -3143,18 +3183,19 @@ void kernel_mul_mv_q4_K_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiisg, + uint sgitg) { const uint16_t kmask1 = 0x3f3f; const uint16_t kmask2 = 0x0f0f; @@ -3265,6 +3306,7 @@ void kernel_mul_mv_q4_K_f32_impl( constant int64_t & ne1, constant uint & r2, constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -3373,25 +3415,26 @@ kernel void kernel_mul_mv_q4_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q4_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_q4_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } void kernel_mul_mv_q5_K_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiisg, + uint sgitg) { const int nb = ne00/QK_K; @@ -3579,25 +3622,26 @@ kernel void kernel_mul_mv_q5_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q5_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_q5_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } void kernel_mul_mv_q6_K_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiisg, + uint sgitg) { const uint8_t kmask1 = 0x03; const uint8_t kmask2 = 0x0C; @@ -3713,7 +3757,7 @@ kernel void kernel_mul_mv_q6_K_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q6_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_q6_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } // ======================= "True" 2-bit @@ -3722,19 +3766,19 @@ void kernel_mul_mv_iq2_xxs_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiisg, + uint sgitg) { const int nb = ne00/QK_K; const int r0 = tgpig.x; @@ -3851,19 +3895,19 @@ void kernel_mul_mv_iq2_xs_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiisg, + uint sgitg) { const int nb = ne00/QK_K; const int r0 = tgpig.x; @@ -3990,19 +4034,19 @@ void kernel_mul_mv_iq3_xxs_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiisg, + uint sgitg) { const int nb = ne00/QK_K; const int r0 = tgpig.x; @@ -4122,19 +4166,19 @@ void kernel_mul_mv_iq3_s_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiisg, + uint sgitg) { const int nb = ne00/QK_K; const int r0 = tgpig.x; @@ -4254,19 +4298,19 @@ void kernel_mul_mv_iq2_s_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiisg, + uint sgitg) { const int nb = ne00/QK_K; const int r0 = tgpig.x; @@ -4387,18 +4431,19 @@ void kernel_mul_mv_iq1_s_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_value, + uint3 tgpig, + uint tiisg, + uint sgitg) { const int nb = ne00/QK_K; const int r0 = tgpig.x; @@ -4476,18 +4521,19 @@ void kernel_mul_mv_iq1_m_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_value, + uint3 tgpig, + uint tiisg, + uint sgitg) { const int nb = ne00/QK_K; const int r0 = tgpig.x; @@ -4584,20 +4630,21 @@ void kernel_mul_mv_iq4_nl_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup float * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values_i8, + uint3 tgpig, + uint tiisg, + uint sgitg) { + threadgroup float * shared_values = (threadgroup float *)shared_values_i8; const int nb = ne00/QK4_NL; const int r0 = tgpig.x; const int r1 = tgpig.y; @@ -4678,20 +4725,21 @@ void kernel_mul_mv_iq4_xs_f32_impl( device const void * src0, device const float * src1, device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup float * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values_i8, + uint3 tgpig, + uint tiisg, + uint sgitg) { + threadgroup float * shared_values = (threadgroup float *)shared_values_i8; const int nb = ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; @@ -4794,7 +4842,7 @@ kernel void kernel_mul_mv_iq1_s_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq1_m_f32")]] @@ -4822,7 +4870,7 @@ kernel void kernel_mul_mv_iq1_m_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg); + kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq4_nl_f32")]] @@ -4846,7 +4894,7 @@ kernel void kernel_mul_mv_iq4_nl_f32( constant int64_t & ne1, constant uint & r2, constant uint & r3, - threadgroup float * shared_values [[threadgroup(0)]], + threadgroup int8_t * shared_values [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -4875,7 +4923,7 @@ kernel void kernel_mul_mv_iq4_xs_f32( constant int64_t & ne1, constant uint & r2, constant uint & r3, - threadgroup float * shared_values [[threadgroup(0)]], + threadgroup int8_t * shared_values [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { @@ -5632,25 +5680,25 @@ void kernel_mul_mm_impl(device const uchar * src0, } } -// same as kernel_mul_mm_impl, but src1 and dst are accessed via indices stored in src1ids +// same as kernel_mul_mm_impl, but src1 and dst are accessed via indices stored in rowids template void kernel_mul_mm_id_impl( device const uchar * src0, device const uchar * src1, - threadgroup short * src1ids, + threadgroup ushort2 * rowids, device float * dst, constant int64_t & ne00, constant int64_t & ne02, constant uint64_t & nb01, constant uint64_t & nb02, + constant int64_t & ne11, constant int64_t & ne12, constant uint64_t & nb10, constant uint64_t & nb11, constant uint64_t & nb12, constant int64_t & ne0, int64_t ne1, - constant uint & r2, - constant uint & r3, + int64_t ne0ne1, threadgroup uchar * shared_memory, uint3 tgpig[[threadgroup_position_in_grid]], uint tiitg[[thread_index_in_threadgroup]], @@ -5661,7 +5709,6 @@ void kernel_mul_mm_id_impl( const uint r0 = tgpig.y; const uint r1 = tgpig.x; - const uint im = tgpig.z; if (r1 * BLOCK_SIZE_N >= ne1) return; @@ -5679,19 +5726,16 @@ void kernel_mul_mm_id_impl( for (int i = 0; i < 8; i++){ c_res[i] = make_filled_simdgroup_matrix(0.f); } - short il = (tiitg % THREAD_PER_ROW); - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - uint offset0 = (i12/r2)*nb02 + (i13/r3)*(nb02*ne02); ushort offset1 = il/nl; - device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01 + offset0) + offset1; + threadgroup const auto & id = rowids[r1 * BLOCK_SIZE_N + thread_col]; + + device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01) + offset1; device const float * y = (device const float *)(src1 - + nb12 * im - + nb11 * src1ids[r1 * BLOCK_SIZE_N + thread_col] + + nb12 * id[1] + + nb11 * (id[0] % ne11) + nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL))); for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) { @@ -5720,11 +5764,11 @@ void kernel_mul_mm_id_impl( for (int ik = 0; ik < BLOCK_SIZE_K / 8; ik++) { for (int i = 0; i < 4; i++) { - simdgroup_load(ma[i],lsma + SG_MAT_SIZE * i); + simdgroup_load(ma[i], lsma + SG_MAT_SIZE * i); } simdgroup_barrier(mem_flags::mem_none); for (int i = 0; i < 2; i++) { - simdgroup_load(mb[i],lsmb + SG_MAT_SIZE * i); + simdgroup_load(mb[i], lsmb + SG_MAT_SIZE * i); } lsma += BLOCK_SIZE_M / SG_MAT_ROW * SG_MAT_SIZE; @@ -5746,11 +5790,13 @@ void kernel_mul_mm_id_impl( threadgroup_barrier(mem_flags::mem_threadgroup); - device float * C = dst + (BLOCK_SIZE_M * r0) + im*ne1*ne0; + device float * C = dst + (BLOCK_SIZE_M * r0); if (sgitg == 0) { - for (int i = 0; i < n_rows; i++) { - for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) { - *(C + i + src1ids[j + r1*BLOCK_SIZE_N] * ne0) = *(temp_str + i + j * BLOCK_SIZE_M); + for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) { + threadgroup const auto & jid = rowids[r1 * BLOCK_SIZE_N + j]; + int joff = jid[0] * ne0 + jid[1] * ne0ne1; + for (int i = 0; i < n_rows; i++) { + *(C + i + joff) = *(temp_str + i + j * BLOCK_SIZE_M); } } } @@ -5805,11 +5851,14 @@ kernel void kernel_mul_mm_id( device const uchar * src1, device float * dst, device const uchar * ids, + constant int64_t & nei0, + constant int64_t & nei1, constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne02, constant uint64_t & nb01, constant uint64_t & nb02, + constant int64_t & ne11, constant int64_t & ne12, constant int64_t & ne13, constant uint64_t & nb10, @@ -5818,47 +5867,52 @@ kernel void kernel_mul_mm_id( constant int64_t & ne0, constant int64_t & ne1, constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, threadgroup uchar * shared_memory [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], uint tiitg[[thread_index_in_threadgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - // expert id - const int32_t id = tgpig.z/(ne12*ne13); - device const uchar * src0 = src0s + id*nb02; + const int32_t i02 = tgpig.z; + tgpig.z = 0; - tgpig.z = tgpig.z%(ne12*ne13); + device const uchar * src0 = src0s + i02*nb02; - // row indices of src1 for expert id - threadgroup short * src1ids = (threadgroup short *)(shared_memory + 8192); + // row indices + threadgroup ushort2 * rowids = (threadgroup ushort2 *)(shared_memory + 8192); + // TODO: parallelize this loop int64_t _ne1 = 0; - for (int64_t i1 = 0; i1 < ne1; i1++) { - if (((device int32_t *) (ids + i1*nbi1))[idx] == id) { - src1ids[_ne1++] = i1; + for (ushort ii1 = 0; ii1 < nei1; ii1++) { + for (ushort ii0 = 0; ii0 < nei0; ii0++) { + int32_t id = ((device int32_t *) (ids + ii1*nbi1))[ii0]; + if (id == i02) { + //if (tiitg == 0) { + rowids[_ne1] = ushort2(ii0, ii1); + //} + _ne1++; + } } } + threadgroup_barrier(mem_flags::mem_threadgroup); + kernel_mul_mm_id_impl( src0, src1, - src1ids, + rowids, dst, ne00, ne02, nb01, nb02, + ne11, ne12, nb10, nb11, nb12, ne0, _ne1, - r2, - r3, + ne0*ne1, shared_memory, tgpig, tiitg, @@ -5919,24 +5973,7 @@ template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_r // matrix-matrix multiplication // -typedef void (mat_mm_t)( - device const uchar * src0, - device const uchar * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne02, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup uchar *, - uint3, uint, uint); +typedef decltype(kernel_mul_mm) mat_mm_t; template [[host_name("kernel_mul_mm_f32_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_f16_f32")]] kernel mat_mm_t kernel_mul_mm; @@ -5968,29 +6005,7 @@ template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_m // indirect matrix-matrix multiplication // -typedef void (mat_mm_id_t)( - device const uchar * src0s, - device const uchar * src1, - device float * dst, - device const uchar * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne02, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - threadgroup uchar *, - uint3, uint, uint); +typedef decltype(kernel_mul_mm_id) mat_mm_id_t; template [[host_name("kernel_mul_mm_id_f32_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_f16_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; @@ -6022,134 +6037,119 @@ template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel // matrix-vector multiplication // -[[host_name("kernel_mul_mv_id_f32_f32")]] -kernel void kernel_mul_mv_id_f32_f32( - device const char * src0s, +typedef void (kernel_mul_mv_impl_t)( + device const char * src0, + device const char * src1, + device float * dst, + int64_t ne00, + int64_t ne01, + int64_t ne02, + uint64_t nb00, + uint64_t nb01, + uint64_t nb02, + int64_t ne10, + int64_t ne11, + int64_t ne12, + uint64_t nb10, + uint64_t nb11, + uint64_t nb12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + uint3 tgpig, + uint tiisg); + +typedef void (kernel_mul_mv2_impl_t)( + device const void * src0, + device const float * src1, + device float * dst, + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne10, + int64_t ne12, + int64_t ne0, + int64_t ne1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiisg, + uint sgitg); + +template +void mmv_fn( + device const char * src0, device const char * src1, device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_f32_f32_impl( - src0, - src1 + bid*nb11, - dst + bid*ne0, - ne00, - ne01, - ne02, - nb00, - nb01, - nb02, - ne10, - ne11, - ne12, - nb10, - nb11, - nb12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg); + int64_t ne00, + int64_t ne01, + int64_t ne02, + uint64_t nb00, + uint64_t nb01, + uint64_t nb02, + int64_t ne10, + int64_t ne11, + int64_t ne12, + int64_t ne13, + uint64_t nb10, + uint64_t nb11, + uint64_t nb12, + int64_t ne0, + int64_t ne1, + uint64_t nb1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiitg, + uint tiisg, + uint sgitg) { + impl_fn(src0,src1,dst,ne00,ne01,ne02,nb00,nb01,nb02,ne10,ne11,ne12,nb10,nb11,nb12,ne0,ne1,r2,r3,tgpig,tiisg); } -[[host_name("kernel_mul_mv_id_f16_f32")]] -kernel void kernel_mul_mv_id_f16_f32( - device const char * src0s, +template +void mmv_fn( + device const char * src0, device const char * src1, device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_f16_f32_impl( - src0, - src1 + bid*nb11, - dst + bid*ne0, - ne00, - ne01, - ne02, - nb00, - nb01, - nb02, - ne10, - ne11, - ne12, - nb10, - nb11, - nb12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg); + int64_t ne00, + int64_t ne01, + int64_t ne02, + uint64_t nb00, + uint64_t nb01, + uint64_t nb02, + int64_t ne10, + int64_t ne11, + int64_t ne12, + int64_t ne13, + uint64_t nb10, + uint64_t nb11, + uint64_t nb12, + int64_t ne0, + int64_t ne1, + uint64_t nb1, + uint r2, + uint r3, + threadgroup int8_t * shared_values, + uint3 tgpig, + uint tiitg, + uint tiisg, + uint sgitg) { + impl_fn(src0,(const device float *)src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,shared_values,tgpig,tiisg,sgitg); } -[[host_name("kernel_mul_mv_id_q8_0_f32")]] -kernel void kernel_mul_mv_id_q8_0_f32( +typedef decltype(mmv_fn) mul_mv_impl_fn_t; + +template +kernel void kernel_mul_mv_id( device const char * src0s, device const char * src1, device float * dst, device const char * ids, + constant int64_t & nei0, + constant int64_t & nei1, constant uint64_t & nbi1, constant int64_t & ne00, constant int64_t & ne01, @@ -6167,1042 +6167,80 @@ kernel void kernel_mul_mv_id_q8_0_f32( constant int64_t & ne0, constant int64_t & ne1, constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_q8_0_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q4_0_f32")]] -kernel void kernel_mul_mv_id_q4_0_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - mul_vec_q_n_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q4_1_f32")]] -kernel void kernel_mul_mv_id_q4_1_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - mul_vec_q_n_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q5_0_f32")]] -kernel void kernel_mul_mv_id_q5_0_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - mul_vec_q_n_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q5_1_f32")]] -kernel void kernel_mul_mv_id_q5_1_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - mul_vec_q_n_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q2_K_f32")]] -kernel void kernel_mul_mv_id_q2_K_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_q2_K_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q3_K_f32")]] -kernel void kernel_mul_mv_id_q3_K_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_q3_K_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q4_K_f32")]] -kernel void kernel_mul_mv_id_q4_K_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_q4_K_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q5_K_f32")]] -kernel void kernel_mul_mv_id_q5_K_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_q5_K_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_q6_K_f32")]] -kernel void kernel_mul_mv_id_q6_K_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_q6_K_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_iq2_xxs_f32")]] -kernel void kernel_mul_mv_id_iq2_xxs_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, threadgroup int8_t * shared_values [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], uint tiitg[[thread_index_in_threadgroup]], uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); + const int iid1 = tgpig.z/nei0; + const int idx = tgpig.z%nei0; - tgpig.z = tgpig.z%(ne12*ne13); + tgpig.z = 0; - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; + const int32_t i02 = ((device const int32_t *) (ids + iid1*nbi1))[idx]; - kernel_mul_mv_iq2_xxs_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, + const int64_t i11 = idx % ne11; + const int64_t i12 = iid1; + + const int64_t i1 = idx; + const int64_t i2 = i12; + + device const char * src0_cur = src0s + i02*nb02; + device const char * src1_cur = src1 + i11*nb11 + i12*nb12; + device float * dst_cur = dst + i1*ne0 + i2*ne1*ne0; + + impl_fn( + /* src0 */ src0_cur, + /* src1 */ src1_cur, + /* dst */ dst_cur, + /* ne00 */ ne00, + /* ne01 */ ne01, + /* ne02 */ 1,//ne02, + /* nb00 */ nb00, + /* nb01 */ nb01, + /* nb02 */ nb02, + /* ne10 */ ne10, + /* ne11 */ 1,//ne11, + /* ne12 */ 1,//ne12, + /* ne13 */ 1,//ne13, + /* nb10 */ nb10, + /* nb11 */ nb11, + /* nb12 */ nb12, + /* ne0 */ ne0, + /* ne1 */ 1,//ne1, + /* nb1 */ nb1, + /* r2 */ 1, + /* r3 */ 1, shared_values, tgpig, + tiitg, tiisg, sgitg); } -[[host_name("kernel_mul_mv_id_iq2_xs_f32")]] -kernel void kernel_mul_mv_id_iq2_xs_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); +typedef decltype(kernel_mul_mv_id>) kernel_mul_mv_id_t; - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq2_xs_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - shared_values, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_iq3_xxs_f32")]] -kernel void kernel_mul_mv_id_iq3_xxs_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq3_xxs_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - shared_values, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_iq3_s_f32")]] -kernel void kernel_mul_mv_id_iq3_s_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq3_s_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - shared_values, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_iq2_s_f32")]] -kernel void kernel_mul_mv_id_iq2_s_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq2_s_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - shared_values, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_iq1_s_f32")]] -kernel void kernel_mul_mv_id_iq1_s_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq1_s_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_iq1_m_f32")]] -kernel void kernel_mul_mv_id_iq1_m_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq1_m_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_iq4_nl_f32")]] -kernel void kernel_mul_mv_id_iq4_nl_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - threadgroup float * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - - kernel_mul_mv_iq4_nl_f32_impl( - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - shared_values, - tgpig, - tiisg, - sgitg); -} - -[[host_name("kernel_mul_mv_id_iq4_xs_f32")]] -kernel void kernel_mul_mv_id_iq4_xs_f32( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - constant uint & r2, - constant uint & r3, - constant int & idx, - threadgroup float * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int64_t bid = tgpig.z/(ne12*ne13); - - tgpig.z = tgpig.z%(ne12*ne13); - - const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; - device const char * src0 = src0s + id*nb02; - -#if QK_K == 64 - kernel_mul_mv_iq4_nl_f32_impl( -#else - kernel_mul_mv_iq4_xs_f32_impl( +template [[host_name("kernel_mul_mv_id_f32_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_f16_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_q8_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_q4_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; +template [[host_name("kernel_mul_mv_id_q4_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; +template [[host_name("kernel_mul_mv_id_q5_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; +template [[host_name("kernel_mul_mv_id_q5_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; +template [[host_name("kernel_mul_mv_id_q2_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_q3_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_q4_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_q5_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_q6_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq1_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq1_m_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq2_xxs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq2_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq3_xxs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq3_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq2_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; +#if QK_K != 64 +template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; #endif - src0, - (device const float *) (src1 + bid*nb11), - dst + bid*ne0, - ne00, - ne01, - ne02, - ne10, - ne12, - ne0, - ne1, - r2, - r3, - shared_values, - tgpig, - tiisg, - sgitg); -} + diff --git a/ggml-quants.c b/ggml-quants.c index 32e84434a..444d1e55e 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -14,47 +14,6 @@ #include // for qsort #include // for GGML_ASSERT -#ifdef __ARM_NEON - -// if YCM cannot find , make a symbolic link to it, for example: -// -// $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/ -// -#include - -#else - -#ifdef __wasm_simd128__ -#include -#else -#if defined(__POWER9_VECTOR__) || defined(__powerpc64__) -#include -#undef bool -#define bool _Bool -#else -#if defined(_MSC_VER) || defined(__MINGW32__) -#include -#else -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) -#if !defined(__riscv) -#include -#endif -#endif -#endif -#endif -#endif -#endif - -#ifdef __riscv_v_intrinsic -#include -#endif - -#undef MIN -#undef MAX - -#define MIN(a, b) ((a) < (b) ? (a) : (b)) -#define MAX(a, b) ((a) > (b) ? (a) : (b)) - #define UNUSED GGML_UNUSED // some compilers don't provide _mm256_set_m128i, e.g. gcc 7 @@ -132,7 +91,7 @@ static inline __m256 sum_i16_pairs_float(const __m256i x) { } static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { -#if defined(__AVXVNNI__) || defined(__AVX512VNNI__) +#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) const __m256i zero = _mm256_setzero_si256(); const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy); return _mm256_cvtepi32_ps(summed_pairs); @@ -276,258 +235,6 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 #endif // __AVX__ || __AVX2__ || __AVX512F__ #endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) -#if defined(__ARM_NEON) - -#ifdef _MSC_VER - -#define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) } - -#else - -#define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) } - -#endif - -#if !defined(__aarch64__) - -// 64-bit compatibility - -// vaddvq_s16 -// vpaddq_s16 -// vpaddq_s32 -// vaddvq_s32 -// vaddvq_f32 -// vmaxvq_f32 -// vcvtnq_s32_f32 -// vzip1_u8 -// vzip2_u8 - -inline static int32_t vaddvq_s16(int16x8_t v) { - return - (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) + - (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) + - (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) + - (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7); -} - -inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) { - int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a)); - int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b)); - return vcombine_s16(a0, b0); -} - -inline static int32x4_t vpaddq_s32(int32x4_t a, int32x4_t b) { - int32x2_t a0 = vpadd_s32(vget_low_s32(a), vget_high_s32(a)); - int32x2_t b0 = vpadd_s32(vget_low_s32(b), vget_high_s32(b)); - return vcombine_s32(a0, b0); -} - -inline static int32_t vaddvq_s32(int32x4_t v) { - return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); -} - -inline static float vaddvq_f32(float32x4_t v) { - return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3); -} - -inline static float vmaxvq_f32(float32x4_t v) { - return - MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), - MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3))); -} - -inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) { - int32x4_t res; - - res[0] = roundf(vgetq_lane_f32(v, 0)); - res[1] = roundf(vgetq_lane_f32(v, 1)); - res[2] = roundf(vgetq_lane_f32(v, 2)); - res[3] = roundf(vgetq_lane_f32(v, 3)); - - return res; -} - -inline static uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) { - uint8x8_t res; - - res[0] = a[0]; res[1] = b[0]; - res[2] = a[1]; res[3] = b[1]; - res[4] = a[2]; res[5] = b[2]; - res[6] = a[3]; res[7] = b[3]; - - return res; -} - -inline static uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) { - uint8x8_t res; - - res[0] = a[4]; res[1] = b[4]; - res[2] = a[5]; res[3] = b[5]; - res[4] = a[6]; res[5] = b[6]; - res[6] = a[7]; res[7] = b[7]; - - return res; -} - -// vld1q_s16_x2 -// vld1q_u8_x2 -// vld1q_u8_x4 -// vld1q_s8_x2 -// vld1q_s8_x4 -// TODO: double-check these work correctly - -typedef struct ggml_int16x8x2_t { - int16x8_t val[2]; -} ggml_int16x8x2_t; - -inline static ggml_int16x8x2_t ggml_vld1q_s16_x2(const int16_t * ptr) { - ggml_int16x8x2_t res; - - res.val[0] = vld1q_s16(ptr + 0); - res.val[1] = vld1q_s16(ptr + 8); - - return res; -} - -typedef struct ggml_uint8x16x2_t { - uint8x16_t val[2]; -} ggml_uint8x16x2_t; - -inline static ggml_uint8x16x2_t ggml_vld1q_u8_x2(const uint8_t * ptr) { - ggml_uint8x16x2_t res; - - res.val[0] = vld1q_u8(ptr + 0); - res.val[1] = vld1q_u8(ptr + 16); - - return res; -} - -typedef struct ggml_uint8x16x4_t { - uint8x16_t val[4]; -} ggml_uint8x16x4_t; - -inline static ggml_uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) { - ggml_uint8x16x4_t res; - - res.val[0] = vld1q_u8(ptr + 0); - res.val[1] = vld1q_u8(ptr + 16); - res.val[2] = vld1q_u8(ptr + 32); - res.val[3] = vld1q_u8(ptr + 48); - - return res; -} - -typedef struct ggml_int8x16x2_t { - int8x16_t val[2]; -} ggml_int8x16x2_t; - -inline static ggml_int8x16x2_t ggml_vld1q_s8_x2(const int8_t * ptr) { - ggml_int8x16x2_t res; - - res.val[0] = vld1q_s8(ptr + 0); - res.val[1] = vld1q_s8(ptr + 16); - - return res; -} - -typedef struct ggml_int8x16x4_t { - int8x16_t val[4]; -} ggml_int8x16x4_t; - -inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) { - ggml_int8x16x4_t res; - - res.val[0] = vld1q_s8(ptr + 0); - res.val[1] = vld1q_s8(ptr + 16); - res.val[2] = vld1q_s8(ptr + 32); - res.val[3] = vld1q_s8(ptr + 48); - - return res; -} - -// NOTE: not tested -inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) { - int8x16_t res; - - res[ 0] = a[b[ 0]]; - res[ 1] = a[b[ 1]]; - res[ 2] = a[b[ 2]]; - res[ 3] = a[b[ 3]]; - res[ 4] = a[b[ 4]]; - res[ 5] = a[b[ 5]]; - res[ 6] = a[b[ 6]]; - res[ 7] = a[b[ 7]]; - res[ 8] = a[b[ 8]]; - res[ 9] = a[b[ 9]]; - res[10] = a[b[10]]; - res[11] = a[b[11]]; - res[12] = a[b[12]]; - res[13] = a[b[13]]; - res[14] = a[b[14]]; - res[15] = a[b[15]]; - - return res; -} - -// NOTE: not tested -inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) { - uint8x16_t res; - - res[ 0] = a[b[ 0]]; - res[ 1] = a[b[ 1]]; - res[ 2] = a[b[ 2]]; - res[ 3] = a[b[ 3]]; - res[ 4] = a[b[ 4]]; - res[ 5] = a[b[ 5]]; - res[ 6] = a[b[ 6]]; - res[ 7] = a[b[ 7]]; - res[ 8] = a[b[ 8]]; - res[ 9] = a[b[ 9]]; - res[10] = a[b[10]]; - res[11] = a[b[11]]; - res[12] = a[b[12]]; - res[13] = a[b[13]]; - res[14] = a[b[14]]; - res[15] = a[b[15]]; - - return res; -} - -#else - -#define ggml_int16x8x2_t int16x8x2_t -#define ggml_uint8x16x2_t uint8x16x2_t -#define ggml_uint8x16x4_t uint8x16x4_t -#define ggml_int8x16x2_t int8x16x2_t -#define ggml_int8x16x4_t int8x16x4_t - -#define ggml_vld1q_s16_x2 vld1q_s16_x2 -#define ggml_vld1q_u8_x2 vld1q_u8_x2 -#define ggml_vld1q_u8_x4 vld1q_u8_x4 -#define ggml_vld1q_s8_x2 vld1q_s8_x2 -#define ggml_vld1q_s8_x4 vld1q_s8_x4 -#define ggml_vqtbl1q_s8 vqtbl1q_s8 -#define ggml_vqtbl1q_u8 vqtbl1q_u8 - -#endif - -#if !defined(__ARM_FEATURE_DOTPROD) - -inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) { - const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b)); - const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b)); - - return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1))); -} - -#else - -#define ggml_vdotq_s32(a, b, c) vdotq_s32(a, b, c) - -#endif - -#endif - #if defined(__ARM_NEON) || defined(__wasm_simd128__) #define B1(c,s,n) 0x ## n ## c , 0x ## n ## s #define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s) @@ -12676,3 +12383,287 @@ void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k) block_iq2_s * restrict y = vy; quantize_row_iq2_s_reference(x, y, k); } + +static bool validate_float(float f, size_t i) { + if (isinf(f)) { + fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i); + return false; + } + + if (isnan(f)) { + fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i); + return false; + } + + return true; +} + +static bool isinf_fp16(ggml_fp16_t f) { + return (f & 0x7c00) == 0x7c00 && (f & 0x03ff) == 0; +} + +static bool isnan_fp16(ggml_fp16_t f) { + return (f & 0x7c00) == 0x7c00 && (f & 0x03ff) != 0; +} + +static bool validate_fp16(ggml_fp16_t f, size_t i) { + if (isinf_fp16(f)) { + fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i); + return false; + } + + if (isnan_fp16(f)) { + fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i); + return false; + } + + return true; +} + +#define VALIDATE_ROW_DATA_D_F16_IMPL(type, data, nb) \ + const type * q = (const type *) (data); \ + for (size_t i = 0; i < (nb); ++i) { \ + if (!validate_fp16(q[i].d, i)) { \ + return false; \ + } \ + } + +#define VALIDATE_ROW_DATA_DM_F16_IMPL(type, data, nb, d, m) \ + const type * q = (const type *) (data); \ + for (size_t i = 0; i < (nb); ++i) { \ + if (!validate_fp16(q[i].d, i) || !validate_fp16(q[i].m, i)) { \ + return false; \ + } \ + } + +bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes) { + if (type < 0 || type >= GGML_TYPE_COUNT) { + fprintf(stderr, "%s: invalid type %d\n", __func__, type); + return false; + } + + if (nbytes % ggml_type_size(type) != 0) { + fprintf(stderr, "%s: invalid size %zu for type %d\n", __func__, nbytes, type); + return false; + } + + const size_t nb = nbytes/ggml_type_size(type); + + switch (type) { + case GGML_TYPE_F16: + { + const ggml_fp16_t * f = (const ggml_fp16_t *) data; + size_t i = 0; +#if defined(__AVX2__) + for (; i + 15 < nb; i += 16) { + __m256i v = _mm256_loadu_si256((const __m256i *)(f + i)); + __m256i vexp = _mm256_and_si256(v, _mm256_set1_epi16(0x7c00)); + __m256i cmp = _mm256_cmpeq_epi16(vexp, _mm256_set1_epi16(0x7c00)); + int mask = _mm256_movemask_epi8(cmp); + if (mask) { + for (size_t j = 0; j < 16; ++j) { + if (!validate_fp16(f[i + j], i + j)) { + return false; + } + } + GGML_UNREACHABLE(); + } + } +#elif defined(__ARM_NEON) + for (; i + 7 < nb; i += 8) { + uint16x8_t v = vld1q_u16(f + i); + uint16x8_t vexp = vandq_u16(v, vdupq_n_u16(0x7c00)); + uint16x8_t cmp = vceqq_u16(vexp, vdupq_n_u16(0x7c00)); + uint64_t mask = vget_lane_u64(vreinterpret_u64_u8(vshrn_n_u16(cmp, 4)), 0); + if (mask) { + for (size_t j = 0; j < 8; ++j) { + if (!validate_fp16(f[i + j], i + j)) { + return false; + } + } + GGML_UNREACHABLE(); + } + } +#endif + for (; i < nb; ++i) { + if (!validate_fp16(f[i], i)) { + return false; + } + } + } break; + case GGML_TYPE_F32: + { + const float * f = (const float *) data; + size_t i = 0; +#if defined(__AVX2__) + for (; i + 7 < nb; i += 8) { + __m256i v = _mm256_loadu_si256((const __m256i *)(f + i)); + __m256i vexp = _mm256_and_si256(v, _mm256_set1_epi32(0x7f800000)); + __m256i cmp = _mm256_cmpeq_epi32(vexp, _mm256_set1_epi32(0x7f800000)); + int mask = _mm256_movemask_epi8(cmp); + if (mask) { + for (size_t j = 0; j < 8; ++j) { + if (!validate_float(f[i + j], i + j)) { + return false; + } + } + GGML_UNREACHABLE(); + } + } +#elif defined(__ARM_NEON) + for (; i + 3 < nb; i += 4) { + uint32x4_t v = vld1q_u32((const uint32_t *)f + i); + uint32x4_t vexp = vandq_u32(v, vdupq_n_u32(0x7f800000)); + uint32x4_t cmp = vceqq_u32(vexp, vdupq_n_u32(0x7f800000)); + uint64_t mask = vget_lane_u64(vreinterpret_u64_u16(vshrn_n_u32(cmp, 8)), 0); + if (mask) { + for (size_t j = 0; j < 4; ++j) { + if (!validate_float(f[i + j], i + j)) { + return false; + } + } + GGML_UNREACHABLE(); + } + } +#endif + for (; i < nb; ++i) { + if (!validate_float(f[i], i)) { + return false; + } + } + } break; + case GGML_TYPE_F64: + { + const double * f = (const double *) data; + for (size_t i = 0; i < nb; ++i) { + if (!validate_float(f[i], i)) { + return false; + } + } + } break; + case GGML_TYPE_Q4_0: + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_q4_0, data, nb); + } break; + case GGML_TYPE_Q4_1: + { + VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_1, data, nb, d, m); + } break; + case GGML_TYPE_Q5_0: + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_0, data, nb); + } break; + case GGML_TYPE_Q5_1: + { + VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_1, data, nb, d, m); + } break; + case GGML_TYPE_Q8_0: + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_q8_0, data, nb); + } break; + case GGML_TYPE_Q2_K: + { + VALIDATE_ROW_DATA_DM_F16_IMPL(block_q2_K, data, nb, d, dmin); + } break; + case GGML_TYPE_Q3_K: + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_q3_K, data, nb); + } break; + case GGML_TYPE_Q4_K: + { + #ifdef GGML_QKK_64 + VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d[0], d[1]); + #else + VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d, dmin); + #endif + } break; + case GGML_TYPE_Q5_K: + { + #ifdef GGML_QKK_64 + VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_K, data, nb); + #else + VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_K, data, nb, d, dmin); + #endif + } break; + case GGML_TYPE_Q6_K: + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_q6_K, data, nb); + } break; + case GGML_TYPE_Q8_K: + { + const block_q8_K * q = (const block_q8_K *) data; + for (size_t i = 0; i < nb; ++i) { + if (!validate_float(q[i].d, i)) { + return false; + } + } + } break; + case GGML_TYPE_IQ1_S: + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_iq1_s, data, nb); + } break; + case GGML_TYPE_IQ1_M: + { + const block_iq1_m * q = (const block_iq1_m *) data; + for (size_t i = 0; i < nb; ++i) { + #if QK_K == 64 + if (!validate_fp16(q[i].d, i)) { + return false; + } + #else + iq1m_scale_t scale; + const uint16_t * sc = (const uint16_t *)q[i].scales; + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + if (!validate_fp16(scale.f16, i)) { + return false; + } + #endif + } + } break; + case GGML_TYPE_IQ2_XXS: + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xxs, data, nb); + } break; + case GGML_TYPE_IQ2_XS: + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xs, data, nb); + } break; + case GGML_TYPE_IQ2_S: + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_s, data, nb); + } break; + case GGML_TYPE_IQ3_XXS: + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_xxs, data, nb); + } break; + + case GGML_TYPE_IQ3_S: + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_s, data, nb); + } break; + case GGML_TYPE_IQ4_XS: + #if QK_K != 64 + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_xs, data, nb); + } break; + #endif + // with QK_K == 64, iq4_xs is iq4_nl + case GGML_TYPE_IQ4_NL: + { + VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb); + } break; + case GGML_TYPE_I8: + case GGML_TYPE_I16: + case GGML_TYPE_I32: + case GGML_TYPE_I64: + // nothing to validate + break; + default: + { + fprintf(stderr, "%s: invalid type %d\n", __func__, type); + return false; + } + } + + return true; +} diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 55a1eedb5..2b76b3ebd 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3154,7 +3154,6 @@ typedef float (*vec_dot_q_mul_mat_sycl_t)( #define SYCL_SCALE_BLOCK_SIZE 256 #define SYCL_CLAMP_BLOCK_SIZE 256 #define SYCL_ROPE_BLOCK_SIZE 256 -#define SYCL_SOFT_MAX_BLOCK_SIZE 1024 #define SYCL_ALIBI_BLOCK_SIZE 32 #define SYCL_DIAG_MASK_INF_BLOCK_SIZE 32 #define SYCL_QUANTIZE_BLOCK_SIZE 256 @@ -13080,11 +13079,13 @@ static void soft_max_f32_sycl(const float * x, const float * mask, const float * const int nrows_y, const float scale, const float max_bias, dpct::queue_ptr stream) { int nth = WARP_SIZE; - while (nth < ncols_x && nth < SYCL_SOFT_MAX_BLOCK_SIZE) nth *= 2; + int max_block_size = g_work_group_size; + while (nth < ncols_x && nth < max_block_size) nth *= 2; + if (nth>max_block_size) nth = max_block_size; + const sycl::range<3> block_dims(1, 1, nth); const sycl::range<3> block_nums(1, 1, nrows_x); const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE); - static_assert(SYCL_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted."); const uint32_t n_head_kv = nrows_x/nrows_y; const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); @@ -13094,6 +13095,12 @@ static void soft_max_f32_sycl(const float * x, const float * mask, const float * const size_t local_mem_size = stream->get_device().get_info(); if (n_local_scratch*sizeof(float) < local_mem_size) { + if (ncols_x > max_block_size) { + soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, n_local_scratch, stream); + return; + } switch (ncols_x) { case 32: soft_max_f32_submitter(x, mask, pos, dst, ncols_x, nrows_y, scale, @@ -13409,11 +13416,16 @@ void print_device_detail(int id, sycl::device &device, std::string device_type) version += std::to_string(prop.get_minor_version()); device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), ""); + std::string name = std::string(prop.get_name()); + name = std::regex_replace(name, std::regex("\\(R\\)"), ""); + name = std::regex_replace(name, std::regex("\\(TM\\)"), ""); - fprintf(stderr, "|%2d|%18s|%45s|%10s|%11d|%8d|%7d|%15lu|\n", id, device_type.c_str(), - prop.get_name(), version.c_str(), prop.get_max_compute_units(), + auto global_mem_size = prop.get_global_mem_size()/1000000; + + fprintf(stderr, "|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(), + name.c_str(), version.c_str(), prop.get_max_compute_units(), prop.get_max_work_group_size(), prop.get_max_sub_group_size(), - prop.get_global_mem_size()); + global_mem_size, device.get_info().c_str()); } void ggml_backend_sycl_print_sycl_devices() { @@ -13421,9 +13433,10 @@ void ggml_backend_sycl_print_sycl_devices() { int device_count = dpct::dev_mgr::instance().device_count(); std::map DeviceNums; fprintf(stderr, "found %d SYCL devices:\n", device_count); - fprintf(stderr, "| | | |Compute |Max compute|Max work|Max sub| |\n"); - fprintf(stderr, "|ID| Device Type| Name|capability|units |group |group |Global mem size|\n"); - fprintf(stderr, "|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|\n"); + fprintf(stderr, "| | | | |Max | |Max |Global | |\n"); + fprintf(stderr, "| | | | |compute|Max work|sub |mem | |\n"); + fprintf(stderr, "|ID| Device Type| Name|Version|units |group |group|size | Driver version|\n"); + fprintf(stderr, "|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|\n"); for (int id = 0; id < device_count; ++id) { sycl::device device = dpct::dev_mgr::instance().get_device(id); sycl::backend backend = device.get_backend(); @@ -15989,73 +16002,76 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst) try { -#if 0 - ggml_sycl_mul_mat_id_sycl(dst); - // TODO: mmq/mmv support -#endif - - const int64_t nb11 = src1->nb[1]; - const int64_t nb1 = dst->nb[1]; - - const struct ggml_tensor * ids = src0; - const int32_t id = ((int32_t *) dst->op_params)[0]; - const int32_t n_as = ((int32_t *) dst->op_params)[1]; - - std::vector ids_host(ggml_nbytes(ids)); - + GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT && + "mul_mat_id does not support split buffers"); + const ggml_tensor *ids = dst->src[2]; const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; - if (ids->backend == GGML_BACKEND_TYPE_GPU) { - const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device]; - SYCL_CHECK(CHECK_TRY_ERROR( - stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)).wait())); - // SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); - } else { - memcpy(ids_host.data(), ids->data, ggml_nbytes(ids)); - } + const size_t nb11 = src1->nb[1]; + const size_t nb1 = dst->nb[1]; - const ggml_tensor_extra_gpu * src1_extra = (const ggml_tensor_extra_gpu *) src1->extra; - const ggml_tensor_extra_gpu * dst_extra = (const ggml_tensor_extra_gpu *) dst->extra; + const int32_t id = ((int32_t *)dst->op_params)[0]; + const int32_t n_as = src0->ne[2]; + std::vector ids_host(ggml_nbytes(ids)); + const char *ids_dev = (const char *)ids->data; + + SYCL_CHECK(CHECK_TRY_ERROR( + stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); + SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); + + const ggml_tensor_extra_gpu *src0_extra = + (const ggml_tensor_extra_gpu *)src0->extra; + const ggml_tensor_extra_gpu *src1_extra = + (const ggml_tensor_extra_gpu *)src1->extra; + const ggml_tensor_extra_gpu *dst_extra = + (const ggml_tensor_extra_gpu *)dst->extra; + + ggml_tensor_extra_gpu src0_row_extra; ggml_tensor_extra_gpu src1_row_extra; ggml_tensor_extra_gpu dst_row_extra; + ggml_tensor src0_row = *src0; ggml_tensor src1_row = *src1; ggml_tensor dst_row = *dst; src1_row.backend = GGML_BACKEND_TYPE_GPU; dst_row.backend = GGML_BACKEND_TYPE_GPU; + src0_row.extra = &src0_row_extra; src1_row.extra = &src1_row_extra; dst_row.extra = &dst_row_extra; - char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ? - (char *) src1->data : (char *) src1_extra->data_device[g_main_device]; - char * dst_original = dst->backend == GGML_BACKEND_TYPE_CPU ? - (char *) dst->data : (char *) dst_extra->data_device[g_main_device]; + char *src0_original = src1->backend == GGML_BACKEND_TYPE_CPU + ? (char *)src0->data + : (char *)src0_extra->data_device[g_main_device]; + char *src1_original = src1->backend == GGML_BACKEND_TYPE_CPU + ? (char *)src1->data + : (char *)src1_extra->data_device[g_main_device]; + char *dst_original = dst->backend == GGML_BACKEND_TYPE_CPU + ? (char *)dst->data + : (char *)dst_extra->data_device[g_main_device]; + + src0_row.ne[2] = 1; + src0_row.ne[3] = 1; + src0_row.nb[3] = src0->nb[2]; if (src1->ne[1] == 1) { - GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); - GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU); - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - //int32_t row_id; - //SYCL_CHECK(syclMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), syclMemcpyDeviceToHost, g_syclStreams[g_main_device][0])); - //SYCL_CHECK(syclStreamSynchronize(g_syclStreams[g_main_device][0])); - - const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + const int32_t row_id = + *(const int32_t *)(ids_host.data() + i01 * ids->nb[1] + + id * ids->nb[0]); GGML_ASSERT(row_id >= 0 && row_id < n_as); - const struct ggml_tensor * src0_row = dst->src[row_id + 2]; + src0_row_extra.data_device[g_main_device] = + src0_original + row_id * src0->nb[2]; + src1_row_extra.data_device[g_main_device] = + src1_original + i01 * src1->nb[1]; + dst_row_extra.data_device[g_main_device] = + dst_original + i01 * dst->nb[1]; - src1_row_extra.data_device[g_main_device] = src1_original + i01*src1->nb[1]; - src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set? - - dst_row_extra.data_device[g_main_device] = dst_original + i01*dst->nb[1]; - dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set? - - ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row); + ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row); } } else { sycl_pool_alloc src1_contiguous(sizeof(float)*ggml_nelements(src1)); @@ -16065,8 +16081,6 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, dst_row_extra.data_device[g_main_device] = dst_contiguous.get(); for (int32_t row_id = 0; row_id < n_as; ++row_id) { - const struct ggml_tensor * src0_row = dst->src[row_id + 2]; - int64_t num_src1_rows = 0; for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); @@ -16079,7 +16093,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, SYCL_CHECK(CHECK_TRY_ERROR( stream->memcpy(src1_contiguous.get() + num_src1_rows * nb11, - src1_original + i01 * nb11, nb11).wait())); + src1_original + i01 * nb11, nb11))); num_src1_rows++; } @@ -16087,6 +16101,9 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, continue; } + src0_row_extra.data_device[g_main_device] = + src0_original + row_id * src0->nb[2]; + src1_row.ne[1] = num_src1_rows; dst_row.ne[1] = num_src1_rows; @@ -16098,7 +16115,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, dst_row.nb[2] = num_src1_rows*nb1; dst_row.nb[3] = num_src1_rows*nb1; - ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row); + ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row); num_src1_rows = 0; for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { @@ -16112,7 +16129,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( dst_original + i01 * nb1, - dst_contiguous.get() + num_src1_rows * nb1, nb1).wait())); + dst_contiguous.get() + num_src1_rows * nb1, nb1))); num_src1_rows++; } } @@ -16814,11 +16831,13 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; SYCL_CHECK( CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); - + char* host_buf = (char*)malloc(size); + memcpy(host_buf, data, size); SYCL_CHECK( CHECK_TRY_ERROR((*stream) - .memcpy((char *)tensor->data + offset, data, size) + .memcpy((char *)tensor->data + offset, host_buf, size) .wait())); + free(host_buf); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -17739,7 +17758,7 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons GGML_CALL static bool ggml_backend_sycl_offload_op(ggml_backend_t backend, const ggml_tensor * op) { const int min_batch_size = 32; - return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS; + return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS && op->op != GGML_OP_MUL_MAT_ID; GGML_UNUSED(backend); } diff --git a/ggml.c b/ggml.c index 73a85dcb9..fbbf927c3 100644 --- a/ggml.c +++ b/ggml.c @@ -4,6 +4,7 @@ #include "ggml-impl.h" #include "ggml-quants.h" #include "ggml.h" +#include "sgemm.h" #if defined(_MSC_VER) || defined(__MINGW32__) #include // using malloc.h with MSC/MINGW @@ -32,6 +33,10 @@ #include #endif +#ifdef __ARM_FEATURE_MATMUL_INT8 +#undef GGML_USE_LLAMAFILE +#endif + #if defined(_MSC_VER) // disable "possible loss of data" to avoid hundreds of casts // we should just be careful :) @@ -853,18 +858,6 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) { // simd mappings // -#if defined(__ARM_NEON) -#if !defined(__aarch64__) - -// 64-bit compatibility - -inline static float vaddvq_f32(float32x4_t v) { - return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3); -} - -#endif -#endif - // we define a common set of C macros which map to specific intrinsics based on the current architecture // we then implement the fundamental computation operations below using only these macros // adding support for new architectures requires to define the corresponding SIMD macros @@ -4573,21 +4566,32 @@ void ggml_mul_mat_set_prec( // ggml_mul_mat_id -// NOTE: id will be removed in the future and instead all the experts listed in ids will be computed -// this will allow computing all the used experts in a single matrix multiplication +/* + c = ggml_mul_mat_id(ctx, as, b, ids); + + as -> [cols, rows, n_expert] + ids -> [n_experts_used, n_tokens] (i32) + b -> [cols, n_expert_used, n_tokens] + c -> [cols, n_expert_used, n_tokens] + + in b, n_experts_used can be broadcasted to match the n_expert_used of ids + + c ~= as[:,:,i] @ b[:,i%r,t], i = ids[e,t] for all e,t in ids +*/ struct ggml_tensor * ggml_mul_mat_id( struct ggml_context * ctx, struct ggml_tensor * as, - struct ggml_tensor * ids, - int id, - struct ggml_tensor * b) { - + struct ggml_tensor * b, + struct ggml_tensor * ids) { + GGML_ASSERT(!ggml_is_transposed(as)); GGML_ASSERT(ids->type == GGML_TYPE_I32); + + GGML_ASSERT(as->ne[3] == 1); // as is 3d (one matrix per expert) + GGML_ASSERT(b->ne[3] == 1); // b is 3d GGML_ASSERT(ids->ne[2] == 1 && ids->ne[3] == 1); // ids is 2d - GGML_ASSERT(ids->ne[1] == b->ne[1]); // must have an expert per b row - GGML_ASSERT(ids->ne[2] == b->ne[2] && ids->ne[3] == b->ne[3]); - GGML_ASSERT(id >= 0 && id < ids->ne[0]); // valid id + GGML_ASSERT(ids->ne[1] == b->ne[2]); // must have an expert list per b row GGML_ASSERT(as->ne[0] == b->ne[0]); // can_mul_mat + GGML_ASSERT(ids->ne[0] % b->ne[1] == 0); // can broadcast bool is_node = false; @@ -4595,11 +4599,9 @@ struct ggml_tensor * ggml_mul_mat_id( is_node = true; } - const int64_t ne[4] = { as->ne[1], b->ne[1], b->ne[2], b->ne[3] }; + const int64_t ne[4] = { as->ne[1], ids->ne[0], b->ne[2], 1 }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - ggml_set_op_params_i32(result, 0, id); - result->op = GGML_OP_MUL_MAT_ID; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = as; @@ -10809,6 +10811,28 @@ static void ggml_compute_forward_mul_mat( } #endif +#if GGML_USE_LLAMAFILE + if (src1_cont) { + for (int64_t i13 = 0; i13 < ne13; i13++) + for (int64_t i12 = 0; i12 < ne12; i12++) + if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), + (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, + nb01/ggml_type_size(src0->type), + (const char *)src1->data + i12*nb12 + i13*nb13, + nb11/ggml_type_size(src1->type), + (char *)dst->data + i12*nb2 + i13*nb3, + nb1/ggml_type_size(dst->type), + ith, nth, + params->type, + src0->type, + src1->type, + dst->type)) + goto UseGgmlGemm1; + return; + } +UseGgmlGemm1:; +#endif + if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; @@ -10840,6 +10864,28 @@ static void ggml_compute_forward_mul_mat( const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; const size_t row_size = ggml_row_size(vec_dot_type, ne10); +#if GGML_USE_LLAMAFILE + if (src1->type != vec_dot_type) { + for (int64_t i13 = 0; i13 < ne13; i13++) + for (int64_t i12 = 0; i12 < ne12; i12++) + if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), + (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, + nb01/ggml_type_size(src0->type), + (const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size, + row_size/ggml_type_size(vec_dot_type), + (char *)dst->data + i12*nb2 + i13*nb3, + nb1/ggml_type_size(dst->type), + ith, nth, + params->type, + src0->type, + vec_dot_type, + dst->type)) + goto UseGgmlGemm2; + return; + } +UseGgmlGemm2:; +#endif + const int64_t nr0 = ne01; // src0 rows const int64_t nr1 = ne1*ne12*ne13; // src1 rows @@ -10957,11 +11003,6 @@ static void ggml_compute_forward_mul_mat_id( enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float; - GGML_ASSERT(ne0 == ne01); - GGML_ASSERT(ne1 == ne11); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == ggml_type_size(type)); GGML_ASSERT(nb10 == ggml_type_size(src1->type)); @@ -10972,22 +11013,21 @@ static void ggml_compute_forward_mul_mat_id( GGML_ASSERT(nb1 <= nb2); GGML_ASSERT(nb2 <= nb3); - // broadcast is not supported with mmid - assert(ne12 == 1); - assert(ne13 == 1); - // row groups - const int id = ggml_get_op_params_i32(dst, 0); - const int n_as = src0->ne[2]; + const int n_ids = ids->ne[0]; // n_expert_used + const int n_as = ne02; // n_expert char * wdata_src1_end = (src1->type == vec_dot_type) ? (char *) params->wdata : (char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t)); - int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as] - int64_t * matrix_rows = matrix_row_counts + n_as; // [n_as][ne11] + struct mmid_row_mapping { + int32_t i1; + int32_t i2; + }; - #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)] + int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as] + struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *)(matrix_row_counts + n_as); // [n_as][ne11] if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { @@ -11011,16 +11051,20 @@ static void ggml_compute_forward_mul_mat_id( } // initialize matrix_row_counts - GGML_ASSERT(wdata == wdata_src1_end); memset(matrix_row_counts, 0, n_as*sizeof(int64_t)); - // group rows by src0 matrix - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]); +#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)] - GGML_ASSERT(row_id >= 0 && row_id < n_as); - MMID_MATRIX_ROW(row_id, matrix_row_counts[row_id]) = i01; - matrix_row_counts[row_id] += 1; + // group rows by src0 matrix + for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { + for (int id = 0; id < n_ids; ++id) { + const int32_t i02 = *(const int32_t *) ((const char *) ids->data + iid1*ids->nb[1] + id*ids->nb[0]); + + assert(i02 >= 0 && i02 < n_as); + + MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = (struct mmid_row_mapping) {id, iid1}; + matrix_row_counts[i02] += 1; + } } return; @@ -11038,15 +11082,13 @@ static void ggml_compute_forward_mul_mat_id( continue; } - size_t src0_offset = cur_a*src0->nb[2]; + const char * src0_cur = (const char *) src0->data + cur_a*nb02; const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; const size_t row_size = ggml_row_size(vec_dot_type, ne10); - const int64_t nr0 = ne01; // src0 rows - const int64_t nr1 = cne1*ne12*ne13; // src1 rows - - //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1); + const int64_t nr0 = ne01; // src0 rows + const int64_t nr1 = cne1; // src1 rows // distribute the thread work across the inner or outer loop based on which one is larger @@ -11065,13 +11107,11 @@ static void ggml_compute_forward_mul_mat_id( const int64_t ir110 = dr1*ith1; const int64_t ir111 = MIN(ir110 + dr1, nr1); - //printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111); - // threads with no work simply yield (not sure if it helps) - if (ir010 >= ir011 || ir110 >= ir111) { - sched_yield(); - continue; - } + //if (ir010 >= ir011 || ir110 >= ir111) { + // sched_yield(); + // continue; + //} // block-tiling attempt const int64_t blck_0 = 16; @@ -11083,20 +11123,16 @@ static void ggml_compute_forward_mul_mat_id( for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) { for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) { for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) { - const int64_t i13 = (ir1/(ne12*cne1)); // Note: currently, src1 is always a matrix - const int64_t i12 = (ir1 - i13*ne12*cne1)/cne1; - const int64_t _i11 = (ir1 - i13*ne12*cne1 - i12*cne1); - const int64_t i11 = MMID_MATRIX_ROW(cur_a, _i11); + const int64_t _i12 = ir1; // logical row index for this expert - // broadcast src0 into src1 - //const int64_t i03 = i13/r3; - //const int64_t i02 = i12/r2; + struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12); + const int id = row_mapping.i1; // selected expert index - const int64_t i1 = i11; - const int64_t i2 = i12; - const int64_t i3 = i13; + const int64_t i11 = id % ne11; + const int64_t i12 = row_mapping.i2; // row index in src1 - const char * src0_row = (const char *) src0->data + src0_offset; + const int64_t i1 = id; // selected expert index + const int64_t i2 = i12; // row // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using @@ -11104,25 +11140,26 @@ static void ggml_compute_forward_mul_mat_id( // TODO: this is a bit of a hack, we should probably have a better way to handle this const char * src1_col = (const char *) wdata + (src1_cont || src1->type != vec_dot_type - ? (i11 + i12*ne11 + i13*ne12*ne11)*row_size - : (i11*nb11 + i12*nb12 + i13*nb13)); + ? (i11 + i12*ne11)*row_size + : (i11*nb11 + i12*nb12)); - float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)); + float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2)); //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); //} for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { - vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_row + ir0*nb01, 0, src1_col, 0, 1); + vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1); } + memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); } } } } - #undef MMID_MATRIX_ROW +#undef MMID_MATRIX_ROW } // ggml_compute_forward_out_prod @@ -18442,7 +18479,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa const int n_as = src0->ne[2]; cur += GGML_PAD(cur, sizeof(int64_t)); // align cur += n_as * sizeof(int64_t); // matrix_row_counts - cur += n_as * src1->ne[1] * sizeof(int64_t); // matrix_rows + cur += n_as * src1->ne[2] * sizeof(int64_t); // matrix_rows } break; case GGML_OP_OUT_PROD: { @@ -20530,8 +20567,34 @@ static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) { return ok; } +static void gguf_free_kv(struct gguf_kv * kv) { + if (kv->key.data) { + GGML_FREE(kv->key.data); + } + + if (kv->type == GGUF_TYPE_STRING) { + if (kv->value.str.data) { + GGML_FREE(kv->value.str.data); + } + } + + if (kv->type == GGUF_TYPE_ARRAY) { + if (kv->value.arr.data) { + if (kv->value.arr.type == GGUF_TYPE_STRING) { + for (uint64_t j = 0; j < kv->value.arr.n; ++j) { + struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j]; + if (str->data) { + GGML_FREE(str->data); + } + } + } + GGML_FREE(kv->value.arr.data); + } + } +} + struct gguf_context * gguf_init_empty(void) { - struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context)); + struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context)); memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic)); ctx->header.version = GGUF_VERSION; @@ -20576,7 +20639,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p bool ok = true; - struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context)); + struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context)); // read the header { @@ -20613,9 +20676,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // read the kv pairs { - ctx->kv = GGML_MALLOC(ctx->header.n_kv * sizeof(struct gguf_kv)); + const uint64_t n_kv = ctx->header.n_kv; - for (uint64_t i = 0; i < ctx->header.n_kv; ++i) { + // header.n_kv will hold the actual value of pairs that were successfully read in the loop below + ctx->header.n_kv = 0; + ctx->kv = GGML_CALLOC(n_kv, sizeof(struct gguf_kv)); + + for (uint64_t i = 0; i < n_kv; ++i) { struct gguf_kv * kv = &ctx->kv[i]; //fprintf(stderr, "%s: reading kv %d\n", __func__, i); @@ -20664,7 +20731,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p return NULL; } - kv->value.arr.data = GGML_MALLOC(kv->value.arr.n * gguf_type_size(kv->value.arr.type)); + kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, gguf_type_size(kv->value.arr.type)); ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type), &offset); } break; @@ -20678,7 +20745,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p return NULL; } - kv->value.arr.data = GGML_MALLOC(kv->value.arr.n * sizeof(struct gguf_str)); + kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, sizeof(struct gguf_str)); for (uint64_t j = 0; j < kv->value.arr.n; ++j) { ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset); @@ -20694,6 +20761,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p if (!ok) { break; } + + ctx->header.n_kv++; } if (!ok) { @@ -20706,7 +20775,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // read the tensor infos { - ctx->infos = GGML_MALLOC(ctx->header.n_tensors * sizeof(struct gguf_tensor_info)); + ctx->infos = GGML_CALLOC(ctx->header.n_tensors, sizeof(struct gguf_tensor_info)); for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; @@ -20727,8 +20796,17 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ok = ok && gguf_fread_el (file, &info->type, sizeof(info->type), &offset); ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset), &offset); + // TODO: return an error instead of crashing with GGML_ASSERT gguf_tensor_info_sanitize(info); + // make sure there is no duplicated tensor names + for (uint64_t j = 0; j < i; ++j) { + if (strcmp(info->name.data, ctx->infos[j].name.data) == 0) { + fprintf(stderr, "%s: duplicated tensor name %s\n", __func__, info->name.data); + ok = false; + } + } + if (!ok) { fprintf(stderr, "%s: failed to read tensor info\n", __func__); fclose(file); @@ -20842,12 +20920,12 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ok = ok && cur != NULL; - ggml_set_name(cur, ctx->infos[i].name.data); - if (!ok) { break; } + ggml_set_name(cur, ctx->infos[i].name.data); + // point the data member to the appropriate location in the binary blob using the tensor infos if (!params.no_alloc) { //cur->data = (char *) data->data + ctx->infos[i].offset - ctx->offset; // offset from start of file @@ -20879,31 +20957,7 @@ void gguf_free(struct gguf_context * ctx) { if (ctx->kv) { // free string memory - not great.. for (uint64_t i = 0; i < ctx->header.n_kv; ++i) { - struct gguf_kv * kv = &ctx->kv[i]; - - if (kv->key.data) { - GGML_FREE(kv->key.data); - } - - if (kv->type == GGUF_TYPE_STRING) { - if (kv->value.str.data) { - GGML_FREE(kv->value.str.data); - } - } - - if (kv->type == GGUF_TYPE_ARRAY) { - if (kv->value.arr.data) { - if (kv->value.arr.type == GGUF_TYPE_STRING) { - for (uint64_t j = 0; j < kv->value.arr.n; ++j) { - struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j]; - if (str->data) { - GGML_FREE(str->data); - } - } - } - GGML_FREE(kv->value.arr.data); - } - } + gguf_free_kv(&ctx->kv[i]); } GGML_FREE(ctx->kv); @@ -20921,7 +20975,7 @@ void gguf_free(struct gguf_context * ctx) { GGML_FREE(ctx->infos); } - GGML_ALIGNED_FREE(ctx); + GGML_FREE(ctx); } const char * gguf_type_name(enum gguf_type type) { @@ -21128,6 +21182,19 @@ static int gguf_get_or_add_key(struct gguf_context * ctx, const char * key) { return n_kv; } +void gguf_remove_key(struct gguf_context * ctx, const char * key) { + const int idx = gguf_find_key(ctx, key); + if (idx >= 0) { + const int n_kv = gguf_get_n_kv(ctx); + gguf_free_kv(&ctx->kv[idx]); + for (int i = idx; i < n_kv-1; ++i) { + ctx->kv[i] = ctx->kv[i+1]; + } + ctx->kv = realloc(ctx->kv, (n_kv - 1) * sizeof(struct gguf_kv)); + ctx->header.n_kv--; + } +} + void gguf_set_val_u8(struct gguf_context * ctx, const char * key, uint8_t val) { const int idx = gguf_get_or_add_key(ctx, key); @@ -21219,7 +21286,7 @@ void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_ty ctx->kv[idx].type = GGUF_TYPE_ARRAY; ctx->kv[idx].value.arr.type = type; ctx->kv[idx].value.arr.n = n; - ctx->kv[idx].value.arr.data = GGML_MALLOC(n*gguf_type_size(type)); + ctx->kv[idx].value.arr.data = GGML_CALLOC(n, gguf_type_size(type)); memcpy(ctx->kv[idx].value.arr.data, data, n*gguf_type_size(type)); } @@ -21229,7 +21296,7 @@ void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char ** ctx->kv[idx].type = GGUF_TYPE_ARRAY; ctx->kv[idx].value.arr.type = GGUF_TYPE_STRING; ctx->kv[idx].value.arr.n = n; - ctx->kv[idx].value.arr.data = GGML_MALLOC(n*sizeof(struct gguf_str)); + ctx->kv[idx].value.arr.data = GGML_CALLOC(n, sizeof(struct gguf_str)); for (int i = 0; i < n; i++) { struct gguf_str * str = &((struct gguf_str *)ctx->kv[idx].value.arr.data)[i]; str->n = strlen(data[i]); @@ -21256,7 +21323,7 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) { case GGUF_TYPE_ARRAY: { if (src->kv[i].value.arr.type == GGUF_TYPE_STRING) { - const char ** data = GGML_MALLOC(src->kv[i].value.arr.n*sizeof(char *)); + const char ** data = GGML_CALLOC(src->kv[i].value.arr.n, sizeof(char *)); for (uint32_t j = 0; j < src->kv[i].value.arr.n; j++) { data[j] = ((struct gguf_str *)src->kv[i].value.arr.data)[j].data; } @@ -21276,6 +21343,10 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) { void gguf_add_tensor( struct gguf_context * ctx, const struct ggml_tensor * tensor) { + if (gguf_find_tensor(ctx, tensor->name) != -1) { + GGML_ASSERT(false && "duplicated tensor name"); + } + const int idx = ctx->header.n_tensors; ctx->infos = realloc(ctx->infos, (idx + 1)*sizeof(struct gguf_tensor_info)); @@ -21344,7 +21415,7 @@ struct gguf_buf { static struct gguf_buf gguf_buf_init(size_t size) { struct gguf_buf buf = { - /*buf.data =*/ size == 0 ? NULL : GGML_MALLOC(size), + /*buf.data =*/ size == 0 ? NULL : GGML_CALLOC(1, size), /*buf.size =*/ size, /*buf.offset =*/ 0, }; diff --git a/ggml.h b/ggml.h index abe3767f2..86e5a8dc5 100644 --- a/ggml.h +++ b/ggml.h @@ -762,6 +762,8 @@ extern "C" { // use this to compute the memory overhead of a tensor GGML_API size_t ggml_tensor_overhead(void); + GGML_API bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes); + // main GGML_API struct ggml_context * ggml_init(struct ggml_init_params params); @@ -1161,13 +1163,11 @@ extern "C" { enum ggml_prec prec); // indirect matrix multiplication - // ggml_mul_mat_id(ctx, as, ids, id, b) ~= ggml_mul_mat(as[ids[id]], b) GGML_API struct ggml_tensor * ggml_mul_mat_id( struct ggml_context * ctx, struct ggml_tensor * as, - struct ggml_tensor * ids, - int id, - struct ggml_tensor * b); + struct ggml_tensor * b, + struct ggml_tensor * ids); // A: m columns, n rows, // B: p columns, n rows, @@ -2289,6 +2289,9 @@ extern "C" { GGML_API char * gguf_get_tensor_name (const struct gguf_context * ctx, int i); GGML_API enum ggml_type gguf_get_tensor_type (const struct gguf_context * ctx, int i); + // removes key if it exists + GGML_API void gguf_remove_key(struct gguf_context * ctx, const char * key); + // overrides existing values or adds a new one GGML_API void gguf_set_val_u8 (struct gguf_context * ctx, const char * key, uint8_t val); GGML_API void gguf_set_val_i8 (struct gguf_context * ctx, const char * key, int8_t val); diff --git a/gguf-py/README.md b/gguf-py/README.md index 22d7ffa52..a04c22759 100644 --- a/gguf-py/README.md +++ b/gguf-py/README.md @@ -21,6 +21,8 @@ pip install gguf [scripts/gguf-convert-endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-convert-endian.py) — Allows converting the endianness of GGUF files. +[scripts/gguf-new-metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-new-metadata.py) — Copies a GGUF file with added/modified/removed metadata values. + ## Development Maintainers who participate in development of this package are advised to install it in editable mode: diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index a6454a10e..6d597bfd9 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -72,6 +72,7 @@ class Keys: class Tokenizer: MODEL = "tokenizer.ggml.model" + PRE = "tokenizer.ggml.pre" LIST = "tokenizer.ggml.tokens" TOKEN_TYPE = "tokenizer.ggml.token_type" TOKEN_TYPE_COUNT = "tokenizer.ggml.token_type_count" # for BERT-style token types @@ -90,6 +91,13 @@ class Keys: HF_JSON = "tokenizer.huggingface.json" RWKV = "tokenizer.rwkv.world" CHAT_TEMPLATE = "tokenizer.chat_template" + CHAT_TEMPLATE_N = "tokenizer.chat_template.{name}" + CHAT_TEMPLATES = "tokenizer.chat_templates" + # FIM/Infill special tokens constants + PREFIX_ID = "tokenizer.ggml.prefix_token_id" + SUFFIX_ID = "tokenizer.ggml.suffix_token_id" + MIDDLE_ID = "tokenizer.ggml.middle_token_id" + EOT_ID = "tokenizer.ggml.eot_token_id" # @@ -115,7 +123,9 @@ class MODEL_ARCH(IntEnum): STABLELM = auto() QWEN = auto() QWEN2 = auto() + QWEN2MOE = auto() PHI2 = auto() + PHI3 = auto() PLAMO = auto() CODESHELL = auto() ORION = auto() @@ -126,44 +136,50 @@ class MODEL_ARCH(IntEnum): MAMBA = auto() XVERSE = auto() COMMAND_R = auto() + DBRX = auto() + OLMO = auto() class MODEL_TENSOR(IntEnum): - TOKEN_EMBD = auto() - TOKEN_EMBD_NORM = auto() - TOKEN_TYPES = auto() - POS_EMBD = auto() - OUTPUT = auto() - OUTPUT_NORM = auto() - ROPE_FREQS = auto() - ATTN_Q = auto() - ATTN_K = auto() - ATTN_V = auto() - ATTN_QKV = auto() - ATTN_OUT = auto() - ATTN_NORM = auto() - ATTN_NORM_2 = auto() - ATTN_OUT_NORM = auto() - ATTN_ROT_EMBD = auto() - FFN_GATE_INP = auto() - FFN_NORM = auto() - FFN_GATE = auto() - FFN_DOWN = auto() - FFN_UP = auto() - FFN_ACT = auto() - FFN_GATE_EXP = auto() - FFN_DOWN_EXP = auto() - FFN_UP_EXP = auto() - ATTN_Q_NORM = auto() - ATTN_K_NORM = auto() - LAYER_OUT_NORM = auto() - SSM_IN = auto() - SSM_CONV1D = auto() - SSM_X = auto() - SSM_DT = auto() - SSM_A = auto() - SSM_D = auto() - SSM_OUT = auto() + TOKEN_EMBD = auto() + TOKEN_EMBD_NORM = auto() + TOKEN_TYPES = auto() + POS_EMBD = auto() + OUTPUT = auto() + OUTPUT_NORM = auto() + ROPE_FREQS = auto() + ATTN_Q = auto() + ATTN_K = auto() + ATTN_V = auto() + ATTN_QKV = auto() + ATTN_OUT = auto() + ATTN_NORM = auto() + ATTN_NORM_2 = auto() + ATTN_OUT_NORM = auto() + ATTN_ROT_EMBD = auto() + FFN_GATE_INP = auto() + FFN_GATE_INP_SHEXP = auto() + FFN_NORM = auto() + FFN_GATE = auto() + FFN_DOWN = auto() + FFN_UP = auto() + FFN_ACT = auto() + FFN_GATE_EXP = auto() + FFN_DOWN_EXP = auto() + FFN_UP_EXP = auto() + FFN_GATE_SHEXP = auto() + FFN_DOWN_SHEXP = auto() + FFN_UP_SHEXP = auto() + ATTN_Q_NORM = auto() + ATTN_K_NORM = auto() + LAYER_OUT_NORM = auto() + SSM_IN = auto() + SSM_CONV1D = auto() + SSM_X = auto() + SSM_DT = auto() + SSM_A = auto() + SSM_D = auto() + SSM_OUT = auto() MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { @@ -184,7 +200,9 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.STABLELM: "stablelm", MODEL_ARCH.QWEN: "qwen", MODEL_ARCH.QWEN2: "qwen2", + MODEL_ARCH.QWEN2MOE: "qwen2moe", MODEL_ARCH.PHI2: "phi2", + MODEL_ARCH.PHI3: "phi3", MODEL_ARCH.PLAMO: "plamo", MODEL_ARCH.CODESHELL: "codeshell", MODEL_ARCH.ORION: "orion", @@ -195,44 +213,50 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.MAMBA: "mamba", MODEL_ARCH.XVERSE: "xverse", MODEL_ARCH.COMMAND_R: "command-r", + MODEL_ARCH.DBRX: "dbrx", + MODEL_ARCH.OLMO: "olmo", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { - MODEL_TENSOR.TOKEN_EMBD: "token_embd", - MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", - MODEL_TENSOR.TOKEN_TYPES: "token_types", - MODEL_TENSOR.POS_EMBD: "position_embd", - MODEL_TENSOR.OUTPUT_NORM: "output_norm", - MODEL_TENSOR.OUTPUT: "output", - MODEL_TENSOR.ROPE_FREQS: "rope_freqs", - MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", - MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", - MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", - MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", - MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", - MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", - MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", - MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", - MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", - MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", - MODEL_TENSOR.ATTN_OUT_NORM: "blk.{bid}.attn_output_norm", - MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp", - MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", - MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", - MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", - MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", - MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn", - MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps", - MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps", - MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps", - MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm", - MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in", - MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d", - MODEL_TENSOR.SSM_X: "blk.{bid}.ssm_x", - MODEL_TENSOR.SSM_DT: "blk.{bid}.ssm_dt", - MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a", - MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d", - MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out", + MODEL_TENSOR.TOKEN_EMBD: "token_embd", + MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", + MODEL_TENSOR.TOKEN_TYPES: "token_types", + MODEL_TENSOR.POS_EMBD: "position_embd", + MODEL_TENSOR.OUTPUT_NORM: "output_norm", + MODEL_TENSOR.OUTPUT: "output", + MODEL_TENSOR.ROPE_FREQS: "rope_freqs", + MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", + MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", + MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", + MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", + MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", + MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", + MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", + MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", + MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", + MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", + MODEL_TENSOR.ATTN_OUT_NORM: "blk.