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Merge remote-tracking branch 'origin/master' into skip-unused-2

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This commit is contained in:
Olivier Chafik 2023-08-30 16:00:44 +01:00
commit d547e05f0e
71 changed files with 6322 additions and 4295 deletions
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44
.devops/lamma-cpp-clblast.srpm.spec → .devops/llama-cpp-clblast.srpm.spec
View file

@ -13,12 +13,13 @@
# It is up to the user to install the correct vendor-specific support.
Name: llama.cpp-clblast
Version: master
Version: %( date "+%%Y%%m%%d" )
Release: 1%{?dist}
Summary: OpenCL Inference of LLaMA model in pure C/C++
Summary: OpenCL Inference of LLaMA model in C/C++
License: MIT
Source0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
BuildRequires: coreutils make gcc-c++ git mesa-libOpenCL-devel
BuildRequires: coreutils make gcc-c++ git mesa-libOpenCL-devel clblast-devel
Requires: clblast
URL: https://github.com/ggerganov/llama.cpp
%define debug_package %{nil}
@ -35,18 +36,43 @@ make -j LLAMA_CLBLAST=1
%install
mkdir -p %{buildroot}%{_bindir}/
cp -p main %{buildroot}%{_bindir}/llamacppclblast
cp -p server %{buildroot}%{_bindir}/llamacppclblastserver
cp -p simple %{buildroot}%{_bindir}/llamacppclblastsimple
cp -p main %{buildroot}%{_bindir}/llamaclblast
cp -p server %{buildroot}%{_bindir}/llamaclblastserver
cp -p simple %{buildroot}%{_bindir}/llamaclblastsimple
mkdir -p %{buildroot}/usr/lib/systemd/system
%{__cat} <<EOF > %{buildroot}/usr/lib/systemd/system/llamaclblast.service
[Unit]
Description=Llama.cpp server, CPU only (no GPU support in this build).
After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
[Service]
Type=simple
EnvironmentFile=/etc/sysconfig/llama
ExecStart=/usr/bin/llamaclblastserver $LLAMA_ARGS
ExecReload=/bin/kill -s HUP $MAINPID
Restart=never
[Install]
WantedBy=default.target
EOF
mkdir -p %{buildroot}/etc/sysconfig
%{__cat} <<EOF > %{buildroot}/etc/sysconfig/llama
LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
EOF
%clean
rm -rf %{buildroot}
rm -rf %{_builddir}/*
%files
%{_bindir}/llamacppclblast
%{_bindir}/llamacppclblastserver
%{_bindir}/llamacppclblastsimple
%{_bindir}/llamaclblast
%{_bindir}/llamaclblastserver
%{_bindir}/llamaclblastsimple
/usr/lib/systemd/system/llamaclblast.service
%config /etc/sysconfig/llama
%pre

26
.devops/lamma-cpp-cublas.srpm.spec → .devops/llama-cpp-cublas.srpm.spec
View file

@ -13,7 +13,7 @@
# It is up to the user to install the correct vendor-specific support.
Name: llama.cpp-cublas
Version: master
Version: %( date "+%%Y%%m%%d" )
Release: 1%{?dist}
Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
License: MIT
@ -40,6 +40,28 @@ cp -p main %{buildroot}%{_bindir}/llamacppcublas
cp -p server %{buildroot}%{_bindir}/llamacppcublasserver
cp -p simple %{buildroot}%{_bindir}/llamacppcublassimple
mkdir -p %{buildroot}/usr/lib/systemd/system
%{__cat} <<EOF > %{buildroot}/usr/lib/systemd/system/llamacublas.service
[Unit]
Description=Llama.cpp server, CPU only (no GPU support in this build).
After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
[Service]
Type=simple
EnvironmentFile=/etc/sysconfig/llama
ExecStart=/usr/bin/llamacppcublasserver $LLAMA_ARGS
ExecReload=/bin/kill -s HUP $MAINPID
Restart=never
[Install]
WantedBy=default.target
EOF
mkdir -p %{buildroot}/etc/sysconfig
%{__cat} <<EOF > %{buildroot}/etc/sysconfig/llama
LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
EOF
%clean
rm -rf %{buildroot}
rm -rf %{_builddir}/*
@ -48,6 +70,8 @@ rm -rf %{_builddir}/*
%{_bindir}/llamacppcublas
%{_bindir}/llamacppcublasserver
%{_bindir}/llamacppcublassimple
/usr/lib/systemd/system/llamacublas.service
%config /etc/sysconfig/llama
%pre

45
.devops/llama-cpp.srpm.spec
View file

@ -6,6 +6,7 @@
# Notes for llama.cpp:
# 1. Tags are currently based on hash - which will not sort asciibetically.
# We need to declare standard versioning if people want to sort latest releases.
# In the meantime, YYYYMMDD format will be used.
# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.
# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.
# Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo
@ -13,12 +14,13 @@
# It is up to the user to install the correct vendor-specific support.
Name: llama.cpp
Version: master
Version: %( date "+%%Y%%m%%d" )
Release: 1%{?dist}
Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
License: MIT
Source0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
BuildRequires: coreutils make gcc-c++ git
BuildRequires: coreutils make gcc-c++ git libstdc++-devel
Requires: libstdc++
URL: https://github.com/ggerganov/llama.cpp
%define debug_package %{nil}
@ -26,27 +28,52 @@ URL: https://github.com/ggerganov/llama.cpp
%description
CPU inference for Meta's Lllama2 models using default options.
Models are not included in this package and must be downloaded separately.
%prep
%autosetup
%setup -n llama.cpp-master
%build
make -j
%install
mkdir -p %{buildroot}%{_bindir}/
cp -p main %{buildroot}%{_bindir}/llamacpp
cp -p server %{buildroot}%{_bindir}/llamacppserver
cp -p simple %{buildroot}%{_bindir}/llamacppsimple
cp -p main %{buildroot}%{_bindir}/llama
cp -p server %{buildroot}%{_bindir}/llamaserver
cp -p simple %{buildroot}%{_bindir}/llamasimple
mkdir -p %{buildroot}/usr/lib/systemd/system
%{__cat} <<EOF > %{buildroot}/usr/lib/systemd/system/llama.service
[Unit]
Description=Llama.cpp server, CPU only (no GPU support in this build).
After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
[Service]
Type=simple
EnvironmentFile=/etc/sysconfig/llama
ExecStart=/usr/bin/llamaserver $LLAMA_ARGS
ExecReload=/bin/kill -s HUP $MAINPID
Restart=never
[Install]
WantedBy=default.target
EOF
mkdir -p %{buildroot}/etc/sysconfig
%{__cat} <<EOF > %{buildroot}/etc/sysconfig/llama
LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
EOF
%clean
rm -rf %{buildroot}
rm -rf %{_builddir}/*
%files
%{_bindir}/llamacpp
%{_bindir}/llamacppserver
%{_bindir}/llamacppsimple
%{_bindir}/llama
%{_bindir}/llamaserver
%{_bindir}/llamasimple
/usr/lib/systemd/system/llama.service
%config /etc/sysconfig/llama
%pre

12
.github/workflows/build.yml vendored
View file

@ -41,6 +41,12 @@ jobs:
run: |
CC=gcc-8 make
- name: Test
id: make_test
run: |
CC=gcc-8 make tests
make test
ubuntu-latest-cmake:
runs-on: ubuntu-latest
@ -157,6 +163,12 @@ jobs:
run: |
make
- name: Test
id: make_test
run: |
make tests
make test
macOS-latest-cmake:
runs-on: macos-latest

43
.github/workflows/gguf-publish.yml vendored Normal file
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@ -0,0 +1,43 @@
# This workflow will upload a Python Package using Twine when a GGUF release is created
# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
# See `gguf-py/README.md` for how to make a release.
# This workflow uses actions that are not certified by GitHub.
# They are provided by a third-party and are governed by
# separate terms of service, privacy policy, and support
# documentation.
name: Upload Python Package
on:
workflow_dispatch:
push:
# Pattern matched against refs/tags
tags:
- 'gguf-v*' # Push events to every version tag
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: '3.9.x'
- name: Install dependencies
run: |
cd gguf-py
python -m pip install poetry
poetry install
- name: Build package
run: poetry build
- name: Publish package
uses: pypa/gh-action-pypi-publish@release/v1
with:
password: ${{ secrets.PYPI_API_TOKEN }}

9
.gitignore vendored
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@ -5,6 +5,7 @@
*.bin
*.exe
*.dll
*.log
.DS_Store
.build/
.cache/
@ -41,6 +42,9 @@ models-mnt
/gguf-llama-simple
/libllama.so
/llama-bench
/baby-llama
/beam-search
/save-load-state
build-info.h
arm_neon.h
compile_commands.json
@ -63,10 +67,13 @@ poetry.toml
# Test binaries
tests/test-grammar-parser
tests/test-llama-grammar
tests/test-double-float
tests/test-grad0
tests/test-opt
tests/test-quantize-fns
tests/test-quantize-perf
tests/test-sampling
tests/test-tokenizer-0
tests/test-tokenizer-0-llama
tests/test-tokenizer-0-falcon
tests/test-tokenizer-1

3
CMakeLists.txt
View file

@ -302,7 +302,7 @@ if (LLAMA_METAL)
set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
add_compile_definitions(GGML_USE_METAL)
add_compile_definitions(GGML_METAL_NDEBUG)
#add_compile_definitions(GGML_METAL_NDEBUG)
# get full path to the file
#add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
@ -407,6 +407,7 @@ if (LLAMA_ALL_WARNINGS)
-Wstrict-prototypes
-Wpointer-arith
-Wmissing-prototypes
-Werror=implicit-int
)
set(cxx_flags
-Wall

88
Makefile
View file

@ -1,11 +1,28 @@
# Define the default target now so that it is always the first target
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test gguf llama-bench
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam-search tests/test-c.o
# Binaries only useful for tests
TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0
TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1
default: $(BUILD_TARGETS)
test:
@echo "Running tests..."
@for test_target in $(TEST_TARGETS); do \
if [ "$$test_target" = "tests/test-tokenizer-0-llama" ]; then \
./$$test_target $(CURDIR)/models/ggml-vocab-llama.gguf; \
elif [ "$$test_target" = "tests/test-tokenizer-0-falcon" ]; then \
continue; \
elif [ "$$test_target" = "tests/test-tokenizer-1" ]; then \
continue; \
else \
./$$test_target; \
fi; \
done
@echo "All tests have been run."
all: $(BUILD_TARGETS) $(TEST_TARGETS)
ifndef UNAME_S
UNAME_S := $(shell uname -s)
endif
@ -64,7 +81,7 @@ endif
# warnings
CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \
-Wmissing-prototypes
-Wmissing-prototypes -Werror=implicit-int
CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar
# OS specific
@ -305,7 +322,7 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
endif # LLAMA_HIPBLAS
ifdef LLAMA_METAL
CFLAGS += -DGGML_USE_METAL -DGGML_METAL_NDEBUG
CFLAGS += -DGGML_USE_METAL #-DGGML_METAL_NDEBUG
CXXFLAGS += -DGGML_USE_METAL
LDFLAGS += -framework Foundation -framework Metal -framework MetalKit
OBJS += ggml-metal.o
@ -331,6 +348,11 @@ ifndef LLAMA_NO_SKIP_UNUSED_LOGITS
CXXFLAGS += -DLLAMA_SKIP_UNUSED_LOGITS
endif
ifdef LLAMA_DISABLE_LOGS
CFLAGS += -DLOG_DISABLE_LOGS
CXXFLAGS += -DLOG_DISABLE_LOGS
endif # LLAMA_DISABLE_LOGS
#
# Print build information
#
@ -361,7 +383,7 @@ OBJS += ggml-alloc.o
llama.o: llama.cpp ggml.h ggml-alloc.h ggml-cuda.h ggml-metal.h llama.h
$(CXX) $(CXXFLAGS) -c $< -o $@
common.o: common/common.cpp common/common.h
common.o: common/common.cpp common/common.h build-info.h common/log.h
$(CXX) $(CXXFLAGS) -c $< -o $@
console.o: common/console.cpp common/console.h
@ -374,7 +396,7 @@ libllama.so: llama.o ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
clean:
rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch convert-llama2c-to-ggml embd-input-test gguf llama-bench build-info.h $(TEST_TARGETS)
rm -vf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h $(BUILD_TARGETS) $(TEST_TARGETS)
#
# Examples
@ -414,18 +436,33 @@ $(LIB_PRE)embdinput$(DSO_EXT): examples/embd-input/embd-input.h examples/embd-in
embd-input-test: $(LIB_PRE)embdinput$(DSO_EXT) examples/embd-input/embd-input-test.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %$(DSO_EXT),$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -lembdinput
gguf: examples/gguf/gguf.cpp build-info.h ggml.o llama.o $(OBJS)
gguf: examples/gguf/gguf.cpp ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp build-info.h ggml.o llama.o common.o $(OBJS)
train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp build-info.h ggml.o llama.o $(OBJS)
convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
llama-bench: examples/llama-bench/llama-bench.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))'
BUILD_TARGETS += metal
endif
ifdef LLAMA_METAL
metal: examples/metal/metal.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
endif
build-info.h: $(wildcard .git/index) scripts/build-info.sh
@sh scripts/build-info.sh > $@.tmp
@if ! cmp -s $@.tmp $@; then \
@ -447,29 +484,38 @@ benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o
vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o common.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-grad0: tests/test-grad0.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-opt: tests/test-opt.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-quantize-fns: tests/test-quantize-fns.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-quantize-perf: tests/test-quantize-perf.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-sampling: tests/test-sampling.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-tokenizer-0: tests/test-tokenizer-0.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-tokenizer-1: tests/test-tokenizer-1.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-c.o: tests/test-c.c llama.h
$(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@

9
README.md
View file

@ -11,6 +11,10 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
### Hot topics
- #### IMPORTANT: Tokenizer fixes and API change (developers and projects using `llama.cpp` built-in tokenization must read): https://github.com/ggerganov/llama.cpp/pull/2810
- GGUFv2 adds support for 64-bit sizes + backwards compatible: https://github.com/ggerganov/llama.cpp/pull/2821
- Added support for Falcon models: https://github.com/ggerganov/llama.cpp/pull/2717
- A new file format has been introduced: [GGUF](https://github.com/ggerganov/llama.cpp/pull/2398)
@ -103,12 +107,13 @@ as the main playground for developing new features for the [ggml](https://github
- Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
- Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
- Node.js: [hlhr202/llama-node](https://github.com/hlhr202/llama-node)
- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp), [hlhr202/llama-node](https://github.com/hlhr202/llama-node)
- Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
- Rust: [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
- Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
- React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
**UI:**
@ -724,8 +729,6 @@ python3 convert.py pygmalion-7b/ --outtype q4_1
- [LLaMA 2 7B chat](https://huggingface.co/TheBloke/Llama-2-7B-chat-GGML)
- [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML)
- [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGML)
- Specify `-eps 1e-5` for best generation quality
- Specify `-gqa 8` for 70B models to work
### Verifying the model files

140
ci/run.sh
View file

@ -196,17 +196,17 @@ 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 3 ) 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 3 ) 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 3 ) 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 3 ) 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 3 ) 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 3 ) 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 3 ) 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 3 ) 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 3 ) 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 3 ) 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 3 ) 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
function check_ppl {
qnt="$1"
@ -233,6 +233,48 @@ function gg_run_open_llama_3b_v2 {
check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
# lora
function compare_ppl {
qnt="$1"
ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then
printf ' - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2"
return 20
fi
printf ' - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2"
return 0
}
path_lora="../models-mnt/open-llama/3B-v2/lora"
path_shakespeare="../models-mnt/shakespeare"
shakespeare="${path_shakespeare}/shakespeare.txt"
lora_shakespeare="${path_lora}/ggml-adapter-model.bin"
gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_config.json
gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_model.bin
gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/shakespeare.txt
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
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
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
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
}
@ -242,6 +284,7 @@ function gg_sum_open_llama_3b_v2 {
gg_printf 'OpenLLaMA 3B-v2:\n'
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
@ -253,6 +296,11 @@ function gg_sum_open_llama_3b_v2 {
gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)"
gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)"
gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)"
gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)"
gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)"
}
# open_llama_7b_v2
@ -310,17 +358,17 @@ function gg_run_open_llama_7b_v2 {
./bin/quantize ${model_f16} ${model_q5_k} q5_k
./bin/quantize ${model_f16} ${model_q6_k} q6_k
(time ./bin/main --model ${model_f16} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/main --model ${model_q8_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/main --model ${model_q4_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/main --model ${model_q4_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/main --model ${model_q5_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/main --model ${model_q5_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/main --model ${model_q2_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/main --model ${model_q3_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/main --model ${model_q4_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/main --model ${model_q5_k} -ngl 999 -s 1234 -n 256 --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} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/main --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/main --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/main --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/main --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/main --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/main --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/main --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/main --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/main --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/main --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --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} -t 1 -ngl 999 -s 1234 -n 256 --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} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@ -359,6 +407,48 @@ function gg_run_open_llama_7b_v2 {
check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
# lora
function compare_ppl {
qnt="$1"
ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then
printf ' - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2"
return 20
fi
printf ' - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2"
return 0
}
path_lora="../models-mnt/open-llama/7B-v2/lora"
path_shakespeare="../models-mnt/shakespeare"
shakespeare="${path_shakespeare}/shakespeare.txt"
lora_shakespeare="${path_lora}/ggml-adapter-model.bin"
gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_config.json
gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_model.bin
gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/shakespeare.txt
python3 ../convert-lora-to-ggml.py ${path_lora}
# f16
(time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
(time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 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
# currently not supported by the CUDA backend
# q8_0
#(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
#(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 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} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
#compare_ppl "q8_0 / f16 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
}
@ -368,6 +458,7 @@ function gg_sum_open_llama_7b_v2 {
gg_printf 'OpenLLaMA 7B-v2:\n'
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
@ -379,6 +470,11 @@ function gg_sum_open_llama_7b_v2 {
gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)"
gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)"
#gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)"
#gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)"
#gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)"
}
## main

393
common/common.cpp
View file

@ -1,15 +1,21 @@
#include "common.h"
#include "build-info.h"
#include "llama.h"
#include <cassert>
#include <iostream>
#include <cstring>
#include <fstream>
#include <string>
#include <iterator>
#include <algorithm>
#include <sstream>
#include <unordered_set>
#include <cassert>
#include <cmath>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iterator>
#include <iostream>
#include <regex>
#include <sstream>
#include <string>
#include <unordered_set>
#include <vector>
#include <cinttypes>
#if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h>
@ -19,11 +25,14 @@
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#define NOMINMAX
#include <codecvt>
#include <locale>
#include <windows.h>
#include <fcntl.h>
#include <io.h>
#else
#include <sys/ioctl.h>
#include <sys/stat.h>
#include <unistd.h>
#endif
@ -93,7 +102,6 @@ void process_escapes(std::string& input) {
bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
bool invalid_param = false;
bool escape_prompt = false;
std::string arg;
gpt_params default_params;
const std::string arg_prefix = "--";
@ -125,8 +133,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.prompt = argv[i];
} else if (arg == "-e") {
escape_prompt = true;
} else if (arg == "-e" || arg == "--escape") {
params.escape = true;
} else if (arg == "--prompt-cache") {
if (++i >= argc) {
invalid_param = true;
@ -415,6 +423,16 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.antiprompt.push_back(argv[i]);
} else if (arg == "-ld" || arg == "--logdir") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.logdir = argv[i];
if (params.logdir.back() != DIRECTORY_SEPARATOR) {
params.logdir += DIRECTORY_SEPARATOR;
}
} else if (arg == "--perplexity") {
params.perplexity = true;
} else if (arg == "--ppl-stride") {
@ -462,6 +480,9 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
}
} else if (arg == "-h" || arg == "--help") {
gpt_print_usage(argc, argv, default_params);
#ifndef LOG_DISABLE_LOGS
log_print_usage();
#endif // LOG_DISABLE_LOGS
exit(0);
} else if (arg == "--random-prompt") {
params.random_prompt = true;
@ -501,6 +522,25 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
std::istreambuf_iterator<char>(),
std::back_inserter(params.grammar)
);
#ifndef LOG_DISABLE_LOGS
// Parse args for logging parameters
} else if ( log_param_single_parse( argv[i] ) ) {
// Do nothing, log_param_single_parse automatically does it's thing
// and returns if a match was found and parsed.
} else if ( log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i] ) ) {
// We have a matching known parameter requiring an argument,
// now we need to check if there is anything after this argv
// and flag invalid_param or parse it.
if (++i >= argc) {
invalid_param = true;
break;
}
if( !log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i-1], argv[i]) ) {
invalid_param = true;
break;
}
// End of Parse args for logging parameters
#endif // LOG_DISABLE_LOGS
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
gpt_print_usage(argc, argv, default_params);
@ -520,7 +560,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
exit(1);
}
if (escape_prompt) {
if (params.escape) {
process_escapes(params.prompt);
process_escapes(params.input_prefix);
process_escapes(params.input_suffix);
@ -546,7 +586,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stdout, " -p PROMPT, --prompt PROMPT\n");
fprintf(stdout, " prompt to start generation with (default: empty)\n");
fprintf(stdout, " -e process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
fprintf(stdout, " -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
fprintf(stdout, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n");
fprintf(stdout, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n");
fprintf(stdout, " not supported with --interactive or other interactive options\n");
@ -627,6 +667,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
fprintf(stdout, " -m FNAME, --model FNAME\n");
fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
fprintf(stdout, " -ld LOGDIR, --logdir LOGDIR\n");
fprintf(stdout, " path under which to save YAML logs (no logging if unset)\n");
fprintf(stdout, "\n");
}
@ -733,12 +775,12 @@ std::vector<llama_token> llama_tokenize(
return result;
}
std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
std::vector<char> result(8, 0);
const int n_tokens = llama_token_to_str(ctx, token, result.data(), result.size());
const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size());
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_token_to_str(ctx, token, result.data(), result.size());
int check = llama_token_to_piece(ctx, token, result.data(), result.size());
GGML_ASSERT(check == -n_tokens);
} else {
result.resize(n_tokens);
@ -746,3 +788,322 @@ std::string llama_token_to_str(const struct llama_context * ctx, llama_token tok
return std::string(result.data(), result.size());
}
std::string llama_detokenize_spm(llama_context * ctx, const std::vector<llama_token> & tokens) {
const llama_token bos_id = llama_token_bos(ctx);
std::string piece;
std::string result;
for (size_t i = 0; i < tokens.size(); ++i) {
piece = llama_token_to_piece(ctx, tokens[i]);
// remove the leading space of the first non-BOS token
if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') {
piece = piece.substr(1);
}
result += piece;
}
return result;
}
std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_token> & tokens) {
std::string piece;
std::string result;
for (size_t i = 0; i < tokens.size(); ++i) {
piece = llama_token_to_piece(ctx, tokens[i]);
result += piece;
}
return result;
}
// returns true if successful, false otherwise
bool create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
std::wstring wpath = converter.from_bytes(path);
// if the path already exists, check whether it's a directory
const DWORD attributes = GetFileAttributesW(wpath.c_str());
if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
return true;
}
size_t pos_slash = 0;
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
const std::wstring subpath = wpath.substr(0, pos_slash);
const wchar_t * test = subpath.c_str();
const bool success = CreateDirectoryW(test, NULL);
if (!success) {
const DWORD error = GetLastError();
// if the path already exists, ensure that it's a directory
if (error == ERROR_ALREADY_EXISTS) {
const DWORD attributes = GetFileAttributesW(subpath.c_str());
if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
return false;
}
} else {
return false;
}
}
pos_slash += 1;
}
return true;
#else
// if the path already exists, check whether it's a directory
struct stat info;
if (stat(path.c_str(), &info) == 0) {
return S_ISDIR(info.st_mode);
}
size_t pos_slash = 1; // skip leading slashes for directory creation
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
const std::string subpath = path.substr(0, pos_slash);
struct stat info;
// if the path already exists, ensure that it's a directory
if (stat(subpath.c_str(), &info) == 0) {
if (!S_ISDIR(info.st_mode)) {
return false;
}
} else {
// create parent directories
const int ret = mkdir(subpath.c_str(), 0755);
if (ret != 0) {
return false;
}
}
pos_slash += 1;
}
return true;
#endif // _WIN32
}
void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data) {
if (data.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
fprintf(stream, "%s: [", prop_name);
for (size_t i = 0; i < data.size() - 1; ++i) {
fprintf(stream, "%e, ", data[i]);
}
fprintf(stream, "%e]\n", data.back());
}
void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data) {
if (data.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
fprintf(stream, "%s: [", prop_name);
for (size_t i = 0; i < data.size() - 1; ++i) {
fprintf(stream, "%d, ", data[i]);
}
fprintf(stream, "%d]\n", data.back());
}
void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data) {
std::string data_str(data == NULL ? "" : data);
if (data_str.empty()) {
fprintf(stream, "%s:\n", prop_name);
return;
}
size_t pos_start = 0;
size_t pos_found = 0;
if (!data_str.empty() && (std::isspace(data_str[0]) || std::isspace(data_str.back()))) {
data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
data_str = "\"" + data_str + "\"";
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
return;
}
if (data_str.find('\n') == std::string::npos) {
fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
return;
}
fprintf(stream, "%s: |\n", prop_name);
while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) {
fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str());
pos_start = pos_found + 1;
}
}
std::string get_sortable_timestamp() {
using clock = std::chrono::system_clock;
const clock::time_point current_time = clock::now();
const time_t as_time_t = clock::to_time_t(current_time);
char timestamp_no_ns[100];
std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
current_time.time_since_epoch() % 1000000000).count();
char timestamp_ns[11];
snprintf(timestamp_ns, 11, "%09" PRId64, ns);
return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
}
void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
fprintf(stream, "build_commit: %s\n", BUILD_COMMIT);
fprintf(stream, "build_number: %d\n", BUILD_NUMBER);
fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false");
fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false");
fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false");
fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false");
fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false");
fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
#ifdef NDEBUG
fprintf(stream, "debug: false\n");
#else
fprintf(stream, "debug: true\n");
#endif // NDEBUG
fprintf(stream, "model_desc: %s\n", model_desc);
fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(lctx));
#ifdef __OPTIMIZE__
fprintf(stream, "optimize: true\n");
#else
fprintf(stream, "optimize: false\n");
#endif // __OPTIMIZE__
fprintf(stream, "time: %s\n", timestamp.c_str());
fprintf(stream, "\n");
fprintf(stream, "###############\n");
fprintf(stream, "# User Inputs #\n");
fprintf(stream, "###############\n");
fprintf(stream, "\n");
fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
dump_string_yaml_multiline(stream, "cfg_negative_prompt", params.cfg_negative_prompt.c_str());
fprintf(stream, "cfg_scale: %f # default: 1.0\n", params.cfg_scale);
fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
fprintf(stream, "export: %s # default: false\n", params.export_cgraph ? "true" : "false");
fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", params.frequency_penalty);
dump_string_yaml_multiline(stream, "grammar", params.grammar.c_str());
fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
fprintf(stream, "hellaswag_tasks: %ld # default: 400\n", params.hellaswag_tasks);
const auto logit_bias_eos = params.logit_bias.find(llama_token_eos(lctx));
const bool ignore_eos = logit_bias_eos != params.logit_bias.end() && logit_bias_eos->second == -INFINITY;
fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str());
fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str());
fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false");
fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
fprintf(stream, "logit_bias:\n");
for (std::pair<llama_token, float> lb : params.logit_bias) {
if (ignore_eos && lb.first == logit_bias_eos->first) {
continue;
}
fprintf(stream, " %d: %f", lb.first, lb.second);
}
fprintf(stream, "lora: %s\n", params.lora_adapter.c_str());
fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
fprintf(stream, "low_vram: %s # default: false\n", params.low_vram ? "true" : "false");
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
fprintf(stream, "memory_f32: %s # default: false\n", !params.memory_f16 ? "true" : "false");
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", params.mirostat);
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", params.mirostat_tau);
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta);
fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str());
fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false");
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers);
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", params.n_probs);
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
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", !params.penalize_nl ? "true" : "false");
fprintf(stream, "numa: %s # default: false\n", params.numa ? "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);
fprintf(stream, "presence_penalty: %f # default: 0.0\n", params.presence_penalty);
dump_string_yaml_multiline(stream, "prompt", params.prompt.c_str());
fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens);
fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false");
fprintf(stream, "repeat_penalty: %f # default: 1.1\n", params.repeat_penalty);
fprintf(stream, "reverse_prompt:\n");
for (std::string ap : params.antiprompt) {
size_t pos = 0;
while ((pos = ap.find('\n', pos)) != std::string::npos) {
ap.replace(pos, 1, "\\n");
pos += 1;
}
fprintf(stream, " - %s\n", ap.c_str());
}
fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
fprintf(stream, "seed: %d # default: -1 (random seed)\n", params.seed);
fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
fprintf(stream, "temp: %f # default: 0.8\n", params.temp);
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES);
dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector);
fprintf(stream, "tfs: %f # default: 1.0\n", params.tfs_z);
fprintf(stream, "threads: %d # default: %d\n", params.n_threads, std::thread::hardware_concurrency());
fprintf(stream, "top_k: %d # default: 40\n", params.top_k);
fprintf(stream, "top_p: %f # default: 0.95\n", params.top_p);
fprintf(stream, "typical_p: %f # default: 1.0\n", params.typical_p);
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
}

43
common/common.h
View file

@ -4,6 +4,9 @@
#include "llama.h"
#define LOG_NO_FILE_LINE_FUNCTION
#include "log.h"
#include <string>
#include <vector>
#include <random>
@ -11,6 +14,12 @@
#include <unordered_map>
#include <tuple>
#ifdef _WIN32
#define DIRECTORY_SEPARATOR '\\'
#else
#define DIRECTORY_SEPARATOR '/'
#endif // _WIN32
//
// CLI argument parsing
//
@ -61,6 +70,7 @@ struct gpt_params {
std::string input_suffix = ""; // string to suffix user inputs with
std::string grammar = ""; // optional BNF-like grammar to constrain sampling
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
std::string logdir = ""; // directory in which to save YAML log files
std::string lora_adapter = ""; // lora adapter path
std::string lora_base = ""; // base model path for the lora adapter
@ -82,6 +92,7 @@ struct gpt_params {
bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it
bool embedding = false; // get only sentence embedding
bool escape = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\"
bool interactive_first = false; // wait for user input immediately
bool multiline_input = false; // reverse the usage of `\`
bool simple_io = false; // improves compatibility with subprocesses and limited consoles
@ -116,11 +127,41 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
// Vocab utils
//
// tokenizes a string into a vector of tokens
// should work similar to Python's `tokenizer.encode`
std::vector<llama_token> llama_tokenize(
struct llama_context * ctx,
const std::string & text,
bool add_bos);
std::string llama_token_to_str(
// tokenizes a token into a piece
// should work similar to Python's `tokenizer.id_to_piece`
std::string llama_token_to_piece(
const struct llama_context * ctx,
llama_token token);
// TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function
// that takes into account the tokenizer type and decides how to handle the leading space
//
// detokenizes a vector of tokens into a string
// should work similar to Python's `tokenizer.decode`
// removes the leading space from the first non-BOS token
std::string llama_detokenize_spm(
llama_context * ctx,
const std::vector<llama_token> & tokens);
// detokenizes a vector of tokens into a string
// should work similar to Python's `tokenizer.decode`
std::string llama_detokenize_bpe(
llama_context * ctx,
const std::vector<llama_token> & tokens);
bool create_directory_with_parents(const std::string & path);
void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data);
void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data);
void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data);
std::string get_sortable_timestamp();
void dump_non_result_info_yaml(
FILE * stream, const gpt_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);

