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Merge branch 'master' into cuda-cublas-opts

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ggml-ci
This commit is contained in:
Georgi Gerganov 2023-12-17 08:20:02 +02:00
commit e75889a9b8
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60 changed files with 9755 additions and 2159 deletions
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3
.editorconfig
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@ -15,6 +15,9 @@ indent_size = 4
[Makefile]
indent_style = tab
[scripts/*.mk]
indent_style = tab
[prompts/*.txt]
insert_final_newline = unset

15
.github/workflows/build.yml vendored
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@ -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

1
.gitignore vendored
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@ -101,3 +101,4 @@ poetry.toml
/tests/test-tokenizer-1-llama
/tests/test-tokenizer-1-bpe
/tests/test-rope
/tests/test-backend-ops

121
CMakeLists.txt
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@ -397,57 +397,102 @@ 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)
(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)
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)
elseif (CCID STREQUAL "GNU")
set(C_FLAGS -Wdouble-promotion)
set(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)
if (CCVER VERSION_GREATER_EQUAL 7.1.0)
set(CXX_FLAGS ${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)
if (CCVER VERSION_GREATER_EQUAL 8.1.0)
set(CXX_FLAGS ${CXX_FLAGS} -Wextra-semi)
endif()
endif()
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()
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}>")
endif()
if (LLAMA_CUBLAS)
set(CUDA_FLAGS ${CXX_FLAGS} -use_fast_math)
if (NOT MSVC)
set(cuda_flags -Wno-pedantic)
endif()
set(cuda_flags ${cxx_flags} -use_fast_math ${cuda_flags})
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})
set(CUDA_FLAGS ${CUDA_FLAGS} -Wno-pedantic)
endif()
add_compile_options("$<$<COMPILE_LANGUAGE:CUDA>:${cuda_flags}>")
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()
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)
@ -471,6 +516,7 @@ 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)
@ -593,6 +639,11 @@ else()
message(STATUS "Unknown architecture")
endif()
if (MINGW)
# Target Windows 8 for PrefetchVirtualMemory
add_compile_definitions(_WIN32_WINNT=0x602)
endif()
#
# POSIX conformance
#

130
Makefile
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@ -8,7 +8,8 @@ BUILD_TARGETS = \
TEST_TARGETS = \
tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama \
tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope
tests/test-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 '' '$(findstring Apple,$(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)
@ -121,8 +108,8 @@ 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
HOST_CXXFLAGS += -Ofast
MK_NVCCFLAGS += -O3
else
MK_CFLAGS += -O3
MK_CXXFLAGS += -O3
@ -219,30 +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) $(LDFLAGS) -Wl,-v 2>&1))'
MK_CPPFLAGS += -DHAVE_BUGGY_APPLE_LINKER
@ -294,7 +257,7 @@ ifndef RISCV
ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
# Use all CPU extensions that are available:
MK_CFLAGS += -march=native -mtune=native
MK_HOST_CXXFLAGS += -march=native -mtune=native
HOST_CXXFLAGS += -march=native -mtune=native
# Usage AVX-only
#MK_CFLAGS += -mfma -mf16c -mavx
@ -305,12 +268,15 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
#MK_CXXFLAGS += -mssse3
endif
ifneq '' '$(findstring mingw,$(shell $(CC) -dumpmachine))'
# The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves.
# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412
# https://github.com/ggerganov/llama.cpp/issues/2922
ifneq '' '$(findstring mingw,$(shell $(CC) -dumpmachine))'
MK_CFLAGS += -Xassembler -muse-unaligned-vector-move
MK_CXXFLAGS += -Xassembler -muse-unaligned-vector-move
# Target Windows 8 for PrefetchVirtualMemory
MK_CPPFLAGS += -D_WIN32_WINNT=0x602
endif
ifneq ($(filter aarch64%,$(UNAME_M)),)
@ -394,61 +360,64 @@ ifdef LLAMA_CUBLAS
MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(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
@ -510,16 +479,22 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h
$(CC) $(CFLAGS) -c $< -o $@
endif # LLAMA_MPI
GF_CC := $(CC)
include scripts/get-flags.mk
# 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 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)
# save CXXFLAGS before we add host-only options
NVCCFLAGS := $(NVCCFLAGS) $(CXXFLAGS) $(CUDA_CXXFLAGS) -Wno-pedantic -Xcompiler "$(HOST_CXXFLAGS)"
override CXXFLAGS += $(HOST_CXXFLAGS)
# 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
@ -729,16 +704,16 @@ tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(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)
@ -746,3 +721,6 @@ tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS)
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)

