vbatts/llama.cpp
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15
.devops/nix/package.nix
View file

@ -1,5 +1,6 @@
{
lib,
glibc,
config,
stdenv,
mkShell,
@ -30,6 +31,11 @@
useRocm ? config.rocmSupport,
useVulkan ? false,
llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
# It's necessary to consistently use backendStdenv when building with CUDA support,
# otherwise we get libstdc++ errors downstream.
effectiveStdenv ? if useCuda then cudaPackages.backendStdenv else stdenv,
enableStatic ? effectiveStdenv.hostPlatform.isStatic
}@inputs:
let
@ -41,10 +47,7 @@ let
versionOlder
;
# It's necessary to consistently use backendStdenv when building with CUDA support,
# otherwise we get libstdc++ errors downstream.
stdenv = throw "Use effectiveStdenv instead";
effectiveStdenv = if useCuda then cudaPackages.backendStdenv else inputs.stdenv;
suffices =
lib.optionals useBlas [ "BLAS" ]
@ -167,6 +170,9 @@ effectiveStdenv.mkDerivation (
# TODO: Replace with autoAddDriverRunpath
# once https://github.com/NixOS/nixpkgs/pull/275241 has been merged
cudaPackages.autoAddOpenGLRunpathHook
]
++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [
glibc.static
];
buildInputs =
@ -181,7 +187,7 @@ effectiveStdenv.mkDerivation (
[
(cmakeBool "LLAMA_NATIVE" false)
(cmakeBool "LLAMA_BUILD_SERVER" true)
(cmakeBool "BUILD_SHARED_LIBS" true)
(cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
(cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
(cmakeBool "LLAMA_BLAS" useBlas)
(cmakeBool "LLAMA_CLBLAST" useOpenCL)
@ -190,6 +196,7 @@ effectiveStdenv.mkDerivation (
(cmakeBool "LLAMA_METAL" useMetalKit)
(cmakeBool "LLAMA_MPI" useMpi)
(cmakeBool "LLAMA_VULKAN" useVulkan)
(cmakeBool "LLAMA_STATIC" enableStatic)
]
++ optionals useCuda [
(

2
.devops/nix/sif.nix
View file

@ -7,7 +7,7 @@
}:
let
optionalInt = cond: x: if cond then x else 0;
optionalInt = cond: x: if cond then x else 0;
in
singularity-tools.buildImage rec {
inherit (llama-cpp) name;

22
.github/workflows/build.yml vendored
View file

@ -145,6 +145,28 @@ jobs:
cd build
ctest -L main --verbose
ubuntu-22-cmake-vulkan:
runs-on: ubuntu-22.04
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential libvulkan-dev
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake -DLLAMA_VULKAN=ON ..
cmake --build . --config Release -j $(nproc)
ubuntu-22-cmake-sycl:
runs-on: ubuntu-22.04

4
.github/workflows/python-check-requirements.yml vendored
View file

@ -3,12 +3,14 @@ name: Python check requirements.txt
on:
push:
paths:
- '.github/workflows/python-check-requirements.yml'
- 'scripts/check-requirements.sh'
- 'convert*.py'
- 'requirements.txt'
- 'requirements/*.txt'
pull_request:
paths:
- '.github/workflows/python-check-requirements.yml'
- 'scripts/check-requirements.sh'
- 'convert*.py'
- 'requirements.txt'
@ -26,4 +28,4 @@ jobs:
with:
python-version: "3.11"
- name: Run check-requirements.sh script
run: bash scripts/check-requirements.sh nocleanup
run: bash scripts/check-requirements.sh

25
.github/workflows/server.yml vendored
View file

@ -3,6 +3,11 @@ name: Server
on:
workflow_dispatch: # allows manual triggering
inputs:
slow_tests:
description: 'Run slow tests'
required: true
type: boolean
push:
branches:
- master
@ -10,6 +15,8 @@ on:
pull_request:
types: [opened, synchronize, reopened]
paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*']
schedule:
- cron: '0 0 * * *'
jobs:
server:
@ -51,7 +58,8 @@ jobs:
cmake \
python3-pip \
wget \
psmisc
psmisc \
language-pack-en
- name: Build
id: cmake_build
@ -70,14 +78,15 @@ jobs:
run: |
pip install -r examples/server/tests/requirements.txt
- name: Download models
id: download_models
run: |
cd examples/server/tests
../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf
- name: Tests
id: server_integration_test
id: server_integration_tests
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' }}
run: |
cd examples/server/tests
PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow

5
Makefile
View file

@ -724,10 +724,9 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h examples/llava/llava.h examples/llava/llava.cpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
$(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual
$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h %.hpp $< examples/llava/clip.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) -o $@ $(LDFLAGS) $(LWINSOCK2)
$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)

21
README-sycl.md
View file

@ -1,6 +1,7 @@
# llama.cpp for SYCL
- [Background](#background)
- [News](#news)
- [OS](#os)
- [Intel GPU](#intel-gpu)
- [Docker](#docker)
@ -25,6 +26,21 @@ The llama.cpp for SYCL is used to support Intel GPUs.
For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
## News
- 2024.3
- Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing.
- Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE.
- Support detecting all GPUs with level-zero and same top **Max compute units**.
- Support OPs
- hardsigmoid
- hardswish
- pool2d
- 2024.1
- Create SYCL backend for Intel GPU.
- Support Windows build
## OS
|OS|Status|Verified|
@ -449,6 +465,7 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
|-|-|-|
|GGML_SYCL_DEVICE|0 (default) or 1|Set the device id used. Check the device ids by default running output|
|GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG|
|ZES_ENABLE_SYSMAN| 0 (default) or 1|Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer|
## Known Issue
@ -458,6 +475,10 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
Solution: add **--no-mmap** or **--mmap 0**.
- Split-mode: [row] is not supported
It's on developing.
## Q&A
- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.

