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Merge branch 'master' into compilade/refactor-kv-cache

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This commit is contained in:
Francis Couture-Harpin 2024-05-12 17:13:31 -04:00
commit b7ec12ebf7
182 changed files with 57387 additions and 40048 deletions
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8
.devops/main-intel.Dockerfile
View File

@ -10,14 +10,12 @@ WORKDIR /app
COPY . .
RUN mkdir build && \
cd build && \
if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
echo "LLAMA_SYCL_F16 is set" && \
export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
fi && \
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
cmake --build . --config Release --target main
cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
cmake --build build --config Release --target main
FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime

6
.devops/main-vulkan.Dockerfile
View File

@ -14,10 +14,8 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key
# Build it
WORKDIR /app
COPY . .
RUN mkdir build && \
cd build && \
cmake .. -DLLAMA_VULKAN=1 && \
cmake --build . --config Release --target main
RUN cmake -B build -DLLAMA_VULKAN=1 && \
cmake --build build --config Release --target main
# Clean up
WORKDIR /

8
.devops/server-intel.Dockerfile
View File

@ -10,14 +10,12 @@ WORKDIR /app
COPY . .
RUN mkdir build && \
cd build && \
if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
echo "LLAMA_SYCL_F16 is set" && \
export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
fi && \
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
cmake --build . --config Release --target server
cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
cmake --build build --config Release --target server
FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime

6
.devops/server-vulkan.Dockerfile
View File

@ -18,10 +18,8 @@ RUN apt-get update && \
# Build it
WORKDIR /app
COPY . .
RUN mkdir build && \
cd build && \
cmake .. -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \
cmake --build . --config Release --target server
RUN cmake -B build -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \
cmake --build build --config Release --target server
# Clean up
WORKDIR /

16
.flake8
View File

@ -1,3 +1,17 @@
[flake8]
max-line-length = 125
ignore = W503
ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503
exclude =
# Do not traverse examples
examples,
# Do not include package initializers
__init__.py,
# No need to traverse our git directory
.git,
# There's no value in checking cache directories
__pycache__,
# No need to include the build path
build,
# This contains builds that we don't want to check
dist # This is generated with `python build .` for package releases
# max-complexity = 10

20
.github/workflows/bench.yml vendored
View File

@ -52,7 +52,19 @@ jobs:
ftype: q4_0
pr_comment_enabled: "true"
if: ${{ github.event.inputs.gpu-series == 'Standard_NC4as_T4_v3' || github.event.schedule || github.event.pull_request || github.head_ref == 'master' || github.ref_name == 'master' || github.event.push.ref == 'refs/heads/master' }}
if: |
inputs.gpu-series == 'Standard_NC4as_T4_v3'
|| (
github.event_name == 'schedule'
&& github.ref_name == 'master'
&& github.repository_owner == 'ggerganov'
)
|| github.event_name == 'pull_request_target'
|| (
github.event_name == 'push'
&& github.event.ref == 'refs/heads/master'
&& github.repository_owner == 'ggerganov'
)
steps:
- name: Clone
id: checkout
@ -96,9 +108,7 @@ jobs:
id: cmake_build
run: |
set -eux
mkdir build
cd build
cmake .. \
cmake -B build \
-DLLAMA_NATIVE=OFF \
-DLLAMA_BUILD_SERVER=ON \
-DLLAMA_CURL=ON \
@ -109,7 +119,7 @@ jobs:
-DLLAMA_FATAL_WARNINGS=OFF \
-DLLAMA_ALL_WARNINGS=OFF \
-DCMAKE_BUILD_TYPE=Release;
cmake --build . --config Release -j $(nproc) --target server
cmake --build build --config Release -j $(nproc) --target server
- name: Download the dataset
id: download_dataset

2
.github/workflows/close-issue.yml vendored
View File

@ -12,7 +12,7 @@ jobs:
steps:
- uses: actions/stale@v5
with:
exempt-issue-labels: "refactor,help wanted,good first issue,research"
exempt-issue-labels: "refactor,help wanted,good first issue,research,bug"
days-before-issue-stale: 30
days-before-issue-close: 14
stale-issue-label: "stale"

3
.github/workflows/python-lint.yml vendored
View File

@ -20,5 +20,4 @@ jobs:
- name: flake8 Lint
uses: py-actions/flake8@v2
with:
ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503"
exclude: "examples/*,examples/*/**,*/**/__init__.py,convert-hf-to-gguf-update.py"
plugins: "flake8-no-print"

12
.github/workflows/server.yml vendored
View File

@ -94,15 +94,13 @@ jobs:
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake .. \
cmake -B build \
-DLLAMA_NATIVE=OFF \
-DLLAMA_BUILD_SERVER=ON \
-DLLAMA_CURL=ON \
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server
cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target server
- name: Tests
@ -143,10 +141,8 @@ jobs:
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake .. -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server
cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server
- name: Python setup
id: setup_python

1
.gitignore vendored
View File

@ -2,6 +2,7 @@
*.a
*.so
*.gguf
*.gguf.json
*.bin
*.exe
*.dll

5
.pre-commit-config.yaml
View File

@ -3,13 +3,14 @@
exclude: prompts/.*.txt
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v3.2.0
rev: v4.6.0
hooks:
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-yaml
- id: check-added-large-files
- repo: https://github.com/PyCQA/flake8
rev: 6.0.0
rev: 7.0.0
hooks:
- id: flake8
additional_dependencies: [flake8-no-print]

27
CMakeLists.txt
View File

@ -103,6 +103,8 @@ set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for
set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
"llama: max. batch size for using peer access")
option(LLAMA_CUDA_NO_PEER_COPY "llama: do not use peer to peer copies" OFF)
option(LLAMA_CUDA_NO_VMM "llama: do not try to use CUDA VMM" OFF)
option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF)
option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF)
@ -294,7 +296,7 @@ if (LLAMA_BLAS)
if (LLAMA_STATIC)
set(BLA_STATIC ON)
endif()
if ($(CMAKE_VERSION) VERSION_GREATER_EQUAL 3.22)
if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22)
set(BLA_SIZEOF_INTEGER 8)
endif()
@ -403,12 +405,16 @@ if (LLAMA_CUDA)
list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu")
add_compile_definitions(GGML_USE_CUDA)
add_compile_definitions(GGML_CUDA_USE_GRAPHS)
if (LLAMA_CUDA_FORCE_DMMV)
add_compile_definitions(GGML_CUDA_FORCE_DMMV)
endif()
if (LLAMA_CUDA_FORCE_MMQ)
add_compile_definitions(GGML_CUDA_FORCE_MMQ)
endif()
if (LLAMA_CUDA_NO_VMM)
add_compile_definitions(GGML_CUDA_NO_VMM)
endif()
add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
if (DEFINED LLAMA_CUDA_DMMV_Y)
@ -425,7 +431,7 @@ if (LLAMA_CUDA)
if (LLAMA_STATIC)
if (WIN32)
# As of 12.3.1 CUDA Tookit for Windows does not offer a static cublas library
# As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt)
else ()
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
@ -434,7 +440,11 @@ if (LLAMA_CUDA)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
endif()
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver)
if (LLAMA_CUDA_NO_VMM)
# No VMM requested, no need to link directly with the cuda driver lib (libcuda.so)
else()
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ...
endif()
if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
# 52 == lowest CUDA 12 standard
@ -1271,17 +1281,6 @@ install(
WORLD_READ
WORLD_EXECUTE
DESTINATION ${CMAKE_INSTALL_BINDIR})
install(
FILES convert-lora-to-ggml.py
PERMISSIONS
OWNER_READ
OWNER_WRITE
OWNER_EXECUTE
GROUP_READ
GROUP_EXECUTE
WORLD_READ
WORLD_EXECUTE
DESTINATION ${CMAKE_INSTALL_BINDIR})
if (LLAMA_METAL)
install(
FILES ggml-metal.metal

5
Makefile
View File

@ -77,11 +77,10 @@ test: $(TEST_TARGETS)
./$$test_target $(CURDIR)/models/ggml-vocab-llama-bpe.gguf; \
./$$test_target $(CURDIR)/models/ggml-vocab-phi-3.gguf; \
./$$test_target $(CURDIR)/models/ggml-vocab-falcon.gguf; \
./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-coder.gguf; \
./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-llm.gguf; \
./$$test_target $(CURDIR)/models/ggml-vocab-bert-bge.gguf; \
./$$test_target $(CURDIR)/models/ggml-vocab-starcoder.gguf; \
./$$test_target $(CURDIR)/models/ggml-vocab-gpt-2.gguf; \
./$$test_target $(CURDIR)/models/ggml-vocab-refact.gguf; \
elif [ "$$test_target" = "tests/test-tokenizer-1-spm" ]; then \
continue; \
elif [ "$$test_target" = "tests/test-tokenizer-1-bpe" ]; then \
@ -434,7 +433,7 @@ ifdef LLAMA_CUDA
else
CUDA_PATH ?= /usr/local/cuda
endif
MK_CPPFLAGS += -DGGML_USE_CUDA -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include
MK_CPPFLAGS += -DGGML_USE_CUDA -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include -DGGML_CUDA_USE_GRAPHS
MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib
OBJS += ggml-cuda.o
OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))

31
README-sycl.md
View File

@ -185,9 +185,8 @@ Upon a successful installation, SYCL is enabled for the available intel devices,
```sh
git clone https://github.com/oneapi-src/oneMKL
cd oneMKL
mkdir -p buildWithCublas && cd buildWithCublas
cmake ../ -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas
make
cmake -B buildWithCublas -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas
cmake --build buildWithCublas --config Release
```
@ -227,16 +226,15 @@ Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA devic
source /opt/intel/oneapi/setvars.sh
# Build LLAMA with MKL BLAS acceleration for intel GPU
mkdir -p build && cd build
# Option 1: Use FP32 (recommended for better performance in most cases)
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# Option 2: Use FP16
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
#build all binary
cmake --build . --config Release -j -v
# build all binary
cmake --build build --config Release -j -v
```
#### Nvidia GPU
@ -248,16 +246,15 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_
export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR
# Build LLAMA with Nvidia BLAS acceleration through SYCL
mkdir -p build && cd build
# Option 1: Use FP32 (recommended for better performance in most cases)
cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# Option 2: Use FP16
cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
#build all binary
cmake --build . --config Release -j -v
# build all binary
cmake --build build --config Release -j -v
```
@ -412,17 +409,15 @@ b. Download & install mingw-w64 make for Windows provided by w64devkit
On the oneAPI command line window, step into the llama.cpp main directory and run the following:
```
mkdir -p build
cd build
@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
# Option 1: Use FP32 (recommended for better performance in most cases)
cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release
cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release
# Option 2: Or FP16
cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
make -j
cmake --build build --config Release -j
```
Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions:

