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Merge remote-tracking branch 'origin/master' into 0cc4m/vulkan-improvements

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0cc4m 2024-03-18 15:37:18 +01:00
commit f315402d9b
99 changed files with 12682 additions and 8505 deletions
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96
.github/workflows/build.yml vendored
View file

@ -48,6 +48,28 @@ jobs:
CC=gcc-8 make tests -j $(nproc)
make test -j $(nproc)
ubuntu-focal-make-curl:
runs-on: ubuntu-20.04
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential gcc-8 libcurl4-openssl-dev
- name: Build
id: make_build
env:
LLAMA_FATAL_WARNINGS: 1
LLAMA_CURL: 1
run: |
CC=gcc-8 make -j $(nproc)
ubuntu-latest-cmake:
runs-on: ubuntu-latest
@ -76,40 +98,40 @@ jobs:
cd build
ctest -L main --verbose --timeout 900
ubuntu-latest-cmake-sanitizer:
runs-on: ubuntu-latest
continue-on-error: true
strategy:
matrix:
sanitizer: [ADDRESS, THREAD, UNDEFINED]
build_type: [Debug, Release]
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
- name: Test
id: cmake_test
run: |
cd build
ctest -L main --verbose --timeout 900
# ubuntu-latest-cmake-sanitizer:
# runs-on: ubuntu-latest
#
# continue-on-error: true
#
# strategy:
# matrix:
# sanitizer: [ADDRESS, THREAD, UNDEFINED]
# build_type: [Debug, Release]
#
# steps:
# - name: Clone
# id: checkout
# uses: actions/checkout@v3
#
# - name: Dependencies
# id: depends
# run: |
# sudo apt-get update
# sudo apt-get install build-essential
#
# - name: Build
# id: cmake_build
# run: |
# mkdir build
# cd build
# cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
# cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
#
# - name: Test
# id: cmake_test
# run: |
# cd build
# ctest -L main --verbose --timeout 900
ubuntu-latest-cmake-mpi:
runs-on: ubuntu-latest
@ -333,6 +355,7 @@ jobs:
mkdir build
cd build
cmake -G Xcode .. \
-DLLAMA_METAL_EMBED_LIBRARY=ON \
-DLLAMA_BUILD_EXAMPLES=OFF \
-DLLAMA_BUILD_TESTS=OFF \
-DLLAMA_BUILD_SERVER=OFF \
@ -361,6 +384,7 @@ jobs:
mkdir build
cd build
cmake -G Xcode .. \
-DLLAMA_METAL_EMBED_LIBRARY=ON \
-DLLAMA_BUILD_EXAMPLES=OFF \
-DLLAMA_BUILD_TESTS=OFF \
-DLLAMA_BUILD_SERVER=OFF \
@ -425,6 +449,8 @@ jobs:
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON'
- build: 'vulkan'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON'
- build: 'arm64'
defines: '-A ARM64 -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
steps:
- name: Clone
@ -520,7 +546,7 @@ jobs:
- name: Test
id: cmake_test
# not all machines have native AVX-512
if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }}
if: ${{ matrix.build != 'arm64' && matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }}
run: |
cd build
ctest -L main -C Release --verbose --timeout 900

22
.github/workflows/close-issue.yml vendored Normal file
View file

@ -0,0 +1,22 @@
name: Close inactive issues
on:
schedule:
- cron: "42 0 * * *"
jobs:
close-issues:
runs-on: ubuntu-latest
permissions:
issues: write
pull-requests: write
steps:
- uses: actions/stale@v5
with:
days-before-issue-stale: 30
days-before-issue-close: 14
stale-issue-label: "stale"
close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale."
days-before-pr-stale: -1
days-before-pr-close: -1
operations-per-run: 1000
repo-token: ${{ secrets.GITHUB_TOKEN }}

83
.github/workflows/server.yml vendored
View file

@ -24,18 +24,15 @@ jobs:
strategy:
matrix:
sanitizer: [ADDRESS, THREAD, UNDEFINED]
build_type: [Debug, Release]
# TODO: temporary disabled due to linux kernel issues
#sanitizer: [ADDRESS, THREAD, UNDEFINED]
sanitizer: [UNDEFINED]
build_type: [Debug]
include:
- build_type: Release
sanitizer: ""
exclude:
- build_type: Release
sanitizer: ADDRESS
- build_type: Release
sanitizer: THREAD
- build_type: Release
sanitizer: UNDEFINED
disabled_on_pr: true
fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
container:
image: ubuntu:latest
@ -47,6 +44,8 @@ jobs:
- name: Clone
id: checkout
uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Dependencies
id: depends
@ -58,8 +57,8 @@ jobs:
cmake \
python3-pip \
wget \
psmisc \
language-pack-en
language-pack-en \
libcurl4-openssl-dev
- name: Build
id: cmake_build
@ -69,6 +68,7 @@ jobs:
cmake .. \
-DLLAMA_NATIVE=OFF \
-DLLAMA_BUILD_SERVER=ON \
-DLLAMA_CURL=ON \
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server
@ -80,13 +80,72 @@ jobs:
- name: Tests
id: server_integration_tests
if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
run: |
cd examples/server/tests
PORT=8888 ./tests.sh
- name: Slow tests
id: server_integration_tests_slow
if: ${{ github.event.schedule != '' && matrix.build_type == 'Release' || github.event.inputs.slow_tests == 'true' }}
if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
run: |
cd examples/server/tests
PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow
server-windows:
runs-on: windows-latest
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
with:
fetch-depth: 0
- name: libCURL
id: get_libcurl
env:
CURL_VERSION: 8.6.0_6
run: |
curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip"
mkdir $env:RUNNER_TEMP/libcurl
tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake .. -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server
- name: Python setup
id: setup_python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Tests dependencies
id: test_dependencies
run: |
pip install -r examples/server/tests/requirements.txt
- name: Copy Libcurl
id: prepare_libcurl
run: |
cp $env:RUNNER_TEMP/libcurl/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll
- name: Tests
id: server_integration_tests
if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
run: |
cd examples/server/tests
behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
- name: Slow tests
id: server_integration_tests_slow
if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
run: |
cd examples/server/tests
behave.exe --stop --no-skipped --no-capture --tags slow

20
.github/workflows/tidy-post.yml vendored
View file

@ -1,20 +0,0 @@
name: clang-tidy review post comments
on:
workflow_dispatch:
workflows: ["clang-tidy-review"]
types:
- completed
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: ZedThree/clang-tidy-review/post@v0.13.0
# lgtm_comment_body, max_comments, and annotations need to be set on the posting workflow in a split setup
with:
# adjust options as necessary
lgtm_comment_body: ''
annotations: false
max_comments: 25

23
.github/workflows/tidy-review.yml vendored
View file

@ -1,23 +0,0 @@
name: clang-tidy-review
on:
pull_request:
branches:
- master
jobs:
clang-tidy-review:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: ZedThree/clang-tidy-review@v0.13.0
id: review
with:
lgtm_comment_body: ''
build_dir: build
cmake_command: cmake . -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=on
split_workflow: true
- uses: ZedThree/clang-tidy-review/upload@v0.13.0

3
.gitignore vendored
View file

@ -25,6 +25,8 @@
.vscode/
.idea/
ggml-metal-embed.metal
lcov-report/
gcovr-report/
@ -45,6 +47,7 @@ models-mnt
/embedding
/gguf
/gguf-llama-simple
/gritlm
/imatrix
/infill
/libllama.so

95
CMakeLists.txt
View file

@ -99,6 +99,7 @@ option(LLAMA_CUDA_F16 "llama: use 16 bit floats for some
set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
"llama: max. batch size for using peer access")
option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF)
option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF)
option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
@ -116,7 +117,9 @@ option(LLAMA_MPI "llama: use MPI"
option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF)
option(LLAMA_SYCL "llama: use SYCL" OFF)
option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF)
set(LLAMA_SYCL_TARGET "INTEL" CACHE STRING "llama: sycl target device")
option(LLAMA_CPU_HBM "llama: use memkind for CPU HBM" OFF)
set(LLAMA_SCHED_MAX_COPIES "4" CACHE STRING "llama: max input copies for pipeline parallelism")
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
@ -146,6 +149,8 @@ set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads REQUIRED)
include(CheckCXXCompilerFlag)
add_compile_definitions(GGML_SCHED_MAX_COPIES=${LLAMA_SCHED_MAX_COPIES})
# enable libstdc++ assertions for debug builds
if (CMAKE_SYSTEM_NAME MATCHES "Linux")
add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
@ -196,63 +201,70 @@ if (LLAMA_METAL)
add_compile_definitions(GGML_METAL_NDEBUG)
endif()
# get full path to the file
#add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
# copy ggml-metal.metal to bin directory
# copy ggml-common.h and ggml-metal.metal to bin directory
configure_file(ggml-common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h COPYONLY)
configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
if (LLAMA_METAL_EMBED_LIBRARY)
enable_language(ASM)
add_compile_definitions(GGML_METAL_EMBED_LIBRARY)
set(METALLIB_SOURCE "${CMAKE_SOURCE_DIR}/ggml-metal.metal")
set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/ggml-common.h")
set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated")
set(EMBED_METALLIB_ASSEMBLY "${CMAKE_BINARY_DIR}/autogenerated/ggml-embed-metallib.s")
# merge ggml-common.h and ggml-metal.metal into a single file
set(METALLIB_EMBED_ASM "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.s")
set(METALLIB_SOURCE_EMBED "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal")
add_custom_command(
OUTPUT ${EMBED_METALLIB_ASSEMBLY}
COMMAND echo ".section __DATA,__ggml_metallib" > ${EMBED_METALLIB_ASSEMBLY}
COMMAND echo ".globl _ggml_metallib_start" >> ${EMBED_METALLIB_ASSEMBLY}
COMMAND echo "_ggml_metallib_start:" >> ${EMBED_METALLIB_ASSEMBLY}
COMMAND echo ".incbin \\\"${METALLIB_SOURCE}\\\"" >> ${EMBED_METALLIB_ASSEMBLY}
COMMAND echo ".globl _ggml_metallib_end" >> ${EMBED_METALLIB_ASSEMBLY}
COMMAND echo "_ggml_metallib_end:" >> ${EMBED_METALLIB_ASSEMBLY}
DEPENDS ${METALLIB_SOURCE}
OUTPUT ${METALLIB_EMBED_ASM}
COMMAND echo "Embedding Metal library"
COMMAND sed -e '/\#include \"ggml-common.h\"/r ${METALLIB_COMMON}' -e '/\#include \"ggml-common.h\"/d' < ${METALLIB_SOURCE} > ${METALLIB_SOURCE_EMBED}
COMMAND echo ".section __DATA,__ggml_metallib" > ${METALLIB_EMBED_ASM}
COMMAND echo ".globl _ggml_metallib_start" >> ${METALLIB_EMBED_ASM}
COMMAND echo "_ggml_metallib_start:" >> ${METALLIB_EMBED_ASM}
COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM}
COMMAND echo ".globl _ggml_metallib_end" >> ${METALLIB_EMBED_ASM}
COMMAND echo "_ggml_metallib_end:" >> ${METALLIB_EMBED_ASM}
DEPENDS ggml-metal.metal ggml-common.h
COMMENT "Generate assembly for embedded Metal library"
)
set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${EMBED_METALLIB_ASSEMBLY})
endif()
if (LLAMA_METAL_SHADER_DEBUG)
# custom command to do the following:
# xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
# xcrun -sdk macosx metallib ggml-metal.air -o default.metallib
#
# note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
# disabling fast math is needed in order to pass tests/test-backend-ops
# note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
# note: unfortunately, we have to call it default.metallib instead of ggml.metallib
# ref: https://github.com/ggerganov/whisper.cpp/issues/1720
set(XC_FLAGS -fno-fast-math -fno-inline -g)
if (LLAMA_QKK_64)
set(XC_FLAGS ${XC_FLAGS} -DQK_K=64)
set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${METALLIB_EMBED_ASM})
else()
if (LLAMA_METAL_SHADER_DEBUG)
# custom command to do the following:
# xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
# xcrun -sdk macosx metallib ggml-metal.air -o default.metallib
#
# note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
# disabling fast math is needed in order to pass tests/test-backend-ops
# note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
# note: unfortunately, we have to call it default.metallib instead of ggml.metallib
# ref: https://github.com/ggerganov/whisper.cpp/issues/1720
set(XC_FLAGS -fno-fast-math -fno-inline -g)
else()
set(XC_FLAGS -O3)
endif()
add_custom_command(
OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
COMMAND xcrun -sdk macosx metallib ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
DEPENDS ggml-metal.metal
COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal
DEPENDS ggml-metal.metal ggml-common.h
COMMENT "Compiling Metal kernels"
)
)
add_custom_target(
ggml-metal ALL
DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
)
endif()
)
endif() # LLAMA_METAL_EMBED_LIBRARY
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
${FOUNDATION_LIBRARY}
@ -533,6 +545,10 @@ if (LLAMA_HIPBLAS)
endif()
if (LLAMA_SYCL)
if (NOT LLAMA_SYCL_TARGET MATCHES "^(INTEL|NVIDIA)$")
message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL or NVIDIA")
endif()
if ( NOT DEFINED ENV{ONEAPI_ROOT})
message(FATAL_ERROR "Not detect ENV {ONEAPI_ROOT}, please install oneAPI & source it, like: source /opt/intel/oneapi/setvars.sh")
endif()
@ -554,6 +570,9 @@ if (LLAMA_SYCL)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib")
if (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
endif()
set(GGML_HEADERS_SYCL ggml-sycl.h)
set(GGML_SOURCES_SYCL ggml-sycl.cpp)
@ -561,7 +580,11 @@ if (LLAMA_SYCL)
if (WIN32)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib)
else()
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
if (LLAMA_SYCL_TARGET STREQUAL "INTEL")
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
elseif (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl pthread m dl onemkl)
endif()
endif()
endif()
@ -1128,6 +1151,8 @@ endif()
add_library(llama
llama.cpp
llama.h
unicode.h
unicode.cpp
)
target_include_directories(llama PUBLIC .)

47
Makefile
View file

@ -2,7 +2,7 @@
BUILD_TARGETS = \
main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \
speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey tests/test-c.o
speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o
# Binaries only useful for tests
TEST_TARGETS = \
@ -167,6 +167,10 @@ ifeq ($(UNAME_S),OpenBSD)
MK_CPPFLAGS += -D_BSD_SOURCE
endif
ifdef LLAMA_SCHED_MAX_COPIES
MK_CPPFLAGS += -DGGML_SCHED_MAX_COPIES=$(LLAMA_SCHED_MAX_COPIES)
endif
ifdef LLAMA_DEBUG
MK_CFLAGS += -O0 -g
MK_CXXFLAGS += -O0 -g
@ -201,6 +205,10 @@ ifdef LLAMA_SERVER_VERBOSE
MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
endif
ifdef LLAMA_SERVER_SSL
MK_CPPFLAGS += -DCPPHTTPLIB_OPENSSL_SUPPORT
MK_LDFLAGS += -lssl -lcrypto
endif
ifdef LLAMA_CODE_COVERAGE
MK_CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase ''
@ -449,7 +457,7 @@ endif # LLAMA_CUDA_PEER_MAX_BATCH_SIZE
ifdef LLAMA_CUDA_CCBIN
MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
endif
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml-common.h
ifdef JETSON_EOL_MODULE_DETECT
$(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
else
@ -545,19 +553,20 @@ endif
endif # LLAMA_METAL
ifdef LLAMA_METAL
ggml-metal.o: ggml-metal.m ggml-metal.h
ggml-metal.o: ggml-metal.m ggml-metal.h ggml.h
$(CC) $(CFLAGS) -c $< -o $@
ifdef LLAMA_METAL_EMBED_LIBRARY
ggml-metal-embed.o: ggml-metal.metal
ggml-metal-embed.o: ggml-metal.metal ggml-common.h
@echo "Embedding Metal library"
@sed -e '/#include "ggml-common.h"/r ggml-common.h' -e '/#include "ggml-common.h"/d' < ggml-metal.metal > ggml-metal-embed.metal
$(eval TEMP_ASSEMBLY=$(shell mktemp))
@echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY)
@echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY)
@echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY)
@echo ".incbin \"$<\"" >> $(TEMP_ASSEMBLY)
@echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY)
@echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY)
@echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY)
@echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY)
@echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY)
@echo ".incbin \"ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)
@echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY)
@echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY)
@$(AS) $(TEMP_ASSEMBLY) -o $@
@rm -f ${TEMP_ASSEMBLY}
endif
@ -586,6 +595,11 @@ include scripts/get-flags.mk
CUDA_CXXFLAGS := $(BASE_CXXFLAGS) $(GF_CXXFLAGS) -Wno-pedantic
endif
ifdef LLAMA_CURL
override CXXFLAGS := $(CXXFLAGS) -DLLAMA_USE_CURL
override LDFLAGS := $(LDFLAGS) -lcurl
endif
#
# Print build information
#
@ -626,12 +640,15 @@ ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h
$(CC) $(CFLAGS) -c $< -o $@
ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h
ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h ggml-common.h
$(CC) $(CFLAGS) -c $< -o $@
OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o
unicode.o: unicode.cpp unicode.h
$(CXX) $(CXXFLAGS) -c $< -o $@
llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o
llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
$(CXX) $(CXXFLAGS) -c $< -o $@
COMMON_H_DEPS = common/common.h common/sampling.h common/log.h
@ -720,6 +737,10 @@ embedding: examples/embedding/embedding.cpp ggml.o llama.o $(C
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
gritlm: examples/gritlm/gritlm.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

1
Package.swift
View file

@ -31,6 +31,7 @@ let package = Package(
sources: [
"ggml.c",
"llama.cpp",
"unicode.cpp",
"ggml-alloc.c",
"ggml-backend.c",
"ggml-quants.c",

26
README-sycl.md
View file

@ -73,6 +73,29 @@ For iGPU, please make sure the shared memory from host memory is enough. For lla
For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+.
## Nvidia GPU
### Verified
|Intel GPU| Status | Verified Model|
|-|-|-|
|Ampere Series| Support| A100|
### oneMKL
The current oneMKL release does not contain the oneMKL cuBlas backend.
As a result for Nvidia GPU's oneMKL must be built from source.
```
git clone https://github.com/oneapi-src/oneMKL
cd oneMKL
mkdir build
cd build
cmake -G Ninja .. -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON
ninja
// Add paths as necessary
```
## Docker
Note:
@ -186,6 +209,9 @@ source /opt/intel/oneapi/setvars.sh
# Or, for FP32:
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# For Nvidia GPUs
cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# Build example/main only
#cmake --build . --config Release --target main

