Merge remote-tracking branch 'upstream/master'
# Resolved Conflicts: # examples/server/README.md # examples/server/server.cpp
This commit is contained in:
Randall Fitzgerald
commit
64a06536cb
37 changed files with 6548 additions and 782 deletions
|
|
@ -11,7 +11,7 @@ shift
|
|||
arg2="$@"
|
||||
|
||||
if [[ $arg1 == '--convert' || $arg1 == '-c' ]]; then
|
||||
python3 ./convert-pth-to-ggml.py $arg2
|
||||
python3 ./convert.py $arg2
|
||||
elif [[ $arg1 == '--quantize' || $arg1 == '-q' ]]; then
|
||||
./quantize $arg2
|
||||
elif [[ $arg1 == '--run' || $arg1 == '-r' ]]; then
|
||||
|
|
@ -32,7 +32,7 @@ else
|
|||
echo " --run (-r): Run a model previously converted into ggml"
|
||||
echo " ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512"
|
||||
echo " --convert (-c): Convert a llama model into ggml"
|
||||
echo " ex: \"/models/7B/\" 1"
|
||||
echo " ex: --outtype f16 \"/models/7B/\" "
|
||||
echo " --quantize (-q): Optimize with quantization process ggml"
|
||||
echo " ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2"
|
||||
echo " --all-in-one (-a): Execute --convert & --quantize"
|
||||
|
|
|
|||
2
.github/workflows/tidy-post.yml
vendored
2
.github/workflows/tidy-post.yml
vendored
|
|
@ -1,7 +1,7 @@
|
|||
name: clang-tidy review post comments
|
||||
|
||||
on:
|
||||
workflow_run:
|
||||
workflow_dispatch:
|
||||
workflows: ["clang-tidy-review"]
|
||||
types:
|
||||
- completed
|
||||
|
|
|
|||
3
.gitignore
vendored
3
.gitignore
vendored
|
|
@ -7,6 +7,7 @@
|
|||
.envrc
|
||||
.swiftpm
|
||||
.venv
|
||||
.clang-tidy
|
||||
.vs/
|
||||
.vscode/
|
||||
|
||||
|
|
@ -17,6 +18,7 @@ build-release/
|
|||
build-static/
|
||||
build-cublas/
|
||||
build-opencl/
|
||||
build-metal/
|
||||
build-no-accel/
|
||||
build-sanitize-addr/
|
||||
build-sanitize-thread/
|
||||
|
|
@ -33,6 +35,7 @@ models/*
|
|||
/benchmark-matmult
|
||||
/vdot
|
||||
/Pipfile
|
||||
/libllama.so
|
||||
|
||||
build-info.h
|
||||
arm_neon.h
|
||||
|
|
|
|||
|
|
@ -71,6 +71,8 @@ option(LLAMA_CUBLAS "llama: use cuBLAS"
|
|||
set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
|
||||
set(LLAMA_CUDA_DMMV_Y "1" CACHE STRING "llama: y block size for dmmv CUDA kernels")
|
||||
option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
|
||||
option(LLAMA_METAL "llama: use Metal" OFF)
|
||||
option(LLAMA_K_QUANTS "llama: use k-quants" ON)
|
||||
|
||||
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
|
||||
|
|
@ -183,7 +185,7 @@ if (LLAMA_CUBLAS)
|
|||
|
||||
enable_language(CUDA)
|
||||
|
||||
set(GGML_CUDA_SOURCES ggml-cuda.cu ggml-cuda.h)
|
||||
set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h)
|
||||
|
||||
add_compile_definitions(GGML_USE_CUBLAS)
|
||||
add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
|
||||
|
|
@ -200,12 +202,41 @@ if (LLAMA_CUBLAS)
|
|||
endif()
|
||||
endif()
|
||||
|
||||
if (LLAMA_METAL)
|
||||
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
|
||||
find_library(METAL_FRAMEWORK Metal REQUIRED)
|
||||
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
|
||||
find_library(METALPERFORMANCE_FRAMEWORK MetalPerformanceShaders REQUIRED)
|
||||
|
||||
set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
|
||||
|
||||
add_compile_definitions(GGML_USE_METAL)
|
||||
add_compile_definitions(GGML_METAL_NDEBUG)
|
||||
|
||||
# get full path to the file
|
||||
#add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
|
||||
|
||||
# copy ggml-metal.metal to bin directory
|
||||
configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)
|
||||
|
||||
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
|
||||
${FOUNDATION_LIBRARY}
|
||||
${METAL_FRAMEWORK}
|
||||
${METALKIT_FRAMEWORK}
|
||||
${METALPERFORMANCE_FRAMEWORK}
|
||||
)
|
||||
endif()
|
||||
|
||||
if (LLAMA_K_QUANTS)
|
||||
set(GGML_SOURCES_EXTRA ${GGML_SOURCES_EXTRA} k_quants.c k_quants.h)
|
||||
endif()
|
||||
|
||||
if (LLAMA_CLBLAST)
|
||||
find_package(CLBlast)
|
||||
if (CLBlast_FOUND)
|
||||
message(STATUS "CLBlast found")
|
||||
|
||||
set(GGML_OPENCL_SOURCES ggml-opencl.cpp ggml-opencl.h)
|
||||
set(GGML_SOURCES_OPENCL ggml-opencl.cpp ggml-opencl.h)
|
||||
|
||||
add_compile_definitions(GGML_USE_CLBLAST)
|
||||
|
||||
|
|
@ -370,8 +401,11 @@ endif()
|
|||
add_library(ggml OBJECT
|
||||
ggml.c
|
||||
ggml.h
|
||||
${GGML_CUDA_SOURCES}
|
||||
${GGML_OPENCL_SOURCES})
|
||||
${GGML_SOURCES_CUDA}
|
||||
${GGML_SOURCES_OPENCL}
|
||||
${GGML_SOURCES_METAL}
|
||||
${GGML_SOURCES_EXTRA}
|
||||
)
|
||||
|
||||
target_include_directories(ggml PUBLIC .)
|
||||
target_compile_features(ggml PUBLIC c_std_11) # don't bump
|
||||
|
|
@ -384,18 +418,22 @@ endif()
|
|||
add_library(llama
|
||||
llama.cpp
|
||||
llama.h
|
||||
llama-util.h)
|
||||
llama-util.h
|
||||
)
|
||||
|
||||
target_include_directories(llama PUBLIC .)
|
||||
target_compile_features(llama PUBLIC cxx_std_11) # don't bump
|
||||
target_link_libraries(llama PRIVATE ggml ${LLAMA_EXTRA_LIBS})
|
||||
target_link_libraries(llama PRIVATE
|
||||
ggml
|
||||
${LLAMA_EXTRA_LIBS}
|
||||
)
|
||||
|
||||
if (BUILD_SHARED_LIBS)
|
||||
set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON)
|
||||
target_compile_definitions(llama PRIVATE LLAMA_SHARED LLAMA_BUILD)
|
||||
endif()
|
||||
|
||||
if (GGML_CUDA_SOURCES)
|
||||
if (GGML_SOURCES_CUDA)
|
||||
message(STATUS "GGML CUDA sources found, configuring CUDA architecture")
|
||||
set_property(TARGET ggml PROPERTY CUDA_ARCHITECTURES OFF)
|
||||
set_property(TARGET ggml PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto")
|
||||
|
|
|
|||
46
Makefile
46
Makefile
|
|
@ -40,8 +40,11 @@ endif
|
|||
#
|
||||
|
||||
# keep standard at C11 and C++11
|
||||
CFLAGS = -I. -O3 -std=c11 -fPIC
|
||||
CXXFLAGS = -I. -I./examples -O3 -std=c++11 -fPIC
|
||||
# -Ofast tends to produce faster code, but may not be available for some compilers.
|
||||
#OPT = -Ofast
|
||||
OPT = -O3
|
||||
CFLAGS = -I. $(OPT) -std=c11 -fPIC
|
||||
CXXFLAGS = -I. -I./examples $(OPT) -std=c++11 -fPIC
|
||||
LDFLAGS =
|
||||
|
||||
ifdef LLAMA_DEBUG
|
||||
|
|
@ -105,6 +108,7 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
|
|||
#CFLAGS += -mfma -mf16c -mavx
|
||||
#CXXFLAGS += -mfma -mf16c -mavx
|
||||
endif
|
||||
|
||||
ifneq ($(filter ppc64%,$(UNAME_M)),)
|
||||
POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
|
||||
ifneq (,$(findstring POWER9,$(POWER9_M)))
|
||||
|
|
@ -116,6 +120,12 @@ ifneq ($(filter ppc64%,$(UNAME_M)),)
|
|||
CXXFLAGS += -std=c++23 -DGGML_BIG_ENDIAN
|
||||
endif
|
||||
endif
|
||||
|
||||
ifndef LLAMA_NO_K_QUANTS
|
||||
CFLAGS += -DGGML_USE_K_QUANTS
|
||||
OBJS += k_quants.o
|
||||
endif
|
||||
|
||||
ifndef LLAMA_NO_ACCELERATE
|
||||
# Mac M1 - include Accelerate framework.
|
||||
# `-framework Accelerate` works on Mac Intel as well, with negliable performance boost (as of the predict time).
|
||||
|
|
@ -123,7 +133,8 @@ ifndef LLAMA_NO_ACCELERATE
|
|||
CFLAGS += -DGGML_USE_ACCELERATE
|
||||
LDFLAGS += -framework Accelerate
|
||||
endif
|
||||
endif
|
||||
endif # LLAMA_NO_ACCELERATE
|
||||
|
||||
ifdef LLAMA_OPENBLAS
|
||||
CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas -I/usr/include/openblas
|
||||
ifneq ($(shell grep -e "Arch Linux" -e "ID_LIKE=arch" /etc/os-release 2>/dev/null),)
|
||||
|
|
@ -131,11 +142,13 @@ ifdef LLAMA_OPENBLAS
|
|||
else
|
||||
LDFLAGS += -lopenblas
|
||||
endif
|
||||
endif
|
||||
endif # LLAMA_OPENBLAS
|
||||
|
||||
ifdef LLAMA_BLIS
|
||||
CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis
|
||||
LDFLAGS += -lblis -L/usr/local/lib
|
||||
endif
|
||||
endif # LLAMA_BLIS
|
||||
|
||||
ifdef LLAMA_CUBLAS
|
||||
CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
|
||||
CXXFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
|
||||
|
|
@ -156,6 +169,7 @@ endif # LLAMA_CUDA_DMMV_Y
|
|||
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
|
||||
$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
|
||||
endif # LLAMA_CUBLAS
|
||||
|
||||
ifdef LLAMA_CLBLAST
|
||||
CFLAGS += -DGGML_USE_CLBLAST
|
||||
CXXFLAGS += -DGGML_USE_CLBLAST
|
||||
|
|
@ -166,28 +180,48 @@ ifdef LLAMA_CLBLAST
|
|||
LDFLAGS += -lclblast -lOpenCL
|
||||
endif
|
||||
OBJS += ggml-opencl.o
|
||||
|
||||
ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
|
||||
$(CXX) $(CXXFLAGS) -c $< -o $@
|
||||
endif
|
||||
endif # LLAMA_CLBLAST
|
||||
|
||||
ifdef LLAMA_METAL
|
||||
CFLAGS += -DGGML_USE_METAL -DGGML_METAL_NDEBUG
|
||||
CXXFLAGS += -DGGML_USE_METAL
|
||||
LDFLAGS += -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
|
||||
OBJS += ggml-metal.o
|
||||
|
||||
ggml-metal.o: ggml-metal.m ggml-metal.h
|
||||
$(CC) $(CFLAGS) -c $< -o $@
|
||||
endif # LLAMA_METAL
|
||||
|
||||
ifneq ($(filter aarch64%,$(UNAME_M)),)
|
||||
# Apple M1, M2, etc.
|
||||
# Raspberry Pi 3, 4, Zero 2 (64-bit)
|
||||
CFLAGS += -mcpu=native
|
||||
CXXFLAGS += -mcpu=native
|
||||
endif
|
||||
|
||||
ifneq ($(filter armv6%,$(UNAME_M)),)
|
||||
# Raspberry Pi 1, Zero
|
||||
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
|
||||
endif
|
||||
|
||||
ifneq ($(filter armv7%,$(UNAME_M)),)
|
||||
# Raspberry Pi 2
|
||||
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
|
||||
endif
|
||||
|
||||
ifneq ($(filter armv8%,$(UNAME_M)),)
|
||||
# Raspberry Pi 3, 4, Zero 2 (32-bit)
|
||||
CFLAGS += -mfp16-format=ieee -mno-unaligned-access
|
||||
endif
|
||||
|
||||
ifdef LLAMA_NO_K_QUANTS
|
||||
k_quants.o: k_quants.c k_quants.h
|
||||
$(CC) $(CFLAGS) -c $< -o $@
|
||||
endif # LLAMA_NO_K_QUANTS
|
||||
|
||||
#
|
||||
# Print build information
|
||||
#
|
||||
|
|
|
|||
57
README.md
57
README.md
|
|
@ -9,9 +9,12 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
|
|||
|
||||
**Hot topics:**
|
||||
|
||||
- Quantization formats `Q4` and `Q8` have changed again (19 May) - [(info)](https://github.com/ggerganov/llama.cpp/pull/1508)
|
||||
- Quantization formats `Q4` and `Q5` have changed - requantize any old models [(info)](https://github.com/ggerganov/llama.cpp/pull/1405)
|
||||
- [Roadmap May 2023](https://github.com/ggerganov/llama.cpp/discussions/1220)
|
||||
- Roadmap June 2023: https://github.com/ggerganov/llama.cpp/discussions/1729
|
||||
- GPU support with Metal (Apple Silicon): https://github.com/ggerganov/llama.cpp/pull/1642
|
||||
- High-quality 2,3,4,5,6-bit quantization: https://github.com/ggerganov/llama.cpp/pull/1684
|
||||
- Multi-GPU support: https://github.com/ggerganov/llama.cpp/pull/1607
|
||||
- Training LLaMA models from scratch: https://github.com/ggerganov/llama.cpp/pull/1652
|
||||
- CPU threading improvements: https://github.com/ggerganov/llama.cpp/pull/1632
|
||||
|
||||
<details>
|
||||
<summary>Table of Contents</summary>
|
||||
|
|
@ -51,11 +54,10 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
|
|||
The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quantization on a MacBook
|
||||
|
||||
- Plain C/C++ implementation without dependencies
|
||||
- Apple silicon first-class citizen - optimized via ARM NEON and Accelerate framework
|
||||
- Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
|
||||
- AVX, AVX2 and AVX512 support for x86 architectures
|
||||
- Mixed F16 / F32 precision
|
||||
- 4-bit, 5-bit and 8-bit integer quantization support
|
||||
- Runs on the CPU
|
||||
- Supports OpenBLAS/Apple BLAS/ARM Performance Lib/ATLAS/BLIS/Intel MKL/NVHPC/ACML/SCSL/SGIMATH and [more](https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors) in BLAS
|
||||
- cuBLAS and CLBlast support
|
||||
|
||||
|
|
@ -236,15 +238,41 @@ In order to build llama.cpp you have three different options.
|
|||
zig build -Drelease-fast
|
||||
```
|
||||
|
||||
### Metal Build
|
||||
|
||||
Using Metal allows the computation to be executed on the GPU for Apple devices:
|
||||
|
||||
- Using `make`:
|
||||
|
||||
```bash
|
||||
LLAMA_METAL=1 make
|
||||
```
|
||||
|
||||
- Using `CMake`:
|
||||
|
||||
```bash
|
||||
mkdir build-metal
|
||||
cd build-metal
|
||||
cmake -DLLAMA_METAL=ON ..
|
||||
cmake --build . --config Release
|
||||
```
|
||||
|
||||
When built with Metal support, you can enable GPU inference with the `--gpu-layers|-ngl` command-line argument.
|
||||
Any value larger than 0 will offload the computation to the GPU. For example:
|
||||
|
||||
```bash
|
||||
./main -m ./models/7B/ggml-model-q4_0.bin -n 128 -ngl 1
|
||||
```
|
||||
|
||||
### BLAS Build
|
||||
|
||||
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:
|
||||
|
||||
- **Accelerate Framework**:
|
||||
- #### Accelerate Framework:
|
||||
|
||||
This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
|
||||
|
||||
- **OpenBLAS**:
|
||||
- #### OpenBLAS:
|
||||
|
||||
This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
|
||||
|
||||
|
|
@ -278,11 +306,11 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
cmake --build . --config Release
|
||||
```
|
||||
|
||||
- **BLIS**
|
||||
- #### BLIS
|
||||
|
||||
Check [BLIS.md](BLIS.md) for more information.
|
||||
|
||||
- **Intel MKL**
|
||||
- #### Intel MKL
|
||||
|
||||
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. You may also specify it by:
|
||||
|
||||
|
|
@ -290,10 +318,10 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
mkdir build
|
||||
cd build
|
||||
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
|
||||
cmake --build . -config Release
|
||||
cmake --build . --config Release
|
||||
```
|
||||
|
||||
- **cuBLAS**
|
||||
- #### cuBLAS
|
||||
|
||||
This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
|
||||
- Using `make`:
|
||||
|
|
@ -310,7 +338,9 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
```
|
||||
Note: Because llama.cpp uses multiple CUDA streams for matrix multiplication results [are not guaranteed to be reproducible](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility). If you need reproducibility, set `GGML_CUDA_MAX_STREAMS` in the file `ggml-cuda.cu` to 1.
|
||||
|
||||
- **CLBlast**
|
||||
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.
|
||||
|
||||
- #### CLBlast
|
||||
|
||||
OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.
|
||||
|
||||
|
|
@ -348,7 +378,7 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
cmake --install . --prefix /some/path
|
||||
```
|
||||
|
||||
Where `/some/path` is where the built library will be installed (default is `/usr/loca`l`).
|
||||
Where `/some/path` is where the built library will be installed (default is `/usr/local`).
|
||||
</details>
|
||||
|
||||
Building:
|
||||
|
|
@ -655,3 +685,4 @@ docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /mode
|
|||
### Docs
|
||||
|
||||
- [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)
|
||||
- [Performance troubleshooting](./docs/token_generation_performance_tips.md)
|
||||
|
|
|
|||
|
|
@ -4,7 +4,9 @@ import argparse
|
|||
|
||||
import convert
|
||||
|
||||
parser = argparse.ArgumentParser(description='Convert a LLaMA model checkpoint to a ggml compatible file')
|
||||
parser = argparse.ArgumentParser(
|
||||
description="""[DEPRECATED - use `convert.py` instead]
|
||||
Convert a LLaMA model checkpoint to a ggml compatible file""")
|
||||
parser.add_argument('dir_model', help='directory containing the model checkpoint')
|
||||
parser.add_argument('ftype', help='file type (0: float32, 1: float16)', type=int, choices=[0, 1], default=1)
|
||||
args = parser.parse_args()
|
||||
|
|
|
|||
40
docs/token_generation_performance_tips.md
Normal file
40
docs/token_generation_performance_tips.md
Normal file
|
|
@ -0,0 +1,40 @@
|
|||
# Token generation performance troubleshooting
|
||||
|
||||
## Verifying that the model is running on the GPU with cuBLAS
|
||||
Make sure you compiled llama with the correct env variables according to [this guide](../README.md#cublas), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
|
||||
```shell
|
||||
./main -m "path/to/model.bin" -ngl 200000 -p "Please sir, may I have some "
|
||||
```
|
||||
|
||||
When running llama, before it starts the inference work, it will output diagnostic information that shows whether cuBLAS is offloading work to the GPU. Look for these lines:
|
||||
```shell
|
||||
llama_model_load_internal: [cublas] offloading 60 layers to GPU
|
||||
llama_model_load_internal: [cublas] offloading output layer to GPU
|
||||
llama_model_load_internal: [cublas] total VRAM used: 17223 MB
|
||||
... rest of inference
|
||||
```
|
||||
|
||||
If you see these lines, then the GPU is being used.
|
||||
|
||||
## Verifying that the CPU is not oversaturated
|
||||
llama accepts a `-t N` (or `--threads N`) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physicial CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down.