178
README.md
View File

@ -2,7 +2,7 @@
![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
[Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
@ -20,7 +20,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
### Hot topics
- **BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920**
- **Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021**
- BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920
- MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387
- Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404
- Fix major bug in Metal batched inference https://github.com/ggerganov/llama.cpp/pull/6225
@ -175,6 +176,7 @@ Unless otherwise noted these projects are open-source with permissive licensing:
- [nat/openplayground](https://github.com/nat/openplayground)
- [Faraday](https://faraday.dev/) (proprietary)
- [LMStudio](https://lmstudio.ai/) (proprietary)
- [Layla](https://play.google.com/store/apps/details?id=com.laylalite) (proprietary)
- [LocalAI](https://github.com/mudler/LocalAI) (MIT)
- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile)
@ -308,6 +310,8 @@ In order to build llama.cpp you have three different options.
make
```
**Note**: for `Debug` builds, run `make LLAMA_DEBUG=1`
- On Windows:
1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
@ -322,12 +326,26 @@ In order to build llama.cpp you have three different options.
- Using `CMake`:
```bash
mkdir build
cd build
cmake ..
cmake --build . --config Release
cmake -B build
cmake --build build --config Release
```
**Note**: for `Debug` builds, there are two cases:
- Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
```bash
cmake -B build -DCMAKE_BUILD_TYPE=Debug
cmake --build build
```
- Multi-config generators (`-G` param set to Visual Studio, XCode...):
```bash
cmake -B build -G "Xcode"
cmake --build build --config Debug
```
- Using `Zig` (version 0.11 or later):
Building for optimization levels and CPU features can be accomplished using standard build arguments, for example AVX2, FMA, F16C,
@ -439,10 +457,8 @@ Building the program with BLAS support may lead to some performance improvements
- Using `CMake` on Linux:
```bash
mkdir build
cd build
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
cmake --build . --config Release
cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
cmake --build build --config Release
```
- #### BLIS
@ -462,11 +478,9 @@ Building the program with BLAS support may lead to some performance improvements
- Using manual oneAPI installation:
By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
```bash
mkdir build
cd build
source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
cmake --build . --config Release
cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
cmake --build build --config Release
```
- Using oneAPI docker image:
@ -487,10 +501,8 @@ Building the program with BLAS support may lead to some performance improvements
- Using `CMake`:
```bash
mkdir build
cd build
cmake .. -DLLAMA_CUDA=ON
cmake --build . --config Release
cmake -B build -DLLAMA_CUDA=ON
cmake --build build --config Release
```
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
@ -517,8 +529,8 @@ Building the program with BLAS support may lead to some performance improvements
- Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
```bash
CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \
cmake -H. -Bbuild -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
&& cmake --build build -- -j 16
cmake -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
&& cmake --build build --config Release -- -j 16
```
On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON"`.
However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
@ -564,15 +576,14 @@ Building the program with BLAS support may lead to some performance improvements
```sh
git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
mkdir OpenCL-SDK/build
cd OpenCL-SDK/build
cmake .. -DBUILD_DOCS=OFF \
cd OpenCL-SDK
cmake -B build -DBUILD_DOCS=OFF \
-DBUILD_EXAMPLES=OFF \
-DBUILD_TESTING=OFF \
-DOPENCL_SDK_BUILD_SAMPLES=OFF \
-DOPENCL_SDK_TEST_SAMPLES=OFF
cmake --build . --config Release
cmake --install . --prefix /some/path
cmake --build build
cmake --install build --prefix /some/path
```
</details>
@ -594,23 +605,23 @@ Building the program with BLAS support may lead to some performance improvements
```cmd
set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64"
git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast\build
cd CLBlast\build
cmake .. -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/CLBlast
cd CLBlast
cmake -B build -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
cmake --build build --config Release
cmake --install build --prefix C:/CLBlast
```
(note: `--config Release` at build time is the default and only relevant for Visual Studio builds - or multi-config Ninja builds)
- <details>
<summary>Unix:</summary>
```sh
git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast/build
cd CLBlast/build
cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
cmake --build . --config Release
cmake --install . --prefix /some/path
cd CLBlast
cmake -B build -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
cmake --build build --config Release
cmake --install build --prefix /some/path
```
Where `/some/path` is where the built library will be installed (default is `/usr/local`).
@ -624,21 +635,17 @@ Building the program with BLAS support may lead to some performance improvements
```
- CMake (Unix):
```sh
mkdir build
cd build
cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
cmake --build . --config Release
cmake -B build -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
cmake --build build --config Release
```
- CMake (Windows):
```cmd
set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast"
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
mkdir build
cd build
cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/LlamaCPP
cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
cmake --build build --config Release
cmake --install build --prefix C:/LlamaCPP
```
##### Running Llama with CLBlast
@ -694,10 +701,8 @@ Building the program with BLAS support may lead to some performance improvements
Then, build llama.cpp using the cmake command below:
```bash
mkdir -p build
cd build
cmake .. -DLLAMA_VULKAN=1
cmake --build . --config Release
cmake -B build -DLLAMA_VULKAN=1
cmake --build build --config Release
# Test the output binary (with "-ngl 33" to offload all layers to GPU)
./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4
@ -709,6 +714,8 @@ Building the program with BLAS support may lead to some performance improvements
To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face.
```bash
# obtain the official LLaMA model weights and place them in ./models
ls ./models
@ -930,17 +937,25 @@ If your issue is with model generation quality, then please at least scan the fo
### Android
#### Build on Android using Termux
[Termux](https://github.com/termux/termux-app#installation) is a method to execute `llama.cpp` on an Android device (no root required).
```
apt update && apt upgrade -y
apt install git make cmake
```
It's recommended to move your model inside the `~/` directory for best performance:
```
cd storage/downloads
mv model.gguf ~/
```
[Get the code](https://github.com/ggerganov/llama.cpp#get-the-code) & [follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`.
#### Building the Project using Android NDK
You can easily run `llama.cpp` on Android device with [termux](https://termux.dev/).
First, install the essential packages for termux:
```
pkg install clang wget git cmake
```
Second, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake:
You can execute the following commands on your computer to avoid downloading the NDK to your mobile. Of course, you can also do this in Termux.
Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake.
Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux:
```
$ mkdir build-android
$ cd build-android
@ -948,7 +963,9 @@ $ export NDK=<your_ndk_directory>
$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
$ make
```
Install [termux](https://termux.dev/) on your device and run `termux-setup-storage` to get access to your SD card.
Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice).
Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
@ -970,53 +987,10 @@ $cd /data/data/com.termux/files/home/bin
$./main -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml
```
Here is a demo of an interactive session running on Pixel 5 phone:
Here's a demo of an interactive session running on Pixel 5 phone:
https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
#### Building the Project using Termux (F-Droid)
Termux from F-Droid offers an alternative route to execute the project on an Android device. This method empowers you to construct the project right from within the terminal, negating the requirement for a rooted device or SD Card.
Outlined below are the directives for installing the project using OpenBLAS and CLBlast. This combination is specifically designed to deliver peak performance on recent devices that feature a GPU.
If you opt to utilize OpenBLAS, you'll need to install the corresponding package.
```
apt install libopenblas
```
Subsequently, if you decide to incorporate CLBlast, you'll first need to install the requisite OpenCL packages:
```
apt install ocl-icd opencl-headers opencl-clhpp clinfo
```
In order to compile CLBlast, you'll need to first clone the respective Git repository, which can be found at this URL: https://github.com/CNugteren/CLBlast. Alongside this, clone this repository into your home directory. Once this is done, navigate to the CLBlast folder and execute the commands detailed below:
```
cmake .
make
cp libclblast.so* $PREFIX/lib
cp ./include/clblast.h ../llama.cpp
```
Following the previous steps, navigate to the LlamaCpp directory. To compile it with OpenBLAS and CLBlast, execute the command provided below:
```
cp /data/data/com.termux/files/usr/include/openblas/cblas.h .
cp /data/data/com.termux/files/usr/include/openblas/openblas_config.h .
make LLAMA_CLBLAST=1 //(sometimes you need to run this command twice)
```
Upon completion of the aforementioned steps, you will have successfully compiled the project. To run it using CLBlast, a slight adjustment is required: a command must be issued to direct the operations towards your device's physical GPU, rather than the virtual one. The necessary command is detailed below:
```
GGML_OPENCL_PLATFORM=0
GGML_OPENCL_DEVICE=0
export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH
```
(Note: some Android devices, like the Zenfone 8, need the following command instead - "export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH". Source: https://www.reddit.com/r/termux/comments/kc3ynp/opencl_working_in_termux_more_in_comments/ )
For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle.
Place your desired model into the `~/llama.cpp/models/` directory and execute the `./main (...)` script.
### Docker
#### Prerequisites

112
ci/run.sh
View File

@ -160,9 +160,8 @@ function gg_run_test_scripts_debug {
set -e
# TODO: too slow, run on dedicated node
(cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
#(cd ./examples/quantize && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
(cd ./examples/quantize && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
set +e
}
@ -336,7 +335,8 @@ function gg_run_open_llama_3b_v2 {
(time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
(time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
function check_ppl {
qnt="$1"
@ -365,47 +365,6 @@ function gg_run_open_llama_3b_v2 {
cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.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 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
(time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
# q8_0
(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
# q8_0 + f16 lora-base
(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
compare_ppl "q8_0 / f16 base shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
set +e
}
@ -416,7 +375,6 @@ function gg_sum_open_llama_3b_v2 {
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.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)"
@ -429,11 +387,6 @@ function gg_sum_open_llama_3b_v2 {
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 '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.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
@ -517,7 +470,10 @@ function gg_run_open_llama_7b_v2 {
(time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
(time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
function check_ppl {
qnt="$1"
@ -546,48 +502,6 @@ function gg_run_open_llama_7b_v2 {
cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.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
}
@ -598,7 +512,6 @@ function gg_sum_open_llama_7b_v2 {
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.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)"
@ -611,11 +524,6 @@ function gg_sum_open_llama_7b_v2 {
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 '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.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)"
}
# bge-small
@ -691,8 +599,10 @@ test $ret -eq 0 && gg_run ctest_release
if [ -z ${GG_BUILD_LOW_PERF} ]; then
test $ret -eq 0 && gg_run embd_bge_small
test $ret -eq 0 && gg_run test_scripts_debug
test $ret -eq 0 && gg_run test_scripts_release
if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then
test $ret -eq 0 && gg_run test_scripts_debug
test $ret -eq 0 && gg_run test_scripts_release
fi
if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
if [ -z ${GG_BUILD_CUDA} ]; then

292
common/common.cpp
View File

@ -1,4 +1,6 @@
#include "common.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "json.hpp"
#include "json-schema-to-grammar.h"
#include "llama.h"
@ -67,7 +69,6 @@
#include <sys/syslimits.h>
#endif
#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
#define LLAMA_CURL_MAX_HEADER_LENGTH 256
#endif // LLAMA_USE_CURL
using json = nlohmann::ordered_json;
@ -77,7 +78,7 @@ int32_t get_num_physical_cores() {
// enumerate the set of thread siblings, num entries is num cores
std::unordered_set<std::string> siblings;
for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
std::ifstream thread_siblings("/sys/devices/system/cpu"
std::ifstream thread_siblings("/sys/devices/system/cpu/cpu"
+ std::to_string(cpu) + "/topology/thread_siblings");
if (!thread_siblings.is_open()) {
break; // no more cpus
@ -893,13 +894,17 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
invalid_param = true;
return true;
}
params.image = argv[i];
params.image.emplace_back(argv[i]);
return true;
}
if (arg == "-i" || arg == "--interactive") {
params.interactive = true;
return true;
}
if (arg == "--interactive-specials") {
params.interactive_specials = true;
return true;
}
if (arg == "--embedding") {
params.embedding = true;
return true;
@ -912,6 +917,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
params.instruct = true;
return true;
}
if (arg == "-cnv" || arg == "--conversation") {
params.conversation = true;
return true;
}
if (arg == "-cml" || arg == "--chatml") {
params.chatml = true;
return true;
@ -948,6 +957,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
params.cont_batching = true;
return true;
}
if (arg == "-fa" || arg == "--flash-attn") {
params.flash_attn = true;
return true;
}
if (arg == "--color") {
params.use_color = true;
return true;
@ -1324,6 +1337,29 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
return false;
}
void gpt_params_handle_model_default(gpt_params & params) {
if (!params.hf_repo.empty()) {
// short-hand to avoid specifying --hf-file -> default it to --model
if (params.hf_file.empty()) {
if (params.model.empty()) {
throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
}
params.hf_file = params.model;
} else if (params.model.empty()) {
params.model = "models/" + string_split(params.hf_file, '/').back();
}
} else if (!params.model_url.empty()) {
if (params.model.empty()) {
auto f = string_split(params.model_url, '#').front();
f = string_split(f, '?').front();
f = string_split(f, '/').back();
params.model = "models/" + f;
}
} else if (params.model.empty()) {
params.model = DEFAULT_MODEL_PATH;
}
}
bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
bool invalid_param = false;
std::string arg;
@ -1335,14 +1371,12 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
throw std::invalid_argument("error: unknown argument: " + arg);
}
}
if (invalid_param) {
throw std::invalid_argument("error: invalid parameter for argument: " + arg);
if (invalid_param) {
throw std::invalid_argument("error: invalid parameter for argument: " + arg);
}
}
if (params.prompt_cache_all &&
@ -1352,10 +1386,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
}
// short-hand to avoid specifying --hf-file -> default it to --model
if (!params.hf_repo.empty() && params.hf_file.empty()) {
params.hf_file = params.model;
}
gpt_params_handle_model_default(params);
if (params.escape) {
process_escapes(params.prompt);
@ -1393,7 +1424,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" -h, --help show this help message and exit\n");
printf(" --version show version and build info\n");
printf(" -i, --interactive run in interactive mode\n");
printf(" --interactive-specials allow special tokens in user text, in interactive mode\n");
printf(" --interactive-first run in interactive mode and wait for input right away\n");
printf(" -cnv, --conversation run in conversation mode (does not print special tokens and suffix/prefix)\n");
printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n");
printf(" -cml, --chatml run in chatml mode (use with ChatML-compatible models)\n");
printf(" --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n");
@ -1494,8 +1527,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences);
printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split);
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
printf(" -fa, --flash-attn enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled");
printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n");
printf(" --image IMAGE_FILE path to an image file. use with multimodal models\n");
printf(" --image IMAGE_FILE path to an image file. use with multimodal models. Specify multiple times for batching\n");
if (llama_supports_mlock()) {
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
}
@ -1548,7 +1582,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" --control-vector-layer-range START END\n");
printf(" layer range to apply the control vector(s) to, start and end inclusive\n");
printf(" -m FNAME, --model FNAME\n");
printf(" model path (default: %s)\n", params.model.c_str());
printf(" model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH);
printf(" -md FNAME, --model-draft FNAME\n");
printf(" draft model for speculative decoding (default: unused)\n");
printf(" -mu MODEL_URL, --model-url MODEL_URL\n");
@ -1866,6 +1900,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.cb_eval = params.cb_eval;
cparams.cb_eval_user_data = params.cb_eval_user_data;
cparams.offload_kqv = !params.no_kv_offload;
cparams.flash_attn = params.flash_attn;
cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
@ -1896,59 +1931,75 @@ void llama_batch_add(
#ifdef LLAMA_USE_CURL
static bool llama_download_file(CURL * curl, const char * url, const char * path) {
static bool starts_with(const std::string & str, const std::string & prefix) {
// While we wait for C++20's std::string::starts_with...
return str.rfind(prefix, 0) == 0;
}
static bool llama_download_file(const std::string & url, const std::string & path) {
// Initialize libcurl
std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
if (!curl) {
fprintf(stderr, "%s: error initializing libcurl\n", __func__);
return false;
}
bool force_download = false;
// Set the URL, allow to follow http redirection
curl_easy_setopt(curl, CURLOPT_URL, url);
curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
#if defined(_WIN32)
// CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
// operating system. Currently implemented under MS-Windows.
curl_easy_setopt(curl, CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
#endif
// Check if the file already exists locally
struct stat model_file_info;
auto file_exists = (stat(path, &model_file_info) == 0);
auto file_exists = (stat(path.c_str(), &model_file_info) == 0);
// If the file exists, check for ${path_model}.etag or ${path_model}.lastModified files
char etag[LLAMA_CURL_MAX_HEADER_LENGTH] = {0};
char etag_path[PATH_MAX] = {0};
snprintf(etag_path, sizeof(etag_path), "%s.etag", path);
char last_modified[LLAMA_CURL_MAX_HEADER_LENGTH] = {0};
char last_modified_path[PATH_MAX] = {0};
snprintf(last_modified_path, sizeof(last_modified_path), "%s.lastModified", path);
// If the file exists, check its JSON metadata companion file.
std::string metadata_path = path + ".json";
nlohmann::json metadata;
std::string etag;
std::string last_modified;
if (file_exists) {
auto * f_etag = fopen(etag_path, "r");
if (f_etag) {
if (!fgets(etag, sizeof(etag), f_etag)) {
fprintf(stderr, "%s: unable to read file %s\n", __func__, etag_path);
} else {
fprintf(stderr, "%s: previous file found %s: %s\n", __func__, etag_path, etag);
// Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
std::ifstream metadata_in(metadata_path);
if (metadata_in.good()) {
try {
metadata_in >> metadata;
fprintf(stderr, "%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
if (metadata.contains("url") && metadata.at("url").is_string()) {
auto previous_url = metadata.at("url").get<std::string>();
if (previous_url != url) {
fprintf(stderr, "%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
return false;
}
}
if (metadata.contains("etag") && metadata.at("etag").is_string()) {
etag = metadata.at("etag");
}
if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
last_modified = metadata.at("lastModified");
}
} catch (const nlohmann::json::exception & e) {
fprintf(stderr, "%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
return false;
}
fclose(f_etag);
}
auto * f_last_modified = fopen(last_modified_path, "r");
if (f_last_modified) {
if (!fgets(last_modified, sizeof(last_modified), f_last_modified)) {
fprintf(stderr, "%s: unable to read file %s\n", __func__, last_modified_path);
} else {
fprintf(stderr, "%s: previous file found %s: %s\n", __func__, last_modified_path,
last_modified);
}
fclose(f_last_modified);
}
} else {
fprintf(stderr, "%s: no previous model file found %s\n", __func__, path.c_str());
}
// Send a HEAD request to retrieve the etag and last-modified headers
struct llama_load_model_from_url_headers {
char etag[LLAMA_CURL_MAX_HEADER_LENGTH] = {0};
char last_modified[LLAMA_CURL_MAX_HEADER_LENGTH] = {0};
std::string etag;
std::string last_modified;
};
llama_load_model_from_url_headers headers;
{
@ -1956,38 +2007,37 @@ static bool llama_download_file(CURL * curl, const char * url, const char * path
auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata;
// Convert header field name to lowercase
for (size_t i = 0; i < n_items && buffer[i] != ':'; ++i) {
buffer[i] = tolower(buffer[i]);
}
static std::regex header_regex("([^:]+): (.*)\r\n");
static std::regex etag_regex("ETag", std::regex_constants::icase);
static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);
const char * etag_prefix = "etag: ";
if (strncmp(buffer, etag_prefix, strlen(etag_prefix)) == 0) {
strncpy(headers->etag, buffer + strlen(etag_prefix), n_items - strlen(etag_prefix) - 2); // Remove CRLF
}
const char * last_modified_prefix = "last-modified: ";
if (strncmp(buffer, last_modified_prefix, strlen(last_modified_prefix)) == 0) {
strncpy(headers->last_modified, buffer + strlen(last_modified_prefix),
n_items - strlen(last_modified_prefix) - 2); // Remove CRLF
std::string header(buffer, n_items);
std::smatch match;
if (std::regex_match(header, match, header_regex)) {
const std::string & key = match[1];
const std::string & value = match[2];
if (std::regex_match(key, match, etag_regex)) {
headers->etag = value;
} else if (std::regex_match(key, match, last_modified_regex)) {
headers->last_modified = value;
}
}
return n_items;
};
curl_easy_setopt(curl, CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 1L); // hide head request progress
curl_easy_setopt(curl, CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
curl_easy_setopt(curl, CURLOPT_HEADERDATA, &headers);
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
CURLcode res = curl_easy_perform(curl);
CURLcode res = curl_easy_perform(curl.get());
if (res != CURLE_OK) {
curl_easy_cleanup(curl);
fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
return false;
}
long http_code = 0;
curl_easy_getinfo(curl, CURLINFO_RESPONSE_CODE, &http_code);
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
if (http_code != 200) {
// HEAD not supported, we don't know if the file has changed
// force trigger downloading
@ -1996,28 +2046,30 @@ static bool llama_download_file(CURL * curl, const char * url, const char * path
}
}
// If the ETag or the Last-Modified headers are different: trigger a new download
bool should_download = !file_exists
|| force_download
|| (strlen(headers.etag) > 0 && strcmp(etag, headers.etag) != 0)
|| (strlen(headers.last_modified) > 0 && strcmp(last_modified, headers.last_modified) != 0);
bool should_download = !file_exists || force_download;
if (!should_download) {
if (!etag.empty() && etag != headers.etag) {
fprintf(stderr, "%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
should_download = true;
} else if (!last_modified.empty() && last_modified != headers.last_modified) {
fprintf(stderr, "%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
should_download = true;
}
}
if (should_download) {
char path_temporary[PATH_MAX] = {0};
snprintf(path_temporary, sizeof(path_temporary), "%s.downloadInProgress", path);
std::string path_temporary = path + ".downloadInProgress";
if (file_exists) {
fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path);
if (remove(path) != 0) {
curl_easy_cleanup(curl);
fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path);
fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
if (remove(path.c_str()) != 0) {
fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path.c_str());
return false;
}
}
// Set the output file
auto * outfile = fopen(path_temporary, "wb");
std::unique_ptr<FILE, decltype(&fclose)> outfile(fopen(path_temporary.c_str(), "wb"), fclose);
if (!outfile) {
curl_easy_cleanup(curl);
fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path);
fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str());
return false;
}
@ -2025,12 +2077,12 @@ static bool llama_download_file(CURL * curl, const char * url, const char * path
auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
return fwrite(data, size, nmemb, (FILE *)fd);
};
curl_easy_setopt(curl, CURLOPT_NOBODY, 0L);
curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
curl_easy_setopt(curl, CURLOPT_WRITEDATA, outfile);
curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L);
curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get());
// display download progress
curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L);
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L);
// helper function to hide password in URL
auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
@ -2049,51 +2101,34 @@ static bool llama_download_file(CURL * curl, const char * url, const char * path
// start the download
fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
llama_download_hide_password_in_url(url).c_str(), path, headers.etag, headers.last_modified);
auto res = curl_easy_perform(curl);
llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
auto res = curl_easy_perform(curl.get());
if (res != CURLE_OK) {
fclose(outfile);
curl_easy_cleanup(curl);
fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
return false;
}
long http_code = 0;
curl_easy_getinfo (curl, CURLINFO_RESPONSE_CODE, &http_code);
curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
if (http_code < 200 || http_code >= 400) {
fclose(outfile);
curl_easy_cleanup(curl);
fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code);
return false;
}
// Clean up
fclose(outfile);
// Causes file to be closed explicitly here before we rename it.
outfile.reset();
// Write the new ETag to the .etag file
if (strlen(headers.etag) > 0) {
auto * etag_file = fopen(etag_path, "w");
if (etag_file) {
fputs(headers.etag, etag_file);
fclose(etag_file);
fprintf(stderr, "%s: file etag saved %s: %s\n", __func__, etag_path, headers.etag);
}
}
// Write the updated JSON metadata file.
metadata.update({
{"url", url},
{"etag", headers.etag},
{"lastModified", headers.last_modified}
});
std::ofstream(metadata_path) << metadata.dump(4);
fprintf(stderr, "%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
// Write the new lastModified to the .etag file
if (strlen(headers.last_modified) > 0) {
auto * last_modified_file = fopen(last_modified_path, "w");
if (last_modified_file) {
fputs(headers.last_modified, last_modified_file);
fclose(last_modified_file);
fprintf(stderr, "%s: file last modified saved %s: %s\n", __func__, last_modified_path,
headers.last_modified);
}
}
if (rename(path_temporary, path) != 0) {
curl_easy_cleanup(curl);
fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary, path);
if (rename(path_temporary.c_str(), path.c_str()) != 0) {
fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
return false;
}
}
@ -2111,15 +2146,7 @@ struct llama_model * llama_load_model_from_url(
return NULL;
}
// Initialize libcurl
auto * curl = curl_easy_init();
if (!curl) {
fprintf(stderr, "%s: error initializing libcurl\n", __func__);
return NULL;
}
if (!llama_download_file(curl, model_url, path_model)) {
if (!llama_download_file(model_url, path_model)) {
return NULL;
}
@ -2133,7 +2160,6 @@ struct llama_model * llama_load_model_from_url(
auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params);
if (!ctx_gguf) {
fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, path_model);
curl_easy_cleanup(curl);
return NULL;
}
@ -2145,8 +2171,6 @@ struct llama_model * llama_load_model_from_url(
gguf_free(ctx_gguf);
}
curl_easy_cleanup(curl);
if (n_split > 1) {
char split_prefix[PATH_MAX] = {0};
char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0};
@ -2177,11 +2201,7 @@ struct llama_model * llama_load_model_from_url(
char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);
auto * curl = curl_easy_init();
bool res = llama_download_file(curl, split_url, split_path);
curl_easy_cleanup(curl);
return res;
return llama_download_file(split_url, split_path);
}, idx));
}
@ -2635,6 +2655,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
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_specials: %s # default: false\n", params.interactive_specials ? "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());
@ -2668,7 +2689,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.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, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
@ -2703,6 +2724,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed);
fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());

15
common/common.h
View File

@ -31,6 +31,8 @@
fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \
} while(0)
#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
// build info
extern int LLAMA_BUILD_NUMBER;
extern char const *LLAMA_COMMIT;
@ -92,7 +94,7 @@ struct gpt_params {
// // sampling parameters
struct llama_sampling_params sparams;
std::string model = "models/7B/ggml-model-f16.gguf"; // model path
std::string model = ""; // model path
std::string model_draft = ""; // draft model for speculative decoding
std::string model_alias = "unknown"; // model alias
std::string model_url = ""; // model url to download
@ -133,11 +135,13 @@ struct gpt_params {
bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
bool kl_divergence = false; // compute KL-divergence
bool kl_divergence = false; // compute KL divergence
bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs
bool interactive = false; // interactive mode
bool interactive_specials = false; // whether to allow special tokens from user, during interactive mode
bool conversation = false; // conversation mode (does not print special tokens and suffix/prefix)
bool chatml = false; // chatml mode (used for models trained on chatml syntax)
bool prompt_cache_all = false; // save user input and generations to prompt cache
bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it
@ -148,6 +152,7 @@ struct gpt_params {
bool multiline_input = false; // reverse the usage of `\`
bool simple_io = false; // improves compatibility with subprocesses and limited consoles
bool cont_batching = true; // insert new sequences for decoding on-the-fly
bool flash_attn = false; // flash attention
bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix
bool ignore_eos = false; // ignore generated EOS tokens
@ -167,10 +172,12 @@ struct gpt_params {
std::string cache_type_v = "f16"; // KV cache data type for the V
// multimodal models (see examples/llava)
std::string mmproj = ""; // path to multimodal projector
std::string image = ""; // path to an image file
std::string mmproj = ""; // path to multimodal projector
std::vector<std::string> image; // path to image file(s)
};
void gpt_params_handle_model_default(gpt_params & params);
bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params);

9
common/grammar-parser.cpp
View File

@ -142,6 +142,9 @@ namespace grammar_parser {
pos++;
last_sym_start = out_elements.size();
while (*pos != '"') {
if (!*pos) {
throw std::runtime_error("unexpected end of input");
}
auto char_pair = parse_char(pos);
pos = char_pair.second;
out_elements.push_back({LLAMA_GRETYPE_CHAR, char_pair.first});
@ -156,6 +159,9 @@ namespace grammar_parser {
}
last_sym_start = out_elements.size();
while (*pos != ']') {
if (!*pos) {
throw std::runtime_error("unexpected end of input");
}
auto char_pair = parse_char(pos);
pos = char_pair.second;
enum llama_gretype type = last_sym_start < out_elements.size()
@ -164,6 +170,9 @@ namespace grammar_parser {
out_elements.push_back({type, char_pair.first});
if (pos[0] == '-' && pos[1] != ']') {
if (!pos[1]) {
throw std::runtime_error("unexpected end of input");
}
auto endchar_pair = parse_char(pos + 1);
pos = endchar_pair.second;
out_elements.push_back({LLAMA_GRETYPE_CHAR_RNG_UPPER, endchar_pair.first});

4
common/json-schema-to-grammar.h
View File

@ -1,4 +1,8 @@
#pragma once
#include "ggml.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "json.hpp"
std::string json_schema_to_grammar(const nlohmann::ordered_json& schema);

4
common/log.h
View File

@ -234,7 +234,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
// INTERNAL, DO NOT USE
// USE LOG() INSTEAD
//
#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER)
#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) || defined(__clang__)
#define LOG_IMPL(str, ...) \
do { \
if (LOG_TARGET != nullptr) \
@ -257,7 +257,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
// INTERNAL, DO NOT USE
// USE LOG_TEE() INSTEAD
//
#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER)
#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) || defined(__clang__)
#define LOG_TEE_IMPL(str, ...) \
do { \
if (LOG_TARGET != nullptr) \

5
common/sampling.cpp
View File

@ -35,6 +35,8 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_
result->prev.resize(params.n_prev);
result->n_valid = 0;
llama_sampling_set_rng_seed(result, params.seed);
return result;
@ -64,6 +66,7 @@ void llama_sampling_reset(llama_sampling_context * ctx) {
std::fill(ctx->prev.begin(), ctx->prev.end(), 0);
ctx->cur.clear();
ctx->n_valid = 0;
}
void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed) {
@ -253,6 +256,8 @@ static llama_token llama_sampling_sample_impl(
}
}
ctx_sampling->n_valid = temp == 0.0f ? 0 : cur_p.size;
return id;
}

1
common/sampling.h
View File

@ -81,6 +81,7 @@ struct llama_sampling_context {
// TODO: replace with ring-buffer
std::vector<llama_token> prev;
std::vector<llama_token_data> cur;
size_t n_valid; // Number of correct top tokens with correct probabilities.
std::mt19937 rng;
};

207
convert-hf-to-gguf-update.py Normal file → Executable file
View File

@ -1,3 +1,5 @@
#!/usr/bin/env python3
# This script downloads the tokenizer models of the specified models from Huggingface and
# generates the get_vocab_base_pre() function for convert-hf-to-gguf.py
#
@ -21,6 +23,7 @@
# TODO: automate the update of convert-hf-to-gguf.py
#
import logging
import os
import requests
import sys
@ -28,49 +31,69 @@ import json
from hashlib import sha256
from enum import IntEnum, auto
from transformers import AutoTokenizer
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger("convert-hf-to-gguf-update")
class TOKENIZER_TYPE(IntEnum):
SPM = auto()
BPE = auto()
WPM = auto()
# TODO: this string has to exercise as much pre-tokenizer functionality as possible
# will be updated with time - contributions welcome
chktxt = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
if len(sys.argv) == 2:
token = sys.argv[1]
if not token.startswith("hf_"):
logger.info("Huggingface token seems invalid")
logger.info("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
sys.exit(1)
else:
print("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
logger.info("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
sys.exit(1)
# TODO: add models here, base models preferred
models = [
{ "name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
{ "name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
{ "name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", },
{ "name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", },
{ "name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
{ "name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
{ "name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
{ "name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
{ "name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
{ "name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
]
{"name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
{"name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
{"name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", },
{"name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", },
{"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
{"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
{"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
{"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
{"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
{"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
{"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
{"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
{"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
{"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
{"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
{"name": "jina-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
{"name": "jina-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
{"name": "jina-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
]
# make directory "models/tokenizers" if it doesn't exist
if not os.path.exists("models/tokenizers"):
os.makedirs("models/tokenizers")
def download_file_with_auth(url, token, save_path):
headers = {"Authorization": f"Bearer {token}"}
response = requests.get(url, headers=headers)
if response.status_code == 200:
with open(save_path, 'wb') as f:
f.write(response.content)
print(f"File {save_path} downloaded successfully")
logger.info(f"File {save_path} downloaded successfully")
else:
print(f"Failed to download file. Status code: {response.status_code}")
logger.info(f"Failed to download file. Status code: {response.status_code}")
# download the tokenizer models
for model in models:
@ -81,10 +104,10 @@ for model in models:
if not os.path.exists(f"models/tokenizers/{name}"):
os.makedirs(f"models/tokenizers/{name}")
else:
print(f"Directory models/tokenizers/{name} already exists - skipping")
logger.info(f"Directory models/tokenizers/{name} already exists - skipping")
continue
print(f"Downloading {name} to models/tokenizers/{name}")
logger.info(f"Downloading {name} to models/tokenizers/{name}")
url = f"{repo}/raw/main/config.json"
save_path = f"models/tokenizers/{name}/config.json"
@ -94,6 +117,14 @@ for model in models:
save_path = f"models/tokenizers/{name}/tokenizer.json"
download_file_with_auth(url, token, save_path)
# if downloaded file is less than 1KB, we likely need to download an LFS instead
if os.path.getsize(save_path) < 1024:
# remove the file
os.remove(save_path)
url = f"{repo}/resolve/main/tokenizer.json"
save_path = f"models/tokenizers/{name}/tokenizer.json"
download_file_with_auth(url, token, save_path)
if tokt == TOKENIZER_TYPE.SPM:
url = f"{repo}/resolve/main/tokenizer.model"
save_path = f"models/tokenizers/{name}/tokenizer.model"
@ -114,77 +145,96 @@ for model in models:
if tokt == TOKENIZER_TYPE.SPM:
continue
# Skip if the tokenizer folder does not exist or there are other download issues previously
if not os.path.exists(f"models/tokenizers/{name}"):
logger.warning(f"Directory for tokenizer {name} not found. Skipping...")
continue
# create the tokenizer
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
try:
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
except OSError as e:
logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
continue # Skip to the next model if the tokenizer can't be loaded
chktok = tokenizer.encode(chktxt)
chkhsh = sha256(str(chktok).encode()).hexdigest()
print(f"model: {name}")
print(f"tokt: {tokt}")
print(f"repo: {model['repo']}")
print(f"chktok: {chktok}")
print(f"chkhsh: {chkhsh}")
logger.info(f"model: {name}")
logger.info(f"tokt: {tokt}")
logger.info(f"repo: {model['repo']}")
logger.info(f"chktok: {chktok}")
logger.info(f"chkhsh: {chkhsh}")
# print the "pre_tokenizer" content from the tokenizer.json
with open(f"models/tokenizers/{name}/tokenizer.json", "r") as f:
with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f:
cfg = json.load(f)
normalizer = cfg["normalizer"]
logger.info("normalizer: " + json.dumps(normalizer, indent=4))
pre_tokenizer = cfg["pre_tokenizer"]
print("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
if "ignore_merges" in cfg["model"]:
logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4))
print(f"\n")
logger.info("")
src_ifs += f" if chkhsh == \"{chkhsh}\":\n"
src_ifs += f" # ref: {model['repo']}\n"
src_ifs += f" res = \"{name}\"\n"
src_func = ""
src_func += " def get_vocab_base_pre(self, tokenizer) -> str:\n"
src_func += " # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that\n"
src_func += " # is specific for the BPE pre-tokenizer used by the model\n"
src_func += " # we will use this unique identifier to write a \"tokenizer.ggml.pre\" entry in the GGUF file which we can\n"
src_func += " # use in llama.cpp to implement the same pre-tokenizer\n"
src_func += "\n"
src_func += f" chktxt = {repr(chktxt)}\n"
src_func += "\n"
src_func += " chktok = tokenizer.encode(chktxt)\n"
src_func += " chkhsh = sha256(str(chktok).encode()).hexdigest()\n"
src_func += "\n"
src_func += " print(f\"chktok: {chktok}\")\n"
src_func += " print(f\"chkhsh: {chkhsh}\")\n"
src_func += "\n"
src_func += " res = None\n"
src_func += "\n"
src_func += " # NOTE: if you get an error here, you need to add the model to the if-elif chain below\n"
src_func += " # don't do this manually - use the convert-hf-to-gguf-update.py script!\n"
src_func += f"{src_ifs}\n"
src_func += " if res is None:\n"
src_func += " print(\"\\n\")\n"
src_func += " print(\"**************************************************************************************\")\n"
src_func += " print(\"** WARNING: The BPE pre-tokenizer was not recognized!\")\n"
src_func += " print(\"** This means that it was not added yet or you are using an older version.\")\n"
src_func += " print(\"** Check convert-hf-to-gguf-update.py and update it accordingly.\")\n"
src_func += " print(\"**\")\n"
src_func += " print(f\"** chkhsh: {chkhsh}\")\n"
src_func += " print(\"**************************************************************************************\")\n"
src_func += " print(\"\\n\")\n"
src_func += " raise NotImplementedError(\"BPE pre-tokenizer was not recognized - update get_vocab_base_pre()\")\n"
src_func += "\n"
src_func += " print(f\"tokenizer.ggml.pre: {res}\")\n"
src_func += " print(f\"chkhsh: {chkhsh}\")\n"
src_func += "\n"
src_func += " return res\n"
src_func = f"""
def get_vocab_base_pre(self, tokenizer) -> str:
# encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
# is specific for the BPE pre-tokenizer used by the model
# we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
# use in llama.cpp to implement the same pre-tokenizer
print(src_func)
chktxt = {repr(chktxt)}
print("\n")
print("!!! Copy-paste the function above into convert-hf-to-gguf.py !!!")
print("\n")
chktok = tokenizer.encode(chktxt)
chkhsh = sha256(str(chktok).encode()).hexdigest()
logger.debug(f"chktok: {{chktok}}")
logger.debug(f"chkhsh: {{chkhsh}}")
res = None
# NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script
# or pull the latest version of the model from Huggingface
# don't edit the hashes manually!
{src_ifs}
if res is None:
logger.warning("\\n")
logger.warning("**************************************************************************************")
logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
logger.warning("** There are 2 possible reasons for this:")
logger.warning("** - the model has not been added to convert-hf-to-gguf-update.py yet")
logger.warning("** - the pre-tokenization config has changed upstream")
logger.warning("** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
logger.warning("** ref: https://github.com/ggerganov/llama.cpp/pull/6920")
logger.warning("**")
logger.warning(f"** chkhsh: {{chkhsh}}")
logger.warning("**************************************************************************************")
logger.warning("\\n")
raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
logger.debug(f"tokenizer.ggml.pre: {{repr(res)}}")
logger.debug(f"chkhsh: {{chkhsh}}")
return res
"""
print(src_func) # noqa: NP100
logger.info("\n")
logger.info("!!! Copy-paste the function above into convert-hf-to-gguf.py !!!")
logger.info("\n")
# generate tests for each tokenizer model
tests = [
"ied 4 ½ months",
"Führer",
"",
" ",
" ",
@ -225,6 +275,7 @@ tests = [
"3333333",
"33333333",
"333333333",
# "Cửa Việt", # llama-bpe fails on this
chktxt,
]
@ -245,11 +296,19 @@ for model in models:
name = model["name"]
tokt = model["tokt"]
# create the tokenizer
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
# Skip if the tokenizer folder does not exist or there are other download issues previously
if not os.path.exists(f"models/tokenizers/{name}"):
logger.warning(f"Directory for tokenizer {name} not found. Skipping...")
continue
with open(f"models/ggml-vocab-{name}.gguf.inp", "w") as f:
# create the tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
except OSError as e:
logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
continue # Skip this model and continue with the next one in the loop
with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f:
for text in tests:
f.write(f"{text}")
f.write("\n__ggml_vocab_test__\n")
@ -261,15 +320,15 @@ for model in models:
f.write(f" {r}")
f.write("\n")
print(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*")
logger.info(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*")
# generate commands for creating vocab files
print("\nRun the following commands to generate the vocab files for testing:\n")
logger.info("\nRun the following commands to generate the vocab files for testing:\n")
for model in models:
name = model["name"]
print(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only")
print(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100
print("\n")
logger.info("\n")

2237
convert-hf-to-gguf.py
View File

File diff suppressed because it is too large Load Diff

51
convert-llama-ggml-to-gguf.py
View File

@ -1,6 +1,7 @@
#!/usr/bin/env python3
from __future__ import annotations
import logging
import argparse
import os
import struct
@ -14,6 +15,8 @@ if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
logger = logging.getLogger("ggml-to-gguf")
class GGMLFormat(IntEnum):
GGML = 0
@ -125,7 +128,6 @@ class Tensor:
self.start_offset = offset
self.len_bytes = n_bytes
offset += n_bytes
# print(n_dims, name_len, dtype, self.dims, self.name, pad)
return offset - orig_offset
@ -175,7 +177,7 @@ class GGMLModel:
offset += self.validate_header(data, offset)
hp = Hyperparameters()
offset += hp.load(data, offset)
print(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}')
logger.info(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}')
self.validate_conversion(hp.ftype)
vocab = Vocab(load_scores = self.file_format > GGMLFormat.GGML)
offset += vocab.load(data, offset, hp.n_vocab)
@ -215,12 +217,12 @@ class GGMLToGGUF:
if float(hp.n_head) / float(x) == gqa:
n_kv_head = x
assert n_kv_head is not None, "Couldn't determine n_kv_head from GQA param"
print(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}')
logger.info(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}')
self.n_kv_head = n_kv_head
self.name_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.LLAMA, ggml_model.hyperparameters.n_layer)
def save(self):
print('* Preparing to save GGUF file')
logger.info('* Preparing to save GGUF file')
gguf_writer = gguf.GGUFWriter(
self.cfg.output,
gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA],
@ -230,11 +232,11 @@ class GGMLToGGUF:
if self.special_vocab is not None:
self.special_vocab.add_to_gguf(gguf_writer)
self.add_tensors(gguf_writer)
print(" gguf: write header")
logger.info(" gguf: write header")
gguf_writer.write_header_to_file()
print(" gguf: write metadata")
logger.info(" gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print(" gguf: write tensors")
logger.info(" gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
@ -250,7 +252,7 @@ class GGMLToGGUF:
name = cfg.name if cfg.name is not None else cfg.input.name
except UnicodeDecodeError:
name = None
print('* Adding model parameters and KV items')
logger.info('* Adding model parameters and KV items')
if name is not None:
gguf_writer.add_name(name)
gguf_writer.add_description(desc)
@ -287,7 +289,7 @@ class GGMLToGGUF:
toktypes = []
if self.vocab_override is not None:
vo = self.vocab_override
print('* Adding vocab item(s)')
logger.info('* Adding vocab item(s)')
for (idx, (vbytes, score, ttype)) in enumerate(vo.all_tokens()):
tokens.append(vbytes)
scores.append(score)
@ -299,7 +301,7 @@ class GGMLToGGUF:
if len(toktypes) > 0:
gguf_writer.add_token_types(toktypes)
return
print(f'* Adding {hp.n_vocab} vocab item(s)')
logger.info(f'* Adding {hp.n_vocab} vocab item(s)')
assert len(self.model.vocab.items) >= 3, 'Cannot handle unexpectedly short model vocab'
for (tokid, (vbytes, vscore)) in enumerate(self.model.vocab.items):
tt = 1 # Normal
@ -334,7 +336,7 @@ class GGMLToGGUF:
def add_tensors(self, gguf_writer):
tensor_map = self.name_map
data = self.data
print(f'* Adding {len(self.model.tensors)} tensor(s)')
logger.info(f'* Adding {len(self.model.tensors)} tensor(s)')
for tensor in self.model.tensors:
name = str(tensor.name, 'UTF-8')
mapped_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
@ -344,7 +346,6 @@ class GGMLToGGUF:
temp = tempdims[1]
tempdims[1] = tempdims[0]
tempdims[0] = temp
# print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}')
gguf_writer.add_tensor(
mapped_name,
data[tensor.start_offset:tensor.start_offset + tensor.len_bytes],
@ -401,33 +402,35 @@ def handle_args():
help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
parser.add_argument("--vocabtype", default="spm,hfft",
help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)")
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
return parser.parse_args()
def main():
cfg = handle_args()
print(f'* Using config: {cfg}')
print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n')
logging.basicConfig(level=logging.DEBUG if cfg.verbose else logging.INFO)
logger.info(f'* Using config: {cfg}')
logger.warning('=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===')
if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'):
print('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".')
logger.info('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".')
data = np.memmap(cfg.input, mode = 'r')
model = GGMLModel()
print('* Scanning GGML input file')
logger.info('* Scanning GGML input file')
offset = model.load(data, 0) # noqa
print(f'* GGML model hyperparameters: {model.hyperparameters}')
logger.info(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, 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}')
logger.info('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.')
logger.info(f'* Overriding params: {params_override}')
logger.info(f'* Overriding vocab: {vocab_override}')
logger.info(f'* Special vocab: {special_vocab}')
else:
print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
logger.warning('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
if model.file_format == GGMLFormat.GGML:
print('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!')
logger.info('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!')
converter = GGMLToGGUF(
model, data, cfg,
params_override = params_override,
@ -435,7 +438,7 @@ def main():
special_vocab = special_vocab
)
converter.save()
print(f'* Successful completion. Output saved to: {cfg.output}')
logger.info(f'* Successful completion. Output saved to: {cfg.output}')
if __name__ == '__main__':

148
convert-lora-to-ggml.py
View File

@ -1,148 +0,0 @@
#!/usr/bin/env python3
from __future__ import annotations
import json
import os
import struct
import sys
from pathlib import Path
from typing import Any, BinaryIO, Sequence
import numpy as np
import torch
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
import gguf
NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
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"]))
# https://opendelta.readthedocs.io/en/latest/modules/deltas.html says that `lora_alpha` is an int
# but some models ship a float value instead
# let's convert to int, but fail if lossless conversion is not possible
assert (
int(params["lora_alpha"]) == params["lora_alpha"]
), "cannot convert float to int losslessly"
fout.write(struct.pack("i", int(params["lora_alpha"])))
def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None:
sname = name.encode("utf-8")
fout.write(
struct.pack(
"iii",
len(shape),
len(sname),
NUMPY_TYPE_TO_FTYPE[data_type.name],
)
)
fout.write(struct.pack("i" * len(shape), *shape[::-1]))
fout.write(sname)
fout.seek((fout.tell() + 31) & -32)
if __name__ == '__main__':
if len(sys.argv) < 2:
print(f"Usage: python {sys.argv[0]} <path> [arch]")
print(
"Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
)
print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
sys.exit(1)
input_json = os.path.join(sys.argv[1], "adapter_config.json")
input_model = os.path.join(sys.argv[1], "adapter_model.bin")
output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
if os.path.exists(input_model):
model = torch.load(input_model, map_location="cpu")
else:
input_model = os.path.join(sys.argv[1], "adapter_model.safetensors")
# lazy import load_file only if lora is in safetensors format.
from safetensors.torch import load_file
model = load_file(input_model, device="cpu")
arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
if arch_name not in gguf.MODEL_ARCH_NAMES.values():
print(f"Error: unsupported architecture {arch_name}")
sys.exit(1)
arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
with open(input_json, "r") as f:
params = json.load(f)
if params["peft_type"] != "LORA":
print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA")
sys.exit(1)
if params["fan_in_fan_out"] is True:
print("Error: param fan_in_fan_out is not supported")
sys.exit(1)
if params["bias"] is not None and params["bias"] != "none":
print("Error: param bias is not supported")
sys.exit(1)
# TODO: these seem to be layers that have been trained but without lora.
# doesn't seem widely used but eventually should be supported
if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0:
print("Error: param modules_to_save is not supported")
sys.exit(1)
with open(output_path, "wb") as fout:
fout.truncate()
write_file_header(fout, params)
for k, v in model.items():
orig_k = k
if k.endswith(".default.weight"):
k = k.replace(".default.weight", ".weight")
if k in ["llama_proj.weight", "llama_proj.bias"]:
continue
if k.endswith("lora_A.weight"):
if v.dtype != torch.float16 and v.dtype != torch.float32:
v = v.float()
v = v.T
else:
v = v.float()
t = v.detach().numpy()
prefix = "base_model.model."
if k.startswith(prefix):
k = k[len(prefix) :]
lora_suffixes = (".lora_A.weight", ".lora_B.weight")
if k.endswith(lora_suffixes):
suffix = k[-len(lora_suffixes[0]):]
k = k[: -len(lora_suffixes[0])]
else:
print(f"Error: unrecognized tensor name {orig_k}")
sys.exit(1)
tname = name_map.get_name(k)
if tname is None:
print(f"Error: could not map tensor name {orig_k}")
print(" Note: the arch parameter must be specified if the model is not llama")
sys.exit(1)
if suffix == ".lora_A.weight":
tname += ".weight.loraA"
elif suffix == ".lora_B.weight":
tname += ".weight.loraB"
else:
assert False
print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
write_tensor_header(fout, tname, t.shape, t.dtype)
t.tofile(fout)
print(f"Converted {input_json} and {input_model} to {output_path}")

28
convert-persimmon-to-gguf.py
View File

@ -1,6 +1,7 @@
#!/usr/bin/env python3
from __future__ import annotations
import logging
import argparse
import os
import sys
@ -14,6 +15,8 @@ if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
logger = logging.getLogger("persimmon-to-gguf")
def _flatten_dict(dct, tensors, prefix=None):
assert isinstance(dct, dict)
@ -30,9 +33,9 @@ def _flatten_dict(dct, tensors, prefix=None):
def _get_sentencepiece_tokenizer_info(dir_model: Path):
tokenizer_path = dir_model / 'adept_vocab.model'
print('gguf: getting sentencepiece tokenizer from', tokenizer_path)
logger.info('getting sentencepiece tokenizer from', tokenizer_path)
tokenizer = SentencePieceProcessor(str(tokenizer_path))
print('gguf: adding tokens')
logger.info('adding tokens')
tokens: list[bytes] = []
scores: list[float] = []
toktypes: list[int] = []
@ -67,8 +70,10 @@ def main():
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("--ckpt-path", type=Path, help="path to persimmon checkpoint .pt file")
parser.add_argument("--model-dir", type=Path, help="directory containing model e.g. 8b_chat_model_release")
parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory")
parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory")
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
args = parser.parse_args()
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
sys.path.append(str(args.adept_inference_dir))
persimmon_model = torch.load(args.ckpt_path)
hparams = persimmon_model['args']
@ -107,7 +112,7 @@ def main():
gguf_writer.add_eos_token_id(71013)
tensor_map = gguf.get_tensor_name_map(arch, block_count)
print(tensor_map)
logger.info(tensor_map)
for name in tensors.keys():
data_torch = tensors[name]
if name.endswith(".self_attention.rotary_emb.inv_freq"):
@ -117,22 +122,21 @@ def main():
data = data_torch.to(torch.float32).squeeze().numpy()
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
if new_name is None:
print("Can not map tensor '" + name + "'")
sys.exit()
raise ValueError(f"Can not map tensor '{name}'")
n_dims = len(data.shape)
print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
logger.debug(f"{new_name}, n_dims = {str(n_dims)}, {str(old_dtype)} --> {str(data.dtype)}")
gguf_writer.add_tensor(new_name, data)
print("gguf: write header")
logger.info("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
logger.info("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensors")
logger.info("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
print(f"gguf: model successfully exported to '{args.outfile}'")
print("")
logger.info(f"gguf: model successfully exported to '{args.outfile}'")
if __name__ == '__main__':

125
convert.py
View File

@ -1,6 +1,7 @@
#!/usr/bin/env python3
from __future__ import annotations
import logging
import argparse
import concurrent.futures
import enum
@ -35,6 +36,8 @@ import gguf
if TYPE_CHECKING:
from typing_extensions import Self, TypeAlias
logger = logging.getLogger("convert")
if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'):
faulthandler.register(signal.SIGUSR1)
@ -281,6 +284,7 @@ class Params:
n_experts = None
n_experts_used = None
f_rope_freq_base = None
n_ff = None
# hack to determine LLaMA v1 vs v2 vs CodeLlama
if config.get("moe"):
@ -305,6 +309,8 @@ class Params:
n_experts_used = config["moe"]["num_experts_per_tok"]
f_rope_freq_base = 1e6
assert n_ff is not None
return Params(
n_vocab = model["tok_embeddings.weight"].shape[0],
n_embd = config["dim"],
@ -459,7 +465,8 @@ class SentencePieceVocab(Vocab):
# not found in alternate location either
raise FileNotFoundError('Cannot find tokenizer.model')
self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
self.sentencepiece_tokenizer = SentencePieceProcessor()
self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer))
vocab_size = self.sentencepiece_tokenizer.vocab_size()
new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
@ -479,23 +486,23 @@ class SentencePieceVocab(Vocab):
def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
tokenizer = self.sentencepiece_tokenizer
for i in range(tokenizer.vocab_size()):
piece = tokenizer.id_to_piece(i)
piece = tokenizer.IdToPiece(i)
text = piece.encode("utf-8")
score: float = tokenizer.get_score(i)
score: float = tokenizer.GetScore(i)
toktype = gguf.TokenType.NORMAL
if tokenizer.is_unknown(i):
if tokenizer.IsUnknown(i):
toktype = gguf.TokenType.UNKNOWN
if tokenizer.is_control(i):
if tokenizer.IsControl(i):
toktype = gguf.TokenType.CONTROL
# NOTE: I think added_tokens are user defined.
# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
# if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED
if tokenizer.is_unused(i):
if tokenizer.IsUnused(i):
toktype = gguf.TokenType.UNUSED
if tokenizer.is_byte(i):
if tokenizer.IsByte(i):
toktype = gguf.TokenType.BYTE
yield text, score, toktype
@ -643,7 +650,6 @@ class LlamaHfVocab(Vocab):
def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
# print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) )
if n_head_kv is not None and n_head != n_head_kv:
n_head = n_head_kv
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
@ -904,7 +910,7 @@ class LazyUnpickler(pickle.Unpickler):
def rebuild_from_type_v2(func, new_type, args, state):
return func(*args)
CLASSES = {
CLASSES: dict[tuple[str, str], type[LazyTensor] | LazyStorageKind] = {
# getattr used here as a workaround for mypy not being smart enough to determine
# the staticmethods have a __func__ attribute.
('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),
@ -1033,12 +1039,12 @@ def check_vocab_size(params: Params, vocab: BaseVocab, pad_vocab: bool = False)
# Check for a vocab size mismatch
if params.n_vocab == vocab.vocab_size:
print("Ignoring added_tokens.json since model matches vocab size without it.")
logger.warning("Ignoring added_tokens.json since model matches vocab size without it.")
return
if pad_vocab and params.n_vocab > vocab.vocab_size:
pad_count = params.n_vocab - vocab.vocab_size
print(
logger.debug(
f"Padding vocab with {pad_count} token(s) - <dummy00001> through <dummy{pad_count:05}>"
)
for i in range(1, pad_count + 1):
@ -1166,7 +1172,7 @@ class OutputFile:
elapsed = time.time() - start
size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
padi = len(str(len(model)))
print(
logger.info(
f"[{i + 1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
)
self.gguf.write_tensor_data(ndarray)
@ -1281,12 +1287,12 @@ def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) ->
# HF models permut or pack some of the tensors, so we need to undo that
for i in itertools.count():
if f"model.layers.{i}.self_attn.q_proj.weight" in model:
print(f"Permuting layer {i}")
logger.debug(f"Permuting layer {i}")
tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head, params.n_head)
tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_head_kv)
# tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
print(f"Unpacking and permuting layer {i}")
logger.debug(f"Unpacking and permuting layer {i}")
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)
@ -1299,15 +1305,15 @@ def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) ->
tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None)
if name_new is None:
if skip_unknown:
print(f"Unexpected tensor name: {name} - skipping")
logger.warning(f"Unexpected tensor name: {name} - skipping")
continue
raise ValueError(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)")
if tensor_type in should_skip:
print(f"skipping tensor {name_new}")
logger.debug(f"skipping tensor {name_new}")
continue
print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
logger.debug(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
out[name_new] = lazy_tensor
return out
@ -1372,7 +1378,7 @@ def load_some_model(path: Path) -> ModelPlus:
paths = find_multifile_paths(path)
models_plus: list[ModelPlus] = []
for path in paths:
print(f"Loading model file {path}")
logger.info(f"Loading model file {path}")
models_plus.append(lazy_load_file(path))
model_plus = merge_multifile_models(models_plus)
@ -1413,7 +1419,7 @@ class VocabFactory:
else:
raise FileNotFoundError(f"Could not find a tokenizer matching any of {vocab_types}")
print(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}")
logger.info(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}")
return vocab
def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) -> tuple[BaseVocab, gguf.SpecialVocab]:
@ -1438,19 +1444,19 @@ def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path:
}[file_type]
ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf"
if ret in model_paths:
sys.stderr.write(
logger.error(
f"Error: Default output path ({ret}) would overwrite the input. "
"Please explicitly specify a path using --outfile.\n")
"Please explicitly specify a path using --outfile.")
sys.exit(1)
return ret
def do_dump_model(model_plus: ModelPlus) -> None:
print(f"model_plus.paths = {model_plus.paths!r}")
print(f"model_plus.format = {model_plus.format!r}")
print(f"model_plus.vocab = {model_plus.vocab!r}")
print(f"model_plus.paths = {model_plus.paths!r}") # noqa: NP100
print(f"model_plus.format = {model_plus.format!r}") # noqa: NP100
print(f"model_plus.vocab = {model_plus.vocab!r}") # noqa: NP100
for name, lazy_tensor in model_plus.model.items():
print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}")
print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") # noqa: NP100
def main(args_in: list[str] | None = None) -> None:
@ -1473,8 +1479,18 @@ def main(args_in: list[str] | None = None) -> None:
parser.add_argument("--big-endian", action="store_true", help="model is executed on big endian machine")
parser.add_argument("--pad-vocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides")
parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing")
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
args = parser.parse_args(args_in)
if args.verbose:
logging.basicConfig(level=logging.DEBUG)
elif args.dump_single or args.dump:
# Avoid printing anything besides the dump output
logging.basicConfig(level=logging.WARNING)
else:
logging.basicConfig(level=logging.INFO)
if args.no_vocab and args.vocab_only:
raise ValueError("--vocab-only does not make sense with --no-vocab")
@ -1491,29 +1507,32 @@ def main(args_in: list[str] | None = None) -> None:
if args.dump:
do_dump_model(model_plus)
return
endianess = gguf.GGUFEndian.LITTLE
if args.big_endian:
endianess = gguf.GGUFEndian.BIG
params = Params.load(model_plus)
if params.n_ctx == -1:
if args.ctx is None:
msg = """\
The model doesn't have a context size, and you didn't specify one with --ctx
Please specify one with --ctx:
- LLaMA v1: --ctx 2048
- LLaMA v2: --ctx 4096"""
parser.error(textwrap.dedent(msg))
params.n_ctx = args.ctx
params = None
if args.pad_vocab or not args.vocab_only:
params = Params.load(model_plus)
if params.n_ctx == -1:
if args.ctx is None:
msg = """\
The model doesn't have a context size, and you didn't specify one with --ctx
Please specify one with --ctx:
- LLaMA v1: --ctx 2048
- LLaMA v2: --ctx 4096"""
parser.error(textwrap.dedent(msg))
params.n_ctx = args.ctx
if args.outtype:
params.ftype = {
"f32": GGMLFileType.AllF32,
"f16": GGMLFileType.MostlyF16,
"q8_0": GGMLFileType.MostlyQ8_0,
}[args.outtype]
if args.outtype:
params.ftype = {
"f32": GGMLFileType.AllF32,
"f16": GGMLFileType.MostlyF16,
"q8_0": GGMLFileType.MostlyQ8_0,
}[args.outtype]
print(f"params = {params}")
logger.info(f"params = {params}")
model_parent_path = model_plus.paths[0].parent
vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
@ -1526,17 +1545,27 @@ def main(args_in: list[str] | None = None) -> None:
if not args.outfile:
raise ValueError("need --outfile if using --vocab-only")
outfile = args.outfile
if params is None:
params = Params(
n_vocab = vocab.vocab_size,
n_embd = 1,
n_layer = 1,
n_ctx = 1,
n_ff = 1,
n_head = 1,
n_head_kv = 1,
f_norm_eps = 1e-5,
)
OutputFile.write_vocab_only(outfile, params, vocab, special_vocab,
endianess=endianess, pad_vocab=args.pad_vocab)
print(f"Wrote {outfile}")
logger.info(f"Wrote {outfile}")
return
if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab:
vocab = model_plus.vocab
print(f"Vocab info: {vocab}")
print(f"Special vocab info: {special_vocab}")
logger.info(f"Vocab info: {vocab}")
logger.info(f"Special vocab info: {special_vocab}")
model = model_plus.model
model = convert_model_names(model, params, args.skip_unknown)
ftype = pick_output_type(model, args.outtype)
@ -1544,11 +1573,11 @@ def main(args_in: list[str] | None = None) -> None:
outfile = args.outfile or default_outfile(model_plus.paths, ftype)
params.ftype = ftype
print(f"Writing {outfile}, format {ftype}")
logger.info(f"Writing {outfile}, format {ftype}")
OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab,
concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab)
print(f"Wrote {outfile}")
logger.info(f"Wrote {outfile}")
if __name__ == '__main__':

2
docs/BLIS.md
View File

@ -23,7 +23,7 @@ Install BLIS:
sudo make install
```
We recommend using openmp since it's easier to modify the cores been used.
We recommend using openmp since it's easier to modify the cores being used.
### llama.cpp compilation

4
docs/HOWTO-add-model.md
View File

@ -96,9 +96,9 @@ NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorc
This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`.
Have a look to existing implementation like `build_llama`, `build_dbrx` or `build_bert`.
Have a look at existing implementation like `build_llama`, `build_dbrx` or `build_bert`.
When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support of missing backend operations can be added in another PR.
When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support for missing backend operations can be added in another PR.
Note: to debug the inference graph: you can use [eval-callback](../examples/eval-callback).