40
README.md
View file

@ -10,16 +10,17 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
### Recent API changes
- [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017
- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328
- [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796
- [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849
### Hot topics
- The `api_like_OAI.py` script has been removed - use `server` instead ([#5766](https://github.com/ggerganov/llama.cpp/issues/5766#issuecomment-1969037761))
- Support for chat templates: [Wiki (contributions welcome)](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
- Support for Gemma models: https://github.com/ggerganov/llama.cpp/pull/5631
- Non-linear quantization IQ4_NL: https://github.com/ggerganov/llama.cpp/pull/5590
- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
- Multi-GPU pipeline parallelizm support https://github.com/ggerganov/llama.cpp/pull/6017
- Looking for contributions to add Deepseek support: https://github.com/ggerganov/llama.cpp/issues/5981
- Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962
- Initial Mamba support has been added: https://github.com/ggerganov/llama.cpp/pull/5328
----
@ -110,6 +111,8 @@ Typically finetunes of the base models below are supported as well.
- [x] [InternLM2](https://huggingface.co/models?search=internlm2)
- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
- [x] [Gemma](https://ai.google.dev/gemma)
- [x] [Mamba](https://github.com/state-spaces/mamba)
- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
**Multimodal models:**
@ -131,6 +134,7 @@ Typically finetunes of the base models below are supported as well.
- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
- JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp)
- JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm)
- Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama)
- Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
- Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
- Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
@ -902,6 +906,9 @@ First, install the essential packages for termux:
pkg install clang wget git cmake
```
Second, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake:
You can execute the following commands on your computer to avoid downloading the NDK to your mobile. Of course, you can also do this in Termux.
```
$ mkdir build-android
$ cd build-android
@ -910,7 +917,28 @@ $ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROI
$ make
```
Install [termux](https://termux.dev/) on your device and run `termux-setup-storage` to get access to your SD card.
Finally, copy the `llama` binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone:
Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
```
$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
$cd /data/data/com.termux/files/home/bin
$chmod +x ./*
```
Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
```
$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/
```
Now, you can start chatting:
```
$cd /data/data/com.termux/files/home/bin
$./main -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml
```
Here is a demo of an interactive session running on Pixel 5 phone:
https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4

15
build.zig
View file

@ -115,6 +115,7 @@ pub fn build(b: *std.build.Builder) !void {
const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c");
const ggml_backend = make.obj("ggml-backend", "ggml-backend.c");
const ggml_quants = make.obj("ggml-quants", "ggml-quants.c");
const unicode = make.obj("unicode", "unicode.cpp");
const llama = make.obj("llama", "llama.cpp");
const buildinfo = make.obj("common", "common/build-info.cpp");
const common = make.obj("common", "common/common.cpp");
@ -125,14 +126,14 @@ pub fn build(b: *std.build.Builder) !void {
const clip = make.obj("clip", "examples/llava/clip.cpp");
const llava = make.obj("llava", "examples/llava/llava.cpp");
_ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, console, grammar_parser });
_ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
_ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
_ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
_ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });
_ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });
_ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, sampling, console, grammar_parser });
_ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo });
_ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo });
_ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo });
_ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, train });
_ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, train });
const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip, llava });
const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, sampling, grammar_parser, clip, llava });
if (server.target.isWindows()) {
server.linkSystemLibrary("ws2_32");
}

13
common/CMakeLists.txt
View file

@ -68,6 +68,17 @@ if (BUILD_SHARED_LIBS)
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
endif()
set(LLAMA_COMMON_EXTRA_LIBS build_info)
# Use curl to download model url
if (LLAMA_CURL)
find_package(CURL REQUIRED)
add_definitions(-DLLAMA_USE_CURL)
include_directories(${CURL_INCLUDE_DIRS})
find_library(CURL_LIBRARY curl REQUIRED)
set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})
endif ()
target_include_directories(${TARGET} PUBLIC .)
target_compile_features(${TARGET} PUBLIC cxx_std_11)
target_link_libraries(${TARGET} PRIVATE build_info PUBLIC llama)
target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama)

2248
common/common.cpp
View file

File diff suppressed because it is too large Load diff

46
common/common.h
View file

@ -37,10 +37,13 @@ extern char const *LLAMA_COMMIT;
extern char const *LLAMA_COMPILER;
extern char const *LLAMA_BUILD_TARGET;
struct llama_control_vector_load_info;
int32_t get_num_physical_cores();
//
// CLI argument parsing
//
int32_t get_num_physical_cores();
struct gpt_params {
uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed
@ -51,7 +54,8 @@ struct gpt_params {
int32_t n_threads_batch_draft = -1;
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 512; // context size
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_draft = 5; // number of tokens to draft during speculative decoding
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
@ -85,6 +89,7 @@ struct gpt_params {
struct llama_sampling_params sparams;
std::string model = "models/7B/ggml-model-f16.gguf"; // model path
std::string model_url = ""; // model url to download
std::string model_draft = ""; // draft model for speculative decoding
std::string model_alias = "unknown"; // model alias
std::string prompt = "";
@ -102,6 +107,11 @@ struct gpt_params {
std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
std::string lora_base = ""; // base model path for the lora adapter
std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale
int32_t control_vector_layer_start = -1; // layer range for control vector
int32_t control_vector_layer_end = -1; // layer range for control vector
int ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
// (which is more convenient to use for plotting)
@ -182,6 +192,9 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params);
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model,
struct llama_model_params params);
// Batch utils
void llama_batch_clear(struct llama_batch & batch);
@ -260,3 +273,32 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80);
// Dump the KV cache view showing individual sequences in each cell (long output).
void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
//
// Embedding utils
//
void llama_embd_normalize(const float * inp, float * out, int n);
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n);
//
// Control vector utils
//
struct llama_control_vector_data {
int n_embd;
// stores data for layers [1, n_layer] where n_layer = data.size() / n_embd
std::vector<float> data;
};
struct llama_control_vector_load_info {
float strength;
std::string fname;
};
// Load control vectors, scale each by strength, and add them together.
// On error, returns {-1, empty}
llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos);

16
common/grammar-parser.cpp
View file

@ -278,6 +278,22 @@ namespace grammar_parser {
while (*pos) {
pos = parse_rule(state, pos);
}
// Validate the state to ensure that all rules are defined
for (const auto & rule : state.rules) {
for (const auto & elem : rule) {
if (elem.type == LLAMA_GRETYPE_RULE_REF) {
// Ensure that the rule at that location exists
if (elem.value >= state.rules.size() || state.rules[elem.value].empty()) {
// Get the name of the rule that is missing
for (const auto & kv : state.symbol_ids) {
if (kv.second == elem.value) {
throw std::runtime_error("Undefined rule identifier '" + kv.first + "'");
}
}
}
}
}
}
return state;
} catch (const std::exception & err) {
fprintf(stderr, "%s: error parsing grammar: %s\n", __func__, err.what());

8
common/log.h
View file

@ -297,7 +297,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
#ifndef _MSC_VER
#define LOG(...) LOG_IMPL(__VA_ARGS__, "")
#else
#define LOG(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "")
#define LOG(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "")
#endif
// Main TEE macro.
@ -311,7 +311,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
#ifndef _MSC_VER
#define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "")
#else
#define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "")
#define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "")
#endif
// LOG macro variants with auto endline.
@ -319,8 +319,8 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
#define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n")
#define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n")
#else
#define LOGLN(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "\n")
#define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "\n")
#define LOGLN(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "\n")
#define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "\n")
#endif
// INTERNAL, DO NOT USE

7
common/sampling.cpp
View file

@ -17,6 +17,13 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_
return nullptr;
}
// Ensure that there is a "root" node.
if (result->parsed_grammar.symbol_ids.find("root") == result->parsed_grammar.symbol_ids.end()) {
fprintf(stderr, "%s: grammar does not contain a 'root' symbol\n", __func__);
delete result;
return nullptr;
}
std::vector<const llama_grammar_element *> grammar_rules(result->parsed_grammar.c_rules());
result->grammar = llama_grammar_init(

137
convert-hf-to-gguf.py
View file

@ -1634,7 +1634,7 @@ in chat mode so that the conversation can end normally.")
self.post_write_tensors(tensor_map, name, data_torch)
@Model.register("BertModel")
@Model.register("BertModel", "CamembertModel")
class BertModel(Model):
model_arch = gguf.MODEL_ARCH.BERT
@ -1847,6 +1847,141 @@ class StarCoder2Model(Model):
model_arch = gguf.MODEL_ARCH.STARCODER2
@Model.register("MambaForCausalLM", "MambaLMHeadModel")
class MambaModel(Model):
model_arch = gguf.MODEL_ARCH.MAMBA
def set_vocab(self):
vocab_size = self.hparams["vocab_size"]
# Round vocab size to next multiple of 8
pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8)
# pad using ceiling division
# ref: https://stackoverflow.com/a/17511341/22827863
vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
self.hparams["vocab_size"] = vocab_size
if (self.dir_model / "tokenizer.json").is_file():
self._set_vocab_gpt2()
else:
# Use the GPT-NeoX tokenizer when no tokenizer files are present
tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf"
print(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
neox_reader = gguf.GGUFReader(tokenizer_path, "r")
field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]))
field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
def set_gguf_parameters(self):
d_model = self.find_hparam(["hidden_size", "d_model"])
d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
# ceiling division
# ref: https://stackoverflow.com/a/17511341/22827863
# ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
# Fail early for models which don't have a block expansion factor of 2
assert d_inner == 2 * d_model
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
self.gguf_writer.add_embedding_length(d_model)
self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
self.gguf_writer.add_block_count(self.hparams["n_layer"])
self.gguf_writer.add_ssm_conv_kernel(d_conv)
self.gguf_writer.add_ssm_inner_size(d_inner)
self.gguf_writer.add_ssm_state_size(d_state)
self.gguf_writer.add_ssm_time_step_rank(dt_rank)
self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
self.gguf_writer.add_file_type(self.ftype)
def write_tensors(self):
block_count = self.hparams["n_layer"]
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
tok_embd = None
tok_embd_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.TOKEN_EMBD] + ".weight"
output_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.OUTPUT] + ".weight"
for name, data_torch in self.get_tensors():
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
if name.endswith(".A_log"):
print("A_log --> A ==> " + new_name)
data_torch = -torch.exp(data_torch)
# assuming token_embd.weight is seen before output.weight
if tok_embd is not None and new_name == output_name:
if torch.equal(tok_embd, data_torch):
print(f"{output_name} is equivalent to {tok_embd_name}, omitting")
continue
if new_name == tok_embd_name:
tok_embd = data_torch
data = data_torch.squeeze().numpy()
n_dims = len(data.shape)
data_dtype = data.dtype
# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data = data.astype(np.float32)
# if f16 desired, convert big float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and new_name.removesuffix(".weight").endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2:
data = data.astype(np.float16)
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
@Model.register("CohereForCausalLM")
class CommandR2Model(Model):
model_arch = gguf.MODEL_ARCH.COMMAND_R
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# max_position_embeddings = 8192 in config.json but model was actually
# trained on 128k context length
self.hparams["max_position_embeddings"] = self.hparams["model_max_length"]
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
###### CONVERSION LOGIC ######

130
convert.py
View file

@ -332,6 +332,9 @@ class Params:
#
class BpeVocab:
tokenizer_model = "gpt2"
name = "bpe"
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())
if isinstance(self.bpe_tokenizer.get('model'), dict):
@ -390,6 +393,9 @@ class BpeVocab:
class SentencePieceVocab:
tokenizer_model = "llama"
name = "spm"
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
added_tokens: dict[str, int]
@ -453,6 +459,9 @@ class SentencePieceVocab:
class HfVocab:
tokenizer_model = "llama"
name = "hfft"
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None = None) -> None:
try:
from transformers import AutoTokenizer
@ -553,7 +562,15 @@ class HfVocab:
return f"<HfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab"
class NoVocab:
tokenizer_model = "no_vocab"
name = "no_vocab"
def __repr__(self) -> str:
return "<NoVocab for a model without integrated vocabulary>"
Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab | NoVocab"
#
@ -935,8 +952,10 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N
# Handle special case where the model's vocab size is not set
if params.n_vocab == -1:
raise ValueError(
f"The model's vocab size is set to -1 in params.json. Please update it manually. Maybe {vocab.vocab_size}?"
f"The model's vocab size is set to -1 in params.json. Please update it manually.{f' Maybe {vocab.vocab_size}?' if hasattr(vocab, 'vocab_size') else ''}"
)
if isinstance(vocab, NoVocab):
return # model has no vocab
# Check for a vocab size mismatch
if params.n_vocab == vocab.vocab_size:
@ -977,6 +996,7 @@ class OutputFile:
name = str(params.path_model.parent).split('/')[-1]
self.gguf.add_name (name)
self.gguf.add_vocab_size (params.n_vocab)
self.gguf.add_context_length (params.n_ctx)
self.gguf.add_embedding_length (params.n_embd)
self.gguf.add_block_count (params.n_layer)
@ -1013,21 +1033,9 @@ class OutputFile:
if params.ftype is not None:
self.gguf.add_file_type(params.ftype)
def handle_tokenizer_model(self, vocab: Vocab) -> str:
# Map the vocab types to the supported tokenizer models
tokenizer_model = {
SentencePieceVocab: "llama",
HfVocab: "llama",
BpeVocab: "gpt2",
}.get(type(vocab))
# Block if vocab type is not predefined
if tokenizer_model is None:
raise ValueError("Unknown vocab type: Not supported")
return tokenizer_model
def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]:
assert not isinstance(vocab, NoVocab)
tokens = []
scores = []
toktypes = []
@ -1043,11 +1051,8 @@ class OutputFile:
return tokens, scores, toktypes
def add_meta_vocab(self, vocab: Vocab) -> None:
# Handle the tokenizer model
tokenizer_model = self.handle_tokenizer_model(vocab)
# Ensure that tokenizer_model is added to the GGUF model
self.gguf.add_tokenizer_model(tokenizer_model)
self.gguf.add_tokenizer_model(vocab.tokenizer_model)
# Extract model vocabulary for model conversion
tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab)
@ -1074,6 +1079,26 @@ class OutputFile:
def write_tensor_info(self) -> None:
self.gguf.write_ti_data_to_file()
def write_tensor_data(self, ftype: GGMLFileType, model: LazyModel, concurrency: int) -> None:
ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency=concurrency)
if ftype == GGMLFileType.MostlyQ8_0:
ndarrays = bounded_parallel_map(
OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency,
use_processpool_executor=True,
)
else:
ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
start = time.time()
for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
elapsed = time.time() - start
size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
padi = len(str(len(model)))
print(
f"[{i + 1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
)
self.gguf.write_tensor_data(ndarray)
def close(self) -> None:
self.gguf.close()
@ -1082,7 +1107,7 @@ class OutputFile:
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)
check_vocab_size(params, vocab, pad_vocab=pad_vocab)
of = OutputFile(fname_out, endianess=endianess)
@ -1120,8 +1145,11 @@ class OutputFile:
# meta data
of.add_meta_arch(params)
of.add_meta_vocab(vocab)
of.add_meta_special_vocab(svocab)
if isinstance(vocab, NoVocab):
of.gguf.add_tokenizer_model(vocab.tokenizer_model)
else:
of.add_meta_vocab(vocab)
of.add_meta_special_vocab(svocab)
# tensor info
for name, lazy_tensor in model.items():
@ -1131,24 +1159,7 @@ class OutputFile:
of.write_tensor_info()
# tensor data
ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)
if ftype == GGMLFileType.MostlyQ8_0:
ndarrays = bounded_parallel_map(
OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency,
use_processpool_executor=True,
)
else:
ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
start = time.time()
for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
elapsed = time.time() - start
size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
padi = len(str(len(model)))
print(
f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
)
of.gguf.write_tensor_data(ndarray)
of.write_tensor_data(ftype, model, concurrency)
of.close()
@ -1156,9 +1167,9 @@ class OutputFile:
def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:
wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type
if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32):
if output_type_str == "f32" or (output_type_str is None and wq_type in (DT_F32, DT_BF16)):
return GGMLFileType.AllF32
if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16):
return GGMLFileType.MostlyF16
if output_type_str == "q8_0":
return GGMLFileType.MostlyQ8_0
@ -1309,8 +1320,8 @@ class VocabFactory:
return vtype, path
raise FileNotFoundError(f"Could not find any of {[self._FILES[vt] for vt in vocab_types]}")
def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: Path) -> gguf.SpecialVocab:
load_merges = vocabtype == "bpe"
def _create_special_vocab(self, vocab: Vocab, model_parent_path: Path) -> gguf.SpecialVocab:
load_merges = vocab.name == "bpe"
n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") else None
return gguf.SpecialVocab(
model_parent_path,
@ -1319,30 +1330,34 @@ class VocabFactory:
n_vocab=n_vocab,
)
def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab:
vocab_type, path = self._select_file(vocab_types)
print(f"Loading vocab file {path!r}, type {vocab_type!r}")
added_tokens_path = path.parent / "added_tokens.json"
vocab: Vocab
if vocab_type == "bpe":
vocab = BpeVocab(
return BpeVocab(
path, added_tokens_path if added_tokens_path.exists() else None
)
elif vocab_type == "spm":
vocab = SentencePieceVocab(
if vocab_type == "spm":
return SentencePieceVocab(
path, added_tokens_path if added_tokens_path.exists() else None
)
elif vocab_type == "hfft":
vocab = HfVocab(
if vocab_type == "hfft":
return HfVocab(
path.parent, added_tokens_path if added_tokens_path.exists() else None
)
raise ValueError(vocab_type)
def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
vocab: Vocab
if len(vocab_types) == 1 and "no_vocab" in vocab_types:
vocab = NoVocab()
else:
raise ValueError(vocab_type)
vocab = self._create_vocab_by_path(vocab_types)
# FIXME: Respect --vocab-dir?
special_vocab = self._create_special_vocab(
vocab,
vocab_type,
model_parent_path,
)
return vocab, special_vocab
@ -1380,6 +1395,7 @@ def main(args_in: list[str] | None = None) -> None:
parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model")
parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--no-vocab", action="store_true", help="store model without the vocab")
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("--vocab-type", help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft")
@ -1392,6 +1408,10 @@ def main(args_in: list[str] | None = None) -> None:
parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing")
args = parser.parse_args(args_in)
if args.no_vocab:
if args.vocab_only:
raise ValueError("no need to specify --vocab-only if using --no-vocab")
args.vocab_type = "no_vocab"
if args.dump_single:
model_plus = lazy_load_file(args.model)
@ -1442,7 +1462,7 @@ def main(args_in: list[str] | None = None) -> None:
print(f"Wrote {outfile}")
return
if model_plus.vocab is not None and args.vocab_dir is None:
if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab:
vocab = model_plus.vocab
print(f"Vocab info: {vocab}")

1
examples/CMakeLists.txt
View file

@ -20,6 +20,7 @@ else()
add_subdirectory(convert-llama2c-to-ggml)
add_subdirectory(embedding)
add_subdirectory(finetune)
add_subdirectory(gritlm)
add_subdirectory(infill)
add_subdirectory(llama-bench)
add_subdirectory(llava)

15
examples/batched-bench/batched-bench.cpp
View file

@ -105,6 +105,9 @@ int main(int argc, char ** argv) {
ctx_params.n_threads = params.n_threads;
ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
// ensure enough sequences are available
ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end());
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
if (ctx == NULL) {
@ -135,6 +138,8 @@ int main(int argc, char ** argv) {
LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
return false;
}
llama_synchronize(ctx);
}
return true;
@ -174,10 +179,10 @@ int main(int argc, char ** argv) {
llama_batch_clear(batch);
const int n_tokens = is_pp_shared ? pp : pl*pp;
for (int i = 0; i < n_tokens; ++i) {
llama_batch_add(batch, 0, i, { 0 }, false);
for (int i = 0; i < pp; ++i) {
for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) {
llama_batch_add(batch, 0, i, { j }, false);
}
}
batch.logits[batch.n_tokens - 1] = true;
@ -192,7 +197,7 @@ int main(int argc, char ** argv) {
if (is_pp_shared) {
for (int32_t i = 1; i < pl; ++i) {
llama_kv_cache_seq_cp(ctx, 0, i, 0, pp);
llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
}
}

3
examples/batched/batched.cpp
View file

@ -80,6 +80,7 @@ int main(int argc, char ** argv) {
ctx_params.seed = 1234;
ctx_params.n_ctx = n_kv_req;
ctx_params.n_batch = std::max(n_len, n_parallel);
ctx_params.n_seq_max = n_parallel;
ctx_params.n_threads = params.n_threads;
ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
@ -132,7 +133,7 @@ int main(int argc, char ** argv) {
// assign the system KV cache to all parallel sequences
// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
for (int32_t i = 1; i < n_parallel; ++i) {
llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens);
llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
}
if (n_parallel > 1) {

6
examples/benchmark/benchmark-matmult.cpp
View file

@ -189,12 +189,10 @@ int main(int argc, char ** argv) {
int32_t nelements = sizex*sizey;
std::vector<int64_t> hist_cur(1 << 4, 0);
// Set up a the benchmark matrices
// printf("Creating new tensor q11 & Running quantize\n");
struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], hist_cur.data(), nullptr);
ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], nullptr);
// Set up a the compute graph
// printf("Creating new tensor q31\n");
@ -207,7 +205,7 @@ int main(int argc, char ** argv) {
// Set up a second graph computation to make sure we override the CPU cache lines
// printf("Creating new tensor q12 & Running quantize\n");
struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], hist_cur.data(), nullptr);
ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], nullptr);
// printf("Creating new tensor q32\n");
struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2);

50
examples/embedding/embedding.cpp
View file

@ -23,17 +23,6 @@ static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & toke
}
}
static void normalize(const float * vec, float * out, int n) {
float norm = 0;
for (int i = 0; i < n; i++) {
norm += vec[i] * vec[i];
}
norm = sqrt(norm);
for (int i = 0; i < n; i++) {
out[i] = vec[i] / norm;
}
}
static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
// clear previous kv_cache values (irrelevant for embeddings)
llama_kv_cache_clear(ctx);
@ -44,7 +33,6 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
fprintf(stderr, "%s : failed to decode\n", __func__);
}
// normalize on copy
for (int i = 0; i < batch.n_tokens; i++) {
if (!batch.logits[i]) {
continue;
@ -61,7 +49,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
}
float * out = output + batch.seq_id[i][0] * n_embd;
normalize(embd, out, n_embd);
llama_embd_normalize(embd, out, n_embd);
}
}
@ -119,18 +107,25 @@ int main(int argc, char ** argv) {
// max batch size
const uint64_t n_batch = params.n_batch;
GGML_ASSERT(params.n_batch == params.n_ctx);
GGML_ASSERT(params.n_batch >= params.n_ctx);
// tokenize the prompts and trim
std::vector<std::vector<int32_t>> inputs;
for (const auto & prompt : prompts) {
auto inp = ::llama_tokenize(ctx, prompt, true);
auto inp = ::llama_tokenize(ctx, prompt, true, false);
if (inp.size() > n_batch) {
inp.resize(n_batch);
}
inputs.push_back(inp);
}
// add eos if not present
for (auto & inp : inputs) {
if (inp.empty() || inp.back() != llama_token_eos(model)) {
inp.push_back(llama_token_eos(model));
}
}
// tokenization stats
if (params.verbose_prompt) {
for (int i = 0; i < (int) inputs.size(); i++) {
@ -179,15 +174,26 @@ int main(int argc, char ** argv) {
float * out = emb + p * n_embd;
batch_decode(ctx, batch, out, s, n_embd);
// print first 3 embeddings
for (int j = 0; j < std::min(3, n_prompts); j++) {
fprintf(stderr, "embedding %d: ", j);
for (int i = 0; i < n_embd; i++) {
fprintf(stderr, "%f ", emb[j * n_embd + i]);
// print the first part of the embeddings
fprintf(stdout, "\n");
for (int j = 0; j < n_prompts; j++) {
fprintf(stdout, "embedding %d: ", j);
for (int i = 0; i < std::min(16, n_embd); i++) {
fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
}
fprintf(stderr, "\n\n");
fprintf(stdout, "\n");
}
// print cosine similarity matrix
fprintf(stdout, "\n");
printf("cosine similarity matrix:\n\n");
for (int i = 0; i < n_prompts; i++) {
for (int j = 0; j < n_prompts; j++) {
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
fprintf(stdout, "%6.2f ", sim);
}
fprintf(stdout, "\n");
}
fprintf(stderr, "\n");
// clean up
llama_print_timings(ctx);

1
examples/gguf/gguf.cpp
View file

@ -211,6 +211,7 @@ static bool gguf_ex_read_1(const std::string & fname) {
for (int j = 0; j < ggml_nelements(cur); ++j) {
if (data[j] != 100 + i) {
fprintf(stderr, "%s: tensor[%d]: data[%d] = %f\n", __func__, i, j, data[j]);
gguf_free(ctx);
return false;
}
}

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

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

62
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@ -0,0 +1,62 @@
## Generative Representational Instruction Tuning (GRIT) Example
[gritlm] a model which can generate embeddings as well as "normal" text
generation depending on the instructions in the prompt.
* Paper: https://arxiv.org/pdf/2402.09906.pdf
### Retrieval-Augmented Generation (RAG) use case
One use case for `gritlm` is to use it with RAG. If we recall how RAG works is
that we take documents that we want to use as context, to ground the large
language model (LLM), and we create token embeddings for them. We then store
these token embeddings in a vector database.
When we perform a query, prompt the LLM, we will first create token embeddings
for the query and then search the vector database to retrieve the most
similar vectors, and return those documents so they can be passed to the LLM as
context. Then the query and the context will be passed to the LLM which will
have to _again_ create token embeddings for the query. But because gritlm is used
the first query can be cached and the second query tokenization generation does
not have to be performed at all.
### Running the example
Download a Grit model:
```console
$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf
```
Run the example using the downloaded model:
```console
$ ./gritlm -m gritlm-7b_q4_1.gguf
Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605
Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103
Cosine similarity between "Generative Representational Instruction Tuning" and "A purely peer-to-peer version of electronic cash w" is: 0.112
Cosine similarity between "Generative Representational Instruction Tuning" and "All text-based language problems can be reduced to" is: 0.547
Oh, brave adventurer, who dared to climb
The lofty peak of Mt. Fuji in the night,
When shadows lurk and ghosts do roam,
And darkness reigns, a fearsome sight.
Thou didst set out, with heart aglow,
To conquer this mountain, so high,
And reach the summit, where the stars do glow,
And the moon shines bright, up in the sky.
Through the mist and fog, thou didst press on,
With steadfast courage, and a steadfast will,
Through the darkness, thou didst not be gone,
But didst climb on, with a steadfast skill.
At last, thou didst reach the summit's crest,
And gazed upon the world below,
And saw the beauty of the night's best,
And felt the peace, that only nature knows.
Oh, brave adventurer, who dared to climb
The lofty peak of Mt. Fuji in the night,
Thou art a hero, in the eyes of all,
For thou didst conquer this mountain, so bright.
```
[gritlm]: https://github.com/ContextualAI/gritlm

215
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@ -0,0 +1,215 @@
#include "common.h"
#include "llama.h"
#include <string>
#include <vector>
// #define GRIT_DEBUG
static std::vector<std::vector<float>> encode(llama_context * ctx, const std::vector<std::string> & sentences, const std::string & instruction) {
std::vector<std::vector<float>> result;
const llama_model * mdl = llama_get_model(ctx);
llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1);
for (uint64_t i = 0; i < sentences.size(); i++) {
llama_batch_clear(batch);
const std::string input_string = instruction + sentences[i];
std::vector<llama_token> inputs = llama_tokenize(mdl, input_string, true, false);
const int32_t n_toks = inputs.size();
// GritLM seems to have EOS = ""
// https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L18
// inputs.push_back(llama_token_eos(mdl));
// we want to ignore instruction tokens for mean pooling
const int32_t n_inst = llama_tokenize(mdl, instruction, true, false).size();
#ifdef GRIT_DEBUG
// debug tokens - should be matching as referenced in the GritLM sample
std::for_each(inputs.begin(), inputs.end(), [&ctx](llama_token t) {
std::printf("[%u:%s]", t, llama_token_to_piece(ctx, t).c_str());
});
std::printf("\n");
#endif
// add input to batch (this increments n_tokens)
for (int32_t j = 0; j < n_toks; j++) {
llama_batch_add(batch, inputs[j], j, { 0 }, j >= n_inst);
}
// clear previous kv_cache values (irrelevant for embeddings)
llama_kv_cache_clear(ctx);
llama_set_causal_attn(ctx, false);
// run model
llama_decode(ctx, batch);
// get embedding dimensions
uint64_t n_embd = llama_n_embd(mdl);
// allocate embedding output
std::vector<float> emb_unorm(n_embd, 0.0f);
// sum up all token embeddings
for (int32_t k = n_inst; k < n_toks; k++) {
float * emb = llama_get_embeddings_ith(ctx, k);
for (uint64_t j = 0; j < n_embd; j++) {
emb_unorm[j] += emb[j];
}
}
// divide by number of tokens (mean pooling)
{
const uint64_t n_sent = n_toks - n_inst;
for (uint64_t j = 0; j < n_embd; j++) {
emb_unorm[j] /= n_sent;
}
}
std::vector<float> emb_norm(emb_unorm.size());
llama_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd);
result.push_back(emb_norm);
#ifdef GRIT_DEBUG
// print out emb_norm
std::printf("embedding %ld: ", i);
for (uint64_t j = 0; j < n_embd; j++) {
std::printf("%.5f ", emb_norm[j]);
}
std::printf("\n\n");
#endif
}
llama_batch_free(batch);
return result;
}
static std::string generate(llama_context * ctx, const std::string & prompt, bool stream) {
std::string result;
const llama_model * mdl = llama_get_model(ctx);
llama_token eos_token = llama_token_eos(mdl);
llama_kv_cache_clear(ctx);
llama_set_causal_attn(ctx, true);
llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
std::vector<llama_token> inputs = llama_tokenize(mdl, prompt, false, true);
int32_t i_current_token = 0;
while (true) {
llama_batch_clear(bat);
auto n_inputs = (int32_t)inputs.size();
for (int32_t i = 0; i < n_inputs; i++) {
llama_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1);
}
inputs.clear();
llama_decode(ctx, bat);
auto logits = llama_get_logits_ith(ctx, bat.n_tokens - 1);
auto candidates = std::vector<llama_token_data>(llama_n_vocab(mdl));
auto n_candidates = (int32_t)candidates.size();
for (int32_t token = 0; token < n_candidates; token++) {
candidates[token] = llama_token_data{ token, logits[token], 0.0f };
}
auto candidates_p = llama_token_data_array{ candidates.data(), candidates.size(), false };
llama_token token = llama_sample_token_greedy(ctx, &candidates_p);
if (token == eos_token) {
break;
}
std::string piece = llama_token_to_piece(ctx, token);
if (stream) {
std::printf("%s", piece.c_str());
std::fflush(stdout);
}
inputs.push_back(token);
result += piece;
}
if (stream) {
std::printf("\n");
}
llama_batch_free(bat);
return result;
}
static std::string gritlm_instruction(const std::string & instruction) {
return !instruction.empty() ? "<|user|>\n" + instruction + "\n<|embed|>\n" : "<|embed|>\n";
}
int main(int argc, char * argv[]) {
gpt_params params;
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}
llama_model_params mparams = llama_model_params_from_gpt_params(params);
llama_context_params cparams = llama_context_params_from_gpt_params(params);
llama_backend_init();
llama_model * mdl = llama_load_model_from_file(params.model.c_str(), mparams);
// create new context - set to embedding mode
cparams.embeddings = true;
llama_context * ctx = llama_new_context_with_model(mdl, cparams);
// ### Embedding/Representation ###
// samples taken from: https://github.com/ContextualAI/gritlm#basic
{
const std::string instruction = "Given a scientific paper title, retrieve the paper's abstract";
const std::vector<std::string> queries = {
"Bitcoin: A Peer-to-Peer Electronic Cash System",
"Generative Representational Instruction Tuning",
};
const std::vector<std::string> documents = {
"A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.",
"All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.",
};
// No need to add instruction for retrieval documents
const std::vector<std::vector<float>> d_rep = encode(ctx, documents, gritlm_instruction(""));
const std::vector<std::vector<float>> q_rep = encode(ctx, queries, gritlm_instruction(instruction));
const int n_embd = llama_n_embd(mdl);
const float cosine_sim_q0_d0 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q0_d1 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);
const float cosine_sim_q1_d0 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q1_d1 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[0].c_str(), cosine_sim_q1_d0);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[1].c_str(), cosine_sim_q1_d1);
}
// ### Generation ###
// GritLM models are not finetuned with system prompts, as you can just include system-like instructions together with your user instruction
{
const std::string prompt = "<|user|>\nPlease write me a poem about my recent hike of Mt. Fuji at midnight in the style of Shakespeare.\n<|assistant|>\n";
std::string response = generate(ctx, prompt, true);
}
llama_free(ctx);
llama_free_model(mdl);
llama_backend_free();
return 0;
}

32
examples/imatrix/imatrix.cpp
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@ -56,13 +56,31 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
const struct ggml_tensor * src0 = t->src[0];
const struct ggml_tensor * src1 = t->src[1];
std::string wname;
{
// remove any prefix and suffixes from the name
// CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight
const char * p = strchr(src0->name, '#');
if (p != NULL) {
p = p + 1;
const char * q = strchr(p, '#');
if (q != NULL) {
wname = std::string(p, q - p);
} else {
wname = p;
}
} else {
wname = src0->name;
}
}
// when ask is true, the scheduler wants to know if we are interested in data from this tensor
// if we return true, a follow-up call will be made with ask=false in which we can do the actual collection
if (ask) {
if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications
if (t->op != GGML_OP_MUL_MAT) return false;
if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
if (!(strncmp(src0->name, "blk.", 4) == 0 || (m_params.collect_output_weight && strcmp(src0->name, "output.weight") == 0))) return false;
if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && wname == "output.weight"))) return false;
return true;
}
@ -94,12 +112,12 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
// this is necessary to guarantee equal number of "ncall" for each tensor
for (int ex = 0; ex < n_as; ++ex) {
src0 = t->src[2 + ex];
auto& e = m_stats[src0->name];
auto& e = m_stats[wname];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
}
else if (e.values.size() != (size_t)src1->ne[0]) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]);
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
exit(1); //GGML_ASSERT(false);
}
// NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger
@ -107,7 +125,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
//if (idx == t->src[0]->ne[0] - 1) ++e.ncall;
++e.ncall;
if (m_params.verbosity > 1) {
printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, src0->name, ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
}
for (int row = 0; row < (int)src1->ne[1]; ++row) {
const int excur = m_ids[row*n_as + idx];
@ -129,17 +147,17 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
}
}
} else {
auto& e = m_stats[src0->name];
auto& e = m_stats[wname];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
}
else if (e.values.size() != (size_t)src1->ne[0]) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]);
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
exit(1); //GGML_ASSERT(false);
}
++e.ncall;
if (m_params.verbosity > 1) {
printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, src0->name, ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
}
for (int row = 0; row < (int)src1->ne[1]; ++row) {
const float * x = data + row * src1->ne[0];

69
examples/llama-bench/llama-bench.cpp
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@ -8,6 +8,7 @@
#include <cstdio>
#include <cstring>
#include <ctime>
#include <cstdlib>
#include <iterator>
#include <map>
#include <numeric>
@ -103,6 +104,7 @@ static std::string get_cpu_info() {
}
}
}
fclose(f);
}
#endif
// TODO: other platforms
@ -112,10 +114,10 @@ static std::string get_cpu_info() {
static std::string get_gpu_info() {
std::string id;
#ifdef GGML_USE_CUBLAS
int count = ggml_cuda_get_device_count();
int count = ggml_backend_cuda_get_device_count();
for (int i = 0; i < count; i++) {
char buf[128];
ggml_cuda_get_device_description(i, buf, sizeof(buf));