13
README.md
View File

@ -10,6 +10,8 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
### Hot topics
- Added Mixtral support: https://github.com/ggerganov/llama.cpp/pull/4406
- **llama.h API change for handling KV cache offloading and data type: https://github.com/ggerganov/llama.cpp/pull/4309**
- Using `llama.cpp` with AWS instances: https://github.com/ggerganov/llama.cpp/discussions/4225
- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
- Collecting Apple Silicon performance stats: https://github.com/ggerganov/llama.cpp/discussions/4167
@ -95,7 +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:**

50
common/common.cpp
View File

@ -278,8 +278,6 @@ 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;
@ -510,6 +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") {
@ -652,6 +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") {
@ -790,6 +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");
@ -858,8 +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");
@ -900,6 +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");
@ -1015,6 +1028,29 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
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();
@ -1024,7 +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;
@ -1035,6 +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;
}
@ -1447,7 +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);

5
common/common.h
View File

@ -100,7 +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
@ -125,6 +124,10 @@ 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

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

6
common/sampling.cpp
View File

@ -113,13 +113,15 @@ std::string llama_sampling_order_print(const llama_sampling_params & params) {
default : break;
}
}
} else result += "-> mirostat ";
} else {
result += "-> mirostat ";
}
return result;
}
// no reasons to expose this function in header
void sampler_queue(
static void sampler_queue(
struct llama_context * ctx_main,
const llama_sampling_params & params,
llama_token_data_array & cur_p,

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];

21
convert-hf-to-gguf.py
View File

@ -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):
@ -170,6 +180,8 @@ class Model:
return StableLMModel
if model_architecture == "QWenLMHeadModel":
return QwenModel
if model_architecture == "MixtralForCausalLM":
return MixtralModel
return Model
def _is_model_safetensors(self) -> bool:
@ -207,6 +219,8 @@ class Model:
return gguf.MODEL_ARCH.STABLELM
if arch == "QWenLMHeadModel":
return gguf.MODEL_ARCH.QWEN
if arch == "MixtralForCausalLM":
return gguf.MODEL_ARCH.LLAMA
raise NotImplementedError(f'Architecture "{arch}" not supported!')
@ -837,6 +851,11 @@ class StableLMModel(Model):
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):

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)

367
convert.py
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
#
@ -158,7 +160,9 @@ class Params:
n_ff: int
n_head: int
n_head_kv: int
f_norm_eps: float
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 = 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()}
for i, _ in enumerate(tokenizer):
yield reverse_vocab[i], 0.0, 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)
text = reverse_vocab[i].encode("utf-8")
yield text, self.get_token_score(i), self.get_token_type(i)
def get_token_type(self, token_id: int) -> gguf.TokenType:
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:
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, gguf.TokenType.USER_DEFINED
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)
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)
@ -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"
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
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")
return path2
if path3.exists():
return path3
print(f"Loading vocab file '{path}', type '{vocabtype}'")
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}")

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]);

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++){

2
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],

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");

8
examples/llava/clip.cpp
View File

@ -514,7 +514,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
ctx_size += padded_size;
if (verbosity >= 3) {
printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, padded_size=%zu, offset=%zu\n", __func__, i,
cur->n_dims, cur->name, tensor_size, padded_size, offset);
ggml_n_dims(cur), cur->name, tensor_size, padded_size, offset);
}
}
}
@ -739,7 +739,7 @@ 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++) {
@ -962,7 +962,7 @@ 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;
@ -1035,7 +1035,7 @@ 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);
}

2
examples/llava/convert-image-encoder-to-gguf.py
View File

@ -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.
"""

2
examples/lookahead/README.md
View File

@ -1,6 +1,6 @@
# llama.cpp/examples/lookahead
Demonstartion of lookahead decoding technique:
Demonstration of lookahead decoding technique:
https://lmsys.org/blog/2023-11-21-lookahead-decoding/

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);