|
||||
|
||||
# Example of runtime flags effect on inference speed benchmark
|
||||
These runs were tested on the following machine:
|
||||
GPU: A6000 (48GB VRAM)
|
||||
CPU: 7 physical cores
|
||||
RAM: 32GB
|
||||
|
||||
Model: `TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.ggmlv3.q4_0.bin` (30B parameters, 4bit quantization, GGML)
|
||||
|
||||
Run command: `./main -m "path/to/model.bin" -p "-p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]`
|
||||
|
||||
Result:
|
||||
|
||||
| command | tokens/second (higher is better) |
|
||||
| - | - |
|
||||
| -ngl 2000000 | N/A (less than 0.1) |
|
||||
| -t 7 | 1.7 |
|
||||
| -t 1 -ngl 2000000 | 5.5 |
|
||||
| -t 7 -ngl 2000000 | 8.7 |
|
||||
| -t 4 -ngl 2000000 | 9.1 |
|
||||
|
|
@ -37,7 +37,10 @@ else()
|
|||
add_subdirectory(save-load-state)
|
||||
add_subdirectory(benchmark)
|
||||
add_subdirectory(baby-llama)
|
||||
if(LLAMA_BUILD_SERVER)
|
||||
if (LLAMA_METAL)
|
||||
add_subdirectory(metal)
|
||||
endif()
|
||||
if (LLAMA_BUILD_SERVER)
|
||||
add_subdirectory(server)
|
||||
endif()
|
||||
endif()
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@
|
|||
#include <algorithm>
|
||||
#include <sstream>
|
||||
#include <unordered_set>
|
||||
#include <regex>
|
||||
|
||||
#if defined(__APPLE__) && defined(__MACH__)
|
||||
#include <sys/types.h>
|
||||
|
|
@ -131,6 +132,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
params.path_prompt_cache = argv[i];
|
||||
} else if (arg == "--prompt-cache-all") {
|
||||
params.prompt_cache_all = true;
|
||||
} else if (arg == "--prompt-cache-ro") {
|
||||
params.prompt_cache_ro = true;
|
||||
} else if (arg == "-f" || arg == "--file") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
|
|
@ -295,10 +298,46 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
|
||||
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
|
||||
#endif
|
||||
} else if (arg == "--main-gpu" || arg == "-mg") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
params.main_gpu = std::stoi(argv[i]);
|
||||
#else
|
||||
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
|
||||
#endif
|
||||
} else if (arg == "--tensor-split" || arg == "-ts") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
std::string arg_next = argv[i];
|
||||
|
||||
// split string by , and /
|
||||
const std::regex regex{R"([,/]+)"};
|
||||
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
|
||||
std::vector<std::string> split_arg{it, {}};
|
||||
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
|
||||
|
||||
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
|
||||
if (i < split_arg.size()) {
|
||||
params.tensor_split[i] = std::stof(split_arg[i]);
|
||||
} else {
|
||||
params.tensor_split[i] = 0.0f;
|
||||
}
|
||||
}
|
||||
#else
|
||||
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
|
||||
#endif // GGML_USE_CUBLAS
|
||||
} else if (arg == "--no-mmap") {
|
||||
params.use_mmap = false;
|
||||
} else if (arg == "--mtest") {
|
||||
params.mem_test = true;
|
||||
} else if (arg == "--export") {
|
||||
params.export_cgraph = true;
|
||||
} else if (arg == "--verbose-prompt") {
|
||||
params.verbose_prompt = true;
|
||||
} else if (arg == "-r" || arg == "--reverse-prompt") {
|
||||
|
|
@ -395,6 +434,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|||
fprintf(stderr, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n");
|
||||
fprintf(stderr, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n");
|
||||
fprintf(stderr, " not supported with --interactive or other interactive options\n");
|
||||
fprintf(stderr, " --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n");
|
||||
fprintf(stderr, " --random-prompt start with a randomized prompt.\n");
|
||||
fprintf(stderr, " --in-prefix STRING string to prefix user inputs with (default: empty)\n");
|
||||
fprintf(stderr, " --in-suffix STRING string to suffix after user inputs with (default: empty)\n");
|
||||
|
|
@ -436,8 +476,12 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|||
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
||||
fprintf(stderr, " -ngl N, --n-gpu-layers N\n");
|
||||
fprintf(stderr, " number of layers to store in VRAM\n");
|
||||
fprintf(stderr, " -ts SPLIT --tensor-split SPLIT\n");
|
||||
fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stderr, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" );
|
||||
#endif
|
||||
fprintf(stderr, " --mtest compute maximum memory usage\n");
|
||||
fprintf(stderr, " --export export the computation graph to 'llama.ggml'\n");
|
||||
fprintf(stderr, " --verbose-prompt print prompt before generation\n");
|
||||
fprintf(stderr, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
|
||||
fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
|
||||
|
|
@ -480,7 +524,10 @@ struct llama_context * llama_init_from_gpt_params(const gpt_params & params) {
|
|||
auto lparams = llama_context_default_params();
|
||||
|
||||
lparams.n_ctx = params.n_ctx;
|
||||
lparams.n_batch = params.n_batch;
|
||||
lparams.n_gpu_layers = params.n_gpu_layers;
|
||||
lparams.main_gpu = params.main_gpu;
|
||||
memcpy(lparams.tensor_split, params.tensor_split, LLAMA_MAX_DEVICES*sizeof(float));
|
||||
lparams.seed = params.seed;
|
||||
lparams.f16_kv = params.memory_f16;
|
||||
lparams.use_mmap = params.use_mmap;
|
||||
|
|
|
|||
|
|
@ -28,6 +28,8 @@ struct gpt_params {
|
|||
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
|
||||
int32_t n_keep = 0; // number of tokens to keep from initial prompt
|
||||
int32_t n_gpu_layers = 0; // number of layers to store in VRAM
|
||||
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
|
||||
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
|
||||
|
||||
// sampling parameters
|
||||
std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
|
||||
|
|
@ -60,6 +62,7 @@ struct gpt_params {
|
|||
bool use_color = false; // use color to distinguish generations and inputs
|
||||
bool interactive = false; // interactive mode
|
||||
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
|
||||
|
||||
bool embedding = false; // get only sentence embedding
|
||||
bool interactive_first = false; // wait for user input immediately
|
||||
|
|
@ -71,6 +74,7 @@ struct gpt_params {
|
|||
bool use_mmap = true; // use mmap for faster loads
|
||||
bool use_mlock = false; // use mlock to keep model in memory
|
||||
bool mem_test = false; // compute maximum memory usage
|
||||
bool export_cgraph = false; // export the computation graph
|
||||
bool verbose_prompt = false; // print prompt tokens before generation
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -286,5 +286,7 @@ These options provide extra functionality and customization when running the LLa
|
|||
- `--verbose-prompt`: Print the prompt before generating text.
|
||||
- `--mtest`: Test the model's functionality by running a series of tests to ensure it's working properly.
|
||||
- `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
|
||||
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
|
||||
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
|
||||
- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
|
||||
- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
|
||||
|
|
|
|||
|
|
@ -134,6 +134,13 @@ int main(int argc, char ** argv) {
|
|||
return 0;
|
||||
}
|
||||
|
||||
// export the cgraph and exit
|
||||
if (params.export_cgraph) {
|
||||
llama_eval_export(ctx, "llama.ggml");
|
||||
llama_free(ctx);
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
std::string path_session = params.path_prompt_cache;
|
||||
std::vector<llama_token> session_tokens;
|
||||
|
|
@ -202,6 +209,13 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
// if we will use the cache for the full prompt without reaching the end of the cache, force
|
||||
// reevaluation of the last token token to recalculate the cached logits
|
||||
if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() &&
|
||||
session_tokens.size() > embd_inp.size()) {
|
||||
session_tokens.resize(embd_inp.size() - 1);
|
||||
}
|
||||
|
||||
// number of tokens to keep when resetting context
|
||||
if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct) {
|
||||
params.n_keep = (int)embd_inp.size();
|
||||
|
|
@ -360,12 +374,6 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
if (i > 0) {
|
||||
// check if we've used up all the prompt but not all cached tokens
|
||||
if (embd.size() == i && n_session_consumed < (int) session_tokens.size()) {
|
||||
// force revaluation of the last token to recalculate logits
|
||||
i--;
|
||||
n_past--;
|
||||
}
|
||||
embd.erase(embd.begin(), embd.begin() + i);
|
||||
}
|
||||
}
|
||||
|
|
@ -409,7 +417,7 @@ int main(int argc, char ** argv) {
|
|||
const bool penalize_nl = params.penalize_nl;
|
||||
|
||||
// optionally save the session on first sample (for faster prompt loading next time)
|
||||
if (!path_session.empty() && need_to_save_session) {
|
||||
if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
|
||||
need_to_save_session = false;
|
||||
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
|
||||
}
|
||||
|
|
@ -622,7 +630,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
if (!path_session.empty() && params.prompt_cache_all) {
|
||||
if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
|
||||
fprintf(stderr, "\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
|
||||
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
|
||||
}
|
||||
|
|
|
|||
3
examples/metal/CMakeLists.txt
Normal file
3
examples/metal/CMakeLists.txt
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
set(TEST_TARGET metal)
|
||||
add_executable(${TEST_TARGET} metal.cpp)
|
||||
target_link_libraries(${TEST_TARGET} PRIVATE ggml)
|
||||
102
examples/metal/metal.cpp
Normal file
102
examples/metal/metal.cpp
Normal file
|
|
@ -0,0 +1,102 @@
|
|||
// Evaluate a statically exported ggml computation graph with Metal
|
||||
//
|
||||
// - First, export a LLaMA graph:
|
||||
//
|
||||
// $ ./bin/main -m ../models/7B/ggml-model-q4_0.bin --export
|
||||
//
|
||||
// - Run this tool to evaluate the exported graph:
|
||||
//
|
||||
// $ ./bin/metal llama.ggml
|
||||
//
|
||||
// The purpose of this tool is mostly for debugging and demonstration purposes.
|
||||
// The main limitation of exporting computation graphs is that their sizes are static which often
|
||||
// can be a problem for real-world applications.
|
||||
//
|
||||
|
||||
#include "ggml.h"
|
||||
#include "ggml-metal.h"
|
||||
|
||||
#include <cstdio>
|
||||
#include <cstring>
|
||||
#include <cstdlib>
|
||||
|
||||
int main(int argc, char ** argv) {
|
||||
ggml_time_init();
|
||||
|
||||
if (argc != 2) {
|
||||
fprintf(stderr, "Usage: %s llama.ggml\n", argv[0]);
|
||||
return -1;
|
||||
}
|
||||
|
||||
const char * fname_cgraph = argv[1];
|
||||
|
||||
// load the compute graph
|
||||
struct ggml_context * ctx_data = NULL;
|
||||
struct ggml_context * ctx_eval = NULL;
|
||||
|
||||
struct ggml_cgraph gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval);
|
||||
gf.n_threads = 1;
|
||||
|
||||
// this allocates all Metal resources and memory buffers
|
||||
auto * ctx_metal = ggml_metal_init();
|
||||
|
||||
ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data));
|
||||
ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval));
|
||||
|
||||
// main
|
||||
{
|
||||
struct ggml_tensor * input = ggml_graph_get_tensor(&gf, "embd");
|
||||
*(int32_t *) input->data = 1; // BOS
|
||||
|
||||
ggml_metal_set_tensor(ctx_metal, input);
|
||||
|
||||
// warmup
|
||||
ggml_metal_graph_compute(ctx_metal, &gf);
|
||||
|
||||
const int n_iter = 16;
|
||||
|
||||
const int64_t t0 = ggml_time_us();
|
||||
|
||||
// the actual inference happens here
|
||||
for (int i = 0; i < n_iter; ++i) {
|
||||
ggml_metal_graph_compute(ctx_metal, &gf);
|
||||
}
|
||||
|
||||
const int64_t t1 = ggml_time_us();
|
||||
|
||||
printf("time: %.2f ms, %.2f ms/tok\n", (t1 - t0) / 1000.0, (t1 - t0) / 1000.0 / n_iter);
|
||||
}
|
||||
|
||||
// debug output
|
||||
{
|
||||
struct ggml_tensor * logits = gf.nodes[gf.n_nodes - 1];
|
||||
ggml_metal_get_tensor(ctx_metal, logits);
|
||||
|
||||
float * ptr = (float *) ggml_get_data(logits);
|
||||
|
||||
printf("logits: ");
|
||||
for (int i = 0; i < 10; i++) {
|
||||
printf("%8.4f ", ptr[i]);
|
||||
}
|
||||
printf("\n");
|
||||
int imax = 0;
|
||||
double sum = 0.0;
|
||||
double vmax = -1e9;
|
||||
for (int i = 0; i < 32000; i++) {
|
||||
sum += (double) ptr[i];
|
||||
if (ptr[i] > vmax) {
|
||||
vmax = ptr[i];
|
||||
imax = i;
|
||||
}
|
||||
}
|
||||
printf("sum: %f, imax = %d, vmax = %f\n", sum, imax, vmax);
|
||||
}
|
||||
|
||||
ggml_metal_free(ctx_metal);
|
||||
|
||||
ggml_free(ctx_data);
|
||||
ggml_free(ctx_eval);
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
|
@ -282,8 +282,9 @@ int main(int argc, char ** argv) {
|
|||
break;
|
||||
}
|
||||
int j;
|
||||
for (j = 0; j < GGML_TYPE_COUNT && strcmp(argv[i], ggml_type_name((ggml_type) j)) != 0; j++) {
|
||||
// find match
|
||||
for (j = 0; j < GGML_TYPE_COUNT; ++j) {
|
||||
const auto * name = ggml_type_name((ggml_type) j);
|
||||
if (name && strcmp(argv[i], name) == 0) break;
|
||||
}
|
||||
if (j < GGML_TYPE_COUNT) {
|
||||
params.include_types.push_back((ggml_type) j);
|
||||
|
|
|
|||
|
|
@ -12,6 +12,18 @@ static const std::map<std::string, llama_ftype> LLAMA_FTYPE_MAP = {
|
|||
{"q5_0", LLAMA_FTYPE_MOSTLY_Q5_0},
|
||||
{"q5_1", LLAMA_FTYPE_MOSTLY_Q5_1},
|
||||
{"q8_0", LLAMA_FTYPE_MOSTLY_Q8_0},
|
||||
{"q2_K", LLAMA_FTYPE_MOSTLY_Q2_K},
|
||||
{"q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M},
|
||||
{"q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S},
|
||||
{"q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M},
|
||||
{"q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L},
|
||||
{"q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M},
|
||||
{"q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S},
|
||||
{"q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M},
|
||||
{"q5_K", LLAMA_FTYPE_MOSTLY_Q5_K_M},
|
||||
{"q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S},
|
||||
{"q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M},
|
||||
{"q6_K", LLAMA_FTYPE_MOSTLY_Q6_K},
|
||||
};
|
||||
|
||||
bool try_parse_ftype(const std::string & ftype_str, llama_ftype & ftype, std::string & ftype_str_out) {
|
||||
|
|
|
|||
|
|
@ -9,6 +9,8 @@ Command line options:
|
|||
- `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
|
||||
- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
|
||||
- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
|
||||
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
|
||||
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
|
||||
- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `512`.
|
||||
- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended.
|
||||
- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
|
||||
|
|
|
|||
|
|
@ -433,6 +433,10 @@ void server_print_usage(int /*argc*/, char **argv, const gpt_params ¶ms, con
|
|||
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
||||
fprintf(stderr, " -ngl N, --n-gpu-layers N\n");
|
||||
fprintf(stderr, " number of layers to store in VRAM\n");
|
||||
fprintf(stderr, " -ts SPLIT --tensor-split SPLIT\n");
|
||||
fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stderr, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
|
||||
#endif
|
||||
fprintf(stderr, " -m FNAME, --model FNAME\n");
|
||||
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
|
||||
|
|
@ -521,6 +525,50 @@ void server_params_parse(int argc, char **argv, server_params &sparams,
|
|||
#else
|
||||
fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
|
||||
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
|
||||
#endif
|
||||
}
|
||||
else if (arg == "--tensor-split" || arg == "-ts")
|
||||
{
|
||||
if (++i >= argc)
|
||||
{
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
std::string arg_next = argv[i];
|
||||
|
||||
// split string by , and /
|
||||
const std::regex regex{ R"([,/]+)" };
|
||||
std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
|
||||
std::vector<std::string> split_arg{ it, {} };
|
||||
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
|
||||
|
||||
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i)
|
||||
{
|
||||
if (i < split_arg.size())
|
||||
{
|
||||
params.tensor_split[i] = std::stof(split_arg[i]);
|
||||
}
|
||||
else
|
||||
{
|
||||
params.tensor_split[i] = 0.0f;
|
||||
}
|
||||
}
|
||||
#else
|
||||
fprintf(stderr, "WARNING: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
|
||||
#endif // GGML_USE_CUBLAS
|
||||
}
|
||||
else if (arg == "--main-gpu" || arg == "-mg")
|
||||
{
|
||||
if (++i >= argc)
|
||||
{
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
params.main_gpu = std::stoi(argv[i]);
|
||||
#else
|
||||
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
|
||||
#endif
|
||||
} else if (arg == "--lora") {
|
||||
if (++i >= argc) {
|
||||
|
|
|
|||
30
flake.lock
generated
30
flake.lock
generated
|
|
@ -1,12 +1,15 @@
|
|||
{
|
||||
"nodes": {
|
||||
"flake-utils": {
|
||||
"inputs": {
|
||||
"systems": "systems"
|
||||
},
|
||||
"locked": {
|
||||
"lastModified": 1676283394,
|
||||
"narHash": "sha256-XX2f9c3iySLCw54rJ/CZs+ZK6IQy7GXNY4nSOyu2QG4=",
|
||||
"lastModified": 1685518550,
|
||||
"narHash": "sha256-o2d0KcvaXzTrPRIo0kOLV0/QXHhDQ5DTi+OxcjO8xqY=",
|
||||
"owner": "numtide",
|
||||
"repo": "flake-utils",
|
||||
"rev": "3db36a8b464d0c4532ba1c7dda728f4576d6d073",
|
||||
"rev": "a1720a10a6cfe8234c0e93907ffe81be440f4cef",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
|
|
@ -17,11 +20,11 @@
|
|||
},
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1678470307,
|
||||
"narHash": "sha256-OEeMUr3ueLIXyW/OaFUX5jUdimyQwMg/7e+/Q0gC/QE=",
|
||||
"lastModified": 1685931219,
|
||||
"narHash": "sha256-8EWeOZ6LKQfgAjB/USffUSELPRjw88A+xTcXnOUvO5M=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "0c4800d579af4ed98ecc47d464a5e7b0870c4b1f",
|
||||
"rev": "7409480d5c8584a1a83c422530419efe4afb0d19",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
|
|
@ -36,6 +39,21 @@
|
|||
"flake-utils": "flake-utils",
|
||||
"nixpkgs": "nixpkgs"
|
||||
}
|
||||
},
|
||||
"systems": {
|
||||
"locked": {
|
||||
"lastModified": 1681028828,
|
||||
"narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=",
|
||||
"owner": "nix-systems",
|
||||
"repo": "default",
|
||||
"rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"owner": "nix-systems",
|
||||
"repo": "default",
|
||||
"type": "github"
|
||||
}
|
||||
}
|
||||
},
|
||||
"root": "root",
|
||||
|
|
|
|||
26
flake.nix
26
flake.nix
|
|
@ -6,6 +6,13 @@
|
|||
outputs = { self, nixpkgs, flake-utils }:
|
||||
flake-utils.lib.eachDefaultSystem (system:
|
||||
let
|
||||
inherit (pkgs.stdenv) isAarch64 isDarwin;
|
||||
inherit (pkgs.lib) optionals;
|
||||
isM1 = isAarch64 && isDarwin;
|
||||
osSpecific =
|
||||
if isM1 then with pkgs.darwin.apple_sdk_11_0.frameworks; [ Accelerate MetalKit MetalPerformanceShaders MetalPerformanceShadersGraph ]
|
||||
else if isDarwin then with pkgs.darwin.apple_sdk.frameworks; [ Accelerate CoreGraphics CoreVideo ]
|
||||
else [ ];
|
||||
pkgs = import nixpkgs {
|
||||
inherit system;
|
||||
};
|
||||
|
|
@ -18,17 +25,22 @@
|
|||
packages.default = pkgs.stdenv.mkDerivation {
|
||||
name = "llama.cpp";
|
||||
src = ./.;
|
||||
postPatch =
|
||||
if isM1 then ''
|
||||
substituteInPlace ./ggml-metal.m \
|
||||
--replace '[[NSBundle mainBundle] pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/ggml-metal.metal\";"
|
||||
'' else "";
|
||||
nativeBuildInputs = with pkgs; [ cmake ];
|
||||
buildInputs = with pkgs; lib.optionals stdenv.isDarwin [
|
||||
darwin.apple_sdk.frameworks.Accelerate
|
||||
];
|
||||
cmakeFlags = with pkgs; lib.optionals (system == "aarch64-darwin") [
|
||||
buildInputs = osSpecific;
|
||||
cmakeFlags = [ "-DLLAMA_BUILD_SERVER=ON" ] ++ (optionals isM1 [
|
||||
"-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1"
|
||||
];
|
||||
"-DLLAMA_METAL=ON"
|
||||
]);
|
||||
installPhase = ''
|
||||
mkdir -p $out/bin
|
||||
mv bin/* $out/bin/
|
||||
mv $out/bin/main $out/bin/llama
|
||||
mv $out/bin/server $out/bin/llama-server
|
||||
|
||||
echo "#!${llama-python}/bin/python" > $out/bin/convert.py
|
||||
cat ${./convert.py} >> $out/bin/convert.py
|
||||
|
|
@ -40,9 +52,7 @@
|
|||
packages = with pkgs; [
|
||||
cmake
|
||||
llama-python
|
||||
] ++ lib.optionals stdenv.isDarwin [
|
||||
darwin.apple_sdk.frameworks.Accelerate
|
||||
];
|
||||
] ++ osSpecific;
|
||||
};
|
||||
}
|
||||
);
|
||||
|
|
|
|||
1726
ggml-cuda.cu
1726
ggml-cuda.cu
File diff suppressed because it is too large
Load diff
15
ggml-cuda.h
15
ggml-cuda.h
|
|
@ -1,10 +1,19 @@
|
|||
#pragma once
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
#define GGML_CUDA_MAX_DEVICES 16
|
||||
|
||||
struct ggml_tensor_extra_gpu {
|
||||
void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
|
||||
};
|
||||
|
||||
void ggml_init_cublas(void);
|
||||
void ggml_cuda_set_tensor_split(const float * tensor_split);
|
||||
|
||||
void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
|
|
@ -15,8 +24,12 @@ void ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens
|
|||
void * ggml_cuda_host_malloc(size_t size);
|
||||
void ggml_cuda_host_free(void * ptr);
|
||||
|
||||
void ggml_cuda_transform_tensor(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensors, size_t offset);
|
||||
void ggml_cuda_free_data(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_set_main_device(int main_device);
|
||||
void ggml_cuda_set_scratch_size(size_t scratch_size);
|
||||
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
|
|
|||
63
ggml-metal.h
Normal file
63
ggml-metal.h
Normal file
|
|
@ -0,0 +1,63 @@
|
|||
// An interface allowing to compute ggml_cgraph with Metal
|
||||
//
|
||||
// This is a fully functional interface that extends ggml with GPU support for Apple devices.
