vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Merge branch 'master' of https://github.com/ggerganov/llama.cpp

Browse source
This commit is contained in:
FSSRepo 2023-12-19 17:06:11 -05:00
commit ffdb10d276
96 changed files with 14025 additions and 2852 deletions
Download patch file Download diff file Expand all files Collapse all files

4
.devops/tools.sh
View file

@ -13,6 +13,8 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
./quantize "$@"
elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
./main "$@"
elif [[ "$arg1" == '--finetune' || "$arg1" == '-f' ]]; then
./finetune "$@"
elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
echo "Converting PTH to GGML..."
for i in `ls $1/$2/ggml-model-f16.bin*`; do
@ -34,6 +36,8 @@ else
echo " ex: --outtype f16 \"/models/7B/\" "
echo " --quantize (-q): Optimize with quantization process ggml"
echo " ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2"
echo " --finetune (-f): Run finetune command to create a lora finetune of the model"
echo " See documentation for finetune for command-line parameters"
echo " --all-in-one (-a): Execute --convert & --quantize"
echo " ex: \"/models/\" 7B"
echo " --server (-s): Run a model on the server"

6
.editorconfig
View file

@ -15,8 +15,14 @@ indent_size = 4
[Makefile]
indent_style = tab
[scripts/*.mk]
indent_style = tab
[prompts/*.txt]
insert_final_newline = unset
[examples/server/public/*]
indent_size = 2
[examples/llama.swiftui/llama.swiftui.xcodeproj/*]
indent_style = tab

26
.github/workflows/build.yml vendored
View file

@ -143,6 +143,9 @@ jobs:
cd build
ctest --verbose
# TODO: build with LLAMA_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know
# how to debug it.
# ref: https://github.com/ggerganov/llama.cpp/actions/runs/7131777249/job/19420981052#step:5:1124
macOS-latest-make:
runs-on: macos-latest
@ -160,14 +163,18 @@ jobs:
- name: Build
id: make_build
run: |
make -j $(sysctl -n hw.logicalcpu)
LLAMA_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu)
- name: Test
id: make_test
run: |
make tests -j $(sysctl -n hw.logicalcpu)
make test -j $(sysctl -n hw.logicalcpu)
LLAMA_NO_METAL=1 make tests -j $(sysctl -n hw.logicalcpu)
LLAMA_NO_METAL=1 make test -j $(sysctl -n hw.logicalcpu)
# TODO: build with LLAMA_METAL=OFF because test-backend-ops fail on "Apple Paravirtual device" and I don't know
# how to debug it.
# ref: https://github.com/ggerganov/llama.cpp/actions/runs/7132125951/job/19422043567?pr=4359#step:5:6584
# would be great if we fix these
macOS-latest-cmake:
runs-on: macos-latest
@ -188,7 +195,7 @@ jobs:
sysctl -a
mkdir build
cd build
cmake ..
cmake -DLLAMA_METAL=OFF ..
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
@ -498,6 +505,17 @@ jobs:
path: |
cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
ios-xcode-build:
runs-on: macos-latest
steps:
- name: Checkout code
uses: actions/checkout@v3
- name: Build Xcode project
run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build
# freeBSD-latest:
# runs-on: macos-12
# steps:

27
.gitignore vendored
View file

@ -47,6 +47,7 @@ models-mnt
/libllama.so
/llama-bench
/llava-cli
/lookahead
/main
/metal
/perplexity
@ -87,15 +88,17 @@ poetry.lock
poetry.toml
# Test binaries
tests/test-grammar-parser
tests/test-llama-grammar
tests/test-double-float
tests/test-grad0
tests/test-opt
tests/test-quantize-fns
tests/test-quantize-perf
tests/test-sampling
tests/test-tokenizer-0-llama
tests/test-tokenizer-0-falcon
tests/test-tokenizer-1-llama
tests/test-tokenizer-1-bpe
/tests/test-grammar-parser
/tests/test-llama-grammar
/tests/test-double-float
/tests/test-grad0
/tests/test-opt
/tests/test-quantize-fns
/tests/test-quantize-perf
/tests/test-sampling
/tests/test-tokenizer-0-llama
/tests/test-tokenizer-0-falcon
/tests/test-tokenizer-1-llama
/tests/test-tokenizer-1-bpe
/tests/test-rope
/tests/test-backend-ops

165
CMakeLists.txt
View file

@ -43,6 +43,7 @@ else()
endif()
# general
option(BUILD_SHARED_LIBS "build shared libraries" OFF)
option(LLAMA_STATIC "llama: static link libraries" OFF)
option(LLAMA_NATIVE "llama: enable -march=native flag" ON)
option(LLAMA_LTO "llama: enable link time optimization" OFF)
@ -96,9 +97,12 @@ option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging"
option(LLAMA_MPI "llama: use MPI" OFF)
option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF)
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_SERVER "llama: build server example" ON)
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_SERVER "llama: build server example" ON)
# Required for relocatable CMake package
include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
#
# Compile flags
@ -112,6 +116,11 @@ set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads REQUIRED)
include(CheckCXXCompilerFlag)
# enable libstdc++ assertions for debug builds
if (CMAKE_SYSTEM_NAME MATCHES "Linux")
add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
endif()
if (NOT MSVC)
if (LLAMA_SANITIZE_THREAD)
add_compile_options(-fsanitize=thread)
@ -161,7 +170,7 @@ if (LLAMA_METAL)
#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)
configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
${FOUNDATION_LIBRARY}
@ -282,7 +291,12 @@ if (LLAMA_CUBLAS)
add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${LLAMA_CUDA_PEER_MAX_BATCH_SIZE})
if (LLAMA_STATIC)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
if (WIN32)
# As of 12.3.1 CUDA Tookit for Windows does not offer a static cublas library
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt)
else ()
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
endif()
else()
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
endif()
@ -388,57 +402,102 @@ if (LLAMA_HIPBLAS)
endif()
endif()
if (LLAMA_ALL_WARNINGS)
if (NOT MSVC)
set(warning_flags -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
set(c_flags -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int -Werror=implicit-function-declaration)
set(cxx_flags -Wmissing-declarations -Wmissing-noreturn)
set(host_cxx_flags "")
function(get_flags CCID CCVER)
set(C_FLAGS "")
set(CXX_FLAGS "")
if (CMAKE_C_COMPILER_ID MATCHES "Clang")
set(warning_flags ${warning_flags} -Wunreachable-code-break -Wunreachable-code-return)
set(host_cxx_flags ${host_cxx_flags} -Wmissing-prototypes -Wextra-semi)
if (CCID MATCHES "Clang")
set(C_FLAGS -Wunreachable-code-break -Wunreachable-code-return)
set(CXX_FLAGS -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi)
if (
(CMAKE_C_COMPILER_ID STREQUAL "Clang" AND CMAKE_C_COMPILER_VERSION VERSION_GREATER_EQUAL 3.8.0) OR
(CMAKE_C_COMPILER_ID STREQUAL "AppleClang" AND CMAKE_C_COMPILER_VERSION VERSION_GREATER_EQUAL 7.3.0)
)
set(c_flags ${c_flags} -Wdouble-promotion)
endif()
elseif (CMAKE_C_COMPILER_ID STREQUAL "GNU")
set(c_flags ${c_flags} -Wdouble-promotion)
set(host_cxx_flags ${host_cxx_flags} -Wno-array-bounds)
if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 7.1.0)
set(host_cxx_flags ${host_cxx_flags} -Wno-format-truncation)
endif()
if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 8.1.0)
set(host_cxx_flags ${host_cxx_flags} -Wextra-semi)
endif()
if (
(CCID STREQUAL "Clang" AND CCVER VERSION_GREATER_EQUAL 3.8.0) OR
(CCID STREQUAL "AppleClang" AND CCVER VERSION_GREATER_EQUAL 7.3.0)
)
set(C_FLAGS ${C_FLAGS} -Wdouble-promotion)
endif()
elseif (CCID STREQUAL "GNU")
set(C_FLAGS -Wdouble-promotion)
set(CXX_FLAGS -Wno-array-bounds)
if (CCVER VERSION_GREATER_EQUAL 7.1.0)
set(CXX_FLAGS ${CXX_FLAGS} -Wno-format-truncation)
endif()
if (CCVER VERSION_GREATER_EQUAL 8.1.0)
set(CXX_FLAGS ${CXX_FLAGS} -Wextra-semi)
endif()
else()
# todo : msvc
endif()
set(c_flags ${c_flags} ${warning_flags})
set(cxx_flags ${cxx_flags} ${warning_flags})
add_compile_options("$<$<COMPILE_LANGUAGE:C>:${c_flags}>"
"$<$<COMPILE_LANGUAGE:CXX>:${cxx_flags}>"
"$<$<COMPILE_LANGUAGE:CXX>:${host_cxx_flags}>")
set(GF_C_FLAGS ${C_FLAGS} PARENT_SCOPE)
set(GF_CXX_FLAGS ${CXX_FLAGS} PARENT_SCOPE)
endfunction()
if (LLAMA_ALL_WARNINGS)
if (NOT MSVC)
set(WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
set(C_FLAGS -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes
-Werror=implicit-int -Werror=implicit-function-declaration)
set(CXX_FLAGS -Wmissing-declarations -Wmissing-noreturn)
set(C_FLAGS ${WARNING_FLAGS} ${C_FLAGS})
set(CXX_FLAGS ${WARNING_FLAGS} ${CXX_FLAGS})
get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION})
add_compile_options("$<$<COMPILE_LANGUAGE:C>:${C_FLAGS};${GF_C_FLAGS}>"
"$<$<COMPILE_LANGUAGE:CXX>:${CXX_FLAGS};${GF_CXX_FLAGS}>")
else()
# todo : msvc
set(C_FLAGS "")
set(CXX_FLAGS "")
endif()
endif()
if (NOT MSVC)
set(cuda_flags -Wno-pedantic)
endif()
set(cuda_flags ${cxx_flags} -use_fast_math ${cuda_flags})
if (LLAMA_CUBLAS)
set(CUDA_FLAGS ${CXX_FLAGS} -use_fast_math)
if (NOT MSVC)
set(CUDA_FLAGS ${CUDA_FLAGS} -Wno-pedantic)
endif()
list(JOIN host_cxx_flags " " cuda_host_flags) # pass host compiler flags as a single argument
if (NOT cuda_host_flags STREQUAL "")
set(cuda_flags ${cuda_flags} -Xcompiler ${cuda_host_flags})
endif()
if (LLAMA_ALL_WARNINGS AND NOT MSVC)
set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c)
if (NOT CMAKE_CUDA_HOST_COMPILER STREQUAL "")
set(NVCC_CMD ${NVCC_CMD} -ccbin ${CMAKE_CUDA_HOST_COMPILER})
endif()
add_compile_options("$<$<COMPILE_LANGUAGE:CUDA>:${cuda_flags}>")
execute_process(
COMMAND ${NVCC_CMD} -Xcompiler --version
OUTPUT_VARIABLE CUDA_CCFULLVER
ERROR_QUIET
)
if (NOT CUDA_CCFULLVER MATCHES clang)
set(CUDA_CCID "GNU")
execute_process(
COMMAND ${NVCC_CMD} -Xcompiler "-dumpfullversion -dumpversion"
OUTPUT_VARIABLE CUDA_CCVER
ERROR_QUIET
)
else()
if (CUDA_CCFULLVER MATCHES Apple)
set(CUDA_CCID "AppleClang")
else()
set(CUDA_CCID "Clang")
endif()
string(REGEX REPLACE "^.* version ([0-9.]*).*$" "\\1" CUDA_CCVER ${CUDA_CCFULLVER})
endif()
message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}")
get_flags(${CUDA_CCID} ${CUDA_CCVER})
list(JOIN GF_CXX_FLAGS " " CUDA_CXX_FLAGS) # pass host compiler flags as a single argument
if (NOT CUDA_CXX_FLAGS STREQUAL "")
set(CUDA_FLAGS ${CUDA_FLAGS} -Xcompiler ${CUDA_CXX_FLAGS})
endif()
endif()
add_compile_options("$<$<COMPILE_LANGUAGE:CUDA>:${CUDA_FLAGS}>")
endif()
if (WIN32)
add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
@ -462,6 +521,7 @@ endif()
execute_process(
COMMAND ${CMAKE_C_COMPILER} ${CMAKE_EXE_LINKER_FLAGS} -Wl,-v
ERROR_VARIABLE output
OUTPUT_QUIET
)
if (output MATCHES "dyld-1015\.7")
add_compile_definitions(HAVE_BUGGY_APPLE_LINKER)
@ -584,6 +644,11 @@ else()
message(STATUS "Unknown architecture")
endif()
if (MINGW)
# Target Windows 8 for PrefetchVirtualMemory
add_compile_definitions(_WIN32_WINNT=0x602)
endif()
#
# POSIX conformance
#
@ -653,11 +718,11 @@ add_library(ggml OBJECT
ggml-backend.h
ggml-quants.c
ggml-quants.h
${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA}
${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA}
${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL}
${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL}
${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI}
${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA}
${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL}
${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI}
${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA}
)
target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES})

188
Makefile
View file

@ -2,13 +2,14 @@
BUILD_TARGETS = \
main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \
speculative infill tokenize benchmark-matmult parallel finetune export-lora tests/test-c.o
speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead tests/test-c.o
# Binaries only useful for tests
TEST_TARGETS = \
tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama \
tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe
tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope \
tests/test-backend-ops
# Code coverage output files
COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
@ -25,20 +26,6 @@ ifndef UNAME_M
UNAME_M := $(shell uname -m)
endif
ifeq '' '$(findstring clang,$(shell $(CC) --version))'
CC_IS_GCC=1
CC_VER := $(shell $(CC) -dumpfullversion -dumpversion | awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }')
else
CC_IS_CLANG=1
ifeq '' '$(findstring Apple LLVM,$(shell $(CC) --version))'
CC_IS_LLVM_CLANG=1
else
CC_IS_APPLE_CLANG=1
endif
CC_VER := $(shell $(CC) --version | sed -n 's/^.* version \([0-9.]*\).*$$/\1/p' \
| awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }')
endif
# Mac OS + Arm can report x86_64
# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
ifeq ($(UNAME_S),Darwin)
@ -120,12 +107,12 @@ MK_CXXFLAGS = -std=c++11 -fPIC
# -Ofast tends to produce faster code, but may not be available for some compilers.
ifdef LLAMA_FAST
MK_CFLAGS += -Ofast
MK_HOST_CXXFLAGS += -Ofast
MK_CUDA_CXXFLAGS += -O3
MK_CFLAGS += -Ofast
HOST_CXXFLAGS += -Ofast
MK_NVCCFLAGS += -O3
else
MK_CFLAGS += -O3
MK_CXXFLAGS += -O3
MK_CFLAGS += -O3
MK_CXXFLAGS += -O3
endif
# clock_gettime came in POSIX.1b (1993)
@ -174,6 +161,10 @@ ifdef LLAMA_DEBUG
MK_CFLAGS += -O0 -g
MK_CXXFLAGS += -O0 -g
MK_LDFLAGS += -g
ifeq ($(UNAME_S),Linux)
MK_CXXFLAGS += -Wp,-D_GLIBCXX_ASSERTIONS
endif
else
MK_CPPFLAGS += -DNDEBUG
endif
@ -215,30 +206,6 @@ MK_CFLAGS += $(WARN_FLAGS) -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmis
-Werror=implicit-function-declaration
MK_CXXFLAGS += $(WARN_FLAGS) -Wmissing-declarations -Wmissing-noreturn
ifeq ($(CC_IS_CLANG), 1)
# clang options
MK_CFLAGS += -Wunreachable-code-break -Wunreachable-code-return
MK_HOST_CXXFLAGS += -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi
ifneq '' '$(and $(CC_IS_LLVM_CLANG),$(filter 1,$(shell expr $(CC_VER) \>= 030800)))'
MK_CFLAGS += -Wdouble-promotion
endif
ifneq '' '$(and $(CC_IS_APPLE_CLANG),$(filter 1,$(shell expr $(CC_VER) \>= 070300)))'
MK_CFLAGS += -Wdouble-promotion
endif
else
# gcc options
MK_CFLAGS += -Wdouble-promotion
MK_HOST_CXXFLAGS += -Wno-array-bounds
ifeq ($(shell expr $(CC_VER) \>= 070100), 1)
MK_HOST_CXXFLAGS += -Wno-format-truncation
endif
ifeq ($(shell expr $(CC_VER) \>= 080100), 1)
MK_HOST_CXXFLAGS += -Wextra-semi
endif
endif
# this version of Apple ld64 is buggy
ifneq '' '$(findstring dyld-1015.7,$(shell $(CC) $(LDFLAGS) -Wl,-v 2>&1))'
MK_CPPFLAGS += -DHAVE_BUGGY_APPLE_LINKER
@ -289,8 +256,8 @@ ifndef RISCV
ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
# Use all CPU extensions that are available:
MK_CFLAGS += -march=native -mtune=native
MK_HOST_CXXFLAGS += -march=native -mtune=native
MK_CFLAGS += -march=native -mtune=native
HOST_CXXFLAGS += -march=native -mtune=native
# Usage AVX-only
#MK_CFLAGS += -mfma -mf16c -mavx
@ -301,12 +268,15 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
#MK_CXXFLAGS += -mssse3
endif
# The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves.
# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412
# https://github.com/ggerganov/llama.cpp/issues/2922
ifneq '' '$(findstring mingw,$(shell $(CC) -dumpmachine))'
# The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves.
# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412
# https://github.com/ggerganov/llama.cpp/issues/2922
MK_CFLAGS += -Xassembler -muse-unaligned-vector-move
MK_CXXFLAGS += -Xassembler -muse-unaligned-vector-move
# Target Windows 8 for PrefetchVirtualMemory
MK_CPPFLAGS += -D_WIN32_WINNT=0x602
endif
ifneq ($(filter aarch64%,$(UNAME_M)),)
@ -390,61 +360,64 @@ ifdef LLAMA_CUBLAS
MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(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
MK_NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math
ifdef LLAMA_DEBUG
MK_NVCCFLAGS += -lineinfo
endif
ifdef LLAMA_CUDA_NVCC
NVCC = $(LLAMA_CUDA_NVCC)
else
NVCC = nvcc
endif #LLAMA_CUDA_NVCC
ifdef CUDA_DOCKER_ARCH
NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH)
else ifdef CUDA_POWER_ARCH
NVCCFLAGS +=
else
NVCCFLAGS += -arch=native
MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH)
else ifndef CUDA_POWER_ARCH
MK_NVCCFLAGS += -arch=native
endif # CUDA_DOCKER_ARCH
ifdef LLAMA_CUDA_FORCE_DMMV
NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
MK_NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
endif # LLAMA_CUDA_FORCE_DMMV
ifdef LLAMA_CUDA_FORCE_MMQ
NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ
MK_NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ
endif # LLAMA_CUDA_FORCE_MMQ
ifdef LLAMA_CUDA_DMMV_X
NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
else
NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
endif # LLAMA_CUDA_DMMV_X
ifdef LLAMA_CUDA_MMV_Y
NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
else ifdef LLAMA_CUDA_DMMV_Y
NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility
MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility
else
NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
endif # LLAMA_CUDA_MMV_Y
ifdef LLAMA_CUDA_F16
NVCCFLAGS += -DGGML_CUDA_F16
MK_NVCCFLAGS += -DGGML_CUDA_F16
endif # LLAMA_CUDA_F16
ifdef LLAMA_CUDA_DMMV_F16
NVCCFLAGS += -DGGML_CUDA_F16
MK_NVCCFLAGS += -DGGML_CUDA_F16
endif # LLAMA_CUDA_DMMV_F16
ifdef LLAMA_CUDA_KQUANTS_ITER
NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
else
NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
endif
ifdef LLAMA_CUDA_PEER_MAX_BATCH_SIZE
NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(LLAMA_CUDA_PEER_MAX_BATCH_SIZE)
MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(LLAMA_CUDA_PEER_MAX_BATCH_SIZE)
else
NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128
MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128
endif # LLAMA_CUDA_PEER_MAX_BATCH_SIZE
#ifdef LLAMA_CUDA_CUBLAS
# NVCCFLAGS += -DGGML_CUDA_CUBLAS
# MK_NVCCFLAGS += -DGGML_CUDA_CUBLAS
#endif # LLAMA_CUDA_CUBLAS
ifdef LLAMA_CUDA_CCBIN
NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
endif
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
$(NVCC) $(NVCCFLAGS) -c $< -o $@
$(NVCC) $(BASE_CXXFLAGS) $(NVCCFLAGS) -Wno-pedantic -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
endif # LLAMA_CUBLAS
ifdef LLAMA_CLBLAST
@ -466,9 +439,15 @@ ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
endif # LLAMA_CLBLAST
ifdef LLAMA_HIPBLAS
ROCM_PATH ?= /opt/rocm
HIPCC ?= $(ROCM_PATH)/bin/hipcc
GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
ifeq ($(wildcard /opt/rocm),)
ROCM_PATH ?= /usr
GPU_TARGETS ?= $(shell $(shell which amdgpu-arch))
else
ROCM_PATH ?= /opt/rocm
GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
endif
HIPCC ?= $(ROCM_PATH)/bin/hipcc
LLAMA_CUDA_DMMV_X ?= 32
LLAMA_CUDA_MMV_Y ?= 1
LLAMA_CUDA_KQUANTS_ITER ?= 2
@ -506,16 +485,22 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h
$(CC) $(CFLAGS) -c $< -o $@
endif # LLAMA_MPI
# combine build flags with cmdline overrides
override CFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(CFLAGS)
override CXXFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS)
override CUDA_CXXFLAGS := $(MK_CUDA_CXXFLAGS) $(CUDA_CXXFLAGS)
override HOST_CXXFLAGS := $(MK_HOST_CXXFLAGS) $(HOST_CXXFLAGS)
override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS)
GF_CC := $(CC)
include scripts/get-flags.mk
# save CXXFLAGS before we add host-only options
NVCCFLAGS := $(NVCCFLAGS) $(CXXFLAGS) $(CUDA_CXXFLAGS) -Wno-pedantic -Xcompiler "$(HOST_CXXFLAGS)"
override CXXFLAGS += $(HOST_CXXFLAGS)
# combine build flags with cmdline overrides
override CFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CFLAGS) $(GF_CFLAGS) $(CFLAGS)
BASE_CXXFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) $(MK_CXXFLAGS) $(CXXFLAGS)
override CXXFLAGS := $(BASE_CXXFLAGS) $(HOST_CXXFLAGS) $(GF_CXXFLAGS)
override NVCCFLAGS := $(MK_NVCCFLAGS) $(NVCCFLAGS)
override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS)
# identify CUDA host compiler
ifdef LLAMA_CUBLAS
GF_CC := $(NVCC) $(NVCCFLAGS) 2>/dev/null .c -Xcompiler
include scripts/get-flags.mk
CUDA_CXXFLAGS := $(GF_CXXFLAGS)
endif
#
# Print build information
@ -648,7 +633,7 @@ beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS)
finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
export-lora: examples/export-lora/export-lora.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
@ -657,6 +642,9 @@ speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS)
parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
ifdef LLAMA_METAL
metal: examples/metal/metal.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
@ -698,41 +686,47 @@ vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-double-float: tests/test-double-float.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-grad0: tests/test-grad0.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-opt: tests/test-opt.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-opt: tests/test-opt.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-c.o: tests/test-c.c llama.h
$(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@
tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

46
Package.swift
View file

@ -2,33 +2,14 @@
import PackageDescription
#if arch(arm) || arch(arm64)
let platforms: [SupportedPlatform]? = [
.macOS(.v12),
.iOS(.v14),
.watchOS(.v4),
.tvOS(.v14)
]
let exclude: [String] = []
let resources: [Resource] = [
.process("ggml-metal.metal")
]
let additionalSources: [String] = ["ggml-metal.m"]
let additionalSettings: [CSetting] = [
.unsafeFlags(["-fno-objc-arc"]),
.define("GGML_USE_METAL")
]
#else
let platforms: [SupportedPlatform]? = nil
let exclude: [String] = ["ggml-metal.metal"]
let resources: [Resource] = []
let additionalSources: [String] = []
let additionalSettings: [CSetting] = []
#endif
let package = Package(
name: "llama",
platforms: platforms,
platforms: [
.macOS(.v12),
.iOS(.v14),
.watchOS(.v4),
.tvOS(.v14)
],
products: [
.library(name: "llama", targets: ["llama"]),
],
@ -36,25 +17,30 @@ let package = Package(
.target(
name: "llama",
path: ".",
exclude: exclude,
exclude: [],
sources: [
"ggml.c",
"llama.cpp",
"ggml-alloc.c",
"ggml-backend.c",
"ggml-quants.c",
] + additionalSources,
resources: resources,
"ggml-metal.m",
],
resources: [
.process("ggml-metal.metal")
],
publicHeadersPath: "spm-headers",
cSettings: [
.unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
.define("GGML_USE_ACCELERATE")
.define("GGML_USE_ACCELERATE"),
.unsafeFlags(["-fno-objc-arc"]),
.define("GGML_USE_METAL"),
// NOTE: NEW_LAPACK will required iOS version 16.4+
// We should consider add this in the future when we drop support for iOS 14
// (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
// .define("ACCELERATE_NEW_LAPACK"),
// .define("ACCELERATE_LAPACK_ILP64")
] + additionalSettings,
],
linkerSettings: [
.linkedFramework("Accelerate")
]