643
common/log.h Normal file
View file

@ -0,0 +1,643 @@
#pragma once
#include <chrono>
#include <cstring>
#include <sstream>
#include <iostream>
#include <thread>
#include <vector>
#include <algorithm>
#include <cinttypes>
// --------------------------------
//
// Basic usage:
//
// --------
//
// The LOG() and LOG_TEE() macros are ready to go by default
// they do not require any initialization.
//
// LOGLN() and LOG_TEELN() are variants which automatically
// include \n character at the end of the log string.
//
// LOG() behaves exactly like printf, by default writing to a logfile.
// LOG_TEE() additionally, prints to the screen too ( mimics Unix tee command ).
//
// Default logfile is named
// "llama.<threadID>.log"
// Default LOG_TEE() secondary output target is
// stderr
//
// Logs can be dynamically disabled or enabled using functions:
// log_disable()
// and
// log_enable()
//
// A log target can be changed with:
// log_set_target( string )
// creating and opening, or re-opening a file by string filename
// or
// log_set_target( FILE* )
// allowing to point at stderr, stdout, or any valid FILE* file handler.
//
// --------
//
// End of Basic usage.
//
// --------------------------------
// Specifies a log target.
// default uses log_handler() with "llama.log" log file
// this can be changed, by defining LOG_TARGET
// like so:
//
// #define LOG_TARGET (a valid FILE*)
// #include "log.h"
//
// or it can be simply redirected to stdout or stderr
// like so:
//
// #define LOG_TARGET stderr
// #include "log.h"
//
// The log target can also be redirected to a diffrent function
// like so:
//
// #define LOG_TARGET log_handler_diffrent()
// #include "log.h"
//
// FILE* log_handler_diffrent()
// {
// return stderr;
// }
//
// or:
//
// #define LOG_TARGET log_handler_another_one("somelog.log")
// #include "log.h"
//
// FILE* log_handler_another_one(char*filename)
// {
// static FILE* logfile = nullptr;
// (...)
// if( !logfile )
// {
// fopen(...)
// }
// (...)
// return logfile
// }
//
#ifndef LOG_TARGET
#define LOG_TARGET log_handler()
#endif
#ifndef LOG_TEE_TARGET
#define LOG_TEE_TARGET stderr
#endif
// Utility to obtain "pid" like unique process id and use it when creating log files.
inline std::string log_get_pid()
{
static std::string pid;
if (pid.empty())
{
// std::this_thread::get_id() is the most portable way of obtaining a "process id"
// it's not the same as "pid" but is unique enough to solve multiple instances
// trying to write to the same log.
std::stringstream ss;
ss << std::this_thread::get_id();
pid = ss.str();
}
return pid;
}
// Utility function for generating log file names with unique id based on thread id.
// invocation with log_filename_generator( "llama", "log" ) creates a string "llama.<number>.log"
// where the number is a runtime id of the current thread.
#define log_filename_generator(log_file_basename, log_file_extension) log_filename_generator_impl(log_file_basename, log_file_extension)
// INTERNAL, DO NOT USE
inline std::string log_filename_generator_impl(const std::string & log_file_basename, const std::string & log_file_extension)
{
std::stringstream buf;
buf << log_file_basename;
buf << ".";
buf << log_get_pid();
buf << ".";
buf << log_file_extension;
return buf.str();
}
#ifndef LOG_DEFAULT_FILE_NAME
#define LOG_DEFAULT_FILE_NAME log_filename_generator("llama", "log")
#endif
// Utility for turning #define values into string literals
// so we can have a define for stderr and
// we can print "stderr" instead of literal stderr, etc.
#define LOG_STRINGIZE1(s) #s
#define LOG_STRINGIZE(s) LOG_STRINGIZE1(s)
#define LOG_TEE_TARGET_STRING LOG_STRINGIZE(LOG_TEE_TARGET)
// Allows disabling timestamps.
// in order to disable, define LOG_NO_TIMESTAMPS
// like so:
//
// #define LOG_NO_TIMESTAMPS
// #include "log.h"
//
#ifndef LOG_NO_TIMESTAMPS
#ifndef _WIN32
#define LOG_TIMESTAMP_FMT "[%" PRIu64 "] "
#define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast<std::chrono::duration<std::uint64_t>>(std::chrono::system_clock::now().time_since_epoch())).count()
#else
#define LOG_TIMESTAMP_FMT "[%" PRIu64 "] "
#define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast<std::chrono::duration<std::uint64_t>>(std::chrono::system_clock::now().time_since_epoch())).count()
#endif
#else
#define LOG_TIMESTAMP_FMT "%s"
#define LOG_TIMESTAMP_VAL ,""
#endif
#ifdef LOG_TEE_TIMESTAMPS
#ifndef _WIN32
#define LOG_TEE_TIMESTAMP_FMT "[%" PRIu64 "] "
#define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast<std::chrono::duration<std::uint64_t>>(std::chrono::system_clock::now().time_since_epoch())).count()
#else
#define LOG_TEE_TIMESTAMP_FMT "[%" PRIu64 "] "
#define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast<std::chrono::duration<std::uint64_t>>(std::chrono::system_clock::now().time_since_epoch())).count()
#endif
#else
#define LOG_TEE_TIMESTAMP_FMT "%s"
#define LOG_TEE_TIMESTAMP_VAL ,""
#endif
// Allows disabling file/line/function prefix
// in order to disable, define LOG_NO_FILE_LINE_FUNCTION
// like so:
//
// #define LOG_NO_FILE_LINE_FUNCTION
// #include "log.h"
//
#ifndef LOG_NO_FILE_LINE_FUNCTION
#ifndef _WIN32
#define LOG_FLF_FMT "[%24s:%5d][%24s] "
#define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
#else
#define LOG_FLF_FMT "[%24s:%5ld][%24s] "
#define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
#endif
#else
#define LOG_FLF_FMT "%s"
#define LOG_FLF_VAL ,""
#endif
#ifdef LOG_TEE_FILE_LINE_FUNCTION
#ifndef _WIN32
#define LOG_TEE_FLF_FMT "[%24s:%5d][%24s] "
#define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
#else
#define LOG_TEE_FLF_FMT "[%24s:%5ld][%24s] "
#define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
#endif
#else
#define LOG_TEE_FLF_FMT "%s"
#define LOG_TEE_FLF_VAL ,""
#endif
// Utility for synchronizing log configuration state
// since std::optional was introduced only in c++17
enum LogTriState
{
LogTriStateSame,
LogTriStateFalse,
LogTriStateTrue
};
// INTERNAL, DO NOT USE
// USE LOG() INSTEAD
//
#ifndef _WIN32
#define LOG_IMPL(str, ...) \
{ \
if (LOG_TARGET != nullptr) \
{ \
fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL, __VA_ARGS__); \
fflush(LOG_TARGET); \
} \
}
#else
#define LOG_IMPL(str, ...) \
{ \
if (LOG_TARGET != nullptr) \
{ \
fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL "", ##__VA_ARGS__); \
fflush(LOG_TARGET); \
} \
}
#endif
// INTERNAL, DO NOT USE
// USE LOG_TEE() INSTEAD
//
#ifndef _WIN32
#define LOG_TEE_IMPL(str, ...) \
{ \
if (LOG_TARGET != nullptr) \
{ \
fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL, __VA_ARGS__); \
fflush(LOG_TARGET); \
} \
if (LOG_TARGET != nullptr && LOG_TARGET != stdout && LOG_TARGET != stderr && LOG_TEE_TARGET != nullptr) \
{ \
fprintf(LOG_TEE_TARGET, LOG_TEE_TIMESTAMP_FMT LOG_TEE_FLF_FMT str "%s" LOG_TEE_TIMESTAMP_VAL LOG_TEE_FLF_VAL, __VA_ARGS__); \
fflush(LOG_TEE_TARGET); \
} \
}
#else
#define LOG_TEE_IMPL(str, ...) \
{ \
if (LOG_TARGET != nullptr) \
{ \
fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL "", ##__VA_ARGS__); \
fflush(LOG_TARGET); \
} \
if (LOG_TARGET != nullptr && LOG_TARGET != stdout && LOG_TARGET != stderr && LOG_TEE_TARGET != nullptr) \
{ \
fprintf(LOG_TEE_TARGET, LOG_TEE_TIMESTAMP_FMT LOG_TEE_FLF_FMT str "%s" LOG_TEE_TIMESTAMP_VAL LOG_TEE_FLF_VAL "", ##__VA_ARGS__); \
fflush(LOG_TEE_TARGET); \
} \
}
#endif
// The '\0' as a last argument, is a trick to bypass the silly
// "warning: ISO C++11 requires at least one argument for the "..." in a variadic macro"
// so we can have a single macro which can be called just like printf.
// Main LOG macro.
// behaves like printf, and supports arguments the exact same way.
//
#ifndef _WIN32
#define LOG(...) LOG_IMPL(__VA_ARGS__, "")
#else
#define LOG(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "")
#endif
// Main TEE macro.
// does the same as LOG
// and
// simultaneously writes stderr.
//
// Secondary target can be changed just like LOG_TARGET
// by defining LOG_TEE_TARGET
//
#ifndef _WIN32
#define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "")
#else
#define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "")
#endif
// LOG macro variants with auto endline.
#ifndef _WIN32
#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")
#endif
// INTERNAL, DO NOT USE
inline FILE *log_handler1_impl(bool change = false, LogTriState disable = LogTriStateSame, const std::string & filename = LOG_DEFAULT_FILE_NAME, FILE *target = nullptr)
{
static bool _initialized{false};
static bool _disabled{(filename.empty() && target == nullptr)};
static std::string log_current_filename{filename};
static FILE *log_current_target{target};
static FILE *logfile = nullptr;
if (change)
{
if (disable == LogTriStateTrue)
{
// Disable primary target
_disabled = true;
}
// If previously disabled, only enable, and keep previous target
else if (disable == LogTriStateFalse)
{
_disabled = false;
}
// Otherwise, process the arguments
else if (log_current_filename != filename || log_current_target != target)
{
_initialized = false;
}
}
if (_initialized)
{
if (_disabled)
{
// Log is disabled
return nullptr;
}
// with fallback in case something went wrong
return logfile ? logfile : stderr;
}
// do the (re)initialization
if (target != nullptr)
{
if (logfile != nullptr && logfile != stdout && logfile != stderr)
{
fclose(logfile);
}
log_current_filename = LOG_DEFAULT_FILE_NAME;
log_current_target = target;
logfile = target;
}
else
{
if (log_current_filename != filename)
{
if (logfile != nullptr && logfile != stdout && logfile != stderr)
{
fclose(logfile);
}
}
logfile = fopen(filename.c_str(), "w");
}
if (!logfile)
{
// Verify whether the file was opened, otherwise fallback to stderr
logfile = stderr;
fprintf(stderr, "Failed to open logfile '%s' with error '%s'\n", filename.c_str(), std::strerror(errno));
fflush(stderr);
// At this point we let the init flag be to true below, and let the target fallback to stderr
// otherwise we would repeatedly fopen() which was already unsuccessful
}
_initialized = true;
return logfile ? logfile : stderr;
}
// INTERNAL, DO NOT USE
inline FILE *log_handler2_impl(bool change = false, LogTriState disable = LogTriStateSame, FILE *target = nullptr, const std::string & filename = LOG_DEFAULT_FILE_NAME)
{
return log_handler1_impl(change, disable, filename, target);
}
// Disables logs entirely at runtime.
// Makes LOG() and LOG_TEE() produce no output,
// untill enabled back.
#define log_disable() log_disable_impl()
// INTERNAL, DO NOT USE
inline FILE *log_disable_impl()
{
return log_handler1_impl(true, LogTriStateTrue);
}
// Enables logs at runtime.
#define log_enable() log_enable_impl()
// INTERNAL, DO NOT USE
inline FILE *log_enable_impl()
{
return log_handler1_impl(true, LogTriStateFalse);
}
// Sets target fir logs, either by a file name or FILE* pointer (stdout, stderr, or any valid FILE*)
#define log_set_target(target) log_set_target_impl(target)
// INTERNAL, DO NOT USE
inline FILE *log_set_target_impl(const std::string & filename) { return log_handler1_impl(true, LogTriStateSame, filename); }
inline FILE *log_set_target_impl(FILE *target) { return log_handler2_impl(true, LogTriStateSame, target); }
// INTERNAL, DO NOT USE
inline FILE *log_handler() { return log_handler1_impl(); }
inline void log_test()
{
log_disable();
LOG("01 Hello World to nobody, because logs are disabled!\n")
log_enable();
LOG("02 Hello World to default output, which is \"%s\" ( Yaaay, arguments! )!\n", LOG_STRINGIZE(LOG_TARGET))
LOG_TEE("03 Hello World to **both** default output and " LOG_TEE_TARGET_STRING "!\n")
log_set_target(stderr);
LOG("04 Hello World to stderr!\n")
LOG_TEE("05 Hello World TEE with double printing to stderr prevented!\n")
log_set_target(LOG_DEFAULT_FILE_NAME);
LOG("06 Hello World to default log file!\n")
log_set_target(stdout);
LOG("07 Hello World to stdout!\n")
log_set_target(LOG_DEFAULT_FILE_NAME);
LOG("08 Hello World to default log file again!\n")
log_disable();
LOG("09 Hello World _1_ into the void!\n")
log_enable();
LOG("10 Hello World back from the void ( you should not see _1_ in the log or the output )!\n")
log_disable();
log_set_target("llama.anotherlog.log");
LOG("11 Hello World _2_ to nobody, new target was selected but logs are still disabled!\n")
log_enable();
LOG("12 Hello World this time in a new file ( you should not see _2_ in the log or the output )?\n")
log_set_target("llama.yetanotherlog.log");
LOG("13 Hello World this time in yet new file?\n")
log_set_target(log_filename_generator("llama_autonamed", "log"));
LOG("14 Hello World in log with generated filename!\n")
#ifdef _WIN32
LOG_TEE("15 Hello msvc TEE without arguments\n")
LOG_TEE("16 Hello msvc TEE with (%d)(%s) arguments\n", 1, "test")
LOG_TEELN("17 Hello msvc TEELN without arguments\n")
LOG_TEELN("18 Hello msvc TEELN with (%d)(%s) arguments\n", 1, "test")
LOG("19 Hello msvc LOG without arguments\n")
LOG("20 Hello msvc LOG with (%d)(%s) arguments\n", 1, "test")
LOGLN("21 Hello msvc LOGLN without arguments\n")
LOGLN("22 Hello msvc LOGLN with (%d)(%s) arguments\n", 1, "test")
#endif
}
inline bool log_param_single_parse(const std::string & param)
{
if ( param == "--log-test")
{
log_test();
return true;
}
if ( param == "--log-disable")
{
log_disable();
return true;
}
if ( param == "--log-enable")
{
log_enable();
return true;
}
return false;
}
inline bool log_param_pair_parse(bool check_but_dont_parse, const std::string & param, const std::string & next = std::string())
{
if ( param == "--log-file")
{
if (!check_but_dont_parse)
{
log_set_target(log_filename_generator(next.empty() ? "unnamed" : next, "log"));
}
return true;
}
return false;
}
inline void log_print_usage()
{
fprintf(stdout, "log options:\n");
/* format
fprintf(stdout, " -h, --help show this help message and exit\n");*/
/* spacing
fprintf(stdout, "__-param----------------Description\n");*/
fprintf(stdout, " --log-test Run simple logging test\n");
fprintf(stdout, " --log-disable Disable trace logs\n");
fprintf(stdout, " --log-enable Enable trace logs\n");
fprintf(stdout, " --log-file Specify a log filename (without extension)\n");
fprintf(stdout, " Log file will be tagged with unique ID and written as \"<name>.<ID>.log\"\n"); /* */
}
#define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv)
// INTERNAL, DO NOT USE
inline void log_dump_cmdline_impl(int argc, char **argv)
{
std::stringstream buf;
for (int i = 0; i < argc; ++i)
{
if (std::string(argv[i]).find(' ') != std::string::npos)
{
buf << " \"" << argv[i] <<"\"";
}
else
{
buf << " " << argv[i];
}
}
LOGLN("Cmd:%s", buf.str().c_str())
}
#define log_tostr(var) log_var_to_string_impl(var).c_str()
inline std::string log_var_to_string_impl(bool var)
{
return var ? "true" : "false";
}
inline std::string log_var_to_string_impl(std::string var)
{
return var;
}
inline std::string log_var_to_string_impl(const std::vector<int> & var)
{
std::stringstream buf;
buf << "[ ";
bool first = true;
for (auto e : var)
{
if (first)
{
first = false;
}
else
{
buf << ", ";
}
buf << std::to_string(e);
}
buf << " ]";
return buf.str();
}
#define LOG_TOKENS_TOSTR_PRETTY(ctx, tokens) \
[&tokens, &ctx]() \
{ \
std::stringstream buf; \
buf << "[ "; \
\
bool first = true; \
for (const auto &token : tokens) \
{ \
if (!first) \
buf << ", "; \
else \
first = false; \
\
auto detokenized = llama_token_to_piece(ctx, token); \
\
detokenized.erase( \
std::remove_if( \
detokenized.begin(), \
detokenized.end(), \
[](const unsigned char c) { return !std::isprint(c); }), \
detokenized.end()); \
\
buf \
<< "'" << detokenized << "'" \
<< ":" << std::to_string(token); \
} \
buf << " ]"; \
\
return buf.str(); \
}() \
.c_str()
#ifdef LOG_DISABLE_LOGS
#undef LOG
#define LOG(...) // dummy stub
#undef LOGLN
#define LOGLN(...) // dummy stub
#undef LOG_TEE
#define LOG_TEE(...) fprintf(stderr, __VA_ARGS__); // convert to normal fprintf
#undef LOG_TEELN
#define LOG_TEELN(...) fprintf(stderr, __VA_ARGS__); // convert to normal fprintf
#undef LOG_DISABLE
#define LOG_DISABLE() // dummy stub
#undef LOG_ENABLE
#define LOG_ENABLE() // dummy stub
#undef LOG_ENABLE
#define LOG_ENABLE() // dummy stub
#undef LOG_SET_TARGET
#define LOG_SET_TARGET(...) // dummy stub
#undef LOG_DUMP_CMDLINE
#define LOG_DUMP_CMDLINE(...) // dummy stub
#endif // LOG_DISABLE_LOGS

170
convert-falcon-hf-to-gguf.py
View file

@ -8,6 +8,7 @@ import struct
import json
import numpy as np
import torch
import argparse
from typing import Any, List
from pathlib import Path
@ -32,11 +33,10 @@ def bytes_to_unicode():
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
return dict(zip(bs, (chr(n) for n in cs)))
def count_model_parts(dir_model: str) -> int:
def count_model_parts(dir_model: Path) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
if filename.startswith("pytorch_model-"):
@ -47,17 +47,22 @@ def count_model_parts(dir_model: str) -> int:
return num_parts
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Convert a Falcon model to a GGML compatible file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1)
return parser.parse_args()
args = parse_args()
dir_model = args.model
ftype = args.ftype
if not dir_model.is_dir():
print(f'Error: {args.model} is not a directory', file = sys.stderr)
sys.exit(1)
# output in the same directory as the model
dir_model = sys.argv[1]
last_dir = os.path.basename(os.path.normpath(dir_model))
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
@ -65,25 +70,21 @@ last_dir = os.path.basename(os.path.normpath(dir_model))
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))
if args.outfile is not None:
fname_out = args.outfile
else:
# output in the same directory as the model by default
fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
sys.exit(1)
print("gguf: loading model "+dir_model.name)
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
print("gguf: loading model "+last_dir)
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
if hparams["architectures"][0] != "RWForCausalLM":
print("Model architecture not supported: " + hparams["architectures"][0])
sys.exit()
sys.exit(1)
# get number of model parts
num_parts = count_model_parts(dir_model)
@ -113,77 +114,58 @@ gguf_writer.add_file_type(ftype)
print("gguf: get tokenizer metadata")
tokens: List[str] = []
tokens: List[bytearray] = []
scores: List[float] = []
toktypes: List[int] = []
merges: List[str] = []
tokenizer_json_file = dir_model / 'tokenizer.json'
if not tokenizer_json_file.is_file():
print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
sys.exit(1)
if Path(dir_model + "/tokenizer.json").is_file():
# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")
# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")
print("gguf: get gpt2 tokenizer merges")
with open(tokenizer_json_file, "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)
with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)
merges = tokenizer_json["model"]["merges"]
print("gguf: get gpt2 tokenizer vocab")
gguf_writer.add_token_merges(merges)
vocab_size = len(tokenizer_json["model"]["vocab"])
print("gguf: get gpt2 tokenizer vocab")
# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
tokenizer = AutoTokenizer.from_pretrained(dir_model)
vocab_size = len(tokenizer_json["model"]["vocab"])
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}
# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
tokenizer = AutoTokenizer.from_pretrained(dir_model)
for i in range(vocab_size):
if i in reverse_vocab:
try:
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
except KeyError:
text = bytearray()
for c in reverse_vocab[i]:
if ord(c) < 256: # single byte character
text.append(byte_decoder[ord(c)])
else: # multibyte special token character
text.extend(c.encode('utf-8'))
else:
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
pad_token = f"[PAD{i}]".encode("utf8")
text = bytearray(pad_token)
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}
tokens.append(text)
scores.append(0.0) # dymmy
toktypes.append(gguf.TokenType.NORMAL) # dummy
for i in range(vocab_size):
if i in reverse_vocab:
try:
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
except KeyError:
text = bytearray()
for c in reverse_vocab[i]:
if ord(c) < 256: # single byte character
text.append(byte_decoder[ord(c)])
else: # multibyte special token character
text.extend(c.encode('utf-8'))
else:
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
pad_token = f"[PAD{i}]".encode("utf8")
text = bytearray(pad_token)
tokens.append(text)
scores.append(0.0) # dymmy
toktypes.append(gguf.TokenType.NORMAL) # dummy
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)
print("gguf: get special token ids")
# Look for special tokens in config.json
if "bos_token_id" in hparams and hparams["bos_token_id"] != None:
gguf_writer.add_bos_token_id(hparams["bos_token_id"])
if "eos_token_id" in hparams and hparams["eos_token_id"] != None:
gguf_writer.add_eos_token_id(hparams["eos_token_id"])
if "unk_token_id" in hparams and hparams["unk_token_id"] != None:
gguf_writer.add_unk_token_id(hparams["unk_token_id"])
if "sep_token_id" in hparams and hparams["sep_token_id"] != None:
gguf_writer.add_sep_token_id(hparams["sep_token_id"])
if "pad_token_id" in hparams and hparams["pad_token_id"] != None:
gguf_writer.add_pad_token_id(hparams["pad_token_id"])
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
special_vocab.add_to_gguf(gguf_writer)
# TENSORS
@ -199,15 +181,17 @@ head_dim = hparams["hidden_size"] // n_head
print("gguf: get tensor metadata")
if num_parts == 0:
part_names = ("pytorch_model.bin",)
part_names = iter(("pytorch_model.bin",))
else:
part_names = (
f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
)
for part_name in part_names:
if args.vocab_only:
break
print("gguf: loading model part '" + part_name + "'")
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
model_part = torch.load(dir_model / part_name, map_location="cpu")
for name in model_part.keys():
data = model_part[name]
@ -238,11 +222,8 @@ for part_name in part_names:
data = data.squeeze().numpy()
# map tensor names
if name.endswith(".weight") and name[:-7] in tensor_map:
name = tensor_map[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
if new_name is None:
print("Can not map tensor '" + name + "'")
sys.exit()
@ -261,19 +242,20 @@ for part_name in part_names:
if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.add_tensor(name, data)
gguf_writer.add_tensor(new_name, data)
print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
if not args.vocab_only:
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
print("gguf: model successfully exported to '" + fname_out + "'")
print(f"gguf: model successfully exported to '{fname_out}'")
print("")

175
convert-gptneox-hf-to-gguf.py
View file

@ -8,6 +8,7 @@ import struct
import json
import numpy as np
import torch
import argparse
from typing import Any, List
from pathlib import Path
@ -34,11 +35,10 @@ def bytes_to_unicode():
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
return dict(zip(bs, (chr(n) for n in cs)))
def count_model_parts(dir_model: str) -> int:
def count_model_parts(dir_model: Path) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
if filename.startswith("pytorch_model-"):
@ -49,17 +49,22 @@ def count_model_parts(dir_model: str) -> int:
return num_parts
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Convert a GPT-NeoX model to a GGML compatible file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1)
return parser.parse_args()
args = parse_args()
dir_model = args.model
ftype = args.ftype
if not dir_model.is_dir():
print(f'Error: {args.model} is not a directory', file = sys.stderr)
sys.exit(1)
# output in the same directory as the model
dir_model = sys.argv[1]
last_dir = os.path.basename(os.path.normpath(dir_model))
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
@ -67,19 +72,15 @@ last_dir = os.path.basename(os.path.normpath(dir_model))
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))
if args.outfile is not None:
fname_out = args.outfile
else:
# output in the same directory as the model by default
fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
sys.exit(1)
print("gguf: loading model "+dir_model.name)
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
print("gguf: loading model "+last_dir)
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
if hparams["architectures"][0] != "GPTNeoXForCausalLM":
@ -97,7 +98,7 @@ print("gguf: get model metadata")
block_count = hparams["num_hidden_layers"]
gguf_writer.add_name(last_dir)
gguf_writer.add_name(dir_model.name)
gguf_writer.add_context_length(hparams["max_position_embeddings"])
gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_block_count(block_count)
@ -111,86 +112,52 @@ gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"])
print("gguf: get tokenizer metadata")
tokens: List[str] = []
merges: List[str] = []
tokens: List[bytearray] = []
tokenizer_json_file = dir_model / 'tokenizer.json'
if not tokenizer_json_file.is_file():
print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
sys.exit(1)
if Path(dir_model + "/tokenizer.json").is_file():
# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")
# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")
print("gguf: get gpt2 tokenizer merges")
with open(tokenizer_json_file, "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)
with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)
merges = tokenizer_json["model"]["merges"]
print("gguf: get gpt2 tokenizer vocab")
gguf_writer.add_token_merges(merges)
vocab_size = len(tokenizer_json["model"]["vocab"])
print("gguf: get gpt2 tokenizer vocab")
# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
tokenizer = AutoTokenizer.from_pretrained(dir_model)
vocab_size = len(tokenizer_json["model"]["vocab"])
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}
# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
tokenizer = AutoTokenizer.from_pretrained(dir_model)
for i in range(vocab_size):
if i in reverse_vocab:
try:
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
except KeyError:
text = bytearray()
for c in reverse_vocab[i]:
if ord(c) < 256: # single byte character
text.append(byte_decoder[ord(c)])
else: # multibyte special token character
text.extend(c.encode('utf-8'))
else:
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
pad_token = f"[PAD{i}]".encode("utf8")
text = bytearray(pad_token)
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}
tokens.append(text)
for i in range(vocab_size):
if i in reverse_vocab:
try:
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
except KeyError:
text = bytearray()
for c in reverse_vocab[i]:
if ord(c) < 256: # single byte character
text.append(byte_decoder[ord(c)])
else: # multibyte special token character
text.extend(c.encode('utf-8'))
else:
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
pad_token = f"[PAD{i}]".encode("utf8")
text = bytearray(pad_token)
tokens.append(text)
gguf_writer.add_token_list(tokens)
if "added_tokens" in tokenizer_json and Path(dir_model + "/tokenizer_config.json").is_file():
print("gguf: get special token ids")
with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
tokenizer_config = json.load(f)
# find special token ids
if "bos_token" in tokenizer_config:
for key in tokenizer_json["added_tokens"]:
if key["content"] == tokenizer_config["bos_token"]:
gguf_writer.add_bos_token_id(key["id"])
if "eos_token" in tokenizer_config:
for key in tokenizer_json["added_tokens"]:
if key["content"] == tokenizer_config["eos_token"]:
gguf_writer.add_eos_token_id(key["id"])
if "unk_token" in tokenizer_config:
for key in tokenizer_json["added_tokens"]:
if key["content"] == tokenizer_config["unk_token"]:
gguf_writer.add_unk_token_id(key["id"])
if "sep_token" in tokenizer_config:
for key in tokenizer_json["added_tokens"]:
if key["content"] == tokenizer_config["sep_token"]:
gguf_writer.add_sep_token_id(key["id"])
if "pad_token" in tokenizer_config:
for key in tokenizer_json["added_tokens"]:
if key["content"] == tokenizer_config["pad_token"]:
gguf_writer.add_pad_token_id(key["id"])
gguf_writer.add_token_list(tokens)
special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
special_vocab.add_to_gguf(gguf_writer)
# TENSORS
@ -200,13 +167,15 @@ tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
print("gguf: get tensor metadata")
if num_parts == 0:
part_names = ("pytorch_model.bin",)
part_names = iter(("pytorch_model.bin",))
else:
part_names = (
f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
)
for part_name in part_names:
if args.vocab_only:
break
print("gguf: loading model part '" + part_name + "'")
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
@ -226,11 +195,8 @@ for part_name in part_names:
data = data.squeeze().numpy()
# map tensor names
if name.endswith(".weight") and name[:-7] in tensor_map:
name = tensor_map[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
if new_name is None:
print("Can not map tensor '" + name + "'")
sys.exit()
@ -249,19 +215,20 @@ for part_name in part_names:
if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.add_tensor(name, data)
gguf_writer.add_tensor(new_name, data)
print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
if not args.vocab_only:
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
print("gguf: model successfully exported to '" + fname_out + "'")
print(f"gguf: model successfully exported to '{fname_out}'")
print("")

200
convert-llama-7b-pth-to-gguf.py
View file

@ -10,8 +10,9 @@ import struct
import json
import numpy as np
import torch
import argparse
from typing import Any, List
from typing import Any, List, TypeAlias
from pathlib import Path
from sentencepiece import SentencePieceProcessor
@ -20,7 +21,7 @@ from sentencepiece import SentencePieceProcessor
NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
def count_model_parts(dir_model: str) -> int:
def count_model_parts(dir_model: Path) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
if filename.startswith("consolidated."):
@ -31,19 +32,22 @@ def count_model_parts(dir_model: str) -> int:
return num_parts
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Convert a PyTorch 7B LLaMA model to a GGML compatible file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1)
return parser.parse_args()
args = parse_args()
dir_model = args.model
ftype = args.ftype
if not dir_model.is_dir():
print(f'Error: {args.model} is not a directory', file = sys.stderr)
sys.exit(1)
# output in the same directory as the model
dir_model = sys.argv[1]
last_dir = os.path.basename(os.path.normpath(dir_model))
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
@ -51,19 +55,15 @@ last_dir = os.path.basename(os.path.normpath(dir_model))
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))
if args.outfile is not None:
fname_out = args.outfile
else:
# output in the same directory as the model by default
fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
sys.exit(1)
print("gguf: loading model "+dir_model.name)
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
print("gguf: loading model "+last_dir)
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
if hparams["architectures"][0] != "LlamaForCausalLM":
@ -107,7 +107,7 @@ else:
sys.exit()
gguf_writer.add_name(last_dir)
gguf_writer.add_name(dir_model.name)
gguf_writer.add_source_hf_repo(hf_repo)
gguf_writer.add_tensor_data_layout("Meta AI original pth")
gguf_writer.add_context_length(ctx_length)
@ -133,109 +133,60 @@ tokens: List[bytes] = []
scores: List[float] = []
toktypes: List[int] = []
if Path(dir_model + "/tokenizer.model").is_file():
# vocab type sentencepiece
print("gguf: get sentencepiece tokenizer vocab and scores")
tokenizer_model_file = dir_model / 'tokenizer.model'
if not tokenizer_model_file.is_file():
print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr)
sys.exit(1)
tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model")
# vocab type sentencepiece
print("gguf: get sentencepiece tokenizer vocab and scores")
for i in range(tokenizer.vocab_size()):
text: bytes
score: float
tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
piece = tokenizer.id_to_piece(i)
text = piece.encode("utf-8")
score = tokenizer.get_score(i)
for i in range(tokenizer.vocab_size()):
text: bytes
score: float
toktype = 1 # defualt to normal token type
if tokenizer.is_unknown(i):
toktype = 2
if tokenizer.is_control(i):
toktype = 3
piece = tokenizer.id_to_piece(i)
text = piece.encode("utf-8")
score = tokenizer.get_score(i)
# toktype = 4 is user-defined = tokens from added_tokens.json
toktype = 1 # defualt to normal token type
if tokenizer.is_unknown(i):
toktype = 2
if tokenizer.is_control(i):
toktype = 3
if tokenizer.is_unused(i):
toktype = 5
if tokenizer.is_byte(i):
toktype = 6
# toktype = 4 is user-defined = tokens from added_tokens.json
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
if tokenizer.is_unused(i):
toktype = 5
if tokenizer.is_byte(i):
toktype = 6
if Path(dir_model + "/added_tokens.json").is_file():
with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f:
addtokens_json = json.load(f)
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
print("gguf: get added tokens")
added_tokens_file = dir_model / 'added_tokens.json'
if added_tokens_file.is_file():
with open(added_tokens_file, "r", encoding="utf-8") as f:
addtokens_json = json.load(f)
for key in addtokens_json:
tokens.append( key.encode("utf-8") )
scores.append(-1000.0)
toktypes.append(4) # user-defined token type
print("gguf: get added tokens")
gguf_writer.add_tokenizer_model("llama")
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)
for key in addtokens_json:
tokens.append( key.encode("utf-8") )
scores.append(-1000.0)
toktypes.append(4) # user-defined token type
gguf_writer.add_tokenizer_model("llama")
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)
print("gguf: get special token ids")
if Path(dir_model + "/tokenizer.json").is_file():
# Look for special tokens in tokenizer.json if it exists
with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
tokenizer = json.load(f)
if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file():
with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
tokenizer_config = json.load(f)
if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["bos_token"]["content"]:
gguf_writer.add_bos_token_id(key["id"])
if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["eos_token"]["content"]:
gguf_writer.add_eos_token_id(key["id"])
if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["unk_token"]["content"]:
gguf_writer.add_unk_token_id(key["id"])
if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["sep_token"]["content"]:
gguf_writer.add_sep_token_id(key["id"])
if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["pad_token"]["content"]:
gguf_writer.add_pad_token_id(key["id"])
else:
# If no tokenizer.json: Look for special tokens in config.json
if "bos_token_id" in hparams and hparams["bos_token_id"] != None:
gguf_writer.add_bos_token_id(hparams["bos_token_id"])
if "eos_token_id" in hparams and hparams["eos_token_id"] != None:
gguf_writer.add_eos_token_id(hparams["eos_token_id"])
if "unk_token_id" in hparams and hparams["unk_token_id"] != None:
gguf_writer.add_unk_token_id(hparams["unk_token_id"])
if "sep_token_id" in hparams and hparams["sep_token_id"] != None:
gguf_writer.add_sep_token_id(hparams["sep_token_id"])
if "pad_token_id" in hparams and hparams["pad_token_id"] != None:
gguf_writer.add_pad_token_id(hparams["pad_token_id"])
special_vocab = gguf.SpecialVocab(dir_model)
special_vocab.add_to_gguf(gguf_writer)
# TENSORS
@ -247,6 +198,8 @@ print("gguf: get tensor metadata")
part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts))
for part_name in part_names:
if args.vocab_only:
break
print("gguf: loading model part '" + part_name + "'")
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
@ -266,11 +219,8 @@ for part_name in part_names:
data = data.squeeze().numpy()
# map tensor names
if name.endswith(".weight") and name[:-7] in tensor_map:
name = tensor_map[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
if new_name is None:
print("Can not map tensor '" + name + "'")
sys.exit()
@ -289,20 +239,20 @@ for part_name in part_names:
if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.add_tensor(name, data)
gguf_writer.add_tensor(new_name, data)
print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
if not args.vocab_only:
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
print("gguf: model successfully exported to '" + fname_out + "'")
print(f"gguf: model successfully exported to '{fname_out}'")
print("")

28
convert-llama-ggmlv3-to-gguf.py
View file

@ -75,7 +75,7 @@ class Tensor:
self.dims = ()
self.dtype = None
self.start_offset = 0
self.len_bytes = 0
self.len_bytes = np.int64(0)
def load(self, data, offset):
orig_offset = offset
@ -134,13 +134,14 @@ class GGMLV3Model:
return offset
class GGMLToGGUF:
def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override = None):
def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override = None, special_vocab = None):
hp = ggml_model.hyperparameters
self.model = ggml_model
self.data = data
self.cfg = cfg
self.params_override = params_override
self.vocab_override = vocab_override
self.special_vocab = special_vocab
if params_override is not None:
n_kv_head = params_override.n_head_kv
else:
@ -162,6 +163,8 @@ class GGMLToGGUF:
gguf_writer = gguf.GGUFWriter(self.cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
self.add_params(gguf_writer)
self.add_vocab(gguf_writer)
if self.special_vocab is not None:
self.special_vocab.add_to_gguf(gguf_writer)
self.add_tensors(gguf_writer)
print(" gguf: write header")
gguf_writer.write_header_to_file()
@ -259,20 +262,13 @@ class GGMLToGGUF:
gguf_writer.add_eos_token_id(2)
def add_tensors(self, gguf_writer):
nm = self.name_map
tensor_map = self.name_map
data = self.data
print(f'* Adding {len(self.model.tensors)} tensor(s)')
for tensor in self.model.tensors:
name = str(tensor.name, 'UTF-8')
if name.endswith('.weight'):
name = name[:-7]
suffix = '.weight'
elif name.endswith('.bias'):
name = name[:-5]
suffix = '.bias'
mapped_name = nm.get(name)
mapped_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
assert mapped_name is not None, f'Bad name {name}'
mapped_name += suffix
tempdims = list(tensor.dims[:])
if len(tempdims) > 1:
temp = tempdims[1]
@ -302,8 +298,10 @@ def handle_metadata(cfg, hp):
else:
raise ValueError('Unable to load metadata')
vocab = convert.load_vocab(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, cfg.vocabtype)
# FIXME: Respect cfg.vocab_dir?
svocab = gguf.SpecialVocab(cfg.model_metadata_dir)
convert.check_vocab_size(params, vocab)
return (params, vocab)
return (params, vocab, svocab)
def handle_args():
parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF')
@ -330,14 +328,16 @@ def main():
print(f'* GGML model hyperparameters: {model.hyperparameters}')
vocab_override = None
params_override = None
special_vocab = None
if cfg.model_metadata_dir is not None:
(params_override, vocab_override) = handle_metadata(cfg, model.hyperparameters)
(params_override, vocab_override, special_vocab) = handle_metadata(cfg, model.hyperparameters)
print('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.')
print(f'* Overriding params: {params_override}')
print(f'* Overriding vocab: {vocab_override}')
print(f'* Special vocab: {special_vocab}')
else:
print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override)
converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override, special_vocab = special_vocab)
converter.save()
print(f'* Successful completion. Output saved to: {cfg.output}')