|
||||
// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.)
|
||||
//
|
||||
// How it works?
|
||||
//
|
||||
// As long as your program can create and evaluate a ggml_cgraph on the CPU, you can use this
|
||||
// interface to evaluate the same graph on the GPU. Instead of using ggml_graph_compute(), you
|
||||
// use ggml_metal_graph_compute() (or ggml_vulkan_graph_compute(), etc.)
|
||||
//
|
||||
// You only need to make sure that all memory buffers that you used during the graph creation
|
||||
// are mapped to the device memory with the ggml_metal_add_buffer() function. This mapping is
|
||||
// used during the graph evaluation to determine the arguments of the compute kernels.
|
||||
//
|
||||
// Synchronization between device and host memory (for example for input and output tensors)
|
||||
// is done with the ggml_metal_set_tensor() and ggml_metal_get_tensor() functions.
|
||||
//
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <stddef.h>
|
||||
#include <stdbool.h>
|
||||
|
||||
// max memory buffers that can be mapped to the device
|
||||
#define GGML_METAL_MAX_BUFFERS 16
|
||||
|
||||
struct ggml_tensor;
|
||||
struct ggml_cgraph;
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
struct ggml_metal_context;
|
||||
|
||||
struct ggml_metal_context * ggml_metal_init(void);
|
||||
void ggml_metal_free(struct ggml_metal_context * ctx);
|
||||
|
||||
// creates a mapping between a host memory buffer and a device memory buffer
|
||||
// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
|
||||
// - the mapping is used during computation to determine the arguments of the compute kernels
|
||||
// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal
|
||||
//
|
||||
bool ggml_metal_add_buffer(
|
||||
struct ggml_metal_context * ctx,
|
||||
const char * name,
|
||||
void * data,
|
||||
size_t size);
|
||||
|
||||
// set data from host memory into the device
|
||||
void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
|
||||
|
||||
// get data from the device into host memory
|
||||
void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
|
||||
|
||||
// same as ggml_graph_compute but uses Metal
|
||||
void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
||||
691
ggml-metal.m
Normal file
691
ggml-metal.m
Normal file
|
|
@ -0,0 +1,691 @@
|
|||
#import "ggml-metal.h"
|
||||
|
||||
#import "ggml.h"
|
||||
|
||||
#import <Foundation/Foundation.h>
|
||||
|
||||
#import <Metal/Metal.h>
|
||||
#import <MetalPerformanceShaders/MetalPerformanceShaders.h>
|
||||
|
||||
#ifdef GGML_METAL_NDEBUG
|
||||
#define metal_printf(...)
|
||||
#else
|
||||
#define metal_printf(...) fprintf(stderr, __VA_ARGS__)
|
||||
#endif
|
||||
|
||||
#define UNUSED(x) (void)(x)
|
||||
|
||||
struct ggml_metal_buffer {
|
||||
const char * name;
|
||||
|
||||
void * data;
|
||||
size_t size;
|
||||
|
||||
id<MTLBuffer> metal;
|
||||
};
|
||||
|
||||
struct ggml_metal_context {
|
||||
float * logits;
|
||||
|
||||
id<MTLDevice> device;
|
||||
id<MTLCommandQueue> queue;
|
||||
id<MTLLibrary> library;
|
||||
|
||||
int n_buffers;
|
||||
struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
|
||||
|
||||
// custom kernels
|
||||
#define GGML_METAL_DECL_KERNEL(name) \
|
||||
id<MTLFunction> function_##name; \
|
||||
id<MTLComputePipelineState> pipeline_##name
|
||||
|
||||
GGML_METAL_DECL_KERNEL(add);
|
||||
GGML_METAL_DECL_KERNEL(mul);
|
||||
GGML_METAL_DECL_KERNEL(mul_row); // TODO: avoid this extra kernel, instead extend the "mul" kernel to support broadcast
|
||||
GGML_METAL_DECL_KERNEL(scale);
|
||||
GGML_METAL_DECL_KERNEL(silu);
|
||||
GGML_METAL_DECL_KERNEL(relu);
|
||||
GGML_METAL_DECL_KERNEL(soft_max);
|
||||
GGML_METAL_DECL_KERNEL(diag_mask_inf);
|
||||
GGML_METAL_DECL_KERNEL(get_rows_f16);
|
||||
GGML_METAL_DECL_KERNEL(get_rows_q4_0);
|
||||
GGML_METAL_DECL_KERNEL(rms_norm);
|
||||
GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
|
||||
GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
|
||||
GGML_METAL_DECL_KERNEL(rope);
|
||||
GGML_METAL_DECL_KERNEL(cpy_f32_f16);
|
||||
GGML_METAL_DECL_KERNEL(cpy_f32_f32);
|
||||
|
||||
#undef GGML_METAL_DECL_KERNEL
|
||||
};
|
||||
|
||||
// MSL code
|
||||
// TODO: move the contents here when ready
|
||||
// for now it is easier to work in a separate file
|
||||
static NSString * const msl_library_source = @"see metal.metal";
|
||||
|
||||
struct ggml_metal_context * ggml_metal_init(void) {
|
||||
fprintf(stderr, "%s: allocating\n", __func__);
|
||||
|
||||
struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
|
||||
|
||||
ctx->device = MTLCreateSystemDefaultDevice();
|
||||
ctx->queue = [ctx->device newCommandQueue];
|
||||
|
||||
// determine if we can use MPS
|
||||
if (MPSSupportsMTLDevice(ctx->device)) {
|
||||
fprintf(stderr, "%s: using MPS\n", __func__);
|
||||
} else {
|
||||
fprintf(stderr, "%s: not using MPS\n", __func__);
|
||||
GGML_ASSERT(false && "MPS not supported");
|
||||
}
|
||||
|
||||
#if 0
|
||||
// compile from source string and show compile log
|
||||
{
|
||||
NSError * error = nil;
|
||||
|
||||
ctx->library = [ctx->device newLibraryWithSource:msl_library_source options:nil error:&error];
|
||||
if (error) {
|
||||
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
#else
|
||||
UNUSED(msl_library_source);
|
||||
|
||||
// read the source from "ggml-metal.metal" into a string and use newLibraryWithSource
|
||||
{
|
||||
NSError * error = nil;
|
||||
|
||||
//NSString * path = [[NSBundle mainBundle] pathForResource:@"../../examples/metal/metal" ofType:@"metal"];
|
||||
NSString * path = [[NSBundle mainBundle] pathForResource:@"ggml-metal" ofType:@"metal"];
|
||||
fprintf(stderr, "%s: loading '%s'\n", __func__, [path UTF8String]);
|
||||
|
||||
NSString * src = [NSString stringWithContentsOfFile:path encoding:NSUTF8StringEncoding error:&error];
|
||||
if (error) {
|
||||
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
|
||||
exit(1);
|
||||
}
|
||||
|
||||
ctx->library = [ctx->device newLibraryWithSource:src options:nil error:&error];
|
||||
if (error) {
|
||||
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
// load kernels
|
||||
{
|
||||
#define GGML_METAL_ADD_KERNEL(name) \
|
||||
ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \
|
||||
ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:nil]; \
|
||||
fprintf(stderr, "%s: loaded %-32s %16p\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name);
|
||||
|
||||
GGML_METAL_ADD_KERNEL(add);
|
||||
GGML_METAL_ADD_KERNEL(mul);
|
||||
GGML_METAL_ADD_KERNEL(mul_row);
|
||||
GGML_METAL_ADD_KERNEL(scale);
|
||||
GGML_METAL_ADD_KERNEL(silu);
|
||||
GGML_METAL_ADD_KERNEL(relu);
|
||||
GGML_METAL_ADD_KERNEL(soft_max);
|
||||
GGML_METAL_ADD_KERNEL(diag_mask_inf);
|
||||
GGML_METAL_ADD_KERNEL(get_rows_f16);
|
||||
GGML_METAL_ADD_KERNEL(get_rows_q4_0);
|
||||
GGML_METAL_ADD_KERNEL(rms_norm);
|
||||
GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
|
||||
GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
|
||||
GGML_METAL_ADD_KERNEL(rope);
|
||||
GGML_METAL_ADD_KERNEL(cpy_f32_f16);
|
||||
GGML_METAL_ADD_KERNEL(cpy_f32_f32);
|
||||
|
||||
#undef GGML_METAL_ADD_KERNEL
|
||||
}
|
||||
|
||||
return ctx;
|
||||
}
|
||||
|
||||
void ggml_metal_free(struct ggml_metal_context * ctx) {
|
||||
fprintf(stderr, "%s: deallocating\n", __func__);
|
||||
|
||||
free(ctx);
|
||||
}
|
||||
|
||||
// finds the Metal buffer that contains the tensor data on the GPU device
|
||||
// the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
|
||||
// Metal buffer based on the host memory pointer
|
||||
//
|
||||
static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) {
|
||||
//fprintf(stderr, "%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
|
||||
|
||||
for (int i = 0; i < ctx->n_buffers; ++i) {
|
||||
const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data;
|
||||
|
||||
if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) {
|
||||
*offs = (size_t) ioffs;
|
||||
|
||||
//fprintf(stderr, "%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs);
|
||||
|
||||
return ctx->buffers[i].metal;
|
||||
}
|
||||
}
|
||||
|
||||
fprintf(stderr, "%s: error: buffer is nil\n", __func__);
|
||||
|
||||
return nil;
|
||||
}
|
||||
|
||||
bool ggml_metal_add_buffer(
|
||||
struct ggml_metal_context * ctx,
|
||||
const char * name,
|
||||
void * data,
|
||||
size_t size) {
|
||||
if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) {
|
||||
fprintf(stderr, "%s: too many buffers\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
if (data) {
|
||||
// verify that the buffer does not overlap with any of the existing buffers
|
||||
for (int i = 0; i < ctx->n_buffers; ++i) {
|
||||
const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data;
|
||||
|
||||
if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) {
|
||||
fprintf(stderr, "%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
size_t page_size = getpagesize();
|
||||
size_t aligned_size = size;
|
||||
if ((aligned_size % page_size) != 0) {
|
||||
aligned_size += (page_size - (aligned_size % page_size));
|
||||
}
|
||||
|
||||
ctx->buffers[ctx->n_buffers].name = name;
|
||||
ctx->buffers[ctx->n_buffers].data = data;
|
||||
ctx->buffers[ctx->n_buffers].size = size;
|
||||
|
||||
if (ctx->device.maxBufferLength < aligned_size) {
|
||||
fprintf(stderr, "%s: buffer '%s' size %zu is larger than buffer maximum of %zu\n", __func__, name, aligned_size, ctx->device.maxBufferLength);
|
||||
return false;
|
||||
}
|
||||
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:aligned_size options:MTLResourceStorageModeShared deallocator:nil];
|
||||
|
||||
if (ctx->buffers[ctx->n_buffers].metal == nil) {
|
||||
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0);
|
||||
return false;
|
||||
} else {
|
||||
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0);
|
||||
}
|
||||
|
||||
++ctx->n_buffers;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void ggml_metal_set_tensor(
|
||||
struct ggml_metal_context * ctx,
|
||||
struct ggml_tensor * t) {
|
||||
metal_printf("%s: set input for tensor '%s'\n", __func__, t->name);
|
||||
|
||||
size_t offs;
|
||||
id<MTLBuffer> id_dst = ggml_metal_get_buffer(ctx, t, &offs);
|
||||
|
||||
memcpy((void *) ((uint8_t *) id_dst.contents + offs), t->data, ggml_nbytes(t));
|
||||
}
|
||||
|
||||
void ggml_metal_get_tensor(
|
||||
struct ggml_metal_context * ctx,
|
||||
struct ggml_tensor * t) {
|
||||
metal_printf("%s: extract results for tensor '%s'\n", __func__, t->name);
|
||||
|
||||
size_t offs;
|
||||
id<MTLBuffer> id_src = ggml_metal_get_buffer(ctx, t, &offs);
|
||||
|
||||
memcpy(t->data, (void *) ((uint8_t *) id_src.contents + offs), ggml_nbytes(t));
|
||||
}
|
||||
|
||||
void ggml_metal_graph_compute(
|
||||
struct ggml_metal_context * ctx,
|
||||
struct ggml_cgraph * gf) {
|
||||
metal_printf("%s: evaluating graph\n", __func__);
|
||||
|
||||
size_t offs_src0 = 0;
|
||||
size_t offs_src1 = 0;
|
||||
size_t offs_dst = 0;
|
||||
|
||||
id<MTLCommandBuffer> command_buffer = [ctx->queue commandBuffer];
|
||||
id<MTLComputeCommandEncoder> encoder = nil;
|
||||
|
||||
for (int i = 0; i < gf->n_nodes; ++i) {
|
||||
//metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
|
||||
|
||||
struct ggml_tensor * src0 = gf->nodes[i]->src0;
|
||||
struct ggml_tensor * src1 = gf->nodes[i]->src1;
|
||||
struct ggml_tensor * dst = gf->nodes[i];
|
||||
|
||||
const int64_t ne00 = src0 ? src0->ne[0] : 0;
|
||||
const int64_t ne01 = src0 ? src0->ne[1] : 0;
|
||||
const int64_t ne02 = src0 ? src0->ne[2] : 0;
|
||||
const int64_t ne03 = src0 ? src0->ne[3] : 0;
|
||||
|
||||
const uint64_t nb00 = src0 ? src0->nb[0] : 0;
|
||||
const uint64_t nb01 = src0 ? src0->nb[1] : 0;
|
||||
const uint64_t nb02 = src0 ? src0->nb[2] : 0;
|
||||
const uint64_t nb03 = src0 ? src0->nb[3] : 0;
|
||||
|
||||
const int64_t ne10 = src1 ? src1->ne[0] : 0;
|
||||
const int64_t ne11 = src1 ? src1->ne[1] : 0;
|
||||
const int64_t ne12 = src1 ? src1->ne[2] : 0;
|
||||
const int64_t ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);
|
||||
|
||||
const uint64_t nb10 = src1 ? src1->nb[0] : 0;
|
||||
const uint64_t nb11 = src1 ? src1->nb[1] : 0;
|
||||
const uint64_t nb12 = src1 ? src1->nb[2] : 0;
|
||||
const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13);
|
||||
|
||||
const int64_t ne0 = dst ? dst->ne[0] : 0;
|
||||
const int64_t ne1 = dst ? dst->ne[1] : 0;
|
||||
const int64_t ne2 = dst ? dst->ne[2] : 0;
|
||||
const int64_t ne3 = dst ? dst->ne[3] : 0;
|
||||
|
||||
const uint64_t nb0 = dst ? dst->nb[0] : 0;
|
||||
const uint64_t nb1 = dst ? dst->nb[1] : 0;
|
||||
const uint64_t nb2 = dst ? dst->nb[2] : 0;
|
||||
const uint64_t nb3 = dst ? dst->nb[3] : 0;
|
||||
|
||||
const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
|
||||
const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
|
||||
const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT;
|
||||
|
||||
id<MTLBuffer> id_src0 = src0 ? ggml_metal_get_buffer(ctx, src0, &offs_src0) : nil;
|
||||
id<MTLBuffer> id_src1 = src1 ? ggml_metal_get_buffer(ctx, src1, &offs_src1) : nil;
|
||||
id<MTLBuffer> id_dst = dst ? ggml_metal_get_buffer(ctx, dst, &offs_dst) : nil;
|
||||
|
||||
//metal_printf("%s: op - %s\n", __func__, ggml_op_name(dst->op));
|
||||
//if (src0) {
|
||||
// metal_printf("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02,
|
||||
// ggml_is_contiguous(src0), src0->name);
|
||||
//}
|
||||
//if (src1) {
|
||||
// metal_printf("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12,
|
||||
// ggml_is_contiguous(src1), src1->name);
|
||||
//}
|
||||
//if (dst) {
|
||||
// metal_printf("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2,
|
||||
// dst->name);
|
||||
//}
|
||||
|
||||
switch (dst->op) {
|
||||
case GGML_OP_RESHAPE:
|
||||
case GGML_OP_VIEW:
|
||||
case GGML_OP_TRANSPOSE:
|
||||
case GGML_OP_PERMUTE:
|
||||
{
|
||||
// noop
|
||||
} break;
|
||||
case GGML_OP_ADD:
|
||||
{
|
||||
if (encoder == nil) {
|
||||
encoder = [command_buffer computeCommandEncoder];
|
||||
}
|
||||
|
||||
[encoder setComputePipelineState:ctx->pipeline_add];
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
|
||||
|
||||
const int64_t n = ggml_nelements(dst);
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
|
||||
} break;
|
||||
case GGML_OP_MUL:
|
||||
{
|
||||
if (encoder == nil) {
|
||||
encoder = [command_buffer computeCommandEncoder];
|
||||
}
|
||||
|
||||
if (ggml_nelements(src1) == ne10) {
|
||||
// src1 is a row
|
||||
[encoder setComputePipelineState:ctx->pipeline_mul_row];
|
||||
} else {
|
||||
[encoder setComputePipelineState:ctx->pipeline_mul];
|
||||
}
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
|
||||
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
|
||||
|
||||
const int64_t n = ggml_nelements(dst);
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
|
||||
} break;
|
||||
case GGML_OP_SCALE:
|
||||
{
|
||||
if (encoder == nil) {
|
||||
encoder = [command_buffer computeCommandEncoder];
|
||||
}
|
||||
|
||||
const float scale = *(const float *) src1->data;
|
||||
|
||||
[encoder setComputePipelineState:ctx->pipeline_scale];
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
|
||||
[encoder setBytes:&scale length:sizeof(scale) atIndex:2];
|
||||
|
||||
const int64_t n = ggml_nelements(dst);
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
|
||||
} break;
|
||||
case GGML_OP_SILU:
|
||||
{
|
||||
if (encoder == nil) {
|
||||
encoder = [command_buffer computeCommandEncoder];
|
||||
}
|
||||
|
||||
[encoder setComputePipelineState:ctx->pipeline_silu];
|
||||
[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_OP_RELU:
|
||||
{
|
||||
if (encoder == nil) {
|
||||
encoder = [command_buffer computeCommandEncoder];
|
||||
}
|
||||
|
||||
[encoder setComputePipelineState:ctx->pipeline_relu];
|
||||
[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_OP_SOFT_MAX:
|
||||
{
|
||||
if (encoder == nil) {
|
||||
encoder = [command_buffer computeCommandEncoder];
|
||||
}
|
||||
|
||||
const int nth = 32;
|
||||
|
||||
[encoder setComputePipelineState:ctx->pipeline_soft_max];
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
|
||||
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
|
||||
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
|
||||
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
|
||||
[encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
|
||||
} break;
|
||||
case GGML_OP_DIAG_MASK_INF:
|
||||
{
|
||||
if (encoder == nil) {
|
||||
encoder = [command_buffer computeCommandEncoder];
|
||||
}
|
||||
|
||||
const int n_past = ((int32_t *)(src1->data))[0];
|
||||
|
||||
[encoder setComputePipelineState:ctx->pipeline_diag_mask_inf];
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
|
||||
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
|
||||
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
|
||||
[encoder setBytes:&n_past length:sizeof(int) atIndex:4];
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
|
||||
} break;
|
||||
case GGML_OP_MUL_MAT:
|
||||
{
|
||||
// TODO: needs to be updated after PR: https://github.com/ggerganov/ggml/pull/224
|
||||
|
||||
GGML_ASSERT(ne00 == ne10);
|
||||
GGML_ASSERT(ne02 == ne12);
|
||||
|
||||
if (ggml_is_contiguous(src0) &&
|
||||
ggml_is_contiguous(src1) &&
|
||||
(src0t == GGML_TYPE_F32 || src0t == GGML_TYPE_F16) && ne11 > 1) {
|
||||
|
||||
if (encoder != nil) {
|
||||
[encoder endEncoding];
|
||||
encoder = nil;
|
||||
}
|
||||
|
||||
MPSDataType src0dt = src0t == GGML_TYPE_F32 ? MPSDataTypeFloat32 : MPSDataTypeFloat16;
|
||||
MPSDataType src1dt = src1t == GGML_TYPE_F32 ? MPSDataTypeFloat32 : MPSDataTypeFloat16;
|
||||
|
||||
// for F32 x F32 we use MPS
|
||||
MPSMatrixDescriptor * desc0 = [MPSMatrixDescriptor
|
||||
matrixDescriptorWithRows:ne01 columns:ne00 rowBytes:src0->nb[1] dataType:src0dt];
|
||||
|
||||
MPSMatrixDescriptor * desc1 = [MPSMatrixDescriptor
|
||||
matrixDescriptorWithRows:ne11 columns:ne10 rowBytes:src1->nb[1] dataType:src1dt];
|
||||
|
||||
MPSMatrixDescriptor * desc = [MPSMatrixDescriptor
|
||||
matrixDescriptorWithRows:ne1 columns:ne0 rowBytes:dst->nb[1] dataType:MPSDataTypeFloat32];
|
||||
|
||||
MPSMatrixMultiplication * mul = [[MPSMatrixMultiplication alloc]
|
||||
initWithDevice:ctx->device transposeLeft:false transposeRight:true
|
||||
resultRows:ne11 resultColumns:ne01 interiorColumns:ne00 alpha:1.0 beta:0.0];
|
||||
|
||||
// we need to do ne02 multiplications
|
||||
// TODO: is there a way to do this in parallel - currently very slow ..