23
README.md
View file

@ -10,7 +10,11 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
### Hot topics
- Collecting Apple Silicon performance stats: https://github.com/ggerganov/llama.cpp/discussions/4167
- Collecting Apple Silicon performance stats:
- M-series: https://github.com/ggerganov/llama.cpp/discussions/4167
- A-series: https://github.com/ggerganov/llama.cpp/discussions/4508
- Added Mixtral support: https://github.com/ggerganov/llama.cpp/pull/4406
- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
----
@ -93,7 +97,18 @@ as the main playground for developing new features for the [ggml](https://github
- [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410)
- [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417)
- [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553)
- [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
- [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586)
- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
**Multimodal models:**
- [x] [Llava 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e)
- [x] [Bakllava](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
- [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
- [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
**Bindings:**
@ -114,6 +129,8 @@ 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)
- [semperai/amica](https://github.com/semperai/amica)
- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)
---
@ -320,7 +337,7 @@ mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128
### BLAS Build
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use:
- #### Accelerate Framework:
@ -892,7 +909,7 @@ Additionally, there the following images, similar to the above:
- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the Gitlab Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
#### Usage

9
common/CMakeLists.txt
View file

@ -11,7 +11,12 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
if(NOT IS_DIRECTORY "${GIT_DIR}")
file(READ ${GIT_DIR} REAL_GIT_DIR_LINK)
string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK})
set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}")
string(FIND "${REAL_GIT_DIR}" "/" SLASH_POS)
if (SLASH_POS EQUAL 0)
set(GIT_DIR "${REAL_GIT_DIR}")
else()
set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}")
endif()
endif()
set(GIT_INDEX "${GIT_DIR}/index")
@ -26,7 +31,7 @@ add_custom_command(
COMMENT "Generating build details from Git"
COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
-DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/build-info.cmake"
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake"
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
VERBATIM

161
common/common.cpp
View file

@ -278,8 +278,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
break;
}
params.yarn_beta_slow = std::stof(argv[i]);
} else if (arg == "--memory-f32") {
params.memory_f16 = false;
} else if (arg == "--samplers") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.samplers_sequence = parse_samplers_input(argv[i]);
} else if (arg == "--sampling-seq") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.samplers_sequence = argv[i];
} else if (arg == "--top-p") {
if (++i >= argc) {
invalid_param = true;
@ -498,6 +508,12 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
params.infill = true;
} else if (arg == "-dkvc" || arg == "--dump-kv-cache") {
params.dump_kv_cache = true;
} else if (arg == "-nkvo" || arg == "--no-kv-offload") {
params.no_kv_offload = true;
} else if (arg == "-ctk" || arg == "--cache-type-k") {
params.cache_type_k = argv[++i];
} else if (arg == "-ctv" || arg == "--cache-type-v") {
params.cache_type_v = argv[++i];
} else if (arg == "--multiline-input") {
params.multiline_input = true;
} else if (arg == "--simple-io") {
@ -640,6 +656,10 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
} else if (arg == "-h" || arg == "--help") {
return false;
} else if (arg == "--version") {
fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
exit(0);
} else if (arg == "--random-prompt") {
params.random_prompt = true;
} else if (arg == "--in-prefix-bos") {
@ -678,6 +698,47 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
std::istreambuf_iterator<char>(),
std::back_inserter(sparams.grammar)
);
} else if (arg == "--override-kv") {
if (++i >= argc) {
invalid_param = true;
break;
}
char * sep = strchr(argv[i], '=');
if (sep == nullptr || sep - argv[i] >= 128) {
fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
struct llama_model_kv_override kvo;
std::strncpy(kvo.key, argv[i], sep - argv[i]);
kvo.key[sep - argv[i]] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_INT;
kvo.int_value = std::atol(sep);
} else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
kvo.float_value = std::atof(sep);
} else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.bool_value = true;
} else if (std::strcmp(sep, "false") == 0) {
kvo.bool_value = false;
} else {
fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
} else {
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
params.kv_overrides.push_back(kvo);
#ifndef LOG_DISABLE_LOGS
// Parse args for logging parameters
} else if ( log_param_single_parse( argv[i] ) ) {
@ -721,6 +782,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
}
}
if (!params.kv_overrides.empty()) {
params.kv_overrides.emplace_back(llama_model_kv_override());
params.kv_overrides.back().key[0] = 0;
}
return true;
}
@ -732,6 +798,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf("\n");
printf("options:\n");
printf(" -h, --help show this help message and exit\n");
printf(" --version show version and build info\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");
@ -761,6 +828,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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, 0 = loaded from model)\n", params.n_ctx);
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
printf(" --samplers samplers that will be used for generation in the order, separated by \';\', for example: \"top_k;tfs;typical;top_p;min_p;temp\"\n");
printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sparams.samplers_sequence.c_str());
printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k);
printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p);
printf(" --min-p N min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)sparams.min_p);
@ -798,8 +867,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
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)sparams.temp);
printf(" --logits-all return logits for all tokens in the batch (default: disabled)\n");
printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
@ -840,6 +907,12 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" --verbose-prompt print prompt before generation\n");
printf(" -dkvc, --dump-kv-cache\n");
printf(" verbose print of the KV cache\n");
printf(" -nkvo, --no-kv-offload\n");
printf(" disable KV offload\n");
printf(" -ctk TYPE, --cache-type-k TYPE\n");
printf(" KV cache data type for K (default: %s)\n", params.cache_type_k.c_str());
printf(" -ctv TYPE, --cache-type-v TYPE\n");
printf(" KV cache data type for V (default: %s)\n", params.cache_type_v.c_str());
printf(" --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n");
printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
printf(" --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n");
@ -850,6 +923,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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(" --override-kv KEY=TYPE:VALUE\n");
printf(" advanced option to override model metadata by key. may be specified multiple times.\n");
printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
printf("\n");
#ifndef LOG_DISABLE_LOGS
log_print_usage();
@ -886,6 +962,48 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
GGML_UNREACHABLE();
}
//
// String parsing
//
std::string parse_samplers_input(std::string input) {
std::string output = "";
// since samplers names are written multiple ways
// make it ready for both system names and input names
std::unordered_map<std::string, char> samplers_symbols {
{"top_k", 'k'},
{"top-k", 'k'},
{"top_p", 'p'},
{"top-p", 'p'},
{"nucleus", 'p'},
{"typical_p", 'y'},
{"typical-p", 'y'},
{"typical", 'y'},
{"min_p", 'm'},
{"min-p", 'm'},
{"tfs_z", 'f'},
{"tfs-z", 'f'},
{"tfs", 'f'},
{"temp", 't'},
{"temperature",'t'}
};
// expected format example: "temp;top_k;tfs_z;typical_p;top_p;min_p"
size_t separator = input.find(';');
while (separator != input.npos) {
std::string name = input.substr(0,separator);
input = input.substr(separator+1);
separator = input.find(';');
if (samplers_symbols.find(name) != samplers_symbols.end()) {
output += samplers_symbols[name];
}
}
if (samplers_symbols.find(input) != samplers_symbols.end()) {
output += samplers_symbols[input];
}
return output;
}
//
// Model utils
//
@ -900,10 +1018,39 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
mparams.tensor_split = params.tensor_split;
mparams.use_mmap = params.use_mmap;
mparams.use_mlock = params.use_mlock;
if (params.kv_overrides.empty()) {
mparams.kv_overrides = NULL;
} else {
GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key");
mparams.kv_overrides = params.kv_overrides.data();
}
return mparams;
}
static ggml_type kv_cache_type_from_str(const std::string & s) {
if (s == "f16") {
return GGML_TYPE_F16;
}
if (s == "q8_0") {
return GGML_TYPE_Q8_0;
}
if (s == "q4_0") {
return GGML_TYPE_Q4_0;
}
if (s == "q4_1") {
return GGML_TYPE_Q4_1;
}
if (s == "q5_0") {
return GGML_TYPE_Q5_0;
}
if (s == "q5_1") {
return GGML_TYPE_Q5_1;
}
throw std::runtime_error("Invalid cache type: " + s);
}
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
auto cparams = llama_context_default_params();
@ -913,7 +1060,6 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
cparams.mul_mat_q = params.mul_mat_q;
cparams.seed = params.seed;
cparams.f16_kv = params.memory_f16;
cparams.logits_all = params.logits_all;
cparams.embedding = params.embedding;
cparams.rope_scaling_type = params.rope_scaling_type;
@ -924,6 +1070,10 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.yarn_beta_fast = params.yarn_beta_fast;
cparams.yarn_beta_slow = params.yarn_beta_slow;
cparams.yarn_orig_ctx = params.yarn_orig_ctx;
cparams.offload_kqv = !params.no_kv_offload;
cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
return cparams;
}
@ -1336,7 +1486,6 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
}
fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
fprintf(stream, "memory_f32: %s # default: false\n", !params.memory_f16 ? "true" : "false");
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);

15
common/common.h
View file

@ -86,6 +86,8 @@ struct gpt_params {
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
std::string logdir = ""; // directory in which to save YAML log files
std::vector<llama_model_kv_override> kv_overrides;
// TODO: avoid tuple, use struct
std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
std::string lora_base = ""; // base model path for the lora adapter
@ -98,7 +100,6 @@ struct gpt_params {
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS
bool memory_f16 = true; // use f16 instead of f32 for memory kv
bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs
bool interactive = false; // interactive mode
@ -123,10 +124,14 @@ struct gpt_params {
bool verbose_prompt = false; // print prompt tokens before generation
bool infill = false; // use infill mode
bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes
bool no_kv_offload = false; // disable KV offloading
std::string cache_type_k = "f16"; // KV cache data type for the K
std::string cache_type_v = "f16"; // KV cache data type for the V
// multimodal models (see examples/llava)
std::string mmproj = ""; // path to multimodal projector
std::string image = ""; // path to an image file
std::string image = ""; // path to an image file
};
bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params);
@ -141,6 +146,12 @@ std::string gpt_random_prompt(std::mt19937 & rng);
void process_escapes(std::string& input);
//
// String parsing
//
std::string parse_samplers_input(std::string input);
//
// Model utils
//

2
common/grammar-parser.cpp
View file

@ -190,7 +190,7 @@ namespace grammar_parser {
pos = parse_space(pos + 1, is_nested);
} else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator
if (last_sym_start == out_elements.size()) {
throw std::runtime_error(std::string("expecting preceeding item to */+/? at ") + pos);
throw std::runtime_error(std::string("expecting preceding item to */+/? at ") + pos);
}
// apply transformation to previous symbol (last_sym_start to end) according to

8
common/log.h
View file

@ -61,13 +61,13 @@
// #define LOG_TARGET stderr
// #include "log.h"
//
// The log target can also be redirected to a diffrent function
// The log target can also be redirected to a different function
// like so:
//
// #define LOG_TARGET log_handler_diffrent()
// #define LOG_TARGET log_handler_different()
// #include "log.h"
//
// FILE* log_handler_diffrent()
// FILE* log_handler_different()
// {
// return stderr;
// }
@ -421,7 +421,7 @@ inline FILE *log_handler2_impl(bool change = false, LogTriState append = LogTriS
// Disables logs entirely at runtime.
// Makes LOG() and LOG_TEE() produce no output,
// untill enabled back.
// until enabled back.
#define log_disable() log_disable_impl()
// INTERNAL, DO NOT USE

62
common/sampling.cpp
View file

@ -99,6 +99,56 @@ std::string llama_sampling_print(const llama_sampling_params & params) {
return std::string(result);
}
std::string llama_sampling_order_print(const llama_sampling_params & params) {
std::string result = "CFG -> Penalties ";
if (params.mirostat == 0) {
for (auto s : params.samplers_sequence) {
switch (s) {
case 'k': result += "-> top_k "; break;
case 'f': result += "-> tfs_z "; break;
case 'y': result += "-> typical_p "; break;
case 'p': result += "-> top_p "; break;
case 'm': result += "-> min_p "; break;
case 't': result += "-> temp "; break;
default : break;
}
}
} else {
result += "-> mirostat ";
}
return result;
}
// no reasons to expose this function in header
static void sampler_queue(
struct llama_context * ctx_main,
const llama_sampling_params & params,
llama_token_data_array & cur_p,
size_t & min_keep) {
const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
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 min_p = params.min_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const std::string & samplers_sequence = params.samplers_sequence;
for (auto s : samplers_sequence) {
switch (s){
case 'k': llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); break;
case 'f': llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); break;
case 'y': llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break;
case 'p': llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break;
case 'm': llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break;
case 't': llama_sample_temp (ctx_main, &cur_p, temp); break;
default : break;
}
}
}
llama_token llama_sampling_sample(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
@ -109,11 +159,6 @@ llama_token llama_sampling_sample(
const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
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 min_p = params.min_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
const float penalty_repeat = params.penalty_repeat;
const float penalty_freq = params.penalty_freq;
@ -188,12 +233,7 @@ llama_token llama_sampling_sample(
// temperature sampling
size_t min_keep = std::max(1, params.n_probs);
llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep);
llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep);
llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep);
llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep);
llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep);
llama_sample_temp (ctx_main, &cur_p, temp);
sampler_queue(ctx_main, params, cur_p, min_keep);
id = llama_sample_token(ctx_main, &cur_p);

36
common/sampling.h
View file

@ -10,22 +10,23 @@
// sampling parameters
typedef struct llama_sampling_params {
int32_t n_prev = 64; // number of previous tokens to remember
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
float temp = 0.80f; // 1.0 = disabled
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.10f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = true; // consider newlines as a repeatable token
int32_t n_prev = 64; // number of previous tokens to remember
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
float temp = 0.80f; // 1.0 = disabled
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.10f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = true; // consider newlines as a repeatable token
std::string samplers_sequence = "kfypmt"; // top_k, tail_free, typical_p, top_p, min_p, temp
std::string grammar; // optional BNF-like grammar to constrain sampling
@ -80,6 +81,9 @@ std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama
// Print sampling parameters into a string
std::string llama_sampling_print(const llama_sampling_params & params);
// Print sampling order into a string
std::string llama_sampling_order_print(const llama_sampling_params & params);
// 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
// Note: When using multiple sequences, it is the caller's responsibility to call

18
common/train.cpp
View file

@ -71,7 +71,7 @@ void free_random_uniform_distribution(struct random_uniform_distribution * rnd)
struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct random_normal_distribution * rnd) {
float scale = 1.0f; // xavier
switch (tensor->n_dims) {
switch (ggml_n_dims(tensor)) {
case 1:
scale /= sqrtf((float) tensor->ne[0]);
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
@ -119,7 +119,7 @@ struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct
}
struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd) {
switch (tensor->n_dims) {
switch (ggml_n_dims(tensor)) {
case 1:
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]);
@ -183,25 +183,27 @@ float fclamp(const float v, const float min, const float max) {
}
void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) {
GGML_ASSERT(tensor->n_dims == 1);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == 1);
GGML_ASSERT(tensor->ne[2] == 1);
GGML_ASSERT(tensor->ne[3] == 1);
}
void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1) {
GGML_ASSERT(tensor->n_dims == 2);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == ne1);
GGML_ASSERT(tensor->ne[2] == 1);
GGML_ASSERT(tensor->ne[3] == 1);
}
void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2) {
GGML_ASSERT(tensor->n_dims == 3);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == ne1);
GGML_ASSERT(tensor->ne[2] == ne2);
GGML_ASSERT(tensor->ne[3] == 1);
}
void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) {
GGML_ASSERT(tensor->n_dims == 4);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == ne1);
GGML_ASSERT(tensor->ne[2] == ne2);
@ -225,8 +227,8 @@ int64_t get_example_targets_batch(
bool sample_random_offsets
) {
GGML_ASSERT(samples_count > 0);
GGML_ASSERT(tokens_input->n_dims == 2);
GGML_ASSERT(target_probs->n_dims == 3);
GGML_ASSERT(ggml_is_matrix(tokens_input));
GGML_ASSERT(ggml_is_3d(target_probs));
int64_t n_vocab = target_probs->ne[0];
int64_t n_tokens = tokens_input->ne[0];
int64_t n_batch = tokens_input->ne[1];

176
convert-hf-to-gguf.py
View file

@ -10,7 +10,7 @@ import re
import sys
from enum import IntEnum
from pathlib import Path
from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast
from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast, Optional
import numpy as np
import torch
@ -59,7 +59,7 @@ class Model:
from safetensors import safe_open
ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
else:
ctx = contextlib.nullcontext(torch.load(self.dir_model / part_name, map_location="cpu"))
ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
with ctx as model_part:
for name in model_part.keys():
@ -77,8 +77,18 @@ class Model:
self.gguf_writer.add_embedding_length(n_embd)
if (n_ff := self.hparams.get("intermediate_size")) is not None:
self.gguf_writer.add_feed_forward_length(n_ff)
if (n_head := self.hparams.get("num_attention_head")) is not None:
if (n_head := self.hparams.get("num_attention_heads")) is not None:
self.gguf_writer.add_head_count(n_head)
if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
self.gguf_writer.add_head_count_kv(n_head_kv)
if (n_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
self.gguf_writer.add_layer_norm_rms_eps(n_rms_eps)
if (n_experts := self.hparams.get("num_local_experts")) is not None:
self.gguf_writer.add_expert_count(n_experts)
if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
self.gguf_writer.add_expert_used_count(n_experts_used)
self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True))
def write_tensors(self):
@ -168,6 +178,12 @@ class Model:
return PersimmonModel
if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
return StableLMModel
if model_architecture == "QWenLMHeadModel":
return QwenModel
if model_architecture == "MixtralForCausalLM":
return MixtralModel
if model_architecture == "PhiForCausalLM":
return Phi2Model
return Model
def _is_model_safetensors(self) -> bool:
@ -203,6 +219,12 @@ class Model:
return gguf.MODEL_ARCH.PERSIMMON
if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
return gguf.MODEL_ARCH.STABLELM
if arch == "QWenLMHeadModel":
return gguf.MODEL_ARCH.QWEN
if arch == "MixtralForCausalLM":
return gguf.MODEL_ARCH.LLAMA
if arch == "PhiForCausalLM":
return gguf.MODEL_ARCH.PHI2
raise NotImplementedError(f'Architecture "{arch}" not supported!')
@ -832,6 +854,154 @@ class StableLMModel(Model):
self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
self.gguf_writer.add_layer_norm_eps(1e-5)
class MixtralModel(Model):
def set_vocab(self):
self._set_vocab_sentencepiece()
class QwenModel(Model):
@staticmethod
def token_bytes_to_string(b):
from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
byte_encoder = bytes_to_unicode()
return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
@staticmethod
def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: Optional[int] = None) -> list[bytes]:
parts = [bytes([b]) for b in token]
while True:
min_idx = None
min_rank = None
for i, pair in enumerate(zip(parts[:-1], parts[1:])):
rank = mergeable_ranks.get(pair[0] + pair[1])
if rank is not None and (min_rank is None or rank < min_rank):
min_idx = i
min_rank = rank
if min_rank is None or (max_rank is not None and min_rank >= max_rank):
break
assert min_idx is not None
parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
return parts
def set_vocab(self):
dir_model = self.dir_model
hparams = self.hparams
tokens: list[bytearray] = []
toktypes: list[int] = []
from transformers import AutoTokenizer # type: ignore[attr-defined]
tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
vocab_size = hparams["vocab_size"]
assert max(tokenizer.get_vocab().values()) < vocab_size
merges = []
vocab = {}
mergeable_ranks = tokenizer.mergeable_ranks
for token, rank in mergeable_ranks.items():
vocab[self.token_bytes_to_string(token)] = rank
if len(token) == 1:
continue
merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
assert len(merged) == 2
merges.append(' '.join(map(self.token_bytes_to_string, merged)))
reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in vocab.items()}
added_vocab = tokenizer.special_tokens
for i in range(vocab_size):
if i not in reverse_vocab:
pad_token = f"[PAD{i}]".encode("utf-8")
tokens.append(bytearray(pad_token))
toktypes.append(gguf.TokenType.USER_DEFINED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.CONTROL)
else:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.NORMAL)
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
special_vocab.merges = merges
special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
self.gguf_writer.add_name("Qwen")
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
def write_tensors(self):
block_count = self.hparams["num_hidden_layers"]
model_kv = dict(self.get_tensors())
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
for name, data_torch in model_kv.items():
# we don't need these
if name.endswith(".rotary_emb.inv_freq"):
continue
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
data = data_torch.squeeze().numpy()
# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
class Phi2Model(Model):
def set_gguf_parameters(self):
block_count = self.hparams["n_layer"]
self.gguf_writer.add_name("Phi2")
self.gguf_writer.add_context_length(self.hparams["n_positions"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_head_count(self.hparams["n_head"])
self.gguf_writer.add_head_count_kv(self.hparams["n_head"])
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"])
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_add_bos_token(False)
###### CONVERSION LOGIC ######

86
convert-lora-to-ggml.py
View file

@ -3,7 +3,6 @@ from __future__ import annotations
import json
import os
import re
import struct
import sys
from typing import Any, BinaryIO, Sequence
@ -11,43 +10,15 @@ from typing import Any, BinaryIO, Sequence
import numpy as np
import torch
from pathlib import Path
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
import gguf
NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
HF_SUBLAYER_TO_GGML = {
"self_attn.q_proj": "attn_q",
"self_attn.k_proj": "attn_k",
"self_attn.v_proj": "attn_v",
"self_attn.o_proj": "attn_output",
"mlp.gate_proj": "ffn_gate",
"mlp.down_proj": "ffn_down",
"mlp.up_proj": "ffn_up",
"input_layernorm": "attn_norm",
"post_attention_layernorm": "ffn_norm",
}
def translate_tensor_name(t: str) -> str:
match = re.match(r".*layers\.(\d+)\.(\w+\.\w+)\.lora_(A|B)\.weight", t)
if match:
nn = match.group(1)
sub_layer = match.group(2)
lora_type = match.group(3)
sub_layer_renamed = HF_SUBLAYER_TO_GGML.get(sub_layer)
if sub_layer_renamed is None:
print(f"Error: unrecognized sub-layer {sub_layer} in tensor {t}")
sys.exit(1)
output_string = (
f"blk.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}"
)
return output_string
else:
print(f"Error: unrecognized tensor {t}")
sys.exit(1)
def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
fout.write(b"ggla"[::-1]) # magic (ggml lora)
fout.write(struct.pack("i", 1)) # file version
@ -61,9 +32,7 @@ def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
fout.write(struct.pack("i", int(params["lora_alpha"])))
def write_tensor_header(
self, name: str, shape: Sequence[int], data_type: np.dtype[Any]
) -> None:
def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None:
sname = name.encode("utf-8")
fout.write(
struct.pack(
@ -78,11 +47,12 @@ def write_tensor_header(
fout.seek((fout.tell() + 31) & -32)
if len(sys.argv) != 2:
print(f"Usage: python {sys.argv[0]} <path>")
if len(sys.argv) < 2:
print(f"Usage: python {sys.argv[0]} <path> [arch]")
print(
"Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
)
print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
sys.exit(1)
input_json = os.path.join(sys.argv[1], "adapter_config.json")
@ -90,6 +60,14 @@ input_model = os.path.join(sys.argv[1], "adapter_model.bin")
output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
model = torch.load(input_model, map_location="cpu")
arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
if arch_name not in gguf.MODEL_ARCH_NAMES.values():
print(f"Error: unsupported architecture {arch_name}")
sys.exit(1)
arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
with open(input_json, "r") as f:
params = json.load(f)
@ -117,6 +95,7 @@ with open(output_path, "wb") as fout:
write_file_header(fout, params)
for k, v in model.items():
orig_k = k
if k.endswith(".default.weight"):
k = k.replace(".default.weight", ".weight")
if k in ["llama_proj.weight", "llama_proj.bias"]:
@ -129,7 +108,32 @@ with open(output_path, "wb") as fout:
v = v.float()
t = v.detach().numpy()
tname = translate_tensor_name(k)
prefix = "base_model.model."
if k.startswith(prefix):
k = k[len(prefix) :]
lora_suffixes = (".lora_A.weight", ".lora_B.weight")
if k.endswith(lora_suffixes):
suffix = k[-len(lora_suffixes[0]):]
k = k[: -len(lora_suffixes[0])]
else:
print(f"Error: unrecognized tensor name {orig_k}")
sys.exit(1)
tname = name_map.get_name(k)
if tname is None:
print(f"Error: could not map tensor name {orig_k}")
print(" Note: the arch parameter must be specified if the model is not llama")
sys.exit(1)
if suffix == ".lora_A.weight":
tname += ".weight.loraA"
elif suffix == ".lora_B.weight":
tname += ".weight.loraB"
else:
assert False
print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
write_tensor_header(fout, tname, t.shape, t.dtype)
t.tofile(fout)

