diff --git a/.clang-tidy b/.clang-tidy index 1a42b9abc..3078beacc 100644 --- a/.clang-tidy +++ b/.clang-tidy @@ -3,6 +3,7 @@ Checks: > bugprone-*, -bugprone-easily-swappable-parameters, -bugprone-implicit-widening-of-multiplication-result, + -bugprone-misplaced-widening-cast, -bugprone-narrowing-conversions, readability-*, -readability-avoid-unconditional-preprocessor-if, @@ -15,4 +16,8 @@ Checks: > -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling, performance-*, portability-*, + misc-*, + -misc-const-correctness, + -misc-non-private-member-variables-in-classes, + -misc-no-recursion, FormatStyle: none diff --git a/.editorconfig b/.editorconfig index 135a7e4bc..f8245b85c 100644 --- a/.editorconfig +++ b/.editorconfig @@ -17,3 +17,6 @@ indent_style = tab [prompts/*.txt] insert_final_newline = unset + +[examples/server/public/*] +indent_size = 2 diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 20fd8c2b5..9d0a6c222 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -18,7 +18,6 @@ on: env: BRANCH_NAME: ${{ github.head_ref || github.ref_name }} GGML_NLOOP: 3 - GGML_NITER: 1 GGML_N_THREADS: 1 jobs: diff --git a/.github/workflows/code-coverage.yml b/.github/workflows/code-coverage.yml new file mode 100644 index 000000000..392db8a08 --- /dev/null +++ b/.github/workflows/code-coverage.yml @@ -0,0 +1,36 @@ +name: Code Coverage +on: [push, pull_request] + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + +jobs: + run: + runs-on: ubuntu-20.04 + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Dependencies + run: | + sudo apt-get update + sudo apt-get install build-essential gcc-8 lcov + + - name: Build + run: CC=gcc-8 make -j LLAMA_CODE_COVERAGE=1 tests + + - name: Run tests + run: CC=gcc-8 make test + + - name: Generate coverage report + run: | + make coverage + make lcov-report + + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v3 + env: + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} + with: + files: lcov-report/coverage.info diff --git a/.gitignore b/.gitignore index 8b5f45a2d..b862a0415 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,10 @@ *.exe *.dll *.log +*.gcov +*.gcno +*.gcda +*.dot .DS_Store .build/ .cache/ @@ -17,6 +21,9 @@ .vs/ .vscode/ +lcov-report/ +gcovr-report/ + build*/ out/ tmp/ @@ -24,27 +31,29 @@ tmp/ models/* models-mnt -/main -/quantize -/quantize-stats -/result -/perplexity -/embedding -/train-text-from-scratch -/convert-llama2c-to-ggml -/simple -/benchmark-matmult -/vdot -/server /Pipfile +/baby-llama +/beam-search +/benchmark-matmult +/convert-llama2c-to-ggml /embd-input-test +/embedding /gguf /gguf-llama-simple /libllama.so /llama-bench -/baby-llama -/beam-search +/main +/metal +/perplexity +/quantize +/quantize-stats +/result /save-load-state +/server +/simple +/speculative +/train-text-from-scratch +/vdot build-info.h arm_neon.h compile_commands.json diff --git a/CMakeLists.txt b/CMakeLists.txt index 1b7cce9f1..f8cee71c8 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -36,6 +36,12 @@ endif() # Option list # +if (APPLE) + set(LLAMA_METAL_DEFAULT ON) +else() + set(LLAMA_METAL_DEFAULT OFF) +endif() + # general option(LLAMA_STATIC "llama: static link libraries" OFF) option(LLAMA_NATIVE "llama: enable -march=native flag" OFF) @@ -76,7 +82,8 @@ option(LLAMA_CUDA_F16 "llama: use 16 bit floats for some set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K") option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF) option(LLAMA_CLBLAST "llama: use CLBlast" OFF) -option(LLAMA_METAL "llama: use Metal" OFF) +option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) +option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF) option(LLAMA_MPI "llama: use MPI" OFF) option(LLAMA_K_QUANTS "llama: use k-quants" ON) option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) @@ -158,6 +165,33 @@ if (APPLE AND LLAMA_ACCELERATE) endif() endif() +if (LLAMA_METAL) + find_library(FOUNDATION_LIBRARY Foundation REQUIRED) + find_library(METAL_FRAMEWORK Metal REQUIRED) + find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) + + message(STATUS "Metal framework found") + + set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h) + + add_compile_definitions(GGML_USE_METAL) + if (LLAMA_METAL_NDEBUG) + add_compile_definitions(GGML_METAL_NDEBUG) + endif() + + # get full path to the file + #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/") + + # copy ggml-metal.metal to bin directory + configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY) + + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} + ${FOUNDATION_LIBRARY} + ${METAL_FRAMEWORK} + ${METALKIT_FRAMEWORK} + ) +endif() + if (LLAMA_BLAS) if (LLAMA_STATIC) set(BLA_STATIC ON) @@ -293,29 +327,6 @@ if (LLAMA_CUBLAS) endif() endif() -if (LLAMA_METAL) - find_library(FOUNDATION_LIBRARY Foundation REQUIRED) - find_library(METAL_FRAMEWORK Metal REQUIRED) - find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) - - set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h) - - add_compile_definitions(GGML_USE_METAL) - #add_compile_definitions(GGML_METAL_NDEBUG) - - # get full path to the file - #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/") - - # copy ggml-metal.metal to bin directory - configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY) - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} - ${FOUNDATION_LIBRARY} - ${METAL_FRAMEWORK} - ${METALKIT_FRAMEWORK} - ) -endif() - if (LLAMA_MPI) cmake_minimum_required(VERSION 3.10) find_package(MPI) @@ -415,7 +426,7 @@ if (LLAMA_ALL_WARNINGS) ) if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU") # g++ only - set(cxx_flags ${cxx_flags} -Wno-format-truncation) + set(cxx_flags ${cxx_flags} -Wno-format-truncation -Wno-array-bounds) endif() else() # todo : msvc @@ -546,6 +557,11 @@ endif() # ggml +if (GGML_USE_CPU_HBM) + add_definitions(-DGGML_USE_CPU_HBM) + find_library(memkind memkind REQUIRED) +endif() + add_library(ggml OBJECT ggml.c ggml.h @@ -561,6 +577,9 @@ add_library(ggml OBJECT target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES}) target_compile_features(ggml PUBLIC c_std_11) # don't bump target_link_libraries(ggml PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS}) +if (GGML_USE_CPU_HBM) + target_link_libraries(ggml PUBLIC memkind) +endif() add_library(ggml_static STATIC $) if (BUILD_SHARED_LIBS) diff --git a/Makefile b/Makefile index 23f050c0d..86e36ba52 100644 --- a/Makefile +++ b/Makefile @@ -1,27 +1,11 @@ # Define the default target now so that it is always the first target -BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam-search tests/test-c.o +BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam-search speculative tests/test-c.o # Binaries only useful for tests TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1 -default: $(BUILD_TARGETS) - -test: - @echo "Running tests..." - @for test_target in $(TEST_TARGETS); do \ - if [ "$$test_target" = "tests/test-tokenizer-0-llama" ]; then \ - ./$$test_target $(CURDIR)/models/ggml-vocab-llama.gguf; \ - elif [ "$$test_target" = "tests/test-tokenizer-0-falcon" ]; then \ - continue; \ - elif [ "$$test_target" = "tests/test-tokenizer-1" ]; then \ - continue; \ - else \ - ./$$test_target; \ - fi; \ - done - @echo "All tests have been run." - -all: $(BUILD_TARGETS) $(TEST_TARGETS) +# Code coverage output files +COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report ifndef UNAME_S UNAME_S := $(shell uname -s) @@ -35,17 +19,13 @@ ifndef UNAME_M UNAME_M := $(shell uname -m) endif -ifdef RISCV_CROSS_COMPILE -CC := riscv64-unknown-linux-gnu-gcc -CXX := riscv64-unknown-linux-gnu-g++ -endif - -CCV := $(shell $(CC) --version | head -n 1) -CXXV := $(shell $(CXX) --version | head -n 1) - # Mac OS + Arm can report x86_64 # ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789 ifeq ($(UNAME_S),Darwin) + ifndef LLAMA_NO_METAL + LLAMA_METAL := 1 + endif + ifneq ($(UNAME_P),arm) SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null) ifeq ($(SYSCTL_M),1) @@ -56,6 +36,60 @@ ifeq ($(UNAME_S),Darwin) endif endif +ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))' +BUILD_TARGETS += metal +endif + +default: $(BUILD_TARGETS) + +test: $(TEST_TARGETS) + @failures=0; \ + for test_target in $(TEST_TARGETS); do \ + if [ "$$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 \ + continue; \ + elif [ "$$test_target" = "tests/test-tokenizer-1" ]; then \ + continue; \ + else \ + echo "Running test $$test_target..."; \ + ./$$test_target; \ + fi; \ + if [ $$? -ne 0 ]; then \ + printf 'Test $$test_target FAILED!\n\n' $$test_target; \ + failures=$$(( failures + 1 )); \ + else \ + printf 'Test %s passed.\n\n' $$test_target; \ + fi; \ + done; \ + if [ $$failures -gt 0 ]; then \ + printf '\n%s tests failed.\n' $$failures; \ + exit 1; \ + fi + @echo 'All tests passed.' + +all: $(BUILD_TARGETS) $(TEST_TARGETS) + +coverage: ## Run code coverage + gcov -pb tests/*.cpp + +lcov-report: coverage ## Generate lcov report + mkdir -p lcov-report + lcov --capture --directory . --output-file lcov-report/coverage.info + genhtml lcov-report/coverage.info --output-directory lcov-report + +gcovr-report: coverage ## Generate gcovr report + mkdir -p gcovr-report + gcovr --root . --html --html-details --output gcovr-report/coverage.html + +ifdef RISCV_CROSS_COMPILE +CC := riscv64-unknown-linux-gnu-gcc +CXX := riscv64-unknown-linux-gnu-g++ +endif + +CCV := $(shell $(CC) --version | head -n 1) +CXXV := $(shell $(CXX) --version | head -n 1) + # # Compile flags # @@ -67,63 +101,47 @@ OPT = -Ofast else OPT = -O3 endif -CFLAGS = -I. $(OPT) -std=c11 -fPIC -CXXFLAGS = -I. -I./common $(OPT) -std=c++11 -fPIC -LDFLAGS = +MK_CPPFLAGS = -I. -Icommon +MK_CFLAGS = $(OPT) -std=c11 -fPIC +MK_CXXFLAGS = $(OPT) -std=c++11 -fPIC +MK_LDFLAGS = ifdef LLAMA_DEBUG - CFLAGS += -O0 -g - CXXFLAGS += -O0 -g - LDFLAGS += -g + MK_CFLAGS += -O0 -g + MK_CXXFLAGS += -O0 -g + MK_LDFLAGS += -g else - CFLAGS += -DNDEBUG - CXXFLAGS += -DNDEBUG + MK_CPPFLAGS += -DNDEBUG endif ifdef LLAMA_SERVER_VERBOSE - CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) + MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) +endif + + +ifdef LLAMA_CODE_COVERAGE + MK_CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase '' endif ifdef LLAMA_DISABLE_LOGS - CFLAGS += -DLOG_DISABLE_LOGS - CXXFLAGS += -DLOG_DISABLE_LOGS + MK_CPPFLAGS += -DLOG_DISABLE_LOGS endif # LLAMA_DISABLE_LOGS # warnings -CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ - -Wmissing-prototypes -Werror=implicit-int -Wno-unused-function -CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar +MK_CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ + -Wmissing-prototypes -Werror=implicit-int -Wno-unused-function +MK_CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar ifeq '' '$(findstring clang++,$(CXX))' # g++ only - CXXFLAGS += -Wno-format-truncation + MK_CXXFLAGS += -Wno-format-truncation -Wno-array-bounds endif # OS specific # TODO: support Windows -ifeq ($(UNAME_S),Linux) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),Darwin) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),FreeBSD) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),NetBSD) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),OpenBSD) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),Haiku) - CFLAGS += -pthread - CXXFLAGS += -pthread +ifneq '' '$(filter $(UNAME_S),Linux Darwin FreeBSD NetBSD OpenBSD Haiku)' + MK_CFLAGS += -pthread + MK_CXXFLAGS += -pthread endif # detect Windows @@ -149,12 +167,11 @@ ifeq ($(_WIN32),1) endif ifdef LLAMA_GPROF - CFLAGS += -pg - CXXFLAGS += -pg + MK_CFLAGS += -pg + MK_CXXFLAGS += -pg endif ifdef LLAMA_PERF - CFLAGS += -DGGML_PERF - CXXFLAGS += -DGGML_PERF + MK_CPPFLAGS += -DGGML_PERF endif # Architecture specific @@ -165,104 +182,102 @@ ifndef RISCV ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64)) # Use all CPU extensions that are available: - CFLAGS += -march=native -mtune=native - CXXFLAGS += -march=native -mtune=native + MK_CFLAGS += -march=native -mtune=native + MK_CXXFLAGS += -march=native -mtune=native # Usage AVX-only - #CFLAGS += -mfma -mf16c -mavx - #CXXFLAGS += -mfma -mf16c -mavx + #MK_CFLAGS += -mfma -mf16c -mavx + #MK_CXXFLAGS += -mfma -mf16c -mavx # Usage SSSE3-only (Not is SSE3!) - #CFLAGS += -mssse3 - #CXXFLAGS += -mssse3 + #MK_CFLAGS += -mssse3 + #MK_CXXFLAGS += -mssse3 endif # The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves. # https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412 # https://github.com/ggerganov/llama.cpp/issues/2922 ifneq '' '$(findstring mingw,$(shell $(CC) -dumpmachine))' - CFLAGS += -Xassembler -muse-unaligned-vector-move - CXXFLAGS += -Xassembler -muse-unaligned-vector-move + MK_CFLAGS += -Xassembler -muse-unaligned-vector-move + MK_CXXFLAGS += -Xassembler -muse-unaligned-vector-move endif ifneq ($(filter aarch64%,$(UNAME_M)),) # Apple M1, M2, etc. # Raspberry Pi 3, 4, Zero 2 (64-bit) - CFLAGS += -mcpu=native - CXXFLAGS += -mcpu=native + MK_CFLAGS += -mcpu=native + MK_CXXFLAGS += -mcpu=native endif ifneq ($(filter armv6%,$(UNAME_M)),) # Raspberry Pi 1, Zero - CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access + MK_CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access + MK_CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access endif ifneq ($(filter armv7%,$(UNAME_M)),) # Raspberry Pi 2 - CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations + MK_CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations + MK_CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations endif ifneq ($(filter armv8%,$(UNAME_M)),) # Raspberry Pi 3, 4, Zero 2 (32-bit) - CFLAGS += -mfp16-format=ieee -mno-unaligned-access + MK_CFLAGS += -mfp16-format=ieee -mno-unaligned-access + MK_CXXFLAGS += -mfp16-format=ieee -mno-unaligned-access endif ifneq ($(filter ppc64%,$(UNAME_M)),) POWER9_M := $(shell grep "POWER9" /proc/cpuinfo) ifneq (,$(findstring POWER9,$(POWER9_M))) - CFLAGS += -mcpu=power9 - CXXFLAGS += -mcpu=power9 - endif - # Require c++23's std::byteswap for big-endian support. - ifeq ($(UNAME_M),ppc64) - CXXFLAGS += -std=c++23 -DGGML_BIG_ENDIAN + MK_CFLAGS += -mcpu=power9 + MK_CXXFLAGS += -mcpu=power9 endif endif else - CFLAGS += -march=rv64gcv -mabi=lp64d - CXXFLAGS += -march=rv64gcv -mabi=lp64d + MK_CFLAGS += -march=rv64gcv -mabi=lp64d + MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d endif ifndef LLAMA_NO_K_QUANTS - CFLAGS += -DGGML_USE_K_QUANTS - CXXFLAGS += -DGGML_USE_K_QUANTS + MK_CPPFLAGS += -DGGML_USE_K_QUANTS OBJS += k_quants.o ifdef LLAMA_QKK_64 - CFLAGS += -DGGML_QKK_64 - CXXFLAGS += -DGGML_QKK_64 + MK_CPPFLAGS += -DGGML_QKK_64 endif endif ifndef LLAMA_NO_ACCELERATE - # Mac M1 - include Accelerate framework. - # `-framework Accelerate` works on Mac Intel as well, with negliable performance boost (as of the predict time). + # Mac OS - include Accelerate framework. + # `-framework Accelerate` works both with Apple Silicon and Mac Intel ifeq ($(UNAME_S),Darwin) - CFLAGS += -DGGML_USE_ACCELERATE - LDFLAGS += -framework Accelerate + MK_CPPFLAGS += -DGGML_USE_ACCELERATE + MK_LDFLAGS += -framework Accelerate endif endif # LLAMA_NO_ACCELERATE ifdef LLAMA_MPI - CFLAGS += -DGGML_USE_MPI -Wno-cast-qual - CXXFLAGS += -DGGML_USE_MPI -Wno-cast-qual + MK_CPPFLAGS += -DGGML_USE_MPI + MK_CFLAGS += -Wno-cast-qual + MK_CXXFLAGS += -Wno-cast-qual OBJS += ggml-mpi.o endif # LLAMA_MPI ifdef LLAMA_OPENBLAS - CFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags openblas) - LDFLAGS += $(shell pkg-config --libs openblas) + MK_CPPFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags-only-I openblas) + MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) + MK_LDFLAGS += $(shell pkg-config --libs openblas) endif # LLAMA_OPENBLAS ifdef LLAMA_BLIS - CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis - LDFLAGS += -lblis -L/usr/local/lib + MK_CPPFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis + MK_LDFLAGS += -lblis -L/usr/local/lib endif # LLAMA_BLIS ifdef LLAMA_CUBLAS - CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include - CXXFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include - LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib + MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include + MK_LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib OBJS += ggml-cuda.o NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math ifdef LLAMA_CUDA_NVCC @@ -313,14 +328,15 @@ endif # LLAMA_CUBLAS ifdef LLAMA_CLBLAST - CFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags clblast OpenCL) - CXXFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags clblast OpenCL) + MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL) + MK_CFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) + MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) # Mac provides OpenCL as a framework ifeq ($(UNAME_S),Darwin) - LDFLAGS += -lclblast -framework OpenCL + MK_LDFLAGS += -lclblast -framework OpenCL else - LDFLAGS += $(shell pkg-config --libs clblast OpenCL) + MK_LDFLAGS += $(shell pkg-config --libs clblast OpenCL) endif OBJS += ggml-opencl.o @@ -335,10 +351,9 @@ ifdef LLAMA_HIPBLAS LLAMA_CUDA_DMMV_X ?= 32 LLAMA_CUDA_MMV_Y ?= 1 LLAMA_CUDA_KQUANTS_ITER ?= 2 - CFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS - CXXFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS - LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib - LDFLAGS += -lhipblas -lamdhip64 -lrocblas + MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS + MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib + MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS)) HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) HIPFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) @@ -353,10 +368,12 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h endif # LLAMA_HIPBLAS ifdef LLAMA_METAL - CFLAGS += -DGGML_USE_METAL #-DGGML_METAL_NDEBUG - CXXFLAGS += -DGGML_USE_METAL - LDFLAGS += -framework Foundation -framework Metal -framework MetalKit - OBJS += ggml-metal.o + MK_CPPFLAGS += -DGGML_USE_METAL + MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit + OBJS += ggml-metal.o +ifdef LLAMA_METAL_NDEBUG + MK_CPPFLAGS += -DGGML_METAL_NDEBUG +endif endif # LLAMA_METAL ifdef LLAMA_METAL @@ -369,11 +386,16 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h $(CC) $(CFLAGS) -c $< -o $@ endif # LLAMA_MPI -ifdef LLAMA_NO_K_QUANTS +ifndef LLAMA_NO_K_QUANTS k_quants.o: k_quants.c k_quants.h $(CC) $(CFLAGS) -c $< -o $@ endif # LLAMA_NO_K_QUANTS +# combine build flags with cmdline overrides +override CFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(CFLAGS) +override CXXFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS) +override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS) + # # Print build information # @@ -417,7 +439,7 @@ libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) clean: - rm -vf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h $(BUILD_TARGETS) $(TEST_TARGETS) + rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) # # Examples @@ -475,9 +497,8 @@ baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o common.o $(OBJS) beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))' -BUILD_TARGETS += metal -endif +speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) ifdef LLAMA_METAL metal: examples/metal/metal.cpp ggml.o $(OBJS) diff --git a/Package.swift b/Package.swift index 73d027c70..96f52c4f0 100644 --- a/Package.swift +++ b/Package.swift @@ -12,9 +12,18 @@ let package = Package( name: "llama", path: ".", exclude: ["ggml-metal.metal"], - sources: ["ggml.c", "llama.cpp"], + sources: [ + "ggml.c", + "llama.cpp", + "ggml-alloc.c", + "k_quants.c" + ], publicHeadersPath: "spm-headers", - cSettings: [.unsafeFlags(["-Wno-shorten-64-to-32"]), .define("GGML_USE_ACCELERATE")], + cSettings: [ + .unsafeFlags(["-Wno-shorten-64-to-32"]), + .define("GGML_USE_K_QUANTS"), + .define("GGML_USE_ACCELERATE") + ], linkerSettings: [ .linkedFramework("Accelerate") ] diff --git a/README.md b/README.md index 5eda5f006..17a5c2cbf 100644 --- a/README.md +++ b/README.md @@ -120,6 +120,7 @@ as the main playground for developing new features for the [ggml](https://github - [nat/openplayground](https://github.com/nat/openplayground) - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) +- [withcatai/catai](https://github.com/withcatai/catai) --- @@ -279,29 +280,11 @@ In order to build llama.cpp you have three different options. ### Metal Build -Using Metal allows the computation to be executed on the GPU for Apple devices: +On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU. +To disable the Metal build at compile time use the `LLAMA_NO_METAL=1` flag or the `LLAMA_METAL=OFF` cmake option. -- Using `make`: - - ```bash - LLAMA_METAL=1 make - ``` - -- Using `CMake`: - - ```bash - mkdir build-metal - cd build-metal - cmake -DLLAMA_METAL=ON .. - cmake --build . --config Release - ``` - -When built with Metal support, you can enable GPU inference with the `--gpu-layers|-ngl` command-line argument. -Any value larger than 0 will offload the computation to the GPU. For example: - -```bash -./main -m ./models/7B/ggml-model-q4_0.gguf -n 128 -ngl 1 -``` +When built with Metal support, you can explicitly disable GPU inference with the `--gpu-layers|-ngl 0` command-line +argument. ### MPI Build diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 000000000..a301c5b2c --- /dev/null +++ b/codecov.yml @@ -0,0 +1,14 @@ +comment: off + +coverage: + status: + project: + default: + target: auto + threshold: 0 + base: auto + patch: + default: + target: auto + threshold: 0 + base: auto diff --git a/common/common.cpp b/common/common.cpp index 41fc59ced..6e5d5b4d5 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -57,7 +57,7 @@ int32_t get_num_physical_cores() { siblings.insert(line); } } - if (siblings.size() > 0) { + if (!siblings.empty()) { return static_cast(siblings.size()); } #elif defined(__APPLE__) && defined(__MACH__) @@ -305,6 +305,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.n_keep = std::stoi(argv[i]); + } else if (arg == "--draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_draft = std::stoi(argv[i]); } else if (arg == "--chunks") { if (++i >= argc) { invalid_param = true; @@ -317,6 +323,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.model = argv[i]; + } else if (arg == "-md" || arg == "--model-draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.model_draft = argv[i]; } else if (arg == "-a" || arg == "--alias") { if (++i >= argc) { invalid_param = true; @@ -572,106 +584,109 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { } void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { - fprintf(stdout, "usage: %s [options]\n", argv[0]); - fprintf(stdout, "\n"); - fprintf(stdout, "options:\n"); - fprintf(stdout, " -h, --help show this help message and exit\n"); - fprintf(stdout, " -i, --interactive run in interactive mode\n"); - fprintf(stdout, " --interactive-first run in interactive mode and wait for input right away\n"); - fprintf(stdout, " -ins, --instruct run in instruction mode (use with Alpaca models)\n"); - fprintf(stdout, " --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n"); - fprintf(stdout, " -r PROMPT, --reverse-prompt PROMPT\n"); - fprintf(stdout, " halt generation at PROMPT, return control in interactive mode\n"); - fprintf(stdout, " (can be specified more than once for multiple prompts).\n"); - fprintf(stdout, " --color colorise output to distinguish prompt and user input from generations\n"); - fprintf(stdout, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n"); - fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - fprintf(stdout, " -p PROMPT, --prompt PROMPT\n"); - fprintf(stdout, " prompt to start generation with (default: empty)\n"); - fprintf(stdout, " -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); - fprintf(stdout, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); - fprintf(stdout, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); - fprintf(stdout, " not supported with --interactive or other interactive options\n"); - fprintf(stdout, " --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n"); - fprintf(stdout, " --random-prompt start with a randomized prompt.\n"); - fprintf(stdout, " --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n"); - fprintf(stdout, " --in-prefix STRING string to prefix user inputs with (default: empty)\n"); - fprintf(stdout, " --in-suffix STRING string to suffix after user inputs with (default: empty)\n"); - fprintf(stdout, " -f FNAME, --file FNAME\n"); - fprintf(stdout, " prompt file to start generation.