|
||||
// TODO: might be possible to offload part of the computation to ANE using Accelerate's CBLAS
|
||||
for (int64_t i02 = 0; i02 < ne02; ++i02) {
|
||||
size_t offs_src0_cur = offs_src0 + i02*nb02;
|
||||
size_t offs_src1_cur = offs_src1 + i02*nb12;
|
||||
size_t offs_dst_cur = offs_dst + i02*nb2;
|
||||
|
||||
MPSMatrix * mat_src0 = [[MPSMatrix alloc] initWithBuffer:id_src0 offset:offs_src0_cur descriptor:desc0];
|
||||
MPSMatrix * mat_src1 = [[MPSMatrix alloc] initWithBuffer:id_src1 offset:offs_src1_cur descriptor:desc1];
|
||||
MPSMatrix * mat_dst = [[MPSMatrix alloc] initWithBuffer:id_dst offset:offs_dst_cur descriptor:desc ];
|
||||
|
||||
[mul encodeToCommandBuffer:command_buffer leftMatrix:mat_src1 rightMatrix:mat_src0 resultMatrix:mat_dst];
|
||||
}
|
||||
} else {
|
||||
if (encoder == nil) {
|
||||
encoder = [command_buffer computeCommandEncoder];
|
||||
}
|
||||
|
||||
int nth0 = 32;
|
||||
int nth1 = 1;
|
||||
|
||||
// use custom matrix x vector kernel
|
||||
switch (src0t) {
|
||||
case GGML_TYPE_F16:
|
||||
{
|
||||
GGML_ASSERT(ne02 == ne12);
|
||||
|
||||
nth0 = 64;
|
||||
nth1 = 1;
|
||||
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
|
||||
} break;
|
||||
case GGML_TYPE_Q4_0:
|
||||
{
|
||||
GGML_ASSERT(ne02 == 1);
|
||||
GGML_ASSERT(ne12 == 1);
|
||||
|
||||
nth0 = 8;
|
||||
nth1 = 4;
|
||||
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32];
|
||||
} break;
|
||||
default: GGML_ASSERT(false && "not implemented");
|
||||
};
|
||||
|
||||
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
|
||||
[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:&nb00 length:sizeof(nb00) atIndex:5];
|
||||
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:6];
|
||||
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:7];
|
||||
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:8];
|
||||
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:9];
|
||||
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:10];
|
||||
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11];
|
||||
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12];
|
||||
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13];
|
||||
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14];
|
||||
|
||||
if (src0t == GGML_TYPE_Q4_0) {
|
||||
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
} else {
|
||||
[encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
|
||||
}
|
||||
}
|
||||
} break;
|
||||
case GGML_OP_GET_ROWS:
|
||||
{
|
||||
if (encoder == nil) {
|
||||
encoder = [command_buffer computeCommandEncoder];
|
||||
}
|
||||
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break;
|
||||
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
|
||||
default: GGML_ASSERT(false && "not implemented");
|
||||
}
|
||||
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
|
||||
[encoder setBytes:&(src0->ne[0]) length:sizeof( int64_t) atIndex:3];
|
||||
[encoder setBytes:&(src0->nb[1]) length:sizeof(uint64_t) atIndex:4];
|
||||
[encoder setBytes:&(dst->nb[1]) length:sizeof(uint64_t) atIndex:5];
|
||||
|
||||
const int64_t n = ggml_nelements(src1);
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
|
||||
} break;
|
||||
case GGML_OP_RMS_NORM:
|
||||
{
|
||||
if (encoder == nil) {
|
||||
encoder = [command_buffer computeCommandEncoder];
|
||||
}
|
||||
|
||||
const float eps = 1e-6f;
|
||||
|
||||
const int nth = 256;
|
||||
|
||||
[encoder setComputePipelineState:ctx->pipeline_rms_norm];
|
||||
[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:&nb01 length:sizeof(uint64_t) atIndex:3];
|
||||
[encoder setBytes:&eps length:sizeof( float) atIndex:4];
|
||||
[encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];
|
||||
|
||||
const int64_t nrows = ggml_nrows(src0);
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
|
||||
} break;
|
||||
case GGML_OP_ROPE:
|
||||
{
|
||||
if (encoder == nil) {
|
||||
encoder = [command_buffer computeCommandEncoder];
|
||||
}
|
||||
|
||||
const int n_dims = ((int32_t *) src1->data)[1];
|
||||
const int mode = ((int32_t *) src1->data)[2];
|
||||
|
||||
const int n_past = ((int32_t *)(src1->data))[0];
|
||||
|
||||
[encoder setComputePipelineState:ctx->pipeline_rope];
|
||||
[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:&n_past length:sizeof( int) atIndex:18];
|
||||
[encoder setBytes:&n_dims length:sizeof( int) atIndex:19];
|
||||
[encoder setBytes:&mode length:sizeof( int) atIndex:20];
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
|
||||
} break;
|
||||
case GGML_OP_CPY:
|
||||
{
|
||||
if (encoder == nil) {
|
||||
encoder = [command_buffer computeCommandEncoder];
|
||||
}
|
||||
|
||||
const int nth = 32;
|
||||
|
||||
switch (src0t) {
|
||||
case GGML_TYPE_F32:
|
||||
{
|
||||
switch (dstt) {
|
||||
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f16]; break;
|
||||
case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32]; break;
|
||||
default: GGML_ASSERT(false && "not implemented");
|
||||
};
|
||||
} break;
|
||||
default: GGML_ASSERT(false && "not implemented");
|
||||
}
|
||||
|
||||
[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 dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
|
||||
} break;
|
||||
default:
|
||||
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
}
|
||||
|
||||
if (encoder != nil) {
|
||||
[encoder endEncoding];
|
||||
encoder = nil;
|
||||
}
|
||||
|
||||
[command_buffer commit];
|
||||
[command_buffer waitUntilCompleted];
|
||||
|
||||
{
|
||||
const double time_elapsed = [command_buffer GPUEndTime] - [command_buffer GPUStartTime];
|
||||
UNUSED(time_elapsed);
|
||||
|
||||
metal_printf("%s: time elapsed = %f ms\n", __func__, time_elapsed * 1000.0);
|
||||
}
|
||||
}
|
||||
505
ggml-metal.metal
Normal file
505
ggml-metal.metal
Normal file
|
|
@ -0,0 +1,505 @@
|
|||
#include <metal_stdlib>
|
||||
|
||||
using namespace metal;
|
||||
|
||||
#define MAX(x, y) ((x) > (y) ? (x) : (y))
|
||||
|
||||
#define QK4_0 32
|
||||
#define QR4_0 2
|
||||
typedef struct {
|
||||
half d; // delta
|
||||
uint8_t qs[QK4_0 / 2]; // nibbles / quants
|
||||
} block_q4_0;
|
||||
|
||||
static void dequantize_row_q4_0(device const block_q4_0 * x, device float * y, int k) {
|
||||
const int qk = QK4_0;
|
||||
|
||||
assert(k % qk == 0);
|
||||
|
||||
const int nb = k / qk;
|
||||
|
||||
for (int i = 0; i < nb; i++) {
|
||||
const half d = x[i].d;
|
||||
|
||||
for (int j = 0; j < qk/2; ++j) {
|
||||
const int x0 = (x[i].qs[j] & 0x0F) - 8;
|
||||
const int x1 = (x[i].qs[j] >> 4) - 8;
|
||||
|
||||
y[i*qk + j + 0 ] = x0*d;
|
||||
y[i*qk + j + qk/2] = x1*d;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_add(
|
||||
device const float * src0,
|
||||
device const float * src1,
|
||||
device float * dst,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
dst[tpig] = src0[tpig] + src1[tpig];
|
||||
}
|
||||
|
||||
kernel void kernel_mul(
|
||||
device const float * src0,
|
||||
device const float * src1,
|
||||
device float * dst,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
dst[tpig] = src0[tpig] * src1[tpig];
|
||||
}
|
||||
|
||||
// assumption: src1 is a row
|
||||
// broadcast src1 into src0
|
||||
kernel void kernel_mul_row(
|
||||
device const float * src0,
|
||||
device const float * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
dst[tpig] = src0[tpig] * src1[tpig % ne00];
|
||||
}
|
||||
|
||||
kernel void kernel_scale(
|
||||
device const float * src0,
|
||||
device float * dst,
|
||||
constant float & scale,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
dst[tpig] = src0[tpig] * scale;
|
||||
}
|
||||
|
||||
kernel void kernel_silu(
|
||||
device const float * src0,
|
||||
device float * dst,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
float x = src0[tpig];
|
||||
dst[tpig] = x / (1.0f + exp(-x));
|
||||
}
|
||||
|
||||
kernel void kernel_relu(
|
||||
device const float * src0,
|
||||
device float * dst,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
dst[tpig] = max(0.0f, src0[tpig]);
|
||||
}
|
||||
|
||||
kernel void kernel_soft_max(
|
||||
device const float * src0,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
threadgroup float * buf [[threadgroup(0)]],
|
||||
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];
|
||||
|
||||
device const float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
|
||||
device float * pdst = dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
|
||||
|
||||
// parallel max
|
||||
buf[tpitg[0]] = -INFINITY;
|
||||
for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
|
||||
buf[tpitg[0]] = MAX(buf[tpitg[0]], psrc0[i00]);
|
||||
}
|
||||
|
||||
// reduce
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
for (uint i = ntg[0]/2; i > 0; i /= 2) {
|
||||
if (tpitg[0] < i) {
|
||||
buf[tpitg[0]] = MAX(buf[tpitg[0]], buf[tpitg[0] + i]);
|
||||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
}
|
||||
|
||||
// broadcast
|
||||
if (tpitg[0] == 0) {
|
||||
buf[0] = buf[0];
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
const float max = buf[0];
|
||||
|
||||
// parallel sum
|
||||
buf[tpitg[0]] = 0.0f;
|
||||
for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
|
||||
buf[tpitg[0]] += exp(psrc0[i00] - max);
|
||||
}
|
||||
|
||||
// reduce
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
for (uint i = ntg[0]/2; i > 0; i /= 2) {
|
||||
if (tpitg[0] < i) {
|
||||
buf[tpitg[0]] += buf[tpitg[0] + i];
|
||||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
}
|
||||
|
||||
// broadcast
|
||||
if (tpitg[0] == 0) {
|
||||
buf[0] = buf[0];
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
const float sum = buf[0];
|
||||
|
||||
for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) {
|
||||
pdst[i00] = exp(psrc0[i00] - max) / sum;
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_diag_mask_inf(
|
||||
device const float * src0,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int & n_past,
|
||||
uint3 tpig[[thread_position_in_grid]]) {
|
||||
const int64_t i02 = tpig[2];
|
||||
const int64_t i01 = tpig[1];
|
||||
const int64_t i00 = tpig[0];
|
||||
|
||||
if (i00 > n_past + i01) {
|
||||
dst[i02*ne01*ne00 + i01*ne00 + i00] = -INFINITY;
|
||||
} else {
|
||||
dst[i02*ne01*ne00 + i01*ne00 + i00] = src0[i02*ne01*ne00 + i01*ne00 + i00];
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_get_rows_f16(
|
||||
device const void * src0,
|
||||
device const int * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb1,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
const int i = tpig;
|
||||
const int r = ((device int32_t *) src1)[i];
|
||||
|
||||
for (int j = 0; j < ne00; j++) {
|
||||
dst[i*nb1 + j] = ((device half *) ((device char *) src0 + r*nb01))[j];
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_get_rows_q4_0(
|
||||
device const void * src0,
|
||||
device const int * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb1,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
const int i = tpig;
|
||||
const int r = ((device int32_t *) src1)[i];
|
||||
|
||||
dequantize_row_q4_0(
|
||||
(device const block_q4_0 *) ((device char *) src0 + r*nb01),
|
||||
(device float *) ((device char *) dst + i*nb1), ne00);
|
||||
}
|
||||
|
||||
kernel void kernel_rms_norm(
|
||||
device const void * src0,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant uint64_t & nb01,
|
||||
constant float & eps,
|
||||
threadgroup float * sum [[threadgroup(0)]],
|
||||
uint tgpig[[threadgroup_position_in_grid]],
|
||||
uint tpitg[[thread_position_in_threadgroup]],
|
||||
uint ntg[[threads_per_threadgroup]]) {
|
||||
device const float * x = (device const float *) ((device const char *) src0 + tgpig*nb01);
|
||||
|
||||
// parallel sum
|
||||
sum[tpitg] = 0.0f;
|
||||
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
|
||||
sum[tpitg] += x[i00] * x[i00];
|
||||
}
|
||||
|
||||
// reduce
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
for (uint i = ntg/2; i > 0; i /= 2) {
|
||||
if (tpitg < i) {
|
||||
sum[tpitg] += sum[tpitg + i];
|
||||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
}
|
||||
|
||||
// broadcast
|
||||
if (tpitg == 0) {
|
||||
sum[0] /= ne00;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
const float mean = sum[0];
|
||||
const float scale = 1.0f/sqrt(mean + eps);
|
||||
|
||||
device float * y = dst + tgpig*ne00;
|
||||
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
|
||||
y[i00] = x[i00] * scale;
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_mul_mat_q4_0_f32(
|
||||
device const void * src0,
|
||||
device const float * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant uint64_t & nb00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb02,
|
||||
constant int64_t & ne10,
|
||||
constant int64_t & ne11,
|
||||
constant uint64_t & nb10,
|
||||
constant uint64_t & nb11,
|
||||
constant uint64_t & nb12,
|
||||
constant int64_t & ne0,
|
||||
constant int64_t & ne1,
|
||||
threadgroup float * sum [[threadgroup(0)]],
|
||||
uint2 tgpig[[threadgroup_position_in_grid]],
|
||||
uint2 tpig[[thread_position_in_grid]],
|
||||
uint2 tpitg[[thread_position_in_threadgroup]],
|
||||
uint2 tptg[[threads_per_threadgroup]]) {
|
||||
const int nb = ne00/QK4_0;
|
||||
|
||||
const int64_t r0 = tgpig.x;
|
||||
const int64_t r1 = tgpig.y;
|
||||
|
||||
device const block_q4_0 * x = (device const block_q4_0 *) src0 + r0*nb;
|
||||
device const float * y = (device const float *) src1 + r1*ne10;
|
||||
|
||||
const uint nth = tptg.x*tptg.y;
|
||||
const uint ith = tptg.y*tpitg.x + tpitg.y;
|
||||
|
||||
sum[ith] = 0.0f;
|
||||
|
||||
for (int i = tpitg.x; i < nb; i += tptg.x) {
|
||||
device const uchar4 * x0p = (device const uchar4 *) (x + i)->qs;
|
||||
device const float4 * y0p = (device const float4 *) (y + i*QK4_0);
|
||||
|
||||
const float d = (float)((x + i)->d);
|
||||
|
||||
const uchar4 x0v = *(x0p + tpitg.y);
|
||||
const float4 y0v = *(y0p + tpitg.y + 0);
|
||||
const float4 y1v = *(y0p + tpitg.y + 4);
|
||||
|
||||
float acc = 0.0f;
|
||||
|
||||
for (int j = 0; j < 4; ++j) {
|
||||
const int x0 = x0v[j] & 0x0F;
|
||||
const int x1 = x0v[j] >> 4;
|
||||
|
||||
const float y0 = y0v[j];
|
||||
const float y1 = y1v[j];
|
||||
|
||||
acc += (x0 - 8)*y0 + (x1 - 8)*y1;
|
||||
}
|
||||
|
||||
sum[ith] += acc*d;
|
||||
}
|
||||
|
||||
// accumulate the sum from all threads in the threadgroup
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
for (uint i = nth/2; i > 0; i /= 2) {
|
||||
if (ith < i) {
|
||||
sum[ith] += sum[ith + i];
|
||||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
}
|
||||
|
||||
if (ith == 0) {
|
||||
dst[r1*ne0 + r0] = sum[0];
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_mul_mat_f16_f32(
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant uint64_t & nb00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb02,
|
||||
constant int64_t & ne10,
|
||||
constant int64_t & ne11,
|
||||
constant uint64_t & nb10,
|
||||
constant uint64_t & nb11,
|
||||
constant uint64_t & nb12,
|
||||
constant int64_t & ne0,
|
||||
constant int64_t & ne1,
|
||||
threadgroup float * sum [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpig[[thread_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 tptg[[threads_per_threadgroup]]) {
|
||||
const int64_t r0 = tgpig.x;
|
||||
const int64_t r1 = tgpig.y;
|
||||
const int64_t im = tgpig.z;
|
||||
|
||||
device const half * x = (device const half *) (src0 + r0*nb01 + im*nb02);
|
||||
device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
|
||||
|
||||
sum[tpitg.x] = 0.0f;
|
||||
|
||||
for (int i = tpitg.x; i < ne00; i += tptg.x) {
|
||||
sum[tpitg.x] += (float) x[i] * (float) y[i];
|
||||
}
|
||||
|
||||
// accumulate the sum from all threads in the threadgroup
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
for (uint i = tptg.x/2; i > 0; i /= 2) {
|
||||
if (tpitg.x < i) {
|
||||
sum[tpitg.x] += sum[tpitg.x + i];
|
||||
}
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
}
|
||||
|
||||
if (tpitg.x == 0) {
|
||||
dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0];
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_rope(
|
||||
device const void * 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 int & n_past,
|
||||
constant int & n_dims,
|
||||
constant int & mode,
|
||||
uint3 tpig[[thread_position_in_grid]]) {
|
||||
const int64_t i3 = tpig[2];
|
||||
const int64_t i2 = tpig[1];
|
||||
const int64_t i1 = tpig[0];
|
||||
|
||||
const bool is_neox = mode & 2;
|
||||
const float theta_scale = pow(10000.0, -2.0f/n_dims);
|
||||
|
||||
const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
|
||||
|
||||
float theta = (float)p;
|
||||
|
||||
if (!is_neox) {
|
||||
for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
|
||||
const float cos_theta = cos(theta);
|
||||
const float sin_theta = sin(theta);
|
||||
|
||||
theta *= theta_scale;
|
||||
|
||||
device const float * const src = (device float *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
|
||||
device float * dst_data = (device float *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
|
||||
const float x0 = src[0];
|
||||
const float x1 = src[1];
|
||||
|
||||
dst_data[0] = x0*cos_theta - x1*sin_theta;
|
||||
dst_data[1] = x0*sin_theta + x1*cos_theta;
|
||||
}
|
||||
} else {
|
||||
// TODO: implement
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_cpy_f32_f16(
|
||||
device const float * src0,
|
||||
device half * 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,
|
||||
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);
|
||||
|
||||
device half * dst_data = (device half *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
|
||||
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];
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_cpy_f32_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,
|
||||
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);
|
||||
|
||||
device float * dst_data = (device float *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
|
||||
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];
|
||||
}
|
||||
}
|
||||
257
ggml-opencl.cpp
257
ggml-opencl.cpp
|
|
@ -3,6 +3,8 @@
|
|||
#include <array>
|
||||
#include <atomic>
|
||||
#include <sstream>
|
||||
#include <vector>
|
||||
#include <limits>
|
||||
|
||||
#define CL_TARGET_OPENCL_VERSION 110
|
||||
#include <clblast.h>
|
||||
|
|
@ -197,6 +199,18 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float
|
|||
}
|
||||
);
|
||||
|
||||
std::string mul_template = MULTILINE_QUOTE(
|
||||
__kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y, const int y_offset, __global TYPE* dst, const int dst_offset, const int ky) {
|
||||
const int i = get_group_id(0)*get_local_size(0) + get_local_id(0);
|
||||
|
||||