203
convert-llama-hf-to-gguf.py
View file

@ -8,8 +8,9 @@ import struct
import json
import numpy as np
import torch
import argparse
from typing import Any, List, Optional
from typing import Any, List, Optional, TypeAlias
from pathlib import Path
from sentencepiece import SentencePieceProcessor
@ -43,40 +44,38 @@ def count_model_parts(dir_model: str) -> int:
return num_parts
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Convert a HuggingFace LLaMA model to a GGML compatible file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1)
return parser.parse_args()
args = parse_args()
dir_model = args.model
ftype = args.ftype
if not dir_model.is_dir():
print(f'Error: {args.model} is not a directory', file = sys.stderr)
sys.exit(1)
# output in the same directory as the model
dir_model = sys.argv[1]
last_dir = os.path.basename(os.path.normpath(dir_model))
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))
if args.outfile is not None:
fname_out = args.outfile
else:
# output in the same directory as the model by default
fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
sys.exit(1)
print("gguf: loading model "+dir_model.name)
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
print("gguf: loading model "+last_dir)
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
if hparams["architectures"][0] != "LlamaForCausalLM":
@ -115,7 +114,7 @@ else:
sys.exit()
gguf_writer.add_name(last_dir)
gguf_writer.add_name(dir_model.name)
gguf_writer.add_source_hf_repo(hf_repo)
gguf_writer.add_tensor_data_layout("Meta AI original pth")
gguf_writer.add_context_length(ctx_length)
@ -141,110 +140,61 @@ tokens: List[bytes] = []
scores: List[float] = []
toktypes: List[int] = []
if Path(dir_model + "/tokenizer.model").is_file():
# vocab type sentencepiece
print("gguf: get sentencepiece tokenizer vocab, scores and token types")
tokenizer_model_file = dir_model / 'tokenizer.model'
if not tokenizer_model_file.is_file():
print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr)
sys.exit(1)
tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model")
# vocab type sentencepiece
print("gguf: get sentencepiece tokenizer vocab, scores and token types")
for i in range(tokenizer.vocab_size()):
text: bytes
score: float
tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
piece = tokenizer.id_to_piece(i)
text = piece.encode("utf-8")
score = tokenizer.get_score(i)
for i in range(tokenizer.vocab_size()):
text: bytes
score: float
toktype = 1 # defualt to normal token type
if tokenizer.is_unknown(i):
toktype = 2
if tokenizer.is_control(i):
toktype = 3
piece = tokenizer.id_to_piece(i)
text = piece.encode("utf-8")
score = tokenizer.get_score(i)
# toktype = 4 is user-defined = tokens from added_tokens.json
toktype = 1 # defualt to normal token type
if tokenizer.is_unknown(i):
toktype = 2
if tokenizer.is_control(i):
toktype = 3
if tokenizer.is_unused(i):
toktype = 5
if tokenizer.is_byte(i):
toktype = 6
# toktype = 4 is user-defined = tokens from added_tokens.json
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
if tokenizer.is_unused(i):
toktype = 5
if tokenizer.is_byte(i):
toktype = 6
if Path(dir_model + "/added_tokens.json").is_file():
with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f:
addtokens_json = json.load(f)
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
print("gguf: get added tokens")
added_tokens_file = dir_model / 'added_tokens.json'
if added_tokens_file.is_file():
with open(added_tokens_file, "r", encoding="utf-8") as f:
addtokens_json = json.load(f)
for key in addtokens_json:
tokens.append( key.encode("utf-8") )
scores.append(-1000.0)
toktypes.append(4) # user-defined token type
print("gguf: get added tokens")
for key in addtokens_json:
tokens.append( key.encode("utf-8") )
scores.append(-1000.0)
toktypes.append(4) # user-defined token type
gguf_writer.add_tokenizer_model("llama")
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)
print("gguf: get special token ids")
if Path(dir_model + "/tokenizer.json").is_file():
# Look for special tokens in tokenizer.json if it exists
with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
tokenizer = json.load(f)
if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file():
with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
tokenizer_config = json.load(f)
if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["bos_token"]["content"]:
gguf_writer.add_bos_token_id(key["id"])
if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["eos_token"]["content"]:
gguf_writer.add_eos_token_id(key["id"])
if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["unk_token"]["content"]:
gguf_writer.add_unk_token_id(key["id"])
if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["sep_token"]["content"]:
gguf_writer.add_sep_token_id(key["id"])
if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["pad_token"]["content"]:
gguf_writer.add_pad_token_id(key["id"])
else:
# If no tokenizer.json: Look for special tokens in config.json
if "bos_token_id" in hparams and hparams["bos_token_id"] != None:
gguf_writer.add_bos_token_id(hparams["bos_token_id"])
if "eos_token_id" in hparams and hparams["eos_token_id"] != None:
gguf_writer.add_eos_token_id(hparams["eos_token_id"])
if "unk_token_id" in hparams and hparams["unk_token_id"] != None:
gguf_writer.add_unk_token_id(hparams["unk_token_id"])
if "sep_token_id" in hparams and hparams["sep_token_id"] != None:
gguf_writer.add_sep_token_id(hparams["sep_token_id"])
if "pad_token_id" in hparams and hparams["pad_token_id"] != None:
gguf_writer.add_pad_token_id(hparams["pad_token_id"])
gguf_writer.add_tokenizer_model("llama")
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model)
special_vocab.add_to_gguf(gguf_writer)
# TENSORS
@ -254,13 +204,15 @@ tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
print("gguf: get tensor metadata")
if num_parts == 0:
part_names = ("pytorch_model.bin",)
part_names = iter(("pytorch_model.bin",))
else:
part_names = (
f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
)
for part_name in part_names:
if args.vocab_only:
break
print("gguf: loading model part '" + part_name + "'")
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
@ -286,11 +238,8 @@ for part_name in part_names:
data = reverse_hf_permute(data, head_count, head_count_kv)
# map tensor names
if name.endswith(".weight") and name[:-7] in tensor_map:
name = tensor_map[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
if new_name is None:
print("Can not map tensor '" + name + "'")
sys.exit()
@ -309,20 +258,20 @@ for part_name in part_names:
if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.add_tensor(name, data)
gguf_writer.add_tensor(new_name, data)
print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
if not args.vocab_only:
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
print("gguf: model successfully exported to '" + fname_out + "'")
print(f"gguf: model successfully exported to '{fname_out}'")
print("")

6
convert-lora-to-ggml.py
View file

@ -4,7 +4,7 @@ import os
import re
import struct
import sys
from typing import Any, Dict, Sequence, TextIO
from typing import Any, Dict, Sequence, BinaryIO
import numpy as np
import torch
@ -46,7 +46,7 @@ def translate_tensor_name(t: str) -> str:
sys.exit(1)
def write_file_header(fout: TextIO, params: Dict[str, Any]) -> None:
def write_file_header(fout: BinaryIO, params: Dict[str, Any]) -> None:
fout.write(b"ggla"[::-1]) # magic (ggml lora)
fout.write(struct.pack("i", 1)) # file version
fout.write(struct.pack("i", params["r"]))
@ -60,7 +60,7 @@ def write_file_header(fout: TextIO, params: Dict[str, Any]) -> None:
def write_tensor_header(
self, name: str, shape: Sequence[int], data_type: np.dtype
self, name: str, shape: Sequence[int], data_type: np.dtype[Any]
) -> None:
sname = name.encode("utf-8")
fout.write(

343
convert.py
View file

@ -3,6 +3,7 @@
import gguf
import argparse
import concurrent.futures
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import copy
import enum
import faulthandler
@ -17,13 +18,14 @@ import re
import signal
import struct
import sys
import time
import zipfile
import numpy as np
from abc import ABCMeta, abstractmethod
from dataclasses import dataclass
from pathlib import Path
from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, TypeVar, Union)
from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set, Tuple, Type, TypeVar, Union)
from sentencepiece import SentencePieceProcessor # type: ignore
if TYPE_CHECKING:
@ -37,30 +39,70 @@ NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
ARCH=gguf.MODEL_ARCH.LLAMA
NAMES=gguf.MODEL_TENSOR_NAMES[ARCH]
DEFAULT_CONCURRENCY = 8
#
# data types
#
@dataclass(frozen=True)
class UnquantizedDataType:
class DataType:
name: str
dtype: 'np.dtype[Any]'
valid_conversions: List[str]
DT_F16 = UnquantizedDataType('F16')
DT_F32 = UnquantizedDataType('F32')
DT_I32 = UnquantizedDataType('I32')
DT_BF16 = UnquantizedDataType('BF16')
def elements_to_bytes(self, n_elements: int) -> int:
return n_elements * self.dtype.itemsize
DataType = Union[UnquantizedDataType]
@dataclass(frozen=True)
class UnquantizedDataType(DataType):
pass
DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = {
DT_BF16: np.dtype(np.uint16),
DT_F16: np.dtype(np.float16),
DT_F32: np.dtype(np.float32),
DT_I32: np.dtype(np.int32),
}
DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])
NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \
{dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()}
@dataclass(frozen=True)
class QuantizedDataType(DataType):
block_size: int
quantized_dtype: 'np.dtype[Any]'
ggml_type: gguf.GGMLQuantizationType
def quantize(self, arr: NDArray) -> NDArray:
raise NotImplementedError(f'Quantization for {self.name} not implemented')
def elements_to_bytes(self, n_elements: int) -> int:
assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'
return self.quantized_dtype.itemsize * (n_elements // self.block_size)
@dataclass(frozen=True)
class Q8_0QuantizedDataType(QuantizedDataType):
# Mini Q8_0 quantization in Python!
def quantize(self, arr: NDArray) -> NDArray:
assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}'
assert arr.dtype == np.float32, f'Bad array type {arr.dtype}'
n_blocks = arr.size // self.block_size
blocks = arr.reshape((n_blocks, self.block_size))
# Much faster implementation of block quantization contributed by @Cebtenzzre
def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[Tuple[Any, Any]]:
d = abs(blocks).max(axis = 1) / np.float32(127)
with np.errstate(divide = 'ignore'):
qs = (blocks / d[:, None]).round()
qs[d == 0] = 0
yield from zip(d, qs)
return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)
DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
dtype = np.dtype(np.float32), valid_conversions = [],
ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))
# Quantized types skipped here because they may also map to np.float32
NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = {}
for dt in (DT_BF16, DT_F16, DT_F32, DT_I32):
if dt.dtype in NUMPY_TYPE_TO_DATA_TYPE:
raise ValueError(f'Invalid duplicate data type {dt}')
NUMPY_TYPE_TO_DATA_TYPE[dt.dtype] = dt
SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
'BF16': DT_BF16,
@ -73,20 +115,22 @@ SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
# TODO: rename to LLAMAFileType
# TODO: move to `gguf.py`
class GGMLFileType(enum.IntEnum):
AllF32 = 0
MostlyF16 = 1 # except 1d tensors
AllF32 = 0
MostlyF16 = 1 # except 1d tensors
MostlyQ8_0 = 7 # except 1d tensors
def type_for_tensor(self, name: str, tensor: 'LazyTensor') -> DataType:
if len(tensor.shape) == 1:
# 1D tensors are always F32.
return DT_F32
elif self == GGMLFileType.AllF32:
return DT_F32
elif self == GGMLFileType.MostlyF16:
return DT_F16
else:
dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)
if dt is None:
raise ValueError(self)
# 1D tensors are always F32.
return dt if len(tensor.shape) > 1 else DT_F32
GGML_FILE_TYPE_TO_DATA_TYPE: Dict[GGMLFileType, DataType] = {
GGMLFileType.AllF32 : DT_F32,
GGMLFileType.MostlyF16 : DT_F16,
GGMLFileType.MostlyQ8_0: DT_Q8_0,
}
#
# hparams loading
@ -170,7 +214,8 @@ class Params:
f_norm_eps = config["rms_norm_eps"]
f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
if "rope_scaling" in config and config["rope_scaling"].get("type") == "linear":
rope_scaling = config.get("rope_scaling")
if isinstance(rope_scaling, dict) and rope_scaling.get("type") == "linear":
f_rope_scale = config["rope_scaling"].get("factor")
else:
f_rope_scale = None
@ -254,8 +299,10 @@ class Params:
params = Params.loadHFTransformerJson(model_plus.model, hf_config_path)
elif orig_config_path.exists():
params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path)
else:
elif model_plus.format != 'none':
params = Params.guessed(model_plus.model)
else:
raise ValueError('Cannot guess params when model format is none')
params.path_model = model_plus.paths[0].parent
@ -308,7 +355,7 @@ class BpeVocab:
yield from self.added_tokens()
def __repr__(self) -> str:
return f"BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
class SentencePieceVocab:
@ -371,7 +418,6 @@ class SentencePieceVocab:
Vocab = Union[BpeVocab, SentencePieceVocab]
#
# data loading
# TODO: reuse (probably move to gguf.py?)
@ -394,14 +440,14 @@ class Tensor(metaclass=ABCMeta):
@abstractmethod
def permute(self, n_head: int, n_head_kv: int) -> 'Tensor': ...
@abstractmethod
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ...
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> 'UnquantizedTensor': ...
@abstractmethod
def part(self, n_part: int) -> 'UnquantizedTensor': ...
@abstractmethod
def to_ggml(self) -> 'GGMLCompatibleTensor': ...
def bf16_to_fp32(bf16_arr: np.ndarray) -> np.ndarray:
def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray:
assert bf16_arr.dtype == np.uint16, f"Input array should be of dtype uint16, but got {bf16_arr.dtype}"
fp32_arr = bf16_arr.astype(np.uint32) << 16
return fp32_arr.view(np.float32)
@ -414,7 +460,7 @@ class UnquantizedTensor(Tensor):
self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]
def astype(self, data_type: DataType) -> Tensor:
dtype = DATA_TYPE_TO_NUMPY[data_type]
dtype = data_type.dtype
if self.data_type == DT_BF16:
self.ndarray = bf16_to_fp32(self.ndarray)
return UnquantizedTensor(self.ndarray.astype(dtype))
@ -422,9 +468,9 @@ class UnquantizedTensor(Tensor):
def to_ggml(self) -> 'UnquantizedTensor':
return self
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor':
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> 'UnquantizedTensor':
r = self.ndarray.shape[0] // 3
return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head))
return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head_kv))
def part(self, n_part: int) -> 'UnquantizedTensor':
r = self.ndarray.shape[0] // 3
@ -453,22 +499,6 @@ def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, conv
GGMLCompatibleTensor = Union[UnquantizedTensor]
class DeferredPermutedTensor(Tensor):
def __init__(self, base: Tensor, n_head: int, n_head_kv: int) -> None:
self.base = base
self.n_head = n_head
self.data_type = self.base.data_type
def astype(self, data_type: DataType) -> Tensor:
return self.base.astype(data_type).permute(self.n_head, self.n_head_kv)
def to_ggml(self) -> GGMLCompatibleTensor:
return self.base.to_ggml().permute(self.n_head, self.n_head_kv)
def permute(self, n_head: int, n_head_kv: int) -> Tensor:
raise Exception("shouldn't permute twice")
@dataclass
class LazyTensor:
_load: Callable[[], Tensor]
@ -478,7 +508,9 @@ class LazyTensor:
def load(self) -> Tensor:
ret = self._load()
assert ret.data_type == self.data_type, (self.data_type, ret.data_type, self.description)
# Should be okay if it maps to the same numpy type?
assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \
(self.data_type, ret.data_type, self.description)
return ret
def astype(self, data_type: DataType) -> 'LazyTensor':
@ -489,8 +521,8 @@ class LazyTensor:
return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}')
def validate_conversion_to(self, data_type: DataType) -> None:
if data_type == self.data_type:
return
if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions:
raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.')
LazyModel = Dict[str, LazyTensor]
@ -500,7 +532,7 @@ LazyModel = Dict[str, LazyTensor]
class ModelPlus:
model: LazyModel
paths: List[Path] # Where this was read from.
format: Literal['ggml', 'torch', 'safetensors']
format: Literal['ggml', 'torch', 'safetensors', 'none']
vocab: Optional[Vocab] # For GGML models (which have vocab built in), the vocab.
@ -566,12 +598,12 @@ def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTe
return lazy_tensor.load().permute(n_head, n_head_kv)
return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)
def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor:
def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_kv: int) -> LazyTensor:
def load() -> Tensor:
return lazy_tensor.load().permute_part(n_part, n_head)
return lazy_tensor.load().permute_part(n_part, n_head, n_head_kv)
s = lazy_tensor.shape.copy()
s[0] = s[0] // 3
return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description)
return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)
def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
def load() -> Tensor:
@ -616,9 +648,7 @@ class LazyUnpickler(pickle.Unpickler):
info = self.zip_file.getinfo(filename)
def load(offset: int, elm_count: int) -> NDArray:
dtype = DATA_TYPE_TO_NUMPY.get(data_type)
if dtype is None:
raise Exception("tensor stored in unsupported format")
dtype = data_type.dtype
fp = self.zip_file.open(info)
fp.seek(offset * dtype.itemsize)
size = elm_count * dtype.itemsize
@ -628,7 +658,7 @@ class LazyUnpickler(pickle.Unpickler):
description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}'
return LazyStorage(load=load, kind=pid[1], description=description)
# @staticmethod
@staticmethod
def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any,
# pyright: ignore[reportSelfClsParameterName]
requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor:
@ -640,13 +670,15 @@ class LazyUnpickler(pickle.Unpickler):
description = f'pickled storage_offset={storage_offset} in {storage.description}'
return LazyTensor(load, list(size), storage.kind.data_type, description)
# @staticmethod
@staticmethod
def rebuild_from_type_v2(func, new_type, args, state):
return func(*args)
CLASSES: Dict[Any, Any] = {
('torch._tensor', '_rebuild_from_type_v2'): rebuild_from_type_v2,
('torch._utils', '_rebuild_tensor_v2'): lazy_rebuild_tensor_v2,
CLASSES: Dict[Tuple[str, str], Any] = {
# getattr used here as a workaround for mypy not being smart enough to detrmine
# the staticmethods have a __func__ attribute.
('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),
('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'),
('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16),
('torch', 'HalfStorage'): LazyStorageKind(DT_F16),
('torch', 'FloatStorage'): LazyStorageKind(DT_F32),
@ -682,7 +714,7 @@ def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:
def convert(info: Dict[str, Any]) -> LazyTensor:
data_type = SAFETENSORS_DATA_TYPES[info['dtype']]
numpy_dtype = DATA_TYPE_TO_NUMPY[data_type]
numpy_dtype = data_type.dtype
shape: List[int] = info['shape']
begin, end = info['data_offsets']
assert 0 <= begin <= end <= len(byte_buf)
@ -722,23 +754,40 @@ def lazy_load_file(path: Path) -> ModelPlus:
In = TypeVar('In')
Out = TypeVar('Out')
def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int) -> Iterable[Out]:
def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: Optional[int] = None, use_processpool_executor: bool = False) -> Iterable[Out]:
'''Parallel map, but with backpressure. If the caller doesn't call `next`
fast enough, this will stop calling `func` at some point rather than
letting results pile up in memory. Specifically, there is a max of one
output value buffered per thread.'''
with concurrent.futures.ThreadPoolExecutor() as executor:
if concurrency < 2:
yield from map(func, iterable)
# Not reached.
iterable = iter(iterable)
executor_class: Union[Type[ThreadPoolExecutor], Type[ProcessPoolExecutor]]
if use_processpool_executor:
executor_class = ProcessPoolExecutor
else:
executor_class = ThreadPoolExecutor
with executor_class(max_workers = max_workers) as executor:
futures: List[concurrent.futures.Future[Out]] = []
items_rev = list(iterable)[::-1]
for i in range(min(concurrency, len(items_rev))):
futures.append(executor.submit(func, items_rev.pop()))
done = False
for _ in range(concurrency):
try:
futures.append(executor.submit(func, next(iterable)))
except StopIteration:
done = True
break
while futures:
result = futures.pop(0).result()
if items_rev:
futures.append(executor.submit(func, items_rev.pop()))
while not done and len(futures) < concurrency:
try:
futures.append(executor.submit(func, next(iterable)))
except StopIteration:
done = True
break
yield result
def check_vocab_size(params: Params, vocab: Vocab) -> None:
if params.n_vocab != vocab.vocab_size:
assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)
@ -762,10 +811,12 @@ class OutputFile:
def add_meta_arch(self, params: Params) -> None:
name = "LLaMA"
# TODO: better logic to determine model name
if (params.n_ctx == 4096):
name = "LLaMA v2"
if params.path_model:
name = str(params.path_model.parent).split('/')[-1]
elif params.path_model:
name = str(params.path_model.parent).split('/')[-1]
self.gguf.add_name (name)
self.gguf.add_context_length (params.n_ctx)
@ -790,25 +841,31 @@ class OutputFile:
tokens = []
scores = []
toktypes = []
# NOTE: `all_tokens` returns the the base vocabulary and added tokens
# TODO: add special tokens?
# NOTE: `all_tokens` returns the base vocabulary and added tokens
for text, score, toktype in vocab.all_tokens():
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
self.gguf.add_tokenizer_model("llama")
if isinstance(vocab, SentencePieceVocab):
self.gguf.add_tokenizer_model("llama")
elif isinstance(vocab, BpeVocab):
self.gguf.add_tokenizer_model("gpt2")
else:
raise ValueError(f'Unknown vocab type: Not BpeVocab or SentencePieceVocab')
self.gguf.add_token_list(tokens)
self.gguf.add_token_scores(scores)
self.gguf.add_token_types(toktypes)
def add_meta_special_vocab(self, svocab: gguf.SpecialVocab) -> None:
svocab.add_to_gguf(self.gguf)
def add_tensor_info(self, name: str, tensor: LazyTensor) -> None:
n_elements = 1
for dim in tensor.shape:
n_elements *= dim
data_type = DATA_TYPE_TO_NUMPY[tensor.data_type]
data_nbytes = n_elements * data_type.itemsize
self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes)
n_elements = int(np.prod(tensor.shape))
raw_dtype = getattr(tensor.data_type, 'ggml_type', None)
data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype
data_nbytes = tensor.data_type.elements_to_bytes(n_elements)
self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype = raw_dtype)
def write_meta(self) -> None:
self.gguf.write_header_to_file()
@ -821,7 +878,7 @@ class OutputFile:
self.gguf.close()
@staticmethod
def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab) -> None:
def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab) -> None:
check_vocab_size(params, vocab)
of = OutputFile(fname_out)
@ -829,12 +886,27 @@ class OutputFile:
# meta data
of.add_meta_arch(params)
of.add_meta_vocab(vocab)
of.add_meta_special_vocab(svocab)
of.write_meta()
of.close()
@staticmethod
def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None:
def do_item(item: Tuple[str, LazyTensor]) -> Tuple[DataType, NDArray]:
name, lazy_tensor = item
tensor = lazy_tensor.load().to_ggml()
return (lazy_tensor.data_type, tensor.ndarray)
@staticmethod
def maybe_do_quantize(item: Tuple[DataType, NDArray]) -> NDArray:
dt, arr = item
if not isinstance(dt, QuantizedDataType):
return arr
return dt.quantize(arr)
@staticmethod
def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY) -> None:
check_vocab_size(params, vocab)
of = OutputFile(fname_out)
@ -842,6 +914,7 @@ class OutputFile:
# meta data
of.add_meta_arch(params)
of.add_meta_vocab(vocab)
of.add_meta_special_vocab(svocab)
# tensor info
for name, lazy_tensor in model.items():
@ -850,16 +923,19 @@ class OutputFile:
of.write_meta()
of.write_tensor_info()
def do_item(item: Tuple[str, LazyTensor]) -> NDArray:
name, lazy_tensor = item
return lazy_tensor.load().to_ggml().ndarray
# tensor data
ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8)
ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)
if ftype == GGMLFileType.MostlyQ8_0:
ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, use_processpool_executor = True)
else:
ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
start = time.time()
for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
elapsed = time.time() - start
size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
padi = len(str(len(model)))
print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type}")
print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}")
of.gguf.write_tensor_data(ndarray)
of.close()
@ -871,6 +947,8 @@ def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFi
return GGMLFileType.AllF32
if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
return GGMLFileType.MostlyF16
if output_type_str == "q8_0":
return GGMLFileType.MostlyQ8_0
name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}
@ -881,7 +959,8 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM
for (name, tensor) in model.items()}
def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
tmap = gguf.get_tensor_name_map(ARCH, params.n_layer)
tmap = gguf.TensorNameMap(ARCH, params.n_layer)
should_skip: Set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
tmp = model
@ -897,28 +976,22 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head)
tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv)
tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2)
del tmp[f"model.layers.{i}.self_attn.W_pack.weight"]
else:
break
out: LazyModel = {}
for name, lazy_tensor in model.items():
name_new = name
if name in tmap:
name_new = tmap[name]
elif name.endswith(".weight") and name[:-7] in tmap:
name_new = tmap[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tmap:
name_new = tmap[name[:-5]] + ".bias"
else:
tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None)
if name_new is None:
raise Exception(f"Unexpected tensor name: {name}")
if gguf.should_skip_tensor_TMP(ARCH, params.n_layer, name_new):
if tensor_type in should_skip:
print(f"skipping tensor {name_new}")
continue
else:
print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type} | {lazy_tensor.shape}")
out[name_new] = lazy_tensor
print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
out[name_new] = lazy_tensor
return out
@ -1022,6 +1095,7 @@ def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path:
namestr = {
GGMLFileType.AllF32: "f32",
GGMLFileType.MostlyF16: "f16",
GGMLFileType.MostlyQ8_0:"q8_0",
}[file_type]
ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf"
if ret in model_paths:
@ -1045,19 +1119,28 @@ def main(args_in: Optional[List[str]] = None) -> None:
parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model")
parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--outtype", choices=["f32", "f16"], help="output format (default: based on input)")
parser.add_argument("--outtype", choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
parser.add_argument("--ctx", type=int, help="model training context (default: based on input)")
parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY)
args = parser.parse_args(args_in)
if args.dump_single:
model_plus = lazy_load_file(args.model)
do_dump_model(model_plus)
return
model_plus = load_some_model(args.model)
if not args.vocab_only:
model_plus = load_some_model(args.model)
else:
model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None)
if args.dump:
do_dump_model(model_plus)
return
params = Params.load(model_plus)
if params.n_ctx == -1:
@ -1072,39 +1155,41 @@ def main(args_in: Optional[List[str]] = None) -> None:
params.ftype = {
"f32": GGMLFileType.AllF32,
"f16": GGMLFileType.MostlyF16,
"q8_0": GGMLFileType.MostlyQ8_0,
}[args.outtype]
print(f"params = {params}")
vocab: Vocab
if args.vocab_only:
vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)
assert args.outfile, "need --outfile if using --vocab-only"
# FIXME: Try to respect vocab_dir somehow?
vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)
special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, load_merges = args.vocabtype == 'bpe')
outfile = args.outfile
OutputFile.write_vocab_only(outfile, params, vocab)
OutputFile.write_vocab_only(outfile, params, vocab, special_vocab)
print(f"Wrote {outfile}")
return
if model_plus.vocab is not None and args.vocab_dir is None:
vocab = model_plus.vocab
else:
if args.dump:
do_dump_model(model_plus)
return
vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
vocab = load_vocab(vocab_dir, args.vocabtype)
# FIXME: Try to respect vocab_dir somehow?
special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, load_merges = args.vocabtype == 'bpe')
if model_plus.vocab is not None and args.vocab_dir is None:
vocab = model_plus.vocab
else:
vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
vocab = load_vocab(vocab_dir, args.vocabtype)
model = model_plus.model
model = convert_model_names(model, params)
ftype = pick_output_type(model, args.outtype)
model = convert_to_output_type(model, ftype)
outfile = args.outfile or default_outfile(model_plus.paths, ftype)
model = model_plus.model
model = convert_model_names(model, params)
ftype = pick_output_type(model, args.outtype)
model = convert_to_output_type(model, ftype)
outfile = args.outfile or default_outfile(model_plus.paths, ftype)
params.ftype = ftype
print(f"Writing {outfile}, format {ftype}")
params.ftype = ftype
print(f"Writing {outfile}, format {ftype}")
OutputFile.write_all(outfile, params, model, vocab)
print(f"Wrote {outfile}")
OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency)
print(f"Wrote {outfile}")
if __name__ == '__main__':

2
examples/CMakeLists.txt
View file

@ -25,7 +25,7 @@ else()
add_subdirectory(simple)
add_subdirectory(embd-input)
add_subdirectory(llama-bench)
add_subdirectory(beam_search)
add_subdirectory(beam-search)
if (LLAMA_METAL)
add_subdirectory(metal)
endif()

4
examples/beam_search/CMakeLists.txt → examples/beam-search/CMakeLists.txt
View file

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

6
examples/beam_search/beam_search.cpp → examples/beam-search/beam-search.cpp
View file

@ -35,7 +35,7 @@ struct ostream_beam_view {
std::ostream& operator<<(std::ostream& os, const ostream_beam_view & obv) {
os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens(";
for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) {
os << llama_token_to_str(obv.ctx, obv.beam_view.tokens[i]);
os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]);
}
return os << ')';
}
@ -156,7 +156,7 @@ int main(int argc, char ** argv)
for( auto id : tokens_list )
{
std::cout << llama_token_to_str(ctx, id);
std::cout << llama_token_to_piece(ctx, id);
}
std::cout << std::flush;
@ -175,7 +175,7 @@ int main(int argc, char ** argv)
std::cout << "\n\n";
for (llama_token const token_id : callback_data.response) {
std::cout << llama_token_to_str(ctx,token_id);
std::cout << llama_token_to_piece(ctx,token_id);
}
std::cout << std::endl;

2
examples/chat.sh
View file

@ -11,6 +11,6 @@ cd ..
#
# "--keep 48" is based on the contents of prompts/chat-with-bob.txt
#
./main -m ./models/7B/ggml-model-q4_0.bin -c 512 -b 1024 -n 256 --keep 48 \
./main -m ./models/llama-7b/ggml-model-q4_0.gguf -c 512 -b 1024 -n 256 --keep 48 \
--repeat_penalty 1.0 --color -i \
-r "User:" -f prompts/chat-with-bob.txt