ggml_backend_cuda_get_device_description(i, buf, sizeof(buf));
id += buf;
if (i < count - 1) {
id += "/";
@ -164,6 +166,7 @@ struct cmd_params {
std::vector<int> n_prompt;
std::vector<int> n_gen;
std::vector<int> n_batch;
std::vector<int> n_ubatch;
std::vector<ggml_type> type_k;
std::vector<ggml_type> type_v;
std::vector<int> n_threads;
@ -183,7 +186,8 @@ static const cmd_params cmd_params_defaults = {
/* model */ {"models/7B/ggml-model-q4_0.gguf"},
/* n_prompt */ {512},
/* n_gen */ {128},
/* n_batch */ {512},
/* n_batch */ {2048},
/* n_ubatch */ {512},
/* type_k */ {GGML_TYPE_F16},
/* type_v */ {GGML_TYPE_F16},
/* n_threads */ {get_num_physical_cores()},
@ -208,6 +212,7 @@ 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(" -ub N, --ubatch-size <n> (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").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());
@ -217,7 +222,7 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
printf(" -ts, --tensor_split <ts0/ts1/..> (default: 0)\n");
printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
printf(" -o, --output <csv|json|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
@ -297,6 +302,13 @@ 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 == "-ub" || arg == "--ubatch-size") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<int>(argv[i], split_delim);
params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end());
} else if (arg == "-ctk" || arg == "--cache-type-k") {
if (++i >= argc) {
invalid_param = true;
@ -455,6 +467,7 @@ 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.n_ubatch.empty()) { params.n_ubatch = cmd_params_defaults.n_ubatch; }
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; }
@ -474,6 +487,7 @@ struct cmd_params_instance {
int n_prompt;
int n_gen;
int n_batch;
int n_ubatch;
ggml_type type_k;
ggml_type type_v;
int n_threads;
@ -511,6 +525,7 @@ struct cmd_params_instance {
cparams.n_ctx = n_prompt + n_gen;
cparams.n_batch = n_batch;
cparams.n_ubatch = n_ubatch;
cparams.type_k = type_k;
cparams.type_v = type_v;
cparams.offload_kqv = !no_kv_offload;
@ -532,6 +547,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
for (const auto & mmp : params.use_mmap)
for (const auto & embd : params.embeddings)
for (const auto & nb : params.n_batch)
for (const auto & nub : params.n_ubatch)
for (const auto & tk : params.type_k)
for (const auto & tv : params.type_v)
for (const auto & nkvo : params.no_kv_offload)
@ -545,6 +561,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .n_prompt = */ n_prompt,
/* .n_gen = */ 0,
/* .n_batch = */ nb,
/* .n_ubatch = */ nub,
/* .type_k = */ tk,
/* .type_v = */ tv,
/* .n_threads = */ nt,
@ -568,6 +585,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .n_prompt = */ 0,
/* .n_gen = */ n_gen,
/* .n_batch = */ nb,
/* .n_ubatch = */ nub,
/* .type_k = */ tk,
/* .type_v = */ tv,
/* .n_threads = */ nt,
@ -604,6 +622,7 @@ struct test {
uint64_t model_size;
uint64_t model_n_params;
int n_batch;
int n_ubatch;
int n_threads;
ggml_type type_k;
ggml_type type_v;
@ -627,6 +646,7 @@ struct test {
model_size = llama_model_size(lmodel);
model_n_params = llama_model_n_params(lmodel);
n_batch = inst.n_batch;
n_ubatch = inst.n_ubatch;
n_threads = inst.n_threads;
type_k = inst.type_k;
type_v = inst.type_v;
@ -705,7 +725,8 @@ struct test {
"cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas",
"cpu_info", "gpu_info",
"model_filename", "model_type", "model_size", "model_n_params",
"n_batch", "n_threads", "type_k", "type_v",
"n_batch", "n_ubatch",
"n_threads", "type_k", "type_v",
"n_gpu_layers", "split_mode",
"main_gpu", "no_kv_offload",
"tensor_split", "use_mmap", "embeddings",
@ -719,7 +740,8 @@ struct test {
enum field_type {STRING, BOOL, INT, FLOAT};
static field_type get_field_type(const std::string & field) {
if (field == "build_number" || field == "n_batch" || field == "n_threads" ||
if (field == "build_number" || field == "n_batch" || field == "n_ubatch" ||
field == "n_threads" ||
field == "model_size" || field == "model_n_params" ||
field == "n_gpu_layers" || field == "main_gpu" ||
field == "n_prompt" || field == "n_gen" ||
@ -759,7 +781,8 @@ struct test {
std::to_string(metal), std::to_string(sycl), 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), ggml_type_name(type_k), ggml_type_name(type_v),
std::to_string(n_batch), std::to_string(n_ubatch),
std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
std::to_string(n_gpu_layers), split_mode_str(split_mode),
std::to_string(main_gpu), std::to_string(no_kv_offload),
tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
@ -957,6 +980,9 @@ struct markdown_printer : public printer {
if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
fields.emplace_back("n_batch");
}
if (params.n_ubatch.size() > 1 || params.n_ubatch != cmd_params_defaults.n_ubatch) {
fields.emplace_back("n_ubatch");
}
if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) {
fields.emplace_back("type_k");
}
@ -1096,25 +1122,40 @@ struct sql_printer : public printer {
};
static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
std::vector<llama_token> tokens(n_batch, llama_token_bos(llama_get_model(ctx)));
int n_processed = 0;
llama_set_n_threads(ctx, n_threads, n_threads);
const llama_model * model = llama_get_model(ctx);
const int32_t n_vocab = llama_n_vocab(model);
std::vector<llama_token> tokens(n_batch);
int n_processed = 0;
while (n_processed < n_prompt) {
int n_tokens = std::min(n_prompt - n_processed, n_batch);
tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
for (int i = 1; i < n_tokens; i++) {
tokens[i] = std::rand() % n_vocab;
}
llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0));
n_processed += n_tokens;
}
llama_synchronize(ctx);
}
static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
llama_token token = llama_token_bos(llama_get_model(ctx));
llama_set_n_threads(ctx, n_threads, n_threads);
const llama_model * model = llama_get_model(ctx);
const int32_t n_vocab = llama_n_vocab(model);
llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
for (int i = 0; i < n_gen; i++) {
llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0));
llama_synchronize(ctx);
token = std::rand() % n_vocab;
}
}
@ -1203,7 +1244,8 @@ int main(int argc, char ** argv) {
// warmup run
if (t.n_prompt > 0) {
test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads);
//test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
}
if (t.n_gen > 0) {
test_gen(ctx, 1, 0, t.n_threads);
@ -1219,6 +1261,7 @@ int main(int argc, char ** argv) {
if (t.n_gen > 0) {
test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);
}
uint64_t t_ns = get_time_ns() - t_start;
t.samples_ns.push_back(t_ns);
}

53
examples/llama.android/app/src/main/cpp/llama-android.cpp
View file

@ -33,6 +33,45 @@ jclass la_int_var;
jmethodID la_int_var_value;
jmethodID la_int_var_inc;
std::string cached_token_chars;
bool is_valid_utf8(const char * string) {
if (!string) {
return true;
}
const unsigned char * bytes = (const unsigned char *)string;
int num;
while (*bytes != 0x00) {
if ((*bytes & 0x80) == 0x00) {
// U+0000 to U+007F
num = 1;
} else if ((*bytes & 0xE0) == 0xC0) {
// U+0080 to U+07FF
num = 2;
} else if ((*bytes & 0xF0) == 0xE0) {
// U+0800 to U+FFFF
num = 3;
} else if ((*bytes & 0xF8) == 0xF0) {
// U+10000 to U+10FFFF
num = 4;
} else {
return false;
}
bytes += 1;
for (int i = 1; i < num; ++i) {
if ((*bytes & 0xC0) != 0x80) {
return false;
}
bytes += 1;
}
}
return true;
}
static void log_callback(ggml_log_level level, const char * fmt, void * data) {
if (level == GGML_LOG_LEVEL_ERROR) __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data);
else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data);
@ -295,6 +334,8 @@ Java_com_example_llama_Llm_completion_1init(
jint n_len
) {
cached_token_chars.clear();
const auto text = env->GetStringUTFChars(jtext, 0);
const auto context = reinterpret_cast<llama_context *>(context_pointer);
const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
@ -372,8 +413,16 @@ Java_com_example_llama_Llm_completion_1loop(
}
auto new_token_chars = llama_token_to_piece(context, new_token_id);
LOGi("new_token_chars: `%s`", new_token_chars.c_str());
auto new_token = env->NewStringUTF(new_token_chars.c_str());
cached_token_chars += new_token_chars;
jstring new_token = nullptr;
if (is_valid_utf8(cached_token_chars.c_str())) {
new_token = env->NewStringUTF(cached_token_chars.c_str());
LOGi("cached: %s, new_token_chars: `%s`, id: %d", cached_token_chars.c_str(), new_token_chars.c_str(), new_token_id);
cached_token_chars.clear();
} else {
new_token = env->NewStringUTF("");
}
llama_batch_clear(*batch);
llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true);

4
examples/llama.android/app/src/main/java/com/example/llama/Llm.kt
View file

@ -71,7 +71,7 @@ class Llm {
batch: Long,
nLen: Int,
ncur: IntVar
): String
): String?
private external fun kv_cache_clear(context: Long)
@ -115,7 +115,7 @@ class Llm {
val ncur = IntVar(completion_init(state.context, state.batch, message, nlen))
while (ncur.value <= nlen) {
val str = completion_loop(state.context, state.batch, nlen, ncur)
if (str.isEmpty()) {
if (str == null) {
break
}
emit(str)

2
examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
View file

@ -221,6 +221,7 @@ actor LlamaContext {
if llama_decode(context, batch) != 0 {
print("llama_decode() failed during prompt")
}
llama_synchronize(context)
let t_pp_end = ggml_time_us()
@ -240,6 +241,7 @@ actor LlamaContext {
if llama_decode(context, batch) != 0 {
print("llama_decode() failed during text generation")
}
llama_synchronize(context)
}
let t_tg_end = ggml_time_us()

18
examples/llava/README.md
View file

@ -63,12 +63,20 @@ Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` director
```console
git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
```
2) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
2) Install the required Python packages:
```sh
pip install -r examples/llava/requirements.txt
```
3) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
```console
python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/
```
- you will find a llava.projector and a llava.clip file in your model directory
3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory:
4) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory:
```console
mkdir vit
cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin
@ -76,18 +84,18 @@ cp ../llava-v1.6-vicuna-7b/llava.projector vit/
curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json
```
4) Create the visual gguf model:
5) Create the visual gguf model:
```console
python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
```
- This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP
5) Then convert the model to gguf format:
6) Then convert the model to gguf format:
```console
python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown
```
6) And finally we can run the llava-cli using the 1.6 model version:
7) And finally we can run the llava-cli using the 1.6 model version:
```console
./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
```

95
examples/llava/clip.cpp
View file

@ -995,6 +995,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
if (!new_clip->ctx_data) {
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
clip_free(new_clip);
gguf_free(ctx);
return nullptr;
}
@ -1002,6 +1003,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
if (!fin) {
printf("cannot open model file for loading tensors\n");
clip_free(new_clip);
gguf_free(ctx);
return nullptr;
}
@ -1023,6 +1025,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
if (!fin) {
printf("%s: failed to seek for tensor %s\n", __func__, name);
clip_free(new_clip);
gguf_free(ctx);
return nullptr;
}
int num_bytes = ggml_nbytes(cur);
@ -1232,16 +1235,16 @@ struct clip_image_f32 * clip_image_f32_init() {
void clip_image_u8_free(struct clip_image_u8 * img) { delete img; }
void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
void clip_image_u8_batch_free(struct clip_image_u8_batch & batch) {
if (batch.size > 0) {
delete[] batch.data;
batch.size = 0;
void clip_image_u8_batch_free(struct clip_image_u8_batch * batch) {
if (batch->size > 0) {
delete[] batch->data;
batch->size = 0;
}
}
void clip_image_f32_batch_free(struct clip_image_f32_batch & batch) {
if (batch.size > 0) {
delete[] batch.data;
batch.size = 0;
void clip_image_f32_batch_free(struct clip_image_f32_batch * batch) {
if (batch->size > 0) {
delete[] batch->data;
batch->size = 0;
}
}
@ -1494,7 +1497,7 @@ static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & im
// returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
// res_imgs memory is being allocated here, previous allocations will be freed if found
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs) {
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
bool pad_to_square = true;
if (!ctx->has_vision_encoder) {
printf("This gguf file seems to have no vision encoder\n");
@ -1506,11 +1509,11 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
pad_to_square = false;
}
// free the previous res_imgs if any set
if (res_imgs.size > 0) {
if (res_imgs->size > 0) {
clip_image_f32_batch_free(res_imgs);
}
res_imgs.data = nullptr;
res_imgs.size = 0;
res_imgs->data = nullptr;
res_imgs->size = 0;
// the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
// see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
@ -1565,11 +1568,11 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
bicubic_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
patches.insert(patches.begin(), image_original_resize);
// clip_image_f32_batch_init(patches.size());
res_imgs.size = patches.size();
res_imgs.data = new clip_image_f32[res_imgs.size];
res_imgs->size = patches.size();
res_imgs->data = new clip_image_f32[res_imgs->size];
int num=0;
for (auto& patch : patches) {
normalize_image_u8_to_f32(patch, &res_imgs.data[num], ctx->image_mean, ctx->image_std);
normalize_image_u8_to_f32(patch, &res_imgs->data[num], ctx->image_mean, ctx->image_std);
num++;
}
@ -1657,9 +1660,9 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
// }
// res_imgs.push_back(res);
res_imgs.size = 1;
res_imgs.data = new clip_image_f32[res_imgs.size];
res_imgs.data[0] = *res;
res_imgs->size = 1;
res_imgs->data = new clip_image_f32[res_imgs->size];
res_imgs->data[0] = *res;
clip_image_f32_free(res);
return true;
@ -1862,7 +1865,6 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
std::vector<uint8_t> work(512);
std::vector<float> conv_buf(512);
std::vector<int64_t> hist_all(1 << 4, 0);
size_t total_size_org = 0;
size_t total_size_new = 0;
@ -1909,6 +1911,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
break;
default:
printf("Please use an input file in f32 or f16\n");
gguf_free(ctx_out);
return false;
}
@ -1917,48 +1920,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
}
new_data = work.data();
std::vector<int64_t> hist_cur(1 << 4, 0);
switch (new_type) {
case GGML_TYPE_Q4_0: {
new_size = ggml_quantize_q4_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q4_1: {
new_size = ggml_quantize_q4_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q5_0: {
new_size = ggml_quantize_q5_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q5_1: {
new_size = ggml_quantize_q5_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q8_0: {
new_size = ggml_quantize_q8_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q2_K: {
new_size = ggml_quantize_q2_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q3_K: {
new_size = ggml_quantize_q3_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q4_K: {
new_size = ggml_quantize_q4_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q5_K: {
new_size = ggml_quantize_q5_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
case GGML_TYPE_Q6_K: {
new_size = ggml_quantize_q6_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
} break;
default: {
fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, new_type);
return false;
}
}
for (size_t j = 0; j < hist_cur.size(); ++j) {
hist_all[j] += hist_cur[j];
}
new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr);
} else {
new_type = cur->type;
new_data = cur->data;
@ -1993,17 +1955,6 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
{
printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
int64_t sum_all = 0;
for (size_t i = 0; i < hist_all.size(); ++i) {
sum_all += hist_all[i];
}
printf("%s: hist: ", __func__);
for (size_t i = 0; i < hist_all.size(); ++i) {
printf("%5.3f ", hist_all[i] / (float)sum_all);
}
printf("\n");
}
return true;

6
examples/llava/clip.h
View file

@ -60,8 +60,8 @@ CLIP_API struct clip_image_f32 * clip_image_f32_init();
CLIP_API void clip_image_u8_free (struct clip_image_u8 * img);
CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch & batch);
CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch & batch);
CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch * batch);
CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch);
CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
@ -69,7 +69,7 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8
CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
/** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */
CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs );
CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);

2
examples/llava/llava.cpp
View file

@ -223,7 +223,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
clip_image_f32_batch img_res_v;
img_res_v.size = 0;
img_res_v.data = nullptr;
if (!clip_image_preprocess(ctx_clip, img, img_res_v)) {
if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) {
fprintf(stderr, "%s: unable to preprocess image\n", __func__);
delete[] img_res_v.data;
return false;

4
examples/llava/llava.h
View file

@ -29,9 +29,9 @@ struct llava_image_embed {
};
/** sanity check for clip <-> llava embed size match */
LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip);
LLAVA_API bool llava_validate_embed_size(const struct llama_context * ctx_llama, const struct clip_ctx * ctx_clip);
LLAVA_API bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
/** build an image embed from image file bytes */
LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);

1
examples/main/README.md
View file

@ -67,6 +67,7 @@ main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt
In this section, we cover the most commonly used options for running the `main` program with the LLaMA models:
- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf).
- `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
- `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models.
- `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.

1
examples/main/main.cpp
View file

@ -878,6 +878,7 @@ int main(int argc, char ** argv) {
const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
const auto line_inp = ::llama_tokenize(ctx, buffer, false, false);
const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());

20
examples/parallel/parallel.cpp
View file

@ -107,6 +107,9 @@ int main(int argc, char ** argv) {
// number of simultaneous "clients" to simulate
const int32_t n_clients = params.n_parallel;
// dedicate one sequence to the system prompt
params.n_parallel += 1;
// requests to simulate
const int32_t n_seq = params.n_sequences;
@ -196,8 +199,8 @@ int main(int argc, char ** argv) {
}
// assign the system KV cache to all parallel sequences
for (int32_t i = 1; i < n_clients; ++i) {
llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system);
for (int32_t i = 1; i <= n_clients; ++i) {
llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
}
LOG_TEE("\n");
@ -221,15 +224,17 @@ int main(int argc, char ** argv) {
client.i_batch = batch.n_tokens;
llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id }, true);
llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id + 1 }, true);
client.n_decoded += 1;
}
if (batch.n_tokens == 0) {
// all sequences have ended - clear the entire KV cache
for (int i = 0; i < n_clients; ++i) {
llama_kv_cache_seq_rm(ctx, i, n_tokens_system, -1);
for (int i = 1; i <= n_clients; ++i) {
llama_kv_cache_seq_rm(ctx, i, -1, -1);
// but keep the system prompt
llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
}
LOG_TEE("%s: clearing the KV cache\n", __func__);
@ -255,7 +260,7 @@ int main(int argc, char ** argv) {
tokens_prompt = ::llama_tokenize(ctx, client.prompt, false);
for (size_t i = 0; i < tokens_prompt.size(); ++i) {
llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id }, false);
llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id + 1 }, false);
}
// extract the logits only for the last token
@ -366,7 +371,8 @@ int main(int argc, char ** argv) {
}
// delete only the generated part of the sequence, i.e. keep the system prompt in the cache
llama_kv_cache_seq_rm(ctx, client.id, n_tokens_system, -1);
llama_kv_cache_seq_rm(ctx, client.id + 1, -1, -1);
llama_kv_cache_seq_cp(ctx, 0, client.id + 1, -1, -1);
const auto t_main_end = ggml_time_us();

149
examples/perplexity/perplexity.cpp
View file

@ -442,7 +442,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
return {tokens, std::exp(nll / count), logit_history, prob_history};
}
static results_perplexity perplexity(llama_context * ctx, const gpt_params & params) {
static results_perplexity perplexity(llama_context * ctx, const gpt_params & params, const int32_t n_ctx) {
if (params.ppl_stride > 0) {
return perplexity_v2(ctx, params);
}
@ -453,7 +453,6 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
// BOS tokens will be added for each chunk before eval
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
const int n_ctx = llama_n_ctx(ctx);
std::ofstream logits_stream;
if (!params.logits_file.empty()) {
@ -499,13 +498,19 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
double nll2 = 0.0;
const int num_batches = (n_ctx + n_batch - 1) / n_batch;
const int n_seq = std::max(1, n_batch / n_ctx);
GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0);
GGML_ASSERT(params.n_ctx == n_seq * n_ctx);
llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1);
std::vector<float> logits;
if (num_batches > 1) {
logits.reserve((size_t)n_ctx * n_vocab);
}
fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
fprintf(stderr, "%s: calculating perplexity over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
@ -518,10 +523,26 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
log_probs.resize(n_ctx * nv);
}
for (int i = 0; i < n_chunk; ++i) {
// We get the logits for all the tokens in the context window (params.n_ctx)
// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,
// calculate the perplexity over the last half of the window (so the model always has
// some context to predict the token).
//
// We rely on the fact that attention in the forward pass only looks at previous
// tokens here, so the logits returned for each token are an accurate representation
// of what the model would have predicted at that point.
//
// Example, we have a context window of 512, we will compute perplexity for each of the
// last 256 tokens. Then, we split the input up into context window size chunks to
// process the entire prompt.
const int first = n_ctx/2;
for (int i = 0; i < n_chunk; i += n_seq) {
const int start = i * n_ctx;
const int end = start + n_ctx;
const int n_seq_batch = std::min(n_seq, n_chunk - i);
const auto t_start = std::chrono::high_resolution_clock::now();
// clear the KV cache
@ -531,34 +552,50 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
const int batch_start = start + j * n_batch;
const int batch_size = std::min(end - batch_start, n_batch);
// save original token and restore it after eval
const auto token_org = tokens[batch_start];
batch.n_tokens = 0;
for (int seq = 0; seq < n_seq_batch; seq++) {
int seq_start = batch_start + seq*n_ctx;
// add BOS token for the first batch of each chunk
if (add_bos && j == 0) {
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
// save original token and restore it after eval
const auto token_org = tokens[seq_start];
// add BOS token for the first batch of each chunk
if (add_bos && j == 0) {
tokens[seq_start] = llama_token_bos(llama_get_model(ctx));
}
for (int k = 0; k < batch_size; ++k) {
const int idx = seq*n_ctx + k;
batch.token[idx] = tokens[seq_start + k];
batch.pos[idx] = j*n_batch + k;
batch.n_seq_id[idx] = 1;
batch.seq_id[idx][0] = seq;
batch.logits[idx] = batch.pos[idx] >= first ? 1 : 0;
}
batch.n_tokens += batch_size;
// restore the original token in case it was set to BOS
tokens[seq_start] = token_org;
}
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
if (llama_decode(ctx, batch)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return {tokens, -1, logit_history, prob_history};
}
// restore the original token in case it was set to BOS
tokens[batch_start] = token_org;
if (num_batches > 1) {
const auto * batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
}
}
const auto t_end = std::chrono::high_resolution_clock::now();
if (i == 0) {
llama_synchronize(ctx);
const auto t_end = std::chrono::high_resolution_clock::now();
const float t_total = std::chrono::duration<float>(t_end - t_start).count();
fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
int total_seconds = (int)(t_total * n_chunk);
int total_seconds = (int)(t_total*n_chunk/n_seq);
if (total_seconds >= 60*60) {
fprintf(stderr, "%d hours ", total_seconds / (60*60));
total_seconds = total_seconds % (60*60);
@ -566,37 +603,31 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
}
// We get the logits for all the tokens in the context window (params.n_ctx)
// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,
// calculate the perplexity over the last half of the window (so the model always has
// some context to predict the token).
//
// We rely on the fact that attention in the forward pass only looks at previous
// tokens here, so the logits returned for each token are an accurate representation
// of what the model would have predicted at that point.
//
// Example, we have a context window of 512, we will compute perplexity for each of the
// last 256 tokens. Then, we split the input up into context window size chunks to
// process the entire prompt.
const int first = n_ctx/2;
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
if (!params.logits_file.empty()) {
process_logits(logits_stream, n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
workers, log_probs, nll, nll2);
} else {
process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
}
count += n_ctx - first - 1;
for (int seq = 0; seq < n_seq_batch; seq++) {
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx);
llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first;
if (!params.logits_file.empty()) {
process_logits(logits_stream, n_vocab, all_logits + first*n_vocab,
tokens_data, n_ctx - 1 - first,
workers, log_probs, nll, nll2);
} else {
process_logits(n_vocab, all_logits + first*n_vocab,
tokens_data, n_ctx - 1 - first,
workers, nll, nll2,
logit_history.data() + start + seq*n_ctx + first,
prob_history.data() + start + seq*n_ctx + first);
}
count += n_ctx - first - 1;
// perplexity is e^(average negative log-likelihood)
if (params.ppl_output_type == 0) {
printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
} else {
double av = nll/count;
double av2 = nll2/count - av*av;
if (av2 > 0) av2 = sqrt(av2/(count-1));
printf("%8d %.4lf %4lf %4lf\n", i*n_ctx, std::exp(nll / count), av, av2);
// perplexity is e^(average negative log-likelihood)
if (params.ppl_output_type == 0) {
printf("[%d]%.4lf,", i + seq + 1, std::exp(nll / count));
} else {
double av = nll/count;
double av2 = nll2/count - av*av;
if (av2 > 0) av2 = sqrt(av2/(count-1));
printf("%8d %.4lf %4lf %4lf\n", i*n_ctx, std::exp(nll / count), av, av2);
}
}
fflush(stdout);
@ -615,6 +646,8 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
printf("Unexpected negative standard deviation of log(prob)\n");
}
llama_batch_free(batch);
return {tokens, ppl, logit_history, prob_history};
}
@ -809,7 +842,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
const int n_batch = params.n_batch;
const int max_tasks_per_batch = 32;
const int max_seq = 4*max_tasks_per_batch;
const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
@ -1086,7 +1119,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
const int n_batch = params.n_batch;
const int max_tasks_per_batch = 128;
const int max_seq = 2*max_tasks_per_batch;
const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
@ -1438,7 +1471,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
const int n_batch = params.n_batch;
const int max_tasks_per_batch = 32;
const int max_seq = 4*max_tasks_per_batch;
const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
@ -1782,13 +1815,24 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
int main(int argc, char ** argv) {
gpt_params params;
params.n_batch = 512;
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}
params.logits_all = true;
params.n_batch = std::min(params.n_batch, params.n_ctx);
const int32_t n_ctx = params.n_ctx;
const bool ppl = !params.hellaswag && !params.winogrande && !params.multiple_choice && !params.kl_divergence;
if (ppl) {
int n_seq = std::max(1, params.n_batch / n_ctx);
int32_t n_kv = n_seq * n_ctx;
params.n_parallel = n_seq;
params.n_ctx = n_kv;
params.n_batch = std::min(params.n_batch, n_kv);
} else {
params.n_batch = std::min(params.n_batch, params.n_ctx);
}
if (params.ppl_stride > 0) {
fprintf(stderr, "Will perform strided perplexity calculation -> adjusting context size from %d to %d\n",
@ -1815,6 +1859,9 @@ int main(int argc, char ** argv) {
llama_model * model;
llama_context * ctx;
// ensure there's at least enough seq_ids for HellaSwag
params.n_parallel = std::max(4, params.n_parallel);
// load the model and apply lora adapter, if any
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == NULL) {
@ -1844,7 +1891,7 @@ int main(int argc, char ** argv) {
} else if (params.kl_divergence) {
kl_divergence(ctx, params);
} else {
results = perplexity(ctx, params);
results = perplexity(ctx, params, n_ctx);
}
llama_print_timings(ctx);

6
examples/server/CMakeLists.txt
View file

@ -1,5 +1,6 @@
set(TARGET server)
option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h)
install(TARGETS ${TARGET} RUNTIME)
@ -7,6 +8,11 @@ target_compile_definitions(${TARGET} PRIVATE
SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
)
target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT})
if (LLAMA_SERVER_SSL)
find_package(OpenSSL REQUIRED)
target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto)
target_compile_definitions(${TARGET} PRIVATE CPPHTTPLIB_OPENSSL_SUPPORT)
endif()
if (WIN32)
TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
endif()

89
examples/server/README.md
View file

@ -20,6 +20,7 @@ The project is under active development, and we are [looking for feedback and co
- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation.
- `--threads-http N`: number of threads in the http server pool to process requests (default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`)
- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf).
- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
@ -42,7 +43,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437
- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
- `--port`: Set the port to listen. Default: `8080`.
- `--path`: path from which to serve static files (default examples/server/public)
- `--path`: path from which to serve static files (default: disabled)
- `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys.
- `--api-key-file`: path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`'s.
- `--embedding`: Enable embedding extraction, Default: disabled.
@ -59,6 +60,10 @@ see https://github.com/ggerganov/llama.cpp/issues/1437
- `--log-disable`: Output logs to stdout only, default: enabled.
- `--log-format FORMAT`: Define the log output to FORMAT: json or text (default: json)
**If compiled with `LLAMA_SERVER_SSL=ON`**
- `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key
- `--ssl-cert-file FNAME`: path to file a PEM-encoded SSL certificate
## Build
server is build alongside everything else from the root of the project
@ -75,6 +80,28 @@ server is build alongside everything else from the root of the project
cmake --build . --config Release
```
## Build with SSL
server can also be built with SSL support using OpenSSL 3
- Using `make`:
```bash
# NOTE: For non-system openssl, use the following:
# CXXFLAGS="-I /path/to/openssl/include"
# LDFLAGS="-L /path/to/openssl/lib"
make LLAMA_SERVER_SSL=true server
```
- Using `CMake`:
```bash
mkdir build
cd build
cmake .. -DLLAMA_SERVER_SSL=ON
make server
```
## Quick Start
To get started right away, run the following command, making sure to use the correct path for the model you have:
@ -97,10 +124,10 @@ You can consume the endpoints with Postman or NodeJS with axios library. You can
### Docker
```bash
docker run -p 8080:8080 -v /path/to/models:/models ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080
docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080
# or, with CUDA:
docker run -p 8080:8080 -v /path/to/models:/models --gpus all ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
```
## Testing with CURL
@ -169,7 +196,11 @@ node index.js
*Options:*
`prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. If the prompt is a string or an array with the first element given as a string, a `bos` token is inserted in the front like `main` does.
`prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true:
- The prompt is a string or an array with the first element given as a string
- The model's `tokenizer.ggml.add_bos_token` metadata is `true`
- The system prompt is empty
`temperature`: Adjust the randomness of the generated text (default: 0.8).
@ -282,7 +313,7 @@ Notice that each `probs` is an array of length `n_probs`.
`content`: Set the text to tokenize.
Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.
Note that a special `BOS` token is never inserted.
- **POST** `/detokenize`: Convert tokens to text.
@ -526,13 +557,55 @@ Run with bash:
bash chat.sh
```
### API like OAI
### OAI-like API
The HTTP server supports OAI-like API
The HTTP server supports OAI-like API: https://github.com/openai/openai-openapi
### API errors
Server returns error in the same format as OAI: https://github.com/openai/openai-openapi
Example of an error:
```json
{
"error": {
"code": 401,
"message": "Invalid API Key",
"type": "authentication_error"
}
}
```
Apart from error types supported by OAI, we also have custom types that are specific to functionalities of llama.cpp:
**When /metrics or /slots endpoint is disabled**
```json
{
"error": {
"code": 501,
"message": "This server does not support metrics endpoint.",
"type": "not_supported_error"
}
}
```
**When the server receives invalid grammar via */completions endpoint**
```json
{
"error": {
"code": 400,
"message": "Failed to parse grammar",
"type": "invalid_request_error"
}
}
```
### Extending or building alternative Web Front End
The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
Read the documentation in `/completion.js` to see convenient ways to access llama.

88
examples/server/bench/README.md Normal file
View file

@ -0,0 +1,88 @@
### Server benchmark tools
Benchmark is using [k6](https://k6.io/).
##### Install k6
Follow instruction from: https://k6.io/docs/get-started/installation/
Example for ubuntu:
```shell
snap install k6
```
#### Download a dataset
This dataset was originally proposed in [vLLM benchmarks](https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md).
```shell
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
```
#### Download a model
Example for PHI-2
```shell
../../../scripts/hf.sh --repo ggml-org/models --file phi-2/ggml-model-q4_0.gguf
```
#### Start the server
The server must answer OAI Chat completion requests on `http://localhost:8080/v1` or according to the environment variable `SERVER_BENCH_URL`.
Example:
```shell
server --host localhost --port 8080 \
--model ggml-model-q4_0.gguf \
--cont-batching \
--metrics \
--parallel 8 \
--batch-size 512 \
--ctx-size 4096 \
--log-format text \
-ngl 33
```
#### Run the benchmark
For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run:
```shell
k6 run script.js --duration 10m --iterations 500 --vus 8
```
The benchmark values can be overridden with:
- `SERVER_BENCH_URL` server url prefix for chat completions, default `http://localhost:8080/v1`
- `SERVER_BENCH_N_PROMPTS` total prompts to randomly select in the benchmark, default `480`
- `SERVER_BENCH_MODEL_ALIAS` model alias to pass in the completion request, default `my-model`
- `SERVER_BENCH_MAX_TOKENS` max tokens to predict, default: `512`
- `SERVER_BENCH_DATASET` path to the benchmark dataset file
- `SERVER_BENCH_MAX_PROMPT_TOKENS` maximum prompt tokens to filter out in the dataset: default `1024`
- `SERVER_BENCH_MAX_CONTEXT` maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens, default `2048`
Note: the local tokenizer is just a string space split, real number of tokens will differ.
Or with [k6 options](https://k6.io/docs/using-k6/k6-options/reference/):
```shell
SERVER_BENCH_N_PROMPTS=500 k6 run script.js --duration 10m --iterations 500 --vus 8
```
To [debug http request](https://k6.io/docs/using-k6/http-debugging/) use `--http-debug="full"`.
#### Metrics
Following metrics are available computed from the OAI chat completions response `usage`:
- `llamacpp_tokens_second` Trend of `usage.total_tokens / request duration`
- `llamacpp_prompt_tokens` Trend of `usage.prompt_tokens`
- `llamacpp_prompt_tokens_total_counter` Counter of `usage.prompt_tokens`
- `llamacpp_completion_tokens` Trend of `usage.completion_tokens`
- `llamacpp_completion_tokens_total_counter` Counter of `usage.completion_tokens`
- `llamacpp_completions_truncated_rate` Rate of completions truncated, i.e. if `finish_reason === 'length'`
- `llamacpp_completions_stop_rate` Rate of completions stopped by the model, i.e. if `finish_reason === 'stop'`
The script will fail if too many completions are truncated, see `llamacpp_completions_truncated_rate`.
K6 metrics might be compared against [server metrics](../README.md), with:
```shell
curl http://localhost:8080/metrics
```

120
examples/server/bench/script.js Normal file
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@ -0,0 +1,120 @@
import http from 'k6/http'
import {check, sleep} from 'k6'
import {SharedArray} from 'k6/data'
import {Counter, Rate, Trend} from 'k6/metrics'
import exec from 'k6/execution';
// Server chat completions prefix
const server_url = __ENV.SERVER_BENCH_URL ? __ENV.SERVER_BENCH_URL : 'http://localhost:8080/v1'
// Number of total prompts in the dataset - default 10m / 10 seconds/request * number of users
const n_prompt = __ENV.SERVER_BENCH_N_PROMPTS ? parseInt(__ENV.SERVER_BENCH_N_PROMPTS) : 600 / 10 * 8
// Model name to request
const model = __ENV.SERVER_BENCH_MODEL_ALIAS ? __ENV.SERVER_BENCH_MODEL_ALIAS : 'my-model'
// Dataset path
const dataset_path = __ENV.SERVER_BENCH_DATASET ? __ENV.SERVER_BENCH_DATASET : './ShareGPT_V3_unfiltered_cleaned_split.json'
// Max tokens to predict
const max_tokens = __ENV.SERVER_BENCH_MAX_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_TOKENS) : 512
// Max prompt tokens
const n_prompt_tokens = __ENV.SERVER_BENCH_MAX_PROMPT_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_PROMPT_TOKENS) : 1024
// Max slot context
const n_ctx_slot = __ENV.SERVER_BENCH_MAX_CONTEXT ? parseInt(__ENV.SERVER_BENCH_MAX_CONTEXT) : 2048
export function setup() {
console.info(`Benchmark config: server_url=${server_url} n_prompt=${n_prompt} model=${model} dataset_path=${dataset_path} max_tokens=${max_tokens}`)
}
const data = new SharedArray('conversations', function () {
const tokenizer = (message) => message.split(/[\s,'".?]/)
return JSON.parse(open(dataset_path))
// Filter out the conversations with less than 2 turns.
.filter(data => data["conversations"].length >= 2)
.filter(data => data["conversations"][0]["from"] === "human")
.map(data => {
return {
prompt: data["conversations"][0]["value"],
n_prompt_tokens: tokenizer(data["conversations"][0]["value"]).length,
n_completion_tokens: tokenizer(data["conversations"][1]["value"]).length,
}
})
// Filter out too short sequences
.filter(conv => conv.n_prompt_tokens >= 4 && conv.n_completion_tokens >= 4)
// Filter out too long sequences.
.filter(conv => conv.n_prompt_tokens <= n_prompt_tokens && conv.n_prompt_tokens + conv.n_completion_tokens <= n_ctx_slot)
// Keep only first n prompts
.slice(0, n_prompt)
})
const llamacpp_prompt_tokens = new Trend('llamacpp_prompt_tokens')
const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens')
const llamacpp_tokens_second = new Trend('llamacpp_tokens_second')
const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter')
const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter')
const llamacpp_completions_truncated_rate = new Rate('llamacpp_completions_truncated_rate')
const llamacpp_completions_stop_rate = new Rate('llamacpp_completions_stop_rate')