10
README.md
View file

@ -8,8 +8,14 @@
Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++
### Recent API changes
- [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
@ -785,7 +791,7 @@ And after 4.45 hours, you will have the final perplexity.
### Interactive mode
If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.
In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
Here is an example of a few-shot interaction, invoked with the command
@ -849,7 +855,7 @@ Sample run:
```
== Running in interactive mode. ==
- Press Ctrl+C to interject at any time.
- Press Return to return control to LLaMa.
- Press Return to return control to LLaMA.
- If you want to submit another line, end your input in '\'.
Below is an instruction that describes a task. Write a response that appropriately completes the request.

37
ci/run.sh
View file

@ -45,7 +45,8 @@ fi
if [ ! -z ${GG_BUILD_SYCL} ]; then
if [ -z ${ONEAPI_ROOT} ]; then
echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:\n source /opt/intel/oneapi/setvars.sh"
echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:"
echo "source /opt/intel/oneapi/setvars.sh"
exit 1
fi
@ -272,19 +273,19 @@ function gg_run_open_llama_3b_v2 {
(time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
(time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
(time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
@ -343,17 +344,17 @@ function gg_run_open_llama_3b_v2 {
python3 ../convert-lora-to-ggml.py ${path_lora}
# f16
(time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
(time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
(time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
(time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
# q8_0
(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
# q8_0 + f16 lora-base
(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
compare_ppl "q8_0 / f16 base shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
set +e

7
common/CMakeLists.txt
View file

@ -19,7 +19,12 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
endif()
endif()
set(GIT_INDEX "${GIT_DIR}/index")
if(EXISTS "${GIT_DIR}/index")
set(GIT_INDEX "${GIT_DIR}/index")
else()
message(WARNING "Git index not found in git repository.")
set(GIT_INDEX "")
endif()
else()
message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.")
set(GIT_INDEX "")

30
common/common.cpp
View file

@ -335,6 +335,16 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
break;
}
params.yarn_beta_slow = std::stof(argv[i]);
} else if (arg == "--pooling") {
if (++i >= argc) {
invalid_param = true;
break;
}
std::string value(argv[i]);
/**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
else { invalid_param = true; break; }
} else if (arg == "--defrag-thold" || arg == "-dt") {
if (++i >= argc) {
invalid_param = true;
@ -503,12 +513,6 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
break;
}
params.n_sequences = std::stoi(argv[i]);
} else if (arg == "--p-accept" || arg == "-pa") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.p_accept = std::stof(argv[i]);
} else if (arg == "--p-split" || arg == "-ps") {
if (++i >= argc) {
invalid_param = true;
@ -640,6 +644,10 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
} else if (arg_next == "layer") {
params.split_mode = LLAMA_SPLIT_MODE_LAYER;
} else if (arg_next == "row") {
#ifdef GGML_USE_SYCL
fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
exit(1);
#endif // GGML_USE_SYCL
params.split_mode = LLAMA_SPLIT_MODE_ROW;
} else {
invalid_param = true;
@ -1010,12 +1018,14 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
printf(" --pooling {none,mean,cls}\n");
printf(" pooling type for embeddings, use model default if unspecified\n");
printf(" -dt N, --defrag-thold N\n");
printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
printf(" --no-penalize-nl do not penalize newline token\n");
printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp);
printf(" --logits-all return logits for all tokens in the batch (default: disabled)\n");
printf(" --all-logits return logits for all tokens in the batch (default: disabled)\n");
printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
printf(" --winogrande compute Winogrande score over random tasks from datafile supplied with -f\n");
@ -1028,7 +1038,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel);
printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences);
printf(" -pa N, --p-accept N speculative decoding accept probability (default: %.1f)\n", (double)params.p_accept);
printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split);
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n");
@ -1281,10 +1290,9 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.n_batch = params.n_batch;
cparams.n_threads = params.n_threads;
cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
cparams.mul_mat_q = params.mul_mat_q;
cparams.seed = params.seed;
cparams.logits_all = params.logits_all;
cparams.embedding = params.embedding;
cparams.embeddings = params.embedding;
cparams.rope_scaling_type = params.rope_scaling_type;
cparams.rope_freq_base = params.rope_freq_base;
cparams.rope_freq_scale = params.rope_freq_scale;
@ -1293,6 +1301,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.yarn_beta_fast = params.yarn_beta_fast;
cparams.yarn_beta_slow = params.yarn_beta_slow;
cparams.yarn_orig_ctx = params.yarn_orig_ctx;
cparams.pooling_type = params.pooling_type;
cparams.defrag_thold = params.defrag_thold;
cparams.offload_kqv = !params.no_kv_offload;
@ -1725,7 +1734,6 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false");
fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false");
fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);

13
common/common.h
View file

@ -43,7 +43,7 @@ extern char const *LLAMA_BUILD_TARGET;
int32_t get_num_physical_cores();
struct gpt_params {
uint32_t seed = -1; // RNG seed
uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed
int32_t n_threads = get_num_physical_cores();
int32_t n_threads_draft = -1;
@ -53,11 +53,10 @@ struct gpt_params {
int32_t n_ctx = 512; // context size
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_draft = 8; // number of tokens to draft during speculative decoding
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)
int32_t n_parallel = 1; // number of parallel sequences to decode
int32_t n_sequences = 1; // number of sequences to decode
float p_accept = 0.5f; // speculative decoding accept probability
float p_split = 0.1f; // speculative decoding split probability
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
@ -76,8 +75,11 @@ struct gpt_params {
float yarn_beta_slow = 1.0f; // YaRN high correction dim
int32_t yarn_orig_ctx = 0; // YaRN original context length
float defrag_thold = -1.0f; // KV cache defragmentation threshold
int32_t rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
// // sampling parameters
struct llama_sampling_params sparams;
@ -115,7 +117,6 @@ struct gpt_params {
bool kl_divergence = false; // compute KL-divergence
bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS
bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs
bool interactive = false; // interactive mode

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

79
common/sampling.cpp
View file

@ -300,6 +300,77 @@ static llama_token llama_sampling_sample_impl(
return id;
}
static llama_token_data_array llama_sample_probability_distribution_impl(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
struct llama_context * ctx_cfg,
const int idx) {
const llama_sampling_params & params = ctx_sampling->params;
const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
const float penalty_repeat = params.penalty_repeat;
const float penalty_freq = params.penalty_freq;
const float penalty_present = params.penalty_present;
const bool penalize_nl = params.penalize_nl;
auto & prev = ctx_sampling->prev;
auto & cur = ctx_sampling->cur;
// Get a pointer to the logits
float * logits = llama_get_logits_ith(ctx_main, idx);
// Declare original_logits at the beginning of the function scope
std::vector<float> original_logits;
// apply params.logit_bias map
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
logits[it->first] += it->second;
}
if (ctx_cfg) {
float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx);
llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
}
cur.clear();
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array cur_p = { cur.data(), cur.size(), false };
// apply penalties
const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev;
const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n);
if (penalty_tokens_used_size) {
const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];
llama_sample_repetition_penalties(ctx_main, &cur_p,
penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size,
penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present);
if (!penalize_nl) {
for (size_t idx = 0; idx < cur_p.size; idx++) {
if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {
cur_p.data[idx].logit = nl_logit;
break;
}
}
}
}
// apply grammar checks
if (ctx_sampling->grammar != NULL) {
llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
}
llama_sample_softmax(ctx_main, &cur_p);
return cur_p;
}
llama_token llama_sampling_sample(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
@ -309,6 +380,14 @@ llama_token llama_sampling_sample(
return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false);
}
llama_token_data_array llama_sampling_probability_distribution(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
struct llama_context * ctx_cfg,
const int idx) {
return llama_sample_probability_distribution_impl(ctx_sampling,ctx_main, ctx_cfg, idx);
}
void llama_sampling_accept(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,

7
common/sampling.h
View file

@ -131,6 +131,13 @@ llama_token llama_sampling_sample(
struct llama_context * ctx_cfg,
int idx = 0);
// returns the probability that token of given id will be sampled
llama_token_data_array llama_sampling_probability_distribution(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
struct llama_context * ctx_cfg,
int idx = 0);
void llama_sampling_accept(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,

230
convert-hf-to-gguf.py
View file

@ -8,9 +8,10 @@ import json
import os
import re
import sys
from abc import ABC, abstractmethod
from enum import IntEnum
from pathlib import Path
from typing import TYPE_CHECKING, Any, ContextManager, Iterator, Sequence, cast
from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterator, Sequence, TypeVar, cast
import numpy as np
import torch
@ -36,7 +37,12 @@ class SentencePieceTokenTypes(IntEnum):
BYTE = 6
class Model:
AnyModel = TypeVar("AnyModel", bound="type[Model]")
class Model(ABC):
_model_classes: dict[str, type[Model]] = {}
def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool):
self.dir_model = dir_model
self.ftype = ftype
@ -47,10 +53,14 @@ class Model:
self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin")
self.part_names = self._get_part_names()
self.hparams = Model.load_hparams(self.dir_model)
self.model_arch = self._get_model_architecture()
self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False)
self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
@property
@abstractmethod
def model_arch(self) -> gguf.MODEL_ARCH:
pass
def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any:
key = next((k for k in keys if k in self.hparams), None)
if key is not None:
@ -96,9 +106,11 @@ class Model:
if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None:
self.gguf_writer.add_head_count_kv(n_head_kv)
if (rope_theta := self.hparams.get("rope_theta")) is not None:
self.gguf_writer.add_rope_freq_base(rope_theta)
if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None:
self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon"], optional=True)) is not None:
if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
self.gguf_writer.add_layer_norm_eps(f_norm_eps)
if (n_experts := self.hparams.get("num_local_experts")) is not None:
self.gguf_writer.add_expert_count(n_experts)
@ -174,53 +186,22 @@ class Model:
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
return json.load(f)
@staticmethod
def from_model_architecture(model_architecture):
if model_architecture == "GPTNeoXForCausalLM":
return GPTNeoXModel
if model_architecture == "BloomForCausalLM":
return BloomModel
if model_architecture == "MPTForCausalLM":
return MPTModel
if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
return BaichuanModel
if model_architecture in ("FalconForCausalLM", "RWForCausalLM"):
return FalconModel
if model_architecture == "GPTBigCodeForCausalLM":
return StarCoderModel
if model_architecture == "GPTRefactForCausalLM":
return RefactModel
if model_architecture == "PersimmonForCausalLM":
return PersimmonModel
if model_architecture in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
return StableLMModel
if model_architecture == "QWenLMHeadModel":
return QwenModel
if model_architecture == "Qwen2ForCausalLM":
return Model
if model_architecture == "MixtralForCausalLM":
return MixtralModel
if model_architecture == "GPT2LMHeadModel":
return GPT2Model
if model_architecture == "PhiForCausalLM":
return Phi2Model
if model_architecture == "PlamoForCausalLM":
return PlamoModel
if model_architecture == "CodeShellForCausalLM":
return CodeShellModel
if model_architecture == "OrionForCausalLM":
return OrionModel
if model_architecture == "InternLM2ForCausalLM":
return InternLM2Model
if model_architecture == "MiniCPMForCausalLM":
return MiniCPMModel
if model_architecture == "BertModel":
return BertModel
if model_architecture == "NomicBertModel":
return NomicBertModel
if model_architecture == "GemmaForCausalLM":
return GemmaModel
return Model
@classmethod
def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
assert names
def func(modelcls: type[Model]):
for name in names:
cls._model_classes[name] = modelcls
return modelcls
return func
@classmethod
def from_model_architecture(cls, arch):
try:
return cls._model_classes[arch]
except KeyError:
raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
def _is_model_safetensors(self) -> bool:
return Model.count_model_parts(self.dir_model, ".safetensors") > 0
@ -235,55 +216,6 @@ class Model:
return ("pytorch_model.bin",)
return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))
def _get_model_architecture(self) -> gguf.MODEL_ARCH:
arch = self.hparams["architectures"][0]
if arch == "GPTNeoXForCausalLM":
return gguf.MODEL_ARCH.GPTNEOX
if arch == "BloomForCausalLM":
return gguf.MODEL_ARCH.BLOOM
if arch == "MPTForCausalLM":
return gguf.MODEL_ARCH.MPT
if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
return gguf.MODEL_ARCH.BAICHUAN
if arch in ("FalconForCausalLM", "RWForCausalLM"):
return gguf.MODEL_ARCH.FALCON
if arch == "GPTBigCodeForCausalLM":
return gguf.MODEL_ARCH.STARCODER
if arch == "GPTRefactForCausalLM":
return gguf.MODEL_ARCH.REFACT
if arch == "PersimmonForCausalLM":
return gguf.MODEL_ARCH.PERSIMMON
if arch in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
return gguf.MODEL_ARCH.STABLELM
if arch == "QWenLMHeadModel":
return gguf.MODEL_ARCH.QWEN
if arch == "Qwen2ForCausalLM":
return gguf.MODEL_ARCH.QWEN2
if arch == "MixtralForCausalLM":
return gguf.MODEL_ARCH.LLAMA
if arch == "GPT2LMHeadModel":
return gguf.MODEL_ARCH.GPT2
if arch == "PhiForCausalLM":
return gguf.MODEL_ARCH.PHI2
if arch == "PlamoForCausalLM":
return gguf.MODEL_ARCH.PLAMO
if arch == "CodeShellForCausalLM":
return gguf.MODEL_ARCH.CODESHELL
if arch == "OrionForCausalLM":
return gguf.MODEL_ARCH.ORION
if arch == "InternLM2ForCausalLM":
return gguf.MODEL_ARCH.INTERNLM2
if arch == "MiniCPMForCausalLM":
return gguf.MODEL_ARCH.MINICPM
if arch == "BertModel":
return gguf.MODEL_ARCH.BERT
if arch == "NomicBertModel":
return gguf.MODEL_ARCH.NOMIC_BERT
if arch == "GemmaForCausalLM":
return gguf.MODEL_ARCH.GEMMA
raise NotImplementedError(f'Architecture "{arch}" not supported!')
def _set_vocab_gpt2(self):
dir_model = self.dir_model
hparams = self.hparams
@ -451,7 +383,10 @@ class Model:
special_vocab.add_to_gguf(self.gguf_writer)
@Model.register("GPTNeoXForCausalLM")
class GPTNeoXModel(Model):
model_arch = gguf.MODEL_ARCH.GPTNEOX
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
@ -468,7 +403,10 @@ class GPTNeoXModel(Model):
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
@Model.register("BloomForCausalLM")
class BloomModel(Model):
model_arch = gguf.MODEL_ARCH.BLOOM
def set_gguf_parameters(self):
self.gguf_writer.add_name("Bloom")
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
@ -560,7 +498,10 @@ class BloomModel(Model):
print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
@Model.register("MPTForCausalLM")
class MPTModel(Model):
model_arch = gguf.MODEL_ARCH.MPT
def set_gguf_parameters(self):
block_count = self.hparams["n_layers"]
self.gguf_writer.add_name(self.dir_model.name)
@ -623,7 +564,10 @@ class MPTModel(Model):
self.gguf_writer.add_tensor(new_name, data)
@Model.register("OrionForCausalLM")
class OrionModel(Model):
model_arch = gguf.MODEL_ARCH.ORION
def set_vocab(self):
self._set_vocab_sentencepiece()
@ -702,7 +646,10 @@ class OrionModel(Model):
self.gguf_writer.add_tensor(new_name, data)
@Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
class BaichuanModel(Model):
model_arch = gguf.MODEL_ARCH.BAICHUAN
def set_vocab(self):
self._set_vocab_sentencepiece()
@ -817,7 +764,10 @@ class BaichuanModel(Model):
return weights[r * n_part:r * n_part + r, ...]
@Model.register("FalconForCausalLM", "RWForCausalLM")
class FalconModel(Model):
model_arch = gguf.MODEL_ARCH.FALCON
def set_gguf_parameters(self):
block_count = self.hparams.get("num_hidden_layers")
if block_count is None:
@ -910,7 +860,10 @@ class FalconModel(Model):
self.gguf_writer.add_tensor(new_name, data)
@Model.register("GPTBigCodeForCausalLM")
class StarCoderModel(Model):
model_arch = gguf.MODEL_ARCH.STARCODER
def set_gguf_parameters(self):
block_count = self.hparams["n_layer"]
@ -925,7 +878,10 @@ class StarCoderModel(Model):
self.gguf_writer.add_file_type(self.ftype)
@Model.register("GPTRefactForCausalLM")
class RefactModel(Model):
model_arch = gguf.MODEL_ARCH.REFACT
def set_gguf_parameters(self):
hidden_dim = self.hparams["n_embd"]
inner_dim = 4 * hidden_dim
@ -1009,7 +965,10 @@ class RefactModel(Model):
self.gguf_writer.add_tensor(new_name, data)
@Model.register("PersimmonForCausalLM")
class PersimmonModel(Model):
model_arch = gguf.MODEL_ARCH.PERSIMMON
def set_gguf_parameters(self):
block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
head_count = self.hparams["num_attention_heads"]
@ -1057,7 +1016,10 @@ class PersimmonModel(Model):
self.gguf_writer.add_tensor(new_name, data)
@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
class StableLMModel(Model):
model_arch = gguf.MODEL_ARCH.STABLELM
def set_vocab(self):
if (self.dir_model / "tokenizer.json").is_file():
self._set_vocab_gpt2()
@ -1081,12 +1043,18 @@ class StableLMModel(Model):
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
@Model.register("MixtralForCausalLM")
class MixtralModel(Model):
model_arch = gguf.MODEL_ARCH.LLAMA
def set_vocab(self):
self._set_vocab_sentencepiece()
@Model.register("MiniCPMForCausalLM")
class MiniCPMModel(Model):
model_arch = gguf.MODEL_ARCH.MINICPM
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
self.gguf_writer.add_name("MiniCPM")
@ -1163,7 +1131,10 @@ class MiniCPMModel(Model):
self.gguf_writer.add_tensor(new_name, data)
@Model.register("QWenLMHeadModel")
class QwenModel(Model):
model_arch = gguf.MODEL_ARCH.QWEN
@staticmethod
def token_bytes_to_string(b):
from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
@ -1243,7 +1214,15 @@ class QwenModel(Model):
self.gguf_writer.add_tensor(new_name, data)
@Model.register("Qwen2ForCausalLM")
class Qwen2Model(Model):
model_arch = gguf.MODEL_ARCH.QWEN2
@Model.register("GPT2LMHeadModel")
class GPT2Model(Model):
model_arch = gguf.MODEL_ARCH.GPT2
def set_gguf_parameters(self):
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_block_count(self.hparams["n_layer"])
@ -1305,7 +1284,10 @@ class GPT2Model(Model):
self.gguf_writer.add_tensor("output.weight", data)
@Model.register("PhiForCausalLM")
class Phi2Model(Model):
model_arch = gguf.MODEL_ARCH.PHI2
def set_gguf_parameters(self):
block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
@ -1327,7 +1309,10 @@ class Phi2Model(Model):
self.gguf_writer.add_add_bos_token(False)
@Model.register("PlamoForCausalLM")
class PlamoModel(Model):
model_arch = gguf.MODEL_ARCH.PLAMO
def set_vocab(self):
self._set_vocab_sentencepiece()
@ -1406,7 +1391,10 @@ class PlamoModel(Model):
self.gguf_writer.add_tensor(new_name, data)
@Model.register("CodeShellForCausalLM")
class CodeShellModel(Model):
model_arch = gguf.MODEL_ARCH.CODESHELL
def set_gguf_parameters(self):
block_count = self.hparams["n_layer"]
@ -1471,7 +1459,10 @@ class CodeShellModel(Model):
print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
@Model.register("InternLM2ForCausalLM")
class InternLM2Model(Model):
model_arch = gguf.MODEL_ARCH.INTERNLM2
def set_vocab(self):
# (TODO): Is there a better way?
# Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character
@ -1643,7 +1634,10 @@ in chat mode so that the conversation can end normally.")
self.post_write_tensors(tensor_map, name, data_torch)
@Model.register("BertModel")
class BertModel(Model):
model_arch = gguf.MODEL_ARCH.BERT
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.vocab_size = None
@ -1653,16 +1647,17 @@ class BertModel(Model):
self.gguf_writer.add_causal_attention(False)
# get pooling path
with open(self.dir_model / "modules.json", encoding="utf-8") as f:
modules = json.load(f)
pooling_path = None
for mod in modules:
if mod["type"] == "sentence_transformers.models.Pooling":
pooling_path = mod["path"]
break
module_path = self.dir_model / "modules.json"
if module_path.is_file():
with open(module_path, encoding="utf-8") as f:
modules = json.load(f)
for mod in modules:
if mod["type"] == "sentence_transformers.models.Pooling":
pooling_path = mod["path"]
break
# get pooling type
pooling_type = gguf.PoolingType.NONE
if pooling_path is not None:
with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f:
pooling = json.load(f)
@ -1672,8 +1667,7 @@ class BertModel(Model):
pooling_type = gguf.PoolingType.CLS
else:
raise NotImplementedError("Only MEAN and CLS pooling types supported")
self.gguf_writer.add_pooling_type(pooling_type.value)
self.gguf_writer.add_pooling_type(pooling_type)
def set_vocab(self):
path = self.dir_model
@ -1749,7 +1743,10 @@ class BertModel(Model):
self.gguf_writer.add_tensor(new_name, data)
@Model.register("NomicBertModel")
class NomicBertModel(BertModel):
model_arch = gguf.MODEL_ARCH.NOMIC_BERT
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@ -1786,7 +1783,10 @@ class NomicBertModel(BertModel):
yield name, data
@Model.register("GemmaForCausalLM")
class GemmaModel(Model):
model_arch = gguf.MODEL_ARCH.GEMMA
def set_vocab(self):
self._set_vocab_sentencepiece()
@ -1811,16 +1811,15 @@ class GemmaModel(Model):
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
for name, data_torch in self.get_tensors():
# ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
if name.endswith("norm.weight"):
data_torch = data_torch + 1
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)
# ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
if name.endswith("norm.weight"):
data_torch = data_torch + 1
data = data_torch.squeeze().numpy()
# map tensor names
@ -1843,6 +1842,11 @@ class GemmaModel(Model):
self.gguf_writer.add_tensor(new_name, data)
@Model.register("Starcoder2ForCausalLM")
class StarCoder2Model(Model):
model_arch = gguf.MODEL_ARCH.STARCODER2
###### CONVERSION LOGIC ######

6
convert-llama-ggml-to-gguf.py
View file

@ -373,7 +373,7 @@ def handle_metadata(cfg, hp):
raise ValueError('Unable to load metadata')
vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir)
vocab_factory = convert.VocabFactory(vocab_path)
vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype, cfg.model_metadata_dir)
vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype.split(","), cfg.model_metadata_dir)
convert.check_vocab_size(params, vocab)
return params, vocab, special_vocab
@ -398,8 +398,8 @@ def handle_args():
help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
parser.add_argument("--vocab-dir", type=Path,
help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm",
help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)")
parser.add_argument("--vocabtype", default="spm,hfft",
help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)")
return parser.parse_args()

85
convert.py
View file

@ -1282,35 +1282,32 @@ def load_some_model(path: Path) -> ModelPlus:
class VocabFactory:
_FILES = {"spm": "tokenizer.model", "bpe": "vocab.json", "hfft": "tokenizer.json"}
def __init__(self, path: Path):
self.path = path
self.files: dict[str, Path | None] = {
"tokenizer.model": None,
"vocab.json": None,
"tokenizer.json": None,
}
self._detect_files()
self.file_paths = self._detect_files()
print(f"Found vocab files: {self.file_paths}")
def _detect_files(self):
for file in self.files.keys():
file_path = self.path / file
parent_file_path = self.path.parent / file
if file_path.exists():
self.files[file] = file_path
elif parent_file_path.exists():
self.files[file] = parent_file_path
print(f"Found vocab files: {self.files}")
def _detect_files(self) -> dict[str, Path | None]:
def locate(file: str) -> Path | None:
if (path := self.path / file).exists():
return path
if (path := self.path.parent / file).exists():
return path
return None
def _select_file(self, vocabtype: str | None) -> Path:
if vocabtype in ["spm", "bpe"]:
for file_key in self.files.keys():
if (file := self.files[file_key]) is not None:
return file
raise FileNotFoundError(f"{vocabtype} vocab not found.")
if vocabtype == "hfft":
# For Hugging Face Fast Tokenizer, return the directory path instead of a specific file
return self.path
raise ValueError(f"Unsupported vocabulary type {vocabtype}")
return {vt: locate(f) for vt, f in self._FILES.items()}
def _select_file(self, vocab_types: list[str]) -> tuple[str, Path]:
for vtype in vocab_types:
try:
path = self.file_paths[vtype]
except KeyError:
raise ValueError(f"Unsupported vocabulary type {vtype}") from None
if path is not None:
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"
@ -1322,30 +1319,30 @@ class VocabFactory:
n_vocab=n_vocab,
)
def load_vocab(self, vocabtype: str, model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
path = self._select_file(vocabtype)
print(f"Loading vocab file '{path}', type '{vocabtype}'")
def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
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 vocabtype == "bpe":
if vocab_type == "bpe":
vocab = BpeVocab(
path, added_tokens_path if added_tokens_path.exists() else None
)
elif vocabtype == "spm":
elif vocab_type == "spm":
vocab = SentencePieceVocab(
path, added_tokens_path if added_tokens_path.exists() else None
)
elif vocabtype == "hfft":
elif vocab_type == "hfft":
vocab = HfVocab(
path, added_tokens_path if added_tokens_path.exists() else None
path.parent, added_tokens_path if added_tokens_path.exists() else None
)
else:
raise ValueError(f"Unsupported vocabulary type {vocabtype}")
raise ValueError(vocab_type)
# FIXME: Respect --vocab-dir?
special_vocab = self._create_special_vocab(
vocab,
vocabtype,
vocab_type,
model_parent_path,
)
return vocab, special_vocab
@ -1379,15 +1376,13 @@ def main(args_in: list[str] | None = None) -> None:
if np.uint32(1) == np.uint32(1).newbyteorder("<"):
# We currently only support Q8_0 output on little endian systems.
output_choices.append("q8_0")
vocab_types = ["spm", "bpe", "hfft"]
parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file")
parser.add_argument("--awq-path", type=Path, help="Path to scale awq cache file", default=None)
parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file")
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("--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", choices=vocab_types, help="The vocabulary format used to define the tokenizer model (default: spm)", default="spm")
parser.add_argument("--vocab-type", help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
parser.add_argument("--ctx", type=int, help="model training context (default: based on input)")
@ -1397,18 +1392,6 @@ 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.awq_path:
sys.path.insert(1, str(Path(__file__).parent / 'awq-py'))
from awq.apply_awq import add_scale_weights # type: ignore[import-not-found]
tmp_model_path = args.model / "weighted_model"
if tmp_model_path.is_dir():
print(f"{tmp_model_path} exists as a weighted model.")
else:
tmp_model_path.mkdir(parents=True, exist_ok=True)
print("Saving new weighted model ...")
add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path))
print(f"Saved weighted model at {tmp_model_path}.")
args.model = tmp_model_path
if args.dump_single:
model_plus = lazy_load_file(args.model)
@ -1448,7 +1431,7 @@ def main(args_in: list[str] | None = None) -> None:
model_parent_path = model_plus.paths[0].parent
vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
vocab_factory = VocabFactory(vocab_path)
vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path)
vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type.split(","), model_parent_path)
if args.vocab_only:
if not args.outfile:

18
examples/batched-bench/batched-bench.cpp
View file

@ -32,16 +32,15 @@ int main(int argc, char ** argv) {
gpt_params params;
if (argc == 1 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>\n" , argv[0]);
printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
printf(" <PP>, <TG> and PL are comma-separated lists of numbers without spaces\n\n");
printf(" example: %s ggml-model-f16.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
printf(" example: %s ggml-model-f16.gguf 2048 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
return 1 ;
}
int n_kv_max = 2048;
int is_pp_shared = 0;
int n_gpu_layers = 0;
int mmq = 0;
std::vector<int> n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, };
std::vector<int> n_tg = { 128, 256, };
@ -65,19 +64,15 @@ int main(int argc, char ** argv) {
}
if (argc >= 6) {
mmq = std::atoi(argv[5]);
n_pp = parse_list(argv[5]);
}
if (argc >= 7) {
n_pp = parse_list(argv[6]);
n_tg = parse_list(argv[6]);
}
if (argc >= 8) {
n_tg = parse_list(argv[7]);
}
if (argc >= 9) {
n_pl = parse_list(argv[8]);
n_pl = parse_list(argv[7]);
}
// init LLM
@ -106,7 +101,6 @@ int main(int argc, char ** argv) {
ctx_params.seed = 1234;
ctx_params.n_ctx = n_kv_max;
ctx_params.n_batch = 512;
ctx_params.mul_mat_q = mmq;
ctx_params.n_threads = params.n_threads;
ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
@ -159,7 +153,7 @@ int main(int argc, char ** argv) {
}
LOG_TEE("\n");
LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG_TEE("\n");
LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");

28
examples/embedding/embedding.cpp
View file

@ -19,11 +19,11 @@ static std::vector<std::string> split_lines(const std::string & s) {
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
for (size_t i = 0; i < tokens.size(); i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, false);
llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
}
}
static void normalize(float * vec, float * out, int n) {
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];
@ -45,10 +45,23 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
}
// normalize on copy
for (int k = 0; k < n_seq; k++) {
float * emb = llama_get_embeddings_ith(ctx, k);
float * out = output + k * n_embd;
normalize(emb, out, n_embd);
for (int i = 0; i < batch.n_tokens; i++) {
if (!batch.logits[i]) {
continue;
}
// try to get sequence embeddings - supported only when pooling_type is not NONE
const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
if (embd == NULL) {
embd = llama_get_embeddings_ith(ctx, i);
if (embd == NULL) {
fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
continue;
}
}
float * out = output + batch.seq_id[i][0] * n_embd;
normalize(embd, out, n_embd);
}
}
@ -132,7 +145,7 @@ int main(int argc, char ** argv) {
// initialize batch
const int n_prompts = prompts.size();
struct llama_batch batch = llama_batch_init(n_batch, 0, n_prompts);
struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
// allocate output
const int n_embd = llama_n_embd(model);
@ -145,6 +158,7 @@ int main(int argc, char ** argv) {
for (int k = 0; k < n_prompts; k++) {
// clamp to n_batch tokens
auto & inp = inputs[k];
const uint64_t n_toks = inp.size();
// encode if at capacity

4
examples/infill/infill.cpp
View file

@ -378,10 +378,10 @@ int main(int argc, char ** argv) {
if (params.interactive) {
const char *control_message;
if (params.multiline_input) {
control_message = " - To return control to LLaMa, end your input with '\\'.\n"
control_message = " - To return control to LLaMA, end your input with '\\'.\n"
" - To return control without starting a new line, end your input with '/'.\n";
} else {
control_message = " - Press Return to return control to LLaMa.\n"
control_message = " - Press Return to return control to LLaMA.\n"
" - To return control without starting a new line, end your input with '/'.\n"
" - If you want to submit another line, end your input with '\\'.\n";
}

1
examples/llama-bench/README.md
View file

@ -35,7 +35,6 @@ options:
-mg, --main-gpu <i> (default: 0)
-nkvo, --no-kv-offload <0|1> (default: 0)
-mmp, --mmap <0|1> (default: 1)
-mmq, --mul-mat-q <0|1> (default: 1)
-ts, --tensor_split <ts0/ts1/..> (default: 0)
-r, --repetitions <n> (default: 5)
-o, --output <csv|json|md|sql> (default: md)

71
examples/llama-bench/llama-bench.cpp
View file

@ -123,20 +123,15 @@ static std::string get_gpu_info() {
}
#endif
#ifdef GGML_USE_SYCL
int device_list[GGML_SYCL_MAX_DEVICES];
ggml_sycl_get_gpu_list(device_list, GGML_SYCL_MAX_DEVICES);
for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) {
if (device_list[i] >0 ){
char buf[128];
ggml_sycl_get_device_description(i, buf, sizeof(buf));
id += buf;
int count = ggml_backend_sycl_get_device_count();
for (int i = 0; i < count; i++) {
char buf[128];
ggml_sycl_get_device_description(i, buf, sizeof(buf));
id += buf;
if (i < count - 1) {
id += "/";
}
}
if (id.length() >2 ) {
id.pop_back();
}
#endif
// TODO: other backends
return id;
@ -176,9 +171,9 @@ struct cmd_params {
std::vector<llama_split_mode> split_mode;
std::vector<int> main_gpu;
std::vector<bool> no_kv_offload;
std::vector<bool> mul_mat_q;
std::vector<std::vector<float>> tensor_split;
std::vector<bool> use_mmap;
std::vector<bool> embeddings;
int reps;
bool verbose;
output_formats output_format;
@ -196,9 +191,9 @@ static const cmd_params cmd_params_defaults = {
/* split_mode */ {LLAMA_SPLIT_MODE_LAYER},
/* main_gpu */ {0},
/* no_kv_offload */ {false},
/* mul_mat_q */ {true},
/* tensor_split */ {std::vector<float>(llama_max_devices(), 0.0f)},
/* use_mmap */ {true},
/* embeddings */ {false},
/* reps */ 5,
/* verbose */ false,
/* output_format */ MARKDOWN
@ -221,7 +216,7 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
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(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").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(" -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));
@ -383,13 +378,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
}
auto p = split<bool>(argv[i], split_delim);
params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
} else if (arg == "-mmq" || arg == "--mul-mat-q") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<bool>(argv[i], split_delim);
params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end());
} else if (arg == "-mmp" || arg == "--mmap") {
if (++i >= argc) {
invalid_param = true;
@ -397,6 +385,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
}
auto p = split<bool>(argv[i], split_delim);
params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end());
} else if (arg == "-embd" || arg == "--embeddings") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<bool>(argv[i], split_delim);