8
examples/convert-llama2c-to-ggml/README.md
View file

@ -12,18 +12,14 @@ usage: ./convert-llama2c-to-ggml [options]
options:
-h, --help show this help message and exit
--copy-vocab-from-model FNAME model path from which to copy vocab (default 'tokenizer.bin')
--copy-vocab-from-model FNAME path of gguf llama model or llama2.c vocabulary from which to copy vocab (default 'models/7B/ggml-model-f16.gguf')
--llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model
--llama2c-output-model FNAME model path to save the converted llama2.c model (default ak_llama_model.bin')
```
An example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows:
`$ ./convert-llama2c-to-ggml --copy-vocab-from-model ../llama2.c/tokenizer.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.ggmlv3.bin`
For now the generated model is in the legacy GGJTv3 format, so you need to convert it to gguf manually:
`$ python ./convert-llama-ggmlv3-to-gguf.py --eps 1e-5 --input stories42M.ggmlv3.bin --output stories42M.gguf.bin`
`$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin`
Now you can use the model with a command like:

341
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View file

@ -10,9 +10,48 @@
#include <ctime>
#include <random>
#include <stdexcept>
#include <sstream>
#include <algorithm>
#include <string>
// GGUF keys & tensor names.
#define KV_GENERAL_ARCHITECTURE "general.architecture"
#define KV_GENERAL_NAME "general.name"
#define KV_TOKENIZER_MODEL "tokenizer.ggml.model"
#define KV_TOKENIZER_LIST "tokenizer.ggml.tokens"
#define KV_TOKENIZER_TOKEN_TYPE "tokenizer.ggml.token_type"
#define KV_TOKENIZER_SCORES "tokenizer.ggml.scores"
#define KV_TOKENIZER_BOS_ID "tokenizer.ggml.bos_token_id"
#define KV_TOKENIZER_EOS_ID "tokenizer.ggml.eos_token_id"
#define KV_TOKENIZER_UNK_ID "tokenizer.ggml.unknown_token_id"
#define KV_TOKENIZER_SEP_ID "tokenizer.ggml.seperator_token_id"
#define KV_TOKENIZER_PAD_ID "tokenizer.ggml.padding_token_id"
#define KV_TOKENIZER_HF_JSON "tokenizer.huggingface.json"
#define KV_CONTEXT_LENGTH "llama.context_length"
#define KV_EMBEDDING_LENGTH "llama.embedding_length"
#define KV_BLOCK_COUNT "llama.block_count"
#define KV_FEED_FORWARD_LENGTH "llama.feed_forward_length"
#define KV_ATTENTION_HEAD_COUNT "llama.attention.head_count"
#define KV_ATTENTION_HEAD_COUNT_KV "llama.attention.head_count_kv"
#define KV_ATTENTION_LAYERNORM_RMS_EPS "llama.attention.layer_norm_rms_epsilon"
#define KV_ROPE_DIMENSION_COUNT "llama.rope.dimension_count"
#define TN_TOKEN_EMBD "token_embd.weight"
#define TN_OUTPUT_NORM "output_norm.weight"
#define TN_OUTPUT "output.weight"
#define TN_ATTN_NORM "blk.%d.attn_norm.weight"
#define TN_ATTN_Q "blk.%d.attn_q.weight"
#define TN_ATTN_K "blk.%d.attn_k.weight"
#define TN_ATTN_V "blk.%d.attn_v.weight"
#define TN_ATTN_OUTPUT "blk.%d.attn_output.weight"
#define TN_FFN_NORM "blk.%d.ffn_norm.weight"
#define TN_FFN_GATE "blk.%d.ffn_gate.weight"
#define TN_FFN_DOWN "blk.%d.ffn_down.weight"
#define TN_FFN_UP "blk.%d.ffn_up.weight"
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
@ -20,6 +59,11 @@
#define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
#define LLAMA_FILE_VERSION_GGJT_V3 3
#define TOKENIZER_NAME "llama"
#define UNKNOWN_TOKEN_ID 0
#define BOS_TOKEN_ID 1
#define EOS_TOKEN_ID 2
//////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc.
typedef struct {
int dim; // transformer dimension
@ -183,6 +227,7 @@ struct my_llama_hparams {
uint32_t n_vocab = 32000;
uint32_t n_ctx = 512; // this is provided as user input?
uint32_t n_embd = 4096;
uint32_t n_ff = 11008;
uint32_t n_mult = 4;
uint32_t n_head = 32;
uint32_t n_layer = 32;
@ -214,6 +259,8 @@ struct my_llama_layer {
struct my_llama_model {
struct ggml_context * ctx = NULL;
std::string name;
my_llama_hparams hparams;
struct ggml_tensor * tok_embeddings;
@ -276,18 +323,13 @@ struct train_params {
int mem_compute1_gb;
};
uint32_t get_n_ff(const struct my_llama_hparams* hparams) {
const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult;
return n_ff;
}
void print_params(struct my_llama_hparams * params) {
printf("%s: n_vocab: %d\n", __func__, params->n_vocab);
printf("%s: n_ctx: %d\n", __func__, params->n_ctx);
printf("%s: n_embd: %d\n", __func__, params->n_embd);
printf("%s: n_mult: %d\n", __func__, params->n_mult);
printf("%s: n_head: %d\n", __func__, params->n_head);
printf("%s: n_ff: %d\n", __func__, get_n_ff(params));
printf("%s: n_ff: %d\n", __func__, params->n_ff);
printf("%s: n_layer: %d\n", __func__, params->n_layer);
printf("%s: n_rot: %d\n", __func__, params->n_rot);
}
@ -299,7 +341,7 @@ void init_model(struct my_llama_model * model) {
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_vocab = hparams.n_vocab;
const uint32_t n_ff = get_n_ff(&hparams);
const uint32_t n_ff = hparams.n_ff;
struct ggml_context * ctx = model->ctx;
model->train_its = 0;
@ -481,21 +523,6 @@ struct llama_file {
return std::string(chars.data(), len);
}
void write_raw(const void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, size, 1, fp);
if (ret != 1) {
throw std::runtime_error(format("write error: %s", strerror(errno)));
}
}
void write_u32(std::uint32_t val) {
write_raw(&val, sizeof(val));
}
~llama_file() {
if (fp) {
std::fclose(fp);
@ -503,30 +530,6 @@ struct llama_file {
}
};
void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
if (tensor == NULL) {
file->write_u32(0);
file->write_u32(0);
file->write_u32(GGML_TYPE_F32);
file->seek((0-file->tell()) & 31, SEEK_CUR);
return;
}
const char * name = ggml_get_name(tensor);
uint32_t name_len = strlen(name);
uint32_t nd = tensor->n_dims;
uint32_t ne[4] = { (uint32_t)tensor->ne[0],
(uint32_t)tensor->ne[1],
(uint32_t)tensor->ne[2],
(uint32_t)tensor->ne[3] };
file->write_u32(nd);
file->write_u32(name_len);
file->write_u32(tensor->type);
file->write_raw(ne, sizeof(ne[0]) * nd);
file->write_raw(name, name_len);
file->seek((0-file->tell()) & 31, SEEK_CUR);
file->write_raw(tensor->data, ggml_nbytes(tensor));
}
bool is_ggml_file(const char *filename) {
llama_file file(filename, "rb");
if (file.size < 4) {
@ -536,48 +539,96 @@ bool is_ggml_file(const char *filename) {
return magic == GGUF_MAGIC;
}
static std::string llama_escape_whitespaces(const std::string& text) {
std::ostringstream out;
for (char c : text) {
if (c == ' ') out << "\xe2\x96\x81";
else out << c;
}
return out.str();
}
void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
#pragma message("TODO: implement reading vocabulary using gguf")
// // heuristic to infer whether vocab is from ggml or from llama2.c vocabulary
// if (is_ggml_file(filename)) {
//
// struct llama_context_params llama_params = llama_context_default_params();
// llama_params.vocab_only = true;
//
// struct llama_model * lmodel = llama_load_model_from_file(filename, llama_params);
// struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params);
//
// const int n_vocab = llama_n_vocab(lctx);
// vocab->id_to_token.resize(n_vocab);
// for (int i=0; i<n_vocab; ++i) {
// vocab->id_to_token[i].text = llama_token_get_text(lctx, i);
// vocab->id_to_token[i].score = llama_token_get_score(lctx, i);
// vocab->id_to_token[i].type = llama_token_get_type(lctx, i);
// vocab->token_to_id.emplace(vocab->id_to_token[i].text, i);
// }
// llama_free(lctx);
// llama_free_model(lmodel);
// } else
{ // assume llama2.c vocabulary
printf("Assuming llama2.c vocabulary since %s is not a ggml file\n", filename);
if (is_ggml_file(filename)) {
struct ggml_context * ctx_data = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ false,
/*.ctx = */ &ctx_data,
};
struct gguf_context * ctx = gguf_init_from_file(filename, params);
GGML_ASSERT(ctx != NULL);
const int model_idx = gguf_find_key(ctx, KV_TOKENIZER_MODEL);
GGML_ASSERT(model_idx >= 0);
std::string tokenizer_name = gguf_get_val_str(ctx, model_idx);
GGML_ASSERT(tokenizer_name == TOKENIZER_NAME);
const int token_idx = gguf_find_key(ctx, KV_TOKENIZER_LIST);
GGML_ASSERT(token_idx >= 0);
const int score_idx = gguf_find_key(ctx, KV_TOKENIZER_SCORES);
GGML_ASSERT(score_idx >= 0);
const float * scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
const int toktype_idx = gguf_find_key(ctx, KV_TOKENIZER_TOKEN_TYPE);
GGML_ASSERT(toktype_idx >= 0);
const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
vocab->id_to_token.resize(n_vocab);
for (uint32_t i = 0; i < n_vocab; i++) {
std::string word = gguf_get_arr_str(ctx, token_idx, i);
vocab->token_to_id[word] = i;
auto & token_data = vocab->id_to_token[i];
token_data.text = std::move(word);
token_data.score = scores[i];
token_data.type = (llama_token_type) toktypes[i];
}
ggml_free(ctx_data);
gguf_free(ctx);
} else {
// assume llama2.c vocabulary
printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename);
llama_file file(filename, "rb");
const int n_vocab = config->vocab_size;
/* uint32_t max_token_length = */ file.read_u32(); // unused
vocab->id_to_token.resize(n_vocab);
for (int i=0; i<n_vocab; ++i) {
for (llama_vocab::id id=0; id<n_vocab; ++id) {
float_t score = file.read_f32();
uint32_t len = file.read_u32();
std::string text = file.read_string(len);
// Special-case handling of <0xXX> single byte tokens.
char byte_val;
if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) {
char cstr[2] = { byte_val, 0 };
text = cstr;
unsigned char byte_val;
llama_vocab::ttype type = LLAMA_TOKEN_TYPE_NORMAL;
if (id == UNKNOWN_TOKEN_ID) {
text = "<unk>";
type = LLAMA_TOKEN_TYPE_UNKNOWN;
} else if (id == BOS_TOKEN_ID) {
text = "<s>";
type = LLAMA_TOKEN_TYPE_CONTROL;
} else if (id == EOS_TOKEN_ID) {
text = "</s>";
type = LLAMA_TOKEN_TYPE_CONTROL;
} else if (text.empty()) {
type = LLAMA_TOKEN_TYPE_CONTROL;
} else if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) {
// Text of byte tokens is already in the expected format.
type = LLAMA_TOKEN_TYPE_BYTE;
} else {
type = LLAMA_TOKEN_TYPE_NORMAL;
}
vocab->id_to_token[i].text = text;
vocab->id_to_token[i].score = score;
vocab->id_to_token[i].type = LLAMA_TOKEN_TYPE_UNDEFINED;
vocab->token_to_id.emplace(text, i);
text = llama_escape_whitespaces(text);
vocab->id_to_token[id].text = text;
vocab->id_to_token[id].score = score;
vocab->id_to_token[id].type = type;
vocab->token_to_id.emplace(text, id);
}
}
}
@ -619,33 +670,6 @@ void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, float * kar
}
void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename) {
struct llama_file file(filename, "wb");
if (file.fp == NULL) {
return;
}
#pragma message("TODO: implement file saving using gguf")
// write_magic
file.write_u32(LLAMA_FILE_MAGIC_GGJT); // magic
file.write_u32(LLAMA_FILE_VERSION_GGJT_V3); // version
// write_hparams
file.write_u32(model->hparams.n_vocab);
file.write_u32(model->hparams.n_embd);
file.write_u32(model->hparams.n_mult);
file.write_u32(model->hparams.n_head);
file.write_u32(model->hparams.n_layer);
file.write_u32(model->hparams.n_rot);
file.write_u32(LLAMA_FTYPE_ALL_F32);
// write_vocab - for now we are just writing the existing BPE voc. assuming karpathy's vocabulary is the same. idk.
uint32_t n_vocab = model->hparams.n_vocab;
for (uint32_t i = 0; i < n_vocab; i++) {
const auto & token_data = vocab->id_to_token.at(i);
file.write_u32((uint32_t) token_data.text.size());
file.write_raw(token_data.text.data(), token_data.text.size());
file.write_raw(&token_data.score, sizeof(token_data.score));
}
// stuff AK weights into GG weights one by one.
// w->token_embedding_table -> model->tok_embeddings
// float* -> struct ggml_tensor
@ -657,9 +681,7 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
// for rms-att-weight
int row_length = model->hparams.n_embd;
const auto & hparams = model->hparams;
//int n_ff = model->hparams.n_embd;
int n_ff = get_n_ff(&hparams);
int n_ff = model->hparams.n_ff;
for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
auto & layer = model->layers[i];
@ -677,28 +699,91 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
stuff_karpathy_weights_into_gg(layer.w2 , &w->w2[i*n_ff*row_length]);
stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]);
}
struct gguf_context * ctx = gguf_init_empty();
std::vector<const char*> tokens;
std::vector<float> scores;
std::vector<llama_token_type> token_types;
for (const llama_vocab::token_data & token_data : vocab->id_to_token) {
tokens.push_back(token_data.text.c_str());
scores.push_back(token_data.score);
token_types.push_back(token_data.type);
}
gguf_set_arr_str(ctx, KV_TOKENIZER_LIST, tokens.data(), tokens.size());
gguf_set_arr_data(ctx, KV_TOKENIZER_SCORES, GGUF_TYPE_FLOAT32, scores.data(), scores.size());
gguf_set_arr_data(ctx, KV_TOKENIZER_TOKEN_TYPE, GGUF_TYPE_INT32, token_types.data(), token_types.size());
gguf_set_val_str(ctx, KV_TOKENIZER_MODEL, TOKENIZER_NAME);
gguf_set_val_str(ctx, KV_GENERAL_ARCHITECTURE, "llama");
gguf_set_val_str(ctx, KV_GENERAL_NAME, "llama");
// special tokens
gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID);
gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID);
gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID);
gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, -1);
gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, -1);
gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx);
gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd);
gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff);
gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);
// n_head_kv is optional, default to n_head
// gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, ...);
gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer);
gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot);
gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
// write tensors
write_tensor(&file, model->tok_embeddings);
write_tensor(&file, model->norm);
write_tensor(&file, model->output); // ?
ggml_set_name(model->tok_embeddings, TN_TOKEN_EMBD);
gguf_add_tensor(ctx, model->tok_embeddings);
ggml_set_name(model->norm, TN_OUTPUT_NORM);
gguf_add_tensor(ctx, model->norm);
ggml_set_name(model->output, TN_OUTPUT);
gguf_add_tensor(ctx, model->output);
for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
auto & layer = model->layers[i];
write_tensor(&file, layer.attention_norm);
write_tensor(&file, layer.wq);
write_tensor(&file, layer.wk);
write_tensor(&file, layer.wv);
write_tensor(&file, layer.wo);
write_tensor(&file, layer.ffn_norm);
write_tensor(&file, layer.w1);
write_tensor(&file, layer.w2);
write_tensor(&file, layer.w3);
ggml_format_name(layer.wq, TN_ATTN_Q, i);
gguf_add_tensor(ctx, layer.wq);
ggml_format_name(layer.wk, TN_ATTN_K, i);
gguf_add_tensor(ctx, layer.wk);
ggml_format_name(layer.wv, TN_ATTN_V, i);
gguf_add_tensor(ctx, layer.wv);
ggml_format_name(layer.wo, TN_ATTN_OUTPUT, i);
gguf_add_tensor(ctx, layer.wo);
ggml_format_name(layer.attention_norm, TN_ATTN_NORM, i);
gguf_add_tensor(ctx, layer.attention_norm);
ggml_format_name(layer.w1, TN_FFN_GATE, i);
gguf_add_tensor(ctx, layer.w1);
ggml_format_name(layer.w2, TN_FFN_DOWN, i);
gguf_add_tensor(ctx, layer.w2);
ggml_format_name(layer.w3, TN_FFN_UP, i);
gguf_add_tensor(ctx, layer.w3);
ggml_format_name(layer.ffn_norm, TN_FFN_NORM, i);
gguf_add_tensor(ctx, layer.ffn_norm);
}
gguf_write_to_file(ctx, filename, false);
gguf_free(ctx);
}
struct train_params get_default_train_params() {
struct train_params params;
params.fn_vocab_model = "tokenizer.bin";
params.fn_vocab_model = "models/7B/ggml-model-f16.gguf";
params.fn_llama2c_output_model = "ak_llama_model.bin";
params.fn_train_data = "shakespeare.txt";
params.fn_checkpoint_in = "checkpoint.bin";
@ -751,7 +836,7 @@ void print_usage(int /*argc*/, char ** argv, const struct train_params * params)
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " --copy-vocab-from-model FNAME llama2.c vocabulary or ggmlv3 model path from which to copy vocab (default '%s')\n", params->fn_vocab_model);
fprintf(stderr, " --copy-vocab-from-model FNAME path of gguf llama model or llama2.c vocabulary from which to copy vocab (default '%s')\n", params->fn_vocab_model);
fprintf(stderr, " --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model\n");
fprintf(stderr, " --llama2c-output-model FNAME model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model);
fprintf(stderr, "\n");
@ -812,6 +897,14 @@ bool params_parse(int argc, char ** argv, struct train_params * params) {
return true;
}
std::string basename(const std::string &path) {
size_t pos = path.find_last_of("/");
if (pos == std::string::npos) {
return path;
}
return path.substr(pos + 1);
}
int main(int argc, char ** argv) {
struct train_params params = get_default_train_params();
if (!params_parse(argc, argv, &params)) {
@ -840,6 +933,7 @@ int main(int argc, char ** argv) {
model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
model.hparams.n_ctx = params.n_ctx;
model.hparams.n_embd = config.dim; //params.n_embd;
model.hparams.n_ff = config.hidden_dim;
model.hparams.n_mult = 32;//params.n_mult;
model.hparams.n_head = config.n_heads; //params.n_head;
model.hparams.n_layer = config.n_layers; //params.n_layer;
@ -853,6 +947,7 @@ int main(int argc, char ** argv) {
model.ctx = ggml_init(lcparams);
init_model(&model);
model.name = basename(params.fn_llama2c_model);
save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model);
printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model);

2
examples/embd-input/embd-input-lib.cpp
View file

@ -214,7 +214,7 @@ const char * sampling(struct MyModel * mymodel) {
if (id == llama_token_eos(ctx)) {
ret = "</s>";
} else {
ret = llama_token_to_str(ctx, id);
ret = llama_token_to_piece(ctx, id);
}
eval_id(mymodel, id);
return ret.c_str();

5
examples/embedding/embedding.cpp
View file

@ -56,9 +56,6 @@ int main(int argc, char ** argv) {
int n_past = 0;
// Add a space in front of the first character to match OG llama tokenizer behavior
params.prompt.insert(0, 1, ' ');
// tokenize the prompt
auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
@ -67,7 +64,7 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str());
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
}
fprintf(stderr, "\n");
}

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

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

3
examples/gguf/gguf.cpp
View file

@ -30,6 +30,9 @@ bool gguf_ex_write(const std::string & fname) {
gguf_set_val_u32 (ctx, "some.parameter.uint32", 0x12345678);
gguf_set_val_i32 (ctx, "some.parameter.int32", -0x12345679);
gguf_set_val_f32 (ctx, "some.parameter.float32", 0.123456789f);
gguf_set_val_u64 (ctx, "some.parameter.uint64", 0x123456789abcdef0ull);
gguf_set_val_i64 (ctx, "some.parameter.int64", -0x123456789abcdef1ll);
gguf_set_val_f64 (ctx, "some.parameter.float64", 0.1234567890123456789);
gguf_set_val_bool(ctx, "some.parameter.bool", true);
gguf_set_val_str (ctx, "some.parameter.string", "hello world");

7
examples/llama-bench/llama-bench.cpp
View file

@ -3,6 +3,9 @@
#include <cassert>
#include <chrono>
#include <cinttypes>
#include <clocale>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <iterator>
@ -10,7 +13,6 @@
#include <numeric>
#include <regex>
#include <sstream>
#include <stdio.h>
#include <string>
#include <vector>
@ -916,6 +918,9 @@ static void llama_null_log_callback(enum llama_log_level level, const char * tex
}
int main(int argc, char ** argv) {
// try to set locale for unicode characters in markdown
setlocale(LC_CTYPE, ".UTF-8");
#if !defined(NDEBUG)
fprintf(stderr, "warning: asserts enabled, performance may be affected\n");
#endif

3
examples/llm.vim
View file

@ -8,7 +8,7 @@ function! Llm()
let buffer_content = join(getline(1, '$'), "\n")
" Create the JSON payload
let json_payload = {"temp":0.72,"top_k":100,"top_p":0.73,"repeat_penalty":1.100000023841858,"n_predict":10,"stream": v:false}
let json_payload = {"temp":0.72,"top_k":100,"top_p":0.73,"repeat_penalty":1.100000023841858,"n_predict":256,"stop": ["\n\n\n"],"stream": v:false}
let json_payload.prompt = buffer_content
" Define the curl command
@ -25,3 +25,4 @@ function! Llm()
endfunction
command! Llm call Llm()
noremap <F2> :Llm<CR>

380
examples/main/main.cpp
View file

@ -4,6 +4,7 @@
#endif
#include "common.h"
#include "console.h"
#include "llama.h"
#include "build-info.h"
@ -17,6 +18,7 @@
#include <ctime>
#include <fstream>
#include <iostream>
#include <sstream>
#include <string>
#include <vector>
@ -36,9 +38,57 @@
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
static llama_context ** g_ctx;
static llama_context ** g_ctx;
static llama_model ** g_model;
static gpt_params * g_params;
static std::vector<llama_token> * g_input_tokens;
static std::ostringstream * g_output_ss;
static std::vector<llama_token> * g_output_tokens;
static bool is_interacting = false;
void write_logfile(
const llama_context * ctx, const gpt_params & params, const llama_model * model,
const std::vector<llama_token> input_tokens, const std::string output, const std::vector<llama_token> output_tokens) {
if (params.logdir.empty()) {
return;
}
const std::string timestamp = get_sortable_timestamp();
const bool success = create_directory_with_parents(params.logdir);
if (!success) {
fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
__func__, params.logdir.c_str());
return;
}
const std::string logfile_path = params.logdir + timestamp + ".yml";
FILE * logfile = fopen(logfile_path.c_str(), "w");
if (logfile == NULL) {
fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
return;
}
fprintf(logfile, "binary: main\n");
char model_desc[128];
llama_model_desc(model, model_desc, sizeof(model_desc));
dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc);
fprintf(logfile, "\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "# Generation Results #\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "\n");
dump_string_yaml_multiline(logfile, "output", output.c_str());
dump_vector_int_yaml(logfile, "output_tokens", output_tokens);
llama_dump_timing_info_yaml(logfile, ctx);
fclose(logfile);
}
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
void sigint_handler(int signo) {
if (signo == SIGINT) {
@ -48,6 +98,7 @@ void sigint_handler(int signo) {
console::cleanup();
printf("\n");
llama_print_timings(*g_ctx);
write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
_exit(130);
}
}
@ -56,11 +107,21 @@ void sigint_handler(int signo) {
int main(int argc, char ** argv) {
gpt_params params;
g_params = &params;
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("main", "log"));
LOG_TEE("Log start\n");
log_dump_cmdline(argc,argv);
#endif // LOG_DISABLE_LOGS
// TODO: Dump params ?
//LOG("Params perplexity: %s\n", LOG_TOSTR(params.perplexity));
// save choice to use color for later
// (note for later: this is a slightly awkward choice)
console::init(params.simple_io, params.use_color);
@ -83,42 +144,45 @@ int main(int argc, char ** argv) {
}
if (params.rope_freq_base != 10000.0) {
fprintf(stderr, "%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base);
LOG_TEE("%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base);
}
if (params.rope_freq_scale != 1.0) {
fprintf(stderr, "%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale);
LOG_TEE("%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale);
}
if (params.n_ctx > 2048) {
// TODO: determine the actual max context of the model (e.g. 4096 for LLaMA v2) and use that instead of 2048
fprintf(stderr, "%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx);
LOG_TEE("%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx);
} else if (params.n_ctx < 8) {
fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__);
LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
params.n_ctx = 8;
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
LOG_TEE("%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
LOG("%s: llama backend init\n", __func__);
llama_backend_init(params.numa);
llama_model * model;
llama_context * ctx;
llama_context * ctx_guidance = NULL;
g_model = &model;
g_ctx = &ctx;
// load the model and apply lora adapter, if any
LOG("%s: load the model and apply lora adapter, if any\n", __func__);
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (params.cfg_scale > 1.f) {
struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
@ -126,14 +190,14 @@ int main(int argc, char ** argv) {
}
if (model == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
LOG_TEE("%s: error: unable to load model\n", __func__);
return 1;
}
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
LOG_TEE("\n");
LOG_TEE("system_info: n_threads = %d / %d | %s\n",
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
}
@ -141,7 +205,7 @@ int main(int argc, char ** argv) {
// uncomment the "used_mem" line in llama.cpp to see the results
if (params.mem_test) {
{
fprintf(stderr, "%s: testing memory usage for n_batch = %d, n_ctx = %d\n", __func__, params.n_batch, params.n_ctx);
LOG_TEE("%s: testing memory usage for n_batch = %d, n_ctx = %d\n", __func__, params.n_batch, params.n_ctx);
const std::vector<llama_token> tmp(params.n_batch, llama_token_bos(ctx));
llama_eval(ctx, tmp.data(), tmp.size(), params.n_ctx, params.n_threads);
@ -167,7 +231,7 @@ int main(int argc, char ** argv) {
std::vector<llama_token> session_tokens;
if (!path_session.empty()) {
fprintf(stderr, "%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
LOG_TEE("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
// fopen to check for existing session
FILE * fp = std::fopen(path_session.c_str(), "rb");
@ -177,31 +241,38 @@ int main(int argc, char ** argv) {
session_tokens.resize(params.n_ctx);
size_t n_token_count_out = 0;
if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
fprintf(stderr, "%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
return 1;
}
session_tokens.resize(n_token_count_out);
llama_set_rng_seed(ctx, params.seed);
fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size());
LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size());
} else {
fprintf(stderr, "%s: session file does not exist, will create\n", __func__);
LOG_TEE("%s: session file does not exist, will create\n", __func__);
}
}
const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
const bool add_bos = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
LOG("add_bos: %d\n", add_bos);
// tokenize the prompt
std::vector<llama_token> embd_inp;
if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {
embd_inp = ::llama_tokenize(ctx, params.prompt, is_spm);
LOG("tokenize the prompt\n");
embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
} else {
LOG("use session tokens\n");
embd_inp = session_tokens;
}
LOG("prompt: \"%s\"\n", log_tostr(params.prompt));
LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp));
// Should not run without any tokens
if (embd_inp.empty()) {
embd_inp.push_back(llama_token_bos(ctx));
LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp));
}
// Tokenize negative prompt
@ -209,24 +280,31 @@ int main(int argc, char ** argv) {
int guidance_offset = 0;
int original_prompt_len = 0;
if (ctx_guidance) {
params.cfg_negative_prompt.insert(0, 1, ' ');
guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, is_spm);
LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(params.cfg_negative_prompt));
guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos);
LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp));
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp));
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, is_spm);
original_prompt_len = original_inp.size();
guidance_offset = (int)guidance_inp.size() - original_prompt_len;
LOG("original_prompt_len: %s", log_tostr(original_prompt_len));
LOG("guidance_offset: %s", log_tostr(guidance_offset));
}
const int n_ctx = llama_n_ctx(ctx);
LOG("n_ctx: %d\n", n_ctx);
if ((int) embd_inp.size() > n_ctx - 4) {
fprintf(stderr, "%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
return 1;
}
// debug message about similarity of saved session, if applicable
size_t n_matching_session_tokens = 0;
if (session_tokens.size()) {
if (session_tokens.size() > 0) {
for (llama_token id : session_tokens) {
if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
break;
@ -234,22 +312,27 @@ int main(int argc, char ** argv) {
n_matching_session_tokens++;
}
if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) {
fprintf(stderr, "%s: using full prompt from session file\n", __func__);
LOG_TEE("%s: using full prompt from session file\n", __func__);
} else if (n_matching_session_tokens >= embd_inp.size()) {
fprintf(stderr, "%s: session file has exact match for prompt!\n", __func__);
LOG_TEE("%s: session file has exact match for prompt!\n", __func__);
} else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
fprintf(stderr, "%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
LOG_TEE("%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
__func__, n_matching_session_tokens, embd_inp.size());
} else {
fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
LOG_TEE("%s: session file matches %zu / %zu tokens of prompt\n",
__func__, n_matching_session_tokens, embd_inp.size());
}
}
LOGLN(
"recalculate the cached logits (check): embd_inp.empty() %s, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu, embd_inp.size() %zu",
log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size());
// if we will use the cache for the full prompt without reaching the end of the cache, force
// reevaluation of the last token token to recalculate the cached logits
if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() &&
session_tokens.size() > embd_inp.size()) {
if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) {
LOGLN("recalculate the cached logits (do): session_tokens.resize( %zu )", embd_inp.size() - 1);
session_tokens.resize(embd_inp.size() - 1);
}
@ -259,9 +342,12 @@ int main(int argc, char ** argv) {
}
// prefix & suffix for instruct mode
const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", is_spm);
const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos);
const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false);
LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx));
LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx));
// in instruct mode, we inject a prefix and a suffix to each input by the user
if (params.instruct) {
params.interactive_first = true;
@ -274,30 +360,30 @@ int main(int argc, char ** argv) {
}
if (params.verbose_prompt) {
fprintf(stderr, "\n");
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
LOG_TEE("\n");
LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
LOG_TEE("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str());
LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
}
if (ctx_guidance) {
fprintf(stderr, "\n");
fprintf(stderr, "%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
fprintf(stderr, "%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
LOG_TEE("\n");
LOG_TEE("%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
for (int i = 0; i < (int) guidance_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_str(ctx, guidance_inp[i]).c_str());
LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
}
}
if (params.n_keep > 0) {
fprintf(stderr, "%s: static prompt based on n_keep: '", __func__);
LOG_TEE("%s: static prompt based on n_keep: '", __func__);
for (int i = 0; i < params.n_keep; i++) {
fprintf(stderr, "%s", llama_token_to_str(ctx, embd_inp[i]).c_str());
LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
}
fprintf(stderr, "'\n");
LOG_TEE("'\n");
}
fprintf(stderr, "\n");
LOG_TEE("\n");
}
if (params.interactive) {
@ -314,30 +400,30 @@ int main(int argc, char ** argv) {
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
#endif
fprintf(stderr, "%s: interactive mode on.\n", __func__);
LOG_TEE("%s: interactive mode on.\n", __func__);
if (params.antiprompt.size()) {
for (auto antiprompt : params.antiprompt) {
fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str());
for (const auto & antiprompt : params.antiprompt) {
LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str());
}
}
if (params.input_prefix_bos) {
fprintf(stderr, "Input prefix with BOS\n");
LOG_TEE("Input prefix with BOS\n");
}
if (!params.input_prefix.empty()) {
fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str());
LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str());
}
if (!params.input_suffix.empty()) {
fprintf(stderr, "Input suffix: '%s'\n", params.input_suffix.c_str());
LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str());
}
}
fprintf(stderr, "sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n",
LOG_TEE("sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n",
params.repeat_last_n, params.repeat_penalty, params.presence_penalty, params.frequency_penalty, params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp, params.mirostat, params.mirostat_eta, params.mirostat_tau);
fprintf(stderr, "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
fprintf(stderr, "\n\n");
LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
LOG_TEE("\n\n");
grammar_parser::parse_state parsed_grammar;
llama_grammar * grammar = NULL;
@ -347,14 +433,14 @@ int main(int argc, char ** argv) {
if (parsed_grammar.rules.empty()) {
return 1;
}
fprintf(stderr, "%s: grammar:\n", __func__);
LOG_TEE("%s: grammar:\n", __func__);
grammar_parser::print_grammar(stderr, parsed_grammar);
fprintf(stderr, "\n");
LOG_TEE("\n");
{
auto it = params.logit_bias.find(llama_token_eos(ctx));
if (it != params.logit_bias.end() && it->second == -INFINITY) {
fprintf(stderr, "%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__);
LOG_TEE("%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__);
}
}
@ -377,11 +463,11 @@ int main(int argc, char ** argv) {
" - 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";
}
fprintf(stderr, "== Running in interactive mode. ==\n"
LOG_TEE("== Running in interactive mode. ==\n");
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
" - Press Ctrl+C to interject at any time.\n"
LOG_TEE( " - Press Ctrl+C to interject at any time.\n");
#endif
"%s\n", control_message);
LOG_TEE( "%s\n", control_message);
is_interacting = params.interactive_first;
}
@ -396,14 +482,19 @@ int main(int argc, char ** argv) {
int n_session_consumed = 0;
int n_past_guidance = 0;
std::vector<int> input_tokens; g_input_tokens = &input_tokens;
std::vector<int> output_tokens; g_output_tokens = &output_tokens;
std::ostringstream output_ss; g_output_ss = &output_ss;
// the first thing we will do is to output the prompt, so set color accordingly
console::set_display(console::prompt);
std::vector<llama_token> embd;
std::vector<llama_token> embd_guidance;
// do one empty run to warm up the model
{
LOG("warming up the model with an empty run\n");
const std::vector<llama_token> tmp = { llama_token_bos(ctx), };
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
llama_reset_timings(ctx);
@ -414,15 +505,17 @@ int main(int argc, char ** argv) {
if (embd.size() > 0) {
// Note: n_ctx - 4 here is to match the logic for commandline prompt handling via
// --prompt or --file which uses the same value.
auto max_embd_size = n_ctx - 4;
int max_embd_size = n_ctx - 4;
// Ensure the input doesn't exceed the context size by truncating embd if necessary.
if ((int)embd.size() > max_embd_size) {
auto skipped_tokens = embd.size() - max_embd_size;
if ((int) embd.size() > max_embd_size) {
const int skipped_tokens = (int) embd.size() - max_embd_size;
embd.resize(max_embd_size);
console::set_display(console::error);
printf("<<input too long: skipped %zu token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
printf("<<input too long: skipped %d token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
console::set_display(console::reset);
fflush(stdout);
embd.resize(max_embd_size);
}
// infinite text generation via context swapping
@ -431,28 +524,26 @@ int main(int argc, char ** argv) {
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
if (params.n_predict == -2) {
fprintf(stderr, "\n\n%s: context full, stopping generation\n", __func__);
LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
break;
}
const int n_left = n_past - params.n_keep;
LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d\n", n_past, n_left, n_ctx, params.n_keep);
// always keep the first token - BOS
n_past = std::max(1, params.n_keep);
n_past = std::max(1, params.n_keep);
n_past_guidance = std::max(1, params.n_keep + guidance_offset);
LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
// insert n_left/2 tokens at the start of embd from last_n_tokens
embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size());
// stop saving session if we run out of context
path_session.clear();
LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
//printf("\n---\n");
//printf("resetting: '");
//for (int i = 0; i < (int) embd.size(); i++) {
// printf("%s", llama_token_to_str(ctx, embd[i]));
//}
//printf("'\n");
//printf("\n---\n");
LOG("clear session path\n");
path_session.clear();
}
// try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
@ -482,7 +573,7 @@ int main(int argc, char ** argv) {
if (ctx_guidance) {
int input_size = 0;
llama_token* input_buf = NULL;
llama_token * input_buf = NULL;
if (n_past_guidance < (int) guidance_inp.size()) {
// Guidance context should have the same data with these modifications:
@ -498,22 +589,19 @@ int main(int argc, char ** argv) {
);
}
input_buf = embd_guidance.data();
input_buf = embd_guidance.data();
input_size = embd_guidance.size();
//fprintf(stderr, "\n---------------------\n");
//for (int i = 0; i < (int) embd_guidance.size(); i++) {
//fprintf(stderr, "%s", llama_token_to_str(ctx, embd_guidance[i]));
//}
//fprintf(stderr, "\n---------------------\n");
LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance));
} else {
input_buf = embd.data();
input_buf = embd.data();
input_size = embd.size();
}
for (int i = 0; i < input_size; i += params.n_batch) {
int n_eval = std::min(input_size - i, params.n_batch);
if (llama_eval(ctx_guidance, input_buf + i, n_eval, n_past_guidance, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
LOG_TEE("%s : failed to eval\n", __func__);
return 1;
}
@ -526,11 +614,17 @@ int main(int argc, char ** argv) {
if (n_eval > params.n_batch) {
n_eval = params.n_batch;
}
LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
LOG_TEE("%s : failed to eval\n", __func__);
return 1;
}
n_past += n_eval;
LOG("n_past = %d\n", n_past);
}
if (embd.size() > 0 && !path_session.empty()) {
@ -543,7 +637,6 @@ int main(int argc, char ** argv) {
embd_guidance.clear();
if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
// out of user input, sample next token
const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
const float top_p = params.top_p;
@ -562,6 +655,8 @@ int main(int argc, char ** argv) {
if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
need_to_save_session = false;
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
LOG("saved session to %s\n", path_session.c_str());
}
llama_token id = 0;
@ -581,50 +676,68 @@ int main(int argc, char ** argv) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
if (ctx_guidance) {
llama_sample_classifier_free_guidance(ctx, &candidates_p, ctx_guidance, params.cfg_scale);
llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale);
}
// Apply penalties
float nl_logit = logits[llama_token_nl(ctx)];
auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
llama_sample_repetition_penalty(ctx, &candidates_p,
llama_sample_repetition_penalty(ctx, &cur_p,
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
last_n_repeat, repeat_penalty);
llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
llama_sample_frequency_and_presence_penalties(ctx, &cur_p,
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
last_n_repeat, alpha_frequency, alpha_presence);
if (!penalize_nl) {
logits[llama_token_nl(ctx)] = nl_logit;
for (size_t idx = 0; idx < cur_p.size; idx++) {
if (cur_p.data[idx].id == llama_token_nl(ctx)) {
cur_p.data[idx].logit = nl_logit;
break;
}
}
}
if (grammar != NULL) {
llama_sample_grammar(ctx, &candidates_p, grammar);
llama_sample_grammar(ctx, &cur_p, grammar);
}
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx, &candidates_p);
id = llama_sample_token_greedy(ctx, &cur_p);
} else {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
llama_sample_temperature(ctx, &cur_p, temp);
id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
llama_sample_temperature(ctx, &cur_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
llama_sample_typical(ctx, &candidates_p, typical_p, 1);
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token(ctx, &candidates_p);
llama_sample_top_k (ctx, &cur_p, top_k, 1);
llama_sample_tail_free (ctx, &cur_p, tfs_z, 1);
llama_sample_typical (ctx, &cur_p, typical_p, 1);
llama_sample_top_p (ctx, &cur_p, top_p, 1);
llama_sample_temperature(ctx, &cur_p, temp);
{
const int n_top = 10;
LOG("top %d candidates:\n", n_top);
for (int i = 0; i < n_top; i++) {
const llama_token id = cur_p.data[i].id;
LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p);
}
}
id = llama_sample_token(ctx, &cur_p);
LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str());
}
}
// printf("`%d`", candidates_p.size);
@ -635,9 +748,10 @@ int main(int argc, char ** argv) {
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_n_tokens));
}
// add it to the context
embd.push_back(id);
// echo this to console
@ -645,8 +759,11 @@ int main(int argc, char ** argv) {
// decrement remaining sampling budget
--n_remain;
LOG("n_remain: %d\n", n_remain);
} else {
// some user input remains from prompt or interaction, forward it to processing
LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed);
while ((int) embd_inp.size() > n_consumed) {
embd.push_back(embd_inp[n_consumed]);
last_n_tokens.erase(last_n_tokens.begin());
@ -661,23 +778,30 @@ int main(int argc, char ** argv) {
// display text
if (input_echo) {
for (auto id : embd) {
printf("%s", llama_token_to_str(ctx, id).c_str());
const std::string token_str = llama_token_to_piece(ctx, id);
printf("%s", token_str.c_str());
if (embd.size() > 1) {
input_tokens.push_back(id);
} else {
output_tokens.push_back(id);
output_ss << token_str;
}
}
fflush(stdout);
}
// reset color to default if we there is no pending user input
if (input_echo && (int)embd_inp.size() == n_consumed) {
if (input_echo && (int) embd_inp.size() == n_consumed) {
console::set_display(console::reset);
}
// if not currently processing queued inputs;
if ((int) embd_inp.size() <= n_consumed) {
// check for reverse prompt
if (params.antiprompt.size()) {
std::string last_output;
for (auto id : last_n_tokens) {
last_output += llama_token_to_str(ctx, id);
last_output += llama_token_to_piece(ctx, id);
}
is_antiprompt = false;
@ -690,7 +814,7 @@ int main(int argc, char ** argv) {
? last_output.length() - static_cast<size_t>(antiprompt.length() + extra_padding)
: 0;
if (last_output.find(antiprompt.c_str(), search_start_pos) != std::string::npos) {
if (last_output.find(antiprompt, search_start_pos) != std::string::npos) {
if (params.interactive) {
is_interacting = true;
console::set_display(console::user_input);
@ -700,10 +824,16 @@ int main(int argc, char ** argv) {
break;
}
}
if (is_antiprompt) {
LOG("found antiprompt: %s\n", last_output.c_str());
}
}
// deal with end of text token in interactive mode
if (last_n_tokens.back() == llama_token_eos(ctx)) {
LOG("found EOS token\n");
if (params.interactive) {
if (params.antiprompt.size() != 0) {
// tokenize and inject first reverse prompt
@ -722,16 +852,20 @@ int main(int argc, char ** argv) {
}
if (n_past > 0 && is_interacting) {
LOG("waiting for user input\n");
if (params.instruct) {
printf("\n> ");
}
if (params.input_prefix_bos) {
LOG("adding input prefix BOS token\n");
embd_inp.push_back(llama_token_bos(ctx));
}
std::string buffer;
if (!params.input_prefix.empty()) {
LOG("appending input prefix: '%s'\n", params.input_prefix.c_str());
buffer += params.input_prefix;
printf("%s", buffer.c_str());
}
@ -751,25 +885,43 @@ int main(int argc, char ** argv) {
if (buffer.length() > 1) {
// append input suffix if any
if (!params.input_suffix.empty()) {
LOG("appending input suffix: '%s'\n", params.input_suffix.c_str());
buffer += params.input_suffix;
printf("%s", params.input_suffix.c_str());
}
LOG("buffer: '%s'\n", buffer.c_str());
const size_t original_size = embd_inp.size();
// instruct mode: insert instruction prefix
if (params.instruct && !is_antiprompt) {
LOG("inserting instruction prefix\n");
n_consumed = embd_inp.size();
embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
}
auto line_inp = ::llama_tokenize(ctx, buffer, false);
const auto line_inp = ::llama_tokenize(ctx, buffer, false);
LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp));
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
// instruct mode: insert response suffix
if (params.instruct) {
LOG("inserting instruction suffix\n");
embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
}
for (size_t i = original_size; i < embd_inp.size(); ++i) {
const llama_token token = embd_inp[i];
output_tokens.push_back(token);
output_ss << llama_token_to_piece(ctx, token);
}
n_remain -= line_inp.size();
LOG("n_remain: %d\n", n_remain);
} else {
LOG("empty line, passing control back\n");
}
input_echo = false; // do not echo this again
@ -793,7 +945,7 @@ int main(int argc, char ** argv) {
// end of text token
if (!embd.empty() && embd.back() == llama_token_eos(ctx) && !(params.instruct || params.interactive)) {
fprintf(stderr, " [end of text]\n");
LOG_TEE(" [end of text]\n");
break;
}
@ -806,11 +958,13 @@ int main(int argc, char ** argv) {
}
if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
fprintf(stderr, "\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
}
llama_print_timings(ctx);
write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
if (ctx_guidance) { llama_free(ctx_guidance); }
llama_free(ctx);
llama_free_model(model);
@ -820,5 +974,9 @@ int main(int argc, char ** argv) {
}
llama_backend_free();
#ifndef LOG_DISABLE_LOGS
LOG_TEE("Log end\n")
#endif // LOG_DISABLE_LOGS
return 0;
}

182
examples/perplexity/perplexity.cpp
View file

@ -3,16 +3,79 @@
#include "build-info.h"
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <sstream>
#include <cstring>
#include <thread>
#include <mutex>
#include <vector>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
struct results_perplexity {
std::vector<llama_token> tokens;
double ppl_value;
std::vector<float> logits;
std::vector<float> probs;
};
struct results_log_softmax {
double log_softmax;
float logit;
float prob;
};
void write_logfile(const llama_context * ctx, const gpt_params & params,
const llama_model * model, const struct results_perplexity & results) {
if (params.logdir.empty()) {
return;
}
if (params.hellaswag) {
fprintf(stderr, "%s: warning: logging results is not implemented for HellaSwag. No files will be written.\n", __func__);
return;
}
const std::string timestamp = get_sortable_timestamp();
const bool success = create_directory_with_parents(params.logdir);
if (!success) {
fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
__func__, params.logdir.c_str());
return;
}
const std::string logfile_path = params.logdir + timestamp + ".yml";
FILE * logfile = fopen(logfile_path.c_str(), "w");
if (logfile == NULL) {
fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
return;
}
fprintf(logfile, "binary: main\n");
char model_desc[128];
llama_model_desc(model, model_desc, sizeof(model_desc));
dump_non_result_info_yaml(logfile, params, ctx, timestamp, results.tokens, model_desc);
fprintf(logfile, "\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "# Perplexity Results #\n");
fprintf(logfile, "######################\n");
fprintf(logfile, "\n");
dump_vector_float_yaml(logfile, "logits", results.logits);
fprintf(logfile, "ppl_value: %f\n", results.ppl_value);
dump_vector_float_yaml(logfile, "probs", results.probs);
llama_dump_timing_info_yaml(logfile, ctx);
fclose(logfile);
}
std::vector<float> softmax(const std::vector<float>& logits) {
std::vector<float> probs(logits.size());
float max_logit = logits[0];
@ -29,20 +92,20 @@ std::vector<float> softmax(const std::vector<float>& logits) {
return probs;
}
float log_softmax(int n_vocab, const float * logits, int tok) {
results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
float max_logit = logits[0];
for (int i = 1; i < n_vocab; ++i) max_logit = std::max(max_logit, logits[i]);
double sum_exp = 0.0;
for (int i = 0; i < n_vocab; ++i) sum_exp += expf(logits[i] - max_logit);
return logits[tok] - max_logit - log(sum_exp);
return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
}
void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread>& workers,
double& nll, double& nll2) {
void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
double & nll, double & nll2, float * logit_history, float * prob_history) {
std::mutex mutex;
int counter = 0;
auto compute = [&mutex, &counter, &nll, &nll2, n_vocab, logits, tokens, n_token] () {
auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
double local_nll = 0, local_nll2 = 0;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
@ -52,34 +115,51 @@ void process_logits(int n_vocab, const float * logits, const int * tokens, int n
break;
}
lock.unlock();
double v = -log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
const double v = -results.log_softmax;
local_nll += v;
local_nll2 += v*v;
logit_history[i] = results.logit;
prob_history[i] = results.prob;
}
};
for (auto& w : workers) w = std::thread(compute);
for (auto & w : workers) w = std::thread(compute);
compute();
for (auto& w : workers) w.join();
for (auto & w : workers) w.join();
}
void perplexity_v2(llama_context * ctx, const gpt_params & params) {
results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) {
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
// Output: `perplexity: 13.5106 [114/114]`
// BOS tokens will be added for each chunk before eval
if (params.ppl_stride <= 0) {
fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride);
return;
}
const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
const bool add_bos = is_spm;
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
if (int(tokens.size()) < 2*params.n_ctx) {
fprintf(stderr, "%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*params.n_ctx,
params.n_ctx);
fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size());
return {std::move(tokens), 0., {}, {}};
}
std::vector<float> logit_history;
std::vector<float> prob_history;
logit_history.resize(tokens.size());
prob_history.resize(tokens.size());
if (params.ppl_stride <= 0) {
fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride);
return {tokens, -1, logit_history, prob_history};
}
const int calc_chunk = params.n_ctx;
@ -88,7 +168,7 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
if (int(tokens.size()) <= calc_chunk) {
fprintf(stderr, "%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__,
tokens.size(), params.n_ctx, params.ppl_stride);
return;
return {tokens, -1, logit_history, prob_history};
}
const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1) / params.ppl_stride;
@ -120,7 +200,7 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
//fprintf(stderr, " Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
//fprintf(stderr, "%s : failed to eval\n", __func__);
return;
return {tokens, -1, logit_history, prob_history};
}
// save original token and restore it after eval
@ -161,6 +241,8 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
logits.begin() + (j + 1) * n_vocab);
const float prob = softmax(tok_logits)[tokens[start + j + 1]];
logit_history[start + j + 1] = tok_logits[tokens[start + j + 1]];
prob_history[start + j + 1] = prob;
nll += -std::log(prob);
++count;
@ -174,12 +256,14 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) {
fflush(stdout);
}
printf("\n");
return {tokens, std::exp(nll / count), logit_history, prob_history};
}
void perplexity(llama_context * ctx, const gpt_params & params) {
results_perplexity perplexity(llama_context * ctx, const gpt_params & params) {
if (params.ppl_stride > 0) {
perplexity_v2(ctx, params);
return;
return perplexity_v2(ctx, params);
}
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
@ -190,9 +274,26 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
const bool add_bos = is_spm;
auto tim1 = std::chrono::high_resolution_clock::now();
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
auto tim2 = std::chrono::high_resolution_clock::now();
fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
if (int(tokens.size()) < 2*params.n_ctx) {
fprintf(stderr, "%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*params.n_ctx,
params.n_ctx);
fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size());
return {std::move(tokens), 0., {}, {}};
}
std::vector<float> logit_history;
logit_history.resize(tokens.size());
std::vector<float> prob_history;
prob_history.resize(tokens.size());
const int n_chunk_max = tokens.size() / params.n_ctx;
@ -232,7 +333,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
return {tokens, -1, logit_history, prob_history};
}
// restore the original token in case it was set to BOS
@ -268,7 +369,8 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
// last 256 tokens. Then, we split the input up into context window size chunks to
// process the entire prompt.
const int first = std::min(512, params.n_ctx/2);
process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first, workers, nll, nll2);
process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first,
workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
count += params.n_ctx - first - 1;
// perplexity is e^(average negative log-likelihood)
@ -283,16 +385,19 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
fflush(stdout);
}
printf("\n");
nll2 /= count;
nll /= count;
const double ppl = exp(nll);
nll2 -= nll * nll;
if (nll2 > 0) {
nll2 = sqrt(nll2/(count-1));
double ppl = exp(nll);
printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
} else {
printf("Unexpected negative standard deviation of log(prob)\n");
}
return {tokens, ppl, logit_history, prob_history};
}
std::vector<float> hellaswag_evaluate_tokens(llama_context * ctx, const std::vector<int>& tokens, int n_past, int n_batch,
@ -351,6 +456,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);
const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
fprintf(stderr, "================================= is_spm = %d\n", is_spm);
// This is needed as usual for LLaMA models
const bool add_bos = is_spm;
@ -391,7 +497,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
hs_data[i].context = prompt_lines[idx*6];
hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
for (size_t j=0; j < 4; j++) {
hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j];
hs_data[i].ending[j] = prompt_lines[idx*6+2+j];
}
// Delete the selected random example from the prompt
@ -406,6 +512,8 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
double acc = 0.0f;
const int n_vocab = llama_n_vocab(ctx);
std::vector<std::vector<int>> ending_tokens(4);
std::vector<float> tok_logits(n_vocab);
for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
@ -413,11 +521,21 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, add_bos);
size_t context_size = context_embd.size();
for (int i = 0; i < 4; ++i) {
ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + " " + hs_data[task_idx].ending[i], add_bos);
for (int k = 0; k < int(context_size); ++k) {
if (ending_tokens[i][k] != context_embd[k]) {
fprintf(stderr, "Oops: ending %d of task %d differs from context at position %d\n",i,int(task_idx),k);
break;
}
}
}
// Do the 1st ending
// In this case we include the context when evaluating
auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);
//auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);
auto query_embd = ending_tokens[0];
auto query_size = query_embd.size();
//printf("First query: %d\n",(int)query_size);
// Stop if query wont fit the ctx window
if (query_size > (size_t)params.n_ctx) {
@ -462,7 +580,8 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
for (size_t ending_idx = 1; ending_idx < 4; ending_idx++) {
// Tokenize the query
query_embd = ::llama_tokenize(ctx, hs_data[task_idx].ending[ending_idx], false);
query_embd.resize(ending_tokens[ending_idx].size() - context_size);
std::memcpy(query_embd.data(), ending_tokens[ending_idx].data() + context_size, query_embd.size()*sizeof(int));
query_size = query_embd.size();
// Stop if query wont fit the ctx window
@ -586,13 +705,16 @@ int main(int argc, char ** argv) {
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
}
struct results_perplexity results;
if (params.hellaswag) {
hellaswag_score(ctx, params);
} else {
perplexity(ctx, params);
results = perplexity(ctx, params);
}
llama_print_timings(ctx);
write_logfile(ctx, params, model, results);
llama_free(ctx);
llama_free_model(model);

4
examples/quantize/quantize.cpp
View file

@ -100,7 +100,7 @@ int main(int argc, char ** argv) {
}
}
if (argc - arg_idx < 3) {
if (argc - arg_idx < 2) {
usage(argv[0]);
}
@ -114,7 +114,7 @@ int main(int argc, char ** argv) {
std::string ftype_str;
if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
std::string fpath;
const size_t pos = fname_inp.find_last_of('/');
const size_t pos = fname_inp.find_last_of("/\\");
if (pos != std::string::npos) {
fpath = fname_inp.substr(0, pos + 1);
}

4
examples/save-load-state/save-load-state.cpp
View file

@ -87,7 +87,7 @@ int main(int argc, char ** argv) {
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
auto next_token = llama_sample_token(ctx, &candidates_p);
auto next_token_str = llama_token_to_str(ctx, next_token);
auto next_token_str = llama_token_to_piece(ctx, next_token);
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str.c_str());
@ -147,7 +147,7 @@ int main(int argc, char ** argv) {
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
auto next_token = llama_sample_token(ctx2, &candidates_p);
auto next_token_str = llama_token_to_str(ctx2, next_token);
auto next_token_str = llama_token_to_piece(ctx2, next_token);
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str.c_str());

29
examples/server/README.md
View file

@ -77,34 +77,31 @@ You need to have [Node.js](https://nodejs.org/en) installed.
```bash
mkdir llama-client
cd llama-client
npm init
npm install axios
```
Create a index.js file and put inside this:
```javascript
const axios = require("axios");
const prompt = `Building a website can be done in 10 simple steps:`;
async function Test() {
let result = await axios.post("http://127.0.0.1:8080/completion", {
prompt,
n_predict: 512,
});
// the response is received until completion finish
console.log(result.data.content);
let response = await fetch("http://127.0.0.1:8080/completion", {
method: 'POST',
body: JSON.stringify({
prompt,
n_predict: 512,
})
})
console.log((await response.json()).content)
}
Test();
Test()
```
And run it:
```bash
node .
node index.js
```
## API Endpoints
@ -167,6 +164,12 @@ node .
Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.
- **POST** `/detokenize`: Convert tokens to text.
*Options:*
`tokens`: Set the tokens to detokenize.
- **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.
*Options:*

39
examples/server/server.cpp
View file

@ -94,7 +94,7 @@ static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
std::string ret;
for (; begin != end; ++begin)
{
ret += llama_token_to_str(ctx, *begin);
ret += llama_token_to_piece(ctx, *begin);
}
return ret;
}
@ -123,7 +123,7 @@ static void server_log(const char *level, const char *function, int line,
// 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_str(ctx, 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)
@ -286,7 +286,6 @@ struct llama_server_context
std::vector<llama_token> p;
if (first)
{
s.insert(0, 1, ' '); // add a space if it's the first
p = ::llama_tokenize(ctx, s, add_bos);
first = false;
}
@ -309,7 +308,6 @@ struct llama_server_context
else
{
auto s = json_prompt.template get<std::string>();
s.insert(0, 1, ' '); // always add a first space
prompt_tokens = ::llama_tokenize(ctx, s, add_bos);
}
@ -566,7 +564,7 @@ struct llama_server_context
if (!embd.empty() && embd.back() == llama_token_eos(ctx))
{
// stopping_word = llama_token_to_str(ctx, embd.back());
// stopping_word = llama_token_to_piece(ctx, embd.back());
has_next_token = false;
stopped_eos = true;
LOG_VERBOSE("eos token found", {});
@ -613,7 +611,7 @@ struct llama_server_context
{
const completion_token_output token_with_probs = nextToken();
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok);
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok);
generated_text += token_text;
if (params.n_probs > 0)
@ -721,7 +719,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n");
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
fprintf(stdout, " -nommq, --no-mul-mat-q\n");
fprintf(stdout, " use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
fprintf(stdout, " Not recommended since this is both slower and uses more VRAM.\n");
@ -1104,6 +1102,12 @@ static json format_tokenizer_response(const std::vector<llama_token> &tokens)
{"tokens", tokens}};
}
static json format_detokenized_response(std::string content)
{
return json{
{"content", content}};
}
template <typename T>
static T json_value(const json &body, const std::string &key, const T &default_value)
{
@ -1248,7 +1252,7 @@ void beam_search_callback(void * callback_data, llama_beams_state beams_state) {
struct token_translator {
llama_context * ctx;
std::string operator()(llama_token tok) const { return llama_token_to_str(ctx, tok); }
std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); }
std::string operator()(completion_token_output cto) const { return (*this)(cto.tok); }
};
@ -1358,7 +1362,7 @@ int main(int argc, char **argv)
while (llama.has_next_token) {
const completion_token_output token_with_probs = llama.doCompletion();
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok);
stop_pos = llama.findStoppingStrings(llama.generated_text,
token_text.size(), STOP_FULL);
@ -1389,7 +1393,7 @@ int main(int argc, char **argv)
if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) {
continue;
}
const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok);
const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok);
size_t pos = std::min(sent_count, llama.generated_text.size());
@ -1501,6 +1505,21 @@ int main(int argc, char **argv)
const json data = format_tokenizer_response(tokens);
return res.set_content(data.dump(), "application/json"); });
svr.Post("/detokenize", [&llama](const Request &req, Response &res)
{
auto lock = llama.lock();
const json body = json::parse(req.body);
std::string content;
if (body.count("tokens") != 0)
{
const std::vector<llama_token> tokens = body["tokens"];
content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend());
}
const json data = format_detokenized_response(content);
return res.set_content(data.dump(), "application/json"); });
svr.Post("/embedding", [&llama](const Request &req, Response &res)
{
auto lock = llama.lock();

4
examples/simple/simple.cpp
View file

@ -63,7 +63,7 @@ int main(int argc, char ** argv) {
fprintf(stderr, "\n\n");
for (auto id : tokens_list) {
fprintf(stderr, "%s", llama_token_to_str(ctx, id).c_str());
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
}
fflush(stderr);
@ -112,7 +112,7 @@ int main(int argc, char ** argv) {
}
// print the new token :
printf("%s", llama_token_to_str(ctx, new_token_id).c_str());
printf("%s", llama_token_to_piece(ctx, new_token_id).c_str());
fflush(stdout);
// push this new token for next evaluation

14
examples/train-text-from-scratch/README.md
View file

@ -8,15 +8,15 @@ wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/s
# train
./bin/train-text-from-scratch \
--vocab-model ../models/ggml-vocab.bin \
--vocab-model ../models/ggml-vocab-llama.gguf \
--ctx 64 --embd 256 --head 8 --layer 16 \
--checkpoint-in chk-shakespeare-256x16.bin \
--checkpoint-out chk-shakespeare-256x16.bin \
--model-out ggml-shakespeare-256x16-f32.bin \
--checkpoint-in chk-shakespeare-256x16.gguf \
--checkpoint-out chk-shakespeare-256x16.gguf \
--model-out ggml-shakespeare-256x16-f32.gguf \
--train-data "shakespeare.txt" \
-t 6 -b 16 -n 32 --seed 1 --adam-iter 16 \
--print-details-interval 0 --predict 16 --use-flash
-t 6 -b 16 --seed 1 --adam-iter 256 \
--no-checkpointing
# predict
./bin/main -m ggml-shakespeare-256x16-f32.bin
./bin/main -m ggml-shakespeare-256x16-f32.gguf
```