export const options = {
thresholds: {
llamacpp_completions_truncated_rate: [
// more than 80% of truncated input will abort the test
{threshold: 'rate < 0.8', abortOnFail: true, delayAbortEval: '1m'},
],
},
duration: '10m',
vus: 8,
}
export default function () {
const conversation = data[exec.scenario.iterationInInstance % data.length]
const payload = {
"messages": [
{
"role": "system",
"content": "You are ChatGPT, an AI assistant.",
},
{
"role": "user",
"content": conversation.prompt,
}
],
"model": model,
"stream": false,
"max_tokens": max_tokens
}
const body = JSON.stringify(payload)
let res = http.post(`${server_url}/chat/completions`, body, {
headers: {'Content-Type': 'application/json'},
timeout: '300s'
})
check(res, {'success completion': (r) => r.status === 200})
if (res.status === 200) {
const completions = res.json()
llamacpp_prompt_tokens.add(completions.usage.prompt_tokens)
llamacpp_prompt_tokens_total_counter.add(completions.usage.prompt_tokens)
llamacpp_completion_tokens.add(completions.usage.completion_tokens)
llamacpp_completion_tokens_total_counter.add(completions.usage.completion_tokens)
llamacpp_completions_truncated_rate.add(completions.choices[0].finish_reason === 'length')
llamacpp_completions_stop_rate.add(completions.choices[0].finish_reason === 'stop')
llamacpp_tokens_second.add(completions.usage.total_tokens / res.timings.duration * 1.e3)
} else {
console.error(`response: ${res.body} request=${payload}`)
}
sleep(0.3)
}

469
examples/server/completion.js.hpp
View file

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};
unsigned int completion_js_len = 5782;
unsigned int completion_js_len = 5796;

20
examples/server/public/completion.js
View file

@ -96,18 +96,18 @@ export async function* llama(prompt, params = {}, config = {}) {
}
}
if (result.error) {
result.error = JSON.parse(result.error);
if (result.error.content.includes('slot unavailable')) {
// Throw an error to be caught by upstream callers
throw new Error('slot unavailable');
} else {
console.error(`llama.cpp error: ${result.error.content}`);
try {
result.error = JSON.parse(result.error);
if (result.error.message.includes('slot unavailable')) {
// Throw an error to be caught by upstream callers
throw new Error('slot unavailable');
} else {
console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`);
}
} catch(e) {
console.error(`llama.cpp error ${result.error}`)
}
}
if (result.error) {
result.error = JSON.parse(result.error);
console.error(`llama.cpp error: ${result.error.content}`);
}
}
}
}

1068
examples/server/server.cpp
View file

File diff suppressed because it is too large Load diff

2
examples/server/tests/README.md
View file

@ -57,7 +57,7 @@ Feature or Scenario must be annotated with `@llama.cpp` to be included in the de
To run a scenario annotated with `@bug`, start:
```shell
DEBUG=ON ./tests.sh --no-skipped --tags bug
DEBUG=ON ./tests.sh --no-skipped --tags bug --stop
```
After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated.

4
examples/server/tests/features/embeddings.feature
View file

@ -4,11 +4,13 @@ Feature: llama.cpp server
Background: Server startup
Given a server listening on localhost:8080
And a model file bert-bge-small/ggml-model-f16.gguf from HF repo ggml-org/models
And a model url https://huggingface.co/ggml-org/models/resolve/main/bert-bge-small/ggml-model-f16.gguf
And a model file ggml-model-f16.gguf
And a model alias bert-bge-small
And 42 as server seed
And 2 slots
And 1024 as batch size
And 1024 as ubatch size
And 2048 KV cache size
And embeddings extraction
Then the server is starting

109
examples/server/tests/features/environment.py
View file

@ -1,9 +1,12 @@
import os
import signal
import socket
import subprocess
import sys
import time
import traceback
from contextlib import closing
from signal import SIGKILL
import psutil
def before_scenario(context, scenario):
@ -19,54 +22,80 @@ def before_scenario(context, scenario):
def after_scenario(context, scenario):
if context.server_process is None:
return
if scenario.status == "failed":
if 'GITHUB_ACTIONS' in os.environ:
print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n\n")
if os.path.isfile('llama.log'):
with closing(open('llama.log', 'r')) as f:
for line in f:
print(line)
if not is_server_listening(context.server_fqdn, context.server_port):
print("\x1b[33;101mERROR: Server stopped listening\x1b[0m")
try:
if 'server_process' not in context or context.server_process is None:
return
if scenario.status == "failed":
if 'GITHUB_ACTIONS' in os.environ:
print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n\n")
if os.path.isfile('llama.log'):
with closing(open('llama.log', 'r')) as f:
for line in f:
print(line)
if not is_server_listening(context.server_fqdn, context.server_port):
print("\x1b[33;101mERROR: Server stopped listening\x1b[0m\n")
if not pid_exists(context.server_process.pid):
assert False, f"Server not running pid={context.server_process.pid} ..."
if not pid_exists(context.server_process.pid):
assert False, f"Server not running pid={context.server_process.pid} ..."
print(f"stopping server pid={context.server_process.pid} ...")
server_graceful_shutdown(context)
# Wait few for socket to free up
time.sleep(0.05)
attempts = 0
while pid_exists(context.server_process.pid) or is_server_listening(context.server_fqdn, context.server_port):
server_kill(context)
time.sleep(0.1)
attempts += 1
if attempts > 5:
server_kill_hard(context)
except:
exc = sys.exception()
print("error in after scenario: \n")
print(exc)
print("*** print_tb: \n")
traceback.print_tb(exc.__traceback__, file=sys.stdout)
def server_graceful_shutdown(context):
print(f"shutting down server pid={context.server_process.pid} ...\n")
if os.name == 'nt':
os.kill(context.server_process.pid, signal.CTRL_C_EVENT)
else:
os.kill(context.server_process.pid, signal.SIGINT)
def server_kill(context):
print(f"killing server pid={context.server_process.pid} ...\n")
context.server_process.kill()
# Wait few for socket to free up
time.sleep(0.05)
attempts = 0
while is_server_listening(context.server_fqdn, context.server_port):
print(f"stopping server pid={context.server_process.pid} ...")
os.kill(context.server_process.pid, SIGKILL)
time.sleep(0.1)
attempts += 1
if attempts > 5:
print(f"Server dangling exits, killing all {context.server_path} ...")
process = subprocess.run(['killall', '-9', context.server_path],
stderr=subprocess.PIPE,
universal_newlines=True)
print(process)
def server_kill_hard(context):
pid = context.server_process.pid
path = context.server_path
print(f"Server dangling exits, hard killing force {pid}={path}...\n")
try:
psutil.Process(pid).kill()
except psutil.NoSuchProcess:
return False
return True
def is_server_listening(server_fqdn, server_port):
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
result = sock.connect_ex((server_fqdn, server_port))
return result == 0
_is_server_listening = result == 0
if _is_server_listening:
print(f"server is listening on {server_fqdn}:{server_port}...\n")
return _is_server_listening
def pid_exists(pid):
"""Check whether pid exists in the current process table."""
import errno
if pid < 0:
return False
try:
os.kill(pid, 0)
except OSError as e:
return e.errno == errno.EPERM
else:
return True
psutil.Process(pid)
except psutil.NoSuchProcess:
return False
return True

5
examples/server/tests/features/parallel.feature
View file

@ -6,8 +6,8 @@ Feature: Parallel
Given a server listening on localhost:8080
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
And 42 as server seed
And 512 as batch size
And 64 KV cache size
And 128 as batch size
And 256 KV cache size
And 2 slots
And continuous batching
Then the server is starting
@ -76,6 +76,7 @@ Feature: Parallel
| disabled | 128 |
| enabled | 64 |
Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969
Given a prompt:
"""

5
examples/server/tests/features/security.feature
View file

@ -39,8 +39,9 @@ Feature: Security
Scenario Outline: CORS Options
When an OPTIONS request is sent from <origin>
Then CORS header <cors_header> is set to <cors_header_value>
Given a user api key llama.cpp
When an OPTIONS request is sent from <origin>
Then CORS header <cors_header> is set to <cors_header_value>
Examples: Headers
| origin | cors_header | cors_header_value |

45
examples/server/tests/features/server.feature
View file

@ -4,17 +4,17 @@ Feature: llama.cpp server
Background: Server startup
Given a server listening on localhost:8080
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
And a model url https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories260K.gguf
And a model file stories260K.gguf
And a model alias tinyllama-2
And 42 as server seed
# KV Cache corresponds to the total amount of tokens
# that can be stored across all independent sequences: #4130
# see --ctx-size and #5568
And 32 KV cache size
And 512 as batch size
And 1 slots
And embeddings extraction
And 32 server max tokens to predict
And 256 KV cache size
And 32 as batch size
And 2 slots
And 64 server max tokens to predict
And prometheus compatible metrics exposed
Then the server is starting
Then the server is healthy
@ -23,17 +23,35 @@ Feature: llama.cpp server
Then the server is ready
And all slots are idle
Scenario Outline: Completion
Given a prompt <prompt>
And <n_predict> max tokens to predict
And a completion request with no api error
Then <n_predicted> tokens are predicted matching <re_content>
And the completion is <truncated> truncated
And <n_prompt> prompt tokens are processed
And prometheus metrics are exposed
And metric llamacpp:tokens_predicted is <n_predicted>
Examples: Prompts
| prompt | n_predict | re_content | n_predicted |
| I believe the meaning of life is | 8 | (read\|going)+ | 8 |
| Write a joke about AI | 64 | (park\|friends\|scared\|always)+ | 32 |
| prompt | n_predict | re_content | n_prompt | n_predicted | truncated |
| I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not |
| Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids)+ | 46 | 64 | not |
Scenario: Completion prompt truncated
Given a prompt:
"""
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
"""
And a completion request with no api error
Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry
And the completion is truncated
And 109 prompt tokens are processed
Scenario Outline: OAI Compatibility
Given a model <model>
@ -43,11 +61,14 @@ Feature: llama.cpp server
And streaming is <enable_streaming>
Given an OAI compatible chat completions request with no api error
Then <n_predicted> tokens are predicted matching <re_content>
And <n_prompt> prompt tokens are processed
And the completion is <truncated> truncated
Examples: Prompts
| model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming |
| llama-2 | Book | What is the best book | 8 | (Mom\|what)+ | 8 | disabled |
| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks\|happy\|bird)+ | 32 | enabled |
| model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated |
| llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not |
| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird)+ | -1 | 64 | enabled | |
Scenario: Tokenize / Detokenize
When tokenizing:

273
examples/server/tests/features/steps/steps.py
View file

@ -5,6 +5,8 @@ import os
import re
import socket
import subprocess
import sys
import threading
import time
from contextlib import closing
from re import RegexFlag
@ -18,7 +20,7 @@ from huggingface_hub import hf_hub_download
from prometheus_client import parser
@step(u"a server listening on {server_fqdn}:{server_port}")
@step("a server listening on {server_fqdn}:{server_port}")
def step_server_config(context, server_fqdn, server_port):
context.server_fqdn = server_fqdn
context.server_port = int(server_port)
@ -32,7 +34,10 @@ def step_server_config(context, server_fqdn, server_port):
context.base_url = f'http://{context.server_fqdn}:{context.server_port}'
context.model_alias = None
context.model_file = None
context.model_url = None
context.n_batch = None
context.n_ubatch = None
context.n_ctx = None
context.n_ga = None
context.n_ga_w = None
@ -57,24 +62,34 @@ def step_server_config(context, server_fqdn, server_port):
context.prompts = []
@step(u'a model file {hf_file} from HF repo {hf_repo}')
@step('a model file {hf_file} from HF repo {hf_repo}')
def step_download_hf_model(context, hf_file, hf_repo):
context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file)
if context.debug:
print(f"model file: {context.model_file}\n")
@step(u'a model alias {model_alias}')
@step('a model file {model_file}')
def step_model_file(context, model_file):
context.model_file = model_file
@step('a model url {model_url}')
def step_model_url(context, model_url):
context.model_url = model_url
@step('a model alias {model_alias}')
def step_model_alias(context, model_alias):
context.model_alias = model_alias
@step(u'{seed:d} as server seed')
@step('{seed:d} as server seed')
def step_seed(context, seed):
context.server_seed = seed
@step(u'{ngl:d} GPU offloaded layers')
@step('{ngl:d} GPU offloaded layers')
def step_n_gpu_layer(context, ngl):
if 'N_GPU_LAYERS' in os.environ:
new_ngl = int(os.environ['N_GPU_LAYERS'])
@ -84,40 +99,44 @@ def step_n_gpu_layer(context, ngl):
context.n_gpu_layer = ngl
@step(u'{n_ctx:d} KV cache size')
@step('{n_ctx:d} KV cache size')
def step_n_ctx(context, n_ctx):
context.n_ctx = n_ctx
@step(u'{n_slots:d} slots')
@step('{n_slots:d} slots')
def step_n_slots(context, n_slots):
context.n_slots = n_slots
@step(u'{n_predict:d} server max tokens to predict')
@step('{n_predict:d} server max tokens to predict')
def step_server_n_predict(context, n_predict):
context.n_server_predict = n_predict
@step(u'continuous batching')
@step('continuous batching')
def step_server_continuous_batching(context):
context.server_continuous_batching = True
@step(u'embeddings extraction')
@step('embeddings extraction')
def step_server_embeddings(context):
context.server_embeddings = True
@step(u'prometheus compatible metrics exposed')
@step('prometheus compatible metrics exposed')
def step_server_metrics(context):
context.server_metrics = True
@step(u"the server is starting")
@step("the server is starting")
def step_start_server(context):
start_server_background(context)
attempts = 0
max_attempts = 20
if 'GITHUB_ACTIONS' in os.environ:
max_attempts *= 2
while True:
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
result = sock.connect_ex((context.server_fqdn, context.server_port))
@ -125,18 +144,19 @@ def step_start_server(context):
print("\x1b[33;46mserver started!\x1b[0m")
return
attempts += 1
if attempts > 20:
if attempts > max_attempts:
assert False, "server not started"
print(f"waiting for server to start, connect error code = {result}...")
time.sleep(0.1)
@step(u"the server is {expecting_status}")
@step("the server is {expecting_status}")
@async_run_until_complete
async def step_wait_for_the_server_to_be_started(context, expecting_status):
match expecting_status:
case 'healthy':
await wait_for_health_status(context, context.base_url, 200, 'ok')
await wait_for_health_status(context, context.base_url, 200, 'ok',
timeout=30)
case 'ready' | 'idle':
await wait_for_health_status(context, context.base_url, 200, 'ok',
@ -160,7 +180,7 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status):
assert False, "unknown status"
@step(u'all slots are {expected_slot_status_string}')
@step('all slots are {expected_slot_status_string}')
@async_run_until_complete
async def step_all_slots_status(context, expected_slot_status_string):
match expected_slot_status_string:
@ -176,7 +196,7 @@ async def step_all_slots_status(context, expected_slot_status_string):
await request_slots_status(context, expected_slots)
@step(u'a completion request with {api_error} api error')
@step('a completion request with {api_error} api error')
@async_run_until_complete
async def step_request_completion(context, api_error):
expect_api_error = api_error == 'raised'
@ -194,115 +214,138 @@ async def step_request_completion(context, api_error):
assert completion == 401, f"completion must be an 401 status code: {completion}"
@step(u'{predicted_n:d} tokens are predicted matching {re_content}')
@step('{predicted_n:d} tokens are predicted matching {re_content}')
def step_n_tokens_predicted_with_content(context, predicted_n, re_content):
assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n, re_content)
context.completion = context.tasks_result.pop()
assert_n_tokens_predicted(context.completion, predicted_n, re_content)
@step(u'{predicted_n:d} tokens are predicted')
@step('{predicted_n:d} tokens are predicted')
def step_n_tokens_predicted(context, predicted_n):
assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n)
context.completion = context.tasks_result.pop()
assert_n_tokens_predicted(context.completion, predicted_n)
@step(u'a user prompt {user_prompt}')
@step('the completion is truncated')
def step_assert_completion_truncated(context):
step_assert_completion_truncated(context, '')
@step('the completion is {truncated} truncated')
def step_assert_completion_truncated(context, truncated):
truncated = truncated != "not"
assert context.completion['truncated'] == truncated, f'{context.completion}'
@step('{n_prompt:d} prompt tokens are processed')
def step_impl(context, n_prompt):
assert n_prompt < 0 or n_prompt == context.completion['timings']['prompt_n'], f"n_prompt={context.completion['timings']['prompt_n']}"
@step('a user prompt {user_prompt}')
def step_user_prompt(context, user_prompt):
context.prompts.append(user_prompt)
context.n_prompts = len(context.prompts)
@step(u'a system prompt {system_prompt}')
@step('a system prompt {system_prompt}')
def step_system_prompt(context, system_prompt):
context.system_prompt = system_prompt
@step(u'a model {model}')
@step('a model {model}')
def step_model(context, model):
context.model = model
@step(u'{max_tokens:d} max tokens to predict')
@step('{max_tokens:d} max tokens to predict')
def step_max_tokens(context, max_tokens):
context.n_predict = max_tokens
@step(u'streaming is {enable_streaming}')
@step('streaming is {enable_streaming}')
def step_streaming(context, enable_streaming):
context.enable_streaming = enable_streaming == 'enabled'
@step(u'a user api key {user_api_key}')
@step('a user api key {user_api_key}')
def step_user_api_key(context, user_api_key):
context.user_api_key = user_api_key
@step(u'no user api key')
@step('no user api key')
def step_no_user_api_key(context):
context.user_api_key = None
@step(u'a user api key ')
@step('a user api key ')
def step_no_user_api_key_space(context):
context.user_api_key = None
@step(u'a server api key {server_api_key}')
@step('a server api key {server_api_key}')
def step_server_api_key(context, server_api_key):
context.server_api_key = server_api_key
@step(u'{n_junk:d} as number of junk')
@step('{n_junk:d} as number of junk')
def step_n_junk(context, n_junk):
context.n_junk = n_junk
@step(u'{n_batch:d} as batch size')
@step('{n_batch:d} as batch size')
def step_n_batch(context, n_batch):
context.n_batch = n_batch
@step(u'{seed:d} as seed')
@step('{n_ubatch:d} as ubatch size')
def step_n_ubatch(context, n_ubatch):
context.n_ubatch = n_ubatch
@step('{seed:d} as seed')
def step_seed(context, seed):
context.seed = seed
@step(u'a prefix prompt')
@step('a prefix prompt')
def step_prompt_prefix(context):
context.prompt_prefix = context.text
context.prompt_prefix = context_text(context)
@step(u'a junk suffix prompt')
@step('a junk suffix prompt')
def step_prompt_junk_suffix(context):
context.prompt_junk_suffix = context.text
context.prompt_junk_suffix = context_text(context)
@step(u'a suffix prompt')
@step('a suffix prompt')
def step_prompt_suffix(context):
context.prompt_suffix = context.text
context.prompt_suffix = context_text(context)
@step(u'{n_ga:d} group attention factor'
u' to extend context size through self-extend')
@step('{n_ga:d} group attention factor'
' to extend context size through self-extend')
def step_impl(context, n_ga):
context.n_ga = n_ga
@step(u'{n_ga_w:d} group attention width to extend context size through self-extend')
@step('{n_ga_w:d} group attention width to extend context size through self-extend')
def step_impl(context, n_ga_w):
context.n_ga_w = n_ga_w
@step(u'a passkey prompt template')
@step('a passkey prompt template')
def step_prompt_passkey(context):
context.prompt_passkey = context.text
context.prompt_passkey = context_text(context)
@step(u'{n_prompts:d} fixed prompts')
@step('{n_prompts:d} fixed prompts')
def step_fixed_prompts(context, n_prompts):
context.prompts.extend([str(0)*(context.n_batch if context.n_batch is not None else 512) for i in range(n_prompts)])
context.n_prompts = n_prompts
@step(u'a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk')
@step('a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk')
def step_prompt_passkey(context, passkey, i_pos):
prompt = ""
for i in range(context.n_junk):
@ -316,7 +359,7 @@ def step_prompt_passkey(context, passkey, i_pos):
context.n_prompts = len(context.prompts)
@step(u'an OAI compatible chat completions request with {api_error} api error')
@step('an OAI compatible chat completions request with {api_error} api error')
@async_run_until_complete
async def step_oai_chat_completions(context, api_error):
if context.debug:
@ -351,19 +394,19 @@ async def step_oai_chat_completions(context, api_error):
print(f"Completion response: {completion}")
@step(u'a prompt')
@step('a prompt')
def step_a_prompt(context):
context.prompts.append(context.text)
context.prompts.append(context_text(context))
context.n_prompts = len(context.prompts)
@step(u'a prompt {prompt}')
@step('a prompt {prompt}')
def step_a_prompt_prompt(context, prompt):
context.prompts.append(prompt)
context.n_prompts = len(context.prompts)
@step(u'concurrent completion requests')
@step('concurrent completion requests')
@async_run_until_complete()
async def step_concurrent_completion_requests(context):
await concurrent_requests(context,
@ -379,7 +422,7 @@ async def step_concurrent_completion_requests(context):
'user_api_key') else None)
@step(u'concurrent OAI completions requests')
@step('concurrent OAI completions requests')
@async_run_until_complete
async def step_oai_chat_completions(context):
await concurrent_requests(context, oai_chat_completions,
@ -399,7 +442,7 @@ async def step_oai_chat_completions(context):
if hasattr(context, 'user_api_key') else None)
@step(u'concurrent OAI completions requests no v1')
@step('concurrent OAI completions requests no v1')
@async_run_until_complete
async def step_oai_chat_completions(context):
await concurrent_requests(context, oai_chat_completions,
@ -422,13 +465,13 @@ async def step_oai_chat_completions(context):
if hasattr(context, 'user_api_key') else None)
@step(u'all prompts are predicted')
@step('all prompts are predicted')
@async_run_until_complete
async def step_all_prompts_are_predicted(context):
await all_prompts_are_predicted(context)
@step(u'all prompts are predicted with {n_expected_predicted:d} tokens')
@step('all prompts are predicted with {n_expected_predicted:d} tokens')
@async_run_until_complete
async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted):
await all_prompts_are_predicted(context, n_expected_predicted)
@ -442,14 +485,14 @@ async def all_prompts_are_predicted(context, expected_predicted_n=None):
assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests"
@step(u'embeddings are computed for')
@step('embeddings are computed for')
@async_run_until_complete
async def step_compute_embedding(context):
context.n_prompts = 1
context.embeddings = await request_embedding(context.text, base_url=context.base_url)
context.embeddings = await request_embedding(context_text(context), base_url=context.base_url)
@step(u'all embeddings are the same')
@step('all embeddings are the same')
@async_run_until_complete
async def step_all_embeddings_are_the_same(context):
n_embedding_requests = await gather_tasks_results(context)
@ -473,7 +516,8 @@ async def step_all_embeddings_are_the_same(context):
print(f"{msg}\n")
assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg
@step(u'embeddings are generated')
@step('embeddings are generated')
def step_assert_embeddings(context):
assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n"
f"context.n_prompts={context.n_prompts}\n"
@ -482,17 +526,17 @@ def step_assert_embeddings(context):
assert_embeddings(embedding)
@step(u'an OAI compatible embeddings computation request for')
@step('an OAI compatible embeddings computation request for')
@async_run_until_complete
async def step_oai_compute_embeddings(context):
context.n_prompts = 1
context.embeddings = await request_oai_embeddings(context.text,
context.embeddings = await request_oai_embeddings(context_text(context),
base_url=context.base_url,
user_api_key=context.user_api_key,
model=context.model)
@step(u'an OAI compatible embeddings computation request for multiple inputs')
@step('an OAI compatible embeddings computation request for multiple inputs')
@async_run_until_complete
async def step_oai_compute_embeddings_multiple_inputs(context):
context.embeddings = await request_oai_embeddings(context.prompts,
@ -502,7 +546,7 @@ async def step_oai_compute_embeddings_multiple_inputs(context):
context.prompts.clear()
@step(u'concurrent embedding requests')
@step('concurrent embedding requests')
@async_run_until_complete()
async def step_concurrent_embedding_requests(context):
await concurrent_requests(context,
@ -511,7 +555,7 @@ async def step_concurrent_embedding_requests(context):
base_url=context.base_url)
@step(u'concurrent OAI embedding requests')
@step('concurrent OAI embedding requests')
@async_run_until_complete()
async def step_concurrent_oai_embedding_requests(context):
await concurrent_requests(context,
@ -522,7 +566,7 @@ async def step_concurrent_oai_embedding_requests(context):
model=context.model)
@step(u'all embeddings are generated')
@step('all embeddings are generated')
@async_run_until_complete()
async def all_embeddings_are_generated(context):
n_embedding_requests = await gather_tasks_results(context)
@ -531,10 +575,10 @@ async def all_embeddings_are_generated(context):
assert_embeddings(context.tasks_result.pop().pop())
@step(u'tokenizing')
@step('tokenizing')
@async_run_until_complete
async def step_tokenize(context):
context.tokenized_text = context.text
context.tokenized_text = context_text(context)
async with aiohttp.ClientSession() as session:
async with session.post(f'{context.base_url}/tokenize',
json={
@ -545,7 +589,7 @@ async def step_tokenize(context):
context.tokens = tokenize_json['tokens']
@step(u'tokens can be detokenize')
@step('tokens can be detokenize')
@async_run_until_complete
async def step_detokenize(context):
assert len(context.tokens) > 0
@ -560,22 +604,23 @@ async def step_detokenize(context):
assert context.tokenized_text == detokenize_json['content'].strip()
@step(u'an OPTIONS request is sent from {origin}')
@step('an OPTIONS request is sent from {origin}')
@async_run_until_complete
async def step_options_request(context, origin):
async with aiohttp.ClientSession() as session:
headers = {'Authorization': f'Bearer {context.user_api_key}', 'Origin': origin}
async with session.options(f'{context.base_url}/v1/chat/completions',
headers={"Origin": origin}) as response:
headers=headers) as response:
assert response.status == 200
context.options_response = response
@step(u'CORS header {cors_header} is set to {cors_header_value}')
@step('CORS header {cors_header} is set to {cors_header_value}')
def step_check_options_header_value(context, cors_header, cors_header_value):
assert context.options_response.headers[cors_header] == cors_header_value
@step(u'prometheus metrics are exposed')
@step('prometheus metrics are exposed')
@async_run_until_complete
async def step_prometheus_metrics_exported(context):
async with aiohttp.ClientSession() as session:
@ -586,15 +631,25 @@ async def step_prometheus_metrics_exported(context):
metric_exported = False
if context.debug:
print(f"/metrics answer:\n{metrics_raw}\n")
context.metrics = {}
for metric in parser.text_string_to_metric_families(metrics_raw):
match metric.name:
case "llamacpp:kv_cache_usage_ratio":
assert len(metric.samples) > 0
metric_exported = True
context.metrics[metric.name] = metric
assert int(metrics_response.headers["Process-Start-Time-Unix"]) > 0, "no header process start time"
assert metric_exported, "No metrics exported"
@step(u'available models')
@step('metric {metric_name} is {metric_value:d}')
def step_assert_metric_value(context, metric_name, metric_value):
if metric_name not in context.metrics:
assert False, f"no metric {metric_name} in {context.metrics.keys()}"
assert context.metrics[metric_name].samples[0].value == metric_value, f"metric: {context.metrics[metric_name]}"
@step('available models')
def step_available_models(context):
# openai client always expects an api_key
openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope'
@ -602,14 +657,14 @@ def step_available_models(context):
context.models = openai.Model.list().data
@step(u'{n_model:d} models are supported')
@step('{n_model:d} models are supported')
def step_supported_models(context, n_model):
if context.debug:
print("server models available:", context.models)
assert len(context.models) == n_model
@step(u'model {i_model:d} is {param} {preposition} {param_value}')
@step('model {i_model:d} is {param} {preposition} {param_value}')
def step_supported_models(context, i_model, param, preposition, param_value):
assert i_model < len(context.models)
model = context.models[i_model]
@ -712,7 +767,8 @@ async def oai_chat_completions(user_prompt,
completion_response = {
'content': '',
'timings': {
'predicted_n': 0
'predicted_n': 0,
'prompt_n': 0
}
}
if async_client:
@ -753,7 +809,8 @@ async def oai_chat_completions(user_prompt,
completion_response = {
'content': chat_completion_raw['choices'][0]['message'],
'timings': {
'predicted_n': chat_completion_raw['usage']['completion_tokens']
'predicted_n': chat_completion_raw['usage']['completion_tokens'],
'prompt_n': chat_completion_raw['usage']['prompt_tokens']
}
}
else:
@ -769,7 +826,7 @@ async def oai_chat_completions(user_prompt,
stream=enable_streaming,
seed=seed
)
except openai.error.APIError as e:
except openai.error.AuthenticationError as e:
if expect_api_error is not None and expect_api_error:
return 401
else:
@ -782,13 +839,16 @@ async def oai_chat_completions(user_prompt,
if 'content' in delta:
completion_response['content'] += delta['content']
completion_response['timings']['predicted_n'] += 1
completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop'
else:
assert len(chat_completion.choices) == 1
completion_response = {
'content': chat_completion.choices[0].message.content,
'timings': {
'predicted_n': chat_completion.usage.completion_tokens
}
'predicted_n': chat_completion.usage.completion_tokens,
'prompt_n': chat_completion.usage.prompt_tokens
},
'truncated': chat_completion.choices[0].finish_reason != 'stop'
}
if debug:
print("OAI response formatted to llama.cpp:", completion_response)
@ -879,7 +939,6 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
f' {n_predicted} <> {expected_predicted_n}')
async def gather_tasks_results(context):
n_tasks = len(context.concurrent_tasks)
if context.debug:
@ -903,6 +962,9 @@ async def wait_for_health_status(context,
print(f"Starting checking for health for expected_health_status={expected_health_status}\n")
interval = 0.5
counter = 0
if 'GITHUB_ACTIONS' in os.environ:
timeout *= 2
async with aiohttp.ClientSession() as session:
while True:
async with await session.get(f'{base_url}/health', params=params) as health_response:
@ -974,17 +1036,32 @@ async def completions_seed(context):
else context.server_seed if hasattr(context, 'server_seed') else None
def context_text(context):
return context.text.replace('\r', '')
def start_server_background(context):
context.server_path = '../../../build/bin/server'
if os.name == 'nt':
context.server_path = '../../../build/bin/Release/server.exe'
else:
context.server_path = '../../../build/bin/server'
if 'LLAMA_SERVER_BIN_PATH' in os.environ:
context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
server_listen_addr = context.server_fqdn
if os.name == 'nt':
server_listen_addr = '0.0.0.0'
server_args = [
'--host', context.server_fqdn,
'--host', server_listen_addr,
'--port', context.server_port,
'--model', context.model_file
]
if context.model_file:
server_args.extend(['--model', context.model_file])
if context.model_url:
server_args.extend(['--model-url', context.model_url])
if context.n_batch:
server_args.extend(['--batch-size', context.n_batch])
if context.n_ubatch:
server_args.extend(['--ubatch-size', context.n_ubatch])
if context.n_gpu_layer:
server_args.extend(['--n-gpu-layers', context.n_gpu_layer])
if context.server_continuous_batching:
@ -1012,7 +1089,31 @@ def start_server_background(context):
if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
server_args.extend(['--log-format', "text"])
print(f"starting server with: {context.server_path} {server_args}\n")
flags = 0
if 'nt' == os.name:
flags |= subprocess.DETACHED_PROCESS
flags |= subprocess.CREATE_NEW_PROCESS_GROUP
flags |= subprocess.CREATE_NO_WINDOW
pkwargs = {
'creationflags': flags,
'stdout': subprocess.PIPE,
'stderr': subprocess.PIPE
}
context.server_process = subprocess.Popen(
[str(arg) for arg in [context.server_path, *server_args]],
close_fds=True)
print(f"server pid={context.server_process.pid}")
**pkwargs)
def log_stdout(process):
for line in iter(process.stdout.readline, b''):
print(line.decode('utf-8'), end='')
thread_stdout = threading.Thread(target=log_stdout, args=(context.server_process,))
thread_stdout.start()
def log_stderr(process):
for line in iter(process.stderr.readline, b''):
print(line.decode('utf-8'), end='', file=sys.stderr)
thread_stderr = threading.Thread(target=log_stderr, args=(context.server_process,))
thread_stderr.start()
print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}")

1
examples/server/tests/requirements.txt
View file

@ -3,4 +3,5 @@ behave~=1.2.6
huggingface_hub~=0.20.3
numpy~=1.24.4
openai~=0.25.0
psutil~=5.9.8
prometheus-client~=0.20.0

75
examples/server/utils.hpp
View file

@ -14,6 +14,17 @@
using json = nlohmann::json;
// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
enum error_type {
ERROR_TYPE_INVALID_REQUEST,
ERROR_TYPE_AUTHENTICATION,
ERROR_TYPE_SERVER,
ERROR_TYPE_NOT_FOUND,
ERROR_TYPE_PERMISSION,
ERROR_TYPE_UNAVAILABLE, // custom error
ERROR_TYPE_NOT_SUPPORTED, // custom error
};
extern bool server_verbose;
extern bool server_log_json;
@ -378,7 +389,7 @@ static json oaicompat_completion_params_parse(
return llama_params;
}
static json format_final_response_oaicompat(const json & request, json result, bool streaming = false) {
static json format_final_response_oaicompat(const json & request, json result, const std::string & completion_id, bool streaming = false) {
bool stopped_word = result.count("stopped_word") != 0;
bool stopped_eos = json_value(result, "stopped_eos", false);
int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
@ -412,7 +423,7 @@ static json format_final_response_oaicompat(const json & request, json result, b
{"prompt_tokens", num_prompt_tokens},
{"total_tokens", num_tokens_predicted + num_prompt_tokens}
}},
{"id", gen_chatcmplid()}
{"id", completion_id}
};
if (server_verbose) {
@ -427,7 +438,7 @@ static json format_final_response_oaicompat(const json & request, json result, b
}
// return value is vector as there is one case where we might need to generate two responses
static std::vector<json> format_partial_response_oaicompat(json result) {
static std::vector<json> format_partial_response_oaicompat(json result, const std::string & completion_id) {
if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
return std::vector<json>({result});
}
@ -471,7 +482,7 @@ static std::vector<json> format_partial_response_oaicompat(json result) {
{"role", "assistant"}
}}}})},
{"created", t},
{"id", gen_chatcmplid()},
{"id", completion_id},
{"model", modelname},
{"object", "chat.completion.chunk"}};
@ -482,7 +493,7 @@ static std::vector<json> format_partial_response_oaicompat(json result) {
{"content", content}}}
}})},
{"created", t},
{"id", gen_chatcmplid()},
{"id", completion_id},
{"model", modelname},
{"object", "chat.completion.chunk"}};
@ -509,7 +520,7 @@ static std::vector<json> format_partial_response_oaicompat(json result) {
json ret = json {
{"choices", choices},
{"created", t},
{"id", gen_chatcmplid()},
{"id", completion_id},
{"model", modelname},
{"object", "chat.completion.chunk"}
};
@ -518,6 +529,16 @@ static std::vector<json> format_partial_response_oaicompat(json result) {
}
static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
json data = json::array();
int i = 0;
for (auto & elem : embeddings) {
data.push_back(json{
{"embedding", json_value(elem, "embedding", json::array())},
{"index", i++},
{"object", "embedding"}
});
}
json res = json {
{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
{"object", "list"},
@ -525,7 +546,7 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
{"prompt_tokens", 0},
{"total_tokens", 0}
}},
{"data", embeddings}
{"data", data}
};
return res;
@ -542,3 +563,43 @@ static json format_detokenized_response(const std::string & content) {
{"content", content}
};
}
static json format_error_response(const std::string & message, const enum error_type type) {
std::string type_str;
int code = 500;
switch (type) {
case ERROR_TYPE_INVALID_REQUEST:
type_str = "invalid_request_error";
code = 400;
break;
case ERROR_TYPE_AUTHENTICATION:
type_str = "authentication_error";
code = 401;
break;
case ERROR_TYPE_NOT_FOUND:
type_str = "not_found_error";
code = 404;
break;
case ERROR_TYPE_SERVER:
type_str = "server_error";
code = 500;
break;
case ERROR_TYPE_PERMISSION:
type_str = "permission_error";
code = 403;
break;
case ERROR_TYPE_NOT_SUPPORTED:
type_str = "not_supported_error";
code = 501;
break;
case ERROR_TYPE_UNAVAILABLE:
type_str = "unavailable_error";
code = 503;
break;
}
return json {
{"code", code},
{"message", message},
{"type", type_str},
};
}

5
examples/sycl/build.sh
View file

@ -13,8 +13,11 @@ source /opt/intel/oneapi/setvars.sh
#for FP32
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
#build example/main only
#build example/main
#cmake --build . --config Release --target main
#build example/llama-bench
#cmake --build . --config Release --target llama-bench
#build all binary
cmake --build . --config Release -v

16
examples/sycl/run-llama2.sh
View file

@ -9,18 +9,28 @@ source /opt/intel/oneapi/setvars.sh
if [ $# -gt 0 ]; then
GGML_SYCL_DEVICE=$1
GGML_SYCL_SINGLE_GPU=1
else
GGML_SYCL_DEVICE=0
fi
echo "use $GGML_SYCL_DEVICE as main GPU"
#export GGML_SYCL_DEBUG=1
#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer.
#use all GPUs with same max compute units
ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
if [ $GGML_SYCL_SINGLE_GPU -eq 1 ]; then
echo "use $GGML_SYCL_DEVICE as main GPU"
#use signle GPU only
ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
else
#use multiple GPUs with same max compute units
ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
fi
#use main GPU only
#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
#use multiple GPUs with same max compute units
#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0

1
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

@ -711,6 +711,7 @@ static bool load_checkpoint_file(const char * filename, struct my_llama_model *
load_checkpoint_gguf(fctx, f_ggml_ctx, model, train);
gguf_free(fctx);
return true;
}

6
flake.lock generated
View file

@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1709237383,
"narHash": "sha256-cy6ArO4k5qTx+l5o+0mL9f5fa86tYUX3ozE1S+Txlds=",
"lastModified": 1709703039,
"narHash": "sha256-6hqgQ8OK6gsMu1VtcGKBxKQInRLHtzulDo9Z5jxHEFY=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "1536926ef5621b09bba54035ae2bb6d806d72ac8",
"rev": "9df3e30ce24fd28c7b3e2de0d986769db5d6225d",
"type": "github"
},
"original": {

119
ggml-alloc.c
View file

@ -61,7 +61,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
}
}
// TODO: GGML_PAD ?
static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
assert(alignment && !(alignment & (alignment - 1))); // power of 2
size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
@ -69,25 +68,14 @@ static size_t aligned_offset(const void * buffer, size_t offset, size_t alignmen
}
// tallocr
struct ggml_tallocr {
ggml_backend_buffer_t buffer;
void * base;
size_t alignment;
size_t offset;
};
ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer) {
ggml_tallocr_t talloc = malloc(sizeof(struct ggml_tallocr));
if (talloc == NULL) {
return NULL;
}
struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer) {
void * base = ggml_backend_buffer_get_base(buffer);
size_t align = ggml_backend_buffer_get_alignment(buffer);
assert(align && !(align & (align - 1))); // power of 2
*talloc = (struct ggml_tallocr) {
struct ggml_tallocr talloc = (struct ggml_tallocr) {