2
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:*

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>
`

113
examples/server/server.cpp
View File

@ -36,6 +36,7 @@ using json = nlohmann::json;
struct server_params
{
std::string hostname = "127.0.0.1";
std::string api_key;
std::string public_path = "examples/server/public";
int32_t port = 8080;
int32_t read_timeout = 600;
@ -376,7 +377,6 @@ struct llama_client_slot
int32_t num_prompt_tokens = 0;
int32_t num_prompt_tokens_processed = 0;
int32_t multibyte_pending = 0;
json prompt;
std::string generated_text;
@ -425,7 +425,6 @@ struct llama_client_slot
stopped_word = false;
stopped_limit = false;
stopping_word = "";
multibyte_pending = 0;
n_past = 0;
sent_count = 0;
sent_token_probs_index = 0;
@ -992,35 +991,36 @@ struct llama_server_context
slot.generated_text += token_str;
slot.has_next_token = true;
if (slot.multibyte_pending > 0)
// check if there is incomplete UTF-8 character at the end
bool incomplete = false;
for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
{
slot.multibyte_pending -= token_str.size();
unsigned char c = slot.generated_text[slot.generated_text.size() - i];
if ((c & 0xC0) == 0x80)
{
// continuation byte: 10xxxxxx
continue;
}
else if (token_str.size() == 1)
{
const char c = token_str[0];
// 2-byte characters: 110xxxxx 10xxxxxx
if ((c & 0xE0) == 0xC0)
{
slot.multibyte_pending = 1;
// 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
// 2-byte character: 110xxxxx ...
incomplete = i < 2;
}
else if ((c & 0xF0) == 0xE0)
{
slot.multibyte_pending = 2;
// 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
// 3-byte character: 1110xxxx ...
incomplete = i < 3;
}
else if ((c & 0xF8) == 0xF0)
{
slot.multibyte_pending = 3;
}
else
{
slot.multibyte_pending = 0;
// 4-byte character: 11110xxx ...
incomplete = i < 4;
}
// else 1-byte character or invalid byte
break;
}
if (slot.multibyte_pending == 0)
if (!incomplete)
{
size_t pos = std::min(slot.sent_count, slot.generated_text.size());
const std::string str_test = slot.generated_text.substr(pos);
@ -1055,7 +1055,7 @@ struct llama_server_context
}
}
if (slot.multibyte_pending > 0 && !slot.has_next_token)
if (incomplete)
{
slot.has_next_token = true;
}
@ -1954,6 +1954,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
printf(" --port PORT port to listen (default (default: %d)\n", sparams.port);
printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n");
printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel);
@ -2003,6 +2004,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
sparams.public_path = argv[i];
}
else if (arg == "--api-key")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
sparams.api_key = argv[i];
}
else if (arg == "--timeout" || arg == "-to")
{
if (++i >= argc)
@ -2108,10 +2118,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
params.yarn_beta_slow = std::stof(argv[i]);
}
else if (arg == "--memory-f32" || arg == "--memory_f32")
{
params.memory_f16 = false;
}
else if (arg == "--threads" || arg == "-t")
{
if (++i >= argc)
@ -2386,7 +2392,9 @@ json oaicompat_completion_params_parse(
llama_params["__oaicompat"] = true;
// Map OpenAI parameters to llama.cpp parameters
llama_params["model"] = json_value(body, "model", std::string("uknown"));
llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
llama_params["cache_prompt"] = json_value(body, "cache_prompt", false);
llama_params["temperature"] = json_value(body, "temperature", 0.8);
llama_params["top_k"] = json_value(body, "top_k", 40);
llama_params["top_p"] = json_value(body, "top_p", 0.95);
@ -2672,6 +2680,32 @@ int main(int argc, char **argv)
httplib::Server svr;
// Middleware for API key validation
auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool {
// If API key is not set, skip validation
if (sparams.api_key.empty()) {
return true;
}
// Check for API key in the header
auto auth_header = req.get_header_value("Authorization");
std::string prefix = "Bearer ";
if (auth_header.substr(0, prefix.size()) == prefix) {
std::string received_api_key = auth_header.substr(prefix.size());
if (received_api_key == sparams.api_key) {
return true; // API key is valid
}
}
// API key is invalid or not provided
res.set_content("Unauthorized: Invalid API Key", "text/plain");
res.status = 401; // Unauthorized
LOG_WARNING("Unauthorized: Invalid API Key", {});
return false;
};
svr.set_default_headers({{"Server", "llama.cpp"},
{"Access-Control-Allow-Origin", "*"},
{"Access-Control-Allow-Headers", "content-type"}});
@ -2714,8 +2748,11 @@ int main(int argc, char **argv)
res.set_content(data.dump(), "application/json");
});
svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res)
svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
{
if (!validate_api_key(req, res)) {
return;
}
json data = json::parse(req.body);
const int task_id = llama.request_completion(data, false, false, -1);
if (!json_value(data, "stream", false)) {
@ -2802,8 +2839,11 @@ int main(int argc, char **argv)
});
// TODO: add mount point without "/v1" prefix -- how?
svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req, httplib::Response &res)
svr.Post("/v1/chat/completions", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
{
if (!validate_api_key(req, res)) {
return;
}
json data = oaicompat_completion_params_parse(json::parse(req.body));
const int task_id = llama.request_completion(data, false, false, -1);
@ -2872,8 +2912,11 @@ int main(int argc, char **argv)
}
});
svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
{
if (!validate_api_key(req, res)) {
return;
}
json data = json::parse(req.body);
const int task_id = llama.request_completion(data, true, false, -1);
if (!json_value(data, "stream", false)) {
@ -3008,11 +3051,15 @@ int main(int argc, char **argv)
svr.set_error_handler([](const httplib::Request &, httplib::Response &res)
{
if (res.status == 401)
{
res.set_content("Unauthorized", "text/plain");
}
if (res.status == 400)
{
res.set_content("Invalid request", "text/plain");
}
else if (res.status != 500)
else if (res.status == 404)
{
res.set_content("File Not Found", "text/plain");
res.status = 404;
@ -3035,11 +3082,15 @@ int main(int argc, char **argv)
// to make it ctrl+clickable:
LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
LOG_INFO("HTTP server listening", {
{"hostname", sparams.hostname},
{"port", sparams.port},
});
std::unordered_map<std::string, std::string> log_data;
log_data["hostname"] = sparams.hostname;
log_data["port"] = std::to_string(sparams.port);
if (!sparams.api_key.empty()) {
log_data["api_key"] = "api_key: ****" + sparams.api_key.substr(sparams.api_key.length() - 4);
}
LOG_INFO("HTTP server listening", log_data);
// run the HTTP server in a thread - see comment below
std::thread t([&]()
{