88
docs/debugging-tests.md Normal file
View File

@ -0,0 +1,88 @@
# Debugging Tests Tips
## How to run & debug a specific test without anything else to keep the feedback loop short?
There is a script called debug-test.sh in the scripts folder whose parameter takes a REGEX and an optional test number.
For example, running the following command will output an interactive list from which you can select a test. It takes this form:
`debug-test.sh [OPTION]... <test_regex> <test_number>`
It will then build & run in the debugger for you.
```bash
./scripts/debug-test.sh test-tokenizer
# Once in the debugger, i.e. at the chevrons prompt, setting a breakpoint could be as follows:
>>> b main
```
For further reference use `debug-test.sh -h` to print help.
&nbsp;
### How does the script work?
If you want to be able to use the concepts contained in the script separately, the important ones are briefly outlined below.
#### Step 1: Reset and Setup folder context
From base of this repository, let's create `build-ci-debug` as our build context.
```bash
rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug
```
#### Step 2: Setup Build Environment and Compile Test Binaries
Setup and trigger a build under debug mode. You may adapt the arguments as needed, but in this case these are sane defaults.
```bash
cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON ..
make -j
```
#### Step 3.1: Identify Test Command for Debugging
The output of this command will give you the command & arguments needed to run GDB.
* `-R test-tokenizer` : looks for all the test files named `test-tokenizer*` (R=Regex)
* `-N` : "show-only" disables test execution & shows test commands that you can feed to GDB.
* `-V` : Verbose Mode
```bash
ctest -R "test-tokenizer" -V -N
```
This may return output similar to below (focusing on key lines to pay attention to):
```bash
...
1: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf"
1: Working Directory: .
Labels: main
Test #1: test-tokenizer-0-llama-spm
...
4: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-falcon.gguf"
4: Working Directory: .
Labels: main
Test #4: test-tokenizer-0-falcon
...
```
So for test #1 we can tell these two pieces of relevant information:
* Test Binary: `~/llama.cpp/build-ci-debug/bin/test-tokenizer-0`
* Test GGUF Model: `~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf`
#### Step 3.2: Run GDB on test command
Based on the ctest 'test command' report above we can then run a gdb session via this command below:
```bash
gdb --args ${Test Binary} ${Test GGUF Model}
```
Example:
```bash
gdb --args ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf"
```

28
examples/batched-bench/batched-bench.cpp
View File

@ -32,7 +32,7 @@ int main(int argc, char ** argv) {
gpt_params params;
if (argc == 1 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [FATTN] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
printf(" <PP>, <TG> and PL are comma-separated lists of numbers without spaces\n\n");
printf(" example: %s ggml-model-f16.gguf 2048 2048 512 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
return 1 ;
@ -41,6 +41,7 @@ int main(int argc, char ** argv) {
int n_kv_max = 2048;
int n_batch = 2048;
int n_ubatch = 512;
bool flash_attn = false;
int is_pp_shared = 0;
int n_gpu_layers = 0;
@ -66,23 +67,27 @@ int main(int argc, char ** argv) {
}
if (argc >= 6) {
is_pp_shared = std::atoi(argv[5]);
flash_attn = std::atoi(argv[5]);
}
if (argc >= 7) {
n_gpu_layers = std::atoi(argv[6]);
is_pp_shared = std::atoi(argv[6]);
}
if (argc >= 8) {
n_pp = parse_list(argv[7]);
n_gpu_layers = std::atoi(argv[7]);
}
if (argc >= 9) {
n_tg = parse_list(argv[8]);
n_pp = parse_list(argv[8]);
}
if (argc >= 10) {
n_pl = parse_list(argv[9]);
n_tg = parse_list(argv[9]);
}
if (argc >= 11) {
n_pl = parse_list(argv[10]);
}
// init LLM
@ -108,10 +113,11 @@ int main(int argc, char ** argv) {
llama_context_params ctx_params = llama_context_default_params();
ctx_params.seed = 1234;
ctx_params.n_ctx = n_kv_max;
ctx_params.n_batch = n_batch;
ctx_params.n_ubatch = n_ubatch;
ctx_params.seed = 1234;
ctx_params.n_ctx = n_kv_max;
ctx_params.n_batch = n_batch;
ctx_params.n_ubatch = n_ubatch;
ctx_params.flash_attn = flash_attn;
ctx_params.n_threads = params.n_threads;
ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
@ -169,7 +175,7 @@ int main(int argc, char ** argv) {
}
LOG_TEE("\n");
LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, flash_attn, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG_TEE("\n");
LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");

2
examples/convert-llama2c-to-ggml/README.md
View File

@ -2,7 +2,7 @@
This example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default.
To convert the model first download the models from the [llma2.c](https://github.com/karpathy/llama2.c) repository:
To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository:
`$ make -j`

12
examples/embedding/embedding.cpp
View File

@ -49,6 +49,12 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
}
float * out = output + batch.seq_id[i][0] * n_embd;
//TODO: I would also add a parameter here to enable normalization or not.
/*fprintf(stdout, "unnormalized_embedding:");
for (int hh = 0; hh < n_embd; hh++) {
fprintf(stdout, "%9.6f ", embd[hh]);
}
fprintf(stdout, "\n");*/
llama_embd_normalize(embd, out, n_embd);
}
}
@ -123,10 +129,12 @@ int main(int argc, char ** argv) {
inputs.push_back(inp);
}
// add SEP if not present
// check if the last token is SEP
// it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true'
for (auto & inp : inputs) {
if (inp.empty() || inp.back() != llama_token_sep(model)) {
inp.push_back(llama_token_sep(model));
fprintf(stderr, "%s: warning: last token in the prompt is not SEP\n", __func__);
fprintf(stderr, "%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__);
}
}

10
examples/eval-callback/eval-callback.cpp
View File

@ -52,15 +52,15 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne
size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
float v;
if (type == GGML_TYPE_F16) {
v = ggml_fp16_to_fp32(*(ggml_fp16_t *) data + i);
v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]);
} else if (type == GGML_TYPE_F32) {
v = *(float *) data + i;
v = *(float *) &data[i];
} else if (type == GGML_TYPE_I32) {
v = (float) *(int32_t *) data + i;
v = (float) *(int32_t *) &data[i];
} else if (type == GGML_TYPE_I16) {
v = (float) *(int16_t *) data + i;
v = (float) *(int16_t *) &data[i];
} else if (type == GGML_TYPE_I8) {
v = (float) *(int8_t *) data + i;
v = (float) *(int8_t *) &data[i];
} else {
GGML_ASSERT(false);
}

2
examples/finetune/finetune.cpp
View File

@ -575,7 +575,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
GGML_ASSERT(tokens_input->type == GGML_TYPE_I32);
auto add_to_f32 = [] (struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16) {
if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16 || a->type == GGML_TYPE_BF16) {
return ggml_add_cast(ctx, a, b, GGML_TYPE_F32);
} else if (a->type == GGML_TYPE_F32) {
return ggml_add(ctx, a, b);

19
examples/gguf-split/gguf-split.cpp
View File

@ -32,6 +32,7 @@ struct split_params {
int n_split_tensors = 128;
std::string input;
std::string output;
bool no_tensor_first_split = false;
bool dry_run = false;
};
@ -49,6 +50,7 @@ static void split_print_usage(const char * executable) {
printf(" --merge merge multiple GGUF to a single GGUF\n");
printf(" --split-max-tensors max tensors in each split (default: %d)\n", default_params.n_split_tensors);
printf(" --split-max-size N(M|G) max size per split\n");
printf(" --no-tensor-first-split do not add tensors to the first split (disabled by default)\n");
printf(" --dry-run only print out a split plan and exit, without writing any new files\n");
printf("\n");
}
@ -100,6 +102,10 @@ static void split_params_parse_ex(int argc, const char ** argv, split_params & p
arg_found = true;
params.dry_run = true;
}
if (arg == "--no-tensor-first-split") {
arg_found = true;
params.no_tensor_first_split = true;
}
if (is_op_set) {
throw std::invalid_argument("error: either --split or --merge can be specified, but not both");
@ -200,10 +206,10 @@ struct split_strategy {
// because we need to know list of tensors for each file in advance, we will build all the ctx_out for all output splits
int i_split = -1;
struct gguf_context * ctx_out = NULL;
auto new_ctx_out = [&]() {
auto new_ctx_out = [&](bool allow_no_tensors) {
i_split++;
if (ctx_out != NULL) {
if (gguf_get_n_tensors(ctx_out) == 0) {
if (gguf_get_n_tensors(ctx_out) == 0 && !allow_no_tensors) {
fprintf(stderr, "error: one of splits have 0 tensors. Maybe size or tensors limit is too small\n");
exit(EXIT_FAILURE);
}
@ -220,7 +226,12 @@ struct split_strategy {
};
// initialize ctx_out for the first split
new_ctx_out();
new_ctx_out(false);
// skip first split if no_tensor_first_split is set
if (params.no_tensor_first_split) {
new_ctx_out(true);
}
// process tensors one by one
size_t curr_tensors_size = 0; // current size by counting only tensors size (without metadata)
@ -230,7 +241,7 @@ struct split_strategy {
size_t n_bytes = GGML_PAD(ggml_nbytes(t), GGUF_DEFAULT_ALIGNMENT);
size_t next_tensors_size = curr_tensors_size + n_bytes;
if (should_split(i, next_tensors_size)) {
new_ctx_out();
new_ctx_out(false);
curr_tensors_size = n_bytes;
} else {
curr_tensors_size = next_tensors_size;

14
examples/gguf-split/tests.sh
View File

@ -55,15 +55,15 @@ $MAIN --model $WORK_PATH/ggml-model-merge.gguf --random-prompt --n-predict 32
echo PASS
echo
# 4. Split with no tensor in metadata
#$SPLIT --split-max-tensors 32 --no-tensor-in-metadata $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors
#echo PASS
#echo
# 4. Split with no tensors in the first split
$SPLIT --split-max-tensors 32 --no-tensor-first-split $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors
echo PASS
echo
# 4b. Test the sharded model is loading properly
#$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf --random-prompt --n-predict 32
#echo PASS
#echo
$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --random-prompt --n-predict 32
echo PASS
echo
# 5. Merge
#$SPLIT --merge $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf $WORK_PATH/ggml-model-merge-2.gguf

36
examples/imatrix/imatrix.cpp
View File

@ -19,6 +19,7 @@
struct Stats {
std::vector<float> values;
std::vector<int> counts;
int ncall = 0;
};
@ -121,12 +122,10 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
auto & e = m_stats[wname];
++e.ncall;
// NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger
// using the following line, we can correct for that if needed by replacing the line above with:
//if (idx == t->src[0]->ne[0] - 1) ++e.ncall;
if (e.values.empty()) {
e.values.resize(src1->ne[0]*n_as, 0);
e.counts.resize(src1->ne[0]*n_as, 0);
}
else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
@ -153,6 +152,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[e_start + j] += x[j]*x[j];
e.counts[e_start + j]++;
}
}
}
@ -170,6 +170,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
auto& e = m_stats[wname];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
e.counts.resize(src1->ne[0], 0);
}
else if (e.values.size() != (size_t)src1->ne[0]) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
@ -183,6 +184,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
const float * x = data + row * src1->ne[0];
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[j] += x[j]*x[j];
e.counts[j]++;
}
}
if (e.ncall > m_last_call) {
@ -222,7 +224,13 @@ void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) co
out.write((const char *) &p.second.ncall, sizeof(p.second.ncall));
int nval = p.second.values.size();
out.write((const char *) &nval, sizeof(nval));
if (nval > 0) out.write((const char *) p.second.values.data(), nval * sizeof(float));
if (nval > 0) {
std::vector<float> tmp(nval);
for (int i = 0; i < nval; i++) {
tmp[i] = (p.second.values[i] / static_cast<float>(p.second.counts[i])) * static_cast<float>(p.second.ncall);
}
out.write((const char*)tmp.data(), nval*sizeof(float));
}
}
// Write the number of call the matrix was computed with
@ -270,14 +278,28 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma
imatrix_data = {};
return false;
}
e.values.resize(nval);
in.read((char*)e.values.data(), nval*sizeof(float));
// When re-called from load_imatrix() with add set, this will already be created.
if (e.values.empty()) {
e.values.resize(nval, 0);
e.counts.resize(nval, 0);
}
std::vector<float> tmp(nval);
in.read((char*)tmp.data(), nval*sizeof(float));
if (in.fail()) {
printf("%s: failed reading data for entry %d\n",__func__,i);
imatrix_data = {};
return false;
}
e.ncall = ncall;
// Recreate the state as expected by save_imatrix(), and corerct for weighted sum.
for (int i = 0; i < nval; i++) {
e.values[i] += tmp[i];
e.counts[i] += ncall;
}
e.ncall += ncall;
}
return true;
}

18
examples/llama-bench/README.md
View File

@ -26,16 +26,21 @@ options:
-m, --model <filename> (default: models/7B/ggml-model-q4_0.gguf)
-p, --n-prompt <n> (default: 512)
-n, --n-gen <n> (default: 128)
-b, --batch-size <n> (default: 512)
-ctk <t>, --cache-type-k <t> (default: f16)
-ctv <t>, --cache-type-v <t> (default: f16)
-t, --threads <n> (default: 112)
-pg <pp,tg> (default: 512,128)
-b, --batch-size <n> (default: 2048)
-ub, --ubatch-size <n> (default: 512)
-ctk, --cache-type-k <t> (default: f16)
-ctv, --cache-type-v <t> (default: f16)
-t, --threads <n> (default: 16)
-ngl, --n-gpu-layers <n> (default: 99)
-sm, --split-mode <none|layer|row> (default: layer)
-mg, --main-gpu <i> (default: 0)
-nkvo, --no-kv-offload <0|1> (default: 0)
-fa, --flash-attn <0|1> (default: 0)
-mmp, --mmap <0|1> (default: 1)
-ts, --tensor_split <ts0/ts1/..> (default: 0)
--numa <distribute|isolate|numactl> (default: disabled)
-embd, --embeddings <0|1> (default: 0)
-ts, --tensor-split <ts0/ts1/..> (default: 0)
-r, --repetitions <n> (default: 5)
-o, --output <csv|json|md|sql> (default: md)
-v, --verbose (default: 0)
@ -43,10 +48,11 @@ options:
Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.
```
llama-bench can perform two types of tests:
llama-bench can perform three types of tests:
- Prompt processing (pp): processing a prompt in batches (`-p`)
- Text generation (tg): generating a sequence of tokens (`-n`)
- Prompt processing + text generation (pg): processing a prompt followed by generating a sequence of tokens (`-pg`)
With the exception of `-r`, `-o` and `-v`, all options can be specified multiple times to run multiple tests. Each pp and tg test is run with all combinations of the specified options. To specify multiple values for an option, the values can be separated by commas (e.g. `-n 16,32`), or the option can be specified multiple times (e.g. `-n 16 -n 32`).

108
examples/llama-bench/llama-bench.cpp
View File

@ -161,10 +161,17 @@ static const char * split_mode_str(llama_split_mode mode) {
}
}
static std::string pair_str(const std::pair<int, int> & p) {
static char buf[32];
snprintf(buf, sizeof(buf), "%d,%d", p.first, p.second);
return buf;
}
struct cmd_params {
std::vector<std::string> model;
std::vector<int> n_prompt;
std::vector<int> n_gen;
std::vector<std::pair<int, int>> n_pg;
std::vector<int> n_batch;
std::vector<int> n_ubatch;
std::vector<ggml_type> type_k;
@ -174,9 +181,11 @@ struct cmd_params {
std::vector<llama_split_mode> split_mode;
std::vector<int> main_gpu;
std::vector<bool> no_kv_offload;
std::vector<bool> flash_attn;
std::vector<std::vector<float>> tensor_split;
std::vector<bool> use_mmap;
std::vector<bool> embeddings;
ggml_numa_strategy numa;
int reps;
bool verbose;
output_formats output_format;
@ -186,6 +195,7 @@ static const cmd_params cmd_params_defaults = {
/* model */ {"models/7B/ggml-model-q4_0.gguf"},
/* n_prompt */ {512},
/* n_gen */ {128},
/* n_pg */ {{512, 128}},
/* n_batch */ {2048},
/* n_ubatch */ {512},
/* type_k */ {GGML_TYPE_F16},
@ -195,9 +205,11 @@ static const cmd_params cmd_params_defaults = {
/* split_mode */ {LLAMA_SPLIT_MODE_LAYER},
/* main_gpu */ {0},
/* no_kv_offload */ {false},
/* flash_attn */ {false},
/* tensor_split */ {std::vector<float>(llama_max_devices(), 0.0f)},
/* use_mmap */ {true},
/* embeddings */ {false},
/* numa */ GGML_NUMA_STRATEGY_DISABLED,
/* reps */ 5,
/* verbose */ false,
/* output_format */ MARKDOWN
@ -211,16 +223,19 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -m, --model <filename> (default: %s)\n", join(cmd_params_defaults.model, ",").c_str());
printf(" -p, --n-prompt <n> (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
printf(" -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
printf(" -pg <pp,tg> (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str());
printf(" -b, --batch-size <n> (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
printf(" -ub N, --ubatch-size <n> (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str());
printf(" -ctk <t>, --cache-type-k <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
printf(" -ctv <t>, --cache-type-v <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
printf(" -ub, --ubatch-size <n> (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str());
printf(" -ctk, --cache-type-k <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
printf(" -ctv, --cache-type-v <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
printf(" -sm, --split-mode <none|layer|row> (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
printf(" -fa, --flash-attn <0|1> (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str());
printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
printf(" --numa <distribute|isolate|numactl> (default: disabled)\n");
printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
@ -298,6 +313,17 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
}
auto p = split<int>(argv[i], split_delim);
params.n_gen.insert(params.n_gen.end(), p.begin(), p.end());
} else if (arg == "-pg") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<std::string>(argv[i], ',');
if (p.size() != 2) {
invalid_param = true;
break;
}
params.n_pg.push_back({std::stoi(p[0]), std::stoi(p[1])});
} else if (arg == "-b" || arg == "--batch-size") {
if (++i >= argc) {
invalid_param = true;
@ -393,6 +419,24 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
}
auto p = split<bool>(argv[i], split_delim);
params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
} else if (arg == "--numa") {
if (++i >= argc) {
invalid_param = true;
break;
} else {
std::string value(argv[i]);
/**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
else { invalid_param = true; break; }
}
} else if (arg == "-fa" || arg == "--flash-attn") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<bool>(argv[i], split_delim);
params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end());
} else if (arg == "-mmp" || arg == "--mmap") {
if (++i >= argc) {
invalid_param = true;
@ -469,6 +513,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
if (params.model.empty()) { params.model = cmd_params_defaults.model; }
if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; }
if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; }
if (params.n_pg.empty()) { params.n_pg = cmd_params_defaults.n_pg; }
if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; }
if (params.n_ubatch.empty()) { params.n_ubatch = cmd_params_defaults.n_ubatch; }
if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; }
@ -477,6 +522,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; }
if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; }
if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
if (params.flash_attn.empty()) { params.flash_attn = cmd_params_defaults.flash_attn; }
if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; }
if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; }
@ -498,6 +544,7 @@ struct cmd_params_instance {
llama_split_mode split_mode;
int main_gpu;
bool no_kv_offload;
bool flash_attn;
std::vector<float> tensor_split;
bool use_mmap;
bool embeddings;
@ -532,6 +579,7 @@ struct cmd_params_instance {
cparams.type_k = type_k;
cparams.type_v = type_v;
cparams.offload_kqv = !no_kv_offload;
cparams.flash_attn = flash_attn;
cparams.embeddings = embeddings;
return cparams;
@ -554,6 +602,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
for (const auto & tk : params.type_k)
for (const auto & tv : params.type_v)
for (const auto & nkvo : params.no_kv_offload)
for (const auto & fa : params.flash_attn)
for (const auto & nt : params.n_threads) {
for (const auto & n_prompt : params.n_prompt) {
if (n_prompt == 0) {
@ -572,6 +621,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .split_mode = */ sm,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
/* .flash_attn = */ fa,
/* .tensor_split = */ ts,
/* .use_mmap = */ mmp,
/* .embeddings = */ embd,
@ -596,6 +646,32 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .split_mode = */ sm,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
/* .flash_attn = */ fa,
/* .tensor_split = */ ts,
/* .use_mmap = */ mmp,
/* .embeddings = */ embd,
};
instances.push_back(instance);
}
for (const auto & n_pg : params.n_pg) {
if (n_pg.first == 0 && n_pg.second == 0) {
continue;
}
cmd_params_instance instance = {
/* .model = */ m,
/* .n_prompt = */ n_pg.first,
/* .n_gen = */ n_pg.second,
/* .n_batch = */ nb,
/* .n_ubatch = */ nub,
/* .type_k = */ tk,
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .split_mode = */ sm,
/* .main_gpu = */ mg,
/* .no_kv_offload= */ nkvo,
/* .flash_attn = */ fa,
/* .tensor_split = */ ts,
/* .use_mmap = */ mmp,
/* .embeddings = */ embd,
@ -633,6 +709,7 @@ struct test {
llama_split_mode split_mode;
int main_gpu;
bool no_kv_offload;
bool flash_attn;
std::vector<float> tensor_split;
bool use_mmap;
bool embeddings;
@ -657,6 +734,7 @@ struct test {
split_mode = inst.split_mode;
main_gpu = inst.main_gpu;
no_kv_offload = inst.no_kv_offload;
flash_attn = inst.flash_attn;
tensor_split = inst.tensor_split;
use_mmap = inst.use_mmap;
embeddings = inst.embeddings;
@ -731,7 +809,7 @@ struct test {
"n_batch", "n_ubatch",
"n_threads", "type_k", "type_v",
"n_gpu_layers", "split_mode",
"main_gpu", "no_kv_offload",
"main_gpu", "no_kv_offload", "flash_attn",
"tensor_split", "use_mmap", "embeddings",
"n_prompt", "n_gen", "test_time",
"avg_ns", "stddev_ns",
@ -753,7 +831,7 @@ struct test {
}
if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" ||
field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
field == "use_mmap" || field == "embeddings") {
field == "flash_attn" || field == "use_mmap" || field == "embeddings") {
return BOOL;
}
if (field == "avg_ts" || field == "stddev_ts") {
@ -787,7 +865,7 @@ struct test {
std::to_string(n_batch), std::to_string(n_ubatch),
std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
std::to_string(n_gpu_layers), split_mode_str(split_mode),
std::to_string(main_gpu), std::to_string(no_kv_offload),
std::to_string(main_gpu), std::to_string(no_kv_offload), std::to_string(flash_attn),
tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
std::to_string(n_prompt), std::to_string(n_gen), test_time,
std::to_string(avg_ns()), std::to_string(stdev_ns()),
@ -933,6 +1011,9 @@ struct markdown_printer : public printer {
if (field == "n_gpu_layers") {
return 3;
}
if (field == "test") {
return 13;
}
int width = std::max((int)field.length(), 10);
@ -955,6 +1036,9 @@ struct markdown_printer : public printer {
if (field == "no_kv_offload") {
return "nkvo";
}
if (field == "flash_attn") {
return "fa";
}
if (field == "use_mmap") {
return "mmap";
}
@ -1001,6 +1085,9 @@ struct markdown_printer : public printer {
if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
fields.emplace_back("no_kv_offload");
}
if (params.flash_attn.size() > 1 || params.flash_attn != cmd_params_defaults.flash_attn) {
fields.emplace_back("flash_attn");
}
if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
fields.emplace_back("tensor_split");
}
@ -1053,12 +1140,11 @@ struct markdown_printer : public printer {
value = test::get_backend();
} else if (field == "test") {
if (t.n_prompt > 0 && t.n_gen == 0) {
snprintf(buf, sizeof(buf), "pp %d", t.n_prompt);
snprintf(buf, sizeof(buf), "pp%d", t.n_prompt);
} else if (t.n_gen > 0 && t.n_prompt == 0) {
snprintf(buf, sizeof(buf), "tg %d", t.n_gen);
snprintf(buf, sizeof(buf), "tg%d", t.n_gen);
} else {
assert(false);
exit(1);
snprintf(buf, sizeof(buf), "pp%d+tg%d", t.n_prompt, t.n_gen);
}
value = buf;
} else if (field == "t/s") {
@ -1191,6 +1277,7 @@ int main(int argc, char ** argv) {
llama_log_set(llama_null_log_callback, NULL);
}
llama_backend_init();
llama_numa_init(params.numa);
// initialize printer
std::unique_ptr<printer> p;
@ -1258,6 +1345,7 @@ int main(int argc, char ** argv) {
llama_kv_cache_clear(ctx);
uint64_t t_start = get_time_ns();
if (t.n_prompt > 0) {
test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
}

2
examples/llava/README.md
View File

@ -56,7 +56,7 @@ python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-pa
python ./convert.py ../llava-v1.5-7b --skip-unknown
```
Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory.
Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory.
## LLaVA 1.6 gguf conversion
1) First clone a LLaVA 1.6 model:

18
examples/llava/clip.cpp
View File

@ -573,13 +573,13 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
struct ggml_tensor * embeddings = inp;
if (ctx->has_class_embedding) {
embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
ggml_set_name(embeddings, "embeddings");
ggml_set_input(embeddings);
embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
embeddings = ggml_acc(ctx0, embeddings, inp,
embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
}
ggml_set_name(embeddings, "embeddings");
ggml_set_input(embeddings);
struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
@ -1846,7 +1846,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
const int image_size = hparams.image_size;
const int patch_size = hparams.patch_size;
const int num_patches = ((image_size / patch_size) * (image_size / patch_size));
const int num_positions = num_patches + 1;
const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0);
{
struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw");
@ -1874,12 +1874,14 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
}
{
struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings");
if (ctx->has_class_embedding) {
struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings");
void* zero_mem = malloc(ggml_nbytes(embeddings));
memset(zero_mem, 0, ggml_nbytes(embeddings));
ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
free(zero_mem);
void* zero_mem = malloc(ggml_nbytes(embeddings));
memset(zero_mem, 0, ggml_nbytes(embeddings));
ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings));
free(zero_mem);
}
}
{

72
examples/llava/llava-cli.cpp
View File

@ -113,11 +113,11 @@ struct llava_context {
};
static void show_additional_info(int /*argc*/, char ** argv) {
LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
LOG_TEE(" note: a lower temperature value like 0.1 is recommended for better quality.\n");
}
static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) {
static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params, const std::string & fname) {
// load and preprocess the image
llava_image_embed * embed = NULL;
@ -133,9 +133,9 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para
}
params->prompt = remove_image_from_prompt(prompt);
} else {
embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str());
embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, fname.c_str());
if (!embed) {
LOG_TEE("%s: is %s really an image file?\n", __func__, params->image.c_str());
fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
return NULL;
}
}
@ -189,6 +189,11 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
LOG_TEE("\n");
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
if (!ctx_sampling) {
fprintf(stderr, "%s: failed to initialize sampling subsystem\n", __func__);
exit(1);
}
std::string response = "";
for (int i = 0; i < max_tgt_len; i++) {
const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past);
@ -207,17 +212,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
printf("\n");
}
static struct llava_context * llava_init(gpt_params * params) {
const char * clip_path = params->mmproj.c_str();
auto prompt = params->prompt;
if (prompt.empty()) {
prompt = "describe the image in detail.";
}
auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
static struct llama_model * llava_init(gpt_params * params) {
llama_backend_init();
llama_numa_init(params->numa);
@ -228,6 +223,19 @@ static struct llava_context * llava_init(gpt_params * params) {
LOG_TEE("%s: error: unable to load model\n" , __func__);
return NULL;
}
return model;
}
static struct llava_context * llava_init_context(gpt_params * params, llama_model * model) {
const char * clip_path = params->mmproj.c_str();
auto prompt = params->prompt;
if (prompt.empty()) {
prompt = "describe the image in detail.";
}
auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
llama_context_params ctx_params = llama_context_params_from_gpt_params(*params);
ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
@ -286,24 +294,30 @@ int main(int argc, char ** argv) {
show_additional_info(argc, argv);
return 1;
}
auto ctx_llava = llava_init(&params);
if (ctx_llava == NULL) {
LOG_TEE("%s: error: failed to init llava\n", __func__);
auto model = llava_init(&params);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
return 1;
}
auto image_embed = load_image(ctx_llava, &params);
if (!image_embed) {
return 1;
for (auto & image : params.image) {
auto ctx_llava = llava_init_context(&params, model);
auto image_embed = load_image(ctx_llava, &params, image);
if (!image_embed) {
std::cerr << "error: failed to load image " << image << ". Terminating\n\n";
return 1;
}
// process the prompt
process_prompt(ctx_llava, image_embed, &params, params.prompt);
llama_print_timings(ctx_llava->ctx_llama);
llava_image_embed_free(image_embed);
ctx_llava->model = NULL;
llava_free(ctx_llava);
}
llama_free_model(model);
// process the prompt
process_prompt(ctx_llava, image_embed, &params, params.prompt);
llama_print_timings(ctx_llava->ctx_llama);
llava_image_embed_free(image_embed);
llava_free(ctx_llava);
return 0;
}

16
examples/main-cmake-pkg/README.md
View File

@ -17,11 +17,9 @@ In this case, CLBlast was already installed so the CMake package is referenced i
```cmd
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
mkdir build
cd build
cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/LlamaCPP
cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64
cmake --build build --config Release
cmake --install build --prefix C:/LlamaCPP
```
### Build main-cmake-pkg
@ -29,9 +27,7 @@ cmake --install . --prefix C:/LlamaCPP
```cmd
cd ..\examples\main-cmake-pkg
mkdir build
cd build
cmake .. -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/MyLlamaApp
cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
cmake --build build --config Release
cmake --install build --prefix C:/MyLlamaApp
```

6
examples/main/README.md
View File

@ -66,7 +66,7 @@ main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt
In this section, we cover the most commonly used options for running the `main` program with the LLaMA models:
- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`; inferred from `--model-url` if set).
- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf).
- `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
- `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models.
@ -143,7 +143,7 @@ The `--ctx-size` option allows you to set the size of the prompt context used by
### Extended Context Size
Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8.
Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model has a context length (max sequence length) of 4096 (4k) and the fine-tuned model has 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8.
- `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model.
@ -286,7 +286,7 @@ These options help improve the performance and memory usage of the LLaMA models.
- `--numa distribute`: Pin an equal proportion of the threads to the cores on each NUMA node. This will spread the load amongst all cores on the system, utilitizing all memory channels at the expense of potentially requiring memory to travel over the slow links between nodes.
- `--numa isolate`: Pin all threads to the NUMA node that the program starts on. This limits the number of cores and amount of memory that can be used, but guarantees all memory access remains local to the NUMA node.
- `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitraty core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus.
- `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitrary core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus.
These flags attempt optimizations that help on some systems with non-uniform memory access. This currently consists of one of the above strategies, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root.