\n"); - fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict); - fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); - fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); - fprintf(stdout, " --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k); - fprintf(stdout, " --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p); - fprintf(stdout, " --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z); - fprintf(stdout, " --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p); - fprintf(stdout, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n); - fprintf(stdout, " --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty); - fprintf(stdout, " --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty); - fprintf(stdout, " --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty); - fprintf(stdout, " --mirostat N use Mirostat sampling.\n"); - fprintf(stdout, " Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); - fprintf(stdout, " (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat); - fprintf(stdout, " --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta); - fprintf(stdout, " --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau); - fprintf(stdout, " -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n"); - fprintf(stdout, " modifies the likelihood of token appearing in the completion,\n"); - fprintf(stdout, " i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"); - fprintf(stdout, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n"); - fprintf(stdout, " --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n"); - fprintf(stdout, " --grammar-file FNAME file to read grammar from\n"); - fprintf(stdout, " --cfg-negative-prompt PROMPT\n"); - fprintf(stdout, " negative prompt to use for guidance. (default: empty)\n"); - fprintf(stdout, " --cfg-negative-prompt-file FNAME\n"); - fprintf(stdout, " negative prompt file to use for guidance. (default: empty)\n"); - fprintf(stdout, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale); - fprintf(stdout, " --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale); - fprintf(stdout, " --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base); - fprintf(stdout, " --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale); - fprintf(stdout, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); - fprintf(stdout, " --no-penalize-nl do not penalize newline token\n"); - fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); - fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n"); - fprintf(stdout, " --temp N temperature (default: %.1f)\n", (double)params.temp); - fprintf(stdout, " --perplexity compute perplexity over each ctx window of the prompt\n"); - fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); - fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); - fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); - fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); + printf("usage: %s [options]\n", argv[0]); + printf("\n"); + printf("options:\n"); + printf(" -h, --help show this help message and exit\n"); + printf(" -i, --interactive run in interactive mode\n"); + printf(" --interactive-first run in interactive mode and wait for input right away\n"); + printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n"); + printf(" --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n"); + printf(" -r PROMPT, --reverse-prompt PROMPT\n"); + printf(" halt generation at PROMPT, return control in interactive mode\n"); + printf(" (can be specified more than once for multiple prompts).\n"); + printf(" --color colorise output to distinguish prompt and user input from generations\n"); + printf(" -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n"); + printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); + printf(" -p PROMPT, --prompt PROMPT\n"); + printf(" prompt to start generation with (default: empty)\n"); + printf(" -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); + printf(" --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); + printf(" --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); + printf(" not supported with --interactive or other interactive options\n"); + printf(" --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n"); + printf(" --random-prompt start with a randomized prompt.\n"); + printf(" --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n"); + printf(" --in-prefix STRING string to prefix user inputs with (default: empty)\n"); + printf(" --in-suffix STRING string to suffix after user inputs with (default: empty)\n"); + printf(" -f FNAME, --file FNAME\n"); + printf(" prompt file to start generation.\n"); + printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict); + printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); + printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); + printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k); + printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p); + printf(" --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z); + printf(" --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p); + printf(" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n); + printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty); + printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty); + printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty); + printf(" --mirostat N use Mirostat sampling.\n"); + printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); + printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat); + printf(" --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta); + printf(" --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau); + printf(" -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n"); + printf(" modifies the likelihood of token appearing in the completion,\n"); + printf(" i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"); + printf(" or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n"); + printf(" --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n"); + printf(" --grammar-file FNAME file to read grammar from\n"); + printf(" --cfg-negative-prompt PROMPT\n"); + printf(" negative prompt to use for guidance. (default: empty)\n"); + printf(" --cfg-negative-prompt-file FNAME\n"); + printf(" negative prompt file to use for guidance. (default: empty)\n"); + printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale); + printf(" --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale); + printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base); + printf(" --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale); + printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); + printf(" --no-penalize-nl do not penalize newline token\n"); + printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); + printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); + printf(" --temp N temperature (default: %.1f)\n", (double)params.temp); + printf(" --perplexity compute perplexity over each ctx window of the prompt\n"); + printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); + printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); + printf(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); + printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft); + printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); if (llama_mlock_supported()) { - fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); + printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } if (llama_mmap_supported()) { - fprintf(stdout, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); + printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } - fprintf(stdout, " --numa attempt optimizations that help on some NUMA systems\n"); - fprintf(stdout, " if run without this previously, it is recommended to drop the system page cache before using this\n"); - fprintf(stdout, " see https://github.com/ggerganov/llama.cpp/issues/1437\n"); + printf(" --numa attempt optimizations that help on some NUMA systems\n"); + printf(" if run without this previously, it is recommended to drop the system page cache before using this\n"); + printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n"); #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - fprintf(stdout, " -ngl N, --n-gpu-layers N\n"); - fprintf(stdout, " number of layers to store in VRAM\n"); - fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n"); - fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); - fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); - fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n"); + printf(" -ngl N, --n-gpu-layers N\n"); + printf(" number of layers to store in VRAM\n"); + printf(" -ts SPLIT --tensor-split SPLIT\n"); + printf(" how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); + printf(" -lv, --low-vram don't allocate VRAM scratch buffer\n"); #ifdef GGML_USE_CUBLAS - fprintf(stdout, " -nommq, --no-mul-mat-q\n"); - fprintf(stdout, " use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n"); - fprintf(stdout, " Not recommended since this is both slower and uses more VRAM.\n"); + printf(" -nommq, --no-mul-mat-q\n"); + printf(" use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n"); + printf(" Not recommended since this is both slower and uses more VRAM.\n"); #endif // GGML_USE_CUBLAS #endif - fprintf(stdout, " --mtest compute maximum memory usage\n"); - fprintf(stdout, " --export export the computation graph to 'llama.ggml'\n"); - fprintf(stdout, " --verbose-prompt print prompt before generation\n"); + printf(" --mtest compute maximum memory usage\n"); + printf(" --export export the computation graph to 'llama.ggml'\n"); + printf(" --verbose-prompt print prompt before generation\n"); fprintf(stderr, " --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n"); - fprintf(stdout, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); - fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - fprintf(stdout, " -m FNAME, --model FNAME\n"); - fprintf(stdout, " model path (default: %s)\n", params.model.c_str()); - fprintf(stdout, " -ld LOGDIR, --logdir LOGDIR\n"); - fprintf(stdout, " path under which to save YAML logs (no logging if unset)\n"); - fprintf(stdout, "\n"); + printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); + printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); + printf(" -m FNAME, --model FNAME\n"); + printf(" model path (default: %s)\n", params.model.c_str()); + printf(" -md FNAME, --model-draft FNAME\n"); + printf(" draft model for speculative decoding (default: %s)\n", params.model.c_str()); + printf(" -ld LOGDIR, --logdir LOGDIR\n"); + printf(" path under which to save YAML logs (no logging if unset)\n"); + printf("\n"); } std::string gpt_random_prompt(std::mt19937 & rng) { @@ -702,7 +717,9 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param lparams.n_ctx = params.n_ctx; lparams.n_batch = params.n_batch; - lparams.n_gpu_layers = params.n_gpu_layers; + if (params.n_gpu_layers != -1) { + lparams.n_gpu_layers = params.n_gpu_layers; + } lparams.main_gpu = params.main_gpu; lparams.tensor_split = params.tensor_split; lparams.low_vram = params.low_vram; @@ -752,6 +769,14 @@ std::tuple llama_init_from_gpt_par params.logit_bias[llama_token_eos(lctx)] = -INFINITY; } + { + LOG("warming up the model with an empty run\n"); + + const std::vector tmp = { llama_token_bos(lctx), llama_token_eos(lctx), }; + llama_eval(lctx, tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, params.n_threads); + llama_reset_timings(lctx); + } + return std::make_tuple(model, lctx); } @@ -824,6 +849,130 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector & last_tokens, + std::vector & candidates, + int idx) { + const int n_ctx = llama_n_ctx(ctx); + const int n_vocab = llama_n_vocab(ctx); + + const float temp = params.temp; + const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k; + const float top_p = params.top_p; + const float tfs_z = params.tfs_z; + const float typical_p = params.typical_p; + const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; + const float repeat_penalty = params.repeat_penalty; + const float alpha_presence = params.presence_penalty; + const float alpha_frequency = params.frequency_penalty; + const int mirostat = params.mirostat; + const float mirostat_tau = params.mirostat_tau; + const