if (i >= get_global_size(0)) {
|
||||
return;
|
||||
}
|
||||
|
||||
dst[dst_offset + i] = x[x_offset + i] * y[y_offset + i%ky];
|
||||
}
|
||||
);
|
||||
|
||||
#define CL_CHECK(err) \
|
||||
do { \
|
||||
cl_int err_ = (err); \
|
||||
|
|
@ -239,6 +253,13 @@ std::array<std::string, 30> dequant_mul_mat_vec_str_values = {
|
|||
"convert_mul_mat_vec_f16", "half", "1", "1", "convert_f16"
|
||||
};
|
||||
|
||||
std::array<std::string, 2> mul_str_keys = {
|
||||
"KERNEL_NAME", "TYPE"
|
||||
};
|
||||
std::array<std::string, 2> mul_str_values = {
|
||||
"mul_f32", "float"
|
||||
};
|
||||
|
||||
std::string& replace(std::string& s, const std::string& from, const std::string& to) {
|
||||
size_t pos = 0;
|
||||
while ((pos = s.find(from, pos)) != std::string::npos) {
|
||||
|
|
@ -261,6 +282,13 @@ std::string generate_kernels() {
|
|||
src << dequant_kernel << '\n';
|
||||
src << dmmv_kernel << '\n';
|
||||
}
|
||||
for (size_t i = 0; i < mul_str_values.size(); i += mul_str_keys.size()) {
|
||||
std::string mul_kernel = mul_template;
|
||||
for (size_t j = 0; j < mul_str_keys.size(); j++) {
|
||||
replace(mul_kernel, mul_str_keys[j], mul_str_values[i + j]);
|
||||
}
|
||||
src << mul_kernel << '\n';
|
||||
}
|
||||
return src.str();
|
||||
}
|
||||
|
||||
|
|
@ -272,6 +300,7 @@ static cl_program program;
|
|||
static cl_kernel convert_row_f16_cl;
|
||||
static cl_kernel dequantize_row_q4_0_cl, dequantize_row_q4_1_cl, dequantize_row_q5_0_cl, dequantize_row_q5_1_cl, dequantize_row_q8_0_cl;
|
||||
static cl_kernel dequantize_mul_mat_vec_q4_0_cl, dequantize_mul_mat_vec_q4_1_cl, dequantize_mul_mat_vec_q5_0_cl, dequantize_mul_mat_vec_q5_1_cl, dequantize_mul_mat_vec_q8_0_cl, convert_mul_mat_vec_f16_cl;
|
||||
static cl_kernel mul_f32_cl;
|
||||
static bool fp16_support;
|
||||
|
||||
static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
|
||||
|
|
@ -508,6 +537,9 @@ void ggml_cl_init(void) {
|
|||
CL_CHECK((dequantize_mul_mat_vec_q5_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_1", &err), err));
|
||||
CL_CHECK((dequantize_mul_mat_vec_q8_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q8_0", &err), err));
|
||||
CL_CHECK((convert_mul_mat_vec_f16_cl = clCreateKernel(program, "convert_mul_mat_vec_f16", &err), err));
|
||||
|
||||
// mul kernel
|
||||
CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err));
|
||||
}
|
||||
|
||||
static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
|
||||
|
|
@ -573,21 +605,44 @@ struct cl_buffer {
|
|||
static cl_buffer g_cl_buffer_pool[MAX_CL_BUFFERS];
|
||||
static std::atomic_flag g_cl_pool_lock = ATOMIC_FLAG_INIT;
|
||||
|
||||
static cl_mem ggml_cl_pool_malloc(size_t size, size_t * actual_size, cl_mem_flags flags) {
|
||||
static cl_mem ggml_cl_pool_malloc(size_t size, size_t * actual_size) {
|
||||
scoped_spin_lock lock(g_cl_pool_lock);
|
||||
cl_int err;
|
||||
|
||||
int best_i = -1;
|
||||
size_t best_size = std::numeric_limits<size_t>::max(); //smallest unused buffer that fits our needs
|
||||
int worst_i = -1;
|
||||
size_t worst_size = 0; //largest unused buffer seen so far
|
||||
for (int i = 0; i < MAX_CL_BUFFERS; ++i) {
|
||||
cl_buffer& b = g_cl_buffer_pool[i];
|
||||
if (b.size > 0 && b.size >= size) {
|
||||
cl_buffer &b = g_cl_buffer_pool[i];
|
||||
if (b.size > 0 && b.size >= size && b.size < best_size)
|
||||
{
|
||||
best_i = i;
|
||||
best_size = b.size;
|
||||
}
|
||||
if (b.size > 0 && b.size > worst_size)
|
||||
{
|
||||
worst_i = i;
|
||||
worst_size = b.size;
|
||||
}
|
||||
}
|
||||
if(best_i!=-1) //found the smallest buffer that fits our needs
|
||||
{
|
||||
cl_buffer& b = g_cl_buffer_pool[best_i];
|
||||
cl_mem mem = b.mem;
|
||||
*actual_size = b.size;
|
||||
b.size = 0;
|
||||
return mem;
|
||||
}
|
||||
if(worst_i!=-1) //no buffer that fits our needs, resize largest one to save memory
|
||||
{
|
||||
cl_buffer& b = g_cl_buffer_pool[worst_i];
|
||||
cl_mem mem = b.mem;
|
||||
b.size = 0;
|
||||
clReleaseMemObject(mem);
|
||||
}
|
||||
cl_mem mem;
|
||||
CL_CHECK((mem = clCreateBuffer(context, flags, size, NULL, &err), err));
|
||||
CL_CHECK((mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err), err));
|
||||
*actual_size = size;
|
||||
return mem;
|
||||
}
|
||||
|
|
@ -644,6 +699,99 @@ static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t o
|
|||
return err;
|
||||
}
|
||||
|
||||
static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
||||
GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
|
||||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t ne01 = src0->ne[1];
|
||||
const int64_t ne02 = src0->ne[2];
|
||||
const int64_t ne03 = src0->ne[2];
|
||||
const int64_t ne0 = ne00 * ne01 * ne02 * ne03;
|
||||
const int64_t ne10 = src1->ne[0];
|
||||
const int64_t ne11 = src1->ne[1];
|
||||
const int64_t ne12 = src1->ne[2];
|
||||
const int64_t ne13 = src1->ne[3];
|
||||
const int64_t nb10 = src1->nb[0];
|
||||
const int nb2 = dst->nb[2];
|
||||
const int nb3 = dst->nb[3];
|
||||
size_t x_size;
|
||||
size_t d_size;
|
||||
|
||||
cl_mem d_X = ggml_cl_pool_malloc(ne0 * sizeof(float), &x_size); // src0
|
||||
cl_mem d_Y = (cl_mem) src1->data; // src1 is already on device, broadcasted.
|
||||
cl_mem d_D = ggml_cl_pool_malloc(ne0 * sizeof(float), &d_size); // dst
|
||||
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
const int i0 = i03*ne02 + i02;
|
||||
|
||||
cl_event ev;
|
||||
|
||||
// copy src0 to device
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, i0, src0, i03, i02, &ev));
|
||||
|
||||
if (nb10 == sizeof(float)) {
|
||||
// Contiguous, avoid overhead from queueing many kernel runs
|
||||
const int64_t i13 = i03%ne13;
|
||||
const int64_t i12 = i02%ne12;
|
||||
const int i1 = i13*ne12*ne11 + i12*ne11;
|
||||
|
||||
cl_int x_offset = 0;
|
||||
cl_int y_offset = i1*ne10;
|
||||
cl_int d_offset = 0;
|
||||
|
||||
size_t global = ne00 * ne01;
|
||||
cl_int ky = ne10;
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X));
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset));
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y));
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset));
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D));
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset));
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky));
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
|
||||
} else {
|
||||
for (int64_t i01 = 0; i01 < ne01; i01++) {
|
||||
const int64_t i13 = i03%ne13;
|
||||
const int64_t i12 = i02%ne12;
|
||||
const int64_t i11 = i01%ne11;
|
||||
const int i1 = i13*ne12*ne11 + i12*ne11 + i11;
|
||||
|
||||
cl_int x_offset = i01*ne00;
|
||||
cl_int y_offset = i1*ne10;
|
||||
cl_int d_offset = i01*ne00;
|
||||
|
||||
// compute
|
||||
size_t global = ne00;
|
||||
cl_int ky = ne10;
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X));
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset));
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y));
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset));
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D));
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset));
|
||||
CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky));
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
|
||||
}
|
||||
}
|
||||
|
||||
CL_CHECK(clReleaseEvent(ev));
|
||||
CL_CHECK(clFinish(queue));
|
||||
|
||||
// copy dst to host
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL));
|
||||
}
|
||||
}
|
||||
ggml_cl_pool_free(d_X, x_size);
|
||||
ggml_cl_pool_free(d_D, d_size);
|
||||
}
|
||||
|
||||
void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
|
||||
ggml_cl_mul_f32(src0, src1, dst);
|
||||
}
|
||||
|
||||
static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
||||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t ne01 = src0->ne[1];
|
||||
|
|
@ -666,18 +814,18 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
size_t y_size;
|
||||
size_t d_size;
|
||||
cl_mem d_X;
|
||||
if (src0->backend == GGML_BACKEND_CL) {
|
||||
if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
|
||||
d_X = (cl_mem) src0->data;
|
||||
} else {
|
||||
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size, CL_MEM_READ_ONLY);
|
||||
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
|
||||
}
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size, CL_MEM_READ_ONLY);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size, CL_MEM_WRITE_ONLY);
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
// copy data to device
|
||||
if (src0->backend != GGML_BACKEND_CL) {
|
||||
if (src0->backend != GGML_BACKEND_GPU) {
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
|
||||
}
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, NULL));
|
||||
|
|
@ -706,7 +854,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
}
|
||||
}
|
||||
|
||||
if (src0->backend != GGML_BACKEND_CL) {
|
||||
if (src0->backend != GGML_BACKEND_GPU) {
|
||||
ggml_cl_pool_free(d_X, x_size);
|
||||
}
|
||||
ggml_cl_pool_free(d_Y, y_size);
|
||||
|
|
@ -742,13 +890,13 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
size_t y_size;
|
||||
size_t d_size;
|
||||
cl_mem d_X;
|
||||
if (src0->backend == GGML_BACKEND_CL) {
|
||||
if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
|
||||
d_X = (cl_mem) src0->data;
|
||||
} else {
|
||||
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size, CL_MEM_READ_ONLY);
|
||||
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
|
||||
}
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * y_ne, &y_size, CL_MEM_READ_ONLY);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * d_ne, &d_size, CL_MEM_WRITE_ONLY);
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * y_ne, &y_size);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * d_ne, &d_size);
|
||||
|
||||
bool src1_cont_rows = nb10 == sizeof(float);
|
||||
bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
|
||||
|
|
@ -756,7 +904,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
// copy src0 to device
|
||||
if (src0->backend != GGML_BACKEND_CL) {
|
||||
if (src0->backend != GGML_BACKEND_GPU) {
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
|
||||
}
|
||||
|
||||
|
|
@ -813,7 +961,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
}
|
||||
}
|
||||
|
||||
if (src0->backend != GGML_BACKEND_CL) {
|
||||
if (src0->backend != GGML_BACKEND_GPU) {
|
||||
ggml_cl_pool_free(d_X, x_size);
|
||||
}
|
||||
ggml_cl_pool_free(d_Y, y_size);
|
||||
|
|
@ -847,57 +995,61 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
|
|||
size_t q_size;
|
||||
cl_mem d_X;
|
||||
if (!mul_mat_vec) {
|
||||
d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size, CL_MEM_READ_WRITE);
|
||||
d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
|
||||
}
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size, CL_MEM_READ_ONLY);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size, CL_MEM_WRITE_ONLY);
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
|
||||
cl_mem d_Q;
|
||||
if (src0->backend == GGML_BACKEND_CPU) {
|
||||
d_Q = ggml_cl_pool_malloc(q_sz, &q_size, CL_MEM_READ_ONLY);
|
||||
d_Q = ggml_cl_pool_malloc(q_sz, &q_size);
|
||||
}
|
||||
|
||||
cl_kernel* to_fp32_cl = ggml_get_to_fp32_cl(type);
|
||||
cl_kernel* dmmv = ggml_get_dequantize_mul_mat_vec_cl(type);
|
||||
GGML_ASSERT(to_fp32_cl != nullptr);
|
||||
|
||||
size_t ev_idx = 0;
|
||||
std::vector<cl_event> events;
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
cl_event ev_sgemm;
|
||||
|
||||
// copy src0 to device if necessary
|
||||
if (src0->backend == GGML_BACKEND_CPU) {
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, NULL));
|
||||
} else if (src0->backend == GGML_BACKEND_CL) {
|
||||
events.emplace_back();
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
|
||||
} else if (src0->backend == GGML_BACKEND_GPU) {
|
||||
d_Q = (cl_mem) src0->data;
|
||||
} else {
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel
|
||||
// copy src1 to device
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, NULL));
|
||||
events.emplace_back();
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, events.data() + ev_idx++));
|
||||
|
||||
// compute
|
||||
const size_t global = ne01 * CL_DMMV_BLOCK_SIZE;
|
||||
const size_t local = CL_DMMV_BLOCK_SIZE;
|
||||
const cl_int ncols = ne00;
|
||||
events.emplace_back();
|
||||
CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q));
|
||||
CL_CHECK(clSetKernelArg(*dmmv, 1, sizeof(float) * local, NULL));
|
||||
CL_CHECK(clSetKernelArg(*dmmv, 2, sizeof(cl_mem), &d_Y));
|
||||
CL_CHECK(clSetKernelArg(*dmmv, 3, sizeof(cl_mem), &d_D));
|
||||
CL_CHECK(clSetKernelArg(*dmmv, 4, sizeof(cl_int), &ncols));
|
||||
CL_CHECK(clFinish(queue));
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, NULL, &global, &local, 0, NULL, &ev_sgemm));
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, NULL, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++));
|
||||
} else { // general dequantization kernel + CLBlast matrix matrix multiplication
|
||||
// convert src0 to fp32 on device
|
||||
const size_t global = x_ne;
|
||||
CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
|
||||
CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
|
||||
CL_CHECK(clFinish(queue));
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, NULL, &global, NULL, 0, NULL, NULL));
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, NULL, &global, NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
|
||||
|
||||
// copy src1 to device
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, NULL));
|
||||
|
||||
events.emplace_back();
|
||||
|
||||
// wait for conversion
|
||||
CL_CHECK(clFinish(queue));
|
||||
|
||||
|
|
@ -910,7 +1062,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
|
|||
d_Y, 0, ne10,
|
||||
beta,
|
||||
d_D, 0, ne01,
|
||||
&queue, &ev_sgemm);
|
||||
&queue, events.data() + ev_idx++);
|
||||
|
||||
if (status != clblast::StatusCode::kSuccess) {
|
||||
GGML_ASSERT(false);
|
||||
|
|
@ -919,8 +1071,13 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
|
|||
|
||||
// copy dst to host
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
|
||||
clReleaseEvent(ev_sgemm);
|
||||
CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &events[events.size() - 1], NULL));
|
||||
for (auto *event : events) {
|
||||
clReleaseEvent(event);
|
||||
}
|
||||
|
||||
ev_idx = 0;
|
||||
events.clear();
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -945,7 +1102,7 @@ bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens
|
|||
if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
|
||||
src1->type == GGML_TYPE_F32 &&
|
||||
dst->type == GGML_TYPE_F32 &&
|
||||
((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_CL)) {
|
||||
((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU)) {
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
@ -1011,7 +1168,7 @@ void ggml_cl_transform_tensor(ggml_tensor * tensor) {
|
|||
const size_t q_sz = ggml_type_size(type) * ne0 * ne1 * ne2 * ne3 / ggml_blck_size(type);
|
||||
|
||||
size_t q_size;
|
||||
cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size, CL_MEM_READ_ONLY);
|
||||
cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size);
|
||||
|
||||
// copy tensor to device
|
||||
for (int64_t i3 = 0; i3 < ne3; i3++) {
|
||||
|
|
@ -1024,5 +1181,35 @@ void ggml_cl_transform_tensor(ggml_tensor * tensor) {
|
|||
CL_CHECK(clFinish(queue));
|
||||
|
||||
tensor->data = dst;
|
||||
tensor->backend = GGML_BACKEND_CL;
|
||||
tensor->backend = GGML_BACKEND_GPU;
|
||||
}
|
||||
|
||||
void ggml_cl_load_data(const char * fname, struct ggml_tensor * tensor, const size_t offset) {
|
||||
cl_int err;
|
||||
FILE * fp = fopen(fname, "rb");
|
||||
|
||||
const size_t size = ggml_nbytes(tensor);
|
||||
|
||||
cl_mem dst;
|
||||
CL_CHECK((dst = clCreateBuffer(context, CL_MEM_READ_ONLY, size, nullptr, &err), err));
|
||||
void * buf_host = malloc(size);
|
||||
|
||||
#ifdef _WIN32
|
||||
int ret = _fseeki64(fp, (__int64) offset, SEEK_SET);
|
||||
#else
|
||||
int ret = fseek(fp, (long) offset, SEEK_SET);
|
||||
#endif
|
||||
GGML_ASSERT(ret == 0); // same
|
||||
|
||||
size_t ret2 = fread(buf_host, size, 1, fp);
|
||||
if (ret2 != 1) {
|
||||
fprintf(stderr, "unexpectedly reached end of file");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
clEnqueueWriteBuffer(queue, dst, CL_TRUE, 0, size, buf_host, 0, nullptr, nullptr);
|
||||
|
||||
tensor->data = dst;
|
||||
free(buf_host);
|
||||
fclose(fp);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@ extern "C" {
|
|||
|
||||
void ggml_cl_init(void);
|
||||
|
||||
void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
|
||||
|
|
@ -16,6 +17,7 @@ void * ggml_cl_host_malloc(size_t size);
|
|||
void ggml_cl_host_free(void * ptr);
|
||||
|
||||
void ggml_cl_transform_tensor(struct ggml_tensor * tensor);
|
||||
void ggml_cl_load_data(const char * fname, struct ggml_tensor * tensor, size_t offset);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
|
|
|||
406
ggml.c
406
ggml.c
|
|
@ -3,6 +3,10 @@
|
|||
|
||||
#include "ggml.h"
|
||||
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
#include "k_quants.h"
|
||||
#endif
|
||||
|
||||
#if defined(_MSC_VER) || defined(__MINGW32__)
|
||||
#include <malloc.h> // using malloc.h with MSC/MINGW
|
||||
#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
|
||||
|
|
@ -21,6 +25,10 @@
|
|||
#include <float.h>
|
||||
#include <limits.h>
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
#include <unistd.h>
|
||||
#endif
|
||||
|
||||
// if C99 - static_assert is noop
|
||||
// ref: https://stackoverflow.com/a/53923785/4039976
|
||||
#ifndef static_assert
|
||||
|
|
@ -121,7 +129,11 @@ typedef void* thread_ret_t;
|
|||
#else
|
||||
inline static void* ggml_aligned_malloc(size_t size) {
|
||||
void* aligned_memory = NULL;
|
||||
#ifdef GGML_USE_METAL
|
||||
int result = posix_memalign(&aligned_memory, getpagesize(), size);
|
||||
#else
|
||||
int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size);
|
||||
#endif
|
||||
if (result != 0) {
|
||||
// Handle allocation failure
|
||||
return NULL;
|
||||
|
|
@ -403,21 +415,27 @@ void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n) {
|
|||
//
|
||||
|
||||
#if defined(_MSC_VER) || defined(__MINGW32__)
|
||||
static int64_t timer_freq;
|
||||
static int64_t timer_freq, timer_start;
|
||||
void ggml_time_init(void) {
|
||||
LARGE_INTEGER frequency;
|
||||
QueryPerformanceFrequency(&frequency);
|
||||
timer_freq = frequency.QuadPart;
|
||||
LARGE_INTEGER t;
|
||||
QueryPerformanceFrequency(&t);
|
||||
timer_freq = t.QuadPart;
|
||||
|
||||
// The multiplication by 1000 or 1000000 below can cause an overflow if timer_freq
|
||||
// and the uptime is high enough.