params.embeddings.insert(params.embeddings.end(), p.begin(), p.end());
} else if (arg == "-ts" || arg == "--tensor-split") {
if (++i >= argc) {
invalid_param = true;
@ -466,9 +461,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; }
if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; }
if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; }
if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; }
if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; }
return params;
@ -486,9 +481,9 @@ struct cmd_params_instance {
llama_split_mode split_mode;
int main_gpu;
bool no_kv_offload;
bool mul_mat_q;
std::vector<float> tensor_split;
bool use_mmap;
bool embeddings;
llama_model_params to_llama_mparams() const {
llama_model_params mparams = llama_model_default_params();
@ -518,8 +513,8 @@ struct cmd_params_instance {
cparams.n_batch = n_batch;
cparams.type_k = type_k;
cparams.type_v = type_v;
cparams.mul_mat_q = mul_mat_q;
cparams.offload_kqv = !no_kv_offload;
cparams.embeddings = embeddings;
return cparams;
}
@ -535,10 +530,10 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
for (const auto & mg : params.main_gpu)
for (const auto & ts : params.tensor_split)
for (const auto & mmp : params.use_mmap)
for (const auto & embd : params.embeddings)
for (const auto & nb : params.n_batch)
for (const auto & tk : params.type_k)
for (const auto & tv : params.type_v)
for (const auto & mmq : params.mul_mat_q)
for (const auto & nkvo : params.no_kv_offload)
for (const auto & nt : params.n_threads) {
for (const auto & n_prompt : params.n_prompt) {
@ -557,9 +552,9 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .split_mode = */ sm,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
/* .mul_mat_q = */ mmq,
/* .tensor_split = */ ts,
/* .use_mmap = */ mmp,
/* .embeddings = */ embd,
};
instances.push_back(instance);
}
@ -580,9 +575,9 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .split_mode = */ sm,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
/* .mul_mat_q = */ mmq,
/* .tensor_split = */ ts,
/* .use_mmap = */ mmp,
/* .embeddings = */ embd,
};
instances.push_back(instance);
}
@ -616,9 +611,9 @@ struct test {
llama_split_mode split_mode;
int main_gpu;
bool no_kv_offload;
bool mul_mat_q;
std::vector<float> tensor_split;
bool use_mmap;
bool embeddings;
int n_prompt;
int n_gen;
std::string test_time;
@ -639,9 +634,9 @@ struct test {
split_mode = inst.split_mode;
main_gpu = inst.main_gpu;
no_kv_offload = inst.no_kv_offload;
mul_mat_q = inst.mul_mat_q;
tensor_split = inst.tensor_split;
use_mmap = inst.use_mmap;
embeddings = inst.embeddings;
n_prompt = inst.n_prompt;
n_gen = inst.n_gen;
// RFC 3339 date-time format
@ -713,7 +708,7 @@ struct test {
"n_batch", "n_threads", "type_k", "type_v",
"n_gpu_layers", "split_mode",
"main_gpu", "no_kv_offload",
"mul_mat_q", "tensor_split", "use_mmap",
"tensor_split", "use_mmap", "embeddings",
"n_prompt", "n_gen", "test_time",
"avg_ns", "stddev_ns",
"avg_ts", "stddev_ts"
@ -733,7 +728,7 @@ struct test {
}
if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" ||
field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
field == "mul_mat_q" || field == "use_mmap") {
field == "use_mmap" || field == "embeddings") {
return BOOL;
}
if (field == "avg_ts" || field == "stddev_ts") {
@ -767,7 +762,7 @@ struct test {
std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
std::to_string(n_gpu_layers), split_mode_str(split_mode),
std::to_string(main_gpu), std::to_string(no_kv_offload),
std::to_string(mul_mat_q), tensor_split_str, std::to_string(use_mmap),
tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
std::to_string(n_prompt), std::to_string(n_gen), test_time,
std::to_string(avg_ns()), std::to_string(stdev_ns()),
std::to_string(avg_ts()), std::to_string(stdev_ts())
@ -931,15 +926,15 @@ struct markdown_printer : public printer {
if (field == "n_threads") {
return "threads";
}
if (field == "mul_mat_q") {
return "mmq";
}
if (field == "no_kv_offload") {
return "nkvo";
}
if (field == "use_mmap") {
return "mmap";
}
if (field == "embeddings") {
return "embd";
}
if (field == "tensor_split") {
return "ts";
}
@ -974,9 +969,6 @@ struct markdown_printer : public printer {
if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) {
fields.emplace_back("split_mode");
}
if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {
fields.emplace_back("mul_mat_q");
}
if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
fields.emplace_back("no_kv_offload");
}
@ -986,6 +978,9 @@ struct markdown_printer : public printer {
if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) {
fields.emplace_back("use_mmap");
}
if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) {
fields.emplace_back("embeddings");
}
fields.emplace_back("test");
fields.emplace_back("t/s");

20
examples/main/main.cpp
View file

@ -511,6 +511,14 @@ int main(int argc, char ** argv) {
std::vector<llama_token> embd;
std::vector<llama_token> embd_guidance;
// tokenized antiprompts
std::vector<std::vector<llama_token>> antiprompt_ids;
antiprompt_ids.reserve(params.antiprompt.size());
for (const std::string & antiprompt : params.antiprompt) {
antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true));
}
struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
@ -769,6 +777,18 @@ int main(int argc, char ** argv) {
}
}
// check for reverse prompt using special tokens
llama_token last_token = llama_sampling_last(ctx_sampling);
for (std::vector<llama_token> ids : antiprompt_ids) {
if (ids.size() == 1 && last_token == ids[0]) {
if (params.interactive) {
is_interacting = true;
}
is_antiprompt = true;
break;
}
}
if (is_antiprompt) {
LOG("found antiprompt: %s\n", last_output.c_str());
}

34
examples/server-embd.py Normal file
View file

@ -0,0 +1,34 @@
import asyncio
import requests
import numpy as np
n = 8
result = []
async def requests_post_async(*args, **kwargs):
return await asyncio.to_thread(requests.post, *args, **kwargs)
async def main():
model_url = "http://127.0.0.1:6900"
responses: list[requests.Response] = await asyncio.gather(*[requests_post_async(
url= f"{model_url}/embedding",
json= {"content": str(0)*1024}
) for i in range(n)])
for response in responses:
embedding = response.json()["embedding"]
print(embedding[-8:])
result.append(embedding)
asyncio.run(main())
# compute cosine similarity
for i in range(n-1):
for j in range(i+1, n):
embedding1 = np.array(result[i])
embedding2 = np.array(result[j])
similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
print(f"Similarity between {i} and {j}: {similarity:.2f}")

4
examples/server/CMakeLists.txt
View file

@ -1,12 +1,12 @@
set(TARGET server)
option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
add_executable(${TARGET} server.cpp oai.hpp utils.hpp json.hpp httplib.h)
add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h)
install(TARGETS ${TARGET} RUNTIME)
target_compile_definitions(${TARGET} PRIVATE
SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
)
target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT})
if (WIN32)
TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
endif()

20
examples/server/README.md
View file

@ -18,6 +18,7 @@ The project is under active development, and we are [looking for feedback and co
- `--threads N`, `-t N`: Set the number of threads to use during generation.
- `-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`).
- `-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.
@ -339,7 +340,7 @@ where:
- `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint.
- `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served.
- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint.
*Options:*
@ -449,7 +450,7 @@ where:
"next_token": {
"has_next_token": true,
"n_remain": -1,
"num_tokens_predicted": 0,
"n_decoded": 0,
"stopped_eos": false,
"stopped_limit": false,
"stopped_word": false,
@ -541,20 +542,7 @@ bash chat.sh
### API like OAI
API example using Python Flask: [api_like_OAI.py](api_like_OAI.py)
This example must be used with server.cpp
```sh
python api_like_OAI.py
```
After running the API server, you can use it in Python by setting the API base URL.
```python
openai.api_base = "http://<Your api-server IP>:port"
```
Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API
The HTTP server supports OAI-like API
### Extending or building alternative Web Front End

228
examples/server/api_like_OAI.py
View file

@ -1,228 +0,0 @@
#!/usr/bin/env python3
import argparse
from flask import Flask, jsonify, request, Response
import urllib.parse
import requests
import time
import json
app = Flask(__name__)
slot_id = -1
parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')
parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ")
parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ")
parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT's RULE: ")
parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1')
parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081)
args = parser.parse_args()
def is_present(json, key):
try:
buf = json[key]
except KeyError:
return False
if json[key] == None:
return False
return True
#convert chat to prompt
def convert_chat(messages):
system_n = args.system_name
user_n = args.user_name
ai_n = args.ai_name
stop = args.stop
prompt = "" + args.chat_prompt + stop
for line in messages:
if (line["role"] == "system"):
prompt += f"{system_n}{line['content']}{stop}"
if (line["role"] == "user"):
prompt += f"{user_n}{line['content']}{stop}"
if (line["role"] == "assistant"):
prompt += f"{ai_n}{line['content']}{stop}"
prompt += ai_n.rstrip()
return prompt
def make_postData(body, chat=False, stream=False):
postData = {}
if (chat):
postData["prompt"] = convert_chat(body["messages"])
else:
postData["prompt"] = body["prompt"]
if(is_present(body, "temperature")): postData["temperature"] = body["temperature"]
if(is_present(body, "top_k")): postData["top_k"] = body["top_k"]
if(is_present(body, "top_p")): postData["top_p"] = body["top_p"]
if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"]
if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"]
if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"]
if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"]
if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"]
if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
if(is_present(body, "seed")): postData["seed"] = body["seed"]
if(is_present(body, "grammar")): postData["grammar"] = body["grammar"]
if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
if (args.stop != ""):
postData["stop"] = [args.stop]
else:
postData["stop"] = []
if(is_present(body, "stop")): postData["stop"] += body["stop"]
postData["n_keep"] = -1
postData["stream"] = stream
postData["cache_prompt"] = True
postData["slot_id"] = slot_id
return postData
def make_resData(data, chat=False, promptToken=[]):
resData = {
"id": "chatcmpl" if (chat) else "cmpl",
"object": "chat.completion" if (chat) else "text_completion",
"created": int(time.time()),
"truncated": data["truncated"],
"model": "LLaMA_CPP",
"usage": {
"prompt_tokens": data["tokens_evaluated"],
"completion_tokens": data["tokens_predicted"],
"total_tokens": data["tokens_evaluated"] + data["tokens_predicted"]
}
}
if (len(promptToken) != 0):
resData["promptToken"] = promptToken
if (chat):
#only one choice is supported
resData["choices"] = [{
"index": 0,
"message": {
"role": "assistant",
"content": data["content"],
},
"finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
}]
else:
#only one choice is supported
resData["choices"] = [{
"text": data["content"],
"index": 0,
"logprobs": None,
"finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
}]
return resData
def make_resData_stream(data, chat=False, time_now = 0, start=False):
resData = {
"id": "chatcmpl" if (chat) else "cmpl",
"object": "chat.completion.chunk" if (chat) else "text_completion.chunk",
"created": time_now,
"model": "LLaMA_CPP",
"choices": [
{
"finish_reason": None,
"index": 0
}
]
}
slot_id = data.get("slot_id")
if (chat):
if (start):
resData["choices"][0]["delta"] = {
"role": "assistant"
}
else:
resData["choices"][0]["delta"] = {
"content": data["content"]
}
if (data["stop"]):
resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
else:
resData["choices"][0]["text"] = data["content"]
if (data["stop"]):
resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
return resData
@app.route('/chat/completions', methods=['POST', 'OPTIONS'])
@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS'])
def chat_completions():
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
return Response(status=403)
if request.method == 'OPTIONS':
return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
body = request.get_json()
stream = False
tokenize = False
if(is_present(body, "stream")): stream = body["stream"]
if(is_present(body, "tokenize")): tokenize = body["tokenize"]
postData = make_postData(body, chat=True, stream=stream)
promptToken = []
if (tokenize):
tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
promptToken = tokenData["tokens"]
if (not stream):
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
print(data.json())
resData = make_resData(data.json(), chat=True, promptToken=promptToken)
return jsonify(resData)
else:
def generate():
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
time_now = int(time.time())
resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
yield 'data: {}\n\n'.format(json.dumps(resData))
for line in data.iter_lines():
if line:
decoded_line = line.decode('utf-8')
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
yield 'data: {}\n\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
@app.route('/completions', methods=['POST', 'OPTIONS'])
@app.route('/v1/completions', methods=['POST', 'OPTIONS'])
def completion():
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
return Response(status=403)
if request.method == 'OPTIONS':
return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
body = request.get_json()
stream = False
tokenize = False
if(is_present(body, "stream")): stream = body["stream"]
if(is_present(body, "tokenize")): tokenize = body["tokenize"]
postData = make_postData(body, chat=False, stream=stream)
promptToken = []
if (tokenize):
tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
promptToken = tokenData["tokens"]
if (not stream):
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
print(data.json())
resData = make_resData(data.json(), chat=False, promptToken=promptToken)
return jsonify(resData)
else:
def generate():
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
time_now = int(time.time())
for line in data.iter_lines():
if line:
decoded_line = line.decode('utf-8')
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
yield 'data: {}\n\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
if __name__ == '__main__':
app.run(args.host, port=args.port)

225
examples/server/oai.hpp
View file

@ -1,225 +0,0 @@
#pragma once
#include <string>
#include <vector>
#include <set>
#include <mutex>
#include <condition_variable>
#include <unordered_map>
#include "json.hpp"
#include "utils.hpp"
#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
using json = nlohmann::json;
inline static json oaicompat_completion_params_parse(
const struct llama_model * model,
const json &body, /* openai api json semantics */
const std::string &chat_template)
{
json llama_params;
llama_params["__oaicompat"] = true;
// Map OpenAI parameters to llama.cpp parameters
//
// For parameters that are defined by the OpenAI documentation (e.g.
// temperature), we explicitly specify OpenAI's intended default; we
// need to do that because sometimes OpenAI disagrees with llama.cpp
//
// https://platform.openai.com/docs/api-reference/chat/create
llama_sampling_params default_sparams;
llama_params["model"] = json_value(body, "model", std::string("unknown"));
llama_params["prompt"] = format_chat(model, chat_template, body["messages"]);
llama_params["cache_prompt"] = json_value(body, "cache_prompt", false);
llama_params["temperature"] = json_value(body, "temperature", 0.0);
llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k);
llama_params["top_p"] = json_value(body, "top_p", 1.0);
llama_params["n_predict"] = json_value(body, "max_tokens", -1);
llama_params["logit_bias"] = json_value(body, "logit_bias",json::object());
llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED);
llama_params["stream"] = json_value(body, "stream", false);
llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat);
llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl);
llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p);
llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z);
if (body.count("grammar") != 0) {
llama_params["grammar"] = json_value(body, "grammar", json::object());
}
// Handle 'stop' field
if (body.contains("stop") && body["stop"].is_string()) {
llama_params["stop"] = json::array({body["stop"].get<std::string>()});
} else {
llama_params["stop"] = json_value(body, "stop", json::array());
}
// Ensure there is ChatML-specific end sequence among stop words
llama_params["stop"].push_back("<|im_end|>");
return llama_params;
}
inline static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false)
{
json result = response.result_json;
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);
int num_prompt_tokens = json_value(result, "tokens_evaluated", 0);
std::string content = json_value(result, "content", std::string(""));
std::string finish_reason = "length";
if (stopped_word || stopped_eos) {
finish_reason = "stop";
}
json choices =
streaming ? json::array({json{{"finish_reason", finish_reason},
{"index", 0},
{"delta", json::object()}}})
: json::array({json{{"finish_reason", finish_reason},
{"index", 0},
{"message", json{{"content", content},
{"role", "assistant"}}}}});
std::time_t t = std::time(0);
json res =
json{{"choices", choices},
{"created", t},
{"model",
json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
{"object", streaming ? "chat.completion.chunk" : "chat.completion"},
{"usage",
json{{"completion_tokens", num_tokens_predicted},
{"prompt_tokens", num_prompt_tokens},
{"total_tokens", num_tokens_predicted + num_prompt_tokens}}},
{"id", gen_chatcmplid()}};
if (server_verbose) {
res["__verbose"] = result;
}
if (result.contains("completion_probabilities")) {
res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
}
return res;
}
// return value is vector as there is one case where we might need to generate two responses
inline static std::vector<json> format_partial_response_oaicompat(const task_result &response) {
json result = response.result_json;
if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
return std::vector<json>({response.result_json});
}
bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
bool stopped_word = json_value(result, "stopped_word", false);
bool stopped_eos = json_value(result, "stopped_eos", false);
bool stopped_limit = json_value(result, "stopped_limit", false);
std::string content = json_value(result, "content", std::string(""));
std::string finish_reason;
if (stopped_word || stopped_eos) {
finish_reason = "stop";
}
if (stopped_limit) {
finish_reason = "length";
}
std::time_t t = std::time(0);
json choices;
if (!finish_reason.empty()) {
choices = json::array({json{{"finish_reason", finish_reason},
{"index", 0},
{"delta", json::object()}}});
} else {
if (first) {
if (content.empty()) {
choices = json::array({json{{"finish_reason", nullptr},
{"index", 0},
{"delta", json{{"role", "assistant"}}}}});
} else {
// We have to send this as two updates to conform to openai behavior
json initial_ret = json{{"choices", json::array({json{
{"finish_reason", nullptr},
{"index", 0},
{"delta", json{
{"role", "assistant"}
}}}})},
{"created", t},
{"id", gen_chatcmplid()},
{"model", modelname},
{"object", "chat.completion.chunk"}};
json second_ret = json{
{"choices", json::array({json{{"finish_reason", nullptr},
{"index", 0},
{"delta", json{
{"content", content}}}
}})},
{"created", t},
{"id", gen_chatcmplid()},
{"model", modelname},
{"object", "chat.completion.chunk"}};
return std::vector<json>({initial_ret, second_ret});
}
} else {
// Some idiosyncrasy in task processing logic makes several trailing calls
// with empty content, we ignore these at the calee site.
if (content.empty()) {
return std::vector<json>({json::object()});
}
choices = json::array({json{
{"finish_reason", nullptr},
{"index", 0},
{"delta",
json{
{"content", content},
}},
}});
}
}
json ret = json{{"choices", choices},
{"created", t},
{"id", gen_chatcmplid()},
{"model", modelname},
{"object", "chat.completion.chunk"}};
return std::vector<json>({ret});
}
inline static json format_embeddings_response_oaicompat(const json &request, const json &embeddings)
{
json res =
json{
{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
{"object", "list"},
{"usage",
json{{"prompt_tokens", 0},
{"total_tokens", 0}}},
{"data", embeddings}
};
return res;
}

4133
examples/server/server.cpp
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50
examples/server/tests/README.md
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@ -1,22 +1,30 @@
# Server tests
Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development) and [behave](https://behave.readthedocs.io/en/latest/):
* [issues.feature](./features/issues.feature) Pending issues scenario
* [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests
* [security.feature](./features/security.feature) Security, CORS and API Key
* [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc...
Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development)
and [behave](https://behave.readthedocs.io/en/latest/):
* [issues.feature](./features/issues.feature) Pending issues scenario
* [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests
* [security.feature](./features/security.feature) Security, CORS and API Key
* [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc...
Tests target GitHub workflows job runners with 4 vCPU.
Requests are using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html) based http client.
Requests are
using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html)
based http client.
Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. To mitigate it, you can increase values in `n_predict`, `kv_size`.
Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail.
To mitigate it, you can increase values in `n_predict`, `kv_size`.
### Install dependencies
`pip install -r requirements.txt`
### Run tests
1. Build the server
```shell
cd ../../..
mkdir build
@ -24,24 +32,36 @@ cd build
cmake ../
cmake --build . --target server
```
2. download required models:
1. `../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf`
3. Start the test: `./tests.sh`
2. Start the test: `./tests.sh`
It's possible to override some scenario steps values with environment variables:
- `PORT` -> `context.server_port` to set the listening port of the server during scenario, default: `8080`
- `LLAMA_SERVER_BIN_PATH` -> to change the server binary path, default: `../../../build/bin/server`
- `DEBUG` -> "ON" to enable steps and server verbose mode `--verbose`
- `SERVER_LOG_FORMAT_JSON` -> if set switch server logs to json format
| variable | description |
|--------------------------|------------------------------------------------------------------------------------------------|
| `PORT` | `context.server_port` to set the listening port of the server during scenario, default: `8080` |
| `LLAMA_SERVER_BIN_PATH` | to change the server binary path, default: `../../../build/bin/server` |
| `DEBUG` | "ON" to enable steps and server verbose mode `--verbose` |
| `SERVER_LOG_FORMAT_JSON` | if set switch server logs to json format |
| `N_GPU_LAYERS` | number of model layers to offload to VRAM `-ngl --n-gpu-layers` |
### Run @bug, @wip or @wrong_usage annotated scenario
Feature or Scenario must be annotated with `@llama.cpp` to be included in the default scope.
- `@bug` annotation aims to link a scenario with a GitHub issue.
- `@wrong_usage` are meant to show user issue that are actually an expected behavior
- `@wip` to focus on a scenario working in progress
- `@slow` heavy test, disabled by default
To run a scenario annotated with `@bug`, start:
`DEBUG=ON ./tests.sh --no-skipped --tags bug`
```shell
DEBUG=ON ./tests.sh --no-skipped --tags bug
```
After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated.
```shell
./tests.sh --no-skipped --tags bug,wrong_usage || echo "should failed but compile"
```

94
examples/server/tests/features/embeddings.feature Normal file
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@ -0,0 +1,94 @@
@llama.cpp
@embeddings
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 alias bert-bge-small
And 42 as server seed
And 2 slots
And 1024 as batch size
And 2048 KV cache size
And embeddings extraction
Then the server is starting
Then the server is healthy
Scenario: Embedding
When embeddings are computed for:
"""
What is the capital of Bulgaria ?
"""
Then embeddings are generated
Scenario: OAI Embeddings compatibility
Given a model bert-bge-small
When an OAI compatible embeddings computation request for:
"""
What is the capital of Spain ?
"""
Then embeddings are generated
Scenario: OAI Embeddings compatibility with multiple inputs
Given a model bert-bge-small
Given a prompt:
"""
In which country Paris is located ?
"""
And a prompt:
"""
Is Madrid the capital of Spain ?
"""
When an OAI compatible embeddings computation request for multiple inputs
Then embeddings are generated
Scenario: Multi users embeddings
Given a prompt:
"""
Write a very long story about AI.
"""
And a prompt:
"""
Write another very long music lyrics.
"""
And a prompt:
"""
Write a very long poem.
"""
And a prompt:
"""
Write a very long joke.
"""
Given concurrent embedding requests
Then the server is busy
Then the server is idle
Then all embeddings are generated
Scenario: Multi users OAI compatibility embeddings
Given a prompt:
"""
In which country Paris is located ?
"""
And a prompt:
"""
Is Madrid the capital of Spain ?
"""
And a prompt:
"""
What is the biggest US city ?
"""
And a prompt:
"""
What is the capital of Bulgaria ?
"""
And a model bert-bge-small
Given concurrent OAI embedding requests
Then the server is busy
Then the server is idle
Then all embeddings are generated
Scenario: All embeddings should be the same
Given 10 fixed prompts
And a model bert-bge-small
Given concurrent OAI embedding requests
Then all embeddings are the same

5
examples/server/tests/features/environment.py
View file

@ -7,7 +7,10 @@ from signal import SIGKILL
def before_scenario(context, scenario):
print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m")
context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON'
if context.debug:
print("DEBUG=ON\n")
print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m\n")
port = 8080
if 'PORT' in os.environ:
port = int(os.environ['PORT'])

1
examples/server/tests/features/issues.feature
View file

@ -1,4 +1,5 @@
# List of ongoing issues
# run with: DEBUG=ON ./tests.sh --no-skipped --tags bug
@bug
Feature: Issues
# No confirmed issue at the moment

51
examples/server/tests/features/parallel.feature
View file

@ -1,14 +1,14 @@
@llama.cpp
@parallel
Feature: Parallel
Background: Server startup
Given a server listening on localhost:8080
And a model file stories260K.gguf
And a model alias tinyllama-2
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 2 slots
And embeddings extraction
And continuous batching
Then the server is starting
Then the server is healthy
@ -98,48 +98,3 @@ Feature: Parallel
Then the server is busy
Then the server is idle
Then all prompts are predicted
Scenario: Multi users embeddings
Given a prompt:
"""
Write a very long story about AI.
"""
And a prompt:
"""
Write another very long music lyrics.
"""
And a prompt:
"""
Write a very long poem.
"""
And a prompt:
"""
Write a very long joke.
"""
Given concurrent embedding requests
Then the server is busy
Then the server is idle
Then all embeddings are generated
Scenario: Multi users OAI compatibility embeddings
Given a prompt:
"""
In which country Paris is located ?
"""
And a prompt:
"""
Is Madrid the capital of Spain ?
"""
And a prompt:
"""
What is the biggest US city ?
"""
And a prompt:
"""
What is the capital of Bulgaria ?
"""
And a model tinyllama-2
Given concurrent OAI embedding requests
Then the server is busy
Then the server is idle
Then all embeddings are generated

55
examples/server/tests/features/passkey.feature Normal file
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@ -0,0 +1,55 @@
# run with: ./tests.sh --no-skipped --tags passkey
@passkey
@slow
Feature: Passkey / Self-extend with context shift
Background: Server startup
Given a server listening on localhost:8080
# Generates a long text of junk and inserts a secret passkey number inside it.
# Then we query the LLM for the secret passkey.
# see #3856 and #4810
Scenario Outline: Passkey
Given a model file <hf_file> from HF repo <hf_repo>
And <n_batch> as batch size
And <n_junk> as number of junk
And <n_predicted> server max tokens to predict
And 42 as seed
And <n_ctx> KV cache size
And 1 slots
And <n_ga> group attention factor to extend context size through self-extend
And <n_ga_w> group attention width to extend context size through self-extend
# Can be override with N_GPU_LAYERS
And <ngl> GPU offloaded layers
Then the server is starting
Then the server is healthy
Given available models
Then model 0 is trained on <n_ctx_train> tokens context
Given a prefix prompt:
"""
here is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there.
"""
And a passkey prompt template:
"""
The pass key is <passkey> Remember it. <passkey> is the pass key.
"""
And a junk suffix prompt:
"""
The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.
"""
And a suffix prompt:
"""
What is the pass key? The pass key is
"""
Given a "<passkey>" passkey challenge prompt with the passkey inserted every <i_pos> junk
And a completion request with no api error
Then <n_predicted> tokens are predicted matching <re_content>
Examples:
| hf_repo | hf_file | n_ctx_train | ngl | n_ctx | n_batch | n_ga | n_ga_w | n_junk | i_pos | passkey | n_predicted | re_content |
| TheBloke/phi-2-GGUF | phi-2.Q4_K_M.gguf | 2048 | 5 | 8192 | 512 | 4 | 512 | 250 | 50 | 42 | 1 | 42 |
| TheBloke/phi-2-GGUF | phi-2.Q4_K_M.gguf | 2048 | 5 | 8192 | 512 | 2 | 512 | 250 | 50 | 42 | 1 | \b((?!42)\w)+\b |
#| TheBloke/Llama-2-7B-GGUF | llama-2-7b.Q2_K.gguf | 4096 | 3 | 16384 | 512 | 4 | 512 | 500 | 300 | 1234 | 5 | 1234 |
#| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768 | 2 | 16384 | 512 | 4 | 512 | 500 | 100 | 0987 | 5 | 0
# 987 |

3
examples/server/tests/features/security.feature
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@ -1,9 +1,10 @@
@llama.cpp
@security
Feature: Security
Background: Server startup with an api key defined
Given a server listening on localhost:8080
And a model file stories260K.gguf
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
And a server api key llama.cpp
Then the server is starting
Then the server is healthy

52
examples/server/tests/features/server.feature
View file

@ -1,15 +1,17 @@
@llama.cpp
@server
Feature: llama.cpp server
Background: Server startup
Given a server listening on localhost:8080
And a model file stories260K.gguf
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
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
@ -27,11 +29,12 @@ Feature: llama.cpp server
And a completion request with no api error
Then <n_predicted> tokens are predicted matching <re_content>
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<or>going)+ | 8 |
| Write a joke about AI | 64 | (park<or>friends<or>scared<or>always)+ | 32 |
| 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 |
Scenario Outline: OAI Compatibility
Given a model <model>
@ -43,38 +46,9 @@ Feature: llama.cpp server
Then <n_predicted> tokens are predicted matching <re_content>
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<or>what)+ | 8 | disabled |
| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks<or>happy<or>bird)+ | 32 | enabled |
Scenario: Embedding
When embeddings are computed for:
"""
What is the capital of Bulgaria ?
"""
Then embeddings are generated
Scenario: OAI Embeddings compatibility
Given a model tinyllama-2
When an OAI compatible embeddings computation request for:
"""
What is the capital of Spain ?
"""
Then embeddings are generated
Scenario: OAI Embeddings compatibility with multiple inputs
Given a model tinyllama-2
Given a prompt:
"""
In which country Paris is located ?
"""
And a prompt:
"""
Is Madrid the capital of Spain ?
"""
When an OAI compatible embeddings computation request for multiple inputs
Then embeddings are generated
| 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 |
Scenario: Tokenize / Detokenize
When tokenizing:
@ -82,3 +56,9 @@ Feature: llama.cpp server
What is the capital of France ?
"""
Then tokens can be detokenize
Scenario: Models available
Given available models
Then 1 models are supported
Then model 0 is identified by tinyllama-2
Then model 0 is trained on 128 tokens context

356
examples/server/tests/features/steps/steps.py
View file

@ -10,9 +10,11 @@ from contextlib import closing
from re import RegexFlag
import aiohttp
import numpy as np
import openai
from behave import step
from behave.api.async_step import async_run_until_complete
from huggingface_hub import hf_hub_download
from prometheus_client import parser
@ -23,20 +25,30 @@ def step_server_config(context, server_fqdn, server_port):
if 'PORT' in os.environ:
context.server_port = int(os.environ['PORT'])
print(f"$PORT set, overriding server port with to {context.server_port}")
if 'FQDN' in os.environ:
context.server_fqdn = os.environ['FQDN']
print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}")
context.base_url = f'http://{context.server_fqdn}:{context.server_port}'
context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON'
context.model_alias = None
context.n_batch = None
context.n_ctx = None
context.n_ga = None
context.n_ga_w = None
context.n_gpu_layer = None
context.n_predict = None
context.n_prompts = 0
context.n_server_predict = None
context.n_slots = None
context.prompt_prefix = None
context.prompt_suffix = None
context.server_api_key = None
context.server_continuous_batching = False
context.server_embeddings = False
context.server_metrics = False
context.server_process = None
context.seed = None
context.server_seed = None
context.user_api_key = None
@ -45,9 +57,11 @@ def step_server_config(context, server_fqdn, server_port):
context.prompts = []
@step(u'a model file {model_file}')
def step_model_file(context, model_file):
context.model_file = model_file
@step(u'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}')
@ -55,24 +69,34 @@ def step_model_alias(context, model_alias):
context.model_alias = model_alias
@step(u'{seed} as server seed')
@step(u'{seed:d} as server seed')
def step_seed(context, seed):
context.server_seed = int(seed)
context.server_seed = seed
@step(u'{n_ctx} KV cache size')
@step(u'{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'])
if context.debug:
print(f"-ngl upgraded from {ngl} to {new_ngl}")
ngl = new_ngl
context.n_gpu_layer = ngl
@step(u'{n_ctx:d} KV cache size')
def step_n_ctx(context, n_ctx):
context.n_ctx = int(n_ctx)
context.n_ctx = n_ctx
@step(u'{n_slots} slots')
@step(u'{n_slots:d} slots')
def step_n_slots(context, n_slots):
context.n_slots = int(n_slots)
context.n_slots = n_slots
@step(u'{n_predict} server max tokens to predict')
@step(u'{n_predict:d} server max tokens to predict')
def step_server_n_predict(context, n_predict):
context.n_server_predict = int(n_predict)
context.n_server_predict = n_predict
@step(u'continuous batching')
@ -116,11 +140,13 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status):
case 'ready' | 'idle':
await wait_for_health_status(context, context.base_url, 200, 'ok',
timeout=10,
params={'fail_on_no_slot': 0, 'include_slots': 0},
slots_idle=context.n_slots,
slots_processing=0,
expected_slots=[{'id': slot_id, 'state': 0}
for slot_id in range(context.n_slots)])