float mirostat_eta = params.mirostat_eta; + const bool penalize_nl = params.penalize_nl; + + llama_token id = 0; + + float * logits = llama_get_logits(ctx) + idx * n_vocab; + + // Apply params.logit_bias map + for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { + logits[it->first] += it->second; + } + + candidates.clear(); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + + llama_token_data_array cur_p = { candidates.data(), candidates.size(), false }; + + if (ctx_guidance) { + llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale); + } + + // apply penalties + if (!last_tokens.empty()) { + const float nl_logit = logits[llama_token_nl(ctx)]; + const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx); + + llama_sample_repetition_penalty(ctx, &cur_p, + last_tokens.data() + last_tokens.size() - last_n_repeat, + last_n_repeat, repeat_penalty); + llama_sample_frequency_and_presence_penalties(ctx, &cur_p, + last_tokens.data() + last_tokens.size() - last_n_repeat, + last_n_repeat, alpha_frequency, alpha_presence); + + if (!penalize_nl) { + for (size_t idx = 0; idx < cur_p.size; idx++) { + if (cur_p.data[idx].id == llama_token_nl(ctx)) { + cur_p.data[idx].logit = nl_logit; + break; + } + } + } + } + + if (grammar != NULL) { + llama_sample_grammar(ctx, &cur_p, grammar); + } + + if (temp <= 0) { + // Greedy sampling + id = llama_sample_token_greedy(ctx, &cur_p); + } else { + if (mirostat == 1) { + static float mirostat_mu = 2.0f * mirostat_tau; + const int mirostat_m = 100; + llama_sample_temperature(ctx, &cur_p, temp); + id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); + } else if (mirostat == 2) { + static float mirostat_mu = 2.0f * mirostat_tau; + llama_sample_temperature(ctx, &cur_p, temp); + id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu); + } else { + // Temperature sampling + llama_sample_top_k (ctx, &cur_p, top_k, 1); + llama_sample_tail_free (ctx, &cur_p, tfs_z, 1); + llama_sample_typical (ctx, &cur_p, typical_p, 1); + llama_sample_top_p (ctx, &cur_p, top_p, 1); + llama_sample_temperature(ctx, &cur_p, temp); + + { + const int n_top = 10; + LOG("top %d candidates:\n", n_top); + + for (int i = 0; i < n_top; i++) { + const llama_token id = cur_p.data[i].id; + LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p); + } + } + + id = llama_sample_token(ctx, &cur_p); + + LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str()); + } + } + // printf("`%d`", candidates_p.size); + + if (grammar != NULL) { + llama_grammar_accept_token(ctx, grammar, id); + } + + return id; +} + +// +// YAML utils +// + // returns true if successful, false otherwise bool create_directory_with_parents(const std::string & path) { #ifdef _WIN32 @@ -1062,9 +1211,10 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta); fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str()); + fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false"); fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); - fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers); + fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers); fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", params.n_probs); fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); diff --git a/common/common.h b/common/common.h index 5a379688e..012bf5e13 100644 --- a/common/common.h +++ b/common/common.h @@ -20,6 +20,9 @@ #define DIRECTORY_SEPARATOR '/' #endif // _WIN32 +#define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0) +#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", ##__VA_ARGS__); exit(1); } while (0) + // // CLI argument parsing // @@ -32,8 +35,9 @@ struct gpt_params { int32_t n_ctx = 512; // context size int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_draft = 16; // number of tokens to draft during speculative decoding int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) - int32_t n_gpu_layers = 0; // number of layers to store in VRAM + int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. @@ -63,6 +67,7 @@ struct gpt_params { float cfg_scale = 1.f; // How strong is guidance std::string model = "models/7B/ggml-model-f16.gguf"; // model path + std::string model_draft = ""; // draft model for speculative decoding std::string model_alias = "unknown"; // model alias std::string prompt = ""; std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state @@ -156,6 +161,40 @@ std::string llama_detokenize_bpe( llama_context * ctx, const std::vector & tokens); +// +// Sampling utils +// + +// this is a common sampling function used across the examples for convenience +// it can serve as a starting point for implementing your own sampling function +// +// required: +// - ctx: context to use for sampling +// - params: sampling parameters +// +// optional: +// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL +// - grammar: grammar to use for sampling, ignore if NULL +// - last_tokens: needed for repetition penalty, ignore if empty +// - idx: sample from llama_get_logits(ctx) + idx * n_vocab +// +// returns: +// - token: sampled token +// - candidates: vector of candidate tokens +// +llama_token llama_sample_token( + struct llama_context * ctx, + struct llama_context * ctx_guidance, + struct llama_grammar * grammar, + const struct gpt_params & params, + const std::vector & last_tokens, + std::vector & candidates, + int idx = 0); + +// +// YAML utils +// + bool create_directory_with_parents(const std::string & path); void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data); void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data); diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp index e76bd11c3..177d1e3a8 100644 --- a/common/grammar-parser.cpp +++ b/common/grammar-parser.cpp @@ -415,6 +415,7 @@ namespace grammar_parser { std::vector parse_state::c_rules() { std::vector ret; + ret.reserve(rules.size()); for (const auto & rule : rules) { ret.push_back(rule.data()); } diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index 271e58972..6ed2b88c6 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-hf-to-gguf.py @@ -55,10 +55,10 @@ def count_model_parts(dir_model: Path) -> int: def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Convert a Falcon model to a GGML compatible file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") - parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) + parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") + parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") + parser.add_argument("ftype", type=int, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1) return parser.parse_args() args = parse_args() diff --git a/convert-llama-ggmlv3-to-gguf.py b/convert-llama-ggml-to-gguf.py similarity index 68% rename from convert-llama-ggmlv3-to-gguf.py rename to convert-llama-ggml-to-gguf.py index 08ba0c490..b5d3e0b3c 100755 --- a/convert-llama-ggmlv3-to-gguf.py +++ b/convert-llama-ggml-to-gguf.py @@ -5,6 +5,7 @@ import argparse import math import struct import sys +from enum import IntEnum from pathlib import Path import numpy as np @@ -34,10 +35,35 @@ GGML_QUANT_SIZES = { gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8), } +class GGMLFormat(IntEnum): + GGML = 0 + GGMF = 1 + GGJT = 2 + +class GGMLFType(IntEnum): + ALL_F32 = 0 + MOSTLY_F16 = 1 + MOSTLY_Q4_0 = 2 + MOSTLY_Q4_1 = 3 + MOSTLY_Q4_1_SOME_F16 = 4 + MOSTLY_Q8_0 = 7 + MOSTLY_Q5_0 = 8 + MOSTLY_Q5_1 = 9 + MOSTLY_Q2_K = 10 + MOSTLY_Q3_K_S = 11 + MOSTLY_Q3_K_M = 12 + MOSTLY_Q3_K_L = 13 + MOSTLY_Q4_K_S = 14 + MOSTLY_Q4_K_M = 15 + MOSTLY_Q5_K_S = 16 + MOSTLY_Q5_K_M = 17 + MOSTLY_Q6_K = 18 + class Hyperparameters: def __init__(self): - self.n_vocab = self.n_embd = self.n_mult = self.n_head = self.n_layer = self.n_rot = self.ftype = 0 - self.n_ff = 0 + self.n_vocab = self.n_embd = self.n_mult = self.n_head = 0 + self.n_layer = self.n_rot = self.n_ff = 0 + self.ftype = GGMLFType.ALL_F32 def set_n_ff(self, model): ff_tensor_idx = model.tensor_map.get(b'layers.0.feed_forward.w1.weight') @@ -53,16 +79,21 @@ class Hyperparameters: self.n_head, self.n_layer, self.n_rot, - self.ftype, + ftype, ) = struct.unpack('<7I', data[offset:offset + (4 * 7)]) + try: + self.ftype = GGMLFType(ftype) + except ValueError: + raise ValueError(f'Invalid ftype {ftype}') return 4 * 7 def __str__(self): - return f'' + return f'' class Vocab: - def __init__(self): + def __init__(self, load_scores = True): self.items = [] + self.load_scores = load_scores def load(self, data, offset, n_vocab): orig_offset = offset @@ -70,20 +101,24 @@ class Vocab: itemlen = struct.unpack(' 3: + raise ValueError(f'Cannot handle unexpected GGJT file version {version}') + self.file_format = GGMLFormat.GGJT + self.format_version = version + return 8 + raise ValueError(f"Unexpected file magic {magic!r}! This doesn't look like a GGML format file.") + + def validate_conversion(self, ftype): + err = '' + if (self.file_format < GGMLFormat.GGJT or self.format_version < 2): + if ftype not in (GGMLFType.ALL_F32, GGMLFType.MOSTLY_F16): + err = 'Quantizations changed in GGJTv2. Can only convert unquantized GGML files older than GGJTv2.' + elif (self.file_format == GGMLFormat.GGJT and self.format_version == 2): + if ftype in ( GGMLFType.MOSTLY_Q4_0, GGMLFType.MOSTLY_Q4_1, + GGMLFType.MOSTLY_Q4_1_SOME_F16, GGMLFType.MOSTLY_Q8_0): + err = 'Q4 and Q8 quantizations changed in GGJTv3.' + if len(err) > 0: + raise ValueError(f'{err} Sorry, your {self.file_format.name}v{self.format_version} file of type {ftype.name} is not eligible for conversion.') def load(self, data, offset): offset += self.validate_header(data, offset) hp = Hyperparameters() offset += hp.load(data, offset) - vocab = Vocab() + print(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}') + self.validate_conversion(hp.ftype) + vocab = Vocab(load_scores = self.file_format > GGMLFormat.GGML) offset += vocab.load(data, offset, hp.n_vocab) tensors: list[Tensor] = [] tensor_map = {} while offset < len(data): - tensor = Tensor() + tensor = Tensor(use_padding = self.file_format > GGMLFormat.GGMF) offset += tensor.load(data, offset) tensor_map[tensor.name] = len(tensors) tensors.append(tensor) @@ -168,7 +235,10 @@ class GGMLToGGUF: def save(self): print('* Preparing to save GGUF file') - gguf_writer = gguf.GGUFWriter(self.cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False) + gguf_writer = gguf.GGUFWriter( + self.cfg.output, + gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], + use_temp_file = False ) self.add_params(gguf_writer) self.add_vocab(gguf_writer) if self.special_vocab is not None: @@ -185,7 +255,10 @@ class GGMLToGGUF: def add_params(self, gguf_writer): hp = self.model.hyperparameters cfg = self.cfg - desc = cfg.desc if cfg.desc is not None else 'converted from legacy GGJTv3 format' + if cfg.desc is not None: + desc = cfg.desc + else: + desc = f'converted from legacy {self.model.file_format.name}v{self.model.format_version} {hp.ftype.name} format' try: # Filenames aren't necessarily valid UTF8. name = cfg.name if cfg.name is not None else cfg.input.name @@ -195,6 +268,7 @@ class GGMLToGGUF: if name is not None: gguf_writer.add_name(name) gguf_writer.add_description(desc) + gguf_writer.add_file_type(int(hp.ftype)) if self.params_override is not None: po = self.params_override assert po.n_embd == hp.n_embd, 'Model hyperparams mismatch' @@ -231,7 +305,8 @@ class GGMLToGGUF: tokens.append(vbytes) scores.append(score) toktypes.append(ttype) - assert len(tokens) == hp.n_vocab, f'Override vocab has a different number of items than hyperparameters - override = {len(tokens)} but n_vocab={hp.n_vocab}' + assert len(tokens) == hp.n_vocab, \ + f'Override vocab has a different number of items than hyperparameters - override = {len(tokens)} but n_vocab={hp.n_vocab}' gguf_writer.add_token_list(tokens) gguf_writer.add_token_scores(scores) if len(toktypes) > 0: @@ -283,7 +358,11 @@ class GGMLToGGUF: tempdims[1] = tempdims[0] tempdims[0] = temp # print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}') - gguf_writer.add_tensor(mapped_name, data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], raw_shape = tempdims, raw_dtype = tensor.dtype) + gguf_writer.add_tensor( + mapped_name, + data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], + raw_shape = tempdims, + raw_dtype = tensor.dtype ) def handle_metadata(cfg, hp): import