{bid}.attn_output_norm", + MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp", + MODEL_TENSOR.FFN_GATE_INP_SHEXP: "blk.{bid}.ffn_gate_inp_shexp", + MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", + MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", + MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", + MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", + MODEL_TENSOR.FFN_GATE_SHEXP: "blk.{bid}.ffn_gate_shexp", + MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp", + MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp", + MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn", + MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps", + MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps", + MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps", + MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm", + MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in", + MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d", + MODEL_TENSOR.SSM_X: "blk.{bid}.ssm_x", + MODEL_TENSOR.SSM_DT: "blk.{bid}.ssm_dt", + MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a", + MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d", + MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out", } MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { @@ -438,6 +462,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_GATE, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, ], MODEL_ARCH.QWEN: [ MODEL_TENSOR.TOKEN_EMBD, @@ -467,6 +493,25 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.QWEN2MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_INP_SHEXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], MODEL_ARCH.PLAMO: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -508,6 +553,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.PHI3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], MODEL_ARCH.CODESHELL: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.POS_EMBD, @@ -642,6 +701,30 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.ATTN_K_NORM, MODEL_TENSOR.ATTN_Q_NORM, ], + MODEL_ARCH.DBRX: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.OLMO: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], # TODO } @@ -806,6 +889,7 @@ GGML_QUANT_SIZES = { GGMLQuantizationType.I32: (1, 4), GGMLQuantizationType.I64: (1, 8), GGMLQuantizationType.F64: (1, 8), + GGMLQuantizationType.IQ1_M: (256, QK_K // 8 + QK_K // 16 + QK_K // 32), } @@ -857,6 +941,7 @@ KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK # tokenization KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL +KEY_TOKENIZER_PRE = Keys.Tokenizer.PRE KEY_TOKENIZER_LIST = Keys.Tokenizer.LIST KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE KEY_TOKENIZER_SCORES = Keys.Tokenizer.SCORES @@ -870,3 +955,7 @@ KEY_TOKENIZER_CLS_ID = Keys.Tokenizer.CLS_ID KEY_TOKENIZER_MASK_ID = Keys.Tokenizer.MASK_ID KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV +KEY_TOKENIZER_PRIFIX_ID = Keys.Tokenizer.PREFIX_ID +KEY_TOKENIZER_SUFFIX_ID = Keys.Tokenizer.SUFFIX_ID +KEY_TOKENIZER_MIDDLE_ID = Keys.Tokenizer.MIDDLE_ID +KEY_TOKENIZER_EOT_ID = Keys.Tokenizer.EOT_ID diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py index 33afac552..2bdb15525 100644 --- a/gguf-py/gguf/gguf_reader.py +++ b/gguf-py/gguf/gguf_reader.py @@ -139,8 +139,13 @@ class GGUFReader: def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int: if field.name in self.fields: - raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}') - self.fields[field.name] = field + # TODO: add option to generate error on duplicate keys + # raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}') + + print(f'Warning: Duplicate key {field.name} at offset {field.offset}') + self.fields[field.name + '_{}'.format(field.offset)] = field + else: + self.fields[field.name] = field return 0 if skip_sum else sum(int(part.nbytes) for part in field.parts) def _get_str(self, offset: int) -> tuple[npt.NDArray[np.uint64], npt.NDArray[np.uint8]]: @@ -234,8 +239,14 @@ class GGUFReader: def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None: tensors = [] + tensor_names = set() # keep track of name to prevent duplicated tensors for field in fields: _name_len, name_data, _n_dims, dims, raw_dtype, offset_tensor = field.parts + # check if there's any tensor having same name already in the list + tensor_name = str(bytes(name_data), encoding = 'utf-8') + if tensor_name in tensor_names: + raise ValueError(f'Found duplicated tensor with name {tensor_name}') + tensor_names.add(tensor_name) ggml_type = GGMLQuantizationType(raw_dtype[0]) n_elems = np.prod(dims) block_size, type_size = GGML_QUANT_SIZES[ggml_type] @@ -267,7 +278,7 @@ class GGUFReader: item_count = n_bytes item_type = np.uint8 tensors.append(ReaderTensor( - name = str(bytes(name_data), encoding = 'utf-8'), + name = tensor_name, tensor_type = ggml_type, shape = dims, n_elements = n_elems, diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index f4c440766..089aece87 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -6,7 +6,8 @@ import struct import tempfile from enum import Enum, auto from io import BufferedWriter -from typing import IO, Any, Sequence +from typing import IO, Any, Sequence, Mapping +from string import ascii_letters, digits import numpy as np @@ -62,6 +63,7 @@ class GGUFWriter: self.kv_data_count = 0 self.ti_data = bytearray() self.ti_data_count = 0 + self.ti_names = set() self.use_temp_file = use_temp_file self.temp_file = None self.tensors = [] @@ -196,6 +198,10 @@ class GGUFWriter: if self.state is not WriterState.EMPTY: raise ValueError(f'Expected output file to be empty, got {self.state}') + if name in self.ti_names: + raise ValueError(f'Duplicated tensor name {name}') + self.ti_names.add(name) + encoded_name = name.encode("utf8") self.ti_data += self._pack("Q", len(encoded_name)) self.ti_data += encoded_name @@ -421,6 +427,9 @@ class GGUFWriter: def add_tokenizer_model(self, model: str) -> None: self.add_string(Keys.Tokenizer.MODEL, model) + def add_tokenizer_pre(self, pre: str) -> None: + self.add_string(Keys.Tokenizer.PRE, pre) + def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: self.add_array(Keys.Tokenizer.LIST, tokens) @@ -466,9 +475,47 @@ class GGUFWriter: def add_add_space_prefix(self, value: bool) -> None: self.add_bool(Keys.Tokenizer.ADD_PREFIX, value) - def add_chat_template(self, value: str) -> None: + def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None: + if isinstance(value, list): + template_default = None + template_names = set() + + for choice in value: + name = choice.get('name', '') + template = choice.get('template') + + # Allowing non-alphanumerical characters in template name is probably not a good idea, so filter it + name = ''.join((c if c in ascii_letters + digits else '_' for c in name)) + + if name and template is not None: + if name == 'default': + template_default = template + else: + template_names.add(name) + self.add_string(Keys.Tokenizer.CHAT_TEMPLATE_N.format(name=name), template) + + if template_names: + self.add_array(Keys.Tokenizer.CHAT_TEMPLATES, list(template_names)) + + if template_default is None: + return + + value = template_default + self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value) + def add_prefix_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.PREFIX_ID, id) + + def add_suffix_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.SUFFIX_ID, id) + + def add_middle_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.MIDDLE_ID, id) + + def add_eot_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.EOT_ID, id) + def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: pack_prefix = '' if not skip_pack_prefix: diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 4f02d298e..e5750d419 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -10,7 +10,7 @@ class TensorNameMap: # Token embeddings MODEL_TENSOR.TOKEN_EMBD: ( "gpt_neox.embed_in", # gptneox - "transformer.wte", # gpt2 gpt-j mpt refact qwen + "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx "transformer.word_embeddings", # falcon "word_embeddings", # bloom "model.embed_tokens", # llama-hf @@ -48,7 +48,7 @@ class TensorNameMap: # Output MODEL_TENSOR.OUTPUT: ( "embed_out", # gptneox - "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba + "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx "output", # llama-pth bloom internlm2 "word_embeddings_for_head", # persimmon "lm_head.linear", # phi2 @@ -60,7 +60,7 @@ class TensorNameMap: "transformer.ln_f", # gpt2 gpt-j falcon "model.norm", # llama-hf baichuan internlm2 "norm", # llama-pth - "transformer.norm_f", # mpt + "transformer.norm_f", # mpt dbrx "ln_f", # refact bloom qwen gpt2 "language_model.encoder.final_layernorm", # persimmon "model.final_layernorm", # persimmon @@ -96,6 +96,7 @@ class TensorNameMap: "model.layers.{bid}.norm", # mamba-qbert "backbone.layers.{bid}.norm", # mamba "transformer.decoder_layer.{bid}.rms_norm", # Grok + "transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx ), # Attention norm 2 @@ -108,6 +109,7 @@ class TensorNameMap: "gpt_neox.layers.{bid}.attention.query_key_value", # gptneox "transformer.h.{bid}.attn.c_attn", # gpt2 qwen "transformer.blocks.{bid}.attn.Wqkv", # mpt + "transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv", # dbrx "transformer.h.{bid}.self_attention.query_key_value", # falcon "h.{bid}.self_attention.query_key_value", # bloom "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon @@ -115,6 +117,7 @@ class TensorNameMap: "h.{bid}.attn.c_attn", # gpt2 "transformer.h.{bid}.mixer.Wqkv", # phi2 "encoder.layers.{bid}.attn.Wqkv", # nomic-bert + "model.layers.{bid}.self_attn.qkv_proj" # phi3 ), # Attention query @@ -152,23 +155,24 @@ class TensorNameMap: # Attention output MODEL_TENSOR.ATTN_OUT: ( - "gpt_neox.layers.{bid}.attention.dense", # gptneox - "transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen - "transformer.blocks.{bid}.attn.out_proj", # mpt - "transformer.h.{bid}.self_attention.dense", # falcon - "h.{bid}.self_attention.dense", # bloom - "model.layers.{bid}.self_attn.o_proj", # llama-hf - "layers.{bid}.attention.wo", # llama-pth - "encoder.layer.{bid}.attention.output.dense", # bert - "transformer.h.{bid}.attn.out_proj", # gpt-j - "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon - "model.layers.{bid}.self_attn.dense", # persimmon - "h.{bid}.attn.c_proj", # gpt2 - "transformer.h.{bid}.mixer.out_proj", # phi2 - "model.layers.layers.{bid}.self_attn.o_proj", # plamo - "model.layers.{bid}.attention.wo", # internlm2 - "encoder.layers.{bid}.attn.out_proj", # nomic-bert - "transformer.decoder_layer.{bid}.multi_head_attention.linear"# Grok + "gpt_neox.layers.{bid}.attention.dense", # gptneox + "transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen + "transformer.blocks.{bid}.attn.out_proj", # mpt + "transformer.h.{bid}.self_attention.dense", # falcon + "h.{bid}.self_attention.dense", # bloom + "model.layers.{bid}.self_attn.o_proj", # llama-hf + "layers.{bid}.attention.wo", # llama-pth + "encoder.layer.{bid}.attention.output.dense", # bert + "transformer.h.{bid}.attn.out_proj", # gpt-j + "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon + "model.layers.{bid}.self_attn.dense", # persimmon + "h.{bid}.attn.c_proj", # gpt2 + "transformer.h.{bid}.mixer.out_proj", # phi2 + "model.layers.layers.{bid}.self_attn.o_proj", # plamo + "model.layers.{bid}.attention.wo", # internlm2 + "encoder.layers.{bid}.attn.out_proj", # nomic-bert + "transformer.decoder_layer.{bid}.multi_head_attention.linear", # Grok + "transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx ), # Attention output norm @@ -176,6 +180,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.output.LayerNorm", # bert "encoder.layers.{bid}.norm1", # nomic-bert "transformer.decoder_layer.{bid}.rms_norm_1", # Grok + "transformer.blocks.{bid}.norm_attn_norm.norm_2", # dbrx ), # Rotary embeddings @@ -202,9 +207,15 @@ class TensorNameMap: ), MODEL_TENSOR.FFN_GATE_INP: ( - "layers.{bid}.feed_forward.gate", # mixtral - "model.layers.{bid}.block_sparse_moe.gate", # mixtral - "transformer.decoder_layer.{bid}.router" # Grok + "layers.{bid}.feed_forward.gate", # mixtral + "model.layers.{bid}.block_sparse_moe.gate", # mixtral + "model.layers.{bid}.mlp.gate", # qwen2moe + "transformer.decoder_layer.{bid}.router", # Grok + "transformer.blocks.{bid}.ffn.router.layer", # dbrx + ), + + MODEL_TENSOR.FFN_GATE_INP_SHEXP: ( + "model.layers.{bid}.mlp.shared_expert_gate", # qwen2moe ), # Feed-forward up @@ -224,6 +235,7 @@ class TensorNameMap: "h.{bid}.mlp.c_fc", # gpt2 "transformer.h.{bid}.mlp.fc1", # phi2 "model.layers.{bid}.mlp.fc1", # phi2 + "model.layers.{bid}.mlp.gate_up_proj", # phi3 "model.layers.layers.{bid}.mlp.up_proj", # plamo "model.layers.{bid}.feed_forward.w3", # internlm2 "encoder.layers.{bid}.mlp.fc11", # nomic-bert @@ -231,8 +243,14 @@ class TensorNameMap: ), MODEL_TENSOR.FFN_UP_EXP: ( - "layers.{bid}.feed_forward.experts.w3", # mixtral (merged) - "transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged) + "layers.{bid}.feed_forward.experts.w3", # mixtral (merged) + "transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged) + "transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx + "model.layers.{bid}.mlp.experts.up_proj", # qwen2moe (merged) + ), + + MODEL_TENSOR.FFN_UP_SHEXP: ( + "model.layers.{bid}.mlp.shared_expert.up_proj", # qwen2moe ), # AWQ-activation gate @@ -251,8 +269,14 @@ class TensorNameMap: ), MODEL_TENSOR.FFN_GATE_EXP: ( - "layers.{bid}.feed_forward.experts.w1", # mixtral (merged) - "transformer.decoder_layer.{bid}.moe.linear" # Grok (merged) + "layers.{bid}.feed_forward.experts.w1", # mixtral (merged) + "transformer.decoder_layer.{bid}.moe.linear", # Grok (merged) + "transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx + "model.layers.{bid}.mlp.experts.gate_proj", # qwen2moe (merged) + ), + + MODEL_TENSOR.FFN_GATE_SHEXP: ( + "model.layers.{bid}.mlp.shared_expert.gate_proj", # qwen2moe ), # Feed-forward down @@ -278,8 +302,14 @@ class TensorNameMap: ), MODEL_TENSOR.FFN_DOWN_EXP: ( - "layers.{bid}.feed_forward.experts.w2", # mixtral (merged) - "transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged) + "layers.{bid}.feed_forward.experts.w2", # mixtral (merged) + "transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged) + "transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx + "model.layers.{bid}.mlp.experts.down_proj", # qwen2moe (merged) + ), + + MODEL_TENSOR.FFN_DOWN_SHEXP: ( + "model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe ), MODEL_TENSOR.ATTN_Q_NORM: ( @@ -358,7 +388,7 @@ class TensorNameMap: if tensor not in MODEL_TENSORS[arch]: continue # TODO: make this configurable - n_experts = 8 + n_experts = 60 for xid in range(n_experts): tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid) self.mapping[tensor_name] = (tensor, tensor_name) diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py index a23136b18..378eaecad 100644 --- a/gguf-py/gguf/vocab.py +++ b/gguf-py/gguf/vocab.py @@ -141,7 +141,7 @@ class SpecialVocab: with open(tokenizer_config_file, encoding = 'utf-8') as f: tokenizer_config = json.load(f) chat_template = tokenizer_config.get('chat_template') - if chat_template is None or isinstance(chat_template, str): + if chat_template is None or isinstance(chat_template, (str, list)): self.chat_template = chat_template else: print( diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index 13cbfffbc..d1d876d6d 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -33,3 +33,4 @@ build-backend = "poetry.core.masonry.api" gguf-convert-endian = "scripts:gguf_convert_endian_entrypoint" gguf-dump = "scripts:gguf_dump_entrypoint" gguf-set-metadata = "scripts:gguf_set_metadata_entrypoint" +gguf-new-metadata = "scripts:gguf_new_metadata_entrypoint" diff --git a/gguf-py/scripts/__init__.py b/gguf-py/scripts/__init__.py index 77132db7a..1ad45639a 100644 --- a/gguf-py/scripts/__init__.py +++ b/gguf-py/scripts/__init__.py @@ -8,5 +8,6 @@ os.environ["NO_LOCAL_GGUF"] = "TRUE" gguf_convert_endian_entrypoint = import_module("scripts.gguf-convert-endian").main gguf_dump_entrypoint = import_module("scripts.gguf-dump").main gguf_set_metadata_entrypoint = import_module("scripts.gguf-set-metadata").main +gguf_new_metadata_entrypoint = import_module("scripts.gguf-new-metadata").main del import_module, os diff --git a/gguf-py/scripts/gguf-new-metadata.py b/gguf-py/scripts/gguf-new-metadata.py new file mode 100644 index 000000000..3444ab418 --- /dev/null +++ b/gguf-py/scripts/gguf-new-metadata.py @@ -0,0 +1,190 @@ +#!/usr/bin/env python3 +import logging +import argparse +import os +import sys +import json +from pathlib import Path + +import numpy as np +from typing import Any, Mapping, Sequence + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +import gguf + +logger = logging.getLogger("gguf-new-metadata") + + +def get_byteorder(reader: gguf.GGUFReader) -> gguf.GGUFEndian: + if np.uint32(1) == np.uint32(1).newbyteorder("<"): + # Host is little endian + host_endian = gguf.GGUFEndian.LITTLE + swapped_endian = gguf.GGUFEndian.BIG + else: + # Sorry PDP or other weird systems that don't use BE or LE. + host_endian = gguf.GGUFEndian.BIG + swapped_endian = gguf.GGUFEndian.LITTLE + + if reader.byte_order == "S": + return swapped_endian + else: + return host_endian + + +def decode_field(field: gguf.ReaderField) -> Any: + if field and field.types: + main_type = field.types[0] + + if main_type == gguf.GGUFValueType.ARRAY: + sub_type = field.types[-1] + + if sub_type == gguf.GGUFValueType.STRING: + return [str(bytes(field.parts[idx]), encoding='utf8') for idx in field.data] + else: + return [pv for idx in field.data for pv in field.parts[idx].tolist()] + if main_type == gguf.GGUFValueType.STRING: + return str(bytes(field.parts[-1]), encoding='utf8') + else: + return field.parts[-1][0] + + return None + + +def get_field_data(reader: gguf.GGUFReader, key: str) -> Any: + field = reader.get_field(key) + + return decode_field(field) + + +def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new_metadata: Mapping[str, str], remove_metadata: Sequence[str]) -> None: + for field in reader.fields.values(): + # Suppress virtual fields and fields written by GGUFWriter + if field.name == gguf.Keys.General.ARCHITECTURE or field.name.startswith('GGUF.'): + logger.debug(f'Suppressing {field.name}') + continue + + # Skip old chat templates if we have new ones + if field.name.startswith(gguf.Keys.Tokenizer.CHAT_TEMPLATE) and gguf.Keys.Tokenizer.CHAT_TEMPLATE in new_metadata: + logger.debug(f'Skipping {field.name}') + continue + + if field.name in remove_metadata: + logger.debug(f'Removing {field.name}') + continue + + old_val = decode_field(field) + val = new_metadata.get(field.name, old_val) + + if field.name in new_metadata: + logger.debug(f'Modifying {field.name}: "{old_val}" -> "{val}"') + del new_metadata[field.name] + elif val is not None: + logger.debug(f'Copying {field.name}') + + if val is not None: + writer.add_key(field.name) + writer.add_val(val, field.types[0]) + + if gguf.Keys.Tokenizer.CHAT_TEMPLATE in new_metadata: + logger.debug('Adding chat template(s)') + writer.add_chat_template(new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE]) + del new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] + + # TODO: Support other types than string? + for key, val in new_metadata.items(): + logger.debug(f'Adding {key}: {val}') + writer.add_key(key) + writer.add_val(val, gguf.GGUFValueType.STRING) + + for tensor in reader.tensors: + # Dimensions are written in reverse order, so flip them first + shape = np.flipud(tensor.shape) + writer.add_tensor_info(tensor.name, shape, tensor.data.dtype, tensor.data.nbytes, tensor.tensor_type) + + writer.write_header_to_file() + writer.write_kv_data_to_file() + writer.write_ti_data_to_file() + + for tensor in reader.tensors: + writer.write_tensor_data(tensor.data) + + writer.close() + + +def main() -> None: + parser = argparse.ArgumentParser(description="Make a copy of a GGUF file with new metadata") + parser.add_argument("input", type=Path, help="GGUF format model input filename") + parser.add_argument("output", type=Path, help="GGUF format model output filename") + parser.add_argument("--general-name", type=str, help="The models general.name") + parser.add_argument("--general-description", type=str, help="The models general.description") + parser.add_argument("--chat-template", type=str, help="Chat template string (or JSON string containing templates)") + parser.add_argument("--chat-template-config", type=Path, help="Config file (tokenizer_config.json) containing chat template(s)") + parser.add_argument("--remove-metadata", action="append", type=str, help="Remove metadata (by key name) from output model") + parser.add_argument("--force", action="store_true", help="Bypass warnings without confirmation") + parser.add_argument("--verbose", action="store_true", help="Increase output verbosity") + args = parser.parse_args(None if len(sys.argv) > 2 else ["--help"]) + + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + + new_metadata = {} + remove_metadata = args.remove_metadata or [] + + if args.general_name: + new_metadata[gguf.Keys.General.NAME] = args.general_name + + if args.general_description: + new_metadata[gguf.Keys.General.DESCRIPTION] = args.general_description + + if args.chat_template: + new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = json.loads(args.chat_template) if args.chat_template.startswith('[') else args.chat_template + + if args.chat_template_config: + with open(args.chat_template_config, 'r') as fp: + config = json.load(fp) + template = config.get('chat_template') + if template: + new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = template + + if remove_metadata: + logger.warning('*** Warning *** Warning *** Warning **') + logger.warning('* Most metadata is required for a fully functional GGUF file,') + logger.warning('* removing crucial metadata may result in a corrupt output file!') + + if not args.force: + logger.warning('* Enter exactly YES if you are positive you want to proceed:') + response = input('YES, I am sure> ') + if response != 'YES': + logger.info("You didn't enter YES. Okay then, see ya!") + sys.exit(0) + + logger.info(f'* Loading: {args.input}') + reader = gguf.GGUFReader(args.input, 'r') + + arch = get_field_data(reader, gguf.Keys.General.ARCHITECTURE) + endianess = get_byteorder(reader) + + if os.path.isfile(args.output) and not args.force: + logger.warning('*** Warning *** Warning *** Warning **') + logger.warning(f'* The "{args.output}" GGUF file already exists, it will be overwritten!') + logger.warning('* Enter exactly YES if you are positive you want to proceed:') + response = input('YES, I am sure> ') + if response != 'YES': + logger.info("You didn't enter YES. Okay then, see ya!") + sys.exit(0) + + logger.info(f'* Writing: {args.output}') + writer = gguf.GGUFWriter(args.output, arch=arch, endianess=endianess) + + alignment = get_field_data(reader, gguf.Keys.General.ALIGNMENT) + if alignment is not None: + logger.debug(f'Setting custom alignment: {alignment}') + writer.data_alignment = alignment + + copy_with_new_metadata(reader, writer, new_metadata, remove_metadata) + + +if __name__ == '__main__': + main() diff --git a/grammars/README.md b/grammars/README.md index e1383fa5c..c924e8d46 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -89,3 +89,13 @@ This guide provides a brief overview. Check out the GBNF files in this directory ``` ./main -m --grammar-file grammars/some-grammar.gbnf -p 'Some prompt' ``` + +## Troubleshooting + +Grammars currently have performance gotchas (see https://github.com/ggerganov/llama.cpp/issues/4218). + +### Efficient optional repetitions + +A common pattern is to allow repetitions of a pattern `x` up to N times. + +While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) will result in extremely slow inference. Instead, you can write `(x (x (x ... (x)?...)?)?)?` (w/ N-deep nesting) diff --git a/llama.cpp b/llama.cpp index f972c3472..92bff6b90 100644 --- a/llama.cpp +++ b/llama.cpp @@ -75,6 +75,7 @@ #include #include #include +#include #include #include #include @@ -105,7 +106,7 @@ #endif #define LLAMA_MAX_NODES 8192 -#define LLAMA_MAX_EXPERTS 8 +#define LLAMA_MAX_EXPERTS 60 // @@ -209,7 +210,9 @@ enum llm_arch { LLM_ARCH_STABLELM, LLM_ARCH_QWEN, LLM_ARCH_QWEN2, + LLM_ARCH_QWEN2MOE, LLM_ARCH_PHI2, + LLM_ARCH_PHI3, LLM_ARCH_PLAMO, LLM_ARCH_CODESHELL, LLM_ARCH_ORION, @@ -220,6 +223,8 @@ enum llm_arch { LLM_ARCH_MAMBA, LLM_ARCH_XVERSE, LLM_ARCH_COMMAND_R, + LLM_ARCH_DBRX, + LLM_ARCH_OLMO, LLM_ARCH_UNKNOWN, }; @@ -241,7 +246,9 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_STABLELM, "stablelm" }, { LLM_ARCH_QWEN, "qwen" }, { LLM_ARCH_QWEN2, "qwen2" }, + { LLM_ARCH_QWEN2MOE, "qwen2moe" }, { LLM_ARCH_PHI2, "phi2" }, + { LLM_ARCH_PHI3, "phi3" }, { LLM_ARCH_PLAMO, "plamo" }, { LLM_ARCH_CODESHELL, "codeshell" }, { LLM_ARCH_ORION, "orion" }, @@ -252,6 +259,8 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_MAMBA, "mamba" }, { LLM_ARCH_XVERSE, "xverse" }, { LLM_ARCH_COMMAND_R, "command-r" }, + { LLM_ARCH_DBRX, "dbrx" }, + { LLM_ARCH_OLMO, "olmo" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -308,6 +317,7 @@ enum llm_kv { LLM_KV_SSM_TIME_STEP_RANK, LLM_KV_TOKENIZER_MODEL, + LLM_KV_TOKENIZER_PRE, LLM_KV_TOKENIZER_LIST, LLM_KV_TOKENIZER_TOKEN_TYPE, LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, @@ -325,6 +335,10 @@ enum llm_kv { LLM_KV_TOKENIZER_ADD_PREFIX, LLM_KV_TOKENIZER_HF_JSON, LLM_KV_TOKENIZER_RWKV, + LLM_KV_TOKENIZER_PREFIX_ID, + LLM_KV_TOKENIZER_SUFFIX_ID, + LLM_KV_TOKENIZER_MIDDLE_ID, + LLM_KV_TOKENIZER_EOT_ID, }; static const std::map LLM_KV_NAMES = { @@ -380,6 +394,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" }, { LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" }, + { LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" }, { LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" }, { LLM_KV_TOKENIZER_TOKEN_TYPE, "tokenizer.ggml.token_type" }, { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, "tokenizer.ggml.token_type_count" }, @@ -397,6 +412,10 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" }, { LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" }, { LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" }, + { LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" }, + { LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" }, + { LLM_KV_TOKENIZER_MIDDLE_ID, "tokenizer.ggml.middle_token_id" }, + { LLM_KV_TOKENIZER_EOT_ID, "tokenizer.ggml.eot_token_id" }, }; struct LLM_KV { @@ -427,6 +446,7 @@ enum llm_tensor { LLM_TENSOR_ATTN_OUT_NORM, LLM_TENSOR_ATTN_ROT_EMBD, LLM_TENSOR_FFN_GATE_INP, + LLM_TENSOR_FFN_GATE_INP_SHEXP, LLM_TENSOR_FFN_NORM, LLM_TENSOR_FFN_GATE, LLM_TENSOR_FFN_DOWN, @@ -438,6 +458,9 @@ enum llm_tensor { LLM_TENSOR_FFN_DOWN_EXPS, // merged experts LLM_TENSOR_FFN_GATE_EXPS, LLM_TENSOR_FFN_UP_EXPS, + LLM_TENSOR_FFN_DOWN_SHEXP, + LLM_TENSOR_FFN_GATE_SHEXP, + LLM_TENSOR_FFN_UP_SHEXP, LLM_TENSOR_ATTN_Q_NORM, LLM_TENSOR_ATTN_K_NORM, LLM_TENSOR_LAYER_OUT_NORM, @@ -700,6 +723,8 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, }, }, { @@ -735,6 +760,28 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_QWEN2MOE, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" }, + { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" }, + { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" }, + { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, + }, + }, { LLM_ARCH_PHI2, { @@ -751,6 +798,23 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_PHI3, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_PLAMO, { @@ -934,6 +998,36 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, }, }, + { + LLM_ARCH_DBRX, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + }, + }, + { + LLM_ARCH_OLMO, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1528,12 +1622,12 @@ struct llama_mlock { }; using llama_mlocks = std::vector>; -static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) { +static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) { std::vector result(8, 0); - const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size()); + const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special); if (n_tokens < 0) { result.resize(-n_tokens); - int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size()); + int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special); GGML_ASSERT(check == -n_tokens); } else { @@ -1638,17 +1732,17 @@ static size_t llama_get_device_memory(int device) { #if defined(GGML_USE_CUDA) size_t total; size_t free; - ggml_backend_cuda_get_device_memory(device, &total, &free); + ggml_backend_cuda_get_device_memory(device, &free, &total); return free; #elif defined(GGML_USE_SYCL) size_t total; size_t free; - ggml_backend_sycl_get_device_memory(device, &total, &free); + ggml_backend_sycl_get_device_memory(device, &free, &total); return free; #elif defined(GGML_USE_VULKAN) size_t total; size_t free; - ggml_backend_vk_get_device_memory(device, &total, &free); + ggml_backend_vk_get_device_memory(device, &free, &total); return free; #else return 1; @@ -1690,6 +1784,7 @@ enum e_model { MODEL_4B, MODEL_7B, MODEL_8B, + MODEL_12B, MODEL_13B, MODEL_14B, MODEL_15B, @@ -1705,6 +1800,10 @@ enum e_model { MODEL_MEDIUM, MODEL_LARGE, MODEL_XL, + MODEL_A2_7B, + MODEL_8x7B, + MODEL_8x22B, + MODEL_16x12B, }; static const size_t kiB = 1024; @@ -1888,6 +1987,12 @@ struct llama_layer { struct ggml_tensor * ffn_down_exps; struct ggml_tensor * ffn_up_exps ; + // ff shared expert (shexp) + struct ggml_tensor * ffn_gate_inp_shexp; + struct ggml_tensor * ffn_gate_shexp; + struct ggml_tensor * ffn_down_shexp; + struct ggml_tensor * ffn_up_shexp; + // ff bias struct ggml_tensor * ffn_down_b; // b2 struct ggml_tensor * ffn_up_b; // b3 @@ -2552,7 +2657,8 @@ struct llama_vocab { ttype type; }; - enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM; + enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM; + enum llama_vocab_pre_type type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; std::unordered_map token_to_id; std::vector id_to_token; @@ -2574,10 +2680,10 @@ struct llama_vocab { int special_add_eos = -1; // -1 unknown, 1 add, 0 don't add. id linefeed_id = 13; - id special_prefix_id = 32007; - id special_middle_id = 32009; - id special_suffix_id = 32008; - id special_eot_id = 32010; + id special_prefix_id = -1; + id special_suffix_id = -1; + id special_middle_id = -1; + id special_eot_id = -1; // TODO: move above after "eos_id", and here add "file separator" token bool add_space_prefix = true; @@ -3670,6 +3776,7 @@ namespace GGUFMeta { case LLAMA_KV_OVERRIDE_TYPE_BOOL: return "bool"; case LLAMA_KV_OVERRIDE_TYPE_INT: return "int"; case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float"; + case LLAMA_KV_OVERRIDE_TYPE_STR: return "str"; } return "unknown"; } @@ -3681,13 +3788,16 @@ namespace GGUFMeta { __func__, override_type_to_str(ovrd->tag), ovrd->key); switch (ovrd->tag) { case LLAMA_KV_OVERRIDE_TYPE_BOOL: { - LLAMA_LOG_INFO("%s\n", ovrd->bool_value ? "true" : "false"); + LLAMA_LOG_INFO("%s\n", ovrd->val_bool ? "true" : "false"); } break; case LLAMA_KV_OVERRIDE_TYPE_INT: { - LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->int_value); + LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->val_i64); } break; case LLAMA_KV_OVERRIDE_TYPE_FLOAT: { - LLAMA_LOG_INFO("%.6f\n", ovrd->float_value); + LLAMA_LOG_INFO("%.6f\n", ovrd->val_f64); + } break; + case LLAMA_KV_OVERRIDE_TYPE_STR: { + LLAMA_LOG_INFO("%s\n", ovrd->val_str); } break; default: // Shouldn't be possible to end up here, but just in case... @@ -3706,7 +3816,7 @@ namespace GGUFMeta { static typename std::enable_if::value, bool>::type try_override(OT & target, const struct llama_model_kv_override * ovrd) { if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, ovrd)) { - target = ovrd->bool_value; + target = ovrd->val_bool; return true; } return false; @@ -3716,7 +3826,7 @@ namespace GGUFMeta { static typename std::enable_if::value && std::is_integral::value, bool>::type try_override(OT & target, const struct llama_model_kv_override * ovrd) { if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, ovrd)) { - target = ovrd->int_value; + target = ovrd->val_i64; return true; } return false; @@ -3726,7 +3836,7 @@ namespace GGUFMeta { static typename std::enable_if::value, bool>::type try_override(T & target, const struct llama_model_kv_override * ovrd) { if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, ovrd)) { - target = ovrd->float_value; + target = ovrd->val_f64; return true; } return false; @@ -3735,12 +3845,11 @@ namespace GGUFMeta { template static typename std::enable_if::value, bool>::type try_override(T & target, const struct llama_model_kv_override * ovrd) { - (void)target; - (void)ovrd; - if (!ovrd) { return false; } - // Currently, we should never end up here so it would be a bug if we do. - throw std::runtime_error(format("Unsupported attempt to override string type for metadata key %s\n", - ovrd ? ovrd->key : "NULL")); + if (validate_override(LLAMA_KV_OVERRIDE_TYPE_STR, ovrd)) { + target = ovrd->val_str; + return true; + } + return false; } static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override * ovrd = nullptr) { @@ -3773,6 +3882,7 @@ struct llama_model_loader { size_t n_bytes = 0; bool use_mmap = false; + bool check_tensors; llama_files files; llama_ftype ftype; @@ -3787,9 +3897,13 @@ struct llama_model_loader { ggml_tensor * tensor; - llama_tensor_weight(uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) { + llama_tensor_weight(const llama_file * file, uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) { const int tensor_idx = gguf_find_tensor(gguf_ctx, name); offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx); + + if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size) { + throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", name)); + } } }; std::vector weights; @@ -3802,7 +3916,7 @@ struct llama_model_loader { std::string arch_name; LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN); - llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) { + llama_model_loader(const std::string & fname, bool use_mmap, bool check_tensors, const struct llama_model_kv_override * param_overrides_p) { int trace = 0; if (getenv("LLAMA_TRACE")) { trace = atoi(getenv("LLAMA_TRACE")); @@ -3828,15 +3942,15 @@ struct llama_model_loader { get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false); llm_kv = LLM_KV(llm_arch_from_string(arch_name)); + files.emplace_back(new llama_file(fname.c_str(), "rb")); + contexts.emplace_back(ctx); + // Save tensors data offset of the main file. // For subsidiary files, `meta` tensor data offset must not be used, // so we build a unified tensors index for weights. for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { - weights.emplace_back(0, cur->name, meta, cur); + weights.emplace_back(files.back().get(), 0, cur->name, meta, cur); } - files.emplace_back(new llama_file(fname.c_str(), "rb")); - contexts.emplace_back(ctx); - uint16_t n_split = 0; get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false); @@ -3870,13 +3984,14 @@ struct llama_model_loader { throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path)); } - // Save tensors data offset info of the shard. - for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { - weights.emplace_back(idx, cur->name, ctx_gguf, cur); - } files.emplace_back(new