492
examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py Normal file
View file

@ -0,0 +1,492 @@
#!/usr/bin/env python3
# train-text-from-scratch checkpoint --> gguf conversion
import argparse
import gguf
import os
import struct
import sys
import numpy as np
from pathlib import Path
# gguf constants
LLM_KV_OPTIMIZER_TYPE = "optimizer.type"
LLM_KV_OPTIMIZER_TYPE_ADAM = "adam"
LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs"
LLM_KV_OPTIMIZER_FILE_VERSION = "optimizer.file_version"
LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT = "optimizer.convergence_past_count"
LLM_KV_OPTIMIZER_PARAMETER_COUNT = "optimizer.parameter_count"
LLM_KV_OPTIMIZER_ITERATION_COUNT = "optimizer.iteration_count"
LLM_KV_OPTIMIZER_JUST_INITIALIZED = "optimizer.just_initialized"
LLM_KV_OPTIMIZER_ADAM_BEST_LOSS = "optimizer.adam.best_loss"
LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS = "optimizer.adam.previous_loss"
LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT = "optimizer.adam.no_improvement_count"
LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count"
LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS = "optimizer.lbfgs.best_loss"
LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP = "optimizer.lbfgs.line_search_step"
LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J = "optimizer.lbfgs.line_search_j"
LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K = "optimizer.lbfgs.line_search_k"
LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END = "optimizer.lbfgs.line_search_end"
LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count"
LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS = "optimizer.adam.first_moments"
LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS = "optimizer.adam.second_moments"
LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values"
LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS = "optimizer.lbfgs.current_parameters"
LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters"
LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS = "optimizer.lbfgs.current_gradients"
LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS = "optimizer.lbfgs.previous_gradients"
LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION = "optimizer.lbfgs.search_direction"
LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES = "optimizer.lbfgs.past_loss_values"
LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA = "optimizer.lbfgs.memory_alpha"
LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS = "optimizer.lbfgs.memory_ys"
LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S = "optimizer.lbfgs.memory_s"
LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y = "optimizer.lbfgs.memory_y"
LLM_KV_TRAINING_FILE_VERSION = "training.file_version"
LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count"
LLM_KV_TRAINING_SAMPLE_COUNT = "training.sample_count"
LLM_KV_TRAINING_TOKEN_COUNT = "training.token_count"
class Tensor:
def __init__(self, dtype='f', ne=None):
if ne is None:
ne = []
self.dtype = dtype
self.ne = ne
self.nbytes = 0
if self.dtype == 'f':
if len(self.ne) == 0:
self.nbytes = 0
else:
self.nbytes = int(np.product(self.ne)) * 4
else:
raise ValueError(f"Unhandled data type '{self.dtype}'")
def load(self, data, offset):
nd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
namelen = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
dtype = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
assert(nd == len(self.ne))
ne = []
for d in range(nd):
n = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
ne.append(n)
assert(tuple(ne) == tuple(self.ne))
if self.dtype == 'f':
assert(dtype == 0)
else:
raise ValueError(f"Unhandled data type '{self.dtype}'")
self.name = bytes(data[offset:offset+namelen]); offset += namelen
# 32-byte alignment
offset += (0 - offset) & 31
self.data = data[offset:offset+self.nbytes]
offset += self.nbytes
return offset
def max_storage_size(self):
result = 0
result += 4 # nd
result += 4 # namelen
result += 4 # dtype
result += len(self.ne)*8 # ne
result += 48 # name (maximum as of commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9)
result += 31 # 32-byte alignment
result += self.nbytes
return result
def save_gguf(self, gguf_writer, name):
gguf_writer.add_tensor(
name=name,
tensor=self.data,
raw_shape=np.array(list(reversed(self.ne))),
raw_dtype=gguf.GGMLQuantizationType.F32)
class OptimizationParamsV0:
def __init__(self):
pass
def load(self, data, offset):
self.type = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.n_threads = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.past = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.delta = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.print_forward_graph = struct.unpack('<?', bytes(data[offset:offset + 1]))[0]; offset += 4 # 32bit-aligned
self.print_backward_graph = struct.unpack('<?', bytes(data[offset:offset + 1]))[0]; offset += 4 # 32bit-aligned
self.adam_n_iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_sched = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_decay = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_alpha = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_beta1 = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_beta2 = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_eps = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_eps_f = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_eps_g = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_n_iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_max_linesearch = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_eps = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_ftol = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_wolfe = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_min_step = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_max_step = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_linesearch = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
return offset
class OptimizationContext:
def __init__(self):
pass
def load(self, data, offset):
self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]
offset += 4
if self.version == 0:
params = OptimizationParamsV0()
offset = params.load(data, offset)
self.past = params.past
self.lbfgs_m = params.lbfgs_m
self.nx = struct.unpack('N', bytes(data[offset:offset + 8]))[0]; offset += 8
self.iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]); offset += 4
self.type = params.type
self.adam_m = Tensor('f', [self.nx])
self.adam_v = Tensor('f', [self.nx])
self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
self.lbfgs_x = Tensor('f', [self.nx])
self.lbfgs_xp = Tensor('f', [self.nx])
self.lbfgs_g = Tensor('f', [self.nx])
self.lbfgs_gp = Tensor('f', [self.nx])
self.lbfgs_d = Tensor('f', [self.nx])
self.lbfgs_pf = Tensor('f', [self.past] if self.past > 0 else [])
self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
self.lbfgs_lms = Tensor('f', [self.nx, self.lbfgs_m])
self.lbfgs_lmy = Tensor('f', [self.nx, self.lbfgs_m])
if self.type == 0:
# these tensors are stored, but we don't need their data
x = Tensor('f', [self.nx])
g = Tensor('f', [self.nx])
g2 = Tensor('f', [self.nx])
mh = Tensor('f', [self.nx])
vh = Tensor('f', [self.nx])
offset = x.load(data, offset)
offset = g.load(data, offset)
offset = g2.load(data, offset)
offset = self.adam_m.load(data, offset)
offset = self.adam_v.load(data, offset)
offset = mh.load(data, offset)
offset = vh.load(data, offset)
offset = self.adam_pf.load(data, offset)
self.adam_fx_best = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_fx_prev = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
elif self.type == 1:
offset = self.lbfgs_x.load(data, offset)
offset = self.lbfgs_xp.load(data, offset)
offset = self.lbfgs_g.load(data, offset)
offset = self.lbfgs_gp.load(data, offset)
offset = self.lbfgs_d.load(data, offset)
offset = self.lbfgs_pf.load(data, offset)
offset = self.lbfgs_lmal.load(data, offset)
offset = self.lbfgs_lmys.load(data, offset)
offset = self.lbfgs_lms.load(data, offset)
offset = self.lbfgs_lmy.load(data, offset)
self.lbfgs_fx_best = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_step = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_j = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_k = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_end = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
else:
raise ValueError('Unknown optimizer type')
elif self.version == 1:
self.past = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.nx = struct.unpack('N', bytes(data[offset:offset + 8]))[0]; offset += 8
self.iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]); offset += 4
self.adam_m = Tensor('f', [self.nx])
self.adam_v = Tensor('f', [self.nx])
self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
self.lbfgs_x = Tensor('f', [self.nx])
self.lbfgs_xp = Tensor('f', [self.nx])
self.lbfgs_g = Tensor('f', [self.nx])
self.lbfgs_gp = Tensor('f', [self.nx])
self.lbfgs_d = Tensor('f', [self.nx])
self.lbfgs_pf = Tensor('f', [self.past] if self.past > 0 else [])
self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
self.lbfgs_lms = Tensor('f', [self.nx, self.lbfgs_m])
self.lbfgs_lmy = Tensor('f', [self.nx, self.lbfgs_m])
# forgot to save type in version 1:
# guess self.type from number of remaining bytes
size_type_0 = 12 + sum([t.max_storage_size() for t in
[self.adam_m, self.adam_v]
+([self.adam_pf] if (self.past > 0) else [])])
size_type_1 = 24 + sum([t.max_storage_size() for t in
[self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g,
self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf,
self.lbfgs_lmal, self.lbfgs_lmys,
self.lbfgs_lms, self.lbfgs_lmy]
+([self.lbfgs_pf] if (self.past > 0) else [])])
# due to alignment padding the size might not by exact
# but the difference in size for both types is significant,
# so we can just use whichever is closest
remaining = len(data) - offset
if abs(remaining - size_type_0) < abs(remaining - size_type_1):
self.type = 0
else:
self.type = 1
if self.type == 0:
offset = self.adam_m.load(data, offset)
offset = self.adam_v.load(data, offset)
offset = self.adam_pf.load(data,offset)
self.adam_fx_best = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_fx_prev = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
elif self.type == 1:
offset = self.lbfgs_x.load(data, offset)
offset = self.lbfgs_xp.load(data, offset)
offset = self.lbfgs_g.load(data, offset)
offset = self.lbfgs_gp.load(data, offset)
offset = self.lbfgs_d.load(data, offset)
offset = self.lbfgs_pf.load(data, offset)
offset = self.lbfgs_lmal.load(data, offset)
offset = self.lbfgs_lmys.load(data, offset)
offset = self.lbfgs_lms.load(data, offset)
offset = self.lbfgs_lmy.load(data, offset)
self.lbfgs_fx_best = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_step = struct.unpack('<f', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_j = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_k = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_end = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0]; offset += 4
else:
raise ValueError('Invalid version of checkpoint file')
return offset
def save_gguf(self, gguf_writer):
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_FILE_VERSION, 0)
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, self.past)
gguf_writer.add_uint64(LLM_KV_OPTIMIZER_PARAMETER_COUNT, self.nx)
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ITERATION_COUNT, self.iter)
gguf_writer.add_bool(LLM_KV_OPTIMIZER_JUST_INITIALIZED, self.just_initialized)
if self.type == 0:
gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM)
gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, self.adam_fx_best)
gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, self.adam_fx_prev)
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, self.adam_n_no_improvement)
self.adam_m.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS)
self.adam_v.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS)
if self.past > 0:
self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES)
elif self.type == 1:
gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS)
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m)
gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best)
gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step)
gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j)
gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k)
gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end)
gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement)
self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS)
self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS)
self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS)
self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS)
self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION)
if self.past > 0:
self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES)
self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA)
self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS)
self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S)
self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y)
else:
raise ValueError('Unknown optimizer type')
class ModelParams:
def __init__(self):
pass
def load(self, data, offset):
self.n_vocab = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.n_embd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.n_mult = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.n_head = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.n_layer = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.n_rot = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
return offset
def get_n_ff(self):
# struct my_llama_model::get_n_ff in train-text-from-scratch.cpp commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9
return ((2*(4*self.n_embd)//3 + self.n_mult - 1)//self.n_mult)*self.n_mult
def save_gguf(self, gguf_writer):
# self.n_vocab not saved
gguf_writer.add_embedding_length(self.n_embd)
gguf_writer.add_head_count(self.n_head)
gguf_writer.add_block_count(self.n_layer)
gguf_writer.add_rope_dimension_count(self.n_rot)
gguf_writer.add_feed_forward_length(self.get_n_ff())
def tensor_name(key, bid=None):
return gguf.MODEL_TENSOR_NAMES[gguf.MODEL_ARCH.LLAMA][key].format(bid=bid) + ".weight"
class Layer:
def __init__(self, params, bid):
self.bid = bid
self.att_norm = Tensor('f', [params.n_embd])
self.wq = Tensor('f', [params.n_embd, params.n_embd])
self.wk = Tensor('f', [params.n_embd, params.n_embd])
self.wv = Tensor('f', [params.n_embd, params.n_embd])
self.wo = Tensor('f', [params.n_embd, params.n_embd])
self.ffn_norm = Tensor('f', [params.n_embd])
self.w1 = Tensor('f', [params.n_embd, params.get_n_ff()])
self.w2 = Tensor('f', [params.get_n_ff(), params.n_embd])
self.w3 = Tensor('f', [params.n_embd, params.get_n_ff()])
def load(self, data, offset):
offset = self.att_norm.load(data, offset)
offset = self.wq.load(data, offset)
offset = self.wk.load(data, offset)
offset = self.wv.load(data, offset)
offset = self.wo.load(data, offset)
offset = self.ffn_norm.load(data, offset)
offset = self.w1.load(data, offset)
offset = self.w2.load(data, offset)
offset = self.w3.load(data, offset)
return offset
def save_gguf(self, gguf_writer):
self.att_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid))
self.wq.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q, self.bid))
self.wk.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K, self.bid))
self.wv.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V, self.bid))
self.wo.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT, self.bid))
self.ffn_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM, self.bid))
self.w1.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE, self.bid))
self.w2.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, self.bid))
self.w3.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP, self.bid))
class Model:
def __init__(self):
self.params = ModelParams()
self.layers = []
def load(self, data, offset):
offset = self.params.load(data, offset)
self.tok_embd = Tensor('f', [self.params.n_embd, self.params.n_vocab])
self.norm = Tensor('f', [self.params.n_embd])
self.output = Tensor('f', [self.params.n_embd, self.params.n_vocab])
offset = self.tok_embd.load(data, offset)
offset = self.norm.load(data, offset)
offset = self.output.load(data, offset)
self.layers.clear()
for bid in range(self.params.n_layer):
layer = Layer(self.params, bid)
offset = layer.load(data, offset)
self.layers.append(layer)
return offset
def save_gguf(self, gguf_writer):
self.params.save_gguf(gguf_writer)
self.tok_embd.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD))
self.norm.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM))
self.output.save_gguf (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT))
for layer in self.layers:
layer.save_gguf(gguf_writer)
class Checkpoint:
def __init__(self):
self.model = Model()
self.opt_ctx = OptimizationContext()
def load(self, data, offset):
magic = bytes(reversed(data[offset:offset + 4])); offset += 4
if magic != b'ggcp':
raise ValueError(f"File header magic indicates, that this is no checkpoint file. Expected 'ggcp', Got '{str(magic)}'")
self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
if self.version != 0:
raise ValueError('Invalid version of checkpoint file')
self.train_its = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.train_samples = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
self.train_tokens = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
offset = self.model.load(data, offset)
offset = self.opt_ctx.load(data, offset)
return offset
def save_gguf(self, gguf_writer):
gguf_writer.add_file_type(gguf.GGMLQuantizationType.F32)
gguf_writer.add_layer_norm_rms_eps(1e-5)
gguf_writer.add_uint32(LLM_KV_TRAINING_FILE_VERSION, 0)
gguf_writer.add_uint32(LLM_KV_TRAINING_ITERATION_COUNT, self.train_its)
gguf_writer.add_uint32(LLM_KV_TRAINING_SAMPLE_COUNT, self.train_samples)
gguf_writer.add_uint32(LLM_KV_TRAINING_TOKEN_COUNT, self.train_tokens)
self.model.save_gguf(gguf_writer)
self.opt_ctx.save_gguf(gguf_writer)
def handle_args():
parser = argparse.ArgumentParser(description = 'Convert train-text-from-scratch checkpoints to GGUF')
parser.add_argument('--input', '-i', type = Path, help = 'Input train checkpoint filename', required=True)
parser.add_argument('--output', '-o', type = Path, help ='Output GGUF filename', required=True)
return parser.parse_args()
def main():
cfg = handle_args()
data = np.memmap(cfg.input, mode = 'r')
chk = Checkpoint()
offset = 0
offset = chk.load(data, offset)
# we should have read all available data
assert(offset == len(data))
gguf_writer = gguf.GGUFWriter(cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
chk.save_gguf(gguf_writer)
print(" gguf: write header")
gguf_writer.write_header_to_file()
print(" gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print(" gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
if __name__ == '__main__':
main()

3504
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

File diff suppressed because it is too large Load diff

12
flake.lock generated
View file

@ -5,11 +5,11 @@
"systems": "systems"
},
"locked": {
"lastModified": 1685518550,
"narHash": "sha256-o2d0KcvaXzTrPRIo0kOLV0/QXHhDQ5DTi+OxcjO8xqY=",
"lastModified": 1692799911,
"narHash": "sha256-3eihraek4qL744EvQXsK1Ha6C3CR7nnT8X2qWap4RNk=",
"owner": "numtide",
"repo": "flake-utils",
"rev": "a1720a10a6cfe8234c0e93907ffe81be440f4cef",
"rev": "f9e7cf818399d17d347f847525c5a5a8032e4e44",
"type": "github"
},
"original": {
@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1685931219,
"narHash": "sha256-8EWeOZ6LKQfgAjB/USffUSELPRjw88A+xTcXnOUvO5M=",
"lastModified": 1692913444,
"narHash": "sha256-1SvMQm2DwofNxXVtNWWtIcTh7GctEVrS/Xel/mdc6iY=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "7409480d5c8584a1a83c422530419efe4afb0d19",
"rev": "18324978d632ffc55ef1d928e81630c620f4f447",
"type": "github"
},
"original": {