/*.buffer = */ buffer,
/*.base = */ base,
/*.alignment = */ align,
@ -96,11 +84,7 @@ ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer) {
return talloc;
}
void ggml_tallocr_free(ggml_tallocr_t talloc) {
free(talloc);
}
void ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor) {
void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor) {
size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor);
size = GGML_PAD(size, talloc->alignment);
@ -354,12 +338,16 @@ struct hash_node {
bool allocated;
};
//
struct tensor_alloc {
size_t offset;
size_t size_max; // 0 = pre-allocated, unused, or view
};
struct leaf_alloc {
int buffer_id;
struct tensor_alloc leaf;
};
struct node_alloc {
int buffer_id;
struct tensor_alloc dst;
@ -378,7 +366,7 @@ struct ggml_gallocr {
struct node_alloc * node_allocs; // [n_nodes]
int n_nodes;
struct tensor_alloc * leaf_allocs; // [n_leafs]
struct leaf_alloc * leaf_allocs; // [n_leafs]
int n_leafs;
};
@ -543,17 +531,28 @@ static int get_node_buffer_id(const int * node_buffer_ids, int i) {
return node_buffer_ids ? node_buffer_ids[i] : 0;
}
static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) {
static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
// clear hash tables
memset(galloc->hash_set.keys, 0, galloc->hash_set.size * sizeof(struct ggml_tensor *));
memset(galloc->hash_values, 0, galloc->hash_set.size * sizeof(struct hash_node));
// allocate leafs
// these may be tensors that the application is not using in the graph, but may still want to allocate for other purposes
for (int i = 0; i < graph->n_leafs; i++) {
struct ggml_tensor * leaf = graph->leafs[i];
ggml_gallocr_allocate_node(galloc, leaf, get_node_buffer_id(leaf_buffer_ids, i));
}
// count number of children and views
// allocate all graph inputs and leafs first to avoid overwriting them
// allocate other graph inputs and leafs first to avoid overwriting them
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
if (ggml_is_view(node)) {
// TODO: better way to add external dependencies
// GGML_OP_NONE does not appear normally in the graph nodes, but is used by ggml-backend to add dependencies to
// control when some tensors are allocated and freed. in this case, the dependencies are in `src`, but the node
// itself is never used and should not be considered a dependency
if (ggml_is_view(node) && node->op != GGML_OP_NONE) {
struct ggml_tensor * view_src = node->view_src;
ggml_gallocr_hash_get(galloc, view_src)->n_views += 1;
}
@ -570,26 +569,13 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
ggml_gallocr_hash_get(galloc, src)->n_children += 1;
// allocate explicit inputs and leafs
if (src->flags & GGML_TENSOR_FLAG_INPUT || src->op == GGML_OP_NONE) {
// allocate explicit inputs
if (src->flags & GGML_TENSOR_FLAG_INPUT) {
ggml_gallocr_allocate_node(galloc, src, get_node_buffer_id(node_buffer_ids, i));
}
}
}
// allocate the remaining leafs that are unused on the graph
// these are effectively static tensors that the application is not using in the graph, but may still want to allocate for other purposes
for (int i = 0; i < graph->n_leafs; i++) {
struct ggml_tensor * leaf = graph->leafs[i];
struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
if (hn->n_children == 0) {
assert(!hn->allocated);
// since buffer ids are only given for nodes, these leafs are always allocated in the first buffer
ggml_gallocr_allocate_node(galloc, leaf, 0);
}
}
// allocate tensors
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
@ -652,7 +638,7 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
}
}
bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) {
bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
size_t hash_size = graph->visited_hash_table.size;
// initialize hash table
@ -676,7 +662,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
}
// allocate in hash table
ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids);
ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids);
// set the node_allocs from the hash table
if (galloc->n_nodes < graph->n_nodes) {
@ -711,15 +697,16 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
}
if (galloc->n_leafs < graph->n_leafs) {
free(galloc->leaf_allocs);
galloc->leaf_allocs = calloc(sizeof(struct tensor_alloc), graph->n_leafs);
galloc->leaf_allocs = calloc(sizeof(galloc->leaf_allocs[0]), graph->n_leafs);
GGML_ASSERT(galloc->leaf_allocs != NULL);
}
galloc->n_leafs = graph->n_leafs;
for (int i = 0; i < graph->n_leafs; i++) {
struct ggml_tensor * leaf = graph->leafs[i];
struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
galloc->leaf_allocs[i].offset = hn->offset;
galloc->leaf_allocs[i].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
galloc->leaf_allocs[i].buffer_id = hn->buffer_id;
galloc->leaf_allocs[i].leaf.offset = hn->offset;
galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
}
// reallocate buffers if needed
@ -727,7 +714,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0;
size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]);
if (new_size > cur_size) {
// even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views
if (new_size > cur_size || galloc->buffers[i] == NULL) {
#ifndef NDEBUG
fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
#endif
@ -744,30 +732,30 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
}
bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
return ggml_gallocr_reserve_n(galloc, graph, NULL);
return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL);
}
static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, struct tensor_alloc * tensor_alloc) {
assert(node->data || node->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max);
static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, int buffer_id, struct tensor_alloc * tensor_alloc) {
assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
if (node->view_src != NULL) {
if (node->buffer == NULL) {
if (tensor->view_src != NULL) {
if (tensor->buffer == NULL) {
assert(tensor_alloc->offset == SIZE_MAX);
if (node->view_src->buffer == NULL) {
if (tensor->view_src->buffer == NULL) {
// this tensor was allocated without ggml-backend
return;
}
ggml_backend_view_init(galloc->buffers[buffer_id], node);
ggml_backend_view_init(galloc->buffers[buffer_id], tensor);
}
} else {
if (node->data == NULL) {
if (tensor->data == NULL) {
assert(tensor_alloc->offset != SIZE_MAX);
assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max);
assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
void * base = ggml_backend_buffer_get_base(galloc->buffers[buffer_id]);
void * addr = (char *)base + tensor_alloc->offset;
ggml_backend_tensor_alloc(galloc->buffers[buffer_id], node, addr);
ggml_backend_tensor_alloc(galloc->buffers[buffer_id], tensor, addr);
} else {
if (node->buffer == NULL) {
if (tensor->buffer == NULL) {
// this tensor was allocated without ggml-backend
return;
}
@ -843,13 +831,18 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
// reset buffers
for (int i = 0; i < galloc->n_buffers; i++) {
// zero size buffers are not allocated
if (galloc->buffers[i] != NULL) {
ggml_backend_buffer_reset(galloc->buffers[i]);
}
}
// allocate the graph tensors from the previous assignments
// leafs
for (int i = 0; i < graph->n_leafs; i++) {
struct ggml_tensor * leaf = graph->leafs[i];
struct leaf_alloc * leaf_alloc = &galloc->leaf_allocs[i];
ggml_gallocr_init_tensor(galloc, leaf, leaf_alloc->buffer_id, &leaf_alloc->leaf);
}
// nodes
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
@ -863,12 +856,6 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
}
ggml_gallocr_init_tensor(galloc, node, node_alloc->buffer_id, &node_alloc->dst);
}
// leafs
for (int i = 0; i < graph->n_leafs; i++) {
struct ggml_tensor * leaf = graph->leafs[i];
struct tensor_alloc * leaf_alloc = &galloc->leaf_allocs[i];
ggml_gallocr_init_tensor(galloc, leaf, 0, leaf_alloc);
}
return true;
}
@ -900,12 +887,12 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
return false;
}
struct ggml_tallocr * tallocr = ggml_tallocr_new(buffer);
struct ggml_tallocr tallocr = ggml_tallocr_new(buffer);
for (struct ggml_tensor * t = first; t != last; t = ggml_get_next_tensor(ctx, t)) {
if (t->data == NULL) {
if (t->view_src == NULL) {
ggml_tallocr_alloc(tallocr, t);
ggml_tallocr_alloc(&tallocr, t);
} else if (t->buffer == NULL) {
ggml_backend_view_init(buffer, t);
}
@ -917,8 +904,6 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
}
}
ggml_tallocr_free(tallocr);
*buffers = realloc(*buffers, sizeof(ggml_backend_buffer_t) * (*n_buffers + 1));
(*buffers)[(*n_buffers)++] = buffer;

18
ggml-alloc.h
View file

@ -11,11 +11,15 @@ typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
typedef struct ggml_backend * ggml_backend_t;
// Tensor allocator
typedef struct ggml_tallocr * ggml_tallocr_t;
struct ggml_tallocr {
ggml_backend_buffer_t buffer;
void * base;
size_t alignment;
size_t offset;
};
GGML_API ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer);
GGML_API void ggml_tallocr_free(ggml_tallocr_t talloc);
GGML_API void ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor);
GGML_API struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer);
GGML_API void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor);
// Graph allocator
/*
@ -50,7 +54,11 @@ GGML_API void ggml_gallocr_free(ggml_gallocr_t galloc);
// not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed
// returns false if the buffer allocation failed
GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
GGML_API bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids);
GGML_API bool ggml_gallocr_reserve_n(
ggml_gallocr_t galloc,
struct ggml_cgraph * graph,
const int * node_buffer_ids,
const int * leaf_buffer_ids);
// automatic reallocation if the topology changes when using a single buffer
// returns false if using multiple buffers and a re-allocation is needed (call ggml_gallocr_reserve_n first to set the node buffers)

22
ggml-backend-impl.h
View file

@ -86,12 +86,12 @@ extern "C" {
// (optional) asynchronous tensor data access
void (*GGML_CALL set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst);
bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
// (optional) complete all pending operations
void (*GGML_CALL synchronize)(ggml_backend_t backend);
// create a plan for ggml_cgraph and free it
// compute graph with a plan (not used currently)
ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
@ -102,16 +102,32 @@ extern "C" {
// check if the backend supports an operation
bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
// check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
// these should be expensive operations with large batch sizes that may benefit from running on this backend
// even if the weight has to be copied from the CPU temporarily
bool (*GGML_CALL offload_op)(ggml_backend_t backend, const struct ggml_tensor * op);
// (optional) event synchronization
ggml_backend_event_t (*GGML_CALL event_new) (ggml_backend_t backend);
void (*GGML_CALL event_free) (ggml_backend_event_t event);
void (*GGML_CALL event_record) (ggml_backend_event_t event);
void (*GGML_CALL event_wait) (ggml_backend_t backend, ggml_backend_event_t event);
void (*GGML_CALL event_synchronize) (ggml_backend_event_t event);
};
struct ggml_backend {
ggml_guid_t guid;
struct ggml_backend_i iface;
ggml_backend_context_t context;
};
struct ggml_backend_event {
ggml_backend_t backend;
void * context;
};
//
// Backend registry
//

723
ggml-backend.c
View file

File diff suppressed because it is too large Load diff

64
ggml-backend.h
View file

@ -9,6 +9,7 @@ extern "C" {
typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
typedef struct ggml_backend_event * ggml_backend_event_t;
typedef struct ggml_backend * ggml_backend_t;
typedef void * ggml_backend_graph_plan_t;
@ -69,14 +70,27 @@ extern "C" {
GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API enum ggml_status ggml_backend_graph_plan_compute (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op);
// 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
// asynchronous copy
// the copy is performed after all the currently queued operations in backend_src
// backend_dst will wait for the copy to complete before performing other operations
// automatic fallback to sync copy if async is not supported
GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
// events
GGML_API ggml_backend_event_t ggml_backend_event_new (ggml_backend_t backend);
GGML_API void ggml_backend_event_free (ggml_backend_event_t event);
GGML_API void ggml_backend_event_record (ggml_backend_event_t event);
GGML_API void ggml_backend_event_synchronize(ggml_backend_event_t event);
GGML_API void ggml_backend_event_wait (ggml_backend_t backend, ggml_backend_event_t event); // wait async on event
//
// CPU backend
@ -123,27 +137,31 @@ extern "C" {
/*
Example usage:
sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, num_backends);
// sched is initialized with measure allocators and cannot be used until allocated with a measure graph
// operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be asigned
// preferrably to run on the same backend as the buffer
ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
// initialize buffers from a measure graph
measure_graph = build_graph(sched); // use the allocr to allocate inputs as needed
sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
// in build_graph:
build_graph(...) {
// manually assign nodes to a backend (optional, should not be needed in most cases)
struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
ggml_backend_sched_set_node_backend(sched, node, backend_gpu);
}
// initialize buffers from a max size graph (optional)
reserve_graph = build_graph(sched, max_batch_size);
// allocate backend buffers from measure graph
ggml_backend_sched_init_measure(sched, measure_graph);
// manually assign nodes to a backend (optional, should not be needed in most cases)
struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu);
// the scheduler is now ready to compute graphs
ggml_backend_sched_reserve(sched, reserve_graph);
// compute
graph = build_graph(sched);
ggml_backend_sched_graph_compute(sched, graph);
// if there are graph inputs:
ggml_backend_sched_reset(sched);
ggml_backend_sched_alloc_graph(sched, graph);
ggml_backend_tensor_set(input_tensor, ...);
ggml_backend_sched_graph_compute(sched, graph);
}
*/
struct ggml_backend_sched;
@ -158,20 +176,26 @@ extern "C" {
typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
// Initialize a backend scheduler
GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size);
GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
// Initialize backend buffers from a measure graph
GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
// Get the number of splits of the last graph
GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
GGML_API int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
GGML_API ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
GGML_API void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
GGML_API ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
// Allocate and compute graph on the backend scheduler
GGML_API bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API void ggml_backend_sched_synchronize(ggml_backend_sched_t sched);
// Reset all assignments and allocators - must be called before changing the node backends
GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched);

1830
ggml-common.h Normal file
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ggml-cuda.cu
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21
ggml-cuda.h
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@ -17,29 +17,17 @@ extern "C" {
#define GGML_CUDA_MAX_DEVICES 16
// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`.
GGML_API GGML_CALL void ggml_init_cublas(void);
// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`.
GGML_API GGML_CALL bool ggml_cublas_loaded(void);
GGML_API GGML_CALL void * ggml_cuda_host_malloc(size_t size);
GGML_API GGML_CALL void ggml_cuda_host_free(void * ptr);
GGML_API GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
GGML_API GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
GGML_API GGML_CALL int ggml_cuda_get_device_count(void);
GGML_API GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size);
// backend API
GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device);
GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend);
// device buffer
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
// split tensor buffer that splits matrices by rows across multiple devices
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split);
// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
@ -47,6 +35,9 @@ GGML_API GGML_CALL int ggml_backend_cuda_get_device_count(void);
GGML_API GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
GGML_API GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size);
GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer);
#ifdef __cplusplus
}
#endif

8
ggml-impl.h
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@ -53,26 +53,30 @@ extern "C" {
//
#include <arm_neon.h>
typedef __fp16 ggml_fp16_internal_t;
#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
#define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
__fp16 tmp;
ggml_fp16_internal_t tmp;
memcpy(&tmp, &h, sizeof(ggml_fp16_t));
return (float)tmp;
}
static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
ggml_fp16_t res;
__fp16 tmp = f;
ggml_fp16_internal_t tmp = f;
memcpy(&res, &tmp, sizeof(ggml_fp16_t));
return res;
}
#else
typedef uint16_t ggml_fp16_internal_t;
#ifdef __wasm_simd128__
#include <wasm_simd128.h>
#else

6
ggml-kompute.cpp
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@ -1951,6 +1951,12 @@ static struct ggml_backend_i kompute_backend_i = {
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_kompute_graph_compute,
/* .supports_op = */ ggml_backend_kompute_supports_op,
/* .offload_op = */ NULL,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
/* .event_synchronize = */ NULL,
};
static ggml_guid_t ggml_backend_kompute_guid() {

63
ggml-metal.m
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@ -280,6 +280,11 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
id<MTLLibrary> metal_library;
// load library
//
// - first check if the library is embedded
// - then check if the library is in the bundle
// - if not found, load the source and compile it
// - if that fails, return NULL
{
NSBundle * bundle = nil;
#ifdef SWIFT_PACKAGE
@ -287,12 +292,21 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
#else
bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
#endif
NSError * error = nil;
NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"];
if (libPath != nil) {
#if GGML_METAL_EMBED_LIBRARY
const bool try_metallib = false;
#else
const bool try_metallib = true;
#endif
NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
if (try_metallib && path_lib != nil) {
// pre-compiled library found
NSURL * libURL = [NSURL fileURLWithPath:libPath];
GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]);
NSURL * libURL = [NSURL fileURLWithPath:path_lib];
GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
metal_library = [ctx->device newLibraryWithURL:libURL error:&error];
if (error) {
GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
@ -305,38 +319,41 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
extern const char ggml_metallib_start[];
extern const char ggml_metallib_end[];
NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
#else
GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
NSString * sourcePath;
NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
NSString * path_source;
NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, ggmlMetalPathResources ? [ggmlMetalPathResources UTF8String] : "nil");
GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil");
if (ggmlMetalPathResources) {
sourcePath = [ggmlMetalPathResources stringByAppendingPathComponent:@"ggml-metal.metal"];
if (path_resource) {
path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"];
} else {
sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
}
if (sourcePath == nil) {
if (path_source == nil) {
GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
sourcePath = @"ggml-metal.metal";
path_source = @"ggml-metal.metal";
}
GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [sourcePath UTF8String]);
NSString * src = [NSString stringWithContentsOfFile:sourcePath encoding:NSUTF8StringEncoding error:&error];
GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);
NSString * src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
if (error) {
GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
return NULL;
}
#endif
#endif // GGML_METAL_EMBED_LIBRARY
@autoreleasepool {
// dictionary of preprocessor macros
NSMutableDictionary * prep = [NSMutableDictionary dictionary];
#ifdef GGML_QKK_64
prep[@"QK_K"] = @(64);
prep[@"GGML_QKK_64"] = @(1);
#endif
MTLCompileOptions* options = [MTLCompileOptions new];
@ -1642,8 +1659,8 @@ static enum ggml_status ggml_metal_graph_compute(
// TODO: make this more general
GGML_ASSERT(n_as <= 8);
// max size of the src1ids array in the kernel stack
GGML_ASSERT(ne11 <= 512);
// max size of the src1ids array in the kernel shared buffer
GGML_ASSERT(ne11 <= 4096);
const int64_t ne20 = src2 ? src2->ne[0] : 0;
const int64_t ne21 = src2 ? src2->ne[1] : 0;
@ -1741,7 +1758,7 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j];
}
[encoder setThreadgroupMemoryLength:8192 atIndex:0];
[encoder setThreadgroupMemoryLength:GGML_PAD(8192 + 2*ne11, 16) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
} else {
@ -2820,6 +2837,12 @@ static struct ggml_backend_i ggml_backend_metal_i = {
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_metal_graph_compute,
/* .supports_op = */ ggml_backend_metal_supports_op,
/* .offload_op = */ NULL,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
/* .event_synchronize = */ NULL,
};
void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) {

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ggml-metal.metal
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ggml-quants.c
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286
ggml-quants.h
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@ -1,248 +1,12 @@
#pragma once
#include "ggml-impl.h"
#define GGML_COMMON_DECL_C
#include "ggml-common.h"
#include "ggml.h"
// GGML internal header
#include <stdint.h>
#include <stddef.h>
#define QK4_0 32
typedef struct {
ggml_fp16_t d; // delta
uint8_t qs[QK4_0 / 2]; // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
#define QK4_1 32
typedef struct {
ggml_fp16_t d; // delta
ggml_fp16_t m; // min
uint8_t qs[QK4_1 / 2]; // nibbles / quants
} block_q4_1;
static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
#define QK5_0 32
typedef struct {
ggml_fp16_t d; // delta
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_0 / 2]; // nibbles / quants
} block_q5_0;
static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
#define QK5_1 32
typedef struct {
ggml_fp16_t d; // delta
ggml_fp16_t m; // min
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_1 / 2]; // nibbles / quants
} block_q5_1;
static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
#define QK8_0 32
typedef struct {
ggml_fp16_t d; // delta
int8_t qs[QK8_0]; // quants
} block_q8_0;
static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
#define QK8_1 32
typedef struct {
float d; // delta
float s; // d * sum(qs[i])
int8_t qs[QK8_1]; // quants
} block_q8_1;
static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
//
// Super-block quantization structures
//
// Super-block size
#ifdef GGML_QKK_64
#define QK_K 64
#define K_SCALE_SIZE 4
#else
#define QK_K 256
#define K_SCALE_SIZE 12
#endif
// 2-bit quantization
// weight is represented as x = a * q + b
// 16 blocks of 16 elements each
// Effectively 2.625 bits per weight
typedef struct {
uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
uint8_t qs[QK_K/4]; // quants
ggml_fp16_t d; // super-block scale for quantized scales
ggml_fp16_t dmin; // super-block scale for quantized mins
} block_q2_K;
static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
// 3-bit quantization
// weight is represented as x = a * q
// 16 blocks of 16 elements each
// Effectively 3.4375 bits per weight
#ifdef GGML_QKK_64
typedef struct {
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
uint8_t scales[2];
ggml_fp16_t d; // super-block scale
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
#else
typedef struct {
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
uint8_t scales[12]; // scales, quantized with 6 bits
ggml_fp16_t d; // super-block scale
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
#endif
// 4-bit quantization
// 8 blocks of 32 elements each
// weight is represented as x = a * q + b
// Effectively 4.5 bits per weight
#ifdef GGML_QKK_64
typedef struct {
ggml_fp16_t d[2]; // super-block scales/mins
uint8_t scales[2]; // 4-bit block scales/mins
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
#else
typedef struct {
ggml_fp16_t d; // super-block scale for quantized scales
ggml_fp16_t dmin; // super-block scale for quantized mins
uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
#endif
// 5-bit quantization
// 8 blocks of 32 elements each
// weight is represented as x = a * q + b
// Effectively 5.5 bits per weight
#ifdef GGML_QKK_64
typedef struct {
ggml_fp16_t d; // super-block scale
int8_t scales[QK_K/16]; // 8-bit block scales
uint8_t qh[QK_K/8]; // quants, high bit
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_K;
static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
#else
typedef struct {
ggml_fp16_t d; // super-block scale for quantized scales
ggml_fp16_t dmin; // super-block scale for quantized mins
uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
uint8_t qh[QK_K/8]; // quants, high bit
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_K;
static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
#endif
// 6-bit quantization
// weight is represented as x = a * q
// 16 blocks of 16 elements each
// Effectively 6.5625 bits per weight
typedef struct {
uint8_t ql[QK_K/2]; // quants, lower 4 bits
uint8_t qh[QK_K/4]; // quants, upper 2 bits
int8_t scales[QK_K/16]; // scales, quantized with 8 bits
ggml_fp16_t d; // super-block scale
} block_q6_K;
static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
// This is only used for intermediate quantization and dot products
typedef struct {
float d; // delta
int8_t qs[QK_K]; // quants
int16_t bsums[QK_K/16]; // sum of quants in groups of 16
} block_q8_K;
static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
// (Almost) "true" 2-bit quantization.
// Due to the need to use blocks as per ggml design, it ends up using
// 2.0625 bpw because of the 16-bit scale for each block of 256.
typedef struct {
ggml_fp16_t d;
uint16_t qs[QK_K/8];
} block_iq2_xxs;
static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding");
// 2.3125 bpw quants
typedef struct {
ggml_fp16_t d;
uint16_t qs[QK_K/8];
uint8_t scales[QK_K/32];
} block_iq2_xs;
static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding");
// 2.5625 bpw quants
typedef struct {
ggml_fp16_t d;
uint8_t qs[QK_K/4];
uint8_t qh[QK_K/32];
uint8_t scales[QK_K/32];
} block_iq2_s;
static_assert(sizeof(block_iq2_s) == sizeof(ggml_fp16_t) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding");
// (Almost) "true" 3-bit quantization.
// Due to the need to use blocks as per ggml design, it ends up using
// 3.0625 bpw because of the 16-bit scale for each block of 256.
typedef struct {
ggml_fp16_t d;
uint8_t qs[3*QK_K/8];
} block_iq3_xxs;
static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
// 3.4375 bpw
#if QK_K == 64
#define IQ3S_N_SCALE 2
#else
#define IQ3S_N_SCALE QK_K/64
#endif
typedef struct {
ggml_fp16_t d;
uint8_t qs[QK_K/4];
uint8_t qh[QK_K/32];
uint8_t signs[QK_K/8];
uint8_t scales[IQ3S_N_SCALE];
} block_iq3_s;
static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding");
typedef struct {
ggml_fp16_t d;
uint8_t qs[QK_K/8];
uint8_t scales[QK_K/16];
} block_iq1_s;
static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding");
// Non-linear quants
#define QK4_NL 32
typedef struct {
ggml_fp16_t d;
uint8_t qs[QK4_NL/2];
} block_iq4_nl;
static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding");
#if QK_K == 64
#define block_iq4_xs block_iq4_nl
//typedef struct block_iq4_nl block_iq4_xs;
#else
typedef struct {
ggml_fp16_t d;
uint16_t scales_h;
uint8_t scales_l[QK_K/64];
uint8_t qs[QK_K/2];
} block_iq4_xs;
static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
#endif
#ifdef __cplusplus
extern "C" {
#endif
@ -261,6 +25,7 @@ void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGM
void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int k);
void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k);
void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k);
void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k);
void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int k);
void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int k);
@ -280,6 +45,7 @@ void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
@ -300,6 +66,7 @@ void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRI
void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
@ -321,6 +88,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
@ -330,26 +98,26 @@ void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const
void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
//
// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
//
size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq2_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq1_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq4_nl (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq4_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq3_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q4_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q5_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q6_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q4_0 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q4_1 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q5_0 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q5_1 (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
void iq2xs_init_impl(enum ggml_type type);
void iq2xs_free_impl(enum ggml_type type);

1286
ggml-sycl.cpp
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File diff suppressed because it is too large Load diff

5
ggml-sycl.h
View file

@ -29,6 +29,11 @@ GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_typ
GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total);
GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id);
// TODO: these are temporary
// ref: https://github.com/ggerganov/llama.cpp/pull/6022#issuecomment-1992615670
GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index);
GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id);
GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode();
#ifdef __cplusplus
}
#endif

52
ggml-vulkan.cpp
View file

@ -709,6 +709,12 @@ static uint32_t ggml_vk_find_queue_family_index(std::vector<vk::QueueFamilyPrope
}
}
// All commands that are allowed on a queue that supports transfer operations are also allowed on a queue that supports either graphics or compute operations.
// Thus, if the capabilities of a queue family include VK_QUEUE_GRAPHICS_BIT or VK_QUEUE_COMPUTE_BIT, then reporting the VK_QUEUE_TRANSFER_BIT capability separately for that queue family is optional.
if (compute_index >= 0) {
return compute_index;
}
std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl;
for(auto &q_family : queue_family_props) {
@ -4186,45 +4192,7 @@ static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool
}
static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) {
std::vector<int64_t> hist_cur(1 << 4, 0);
switch(quant) {
case GGML_TYPE_F32:
memcpy(to, from, sizeof(float) * ne);
break;
case GGML_TYPE_Q4_0:
ggml_quantize_q4_0(from, to, ne, ne, hist_cur.data());
break;
case GGML_TYPE_Q4_1:
ggml_quantize_q4_1(from, to, ne, ne, hist_cur.data());
break;
case GGML_TYPE_Q5_0:
ggml_quantize_q5_0(from, to, ne, ne, hist_cur.data());
break;
case GGML_TYPE_Q5_1:
ggml_quantize_q5_1(from, to, ne, ne, hist_cur.data());
break;
case GGML_TYPE_Q8_0:
ggml_quantize_q8_0(from, to, ne, ne, hist_cur.data());
break;
case GGML_TYPE_Q2_K:
ggml_quantize_q2_K(from, to, ne, ne, hist_cur.data());
break;
case GGML_TYPE_Q3_K:
ggml_quantize_q3_K(from, to, ne, ne, hist_cur.data());
break;
case GGML_TYPE_Q4_K:
ggml_quantize_q4_K(from, to, ne, ne, hist_cur.data());
break;
case GGML_TYPE_Q5_K:
ggml_quantize_q5_K(from, to, ne, ne, hist_cur.data());
break;
case GGML_TYPE_Q6_K:
ggml_quantize_q6_K(from, to, ne, ne, hist_cur.data());
break;
default:
GGML_ASSERT(false);
}
ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr);
}
static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) {
@ -5875,6 +5843,12 @@ static ggml_backend_i ggml_backend_vk_interface = {
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_vk_graph_compute,
/* .supports_op = */ ggml_backend_vk_supports_op,
/* .offload_op = */ NULL,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
/* .event_synchronize = */ NULL,
};
static ggml_guid_t ggml_backend_vk_guid() {

966
ggml.c
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File diff suppressed because it is too large Load diff

108
ggml.h
View file

@ -337,24 +337,24 @@ extern "C" {
struct ggml_object;
struct ggml_context;
// NOTE: always add types at the end of the enum to keep backward compatibility
enum ggml_type {
GGML_TYPE_F32 = 0,
GGML_TYPE_F16 = 1,
GGML_TYPE_Q4_0 = 2,
GGML_TYPE_Q4_1 = 3,
GGML_TYPE_F32 = 0,
GGML_TYPE_F16 = 1,
GGML_TYPE_Q4_0 = 2,
GGML_TYPE_Q4_1 = 3,
// GGML_TYPE_Q4_2 = 4, support has been removed
// GGML_TYPE_Q4_3 (5) support has been removed
GGML_TYPE_Q5_0 = 6,
GGML_TYPE_Q5_1 = 7,
GGML_TYPE_Q8_0 = 8,
GGML_TYPE_Q8_1 = 9,
// k-quantizations
GGML_TYPE_Q2_K = 10,
GGML_TYPE_Q3_K = 11,
GGML_TYPE_Q4_K = 12,
GGML_TYPE_Q5_K = 13,
GGML_TYPE_Q6_K = 14,
GGML_TYPE_Q8_K = 15,
// GGML_TYPE_Q4_3 = 5, support has been removed
GGML_TYPE_Q5_0 = 6,
GGML_TYPE_Q5_1 = 7,
GGML_TYPE_Q8_0 = 8,
GGML_TYPE_Q8_1 = 9,
GGML_TYPE_Q2_K = 10,
GGML_TYPE_Q3_K = 11,
GGML_TYPE_Q4_K = 12,
GGML_TYPE_Q5_K = 13,
GGML_TYPE_Q6_K = 14,
GGML_TYPE_Q8_K = 15,
GGML_TYPE_IQ2_XXS = 16,
GGML_TYPE_IQ2_XS = 17,
GGML_TYPE_IQ3_XXS = 18,
@ -363,9 +363,11 @@ extern "C" {
GGML_TYPE_IQ3_S = 21,
GGML_TYPE_IQ2_S = 22,
GGML_TYPE_IQ4_XS = 23,
GGML_TYPE_I8,
GGML_TYPE_I16,
GGML_TYPE_I32,
GGML_TYPE_I8 = 24,
GGML_TYPE_I16 = 25,
GGML_TYPE_I32 = 26,
GGML_TYPE_I64 = 27,
GGML_TYPE_F64 = 28,
GGML_TYPE_COUNT,
};
@ -383,20 +385,20 @@ extern "C" {
// model file types
enum ggml_ftype {
GGML_FTYPE_UNKNOWN = -1,
GGML_FTYPE_ALL_F32 = 0,
GGML_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
GGML_FTYPE_UNKNOWN = -1,
GGML_FTYPE_ALL_F32 = 0,
GGML_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
GGML_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
GGML_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
GGML_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors
GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors
GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
GGML_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
GGML_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
GGML_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors
GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors
GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors
GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ2_XS = 16, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors
@ -472,6 +474,8 @@ extern "C" {
GGML_OP_FLASH_ATTN,
GGML_OP_FLASH_FF,
GGML_OP_FLASH_ATTN_BACK,
GGML_OP_SSM_CONV,
GGML_OP_SSM_SCAN,
GGML_OP_WIN_PART,
GGML_OP_WIN_UNPART,
GGML_OP_GET_REL_POS,
@ -1728,6 +1732,23 @@ extern "C" {
struct ggml_tensor * c0,
struct ggml_tensor * c1);
GGML_API struct ggml_tensor * ggml_ssm_conv(
struct ggml_context * ctx,
struct ggml_tensor * s,
struct ggml_tensor * x,
struct ggml_tensor * c,
struct ggml_tensor * sq);
GGML_API struct ggml_tensor * ggml_ssm_scan(
struct ggml_context * ctx,
struct ggml_tensor * s,
struct ggml_tensor * x,
struct ggml_tensor * dt,
struct ggml_tensor * A,
struct ggml_tensor * B,
struct ggml_tensor * C,
struct ggml_tensor * sq);
// partition into non-overlapping windows with padding if needed
// example:
// a: 768 64 64 1
@ -2175,25 +2196,18 @@ extern "C" {
GGML_API void ggml_quantize_init(enum ggml_type type);
GGML_API void ggml_quantize_free(void);
// TODO: these would probably get removed in favor of the more general ggml_quantize_chunk
GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
// some quantization type cannot be used without an importance matrix
GGML_API bool ggml_quantize_requires_imatrix(enum ggml_type type);
// calls ggml_quantize_init internally (i.e. can allocate memory)
GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst,
int start, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
GGML_API size_t ggml_quantize_chunk(
enum ggml_type type,
const float * src,
void * dst,
int start,
int nrows,
int n_per_row,
const float * imatrix);
//
// gguf

68
gguf-py/gguf/constants.py
View file

@ -32,6 +32,7 @@ class Keys:
FILE_TYPE = "general.file_type"
class LLM:
VOCAB_SIZE = "{arch}.vocab_size"
CONTEXT_LENGTH = "{arch}.context_length"
EMBEDDING_LENGTH = "{arch}.embedding_length"
BLOCK_COUNT = "{arch}.block_count"
@ -41,6 +42,7 @@ class Keys:
EXPERT_COUNT = "{arch}.expert_count"
EXPERT_USED_COUNT = "{arch}.expert_used_count"
POOLING_TYPE = "{arch}.pooling_type"
LOGIT_SCALE = "{arch}.logit_scale"
class Attention:
HEAD_COUNT = "{arch}.attention.head_count"
@ -61,6 +63,12 @@ class Keys:
SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
SCALING_FINETUNED = "{arch}.rope.scaling.finetuned"
class SSM:
CONV_KERNEL = "{arch}.ssm.conv_kernel"
INNER_SIZE = "{arch}.ssm.inner_size"
STATE_SIZE = "{arch}.ssm.state_size"
TIME_STEP_RANK = "{arch}.ssm.time_step_rank"
class Tokenizer:
MODEL = "tokenizer.ggml.model"
LIST = "tokenizer.ggml.tokens"
@ -113,6 +121,8 @@ class MODEL_ARCH(IntEnum):
MINICPM = auto()
GEMMA = auto()
STARCODER2 = auto()
MAMBA = auto()
COMMAND_R = auto()
class MODEL_TENSOR(IntEnum):
@ -144,6 +154,13 @@ class MODEL_TENSOR(IntEnum):
ATTN_Q_NORM = auto()
ATTN_K_NORM = auto()
LAYER_OUT_NORM = auto()
SSM_IN = auto()
SSM_CONV1D = auto()
SSM_X = auto()
SSM_DT = auto()
SSM_A = auto()
SSM_D = auto()
SSM_OUT = auto()
MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@ -171,6 +188,8 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.MINICPM: "minicpm",
MODEL_ARCH.GEMMA: "gemma",
MODEL_ARCH.STARCODER2: "starcoder2",
MODEL_ARCH.MAMBA: "mamba",
MODEL_ARCH.COMMAND_R: "command-r",
}
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -202,6 +221,13 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down.{xid}",
MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up.{xid}",
MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm",
MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in",
MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d",
MODEL_TENSOR.SSM_X: "blk.{bid}.ssm_x",
MODEL_TENSOR.SSM_DT: "blk.{bid}.ssm_dt",
MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a",
MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d",
MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out",
}
MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@ -543,6 +569,31 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.MAMBA: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.SSM_IN,
MODEL_TENSOR.SSM_CONV1D,
MODEL_TENSOR.SSM_X,
MODEL_TENSOR.SSM_DT,
MODEL_TENSOR.SSM_A,
MODEL_TENSOR.SSM_D,
MODEL_TENSOR.SSM_OUT,
],
MODEL_ARCH.COMMAND_R: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
# TODO
}
@ -626,6 +677,11 @@ class GGMLQuantizationType(IntEnum):
IQ3_S = 21
IQ2_S = 22
IQ4_XS = 23
I8 = 24
I16 = 25
I32 = 26
I64 = 27
F64 = 28
class GGUFEndian(IntEnum):
@ -692,6 +748,11 @@ GGML_QUANT_SIZES = {
GGMLQuantizationType.IQ3_S: (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4),
GGMLQuantizationType.IQ2_S: (256, 2 + QK_K // 4 + QK_K // 16),
GGMLQuantizationType.IQ4_XS: (256, 2 + 2 + QK_K // 2 + QK_K // 64),
GGMLQuantizationType.I8: (1, 1),
GGMLQuantizationType.I16: (1, 2),
GGMLQuantizationType.I32: (1, 4),
GGMLQuantizationType.I64: (1, 8),
GGMLQuantizationType.F64: (1, 8),
}
@ -711,6 +772,7 @@ KEY_GENERAL_SOURCE_HF_REPO = Keys.General.SOURCE_HF_REPO
KEY_GENERAL_FILE_TYPE = Keys.General.FILE_TYPE
# LLM
KEY_VOCAB_SIZE = Keys.LLM.VOCAB_SIZE
KEY_CONTEXT_LENGTH = Keys.LLM.CONTEXT_LENGTH
KEY_EMBEDDING_LENGTH = Keys.LLM.EMBEDDING_LENGTH
KEY_BLOCK_COUNT = Keys.LLM.BLOCK_COUNT
@ -734,6 +796,12 @@ KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR
KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN
KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED
# SSM
KEY_SSM_CONV_KERNEL = Keys.SSM.CONV_KERNEL
KEY_SSM_INNER_SIZE = Keys.SSM.INNER_SIZE
KEY_SSM_STATE_SIZE = Keys.SSM.STATE_SIZE
KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK
# tokenization
KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL
KEY_TOKENIZER_LIST = Keys.Tokenizer.LIST

23
gguf-py/gguf/gguf_reader.py
View file

@ -242,12 +242,27 @@ class GGUFReader:
n_bytes = n_elems * type_size // block_size
data_offs = int(start_offs + offset_tensor[0])
item_type: npt.DTypeLike
if ggml_type == GGMLQuantizationType.F32:
item_count = n_elems
item_type = np.float32
elif ggml_type == GGMLQuantizationType.F16:
if ggml_type == GGMLQuantizationType.F16:
item_count = n_elems
item_type = np.float16
elif ggml_type == GGMLQuantizationType.F32:
item_count = n_elems
item_type = np.float32
elif ggml_type == GGMLQuantizationType.F64:
item_count = n_elems
item_type = np.float64
elif ggml_type == GGMLQuantizationType.I8:
item_count = n_elems
item_type = np.int8
elif ggml_type == GGMLQuantizationType.I16:
item_count = n_elems
item_type = np.int16
elif ggml_type == GGMLQuantizationType.I32:
item_count = n_elems
item_type = np.int32
elif ggml_type == GGMLQuantizationType.I64:
item_count = n_elems
item_type = np.int64
else:
item_count = n_bytes
item_type = np.uint8

38
gguf-py/gguf/gguf_writer.py
View file

@ -196,9 +196,6 @@ class GGUFWriter:
if self.state is not WriterState.EMPTY:
raise ValueError(f'Expected output file to be empty, got {self.state}')
if raw_dtype is None and tensor_dtype not in (np.float32, np.float16):
raise ValueError("Only F32 and F16 tensors are supported for now")
encoded_name = name.encode("utf8")
self.ti_data += self._pack("Q", len(encoded_name))
self.ti_data += encoded_name
@ -207,7 +204,22 @@ class GGUFWriter:
for i in range(n_dims):
self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i])
if raw_dtype is None:
dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
if tensor_dtype == np.float16:
dtype = GGMLQuantizationType.F16
elif tensor_dtype == np.float32:
dtype = GGMLQuantizationType.F32
elif tensor_dtype == np.float64:
dtype = GGMLQuantizationType.F64
elif tensor_dtype == np.int8:
dtype = GGMLQuantizationType.I8
elif tensor_dtype == np.int16:
dtype = GGMLQuantizationType.I16
elif tensor_dtype == np.int32:
dtype = GGMLQuantizationType.I32
elif tensor_dtype == np.int64:
dtype = GGMLQuantizationType.I64
else:
raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now")
else:
dtype = raw_dtype
self.ti_data += self._pack("I", dtype)
@ -313,6 +325,9 @@ class GGUFWriter:
self.data_alignment = alignment
self.add_uint32(Keys.General.ALIGNMENT, alignment)
def add_vocab_size(self, size: int) -> None:
self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size)
def add_context_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length)
@ -346,6 +361,9 @@ class GGUFWriter:
def add_clamp_kqv(self, value: float) -> None:
self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
def add_logit_scale(self, value: float) -> None:
self.add_float32(Keys.LLM.LOGIT_SCALE.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)
@ -382,6 +400,18 @@ class GGUFWriter:
def add_rope_scaling_finetuned(self, value: bool) -> None:
self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value)
def add_ssm_conv_kernel(self, value: int) -> None:
self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value)
def add_ssm_inner_size(self, value: int) -> None:
self.add_uint32(Keys.SSM.INNER_SIZE.format(arch=self.arch), value)
def add_ssm_state_size(self, value: int) -> None:
self.add_uint32(Keys.SSM.STATE_SIZE.format(arch=self.arch), value)
def add_ssm_time_step_rank(self, value: int) -> None:
self.add_uint32(Keys.SSM.TIME_STEP_RANK.format(arch=self.arch), value)
def add_tokenizer_model(self, model: str) -> None:
self.add_string(Keys.Tokenizer.MODEL, model)

46
gguf-py/gguf/tensor_mapping.py
View file

@ -20,6 +20,9 @@ class TensorNameMap:
"wte", # gpt2
"transformer.embd.wte", # phi2
"model.tok_embeddings", # internlm2
"model.embedding", # mamba-qbert
"backbone.embedding", # mamba
"backbone.embeddings", # mamba-hf
),
# Token type embeddings
@ -44,7 +47,7 @@ class TensorNameMap:
# Output
MODEL_TENSOR.OUTPUT: (
"embed_out", # gptneox
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba
"output", # llama-pth bloom internlm2
"word_embeddings_for_head", # persimmon
"lm_head.linear", # phi2
@ -61,6 +64,8 @@ class TensorNameMap:
"language_model.encoder.final_layernorm", # persimmon
"model.final_layernorm", # persimmon
"lm_head.ln", # phi2
"model.norm_f", # mamba-qbert
"backbone.norm_f", # mamba
),
# Rope frequencies
@ -86,6 +91,8 @@ class TensorNameMap:
"transformer.h.{bid}.ln", # phi2
"model.layers.layers.{bid}.norm", # plamo
"model.layers.{bid}.attention_norm", # internlm2
"model.layers.{bid}.norm", # mamba-qbert
"backbone.layers.{bid}.norm", # mamba
),
# Attention norm 2
@ -282,7 +289,42 @@ class TensorNameMap:
MODEL_TENSOR.LAYER_OUT_NORM: (
"encoder.layer.{bid}.output.LayerNorm", # bert
"encoder.layers.{bid}.norm2", # nomic-bert
)
),
MODEL_TENSOR.SSM_IN: (
"model.layers.{bid}.in_proj",
"backbone.layers.{bid}.mixer.in_proj",
),
MODEL_TENSOR.SSM_CONV1D: (
"model.layers.{bid}.conv1d",
"backbone.layers.{bid}.mixer.conv1d",
),
MODEL_TENSOR.SSM_X: (
"model.layers.{bid}.x_proj",
"backbone.layers.{bid}.mixer.x_proj",
),
MODEL_TENSOR.SSM_DT: (
"model.layers.{bid}.dt_proj",
"backbone.layers.{bid}.mixer.dt_proj",
),
MODEL_TENSOR.SSM_A: (
"model.layers.{bid}.A_log",
"backbone.layers.{bid}.mixer.A_log",
),
MODEL_TENSOR.SSM_D: (
"model.layers.{bid}.D",
"backbone.layers.{bid}.mixer.D",
),
MODEL_TENSOR.SSM_OUT: (
"model.layers.{bid}.out_proj",
"backbone.layers.{bid}.mixer.out_proj",
),
}
mapping: dict[str, tuple[MODEL_TENSOR, str]]

2
gguf-py/pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "gguf"
version = "0.7.0"
version = "0.8.0"
description = "Read and write ML models in GGUF for GGML"
authors = ["GGML <ggml@ggml.ai>"]
packages = [

2573
llama.cpp
View file

File diff suppressed because it is too large Load diff

59
llama.h
View file

@ -59,9 +59,10 @@ extern "C" {
typedef int32_t llama_seq_id;
enum llama_vocab_type {
LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece
LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding
LLAMA_VOCAB_TYPE_WPM = 2, // WordPiece
LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
LLAMA_VOCAB_TYPE_SPM = 1, // SentencePiece
LLAMA_VOCAB_TYPE_BPE = 2, // Byte Pair Encoding
LLAMA_VOCAB_TYPE_WPM = 3, // WordPiece
};
// note: these values should be synchronized with ggml_rope
@ -234,7 +235,9 @@ extern "C" {
struct llama_context_params {
uint32_t seed; // RNG seed, -1 for random
uint32_t n_ctx; // text context, 0 = from model
uint32_t n_batch; // prompt processing maximum batch size
uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode
uint32_t n_ubatch; // physical maximum batch size
uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models)
uint32_t n_threads; // number of threads to use for generation
uint32_t n_threads_batch; // number of threads to use for batch processing
@ -277,7 +280,7 @@ extern "C" {
bool allow_requantize; // allow quantizing non-f32/f16 tensors
bool quantize_output_tensor; // quantize output.weight
bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
bool pure; // disable k-quant mixtures and quantize all tensors to the same type
bool pure; // quantize all tensors to the default type
void * imatrix; // pointer to importance matrix data
} llama_model_quantize_params;
@ -376,6 +379,8 @@ extern "C" {
LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx);
LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx);
LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model);
@ -383,6 +388,7 @@ extern "C" {
LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
LLAMA_API int32_t llama_n_embd (const struct llama_model * model);
LLAMA_API int32_t llama_n_layer (const struct llama_model * model);
// Get the model's RoPE frequency scaling factor
LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
@ -430,10 +436,24 @@ extern "C" {
// Returns 0 on success
LLAMA_API int32_t llama_model_apply_lora_from_file(
const struct llama_model * model,
const char * path_lora,
float scale,
const char * path_base_model,
int32_t n_threads);
const char * path_lora,
float scale,
const char * path_base_model,
int32_t n_threads);
// Apply a loaded control vector to a llama_context, or if data is NULL, clear
// the currently loaded vector.
// n_embd should be the size of a single layer's control, and data should point
// to an n_embd x n_layers buffer starting from layer 1.
// il_start and il_end are the layer range the vector should apply to (both inclusive)
// See llama_control_vector_load in common to load a control vector.
LLAMA_API int32_t llama_control_vector_apply(
struct llama_context * lctx,
const float * data,
size_t len,
int32_t n_embd,
int32_t il_start,
int32_t il_end);
//
// KV cache
@ -454,7 +474,7 @@ extern "C" {
// Maximum number of sequences that can exist in a cell. It's not an error
// if there are more sequences in a cell than this value, however they will
// not be visible in the view cells_sequences.
int32_t n_max_seq;
int32_t n_seq_max;
// Number of tokens in the cache. For example, if there are two populated
// cells, the first with 1 sequence id in it and the second with 2 sequence
@ -474,12 +494,12 @@ extern "C" {
// Information for an individual cell.
struct llama_kv_cache_view_cell * cells;
// The sequences for each cell. There will be n_max_seq items per cell.
// The sequences for each cell. There will be n_seq_max items per cell.
llama_seq_id * cells_sequences;
};
// Create an empty KV cache view. (use only for debugging purposes)
LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq);
LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max);
// Free a KV cache view. (use only for debugging purposes)
LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
@ -502,7 +522,7 @@ extern "C" {
// seq_id < 0 : match any sequence
// p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_cache_seq_rm(
LLAMA_API bool llama_kv_cache_seq_rm(
struct llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
@ -641,9 +661,18 @@ extern "C" {
// n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
// Set whether to use causal attention or not
// If set to true, the model will only attend to the past tokens
LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
// Set abort callback
LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
// Wait until all computations are finished
// This is automatically done when using one of the functions below to obtain the computation results
// and is not necessary to call it explicitly in most cases
LLAMA_API void llama_synchronize(struct llama_context * ctx);
// Token logits obtained from the last call to llama_decode()
// The logits for the last token are stored in the last row
// Logits for which llama_batch.logits[i] == 0 are undefined
@ -702,7 +731,7 @@ extern "C" {
/// @details Convert the provided text into tokens.
/// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
/// @return Returns the number of tokens on success, no more than n_max_tokens
/// @return Returns the number of tokens on success, no more than n_tokens_max
/// @return Returns a negative number on failure - the number of tokens that would have been returned
/// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
/// Does not insert a leading space.
@ -711,7 +740,7 @@ extern "C" {
const char * text,
int32_t text_len,
llama_token * tokens,
int32_t n_max_tokens,
int32_t n_tokens_max,
bool add_bos,
bool special);

2
scripts/sync-ggml-am.sh
View file

@ -94,6 +94,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
# src/ggml-alloc.c -> ggml-alloc.c
# src/ggml-backend-impl.h -> ggml-backend-impl.h
# src/ggml-backend.c -> ggml-backend.c
# src/ggml-common.h -> ggml-common.h
# src/ggml-cuda.cu -> ggml-cuda.cu
# src/ggml-cuda.h -> ggml-cuda.h
# src/ggml-impl.h -> ggml-impl.h
@ -126,6 +127,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
-e 's/src\/ggml-alloc\.c/ggml-alloc.c/g' \
-e 's/src\/ggml-backend-impl\.h/ggml-backend-impl.h/g' \
-e 's/src\/ggml-backend\.c/ggml-backend.c/g' \
-e 's/src\/ggml-common\.h/ggml-common.h/g' \
-e 's/src\/ggml-cuda\.cu/ggml-cuda.cu/g' \
-e 's/src\/ggml-cuda\.h/ggml-cuda.h/g' \
-e 's/src\/ggml-impl\.h/ggml-impl.h/g' \

2
scripts/sync-ggml.last
View file

@ -1 +1 @@
8695910a39102609073d0e099aa7c97d6bcb3bf9
43a6d4af1971ee2912ff7bc2404011ff327b6a60

1
scripts/sync-ggml.sh
View file

@ -4,6 +4,7 @@ cp -rpv ../ggml/src/ggml.c ./ggml.c
cp -rpv ../ggml/src/ggml-alloc.c ./ggml-alloc.c
cp -rpv ../ggml/src/ggml-backend-impl.h ./ggml-backend-impl.h
cp -rpv ../ggml/src/ggml-backend.c ./ggml-backend.c
cp -rpv ../ggml/src/ggml-common.h ./ggml-common.h
cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu
cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h
cp -rpv ../ggml/src/ggml-impl.h ./ggml-impl.h

1
tests/.gitignore vendored
View file

@ -1,3 +1,4 @@
*
!*.*
*.o
ggml-common.h

23
tests/test-backend-ops.cpp
View file

@ -53,7 +53,6 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
} else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16) {
GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0);
std::vector<uint8_t> dataq(ggml_row_size(tensor->type, size));
int64_t hist[16];
std::vector<float> imatrix(tensor->ne[0], 1.0f); // dummy importance matrix
const float * im = imatrix.data();
if (!ggml_quantize_requires_imatrix(tensor->type)) {
@ -63,7 +62,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
im = nullptr;
}
}
ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], hist, im);
ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im);
ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
} else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
// This is going to create some weird integers though.
@ -2223,8 +2222,8 @@ static void usage(char ** argv) {
int main(int argc, char ** argv) {
test_mode mode = MODE_TEST;
const char * op_name = NULL;
const char * backend = NULL;
const char * op_name_filter = NULL;
const char * backend_filter = NULL;
for (int i = 1; i < argc; i++) {
if (strcmp(argv[i], "test") == 0) {
@ -2233,14 +2232,14 @@ int main(int argc, char ** argv) {
mode = MODE_PERF;
} else if (strcmp(argv[i], "-o") == 0) {
if (i + 1 < argc) {
op_name = argv[++i];
op_name_filter = argv[++i];
} else {
usage(argv);
return 1;
}
} else if (strcmp(argv[i], "-b") == 0) {
if (i + 1 < argc) {
backend = argv[++i];
backend_filter = argv[++i];
} else {
usage(argv);
return 1;
@ -2259,7 +2258,7 @@ int main(int argc, char ** argv) {
for (size_t i = 0; i < ggml_backend_reg_get_count(); i++) {
printf("Backend %zu/%zu (%s)\n", i + 1, ggml_backend_reg_get_count(), ggml_backend_reg_get_name(i));
if (backend != NULL && strcmp(backend, ggml_backend_reg_get_name(i)) != 0) {
if (backend_filter != NULL && strcmp(backend_filter, ggml_backend_reg_get_name(i)) != 0) {
printf(" Skipping\n");
n_ok++;
continue;
@ -2267,9 +2266,17 @@ int main(int argc, char ** argv) {
ggml_backend_t backend = ggml_backend_reg_init_backend(i, NULL);
GGML_ASSERT(backend != NULL);
if (backend_filter == NULL && ggml_backend_is_cpu(backend)) {
printf(" Skipping CPU backend\n");
ggml_backend_free(backend);
n_ok++;
continue;
}
printf(" Backend name: %s\n", ggml_backend_name(backend));
bool ok = test_backend(backend, mode, op_name);
bool ok = test_backend(backend, mode, op_name_filter);
printf(" Backend %s: ", ggml_backend_name(backend));
if (ok) {

4
tests/test-chat-template.cpp
View file

@ -31,6 +31,8 @@ int main(void) {
"{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}",
// google/gemma-7b-it
"{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\\n' + message['content'] | trim + '<end_of_turn>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}",
// OrionStarAI/Orion-14B-Chat
"{% for message in messages %}{% if loop.first %}{{ bos_token }}{% endif %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\\n\\nAssistant: ' + eos_token }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}",
};
std::vector<std::string> expected_output = {
// teknium/OpenHermes-2.5-Mistral-7B
@ -45,6 +47,8 @@ int main(void) {
"system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n I am an assistant </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
// google/gemma-7b-it
"<start_of_turn>user\nYou are a helpful assistant\n\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nWho are you<end_of_turn>\n<start_of_turn>model\nI am an assistant<end_of_turn>\n<start_of_turn>user\nAnother question<end_of_turn>\n<start_of_turn>model\n",
// OrionStarAI/Orion-14B-Chat
"Human: You are a helpful assistant\n\nHello\n\nAssistant: </s>Hi there</s>Human: Who are you\n\nAssistant: </s> I am an assistant </s>Human: Another question\n\nAssistant: </s>",
};
std::vector<char> formatted_chat(1024);
int32_t res;

4
tests/test-tokenizer-1-bpe.cpp
View file

@ -64,7 +64,7 @@ int main(int argc, char **argv) {
for (int i = 0; i < n_vocab; ++i) {
std::string str = llama_detokenize_bpe(ctx, std::vector<int>(1, i));
try {
auto cps = codepoints_from_utf8(str);
auto cps = unicode_cpts_from_utf8(str);
std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
std::string check = llama_detokenize_bpe(ctx, tokens);
if (check != str) {
@ -97,7 +97,7 @@ int main(int argc, char **argv) {
continue;
}
std::string str = codepoint_to_utf8(cp);
std::string str = unicode_cpt_to_utf8(cp);
std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
std::string check = llama_detokenize_bpe(ctx, tokens);
if (cp != 9601 && str != check) {

2
tests/test-tokenizer-1-llama.cpp
View file

@ -85,7 +85,7 @@ int main(int argc, char **argv) {
continue;
}
std::string str = codepoint_to_utf8(cp);
std::string str = unicode_cpt_to_utf8(cp);
std::vector<llama_token> tokens = llama_tokenize(ctx, str, false);
std::string check = llama_detokenize_spm(ctx, tokens);
if (cp != 9601 && str != check) {

1672
unicode.cpp Normal file
View file

File diff suppressed because it is too large Load diff

790
unicode.h
View file

@ -1,784 +1,26 @@
#pragma once
#pragma once
#include <cassert>
#include <map>
#include <stdexcept>
#include <cstdint>
#include <string>
#include <unordered_map>
#include <vector>
static const std::vector<std::pair<uint32_t, uint32_t>> digit_ranges = {
{0x30, 0x39}, {0xB2, 0xB3}, {0xB9, 0xB9}, {0x660, 0x669}, {0x6F0, 0x6F9}, {0x7C0, 0x7C9}, {0x966, 0x96F}, {0x9E6, 0x9EF}, {0xA66, 0xA6F}, {0xAE6, 0xAEF}, {0xB66, 0xB6F}, {0xBE6, 0xBEF}, {0xC66, 0xC6F},
{0xCE6, 0xCEF}, {0xD66, 0xD6F}, {0xDE6, 0xDEF}, {0xE50, 0xE59}, {0xED0, 0xED9}, {0xF20, 0xF29}, {0x1040, 0x1049}, {0x1090, 0x1099}, {0x1369, 0x1371}, {0x17E0, 0x17E9}, {0x1810, 0x1819}, {0x1946, 0x194F},
{0x19D0, 0x19DA}, {0x1A80, 0x1A89}, {0x1A90, 0x1A99}, {0x1B50, 0x1B59}, {0x1BB0, 0x1BB9}, {0x1C40, 0x1C49}, {0x1C50, 0x1C59}, {0x2070, 0x2070}, {0x2074, 0x2079}, {0x2080, 0x2089}, {0x2460, 0x2468},
{0x2474, 0x247C}, {0x2488, 0x2490}, {0x24EA, 0x24EA}, {0x24F5, 0x24FD}, {0x24FF, 0x24FF}, {0x2776, 0x277E}, {0x2780, 0x2788}, {0x278A, 0x2792}, {0xA620, 0xA629}, {0xA8D0, 0xA8D9}, {0xA900, 0xA909},
{0xA9D0, 0xA9D9}, {0xA9F0, 0xA9F9}, {0xAA50, 0xAA59}, {0xABF0, 0xABF9}, {0xFF10, 0xFF19}, {0x104A0, 0x104A9}, {0x10A40, 0x10A43}, {0x10D30, 0x10D39}, {0x10E60, 0x10E68}, {0x11052, 0x1105A},
{0x11066, 0x1106F}, {0x110F0, 0x110F9}, {0x11136, 0x1113F}, {0x111D0, 0x111D9}, {0x112F0, 0x112F9}, {0x11450, 0x11459}, {0x114D0, 0x114D9}, {0x11650, 0x11659}, {0x116C0, 0x116C9}, {0x11730, 0x11739},
{0x118E0, 0x118E9}, {0x11950, 0x11959}, {0x11C50, 0x11C59}, {0x11D50, 0x11D59}, {0x11DA0, 0x11DA9}, {0x16A60, 0x16A69}, {0x16B50, 0x16B59}, {0x1D7CE, 0x1D7FF}, {0x1E140, 0x1E149}, {0x1E2F0, 0x1E2F9},
{0x1E950, 0x1E959}, {0x1F100, 0x1F10A}, {0x1FBF0, 0x1FBF9},
};
static const std::vector<std::pair<uint32_t, uint32_t>> letter_ranges = {
{0x41, 0x5A}, {0x61, 0x7A}, {0xAA, 0xAA}, {0xB5, 0xB5}, {0xBA, 0xBA}, {0xC0, 0xD6}, {0xD8, 0xF6}, {0xF8, 0x2C1}, {0x2C6, 0x2D1}, {0x2E0, 0x2E4}, {0x2EC, 0x2EC}, {0x2EE, 0x2EE}, {0x370, 0x374},
{0x376, 0x377}, {0x37A, 0x37D}, {0x37F, 0x37F}, {0x386, 0x386}, {0x388, 0x38A}, {0x38C, 0x38C}, {0x38E, 0x3A1}, {0x3A3, 0x3F5}, {0x3F7, 0x481}, {0x48A, 0x52F}, {0x531, 0x556}, {0x559, 0x559},
{0x560, 0x588}, {0x5D0, 0x5EA}, {0x5EF, 0x5F2}, {0x620, 0x64A}, {0x66E, 0x66F}, {0x671, 0x6D3}, {0x6D5, 0x6D5}, {0x6E5, 0x6E6}, {0x6EE, 0x6EF}, {0x6FA, 0x6FC}, {0x6FF, 0x6FF}, {0x710, 0x710},
{0x712, 0x72F}, {0x74D, 0x7A5}, {0x7B1, 0x7B1}, {0x7CA, 0x7EA}, {0x7F4, 0x7F5}, {0x7FA, 0x7FA}, {0x800, 0x815}, {0x81A, 0x81A}, {0x824, 0x824}, {0x828, 0x828}, {0x840, 0x858}, {0x860, 0x86A},
{0x8A0, 0x8B4}, {0x8B6, 0x8C7}, {0x904, 0x939}, {0x93D, 0x93D}, {0x950, 0x950}, {0x958, 0x961}, {0x971, 0x980}, {0x985, 0x98C}, {0x98F, 0x990}, {0x993, 0x9A8}, {0x9AA, 0x9B0}, {0x9B2, 0x9B2},
{0x9B6, 0x9B9}, {0x9BD, 0x9BD}, {0x9CE, 0x9CE}, {0x9DC, 0x9DD}, {0x9DF, 0x9E1}, {0x9F0, 0x9F1}, {0x9FC, 0x9FC}, {0xA05, 0xA0A}, {0xA0F, 0xA10}, {0xA13, 0xA28}, {0xA2A, 0xA30}, {0xA32, 0xA33},
{0xA35, 0xA36}, {0xA38, 0xA39}, {0xA59, 0xA5C}, {0xA5E, 0xA5E}, {0xA72, 0xA74}, {0xA85, 0xA8D}, {0xA8F, 0xA91}, {0xA93, 0xAA8}, {0xAAA, 0xAB0}, {0xAB2, 0xAB3}, {0xAB5, 0xAB9}, {0xABD, 0xABD},
{0xAD0, 0xAD0}, {0xAE0, 0xAE1}, {0xAF9, 0xAF9}, {0xB05, 0xB0C}, {0xB0F, 0xB10}, {0xB13, 0xB28}, {0xB2A, 0xB30}, {0xB32, 0xB33}, {0xB35, 0xB39}, {0xB3D, 0xB3D}, {0xB5C, 0xB5D}, {0xB5F, 0xB61},
{0xB71, 0xB71}, {0xB83, 0xB83}, {0xB85, 0xB8A}, {0xB8E, 0xB90}, {0xB92, 0xB95}, {0xB99, 0xB9A}, {0xB9C, 0xB9C}, {0xB9E, 0xB9F}, {0xBA3, 0xBA4}, {0xBA8, 0xBAA}, {0xBAE, 0xBB9}, {0xBD0, 0xBD0},
{0xC05, 0xC0C}, {0xC0E, 0xC10}, {0xC12, 0xC28}, {0xC2A, 0xC39}, {0xC3D, 0xC3D}, {0xC58, 0xC5A}, {0xC60, 0xC61}, {0xC80, 0xC80}, {0xC85, 0xC8C}, {0xC8E, 0xC90}, {0xC92, 0xCA8}, {0xCAA, 0xCB3},
{0xCB5, 0xCB9}, {0xCBD, 0xCBD}, {0xCDE, 0xCDE}, {0xCE0, 0xCE1}, {0xCF1, 0xCF2}, {0xD04, 0xD0C}, {0xD0E, 0xD10}, {0xD12, 0xD3A}, {0xD3D, 0xD3D}, {0xD4E, 0xD4E}, {0xD54, 0xD56}, {0xD5F, 0xD61},
{0xD7A, 0xD7F}, {0xD85, 0xD96}, {0xD9A, 0xDB1}, {0xDB3, 0xDBB}, {0xDBD, 0xDBD}, {0xDC0, 0xDC6}, {0xE01, 0xE30}, {0xE32, 0xE33}, {0xE40, 0xE46}, {0xE81, 0xE82}, {0xE84, 0xE84}, {0xE86, 0xE8A},
{0xE8C, 0xEA3}, {0xEA5, 0xEA5}, {0xEA7, 0xEB0}, {0xEB2, 0xEB3}, {0xEBD, 0xEBD}, {0xEC0, 0xEC4}, {0xEC6, 0xEC6}, {0xEDC, 0xEDF}, {0xF00, 0xF00}, {0xF40, 0xF47}, {0xF49, 0xF6C}, {0xF88, 0xF8C},
{0x1000, 0x102A}, {0x103F, 0x103F}, {0x1050, 0x1055}, {0x105A, 0x105D}, {0x1061, 0x1061}, {0x1065, 0x1066}, {0x106E, 0x1070}, {0x1075, 0x1081}, {0x108E, 0x108E}, {0x10A0, 0x10C5}, {0x10C7, 0x10C7},
{0x10CD, 0x10CD}, {0x10D0, 0x10FA}, {0x10FC, 0x1248}, {0x124A, 0x124D}, {0x1250, 0x1256}, {0x1258, 0x1258}, {0x125A, 0x125D}, {0x1260, 0x1288}, {0x128A, 0x128D}, {0x1290, 0x12B0}, {0x12B2, 0x12B5},
{0x12B8, 0x12BE}, {0x12C0, 0x12C0}, {0x12C2, 0x12C5}, {0x12C8, 0x12D6}, {0x12D8, 0x1310}, {0x1312, 0x1315}, {0x1318, 0x135A}, {0x1380, 0x138F}, {0x13A0, 0x13F5}, {0x13F8, 0x13FD}, {0x1401, 0x166C},
{0x166F, 0x167F}, {0x1681, 0x169A}, {0x16A0, 0x16EA}, {0x16F1, 0x16F8}, {0x1700, 0x170C}, {0x170E, 0x1711}, {0x1720, 0x1731}, {0x1740, 0x1751}, {0x1760, 0x176C}, {0x176E, 0x1770}, {0x1780, 0x17B3},
{0x17D7, 0x17D7}, {0x17DC, 0x17DC}, {0x1820, 0x1878}, {0x1880, 0x1884}, {0x1887, 0x18A8}, {0x18AA, 0x18AA}, {0x18B0, 0x18F5}, {0x1900, 0x191E}, {0x1950, 0x196D}, {0x1970, 0x1974}, {0x1980, 0x19AB},
{0x19B0, 0x19C9}, {0x1A00, 0x1A16}, {0x1A20, 0x1A54}, {0x1AA7, 0x1AA7}, {0x1B05, 0x1B33}, {0x1B45, 0x1B4B}, {0x1B83, 0x1BA0}, {0x1BAE, 0x1BAF}, {0x1BBA, 0x1BE5}, {0x1C00, 0x1C23}, {0x1C4D, 0x1C4F},
{0x1C5A, 0x1C7D}, {0x1C80, 0x1C88}, {0x1C90, 0x1CBA}, {0x1CBD, 0x1CBF}, {0x1CE9, 0x1CEC}, {0x1CEE, 0x1CF3}, {0x1CF5, 0x1CF6}, {0x1CFA, 0x1CFA}, {0x1D00, 0x1DBF}, {0x1E00, 0x1F15}, {0x1F18, 0x1F1D},
{0x1F20, 0x1F45}, {0x1F48, 0x1F4D}, {0x1F50, 0x1F57}, {0x1F59, 0x1F59}, {0x1F5B, 0x1F5B}, {0x1F5D, 0x1F5D}, {0x1F5F, 0x1F7D}, {0x1F80, 0x1FB4}, {0x1FB6, 0x1FBC}, {0x1FBE, 0x1FBE}, {0x1FC2, 0x1FC4},
{0x1FC6, 0x1FCC}, {0x1FD0, 0x1FD3}, {0x1FD6, 0x1FDB}, {0x1FE0, 0x1FEC}, {0x1FF2, 0x1FF4}, {0x1FF6, 0x1FFC}, {0x2071, 0x2071}, {0x207F, 0x207F}, {0x2090, 0x209C}, {0x2102, 0x2102}, {0x2107, 0x2107},
{0x210A, 0x2113}, {0x2115, 0x2115}, {0x2119, 0x211D}, {0x2124, 0x2124}, {0x2126, 0x2126}, {0x2128, 0x2128}, {0x212A, 0x212D}, {0x212F, 0x2139}, {0x213C, 0x213F}, {0x2145, 0x2149}, {0x214E, 0x214E},
{0x2183, 0x2184}, {0x2C00, 0x2C2E}, {0x2C30, 0x2C5E}, {0x2C60, 0x2CE4}, {0x2CEB, 0x2CEE}, {0x2CF2, 0x2CF3}, {0x2D00, 0x2D25}, {0x2D27, 0x2D27}, {0x2D2D, 0x2D2D}, {0x2D30, 0x2D67}, {0x2D6F, 0x2D6F},
{0x2D80, 0x2D96}, {0x2DA0, 0x2DA6}, {0x2DA8, 0x2DAE}, {0x2DB0, 0x2DB6}, {0x2DB8, 0x2DBE}, {0x2DC0, 0x2DC6}, {0x2DC8, 0x2DCE}, {0x2DD0, 0x2DD6}, {0x2DD8, 0x2DDE}, {0x2E2F, 0x2E2F}, {0x3005, 0x3006},
{0x3031, 0x3035}, {0x303B, 0x303C}, {0x3041, 0x3096}, {0x309D, 0x309F}, {0x30A1, 0x30FA}, {0x30FC, 0x30FF}, {0x3105, 0x312F}, {0x3131, 0x318E}, {0x31A0, 0x31BF}, {0x31F0, 0x31FF}, {0x3400, 0x4DBF},
{0x4E00, 0x9FFC}, {0xA000, 0xA48C}, {0xA4D0, 0xA4FD}, {0xA500, 0xA60C}, {0xA610, 0xA61F}, {0xA62A, 0xA62B}, {0xA640, 0xA66E}, {0xA67F, 0xA69D}, {0xA6A0, 0xA6E5}, {0xA717, 0xA71F}, {0xA722, 0xA788},
{0xA78B, 0xA7BF}, {0xA7C2, 0xA7CA}, {0xA7F5, 0xA801}, {0xA803, 0xA805}, {0xA807, 0xA80A}, {0xA80C, 0xA822}, {0xA840, 0xA873}, {0xA882, 0xA8B3}, {0xA8F2, 0xA8F7}, {0xA8FB, 0xA8FB}, {0xA8FD, 0xA8FE},
{0xA90A, 0xA925}, {0xA930, 0xA946}, {0xA960, 0xA97C}, {0xA984, 0xA9B2}, {0xA9CF, 0xA9CF}, {0xA9E0, 0xA9E4}, {0xA9E6, 0xA9EF}, {0xA9FA, 0xA9FE}, {0xAA00, 0xAA28}, {0xAA40, 0xAA42}, {0xAA44, 0xAA4B},
{0xAA60, 0xAA76}, {0xAA7A, 0xAA7A}, {0xAA7E, 0xAAAF}, {0xAAB1, 0xAAB1}, {0xAAB5, 0xAAB6}, {0xAAB9, 0xAABD}, {0xAAC0, 0xAAC0}, {0xAAC2, 0xAAC2}, {0xAADB, 0xAADD}, {0xAAE0, 0xAAEA}, {0xAAF2, 0xAAF4},
{0xAB01, 0xAB06}, {0xAB09, 0xAB0E}, {0xAB11, 0xAB16}, {0xAB20, 0xAB26}, {0xAB28, 0xAB2E}, {0xAB30, 0xAB5A}, {0xAB5C, 0xAB69}, {0xAB70, 0xABE2}, {0xAC00, 0xD7A3}, {0xD7B0, 0xD7C6}, {0xD7CB, 0xD7FB},
{0xF900, 0xFA6D}, {0xFA70, 0xFAD9}, {0xFB00, 0xFB06}, {0xFB13, 0xFB17}, {0xFB1D, 0xFB1D}, {0xFB1F, 0xFB28}, {0xFB2A, 0xFB36}, {0xFB38, 0xFB3C}, {0xFB3E, 0xFB3E}, {0xFB40, 0xFB41}, {0xFB43, 0xFB44},
{0xFB46, 0xFBB1}, {0xFBD3, 0xFD3D}, {0xFD50, 0xFD8F}, {0xFD92, 0xFDC7}, {0xFDF0, 0xFDFB}, {0xFE70, 0xFE74}, {0xFE76, 0xFEFC}, {0xFF21, 0xFF3A}, {0xFF41, 0xFF5A}, {0xFF66, 0xFFBE}, {0xFFC2, 0xFFC7},
{0xFFCA, 0xFFCF}, {0xFFD2, 0xFFD7}, {0xFFDA, 0xFFDC}, {0x10000, 0x1000B}, {0x1000D, 0x10026}, {0x10028, 0x1003A}, {0x1003C, 0x1003D}, {0x1003F, 0x1004D}, {0x10050, 0x1005D}, {0x10080, 0x100FA},
{0x10280, 0x1029C}, {0x102A0, 0x102D0}, {0x10300, 0x1031F}, {0x1032D, 0x10340}, {0x10342, 0x10349}, {0x10350, 0x10375}, {0x10380, 0x1039D}, {0x103A0, 0x103C3}, {0x103C8, 0x103CF}, {0x10400, 0x1049D},
{0x104B0, 0x104D3}, {0x104D8, 0x104FB}, {0x10500, 0x10527}, {0x10530, 0x10563}, {0x10600, 0x10736}, {0x10740, 0x10755}, {0x10760, 0x10767}, {0x10800, 0x10805}, {0x10808, 0x10808}, {0x1080A, 0x10835},
{0x10837, 0x10838}, {0x1083C, 0x1083C}, {0x1083F, 0x10855}, {0x10860, 0x10876}, {0x10880, 0x1089E}, {0x108E0, 0x108F2}, {0x108F4, 0x108F5}, {0x10900, 0x10915}, {0x10920, 0x10939}, {0x10980, 0x109B7},
{0x109BE, 0x109BF}, {0x10A00, 0x10A00}, {0x10A10, 0x10A13}, {0x10A15, 0x10A17}, {0x10A19, 0x10A35}, {0x10A60, 0x10A7C}, {0x10A80, 0x10A9C}, {0x10AC0, 0x10AC7}, {0x10AC9, 0x10AE4}, {0x10B00, 0x10B35},
{0x10B40, 0x10B55}, {0x10B60, 0x10B72}, {0x10B80, 0x10B91}, {0x10C00, 0x10C48}, {0x10C80, 0x10CB2}, {0x10CC0, 0x10CF2}, {0x10D00, 0x10D23}, {0x10E80, 0x10EA9}, {0x10EB0, 0x10EB1}, {0x10F00, 0x10F1C},
{0x10F27, 0x10F27}, {0x10F30, 0x10F45}, {0x10FB0, 0x10FC4}, {0x10FE0, 0x10FF6}, {0x11003, 0x11037}, {0x11083, 0x110AF}, {0x110D0, 0x110E8}, {0x11103, 0x11126}, {0x11144, 0x11144}, {0x11147, 0x11147},
{0x11150, 0x11172}, {0x11176, 0x11176}, {0x11183, 0x111B2}, {0x111C1, 0x111C4}, {0x111DA, 0x111DA}, {0x111DC, 0x111DC}, {0x11200, 0x11211}, {0x11213, 0x1122B}, {0x11280, 0x11286}, {0x11288, 0x11288},
{0x1128A, 0x1128D}, {0x1128F, 0x1129D}, {0x1129F, 0x112A8}, {0x112B0, 0x112DE}, {0x11305, 0x1130C}, {0x1130F, 0x11310}, {0x11313, 0x11328}, {0x1132A, 0x11330}, {0x11332, 0x11333}, {0x11335, 0x11339},
{0x1133D, 0x1133D}, {0x11350, 0x11350}, {0x1135D, 0x11361}, {0x11400, 0x11434}, {0x11447, 0x1144A}, {0x1145F, 0x11461}, {0x11480, 0x114AF}, {0x114C4, 0x114C5}, {0x114C7, 0x114C7}, {0x11580, 0x115AE},
{0x115D8, 0x115DB}, {0x11600, 0x1162F}, {0x11644, 0x11644}, {0x11680, 0x116AA}, {0x116B8, 0x116B8}, {0x11700, 0x1171A}, {0x11800, 0x1182B}, {0x118A0, 0x118DF}, {0x118FF, 0x11906}, {0x11909, 0x11909},
{0x1190C, 0x11913}, {0x11915, 0x11916}, {0x11918, 0x1192F}, {0x1193F, 0x1193F}, {0x11941, 0x11941}, {0x119A0, 0x119A7}, {0x119AA, 0x119D0}, {0x119E1, 0x119E1}, {0x119E3, 0x119E3}, {0x11A00, 0x11A00},
{0x11A0B, 0x11A32}, {0x11A3A, 0x11A3A}, {0x11A50, 0x11A50}, {0x11A5C, 0x11A89}, {0x11A9D, 0x11A9D}, {0x11AC0, 0x11AF8}, {0x11C00, 0x11C08}, {0x11C0A, 0x11C2E}, {0x11C40, 0x11C40}, {0x11C72, 0x11C8F},
{0x11D00, 0x11D06}, {0x11D08, 0x11D09}, {0x11D0B, 0x11D30}, {0x11D46, 0x11D46}, {0x11D60, 0x11D65}, {0x11D67, 0x11D68}, {0x11D6A, 0x11D89}, {0x11D98, 0x11D98}, {0x11EE0, 0x11EF2}, {0x11FB0, 0x11FB0},
{0x12000, 0x12399}, {0x12480, 0x12543}, {0x13000, 0x1342E}, {0x14400, 0x14646}, {0x16800, 0x16A38}, {0x16A40, 0x16A5E}, {0x16AD0, 0x16AED}, {0x16B00, 0x16B2F}, {0x16B40, 0x16B43}, {0x16B63, 0x16B77},
{0x16B7D, 0x16B8F}, {0x16E40, 0x16E7F}, {0x16F00, 0x16F4A}, {0x16F50, 0x16F50}, {0x16F93, 0x16F9F}, {0x16FE0, 0x16FE1}, {0x16FE3, 0x16FE3}, {0x17000, 0x187F7}, {0x18800, 0x18CD5}, {0x18D00, 0x18D08},
{0x1B000, 0x1B11E}, {0x1B150, 0x1B152}, {0x1B164, 0x1B167}, {0x1B170, 0x1B2FB}, {0x1BC00, 0x1BC6A}, {0x1BC70, 0x1BC7C}, {0x1BC80, 0x1BC88}, {0x1BC90, 0x1BC99}, {0x1D400, 0x1D454}, {0x1D456, 0x1D49C},
{0x1D49E, 0x1D49F}, {0x1D4A2, 0x1D4A2}, {0x1D4A5, 0x1D4A6}, {0x1D4A9, 0x1D4AC}, {0x1D4AE, 0x1D4B9}, {0x1D4BB, 0x1D4BB}, {0x1D4BD, 0x1D4C3}, {0x1D4C5, 0x1D505}, {0x1D507, 0x1D50A}, {0x1D50D, 0x1D514},
{0x1D516, 0x1D51C}, {0x1D51E, 0x1D539}, {0x1D53B, 0x1D53E}, {0x1D540, 0x1D544}, {0x1D546, 0x1D546}, {0x1D54A, 0x1D550}, {0x1D552, 0x1D6A5}, {0x1D6A8, 0x1D6C0}, {0x1D6C2, 0x1D6DA}, {0x1D6DC, 0x1D6FA},
{0x1D6FC, 0x1D714}, {0x1D716, 0x1D734}, {0x1D736, 0x1D74E}, {0x1D750, 0x1D76E}, {0x1D770, 0x1D788}, {0x1D78A, 0x1D7A8}, {0x1D7AA, 0x1D7C2}, {0x1D7C4, 0x1D7CB}, {0x1E100, 0x1E12C}, {0x1E137, 0x1E13D},
{0x1E14E, 0x1E14E}, {0x1E2C0, 0x1E2EB}, {0x1E800, 0x1E8C4}, {0x1E900, 0x1E943}, {0x1E94B, 0x1E94B}, {0x1EE00, 0x1EE03}, {0x1EE05, 0x1EE1F}, {0x1EE21, 0x1EE22}, {0x1EE24, 0x1EE24}, {0x1EE27, 0x1EE27},
{0x1EE29, 0x1EE32}, {0x1EE34, 0x1EE37}, {0x1EE39, 0x1EE39}, {0x1EE3B, 0x1EE3B}, {0x1EE42, 0x1EE42}, {0x1EE47, 0x1EE47}, {0x1EE49, 0x1EE49}, {0x1EE4B, 0x1EE4B}, {0x1EE4D, 0x1EE4F}, {0x1EE51, 0x1EE52},
{0x1EE54, 0x1EE54}, {0x1EE57, 0x1EE57}, {0x1EE59, 0x1EE59}, {0x1EE5B, 0x1EE5B}, {0x1EE5D, 0x1EE5D}, {0x1EE5F, 0x1EE5F}, {0x1EE61, 0x1EE62}, {0x1EE64, 0x1EE64}, {0x1EE67, 0x1EE6A}, {0x1EE6C, 0x1EE72},
{0x1EE74, 0x1EE77}, {0x1EE79, 0x1EE7C}, {0x1EE7E, 0x1EE7E}, {0x1EE80, 0x1EE89}, {0x1EE8B, 0x1EE9B}, {0x1EEA1, 0x1EEA3}, {0x1EEA5, 0x1EEA9}, {0x1EEAB, 0x1EEBB}, {0x20000, 0x2A6DD}, {0x2A700, 0x2B734},
{0x2B740, 0x2B81D}, {0x2B820, 0x2CEA1}, {0x2CEB0, 0x2EBE0}, {0x2F800, 0x2FA1D}, {0x30000, 0x3134A},
};
static const std::vector<std::pair<uint32_t, uint32_t>> whitespace_ranges = {
{0x9, 0xD}, {0x1C, 0x20}, {0x85, 0x85}, {0xA0, 0xA0}, {0x1680, 0x1680}, {0x2000, 0x200A}, {0x2028, 0x2029}, {0x202F, 0x202F}, {0x205F, 0x205F}, {0x3000, 0x3000},
};
static const std::vector<std::pair<uint32_t, uint32_t>> accent_mark_ranges = {
{0x300, 0x36F}, {0x483, 0x489}, {0x591, 0x5BD}, {0x5BF, 0x5BF}, {0x5C1, 0x5C2}, {0x5C4, 0x5C5}, {0x5C7, 0x5C7}, {0x610, 0x61A}, {0x64B, 0x65F}, {0x670, 0x670}, {0x6D6, 0x6DC}, {0x6DF, 0x6E4},
{0x6E7, 0x6E8}, {0x6EA, 0x6ED}, {0x711, 0x711}, {0x730, 0x74A}, {0x7A6, 0x7B0}, {0x7EB, 0x7F3}, {0x7FD, 0x7FD}, {0x816, 0x819}, {0x81B, 0x823}, {0x825, 0x827}, {0x829, 0x82D}, {0x859, 0x85B},
{0x8D3, 0x8E1}, {0x8E3, 0x903}, {0x93A, 0x93C}, {0x93E, 0x94F}, {0x951, 0x957}, {0x962, 0x963}, {0x981, 0x983}, {0x9BC, 0x9BC}, {0x9BE, 0x9C4}, {0x9C7, 0x9C8}, {0x9CB, 0x9CD}, {0x9D7, 0x9D7},
{0x9E2, 0x9E3}, {0x9FE, 0x9FE}, {0xA01, 0xA03}, {0xA3C, 0xA3C}, {0xA3E, 0xA42}, {0xA47, 0xA48}, {0xA4B, 0xA4D}, {0xA51, 0xA51}, {0xA70, 0xA71}, {0xA75, 0xA75}, {0xA81, 0xA83}, {0xABC, 0xABC},
{0xABE, 0xAC5}, {0xAC7, 0xAC9}, {0xACB, 0xACD}, {0xAE2, 0xAE3}, {0xAFA, 0xAFF}, {0xB01, 0xB03}, {0xB3C, 0xB3C}, {0xB3E, 0xB44}, {0xB47, 0xB48}, {0xB4B, 0xB4D}, {0xB55, 0xB57}, {0xB62, 0xB63},
{0xB82, 0xB82}, {0xBBE, 0xBC2}, {0xBC6, 0xBC8}, {0xBCA, 0xBCD}, {0xBD7, 0xBD7}, {0xC00, 0xC04}, {0xC3E, 0xC44}, {0xC46, 0xC48}, {0xC4A, 0xC4D}, {0xC55, 0xC56}, {0xC62, 0xC63}, {0xC81, 0xC83},
{0xCBC, 0xCBC}, {0xCBE, 0xCC4}, {0xCC6, 0xCC8}, {0xCCA, 0xCCD}, {0xCD5, 0xCD6}, {0xCE2, 0xCE3}, {0xD00, 0xD03}, {0xD3B, 0xD3C}, {0xD3E, 0xD44}, {0xD46, 0xD48}, {0xD4A, 0xD4D}, {0xD57, 0xD57},
{0xD62, 0xD63}, {0xD81, 0xD83}, {0xDCA, 0xDCA}, {0xDCF, 0xDD4}, {0xDD6, 0xDD6}, {0xDD8, 0xDDF}, {0xDF2, 0xDF3}, {0xE31, 0xE31}, {0xE34, 0xE3A}, {0xE47, 0xE4E}, {0xEB1, 0xEB1}, {0xEB4, 0xEBC},
{0xEC8, 0xECD}, {0xF18, 0xF19}, {0xF35, 0xF35}, {0xF37, 0xF37}, {0xF39, 0xF39}, {0xF3E, 0xF3F}, {0xF71, 0xF84}, {0xF86, 0xF87}, {0xF8D, 0xF97}, {0xF99, 0xFBC}, {0xFC6, 0xFC6}, {0x102B, 0x103E},
{0x1056, 0x1059}, {0x105E, 0x1060}, {0x1062, 0x1064}, {0x1067, 0x106D}, {0x1071, 0x1074}, {0x1082, 0x108D}, {0x108F, 0x108F}, {0x109A, 0x109D}, {0x135D, 0x135F}, {0x1712, 0x1714}, {0x1732, 0x1734},
{0x1752, 0x1753}, {0x1772, 0x1773}, {0x17B4, 0x17D3}, {0x17DD, 0x17DD}, {0x180B, 0x180D}, {0x1885, 0x1886}, {0x18A9, 0x18A9}, {0x1920, 0x192B}, {0x1930, 0x193B}, {0x1A17, 0x1A1B}, {0x1A55, 0x1A5E},
{0x1A60, 0x1A7C}, {0x1A7F, 0x1A7F}, {0x1AB0, 0x1AC0}, {0x1B00, 0x1B04}, {0x1B34, 0x1B44}, {0x1B6B, 0x1B73}, {0x1B80, 0x1B82}, {0x1BA1, 0x1BAD}, {0x1BE6, 0x1BF3}, {0x1C24, 0x1C37}, {0x1CD0, 0x1CD2},
{0x1CD4, 0x1CE8}, {0x1CED, 0x1CED}, {0x1CF4, 0x1CF4}, {0x1CF7, 0x1CF9}, {0x1DC0, 0x1DF9}, {0x1DFB, 0x1DFF}, {0x20D0, 0x20F0}, {0x2CEF, 0x2CF1}, {0x2D7F, 0x2D7F}, {0x2DE0, 0x2DFF}, {0x302A, 0x302F},
{0x3099, 0x309A}, {0xA66F, 0xA672}, {0xA674, 0xA67D}, {0xA69E, 0xA69F}, {0xA6F0, 0xA6F1}, {0xA802, 0xA802}, {0xA806, 0xA806}, {0xA80B, 0xA80B}, {0xA823, 0xA827}, {0xA82C, 0xA82C}, {0xA880, 0xA881},
{0xA8B4, 0xA8C5}, {0xA8E0, 0xA8F1}, {0xA8FF, 0xA8FF}, {0xA926, 0xA92D}, {0xA947, 0xA953}, {0xA980, 0xA983}, {0xA9B3, 0xA9C0}, {0xA9E5, 0xA9E5}, {0xAA29, 0xAA36}, {0xAA43, 0xAA43}, {0xAA4C, 0xAA4D},
{0xAA7B, 0xAA7D}, {0xAAB0, 0xAAB0}, {0xAAB2, 0xAAB4}, {0xAAB7, 0xAAB8}, {0xAABE, 0xAABF}, {0xAAC1, 0xAAC1}, {0xAAEB, 0xAAEF}, {0xAAF5, 0xAAF6}, {0xABE3, 0xABEA}, {0xABEC, 0xABED}, {0xFB1E, 0xFB1E},
{0xFE00, 0xFE0F}, {0xFE20, 0xFE2F}, {0x101FD, 0x101FD}, {0x102E0, 0x102E0}, {0x10376, 0x1037A}, {0x10A01, 0x10A03}, {0x10A05, 0x10A06}, {0x10A0C, 0x10A0F}, {0x10A38, 0x10A3A}, {0x10A3F, 0x10A3F},
{0x10AE5, 0x10AE6}, {0x10D24, 0x10D27}, {0x10EAB, 0x10EAC}, {0x10F46, 0x10F50}, {0x11000, 0x11002}, {0x11038, 0x11046}, {0x1107F, 0x11082}, {0x110B0, 0x110BA}, {0x11100, 0x11102}, {0x11127, 0x11134},
{0x11145, 0x11146}, {0x11173, 0x11173}, {0x11180, 0x11182}, {0x111B3, 0x111C0}, {0x111C9, 0x111CC}, {0x111CE, 0x111CF}, {0x1122C, 0x11237}, {0x1123E, 0x1123E}, {0x112DF, 0x112EA}, {0x11300, 0x11303},
{0x1133B, 0x1133C}, {0x1133E, 0x11344}, {0x11347, 0x11348}, {0x1134B, 0x1134D}, {0x11357, 0x11357}, {0x11362, 0x11363}, {0x11366, 0x1136C}, {0x11370, 0x11374}, {0x11435, 0x11446}, {0x1145E, 0x1145E},
{0x114B0, 0x114C3}, {0x115AF, 0x115B5}, {0x115B8, 0x115C0}, {0x115DC, 0x115DD}, {0x11630, 0x11640}, {0x116AB, 0x116B7}, {0x1171D, 0x1172B}, {0x1182C, 0x1183A}, {0x11930, 0x11935}, {0x11937, 0x11938},
{0x1193B, 0x1193E}, {0x11940, 0x11940}, {0x11942, 0x11943}, {0x119D1, 0x119D7}, {0x119DA, 0x119E0}, {0x119E4, 0x119E4}, {0x11A01, 0x11A0A}, {0x11A33, 0x11A39}, {0x11A3B, 0x11A3E}, {0x11A47, 0x11A47},
{0x11A51, 0x11A5B}, {0x11A8A, 0x11A99}, {0x11C2F, 0x11C36}, {0x11C38, 0x11C3F}, {0x11C92, 0x11CA7}, {0x11CA9, 0x11CB6}, {0x11D31, 0x11D36}, {0x11D3A, 0x11D3A}, {0x11D3C, 0x11D3D}, {0x11D3F, 0x11D45},
{0x11D47, 0x11D47}, {0x11D8A, 0x11D8E}, {0x11D90, 0x11D91}, {0x11D93, 0x11D97}, {0x11EF3, 0x11EF6}, {0x16AF0, 0x16AF4}, {0x16B30, 0x16B36}, {0x16F4F, 0x16F4F}, {0x16F51, 0x16F87}, {0x16F8F, 0x16F92},
{0x16FE4, 0x16FE4}, {0x16FF0, 0x16FF1}, {0x1BC9D, 0x1BC9E}, {0x1D165, 0x1D169}, {0x1D16D, 0x1D172}, {0x1D17B, 0x1D182}, {0x1D185, 0x1D18B}, {0x1D1AA, 0x1D1AD}, {0x1D242, 0x1D244}, {0x1DA00, 0x1DA36},
{0x1DA3B, 0x1DA6C}, {0x1DA75, 0x1DA75}, {0x1DA84, 0x1DA84}, {0x1DA9B, 0x1DA9F}, {0x1DAA1, 0x1DAAF}, {0x1E000, 0x1E006}, {0x1E008, 0x1E018}, {0x1E01B, 0x1E021}, {0x1E023, 0x1E024}, {0x1E026, 0x1E02A},
{0x1E130, 0x1E136}, {0x1E2EC, 0x1E2EF}, {0x1E8D0, 0x1E8D6}, {0x1E944, 0x1E94A}, {0xE0100, 0xE01EF},
};
static const std::vector<std::pair<uint32_t, uint32_t>> punctuation_ranges = {
{0x21, 0x23}, {0x25, 0x2A}, {0x2C, 0x2F}, {0x3A, 0x3B}, {0x3F, 0x40}, {0x5B, 0x5D}, {0x5F, 0x5F}, {0x7B, 0x7B}, {0x7D, 0x7D}, {0xA1, 0xA1}, {0xA7, 0xA7}, {0xAB, 0xAB}, {0xB6, 0xB7}, {0xBB, 0xBB},
{0xBF, 0xBF}, {0x37E, 0x37E}, {0x387, 0x387}, {0x55A, 0x55F}, {0x589, 0x58A}, {0x5BE, 0x5BE}, {0x5C0, 0x5C0}, {0x5C3, 0x5C3}, {0x5C6, 0x5C6}, {0x5F3, 0x5F4}, {0x609, 0x60A}, {0x60C, 0x60D},
{0x61B, 0x61B}, {0x61E, 0x61F}, {0x66A, 0x66D}, {0x6D4, 0x6D4}, {0x700, 0x70D}, {0x7F7, 0x7F9}, {0x830, 0x83E}, {0x85E, 0x85E}, {0x964, 0x965}, {0x970, 0x970}, {0x9FD, 0x9FD}, {0xA76, 0xA76},
{0xAF0, 0xAF0}, {0xC77, 0xC77}, {0xC84, 0xC84}, {0xDF4, 0xDF4}, {0xE4F, 0xE4F}, {0xE5A, 0xE5B}, {0xF04, 0xF12}, {0xF14, 0xF14}, {0xF3A, 0xF3D}, {0xF85, 0xF85}, {0xFD0, 0xFD4}, {0xFD9, 0xFDA},
{0x104A, 0x104F}, {0x10FB, 0x10FB}, {0x1360, 0x1368}, {0x1400, 0x1400}, {0x166E, 0x166E}, {0x169B, 0x169C}, {0x16EB, 0x16ED}, {0x1735, 0x1736}, {0x17D4, 0x17D6}, {0x17D8, 0x17DA}, {0x1800, 0x180A},
{0x1944, 0x1945}, {0x1A1E, 0x1A1F}, {0x1AA0, 0x1AA6}, {0x1AA8, 0x1AAD}, {0x1B5A, 0x1B60}, {0x1BFC, 0x1BFF}, {0x1C3B, 0x1C3F}, {0x1C7E, 0x1C7F}, {0x1CC0, 0x1CC7}, {0x1CD3, 0x1CD3}, {0x2010, 0x2027},
{0x2030, 0x2043}, {0x2045, 0x2051}, {0x2053, 0x205E}, {0x207D, 0x207E}, {0x208D, 0x208E}, {0x2308, 0x230B}, {0x2329, 0x232A}, {0x2768, 0x2775}, {0x27C5, 0x27C6}, {0x27E6, 0x27EF}, {0x2983, 0x2998},
{0x29D8, 0x29DB}, {0x29FC, 0x29FD}, {0x2CF9, 0x2CFC}, {0x2CFE, 0x2CFF}, {0x2D70, 0x2D70}, {0x2E00, 0x2E2E}, {0x2E30, 0x2E4F}, {0x2E52, 0x2E52}, {0x3001, 0x3003}, {0x3008, 0x3011}, {0x3014, 0x301F},
{0x3030, 0x3030}, {0x303D, 0x303D}, {0x30A0, 0x30A0}, {0x30FB, 0x30FB}, {0xA4FE, 0xA4FF}, {0xA60D, 0xA60F}, {0xA673, 0xA673}, {0xA67E, 0xA67E}, {0xA6F2, 0xA6F7}, {0xA874, 0xA877}, {0xA8CE, 0xA8CF},
{0xA8F8, 0xA8FA}, {0xA8FC, 0xA8FC}, {0xA92E, 0xA92F}, {0xA95F, 0xA95F}, {0xA9C1, 0xA9CD}, {0xA9DE, 0xA9DF}, {0xAA5C, 0xAA5F}, {0xAADE, 0xAADF}, {0xAAF0, 0xAAF1}, {0xABEB, 0xABEB}, {0xFD3E, 0xFD3F},
{0xFE10, 0xFE19}, {0xFE30, 0xFE52}, {0xFE54, 0xFE61}, {0xFE63, 0xFE63}, {0xFE68, 0xFE68}, {0xFE6A, 0xFE6B}, {0xFF01, 0xFF03}, {0xFF05, 0xFF0A}, {0xFF0C, 0xFF0F}, {0xFF1A, 0xFF1B}, {0xFF1F, 0xFF20},
{0xFF3B, 0xFF3D}, {0xFF3F, 0xFF3F}, {0xFF5B, 0xFF5B}, {0xFF5D, 0xFF5D}, {0xFF5F, 0xFF65}, {0x10100, 0x10102}, {0x1039F, 0x1039F}, {0x103D0, 0x103D0}, {0x1056F, 0x1056F}, {0x10857, 0x10857},
{0x1091F, 0x1091F}, {0x1093F, 0x1093F}, {0x10A50, 0x10A58}, {0x10A7F, 0x10A7F}, {0x10AF0, 0x10AF6}, {0x10B39, 0x10B3F}, {0x10B99, 0x10B9C}, {0x10EAD, 0x10EAD}, {0x10F55, 0x10F59}, {0x11047, 0x1104D},
{0x110BB, 0x110BC}, {0x110BE, 0x110C1}, {0x11140, 0x11143}, {0x11174, 0x11175}, {0x111C5, 0x111C8}, {0x111CD, 0x111CD}, {0x111DB, 0x111DB}, {0x111DD, 0x111DF}, {0x11238, 0x1123D}, {0x112A9, 0x112A9},
{0x1144B, 0x1144F}, {0x1145A, 0x1145B}, {0x1145D, 0x1145D}, {0x114C6, 0x114C6}, {0x115C1, 0x115D7}, {0x11641, 0x11643}, {0x11660, 0x1166C}, {0x1173C, 0x1173E}, {0x1183B, 0x1183B}, {0x11944, 0x11946},
{0x119E2, 0x119E2}, {0x11A3F, 0x11A46}, {0x11A9A, 0x11A9C}, {0x11A9E, 0x11AA2}, {0x11C41, 0x11C45}, {0x11C70, 0x11C71}, {0x11EF7, 0x11EF8}, {0x11FFF, 0x11FFF}, {0x12470, 0x12474}, {0x16A6E, 0x16A6F},
{0x16AF5, 0x16AF5}, {0x16B37, 0x16B3B}, {0x16B44, 0x16B44}, {0x16E97, 0x16E9A}, {0x16FE2, 0x16FE2}, {0x1BC9F, 0x1BC9F}, {0x1DA87, 0x1DA8B}, {0x1E95E, 0x1E95F},
};
static const std::vector<std::pair<uint32_t, uint32_t>> symbol_ranges = {
{0x24, 0x24}, {0x2B, 0x2B}, {0x3C, 0x3E}, {0x5E, 0x5E}, {0x60, 0x60}, {0x7C, 0x7C}, {0x7E, 0x7E}, {0xA2, 0xA6}, {0xA8, 0xA9}, {0xAC, 0xAC}, {0xAE, 0xB1}, {0xB4, 0xB4}, {0xB8, 0xB8}, {0xD7, 0xD7},
{0xF7, 0xF7}, {0x2C2, 0x2C5}, {0x2D2, 0x2DF}, {0x2E5, 0x2EB}, {0x2ED, 0x2ED}, {0x2EF, 0x2FF}, {0x375, 0x375}, {0x384, 0x385}, {0x3F6, 0x3F6}, {0x482, 0x482}, {0x58D, 0x58F}, {0x606, 0x608},
{0x60B, 0x60B}, {0x60E, 0x60F}, {0x6DE, 0x6DE}, {0x6E9, 0x6E9}, {0x6FD, 0x6FE}, {0x7F6, 0x7F6}, {0x7FE, 0x7FF}, {0x9F2, 0x9F3}, {0x9FA, 0x9FB}, {0xAF1, 0xAF1}, {0xB70, 0xB70}, {0xBF3, 0xBFA},
{0xC7F, 0xC7F}, {0xD4F, 0xD4F}, {0xD79, 0xD79}, {0xE3F, 0xE3F}, {0xF01, 0xF03}, {0xF13, 0xF13}, {0xF15, 0xF17}, {0xF1A, 0xF1F}, {0xF34, 0xF34}, {0xF36, 0xF36}, {0xF38, 0xF38}, {0xFBE, 0xFC5},