llama_file(split_path, "rb")); contexts.emplace_back(ctx); + // Save tensors data offset info of the shard. + for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { + weights.emplace_back(files.back().get(), idx, cur->name, ctx_gguf, cur); + } + gguf_free(ctx_gguf); } @@ -3898,9 +4013,17 @@ struct llama_model_loader { fver = (enum llama_fver) gguf_get_version(meta); + std::set tensor_names; for (auto & w : weights) { n_elements += ggml_nelements(w.tensor); n_bytes += ggml_nbytes(w.tensor); + // make sure there is no duplicated tensor names + const std::string name(w.tensor->name); + auto found = tensor_names.find(name); + if (found != tensor_names.end()) { + throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", w.tensor->name)); + } + tensor_names.insert(name); } LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n", @@ -4006,6 +4129,7 @@ struct llama_model_loader { } this->use_mmap = use_mmap; + this->check_tensors = check_tensors; } ~llama_model_loader() { @@ -4085,6 +4209,10 @@ struct llama_model_loader { return nullptr; } + const llama_tensor_weight * get_weight(int i) const { + return get_weight(get_tensor_name(i)); + } + const llama_tensor_weight & require_weight(const char * name) const { const llama_tensor_weight * weight = get_weight(name); if (!weight) { @@ -4260,6 +4388,10 @@ struct llama_model_loader { file->seek(w.offs, SEEK_SET); file->read_raw(cur->data, ggml_nbytes(cur)); } + + if (check_tensors && !ggml_validate_row_data(cur->type, cur->data, ggml_nbytes(cur))) { + throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur))); + } } size_t size_done = 0; @@ -4276,6 +4408,8 @@ struct llama_model_loader { GGML_ASSERT(size_data != 0 && "call init_mappings() first"); std::vector> read_buf; + std::vector>> validation_result; + for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) { const auto * weight = get_weight(ggml_get_name(cur)); if (weight == nullptr) { @@ -4297,37 +4431,66 @@ struct llama_model_loader { if (bufs_mmap.count(weight->idx)) { buf_mmap = bufs_mmap.at(weight->idx); } + uint8_t * data = (uint8_t *) mapping->addr + weight->offs; + + if (check_tensors) { + validation_result.emplace_back(std::async(std::launch::async, [cur, data, n_size] { + return std::make_pair(cur, ggml_validate_row_data(cur->type, data, n_size)); + })); + } + GGML_ASSERT(buf_mmap || cur->data); // either we have a buffer to allocate the tensor in, or it is already allocated if (buf_mmap && cur->data == nullptr) { - ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + weight->offs); + ggml_backend_tensor_alloc(buf_mmap, cur, data); if (lmlocks) { const auto & lmlock = lmlocks->at(weight->idx); - lmlock->grow_to(weight->offs + ggml_nbytes(cur)); + lmlock->grow_to(weight->offs + n_size); } auto & mmap_used = mmaps_used[weight->idx]; mmap_used.first = std::min(mmap_used.first, weight->offs); mmap_used.second = std::max(mmap_used.second, weight->offs + n_size); } else { - ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + weight->offs, 0, n_size); + ggml_backend_tensor_set(cur, data, 0, n_size); } } else { GGML_ASSERT(weight->idx < files.size()); const auto & file = files.at(weight->idx); if (ggml_backend_buffer_is_host(cur->buffer)) { file->seek(weight->offs, SEEK_SET); - file->read_raw(cur->data, ggml_nbytes(cur)); + file->read_raw(cur->data, n_size); + if (check_tensors) { + validation_result.emplace_back(std::async(std::launch::async, [cur, n_size] { + return std::make_pair(cur, ggml_validate_row_data(cur->type, cur->data, n_size)); + })); + } } else { - read_buf.resize(ggml_nbytes(cur)); + read_buf.resize(n_size); file->seek(weight->offs, SEEK_SET); - file->read_raw(read_buf.data(), ggml_nbytes(cur)); + file->read_raw(read_buf.data(), n_size); ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size); + if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) { + throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur))); + } } } size_done += n_size; } + // check validation results + bool validation_failed = false; + for (auto & future : validation_result) { + auto result = future.get(); + if (!result.second) { + LLAMA_LOG_ERROR("%s: tensor '%s' has invalid data\n", __func__, ggml_get_name(result.first)); + validation_failed = true; + } + } + if (validation_failed) { + throw std::runtime_error("found tensors with invalid data"); + } + // check if this is the last call and do final cleanup if (size_done >= size_data) { // unmap offloaded tensors and metadata @@ -4433,6 +4596,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_3B: return "3B"; case MODEL_7B: return "7B"; case MODEL_8B: return "8B"; + case MODEL_12B: return "12B"; case MODEL_13B: return "13B"; case MODEL_14B: return "14B"; case MODEL_15B: return "15B"; @@ -4448,6 +4612,10 @@ static const char * llama_model_type_name(e_model type) { case MODEL_MEDIUM: return "0.4B"; case MODEL_LARGE: return "0.8B"; case MODEL_XL: return "1.5B"; + case MODEL_A2_7B: return "A2.7B"; + case MODEL_8x7B: return "8x7B"; + case MODEL_8x22B: return "8x22B"; + case MODEL_16x12B: return "16x12B"; default: return "?B"; } } @@ -4562,15 +4730,23 @@ static void llm_load_hparams( { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - switch (hparams.n_layer) { - case 22: model.type = e_model::MODEL_1B; break; - case 26: model.type = e_model::MODEL_3B; break; - case 32: model.type = e_model::MODEL_7B; break; - case 40: model.type = e_model::MODEL_13B; break; - case 48: model.type = e_model::MODEL_34B; break; - case 60: model.type = e_model::MODEL_30B; break; - case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break; - default: model.type = e_model::MODEL_UNKNOWN; + if (hparams.n_expert == 8) { + switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_8x7B; break; + case 56: model.type = e_model::MODEL_8x22B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } else { + switch (hparams.n_layer) { + case 22: model.type = e_model::MODEL_1B; break; + case 26: model.type = e_model::MODEL_3B; break; + case 32: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_7B : e_model::MODEL_8B; break; // LLaMa 8B v3 uses GQA + case 40: model.type = e_model::MODEL_13B; break; + case 48: model.type = e_model::MODEL_34B; break; + case 60: model.type = e_model::MODEL_30B; break; + case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } } } break; case LLM_ARCH_MINICPM: @@ -4712,6 +4888,7 @@ static void llm_load_hparams( switch (hparams.n_layer) { case 24: model.type = e_model::MODEL_1B; break; case 32: model.type = e_model::MODEL_3B; break; + case 40: model.type = e_model::MODEL_12B; break; default: model.type = e_model::MODEL_UNKNOWN; } } break; @@ -4736,10 +4913,28 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_QWEN2MOE: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + switch (hparams.n_layer) { + case 24: model.type = e_model::MODEL_A2_7B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; case LLM_ARCH_PHI2: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + switch (hparams.n_layer) { + case 24: model.type = e_model::MODEL_1B; break; + case 32: model.type = e_model::MODEL_3B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; + case LLM_ARCH_PHI3: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + switch (hparams.n_layer) { case 24: model.type = e_model::MODEL_1B; break; case 32: model.type = e_model::MODEL_3B; break; @@ -4861,6 +5056,28 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_DBRX: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv); + + switch (hparams.n_layer) { + case 40: model.type = e_model::MODEL_16x12B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; + case LLM_ARCH_OLMO: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false); + + switch (hparams.n_layer) { + case 22: model.type = e_model::MODEL_1B; break; + case 32: model.type = e_model::MODEL_7B; break; + case 80: model.type = e_model::MODEL_70B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -4890,11 +5107,13 @@ static void llm_load_vocab( // determine vocab type { - std::string tokenizer_name; + std::string tokenizer_model; + std::string tokenizer_pre; - ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_name); + ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model); + ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false); - if (tokenizer_name == "no_vocab") { + if (tokenizer_model == "no_vocab") { vocab.type = LLAMA_VOCAB_TYPE_NONE; // default special tokens @@ -4908,7 +5127,7 @@ static void llm_load_vocab( vocab.linefeed_id = -1; return; - } else if (tokenizer_name == "llama") { + } else if (tokenizer_model == "llama") { vocab.type = LLAMA_VOCAB_TYPE_SPM; // default special tokens @@ -4920,13 +5139,60 @@ static void llm_load_vocab( vocab.special_cls_id = -1; vocab.special_mask_id = -1; + // For Fill-In-the-Middle (FIM)/infill models which where converted + // prior to support of FIM special tokens in GGUF, the following + // will allow those models to continue to work. The general names + // of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and + // CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once + // new versions of these models have been published. + std::string gen_name; + ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false); + + std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(), + [](unsigned char c){ return std::tolower(c); }); + + if (gen_name.find("code") != std::string::npos) { + if (model.arch == LLM_ARCH_LLAMA) { + vocab.special_prefix_id = 32007; + vocab.special_suffix_id = 32008; + vocab.special_middle_id = 32009; + vocab.special_eot_id = 32010; + } else if (model.arch == LLM_ARCH_GEMMA) { + vocab.special_prefix_id = 67; + vocab.special_suffix_id = 69; + vocab.special_middle_id = 68; + // TODO: this is not EOT, it is "file separator" token, needs fix + // https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572 + //vocab.special_eot_id = 70; + vocab.special_eot_id = 107; + } + } + const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str()); if (add_space_prefix_keyidx != -1) { vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx); } // The default value of add_space_prefix is true. - } else if (tokenizer_name == "gpt2") { - vocab.type = LLAMA_VOCAB_TYPE_BPE; + } else if (tokenizer_model == "bert") { + vocab.type = LLAMA_VOCAB_TYPE_WPM; + // default special tokens + vocab.special_bos_id = -1; + vocab.special_eos_id = -1; + vocab.special_unk_id = 100; + vocab.special_sep_id = 102; + vocab.special_pad_id = 0; + vocab.special_cls_id = 101; + vocab.special_mask_id = 103; + vocab.add_space_prefix = false; + } else { + if (tokenizer_model == "gpt2") { + vocab.type = LLAMA_VOCAB_TYPE_BPE; + } else { + LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str()); + LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__); + vocab.type = LLAMA_VOCAB_TYPE_SPM; + return; + } // read bpe merges and populate bpe ranks const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str()); if (merges_keyidx == -1) { @@ -4960,23 +5226,50 @@ static void llm_load_vocab( vocab.special_pad_id = -1; vocab.special_cls_id = -1; vocab.special_mask_id = -1; - } else if (tokenizer_name == "bert") { - vocab.type = LLAMA_VOCAB_TYPE_WPM; + } - // default special tokens - vocab.special_bos_id = -1; - vocab.special_eos_id = -1; - vocab.special_unk_id = 100; - vocab.special_sep_id = 102; - vocab.special_pad_id = 0; - vocab.special_cls_id = 101; - vocab.special_mask_id = 103; - vocab.add_space_prefix = false; + // for now, only BPE models have pre-tokenizers + if (vocab.type == LLAMA_VOCAB_TYPE_BPE) { + if (tokenizer_pre.empty()) { + LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__); + LLAMA_LOG_WARN("%s: \n", __func__); + LLAMA_LOG_WARN("%s: ************************************ \n", __func__); + LLAMA_LOG_WARN("%s: GENERATION QUALITY WILL BE DEGRADED! \n", __func__); + LLAMA_LOG_WARN("%s: CONSIDER REGENERATING THE MODEL \n", __func__); + LLAMA_LOG_WARN("%s: ************************************ \n", __func__); + LLAMA_LOG_WARN("%s: \n", __func__); + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; + } else if ( + tokenizer_pre == "default") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; + } else if ( + tokenizer_pre == "llama3" || + tokenizer_pre == "llama-v3" || + tokenizer_pre == "llama-bpe") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3; + } else if ( + tokenizer_pre == "deepseek-llm") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM; + } else if ( + tokenizer_pre == "deepseek-coder") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER; + } else if ( + tokenizer_pre == "falcon") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON; + } else if ( + tokenizer_pre == "mpt") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MPT; + } else if ( + tokenizer_pre == "starcoder") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STARCODER; + } else if ( + tokenizer_pre == "gpt-2") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2; + } else { + throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); + } } else { - LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str()); - LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__); - - vocab.type = LLAMA_VOCAB_TYPE_SPM; + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; } } @@ -5033,14 +5326,19 @@ static void llm_load_vocab( // special tokens { const std::vector> special_token_types = { - { LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id }, - { LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id }, - { LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id }, - { LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id }, - { LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id }, - { LLM_KV_TOKENIZER_CLS_ID, vocab.special_cls_id }, - { LLM_KV_TOKENIZER_MASK_ID, vocab.special_mask_id }, + { LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id }, + { LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id }, + { LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id }, + { LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id }, + { LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id }, + { LLM_KV_TOKENIZER_CLS_ID, vocab.special_cls_id }, + { LLM_KV_TOKENIZER_MASK_ID, vocab.special_mask_id }, + { LLM_KV_TOKENIZER_PREFIX_ID, vocab.special_prefix_id }, + { LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_suffix_id }, + { LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_middle_id }, + { LLM_KV_TOKENIZER_EOT_ID, vocab.special_eot_id }, }; + for (const auto & it : special_token_types) { const std::string & key = kv(std::get<0>(it)); int32_t & id = std::get<1>(it); @@ -5055,7 +5353,6 @@ static void llm_load_vocab( } else { id = new_id; } - } // Handle add_bos_token and add_eos_token @@ -5069,6 +5366,28 @@ static void llm_load_vocab( vocab.special_add_eos = int(temp); } } + + // find EOT token: "<|eot_id|>", "<|im_end|>", "", etc. + // + // TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOT_ID + // for now, we apply this workaround to find the EOT token based on its text + if (vocab.special_eot_id == -1) { + for (const auto & t : vocab.token_to_id) { + if ( + // TODO: gemma "" is exported as a normal token, so the following check does not work + // need to fix convert script + //vocab.id_to_token[t.second].type == LLAMA_TOKEN_TYPE_CONTROL && + (t.first == "<|eot_id|>" || + t.first == "<|im_end|>" || + t.first == "<|end|>" || + t.first == "" + ) + ) { + vocab.special_eot_id = t.second; + break; + } + } + } } // build special tokens cache @@ -5231,14 +5550,19 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, model.name.c_str()); // special tokens - if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); } - if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); } - if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); } - if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); } - if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); } - if (vocab.special_cls_id != -1) { LLAMA_LOG_INFO( "%s: CLS token = %d '%s'\n", __func__, vocab.special_cls_id, vocab.id_to_token[vocab.special_cls_id].text.c_str() ); } - if (vocab.special_mask_id != -1) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); } - if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); } + if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); } + if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); } + if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); } + if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); } + if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); } + if (vocab.special_cls_id != -1) { LLAMA_LOG_INFO( "%s: CLS token = %d '%s'\n", __func__, vocab.special_cls_id, vocab.id_to_token[vocab.special_cls_id].text.c_str() ); } + if (vocab.special_mask_id != -1) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); } + + if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); } + if (vocab.special_prefix_id != -1) { LLAMA_LOG_INFO( "%s: PRE token = %d '%s'\n", __func__, vocab.special_prefix_id, vocab.id_to_token[vocab.special_prefix_id].text.c_str() ); } + if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); } + if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); } + if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); } } // Returns false if cancelled by progress_callback @@ -5256,6 +5580,13 @@ static bool llm_load_tensors( auto & hparams = model.hparams; +#ifdef GGML_USE_SYCL + // disable MoE with SYCL until mul_mat_id is updated + if (hparams.n_expert > 0) { + n_gpu_layers = 0; + } +#endif + model.split_mode = split_mode; model.main_gpu = main_gpu; model.n_gpu_layers = n_gpu_layers; @@ -5353,7 +5684,7 @@ static bool llm_load_tensors( size_t ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output // for moe merged tensors - ctx_size += ggml_tensor_overhead()*hparams.n_expert*n_layer; + ctx_size += ggml_tensor_overhead()*n_layer*3; std::map ctx_map; for (auto & it : buft_layer_count) { @@ -5549,6 +5880,39 @@ static bool llm_load_tensors( layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); } } break; + case LLM_ARCH_DBRX: + { + if (n_expert == 0) { + throw std::runtime_error("DBRX model cannot have zero experts"); + } + + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); + + layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}); + layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}); + layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); + } + } break; case LLM_ARCH_BAICHUAN: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); @@ -5863,8 +6227,13 @@ static bool llm_load_tensors( layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false); layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false); - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + // optional q and k layernorms, present in StableLM 2 12B + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, false); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, false); + + // optional FFN norm, not present in StableLM 2 12B which uses parallel residual + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, false); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false); layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); @@ -5907,7 +6276,13 @@ static bool llm_load_tensors( // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } } for (int i = 0; i < n_layer; ++i) { @@ -5935,6 +6310,54 @@ static bool llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; + case LLM_ARCH_QWEN2MOE: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + // optional bias tensors + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + + layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + + GGML_ASSERT(hparams.n_expert > 0); + GGML_ASSERT(hparams.n_expert_used > 0); + + // MoE branch + auto n_ff_exp = n_ff / hparams.n_expert_used; + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); + layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}); + layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); + + // Shared expert branch + layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}); + layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff}); + layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff, n_embd}); + layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff}); + } + } break; case LLM_ARCH_PHI2: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); @@ -5980,6 +6403,33 @@ static bool llm_load_tensors( layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; + case LLM_ARCH_PHI3: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }); + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context* ctx_layer = ctx_for_layer(i); + ggml_context* ctx_split = ctx_for_layer_split(i); + + auto& layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }); + + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, false); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }); + } + } break; case LLM_ARCH_PLAMO: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); @@ -6328,6 +6778,37 @@ static bool llm_load_tensors( layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + } + } break; + case LLM_ARCH_OLMO: // adapted from LLM_ARCH_LLAMA with norm params removed + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); @@ -6483,7 +6964,7 @@ static bool llm_load_tensors( // Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) { try { - llama_model_loader ml(fname, params.use_mmap, params.kv_overrides); + llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides); model.hparams.vocab_only = params.vocab_only; @@ -6768,6 +7249,100 @@ static struct ggml_tensor * llm_build_ffn( return cur; } +static struct ggml_tensor * llm_build_moe_ffn( + struct ggml_context * ctx, + struct ggml_tensor * cur, + struct ggml_tensor * gate_inp, + struct ggml_tensor * up_exps, + struct ggml_tensor * gate_exps, + struct ggml_tensor * down_exps, + int64_t n_expert, + int64_t n_expert_used, + llm_ffn_op_type type_op, + bool norm_w, + const llm_build_cb & cb, + int il) { + int64_t n_embd = cur->ne[0]; + int64_t n_tokens = cur->ne[1]; + + ggml_tensor * logits = ggml_mul_mat(ctx, gate_inp, cur); // [n_expert, n_tokens] + cb(logits, "ffn_moe_logits", il); + + ggml_tensor * probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens] + cb(probs, "ffn_moe_probs", il); + + // select experts + ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_expert_used); // [n_expert_used, n_tokens] + cb(selected_experts->src[0], "ffn_moe_argsort", il); + cb(selected_experts, "ffn_moe_topk", il); + + ggml_tensor * weights = ggml_get_rows(ctx, + ggml_reshape_3d(ctx, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens] + cb(weights, "ffn_moe_weights", il); + + if (norm_w) { + weights = ggml_reshape_2d(ctx, weights, n_expert_used, n_tokens); + + ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights); // [1, n_tokens] + cb(weights_sum, "ffn_moe_weights_sum", il); + + weights = ggml_div(ctx, weights, weights_sum); // [n_expert_used, n_tokens] + cb(weights, "ffn_moe_weights_norm", il); + + weights = ggml_reshape_3d(ctx, weights, 1, n_expert_used, n_tokens); + } + + cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens); + ggml_tensor * up = ggml_mul_mat_id(ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens] + cb(up, "ffn_moe_up", il); + + ggml_tensor * gate = ggml_mul_mat_id(ctx, gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens] + cb(gate, "ffn_moe_gate", il); + + switch (type_op) { + case LLM_FFN_SILU: + { + gate = ggml_silu(ctx, gate); + cb(gate, "ffn_moe_silu", il); + } break; + case LLM_FFN_GELU: + { + gate = ggml_gelu(ctx, gate); + cb(gate, "ffn_moe_gelu", il); + } break; + default: + GGML_ASSERT(false); + } + + ggml_tensor * par = ggml_mul(ctx, up, gate); // [n_ff, n_expert_used, n_tokens] + cb(par, "ffn_moe_gate_par", il); + + ggml_tensor * experts = ggml_mul_mat_id(ctx, down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens] + cb(experts, "ffn_moe_down", il); + + experts = ggml_mul(ctx, experts, weights); + + // aggregate experts + ggml_tensor * moe_out = nullptr; + for (int i = 0; i < n_expert_used; ++i) { + ggml_tensor * cur_expert = ggml_view_2d(ctx, experts, n_embd, n_tokens, + experts->nb[2], i*experts->nb[1]); + + if (i == 0) { + moe_out = cur_expert; + } else { + moe_out = ggml_add(ctx, moe_out, cur_expert); + } + } + + if (n_expert_used == 1) { + // avoid returning a non-contiguous tensor + moe_out = ggml_cont(ctx, moe_out); + } + + return moe_out; +} + // if max_alibi_bias > 0 then apply ALiBi static struct ggml_tensor * llm_build_kqv( struct ggml_context * ctx, @@ -6806,7 +7381,7 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); cb(kq, "kq", il); - if (model.arch == LLM_ARCH_PHI2) { + if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) { // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847 ggml_mul_mat_set_prec(kq, GGML_PREC_F32); @@ -7294,62 +7869,15 @@ struct llm_build_context { LLM_NORM_RMS, cb, il); cb(cur, "ffn_norm", il); - ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts] - cb(logits, "ffn_moe_logits", il); - - ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts] - cb(probs, "ffn_moe_probs", il); - - // select experts - ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok] - cb(selected_experts->src[0], "ffn_moe_argsort", il); - - ggml_tensor * weights = ggml_get_rows(ctx0, - ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts); - cb(weights, "ffn_moe_weights", il); - - weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok] - - ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights); - cb(weights_sum, "ffn_moe_weights_sum", il); - - weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok] - cb(weights, "ffn_moe_weights_norm", il); - - // compute expert outputs - ggml_tensor * moe_out = nullptr; - - for (int i = 0; i < n_expert_used; ++i) { - ggml_tensor * cur_expert; - - ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur); - cb(cur_up, "ffn_moe_up", il); - - ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur); - cb(cur_gate, "ffn_moe_gate", il); - - cur_gate = ggml_silu(ctx0, cur_gate); - cb(cur_gate, "ffn_moe_silu", il); - - cur_expert = ggml_mul(ctx0, cur_up, cur_gate); - cb(cur_expert, "ffn_moe_gate_par", il); - - cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd] - cb(cur_expert, "ffn_moe_down", il); - - cur_expert = ggml_mul(ctx0, cur_expert, - ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0])); - cb(cur_expert, "ffn_moe_weighted", il); - - if (i == 0) { - moe_out = cur_expert; - } else { - moe_out = ggml_add(ctx0, moe_out, cur_expert); - cb(moe_out, "ffn_moe_out", il); - } - } - - cur = moe_out; + cur = llm_build_moe_ffn(ctx0, cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + cb, il); + cb(cur, "ffn_moe_out", il); } cur = ggml_add(ctx0, cur, ffn_inp); @@ -7828,63 +8356,15 @@ struct llm_build_context { LLM_NORM_RMS, cb, il); cb(cur, "ffn_norm", il); - ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts] - cb(logits, "ffn_moe_logits", il); - - ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts] - cb(probs, "ffn_moe_probs", il); - - // select experts - ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok] - cb(selected_experts->src[0], "ffn_moe_argsort", il); - - ggml_tensor * weights = ggml_get_rows(ctx0, - ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts); - cb(weights, "ffn_moe_weights", il); - - weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok] - - ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights); - cb(weights_sum, "ffn_moe_weights_sum", il); - - weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok] - cb(weights, "ffn_moe_weights_norm", il); - - // compute expert outputs - ggml_tensor * moe_out = nullptr; - - for (int i = 0; i < n_expert_used; ++i) { - ggml_tensor * cur_expert; - - ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur); - cb(cur_up, "ffn_moe_up", il); - - ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur); - cb(cur_gate, "ffn_moe_gate", il); - - //GeLU - cur_gate = ggml_gelu(ctx0, cur_gate); - cb(cur_gate, "ffn_moe_gelu", il); - - cur_expert = ggml_mul(ctx0, cur_up, cur_gate); - cb(cur_expert, "ffn_moe_gate_par", il); - - cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd] - cb(cur_expert, "ffn_moe_down", il); - - cur_expert = ggml_mul(ctx0, cur_expert, - ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0])); - cb(cur_expert, "ffn_moe_weighted", il); - - if (i == 0) { - moe_out = cur_expert; - } else { - moe_out = ggml_add(ctx0, moe_out, cur_expert); - cb(moe_out, "ffn_moe_out", il); - } - } - - cur = moe_out; + cur = llm_build_moe_ffn(ctx0, cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + n_expert, n_expert_used, + LLM_FFN_GELU, true, + cb, il); + cb(cur, "ffn_moe_out", il); // Grok // if layer_out_norm is present then apply it before adding the input @@ -7896,7 +8376,6 @@ struct llm_build_context { cb(cur, "layer_out_norm", il); } - cur = ggml_add(ctx0, cur, ffn_inp); cb(cur, "ffn_out", il); @@ -7932,6 +8411,134 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_dbrx() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + struct ggml_tensor * Qcur = nullptr; + struct ggml_tensor * Kcur = nullptr; + struct ggml_tensor * Vcur = nullptr; + + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv); + cb(cur, "wqkv_clamped", il); + + Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + // MoE branch + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].attn_out_norm, NULL, + LLM_NORM, cb, il); + cb(cur, "attn_out_norm", il); + + cur = llm_build_moe_ffn(ctx0, cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + cb, il); + cb(cur, "ffn_moe_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); + if (layer_dir != nullptr) { + cur = ggml_add(ctx0, cur, layer_dir); + } + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + struct ggml_cgraph * build_starcoder() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -8784,7 +9391,7 @@ struct llm_build_context { struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; + // norm cur = llm_build_norm(ctx0, inpL, hparams, @@ -8793,6 +9400,8 @@ struct llm_build_context { LLM_NORM, cb, il); cb(cur, "attn_norm", il); + struct ggml_tensor * inpSA = cur; + // self-attention { // compute Q and K and RoPE them @@ -8817,15 +9426,36 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + cb(Qcur, "Qcur", il); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + cb(Kcur, "Kcur", il); + + if (model.layers[il].attn_q_norm) { + Qcur = llm_build_norm(ctx0, Qcur, hparams, + model.layers[il].attn_q_norm, + NULL, + LLM_NORM, cb, il); + cb(Qcur, "Qcur", il); + } + if (model.layers[il].attn_k_norm) { + Kcur = llm_build_norm(ctx0, Kcur, hparams, + model.layers[il].attn_k_norm, + NULL, + LLM_NORM, cb, il); + cb(Kcur, "Kcur", il); + } + + Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8840,20 +9470,25 @@ struct llm_build_context { // skip computing output for unused tokens struct ggml_tensor * inp_out_ids = build_inp_out_ids(); cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); } - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); cb(ffn_inp, "ffn_inp", il); // feed-forward network { - cur = llm_build_norm(ctx0, ffn_inp, hparams, - model.layers[il].ffn_norm, - model.layers[il].ffn_norm_b, - LLM_NORM, cb, il); - cb(cur, "ffn_norm", il); - + if (model.layers[il].ffn_norm) { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + } else { + // parallel residual + cur = inpSA; + } cur = llm_build_ffn(ctx0, cur, model.layers[il].ffn_up, NULL, model.layers[il].ffn_gate, NULL, @@ -9043,12 +9678,6 @@ struct llm_build_context { Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); cb(Vcur, "Vcur", il); - // these nodes are added to the graph together so that they are not reordered - // by doing so, the number of splits in the graph is reduced - ggml_build_forward_expand(gf, Qcur); - ggml_build_forward_expand(gf, Kcur); - ggml_build_forward_expand(gf, Vcur); - Qcur = ggml_rope_custom( ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, @@ -9115,6 +9744,150 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_qwen2moe() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self_attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // MoE branch + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + ggml_tensor * moe_out = + llm_build_moe_ffn(ctx0, cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + n_expert, n_expert_used, + LLM_FFN_SILU, false, + cb, il); + cb(cur, "ffn_moe_out", il); + + // FFN shared expert + { + ggml_tensor * cur_gate_inp = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp_shexp, cur); + cb(cur_gate_inp, "ffn_shexp_gate_inp", il); + + // sigmoid + ggml_tensor * cur_gate = ggml_div(ctx0, ggml_silu(ctx0, cur_gate_inp), cur_gate_inp); + cb(cur_gate, "ffn_shexp_gate", il); + + ggml_tensor * cur_ffn = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up_shexp, NULL, + model.layers[il].ffn_gate_shexp, NULL, + model.layers[il].ffn_down_shexp, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur_ffn, "ffn_shexp", il); + + ggml_tensor * ffn_shexp_out = ggml_mul(ctx0, cur_ffn, cur_gate); + cb(ffn_shexp_out, "ffn_shexp_out", il); + + moe_out = ggml_add(ctx0, moe_out, ffn_shexp_out); + cb(moe_out, "ffn_out", il); + + cur = moe_out; + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + struct ggml_cgraph * build_phi2() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -9232,12 +10005,140 @@ struct llm_build_context { cur = ggml_add(ctx0, cur, model.output_b); cb(cur, "result_output", -1); + ggml_build_forward_expand(gf, cur); + return gf; + } + + struct ggml_cgraph * build_phi3() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + auto residual = inpL; + + // self-attention + { + struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + NULL, + LLM_NORM_RMS, cb, il); + cb(attn_norm_output, "attn_norm", il); + + struct ggml_tensor * Qcur = nullptr; + struct ggml_tensor * Kcur = nullptr; + struct ggml_tensor * Vcur = nullptr; + + if (model.layers[il].wqkv) { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output); + cb(cur, "wqkv", il); + + Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0 * sizeof(float) * (n_embd))); + Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd))); + Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa))); + } + else { + Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq); + Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk); + Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv); + } + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = ggml_rope_custom( + ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head))); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor* inp_out_ids = build_inp_out_ids(); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + residual = ggml_get_rows(ctx0, residual, inp_out_ids); + } + + cur = ggml_add(ctx0, cur, residual); + residual = cur; + + cur = llm_build_norm(ctx0, cur, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + // FF + // special-case: the up and gate tensors are merged into a single tensor + // TOOD: support into llm_build_ffn + { + struct ggml_tensor* up = ggml_mul_mat(ctx0, model.layers[il].ffn_up, cur); + cb(up, "ffn_up", il); + + auto g = ggml_cont(ctx0, ggml_view_2d(ctx0, up, up->ne[0] / 2, up->ne[1], ggml_row_size(up->type, up->ne[0]), 0)); + auto y = ggml_cont(ctx0, ggml_view_2d(ctx0, up, up->ne[0] / 2, up->ne[1], ggml_row_size(up->type, up->ne[0]), up->nb[1] / 2)); + + y = ggml_mul(ctx0, y, ggml_silu(ctx0, g)); + cb(y, "ffn_gate", il); + + auto down = ggml_mul_mat(ctx0, model.layers[il].ffn_down, y); + cb(down, "ffn_down", il); + + cur = down; + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, residual, cur); + cb(cur, "l_out", il); + + inpL = cur; + } + + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, + NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); ggml_build_forward_expand(gf, cur); return gf; } + struct ggml_cgraph * build_plamo() { struct ggml_cgraph * gf = ggml_new_graph(ctx0); @@ -10454,6 +11355,139 @@ struct llm_build_context { return gf; } + + // ref: https://allenai.org/olmo + // based on the original build_llama() function, changes: + // * non-parametric layer norm + // * clamp qkv + // * removed bias + // * removed MoE + struct ggml_cgraph * build_olmo() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + NULL, NULL, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (hparams.f_clamp_kqv > 0.0f) { + Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (hparams.f_clamp_kqv > 0.0f) { + Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (hparams.f_clamp_kqv > 0.0f) { + Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, nullptr, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_norm(ctx0, ffn_inp, hparams, + NULL, NULL, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); + if (layer_dir != nullptr) { + cur = ggml_add(ctx0, cur, layer_dir); + } + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + NULL, NULL, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -10586,10 +11620,18 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_qwen2(); } break; + case LLM_ARCH_QWEN2MOE: + { + result = llm.build_qwen2moe(); + } break; case LLM_ARCH_PHI2: { result = llm.build_phi2(); } break; + case LLM_ARCH_PHI3: + { + result = llm.build_phi3(); + } break; case LLM_ARCH_PLAMO: { result = llm.build_plamo(); @@ -10634,6 +11676,14 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_command_r(); } break; + case LLM_ARCH_DBRX: + { + result = llm.build_dbrx(); + } break; + case LLM_ARCH_OLMO: + { + result = llm.build_olmo(); + } break; default: GGML_ASSERT(false); } @@ -11356,6 +12406,10 @@ static int llama_decode_internal( } } + // Reset state for the next token before backend sync, to allow the CPU activities in the reset to + // overlap with device computation. + ggml_backend_sched_reset(lctx.sched); + return 0; } @@ -11679,7 +12733,7 @@ static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) { } case LLAMA_VOCAB_TYPE_BPE: { GGML_ASSERT(false); - return unicode_utf8_to_byte(token_data.text); + return unicode_utf8_to_byte(token_data.text); // TODO: why is this here after GGML_ASSERT? } case LLAMA_VOCAB_TYPE_WPM: { GGML_ASSERT(false); @@ -11901,7 +12955,79 @@ struct llm_tokenizer_bpe { void tokenize(const std::string & text, std::vector & output) { int final_prev_index = -1; - auto word_collection = bpe_gpt2_preprocess(text); + + std::vector word_collection; + switch (vocab.type) { + case LLAMA_VOCAB_TYPE_BPE: + switch (vocab.type_pre) { + case LLAMA_VOCAB_PRE_TYPE_LLAMA3: + word_collection = unicode_regex_split(text, { + // original regex from tokenizer.json + //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", + + // adapted: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2080233989 + "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", + }); + break; + case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM: + word_collection = unicode_regex_split(text, { + "[\r\n]", + "\\s?