convert @@ -305,32 +384,46 @@ def handle_metadata(cfg, hp): params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path) else: raise ValueError('Unable to load metadata') - vocab = convert.load_vocab(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, cfg.vocabtype) + vocab = convert.load_vocab( + cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, + cfg.vocabtype ) # FIXME: Respect cfg.vocab_dir? svocab = gguf.SpecialVocab(cfg.model_metadata_dir) convert.check_vocab_size(params, vocab) return (params, vocab, svocab) def handle_args(): - parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF') - parser.add_argument('--input', '-i', type = Path, required = True, help = 'Input GGMLv3 filename') - parser.add_argument('--output', '-o', type = Path, required = True, help ='Output GGUF filename') - parser.add_argument('--name', help = 'Set model name') - parser.add_argument('--desc', help = 'Set model description') - parser.add_argument('--gqa', type = int, default = 1, help = 'grouped-query attention factor (use 8 for LLaMA2 70B)') - parser.add_argument('--eps', default = '5.0e-06', help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2') - parser.add_argument('--context-length', '-c', type=int, default = 2048, help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096') - parser.add_argument('--model-metadata-dir', '-m', type = Path, help ='Load HuggingFace/.pth vocab and metadata from the specified directory') - parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir") - parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)", default="spm") + parser = argparse.ArgumentParser(description = 'Convert GGML models to GGUF') + parser.add_argument('--input', '-i', type = Path, required = True, + help = 'Input GGMLv3 filename') + parser.add_argument('--output', '-o', type = Path, required = True, + help ='Output GGUF filename') + parser.add_argument('--name', + help = 'Set model name') + parser.add_argument('--desc', + help = 'Set model description') + parser.add_argument('--gqa', type = int, default = 1, + help = 'grouped-query attention factor (use 8 for LLaMA2 70B)') + parser.add_argument('--eps', default = '5.0e-06', + help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2') + parser.add_argument('--context-length', '-c', type=int, default = 2048, + help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096') + parser.add_argument('--model-metadata-dir', '-m', type = Path, + help ='Load HuggingFace/.pth vocab and metadata from the specified directory') + parser.add_argument("--vocab-dir", type=Path, + help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir") + parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm", + help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)") return parser.parse_args() def main(): cfg = handle_args() print(f'* Using config: {cfg}') print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n') + if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'): + print('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".') data = np.memmap(cfg.input, mode = 'r') - model = GGMLV3Model() + model = GGMLModel() print('* Scanning GGML input file') offset = model.load(data, 0) print(f'* GGML model hyperparameters: {model.hyperparameters}') @@ -345,7 +438,12 @@ def main(): print(f'* Special vocab: {special_vocab}') else: print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n') - converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override, special_vocab = special_vocab) + if model.file_format == GGMLFormat.GGML: + print('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!') + converter = GGMLToGGUF(model, data, cfg, + params_override = params_override, + vocab_override = vocab_override, + special_vocab = special_vocab ) converter.save() print(f'* Successful completion. Output saved to: {cfg.output}') diff --git a/convert.py b/convert.py index 6c89b5ecc..79a7cd52b 100755 --- a/convert.py +++ b/convert.py @@ -266,7 +266,7 @@ class Params: f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None # hack to determine LLaMA v1 vs v2 vs CodeLlama - if f_rope_freq_base and f_rope_freq_base == 1000000: + if f_rope_freq_base == 1000000: # CodeLlama n_ctx = 16384 elif config["norm_eps"] == 1e-05: @@ -323,15 +323,27 @@ class BpeVocab: self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read()) added_tokens: dict[str, int] if fname_added_tokens is not None: + # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) else: - added_tokens = {} + # Fall back to trying to find the added tokens in tokenizer.json + tokenizer_json_file = fname_tokenizer.parent / 'tokenizer.json' + if not tokenizer_json_file.is_file(): + added_tokens = {} + else: + tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8")) + added_tokens = dict( + (item['content'], item['id']) + for item in tokenizer_json.get('added_tokens', []) + # Added tokens here can be duplicates of the main vocabulary. + if item['content'] not in self.bpe_tokenizer ) vocab_size: int = len(self.bpe_tokenizer) expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) actual_ids = sorted(added_tokens.values()) if expected_ids != actual_ids: - raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}") + expected_end_id = vocab_size + len(actual_ids) - 1 + raise Exception(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}") items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) self.added_tokens_list = [text for (text, idx) in items] @@ -345,10 +357,22 @@ class BpeVocab: from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import] byte_encoder = tokenization_gpt2.bytes_to_unicode() byte_decoder = {v: k for k, v in byte_encoder.items()} + score = 0.0 for i, item in enumerate(tokenizer): text: bytes = item.encode("utf-8") - score: float = -i - yield text, score, gguf.TokenType.USER_DEFINED + # FIXME: These shouldn't be hardcoded, but it's probably better than the current behavior? + if i <= 258 and text.startswith(b'<') and text.endswith(b'>'): + if i == 0 and text == b'': + toktype = gguf.TokenType.UNKNOWN + elif i == 1 or i == 2: + toktype = gguf.TokenType.CONTROL + elif i >= 3 and text.startswith(b'<0x'): + toktype = gguf.TokenType.BYTE + else: + toktype = gguf.TokenType.NORMAL + else: + toktype = gguf.TokenType.NORMAL + yield text, score, toktype def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: @@ -649,7 +673,7 @@ class LazyUnpickler(pickle.Unpickler): assert isinstance(pid[1], LazyStorageKind) data_type = pid[1].data_type filename_stem = pid[2] - filename = self.data_base_path + '/' + filename_stem + filename = f'{self.data_base_path}/{filename_stem}' info = self.zip_file.getinfo(filename) def load(offset: int, elm_count: int) -> NDArray: @@ -665,7 +689,6 @@ class LazyUnpickler(pickle.Unpickler): @staticmethod def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any, - # pyright: ignore[reportSelfClsParameterName] requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor: assert isinstance(storage, LazyStorage) @@ -818,9 +841,9 @@ class OutputFile: name = "LLaMA" # TODO: better logic to determine model name - if (params.n_ctx == 4096): + if params.n_ctx == 4096: name = "LLaMA v2" - elif params.path_model: + elif params.path_model is not None: name = str(params.path_model.parent).split('/')[-1] self.gguf.add_name (name) @@ -833,13 +856,13 @@ class OutputFile: self.gguf.add_head_count_kv (params.n_head_kv) self.gguf.add_layer_norm_rms_eps (params.f_norm_eps) - if params.f_rope_freq_base: + if params.f_rope_freq_base is not None: self.gguf.add_rope_freq_base(params.f_rope_freq_base) - if params.f_rope_scale: + if params.f_rope_scale is not None: self.gguf.add_rope_scale_linear(params.f_rope_scale) - if params.ftype: + if params.ftype is not None: self.gguf.add_file_type(params.ftype) def add_meta_vocab(self, vocab: Vocab) -> None: diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 6e65eb087..884c42764 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -23,6 +23,7 @@ else() add_subdirectory(train-text-from-scratch) add_subdirectory(convert-llama2c-to-ggml) add_subdirectory(simple) + add_subdirectory(speculative) add_subdirectory(embd-input) add_subdirectory(llama-bench) add_subdirectory(beam-search) diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 9e856c21a..293b455d0 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -1,5 +1,6 @@ #include "ggml.h" #include "llama.h" +#include "common.h" #include #include @@ -499,10 +500,10 @@ struct llama_file { errno = 0; std::size_t ret = std::fread(ptr, size, 1, fp); if (ferror(fp)) { - throw std::runtime_error(format("read error: %s", strerror(errno))); + die_fmt("fread failed: %s", strerror(errno)); } if (ret != 1) { - throw std::runtime_error(std::string("unexpectedly reached end of file")); + die("unexpectedly reached end of file"); } } @@ -597,8 +598,7 @@ void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename); llama_file file(filename, "rb"); if (!file.fp) { - fprintf(stderr, "error: %s: %s\n", strerror(errno), filename); - exit(1); + die_fmt("%s: %s", strerror(errno), filename); } const int n_vocab = config->vocab_size; /* uint32_t max_token_length = */ file.read_u32(); // unused diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp index 036bdb398..87aac3479 100644 --- a/examples/embd-input/embd-input-lib.cpp +++ b/examples/embd-input/embd-input-lib.cpp @@ -23,7 +23,7 @@ extern "C" { struct MyModel* create_mymodel(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return nullptr; } diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 93d583b5c..49ab3e063 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -11,7 +11,7 @@ int main(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return 1; } diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp index cda517bde..a34010f10 100644 --- a/examples/gguf/gguf.cpp +++ b/examples/gguf/gguf.cpp @@ -76,7 +76,7 @@ bool gguf_ex_write(const std::string & fname) { gguf_write_to_file(ctx, fname.c_str(), false); - fprintf(stdout, "%s: wrote file '%s;\n", __func__, fname.c_str()); + printf("%s: wrote file '%s;\n", __func__, fname.c_str()); ggml_free(ctx_data); gguf_free(ctx); @@ -93,20 +93,20 @@ bool gguf_ex_read_0(const std::string & fname) { struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params); - fprintf(stdout, "%s: version: %d\n", __func__, gguf_get_version(ctx)); - fprintf(stdout, "%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); - fprintf(stdout, "%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx)); + printf("%s: version: %d\n", __func__, gguf_get_version(ctx)); + printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); + printf("%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx)); // kv { const int n_kv = gguf_get_n_kv(ctx); - fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv); + printf("%s: n_kv: %d\n", __func__, n_kv); for (int i = 0; i < n_kv; ++i) { const char * key = gguf_get_key(ctx, i); - fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key); + printf("%s: kv[%d]: key = %s\n", __func__, i, key); } } @@ -116,10 +116,10 @@ bool gguf_ex_read_0(const std::string & fname) { const int keyidx = gguf_find_key(ctx, findkey); if (keyidx == -1) { - fprintf(stdout, "%s: find key: %s not found.\n", __func__, findkey); + printf("%s: find key: %s not found.