{0xFC7, 0xFCC}, {0xFCE, 0xFCF}, {0xFD5, 0xFD8}, {0x109E, 0x109F}, {0x1390, 0x1399}, {0x166D, 0x166D}, {0x17DB, 0x17DB}, {0x1940, 0x1940}, {0x19DE, 0x19FF}, {0x1B61, 0x1B6A}, {0x1B74, 0x1B7C},
{0x1FBD, 0x1FBD}, {0x1FBF, 0x1FC1}, {0x1FCD, 0x1FCF}, {0x1FDD, 0x1FDF}, {0x1FED, 0x1FEF}, {0x1FFD, 0x1FFE}, {0x2044, 0x2044}, {0x2052, 0x2052}, {0x207A, 0x207C}, {0x208A, 0x208C}, {0x20A0, 0x20BF},
{0x2100, 0x2101}, {0x2103, 0x2106}, {0x2108, 0x2109}, {0x2114, 0x2114}, {0x2116, 0x2118}, {0x211E, 0x2123}, {0x2125, 0x2125}, {0x2127, 0x2127}, {0x2129, 0x2129}, {0x212E, 0x212E}, {0x213A, 0x213B},
{0x2140, 0x2144}, {0x214A, 0x214D}, {0x214F, 0x214F}, {0x218A, 0x218B}, {0x2190, 0x2307}, {0x230C, 0x2328}, {0x232B, 0x2426}, {0x2440, 0x244A}, {0x249C, 0x24E9}, {0x2500, 0x2767}, {0x2794, 0x27C4},
{0x27C7, 0x27E5}, {0x27F0, 0x2982}, {0x2999, 0x29D7}, {0x29DC, 0x29FB}, {0x29FE, 0x2B73}, {0x2B76, 0x2B95}, {0x2B97, 0x2BFF}, {0x2CE5, 0x2CEA}, {0x2E50, 0x2E51}, {0x2E80, 0x2E99}, {0x2E9B, 0x2EF3},
{0x2F00, 0x2FD5}, {0x2FF0, 0x2FFB}, {0x3004, 0x3004}, {0x3012, 0x3013}, {0x3020, 0x3020}, {0x3036, 0x3037}, {0x303E, 0x303F}, {0x309B, 0x309C}, {0x3190, 0x3191}, {0x3196, 0x319F}, {0x31C0, 0x31E3},
{0x3200, 0x321E}, {0x322A, 0x3247}, {0x3250, 0x3250}, {0x3260, 0x327F}, {0x328A, 0x32B0}, {0x32C0, 0x33FF}, {0x4DC0, 0x4DFF}, {0xA490, 0xA4C6}, {0xA700, 0xA716}, {0xA720, 0xA721}, {0xA789, 0xA78A},
{0xA828, 0xA82B}, {0xA836, 0xA839}, {0xAA77, 0xAA79}, {0xAB5B, 0xAB5B}, {0xAB6A, 0xAB6B}, {0xFB29, 0xFB29}, {0xFBB2, 0xFBC1}, {0xFDFC, 0xFDFD}, {0xFE62, 0xFE62}, {0xFE64, 0xFE66}, {0xFE69, 0xFE69},
{0xFF04, 0xFF04}, {0xFF0B, 0xFF0B}, {0xFF1C, 0xFF1E}, {0xFF3E, 0xFF3E}, {0xFF40, 0xFF40}, {0xFF5C, 0xFF5C}, {0xFF5E, 0xFF5E}, {0xFFE0, 0xFFE6}, {0xFFE8, 0xFFEE}, {0xFFFC, 0xFFFD}, {0x10137, 0x1013F},
{0x10179, 0x10189}, {0x1018C, 0x1018E}, {0x10190, 0x1019C}, {0x101A0, 0x101A0}, {0x101D0, 0x101FC}, {0x10877, 0x10878}, {0x10AC8, 0x10AC8}, {0x1173F, 0x1173F}, {0x11FD5, 0x11FF1}, {0x16B3C, 0x16B3F},
{0x16B45, 0x16B45}, {0x1BC9C, 0x1BC9C}, {0x1D000, 0x1D0F5}, {0x1D100, 0x1D126}, {0x1D129, 0x1D164}, {0x1D16A, 0x1D16C}, {0x1D183, 0x1D184}, {0x1D18C, 0x1D1A9}, {0x1D1AE, 0x1D1E8}, {0x1D200, 0x1D241},
{0x1D245, 0x1D245}, {0x1D300, 0x1D356}, {0x1D6C1, 0x1D6C1}, {0x1D6DB, 0x1D6DB}, {0x1D6FB, 0x1D6FB}, {0x1D715, 0x1D715}, {0x1D735, 0x1D735}, {0x1D74F, 0x1D74F}, {0x1D76F, 0x1D76F}, {0x1D789, 0x1D789},
{0x1D7A9, 0x1D7A9}, {0x1D7C3, 0x1D7C3}, {0x1D800, 0x1D9FF}, {0x1DA37, 0x1DA3A}, {0x1DA6D, 0x1DA74}, {0x1DA76, 0x1DA83}, {0x1DA85, 0x1DA86}, {0x1E14F, 0x1E14F}, {0x1E2FF, 0x1E2FF}, {0x1ECAC, 0x1ECAC},
{0x1ECB0, 0x1ECB0}, {0x1ED2E, 0x1ED2E}, {0x1EEF0, 0x1EEF1}, {0x1F000, 0x1F02B}, {0x1F030, 0x1F093}, {0x1F0A0, 0x1F0AE}, {0x1F0B1, 0x1F0BF}, {0x1F0C1, 0x1F0CF}, {0x1F0D1, 0x1F0F5}, {0x1F10D, 0x1F1AD},
{0x1F1E6, 0x1F202}, {0x1F210, 0x1F23B}, {0x1F240, 0x1F248}, {0x1F250, 0x1F251}, {0x1F260, 0x1F265}, {0x1F300, 0x1F6D7}, {0x1F6E0, 0x1F6EC}, {0x1F6F0, 0x1F6FC}, {0x1F700, 0x1F773}, {0x1F780, 0x1F7D8},
{0x1F7E0, 0x1F7EB}, {0x1F800, 0x1F80B}, {0x1F810, 0x1F847}, {0x1F850, 0x1F859}, {0x1F860, 0x1F887}, {0x1F890, 0x1F8AD}, {0x1F8B0, 0x1F8B1}, {0x1F900, 0x1F978}, {0x1F97A, 0x1F9CB}, {0x1F9CD, 0x1FA53},
{0x1FA60, 0x1FA6D}, {0x1FA70, 0x1FA74}, {0x1FA78, 0x1FA7A}, {0x1FA80, 0x1FA86}, {0x1FA90, 0x1FAA8}, {0x1FAB0, 0x1FAB6}, {0x1FAC0, 0x1FAC2}, {0x1FAD0, 0x1FAD6}, {0x1FB00, 0x1FB92}, {0x1FB94, 0x1FBCA},
};
static const std::vector<std::pair<uint32_t, uint32_t>> control_ranges = {
{0x0, 0x8}, {0xE, 0x1B}, {0x7F, 0x84}, {0x86, 0x9F}, {0xAD, 0xAD}, {0x378, 0x379}, {0x380, 0x383}, {0x38B, 0x38B}, {0x38D, 0x38D}, {0x3A2, 0x3A2}, {0x530, 0x530}, {0x557, 0x558}, {0x58B, 0x58C},
{0x590, 0x590}, {0x5C8, 0x5CF}, {0x5EB, 0x5EE}, {0x5F5, 0x605}, {0x61C, 0x61D}, {0x6DD, 0x6DD}, {0x70E, 0x70F}, {0x74B, 0x74C}, {0x7B2, 0x7BF}, {0x7FB, 0x7FC}, {0x82E, 0x82F}, {0x83F, 0x83F},
{0x85C, 0x85D}, {0x85F, 0x85F}, {0x86B, 0x89F}, {0x8B5, 0x8B5}, {0x8C8, 0x8D2}, {0x8E2, 0x8E2}, {0x984, 0x984}, {0x98D, 0x98E}, {0x991, 0x992}, {0x9A9, 0x9A9}, {0x9B1, 0x9B1}, {0x9B3, 0x9B5},
{0x9BA, 0x9BB}, {0x9C5, 0x9C6}, {0x9C9, 0x9CA}, {0x9CF, 0x9D6}, {0x9D8, 0x9DB}, {0x9DE, 0x9DE}, {0x9E4, 0x9E5}, {0x9FF, 0xA00}, {0xA04, 0xA04}, {0xA0B, 0xA0E}, {0xA11, 0xA12}, {0xA29, 0xA29},
{0xA31, 0xA31}, {0xA34, 0xA34}, {0xA37, 0xA37}, {0xA3A, 0xA3B}, {0xA3D, 0xA3D}, {0xA43, 0xA46}, {0xA49, 0xA4A}, {0xA4E, 0xA50}, {0xA52, 0xA58}, {0xA5D, 0xA5D}, {0xA5F, 0xA65}, {0xA77, 0xA80},
{0xA84, 0xA84}, {0xA8E, 0xA8E}, {0xA92, 0xA92}, {0xAA9, 0xAA9}, {0xAB1, 0xAB1}, {0xAB4, 0xAB4}, {0xABA, 0xABB}, {0xAC6, 0xAC6}, {0xACA, 0xACA}, {0xACE, 0xACF}, {0xAD1, 0xADF}, {0xAE4, 0xAE5},
{0xAF2, 0xAF8}, {0xB00, 0xB00}, {0xB04, 0xB04}, {0xB0D, 0xB0E}, {0xB11, 0xB12}, {0xB29, 0xB29}, {0xB31, 0xB31}, {0xB34, 0xB34}, {0xB3A, 0xB3B}, {0xB45, 0xB46}, {0xB49, 0xB4A}, {0xB4E, 0xB54},
{0xB58, 0xB5B}, {0xB5E, 0xB5E}, {0xB64, 0xB65}, {0xB78, 0xB81}, {0xB84, 0xB84}, {0xB8B, 0xB8D}, {0xB91, 0xB91}, {0xB96, 0xB98}, {0xB9B, 0xB9B}, {0xB9D, 0xB9D}, {0xBA0, 0xBA2}, {0xBA5, 0xBA7},
{0xBAB, 0xBAD}, {0xBBA, 0xBBD}, {0xBC3, 0xBC5}, {0xBC9, 0xBC9}, {0xBCE, 0xBCF}, {0xBD1, 0xBD6}, {0xBD8, 0xBE5}, {0xBFB, 0xBFF}, {0xC0D, 0xC0D}, {0xC11, 0xC11}, {0xC29, 0xC29}, {0xC3A, 0xC3C},
{0xC45, 0xC45}, {0xC49, 0xC49}, {0xC4E, 0xC54}, {0xC57, 0xC57}, {0xC5B, 0xC5F}, {0xC64, 0xC65}, {0xC70, 0xC76}, {0xC8D, 0xC8D}, {0xC91, 0xC91}, {0xCA9, 0xCA9}, {0xCB4, 0xCB4}, {0xCBA, 0xCBB},
{0xCC5, 0xCC5}, {0xCC9, 0xCC9}, {0xCCE, 0xCD4}, {0xCD7, 0xCDD}, {0xCDF, 0xCDF}, {0xCE4, 0xCE5}, {0xCF0, 0xCF0}, {0xCF3, 0xCFF}, {0xD0D, 0xD0D}, {0xD11, 0xD11}, {0xD45, 0xD45}, {0xD49, 0xD49},
{0xD50, 0xD53}, {0xD64, 0xD65}, {0xD80, 0xD80}, {0xD84, 0xD84}, {0xD97, 0xD99}, {0xDB2, 0xDB2}, {0xDBC, 0xDBC}, {0xDBE, 0xDBF}, {0xDC7, 0xDC9}, {0xDCB, 0xDCE}, {0xDD5, 0xDD5}, {0xDD7, 0xDD7},
{0xDE0, 0xDE5}, {0xDF0, 0xDF1}, {0xDF5, 0xE00}, {0xE3B, 0xE3E}, {0xE5C, 0xE80}, {0xE83, 0xE83}, {0xE85, 0xE85}, {0xE8B, 0xE8B}, {0xEA4, 0xEA4}, {0xEA6, 0xEA6}, {0xEBE, 0xEBF}, {0xEC5, 0xEC5},
{0xEC7, 0xEC7}, {0xECE, 0xECF}, {0xEDA, 0xEDB}, {0xEE0, 0xEFF}, {0xF48, 0xF48}, {0xF6D, 0xF70}, {0xF98, 0xF98}, {0xFBD, 0xFBD}, {0xFCD, 0xFCD}, {0xFDB, 0xFFF}, {0x10C6, 0x10C6}, {0x10C8, 0x10CC},
{0x10CE, 0x10CF}, {0x1249, 0x1249}, {0x124E, 0x124F}, {0x1257, 0x1257}, {0x1259, 0x1259}, {0x125E, 0x125F}, {0x1289, 0x1289}, {0x128E, 0x128F}, {0x12B1, 0x12B1}, {0x12B6, 0x12B7}, {0x12BF, 0x12BF},
{0x12C1, 0x12C1}, {0x12C6, 0x12C7}, {0x12D7, 0x12D7}, {0x1311, 0x1311}, {0x1316, 0x1317}, {0x135B, 0x135C}, {0x137D, 0x137F}, {0x139A, 0x139F}, {0x13F6, 0x13F7}, {0x13FE, 0x13FF}, {0x169D, 0x169F},
{0x16F9, 0x16FF}, {0x170D, 0x170D}, {0x1715, 0x171F}, {0x1737, 0x173F}, {0x1754, 0x175F}, {0x176D, 0x176D}, {0x1771, 0x1771}, {0x1774, 0x177F}, {0x17DE, 0x17DF}, {0x17EA, 0x17EF}, {0x17FA, 0x17FF},
{0x180E, 0x180F}, {0x181A, 0x181F}, {0x1879, 0x187F}, {0x18AB, 0x18AF}, {0x18F6, 0x18FF}, {0x191F, 0x191F}, {0x192C, 0x192F}, {0x193C, 0x193F}, {0x1941, 0x1943}, {0x196E, 0x196F}, {0x1975, 0x197F},
{0x19AC, 0x19AF}, {0x19CA, 0x19CF}, {0x19DB, 0x19DD}, {0x1A1C, 0x1A1D}, {0x1A5F, 0x1A5F}, {0x1A7D, 0x1A7E}, {0x1A8A, 0x1A8F}, {0x1A9A, 0x1A9F}, {0x1AAE, 0x1AAF}, {0x1AC1, 0x1AFF}, {0x1B4C, 0x1B4F},
{0x1B7D, 0x1B7F}, {0x1BF4, 0x1BFB}, {0x1C38, 0x1C3A}, {0x1C4A, 0x1C4C}, {0x1C89, 0x1C8F}, {0x1CBB, 0x1CBC}, {0x1CC8, 0x1CCF}, {0x1CFB, 0x1CFF}, {0x1DFA, 0x1DFA}, {0x1F16, 0x1F17}, {0x1F1E, 0x1F1F},
{0x1F46, 0x1F47}, {0x1F4E, 0x1F4F}, {0x1F58, 0x1F58}, {0x1F5A, 0x1F5A}, {0x1F5C, 0x1F5C}, {0x1F5E, 0x1F5E}, {0x1F7E, 0x1F7F}, {0x1FB5, 0x1FB5}, {0x1FC5, 0x1FC5}, {0x1FD4, 0x1FD5}, {0x1FDC, 0x1FDC},
{0x1FF0, 0x1FF1}, {0x1FF5, 0x1FF5}, {0x1FFF, 0x1FFF}, {0x200B, 0x200F}, {0x202A, 0x202E}, {0x2060, 0x206F}, {0x2072, 0x2073}, {0x208F, 0x208F}, {0x209D, 0x209F}, {0x20C0, 0x20CF}, {0x20F1, 0x20FF},
{0x218C, 0x218F}, {0x2427, 0x243F}, {0x244B, 0x245F}, {0x2B74, 0x2B75}, {0x2B96, 0x2B96}, {0x2C2F, 0x2C2F}, {0x2C5F, 0x2C5F}, {0x2CF4, 0x2CF8}, {0x2D26, 0x2D26}, {0x2D28, 0x2D2C}, {0x2D2E, 0x2D2F},
{0x2D68, 0x2D6E}, {0x2D71, 0x2D7E}, {0x2D97, 0x2D9F}, {0x2DA7, 0x2DA7}, {0x2DAF, 0x2DAF}, {0x2DB7, 0x2DB7}, {0x2DBF, 0x2DBF}, {0x2DC7, 0x2DC7}, {0x2DCF, 0x2DCF}, {0x2DD7, 0x2DD7}, {0x2DDF, 0x2DDF},
{0x2E53, 0x2E7F}, {0x2E9A, 0x2E9A}, {0x2EF4, 0x2EFF}, {0x2FD6, 0x2FEF}, {0x2FFC, 0x2FFF}, {0x3040, 0x3040}, {0x3097, 0x3098}, {0x3100, 0x3104}, {0x3130, 0x3130}, {0x318F, 0x318F}, {0x31E4, 0x31EF},
{0x321F, 0x321F}, {0x9FFD, 0x9FFF}, {0xA48D, 0xA48F}, {0xA4C7, 0xA4CF}, {0xA62C, 0xA63F}, {0xA6F8, 0xA6FF}, {0xA7C0, 0xA7C1}, {0xA7CB, 0xA7F4}, {0xA82D, 0xA82F}, {0xA83A, 0xA83F}, {0xA878, 0xA87F},
{0xA8C6, 0xA8CD}, {0xA8DA, 0xA8DF}, {0xA954, 0xA95E}, {0xA97D, 0xA97F}, {0xA9CE, 0xA9CE}, {0xA9DA, 0xA9DD}, {0xA9FF, 0xA9FF}, {0xAA37, 0xAA3F}, {0xAA4E, 0xAA4F}, {0xAA5A, 0xAA5B}, {0xAAC3, 0xAADA},
{0xAAF7, 0xAB00}, {0xAB07, 0xAB08}, {0xAB0F, 0xAB10}, {0xAB17, 0xAB1F}, {0xAB27, 0xAB27}, {0xAB2F, 0xAB2F}, {0xAB6C, 0xAB6F}, {0xABEE, 0xABEF}, {0xABFA, 0xABFF}, {0xD7A4, 0xD7AF}, {0xD7C7, 0xD7CA},
{0xD7FC, 0xF8FF}, {0xFA6E, 0xFA6F}, {0xFADA, 0xFAFF}, {0xFB07, 0xFB12}, {0xFB18, 0xFB1C}, {0xFB37, 0xFB37}, {0xFB3D, 0xFB3D}, {0xFB3F, 0xFB3F}, {0xFB42, 0xFB42}, {0xFB45, 0xFB45}, {0xFBC2, 0xFBD2},
{0xFD40, 0xFD4F}, {0xFD90, 0xFD91}, {0xFDC8, 0xFDEF}, {0xFDFE, 0xFDFF}, {0xFE1A, 0xFE1F}, {0xFE53, 0xFE53}, {0xFE67, 0xFE67}, {0xFE6C, 0xFE6F}, {0xFE75, 0xFE75}, {0xFEFD, 0xFF00}, {0xFFBF, 0xFFC1},
{0xFFC8, 0xFFC9}, {0xFFD0, 0xFFD1}, {0xFFD8, 0xFFD9}, {0xFFDD, 0xFFDF}, {0xFFE7, 0xFFE7}, {0xFFEF, 0xFFFB}, {0xFFFE, 0xFFFF}, {0x1000C, 0x1000C}, {0x10027, 0x10027}, {0x1003B, 0x1003B},
{0x1003E, 0x1003E}, {0x1004E, 0x1004F}, {0x1005E, 0x1007F}, {0x100FB, 0x100FF}, {0x10103, 0x10106}, {0x10134, 0x10136}, {0x1018F, 0x1018F}, {0x1019D, 0x1019F}, {0x101A1, 0x101CF}, {0x101FE, 0x1027F},
{0x1029D, 0x1029F}, {0x102D1, 0x102DF}, {0x102FC, 0x102FF}, {0x10324, 0x1032C}, {0x1034B, 0x1034F}, {0x1037B, 0x1037F}, {0x1039E, 0x1039E}, {0x103C4, 0x103C7}, {0x103D6, 0x103FF}, {0x1049E, 0x1049F},
{0x104AA, 0x104AF}, {0x104D4, 0x104D7}, {0x104FC, 0x104FF}, {0x10528, 0x1052F}, {0x10564, 0x1056E}, {0x10570, 0x105FF}, {0x10737, 0x1073F}, {0x10756, 0x1075F}, {0x10768, 0x107FF}, {0x10806, 0x10807},
{0x10809, 0x10809}, {0x10836, 0x10836}, {0x10839, 0x1083B}, {0x1083D, 0x1083E}, {0x10856, 0x10856}, {0x1089F, 0x108A6}, {0x108B0, 0x108DF}, {0x108F3, 0x108F3}, {0x108F6, 0x108FA}, {0x1091C, 0x1091E},
{0x1093A, 0x1093E}, {0x10940, 0x1097F}, {0x109B8, 0x109BB}, {0x109D0, 0x109D1}, {0x10A04, 0x10A04}, {0x10A07, 0x10A0B}, {0x10A14, 0x10A14}, {0x10A18, 0x10A18}, {0x10A36, 0x10A37}, {0x10A3B, 0x10A3E},
{0x10A49, 0x10A4F}, {0x10A59, 0x10A5F}, {0x10AA0, 0x10ABF}, {0x10AE7, 0x10AEA}, {0x10AF7, 0x10AFF}, {0x10B36, 0x10B38}, {0x10B56, 0x10B57}, {0x10B73, 0x10B77}, {0x10B92, 0x10B98}, {0x10B9D, 0x10BA8},
{0x10BB0, 0x10BFF}, {0x10C49, 0x10C7F}, {0x10CB3, 0x10CBF}, {0x10CF3, 0x10CF9}, {0x10D28, 0x10D2F}, {0x10D3A, 0x10E5F}, {0x10E7F, 0x10E7F}, {0x10EAA, 0x10EAA}, {0x10EAE, 0x10EAF}, {0x10EB2, 0x10EFF},
{0x10F28, 0x10F2F}, {0x10F5A, 0x10FAF}, {0x10FCC, 0x10FDF}, {0x10FF7, 0x10FFF}, {0x1104E, 0x11051}, {0x11070, 0x1107E}, {0x110BD, 0x110BD}, {0x110C2, 0x110CF}, {0x110E9, 0x110EF}, {0x110FA, 0x110FF},
{0x11135, 0x11135}, {0x11148, 0x1114F}, {0x11177, 0x1117F}, {0x111E0, 0x111E0}, {0x111F5, 0x111FF}, {0x11212, 0x11212}, {0x1123F, 0x1127F}, {0x11287, 0x11287}, {0x11289, 0x11289}, {0x1128E, 0x1128E},
{0x1129E, 0x1129E}, {0x112AA, 0x112AF}, {0x112EB, 0x112EF}, {0x112FA, 0x112FF}, {0x11304, 0x11304}, {0x1130D, 0x1130E}, {0x11311, 0x11312}, {0x11329, 0x11329}, {0x11331, 0x11331}, {0x11334, 0x11334},
{0x1133A, 0x1133A}, {0x11345, 0x11346}, {0x11349, 0x1134A}, {0x1134E, 0x1134F}, {0x11351, 0x11356}, {0x11358, 0x1135C}, {0x11364, 0x11365}, {0x1136D, 0x1136F}, {0x11375, 0x113FF}, {0x1145C, 0x1145C},
{0x11462, 0x1147F}, {0x114C8, 0x114CF}, {0x114DA, 0x1157F}, {0x115B6, 0x115B7}, {0x115DE, 0x115FF}, {0x11645, 0x1164F}, {0x1165A, 0x1165F}, {0x1166D, 0x1167F}, {0x116B9, 0x116BF}, {0x116CA, 0x116FF},
{0x1171B, 0x1171C}, {0x1172C, 0x1172F}, {0x11740, 0x117FF}, {0x1183C, 0x1189F}, {0x118F3, 0x118FE}, {0x11907, 0x11908}, {0x1190A, 0x1190B}, {0x11914, 0x11914}, {0x11917, 0x11917}, {0x11936, 0x11936},
{0x11939, 0x1193A}, {0x11947, 0x1194F}, {0x1195A, 0x1199F}, {0x119A8, 0x119A9}, {0x119D8, 0x119D9}, {0x119E5, 0x119FF}, {0x11A48, 0x11A4F}, {0x11AA3, 0x11ABF}, {0x11AF9, 0x11BFF}, {0x11C09, 0x11C09},
{0x11C37, 0x11C37}, {0x11C46, 0x11C4F}, {0x11C6D, 0x11C6F}, {0x11C90, 0x11C91}, {0x11CA8, 0x11CA8}, {0x11CB7, 0x11CFF}, {0x11D07, 0x11D07}, {0x11D0A, 0x11D0A}, {0x11D37, 0x11D39}, {0x11D3B, 0x11D3B},
{0x11D3E, 0x11D3E}, {0x11D48, 0x11D4F}, {0x11D5A, 0x11D5F}, {0x11D66, 0x11D66}, {0x11D69, 0x11D69}, {0x11D8F, 0x11D8F}, {0x11D92, 0x11D92}, {0x11D99, 0x11D9F}, {0x11DAA, 0x11EDF}, {0x11EF9, 0x11FAF},
{0x11FB1, 0x11FBF}, {0x11FF2, 0x11FFE}, {0x1239A, 0x123FF}, {0x1246F, 0x1246F}, {0x12475, 0x1247F}, {0x12544, 0x12FFF}, {0x1342F, 0x143FF}, {0x14647, 0x167FF}, {0x16A39, 0x16A3F}, {0x16A5F, 0x16A5F},
{0x16A6A, 0x16A6D}, {0x16A70, 0x16ACF}, {0x16AEE, 0x16AEF}, {0x16AF6, 0x16AFF}, {0x16B46, 0x16B4F}, {0x16B5A, 0x16B5A}, {0x16B62, 0x16B62}, {0x16B78, 0x16B7C}, {0x16B90, 0x16E3F}, {0x16E9B, 0x16EFF},
{0x16F4B, 0x16F4E}, {0x16F88, 0x16F8E}, {0x16FA0, 0x16FDF}, {0x16FE5, 0x16FEF}, {0x16FF2, 0x16FFF}, {0x187F8, 0x187FF}, {0x18CD6, 0x18CFF}, {0x18D09, 0x1AFFF}, {0x1B11F, 0x1B14F}, {0x1B153, 0x1B163},
{0x1B168, 0x1B16F}, {0x1B2FC, 0x1BBFF}, {0x1BC6B, 0x1BC6F}, {0x1BC7D, 0x1BC7F}, {0x1BC89, 0x1BC8F}, {0x1BC9A, 0x1BC9B}, {0x1BCA0, 0x1CFFF}, {0x1D0F6, 0x1D0FF}, {0x1D127, 0x1D128}, {0x1D173, 0x1D17A},
{0x1D1E9, 0x1D1FF}, {0x1D246, 0x1D2DF}, {0x1D2F4, 0x1D2FF}, {0x1D357, 0x1D35F}, {0x1D379, 0x1D3FF}, {0x1D455, 0x1D455}, {0x1D49D, 0x1D49D}, {0x1D4A0, 0x1D4A1}, {0x1D4A3, 0x1D4A4}, {0x1D4A7, 0x1D4A8},
{0x1D4AD, 0x1D4AD}, {0x1D4BA, 0x1D4BA}, {0x1D4BC, 0x1D4BC}, {0x1D4C4, 0x1D4C4}, {0x1D506, 0x1D506}, {0x1D50B, 0x1D50C}, {0x1D515, 0x1D515}, {0x1D51D, 0x1D51D}, {0x1D53A, 0x1D53A}, {0x1D53F, 0x1D53F},
{0x1D545, 0x1D545}, {0x1D547, 0x1D549}, {0x1D551, 0x1D551}, {0x1D6A6, 0x1D6A7}, {0x1D7CC, 0x1D7CD}, {0x1DA8C, 0x1DA9A}, {0x1DAA0, 0x1DAA0}, {0x1DAB0, 0x1DFFF}, {0x1E007, 0x1E007}, {0x1E019, 0x1E01A},
{0x1E022, 0x1E022}, {0x1E025, 0x1E025}, {0x1E02B, 0x1E0FF}, {0x1E12D, 0x1E12F}, {0x1E13E, 0x1E13F}, {0x1E14A, 0x1E14D}, {0x1E150, 0x1E2BF}, {0x1E2FA, 0x1E2FE}, {0x1E300, 0x1E7FF}, {0x1E8C5, 0x1E8C6},
{0x1E8D7, 0x1E8FF}, {0x1E94C, 0x1E94F}, {0x1E95A, 0x1E95D}, {0x1E960, 0x1EC70}, {0x1ECB5, 0x1ED00}, {0x1ED3E, 0x1EDFF}, {0x1EE04, 0x1EE04}, {0x1EE20, 0x1EE20}, {0x1EE23, 0x1EE23}, {0x1EE25, 0x1EE26},
{0x1EE28, 0x1EE28}, {0x1EE33, 0x1EE33}, {0x1EE38, 0x1EE38}, {0x1EE3A, 0x1EE3A}, {0x1EE3C, 0x1EE41}, {0x1EE43, 0x1EE46}, {0x1EE48, 0x1EE48}, {0x1EE4A, 0x1EE4A}, {0x1EE4C, 0x1EE4C}, {0x1EE50, 0x1EE50},
{0x1EE53, 0x1EE53}, {0x1EE55, 0x1EE56}, {0x1EE58, 0x1EE58}, {0x1EE5A, 0x1EE5A}, {0x1EE5C, 0x1EE5C}, {0x1EE5E, 0x1EE5E}, {0x1EE60, 0x1EE60}, {0x1EE63, 0x1EE63}, {0x1EE65, 0x1EE66}, {0x1EE6B, 0x1EE6B},
{0x1EE73, 0x1EE73}, {0x1EE78, 0x1EE78}, {0x1EE7D, 0x1EE7D}, {0x1EE7F, 0x1EE7F}, {0x1EE8A, 0x1EE8A}, {0x1EE9C, 0x1EEA0}, {0x1EEA4, 0x1EEA4}, {0x1EEAA, 0x1EEAA}, {0x1EEBC, 0x1EEEF}, {0x1EEF2, 0x1EFFF},
{0x1F02C, 0x1F02F}, {0x1F094, 0x1F09F}, {0x1F0AF, 0x1F0B0}, {0x1F0C0, 0x1F0C0}, {0x1F0D0, 0x1F0D0}, {0x1F0F6, 0x1F0FF}, {0x1F1AE, 0x1F1E5}, {0x1F203, 0x1F20F}, {0x1F23C, 0x1F23F}, {0x1F249, 0x1F24F},
{0x1F252, 0x1F25F}, {0x1F266, 0x1F2FF}, {0x1F6D8, 0x1F6DF}, {0x1F6ED, 0x1F6EF}, {0x1F6FD, 0x1F6FF}, {0x1F774, 0x1F77F}, {0x1F7D9, 0x1F7DF}, {0x1F7EC, 0x1F7FF}, {0x1F80C, 0x1F80F}, {0x1F848, 0x1F84F},
{0x1F85A, 0x1F85F}, {0x1F888, 0x1F88F}, {0x1F8AE, 0x1F8AF}, {0x1F8B2, 0x1F8FF}, {0x1F979, 0x1F979}, {0x1F9CC, 0x1F9CC}, {0x1FA54, 0x1FA5F}, {0x1FA6E, 0x1FA6F}, {0x1FA75, 0x1FA77}, {0x1FA7B, 0x1FA7F},
{0x1FA87, 0x1FA8F}, {0x1FAA9, 0x1FAAF}, {0x1FAB7, 0x1FABF}, {0x1FAC3, 0x1FACF}, {0x1FAD7, 0x1FAFF}, {0x1FB93, 0x1FB93}, {0x1FBCB, 0x1FBEF}, {0x1FBFA, 0x1FFFF}, {0x2A6DE, 0x2A6FF}, {0x2B735, 0x2B73F},
{0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF},
};
static const std::multimap<uint32_t, uint32_t> nfd_map = {
{0xC0, 0x41}, {0xC0, 0x300}, {0xC1, 0x41}, {0xC1, 0x301}, {0xC2, 0x41}, {0xC2, 0x302}, {0xC3, 0x41}, {0xC3, 0x303}, {0xC4, 0x41}, {0xC4, 0x308}, {0xC5, 0x41}, {0xC5, 0x30A}, {0xC7, 0x43},
{0xC7, 0x327}, {0xC8, 0x45}, {0xC8, 0x300}, {0xC9, 0x45}, {0xC9, 0x301}, {0xCA, 0x45}, {0xCA, 0x302}, {0xCB, 0x45}, {0xCB, 0x308}, {0xCC, 0x49}, {0xCC, 0x300}, {0xCD, 0x49}, {0xCD, 0x301},
{0xCE, 0x49}, {0xCE, 0x302}, {0xCF, 0x49}, {0xCF, 0x308}, {0xD1, 0x4E}, {0xD1, 0x303}, {0xD2, 0x4F}, {0xD2, 0x300}, {0xD3, 0x4F}, {0xD3, 0x301}, {0xD4, 0x4F}, {0xD4, 0x302}, {0xD5, 0x4F},
{0xD5, 0x303}, {0xD6, 0x4F}, {0xD6, 0x308}, {0xD9, 0x55}, {0xD9, 0x300}, {0xDA, 0x55}, {0xDA, 0x301}, {0xDB, 0x55}, {0xDB, 0x302}, {0xDC, 0x55}, {0xDC, 0x308}, {0xDD, 0x59}, {0xDD, 0x301},
{0xE0, 0x61}, {0xE0, 0x300}, {0xE1, 0x61}, {0xE1, 0x301}, {0xE2, 0x61}, {0xE2, 0x302}, {0xE3, 0x61}, {0xE3, 0x303}, {0xE4, 0x61}, {0xE4, 0x308}, {0xE5, 0x61}, {0xE5, 0x30A}, {0xE7, 0x63},
{0xE7, 0x327}, {0xE8, 0x65}, {0xE8, 0x300}, {0xE9, 0x65}, {0xE9, 0x301}, {0xEA, 0x65}, {0xEA, 0x302}, {0xEB, 0x65}, {0xEB, 0x308}, {0xEC, 0x69}, {0xEC, 0x300}, {0xED, 0x69}, {0xED, 0x301},
{0xEE, 0x69}, {0xEE, 0x302}, {0xEF, 0x69}, {0xEF, 0x308}, {0xF1, 0x6E}, {0xF1, 0x303}, {0xF2, 0x6F}, {0xF2, 0x300}, {0xF3, 0x6F}, {0xF3, 0x301}, {0xF4, 0x6F}, {0xF4, 0x302}, {0xF5, 0x6F},
{0xF5, 0x303}, {0xF6, 0x6F}, {0xF6, 0x308}, {0xF9, 0x75}, {0xF9, 0x300}, {0xFA, 0x75}, {0xFA, 0x301}, {0xFB, 0x75}, {0xFB, 0x302}, {0xFC, 0x75}, {0xFC, 0x308}, {0xFD, 0x79}, {0xFD, 0x301},
{0xFF, 0x79}, {0xFF, 0x308}, {0x100, 0x41}, {0x100, 0x304}, {0x101, 0x61}, {0x101, 0x304}, {0x102, 0x41}, {0x102, 0x306}, {0x103, 0x61}, {0x103, 0x306}, {0x104, 0x41}, {0x104, 0x328}, {0x105, 0x61},
{0x105, 0x328}, {0x106, 0x43}, {0x106, 0x301}, {0x107, 0x63}, {0x107, 0x301}, {0x108, 0x43}, {0x108, 0x302}, {0x109, 0x63}, {0x109, 0x302}, {0x10A, 0x43}, {0x10A, 0x307}, {0x10B, 0x63},
{0x10B, 0x307}, {0x10C, 0x43}, {0x10C, 0x30C}, {0x10D, 0x63}, {0x10D, 0x30C}, {0x10E, 0x44}, {0x10E, 0x30C}, {0x10F, 0x64}, {0x10F, 0x30C}, {0x112, 0x45}, {0x112, 0x304}, {0x113, 0x65},
{0x113, 0x304}, {0x114, 0x45}, {0x114, 0x306}, {0x115, 0x65}, {0x115, 0x306}, {0x116, 0x45}, {0x116, 0x307}, {0x117, 0x65}, {0x117, 0x307}, {0x118, 0x45}, {0x118, 0x328}, {0x119, 0x65},
{0x119, 0x328}, {0x11A, 0x45}, {0x11A, 0x30C}, {0x11B, 0x65}, {0x11B, 0x30C}, {0x11C, 0x47}, {0x11C, 0x302}, {0x11D, 0x67}, {0x11D, 0x302}, {0x11E, 0x47}, {0x11E, 0x306}, {0x11F, 0x67},
{0x11F, 0x306}, {0x120, 0x47}, {0x120, 0x307}, {0x121, 0x67}, {0x121, 0x307}, {0x122, 0x47}, {0x122, 0x327}, {0x123, 0x67}, {0x123, 0x327}, {0x124, 0x48}, {0x124, 0x302}, {0x125, 0x68},
{0x125, 0x302}, {0x128, 0x49}, {0x128, 0x303}, {0x129, 0x69}, {0x129, 0x303}, {0x12A, 0x49}, {0x12A, 0x304}, {0x12B, 0x69}, {0x12B, 0x304}, {0x12C, 0x49}, {0x12C, 0x306}, {0x12D, 0x69},
{0x12D, 0x306}, {0x12E, 0x49}, {0x12E, 0x328}, {0x12F, 0x69}, {0x12F, 0x328}, {0x130, 0x49}, {0x130, 0x307}, {0x134, 0x4A}, {0x134, 0x302}, {0x135, 0x6A}, {0x135, 0x302}, {0x136, 0x4B},
{0x136, 0x327}, {0x137, 0x6B}, {0x137, 0x327}, {0x139, 0x4C}, {0x139, 0x301}, {0x13A, 0x6C}, {0x13A, 0x301}, {0x13B, 0x4C}, {0x13B, 0x327}, {0x13C, 0x6C}, {0x13C, 0x327}, {0x13D, 0x4C},
{0x13D, 0x30C}, {0x13E, 0x6C}, {0x13E, 0x30C}, {0x143, 0x4E}, {0x143, 0x301}, {0x144, 0x6E}, {0x144, 0x301}, {0x145, 0x4E}, {0x145, 0x327}, {0x146, 0x6E}, {0x146, 0x327}, {0x147, 0x4E},
{0x147, 0x30C}, {0x148, 0x6E}, {0x148, 0x30C}, {0x14C, 0x4F}, {0x14C, 0x304}, {0x14D, 0x6F}, {0x14D, 0x304}, {0x14E, 0x4F}, {0x14E, 0x306}, {0x14F, 0x6F}, {0x14F, 0x306}, {0x150, 0x4F},
{0x150, 0x30B}, {0x151, 0x6F}, {0x151, 0x30B}, {0x154, 0x52}, {0x154, 0x301}, {0x155, 0x72}, {0x155, 0x301}, {0x156, 0x52}, {0x156, 0x327}, {0x157, 0x72}, {0x157, 0x327}, {0x158, 0x52},
{0x158, 0x30C}, {0x159, 0x72}, {0x159, 0x30C}, {0x15A, 0x53}, {0x15A, 0x301}, {0x15B, 0x73}, {0x15B, 0x301}, {0x15C, 0x53}, {0x15C, 0x302}, {0x15D, 0x73}, {0x15D, 0x302}, {0x15E, 0x53},
{0x15E, 0x327}, {0x15F, 0x73}, {0x15F, 0x327}, {0x160, 0x53}, {0x160, 0x30C}, {0x161, 0x73}, {0x161, 0x30C}, {0x162, 0x54}, {0x162, 0x327}, {0x163, 0x74}, {0x163, 0x327}, {0x164, 0x54},
{0x164, 0x30C}, {0x165, 0x74}, {0x165, 0x30C}, {0x168, 0x55}, {0x168, 0x303}, {0x169, 0x75}, {0x169, 0x303}, {0x16A, 0x55}, {0x16A, 0x304}, {0x16B, 0x75}, {0x16B, 0x304}, {0x16C, 0x55},
{0x16C, 0x306}, {0x16D, 0x75}, {0x16D, 0x306}, {0x16E, 0x55}, {0x16E, 0x30A}, {0x16F, 0x75}, {0x16F, 0x30A}, {0x170, 0x55}, {0x170, 0x30B}, {0x171, 0x75}, {0x171, 0x30B}, {0x172, 0x55},
{0x172, 0x328}, {0x173, 0x75}, {0x173, 0x328}, {0x174, 0x57}, {0x174, 0x302}, {0x175, 0x77}, {0x175, 0x302}, {0x176, 0x59}, {0x176, 0x302}, {0x177, 0x79}, {0x177, 0x302}, {0x178, 0x59},
{0x178, 0x308}, {0x179, 0x5A}, {0x179, 0x301}, {0x17A, 0x7A}, {0x17A, 0x301}, {0x17B, 0x5A}, {0x17B, 0x307}, {0x17C, 0x7A}, {0x17C, 0x307}, {0x17D, 0x5A}, {0x17D, 0x30C}, {0x17E, 0x7A},
{0x17E, 0x30C}, {0x1A0, 0x4F}, {0x1A0, 0x31B}, {0x1A1, 0x6F}, {0x1A1, 0x31B}, {0x1AF, 0x55}, {0x1AF, 0x31B}, {0x1B0, 0x75}, {0x1B0, 0x31B}, {0x1CD, 0x41}, {0x1CD, 0x30C}, {0x1CE, 0x61},
{0x1CE, 0x30C}, {0x1CF, 0x49}, {0x1CF, 0x30C}, {0x1D0, 0x69}, {0x1D0, 0x30C}, {0x1D1, 0x4F}, {0x1D1, 0x30C}, {0x1D2, 0x6F}, {0x1D2, 0x30C}, {0x1D3, 0x55}, {0x1D3, 0x30C}, {0x1D4, 0x75},
{0x1D4, 0x30C}, {0x1D5, 0x55}, {0x1D5, 0x308}, {0x1D5, 0x304}, {0x1D6, 0x75}, {0x1D6, 0x308}, {0x1D6, 0x304}, {0x1D7, 0x55}, {0x1D7, 0x308}, {0x1D7, 0x301}, {0x1D8, 0x75}, {0x1D8, 0x308},
{0x1D8, 0x301}, {0x1D9, 0x55}, {0x1D9, 0x308}, {0x1D9, 0x30C}, {0x1DA, 0x75}, {0x1DA, 0x308}, {0x1DA, 0x30C}, {0x1DB, 0x55}, {0x1DB, 0x308}, {0x1DB, 0x300}, {0x1DC, 0x75}, {0x1DC, 0x308},
{0x1DC, 0x300}, {0x1DE, 0x41}, {0x1DE, 0x308}, {0x1DE, 0x304}, {0x1DF, 0x61}, {0x1DF, 0x308}, {0x1DF, 0x304}, {0x1E0, 0x41}, {0x1E0, 0x307}, {0x1E0, 0x304}, {0x1E1, 0x61}, {0x1E1, 0x307},
{0x1E1, 0x304}, {0x1E2, 0xC6}, {0x1E2, 0x304}, {0x1E3, 0xE6}, {0x1E3, 0x304}, {0x1E6, 0x47}, {0x1E6, 0x30C}, {0x1E7, 0x67}, {0x1E7, 0x30C}, {0x1E8, 0x4B}, {0x1E8, 0x30C}, {0x1E9, 0x6B},
{0x1E9, 0x30C}, {0x1EA, 0x4F}, {0x1EA, 0x328}, {0x1EB, 0x6F}, {0x1EB, 0x328}, {0x1EC, 0x4F}, {0x1EC, 0x328}, {0x1EC, 0x304}, {0x1ED, 0x6F}, {0x1ED, 0x328}, {0x1ED, 0x304}, {0x1EE, 0x1B7},
{0x1EE, 0x30C}, {0x1EF, 0x292}, {0x1EF, 0x30C}, {0x1F0, 0x6A}, {0x1F0, 0x30C}, {0x1F4, 0x47}, {0x1F4, 0x301}, {0x1F5, 0x67}, {0x1F5, 0x301}, {0x1F8, 0x4E}, {0x1F8, 0x300}, {0x1F9, 0x6E},
{0x1F9, 0x300}, {0x1FA, 0x41}, {0x1FA, 0x30A}, {0x1FA, 0x301}, {0x1FB, 0x61}, {0x1FB, 0x30A}, {0x1FB, 0x301}, {0x1FC, 0xC6}, {0x1FC, 0x301}, {0x1FD, 0xE6}, {0x1FD, 0x301}, {0x1FE, 0xD8},
{0x1FE, 0x301}, {0x1FF, 0xF8}, {0x1FF, 0x301}, {0x200, 0x41}, {0x200, 0x30F}, {0x201, 0x61}, {0x201, 0x30F}, {0x202, 0x41}, {0x202, 0x311}, {0x203, 0x61}, {0x203, 0x311}, {0x204, 0x45},
{0x204, 0x30F}, {0x205, 0x65}, {0x205, 0x30F}, {0x206, 0x45}, {0x206, 0x311}, {0x207, 0x65}, {0x207, 0x311}, {0x208, 0x49}, {0x208, 0x30F}, {0x209, 0x69}, {0x209, 0x30F}, {0x20A, 0x49},
{0x20A, 0x311}, {0x20B, 0x69}, {0x20B, 0x311}, {0x20C, 0x4F}, {0x20C, 0x30F}, {0x20D, 0x6F}, {0x20D, 0x30F}, {0x20E, 0x4F}, {0x20E, 0x311}, {0x20F, 0x6F}, {0x20F, 0x311}, {0x210, 0x52},
{0x210, 0x30F}, {0x211, 0x72}, {0x211, 0x30F}, {0x212, 0x52}, {0x212, 0x311}, {0x213, 0x72}, {0x213, 0x311}, {0x214, 0x55}, {0x214, 0x30F}, {0x215, 0x75}, {0x215, 0x30F}, {0x216, 0x55},
{0x216, 0x311}, {0x217, 0x75}, {0x217, 0x311}, {0x218, 0x53}, {0x218, 0x326}, {0x219, 0x73}, {0x219, 0x326}, {0x21A, 0x54}, {0x21A, 0x326}, {0x21B, 0x74}, {0x21B, 0x326}, {0x21E, 0x48},
{0x21E, 0x30C}, {0x21F, 0x68}, {0x21F, 0x30C}, {0x226, 0x41}, {0x226, 0x307}, {0x227, 0x61}, {0x227, 0x307}, {0x228, 0x45}, {0x228, 0x327}, {0x229, 0x65}, {0x229, 0x327}, {0x22A, 0x4F},
{0x22A, 0x308}, {0x22A, 0x304}, {0x22B, 0x6F}, {0x22B, 0x308}, {0x22B, 0x304}, {0x22C, 0x4F}, {0x22C, 0x303}, {0x22C, 0x304}, {0x22D, 0x6F}, {0x22D, 0x303}, {0x22D, 0x304}, {0x22E, 0x4F},
{0x22E, 0x307}, {0x22F, 0x6F}, {0x22F, 0x307}, {0x230, 0x4F}, {0x230, 0x307}, {0x230, 0x304}, {0x231, 0x6F}, {0x231, 0x307}, {0x231, 0x304}, {0x232, 0x59}, {0x232, 0x304}, {0x233, 0x79},
{0x233, 0x304}, {0x340, 0x300}, {0x341, 0x301}, {0x343, 0x313}, {0x344, 0x308}, {0x344, 0x301}, {0x374, 0x2B9}, {0x37E, 0x3B}, {0x385, 0xA8}, {0x385, 0x301}, {0x386, 0x391}, {0x386, 0x301},
{0x387, 0xB7}, {0x388, 0x395}, {0x388, 0x301}, {0x389, 0x397}, {0x389, 0x301}, {0x38A, 0x399}, {0x38A, 0x301}, {0x38C, 0x39F}, {0x38C, 0x301}, {0x38E, 0x3A5}, {0x38E, 0x301}, {0x38F, 0x3A9},
{0x38F, 0x301}, {0x390, 0x3B9}, {0x390, 0x308}, {0x390, 0x301}, {0x3AA, 0x399}, {0x3AA, 0x308}, {0x3AB, 0x3A5}, {0x3AB, 0x308}, {0x3AC, 0x3B1}, {0x3AC, 0x301}, {0x3AD, 0x3B5}, {0x3AD, 0x301},
{0x3AE, 0x3B7}, {0x3AE, 0x301}, {0x3AF, 0x3B9}, {0x3AF, 0x301}, {0x3B0, 0x3C5}, {0x3B0, 0x308}, {0x3B0, 0x301}, {0x3CA, 0x3B9}, {0x3CA, 0x308}, {0x3CB, 0x3C5}, {0x3CB, 0x308}, {0x3CC, 0x3BF},
{0x3CC, 0x301}, {0x3CD, 0x3C5}, {0x3CD, 0x301}, {0x3CE, 0x3C9}, {0x3CE, 0x301}, {0x3D3, 0x3D2}, {0x3D3, 0x301}, {0x3D4, 0x3D2}, {0x3D4, 0x308}, {0x400, 0x415}, {0x400, 0x300}, {0x401, 0x415},
{0x401, 0x308}, {0x403, 0x413}, {0x403, 0x301}, {0x407, 0x406}, {0x407, 0x308}, {0x40C, 0x41A}, {0x40C, 0x301}, {0x40D, 0x418}, {0x40D, 0x300}, {0x40E, 0x423}, {0x40E, 0x306}, {0x419, 0x418},
{0x419, 0x306}, {0x439, 0x438}, {0x439, 0x306}, {0x450, 0x435}, {0x450, 0x300}, {0x451, 0x435}, {0x451, 0x308}, {0x453, 0x433}, {0x453, 0x301}, {0x457, 0x456}, {0x457, 0x308}, {0x45C, 0x43A},
{0x45C, 0x301}, {0x45D, 0x438}, {0x45D, 0x300}, {0x45E, 0x443}, {0x45E, 0x306}, {0x476, 0x474}, {0x476, 0x30F}, {0x477, 0x475}, {0x477, 0x30F}, {0x4C1, 0x416}, {0x4C1, 0x306}, {0x4C2, 0x436},
{0x4C2, 0x306}, {0x4D0, 0x410}, {0x4D0, 0x306}, {0x4D1, 0x430}, {0x4D1, 0x306}, {0x4D2, 0x410}, {0x4D2, 0x308}, {0x4D3, 0x430}, {0x4D3, 0x308}, {0x4D6, 0x415}, {0x4D6, 0x306}, {0x4D7, 0x435},
{0x4D7, 0x306}, {0x4DA, 0x4D8}, {0x4DA, 0x308}, {0x4DB, 0x4D9}, {0x4DB, 0x308}, {0x4DC, 0x416}, {0x4DC, 0x308}, {0x4DD, 0x436}, {0x4DD, 0x308}, {0x4DE, 0x417}, {0x4DE, 0x308}, {0x4DF, 0x437},
{0x4DF, 0x308}, {0x4E2, 0x418}, {0x4E2, 0x304}, {0x4E3, 0x438}, {0x4E3, 0x304}, {0x4E4, 0x418}, {0x4E4, 0x308}, {0x4E5, 0x438}, {0x4E5, 0x308}, {0x4E6, 0x41E}, {0x4E6, 0x308}, {0x4E7, 0x43E},
{0x4E7, 0x308}, {0x4EA, 0x4E8}, {0x4EA, 0x308}, {0x4EB, 0x4E9}, {0x4EB, 0x308}, {0x4EC, 0x42D}, {0x4EC, 0x308}, {0x4ED, 0x44D}, {0x4ED, 0x308}, {0x4EE, 0x423}, {0x4EE, 0x304}, {0x4EF, 0x443},
{0x4EF, 0x304}, {0x4F0, 0x423}, {0x4F0, 0x308}, {0x4F1, 0x443}, {0x4F1, 0x308}, {0x4F2, 0x423}, {0x4F2, 0x30B}, {0x4F3, 0x443}, {0x4F3, 0x30B}, {0x4F4, 0x427}, {0x4F4, 0x308}, {0x4F5, 0x447},
{0x4F5, 0x308}, {0x4F8, 0x42B}, {0x4F8, 0x308}, {0x4F9, 0x44B}, {0x4F9, 0x308}, {0x622, 0x627}, {0x622, 0x653}, {0x623, 0x627}, {0x623, 0x654}, {0x624, 0x648}, {0x624, 0x654}, {0x625, 0x627},
{0x625, 0x655}, {0x626, 0x64A}, {0x626, 0x654}, {0x6C0, 0x6D5}, {0x6C0, 0x654}, {0x6C2, 0x6C1}, {0x6C2, 0x654}, {0x6D3, 0x6D2}, {0x6D3, 0x654}, {0x929, 0x928}, {0x929, 0x93C}, {0x931, 0x930},
{0x931, 0x93C}, {0x934, 0x933}, {0x934, 0x93C}, {0x958, 0x915}, {0x958, 0x93C}, {0x959, 0x916}, {0x959, 0x93C}, {0x95A, 0x917}, {0x95A, 0x93C}, {0x95B, 0x91C}, {0x95B, 0x93C}, {0x95C, 0x921},
{0x95C, 0x93C}, {0x95D, 0x922}, {0x95D, 0x93C}, {0x95E, 0x92B}, {0x95E, 0x93C}, {0x95F, 0x92F}, {0x95F, 0x93C}, {0x9CB, 0x9C7}, {0x9CB, 0x9BE}, {0x9CC, 0x9C7}, {0x9CC, 0x9D7}, {0x9DC, 0x9A1},
{0x9DC, 0x9BC}, {0x9DD, 0x9A2}, {0x9DD, 0x9BC}, {0x9DF, 0x9AF}, {0x9DF, 0x9BC}, {0xA33, 0xA32}, {0xA33, 0xA3C}, {0xA36, 0xA38}, {0xA36, 0xA3C}, {0xA59, 0xA16}, {0xA59, 0xA3C}, {0xA5A, 0xA17},
{0xA5A, 0xA3C}, {0xA5B, 0xA1C}, {0xA5B, 0xA3C}, {0xA5E, 0xA2B}, {0xA5E, 0xA3C}, {0xB48, 0xB47}, {0xB48, 0xB56}, {0xB4B, 0xB47}, {0xB4B, 0xB3E}, {0xB4C, 0xB47}, {0xB4C, 0xB57}, {0xB5C, 0xB21},
{0xB5C, 0xB3C}, {0xB5D, 0xB22}, {0xB5D, 0xB3C}, {0xB94, 0xB92}, {0xB94, 0xBD7}, {0xBCA, 0xBC6}, {0xBCA, 0xBBE}, {0xBCB, 0xBC7}, {0xBCB, 0xBBE}, {0xBCC, 0xBC6}, {0xBCC, 0xBD7}, {0xC48, 0xC46},
{0xC48, 0xC56}, {0xCC0, 0xCBF}, {0xCC0, 0xCD5}, {0xCC7, 0xCC6}, {0xCC7, 0xCD5}, {0xCC8, 0xCC6}, {0xCC8, 0xCD6}, {0xCCA, 0xCC6}, {0xCCA, 0xCC2}, {0xCCB, 0xCC6}, {0xCCB, 0xCC2}, {0xCCB, 0xCD5},
{0xD4A, 0xD46}, {0xD4A, 0xD3E}, {0xD4B, 0xD47}, {0xD4B, 0xD3E}, {0xD4C, 0xD46}, {0xD4C, 0xD57}, {0xDDA, 0xDD9}, {0xDDA, 0xDCA}, {0xDDC, 0xDD9}, {0xDDC, 0xDCF}, {0xDDD, 0xDD9}, {0xDDD, 0xDCF},
{0xDDD, 0xDCA}, {0xDDE, 0xDD9}, {0xDDE, 0xDDF}, {0xF43, 0xF42}, {0xF43, 0xFB7}, {0xF4D, 0xF4C}, {0xF4D, 0xFB7}, {0xF52, 0xF51}, {0xF52, 0xFB7}, {0xF57, 0xF56}, {0xF57, 0xFB7}, {0xF5C, 0xF5B},
{0xF5C, 0xFB7}, {0xF69, 0xF40}, {0xF69, 0xFB5}, {0xF73, 0xF71}, {0xF73, 0xF72}, {0xF75, 0xF71}, {0xF75, 0xF74}, {0xF76, 0xFB2}, {0xF76, 0xF80}, {0xF78, 0xFB3}, {0xF78, 0xF80}, {0xF81, 0xF71},
{0xF81, 0xF80}, {0xF93, 0xF92}, {0xF93, 0xFB7}, {0xF9D, 0xF9C}, {0xF9D, 0xFB7}, {0xFA2, 0xFA1}, {0xFA2, 0xFB7}, {0xFA7, 0xFA6}, {0xFA7, 0xFB7}, {0xFAC, 0xFAB}, {0xFAC, 0xFB7}, {0xFB9, 0xF90},
{0xFB9, 0xFB5}, {0x1026, 0x1025}, {0x1026, 0x102E}, {0x1B06, 0x1B05}, {0x1B06, 0x1B35}, {0x1B08, 0x1B07}, {0x1B08, 0x1B35}, {0x1B0A, 0x1B09}, {0x1B0A, 0x1B35}, {0x1B0C, 0x1B0B}, {0x1B0C, 0x1B35},
{0x1B0E, 0x1B0D}, {0x1B0E, 0x1B35}, {0x1B12, 0x1B11}, {0x1B12, 0x1B35}, {0x1B3B, 0x1B3A}, {0x1B3B, 0x1B35}, {0x1B3D, 0x1B3C}, {0x1B3D, 0x1B35}, {0x1B40, 0x1B3E}, {0x1B40, 0x1B35}, {0x1B41, 0x1B3F},
{0x1B41, 0x1B35}, {0x1B43, 0x1B42}, {0x1B43, 0x1B35}, {0x1E00, 0x41}, {0x1E00, 0x325}, {0x1E01, 0x61}, {0x1E01, 0x325}, {0x1E02, 0x42}, {0x1E02, 0x307}, {0x1E03, 0x62}, {0x1E03, 0x307},
{0x1E04, 0x42}, {0x1E04, 0x323}, {0x1E05, 0x62}, {0x1E05, 0x323}, {0x1E06, 0x42}, {0x1E06, 0x331}, {0x1E07, 0x62}, {0x1E07, 0x331}, {0x1E08, 0x43}, {0x1E08, 0x327}, {0x1E08, 0x301}, {0x1E09, 0x63},
{0x1E09, 0x327}, {0x1E09, 0x301}, {0x1E0A, 0x44}, {0x1E0A, 0x307}, {0x1E0B, 0x64}, {0x1E0B, 0x307}, {0x1E0C, 0x44}, {0x1E0C, 0x323}, {0x1E0D, 0x64}, {0x1E0D, 0x323}, {0x1E0E, 0x44}, {0x1E0E, 0x331},
{0x1E0F, 0x64}, {0x1E0F, 0x331}, {0x1E10, 0x44}, {0x1E10, 0x327}, {0x1E11, 0x64}, {0x1E11, 0x327}, {0x1E12, 0x44}, {0x1E12, 0x32D}, {0x1E13, 0x64}, {0x1E13, 0x32D}, {0x1E14, 0x45}, {0x1E14, 0x304},
{0x1E14, 0x300}, {0x1E15, 0x65}, {0x1E15, 0x304}, {0x1E15, 0x300}, {0x1E16, 0x45}, {0x1E16, 0x304}, {0x1E16, 0x301}, {0x1E17, 0x65}, {0x1E17, 0x304}, {0x1E17, 0x301}, {0x1E18, 0x45}, {0x1E18, 0x32D},
{0x1E19, 0x65}, {0x1E19, 0x32D}, {0x1E1A, 0x45}, {0x1E1A, 0x330}, {0x1E1B, 0x65}, {0x1E1B, 0x330}, {0x1E1C, 0x45}, {0x1E1C, 0x327}, {0x1E1C, 0x306}, {0x1E1D, 0x65}, {0x1E1D, 0x327}, {0x1E1D, 0x306},
{0x1E1E, 0x46}, {0x1E1E, 0x307}, {0x1E1F, 0x66}, {0x1E1F, 0x307}, {0x1E20, 0x47}, {0x1E20, 0x304}, {0x1E21, 0x67}, {0x1E21, 0x304}, {0x1E22, 0x48}, {0x1E22, 0x307}, {0x1E23, 0x68}, {0x1E23, 0x307},
{0x1E24, 0x48}, {0x1E24, 0x323}, {0x1E25, 0x68}, {0x1E25, 0x323}, {0x1E26, 0x48}, {0x1E26, 0x308}, {0x1E27, 0x68}, {0x1E27, 0x308}, {0x1E28, 0x48}, {0x1E28, 0x327}, {0x1E29, 0x68}, {0x1E29, 0x327},
{0x1E2A, 0x48}, {0x1E2A, 0x32E}, {0x1E2B, 0x68}, {0x1E2B, 0x32E}, {0x1E2C, 0x49}, {0x1E2C, 0x330}, {0x1E2D, 0x69}, {0x1E2D, 0x330}, {0x1E2E, 0x49}, {0x1E2E, 0x308}, {0x1E2E, 0x301}, {0x1E2F, 0x69},
{0x1E2F, 0x308}, {0x1E2F, 0x301}, {0x1E30, 0x4B}, {0x1E30, 0x301}, {0x1E31, 0x6B}, {0x1E31, 0x301}, {0x1E32, 0x4B}, {0x1E32, 0x323}, {0x1E33, 0x6B}, {0x1E33, 0x323}, {0x1E34, 0x4B}, {0x1E34, 0x331},
{0x1E35, 0x6B}, {0x1E35, 0x331}, {0x1E36, 0x4C}, {0x1E36, 0x323}, {0x1E37, 0x6C}, {0x1E37, 0x323}, {0x1E38, 0x4C}, {0x1E38, 0x323}, {0x1E38, 0x304}, {0x1E39, 0x6C}, {0x1E39, 0x323}, {0x1E39, 0x304},
{0x1E3A, 0x4C}, {0x1E3A, 0x331}, {0x1E3B, 0x6C}, {0x1E3B, 0x331}, {0x1E3C, 0x4C}, {0x1E3C, 0x32D}, {0x1E3D, 0x6C}, {0x1E3D, 0x32D}, {0x1E3E, 0x4D}, {0x1E3E, 0x301}, {0x1E3F, 0x6D}, {0x1E3F, 0x301},
{0x1E40, 0x4D}, {0x1E40, 0x307}, {0x1E41, 0x6D}, {0x1E41, 0x307}, {0x1E42, 0x4D}, {0x1E42, 0x323}, {0x1E43, 0x6D}, {0x1E43, 0x323}, {0x1E44, 0x4E}, {0x1E44, 0x307}, {0x1E45, 0x6E}, {0x1E45, 0x307},
{0x1E46, 0x4E}, {0x1E46, 0x323}, {0x1E47, 0x6E}, {0x1E47, 0x323}, {0x1E48, 0x4E}, {0x1E48, 0x331}, {0x1E49, 0x6E}, {0x1E49, 0x331}, {0x1E4A, 0x4E}, {0x1E4A, 0x32D}, {0x1E4B, 0x6E}, {0x1E4B, 0x32D},
{0x1E4C, 0x4F}, {0x1E4C, 0x303}, {0x1E4C, 0x301}, {0x1E4D, 0x6F}, {0x1E4D, 0x303}, {0x1E4D, 0x301}, {0x1E4E, 0x4F}, {0x1E4E, 0x303}, {0x1E4E, 0x308}, {0x1E4F, 0x6F}, {0x1E4F, 0x303}, {0x1E4F, 0x308},
{0x1E50, 0x4F}, {0x1E50, 0x304}, {0x1E50, 0x300}, {0x1E51, 0x6F}, {0x1E51, 0x304}, {0x1E51, 0x300}, {0x1E52, 0x4F}, {0x1E52, 0x304}, {0x1E52, 0x301}, {0x1E53, 0x6F}, {0x1E53, 0x304}, {0x1E53, 0x301},
{0x1E54, 0x50}, {0x1E54, 0x301}, {0x1E55, 0x70}, {0x1E55, 0x301}, {0x1E56, 0x50}, {0x1E56, 0x307}, {0x1E57, 0x70}, {0x1E57, 0x307}, {0x1E58, 0x52}, {0x1E58, 0x307}, {0x1E59, 0x72}, {0x1E59, 0x307},
{0x1E5A, 0x52}, {0x1E5A, 0x323}, {0x1E5B, 0x72}, {0x1E5B, 0x323}, {0x1E5C, 0x52}, {0x1E5C, 0x323}, {0x1E5C, 0x304}, {0x1E5D, 0x72}, {0x1E5D, 0x323}, {0x1E5D, 0x304}, {0x1E5E, 0x52}, {0x1E5E, 0x331},
{0x1E5F, 0x72}, {0x1E5F, 0x331}, {0x1E60, 0x53}, {0x1E60, 0x307}, {0x1E61, 0x73}, {0x1E61, 0x307}, {0x1E62, 0x53}, {0x1E62, 0x323}, {0x1E63, 0x73}, {0x1E63, 0x323}, {0x1E64, 0x53}, {0x1E64, 0x301},
{0x1E64, 0x307}, {0x1E65, 0x73}, {0x1E65, 0x301}, {0x1E65, 0x307}, {0x1E66, 0x53}, {0x1E66, 0x30C}, {0x1E66, 0x307}, {0x1E67, 0x73}, {0x1E67, 0x30C}, {0x1E67, 0x307}, {0x1E68, 0x53}, {0x1E68, 0x323},
{0x1E68, 0x307}, {0x1E69, 0x73}, {0x1E69, 0x323}, {0x1E69, 0x307}, {0x1E6A, 0x54}, {0x1E6A, 0x307}, {0x1E6B, 0x74}, {0x1E6B, 0x307}, {0x1E6C, 0x54}, {0x1E6C, 0x323}, {0x1E6D, 0x74}, {0x1E6D, 0x323},
{0x1E6E, 0x54}, {0x1E6E, 0x331}, {0x1E6F, 0x74}, {0x1E6F, 0x331}, {0x1E70, 0x54}, {0x1E70, 0x32D}, {0x1E71, 0x74}, {0x1E71, 0x32D}, {0x1E72, 0x55}, {0x1E72, 0x324}, {0x1E73, 0x75}, {0x1E73, 0x324},
{0x1E74, 0x55}, {0x1E74, 0x330}, {0x1E75, 0x75}, {0x1E75, 0x330}, {0x1E76, 0x55}, {0x1E76, 0x32D}, {0x1E77, 0x75}, {0x1E77, 0x32D}, {0x1E78, 0x55}, {0x1E78, 0x303}, {0x1E78, 0x301}, {0x1E79, 0x75},
{0x1E79, 0x303}, {0x1E79, 0x301}, {0x1E7A, 0x55}, {0x1E7A, 0x304}, {0x1E7A, 0x308}, {0x1E7B, 0x75}, {0x1E7B, 0x304}, {0x1E7B, 0x308}, {0x1E7C, 0x56}, {0x1E7C, 0x303}, {0x1E7D, 0x76}, {0x1E7D, 0x303},
{0x1E7E, 0x56}, {0x1E7E, 0x323}, {0x1E7F, 0x76}, {0x1E7F, 0x323}, {0x1E80, 0x57}, {0x1E80, 0x300}, {0x1E81, 0x77}, {0x1E81, 0x300}, {0x1E82, 0x57}, {0x1E82, 0x301}, {0x1E83, 0x77}, {0x1E83, 0x301},
{0x1E84, 0x57}, {0x1E84, 0x308}, {0x1E85, 0x77}, {0x1E85, 0x308}, {0x1E86, 0x57}, {0x1E86, 0x307}, {0x1E87, 0x77}, {0x1E87, 0x307}, {0x1E88, 0x57}, {0x1E88, 0x323}, {0x1E89, 0x77}, {0x1E89, 0x323},
{0x1E8A, 0x58}, {0x1E8A, 0x307}, {0x1E8B, 0x78}, {0x1E8B, 0x307}, {0x1E8C, 0x58}, {0x1E8C, 0x308}, {0x1E8D, 0x78}, {0x1E8D, 0x308}, {0x1E8E, 0x59}, {0x1E8E, 0x307}, {0x1E8F, 0x79}, {0x1E8F, 0x307},
{0x1E90, 0x5A}, {0x1E90, 0x302}, {0x1E91, 0x7A}, {0x1E91, 0x302}, {0x1E92, 0x5A}, {0x1E92, 0x323}, {0x1E93, 0x7A}, {0x1E93, 0x323}, {0x1E94, 0x5A}, {0x1E94, 0x331}, {0x1E95, 0x7A}, {0x1E95, 0x331},
{0x1E96, 0x68}, {0x1E96, 0x331}, {0x1E97, 0x74}, {0x1E97, 0x308}, {0x1E98, 0x77}, {0x1E98, 0x30A}, {0x1E99, 0x79}, {0x1E99, 0x30A}, {0x1E9B, 0x17F}, {0x1E9B, 0x307}, {0x1EA0, 0x41}, {0x1EA0, 0x323},
{0x1EA1, 0x61}, {0x1EA1, 0x323}, {0x1EA2, 0x41}, {0x1EA2, 0x309}, {0x1EA3, 0x61}, {0x1EA3, 0x309}, {0x1EA4, 0x41}, {0x1EA4, 0x302}, {0x1EA4, 0x301}, {0x1EA5, 0x61}, {0x1EA5, 0x302}, {0x1EA5, 0x301},
{0x1EA6, 0x41}, {0x1EA6, 0x302}, {0x1EA6, 0x300}, {0x1EA7, 0x61}, {0x1EA7, 0x302}, {0x1EA7, 0x300}, {0x1EA8, 0x41}, {0x1EA8, 0x302}, {0x1EA8, 0x309}, {0x1EA9, 0x61}, {0x1EA9, 0x302}, {0x1EA9, 0x309},
{0x1EAA, 0x41}, {0x1EAA, 0x302}, {0x1EAA, 0x303}, {0x1EAB, 0x61}, {0x1EAB, 0x302}, {0x1EAB, 0x303}, {0x1EAC, 0x41}, {0x1EAC, 0x323}, {0x1EAC, 0x302}, {0x1EAD, 0x61}, {0x1EAD, 0x323}, {0x1EAD, 0x302},
{0x1EAE, 0x41}, {0x1EAE, 0x306}, {0x1EAE, 0x301}, {0x1EAF, 0x61}, {0x1EAF, 0x306}, {0x1EAF, 0x301}, {0x1EB0, 0x41}, {0x1EB0, 0x306}, {0x1EB0, 0x300}, {0x1EB1, 0x61}, {0x1EB1, 0x306}, {0x1EB1, 0x300},
{0x1EB2, 0x41}, {0x1EB2, 0x306}, {0x1EB2, 0x309}, {0x1EB3, 0x61}, {0x1EB3, 0x306}, {0x1EB3, 0x309}, {0x1EB4, 0x41}, {0x1EB4, 0x306}, {0x1EB4, 0x303}, {0x1EB5, 0x61}, {0x1EB5, 0x306}, {0x1EB5, 0x303},
{0x1EB6, 0x41}, {0x1EB6, 0x323}, {0x1EB6, 0x306}, {0x1EB7, 0x61}, {0x1EB7, 0x323}, {0x1EB7, 0x306}, {0x1EB8, 0x45}, {0x1EB8, 0x323}, {0x1EB9, 0x65}, {0x1EB9, 0x323}, {0x1EBA, 0x45}, {0x1EBA, 0x309},
{0x1EBB, 0x65}, {0x1EBB, 0x309}, {0x1EBC, 0x45}, {0x1EBC, 0x303}, {0x1EBD, 0x65}, {0x1EBD, 0x303}, {0x1EBE, 0x45}, {0x1EBE, 0x302}, {0x1EBE, 0x301}, {0x1EBF, 0x65}, {0x1EBF, 0x302}, {0x1EBF, 0x301},
{0x1EC0, 0x45}, {0x1EC0, 0x302}, {0x1EC0, 0x300}, {0x1EC1, 0x65}, {0x1EC1, 0x302}, {0x1EC1, 0x300}, {0x1EC2, 0x45}, {0x1EC2, 0x302}, {0x1EC2, 0x309}, {0x1EC3, 0x65}, {0x1EC3, 0x302}, {0x1EC3, 0x309},
{0x1EC4, 0x45}, {0x1EC4, 0x302}, {0x1EC4, 0x303}, {0x1EC5, 0x65}, {0x1EC5, 0x302}, {0x1EC5, 0x303}, {0x1EC6, 0x45}, {0x1EC6, 0x323}, {0x1EC6, 0x302}, {0x1EC7, 0x65}, {0x1EC7, 0x323}, {0x1EC7, 0x302},
{0x1EC8, 0x49}, {0x1EC8, 0x309}, {0x1EC9, 0x69}, {0x1EC9, 0x309}, {0x1ECA, 0x49}, {0x1ECA, 0x323}, {0x1ECB, 0x69}, {0x1ECB, 0x323}, {0x1ECC, 0x4F}, {0x1ECC, 0x323}, {0x1ECD, 0x6F}, {0x1ECD, 0x323},
{0x1ECE, 0x4F}, {0x1ECE, 0x309}, {0x1ECF, 0x6F}, {0x1ECF, 0x309}, {0x1ED0, 0x4F}, {0x1ED0, 0x302}, {0x1ED0, 0x301}, {0x1ED1, 0x6F}, {0x1ED1, 0x302}, {0x1ED1, 0x301}, {0x1ED2, 0x4F}, {0x1ED2, 0x302},
{0x1ED2, 0x300}, {0x1ED3, 0x6F}, {0x1ED3, 0x302}, {0x1ED3, 0x300}, {0x1ED4, 0x4F}, {0x1ED4, 0x302}, {0x1ED4, 0x309}, {0x1ED5, 0x6F}, {0x1ED5, 0x302}, {0x1ED5, 0x309}, {0x1ED6, 0x4F}, {0x1ED6, 0x302},
{0x1ED6, 0x303}, {0x1ED7, 0x6F}, {0x1ED7, 0x302}, {0x1ED7, 0x303}, {0x1ED8, 0x4F}, {0x1ED8, 0x323}, {0x1ED8, 0x302}, {0x1ED9, 0x6F}, {0x1ED9, 0x323}, {0x1ED9, 0x302}, {0x1EDA, 0x4F}, {0x1EDA, 0x31B},
{0x1EDA, 0x301}, {0x1EDB, 0x6F}, {0x1EDB, 0x31B}, {0x1EDB, 0x301}, {0x1EDC, 0x4F}, {0x1EDC, 0x31B}, {0x1EDC, 0x300}, {0x1EDD, 0x6F}, {0x1EDD, 0x31B}, {0x1EDD, 0x300}, {0x1EDE, 0x4F}, {0x1EDE, 0x31B},
{0x1EDE, 0x309}, {0x1EDF, 0x6F}, {0x1EDF, 0x31B}, {0x1EDF, 0x309}, {0x1EE0, 0x4F}, {0x1EE0, 0x31B}, {0x1EE0, 0x303}, {0x1EE1, 0x6F}, {0x1EE1, 0x31B}, {0x1EE1, 0x303}, {0x1EE2, 0x4F}, {0x1EE2, 0x31B},
{0x1EE2, 0x323}, {0x1EE3, 0x6F}, {0x1EE3, 0x31B}, {0x1EE3, 0x323}, {0x1EE4, 0x55}, {0x1EE4, 0x323}, {0x1EE5, 0x75}, {0x1EE5, 0x323}, {0x1EE6, 0x55}, {0x1EE6, 0x309}, {0x1EE7, 0x75}, {0x1EE7, 0x309},
{0x1EE8, 0x55}, {0x1EE8, 0x31B}, {0x1EE8, 0x301}, {0x1EE9, 0x75}, {0x1EE9, 0x31B}, {0x1EE9, 0x301}, {0x1EEA, 0x55}, {0x1EEA, 0x31B}, {0x1EEA, 0x300}, {0x1EEB, 0x75}, {0x1EEB, 0x31B}, {0x1EEB, 0x300},
{0x1EEC, 0x55}, {0x1EEC, 0x31B}, {0x1EEC, 0x309}, {0x1EED, 0x75}, {0x1EED, 0x31B}, {0x1EED, 0x309}, {0x1EEE, 0x55}, {0x1EEE, 0x31B}, {0x1EEE, 0x303}, {0x1EEF, 0x75}, {0x1EEF, 0x31B}, {0x1EEF, 0x303},
{0x1EF0, 0x55}, {0x1EF0, 0x31B}, {0x1EF0, 0x323}, {0x1EF1, 0x75}, {0x1EF1, 0x31B}, {0x1EF1, 0x323}, {0x1EF2, 0x59}, {0x1EF2, 0x300}, {0x1EF3, 0x79}, {0x1EF3, 0x300}, {0x1EF4, 0x59}, {0x1EF4, 0x323},
{0x1EF5, 0x79}, {0x1EF5, 0x323}, {0x1EF6, 0x59}, {0x1EF6, 0x309}, {0x1EF7, 0x79}, {0x1EF7, 0x309}, {0x1EF8, 0x59}, {0x1EF8, 0x303}, {0x1EF9, 0x79}, {0x1EF9, 0x303}, {0x1F00, 0x3B1}, {0x1F00, 0x313},
{0x1F01, 0x3B1}, {0x1F01, 0x314}, {0x1F02, 0x3B1}, {0x1F02, 0x313}, {0x1F02, 0x300}, {0x1F03, 0x3B1}, {0x1F03, 0x314}, {0x1F03, 0x300}, {0x1F04, 0x3B1}, {0x1F04, 0x313}, {0x1F04, 0x301},
{0x1F05, 0x3B1}, {0x1F05, 0x314}, {0x1F05, 0x301}, {0x1F06, 0x3B1}, {0x1F06, 0x313}, {0x1F06, 0x342}, {0x1F07, 0x3B1}, {0x1F07, 0x314}, {0x1F07, 0x342}, {0x1F08, 0x391}, {0x1F08, 0x313},
{0x1F09, 0x391}, {0x1F09, 0x314}, {0x1F0A, 0x391}, {0x1F0A, 0x313}, {0x1F0A, 0x300}, {0x1F0B, 0x391}, {0x1F0B, 0x314}, {0x1F0B, 0x300}, {0x1F0C, 0x391}, {0x1F0C, 0x313}, {0x1F0C, 0x301},
{0x1F0D, 0x391}, {0x1F0D, 0x314}, {0x1F0D, 0x301}, {0x1F0E, 0x391}, {0x1F0E, 0x313}, {0x1F0E, 0x342}, {0x1F0F, 0x391}, {0x1F0F, 0x314}, {0x1F0F, 0x342}, {0x1F10, 0x3B5}, {0x1F10, 0x313},
{0x1F11, 0x3B5}, {0x1F11, 0x314}, {0x1F12, 0x3B5}, {0x1F12, 0x313}, {0x1F12, 0x300}, {0x1F13, 0x3B5}, {0x1F13, 0x314}, {0x1F13, 0x300}, {0x1F14, 0x3B5}, {0x1F14, 0x313}, {0x1F14, 0x301},
{0x1F15, 0x3B5}, {0x1F15, 0x314}, {0x1F15, 0x301}, {0x1F18, 0x395}, {0x1F18, 0x313}, {0x1F19, 0x395}, {0x1F19, 0x314}, {0x1F1A, 0x395}, {0x1F1A, 0x313}, {0x1F1A, 0x300}, {0x1F1B, 0x395},
{0x1F1B, 0x314}, {0x1F1B, 0x300}, {0x1F1C, 0x395}, {0x1F1C, 0x313}, {0x1F1C, 0x301}, {0x1F1D, 0x395}, {0x1F1D, 0x314}, {0x1F1D, 0x301}, {0x1F20, 0x3B7}, {0x1F20, 0x313}, {0x1F21, 0x3B7},
{0x1F21, 0x314}, {0x1F22, 0x3B7}, {0x1F22, 0x313}, {0x1F22, 0x300}, {0x1F23, 0x3B7}, {0x1F23, 0x314}, {0x1F23, 0x300}, {0x1F24, 0x3B7}, {0x1F24, 0x313}, {0x1F24, 0x301}, {0x1F25, 0x3B7},
{0x1F25, 0x314}, {0x1F25, 0x301}, {0x1F26, 0x3B7}, {0x1F26, 0x313}, {0x1F26, 0x342}, {0x1F27, 0x3B7}, {0x1F27, 0x314}, {0x1F27, 0x342}, {0x1F28, 0x397}, {0x1F28, 0x313}, {0x1F29, 0x397},
{0x1F29, 0x314}, {0x1F2A, 0x397}, {0x1F2A, 0x313}, {0x1F2A, 0x300}, {0x1F2B, 0x397}, {0x1F2B, 0x314}, {0x1F2B, 0x300}, {0x1F2C, 0x397}, {0x1F2C, 0x313}, {0x1F2C, 0x301}, {0x1F2D, 0x397},