[A-Za-zµÀ-ÖØ-öø-ƺƼ-ƿDŽ-ʓʕ-ʯͰ-ͳͶͷͻ-ͽͿΆΈ-ΊΌΎ-ΡΣ-ϵϷ-ҁҊ-ԯԱ-ՖႠ-ჅᎠ-Ᏽᏸ-ᏽᲐ-ᲺᲽ-Ჿᴀ-ᴫᵫ-ᵷᵹ-ᶚḀ-ἕἘ-Ἕἠ-ὅὈ-Ὅὐ-ὗὙὛὝὟ-ώᾀ-ᾴᾶ-ᾼιῂ-ῄῆ-ῌῐ-ΐῖ-Ίῠ-Ῥῲ-ῴῶ-ῼℂℇℊ-ℓℕℙ-ℝℤΩℨK-ℭℯ-ℴℹℼ-ℿⅅ-ⅉⅎↃↄⰀ-ⱻⱾ-ⳤⳫ-ⳮⳲⳳꙀ-ꙭꚀ-ꚛꜢ-ꝯꝱ-ꞇꞋ-ꞎꭰ-ꮿff-stﬓ-ﬗＡ-Ｚａ-ｚ𐐀-𐑏𐒰-𐓓𐓘-𐓻𐲀-𐲲𐳀-𐳲𑢠-𑣟𞤀-𞥃]+", + "\\s?[!-/:-~！-／：-～‘-‟　-。]+", + "\\s+$", + "[一-龥ࠀ-一가-퟿]+", + "\\p{N}+", + }); + break; + case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER: + word_collection = unicode_regex_split(text, { + "[\r\n]", + "\\s?\\p{L}+", + "\\s?\\p{P}+", + "[一-龥ࠀ-一가-퟿]+", + "\\p{N}+", + }); + break; + case LLAMA_VOCAB_PRE_TYPE_FALCON: + word_collection = unicode_regex_split(text, { + "[\\p{P}\\$\\+<=>\\^~\\|]+", + "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", + "\\p{N}+", + "[0-9][0-9][0-9]", + }); + break; + case LLAMA_VOCAB_PRE_TYPE_MPT: + // TODO: MPT pre-tokenization regexes are unknown + // the following are close, but not exact. run the following: + // ./bin/test-tokenizer-0 ../models/ggml-vocab-mpt.gguf + GGML_ASSERT("MPT pre-tokenization regexes are unknown - fixes needed"); + word_collection = unicode_regex_split(text, { + "\\s?\\p{L}+", + "\\s?\\p{P}+", + "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", + }); + break; + case LLAMA_VOCAB_PRE_TYPE_STARCODER: + case LLAMA_VOCAB_PRE_TYPE_GPT2: + word_collection = unicode_regex_split(text, { + "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", + }); + break; + default: + // default regex for BPE tokenization pre-processing + word_collection = unicode_regex_split(text, { + "[\\p{P}\\$\\+<=>\\^~\\|]+", + "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", + "\\p{N}+", + "[0-9][0-9][0-9]", + }); + break; + } + break; + default: + GGML_ASSERT(false); + break; + } symbols_final.clear(); @@ -11958,7 +13084,7 @@ struct llm_tokenizer_bpe { add_new_bigram(bigram.left, left_symbol.next); // right side of current symbol } - // add the fnished tokens to the final list keeping correct order for next and prev + // add the finished tokens to the final list keeping correct order for next and prev for (auto & sym : symbols) { if (sym.n > 0) { sym.prev = final_prev_index; @@ -12028,145 +13154,6 @@ private: work_queue.push(bigram); } - std::vector bpe_gpt2_preprocess(const std::string & text) { - std::vector bpe_words; - std::vector bpe_encoded_words; - - std::string token = ""; - // GPT2 system regex: 's|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+ - bool collecting_numeric = false; - bool collecting_letter = false; - bool collecting_special = false; - bool collecting_whitespace_lookahead = false; - bool collecting = false; - - std::vector text_utf; - text_utf.reserve(text.size()); - bpe_words.reserve(text.size()); - bpe_encoded_words.reserve(text.size()); - - const auto cpts = unicode_cpts_from_utf8(text); - for (size_t i = 0; i < cpts.size(); ++i) - text_utf.emplace_back(unicode_cpt_to_utf8(cpts[i])); - - for (int i = 0; i < (int)text_utf.size(); i++) { - const std::string & utf_char = text_utf[i]; - bool split_condition = false; - int bytes_remain = text_utf.size() - i; - // forward backward lookups - const std::string & utf_char_next = (i + 1 < (int)text_utf.size()) ? text_utf[i + 1] : ""; - const std::string & utf_char_next_next = (i + 2 < (int)text_utf.size()) ? text_utf[i + 2] : ""; - - // handling contractions - if (!split_condition && bytes_remain >= 2) { - // 's|'t|'m|'d - if (utf_char == "\'" && (utf_char_next == "s" || utf_char_next == "t" || utf_char_next == "m" || utf_char_next == "d")) { - split_condition = true; - } - if (split_condition) { - if (token.size()) { - bpe_words.emplace_back(token); // push previous content as token - } - token = utf_char + utf_char_next; - bpe_words.emplace_back(token); - token = ""; - i++; - continue; - } - } - if (!split_condition && bytes_remain >= 3) { - // 're|'ve|'ll - if (utf_char == "\'" && ( - (utf_char_next == "r" && utf_char_next_next == "e") || - (utf_char_next == "v" && utf_char_next_next == "e") || - (utf_char_next == "l" && utf_char_next_next == "l")) - ) { - split_condition = true; - } - if (split_condition) { - // current token + next token can be defined - if (token.size()) { - bpe_words.emplace_back(token); // push previous content as token - } - token = utf_char + utf_char_next + utf_char_next_next; - bpe_words.emplace_back(token); // the contraction - token = ""; - i += 2; - continue; - } - } - - if (!split_condition && !collecting) { - if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) { - collecting_letter = true; - collecting = true; - } - else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) { - collecting_numeric = true; - collecting = true; - } - else if ( - ((unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (unicode_cpt_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) || - (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE) - ) { - collecting_special = true; - collecting = true; - } - else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) { - collecting_whitespace_lookahead = true; - collecting = true; - } - else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) { - split_condition = true; - } - } - else if (!split_condition && collecting) { - if (collecting_letter && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER) { - split_condition = true; - } - else if (collecting_numeric && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) { - split_condition = true; - } - else if (collecting_special && (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) { - split_condition = true; - } - else if (collecting_whitespace_lookahead && (unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) { - split_condition = true; - } - } - - if (utf_char_next == "") { - split_condition = true; // final - token += utf_char; - } - - if (split_condition) { - if (token.size()) { - bpe_words.emplace_back(token); - } - token = utf_char; - collecting = false; - collecting_letter = false; - collecting_numeric = false; - collecting_special = false; - collecting_whitespace_lookahead = false; - } - else { - token += utf_char; - } - } - - for (std::string & word : bpe_words) { - std::string encoded_token = ""; - for (char & c : word) { - encoded_token += unicode_byte_to_utf8(c); - } - bpe_encoded_words.emplace_back(encoded_token); - } - - return bpe_encoded_words; - } - const llama_vocab & vocab; std::vector symbols; @@ -12486,7 +13473,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & } break; case LLAMA_VOCAB_TYPE_BPE: { - if (add_special && vocab.special_add_bos == 1) { + if (add_special && vocab.special_add_bos != 0) { GGML_ASSERT(vocab.special_bos_id != -1); output.push_back(vocab.special_bos_id); } @@ -12698,7 +13685,9 @@ static void llama_grammar_advance_stack( std::vector> & new_stacks) { if (stack.empty()) { - new_stacks.emplace_back(stack); + if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) { + new_stacks.emplace_back(stack); + } return; } @@ -12735,7 +13724,10 @@ static void llama_grammar_advance_stack( } case LLAMA_GRETYPE_CHAR: case LLAMA_GRETYPE_CHAR_NOT: - new_stacks.emplace_back(stack); + if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) { + // only add the stack if it's not a duplicate of one we already have + new_stacks.emplace_back(stack); + } break; default: // end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range @@ -12749,12 +13741,13 @@ static void llama_grammar_advance_stack( // be positioned at a character range (see `llama_grammar_advance_stack`), and // produces the N possible stacks if the given char is accepted at those // positions -std::vector> llama_grammar_accept( +void llama_grammar_accept( const std::vector> & rules, const std::vector> & stacks, - const uint32_t chr) { + const uint32_t chr, + std::vector> & new_stacks) { - std::vector> new_stacks; + new_stacks.clear(); for (const auto & stack : stacks) { if (stack.empty()) { @@ -12773,8 +13766,6 @@ std::vector> llama_grammar_accept( llama_grammar_advance_stack(rules, new_stack, new_stacks); } } - - return new_stacks; } static std::vector llama_grammar_reject_candidates( @@ -12788,6 +13779,7 @@ static std::vector llama_grammar_reject_candidates_for_ const std::vector & candidates) { std::vector rejects; + rejects.reserve(candidates.size()); if (stack.empty()) { for (const auto & tok : candidates) { @@ -12801,6 +13793,8 @@ static std::vector llama_grammar_reject_candidates_for_ const llama_grammar_element * stack_pos = stack.back(); std::vector next_candidates; + next_candidates.reserve(candidates.size()); + for (const auto & tok : candidates) { if (*tok.code_points == 0) { // reached end of full codepoints in token, reject iff it ended in a partial sequence @@ -13386,16 +14380,14 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c GGML_ASSERT(ctx); const int64_t t_start_sample_us = ggml_time_us(); - bool allow_eos = false; + bool allow_eog = false; for (const auto & stack : grammar->stacks) { if (stack.empty()) { - allow_eos = true; + allow_eog = true; break; } } - const llama_token eos = llama_token_eos(&ctx->model); - std::vector, llama_partial_utf8>> candidates_decoded; candidates_decoded.reserve(candidates->size); std::vector candidates_grammar; @@ -13403,9 +14395,10 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c for (size_t i = 0; i < candidates->size; ++i) { const llama_token id = candidates->data[i].id; - const std::string piece = llama_token_to_piece(ctx, id); - if (id == eos) { - if (!allow_eos) { + const std::string piece = llama_token_to_piece(ctx, id, false); + + if (llama_token_is_eog(&ctx->model, id)) { + if (!allow_eog) { candidates->data[i].logit = -INFINITY; } } else if (piece.empty() || piece[0] == 0) { @@ -13568,7 +14561,7 @@ llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_da return result; } -llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) { +llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng) { GGML_ASSERT(ctx); const int64_t t_start_sample_us = ggml_time_us(); @@ -13581,7 +14574,6 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra } std::discrete_distribution<> dist(probs.begin(), probs.end()); - auto & rng = ctx->rng; int idx = dist(rng); llama_token result = candidates->data[idx].id; @@ -13591,10 +14583,14 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra return result; } +llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) { + return llama_sample_token_with_rng(ctx, candidates, ctx->rng); +} + void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) { const int64_t t_start_sample_us = ggml_time_us(); - if (token == llama_token_eos(&ctx->model)) { + if (llama_token_is_eog(&ctx->model, token)) { for (const auto & stack : grammar->stacks) { if (stack.empty()) { return; @@ -13603,13 +14599,15 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar GGML_ASSERT(false); } - const std::string piece = llama_token_to_piece(ctx, token); + const std::string piece = llama_token_to_piece(ctx, token, false); // Note terminating 0 in decoded string const auto decoded = decode_utf8(piece, grammar->partial_utf8); const auto & code_points = decoded.first; + std::vector> tmp_new_stacks; for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { - grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it); + llama_grammar_accept(grammar->rules, grammar->stacks, *it, tmp_new_stacks); + grammar->stacks = tmp_new_stacks; } grammar->partial_utf8 = decoded.second; GGML_ASSERT(!grammar->stacks.empty()); @@ -13743,6 +14741,11 @@ struct llama_beam_search_data { } llama_logit_info logit_info(ctx); std::vector next_tokens = logit_info.top_k(n_beams); + + // Clear the kv slot so that other beams may try different tokens at this position. The llama_decode() + // call in loop() will conclusively fill in the kv slot once the beams converge at this position. + llama_kv_cache_seq_rm(ctx, 0, n_past, -1); + size_t i=0; if (next_beams.size() < n_beams) { for (; next_beams.size() < n_beams ; ++i) { @@ -14242,14 +15245,20 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n } static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector & workers, const int nthread) { + if (nthread < 2) { + // single-thread + size_t new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix); + if (!ggml_validate_row_data(new_type, new_data, new_size)) { + throw std::runtime_error("quantized data validation failed"); + } + return new_size; + } + std::mutex mutex; int64_t counter = 0; size_t new_size = 0; - if (nthread < 2) { - // single-thread - return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix); - } - auto compute = [&mutex, &counter, &new_size, new_type, f32_data, new_data, chunk_size, + bool valid = true; + auto compute = [&mutex, &counter, &new_size, &valid, new_type, f32_data, new_data, chunk_size, nrows, n_per_row, imatrix]() { const int64_t nrows_per_chunk = chunk_size / n_per_row; size_t local_size = 0; @@ -14264,7 +15273,17 @@ static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const floa } lock.unlock(); const int64_t this_nrow = std::min(nrows - first_row, nrows_per_chunk); - local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix); + size_t this_size = ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix); + local_size += this_size; + + // validate the quantized data + const size_t row_size = ggml_row_size(new_type, n_per_row); + void * this_data = (char *) new_data + first_row * row_size; + if (!ggml_validate_row_data(new_type, this_data, this_size)) { + std::unique_lock lock(mutex); + valid = false; + break; + } } }; for (int it = 0; it < nthread - 1; ++it) { @@ -14273,6 +15292,9 @@ static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const floa compute(); for (auto & w : workers) { w.join(); } workers.clear(); + if (!valid) { + throw std::runtime_error("quantized data validation failed"); + } return new_size; } @@ -14335,7 +15357,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s auto v = (std::vector*)params->kv_overrides; kv_overrides = v->data(); } - llama_model_loader ml(fname_inp, use_mmap, kv_overrides); + llama_model_loader ml(fname_inp, use_mmap, /*check_tensors*/ true, kv_overrides); ml.init_mappings(false); // no prefetching llama_model model; @@ -14363,17 +15385,23 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s gguf_set_kv (ctx_out, ml.meta); gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION); gguf_set_val_u32(ctx_out, "general.file_type", ftype); + // Remove split metadata + gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_NO).c_str()); + gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str()); + gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str()); if (params->kv_overrides) { const std::vector & overrides = *(const std::vector *)params->kv_overrides; for (auto & o : overrides) { if (o.key[0] == 0) break; if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) { - gguf_set_val_f32(ctx_out, o.key, o.float_value); + gguf_set_val_f32(ctx_out, o.key, o.val_f64); } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) { - gguf_set_val_i32(ctx_out, o.key, o.int_value); + gguf_set_val_i32(ctx_out, o.key, o.val_i64); } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) { - gguf_set_val_bool(ctx_out, o.key, o.bool_value); + gguf_set_val_bool(ctx_out, o.key, o.val_bool); + } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) { + gguf_set_val_str(ctx_out, o.key, o.val_str); } else { LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key); } @@ -14415,26 +15443,74 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s std::vector> work; std::vector> f32_conv_buf; + uint16_t n_split = 1; + // Assume split index is continuous + if (params->keep_split) { + for (int i = 0; i < ml.n_tensors; ++i) { + n_split = std::max(uint16_t(ml.get_weight(i)->idx+1), n_split); + } + } + std::vector ctx_outs(n_split, NULL); + ctx_outs[0] = ctx_out; + // populate the original tensors so we get an initial meta data for (int i = 0; i < ml.n_tensors; ++i) { - const struct ggml_tensor * meta = ml.get_tensor_meta(i); - gguf_add_tensor(ctx_out, meta); + auto weight = ml.get_weight(i); + uint16_t i_split = params->keep_split ? weight->idx : 0; + struct ggml_tensor * tensor = weight->tensor; + if (ctx_outs[i_split] == NULL) { + ctx_outs[i_split] = gguf_init_empty(); + } + gguf_add_tensor(ctx_outs[i_split], tensor); } - std::ofstream fout(fname_out, std::ios::binary); - fout.exceptions(std::ofstream::failbit); // fail fast on write errors + // Set split info if needed + if (n_split > 1) { + for (size_t i = 0; i < ctx_outs.size(); ++i) { + gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i); + gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split); + gguf_set_val_i32(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors); + } + } - const size_t meta_size = gguf_get_meta_size(ctx_out); + int cur_split = -1; + std::ofstream fout; + auto close_ofstream = [&]() { + // Write metadata and close file handler + if (fout.is_open()) { + fout.seekp(0); + std::vector data(gguf_get_meta_size(ctx_outs[cur_split])); + gguf_get_meta_data(ctx_outs[cur_split], data.data()); + fout.write((const char *) data.data(), data.size()); + fout.close(); + } + }; + auto new_ofstream = [&](int index) { + cur_split = index; + GGML_ASSERT(ctx_outs[cur_split] && "Find uninitialized gguf_context"); + std::string fname = fname_out; + if (params->keep_split) { + char split_path[PATH_MAX] = {0}; + llama_split_path(split_path, sizeof(split_path), fname_out.c_str(), cur_split, n_split); + fname = std::string(split_path); + } - LLAMA_LOG_INFO("%s: meta size = %zu bytes\n", __func__, meta_size); - - // placeholder for the meta data - ::zeros(fout, meta_size); + fout = std::ofstream(fname, std::ios::binary); + fout.exceptions(std::ofstream::failbit); // fail fast on write errors + const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split]); + // placeholder for the meta data + ::zeros(fout, meta_size); + }; const auto tn = LLM_TN(model.arch); - + new_ofstream(0); for (int i = 0; i < ml.n_tensors; ++i) { - struct ggml_tensor * tensor = ml.get_tensor_meta(i); + auto weight = ml.get_weight(i); + struct ggml_tensor * tensor = weight->tensor; + if (weight->idx != cur_split && params->keep_split) { + close_ofstream(); + new_ofstream(weight->idx); + } const std::string name = ggml_get_name(tensor); @@ -14589,26 +15665,18 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s total_size_new += new_size; // update the gguf meta data as we go - gguf_set_tensor_type(ctx_out, name.c_str(), new_type); - gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size); + gguf_set_tensor_type(ctx_outs[cur_split], name.c_str(), new_type); + gguf_set_tensor_data(ctx_outs[cur_split], name.c_str(), new_data, new_size); // write tensor data + padding fout.write((const char *) new_data, new_size); zeros(fout, GGML_PAD(new_size, align) - new_size); } - - // go back to beginning of file and write the updated meta data - { - fout.seekp(0); - std::vector data(gguf_get_meta_size(ctx_out)); - gguf_get_meta_data(ctx_out, data.data()); - fout.write((const char *) data.data(), data.size()); + close_ofstream(); + for (auto & c:ctx_outs) { + gguf_free(c); } - fout.close(); - - gguf_free(ctx_out); - LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0); @@ -14652,7 +15720,7 @@ static int llama_apply_lora_from_file_internal( std::unique_ptr ml; if (path_base_model) { LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model); - ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ nullptr)); + ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*check_tensors*/ false, /*kv_overrides*/ nullptr)); ml->init_mappings(/*prefetch*/ false); // no prefetching } @@ -14911,6 +15979,7 @@ struct llama_model_params llama_model_default_params() { /*.vocab_only =*/ false, /*.use_mmap =*/ true, /*.use_mlock =*/ false, + /*.check_tensors =*/ false, }; #ifdef GGML_USE_METAL @@ -14964,6 +16033,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { /*.quantize_output_tensor =*/ true, /*.only_copy =*/ false, /*.pure =*/ false, + /*.keep_split =*/ false, /*.imatrix =*/ nullptr, /*.kv_overrides =*/ nullptr, }; @@ -15468,18 +16538,22 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_MINICPM: case LLM_ARCH_XVERSE: case LLM_ARCH_COMMAND_R: + case LLM_ARCH_OLMO: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 case LLM_ARCH_FALCON: case LLM_ARCH_GROK: + case LLM_ARCH_DBRX: case LLM_ARCH_PERSIMMON: case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: case LLM_ARCH_STABLELM: case LLM_ARCH_QWEN: case LLM_ARCH_QWEN2: + case LLM_ARCH_QWEN2MOE: case LLM_ARCH_PHI2: + case LLM_ARCH_PHI3: case LLM_ARCH_GEMMA: case LLM_ARCH_STARCODER2: return LLAMA_ROPE_TYPE_NEOX; @@ -15493,6 +16567,10 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { return LLAMA_ROPE_TYPE_NONE; } +enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx) { + return ctx->cparams.pooling_type; +} + int32_t llama_n_vocab(const struct llama_model * model) { return model->hparams.n_vocab; } @@ -16028,6 +17106,8 @@ struct llama_data_file_context : llama_data_context { * */ static void llama_state_get_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) { + llama_synchronize(ctx); + // copy rng { std::ostringstream rng_ss; @@ -16170,6 +17250,8 @@ size_t llama_state_get_data(struct llama_context * ctx, uint8_t * dst) { // Sets the state reading from the specified source address size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) { + llama_synchronize(ctx); + const uint8_t * inp = src; // set rng @@ -16206,6 +17288,8 @@ size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) { GGML_ASSERT((uint32_t) id < ctx->cparams.n_batch); ctx->output_ids[id] = i; } + + ctx->n_outputs = n_outputs; } } @@ -16463,6 +17547,8 @@ size_t llama_state_seq_get_size(struct llama_context* ctx, llama_seq_id seq_id) } static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llama_data_context & data_ctx, llama_seq_id seq_id) { + llama_synchronize(ctx); + const auto & kv_self = ctx->cache.kv; GGML_ASSERT(ctx->cache.rs.size == 0); // not implemented @@ -16580,6 +17666,8 @@ size_t llama_state_seq_get_data(struct llama_context* ctx, uint8_t* dst, llama_s } size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, llama_seq_id dest_seq_id) { + llama_synchronize(ctx); + auto & cache = ctx->cache; auto & kv_self = cache.kv; GGML_ASSERT(cache.rs.size == 0); // not implemented @@ -17040,6 +18128,13 @@ llama_token_type llama_token_get_type(const struct llama_model * model, llama_to return model->vocab.id_to_token[token].type; } +bool llama_token_is_eog(const struct llama_model * model, llama_token token) { + return token != -1 && ( + token == llama_token_eos(model) || + token == llama_token_eot(model) + ); +} + llama_token llama_token_bos(const struct llama_model * model) { return model->vocab.special_bos_id; } @@ -17117,7 +18212,7 @@ static std::string llama_decode_text(const std::string & text) { } // does not write null-terminator to buf -int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) { +int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) { if (0 <= token && token < llama_n_vocab(model)) { switch (llama_vocab_get_type(model->vocab)) { case LLAMA_VOCAB_TYPE_WPM: @@ -17132,7 +18227,9 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token } memcpy(buf, result.c_str(), result.length()); return result.length(); - } else if (llama_is_user_defined_token(model->vocab, token)) { + } else if ( + (llama_is_user_defined_token(model->vocab, token)) || + (llama_is_control_token (model->vocab, token) && special)) { std::string result = model->vocab.id_to_token[token].text; if (length < (int) result.length()) { return -(int) result.length(); @@ -17145,8 +18242,6 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token } memcpy(buf, "\xe2\x96\x85", 3); return 3; - } else if (llama_is_control_token(model->vocab, token)) { - ; } else if (llama_is_byte_token(model->vocab, token)) { if (length < 1) { return -1; @@ -17167,15 +18262,15 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token } memcpy(buf, result.c_str(), result.length()); return result.length(); - } else if (llama_is_user_defined_token(model->vocab, token)) { + } else if ( + (llama_is_user_defined_token(model->vocab, token)) || + (llama_is_control_token (model->vocab, token) && special)) { std::string result = model->vocab.id_to_token[token].text; if (length < (int) result.length()) { return -(int) result.length(); } memcpy(buf, result.c_str(), result.length()); return result.length(); - } else if (llama_is_control_token(model->vocab, token)) { - ; } break; } @@ -17358,6 +18453,39 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "### Response:\n"; } + } else if (tmpl == "command-r" || (tmpl.find("<|START_OF_TURN_TOKEN|>") != std::string::npos && tmpl.find("<|USER_TOKEN|>") != std::string::npos)) { + // CohereForAI/c4ai-command-r-plus + for (auto message : chat) { + std::string role(message->role); + if (role == "system") { + ss << "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>"; + } else if (role == "user") { + ss << "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>"; + } else if (role == "assistant") { + ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>"; + } + } + if (add_ass) { + ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"; + } + } else if (tmpl == "llama3" || (tmpl.find("<|start_header_id|>") != std::string::npos && tmpl.find("<|end_header_id|>") != std::string::npos)) { + // Llama 3 + for (auto message : chat) { + std::string role(message->role); + ss << "<|start_header_id|>" << role << "<|end_header_id|>\n\n" << trim(message->content) << "<|eot_id|>"; + } + if (add_ass) { + ss << "<|start_header_id|>assistant<|end_header_id|>\n\n"; + } + } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos )) { + // Phi 3 + for (auto message : chat) { + std::string role(message->role); + ss << "<|" << role << "|>\n" << trim(message->content) << "<|end|>\n"; + } + if (add_ass) { + ss << "<|assistant|>\n"; + } } else { // template not supported return -1; @@ -17490,6 +18618,11 @@ const char * llama_print_system_info(void) { s += "SSSE3 = " + std::to_string(ggml_cpu_has_ssse3()) + " | "; s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | "; s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | "; +#ifdef GGML_USE_LLAMAFILE + s += "LLAMAFILE = 1 | "; +#else + s += "LLAMAFILE = 0 | "; +#endif return s.c_str(); } diff --git a/llama.h b/llama.h index c211ca592..fa6d0b586 100644 --- a/llama.h +++ b/llama.h @@ -69,6 +69,18 @@ extern "C" { LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece }; + // pre-tokenization types + enum llama_vocab_pre_type { + LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0, + LLAMA_VOCAB_PRE_TYPE_LLAMA3 = 1, + LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM = 2, + LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER = 3, + LLAMA_VOCAB_PRE_TYPE_FALCON = 4, + LLAMA_VOCAB_PRE_TYPE_MPT = 5, + LLAMA_VOCAB_PRE_TYPE_STARCODER = 6, + LLAMA_VOCAB_PRE_TYPE_GPT2 = 7, + }; + // note: these values should be synchronized with ggml_rope // TODO: maybe move this enum to ggml.h (ggml_rope_type) enum llama_rope_type { @@ -195,15 +207,19 @@ extern "C" { LLAMA_KV_OVERRIDE_TYPE_INT, LLAMA_KV_OVERRIDE_TYPE_FLOAT, LLAMA_KV_OVERRIDE_TYPE_BOOL, + LLAMA_KV_OVERRIDE_TYPE_STR, }; struct llama_model_kv_override { - char key[128]; enum llama_model_kv_override_type tag; + + char key[128]; + union { - int64_t int_value; - double float_value; - bool bool_value; + int64_t val_i64; + double val_f64; + bool val_bool; + char val_str[128]; }; }; @@ -232,9 +248,10 @@ extern "C" { const struct llama_model_kv_override * kv_overrides; // Keep the booleans together to avoid misalignment during copy-by-value. - bool vocab_only; // only load the vocabulary, no weights - bool use_mmap; // use mmap if possible - bool use_mlock; // force system to keep model in RAM + bool vocab_only; // only load the vocabulary, no weights + bool use_mmap; // use mmap if possible + bool use_mlock; // force system to keep model in RAM + bool check_tensors; // validate model tensor data }; struct llama_context_params { @@ -288,6 +305,7 @@ extern "C" { bool quantize_output_tensor; // quantize output.weight bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored bool pure; // quantize all tensors to the default type + bool keep_split; // quantize to the same number of shards void * imatrix; // pointer to importance matrix data void * kv_overrides; // pointer to vector containing overrides } llama_model_quantize_params; @@ -390,8 +408,10 @@ extern "C" { LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx); LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx); - LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model); - LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model); + LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx); + + LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model); + LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model); LLAMA_API int32_t llama_n_vocab (const struct llama_model * model); LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model); @@ -833,6 +853,9 @@ extern "C" { LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token); + // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.) + LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token); + // Special tokens LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence @@ -846,7 +869,7 @@ extern "C" { // Returns -1 if unknown, 1 for true or 0 for false. LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model); - // codellama infill tokens + // Codellama infill tokens LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix @@ -875,11 +898,13 @@ extern "C" { // Uses the vocabulary in the provided context. // Does not write null terminator to the buffer. // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens. + // @param special If true, special tokens are rendered in the output. LLAMA_API int32_t llama_token_to_piece( const struct llama_model * model, llama_token token, char * buf, - int32_t length); + int32_t length, + bool special); /// Apply chat template. Inspired by hf apply_chat_template() on python. /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model" @@ -1032,7 +1057,7 @@ extern "C" { struct llama_context * ctx, llama_token_data_array * candidates); - /// @details Randomly selects a token from the candidates based on their probabilities. + /// @details Randomly selects a token from the candidates based on their probabilities using the RNG of ctx. LLAMA_API llama_token llama_sample_token( struct llama_context * ctx, llama_token_data_array * candidates); @@ -1119,8 +1144,9 @@ extern "C" { // Internal API to be implemented by llama.cpp and used by tests/benchmarks only #ifdef LLAMA_API_INTERNAL -#include +#include #include +#include struct ggml_tensor; @@ -1147,15 +1173,20 @@ const std::vector> & llama_internal struct llama_context * ctx ); -std::vector> llama_grammar_accept( +void llama_grammar_accept( const std::vector> & rules, const std::vector> & stacks, - const uint32_t chr); + const uint32_t chr, + std::vector> & new_stacks); std::pair, llama_partial_utf8> decode_utf8( const std::string & src, llama_partial_utf8 partial_start); +// Randomly selects a token from the candidates based on their probabilities using given std::mt19937. +// This is a temporary workaround in order to fix race conditions when sampling with multiple sequences. +llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng); + #endif // LLAMA_API_INTERNAL #endif // LLAMA_H diff --git a/media/matmul.png b/media/matmul.png new file mode 100644 index 000000000..786a20492 Binary files /dev/null and b/media/matmul.png differ diff --git a/media/matmul.svg b/media/matmul.svg new file mode 100644 index 000000000..1d6cb4bb7 --- /dev/null +++ b/media/matmul.svg @@ -0,0 +1,1238 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ARow-major + BTColumn-major + CT=ABTColumn-major + + ne00 + + ne01 + + ne1 + + ne0 + + ne10 + + ne11 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + BRow-major + ATColumn-major + C=BATRow-major + + ne10 + + ne11 + + ne0 + + ne1 + + ne00 + + ne01 + + + diff --git a/models/ggml-vocab-bert-bge.gguf b/models/ggml-vocab-bert-bge.gguf new file mode 100644 index 000000000..b2cbd5df6 Binary files /dev/null and b/models/ggml-vocab-bert-bge.gguf