|
||||
// We subtract the program start time to reduce the likelihood of that happening.
|
||||
QueryPerformanceCounter(&t);
|
||||
timer_start = t.QuadPart;
|
||||
}
|
||||
int64_t ggml_time_ms(void) {
|
||||
LARGE_INTEGER t;
|
||||
QueryPerformanceCounter(&t);
|
||||
return (t.QuadPart * 1000) / timer_freq;
|
||||
return ((t.QuadPart-timer_start) * 1000) / timer_freq;
|
||||
}
|
||||
int64_t ggml_time_us(void) {
|
||||
LARGE_INTEGER t;
|
||||
QueryPerformanceCounter(&t);
|
||||
return (t.QuadPart * 1000000) / timer_freq;
|
||||
return ((t.QuadPart-timer_start) * 1000000) / timer_freq;
|
||||
}
|
||||
#else
|
||||
void ggml_time_init(void) {}
|
||||
|
|
@ -1565,6 +1583,48 @@ static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
|
|||
.vec_dot_q = NULL, // TODO
|
||||
.vec_dot_type = GGML_TYPE_Q8_1,
|
||||
},
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
[GGML_TYPE_Q2_K] = {
|
||||
.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q2_K,
|
||||
.quantize_row_q = quantize_row_q2_K,
|
||||
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q2_K_reference,
|
||||
.quantize_row_q_dot = quantize_row_q8_K,
|
||||
.vec_dot_q = ggml_vec_dot_q2_K_q8_K,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
},
|
||||
[GGML_TYPE_Q3_K] = {
|
||||
.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q3_K,
|
||||
.quantize_row_q = quantize_row_q3_K,
|
||||
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q3_K_reference,
|
||||
.quantize_row_q_dot = quantize_row_q8_K,
|
||||
.vec_dot_q = ggml_vec_dot_q3_K_q8_K,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
},
|
||||
[GGML_TYPE_Q4_K] = {
|
||||
.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q4_K,
|
||||
.quantize_row_q = quantize_row_q4_K,
|
||||
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_K_reference,
|
||||
.quantize_row_q_dot = quantize_row_q8_K,
|
||||
.vec_dot_q = ggml_vec_dot_q4_K_q8_K,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
},
|
||||
[GGML_TYPE_Q5_K] = {
|
||||
.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q5_K,
|
||||
.quantize_row_q = quantize_row_q5_K,
|
||||
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_K_reference,
|
||||
.quantize_row_q_dot = quantize_row_q8_K,
|
||||
.vec_dot_q = ggml_vec_dot_q5_K_q8_K,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
},
|
||||
[GGML_TYPE_Q6_K] = {
|
||||
.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q6_K,
|
||||
.quantize_row_q = quantize_row_q6_K,
|
||||
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q6_K_reference,
|
||||
.quantize_row_q_dot = quantize_row_q8_K,
|
||||
.vec_dot_q = ggml_vec_dot_q6_K_q8_K,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
},
|
||||
#endif
|
||||
};
|
||||
|
||||
// For internal test use
|
||||
|
|
@ -3444,11 +3504,19 @@ static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = {
|
|||
[GGML_TYPE_Q5_1] = QK5_1,
|
||||
[GGML_TYPE_Q8_0] = QK8_0,
|
||||
[GGML_TYPE_Q8_1] = QK8_1,
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
[GGML_TYPE_Q2_K] = QK_K,
|
||||
[GGML_TYPE_Q3_K] = QK_K,
|
||||
[GGML_TYPE_Q4_K] = QK_K,
|
||||
[GGML_TYPE_Q5_K] = QK_K,
|
||||
[GGML_TYPE_Q6_K] = QK_K,
|
||||
[GGML_TYPE_Q8_K] = QK_K,
|
||||
#endif
|
||||
[GGML_TYPE_I8] = 1,
|
||||
[GGML_TYPE_I16] = 1,
|
||||
[GGML_TYPE_I32] = 1,
|
||||
};
|
||||
static_assert(GGML_TYPE_COUNT == 13, "GGML_BLCK_SIZE is outdated");
|
||||
static_assert(GGML_TYPE_COUNT == 19, "GGML_BLCK_SIZE is outdated");
|
||||
|
||||
static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
|
||||
[GGML_TYPE_F32] = sizeof(float),
|
||||
|
|
@ -3459,11 +3527,19 @@ static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
|
|||
[GGML_TYPE_Q5_1] = sizeof(block_q5_1),
|
||||
[GGML_TYPE_Q8_0] = sizeof(block_q8_0),
|
||||
[GGML_TYPE_Q8_1] = sizeof(block_q8_1),
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
[GGML_TYPE_Q2_K] = sizeof(block_q2_K),
|
||||
[GGML_TYPE_Q3_K] = sizeof(block_q3_K),
|
||||
[GGML_TYPE_Q4_K] = sizeof(block_q4_K),
|
||||
[GGML_TYPE_Q5_K] = sizeof(block_q5_K),
|
||||
[GGML_TYPE_Q6_K] = sizeof(block_q6_K),
|
||||
[GGML_TYPE_Q8_K] = sizeof(block_q8_K),
|
||||
#endif
|
||||
[GGML_TYPE_I8] = sizeof(int8_t),
|
||||
[GGML_TYPE_I16] = sizeof(int16_t),
|
||||
[GGML_TYPE_I32] = sizeof(int32_t),
|
||||
};
|
||||
static_assert(GGML_TYPE_COUNT == 13, "GGML_TYPE_SIZE is outdated");
|
||||
static_assert(GGML_TYPE_COUNT == 19, "GGML_TYPE_SIZE is outdated");
|
||||
|
||||
|
||||
static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
|
||||
|
|
@ -3475,11 +3551,17 @@ static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
|
|||
[GGML_TYPE_Q5_1] = "q5_1",
|
||||
[GGML_TYPE_Q8_0] = "q8_0",
|
||||
[GGML_TYPE_Q8_1] = "q8_1",
|
||||
[GGML_TYPE_Q2_K] = "q2_K",
|
||||
[GGML_TYPE_Q3_K] = "q3_K",
|
||||
[GGML_TYPE_Q4_K] = "q4_K",
|
||||
[GGML_TYPE_Q5_K] = "q5_K",
|
||||
[GGML_TYPE_Q6_K] = "q6_K",
|
||||
[GGML_TYPE_Q8_K] = "q8_K",
|
||||
[GGML_TYPE_I8] = "i8",
|
||||
[GGML_TYPE_I16] = "i16",
|
||||
[GGML_TYPE_I32] = "i32",
|
||||
};
|
||||
static_assert(GGML_TYPE_COUNT == 13, "GGML_TYPE_NAME is outdated");
|
||||
static_assert(GGML_TYPE_COUNT == 19, "GGML_TYPE_NAME is outdated");
|
||||
|
||||
static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
|
||||
[GGML_TYPE_F32] = false,
|
||||
|
|
@ -3490,11 +3572,17 @@ static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
|
|||
[GGML_TYPE_Q5_1] = true,
|
||||
[GGML_TYPE_Q8_0] = true,
|
||||
[GGML_TYPE_Q8_1] = true,
|
||||
[GGML_TYPE_Q2_K] = true,
|
||||
[GGML_TYPE_Q3_K] = true,
|
||||
[GGML_TYPE_Q4_K] = true,
|
||||
[GGML_TYPE_Q5_K] = true,
|
||||
[GGML_TYPE_Q6_K] = true,
|
||||
[GGML_TYPE_Q8_K] = true,
|
||||
[GGML_TYPE_I8] = false,
|
||||
[GGML_TYPE_I16] = false,
|
||||
[GGML_TYPE_I32] = false,
|
||||
};
|
||||
static_assert(GGML_TYPE_COUNT == 13, "GGML_IS_QUANTIZED is outdated");
|
||||
static_assert(GGML_TYPE_COUNT == 19, "GGML_IS_QUANTIZED is outdated");
|
||||
|
||||
static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
|
||||
"NONE",
|
||||
|
|
@ -3647,26 +3735,6 @@ struct ggml_context_container {
|
|||
struct ggml_context context;
|
||||
};
|
||||
|
||||
//
|
||||
// compute types
|
||||
//
|
||||
|
||||
enum ggml_task_type {
|
||||
GGML_TASK_INIT = 0,
|
||||
GGML_TASK_COMPUTE,
|
||||
GGML_TASK_FINALIZE,
|
||||
};
|
||||
|
||||
struct ggml_compute_params {
|
||||
enum ggml_task_type type;
|
||||
|
||||
int ith, nth;
|
||||
|
||||
// work buffer for all threads
|
||||
size_t wsize;
|
||||
void * wdata;
|
||||
};
|
||||
|
||||
//
|
||||
// ggml state
|
||||
//
|
||||
|
|
@ -3723,7 +3791,7 @@ int64_t ggml_nelements(const struct ggml_tensor * tensor) {
|
|||
return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
|
||||
}
|
||||
|
||||
int ggml_nrows(const struct ggml_tensor * tensor) {
|
||||
int64_t ggml_nrows(const struct ggml_tensor * tensor) {
|
||||
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
||||
|
||||
return tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
|
||||
|
|
@ -3732,7 +3800,20 @@ int ggml_nrows(const struct ggml_tensor * tensor) {
|
|||
size_t ggml_nbytes(const struct ggml_tensor * tensor) {
|
||||
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
||||
|
||||
return (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type];
|
||||
// this should handle cases where the tensor is not contiguous in memory
|
||||
// probaby just:
|
||||
//
|
||||
// return tensor->ne[3]*tensor->nb[3]
|
||||
//
|
||||
// is enough, but just in case, adding the second part
|
||||
|
||||
return MAX(tensor->ne[3]*tensor->nb[3], (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type]);
|
||||
}
|
||||
|
||||
size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
|
||||
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
||||
|
||||
return (nrows_split*tensor->ne[0]*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type];
|
||||
}
|
||||
|
||||
int ggml_blck_size(enum ggml_type type) {
|
||||
|
|
@ -3801,6 +3882,11 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
|
|||
case GGML_FTYPE_MOSTLY_Q5_0: wtype = GGML_TYPE_Q5_0; break;
|
||||
case GGML_FTYPE_MOSTLY_Q5_1: wtype = GGML_TYPE_Q5_1; break;
|
||||
case GGML_FTYPE_MOSTLY_Q8_0: wtype = GGML_TYPE_Q8_0; break;
|
||||
case GGML_FTYPE_MOSTLY_Q2_K: wtype = GGML_TYPE_Q2_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q3_K: wtype = GGML_TYPE_Q3_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q4_K: wtype = GGML_TYPE_Q4_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q5_K: wtype = GGML_TYPE_Q5_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break;
|
||||
case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break;
|
||||
case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
|
||||
}
|
||||
|
|
@ -3814,11 +3900,11 @@ size_t ggml_tensor_overhead(void) {
|
|||
return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE + 16;
|
||||
}
|
||||
|
||||
static inline bool ggml_is_transposed(const struct ggml_tensor * tensor) {
|
||||
bool ggml_is_transposed(const struct ggml_tensor * tensor) {
|
||||
return tensor->nb[0] > tensor->nb[1];
|
||||
}
|
||||
|
||||
static inline bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
|
||||
bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
|
||||
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
||||
|
||||
return
|
||||
|
|
@ -4157,6 +4243,7 @@ struct ggml_tensor * ggml_new_tensor_impl(
|
|||
/*.perf_time_us =*/ 0,
|
||||
/*.data =*/ (data == NULL && !ctx->no_alloc) ? (void *)(result + 1) : data,
|
||||
/*.name =*/ { 0 },
|
||||
/*.extra =*/ NULL,
|
||||
/*.pad =*/ { 0 },
|
||||
};
|
||||
|
||||
|
|
@ -5802,10 +5889,18 @@ struct ggml_tensor * ggml_view_1d(
|
|||
|
||||
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, &ne0, (char *) a->data + offset);
|
||||
|
||||
ggml_scratch_save(ctx);
|
||||
|
||||
struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2);
|
||||
memcpy(offs->data, &offset, 2*sizeof(int32_t));
|
||||
|
||||
ggml_scratch_load(ctx);
|
||||
|
||||
result->op = GGML_OP_VIEW;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src0 = a;
|
||||
result->src1 = NULL;
|
||||
result->opt[0] = offs;
|
||||
|
||||
if (is_node) {
|
||||
memcpy(result->padding, &offset, sizeof(offset));
|
||||
|
|
@ -5834,6 +5929,13 @@ struct ggml_tensor * ggml_view_2d(
|
|||
|
||||
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, (char *) a->data + offset);
|
||||
|
||||
ggml_scratch_save(ctx);
|
||||
|
||||
struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2);
|
||||
memcpy(offs->data, &offset, 2*sizeof(int32_t));
|
||||
|
||||
ggml_scratch_load(ctx);
|
||||
|
||||
result->nb[1] = nb1;
|
||||
result->nb[2] = result->nb[1]*ne1;
|
||||
result->nb[3] = result->nb[2];
|
||||
|
|
@ -5842,6 +5944,7 @@ struct ggml_tensor * ggml_view_2d(
|
|||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src0 = a;
|
||||
result->src1 = NULL;
|
||||
result->opt[0] = offs;
|
||||
|
||||
if (is_node) {
|
||||
memcpy(result->padding, &offset, sizeof(offset));
|
||||
|
|
@ -5872,6 +5975,13 @@ struct ggml_tensor * ggml_view_3d(
|
|||
|
||||
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, (char *) a->data + offset);
|
||||
|
||||
ggml_scratch_save(ctx);
|
||||
|
||||
struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2);
|
||||
memcpy(offs->data, &offset, 2*sizeof(int32_t));
|
||||
|
||||
ggml_scratch_load(ctx);
|
||||
|
||||
result->nb[1] = nb1;
|
||||
result->nb[2] = nb2;
|
||||
result->nb[3] = result->nb[2]*ne2;
|
||||
|
|
@ -5880,6 +5990,7 @@ struct ggml_tensor * ggml_view_3d(
|
|||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src0 = a;
|
||||
result->src1 = NULL;
|
||||
result->opt[0] = offs;
|
||||
|
||||
if (is_node) {
|
||||
memcpy(result->padding, &offset, sizeof(offset));
|
||||
|
|
@ -5912,6 +6023,13 @@ struct ggml_tensor * ggml_view_4d(
|
|||
|
||||
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, (char *) a->data + offset);
|
||||
|
||||
ggml_scratch_save(ctx);
|
||||
|
||||
struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2);
|
||||
memcpy(offs->data, &offset, 2*sizeof(int32_t));
|
||||
|
||||
ggml_scratch_load(ctx);
|
||||
|
||||
result->nb[1] = nb1;
|
||||
result->nb[2] = nb2;
|
||||
result->nb[3] = nb3;
|
||||
|
|
@ -5920,6 +6038,7 @@ struct ggml_tensor * ggml_view_4d(
|
|||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src0 = a;
|
||||
result->src1 = NULL;
|
||||
result->opt[0] = offs;
|
||||
|
||||
if (is_node) {
|
||||
memcpy(result->padding, &offset, sizeof(offset));
|
||||
|
|
@ -7584,6 +7703,11 @@ static void ggml_compute_forward_add(
|
|||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
{
|
||||
ggml_compute_forward_add_q_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
|
|
@ -7887,6 +8011,11 @@ static void ggml_compute_forward_add1(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
{
|
||||
ggml_compute_forward_add1_q_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
|
|
@ -8009,6 +8138,11 @@ static void ggml_compute_forward_acc(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
|
|
@ -8127,10 +8261,10 @@ static void ggml_compute_forward_mul_f32(
|
|||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
if (src1->backend == GGML_BACKEND_CUDA) {
|
||||
#ifdef GGML_USE_CLBLAST
|
||||
if (src1->backend == GGML_BACKEND_GPU) {
|
||||
if (ith == 0) {
|
||||
ggml_cuda_mul(src0, src1, dst);
|
||||
ggml_cl_mul(src0, src1, dst);
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
|
@ -9245,7 +9379,7 @@ static void ggml_compute_forward_rms_norm_f32(
|
|||
sum += (ggml_float)(x[i00] * x[i00]);
|
||||
}
|
||||
|
||||
float mean = sum/ne00;
|
||||
const float mean = sum/ne00;
|
||||
|
||||
float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
|
||||
|
||||
|
|
@ -9568,14 +9702,7 @@ static void ggml_compute_forward_mul_mat_f32(
|
|||
// nb01 >= nb00 - src0 is not transposed
|
||||
// compute by src0 rows
|
||||
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
if (ggml_cuda_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
|
|
@ -9740,14 +9867,7 @@ static void ggml_compute_forward_mul_mat_f16_f32(
|
|||
// nb01 >= nb00 - src0 is not transposed
|
||||
// compute by src0 rows
|
||||
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
if (ggml_cuda_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
|
|
@ -9952,14 +10072,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
// nb01 >= nb00 - src0 is not transposed
|
||||
// compute by src0 rows
|
||||
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
if (ggml_cuda_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
|
|
@ -10102,6 +10215,11 @@ static void ggml_compute_forward_mul_mat(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
{
|
||||
ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
|
|
@ -10285,6 +10403,11 @@ static void ggml_compute_forward_set(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
|
|
@ -10450,6 +10573,11 @@ static void ggml_compute_forward_get_rows(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
{
|
||||
ggml_compute_forward_get_rows_q(params, src0, src1, dst);
|
||||
} break;
|
||||
|
|
@ -10996,6 +11124,12 @@ static void ggml_compute_forward_alibi(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_Q8_K:
|
||||
case GGML_TYPE_I8:
|
||||
case GGML_TYPE_I16:
|
||||
case GGML_TYPE_I32:
|
||||
|
|
@ -11067,6 +11201,12 @@ static void ggml_compute_forward_clamp(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_Q8_K:
|
||||
case GGML_TYPE_I8:
|
||||
case GGML_TYPE_I16:
|
||||
case GGML_TYPE_I32:
|
||||
|
|
@ -12885,6 +13025,15 @@ static void ggml_compute_forward_map_binary(
|
|||
static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
|
||||
GGML_ASSERT(params);
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
bool skip_cpu = ggml_cuda_compute_forward(params, tensor);
|
||||
if (skip_cpu) {
|
||||
return;
|
||||
}
|
||||
GGML_ASSERT(tensor->src0->backend == GGML_BACKEND_CPU);
|
||||
GGML_ASSERT(tensor->src1 == NULL || tensor->src1->backend == GGML_BACKEND_CPU);
|
||||
#endif // GGML_USE_CUBLAS
|
||||
|
||||
switch (tensor->op) {
|
||||
case GGML_OP_DUP:
|
||||
{
|
||||
|
|
@ -14191,7 +14340,6 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
|
|||
if (ggml_cuda_can_mul_mat(node->src0, node->src1, node)) {
|
||||
node->n_tasks = 1; // TODO: this actually is doing nothing
|
||||
// the threads are still spinning
|
||||
cur = ggml_cuda_mul_mat_get_wsize(node->src0, node->src1, node);
|
||||
}
|
||||
else
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
|
|
@ -14581,11 +14729,11 @@ static void ggml_graph_export_leaf(const struct ggml_tensor * tensor, FILE * fou
|
|||
const int64_t * ne = tensor->ne;
|
||||
const size_t * nb = tensor->nb;
|
||||
|
||||
fprintf(fout, "%-6s %-12s %8d %8lld %8lld %8lld %8lld %16zu %16zu %16zu %16zu %16p %16s\n",
|
||||
fprintf(fout, "%-6s %-12s %8d %8d %d %d %d %16zu %16zu %16zu %16zu %16p %32s\n",
|
||||
ggml_type_name(tensor->type),
|
||||
ggml_op_name (tensor->op),
|
||||
tensor->n_dims,
|
||||
ne[0], ne[1], ne[2], ne[3],
|
||||
(int) ne[0], (int) ne[1], (int) ne[2], (int) ne[3],
|
||||
nb[0], nb[1], nb[2], nb[3],
|
||||
tensor->data,
|
||||
tensor->name);
|
||||
|
|
@ -14595,12 +14743,12 @@ static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char
|
|||
const int64_t * ne = tensor->ne;
|
||||
const size_t * nb = tensor->nb;
|
||||
|
||||
fprintf(fout, "%-6s %-6s %-12s %8d %8lld %8lld %8lld %8lld %16zu %16zu %16zu %16zu %8d %16p %16s\n",
|
||||
fprintf(fout, "%-6s %-6s %-12s %8d %d %d %d %d %16zu %16zu %16zu %16zu %8d %16p %32s\n",
|
||||
arg,
|
||||
ggml_type_name(tensor->type),
|
||||
ggml_op_name (tensor->op),
|
||||
tensor->n_dims,
|
||||
ne[0], ne[1], ne[2], ne[3],
|
||||
(int) ne[0], (int) ne[1], (int) ne[2], (int) ne[3],
|
||||
nb[0], nb[1], nb[2], nb[3],
|
||||
tensor->n_tasks,
|
||||
tensor->data,
|
||||
|
|
@ -14608,8 +14756,8 @@ static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char
|
|||
}
|
||||
|
||||
void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
|
||||
assert(cgraph->work == NULL);
|
||||
assert(cgraph->work_size == 0);
|
||||
//assert(cgraph->work == NULL);
|
||||
//assert(cgraph->work_size == 0);
|
||||
|
||||
uint64_t size_eval = 0;
|
||||
|
||||
|
|
@ -14628,7 +14776,7 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
|
|||
fprintf(fout, "%-16s %8d\n", "version", GGML_FILE_VERSION);
|
||||
fprintf(fout, "%-16s %8d\n", "leafs", cgraph->n_leafs);
|
||||
fprintf(fout, "%-16s %8d\n", "nodes", cgraph->n_nodes);
|
||||