403
convert.py Normal file → Executable file
View file

@ -10,6 +10,7 @@ import itertools
import json
import math
import mmap
import os
import pickle
import re
import signal
@ -18,15 +19,15 @@ import sys
import time
import zipfile
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from dataclasses import dataclass
from pathlib import Path
from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar
from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, Optional, TypeVar, cast
import numpy as np
from sentencepiece import SentencePieceProcessor
import os
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
@ -42,6 +43,7 @@ NDArray: TypeAlias = 'np.ndarray[Any, Any]'
ARCH = gguf.MODEL_ARCH.LLAMA
DEFAULT_CONCURRENCY = 8
#
# data types
#
@ -62,10 +64,10 @@ class UnquantizedDataType(DataType):
pass
DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])
DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])
@dataclass(frozen=True)
@ -151,14 +153,16 @@ GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
@dataclass
class Params:
n_vocab: int
n_embd: int
n_layer: int
n_ctx: int
n_ff: int
n_head: int
n_head_kv: int
f_norm_eps: float
n_vocab: int
n_embd: int
n_layer: int
n_ctx: int
n_ff: int
n_head: int
n_head_kv: int
n_experts: int | None = None
n_experts_used: int | None = None
f_norm_eps: float | None = None
rope_scaling_type: gguf.RopeScalingType | None = None
f_rope_freq_base: float | None = None
@ -233,6 +237,13 @@ class Params:
raise Exception("failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files.")
n_experts = None
n_experts_used = None
if "num_local_experts" in config:
n_experts = config["num_local_experts"]
n_experts_used = config["num_experts_per_tok"]
return Params(
n_vocab = config["vocab_size"],
n_embd = config["hidden_size"],
@ -241,6 +252,8 @@ class Params:
n_ff = config["intermediate_size"],
n_head = (n_head := config["num_attention_heads"]),
n_head_kv = config.get("num_key_value_heads", n_head),
n_experts = n_experts,
n_experts_used = n_experts_used,
f_norm_eps = config["rms_norm_eps"],
f_rope_freq_base = config.get("rope_theta"),
rope_scaling_type = rope_scaling_type,
@ -255,8 +268,15 @@ class Params:
def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
config = json.load(open(config_path))
n_experts = None
n_experts_used = None
f_rope_freq_base = None
# hack to determine LLaMA v1 vs v2 vs CodeLlama
if config.get("rope_theta") == 1000000:
if config.get("moe"):
# Mixtral
n_ctx = 32768
elif config.get("rope_theta") == 1000000:
# CodeLlama
n_ctx = 16384
elif config["norm_eps"] == 1e-05:
@ -266,16 +286,27 @@ class Params:
# LLaMA v1
n_ctx = 2048
if "layers.0.feed_forward.w1.weight" in model:
n_ff = model["layers.0.feed_forward.w1.weight"].shape[0]
if config.get("moe"):
n_ff = model["layers.0.feed_forward.experts.0.w1.weight"].shape[0]
n_experts = config["moe"]["num_experts"]
n_experts_used = config["moe"]["num_experts_per_tok"]
f_rope_freq_base = 1e6
return Params(
n_vocab = config.get("vocab_size", model["tok_embeddings.weight"].shape[0]),
n_vocab = model["tok_embeddings.weight"].shape[0],
n_embd = config["dim"],
n_layer = config["n_layers"],
n_ctx = n_ctx,
n_ff = model["layers.0.feed_forward.w1.weight"].shape[0],
n_ff = n_ff,
n_head = (n_head := config["n_heads"]),
n_head_kv = config.get("n_kv_heads", n_head),
n_experts = n_experts,
n_experts_used = n_experts_used,
f_norm_eps = config["norm_eps"],
f_rope_freq_base = config.get("rope_theta"),
f_rope_freq_base = config.get("rope_theta", f_rope_freq_base),
)
@staticmethod
@ -297,127 +328,138 @@ class Params:
return params
#
# vocab
#
class VocabLoader:
def __init__(self, params: Params, fname_tokenizer: Path) -> None:
try:
from transformers import AutoTokenizer
except ImportError as e:
raise ImportError(
"To use VocabLoader, please install the `transformers` package. "
"You can install it with `pip install transformers`."
) from e
class BpeVocab:
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
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"))
try:
self.tokenizer = AutoTokenizer.from_pretrained(str(fname_tokenizer), trust_remote_code=True)
except ValueError:
self.tokenizer = AutoTokenizer.from_pretrained(str(fname_tokenizer), use_fast=False, trust_remote_code=True)
self.added_tokens_dict: OrderedDict[str, int] = OrderedDict()
for tok, tokidx in sorted(self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]):
if tokidx >= params.n_vocab or tokidx < self.tokenizer.vocab_size:
continue
self.added_tokens_dict[tok] = tokidx
self.unk_token_id: int = self.tokenizer.unk_token_id
self.specials: dict[str, int] = {
tok: self.tokenizer.get_vocab()[tok]
for tok in self.tokenizer.all_special_tokens
}
self.special_ids: set[int] = set(self.tokenizer.all_special_ids)
self.vocab_size_base: int = self.tokenizer.vocab_size
self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_dict)
self.fname_tokenizer: Path = fname_tokenizer
vocab_file = "tokenizer.model"
path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file)
if path_candidate is not None:
self.spm = SentencePieceProcessor(str(path_candidate))
print(self.spm.vocab_size(), self.vocab_size_base)
else:
# 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)
self.spm = None
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:
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}")
def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
tokenizer = self.tokenizer
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.get_vocab().items()}
added_tokens_ids = set(self.added_tokens_dict.values())
items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
self.added_tokens_list = [text for (text, idx) in items]
self.vocab_size_base: int = vocab_size
self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
self.fname_added_tokens = fname_added_tokens
for i in range(self.vocab_size_base):
if i in added_tokens_ids:
continue
def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
tokenizer = self.bpe_tokenizer
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}
text = reverse_vocab[i].encode("utf-8")
yield text, self.get_token_score(i), self.get_token_type(i)
for i, _ in enumerate(tokenizer):
yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL
def get_token_type(self, token_id: int) -> gguf.TokenType:
toktype = gguf.TokenType.NORMAL
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
score = -1000.0
yield text.encode("utf-8"), score, gguf.TokenType.CONTROL
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
yield from self.bpe_tokens()
yield from self.added_tokens()
def __repr__(self) -> str:
return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
class SentencePieceVocab:
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
added_tokens: dict[str, int]
if fname_added_tokens is not None:
added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
else:
added_tokens = {}
vocab_size: int = self.sentencepiece_tokenizer.vocab_size()
new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
actual_new_ids = sorted(new_tokens.keys())
if expected_new_ids != actual_new_ids:
raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
# Token pieces that were added to the base vocabulary.
self.added_tokens_list = [new_tokens[id] for id in actual_new_ids]
self.vocab_size_base = vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
self.fname_added_tokens = fname_added_tokens
def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
tokenizer = self.sentencepiece_tokenizer
for i in range(tokenizer.vocab_size()):
piece = tokenizer.id_to_piece(i)
text: bytes = piece.encode("utf-8")
score: float = tokenizer.get_score(i)
toktype = gguf.TokenType.NORMAL
if tokenizer.is_unknown(i):
if self.spm is not None and token_id < self.spm.vocab_size():
if self.spm.is_unknown(token_id):
toktype = gguf.TokenType.UNKNOWN
if tokenizer.is_control(i):
if self.spm.is_control(token_id):
toktype = gguf.TokenType.CONTROL
if self.spm.is_unused(token_id):
toktype = gguf.TokenType.UNUSED
if self.spm.is_byte(token_id):
toktype = gguf.TokenType.BYTE
else:
if token_id == self.unk_token_id:
toktype = gguf.TokenType.UNKNOWN
if token_id in self.special_ids:
toktype = gguf.TokenType.CONTROL
# NOTE: I think added_tokens are user defined.
# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
# if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED
return toktype
if tokenizer.is_unused(i):
toktype = gguf.TokenType.UNUSED
if tokenizer.is_byte(i):
toktype = gguf.TokenType.BYTE
yield text, score, toktype
def get_token_score(self, token_id: int) -> float:
if self.spm is not None and token_id < self.spm.vocab_size():
return cast(float, self.spm.get_score(token_id))
return 0.0
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
score = -1000.0
yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED
for text in self.added_tokens_dict:
if text in self.specials:
toktype = self.get_token_type(self.specials[text])
score = self.get_token_score(self.specials[text])
else:
toktype = gguf.TokenType.USER_DEFINED
score = -1000.0
yield text.encode("utf-8"), score, toktype
def has_newline_token(self) -> bool:
return '<0x0A>' in self.tokenizer.vocab or '\n' in self.tokenizer.vocab
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
yield from self.sentencepiece_tokens()
yield from self.hf_tokens()
yield from self.added_tokens()
def get_vocab_type(self) -> str:
path_candidates = []
vocab_file = "tokenizer.model"
path_candidates.append(vocab_file)
path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file)
if path_candidate is not None:
return "llama"
vocab_file = "vocab.json"
path_candidates.append(vocab_file)
path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file)
if path_candidate is not None:
return "gpt2"
vocab_file = "tokenizer.json"
path_candidates.append(vocab_file)
path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file)
if path_candidate:
if not self.has_newline_token():
return "gpt2"
return "llama"
raise FileNotFoundError(
f"Could not find {path_candidates} in {self.fname_tokenizer} or its parent; "
"if it's in another directory, pass the directory as --vocab-dir"
)
def __repr__(self) -> str:
return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
return f"<VocabLoader with {self.vocab_size_base} base tokens and {len(self.added_tokens_dict)} added tokens>"
Vocab: TypeAlias = 'BpeVocab | SentencePieceVocab'
Vocab: TypeAlias = 'VocabLoader'
#
# data loading
@ -585,7 +627,7 @@ def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus:
if any("model.embed_tokens.weight" in mp.model for mp in models_plus):
# Transformers models put different tensors in different files, but
# don't split indivdual tensors between files.
# don't split individual tensors between files.
model: LazyModel = {}
for mp in models_plus:
model.update(mp.model)
@ -678,7 +720,7 @@ class LazyUnpickler(pickle.Unpickler):
return func(*args)
CLASSES: dict[tuple[str, str], Any] = {
# getattr used here as a workaround for mypy not being smart enough to detrmine
# getattr used here as a workaround for mypy not being smart enough to determine
# the staticmethods have a __func__ attribute.
('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),
('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'),
@ -794,20 +836,27 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc
yield result
def check_vocab_size(params: Params, vocab: Vocab) -> None:
def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> None:
if params.n_vocab != vocab.vocab_size:
assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)
if params.n_vocab == vocab.vocab_size_base:
if params.n_vocab == vocab.vocab_size:
print("Ignoring added_tokens.json since model matches vocab size without it.")
vocab.added_tokens_list = []
vocab.vocab_size = vocab.vocab_size_base
vocab.added_tokens_dict = OrderedDict()
vocab.vocab_size = vocab.vocab_size
return
if pad_vocab and params.n_vocab > vocab.vocab_size:
pad_count = params.n_vocab - vocab.vocab_size
print(f'Padding vocab with {pad_count} token(s) - <dummy00001> through <dummy{pad_count:05}>')
for i in range(1, (params.n_vocab - vocab.vocab_size) + 1):
vocab.added_tokens_dict[f'<dummy{i:05}>'] = -1
vocab.vocab_size = params.n_vocab
return
msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}"
if vocab.fname_added_tokens is not None:
msg += f" combined with {vocab.fname_added_tokens}"
msg += f" has {vocab.vocab_size})."
if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20 and vocab.fname_added_tokens is None:
if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20:
msg += f" Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})."
if vocab.vocab_size < params.n_vocab:
msg += " Possibly try using the --padvocab option."
raise Exception(msg)
@ -832,7 +881,17 @@ class OutputFile:
self.gguf.add_rope_dimension_count(params.n_embd // params.n_head)
self.gguf.add_head_count (params.n_head)
self.gguf.add_head_count_kv (params.n_head_kv)
self.gguf.add_layer_norm_rms_eps (params.f_norm_eps)
if params.n_experts:
self.gguf.add_expert_count(params.n_experts)
if params.n_experts_used:
self.gguf.add_expert_used_count(params.n_experts_used)
if params.f_norm_eps:
self.gguf.add_layer_norm_rms_eps(params.f_norm_eps)
else:
raise ValueError('f_norm_eps is None')
if params.f_rope_freq_base is not None:
self.gguf.add_rope_freq_base(params.f_rope_freq_base)
@ -861,12 +920,8 @@ class OutputFile:
scores.append(score)
toktypes.append(toktype)
if isinstance(vocab, SentencePieceVocab):
self.gguf.add_tokenizer_model("llama")
elif isinstance(vocab, BpeVocab):
self.gguf.add_tokenizer_model("gpt2")
else:
raise ValueError('Unknown vocab type: Not BpeVocab or SentencePieceVocab')
vocab_type = vocab.get_vocab_type()
self.gguf.add_tokenizer_model(vocab_type)
self.gguf.add_token_list(tokens)
self.gguf.add_token_scores(scores)
self.gguf.add_token_types(toktypes)
@ -892,8 +947,12 @@ class OutputFile:
self.gguf.close()
@staticmethod
def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
check_vocab_size(params, vocab)
def write_vocab_only(
fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab,
endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
pad_vocab: bool = False,
) -> None:
check_vocab_size(params, vocab, pad_vocab = pad_vocab)
of = OutputFile(fname_out, endianess=endianess)
@ -920,8 +979,13 @@ class OutputFile:
return dt.quantize(arr)
@staticmethod
def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
check_vocab_size(params, vocab)
def write_all(
fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab,
concurrency: int = DEFAULT_CONCURRENCY,
endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
pad_vocab: bool = False,
) -> None:
check_vocab_size(params, vocab, pad_vocab = pad_vocab)
of = OutputFile(fname_out, endianess=endianess)
@ -956,7 +1020,7 @@ class OutputFile:
def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:
wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) +".weight"].data_type
wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type
if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32):
return GGMLFileType.AllF32
@ -1079,35 +1143,17 @@ def load_some_model(path: Path) -> ModelPlus:
return model_plus
def load_vocab(path: Path, vocabtype: str | None) -> Vocab:
# Be extra-friendly and accept either a file or a directory. Also, if it's
# a directory, it might be the model directory, and tokenizer.model might
# be in the parent of that.
if path.is_dir():
vocab_file = "tokenizer.model"
if vocabtype == 'bpe':
vocab_file = "vocab.json"
path2 = path / vocab_file
# Use `.parent` instead of /.. to handle the symlink case better.
path3 = path.parent / vocab_file
if path2.exists():
path = path2
elif path3.exists():
path = path3
else:
raise FileNotFoundError(
f"Could not find {vocab_file} in {path} or its parent; "
"if it's in another directory, pass the directory as --vocab-dir")
def find_vocab_file_path(path: Path, vocab_file: str) -> Optional[Path]:
path2 = path / vocab_file
# Use `.parent` instead of /.. to handle the symlink case better.
path3 = path.parent / vocab_file
print(f"Loading vocab file '{path}', type '{vocabtype}'")
if path2.exists():
return path2
if path3.exists():
return path3
added_tokens_path = path.parent / "added_tokens.json"
if vocabtype == "bpe":
return BpeVocab(path, added_tokens_path if added_tokens_path.exists() else None)
elif vocabtype == "spm":
return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None)
else:
raise ValueError(f"Unsupported vocabulary type {vocabtype}")
return None
def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path:
@ -1145,11 +1191,11 @@ def main(args_in: list[str] | None = None) -> None:
parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin, *.safetensors)")
parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
parser.add_argument("--ctx", type=int, help="model training context (default: based on input)")
parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY)
parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine")
parser.add_argument("--padvocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides")
args = parser.parse_args(args_in)
if args.dump_single:
@ -1192,12 +1238,13 @@ def main(args_in: list[str] | None = None) -> None:
if not args.outfile:
raise ValueError("need --outfile if using --vocab-only")
# FIXME: Try to respect vocab_dir somehow?
vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)
vocab = VocabLoader(params, args.vocab_dir or args.model)
special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
load_merges = args.vocabtype == 'bpe',
load_merges = True,
n_vocab = vocab.vocab_size)
outfile = args.outfile
OutputFile.write_vocab_only(outfile, params, vocab, special_vocab)
OutputFile.write_vocab_only(outfile, params, vocab, special_vocab,
endianess = endianess, pad_vocab = args.padvocab)
print(f"Wrote {outfile}")
return
@ -1205,12 +1252,15 @@ def main(args_in: list[str] | None = None) -> None:
vocab = model_plus.vocab
else:
vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
vocab = load_vocab(vocab_dir, args.vocabtype)
vocab = VocabLoader(params, vocab_dir)
# FIXME: Try to respect vocab_dir somehow?
print(f"Vocab info: {vocab}")
special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
load_merges = args.vocabtype == 'bpe',
load_merges = True,
n_vocab = vocab.vocab_size)
print(f"Special vocab info: {special_vocab}")
model = model_plus.model
model = convert_model_names(model, params)
ftype = pick_output_type(model, args.outtype)
@ -1220,7 +1270,8 @@ def main(args_in: list[str] | None = None) -> None:
params.ftype = ftype
print(f"Writing {outfile}, format {ftype}")
OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency, endianess=endianess)
OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab,
concurrency = args.concurrency, endianess = endianess, pad_vocab = args.padvocab)
print(f"Wrote {outfile}")

1
examples/CMakeLists.txt
View file

@ -32,6 +32,7 @@ else()
add_subdirectory(save-load-state)
add_subdirectory(simple)
add_subdirectory(speculative)
add_subdirectory(lookahead)
add_subdirectory(train-text-from-scratch)
if (LLAMA_METAL)
add_subdirectory(metal)

18
examples/baby-llama/baby-llama.cpp
View file

@ -1258,9 +1258,9 @@ static struct ggml_tensor * forward_lora(
}
static void sample_softmax(struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) {
assert(logits->n_dims == 2);
assert(probs->n_dims == 2);
assert(best_samples->n_dims == 1);
assert(ggml_is_matrix(logits));
assert(ggml_is_matrix(probs));
assert(ggml_is_vector(best_samples));
assert(logits->ne[1] == best_samples->ne[0]);
assert(logits->ne[0] == probs->ne[0]);
assert(logits->ne[1] == probs->ne[1]);
@ -1292,9 +1292,9 @@ static void sample_softmax_batch(
struct ggml_context * ctx, struct ggml_tensor * logits, struct ggml_tensor * probs,
struct ggml_tensor * best_samples
) {
GGML_ASSERT(best_samples->n_dims == 2);
GGML_ASSERT(logits->n_dims == 3);
GGML_ASSERT(probs->n_dims == 3);
GGML_ASSERT(ggml_is_matrix(best_samples));
GGML_ASSERT(ggml_is_3d(logits));
GGML_ASSERT(ggml_is_3d(probs));
int n_tokens = best_samples->ne[0];
int n_batch = best_samples->ne[1];
int n_vocab = logits->ne[0];
@ -1334,7 +1334,7 @@ static void print_row(struct ggml_tensor * probs, int i) {
}
static void print_matrix(struct ggml_tensor * probs) {
assert(probs->n_dims == 2);
assert(ggml_is_matrix(probs));
for (int i = 0; i < probs->ne[1]; ++i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);
@ -1386,8 +1386,8 @@ static void get_example_targets(int example_id, struct ggml_tensor * tokens_inpu
static void get_example_targets_batch(
struct ggml_context * ctx, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets
) {
GGML_ASSERT(tokens_input->n_dims == 2);
GGML_ASSERT( targets->n_dims == 3);
GGML_ASSERT(ggml_is_matrix(tokens_input));
GGML_ASSERT(ggml_is_3d(targets));
int n_tokens = tokens_input->ne[0];
int n_batch = tokens_input->ne[1];
GGML_ASSERT(n_tokens == targets->ne[1]);

2
examples/batched-bench/batched-bench.cpp
View file

@ -155,7 +155,7 @@ int main(int argc, char ** argv) {
}
LOG_TEE("\n");
LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq);
LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %d, n_threads_batch = %d\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG_TEE("\n");
LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");

2
examples/batched.swift/README.md
View file

@ -1,4 +1,4 @@
This is a swift clone of `examples/batched`.
$ `make`
$ `./swift MODEL_PATH [PROMPT] [PARALLEL]`
$ `./batched_swift MODEL_PATH [PROMPT] [PARALLEL]`

15
examples/batched.swift/Sources/main.swift
View file

@ -215,9 +215,10 @@ print("decoded \(n_decode) tokens in \(String(format: "%.2f", Double(t_main_end
llama_print_timings(context)
private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
let n_tokens = text.count + (add_bos ? 1 : 0)
let utf8Count = text.utf8.count
let n_tokens = utf8Count + (add_bos ? 1 : 0)
let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
let tokenCount = llama_tokenize(model, text, Int32(text.count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
var swiftTokens: [llama_token] = []
for i in 0 ..< tokenCount {
swiftTokens.append(tokens[Int(i)])
@ -230,18 +231,15 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
var result = [CChar](repeating: 0, count: 8)
let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))
if nTokens < 0 {
if result.count >= -Int(nTokens) {
result.removeLast(-Int(nTokens))
} else {
result.removeAll()
}
let actualTokensCount = -Int(nTokens)
result = .init(repeating: 0, count: actualTokensCount)
let check = llama_token_to_piece(
model,
token,
&result,
Int32(result.count)
)
assert(check == nTokens)
assert(check == actualTokensCount)
} else {
result.removeLast(result.count - Int(nTokens))
}
@ -259,5 +257,4 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
buffer = []
return bufferString
}
return nil
}

14
examples/benchmark/benchmark-matmult.cpp
View file

@ -129,13 +129,13 @@ int main(int argc, char ** argv) {
const ggml_type qtype = GGML_TYPE_Q4_1;
size_t ctx_size = 0;
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32);
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32);
ctx_size += sizex*sizez*ggml_type_sizef(GGML_TYPE_F32);
ctx_size += sizex*sizey*ggml_type_sizef(qtype);
ctx_size += sizex*sizey*ggml_type_sizef(qtype);
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS
ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey);
ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey);
ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizez);
ctx_size += ggml_row_size(qtype, sizex*sizey);
ctx_size += ggml_row_size(qtype, sizex*sizey);
ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey); // BLAS
ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey); // BLAS
ctx_size += 1024*1024*16;
printf("Allocating Memory of size %zi bytes, %zi MB\n",ctx_size, (ctx_size/1024/1024));

4
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View file

@ -427,7 +427,7 @@ static void print_row(struct ggml_tensor * probs, int i) {
}
static void print_matrix(struct ggml_tensor * probs) {
assert(probs->n_dims == 2);
assert(ggml_is_matrix(probs));
for (int i = 0; i < probs->ne[1]; ++i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = get_f32_2d(probs, k, i);
@ -639,7 +639,7 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab
static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) {
int ct;
switch (gg_weights->n_dims){
switch (ggml_n_dims(gg_weights)) {
case 1:
ct = 0;
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++){