differ diff --git a/models/ggml-vocab-bert-bge.gguf.inp b/models/ggml-vocab-bert-bge.gguf.inp new file mode 100644 index 000000000..0389f00c7 --- /dev/null +++ b/models/ggml-vocab-bert-bge.gguf.inp @@ -0,0 +1,102 @@ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-bert-bge.gguf.out b/models/ggml-vocab-bert-bge.gguf.out new file mode 100644 index 000000000..969552e1c --- /dev/null +++ b/models/ggml-vocab-bert-bge.gguf.out @@ -0,0 +1,41 @@ + + + + + + + + + + 7592 2088 + 7592 2088 + 7592 2088 + 7592 2088 + 7592 2088 999 + 7592 1010 2088 999 + 7592 1010 2088 999 + 2023 2003 100 1012 18133 2361 + 1059 2692 18139 1021 8525 28418 2243 16233 20952 6979 + 1192 15290 29754 14150 1192 10260 1181 29755 29436 29741 10260 16856 29747 23925 10325 + 100 + 100 1006 3671 1007 100 1006 3674 7861 29147 2483 9530 16280 23854 1007 100 1006 2069 7861 29147 2072 2008 2038 2049 2219 19204 1007 + 7592 + 7592 + 7592 + 7592 + 7592 + 7592 7592 + 1006 + 1027 + 1005 3690 + 7592 1010 1061 1005 2035 999 2129 2024 2017 100 1029 1855 100 100 6207 100 100 14677 23632 22203 1811 1995 + 1017 + 3943 + 21211 + 21211 2509 + 21211 22394 + 21211 22394 2509 + 21211 22394 22394 + 21211 22394 22394 2509 + 21211 22394 22394 22394 + 100 1006 3671 1007 100 1006 3674 7861 29147 2483 9530 16280 23854 1007 100 100 1017 3943 21211 21211 2509 21211 22394 21211 22394 2509 21211 22394 22394 21211 22394 22394 2509 1017 1012 1017 1017 1012 1012 1017 1017 1012 1012 1012 1017 100 1029 1855 100 100 6207 100 100 14677 23632 22203 1811 1995 1011 1011 1011 1011 1011 1011 1027 1027 1027 1027 1027 1027 1027 1192 15290 29754 14150 1192 10260 1181 29755 29436 29741 10260 16856 29747 23925 10325 1005 1005 1005 1005 1005 1005 1036 1036 1036 1036 1036 1036 1036 1000 1000 1000 1000 1012 1012 1012 1012 1012 1012 999 999 999 999 999 999 1029 1029 1029 1029 1029 1029 1045 1005 2310 2042 1005 2409 2002 1005 1055 2045 1010 1005 2128 2017 2469 1029 1005 1049 2025 2469 1045 1005 2222 2191 2009 1010 1005 1040 2017 2066 2070 5572 1029 2057 1005 2310 1037 1005 2222 diff --git a/models/ggml-vocab-deepseek-coder.gguf b/models/ggml-vocab-deepseek-coder.gguf new file mode 100644 index 000000000..6728cd747 Binary files /dev/null and b/models/ggml-vocab-deepseek-coder.gguf differ diff --git a/models/ggml-vocab-deepseek-coder.gguf.inp b/models/ggml-vocab-deepseek-coder.gguf.inp new file mode 100644 index 000000000..0389f00c7 --- /dev/null +++ b/models/ggml-vocab-deepseek-coder.gguf.inp @@ -0,0 +1,102 @@ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-deepseek-coder.gguf.out b/models/ggml-vocab-deepseek-coder.gguf.out new file mode 100644 index 000000000..8ef585c78 --- /dev/null +++ b/models/ggml-vocab-deepseek-coder.gguf.out @@ -0,0 +1,41 @@ + + 207 + 243 + 315 + 184 + 185 + 185 185 + 185 185 185 + 184 185 + 17535 1835 + 414 9489 1835 + 17535 5414 + 414 9489 5414 + 414 9489 5414 0 + 17535 11 1835 0 + 414 9489 11 1835 0 + 437 317 12394 99 234 13 14789 + 86 15 19 23 207 22 83 3963 27659 26078 3934 14072 + 1593 6478 616 2251 14994 + 155 239 209 155 239 114 155 239 228 155 240 220 155 239 224 155 240 211 155 239 231 155 239 115 155 239 240 155 240 210 155 239 240 155 239 95 155 239 114 155 239 214 155 239 210 155 239 236 155 239 214 155 240 210 155 239 218 + 10047 235 209 334 8760 8 12394 233 114 350 222 10047 221 104 169 116 224 334 4684 3909 992 24330 262 29651 612 8 207 156 237 214 334 5950 992 78 12896 344 638 891 1372 10736 8 + 17535 + 414 9489 + 207 414 9489 + 243 414 9489 + 315 414 9489 + 315 414 9489 185 315 414 9489 + 334 + 185 405 + 6 2895 + 17535 11 320 6 435 0 1717 417 340 12394 233 210 3015 19100 608 9413 2668 16 18 16 19 16 20 16 1393 169 121 239 + 18 + 18 18 + 18 18 18 + 18 18 18 18 + 18 18 18 18 18 + 18 18 18 18 18 18 + 18 18 18 18 18 18 18 + 18 18 18 18 18 18 18 18 + 18 18 18 18 18 18 18 18 18 + 185 207 185 185 207 185 185 185 207 12405 459 22758 185 243 185 315 185 251 185 730 185 10047 235 209 334 8760 8 12394 233 114 350 222 10047 221 104 169 116 224 334 4684 3909 992 24330 262 29651 612 8 207 156 237 214 12394 99 234 10047 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 524 18 207 18 1202 18 207 155 239 209 155 239 114 155 239 228 155 240 220 155 239 224 155 240 211 155 239 231 155 239 115 155 239 240 155 240 210 155 239 240 155 239 95 155 239 114 155 239 214 10047 233 210 3015 19100 608 9413 2668 16 18 16 19 16 20 16 1393 169 121 239 18155 374 17194 28 2861 6478 616 2251 14994 31269 4191 6 4686 4686 10252 3358 3358 3409 524 15330 3023 15031 5668 303 6 312 798 651 83 839 362 6 82 741 11 651 1369 340 2037 30 651 44 441 2037 303 6 642 1098 359 11 651 35 340 833 738 10860 30 998 6 10709 245 6 75 43 diff --git a/models/ggml-vocab-deepseek-llm.gguf b/models/ggml-vocab-deepseek-llm.gguf new file mode 100644 index 000000000..5d66091c4 Binary files /dev/null and b/models/ggml-vocab-deepseek-llm.gguf differ diff --git a/models/ggml-vocab-deepseek-llm.gguf.inp b/models/ggml-vocab-deepseek-llm.gguf.inp new file mode 100644 index 000000000..0389f00c7 --- /dev/null +++ b/models/ggml-vocab-deepseek-llm.gguf.inp @@ -0,0 +1,102 @@ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-deepseek-llm.gguf.out b/models/ggml-vocab-deepseek-llm.gguf.out new file mode 100644 index 000000000..0ea9d66e3 --- /dev/null +++ b/models/ggml-vocab-deepseek-llm.gguf.out @@ -0,0 +1,41 @@ + + 207 + 243 + 300 + 184 + 185 + 185 185 + 185 185 185 + 184 185 + 17464 1843 + 37727 1843 + 17464 5427 + 37727 5427 + 37727 5427 0 + 17464 11 1843 0 + 37727 11 1843 0 + 437 317 12356 99 234 13 14743 + 86 15 19 23 207 22 83 3970 27519 26016 3944 14025 + 1603 6476 620 91754 + 71374 209 71374 114 71374 228 155 240 220 71374 224 155 240 211 71374 231 71374 115 71374 240 155 240 210 71374 240 71374 95 71374 114 71374 214 71374 210 71374 236 71374 214 155 240 210 71374 218 + 10044 95300 334 8754 8 33701 114 350 222 10044 221 104 46713 334 34732 996 24250 262 80923 8 207 37103 214 334 5956 89213 344 643 895 1377 10728 8 + 17464 + 37727 + 207 37727 + 243 37727 + 300 37727 + 300 37727 185 300 37727 + 334 + 185 403 + 6 2906 + 17464 11 320 6 436 0 1724 418 340 33701 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239 + 18 + 18 18 + 18 18 18 + 18 18 18 18 + 18 18 18 18 18 + 18 18 18 18 18 18 + 18 18 18 18 18 18 18 + 18 18 18 18 18 18 18 18 + 18 18 18 18 18 18 18 18 18 + 185 207 185 185 207 185 185 185 207 11969 486 22504 185 243 185 300 185 251 185 663 185 10044 95300 334 8754 8 33701 114 350 222 10044 221 104 46713 334 34732 996 24250 262 80923 8 207 37103 214 12356 99 234 10044 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 526 18 207 18 1204 18 207 71374 209 71374 114 71374 228 155 240 220 71374 224 155 240 211 71374 231 71374 115 71374 240 155 240 210 71374 240 71374 95 71374 114 71374 214 71899 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239 78827 55170 76659 620 91754 31116 36804 4885 4885 10897 4390 4390 41047 15278 3033 14986 5675 304 6 313 803 655 33326 362 6 82 745 11 655 1374 340 2049 30 655 44 441 2049 304 6 647 1099 359 11 655 35 340 837 742 10842 30 1003 6 10699 245 6 75 43 diff --git a/models/ggml-vocab-falcon.gguf b/models/ggml-vocab-falcon.gguf index d4ea2e822..334d50da5 100644 Binary files a/models/ggml-vocab-falcon.gguf and b/models/ggml-vocab-falcon.gguf differ diff --git a/models/ggml-vocab-falcon.gguf.inp b/models/ggml-vocab-falcon.gguf.inp new file mode 100644 index 000000000..0389f00c7 --- /dev/null +++ b/models/ggml-vocab-falcon.gguf.inp @@ -0,0 +1,102 @@ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-falcon.gguf.out b/models/ggml-vocab-falcon.gguf.out new file mode 100644 index 000000000..cb8da7b1a --- /dev/null +++ b/models/ggml-vocab-falcon.gguf.out @@ -0,0 +1,41 @@ + + 204 + 258 + 466 + 192 + 193 + 1001 + 11331 + 19125 + 9856 1079 + 23090 1079 + 9856 2889 + 23090 2889 + 23090 2889 12 + 9856 23 1079 12 + 23090 23 1079 12 + 414 304 3346 111 231 25 29247 + 98 55866 204 34 16682 7149 36190 6869 11481 + 150 133 6207 151 215 150 134 5052 133 6279 5052 223 151 216 49679 123 53110 47043 7795 + 38154 206 38154 126 38154 225 167 237 217 38154 221 167 237 208 38154 228 38154 127 38154 237 167 237 207 38154 237 38154 107 38154 126 38154 211 38154 207 38154 233 38154 211 167 237 207 38154 215 + 2571 232 206 204 19 11003 20 8196 126 283 219 48778 116 13392 204 19 51831 732 63209 1741 7955 522 20 22438 211 204 19 7927 53360 325 504 701 946 10930 20 + 9856 + 23090 + 204 23090 + 258 23090 + 466 23090 + 466 23090 742 23090 + 204 19 + 1212 40 + 18 4932 + 9856 23 291 18 436 12 1265 362 299 8196 207 204 42 50087 123 2727 20300 32022 133 234 17419 30137 28 7858 181 133 236 + 30 + 3138 + 22287 + 22287 30 + 22287 3138 + 22287 22287 + 22287 22287 30 + 22287 22287 3138 + 22287 22287 22287 + 1212 4824 1001 1212 192 204 663 49453 2069 742 561 1501 193 2571 232 206 204 19 11003 20 8196 126 283 219 48778 116 13392 204 19 51831 732 63209 1741 7955 522 20 22438 211 3346 111 231 2571 111 231 204 30 204 3138 204 22287 204 22287 30 204 22287 3138 204 22287 22287 204 22287 22287 30 204 22287 22287 3138 204 30 25 30 204 30 513 30 204 30 951 30 27171 236 206 38154 126 38154 225 167 237 217 38154 221 167 237 208 38154 228 38154 127 38154 237 167 237 207 38154 237 38154 107 38154 126 38154 211 20589 207 204 42 50087 123 2727 20300 32022 133 234 17419 30137 28 7858 181 133 236 204 37057 2228 10666 5052 133 6207 151 215 150 134 5052 133 6279 5052 223 151 216 49679 123 53110 47043 7795 204 7544 7544 7544 8543 8543 17593 3513 3513 12844 51520 17664 4247 295 18 298 650 204 18 95 693 332 18 94 629 23 204 18 1553 299 1310 42 204 18 56 416 1310 295 18 567 717 334 23 204 18 47 299 606 596 6696 42 703 18 16139 241 18 87 55 diff --git a/models/ggml-vocab-gpt-2.gguf b/models/ggml-vocab-gpt-2.gguf new file mode 100644 index 000000000..5ea85cf52 Binary files /dev/null and b/models/ggml-vocab-gpt-2.gguf differ diff --git a/models/ggml-vocab-gpt-2.gguf.inp b/models/ggml-vocab-gpt-2.gguf.inp new file mode 100644 index 000000000..0389f00c7 --- /dev/null +++ b/models/ggml-vocab-gpt-2.gguf.inp @@ -0,0 +1,102 @@ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-gpt-2.gguf.out b/models/ggml-vocab-gpt-2.gguf.out new file mode 100644 index 000000000..9986f38e4 --- /dev/null +++ b/models/ggml-vocab-gpt-2.gguf.out @@ -0,0 +1,41 @@ + + 220 + 220 220 + 220 220 220 + 197 + 198 + 628 + 628 198 + 197 198 + 15496 995 + 18435 995 + 15496 2159 + 18435 2159 + 18435 2159 0 + 15496 11 995 0 + 18435 11 995 0 + 428 318 12520 99 247 13 20322 + 86 47202 767 28047 45961 288 82 7568 13415 + 22177 16843 141 231 15166 12466 121 16142 12466 239 141 232 30143 140 111 16142 21169 21727 31583 18849 + 157 252 222 157 252 114 157 252 241 157 253 233 157 252 237 157 253 224 157 252 244 157 252 115 157 252 253 157 253 223 157 252 253 157 252 95 157 252 114 157 252 227 157 252 223 157 252 249 157 252 227 157 253 223 157 252 231 + 8582 248 222 357 11265 8 30325 114 447 235 8582 234 104 37929 357 48101 795 13210 271 1673 36686 515 8 14519 227 357 8807 44805 326 468 663 898 11241 8 + 15496 + 18435 + 220 18435 + 220 220 18435 + 220 220 220 18435 + 220 220 220 18435 198 220 220 220 18435 + 357 + 198 796 + 6 6980 + 15496 11 331 6 439 0 1374 389 345 30325 223 5633 22755 239 46349 111 28839 101 18040 32432 98 43291 1485 1415 24309 25465 171 121 252 + 18 + 2091 + 20370 + 24840 + 2091 20370 + 24840 2091 + 24840 20370 + 24840 24840 + 24840 2091 20370 + 198 220 628 220 628 198 220 197 220 197 197 220 197 198 220 220 198 220 220 220 198 220 220 220 220 198 220 220 220 220 220 198 8582 248 222 357 11265 8 30325 114 447 235 8582 234 104 37929 357 48101 795 13210 271 1673 36686 515 8 14519 227 12520 99 247 8582 99 247 513 4747 23460 513 20370 23460 2091 23460 20370 23460 24840 23460 2091 20370 513 13 18 513 492 18 513 986 18 28053 252 222 157 252 114 157 252 241 157 253 233 157 252 237 157 253 224 157 252 244 157 252 115 157 252 253 157 253 223 157 252 253 157 252 95 157 252 114 157 252 227 47249 223 5633 22755 239 46349 111 28839 101 18040 32432 98 43291 1485 1415 24309 25465 171 121 252 40103 1421 18604 12466 121 16843 141 231 15166 12466 121 16142 12466 239 141 232 30143 140 111 16142 21169 21727 31583 18849 705 39115 6 33153 15506 63 15931 15931 16317 13896 3228 9805 3548 314 1053 587 705 44040 339 338 612 11 705 2200 345 1654 30 705 44 407 1654 314 1183 787 340 11 705 35 345 588 617 8887 30 775 6 26979 257 6 75 43 diff --git a/models/ggml-vocab-llama-bpe.gguf b/models/ggml-vocab-llama-bpe.gguf new file mode 100644 index 000000000..e51a99118 Binary files /dev/null and b/models/ggml-vocab-llama-bpe.gguf differ diff --git a/models/ggml-vocab-llama-bpe.gguf.inp b/models/ggml-vocab-llama-bpe.gguf.inp new file mode 100644 index 000000000..0389f00c7 --- /dev/null +++ b/models/ggml-vocab-llama-bpe.gguf.inp @@ -0,0 +1,102 @@ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-llama-bpe.gguf.out b/models/ggml-vocab-llama-bpe.gguf.out new file mode 100644 index 000000000..4d903e1cd --- /dev/null +++ b/models/ggml-vocab-llama-bpe.gguf.out @@ -0,0 +1,41 @@ + + 220 + 256 + 262 + 197 + 198 + 271 + 1432 + 1602 + 9906 1917 + 22691 1917 + 9906 4435 + 22691 4435 + 22691 4435 0 + 9906 11 1917 0 + 22691 11 1917 0 + 420 374 11410 99 247 13 11055 + 86 23904 220 22 83 2005 42908 11729 3013 17156 + 79862 102118 13373 64571 34694 3114 112203 80112 + 21549 222 98629 241 45358 233 21549 237 45358 224 21549 244 21549 115 21549 253 45358 223 21549 253 21549 95 98629 227 21549 223 21549 249 21549 227 45358 223 21549 231 + 9468 248 222 320 8416 8 27623 114 102470 9468 234 104 31643 320 36773 100166 98634 8 26602 227 320 3323 43465 430 706 1202 1866 4037 8 + 9906 + 22691 + 220 22691 + 256 22691 + 262 22691 + 262 22691 198 262 22691 + 320 + 198 284 + 6 11639 + 9906 11 379 65948 0 2650 527 499 27623 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909 + 18 + 1644 + 8765 + 8765 18 + 8765 1644 + 8765 8765 + 8765 8765 18 + 8765 8765 1644 + 8765 8765 8765 + 198 4815 15073 66597 8004 1602 2355 79772 11187 9468 248 222 320 8416 8 27623 114 102470 9468 234 104 31643 320 36773 100166 98634 8 26602 227 11410 99 247 9468 99 247 220 18 220 1644 220 8765 220 8765 18 220 8765 1644 220 8765 8765 220 8765 8765 18 220 8765 8765 1644 220 18 13 18 220 18 497 18 220 18 1131 18 220 21549 222 98629 241 45358 233 21549 237 45358 224 21549 244 21549 115 21549 253 45358 223 21549 253 21549 95 98629 227 76460 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909 56560 54337 19175 102118 13373 64571 34694 3114 112203 80112 3436 106451 14196 14196 74694 3089 3089 29249 17523 3001 27708 7801 358 3077 1027 364 83 820 568 596 1070 11 364 793 499 2771 30 364 44 539 2771 358 3358 1304 433 11 364 35 499 1093 1063 15600 30 1226 6 43712 264 64966 43 diff --git a/models/ggml-vocab-llama.gguf b/models/ggml-vocab-llama-spm.gguf similarity index 99% rename from models/ggml-vocab-llama.gguf rename to models/ggml-vocab-llama-spm.gguf index 549eed8c5..658295a5d 100644 Binary files a/models/ggml-vocab-llama.gguf and b/models/ggml-vocab-llama-spm.gguf differ diff --git a/models/ggml-vocab-llama-spm.gguf.inp b/models/ggml-vocab-llama-spm.gguf.inp new file mode 100644 index 000000000..0389f00c7 --- /dev/null +++ b/models/ggml-vocab-llama-spm.gguf.inp @@ -0,0 +1,102 @@ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-llama-spm.gguf.out b/models/ggml-vocab-llama-spm.gguf.out new file mode 100644 index 000000000..15d00b106 --- /dev/null +++ b/models/ggml-vocab-llama-spm.gguf.out @@ -0,0 +1,41 @@ + + 259 + 1678 + 268 + 29871 12 + 29871 13 + 29871 13 13 + 29871 13 13 13 + 29871 12 13 + 15043 3186 + 29871 15043 3186 + 15043 2787 + 29871 15043 2787 + 29871 15043 2787 29991 + 15043 29892 3186 29991 + 29871 15043 29892 3186 29991 + 29871 445 338 29871 243 162 169 156 29889 8223 + 281 29900 29946 29947 29871 29955 9161 13535 18031 2176 6905 + 1538 4851 665 1386 29713 1305 + 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 228 161 132 228 161 158 228 161 136 228 162 132 228 161 140 + 29871 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 313 6194 953 29877 2397 393 756 967 1914 5993 29897 + 15043 + 29871 15043 + 259 15043 + 1678 15043 + 268 15043 + 268 15043 13 1678 15043 + 29871 313 + 29871 13 353 + 525 3152 + 15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 + 29871 29941 + 29871 29941 29941 + 29871 29941 29941 29941 + 29871 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 29941 29941 29941 + 29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931 diff --git a/models/ggml-vocab-mpt.gguf b/models/ggml-vocab-mpt.gguf index 6affa34bd..f42f56dec 100644 Binary files a/models/ggml-vocab-mpt.gguf and b/models/ggml-vocab-mpt.gguf differ diff --git a/models/ggml-vocab-mpt.gguf.inp b/models/ggml-vocab-mpt.gguf.inp new file mode 100644 index 000000000..0389f00c7 --- /dev/null +++ b/models/ggml-vocab-mpt.gguf.inp @@ -0,0 +1,102 @@ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-mpt.gguf.out b/models/ggml-vocab-mpt.gguf.out new file mode 100644 index 000000000..1f4b0eb3a --- /dev/null +++ b/models/ggml-vocab-mpt.gguf.out @@ -0,0 +1,41 @@ + + 209 + 50276 + 50275 + 186 + 187 + 535 + 2756 + 186 187 + 12092 1533 + 24387 1533 + 12092 3645 + 24387 3645 + 24387 3645 2 + 12092 13 1533 2 + 24387 13 1533 2 + 436 310 22692 101 236 15 14161 + 88 27244 818 16853 16392 20505 4989 11917 + 32520 11514 1068 8713 38177 13396 3415 9925 12559 10453 1389 + 18081 211 18081 116 18081 230 39936 222 18081 226 39936 213 18081 233 18081 117 18081 242 39936 212 18081 242 18081 97 18081 116 18081 216 18081 212 18081 238 18081 216 39936 212 18081 220 + 14931 237 211 313 6320 10 49042 116 325 224 14931 223 106 171 118 226 313 34263 802 13511 261 32147 456 10 3384 239 216 313 7483 802 80 8020 326 556 697 1211 10669 10 + 12092 + 24387 + 50276 12092 + 50275 12092 + 50274 12092 + 50274 12092 187 50274 12092 + 313 + 187 426 + 8 8685 + 12092 13 340 8 455 2 1359 403 368 49042 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241 + 20 + 1610 + 20084 + 26409 + 1610 20084 + 26409 1610 + 26409 20084 + 26409 26409 + 26409 1610 20084 + 586 1744 33525 186 209 623 28910 187 50276 187 50275 187 50274 187 50273 187 14931 237 211 313 6320 10 49042 116 325 224 14931 223 106 171 118 226 313 34263 802 13511 261 32147 456 10 3384 239 216 22692 101 236 14931 101 236 495 5922 30057 495 20084 495 26409 30057 20084 495 26409 1610 495 26409 20084 495 15 20 495 537 20 495 1051 20 209 18081 211 18081 116 18081 230 39936 222 18081 226 39936 213 18081 233 18081 117 18081 242 39936 212 18081 242 18081 97 18081 116 18081 216 14931 235 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241 16081 6877 12880 11514 1068 8713 38177 13396 3415 9925 12559 10453 1389 42011 35033 34842 11202 9739 9739 33021 18963 4672 25561 8220 309 1849 644 686 42618 344 434 627 13 686 1848 368 2119 32 686 46 417 2119 309 1833 1056 352 13 686 37 368 751 690 10331 32 844 8 31516 247 8 77 45 diff --git a/models/ggml-vocab-phi-3.gguf b/models/ggml-vocab-phi-3.gguf new file mode 100644 index 000000000..72fdb409c Binary files /dev/null and b/models/ggml-vocab-phi-3.gguf differ diff --git a/models/ggml-vocab-phi-3.gguf.inp b/models/ggml-vocab-phi-3.gguf.inp new file mode 100644 index 000000000..0389f00c7 --- /dev/null +++ b/models/ggml-vocab-phi-3.gguf.inp @@ -0,0 +1,102 @@ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-phi-3.gguf.out b/models/ggml-vocab-phi-3.gguf.out new file mode 100644 index 000000000..15d00b106 --- /dev/null +++ b/models/ggml-vocab-phi-3.gguf.out @@ -0,0 +1,41 @@ + + 259 + 1678 + 268 + 29871 12 + 29871 13 + 29871 13 13 + 29871 13 13 13 + 29871 12 13 + 15043 3186 + 29871 15043 3186 + 15043 2787 + 29871 15043 2787 + 29871 15043 2787 29991 + 15043 29892 3186 29991 + 29871 15043 29892 3186 29991 + 29871 445 338 29871 243 162 169 156 29889 8223 + 281 29900 29946 29947 29871 29955 9161 13535 18031 2176 6905 + 1538 4851 665 1386 29713 1305 + 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 228 161 132 228 161 158 228 161 136 228 162 132 228 161 140 + 29871 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 313 6194 953 29877 2397 393 756 967 1914 5993 29897 + 15043 + 29871 15043 + 259 15043 + 1678 15043 + 268 15043 + 268 15043 13 1678 15043 + 29871 313 + 29871 13 353 + 525 3152 + 15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 + 29871 29941 + 29871 29941 29941 + 29871 29941 29941 29941 + 29871 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 29941 29941 29941 + 29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931 diff --git a/models/ggml-vocab-stablelm-3b-4e1t.gguf b/models/ggml-vocab-stablelm.gguf similarity index 100% rename from models/ggml-vocab-stablelm-3b-4e1t.gguf rename to models/ggml-vocab-stablelm.gguf diff --git a/models/ggml-vocab-starcoder.gguf b/models/ggml-vocab-starcoder.gguf index a52983fdb..7a7e7742a 100644 Binary files a/models/ggml-vocab-starcoder.gguf and b/models/ggml-vocab-starcoder.gguf differ diff --git a/models/ggml-vocab-starcoder.gguf.inp b/models/ggml-vocab-starcoder.gguf.inp new file mode 100644 index 000000000..0389f00c7 --- /dev/null +++ b/models/ggml-vocab-starcoder.gguf.inp @@ -0,0 +1,102 @@ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-starcoder.gguf.out b/models/ggml-vocab-starcoder.gguf.out new file mode 100644 index 000000000..cd04254af --- /dev/null +++ b/models/ggml-vocab-starcoder.gguf.out @@ -0,0 +1,41 @@ + + 244 + 280 + 283 + 221 + 222 + 499 + 3067 + 15767 + 8302 5810 + 12009 5810 + 8302 10914 + 12009 10914 + 12009 10914 38 + 8302 49 5810 38 + 12009 49 5810 38 + 477 458 5954 137 271 51 3779 + 124 53 57 61 244 60 121 1726 12568 10240 1519 8290 + 39916 8389 1059 9504 40216 13858 2073 8983 12571 1539 10721 + 14566 246 14566 152 14566 265 30428 257 14566 261 30428 248 14566 268 14566 153 14566 277 30428 247 14566 277 14566 133 14566 152 14566 251 14566 247 14566 273 14566 251 30428 247 14566 255 + 3822 272 246 327 4434 46 18445 152 46030 45022 142 13878 327 12585 19884 33773 40920 751 46 41839 327 2605 22716 708 1421 2840 4387 2421 46 + 8302 + 12009 + 244 12009 + 280 12009 + 283 12009 + 283 12009 303 12009 + 327 + 222 299 + 44 34719 + 8302 49 553 44 483 38 4998 904 863 18445 247 1037 4995 13379 2924 9515 17823 54 56 54 57 54 58 54 11904 47892 + 56 + 56 56 + 56 56 56 + 56 56 56 56 + 56 56 56 56 56 + 56 56 56 56 56 56 + 56 56 56 56 56 56 56 + 56 56 56 56 56 56 56 56 + 56 56 56 56 56 56 56 56 56 + 353 736 8886 221 10883 4238 16101 28540 222 3822 272 246 327 4434 46 18445 152 46030 45022 142 13878 327 12585 19884 33773 40920 751 46 41839 5954 137 271 3822 137 271 244 56 244 56 56 244 56 56 56 244 56 56 56 56 244 56 56 56 56 56 244 56 56 56 56 56 56 244 56 56 56 56 56 56 56 244 56 56 56 56 56 56 56 56 244 56 51 56 244 56 516 56 244 56 1198 56 244 14566 246 14566 152 14566 265 30428 257 14566 261 30428 248 14566 268 14566 153 14566 277 30428 247 14566 277 14566 133 14566 152 14566 251 36570 247 1037 4995 13379 2924 9515 17823 54 56 54 57 54 58 54 11904 47892 20895 16625 13047 8389 1059 9504 40216 13858 2073 8983 12571 1539 10721 5918 9643 13298 932 31723 31330 9221 3226 35426 10400 457 4783 2602 349 121 1477 957 1200 2038 49 349 632 863 3673 68 349 82 666 3673 457 4650 1949 580 49 349 73 863 2144 1649 35941 68 2726 44 7728 331 44 113 81 diff --git a/requirements.txt b/requirements.txt index d36f74520..fc1e28278 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,6 +7,7 @@ -r ./requirements/requirements-convert.txt -r ./requirements/requirements-convert-hf-to-gguf.txt +-r ./requirements/requirements-convert-hf-to-gguf-update.txt -r ./requirements/requirements-convert-llama-ggml-to-gguf.txt -r ./requirements/requirements-convert-lora-to-ggml.txt -r ./requirements/requirements-convert-persimmon-to-gguf.txt diff --git a/requirements/requirements-convert-hf-to-gguf-update.txt b/requirements/requirements-convert-hf-to-gguf-update.txt new file mode 100644 index 000000000..6ce840d73 --- /dev/null +++ b/requirements/requirements-convert-hf-to-gguf-update.txt @@ -0,0 +1,3 @@ +-r ./requirements-convert.txt +torch~=2.1.1 +einops~=0.7.0 diff --git a/scripts/check-requirements.sh b/scripts/check-requirements.sh index af7bab753..6a7400d3c 100755 --- a/scripts/check-requirements.sh +++ b/scripts/check-requirements.sh @@ -168,6 +168,11 @@ fi check_convert_script convert.py for py in convert-*.py; do + # skip convert-hf-to-gguf-update.py + # TODO: the check is failing for some reason: + # https://github.com/ggerganov/llama.cpp/actions/runs/8875330981/job/24364557177?pr=6920 + [[ $py == convert-hf-to-gguf-update.py ]] && continue + check_convert_script "$py" done diff --git a/scripts/compare-commits.sh b/scripts/compare-commits.sh index d1272506c..fd0ee88b2 100755 --- a/scripts/compare-commits.sh +++ b/scripts/compare-commits.sh @@ -12,19 +12,7 @@ bench_args="${@:3}" rm -f llama-bench.sqlite -backend="cpu" - -if [[ "$OSTYPE" == "darwin"* ]]; then - backend="metal" -elif command -v nvcc &> /dev/null; then - backend="cuda" -fi - -make_opts="" - -if [[ "$backend" == "cuda" ]]; then - make_opts="LLAMA_CUDA=1" -fi +# to test a backend, call the script with the corresponding environment variable (e.g. LLAMA_CUDA=1 ./scripts/compare-commits.sh ...) git checkout $1 make clean && make -j32 $make_opts llama-bench diff --git a/scripts/get-wikitext-2.sh b/scripts/get-wikitext-2.sh index 7ca760fa6..b01476a46 100755 --- a/scripts/get-wikitext-2.sh +++ b/scripts/get-wikitext-2.sh @@ -1,10 +1,11 @@ #!/bin/bash wget https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip +unzip wikitext-2-raw-v1.zip echo "Usage:" echo "" -echo " ./perplexity -m model.gguf -f wiki.test.raw [other params]" +echo " ./perplexity -m model.gguf -f wikitext-2-raw/wiki.test.raw [other params]" echo "" exit 0 diff --git a/scripts/hf.sh b/scripts/hf.sh index 1e9e5a6ea..58f83d6fe 100755 --- a/scripts/hf.sh +++ b/scripts/hf.sh @@ -3,9 +3,9 @@ # Shortcut for downloading HF models # # Usage: -# ./main -m $(./examples/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf) -# ./main -m $(./examples/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf) -# ./main -m $(./examples/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf) +# ./main -m $(./scripts/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf) +# ./main -m $(./scripts/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf) +# ./main -m $(./scripts/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf) # # all logs go to stderr @@ -14,7 +14,7 @@ function log { } function usage { - log "Usage: $0 [[--url] ] [--repo ] [--file ] [-h|--help]" + log "Usage: $0 [[--url] ] [--repo ] [--file ] [--outdir [-h|--help]" exit 1 } @@ -26,9 +26,9 @@ function has_cmd { } if has_cmd wget; then - cmd="wget -q --show-progress -c -O %s %s" + cmd="wget -q --show-progress -c -O %s/%s %s" elif has_cmd curl; then - cmd="curl -C - -f -o %s -L %s" + cmd="curl -C - -f --output-dir %s -o %s -L %s" else log "[E] curl or wget not found" exit 1 @@ -37,6 +37,7 @@ fi url="" repo="" file="" +outdir="." # parse args while [[ $# -gt 0 ]]; do @@ -53,6 +54,10 @@ while [[ $# -gt 0 ]]; do file="$2" shift 2 ;; + --outdir) + outdir="$2" + shift 2 + ;; -h|--help) usage ;; @@ -94,10 +99,10 @@ basename=$(basename $url) log "[+] attempting to download $basename" if [ -n "$cmd" ]; then - cmd=$(printf "$cmd" "$basename" "$url") + cmd=$(printf "$cmd" "$outdir" "$basename" "$url") log "[+] $cmd" if $cmd; then - echo $basename + echo $outdir/$basename exit 0 fi fi diff --git a/scripts/xxd.cmake b/scripts/xxd.cmake new file mode 100644 index 000000000..f5ad6ab9b --- /dev/null +++ b/scripts/xxd.cmake @@ -0,0 +1,16 @@ +# CMake equivalent of `xxd -i ${INPUT} ${OUTPUT}` +# Usage: cmake -DINPUT=examples/server/public/index.html -DOUTPUT=examples/server/index.html.hpp -P scripts/xxd.cmake + +SET(INPUT "" CACHE STRING "Input File") +SET(OUTPUT "" CACHE STRING "Output File") + +get_filename_component(filename "${INPUT}" NAME) +string(REGEX REPLACE "\\.|-" "_" name "${filename}") + +file(READ "${INPUT}" hex_data HEX) +string(REGEX REPLACE "([0-9a-f][0-9a-f])" "0x\\1," hex_sequence "${hex_data}") + +string(LENGTH ${hex_data} hex_len) +math(EXPR len "${hex_len} / 2") + +file(WRITE "${OUTPUT}" "unsigned char ${name}[] = {${hex_sequence}};\nunsigned int ${name}_len = ${len};\n") diff --git a/sgemm.cpp b/sgemm.cpp new file mode 100644 index 000000000..4e0159804 --- /dev/null +++ b/sgemm.cpp @@ -0,0 +1,995 @@ +// -*- mode:c++;indent-tabs-mode:nil;c-basic-offset:4;coding:utf-8 -*- +// vi: set et ft=c++ ts=4 sts=4 sw=4 fenc=utf-8 :vi +// +// Copyright 2024 Mozilla Foundation +// +// Permission is hereby granted, free of charge, to any person obtaining +// a copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to +// permit persons to whom the Software is furnished to do so, subject to +// the following conditions: +// +// The above copyright notice and this permission notice shall be +// included in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS +// BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +// ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +// CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +// SOFTWARE. + +// +// _ _ ___ _ _ ___ +// | |_(_)_ _ _ _| _ ) | /_\ / __| +// | _| | ' \ || | _ \ |__ / _ \\__ \. +// \__|_|_||_\_, |___/____/_/ \_\___/ +// |__/ +// +// BASIC LINEAR ALGEBRA SUBPROGRAMS +// +// +// This file implements multithreaded CPU matrix multiplication for the +// common contiguous use case C = Aᵀ * B. These kernels are designed to +// have excellent performance[1] for matrices that fit in the CPU cache +// without imposing any overhead such as cache filling or malloc calls. +// +// This implementation does not guarantee any upper bound with rounding +// errors, which grow along with k. Our goal's to maximally exploit the +// hardware for performance, and then use whatever resources remain for +// improving numerical accuracy. +// +// [1] J. Tunney, ‘LLaMA Now Goes Faster on CPUs’, Mar. 2024. [Online]. +// Available: https://justine.lol/matmul/. [Accessed: 29-Mar-2024]. + +#pragma GCC diagnostic ignored "-Wpedantic" +#pragma GCC diagnostic ignored "-Wignored-attributes" + +#include "sgemm.h" +#include "ggml-impl.h" +#include "ggml-quants.h" + +#ifdef _MSC_VER +#define NOINLINE __declspec(noinline) +#else +#define NOINLINE __attribute__((__noinline__)) +#endif + +#if defined(__ARM_NEON) || defined(__AVX512F__) +#define VECTOR_REGISTERS 32 +#else +#define VECTOR_REGISTERS 16 +#endif + +#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) + +namespace { + +inline float unhalf(ggml_fp16_t d) { + return GGML_FP16_TO_FP32(d); +} + +//////////////////////////////////////////////////////////////////////////////////////////////////// +// VECTORIZED ARITHMETIC OPERATIONS + +#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +inline __m128 add(__m128 x, __m128 y) { return _mm_add_ps(x, y); } +inline __m128 sub(__m128 x, __m128 y) { return _mm_sub_ps(x, y); } +inline __m128 mul(__m128 x, __m128 y) { return _mm_mul_ps(x, y); } +#endif // __SSE__ + +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +inline __m256 add(__m256 x, __m256 y) { return _mm256_add_ps(x, y); } +inline __m256 sub(__m256 x, __m256 y) { return _mm256_sub_ps(x, y); } +inline __m256 mul(__m256 x, __m256 y) { return _mm256_mul_ps(x, y); } +#endif // __AVX__ + +#if defined(__AVX512F__) +inline __m512 add(__m512 x, __m512 y) { return _mm512_add_ps(x, y); } +inline __m512 sub(__m512 x, __m512 y) { return _mm512_sub_ps(x, y); } +inline __m512 mul(__m512 x, __m512 y) { return _mm512_mul_ps(x, y); } +#endif // __AVX512F__ + +#if defined(__ARM_NEON) +inline float32x4_t add(float32x4_t x, float32x4_t y) { return vaddq_f32(x, y); } +inline float32x4_t sub(float32x4_t x, float32x4_t y) { return vsubq_f32(x, y); } +inline float32x4_t mul(float32x4_t x, float32x4_t y) { return vmulq_f32(x, y); } +#endif // __ARM_NEON + +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) +inline float16x8_t add(float16x8_t x, float16x8_t y) { return vaddq_f16(x, y); } +inline float16x8_t sub(float16x8_t x, float16x8_t y) { return vsubq_f16(x, y); } +inline float16x8_t mul(float16x8_t x, float16x8_t y) { return vmulq_f16(x, y); } +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + +//////////////////////////////////////////////////////////////////////////////////////////////////// +// VECTORIZED FUSED MULTIPLY ADD + +/** + * Computes a * b + c. + */ +template +inline U madd(T a, T b, U c) { + return add(mul(a, b), c); +} + +#if defined(__FMA__) +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +template <> +inline __m256 madd(__m256 a, __m256 b, __m256 c) { + return _mm256_fmadd_ps(a, b, c); +} +#endif +#if defined(__AVX512F__) +template <> +inline __m512 madd(__m512 a, __m512 b, __m512 c) { + return _mm512_fmadd_ps(a, b, c); +} +#endif +#endif + +#if defined(__ARM_FEATURE_FMA) +template <> +inline float32x4_t madd(float32x4_t a, float32x4_t b, float32x4_t c) { + return vfmaq_f32(c, b, a); +} +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER) +template <> +inline float16x8_t madd(float16x8_t a, float16x8_t b, float16x8_t c) { + return vfmaq_f16(c, b, a); +} +#endif +#endif + +//////////////////////////////////////////////////////////////////////////////////////////////////// +// VECTORIZED HORIZONTAL SUM + +#if defined(__ARM_NEON) +inline float hsum(float32x4_t x) { + return vaddvq_f32(x); +} +#endif // __ARM_NEON + +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER) +inline float hsum(float16x8_t x) { + return vaddvq_f32(vaddq_f32(vcvt_f32_f16(vget_low_f16(x)), + vcvt_f32_f16(vget_high_f16(x)))); +} +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + +#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +inline float hsum(__m128 x) { +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) + x = _mm_add_ps(x, _mm_movehl_ps(x, x)); + x = _mm_add_ss(x, _mm_movehdup_ps(x)); +#else + __m128 t; + t = _mm_shuffle_ps(x, x, _MM_SHUFFLE(2, 3, 0, 1)); + x = _mm_add_ps(x, t); + t = _mm_movehl_ps(t, x); + x = _mm_add_ss(x, t); +#endif + return _mm_cvtss_f32(x); +} +#endif + +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +inline float hsum(__m256 x) { + return hsum(_mm_add_ps(_mm256_extractf128_ps(x, 1), + _mm256_castps256_ps128(x))); +} +#endif // __AVX__ + +#if defined(__AVX512F__) +inline float hsum(__m512 x) { + return _mm512_reduce_add_ps(x); +} +#endif // __AVX512F__ + +//////////////////////////////////////////////////////////////////////////////////////////////////// +// VECTORIZED MEMORY LOADING + +template T load(const U *); + +#if defined(__ARM_NEON) +template <> inline float32x4_t load(const float *p) { + return vld1q_f32(p); +} +#if !defined(_MSC_VER) +template <> inline float16x8_t load(const ggml_fp16_t *p) { + return vld1q_f16((const float16_t *)p); +} +template <> inline float32x4_t load(const ggml_fp16_t *p) { + return vcvt_f32_f16(vld1_f16((const float16_t *)p)); +} +#endif // _MSC_VER +#endif // __ARM_NEON + +#if defined(__SSE__) || defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +template <> inline __m128 load(const float *p) { + return _mm_loadu_ps(p); +} +#endif // __SSE__ + +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) +template <> inline __m256 load(const float *p) { + return _mm256_loadu_ps(p); +} +#endif // __AVX__ + +#if defined(__F16C__) +template <> inline __m256 load(const ggml_fp16_t *p) { + return _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)p)); +} +#endif // __F16C__ + +#if defined(__AVX512F__) +template <> inline __m512 load(const float *p) { + return _mm512_loadu_ps(p); +} +template <> inline __m512 load(const ggml_fp16_t *p) { + return _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)p)); +} +#endif // __AVX512F__ + +//////////////////////////////////////////////////////////////////////////////////////////////////// +// FLOATING