23
examples/main/main.cpp
View File

@ -324,7 +324,7 @@ int main(int argc, char ** argv) {
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
// reevaluation of the last token to recalculate the cached logits
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);
@ -362,6 +362,9 @@ int main(int argc, char ** argv) {
params.interactive_first = true;
params.antiprompt.emplace_back("<|im_start|>user\n");
}
else if (params.conversation) {
params.interactive_first = true;
}
// enable interactive mode if interactive start is specified
if (params.interactive_first) {
@ -520,6 +523,10 @@ int main(int argc, char ** argv) {
}
struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
if (!ctx_sampling) {
fprintf(stderr, "%s: failed to initialize sampling subsystem\n", __func__);
exit(1);
}
while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
// predict
@ -544,7 +551,7 @@ int main(int argc, char ** argv) {
// if we run out of context:
// - take the n_keep first tokens from the original prompt (via n_past)
// - 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 (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) >= n_ctx) {
if (params.n_predict == -2) {
LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
break;
@ -733,7 +740,7 @@ int main(int argc, char ** argv) {
// display text
if (input_echo && display) {
for (auto id : embd) {
const std::string token_str = llama_token_to_piece(ctx, id);
const std::string token_str = llama_token_to_piece(ctx, id, !params.conversation);
printf("%s", token_str.c_str());
if (embd.size() > 1) {
@ -796,7 +803,7 @@ int main(int argc, char ** argv) {
// deal with end of generation tokens in interactive mode
if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
LOG("found EOS token\n");
LOG("found an EOG token\n");
if (params.interactive) {
if (!params.antiprompt.empty()) {
@ -816,7 +823,7 @@ int main(int argc, char ** argv) {
if (n_past > 0 && is_interacting) {
LOG("waiting for user input\n");
if (params.instruct || params.chatml) {
if (params.conversation || params.instruct || params.chatml) {
printf("\n> ");
}
@ -826,7 +833,7 @@ int main(int argc, char ** argv) {
}
std::string buffer;
if (!params.input_prefix.empty()) {
if (!params.input_prefix.empty() && !params.conversation) {
LOG("appending input prefix: '%s'\n", params.input_prefix.c_str());
printf("%s", params.input_prefix.c_str());
}
@ -850,7 +857,7 @@ int main(int argc, char ** argv) {
// Entering a empty line lets the user pass control back
if (buffer.length() > 1) {
// append input suffix if any
if (!params.input_suffix.empty()) {
if (!params.input_suffix.empty() && !params.conversation) {
LOG("appending input suffix: '%s'\n", params.input_suffix.c_str());
printf("%s", params.input_suffix.c_str());
}
@ -876,7 +883,7 @@ int main(int argc, char ** argv) {
}
const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
const auto line_inp = ::llama_tokenize(ctx, buffer, false, false);
const auto line_inp = ::llama_tokenize(ctx, buffer, false, params.interactive_specials);
const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());

118
examples/perplexity/README.md
View File

@ -1,8 +1,118 @@
# perplexity
# Perplexity
TODO
The `perplexity` example can be used to calculate the so-called perplexity value of a language model over a given text corpus.
Perplexity measures how well the model can predict the next token with lower values being better.
Note that perplexity is **not** directly comparable between models, especially if they use different tokenizers.
Also note that finetunes typically result in a higher perplexity value even though the human-rated quality of outputs increases.
Within llama.cpp the perplexity of base models is used primarily to judge the quality loss from e.g. quantized models vs. FP16.
The convention among contributors is to use the Wikitext-2 test set for testing unless noted otherwise (can be obtained with `scripts/get-wikitext-2.sh`).
By default only the mean perplexity value and the corresponding uncertainty is calculated.
The uncertainty is determined empirically by assuming a Gaussian distribution of the "correct" logits per and then applying error propagation.
More statistics can be obtained by recording the logits from the FP16 version of a model.
To do this, supply `perplexity` with `--kl-divergence-base path/to/logit/binary/file.kld`.
The program will then record all logits and save them to the provided path in binary format.
**The logit file will be very large, 11 GiB for LLaMA 2 or 37 GiB for LLaMA 3 when using the Wikitext-2 test set.**
Once you have the file, supply `perplexity` with the quantized model, the logits file via `--kl-divergence-base`,
and finally the `--kl-divergence` argument to indicate that the program should calculate the so-called Kullback-Leibler divergence.
This is a measure of how similar the FP16 and the quantized logit distributions are with a value of 0 indicating that the distribution are the same.
The uncertainty on the mean KL divergence is calculated by assuming the KL divergence per token follows a Gaussian distribution.
In addition to the KL divergence the following statistics are calculated with `--kl-divergence`:
* Ratio of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated. The logarithm of this metric is also calculated and printed, it is 0 if the logit distributions are the same.
* Difference of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated.
* Mean change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse.
* Pearson correlation coefficient of the "correct" token probabilites between models.
* Percentiles of change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse. Can be used to judge noise vs. quality loss from quantization. If the percentiles are symmetric then the quantization is essentially just adding noise. If the negative values are significantly larger than the positive values then this indicates that the model is actually becoming worse from the quantization.
* The root mean square of the change in token probabilities. If you were to assume that the quantization simply causes Gaussian noise on the token probabilities then this would be the standard deviation of said noise. The uncertainty on the value is calculated that the change in token probabilities follows a Gaussian distribution. Related discussion: https://github.com/ggerganov/llama.cpp/discussions/2875 .
* Same top p: Percentage of how often the token was assigned the highest probabilites by both models. The uncertainty is calculated from the Gaussian approximation of the binomial distribution.
## LLaMA 3 8b Scoreboard
Results are sorted by Kullback-Leibler divergence relative to FP16.
The "WT" importance matrices were created using varying numbers of Wikitext tokens and can be found [here](https://huggingface.co/JohannesGaessler/llama.cpp_importance_matrices/blob/main/imatrix-llama_3-8b-f16-2.7m_tokens.dat).
| Quantization | imatrix | Model size [GiB] | PPL | ΔPPL | KLD | Mean Δp | RMS Δp |
|--------------|---------|------------------|------------------------|------------------------|-----------------------|-------------------|------------------|
| f16 | None | 14.97 | 6.233160 ± 0.037828 | - | - | - | - |
| q8_0 | None | 7.96 | 6.234284 ± 0.037878 | 0.002650 ± 0.001006 | 0.001355 ± 0.000006 | -0.019 ± 0.003 % | 1.198 ± 0.007 % |
| q6_K | None | 6.14 | 6.253382 ± 0.038078 | 0.021748 ± 0.001852 | 0.005452 ± 0.000035 | -0.007 ± 0.006 % | 2.295 ± 0.019 % |
| q5_K_M | None | 5.33 | 6.288607 ± 0.038338 | 0.056974 ± 0.002598 | 0.010762 ± 0.000079 | -0.114 ± 0.008 % | 3.160 ± 0.031 % |
| q5_K_S | None | 5.21 | 6.336598 ± 0.038755 | 0.104964 ± 0.003331 | 0.016595 ± 0.000122 | -0.223 ± 0.010 % | 3.918 ± 0.036 % |
| q5_1 | None | 5.65 | 6.337857 ± 0.038677 | 0.106223 ± 0.003476 | 0.018045 ± 0.000139 | -0.287 ± 0.011 % | 4.123 ± 0.039 % |
| q5_0 | None | 5.21 | 6.363224 ± 0.038861 | 0.131591 ± 0.003894 | 0.022239 ± 0.000166 | -0.416 ± 0.012 % | 4.634 ± 0.043 % |
| q4_K_M | WT 10m | 4.58 | 6.382937 ± 0.039055 | 0.151303 ± 0.004429 | 0.028152 ± 0.000240 | -0.389 ± 0.014 % | 5.251 ± 0.049 % |
| q4_K_M | None | 4.58 | 6.407115 ± 0.039119 | 0.175482 ± 0.004620 | 0.031273 ± 0.000238 | -0.596 ± 0.014 % | 5.519 ± 0.050 % |
| q4_K_S | WT 10m | 4.37 | 6.409697 ± 0.039189 | 0.178064 ± 0.004744 | 0.031951 ± 0.000259 | -0.531 ± 0.015 % | 5.645 ± 0.051 % |
| iq4_NL | WT 10m | 4.35 | 6.455593 ± 0.039630 | 0.223959 ± 0.005201 | 0.035742 ± 0.000288 | -0.590 ± 0.016 % | 5.998 ± 0.054 % |
| iq4_XS | WT 10m | 4.14 | 6.459705 ± 0.039595 | 0.228071 ± 0.005207 | 0.036334 ± 0.000284 | -0.668 ± 0.016 % | 6.044 ± 0.054 % |
| q4_K_S | None | 4.37 | 6.500529 ± 0.039778 | 0.268895 ± 0.005638 | 0.043136 ± 0.000314 | -0.927 ± 0.017 % | 6.562 ± 0.055 % |
| q4_1 | None | 4.78 | 6.682737 ± 0.041285 | 0.451103 ± 0.008030 | 0.071683 ± 0.000505 | -0.927 ± 0.017 % | 8.512 ± 0.063 % |
| q4_0 | None | 4.34 | 6.700147 ± 0.041226 | 0.468514 ± 0.007951 | 0.071940 ± 0.000491 | -1.588 ± 0.022 % | 8.434 ± 0.061 % |
| q3_K_L | WT 10m | 4.03 | 6.671223 ± 0.041427 | 0.439590 ± 0.008154 | 0.073077 ± 0.000529 | -0.940 ± 0.023 % | 8.662 ± 0.064 % |
| q3_K_M | WT 10m | 3.74 | 6.734255 ± 0.041838 | 0.502622 ± 0.008901 | 0.084358 ± 0.000588 | -1.198 ± 0.024 % | 9.292 ± 0.065 % |
| q3_K_L | None | 4.03 | 6.787876 ± 0.042104 | 0.556242 ± 0.009171 | 0.087176 ± 0.000614 | -1.532 ± 0.025 % | 9.432 ± 0.067 % |
| q3_K_M | None | 3.74 | 6.888498 ± 0.042669 | 0.656864 ± 0.010071 | 0.101913 ± 0.000677 | -1.990 ± 0.026 % | 10.203 ± 0.068 % |
| iq3_M | WT 10m | 3.53 | 6.898327 ± 0.041643 | 0.666694 ± 0.009449 | 0.102534 ± 0.000663 | -3.178 ± 0.026 % | 10.513 ± 0.066 % |
| iq3_S | WT 10m | 3.42 | 6.965501 ± 0.042406 | 0.733867 ± 0.010245 | 0.111278 ± 0.000710 | -3.066 ± 0.027 % | 10.845 ± 0.068 % |
| iq3_XS | WT 10m | 3.28 | 7.163043 ± 0.043772 | 0.931409 ± 0.012084 | 0.138693 ± 0.000857 | -3.667 ± 0.031 % | 12.148 ± 0.070 % |
| iq3_XXS | WT 10m | 3.05 | 7.458436 ± 0.046404 | 1.226803 ± 0.015234 | 0.183625 ± 0.001042 | -3.918 ± 0.035 % | 13.836 ± 0.074 % |
| q3_K_S | WT 10m | 3.41 | 7.602878 ± 0.046848 | 1.371244 ± 0.015688 | 0.199821 ± 0.001008 | -5.046 ± 0.037 % | 14.980 ± 0.070 % |
| q3_K_S | None | 3.41 | 7.863786 ± 0.048885 | 1.632152 ± 0.017733 | 0.228217 ± 0.001079 | -5.604 ± 0.038 % | 15.541 ± 0.070 % |
| iq2_M | WT 10m | 2.74 | 8.600799 ± 0.055124 | 2.369166 ± 0.025244 | 0.325989 ± 0.00160 | -6.463 ± 0.046 % | 18.519 ± 0.080 % |
| q2_K | WT 10k | 2.96 | 8.652290 ± 0.055572 | 2.420657 ± 0.025587 | 0.331393 ± 0.001562 | -6.606 ± 0.046 % | 18.790 ± 0.078 % |
| q2_K | WT 100k | 2.96 | 8.641993 ± 0.055406 | 2.410359 ± 0.025495 | 0.331672 ± 0.001569 | -6.628 ± 0.047 % | 18.856 ± 0.078 % |
| q2_K | WT 10m | 2.96 | 8.647825 ± 0.055610 | 2.416191 ± 0.025683 | 0.332223 ± 0.001572 | -6.500 ± 0.047 % | 18.881 ± 0.078 % |
| q2_K | WT 1m | 2.96 | 8.674365 ± 0.055743 | 2.442732 ± 0.025843 | 0.335308 ± 0.001576 | -6.634 ± 0.047 % | 19.009 ± 0.079 % |
| q2_K | WT 1k | 2.96 | 8.682605 ± 0.055916 | 2.450972 ± 0.026069 | 0.337093 ± 0.001596 | -6.596 ± 0.047 % | 18.977 ± 0.079 % |
| q2_K_S | WT 10m | 2.96 | 9.323778 ± 0.061551 | 3.092145 ± 0.031914 | 0.403360 ± 0.001787 | -7.131 ± 0.049 % | 20.050 ± 0.081 % |
| q2_K_S | WT 1m | 2.96 | 9.329321 ± 0.061378 | 3.097688 ± 0.031816 | 0.403590 ± 0.001797 | -7.289 ± 0.049 % | 20.123 ± 0.081 % |
| q2_K_S | WT 100k | 2.96 | 9.362973 ± 0.061740 | 3.131339 ± 0.032169 | 0.408367 ± 0.001802 | -7.198 ± 0.050 % | 20.132 ± 0.081 % |
| q2_K_S | WT 10k | 2.96 | 9.376479 ± 0.062045 | 3.144846 ± 0.032464 | 0.408662 ± 0.001819 | -7.141 ± 0.050 % | 20.120 ± 0.081 % |
| q2_K_S | WT 1k | 2.96 | 9.415200 ± 0.062475 | 3.183567 ± 0.032993 | 0.415865 ± 0.001846 | -7.153 ± 0.050 % | 20.311 ± 0.082 % |
| iq2_S | WT 10m | 2.56 | 9.650781 ± 0.063209 | 3.419148 ± 0.034017 | 0.439197 ± 0.001976 | -8.319 ± 0.052 % | 21.491 ± 0.083 % |
| q2_K | None | 2.96 | 9.751568 ± 0.063312 | 3.519934 ± 0.033863 | 0.445132 ± 0.001835 | -9.123 ± 0.051 % | 21.421 ± 0.079 % |
| iq2_XS | WT 10m | 2.43 | 10.761424 ± 0.071056 | 4.529791 ± 0.042229 | 0.546290 ± 0.002133 | -10.576 ± 0.056 % | 23.872 ± 0.082 % |
| iq2_XXS | WT 10m | 2.24 | 14.091782 ± 0.098396 | 7.860148 ± 0.070752 | 0.812022 ± 0.002741 | -14.363 ± 0.065 % | 28.576 ± 0.084 % |
| iq1_M | WT 10m | 2.01 | 25.493722 ± 0.177903 | 19.262089 ± 0.152396 | 1.393084 ± 0.003529 | -24.672 ± 0.077 % | 38.287 ± 0.084 % |
| iq1_S | WT 1m | 1.88 | 58.097760 ± 0.438604 | 51.866126 ± 0.416604 | 2.211278 ± 0.004688 | -32.471 ± 0.087 % | 46.418 ± 0.085 % |
| iq1_S | WT 1k | 1.88 | 58.267851 ± 0.446208 | 52.036218 ± 0.424373 | 2.214858 ± 0.004778 | -31.880 ± 0.089 % | 46.330 ± 0.086 % |
| iq1_S | WT 100k | 1.88 | 58.581498 ± 0.453145 | 52.349864 ± 0.431360 | 2.220834 ± 0.004818 | -32.261 ± 0.089 % | 46.002 ± 0.086 % |
| iq1_S | WT 10m | 1.88 | 60.694593 ± 0.471290 | 54.462959 ± 0.449644 | 2.254554 ± 0.004868 | -31.973 ± 0.088 % | 46.271 ± 0.086 % |
| iq1_S | WT 10k | 1.88 | 63.221324 ± 0.493077 | 56.989691 ± 0.471423 | 2.293527 ± 0.004885 | -32.261 ± 0.089 % | 46.562 ± 0.086 % |
There seems to be no consistent improvement from using more Wikitext tokens for the importance matrix.
K-quants score better on mean Δp than the legacy quants than e.g. KL divergence would suggest.
## LLaMA 2 vs. LLaMA 3 Quantization comparison
| Metric | L2 7b q2_K | L3 8b q2_K | L2 7b q4_K_M | L3 8b q4_K_M | L2 7b q6_K | L3 8b q6_K | L2 7b q8_0 | L3 8b q8_0 |
|-----------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|
| Mean PPL | 5.794552 ± 0.032298 | 9.751568 ± 0.063312 | 5.877078 ± 0.032781 | 6.407115 ± 0.039119 | 5.808494 ± 0.032425 | 6.253382 ± 0.038078 | 5.798542 ± 0.032366 | 6.234284 ± 0.037878 |
| Mean PPL ratio | 1.107955 ± 0.001427 | 1.564849 ± 0.004525 | 1.014242 ± 0.000432 | 1.028160 ± 0.000723 | 1.002406 ± 0.000191 | 1.003490 ± 0.000296 | 1.000689 ± 0.000107 | 1.000425 ± 0.000161 |
| Mean ΔPPL | 0.625552 ± 0.008725 | 3.519934 ± 0.033863 | 0.082526 ± 0.002530 | 0.175482 ± 0.004620 | 0.013941 ± 0.001110 | 0.021748 ± 0.001852 | 0.003990 ± 0.000624 | 0.002650 ± 0.001006 |
| PPL correlation | 97.36% | 89.62% | 99.71% | 99.34% | 99.94% | 99.88% | 99.98% | 99.96% |
| Mean KLD | 0.108903 ± 0.000645 | 0.445132 ± 0.001835 | 0.012686 ± 0.000079 | 0.031273 ± 0.000238 | 0.002098 ± 0.000014 | 0.005452 ± 0.000035 | 0.000369 ± 0.000007 | 0.001355 ± 0.000006 |
| Mean Δp | -2.710 ± 0.023 % | -9.123 ± 0.051 % | -0.416 ± 0.008 % | -0.596 ± 0.014 % | -0.035 ± 0.003 % | -0.007 ± 0.006 % | -0.005 ± 0.002 % | -0.019 ± 0.003 % |
| Maximum Δp | 85.136% | 94.268% | 45.209% | 95.054% | 23.593% | 53.601% | 43.925% | 28.734% |
| 99.9% Δp | 37.184% | 50.003% | 17.461% | 27.084% | 7.798% | 13.613% | 3.387% | 6.402% |
| 99.0% Δp | 18.131% | 25.875% | 7.798% | 12.084% | 3.838% | 6.407% | 1.867% | 3.544% |
| Median Δp | -0.391% | -2.476% | -0.026% | -0.024% | -0.001% | 0.000% | -0.000% | -0.000% |
| 1.0% Δp | -39.762% | -87.173% | -11.433% | -19.567% | -4.222% | -6.767% | -1.862% | -3.698% |
| 0.1% Δp | -79.002% | -98.897% | -26.433% | -56.054% | -9.091% | -16.584% | -3.252% | -6.579% |
| Minimum Δp | -99.915% | -99.965% | -83.383% | -98.699% | -43.142% | -68.487% | -9.343% | -24.301% |
| RMS Δp | 9.762 ± 0.053 % | 21.421 ± 0.079 % | 3.252 ± 0.024 % | 5.519 ± 0.050 % | 1.339 ± 0.010 % | 2.295 ± 0.019 % | 0.618 ± 0.011 % | 1.198 ± 0.007 % |
| Same top p | 85.584 ± 0.086 % | 71.138 ± 0.119 % | 94.665 ± 0.055 % | 91.901 ± 0.072 % | 97.520 ± 0.038 % | 96.031 ± 0.051 % | 98.846 ± 0.026 % | 97.674 ± 0.040 % |
## Old Numbers
<details>
<summary>Llama 2 70B Scoreboard</summary>
## Llama 2 70B Scorechart
| Quantization | Model size (GiB) | Perplexity | Delta to fp16 |
|--------------|------------------|------------|---------------|
| Q4_0 | 36.20 | 3.5550 | 3.61% |
@ -18,3 +128,5 @@ TODO
| Q5_K_M | 45.41 | 3.4451 | 0.40% |
| Q6_K | 52.70 | 3.4367 | 0.16% |
| fp16 | 128.5 | 3.4313 | - |
</details>

230
examples/perplexity/perplexity.cpp
View File

@ -216,17 +216,22 @@ static void process_logits(std::ostream& out, int n_vocab, const float * logits,
}
struct kl_divergence_result {
double sum_nll = 0;
double sum_nll2 = 0;
double sum_kld = 0;
double sum_kld2 = 0;
double sum_nll_diff = 0;
double sum_nll_diff2 = 0;
size_t n_same_top = 0;
size_t count = 0;
double sum_nll = 0.0;
double sum_nll2 = 0.0;
double sum_nll_base = 0.0;
double sum_nll_base2 = 0.0;
double sum_nll_nll_base = 0.0;
double sum_kld = 0.0;
double sum_kld2 = 0.0;
double sum_p_diff = 0.0;
double sum_p_diff2 = 0.0;
double sum_p_diff4 = 0.0;
float max_p_diff = 0.0f;
size_t n_same_top = 0.0;
size_t count = 0.0;
};
static double log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) {
static std::pair<double, float> log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) {
float max_logit = logits[0];
int imax = 0;
for (int i = 1; i < n_vocab; ++i) {
@ -244,12 +249,17 @@ static double log_softmax(int n_vocab, const float * logits, const uint16_t * ba
const float scale = d[0];
const float min_log_prob = d[1];
base_log_prob += 4;
float nll = max_logit + log_sum_exp - logits[tok];
const float nll = max_logit + log_sum_exp - logits[tok];
kld.sum_nll += nll;
kld.sum_nll2 += nll*nll;
nll += (scale*base_log_prob[tok] + min_log_prob);
kld.sum_nll_diff += nll;
kld.sum_nll_diff2 += nll*nll;
const float nll_base = -(scale*base_log_prob[tok] + min_log_prob);
kld.sum_nll_base += nll_base;
kld.sum_nll_base2 += nll_base*nll_base;
kld.sum_nll_nll_base += nll*nll_base;
max_logit += log_sum_exp;
double sum = 0;
int imax_base = -1;
@ -269,34 +279,50 @@ static double log_softmax(int n_vocab, const float * logits, const uint16_t * ba
kld.sum_kld2 += sum*sum;
++kld.count;
if (imax == imax_base) ++kld.n_same_top;
return sum;
const float p_base = expf(-nll_base);
const float p = expf(-nll);
const float p_diff = p - p_base;
kld.sum_p_diff += p_diff;
const double p_diff2 = p_diff*p_diff;
kld.sum_p_diff2 += p_diff2;
kld.sum_p_diff4 += p_diff2*p_diff2;
kld.max_p_diff = std::max(kld.max_p_diff, std::fabs(p_diff));
return std::make_pair(sum, p_diff);
}
static void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token,
std::vector<std::thread> & workers, const std::vector<uint16_t> & base_log_probs, kl_divergence_result & kld,
float * kld_values) {
float * kld_values, float * p_diff_values) {
std::mutex mutex;
const int nv = 2*((n_vocab + 1)/2) + 4;
int counter = 0;
auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values] () {
auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values, p_diff_values] () {
kl_divergence_result local_kld;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
int i = counter++;
if (i >= n_token) {
kld.sum_nll += local_kld.sum_nll;
kld.sum_nll2 += local_kld.sum_nll2;
kld.sum_kld += local_kld.sum_kld;
kld.sum_kld2 += local_kld.sum_kld2;
kld.sum_nll_diff += local_kld.sum_nll_diff;
kld.sum_nll_diff2 += local_kld.sum_nll_diff2;
kld.n_same_top += local_kld.n_same_top;
kld.count += local_kld.count;
kld.sum_nll += local_kld.sum_nll;
kld.sum_nll2 += local_kld.sum_nll2;
kld.sum_nll_base += local_kld.sum_nll_base;
kld.sum_nll_base2 += local_kld.sum_nll_base2;
kld.sum_nll_nll_base += local_kld.sum_nll_nll_base;
kld.sum_kld += local_kld.sum_kld;
kld.sum_kld2 += local_kld.sum_kld2;
kld.sum_p_diff += local_kld.sum_p_diff;
kld.sum_p_diff2 += local_kld.sum_p_diff2;
kld.sum_p_diff4 += local_kld.sum_p_diff4;
kld.n_same_top += local_kld.n_same_top;
kld.max_p_diff = std::max(kld.max_p_diff, local_kld.max_p_diff);
kld.count += local_kld.count;
break;
}
lock.unlock();
double v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
kld_values[i] = (float)v;
std::pair<double, float> v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
kld_values[i] = (float)v.first;
p_diff_values[i] = v.second;
}
};
for (auto & w : workers) {
@ -1711,7 +1737,8 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv);
std::vector<float> kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
std::vector<float> kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
std::vector<float> p_diff_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
std::vector<float> logits;
if (num_batches > 1) {
logits.reserve(n_ctx * n_vocab);
@ -1728,9 +1755,18 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
df = df > 0 && count > 10 ? sqrt(df/(count-1)) : 0.;
return std::make_pair(f, df);
};
auto covariance = [] (double suma, double sumb, double sumab, size_t count) {
if (count < 10) {
return 0.0;
}
double var = sumab/count - (suma/count)*(sumb/count);
var /= count - 1;
return var;
};
kl_divergence_result kld;
auto kld_ptr = kld_values.data();
auto kld_ptr = kld_values.data();
auto p_diff_ptr = p_diff_values.data();
for (int i = 0; i < n_chunk; ++i) {
const int start = i * n_ctx;
@ -1785,24 +1821,42 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
}
fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
printf("\nchunk PPL ln(PPL(Q)/PPL(base)) KL-Divergence Same top\n");
printf("\nchunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p\n");
}
const int first = n_ctx/2;
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
workers, log_probs_uint16, kld, kld_ptr);
kld_ptr += n_ctx - 1 - first;
workers, log_probs_uint16, kld, kld_ptr, p_diff_ptr);
p_diff_ptr += n_ctx - 1 - first;
kld_ptr += n_ctx - 1 - first;
auto ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
auto log_ppl_ratio = mean_and_uncertainty(kld.sum_nll_diff, kld.sum_nll_diff2, kld.count);
auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
auto p_top = 1.*kld.n_same_top/kld.count;
auto d_p_top = sqrt(p_top*(1 - p_top)/(kld.count - 1));
printf("%4d", i+1);
printf("%4d %10.4lf %10.5lf ± %10.5f %10.5f ± %10.5lf %.5f ± %.5f\n", i+1, exp(ppl.first),
log_ppl_ratio.first, log_ppl_ratio.second, kl_div.first, kl_div.second,
p_top, d_p_top);
auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
const double ppl_val = exp(log_ppl.first);
const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 )
printf(" %9.4lf ± %9.4lf", ppl_val, ppl_unc);
auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count);
const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count);
const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first;
const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov);
printf(" %10.5lf ± %10.5lf", log_ppl_ratio_val, log_ppl_ratio_unc);
auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
printf(" %10.5lf ± %10.5lf", kl_div.first, kl_div.second);
auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count);
const double p_diff_rms_val = sqrt(p_diff_mse.first);
const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second;
printf(" %6.3lf ± %6.3lf %%", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc);
double p_top_val = 1.*kld.n_same_top/kld.count;
double p_top_unc = sqrt(p_top_val*(1 - p_top_val)/(kld.count - 1));
printf(" %6.3lf ± %6.3lf %%", 100.0*p_top_val, 100.0*p_top_unc);
printf("\n");
fflush(stdout);
@ -1813,31 +1867,97 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
if (kld.count < 100) return; // we do not wish to do statistics on so few values
std::sort(kld_values.begin(), kld_values.end());
std::sort(p_diff_values.begin(), p_diff_values.end());
printf("===== KL-divergence statistics\n");
printf("====== Perplexity statistics ======\n");
auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
const double ppl_val = exp(log_ppl.first);
const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 )
printf("Mean PPL(Q) : %10.6lf ± %10.6lf\n", ppl_val, ppl_unc);
auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count);
const double ppl_base_val = exp(log_ppl_base.first);
const double ppl_base_unc = ppl_base_val * log_ppl_base.second; // ppl_base_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_base.second ** 2 )
printf("Mean PPL(base) : %10.6lf ± %10.6lf\n", ppl_base_val, ppl_base_unc);
const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count);
// printf("Cov(ln(PPL(Q)), ln(PPL(base))): %10.6lf\n", log_ppl_cov);
const double log_ppl_cor = log_ppl_cov / (log_ppl.second*log_ppl_base.second);
printf("Cor(ln(PPL(Q)), ln(PPL(base))): %6.2lf%%\n", 100.0*log_ppl_cor);
const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first;
const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov);
printf("Mean ln(PPL(Q)/PPL(base)) : %10.6lf ± %10.6lf\n", log_ppl_ratio_val, log_ppl_ratio_unc);
const double ppl_ratio_val = exp(log_ppl_ratio_val);
const double ppl_ratio_unc = ppl_ratio_val * log_ppl_ratio_unc; // ppl_ratio_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_ratio.second ** 2 )
printf("Mean PPL(Q)/PPL(base) : %10.6lf ± %10.6lf\n", ppl_ratio_val, ppl_ratio_unc);
const double ppl_cov = ppl_val * ppl_base_val * log_ppl_cov;
const double ppl_diff_val = ppl_val - ppl_base_val;
const double ppl_diff_unc = sqrt(ppl_unc*ppl_unc + ppl_base_unc*ppl_base_unc - 2.0*ppl_cov);
printf("Mean PPL(Q)-PPL(base) : %10.6lf ± %10.6lf\n", ppl_diff_val, ppl_diff_unc);
printf("\n");
printf("====== KL divergence statistics ======\n");
auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
printf("Average: %10.6f ±%10.6lf\n", kl_div.first, kl_div.second);
printf("Mean KLD: %10.6lf ± %10.6lf\n", kl_div.first, kl_div.second);
auto kld_median = kld_values.size()%2 == 0 ? 0.5f*(kld_values[kld_values.size()/2] + kld_values[kld_values.size()/2-1])
: kld_values[kld_values.size()/2];
printf("Median : %10.6f\n", kld_median);
auto percentile = [&kld_values] (float fraction) {
if (fraction <= 0) return kld_values.front();
if (fraction >= 1) return kld_values.back();
float p = fraction*(kld_values.size() - 1);
auto percentile = [] (std::vector<float> values, float fraction) {
if (fraction <= 0) return values.front();
if (fraction >= 1) return values.back();
float p = fraction*(values.size() - 1);
size_t ip = size_t(p); p -= ip;
return (1 - p)*kld_values[ip] + p*kld_values[std::min(ip+1, kld_values.size()-1)];
return (1 - p)*values[ip] + p*values[std::min(ip+1, values.size()-1)];
};
printf("Maximum: %10.6f\n", kld_values.back());
printf("KLD_99 : %10.6f\n", percentile(0.99f));
printf("KLD_95 : %10.6f\n", percentile(0.95f));
printf("KLD_90 : %10.6f\n", percentile(0.90f));
printf("Maximum KLD: %10.6f\n", kld_values.back());
printf("99.9%% KLD: %10.6f\n", percentile(kld_values, 0.999f));
printf("99.0%% KLD: %10.6f\n", percentile(kld_values, 0.990f));
printf("99.0%% KLD: %10.6f\n", percentile(kld_values, 0.990f));
printf("Median KLD: %10.6f\n", kld_median);
printf("10.0%% KLD: %10.6f\n", percentile(kld_values, 0.100f));
printf(" 5.0%% KLD: %10.6f\n", percentile(kld_values, 0.050f));
printf(" 1.0%% KLD: %10.6f\n", percentile(kld_values, 0.010f));
printf("Minimum KLD: %10.6f\n", kld_values.front());
printf("Minimum: %10.6f\n", kld_values.front());
printf("KLD_01 : %10.6f\n", percentile(0.01f));
printf("KLD_05 : %10.6f\n", percentile(0.05f));
printf("KLD_10 : %10.6f\n", percentile(0.10f));
printf("\n");
printf("====== Token probability statistics ======\n");
auto p_diff = mean_and_uncertainty(kld.sum_p_diff, kld.sum_p_diff2, kld.count);
printf("Mean Δp: %6.3lf ± %5.3lf %%\n", 100.0*p_diff.first, 100.0*p_diff.second);
auto p_diff_median = p_diff_values.size()%2 == 0 ? 0.5f*(p_diff_values[p_diff_values.size()/2] + p_diff_values[p_diff_values.size()/2-1])
: p_diff_values[p_diff_values.size()/2];
printf("Maximum Δp: %6.3lf%%\n", 100.0*p_diff_values.back());
printf("99.9%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.999f));
printf("99.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.990f));
printf("95.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.950f));
printf("90.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.900f));
printf("75.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.750f));
printf("Median Δp: %6.3lf%%\n", 100.0*p_diff_median);
printf("25.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.250f));
printf("10.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.100f));
printf(" 5.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.050f));
printf(" 1.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.010f));
printf(" 0.1%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.001f));
printf("Minimum Δp: %6.3lf%%\n", 100.0*p_diff_values.front());
auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count);
// printf("MSE Δp : %10.6lf ± %10.6lf\n", p_diff_mse.first, p_diff_mse.second);
const double p_diff_rms_val = sqrt(p_diff_mse.first);
const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second;
printf("RMS Δp : %6.3lf ± %5.3lf %%\n", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc);
const double same_top_p = 1.0*kld.n_same_top/kld.count;
printf("Same top p: %6.3lf ± %5.3lf %%\n", 100.0*same_top_p, 100.0*sqrt(same_top_p*(1.0 - same_top_p)/(kld.count - 1)));
}

2
examples/quantize-stats/quantize-stats.cpp
View File

@ -23,7 +23,7 @@
#endif
struct quantize_stats_params {
std::string model = "models/7B/ggml-model-f16.gguf";
std::string model = DEFAULT_MODEL_PATH;
bool verbose = false;
bool per_layer_stats = false;
bool print_histogram = false;

3
examples/quantize/quantize.cpp
View File

@ -46,7 +46,8 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", },
{ "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 5.15G, +0.0008 ppl @ LLaMA-v1-7B", },
{ "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", },
{ "F16", LLAMA_FTYPE_MOSTLY_F16, "13.00G @ 7B", },
{ "F16", LLAMA_FTYPE_MOSTLY_F16, "14.00G, -0.0020 ppl @ Mistral-7B", },
{ "BF16", LLAMA_FTYPE_MOSTLY_BF16, "14.00G, -0.0050 ppl @ Mistral-7B", },
{ "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", },
// Note: Ensure COPY comes after F32 to avoid ftype 0 from matching.
{ "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", },

29
examples/server/README.md
View File

@ -62,6 +62,18 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/
- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name. Default: template taken from model's metadata. We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
- `--log-disable`: Output logs to stdout only, not to `llama.log`. Default: enabled
- `--log-format FORMAT`: Define the log output to FORMAT: json or text Default: `json`
- `--rope-scaling` : RoPE scaling method. Defaults to linear unless otherwise specified by the model. Options are `none`, `linear`, `yarn`
- `--rope-freq-base N` : RoPE frequency base (default: loaded from model)
- `--rope-freq-scale N`: RoPE frequency scaling factor, expands context by a factor of 1/N (e.g. 0.25)
- `--yarn-ext-factor N` : YaRN: extrapolation mix factor (Default: 1.0, 0.0 = full interpolation)
- `--yarn-attn-factor N` : YaRN: scale sqrt(t) or attention magnitude (default: 1.0)
- `--yarn-beta-slow N`: YaRN: High correction dim or alpha (default: 1.0)
- `--yarn-beta-fast N`: YaRN: low correction dim or beta (default: 32.0)
- `--pooling` : Pooling type for embeddings, use model default if unspecified. Options are `none`, `mean`, `cls`
- `-dt N`, `--defrag-thold N`: KV cache defragmentation threshold (default: -1.0, < 0 = disabled)
- `-fa`, `--flash-attn` : enable flash attention (default: disabled).
- `-ctk TYPE`, `--cache-type-k TYPE` : KV cache data type for K (default: `f16`, options `f32`, `f16`, `q8_0`, `q4_0`, `q4_1`, `iq4_nl`, `q5_0`, or `q5_1`)
- `-ctv TYPE`, `--cache-type-v TYPE` : KV cache type for V (default `f16`, see `-ctk` for options)
**If compiled with `LLAMA_SERVER_SSL=ON`**
- `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key
@ -74,15 +86,18 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/
- Using `make`:
```bash
make
make server
```
- Using `CMake`:
```bash
cmake --build . --config Release
cmake -B build
cmake --build build --config Release -t server
```
Binary is at `./build/bin/server`
## Build with SSL
`server` can also be built with SSL support using OpenSSL 3
@ -99,10 +114,8 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/
- Using `CMake`:
```bash
mkdir build
cd build
cmake .. -DLLAMA_SERVER_SSL=ON
make server
cmake -B build -DLLAMA_SERVER_SSL=ON
cmake --build build --config Release -t server
```
## Quick Start
@ -259,7 +272,7 @@ node index.js
`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. Default: `[]`
`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token. Default: `0`
`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token given the sampling settings. Note that for temperature < 0 the tokens are sampled greedily but token probabilities are still being calculated via a simple softmax of the logits without considering any other sampler settings. Default: `0`
`min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0`
@ -318,7 +331,7 @@ Notice that each `probs` is an array of length `n_probs`.
`content`: Set the text to tokenize.
Note that a special `BOS` token is never inserted.
`add_special`: Boolean indicating if special tokens, i.e. `BOS`, should be inserted. Default: `false`
- **POST** `/detokenize`: Convert tokens to text.