17
examples/finetune/finetune.cpp
View file

@ -1110,7 +1110,7 @@ static void write_tensor(struct llama_file * file, struct ggml_tensor * tensor,
name = ggml_get_name(tensor);
}
uint32_t name_len = strlen(name);
uint32_t nd = tensor->n_dims;
uint32_t nd = ggml_n_dims(tensor);
uint32_t ne[4] = { (uint32_t)tensor->ne[0],
(uint32_t)tensor->ne[1],
(uint32_t)tensor->ne[2],
@ -1620,8 +1620,6 @@ int main(int argc, char ** argv) {
opt->params.adam.gclip = params.common.adam_gclip;
opt->params.adam.eps_f = params.common.adam_eps_f;
ggml_allocr * alloc = NULL;
printf("%s: init model\n", __func__);
bool existed = load_checkpoint_lora_file(params.common.fn_checkpoint_in, &model, &lora, train);
@ -1725,10 +1723,9 @@ int main(int argc, char ** argv) {
// allocate input tensors
mem_input_data.resize(max_input_size);
alloc = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);
ggml_allocr_alloc(alloc, tokens_input);
ggml_allocr_alloc(alloc, target_probs);
ggml_allocr_free(alloc);
ggml_allocr_t alloc_inps = ggml_allocr_new(mem_input_data.data(), mem_input_data.size(), tensor_alignment);
ggml_allocr_alloc(alloc_inps, tokens_input);
ggml_allocr_alloc(alloc_inps, target_probs);
// context for compute tensors without their data
const size_t estimated_compute_size_wo_data = (
@ -1755,7 +1752,7 @@ int main(int argc, char ** argv) {
// find best evaluation order
for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
ctx_compute = ggml_init(ctx_compute_params);
alloc = ggml_allocr_new_measure(tensor_alignment);
ggml_allocr_t alloc = ggml_allocr_new_measure(tensor_alignment);
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = (enum ggml_cgraph_eval_order) order;
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
@ -1788,7 +1785,7 @@ int main(int argc, char ** argv) {
// allocate compute tensors
mem_compute_data.resize(max_compute_size);
ctx_compute = ggml_init(ctx_compute_params);
alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
ggml_allocr_t alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = best_order;
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
@ -1804,6 +1801,8 @@ int main(int argc, char ** argv) {
params.common.use_checkpointing
);
ggml_allocr_free(alloc);
ggml_allocr_free(alloc_inps);
// tokenize data
std::vector<llama_token> train_tokens;

2
examples/gguf/gguf.cpp
View file

@ -195,7 +195,7 @@ static bool gguf_ex_read_1(const std::string & fname) {
struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
printf("%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, ggml_n_dims(cur), cur->name, cur->data);
// print first 10 elements
const float * data = (const float *) cur->data;

113
examples/llama-bench/llama-bench.cpp
View file

@ -53,6 +53,13 @@ static std::vector<T> split(const std::string & str, char delim) {
return values;
}
template<typename T, typename F>
static std::vector<std::string> transform_to_str(const std::vector<T> & values, F f) {
std::vector<std::string> str_values;
std::transform(values.begin(), values.end(), std::back_inserter(str_values), f);
return str_values;
}
template<typename T>
static T avg(const std::vector<T> & v) {
if (v.empty()) {
@ -126,7 +133,8 @@ struct cmd_params {
std::vector<int> n_prompt;
std::vector<int> n_gen;
std::vector<int> n_batch;
std::vector<bool> f32_kv;
std::vector<ggml_type> type_k;
std::vector<ggml_type> type_v;
std::vector<int> n_threads;
std::vector<int> n_gpu_layers;
std::vector<int> main_gpu;
@ -142,7 +150,8 @@ static const cmd_params cmd_params_defaults = {
/* n_prompt */ {512},
/* n_gen */ {128},
/* n_batch */ {512},
/* f32_kv */ {false},
/* type_k */ {GGML_TYPE_F16},
/* type_v */ {GGML_TYPE_F16},
/* n_threads */ {get_num_physical_cores()},
/* n_gpu_layers */ {99},
/* main_gpu */ {0},
@ -162,7 +171,8 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -p, --n-prompt <n> (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
printf(" -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
printf(" -b, --batch-size <n> (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(" -ctk <t>, --cache-type-k <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
printf(" -ctv <t>, --cache-type-v <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
@ -173,9 +183,32 @@ static void print_usage(int /* argc */, char ** argv) {
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");
}
static ggml_type ggml_type_from_name(const std::string & s) {
if (s == "f16") {
return GGML_TYPE_F16;
}
if (s == "q8_0") {
return GGML_TYPE_Q8_0;
}
if (s == "q4_0") {
return GGML_TYPE_Q4_0;
}
if (s == "q4_1") {
return GGML_TYPE_Q4_1;
}
if (s == "q5_0") {
return GGML_TYPE_Q5_0;
}
if (s == "q5_1") {
return GGML_TYPE_Q5_1;
}
return GGML_TYPE_COUNT;
}
static cmd_params parse_cmd_params(int argc, char ** argv) {
cmd_params params;
std::string arg;
@ -224,13 +257,38 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
}
auto p = split<int>(argv[i], split_delim);
params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
} else if (arg == "--memory-f32") {
} else if (arg == "-ctk" || arg == "--cache-type-k") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<int>(argv[i], split_delim);
params.f32_kv.insert(params.f32_kv.end(), p.begin(), p.end());
auto p = split<std::string>(argv[i], split_delim);
std::vector<ggml_type> types;
for (const auto & t : p) {
ggml_type gt = ggml_type_from_name(t);
if (gt == GGML_TYPE_COUNT) {
invalid_param = true;
break;
}
types.push_back(gt);
}
params.type_k.insert(params.type_k.end(), types.begin(), types.end());
} else if (arg == "-ctv" || arg == "--cache-type-v") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<std::string>(argv[i], split_delim);
std::vector<ggml_type> types;
for (const auto & t : p) {
ggml_type gt = ggml_type_from_name(t);
if (gt == GGML_TYPE_COUNT) {
invalid_param = true;
break;
}
types.push_back(gt);
}
params.type_v.insert(params.type_v.end(), types.begin(), types.end());
} else if (arg == "-t" || arg == "--threads") {
if (++i >= argc) {
invalid_param = true;
@ -321,7 +379,8 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; }
if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; }
if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; }
if (params.f32_kv.empty()) { params.f32_kv = cmd_params_defaults.f32_kv; }
if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; }
if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; }
if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; }
if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
@ -336,7 +395,8 @@ struct cmd_params_instance {
int n_prompt;
int n_gen;
int n_batch;
bool f32_kv;
ggml_type type_k;
ggml_type type_v;
int n_threads;
int n_gpu_layers;
int main_gpu;
@ -365,7 +425,8 @@ struct cmd_params_instance {
cparams.n_ctx = n_prompt + n_gen;
cparams.n_batch = n_batch;
cparams.f16_kv = !f32_kv;
cparams.type_k = type_k;
cparams.type_v = type_v;
cparams.mul_mat_q = mul_mat_q;
return cparams;
@ -380,7 +441,8 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
for (const auto & mg : params.main_gpu)
for (const auto & ts : params.tensor_split)
for (const auto & nb : params.n_batch)
for (const auto & fk : params.f32_kv)
for (const auto & tk : params.type_k)
for (const auto & tv : params.type_v)
for (const auto & mmq : params.mul_mat_q)
for (const auto & nt : params.n_threads) {
cmd_params_instance instance = {
@ -388,7 +450,8 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
/* .n_prompt = */ n_prompt,
/* .n_gen = */ n_gen,
/* .n_batch = */ nb,
/* .f32_kv = */ fk,
/* .type_k = */ tk,
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
@ -410,7 +473,8 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
for (const auto & mg : params.main_gpu)
for (const auto & ts : params.tensor_split)
for (const auto & nb : params.n_batch)
for (const auto & fk : params.f32_kv)
for (const auto & tk : params.type_k)
for (const auto & tv : params.type_v)
for (const auto & mmq : params.mul_mat_q)
for (const auto & nt : params.n_threads) {
for (const auto & n_prompt : params.n_prompt) {
@ -422,7 +486,8 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .n_prompt = */ n_prompt,
/* .n_gen = */ 0,
/* .n_batch = */ nb,
/* .f32_kv = */ fk,
/* .type_k = */ tk,
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
@ -441,7 +506,8 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .n_prompt = */ 0,
/* .n_gen = */ n_gen,
/* .n_batch = */ nb,
/* .f32_kv = */ fk,
/* .type_k = */ tk,
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
@ -489,7 +555,8 @@ struct test {
uint64_t model_n_params;
int n_batch;
int n_threads;
bool f32_kv;
ggml_type type_k;
ggml_type type_v;
int n_gpu_layers;
int main_gpu;
bool mul_mat_q;
@ -508,7 +575,8 @@ struct test {
model_n_params = llama_model_n_params(lmodel);
n_batch = inst.n_batch;
n_threads = inst.n_threads;
f32_kv = inst.f32_kv;
type_k = inst.type_k;
type_v = inst.type_v;
n_gpu_layers = inst.n_gpu_layers;
main_gpu = inst.main_gpu;
mul_mat_q = inst.mul_mat_q;
@ -571,7 +639,7 @@ struct test {
"cuda", "opencl", "metal", "gpu_blas", "blas",
"cpu_info", "gpu_info",
"model_filename", "model_type", "model_size", "model_n_params",
"n_batch", "n_threads", "f16_kv",
"n_batch", "n_threads", "type_k", "type_v",
"n_gpu_layers", "main_gpu", "mul_mat_q", "tensor_split",
"n_prompt", "n_gen", "test_time",
"avg_ns", "stddev_ns",
@ -621,7 +689,7 @@ struct test {
std::to_string(cuda), std::to_string(opencl), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas),
cpu_info, gpu_info,
model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
std::to_string(n_batch), std::to_string(n_threads), std::to_string(!f32_kv),
std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), tensor_split_str,
std::to_string(n_prompt), std::to_string(n_gen), test_time,
std::to_string(avg_ns()), std::to_string(stdev_ns()),
@ -805,8 +873,11 @@ struct markdown_printer : public printer {
if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
fields.push_back("n_batch");
}
if (params.f32_kv.size() > 1 || params.f32_kv != cmd_params_defaults.f32_kv) {
fields.push_back("f16_kv");
if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) {
fields.push_back("type_k");
}
if (params.type_v.size() > 1 || params.type_v != cmd_params_defaults.type_v) {
fields.push_back("type_v");
}
if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) {
fields.push_back("main_gpu");

2
examples/llama.swiftui/.gitignore vendored Normal file
View file

@ -0,0 +1,2 @@
xcuserdata
xcshareddata

7
examples/llama.swiftui/README.md Normal file
View file

@ -0,0 +1,7 @@
# llama.swiftui
Local inference of llama.cpp on an iPhone.
So far I only tested with starcoder 1B model, but it can most likely handle 7B models as well.
https://github.com/bachittle/llama.cpp/assets/39804642/e290827a-4edb-4093-9642-2a5e399ec545

340
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift Normal file
View file

@ -0,0 +1,340 @@
import Foundation
// import llama
enum LlamaError: Error {
case couldNotInitializeContext
}
func llama_batch_clear(_ batch: inout llama_batch) {
batch.n_tokens = 0
}
func llama_batch_add(_ batch: inout llama_batch, _ id: llama_token, _ pos: llama_pos, _ seq_ids: [llama_seq_id], _ logits: Bool) {
batch.token [Int(batch.n_tokens)] = id
batch.pos [Int(batch.n_tokens)] = pos
batch.n_seq_id[Int(batch.n_tokens)] = Int32(seq_ids.count)
for i in 0..<seq_ids.count {
batch.seq_id[Int(batch.n_tokens)]![Int(i)] = seq_ids[i]
}
batch.logits [Int(batch.n_tokens)] = logits ? 1 : 0
batch.n_tokens += 1
}
actor LlamaContext {
private var model: OpaquePointer
private var context: OpaquePointer
private var batch: llama_batch
private var tokens_list: [llama_token]
/// This variable is used to store temporarily invalid cchars
private var temporary_invalid_cchars: [CChar]
var n_len: Int32 = 64
var n_cur: Int32 = 0
var n_decode: Int32 = 0
init(model: OpaquePointer, context: OpaquePointer) {
self.model = model
self.context = context
self.tokens_list = []
self.batch = llama_batch_init(512, 0, 1)
self.temporary_invalid_cchars = []
}
deinit {
llama_batch_free(batch)
llama_free(context)
llama_free_model(model)
llama_backend_free()
}
static func create_context(path: String) throws -> LlamaContext {
llama_backend_init(false)
var model_params = llama_model_default_params()
#if targetEnvironment(simulator)
model_params.n_gpu_layers = 0
print("Running on simulator, force use n_gpu_layers = 0")
#endif
let model = llama_load_model_from_file(path, model_params)
guard let model else {
print("Could not load model at \(path)")
throw LlamaError.couldNotInitializeContext
}
let n_threads = max(1, min(8, ProcessInfo.processInfo.processorCount - 2))
print("Using \(n_threads) threads")
var ctx_params = llama_context_default_params()
ctx_params.seed = 1234
ctx_params.n_ctx = 2048
ctx_params.n_threads = UInt32(n_threads)
ctx_params.n_threads_batch = UInt32(n_threads)
let context = llama_new_context_with_model(model, ctx_params)
guard let context else {
print("Could not load context!")
throw LlamaError.couldNotInitializeContext
}
return LlamaContext(model: model, context: context)
}
func model_info() -> String {
let result = UnsafeMutablePointer<Int8>.allocate(capacity: 256)
result.initialize(repeating: Int8(0), count: 256)
defer {
result.deallocate()
}
// TODO: this is probably very stupid way to get the string from C
let nChars = llama_model_desc(model, result, 256)
let bufferPointer = UnsafeBufferPointer(start: result, count: Int(nChars))
var SwiftString = ""
for char in bufferPointer {
SwiftString.append(Character(UnicodeScalar(UInt8(char))))
}
return SwiftString
}
func get_n_tokens() -> Int32 {
return batch.n_tokens;
}
func completion_init(text: String) {
print("attempting to complete \"\(text)\"")
tokens_list = tokenize(text: text, add_bos: true)
temporary_invalid_cchars = []
let n_ctx = llama_n_ctx(context)
let n_kv_req = tokens_list.count + (Int(n_len) - tokens_list.count)
print("\n n_len = \(n_len), n_ctx = \(n_ctx), n_kv_req = \(n_kv_req)")
if n_kv_req > n_ctx {
print("error: n_kv_req > n_ctx, the required KV cache size is not big enough")
}
for id in tokens_list {
print(String(cString: token_to_piece(token: id) + [0]))
}
llama_batch_clear(&batch)
for i1 in 0..<tokens_list.count {
let i = Int(i1)
llama_batch_add(&batch, tokens_list[i], Int32(i), [0], false)
}
batch.logits[Int(batch.n_tokens) - 1] = 1 // true
if llama_decode(context, batch) != 0 {
print("llama_decode() failed")
}
n_cur = batch.n_tokens
}
func completion_loop() -> String {
var new_token_id: llama_token = 0
let n_vocab = llama_n_vocab(model)
let logits = llama_get_logits_ith(context, batch.n_tokens - 1)
var candidates = Array<llama_token_data>()
candidates.reserveCapacity(Int(n_vocab))
for token_id in 0..<n_vocab {
candidates.append(llama_token_data(id: token_id, logit: logits![Int(token_id)], p: 0.0))
}
candidates.withUnsafeMutableBufferPointer() { buffer in
var candidates_p = llama_token_data_array(data: buffer.baseAddress, size: buffer.count, sorted: false)
new_token_id = llama_sample_token_greedy(context, &candidates_p)
}
if new_token_id == llama_token_eos(context) || n_cur == n_len {
print("\n")
let new_token_str = String(cString: temporary_invalid_cchars + [0])
temporary_invalid_cchars.removeAll()
return new_token_str
}
let new_token_cchars = token_to_piece(token: new_token_id)
temporary_invalid_cchars.append(contentsOf: new_token_cchars)
let new_token_str: String
if let string = String(validatingUTF8: temporary_invalid_cchars + [0]) {
temporary_invalid_cchars.removeAll()
new_token_str = string
} else if (0 ..< temporary_invalid_cchars.count).contains(where: {$0 != 0 && String(validatingUTF8: Array(temporary_invalid_cchars.suffix($0)) + [0]) != nil}) {
// in this case, at least the suffix of the temporary_invalid_cchars can be interpreted as UTF8 string
let string = String(cString: temporary_invalid_cchars + [0])
temporary_invalid_cchars.removeAll()
new_token_str = string
} else {
new_token_str = ""
}
print(new_token_str)
// tokens_list.append(new_token_id)
llama_batch_clear(&batch)
llama_batch_add(&batch, new_token_id, n_cur, [0], true)
n_decode += 1
n_cur += 1
if llama_decode(context, batch) != 0 {
print("failed to evaluate llama!")
}
return new_token_str
}
func bench(pp: Int, tg: Int, pl: Int, nr: Int = 1) -> String {
var pp_avg: Double = 0
var tg_avg: Double = 0
var pp_std: Double = 0
var tg_std: Double = 0
for _ in 0..<nr {
// bench prompt processing
llama_batch_clear(&batch)
let n_tokens = pp
for i in 0..<n_tokens {
llama_batch_add(&batch, 0, Int32(i), [0], false)
}
batch.logits[Int(batch.n_tokens) - 1] = 1 // true
llama_kv_cache_clear(context)
let t_pp_start = ggml_time_us()
if llama_decode(context, batch) != 0 {
print("llama_decode() failed during prompt")
}
let t_pp_end = ggml_time_us()
// bench text generation
llama_kv_cache_clear(context)
let t_tg_start = ggml_time_us()
for i in 0..<tg {
llama_batch_clear(&batch)
for j in 0..<pl {
llama_batch_add(&batch, 0, Int32(i), [Int32(j)], true)
}
if llama_decode(context, batch) != 0 {
print("llama_decode() failed during text generation")
}
}
let t_tg_end = ggml_time_us()
llama_kv_cache_clear(context)
let t_pp = Double(t_pp_end - t_pp_start) / 1000000.0
let t_tg = Double(t_tg_end - t_tg_start) / 1000000.0
let speed_pp = Double(pp) / t_pp
let speed_tg = Double(pl*tg) / t_tg
pp_avg += speed_pp
tg_avg += speed_tg
pp_std += speed_pp * speed_pp
tg_std += speed_tg * speed_tg
print("pp \(speed_pp) t/s, tg \(speed_tg) t/s")
}
pp_avg /= Double(nr)
tg_avg /= Double(nr)
if nr > 1 {
pp_std = sqrt(pp_std / Double(nr - 1) - pp_avg * pp_avg * Double(nr) / Double(nr - 1))
tg_std = sqrt(tg_std / Double(nr - 1) - tg_avg * tg_avg * Double(nr) / Double(nr - 1))
} else {
pp_std = 0
tg_std = 0
}
let model_desc = model_info();
let model_size = String(format: "%.2f GiB", Double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0);
let model_n_params = String(format: "%.2f B", Double(llama_model_n_params(model)) / 1e9);
let backend = "Metal";
let pp_avg_str = String(format: "%.2f", pp_avg);
let tg_avg_str = String(format: "%.2f", tg_avg);
let pp_std_str = String(format: "%.2f", pp_std);
let tg_std_str = String(format: "%.2f", tg_std);
var result = ""
result += String("| model | size | params | backend | test | t/s |\n")
result += String("| --- | --- | --- | --- | --- | --- |\n")
result += String("| \(model_desc) | \(model_size) | \(model_n_params) | \(backend) | pp \(pp) | \(pp_avg_str) ± \(pp_std_str) |\n")
result += String("| \(model_desc) | \(model_size) | \(model_n_params) | \(backend) | tg \(tg) | \(tg_avg_str) ± \(tg_std_str) |\n")
return result;
}
func clear() {
tokens_list.removeAll()
temporary_invalid_cchars.removeAll()
llama_kv_cache_clear(context)
}
private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
let utf8Count = text.utf8.count
let n_tokens = utf8Count + (add_bos ? 1 : 0) + 1
let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)
var swiftTokens: [llama_token] = []
for i in 0..<tokenCount {
swiftTokens.append(tokens[Int(i)])
}
tokens.deallocate()
return swiftTokens
}
/// - note: The result does not contain null-terminator
private func token_to_piece(token: llama_token) -> [CChar] {
let result = UnsafeMutablePointer<Int8>.allocate(capacity: 8)
result.initialize(repeating: Int8(0), count: 8)
defer {
result.deallocate()
}
let nTokens = llama_token_to_piece(model, token, result, 8)
if nTokens < 0 {
let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
newResult.initialize(repeating: Int8(0), count: Int(-nTokens))
defer {
newResult.deallocate()
}
let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens)
let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
return Array(bufferPointer)
} else {
let bufferPointer = UnsafeBufferPointer(start: result, count: Int(nTokens))
return Array(bufferPointer)
}
}
}

5
examples/llama.swiftui/llama.cpp.swift/bridging-header.h Normal file
View file

@ -0,0 +1,5 @@
//
// Use this file to import your target's public headers that you would like to expose to Swift.
//
#import "llama.h"

483
examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj Normal file
View file