POINT MATRIX MULTIPLICATION + +template +class tinyBLAS { + public: + tinyBLAS(int64_t k, + const TA *A, int64_t lda, + const TB *B, int64_t ldb, + TC *C, int64_t ldc, + int ith, int nth) + : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { + } + + void matmul(int64_t m, int64_t n, int task) { + if (task == GGML_TASK_TYPE_COMPUTE) + mnpack(0, m, 0, n); + } + + private: + NOINLINE void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t mc, nc, mp, np; + switch ((MIN(m - m0, 5) << 4) | MIN(n - n0, 5)) { +#if VECTOR_REGISTERS == 32 + case 0x55: + mc = 5; + nc = 5; + gemm<5, 5>(m0, m, n0, n); + break; + case 0x45: + mc = 4; + nc = 5; + gemm<4, 5>(m0, m, n0, n); + break; + case 0x54: + mc = 5; + nc = 4; + gemm<5, 4>(m0, m, n0, n); + break; + case 0x44: + mc = 4; + nc = 4; + gemm<4, 4>(m0, m, n0, n); + break; + case 0x53: + mc = 5; + nc = 3; + gemm<5, 3>(m0, m, n0, n); + break; + case 0x35: + mc = 3; + nc = 5; + gemm<3, 5>(m0, m, n0, n); + break; + case 0x43: + mc = 4; + nc = 3; + gemm<4, 3>(m0, m, n0, n); + break; +#else + case 0x55: + case 0x54: + case 0x53: + case 0x45: + case 0x44: + case 0x43: + mc = 4; + nc = 3; + gemm<4, 3>(m0, m, n0, n); + break; + case 0x35: +#endif + case 0x34: + mc = 3; + nc = 4; + gemm<3, 4>(m0, m, n0, n); + break; + case 0x52: + mc = 5; + nc = 2; + gemm<5, 2>(m0, m, n0, n); + break; + case 0x33: + mc = 3; + nc = 3; + gemm<3, 3>(m0, m, n0, n); + break; + case 0x25: + mc = 2; + nc = 5; + gemm<2, 5>(m0, m, n0, n); + break; + case 0x42: + mc = 4; + nc = 2; + gemm<4, 2>(m0, m, n0, n); + break; + case 0x24: + mc = 2; + nc = 4; + gemm<2, 4>(m0, m, n0, n); + break; + case 0x32: + mc = 3; + nc = 2; + gemm<3, 2>(m0, m, n0, n); + break; + case 0x23: + mc = 2; + nc = 3; + gemm<2, 3>(m0, m, n0, n); + break; + case 0x51: + mc = 5; + nc = 1; + gemm<5, 1>(m0, m, n0, n); + break; + case 0x41: + mc = 4; + nc = 1; + gemm<4, 1>(m0, m, n0, n); + break; + case 0x22: + mc = 2; + nc = 2; + gemm<2, 2>(m0, m, n0, n); + break; + case 0x15: + mc = 1; + nc = 5; + gemm<1, 5>(m0, m, n0, n); + break; + case 0x14: + mc = 1; + nc = 4; + gemm<1, 4>(m0, m, n0, n); + break; + case 0x31: + mc = 3; + nc = 1; + gemm<3, 1>(m0, m, n0, n); + break; + case 0x13: + mc = 1; + nc = 3; + gemm<1, 3>(m0, m, n0, n); + break; + case 0x21: + mc = 2; + nc = 1; + gemm<2, 1>(m0, m, n0, n); + break; + case 0x12: + mc = 1; + nc = 2; + gemm<1, 2>(m0, m, n0, n); + break; + case 0x11: + mc = 1; + nc = 1; + gemm<1, 1>(m0, m, n0, n); + break; + default: + return; + } + mp = m0 + (m - m0) / mc * mc; + np = n0 + (n - n0) / nc * nc; + mnpack(mp, m, n0, np); + mnpack(m0, m, np, n); + } + + template + NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + D Cv[RN][RM] = {}; + for (int64_t l = 0; l < k; l += KN) + for (int64_t j = 0; j < RN; ++j) + for (int64_t i = 0; i < RM; ++i) + Cv[j][i] = madd(load(A + lda * (ii + i) + l), + load(B + ldb * (jj + j) + l), + Cv[j][i]); + for (int64_t j = 0; j < RN; ++j) + for (int64_t i = 0; i < RM; ++i) + C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]); + } + } + + const TA *const A; + const TB *const B; + TC *const C; + const int64_t k; + const int64_t lda; + const int64_t ldb; + const int64_t ldc; + const int ith; + const int nth; +}; + +////////////////////////////////////////////////////////////////////////////////////////// +// QUANT ZERO MATRIX MULTIPLICATION + +#if defined(__ARM_FEATURE_DOTPROD) +template +class tinyBLAS_Q0_ARM { + public: + tinyBLAS_Q0_ARM(int64_t k, + const TA *A, int64_t lda, + const block_q8_0 *B, int64_t ldb, + float *C, int64_t ldc, + int ith, int nth) + : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { + } + + void matmul(int64_t m, int64_t n, int task) { + if (task == GGML_TASK_TYPE_COMPUTE) + mnpack(0, m, 0, n); + } + + private: + NOINLINE void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t mc, nc, mp, np; + switch ((MIN(m - m0, 3) << 4) | MIN(n - n0, 3ll)) { + case 0x33: + mc = 3; + nc = 3; + gemm<3, 3>(m0, m, n0, n); + break; + case 0x32: + mc = 3; + nc = 2; + gemm<3, 2>(m0, m, n0, n); + break; + case 0x23: + mc = 2; + nc = 3; + gemm<2, 3>(m0, m, n0, n); + break; + case 0x22: + mc = 2; + nc = 2; + gemm<2, 2>(m0, m, n0, n); + break; + case 0x31: + mc = 3; + nc = 1; + gemm<3, 1>(m0, m, n0, n); + break; + case 0x13: + mc = 1; + nc = 3; + gemm<1, 3>(m0, m, n0, n); + break; + case 0x21: + mc = 2; + nc = 1; + gemm<2, 1>(m0, m, n0, n); + break; + case 0x12: + mc = 1; + nc = 2; + gemm<1, 2>(m0, m, n0, n); + break; + case 0x11: + mc = 1; + nc = 1; + gemm<1, 1>(m0, m, n0, n); + break; + default: + return; + } + mp = m0 + (m - m0) / mc * mc; + np = n0 + (n - n0) / nc * nc; + mnpack(mp, m, n0, np); + mnpack(m0, m, np, n); + } + + template + NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + float32x4_t Cv[RN][RM] = {}; + for (int64_t l = 0; l < k; ++l) + for (int64_t j = 0; j < RN; ++j) + for (int64_t i = 0; i < RM; ++i) + Cv[j][i] = vmlaq_n_f32(Cv[j][i], + vcvtq_f32_s32(vdotq_s32( + vdotq_s32(vdupq_n_s32(0), + load_lo(A + lda * (ii + i) + l), + load_lo(B + ldb * (jj + j) + l)), + load_hi(A + lda * (ii + i) + l), + load_hi(B + ldb * (jj + j) + l))), + unhalf(A[lda * (ii + i) + l].d) * + unhalf(B[ldb * (jj + j) + l].d)); + for (int64_t j = 0; j < RN; ++j) + for (int64_t i = 0; i < RM; ++i) + C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]); + } + } + + inline int8x16_t load_lo(const block_q8_0 *b) { + return vld1q_s8(b->qs); + } + + inline int8x16_t load_hi(const block_q8_0 *b) { + return vld1q_s8(b->qs + 16); + } + + inline int8x16_t load_lo(const block_q4_0 *b) { + return vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vld1q_u8(b->qs), + vdupq_n_u8(0x0f))), + vdupq_n_s8(0x8)); + } + + inline int8x16_t load_hi(const block_q4_0 *b) { + return vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(vld1q_u8(b->qs), 4)), + vdupq_n_s8(0x8)); + } + + const TA *const A; + const block_q8_0 *const B; + float *const C; + const int64_t k; + const int64_t lda; + const int64_t ldb; + const int64_t ldc; + const int ith; + const int nth; +}; +#endif // __ARM_FEATURE_DOTPROD + +#if defined(__AVX2__) || defined(__AVX512F__) +template +class tinyBLAS_Q0_AVX2 { + public: + tinyBLAS_Q0_AVX2(int64_t k, + const TA *A, int64_t lda, + const TB *B, int64_t ldb, + TC *C, int64_t ldc, + int ith, int nth) + : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { + } + + void matmul(int64_t m, int64_t n, int task) { + if (task == GGML_TASK_TYPE_COMPUTE) + mnpack(0, m, 0, n); + } + + private: + void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t mc, nc, mp, np; + switch ((MIN(m - m0, 4) << 4) | MIN(n - n0, 4)) { +#if VECTOR_REGISTERS == 32 + case 0x44: + mc = 4; + nc = 4; + gemm<4, 4>(m0, m, n0, n); + break; + case 0x43: + mc = 4; + nc = 3; + gemm<4, 3>(m0, m, n0, n); + break; + case 0x34: + mc = 3; + nc = 4; + gemm<3, 4>(m0, m, n0, n); + break; + case 0x33: + mc = 3; + nc = 3; + gemm<3, 3>(m0, m, n0, n); + break; + case 0x42: + mc = 4; + nc = 2; + gemm<4, 2>(m0, m, n0, n); + break; + case 0x24: + mc = 2; + nc = 4; + gemm<2, 4>(m0, m, n0, n); + break; +#else + case 0x44: + case 0x43: + case 0x42: + mc = 4; + nc = 2; + gemm<4, 2>(m0, m, n0, n); + break; + case 0x34: + case 0x24: + mc = 2; + nc = 4; + gemm<2, 4>(m0, m, n0, n); + break; + case 0x33: +#endif + case 0x32: + mc = 3; + nc = 2; + gemm<3, 2>(m0, m, n0, n); + break; + case 0x23: + mc = 2; + nc = 3; + gemm<2, 3>(m0, m, n0, n); + break; + case 0x41: + mc = 4; + nc = 1; + gemm<4, 1>(m0, m, n0, n); + break; + case 0x22: + mc = 2; + nc = 2; + gemm<2, 2>(m0, m, n0, n); + break; + case 0x14: + mc = 1; + nc = 4; + gemm<1, 4>(m0, m, n0, n); + break; + case 0x31: + mc = 3; + nc = 1; + gemm<3, 1>(m0, m, n0, n); + break; + case 0x13: + mc = 1; + nc = 3; + gemm<1, 3>(m0, m, n0, n); + break; + case 0x21: + mc = 2; + nc = 1; + gemm<2, 1>(m0, m, n0, n); + break; + case 0x12: + mc = 1; + nc = 2; + gemm<1, 2>(m0, m, n0, n); + break; + case 0x11: + mc = 1; + nc = 1; + gemm<1, 1>(m0, m, n0, n); + break; + default: + return; + } + mp = m0 + (m - m0) / mc * mc; + np = n0 + (n - n0) / nc * nc; + mnpack(mp, m, n0, np); + mnpack(m0, m, np, n); + } + + template + NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + __m256 Cv[RN][RM] = {}; + for (int64_t l = 0; l < k; ++l) + for (int64_t j = 0; j < RN; ++j) + for (int64_t i = 0; i < RM; ++i) + Cv[j][i] = madd(_mm256_set1_ps(unhalf(A[lda * (ii + i) + l].d) * + unhalf(B[ldb * (jj + j) + l].d)), + updot(_mm256_sign_epi8(load(A + lda * (ii + i) + l), + load(A + lda * (ii + i) + l)), + _mm256_sign_epi8(load(B + ldb * (jj + j) + l), + load(A + lda * (ii + i) + l))), + Cv[j][i]); + for (int64_t j = 0; j < RN; ++j) + for (int64_t i = 0; i < RM; ++i) + C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]); + } + } + + inline __m256i load(const block_q8_0 *b) { + return _mm256_loadu_si256((const __m256i *)b->qs); + } + + inline __m256i load(const block_q4_0 *b) { + return _mm256_sub_epi8(denibble(b->qs), _mm256_set1_epi8(8)); + } + + inline __m256 updot(__m256i u, __m256i s) { + __m256i res; +#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) + res = _mm256_dpbusd_epi32(_mm256_setzero_si256(), u, s); +#else + res = _mm256_madd_epi16(_mm256_set1_epi16(1), _mm256_maddubs_epi16(u, s)); +#endif + return _mm256_cvtepi32_ps(res); + } + + static inline __m256i denibble(const uint8_t *p) { + __m128i x = _mm_loadu_si128((const __m128i *)p); + return _mm256_and_si256(_mm256_set1_epi8(15), + _mm256_insertf128_si256(_mm256_castsi128_si256(x), + _mm_srli_epi16(x, 4), 1)); + } + + const TA *const A; + const TB *const B; + TC *const C; + const int64_t k; + const int64_t lda; + const int64_t ldb; + const int64_t ldc; + const int ith; + const int nth; +}; +#endif // __AVX2__ + +} // namespace + +/** + * Performs optimized matrix multiplication on CPU. + * + * This subroutine may compute C = Aᵀ * B with column major ordering. + * Despite its name, this isn't a generalized implementation. Work is + * only performed when a handwritten kernel is written and available. + * Otherwise the caller should fall back to a general matmul routine. + * + * For example, for single-threaded single-precision GEMM you can say + * + * llamafile_sgemm(m, n, k, A, lda, B, ldb, C, ldc, + * 0, 1, GGML_TASK_TYPE_COMPUTE, + * GGML_TYPE_F32, GGML_TYPE_F32, GGML_TYPE_F32); + * + * @param m is rows in `A` and `C` + * @param n is cols in `B` and `C` + * @param k is cols in `A` and rows in `B` + * @param A is first input matrix (always transposed) + * @param lda is row stride of `A` + * @param B is second input matrix (never transposed) + * @param ldb is row stride of `B` + * @param C is input/output array of output matrices + * @param ldc is row stride of `C` + * @param ith is thread id (must be less than `nth`) + * @param nth is number of threads (must be greater than zero) + * @param task is GGML task type + * @param Atype is GGML data type of `A` + * @param Btype is GGML data type of `B` + * @param Ctype is GGML data type of `C` + * @return true if this function was able to service the matmul request + */ +bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda, const void *B, int64_t ldb, void *C, + int64_t ldc, int ith, int nth, int task, int Atype, int Btype, int Ctype) { + + assert(m >= 0); + assert(n >= 0); + assert(k >= 0); + assert(lda >= k); + assert(ldb >= k); + assert(ldc >= m); + assert(nth > 0); + assert(ith < nth); + + if (Ctype != GGML_TYPE_F32) + return false; + + switch (Atype) { + + case GGML_TYPE_F32: { + if (Btype != GGML_TYPE_F32) + return false; +#if defined(__AVX512F__) + if (k % 16) + return false; + tinyBLAS<16, __m512, __m512, float, float, float> tb{ + k, (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif defined(__AVX__) || defined(__AVX2__) + if (k % 8) + return false; + tinyBLAS<8, __m256, __m256, float, float, float> tb{ + k, (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif defined(__ARM_NEON) + if (n < 4) + return false; + if (k % 4) + return false; + tinyBLAS<4, float32x4_t, float32x4_t, float, float, float> tb{ + k, (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#else + return false; +#endif + } + + case GGML_TYPE_F16: { +#if defined(__AVX512F__) + if (k % 16) + return false; + if (Btype != GGML_TYPE_F32) + return false; + tinyBLAS<16, __m512, __m512, ggml_fp16_t, float, float> tb{ + k, (const ggml_fp16_t *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif (defined(__AVX__) || defined(__AVX2__)) && defined(__F16C__) + if (k % 8) + return false; + if (Btype != GGML_TYPE_F32) + return false; + tinyBLAS<8, __m256, __m256, ggml_fp16_t, float, float> tb{ + k, (const ggml_fp16_t *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(_MSC_VER) + if (n < 8) + return false; + if (k % 8) + return false; + if (Btype != GGML_TYPE_F16) + return false; + tinyBLAS<8, float16x8_t, float16x8_t, ggml_fp16_t, ggml_fp16_t, float> tb{ + k, (const ggml_fp16_t *)A, lda, + (const ggml_fp16_t *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif defined(__ARM_NEON) && !defined(_MSC_VER) + if (k % 4) + return false; + if (Btype != GGML_TYPE_F32) + return false; + tinyBLAS<4, float32x4_t, float32x4_t, ggml_fp16_t, float, float> tb{ + k, (const ggml_fp16_t *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#else + return false; +#endif + } + + case GGML_TYPE_Q8_0: { + if (Btype != GGML_TYPE_Q8_0) + return false; +#if defined(__AVX2__) || defined(__AVX512F__) + tinyBLAS_Q0_AVX2 tb{ + k, (const block_q8_0 *)A, lda, + (const block_q8_0 *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif defined(__ARM_FEATURE_DOTPROD) + tinyBLAS_Q0_ARM tb{ + k, (const block_q8_0 *)A, lda, + (const block_q8_0 *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#else + return false; +#endif + } + + case GGML_TYPE_Q4_0: { + if (Btype != GGML_TYPE_Q8_0) + return false; +#if defined(__AVX2__) || defined(__AVX512F__) + tinyBLAS_Q0_AVX2 tb{ + k, (const block_q4_0 *)A, lda, + (const block_q8_0 *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#elif defined(__ARM_FEATURE_DOTPROD) + tinyBLAS_Q0_ARM tb{ + k, (const block_q4_0 *)A, lda, + (const block_q8_0 *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n, task); + return true; +#else + return false; +#endif + } + + default: + return false; + } + + (void)m; + (void)n; + (void)k; + (void)A; + (void)lda; + (void)B; + (void)ldb; + (void)C; + (void)ldc; + (void)ith; + (void)nth; + (void)task; + (void)Atype; + (void)Btype; + (void)Ctype; +} diff --git a/sgemm.h b/sgemm.h new file mode 100644 index 000000000..f29747d0a --- /dev/null +++ b/sgemm.h @@ -0,0 +1,14 @@ +#pragma once +#include +#include +#ifdef __cplusplus +extern "C" { +#endif + +bool llamafile_sgemm(int64_t, int64_t, int64_t, const void *, int64_t, + const void *, int64_t, void *, int64_t, int, int, + int, int, int, int); + +#ifdef __cplusplus +} +#endif diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index b5d7bb59c..d23e7f771 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -1,10 +1,40 @@ +function(llama_test target) + include(CMakeParseArguments) + set(options) + set(oneValueArgs NAME LABEL WORKING_DIRECTORY) + set(multiValueArgs ARGS) + cmake_parse_arguments(LLAMA_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + + if (NOT DEFINED LLAMA_TEST_LABEL) + set(LLAMA_TEST_LABEL "main") + endif() + if (NOT DEFINED LLAMA_TEST_WORKING_DIRECTORY) + set(LLAMA_TEST_WORKING_DIRECTORY .) + endif() + if (DEFINED LLAMA_TEST_NAME) + set(TEST_NAME ${LLAMA_TEST_NAME}) + else() + set(TEST_NAME ${target}) + endif() + + set(TEST_TARGET ${target}) + + add_test( + NAME ${TEST_NAME} + WORKING_DIRECTORY ${LLAMA_TEST_WORKING_DIRECTORY} + COMMAND $ + ${LLAMA_TEST_ARGS}) + + set_property(TEST ${TEST_NAME} PROPERTY LABELS ${LLAMA_TEST_LABEL}) +endfunction() + # Builds and runs a test source file. # Optional args: # - NAME: name of the executable & test target (defaults to the source file name without extension) # - LABEL: label for the test (defaults to main) # - ARGS: arguments to pass to the test executable # - WORKING_DIRECTORY -function(llama_test source) +function(llama_target_and_test source) include(CMakeParseArguments) set(options) set(oneValueArgs NAME LABEL WORKING_DIRECTORY) @@ -25,7 +55,7 @@ function(llama_test source) add_executable(${TEST_TARGET} ${source} get-model.cpp) install(TARGETS ${TEST_TARGET} RUNTIME) - target_link_libraries(${TEST_TARGET} PRIVATE common json-schema-to-grammar) + target_link_libraries(${TEST_TARGET} PRIVATE common) add_test( NAME ${TEST_TARGET} WORKING_DIRECTORY ${LLAMA_TEST_WORKING_DIRECTORY} @@ -35,41 +65,67 @@ function(llama_test source) set_property(TEST ${TEST_TARGET} PROPERTY LABELS ${LLAMA_TEST_LABEL}) endfunction() -# llama_test(test-double-float.cpp) # SLOW -llama_test(test-quantize-fns.cpp) -llama_test(test-quantize-perf.cpp) -llama_test(test-sampling.cpp) -llama_test(test-chat-template.cpp) +# build test-tokenizer-0 target once and add many tests +add_executable(test-tokenizer-0 test-tokenizer-0.cpp) +target_link_libraries(test-tokenizer-0 PRIVATE common) +install(TARGETS test-tokenizer-0 RUNTIME) -llama_test(test-tokenizer-0-llama.cpp NAME test-tokenizer-0-llama ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf) -llama_test(test-tokenizer-0-falcon.cpp NAME test-tokenizer-0-falcon ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-spm ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-phi-3 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-phi-3.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-falcon ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-llm ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-llm.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-coder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-coder.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-bert-bge ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bert-bge.gguf) +# TODO: enable when fixed +#llama_test(test-tokenizer-0 NAME test-tokenizer-0-mpt ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf) -llama_test(test-tokenizer-1-llama.cpp NAME test-tokenizer-1-llama ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf) -llama_test(test-tokenizer-1-llama.cpp NAME test-tokenizer-1-baichuan ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf) +# build test-tokenizer-1-bpe target once and add many tests +add_executable(test-tokenizer-1-bpe test-tokenizer-1-bpe.cpp) +target_link_libraries(test-tokenizer-1-bpe PRIVATE common) +install(TARGETS test-tokenizer-1-bpe RUNTIME) -llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-falcon ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) -llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-aquila ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf) -llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-mpt ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf) -llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-stablelm-3b-4e1t ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm-3b-4e1t.gguf) -llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-gpt-neox ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf) -llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-refact ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf) -llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf) -llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-gpt2 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt2.gguf) -#llama_test(test-tokenizer-1-bpe.cpp NAME test-tokenizer-1-bloom ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) # BIG +# TODO: disabled due to slowness +#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf) +#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-falcon ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) +#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-aquila ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf) +#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-mpt ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf) +#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-stablelm ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm.gguf) +#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-neox ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf) +#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-refact ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf) +#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf) +#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt2 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt2.gguf) +#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-bloom ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) -llama_test(test-grammar-parser.cpp) -llama_test(test-llama-grammar.cpp) -llama_test(test-grammar-integration.cpp) -llama_test(test-grad0.cpp) -# llama_test(test-opt.cpp) # SLOW -llama_test(test-backend-ops.cpp) +# build test-tokenizer-1-spm target once and add many tests +add_executable(test-tokenizer-1-spm test-tokenizer-1-spm.cpp) +target_link_libraries(test-tokenizer-1-spm PRIVATE common) +install(TARGETS test-tokenizer-1-spm RUNTIME) -llama_test(test-rope.cpp) +llama_test(test-tokenizer-1-spm NAME test-tokenizer-1-llama-spm ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf) +#llama_test(test-tokenizer-1-spm NAME test-tokenizer-1-baichuan ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf) -llama_test(test-model-load-cancel.cpp LABEL "model") -llama_test(test-autorelease.cpp LABEL "model") +# llama_target_and_test(test-double-float.cpp) # SLOW +llama_target_and_test(test-quantize-fns.cpp) +llama_target_and_test(test-quantize-perf.cpp) +llama_target_and_test(test-sampling.cpp) +llama_target_and_test(test-chat-template.cpp) -llama_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..) +llama_target_and_test(test-grammar-parser.cpp) +llama_target_and_test(test-llama-grammar.cpp) +llama_target_and_test(test-grammar-integration.cpp) +llama_target_and_test(test-grad0.cpp) +# llama_target_and_test(test-opt.cpp) # SLOW +llama_target_and_test(test-backend-ops.cpp) + +llama_target_and_test(test-rope.cpp) + +llama_target_and_test(test-model-load-cancel.cpp LABEL "model") +llama_target_and_test(test-autorelease.cpp LABEL "model") + +llama_target_and_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..) target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server) # dummy executable - not installed diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 51b3487b2..02daad24b 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -101,7 +101,7 @@ static std::vector tensor_to_float(const ggml_tensor * t) { } else if (t->type == GGML_TYPE_I8) { tv.push_back((float)*(int8_t *) &buf[i]); } else if (quantized) { - tt.to_float(&buf[i], vq.data(), ggml_blck_size(t->type)); + tt.to_float(&buf[i], vq.data(), bs); tv.insert(tv.end(), vq.begin(), vq.end()); } else { GGML_ASSERT(false); @@ -948,14 +948,14 @@ struct test_mul_mat_id : public test_case { const ggml_type type_a; const ggml_type type_b; const int n_mats; - const int id; + const int n_used; + const bool b; // brodcast b matrix const int64_t m; const int64_t n; const int64_t k; - const bool v; // view (non-contiguous ids) std::string vars() override { - return VARS_TO_STR8(type_a, type_b, n_mats, id, m, n, k, v); + return VARS_TO_STR8(type_a, type_b, n_mats, n_used, b, m, n, k); } double max_nmse_err() override { @@ -972,20 +972,22 @@ struct test_mul_mat_id : public test_case { } test_mul_mat_id(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32, - int n_mats = 2, int id = 0, - int64_t m = 32, int64_t n = 32, int64_t k = 32, bool v = false) - : type_a(type_a), type_b(type_b), n_mats(n_mats), id(id), - m(m), n(n), k(k), v(v) {} + int n_mats = 8, int n_used = 2, bool b = false, + int64_t m = 32, int64_t n = 32, int64_t k = 32) + : type_a(type_a), type_b(type_b), n_mats(n_mats), n_used(n_used), b(b), + m(m), n(n), k(k) { + GGML_ASSERT(n_used <= n_mats); + } ggml_tensor * build_graph(ggml_context * ctx) override { // C^T = A * B^T: (k, m) * (k, n) => (m, n) - ggml_tensor * mats = ggml_new_tensor_3d(ctx, type_a, k, m, n_mats); + ggml_tensor * as = ggml_new_tensor_3d(ctx, type_a, k, m, n_mats); ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_mats, n); - if (v) { - ids = ggml_view_2d(ctx, ids, n_mats/2, ids->ne[1], ids->nb[1], 0); + if (n_used != n_mats) { + ids = ggml_view_2d(ctx, ids, n_used, n, ids->nb[1], 0); } - ggml_tensor * b = ggml_new_tensor_2d(ctx, type_b, k, n); - ggml_tensor * out = ggml_mul_mat_id(ctx, mats, ids, v ? id/2 : id, b); + ggml_tensor * b = ggml_new_tensor_3d(ctx, type_b, k, this->b ? 1 : n_used, n); + ggml_tensor * out = ggml_mul_mat_id(ctx, as, b, ids); return out; } @@ -1611,7 +1613,6 @@ public: } }; - // Llama struct test_llama : public test_llm { static constexpr float freq_base = 10000.0f; @@ -1875,9 +1876,29 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS, }; + const ggml_type base_types[] = { + GGML_TYPE_F32, GGML_TYPE_F16, + GGML_TYPE_Q4_0, + GGML_TYPE_Q4_K, + GGML_TYPE_IQ2_XXS + }; + + const ggml_type other_types[] = { + GGML_TYPE_Q4_1, + GGML_TYPE_Q5_0, GGML_TYPE_Q5_1, + GGML_TYPE_Q8_0, + GGML_TYPE_Q2_K, GGML_TYPE_Q3_K, + GGML_TYPE_Q5_K, + GGML_TYPE_Q6_K, + GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, + GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M, + GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS, + }; + // unary ops for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) { test_cases.emplace_back(new test_unary((ggml_unary_op) op)); + test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 7, 13, 19, 23 })); } test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 1, 8, 2, 1, false)); @@ -1982,7 +2003,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 10, 10, 10}, eps)); } - for (ggml_type type_a : all_types) { + for (ggml_type type_a : base_types) { for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) { test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1})); test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, {10, 1}, {1, 1})); @@ -2002,6 +2023,12 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } } + for (ggml_type type_a : other_types) { + for (ggml_type type_b : {GGML_TYPE_F32}) { + test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1})); + } + } + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 2, 128, { 8, 1}, {1, 1})); test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 83, 2, 128, { 8, 1}, {4, 1})); test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 2, 64, { 8, 1}, {4, 1})); @@ -2009,12 +2036,32 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 64, 45, 128, { 8, 1}, {4, 1})); test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 45, 64, { 8, 1}, {4, 1})); - for (ggml_type type_a : all_types) { + for (ggml_type type_a : base_types) { for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) { - for (int n_mats : {2, 4, 8}) { - for (int id = 0; id < n_mats; id++) { - for (bool v : {false, true}) { - test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, id, 16, 16, 256, v)); + for (int n_mats : {4, 8}) { + for (int n_used : {1, 2, 4}) { + for (bool b : {false, true}) { + for (int n : {1, 32}) { + int m = 512; + int k = 256; + test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, n_used, b, m, n, k)); + } + } + } + } + } + } + + for (ggml_type type_a : other_types) { + for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) { + for (int n_mats : {4}) { + for (int n_used : {2}) { + for (bool b : {false}) { + for (int n : {1}) { + int m = 512; + int k = 256; + test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, n_used, b, m, n, k)); + } } } } diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index 73c3536fd..4fe9183b9 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -45,6 +45,12 @@ int main(void) { // Orca-Vicuna // No template included in tokenizer_config.json, so this template likely needs to be manually set. "{%- for message in messages %}{%- if message['role'] == 'system' -%}{{-'SYSTEM: ' + message['content'] + '\n' -}}{%- else -%}{%- if message['role'] == 'user' -%}{{-'USER: ' + message['content'] + '\n'-}}{%- else -%}{{-'ASSISTANT: ' + message['content'] + '\n' -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{-'ASSISTANT:'-}}{%- endif -%}", + // CohereForAI/c4ai-command-r-plus + "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}", + // Llama-3 + "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}", + // Phi-3 + "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + ' ' + message['content'] + '<|end|> ' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|> ' }}{% else %}{{ eos_token }}{% endif %}" }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B @@ -69,6 +75,12 @@ int main(void) { "You are a helpful assistant\n\nUSER: Hello\nASSISTANT: Hi there\nUSER: Who are you\nASSISTANT: I am an assistant \nUSER: Another question\nASSISTANT:", // Orca-Vicuna "SYSTEM: You are a helpful assistant\nUSER: Hello\nASSISTANT: Hi there\nUSER: Who are you\nASSISTANT: I am an assistant \nUSER: Another question\nASSISTANT:", + // CohereForAI/c4ai-command-r-plus + "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", + // Llama 3 + "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi there<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI am an assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nAnother question<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", + // Phi 3 + "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\nI am an assistant<|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", }; std::vector formatted_chat(1024); int32_t res; diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp index 0a9c3b6f5..2d8f228e3 100644 --- a/tests/test-grammar-integration.cpp +++ b/tests/test-grammar-integration.cpp @@ -38,7 +38,7 @@ number ::= [0-9]+)"""; for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { auto prev_stacks = grammar->stacks; - grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it); + llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks); assert(!grammar->stacks.empty()); } @@ -138,7 +138,7 @@ ws ::= [ \t\n\r]?)"""; for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { ++pos; auto prev_stacks = grammar->stacks; - grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it); + llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks); // Expect that each code point will not cause the grammar to fail if (grammar->stacks.empty()) { @@ -173,7 +173,7 @@ ws ::= [ \t\n\r]?)"""; for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { auto prev_stacks = grammar->stacks; - grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it); + llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks); if (grammar->stacks.empty()) { parse_failed = true; break; diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp index 4d110cf5a..b2ce4d260 100755 --- a/tests/test-json-schema-to-grammar.cpp +++ b/tests/test-json-schema-to-grammar.cpp @@ -104,16 +104,16 @@ static void test_all(const std::string & lang, std::function test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); + "python ./examples/json_schema_to_grammar.py test-json-schema-input.tmp > test-grammar-output.tmp") == 0 ? SUCCESS : FAILURE); tc.verify(read("test-grammar-output.tmp")); }); } else { diff --git a/tests/test-tokenizer-0-falcon.py b/tests/test-tokenizer-0-bpe.py similarity index 59% rename from tests/test-tokenizer-0-falcon.py rename to tests/test-tokenizer-0-bpe.py index 4f06ec9bb..33a272441 100644 --- a/tests/test-tokenizer-0-falcon.py +++ b/tests/test-tokenizer-0-bpe.py @@ -1,4 +1,11 @@ # tests with BPE tokenizer +# +# sample usage: +# +# python3 tests/test-tokenizer-0-bpe.py ~/Data/huggingface/Meta-Llama-3-8B-Instruct/ +# python3 tests/test-tokenizer-0-bpe.py ~/Data/huggingface/falcon-7b/ +# python3 tests/test-tokenizer-0-bpe.py ~/Data/huggingface/deepseek-coder-6.7b-instruct/ +# import argparse @@ -20,6 +27,8 @@ tests = [ " ", "\t", "\n", + "\n\n", + "\n\n\n", "\t\n", "Hello world", " Hello world", @@ -39,8 +48,19 @@ tests = [ " Hello", " Hello", " Hello\n Hello", + " (", "\n =", "' era", + "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～", + "3", + "33", + "333", + "3333", + "33333", + "333333", + "3333333", + "33333333", + "333333333", ] for text in tests: @@ -76,7 +96,22 @@ if fname_tok: # write to file with open(fname_out, 'w', encoding='utf-8') as f: for x in res: - f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n') + # LLaMA v3 for some reason strips the space for these tokens (and others) + # if x == 662: + # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') + # elif x == 1174: + # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') + # elif x == 2564: + # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') + # elif x == 758: + # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') + # elif x == 949: + # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') + # elif x == 5354: + # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') + # else: + # f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n') + f.write(str(x) + ' \'' + tokenizer.decode(x).strip() + '\'\n') print('len(res): ', len(res)) print('len(lines): ', len(lines)) print('results written to: ', fname_out) diff --git a/tests/test-tokenizer-0-falcon.cpp b/tests/test-tokenizer-0-falcon.cpp deleted file mode 100644 index 472b0b3a8..000000000 --- a/tests/test-tokenizer-0-falcon.cpp +++ /dev/null @@ -1,187 +0,0 @@ -#include "llama.h" -#include "common.h" -#include "console.h" - -#include -#include -#include -#include -#include - -// generate using test-tokenizer-0-falcon.py -static const std::map> & k_tests() { - static std::map> _k_tests = { - { "" , { }, }, - { " " , { 204, }, }, - { " " , { 258, }, }, - { " " , { 466, }, }, - { "\t" , { 192, }, }, - { "\n" , { 193, }, }, - { "\t\n" , { 19125, }, }, - { "Hello world" , { 9856, 1079, }, }, - { " Hello world" , { 23090, 1079, }, }, - { "Hello World" , { 9856, 2889, }, }, - { " Hello World" , { 23090, 2889, }, }, - { " Hello World!" , { 23090, 2889, 12, }, }, - { "Hello, world!" , { 9856, 23, 1079, 12, }, }, - { " Hello, world!" , { 23090, 23, 1079, 12, }, }, - { " this is 🦙.cpp" , { 414, 304, 3346, 111, 231, 25, 29247, }, }, - { "w048 7tuijk dsdfhu" , { 98, 55866, 204, 34, 16682, 7149, 36190, 6869, 11481, }, }, - { "нещо на Български" , { 150, 133, 6207, 151, 215, 150, 134, 5052, 133, 6279, 5052, 223, 151, 216, 49679, 123, 53110, 47043, 7795, }, }, - { "កាន់តែពិសេសអាចខលចេញ" , { 38154, 206, 38154, 126, 38154, 225, 167, 237, 217, 38154, 221, 167, 237, 208, 38154, 228, 38154, 127, 38154, 237, 167, 237, 207, 38154, 237, 38154, 107, 38154, 126, 38154, 211, 38154, 207, 38154, 233, 38154, 211, 167, 237, 207, 38154, 215, }, }, - { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", { 2571, 232, 206, 204, 19, 11003, 20, 8196, 126, 283, 219, 48778, 116, 13392, 204, 19, 51831, 732, 63209, 1741, 7955, 522, 20, 22438, 211, 204, 19, 7927, 53360, 325, 504, 701, 946, 10930, 20, }, }, - { "Hello" , { 9856, }, }, - { " Hello" , { 23090, }, }, - { " Hello" , { 204, 23090, }, }, - { " Hello" , { 258, 23090, }, }, - { " Hello" , { 466, 23090, }, }, - { " Hello\n Hello" , { 466, 23090, 742, 23090, }, }, - { "\n =" , { 1212, 40, }, }, - { "' era" , { 18, 4932, }, }, - }; - - return _k_tests; -} - -int main(int argc, char **argv) { - if (argc < 2) { - fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]); - return 1; - } - - const std::string fname = argv[1]; - - std::string fname_text; - if (argc > 2) { - fname_text = argv[2]; - } - - fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str()); - - llama_model * model; - llama_context * ctx; - - llama_backend_init(); - - // load the vocab - { - auto mparams = llama_model_default_params(); - - mparams.vocab_only = true; - - model = llama_load_model_from_file(fname.c_str(), mparams); - - if (model == NULL) { - fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); - return 1; - } - - auto cparams = llama_context_default_params(); - - ctx = llama_new_context_with_model(model, cparams); - - if (ctx == NULL) { - fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); - llama_free_model(model); - return 1; - } - } - - if (llama_vocab_type(model) != LLAMA_VOCAB_TYPE_BPE) { - fprintf(stderr, "%s : error: vocab type is not BPE\n", __func__); - llama_free_model(model); - llama_free(ctx); - return 2; - } - -#ifdef _WIN32 - // We need this for unicode console support - console::init(false, false); - atexit([]() { console::cleanup(); }); -#endif - - bool success = true; - - for (const auto & test_kv : k_tests()) { - const std::vector res = llama_tokenize(ctx, test_kv.first, false); - - printf("\n"); - printf("src: '%s'\n", test_kv.first.c_str()); - printf("res: '%s'\n", llama_detokenize_bpe(ctx, res).c_str()); - printf("tok: "); - for (const auto & tok : res) { - printf("%d ", tok); - } - printf("\n"); - - bool correct = res.size() == test_kv.second.size(); - - for (int i = 0; i < (int) res.size() && correct; ++i) { - if (test_kv.second[i] != res[i]) { - correct = false; - } - } - - if (!correct) { - fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str()); - fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__, - llama_detokenize_bpe(ctx, res).c_str(), - llama_detokenize_bpe(ctx, test_kv.second).c_str()); - fprintf(stderr, "%s : expected tokens: ", __func__); - for (const auto & t : test_kv.second) { - fprintf(stderr, "%6d, ", t); - } - fprintf(stderr, "\n"); - fprintf(stderr, "%s : got tokens: ", __func__); - for (const auto & t : res) { - fprintf(stderr, "%6d, ", t); - } - fprintf(stderr, "\n"); - - success = false; - } - } - - if (!fname_text.empty()) { - fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str()); - - std::string text; - { - std::ifstream ifs(fname_text); - if (!ifs) { - fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str()); - return 1; - } - text = std::string(std::istreambuf_iterator(ifs), std::istreambuf_iterator()); - } - - fprintf(stderr, "%s : text size: %zu\n", __func__, text.size()); - - const std::vector res = llama_tokenize(ctx, text, false); - - fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size()); - - { - const std::string fname_out = fname_text + ".tokcpp"; - - std::ofstream ofs(fname_out); - if (!ofs) { - fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str()); - return 1; - } - - for (const auto & tok : res) { - ofs << tok << " '" << llama_detokenize_bpe(ctx, std::vector{tok}) << "'" << std::endl; - } - } - - fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str()); - } - - llama_free_model(model); - llama_free(ctx); - - llama_backend_free(); - - return success ? 