1
examples/server/bench/bench.py
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@ -268,6 +268,7 @@ def start_server_background(args):
server_args.extend(['--defrag-thold', "0.1"])
server_args.append('--cont-batching')
server_args.append('--metrics')
server_args.append('--flash-attn')
server_args.extend(['--log-format', "text"])
args = [str(arg) for arg in [server_path, *server_args]]
print(f"bench: starting server with: {' '.join(args)}")

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139
examples/server/server.cpp
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@ -12,6 +12,8 @@
// increase max payload length to allow use of larger context size
#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
#include "httplib.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "json.hpp"
// auto generated files (update with ./deps.sh)
@ -649,9 +651,6 @@ struct server_context {
std::string system_prompt;
std::vector<llama_token> system_tokens;
std::string name_user; // this should be the antiprompt
std::string name_assistant;
// slots / clients
std::vector<server_slot> slots;
json default_generation_settings_for_props;
@ -671,6 +670,8 @@ struct server_context {
llama_free_model(model);
model = nullptr;
}
llama_batch_free(batch);
}
bool load_model(const gpt_params & params_) {
@ -859,7 +860,7 @@ struct server_context {
slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
// process "json_schema" and "grammar"
if (data.contains("json_schema") && !data["json_schema"].is_null() && data.contains("grammar") && !data["grammar"].is_null()) {
if (data.contains("json_schema") && !data.at("json_schema").is_null() && data.contains("grammar") && !data.at("grammar").is_null()) {
send_error(task, "Either \"json_schema\" or \"grammar\" can be specified, but not both", ERROR_TYPE_INVALID_REQUEST);
return false;
} else if (data.contains("json_schema") && !data.contains("grammar")) {
@ -1096,15 +1097,11 @@ struct server_context {
system_need_update = false;
}
void system_prompt_set(const json & sys_props) {
system_prompt = sys_props.value("prompt", "");
name_user = sys_props.value("anti_prompt", "");
name_assistant = sys_props.value("assistant_name", "");
bool system_prompt_set(const std::string & sys_prompt) {
system_prompt = sys_prompt;
LOG_VERBOSE("system prompt process", {
{"system_prompt", system_prompt},
{"name_user", name_user},
{"name_assistant", name_assistant},
});
// release all slots
@ -1113,6 +1110,7 @@ struct server_context {
}
system_need_update = true;
return true;
}
bool process_token(completion_token_output & result, server_slot & slot) {
@ -1383,9 +1381,10 @@ struct server_context {
if (!slot.params.stream && slot.stopped_word) {
const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false);
size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size());
probs = std::vector<completion_token_output>(
slot.generated_token_probs.begin(),
slot.generated_token_probs.end() - stop_word_toks.size());
slot.generated_token_probs.end() - safe_offset);
} else {
probs = std::vector<completion_token_output>(
slot.generated_token_probs.begin(),
@ -1511,7 +1510,7 @@ struct server_context {
// add subtasks
for (int i = 0; i < prompt_count; i++) {
json subtask_data = multiprompt_task.data;
subtask_data["prompt"] = subtask_data["prompt"][i];
subtask_data["prompt"] = subtask_data.at("prompt")[i];
// subtasks inherit everything else (infill mode, embedding mode, etc.)
request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill, multiprompt_task.embedding);
@ -1531,7 +1530,8 @@ struct server_context {
}
if (task.data.contains("system_prompt")) {
system_prompt_set(task.data["system_prompt"]);
std::string sys_prompt = json_value(task.data, "system_prompt", std::string());
system_prompt_set(sys_prompt);
for (server_slot & slot : slots) {
slot.n_past = 0;
@ -1643,7 +1643,7 @@ struct server_context {
} break;
case SERVER_TASK_TYPE_SLOT_SAVE:
{
int id_slot = task.data["id_slot"];
int id_slot = task.data.at("id_slot");
server_slot * slot = get_slot(id_slot);
if (slot == nullptr) {
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
@ -1653,8 +1653,8 @@ struct server_context {
const size_t token_count = slot->cache_tokens.size();
const int64_t t_start = ggml_time_us();
std::string filename = task.data["filename"];
std::string filepath = task.data["filepath"];
std::string filename = task.data.at("filename");
std::string filepath = task.data.at("filepath");
const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count);
@ -1678,7 +1678,7 @@ struct server_context {
} break;
case SERVER_TASK_TYPE_SLOT_RESTORE:
{
int id_slot = task.data["id_slot"];
int id_slot = task.data.at("id_slot");
server_slot * slot = get_slot(id_slot);
if (slot == nullptr) {
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
@ -1687,8 +1687,8 @@ struct server_context {
const int64_t t_start = ggml_time_us();
std::string filename = task.data["filename"];
std::string filepath = task.data["filepath"];
std::string filename = task.data.at("filename");
std::string filepath = task.data.at("filepath");
slot->cache_tokens.resize(slot->n_ctx);
size_t token_count = 0;
@ -1720,7 +1720,7 @@ struct server_context {
} break;
case SERVER_TASK_TYPE_SLOT_ERASE:
{
int id_slot = task.data["id_slot"];
int id_slot = task.data.at("id_slot");
server_slot * slot = get_slot(id_slot);
if (slot == nullptr) {
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
@ -2265,17 +2265,31 @@ struct server_context {
llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
result.tok = id;
const int32_t n_probs = slot.sparams.n_probs;
if (slot.sparams.temp <= 0 && n_probs > 0) {
// for llama_sample_token_greedy we need to sort candidates
llama_sample_softmax(ctx, &cur_p);
}
const size_t n_probs = std::min(cur_p.size, (size_t) slot.sparams.n_probs);
if (n_probs > 0) {
const size_t n_valid = slot.ctx_sampling->n_valid;
for (size_t i = 0; i < std::min(cur_p.size, (size_t) n_probs); ++i) {
result.probs.push_back({
cur_p.data[i].id,
cur_p.data[i].p
});
// Make sure at least n_probs top tokens are at the front of the vector:
if (slot.sparams.temp == 0.0f && n_probs > n_valid) {
llama_sample_top_k(ctx, &cur_p, n_probs, 0);
}
if (slot.sparams.temp == 0.0f) {
// With greedy sampling the probabilities have possibly not been calculated.
for (size_t i = 0; i < n_probs; ++i) {
result.probs.push_back({
cur_p.data[i].id,
i == 0 ? 1.0f : 0.0f
});
}
} else {
for (size_t i = 0; i < n_probs; ++i) {
result.probs.push_back({
cur_p.data[i].id,
i >= n_valid ? 0.0f : cur_p.data[i].p // Tokens filtered out due to e.g. top_k have 0 probability.
});
}
}
}
if (!process_token(result, slot)) {
@ -2353,7 +2367,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
printf(" disable KV offload\n");
}
printf(" -m FNAME, --model FNAME\n");
printf(" model path (default: %s)\n", params.model.c_str());
printf(" model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH);
printf(" -mu MODEL_URL, --model-url MODEL_URL\n");
printf(" model download url (default: unused)\n");
printf(" -hfr REPO, --hf-repo REPO\n");
@ -2377,6 +2391,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
printf(" --embeddings enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel);
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: enabled)\n");
printf(" -fa, --flash-attn enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled");
printf(" -spf FNAME, --system-prompt-file FNAME\n");
printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
printf(" -ctk TYPE, --cache-type-k TYPE\n");
@ -2742,6 +2757,8 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
params.embedding = true;
} else if (arg == "-cb" || arg == "--cont-batching") {
params.cont_batching = true;
} else if (arg == "-fa" || arg == "--flash-attn") {
params.flash_attn = true;
} else if (arg == "-np" || arg == "--parallel") {
if (++i >= argc) {
invalid_param = true;
@ -2835,6 +2852,8 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
}
}
gpt_params_handle_model_default(params);
if (!params.kv_overrides.empty()) {
params.kv_overrides.emplace_back();
params.kv_overrides.back().key[0] = 0;
@ -2896,7 +2915,7 @@ int main(int argc, char ** argv) {
server_params_parse(argc, argv, sparams, params);
if (!sparams.system_prompt.empty()) {
ctx_server.system_prompt_set(json::parse(sparams.system_prompt));
ctx_server.system_prompt_set(sparams.system_prompt);
}
if (params.model_alias == "unknown") {
@ -3116,8 +3135,8 @@ int main(int argc, char ** argv) {
server_task_result result = ctx_server.queue_results.recv(task.id);
ctx_server.queue_results.remove_waiting_task_id(task.id);
const int n_idle_slots = result.data["idle"];
const int n_processing_slots = result.data["processing"];
const int n_idle_slots = result.data.at("idle");
const int n_processing_slots = result.data.at("processing");
json health = {
{"status", "ok"},
@ -3127,7 +3146,7 @@ int main(int argc, char ** argv) {
res.status = 200; // HTTP OK
if (sparams.slots_endpoint && req.has_param("include_slots")) {
health["slots"] = result.data["slots"];
health["slots"] = result.data.at("slots");
}
if (n_idle_slots == 0) {
@ -3171,7 +3190,7 @@ int main(int argc, char ** argv) {
server_task_result result = ctx_server.queue_results.recv(task.id);
ctx_server.queue_results.remove_waiting_task_id(task.id);
res.set_content(result.data["slots"].dump(), "application/json");
res.set_content(result.data.at("slots").dump(), "application/json");
res.status = 200; // HTTP OK
};
@ -3198,32 +3217,32 @@ int main(int argc, char ** argv) {
json data = result.data;
const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"];
const uint64_t t_prompt_processing = data["t_prompt_processing"];
const uint64_t n_prompt_tokens_processed = data.at("n_prompt_tokens_processed");
const uint64_t t_prompt_processing = data.at("t_prompt_processing");
const uint64_t n_tokens_predicted = data["n_tokens_predicted"];
const uint64_t t_tokens_generation = data["t_tokens_generation"];
const uint64_t n_tokens_predicted = data.at("n_tokens_predicted");
const uint64_t t_tokens_generation = data.at("t_tokens_generation");
const int32_t kv_cache_used_cells = data["kv_cache_used_cells"];
const int32_t kv_cache_used_cells = data.at("kv_cache_used_cells");
// metrics definition: https://prometheus.io/docs/practices/naming/#metric-names
json all_metrics_def = json {
{"counter", {{
{"name", "prompt_tokens_total"},
{"help", "Number of prompt tokens processed."},
{"value", (uint64_t) data["n_prompt_tokens_processed_total"]}
{"value", (uint64_t) data.at("n_prompt_tokens_processed_total")}
}, {
{"name", "prompt_seconds_total"},
{"help", "Prompt process time"},
{"value", (uint64_t) data["t_prompt_processing_total"] / 1.e3}
{"value", (uint64_t) data.at("t_prompt_processing_total") / 1.e3}
}, {
{"name", "tokens_predicted_total"},
{"help", "Number of generation tokens processed."},
{"value", (uint64_t) data["n_tokens_predicted_total"]}
{"value", (uint64_t) data.at("n_tokens_predicted_total")}
}, {
{"name", "tokens_predicted_seconds_total"},
{"help", "Predict process time"},
{"value", (uint64_t) data["t_tokens_generation_total"] / 1.e3}
{"value", (uint64_t) data.at("t_tokens_generation_total") / 1.e3}
}}},
{"gauge", {{
{"name", "prompt_tokens_seconds"},
@ -3240,15 +3259,15 @@ int main(int argc, char ** argv) {
},{
{"name", "kv_cache_tokens"},
{"help", "KV-cache tokens."},
{"value", (uint64_t) data["kv_cache_tokens_count"]}
{"value", (uint64_t) data.at("kv_cache_tokens_count")}
},{
{"name", "requests_processing"},
{"help", "Number of request processing."},
{"value", (uint64_t) data["processing"]}
{"value", (uint64_t) data.at("processing")}
},{
{"name", "requests_deferred"},
{"help", "Number of request deferred."},
{"value", (uint64_t) data["deferred"]}
{"value", (uint64_t) data.at("deferred")}
}}}
};
@ -3259,8 +3278,8 @@ int main(int argc, char ** argv) {
const auto & metrics_def = el.value();
for (const auto & metric_def : metrics_def) {
const std::string name = metric_def["name"];
const std::string help = metric_def["help"];
const std::string name = metric_def.at("name");
const std::string help = metric_def.at("help");
auto value = json_value(metric_def, "value", 0.);
prometheus << "# HELP llamacpp:" << name << " " << help << "\n"
@ -3269,7 +3288,7 @@ int main(int argc, char ** argv) {
}
}
const int64_t t_start = data["t_start"];
const int64_t t_start = data.at("t_start");
res.set_header("Process-Start-Time-Unix", std::to_string(t_start));
res.set_content(prometheus.str(), "text/plain; version=0.0.4");
@ -3278,7 +3297,7 @@ int main(int argc, char ** argv) {
const auto handle_slots_save = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) {
json request_data = json::parse(req.body);
std::string filename = request_data["filename"];
std::string filename = request_data.at("filename");
if (!validate_file_name(filename)) {
res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
return;
@ -3308,7 +3327,7 @@ int main(int argc, char ** argv) {
const auto handle_slots_restore = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) {
json request_data = json::parse(req.body);
std::string filename = request_data["filename"];
std::string filename = request_data.at("filename");
if (!validate_file_name(filename)) {
res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
return;
@ -3385,8 +3404,7 @@ int main(int argc, char ** argv) {
const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
json data = {
{ "user_name", ctx_server.name_user.c_str() },
{ "assistant_name", ctx_server.name_assistant.c_str() },
{ "system_prompt", ctx_server.system_prompt.c_str() },
{ "default_generation_settings", ctx_server.default_generation_settings_for_props },
{ "total_slots", ctx_server.params.n_parallel }
};
@ -3627,7 +3645,8 @@ int main(int argc, char ** argv) {
std::vector<llama_token> tokens;
if (body.count("content") != 0) {
tokens = ctx_server.tokenize(body["content"], false);
const bool add_special = json_value(body, "add_special", false);
tokens = ctx_server.tokenize(body.at("content"), add_special);
}
const json data = format_tokenizer_response(tokens);
return res.set_content(data.dump(), "application/json; charset=utf-8");
@ -3639,7 +3658,7 @@ int main(int argc, char ** argv) {
std::string content;
if (body.count("tokens") != 0) {
const std::vector<llama_token> tokens = body["tokens"];
const std::vector<llama_token> tokens = body.at("tokens");
content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend());
}
@ -3662,10 +3681,10 @@ int main(int argc, char ** argv) {
json prompt;
if (body.count("input") != 0) {
is_openai = true;
prompt = body["input"];
prompt = body.at("input");
} else if (body.count("content") != 0) {
// with "content", we only support single prompt
prompt = std::vector<std::string>{body["content"]};
prompt = std::vector<std::string>{body.at("content")};
} else {
res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
return;
@ -3684,7 +3703,7 @@ int main(int argc, char ** argv) {
if (!result.error) {
if (result.data.count("results")) {
// result for multi-task
responses = result.data["results"];
responses = result.data.at("results");
} else {
// result for single task
responses = std::vector<json>{result.data};

2
examples/server/tests/features/embeddings.feature
View File

@ -5,7 +5,7 @@ Feature: llama.cpp server
Background: Server startup
Given a server listening on localhost:8080
And a model url https://huggingface.co/ggml-org/models/resolve/main/bert-bge-small/ggml-model-f16.gguf
And a model file ggml-model-f16.gguf
And a model file bert-bge-small.gguf
And a model alias bert-bge-small
And 42 as server seed
And 2 slots

90
examples/server/tests/features/results.feature
View File

@ -7,44 +7,16 @@ Feature: Results
And a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models
And a model file test-model-00001-of-00003.gguf
And 128 as batch size
And 256 KV cache size
And 1024 KV cache size
And 128 max tokens to predict
Scenario Outline: Multi users completion
Given <n_slots> slots
And continuous batching
Scenario Outline: consistent results with same seed
Given <n_slots> slots
Then the server is starting
Then the server is healthy
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given 42 as seed
And a prompt:
"""
Write a very long story about AI.
"""
Given 4 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 42
Given concurrent completion requests
Then the server is busy
@ -55,3 +27,55 @@ Feature: Results
| n_slots |
| 1 |
| 2 |
Scenario Outline: different results with different seed
Given <n_slots> slots
Then the server is starting
Then the server is healthy
Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 42
Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 43
Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 44
Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 45
Given concurrent completion requests
Then the server is busy
Then the server is idle
And all slots are idle
Then all predictions are different
Examples:
| n_slots |
| 1 |
| 2 |
Scenario Outline: consistent results with same seed and varying batch size
Given 4 slots
And <temp> temperature
# And 0 as draft
Then the server is starting
Then the server is healthy
Given 1 prompts "Write a very long story about AI." with seed 42
And concurrent completion requests
# Then the server is busy # Not all slots will be utilized.
Then the server is idle
And all slots are idle
Given <n_parallel> prompts "Write a very long story about AI." with seed 42
And concurrent completion requests
# Then the server is busy # Not all slots will be utilized.
Then the server is idle
And all slots are idle
Then all predictions are equal
Examples:
| n_parallel | temp |
| 1 | 0.0 |
| 2 | 0.0 |
| 4 | 0.0 |
| 1 | 1.0 |
# FIXME: These tests fail on master. The problem seems to be the unified KV cache.
# See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227
# and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 .
# | 2 | 1.0 |
# | 4 | 1.0 |

14
examples/server/tests/features/server.feature
View File

@ -7,6 +7,7 @@ Feature: llama.cpp server
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
And a model file test-model.gguf
And a model alias tinyllama-2
And BOS token is 1
And 42 as server seed
# KV Cache corresponds to the total amount of tokens
# that can be stored across all independent sequences: #4130
@ -91,7 +92,18 @@ Feature: llama.cpp server
"""
What is the capital of France ?
"""
Then tokens can be detokenize
Then tokens can be detokenized
And tokens do not begin with BOS
Scenario: Tokenize w/ BOS
Given adding special tokens
When tokenizing:
"""
What is the capital of Germany?
"""
Then tokens begin with BOS
Given first token is removed
Then tokens can be detokenized
Scenario: Models available
Given available models

182
examples/server/tests/features/steps/steps.py
View File

@ -65,6 +65,7 @@ def step_server_config(context, server_fqdn, server_port):
context.server_seed = None
context.user_api_key = None
context.response_format = None
context.temperature = None
context.tasks_result = []
context.concurrent_tasks = []
@ -232,15 +233,17 @@ async def step_all_slots_status(context, expected_slot_status_string):
@async_run_until_complete
async def step_request_completion(context, api_error):
expect_api_error = api_error == 'raised'
seeds = await completions_seed(context, num_seeds=1)
completion = await request_completion(context.prompts.pop(),
seeds[0] if seeds is not None else seeds,
context.base_url,
debug=context.debug,
n_predict=context.n_predict,
cache_prompt=context.cache_prompt,
id_slot=context.id_slot,
seed=await completions_seed(context),
expect_api_error=expect_api_error,
user_api_key=context.user_api_key)
user_api_key=context.user_api_key,
temperature=context.temperature)
context.tasks_result.append(completion)
if context.debug:
print(f"Completion response: {completion}")
@ -269,6 +272,15 @@ async def step_predictions_equal(context):
context.tasks_result = []
@step('all predictions are different')
@async_run_until_complete
async def step_predictions_equal(context):
n_completions = await gather_tasks_results(context)
assert n_completions >= 2, "need at least 2 completions"
assert_all_predictions_different(context.tasks_result)
context.tasks_result = []
@step('the completion is truncated')
def step_assert_completion_truncated(context):
step_assert_completion_truncated(context, '')
@ -311,6 +323,11 @@ def step_response_format(context, response_format):
context.response_format = json.loads(response_format)
@step('{temperature:f} temperature')
def step_temperature(context, temperature):
context.temperature = temperature
@step('streaming is {enable_streaming}')
def step_streaming(context, enable_streaming):
context.enable_streaming = enable_streaming == 'enabled'
@ -353,7 +370,15 @@ def step_n_ubatch(context, n_ubatch):
@step('{seed:d} as seed')
def step_seed(context, seed):
context.seed = seed
if context.seed is None:
context.seed = [seed]
else:
context.seed.append(seed)
@step('BOS token is {bos:d}')
def step_bos_token(context, bos):
context.bos = bos
@step('a prefix prompt')
@ -413,7 +438,9 @@ async def step_oai_chat_completions(context, api_error):
if context.debug:
print(f"Submitting OAI compatible completions request...")
expect_api_error = api_error == 'raised'
seeds = await completions_seed(context, num_seeds=1),
completion = await oai_chat_completions(context.prompts.pop(),
seeds[0] if seeds is not None else seeds,
context.system_prompt,
context.base_url,
'/v1/chat',
@ -429,8 +456,6 @@ async def step_oai_chat_completions(context, api_error):
response_format=context.response_format
if hasattr(context, 'response_format') else None,
seed=await completions_seed(context),
user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None,
@ -457,20 +482,31 @@ def step_a_prompt_prompt(context, prompt):
context.n_prompts = len(context.prompts)
@step('{num_prompts:d} prompts {prompt} with seed {seed:d}')
def step_many_prompts(context, num_prompts, prompt, seed):
if context.seed is None:
context.seed = []
for _ in range(num_prompts):
context.seed.append(seed)
context.prompts.append(prompt)
context.n_prompts = len(context.prompts)
@step('concurrent completion requests')
@async_run_until_complete()
async def step_concurrent_completion_requests(context):
await concurrent_requests(context,
request_completion,
# prompt is inserted automatically
context.base_url,
debug=context.debug,
prompt_prefix=context.prompt_prefix,
prompt_suffix=context.prompt_suffix,
n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
seed=await completions_seed(context),
user_api_key=context.user_api_key if hasattr(context,
'user_api_key') else None)
await concurrent_requests(
context,
request_completion,
# prompt is inserted automatically
context.base_url,
debug=context.debug,
prompt_prefix=context.prompt_prefix,
prompt_suffix=context.prompt_suffix,
n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None,
temperature=context.temperature,
)
@step('concurrent OAI completions requests')
@ -490,7 +526,6 @@ async def step_oai_chat_completions(context):
if hasattr(context, 'enable_streaming') else None,
response_format=context.response_format
if hasattr(context, 'response_format') else None,
seed=await completions_seed(context),
user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None)
@ -512,10 +547,6 @@ async def step_oai_chat_completions(context):
if hasattr(context, 'enable_streaming') else None,
response_format=context.response_format
if hasattr(context, 'response_format') else None,
seed=context.seed
if hasattr(context, 'seed') else
context.server_seed
if hasattr(context, 'server_seed') else None,
user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None)
@ -544,7 +575,7 @@ async def all_prompts_are_predicted(context, expected_predicted_n=None):
@async_run_until_complete
async def step_compute_embedding(context):
context.n_prompts = 1
context.embeddings = await request_embedding(context_text(context), base_url=context.base_url)
context.embeddings = await request_embedding(context_text(context), None, base_url=context.base_url)
@step('all embeddings are the same')
@ -585,7 +616,7 @@ def step_assert_embeddings(context):
@async_run_until_complete
async def step_oai_compute_embeddings(context):
context.n_prompts = 1
context.embeddings = await request_oai_embeddings(context_text(context),
context.embeddings = await request_oai_embeddings(context_text(context), None,
base_url=context.base_url,
user_api_key=context.user_api_key,
model=context.model)
@ -594,7 +625,7 @@ async def step_oai_compute_embeddings(context):
@step('an OAI compatible embeddings computation request for multiple inputs')
@async_run_until_complete
async def step_oai_compute_embeddings_multiple_inputs(context):
context.embeddings = await request_oai_embeddings(context.prompts,
context.embeddings = await request_oai_embeddings(context.prompts, None,
base_url=context.base_url,
user_api_key=context.user_api_key,
model=context.model)
@ -630,21 +661,29 @@ async def all_embeddings_are_generated(context):
assert_embeddings(context.tasks_result.pop().pop())
@step('adding special tokens')
def step_tokenize_set_add_special(context):
context.tokenize_add_special = True
@step('tokenizing')
@async_run_until_complete
async def step_tokenize(context):
context.tokenized_text = context_text(context)
async with aiohttp.ClientSession() as session:
tokenize_args = {
"content": context.tokenized_text,
}
if getattr(context, 'tokenize_add_special', None) is not None:
tokenize_args['add_special'] = context.tokenize_add_special
async with session.post(f'{context.base_url}/tokenize',
json={
"content": context.tokenized_text,
}) as response:
json=tokenize_args) as response:
assert response.status == 200
tokenize_json = await response.json()
context.tokens = tokenize_json['tokens']
@step('tokens can be detokenize')
@step('tokens can be detokenized')
@async_run_until_complete
async def step_detokenize(context):
assert len(context.tokens) > 0
@ -659,6 +698,21 @@ async def step_detokenize(context):
assert context.tokenized_text == detokenize_json['content'].strip()
@step('tokens begin with BOS')
def step_strings_for_tokenization(context):
assert context.tokens[0] == context.bos
@step('tokens do not begin with BOS')
def step_strings_for_tokenization(context):
assert context.tokens[0] != context.bos
@step('first token is removed')
def step_strings_for_tokenization(context):
context.tokens = context.tokens[1:]
@step('an OPTIONS request is sent from {origin}')
@async_run_until_complete
async def step_options_request(context, origin):
@ -740,8 +794,9 @@ async def concurrent_requests(context, f_completion, *args, **kwargs):
if context.debug:
print(f"starting {context.n_prompts} concurrent completion requests...")
assert context.n_prompts > 0
seeds = await completions_seed(context)
for prompt_no in range(context.n_prompts):
shifted_args = [context.prompts.pop(), *args]
shifted_args = [context.prompts.pop(), seeds[prompt_no], *args]
context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
await asyncio.sleep(0.1)
@ -781,6 +836,7 @@ def step_server_responds_with_status_code(context, status_code):
async def request_completion(prompt,
seed,
base_url,
debug=False,
prompt_prefix=None,
@ -788,9 +844,9 @@ async def request_completion(prompt,
n_predict=None,
cache_prompt=False,
id_slot=None,
seed=None,
expect_api_error=None,
user_api_key=None):
user_api_key=None,
temperature=None):
if debug:
print(f"Sending completion request: {prompt}")
origin = "my.super.domain"
@ -811,7 +867,8 @@ async def request_completion(prompt,
"n_predict": n_predict if n_predict is not None else -1,
"cache_prompt": cache_prompt,
"id_slot": id_slot,
"seed": seed if seed is not None else 42
"seed": seed if seed is not None else 42,
"temperature": temperature if temperature is not None else "0.8f",
},
headers=headers,
timeout=3600) as response:
@ -824,6 +881,7 @@ async def request_completion(prompt,
async def oai_chat_completions(user_prompt,
seed,
system_prompt,
base_url,
base_path,
@ -833,7 +891,6 @@ async def oai_chat_completions(user_prompt,
n_predict=None,
enable_streaming=None,
response_format=None,
seed=None,
user_api_key=None,
expect_api_error=None):
if debug:
@ -882,7 +939,7 @@ async def oai_chat_completions(user_prompt,
while event_received:
event_received = False
async for line_in_bytes in response.content:
line = line_in_bytes.decode('utf8')
line = line_in_bytes.decode('utf-8')
line = line.rstrip('\n').rstrip('\r')
if line == '':
continue
@ -952,7 +1009,7 @@ async def oai_chat_completions(user_prompt,
return completion_response
async def request_embedding(content, base_url=None):
async def request_embedding(content, seed, base_url=None):
async with aiohttp.ClientSession() as session:
async with session.post(f'{base_url}/embedding',
json={
@ -963,7 +1020,7 @@ async def request_embedding(content, base_url=None):
return [response_json['embedding']]
async def request_oai_embeddings(input,
async def request_oai_embeddings(input, seed,
base_url=None, user_api_key=None,
model=None, async_client=False):
# openai client always expects an api_key
@ -1036,21 +1093,31 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
f' {n_predicted} <> {expected_predicted_n}')
def assert_all_predictions_equal(completion_responses):
content_0 = completion_responses[0]['content']
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
print(f"content 0: {content_0}")
for i, response_i in enumerate(completion_responses):
content_i = response_i['content']
print(f"content {i}: {content_i}")
for i, response_i in enumerate(completion_responses):
content_i = response_i['content']
for j, response_j in enumerate(completion_responses):
if i == j:
continue
content_j = response_j['content']
assert content_i == content_j, "contents not equal"
i = 1
for response in completion_responses[1:]:
content = response['content']
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
print(f"content {i}: {content}")
assert content == content_0, "contents not equal"
i += 1
def assert_all_predictions_different(completion_responses):
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
for i, response_i in enumerate(completion_responses):
content_i = response_i['content']
print(f"content {i}: {content_i}")
for i, response_i in enumerate(completion_responses):
content_i = response_i['content']
for j, response_j in enumerate(completion_responses):
if i == j:
continue
content_j = response_j['content']
assert content_i != content_j, "contents not different"
async def gather_tasks_results(context):
@ -1145,9 +1212,22 @@ def assert_slots_status(slots, expected_slots):
f" = {expected[key]} != {slot[key]}")
async def completions_seed(context):
return context.seed if hasattr(context, 'seed') and context.seed is not None \
else context.server_seed if hasattr(context, 'server_seed') else None
async def completions_seed(context, num_seeds=None):
if hasattr(context, "seed") and context.seed is not None:
assert len(context.seed) == context.n_prompts
if num_seeds is None:
num_seeds = context.n_prompts
assert num_seeds <= context.n_prompts
seeds = context.seed[:num_seeds]
context.seed = context.seed[num_seeds:] if num_seeds < context.n_prompts else None
return seeds
if hasattr(context, "server_seed") and context.server_seed is not None:
if num_seeds is None:
return [context.server_seed] * context.n_prompts
else:
return [context.server_seed] * num_seeds
return None
def context_text(context):

5
examples/server/themes/README.md Normal file
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@ -0,0 +1,5 @@
# LLaMA.cpp Server Wild Theme
Simple themes directory of sample "public" directories. To try any of these add --path to your run like `server --path=wild`.
![image](wild/wild.png)

7
examples/server/themes/buttons-top/README.md Normal file
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@ -0,0 +1,7 @@
# LLaMA.cpp Server Buttons Top Theme
Simple tweaks to the UI. Chat buttons at the top of the page instead of bottom so you can hit Stop instead of chasing it down the page.
To use simply run server with `--path=themes/buttons_top`
![image](buttons_top.png)

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@ -0,0 +1,5 @@
# LLaMA.cpp Server Wild Theme
Simple tweaks to the UI. To use simply run server with `--path=themes/wild`
![image](wild.png)

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32
examples/server/utils.hpp
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@ -3,6 +3,8 @@
#include "llama.h"
#include "common.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "json.hpp"
#include <string>
@ -49,18 +51,18 @@ extern bool server_log_json;
#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra);
static inline void server_log(const char * level, const char * function, int line, const char * message, const json & extra);
template <typename T>
static T json_value(const json &body, const std::string &key, const T &default_value) {
static T json_value(const json & body, const std::string & key, const T & default_value) {
// Fallback null to default value
if (body.contains(key) && !body.at(key).is_null()){
if (body.contains(key) && !body.at(key).is_null()) {
try {
return body.value(key, default_value);
}
catch (nlohmann::json_abi_v3_11_3::detail::type_error const&){
std::string message = "Wrong type supplied for parameter '" + key + "'. Expected '" + typeid(default_value).name() + "', using default value.";
server_log("WARN", __func__, __LINE__, message.c_str(), body);
return body.at(key);
} catch (NLOHMANN_JSON_NAMESPACE::detail::type_error const &) {
std::stringstream ss;
ss << "Wrong type supplied for parameter '" << key << "'. Expected '" << json(default_value).type_name() << "', using default value.";
LOG_WARNING(ss.str().c_str(), body);
return default_value;
}
} else {
@ -68,16 +70,16 @@ static T json_value(const json &body, const std::string &key, const T &default_v
}
}
static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) {
static inline void server_log(const char * level, const char * function, int line, const char * message, const json & extra) {
std::stringstream ss_tid;
ss_tid << std::this_thread::get_id();
json log = nlohmann::ordered_json{
json log = json{
{"tid", ss_tid.str()},
{"timestamp", time(nullptr)},
};
if (server_log_json) {
log.merge_patch( {
log.merge_patch({
{"level", level},
{"function", function},
{"line", line},
@ -98,7 +100,7 @@ static inline void server_log(const char *level, const char *function, int line,
}
std::stringstream ss;
ss << buf << " |";
for (const auto& el : log.items())
for (const auto & el : log.items())
{
const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace);
ss << " " << el.key() << "=" << value;
@ -373,11 +375,11 @@ static json oaicompat_completion_params_parse(
llama_params["top_p"] = json_value(body, "top_p", 1.0);
// Apply chat template to the list of messages
llama_params["prompt"] = format_chat(model, chat_template, body["messages"]);
llama_params["prompt"] = format_chat(model, chat_template, body.at("messages"));
// Handle "stop" field
if (body.contains("stop") && body["stop"].is_string()) {
llama_params["stop"] = json::array({body["stop"].get<std::string>()});
if (body.contains("stop") && body.at("stop").is_string()) {
llama_params["stop"] = json::array({body.at("stop").get<std::string>()});
} else {
llama_params["stop"] = json_value(body, "stop", json::array());
}