2
examples/speculative/README.md
View File

@ -1,6 +1,6 @@
# llama.cpp/examples/speculative
Demonstartion of speculative decoding and tree-based speculative decoding techniques
Demonstration of speculative decoding and tree-based speculative decoding techniques
More info:

15
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);
if (!params.use_color) {
printf("%s", token_str.c_str());
fflush(stdout);
}
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);

49
ggml-alloc.c
View File

@ -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

63
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 (*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;
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

749
ggml-backend.c
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75
ggml-backend.h
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@ -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_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 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

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ggml-cuda.cu
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10
ggml-cuda.h
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@ -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
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@ -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
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@ -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
}

1179
ggml-metal.m
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2435
ggml-metal.metal
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4
ggml-quants.c
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@ -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);

981
ggml.c
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92
ggml.h
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@ -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
@ -283,6 +283,20 @@
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
@ -381,6 +395,7 @@ extern "C" {
GGML_OP_GROUP_NORM,
GGML_OP_MUL_MAT,
GGML_OP_MUL_MAT_ID,
GGML_OP_OUT_PROD,
GGML_OP_SCALE,
@ -407,8 +422,10 @@ 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,
@ -448,7 +465,8 @@ 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 {
@ -484,7 +502,6 @@ 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)
@ -516,7 +533,7 @@ 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);
@ -621,16 +638,22 @@ 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 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);
@ -641,6 +664,11 @@ 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);
@ -773,6 +801,9 @@ 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,
@ -937,15 +968,14 @@ 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);
@ -1027,6 +1057,16 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
// 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
@ -1234,6 +1274,7 @@ 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,
@ -1520,6 +1561,32 @@ 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,
@ -1581,7 +1648,6 @@ extern "C" {
int kh);
// used in sam
GGML_API struct ggml_tensor * ggml_add_rel_pos(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -1756,7 +1822,7 @@ 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
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@ -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:

16
gguf-py/gguf/constants.py
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@ -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"
@ -111,10 +113,14 @@ 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()
@ -154,10 +160,14 @@ 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]] = {
@ -172,10 +182,14 @@ 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,

6
gguf-py/gguf/gguf_writer.py
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@ -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)

27
gguf-py/gguf/tensor_mapping.py
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@ -149,6 +149,11 @@ 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
@ -164,6 +169,11 @@ class TensorNameMap:
"transformer.h.{bid}.mlp.w1", # qwen
),
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
@ -171,6 +181,11 @@ class TensorNameMap:
"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
@ -185,6 +200,11 @@ class TensorNameMap:
"language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon
),
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: (
"language_model.encoder.layers.{bid}.self_attention.q_layernorm",
),
@ -213,10 +233,13 @@ 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)
# 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)
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:

2
gguf-py/pyproject.toml
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@ -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 = [

767
llama.cpp
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10
llama.h
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@ -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.
@ -211,11 +212,14 @@ extern "C" {
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 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

2
requirements.txt
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@ -1,3 +1,5 @@
numpy==1.24.4
sentencepiece==0.1.98
transformers>=4.34.0
gguf>=0.1.0
protobuf>=4.21.0

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

1
scripts/sync-ggml.sh
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@ -22,3 +22,4 @@ 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-backend-ops.cpp ./tests/test-backend-ops.cpp

8
tests/CMakeLists.txt
View File

@ -22,13 +22,17 @@ 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_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)
@ -38,10 +42,12 @@ llama_test_executable(test-tokenizer-1-gpt-neox test-tokenizer-1-bpe.cpp ${CMAKE
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)

1688
tests/test-backend-ops.cpp Normal file
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File diff suppressed because it is too large Load Diff

2
tests/test-grad0.cpp
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@ -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");