\n", __func__, findkey); } else { const char * key_value = gguf_get_val_str(ctx, keyidx); - fprintf(stdout, "%s: find key: %s found, kv[%d] value = %s\n", __func__, findkey, keyidx, key_value); + printf("%s: find key: %s found, kv[%d] value = %s\n", __func__, findkey, keyidx, key_value); } } @@ -127,13 +127,13 @@ bool gguf_ex_read_0(const std::string & fname) { { const int n_tensors = gguf_get_n_tensors(ctx); - fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors); + printf("%s: n_tensors: %d\n", __func__, n_tensors); for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name (ctx, i); const size_t offset = gguf_get_tensor_offset(ctx, i); - fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); + printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); } } @@ -153,20 +153,20 @@ bool gguf_ex_read_1(const std::string & fname) { struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params); - fprintf(stdout, "%s: version: %d\n", __func__, gguf_get_version(ctx)); - fprintf(stdout, "%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); - fprintf(stdout, "%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx)); + printf("%s: version: %d\n", __func__, gguf_get_version(ctx)); + printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); + printf("%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx)); // kv { const int n_kv = gguf_get_n_kv(ctx); - fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv); + printf("%s: n_kv: %d\n", __func__, n_kv); for (int i = 0; i < n_kv; ++i) { const char * key = gguf_get_key(ctx, i); - fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key); + printf("%s: kv[%d]: key = %s\n", __func__, i, key); } } @@ -174,13 +174,13 @@ bool gguf_ex_read_1(const std::string & fname) { { const int n_tensors = gguf_get_n_tensors(ctx); - fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors); + printf("%s: n_tensors: %d\n", __func__, n_tensors); for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name (ctx, i); const size_t offset = gguf_get_tensor_offset(ctx, i); - fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); + printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); } } @@ -189,13 +189,13 @@ bool gguf_ex_read_1(const std::string & fname) { const int n_tensors = gguf_get_n_tensors(ctx); for (int i = 0; i < n_tensors; ++i) { - fprintf(stdout, "%s: reading tensor %d data\n", __func__, i); + printf("%s: reading tensor %d data\n", __func__, i); const char * name = gguf_get_tensor_name(ctx, i); struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name); - fprintf(stdout, "%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, cur->n_dims, cur->name, cur->data); + printf("%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, cur->n_dims, cur->name, cur->data); // print first 10 elements const float * data = (const float *) cur->data; @@ -219,7 +219,7 @@ bool gguf_ex_read_1(const std::string & fname) { } } - fprintf(stdout, "%s: ctx_data size: %zu\n", __func__, ggml_get_mem_size(ctx_data)); + printf("%s: ctx_data size: %zu\n", __func__, ggml_get_mem_size(ctx_data)); ggml_free(ctx_data); gguf_free(ctx); @@ -229,7 +229,7 @@ bool gguf_ex_read_1(const std::string & fname) { int main(int argc, char ** argv) { if (argc < 3) { - fprintf(stdout, "usage: %s data.gguf r|w\n", argv[0]); + printf("usage: %s data.gguf r|w\n", argv[0]); return -1; } diff --git a/examples/gptneox-wip/falcon-main.cpp b/examples/gptneox-wip/falcon-main.cpp index 43b6a29f3..7f9a1620b 100644 --- a/examples/gptneox-wip/falcon-main.cpp +++ b/examples/gptneox-wip/falcon-main.cpp @@ -305,9 +305,9 @@ struct ggml_tensor * get_tensor_ex( struct ggml_context * ctx, std::string name) struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str()); if( cur == NULL ) { - fprintf(stdout, "%s: tensor '%s' not found!\n", __func__, name.c_str()); + printf("%s: tensor '%s' not found!\n", __func__, name.c_str()); } else { -// fprintf(stdout, "%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name); +// printf("%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name); } return cur; @@ -333,21 +333,21 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ return false; } - fprintf(stdout, "%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx)); - fprintf(stdout, "%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx)); - fprintf(stdout, "%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx)); + printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx)); + printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx)); + printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx)); // print all kv #if 0 { const int n_kv = gguf_get_n_kv(ggufctx); - fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv); + printf("%s: n_kv: %d\n", __func__, n_kv); for (int i = 0; i < n_kv; ++i) { const char * key = gguf_get_key(ggufctx, i); - fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key); + printf("%s: kv[%d]: key = %s\n", __func__, i, key); } } #endif @@ -357,21 +357,21 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ int keyidx; keyidx = gguf_find_key(ggufctx, "general.name"); - if (keyidx != -1) { fprintf(stdout, "%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.description"); - if (keyidx != -1) { fprintf(stdout, "%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.author"); - if (keyidx != -1) { fprintf(stdout, "%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.license"); - if (keyidx != -1) { fprintf(stdout, "%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.architecture"); - if (keyidx != -1) { fprintf(stdout, "%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.file_type"); - if (keyidx != -1) { fprintf(stdout, "%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout"); - if (keyidx != -1) { fprintf(stdout, "%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.source.hugginface.repository"); - if (keyidx != -1) { fprintf(stdout, "%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } } // check required metadata @@ -382,11 +382,11 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ keyidx = gguf_find_key(ggufctx, "general.architecture"); if (keyidx != -1) { if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "falcon") != 0) { - fprintf(stdout, "%s: model architecture not supported!\n", __func__); + printf("%s: model architecture not supported!\n", __func__); return false; } } else { - fprintf(stdout, "%s: gguf model architecture not found!\n", __func__); + printf("%s: gguf model architecture not found!\n", __func__); return false; } @@ -394,11 +394,11 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ keyidx = gguf_find_key(ggufctx, "falcon.tensor_data_layout"); if (keyidx != -1) { if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "jploski") != 0) { - fprintf(stdout, "%s: model tensor data layout not supported!\n", __func__); + printf("%s: model tensor data layout not supported!\n", __func__); return false; } } else { - fprintf(stdout, "%s: gguf model tensor data layout not found!\n", __func__); + printf("%s: gguf model tensor data layout not found!\n", __func__); return false; } @@ -455,11 +455,11 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ if (keyidx != -1) { if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gpt2") != 0) { - fprintf(stdout, "%s: tokenizer model not supported!\n", __func__); + printf("%s: tokenizer model not supported!\n", __func__); return false; } } else { - fprintf(stdout, "%s: tokenizer model not found!\n", __func__); + printf("%s: tokenizer model not found!\n", __func__); return false; } @@ -467,22 +467,22 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens"); if (tokens_keyidx == -1) { - fprintf(stdout, "%s: gpt2 tokenizer vocab not found!\n", __func__); + printf("%s: gpt2 tokenizer vocab not found!\n", __func__); return false; } int merges_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.merges"); if (merges_keyidx == -1) { - fprintf(stdout, "%s: gpt2 tokenizer merges not found!\n", __func__); + printf("%s: gpt2 tokenizer merges not found!\n", __func__); return false; } hparams.n_vocab = gguf_get_arr_n(ggufctx,tokens_keyidx); hparams.n_merges = gguf_get_arr_n(ggufctx,merges_keyidx); - fprintf(stdout, "%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab); - fprintf(stdout, "%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges); + printf("%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab); + printf("%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges); for (size_t i = 0; i < hparams.n_vocab; i++) { std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i); @@ -523,12 +523,12 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.separator_token_id"); if( keyidx != -1 ) { vocab.special_sep_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.padding_token_id"); if( keyidx != -1 ) { vocab.special_pad_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - if( vocab.special_bos_id != -1 ) { fprintf(stdout, "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); } - if( vocab.special_eos_id != -1 ) { fprintf(stdout, "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); } - if( vocab.special_unk_id != -1 ) { fprintf(stdout, "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); } - if( vocab.special_sep_id != -1 ) { fprintf(stdout, "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); } - if( vocab.special_pad_id != -1 ) { fprintf(stdout, "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); } - if( vocab.linefeed_id != -1 ) { fprintf(stdout, "%s: LF token = %d\n", __func__, vocab.linefeed_id ); } + if( vocab.special_bos_id != -1 ) { printf("%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); } + if( vocab.special_eos_id != -1 ) { printf("%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); } + if( vocab.special_unk_id != -1 ) { printf("%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); } + if( vocab.special_sep_id != -1 ) { printf("%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); } + if( vocab.special_pad_id != -1 ) { printf("%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); } + if( vocab.linefeed_id != -1 ) { printf("%s: LF token = %d\n", __func__, vocab.linefeed_id ); } } @@ -543,13 +543,13 @@ bool falcon_model_load(const std::string & fname, falcon_model & model, gpt2bpe_ { const int n_tensors = gguf_get_n_tensors(ggufctx); - fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors); + printf("%s: n_tensors: %d\n", __func__, n_tensors); for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name (ggufctx, i); const size_t offset = gguf_get_tensor_offset(ggufctx, i); - fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); + printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); } } #endif @@ -953,7 +953,7 @@ int main(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return 1; } diff --git a/examples/gptneox-wip/gptneox-main.cpp b/examples/gptneox-wip/gptneox-main.cpp index 04af50245..55eba0cdc 100644 --- a/examples/gptneox-wip/gptneox-main.cpp +++ b/examples/gptneox-wip/gptneox-main.cpp @@ -318,9 +318,9 @@ struct ggml_tensor * get_tensor_ex( struct ggml_context * ctx, std::string name) struct ggml_tensor * cur = ggml_get_tensor(ctx, name.c_str()); if( cur == NULL ) { - fprintf(stdout, "%s: tensor '%s' not found!\n", __func__, name.c_str()); + printf("%s: tensor '%s' not found!\n", __func__, name.c_str()); } else { -// fprintf(stdout, "%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name); +// printf("%s: n_dims = %d, name = '%s'\n", __func__, cur->n_dims, cur->name); } return cur; @@ -346,21 +346,21 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 return false; } - fprintf(stdout, "%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx)); - fprintf(stdout, "%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx)); - fprintf(stdout, "%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx)); + printf("%s: gguf version = %d\n", __func__, gguf_get_version(ggufctx)); + printf("%s: gguf alignment = %zu\n", __func__, gguf_get_alignment(ggufctx)); + printf("%s: gguf data offset = %zu\n", __func__, gguf_get_data_offset(ggufctx)); // print all kv #if 0 { const int n_kv = gguf_get_n_kv(ggufctx); - fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv); + printf("%s: n_kv: %d\n", __func__, n_kv); for (int i = 0; i < n_kv; ++i) { const char * key = gguf_get_key(ggufctx, i); - fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key); + printf("%s: kv[%d]: key = %s\n", __func__, i, key); } } #endif @@ -370,21 +370,21 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 int keyidx; keyidx = gguf_find_key(ggufctx, "general.name"); - if (keyidx != -1) { fprintf(stdout, "%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model name = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.description"); - if (keyidx != -1) { fprintf(stdout, "%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model description = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.author"); - if (keyidx != -1) { fprintf(stdout, "%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model author = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.license"); - if (keyidx != -1) { fprintf(stdout, "%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model license = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.architecture"); - if (keyidx != -1) { fprintf(stdout, "%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model architecture = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.file_type"); - if (keyidx != -1) { fprintf(stdout, "%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model file type = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "gptneox.tensor_data_layout"); - if (keyidx != -1) { fprintf(stdout, "%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model data layout = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } keyidx = gguf_find_key(ggufctx, "general.source.hugginface.repository"); - if (keyidx != -1) { fprintf(stdout, "%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } + if (keyidx != -1) { printf("%s: model source HF repo = %s\n", __func__, gguf_get_val_str(ggufctx, keyidx)); } } // check required metadata @@ -395,11 +395,11 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 keyidx = gguf_find_key(ggufctx, "general.architecture"); if (keyidx != -1) { if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gptneox") != 0) { - fprintf(stdout, "%s: model architecture not supported!