{0x1F2D, 0x314}, {0x1F2D, 0x301}, {0x1F2E, 0x397}, {0x1F2E, 0x313}, {0x1F2E, 0x342}, {0x1F2F, 0x397}, {0x1F2F, 0x314}, {0x1F2F, 0x342}, {0x1F30, 0x3B9}, {0x1F30, 0x313}, {0x1F31, 0x3B9},
{0x1F31, 0x314}, {0x1F32, 0x3B9}, {0x1F32, 0x313}, {0x1F32, 0x300}, {0x1F33, 0x3B9}, {0x1F33, 0x314}, {0x1F33, 0x300}, {0x1F34, 0x3B9}, {0x1F34, 0x313}, {0x1F34, 0x301}, {0x1F35, 0x3B9},
{0x1F35, 0x314}, {0x1F35, 0x301}, {0x1F36, 0x3B9}, {0x1F36, 0x313}, {0x1F36, 0x342}, {0x1F37, 0x3B9}, {0x1F37, 0x314}, {0x1F37, 0x342}, {0x1F38, 0x399}, {0x1F38, 0x313}, {0x1F39, 0x399},
{0x1F39, 0x314}, {0x1F3A, 0x399}, {0x1F3A, 0x313}, {0x1F3A, 0x300}, {0x1F3B, 0x399}, {0x1F3B, 0x314}, {0x1F3B, 0x300}, {0x1F3C, 0x399}, {0x1F3C, 0x313}, {0x1F3C, 0x301}, {0x1F3D, 0x399},
{0x1F3D, 0x314}, {0x1F3D, 0x301}, {0x1F3E, 0x399}, {0x1F3E, 0x313}, {0x1F3E, 0x342}, {0x1F3F, 0x399}, {0x1F3F, 0x314}, {0x1F3F, 0x342}, {0x1F40, 0x3BF}, {0x1F40, 0x313}, {0x1F41, 0x3BF},
{0x1F41, 0x314}, {0x1F42, 0x3BF}, {0x1F42, 0x313}, {0x1F42, 0x300}, {0x1F43, 0x3BF}, {0x1F43, 0x314}, {0x1F43, 0x300}, {0x1F44, 0x3BF}, {0x1F44, 0x313}, {0x1F44, 0x301}, {0x1F45, 0x3BF},
{0x1F45, 0x314}, {0x1F45, 0x301}, {0x1F48, 0x39F}, {0x1F48, 0x313}, {0x1F49, 0x39F}, {0x1F49, 0x314}, {0x1F4A, 0x39F}, {0x1F4A, 0x313}, {0x1F4A, 0x300}, {0x1F4B, 0x39F}, {0x1F4B, 0x314},
{0x1F4B, 0x300}, {0x1F4C, 0x39F}, {0x1F4C, 0x313}, {0x1F4C, 0x301}, {0x1F4D, 0x39F}, {0x1F4D, 0x314}, {0x1F4D, 0x301}, {0x1F50, 0x3C5}, {0x1F50, 0x313}, {0x1F51, 0x3C5}, {0x1F51, 0x314},
{0x1F52, 0x3C5}, {0x1F52, 0x313}, {0x1F52, 0x300}, {0x1F53, 0x3C5}, {0x1F53, 0x314}, {0x1F53, 0x300}, {0x1F54, 0x3C5}, {0x1F54, 0x313}, {0x1F54, 0x301}, {0x1F55, 0x3C5}, {0x1F55, 0x314},
{0x1F55, 0x301}, {0x1F56, 0x3C5}, {0x1F56, 0x313}, {0x1F56, 0x342}, {0x1F57, 0x3C5}, {0x1F57, 0x314}, {0x1F57, 0x342}, {0x1F59, 0x3A5}, {0x1F59, 0x314}, {0x1F5B, 0x3A5}, {0x1F5B, 0x314},
{0x1F5B, 0x300}, {0x1F5D, 0x3A5}, {0x1F5D, 0x314}, {0x1F5D, 0x301}, {0x1F5F, 0x3A5}, {0x1F5F, 0x314}, {0x1F5F, 0x342}, {0x1F60, 0x3C9}, {0x1F60, 0x313}, {0x1F61, 0x3C9}, {0x1F61, 0x314},
{0x1F62, 0x3C9}, {0x1F62, 0x313}, {0x1F62, 0x300}, {0x1F63, 0x3C9}, {0x1F63, 0x314}, {0x1F63, 0x300}, {0x1F64, 0x3C9}, {0x1F64, 0x313}, {0x1F64, 0x301}, {0x1F65, 0x3C9}, {0x1F65, 0x314},
{0x1F65, 0x301}, {0x1F66, 0x3C9}, {0x1F66, 0x313}, {0x1F66, 0x342}, {0x1F67, 0x3C9}, {0x1F67, 0x314}, {0x1F67, 0x342}, {0x1F68, 0x3A9}, {0x1F68, 0x313}, {0x1F69, 0x3A9}, {0x1F69, 0x314},
{0x1F6A, 0x3A9}, {0x1F6A, 0x313}, {0x1F6A, 0x300}, {0x1F6B, 0x3A9}, {0x1F6B, 0x314}, {0x1F6B, 0x300}, {0x1F6C, 0x3A9}, {0x1F6C, 0x313}, {0x1F6C, 0x301}, {0x1F6D, 0x3A9}, {0x1F6D, 0x314},
{0x1F6D, 0x301}, {0x1F6E, 0x3A9}, {0x1F6E, 0x313}, {0x1F6E, 0x342}, {0x1F6F, 0x3A9}, {0x1F6F, 0x314}, {0x1F6F, 0x342}, {0x1F70, 0x3B1}, {0x1F70, 0x300}, {0x1F71, 0x3B1}, {0x1F71, 0x301},
{0x1F72, 0x3B5}, {0x1F72, 0x300}, {0x1F73, 0x3B5}, {0x1F73, 0x301}, {0x1F74, 0x3B7}, {0x1F74, 0x300}, {0x1F75, 0x3B7}, {0x1F75, 0x301}, {0x1F76, 0x3B9}, {0x1F76, 0x300}, {0x1F77, 0x3B9},
{0x1F77, 0x301}, {0x1F78, 0x3BF}, {0x1F78, 0x300}, {0x1F79, 0x3BF}, {0x1F79, 0x301}, {0x1F7A, 0x3C5}, {0x1F7A, 0x300}, {0x1F7B, 0x3C5}, {0x1F7B, 0x301}, {0x1F7C, 0x3C9}, {0x1F7C, 0x300},
{0x1F7D, 0x3C9}, {0x1F7D, 0x301}, {0x1F80, 0x3B1}, {0x1F80, 0x313}, {0x1F80, 0x345}, {0x1F81, 0x3B1}, {0x1F81, 0x314}, {0x1F81, 0x345}, {0x1F82, 0x3B1}, {0x1F82, 0x313}, {0x1F82, 0x300},
{0x1F82, 0x345}, {0x1F83, 0x3B1}, {0x1F83, 0x314}, {0x1F83, 0x300}, {0x1F83, 0x345}, {0x1F84, 0x3B1}, {0x1F84, 0x313}, {0x1F84, 0x301}, {0x1F84, 0x345}, {0x1F85, 0x3B1}, {0x1F85, 0x314},
{0x1F85, 0x301}, {0x1F85, 0x345}, {0x1F86, 0x3B1}, {0x1F86, 0x313}, {0x1F86, 0x342}, {0x1F86, 0x345}, {0x1F87, 0x3B1}, {0x1F87, 0x314}, {0x1F87, 0x342}, {0x1F87, 0x345}, {0x1F88, 0x391},
{0x1F88, 0x313}, {0x1F88, 0x345}, {0x1F89, 0x391}, {0x1F89, 0x314}, {0x1F89, 0x345}, {0x1F8A, 0x391}, {0x1F8A, 0x313}, {0x1F8A, 0x300}, {0x1F8A, 0x345}, {0x1F8B, 0x391}, {0x1F8B, 0x314},
{0x1F8B, 0x300}, {0x1F8B, 0x345}, {0x1F8C, 0x391}, {0x1F8C, 0x313}, {0x1F8C, 0x301}, {0x1F8C, 0x345}, {0x1F8D, 0x391}, {0x1F8D, 0x314}, {0x1F8D, 0x301}, {0x1F8D, 0x345}, {0x1F8E, 0x391},
{0x1F8E, 0x313}, {0x1F8E, 0x342}, {0x1F8E, 0x345}, {0x1F8F, 0x391}, {0x1F8F, 0x314}, {0x1F8F, 0x342}, {0x1F8F, 0x345}, {0x1F90, 0x3B7}, {0x1F90, 0x313}, {0x1F90, 0x345}, {0x1F91, 0x3B7},
{0x1F91, 0x314}, {0x1F91, 0x345}, {0x1F92, 0x3B7}, {0x1F92, 0x313}, {0x1F92, 0x300}, {0x1F92, 0x345}, {0x1F93, 0x3B7}, {0x1F93, 0x314}, {0x1F93, 0x300}, {0x1F93, 0x345}, {0x1F94, 0x3B7},
{0x1F94, 0x313}, {0x1F94, 0x301}, {0x1F94, 0x345}, {0x1F95, 0x3B7}, {0x1F95, 0x314}, {0x1F95, 0x301}, {0x1F95, 0x345}, {0x1F96, 0x3B7}, {0x1F96, 0x313}, {0x1F96, 0x342}, {0x1F96, 0x345},
{0x1F97, 0x3B7}, {0x1F97, 0x314}, {0x1F97, 0x342}, {0x1F97, 0x345}, {0x1F98, 0x397}, {0x1F98, 0x313}, {0x1F98, 0x345}, {0x1F99, 0x397}, {0x1F99, 0x314}, {0x1F99, 0x345}, {0x1F9A, 0x397},
{0x1F9A, 0x313}, {0x1F9A, 0x300}, {0x1F9A, 0x345}, {0x1F9B, 0x397}, {0x1F9B, 0x314}, {0x1F9B, 0x300}, {0x1F9B, 0x345}, {0x1F9C, 0x397}, {0x1F9C, 0x313}, {0x1F9C, 0x301}, {0x1F9C, 0x345},
{0x1F9D, 0x397}, {0x1F9D, 0x314}, {0x1F9D, 0x301}, {0x1F9D, 0x345}, {0x1F9E, 0x397}, {0x1F9E, 0x313}, {0x1F9E, 0x342}, {0x1F9E, 0x345}, {0x1F9F, 0x397}, {0x1F9F, 0x314}, {0x1F9F, 0x342},
{0x1F9F, 0x345}, {0x1FA0, 0x3C9}, {0x1FA0, 0x313}, {0x1FA0, 0x345}, {0x1FA1, 0x3C9}, {0x1FA1, 0x314}, {0x1FA1, 0x345}, {0x1FA2, 0x3C9}, {0x1FA2, 0x313}, {0x1FA2, 0x300}, {0x1FA2, 0x345},
{0x1FA3, 0x3C9}, {0x1FA3, 0x314}, {0x1FA3, 0x300}, {0x1FA3, 0x345}, {0x1FA4, 0x3C9}, {0x1FA4, 0x313}, {0x1FA4, 0x301}, {0x1FA4, 0x345}, {0x1FA5, 0x3C9}, {0x1FA5, 0x314}, {0x1FA5, 0x301},
{0x1FA5, 0x345}, {0x1FA6, 0x3C9}, {0x1FA6, 0x313}, {0x1FA6, 0x342}, {0x1FA6, 0x345}, {0x1FA7, 0x3C9}, {0x1FA7, 0x314}, {0x1FA7, 0x342}, {0x1FA7, 0x345}, {0x1FA8, 0x3A9}, {0x1FA8, 0x313},
{0x1FA8, 0x345}, {0x1FA9, 0x3A9}, {0x1FA9, 0x314}, {0x1FA9, 0x345}, {0x1FAA, 0x3A9}, {0x1FAA, 0x313}, {0x1FAA, 0x300}, {0x1FAA, 0x345}, {0x1FAB, 0x3A9}, {0x1FAB, 0x314}, {0x1FAB, 0x300},
{0x1FAB, 0x345}, {0x1FAC, 0x3A9}, {0x1FAC, 0x313}, {0x1FAC, 0x301}, {0x1FAC, 0x345}, {0x1FAD, 0x3A9}, {0x1FAD, 0x314}, {0x1FAD, 0x301}, {0x1FAD, 0x345}, {0x1FAE, 0x3A9}, {0x1FAE, 0x313},
{0x1FAE, 0x342}, {0x1FAE, 0x345}, {0x1FAF, 0x3A9}, {0x1FAF, 0x314}, {0x1FAF, 0x342}, {0x1FAF, 0x345}, {0x1FB0, 0x3B1}, {0x1FB0, 0x306}, {0x1FB1, 0x3B1}, {0x1FB1, 0x304}, {0x1FB2, 0x3B1},
{0x1FB2, 0x300}, {0x1FB2, 0x345}, {0x1FB3, 0x3B1}, {0x1FB3, 0x345}, {0x1FB4, 0x3B1}, {0x1FB4, 0x301}, {0x1FB4, 0x345}, {0x1FB6, 0x3B1}, {0x1FB6, 0x342}, {0x1FB7, 0x3B1}, {0x1FB7, 0x342},
{0x1FB7, 0x345}, {0x1FB8, 0x391}, {0x1FB8, 0x306}, {0x1FB9, 0x391}, {0x1FB9, 0x304}, {0x1FBA, 0x391}, {0x1FBA, 0x300}, {0x1FBB, 0x391}, {0x1FBB, 0x301}, {0x1FBC, 0x391}, {0x1FBC, 0x345},
{0x1FBE, 0x3B9}, {0x1FC1, 0xA8}, {0x1FC1, 0x342}, {0x1FC2, 0x3B7}, {0x1FC2, 0x300}, {0x1FC2, 0x345}, {0x1FC3, 0x3B7}, {0x1FC3, 0x345}, {0x1FC4, 0x3B7}, {0x1FC4, 0x301}, {0x1FC4, 0x345},
{0x1FC6, 0x3B7}, {0x1FC6, 0x342}, {0x1FC7, 0x3B7}, {0x1FC7, 0x342}, {0x1FC7, 0x345}, {0x1FC8, 0x395}, {0x1FC8, 0x300}, {0x1FC9, 0x395}, {0x1FC9, 0x301}, {0x1FCA, 0x397}, {0x1FCA, 0x300},
{0x1FCB, 0x397}, {0x1FCB, 0x301}, {0x1FCC, 0x397}, {0x1FCC, 0x345}, {0x1FCD, 0x1FBF}, {0x1FCD, 0x300}, {0x1FCE, 0x1FBF}, {0x1FCE, 0x301}, {0x1FCF, 0x1FBF}, {0x1FCF, 0x342}, {0x1FD0, 0x3B9},
{0x1FD0, 0x306}, {0x1FD1, 0x3B9}, {0x1FD1, 0x304}, {0x1FD2, 0x3B9}, {0x1FD2, 0x308}, {0x1FD2, 0x300}, {0x1FD3, 0x3B9}, {0x1FD3, 0x308}, {0x1FD3, 0x301}, {0x1FD6, 0x3B9}, {0x1FD6, 0x342},
{0x1FD7, 0x3B9}, {0x1FD7, 0x308}, {0x1FD7, 0x342}, {0x1FD8, 0x399}, {0x1FD8, 0x306}, {0x1FD9, 0x399}, {0x1FD9, 0x304}, {0x1FDA, 0x399}, {0x1FDA, 0x300}, {0x1FDB, 0x399}, {0x1FDB, 0x301},
{0x1FDD, 0x1FFE}, {0x1FDD, 0x300}, {0x1FDE, 0x1FFE}, {0x1FDE, 0x301}, {0x1FDF, 0x1FFE}, {0x1FDF, 0x342}, {0x1FE0, 0x3C5}, {0x1FE0, 0x306}, {0x1FE1, 0x3C5}, {0x1FE1, 0x304}, {0x1FE2, 0x3C5},
{0x1FE2, 0x308}, {0x1FE2, 0x300}, {0x1FE3, 0x3C5}, {0x1FE3, 0x308}, {0x1FE3, 0x301}, {0x1FE4, 0x3C1}, {0x1FE4, 0x313}, {0x1FE5, 0x3C1}, {0x1FE5, 0x314}, {0x1FE6, 0x3C5}, {0x1FE6, 0x342},
{0x1FE7, 0x3C5}, {0x1FE7, 0x308}, {0x1FE7, 0x342}, {0x1FE8, 0x3A5}, {0x1FE8, 0x306}, {0x1FE9, 0x3A5}, {0x1FE9, 0x304}, {0x1FEA, 0x3A5}, {0x1FEA, 0x300}, {0x1FEB, 0x3A5}, {0x1FEB, 0x301},
{0x1FEC, 0x3A1}, {0x1FEC, 0x314}, {0x1FED, 0xA8}, {0x1FED, 0x300}, {0x1FEE, 0xA8}, {0x1FEE, 0x301}, {0x1FEF, 0x60}, {0x1FF2, 0x3C9}, {0x1FF2, 0x300}, {0x1FF2, 0x345}, {0x1FF3, 0x3C9}, {0x1FF3, 0x345},
{0x1FF4, 0x3C9}, {0x1FF4, 0x301}, {0x1FF4, 0x345}, {0x1FF6, 0x3C9}, {0x1FF6, 0x342}, {0x1FF7, 0x3C9}, {0x1FF7, 0x342}, {0x1FF7, 0x345}, {0x1FF8, 0x39F}, {0x1FF8, 0x300}, {0x1FF9, 0x39F},
{0x1FF9, 0x301}, {0x1FFA, 0x3A9}, {0x1FFA, 0x300}, {0x1FFB, 0x3A9}, {0x1FFB, 0x301}, {0x1FFC, 0x3A9}, {0x1FFC, 0x345}, {0x1FFD, 0xB4}, {0x2000, 0x2002}, {0x2001, 0x2003}, {0x2126, 0x3A9},
{0x212A, 0x4B}, {0x212B, 0x41}, {0x212B, 0x30A}, {0x219A, 0x2190}, {0x219A, 0x338}, {0x219B, 0x2192}, {0x219B, 0x338}, {0x21AE, 0x2194}, {0x21AE, 0x338}, {0x21CD, 0x21D0}, {0x21CD, 0x338},
{0x21CE, 0x21D4}, {0x21CE, 0x338}, {0x21CF, 0x21D2}, {0x21CF, 0x338}, {0x2204, 0x2203}, {0x2204, 0x338}, {0x2209, 0x2208}, {0x2209, 0x338}, {0x220C, 0x220B}, {0x220C, 0x338}, {0x2224, 0x2223},
{0x2224, 0x338}, {0x2226, 0x2225}, {0x2226, 0x338}, {0x2241, 0x223C}, {0x2241, 0x338}, {0x2244, 0x2243}, {0x2244, 0x338}, {0x2247, 0x2245}, {0x2247, 0x338}, {0x2249, 0x2248}, {0x2249, 0x338},
{0x2260, 0x3D}, {0x2260, 0x338}, {0x2262, 0x2261}, {0x2262, 0x338}, {0x226D, 0x224D}, {0x226D, 0x338}, {0x226E, 0x3C}, {0x226E, 0x338}, {0x226F, 0x3E}, {0x226F, 0x338}, {0x2270, 0x2264},
{0x2270, 0x338}, {0x2271, 0x2265}, {0x2271, 0x338}, {0x2274, 0x2272}, {0x2274, 0x338}, {0x2275, 0x2273}, {0x2275, 0x338}, {0x2278, 0x2276}, {0x2278, 0x338}, {0x2279, 0x2277}, {0x2279, 0x338},
{0x2280, 0x227A}, {0x2280, 0x338}, {0x2281, 0x227B}, {0x2281, 0x338}, {0x2284, 0x2282}, {0x2284, 0x338}, {0x2285, 0x2283}, {0x2285, 0x338}, {0x2288, 0x2286}, {0x2288, 0x338}, {0x2289, 0x2287},
{0x2289, 0x338}, {0x22AC, 0x22A2}, {0x22AC, 0x338}, {0x22AD, 0x22A8}, {0x22AD, 0x338}, {0x22AE, 0x22A9}, {0x22AE, 0x338}, {0x22AF, 0x22AB}, {0x22AF, 0x338}, {0x22E0, 0x227C}, {0x22E0, 0x338},
{0x22E1, 0x227D}, {0x22E1, 0x338}, {0x22E2, 0x2291}, {0x22E2, 0x338}, {0x22E3, 0x2292}, {0x22E3, 0x338}, {0x22EA, 0x22B2}, {0x22EA, 0x338}, {0x22EB, 0x22B3}, {0x22EB, 0x338}, {0x22EC, 0x22B4},
{0x22EC, 0x338}, {0x22ED, 0x22B5}, {0x22ED, 0x338}, {0x2329, 0x3008}, {0x232A, 0x3009}, {0x2ADC, 0x2ADD}, {0x2ADC, 0x338}, {0x304C, 0x304B}, {0x304C, 0x3099}, {0x304E, 0x304D}, {0x304E, 0x3099},
{0x3050, 0x304F}, {0x3050, 0x3099}, {0x3052, 0x3051}, {0x3052, 0x3099}, {0x3054, 0x3053}, {0x3054, 0x3099}, {0x3056, 0x3055}, {0x3056, 0x3099}, {0x3058, 0x3057}, {0x3058, 0x3099}, {0x305A, 0x3059},
{0x305A, 0x3099}, {0x305C, 0x305B}, {0x305C, 0x3099}, {0x305E, 0x305D}, {0x305E, 0x3099}, {0x3060, 0x305F}, {0x3060, 0x3099}, {0x3062, 0x3061}, {0x3062, 0x3099}, {0x3065, 0x3064}, {0x3065, 0x3099},
{0x3067, 0x3066}, {0x3067, 0x3099}, {0x3069, 0x3068}, {0x3069, 0x3099}, {0x3070, 0x306F}, {0x3070, 0x3099}, {0x3071, 0x306F}, {0x3071, 0x309A}, {0x3073, 0x3072}, {0x3073, 0x3099}, {0x3074, 0x3072},
{0x3074, 0x309A}, {0x3076, 0x3075}, {0x3076, 0x3099}, {0x3077, 0x3075}, {0x3077, 0x309A}, {0x3079, 0x3078}, {0x3079, 0x3099}, {0x307A, 0x3078}, {0x307A, 0x309A}, {0x307C, 0x307B}, {0x307C, 0x3099},
{0x307D, 0x307B}, {0x307D, 0x309A}, {0x3094, 0x3046}, {0x3094, 0x3099}, {0x309E, 0x309D}, {0x309E, 0x3099}, {0x30AC, 0x30AB}, {0x30AC, 0x3099}, {0x30AE, 0x30AD}, {0x30AE, 0x3099}, {0x30B0, 0x30AF},
{0x30B0, 0x3099}, {0x30B2, 0x30B1}, {0x30B2, 0x3099}, {0x30B4, 0x30B3}, {0x30B4, 0x3099}, {0x30B6, 0x30B5}, {0x30B6, 0x3099}, {0x30B8, 0x30B7}, {0x30B8, 0x3099}, {0x30BA, 0x30B9}, {0x30BA, 0x3099},
{0x30BC, 0x30BB}, {0x30BC, 0x3099}, {0x30BE, 0x30BD}, {0x30BE, 0x3099}, {0x30C0, 0x30BF}, {0x30C0, 0x3099}, {0x30C2, 0x30C1}, {0x30C2, 0x3099}, {0x30C5, 0x30C4}, {0x30C5, 0x3099}, {0x30C7, 0x30C6},
{0x30C7, 0x3099}, {0x30C9, 0x30C8}, {0x30C9, 0x3099}, {0x30D0, 0x30CF}, {0x30D0, 0x3099}, {0x30D1, 0x30CF}, {0x30D1, 0x309A}, {0x30D3, 0x30D2}, {0x30D3, 0x3099}, {0x30D4, 0x30D2}, {0x30D4, 0x309A},
{0x30D6, 0x30D5}, {0x30D6, 0x3099}, {0x30D7, 0x30D5}, {0x30D7, 0x309A}, {0x30D9, 0x30D8}, {0x30D9, 0x3099}, {0x30DA, 0x30D8}, {0x30DA, 0x309A}, {0x30DC, 0x30DB}, {0x30DC, 0x3099}, {0x30DD, 0x30DB},
{0x30DD, 0x309A}, {0x30F4, 0x30A6}, {0x30F4, 0x3099}, {0x30F7, 0x30EF}, {0x30F7, 0x3099}, {0x30F8, 0x30F0}, {0x30F8, 0x3099}, {0x30F9, 0x30F1}, {0x30F9, 0x3099}, {0x30FA, 0x30F2}, {0x30FA, 0x3099},
{0x30FE, 0x30FD}, {0x30FE, 0x3099}, {0xF900, 0x8C48}, {0xF901, 0x66F4}, {0xF902, 0x8ECA}, {0xF903, 0x8CC8}, {0xF904, 0x6ED1}, {0xF905, 0x4E32}, {0xF906, 0x53E5}, {0xF907, 0x9F9C}, {0xF908, 0x9F9C},
{0xF909, 0x5951}, {0xF90A, 0x91D1}, {0xF90B, 0x5587}, {0xF90C, 0x5948}, {0xF90D, 0x61F6}, {0xF90E, 0x7669}, {0xF90F, 0x7F85}, {0xF910, 0x863F}, {0xF911, 0x87BA}, {0xF912, 0x88F8}, {0xF913, 0x908F},
{0xF914, 0x6A02}, {0xF915, 0x6D1B}, {0xF916, 0x70D9}, {0xF917, 0x73DE}, {0xF918, 0x843D}, {0xF919, 0x916A}, {0xF91A, 0x99F1}, {0xF91B, 0x4E82}, {0xF91C, 0x5375}, {0xF91D, 0x6B04}, {0xF91E, 0x721B},
{0xF91F, 0x862D}, {0xF920, 0x9E1E}, {0xF921, 0x5D50}, {0xF922, 0x6FEB}, {0xF923, 0x85CD}, {0xF924, 0x8964}, {0xF925, 0x62C9}, {0xF926, 0x81D8}, {0xF927, 0x881F}, {0xF928, 0x5ECA}, {0xF929, 0x6717},
{0xF92A, 0x6D6A}, {0xF92B, 0x72FC}, {0xF92C, 0x90CE}, {0xF92D, 0x4F86}, {0xF92E, 0x51B7}, {0xF92F, 0x52DE}, {0xF930, 0x64C4}, {0xF931, 0x6AD3}, {0xF932, 0x7210}, {0xF933, 0x76E7}, {0xF934, 0x8001},
{0xF935, 0x8606}, {0xF936, 0x865C}, {0xF937, 0x8DEF}, {0xF938, 0x9732}, {0xF939, 0x9B6F}, {0xF93A, 0x9DFA}, {0xF93B, 0x788C}, {0xF93C, 0x797F}, {0xF93D, 0x7DA0}, {0xF93E, 0x83C9}, {0xF93F, 0x9304},
{0xF940, 0x9E7F}, {0xF941, 0x8AD6}, {0xF942, 0x58DF}, {0xF943, 0x5F04}, {0xF944, 0x7C60}, {0xF945, 0x807E}, {0xF946, 0x7262}, {0xF947, 0x78CA}, {0xF948, 0x8CC2}, {0xF949, 0x96F7}, {0xF94A, 0x58D8},
{0xF94B, 0x5C62}, {0xF94C, 0x6A13}, {0xF94D, 0x6DDA}, {0xF94E, 0x6F0F}, {0xF94F, 0x7D2F}, {0xF950, 0x7E37}, {0xF951, 0x964B}, {0xF952, 0x52D2}, {0xF953, 0x808B}, {0xF954, 0x51DC}, {0xF955, 0x51CC},
{0xF956, 0x7A1C}, {0xF957, 0x7DBE}, {0xF958, 0x83F1}, {0xF959, 0x9675}, {0xF95A, 0x8B80}, {0xF95B, 0x62CF}, {0xF95C, 0x6A02}, {0xF95D, 0x8AFE}, {0xF95E, 0x4E39}, {0xF95F, 0x5BE7}, {0xF960, 0x6012},
{0xF961, 0x7387}, {0xF962, 0x7570}, {0xF963, 0x5317}, {0xF964, 0x78FB}, {0xF965, 0x4FBF}, {0xF966, 0x5FA9}, {0xF967, 0x4E0D}, {0xF968, 0x6CCC}, {0xF969, 0x6578}, {0xF96A, 0x7D22}, {0xF96B, 0x53C3},
{0xF96C, 0x585E}, {0xF96D, 0x7701}, {0xF96E, 0x8449}, {0xF96F, 0x8AAA}, {0xF970, 0x6BBA}, {0xF971, 0x8FB0}, {0xF972, 0x6C88}, {0xF973, 0x62FE}, {0xF974, 0x82E5}, {0xF975, 0x63A0}, {0xF976, 0x7565},
{0xF977, 0x4EAE}, {0xF978, 0x5169}, {0xF979, 0x51C9}, {0xF97A, 0x6881}, {0xF97B, 0x7CE7}, {0xF97C, 0x826F}, {0xF97D, 0x8AD2}, {0xF97E, 0x91CF}, {0xF97F, 0x52F5}, {0xF980, 0x5442}, {0xF981, 0x5973},
{0xF982, 0x5EEC}, {0xF983, 0x65C5}, {0xF984, 0x6FFE}, {0xF985, 0x792A}, {0xF986, 0x95AD}, {0xF987, 0x9A6A}, {0xF988, 0x9E97}, {0xF989, 0x9ECE}, {0xF98A, 0x529B}, {0xF98B, 0x66C6}, {0xF98C, 0x6B77},
{0xF98D, 0x8F62}, {0xF98E, 0x5E74}, {0xF98F, 0x6190}, {0xF990, 0x6200}, {0xF991, 0x649A}, {0xF992, 0x6F23}, {0xF993, 0x7149}, {0xF994, 0x7489}, {0xF995, 0x79CA}, {0xF996, 0x7DF4}, {0xF997, 0x806F},
{0xF998, 0x8F26}, {0xF999, 0x84EE}, {0xF99A, 0x9023}, {0xF99B, 0x934A}, {0xF99C, 0x5217}, {0xF99D, 0x52A3}, {0xF99E, 0x54BD}, {0xF99F, 0x70C8}, {0xF9A0, 0x88C2}, {0xF9A1, 0x8AAA}, {0xF9A2, 0x5EC9},
{0xF9A3, 0x5FF5}, {0xF9A4, 0x637B}, {0xF9A5, 0x6BAE}, {0xF9A6, 0x7C3E}, {0xF9A7, 0x7375}, {0xF9A8, 0x4EE4}, {0xF9A9, 0x56F9}, {0xF9AA, 0x5BE7}, {0xF9AB, 0x5DBA}, {0xF9AC, 0x601C}, {0xF9AD, 0x73B2},
{0xF9AE, 0x7469}, {0xF9AF, 0x7F9A}, {0xF9B0, 0x8046}, {0xF9B1, 0x9234}, {0xF9B2, 0x96F6}, {0xF9B3, 0x9748}, {0xF9B4, 0x9818}, {0xF9B5, 0x4F8B}, {0xF9B6, 0x79AE}, {0xF9B7, 0x91B4}, {0xF9B8, 0x96B8},
{0xF9B9, 0x60E1}, {0xF9BA, 0x4E86}, {0xF9BB, 0x50DA}, {0xF9BC, 0x5BEE}, {0xF9BD, 0x5C3F}, {0xF9BE, 0x6599}, {0xF9BF, 0x6A02}, {0xF9C0, 0x71CE}, {0xF9C1, 0x7642}, {0xF9C2, 0x84FC}, {0xF9C3, 0x907C},
{0xF9C4, 0x9F8D}, {0xF9C5, 0x6688}, {0xF9C6, 0x962E}, {0xF9C7, 0x5289}, {0xF9C8, 0x677B}, {0xF9C9, 0x67F3}, {0xF9CA, 0x6D41}, {0xF9CB, 0x6E9C}, {0xF9CC, 0x7409}, {0xF9CD, 0x7559}, {0xF9CE, 0x786B},
{0xF9CF, 0x7D10}, {0xF9D0, 0x985E}, {0xF9D1, 0x516D}, {0xF9D2, 0x622E}, {0xF9D3, 0x9678}, {0xF9D4, 0x502B}, {0xF9D5, 0x5D19}, {0xF9D6, 0x6DEA}, {0xF9D7, 0x8F2A}, {0xF9D8, 0x5F8B}, {0xF9D9, 0x6144},
{0xF9DA, 0x6817}, {0xF9DB, 0x7387}, {0xF9DC, 0x9686}, {0xF9DD, 0x5229}, {0xF9DE, 0x540F}, {0xF9DF, 0x5C65}, {0xF9E0, 0x6613}, {0xF9E1, 0x674E}, {0xF9E2, 0x68A8}, {0xF9E3, 0x6CE5}, {0xF9E4, 0x7406},
{0xF9E5, 0x75E2}, {0xF9E6, 0x7F79}, {0xF9E7, 0x88CF}, {0xF9E8, 0x88E1}, {0xF9E9, 0x91CC}, {0xF9EA, 0x96E2}, {0xF9EB, 0x533F}, {0xF9EC, 0x6EBA}, {0xF9ED, 0x541D}, {0xF9EE, 0x71D0}, {0xF9EF, 0x7498},
{0xF9F0, 0x85FA}, {0xF9F1, 0x96A3}, {0xF9F2, 0x9C57}, {0xF9F3, 0x9E9F}, {0xF9F4, 0x6797}, {0xF9F5, 0x6DCB}, {0xF9F6, 0x81E8}, {0xF9F7, 0x7ACB}, {0xF9F8, 0x7B20}, {0xF9F9, 0x7C92}, {0xF9FA, 0x72C0},
{0xF9FB, 0x7099}, {0xF9FC, 0x8B58}, {0xF9FD, 0x4EC0}, {0xF9FE, 0x8336}, {0xF9FF, 0x523A}, {0xFA00, 0x5207}, {0xFA01, 0x5EA6}, {0xFA02, 0x62D3}, {0xFA03, 0x7CD6}, {0xFA04, 0x5B85}, {0xFA05, 0x6D1E},
{0xFA06, 0x66B4}, {0xFA07, 0x8F3B}, {0xFA08, 0x884C}, {0xFA09, 0x964D}, {0xFA0A, 0x898B}, {0xFA0B, 0x5ED3}, {0xFA0C, 0x5140}, {0xFA0D, 0x55C0}, {0xFA10, 0x585A}, {0xFA12, 0x6674}, {0xFA15, 0x51DE},
{0xFA16, 0x732A}, {0xFA17, 0x76CA}, {0xFA18, 0x793C}, {0xFA19, 0x795E}, {0xFA1A, 0x7965}, {0xFA1B, 0x798F}, {0xFA1C, 0x9756}, {0xFA1D, 0x7CBE}, {0xFA1E, 0x7FBD}, {0xFA20, 0x8612}, {0xFA22, 0x8AF8},
{0xFA25, 0x9038}, {0xFA26, 0x90FD}, {0xFA2A, 0x98EF}, {0xFA2B, 0x98FC}, {0xFA2C, 0x9928}, {0xFA2D, 0x9DB4}, {0xFA2E, 0x90DE}, {0xFA2F, 0x96B7}, {0xFA30, 0x4FAE}, {0xFA31, 0x50E7}, {0xFA32, 0x514D},
{0xFA33, 0x52C9}, {0xFA34, 0x52E4}, {0xFA35, 0x5351}, {0xFA36, 0x559D}, {0xFA37, 0x5606}, {0xFA38, 0x5668}, {0xFA39, 0x5840}, {0xFA3A, 0x58A8}, {0xFA3B, 0x5C64}, {0xFA3C, 0x5C6E}, {0xFA3D, 0x6094},
{0xFA3E, 0x6168}, {0xFA3F, 0x618E}, {0xFA40, 0x61F2}, {0xFA41, 0x654F}, {0xFA42, 0x65E2}, {0xFA43, 0x6691}, {0xFA44, 0x6885}, {0xFA45, 0x6D77}, {0xFA46, 0x6E1A}, {0xFA47, 0x6F22}, {0xFA48, 0x716E},
{0xFA49, 0x722B}, {0xFA4A, 0x7422}, {0xFA4B, 0x7891}, {0xFA4C, 0x793E}, {0xFA4D, 0x7949}, {0xFA4E, 0x7948}, {0xFA4F, 0x7950}, {0xFA50, 0x7956}, {0xFA51, 0x795D}, {0xFA52, 0x798D}, {0xFA53, 0x798E},
{0xFA54, 0x7A40}, {0xFA55, 0x7A81}, {0xFA56, 0x7BC0}, {0xFA57, 0x7DF4}, {0xFA58, 0x7E09}, {0xFA59, 0x7E41}, {0xFA5A, 0x7F72}, {0xFA5B, 0x8005}, {0xFA5C, 0x81ED}, {0xFA5D, 0x8279}, {0xFA5E, 0x8279},
{0xFA5F, 0x8457}, {0xFA60, 0x8910}, {0xFA61, 0x8996}, {0xFA62, 0x8B01}, {0xFA63, 0x8B39}, {0xFA64, 0x8CD3}, {0xFA65, 0x8D08}, {0xFA66, 0x8FB6}, {0xFA67, 0x9038}, {0xFA68, 0x96E3}, {0xFA69, 0x97FF},
{0xFA6A, 0x983B}, {0xFA6B, 0x6075}, {0xFA6C, 0x242EE}, {0xFA6D, 0x8218}, {0xFA70, 0x4E26}, {0xFA71, 0x51B5}, {0xFA72, 0x5168}, {0xFA73, 0x4F80}, {0xFA74, 0x5145}, {0xFA75, 0x5180}, {0xFA76, 0x52C7},
{0xFA77, 0x52FA}, {0xFA78, 0x559D}, {0xFA79, 0x5555}, {0xFA7A, 0x5599}, {0xFA7B, 0x55E2}, {0xFA7C, 0x585A}, {0xFA7D, 0x58B3}, {0xFA7E, 0x5944}, {0xFA7F, 0x5954}, {0xFA80, 0x5A62}, {0xFA81, 0x5B28},
{0xFA82, 0x5ED2}, {0xFA83, 0x5ED9}, {0xFA84, 0x5F69}, {0xFA85, 0x5FAD}, {0xFA86, 0x60D8}, {0xFA87, 0x614E}, {0xFA88, 0x6108}, {0xFA89, 0x618E}, {0xFA8A, 0x6160}, {0xFA8B, 0x61F2}, {0xFA8C, 0x6234},
{0xFA8D, 0x63C4}, {0xFA8E, 0x641C}, {0xFA8F, 0x6452}, {0xFA90, 0x6556}, {0xFA91, 0x6674}, {0xFA92, 0x6717}, {0xFA93, 0x671B}, {0xFA94, 0x6756}, {0xFA95, 0x6B79}, {0xFA96, 0x6BBA}, {0xFA97, 0x6D41},
{0xFA98, 0x6EDB}, {0xFA99, 0x6ECB}, {0xFA9A, 0x6F22}, {0xFA9B, 0x701E}, {0xFA9C, 0x716E}, {0xFA9D, 0x77A7}, {0xFA9E, 0x7235}, {0xFA9F, 0x72AF}, {0xFAA0, 0x732A}, {0xFAA1, 0x7471}, {0xFAA2, 0x7506},
{0xFAA3, 0x753B}, {0xFAA4, 0x761D}, {0xFAA5, 0x761F}, {0xFAA6, 0x76CA}, {0xFAA7, 0x76DB}, {0xFAA8, 0x76F4}, {0xFAA9, 0x774A}, {0xFAAA, 0x7740}, {0xFAAB, 0x78CC}, {0xFAAC, 0x7AB1}, {0xFAAD, 0x7BC0},
{0xFAAE, 0x7C7B}, {0xFAAF, 0x7D5B}, {0xFAB0, 0x7DF4}, {0xFAB1, 0x7F3E}, {0xFAB2, 0x8005}, {0xFAB3, 0x8352}, {0xFAB4, 0x83EF}, {0xFAB5, 0x8779}, {0xFAB6, 0x8941}, {0xFAB7, 0x8986}, {0xFAB8, 0x8996},
{0xFAB9, 0x8ABF}, {0xFABA, 0x8AF8}, {0xFABB, 0x8ACB}, {0xFABC, 0x8B01}, {0xFABD, 0x8AFE}, {0xFABE, 0x8AED}, {0xFABF, 0x8B39}, {0xFAC0, 0x8B8A}, {0xFAC1, 0x8D08}, {0xFAC2, 0x8F38}, {0xFAC3, 0x9072},
{0xFAC4, 0x9199}, {0xFAC5, 0x9276}, {0xFAC6, 0x967C}, {0xFAC7, 0x96E3}, {0xFAC8, 0x9756}, {0xFAC9, 0x97DB}, {0xFACA, 0x97FF}, {0xFACB, 0x980B}, {0xFACC, 0x983B}, {0xFACD, 0x9B12}, {0xFACE, 0x9F9C},
{0xFACF, 0x2284A}, {0xFAD0, 0x22844}, {0xFAD1, 0x233D5}, {0xFAD2, 0x3B9D}, {0xFAD3, 0x4018}, {0xFAD4, 0x4039}, {0xFAD5, 0x25249}, {0xFAD6, 0x25CD0}, {0xFAD7, 0x27ED3}, {0xFAD8, 0x9F43},
{0xFAD9, 0x9F8E}, {0xFB1D, 0x5D9}, {0xFB1D, 0x5B4}, {0xFB1F, 0x5F2}, {0xFB1F, 0x5B7}, {0xFB2A, 0x5E9}, {0xFB2A, 0x5C1}, {0xFB2B, 0x5E9}, {0xFB2B, 0x5C2}, {0xFB2C, 0x5E9}, {0xFB2C, 0x5BC},
{0xFB2C, 0x5C1}, {0xFB2D, 0x5E9}, {0xFB2D, 0x5BC}, {0xFB2D, 0x5C2}, {0xFB2E, 0x5D0}, {0xFB2E, 0x5B7}, {0xFB2F, 0x5D0}, {0xFB2F, 0x5B8}, {0xFB30, 0x5D0}, {0xFB30, 0x5BC}, {0xFB31, 0x5D1},
{0xFB31, 0x5BC}, {0xFB32, 0x5D2}, {0xFB32, 0x5BC}, {0xFB33, 0x5D3}, {0xFB33, 0x5BC}, {0xFB34, 0x5D4}, {0xFB34, 0x5BC}, {0xFB35, 0x5D5}, {0xFB35, 0x5BC}, {0xFB36, 0x5D6}, {0xFB36, 0x5BC},
{0xFB38, 0x5D8}, {0xFB38, 0x5BC}, {0xFB39, 0x5D9}, {0xFB39, 0x5BC}, {0xFB3A, 0x5DA}, {0xFB3A, 0x5BC}, {0xFB3B, 0x5DB}, {0xFB3B, 0x5BC}, {0xFB3C, 0x5DC}, {0xFB3C, 0x5BC}, {0xFB3E, 0x5DE},
{0xFB3E, 0x5BC}, {0xFB40, 0x5E0}, {0xFB40, 0x5BC}, {0xFB41, 0x5E1}, {0xFB41, 0x5BC}, {0xFB43, 0x5E3}, {0xFB43, 0x5BC}, {0xFB44, 0x5E4}, {0xFB44, 0x5BC}, {0xFB46, 0x5E6}, {0xFB46, 0x5BC},
{0xFB47, 0x5E7}, {0xFB47, 0x5BC}, {0xFB48, 0x5E8}, {0xFB48, 0x5BC}, {0xFB49, 0x5E9}, {0xFB49, 0x5BC}, {0xFB4A, 0x5EA}, {0xFB4A, 0x5BC}, {0xFB4B, 0x5D5}, {0xFB4B, 0x5B9}, {0xFB4C, 0x5D1},
{0xFB4C, 0x5BF}, {0xFB4D, 0x5DB}, {0xFB4D, 0x5BF}, {0xFB4E, 0x5E4}, {0xFB4E, 0x5BF}, {0x1109A, 0x11099}, {0x1109A, 0x110BA}, {0x1109C, 0x1109B}, {0x1109C, 0x110BA}, {0x110AB, 0x110A5},
{0x110AB, 0x110BA}, {0x1112E, 0x11131}, {0x1112E, 0x11127}, {0x1112F, 0x11132}, {0x1112F, 0x11127}, {0x1134B, 0x11347}, {0x1134B, 0x1133E}, {0x1134C, 0x11347}, {0x1134C, 0x11357}, {0x114BB, 0x114B9},
{0x114BB, 0x114BA}, {0x114BC, 0x114B9}, {0x114BC, 0x114B0}, {0x114BE, 0x114B9}, {0x114BE, 0x114BD}, {0x115BA, 0x115B8}, {0x115BA, 0x115AF}, {0x115BB, 0x115B9}, {0x115BB, 0x115AF}, {0x1D15E, 0x1D157},
{0x1D15E, 0x1D165}, {0x1D15F, 0x1D158}, {0x1D15F, 0x1D165}, {0x1D160, 0x1D158}, {0x1D160, 0x1D165}, {0x1D160, 0x1D16E}, {0x1D161, 0x1D158}, {0x1D161, 0x1D165}, {0x1D161, 0x1D16F}, {0x1D162, 0x1D158},
{0x1D162, 0x1D165}, {0x1D162, 0x1D170}, {0x1D163, 0x1D158}, {0x1D163, 0x1D165}, {0x1D163, 0x1D171}, {0x1D164, 0x1D158}, {0x1D164, 0x1D165}, {0x1D164, 0x1D172}, {0x1D1BB, 0x1D1B9}, {0x1D1BB, 0x1D165},
{0x1D1BC, 0x1D1BA}, {0x1D1BC, 0x1D165}, {0x1D1BD, 0x1D1B9}, {0x1D1BD, 0x1D165}, {0x1D1BD, 0x1D16E}, {0x1D1BE, 0x1D1BA}, {0x1D1BE, 0x1D165}, {0x1D1BE, 0x1D16E}, {0x1D1BF, 0x1D1B9}, {0x1D1BF, 0x1D165},
{0x1D1BF, 0x1D16F}, {0x1D1C0, 0x1D1BA}, {0x1D1C0, 0x1D165}, {0x1D1C0, 0x1D16F}, {0x2F800, 0x4E3D}, {0x2F801, 0x4E38}, {0x2F802, 0x4E41}, {0x2F803, 0x20122}, {0x2F804, 0x4F60}, {0x2F805, 0x4FAE},
{0x2F806, 0x4FBB}, {0x2F807, 0x5002}, {0x2F808, 0x507A}, {0x2F809, 0x5099}, {0x2F80A, 0x50E7}, {0x2F80B, 0x50CF}, {0x2F80C, 0x349E}, {0x2F80D, 0x2063A}, {0x2F80E, 0x514D}, {0x2F80F, 0x5154},
{0x2F810, 0x5164}, {0x2F811, 0x5177}, {0x2F812, 0x2051C}, {0x2F813, 0x34B9}, {0x2F814, 0x5167}, {0x2F815, 0x518D}, {0x2F816, 0x2054B}, {0x2F817, 0x5197}, {0x2F818, 0x51A4}, {0x2F819, 0x4ECC},
{0x2F81A, 0x51AC}, {0x2F81B, 0x51B5}, {0x2F81C, 0x291DF}, {0x2F81D, 0x51F5}, {0x2F81E, 0x5203}, {0x2F81F, 0x34DF}, {0x2F820, 0x523B}, {0x2F821, 0x5246}, {0x2F822, 0x5272}, {0x2F823, 0x5277},
{0x2F824, 0x3515}, {0x2F825, 0x52C7}, {0x2F826, 0x52C9}, {0x2F827, 0x52E4}, {0x2F828, 0x52FA}, {0x2F829, 0x5305}, {0x2F82A, 0x5306}, {0x2F82B, 0x5317}, {0x2F82C, 0x5349}, {0x2F82D, 0x5351},
{0x2F82E, 0x535A}, {0x2F82F, 0x5373}, {0x2F830, 0x537D}, {0x2F831, 0x537F}, {0x2F832, 0x537F}, {0x2F833, 0x537F}, {0x2F834, 0x20A2C}, {0x2F835, 0x7070}, {0x2F836, 0x53CA}, {0x2F837, 0x53DF},
{0x2F838, 0x20B63}, {0x2F839, 0x53EB}, {0x2F83A, 0x53F1}, {0x2F83B, 0x5406}, {0x2F83C, 0x549E}, {0x2F83D, 0x5438}, {0x2F83E, 0x5448}, {0x2F83F, 0x5468}, {0x2F840, 0x54A2}, {0x2F841, 0x54F6},
{0x2F842, 0x5510}, {0x2F843, 0x5553}, {0x2F844, 0x5563}, {0x2F845, 0x5584}, {0x2F846, 0x5584}, {0x2F847, 0x5599}, {0x2F848, 0x55AB}, {0x2F849, 0x55B3}, {0x2F84A, 0x55C2}, {0x2F84B, 0x5716},
{0x2F84C, 0x5606}, {0x2F84D, 0x5717}, {0x2F84E, 0x5651}, {0x2F84F, 0x5674}, {0x2F850, 0x5207}, {0x2F851, 0x58EE}, {0x2F852, 0x57CE}, {0x2F853, 0x57F4}, {0x2F854, 0x580D}, {0x2F855, 0x578B},
{0x2F856, 0x5832}, {0x2F857, 0x5831}, {0x2F858, 0x58AC}, {0x2F859, 0x214E4}, {0x2F85A, 0x58F2}, {0x2F85B, 0x58F7}, {0x2F85C, 0x5906}, {0x2F85D, 0x591A}, {0x2F85E, 0x5922}, {0x2F85F, 0x5962},
{0x2F860, 0x216A8}, {0x2F861, 0x216EA}, {0x2F862, 0x59EC}, {0x2F863, 0x5A1B}, {0x2F864, 0x5A27}, {0x2F865, 0x59D8}, {0x2F866, 0x5A66}, {0x2F867, 0x36EE}, {0x2F868, 0x36FC}, {0x2F869, 0x5B08},
{0x2F86A, 0x5B3E}, {0x2F86B, 0x5B3E}, {0x2F86C, 0x219C8}, {0x2F86D, 0x5BC3}, {0x2F86E, 0x5BD8}, {0x2F86F, 0x5BE7}, {0x2F870, 0x5BF3}, {0x2F871, 0x21B18}, {0x2F872, 0x5BFF}, {0x2F873, 0x5C06},
{0x2F874, 0x5F53}, {0x2F875, 0x5C22}, {0x2F876, 0x3781}, {0x2F877, 0x5C60}, {0x2F878, 0x5C6E}, {0x2F879, 0x5CC0}, {0x2F87A, 0x5C8D}, {0x2F87B, 0x21DE4}, {0x2F87C, 0x5D43}, {0x2F87D, 0x21DE6},
{0x2F87E, 0x5D6E}, {0x2F87F, 0x5D6B}, {0x2F880, 0x5D7C}, {0x2F881, 0x5DE1}, {0x2F882, 0x5DE2}, {0x2F883, 0x382F}, {0x2F884, 0x5DFD}, {0x2F885, 0x5E28}, {0x2F886, 0x5E3D}, {0x2F887, 0x5E69},
{0x2F888, 0x3862}, {0x2F889, 0x22183}, {0x2F88A, 0x387C}, {0x2F88B, 0x5EB0}, {0x2F88C, 0x5EB3}, {0x2F88D, 0x5EB6}, {0x2F88E, 0x5ECA}, {0x2F88F, 0x2A392}, {0x2F890, 0x5EFE}, {0x2F891, 0x22331},
{0x2F892, 0x22331}, {0x2F893, 0x8201}, {0x2F894, 0x5F22}, {0x2F895, 0x5F22}, {0x2F896, 0x38C7}, {0x2F897, 0x232B8}, {0x2F898, 0x261DA}, {0x2F899, 0x5F62}, {0x2F89A, 0x5F6B}, {0x2F89B, 0x38E3},
{0x2F89C, 0x5F9A}, {0x2F89D, 0x5FCD}, {0x2F89E, 0x5FD7}, {0x2F89F, 0x5FF9}, {0x2F8A0, 0x6081}, {0x2F8A1, 0x393A}, {0x2F8A2, 0x391C}, {0x2F8A3, 0x6094}, {0x2F8A4, 0x226D4}, {0x2F8A5, 0x60C7},
{0x2F8A6, 0x6148}, {0x2F8A7, 0x614C}, {0x2F8A8, 0x614E}, {0x2F8A9, 0x614C}, {0x2F8AA, 0x617A}, {0x2F8AB, 0x618E}, {0x2F8AC, 0x61B2}, {0x2F8AD, 0x61A4}, {0x2F8AE, 0x61AF}, {0x2F8AF, 0x61DE},
{0x2F8B0, 0x61F2}, {0x2F8B1, 0x61F6}, {0x2F8B2, 0x6210}, {0x2F8B3, 0x621B}, {0x2F8B4, 0x625D}, {0x2F8B5, 0x62B1}, {0x2F8B6, 0x62D4}, {0x2F8B7, 0x6350}, {0x2F8B8, 0x22B0C}, {0x2F8B9, 0x633D},
{0x2F8BA, 0x62FC}, {0x2F8BB, 0x6368}, {0x2F8BC, 0x6383}, {0x2F8BD, 0x63E4}, {0x2F8BE, 0x22BF1}, {0x2F8BF, 0x6422}, {0x2F8C0, 0x63C5}, {0x2F8C1, 0x63A9}, {0x2F8C2, 0x3A2E}, {0x2F8C3, 0x6469},
{0x2F8C4, 0x647E}, {0x2F8C5, 0x649D}, {0x2F8C6, 0x6477}, {0x2F8C7, 0x3A6C}, {0x2F8C8, 0x654F}, {0x2F8C9, 0x656C}, {0x2F8CA, 0x2300A}, {0x2F8CB, 0x65E3}, {0x2F8CC, 0x66F8}, {0x2F8CD, 0x6649},
{0x2F8CE, 0x3B19}, {0x2F8CF, 0x6691}, {0x2F8D0, 0x3B08}, {0x2F8D1, 0x3AE4}, {0x2F8D2, 0x5192}, {0x2F8D3, 0x5195}, {0x2F8D4, 0x6700}, {0x2F8D5, 0x669C}, {0x2F8D6, 0x80AD}, {0x2F8D7, 0x43D9},
{0x2F8D8, 0x6717}, {0x2F8D9, 0x671B}, {0x2F8DA, 0x6721}, {0x2F8DB, 0x675E}, {0x2F8DC, 0x6753}, {0x2F8DD, 0x233C3}, {0x2F8DE, 0x3B49}, {0x2F8DF, 0x67FA}, {0x2F8E0, 0x6785}, {0x2F8E1, 0x6852},
{0x2F8E2, 0x6885}, {0x2F8E3, 0x2346D}, {0x2F8E4, 0x688E}, {0x2F8E5, 0x681F}, {0x2F8E6, 0x6914}, {0x2F8E7, 0x3B9D}, {0x2F8E8, 0x6942}, {0x2F8E9, 0x69A3}, {0x2F8EA, 0x69EA}, {0x2F8EB, 0x6AA8},
{0x2F8EC, 0x236A3}, {0x2F8ED, 0x6ADB}, {0x2F8EE, 0x3C18}, {0x2F8EF, 0x6B21}, {0x2F8F0, 0x238A7}, {0x2F8F1, 0x6B54}, {0x2F8F2, 0x3C4E}, {0x2F8F3, 0x6B72}, {0x2F8F4, 0x6B9F}, {0x2F8F5, 0x6BBA},
{0x2F8F6, 0x6BBB}, {0x2F8F7, 0x23A8D}, {0x2F8F8, 0x21D0B}, {0x2F8F9, 0x23AFA}, {0x2F8FA, 0x6C4E}, {0x2F8FB, 0x23CBC}, {0x2F8FC, 0x6CBF}, {0x2F8FD, 0x6CCD}, {0x2F8FE, 0x6C67}, {0x2F8FF, 0x6D16},
{0x2F900, 0x6D3E}, {0x2F901, 0x6D77}, {0x2F902, 0x6D41}, {0x2F903, 0x6D69}, {0x2F904, 0x6D78}, {0x2F905, 0x6D85}, {0x2F906, 0x23D1E}, {0x2F907, 0x6D34}, {0x2F908, 0x6E2F}, {0x2F909, 0x6E6E},
{0x2F90A, 0x3D33}, {0x2F90B, 0x6ECB}, {0x2F90C, 0x6EC7}, {0x2F90D, 0x23ED1}, {0x2F90E, 0x6DF9}, {0x2F90F, 0x6F6E}, {0x2F910, 0x23F5E}, {0x2F911, 0x23F8E}, {0x2F912, 0x6FC6}, {0x2F913, 0x7039},
{0x2F914, 0x701E}, {0x2F915, 0x701B}, {0x2F916, 0x3D96}, {0x2F917, 0x704A}, {0x2F918, 0x707D}, {0x2F919, 0x7077}, {0x2F91A, 0x70AD}, {0x2F91B, 0x20525}, {0x2F91C, 0x7145}, {0x2F91D, 0x24263},
{0x2F91E, 0x719C}, {0x2F91F, 0x243AB}, {0x2F920, 0x7228}, {0x2F921, 0x7235}, {0x2F922, 0x7250}, {0x2F923, 0x24608}, {0x2F924, 0x7280}, {0x2F925, 0x7295}, {0x2F926, 0x24735}, {0x2F927, 0x24814},
{0x2F928, 0x737A}, {0x2F929, 0x738B}, {0x2F92A, 0x3EAC}, {0x2F92B, 0x73A5}, {0x2F92C, 0x3EB8}, {0x2F92D, 0x3EB8}, {0x2F92E, 0x7447}, {0x2F92F, 0x745C}, {0x2F930, 0x7471}, {0x2F931, 0x7485},
{0x2F932, 0x74CA}, {0x2F933, 0x3F1B}, {0x2F934, 0x7524}, {0x2F935, 0x24C36}, {0x2F936, 0x753E}, {0x2F937, 0x24C92}, {0x2F938, 0x7570}, {0x2F939, 0x2219F}, {0x2F93A, 0x7610}, {0x2F93B, 0x24FA1},
{0x2F93C, 0x24FB8}, {0x2F93D, 0x25044}, {0x2F93E, 0x3FFC}, {0x2F93F, 0x4008}, {0x2F940, 0x76F4}, {0x2F941, 0x250F3}, {0x2F942, 0x250F2}, {0x2F943, 0x25119}, {0x2F944, 0x25133}, {0x2F945, 0x771E},
{0x2F946, 0x771F}, {0x2F947, 0x771F}, {0x2F948, 0x774A}, {0x2F949, 0x4039}, {0x2F94A, 0x778B}, {0x2F94B, 0x4046}, {0x2F94C, 0x4096}, {0x2F94D, 0x2541D}, {0x2F94E, 0x784E}, {0x2F94F, 0x788C},
{0x2F950, 0x78CC}, {0x2F951, 0x40E3}, {0x2F952, 0x25626}, {0x2F953, 0x7956}, {0x2F954, 0x2569A}, {0x2F955, 0x256C5}, {0x2F956, 0x798F}, {0x2F957, 0x79EB}, {0x2F958, 0x412F}, {0x2F959, 0x7A40},
{0x2F95A, 0x7A4A}, {0x2F95B, 0x7A4F}, {0x2F95C, 0x2597C}, {0x2F95D, 0x25AA7}, {0x2F95E, 0x25AA7}, {0x2F95F, 0x7AEE}, {0x2F960, 0x4202}, {0x2F961, 0x25BAB}, {0x2F962, 0x7BC6}, {0x2F963, 0x7BC9},
{0x2F964, 0x4227}, {0x2F965, 0x25C80}, {0x2F966, 0x7CD2}, {0x2F967, 0x42A0}, {0x2F968, 0x7CE8}, {0x2F969, 0x7CE3}, {0x2F96A, 0x7D00}, {0x2F96B, 0x25F86}, {0x2F96C, 0x7D63}, {0x2F96D, 0x4301},
{0x2F96E, 0x7DC7}, {0x2F96F, 0x7E02}, {0x2F970, 0x7E45}, {0x2F971, 0x4334}, {0x2F972, 0x26228}, {0x2F973, 0x26247}, {0x2F974, 0x4359}, {0x2F975, 0x262D9}, {0x2F976, 0x7F7A}, {0x2F977, 0x2633E},
{0x2F978, 0x7F95}, {0x2F979, 0x7FFA}, {0x2F97A, 0x8005}, {0x2F97B, 0x264DA}, {0x2F97C, 0x26523}, {0x2F97D, 0x8060}, {0x2F97E, 0x265A8}, {0x2F97F, 0x8070}, {0x2F980, 0x2335F}, {0x2F981, 0x43D5},
{0x2F982, 0x80B2}, {0x2F983, 0x8103}, {0x2F984, 0x440B}, {0x2F985, 0x813E}, {0x2F986, 0x5AB5}, {0x2F987, 0x267A7}, {0x2F988, 0x267B5}, {0x2F989, 0x23393}, {0x2F98A, 0x2339C}, {0x2F98B, 0x8201},
{0x2F98C, 0x8204}, {0x2F98D, 0x8F9E}, {0x2F98E, 0x446B}, {0x2F98F, 0x8291}, {0x2F990, 0x828B}, {0x2F991, 0x829D}, {0x2F992, 0x52B3}, {0x2F993, 0x82B1}, {0x2F994, 0x82B3}, {0x2F995, 0x82BD},
{0x2F996, 0x82E6}, {0x2F997, 0x26B3C}, {0x2F998, 0x82E5}, {0x2F999, 0x831D}, {0x2F99A, 0x8363}, {0x2F99B, 0x83AD}, {0x2F99C, 0x8323}, {0x2F99D, 0x83BD}, {0x2F99E, 0x83E7}, {0x2F99F, 0x8457},
{0x2F9A0, 0x8353}, {0x2F9A1, 0x83CA}, {0x2F9A2, 0x83CC}, {0x2F9A3, 0x83DC}, {0x2F9A4, 0x26C36}, {0x2F9A5, 0x26D6B}, {0x2F9A6, 0x26CD5}, {0x2F9A7, 0x452B}, {0x2F9A8, 0x84F1}, {0x2F9A9, 0x84F3},
{0x2F9AA, 0x8516}, {0x2F9AB, 0x273CA}, {0x2F9AC, 0x8564}, {0x2F9AD, 0x26F2C}, {0x2F9AE, 0x455D}, {0x2F9AF, 0x4561}, {0x2F9B0, 0x26FB1}, {0x2F9B1, 0x270D2}, {0x2F9B2, 0x456B}, {0x2F9B3, 0x8650},
{0x2F9B4, 0x865C}, {0x2F9B5, 0x8667}, {0x2F9B6, 0x8669}, {0x2F9B7, 0x86A9}, {0x2F9B8, 0x8688}, {0x2F9B9, 0x870E}, {0x2F9BA, 0x86E2}, {0x2F9BB, 0x8779}, {0x2F9BC, 0x8728}, {0x2F9BD, 0x876B},
{0x2F9BE, 0x8786}, {0x2F9BF, 0x45D7}, {0x2F9C0, 0x87E1}, {0x2F9C1, 0x8801}, {0x2F9C2, 0x45F9}, {0x2F9C3, 0x8860}, {0x2F9C4, 0x8863}, {0x2F9C5, 0x27667}, {0x2F9C6, 0x88D7}, {0x2F9C7, 0x88DE},
{0x2F9C8, 0x4635}, {0x2F9C9, 0x88FA}, {0x2F9CA, 0x34BB}, {0x2F9CB, 0x278AE}, {0x2F9CC, 0x27966}, {0x2F9CD, 0x46BE}, {0x2F9CE, 0x46C7}, {0x2F9CF, 0x8AA0}, {0x2F9D0, 0x8AED}, {0x2F9D1, 0x8B8A},
{0x2F9D2, 0x8C55}, {0x2F9D3, 0x27CA8}, {0x2F9D4, 0x8CAB}, {0x2F9D5, 0x8CC1}, {0x2F9D6, 0x8D1B}, {0x2F9D7, 0x8D77}, {0x2F9D8, 0x27F2F}, {0x2F9D9, 0x20804}, {0x2F9DA, 0x8DCB}, {0x2F9DB, 0x8DBC},
{0x2F9DC, 0x8DF0}, {0x2F9DD, 0x208DE}, {0x2F9DE, 0x8ED4}, {0x2F9DF, 0x8F38}, {0x2F9E0, 0x285D2}, {0x2F9E1, 0x285ED}, {0x2F9E2, 0x9094}, {0x2F9E3, 0x90F1}, {0x2F9E4, 0x9111}, {0x2F9E5, 0x2872E},
{0x2F9E6, 0x911B}, {0x2F9E7, 0x9238}, {0x2F9E8, 0x92D7}, {0x2F9E9, 0x92D8}, {0x2F9EA, 0x927C}, {0x2F9EB, 0x93F9}, {0x2F9EC, 0x9415}, {0x2F9ED, 0x28BFA}, {0x2F9EE, 0x958B}, {0x2F9EF, 0x4995},
{0x2F9F0, 0x95B7}, {0x2F9F1, 0x28D77}, {0x2F9F2, 0x49E6}, {0x2F9F3, 0x96C3}, {0x2F9F4, 0x5DB2}, {0x2F9F5, 0x9723}, {0x2F9F6, 0x29145}, {0x2F9F7, 0x2921A}, {0x2F9F8, 0x4A6E}, {0x2F9F9, 0x4A76},
{0x2F9FA, 0x97E0}, {0x2F9FB, 0x2940A}, {0x2F9FC, 0x4AB2}, {0x2F9FD, 0x29496}, {0x2F9FE, 0x980B}, {0x2F9FF, 0x980B}, {0x2FA00, 0x9829}, {0x2FA01, 0x295B6}, {0x2FA02, 0x98E2}, {0x2FA03, 0x4B33},
{0x2FA04, 0x9929}, {0x2FA05, 0x99A7}, {0x2FA06, 0x99C2}, {0x2FA07, 0x99FE}, {0x2FA08, 0x4BCE}, {0x2FA09, 0x29B30}, {0x2FA0A, 0x9B12}, {0x2FA0B, 0x9C40}, {0x2FA0C, 0x9CFD}, {0x2FA0D, 0x4CCE},
{0x2FA0E, 0x4CED}, {0x2FA0F, 0x9D67}, {0x2FA10, 0x2A0CE}, {0x2FA11, 0x4CF8}, {0x2FA12, 0x2A105}, {0x2FA13, 0x2A20E}, {0x2FA14, 0x2A291}, {0x2FA15, 0x9EBB}, {0x2FA16, 0x4D56}, {0x2FA17, 0x9EF9},
{0x2FA18, 0x9EFE}, {0x2FA19, 0x9F05}, {0x2FA1A, 0x9F0F}, {0x2FA1B, 0x9F16}, {0x2FA1D, 0x2A600},
};
static std::string codepoint_to_utf8(uint32_t cp) {
std::string result;
if (/* 0x00 <= cp && */ cp <= 0x7f) {
result.push_back(cp);
}
else if (0x80 <= cp && cp <= 0x7ff) {
result.push_back(0xc0 | ((cp >> 6) & 0x1f));
result.push_back(0x80 | (cp & 0x3f));
}
else if (0x800 <= cp && cp <= 0xffff) {
result.push_back(0xe0 | ((cp >> 12) & 0x0f));
result.push_back(0x80 | ((cp >> 6) & 0x3f));
result.push_back(0x80 | (cp & 0x3f));
}
else if (0x10000 <= cp && cp <= 0x10ffff) {
result.push_back(0xf0 | ((cp >> 18) & 0x07));
result.push_back(0x80 | ((cp >> 12) & 0x3f));
result.push_back(0x80 | ((cp >> 6) & 0x3f));
result.push_back(0x80 | (cp & 0x3f));
}
else {
throw std::invalid_argument("invalid codepoint");
}
return result;
}
static std::string codepoints_to_utf8(const std::vector<uint32_t> & cps) {
std::string result;
for (size_t i = 0; i < cps.size(); ++i) {
result.append(codepoint_to_utf8(cps[i]));
}
return result;
}
static uint32_t codepoint_from_utf8(const std::string & utf8, size_t & offset) {
assert(offset < utf8.size());
if (!(utf8[offset + 0] & 0x80)) {
auto result = utf8[offset + 0];
offset += 1;
return result;
}
if (!(utf8[offset + 0] & 0x40)) {
throw std::invalid_argument("invalid character");
}
if (!(utf8[offset + 0] & 0x20)) {
if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80)) {
throw std::invalid_argument("invalid character");
}
auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f);
offset += 2;
return result;
}
if (!(utf8[offset + 0] & 0x10)) {
if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80)) {
throw std::invalid_argument("invalid character");
}
auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f);
offset += 3;
return result;
}
if (!(utf8[offset + 0] & 0x08)) {
if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80)) {
throw std::invalid_argument("invalid character");
}
auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f);
offset += 4;
return result;
}
throw std::invalid_argument("invalid string");
}
static std::vector<uint32_t> codepoints_from_utf8(const std::string & utf8) {
std::vector<uint32_t> result;
size_t offset = 0;
while (offset < utf8.size()) {
result.push_back(codepoint_from_utf8(utf8, offset));
}
return result;
}
static std::vector<uint16_t> codepoint_to_utf16(uint32_t cp) {
std::vector<uint16_t> result;
if (/* 0x0000 <= cp && */ cp <= 0xffff) {
result.emplace_back(cp);
}
else if (0x10000 <= cp && cp <= 0x10ffff) {
result.emplace_back(0xd800 | ((cp - 0x10000) >> 10));
result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff));
}
else {
throw std::invalid_argument("invalid codepoint");
}
return result;
}
static std::vector<uint16_t> codepoints_to_utf16(const std::vector<uint32_t> & cps) {
std::vector<uint16_t> result;
for (size_t i = 0; i < cps.size(); ++i) {
auto temp = codepoint_to_utf16(cps[i]);
result.insert(result.end(), temp.begin(), temp.end());
}
return result;
}
static uint32_t codepoint_from_utf16(const std::vector<uint16_t> & utf16, size_t & offset) {
assert(offset < utf16.size());
if (((utf16[0] >> 10) << 10) != 0xd800) {
auto result = utf16[offset + 0];
offset += 1;
return result;
}
if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) {
throw std::invalid_argument("invalid character");
}
auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff));
offset += 2;
return result;
}
static std::vector<uint32_t> codepoints_from_utf16(const std::vector<uint16_t> & utf16) {
std::vector<uint32_t> result;
size_t offset = 0;
while (offset < utf16.size()) {
result.push_back(codepoint_from_utf16(utf16, offset));
}
return result;
}
#define CODEPOINT_TYPE_UNIDENTIFIED 0
#define CODEPOINT_TYPE_DIGIT 1
#define CODEPOINT_TYPE_LETTER 2
#define CODEPOINT_TYPE_WHITESPACE 3
#define CODEPOINT_TYPE_ACCENT_MARK 4
#define CODEPOINT_TYPE_PUNCTUATION 5
#define CODEPOINT_TYPE_SYMBOL 6
#define CODEPOINT_TYPE_CONTROL 7
#define CODEPOINT_TYPE_DIGIT 1
#define CODEPOINT_TYPE_LETTER 2
#define CODEPOINT_TYPE_WHITESPACE 3
#define CODEPOINT_TYPE_ACCENT_MARK 4
#define CODEPOINT_TYPE_PUNCTUATION 5
#define CODEPOINT_TYPE_SYMBOL 6
#define CODEPOINT_TYPE_CONTROL 7
static std::unordered_map<uint32_t, int> codepoint_type_map() {
std::unordered_map<uint32_t, int> codepoint_types;
for (auto p : digit_ranges) {
for (auto i = p.first; i <= p.second; ++ i) {
codepoint_types[i] = CODEPOINT_TYPE_DIGIT;
}
}
for (auto p : letter_ranges) {
for (auto i = p.first; i <= p.second; ++ i) {
codepoint_types[i] = CODEPOINT_TYPE_LETTER;
}
}
for (auto p : whitespace_ranges) {
for (auto i = p.first; i <= p.second; ++ i) {
codepoint_types[i] = CODEPOINT_TYPE_WHITESPACE;
}
}
for (auto p : accent_mark_ranges) {
for (auto i = p.first; i <= p.second; ++ i) {
codepoint_types[i] = CODEPOINT_TYPE_ACCENT_MARK;
}
}
for (auto p : punctuation_ranges) {
for (auto i = p.first; i <= p.second; ++ i) {
codepoint_types[i] = CODEPOINT_TYPE_PUNCTUATION;
}
}
for (auto p : symbol_ranges) {
for (auto i = p.first; i <= p.second; ++i) {
codepoint_types[i] = CODEPOINT_TYPE_SYMBOL;
}
}
for (auto p : control_ranges) {
for (auto i = p.first; i <= p.second; ++ i) {
codepoint_types[i] = CODEPOINT_TYPE_CONTROL;
}
}
return codepoint_types;
}
std::string unicode_cpt_to_utf8(uint32_t cp);
std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8);
static int codepoint_type(uint32_t cp) {
static std::unordered_map<uint32_t, int> codepoint_types = codepoint_type_map();
const auto it = codepoint_types.find(cp);
return it == codepoint_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : it->second;
}
std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts);
static int codepoint_type(const std::string & utf8) {
if (utf8.length() == 0) {
return CODEPOINT_TYPE_UNIDENTIFIED;
}
size_t offset = 0;
return codepoint_type(codepoint_from_utf8(utf8, offset));
}
int unicode_cpt_type(uint32_t cp);
int unicode_cpt_type(const std::string & utf8);
static std::unordered_map<uint8_t, std::string> bytes_to_unicode_map_bpe() {
std::unordered_map<uint8_t, std::string> map;
for (int ch = u'!'; ch <= u'~'; ++ch) {
assert(0 <= ch && ch < 256);
map[ch] = codepoint_to_utf8(ch);
}
for (int ch = u'¡'; ch <= u'¬'; ++ch) {
assert(0 <= ch && ch < 256);
map[ch] = codepoint_to_utf8(ch);
}
for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
assert(0 <= ch && ch < 256);
map[ch] = codepoint_to_utf8(ch);
}
auto n = 0;
for (int ch = 0; ch < 256; ++ch) {
if (map.find(ch) == map.end()) {
map[ch] = codepoint_to_utf8(256 + n);
++n;
}
}
return map;
}
static std::string bytes_to_unicode_bpe(uint8_t byte) {
static std::unordered_map<uint8_t, std::string> map = bytes_to_unicode_map_bpe();
return map.at(byte);
}
static std::unordered_map<std::string, uint8_t> unicode_to_bytes_map_bpe() {
std::unordered_map<std::string, uint8_t> map;
for (int ch = u'!'; ch <= u'~'; ++ch) {
assert(0 <= ch && ch < 256);
map[codepoint_to_utf8(ch)] = ch;
}
for (int ch = u'¡'; ch <= u'¬'; ++ch) {
assert(0 <= ch && ch < 256);
map[codepoint_to_utf8(ch)] = ch;
}
for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
assert(0 <= ch && ch < 256);
map[codepoint_to_utf8(ch)] = ch;
}
auto n = 0;
for (int ch = 0; ch < 256; ++ch) {
if (map.find(codepoint_to_utf8(ch)) == map.end()) {
map[codepoint_to_utf8(256 + n)] = ch;
++n;
}
}
return map;
}
static uint8_t unicode_to_bytes_bpe(const std::string & utf8) {
static std::unordered_map<std::string, uint8_t> map = unicode_to_bytes_map_bpe();
return map.at(utf8);
}
std::string unicode_byte_to_utf8(uint8_t byte);
uint8_t unicode_utf8_to_byte(const std::string & utf8);