54
flake.nix
View file

@ -6,6 +6,9 @@
outputs = { self, nixpkgs, flake-utils }:
flake-utils.lib.eachDefaultSystem (system:
let
name = "llama.cpp";
src = ./.;
meta.mainProgram = "llama";
inherit (pkgs.stdenv) isAarch32 isAarch64 isDarwin;
buildInputs = with pkgs; [ openmpi ];
osSpecific = with pkgs; buildInputs ++
@ -21,11 +24,17 @@
CoreGraphics
CoreVideo
]
else if isDarwin then
with pkgs.darwin.apple_sdk.frameworks; [
Accelerate
CoreGraphics
CoreVideo
]
else
with pkgs; [ openblas ]
);
pkgs = import nixpkgs { inherit system; };
nativeBuildInputs = with pkgs; [ cmake pkgconfig ];
nativeBuildInputs = with pkgs; [ cmake ninja pkgconfig ];
llama-python =
pkgs.python3.withPackages (ps: with ps; [ numpy sentencepiece ]);
postPatch = ''
@ -38,35 +47,35 @@
mv $out/bin/server $out/bin/llama-server
'';
cmakeFlags = [ "-DLLAMA_BUILD_SERVER=ON" "-DLLAMA_MPI=ON" "-DBUILD_SHARED_LIBS=ON" "-DCMAKE_SKIP_BUILD_RPATH=ON" ];
in {
in
{
packages.default = pkgs.stdenv.mkDerivation {
name = "llama.cpp";
src = ./.;
postPatch = postPatch;
nativeBuildInputs = nativeBuildInputs;
buildInputs = osSpecific;
inherit name src meta postPatch nativeBuildInputs buildInputs postInstall;
cmakeFlags = cmakeFlags
++ (if isAarch64 && isDarwin then [
"-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1"
"-DLLAMA_METAL=ON"
] else [
"-DLLAMA_BLAS=ON"
"-DLLAMA_BLAS_VENDOR=OpenBLAS"
"-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1"
"-DLLAMA_METAL=ON"
] else [
"-DLLAMA_BLAS=ON"
"-DLLAMA_BLAS_VENDOR=OpenBLAS"
]);
postInstall = postInstall;
meta.mainProgram = "llama";
};
packages.opencl = pkgs.stdenv.mkDerivation {
name = "llama.cpp";
src = ./.;
postPatch = postPatch;
nativeBuildInputs = nativeBuildInputs;
inherit name src meta postPatch nativeBuildInputs postInstall;
buildInputs = with pkgs; buildInputs ++ [ clblast ];
cmakeFlags = cmakeFlags ++ [
"-DLLAMA_CLBLAST=ON"
];
postInstall = postInstall;
meta.mainProgram = "llama";
};
packages.rocm = pkgs.stdenv.mkDerivation {
inherit name src meta postPatch nativeBuildInputs postInstall;
buildInputs = with pkgs; buildInputs ++ [ hip hipblas rocblas ];
cmakeFlags = cmakeFlags ++ [
"-DLLAMA_HIPBLAS=1"
"-DCMAKE_C_COMPILER=hipcc"
"-DCMAKE_CXX_COMPILER=hipcc"
"-DCMAKE_POSITION_INDEPENDENT_CODE=ON"
];
};
apps.llama-server = {
type = "app";
@ -80,8 +89,13 @@
type = "app";
program = "${self.packages.${system}.default}/bin/llama";
};
apps.quantize = {
type = "app";
program = "${self.packages.${system}.default}/bin/quantize";
};
apps.default = self.apps.${system}.llama;
devShells.default = pkgs.mkShell {
buildInputs = [ llama-python ];
packages = nativeBuildInputs ++ osSpecific;
};
});

63
ggml-alloc.c
View file

@ -107,6 +107,10 @@ static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct g
}
void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
#ifdef GGML_ALLOCATOR_DEBUG
GGML_ASSERT(ggml_is_view(tensor) == false); // views generally get data pointer from one of their sources
GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated
#endif
size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
size = aligned_offset(NULL, size, alloc->alignment);
@ -268,7 +272,7 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment)
/*.parse_seq = */ {0},
/*.parse_seq_len = */ 0,
#ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ = {0},
/*.allocated_tensors = */ {0},
#endif
};
@ -297,7 +301,7 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
/*.parse_seq = */ {0},
/*.parse_seq_len = */ 0,
#ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ = {0},
/*.allocated_tensors = */ {0},
#endif
};
@ -317,8 +321,7 @@ bool ggml_allocr_is_measure(struct ggml_allocr * alloc) {
//////////// compute graph allocator
static bool ggml_is_view(struct ggml_tensor * t) {
return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE ||
t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY;
return t->view_src != NULL;
}
static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
@ -336,28 +339,6 @@ static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml
return true;
}
static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) {
switch (t->op) {
case GGML_OP_PERMUTE:
case GGML_OP_RESHAPE:
case GGML_OP_TRANSPOSE:
case GGML_OP_VIEW:
return t->src[0];
case GGML_OP_CPY:
return t->src[1];
default:
return NULL;
}
}
static struct ggml_tensor * get_view_source(struct ggml_tensor * t) {
struct ggml_tensor * parent = t;
do {
parent = get_view_parent(parent);
} while (ggml_is_view(parent));
return parent;
}
static bool ggml_op_can_inplace(enum ggml_op op) {
switch (op) {
case GGML_OP_SCALE:
@ -365,7 +346,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_ADD:
case GGML_OP_ADD1:
case GGML_OP_ACC:
case GGML_OP_SUB:
case GGML_OP_MUL:
case GGML_OP_DIV:
@ -375,7 +355,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
case GGML_OP_UNARY:
case GGML_OP_ROPE:
case GGML_OP_RMS_NORM:
case GGML_OP_SET:
case GGML_OP_SOFT_MAX:
case GGML_OP_CONT:
return true;
@ -389,24 +368,8 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
struct hash_node * ht = alloc->hash_table;
if (node->data == NULL) {
if (ggml_is_view(node)) {
size_t offset;
switch(node->op) {
case GGML_OP_VIEW:
memcpy(&offset, node->op_params, sizeof(size_t));
node->data = (char *) node->src[0]->data + offset;
break;
case GGML_OP_PERMUTE:
case GGML_OP_RESHAPE:
case GGML_OP_TRANSPOSE:
node->data = node->src[0]->data;
break;
case GGML_OP_CPY:
node->data = node->src[1]->data;
break;
default:
GGML_ASSERT(!"unknown view op");
break;
}
assert(node->view_src->data != NULL);
node->data = (char *)node->view_src->data + node->view_offs;
} else {
// see if we can reuse a parent's buffer (inplace)
if (ggml_op_can_inplace(node->op)) {
@ -426,7 +389,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
struct hash_node * p_hn = hash_get(ht, parent);
if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
if (ggml_is_view(parent)) {
struct ggml_tensor * view_src = get_view_source(parent);
struct ggml_tensor * view_src = parent->view_src;
struct hash_node * view_src_hn = hash_get(ht, view_src);
if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
// TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
@ -468,7 +431,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
struct ggml_tensor * node = gf->nodes[i];
if (ggml_is_view(node)) {
struct ggml_tensor * view_src = get_view_source(node);
struct ggml_tensor * view_src = node->view_src;
hash_get(ht, view_src)->n_views += 1;
}
@ -553,10 +516,10 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
if (p_hn->n_children == 0 && p_hn->n_views == 0) {
if (ggml_is_view(parent)) {
struct ggml_tensor * view_src = get_view_source(parent);
struct ggml_tensor * view_src = parent->view_src;
struct hash_node * view_src_hn = hash_get(ht, view_src);
view_src_hn->n_views -= 1;
AT_PRINTF("view_src %s\n", view_src->name);
AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views);
if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
ggml_allocator_free_tensor(alloc, view_src);
}

34
ggml-cuda.cu
View file

@ -306,11 +306,11 @@ typedef struct {
#define QI4_K (QK_K / (4*QR4_K))
#ifdef GGML_QKK_64
typedef struct {
half d[2]; // super-block scales/mins
half dm[2]; // super-block scales/mins
uint8_t scales[2]; // 4-bit block scales/mins
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
static_assert(sizeof(block_q4_K) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding");
#else
typedef struct {
half2 dm; // super-block scale for quantized scales/mins
@ -737,8 +737,8 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, float
const int tid = threadIdx.x;
const uint8_t * q = x[i].qs;
float * y = yy + i*QK_K;
const float d = (float)x[i].d[0];
const float m = (float)x[i].d[1];
const float d = (float)x[i].dm[0];
const float m = (float)x[i].dm[1];
y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >> 4) - m * (x[i].scales[1] >> 4);
#endif
@ -1155,8 +1155,8 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx,
const uint16_t * a = (const uint16_t *)x[i].scales;
aux16[0] = a[0] & 0x0f0f;
aux16[1] = (a[0] >> 4) & 0x0f0f;
const float d = (float)x[i].d[0];
const float m = (float)x[i].d[1];
const float d = (float)x[i].dm[0];
const float m = (float)x[i].dm[1];
float sum = 0.f;
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
@ -2845,8 +2845,8 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
aux16[0] = a[0] & 0x0f0f;
aux16[1] = (a[0] >> 4) & 0x0f0f;
const float dall = bq4_K->d[0];
const float dmin = bq4_K->d[1];
const float dall = bq4_K->dm[0];
const float dmin = bq4_K->dm[1];
const float d8_1 = __low2float(bq8_1[0].ds);
const float d8_2 = __low2float(bq8_1[1].ds);
@ -2929,7 +2929,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
#if QK_K == 256
x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
#else
x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
#endif
}
#pragma unroll
@ -3119,7 +3123,9 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
#if QK_K == 256
x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
#endif
}
#pragma unroll
@ -4709,6 +4715,8 @@ static void ggml_mul_mat_q3_K_q8_1_cuda(
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
#if QK_K == 256
int id;
CUDA_CHECK(cudaGetDevice(&id));
const int compute_capability = g_compute_capabilities[id];
@ -4740,6 +4748,7 @@ static void ggml_mul_mat_q3_K_q8_1_cuda(
mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
}
#endif
}
static void ggml_mul_mat_q4_K_q8_1_cuda(
@ -4899,8 +4908,8 @@ static void scale_f32_cuda(const float * x, float * dst, const float scale, cons
static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
GGML_ASSERT(nrows % 2 == 0); // GG: is this assert really needed? I don't see why
const dim3 block_dims(1, 2*CUDA_ROPE_BLOCK_SIZE, 1);
GGML_ASSERT(ncols % 2 == 0);
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
const dim3 block_nums(nrows, num_blocks_x, 1);
rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
@ -4908,7 +4917,8 @@ static void rope_f32_cuda(const float * x, float * dst, const int ncols, const i
static void rope_neox_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
const dim3 block_dims(1, 2*CUDA_ROPE_BLOCK_SIZE, 1);
GGML_ASSERT(ncols % 2 == 0);
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
const dim3 block_nums(nrows, num_blocks_x, 1);
rope_neox_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
@ -6328,9 +6338,11 @@ void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml
void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
const int mode = ((int32_t *) dst->op_params)[2];
const bool is_glm = mode & 4;
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, !is_glm); // flatten support not implemented for glm
}

1
ggml-metal.h
View file

@ -24,6 +24,7 @@
// max memory buffers that can be mapped to the device
#define GGML_METAL_MAX_BUFFERS 16
#define GGML_METAL_MAX_COMMAND_BUFFERS 32
struct ggml_tensor;
struct ggml_cgraph;

169
ggml-metal.m
View file

@ -11,6 +11,7 @@
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))
// TODO: temporary - reuse llama.cpp logging
#ifdef GGML_METAL_NDEBUG
#define metal_printf(...)
#else
@ -33,12 +34,15 @@ struct ggml_metal_buffer {
struct ggml_metal_context {
int n_cb;
float * logits;
id<MTLDevice> device;
id<MTLCommandQueue> queue;
id<MTLLibrary> library;
id<MTLCommandBuffer> command_buffers [GGML_METAL_MAX_COMMAND_BUFFERS];
id<MTLComputeCommandEncoder> command_encoders[GGML_METAL_MAX_COMMAND_BUFFERS];
dispatch_queue_t d_queue;
int n_buffers;
struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
@ -110,16 +114,17 @@ static NSString * const msl_library_source = @"see metal.metal";
@end
struct ggml_metal_context * ggml_metal_init(int n_cb) {
fprintf(stderr, "%s: allocating\n", __func__);
metal_printf("%s: allocating\n", __func__);
struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
ctx->n_cb = n_cb;
ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
ctx->device = MTLCreateSystemDefaultDevice();
ctx->queue = [ctx->device newCommandQueue];
ctx->n_buffers = 0;
ctx->concur_list_len = 0;
ctx->d_queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
#if 0
// compile from source string and show compile log
@ -128,7 +133,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
ctx->library = [ctx->device newLibraryWithSource:msl_library_source options:nil error:&error];
if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]);
return NULL;
}
}
@ -142,11 +147,11 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
//NSString * path = [[NSBundle mainBundle] pathForResource:@"../../examples/metal/metal" ofType:@"metal"];
NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
NSString * path = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
fprintf(stderr, "%s: loading '%s'\n", __func__, [path UTF8String]);
metal_printf("%s: loading '%s'\n", __func__, [path UTF8String]);
NSString * src = [NSString stringWithContentsOfFile:path encoding:NSUTF8StringEncoding error:&error];
if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]);
return NULL;
}
@ -158,7 +163,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
ctx->library = [ctx->device newLibraryWithSource:src options:nil error:&error];
#endif
if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]);
return NULL;
}
}
@ -170,11 +175,11 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
#define GGML_METAL_ADD_KERNEL(name) \
ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \
ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \
fprintf(stderr, "%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \
metal_printf("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \
(int) ctx->pipeline_##name.maxTotalThreadsPerThreadgroup, \
(int) ctx->pipeline_##name.threadExecutionWidth); \
if (error) { \
fprintf(stderr, "%s: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
metal_printf("%s: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
return NULL; \
}
@ -226,22 +231,80 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
#undef GGML_METAL_ADD_KERNEL
}
fprintf(stderr, "%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
fprintf(stderr, "%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
metal_printf("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
metal_printf("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
if (ctx->device.maxTransferRate != 0) {
fprintf(stderr, "%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
metal_printf("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
} else {
fprintf(stderr, "%s: maxTransferRate = built-in GPU\n", __func__);
metal_printf("%s: maxTransferRate = built-in GPU\n", __func__);
}
return ctx;
}
void ggml_metal_free(struct ggml_metal_context * ctx) {
fprintf(stderr, "%s: deallocating\n", __func__);
metal_printf("%s: deallocating\n", __func__);
#define GGML_METAL_DEL_KERNEL(name) \
[ctx->function_##name release]; \
[ctx->pipeline_##name release];
GGML_METAL_DEL_KERNEL(add);
GGML_METAL_DEL_KERNEL(add_row);
GGML_METAL_DEL_KERNEL(mul);
GGML_METAL_DEL_KERNEL(mul_row);
GGML_METAL_DEL_KERNEL(scale);
GGML_METAL_DEL_KERNEL(silu);
GGML_METAL_DEL_KERNEL(relu);
GGML_METAL_DEL_KERNEL(gelu);
GGML_METAL_DEL_KERNEL(soft_max);
GGML_METAL_DEL_KERNEL(diag_mask_inf);
GGML_METAL_DEL_KERNEL(get_rows_f16);
GGML_METAL_DEL_KERNEL(get_rows_q4_0);
GGML_METAL_DEL_KERNEL(get_rows_q4_1);
GGML_METAL_DEL_KERNEL(get_rows_q8_0);
GGML_METAL_DEL_KERNEL(get_rows_q2_K);
GGML_METAL_DEL_KERNEL(get_rows_q3_K);
GGML_METAL_DEL_KERNEL(get_rows_q4_K);
GGML_METAL_DEL_KERNEL(get_rows_q5_K);
GGML_METAL_DEL_KERNEL(get_rows_q6_K);
GGML_METAL_DEL_KERNEL(rms_norm);
GGML_METAL_DEL_KERNEL(norm);
GGML_METAL_DEL_KERNEL(mul_mat_f16_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q4_1_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q8_0_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q2_K_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q3_K_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q4_K_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q5_K_f32);
GGML_METAL_DEL_KERNEL(mul_mat_q6_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_f16_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q4_0_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q8_0_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q4_1_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q2_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q3_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32);
GGML_METAL_DEL_KERNEL(rope);
GGML_METAL_DEL_KERNEL(alibi_f32);
GGML_METAL_DEL_KERNEL(cpy_f32_f16);
GGML_METAL_DEL_KERNEL(cpy_f32_f32);
GGML_METAL_DEL_KERNEL(cpy_f16_f16);
#undef GGML_METAL_DEL_KERNEL
for (int i = 0; i < ctx->n_buffers; ++i) {
[ctx->buffers[i].metal release];
}
[ctx->library release];
[ctx->queue release];
[ctx->device release];
dispatch_release(ctx->d_queue);
free(ctx);
}
@ -249,7 +312,7 @@ void * ggml_metal_host_malloc(size_t n) {
void * data = NULL;
const int result = posix_memalign((void **) &data, getpagesize(), n);
if (result != 0) {
fprintf(stderr, "%s: error: posix_memalign failed\n", __func__);
metal_printf("%s: error: posix_memalign failed\n", __func__);
return NULL;
}
@ -261,7 +324,7 @@ void ggml_metal_host_free(void * data) {
}
void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
ctx->n_cb = n_cb;
ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
}
int ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
@ -277,7 +340,7 @@ int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) {
// Metal buffer based on the host memory pointer
//
static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) {
//fprintf(stderr, "%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
//metal_printf("%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
const int64_t tsize = ggml_nbytes(t);
@ -288,13 +351,13 @@ static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru
if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) {
*offs = (size_t) ioffs;
//fprintf(stderr, "%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs);
//metal_printf("%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs);
return ctx->buffers[i].metal;
}
}
fprintf(stderr, "%s: error: buffer is nil\n", __func__);
metal_printf("%s: error: buffer is nil\n", __func__);
return nil;
}
@ -306,7 +369,7 @@ bool ggml_metal_add_buffer(
size_t size,
size_t max_size) {
if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) {
fprintf(stderr, "%s: too many buffers\n", __func__);
metal_printf("%s: too many buffers\n", __func__);
return false;
}
@ -316,7 +379,7 @@ bool ggml_metal_add_buffer(
const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data;
if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) {
fprintf(stderr, "%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name);
metal_printf("%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name);
return false;
}
}
@ -337,11 +400,11 @@ bool ggml_metal_add_buffer(
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
if (ctx->buffers[ctx->n_buffers].metal == nil) {
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
metal_printf("%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
return false;
}
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0);
metal_printf("%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0);
++ctx->n_buffers;
} else {
@ -361,27 +424,27 @@ bool ggml_metal_add_buffer(
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
if (ctx->buffers[ctx->n_buffers].metal == nil) {
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
metal_printf("%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
return false;
}
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
metal_printf("%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
if (i + size_step < size) {
fprintf(stderr, "\n");
metal_printf("\n");
}
++ctx->n_buffers;
}
}
fprintf(stderr, ", (%8.2f / %8.2f)",
metal_printf(", (%8.2f / %8.2f)",
ctx->device.currentAllocatedSize / 1024.0 / 1024.0,
ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) {
fprintf(stderr, ", warning: current allocated size is greater than the recommended max working set size\n");
metal_printf(", warning: current allocated size is greater than the recommended max working set size\n");
} else {
fprintf(stderr, "\n");
metal_printf("\n");
}
}
@ -391,8 +454,6 @@ bool ggml_metal_add_buffer(
void ggml_metal_set_tensor(
struct ggml_metal_context * ctx,
struct ggml_tensor * t) {
metal_printf("%s: set input for tensor '%s'\n", __func__, t->name);
size_t offs;
id<MTLBuffer> id_dst = ggml_metal_get_buffer(ctx, t, &offs);
@ -402,8 +463,6 @@ void ggml_metal_set_tensor(
void ggml_metal_get_tensor(
struct ggml_metal_context * ctx,
struct ggml_tensor * t) {
metal_printf("%s: extract results for tensor '%s'\n", __func__, t->name);
size_t offs;
id<MTLBuffer> id_src = ggml_metal_get_buffer(ctx, t, &offs);
@ -498,14 +557,14 @@ void ggml_metal_graph_find_concurrency(
}
if (ctx->concur_list_len > GGML_MAX_CONCUR) {
fprintf(stderr, "%s: too many elements for metal ctx->concur_list!\n", __func__);
metal_printf("%s: too many elements for metal ctx->concur_list!\n", __func__);
}
}
void ggml_metal_graph_compute(
struct ggml_metal_context * ctx,
struct ggml_cgraph * gf) {
metal_printf("%s: evaluating graph\n", __func__);
@autoreleasepool {
// if there is ctx->concur_list, dispatch concurrently
// else fallback to serial dispatch
@ -521,29 +580,25 @@ void ggml_metal_graph_compute(
const int n_cb = ctx->n_cb;
NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb];
for (int i = 0; i < n_cb; ++i) {
command_buffers[i] = [ctx->queue commandBuffer];
ctx->command_buffers[i] = [ctx->queue commandBuffer];
// enqueue the command buffers in order to specify their execution order
[command_buffers[i] enqueue];
}
[ctx->command_buffers[i] enqueue];
// TODO: is this the best way to start threads?
dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
ctx->command_encoders[i] = [ctx->command_buffers[i] computeCommandEncoderWithDescriptor: edesc];
}
for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb;
dispatch_async(queue, ^{
dispatch_async(ctx->d_queue, ^{
size_t offs_src0 = 0;
size_t offs_src1 = 0;
size_t offs_dst = 0;
id<MTLCommandBuffer> command_buffer = command_buffers[cb_idx];
id<MTLComputeCommandEncoder> encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
id<MTLCommandBuffer> command_buffer = ctx->command_buffers[cb_idx];
id<MTLComputeCommandEncoder> encoder = ctx->command_encoders[cb_idx];
const int node_start = (cb_idx + 0) * n_nodes_per_cb;
const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes);
@ -556,7 +611,7 @@ void ggml_metal_graph_compute(
continue;
}
metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
//metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
struct ggml_tensor * src0 = gf->nodes[i]->src[0];
struct ggml_tensor * src1 = gf->nodes[i]->src[1];
@ -704,7 +759,7 @@ void ggml_metal_graph_compute(
} break;
default:
{
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
metal_printf("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
GGML_ASSERT(false);
}
} break;
@ -863,7 +918,7 @@ void ggml_metal_graph_compute(
} break;
default:
{
fprintf(stderr, "Asserting on type %d\n",(int)src0t);
metal_printf("Asserting on type %d\n",(int)src0t);
GGML_ASSERT(false && "not implemented");
}
};
@ -1101,7 +1156,7 @@ void ggml_metal_graph_compute(
} break;
default:
{
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
metal_printf("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
GGML_ASSERT(false);
}
}
@ -1117,17 +1172,19 @@ void ggml_metal_graph_compute(
}
// wait for all threads to finish
dispatch_barrier_sync(queue, ^{});
[command_buffers[n_cb - 1] waitUntilCompleted];
dispatch_barrier_sync(ctx->d_queue, ^{});
// check status of command buffers
// needed to detect if the device ran out-of-memory for example (#1881)
for (int i = 0; i < n_cb; i++) {
MTLCommandBufferStatus status = (MTLCommandBufferStatus) [command_buffers[i] status];
[ctx->command_buffers[i] waitUntilCompleted];
MTLCommandBufferStatus status = (MTLCommandBufferStatus) [ctx->command_buffers[i] status];
if (status != MTLCommandBufferStatusCompleted) {
fprintf(stderr, "%s: command buffer %d failed with status %lu\n", __func__, i, status);
metal_printf("%s: command buffer %d failed with status %lu\n", __func__, i, status);
GGML_ASSERT(false);
}
}
}
}

754
ggml.c
View file

File diff suppressed because it is too large Load diff

64
ggml.h
View file

@ -130,13 +130,16 @@
// The data of the tensor is accessed via the "data" pointer. For example:
//
// {
// struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 2, 3);
// const int nx = 2;
// const int ny = 3;
//
// // a[2, 1] = 1.0f;
// *(float *) ((char *) a->data + 2*a->nb[1] + 1*a->nb[0]) = 1.0f;
// struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, ny);
//
// // a[0, 2] = 2.0f;
// *(float *) ((char *) a->data + 0*a->nb[1] + 2*a->nb[0]) = 2.0f;
// for (int y = 0; y < ny; y++) {
// for (int x = 0; x < nx; x++) {
// *(float *) ((char *) a->data + y*a->nb[1] + x*a->nb[0]) = x + y;
// }
// }
//
// ...
// }
@ -211,12 +214,17 @@
#define GGML_MAX_OP_PARAMS 32
#define GGML_DEFAULT_N_THREADS 4
#if UINTPTR_MAX == 0xFFFFFFFF
#define GGML_MEM_ALIGN 4
#else
#define GGML_MEM_ALIGN 16
#endif
#define GGML_EXIT_SUCCESS 0
#define GGML_EXIT_ABORTED 1
#define GGUF_MAGIC 0x46554747 // "GGUF"
#define GGUF_VERSION 1
#define GGUF_VERSION 2
#define GGUF_DEFAULT_ALIGNMENT 32
@ -471,6 +479,9 @@ extern "C" {
int64_t perf_cycles;
int64_t perf_time_us;
struct ggml_tensor * view_src;
size_t view_offs;
void * data;
char name[GGML_MAX_NAME];
@ -653,7 +664,7 @@ extern "C" {
GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, const struct ggml_tensor * src);
GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src);
GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
@ -944,11 +955,11 @@ extern "C" {
// a - x
// b - dy
// TODO: update with configurable eps
GGML_API struct ggml_tensor * ggml_rms_norm_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * b,
float eps);
// A: n columns, m rows
// B: n columns, p rows (i.e. we transpose it internally)
@ -1604,7 +1615,8 @@ extern "C" {
struct ggml_tensor * tensor);
GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);
GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
@ -1669,6 +1681,8 @@ extern "C" {
GGML_LINESEARCH_INVALID_PARAMETERS,
};
typedef void (*ggml_opt_callback)(void * data, float * sched);
// optimization parameters
//
// see ggml.c (ggml_opt_default_params) for default values
@ -1704,12 +1718,14 @@ extern "C" {
float sched; // schedule multiplier (fixed, decay or warmup)
float decay; // weight decay for AdamW, use 0.0f to disable
int decay_min_ndim; // minimum number of tensor dimension to apply weight decay
float alpha; // learning rate
float beta1;
float beta2;
float eps; // epsilon for numerical stability
float eps_f; // epsilon for convergence test
float eps_g; // epsilon for convergence test
float gclip; // gradient clipping
} adam;
// LBFGS parameters
@ -1737,14 +1753,12 @@ extern "C" {
bool just_initialized;
float loss_before;
float loss_after;
struct {
struct ggml_tensor * x; // view of the parameters
struct ggml_tensor * g1; // gradient
struct ggml_tensor * g2; // gradient squared
struct ggml_tensor * m; // first moment
struct ggml_tensor * v; // second moment
struct ggml_tensor * mh; // first moment hat
struct ggml_tensor * vh; // second moment hat
struct ggml_tensor * pf; // past function values
float fx_best;
float fx_prev;
@ -1781,10 +1795,10 @@ extern "C" {
// initialize optimizer context
GGML_API void ggml_opt_init(
struct ggml_context * ctx,
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_opt_params params,
int64_t nx);
struct ggml_opt_params params,
int64_t nx);
// continue optimizing the function defined by the tensor f
GGML_API enum ggml_opt_result ggml_opt_resume(
@ -1798,7 +1812,9 @@ extern "C" {
struct ggml_opt_context * opt,
struct ggml_tensor * f,
struct ggml_cgraph * gf,
struct ggml_cgraph * gb);
struct ggml_cgraph * gb,
ggml_opt_callback callback,
void * callback_data);
//
// quantization
@ -1827,6 +1843,9 @@ extern "C" {
GGUF_TYPE_BOOL = 7,
GGUF_TYPE_STRING = 8,
GGUF_TYPE_ARRAY = 9,
GGUF_TYPE_UINT64 = 10,
GGUF_TYPE_INT64 = 11,
GGUF_TYPE_FLOAT64 = 12,
GGUF_TYPE_COUNT, // marks the end of the enum
};
@ -1867,6 +1886,9 @@ extern "C" {
GGML_API uint32_t gguf_get_val_u32 (struct gguf_context * ctx, int i);
GGML_API int32_t gguf_get_val_i32 (struct gguf_context * ctx, int i);
GGML_API float gguf_get_val_f32 (struct gguf_context * ctx, int i);
GGML_API uint64_t gguf_get_val_u64 (struct gguf_context * ctx, int i);
GGML_API int64_t gguf_get_val_i64 (struct gguf_context * ctx, int i);
GGML_API double gguf_get_val_f64 (struct gguf_context * ctx, int i);
GGML_API bool gguf_get_val_bool(struct gguf_context * ctx, int i);
GGML_API const char * gguf_get_val_str (struct gguf_context * ctx, int i);
GGML_API int gguf_get_arr_n (struct gguf_context * ctx, int i);
@ -1886,6 +1908,9 @@ extern "C" {
GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);
GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t val);
GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float val);
GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val);
GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t val);
GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double val);
GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val);
GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n);
@ -1944,6 +1969,7 @@ extern "C" {
GGML_API int ggml_cpu_has_clblast (void);
GGML_API int ggml_cpu_has_gpublas (void);
GGML_API int ggml_cpu_has_sse3 (void);
GGML_API int ggml_cpu_has_ssse3 (void);
GGML_API int ggml_cpu_has_vsx (void);
//