@ -0,0 +1,483 @@
// !$*UTF8*$!
{
archiveVersion = 1;
classes = {
};
objectVersion = 56;
objects = {
/* Begin PBXBuildFile section */
542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */ = {isa = PBXBuildFile; fileRef = 542376072B0D9BFB008E6A1C /* ggml-quants.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */ = {isa = PBXBuildFile; fileRef = 5423760A2B0D9C4B008E6A1C /* ggml-backend.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
542378792ACE3F3500834A7B /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; };
542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09B2AC8723900A8AEE9 /* ggml.c */; settings = {COMPILER_FLAGS = "-DGGML_USE_ACCELERATE -DGGML_USE_METAL -DGGML_USE_K_QUANTS -O3"; }; };
542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 542EA0A12AC8729100A8AEE9 /* llama.cpp */; settings = {COMPILER_FLAGS = "-DGGML_USE_K_QUANTS -DGGML_USE_METAL -O3"; }; };
549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; };
549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */ = {isa = PBXBuildFile; fileRef = 549479C52AC9E0F200E0F78B /* ggml-metal.m */; settings = {COMPILER_FLAGS = "-fno-objc-arc -DGGML_SWIFT -DGGML_USE_METAL -O3"; }; };
7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */; };
8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */; };
8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83782AC328BD0096AF73 /* ContentView.swift */; };
8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 8A1C837A2AC328BE0096AF73 /* Assets.xcassets */; };
8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */; };
8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 8A39BE092AC7601000BFEB40 /* Accelerate.framework */; };
8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; };
8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; };
8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; };
/* End PBXBuildFile section */
/* Begin PBXFileReference section */
542376062B0D9BEA008E6A1C /* ggml-quants.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-quants.h"; path = "../../ggml-quants.h"; sourceTree = "<group>"; };
542376072B0D9BFB008E6A1C /* ggml-quants.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-quants.c"; path = "../../ggml-quants.c"; sourceTree = "<group>"; };
542376092B0D9C40008E6A1C /* ggml-backend.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; name = "ggml-backend.h"; path = "../../ggml-backend.h"; sourceTree = "<group>"; };
5423760A2B0D9C4B008E6A1C /* ggml-backend.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-backend.c"; path = "../../ggml-backend.c"; sourceTree = "<group>"; };
542EA09B2AC8723900A8AEE9 /* ggml.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = ggml.c; path = ../../ggml.c; sourceTree = "<group>"; };
542EA09C2AC8723900A8AEE9 /* ggml.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = ggml.h; path = ../../ggml.h; sourceTree = "<group>"; };
542EA09E2AC8725700A8AEE9 /* ggml-alloc.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-alloc.h"; path = "../../ggml-alloc.h"; sourceTree = "<group>"; };
542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-alloc.c"; path = "../../ggml-alloc.c"; sourceTree = "<group>"; };
542EA0A12AC8729100A8AEE9 /* llama.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; name = llama.cpp; path = ../../llama.cpp; sourceTree = "<group>"; };
542EA0A22AC8729100A8AEE9 /* llama.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = llama.h; path = ../../llama.h; sourceTree = "<group>"; };
549479C52AC9E0F200E0F78B /* ggml-metal.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; name = "ggml-metal.m"; path = "../../ggml-metal.m"; sourceTree = "<group>"; };
549479C62AC9E0F200E0F78B /* ggml-metal.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-metal.h"; path = "../../ggml-metal.h"; sourceTree = "<group>"; };
549479C82AC9E10B00E0F78B /* ggml-metal.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; name = "ggml-metal.metal"; path = "../../ggml-metal.metal"; sourceTree = "<group>"; };
549479CA2AC9E16000E0F78B /* Metal.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Metal.framework; path = System/Library/Frameworks/Metal.framework; sourceTree = SDKROOT; };
7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = DownloadButton.swift; sourceTree = "<group>"; };
8A08D20A2AC73B1500FE6CD4 /* bridging-header.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = "bridging-header.h"; sourceTree = "<group>"; };
8A1C83732AC328BD0096AF73 /* llama.swiftui.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = llama.swiftui.app; sourceTree = BUILT_PRODUCTS_DIR; };
8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = llama_swiftuiApp.swift; sourceTree = "<group>"; };
8A1C83782AC328BD0096AF73 /* ContentView.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ContentView.swift; sourceTree = "<group>"; };
8A1C837A2AC328BE0096AF73 /* Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = Assets.xcassets; sourceTree = "<group>"; };
8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = "Preview Assets.xcassets"; sourceTree = "<group>"; };
8A39BE092AC7601000BFEB40 /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = System/Library/Frameworks/Accelerate.framework; sourceTree = SDKROOT; };
8A3F84232AC4C891005E2EE8 /* models */ = {isa = PBXFileReference; lastKnownFileType = folder; name = models; path = llama.swiftui/Resources/models; sourceTree = "<group>"; };
8A907F322AC7134E006146EA /* LibLlama.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LibLlama.swift; sourceTree = "<group>"; };
8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LlamaState.swift; sourceTree = "<group>"; };
/* End PBXFileReference section */
/* Begin PBXFrameworksBuildPhase section */
8A1C83702AC328BD0096AF73 /* Frameworks */ = {
isa = PBXFrameworksBuildPhase;
buildActionMask = 2147483647;
files = (
549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */,
8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */,
);
runOnlyForDeploymentPostprocessing = 0;
};
/* End PBXFrameworksBuildPhase section */
/* Begin PBXGroup section */
8A08D1F62AC7383900FE6CD4 /* llama.cpp */ = {
isa = PBXGroup;
children = (
5423760A2B0D9C4B008E6A1C /* ggml-backend.c */,
542376092B0D9C40008E6A1C /* ggml-backend.h */,
542376062B0D9BEA008E6A1C /* ggml-quants.h */,
542376072B0D9BFB008E6A1C /* ggml-quants.c */,
549479C82AC9E10B00E0F78B /* ggml-metal.metal */,
549479C62AC9E0F200E0F78B /* ggml-metal.h */,
549479C52AC9E0F200E0F78B /* ggml-metal.m */,
542EA09B2AC8723900A8AEE9 /* ggml.c */,
542EA09C2AC8723900A8AEE9 /* ggml.h */,
542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */,
542EA09E2AC8725700A8AEE9 /* ggml-alloc.h */,
542EA0A12AC8729100A8AEE9 /* llama.cpp */,
542EA0A22AC8729100A8AEE9 /* llama.h */,
);
name = llama.cpp;
sourceTree = "<group>";
};
8A1C836A2AC328BD0096AF73 = {
isa = PBXGroup;
children = (
8A08D1F62AC7383900FE6CD4 /* llama.cpp */,
8A907F312AC7134E006146EA /* llama.cpp.swift */,
8A3F84232AC4C891005E2EE8 /* models */,
8A1C83752AC328BD0096AF73 /* llama.swiftui */,
8A1C83742AC328BD0096AF73 /* Products */,
8A39BE082AC7601000BFEB40 /* Frameworks */,
);
sourceTree = "<group>";
};
8A1C83742AC328BD0096AF73 /* Products */ = {
isa = PBXGroup;
children = (
8A1C83732AC328BD0096AF73 /* llama.swiftui.app */,
);
name = Products;
sourceTree = "<group>";
};
8A1C83752AC328BD0096AF73 /* llama.swiftui */ = {
isa = PBXGroup;
children = (
8A3F84102AC4BD85005E2EE8 /* Resources */,
8A9F7C4B2AC332DC008AE1EA /* Models */,
8A9F7C4A2AC332BF008AE1EA /* UI */,
8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */,
8A1C837A2AC328BE0096AF73 /* Assets.xcassets */,
8A1C837C2AC328BE0096AF73 /* Preview Content */,
);
path = llama.swiftui;
sourceTree = "<group>";
};
8A1C837C2AC328BE0096AF73 /* Preview Content */ = {
isa = PBXGroup;
children = (
8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */,
);
path = "Preview Content";
sourceTree = "<group>";
};
8A39BE082AC7601000BFEB40 /* Frameworks */ = {
isa = PBXGroup;
children = (
549479CA2AC9E16000E0F78B /* Metal.framework */,
8A39BE092AC7601000BFEB40 /* Accelerate.framework */,
);
name = Frameworks;
sourceTree = "<group>";
};
8A3F84102AC4BD85005E2EE8 /* Resources */ = {
isa = PBXGroup;
children = (
8A3F84112AC4BD8C005E2EE8 /* models */,
);
path = Resources;
sourceTree = "<group>";
};
8A3F84112AC4BD8C005E2EE8 /* models */ = {
isa = PBXGroup;
children = (
);
path = models;
sourceTree = "<group>";
};
8A907F312AC7134E006146EA /* llama.cpp.swift */ = {
isa = PBXGroup;
children = (
8A08D20A2AC73B1500FE6CD4 /* bridging-header.h */,
8A907F322AC7134E006146EA /* LibLlama.swift */,
);
path = llama.cpp.swift;
sourceTree = "<group>";
};
8A9F7C4A2AC332BF008AE1EA /* UI */ = {
isa = PBXGroup;
children = (
7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */,
8A1C83782AC328BD0096AF73 /* ContentView.swift */,
);
path = UI;
sourceTree = "<group>";
};
8A9F7C4B2AC332DC008AE1EA /* Models */ = {
isa = PBXGroup;
children = (
8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */,
);
path = Models;
sourceTree = "<group>";
};
/* End PBXGroup section */
/* Begin PBXNativeTarget section */
8A1C83722AC328BD0096AF73 /* llama.swiftui */ = {
isa = PBXNativeTarget;
buildConfigurationList = 8A1C83812AC328BE0096AF73 /* Build configuration list for PBXNativeTarget "llama.swiftui" */;
buildPhases = (
8A1C836F2AC328BD0096AF73 /* Sources */,
8A1C83702AC328BD0096AF73 /* Frameworks */,
8A1C83712AC328BD0096AF73 /* Resources */,
);
buildRules = (
);
dependencies = (
);
name = llama.swiftui;
packageProductDependencies = (
);
productName = llama.swiftui;
productReference = 8A1C83732AC328BD0096AF73 /* llama.swiftui.app */;
productType = "com.apple.product-type.application";
};
/* End PBXNativeTarget section */
/* Begin PBXProject section */
8A1C836B2AC328BD0096AF73 /* Project object */ = {
isa = PBXProject;
attributes = {
BuildIndependentTargetsInParallel = 1;
LastSwiftUpdateCheck = 1500;
LastUpgradeCheck = 1500;
TargetAttributes = {
8A1C83722AC328BD0096AF73 = {
CreatedOnToolsVersion = 15.0;
LastSwiftMigration = 1500;
};
};
};
buildConfigurationList = 8A1C836E2AC328BD0096AF73 /* Build configuration list for PBXProject "llama.swiftui" */;
compatibilityVersion = "Xcode 14.0";
developmentRegion = en;
hasScannedForEncodings = 0;
knownRegions = (
en,
Base,
);
mainGroup = 8A1C836A2AC328BD0096AF73;
packageReferences = (
);
productRefGroup = 8A1C83742AC328BD0096AF73 /* Products */;
projectDirPath = "";
projectRoot = "";
targets = (
8A1C83722AC328BD0096AF73 /* llama.swiftui */,
);
};
/* End PBXProject section */
/* Begin PBXResourcesBuildPhase section */
8A1C83712AC328BD0096AF73 /* Resources */ = {
isa = PBXResourcesBuildPhase;
buildActionMask = 2147483647;
files = (
542378792ACE3F3500834A7B /* ggml-metal.metal in Resources */,
8A3F84242AC4C891005E2EE8 /* models in Resources */,
8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */,
8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */,
);
runOnlyForDeploymentPostprocessing = 0;
};
/* End PBXResourcesBuildPhase section */
/* Begin PBXSourcesBuildPhase section */
8A1C836F2AC328BD0096AF73 /* Sources */ = {
isa = PBXSourcesBuildPhase;
buildActionMask = 2147483647;
files = (
542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */,
549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */,
542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */,
8A907F332AC7138A006146EA /* LibLlama.swift in Sources */,
542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */,
8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */,
8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */,
8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */,
7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */,
542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */,
5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */,
);
runOnlyForDeploymentPostprocessing = 0;
};
/* End PBXSourcesBuildPhase section */
/* Begin XCBuildConfiguration section */
8A1C837F2AC328BE0096AF73 /* Debug */ = {
isa = XCBuildConfiguration;
buildSettings = {
ALWAYS_SEARCH_USER_PATHS = NO;
ASSETCATALOG_COMPILER_GENERATE_SWIFT_ASSET_SYMBOL_EXTENSIONS = YES;
CLANG_ANALYZER_NONNULL = YES;
CLANG_ANALYZER_NUMBER_OBJECT_CONVERSION = YES_AGGRESSIVE;
CLANG_CXX_LANGUAGE_STANDARD = "gnu++20";
CLANG_ENABLE_MODULES = YES;
CLANG_ENABLE_OBJC_ARC = YES;
CLANG_ENABLE_OBJC_WEAK = YES;
CLANG_WARN_BLOCK_CAPTURE_AUTORELEASING = YES;
CLANG_WARN_BOOL_CONVERSION = YES;
CLANG_WARN_COMMA = YES;
CLANG_WARN_CONSTANT_CONVERSION = YES;
CLANG_WARN_DEPRECATED_OBJC_IMPLEMENTATIONS = YES;
CLANG_WARN_DIRECT_OBJC_ISA_USAGE = YES_ERROR;
CLANG_WARN_DOCUMENTATION_COMMENTS = YES;
CLANG_WARN_EMPTY_BODY = YES;
CLANG_WARN_ENUM_CONVERSION = YES;
CLANG_WARN_INFINITE_RECURSION = YES;
CLANG_WARN_INT_CONVERSION = YES;
CLANG_WARN_NON_LITERAL_NULL_CONVERSION = YES;
CLANG_WARN_OBJC_IMPLICIT_RETAIN_SELF = YES;
CLANG_WARN_OBJC_LITERAL_CONVERSION = YES;
CLANG_WARN_OBJC_ROOT_CLASS = YES_ERROR;
CLANG_WARN_QUOTED_INCLUDE_IN_FRAMEWORK_HEADER = YES;
CLANG_WARN_RANGE_LOOP_ANALYSIS = YES;
CLANG_WARN_STRICT_PROTOTYPES = YES;
CLANG_WARN_SUSPICIOUS_MOVE = YES;
CLANG_WARN_UNGUARDED_AVAILABILITY = YES_AGGRESSIVE;
CLANG_WARN_UNREACHABLE_CODE = YES;
CLANG_WARN__DUPLICATE_METHOD_MATCH = YES;
COPY_PHASE_STRIP = NO;
DEBUG_INFORMATION_FORMAT = dwarf;
ENABLE_STRICT_OBJC_MSGSEND = YES;
ENABLE_TESTABILITY = YES;
ENABLE_USER_SCRIPT_SANDBOXING = YES;
GCC_C_LANGUAGE_STANDARD = gnu17;
GCC_DYNAMIC_NO_PIC = NO;
GCC_NO_COMMON_BLOCKS = YES;
GCC_OPTIMIZATION_LEVEL = 0;
GCC_PREPROCESSOR_DEFINITIONS = (
"DEBUG=1",
"$(inherited)",
);
GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
GCC_WARN_UNDECLARED_SELECTOR = YES;
GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE;
GCC_WARN_UNUSED_FUNCTION = YES;
GCC_WARN_UNUSED_VARIABLE = YES;
IPHONEOS_DEPLOYMENT_TARGET = 17.0;
LOCALIZATION_PREFERS_STRING_CATALOGS = YES;
MTL_ENABLE_DEBUG_INFO = INCLUDE_SOURCE;
MTL_FAST_MATH = YES;
ONLY_ACTIVE_ARCH = YES;
SDKROOT = iphoneos;
SWIFT_ACTIVE_COMPILATION_CONDITIONS = "DEBUG $(inherited)";
SWIFT_OPTIMIZATION_LEVEL = "-Onone";
};
name = Debug;
};
8A1C83802AC328BE0096AF73 /* Release */ = {
isa = XCBuildConfiguration;
buildSettings = {
ALWAYS_SEARCH_USER_PATHS = NO;
ASSETCATALOG_COMPILER_GENERATE_SWIFT_ASSET_SYMBOL_EXTENSIONS = YES;
CLANG_ANALYZER_NONNULL = YES;
CLANG_ANALYZER_NUMBER_OBJECT_CONVERSION = YES_AGGRESSIVE;
CLANG_CXX_LANGUAGE_STANDARD = "gnu++20";
CLANG_ENABLE_MODULES = YES;
CLANG_ENABLE_OBJC_ARC = YES;
CLANG_ENABLE_OBJC_WEAK = YES;
CLANG_WARN_BLOCK_CAPTURE_AUTORELEASING = YES;
CLANG_WARN_BOOL_CONVERSION = YES;
CLANG_WARN_COMMA = YES;
CLANG_WARN_CONSTANT_CONVERSION = YES;
CLANG_WARN_DEPRECATED_OBJC_IMPLEMENTATIONS = YES;
CLANG_WARN_DIRECT_OBJC_ISA_USAGE = YES_ERROR;
CLANG_WARN_DOCUMENTATION_COMMENTS = YES;
CLANG_WARN_EMPTY_BODY = YES;
CLANG_WARN_ENUM_CONVERSION = YES;
CLANG_WARN_INFINITE_RECURSION = YES;
CLANG_WARN_INT_CONVERSION = YES;
CLANG_WARN_NON_LITERAL_NULL_CONVERSION = YES;
CLANG_WARN_OBJC_IMPLICIT_RETAIN_SELF = YES;
CLANG_WARN_OBJC_LITERAL_CONVERSION = YES;
CLANG_WARN_OBJC_ROOT_CLASS = YES_ERROR;
CLANG_WARN_QUOTED_INCLUDE_IN_FRAMEWORK_HEADER = YES;
CLANG_WARN_RANGE_LOOP_ANALYSIS = YES;
CLANG_WARN_STRICT_PROTOTYPES = YES;
CLANG_WARN_SUSPICIOUS_MOVE = YES;
CLANG_WARN_UNGUARDED_AVAILABILITY = YES_AGGRESSIVE;
CLANG_WARN_UNREACHABLE_CODE = YES;
CLANG_WARN__DUPLICATE_METHOD_MATCH = YES;
COPY_PHASE_STRIP = NO;
DEBUG_INFORMATION_FORMAT = "dwarf-with-dsym";
ENABLE_NS_ASSERTIONS = NO;
ENABLE_STRICT_OBJC_MSGSEND = YES;
ENABLE_USER_SCRIPT_SANDBOXING = YES;
GCC_C_LANGUAGE_STANDARD = gnu17;
GCC_NO_COMMON_BLOCKS = YES;
GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
GCC_WARN_UNDECLARED_SELECTOR = YES;
GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE;
GCC_WARN_UNUSED_FUNCTION = YES;
GCC_WARN_UNUSED_VARIABLE = YES;
IPHONEOS_DEPLOYMENT_TARGET = 17.0;
LOCALIZATION_PREFERS_STRING_CATALOGS = YES;
MTL_ENABLE_DEBUG_INFO = NO;
MTL_FAST_MATH = YES;
SDKROOT = iphoneos;
SWIFT_COMPILATION_MODE = wholemodule;
VALIDATE_PRODUCT = YES;
};
name = Release;
};
8A1C83822AC328BE0096AF73 /* Debug */ = {
isa = XCBuildConfiguration;
buildSettings = {
ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
CLANG_ENABLE_MODULES = YES;
CODE_SIGN_STYLE = Automatic;
CURRENT_PROJECT_VERSION = 1;
DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
DEVELOPMENT_TEAM = STLSG3FG8Q;
ENABLE_PREVIEWS = YES;
GENERATE_INFOPLIST_FILE = YES;
INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES;
INFOPLIST_KEY_UIApplicationSupportsIndirectInputEvents = YES;
INFOPLIST_KEY_UILaunchScreen_Generation = YES;
INFOPLIST_KEY_UISupportedInterfaceOrientations_iPad = "UIInterfaceOrientationPortrait UIInterfaceOrientationPortraitUpsideDown UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
INFOPLIST_KEY_UISupportedInterfaceOrientations_iPhone = "UIInterfaceOrientationPortrait UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
IPHONEOS_DEPLOYMENT_TARGET = 16.0;
LD_RUNPATH_SEARCH_PATHS = (
"$(inherited)",
"@executable_path/Frameworks",
);
MARKETING_VERSION = 1.0;
PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
PRODUCT_NAME = "$(TARGET_NAME)";
SWIFT_EMIT_LOC_STRINGS = YES;
SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
SWIFT_OPTIMIZATION_LEVEL = "-Onone";
SWIFT_VERSION = 5.0;
TARGETED_DEVICE_FAMILY = "1,2";
};
name = Debug;
};
8A1C83832AC328BE0096AF73 /* Release */ = {
isa = XCBuildConfiguration;
buildSettings = {
ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
CLANG_ENABLE_MODULES = YES;
CODE_SIGN_STYLE = Automatic;
CURRENT_PROJECT_VERSION = 1;
DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
DEVELOPMENT_TEAM = STLSG3FG8Q;
ENABLE_PREVIEWS = YES;
GENERATE_INFOPLIST_FILE = YES;
INFOPLIST_KEY_UIApplicationSceneManifest_Generation = YES;
INFOPLIST_KEY_UIApplicationSupportsIndirectInputEvents = YES;
INFOPLIST_KEY_UILaunchScreen_Generation = YES;
INFOPLIST_KEY_UISupportedInterfaceOrientations_iPad = "UIInterfaceOrientationPortrait UIInterfaceOrientationPortraitUpsideDown UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
INFOPLIST_KEY_UISupportedInterfaceOrientations_iPhone = "UIInterfaceOrientationPortrait UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
IPHONEOS_DEPLOYMENT_TARGET = 16.0;
LD_RUNPATH_SEARCH_PATHS = (
"$(inherited)",
"@executable_path/Frameworks",
);
MARKETING_VERSION = 1.0;
PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
PRODUCT_NAME = "$(TARGET_NAME)";
SWIFT_EMIT_LOC_STRINGS = YES;
SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
SWIFT_VERSION = 5.0;
TARGETED_DEVICE_FAMILY = "1,2";
};
name = Release;
};
/* End XCBuildConfiguration section */
/* Begin XCConfigurationList section */
8A1C836E2AC328BD0096AF73 /* Build configuration list for PBXProject "llama.swiftui" */ = {
isa = XCConfigurationList;
buildConfigurations = (
8A1C837F2AC328BE0096AF73 /* Debug */,
8A1C83802AC328BE0096AF73 /* Release */,
);
defaultConfigurationIsVisible = 0;
defaultConfigurationName = Release;
};
8A1C83812AC328BE0096AF73 /* Build configuration list for PBXNativeTarget "llama.swiftui" */ = {
isa = XCConfigurationList;
buildConfigurations = (
8A1C83822AC328BE0096AF73 /* Debug */,
8A1C83832AC328BE0096AF73 /* Release */,
);
defaultConfigurationIsVisible = 0;
defaultConfigurationName = Release;
};
/* End XCConfigurationList section */
};
rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */;
}

7
examples/llama.swiftui/llama.swiftui.xcodeproj/project.xcworkspace/contents.xcworkspacedata generated Normal file
View file

@ -0,0 +1,7 @@
<?xml version="1.0" encoding="UTF-8"?>
<Workspace
version = "1.0">
<FileRef
location = "self:">
</FileRef>
</Workspace>

8
examples/llama.swiftui/llama.swiftui.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist Normal file
View file

@ -0,0 +1,8 @@
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>IDEDidComputeMac32BitWarning</key>
<true/>
</dict>
</plist>

11
examples/llama.swiftui/llama.swiftui/Assets.xcassets/AccentColor.colorset/Contents.json Normal file
View file

@ -0,0 +1,11 @@
{
"colors" : [
{
"idiom" : "universal"
}
],
"info" : {
"author" : "xcode",
"version" : 1
}
}

13
examples/llama.swiftui/llama.swiftui/Assets.xcassets/AppIcon.appiconset/Contents.json Normal file
View file

@ -0,0 +1,13 @@
{
"images" : [
{
"idiom" : "universal",
"platform" : "ios",
"size" : "1024x1024"
}
],
"info" : {
"author" : "xcode",
"version" : 1
}
}

6
examples/llama.swiftui/llama.swiftui/Assets.xcassets/Contents.json Normal file
View file

@ -0,0 +1,6 @@
{
"info" : {
"author" : "xcode",
"version" : 1
}
}

85
examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift Normal file
View file

@ -0,0 +1,85 @@
import Foundation
@MainActor
class LlamaState: ObservableObject {
@Published var messageLog = ""
@Published var cacheCleared = false
private var llamaContext: LlamaContext?
private var defaultModelUrl: URL? {
Bundle.main.url(forResource: "ggml-model", withExtension: "gguf", subdirectory: "models")
// Bundle.main.url(forResource: "llama-2-7b-chat", withExtension: "Q2_K.gguf", subdirectory: "models")
}
init() {
do {
try loadModel(modelUrl: defaultModelUrl)
} catch {
messageLog += "Error!\n"
}
}
func loadModel(modelUrl: URL?) throws {
messageLog += "Loading model...\n"
if let modelUrl {
llamaContext = try LlamaContext.create_context(path: modelUrl.path())
messageLog += "Loaded model \(modelUrl.lastPathComponent)\n"
} else {
messageLog += "Could not locate model\n"
}
}
func complete(text: String) async {
guard let llamaContext else {
return
}
await llamaContext.completion_init(text: text)
messageLog += "\(text)"
while await llamaContext.n_cur <= llamaContext.n_len {
let result = await llamaContext.completion_loop()
messageLog += "\(result)"
}
await llamaContext.clear()
messageLog += "\n\ndone\n"
}
func bench() async {
guard let llamaContext else {
return
}
messageLog += "\n"
messageLog += "Running benchmark...\n"
messageLog += "Model info: "
messageLog += await llamaContext.model_info() + "\n"
let t_start = DispatchTime.now().uptimeNanoseconds
await llamaContext.bench(pp: 8, tg: 4, pl: 1) // heat up
let t_end = DispatchTime.now().uptimeNanoseconds
let t_heat = Double(t_end - t_start) / 1_000_000_000.0
messageLog += "Heat up time: \(t_heat) seconds, please wait...\n"
// if more than 5 seconds, then we're probably running on a slow device
if t_heat > 5.0 {
messageLog += "Heat up time is too long, aborting benchmark\n"
return
}
let result = await llamaContext.bench(pp: 512, tg: 128, pl: 1, nr: 3)
messageLog += "\(result)"
messageLog += "\n"
}
func clear() async {
guard let llamaContext else {
return
}
await llamaContext.clear()
messageLog = ""
}
}

6
examples/llama.swiftui/llama.swiftui/Preview Content/Preview Assets.xcassets/Contents.json Normal file
View file

@ -0,0 +1,6 @@
{
"info" : {
"author" : "xcode",
"version" : 1
}
}

0
examples/llama.swiftui/llama.swiftui/Resources/models/.gitignore vendored Normal file
View file

161
examples/llama.swiftui/llama.swiftui/UI/ContentView.swift Normal file
View file