for slot_id in
range(context.n_slots if context.n_slots else 1)])
case 'busy':
await wait_for_health_status(context, context.base_url, 503,
'no slot available',
@ -128,7 +154,8 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status):
slots_idle=0,
slots_processing=context.n_slots,
expected_slots=[{'id': slot_id, 'state': 1}
for slot_id in range(context.n_slots)])
for slot_id in
range(context.n_slots if context.n_slots else 1)])
case _:
assert False, "unknown status"
@ -157,29 +184,30 @@ async def step_request_completion(context, api_error):
context.base_url,
debug=context.debug,
n_predict=context.n_predict,
server_seed=context.server_seed,
seed=await completions_seed(context),
expect_api_error=expect_api_error,
user_api_key=context.user_api_key)
context.tasks_result.append(completion)
if context.debug:
print(f"Completion response: {completion}")
print(f"Completion response: {completion}\n")
if expect_api_error:
assert completion == 401, f"completion must be an 401 status code: {completion}"
@step(u'{predicted_n} tokens are predicted matching {re_content}')
@step(u'{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(), int(predicted_n), re_content)
assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n, re_content)
@step(u'{predicted_n} tokens are predicted')
@step(u'{predicted_n:d} tokens are predicted')
def step_n_tokens_predicted(context, predicted_n):
assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n))
assert_n_tokens_predicted(context.tasks_result.pop(), predicted_n)
@step(u'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}')
@ -192,9 +220,9 @@ def step_model(context, model):
context.model = model
@step(u'{max_tokens} max tokens to predict')
@step(u'{max_tokens:d} max tokens to predict')
def step_max_tokens(context, max_tokens):
context.n_predict = int(max_tokens)
context.n_predict = max_tokens
@step(u'streaming is {enable_streaming}')
@ -222,11 +250,77 @@ 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')
def step_n_junk(context, n_junk):
context.n_junk = n_junk
@step(u'{n_batch:d} as batch size')
def step_n_batch(context, n_batch):
context.n_batch = n_batch
@step(u'{seed:d} as seed')
def step_seed(context, seed):
context.seed = seed
@step(u'a prefix prompt')
def step_prompt_prefix(context):
context.prompt_prefix = context.text
@step(u'a junk suffix prompt')
def step_prompt_junk_suffix(context):
context.prompt_junk_suffix = context.text
@step(u'a suffix prompt')
def step_prompt_suffix(context):
context.prompt_suffix = context.text
@step(u'{n_ga:d} group attention factor'
u' 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')
def step_impl(context, n_ga_w):
context.n_ga_w = n_ga_w
@step(u'a passkey prompt template')
def step_prompt_passkey(context):
context.prompt_passkey = context.text
@step(u'{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')
def step_prompt_passkey(context, passkey, i_pos):
prompt = ""
for i in range(context.n_junk):
if i % context.n_junk == i_pos:
prompt += context.prompt_passkey # the passkey is already substituted
prompt += context.prompt_junk_suffix
if context.debug:
passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m"
print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```\n")
context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix)
context.n_prompts = len(context.prompts)
@step(u'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:
print(f"Submitting OAI compatible completions request...")
print(f"Submitting OAI compatible completions request...\n")
expect_api_error = api_error == 'raised'
completion = await oai_chat_completions(context.prompts.pop(),
context.system_prompt,
@ -241,8 +335,7 @@ async def step_oai_chat_completions(context, api_error):
enable_streaming=context.enable_streaming
if hasattr(context, 'enable_streaming') else None,
server_seed=context.server_seed
if hasattr(context, 'server_seed') else None,
seed=await completions_seed(context),
user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None,
@ -261,11 +354,13 @@ async def step_oai_chat_completions(context, api_error):
@step(u'a prompt')
def step_a_prompt(context):
context.prompts.append(context.text)
context.n_prompts = len(context.prompts)
@step(u'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')
@ -276,8 +371,10 @@ async def step_concurrent_completion_requests(context):
# prompt is inserted automatically
context.base_url,
debug=context.debug,
prompt_prefix=context.prompt_prefix,
prompt_suffix=context.prompt_suffix,
n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
server_seed=context.server_seed if hasattr(context, 'server_seed') else None,
seed=await completions_seed(context),
user_api_key=context.user_api_key if hasattr(context,
'user_api_key') else None)
@ -297,8 +394,7 @@ async def step_oai_chat_completions(context):
if hasattr(context, 'n_predict') else None,
enable_streaming=context.enable_streaming
if hasattr(context, 'enable_streaming') else None,
server_seed=context.server_seed
if hasattr(context, 'server_seed') else None,
seed=await completions_seed(context),
user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None)
@ -318,7 +414,9 @@ async def step_oai_chat_completions(context):
if hasattr(context, 'n_predict') else None,
enable_streaming=context.enable_streaming
if hasattr(context, 'enable_streaming') else None,
server_seed=context.server_seed
seed=context.seed
if hasattr(context, 'seed') else
context.server_seed
if hasattr(context, 'server_seed') else None,
user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None)
@ -330,11 +428,10 @@ async def step_all_prompts_are_predicted(context):
await all_prompts_are_predicted(context)
@step(u'all prompts are predicted with {n_predict} tokens')
@step(u'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_predict):
expected_predicted_n = int(n_predict)
await all_prompts_are_predicted(context, expected_predicted_n)
async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted):
await all_prompts_are_predicted(context, n_expected_predicted)
async def all_prompts_are_predicted(context, expected_predicted_n=None):
@ -348,25 +445,47 @@ async def all_prompts_are_predicted(context, expected_predicted_n=None):
@step(u'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)
@step(u'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)
assert n_embedding_requests > 0
embeddings = []
for i in range(n_embedding_requests):
embedding = context.tasks_result.pop().pop()
embeddings.append(embedding)
assert_embeddings(embedding)
n = len(embeddings)
for i in range(n-1):
for j in range(i+1, n):
embedding1 = np.array(embeddings[i])
embedding2 = np.array(embeddings[j])
if context.debug:
print(f"embedding1: {embedding1[-8:]}\n")
print(f"embedding2: {embedding2[-8:]}\n")
similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
msg = f"Similarity between {i} and {j}: {similarity:.10f}"
if context.debug:
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')
def step_assert_embeddings(context):
if len(context.prompts) == 0:
assert_embeddings(context.embeddings)
else:
assert len(context.embeddings) == len(context.prompts), (f"unexpected response:\n"
f"context.prompts={context.prompts}\n"
f"context.embeddings={context.embeddings}")
for embedding in context.embeddings:
context.prompts.pop()
assert_embeddings(embedding)
assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n"
f"context.n_prompts={context.n_prompts}\n"
f"context.embeddings={context.embeddings}")
for embedding in context.embeddings:
assert_embeddings(embedding)
@step(u'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,
base_url=context.base_url,
user_api_key=context.user_api_key,
@ -380,6 +499,7 @@ async def step_oai_compute_embeddings_multiple_inputs(context):
base_url=context.base_url,
user_api_key=context.user_api_key,
model=context.model)
context.prompts.clear()
@step(u'concurrent embedding requests')
@ -406,9 +526,9 @@ async def step_concurrent_oai_embedding_requests(context):
@async_run_until_complete()
async def all_embeddings_are_generated(context):
n_embedding_requests = await gather_tasks_results(context)
assert n_embedding_requests > 0
assert n_embedding_requests == context.n_prompts
for i in range(n_embedding_requests):
assert_embeddings(context.tasks_result.pop())
assert_embeddings(context.tasks_result.pop().pop())
@step(u'tokenizing')
@ -464,20 +584,63 @@ async def step_prometheus_metrics_exported(context):
assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4"
metrics_raw = await metrics_response.text()
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"
async def concurrent_requests(context, f_completion, *args, **kwargs):
n_prompts = len(context.prompts)
@step(u'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(u'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'
openai.api_base = f'{context.base_url}/v1'
context.models = openai.Model.list().data
@step(u'{n_model:d} models are supported')
def step_supported_models(context, n_model):
if context.debug:
print(f"starting {n_prompts} concurrent completion requests...")
assert n_prompts > 0
for prompt_no in range(n_prompts):
print("server models available:", context.models)
assert len(context.models) == n_model
@step(u'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]
param_value = param_value.split(' ', 1)[0]
match param:
case 'identified':
value = model.id
case 'trained':
value = str(model.meta.n_ctx_train)
case _:
assert False, "param {param} not supported"
assert param_value == value, f"model param {param} {value} != {param_value}"
async def concurrent_requests(context, f_completion, *args, **kwargs):
context.n_prompts = len(context.prompts)
if context.debug:
print(f"starting {context.n_prompts} concurrent completion requests...")
assert context.n_prompts > 0
for prompt_no in range(context.n_prompts):
shifted_args = [context.prompts.pop(), *args]
context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
await asyncio.sleep(0.1)
@ -486,8 +649,10 @@ async def concurrent_requests(context, f_completion, *args, **kwargs):
async def request_completion(prompt,
base_url,
debug=False,
prompt_prefix=None,
prompt_suffix=None,
n_predict=None,
server_seed=None,
seed=None,
expect_api_error=None,
user_api_key=None):
if debug:
@ -504,11 +669,14 @@ async def request_completion(prompt,
async with aiohttp.ClientSession() as session:
async with session.post(f'{base_url}/completion',
json={
"input_prefix": prompt_prefix,
"prompt": prompt,
"n_predict": int(n_predict) if n_predict is not None else -1,
"seed": server_seed if server_seed is not None else 42
"input_suffix": prompt_suffix,
"n_predict": n_predict if n_predict is not None else -1,
"seed": seed if seed is not None else 42
},
headers=headers) as response:
headers=headers,
timeout=3600) as response:
if expect_api_error is None or not expect_api_error:
assert response.status == 200
assert response.headers['Access-Control-Allow-Origin'] == origin
@ -526,14 +694,14 @@ async def oai_chat_completions(user_prompt,
model=None,
n_predict=None,
enable_streaming=None,
server_seed=None,
seed=None,
user_api_key=None,
expect_api_error=None):
if debug:
print(f"Sending OAI Chat completions request: {user_prompt}")
# openai client always expects an api key
user_api_key = user_api_key if user_api_key is not None else 'nope'
seed = server_seed if server_seed is not None else 42
seed = seed if seed is not None else 42
enable_streaming = enable_streaming if enable_streaming is not None else False
payload = {
"messages": [
@ -645,7 +813,7 @@ async def request_embedding(content, base_url=None):
}) as response:
assert response.status == 200
response_json = await response.json()
return response_json['embedding']
return [response_json['embedding']]
async def request_oai_embeddings(input,
@ -655,6 +823,7 @@ async def request_oai_embeddings(input,
user_api_key = user_api_key if user_api_key is not None else 'nope'
if async_client:
origin = 'llama.cpp'
headers=[]
if user_api_key is not None:
headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
async with aiohttp.ClientSession() as session:
@ -663,14 +832,21 @@ async def request_oai_embeddings(input,
"input": input,
"model": model,
},
headers=headers) as response:
headers=headers,
timeout=3600) as response:
assert response.status == 200, f"received status code not expected: {response.status}"
assert response.headers['Access-Control-Allow-Origin'] == origin
assert response.headers['Content-Type'] == "application/json; charset=utf-8"
response_json = await response.json()
assert response_json['model'] == model, f"invalid model received: {response_json['model']}"
assert response_json['object'] == 'list'
return response_json['data']
if isinstance(input, collections.abc.Sequence):
embeddings = []
for an_oai_embeddings in response_json['data']:
embeddings.append(an_oai_embeddings['embedding'])
else:
embeddings = [response_json['data']['embedding']]
return embeddings
else:
openai.api_key = user_api_key
openai.api_base = f'{base_url}/v1'
@ -684,7 +860,7 @@ async def request_oai_embeddings(input,
for an_oai_embeddings in oai_embeddings.data:
embeddings.append(an_oai_embeddings.embedding)
else:
embeddings = oai_embeddings.data.embedding
embeddings = [oai_embeddings.data.embedding]
return embeddings
@ -692,20 +868,31 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
content = completion_response['content']
n_predicted = completion_response['timings']['predicted_n']
assert len(content) > 0, "no token predicted"
if expected_predicted_n is not None:
if re_content is not None:
p = re.compile(re_content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL)
matches = p.finditer(content)
last_match = 0
highlighted = ''
for match in matches:
start, end = match.span()
highlighted += content[last_match: start]
highlighted += '\x1b[33m'
highlighted += content[start: end]
highlighted += '\x1b[0m'
last_match = end
highlighted += content[last_match:]
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
print(f"Checking completion response: {highlighted}\n")
assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```'
if expected_predicted_n and expected_predicted_n > 0:
assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:'
f' {n_predicted} <> {expected_predicted_n}')
if re_content is not None:
re_content = '^.*' + re_content.replace('<or>', '|') + '.*$'
assert re.match(re_content, content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL), (
f'invalid tokens predicted:'
f' ```\n{content}\n``` do not match /{re_content}/')
async def gather_tasks_results(context):
n_tasks = len(context.concurrent_tasks)
if context.debug:
print(f"Waiting for all {n_tasks} tasks results...")
print(f"Waiting for all {n_tasks} tasks results...\n")
for task_no in range(n_tasks):
context.tasks_result.append(await context.concurrent_tasks.pop())
n_completions = len(context.tasks_result)
@ -716,15 +903,13 @@ async def wait_for_health_status(context,
base_url,
expected_http_status_code,
expected_health_status,
timeout=3,
params=None,
slots_idle=None,
slots_processing=None,
expected_slots=None):
if context.debug:
print(f"Starting checking for health for expected_health_status={expected_health_status}")
timeout = 3 # seconds
if expected_health_status == 'ok':
timeout = 10 # CI slow inference
print(f"Starting checking for health for expected_health_status={expected_health_status}\n")
interval = 0.5
counter = 0
async with aiohttp.ClientSession() as session:
@ -734,7 +919,7 @@ async def wait_for_health_status(context,
health = await health_response.json()
if context.debug:
print(f"HEALTH - response for expected health status='{expected_health_status}' on "
f"'{base_url}/health'?{params} is {health}")
f"'{base_url}/health'?{params} is {health}\n")
if (status_code == expected_http_status_code
and health['status'] == expected_health_status
and (slots_idle is None or health['slots_idle'] == slots_idle)
@ -757,7 +942,7 @@ async def wait_for_health_status(context,
if expected_http_status_code == 503:
if len(context.tasks_result) == 0:
print("\x1b[5;37;43mWARNING: forcing concurrent tasks,"
" busy health check missed, probably too fast inference\x1b[0m")
" busy health check missed, probably too fast inference\x1b[0m\n")
n_completions = await gather_tasks_results(context)
if n_completions > 0:
return
@ -769,6 +954,8 @@ def assert_embeddings(embeddings):
assert len(embeddings) > 0
embeddings_computed = False
for emb in embeddings:
if not isinstance(emb, float):
assert False, f"Bad embeddings: {embeddings}"
if emb != 0:
embeddings_computed = True
assert embeddings_computed, f"Embeddings: {embeddings}"
@ -791,6 +978,11 @@ def assert_slots_status(slots, expected_slots):
f" = {expected[key]} != {slot[key]}")
async def completions_seed(context):
return context.seed if hasattr(context, 'seed') and context.seed is not None \
else context.server_seed if hasattr(context, 'server_seed') else None
def start_server_background(context):
context.server_path = '../../../build/bin/server'
if 'LLAMA_SERVER_BIN_PATH' in os.environ:
@ -800,27 +992,35 @@ def start_server_background(context):
'--port', context.server_port,
'--model', context.model_file
]
if context.n_batch:
server_args.extend(['--batch-size', context.n_batch])
if context.n_gpu_layer:
server_args.extend(['--n-gpu-layers', context.n_gpu_layer])
if context.server_continuous_batching:
server_args.append('--cont-batching')
if context.server_embeddings:
server_args.append('--embedding')
if context.server_metrics:
server_args.append('--metrics')
if context.model_alias is not None:
if context.model_alias:
server_args.extend(['--alias', context.model_alias])
if context.n_ctx is not None:
if context.n_ctx:
server_args.extend(['--ctx-size', context.n_ctx])
if context.n_slots is not None:
if context.n_slots:
server_args.extend(['--parallel', context.n_slots])
if context.n_server_predict is not None:
if context.n_server_predict:
server_args.extend(['--n-predict', context.n_server_predict])
if context.server_api_key is not None:
if context.server_api_key:
server_args.extend(['--api-key', context.server_api_key])
if context.n_ga:
server_args.extend(['--grp-attn-n', context.n_ga])
if context.n_ga_w:
server_args.extend(['--grp-attn-w', context.n_ga_w])
if context.debug:
server_args.append('--verbose')
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)
print(f"starting server with: {context.server_path} {server_args}\n")
context.server_process = subprocess.Popen(
[str(arg) for arg in [context.server_path, *server_args]],
close_fds=True)

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

@ -1,4 +1,4 @@
# run with ./test.sh --tags wrong_usage
# run with: ./tests.sh --no-skipped --tags wrong_usage
@wrong_usage
Feature: Wrong usage of llama.cpp server
@ -7,7 +7,7 @@ Feature: Wrong usage of llama.cpp server
# or pass n_predict/max_tokens in the request.
Scenario: Infinite loop
Given a server listening on localhost:8080
And a model file stories260K.gguf
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
# Uncomment below to fix the issue
#And 64 server max tokens to predict
Then the server is starting
@ -18,4 +18,5 @@ Feature: Wrong usage of llama.cpp server
# Uncomment below to fix the issue
#And 128 max tokens to predict
Given concurrent completion requests
Then the server is idle
Then all prompts are predicted

2
examples/server/tests/requirements.txt
View file

@ -1,4 +1,6 @@
aiohttp~=3.9.3
behave~=1.2.6
huggingface_hub~=0.20.3
numpy~=1.24.4
openai~=0.25.0
prometheus-client~=0.20.0

2
examples/server/tests/tests.sh
View file

@ -5,7 +5,7 @@ set -eu
if [ $# -lt 1 ]
then
# Start @llama.cpp scenario
behave --summary --stop --no-capture --exclude 'issues|wrong_usages' --tags llama.cpp
behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
else
behave "$@"
fi

760
examples/server/utils.hpp
View file

@ -1,15 +1,16 @@
#pragma once
#include <string>
#include <vector>
#include <set>
#include <mutex>
#include <condition_variable>
#include <unordered_map>
#include "llama.h"
#include "common.h"
#include "json.hpp"
#include "../llava/clip.h"
#include <string>
#include <vector>
#include <sstream>
#include <random>
#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
using json = nlohmann::json;
@ -37,125 +38,35 @@ extern bool server_log_json;
#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
//
// parallel
//
template <typename T>
static T json_value(const json &body, const std::string &key, const T &default_value) {
// Fallback null to default value
return body.contains(key) && !body.at(key).is_null()
? body.value(key, default_value)
: default_value;
}
enum server_state {
SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet
SERVER_STATE_READY, // Server is ready and model is loaded
SERVER_STATE_ERROR // An error occurred, load_model failed
};
enum task_type {
TASK_TYPE_COMPLETION,
TASK_TYPE_CANCEL,
TASK_TYPE_NEXT_RESPONSE,
TASK_TYPE_METRICS
};
struct task_server {
int id = -1; // to be filled by llama_server_queue
int target_id;
task_type type;
json data;
bool infill_mode = false;
bool embedding_mode = false;
int multitask_id = -1;
};
struct task_result {
int id;
int multitask_id = -1;
bool stop;
bool error;
json result_json;
};
struct task_multi {
int id;
std::set<int> subtasks_remaining{};
std::vector<task_result> results{};
};
// TODO: can become bool if we can't find use of more states
enum slot_state
{
IDLE,
PROCESSING,
};
enum slot_command
{
NONE,
LOAD_PROMPT,
RELEASE,
};
struct slot_params
{
bool stream = true;
bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt
uint32_t seed = -1; // RNG seed
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_predict = -1; // new tokens to predict
std::vector<std::string> antiprompt;
json input_prefix;
json input_suffix;
};
struct slot_image
{
int32_t id;
bool request_encode_image = false;
float * image_embedding = nullptr;
int32_t image_tokens = 0;
clip_image_u8 * img_data;
std::string prefix_prompt; // before of this image
};
// completion token output with probabilities
struct completion_token_output
{
struct token_prob
{
llama_token tok;
float prob;
};
std::vector<token_prob> probs;
llama_token tok;
std::string text_to_send;
};
static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra)
{
static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) {
std::stringstream ss_tid;
ss_tid << std::this_thread::get_id();
json log = nlohmann::ordered_json{
{"tid", ss_tid.str()},
{"tid", ss_tid.str()},
{"timestamp", time(nullptr)},
};
if (server_log_json) {
log.merge_patch(
{
{"level", level},
{"function", function},
{"line", line},
{"msg", message},
});
log.merge_patch( {
{"level", level},
{"function", function},
{"line", line},
{"msg", message},
});
if (!extra.empty()) {
log.merge_patch(extra);
}
std::cout << log.dump(-1, ' ', false, json::error_handler_t::replace) << "\n" << std::flush;
printf("%s\n", log.dump(-1, ' ', false, json::error_handler_t::replace).c_str());
} else {
char buf[1024];
snprintf(buf, 1024, "%4s [%24s] %s", level, function, message);
@ -168,8 +79,7 @@ static inline void server_log(const char *level, const char *function, int line,
for (const auto& el : log.items())
{
const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace);
snprintf(buf, 1024, " %s=%s", el.key().c_str(), value.c_str());
ss << buf;
ss << " " << el.key() << "=" << value;
}
const std::string str = ss.str();
@ -179,36 +89,25 @@ static inline void server_log(const char *level, const char *function, int line,
}
//
// server utils
// chat template utils
//
template <typename T>
static T json_value(const json &body, const std::string &key, const T &default_value)
{
// Fallback null to default value
return body.contains(key) && !body.at(key).is_null()
? body.value(key, default_value)
: default_value;
}
// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
inline bool verify_custom_template(const std::string & tmpl) {
llama_chat_message chat[] = {{"user", "test"}};
std::vector<char> buf(1);
int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, buf.data(), buf.size());
int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
return res >= 0;
}
// Format given chat. If tmpl is empty, we take the template from model metadata
inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages)
{
inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
size_t alloc_size = 0;
// vector holding all allocated string to be passed to llama_chat_apply_template
std::vector<std::string> str(messages.size() * 2);
std::vector<llama_chat_message> chat(messages.size());
for (size_t i = 0; i < messages.size(); ++i) {
auto &curr_msg = messages[i];
const auto & curr_msg = messages[i];
str[i*2 + 0] = json_value(curr_msg, "role", std::string(""));
str[i*2 + 1] = json_value(curr_msg, "content", std::string(""));
alloc_size += str[i*2 + 1].length();
@ -228,252 +127,13 @@ inline std::string format_chat(const struct llama_model * model, const std::stri
res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
}
std::string formatted_chat(buf.data(), res);
const std::string formatted_chat(buf.data(), res);
LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}});
return formatted_chat;
}
//
// work queue utils
//
struct llama_server_queue {
int id = 0;
std::mutex mutex_tasks;
bool running;
// queues
std::vector<task_server> queue_tasks;
std::vector<task_server> queue_tasks_deferred;
std::vector<task_multi> queue_multitasks;
std::condition_variable condition_tasks;
// callback functions
std::function<void(task_server&)> callback_new_task;
std::function<void(task_multi&)> callback_finish_multitask;
std::function<void(void)> callback_all_task_finished;
// Add a new task to the end of the queue
int post(task_server task) {
std::unique_lock<std::mutex> lock(mutex_tasks);
if (task.id == -1) {
task.id = id++;
LOG_VERBOSE("new task id", {{"new_id", task.id}});
}
queue_tasks.push_back(std::move(task));
condition_tasks.notify_one();
return task.id;
}
// Add a new task, but defer until one slot is available
void defer(task_server task) {
std::unique_lock<std::mutex> lock(mutex_tasks);
queue_tasks_deferred.push_back(std::move(task));
}
// Get the next id for creating anew task
int get_new_id() {
std::unique_lock<std::mutex> lock(mutex_tasks);
int new_id = id++;
LOG_VERBOSE("new task id", {{"new_id", new_id}});
return new_id;
}
// Register function to process a new task
void on_new_task(std::function<void(task_server&)> callback) {
callback_new_task = callback;
}
// Register function to process a multitask
void on_finish_multitask(std::function<void(task_multi&)> callback) {
callback_finish_multitask = callback;
}
// Register the function to be called when the batch of tasks is finished
void on_all_tasks_finished(std::function<void(void)> callback) {
callback_all_task_finished = callback;
}
// Call when the state of one slot is changed
void notify_slot_changed() {
// move deferred tasks back to main loop
std::unique_lock<std::mutex> lock(mutex_tasks);
for (auto & task : queue_tasks_deferred) {
queue_tasks.push_back(std::move(task));
}
queue_tasks_deferred.clear();
}
// end the start_loop routine
void terminate() {
{
std::unique_lock<std::mutex> lock(mutex_tasks);
running = false;
}
condition_tasks.notify_all();
}
// Start the main loop.
void start_loop() {
running = true;
while (true) {
LOG_VERBOSE("new task may arrive", {});
{
while (true)
{
std::unique_lock<std::mutex> lock(mutex_tasks);
if (queue_tasks.empty()) {
lock.unlock();
break;
}
task_server task = queue_tasks.front();
queue_tasks.erase(queue_tasks.begin());
lock.unlock();
LOG_VERBOSE("callback_new_task", {{"task_id", task.id}});
callback_new_task(task);
}
LOG_VERBOSE("callback_all_task_finished", {});
// process and update all the multitasks
auto queue_iterator = queue_multitasks.begin();
while (queue_iterator != queue_multitasks.end())
{
if (queue_iterator->subtasks_remaining.empty())
{
// all subtasks done == multitask is done
task_multi current_multitask = *queue_iterator;
callback_finish_multitask(current_multitask);
// remove this multitask
queue_iterator = queue_multitasks.erase(queue_iterator);
}
else
{
++queue_iterator;
}
}
// all tasks in the current loop is finished
callback_all_task_finished();
}
LOG_VERBOSE("wait for new task", {});
// wait for new task
{
std::unique_lock<std::mutex> lock(mutex_tasks);
if (queue_tasks.empty()) {
if (!running) {
LOG_VERBOSE("ending start_loop", {});
return;
}
condition_tasks.wait(lock, [&]{
return (!queue_tasks.empty() || !running);
});
}
}
}
}
//
// functions to manage multitasks
//
// add a multitask by specifying the id of all subtask (subtask is a task_server)
void add_multitask(int multitask_id, std::vector<int>& sub_ids)
{
std::lock_guard<std::mutex> lock(mutex_tasks);
task_multi multi;
multi.id = multitask_id;
std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
queue_multitasks.push_back(multi);
}
// updatethe remaining subtasks, while appending results to multitask
void update_multitask(int multitask_id, int subtask_id, task_result& result)
{
std::lock_guard<std::mutex> lock(mutex_tasks);
for (auto& multitask : queue_multitasks)
{
if (multitask.id == multitask_id)
{
multitask.subtasks_remaining.erase(subtask_id);
multitask.results.push_back(result);
}
}
}
};
struct llama_server_response {
typedef std::function<void(int, int, task_result&)> callback_multitask_t;
callback_multitask_t callback_update_multitask;
// for keeping track of all tasks waiting for the result
std::set<int> waiting_task_ids;
// the main result queue
std::vector<task_result> queue_results;
std::mutex mutex_results;
std::condition_variable condition_results;
void add_waiting_task_id(int task_id) {
LOG_VERBOSE("waiting for task id", {{"task_id", task_id}});
std::unique_lock<std::mutex> lock(mutex_results);
waiting_task_ids.insert(task_id);
}
void remove_waiting_task_id(int task_id) {
LOG_VERBOSE("remove waiting for task id", {{"task_id", task_id}});
std::unique_lock<std::mutex> lock(mutex_results);
waiting_task_ids.erase(task_id);
}
// This function blocks the thread until there is a response for this task_id
task_result recv(int task_id) {
while (true)
{
std::unique_lock<std::mutex> lock(mutex_results);
condition_results.wait(lock, [&]{
return !queue_results.empty();
});
for (int i = 0; i < (int) queue_results.size(); i++)
{
if (queue_results[i].id == task_id)
{
assert(queue_results[i].multitask_id == -1);
task_result res = queue_results[i];
queue_results.erase(queue_results.begin() + i);
return res;
}
}
}
// should never reach here
}
// Register the function to update multitask
void on_multitask_update(callback_multitask_t callback) {
callback_update_multitask = callback;
}
// Send a new result to a waiting task_id
void send(task_result result) {
std::unique_lock<std::mutex> lock(mutex_results);
LOG_VERBOSE("send new result", {{"task_id", result.id}});
for (auto& task_id : waiting_task_ids) {
// LOG_TEE("waiting task id %i \n", task_id);
// for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
if (result.multitask_id == task_id)
{
LOG_VERBOSE("callback_update_multitask", {{"task_id", task_id}});
callback_update_multitask(task_id, result.id, result);
continue;
}
if (result.id == task_id)
{
LOG_VERBOSE("queue_results.push_back", {{"task_id", task_id}});
queue_results.push_back(result);
condition_results.notify_all();
return;
}
}
}
};
//
// base64 utils (TODO: move to common in the future)
//
@ -483,13 +143,11 @@ static const std::string base64_chars =
"abcdefghijklmnopqrstuvwxyz"
"0123456789+/";
static inline bool is_base64(uint8_t c)
{
static inline bool is_base64(uint8_t c) {
return (isalnum(c) || (c == '+') || (c == '/'));
}
static inline std::vector<uint8_t> base64_decode(const std::string & encoded_string)
{
static inline std::vector<uint8_t> base64_decode(const std::string & encoded_string) {
int i = 0;
int j = 0;
int in_ = 0;
@ -501,13 +159,10 @@ static inline std::vector<uint8_t> base64_decode(const std::string & encoded_str
std::vector<uint8_t> ret;
while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_]))
{
while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) {
char_array_4[i++] = encoded_string[in_]; in_++;
if (i == 4)
{
for (i = 0; i <4; i++)
{
if (i == 4) {
for (i = 0; i < 4; i++) {
char_array_4[i] = base64_chars.find(char_array_4[i]);
}
@ -515,23 +170,20 @@ static inline std::vector<uint8_t> base64_decode(const std::string & encoded_str
char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
for (i = 0; (i < 3); i++)
{
for (i = 0; (i < 3); i++) {
ret.push_back(char_array_3[i]);
}
i = 0;
}
}
if (i)
{
for (j = i; j <4; j++)
{
if (i) {
for (j = i; j < 4; j++) {
char_array_4[j] = 0;
}
for (j = 0; j <4; j++)
{
for (j = 0; j < 4; j++) {
char_array_4[j] = base64_chars.find(char_array_4[j]);
}
@ -539,8 +191,7 @@ static inline std::vector<uint8_t> base64_decode(const std::string & encoded_str
char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
for (j = 0; (j < i - 1); j++)
{
for (j = 0; j < i - 1; j++) {
ret.push_back(char_array_3[j]);
}
}
@ -552,8 +203,7 @@ static inline std::vector<uint8_t> base64_decode(const std::string & encoded_str
// random string / id
//
static std::string random_string()
{
static std::string random_string() {
static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");
std::random_device rd;
@ -568,9 +218,327 @@ static std::string random_string()
return result;
}
static std::string gen_chatcmplid()
{
static std::string gen_chatcmplid() {
std::stringstream chatcmplid;
chatcmplid << "chatcmpl-" << random_string();
return chatcmplid.str();
}
//
// other common utils
//
static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
size_t i;
for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
return i;
}
static bool ends_with(const std::string & str, const std::string & suffix) {
return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
}
static size_t find_partial_stop_string(const std::string &stop, const std::string &text) {
if (!text.empty() && !stop.empty()) {
const char text_last_char = text.back();
for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
if (stop[char_index] == text_last_char) {
const std::string current_partial = stop.substr(0, char_index + 1);
if (ends_with(text, current_partial)) {
return text.size() - char_index - 1;
}
}
}
}
return std::string::npos;
}
// TODO: reuse llama_detokenize
template <class Iter>
static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
std::string ret;
for (; begin != end; ++begin) {
ret += llama_token_to_piece(ctx, *begin);
}
return ret;
}
// format incomplete utf-8 multibyte character for output
static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
// if the size is 1 and first bit is 1, meaning it's a partial character
// (size > 1 meaning it's already a known token)
if (out.size() == 1 && (out[0] & 0x80) == 0x80) {
std::stringstream ss;
ss << std::hex << (out[0] & 0xff);
std::string res(ss.str());
out = "byte: \\x" + res;
}
return out;
}
struct completion_token_output {
llama_token tok;
std::string text_to_send;
struct token_prob {
llama_token tok;
float prob;
};
std::vector<token_prob> probs;
};
// convert a vector of completion_token_output to json
static json probs_vector_to_json(const llama_context * ctx, const std::vector<completion_token_output> & probs) {
json out = json::array();
for (const auto & prob : probs) {
json probs_for_token = json::array();
for (const auto & p : prob.probs) {
const std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
probs_for_token.push_back(json {
{"tok_str", tok_str},
{"prob", p.prob},
});
}
const std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
out.push_back(json {
{"content", tok_str},
{"probs", probs_for_token},
});
}
return out;
}
//
// OAI utils
//
static json oaicompat_completion_params_parse(
const struct llama_model * model,
const json & body, /* openai api json semantics */
const std::string & chat_template) {
json llama_params;
llama_params["__oaicompat"] = true;
// Map OpenAI parameters to llama.cpp parameters
//
// For parameters that are defined by the OpenAI documentation (e.g.
// temperature), we explicitly specify OpenAI's intended default; we
// need to do that because sometimes OpenAI disagrees with llama.cpp
//
// https://platform.openai.com/docs/api-reference/chat/create
llama_sampling_params default_sparams;