2
examples/sycl/README.md
View File

@ -1,6 +1,6 @@
# llama.cpp/example/sycl
This example program provide the tools for llama.cpp for SYCL on Intel GPU.
This example program provides the tools for llama.cpp for SYCL on Intel GPU.
## Tool

30
flake.lock generated
View File

@ -5,11 +5,11 @@
"nixpkgs-lib": "nixpkgs-lib"
},
"locked": {
"lastModified": 1712014858,
"narHash": "sha256-sB4SWl2lX95bExY2gMFG5HIzvva5AVMJd4Igm+GpZNw=",
"lastModified": 1714641030,
"narHash": "sha256-yzcRNDoyVP7+SCNX0wmuDju1NUCt8Dz9+lyUXEI0dbI=",
"owner": "hercules-ci",
"repo": "flake-parts",
"rev": "9126214d0a59633752a136528f5f3b9aa8565b7d",
"rev": "e5d10a24b66c3ea8f150e47dfdb0416ab7c3390e",
"type": "github"
},
"original": {
@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1714076141,
"narHash": "sha256-Drmja/f5MRHZCskS6mvzFqxEaZMeciScCTFxWVLqWEY=",
"lastModified": 1714635257,
"narHash": "sha256-4cPymbty65RvF1DWQfc+Bc8B233A1BWxJnNULJKQ1EY=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "7bb2ccd8cdc44c91edba16c48d2c8f331fb3d856",
"rev": "63c3a29ca82437c87573e4c6919b09a24ea61b0f",
"type": "github"
},
"original": {
@ -36,20 +36,14 @@
},
"nixpkgs-lib": {
"locked": {
"dir": "lib",
"lastModified": 1711703276,
"narHash": "sha256-iMUFArF0WCatKK6RzfUJknjem0H9m4KgorO/p3Dopkk=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "d8fe5e6c92d0d190646fb9f1056741a229980089",
"type": "github"
"lastModified": 1714640452,
"narHash": "sha256-QBx10+k6JWz6u7VsohfSw8g8hjdBZEf8CFzXH1/1Z94=",
"type": "tarball",
"url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz"
},
"original": {
"dir": "lib",
"owner": "NixOS",
"ref": "nixos-unstable",
"repo": "nixpkgs",
"type": "github"
"type": "tarball",
"url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz"
}
},
"root": {

4
ggml-backend.c
View File

@ -1182,9 +1182,9 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
static char * fmt_size(size_t size) {
static char buffer[128];
if (size >= 1024*1024) {
sprintf(buffer, "%zuM", size/1024/1024);
snprintf(buffer, sizeof(buffer), "%zuM", size/1024/1024);
} else {
sprintf(buffer, "%zuK", size/1024);
snprintf(buffer, sizeof(buffer), "%zuK", size/1024);
}
return buffer;
}

330
ggml-cuda.cu
View File

@ -4,7 +4,6 @@
#include "ggml-cuda/common.cuh"
#include "ggml-cuda/acc.cuh"
#include "ggml-cuda/alibi.cuh"
#include "ggml-cuda/arange.cuh"
#include "ggml-cuda/argsort.cuh"
#include "ggml-cuda/binbcast.cuh"
@ -14,6 +13,7 @@
#include "ggml-cuda/cpy.cuh"
#include "ggml-cuda/diagmask.cuh"
#include "ggml-cuda/dmmv.cuh"
#include "ggml-cuda/fattn.cuh"
#include "ggml-cuda/getrows.cuh"
#include "ggml-cuda/im2col.cuh"
#include "ggml-cuda/mmq.cuh"
@ -112,7 +112,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
for (int id = 0; id < info.device_count; ++id) {
int device_vmm = 0;
#if !defined(GGML_USE_HIPBLAS)
#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
CUdevice device;
CU_CHECK(cuDeviceGet(&device, id));
CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device));
@ -140,6 +140,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
info.devices[id].cc = 100*prop.major + 10*prop.minor;
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
info.devices[id].smpb = prop.sharedMemPerBlock;
info.devices[id].nsm = prop.multiProcessorCount;
}
for (int id = 0; id < info.device_count; ++id) {
@ -257,7 +258,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
};
// pool with virtual memory
#if !defined(GGML_USE_HIPBLAS)
#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB
@ -354,7 +355,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
#endif // !defined(GGML_USE_HIPBLAS)
std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(int device) {
#if !defined(GGML_USE_HIPBLAS)
#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM)
if (ggml_cuda_info().devices[device].vmm) {
return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_vmm(device));
}
@ -1645,7 +1646,7 @@ static void ggml_cuda_op_mul_mat(
}
}
static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer));
GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
@ -1668,7 +1669,7 @@ static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const gg
ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
}
static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(!ggml_is_transposed(src0));
GGML_ASSERT(!ggml_is_transposed(src1));
GGML_ASSERT(!ggml_is_permuted(src0));
@ -2203,6 +2204,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_UNARY_OP_RELU:
ggml_cuda_op_relu(ctx, dst);
break;
case GGML_UNARY_OP_SIGMOID:
ggml_cuda_op_sigmoid(ctx, dst);
break;
case GGML_UNARY_OP_HARDSIGMOID:
ggml_cuda_op_hardsigmoid(ctx, dst);
break;
@ -2275,9 +2279,6 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_ROPE:
ggml_cuda_op_rope(ctx, dst);
break;
case GGML_OP_ALIBI:
ggml_cuda_op_alibi(ctx, dst);
break;
case GGML_OP_IM2COL:
ggml_cuda_op_im2col(ctx, dst);
break;
@ -2290,6 +2291,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_ARGSORT:
ggml_cuda_op_argsort(ctx, dst);
break;
case GGML_OP_FLASH_ATTN_EXT:
ggml_cuda_flash_attn_ext(ctx, dst);
break;
default:
return false;
}
@ -2405,44 +2409,318 @@ GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
GGML_UNUSED(backend);
}
static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
graph_node_properties->node_address = node->data;
graph_node_properties->node_op = node->op;
for (int i = 0; i < GGML_MAX_DIMS; i++) {
graph_node_properties->ne[i] = node->ne[i];
graph_node_properties->nb[i] = node->nb[i];
}
for (int i = 0; i < GGML_MAX_SRC; i++) {
graph_node_properties->src_address[i] = node->src[i] ? node->src[i]->data : nullptr;
}
}
static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
if (node->data != graph_node_properties->node_address &&
node->op != GGML_OP_CPY &&
node->op != GGML_OP_VIEW) {
return false;
}
if (node->op != graph_node_properties->node_op) {
return false;
}
for (int i = 0; i < GGML_MAX_DIMS; i++) {
if (node->ne[i] != graph_node_properties->ne[i]) {
return false;
}
if (node->nb[i] != graph_node_properties->nb[i]) {
return false;
}
}
for (int i = 0; i < GGML_MAX_SRC; i++) {
if (node->src[i] &&
node->src[i]->data != graph_node_properties->src_address[i] &&
node->op != GGML_OP_CPY &&
node->op != GGML_OP_VIEW
) {
return false;
}
}
return true;
}
GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
ggml_cuda_set_device(cuda_ctx->device);
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
#ifdef USE_CUDA_GRAPH
static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr);
if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
continue;
// Objects required for CUDA Graph
if (cuda_ctx->cuda_graph == nullptr) {
cuda_ctx->cuda_graph.reset(new ggml_cuda_graph());
}
bool use_cuda_graph = true;
bool cuda_graph_update_required = false;
// pointer to CUDA cpy kernel, which is required to identify
// kernel parameters which need updated in the graph for each token
void * ggml_cuda_cpy_fn_ptr = nullptr;
if (cuda_ctx->cuda_graph->graph == nullptr) {
if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to GPU architecture\n", __func__);
#endif
}
}
// Disable CUDA graphs in presence of env var, old GPU, use-case which is changing too rapidly,
// or previous graph capture failure.
// Also disable for multi-gpu for now. TO DO investigate
if (disable_cuda_graphs_due_to_env
|| cuda_ctx->cuda_graph->disable_due_to_gpu_arch
|| cuda_ctx->cuda_graph->disable_due_to_too_many_updates
|| cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture) {
use_cuda_graph = false;
}
if (use_cuda_graph) {
if (cuda_ctx->cuda_graph->instance == nullptr) {
cuda_graph_update_required = true;
}
// Check if the graph size has changed
if (cuda_ctx->cuda_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
cuda_graph_update_required = true;
cuda_ctx->cuda_graph->ggml_graph_properties.resize(cgraph->n_nodes);
}
// Loop over nodes in GGML graph to determine if CUDA graph update is required
// and store properties to allow this comparison for the next token
for (int i = 0; i < cgraph->n_nodes; i++) {
bool has_matching_properties = true;
if (!cuda_graph_update_required) {
has_matching_properties = ggml_graph_node_has_matching_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
}
if (!has_matching_properties) {
cuda_graph_update_required = true;
}
set_ggml_graph_node_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]);
}
// Loop over nodes in GGML graph to obtain info needed for CUDA graph
cuda_ctx->cuda_graph->updated_kernel_arg.clear();
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
if (node->src[0] && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) {
use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
#ifndef NDEBUG
assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
for (int j = 0; j < GGML_MAX_SRC; j++) {
if (node->src[j] != nullptr) {
assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer));
fprintf(stderr, "%s: disabling CUDA graphs due to split buffer\n", __func__);
#endif
}
if (node->op == GGML_OP_MUL_MAT_ID) {
use_cuda_graph = false; // This node type is not supported by CUDA graph capture
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
#endif
}
if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1) {
// disable CUDA graphs for batch size > 1 for now.
// Changes in batch size or context size can cause changes to the grid size of some kernels.
use_cuda_graph = false;
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
#endif
}
if (node->op == GGML_OP_CPY) {
// store the copy op parameter which changes with each token.
cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data));
if (ggml_cuda_cpy_fn_ptr == nullptr) {
// store a pointer to the copy op CUDA kernel to identify it later
ggml_cuda_cpy_fn_ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
}
}
if (!use_cuda_graph) {
break;
}
}
// Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates.
if (cuda_graph_update_required) {
cuda_ctx->cuda_graph->number_consecutive_updates++;
} else {
cuda_ctx->cuda_graph->number_consecutive_updates = 0;
}
if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
#endif
}
}
if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture
CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed));
}
#else
bool use_cuda_graph = false;
bool cuda_graph_update_required = false;
#endif // USE_CUDA_GRAPH
bool graph_evaluated_or_captured = false;
while (!graph_evaluated_or_captured) {
// Only perform the graph execution if CUDA graphs are not enabled, or we are capturing the graph.
// With the use of CUDA graphs, the execution will be performed by the graph launch.
if (!use_cuda_graph || cuda_graph_update_required) {
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
continue;
}
#ifndef NDEBUG
assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
for (int j = 0; j < GGML_MAX_SRC; j++) {
if (node->src[j] != nullptr) {
assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer));
}
}
#endif
bool ok = ggml_cuda_compute_forward(*cuda_ctx, node);
if (!ok) {
fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
bool ok = ggml_cuda_compute_forward(*cuda_ctx, node);
if (!ok) {
fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
}
GGML_ASSERT(ok);
}
}
GGML_ASSERT(ok);
#ifdef USE_CUDA_GRAPH
if (use_cuda_graph && cuda_graph_update_required) { // End CUDA graph capture
if (cuda_ctx->cuda_graph->graph != nullptr) {
CUDA_CHECK(cudaGraphDestroy(cuda_ctx->cuda_graph->graph));
cuda_ctx->cuda_graph->graph = nullptr;
}
CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph));
#if 0
if (disable_cuda_graphs_due_to_failed_capture) {
use_cuda_graph = false;
cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true;
#ifndef NDEBUG
fprintf(stderr, "%s: disabling CUDA graphs due to failed graph capture\n", __func__);
#endif
} else {
graph_evaluated_or_captured = true; // CUDA graph has been captured
}
#endif
graph_evaluated_or_captured = true; // CUDA graph has been captured
} else {
graph_evaluated_or_captured = true; // ggml graph has been directly evaluated
}
}
if (use_cuda_graph) {
if (cuda_ctx->cuda_graph->instance == nullptr) { // Create executable graph from captured graph.
CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
}
// Perform update to graph (if required for this token), and change copy parameter (required for every token)
if (cuda_graph_update_required) {
// Extract nodes from graph
if (cuda_ctx->cuda_graph->num_nodes == 0) {
// First call with null argument gets number of nodes in graph
CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes));
}
// Subsequent call with non-null argument gets nodes
cuda_ctx->cuda_graph->nodes.resize(cuda_ctx->cuda_graph->num_nodes);
cuda_ctx->cuda_graph->params.resize(cuda_ctx->cuda_graph->num_nodes);
if (cuda_ctx->cuda_graph->num_nodes > 0) {
CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, cuda_ctx->cuda_graph->nodes.data(), &cuda_ctx->cuda_graph->num_nodes));
// Loop over nodes, and extract kernel parameters from each node
for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
cudaGraphNodeType node_type;
CUDA_CHECK(cudaGraphNodeGetType(cuda_ctx->cuda_graph->nodes[i], &node_type));
if (node_type == cudaGraphNodeTypeKernel) {
cudaError_t stat = cudaGraphKernelNodeGetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]); // Get params using runtime
if (stat == cudaErrorInvalidDeviceFunction) {
// Fails due to incorrect handling by CUDA runtime of CUDA BLAS node.
// We don't need to update blas nodes, so clear error and move on.
cudaGetLastError();
} else {
GGML_ASSERT(stat == cudaSuccess);
}
}
}
}
}
// One of the arguments to the copy kernel is updated for each token, hence we need to
// replace that argument with the updated value in the CUDA graph
if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured
int k = 0;
for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
if (cuda_ctx->cuda_graph->params[i].func == ggml_cuda_cpy_fn_ptr) {
char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
}
}
}
// Update graph executable
cudaGraphExecUpdateResultInfo result_info;
cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
if (stat == cudaErrorGraphExecUpdateFailure) {
#ifndef NDEBUG
fprintf(stderr, "%s: CUDA graph update failed\n", __func__);
#endif
// The pre-existing graph exec cannot be updated due to violated constraints
// so instead clear error and re-instantiate
cudaGetLastError();
CUDA_CHECK(cudaGraphExecDestroy(cuda_ctx->cuda_graph->instance));
cuda_ctx->cuda_graph->instance = nullptr;
CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0));
} else {
GGML_ASSERT(stat == cudaSuccess);
}
// Launch graph
CUDA_CHECK(cudaGraphLaunch(cuda_ctx->cuda_graph->instance, cuda_ctx->stream()));
#else
graph_evaluated_or_captured = true;
#endif // USE_CUDA_GRAPH
}
return GGML_STATUS_SUCCESS;
}
GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
switch (op->op) {
case GGML_OP_UNARY:
switch (ggml_get_unary_op(op)) {
case GGML_UNARY_OP_GELU:
case GGML_UNARY_OP_SILU:
case GGML_UNARY_OP_RELU:
case GGML_UNARY_OP_SIGMOID:
case GGML_UNARY_OP_HARDSIGMOID:
case GGML_UNARY_OP_HARDSWISH:
case GGML_UNARY_OP_GELU_QUICK:
@ -2552,7 +2830,6 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_SOFT_MAX:
case GGML_OP_ROPE:
case GGML_OP_ALIBI:
case GGML_OP_IM2COL:
case GGML_OP_POOL_2D:
case GGML_OP_SUM_ROWS:
@ -2565,6 +2842,15 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_LEAKY_RELU:
return true;
case GGML_OP_FLASH_ATTN_EXT:
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
return op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128;
#else
if (op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128) {
return true;
}
return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA;
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
default:
return false;
}

63
ggml-cuda/alibi.cu
View File

@ -1,63 +0,0 @@
#include "alibi.cuh"
static __global__ void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows,
const int n_heads_log2_floor, const float m0, const float m1) {
const int col = blockDim.x*blockIdx.x + threadIdx.x;
if (col >= ncols) {
return;
}
const int row = blockDim.y*blockIdx.y + threadIdx.y;
const int i = row*ncols + col;
const int k = row/k_rows;
float m_k;
if (k < n_heads_log2_floor) {
m_k = powf(m0, k + 1);
} else {
m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
}
dst[i] = col * m_k + x[i];
}
static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows,
const int k_rows, const int n_heads_log2_floor, const float m0,
const float m1, cudaStream_t stream) {
const dim3 block_dims(CUDA_ALIBI_BLOCK_SIZE, 1, 1);
const int num_blocks_x = (ncols + CUDA_ALIBI_BLOCK_SIZE - 1) / (CUDA_ALIBI_BLOCK_SIZE);
const dim3 block_nums(num_blocks_x, nrows, 1);
alibi_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, k_rows, n_heads_log2_floor, m0, m1);
}
void ggml_cuda_op_alibi(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const float * src0_d = (const float *)src0->data;
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t nrows = ggml_nrows(src0);
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_head = ((int32_t *) dst->op_params)[1];
float max_bias;
memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
//GGML_ASSERT(ne01 + n_past == ne00);
GGML_ASSERT(n_head == ne02);
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
alibi_f32_cuda(src0_d, dst_d, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, stream);
}

5
ggml-cuda/alibi.cuh
View File

@ -1,5 +0,0 @@
#include "common.cuh"
#define CUDA_ALIBI_BLOCK_SIZE 32
void ggml_cuda_op_alibi(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

1
ggml-cuda/clamp.cu
View File

@ -31,5 +31,4 @@ void ggml_cuda_op_clamp(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
clamp_f32_cuda(src0_d, dst_d, min, max, ggml_nelements(src0), stream);
CUDA_CHECK(cudaGetLastError());
}

263
ggml-cuda/common.cuh
View File

@ -19,6 +19,7 @@
#include <cassert>
#include <cfloat>
#include <string>
#include <vector>
#if defined(GGML_USE_HIPBLAS)
#include <hip/hip_runtime.h>
@ -137,11 +138,13 @@
#define STRINGIZE(...) STRINGIZE_IMPL(__VA_ARGS__)
#define WARP_SIZE 32
#define CUDART_HMAX 11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed)
#define CUDART_HMAX 11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed)
#define CUDART_HMASK 12000 // CUDA 12.0, min. ver. for half2 -> uint mask comparisons
#define CC_PASCAL 600
#define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
#define CC_VOLTA 700
#define CC_AMPERE 800
#define CC_OFFSET_AMD 1000000
#define CC_RDNA1 (CC_OFFSET_AMD + 1010)
#define CC_RDNA2 (CC_OFFSET_AMD + 1030)
@ -231,83 +234,6 @@ typedef float dfloat; // dequantize float
typedef float2 dfloat2;
#endif //GGML_CUDA_F16
[[noreturn]]
static __device__ void no_device_code(
const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n",
file_name, line, function_name, arch);
GGML_UNUSED(arch_list);
#else
printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n",
file_name, line, function_name, arch, arch_list);
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
__trap();
GGML_UNUSED(no_device_code); // suppress unused function warning
}
#ifdef __CUDA_ARCH__
#define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__))
#else
#define NO_DEVICE_CODE //GGML_ASSERT(false && "NO_DEVICE_CODE not valid in host code.")
#endif // __CUDA_ARCH__
static __device__ __forceinline__ float warp_reduce_sum(float x) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x += __shfl_xor_sync(0xffffffff, x, mask, 32);
}
return x;
}
static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
}
return a;
}
#ifdef GGML_CUDA_F16
static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32));
}
return a;
#else
GGML_UNUSED(a);
NO_DEVICE_CODE;
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
}
#endif // GGML_CUDA_F16
static __device__ __forceinline__ float warp_reduce_max(float x) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
}
return x;
}
//static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
//#pragma unroll
// for (int mask = 16; mask > 0; mask >>= 1) {
// x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
// }
// return x;
//#else
// GGML_UNUSED(x);
// NO_DEVICE_CODE;
//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
//}
#if defined(GGML_USE_HIPBLAS)
#define __CUDA_ARCH__ 1300
@ -391,6 +317,147 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
}
#endif // defined(GGML_USE_HIPBLAS)
#define FP16_AVAILABLE (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL
#define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA
static bool fast_fp16_available(const int cc) {
return cc >= CC_PASCAL && cc != 610;
}
static bool fp16_mma_available(const int cc) {
return cc < CC_OFFSET_AMD && cc >= CC_VOLTA;
}
[[noreturn]]
static __device__ void no_device_code(
const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n",
file_name, line, function_name, arch);
GGML_UNUSED(arch_list);
#else
printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n",
file_name, line, function_name, arch, arch_list);
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
__trap();
GGML_UNUSED(no_device_code); // suppress unused function warning
}
#ifdef __CUDA_ARCH__
#define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__))
#else
#define NO_DEVICE_CODE //GGML_ASSERT(false && "NO_DEVICE_CODE not valid in host code.")
#endif // __CUDA_ARCH__
static __device__ __forceinline__ float warp_reduce_sum(float x) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x += __shfl_xor_sync(0xffffffff, x, mask, 32);
}
return x;
}
static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
}
return a;
}
static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
#if FP16_AVAILABLE
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
const half2 a_other = __shfl_xor_sync(0xffffffff, a, mask, 32);
reinterpret_cast<half&>(a.x) += __low2half(a_other);
reinterpret_cast<half&>(a.y) += __high2half(a_other);
}
return a;
#else
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32));
}
return a;
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
#else
NO_DEVICE_CODE;
return a;
#endif // FP16_AVAILABLE
}
static __device__ __forceinline__ float warp_reduce_max(float x) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
}
return x;
}
static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) {
#if FP16_AVAILABLE
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX
return __float2half(fmaxf(__half2float(a), __half2float(b)));
#else
return __hmax(a, b);
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX
#else
NO_DEVICE_CODE;
GGML_UNUSED(b);
return a;
#endif // FP16_AVAILABLE
}
static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const half2 b) {
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
#if CUDART_VERSION >= CUDART_HMAX
return __hmax2(a, b);
#else
half2 ret;
reinterpret_cast<half&>(ret.x) = __float2half(fmaxf( __low2float(a), __low2float(b)));
reinterpret_cast<half&>(ret.y) = __float2half(fmaxf(__high2float(a), __high2float(b)));
return ret;
#endif // CUDART_VERSION >= CUDART_HMAX
#else
GGML_UNUSED(a);
GGML_UNUSED(b);
NO_DEVICE_CODE;
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
}
static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
}
return x;
#else
GGML_UNUSED(x);
NO_DEVICE_CODE;
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
}
#if CUDART_VERSION < CUDART_HMASK
static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half2 b) {
const uint32_t mask_low = 0x0000FFFF * (float( __low2half(a)) > float( __low2half(b)));
const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b)));
return mask_low | mask_high;
}
#endif // CUDART_VERSION < 12000
// TODO: move to ggml-common.h
static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
@ -404,6 +471,7 @@ struct ggml_cuda_device_info {
struct cuda_device_info {
int cc; // compute capability
int nsm; // number of streaming multiprocessors
size_t smpb; // max. shared memory per block
bool vmm; // virtual memory support
size_t vmm_granularity; // granularity of virtual memory
@ -479,6 +547,43 @@ struct ggml_tensor_extra_gpu {
cudaEvent_t events[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS]; // events for synchronizing multiple GPUs
};
#if (CUDART_VERSION >= 12000) && defined(GGML_CUDA_USE_GRAPHS)
#define USE_CUDA_GRAPH
#endif
struct ggml_graph_node_properties {
void * node_address;
ggml_op node_op;
int64_t ne[GGML_MAX_DIMS];
size_t nb[GGML_MAX_DIMS];
void * src_address[GGML_MAX_SRC];
};
struct ggml_cuda_graph {
#ifdef USE_CUDA_GRAPH
~ggml_cuda_graph() {
if (instance != nullptr) {
CUDA_CHECK(cudaGraphExecDestroy(instance));
}
if (graph != nullptr) {
CUDA_CHECK(cudaGraphDestroy(graph));
}
}
cudaGraph_t graph = nullptr;
cudaGraphExec_t instance = nullptr;
size_t num_nodes = 0;
std::vector<cudaGraphNode_t> nodes;
std::vector<cudaKernelNodeParams> params;
bool disable_due_to_gpu_arch = false;
bool disable_due_to_too_many_updates = false;
bool disable_due_to_failed_graph_capture = false;
int number_consecutive_updates = 0;
std::vector<ggml_graph_node_properties> ggml_graph_properties;
std::vector<char **> updated_kernel_arg;
#endif
};
struct ggml_backend_cuda_context {
int device;
std::string name;
@ -487,6 +592,8 @@ struct ggml_backend_cuda_context {
cudaStream_t streams[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { { nullptr } };
cublasHandle_t cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
std::unique_ptr<ggml_cuda_graph> cuda_graph;
explicit ggml_backend_cuda_context(int device) :
device(device),
name(GGML_CUDA_NAME + std::to_string(device)) {

4
ggml-cuda/convert.cu
View File

@ -727,7 +727,6 @@ static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict_
}
to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
int id;
switch (type) {
case GGML_TYPE_Q4_0:
return dequantize_row_q4_0_cuda;
@ -738,8 +737,7 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
case GGML_TYPE_Q5_1:
return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
case GGML_TYPE_Q8_0:
CUDA_CHECK(cudaGetDevice(&id));
if (ggml_cuda_info().devices[id].cc >= CC_PASCAL) {
if (ggml_cuda_info().devices[ggml_cuda_get_device()].cc >= CC_PASCAL) {
return dequantize_block_q8_0_f16_cuda;
}
return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;

29
ggml-cuda/cpy.cu
View File

@ -459,3 +459,32 @@ void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
ggml_cuda_cpy(ctx, src0, dst);
}
void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
return (void*) cpy_f32_f16<cpy_1_f32_f32>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) {
return (void*) cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) {
return (void*) cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) {
return (void*) cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) {
return (void*) cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) {
return (void*) cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>;
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) {
return (void*) cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>;
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
return (void*) cpy_f32_f16<cpy_1_f32_f16>;
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
return (void*) cpy_f32_f16<cpy_1_f16_f32>;
} else {
fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
ggml_type_name(src0->type), ggml_type_name(src1->type));
GGML_ASSERT(false);
}
}

2
ggml-cuda/cpy.cuh
View File

@ -5,3 +5,5 @@
void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1);
void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1);

47
ggml-cuda/fattn-common.cuh Normal file
View File

@ -0,0 +1,47 @@
#define FATTN_KQ_STRIDE 256
#define HALF_MAX_HALF __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction.
#define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs.
template<int D, int parallel_blocks> // D == head size
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(D, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
static __global__ void flash_attn_combine_results(
const float * __restrict__ VKQ_parts,
const float2 * __restrict__ VKQ_meta,
float * __restrict__ dst) {
VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x;
VKQ_meta += parallel_blocks * gridDim.y*blockIdx.x;
dst += D * gridDim.y*blockIdx.x;
const int tid = threadIdx.x;
__builtin_assume(tid < D);
__shared__ float2 meta[parallel_blocks];
if (tid < 2*parallel_blocks) {
((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid];
}
__syncthreads();
float kqmax = meta[0].x;
#pragma unroll
for (int l = 1; l < parallel_blocks; ++l) {
kqmax = max(kqmax, meta[l].x);
}
float VKQ_numerator = 0.0f;
float VKQ_denominator = 0.0f;
#pragma unroll
for (int l = 0; l < parallel_blocks; ++l) {
const float diff = meta[l].x - kqmax;
const float KQ_max_scale = expf(diff);
const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD);
*((uint32_t *) &KQ_max_scale) &= ftz_mask;
VKQ_numerator += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid];
VKQ_denominator += KQ_max_scale * meta[l].y;
}
dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator;
}

430
ggml-cuda/fattn-vec-f16.cu Normal file
View File

@ -0,0 +1,430 @@
#include "common.cuh"
#include "fattn-common.cuh"
#include "fattn-vec-f16.cuh"
template<int D, int ncols, int parallel_blocks> // D == head size
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(D, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
static __global__ void flash_attn_vec_ext_f16(
const char * __restrict__ Q,
const char * __restrict__ K,
const char * __restrict__ V,
const char * __restrict__ mask,
float * __restrict__ dst,
float2 * __restrict__ dst_meta,
const float scale,
const float max_bias,
const float m0,
const float m1,
const uint32_t n_head_log2,
const int ne00,
const int ne01,
const int ne02,
const int ne03,
const int ne10,
const int ne11,
const int ne12,
const int ne13,
const int ne31,
const int nb31,
const int nb01,
const int nb02,
const int nb03,
const int nb11,
const int nb12,
const int nb13,
const int ne0,
const int ne1,
const int ne2,
const int ne3) {
#if FP16_AVAILABLE
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0);
const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio));
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
const half * maskh = (const half *) mask + ne11*ic0;
const int stride_KV = nb11 / sizeof(half);
const int stride_KV2 = nb11 / sizeof(half2);
half slopeh = __float2half(1.0f);
// ALiBi
if (max_bias > 0.0f) {
const int h = blockIdx.y;
const float base = h < n_head_log2 ? m0 : m1;
const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
slopeh = __float2half(powf(base, exph));
}
static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
constexpr int nwarps = D / WARP_SIZE;
const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
__builtin_assume(tid < D);
__shared__ half KQ[ncols*D];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
KQ[j*D + tid] = -HALF_MAX_HALF;
}
half2 * KQ2 = (half2 *) KQ;
half kqmax[ncols];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqmax[j] = -HALF_MAX_HALF;
}
half kqsum[ncols] = {0.0f};
__shared__ half kqmax_shared[ncols][WARP_SIZE];
__shared__ half kqsum_shared[ncols][WARP_SIZE];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
if (threadIdx.y == 0) {
kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF;
kqsum_shared[j][threadIdx.x] = 0.0f;
}
}
__syncthreads();
// Convert Q to half2 and store in registers:
half2 Q_h2[ncols][D/(2*WARP_SIZE)];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i];
Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y);
}
}
half2 VKQ[ncols] = {{0.0f, 0.0f}};
const int k_start = parallel_blocks == 1 ? 0 : ip*D;
for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
// Calculate KQ tile and keep track of new maximum KQ values:
// For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
// see https://github.com/ggerganov/llama.cpp/pull/7061 .
// Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
half kqmax_new = kqmax[0];
half kqmax_new_arr[ncols];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqmax_new_arr[j] = kqmax[j];
}
#pragma unroll
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
const int i_KQ = i_KQ_0 + threadIdx.y;
if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
break;
}
half2 sum2[ncols] = {{0.0f, 0.0f}};
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE];
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
sum2[j] = warp_reduce_sum(sum2[j]);
half sum = __low2half(sum2[j]) + __high2half(sum2[j]);
sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
if (ncols == 1) {
kqmax_new = ggml_cuda_hmax(kqmax_new, sum);
} else {
kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum);
}
if (threadIdx.x == 0) {
KQ[j*D + i_KQ] = sum;
}
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j];
kqmax_new_j = warp_reduce_max(kqmax_new_j);
if (threadIdx.x == 0) {
kqmax_shared[j][threadIdx.y] = kqmax_new_j;
}
}
__syncthreads();
#pragma unroll
for (int j = 0; j < ncols; ++j) {
half kqmax_new_j = kqmax_shared[j][threadIdx.x];
kqmax_new_j = warp_reduce_max(kqmax_new_j);
const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
kqmax[j] = kqmax_new_j;
const half val = hexp(KQ[j*D + tid] - kqmax[j]);
kqsum[j] = kqsum[j]*KQ_max_scale + val;
KQ[j*D + tid] = val;
VKQ[j] *= __half2half2(KQ_max_scale);
}
__syncthreads();
#pragma unroll
for (int k0 = 0; k0 < D; k0 += 2) {
if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
break;
}
half2 V_k;
reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid];
reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
VKQ[j] += V_k*KQ2[j*(D/2) + k0/2];
}
}
__syncthreads();
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqsum[j] = warp_reduce_sum(kqsum[j]);
if (threadIdx.x == 0) {
kqsum_shared[j][threadIdx.y] = kqsum[j];
}
}
__syncthreads();
#pragma unroll
for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ]));
if (parallel_blocks == 1) {
dst_val /= kqsum[j_VKQ];
}
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
}
if (parallel_blocks != 1 && tid != 0) {
#pragma unroll
for (int j = 0; j < ncols; ++j) {
dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]);
}
}
#else
NO_DEVICE_CODE;
#endif // FP16_AVAILABLE
}
template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_f16(
const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
ggml_cuda_pool & pool, cudaStream_t main_stream
) {
ggml_cuda_pool_alloc<float> dst_tmp(pool);
ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
if (parallel_blocks > 1) {
dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
}
constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE;
const dim3 block_dim(WARP_SIZE, nwarps, 1);
const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]);
const int shmem = 0;
float scale = 1.0f;
float max_bias = 0.0f;
memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float));
memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));
const uint32_t n_head = Q->ne[2];
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>
<<<blocks_num, block_dim, shmem, main_stream>>> (
(const char *) Q->data,
(const char *) K->data,
(const char *) V->data,
mask ? ((const char *) mask->data) : nullptr,
parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
scale, max_bias, m0, m1, n_head_log2,
Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
K->ne[0], K->ne[1], K->ne[2], K->ne[3],
mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0,
Q->nb[1], Q->nb[2], Q->nb[3],
K->nb[1], K->nb[2], K->nb[3],
KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
);
CUDA_CHECK(cudaGetLastError());
if (parallel_blocks == 1) {
return;
}
const dim3 block_dim_combine(D, 1, 1);
const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
const int shmem_combine = 0;
flash_attn_combine_results<D, parallel_blocks>
<<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
(dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
CUDA_CHECK(cudaGetLastError());
}
void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * Q = dst->src[0];
const ggml_tensor * K = dst->src[1];
const ggml_tensor * V = dst->src[2];
const ggml_tensor * mask = dst->src[3];
ggml_tensor * KQV = dst;
const int32_t precision = KQV->op_params[2];
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
constexpr int cols_per_block = 1;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 256:
launch_fattn_vec_f16<256, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
}
void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * Q = dst->src[0];
const ggml_tensor * K = dst->src[1];
const ggml_tensor * V = dst->src[2];
const ggml_tensor * mask = dst->src[3];
ggml_tensor * KQV = dst;
const int32_t precision = KQV->op_params[2];
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
if (Q->ne[1] == 1) {
constexpr int cols_per_block = 1;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] == 2) {
constexpr int cols_per_block = 2;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] <= 4) {
constexpr int cols_per_block = 4;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] <= 8) {
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 1;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
}

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ggml-cuda/fattn-vec-f16.cuh Normal file
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#include "common.cuh"
void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