@ -0,0 +1,161 @@
import SwiftUI
struct ContentView: View {
@StateObject var llamaState = LlamaState()
@State private var multiLineText = ""
private static func cleanupModelCaches() {
// Delete all models (*.gguf)
let fileManager = FileManager.default
let documentsUrl = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0]
do {
let fileURLs = try fileManager.contentsOfDirectory(at: documentsUrl, includingPropertiesForKeys: nil)
for fileURL in fileURLs {
if fileURL.pathExtension == "gguf" {
try fileManager.removeItem(at: fileURL)
}
}
} catch {
print("Error while enumerating files \(documentsUrl.path): \(error.localizedDescription)")
}
}
var body: some View {
VStack {
ScrollView(.vertical, showsIndicators: true) {
Text(llamaState.messageLog)
.font(.system(size: 12))
.frame(maxWidth: .infinity, alignment: .leading)
.padding()
.onTapGesture {
UIApplication.shared.sendAction(#selector(UIResponder.resignFirstResponder), to: nil, from: nil, for: nil)
}
}
TextEditor(text: $multiLineText)
.frame(height: 80)
.padding()
.border(Color.gray, width: 0.5)
HStack {
Button("Send") {
sendText()
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
Button("Bench") {
bench()
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
Button("Clear") {
clear()
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
Button("Copy") {
UIPasteboard.general.string = llamaState.messageLog
}
.padding(8)
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
}
VStack {
DownloadButton(
llamaState: llamaState,
modelName: "TinyLlama-1.1B (Q4_0, 0.6 GiB)",
modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",
filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf"
)
.font(.system(size: 12))
.padding(.top, 4)
.frame(maxWidth: .infinity, alignment: .leading)
DownloadButton(
llamaState: llamaState,
modelName: "TinyLlama-1.1B (Q8_0, 1.1 GiB)",
modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q8_0.gguf?download=true",
filename: "tinyllama-1.1b-1t-openorca.Q8_0.gguf"
)
.font(.system(size: 12))
DownloadButton(
llamaState: llamaState,
modelName: "TinyLlama-1.1B (F16, 2.2 GiB)",
modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true",
filename: "tinyllama-1.1b-f16.gguf"
)
.font(.system(size: 12))
.frame(maxWidth: .infinity, alignment: .leading)
DownloadButton(
llamaState: llamaState,
modelName: "Phi-2.7B (Q4_0, 1.6 GiB)",
modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true",
filename: "phi-2-q4_0.gguf"
)
.font(.system(size: 12))
DownloadButton(
llamaState: llamaState,
modelName: "Phi-2.7B (Q8_0, 2.8 GiB)",
modelUrl: "https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q8_0.gguf?download=true",
filename: "phi-2-q8_0.gguf"
)
.font(.system(size: 12))
.frame(maxWidth: .infinity, alignment: .leading)
DownloadButton(
llamaState: llamaState,
modelName: "Mistral-7B-v0.1 (Q4_0, 3.8 GiB)",
modelUrl: "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/resolve/main/mistral-7b-v0.1.Q4_0.gguf?download=true",
filename: "mistral-7b-v0.1.Q4_0.gguf"
)
.font(.system(size: 12))
Button("Clear downloaded models") {
ContentView.cleanupModelCaches()
llamaState.cacheCleared = true
}
.padding(8)
.font(.system(size: 12))
}
}
.padding()
}
func sendText() {
Task {
await llamaState.complete(text: multiLineText)
multiLineText = ""
}
}
func bench() {
Task {
await llamaState.bench()
}
}
func clear() {
Task {
await llamaState.clear()
}
}
}
//#Preview {
// ContentView()
//}

122
examples/llama.swiftui/llama.swiftui/UI/DownloadButton.swift Normal file
View file

@ -0,0 +1,122 @@
import SwiftUI
struct DownloadButton: View {
@ObservedObject private var llamaState: LlamaState
private var modelName: String
private var modelUrl: String
private var filename: String
@State private var status: String
@State private var downloadTask: URLSessionDownloadTask?
@State private var progress = 0.0
@State private var observation: NSKeyValueObservation?
private static func getFileURL(filename: String) -> URL {
FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0].appendingPathComponent(filename)
}
private func checkFileExistenceAndUpdateStatus() {
}
init(llamaState: LlamaState, modelName: String, modelUrl: String, filename: String) {
self.llamaState = llamaState
self.modelName = modelName
self.modelUrl = modelUrl
self.filename = filename
let fileURL = DownloadButton.getFileURL(filename: filename)
status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download"
}
private func download() {
status = "downloading"
print("Downloading model \(modelName) from \(modelUrl)")
guard let url = URL(string: modelUrl) else { return }
let fileURL = DownloadButton.getFileURL(filename: filename)
downloadTask = URLSession.shared.downloadTask(with: url) { temporaryURL, response, error in
if let error = error {
print("Error: \(error.localizedDescription)")
return
}
guard let response = response as? HTTPURLResponse, (200...299).contains(response.statusCode) else {
print("Server error!")
return
}
do {
if let temporaryURL = temporaryURL {
try FileManager.default.copyItem(at: temporaryURL, to: fileURL)
print("Writing to \(filename) completed")
llamaState.cacheCleared = false
status = "downloaded"
}
} catch let err {
print("Error: \(err.localizedDescription)")
}
}
observation = downloadTask?.progress.observe(\.fractionCompleted) { progress, _ in
self.progress = progress.fractionCompleted
}
downloadTask?.resume()
}
var body: some View {
VStack {
if status == "download" {
Button(action: download) {
Text("Download " + modelName)
}
} else if status == "downloading" {
Button(action: {
downloadTask?.cancel()
status = "download"
}) {
Text("\(modelName) (Downloading \(Int(progress * 100))%)")
}
} else if status == "downloaded" {
Button(action: {
let fileURL = DownloadButton.getFileURL(filename: filename)
if !FileManager.default.fileExists(atPath: fileURL.path) {
download()
return
}
do {
try llamaState.loadModel(modelUrl: fileURL)
} catch let err {
print("Error: \(err.localizedDescription)")
}
}) {
Text("\(modelName) (Downloaded)")
}
} else {
Text("Unknown status")
}
}
.onDisappear() {
downloadTask?.cancel()
}
.onChange(of: llamaState.cacheCleared) { newValue in
if newValue {
downloadTask?.cancel()
let fileURL = DownloadButton.getFileURL(filename: filename)
status = FileManager.default.fileExists(atPath: fileURL.path) ? "downloaded" : "download"
}
}
}
}
// #Preview {
// DownloadButton(
// llamaState: LlamaState(),
// modelName: "TheBloke / TinyLlama-1.1B-1T-OpenOrca-GGUF (Q4_0)",
// modelUrl: "https://huggingface.co/TheBloke/TinyLlama-1.1B-1T-OpenOrca-GGUF/resolve/main/tinyllama-1.1b-1t-openorca.Q4_0.gguf?download=true",
// filename: "tinyllama-1.1b-1t-openorca.Q4_0.gguf"
// )
// }

10
examples/llama.swiftui/llama.swiftui/llama_swiftuiApp.swift Normal file
View file

@ -0,0 +1,10 @@
import SwiftUI
@main
struct llama_swiftuiApp: App {
var body: some Scene {
WindowGroup {
ContentView()
}
}
}

8
examples/llava/clip.cpp
View file

@ -508,7 +508,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
buffer_size += tensor_size;
if (verbosity >= 3) {
printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu\n", __func__, i,
cur->n_dims, cur->name, tensor_size, offset);
ggml_n_dims(cur), cur->name, tensor_size, offset);
}
}
}
@ -762,7 +762,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
temp->ny = longer_side;
temp->size = 3 * longer_side * longer_side;
temp->data = new uint8_t[temp->size]();
uint8_t bc[3] = {122, 116, 104}; // bakground color in RGB from LLaVA
uint8_t bc[3] = {122, 116, 104}; // background color in RGB from LLaVA
// fill with background color
for (size_t i = 0; i < temp->size; i++) {
@ -979,7 +979,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
}
// quantize only 2D tensors
quantize &= (cur->n_dims == 2);
quantize &= (ggml_n_dims(cur) == 2);
if (quantize) {
new_type = type;
@ -1052,7 +1052,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
fout.put(0);
}
printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), cur->n_dims, quantize,
printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
}

54
examples/llava/convert-image-encoder-to-gguf.py
View file

@ -5,7 +5,7 @@ import json
import torch
import numpy as np
from gguf import *
from transformers import CLIPModel, CLIPProcessor
from transformers import CLIPModel, CLIPProcessor, CLIPVisionModel
TEXT = "clip.text"
VISION = "clip.vision"
@ -51,7 +51,7 @@ def bytes_to_unicode():
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
@ -78,11 +78,19 @@ ap.add_argument("--text-only", action="store_true", required=False,
help="Save a text-only model. It can't be used to encode images")
ap.add_argument("--vision-only", action="store_true", required=False,
help="Save a vision-only model. It can't be used to encode texts")
ap.add_argument("--clip_model_is_vision", action="store_true", required=False,
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
ap.add_argument("--image-mean", nargs=3, type=float, required=False, help="Override image mean values")
ap.add_argument("--image-std", nargs=3, type=float, required=False, help="Override image std values")
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
default_image_mean = [0.48145466, 0.4578275, 0.40821073]
default_image_std = [0.26862954, 0.26130258, 0.27577711]
ap.add_argument('--image_mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
ap.add_argument('--image_std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
# with proper
args = ap.parse_args()
@ -96,15 +104,22 @@ if args.use_f32:
# output in the same directory as the model if output_dir is None
dir_model = args.model_dir
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
vocab = json.load(f)
tokens = [key for key in vocab]
if args.clip_model_is_vision:
vocab = None
tokens = None
else:
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
vocab = json.load(f)
tokens = [key for key in vocab]
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
config = json.load(f)
v_hparams = config["vision_config"]
t_hparams = config["text_config"]
if args.clip_model_is_vision:
v_hparams = config
t_hparams = None
else:
v_hparams = config["vision_config"]
t_hparams = config["text_config"]
# possible data types
# ftype == 0 -> float32
@ -117,9 +132,12 @@ ftype = 1
if args.use_f32:
ftype = 0
model = CLIPModel.from_pretrained(dir_model)
processor = CLIPProcessor.from_pretrained(dir_model)
if args.clip_model_is_vision:
model = CLIPVisionModel.from_pretrained(dir_model)
processor = None
else:
model = CLIPModel.from_pretrained(dir_model)
processor = CLIPProcessor.from_pretrained(dir_model)
fname_middle = None
has_text_encoder = True
@ -128,13 +146,13 @@ has_llava_projector = False
if args.text_only:
fname_middle = "text-"
has_vision_encoder = False
elif args.vision_only:
fname_middle = "vision-"
has_text_encoder = False
elif args.llava_projector is not None:
fname_middle = "mmproj-"
has_text_encoder = False
has_llava_projector = True
elif args.vision_only:
fname_middle = "vision-"
has_text_encoder = False
else:
fname_middle = ""
@ -182,8 +200,12 @@ if has_vision_encoder:
block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"]
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)
image_mean = processor.image_processor.image_mean if args.image_mean is None else args.image_mean
image_std = processor.image_processor.image_std if args.image_std is None else args.image_std
if processor is not None:
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
else:
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
image_std = args.image_std if args.image_std is not None else default_image_std
fout.add_array("clip.vision.image_mean", image_mean)
fout.add_array("clip.vision.image_std", image_std)

5
examples/lookahead/CMakeLists.txt Normal file
View file

@ -0,0 +1,5 @@
set(TARGET lookahead)
add_executable(${TARGET} lookahead.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

7
examples/lookahead/README.md Normal file
View file

@ -0,0 +1,7 @@
# llama.cpp/examples/lookahead
Demonstration of lookahead decoding technique:
https://lmsys.org/blog/2023-11-21-lookahead-decoding/
More info: https://github.com/ggerganov/llama.cpp/pull/4207

487
examples/lookahead/lookahead.cpp Normal file
View file

@ -0,0 +1,487 @@
#include "common.h"
#include "llama.h"
#include <cmath>
#include <cstdio>
#include <string>
#include <vector>
struct ngram_data {
bool active = false;
llama_seq_id seq_id = -1;
std::vector<int> i_batch;
std::vector<llama_token> tokens;
};
// n-gram container
struct ngram_container {
ngram_container(int n_vocab, int N, int G) {
cnt.resize(n_vocab);
head.resize(n_vocab);
tokens.resize(n_vocab * G * (N - 1));
}
int n_total = 0;
std::vector<int> cnt;
std::vector<int> head;
// [n_vocab][G][N - 1]
// for each token of the vocab, keep a ring-buffer of capacity G of n-grams of size N - 1
std::vector<llama_token> tokens;
};
int main(int argc, char ** argv) {
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
const int W = 15; // lookahead window
const int N = 5; // n-gram size
const int G = 15; // max verification n-grams
const bool dump_kv_cache = params.dump_kv_cache;
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("lookahead", "log"));
LOG_TEE("Log start\n");
log_dump_cmdline(argc, argv);
#endif // LOG_DISABLE_LOGS
// init llama.cpp
llama_backend_init(params.numa);
llama_model * model = NULL;
llama_context * ctx = NULL;
// load the target model
std::tie(model, ctx) = llama_init_from_gpt_params(params);
// Tokenize the prompt
const bool add_bos = llama_should_add_bos_token(model);
LOG("add_bos tgt: %d\n", add_bos);
std::vector<llama_token> inp;
std::vector<llama_token> all;
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
all = inp;
const int max_context_size = llama_n_ctx(ctx);
const int max_tokens_list_size = max_context_size - 4;
if ((int) inp.size() > max_tokens_list_size) {
fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
return 1;
}
fprintf(stderr, "\n\n");
for (auto id : inp) {
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
}
fflush(stderr);
const int n_input = inp.size();
const auto t_enc_start = ggml_time_us();
// eval the prompt
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
for (int s = 1; s < W + G + 1; ++s) {
llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
}
const auto t_enc_end = ggml_time_us();
int n_predict = 0;
int n_accept = 0;
int n_past = inp.size();
llama_token id = 0;
// used to determine end of generation
bool has_eos = false;
// for each decoded batch, we have at most W + G + 1 distinct sequences:
// seq_id == 0 : the current input token
// seq_id [1, W] : tokens from the past N - 1 Jacobi iterations
// seq_id [W + 1, W + G] : verification n-grams
llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1);
// target model sampling context
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
// verification n-grams
std::vector<ngram_data> ngrams_cur(G);
// tokens for the past N - 1 Jacobi iterations
std::vector<llama_token> tokens_j_prev(W);
std::vector<std::vector<llama_token>> tokens_j(N - 1);
for (int j = 0; j < N - 1; j++) {
tokens_j[j].resize(W);
for (int i = 0; i < W; i++) {
// there are different ways to init these tokens
if (0) {
// initialize randomly from the prompt tokens
tokens_j[j][i] = all[1 + rand() % (all.size() - 1)];
} else {
// initialize with a sequence of increasing numbers
tokens_j[j][i] = 100 + i;
}
}
}
std::vector<llama_seq_id> seq_id_look;
// the input token belongs both to all sequences
std::vector<llama_seq_id> seq_id_all(W + G + 1);
for (int i = 0; i < W + G + 1; i++) {
seq_id_all[i] = i;
}
// here we keep adding new n-grams as we go
ngram_container ngrams_observed(llama_n_vocab(model), N, G);
// debug
struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1);
const auto t_dec_start = ggml_time_us();
// sample first token
{
id = llama_sampling_sample(ctx_sampling, ctx, NULL, 0);
llama_sampling_accept(ctx_sampling, ctx, id, true);
{
const std::string token_str = llama_token_to_piece(ctx, id);
printf("%s", token_str.c_str());
fflush(stdout);
}
}
while (true) {
// debug
if (dump_kv_cache) {
llama_kv_cache_view_update(ctx, &kvc_view);
dump_kv_cache_view_seqs(kvc_view, 40);
}
// build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/
//
// Example for W = 5, N = 4, G = 2:
// (I = input, L = lookahead, V = verification)
//
// Batch: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
// T: -2 -2 -2 -2 -1 -1 -1 -1 -1 0 0 0 0 0 0
// Info: I L L L L L L L L L L L L L L V V V V V V
// Pos: 0 1 2 3 4 1 2 3 4 5 2 3 4 5 6 1 2 3 1 2 3 (+ n_past)
// Logits: 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
// ---------------------------------------------------------------------
// Seq: 0
// 1 1 1
// 2 2 2 2
// 3 3 3 3 3
// 4 4 4 4 4 4
// 5 5 5 5 5 5 5
// 6 6 6 6
// 7 7 7 7
// ---------------------------------------------------------------------
// | | | | | | | | | | |
// V V V V V | | | | | |
// j_tokens | | | | | |
// V V V V V V
// id
{
llama_batch_clear(batch);
// current token - first token of the first level
llama_batch_add(batch, id, n_past, seq_id_all, true);
// verification n-grams - queue this before the lookahead tokens for less KV cache fragmentation
{
const int g_cur = ngrams_observed.cnt[id];
ngrams_cur.resize(g_cur);
for (int g = 0; g < g_cur; g++) {
ngrams_cur[g].active = true;
ngrams_cur[g].tokens.resize(N);
ngrams_cur[g].i_batch.resize(N);
ngrams_cur[g].seq_id = W + 1 + g;
ngrams_cur[g].i_batch[0] = 0;
ngrams_cur[g].tokens [0] = id;
}
for (int j = 0; j < N - 1; j++) {
for (int g = 0; g < g_cur; g++) {
const int idx = id*(N - 1)*G + g*(N - 1);
const llama_token t = ngrams_observed.tokens[idx + j];
ngrams_cur[g].tokens [j + 1] = t;
ngrams_cur[g].i_batch[j + 1] = batch.n_tokens;
llama_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true);
}
}
}
// fill the remaining W - 1 tokens for the first level
for (int i = 1; i < W; i++) {
seq_id_look.resize(W - i);
for (int j = 0; j < W - i; j++) {
seq_id_look[j] = i + j + 1;
}
llama_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false);
}
// fill the rest of the levels
for (int j = 1; j < N - 1; j++) {
for (int i = 0; i < W; i++) {
llama_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2);
}
}
}
if (llama_decode(ctx, batch) != 0) {
fprintf(stderr, "\n\n%s: error: llama_decode failed - increase KV cache size\n", __func__);
return 1;
}
int seq_id_best = 0;
for (int v = 0; v < N; ++v) {
int i_batch = 0;
// if no active ngrams are left, it means the sampled token does not pass the verification
if (v > 0) {
for (int g = 0; g < (int) ngrams_cur.size(); g++) {
if (ngrams_cur[g].active) {
i_batch = ngrams_cur[g].i_batch[v];
seq_id_best = ngrams_cur[g].seq_id;
++n_accept;
break;
}
}
// no more matches -> create a new batch
if (i_batch == 0) {
break;
}
}
// sample the next token
id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_batch);
llama_sampling_accept(ctx_sampling, ctx, id, true);
// print
{
const std::string token_str = llama_token_to_piece(ctx, id);
if (v == 0) {
printf("%s", token_str.c_str());
} else {
// print light cyan
printf("\033[0;96m%s\033[0m", token_str.c_str());
}
fflush(stdout);
if (id == llama_token_eos(model)) {
has_eos = true;
}
all.push_back(id);
}
++n_predict;
++n_past;
if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
break;
}
// verify across active n-grams
for (int g = 0; g < (int) ngrams_cur.size(); g++) {
if (ngrams_cur[g].active) {
if (v == N - 1) {
ngrams_cur[g].active = false;
} else {
if (id != ngrams_cur[g].tokens[v + 1]) {
ngrams_cur[g].active = false;
}
}
}
}
// print known n-grams starting with token id (debug)
if (0 && v == 0) {
if (ngrams_observed.cnt[id] > 0) {
printf("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], llama_token_to_piece(ctx, id).c_str());
}
for (int i = 0; i < ngrams_observed.cnt[id]; i++) {
printf(" - ngram %2d: ", i);
const int idx = id*(N - 1)*G + i*(N - 1);
for (int j = 0; j < N - 1; j++) {
const std::string token_str = llama_token_to_piece(ctx, ngrams_observed.tokens[idx + j]);
printf("%s", token_str.c_str());
}
printf("\n");
}
}
// update lookahead tokens
{
for (int i = 0; i < W; i++) {
tokens_j_prev[i] = tokens_j[0][i];
}
for (int j = 0; j < N - 2; j++) {
tokens_j[j] = tokens_j[j + 1];
}
if (v == 0) {
// sample from the last level
for (int i = 0; i < W; i++) {
tokens_j[N - 2][i] = llama_sampling_sample(ctx_sampling, ctx, NULL, ngrams_cur.size()*(N-1) + W*(N - 2) + i);
}
} else {
for (int i = 0; i < W; i++) {
// there are different ways to init these tokens
if (0) {
// random init
tokens_j[N - 2][i] = all[1 + rand() % (all.size() - 1)];
} else {
// init from the previous level
tokens_j[N - 2][i] = tokens_j[0][i];
}
}
}
}
// update observed ngrams
if (v == 0) {
// the first token of the n-gram is determined by the index in the container so it is not stored
std::vector<llama_token> ngram(N - 1);
// n-gram generation
// ref: https://github.com/hao-ai-lab/LookaheadDecoding/issues/14#issuecomment-1826198518
for (int f = 0; f < W; ++f) {
const int ft = tokens_j_prev[f]; // first token of the n-gram
for (int j = 0; j < N - 1; ++j) {
ngram[j] = tokens_j[j][f];
}
// filter-out repeating n-grams
{
bool is_unique = true;
for (int k = 0; k < ngrams_observed.cnt[ft]; ++k) {
const int idx = ft*(N - 1)*G + k*(N - 1);
bool is_match = true;
for (int j = 0; j < N - 1; ++j) {
if (ngrams_observed.tokens[idx + j] != ngram[j]) {
is_match = false;
break;
}
}
if (is_match) {
is_unique = false;
break;
}
}
if (!is_unique) {
continue;
}
}
const int head = ngrams_observed.head[ft];
const int idx = ft*(N - 1)*G + head*(N - 1);
for (int i = 0; i < N - 1; i++) {
ngrams_observed.tokens[idx + i] = ngram[i];
}
ngrams_observed.cnt[ft] = std::min(G, ngrams_observed.cnt[ft] + 1);
ngrams_observed.head[ft] = (head + 1) % G;
ngrams_observed.n_total++;
}
}
}
if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
break;
}
// KV cache management
// if no verification token matched, we simply remove all cells from this batch -> no fragmentation
llama_kv_cache_seq_rm(ctx, -1, n_past, -1);
if (seq_id_best != 0) {
// if a verification token matched, we keep the best sequence and remove the rest
// this leads to some KV cache fragmentation
llama_kv_cache_seq_keep(ctx, seq_id_best);
llama_kv_cache_seq_cp (ctx, seq_id_best, 0, -1, -1);
llama_kv_cache_seq_rm (ctx, seq_id_best, -1, -1);
for (int s = 1; s < W + G + 1; ++s) {
llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
}
}
}
auto t_dec_end = ggml_time_us();
LOG_TEE("\n\n");
LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
LOG_TEE("\n");
LOG_TEE("W = %2d\n", W);
LOG_TEE("N = %2d\n", N);
LOG_TEE("G = %2d\n", G);
LOG_TEE("\n");
LOG_TEE("n_predict = %d\n", n_predict);
LOG_TEE("n_accept = %d\n", n_accept);
llama_print_timings(ctx);
llama_kv_cache_view_free(&kvc_view);
llama_sampling_free(ctx_sampling);
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
fprintf(stderr, "\n\n");
return 0;
}

8
examples/main/main.cpp
View file

@ -100,6 +100,12 @@ static void sigint_handler(int signo) {
}
#endif
static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
(void) level;
(void) user_data;
LOG_TEE("%s", text);
}
int main(int argc, char ** argv) {
gpt_params params;
g_params = &params;
@ -113,6 +119,7 @@ int main(int argc, char ** argv) {
log_set_target(log_filename_generator("main", "log"));
LOG_TEE("Log start\n");
log_dump_cmdline(argc, argv);
llama_log_set(llama_log_callback_logTee, nullptr);
#endif // LOG_DISABLE_LOGS
// TODO: Dump params ?
@ -430,6 +437,7 @@ int main(int argc, char ** argv) {
}
}
LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str());
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");

1
examples/quantize-stats/quantize-stats.cpp
View file

@ -321,7 +321,6 @@ int main(int argc, char ** argv) {
auto cparams = llama_context_default_params();
cparams.n_ctx = 256;
cparams.seed = 1;
cparams.f16_kv = false;
ctx = llama_new_context_with_model(model, cparams);

51
examples/server/README.md
View file

@ -222,7 +222,7 @@ node index.js
`content`: Set the text to process.
**POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream.
- **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream.
*Options:*
@ -234,6 +234,55 @@ node index.js
- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served.
*Options:*
See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such are `mirostat` are supported.
*Examples:*
You can use either Python `openai` library with appropriate checkpoints:
```python
import openai
client = openai.OpenAI(
base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
api_key = "sk-no-key-required"
)
completion = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."},
{"role": "user", "content": "Write a limerick about python exceptions"}
]
)
print(completion.choices[0].message)
```
... or raw HTTP requests:
```shell
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer no-key" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "system",
"content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."
},
{
"role": "user",
"content": "Write a limerick about python exceptions"
}
]
}'
```
## More examples
### Change system prompt on runtime