llama_params["model"] = json_value(body, "model", std::string("unknown"));
llama_params["prompt"] = format_chat(model, chat_template, body["messages"]);
llama_params["cache_prompt"] = json_value(body, "cache_prompt", false);
llama_params["temperature"] = json_value(body, "temperature", 0.0);
llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k);
llama_params["top_p"] = json_value(body, "top_p", 1.0);
llama_params["n_predict"] = json_value(body, "max_tokens", -1);
llama_params["logit_bias"] = json_value(body, "logit_bias", json::object());
llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED);
llama_params["stream"] = json_value(body, "stream", false);
llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat);
llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl);
llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p);
llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z);
if (body.count("grammar") != 0) {
llama_params["grammar"] = json_value(body, "grammar", json::object());
}
// Handle 'stop' field
if (body.contains("stop") && body["stop"].is_string()) {
llama_params["stop"] = json::array({body["stop"].get<std::string>()});
} else {
llama_params["stop"] = json_value(body, "stop", json::array());
}
// Ensure there is ChatML-specific end sequence among stop words
llama_params["stop"].push_back("<|im_end|>");
return llama_params;
}
static json format_final_response_oaicompat(const json & request, json result, 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);
int num_prompt_tokens = json_value(result, "tokens_evaluated", 0);
std::string content = json_value(result, "content", std::string(""));
std::string finish_reason = "length";
if (stopped_word || stopped_eos) {
finish_reason = "stop";
}
json choices =
streaming ? json::array({json{{"finish_reason", finish_reason},
{"index", 0},
{"delta", json::object()}}})
: json::array({json{{"finish_reason", finish_reason},
{"index", 0},
{"message", json{{"content", content},
{"role", "assistant"}}}}});
std::time_t t = std::time(0);
json res = json {
{"choices", choices},
{"created", t},
{"model",
json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
{"object", streaming ? "chat.completion.chunk" : "chat.completion"},
{"usage", json {
{"completion_tokens", num_tokens_predicted},
{"prompt_tokens", num_prompt_tokens},
{"total_tokens", num_tokens_predicted + num_prompt_tokens}
}},
{"id", gen_chatcmplid()}
};
if (server_verbose) {
res["__verbose"] = result;
}
if (result.contains("completion_probabilities")) {
res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
}
return res;
}
// 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) {
if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
return std::vector<json>({result});
}
bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
bool stopped_word = json_value(result, "stopped_word", false);
bool stopped_eos = json_value(result, "stopped_eos", false);
bool stopped_limit = json_value(result, "stopped_limit", false);
std::string content = json_value(result, "content", std::string(""));
std::string finish_reason;
if (stopped_word || stopped_eos) {
finish_reason = "stop";
}
if (stopped_limit) {
finish_reason = "length";
}
std::time_t t = std::time(0);
json choices;
if (!finish_reason.empty()) {
choices = json::array({json{{"finish_reason", finish_reason},
{"index", 0},
{"delta", json::object()}}});
} else {
if (first) {
if (content.empty()) {
choices = json::array({json{{"finish_reason", nullptr},
{"index", 0},
{"delta", json{{"role", "assistant"}}}}});
} else {
// We have to send this as two updates to conform to openai behavior
json initial_ret = json{{"choices", json::array({json{
{"finish_reason", nullptr},
{"index", 0},
{"delta", json{
{"role", "assistant"}
}}}})},
{"created", t},
{"id", gen_chatcmplid()},
{"model", modelname},
{"object", "chat.completion.chunk"}};
json second_ret = json{
{"choices", json::array({json{{"finish_reason", nullptr},
{"index", 0},
{"delta", json{
{"content", content}}}
}})},
{"created", t},
{"id", gen_chatcmplid()},
{"model", modelname},
{"object", "chat.completion.chunk"}};
return std::vector<json>({initial_ret, second_ret});
}
} else {
// Some idiosyncrasy in task processing logic makes several trailing calls
// with empty content, we ignore these at the calee site.
if (content.empty()) {
return std::vector<json>({json::object()});
}
choices = json::array({json{
{"finish_reason", nullptr},
{"index", 0},
{"delta",
json{
{"content", content},
}},
}});
}
}
json ret = json {
{"choices", choices},
{"created", t},
{"id", gen_chatcmplid()},
{"model", modelname},
{"object", "chat.completion.chunk"}
};
return std::vector<json>({ret});
}
static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
json res = json {
{"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
{"object", "list"},
{"usage", json {
{"prompt_tokens", 0},
{"total_tokens", 0}
}},
{"data", embeddings}
};
return res;
}
static json format_tokenizer_response(const std::vector<llama_token> & tokens) {
return json {
{"tokens", tokens}
};
}
static json format_detokenized_response(const std::string & content) {
return json {
{"content", content}
};
}

1
examples/speculative/README.md
View file

@ -6,3 +6,4 @@ More info:
- https://github.com/ggerganov/llama.cpp/pull/2926
- https://github.com/ggerganov/llama.cpp/pull/3624
- https://github.com/ggerganov/llama.cpp/pull/5625

224
examples/speculative/speculative.cpp
View file

@ -5,6 +5,7 @@
#include <cstdio>
#include <string>
#include <vector>
#include <set>
#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 100
#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
@ -18,6 +19,7 @@ struct seq_draft {
std::vector<int> i_batch_tgt;
std::vector<llama_token> tokens;
std::vector<std::vector<llama_token_data>> dists;
struct llama_sampling_context * ctx_sampling;
};
@ -37,12 +39,15 @@ int main(int argc, char ** argv) {
// max number of parallel drafting sequences (i.e. tree branches)
const int n_seq_dft = params.n_parallel;
// probability threshold for accepting a token from the draft model
const float p_accept = params.p_accept;
// probability threshold for splitting a draft branch (only for n_seq_dft > 1)
const float p_split = params.p_split;
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
std::default_random_engine rng(params.seed);
std::uniform_real_distribution<> u_dist;
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("speculative", "log"));
LOG_TEE("Log start\n");
@ -166,7 +171,9 @@ int main(int argc, char ** argv) {
std::vector<seq_draft> drafts(n_seq_dft);
params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar
params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model
if (params.sparams.temp == 0) {
params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model
}
for (int s = 0; s < n_seq_dft; ++s) {
drafts[s].ctx_sampling = llama_sampling_init(params.sparams);
@ -182,12 +189,15 @@ int main(int argc, char ** argv) {
drafts[0].i_batch_tgt[0] = 0;
while (true) {
std::set<int> active_seqs = {};
// print current draft sequences
for (int s = 0; s < n_seq_dft; ++s) {
if (!drafts[s].active) {
continue;
}
active_seqs.insert(s);
const auto & tokens = drafts[s].tokens;
LOG("draft %d: %s\n", s, LOG_TOKENS_TOSTR_PRETTY(ctx_dft, tokens).c_str());
@ -196,48 +206,156 @@ int main(int argc, char ** argv) {
int i_dft = 0;
int s_keep = 0;
llama_token token_id;
std::string token_str;
// loop until we fail to accept a drafted token or we run out of drafted tokens
while (true) {
LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
// sample from the target model
llama_token id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
llama_sampling_accept(ctx_sampling, ctx_tgt, id, true);
//LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str());
const std::string token_str = llama_token_to_piece(ctx_tgt, id);
if (!params.use_color) {
printf("%s", token_str.c_str());
}
if (id == llama_token_eos(model_tgt)) {
has_eos = true;
}
++n_predict;
// check if the target token matches any of the drafts
// for stochastic sampling, attempt to match the token with the drafted tokens
{
bool matches = false;
bool accept = false;
if (params.sparams.temp > 0) {
// stochastic verification
for (int s = 0; s < n_seq_dft; ++s) {
if (!drafts[s].active) {
continue;
llama_token_data_array dist_tgt = llama_sampling_probability_distribution(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
float p_tgt = 0, p_dft = 0;
// GGML_ASSERT(dist_tgt.size() == dist_dft.size());
while (active_seqs.size() > 0) {
// randomly select a sequence to verify from active sequences
std::uniform_int_distribution<unsigned int> u_int_dist(0, active_seqs.size() - 1);
int s = *std::next(active_seqs.begin(), u_int_dist(rng));
if (i_dft >= (int) drafts[s].tokens.size()) {
drafts[s].active = false;
active_seqs.erase(s);
continue;
}
if (accept) {
// if we already accepted a token, we can skip the rest
if (drafts[s].tokens[i_dft] != drafts[s_keep].tokens[i_dft]) {
drafts[s].active = false;
active_seqs.erase(s);
}
continue;
}
LOG("verifying sequence #%d at pos #%d from %d active sequence(s)\n", s, i_dft, (int) active_seqs.size());
float r = u_dist(rng);
llama_token_data_array dist_dft = { drafts[s].dists[i_dft].data() , drafts[s].dists[i_dft].size(), true };
// acquire the token probabilities assigned by the draft and target models
for (size_t i = 0; i < dist_tgt.size; i++) {
if (dist_tgt.data[i].id == drafts[s].tokens[i_dft]) {
p_tgt = dist_tgt.data[i].p;
}
if (dist_dft.data[i].id == drafts[s].tokens[i_dft]) {
p_dft = dist_dft.data[i].p;
}
if (p_tgt && p_dft) {
break;
}
}
LOG("r = %f, p_dft = %f, p_tgt = %f\n", r, p_dft, p_tgt);
if (r <= p_tgt / p_dft) {
s_keep = s;
accept = true;
token_id = drafts[s].tokens[i_dft];
token_str = llama_token_to_piece(ctx_tgt, token_id);
llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true);
LOG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str());
break;
} else {
LOG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], llama_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str());
drafts[s].active = false;
// calculate residual probability
GGML_ASSERT(dist_tgt.sorted);
GGML_ASSERT(dist_dft.sorted);
float sum_probs = 0.0f;
// sort dist by id
std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) {
return a.id < b.id;
});
std::sort(dist_dft.data, dist_dft.data + dist_dft.size, [](const llama_token_data &a, const llama_token_data &b) {
return a.id < b.id;
});
for (size_t i = 0; i < dist_tgt.size; i++) {
dist_tgt.data[i].p = std::max(0.0f, dist_tgt.data[i].p - dist_dft.data[i].p);
sum_probs += dist_tgt.data[i].p;
}
for (size_t i = 0; i < dist_tgt.size; i++) {
dist_tgt.data[i].p /= sum_probs;
}
// sort dist_tgt by p desc
std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) {
return a.p > b.p;
});
}
active_seqs.erase(s);
for(int i = 0; i < n_seq_dft; i++) {
if (i == s) {
continue;
}
if (drafts[i].tokens[i_dft] == drafts[s].tokens[i_dft]) {
// synchronize active status for sequences with the same drafted token
drafts[i].active = drafts[i].active && accept;
if (!drafts[i].active) {
active_seqs.erase(s);
}
}
}
}
if (i_dft < (int) drafts[s].tokens.size() && id == drafts[s].tokens[i_dft]) {
LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, id, token_str.c_str());
if (!accept) {
// all drafted tokens were rejected
// sample from the target model
LOG("all drafted tokens were rejected, sampling from residual distribution\n");
token_id = llama_sample_token(ctx_tgt, &dist_tgt);
llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true);
token_str = llama_token_to_piece(ctx_tgt, token_id);
}
s_keep = s;
matches = true;
} else {
drafts[s].active = false;
} else {
// greedy verification
// sample from the target model
LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
token_id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true);
//LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str());
token_str = llama_token_to_piece(ctx_tgt, token_id);
for (int s = 0; s < n_seq_dft; ++s) {
if (!drafts[s].active) {
continue;
}
if (i_dft < (int) drafts[s].tokens.size() && token_id == drafts[s].tokens[i_dft]) {
LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, token_id, token_str.c_str());
s_keep = s;
accept = true;
} else {
drafts[s].active = false;
}
}
}
if (matches) {
if (token_id == llama_token_eos(model_tgt)) {
has_eos = true;
}
++n_predict;
if (accept) {
++n_accept;
++n_past_tgt;
++n_past_dft;
@ -245,17 +363,21 @@ int main(int argc, char ** argv) {
if (params.use_color) {
// Color token according to its origin sequence
printf("\u001b[%dm%s\u001b[37m", (36 - s_keep % 6), token_str.c_str());
fflush(stdout);
} else {
printf("%s", token_str.c_str());
}
fflush(stdout);
continue;
} else {
printf("%s", token_str.c_str());
fflush(stdout);
break;
}
}
if (params.use_color) {
printf("%s", token_str.c_str());
}
fflush(stdout);
}
LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
{
LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", token_id, token_str.c_str());
// TODO: simplify
{
@ -275,21 +397,21 @@ int main(int argc, char ** argv) {
drafts[s].active = false;
drafts[s].tokens.clear();
drafts[s].i_batch_tgt.clear();
drafts[s].dists.clear();
}
// note: will be erased after the speculation phase
drafts[0].tokens.push_back(id);
drafts[0].tokens.push_back(token_id);
drafts[0].dists.push_back(std::vector<llama_token_data>());
drafts[0].i_batch_tgt.push_back(0);
llama_batch_clear(batch_dft);
llama_batch_add (batch_dft, id, n_past_dft, { 0 }, true);
llama_batch_add (batch_dft, token_id, n_past_dft, { 0 }, true);
llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
// LOG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str());
llama_decode (ctx_dft, batch_dft);
llama_decode(ctx_dft, batch_dft);
++n_past_dft;
break;
}
if (n_predict > params.n_predict || has_eos) {
@ -334,12 +456,6 @@ int main(int argc, char ** argv) {
k, s, i, cur_p[k].id, cur_p[k].p, llama_token_to_piece(ctx_dft, cur_p[k].id).c_str());
}
if (cur_p[0].p < p_accept) {
LOG("stopping drafting for seq %3d, probability too low: %.3f < %.3f\n", s, cur_p[0].p, p_accept);
drafts[s].drafting = false;
continue;
}
std::vector<int> sa(1, s);
// attempt to split the branch if the probability is high enough
@ -367,6 +483,7 @@ int main(int argc, char ** argv) {
drafts[n_seq_cur].skip = true;
drafts[n_seq_cur].tokens = drafts[s].tokens;
drafts[n_seq_cur].dists = drafts[s].dists;
drafts[n_seq_cur].i_batch_dft = drafts[s].i_batch_dft;
drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt;
@ -389,6 +506,8 @@ int main(int argc, char ** argv) {
llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id, true);
drafts[s].tokens.push_back(id);
// save cur_p.data into drafts[s].dists
drafts[s].dists.push_back(cur_p);
// add unique drafted tokens to the target batch
drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens);
@ -440,6 +559,7 @@ int main(int argc, char ** argv) {
}
drafts[s].tokens.erase(drafts[s].tokens.begin());
drafts[s].dists.erase(drafts[s].dists.begin());
}
}

2
examples/sycl/ls-sycl-device.cpp
View file

@ -7,7 +7,7 @@
#include "ggml-sycl.h"
int main(int argc, char ** argv) {
int main() {
ggml_backend_sycl_print_sycl_devices();
return 0;
}

17
examples/sycl/run-llama2.sh
View file

@ -8,12 +8,19 @@ INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
source /opt/intel/oneapi/setvars.sh
if [ $# -gt 0 ]; then
export GGML_SYCL_DEVICE=$1
GGML_SYCL_DEVICE=$1
else
export GGML_SYCL_DEVICE=0
GGML_SYCL_DEVICE=0
fi
echo GGML_SYCL_DEVICE=$GGML_SYCL_DEVICE
echo "use $GGML_SYCL_DEVICE as main GPU"
#export GGML_SYCL_DEBUG=1
./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
#./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 5 -e -ngl 33 -t 1 -s 0
#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer.
#use all GPUs with same max compute units
ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
#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

18
flake.lock generated
View file

@ -5,11 +5,11 @@
"nixpkgs-lib": "nixpkgs-lib"
},
"locked": {
"lastModified": 1706830856,
"narHash": "sha256-a0NYyp+h9hlb7ddVz4LUn1vT/PLwqfrWYcHMvFB1xYg=",
"lastModified": 1709336216,
"narHash": "sha256-Dt/wOWeW6Sqm11Yh+2+t0dfEWxoMxGBvv3JpIocFl9E=",
"owner": "hercules-ci",
"repo": "flake-parts",
"rev": "b253292d9c0a5ead9bc98c4e9a26c6312e27d69f",
"rev": "f7b3c975cf067e56e7cda6cb098ebe3fb4d74ca2",
"type": "github"
},
"original": {
@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1708655239,
"narHash": "sha256-ZrP/yACUvDB+zbqYJsln4iwotbH6CTZiTkANJ0AgDv4=",
"lastModified": 1709237383,
"narHash": "sha256-cy6ArO4k5qTx+l5o+0mL9f5fa86tYUX3ozE1S+Txlds=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "cbc4211f0afffe6dfd2478a62615dd5175a13f9a",
"rev": "1536926ef5621b09bba54035ae2bb6d806d72ac8",
"type": "github"
},
"original": {
@ -37,11 +37,11 @@
"nixpkgs-lib": {
"locked": {
"dir": "lib",
"lastModified": 1706550542,
"narHash": "sha256-UcsnCG6wx++23yeER4Hg18CXWbgNpqNXcHIo5/1Y+hc=",
"lastModified": 1709237383,
"narHash": "sha256-cy6ArO4k5qTx+l5o+0mL9f5fa86tYUX3ozE1S+Txlds=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "97b17f32362e475016f942bbdfda4a4a72a8a652",
"rev": "1536926ef5621b09bba54035ae2bb6d806d72ac8",
"type": "github"
},
"original": {

10
flake.nix
View file

@ -107,11 +107,12 @@
# ```
#
# Cf. https://nixos.org/manual/nix/unstable/command-ref/new-cli/nix3-flake.html?highlight=flake#flake-format
flake.overlays.default =
(final: prev: {
flake.overlays.default = (
final: prev: {
llamaPackages = final.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
inherit (final.llamaPackages) llama-cpp;
});
}
);
systems = [
"aarch64-darwin"
@ -131,6 +132,9 @@
...
}:
{
# For standardised reproducible formatting with `nix fmt`
formatter = pkgs.nixfmt-rfc-style;
# Unlike `.#packages`, legacyPackages may contain values of
# arbitrary types (including nested attrsets) and may even throw
# exceptions. This attribute isn't recursed into by `nix flake

7
ggml-backend-impl.h
View file

@ -91,13 +91,14 @@ extern "C" {
// (optional) complete all pending operations
void (*GGML_CALL synchronize)(ggml_backend_t backend);
// compute graph with a plan
// create a plan for ggml_cgraph and free it
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);
void (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
// compute graph with a plan
enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
// compute graph without a plan (async)
bool (*GGML_CALL graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
enum ggml_status (*GGML_CALL graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
// check if the backend supports an operation
bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);

39
ggml-backend.c
View file

@ -262,11 +262,11 @@ void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_pla
backend->iface.graph_plan_free(backend, plan);
}
void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
backend->iface.graph_plan_compute(backend, plan);
enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
return backend->iface.graph_plan_compute(backend, plan);
}
bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
return backend->iface.graph_compute(backend, cgraph);
}
@ -732,15 +732,15 @@ GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, g
GGML_UNUSED(backend);
}
GGML_CALL static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
GGML_CALL static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
GGML_UNUSED(backend);
}
GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
@ -755,8 +755,7 @@ GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, str
cplan.abort_callback = cpu_ctx->abort_callback;
cplan.abort_callback_data = cpu_ctx->abort_callback_data;
ggml_graph_compute(cgraph, &cplan);
return true;
return ggml_graph_compute(cgraph, &cplan);
}
GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
@ -1437,7 +1436,7 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
return true;
}
static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
uint64_t copy_us[GGML_MAX_BACKENDS] = {0};
uint64_t compute_us[GGML_MAX_BACKENDS] = {0};
@ -1472,8 +1471,9 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
uint64_t compute_start_us = ggml_time_us();
if (!sched->callback_eval) {
if (!ggml_backend_graph_compute(split_backend, &split->graph)) {
return false;
enum ggml_status ec = ggml_backend_graph_compute(split_backend, &split->graph);
if (ec != GGML_STATUS_SUCCESS) {
return ec;
}
//ggml_backend_synchronize(split_backend); // necessary to measure compute time
} else {
@ -1494,8 +1494,9 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1);
if (!ggml_backend_graph_compute(split_backend, &gv)) {
return false;
enum ggml_status ec = ggml_backend_graph_compute(split_backend, &gv);
if (ec != GGML_STATUS_SUCCESS) {
return ec;
}
if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) {
@ -1519,7 +1520,7 @@ static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
}
#endif
return true;
return GGML_STATUS_SUCCESS;
}
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) {
@ -1581,7 +1582,7 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph *
return true;
}
bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS);
if (!sched->is_reset) {
@ -1590,14 +1591,10 @@ bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cg
ggml_backend_sched_split_graph(sched, graph);
if (!ggml_backend_sched_alloc_splits(sched)) {
return false;
return GGML_STATUS_ALLOC_FAILED;
}
if (!ggml_backend_sched_compute_splits(sched)) {
return false;
}
return true;
return ggml_backend_sched_compute_splits(sched);
}
void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {

31
ggml-backend.h
View file

@ -66,12 +66,13 @@ extern "C" {
GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create (ggml_backend_t backend, struct ggml_cgraph * cgraph);
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 void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API bool ggml_backend_graph_compute (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 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 bool ggml_backend_supports_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);
@ -157,26 +158,26 @@ 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 void ggml_backend_sched_free(ggml_backend_sched_t sched);
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 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);
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_splits(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 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_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);
// Allocate and compute graph on the backend scheduler
GGML_API bool ggml_backend_sched_graph_compute(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);
// 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);
GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched);
// Set a callback to be called for each resulting node during graph compute
GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
//
// Utils

375
ggml-cuda.cu
View file

@ -616,6 +616,8 @@ static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + Q
#define CUDA_UPSCALE_BLOCK_SIZE 256
#define CUDA_CONCAT_BLOCK_SIZE 256
#define CUDA_PAD_BLOCK_SIZE 256
#define CUDA_ARANGE_BLOCK_SIZE 256
#define CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE 256
#define CUDA_ACC_BLOCK_SIZE 256
#define CUDA_IM2COL_BLOCK_SIZE 256
#define CUDA_POOL2D_BLOCK_SIZE 256
@ -990,17 +992,21 @@ static __global__ void concat_f32(const float * x,const float * y, float * dst,
nidx +
blockIdx.y * ne0 +
blockIdx.z * ne0 * gridDim.y;
dst[offset_dst] = x[offset_src];
dst[offset_dst] = x[offset_src];
} else {
int offset_src =
nidx +
blockIdx.y * ne0 +
(blockIdx.z - ne02) * ne0 * gridDim.y;
dst[offset_dst] = y[offset_src];
dst[offset_dst] = y[offset_src];
}
}
static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int nb02, const int scale_factor) {
static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int ne00xne01, const int scale_factor) {
// blockIdx.z: idx of ne02*ne03
// blockIdx.y: idx of ne01*scale_factor， aka ne1
// blockIDx.x: idx of ne00*scale_factor / BLOCK_SIZE
// ne00xne01: ne00 * ne01
int ne0 = ne00 * scale_factor;
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
if (nidx >= ne0) {
@ -1012,7 +1018,7 @@ static __global__ void upscale_f32(const float * x, float * dst, const int ne00,
int offset_src =
i00 +
i01 * ne00 +
blockIdx.z * nb02;
blockIdx.z * ne00xne01;
int offset_dst =
nidx +
blockIdx.y * ne0 +
@ -1020,7 +1026,10 @@ static __global__ void upscale_f32(const float * x, float * dst, const int ne00,
dst[offset_dst] = x[offset_src];
}
static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02) {
static __global__ void pad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02, const int ne03) {
// blockIdx.z: idx of ne2*ne3, aka ne02*ne03
// blockIdx.y: idx of ne1
// blockIDx.x: idx of ne0 / BLOCK_SIZE
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
if (nidx >= ne0) {
return;
@ -1031,19 +1040,53 @@ static __global__ void pad_f32(const float * x, float * dst, const int ne0, cons
nidx +
blockIdx.y * ne0 +
blockIdx.z * ne0 * gridDim.y;
if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02) {
if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02*ne03) {
int offset_src =
nidx +
blockIdx.y * ne00 +
blockIdx.z * ne00 * ne01;
dst[offset_dst] = x[offset_src];
dst[offset_dst] = x[offset_src];
} else {
dst[offset_dst] = 0.0f;
}
}
static __global__ void arange_f32(float * dst, const int ne0, const float start, const float step) {
// blockIDx.x: idx of ne0 / BLOCK_SIZE
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
if (nidx >= ne0) {
return;
}
dst[nidx] = start + step * nidx;
}
static __global__ void timestep_embedding_f32(const float * timesteps, float * dst, const int nb1, const int dim, const int max_period) {
// blockIDx.y: idx of timesteps->ne[0]
// blockIDx.x: idx of ((dim + 1) / 2) / BLOCK_SIZE
int i = blockIdx.y;
int j = threadIdx.x + blockIdx.x * blockDim.x;
float * embed_data = (float *)((char *)dst + i*nb1);
if (dim % 2 != 0 && j == ((dim + 1) / 2)) {
embed_data[dim] = 0.f;
}
int half = dim / 2;
if (j >= half) {
return;
}
float timestep = timesteps[i];
float freq = (float)expf(-logf(max_period) * j / half);
float arg = timestep * freq;
embed_data[j] = cosf(arg);
embed_data[j + half] = sinf(arg);
}
template <int block_size>
static __global__ void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps) {
// blockIdx.x: num_groups idx
// threadIdx.x: block_size idx
int start = blockIdx.x * group_size;
int end = start + group_size;
@ -2018,74 +2061,73 @@ static const __device__ uint32_t iq3xxs_grid[256] = {
0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
};
static const __device__ uint32_t iq3xs_grid[512] = {
0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14,
0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414,
0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24,
0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c,
0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c,
0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34,
0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c,
0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414,
0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c,
0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404,
0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434,
0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c,
0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404,
0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414,
0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414,
0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404,
0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c,
0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c,
0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404,
0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e,
0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14,
0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c,
0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424,
0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c,
0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c,
0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e,
0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e,
0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e,
0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424,
0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e,
0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424,
0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404,
0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c,
0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e,
0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c,
0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c,
0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c,
0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404,
0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04,
0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c,
0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414,
0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c,
0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c,
0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424,
0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c,
0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c,
0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414,
0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c,
0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e,
0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04,
0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424,
0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14,
0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34,
0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c,
0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434,
0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c,
0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424,
0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24,
0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24,
0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e,
0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c,
0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c,
0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c,
0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404,
static const __device__ uint32_t iq3s_grid[512] = {
0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305,
0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905,
0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09,
0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b,
0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b,
0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d,
0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03,
0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505,
0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03,
0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901,
0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d,
0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303,
0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501,
0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105,
0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505,
0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101,
0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707,
0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b,
0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01,
0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f,
0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305,
0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103,
0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509,
0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503,
0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b,
0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f,
0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f,
0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f,
0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109,
0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f,
0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509,
0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501,
0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303,
0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f,
0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907,
0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703,
0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03,
0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01,
0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01,
0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903,
0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505,
0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b,
0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107,
0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509,
0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303,
0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103,
0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05,
0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b,
0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f,
0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701,
0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909,
0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305,
0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d,
0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b,
0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d,
0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307,
0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09,
0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309,
0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709,
0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f,
0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303,
0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503,
0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b,
0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101,
};
static const __device__ uint64_t iq1s_grid[512] = {
0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000,
0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01,
@ -2392,9 +2434,9 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_
const int ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 8*il;
const uint8_t * qs = x[i].qs + 8*ib;
const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256)));
const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)) * 0.5f;
const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256)));
const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf));
const uint8_t signs = x[i].signs[4*ib + il];
for (int j = 0; j < 4; ++j) {
y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
@ -5211,8 +5253,8 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1(
const int8_t * q8 = bq8_1[ib32].qs;
int sumi = 0;
for (int l = 0; l < 4; ++l) {
const uint32_t * grid1 = iq3xs_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256));
const uint32_t * grid2 = iq3xs_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256));
const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256));
const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256));
uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >> 4) * 0x01010101) & 0x08040201, 0x08040201);
const int grid_l = __vsub4(grid1[0] ^ signs0, signs0);
@ -5221,7 +5263,7 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1(
sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
q8 += 8;
}
const float d = (float)bq2->d * (0.5f + ((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds) * 0.5f;
const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds);
return d * sumi;
#else
assert(false);
@ -6449,7 +6491,7 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
const int nb12, const int nb13) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