23
gguf-py/README.md
View file

@ -27,8 +27,25 @@ In this case, upgrade Pip to the latest:
pip install --upgrade pip
```
## Publishing
To publish the package, you need to have `twine` and `build` installed:
## Automatic publishing with CI
There's a GitHub workflow to make a release automatically upon creation of tags in a specified format.
1. Bump the version in `pyproject.toml`.
2. Create a tag named `gguf-vx.x.x` where `x.x.x` is the semantic version number.
```sh
git tag -a gguf-v1.0.0 -m "Version 1.0 release"
```
3. Push the tags.
```sh
git push origin --tags
```
## Manual publishing
If you want to publish the package manually for any reason, you need to have `twine` and `build` installed:
```sh
pip install build twine
@ -36,7 +53,7 @@ pip install build twine
Then, folow these steps to release a new version:
1. Update the version in `pyproject.toml`.
1. Bump the version in `pyproject.toml`.
2. Build the package:
```sh

575
gguf-py/gguf/gguf.py
View file

@ -4,16 +4,20 @@ import sys
import struct
import tempfile
import numpy as np
import json
import os
from pathlib import Path
from enum import IntEnum, auto
from typing import Any, IO, List, Optional
from io import BufferedWriter
from typing import Any, BinaryIO, Callable, IO, Dict, List, Optional, Sequence, Tuple, Union
#
# constants
#
GGUF_MAGIC = 0x46554747
GGUF_VERSION = 1
GGUF_VERSION = 2
GGUF_DEFAULT_ALIGNMENT = 32
# general
@ -71,35 +75,35 @@ KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world"
class MODEL_ARCH(IntEnum):
LLAMA = auto()
FALCON = auto()
GPT2 = auto()
GPTJ = auto()
GPTNEOX = auto()
MPT = auto()
LLAMA : int = auto()
FALCON : int = auto()
GPT2 : int = auto()
GPTJ : int = auto()
GPTNEOX: int = auto()
MPT : int = auto()
class MODEL_TENSOR(IntEnum):
TOKEN_EMBD = auto()
POS_EMBD = auto()
OUTPUT = auto()
OUTPUT_NORM = auto()
ROPE_FREQS = auto()
ATTN_Q = auto()
ATTN_K = auto()
ATTN_V = auto()
ATTN_QKV = auto()
ATTN_OUT = auto()
ATTN_NORM = auto()
ATTN_NORM_2 = auto()
ATTN_ROT_EMBD = auto()
FFN_GATE = auto()
FFN_DOWN = auto()
FFN_UP = auto()
FFN_NORM = auto()
TOKEN_EMBD : int = auto()
POS_EMBD : int = auto()
OUTPUT : int = auto()
OUTPUT_NORM : int = auto()
ROPE_FREQS : int = auto()
ATTN_Q : int = auto()
ATTN_K : int = auto()
ATTN_V : int = auto()
ATTN_QKV : int = auto()
ATTN_OUT : int = auto()
ATTN_NORM : int = auto()
ATTN_NORM_2 : int = auto()
ATTN_ROT_EMBD: int = auto()
FFN_GATE : int = auto()
FFN_DOWN : int = auto()
FFN_UP : int = auto()
FFN_NORM : int = auto()
MODEL_ARCH_NAMES = {
MODEL_ARCH_NAMES: Dict[MODEL_ARCH, str] = {
MODEL_ARCH.LLAMA: "llama",
MODEL_ARCH.FALCON: "falcon",
MODEL_ARCH.GPT2: "gpt2",
@ -108,7 +112,7 @@ MODEL_ARCH_NAMES = {
MODEL_ARCH.MPT: "mpt",
}
MODEL_TENSOR_NAMES = {
MODEL_TENSOR_NAMES: Dict[MODEL_ARCH, Dict[MODEL_TENSOR, str]] = {
MODEL_ARCH.LLAMA: {
MODEL_TENSOR.TOKEN_EMBD: "token_embd",
MODEL_TENSOR.OUTPUT_NORM: "output_norm",
@ -154,7 +158,7 @@ MODEL_TENSOR_NAMES = {
}
# tensors that will not be serialized
MODEL_TENSOR_SKIP = {
MODEL_TENSOR_SKIP: Dict[MODEL_ARCH, List[MODEL_TENSOR]] = {
MODEL_ARCH.LLAMA: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
@ -162,167 +166,198 @@ MODEL_TENSOR_SKIP = {
}
# TODO: the following helper functions should be removed
# instead, get_tensor_name_map should return tuples of (name, MODEL_TENSOR)
# however, my Python is very bad, and I couldn't figure out how to do this, hence these functions
# REMOVE
def should_skip_tensor_TMP(arch: MODEL_ARCH, n_blocks: int, name: str) -> bool:
for skip in MODEL_TENSOR_SKIP.get(arch, []):
for i in range(n_blocks):
if name == MODEL_TENSOR_NAMES[arch][skip].format(bid=i):
return True
class TensorNameMap:
mappings_cfg: Dict[MODEL_TENSOR, Tuple[str, ...]] = {
# Token embeddings
MODEL_TENSOR.TOKEN_EMBD: (
"gpt_neox.embed_in", # gptneox
"transformer.wte", # gpt2 mpt
"transformer.word_embeddings", # falcon
"model.embed_tokens", # llama-hf
"tok_embeddings", # llama-pth
),
return False
# Position embeddings
MODEL_TENSOR.POS_EMBD: (
"transformer.wpe", # gpt2
),
# Output
MODEL_TENSOR.OUTPUT: (
"embed_out", # gptneox
"lm_head", # gpt2 mpt falcon llama-hf
"output", # llama-pth
),
def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> dict:
tensor_map = {}
# Output norm
MODEL_TENSOR.OUTPUT_NORM: (
"gpt_neox.final_layer_norm", # gptneox
"transformer.ln_f", # gpt2 falcon
"model.norm", # llama-hf
"norm", # llama-pth
),
# Token embeddings
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.TOKEN_EMBD, None)
# Rope frequencies
MODEL_TENSOR.ROPE_FREQS: (
"rope.freqs", # llama-pth
),
}
tensor_map["gpt_neox.embed_in"] = mapped_to # gptneox
tensor_map["transformer.wte"] = mapped_to # gpt2 mpt
tensor_map["transformer.word_embeddings"] = mapped_to # falcon
tensor_map["model.embed_tokens"] = mapped_to # llama-hf
tensor_map["tok_embeddings"] = mapped_to # llama-pth
# Position embeddings
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.POS_EMBD, None)
tensor_map["transformer.wpe"] = mapped_to # gpt2
# Output
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT, None)
tensor_map["embed_out"] = mapped_to # gptneox
tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
tensor_map["output"] = mapped_to # llama-pth
# Output norm
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT_NORM, None)
tensor_map["gpt_neox.final_layer_norm"] = mapped_to # gptneox
tensor_map["transformer.ln_f"] = mapped_to # gpt2 falcon
tensor_map["transformer.norm_f"] = mapped_to # mpt
tensor_map["model.norm"] = mapped_to # llama-hf
tensor_map["norm"] = mapped_to # llama-pth
# Rope frequencies
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ROPE_FREQS, None)
tensor_map["rope.freqs"] = mapped_to # llama-pth
# Attention and feed-forward blocks
for i in range(0, n_blocks):
block_mappings_cfg: Dict[MODEL_TENSOR, Tuple[str, ...]] = {
# Attention norm
# TODO: is there are simpler way to write these 2 lines in Python?
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM, None)
mapped_to = mapped_to.format(bid=i) if mapped_to else None
tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".ln_1"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".norm_1"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".input_layernorm"] = mapped_to # falcon7b
tensor_map["transformer.h."+str(i)+".ln_mlp"] = mapped_to # falcon40b
tensor_map["model.layers."+str(i)+".input_layernorm"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention_norm"] = mapped_to # llama-pth
MODEL_TENSOR.ATTN_NORM: (
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
"transformer.h.{bid}.ln_1", # gpt2
"transformer.blocks.{bid}.norm_1", # mpt
"transformer.h.{bid}.input_layernorm", # falcon7b
"transformer.h.{bid}.ln_mlp", # falcon40b
"model.layers.{bid}.input_layernorm", # llama-hf
"layers.{bid}.attention_norm", # llama-pth
),
# Attention norm 2
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM_2, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["transformer.h."+str(i)+".ln_attn"] = mapped_to # falcon40b
MODEL_TENSOR.ATTN_NORM_2: (
"transformer.h.{bid}.ln_attn", # falcon40b
),
# Attention query-key-value
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_QKV, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".attn.c_attn"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to # falcon
MODEL_TENSOR.ATTN_QKV: (
"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
"transformer.h.{bid}.attn.c_attn", # gpt2
"transformer.blocks.{bid}.attn.Wqkv", # mpt
"transformer.h.{bid}.self_attention.query_key_value", # falcon
),
# Attention query
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_Q, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wq"] = mapped_to # llama-pth
MODEL_TENSOR.ATTN_Q: (
"model.layers.{bid}.self_attn.q_proj", # llama-hf
"layers.{bid}.attention.wq", # llama-pth
),
# Attention key
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_K, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wk"] = mapped_to # llama-pth
MODEL_TENSOR.ATTN_K: (
"model.layers.{bid}.self_attn.k_proj", # llama-hf
"layers.{bid}.attention.wk", # llama-pth
),
# Attention value
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_V, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wv"] = mapped_to # llama-pth
MODEL_TENSOR.ATTN_V: (
"model.layers.{bid}.self_attn.v_proj", # llama-hf
"layers.{bid}.attention.wv", # llama-pth
),
# Attention output
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_OUT, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".attention.dense"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".attn.c_proj"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".attn.out_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".self_attention.dense"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".self_attn.o_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wo"] = mapped_to # llama-pth
MODEL_TENSOR.ATTN_OUT: (
"gpt_neox.layers.{bid}.attention.dense", # gptneox
"transformer.h.{bid}.attn.c_proj", # gpt2
"transformer.blocks.{bid}.attn.out_proj", # mpt
"transformer.h.{bid}.self_attention.dense", # falcon
"model.layers.{bid}.self_attn.o_proj", # llama-hf
"layers.{bid}.attention.wo", # llama-pth
),
# Rotary embeddings
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_ROT_EMBD, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.rotary_emb.inv_freq"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.inner_attention.rope.freqs"] = mapped_to # llama-pth
MODEL_TENSOR.ATTN_ROT_EMBD: (
"model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf
"layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth
),
# Feed-forward norm
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_NORM, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".ln_2"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".norm_2"] = mapped_to # mpt
tensor_map["model.layers."+str(i)+".post_attention_layernorm"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".ffn_norm"] = mapped_to # llama-pth
MODEL_TENSOR.FFN_NORM: (
"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
"transformer.h.{bid}.ln_2", # gpt2
"transformer.blocks.{bid}.norm_2", # mpt
"model.layers.{bid}.post_attention_layernorm", # llama-hf
"layers.{bid}.ffn_norm", # llama-pth
),
# Feed-forward up
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_UP, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".mlp.c_fc"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".mlp.up_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w3"] = mapped_to # llama-pth
MODEL_TENSOR.FFN_UP: (
"gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox
"transformer.h.{bid}.mlp.c_fc", # gpt2
"transformer.blocks.{bid}.ffn.up_proj", # mpt
"transformer.h.{bid}.mlp.dense_h_to_4h", # falcon
"model.layers.{bid}.mlp.up_proj", # llama-hf
"layers.{bid}.feed_forward.w3", # llama-pth
),
# Feed-forward gate
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_GATE, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w1"] = mapped_to # llama-pth
MODEL_TENSOR.FFN_GATE: (
"model.layers.{bid}.mlp.gate_proj", # llama-hf
"layers.{bid}.feed_forward.w1", # llama-pth
),
# Feed-forward down
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_DOWN, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
MODEL_TENSOR.FFN_DOWN: (
"gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
"transformer.h.{bid}.mlp.c_proj", # gpt2
"transformer.blocks.{bid}.ffn.down_proj", # mpt
"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
"model.layers.{bid}.mlp.down_proj", # llama-hf
"layers.{bid}.feed_forward.w2", # llama-pth
),
}
tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".mlp.c_proj"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".mlp.down_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w2"] = mapped_to # llama-pth
mapping: Dict[str, Tuple[MODEL_TENSOR, str]]
return tensor_map
tensor_names: Dict[MODEL_TENSOR, str]
def __init__(self, arch: MODEL_ARCH, n_blocks: int):
mapping = self.mapping = {}
tensor_names = self.tensor_names = MODEL_TENSOR_NAMES[arch]
for tensor, keys in self.mappings_cfg.items():
tensor_name = tensor_names.get(tensor)
if tensor_name is None:
continue
for key in keys:
mapping[key] = (tensor, tensor_name)
for bid in range(n_blocks):
for tensor, keys in self.block_mappings_cfg.items():
tensor_name = tensor_names.get(tensor)
if tensor_name is None:
continue
tensor_name = tensor_name.format(bid = bid)
for key in keys:
key = key.format(bid = bid)
mapping[key] = (tensor, tensor_name)
def get_type_and_name(self, key: str, try_suffixes: Sequence[str]) -> Optional[Tuple[MODEL_TENSOR, str]]:
result = self.mapping.get(key)
if result is not None:
return result
for suffix in try_suffixes:
if key.endswith(suffix):
result = self.mapping.get(key[:-len(suffix)])
if result is not None:
return (result[0], result[1] + suffix)
return None
def get_name(self, key: str, try_suffixes: Sequence[str]) -> Optional[str]:
result = self.get_type_and_name(key, try_suffixes = try_suffixes)
if result is None:
return None
return result[1]
def get_type(self, key: str, try_suffixes: Sequence[str]) -> Optional[MODEL_TENSOR]:
result = self.get_type_and_name(key, try_suffixes = try_suffixes)
if result is None:
return None
return result[0]
def __getitem__(self, key: str) -> str:
try:
return self.mapping[key][1]
except KeyError:
raise KeyError(key)
def __contains__(self, key: str) -> bool:
return key in self.mapping
def __repr__(self) -> str:
return repr(self.mapping)
def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap:
return TensorNameMap(arch, n_blocks)
class TokenType(IntEnum):
NORMAL = 1
@ -365,6 +400,9 @@ class GGUFValueType(IntEnum):
BOOL = 7
STRING = 8
ARRAY = 9
UINT64 = 10
INT64 = 11
FLOAT64 = 12
@staticmethod
def get_type(val):
@ -378,21 +416,28 @@ class GGUFValueType(IntEnum):
return GGUFValueType.BOOL
elif isinstance(val, int):
return GGUFValueType.INT32
# TODO: need help with 64-bit types in Python
else:
print("Unknown type: "+str(type(val)))
sys.exit()
class GGUFWriter:
def __init__(self, path: str, arch: str, use_temp_file = True):
fout: BufferedWriter
arch: str
offset_tensor = 0
data_alignment = GGUF_DEFAULT_ALIGNMENT
kv_data = b""
kv_data_count = 0
ti_data = b""
ti_data_count = 0
use_temp_file: bool
temp_file: Optional[tempfile.SpooledTemporaryFile[bytes]] = None
tensors: List[Tuple[np.ndarray[Any, Any], int]]
def __init__(self, path: Union[os.PathLike[str], str], arch: str, use_temp_file = True):
self.fout = open(path, "wb")
self.arch = arch
self.offset_tensor = 0
self.data_alignment = GGUF_DEFAULT_ALIGNMENT
self.kv_data = b""
self.kv_data_count = 0
self.ti_data = b""
self.ti_data_count = 0
self.add_architecture()
self.use_temp_file = use_temp_file
self.tensors = []
@ -400,8 +445,8 @@ class GGUFWriter:
def write_header_to_file(self):
self.fout.write(struct.pack("<I", GGUF_MAGIC))
self.fout.write(struct.pack("<I", GGUF_VERSION))
self.fout.write(struct.pack("<I", self.ti_data_count))
self.fout.write(struct.pack("<I", self.kv_data_count))
self.fout.write(struct.pack("<Q", self.ti_data_count))
self.fout.write(struct.pack("<Q", self.kv_data_count))
self.flush()
# print("tensors " + str(self.ti_data_count) + " kv " + str(self.kv_data_count))
@ -444,6 +489,18 @@ class GGUFWriter:
self.add_key(key)
self.add_val(val, GGUFValueType.FLOAT32)
def add_uint64(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.UINT64)
def add_int64(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.INT64)
def add_float64(self, key: str, val: float):
self.add_key(key)
self.add_val(val, GGUFValueType.FLOAT64)
def add_bool(self, key: str, val: bool):
self.add_key(key)
self.add_val(val, GGUFValueType.BOOL)
@ -454,14 +511,27 @@ class GGUFWriter:
self.add_key(key)
self.add_val(val, GGUFValueType.STRING)
def add_array(self, key: str, val: list):
if not isinstance(val, list):
raise ValueError("Value must be a list for array type")
def add_array(self, key: str, val: Sequence[Any]):
if not isinstance(val, Sequence):
raise ValueError("Value must be a sequence for array type")
self.add_key(key)
self.add_val(val, GGUFValueType.ARRAY)
def add_val(self: str, val: Any, vtype: GGUFValueType = None, add_vtype: bool = True):
_simple_value_packing = {
GGUFValueType.UINT8: "<B",
GGUFValueType.INT8: "<b",
GGUFValueType.UINT16: "<H",
GGUFValueType.INT16: "<h",
GGUFValueType.UINT32: "<I",
GGUFValueType.INT32: "<i",
GGUFValueType.FLOAT32: "<f",
GGUFValueType.UINT64: "<Q",
GGUFValueType.INT64: "<q",
GGUFValueType.FLOAT64: "<d",
GGUFValueType.BOOL: "?" ,
}
def add_val(self, val: Any, vtype: Optional[GGUFValueType] = None, add_vtype: bool = True):
if vtype is None:
vtype = GGUFValueType.get_type(val)
@ -469,50 +539,38 @@ class GGUFWriter:
self.kv_data += struct.pack("<I", vtype)
self.kv_data_count += 1
if vtype == GGUFValueType.UINT8:
self.kv_data += struct.pack("<B", val)
elif vtype == GGUFValueType.INT8:
self.kv_data += struct.pack("<b", val)
elif vtype == GGUFValueType.UINT16:
self.kv_data += struct.pack("<H", val)
elif vtype == GGUFValueType.INT16:
self.kv_data += struct.pack("<h", val)
elif vtype == GGUFValueType.UINT32:
self.kv_data += struct.pack("<I", val)
elif vtype == GGUFValueType.INT32:
self.kv_data += struct.pack("<i", val)
elif vtype == GGUFValueType.FLOAT32:
self.kv_data += struct.pack("<f", val)
elif vtype == GGUFValueType.BOOL:
self.kv_data += struct.pack("?", val)
pack_fmt = self._simple_value_packing.get(vtype)
if pack_fmt is not None:
self.kv_data += struct.pack(pack_fmt, val)
elif vtype == GGUFValueType.STRING:
encoded_val = val.encode("utf8") if isinstance(val, str) else val
self.kv_data += struct.pack("<I", len(encoded_val))
self.kv_data += struct.pack("<Q", len(encoded_val))
self.kv_data += encoded_val
elif vtype == GGUFValueType.ARRAY:
ltype = set([GGUFValueType.get_type(item) for item in val])
assert len(ltype) == 1, "All items in a GGUF array should be of the same type"
self.kv_data += struct.pack("<I", list(ltype)[0])
self.kv_data += struct.pack("<I", len(val))
elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and len(val) > 0:
ltype = GGUFValueType.get_type(val[0])
if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]):
raise ValueError("All items in a GGUF array should be of the same type")
self.kv_data += struct.pack("<I", ltype)
self.kv_data += struct.pack("<Q", len(val))
for item in val:
self.add_val(item, add_vtype=False)
else:
raise ValueError("Invalid GGUF metadata value type")
raise ValueError("Invalid GGUF metadata value type or value")
@staticmethod
def ggml_pad(x: int, n: int) -> int:
return ((x + n - 1) // n) * n
def add_tensor_info(self, name: str, tensor_shape: np.ndarray, tensor_dtype: np.dtype, tensor_nbytes: int, raw_dtype: Optional[GGMLQuantizationType] = None):
def add_tensor_info(self, name: str, tensor_shape: Sequence[int], tensor_dtype: Union[np.dtype[np.float16], np.dtype[np.float32]], tensor_nbytes: int, raw_dtype: Optional[GGMLQuantizationType] = None):
assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
encoded_name = name.encode("utf8")
self.ti_data += struct.pack("<I", len(encoded_name))
self.ti_data += struct.pack("<Q", len(encoded_name))
self.ti_data += encoded_name
n_dims = len(tensor_shape)
self.ti_data += struct.pack("<I", n_dims)
for i in range(n_dims):
self.ti_data += struct.pack("<I", tensor_shape[n_dims - 1 - i])
self.ti_data += struct.pack("<Q", tensor_shape[n_dims - 1 - i])
if raw_dtype is None:
dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
else:
@ -522,16 +580,18 @@ class GGUFWriter:
self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
self.ti_data_count += 1
def add_tensor(self, name: str, tensor: np.ndarray, raw_shape: Optional[np.ndarray] = None, raw_dtype: Optional[GGMLQuantizationType] = None):
if self.use_temp_file and not hasattr(self, "temp_file"):
self.temp_file = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
self.temp_file.seek(0)
def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Optional[Sequence[int]] = None, raw_dtype: Optional[GGMLQuantizationType] = None):
if self.use_temp_file and self.temp_file is None:
fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
fp.seek(0)
self.temp_file = fp
self.add_tensor_info(name, raw_shape if raw_shape is not None else tensor.shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype)
shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape
self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype)
pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
if not self.use_temp_file:
if self.temp_file is None:
self.tensors.append((tensor, pad))
return
@ -540,25 +600,22 @@ class GGUFWriter:
if pad != 0:
self.temp_file.write(bytes([0] * pad))
def write_tensor_data(self, tensor: np.ndarray):
pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
def write_padding(self, fp: BinaryIO, n: int, align: Optional[int] = None):
pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n
if pad != 0:
self.fout.write(bytes([0] * pad))
fp.write(bytes([0] * pad))
def write_tensor_data(self, tensor: np.ndarray[Any, Any]):
self.write_padding(self.fout, self.fout.tell())
tensor.tofile(self.fout)
pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
if pad != 0:
self.fout.write(bytes([0] * pad))
self.write_padding(self.fout, tensor.nbytes)
def write_tensors_to_file(self):
self.write_ti_data_to_file()
pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
if pad != 0:
self.fout.write(bytes([0] * pad))
self.write_padding(self.fout, self.fout.tell())
if not self.use_temp_file:
if self.temp_file is None:
for (currtensor, currpad) in self.tensors:
currtensor.tofile(self.fout)
if currpad != 0:
@ -632,10 +689,6 @@ class GGUFWriter:
self.add_bool(
KEY_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
def add_tensor_data_layout(self, layout: str):
self.add_string(
KEY_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
def add_head_count(self, count: int):
self.add_uint32(
KEY_ATTENTION_HEAD_COUNT.format(arch=self.arch), count)
@ -673,16 +726,16 @@ class GGUFWriter:
def add_tokenizer_model(self, model: str):
self.add_string(KEY_TOKENIZER_MODEL, model)
def add_token_list(self, tokens: List):
def add_token_list(self, tokens: Union[Sequence[str], Sequence[bytes], Sequence[bytearray]]):
self.add_array(KEY_TOKENIZER_LIST, tokens)
def add_token_merges(self, merges: List):
def add_token_merges(self, merges: Union[Sequence[str], Sequence[bytes], Sequence[bytearray]]):
self.add_array(KEY_TOKENIZER_MERGES, merges)
def add_token_types(self, types: List[int]):
def add_token_types(self, types: Union[Sequence[TokenType], Sequence[int]]):
self.add_array(KEY_TOKENIZER_TOKEN_TYPE, types)
def add_token_scores(self, scores: List[float]):
def add_token_scores(self, scores: Sequence[float]):
self.add_array(KEY_TOKENIZER_SCORES, scores)
def add_bos_token_id(self, id: int):
@ -701,6 +754,84 @@ class GGUFWriter:
self.add_uint32(KEY_TOKENIZER_PAD_ID, id)
class SpecialVocab:
load_merges: bool = False
merges: List[str] = []
special_token_types: Tuple[str, ...] = tuple(('bos', 'eos', 'unk', 'sep', 'pad'))
special_token_ids: Dict[str, int] = {}
def __init__(self, path: Path, load_merges: bool = False, special_token_types: Optional[Tuple[str, ...]] = None):
self.special_token_ids = {}
self.load_merges = load_merges
if special_token_types is not None:
self.special_token_types = special_token_types
self.load(path)
def load(self, path: Path):
if not self.try_load_from_tokenizer_json(path):
self.try_load_from_config_json(path)
def try_load_from_tokenizer_json(self, path: Path) -> bool:
tokenizer_file = path / 'tokenizer.json'
if not tokenizer_file.is_file():
return False
with open(tokenizer_file, 'r', encoding = 'utf-8') as f:
tokenizer = json.load(f)
if self.load_merges:
merges = tokenizer.get('model', {}).get('merges')
if isinstance(merges, list) and len(merges) > 0 and isinstance(merges[0], str):
self.merges = merges
tokenizer_config_file = path / 'tokenizer_config.json'
added_tokens = tokenizer.get('added_tokens')
if added_tokens is None or not tokenizer_config_file.is_file():
return True
with open(tokenizer_config_file, 'r', encoding = 'utf-8') as f:
tokenizer_config = json.load(f)
for typ in self.special_token_types:
entry = tokenizer_config.get(f'{typ}_token')
if isinstance(entry, str):
tc_content = entry
elif isinstance(entry, dict):
entry_content = entry.get('content')
if not isinstance(entry_content, str):
continue
tc_content = entry_content
else:
continue
for maybe_token_id in (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content):
if isinstance(maybe_token_id, int):
self.special_token_ids[typ] = maybe_token_id
break
return True
def try_load_from_config_json(self, path: Path) -> bool:
config_file = path / 'config.json'
if not config_file.is_file():
return False
with open(config_file, 'r', encoding = 'utf-8') as f:
config = json.load(f)
for typ in self.special_token_types:
maybe_token_id = config.get(f'{typ}_token_id')
if isinstance(maybe_token_id, int):
self.special_token_ids[typ] = maybe_token_id
return True
def add_to_gguf(self, gw: GGUFWriter):
if len(self.merges) > 0:
print(f'gguf: Adding {len(self.merges)} merge(s).')
gw.add_token_merges(self.merges)
for typ, tokid in self.special_token_ids.items():
handler: Optional[Callable[[int], None]] = getattr(gw, f'add_{typ}_token_id', None)
if handler is None:
print(f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping')
continue
print(f'gguf: Setting special token type {typ} to {tokid}')
handler(tokid)
def __repr__(self):
return f'<SpecialVocab with {len(self.merges)} merges and special tokens {self.special_token_ids if self.special_token_ids else "unset"}>'
# Example usage:
if __name__ == "__main__":
# Example usage with a file

0
gguf-py/gguf/py.typed Normal file
View file

1
gguf-py/pyproject.toml
View file

@ -5,6 +5,7 @@ description = "Write ML models in GGUF for GGML"
authors = ["GGML <ggml@ggml.ai>"]
packages = [
{include = "gguf"},
{include = "gguf/py.typed"},
]
readme = "README.md"
homepage = "https://ggml.ai"

4
k_quants.c
View file

@ -2694,13 +2694,13 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
const __m256i q8l = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
__m256i p16l = _mm256_maddubs_epi16(q4l, q8l);
p16l = _mm256_madd_epi16(scale_l, p16l);
sumi = _mm256_add_epi32(sumi, p16l);
const __m256i q8h = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
__m256i p16h = _mm256_maddubs_epi16(q4h, q8h);
p16h = _mm256_madd_epi16(scale_h, p16h);
sumi = _mm256_add_epi32(sumi, p16h);
const __m256i sumj = _mm256_add_epi32(p16l, p16h);
sumi = _mm256_add_epi32(sumi, sumj);
}
__m256 vd = _mm256_set1_ps(d);

252
llama.cpp
View file

@ -1,9 +1,6 @@
// Defines fileno on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#include <cstddef>
#include <cstdint>
#include <cstdio>
#endif
#include "llama.h"
@ -62,6 +59,9 @@
#include <cinttypes>
#include <climits>
#include <cstdarg>
#include <cstddef>
#include <cstdint>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
@ -114,12 +114,17 @@ static size_t utf8_len(char src) {
}
void replace_all(std::string & s, const std::string & search, const std::string & replace) {
for (size_t pos = 0; ; pos += replace.length()) {
pos = s.find(search, pos);
if (pos == std::string::npos) break;
s.erase(pos, search.length());
s.insert(pos, replace);
std::string result;
for (size_t pos = 0; ; pos += search.length()) {
auto new_pos = s.find(search, pos);
if (new_pos == std::string::npos) {
result += s.substr(pos, s.size() - pos);
break;
}
result += s.substr(pos, new_pos - pos) + replace;
pos = new_pos;
}
s = std::move(result);
}
static void zeros(std::ofstream & file, size_t n) {
@ -796,12 +801,12 @@ static void llama_nop(struct ggml_tensor * tensor) { // don't offload by default
(void) tensor;
}
static std::string llama_token_to_text(const struct llama_context * ctx, llama_token token) {
static std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
std::vector<char> result(8, 0);
const int n_tokens = llama_token_to_str(ctx, token, result.data(), result.size());
const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size());
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_token_to_str(ctx, token, result.data(), result.size());
int check = llama_token_to_piece(ctx, token, result.data(), result.size());
GGML_ASSERT(check == -n_tokens);
} else {
result.resize(n_tokens);
@ -955,10 +960,10 @@ struct llama_vocab {
id linefeed_id = 13;
int find_bpe_rank(std::string token_left, std::string token_right) const {
replace_all(token_left, " ", "Ġ");
replace_all(token_left, "\n", "Ċ");
replace_all(token_right, " ", "Ġ");
replace_all(token_right, "\n", "Ċ");
replace_all(token_left, " ", "\u0120");
replace_all(token_left, "\n", "\u010A");
replace_all(token_right, " ", "\u0120");
replace_all(token_right, "\n", "\u010A");
auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
if (it == bpe_ranks.end()) {
@ -1144,11 +1149,13 @@ static bool llama_kv_cache_init(
enum llama_fver {
GGUF_FILE_VERSION_V1 = 1,
GGUF_FILE_VERSION_V2 = 2,
};
static const char * llama_file_version_name(llama_fver version) {
switch (version) {
case GGUF_FILE_VERSION_V1: return "GGUF V1 (latest)";
case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)";
case GGUF_FILE_VERSION_V2: return "GGUF V2 (latest)";
}
return "unknown";
@ -1635,7 +1642,8 @@ static void llm_load_hparams(
}
// TODO: This should probably be in llama.h
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos, bool escape);
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos);
static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);
static void llm_load_vocab(
llama_model_loader & ml,
@ -1737,7 +1745,11 @@ static void llm_load_vocab(
}
// determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false, false)[0];
if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
} else {
vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false)[0];
}
// special tokens
GGUF_GET_KEY(ctx, vocab.special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID));
@ -2660,18 +2672,20 @@ static struct ggml_cgraph * llm_build_falcon(
const size_t wsize = ggml_type_size(cur->type);
struct ggml_tensor * tmpq = ggml_view_3d(
// TODO: these 2 ggml_conts are technically not needed, but we add them until CUDA support for
// non-contiguous views is added for the rope operator
struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_3d(
ctx0, cur, n_embd_head, n_head, N,
wsize * n_embd_head,
wsize * n_embd_head * (n_head + 2 * n_head_kv),
0);
0));
offload_func_kq(tmpq);
struct ggml_tensor * tmpk = ggml_view_3d(
struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_3d(
ctx0, cur, n_embd_head, n_head_kv, N,
wsize * n_embd_head,
wsize * n_embd_head * (n_head + 2 * n_head_kv),
wsize * n_embd_head * n_head);
wsize * n_embd_head * n_head));
offload_func_kq(tmpk);
struct ggml_tensor * tmpv = ggml_view_3d(
@ -2856,7 +2870,6 @@ static bool llama_eval_internal(
GGML_ASSERT(n_tokens > 0);
GGML_ASSERT(n_past >= 0);
GGML_ASSERT(n_threads > 0);
// TODO: keep the values of n_batch and n_ctx
// GGML_ASSERT(n_tokens <= n_batch);
// GGML_ASSERT(n_past + n_tokens <= n_ctx);
@ -2867,6 +2880,8 @@ static bool llama_eval_internal(
ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
#endif
GGML_ASSERT(n_threads > 0);
const int N = n_tokens;
const auto & model = lctx.model;
@ -3058,16 +3073,8 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
return vocab.token_to_id.at(buf);
}
static std::string llama_escape_whitespace(const std::string& text) {
std::string result = "\xe2\x96\x81";
for (size_t offs = 0; offs < text.length(); ++offs) {
if (text[offs] == ' ') {
result += "\xe2\x96\x81";
} else {
result += text[offs];
}
}
return result;
static void llama_escape_whitespace(std::string & text) {
replace_all(text, " ", "\xe2\x96\x81");
}
static void llama_unescape_whitespace(std::string & word) {
@ -3236,7 +3243,7 @@ private:
struct llm_bigram_bpe {
struct comparator {
bool operator()(llm_bigram_bpe & l, llm_bigram_bpe & r) {
bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
}
};
@ -3251,7 +3258,7 @@ struct llm_bigram_bpe {
};
struct llm_tokenizer_bpe {
llm_tokenizer_bpe(const llama_vocab & vocab, bool g2ws): vocab(vocab) { flag_g2ws = g2ws; }
llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {}
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
int final_prev_index = -1;
@ -3384,26 +3391,23 @@ private:
}
// probably not 100% correct
// TODO: this is quite slow - how to make it more efficient?
static std::vector<std::string> bpe_gpt2_preprocess(std::string text) {
static std::vector<std::string> bpe_gpt2_preprocess(const std::string & text) {
std::vector<std::string> words;
// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
const std::string pattern = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
const std::regex re(pattern);
std::smatch m;
while (std::regex_search(text, m, re)) {
for (auto x : m) {
words.push_back(x);
}
text = m.suffix();
auto words_begin = std::sregex_iterator(text.begin(), text.end(), re);
auto words_end = std::sregex_iterator();
auto n_words = std::distance(words_begin, words_end);
words.reserve(n_words);
for (auto it = words_begin; it != words_end; ++it) {
words.push_back(it->str());
}
return words;
}
bool flag_g2ws = false;
}
const llama_vocab & vocab;
@ -3413,9 +3417,18 @@ private:
llm_bigram_bpe::queue work_queue;
};
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos, bool escape) {
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos) {
std::vector<llama_vocab::id> output;
// OG tokenizer behavior:
//
// tokenizer.encode('', add_bos=True) returns [1]
// tokenizer.encode('', add_bos=False) returns []
if (bos && vocab.special_bos_id != -1) {
output.push_back(vocab.special_bos_id);
}
if (raw_text.empty()) {
return output;
}
@ -3423,29 +3436,16 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
switch (vocab.type) {
case LLAMA_VOCAB_TYPE_SPM:
{
// without adding this leading whitespace, we do not get the same results as the original tokenizer
raw_text = " " + raw_text;
llm_tokenizer_spm tokenizer(vocab);
if (bos) {
output.push_back(vocab.special_bos_id);
}
std::string text;
if (escape) {
text = llama_escape_whitespace(raw_text);
} else {
text = raw_text;
}
tokenizer.tokenize(text, output);
llama_escape_whitespace(raw_text);
tokenizer.tokenize(raw_text, output);
} break;
case LLAMA_VOCAB_TYPE_BPE:
{
llm_tokenizer_bpe tokenizer(vocab, escape);
if (bos && vocab.special_bos_id != -1) {
output.push_back(vocab.special_bos_id);
}
llm_tokenizer_bpe tokenizer(vocab);
tokenizer.tokenize(raw_text, output);
} break;
};
@ -3940,7 +3940,7 @@ void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array *
// Calculate absolute value of second derivatives
for (size_t i = 0; i < second_derivatives.size(); ++i) {
second_derivatives[i] = abs(second_derivatives[i]);
second_derivatives[i] = std::abs(second_derivatives[i]);
}
// Normalize the second derivatives
@ -4131,16 +4131,16 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
std::vector<llama_grammar_candidate> candidates_grammar;
for (size_t i = 0; i < candidates->size; ++i) {
const llama_token id = candidates->data[i].id;
const std::string text = llama_token_to_text(ctx, id);
const llama_token id = candidates->data[i].id;
const std::string piece = llama_token_to_str(ctx, id);
if (id == eos) {
if (!allow_eos) {
candidates->data[i].logit = -INFINITY;
}
} else if (text.empty() || text[0] == 0) {
} else if (piece.empty() || piece[0] == 0) {
candidates->data[i].logit = -INFINITY;
} else {
candidates_decoded.push_back(decode_utf8(text.c_str(), grammar->partial_utf8));
candidates_decoded.push_back(decode_utf8(piece.c_str(), grammar->partial_utf8));
candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
}
}
@ -4344,10 +4344,10 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
GGML_ASSERT(false);
}
const std::string text = llama_token_to_text(ctx, token);
const std::string piece = llama_token_to_str(ctx, token);
// Note terminating 0 in decoded string
const auto decoded = decode_utf8(text.c_str(), grammar->partial_utf8);
const auto decoded = decode_utf8(piece.c_str(), grammar->partial_utf8);
const auto & code_points = decoded.first;
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
@ -4706,6 +4706,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname_inp, /*use_mmap*/ false));
llama_model model;
llm_load_arch(*ml, model);
llm_load_hparams(*ml, model, 0, 0, 0);
const size_t align = GGUF_DEFAULT_ALIGNMENT;
struct gguf_context * ctx_out = gguf_init_empty();
@ -4731,6 +4735,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
++n_feed_forward_w2;
}
}
if (n_attention_wv != n_feed_forward_w2 || (uint32_t)n_attention_wv != model.hparams.n_layer) {
LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n",
__func__, n_attention_wv, n_feed_forward_w2, model.hparams.n_layer);
}
int i_attention_wv = 0;
int i_feed_forward_w2 = 0;
@ -4807,8 +4815,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
int nx = tensor->ne[0];
int ny = tensor->ne[1];
if (nx % QK_K == 0 && ny % QK_K == 0) {
if (model.arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
new_type = GGML_TYPE_Q8_0;
}
else if (new_type != GGML_TYPE_Q8_0) {
new_type = GGML_TYPE_Q6_K;
}
} else if (name.find("attn_v.weight") != std::string::npos) {
@ -4822,21 +4832,49 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
(i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
if (model.type == MODEL_70B) {
// In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