47
examples/server/api_like_OAI.py
View file

@ -11,10 +11,10 @@ app = Flask(__name__)
slot_id = -1
parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')
parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: '\\nUSER: ')", default="\\nUSER: ")
parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: '\\nASSISTANT: ')", default="\\nASSISTANT: ")
parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: '\\nASSISTANT's RULE: ')", default="\\nASSISTANT's RULE: ")
parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')
parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ")
parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ")
parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT's RULE: ")
parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
@ -34,19 +34,19 @@ def is_present(json, key):
#convert chat to prompt
def convert_chat(messages):
prompt = "" + args.chat_prompt.replace("\\n", "\n")
system_n = args.system_name.replace("\\n", "\n")
user_n = args.user_name.replace("\\n", "\n")
ai_n = args.ai_name.replace("\\n", "\n")
stop = args.stop.replace("\\n", "\n")
system_n = args.system_name
user_n = args.user_name
ai_n = args.ai_name
stop = args.stop
prompt = "" + args.chat_prompt + stop
for line in messages:
if (line["role"] == "system"):
prompt += f"{system_n}{line['content']}"
prompt += f"{system_n}{line['content']}{stop}"
if (line["role"] == "user"):
prompt += f"{user_n}{line['content']}"
prompt += f"{user_n}{line['content']}{stop}"
if (line["role"] == "assistant"):
prompt += f"{ai_n}{line['content']}{stop}"
prompt += ai_n.rstrip()
@ -70,6 +70,7 @@ def make_postData(body, chat=False, stream=False):
if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
if(is_present(body, "seed")): postData["seed"] = body["seed"]
if(is_present(body, "grammar")): postData["grammar"] = body["grammar"]
if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
if (args.stop != ""):
postData["stop"] = [args.stop]
@ -130,7 +131,7 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False):
}
]
}
slot_id = data["slot_id"]
slot_id = data.get("slot_id")
if (chat):
if (start):
resData["choices"][0]["delta"] = {
@ -150,11 +151,13 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False):
return resData
@app.route('/chat/completions', methods=['POST'])
@app.route('/v1/chat/completions', methods=['POST'])
@app.route('/chat/completions', methods=['POST', 'OPTIONS'])
@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS'])
def chat_completions():
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
return Response(status=403)
if request.method == 'OPTIONS':
return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
body = request.get_json()
stream = False
tokenize = False
@ -177,20 +180,22 @@ def chat_completions():
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
time_now = int(time.time())
resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
yield 'data: {}\n'.format(json.dumps(resData))
yield 'data: {}\n\n'.format(json.dumps(resData))
for line in data.iter_lines():
if line:
decoded_line = line.decode('utf-8')
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
yield 'data: {}\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream')
yield 'data: {}\n\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
@app.route('/completions', methods=['POST'])
@app.route('/v1/completions', methods=['POST'])
@app.route('/completions', methods=['POST', 'OPTIONS'])
@app.route('/v1/completions', methods=['POST', 'OPTIONS'])
def completion():
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
return Response(status=403)
if request.method == 'OPTIONS':
return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
body = request.get_json()
stream = False
tokenize = False
@ -216,8 +221,8 @@ def completion():
if line:
decoded_line = line.decode('utf-8')
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
yield 'data: {}\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream')
yield 'data: {}\n\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
if __name__ == '__main__':
app.run(args.host, port=args.port)

2
examples/server/json.hpp
View file

@ -11227,7 +11227,7 @@ class binary_reader
}
if (is_ndarray) // ndarray dimensional vector can only contain integers, and can not embed another array
{
return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read, exception_message(input_format, "ndarray dimentional vector is not allowed", "size"), nullptr));
return sax->parse_error(chars_read, get_token_string(), parse_error::create(113, chars_read, exception_message(input_format, "ndarray dimensional vector is not allowed", "size"), nullptr));
}
std::vector<size_t> dim;
if (JSON_HEDLEY_UNLIKELY(!get_ubjson_ndarray_size(dim)))

5
examples/server/public/completion.js
View file

@ -34,7 +34,8 @@ export async function* llama(prompt, params = {}, config = {}) {
headers: {
'Connection': 'keep-alive',
'Content-Type': 'application/json',
'Accept': 'text/event-stream'
'Accept': 'text/event-stream',
...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {})
},
signal: controller.signal,
});
@ -114,7 +115,7 @@ export async function* llama(prompt, params = {}, config = {}) {
return content;
}
// Call llama, return an event target that you can subcribe to
// Call llama, return an event target that you can subscribe to
//
// Example:
//

17
examples/server/public/index.html
View file

@ -223,7 +223,7 @@
repeat_last_n: 256, // 0 = disable penalty, -1 = context size
repeat_penalty: 1.18, // 1.0 = disabled
top_k: 40, // <= 0 to use vocab size
top_p: 0.5, // 1.0 = disabled
top_p: 0.95, // 1.0 = disabled
min_p: 0.05, // 0 = disabled
tfs_z: 1.0, // 1.0 = disabled
typical_p: 1.0, // 1.0 = disabled
@ -235,10 +235,11 @@
grammar: '',
n_probs: 0, // no completion_probabilities,
image_data: [],
cache_prompt: true
cache_prompt: true,
api_key: ''
})
/* START: Support for storing prompt templates and parameters in borwser LocalStorage */
/* START: Support for storing prompt templates and parameters in browsers LocalStorage */
const local_storage_storageKey = "llamacpp_server_local_storage";
@ -282,7 +283,7 @@
let importedTemplates = local_storage_getDataAsObject('user_templates')
if (importedTemplates) {
// saved templates were successfuly imported.
// saved templates were successfully imported.
console.log('Processing saved templates and updating default template')
params.value = { ...params.value, image_data: [] };
@ -303,7 +304,7 @@
}
function userTemplateResetToDefault() {
console.log('Reseting themplate to default')
console.log('Resetting template to default')
selectedUserTemplate.value.name = 'default';
selectedUserTemplate.value.data = savedUserTemplates.value['default'];
}
@ -762,7 +763,7 @@
<fieldset class="two">
${IntField({ label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict })}
${FloatField({ label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
@ -790,6 +791,10 @@
<fieldset>
${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })}
</fieldset>
<fieldset>
<label for="api_key">API Key</label>
<input type="text" name="api_key" value="${params.value.api_key}" placeholder="Enter API key" oninput=${updateParams} />
</fieldset>
</details>
</form>
`

669
examples/server/server.cpp
View file

File diff suppressed because it is too large Load diff

2
examples/simple/simple.cpp
View file

@ -75,7 +75,7 @@ int main(int argc, char ** argv) {
// make sure the KV cache is big enough to hold all the prompt and generated tokens
if (n_kv_req > n_ctx) {
LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
LOG_TEE("%s: either reduce n_parallel or increase n_ctx\n", __func__);
LOG_TEE("%s: either reduce n_len or increase n_ctx\n", __func__);
return 1;
}

8
examples/speculative/README.md Normal file
View file

@ -0,0 +1,8 @@
# llama.cpp/examples/speculative
Demonstration of speculative decoding and tree-based speculative decoding techniques
More info:
- https://github.com/ggerganov/llama.cpp/pull/2926
- https://github.com/ggerganov/llama.cpp/pull/3624

17
examples/speculative/speculative.cpp
View file

@ -203,8 +203,9 @@ int main(int argc, char ** argv) {
const std::string token_str = llama_token_to_piece(ctx_tgt, id);
printf("%s", token_str.c_str());
fflush(stdout);
if (!params.use_color) {
printf("%s", token_str.c_str());
}
if (id == llama_token_eos(model_tgt)) {
has_eos = true;
@ -236,10 +237,18 @@ int main(int argc, char ** argv) {
++n_past_tgt;
++n_past_dft;
++i_dft;
if (params.use_color) {
// Color token according to its origin sequence
printf("\u001b[%dm%s\u001b[37m", (36 - s_keep % 6), token_str.c_str());
fflush(stdout);
}
continue;
}
}
if (params.use_color) {
printf("%s", token_str.c_str());
}
fflush(stdout);
LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
@ -419,7 +428,7 @@ int main(int argc, char ** argv) {
++n_past_tgt;
}
// the first token is always proposed by the traget model before the speculation loop so we erase it here
// the first token is always proposed by the target model before the speculation loop so we erase it here
for (int s = 0; s < n_seq_dft; ++s) {
if (!drafts[s].active) {
continue;

4
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

@ -1295,10 +1295,6 @@ int main(int argc, char ** argv) {
opt_cb_data.last_save_iter = opt->iter;
}
if (alloc) {
ggml_allocr_free(alloc);
}
ggml_free(opt->ctx);
free_train_state(train);
ggml_free(model.ctx);

51
ggml-alloc.c
View file

@ -137,7 +137,7 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
#ifdef GGML_ALLOCATOR_DEBUG
add_allocated_tensor(alloc, tensor);
size_t cur_max = (char*)addr - (char*)alloc->data + size;
size_t cur_max = (char*)addr - (char*)alloc->base + size;
if (cur_max > alloc->max_size) {
printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
for (int i = 0; i < 1024; i++) {
@ -168,10 +168,6 @@ static void ggml_tallocr_free_tensor(ggml_tallocr_t alloc, struct ggml_tensor *
size = aligned_offset(NULL, size, alloc->alignment);
AT_PRINTF("%s: freeing %s at %p (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, ptr, size, alloc->n_free_blocks);
if (!alloc->measure) {
ggml_backend_buffer_free_tensor(alloc->buffer, tensor);
}
#ifdef GGML_ALLOCATOR_DEBUG
remove_allocated_tensor(alloc, tensor);
#endif
@ -237,7 +233,7 @@ void ggml_tallocr_reset(ggml_tallocr_t alloc) {
}
ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment) {
struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(NULL, data, size);
struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(data, size);
ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr));
@ -449,7 +445,6 @@ static ggml_tallocr_t node_tallocr(ggml_gallocr_t galloc, struct ggml_tensor * n
static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool update_backend) {
ggml_tallocr_t alloc = node_tallocr(galloc, view);
//printf("init_view: %s from src %s\n", view->name, view->view_src->name);
GGML_ASSERT(view->view_src != NULL && view->view_src->data != NULL);
if (update_backend) {
view->backend = view->view_src->backend;
@ -459,7 +454,7 @@ static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool upd
// FIXME: the view should be initialized by the owning buffer, but currently this breaks the CUDA backend
// due to the ggml_tensor_extra_gpu ring buffer overwriting the KV cache extras
assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->backend == alloc->buffer->backend);
assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->buft == alloc->buffer->buft);
if (!alloc->measure) {
ggml_backend_buffer_init_tensor(alloc->buffer, view);
@ -765,3 +760,43 @@ size_t ggml_allocr_max_size(ggml_allocr_t alloc) {
size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph) {
return ggml_gallocr_alloc_graph(alloc->galloc, alloc->talloc, graph);
}
// utils
ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
GGML_ASSERT(ggml_get_no_alloc(ctx) == true);
size_t alignment = ggml_backend_buft_get_alignment(buft);
size_t nbytes = 0;
for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->data == NULL && t->view_src == NULL) {
nbytes += GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment);
}
}
if (nbytes == 0) {
fprintf(stderr, "%s: no tensors to allocate\n", __func__);
return NULL;
}
ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, nbytes);
ggml_tallocr_t tallocr = ggml_tallocr_new_from_buffer(buffer);
for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->data == NULL) {
if (t->view_src == NULL) {
ggml_tallocr_alloc(tallocr, t);
} else {
ggml_backend_view_init(buffer, t);
}
}
}
ggml_tallocr_free(tallocr);
return buffer;
}
ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend) {
return ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_get_default_buffer_type(backend));
}

9
ggml-alloc.h
View file

@ -8,6 +8,7 @@ extern "C" {
struct ggml_backend;
struct ggml_backend_buffer;
struct ggml_backend_buffer_type;
//
// Legacy API
@ -42,7 +43,7 @@ GGML_API size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph
// ggml-backend v2 API
//
// Seperate tensor and graph allocator objects
// Separate tensor and graph allocator objects
// This is necessary for multi-backend allocation because the graph allocator needs to use multiple tensor allocators
// The original API is kept as a wrapper around the new API
@ -80,6 +81,12 @@ GGML_API void ggml_gallocr_alloc_graph_n(
struct ggml_hash_set hash_set,
ggml_tallocr_t * hash_node_talloc);
// Utils
// Create a buffer and allocate all the tensors in a ggml_context
GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, struct ggml_backend_buffer_type * buft);
GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, struct ggml_backend * backend);
#ifdef __cplusplus
}
#endif

67
ggml-backend-impl.h
View file

@ -12,31 +12,50 @@ extern "C" {
// Backend buffer
//
// buffer type
typedef void * ggml_backend_buffer_type_context_t;
struct ggml_backend_buffer_type_i {
ggml_backend_buffer_t (*alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size);
size_t (*get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment
size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
bool (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
};
struct ggml_backend_buffer_type {
struct ggml_backend_buffer_type_i iface;
ggml_backend_buffer_type_context_t context;
};
// buffer
typedef void * ggml_backend_buffer_context_t;
struct ggml_backend_buffer_i {
void (*free_buffer) (ggml_backend_buffer_t buffer);
void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer
size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback
void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback
void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback
void (*free_buffer)(ggml_backend_buffer_t buffer);
//void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
void * (*get_base) (ggml_backend_buffer_t buffer);
void (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
// (optional) copy tensor between different buffer-type, allow for single-copy tranfers
void (*cpy_tensor_from)(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*cpy_tensor_to) (ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
};
struct ggml_backend_buffer {
struct ggml_backend_buffer_i iface;
ggml_backend_t backend;
struct ggml_backend_buffer_i iface;
ggml_backend_buffer_type_t buft;
ggml_backend_buffer_context_t context;
size_t size;
};
GGML_API ggml_backend_buffer_t ggml_backend_buffer_init(
struct ggml_backend * backend,
ggml_backend_buffer_t ggml_backend_buffer_init(
ggml_backend_buffer_type_t buft,
struct ggml_backend_buffer_i iface,
ggml_backend_buffer_context_t context,
size_t size);
//
// Backend
//
@ -49,20 +68,17 @@ extern "C" {
void (*free)(ggml_backend_t backend);
// buffer allocation
ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size);
ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend);
// get buffer alignment
size_t (*get_alignment)(ggml_backend_t backend);
// tensor data access
// these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize
// (optional) asynchroneous tensor data access
void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
void (*synchronize) (ggml_backend_t backend);
// (optional) copy tensor between different backends, allow for single-copy tranfers
void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
// (optional) asynchroneous tensor copy
void (*cpy_tensor_from_async)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*cpy_tensor_to_async) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*synchronize) (ggml_backend_t backend);
// compute graph with a plan
ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
@ -82,6 +98,15 @@ extern "C" {
ggml_backend_context_t context;
};
//
// Backend registry
//
typedef ggml_backend_t (*ggml_backend_init_fn)(const char * params, void * user_data);
void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
#ifdef __cplusplus
}
#endif

771
ggml-backend.c
View file

File diff suppressed because it is too large Load diff

79
ggml-backend.h
View file

@ -7,41 +7,44 @@
extern "C" {
#endif
typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
typedef struct ggml_backend * ggml_backend_t;
typedef void * ggml_backend_graph_plan_t;
//
// Backend buffer
//
struct ggml_backend_buffer;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
// buffer type
GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size);
GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft);
GGML_API size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
// backend buffer functions
// buffer
GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API void ggml_backend_buffer_free_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer);
//
// Backend
//
struct ggml_backend;
typedef struct ggml_backend * ggml_backend_t;
typedef void * ggml_backend_graph_plan_t;
GGML_API ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor);
GGML_API const char * ggml_backend_name(ggml_backend_t backend);
GGML_API void ggml_backend_free(ggml_backend_t backend);
GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
GGML_API ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend);
GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
GGML_API void ggml_backend_tensor_set_async( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
@ -57,6 +60,7 @@ extern "C" {
// tensor copy between different backends
GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); // automatic fallback to sync copy
//
// CPU backend
@ -68,8 +72,23 @@ extern "C" {
GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
// Create a backend buffer from an existing pointer
GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size);
GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
//
// Backend registry
//
// The backend registry is a registry of all the available backends, and allows initializing backends in a generic way
GGML_API size_t ggml_backend_reg_get_count(void);
GGML_API size_t ggml_backend_reg_find_by_name(const char * name);
GGML_API ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str); // str is name[:params]
GGML_API const char * ggml_backend_reg_get_name(size_t i);
GGML_API ggml_backend_t ggml_backend_reg_init_backend(size_t i, const char * params); // params is backend-specific
GGML_API ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i);
GGML_API ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size);
//
// Backend scheduler
@ -131,6 +150,32 @@ extern "C" {
ggml_backend_sched_t sched,
struct ggml_cgraph * graph);
//
// Utils
//
struct ggml_backend_graph_copy {
ggml_backend_buffer_t buffer;
struct ggml_context * ctx_allocated;
struct ggml_context * ctx_unallocated;
struct ggml_cgraph * graph;
};
// Copy a graph to a different backend
GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
GGML_API void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
// Compare the output of two backends
GGML_API void ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
// Tensor initialization
GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
GGML_API void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
#ifdef __cplusplus
}
#endif

2531
ggml-cuda.cu
View file

File diff suppressed because it is too large Load diff

10
ggml-cuda.h
View file

@ -49,7 +49,15 @@ GGML_API int ggml_cuda_get_device_count(void);
GGML_API void ggml_cuda_get_device_description(int device, char * description, size_t description_size);
// backend API
GGML_API ggml_backend_t ggml_backend_cuda_init(void); // TODO: take a list of devices to use
GGML_API ggml_backend_t ggml_backend_cuda_init(int device);
GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend);
GGML_API int ggml_backend_cuda_get_device(ggml_backend_t backend);
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
// pinned host buffer for use with CPU backend for faster copies between CPU and GPU
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
#ifdef __cplusplus
}

2
ggml-impl.h
View file

@ -232,7 +232,7 @@ bool ggml_hash_contains (const struct ggml_hash_set hash_set, struct ggml
// returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted
size_t ggml_hash_find (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
// returns GGML_HAHSHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
// returns GGML_HASHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
size_t ggml_hash_insert ( struct ggml_hash_set hash_set, struct ggml_tensor * key);
// return index, asserts if table is full

6
ggml-metal.h
View file

@ -99,6 +99,12 @@ GGML_API ggml_backend_t ggml_backend_metal_init(void);
GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
// helper to check if the device supports a specific family
// ideally, the user code should be doing these checks
// ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
GGML_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family);
#ifdef __cplusplus
}

1257
ggml-metal.m
View file

File diff suppressed because it is too large Load diff

2668
ggml-metal.metal
View file

File diff suppressed because it is too large Load diff

12
ggml-opencl.cpp
View file

@ -1,20 +1,18 @@
#include "ggml.h"
#include "ggml-opencl.h"
#include <array>
#include <atomic>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <limits>
#include <sstream>
#include <vector>
#include <limits>
#define CL_TARGET_OPENCL_VERSION 110
#include <clblast.h>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include "ggml.h"
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

4
ggml-quants.c
View file

@ -3114,7 +3114,7 @@ void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restri
size_t vl = __riscv_vsetvl_e8m1(qk/2);
// These tempory registers are for masking and shift operations
// These temporary registers are for masking and shift operations
vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl);
vuint32m2_t vt_2 = __riscv_vsll_vv_u32m2(__riscv_vmv_v_x_u32m2(1, vl), vt_1, vl);
@ -4757,7 +4757,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
vl = 16;
// retreive lane to multiply with scale
// retrieve lane to multiply with scale
vint32m2_t aux0_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 0), (scale[0]), vl);
vint32m2_t aux0_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 1), (scale[1]), vl);
vint32m2_t aux1_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 0), (scale[2]), vl);

1153
ggml.c
View file

File diff suppressed because it is too large Load diff

123
ggml.h
View file

@ -215,9 +215,9 @@
#define GGML_QNT_VERSION_FACTOR 1000 // do not change this
#define GGML_MAX_DIMS 4
#define GGML_MAX_PARAMS 1024
#define GGML_MAX_PARAMS 2048
#define GGML_MAX_CONTEXTS 64
#define GGML_MAX_SRC 6
#define GGML_MAX_SRC 10
#define GGML_MAX_NAME 64
#define GGML_MAX_OP_PARAMS 64
#define GGML_DEFAULT_N_THREADS 4
@ -244,11 +244,10 @@
#define GGML_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
fflush(stderr); \
fflush(stdout); \
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
ggml_print_backtrace(); \
exit(1); \
abort(); \
} \
} while (0)
@ -284,13 +283,27 @@
const type prefix##3 = (pointer)->array[3]; \
GGML_UNUSED(prefix##3);
#define GGML_TENSOR_UNARY_OP_LOCALS \
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
#define GGML_TENSOR_BINARY_OP_LOCALS \
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) \
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
#ifdef __cplusplus
extern "C" {
#endif
#if defined(__ARM_NEON) && defined(__CUDACC__)
typedef half ggml_fp16_t;
#elif defined(__ARM_NEON)
#elif defined(__ARM_NEON) && !defined(_MSC_VER)
typedef __fp16 ggml_fp16_t;
#else
typedef uint16_t ggml_fp16_t;
@ -330,6 +343,12 @@ extern "C" {
GGML_TYPE_COUNT,
};
// precision
enum ggml_prec {
GGML_PREC_DEFAULT,
GGML_PREC_F32,
};
enum ggml_backend_type {
GGML_BACKEND_CPU = 0,
GGML_BACKEND_GPU = 10,
@ -382,6 +401,7 @@ extern "C" {
GGML_OP_GROUP_NORM,
GGML_OP_MUL_MAT,
GGML_OP_MUL_MAT_ID,
GGML_OP_OUT_PROD,
GGML_OP_SCALE,
@ -408,8 +428,10 @@ extern "C" {
GGML_OP_CONV_TRANSPOSE_2D,
GGML_OP_POOL_1D,
GGML_OP_POOL_2D,
GGML_OP_UPSCALE, // nearest interpolate
GGML_OP_PAD,
GGML_OP_ARGSORT,
GGML_OP_LEAKY_RELU,
GGML_OP_FLASH_ATTN,
GGML_OP_FLASH_FF,
@ -449,7 +471,8 @@ extern "C" {
GGML_UNARY_OP_GELU,
GGML_UNARY_OP_GELU_QUICK,
GGML_UNARY_OP_SILU,
GGML_UNARY_OP_LEAKY
GGML_UNARY_OP_COUNT,
};
enum ggml_object_type {
@ -485,7 +508,6 @@ extern "C" {
struct ggml_backend_buffer * buffer;
int n_dims;
int64_t ne[GGML_MAX_DIMS]; // number of elements
size_t nb[GGML_MAX_DIMS]; // stride in bytes:
// nb[0] = ggml_type_size(type)
@ -517,7 +539,7 @@ extern "C" {
void * extra; // extra things e.g. for ggml-cuda.cu
char padding[12];
char padding[8];
};
static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
@ -622,16 +644,22 @@ extern "C" {
GGML_API int64_t ggml_nrows (const struct ggml_tensor * tensor);
GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor);
GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
GGML_API size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split);
GGML_API int ggml_blck_size (enum ggml_type type);
GGML_API size_t ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block
GGML_API float ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
GGML_API int ggml_blck_size(enum ggml_type type);
GGML_API size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block
GGML_API size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
GGML_DEPRECATED(
GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
"use ggml_row_size() instead");
GGML_API const char * ggml_type_name(enum ggml_type type);
GGML_API const char * ggml_op_name (enum ggml_op op);
GGML_API const char * ggml_op_symbol(enum ggml_op op);
GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op);
GGML_API const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name
GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);
GGML_API bool ggml_is_quantized(enum ggml_type type);
@ -642,6 +670,11 @@ extern "C" {
GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor);
GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor);
GGML_API bool ggml_is_vector (const struct ggml_tensor * tensor);
GGML_API bool ggml_is_matrix (const struct ggml_tensor * tensor);
GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor);
GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars
GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
@ -774,6 +807,9 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
// dst = a
// view(dst, nb1, nb2, nb3, offset) += b
// return dst
GGML_API struct ggml_tensor * ggml_acc(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -938,15 +974,14 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_leaky(
GGML_API struct ggml_tensor * ggml_leaky_relu(
struct ggml_context * ctx,
struct ggml_tensor * a);
struct ggml_tensor * a, float negative_slope, bool inplace);
GGML_API struct ggml_tensor * ggml_relu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
// TODO: double-check this computation is correct
GGML_API struct ggml_tensor * ggml_gelu(
struct ggml_context * ctx,
struct ggml_tensor * a);
@ -1028,6 +1063,22 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
// change the precision of a matrix multiplication
// set to GGML_PREC_F32 for higher precision (useful for phi-2)
GGML_API void ggml_mul_mat_set_prec(
struct ggml_tensor * a,
enum ggml_prec prec);
// indirect matrix multiplication
// ggml_mul_mat_id(ctx, as, ids, id, b) ~= ggml_mul_mat(as[ids[id]], b)
GGML_API struct ggml_tensor * ggml_mul_mat_id(
struct ggml_context * ctx,
struct ggml_tensor * const as[],
int n_as,
struct ggml_tensor * ids,
int id,
struct ggml_tensor * b);
// A: m columns, n rows,
// B: p columns, n rows,
// result is m columns, p rows
@ -1235,6 +1286,7 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
// supports 3D: a->ne[2] == b->ne[1]
GGML_API struct ggml_tensor * ggml_get_rows(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -1283,6 +1335,14 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
// fused soft_max(a*scale + mask)
// mask is optional
GGML_API struct ggml_tensor * ggml_soft_max_ext(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * mask,
float scale);
GGML_API struct ggml_tensor * ggml_soft_max_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -1513,6 +1573,32 @@ extern "C" {
struct ggml_tensor * a,
int scale_factor);
// pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0]
GGML_API struct ggml_tensor * ggml_pad(
struct ggml_context * ctx,
struct ggml_tensor * a,
int p0,
int p1,
int p2,
int p3);
// sort rows
enum ggml_sort_order {
GGML_SORT_ASC,
GGML_SORT_DESC,
};
GGML_API struct ggml_tensor * ggml_argsort(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_sort_order order);
// top k elements per row
GGML_API struct ggml_tensor * ggml_top_k(
struct ggml_context * ctx,
struct ggml_tensor * a,
int k);
GGML_API struct ggml_tensor * ggml_flash_attn(
struct ggml_context * ctx,
struct ggml_tensor * q,
@ -1574,7 +1660,6 @@ extern "C" {
int kh);
// used in sam
GGML_API struct ggml_tensor * ggml_add_rel_pos(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -1749,7 +1834,7 @@ extern "C" {
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
GGML_API struct ggml_cgraph * ggml_new_graph_custom (struct ggml_context * ctx, size_t size, bool grads);
GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
GGML_API struct ggml_cgraph * ggml_graph_view (struct ggml_context * ctx, struct ggml_cgraph * cgraph, int i0, int i1);
GGML_API struct ggml_cgraph ggml_graph_view (struct ggml_cgraph * cgraph, int i0, int i1);
GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst);
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // zero grads
GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph);