const int64_t i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= ne) {
return;
@ -6457,17 +6499,17 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
// determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
// then combine those indices with the corresponding byte offsets to get the total offsets
const int i03 = i/(ne00 * ne01 * ne02);
const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
const int64_t i03 = i/(ne00 * ne01 * ne02);
const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
const int64_t i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
const int64_t x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
const int i13 = i/(ne10 * ne11 * ne12);
const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
const int64_t i13 = i/(ne10 * ne11 * ne12);
const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13;
cpy_1(cx + x_offset, cdst + dst_offset);
}
@ -6905,6 +6947,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f
// find the sum of exps in the block
tmp = warp_reduce_sum(tmp);
if (block_size > WARP_SIZE) {
__syncthreads();
if (warp_id == 0) {
buf_iw[lane_id] = 0.0f;
}
@ -6956,23 +6999,23 @@ static __global__ void clamp_f32(const float * x, float * dst, const float min,
template <typename T>
static __global__ void im2col_kernel(
const float * x, T * dst, int batch_offset,
int offset_delta, int IC, int IW, int IH, int OH, int OW, int KW, int KH, int pelements, int CHW,
const float * x, T * dst, int64_t batch_offset,
int64_t offset_delta, int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW,
int s0, int s1, int p0, int p1, int d0, int d1) {
const int i = threadIdx.x + blockIdx.x * blockDim.x;
const int64_t i = threadIdx.x + blockIdx.x * blockDim.x;
if (i >= pelements) {
return;
}
const int ksize = OW * (KH > 1 ? KW : 1);
const int kx = i / ksize;
const int kd = kx * ksize;
const int ky = (i - kd) / OW;
const int ix = i % OW;
const int64_t ksize = OW * (KH > 1 ? KW : 1);
const int64_t kx = i / ksize;
const int64_t kd = kx * ksize;
const int64_t ky = (i - kd) / OW;
const int64_t ix = i % OW;
const int oh = blockIdx.y;
const int batch = blockIdx.z / IC;
const int ic = blockIdx.z % IC;
const int64_t oh = blockIdx.y;
const int64_t batch = blockIdx.z / IC;
const int64_t ic = blockIdx.z % IC;
const int64_t iiw = ix * s0 + kx * d0 - p0;
const int64_t iih = oh * s1 + ky * d1 - p1;
@ -7298,19 +7341,33 @@ static void concat_f32_cuda(const float * x, const float * y, float * dst, const
concat_f32<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
}
static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int scale_factor, cudaStream_t stream) {
static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int ne03,
const int scale_factor, cudaStream_t stream) {
int ne0 = (ne00 * scale_factor);
int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE;
dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02);
dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02*ne03);
upscale_f32<<<gridDim, CUDA_UPSCALE_BLOCK_SIZE, 0, stream>>>(x, dst, ne00, ne00 * ne01, scale_factor);
}
static void pad_f32_cuda(const float * x, float * dst,
const int ne00, const int ne01, const int ne02,
const int ne0, const int ne1, const int ne2, cudaStream_t stream) {
const int ne00, const int ne01, const int ne02, const int ne03,
const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) {
int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE;
dim3 gridDim(num_blocks, ne1, ne2);
pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(x, dst, ne0, ne00, ne01, ne02);
dim3 gridDim(num_blocks, ne1, ne2*ne3);
pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(x, dst, ne0, ne00, ne01, ne02, ne03);
}
static void arange_f32_cuda(float * dst, const int ne0, const float start, const float step, cudaStream_t stream) {
int num_blocks = (ne0 + CUDA_ARANGE_BLOCK_SIZE - 1) / CUDA_ARANGE_BLOCK_SIZE;
arange_f32<<<num_blocks, CUDA_ARANGE_BLOCK_SIZE, 0, stream>>>(dst, ne0, start, step);
}
static void timestep_embedding_f32_cuda(const float * x, float * dst, const int ne00, const int nb1,
const int dim, const int max_period, cudaStream_t stream) {
int half_ceil = (dim + 1) / 2;
int num_blocks = (half_ceil + CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE;
dim3 gridDim(num_blocks, ne00, 1);
timestep_embedding_f32<<<gridDim, CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE, 0, stream>>>(x, dst, nb1, dim, max_period);
}
static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
@ -8443,8 +8500,8 @@ static void soft_max_f32_cuda(const float * x, const float * mask, const float *
template <typename T>
static void im2col_cuda(const float* x, T* dst,
int IW, int IH, int OW, int OH, int KW, int KH, int IC,
int batch, int batch_offset, int offset_delta,
int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
int64_t batch, int64_t batch_offset, int64_t offset_delta,
int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
const int parallel_elements = OW * KW * KH;
const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
@ -9123,7 +9180,7 @@ static void ggml_cuda_op_group_norm(
int num_groups = dst->op_params[0];
int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
group_norm_f32_cuda(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream);
group_norm_f32_cuda(src0_dd, dst_dd, num_groups * src0->ne[3], group_size, ggml_nelements(src0), main_stream);
(void) src1;
(void) dst;
@ -9156,7 +9213,7 @@ static void ggml_cuda_op_upscale(
const int scale_factor = dst->op_params[0];
upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream);
upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], scale_factor, main_stream);
(void) src1;
(void) dst;
@ -9172,8 +9229,49 @@ static void ggml_cuda_op_pad(
GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
pad_f32_cuda(src0_dd, dst_dd,
src0->ne[0], src0->ne[1], src0->ne[2],
dst->ne[0], dst->ne[1], dst->ne[2], main_stream);
src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], main_stream);
(void) src1;
(void) dst;
(void) src1_dd;
}
static void ggml_cuda_op_arange(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
GGML_ASSERT(dst->type == GGML_TYPE_F32);
float start;
float stop;
float step;
memcpy(&start, (float *)dst->op_params + 0, sizeof(float));
memcpy(&stop, (float *)dst->op_params + 1, sizeof(float));
memcpy(&step, (float *)dst->op_params + 2, sizeof(float));
int64_t steps = (int64_t)ceil((stop - start) / step);
GGML_ASSERT(ggml_nelements(dst) == steps);
arange_f32_cuda(dst_dd, dst->ne[0], start, step, main_stream);
(void) src0;
(void) src1;
(void) src0_dd;
(void) src1_dd;
}
static void ggml_cuda_op_timestep_embedding(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
const int dim = dst->op_params[0];
const int max_period = dst->op_params[1];
timestep_embedding_f32_cuda(src0_dd, dst_dd, src0->ne[0], dst->nb[1], dim, max_period, main_stream);
(void) src1;
(void) dst;
@ -10458,6 +10556,45 @@ static void ggml_cuda_pad(const ggml_tensor * src0, const ggml_tensor * src1, gg
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pad);
}
static void ggml_cuda_arange(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU;
// dd = data device
float * src0_ddf = nullptr;
float * src1_ddf = nullptr;
float * dst_ddf = nullptr;
cuda_pool_alloc<float> dst_f;
ggml_cuda_set_device(g_main_device);
cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
if (dst_on_device) {
dst_ddf = (float *) dst_extra->data_device[g_main_device];
} else {
dst_ddf = dst_f.alloc(ggml_nelements(dst));
}
// do the computation
ggml_cuda_op_arange(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream);
CUDA_CHECK(cudaGetLastError());
// copy dst to host if necessary
if (!dst_on_device) {
CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream));
}
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
CUDA_CHECK(cudaDeviceSynchronize());
}
}
static void ggml_cuda_timestep_embedding(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_timestep_embedding);
}
static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm);
}
@ -11358,6 +11495,12 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st
case GGML_OP_PAD:
func = ggml_cuda_pad;
break;
case GGML_OP_ARANGE:
func = ggml_cuda_arange;
break;
case GGML_OP_TIMESTEP_EMBEDDING:
func = ggml_cuda_timestep_embedding;
break;
case GGML_OP_LEAKY_RELU:
func = ggml_cuda_leaky_relu;
break;
@ -12098,7 +12241,7 @@ GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
UNUSED(backend);
}
GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
ggml_cuda_set_main_device(cuda_ctx->device);
@ -12134,7 +12277,7 @@ GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, gg
GGML_ASSERT(ok);
}
return true;
return GGML_STATUS_SUCCESS;
}
GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
@ -12253,6 +12396,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_OP_GROUP_NORM:
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_LEAKY_RELU:
return true;
default:

4
ggml-kompute.cpp
View file

@ -1927,10 +1927,10 @@ static ggml_backend_buffer_type_t ggml_backend_kompute_get_default_buffer_type(g
return ggml_backend_kompute_buffer_type(ctx->device);
}
static bool ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
ggml_vk_graph_compute(ctx, cgraph);
return true;
return GGML_STATUS_SUCCESS;
}
static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {

70
ggml-metal.m
View file

@ -163,6 +163,8 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_IM2COL_F32,
GGML_METAL_KERNEL_TYPE_UPSCALE_F32,
GGML_METAL_KERNEL_TYPE_PAD_F32,
GGML_METAL_KERNEL_TYPE_ARANGE_F32,
GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,
GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,
GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,
@ -569,6 +571,8 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true);
@ -697,6 +701,8 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
return false;
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_ARGSORT:
case GGML_OP_LEAKY_RELU:
return true;
@ -742,7 +748,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
}
}
static bool ggml_metal_graph_compute(
static enum ggml_status ggml_metal_graph_compute(
struct ggml_metal_context * ctx,
struct ggml_cgraph * gf) {
@ -1091,7 +1097,8 @@ static bool ggml_metal_graph_compute(
{
GGML_ASSERT(ggml_is_contiguous(src0));
const float scale = *(const float *) dst->op_params;
float scale;
memcpy(&scale, dst->op_params, sizeof(scale));
int64_t n = ggml_nelements(dst);
@ -1250,11 +1257,15 @@ static bool ggml_metal_graph_compute(
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline;
}
const float scale = ((float *) dst->op_params)[0];
const float max_bias = ((float *) dst->op_params)[1];
float scale;
float max_bias;
memcpy(&scale, ((int32_t *) dst->op_params) + 0, sizeof(scale));
memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias));
const int64_t nrows_x = ggml_nrows(src0);
const int64_t nrows_y = src0->ne[1];
const uint32_t n_head_kv = nrows_x/nrows_y;
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
@ -2086,6 +2097,7 @@ static bool ggml_metal_graph_compute(
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_head = ((int32_t *) dst->op_params)[1];
float max_bias;
memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
@ -2300,6 +2312,50 @@ static bool ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ARANGE:
{
GGML_ASSERT(dst->type == GGML_TYPE_F32);
float start;
float step;
memcpy(&start, ((int32_t *) dst->op_params) + 0, sizeof(float));
memcpy(&step, ((int32_t *) dst->op_params) + 2, sizeof(float));
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_dst offset:offs_dst atIndex:0];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:1];
[encoder setBytes:&start length:sizeof(start) atIndex:2];
[encoder setBytes:&step length:sizeof(step) atIndex:3];
const int nth = MIN(1024, ne0);
[encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_TIMESTEP_EMBEDDING:
{
GGML_ASSERT(src0->type == GGML_TYPE_F32);
const int dim = dst->op_params[0];
const int max_period = dst->op_params[1];
const int half = dim / 2;
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:2];
[encoder setBytes:&dim length:sizeof(dim) atIndex:3];
[encoder setBytes:&max_period length:sizeof(max_period) atIndex:4];
const int nth = MIN(1024, half);
[encoder dispatchThreadgroups:MTLSizeMake(ne00, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ARGSORT:
{
GGML_ASSERT(src0->type == GGML_TYPE_F32);
@ -2428,7 +2484,7 @@ static bool ggml_metal_graph_compute(
MTLCommandBufferStatus status = [command_buffer status];
if (status != MTLCommandBufferStatusCompleted) {
GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
return false;
return GGML_STATUS_FAILED;
}
}
@ -2437,7 +2493,7 @@ static bool ggml_metal_graph_compute(
}
}
return true;
return GGML_STATUS_SUCCESS;
}
////////////////////////////////////////////////////////////////////////////////
@ -2739,7 +2795,7 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffe
UNUSED(backend);
}
GGML_CALL static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
GGML_CALL static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
return ggml_metal_graph_compute(metal_ctx, cgraph);

195
ggml-metal.metal
View file

@ -1959,6 +1959,49 @@ kernel void kernel_pad_f32(
}
}
kernel void kernel_arange_f32(
device char * dst,
constant int64_t & ne0,
constant float & start,
constant float & step,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
device float * dst_ptr = (device float *) dst;
for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
dst_ptr[i0] = start + step * i0;
}
}
kernel void kernel_timestep_embedding_f32(
device const char * src0,
device char * dst,
constant uint64_t & nb1,
constant int & dim,
constant int & max_period,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
int i = tgpig.x;
device float * embed_data = (device float *)(dst + i*nb1);
int half_ = dim / 2;
for (int j = tpitg.x; j < half_; j += ntg.x) {
float timestep = ((device float *)src0)[i];
float freq = (float)exp(-log((float)max_period) * j / half_);
float arg = timestep * freq;
embed_data[j ] = cos(arg);
embed_data[j + half_] = sin(arg);
}
if (dim % 2 != 0 && tpitg.x == 0) {
embed_data[dim] = 0.f;
}
}
// bitonic sort implementation following the CUDA kernels as reference
typedef void (argsort_t)(
device const float * x,
@ -4087,71 +4130,71 @@ constexpr constant static uint32_t iq3xxs_grid[256] = {
0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
};
constexpr constant static uint32_t iq3xs_grid[512] = {
0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14,
0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414,
0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24,
0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c,
0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c,
0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34,
0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c,
0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414,
0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c,
0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404,
0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434,
0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c,
0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404,
0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414,
0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414,
0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404,
0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c,
0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c,
0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404,
0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e,
0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14,
0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c,
0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424,
0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c,
0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c,
0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e,
0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e,
0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e,
0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424,
0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e,
0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424,
0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404,
0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c,
0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e,
0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c,
0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c,
0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c,
0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404,
0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04,
0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c,
0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414,
0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c,
0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c,
0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424,
0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c,
0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c,
0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414,
0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c,
0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e,
0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04,
0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424,
0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14,
0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34,
0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c,
0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434,
0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c,
0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424,
0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24,
0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24,
0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e,
0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c,
0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c,
0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c,
0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404,
constexpr constant static uint32_t iq3s_grid[512] = {
0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305,
0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905,
0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09,
0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b,
0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b,
0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d,
0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03,
0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505,
0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03,
0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901,
0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d,
0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303,
0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501,
0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105,
0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505,
0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101,
0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707,
0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b,
0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01,
0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f,
0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305,
0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103,
0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509,
0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503,
0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b,
0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f,
0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f,
0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f,
0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109,
0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f,
0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509,
0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501,
0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303,
0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f,
0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907,
0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703,
0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03,
0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01,
0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01,
0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903,
0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505,
0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b,
0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107,
0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509,
0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303,
0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103,
0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05,
0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b,
0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f,
0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701,
0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909,
0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305,
0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d,
0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b,
0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d,
0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307,
0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09,
0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309,
0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709,
0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f,
0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303,
0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503,
0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b,
0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101,
};
#define NGRID_IQ1S 512
@ -4742,7 +4785,7 @@ void kernel_mul_mv_iq3_s_f32_impl(
{
int nval = 8;
int pos = (32*sgitg + tiisg)*nval;
for (int i = 0; i < nval; ++i) values[pos + i] = iq3xs_grid[pos + i];
for (int i = 0; i < nval; ++i) values[pos + i] = iq3s_grid[pos + i];
threadgroup_barrier(mem_flags::mem_threadgroup);
}
@ -4769,12 +4812,14 @@ void kernel_mul_mv_iq3_s_f32_impl(
for (int row = 0; row < N_DST; row++) {
const float db = dh[0];
const float d = db * (0.5f + ((sc[0] >> 4*(ib%2)) & 0xf));
const float d = db * (1 + 2*((sc[0] >> 4*(ib%2)) & 0xf));
float2 sum = {0};
for (int l = 0; l < 4; ++l) {
const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256)));
const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256)));
const threadgroup uint32_t * table1 = qh[0] & kmask_iq2xs[2*l+0] ? values + 256 : values;
const threadgroup uint32_t * table2 = qh[0] & kmask_iq2xs[2*l+1] ? values + 256 : values;
const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(table1 + qs[2*l+0]);
const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(table2 + qs[2*l+1]);
for (int j = 0; j < 4; ++j) {
sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l] & kmask_iq2xs[j+0]);
sum[1] += yl[8*l + j + 4] * grid2[j] * select(1, -1, signs[l] & kmask_iq2xs[j+4]);
@ -4795,7 +4840,7 @@ void kernel_mul_mv_iq3_s_f32_impl(
for (int row = 0; row < N_DST; ++row) {
all_sum = simd_sum(sumf[row]);
if (tiisg == 0) {
dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.5f;
dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
}
}
}
@ -5685,15 +5730,15 @@ void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 &
device const uint8_t * qs = xb->qs + 8*ib32;
device const uint8_t * signs = xb->signs + 4*ib32 + 2*il;
const uint8_t qh = xb->qh[ib32] >> 4*il;
const float dl = d * (0.5f + ((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * 0.5f;
constant uint8_t * grid1 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+0] | ((qh << 8) & 256)));
constant uint8_t * grid2 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+1] | ((qh << 7) & 256)));
const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf));
constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256)));
constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256)));
for (int i = 0; i < 4; ++i) {
reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]);
reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]);
}
grid1 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+2] | ((qh << 6) & 256)));
grid2 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+3] | ((qh << 5) & 256)));
grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256)));
grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256)));
for (int i = 0; i < 4; ++i) {
reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]);
reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]);

4
ggml-opencl.cpp
View file

@ -2231,7 +2231,7 @@ static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(gg
GGML_UNUSED(backend);
}
static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) {
static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) {
for (int i = 0; i < graph->n_nodes; ++i) {
ggml_tensor * node = graph->nodes[i];
switch (node->op) {
@ -2246,7 +2246,7 @@ static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgrap
}
}
return true;
return GGML_STATUS_SUCCESS;
GGML_UNUSED(backend);
}

311
ggml-quants.c
View file

@ -51,6 +51,7 @@
#define UNUSED GGML_UNUSED
// some compilers don't provide _mm256_set_m128i, e.g. gcc 7
#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1)
#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
@ -463,8 +464,8 @@ inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) {
}
// NOTE: not tested
inline static int8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) {
int8x16_t res;
inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) {
uint8x16_t res;
res[ 0] = a[b[ 0]];
res[ 1] = a[b[ 1]];
@ -3818,71 +3819,71 @@ static const uint32_t iq3xxs_grid[256] = {
0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
};
static const uint32_t iq3xs_grid[512] = {
0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14,
0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414,
0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24,
0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c,
0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c,
0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34,
0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c,
0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414,
0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c,
0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404,
0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434,
0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c,
0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404,
0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414,
0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414,
0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404,
0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c,
0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c,
0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404,
0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e,
0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14,
0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c,
0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424,
0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c,
0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c,
0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e,
0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e,
0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e,
0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424,
0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e,
0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424,
0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404,
0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c,
0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e,
0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c,
0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c,
0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c,
0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404,
0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04,
0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c,
0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414,
0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c,
0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c,
0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424,
0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c,
0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c,
0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414,
0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c,
0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e,
0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04,
0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424,
0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14,
0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34,
0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c,
0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434,
0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c,
0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424,
0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24,
0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24,
0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e,
0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c,
0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c,
0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c,
0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404,
static const uint32_t iq3s_grid[512] = {
0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305,
0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905,
0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09,
0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b,
0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b,
0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d,
0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03,
0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505,
0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03,
0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901,
0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d,
0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303,
0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501,
0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105,
0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505,
0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101,
0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707,
0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b,
0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01,
0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f,
0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305,
0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103,
0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509,
0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503,
0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b,
0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f,
0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f,
0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f,
0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109,
0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f,
0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509,
0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501,
0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303,
0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f,
0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907,
0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703,
0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03,
0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01,
0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01,
0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903,
0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505,
0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b,
0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107,
0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509,
0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303,
0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103,
0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05,
0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b,
0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f,
0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701,
0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909,
0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305,
0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d,
0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b,
0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d,
0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307,
0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09,
0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309,
0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709,
0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f,
0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303,
0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503,
0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b,
0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101,
};
#define NGRID_IQ2XXS 512
@ -4162,11 +4163,11 @@ void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, in
const uint8_t * signs = x[i].signs;
for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
const float db1 = d * (0.5f + (x[i].scales[ib32/2] & 0xf)) * 0.5f;
const float db2 = d * (0.5f + (x[i].scales[ib32/2] >> 4)) * 0.5f;
const float db1 = d * (1 + 2*(x[i].scales[ib32/2] & 0xf));
const float db2 = d * (1 + 2*(x[i].scales[ib32/2] >> 4));
for (int l = 0; l < 4; ++l) {
const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256)));
const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256)));
for (int j = 0; j < 4; ++j) {
y[j+0] = db1 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f);
y[j+4] = db1 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f);
@ -4176,8 +4177,8 @@ void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, in
qs += 8;
signs += 4;
for (int l = 0; l < 4; ++l) {
const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[1] << (8-2*l)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[1] << (7-2*l)) & 256)));
const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[1] << (8-2*l)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[1] << (7-2*l)) & 256)));
for (int j = 0; j < 4; ++j) {
y[j+0] = db2 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f);
y[j+4] = db2 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f);
@ -9563,7 +9564,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void *
const __m128i odd_bits = _mm_shuffle_epi8(bit_helper, partial_sign_bits_for_counting);
const __m128i full_sign_bits = _mm_or_si128(partial_sign_bits, odd_bits);
const __m256i full_signs = _mm256_set_m128i(full_sign_bits, full_sign_bits);
const __m256i full_signs = MM256_SET_M128I(full_sign_bits, full_sign_bits);
const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)y[i].qs);
const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)(y[i].qs+32));
@ -9585,8 +9586,8 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void *
const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1);
const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2);
const __m256i sc1 = _mm256_set_m128i(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1));
const __m256i sc2 = _mm256_set_m128i(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1));
const __m256i sc1 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1));
const __m256i sc2 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1));
const __m256i sum = _mm256_add_epi32(_mm256_madd_epi16(sc1, dot1), _mm256_madd_epi16(sc2, dot2));
@ -9653,8 +9654,8 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void *