\n", __func__); + printf("%s: model architecture not supported!\n", __func__); return false; } } else { - fprintf(stdout, "%s: gguf model architecture not found!\n", __func__); + printf("%s: gguf model architecture not found!\n", __func__); return false; } @@ -456,11 +456,11 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 if (keyidx != -1) { if ( strcmp(gguf_get_val_str(ggufctx, keyidx), "gpt2") != 0) { - fprintf(stdout, "%s: tokenizer model not supported!\n", __func__); + printf("%s: tokenizer model not supported!\n", __func__); return false; } } else { - fprintf(stdout, "%s: tokenizer model not found!\n", __func__); + printf("%s: tokenizer model not found!\n", __func__); return false; } @@ -468,22 +468,22 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 int tokens_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.tokens"); if (tokens_keyidx == -1) { - fprintf(stdout, "%s: gpt2 tokenizer vocab not found!\n", __func__); + printf("%s: gpt2 tokenizer vocab not found!\n", __func__); return false; } int merges_keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.merges"); if (merges_keyidx == -1) { - fprintf(stdout, "%s: gpt2 tokenizer merges not found!\n", __func__); + printf("%s: gpt2 tokenizer merges not found!\n", __func__); return false; } hparams.n_vocab = gguf_get_arr_n(ggufctx,tokens_keyidx); hparams.n_merges = gguf_get_arr_n(ggufctx,merges_keyidx); - fprintf(stdout, "%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab); - fprintf(stdout, "%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges); + printf("%s: gpt2 tokenizer vocab = %zu\n", __func__, hparams.n_vocab); + printf("%s: gpt2 tokenizer merges = %zu\n", __func__, hparams.n_merges); for (size_t i = 0; i < hparams.n_vocab; i++) { std::string word = gguf_get_arr_str(ggufctx, tokens_keyidx, i); @@ -524,12 +524,12 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.separator_token_id"); if( keyidx != -1 ) { vocab.special_sep_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } keyidx = gguf_find_key(ggufctx, "tokenizer.ggml.padding_token_id"); if( keyidx != -1 ) { vocab.special_pad_id = (int32_t)gguf_get_val_u32(ggufctx, keyidx); } - if( vocab.special_bos_id != -1 ) { fprintf(stdout, "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); } - if( vocab.special_eos_id != -1 ) { fprintf(stdout, "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); } - if( vocab.special_unk_id != -1 ) { fprintf(stdout, "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); } - if( vocab.special_sep_id != -1 ) { fprintf(stdout, "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); } - if( vocab.special_pad_id != -1 ) { fprintf(stdout, "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); } - if( vocab.linefeed_id != -1 ) { fprintf(stdout, "%s: LF token = %d\n", __func__, vocab.linefeed_id ); } + if( vocab.special_bos_id != -1 ) { printf("%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].c_str() ); } + if( vocab.special_eos_id != -1 ) { printf("%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].c_str() ); } + if( vocab.special_unk_id != -1 ) { printf("%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].c_str() ); } + if( vocab.special_sep_id != -1 ) { printf("%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].c_str() ); } + if( vocab.special_pad_id != -1 ) { printf("%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].c_str() ); } + if( vocab.linefeed_id != -1 ) { printf("%s: LF token = %d\n", __func__, vocab.linefeed_id ); } } @@ -543,13 +543,13 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 { const int n_tensors = gguf_get_n_tensors(ggufctx); - fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors); + printf("%s: n_tensors: %d\n", __func__, n_tensors); for (int i = 0; i < n_tensors; ++i) { const char * name = gguf_get_tensor_name (ggufctx, i); const size_t offset = gguf_get_tensor_offset(ggufctx, i); - fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); + printf("%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset); } } #endif @@ -660,9 +660,10 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 ggml_tensor * gpt_neox_ff( const gpt_neox_block &block, ggml_context * ctx0, - ggml_tensor * inp) { + ggml_tensor * inp, + const gpt_neox_hparams &hparams) { - ggml_tensor * cur = ggml_norm(ctx0, inp); + ggml_tensor * cur = ggml_norm(ctx0, inp, hparams.norm_eps); cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, block.ln_2_g, cur), cur), ggml_repeat(ctx0, block.ln_2_b, cur)); cur = ggml_mul_mat(ctx0, block.c_mlp_fc_w, cur); @@ -753,7 +754,7 @@ bool gpt_neox_eval( // self-attention { { - cur = ggml_norm(ctx0, inpL); + cur = ggml_norm(ctx0, inpL, hparams.norm_eps); cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.blocks[il].ln_1_g, cur), cur), @@ -844,7 +845,7 @@ bool gpt_neox_eval( if (hparams.par_res == 0) { struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpL); - cur = gpt_neox_ff(model.blocks[il], ctx0, inpFF); + cur = gpt_neox_ff(model.blocks[il], ctx0, inpFF, hparams); // input for next layer inpL = ggml_add(ctx0, cur, inpFF); @@ -853,7 +854,7 @@ bool gpt_neox_eval( // this is independent of the self-attention result, so it could be done in parallel to the self-attention // note here we pass inpL instead of cur - cur = gpt_neox_ff(model.blocks[il], ctx0, inpL); + cur = gpt_neox_ff(model.blocks[il], ctx0, inpL, hparams); // layer input + FF cur = ggml_add(ctx0, cur, inpFF); @@ -867,7 +868,7 @@ bool gpt_neox_eval( // norm { - inpL = ggml_norm(ctx0, inpL); + inpL = ggml_norm(ctx0, inpL, hparams.norm_eps); // inpL = ln_f_g*inpL + ln_f_b inpL = ggml_add(ctx0, @@ -924,7 +925,7 @@ int main(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return 1; } diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp old mode 100755 new mode 100644 index bf3a487ab..dedaa34fd --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -165,26 +165,26 @@ static const cmd_params cmd_params_defaults = { }; static void print_usage(int /* argc */, char ** argv) { - fprintf(stdout, "usage: %s [options]\n", argv[0]); - fprintf(stdout, "\n"); - fprintf(stdout, "options:\n"); - fprintf(stdout, " -h, --help\n"); - fprintf(stdout, " -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); - fprintf(stdout, " -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); - fprintf(stdout, " -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); - fprintf(stdout, " -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); - fprintf(stdout, " --memory-f32 <0|1> (default: %s)\n", join(cmd_params_defaults.f32_kv, ",").c_str()); - fprintf(stdout, " -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); - fprintf(stdout, " -ngl N, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); - fprintf(stdout, " -mg i, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); - fprintf(stdout, " -lv, --low-vram <0|1> (default: %s)\n", join(cmd_params_defaults.low_vram, ",").c_str()); - fprintf(stdout, " -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); - fprintf(stdout, " -ts, --tensor_split \n"); - fprintf(stdout, " -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); - fprintf(stdout, " -o, --output (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : cmd_params_defaults.output_format == MARKDOWN ? "md" : "sql"); - fprintf(stdout, " -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); - fprintf(stdout, "\n"); - fprintf(stdout, "Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n"); + printf("usage: %s [options]\n", argv[0]); + printf("\n"); + printf("options:\n"); + printf(" -h, --help\n"); + printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); + printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); + printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); + printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); + printf(" --memory-f32 <0|1> (default: %s)\n", join(cmd_params_defaults.f32_kv, ",").c_str()); + printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); + printf(" -ngl N, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -mg i, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); + printf(" -lv, --low-vram <0|1> (default: %s)\n", join(cmd_params_defaults.low_vram, ",").c_str()); + printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); + printf(" -ts, --tensor_split \n"); + printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); + printf(" -o, --output (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : cmd_params_defaults.output_format == MARKDOWN ? "md" : "sql"); + printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); + printf("\n"); + printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n"); } @@ -986,7 +986,12 @@ int main(int argc, char ** argv) { test t(inst, lmodel, ctx); // warmup run - test_gen(ctx, 1, 0, t.n_threads); + if (t.n_prompt > 0) { + test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads); + } + if (t.n_gen > 0) { + test_gen(ctx, 1, 0, t.n_threads); + } for (int i = 0; i < params.reps; i++) { uint64_t t_start = get_time_ns(); diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 7117db4b0..c9ca7719b 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -48,8 +48,9 @@ static bool is_interacting = false; void write_logfile( const llama_context * ctx, const gpt_params & params, const llama_model * model, - const std::vector input_tokens, const std::string output, const std::vector output_tokens) { - + const std::vector & input_tokens, const std::string & output, + const std::vector & output_tokens +) { if (params.logdir.empty()) { return; } @@ -109,14 +110,14 @@ int main(int argc, char ** argv) { gpt_params params; g_params = ¶ms; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return 1; } #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("main", "log")); LOG_TEE("Log start\n"); - log_dump_cmdline(argc,argv); + log_dump_cmdline(argc, argv); #endif // LOG_DISABLE_LOGS // TODO: Dump params ? @@ -151,14 +152,6 @@ int main(int argc, char ** argv) { LOG_TEE("%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale); } - if (params.n_ctx > 2048) { - // TODO: determine the actual max context of the model (e.g. 4096 for LLaMA v2) and use that instead of 2048 - LOG_TEE("%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx); - } else if (params.n_ctx < 8) { - LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__); - params.n_ctx = 8; - } - LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); if (params.seed == LLAMA_DEFAULT_SEED) { @@ -194,6 +187,13 @@ int main(int argc, char ** argv) { return 1; } + if (params.n_ctx > llama_n_ctx(ctx)) { + LOG_TEE("%s: warning: base model only supports context sizes no greater than %d tokens (%d specified)\n", __func__, llama_n_ctx(ctx), params.n_ctx); + } else if (params.n_ctx < 8) { + LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__); + params.n_ctx = 8; + } + // print system information { LOG_TEE("\n"); @@ -304,7 +304,7 @@ int main(int argc, char ** argv) { // debug message about similarity of saved session, if applicable size_t n_matching_session_tokens = 0; - if (session_tokens.size() > 0) { + if (!session_tokens.empty()) { for (llama_token id : session_tokens) { if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) { break; @@ -402,7 +402,7 @@ int main(int argc, char ** argv) { LOG_TEE("%s: interactive mode on.