fprintf(fout, "%-16s %8llu\n", "eval", size_eval);
|
||||
fprintf(fout, "%-16s %8d\n", "eval", (int) size_eval);
|
||||
|
||||
// header
|
||||
fprintf(fout, "\n");
|
||||
|
|
@ -14830,7 +14978,6 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
|
|||
// read file into data
|
||||
{
|
||||
FILE * fin = fopen(fname, "rb");
|
||||
|
||||
if (!fin) {
|
||||
fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
|
||||
return result;
|
||||
|
|
@ -14862,7 +15009,11 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
|
|||
|
||||
data = ggml_new_tensor_1d(*ctx_data, GGML_TYPE_I8, fsize);
|
||||
|
||||
fread(data->data, sizeof(char), fsize, fin);
|
||||
const size_t ret = fread(data->data, sizeof(char), fsize, fin);
|
||||
if (ret != fsize) {
|
||||
fprintf(stderr, "%s: failed to read %s\n", __func__, fname);
|
||||
return result;
|
||||
}
|
||||
|
||||
fclose(fin);
|
||||
}
|
||||
|
|
@ -14970,6 +15121,8 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
|
|||
op = *(const uint32_t *) ptr; ptr += sizeof(op);
|
||||
n_dims = *(const uint32_t *) ptr; ptr += sizeof(n_dims);
|
||||
|
||||
enum ggml_op eop = (enum ggml_op) op;
|
||||
|
||||
int64_t ne[GGML_MAX_DIMS];
|
||||
size_t nb[GGML_MAX_DIMS];
|
||||
|
||||
|
|
@ -14984,42 +15137,77 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
|
|||
nb[j] = nb_cur;
|
||||
}
|
||||
|
||||
struct ggml_tensor * tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, n_dims, ne);
|
||||
uint64_t ptr_cur = *(const uint64_t *) ptr; ptr += sizeof(ptr_cur); // TODO: not yet used
|
||||
|
||||
tensor->op = (enum ggml_op) op;
|
||||
const char * ptr_name = ptr; ptr += GGML_MAX_NAME;
|
||||
|
||||
uint64_t ptr_cur = *(const uint64_t *) ptr; ptr += sizeof(ptr_cur);
|
||||
const int32_t * ptr_arg_idx = (const int32_t *) ptr; ptr += (2 + GGML_MAX_OPT)*sizeof(int32_t);
|
||||
|
||||
memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME;
|
||||
|
||||
for (int j = 0; j < GGML_MAX_DIMS; ++j) {
|
||||
tensor->nb[j] = nb[j];
|
||||
}
|
||||
struct ggml_tensor * args[2 + GGML_MAX_OPT] = { NULL };
|
||||
|
||||
// parse args
|
||||
{
|
||||
struct ggml_tensor ** args[2 + GGML_MAX_OPT] = {
|
||||
&tensor->src0,
|
||||
&tensor->src1,
|
||||
};
|
||||
|
||||
for (int j = 0; j < GGML_MAX_OPT; ++j) {
|
||||
args[2 + j] = &tensor->opt[j];
|
||||
}
|
||||
|
||||
for (int j = 0; j < 2 + GGML_MAX_OPT; ++j) {
|
||||
const int32_t arg_idx = *(const int32_t *) ptr; ptr += sizeof(arg_idx);
|
||||
const int32_t arg_idx = ptr_arg_idx[j];
|
||||
|
||||
if (arg_idx == -1) {
|
||||
continue;
|
||||
}
|
||||
|
||||
if (arg_idx < GGML_MAX_NODES) {
|
||||
*args[j] = result.leafs[arg_idx];
|
||||
args[j] = result.leafs[arg_idx];
|
||||
} else {
|
||||
*args[j] = result.nodes[arg_idx - GGML_MAX_NODES];
|
||||
args[j] = result.nodes[arg_idx - GGML_MAX_NODES];
|
||||
}
|
||||
}
|
||||
|
||||
// create the tensor
|
||||
// "view" operations are handled differently
|
||||
// TODO: handle inplace ops - currently a copy is always made
|
||||
|
||||
struct ggml_tensor * tensor = NULL;
|
||||
|
||||
switch (eop) {
|
||||
// TODO: implement other view ops
|
||||
case GGML_OP_RESHAPE:
|
||||
{
|
||||
tensor = ggml_reshape_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3]);
|
||||
} break;
|
||||
case GGML_OP_VIEW:
|
||||
{
|
||||
tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0);
|
||||
|
||||
uint64_t offs;
|
||||
memcpy(&offs, args[2]->data, sizeof(offs));
|
||||
|
||||
tensor->data = ((char *) tensor->data) + offs;
|
||||
} break;
|
||||
case GGML_OP_TRANSPOSE:
|
||||
{
|
||||
tensor = ggml_transpose(*ctx_eval, args[0]);
|
||||
} break;
|
||||
case GGML_OP_PERMUTE:
|
||||
{
|
||||
tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0);
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, n_dims, ne);
|
||||
|
||||
tensor->op = eop;
|
||||
} break;
|
||||
}
|
||||
|
||||
memcpy(tensor->name, ptr_name, GGML_MAX_NAME);
|
||||
|
||||
for (int j = 0; j < GGML_MAX_DIMS; ++j) {
|
||||
tensor->nb[j] = nb[j];
|
||||
}
|
||||
|
||||
tensor->src0 = args[0];
|
||||
tensor->src1 = args[1];
|
||||
|
||||
for (int j = 0; j < GGML_MAX_OPT; ++j) {
|
||||
tensor->opt[j] = args[2 + j];
|
||||
}
|
||||
|
||||
result.nodes[i] = tensor;
|
||||
|
|
@ -16070,6 +16258,38 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
|
|||
block_q8_0 * block = (block_q8_0*)dst + start / QK8_0;
|
||||
result = ggml_quantize_q8_0(src + start, block, n, n, hist);
|
||||
} break;
|
||||
#ifdef GGML_USE_K_QUANTS
|
||||
case GGML_TYPE_Q2_K:
|
||||
{
|
||||
GGML_ASSERT(start % QK_K == 0);
|
||||
block_q2_K * block = (block_q2_K*)dst + start / QK_K;
|
||||
result = ggml_quantize_q2_K(src + start, block, n, n, hist);
|
||||
} break;
|
||||
case GGML_TYPE_Q3_K:
|
||||
{
|
||||
GGML_ASSERT(start % QK_K == 0);
|
||||
block_q3_K * block = (block_q3_K*)dst + start / QK_K;
|
||||
result = ggml_quantize_q3_K(src + start, block, n, n, hist);
|
||||
} break;
|
||||
case GGML_TYPE_Q4_K:
|
||||
{
|
||||
GGML_ASSERT(start % QK_K == 0);
|
||||
block_q4_K * block = (block_q4_K*)dst + start / QK_K;
|
||||
result = ggml_quantize_q4_K(src + start, block, n, n, hist);
|
||||
} break;
|
||||
case GGML_TYPE_Q5_K:
|
||||
{
|
||||
GGML_ASSERT(start % QK_K == 0);
|
||||
block_q5_K * block = (block_q5_K*)dst + start / QK_K;
|
||||
result = ggml_quantize_q5_K(src + start, block, n, n, hist);
|
||||
} break;
|
||||
case GGML_TYPE_Q6_K:
|
||||
{
|
||||
GGML_ASSERT(start % QK_K == 0);
|
||||
block_q6_K * block = (block_q6_K*)dst + start / QK_K;
|
||||
result = ggml_quantize_q6_K(src + start, block, n, n, hist);
|
||||
} break;
|
||||
#endif
|
||||
default:
|
||||
assert(false);
|
||||
}
|
||||
|
|
|
|||
46
ggml.h
46
ggml.h
|
|
@ -241,6 +241,13 @@ extern "C" {
|
|||
GGML_TYPE_Q5_1 = 7,
|
||||
GGML_TYPE_Q8_0 = 8,
|
||||
GGML_TYPE_Q8_1 = 9,
|
||||
// k-quantizations
|
||||
GGML_TYPE_Q2_K = 10,
|
||||
GGML_TYPE_Q3_K = 11,
|
||||
GGML_TYPE_Q4_K = 12,
|
||||
GGML_TYPE_Q5_K = 13,
|
||||
GGML_TYPE_Q6_K = 14,
|
||||
GGML_TYPE_Q8_K = 15,
|
||||
GGML_TYPE_I8,
|
||||
GGML_TYPE_I16,
|
||||
GGML_TYPE_I32,
|
||||
|
|
@ -249,8 +256,8 @@ extern "C" {
|
|||
|
||||
enum ggml_backend {
|
||||
GGML_BACKEND_CPU = 0,
|
||||
GGML_BACKEND_CUDA = 1,
|
||||
GGML_BACKEND_CL = 2,
|
||||
GGML_BACKEND_GPU = 10,
|
||||
GGML_BACKEND_GPU_SPLIT = 20,
|
||||
};
|
||||
|
||||
// model file types
|
||||
|
|
@ -264,6 +271,11 @@ extern "C" {
|
|||
GGML_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
|
||||
};
|
||||
|
||||
// available tensor operations:
|
||||
|
|
@ -375,7 +387,9 @@ extern "C" {
|
|||
|
||||
char name[GGML_MAX_NAME];
|
||||
|
||||
char padding[16];
|
||||
void * extra; // extra things e.g. for ggml-cuda.cu
|
||||
|
||||
char padding[4];
|
||||
};
|
||||
|
||||
static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
|
||||
|
|
@ -413,6 +427,25 @@ extern "C" {
|
|||
bool no_alloc; // don't allocate memory for the tensor data
|
||||
};
|
||||
|
||||
|
||||
// compute types
|
||||
enum ggml_task_type {
|
||||
GGML_TASK_INIT = 0,
|
||||
GGML_TASK_COMPUTE,
|
||||
GGML_TASK_FINALIZE,
|
||||
};
|
||||
|
||||
struct ggml_compute_params {
|
||||
enum ggml_task_type type;
|
||||
|
||||
// ith = thread index, nth = number of threads
|
||||
int ith, nth;
|
||||
|
||||
// work buffer for all threads
|
||||
size_t wsize;
|
||||
void * wdata;
|
||||
};
|
||||
|
||||
// misc
|
||||
|
||||
GGML_API void ggml_time_init(void); // call this once at the beginning of the program
|
||||
|
|
@ -424,8 +457,10 @@ extern "C" {
|
|||
GGML_API void ggml_print_object (const struct ggml_object * obj);
|
||||
GGML_API void ggml_print_objects(const struct ggml_context * ctx);
|
||||
|
||||
GGML_API int64_t ggml_nelements(const struct ggml_tensor * tensor);
|
||||
GGML_API int64_t ggml_nelements (const struct ggml_tensor * tensor);
|
||||
GGML_API int64_t ggml_nrows (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split);
|
||||
|
||||
GGML_API int ggml_blck_size (enum ggml_type type);
|
||||
GGML_API size_t ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block
|
||||
|
|
@ -441,6 +476,9 @@ extern "C" {
|
|||
// TODO: temporary until model loading of ggml examples is refactored
|
||||
GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
|
||||
|
||||
GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
|
||||
|
||||
// use this to compute the memory overhead of a tensor
|
||||
GGML_API size_t ggml_tensor_overhead(void);
|
||||
|
||||
|
|
|
|||
2246
k_quants.c
Normal file
2246
k_quants.c
Normal file
File diff suppressed because it is too large
Load diff
122
k_quants.h
Normal file
122
k_quants.h
Normal file
|
|
@ -0,0 +1,122 @@
|
|||
#pragma once
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#include <stdint.h>
|
||||
#include <assert.h>
|
||||
#include <stddef.h>
|
||||
|
||||
// Super-block size
|
||||
#define QK_K 256
|
||||
|
||||
//
|
||||
// Super-block quantization structures
|
||||
//
|
||||
|
||||
// 2-bit quantization
|
||||
// weight is represented as x = a * q + b
|
||||
// 16 blocks of 16 elemenets each
|
||||
// Effectively 2.5625 bits per weight
|
||||
typedef struct {
|
||||
uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
|
||||
uint8_t qs[QK_K/4]; // quants
|
||||
ggml_fp16_t d; // super-block scale for quantized scales
|
||||
ggml_fp16_t dmin; // super-block scale for quantized mins
|
||||
} block_q2_K;
|
||||
static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
|
||||
|
||||
// 3-bit quantization
|
||||
// weight is represented as x = a * q
|
||||
// 16 blocks of 16 elemenets each
|
||||
// Effectively 3.4375 bits per weight
|
||||
typedef struct {
|
||||
uint8_t hmask[QK_K/8]; // quants - high bit
|
||||
uint8_t qs[QK_K/4]; // quants - low 2 bits
|
||||
uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
|
||||
ggml_fp16_t d; // super-block scale
|
||||
} block_q3_K;
|
||||
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + 11 * QK_K / 64, "wrong q3_K block size/padding");
|
||||
|
||||
// 4-bit quantization
|
||||
// 16 blocks of 32 elements each
|
||||
// weight is represented as x = a * q + b
|
||||
// Effectively 4.5 bits per weight
|
||||
typedef struct {
|
||||
ggml_fp16_t d; // super-block scale for quantized scales
|
||||
ggml_fp16_t dmin; // super-block scale for quantized mins
|
||||
uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits
|
||||
uint8_t qs[QK_K/2]; // 4--bit quants
|
||||
} block_q4_K;
|
||||
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding");
|
||||
|
||||
// 5-bit quantization
|
||||
// 16 blocks of 32 elements each
|
||||
// weight is represented as x = a * q + b
|
||||
// Effectively 5.5 bits per weight
|
||||
typedef struct {
|
||||
ggml_fp16_t d; // super-block scale for quantized scales
|
||||
ggml_fp16_t dmin; // super-block scale for quantized mins
|
||||
uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits
|
||||
uint8_t qh[QK_K/8]; // quants, high bit
|
||||
uint8_t qs[QK_K/2]; // quants, low 4 bits
|
||||
} block_q5_K;
|
||||
static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
|
||||
|
||||
// 6-bit quantization
|
||||
// weight is represented as x = a * q
|
||||
// 16 blocks of 16 elemenets each
|
||||
// Effectively 6.5625 bits per weight
|
||||
typedef struct {
|
||||
uint8_t ql[QK_K/2]; // quants, lower 4 bits
|
||||
uint8_t qh[QK_K/4]; // quants, upper 2 bits
|
||||
int8_t scales[QK_K/16]; // scales, quantized with 8 bits
|
||||
ggml_fp16_t d; // super-block scale
|
||||
} block_q6_K;
|
||||
static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
|
||||
|
||||
// This is only used for intermediate quantization and dot products
|
||||
typedef struct {
|
||||
float d; // delta
|
||||
int8_t qs[QK_K]; // quants
|
||||
int16_t bsums[QK_K/16]; // sum of quants in groups of 16
|
||||
} block_q8_K;
|
||||
static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
|
||||
|
||||
|
||||
// Quantization
|
||||
void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k);
|
||||
void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k);
|
||||
void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k);
|
||||
void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k);
|
||||
void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
|
||||
void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
|
||||
|
||||
void quantize_row_q2_K(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q3_K(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q5_K(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
|
||||
|
||||
// Dequantization
|
||||
void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);
|
||||
|
||||
// Dot product
|
||||
void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
|
||||
// Quantization with histogram collection
|
||||
size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
|
||||
16
llama-util.h
16
llama-util.h
|
|
@ -405,13 +405,29 @@ struct llama_buffer {
|
|||
llama_buffer() = default;
|
||||
|
||||
void resize(size_t len) {
|
||||
#ifdef GGML_USE_METAL
|
||||
free(addr);
|
||||
int result = posix_memalign((void **) &addr, getpagesize(), len);
|
||||
if (result == 0) {
|
||||
memset(addr, 0, len);
|
||||
}
|
||||
else {
|
||||
addr = NULL;
|
||||
}
|
||||
#else
|
||||
delete[] addr;
|
||||
addr = new uint8_t[len];
|
||||
#endif
|
||||
size = len;
|
||||
}
|
||||
|
||||
~llama_buffer() {
|
||||
#ifdef GGML_USE_METAL
|
||||
free(addr);
|
||||
#else
|
||||
delete[] addr;
|
||||
#endif
|
||||
addr = NULL;
|
||||
}
|
||||
|
||||
// disable copy and move
|
||||
|
|
|
|||
475
llama.cpp
475
llama.cpp
|
|
@ -16,6 +16,10 @@
|
|||
#include "ggml-opencl.h"
|
||||
#endif
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
#include "ggml-metal.h"
|
||||
#endif
|
||||
|
||||
#include <array>
|
||||
#include <ctime>
|
||||
#include <cinttypes>
|
||||
|
|
@ -49,17 +53,22 @@ enum e_model {
|
|||
MODEL_65B,
|
||||
};
|
||||
|
||||
|
||||
static const size_t MB = 1024*1024;
|
||||
|
||||
// computed for n_ctx == 2048
|
||||
// TODO: dynamically determine these sizes
|
||||
// needs modifications in ggml
|
||||
|
||||
typedef void (*offload_func_t)(struct ggml_tensor * tensor);
|
||||
|
||||
void llama_nop(struct ggml_tensor * tensor) { // don't offload by default
|
||||
(void) tensor;
|
||||
}
|
||||
|
||||
static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0()
|
||||
{
|
||||
static std::map<e_model, size_t> k_sizes = {
|
||||
{ MODEL_3B, 128ull * MB },
|
||||
{ MODEL_3B, 256ull * MB },
|
||||
{ MODEL_7B, 512ull * MB },
|
||||
{ MODEL_13B, 512ull * MB },
|
||||
{ MODEL_30B, 512ull * MB },
|
||||
|
|
@ -71,7 +80,7 @@ static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0()
|
|||
static const std::map<e_model, size_t> & MEM_REQ_SCRATCH1()
|
||||
{
|
||||
static std::map<e_model, size_t> k_sizes = {
|
||||
{ MODEL_3B, 128ull * MB },
|
||||
{ MODEL_3B, 256ull * MB },
|
||||
{ MODEL_7B, 512ull * MB },
|
||||
{ MODEL_13B, 512ull * MB },
|
||||
{ MODEL_30B, 512ull * MB },
|
||||
|
|
@ -170,6 +179,7 @@ struct llama_model {
|
|||
struct ggml_tensor * output;
|
||||
|
||||
std::vector<llama_layer> layers;
|
||||
int n_gpu_layers;
|
||||
|
||||
// context
|
||||
struct ggml_context * ctx = NULL;
|
||||
|
|
@ -195,6 +205,12 @@ struct llama_model {
|
|||
if (ctx) {
|
||||
ggml_free(ctx);
|
||||
}
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
|
||||
ggml_cuda_free_data(tensors_by_name[i].second);
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
}
|
||||
};
|
||||
|
||||
|
|
@ -243,6 +259,10 @@ struct llama_context {
|
|||
llama_ctx_buffer buf_compute;
|
||||
llama_ctx_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
ggml_metal_context * ctx_metal = NULL;
|
||||
#endif
|
||||
|
||||
int buf_last = 0;
|
||||
size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
|
||||
|
||||
|
|
@ -282,15 +302,15 @@ template <typename T>
|
|||
static T checked_mul(T a, T b) {
|
||||
T ret = a * b;
|
||||
if (a != 0 && ret / a != b) {
|
||||
throw format("overflow multiplying %llu * %llu",
|
||||
(unsigned long long) a, (unsigned long long) b);
|
||||
throw std::runtime_error(format("overflow multiplying %llu * %llu",
|
||||
(unsigned long long) a, (unsigned long long) b));
|
||||
}
|
||||
return ret;
|
||||
}
|
||||
|
||||
static size_t checked_div(size_t a, size_t b) {
|
||||
if (b == 0 || a % b != 0) {
|
||||
throw format("error dividing %zu / %zu", a, b);
|
||||
throw std::runtime_error(format("error dividing %zu / %zu", a, b));
|
||||
}
|
||||
return a / b;
|
||||
}
|
||||
|
|
@ -354,7 +374,7 @@ struct llama_load_tensor {
|
|||
const auto & first_shard = shards.at(0);
|
||||
for (const auto & shard : shards) {
|
||||
if (shard.type != first_shard.type) {
|
||||
throw format("inconsistent tensor shard type in '%s'", name.c_str());
|
||||
throw std::runtime_error(format("inconsistent tensor shard type in '%s'", name.c_str()));
|
||||
}
|
||||
}
|
||||
type = first_shard.type;
|
||||
|
|
@ -377,8 +397,8 @@ struct llama_load_tensor {
|
|||
const auto & first_shard = shards.at(0);
|
||||
for (const auto & shard : shards) {
|
||||
if (shard.ne != first_shard.ne) {
|
||||
throw format("inconsistent tensor shard shape in '%s': first was %s, other was %s",
|
||||
name.c_str(), llama_format_tensor_shape(first_shard.ne).c_str(), llama_format_tensor_shape(shard.ne).c_str());
|
||||
throw std::runtime_error(format("inconsistent tensor shard shape in '%s': first was %s, other was %s",
|
||||
name.c_str(), llama_format_tensor_shape(first_shard.ne).c_str(), llama_format_tensor_shape(shard.ne).c_str()));
|
||||
}
|
||||
}
|
||||
ne = first_shard.ne;
|
||||
|
|
@ -456,8 +476,8 @@ struct llama_file_loader {
|
|||
}
|
||||
}
|
||||
|
||||
throw format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?",
|
||||
magic, version);
|
||||
throw std::runtime_error(format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?",
|
||||
magic, version));
|
||||
}
|
||||
void read_hparams() {
|
||||
hparams.n_vocab = file.read_u32();
|
||||
|
|
@ -497,7 +517,7 @@ struct llama_file_loader {
|
|||
file.read_raw(shard.ne.data(), sizeof(shard.ne[0]) * n_dims);
|
||||
std::string name = file.read_string(name_len);
|
||||
if (n_dims < 1 || n_dims > 2) {
|
||||
throw format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims);
|
||||
throw std::runtime_error(format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims));
|
||||
}
|
||||
switch (shard.type) {
|
||||
case GGML_TYPE_F32:
|
||||
|
|
@ -507,9 +527,14 @@ struct llama_file_loader {