const __m128i full_signs_l = _mm256_castsi256_si128(full_sign_bits);
const __m128i full_signs_h = _mm256_extractf128_si256(full_sign_bits, 1);
const __m256i full_signs_1 = _mm256_set_m128i(full_signs_l, full_signs_l);
const __m256i full_signs_2 = _mm256_set_m128i(full_signs_h, full_signs_h);
const __m256i full_signs_1 = MM256_SET_M128I(full_signs_l, full_signs_l);
const __m256i full_signs_2 = MM256_SET_M128I(full_signs_h, full_signs_h);
__m256i signs;
signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_1);
@ -10089,18 +10090,34 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v
#if defined(__ARM_NEON)
typedef union {
uint16x8_t vec_index;
uint16_t index[8];
} vec_index_t;
static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
};
static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,};
const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1);
const uint8x16_t mask2 = vld1q_u8(k_mask2);
static const int16_t k_shift[8] = {8, 7, 6, 5, 4, 3, 2, 1};
const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1);
const uint8x16_t mask2 = vld1q_u8(k_mask2);
const int16x8_t hshift = vld1q_s16(k_shift);
const uint16x8_t m256 = vdupq_n_u16(256);
const uint8x16_t m1 = vdupq_n_u8(1);
uint8x16x2_t vs;
ggml_int8x16x4_t q3s;
ggml_int8x16x4_t q8b;
vec_index_t idx;
#if QK_K == 256
uint32_t scales32[2];
const uint8_t * scales8 = (const uint8_t *)scales32;
#endif
float sumf = 0;
for (int i = 0; i < nb; ++i) {
@ -10109,47 +10126,63 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v
const uint8_t * restrict qh = x[i].qh;
const uint16_t * restrict signs = (const uint16_t *)x[i].signs;
const int8_t * restrict q8 = y[i].qs;
#if QK_K == 256
memcpy(scales32, x[i].scales, 4);
scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101;
scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101;
#endif
int sumi1 = 0, sumi2 = 0;
for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
const uint32x4_t aux32x4_0 = {iq3xs_grid[qs[ 0] | ((qh[ib32+0] << 8) & 256)], iq3xs_grid[qs[ 1] | ((qh[ib32+0] << 7) & 256)],
iq3xs_grid[qs[ 2] | ((qh[ib32+0] << 6) & 256)], iq3xs_grid[qs[ 3] | ((qh[ib32+0] << 5) & 256)]};
const uint32x4_t aux32x4_1 = {iq3xs_grid[qs[ 4] | ((qh[ib32+0] << 4) & 256)], iq3xs_grid[qs[ 5] | ((qh[ib32+0] << 3) & 256)],
iq3xs_grid[qs[ 6] | ((qh[ib32+0] << 2) & 256)], iq3xs_grid[qs[ 7] | ((qh[ib32+0] << 1) & 256)]};
const uint32x4_t aux32x4_2 = {iq3xs_grid[qs[ 8] | ((qh[ib32+1] << 8) & 256)], iq3xs_grid[qs[ 9] | ((qh[ib32+1] << 7) & 256)],
iq3xs_grid[qs[10] | ((qh[ib32+1] << 6) & 256)], iq3xs_grid[qs[11] | ((qh[ib32+1] << 5) & 256)]};
const uint32x4_t aux32x4_3 = {iq3xs_grid[qs[12] | ((qh[ib32+1] << 4) & 256)], iq3xs_grid[qs[13] | ((qh[ib32+1] << 3) & 256)],
iq3xs_grid[qs[14] | ((qh[ib32+1] << 2) & 256)], iq3xs_grid[qs[15] | ((qh[ib32+1] << 1) & 256)]};
qs += 16;
const uint8x16_t idx_l = vld1q_u8(qs); qs += 16;
idx.vec_index = vorrq_u16(vmovl_u8(vget_low_u8 (idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+0]), hshift), m256));
const uint32x4_t aux32x4_0 = {iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]],
iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]};
const uint32x4_t aux32x4_1 = {iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]],
iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]};
idx.vec_index = vorrq_u16(vmovl_u8(vget_high_u8(idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+1]), hshift), m256));
const uint32x4_t aux32x4_2 = {iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]],
iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]};
const uint32x4_t aux32x4_3 = {iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]],
iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]};
vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16)));
vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
vs.val[0] = vceqq_u8(vs.val[0], mask2);
vs.val[1] = vceqq_u8(vs.val[1], mask2);
vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1);
vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1);
q3s.val[0] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[0], vreinterpretq_u8_u32(aux32x4_0))), vreinterpretq_s8_u8(vs.val[0]));
q3s.val[1] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_1))), vreinterpretq_s8_u8(vs.val[1]));
q3s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_0));
q3s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_1));
vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16)));
vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
vs.val[0] = vceqq_u8(vs.val[0], mask2);
vs.val[1] = vceqq_u8(vs.val[1], mask2);
vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1);
vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1);
signs += 4;
q3s.val[2] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[0], vreinterpretq_u8_u32(aux32x4_2))), vreinterpretq_s8_u8(vs.val[0]));
q3s.val[3] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_3))), vreinterpretq_s8_u8(vs.val[1]));
q3s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_2));
q3s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_3));
const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]);
const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]);
#if QK_K == 256
sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0];
sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4];
#else
sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32/2] & 0xf));
sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32/2] >> 4));
#endif
}
sumf += d*(sumi1 + sumi2);
}
*s = 0.25f * sumf;
*s = sumf;
#elif defined(__AVX2__)
@ -10164,6 +10197,16 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v
const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1);
const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2);
const __m256i idx_shift = _mm256_set_epi32(1, 2, 3, 4, 5, 6, 7, 8);
const __m256i idx_mask = _mm256_set1_epi32(256);
typedef union {
__m256i vec[2];
uint32_t index[16];
} index_t;
index_t idx;
__m256 accumf = _mm256_setzero_ps();
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
@ -10176,24 +10219,25 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v
for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
const __m256i q2_1 = _mm256_set_epi32(iq3xs_grid[qs[7] | ((qh[ib32+0] << 1) & 256)],
iq3xs_grid[qs[6] | ((qh[ib32+0] << 2) & 256)],
iq3xs_grid[qs[5] | ((qh[ib32+0] << 3) & 256)],
iq3xs_grid[qs[4] | ((qh[ib32+0] << 4) & 256)],
iq3xs_grid[qs[3] | ((qh[ib32+0] << 5) & 256)],
iq3xs_grid[qs[2] | ((qh[ib32+0] << 6) & 256)],
iq3xs_grid[qs[1] | ((qh[ib32+0] << 7) & 256)],
iq3xs_grid[qs[0] | ((qh[ib32+0] << 8) & 256)]);
qs += 8;
const __m256i q2_2 = _mm256_set_epi32(iq3xs_grid[qs[7] | ((qh[ib32+1] << 1) & 256)],
iq3xs_grid[qs[6] | ((qh[ib32+1] << 2) & 256)],
iq3xs_grid[qs[5] | ((qh[ib32+1] << 3) & 256)],
iq3xs_grid[qs[4] | ((qh[ib32+1] << 4) & 256)],
iq3xs_grid[qs[3] | ((qh[ib32+1] << 5) & 256)],
iq3xs_grid[qs[2] | ((qh[ib32+1] << 6) & 256)],
iq3xs_grid[qs[1] | ((qh[ib32+1] << 7) & 256)],
iq3xs_grid[qs[0] | ((qh[ib32+1] << 8) & 256)]);
qs += 8;
const __m256i idx_l = _mm256_cvtepu8_epi16(_mm_loadu_si128((const __m128i *)qs)); qs += 16;
idx.vec[0] = _mm256_set1_epi32(qh[ib32+0]);
idx.vec[1] = _mm256_set1_epi32(qh[ib32+1]);
idx.vec[0] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[0], idx_shift), idx_mask);
idx.vec[1] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[1], idx_shift), idx_mask);
idx.vec[0] = _mm256_or_si256(idx.vec[0], _mm256_cvtepi16_epi32(_mm256_castsi256_si128(idx_l)));
idx.vec[1] = _mm256_or_si256(idx.vec[1], _mm256_cvtepi16_epi32(_mm256_extractf128_si256(idx_l, 1)));
// At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange.
//const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4);
//const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4);
const __m256i q2_1 = _mm256_set_epi32(
iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]],
iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]]
);
const __m256i q2_2 = _mm256_set_epi32(
iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]],
iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]]
);
__m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16));
aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
@ -10221,7 +10265,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v
}
*s = 0.25f * hsum_float_8(accumf);
*s = hsum_float_8(accumf);
#else
@ -10238,8 +10282,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v
const uint32_t ls2 = 2*(x[i].scales[ib32/2] >> 4) + 1;
int32_t sumi = 0;
for (int l = 0; l < 4; ++l) {
const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256)));
const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256)));
for (int j = 0; j < 4; ++j) {
sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
@ -10251,8 +10295,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v
bsum += sumi * ls1;
sumi = 0;
for (int l = 0; l < 4; ++l) {
const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256)));
const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256)));
for (int j = 0; j < 4; ++j) {
sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
@ -10265,7 +10309,7 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const v
}
sumf += d * bsum;
}
*s = 0.25f * sumf;
*s = sumf;
#endif
}
@ -10508,10 +10552,10 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[1].qs);
const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[0].qs);
const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[1].qs);
const __m256i q4b_1 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
_mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
const __m256i q4b_2 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
_mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)));
const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
_mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
_mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)));
const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1);
const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
const __m256i p_1 = _mm256_madd_epi16(p16_1, mone);
@ -10618,10 +10662,10 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void *
const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)qs); qs += 16;
const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
const __m256i q4b_1 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
_mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
const __m256i q4b_2 = _mm256_set_m128i(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
_mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)));
const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
_mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
_mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)));
const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1);
const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32;
@ -11912,7 +11956,8 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo
}
float best = 0;
float scale = max/(2*kMaxQ-1);
for (int is = -15; is <= 15; ++is) {
for (int k = 0; k < bs4; ++k) is_on_grid[k] = false;
for (int is = -9; is <= 9; ++is) {
float id = (2*kMaxQ-1+is*0.2f)/max;
float this_scale = 1/id;
for (int k = 0; k < bs4; ++k) {
@ -11948,7 +11993,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo
if (n_not_ongrid > 0 && scale > 0) {
float id = 1/scale;
for (int k = 0; k < bs4; ++k) {
if (is_on_grid[k]) continue;
//if (is_on_grid[k]) continue;
uint16_t u = 0;
for (int i = 0; i < 4; ++i) {
int l = nearest_int(0.5f*(id*xval[4*k+i]-1));
@ -12004,7 +12049,7 @@ static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, vo
}
float d = max_scale/31;
y[ibl].d = GGML_FP32_TO_FP16(d);
y[ibl].d = GGML_FP32_TO_FP16(d * 1.033f);
float id = 1/d;
for (int ib = 0; ib < QK_K/block_size; ib += 2) {
int l1 = nearest_int(0.5f*(id*scales[ib+0]-1));

4303
ggml-sycl.cpp
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5
ggml-sycl.h
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@ -24,6 +24,11 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void);
GGML_API void ggml_backend_sycl_print_sycl_devices(void);
GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len);
GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description, size_t description_size);
GGML_API GGML_CALL int ggml_backend_sycl_get_device_count();
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split);
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);
#ifdef __cplusplus
}
#endif

86931
ggml-vulkan-shaders.hpp
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2158
ggml-vulkan.cpp
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1
ggml-vulkan.h
View file

@ -10,6 +10,7 @@ extern "C" {
#define GGML_VK_NAME "Vulkan"
#define GGML_VK_MAX_DEVICES 16
GGML_API void ggml_vk_instance_init(void);
GGML_API void ggml_vk_init_cpu_assist(void);
GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node);

242
ggml.c
View file

@ -320,6 +320,17 @@ static ggml_fp16_t ggml_table_exp_f16[1 << 16];
// precomputed f32 table for f16 (256 KB) (ggml-impl.h)
float ggml_table_f32_f16[1 << 16];
const char * ggml_status_to_string(enum ggml_status status) {
switch (status) {
case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)";
case GGML_STATUS_FAILED: return "GGML status: error (operation failed)";
case GGML_STATUS_SUCCESS: return "GGML status: success";
case GGML_STATUS_ABORTED: return "GGML status: warning (operation aborted)";
}
return "GGML status: unknown";
}
// note: do not use these inside ggml.c
// these are meant to be used via the ggml.h API
float ggml_fp16_to_fp32(ggml_fp16_t x) {
@ -1822,6 +1833,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"POOL_2D",
"UPSCALE",
"PAD",
"ARANGE",
"TIMESTEP_EMBEDDING",
"ARGSORT",
"LEAKY_RELU",
@ -1850,7 +1863,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"CROSS_ENTROPY_LOSS_BACK",
};
static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72");
static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -1908,6 +1921,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"pool_2d(x)",
"upscale(x)",
"pad(x)",
"arange(start, stop, step)",
"timestep_embedding(timesteps, dim, max_period)",
"argsort(x)",
"leaky_relu(x)",
@ -1936,7 +1951,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"cross_entropy_loss_back(x,y)",
};
static_assert(GGML_OP_COUNT == 72, "GGML_OP_COUNT != 72");
static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@ -2139,7 +2154,10 @@ void ggml_numa_init(enum ggml_numa_strategy numa_flag) {
getcpu_ret = getcpu(&current_cpu, &g_state.numa.current_node);
#else
// old glibc doesn't have a wrapper for this call. Fall back on direct syscall
getcpu_ret = syscall(SYS_getcpu,&current_cpu,&g_state.numa.current_node);
# if !defined(SYS_getcpu) && defined(SYS_get_cpu)
# define SYS_getcpu SYS_get_cpu // some older glibc versions use this name
# endif
getcpu_ret = syscall(SYS_getcpu, &current_cpu, &g_state.numa.current_node);
#endif
if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) {
@ -2895,11 +2913,21 @@ static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_
return ((const int32_t *)(tensor->op_params))[i];
}
static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) {
assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
return ((const float *)(tensor->op_params))[i];
}
static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) {
assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t));
((int32_t *)(tensor->op_params))[i] = value;
}
static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) {
assert(i < GGML_MAX_OP_PARAMS / sizeof(float));
((float *)(tensor->op_params))[i] = value;
}
struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
memset(tensor->data, 0, ggml_nbytes(tensor));
return tensor;
@ -5898,6 +5926,55 @@ struct ggml_tensor * ggml_upscale(
return ggml_upscale_impl(ctx, a, scale_factor);
}
struct ggml_tensor * ggml_arange(
struct ggml_context * ctx,
float start,
float stop,
float step) {
GGML_ASSERT(stop > start);
const int64_t steps = (int64_t) ceilf((stop - start) / step);
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, steps);
result->op = GGML_OP_ARANGE;
ggml_set_op_params_f32(result, 0, start);
ggml_set_op_params_f32(result, 1, stop);
ggml_set_op_params_f32(result, 2, step);
return result;
}
struct ggml_tensor * ggml_timestep_embedding(
struct ggml_context * ctx,
struct ggml_tensor * timesteps,
int dim,
int max_period) {
bool is_node = false;
if (timesteps->grad) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
int actual_dim = dim;
if (dim % 2 != 0) {
actual_dim = dim + 1;
}
struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, actual_dim, timesteps->ne[0]);
result->op = GGML_OP_TIMESTEP_EMBEDDING;
ggml_set_op_params_i32(result, 0, dim);
ggml_set_op_params_i32(result, 1, max_period);
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = timesteps;
return result;
}
// ggml_argsort
struct ggml_tensor * ggml_argsort(
@ -10231,7 +10308,7 @@ static void ggml_compute_forward_group_norm_f32(
int n_channels = src0->ne[2];
int n_groups = dst->op_params[0];
int n_channels_per_group = (n_channels + n_groups - 1) / n_groups;
for (int i = ith; i < n_groups; i+=nth) {
for (int i = ith; i < n_groups; i += nth) {
int start = i * n_channels_per_group;
int end = start + n_channels_per_group;
if (end > n_channels) {
@ -10245,28 +10322,32 @@ static void ggml_compute_forward_group_norm_f32(
for (int64_t i01 = 0; i01 < ne01; i01++) {
const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
ggml_float sumr = 0.0;
for (int64_t i00 = 0; i00 < ne00; i00++) {
sum += (ggml_float)x[i00];
sumr += (ggml_float)x[i00];
}
sum += sumr;
}
}
float mean = sum / (ne00 * ne01 * step);
ggml_float sum2 = 0.0;
const float mean = sum / (ne00 * ne01 * step);
ggml_float sum2 = 0.0;
for (int64_t i02 = start; i02 < end; i02++) {
for (int64_t i01 = 0; i01 < ne01; i01++) {
const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03);
float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3);
ggml_float sumr = 0.0;
for (int64_t i00 = 0; i00 < ne00; i00++) {
float v = x[i00] - mean;
y[i00] = v;
sum2 += (ggml_float)(v * v);
sumr += (ggml_float)(v * v);
}
sum2 += sumr;
}
}
float variance = sum2 / (ne00 * ne01 * step);
const float variance = sum2 / (ne00 * ne01 * step);
const float scale = 1.0f / sqrtf(variance + eps);
for (int64_t i02 = start; i02 < end; i02++) {
@ -13547,6 +13628,106 @@ static void ggml_compute_forward_pad(
}
}
// ggml_compute_forward_arange
static void ggml_compute_forward_arange_f32(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return;
}
GGML_ASSERT(dst->nb[0] == sizeof(float));
const int ith = params->ith;
const int nth = params->nth;
const float start = ggml_get_op_params_f32(dst, 0);
const float stop = ggml_get_op_params_f32(dst, 1);
const float step = ggml_get_op_params_f32(dst, 2);
const int64_t steps = (int64_t) ceilf((stop - start) / step);
GGML_ASSERT(ggml_nelements(dst) == steps);
for (int64_t i = ith; i < steps; i+= nth) {
float value = start + step * i;
((float *)dst->data)[i] = value;
}
}
static void ggml_compute_forward_arange(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
switch (dst->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_arange_f32(params, dst);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}
static void ggml_compute_forward_timestep_embedding_f32(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return;
}
const struct ggml_tensor * src0 = dst->src[0];
GGML_ASSERT(src0->nb[0] == sizeof(float));
const int ith = params->ith;
const int nth = params->nth;
GGML_TENSOR_UNARY_OP_LOCALS
const int dim = ggml_get_op_params_i32(dst, 0);
const int max_period = ggml_get_op_params_i32(dst, 1);
int half = dim / 2;
for (int64_t i = 0; i < ne00; i++) {
float * embed_data = (float *)((char *) dst->data + i*nb1);
for (int64_t j = ith; j < half; j += nth) {
float timestep = ((float *)src0->data)[i];
float freq = (float)expf(-logf(max_period) * j / half);
float arg = timestep * freq;
embed_data[j] = cosf(arg);
embed_data[j + half] = sinf(arg);
}
if (dim % 2 != 0 && ith == 0) {
embed_data[dim] = 0.f;
}
}
}
static void ggml_compute_forward_timestep_embedding(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_timestep_embedding_f32(params, dst);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_argsort
static void ggml_compute_forward_argsort_f32(
@ -15615,6 +15796,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_pad(params, tensor);
} break;
case GGML_OP_ARANGE:
{
ggml_compute_forward_arange(params, tensor);
} break;
case GGML_OP_TIMESTEP_EMBEDDING:
{
ggml_compute_forward_timestep_embedding(params, tensor);
} break;
case GGML_OP_ARGSORT:
{
ggml_compute_forward_argsort(params, tensor);
@ -16617,6 +16806,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_ARANGE:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_TIMESTEP_EMBEDDING:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_ARGSORT:
{
GGML_ASSERT(false); // TODO: not implemented
@ -17217,6 +17414,7 @@ struct ggml_compute_state {
ggml_thread_t thrd;
int ith;
struct ggml_compute_state_shared * shared;
enum ggml_status ec;
};
static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) {
@ -17368,6 +17566,14 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
{
n_tasks = n_threads;
} break;
case GGML_OP_ARANGE:
{
n_tasks = n_threads;
} break;
case GGML_OP_TIMESTEP_EMBEDDING:
{
n_tasks = n_threads;
} break;
case GGML_OP_ARGSORT:
{
n_tasks = n_threads;
@ -17502,7 +17708,8 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
while (true) {
if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
state->shared->node_n += 1;
return (thread_ret_t) GGML_EXIT_ABORTED;
state->ec = GGML_STATUS_ABORTED;
return 0;
}
if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
@ -17624,7 +17831,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
}
}
return GGML_EXIT_SUCCESS;
return 0;
}
struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threads) {
@ -17820,7 +18027,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
return cplan;
}
int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
{
GGML_ASSERT(cplan);
GGML_ASSERT(cplan->n_threads > 0);
@ -17864,6 +18071,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
.thrd = 0,
.ith = j,
.shared = &state_shared,
.ec = GGML_STATUS_SUCCESS,
};
const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]);
@ -17874,12 +18082,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
workers[0].ith = 0;
workers[0].shared = &state_shared;
workers[0].ec = GGML_STATUS_SUCCESS;
const int64_t perf_start_cycles = ggml_perf_cycles();
const int64_t perf_start_time_us = ggml_perf_time_us();
// this is a work thread too
int compute_status = (size_t) ggml_graph_compute_thread(&workers[0]);
ggml_graph_compute_thread(&workers[0]);
enum ggml_status compute_status = workers[0].ec;
// don't leave affinity set on the main thread
clear_numa_thread_affinity();
@ -17889,6 +18099,8 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
for (int j = 1; j < n_threads; j++) {
const int rc = ggml_thread_join(workers[j].thrd, NULL);
GGML_ASSERT(rc == 0);
if (workers[j].ec != GGML_STATUS_SUCCESS)
compute_status = workers[j].ec;
}
}
@ -17916,14 +18128,14 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
return compute_status;
}
void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) {
struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads);
struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size);
cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs;
ggml_graph_compute(cgraph, &cplan);
return ggml_graph_compute(cgraph, &cplan);
}
struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) {

34
ggml.h
View file

@ -315,6 +315,16 @@
extern "C" {
#endif
enum ggml_status {
GGML_STATUS_ALLOC_FAILED = -2,
GGML_STATUS_FAILED = -1,
GGML_STATUS_SUCCESS = 0,
GGML_STATUS_ABORTED = 1,
};
// get ggml_status name string
GGML_API GGML_CALL const char * ggml_status_to_string(enum ggml_status status);
typedef uint16_t ggml_fp16_t;
// convert FP16 <-> FP32
@ -454,6 +464,8 @@ extern "C" {
GGML_OP_POOL_2D,
GGML_OP_UPSCALE, // nearest interpolate
GGML_OP_PAD,
GGML_OP_ARANGE,
GGML_OP_TIMESTEP_EMBEDDING,
GGML_OP_ARGSORT,
GGML_OP_LEAKY_RELU,
@ -1661,6 +1673,15 @@ extern "C" {
int p2,
int p3);
// Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
// timesteps: [N,]
// return: [N, dim]
GGML_API struct ggml_tensor * ggml_timestep_embedding(
struct ggml_context * ctx,
struct ggml_tensor * timesteps,
int dim,
int max_period);
// sort rows
enum ggml_sort_order {
GGML_SORT_ORDER_ASC,
@ -1672,6 +1693,12 @@ extern "C" {
struct ggml_tensor * a,
enum ggml_sort_order order);
GGML_API struct ggml_tensor * ggml_arange(
struct ggml_context * ctx,
float start,
float stop,
float step);
// top k elements per row
GGML_API struct ggml_tensor * ggml_top_k(
struct ggml_context * ctx,
@ -1923,12 +1950,11 @@ extern "C" {
// ggml_graph_plan() has to be called before ggml_graph_compute()
// when plan.work_size > 0, caller must allocate memory for plan.work_data
GGML_API struct ggml_cplan ggml_graph_plan (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
GGML_API int ggml_graph_compute( struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
GGML_API struct ggml_cplan ggml_graph_plan (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
GGML_API enum ggml_status ggml_graph_compute ( struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
// same as ggml_graph_compute() but the work data is allocated as a part of the context
// note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
GGML_API void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
GGML_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);

1207
ggml_vk_generate_shaders.py
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File diff suppressed because it is too large Load diff

93
gguf-py/gguf/constants.py
View file

@ -112,6 +112,7 @@ class MODEL_ARCH(IntEnum):
INTERNLM2 = auto()
MINICPM = auto()
GEMMA = auto()
STARCODER2 = auto()
class MODEL_TENSOR(IntEnum):
@ -169,6 +170,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.INTERNLM2: "internlm2",
MODEL_ARCH.MINICPM: "minicpm",
MODEL_ARCH.GEMMA: "gemma",
MODEL_ARCH.STARCODER2: "starcoder2",
}
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -526,6 +528,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_NORM,
],
MODEL_ARCH.STARCODER2: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
# TODO
}
@ -554,6 +571,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
MODEL_ARCH.STARCODER2: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
}
#
@ -583,20 +604,28 @@ class PoolingType(IntEnum):
class GGMLQuantizationType(IntEnum):
F32 = 0
F16 = 1
Q4_0 = 2
Q4_1 = 3
Q5_0 = 6
Q5_1 = 7
Q8_0 = 8
Q8_1 = 9
Q2_K = 10
Q3_K = 11
Q4_K = 12
Q5_K = 13
Q6_K = 14
Q8_K = 15
F32 = 0
F16 = 1
Q4_0 = 2
Q4_1 = 3
Q5_0 = 6
Q5_1 = 7
Q8_0 = 8
Q8_1 = 9
Q2_K = 10
Q3_K = 11
Q4_K = 12
Q5_K = 13
Q6_K = 14
Q8_K = 15
IQ2_XXS = 16
IQ2_XS = 17
IQ3_XXS = 18
IQ1_S = 19
IQ4_NL = 20
IQ3_S = 21
IQ2_S = 22
IQ4_XS = 23
class GGUFEndian(IntEnum):
@ -641,20 +670,28 @@ class GGUFValueType(IntEnum):
QK_K = 256
# Items here are (block size, type size)
GGML_QUANT_SIZES = {
GGMLQuantizationType.F32: (1, 4),
GGMLQuantizationType.F16: (1, 2),
GGMLQuantizationType.Q4_0: (32, 2 + 16),
GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16),
GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16),
GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16),
GGMLQuantizationType.Q8_0: (32, 2 + 32),
GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32),
GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4),
GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12),
GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12),
GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8),
GGMLQuantizationType.F32: (1, 4),
GGMLQuantizationType.F16: (1, 2),
GGMLQuantizationType.Q4_0: (32, 2 + 16),
GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16),
GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16),
GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16),
GGMLQuantizationType.Q8_0: (32, 2 + 32),
GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32),
GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4),
GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12),
GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12),
GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8),
GGMLQuantizationType.IQ2_XXS: (256, 2 + QK_K // 4),
GGMLQuantizationType.IQ2_XS: (256, 2 + QK_K // 4 + QK_K // 32),
GGMLQuantizationType.IQ3_XXS: (256, 2 + QK_K // 4 + QK_K // 8),
GGMLQuantizationType.IQ1_S: (256, 2 + QK_K // 8 + QK_K // 16),
GGMLQuantizationType.IQ4_NL: (32, 2 + 16),
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),
}

2
gguf-py/gguf/gguf_writer.py
View file

@ -362,7 +362,7 @@ class GGUFWriter:
self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)
def add_pooling_type(self, value: PoolingType) -> None:
self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value)
self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
def add_rope_dimension_count(self, count: int) -> None:
self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count)

2
gguf-py/gguf/tensor_mapping.py
View file

@ -210,6 +210,7 @@ class TensorNameMap:
"model.layers.layers.{bid}.mlp.up_proj", # plamo
"model.layers.{bid}.feed_forward.w3", # internlm2
"encoder.layers.{bid}.mlp.fc11", # nomic-bert
"model.layers.{bid}.mlp.c_fc", # starcoder2
),
MODEL_TENSOR.FFN_UP_EXP: (
@ -256,6 +257,7 @@ class TensorNameMap:
"model.layers.layers.{bid}.mlp.down_proj", # plamo
"model.layers.{bid}.feed_forward.w2", # internlm2
"encoder.layers.{bid}.mlp.fc2", # nomic-bert
"model.layers.{bid}.mlp.c_proj", # starcoder2
),
MODEL_TENSOR.FFN_DOWN_EXP: (

2
grammars/json.gbnf
View file

@ -15,7 +15,7 @@ array ::=
string ::=
"\"" (
[^"\\] |
[^"\\\x7F\x00-\x1F] |
"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
)* "\"" ws

2
grammars/json_arr.gbnf
View file

@ -24,7 +24,7 @@ array ::=
string ::=
"\"" (
[^"\\] |
[^"\\\x7F\x00-\x1F] |
"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
)* "\"" ws

881
llama.cpp
View file

File diff suppressed because it is too large Load diff

41
llama.h
View file

@ -129,6 +129,7 @@ extern "C" {
};
enum llama_pooling_type {
LLAMA_POOLING_TYPE_UNSPECIFIED = -1,
LLAMA_POOLING_TYPE_NONE = 0,
LLAMA_POOLING_TYPE_MEAN = 1,
LLAMA_POOLING_TYPE_CLS = 2,
@ -162,7 +163,7 @@ extern "C" {
// - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
// - pos : the positions of the respective token in the sequence
// - seq_id : the sequence to which the respective token belongs
// - logits : if zero, the logits for the respective token will not be output
// - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output
//
typedef struct llama_batch {
int32_t n_tokens;
@ -172,7 +173,7 @@ extern "C" {
llama_pos * pos;
int32_t * n_seq_id;
llama_seq_id ** seq_id;
int8_t * logits;
int8_t * logits; // TODO: rename this to "output"
// NOTE: helpers for smooth API transition - can be deprecated in the future
// for future-proof code, use the above fields instead and ignore everything below
@ -236,7 +237,10 @@ extern "C" {
uint32_t n_batch; // prompt processing maximum batch size
uint32_t n_threads; // number of threads to use for generation
uint32_t n_threads_batch; // number of threads to use for batch processing
int32_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id
// (ignored if no pooling layer)
// ref: https://github.com/ggerganov/llama.cpp/pull/2054
float rope_freq_base; // RoPE base frequency, 0 = from model
@ -255,11 +259,15 @@ extern "C" {
enum ggml_type type_v; // data type for V cache
// Keep the booleans together to avoid misalignment during copy-by-value.
bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
bool embedding; // embedding mode only
bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
bool embeddings; // if true, extract embeddings (together with logits)
bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
bool do_pooling; // whether to pool (sum) embedding results by sequence id (ignored if no pooling layer)
// Abort callback
// if it returns true, execution of llama_decode() will be aborted
// currently works only with CPU execution
ggml_abort_callback abort_callback;
void * abort_callback_data;
};
// model quantization parameters
@ -575,7 +583,7 @@ extern "C" {
// Returns the number of bytes read
LLAMA_API size_t llama_set_state_data(
struct llama_context * ctx,
uint8_t * src);
const uint8_t * src);
// Save/load session file
LLAMA_API bool llama_load_session_file(
@ -633,7 +641,10 @@ 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);
// Token logits obtained from the last call to llama_eval()
// Set abort callback
LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
// 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
// Rows: n_tokens provided with llama_batch
@ -644,14 +655,20 @@ extern "C" {
// llama_get_logits(ctx) + i*n_vocab
LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
// Get the embeddings for the input
// shape: [n_embd] (1-dimensional)
// Get all output token embeddings
// shape: [n_tokens*n_embd] (1-dimensional)
LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
// Get the embeddings for the ith sequence
// Get the embeddings for the ith token
// llama_get_embeddings(ctx) + i*n_embd
// shape: [n_embd] (1-dimensional)
LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
// Get the embeddings for a sequence id
// Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE
// shape: [n_embd] (1-dimensional)
LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id);
//
// Vocab
//

1
requirements/requirements-convert-hf-to-gguf.txt
View file

@ -1,2 +1,3 @@
-r ./requirements-convert.txt
torch~=2.1.1
einops~=0.7.0

4
scripts/compare-llama-bench.py
View file

@ -18,7 +18,7 @@ except ImportError as e:
KEY_PROPERTIES = [
"cpu_info", "gpu_info", "n_gpu_layers", "main_gpu", "cuda", "opencl", "metal", "gpu_blas",
"blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads",
"type_k", "type_v", "no_kv_offload", "mul_mat_q", "tensor_split", "n_prompt", "n_gen"
"type_k", "type_v", "no_kv_offload", "tensor_split", "n_prompt", "n_gen"
]
# Properties that are boolean and are converted to Yes/No for the table:
@ -31,7 +31,7 @@ PRETTY_NAMES = {
"model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters",
"n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type",
"n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO",
"mul_mat_q": "MMQ", "tensor_split": "Tensor split"
"tensor_split": "Tensor split"
}
DEFAULT_SHOW = ["model_type"] # Always show these properties by default.

213
scripts/pod-llama.sh Normal file
View file

@ -0,0 +1,213 @@
#!/bin/bash
#
# Use this script only on fresh pods (runpod.io)!
# Otherwise, it can break your environment!
#
if [ -z "$1" ]; then
echo "Usage: $0 <data>"
echo " 0: no models"
echo " 1: tinyllama-1b"
echo " 2: codellama-7b"
echo " 3: codellama-13b"
echo " 4: codellama-34b"
echo " 5: codellama-7b-instruct"
echo " 6: codellama-13b-instruct"
echo " 7: codellama-34b-instruct"
exit 1
fi
set -x
# setup deps
apt-get update
apt-get install -y git-lfs cmake cmake-curses-gui vim ruby
git-lfs install
if [ ! -d "/workspace" ]; then
ln -sfn $(pwd) /workspace
fi
# download data
cd /workspace
# this is useful to git clone repos without doubling the disk size due to .git
git clone https://github.com/iboB/git-lfs-download
ln -sfn /workspace/git-lfs-download/git-lfs-download /usr/local/bin/git-lfs-download
# llama.cpp
cd /workspace
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
LLAMA_CUBLAS=1 make -j
ln -sfn /workspace/TinyLlama-1.1B-Chat-v0.3 ./models/tinyllama-1b
ln -sfn /workspace/CodeLlama-7b-hf ./models/codellama-7b
ln -sfn /workspace/CodeLlama-13b-hf ./models/codellama-13b
ln -sfn /workspace/CodeLlama-34b-hf ./models/codellama-34b
ln -sfn /workspace/CodeLlama-7b-Instruct-hf ./models/codellama-7b-instruct
ln -sfn /workspace/CodeLlama-13b-Instruct-hf ./models/codellama-13b-instruct
ln -sfn /workspace/CodeLlama-34b-Instruct-hf ./models/codellama-34b-instruct
pip install -r requirements.txt
# cmake
cd /workspace/llama.cpp