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ggml-cuda/fattn-vec-f32.cu Normal file
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#include "common.cuh"
#include "fattn-common.cuh"
#include "fattn-vec-f32.cuh"
template<int D, int ncols, int parallel_blocks> // D == head size
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(D, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
static __global__ void flash_attn_vec_ext_f32(
const char * __restrict__ Q,
const char * __restrict__ K,
const char * __restrict__ V,
const char * __restrict__ mask,
float * __restrict__ dst,
float2 * __restrict__ dst_meta,
const float scale,
const float max_bias,
const float m0,
const float m1,
const uint32_t n_head_log2,
const int ne00,
const int ne01,
const int ne02,
const int ne03,
const int ne10,
const int ne11,
const int ne12,
const int ne13,
const int ne31,
const int nb31,
const int nb01,
const int nb02,
const int nb03,
const int nb11,
const int nb12,
const int nb13,
const int ne0,
const int ne1,
const int ne2,
const int ne3) {
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0);
const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio));
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
const half * maskh = (const half *) mask + ne11*ic0;
const int stride_KV = nb11 / sizeof(half);
const int stride_KV2 = nb11 / sizeof(half2);
float slope = 1.0f;
// ALiBi
if (max_bias > 0.0f) {
const int h = blockIdx.y;
const float base = h < n_head_log2 ? m0 : m1;
const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
slope = powf(base, exph);
}
static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
constexpr int nwarps = D / WARP_SIZE;
const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
__builtin_assume(tid < D);
__shared__ float KQ[ncols*D];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
KQ[j*D + tid] = -FLT_MAX/2.0f;
}
float kqmax[ncols];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqmax[j] = -FLT_MAX/2.0f;
}
float kqsum[ncols] = {0.0f};
__shared__ float kqmax_shared[ncols][WARP_SIZE];
__shared__ float kqsum_shared[ncols][WARP_SIZE];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
if (threadIdx.y == 0) {
kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f;
kqsum_shared[j][threadIdx.x] = 0.0f;
}
}
__syncthreads();
// Convert Q to half2 and store in registers:
float2 Q_h2[ncols][D/(2*WARP_SIZE)];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
Q_h2[j][i0/WARP_SIZE] = Q_f2[j*(nb01/sizeof(float2)) + i];
Q_h2[j][i0/WARP_SIZE].x *= scale;
Q_h2[j][i0/WARP_SIZE].y *= scale;
}
}
float VKQ[ncols] = {0.0f};
const int k_start = parallel_blocks == 1 ? 0 : ip*D;
for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
// Calculate KQ tile and keep track of new maximum KQ values:
float kqmax_new_arr[ncols];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqmax_new_arr[j] = kqmax[j];
}
#pragma unroll
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
const int i_KQ = i_KQ_0 + threadIdx.y;
if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
break;
}
float sum[ncols] = {0.0f};
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
sum[j] += __low2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].x;
sum[j] += __high2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].y;
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
sum[j] = warp_reduce_sum(sum[j]);
sum[j] += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum[j]);
if (threadIdx.x == 0) {
KQ[j*D + i_KQ] = sum[j];
}
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
float kqmax_new_j = kqmax_new_arr[j];
kqmax_new_j = warp_reduce_max(kqmax_new_j);
if (threadIdx.x == 0) {
kqmax_shared[j][threadIdx.y] = kqmax_new_j;
}
}
__syncthreads();
#pragma unroll
for (int j = 0; j < ncols; ++j) {
float kqmax_new_j = kqmax_shared[j][threadIdx.x];
kqmax_new_j = warp_reduce_max(kqmax_new_j);
const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j);
kqmax[j] = kqmax_new_j;
const float val = expf(KQ[j*D + tid] - kqmax[j]);
kqsum[j] = kqsum[j]*KQ_max_scale + val;
KQ[j*D + tid] = val;
VKQ[j] *= KQ_max_scale;
}
__syncthreads();
#pragma unroll
for (int k = 0; k < D; ++k) {
if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) {
break;
}
const float V_ki = __half2float(V_h[(k_VKQ_0 + k)*stride_KV + tid]);
#pragma unroll
for (int j = 0; j < ncols; ++j) {
VKQ[j] += V_ki*KQ[j*D + k];
}
}
__syncthreads();
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqsum[j] = warp_reduce_sum(kqsum[j]);
if (threadIdx.x == 0) {
kqsum_shared[j][threadIdx.y] = kqsum[j];
}
}
__syncthreads();
#pragma unroll
for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
float dst_val = VKQ[j_VKQ];
if (parallel_blocks == 1) {
dst_val /= kqsum[j_VKQ];
}
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
}
if (parallel_blocks != 1 && tid != 0) {
#pragma unroll
for (int j = 0; j < ncols; ++j) {
dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]);
}
}
}
template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_f32(
const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
ggml_cuda_pool & pool, cudaStream_t main_stream
) {
ggml_cuda_pool_alloc<float> dst_tmp(pool);
ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
if (parallel_blocks > 1) {
dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
}
constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE;
const dim3 block_dim(WARP_SIZE, nwarps, 1);
const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]);
const int shmem = 0;
float scale = 1.0f;
float max_bias = 0.0f;
memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float));
memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));
const uint32_t n_head = Q->ne[2];
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks>
<<<blocks_num, block_dim, shmem, main_stream>>> (
(const char *) Q->data,
(const char *) K->data,
(const char *) V->data,
mask ? ((const char *) mask->data) : nullptr,
parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
scale, max_bias, m0, m1, n_head_log2,
Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
K->ne[0], K->ne[1], K->ne[2], K->ne[3],
mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0,
Q->nb[1], Q->nb[2], Q->nb[3],
K->nb[1], K->nb[2], K->nb[3],
KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
);
CUDA_CHECK(cudaGetLastError());
if (parallel_blocks == 1) {
return;
}
const dim3 block_dim_combine(D, 1, 1);
const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
const int shmem_combine = 0;
flash_attn_combine_results<D, parallel_blocks>
<<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
(dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
CUDA_CHECK(cudaGetLastError());
}
void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * Q = dst->src[0];
const ggml_tensor * K = dst->src[1];
const ggml_tensor * V = dst->src[2];
const ggml_tensor * mask = dst->src[3];
ggml_tensor * KQV = dst;
GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
if (Q->ne[1] == 1) {
constexpr int cols_per_block = 1;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] == 2) {
constexpr int cols_per_block = 2;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] <= 4) {
constexpr int cols_per_block = 4;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] <= 8) {
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 1;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
}

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ggml-cuda/fattn-vec-f32.cuh Normal file
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#include "common.cuh"
void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

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#include "common.cuh"
#include "fattn-common.cuh"
#include "fattn-vec-f16.cuh"
#include "fattn-vec-f32.cuh"
#include "fattn.cuh"
#include <cstdint>
#if FP16_MMA_AVAILABLE
#include <mma.h>
#endif
// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t>
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(nwarps*WARP_SIZE, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
static __global__ void flash_attn_ext_f16(
const char * __restrict__ Q,
const char * __restrict__ K,
const char * __restrict__ V,
const char * __restrict__ mask,
float * __restrict__ dst,
float2 * __restrict__ dst_meta,
const float scale,
const float max_bias,
const float m0,
const float m1,
const uint32_t n_head_log2,
const int ne00,
const int ne01,
const int ne02,
const int ne03,
const int ne10,
const int ne11,
const int ne12,
const int ne13,
const int ne31,
const int nb31,
const int nb01,
const int nb02,
const int nb03,
const int nb11,
const int nb12,
const int nb13,
const int ne0,
const int ne1,
const int ne2,
const int ne3) {
#if FP16_MMA_AVAILABLE
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on.
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE.");
static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16.");
constexpr int frag_m = ncols == 8 ? 32 : 16;
constexpr int frag_n = ncols == 8 ? 8 : 16;
static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::row_major> frag_a_K;
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_a_V;
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_b;
typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t> frag_c_KQ;
typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, half> frag_c_VKQ;
constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel.
constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps.");
// Pad internal representation of KQ, KQV to reduce shared memory bank conflicts:
constexpr int D_padded = D + 8;
constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0);
const half * K_h = (const half *) (K + nb12*(blockIdx.y / gqa_ratio));
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
const half * maskh = (const half *) mask + (nb31/sizeof(half))* ic0;
const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2);
const int stride_Q = nb01 / sizeof(float);
const int stride_KV = nb11 / sizeof(half);
half slopeh = __float2half(1.0f);
half2 slope2 = make_half2(1.0f, 1.0f);
// ALiBi
if (max_bias > 0.0f) {
const int h = blockIdx.y;
const float base = h < n_head_log2 ? m0 : m1;
const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
slopeh = __float2half(powf(base, exph));
slope2 = make_half2(slopeh, slopeh);
}
frag_b Q_b[D/16][ncols/frag_n];
// A single buffer for temporarily holding tiles of KQ and VKQ parts:
constexpr int mem_KQ = ncols*kqs_padded*kqar;
constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded;
__shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
float * KQ_f = (float *) KQ;
half2 * KQ2 = (half2 *) KQ;
float KQ_rowsum_f[ncols/nwarps] = {0.0f};
float KQ_max_f[ncols/nwarps];
float KQ_max_scale_f[ncols/nwarps] = {0.0f};
#pragma unroll
for (int j = 0; j < ncols/nwarps; ++j) {
KQ_max_f[j] = -FLT_MAX/2.0f;
}
half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}};
half2 KQ_max_h2[ncols/nwarps];
half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}};
#pragma unroll
for (int j = 0; j < ncols/nwarps; ++j) {
KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF);
}
__shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
half2 * VKQ2 = (half2 *) VKQ;
#pragma unroll
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
const int j = j0 + threadIdx.y;
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
break;
}
VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f);
}
}
// Convert Q to half and apply scale, temporarily store in KQ:
#pragma unroll
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
const int j = j0 + threadIdx.y;
#pragma unroll
for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
if (i0 + WARP_SIZE > D && i >= D) {
break;
}
KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f;
}
}
__syncthreads();
// Load Q into tensor core fragments/registers since it will be used frequently:
#pragma unroll
for (int i0 = 0; i0 < D; i0 += 16) {
#pragma unroll
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
}
}
__syncthreads();
// Iterate over ne11 == previous tokens:
for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) {
// Calculate tile of KQ:
#pragma unroll
for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) {
frag_c_KQ KQ_c[ncols/frag_n];
#pragma unroll
for (int j = 0; j < ncols/frag_n; ++j) {
nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f);
}
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
frag_a_K K_a;
nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
#pragma unroll
for (int j = 0; j < ncols/frag_n; ++j) {
nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
}
}
#pragma unroll
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major);
}
}
__syncthreads();
// Calculate softmax for each KQ column using the current max. value.
// The divisor is stored in KQ_rowsum and will be applied at the end.
#pragma unroll
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
const int j = j0 + threadIdx.y;
if (std::is_same<KQ_acc_t, float>::value) {
float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE];
#pragma unroll
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
const int k = k0 + threadIdx.x;
KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
}
float KQ_max_new = KQ_max_f[j0/nwarps];
#pragma unroll
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
const int k = k0 + threadIdx.x;
KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f;
KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]);
}
KQ_max_new = warp_reduce_max(KQ_max_new);
const float diff = KQ_max_f[j0/nwarps] - KQ_max_new;
KQ_max_scale_f[j0/nwarps] = expf(diff);
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
KQ_max_scale_f[j0/nwarps] = 0.0f;
}
KQ_max_f[j0/nwarps] = KQ_max_new;
float KQ_rowsum_add = 0.0f;
#pragma unroll
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
const int k = k0 + threadIdx.x;
const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps];
KQ_f_tmp[k0/WARP_SIZE] = expf(diff);
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
KQ_f_tmp[k0/WARP_SIZE] = 0.0f;
}
KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE];
KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE];
}
KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add;
} else {
half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)];
#pragma unroll
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
const int k = k0 + threadIdx.x;
KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
}
half2 KQ_max_new = KQ_max_h2[j0/nwarps];
#pragma unroll
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
const int k = k0 + threadIdx.x;
KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
}
KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
*((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask;
KQ_max_h2[j0/nwarps] = KQ_max_new;
half2 KQ_rowsum_add = make_half2(0.0f, 0.0f);
#pragma unroll
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
const int k = k0 + threadIdx.x;
const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps];
KQ2_tmp[k0/WARP_SIZE] = h2exp(diff);
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
*((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask;
KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE];
KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE];
}
KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add;
}
}
__syncthreads();
frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n];
#pragma unroll
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
#pragma unroll
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
nvcuda::wmma::load_matrix_sync(
KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
KQ + j0*(kqar*kqs_padded) + k,
kqar*kqs_padded);
}
}
frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n];
#pragma unroll
for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) {
#pragma unroll
for (int j = 0; j < ncols/frag_n; ++j) {
nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f);
}
#pragma unroll
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
frag_a_V v_a;
nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
#pragma unroll
for (int j = 0; j < ncols/frag_n; ++j) {
nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
}
}
}
__syncthreads();
const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded);
#pragma unroll
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) {
#pragma unroll
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
nvcuda::wmma::store_matrix_sync(
KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
D_padded, nvcuda::wmma::mem_col_major);
}
}
__syncthreads();
#pragma unroll
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
const int j = j0 + threadIdx.y;
half2 VKQ_scale;
if (std::is_same<KQ_acc_t, float>::value) {
VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]);
} else {
VKQ_scale = KQ_max_scale_h2[j0/nwarps];
}
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
break;
}
half2 VKQ_add = make_half2(0.0f, 0.0f);
#pragma unroll
for (int l = 0; l < VKQ_ratio; ++l) {
VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i];
}
VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add;
}
}
__syncthreads();
}
#pragma unroll
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
const int j_VKQ = j0 + threadIdx.y;
if (ic0 + j_VKQ >= ne01) {
return;
}
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
float KQ_rowsum_j;
if (std::is_same<KQ_acc_t, float>::value) {
KQ_rowsum_j = KQ_rowsum_f[j0/nwarps];
} else {
KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]);
}
#pragma unroll
for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
if (i0 + WARP_SIZE > D && i >= D) {
break;
}
float dst_val = VKQ[j_VKQ*D_padded + i];
if (parallel_blocks == 1) {
dst_val /= KQ_rowsum_j;
}
dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val;
}
if (parallel_blocks == 1 || threadIdx.x != 0) {
continue;
}
float2 dst_meta_val;
if (std::is_same<KQ_acc_t, float>::value) {
dst_meta_val.x = KQ_max_f[j0/nwarps];
} else {
dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]);
}
dst_meta_val.y = KQ_rowsum_j;
dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val;
}
#else
NO_DEVICE_CODE;
#endif // FP16_MMA_AVAILABLE
}
constexpr int get_max_power_of_2(int x) {
return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
}
static_assert(get_max_power_of_2(1) == 1, "Test failed.");
static_assert(get_max_power_of_2(2) == 2, "Test failed.");
static_assert(get_max_power_of_2(4) == 4, "Test failed.");
static_assert(get_max_power_of_2(6) == 2, "Test failed.");
// Number of VKQ rows calculated in parallel:
constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) {
return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m;
}
static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed.");
static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed.");
static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed.");
static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed.");
static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed.");
static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed.");
static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed.");
static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed.");
static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed.");
template <int D, int cols_per_block, int nwarps, int parallel_blocks, typename KQ_acc_t> void launch_fattn_f16_impl(
const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
ggml_cuda_pool & pool, cudaStream_t main_stream
) {
ggml_cuda_pool_alloc<float> dst_tmp(pool);
ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
if (parallel_blocks > 1) {
dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
}
constexpr int frag_m = (cols_per_block) == 8 && (D) % 32 == 0 ? 32 : 16;
const dim3 block_dim(WARP_SIZE, nwarps, 1);
const dim3 blocks_num(parallel_blocks*(Q->ne[1] + cols_per_block - 1) / cols_per_block, Q->ne[2], Q->ne[3]);
const int shmem = 0;
float scale = 1.0f;
float max_bias = 0.0f;
memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float));
memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));
const uint32_t n_head = Q->ne[2];
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>
<<<blocks_num, block_dim, shmem, main_stream>>> (
(const char *) Q->data,
(const char *) K->data,
(const char *) V->data,
mask ? ((const char *) mask->data) : nullptr,
(parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
scale, max_bias, m0, m1, n_head_log2,
Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
K->ne[0], K->ne[1], K->ne[2], K->ne[3],
mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0,
Q->nb[1], Q->nb[2], Q->nb[3],
K->nb[1], K->nb[2], K->nb[3],
KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
);
CUDA_CHECK(cudaGetLastError());
if ((parallel_blocks) == 1) {
return;
}
const dim3 block_dim_combine(D, 1, 1);
const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
const int shmem_combine = 0;
flash_attn_combine_results<D, parallel_blocks>
<<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
(dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
CUDA_CHECK(cudaGetLastError());
}
template <int D, int cols_per_block, int nwarps, typename KQ_acc_t> void launch_fattn_f16(
const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
const int nsm, ggml_cuda_pool & pool, cudaStream_t main_stream
) {
const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
if (4*blocks_num_pb1 < 2*nsm) {
launch_fattn_f16_impl<D, cols_per_block, nwarps, 4, KQ_acc_t>(Q, K, V, KQV, mask, pool, main_stream);
return;
}
if (2*blocks_num_pb1 < 2*nsm) {
launch_fattn_f16_impl<D, cols_per_block, nwarps, 2, KQ_acc_t>(Q, K, V, KQV, mask, pool, main_stream);
return;
}
launch_fattn_f16_impl<D, cols_per_block, nwarps, 1, KQ_acc_t>(Q, K, V, KQV, mask, pool, main_stream);
}
void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * Q = dst->src[0];
const ggml_tensor * K = dst->src[1];
const ggml_tensor * V = dst->src[2];
const ggml_tensor * mask = dst->src[3];
ggml_tensor * KQV = dst;
GGML_ASSERT(Q->type == GGML_TYPE_F32);
GGML_ASSERT(K->type == GGML_TYPE_F16);
GGML_ASSERT(V->type == GGML_TYPE_F16);
GGML_ASSERT(KQV->type == GGML_TYPE_F32);
GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16);
GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) &&
"the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big");
GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");
ggml_cuda_set_device(ctx.device);
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
const int32_t precision = KQV->op_params[2];
if (!fast_fp16_available(cc)) {
ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
return;
}
if (!fp16_mma_available(cc)) {
ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst);
return;
}
if (precision != GGML_PREC_DEFAULT) {
if (Q->ne[1] == 1 && (Q->ne[0] == 64 || Q->ne[0] == 128)) {
ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
return;
}
if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
constexpr int cols_per_block = 16;
constexpr int nwarps = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 80:
launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 96:
launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 112:
launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 256:
launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
} else {
constexpr int cols_per_block = 32;
constexpr int nwarps = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 80:
launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 96:
launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 112:
launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
// case 256:
// launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
// break;
default:
GGML_ASSERT(false);
break;
}
}
return;
}
if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) {
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
return;
}
if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
constexpr int cols_per_block = 8;
constexpr int nwarps = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 96:
launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 256:
launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] <= 32) {
constexpr int cols_per_block = 16;
constexpr int nwarps = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 80:
launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 96:
launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 112:
launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 256:
launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
constexpr int cols_per_block = 32;
constexpr int nwarps = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 80:
launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 96:
launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 112:
launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
case 256:
launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}

3
ggml-cuda/fattn.cuh Normal file
View File

@ -0,0 +1,3 @@
#include "common.cuh"
void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

30
ggml-cuda/mmq.cu
View File

@ -1735,8 +1735,7 @@ static void ggml_mul_mat_q4_0_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) {
int id;
CUDA_CHECK(cudaGetDevice(&id));
int id = ggml_cuda_get_device();
const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps;
@ -1780,8 +1779,7 @@ static void ggml_mul_mat_q4_1_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) {
int id;
CUDA_CHECK(cudaGetDevice(&id));
int id = ggml_cuda_get_device();
const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps;
@ -1825,8 +1823,7 @@ static void ggml_mul_mat_q5_0_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) {
int id;
CUDA_CHECK(cudaGetDevice(&id));
int id = ggml_cuda_get_device();
const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps;
@ -1870,8 +1867,7 @@ static void ggml_mul_mat_q5_1_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) {
int id;
CUDA_CHECK(cudaGetDevice(&id));
int id = ggml_cuda_get_device();
const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps;
@ -1915,8 +1911,7 @@ static void ggml_mul_mat_q8_0_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) {
int id;
CUDA_CHECK(cudaGetDevice(&id));
int id = ggml_cuda_get_device();
const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps;
@ -1960,8 +1955,7 @@ static void ggml_mul_mat_q2_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) {
int id;
CUDA_CHECK(cudaGetDevice(&id));
int id = ggml_cuda_get_device();
const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps;
@ -2007,8 +2001,7 @@ static void ggml_mul_mat_q3_K_q8_1_cuda(
#if QK_K == 256
int id;
CUDA_CHECK(cudaGetDevice(&id));
int id = ggml_cuda_get_device();
const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps;
@ -2053,8 +2046,7 @@ static void ggml_mul_mat_q4_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) {
int id;
CUDA_CHECK(cudaGetDevice(&id));
int id = ggml_cuda_get_device();
const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps;
@ -2098,8 +2090,7 @@ static void ggml_mul_mat_q5_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) {
int id;
CUDA_CHECK(cudaGetDevice(&id));
int id = ggml_cuda_get_device();
const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps;
@ -2143,8 +2134,7 @@ static void ggml_mul_mat_q6_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) {
int id;
CUDA_CHECK(cudaGetDevice(&id));
int id = ggml_cuda_get_device();
const int compute_capability = ggml_cuda_info().devices[id].cc;
int mmq_x, mmq_y, nwarps;

6
ggml-cuda/mmvq.cu
View File

@ -89,8 +89,7 @@ static void mul_mat_vec_q_cuda(
GGML_ASSERT(ncols_x % qk == 0);
GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE);
int id;
CUDA_CHECK(cudaGetDevice(&id));
int id = ggml_cuda_get_device();
int64_t nwarps = 1;
int64_t rows_per_cuda_block = 1;
@ -328,8 +327,7 @@ void ggml_cuda_op_mul_mat_vec_q(
const int64_t ne0 = dst->ne[0];
int id;
CUDA_CHECK(cudaGetDevice(&id));
int id = ggml_cuda_get_device();
// the main device has a larger memory buffer to hold the results from all GPUs
// nrows_dst == nrows of the matrix that the kernel writes into

1
ggml-cuda/scale.cu
View File

@ -28,5 +28,4 @@ void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
memcpy(&scale, dst->op_params, sizeof(float));
scale_f32_cuda(src0_d, dst_d, scale, ggml_nelements(src0), stream);
CUDA_CHECK(cudaGetLastError());
}

71
ggml-cuda/softmax.cu
View File

@ -1,7 +1,17 @@
#include "softmax.cuh"
template <bool vals_smem, int ncols_template, int block_size_template>
static __global__ void soft_max_f32(const float * x, const float * mask, const float * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
template <typename T>
static __device__ __forceinline__ float t2f32(T val) {
return (float) val;
}
template <>
__device__ float __forceinline__ t2f32<half>(half val) {
return __half2float(val);
}
template <bool vals_smem, int ncols_template, int block_size_template, typename T>
static __global__ void soft_max_f32(const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
const int tid = threadIdx.x;
@ -13,16 +23,16 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f
const int warp_id = threadIdx.x / WARP_SIZE;
const int lane_id = threadIdx.x % WARP_SIZE;
float slope = 0.0f;
float slope = 1.0f;
// ALiBi
if (max_bias > 0.0f) {
const int h = rowx/nrows_y; // head index
const float base = h < n_head_log2 ? m0 : m1;
const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
slope = powf(base, exp);
slope = powf(base, exph);
}
extern __shared__ float data_soft_max_f32[];
@ -43,7 +53,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f
const int64_t ix = (int64_t)rowx*ncols + col;
const int64_t iy = (int64_t)rowy*ncols + col;
const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f);
const float val = x[ix]*scale + (mask ? slope*t2f32(mask[iy]) : 0.0f);
vals[col] = val;
max_val = max(max_val, val);
@ -114,7 +124,8 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f
}
}
static void soft_max_f32_cuda(const float * x, const float * mask, const float * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) {
template<typename T>
static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) {
int nth = WARP_SIZE;
while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
const dim3 block_dims(nth, 1, 1);
@ -122,8 +133,8 @@ static void soft_max_f32_cuda(const float * x, const float * mask, const float *
const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float);
static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted.");
const uint32_t n_head_kv = nrows_x/nrows_y;
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
const uint32_t n_head = nrows_x/nrows_y;
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
@ -131,51 +142,53 @@ static void soft_max_f32_cuda(const float * x, const float * mask, const float *
if (shmem < ggml_cuda_info().devices[ggml_cuda_get_device()].smpb) {
switch (ncols_x) {
case 32:
soft_max_f32<true, 32, 32><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
soft_max_f32<true, 32, 32><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
break;
case 64:
soft_max_f32<true, 64, 64><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
soft_max_f32<true, 64, 64><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
break;
case 128:
soft_max_f32<true, 128, 128><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
soft_max_f32<true, 128, 128><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
break;
case 256:
soft_max_f32<true, 256, 256><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
soft_max_f32<true, 256, 256><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
break;
case 512:
soft_max_f32<true, 512, 512><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
soft_max_f32<true, 512, 512><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
break;
case 1024:
soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
break;
case 2048:
soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
break;
case 4096:
soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
break;
default:
soft_max_f32<true, 0, 0><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
soft_max_f32<true, 0, 0><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
break;
}
} else {
const size_t shmem_low = WARP_SIZE*sizeof(float);
soft_max_f32<false, 0, 0><<<block_nums, block_dims, shmem_low, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
soft_max_f32<false, 0, 0><<<block_nums, block_dims, shmem_low, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
}
}
void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
const float * src0_d = (const float *)src0->data;
const float * src1_d = src1 ? (const float *)src1->data : nullptr;
const void * src1_d = src1 ? (const void *)src1->data : nullptr;
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
const int64_t ne00 = src0->ne[0];
const int64_t nrows_x = ggml_nrows(src0);
@ -187,15 +200,15 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
// positions tensor
float * src2_dd = nullptr;
const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
ggml_tensor * src2 = dst->src[2];
const bool use_src2 = src2 != nullptr;
if (use_f16) {
const half * src1_dd = (const half *)src1_d;
if (use_src2) {
src2_dd = (float *)src2->data;
soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
} else {
const float * src1_dd = (const float *)src1_d;
soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
}
soft_max_f32_cuda(src0_d, src1_d, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
}

26
ggml-cuda/unary.cu
View File

@ -48,6 +48,15 @@ static __global__ void relu_f32(const float * x, float * dst, const int k) {
dst[i] = fmaxf(x[i], 0);
}
static __global__ void sigmoid_f32(const float * x, float * dst, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= k) {
return;
}
dst[i] = 1.0f / (1.0f + expf(-x[i]));
}
static __global__ void hardsigmoid_f32(const float * x, float * dst, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
@ -108,6 +117,11 @@ static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_
relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
}
static void sigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_SIGMOID_BLOCK_SIZE - 1) / CUDA_SIGMOID_BLOCK_SIZE;
sigmoid_f32<<<num_blocks, CUDA_SIGMOID_BLOCK_SIZE, 0, stream>>>(x, dst, k);
}
static void hardsigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_HARDSIGMOID_BLOCK_SIZE - 1) / CUDA_HARDSIGMOID_BLOCK_SIZE;
hardsigmoid_f32<<<num_blocks, CUDA_HARDSIGMOID_BLOCK_SIZE, 0, stream>>>(x, dst, k);
@ -188,6 +202,18 @@ void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
relu_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
}
void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const float * src0_d = (const float *)src0->data;
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
sigmoid_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
}
void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const float * src0_d = (const float *)src0->data;

3
ggml-cuda/unary.cuh
View File

@ -4,6 +4,7 @@
#define CUDA_SILU_BLOCK_SIZE 256
#define CUDA_TANH_BLOCK_SIZE 256
#define CUDA_RELU_BLOCK_SIZE 256
#define CUDA_SIGMOID_BLOCK_SIZE 256
#define CUDA_HARDSIGMOID_BLOCK_SIZE 256
#define CUDA_HARDSWISH_BLOCK_SIZE 256
#define CUDA_SQR_BLOCK_SIZE 256
@ -18,6 +19,8 @@ void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

79
ggml-impl.h
View File

@ -17,6 +17,83 @@
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))
/**
* Converts brain16 to float32.
*
* The bfloat16 floating point format has the following structure:
*
* ┌sign
* │
* │ ┌exponent
* │ │
* │ │ ┌mantissa
* │ │ │
* │┌──┴───┐┌─┴───┐
* 0b0000000000000000 brain16
*
* Since bf16 has the same number of exponent bits as a 32bit float,
* encoding and decoding numbers becomes relatively straightforward.
*
* ┌sign
* │
* │ ┌exponent
* │ │
* │ │ ┌mantissa
* │ │ │
* │┌──┴───┐┌─┴───────────────────┐
* 0b00000000000000000000000000000000 IEEE binary32
*
* For comparison, the standard fp16 format has fewer exponent bits.
*
* ┌sign
* │
* │ ┌exponent
* │ │
* │ │ ┌mantissa
* │ │ │
* │┌─┴─┐┌─┴──────┐
* 0b0000000000000000 IEEE binary16
*
* @see IEEE 754-2008
*/
static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) {
union {
float f;
uint32_t i;
} u;
u.i = (uint32_t)h.bits << 16;
return u.f;
}
/**
* Converts float32 to brain16.
*
* This function is binary identical to AMD Zen4 VCVTNEPS2BF16.
* Subnormals shall be flushed to zero, and NANs will be quiet.
* This code should vectorize nicely if using modern compilers.
*/
static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
ggml_bf16_t h;
union {
float f;
uint32_t i;
} u;
u.f = s;
if ((u.i & 0x7fffffff) > 0x7f800000) { /* nan */
h.bits = (u.i >> 16) | 64; /* force to quiet */
return h;
}
if (!(u.i & 0x7f800000)) { /* subnormal */
h.bits = (u.i & 0x80000000) >> 16; /* flush to zero */
return h;
}
h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16;
return h;
}
#define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x)
#define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x)
#ifdef __cplusplus
extern "C" {
#endif
@ -313,7 +390,7 @@ inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b)
#endif // defined(__ARM_NEON)
#if defined(__ARM_NEON) && !defined(__MSC_VER)
#if defined(__ARM_NEON) && !defined(_MSC_VER)
#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)

15
ggml-kompute.cpp
View File

@ -1427,6 +1427,7 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
for (int i = node_start; i < node_end; ++i) {
struct ggml_tensor * src0 = gf->nodes[i]->src[0];
struct ggml_tensor * src1 = gf->nodes[i]->src[1];
struct ggml_tensor * src2 = gf->nodes[i]->src[2]; GGML_UNUSED(src2);
struct ggml_tensor * dst = gf->nodes[i];
GGML_ASSERT(dst->data != nullptr);
@ -1558,7 +1559,19 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
case GGML_OP_SOFT_MAX:
{
float scale;
memcpy(&scale, dst->op_params, sizeof(float));
float max_bias;
memcpy(&scale, (float *)dst->op_params + 0, sizeof(float));
memcpy(&max_bias, (float *)dst->op_params + 1, sizeof(float));
#pragma message("TODO: add ggml_vk_soft_max() F16 src1 support")
#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021")
GGML_ASSERT(!src1 || src1t == GGML_TYPE_F32);
#pragma message("TODO: add ALiBi support")
#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/7192")
GGML_ASSERT(max_bias == 0.0f);
ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale);
} break;
case GGML_OP_DIAG_MASK_INF:

709
ggml-metal.m
View File

@ -40,14 +40,17 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_CLAMP,
GGML_METAL_KERNEL_TYPE_TANH,
GGML_METAL_KERNEL_TYPE_RELU,
GGML_METAL_KERNEL_TYPE_SIGMOID,
GGML_METAL_KERNEL_TYPE_GELU,
GGML_METAL_KERNEL_TYPE_GELU_4,
GGML_METAL_KERNEL_TYPE_GELU_QUICK,
GGML_METAL_KERNEL_TYPE_GELU_QUICK_4,
GGML_METAL_KERNEL_TYPE_SILU,
GGML_METAL_KERNEL_TYPE_SILU_4,
GGML_METAL_KERNEL_TYPE_SOFT_MAX,
GGML_METAL_KERNEL_TYPE_SOFT_MAX_4,
GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16,
GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4,
GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32,
GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4,
GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,
GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8,
GGML_METAL_KERNEL_TYPE_GET_ROWS_F32,
@ -167,7 +170,6 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_ROPE_F32,
GGML_METAL_KERNEL_TYPE_ROPE_F16,
GGML_METAL_KERNEL_TYPE_ALIBI_F32,
GGML_METAL_KERNEL_TYPE_IM2COL_F16,
GGML_METAL_KERNEL_TYPE_IM2COL_F32,
GGML_METAL_KERNEL_TYPE_UPSCALE_F32,
@ -177,6 +179,14 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,
GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,
GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
GGML_METAL_KERNEL_TYPE_CPY_F32_F32,
GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,
@ -255,11 +265,20 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
static void * ggml_metal_host_malloc(size_t n) {
void * data = NULL;
#if TARGET_OS_OSX
kern_return_t err = vm_allocate((vm_map_t) mach_task_self(), (void *) &data, n, VM_FLAGS_ANYWHERE);
if (err != KERN_SUCCESS) {
GGML_METAL_LOG_ERROR("%s: error: vm_allocate failed\n", __func__);
return NULL;
}
#else
const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n);
if (result != 0) {
GGML_METAL_LOG_ERROR("%s: error: posix_memalign failed\n", __func__);
return NULL;
}
#endif
return data;
}
@ -443,7 +462,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
}
/*
GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
GGML_METAL_LOG_INFO("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
(int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \
(int) kernel->pipeline.threadExecutionWidth); \
*/
@ -459,172 +478,182 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
return NULL; \
} \
} else { \
GGML_METAL_LOG_WARN("%s: skipping %-32s (not supported)\n", __func__, "kernel_"#name); \
GGML_METAL_LOG_WARN("%s: skipping %-40s (not supported)\n", __func__, "kernel_"#name); \
}
// simd_sum and simd_max requires MTLGPUFamilyApple7
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD, add, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW, add_row, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL, mul, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX, soft_max, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, soft_max_4, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, diag_mask_inf_8, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, get_rows_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, get_rows_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, get_rows_q4_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, get_rows_q4_1, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, get_rows_q5_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, get_rows_q5_1, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, get_rows_q8_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, get_rows_q2_K, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, get_rows_q3_K, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, get_rows_q4_K, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, get_rows_q5_K, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, get_rows_q6_K, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M, get_rows_iq1_m, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction);
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction);
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, ctx->support_simdgroup_reduction);
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, cpy_f32_iq4_nl, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD, add, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW, add_row, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL, mul, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIGMOID, sigmoid, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, soft_max_f16, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, soft_max_f16_4, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, soft_max_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4, soft_max_f32_4, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, diag_mask_inf_8, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, get_rows_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, get_rows_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, get_rows_q4_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, get_rows_q4_1, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, get_rows_q5_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, get_rows_q5_1, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, get_rows_q8_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, get_rows_q2_K, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, get_rows_q3_K, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, get_rows_q4_K, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, get_rows_q5_K, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, get_rows_q6_K, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M, get_rows_iq1_m, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction);
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction);
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, ctx->support_simdgroup_reduction);
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, cpy_f32_iq4_nl, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true);
}
[metal_library release];
@ -703,6 +732,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
switch (ggml_get_unary_op(op)) {
case GGML_UNARY_OP_TANH:
case GGML_UNARY_OP_RELU:
case GGML_UNARY_OP_SIGMOID:
case GGML_UNARY_OP_GELU:
case GGML_UNARY_OP_GELU_QUICK:
case GGML_UNARY_OP_SILU:
@ -730,7 +760,6 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
case GGML_OP_GROUP_NORM:
return ctx->support_simdgroup_reduction;
case GGML_OP_NORM:
case GGML_OP_ALIBI:
case GGML_OP_ROPE:
case GGML_OP_IM2COL:
return true;
@ -744,6 +773,8 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
case GGML_OP_ARGSORT:
case GGML_OP_LEAKY_RELU:
return true;
case GGML_OP_FLASH_ATTN_EXT:
return ctx->support_simdgroup_mm; // TODO: over-restricted for vec-kernels
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
return ctx->support_simdgroup_reduction &&
@ -782,7 +813,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_GET_ROWS:
{
return op->ne[3] == 1;
return op->src[0]->type != GGML_TYPE_BF16 && op->ne[3] == 1;
}
default:
return false;
@ -1164,24 +1195,24 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_CLAMP:
{
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline;
{
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline;
float min;
float max;
memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float));
memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float));
float min;
float max;
memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float));
memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float));
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&min length:sizeof(min) atIndex:2];
[encoder setBytes:&max length:sizeof(max) atIndex:3];
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&min length:sizeof(min) atIndex:2];
[encoder setBytes:&max length:sizeof(max) atIndex:3];
const int64_t n = ggml_nelements(dst);
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_UNARY:
switch (ggml_get_unary_op(gf->nodes[i])) {
// we are not taking into account the strides, so for now require contiguous tensors
@ -1209,6 +1240,18 @@ static enum ggml_status ggml_metal_graph_compute(
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_UNARY_OP_SIGMOID:
{
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIGMOID].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_UNARY_OP_GELU:
@ -1326,20 +1369,32 @@ static enum ggml_status ggml_metal_graph_compute(
} break;
case GGML_OP_SOFT_MAX:
{
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32);
int nth = 32; // SIMD width
id<MTLComputePipelineState> pipeline = nil;
const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
if (ne00%4 == 0) {
while (nth < ne00/4 && nth < 256) {
nth *= 2;
}
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline;
if (use_f16) {
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4].pipeline;
} else {
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4].pipeline;
}
} else {
while (nth < ne00 && nth < 1024) {
nth *= 2;
}
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline;
if (use_f16) {
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16].pipeline;
} else {
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32].pipeline;
}
}
float scale;
@ -1351,8 +1406,8 @@ static enum ggml_status ggml_metal_graph_compute(
const int64_t nrows_x = ggml_nrows(src0);
const int64_t nrows_y = src0->ne[1];
const uint32_t n_head_kv = nrows_x/nrows_y;
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
const uint32_t n_head = nrows_x/nrows_y;
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
@ -1364,20 +1419,15 @@ static enum ggml_status ggml_metal_graph_compute(
} else {
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
}
if (id_src2) {
[encoder setBuffer:id_src2 offset:offs_src2 atIndex:2];
} else {
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:2];
}
[encoder setBuffer:id_dst offset:offs_dst atIndex:3];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:4];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:5];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:6];
[encoder setBytes:&scale length:sizeof(scale) atIndex:7];
[encoder setBytes:&max_bias length:sizeof(max_bias) atIndex:8];
[encoder setBytes:&m0 length:sizeof(m0) atIndex:9];
[encoder setBytes:&m1 length:sizeof(m1) atIndex:10];
[encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:11];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5];
[encoder setBytes:&scale length:sizeof(scale) atIndex:6];
[encoder setBytes:&max_bias length:sizeof(max_bias) atIndex:7];
[encoder setBytes:&m0 length:sizeof(m0) atIndex:8];
[encoder setBytes:&m1 length:sizeof(m1) atIndex:9];
[encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:10];
[encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
@ -2182,49 +2232,6 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ALIBI:
{
GGML_ASSERT((src0t == GGML_TYPE_F32));
const int nth = MIN(1024, ne00);
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_head = ((int32_t *) dst->op_params)[1];
float max_bias;
memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ALIBI_F32].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
[encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
[encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12];
[encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&m0 length:sizeof( float) atIndex:18];
[encoder setBytes:&m1 length:sizeof( float) atIndex:19];
[encoder setBytes:&n_heads_log2_floor length:sizeof(int) atIndex:20];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ROPE:
{
GGML_ASSERT(ne10 == ne02);
@ -2503,6 +2510,173 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_FLASH_ATTN_EXT:
{
GGML_ASSERT(ne00 % 4 == 0);
GGML_ASSERT(src0->type == GGML_TYPE_F32);
struct ggml_tensor * src3 = gf->nodes[i]->src[3];
GGML_ASSERT(ggml_are_same_shape(src1, src2));
GGML_ASSERT(src3);
size_t offs_src3 = 0;
id<MTLBuffer> id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil;
GGML_ASSERT(!src3 || src3->type == GGML_TYPE_F16);
GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) &&
"the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big");
const int64_t ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30);
//const int64_t ne31 = src3 ? src3->ne[1] : 0;
const int64_t ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32);
const int64_t ne33 = src3 ? src3->ne[3] : 0; GGML_UNUSED(ne33);
const uint64_t nb30 = src3 ? src3->nb[0] : 0; GGML_UNUSED(nb30);
const uint64_t nb31 = src3 ? src3->nb[1] : 0;
const uint64_t nb32 = src3 ? src3->nb[2] : 0; GGML_UNUSED(nb32);
const uint64_t nb33 = src3 ? src3->nb[3] : 0; GGML_UNUSED(nb33);
const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t);
float scale;
float max_bias;
memcpy(&scale, ((int32_t *) dst->op_params) + 0, sizeof(scale));
memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias));
const uint32_t n_head = src0->ne[2];
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
id<MTLComputePipelineState> pipeline = nil;
bool use_vec_kernel = false;
if (ne01 >= 4 || (ne00%128 != 0)) {
switch (ne00) {
case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break;
case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break;
case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break;
case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break;
case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break;
case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break;
default:
{
GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
GGML_METAL_LOG_ERROR("add template specialization for this size\n");
GGML_ASSERT(false && "add template specialization for this size");
}
}
} else {
use_vec_kernel = true;
switch (ne00) {
case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break;
case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break;
default:
{
GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
GGML_METAL_LOG_ERROR("add template specialization for this size\n");
GGML_ASSERT(false && "add template specialization for this size");
}
}
}
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_src2 offset:offs_src2 atIndex:2];
[encoder setBuffer:id_src3 offset:offs_src3 atIndex:3];
[encoder setBuffer:id_dst offset:offs_dst atIndex:4];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:5];
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:6];
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:7];
[encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:8];
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:10];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:11];
[encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:12];
[encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:13];
[encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:14];
[encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:15];
[encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:16];
[encoder setBytes:&nb10 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:18];
[encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:19];
[encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:20];
[encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:21];
[encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:22];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:23];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:24];
[encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:25];
[encoder setBytes:&scale length:sizeof( float) atIndex:26];
[encoder setBytes:&max_bias length:sizeof( float) atIndex:27];
[encoder setBytes:&m0 length:sizeof(m0) atIndex:28];
[encoder setBytes:&m1 length:sizeof(m1) atIndex:29];
[encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:30];
if (!use_vec_kernel) {
// half8x8 kernel
const int64_t nqptg = 8; // queries per threadgroup !! sync with kernel template arguments !!
const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!
GGML_ASSERT(nqptg <= 32);
GGML_ASSERT(nqptg % 8 == 0);
GGML_ASSERT(ncpsg % 32 == 0);
int64_t nsgmax = 2;
while (true) {
const size_t smem = nqptg*(ne00 + 2*nsgmax*(ncpsg + nqptg))*(sizeof(float)/2);
if (smem > ctx->device.maxThreadgroupMemoryLength) {
break;
}
nsgmax *= 2;
}
nsgmax /= 2;
// simdgroups per threadgroup (a.k.a. warps)
const int64_t nsg = ne01 <= nqptg ? MAX(4, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))) : 4;
const size_t smem = nqptg*(ne00 + 2*nsg*(ncpsg + nqptg))*(sizeof(float)/2);
//printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength);
GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength);
[encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
} else {
// half1x4 kernel
const int64_t nqptg = 1; // queries per threadgroup !! sync with kernel template arguments !!
const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!
GGML_ASSERT(nqptg <= 32);
GGML_ASSERT(nqptg % 1 == 0);
GGML_ASSERT(ncpsg % 32 == 0);
// simdgroups per threadgroup (a.k.a. warps)
const int64_t nsgt = MAX(2, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32));
int64_t nsg = 1;
while (nsg <= nsgt) {
nsg *= 2;
}
nsg /= 2;
const size_t smem = (nqptg*(ne00 + 2*nsg*(ncpsg + nqptg)) + nsg*ne00)*(sizeof(float)/2);
//printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength);
GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength);
[encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
}
} break;
case GGML_OP_DUP:
case GGML_OP_CPY:
case GGML_OP_CONT:
@ -2590,6 +2764,11 @@ static enum ggml_status ggml_metal_graph_compute(
MTLCommandBufferStatus status = [command_buffer status];
if (status != MTLCommandBufferStatusCompleted) {
GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
if (status == MTLCommandBufferStatusError) {
NSString * error_code = [command_buffer error].localizedDescription;
GGML_METAL_LOG_INFO("error: %s\n", [error_code UTF8String]);
}
return GGML_STATUS_FAILED;
}
}
@ -2646,7 +2825,11 @@ GGML_CALL static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_
ggml_backend_metal_free_device();
if (ctx->owned) {
#if TARGET_OS_OSX
vm_deallocate((vm_map_t)mach_task_self(), (vm_address_t)ctx->all_data, ctx->all_size);
#else
free(ctx->all_data);
#endif
}
free(ctx);
@ -2706,10 +2889,13 @@ GGML_CALL static const char * ggml_backend_metal_buffer_type_get_name(ggml_backe
UNUSED(buft);
}
static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device) {
static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device, size_t size_aligned) {
#ifndef GGML_METAL_NDEBUG
#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
if (@available(macOS 10.12, iOS 16.0, *)) {
GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f / %8.2f)",
__func__,
size_aligned / 1024.0 / 1024.0,
device.currentAllocatedSize / 1024.0 / 1024.0,
device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
@ -2719,10 +2905,15 @@ static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device) {
GGML_METAL_LOG_INFO("\n");
}
} else {
GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0);
GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f)\n",
__func__,
size_aligned / 1024.0 / 1024.0,
device.currentAllocatedSize / 1024.0 / 1024.0);
}
#endif
#endif
UNUSED(device);
UNUSED(size_aligned);
}
GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
@ -2742,22 +2933,23 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buff
ctx->owned = true;
ctx->n_buffers = 1;
ctx->buffers[0].data = ctx->all_data;
ctx->buffers[0].size = size;
ctx->buffers[0].metal = [device newBufferWithBytesNoCopy:ctx->all_data
length:size_aligned
options:MTLResourceStorageModeShared
deallocator:nil];
if (ctx->all_data != NULL) {
ctx->buffers[0].data = ctx->all_data;
ctx->buffers[0].size = size;
ctx->buffers[0].metal = [device newBufferWithBytesNoCopy:ctx->all_data
length:size_aligned
options:MTLResourceStorageModeShared
deallocator:nil];
}
if (ctx->buffers[0].metal == nil) {
if (ctx->all_data == NULL || ctx->buffers[0].metal == nil) {
GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
free(ctx);
ggml_backend_metal_free_device();
return NULL;
}
GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0);
ggml_backend_metal_log_allocated_size(device);
//ggml_backend_metal_log_allocated_size(device, size_aligned);
return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size);
}
@ -2844,7 +3036,7 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
return false;
}
GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0);
ggml_backend_metal_log_allocated_size(device, size_aligned);
++ctx->n_buffers;
} else {
@ -2867,7 +3059,8 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
return false;
}
GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, offs = %12ld", __func__, size_step_aligned / 1024.0 / 1024.0, i);
ggml_backend_metal_log_allocated_size(device, size_step_aligned);
if (i + size_step < size) {
GGML_METAL_LOG_INFO("\n");
}
@ -2876,8 +3069,6 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
}
}
ggml_backend_metal_log_allocated_size(device);
return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size);
}

775
ggml-metal.metal
View File

@ -229,6 +229,13 @@ kernel void kernel_relu(
dst[tpig] = max(0.0f, src0[tpig]);
}
kernel void kernel_sigmoid(
device const float * src0,
device float * dst,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = 1.0f / (1.0f + exp(-src0[tpig]));
}
kernel void kernel_tanh(
device const float * src0,
device float * dst,
@ -352,11 +359,11 @@ kernel void kernel_sum_rows(
dst_row[0] = row_sum;
}
template<typename T>
kernel void kernel_soft_max(
device const float * src0,
device const float * src1,
device const float * src2,
device float * dst,
device const char * src0,
device const char * src1,
device char * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
@ -375,12 +382,11 @@ kernel void kernel_soft_max(
const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01;
const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01);
device const float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
device const float * pmask = src1 != src0 ? src1 + i01*ne00 : nullptr;
device const float * ppos = src2 != src0 ? src2 : nullptr;
device float * pdst = dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
device const float * psrc0 = (device const float *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
device const T * pmask = src1 != src0 ? (device const T *) src1 + i01*ne00 : nullptr;
device float * pdst = (device float *) dst + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
float slope = 0.0f;
float slope = 1.0f;
// ALiBi
if (max_bias > 0.0f) {
@ -396,7 +402,7 @@ kernel void kernel_soft_max(
float lmax = -INFINITY;
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f));
lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
}
// find the max value in the block
@ -421,7 +427,7 @@ kernel void kernel_soft_max(
// parallel sum
float lsum = 0.0f;
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)) - max_val);
const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)) - max_val);
lsum += exp_psrc0;
pdst[i00] = exp_psrc0;
}
@ -456,11 +462,11 @@ kernel void kernel_soft_max(
}
}
template<typename T>
kernel void kernel_soft_max_4(
device const float * src0,
device const float * src1,
device const float * src2,
device float * dst,
device const char * src0,
device const char * src1,
device char * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
@ -479,12 +485,11 @@ kernel void kernel_soft_max_4(
const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01;
const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01);
device const float4 * psrc4 = (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
device const float4 * pmask = src1 != src0 ? (device const float4 *)(src1 + i01*ne00) : nullptr;
device const float4 * ppos = src2 != src0 ? (device const float4 *)(src2) : nullptr;
device float4 * pdst4 = (device float4 *)(dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
device const float4 * psrc4 = (device const float4 *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4;
device const T * pmask = src1 != src0 ? (device const T *) src1 + i01*ne00/4 : nullptr;
device float4 * pdst4 = (device float4 *) dst + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4;
float slope = 0.0f;
float slope = 1.0f;
if (max_bias > 0.0f) {
const int64_t h = i02;
@ -499,7 +504,7 @@ kernel void kernel_soft_max_4(
float4 lmax4 = -INFINITY;
for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f));
lmax4 = fmax(lmax4, psrc4[i00]*scale + (float4)((pmask ? slope*pmask[i00] : 0.0f)));
}
const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3]));
@ -525,7 +530,7 @@ kernel void kernel_soft_max_4(
// parallel sum
float4 lsum4 = 0.0f;
for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)) - max_val);
const float4 exp_psrc4 = exp((psrc4[i00]*scale + (float4)((pmask ? slope*pmask[i00] : 0.0f))) - max_val);
lsum4 += exp_psrc4;
pdst4[i00] = exp_psrc4;
}
@ -562,6 +567,14 @@ kernel void kernel_soft_max_4(
}
}
typedef decltype(kernel_soft_max<float>) kernel_soft_max_t;
typedef decltype(kernel_soft_max_4<float4>) kernel_soft_max_4_t;
template [[host_name("kernel_soft_max_f16")]] kernel kernel_soft_max_t kernel_soft_max<half>;
template [[host_name("kernel_soft_max_f32")]] kernel kernel_soft_max_t kernel_soft_max<float>;
template [[host_name("kernel_soft_max_f16_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4<half4>;
template [[host_name("kernel_soft_max_f32_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4<float4>;
kernel void kernel_diag_mask_inf(
device const float * src0,
device float * dst,
@ -1585,60 +1598,6 @@ kernel void kernel_mul_mv_f16_f32_l4(
}
}
kernel void kernel_alibi_f32(
device const float * src0,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
constant float & m0,
constant float & m1,
constant int & n_heads_log2_floor,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i03 = tgpig[2];
const int64_t i02 = tgpig[1];
const int64_t i01 = tgpig[0];
const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
const int64_t i3 = n / (ne2*ne1*ne0);
const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
//const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
const int64_t k = i3*ne3 + i2;
float m_k;
if (k < n_heads_log2_floor) {
m_k = pow(m0, k + 1);
} else {
m_k = pow(m1, 2 * (k - n_heads_log2_floor) + 1);
}
device char * dst_row = (device char *) dst + i3*nb3 + i2*nb2 + i1*nb1;
device const char * src_row = (device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01;
for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
const float src_v = *(device float *)(src_row + i00*nb00);
device float * dst_v = (device float *)(dst_row + i00*nb0);
*dst_v = i00 * m_k + src_v;
}
}
static float rope_yarn_ramp(const float low, const float high, const int i0) {
const float y = (i0 / 2 - low) / max(0.001f, high - low);
return 1.0f - min(1.0f, max(0.0f, y));
@ -2084,6 +2043,667 @@ kernel void kernel_leaky_relu_f32(
dst[tpig] = src0[tpig] > 0.0f ? src0[tpig] : src0[tpig] * slope;
}
typedef void (flash_attn_ext_f16_t)(
device const char * q,
device const char * k,
device const char * v,
device const char * mask,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant int64_t & ne13,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant uint64_t & nb13,
constant uint64_t & nb31,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant float & scale,
constant float & max_bias,
constant float & m0,
constant float & m1,
constant uint32_t & n_head_log2,
threadgroup half * shared,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]]);
// ref: https://arxiv.org/pdf/2307.08691.pdf
template<int64_t D, int64_t Q = 8, int64_t C = 32> // head size, queries per threadgroup, cache items per threadgroup
kernel void kernel_flash_attn_ext_f16(
device const char * q,
device const char * k,
device const char * v,
device const char * mask,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant int64_t & ne13,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant uint64_t & nb13,
constant uint64_t & nb31,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant float & scale,
constant float & max_bias,
constant float & m0,
constant float & m1,
constant uint32_t & n_head_log2,
threadgroup half * shared [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]]) {
const short nsg = ntg.y; // number of simdgroups
const short iq3 = tgpig[2];
const short iq2 = tgpig[1];
const short iq1 = tgpig[0]*Q;
const short D4 = D/4;
const short D8 = D/8;
//const short Q8 = Q/8;
const short NW = N_SIMDWIDTH;
const short SH = (C + Q); // shared memory per simdgroup in (half)
const short T = D + 2*nsg*SH; // shared memory size per query in (half)
const short TF = T/2; // shared memory size per query in (float)
const short T4 = T/4; // shared memory size per query in (half4)
threadgroup half * sq = (threadgroup half *) (shared + 0*D); // holds the query data
threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4
threadgroup float * ss = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix
// store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper)
simdgroup_half8x8 lo[D8];
// load heads from Q to shared memory
for (short j = sgitg; j < Q; j += nsg) {
device const float4 * q4 = (device const float4 *) ((device const char *) q + ((iq1 + j)*nb01 + iq2*nb02 + iq3*nb03));
for (short i = tiisg; i < D4; i += NW) {
if (iq1 + j < ne01) {
sq4[j*T4 + i] = (half4) q4[i];
} else {
sq4[j*T4 + i] = 0.0h;
}
}
}
// zero out lo
for (short i = 0; i < D8; ++i) {
lo[i] = make_filled_simdgroup_matrix<half, 8>(0.0h);
}
// zero out shared memory SH
for (short j = 0; j < Q; ++j) {
for (short i = tiisg; i < SH; i += NW) {
ss[j*TF + i] = 0.0f;
}
}
threadgroup_barrier(mem_flags::mem_threadgroup);
{
float S[Q] = { [0 ... Q-1] = 0.0h };
float M[Q] = { [0 ... Q-1] = -FLT_MAX/2 };
// assume K and V are same shape
const short ne22 = ne12;
const short ne23 = ne13;
const uint nb21 = nb11;
const uint nb22 = nb12;
const uint nb23 = nb13;
// broadcast
const short rk2 = ne02/ne12;
const short rk3 = ne03/ne13;
const short rv2 = ne02/ne22;
const short rv3 = ne03/ne23;
// k indices
const short ik2 = iq2/rk2;
const short ik3 = iq3/rk3;
// v indices
const short iv2 = iq2/rv2;
const short iv3 = iq3/rv3;
// load the queries from shared memory into local memory
simdgroup_half8x8 mq[D8];
for (short i = 0; i < D8; ++i) {
simdgroup_load(mq[i], sq + i*8, T);
}
// pointer to the mask
device const half * mp = (device const half *) (mask + iq1*nb31);
// prepare diagonal scale matrix
simdgroup_float8x8 mscale(scale);
// prepare diagonal slope matrix
simdgroup_float8x8 mslope(1.0f);
// ALiBi
if (max_bias > 0.0f) {
const uint32_t h = iq2;
const float base = h < n_head_log2 ? m0 : m1;
const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
mslope = simdgroup_float8x8(pow(base, exph));
}
// loop over the KV cache
// each simdgroup handles blocks of Q rows and C columns
for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) {
const int ic = ic0 + C*sgitg;
if (ic >= ne11) {
break;
}
// Q*K^T
{
for (short cc = 0; cc < C/8; ++cc) {
simdgroup_float8x8 mqk = make_filled_simdgroup_matrix<float, 8>(0.h);
device const half * pk = (device const half *) ((device const char *) k + ((ic + 8*cc)*nb11 + ik2*nb12 + ik3*nb13));
for (short i = 0; i < D8; ++i) {
simdgroup_half8x8 mk;
simdgroup_load(mk, pk + i*8, nb11/sizeof(half), 0, true); // transpose
simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk);
}
// mqk = mqk*scale + mask*slope
simdgroup_half8x8 mm;
simdgroup_load(mm, mp + ic + 8*cc, nb31/sizeof(half), 0, false);
simdgroup_multiply(mm, mslope, mm);
simdgroup_multiply_accumulate(mqk, mqk, mscale, mm);
simdgroup_store(mqk, ss + 8*cc, TF, 0, false);
}
}
// used to detect blocks full of -INF
float smax = -INFINITY;
// online softmax
{
float ms[Q];
for (short j = 0; j < Q; ++j) {
const short p = tiisg;
const float m = M[j];
const float s = ss[j*TF + p];
smax = simd_max(max(smax, s));
M[j] = simd_max(max(M[j], s));
ms[j] = exp(m - M[j]);
const float vs = exp(s - M[j]);
S[j] = S[j]*ms[j] + simd_sum(vs);
// the P matrix from the paper (Q rows, C columns)
ss[j*TF + p] = vs;
}
// create a QxQ diagonal matrix for rescaling the output
if (tiisg < Q) {
ss[tiisg*TF + C + tiisg] = ms[tiisg];
}
}
// skip -INF blocks
if (smax == -INFINITY) {
continue;
}
// O = diag(ms)*O
{
simdgroup_float8x8 mm;
simdgroup_load(mm, ss + C, TF, 0, false);
for (short i = 0; i < D8; ++i) {
simdgroup_multiply(lo[i], mm, lo[i]);
}
}
// O = O + (Q*K^T)*V
{
for (short cc = 0; cc < C/8; ++cc) {
device const half * pv = (device const half *) ((device const char *) v + ((ic + 8*cc)*nb21 + iv2*nb22 + iv3*nb23));
for (short i = 0; i < D8; ++i) {
simdgroup_half8x8 mk;
simdgroup_load(mk, pv + i*8, nb21/sizeof(half), 0, false);
simdgroup_float8x8 mv;
simdgroup_load(mv, ss + 8*cc, TF, 0, false);
simdgroup_multiply_accumulate(lo[i], mv, mk, lo[i]);
}
}
}
}
// these are needed for reducing the results from the simdgroups (reuse the ss buffer)
for (short j = 0; j < Q; ++j) {
if (tiisg == 0) {
ss[j*TF + 0] = S[j];
ss[j*TF + 1] = M[j];
}
}
}
// reduce the warps sequentially
for (short sg = 1; sg < nsg; ++sg) {
float S = { 0.0h };
float M = { -FLT_MAX/2 };
threadgroup_barrier(mem_flags::mem_threadgroup);
// each simdgroup stores its output to shared memory, reusing sq
if (sgitg == sg) {
for (short i = 0; i < D8; ++i) {
simdgroup_store(lo[i], sq + i*8, T, 0, false);
}
}
threadgroup_barrier(mem_flags::mem_threadgroup);
// the first simdgroup accumulates the results from the other simdgroups
if (sgitg == 0) {
for (short j = 0; j < Q; ++j) {
const float S0 = ss[j*TF + 0];
const float S1 = ss[j*TF + sg*SH + 0];
const float M0 = ss[j*TF + 1];
const float M1 = ss[j*TF + sg*SH + 1];
M = max(M0, M1);
const float ms0 = exp(M0 - M);
const float ms1 = exp(M1 - M);
S = S0*ms0 + S1*ms1;
if (tiisg == 0) {
ss[j*TF + 0] = S;
ss[j*TF + 1] = M;
ss[j*TF + C + j ] = ms0;
ss[j*TF + C + j + sg*SH] = ms1;
}
}
// O_0 = diag(ms0)*O_0 + diag(ms1)*O_1
{
simdgroup_half8x8 t;
simdgroup_float8x8 ms0;
simdgroup_float8x8 ms1;
simdgroup_load(ms0, ss + C, TF, 0, false);
simdgroup_load(ms1, ss + C + sg*SH, TF, 0, false);
for (short i = 0; i < D8; ++i) {
simdgroup_load (t, sq + i*8, T, 0, false);
simdgroup_multiply(t, ms1, t);
simdgroup_multiply_accumulate(lo[i], ms0, lo[i], t);
}
}
}
}
// store result to shared memory (reuse sq)
if (sgitg == 0) {
for (short i = 0; i < D8; ++i) {
simdgroup_store(lo[i], sq + i*8, T, 0, false);
}
}
device float4 * dst4 = (device float4 *) dst;
// final rescale with 1/S and store to global memory
if (sgitg == 0) {
for (short j = 0; j < Q && iq1 + j < ne01; ++j) {
const float S = ss[j*TF + 0];
for (short i = tiisg; i < D4; i += NW) {
dst4[(iq3*ne2*ne1 + iq2 + (iq1 + j)*ne1)*D4 + i] = (float4) sq4[j*T4 + i]/S;
}
}
}
}
template [[host_name("kernel_flash_attn_ext_f16_h64" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<64>;
template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<80>;
template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<96>;
template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<112>;
template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<128>;
template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>;
template<int64_t D, int64_t Q = 1, int64_t C = 32> // head size, queries per threadgroup, cache items per threadgroup
kernel void kernel_flash_attn_ext_vec_f16(
device const char * q,
device const char * k,
device const char * v,
device const char * mask,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant int64_t & ne13,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant uint64_t & nb13,
constant uint64_t & nb31,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant float & scale,
constant float & max_bias,
constant float & m0,
constant float & m1,
constant uint32_t & n_head_log2,
threadgroup half * shared [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]]) {
const short nsg = ntg.y; // number of simdgroups
const short iq3 = tgpig[2];
const short iq2 = tgpig[1];
const short iq1 = tgpig[0];
const short D4 = D/4;
const short NW = N_SIMDWIDTH;
const short SH = (C + Q); // shared memory per simdgroup in (half)
const short T = D + 2*nsg*SH; // shared memory size per query in (half)
float slope = 1.0f;
// ALiBi
if (max_bias > 0.0f) {
const uint32_t h = iq2;
const float base = h < n_head_log2 ? m0 : m1;
const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
slope = pow(base, exp);
}
//threadgroup half * sq = (threadgroup half *) (shared + 0*D); // holds the query data
threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4
threadgroup float * ss = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix
threadgroup float4 * ss4 = (threadgroup float4 *) (shared + 2*sgitg*SH + 1*D); // same as above but in half4
threadgroup half4 * sr4 = (threadgroup half4 *) (shared + sgitg*D + 1*T); // scratch buffer for the results
// store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper)
half4 lo[D4/NW];
// load heads from Q to shared memory
device const float4 * q4 = (device const float4 *) ((device const char *) q + (iq1*nb01 + iq2*nb02 + iq3*nb03));
for (short i = tiisg; i < D4; i += NW) {
if (iq1 < ne01) {
sq4[i] = (half4) q4[i];
} else {
sq4[i] = 0.0h;
}
}
// zero out lo
for (short i = tiisg; i < D4; i += NW) {
lo[i/NW] = 0.0h;
}
// zero out shared memory SH
for (short i = tiisg; i < SH/4; i += NW) {
ss4[i] = 0.0h;
}
threadgroup_barrier(mem_flags::mem_threadgroup);
{
float S = { 0.0h };
float M = { -FLT_MAX/2 };
// assume K and V are same shape
const short ne22 = ne12;
const short ne23 = ne13;
const uint nb21 = nb11;
const uint nb22 = nb12;
const uint nb23 = nb13;
// broadcast
const short rk2 = ne02/ne12;
const short rk3 = ne03/ne13;
const short rv2 = ne02/ne22;
const short rv3 = ne03/ne23;
// k indices
const short ik2 = iq2 / rk2;
const short ik3 = iq3 / rk3;
// v indices
const short iv2 = iq2 / rv2;
const short iv3 = iq3 / rv3;
// load the queries from shared memory into local memory
half4 mq[D4];
for (short ii = 0; ii < D4; ii += NW) {
short i = ii + tiisg;
mq[i] = sq4[i];
}
// pointer to the mask
device const half4 * mp4 = (device const half4 *) (mask + iq1*nb31);
// loop over the KV cache
// each simdgroup handles blocks of Q rows and C columns
for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) {
const int ic = ic0 + C*sgitg;
if (ic >= ne11) {
break;
}
// Q*K^T
{
#pragma unroll
for (short cc = 0; cc < C/4; ++cc) {
float4 mqk = { 0.0h };
device const half4 * pk4 = (device const half4 *) ((device const char *) k + ((ic + 4*cc)*nb11 + ik2*nb12 + ik3*nb13));
#pragma unroll
for (short ii = 0; ii < D4; ii += NW) {
const short i = ii + tiisg;
half4x4 mk;
mk[0] = pk4[i + 0*(nb11/8)];
mk[1] = pk4[i + 1*(nb11/8)];
mk[2] = pk4[i + 2*(nb11/8)];
mk[3] = pk4[i + 3*(nb11/8)];
mqk += (float4) (mq[i] * mk);
}
// reduce the results from the threads in the simdgroup
mqk += simd_shuffle_down(mqk, 16);
mqk += simd_shuffle_down(mqk, 8);
mqk += simd_shuffle_down(mqk, 4);
mqk += simd_shuffle_down(mqk, 2);
mqk += simd_shuffle_down(mqk, 1);
// mqk = mqk*scale + mask*slope
if (tiisg == 0) {
float4 mm = (float4) mp4[ic/4 + cc];
mqk = mqk*scale + mm*slope;
ss4[cc] = mqk;
}
}
}
// online softmax
{
const short p = tiisg;
const float m = M;
const float s = ss[p];
M = simd_max(max(M, s));
const float ms = exp(m - M);
const float vs = exp(s - M);
S = S*ms + simd_sum(vs);
// the P matrix from the paper (Q rows, C columns)
ss[p] = vs;
// O = diag(ms)*O
#pragma unroll
for (short ii = 0; ii < D4; ii += NW) {
const short i = ii + tiisg;
lo[i/NW] *= ms;
}
}
// O = O + (Q*K^T)*V
{
#pragma unroll
for (short cc = 0; cc < C/4; ++cc) {
device const half4 * pv4 = (device const half4 *) ((device const char *) v + ((ic + 4*cc)*nb21 + iv2*nb22 + iv3*nb23));
#pragma unroll
for (short ii = 0; ii < D4; ii += NW) {
const short i = ii + tiisg;
lo[i/NW] += pv4[i + 0*(nb21/8)] * ss[4*cc + 0];
lo[i/NW] += pv4[i + 1*(nb21/8)] * ss[4*cc + 1];
lo[i/NW] += pv4[i + 2*(nb21/8)] * ss[4*cc + 2];
lo[i/NW] += pv4[i + 3*(nb21/8)] * ss[4*cc + 3];
}
}
}
}
// these are needed for reducing the results from the simdgroups (reuse the ss buffer)
if (tiisg == 0) {
ss[0] = S;
ss[1] = M;
}
}
// store results to shared memory
for (short ii = 0; ii < D4; ii += NW) {
short i = ii + tiisg;
sr4[i] = lo[ii/NW];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
// parallel reduce
for (short r = nsg/2; r > 0; r >>= 1) {
if (sgitg < r) {
const float S0 = ss[ 0];
const float S1 = ss[r*SH + 0];
const float M0 = ss[ 1];
const float M1 = ss[r*SH + 1];
const float M = max(M0, M1);
const float ms0 = exp(M0 - M);
const float ms1 = exp(M1 - M);
const float S = S0*ms0 + S1*ms1;
if (tiisg == 0) {
ss[0] = S;
ss[1] = M;
}
// O_0 = diag(ms0)*O_0 + diag(ms1)*O_1
for (short ii = 0; ii < D4; ii += NW) {
short i = ii + tiisg;
sr4[i] = sr4[i]*ms0 + sr4[i + r*D4]*ms1;
}
}
threadgroup_barrier(mem_flags::mem_threadgroup);
}
device float4 * dst4 = (device float4 *) dst;
// final rescale with 1/S and store to global memory
if (sgitg == 0) {
const float S = ss[0];
for (short ii = 0; ii < D4; ii += NW) {
short i = ii + tiisg;
dst4[(iq3*ne2*ne1 + iq2 + (iq1)*ne1)*D4 + i] = (float4) sr4[i]/S;
}
}
}
template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>;
template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>;
kernel void kernel_cpy_f16_f16(
device const half * src0,
device half * dst,
@ -2204,7 +2824,8 @@ kernel void kernel_cpy_f32_f16(
for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
dst_data[i00] = src[0];
// TODO: is there a better way to handle -INFINITY?
dst_data[i00] = src[0] == -INFINITY ? -MAXHALF : src[0];
}
}

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