// 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
// nearly negligible increase in model size by quantizing this tensor with more bits:
if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
}
++i_attention_wv;
} else if (name.find("ffn_down.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
new_type = i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
new_type = i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K
: model.arch != LLM_ARCH_FALCON || use_more_bits(i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q4_K
: GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
new_type = model.arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
if (model.arch == LLM_ARCH_FALCON) {
new_type = i_feed_forward_w2 < 2 ? GGML_TYPE_Q6_K :
use_more_bits(i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
} else {
if (use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
}
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && model.arch != LLM_ARCH_FALCON && i_feed_forward_w2 < 4) {
new_type = GGML_TYPE_Q5_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_feed_forward_w2 < 4) new_type = GGML_TYPE_Q5_K;
++i_feed_forward_w2;
} else if (name.find("attn_output.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
if (model.arch != LLM_ARCH_FALCON) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
}
}
else if (name.find("attn_qkv.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
}
else if (name.find("ffn_gate.weight") != std::string::npos || name.find("ffn_up.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
@ -4851,8 +4889,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) {
int nx = tensor->ne[0];
int ny = tensor->ne[1];
if (nx % QK_K != 0 || ny % QK_K != 0) {
LLAMA_LOG_INFO("\n\nTensor sizes %d x %d are not divisible by %d, required for k-quants.\n",nx,ny,QK_K);
if (nx % QK_K != 0) {
LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for k-quants\n", __func__, nx, ny, QK_K);
convert_incompatible_tensor = true;
}
}
@ -6127,8 +6165,7 @@ int llama_tokenize_with_model(
llama_token * tokens,
int n_max_tokens,
bool add_bos) {
auto escape = llama_vocab_get_type(model->vocab) == LLAMA_VOCAB_TYPE_SPM;
auto res = llama_tokenize_internal(model->vocab, text, add_bos, escape);
auto res = llama_tokenize_internal(model->vocab, text, add_bos);
if (n_max_tokens < (int) res.size()) {
LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
@ -6142,12 +6179,12 @@ int llama_tokenize_with_model(
return res.size();
}
int llama_token_to_str(const struct llama_context * ctx, llama_token token, char * buf, int length) {
return llama_token_to_str_with_model(&ctx->model, token, buf, length);
int llama_token_to_piece(const struct llama_context * ctx, llama_token token, char * buf, int length) {
return llama_token_to_piece_with_model(&ctx->model, token, buf, length);
}
// does not write null-terminator to str
int llama_token_to_str_with_model(const struct llama_model * model, llama_token token, char * buf, int length) {
// does not write null-terminator to buf
int llama_token_to_piece_with_model(const struct llama_model * model, llama_token token, char * buf, int length) {
if (0 <= token && token < llama_model_n_vocab(model)) {
if (llama_is_normal_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
@ -6235,11 +6272,40 @@ const char * llama_print_system_info(void) {
s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | ";
s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | ";
s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | ";
s += "SSSE3 = " + std::to_string(ggml_cpu_has_ssse3()) + " | ";
s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | ";
return s.c_str();
}
void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
fprintf(stream, "\n");
fprintf(stream, "###########\n");
fprintf(stream, "# Timings #\n");
fprintf(stream, "###########\n");
fprintf(stream, "\n");
fprintf(stream, "mst_eval: %.2f # ms / token during generation\n",
1.0e-3 * ctx->t_eval_us / ctx->n_eval);
fprintf(stream, "mst_p_eval: %.2f # ms / token during prompt processing\n",
1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval);
fprintf(stream, "mst_sample: %.2f # ms / token during sampling\n",
1.0e-3 * ctx->t_sample_us / ctx->n_sample);
fprintf(stream, "n_eval: %d # number of tokens generated (excluding the first one)\n", ctx->n_eval);
fprintf(stream, "n_p_eval: %d # number of tokens processed in batches at the beginning\n", ctx->n_p_eval);
fprintf(stream, "n_sample: %d # number of sampled tokens\n", ctx->n_sample);
fprintf(stream, "t_eval_us: %" PRId64 " # total microseconds spent generating tokens\n", ctx->t_eval_us);
fprintf(stream, "t_load_us: %" PRId64 " # total microseconds spent loading the model\n", ctx->t_load_us);
fprintf(stream, "t_p_eval_us: %" PRId64 " # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
fprintf(stream, "t_sample_us: %" PRId64 " # total microseconds spent sampling\n", ctx->t_sample_us);
fprintf(stream, "ts_eval: %.2f # tokens / second during generation\n",
1.0e6 * ctx->n_eval / ctx->t_eval_us);
fprintf(stream, "ts_p_eval: %.2f # tokens / second during prompt processing\n",
1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us);
fprintf(stream, "ts_sample: %.2f # tokens / second during sampling\n",
1.0e6 * ctx->n_sample / ctx->t_sample_us);
}
// For internal test use
const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) {
return ctx->model.tensors_by_name;
@ -6250,10 +6316,6 @@ void llama_log_set(llama_log_callback log_callback, void * user_data) {
g_state.log_callback_user_data = user_data;
}
#if defined(_MSC_VER) && !defined(vsnprintf)
#define vsnprintf _vsnprintf
#endif
static void llama_log_internal_v(llama_log_level level, const char * format, va_list args) {
va_list args_copy;
va_copy(args_copy, args);

15
llama.h
View file

@ -10,6 +10,7 @@
#endif // GGML_USE_CUBLAS
#include <stddef.h>
#include <stdint.h>
#include <stdio.h>
#include <stdbool.h>
#ifdef LLAMA_SHARED
@ -381,15 +382,17 @@ extern "C" {
int n_max_tokens,
bool add_bos);
// Token Id -> String. Uses the vocabulary in the provided context
// Does not write null terminator to the buffer
LLAMA_API int llama_token_to_str(
// Token Id -> Piece.
// Uses the vocabulary in the provided context.
// Does not write null terminator to the buffer.
// User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
LLAMA_API int llama_token_to_piece(
const struct llama_context * ctx,
llama_token token,
char * buf,
int length);
LLAMA_API int llama_token_to_str_with_model(
LLAMA_API int llama_token_to_piece_with_model(
const struct llama_model * model,
llama_token token,
char * buf,
@ -494,7 +497,7 @@ extern "C" {
// Type of pointer to the beam_search_callback function.
// void* callback_data is any custom data passed to llama_beam_search, that is subsequently
// passed back to beam_search_callback. This avoids having to use global variables in the callback.
typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, llama_beams_state);
typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
/// @details Deterministically returns entire sentence constructed by a beam search.
/// @param ctx Pointer to the llama_context.
@ -518,6 +521,8 @@ extern "C" {
// If this is not called, or NULL is supplied, everything is output on stderr.
LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
#ifdef __cplusplus
}
#endif

140
run_with_preset.py Executable file
View file

@ -0,0 +1,140 @@
#!/usr/bin/env python3
import argparse
import os
import subprocess
import sys
import yaml
CLI_ARGS_MAIN_PERPLEXITY = [
"batch-size", "cfg-negative-prompt", "cfg-scale", "chunks", "color", "ctx-size", "escape",
"export", "file", "frequency-penalty", "grammar", "grammar-file", "hellaswag",
"hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix", "instruct",
"interactive", "interactive-first", "keep", "logdir", "logit-bias", "lora", "lora-base",
"low-vram", "main-gpu", "memory-f32", "mirostat", "mirostat-ent", "mirostat-lr", "mlock",
"model", "mtest", "multiline-input", "n-gpu-layers", "n-predict", "no-mmap", "no-mul-mat-q",
"np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt",
"prompt-cache", "prompt-cache-all", "prompt-cache-ro", "random-prompt", "repeat-last-n",
"repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed",
"simple-io", "tensor-split", "threads", "temp", "tfs", "top-k", "top-p", "typical",
"verbose-prompt"
]
CLI_ARGS_LLAMA_BENCH = [
"batch-size", "memory-f32", "low-vram", "model", "mul-mat-q", "n-gen", "n-gpu-layers",
"n-prompt", "output", "repetitions", "tensor-split", "threads", "verbose"
]
CLI_ARGS_SERVER = [
"alias", "batch-size", "ctx-size", "embedding", "host", "memory-f32", "lora", "lora-base",
"low-vram", "main-gpu", "mlock", "model", "n-gpu-layers", "n-probs", "no-mmap", "no-mul-mat-q",
"numa", "path", "port", "rope-freq-base", "timeout", "rope-freq-scale", "tensor-split",
"threads", "verbose"
]
description = """Run llama.cpp binaries with presets from YAML file(s).
To specify which binary should be run, specify the "binary" property (main, perplexity, llama-bench, and server are supported).
To get a preset file template, run a llama.cpp binary with the "--logdir" CLI argument.
Formatting considerations:
- The YAML property names are the same as the CLI argument names of the corresponding binary.
- Properties must use the long name of their corresponding llama.cpp CLI arguments.
- Like the llama.cpp binaries the property names do not differentiate between hyphens and underscores.
- Flags must be defined as "<PROPERTY_NAME>: true" to be effective.
- To define the logit_bias property, the expected format is "<TOKEN_ID>: <BIAS>" in the "logit_bias" namespace.
- To define multiple "reverse_prompt" properties simultaneously the expected format is a list of strings.
- To define a tensor split, pass a list of floats.
"""
usage = "run_with_preset.py [-h] [yaml_files ...] [--<ARG_NAME> <ARG_VALUE> ...]"
epilog = (" --<ARG_NAME> specify additional CLI ars to be passed to the binary (override all preset files). "
"Unknown args will be ignored.")
parser = argparse.ArgumentParser(
description=description, usage=usage, epilog=epilog, formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument("-bin", "--binary", help="The binary to run.")
parser.add_argument("yaml_files", nargs="*",
help="Arbitrary number of YAML files from which to read preset values. "
"If two files specify the same values the later one will be used.")
known_args, unknown_args = parser.parse_known_args()
if not known_args.yaml_files and not unknown_args:
parser.print_help()
sys.exit(0)
props = dict()
for yaml_file in known_args.yaml_files:
with open(yaml_file, "r") as f:
props.update(yaml.load(f, yaml.SafeLoader))
props = {prop.replace("_", "-"): val for prop, val in props.items()}
binary = props.pop("binary", "main")
if known_args.binary:
binary = known_args.binary
if os.path.exists(f"./{binary}"):
binary = f"./{binary}"
if binary.lower().endswith("main") or binary.lower().endswith("perplexity"):
cli_args = CLI_ARGS_MAIN_PERPLEXITY
elif binary.lower().endswith("llama-bench"):
cli_args = CLI_ARGS_LLAMA_BENCH
elif binary.lower().endswith("server"):
cli_args = CLI_ARGS_SERVER
else:
print(f"Unknown binary: {binary}")
sys.exit(1)
command_list = [binary]
for cli_arg in cli_args:
value = props.pop(cli_arg, None)
if not value or value == -1:
continue
if cli_arg == "logit-bias":
for token, bias in value.items():
command_list.append("--logit-bias")
command_list.append(f"{token}{bias:+}")
continue
if cli_arg == "reverse-prompt" and not isinstance(value, str):
for rp in value:
command_list.append("--reverse-prompt")
command_list.append(str(rp))
continue
command_list.append(f"--{cli_arg}")
if cli_arg == "tensor-split":
command_list.append(",".join([str(v) for v in value]))
continue
value = str(value)
if value != "True":
command_list.append(str(value))
num_unused = len(props)
if num_unused > 10:
print(f"The preset file contained a total of {num_unused} unused properties.")
elif num_unused > 0:
print("The preset file contained the following unused properties:")
for prop, value in props.items():
print(f" {prop}: {value}")
command_list += unknown_args
sp = subprocess.Popen(command_list)
while sp.returncode is None:
try:
sp.wait()
except KeyboardInterrupt:
pass
sys.exit(sp.returncode)

26
scripts/convert-gg.sh Executable file
View file

@ -0,0 +1,26 @@
#!/bin/bash
set -e
# LLaMA v1
python3 convert.py ../llama1/7B --outfile models/llama-7b/ggml-model-f16.gguf --outtype f16
python3 convert.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16
python3 convert.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16
python3 convert.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16
# LLaMA v2
python3 convert.py ../llama2/llama-2-7b --outfile models/llama-7b-v2/ggml-model-f16.gguf --outtype f16
python3 convert.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16
python3 convert.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16
# Code Llama
python3 convert.py ../codellama/CodeLlama-7b/ --outfile models/codellama-7b/ggml-model-f16.gguf --outtype f16
python3 convert.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16
python3 convert.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16
# Falcon
python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-7b 1
mv -v ../falcon/falcon-7b/ggml-model-f16.gguf models/falcon-7b/ggml-model-f16.gguf
python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-40b 1
mv -v ../falcon/falcon-40b/ggml-model-f16.gguf models/falcon-40b/ggml-model-f16.gguf

3
scripts/qnt-all.sh
View file

@ -20,6 +20,9 @@ fi
model="$1"
out="../tmp/results-${model}"
set -o pipefail
set -e
mkdir -p ${out}
for q in ${qnt[@]}; do

3
scripts/run-all-perf.sh
View file

@ -20,6 +20,9 @@ fi
model="$1"
out="../tmp/results-${model}"
set -o pipefail
set -e
mkdir -p ${out}
mstr=""

3
scripts/run-all-ppl.sh
View file

@ -17,6 +17,9 @@ if [ ! -z "$3" ]; then
args="$3"
fi
set -o pipefail
set -e
model="$1"
out="../tmp/results-${model}"

11
tests/CMakeLists.txt
View file

@ -25,8 +25,10 @@ endfunction()
llama_build_and_test_executable(test-quantize-fns.cpp)
llama_build_and_test_executable(test-quantize-perf.cpp)
llama_build_and_test_executable(test-sampling.cpp)
llama_build_executable(test-tokenizer-0.cpp)
llama_test_executable (test-tokenizer-0.llama test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_build_executable(test-tokenizer-0-llama.cpp)
llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_build_executable(test-tokenizer-0-falcon.cpp)
#llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
llama_build_executable(test-tokenizer-1.cpp)
# test-tokenizer-1 requires a BPE vocab. re-enable when we have one.
#llama_test_executable (test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
@ -35,3 +37,8 @@ llama_build_and_test_executable(test-grammar-parser.cpp)
llama_build_and_test_executable(test-llama-grammar.cpp)
llama_build_and_test_executable(test-grad0.cpp) # SLOW
# llama_build_and_test_executable(test-opt.cpp) # SLOW
# dummy executable - not installed
get_filename_component(TEST_TARGET test-c.c NAME_WE)
add_executable(${TEST_TARGET} test-c.c)
target_link_libraries(${TEST_TARGET} PRIVATE llama)

3
tests/test-c.c Normal file
View file

@ -0,0 +1,3 @@
#include "llama.h"
int main(void) {}

52
tests/test-grad0.cpp
View file

@ -275,14 +275,14 @@ static bool check_gradient(
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
const float f0 = ggml_get_f32_1d(f, 0);
const double f0 = ggml_get_f32_1d(f, 0);
ggml_set_f32_1d(x[i], k, xm);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
const float f1 = ggml_get_f32_1d(f, 0);
const float g0 = (f0 - f1)/(2.0f*eps);
const double f1 = ggml_get_f32_1d(f, 0);
const double g0 = (f0 - f1)/(2.0*(double) eps);
ggml_set_f32_1d(x[i], k, x0);
@ -292,10 +292,10 @@ static bool check_gradient(
ggml_graph_compute_with_ctx(ctx0, &gb, n_threads);
const float g1 = ggml_get_f32_1d(x[i]->grad, k);
const double g1 = ggml_get_f32_1d(x[i]->grad, k);
const float error_abs = fabsf(g0 - g1);
const float error_rel = g0 != 0 ? fabsf(g0 - g1)/fabsf(g0) : 0;
const double error_abs = fabs(g0 - g1);
const double error_rel = g0 != 0 ? fabs(g0 - g1)/fabs(g0) : 0;
if (error_abs > max_error_abs || error_rel > max_error_rel) {
printf("%s: ndims=%d, i=%d, k=%d, x0=%f, xm=%f, xp=%f, f0=%f, f1=%f, g0=%f, g1=%f, eps=%f, error_abs=%f, error_rel=%f\n",
@ -531,7 +531,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqrt(ctx0, x[0]));
check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f);
check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, 2e-2f, 1e-1f);
}
}
@ -1345,9 +1345,18 @@ int main(int argc, const char ** argv) {
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
struct ggml_tensor * f = ggml_sum(ctx0, ggml_soft_max(ctx0, x[0]));
float eps = 1e-6f;
// dont use only sum as aggregation, because sum of softmax is always 1 -> finite differences should not work
// instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0)
struct ggml_tensor * f = ggml_sum(ctx0,
ggml_log(ctx0,
ggml_add1(ctx0,
ggml_scale(ctx0,
ggml_soft_max(ctx0, x[0]),
ggml_new_f32(ctx0, 1.0f - eps)),
ggml_new_f32(ctx0, eps))));
check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 2e-1f, INFINITY);
}
}
@ -1358,15 +1367,26 @@ int main(int argc, const char ** argv) {
int64_t ne2[4];
get_random_dims(ne2, 4);
for (int ndims = 1; ndims <= 3; ++ndims) {
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
for (int ndims = 1; ndims <= 4; ++ndims) {
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -0.1f, 0.1f);
x[1] = get_random_tensor_f32(ctx0, ndims, ne2, 0.0f, 1.0f);
// the second argument to cross_entropy_loss must sum up to 1 for each row
int nr = ggml_nrows(x[1]);
int nc = ggml_nelements(x[1]) / nr;
for (int ir = 0; ir < nr; ++ir) {
float sum = 0;
for (int ic = 0; ic < nc; ++ic) {
sum += ((float *) x[1]->data)[ic + ir*nc];
}
for (int ic = 0; ic < nc; ++ic) {
((float *) x[1]->data)[ic + ir*nc] /= sum;
}
}
ggml_set_param(ctx0, x[0]);
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cross_entropy_loss(ctx0, x[0], x[1]));
struct ggml_tensor * f = ggml_cross_entropy_loss(ctx0, x[0], x[1]);
check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-1f, 1e-2f, INFINITY);
// finite differences regularly fails!
check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-4f, 1e-3f, INFINITY);
}
}
@ -1473,7 +1493,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY);
}
}
}
@ -1514,7 +1534,7 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY);
}
}
}

178
tests/test-tokenizer-0-falcon.cpp Normal file
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@ -0,0 +1,178 @@
#include "llama.h"
#include "common.h"
#include <cstdio>
#include <string>
#include <map>
#include <vector>
#include <fstream>
// generate using test-tokenizer-0-falcon.py
static const std::map<std::string, std::vector<llama_token>> & k_tests() {
static std::map<std::string, std::vector<llama_token>> _k_tests = {
{ "" , { }, },
{ " " , { 204, }, },
{ " " , { 258, }, },
{ " " , { 466, }, },
{ "\t" , { 192, }, },
{ "\n" , { 193, }, },
{ "\t\n" , { 19125, }, },
{ "Hello world" , { 9856, 1079, }, },
{ " Hello world" , { 23090, 1079, }, },
{ "Hello World" , { 9856, 2889, }, },
{ " Hello World" , { 23090, 2889, }, },
{ " Hello World!" , { 23090, 2889, 12, }, },
{ "Hello, world!" , { 9856, 23, 1079, 12, }, },
{ " Hello, world!" , { 23090, 23, 1079, 12, }, },
{ " this is 🦙.cpp" , { 414, 304, 3346, 111, 231, 25, 29247, }, },
{ "w048 7tuijk dsdfhu" , { 98, 55866, 204, 34, 16682, 7149, 36190, 6869, 11481, }, },
{ "нещо на Български" , { 150, 133, 6207, 151, 215, 150, 134, 5052, 133, 6279, 5052, 223, 151, 216, 49679, 123, 53110, 47043, 7795, }, },
{ "កាន់តែពិសេសអាចខលចេញ" , { 38154, 206, 38154, 126, 38154, 225, 167, 237, 217, 38154, 221, 167, 237, 208, 38154, 228, 38154, 127, 38154, 237, 167, 237, 207, 38154, 237, 38154, 107, 38154, 126, 38154, 211, 38154, 207, 38154, 233, 38154, 211, 167, 237, 207, 38154, 215, }, },
{ "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", { 2571, 232, 206, 204, 19, 11003, 20, 8196, 126, 283, 219, 48778, 116, 13392, 204, 19, 51831, 732, 63209, 1741, 7955, 522, 20, 22438, 211, 204, 19, 7927, 53360, 325, 504, 701, 946, 10930, 20, }, },
{ "Hello" , { 9856, }, },
{ " Hello" , { 23090, }, },
{ " Hello" , { 204, 23090, }, },
{ " Hello" , { 258, 23090, }, },
{ " Hello" , { 466, 23090, }, },
{ " Hello\n Hello" , { 466, 23090, 742, 23090, }, },
};
return _k_tests;
}
int main(int argc, char **argv) {
if (argc < 2) {
fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]);
return 1;
}
const std::string fname = argv[1];
std::string fname_text;
if (argc > 2) {
fname_text = argv[2];
}
fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
llama_model * model;
llama_context * ctx;
llama_backend_init(false);
// load the vocab
{
auto lparams = llama_context_default_params();
lparams.vocab_only = true;
model = llama_load_model_from_file(fname.c_str(), lparams);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
return 1;
}
ctx = llama_new_context_with_model(model, lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
llama_free_model(model);
return 1;
}
}
if (llama_vocab_type(ctx) != LLAMA_VOCAB_TYPE_BPE) {
fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__);
llama_free_model(model);
llama_free(ctx);
return 2;
}
bool success = true;
for (const auto & test_kv : k_tests()) {
const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, false);
printf("\n");
printf("src: '%s'\n", test_kv.first.c_str());
printf("res: '%s'\n", llama_detokenize_bpe(ctx, res).c_str());
printf("tok: ");
for (const auto & tok : res) {
printf("%d ", tok);
}
printf("\n");
bool correct = res.size() == test_kv.second.size();
for (int i = 0; i < (int) res.size() && correct; ++i) {
if (test_kv.second[i] != res[i]) {
correct = false;
}
}
if (!correct) {
fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
llama_detokenize_bpe(ctx, res).c_str(),
llama_detokenize_bpe(ctx, test_kv.second).c_str());
fprintf(stderr, "%s : expected tokens: ", __func__);
for (const auto & t : test_kv.second) {
fprintf(stderr, "%6d, ", t);
}
fprintf(stderr, "\n");
fprintf(stderr, "%s : got tokens: ", __func__);
for (const auto & t : res) {
fprintf(stderr, "%6d, ", t);
}
fprintf(stderr, "\n");
success = false;
}
}
if (!fname_text.empty()) {
fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
std::string text;
{
std::ifstream ifs(fname_text);
if (!ifs) {
fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
return 1;
}
text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
}
fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
const std::vector<llama_token> res = llama_tokenize(ctx, text, true);
fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
{
const std::string fname_out = fname_text + ".tokcpp";
std::ofstream ofs(fname_out);
if (!ofs) {
fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
return 1;
}
for (const auto & tok : res) {
ofs << tok << " ";
}
ofs << "\n";
}
fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
}
llama_free_model(model);
llama_free(ctx);
llama_backend_free();
return success ? 0 : 3;
}

83
tests/test-tokenizer-0-falcon.py Normal file
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@ -0,0 +1,83 @@
# tests with BPE tokenizer
import os
import sys
import argparse
from transformers import AutoTokenizer
parser = argparse.ArgumentParser()
parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
parser.add_argument("--fname-tok", help="path to a text file to tokenize")
args = parser.parse_args()
dir_tokenizer = args.dir_tokenizer
tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
tests = [
"",
" ",
" ",
" ",
"\t",
"\n",
"\t\n",
"Hello world",
" Hello world",
"Hello World",
" Hello World",
" Hello World!",
"Hello, world!",
" Hello, world!",
" this is 🦙.cpp",
"w048 7tuijk dsdfhu",
"нещо на Български",
"កាន់តែពិសេសអាចខលចេញ",
"🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
"Hello",
" Hello",
" Hello",
" Hello",
" Hello",
" Hello\n Hello",
]
for text in tests:
print('text: ', text)
print(tokenizer.encode(text))
print(tokenizer.decode(tokenizer.encode(text)))
print("\n\ntests for C++:\n")
for text in tests:
res = tokenizer.encode(text)
k = text.replace('\n', '\\n')
k = k.replace('\t', '\\t')
k = '"' + k + '"'
print("{ %-24s, { " % k, end='')
for x in res:
print("%7d," % x, end='')
print(" }, },")
print(tokenizer.encode('hello'))
print(tokenizer.encode('world'))
print(tokenizer.encode(' world'))
print(tokenizer.encode('hello world'))
fname_tok = args.fname_tok
if fname_tok:
print('tokenizing file: ', fname_tok)
fname_out = fname_tok + '.tok'
with open(fname_tok, 'r') as f:
lines = f.readlines()
s = ''.join(lines)
res = tokenizer.encode(s)
# write to file
with open(fname_out, 'w') as f:
for x in res:
f.write(str(x) + ' ')
f.write('\n')
print('len(res): ', len(res))
print('len(lines): ', len(lines))
print('results written to: ', fname_out)

182
tests/test-tokenizer-0-llama.cpp Normal file
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@ -0,0 +1,182 @@
#include "llama.h"
#include "common.h"
#include <cstdio>
#include <string>
#include <map>
#include <vector>
#include <fstream>
// generate using test-tokenizer-0-llama.py
static const std::map<std::string, std::vector<llama_token>> & k_tests() {
static std::map<std::string, std::vector<llama_token>> _k_tests = {
{ "" , { }, },
{ " " , { 259, }, },
{ " " , { 1678, }, },
{ " " , { 268, }, },
{ "\t" , { 29871, 12, }, },
{ "\n" , { 29871, 13, }, },
{ "\t\n" , { 29871, 12, 13, }, },
{ "Hello world" , { 15043, 3186, }, },
{ " Hello world" , { 29871, 15043, 3186, }, },
{ "Hello World" , { 15043, 2787, }, },
{ " Hello World" , { 29871, 15043, 2787, }, },
{ " Hello World!" , { 29871, 15043, 2787, 29991, }, },
{ "Hello, world!" , { 15043, 29892, 3186, 29991, }, },
{ " Hello, world!" , { 29871, 15043, 29892, 3186, 29991, }, },
{ " this is 🦙.cpp" , { 29871, 445, 338, 29871, 243, 162, 169, 156, 29889, 8223, }, },
{ "w048 7tuijk dsdfhu" , { 281, 29900, 29946, 29947, 29871, 29955, 9161, 13535, 18031, 2176, 6905, }, },
{ "нещо на Български" , { 1538, 4851, 665, 1386, 29713, 1305, }, },
{ "កាន់តែពិសេសអាចខលចេញ" , { 29871, 31849, 31324, 31934, 228, 162, 142, 228, 161, 146, 228, 162, 133, 228, 161, 153, 228, 161, 186, 31708, 228, 162, 132, 31708, 228, 161, 165, 31324, 228, 161, 136, 228, 161, 132, 228, 161, 158, 228, 161, 136, 228, 162, 132, 228, 161, 140, }, },
{ "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", { 29871, 243, 162, 157, 131, 313, 8945, 29897, 29871, 243, 162, 155, 185, 30722, 243, 162, 143, 174, 30598, 313, 20787, 953, 3848, 275, 16125, 630, 29897, 29871, 31681, 313, 6194, 953, 29877, 2397, 393, 756, 967, 1914, 5993, 29897, }, },
{ "Hello" , { 15043, }, },
{ " Hello" , { 29871, 15043, }, },
{ " Hello" , { 259, 15043, }, },
{ " Hello" , { 1678, 15043, }, },
{ " Hello" , { 268, 15043, }, },
{ " Hello\n Hello" , { 268, 15043, 13, 1678, 15043, }, },
};
return _k_tests;
}
int main(int argc, char **argv) {
if (argc < 2) {
fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]);
return 1;
}
const std::string fname = argv[1];
std::string fname_text;
if (argc > 2) {
fname_text = argv[2];
}
fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
llama_model * model;
llama_context * ctx;
llama_backend_init(false);
// load the vocab
{
auto lparams = llama_context_default_params();
lparams.vocab_only = true;
model = llama_load_model_from_file(fname.c_str(), lparams);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
return 1;
}
ctx = llama_new_context_with_model(model, lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
llama_free_model(model);
return 1;
}
}
if (llama_vocab_type(ctx) != LLAMA_VOCAB_TYPE_SPM) {
fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__);
llama_free_model(model);
llama_free(ctx);
return 2;
}
bool success = true;
for (const auto & test_kv : k_tests()) {
const std::vector<llama_token> res_bos = llama_tokenize(ctx, test_kv.first, true);
const std::vector<llama_token> res_nobos = llama_tokenize(ctx, test_kv.first, false);
printf("\n");
printf("src: '%s'\n", test_kv.first.c_str());
printf("res: '%s'\n", llama_detokenize_spm(ctx, res_bos).c_str());
printf("tok: ");
for (const auto & tok : res_bos) {
printf("%d ", tok);
}
printf("\n");
bool correct = res_nobos.size() == test_kv.second.size() && res_bos.size() == res_nobos.size() + 1 && res_bos[0] == 1;
for (int i = 0; i < (int) res_nobos.size() && correct; ++i) {
if (test_kv.second[i] != res_bos[i + 1]) {
correct = false;
}
if (test_kv.second[i] != res_nobos[i]) {
correct = false;
}
}
if (!correct) {
fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
llama_detokenize_spm(ctx, res_nobos).c_str(),
llama_detokenize_spm(ctx, test_kv.second).c_str());
fprintf(stderr, "%s : expected tokens: ", __func__);
for (const auto & t : test_kv.second) {
fprintf(stderr, "%6d, ", t);
}
fprintf(stderr, "\n");
fprintf(stderr, "%s : got tokens: ", __func__);
for (const auto & t : res_nobos) {
fprintf(stderr, "%6d, ", t);
}
fprintf(stderr, "\n");
success = false;
}
}
if (!fname_text.empty()) {
fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
std::string text;
{
std::ifstream ifs(fname_text);
if (!ifs) {
fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
return 1;
}
text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
}
fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
const std::vector<llama_token> res = llama_tokenize(ctx, text, true);
fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
{
const std::string fname_out = fname_text + ".tokcpp";
std::ofstream ofs(fname_out);
if (!ofs) {
fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
return 1;
}
for (const auto & tok : res) {
ofs << tok << " ";
}
ofs << "\n";
}
fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
}
llama_free_model(model);
llama_free(ctx);
llama_backend_free();
return success ? 0 : 3;
}

95
tests/test-tokenizer-0-llama.py Normal file
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@ -0,0 +1,95 @@
# tests with SPM tokenizer
import os
import sys
import argparse
from sentencepiece import SentencePieceProcessor
parser = argparse.ArgumentParser()
parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
parser.add_argument("--fname-tok", help="path to a text file to tokenize")
args = parser.parse_args()
dir_tokenizer = args.dir_tokenizer
tokenizer = SentencePieceProcessor(dir_tokenizer + '/tokenizer.model')
tests = [
"",
" ",
" ",
" ",
"\t",
"\n",
"\t\n",
"Hello world",
" Hello world",
"Hello World",
" Hello World",
" Hello World!",
"Hello, world!",
" Hello, world!",
" this is 🦙.cpp",
"w048 7tuijk dsdfhu",
"нещо на Български",
"កាន់តែពិសេសអាចខលចេញ",
"🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
"Hello",
" Hello",
" Hello",
" Hello",
" Hello",
" Hello\n Hello",
]
for text in tests:
print('text: ', text)
print('\nwith bos:')
print(tokenizer.encode(text, add_bos=True))
print(tokenizer.decode(tokenizer.encode(text, add_bos=True)))
print('\nwithout bos:')
print(tokenizer.encode(text, add_bos=False))
print(tokenizer.decode(tokenizer.encode(text, add_bos=False)))
print("'" + tokenizer.id_to_piece(15043) + "'") # '_Hello'
print("'" + tokenizer.id_to_piece(29871) + "'") # '_'
print("'" + tokenizer.decode([15043]) + "'") # 'Hello'
print("'" + tokenizer.decode([15043, 15043]) + "'") # 'Hello Hello'
print("'" + tokenizer.decode([29871, 15043]) + "'") # ' Hello'
print("'" + tokenizer.decode([29871, 15043, 29871, 15043]) + "'") # ' Hello Hello'
print("\n\ntests for C++:\n")
for text in tests:
res = tokenizer.encode(text, add_bos=False)
k = text.replace('\n', '\\n')
k = k.replace('\t', '\\t')
k = '"' + k + '"'
print("{ %-24s, { " % k, end='')
for x in res:
print("%7d," % x, end='')
print(" }, },")
print(tokenizer.encode('hello'))
print(tokenizer.encode('world'))
print(tokenizer.encode(' world'))
print(tokenizer.encode('hello world'))
fname_tok = args.fname_tok
if fname_tok:
print('tokenizing file: ', fname_tok)
fname_out = fname_tok + '.tok'
with open(fname_tok, 'r') as f:
lines = f.readlines()
s = ''.join(lines)
res = tokenizer.encode(s, add_bos=True)
# write to file
with open(fname_out, 'w') as f:
for x in res:
f.write(str(x) + ' ')
f.write('\n')
print('len(res): ', len(res))
print('len(lines): ', len(lines))
print('results written to: ', fname_out)

140
tests/test-tokenizer-0.cpp
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@ -1,140 +0,0 @@
#include "llama.h"
#include "common.h"
#include <cstdio>
#include <string>
#include <map>
#include <vector>
static std::string unescape_whitespace(llama_context* ctx, const std::vector<llama_token>& tokens) {
std::string result;
for (size_t i = 0; i < tokens.size(); ++i) {
result += llama_token_to_str(ctx, tokens[i]);
}
return result;
}
static const std::map<std::string, std::vector<llama_token>> & k_tests() {
static std::map<std::string, std::vector<llama_token>> _k_tests = {
{ " ", {1, 259, }, },
{ " ", { 1, 1678, }, },
{ " ", { 1, 268, }, },
{ "\t", { 1, 29871, 12, }, },
{ "\n", { 1, 29871, 13, }, },
{ "\t\n", { 1, 29871, 12, 13, }, },
{ "Hello world", { 1, 15043, 3186, }, },
{ " Hello world", { 1, 29871, 15043, 3186, }, },
{ "Hello World", { 1, 15043, 2787, }, },
{ " Hello World", { 1, 29871, 15043, 2787, }, },
{ " Hello World!", { 1, 29871, 15043, 2787, 29991, }, },
{ " this is 🦙.cpp", { 1, 29871, 445, 338, 29871, 243, 162, 169, 156, 29889, 8223, }, },
{ "w048 7tuijk dsdfhu", { 1, 281, 29900, 29946, 29947, 29871, 29955, 9161, 13535, 18031, 2176, 6905, }, },
{ "нещо на Български", { 1, 1538, 4851, 665, 1386, 29713, 1305, }, },
{ "កាន់តែពិសេសអាចខលចេញ", { 1, 29871, 31849, 31324, 31934, 228, 162, 142, 228, 161,
146, 228, 162, 133, 228, 161, 153, 228, 161, 186,
31708, 228, 162, 132, 31708, 228, 161, 165, 31324, 228,
161, 136, 228, 161, 132, 228, 161, 158, 228, 161,
136, 228, 162, 132, 228, 161, 140, }, },
{ "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
{ 1, 29871, 243, 162, 157, 131, 313, 8945, 29897, 29871,
243, 162, 155, 185, 30722, 243, 162, 143, 174, 30598,
313, 20787, 953, 3848, 275, 16125, 630, 29897, 29871, 31681,
313, 6194, 953, 29877, 2397, 393, 756, 967, 1914, 5993, 29897, }, },
{ "Hello", { 1, 15043 }, },
{ " Hello", { 1, 29871, 15043 }, },
{ " Hello", { 1, 259, 15043 }, },
{ " Hello", { 1, 1678, 15043 }, },
{ " Hello", { 1, 268, 15043 }, },
{ " Hello\n Hello", { 1, 268, 15043, 13, 1678, 15043 }, },
};
return _k_tests;
}
int main(int argc, char **argv) {
if (argc < 2) {
fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
return 1;
}
const std::string fname = argv[1];
fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
llama_model * model;
llama_context * ctx;
llama_backend_init(false);
// load the vocab
{
auto lparams = llama_context_default_params();
lparams.vocab_only = true;
model = llama_load_model_from_file(fname.c_str(), lparams);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
return 1;
}
ctx = llama_new_context_with_model(model, lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
llama_free_model(model);
return 1;
}
}
const int n_vocab = llama_n_vocab(ctx);
if (n_vocab != 32000) {
fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab);
llama_free_model(model);
llama_free(ctx);
return 2;
}
bool success = true;
for (const auto & test_kv : k_tests()) {
std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, true);
fprintf(stderr, "%s : '%s' tokenized to '%s'\n",
__func__, test_kv.first.c_str(), unescape_whitespace(ctx, res).c_str());
bool correct = res.size() == test_kv.second.size();
for (int i = 0; i < (int) res.size() && correct; ++i) {
if (res[i] != test_kv.second[i]) {
correct = false;
}
}
if (!correct) {
fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
unescape_whitespace(ctx, res).c_str(), unescape_whitespace(ctx, test_kv.second).c_str());
fprintf(stderr, "%s : expected tokens: ", __func__);
for (const auto & t : test_kv.second) {
fprintf(stderr, "%6d, ", t);
}
fprintf(stderr, "\n");
fprintf(stderr, "%s : got tokens: ", __func__);
for (const auto & t : res) {
fprintf(stderr, "%6d, ", t);
}
fprintf(stderr, "\n");
success = false;
}
}
llama_free_model(model);
llama_free(ctx);
llama_backend_free();
return success ? 0 : 3;
}

14
tests/test-tokenizer-1.cpp
View file

@ -22,14 +22,6 @@ static std::string escape_whitespace(const std::string& text) {
return result;
}
static std::string unescape_whitespace(llama_context * ctx, const std::vector<llama_token> & tokens) {
std::string result;
for (size_t i = 0; i < tokens.size(); ++i) {
result += llama_token_to_str(ctx, tokens[i]);
}
return result;
}
int main(int argc, char **argv) {
if (argc < 2) {
fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
@ -72,13 +64,13 @@ int main(int argc, char **argv) {
const int n_vocab = llama_n_vocab(ctx);
for (int i = 0; i < n_vocab; ++i) {
std::string forward = llama_token_to_str(ctx, i);
std::string forward = llama_token_to_piece(ctx, i);
std::vector<llama_token> tokens = llama_tokenize(ctx, forward, false);
if (tokens.size() == 1) {
if (i != tokens[0]) {
std::string backward = llama_token_to_str(ctx, tokens[0]);
std::string backward = llama_token_to_piece(ctx, tokens[0]);
fprintf(stderr, "%s : error: token %d is string %s but bpe returns token %d %s\n",
__func__, i, llama_token_to_str(ctx, i).c_str(), tokens[0], backward.c_str());
__func__, i, llama_token_to_piece(ctx, i).c_str(), tokens[0], backward.c_str());
return 2;
}
}