\n", __func__); - if (params.antiprompt.size()) { + if (!params.antiprompt.empty()) { for (const auto & antiprompt : params.antiprompt) { LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str()); } @@ -425,8 +425,9 @@ int main(int argc, char ** argv) { LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); LOG_TEE("\n\n"); + struct llama_grammar * grammar = NULL; grammar_parser::parse_state parsed_grammar; - llama_grammar * grammar = NULL; + if (!params.grammar.empty()) { parsed_grammar = grammar_parser::parse(params.grammar.c_str()); // will be empty (default) if there are parse errors @@ -450,8 +451,8 @@ int main(int argc, char ** argv) { } // TODO: replace with ring-buffer - std::vector last_n_tokens(n_ctx); - std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); + std::vector last_tokens(n_ctx); + std::fill(last_tokens.begin(), last_tokens.end(), 0); if (params.interactive) { const char *control_message; @@ -492,17 +493,14 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; - { - LOG("warming up the model with an empty run\n"); + const int n_vocab = llama_n_vocab(ctx); - const std::vector tmp = { llama_token_bos(ctx), }; - llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads); - llama_reset_timings(ctx); - } + std::vector candidates; + candidates.reserve(n_vocab); while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict - if (embd.size() > 0) { + if (!embd.empty()) { // Note: n_ctx - 4 here is to match the logic for commandline prompt handling via // --prompt or --file which uses the same value. int max_embd_size = n_ctx - 4; @@ -537,8 +535,8 @@ int main(int argc, char ** argv) { LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance); - // insert n_left/2 tokens at the start of embd from last_n_tokens - embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size()); + // insert n_left/2 tokens at the start of embd from last_tokens + embd.insert(embd.begin(), last_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_tokens.end() - embd.size()); LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd)); @@ -627,7 +625,7 @@ int main(int argc, char ** argv) { LOG("n_past = %d\n", n_past); } - if (embd.size() > 0 && !path_session.empty()) { + if (!embd.empty() && !path_session.empty()) { session_tokens.insert(session_tokens.end(), embd.begin(), embd.end()); n_session_consumed = session_tokens.size(); } @@ -637,20 +635,6 @@ int main(int argc, char ** argv) { embd_guidance.clear(); if ((int) embd_inp.size() <= n_consumed && !is_interacting) { - const float temp = params.temp; - const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k; - const float top_p = params.top_p; - const float tfs_z = params.tfs_z; - const float typical_p = params.typical_p; - const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; - const float repeat_penalty = params.repeat_penalty; - const float alpha_presence = params.presence_penalty; - const float alpha_frequency = params.frequency_penalty; - const int mirostat = params.mirostat; - const float mirostat_tau = params.mirostat_tau; - const float mirostat_eta = params.mirostat_eta; - const bool penalize_nl = params.penalize_nl; - // optionally save the session on first sample (for faster prompt loading next time) if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) { need_to_save_session = false; @@ -659,98 +643,12 @@ int main(int argc, char ** argv) { LOG("saved session to %s\n", path_session.c_str()); } - llama_token id = 0; + const llama_token id = llama_sample_token(ctx, ctx_guidance, grammar, params, last_tokens, candidates); - { - auto logits = llama_get_logits(ctx); - auto n_vocab = llama_n_vocab(ctx); + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(id); - // Apply params.logit_bias map - for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { - logits[it->first] += it->second; - } - - std::vector candidates; - candidates.reserve(n_vocab); - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); - } - - llama_token_data_array cur_p = { candidates.data(), candidates.size(), false }; - - if (ctx_guidance) { - llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale); - } - - // Apply penalties - float nl_logit = logits[llama_token_nl(ctx)]; - auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx); - llama_sample_repetition_penalty(ctx, &cur_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, repeat_penalty); - llama_sample_frequency_and_presence_penalties(ctx, &cur_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, alpha_frequency, alpha_presence); - if (!penalize_nl) { - for (size_t idx = 0; idx < cur_p.size; idx++) { - if (cur_p.data[idx].id == llama_token_nl(ctx)) { - cur_p.data[idx].logit = nl_logit; - break; - } - } - } - - if (grammar != NULL) { - llama_sample_grammar(ctx, &cur_p, grammar); - } - - if (temp <= 0) { - // Greedy sampling - id = llama_sample_token_greedy(ctx, &cur_p); - } else { - if (mirostat == 1) { - static float mirostat_mu = 2.0f * mirostat_tau; - const int mirostat_m = 100; - llama_sample_temperature(ctx, &cur_p, temp); - id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); - } else if (mirostat == 2) { - static float mirostat_mu = 2.0f * mirostat_tau; - llama_sample_temperature(ctx, &cur_p, temp); - id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu); - } else { - // Temperature sampling - llama_sample_top_k (ctx, &cur_p, top_k, 1); - llama_sample_tail_free (ctx, &cur_p, tfs_z, 1); - llama_sample_typical (ctx, &cur_p, typical_p, 1); - llama_sample_top_p (ctx, &cur_p, top_p, 1); - llama_sample_temperature(ctx, &cur_p, temp); - - { - const int n_top = 10; - LOG("top %d candidates:\n", n_top); - - for (int i = 0; i < n_top; i++) { - const llama_token id = cur_p.data[i].id; - LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p); - } - } - - id = llama_sample_token(ctx, &cur_p); - - LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str()); - } - } - // printf("`%d`", candidates_p.size); - - if (grammar != NULL) { - llama_grammar_accept_token(ctx, grammar, id); - } - - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(id); - - LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_n_tokens)); - } + LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_tokens)); embd.push_back(id); @@ -766,8 +664,8 @@ int main(int argc, char ** argv) { LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); while ((int) embd_inp.size() > n_consumed) { embd.push_back(embd_inp[n_consumed]); - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(embd_inp[n_consumed]); + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(embd_inp[n_consumed]); ++n_consumed; if ((int) embd.size() >= params.n_batch) { break; @@ -798,9 +696,9 @@ int main(int argc, char ** argv) { // if not currently processing queued inputs; if ((int) embd_inp.size() <= n_consumed) { // check for reverse prompt - if (params.antiprompt.size()) { + if (!params.antiprompt.empty()) { std::string last_output; - for (auto id : last_n_tokens) { + for (auto id : last_tokens) { last_output += llama_token_to_piece(ctx, id); } @@ -831,11 +729,11 @@ int main(int argc, char ** argv) { } // deal with end of text token in interactive mode - if (last_n_tokens.back() == llama_token_eos(ctx)) { + if (last_tokens.back() == llama_token_eos(ctx)) { LOG("found EOS token\n"); if (params.interactive) { - if (params.antiprompt.size() != 0) { + if (!params.antiprompt.empty()) { // tokenize and inject first reverse prompt const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false); embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end()); @@ -933,7 +831,7 @@ int main(int argc, char ** argv) { if (grammar != NULL) { llama_grammar_free(grammar); - std::vector grammar_rules( parsed_grammar.c_rules()); + std::vector grammar_rules(parsed_grammar.c_rules()); grammar = llama_grammar_init( grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 7c02b6d40..1b760683b 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -368,7 +368,7 @@ results_perplexity perplexity(llama_context * ctx, const gpt_params & params) { // Example, we have a context window of 512, we will compute perplexity for each of the // last 256 tokens. Then, we split the input up into context window size chunks to // process the entire prompt. - const int first = std::min(512, params.n_ctx/2); + const int first = params.n_ctx/2; process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first, workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first); count += params.n_ctx - first - 1; @@ -655,7 +655,7 @@ int main(int argc, char ** argv) { gpt_params params; params.n_batch = 512; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return 1; } @@ -668,11 +668,6 @@ int main(int argc, char ** argv) { params.n_ctx += params.ppl_stride/2; } - if (params.n_ctx > 2048) { - fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);" - "expect poor results\n", __func__, params.n_ctx); - } - fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); if (params.seed == LLAMA_DEFAULT_SEED) { @@ -698,6 +693,11 @@ int main(int argc, char ** argv) { return 1; } + if (params.n_ctx > llama_n_ctx(ctx)) { + fprintf(stderr, "%s: warning: model might not support context sizes greater than %d tokens (%d specified);" + "expect poor results\n", __func__, llama_n_ctx(ctx), params.n_ctx); + } + // print system information { fprintf(stderr, "\n"); diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index 06ce18f09..6ce03ba7b 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -71,7 +71,7 @@ void quantize_stats_print_usage(int /*argc*/, char ** argv) { } // Check if a layer is included/excluded by command line -bool layer_included(const quantize_stats_params params, const std::string & layer) { +bool layer_included(const quantize_stats_params & params, const std::string & layer) { for (const auto& excluded : params.exclude_layers) { if (std::regex_search(layer, std::regex(excluded))) { return false; diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index c174be069..1bf182482 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -143,10 +143,9 @@ int main(int argc, char ** argv) { if (!try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) { fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]); return 1; - } else { - if (ftype_str == "COPY") { - params.only_copy = true; - } + } + if (ftype_str == "COPY") { + params.only_copy = true; } arg_idx++; } diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 573bc4ef9..14e9501ca 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -13,7 +13,7 @@ int main(int argc, char ** argv) { params.repeat_last_n = 64; params.prompt = "The quick brown fox"; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { return 1; } @@ -44,7 +44,7 @@ int main(int argc, char ** argv) { llama_free_model(model); return 1; } - auto tokens = llama_tokenize(ctx, params.prompt.c_str(), true); + auto tokens = llama_tokenize(ctx, params.prompt, true); auto n_prompt_tokens = tokens.size(); if (n_prompt_tokens < 1) { fprintf(stderr, "%s : failed to tokenize prompt\n", __func__); diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 84e6f97ba..f30232929 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -210,1024 +210,1120 @@ unsigned char index_html[] = { 0x66, 0x6f, 0x6e, 0x74, 0x2d, 0x73, 0x69, 0x7a, 0x65, 0x3a, 0x20, 0x38, 0x30, 0x25, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x38, 0x38, 0x38, 0x3b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x40, 0x6d, - 0x65, 0x64, 0x69, 0x61, 0x20, 0x28, 0x70, 0x72, 0x65, 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#eeeeeeff; + background-size: 50% 100%; + background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1)); + animation: loading-bg-wipe 2s linear infinite; + } + @media (prefers-color-scheme: dark) { + .loading { + --loading-color-1: #22222200; + --loading-color-2: #222222ff; + } .popover-content { background-color: black; } @@ -321,7 +343,10 @@ const llamaStats = signal(null) const controller = signal(null) - const generating = computed(() => controller.value == null ) + // currently generating a completion? + const generating = computed(() => controller.value != null) + + // has the user started a chat? const chatStarted = computed(() => session.value.transcript.length > 0) const transcriptUpdate = (transcript) => { @@ -430,11 +455,19 @@ return html`
-