mkdir build-cublas
cd build-cublas
cmake -DLLAMA_CUBLAS=1 ../
make -j
if [ "$1" -eq "0" ]; then
exit 0
fi
# more models
if [ "$1" -eq "1" ]; then
cd /workspace
git-lfs-download https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3
cd /workspace/llama.cpp
python3 convert.py ./models/tinyllama-1b --outfile ./models/tinyllama-1b/ggml-model-f16.gguf --outtype f16
./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0
./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k
./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q8_0.gguf q8_0
fi
if [ "$1" -eq "2" ]; then
cd /workspace
git-lfs-download https://huggingface.co/codellama/CodeLlama-7b-hf --without *safetensors*
rm -v ./CodeLlama-7b-hf/*safetensors*
cd /workspace/llama.cpp
python3 convert.py ./models/codellama-7b --outfile ./models/codellama-7b/ggml-model-f16.gguf --outtype f16
./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0
./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k
./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q8_0.gguf q8_0
fi
if [ "$1" -eq "3" ]; then
cd /workspace
git-lfs-download https://huggingface.co/codellama/CodeLlama-13b-hf --without *safetensors*
rm -v ./CodeLlama-13b-hf/*safetensors*
cd /workspace/llama.cpp
python3 convert.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16
./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0
./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k
./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q8_0.gguf q8_0
fi
if [ "$1" -eq "4" ]; then
cd /workspace
git-lfs-download https://huggingface.co/codellama/CodeLlama-34b-hf --without *safetensors*
rm -v ./CodeLlama-34b-hf/*safetensors*
cd /workspace/llama.cpp
python3 convert.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16
./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0
./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k
./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q8_0.gguf q8_0
fi
if [ "$1" -eq "5" ]; then
cd /workspace
git-lfs-download https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf --without *safetensors*
rm -v ./CodeLlama-7b-Instruct-hf/*safetensors*
cd /workspace/llama.cpp
python3 convert.py ./models/codellama-7b-instruct --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf --outtype f16
./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0
./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k
./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q8_0.gguf q8_0
fi
if [ "$1" -eq "6" ]; then
cd /workspace
git-lfs-download https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf --without *safetensors*
rm -v ./CodeLlama-13b-Instruct-hf/*safetensors*
cd /workspace/llama.cpp
python3 convert.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16
./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0
./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k
./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q8_0.gguf q8_0
fi
if [ "$1" -eq "7" ]; then
cd /workspace
git-lfs-download https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf --without *safetensors*
rm -v ./CodeLlama-34b-Instruct-hf/*safetensors*
cd /workspace/llama.cpp
python3 convert.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16
./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0
./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k
./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q8_0.gguf q8_0
fi
if [ "$1" -eq "1" ]; then
# perf + perplexity
cd /workspace/llama.cpp/build-cublas
make -j && ../scripts/run-all-perf.sh tinyllama-1b "f16" "-ngl 99 -t 1 -p 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,32,64,128,256,512,1024,2048 -n 128"
../scripts/get-wikitext-2.sh
unzip wikitext-2-raw-v1.zip
make -j && ./bin/perplexity -m ../models/tinyllama-1b/ggml-model-f16.gguf -f ./wikitext-2-raw/wiki.test.raw -ngl 100 --chunks 32
# batched
cd /workspace/llama.cpp
LLAMA_CUBLAS=1 make -j && ./batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999
# batched-bench
cd /workspace/llama.cpp
LLAMA_CUBLAS=1 make -j && ./batched-bench ./models/tinyllama-1b/ggml-model-f16.gguf 4608 1 99 0 512 128 1,2,3,4,5,6,7,8,16,32
# parallel
cd /workspace/llama.cpp
LLAMA_CUBLAS=1 make -j && ./parallel -m ./models/tinyllama-1b/ggml-model-f16.gguf -t 1 -ngl 100 -c 4096 -b 512 -s 1 -np 8 -ns 128 -n 100 -cb
fi
# speculative
#if [ "$1" -eq "7" ]; then
# cd /workspace/llama.cpp
#
# LLAMA_CUBLAS=1 make -j && ./speculative -m ./models/codellama-34b-instruct/ggml-model-f16.gguf -md ./models/codellama-7b-instruct/ggml-model-q4_0.gguf -p "# Dijkstra's shortest path algorithm in Python (4 spaces indentation) + complexity analysis:\n\n" -e -ngl 999 -ngld 999 -t 4 -n 512 -c 4096 -s 21 --draft 16 -np 1 --temp 0.0
#fi
# more benches
#LLAMA_CUBLAS=1 make -j && ./batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1
#LLAMA_CUBLAS=1 make -j && ./batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1

2
scripts/sync-ggml.last
View file

@ -1 +1 @@
b458250b736a7473f7ff3560d47c93f1644f3290
8695910a39102609073d0e099aa7c97d6bcb3bf9

46
tests/test-backend-ops.cpp
View file

@ -1412,6 +1412,50 @@ struct test_pad : public test_case {
}
};
// GGML_OP_ARANGE
struct test_arange : public test_case {
const ggml_type type;
const float start;
const float stop;
const float step;
std::string vars() override {
return VARS_TO_STR4(type, start, stop, step);
}
test_arange(ggml_type type = GGML_TYPE_F32,
float start = 0.f, float stop = 10.f, float step = 1.f)
: type(type), start(start), stop(stop), step(step) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * out = ggml_arange(ctx, start, stop, step);
return out;
}
};
// GGML_OP_TIMESTEP_EMBEDDING
struct test_timestep_embedding : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne_a;
const int dim;
const int max_period;
std::string vars() override {
return VARS_TO_STR4(type, ne_a, dim, max_period);
}
test_timestep_embedding(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne_a = {2, 1, 1, 1},
int dim = 320, int max_period=10000)
: type(type), ne_a(ne_a), dim(dim), max_period(max_period) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
ggml_tensor * out = ggml_timestep_embedding(ctx, a, dim, max_period);
return out;
}
};
// GGML_OP_LEAKY_RELU
struct test_leaky_relu : public test_case {
const ggml_type type;
@ -2126,6 +2170,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_group_norm());
test_cases.emplace_back(new test_acc());
test_cases.emplace_back(new test_pad());
test_cases.emplace_back(new test_arange());
test_cases.emplace_back(new test_timestep_embedding());
test_cases.emplace_back(new test_leaky_relu());
// these tests are disabled to save execution time, but they can be handy for debugging

566
unicode.h
View file

@ -1,6 +1,7 @@
#pragma once
#include <cassert>
#include <map>
#include <stdexcept>
#include <string>
#include <unordered_map>
@ -223,266 +224,311 @@ static const std::vector<std::pair<uint32_t, uint32_t>> control_ranges = {
{0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF},
};
static const std::unordered_map<uint32_t, std::vector<uint32_t>> nfd_map = {
{0xC0, {0x41, 0x300}}, {0xC1, {0x41, 0x301}}, {0xC2, {0x41, 0x302}}, {0xC3, {0x41, 0x303}}, {0xC4, {0x41, 0x308}}, {0xC5, {0x41, 0x30A}}, {0xC7, {0x43, 0x327}}, {0xC8, {0x45, 0x300}},
{0xC9, {0x45, 0x301}}, {0xCA, {0x45, 0x302}}, {0xCB, {0x45, 0x308}}, {0xCC, {0x49, 0x300}}, {0xCD, {0x49, 0x301}}, {0xCE, {0x49, 0x302}}, {0xCF, {0x49, 0x308}}, {0xD1, {0x4E, 0x303}},
{0xD2, {0x4F, 0x300}}, {0xD3, {0x4F, 0x301}}, {0xD4, {0x4F, 0x302}}, {0xD5, {0x4F, 0x303}}, {0xD6, {0x4F, 0x308}}, {0xD9, {0x55, 0x300}}, {0xDA, {0x55, 0x301}}, {0xDB, {0x55, 0x302}},
{0xDC, {0x55, 0x308}}, {0xDD, {0x59, 0x301}}, {0xE0, {0x61, 0x300}}, {0xE1, {0x61, 0x301}}, {0xE2, {0x61, 0x302}}, {0xE3, {0x61, 0x303}}, {0xE4, {0x61, 0x308}}, {0xE5, {0x61, 0x30A}},
{0xE7, {0x63, 0x327}}, {0xE8, {0x65, 0x300}}, {0xE9, {0x65, 0x301}}, {0xEA, {0x65, 0x302}}, {0xEB, {0x65, 0x308}}, {0xEC, {0x69, 0x300}}, {0xED, {0x69, 0x301}}, {0xEE, {0x69, 0x302}},
{0xEF, {0x69, 0x308}}, {0xF1, {0x6E, 0x303}}, {0xF2, {0x6F, 0x300}}, {0xF3, {0x6F, 0x301}}, {0xF4, {0x6F, 0x302}}, {0xF5, {0x6F, 0x303}}, {0xF6, {0x6F, 0x308}}, {0xF9, {0x75, 0x300}},
{0xFA, {0x75, 0x301}}, {0xFB, {0x75, 0x302}}, {0xFC, {0x75, 0x308}}, {0xFD, {0x79, 0x301}}, {0xFF, {0x79, 0x308}}, {0x100, {0x41, 0x304}}, {0x101, {0x61, 0x304}}, {0x102, {0x41, 0x306}},
{0x103, {0x61, 0x306}}, {0x104, {0x41, 0x328}}, {0x105, {0x61, 0x328}}, {0x106, {0x43, 0x301}}, {0x107, {0x63, 0x301}}, {0x108, {0x43, 0x302}}, {0x109, {0x63, 0x302}}, {0x10A, {0x43, 0x307}},
{0x10B, {0x63, 0x307}}, {0x10C, {0x43, 0x30C}}, {0x10D, {0x63, 0x30C}}, {0x10E, {0x44, 0x30C}}, {0x10F, {0x64, 0x30C}}, {0x112, {0x45, 0x304}}, {0x113, {0x65, 0x304}}, {0x114, {0x45, 0x306}},
{0x115, {0x65, 0x306}}, {0x116, {0x45, 0x307}}, {0x117, {0x65, 0x307}}, {0x118, {0x45, 0x328}}, {0x119, {0x65, 0x328}}, {0x11A, {0x45, 0x30C}}, {0x11B, {0x65, 0x30C}}, {0x11C, {0x47, 0x302}},
{0x11D, {0x67, 0x302}}, {0x11E, {0x47, 0x306}}, {0x11F, {0x67, 0x306}}, {0x120, {0x47, 0x307}}, {0x121, {0x67, 0x307}}, {0x122, {0x47, 0x327}}, {0x123, {0x67, 0x327}}, {0x124, {0x48, 0x302}},
{0x125, {0x68, 0x302}}, {0x128, {0x49, 0x303}}, {0x129, {0x69, 0x303}}, {0x12A, {0x49, 0x304}}, {0x12B, {0x69, 0x304}}, {0x12C, {0x49, 0x306}}, {0x12D, {0x69, 0x306}}, {0x12E, {0x49, 0x328}},
{0x12F, {0x69, 0x328}}, {0x130, {0x49, 0x307}}, {0x134, {0x4A, 0x302}}, {0x135, {0x6A, 0x302}}, {0x136, {0x4B, 0x327}}, {0x137, {0x6B, 0x327}}, {0x139, {0x4C, 0x301}}, {0x13A, {0x6C, 0x301}},
{0x13B, {0x4C, 0x327}}, {0x13C, {0x6C, 0x327}}, {0x13D, {0x4C, 0x30C}}, {0x13E, {0x6C, 0x30C}}, {0x143, {0x4E, 0x301}}, {0x144, {0x6E, 0x301}}, {0x145, {0x4E, 0x327}}, {0x146, {0x6E, 0x327}},
{0x147, {0x4E, 0x30C}}, {0x148, {0x6E, 0x30C}}, {0x14C, {0x4F, 0x304}}, {0x14D, {0x6F, 0x304}}, {0x14E, {0x4F, 0x306}}, {0x14F, {0x6F, 0x306}}, {0x150, {0x4F, 0x30B}}, {0x151, {0x6F, 0x30B}},
{0x154, {0x52, 0x301}}, {0x155, {0x72, 0x301}}, {0x156, {0x52, 0x327}}, {0x157, {0x72, 0x327}}, {0x158, {0x52, 0x30C}}, {0x159, {0x72, 0x30C}}, {0x15A, {0x53, 0x301}}, {0x15B, {0x73, 0x301}},
{0x15C, {0x53, 0x302}}, {0x15D, {0x73, 0x302}}, {0x15E, {0x53, 0x327}}, {0x15F, {0x73, 0x327}}, {0x160, {0x53, 0x30C}}, {0x161, {0x73, 0x30C}}, {0x162, {0x54, 0x327}}, {0x163, {0x74, 0x327}},
{0x164, {0x54, 0x30C}}, {0x165, {0x74, 0x30C}}, {0x168, {0x55, 0x303}}, {0x169, {0x75, 0x303}}, {0x16A, {0x55, 0x304}}, {0x16B, {0x75, 0x304}}, {0x16C, {0x55, 0x306}}, {0x16D, {0x75, 0x306}},
{0x16E, {0x55, 0x30A}}, {0x16F, {0x75, 0x30A}}, {0x170, {0x55, 0x30B}}, {0x171, {0x75, 0x30B}}, {0x172, {0x55, 0x328}}, {0x173, {0x75, 0x328}}, {0x174, {0x57, 0x302}}, {0x175, {0x77, 0x302}},
{0x176, {0x59, 0x302}}, {0x177, {0x79, 0x302}}, {0x178, {0x59, 0x308}}, {0x179, {0x5A, 0x301}}, {0x17A, {0x7A, 0x301}}, {0x17B, {0x5A, 0x307}}, {0x17C, {0x7A, 0x307}}, {0x17D, {0x5A, 0x30C}},
{0x17E, {0x7A, 0x30C}}, {0x1A0, {0x4F, 0x31B}}, {0x1A1, {0x6F, 0x31B}}, {0x1AF, {0x55, 0x31B}}, {0x1B0, {0x75, 0x31B}}, {0x1CD, {0x41, 0x30C}}, {0x1CE, {0x61, 0x30C}}, {0x1CF, {0x49, 0x30C}},
{0x1D0, {0x69, 0x30C}}, {0x1D1, {0x4F, 0x30C}}, {0x1D2, {0x6F, 0x30C}}, {0x1D3, {0x55, 0x30C}}, {0x1D4, {0x75, 0x30C}}, {0x1D5, {0x55, 0x308, 0x304}}, {0x1D6, {0x75, 0x308, 0x304}},
{0x1D7, {0x55, 0x308, 0x301}}, {0x1D8, {0x75, 0x308, 0x301}}, {0x1D9, {0x55, 0x308, 0x30C}}, {0x1DA, {0x75, 0x308, 0x30C}}, {0x1DB, {0x55, 0x308, 0x300}}, {0x1DC, {0x75, 0x308, 0x300}},
{0x1DE, {0x41, 0x308, 0x304}}, {0x1DF, {0x61, 0x308, 0x304}}, {0x1E0, {0x41, 0x307, 0x304}}, {0x1E1, {0x61, 0x307, 0x304}}, {0x1E2, {0xC6, 0x304}}, {0x1E3, {0xE6, 0x304}}, {0x1E6, {0x47, 0x30C}},
{0x1E7, {0x67, 0x30C}}, {0x1E8, {0x4B, 0x30C}}, {0x1E9, {0x6B, 0x30C}}, {0x1EA, {0x4F, 0x328}}, {0x1EB, {0x6F, 0x328}}, {0x1EC, {0x4F, 0x328, 0x304}}, {0x1ED, {0x6F, 0x328, 0x304}},
{0x1EE, {0x1B7, 0x30C}}, {0x1EF, {0x292, 0x30C}}, {0x1F0, {0x6A, 0x30C}}, {0x1F4, {0x47, 0x301}}, {0x1F5, {0x67, 0x301}}, {0x1F8, {0x4E, 0x300}}, {0x1F9, {0x6E, 0x300}}, {0x1FA, {0x41, 0x30A, 0x301}},
{0x1FB, {0x61, 0x30A, 0x301}}, {0x1FC, {0xC6, 0x301}}, {0x1FD, {0xE6, 0x301}}, {0x1FE, {0xD8, 0x301}}, {0x1FF, {0xF8, 0x301}}, {0x200, {0x41, 0x30F}}, {0x201, {0x61, 0x30F}}, {0x202, {0x41, 0x311}},
{0x203, {0x61, 0x311}}, {0x204, {0x45, 0x30F}}, {0x205, {0x65, 0x30F}}, {0x206, {0x45, 0x311}}, {0x207, {0x65, 0x311}}, {0x208, {0x49, 0x30F}}, {0x209, {0x69, 0x30F}}, {0x20A, {0x49, 0x311}},
{0x20B, {0x69, 0x311}}, {0x20C, {0x4F, 0x30F}}, {0x20D, {0x6F, 0x30F}}, {0x20E, {0x4F, 0x311}}, {0x20F, {0x6F, 0x311}}, {0x210, {0x52, 0x30F}}, {0x211, {0x72, 0x30F}}, {0x212, {0x52, 0x311}},
{0x213, {0x72, 0x311}}, {0x214, {0x55, 0x30F}}, {0x215, {0x75, 0x30F}}, {0x216, {0x55, 0x311}}, {0x217, {0x75, 0x311}}, {0x218, {0x53, 0x326}}, {0x219, {0x73, 0x326}}, {0x21A, {0x54, 0x326}},
{0x21B, {0x74, 0x326}}, {0x21E, {0x48, 0x30C}}, {0x21F, {0x68, 0x30C}}, {0x226, {0x41, 0x307}}, {0x227, {0x61, 0x307}}, {0x228, {0x45, 0x327}}, {0x229, {0x65, 0x327}}, {0x22A, {0x4F, 0x308, 0x304}},
{0x22B, {0x6F, 0x308, 0x304}}, {0x22C, {0x4F, 0x303, 0x304}}, {0x22D, {0x6F, 0x303, 0x304}}, {0x22E, {0x4F, 0x307}}, {0x22F, {0x6F, 0x307}}, {0x230, {0x4F, 0x307, 0x304}},
{0x231, {0x6F, 0x307, 0x304}}, {0x232, {0x59, 0x304}}, {0x233, {0x79, 0x304}}, {0x340, {0x300}}, {0x341, {0x301}}, {0x343, {0x313}}, {0x344, {0x308, 0x301}}, {0x374, {0x2B9}}, {0x37E, {0x3B}},
{0x385, {0xA8, 0x301}}, {0x386, {0x391, 0x301}}, {0x387, {0xB7}}, {0x388, {0x395, 0x301}}, {0x389, {0x397, 0x301}}, {0x38A, {0x399, 0x301}}, {0x38C, {0x39F, 0x301}}, {0x38E, {0x3A5, 0x301}},
{0x38F, {0x3A9, 0x301}}, {0x390, {0x3B9, 0x308, 0x301}}, {0x3AA, {0x399, 0x308}}, {0x3AB, {0x3A5, 0x308}}, {0x3AC, {0x3B1, 0x301}}, {0x3AD, {0x3B5, 0x301}}, {0x3AE, {0x3B7, 0x301}},
{0x3AF, {0x3B9, 0x301}}, {0x3B0, {0x3C5, 0x308, 0x301}}, {0x3CA, {0x3B9, 0x308}}, {0x3CB, {0x3C5, 0x308}}, {0x3CC, {0x3BF, 0x301}}, {0x3CD, {0x3C5, 0x301}}, {0x3CE, {0x3C9, 0x301}},
{0x3D3, {0x3D2, 0x301}}, {0x3D4, {0x3D2, 0x308}}, {0x400, {0x415, 0x300}}, {0x401, {0x415, 0x308}}, {0x403, {0x413, 0x301}}, {0x407, {0x406, 0x308}}, {0x40C, {0x41A, 0x301}}, {0x40D, {0x418, 0x300}},
{0x40E, {0x423, 0x306}}, {0x419, {0x418, 0x306}}, {0x439, {0x438, 0x306}}, {0x450, {0x435, 0x300}}, {0x451, {0x435, 0x308}}, {0x453, {0x433, 0x301}}, {0x457, {0x456, 0x308}}, {0x45C, {0x43A, 0x301}},
{0x45D, {0x438, 0x300}}, {0x45E, {0x443, 0x306}}, {0x476, {0x474, 0x30F}}, {0x477, {0x475, 0x30F}}, {0x4C1, {0x416, 0x306}}, {0x4C2, {0x436, 0x306}}, {0x4D0, {0x410, 0x306}}, {0x4D1, {0x430, 0x306}},
{0x4D2, {0x410, 0x308}}, {0x4D3, {0x430, 0x308}}, {0x4D6, {0x415, 0x306}}, {0x4D7, {0x435, 0x306}}, {0x4DA, {0x4D8, 0x308}}, {0x4DB, {0x4D9, 0x308}}, {0x4DC, {0x416, 0x308}}, {0x4DD, {0x436, 0x308}},
{0x4DE, {0x417, 0x308}}, {0x4DF, {0x437, 0x308}}, {0x4E2, {0x418, 0x304}}, {0x4E3, {0x438, 0x304}}, {0x4E4, {0x418, 0x308}}, {0x4E5, {0x438, 0x308}}, {0x4E6, {0x41E, 0x308}}, {0x4E7, {0x43E, 0x308}},
{0x4EA, {0x4E8, 0x308}}, {0x4EB, {0x4E9, 0x308}}, {0x4EC, {0x42D, 0x308}}, {0x4ED, {0x44D, 0x308}}, {0x4EE, {0x423, 0x304}}, {0x4EF, {0x443, 0x304}}, {0x4F0, {0x423, 0x308}}, {0x4F1, {0x443, 0x308}},
{0x4F2, {0x423, 0x30B}}, {0x4F3, {0x443, 0x30B}}, {0x4F4, {0x427, 0x308}}, {0x4F5, {0x447, 0x308}}, {0x4F8, {0x42B, 0x308}}, {0x4F9, {0x44B, 0x308}}, {0x622, {0x627, 0x653}}, {0x623, {0x627, 0x654}},
{0x624, {0x648, 0x654}}, {0x625, {0x627, 0x655}}, {0x626, {0x64A, 0x654}}, {0x6C0, {0x6D5, 0x654}}, {0x6C2, {0x6C1, 0x654}}, {0x6D3, {0x6D2, 0x654}}, {0x929, {0x928, 0x93C}}, {0x931, {0x930, 0x93C}},
{0x934, {0x933, 0x93C}}, {0x958, {0x915, 0x93C}}, {0x959, {0x916, 0x93C}}, {0x95A, {0x917, 0x93C}}, {0x95B, {0x91C, 0x93C}}, {0x95C, {0x921, 0x93C}}, {0x95D, {0x922, 0x93C}}, {0x95E, {0x92B, 0x93C}},
{0x95F, {0x92F, 0x93C}}, {0x9CB, {0x9C7, 0x9BE}}, {0x9CC, {0x9C7, 0x9D7}}, {0x9DC, {0x9A1, 0x9BC}}, {0x9DD, {0x9A2, 0x9BC}}, {0x9DF, {0x9AF, 0x9BC}}, {0xA33, {0xA32, 0xA3C}}, {0xA36, {0xA38, 0xA3C}},
{0xA59, {0xA16, 0xA3C}}, {0xA5A, {0xA17, 0xA3C}}, {0xA5B, {0xA1C, 0xA3C}}, {0xA5E, {0xA2B, 0xA3C}}, {0xB48, {0xB47, 0xB56}}, {0xB4B, {0xB47, 0xB3E}}, {0xB4C, {0xB47, 0xB57}}, {0xB5C, {0xB21, 0xB3C}},
{0xB5D, {0xB22, 0xB3C}}, {0xB94, {0xB92, 0xBD7}}, {0xBCA, {0xBC6, 0xBBE}}, {0xBCB, {0xBC7, 0xBBE}}, {0xBCC, {0xBC6, 0xBD7}}, {0xC48, {0xC46, 0xC56}}, {0xCC0, {0xCBF, 0xCD5}}, {0xCC7, {0xCC6, 0xCD5}},
{0xCC8, {0xCC6, 0xCD6}}, {0xCCA, {0xCC6, 0xCC2}}, {0xCCB, {0xCC6, 0xCC2, 0xCD5}}, {0xD4A, {0xD46, 0xD3E}}, {0xD4B, {0xD47, 0xD3E}}, {0xD4C, {0xD46, 0xD57}}, {0xDDA, {0xDD9, 0xDCA}},
{0xDDC, {0xDD9, 0xDCF}}, {0xDDD, {0xDD9, 0xDCF, 0xDCA}}, {0xDDE, {0xDD9, 0xDDF}}, {0xF43, {0xF42, 0xFB7}}, {0xF4D, {0xF4C, 0xFB7}}, {0xF52, {0xF51, 0xFB7}}, {0xF57, {0xF56, 0xFB7}},
{0xF5C, {0xF5B, 0xFB7}}, {0xF69, {0xF40, 0xFB5}}, {0xF73, {0xF71, 0xF72}}, {0xF75, {0xF71, 0xF74}}, {0xF76, {0xFB2, 0xF80}}, {0xF78, {0xFB3, 0xF80}}, {0xF81, {0xF71, 0xF80}}, {0xF93, {0xF92, 0xFB7}},
{0xF9D, {0xF9C, 0xFB7}}, {0xFA2, {0xFA1, 0xFB7}}, {0xFA7, {0xFA6, 0xFB7}}, {0xFAC, {0xFAB, 0xFB7}}, {0xFB9, {0xF90, 0xFB5}}, {0x1026, {0x1025, 0x102E}}, {0x1B06, {0x1B05, 0x1B35}},
{0x1B08, {0x1B07, 0x1B35}}, {0x1B0A, {0x1B09, 0x1B35}}, {0x1B0C, {0x1B0B, 0x1B35}}, {0x1B0E, {0x1B0D, 0x1B35}}, {0x1B12, {0x1B11, 0x1B35}}, {0x1B3B, {0x1B3A, 0x1B35}}, {0x1B3D, {0x1B3C, 0x1B35}},
{0x1B40, {0x1B3E, 0x1B35}}, {0x1B41, {0x1B3F, 0x1B35}}, {0x1B43, {0x1B42, 0x1B35}}, {0x1E00, {0x41, 0x325}}, {0x1E01, {0x61, 0x325}}, {0x1E02, {0x42, 0x307}}, {0x1E03, {0x62, 0x307}},
{0x1E04, {0x42, 0x323}}, {0x1E05, {0x62, 0x323}}, {0x1E06, {0x42, 0x331}}, {0x1E07, {0x62, 0x331}}, {0x1E08, {0x43, 0x327, 0x301}}, {0x1E09, {0x63, 0x327, 0x301}}, {0x1E0A, {0x44, 0x307}},
{0x1E0B, {0x64, 0x307}}, {0x1E0C, {0x44, 0x323}}, {0x1E0D, {0x64, 0x323}}, {0x1E0E, {0x44, 0x331}}, {0x1E0F, {0x64, 0x331}}, {0x1E10, {0x44, 0x327}}, {0x1E11, {0x64, 0x327}}, {0x1E12, {0x44, 0x32D}},
{0x1E13, {0x64, 0x32D}}, {0x1E14, {0x45, 0x304, 0x300}}, {0x1E15, {0x65, 0x304, 0x300}}, {0x1E16, {0x45, 0x304, 0x301}}, {0x1E17, {0x65, 0x304, 0x301}}, {0x1E18, {0x45, 0x32D}},
{0x1E19, {0x65, 0x32D}}, {0x1E1A, {0x45, 0x330}}, {0x1E1B, {0x65, 0x330}}, {0x1E1C, {0x45, 0x327, 0x306}}, {0x1E1D, {0x65, 0x327, 0x306}}, {0x1E1E, {0x46, 0x307}}, {0x1E1F, {0x66, 0x307}},
{0x1E20, {0x47, 0x304}}, {0x1E21, {0x67, 0x304}}, {0x1E22, {0x48, 0x307}}, {0x1E23, {0x68, 0x307}}, {0x1E24, {0x48, 0x323}}, {0x1E25, {0x68, 0x323}}, {0x1E26, {0x48, 0x308}}, {0x1E27, {0x68, 0x308}},
{0x1E28, {0x48, 0x327}}, {0x1E29, {0x68, 0x327}}, {0x1E2A, {0x48, 0x32E}}, {0x1E2B, {0x68, 0x32E}}, {0x1E2C, {0x49, 0x330}}, {0x1E2D, {0x69, 0x330}}, {0x1E2E, {0x49, 0x308, 0x301}},
{0x1E2F, {0x69, 0x308, 0x301}}, {0x1E30, {0x4B, 0x301}}, {0x1E31, {0x6B, 0x301}}, {0x1E32, {0x4B, 0x323}}, {0x1E33, {0x6B, 0x323}}, {0x1E34, {0x4B, 0x331}}, {0x1E35, {0x6B, 0x331}},
{0x1E36, {0x4C, 0x323}}, {0x1E37, {0x6C, 0x323}}, {0x1E38, {0x4C, 0x323, 0x304}}, {0x1E39, {0x6C, 0x323, 0x304}}, {0x1E3A, {0x4C, 0x331}}, {0x1E3B, {0x6C, 0x331}}, {0x1E3C, {0x4C, 0x32D}},
{0x1E3D, {0x6C, 0x32D}}, {0x1E3E, {0x4D, 0x301}}, {0x1E3F, {0x6D, 0x301}}, {0x1E40, {0x4D, 0x307}}, {0x1E41, {0x6D, 0x307}}, {0x1E42, {0x4D, 0x323}}, {0x1E43, {0x6D, 0x323}}, {0x1E44, {0x4E, 0x307}},
{0x1E45, {0x6E, 0x307}}, {0x1E46, {0x4E, 0x323}}, {0x1E47, {0x6E, 0x323}}, {0x1E48, {0x4E, 0x331}}, {0x1E49, {0x6E, 0x331}}, {0x1E4A, {0x4E, 0x32D}}, {0x1E4B, {0x6E, 0x32D}},
{0x1E4C, {0x4F, 0x303, 0x301}}, {0x1E4D, {0x6F, 0x303, 0x301}}, {0x1E4E, {0x4F, 0x303, 0x308}}, {0x1E4F, {0x6F, 0x303, 0x308}}, {0x1E50, {0x4F, 0x304, 0x300}}, {0x1E51, {0x6F, 0x304, 0x300}},
{0x1E52, {0x4F, 0x304, 0x301}}, {0x1E53, {0x6F, 0x304, 0x301}}, {0x1E54, {0x50, 0x301}}, {0x1E55, {0x70, 0x301}}, {0x1E56, {0x50, 0x307}}, {0x1E57, {0x70, 0x307}}, {0x1E58, {0x52, 0x307}},
{0x1E59, {0x72, 0x307}}, {0x1E5A, {0x52, 0x323}}, {0x1E5B, {0x72, 0x323}}, {0x1E5C, {0x52, 0x323, 0x304}}, {0x1E5D, {0x72, 0x323, 0x304}}, {0x1E5E, {0x52, 0x331}}, {0x1E5F, {0x72, 0x331}},
{0x1E60, {0x53, 0x307}}, {0x1E61, {0x73, 0x307}}, {0x1E62, {0x53, 0x323}}, {0x1E63, {0x73, 0x323}}, {0x1E64, {0x53, 0x301, 0x307}}, {0x1E65, {0x73, 0x301, 0x307}}, {0x1E66, {0x53, 0x30C, 0x307}},
{0x1E67, {0x73, 0x30C, 0x307}}, {0x1E68, {0x53, 0x323, 0x307}}, {0x1E69, {0x73, 0x323, 0x307}}, {0x1E6A, {0x54, 0x307}}, {0x1E6B, {0x74, 0x307}}, {0x1E6C, {0x54, 0x323}}, {0x1E6D, {0x74, 0x323}},
{0x1E6E, {0x54, 0x331}}, {0x1E6F, {0x74, 0x331}}, {0x1E70, {0x54, 0x32D}}, {0x1E71, {0x74, 0x32D}}, {0x1E72, {0x55, 0x324}}, {0x1E73, {0x75, 0x324}}, {0x1E74, {0x55, 0x330}}, {0x1E75, {0x75, 0x330}},
{0x1E76, {0x55, 0x32D}}, {0x1E77, {0x75, 0x32D}}, {0x1E78, {0x55, 0x303, 0x301}}, {0x1E79, {0x75, 0x303, 0x301}}, {0x1E7A, {0x55, 0x304, 0x308}}, {0x1E7B, {0x75, 0x304, 0x308}},
{0x1E7C, {0x56, 0x303}}, {0x1E7D, {0x76, 0x303}}, {0x1E7E, {0x56, 0x323}}, {0x1E7F, {0x76, 0x323}}, {0x1E80, {0x57, 0x300}}, {0x1E81, {0x77, 0x300}}, {0x1E82, {0x57, 0x301}}, {0x1E83, {0x77, 0x301}},
{0x1E84, {0x57, 0x308}}, {0x1E85, {0x77, 0x308}}, {0x1E86, {0x57, 0x307}}, {0x1E87, {0x77, 0x307}}, {0x1E88, {0x57, 0x323}}, {0x1E89, {0x77, 0x323}}, {0x1E8A, {0x58, 0x307}}, {0x1E8B, {0x78, 0x307}},
{0x1E8C, {0x58, 0x308}}, {0x1E8D, {0x78, 0x308}}, {0x1E8E, {0x59, 0x307}}, {0x1E8F, {0x79, 0x307}}, {0x1E90, {0x5A, 0x302}}, {0x1E91, {0x7A, 0x302}}, {0x1E92, {0x5A, 0x323}}, {0x1E93, {0x7A, 0x323}},
{0x1E94, {0x5A, 0x331}}, {0x1E95, {0x7A, 0x331}}, {0x1E96, {0x68, 0x331}}, {0x1E97, {0x74, 0x308}}, {0x1E98, {0x77, 0x30A}}, {0x1E99, {0x79, 0x30A}}, {0x1E9B, {0x17F, 0x307}}, {0x1EA0, {0x41, 0x323}},
{0x1EA1, {0x61, 0x323}}, {0x1EA2, {0x41, 0x309}}, {0x1EA3, {0x61, 0x309}}, {0x1EA4, {0x41, 0x302, 0x301}}, {0x1EA5, {0x61, 0x302, 0x301}}, {0x1EA6, {0x41, 0x302, 0x300}},
{0x1EA7, {0x61, 0x302, 0x300}}, {0x1EA8, {0x41, 0x302, 0x309}}, {0x1EA9, {0x61, 0x302, 0x309}}, {0x1EAA, {0x41, 0x302, 0x303}}, {0x1EAB, {0x61, 0x302, 0x303}}, {0x1EAC, {0x41, 0x323, 0x302}},
{0x1EAD, {0x61, 0x323, 0x302}}, {0x1EAE, {0x41, 0x306, 0x301}}, {0x1EAF, {0x61, 0x306, 0x301}}, {0x1EB0, {0x41, 0x306, 0x300}}, {0x1EB1, {0x61, 0x306, 0x300}}, {0x1EB2, {0x41, 0x306, 0x309}},
{0x1EB3, {0x61, 0x306, 0x309}}, {0x1EB4, {0x41, 0x306, 0x303}}, {0x1EB5, {0x61, 0x306, 0x303}}, {0x1EB6, {0x41, 0x323, 0x306}}, {0x1EB7, {0x61, 0x323, 0x306}}, {0x1EB8, {0x45, 0x323}},
{0x1EB9, {0x65, 0x323}}, {0x1EBA, {0x45, 0x309}}, {0x1EBB, {0x65, 0x309}}, {0x1EBC, {0x45, 0x303}}, {0x1EBD, {0x65, 0x303}}, {0x1EBE, {0x45, 0x302, 0x301}}, {0x1EBF, {0x65, 0x302, 0x301}},
{0x1EC0, {0x45, 0x302, 0x300}}, {0x1EC1, {0x65, 0x302, 0x300}}, {0x1EC2, {0x45, 0x302, 0x309}}, {0x1EC3, {0x65, 0x302, 0x309}}, {0x1EC4, {0x45, 0x302, 0x303}}, {0x1EC5, {0x65, 0x302, 0x303}},
{0x1EC6, {0x45, 0x323, 0x302}}, {0x1EC7, {0x65, 0x323, 0x302}}, {0x1EC8, {0x49, 0x309}}, {0x1EC9, {0x69, 0x309}}, {0x1ECA, {0x49, 0x323}}, {0x1ECB, {0x69, 0x323}}, {0x1ECC, {0x4F, 0x323}},
{0x1ECD, {0x6F, 0x323}}, {0x1ECE, {0x4F, 0x309}}, {0x1ECF, {0x6F, 0x309}}, {0x1ED0, {0x4F, 0x302, 0x301}}, {0x1ED1, {0x6F, 0x302, 0x301}}, {0x1ED2, {0x4F, 0x302, 0x300}},
{0x1ED3, {0x6F, 0x302, 0x300}}, {0x1ED4, {0x4F, 0x302, 0x309}}, {0x1ED5, {0x6F, 0x302, 0x309}}, {0x1ED6, {0x4F, 0x302, 0x303}}, {0x1ED7, {0x6F, 0x302, 0x303}}, {0x1ED8, {0x4F, 0x323, 0x302}},
{0x1ED9, {0x6F, 0x323, 0x302}}, {0x1EDA, {0x4F, 0x31B, 0x301}}, {0x1EDB, {0x6F, 0x31B, 0x301}}, {0x1EDC, {0x4F, 0x31B, 0x300}}, {0x1EDD, {0x6F, 0x31B, 0x300}}, {0x1EDE, {0x4F, 0x31B, 0x309}},
{0x1EDF, {0x6F, 0x31B, 0x309}}, {0x1EE0, {0x4F, 0x31B, 0x303}}, {0x1EE1, {0x6F, 0x31B, 0x303}}, {0x1EE2, {0x4F, 0x31B, 0x323}}, {0x1EE3, {0x6F, 0x31B, 0x323}}, {0x1EE4, {0x55, 0x323}},
{0x1EE5, {0x75, 0x323}}, {0x1EE6, {0x55, 0x309}}, {0x1EE7, {0x75, 0x309}}, {0x1EE8, {0x55, 0x31B, 0x301}}, {0x1EE9, {0x75, 0x31B, 0x301}}, {0x1EEA, {0x55, 0x31B, 0x300}},
{0x1EEB, {0x75, 0x31B, 0x300}}, {0x1EEC, {0x55, 0x31B, 0x309}}, {0x1EED, {0x75, 0x31B, 0x309}}, {0x1EEE, {0x55, 0x31B, 0x303}}, {0x1EEF, {0x75, 0x31B, 0x303}}, {0x1EF0, {0x55, 0x31B, 0x323}},
{0x1EF1, {0x75, 0x31B, 0x323}}, {0x1EF2, {0x59, 0x300}}, {0x1EF3, {0x79, 0x300}}, {0x1EF4, {0x59, 0x323}}, {0x1EF5, {0x79, 0x323}}, {0x1EF6, {0x59, 0x309}}, {0x1EF7, {0x79, 0x309}},
{0x1EF8, {0x59, 0x303}}, {0x1EF9, {0x79, 0x303}}, {0x1F00, {0x3B1, 0x313}}, {0x1F01, {0x3B1, 0x314}}, {0x1F02, {0x3B1, 0x313, 0x300}}, {0x1F03, {0x3B1, 0x314, 0x300}}, {0x1F04, {0x3B1, 0x313, 0x301}},
{0x1F05, {0x3B1, 0x314, 0x301}}, {0x1F06, {0x3B1, 0x313, 0x342}}, {0x1F07, {0x3B1, 0x314, 0x342}}, {0x1F08, {0x391, 0x313}}, {0x1F09, {0x391, 0x314}}, {0x1F0A, {0x391, 0x313, 0x300}},
{0x1F0B, {0x391, 0x314, 0x300}}, {0x1F0C, {0x391, 0x313, 0x301}}, {0x1F0D, {0x391, 0x314, 0x301}}, {0x1F0E, {0x391, 0x313, 0x342}}, {0x1F0F, {0x391, 0x314, 0x342}}, {0x1F10, {0x3B5, 0x313}},
{0x1F11, {0x3B5, 0x314}}, {0x1F12, {0x3B5, 0x313, 0x300}}, {0x1F13, {0x3B5, 0x314, 0x300}}, {0x1F14, {0x3B5, 0x313, 0x301}}, {0x1F15, {0x3B5, 0x314, 0x301}}, {0x1F18, {0x395, 0x313}},
{0x1F19, {0x395, 0x314}}, {0x1F1A, {0x395, 0x313, 0x300}}, {0x1F1B, {0x395, 0x314, 0x300}}, {0x1F1C, {0x395, 0x313, 0x301}}, {0x1F1D, {0x395, 0x314, 0x301}}, {0x1F20, {0x3B7, 0x313}},
{0x1F21, {0x3B7, 0x314}}, {0x1F22, {0x3B7, 0x313, 0x300}}, {0x1F23, {0x3B7, 0x314, 0x300}}, {0x1F24, {0x3B7, 0x313, 0x301}}, {0x1F25, {0x3B7, 0x314, 0x301}}, {0x1F26, {0x3B7, 0x313, 0x342}},
{0x1F27, {0x3B7, 0x314, 0x342}}, {0x1F28, {0x397, 0x313}}, {0x1F29, {0x397, 0x314}}, {0x1F2A, {0x397, 0x313, 0x300}}, {0x1F2B, {0x397, 0x314, 0x300}}, {0x1F2C, {0x397, 0x313, 0x301}},
{0x1F2D, {0x397, 0x314, 0x301}}, {0x1F2E, {0x397, 0x313, 0x342}}, {0x1F2F, {0x397, 0x314, 0x342}}, {0x1F30, {0x3B9, 0x313}}, {0x1F31, {0x3B9, 0x314}}, {0x1F32, {0x3B9, 0x313, 0x300}},
{0x1F33, {0x3B9, 0x314, 0x300}}, {0x1F34, {0x3B9, 0x313, 0x301}}, {0x1F35, {0x3B9, 0x314, 0x301}}, {0x1F36, {0x3B9, 0x313, 0x342}}, {0x1F37, {0x3B9, 0x314, 0x342}}, {0x1F38, {0x399, 0x313}},
{0x1F39, {0x399, 0x314}}, {0x1F3A, {0x399, 0x313, 0x300}}, {0x1F3B, {0x399, 0x314, 0x300}}, {0x1F3C, {0x399, 0x313, 0x301}}, {0x1F3D, {0x399, 0x314, 0x301}}, {0x1F3E, {0x399, 0x313, 0x342}},
{0x1F3F, {0x399, 0x314, 0x342}}, {0x1F40, {0x3BF, 0x313}}, {0x1F41, {0x3BF, 0x314}}, {0x1F42, {0x3BF, 0x313, 0x300}}, {0x1F43, {0x3BF, 0x314, 0x300}}, {0x1F44, {0x3BF, 0x313, 0x301}},
{0x1F45, {0x3BF, 0x314, 0x301}}, {0x1F48, {0x39F, 0x313}}, {0x1F49, {0x39F, 0x314}}, {0x1F4A, {0x39F, 0x313, 0x300}}, {0x1F4B, {0x39F, 0x314, 0x300}}, {0x1F4C, {0x39F, 0x313, 0x301}},
{0x1F4D, {0x39F, 0x314, 0x301}}, {0x1F50, {0x3C5, 0x313}}, {0x1F51, {0x3C5, 0x314}}, {0x1F52, {0x3C5, 0x313, 0x300}}, {0x1F53, {0x3C5, 0x314, 0x300}}, {0x1F54, {0x3C5, 0x313, 0x301}},
{0x1F55, {0x3C5, 0x314, 0x301}}, {0x1F56, {0x3C5, 0x313, 0x342}}, {0x1F57, {0x3C5, 0x314, 0x342}}, {0x1F59, {0x3A5, 0x314}}, {0x1F5B, {0x3A5, 0x314, 0x300}}, {0x1F5D, {0x3A5, 0x314, 0x301}},
{0x1F5F, {0x3A5, 0x314, 0x342}}, {0x1F60, {0x3C9, 0x313}}, {0x1F61, {0x3C9, 0x314}}, {0x1F62, {0x3C9, 0x313, 0x300}}, {0x1F63, {0x3C9, 0x314, 0x300}}, {0x1F64, {0x3C9, 0x313, 0x301}},
{0x1F65, {0x3C9, 0x314, 0x301}}, {0x1F66, {0x3C9, 0x313, 0x342}}, {0x1F67, {0x3C9, 0x314, 0x342}}, {0x1F68, {0x3A9, 0x313}}, {0x1F69, {0x3A9, 0x314}}, {0x1F6A, {0x3A9, 0x313, 0x300}},
{0x1F6B, {0x3A9, 0x314, 0x300}}, {0x1F6C, {0x3A9, 0x313, 0x301}}, {0x1F6D, {0x3A9, 0x314, 0x301}}, {0x1F6E, {0x3A9, 0x313, 0x342}}, {0x1F6F, {0x3A9, 0x314, 0x342}}, {0x1F70, {0x3B1, 0x300}},
{0x1F71, {0x3B1, 0x301}}, {0x1F72, {0x3B5, 0x300}}, {0x1F73, {0x3B5, 0x301}}, {0x1F74, {0x3B7, 0x300}}, {0x1F75, {0x3B7, 0x301}}, {0x1F76, {0x3B9, 0x300}}, {0x1F77, {0x3B9, 0x301}},
{0x1F78, {0x3BF, 0x300}}, {0x1F79, {0x3BF, 0x301}}, {0x1F7A, {0x3C5, 0x300}}, {0x1F7B, {0x3C5, 0x301}}, {0x1F7C, {0x3C9, 0x300}}, {0x1F7D, {0x3C9, 0x301}}, {0x1F80, {0x3B1, 0x313, 0x345}},
{0x1F81, {0x3B1, 0x314, 0x345}}, {0x1F82, {0x3B1, 0x313, 0x300, 0x345}}, {0x1F83, {0x3B1, 0x314, 0x300, 0x345}}, {0x1F84, {0x3B1, 0x313, 0x301, 0x345}}, {0x1F85, {0x3B1, 0x314, 0x301, 0x345}},
{0x1F86, {0x3B1, 0x313, 0x342, 0x345}}, {0x1F87, {0x3B1, 0x314, 0x342, 0x345}}, {0x1F88, {0x391, 0x313, 0x345}}, {0x1F89, {0x391, 0x314, 0x345}}, {0x1F8A, {0x391, 0x313, 0x300, 0x345}},
{0x1F8B, {0x391, 0x314, 0x300, 0x345}}, {0x1F8C, {0x391, 0x313, 0x301, 0x345}}, {0x1F8D, {0x391, 0x314, 0x301, 0x345}}, {0x1F8E, {0x391, 0x313, 0x342, 0x345}}, {0x1F8F, {0x391, 0x314, 0x342, 0x345}},
{0x1F90, {0x3B7, 0x313, 0x345}}, {0x1F91, {0x3B7, 0x314, 0x345}}, {0x1F92, {0x3B7, 0x313, 0x300, 0x345}}, {0x1F93, {0x3B7, 0x314, 0x300, 0x345}}, {0x1F94, {0x3B7, 0x313, 0x301, 0x345}},
{0x1F95, {0x3B7, 0x314, 0x301, 0x345}}, {0x1F96, {0x3B7, 0x313, 0x342, 0x345}}, {0x1F97, {0x3B7, 0x314, 0x342, 0x345}}, {0x1F98, {0x397, 0x313, 0x345}}, {0x1F99, {0x397, 0x314, 0x345}},
{0x1F9A, {0x397, 0x313, 0x300, 0x345}}, {0x1F9B, {0x397, 0x314, 0x300, 0x345}}, {0x1F9C, {0x397, 0x313, 0x301, 0x345}}, {0x1F9D, {0x397, 0x314, 0x301, 0x345}}, {0x1F9E, {0x397, 0x313, 0x342, 0x345}},
{0x1F9F, {0x397, 0x314, 0x342, 0x345}}, {0x1FA0, {0x3C9, 0x313, 0x345}}, {0x1FA1, {0x3C9, 0x314, 0x345}}, {0x1FA2, {0x3C9, 0x313, 0x300, 0x345}}, {0x1FA3, {0x3C9, 0x314, 0x300, 0x345}},
{0x1FA4, {0x3C9, 0x313, 0x301, 0x345}}, {0x1FA5, {0x3C9, 0x314, 0x301, 0x345}}, {0x1FA6, {0x3C9, 0x313, 0x342, 0x345}}, {0x1FA7, {0x3C9, 0x314, 0x342, 0x345}}, {0x1FA8, {0x3A9, 0x313, 0x345}},
{0x1FA9, {0x3A9, 0x314, 0x345}}, {0x1FAA, {0x3A9, 0x313, 0x300, 0x345}}, {0x1FAB, {0x3A9, 0x314, 0x300, 0x345}}, {0x1FAC, {0x3A9, 0x313, 0x301, 0x345}}, {0x1FAD, {0x3A9, 0x314, 0x301, 0x345}},
{0x1FAE, {0x3A9, 0x313, 0x342, 0x345}}, {0x1FAF, {0x3A9, 0x314, 0x342, 0x345}}, {0x1FB0, {0x3B1, 0x306}}, {0x1FB1, {0x3B1, 0x304}}, {0x1FB2, {0x3B1, 0x300, 0x345}}, {0x1FB3, {0x3B1, 0x345}},
{0x1FB4, {0x3B1, 0x301, 0x345}}, {0x1FB6, {0x3B1, 0x342}}, {0x1FB7, {0x3B1, 0x342, 0x345}}, {0x1FB8, {0x391, 0x306}}, {0x1FB9, {0x391, 0x304}}, {0x1FBA, {0x391, 0x300}}, {0x1FBB, {0x391, 0x301}},
{0x1FBC, {0x391, 0x345}}, {0x1FBE, {0x3B9}}, {0x1FC1, {0xA8, 0x342}}, {0x1FC2, {0x3B7, 0x300, 0x345}}, {0x1FC3, {0x3B7, 0x345}}, {0x1FC4, {0x3B7, 0x301, 0x345}}, {0x1FC6, {0x3B7, 0x342}},
{0x1FC7, {0x3B7, 0x342, 0x345}}, {0x1FC8, {0x395, 0x300}}, {0x1FC9, {0x395, 0x301}}, {0x1FCA, {0x397, 0x300}}, {0x1FCB, {0x397, 0x301}}, {0x1FCC, {0x397, 0x345}}, {0x1FCD, {0x1FBF, 0x300}},
{0x1FCE, {0x1FBF, 0x301}}, {0x1FCF, {0x1FBF, 0x342}}, {0x1FD0, {0x3B9, 0x306}}, {0x1FD1, {0x3B9, 0x304}}, {0x1FD2, {0x3B9, 0x308, 0x300}}, {0x1FD3, {0x3B9, 0x308, 0x301}}, {0x1FD6, {0x3B9, 0x342}},
{0x1FD7, {0x3B9, 0x308, 0x342}}, {0x1FD8, {0x399, 0x306}}, {0x1FD9, {0x399, 0x304}}, {0x1FDA, {0x399, 0x300}}, {0x1FDB, {0x399, 0x301}}, {0x1FDD, {0x1FFE, 0x300}}, {0x1FDE, {0x1FFE, 0x301}},
{0x1FDF, {0x1FFE, 0x342}}, {0x1FE0, {0x3C5, 0x306}}, {0x1FE1, {0x3C5, 0x304}}, {0x1FE2, {0x3C5, 0x308, 0x300}}, {0x1FE3, {0x3C5, 0x308, 0x301}}, {0x1FE4, {0x3C1, 0x313}}, {0x1FE5, {0x3C1, 0x314}},
{0x1FE6, {0x3C5, 0x342}}, {0x1FE7, {0x3C5, 0x308, 0x342}}, {0x1FE8, {0x3A5, 0x306}}, {0x1FE9, {0x3A5, 0x304}}, {0x1FEA, {0x3A5, 0x300}}, {0x1FEB, {0x3A5, 0x301}}, {0x1FEC, {0x3A1, 0x314}},
{0x1FED, {0xA8, 0x300}}, {0x1FEE, {0xA8, 0x301}}, {0x1FEF, {0x60}}, {0x1FF2, {0x3C9, 0x300, 0x345}}, {0x1FF3, {0x3C9, 0x345}}, {0x1FF4, {0x3C9, 0x301, 0x345}}, {0x1FF6, {0x3C9, 0x342}},
{0x1FF7, {0x3C9, 0x342, 0x345}}, {0x1FF8, {0x39F, 0x300}}, {0x1FF9, {0x39F, 0x301}}, {0x1FFA, {0x3A9, 0x300}}, {0x1FFB, {0x3A9, 0x301}}, {0x1FFC, {0x3A9, 0x345}}, {0x1FFD, {0xB4}}, {0x2000, {0x2002}},
{0x2001, {0x2003}}, {0x2126, {0x3A9}}, {0x212A, {0x4B}}, {0x212B, {0x41, 0x30A}}, {0x219A, {0x2190, 0x338}}, {0x219B, {0x2192, 0x338}}, {0x21AE, {0x2194, 0x338}}, {0x21CD, {0x21D0, 0x338}},
{0x21CE, {0x21D4, 0x338}}, {0x21CF, {0x21D2, 0x338}}, {0x2204, {0x2203, 0x338}}, {0x2209, {0x2208, 0x338}}, {0x220C, {0x220B, 0x338}}, {0x2224, {0x2223, 0x338}}, {0x2226, {0x2225, 0x338}},
{0x2241, {0x223C, 0x338}}, {0x2244, {0x2243, 0x338}}, {0x2247, {0x2245, 0x338}}, {0x2249, {0x2248, 0x338}}, {0x2260, {0x3D, 0x338}}, {0x2262, {0x2261, 0x338}}, {0x226D, {0x224D, 0x338}},
{0x226E, {0x3C, 0x338}}, {0x226F, {0x3E, 0x338}}, {0x2270, {0x2264, 0x338}}, {0x2271, {0x2265, 0x338}}, {0x2274, {0x2272, 0x338}}, {0x2275, {0x2273, 0x338}}, {0x2278, {0x2276, 0x338}},
{0x2279, {0x2277, 0x338}}, {0x2280, {0x227A, 0x338}}, {0x2281, {0x227B, 0x338}}, {0x2284, {0x2282, 0x338}}, {0x2285, {0x2283, 0x338}}, {0x2288, {0x2286, 0x338}}, {0x2289, {0x2287, 0x338}},
{0x22AC, {0x22A2, 0x338}}, {0x22AD, {0x22A8, 0x338}}, {0x22AE, {0x22A9, 0x338}}, {0x22AF, {0x22AB, 0x338}}, {0x22E0, {0x227C, 0x338}}, {0x22E1, {0x227D, 0x338}}, {0x22E2, {0x2291, 0x338}},
{0x22E3, {0x2292, 0x338}}, {0x22EA, {0x22B2, 0x338}}, {0x22EB, {0x22B3, 0x338}}, {0x22EC, {0x22B4, 0x338}}, {0x22ED, {0x22B5, 0x338}}, {0x2329, {0x3008}}, {0x232A, {0x3009}},
{0x2ADC, {0x2ADD, 0x338}}, {0x304C, {0x304B, 0x3099}}, {0x304E, {0x304D, 0x3099}}, {0x3050, {0x304F, 0x3099}}, {0x3052, {0x3051, 0x3099}}, {0x3054, {0x3053, 0x3099}}, {0x3056, {0x3055, 0x3099}},
{0x3058, {0x3057, 0x3099}}, {0x305A, {0x3059, 0x3099}}, {0x305C, {0x305B, 0x3099}}, {0x305E, {0x305D, 0x3099}}, {0x3060, {0x305F, 0x3099}}, {0x3062, {0x3061, 0x3099}}, {0x3065, {0x3064, 0x3099}},
{0x3067, {0x3066, 0x3099}}, {0x3069, {0x3068, 0x3099}}, {0x3070, {0x306F, 0x3099}}, {0x3071, {0x306F, 0x309A}}, {0x3073, {0x3072, 0x3099}}, {0x3074, {0x3072, 0x309A}}, {0x3076, {0x3075, 0x3099}},
{0x3077, {0x3075, 0x309A}}, {0x3079, {0x3078, 0x3099}}, {0x307A, {0x3078, 0x309A}}, {0x307C, {0x307B, 0x3099}}, {0x307D, {0x307B, 0x309A}}, {0x3094, {0x3046, 0x3099}}, {0x309E, {0x309D, 0x3099}},
{0x30AC, {0x30AB, 0x3099}}, {0x30AE, {0x30AD, 0x3099}}, {0x30B0, {0x30AF, 0x3099}}, {0x30B2, {0x30B1, 0x3099}}, {0x30B4, {0x30B3, 0x3099}}, {0x30B6, {0x30B5, 0x3099}}, {0x30B8, {0x30B7, 0x3099}},
{0x30BA, {0x30B9, 0x3099}}, {0x30BC, {0x30BB, 0x3099}}, {0x30BE, {0x30BD, 0x3099}}, {0x30C0, {0x30BF, 0x3099}}, {0x30C2, {0x30C1, 0x3099}}, {0x30C5, {0x30C4, 0x3099}}, {0x30C7, {0x30C6, 0x3099}},
{0x30C9, {0x30C8, 0x3099}}, {0x30D0, {0x30CF, 0x3099}}, {0x30D1, {0x30CF, 0x309A}}, {0x30D3, {0x30D2, 0x3099}}, {0x30D4, {0x30D2, 0x309A}}, {0x30D6, {0x30D5, 0x3099}}, {0x30D7, {0x30D5, 0x309A}},
{0x30D9, {0x30D8, 0x3099}}, {0x30DA, {0x30D8, 0x309A}}, {0x30DC, {0x30DB, 0x3099}}, {0x30DD, {0x30DB, 0x309A}}, {0x30F4, {0x30A6, 0x3099}}, {0x30F7, {0x30EF, 0x3099}}, {0x30F8, {0x30F0, 0x3099}},
{0x30F9, {0x30F1, 0x3099}}, {0x30FA, {0x30F2, 0x3099}}, {0x30FE, {0x30FD, 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, 0x5B4}}, {0xFB1F, {0x5F2, 0x5B7}}, {0xFB2A, {0x5E9, 0x5C1}},
{0xFB2B, {0x5E9, 0x5C2}}, {0xFB2C, {0x5E9, 0x5BC, 0x5C1}}, {0xFB2D, {0x5E9, 0x5BC, 0x5C2}}, {0xFB2E, {0x5D0, 0x5B7}}, {0xFB2F, {0x5D0, 0x5B8}}, {0xFB30, {0x5D0, 0x5BC}}, {0xFB31, {0x5D1, 0x5BC}},
{0xFB32, {0x5D2, 0x5BC}}, {0xFB33, {0x5D3, 0x5BC}}, {0xFB34, {0x5D4, 0x5BC}}, {0xFB35, {0x5D5, 0x5BC}}, {0xFB36, {0x5D6, 0x5BC}}, {0xFB38, {0x5D8, 0x5BC}}, {0xFB39, {0x5D9, 0x5BC}},
{0xFB3A, {0x5DA, 0x5BC}}, {0xFB3B, {0x5DB, 0x5BC}}, {0xFB3C, {0x5DC, 0x5BC}}, {0xFB3E, {0x5DE, 0x5BC}}, {0xFB40, {0x5E0, 0x5BC}}, {0xFB41, {0x5E1, 0x5BC}}, {0xFB43, {0x5E3, 0x5BC}},
{0xFB44, {0x5E4, 0x5BC}}, {0xFB46, {0x5E6, 0x5BC}}, {0xFB47, {0x5E7, 0x5BC}}, {0xFB48, {0x5E8, 0x5BC}}, {0xFB49, {0x5E9, 0x5BC}}, {0xFB4A, {0x5EA, 0x5BC}}, {0xFB4B, {0x5D5, 0x5B9}},
{0xFB4C, {0x5D1, 0x5BF}}, {0xFB4D, {0x5DB, 0x5BF}}, {0xFB4E, {0x5E4, 0x5BF}}, {0x1109A, {0x11099, 0x110BA}}, {0x1109C, {0x1109B, 0x110BA}}, {0x110AB, {0x110A5, 0x110BA}},
{0x1112E, {0x11131, 0x11127}}, {0x1112F, {0x11132, 0x11127}}, {0x1134B, {0x11347, 0x1133E}}, {0x1134C, {0x11347, 0x11357}}, {0x114BB, {0x114B9, 0x114BA}}, {0x114BC, {0x114B9, 0x114B0}},
{0x114BE, {0x114B9, 0x114BD}}, {0x115BA, {0x115B8, 0x115AF}}, {0x115BB, {0x115B9, 0x115AF}}, {0x1D15E, {0x1D157, 0x1D165}}, {0x1D15F, {0x1D158, 0x1D165}}, {0x1D160, {0x1D158, 0x1D165, 0x1D16E}},
{0x1D161, {0x1D158, 0x1D165, 0x1D16F}}, {0x1D162, {0x1D158, 0x1D165, 0x1D170}}, {0x1D163, {0x1D158, 0x1D165, 0x1D171}}, {0x1D164, {0x1D158, 0x1D165, 0x1D172}}, {0x1D1BB, {0x1D1B9, 0x1D165}},
{0x1D1BC, {0x1D1BA, 0x1D165}}, {0x1D1BD, {0x1D1B9, 0x1D165, 0x1D16E}}, {0x1D1BE, {0x1D1BA, 0x1D165, 0x1D16E}}, {0x1D1BF, {0x1D1B9, 0x1D165, 0x1D16F}}, {0x1D1C0, {0x1D1BA, 0x1D165, 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 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) {