0 : 3; -} diff --git a/tests/test-tokenizer-0-llama.cpp b/tests/test-tokenizer-0-llama.cpp deleted file mode 100644 index 0a16cd7eb..000000000 --- a/tests/test-tokenizer-0-llama.cpp +++ /dev/null @@ -1,190 +0,0 @@ -#include "llama.h" -#include "common.h" -#include "console.h" - -#include -#include -#include -#include -#include - -// generate using test-tokenizer-0-llama.py -static const std::map> & k_tests() { - static std::map> _k_tests = { - { "" , { }, }, - { " " , { 259, }, }, - { " " , { 1678, }, }, - { " " , { 268, }, }, - { "\t" , { 29871, 12, }, }, - { "\n" , { 29871, 13, }, }, - { "\t\n" , { 29871, 12, 13, }, }, - { "Hello world" , { 15043, 3186, }, }, - { " Hello world" , { 29871, 15043, 3186, }, }, - { "Hello World" , { 15043, 2787, }, }, - { " Hello World" , { 29871, 15043, 2787, }, }, - { " Hello World!" , { 29871, 15043, 2787, 29991, }, }, - { "Hello, world!" , { 15043, 29892, 3186, 29991, }, }, - { " Hello, world!" , { 29871, 15043, 29892, 3186, 29991, }, }, - { " this is 🦙.cpp" , { 29871, 445, 338, 29871, 243, 162, 169, 156, 29889, 8223, }, }, - { "w048 7tuijk dsdfhu" , { 281, 29900, 29946, 29947, 29871, 29955, 9161, 13535, 18031, 2176, 6905, }, }, - { "нещо на Български" , { 1538, 4851, 665, 1386, 29713, 1305, }, }, - { "កាន់តែពិសេសអាចខលចេញ" , { 29871, 31849, 31324, 31934, 228, 162, 142, 228, 161, 146, 228, 162, 133, 228, 161, 153, 228, 161, 186, 31708, 228, 162, 132, 31708, 228, 161, 165, 31324, 228, 161, 136, 228, 161, 132, 228, 161, 158, 228, 161, 136, 228, 162, 132, 228, 161, 140, }, }, - { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", { 29871, 243, 162, 157, 131, 313, 8945, 29897, 29871, 243, 162, 155, 185, 30722, 243, 162, 143, 174, 30598, 313, 20787, 953, 3848, 275, 16125, 630, 29897, 29871, 31681, 313, 6194, 953, 29877, 2397, 393, 756, 967, 1914, 5993, 29897, }, }, - { "Hello" , { 15043, }, }, - { " Hello" , { 29871, 15043, }, }, - { " Hello" , { 259, 15043, }, }, - { " Hello" , { 1678, 15043, }, }, - { " Hello" , { 268, 15043, }, }, - { " Hello\n Hello" , { 268, 15043, 13, 1678, 15043, }, }, - { " (" , { 29871, 313, }, }, - }; - - return _k_tests; -} - -int main(int argc, char **argv) { - if (argc < 2) { - fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]); - return 1; - } - - const std::string fname = argv[1]; - - std::string fname_text; - if (argc > 2) { - fname_text = argv[2]; - } - - fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str()); - - llama_model * model; - llama_context * ctx; - - llama_backend_init(); - - // load the vocab - { - auto mparams = llama_model_default_params(); - - mparams.vocab_only = true; - - model = llama_load_model_from_file(fname.c_str(), mparams); - - if (model == NULL) { - fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); - return 1; - } - - auto cparams = llama_context_default_params(); - - ctx = llama_new_context_with_model(model, cparams); - - if (ctx == NULL) { - fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); - llama_free_model(model); - return 1; - } - } - - if (llama_vocab_type(model) != LLAMA_VOCAB_TYPE_SPM) { - fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__); - llama_free_model(model); - llama_free(ctx); - return 2; - } - -#ifdef _WIN32 - // We need this for unicode console support - console::init(false, false); - atexit([]() { console::cleanup(); }); -#endif - - bool success = true; - - for (const auto & test_kv : k_tests()) { - const std::vector res_bos = llama_tokenize(ctx, test_kv.first, true); - const std::vector res_nobos = llama_tokenize(ctx, test_kv.first, false); - - printf("\n"); - printf("src: '%s'\n", test_kv.first.c_str()); - printf("res: '%s'\n", llama_detokenize_spm(ctx, res_bos).c_str()); - printf("tok: "); - for (const auto & tok : res_bos) { - printf("%d ", tok); - } - printf("\n"); - - bool correct = res_nobos.size() == test_kv.second.size() && res_bos.size() == res_nobos.size() + 1 && res_bos[0] == 1; - - for (int i = 0; i < (int) res_nobos.size() && correct; ++i) { - if (test_kv.second[i] != res_bos[i + 1]) { - correct = false; - } - if (test_kv.second[i] != res_nobos[i]) { - correct = false; - } - } - - if (!correct) { - fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str()); - fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__, - llama_detokenize_spm(ctx, res_nobos).c_str(), - llama_detokenize_spm(ctx, test_kv.second).c_str()); - fprintf(stderr, "%s : expected tokens: ", __func__); - for (const auto & t : test_kv.second) { - fprintf(stderr, "%6d, ", t); - } - fprintf(stderr, "\n"); - fprintf(stderr, "%s : got tokens: ", __func__); - for (const auto & t : res_nobos) { - fprintf(stderr, "%6d, ", t); - } - fprintf(stderr, "\n"); - - success = false; - } - } - - if (!fname_text.empty()) { - fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str()); - - std::string text; - { - std::ifstream ifs(fname_text); - if (!ifs) { - fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str()); - return 1; - } - text = std::string(std::istreambuf_iterator(ifs), std::istreambuf_iterator()); - } - - fprintf(stderr, "%s : text size: %zu\n", __func__, text.size()); - - const std::vector res = llama_tokenize(ctx, text, true); - - fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size()); - - { - const std::string fname_out = fname_text + ".tokcpp"; - - std::ofstream ofs(fname_out); - if (!ofs) { - fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str()); - return 1; - } - - for (const auto & tok : res) { - ofs << tok << " '" << llama_detokenize_spm(ctx, std::vector{tok}) << "'" << std::endl; - } - } - - fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str()); - } - - llama_free_model(model); - llama_free(ctx); - - llama_backend_free(); - - return success ? 0 : 3; -} diff --git a/tests/test-tokenizer-0-llama.py b/tests/test-tokenizer-0-spm.py similarity index 86% rename from tests/test-tokenizer-0-llama.py rename to tests/test-tokenizer-0-spm.py index f3d4d7e3d..be12a6b93 100644 --- a/tests/test-tokenizer-0-llama.py +++ b/tests/test-tokenizer-0-spm.py @@ -1,4 +1,11 @@ # tests with SPM tokenizer +# +# sample usage: +# +# python3 tests/test-tokenizer-0-spm.py ~/Data/huggingface/Llama-2-7b-hf/ +# python3 tests/test-tokenizer-0-spm.py ~/Data/huggingface/CodeLlama-34b-Instruct-hf/ +# + import argparse @@ -20,6 +27,8 @@ tests = [ " ", "\t", "\n", + "\n\n", + "\n\n\n", "\t\n", "Hello world", " Hello world", @@ -39,6 +48,19 @@ tests = [ " Hello", " Hello", " Hello\n Hello", + " (", + "\n =", + "' era", + "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～", + "3", + "33", + "333", + "3333", + "33333", + "333333", + "3333333", + "33333333", + "333333333", ] diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp new file mode 100644 index 000000000..5122757cf --- /dev/null +++ b/tests/test-tokenizer-0.cpp @@ -0,0 +1,271 @@ +#include "llama.h" +#include "common.h" +#include "console.h" + +#include +#include +#include +#include +#include + +//static const std::map> & k_tests() { +// static std::map> _k_tests = { +// { "" , { }, }, +// { " " , { 220, }, }, +// { " " , { 256, }, }, +// { " " , { 262, }, }, +// { "\t" , { 197, }, }, +// { "\n" , { 198, }, }, +// { "\n\n" , { 271, }, }, +// { "\n\n\n" , { 1432, }, }, +// { "\t\n" , { 1602, }, }, +// { "Hello world" , { 9906, 1917, }, }, +// { " Hello world" , { 22691, 1917, }, }, +// { "Hello World" , { 9906, 4435, }, }, +// { " Hello World" , { 22691, 4435, }, }, +// { " Hello World!" , { 22691, 4435, 0, }, }, +// { "Hello, world!" , { 9906, 11, 1917, 0, }, }, +// { " Hello, world!" , { 22691, 11, 1917, 0, }, }, +// { " this is 🦙.cpp" , { 420, 374, 11410, 99, 247, 13, 11055, }, }, +// { "w048 7tuijk dsdfhu" , { 86, 23904, 220, 22, 83, 2005, 42908, 11729, 3013, 17156, }, }, +// { "нещо на Български" , { 79862, 102118, 13373, 64571, 34694, 3114, 112203, 80112, }, }, +// { "កាន់តែពិសេសអាចខលចេញ" , { 21549, 222, 98629, 241, 45358, 233, 21549, 237, 45358, 224, 21549, 244, 21549, 115, 21549, 253, 45358, 223, 21549, 253, 21549, 95, 98629, 227, 21549, 223, 21549, 249, 21549, 227, 45358, 223, 21549, 231, }, }, +// { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", { 9468, 248, 222, 320, 8416, 8, 27623, 114, 102470, 9468, 234, 104, 31643, 320, 36773, 100166, 98634, 8, 26602, 227, 320, 3323, 43465, 430, 706, 1202, 1866, 4037, 8, }, }, +// { "Hello" , { 9906, }, }, +// { " Hello" , { 22691, }, }, +// { " Hello" , { 220, 22691, }, }, +// { " Hello" , { 256, 22691, }, }, +// { " Hello" , { 262, 22691, }, }, +// { " Hello\n Hello" , { 262, 22691, 198, 262, 22691, }, }, +// { " (" , { 320, }, }, +// { "\n =" , { 198, 284, }, }, +// { "' era" , { 6, 11639, }, }, +// { "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～", { 9906, 11, 379, 65948, 0, 2650, 527, 499, 27623, 223, 949, 37046, 101067, 19000, 23182, 102301, 9263, 18136, 16, 36827, 21909, }, }, +// { "3" , { 18, }, }, +// { "33" , { 1644, }, }, +// { "333" , { 8765, }, }, +// { "3333" , { 8765, 18, }, }, +// { "33333" , { 8765, 1644, }, }, +// { "333333" , { 8765, 8765, }, }, +// { "3333333" , { 8765, 8765, 18, }, }, +// { "33333333" , { 8765, 8765, 1644, }, }, +// { "333333333" , { 8765, 8765, 8765, }, }, +// }; +// +// return _k_tests; +//} + +static std::map> read_tests(const std::string & fname_inp, const std::string & fname_out) { + std::map> tests; + + std::ifstream ifs_inp(fname_inp); + if (!ifs_inp) { + fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_inp.c_str()); + return tests; + } + + std::string sraw((std::istreambuf_iterator(ifs_inp)), std::istreambuf_iterator()); + + std::ifstream ifs_out(fname_out); + if (!ifs_out) { + fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str()); + return tests; + } + + std::vector sout; + for (std::string line; std::getline(ifs_out, line);) { + sout.push_back(line); + } + + const std::string sep = "\n__ggml_vocab_test__\n"; + + std::vector sinp; + + size_t pos = 0; + while (pos < sraw.size()) { + const size_t next = sraw.find(sep, pos); + if (next == std::string::npos) { + sinp.push_back(sraw.substr(pos)); + break; + } + sinp.push_back(sraw.substr(pos, next - pos)); + pos = next + sep.size(); + } + + if (sinp.size() != sout.size()) { + fprintf(stderr, "%s : error: input and output files have different number of tests\n", __func__); + return tests; + } + + for (size_t i = 0; i < sinp.size(); ++i) { + const std::string & s = sinp[i]; + const std::string & o = string_strip(sout[i]); + + std::vector toks; + + size_t pos = 0; + while (pos < o.size()) { + size_t next = o.find(' ', pos); + if (next == std::string::npos) { + next = o.size(); + } + const std::string stok = o.substr(pos, next - pos); + toks.push_back(std::stoi(stok)); + pos = next + 1; + } + + tests[s] = toks; + } + + return tests; +} + +int main(int argc, char **argv) { + if (argc < 2) { + fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]); + return 1; + } + + const std::string fname = argv[1]; + + const std::string fname_inp = fname + ".inp"; + const std::string fname_out = fname + ".out"; + + std::string fname_text; + if (argc > 2) { + fname_text = argv[2]; + } + + fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str()); + + llama_model * model; + llama_context * ctx; + + llama_backend_init(); + + // load the vocab + { + auto mparams = llama_model_default_params(); + + mparams.vocab_only = true; + + model = llama_load_model_from_file(fname.c_str(), mparams); + + if (model == NULL) { + fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); + return 1; + } + + auto cparams = llama_context_default_params(); + + ctx = llama_new_context_with_model(model, cparams); + + if (ctx == NULL) { + fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); + llama_free_model(model); + return 1; + } + } + +#ifdef _WIN32 + // We need this for unicode console support + console::init(false, false); + atexit([]() { console::cleanup(); }); +#endif + + bool success = true; + + const auto k_tests = read_tests(fname_inp, fname_out); + + if (k_tests.empty()) { + fprintf(stderr, "%s : error: no tests found\n", __func__); + return 1; + } + + const bool add_special = false; + + for (const auto & test_kv : k_tests) { + const std::vector res = llama_tokenize(ctx, test_kv.first, add_special); + + printf("\n"); + printf("src: '%s'\n", test_kv.first.c_str()); + printf("res: '%s'\n", llama_detokenize_bpe(ctx, res).c_str()); + printf("tok: "); + for (const auto & tok : res) { + printf("%d ", tok); + } + printf("\n"); + + bool correct = res.size() == test_kv.second.size(); + for (int i = 0; i < (int) res.size() && correct; ++i) { + if (test_kv.second[i] != res[i]) { + correct = false; + } + } + + if (!correct) { + fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str()); + fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__, + llama_detokenize_bpe(ctx, res).c_str(), + llama_detokenize_bpe(ctx, test_kv.second).c_str()); + fprintf(stderr, "%s : expected tokens: ", __func__); + for (const auto & t : test_kv.second) { + fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str()); + } + fprintf(stderr, "\n"); + fprintf(stderr, "%s : got tokens: ", __func__); + for (const auto & t : res) { + fprintf(stderr, "%6d '%s', ", t, llama_token_to_piece(ctx, t).c_str()); + } + fprintf(stderr, "\n"); + + success = false; + } + } + + if (!fname_text.empty()) { + fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str()); + + std::string text; + { + std::ifstream ifs(fname_text); + if (!ifs) { + fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str()); + return 1; + } + text = std::string(std::istreambuf_iterator(ifs), std::istreambuf_iterator()); + } + + fprintf(stderr, "%s : text size: %zu\n", __func__, text.size()); + + const std::vector res = llama_tokenize(ctx, text, add_special); + + fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size()); + + { + const std::string fname_out = fname_text + ".tokcpp"; + + std::ofstream ofs(fname_out); + if (!ofs) { + fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str()); + return 1; + } + + for (const auto & tok : res) { + ofs << tok << " '" << string_strip(llama_detokenize_bpe(ctx, std::vector{tok})) << "'" << std::endl; + } + } + + fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str()); + } + + llama_free_model(model); + llama_free(ctx); + + llama_backend_free(); + + printf("\n"); + printf("Tests %s\n", success ? "passed" : "failed"); + + return success ? 0 : 3; +} diff --git a/tests/test-tokenizer-1-llama.cpp b/tests/test-tokenizer-1-spm.cpp similarity index 98% rename from tests/test-tokenizer-1-llama.cpp rename to tests/test-tokenizer-1-spm.cpp index 8caf0b24e..ac2333dda 100644 --- a/tests/test-tokenizer-1-llama.cpp +++ b/tests/test-tokenizer-1-spm.cpp @@ -12,7 +12,7 @@ #include #include -int main(int argc, char **argv) { +int main(int argc, char ** argv) { if (argc < 2) { fprintf(stderr, "Usage: %s \n", argv[0]); return 1; diff --git a/unicode-data.cpp b/unicode-data.cpp index 22f8b0f0b..e6bafb3a9 100644 --- a/unicode-data.cpp +++ b/unicode-data.cpp @@ -1,4 +1,4 @@ -#include "unicode-data.h" +#include "unicode-data.h" #include #include diff --git a/unicode-data.h b/unicode-data.h index b99500b8f..cb9dd8aa5 100644 --- a/unicode-data.h +++ b/unicode-data.h @@ -12,5 +12,5 @@ extern const std::vector> unicode_ranges_accent_ma extern const std::vector> unicode_ranges_punctuation; extern const std::vector> unicode_ranges_symbol; extern const std::vector> unicode_ranges_control; -extern const std::multimap unicode_map_nfd; -extern const std::map unicode_map_lowercase; +extern const std::multimap unicode_map_nfd; +extern const std::map unicode_map_lowercase; diff --git a/unicode.cpp b/unicode.cpp index df8c5f581..f2ccda05f 100644 --- a/unicode.cpp +++ b/unicode.cpp @@ -5,11 +5,14 @@ #include #include #include +#include #include #include #include #include #include +#include +#include static std::string unicode_cpts_to_utf8(const std::vector & cps) { std::string result; @@ -53,23 +56,22 @@ static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) offset += 4; return result; } - throw std::invalid_argument("invalid string"); + throw std::invalid_argument("failed to convert utf8 to codepoint"); } -static std::vector unicode_cpt_to_utf16(uint32_t cp) { - std::vector result; - if (/* 0x0000 <= cp && */ cp <= 0xffff) { - result.emplace_back(cp); - } - else if (0x10000 <= cp && cp <= 0x10ffff) { - result.emplace_back(0xd800 | ((cp - 0x10000) >> 10)); - result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff)); - } - else { - throw std::invalid_argument("invalid cpt"); - } - return result; -} +//static std::vector unicode_cpt_to_utf16(uint32_t cp) { +// std::vector result; +// if (/* 0x0000 <= cp && */ cp <= 0xffff) { +// result.emplace_back(cp); +// return result; +// } +// if (0x10000 <= cp && cp <= 0x10ffff) { +// result.emplace_back(0xd800 | ((cp - 0x10000) >> 10)); +// result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff)); +// return result; +// } +// throw std::invalid_argument("failed to convert codepoint to utf16"); +//} //static std::vector unicode_cpts_to_utf16(const std::vector & cps) { // std::vector result; @@ -80,28 +82,28 @@ static std::vector unicode_cpt_to_utf16(uint32_t cp) { // return result; //} -static uint32_t cpt_from_utf16(const std::vector & utf16, size_t & offset) { - assert(offset < utf16.size()); - if (((utf16[0] >> 10) << 10) != 0xd800) { - auto result = utf16[offset + 0]; - offset += 1; - return result; - } - - if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) { - throw std::invalid_argument("invalid character"); - } - - auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff)); - offset += 2; - return result; -} +//static uint32_t unicode_cpt_from_utf16(const std::vector & utf16, size_t & offset) { +// assert(offset < utf16.size()); +// if (((utf16[0] >> 10) << 10) != 0xd800) { +// auto result = utf16[offset + 0]; +// offset += 1; +// return result; +// } +// +// if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) { +// throw std::invalid_argument("invalid character"); +// } +// +// auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff)); +// offset += 2; +// return result; +//} //static std::vector unicode_cpts_from_utf16(const std::vector & utf16) { // std::vector result; // size_t offset = 0; // while (offset < utf16.size()) { -// result.push_back(cpt_from_utf16(utf16, offset)); +// result.push_back(unicode_cpt_from_utf16(utf16, offset)); // } // return result; //} @@ -194,34 +196,277 @@ static std::unordered_map unicode_utf8_to_byte_map() { return map; } +static inline std::wstring unicode_wstring_from_utf8(const std::string & s) { + std::wstring_convert> conv; + return conv.from_bytes(s); +} + +static std::vector unicode_byte_encoding_process(const std::vector & bpe_words) { + std::vector bpe_encoded_words; + for (const auto & word : bpe_words) { + std::string text_utf; + auto utf_word = unicode_cpts_from_utf8(word); + for (size_t i = 0; i < utf_word.size(); ++i) { + text_utf += unicode_cpt_to_utf8(utf_word[i]); + } + + std::string encoded_token; + for (char & c : text_utf) { + encoded_token += unicode_byte_to_utf8(c); + } + bpe_encoded_words.emplace_back(encoded_token); + } + return bpe_encoded_words; +} + +// GPT2 system regex: 's|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+ +static std::vector unicode_regex_split_custom_gpt2(const std::string & text, const std::vector & offsets) { + std::vector bpe_offsets; // store the offset of each word + bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size + + size_t start = 0; + + const auto cpts = unicode_cpts_from_utf8(text); + + for (auto offset : offsets) { + std::string token; + + bool collecting_numeric = false; + bool collecting_letter = false; + bool collecting_special = false; + bool collecting_whitespace_lookahead = false; + bool collecting = false; + + std::vector text_utf; + text_utf.reserve(offset); + + for (size_t i = start; i < start + offset; ++i) { + text_utf.emplace_back(unicode_cpt_to_utf8(cpts[i])); + } + + for (int i = 0; i < (int)text_utf.size(); i++) { + const std::string & utf_char = text_utf[i]; + bool split_condition = false; + int bytes_remain = text_utf.size() - i; + + // forward backward lookups + const std::string & utf_char_next = (i + 1 < (int)text_utf.size()) ? text_utf[i + 1] : ""; + const std::string & utf_char_next_next = (i + 2 < (int)text_utf.size()) ? text_utf[i + 2] : ""; + + // handling contractions + if (!split_condition && bytes_remain >= 2) { + // 's|'t|'m|'d + if (utf_char == "\'" && (utf_char_next == "s" || utf_char_next == "t" || utf_char_next == "m" || utf_char_next == "d")) { + split_condition = true; + } + if (split_condition) { + if (token.size()) { + bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size()); + } + token = utf_char + utf_char_next; + bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size()); + token = ""; + i++; + continue; + } + } + if (!split_condition && bytes_remain >= 3) { + // 're|'ve|'ll + if (utf_char == "\'" && ( + (utf_char_next == "r" && utf_char_next_next == "e") || + (utf_char_next == "v" && utf_char_next_next == "e") || + (utf_char_next == "l" && utf_char_next_next == "l")) + ) { + split_condition = true; + } + if (split_condition) { + // current token + next token can be defined + if (token.size()) { + bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size()); + } + token = utf_char; + token += utf_char_next; + token += utf_char_next_next; + + bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size()); + token = ""; + i += 2; + continue; + } + } + + if (!split_condition && !collecting) { + if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || (token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) { + collecting_letter = true; + collecting = true; + } + else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || (token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) { + collecting_numeric = true; + collecting = true; + } + else if ( + ((unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (unicode_cpt_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) || + (token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE) + ) { + collecting_special = true; + collecting = true; + } + else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) { + collecting_whitespace_lookahead = true; + collecting = true; + } + else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) { + split_condition = true; + } + } + else if (!split_condition && collecting) { + if (collecting_letter && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER) { + split_condition = true; + } + else if (collecting_numeric && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) { + split_condition = true; + } + else if (collecting_special && (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) { + split_condition = true; + } + else if (collecting_whitespace_lookahead && (unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) { + split_condition = true; + } + } + + if (utf_char_next == "") { + split_condition = true; // final + token += utf_char; + } + + if (split_condition) { + if (token.size()) { + bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size()); + } + token = utf_char; + collecting = false; + collecting_letter = false; + collecting_numeric = false; + collecting_special = false; + collecting_whitespace_lookahead = false; + } + else { + token += utf_char; + } + } + + start += offset; + } + + return bpe_offsets; +} + +// use std::wregex to split the text +static std::vector unicode_regex_split_stl(const std::wstring & wtext, const std::wstring & regex_expr, const std::vector & offsets) { + std::wregex expr(regex_expr); + std::vector bpe_offsets; // store the offset of each word + bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size + size_t start = 0; + for (auto offset : offsets) { + std::wcregex_iterator it(wtext.data() + start, wtext.data() + start + offset, expr); + std::wcregex_iterator end; + + int64_t start_idx = 0; + while (it != end) { + std::wcmatch match = *it; + if (match.position() > start_idx) { + bpe_offsets.emplace_back(match.position() - start_idx); + } + bpe_offsets.emplace_back(match.length()); + start_idx = match.position() + match.length(); + ++it; + } + + if (start_idx < (int64_t) offset) { + bpe_offsets.emplace_back(offset - start_idx); + } + start += offset; + } + + return bpe_offsets; +} + +// use std::regex to split the text +static std::vector unicode_regex_split_stl(const std::string & text, const std::string & regex_expr, const std::vector & offsets) { + std::regex expr(regex_expr); + std::vector bpe_offsets; // store the offset of each word + bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size + size_t start = 0; + for (auto offset : offsets) { + std::cregex_iterator it(text.data() + start, text.data() + start + offset, expr); + std::cregex_iterator end; + + int64_t start_idx = 0; + while (it != end) { + std::cmatch match = *it; + if (match.position() > start_idx) { + bpe_offsets.emplace_back(match.position() - start_idx); + } + bpe_offsets.emplace_back(match.length()); + start_idx = match.position() + match.length(); + ++it; + } + + if (start_idx < (int64_t) offset) { + bpe_offsets.emplace_back(offset - start_idx); + } + start += offset; + } + + return bpe_offsets; +} + +static std::vector unicode_regex_split_custom(const std::string & text, const std::string & regex_expr, const std::vector & offsets) { + std::vector bpe_offsets; + + (void)(text); + (void)(regex_expr); + (void)(offsets); + // TODO: this implementation is actually wrong, uncomment and run: + // make -j && ./bin/test-tokenizer-0 ../models/ggml-vocab-gpt-2.gguf + //if (regex_expr == "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)") { + // bpe_offsets = unicode_regex_split_custom_gpt2(text, offsets); + //} + + return bpe_offsets; +} + // // interface // std::string unicode_cpt_to_utf8(uint32_t cp) { std::string result; + if (/* 0x00 <= cp && */ cp <= 0x7f) { result.push_back(cp); + return result; } - else if (0x80 <= cp && cp <= 0x7ff) { + if (0x80 <= cp && cp <= 0x7ff) { result.push_back(0xc0 | ((cp >> 6) & 0x1f)); result.push_back(0x80 | (cp & 0x3f)); + return result; } - else if (0x800 <= cp && cp <= 0xffff) { + if (0x800 <= cp && cp <= 0xffff) { result.push_back(0xe0 | ((cp >> 12) & 0x0f)); result.push_back(0x80 | ((cp >> 6) & 0x3f)); result.push_back(0x80 | (cp & 0x3f)); + return result; } - else if (0x10000 <= cp && cp <= 0x10ffff) { + if (0x10000 <= cp && cp <= 0x10ffff) { result.push_back(0xf0 | ((cp >> 18) & 0x07)); result.push_back(0x80 | ((cp >> 12) & 0x3f)); result.push_back(0x80 | ((cp >> 6) & 0x3f)); result.push_back(0x80 | (cp & 0x3f)); + return result; } - else { - throw std::invalid_argument("invalid codepoint"); - } - return result; + + throw std::invalid_argument("invalid codepoint"); } std::vector unicode_cpts_normalize_nfd(const std::vector & cpts) { @@ -275,3 +520,167 @@ char32_t unicode_tolower(char32_t cp) { auto it = unicode_map_lowercase.find(cp); return it == unicode_map_lowercase.end() ? cp : it->second; } + +std::vector unicode_regex_split(const std::string & text, const std::vector & regex_exprs) { + // unicode categories + static const std::map k_ucat_enum = { + { "\\p{N}", CODEPOINT_TYPE_DIGIT }, + { "\\p{L}", CODEPOINT_TYPE_LETTER }, + { "\\p{P}", CODEPOINT_TYPE_PUNCTUATION }, + }; + + static const std::map k_ucat_cpt = { + { CODEPOINT_TYPE_DIGIT, 0xD1 }, + { CODEPOINT_TYPE_LETTER, 0xD2 }, + { CODEPOINT_TYPE_PUNCTUATION, 0xD3 }, + }; + + static const std::map k_ucat_map = { + { CODEPOINT_TYPE_DIGIT, "\x30-\x39" }, // 0-9 + { CODEPOINT_TYPE_LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z + { CODEPOINT_TYPE_PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\} + }; + + // compute collapsed codepoints only if needed by at least one regex + bool need_collapse = false; + for (auto & regex_expr : regex_exprs) { + // search for unicode categories + for (const auto & ucat : k_ucat_enum) { + if (std::string::npos != regex_expr.find(ucat.first)) { + need_collapse = true; + break; + } + } + } + + const auto cpts = unicode_cpts_from_utf8(text); + + // generate a "collapsed" representation of the text, where all codepoints are replaced by a single byte + // ref: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2081479935 + std::string text_collapsed; + if (need_collapse) { + // collapse all unicode categories + text_collapsed.resize(cpts.size()); + + for (size_t i = 0; i < cpts.size(); ++i) { + // keep single-byte codepoints as is + if (cpts[i] < 128) { + text_collapsed[i] = cpts[i]; + continue; + } + + const int cpt_type = unicode_cpt_type(cpts[i]); + + if (k_ucat_cpt.find(cpt_type) != k_ucat_cpt.end()) { + text_collapsed[i] = k_ucat_cpt.at(cpt_type); + } else { + text_collapsed[i] = (char) 0xD0; // fallback + } + } + } + + std::vector bpe_offsets = { cpts.size() }; + + for (auto & regex_expr : regex_exprs) { + // first, see if we have an efficient custom regex implementation + auto tmp = unicode_regex_split_custom(text, regex_expr, bpe_offsets); + + if (!tmp.empty()) { + bpe_offsets = std::move(tmp); + continue; + } + + // fallback to general-purpose std::regex / std::wregex + try { + // if a unicode category is used in the regex, we use the collapsed text and replace the unicode category + // with the corresponding collapsed representation + bool use_collapsed = false; + for (auto & ucat : k_ucat_enum) { + if (std::string::npos != regex_expr.find(ucat.first)) { + use_collapsed = true; + break; + } + } + + if (use_collapsed) { + // sanity-check that the original regex does not contain any non-ASCII characters + const auto cpts_regex = unicode_cpts_from_utf8(regex_expr); + for (size_t i = 0; i < cpts_regex.size(); ++i) { + if (cpts_regex[i] >= 128) { + throw std::runtime_error("Regex includes both unicode categories and non-ASCII characters - not supported"); + } + } + + // generate a collapsed representation of the regex + std::string regex_expr_collapsed; + + // track if we are inside [], because nested [] are not allowed + bool inside = false; + for (size_t i = 0; i < regex_expr.size(); ++i) { + if (regex_expr[i] == '[' && (i == 0 || regex_expr[i - 1] != '\\')) { + regex_expr_collapsed += '['; + inside = true; + continue; + } + + if (inside && regex_expr[i] == ']' && regex_expr[i - 1] != '\\') { + regex_expr_collapsed += ']'; + inside = false; + continue; + } + + if (regex_expr[i + 0] == '\\' && i + 4 < regex_expr.size() && + regex_expr[i + 1] == 'p' && + regex_expr[i + 2] == '{' && + regex_expr[i + 4] == '}') { + const std::string pat = regex_expr.substr(i, 5); + if (k_ucat_enum.find(pat) != k_ucat_enum.end()) { + if (!inside) { + regex_expr_collapsed += '['; + } + regex_expr_collapsed += k_ucat_cpt.at(k_ucat_enum.at(pat)); + regex_expr_collapsed += k_ucat_map.at(k_ucat_enum.at(pat)); + if (!inside) { + regex_expr_collapsed += ']'; + } + i += 4; + continue; + } + } + + regex_expr_collapsed += regex_expr[i]; + } + + //printf("text_collapsed: %s\n", text_collapsed.c_str()); + //printf("regex_expr_collapsed: %s\n", regex_expr_collapsed.c_str()); + bpe_offsets = unicode_regex_split_stl(text_collapsed, regex_expr_collapsed, bpe_offsets); + } else { + // no unicode category used, we can use std::wregex directly + const std::wstring wtext = unicode_wstring_from_utf8(text); + const std::wstring wregex_expr = unicode_wstring_from_utf8(regex_expr); + + //printf("text: %s\n", text.c_str()); + //printf("regex_expr: %s\n", regex_expr.c_str()); + bpe_offsets = unicode_regex_split_stl(wtext, wregex_expr, bpe_offsets); + } + } catch (std::regex_error & e) { + fprintf(stderr, "Failed to process regex: '%s'\n", regex_expr.c_str()); + fprintf(stderr, "Regex error: %s\n", e.what()); + throw std::runtime_error("Failed to process regex"); + } + } + + std::vector bpe_words; + bpe_words.reserve(bpe_offsets.size()); // reserve memory for the approximate size + + size_t start = 0; + for (size_t & offset : bpe_offsets) { + bpe_words.emplace_back(); + for (size_t i = start; i < start + offset; ++i) { + bpe_words.back() += unicode_cpt_to_utf8(cpts[i]); + } + start += offset; + } + + return unicode_byte_encoding_process(bpe_words); +} diff --git a/unicode.h b/unicode.h index 6a0be393a..ce2bcef5a 100644 --- a/unicode.h +++ b/unicode.h @@ -24,5 +24,6 @@ int unicode_cpt_type(const std::string & utf8); std::string unicode_byte_to_utf8(uint8_t byte); uint8_t unicode_utf8_to_byte(const std::string & utf8); -// simple tolower that only implements one-to-one mapping, not one-to-many char32_t unicode_tolower(char32_t cp); + +std::vector unicode_regex_split(const std::string & text, const std::vector & regex_exprs);