2
gguf-py/README.md
View file

@ -61,7 +61,7 @@ If you want to publish the package manually for any reason, you need to have `tw
pip install build twine
```
Then, folow these steps to release a new version:
Then, follow these steps to release a new version:
1. Bump the version in `pyproject.toml`.
2. Build the package:

49
gguf-py/gguf/constants.py
View file

@ -38,6 +38,8 @@ class Keys:
FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
EXPERT_COUNT = "{arch}.expert_count"
EXPERT_USED_COUNT = "{arch}.expert_used_count"
class Attention:
HEAD_COUNT = "{arch}.attention.head_count"
@ -92,6 +94,8 @@ class MODEL_ARCH(IntEnum):
BERT = auto()
BLOOM = auto()
STABLELM = auto()
QWEN = auto()
PHI2 = auto()
class MODEL_TENSOR(IntEnum):
@ -110,10 +114,14 @@ class MODEL_TENSOR(IntEnum):
ATTN_NORM = auto()
ATTN_NORM_2 = auto()
ATTN_ROT_EMBD = auto()
FFN_GATE_INP = auto()
FFN_NORM = auto()
FFN_GATE = auto()
FFN_DOWN = auto()
FFN_UP = auto()
FFN_NORM = auto()
FFN_GATE_EXP = auto()
FFN_DOWN_EXP = auto()
FFN_UP_EXP = auto()
ATTN_Q_NORM = auto()
ATTN_K_NORM = auto()
@ -132,6 +140,8 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.BERT: "bert",
MODEL_ARCH.BLOOM: "bloom",
MODEL_ARCH.STABLELM: "stablelm",
MODEL_ARCH.QWEN: "qwen",
MODEL_ARCH.PHI2: "phi2",
}
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -152,10 +162,14 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm",
MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm",
MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp",
MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate",
MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate.{xid}",
MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down.{xid}",
MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up.{xid}",
}
MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@ -170,10 +184,14 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_GATE_INP,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_GATE_EXP,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
],
MODEL_ARCH.GPTNEOX: [
MODEL_TENSOR.TOKEN_EMBD,
@ -317,9 +335,34 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.QWEN: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.GPT2: [
# TODO
],
MODEL_ARCH.PHI2: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
]
# TODO
}
@ -336,6 +379,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_ARCH.PERSIMMON: [
MODEL_TENSOR.ROPE_FREQS,
],
MODEL_ARCH.QWEN: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
}
#

6
gguf-py/gguf/gguf_writer.py
View file

@ -339,6 +339,12 @@ class GGUFWriter:
def add_clamp_kqv(self, value: float) -> None:
self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
def add_expert_count(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count)
def add_expert_used_count(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count)
def add_layer_norm_eps(self, value: float) -> None:
self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)

63
gguf-py/gguf/tensor_mapping.py
View file

@ -10,13 +10,14 @@ class TensorNameMap:
# Token embeddings
MODEL_TENSOR.TOKEN_EMBD: (
"gpt_neox.embed_in", # gptneox
"transformer.wte", # gpt2 gpt-j mpt refact
"transformer.wte", # gpt2 gpt-j mpt refact qwen
"transformer.word_embeddings", # falcon
"word_embeddings", # bloom
"model.embed_tokens", # llama-hf
"tok_embeddings", # llama-pth
"embeddings.word_embeddings", # bert
"language_model.embedding.word_embeddings", # persimmon
"transformer.embd.wte", # phi2
),
# Token type embeddings
@ -38,9 +39,10 @@ class TensorNameMap:
# Output
MODEL_TENSOR.OUTPUT: (
"embed_out", # gptneox
"lm_head", # gpt2 mpt falcon llama-hf baichuan
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen
"output", # llama-pth bloom
"word_embeddings_for_head", # persimmon
"lm_head.linear", # phi2
),
# Output norm
@ -51,8 +53,9 @@ class TensorNameMap:
"norm", # llama-pth
"embeddings.LayerNorm", # bert
"transformer.norm_f", # mpt
"ln_f", # refact bloom
"ln_f", # refact bloom qwen
"language_model.encoder.final_layernorm", # persimmon
"lm_head.ln", # phi2
),
# Rope frequencies
@ -65,7 +68,7 @@ class TensorNameMap:
# Attention norm
MODEL_TENSOR.ATTN_NORM: (
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen
"transformer.blocks.{bid}.norm_1", # mpt
"transformer.h.{bid}.input_layernorm", # falcon7b
"h.{bid}.input_layernorm", # bloom
@ -75,6 +78,7 @@ class TensorNameMap:
"encoder.layer.{bid}.attention.output.LayerNorm", # bert
"language_model.encoder.layers.{bid}.input_layernorm", # persimmon
"model.layers.{bid}.ln1", # yi
"transformer.h.{bid}.ln", # phi2
),
# Attention norm 2
@ -85,11 +89,12 @@ class TensorNameMap:
# Attention query-key-value
MODEL_TENSOR.ATTN_QKV: (
"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
"transformer.h.{bid}.attn.c_attn", # gpt2
"transformer.h.{bid}.attn.c_attn", # gpt2 qwen
"transformer.blocks.{bid}.attn.Wqkv", # mpt
"transformer.h.{bid}.self_attention.query_key_value", # falcon
"h.{bid}.self_attention.query_key_value", # bloom
"language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
"transformer.h.{bid}.mixer.Wqkv", # phi2
),
# Attention query
@ -119,7 +124,7 @@ class TensorNameMap:
# Attention output
MODEL_TENSOR.ATTN_OUT: (
"gpt_neox.layers.{bid}.attention.dense", # gptneox
"transformer.h.{bid}.attn.c_proj", # gpt2 refact
"transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen
"transformer.blocks.{bid}.attn.out_proj", # mpt
"transformer.h.{bid}.self_attention.dense", # falcon
"h.{bid}.self_attention.dense", # bloom
@ -128,6 +133,7 @@ class TensorNameMap:
"encoder.layer.{bid}.attention.output.dense", # bert
"transformer.h.{bid}.attn.out_proj", # gpt-j
"language_model.encoder.layers.{bid}.self_attention.dense", # persimmon
"transformer.h.{bid}.mixer.out_proj", # phi2
),
# Rotary embeddings
@ -139,7 +145,7 @@ class TensorNameMap:
# Feed-forward norm
MODEL_TENSOR.FFN_NORM: (
"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
"transformer.h.{bid}.ln_2", # gpt2 refact
"transformer.h.{bid}.ln_2", # gpt2 refact qwen
"h.{bid}.post_attention_layernorm", # bloom
"transformer.blocks.{bid}.norm_2", # mpt
"model.layers.{bid}.post_attention_layernorm", # llama-hf
@ -149,6 +155,11 @@ class TensorNameMap:
"model.layers.{bid}.ln2", # yi
),
MODEL_TENSOR.FFN_GATE_INP: (
"layers.{bid}.feed_forward.gate", # mixtral
"model.layers.{bid}.block_sparse_moe.gate", # mixtral
),
# Feed-forward up
MODEL_TENSOR.FFN_UP: (
"gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox
@ -161,18 +172,31 @@ class TensorNameMap:
"encoder.layer.{bid}.intermediate.dense", # bert
"transformer.h.{bid}.mlp.fc_in", # gpt-j
"language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
"transformer.h.{bid}.mlp.w1", # qwen
"transformer.h.{bid}.mlp.fc1", # phi2
),
MODEL_TENSOR.FFN_UP_EXP: (
"layers.{bid}.feed_forward.experts.{xid}.w3", # mixtral
"model.layers.{bid}.block_sparse_moe.experts.{xid}.w3", # mixtral
),
# Feed-forward gate
MODEL_TENSOR.FFN_GATE: (
"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
"layers.{bid}.feed_forward.w1", # llama-pth
"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
"layers.{bid}.feed_forward.w1", # llama-pth
"transformer.h.{bid}.mlp.w2", # qwen
),
MODEL_TENSOR.FFN_GATE_EXP: (
"layers.{bid}.feed_forward.experts.{xid}.w1", # mixtral
"model.layers.{bid}.block_sparse_moe.experts.{xid}.w1", # mixtral
),
# Feed-forward down
MODEL_TENSOR.FFN_DOWN: (
"gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
"transformer.h.{bid}.mlp.c_proj", # gpt2 refact
"transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen
"transformer.blocks.{bid}.ffn.down_proj", # mpt
"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
"h.{bid}.mlp.dense_4h_to_h", # bloom
@ -181,6 +205,12 @@ class TensorNameMap:
"encoder.layer.{bid}.output.dense", # bert
"transformer.h.{bid}.mlp.fc_out", # gpt-j
"language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon
"transformer.h.{bid}.mlp.fc2", # phi2
),
MODEL_TENSOR.FFN_DOWN_EXP: (
"layers.{bid}.feed_forward.experts.{xid}.w2", # mixtral
"model.layers.{bid}.block_sparse_moe.experts.{xid}.w2", # mixtral
),
MODEL_TENSOR.ATTN_Q_NORM: (
@ -211,11 +241,14 @@ class TensorNameMap:
for tensor, keys in self.block_mappings_cfg.items():
if tensor not in MODEL_TENSORS[arch]:
continue
tensor_name = TENSOR_NAMES[tensor].format(bid = bid)
self.mapping[tensor_name] = (tensor, tensor_name)
for key in keys:
key = key.format(bid = bid)
self.mapping[key] = (tensor, tensor_name)
# TODO: make this configurable
n_experts = 8
for xid in range(n_experts):
tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid)
self.mapping[tensor_name] = (tensor, tensor_name)
for key in keys:
key = key.format(bid = bid, xid = xid)
self.mapping[key] = (tensor, tensor_name)
def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None:
result = self.mapping.get(key)

4
gguf-py/gguf/vocab.py
View file

@ -109,8 +109,10 @@ class SpecialVocab:
return True
def _set_special_token(self, typ: str, tid: Any) -> None:
if not isinstance(tid, int) or tid < 0:
if not isinstance(tid, int):
return
if tid < 0:
raise ValueError(f'invalid value for special token type {typ}: {tid}')
if self.n_vocab is None or tid < self.n_vocab:
if typ in self.special_token_ids:
return

2
gguf-py/pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "gguf"
version = "0.6.0"
version = "0.7.0"
description = "Read and write ML models in GGUF for GGML"
authors = ["GGML <ggml@ggml.ai>"]
packages = [

1837
llama.cpp
View file

File diff suppressed because it is too large Load diff

36
llama.h
View file

@ -39,10 +39,11 @@
#define LLAMA_MAX_RNG_STATE (64*1024)
#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
#define LLAMA_SESSION_VERSION 2
#define LLAMA_SESSION_VERSION 3
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
@ -158,6 +159,22 @@ extern "C" {
llama_seq_id all_seq_id; // used if seq_id == NULL
} llama_batch;
enum llama_model_kv_override_type {
LLAMA_KV_OVERRIDE_INT,
LLAMA_KV_OVERRIDE_FLOAT,
LLAMA_KV_OVERRIDE_BOOL,
};
struct llama_model_kv_override {
char key[128];
enum llama_model_kv_override_type tag;
union {
int64_t int_value;
double float_value;
bool bool_value;
};
};
struct llama_model_params {
int32_t n_gpu_layers; // number of layers to store in VRAM
int32_t main_gpu; // the GPU that is used for scratch and small tensors
@ -165,9 +182,13 @@ extern "C" {
// called with a progress value between 0 and 1, pass NULL to disable
llama_progress_callback progress_callback;
// context pointer passed to the progress callback
void * progress_callback_user_data;
// override key-value pairs of the model meta data
const struct llama_model_kv_override * kv_overrides;
// Keep the booleans together to avoid misalignment during copy-by-value.
bool vocab_only; // only load the vocabulary, no weights
bool use_mmap; // use mmap if possible
@ -185,17 +206,20 @@ extern "C" {
// ref: https://github.com/ggerganov/llama.cpp/pull/2054
float rope_freq_base; // RoPE base frequency, 0 = from model
float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
float yarn_ext_factor; // YaRN extrapolation mix factor, NaN = from model
float yarn_ext_factor; // YaRN extrapolation mix factor, negative = from model
float yarn_attn_factor; // YaRN magnitude scaling factor
float yarn_beta_fast; // YaRN low correction dim
float yarn_beta_slow; // YaRN high correction dim
uint32_t yarn_orig_ctx; // YaRN original context size
enum ggml_type type_k; // data type for K cache
enum ggml_type type_v; // data type for V cache
// Keep the booleans together to avoid misalignment during copy-by-value.
bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
bool f16_kv; // use fp16 for KV cache, fp32 otherwise
bool logits_all; // the llama_eval() call computes all logits, not just the last one
bool embedding; // embedding mode only
bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
bool embedding; // embedding mode only
bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
};
// model quantization parameters

1
prompts/chat-with-qwen.txt Normal file
View file

@ -0,0 +1 @@
You are a helpful assistant.

3
requirements-hf-to-gguf.txt Normal file
View file

@ -0,0 +1,3 @@
-r requirements.txt
torch==2.1.1
transformers==4.35.2

2
requirements.txt
View file

@ -1,3 +1,5 @@
numpy==1.24.4
sentencepiece==0.1.98
transformers>=4.34.0
gguf>=0.1.0
protobuf>=4.21.0

22
scripts/build-info.cmake
View file

@ -1,5 +1,3 @@
set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
set(BUILD_NUMBER 0)
set(BUILD_COMMIT "unknown")
set(BUILD_COMPILER "unknown")
@ -58,23 +56,3 @@ else()
)
set(BUILD_TARGET ${OUT})
endif()
# Only write the build info if it changed
if(EXISTS ${OUTPUT_FILE})
file(READ ${OUTPUT_FILE} CONTENTS)
string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
set(OLD_COMMIT ${CMAKE_MATCH_1})
string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
set(OLD_COMPILER ${CMAKE_MATCH_1})
string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
set(OLD_TARGET ${CMAKE_MATCH_1})
if (
NOT OLD_COMMIT STREQUAL BUILD_COMMIT OR
NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
NOT OLD_TARGET STREQUAL BUILD_TARGET
)
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
endif()
else()
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
endif()

24
scripts/gen-build-info-cpp.cmake Normal file
View file

@ -0,0 +1,24 @@
include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
# Only write the build info if it changed
if(EXISTS ${OUTPUT_FILE})
file(READ ${OUTPUT_FILE} CONTENTS)
string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
set(OLD_COMMIT ${CMAKE_MATCH_1})
string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
set(OLD_COMPILER ${CMAKE_MATCH_1})
string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
set(OLD_TARGET ${CMAKE_MATCH_1})
if (
NOT OLD_COMMIT STREQUAL BUILD_COMMIT OR
NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
NOT OLD_TARGET STREQUAL BUILD_TARGET
)
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
endif()
else()
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
endif()

38
scripts/get-flags.mk Normal file
View file

@ -0,0 +1,38 @@
ifeq '' '$(findstring clang,$(shell $(GF_CC) --version))'
GF_CC_IS_GCC = 1
GF_CC_VER := $(shell { $(GF_CC) -dumpfullversion 2>/dev/null || $(GF_CC) -dumpversion; } | awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }')
else
GF_CC_IS_CLANG = 1
ifeq '' '$(findstring Apple,$(shell $(GF_CC) --version))'
GF_CC_IS_LLVM_CLANG = 1
else
GF_CC_IS_APPLE_CLANG = 1
endif
GF_CC_VER := \
$(shell $(GF_CC) --version | sed -n 's/^.* version \([0-9.]*\).*$$/\1/p' \
| awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }')
endif
ifeq ($(GF_CC_IS_CLANG), 1)
# clang options
GF_CFLAGS = -Wunreachable-code-break -Wunreachable-code-return
GF_CXXFLAGS = -Wunreachable-code-break -Wunreachable-code-return -Wmissing-prototypes -Wextra-semi
ifneq '' '$(and $(GF_CC_IS_LLVM_CLANG),$(filter 1,$(shell expr $(GF_CC_VER) \>= 030800)))'
GF_CFLAGS += -Wdouble-promotion
endif
ifneq '' '$(and $(GF_CC_IS_APPLE_CLANG),$(filter 1,$(shell expr $(GF_CC_VER) \>= 070300)))'
GF_CFLAGS += -Wdouble-promotion
endif
else
# gcc options
GF_CFLAGS = -Wdouble-promotion
GF_CXXFLAGS = -Wno-array-bounds
ifeq ($(shell expr $(GF_CC_VER) \>= 070100), 1)
GF_CXXFLAGS += -Wno-format-truncation
endif
ifeq ($(shell expr $(GF_CC_VER) \>= 080100), 1)
GF_CXXFLAGS += -Wextra-semi
endif
endif

5
scripts/sync-ggml.sh
View file

@ -20,5 +20,6 @@ cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h
cp -rpv ../ggml/include/ggml/ggml-alloc.h ./ggml-alloc.h
cp -rpv ../ggml/include/ggml/ggml-backend.h ./ggml-backend.h
cp -rpv ../ggml/tests/test-opt.cpp ./tests/test-opt.cpp
cp -rpv ../ggml/tests/test-grad0.cpp ./tests/test-grad0.cpp
cp -rpv ../ggml/tests/test-opt.cpp ./tests/test-opt.cpp
cp -rpv ../ggml/tests/test-grad0.cpp ./tests/test-grad0.cpp
cp -rpv ../ggml/tests/test-backend-ops.cpp ./tests/test-backend-ops.cpp

28
tests/CMakeLists.txt
View file

@ -22,26 +22,32 @@ endfunction()
llama_build_and_test_executable(test-quantize-fns.cpp)
llama_build_and_test_executable(test-quantize-perf.cpp)
llama_build_and_test_executable(test-sampling.cpp)
llama_build_executable(test-tokenizer-0-llama.cpp)
llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_build_executable(test-tokenizer-0-falcon.cpp)
llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
llama_build_executable(test-tokenizer-1-llama.cpp)
llama_test_executable (test-tokenizer-1-llama test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_test_executable(test-tokenizer-1-baichuan test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
llama_test_executable (test-tokenizer-1-llama test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_test_executable (test-tokenizer-1-baichuan test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
llama_build_executable(test-tokenizer-1-bpe.cpp)
llama_test_executable (test-tokenizer-1-falcon test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
llama_test_executable(test-tokenizer-1-aquila test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
llama_test_executable(test-tokenizer-1-mpt test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
llama_test_executable(test-tokenizer-1-stablelm-3b-4e1t test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm-3b-4e1t.gguf)
llama_test_executable(test-tokenizer-1-gpt-neox test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
llama_test_executable(test-tokenizer-1-refact test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
llama_test_executable(test-tokenizer-1-starcoder test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
# llama_test_executable(test-tokenizer-1-bloom test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) # BIG
llama_test_executable (test-tokenizer-1-falcon test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
llama_test_executable (test-tokenizer-1-aquila test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
llama_test_executable (test-tokenizer-1-mpt test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
llama_test_executable (test-tokenizer-1-stablelm-3b-4e1t test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm-3b-4e1t.gguf)
llama_test_executable (test-tokenizer-1-gpt-neox test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
llama_test_executable (test-tokenizer-1-refact test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
llama_test_executable (test-tokenizer-1-starcoder test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
# llama_test_executable (test-tokenizer-1-bloom test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) # BIG
llama_build_and_test_executable(test-grammar-parser.cpp)
llama_build_and_test_executable(test-llama-grammar.cpp)
llama_build_and_test_executable(test-grad0.cpp) # SLOW
llama_build_and_test_executable(test-grad0.cpp)
# llama_build_and_test_executable(test-opt.cpp) # SLOW
llama_build_and_test_executable(test-backend-ops.cpp)
llama_build_and_test_executable(test-rope.cpp)

1689
tests/test-backend-ops.cpp Normal file
View file

File diff suppressed because it is too large Load diff

2
tests/test-grad0.cpp
View file

@ -1,4 +1,4 @@
#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnings on Windows
#include "ggml.h"
#include <cmath>

14
tests/test-quantize-perf.cpp
View file

@ -117,7 +117,7 @@ static void usage(char * argv[]) {
printf(" --size SIZE set test size, divisible by 32 (L1_SIZE:%d)\n", L1_SIZE);
printf(" -3 use size as L1, L2, L3 sizes (L1:%d L2:%d L3:%d)\n", L1_SIZE, L2_SIZE, L3_SIZE);
printf(" -4 use size as L1, L2, L3, MEM sizes (L1:%d L2:%d L3:%d MEM:%d)\n", L1_SIZE, L2_SIZE, L3_SIZE, MEM_SIZE);
printf(" --op OP set test opration as quantize_row_q_reference, quantize_row_q, dequantize_row_q,\n");
printf(" --op OP set test operation as quantize_row_q_reference, quantize_row_q, dequantize_row_q,\n");
printf(" quantize_row_q_dot, vec_dot_q (all)\n");
printf(" --type TYPE set test type as");
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
@ -202,7 +202,7 @@ int main(int argc, char * argv[]) {
}
int alignment = std::stoi(argv[i]);
if (alignment < 0 || alignment > MAX_ALIGNMENT) {
fprintf(stderr, "error: aligment-offset must be less than %d\n", MAX_ALIGNMENT);
fprintf(stderr, "error: alignment-offset must be less than %d\n", MAX_ALIGNMENT);
invalid_param = true;
break;
}
@ -286,7 +286,7 @@ int main(int argc, char * argv[]) {
qfns.from_float_reference(test_data1, test_q1, size);
return test_q1[0];
};
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
size_t quantized_size = ggml_row_size(type, size);
benchmark_function(size, quantized_size, iterations, quantize_fn);
}
printf("\n");
@ -300,7 +300,7 @@ int main(int argc, char * argv[]) {
qfns.from_float(test_data1, test_q1, size);
return test_q1[0];
};
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
size_t quantized_size = ggml_row_size(type, size);
benchmark_function(size, quantized_size, iterations, quantize_fn);
}
printf("\n");
@ -315,7 +315,7 @@ int main(int argc, char * argv[]) {
qfns.to_float(test_q1, test_out, size);
return test_out[0];
};
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
size_t quantized_size = ggml_row_size(type, size);
benchmark_function(size, quantized_size, iterations, quantize_fn);
}
printf("\n");
@ -330,7 +330,7 @@ int main(int argc, char * argv[]) {
vdot.from_float(test_data1, test_q1, size);
return test_q1[0];
};
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
size_t quantized_size = ggml_row_size(type, size);
benchmark_function(size, quantized_size, iterations, quantize_fn);
}
printf("\n");
@ -347,7 +347,7 @@ int main(int argc, char * argv[]) {
qfns.vec_dot(size, &result, test_q1, test_q2);
return result;
};
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
size_t quantized_size = ggml_row_size(type, size);
benchmark_function(size, quantized_size, iterations, quantize_fn);
}
printf("\n");