|
|||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
break;
|
||||
default: {
|
||||
throw format("unrecognized tensor type %u\n", shard.type);
|
||||
throw std::runtime_error(format("unrecognized tensor type %u\n", shard.type));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -582,6 +607,11 @@ struct llama_file_saver {
|
|||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
break;
|
||||
default: LLAMA_ASSERT(false);
|
||||
}
|
||||
|
|
@ -613,7 +643,7 @@ struct llama_model_loader {
|
|||
auto * ith_file = new llama_file_loader(fname.c_str(), i, tensors_map);
|
||||
file_loaders.emplace_back(ith_file);
|
||||
if (ith_file->hparams != first_file->hparams) {
|
||||
throw format("llama.cpp: hparams inconsistent between files");
|
||||
throw std::runtime_error(format("llama.cpp: hparams inconsistent between files"));
|
||||
}
|
||||
}
|
||||
if (!llama_mmap::SUPPORTED) {
|
||||
|
|
@ -643,7 +673,7 @@ struct llama_model_loader {
|
|||
uint32_t guess_n_parts() const {
|
||||
auto it = tensors_map.name_to_idx.find("tok_embeddings.weight");
|
||||
if (it == tensors_map.name_to_idx.end()) {
|
||||
throw std::string("missing tok_embeddings.weight");
|
||||
throw std::runtime_error(std::string("missing tok_embeddings.weight"));
|
||||
}
|
||||
const llama_load_tensor & lt = tensors_map.tensors.at(it->second);
|
||||
return file_loaders.at(0)->hparams.n_embd / lt.shards.at(0).ne.at(0);
|
||||
|
|
@ -660,12 +690,12 @@ struct llama_model_loader {
|
|||
struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) {
|
||||
auto it = tensors_map.name_to_idx.find(name);
|
||||
if (it == tensors_map.name_to_idx.end()) {
|
||||
throw format("llama.cpp: tensor '%s' is missing from model", name.c_str());
|
||||
throw std::runtime_error(std::runtime_error(format("llama.cpp: tensor '%s' is missing from model", name.c_str())));
|
||||
}
|
||||
llama_load_tensor & lt = tensors_map.tensors.at(it->second);
|
||||
if (lt.ne != ne) {
|
||||
throw format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s",
|
||||
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str());
|
||||
throw std::runtime_error(format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s",
|
||||
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str()));
|
||||
}
|
||||
|
||||
return get_tensor_for(lt, backend);
|
||||
|
|
@ -681,6 +711,7 @@ struct llama_model_loader {
|
|||
}
|
||||
ggml_set_name(tensor, lt.name.c_str());
|
||||
LLAMA_ASSERT(lt.ggml_tensor == NULL); // if this fails, we called get_tensor twice on the same tensor
|
||||
|
||||
tensor->backend = backend;
|
||||
lt.ggml_tensor = tensor;
|
||||
num_ggml_tensors_created++;
|
||||
|
|
@ -689,7 +720,7 @@ struct llama_model_loader {
|
|||
|
||||
void done_getting_tensors() const {
|
||||
if (num_ggml_tensors_created != tensors_map.tensors.size()) {
|
||||
throw std::string("llama.cpp: file contained more tensors than expected");
|
||||
throw std::runtime_error(std::string("llama.cpp: file contained more tensors than expected"));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -833,7 +864,10 @@ static bool kv_cache_init(
|
|||
struct llama_context_params llama_context_default_params() {
|
||||
struct llama_context_params result = {
|
||||
/*.n_ctx =*/ 512,
|
||||
/*.n_batch =*/ 512,
|
||||
/*.gpu_layers =*/ 0,
|
||||
/*.main_gpu =*/ 0,
|
||||
/*.tensor_split =*/ {0},
|
||||
/*.seed =*/ -1,
|
||||
/*.f16_kv =*/ true,
|
||||
/*.logits_all =*/ false,
|
||||
|
|
@ -898,6 +932,16 @@ static const char *llama_ftype_name(enum llama_ftype ftype) {
|
|||
case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
|
||||
case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";
|
||||
// K-quants
|
||||
case LLAMA_FTYPE_MOSTLY_Q2_K: return "mostly Q2_K";
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small";
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium";
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large";
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small";
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium";
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small";
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium";
|
||||
case LLAMA_FTYPE_MOSTLY_Q6_K: return "mostly Q6_K";
|
||||
default: return "unknown, may not work";
|
||||
}
|
||||
}
|
||||
|
|
@ -917,7 +961,10 @@ static void llama_model_load_internal(
|
|||
const std::string & fname,
|
||||
llama_context & lctx,
|
||||
int n_ctx,
|
||||
int n_batch,
|
||||
int n_gpu_layers,
|
||||
int main_gpu,
|
||||
const float * tensor_split,
|
||||
ggml_type memory_type,
|
||||
bool use_mmap,
|
||||
bool use_mlock,
|
||||
|
|
@ -932,9 +979,9 @@ static void llama_model_load_internal(
|
|||
lctx.vocab = std::move(ml->file_loaders.at(0)->vocab);
|
||||
auto & model = lctx.model;
|
||||
model.hparams = ml->file_loaders.at(0)->hparams;
|
||||
model.n_gpu_layers = n_gpu_layers;
|
||||
llama_file_version file_version = ml->file_loaders.at(0)->file_version;
|
||||
auto & hparams = model.hparams;
|
||||
uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
|
||||
|
||||
{
|
||||
switch (hparams.n_layer) {
|
||||
|
|
@ -948,6 +995,8 @@ static void llama_model_load_internal(
|
|||
hparams.n_ctx = n_ctx;
|
||||
}
|
||||
|
||||
const uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
|
||||
|
||||
{
|
||||
fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version));
|
||||
fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab);
|
||||
|
|
@ -967,7 +1016,7 @@ static void llama_model_load_internal(
|
|||
if (hparams.ftype != LLAMA_FTYPE_ALL_F32 &&
|
||||
hparams.ftype != LLAMA_FTYPE_MOSTLY_F16 &&
|
||||
hparams.ftype != LLAMA_FTYPE_MOSTLY_Q8_0) {
|
||||
throw format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1405)");
|
||||
throw std::runtime_error(format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1405)"));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -975,7 +1024,7 @@ static void llama_model_load_internal(
|
|||
if (hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
|
||||
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_1 ||
|
||||
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q8_0) {
|
||||
throw format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1508)");
|
||||
throw std::runtime_error(format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1508)"));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -1006,18 +1055,28 @@ static void llama_model_load_internal(
|
|||
|
||||
model.ctx = ggml_init(params);
|
||||
if (!model.ctx) {
|
||||
throw format("ggml_init() failed");
|
||||
throw std::runtime_error(format("ggml_init() failed"));
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CUDA
|
||||
(void) main_gpu;
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
fprintf(stderr, "%s: using CUDA for GPU acceleration\n", __func__);
|
||||
ggml_cuda_set_main_device(main_gpu);
|
||||
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
|
||||
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
fprintf(stderr, "%s: using OpenCL for GPU acceleration\n", __func__);
|
||||
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
|
||||
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU
|
||||
#else
|
||||
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU
|
||||
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_CPU
|
||||
#endif
|
||||
|
||||
// prepare memory for the weights
|
||||
size_t vram_total = 0;
|
||||
size_t vram_weights = 0;
|
||||
size_t vram_scratch = 0;
|
||||
{
|
||||
const uint32_t n_embd = hparams.n_embd;
|
||||
const uint32_t n_layer = hparams.n_layer;
|
||||
|
|
@ -1032,7 +1091,7 @@ static void llama_model_load_internal(
|
|||
{
|
||||
ggml_backend backend_output;
|
||||
if (n_gpu_layers > int(n_layer)) { // NOLINT
|
||||
backend_output = LLAMA_BACKEND_OFFLOAD;
|
||||
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
|
||||
} else {
|
||||
backend_output = GGML_BACKEND_CPU;
|
||||
}
|
||||
|
|
@ -1044,7 +1103,8 @@ static void llama_model_load_internal(
|
|||
|
||||
model.layers.resize(n_layer);
|
||||
for (uint32_t i = 0; i < n_layer; ++i) {
|
||||
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
|
||||
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
|
||||
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
|
||||
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
|
|
@ -1052,19 +1112,19 @@ static void llama_model_load_internal(
|
|||
|
||||
layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd}, backend);
|
||||
|
||||
layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd}, backend);
|
||||
layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd}, backend);
|
||||
layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd}, backend);
|
||||
layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd}, backend);
|
||||
layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd}, backend_split);
|
||||
layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd}, backend_split);
|
||||
layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd}, backend_split);
|
||||
layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd}, backend_split);
|
||||
|
||||
layer.ffn_norm = ml->get_tensor(layers_i + ".ffn_norm.weight", {n_embd}, backend);
|
||||
|
||||
layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}, backend);
|
||||
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}, backend);
|
||||
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend);
|
||||
layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}, backend_split);
|
||||
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}, backend_split);
|
||||
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend_split);
|
||||
|
||||
if (backend == GGML_BACKEND_CUDA) {
|
||||
vram_total +=
|
||||
if (backend == GGML_BACKEND_GPU) {
|
||||
vram_weights +=
|
||||
ggml_nbytes(layer.attention_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
|
||||
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.attention_norm) +
|
||||
ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
|
||||
|
|
@ -1081,10 +1141,10 @@ static void llama_model_load_internal(
|
|||
// this is the total memory required to run the inference
|
||||
const size_t mem_required =
|
||||
ctx_size +
|
||||
mmapped_size - vram_total + // weights in VRAM not in memory
|
||||
mmapped_size - vram_weights + // weights in VRAM not in memory
|
||||
MEM_REQ_SCRATCH0().at(model.type) +
|
||||
MEM_REQ_SCRATCH1().at(model.type) +
|
||||
MEM_REQ_EVAL().at(model.type);
|
||||
MEM_REQ_EVAL().at (model.type);
|
||||
|
||||
// this is the memory required by one llama_state
|
||||
const size_t mem_required_state =
|
||||
|
|
@ -1093,15 +1153,25 @@ static void llama_model_load_internal(
|
|||
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
|
||||
mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
|
||||
|
||||
(void) vram_scratch;
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
vram_scratch = n_batch * MB;
|
||||
ggml_cuda_set_scratch_size(vram_scratch);
|
||||
if (n_gpu_layers > 0) {
|
||||
fprintf(stderr, "%s: allocating batch_size x 1 MB = %ld MB VRAM for the scratch buffer\n",
|
||||
__func__, vram_scratch / MB);
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
|
||||
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
|
||||
|
||||
fprintf(stderr, "%s: [cublas] offloading %d layers to GPU\n", __func__, n_gpu);
|
||||
fprintf(stderr, "%s: offloading %d layers to GPU\n", __func__, n_gpu);
|
||||
if (n_gpu_layers > (int) hparams.n_layer) {
|
||||
fprintf(stderr, "%s: [cublas] offloading output layer to GPU\n", __func__);
|
||||
fprintf(stderr, "%s: offloading output layer to GPU\n", __func__);
|
||||
}
|
||||
fprintf(stderr, "%s: [cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
|
||||
#elif !defined(GGML_USE_CLBLAST)
|
||||
fprintf(stderr, "%s: total VRAM used: %zu MB\n",
|
||||
__func__, (vram_weights + vram_scratch + MB - 1) / MB); // round up
|
||||
#else
|
||||
(void) n_gpu_layers;
|
||||
#endif
|
||||
}
|
||||
|
|
@ -1113,8 +1183,10 @@ static void llama_model_load_internal(
|
|||
|
||||
ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL);
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
{
|
||||
ggml_cuda_set_tensor_split(tensor_split);
|
||||
|
||||
size_t done_size = 0;
|
||||
size_t data_size = 0;
|
||||
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
|
||||
|
|
@ -1124,7 +1196,8 @@ static void llama_model_load_internal(
|
|||
}
|
||||
}
|
||||
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
|
||||
if (lt.ggml_tensor->backend != GGML_BACKEND_CUDA) {
|
||||
ggml_backend backend = lt.ggml_tensor->backend;
|
||||
if (backend != GGML_BACKEND_GPU && backend != GGML_BACKEND_GPU_SPLIT) {
|
||||
continue;
|
||||
}
|
||||
if (progress_callback) {
|
||||
|
|
@ -1136,30 +1209,28 @@ static void llama_model_load_internal(
|
|||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
{
|
||||
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
|
||||
|
||||
fprintf(stderr, "ggml_opencl: offloading %d layers to GPU\n", n_gpu);
|
||||
|
||||
size_t vram_total = 0;
|
||||
|
||||
for (int i = 0; i < n_gpu; ++i) {
|
||||
const auto & layer = model.layers[i];
|
||||
|
||||
ggml_cl_transform_tensor(layer.wq); vram_total += ggml_nbytes(layer.wq);
|
||||
ggml_cl_transform_tensor(layer.wk); vram_total += ggml_nbytes(layer.wk);
|
||||
ggml_cl_transform_tensor(layer.wv); vram_total += ggml_nbytes(layer.wv);
|
||||
ggml_cl_transform_tensor(layer.wo); vram_total += ggml_nbytes(layer.wo);
|
||||
ggml_cl_transform_tensor(layer.w1); vram_total += ggml_nbytes(layer.w1);
|
||||
ggml_cl_transform_tensor(layer.w2); vram_total += ggml_nbytes(layer.w2);
|
||||
ggml_cl_transform_tensor(layer.w3); vram_total += ggml_nbytes(layer.w3);
|
||||
size_t done_size = 0;
|
||||
size_t data_size = 0;
|
||||
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
|
||||
data_size += lt.size;
|
||||
if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) {
|
||||
done_size += lt.size;
|
||||
}
|
||||
if (n_gpu_layers > (int) hparams.n_layer) {
|
||||
fprintf(stderr, "ggml_opencl: offloading output layer to GPU\n");
|
||||
ggml_cl_transform_tensor(model.output); vram_total += ggml_nbytes(model.output);
|
||||
}
|
||||
|
||||
fprintf(stderr, "ggml_opencl: total VRAM used: %zu MB\n", vram_total / 1024 / 1024);
|
||||
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
|
||||
if (lt.ggml_tensor->backend != GGML_BACKEND_GPU) {
|
||||
continue;
|
||||
}
|
||||
if (progress_callback) {
|
||||
progress_callback((float) done_size / data_size, progress_callback_user_data);
|
||||
}
|
||||
ggml_cl_load_data(fname.c_str(), lt.ggml_tensor, lt.shards.at(0).file_off);
|
||||
done_size += lt.size;
|
||||
}
|
||||
}
|
||||
#else
|
||||
(void) n_batch;
|
||||
(void) tensor_split;
|
||||
#endif
|
||||
|
||||
if (progress_callback) {
|
||||
|
|
@ -1177,7 +1248,10 @@ static bool llama_model_load(
|
|||
const std::string & fname,
|
||||
llama_context & lctx,
|
||||
int n_ctx,
|
||||
int n_batch,
|
||||
int n_gpu_layers,
|
||||
int main_gpu,
|
||||
float * tensor_split,
|
||||
ggml_type memory_type,
|
||||
bool use_mmap,
|
||||
bool use_mlock,
|
||||
|
|
@ -1185,11 +1259,11 @@ static bool llama_model_load(
|
|||
llama_progress_callback progress_callback,
|
||||
void *progress_callback_user_data) {
|
||||
try {
|
||||
llama_model_load_internal(fname, lctx, n_ctx, n_gpu_layers, memory_type, use_mmap, use_mlock,
|
||||
vocab_only, progress_callback, progress_callback_user_data);
|
||||
llama_model_load_internal(fname, lctx, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, memory_type,
|
||||
use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
|
||||
return true;
|
||||
} catch (const std::string & err) {
|
||||
fprintf(stderr, "error loading model: %s\n", err.c_str());
|
||||
} catch (const std::exception & err) {
|
||||
fprintf(stderr, "error loading model: %s\n", err.what());
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
|
@ -1200,13 +1274,15 @@ static bool llama_model_load(
|
|||
// - tokens: new batch of tokens to process
|
||||
// - n_past: the context size so far
|
||||
// - n_threads: number of threads to use
|
||||
// - cgraph_fname: filename of the exported computation graph
|
||||
//
|
||||
static bool llama_eval_internal(
|
||||
llama_context & lctx,
|
||||
const llama_token * tokens,
|
||||
const int n_tokens,
|
||||
const int n_past,
|
||||
const int n_threads) {
|
||||
const int n_threads,
|
||||
const char * cgraph_fname) {
|
||||
|
||||
// enforce that the first token is BOS
|
||||
if (n_past == 0 && tokens[0] != llama_token_bos()) {
|
||||
|
|
@ -1231,6 +1307,7 @@ static bool llama_eval_internal(
|
|||
const int n_head = hparams.n_head;
|
||||
const int n_vocab = hparams.n_vocab;
|
||||
const int n_rot = hparams.n_embd/hparams.n_head;
|
||||
const int n_gpu_layers = model.n_gpu_layers;
|
||||
|
||||
auto & mem_per_token = lctx.mem_per_token;
|
||||
auto & buf_compute = lctx.buf_compute;
|
||||
|
|
@ -1252,40 +1329,66 @@ static bool llama_eval_internal(
|
|||
ggml_set_name(embd, "embd");
|
||||
memcpy(embd->data, tokens, N*ggml_element_size(embd));
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
struct ggml_tensor * inpSA = inpL;
|
||||
const int i_gpu_start = n_layer - n_gpu_layers;
|
||||
(void) i_gpu_start;
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
offload_func_t offload_func = llama_nop;
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
if (il >= i_gpu_start) {
|
||||
offload_func = ggml_cuda_assign_buffers; // sets the output backend to GPU
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
|
||||
struct ggml_tensor * inpSA = inpL;
|
||||
|
||||
lctx.use_buf(ctx0, 0);
|
||||
|
||||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, inpL);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "rms_norm_0");
|
||||
|
||||
// cur = cur*attention_norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.layers[il].attention_norm);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "attention_norm_0");
|
||||
}
|
||||
|
||||
// self-attention
|
||||
{
|
||||
// compute Q and K and RoPE them
|
||||
struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
|
||||
struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
|
||||
ggml_set_name(Qcur, "Qcur");
|
||||
struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
||||
// offload_func(tmpq);
|
||||
ggml_set_name(tmpq, "tmpq");
|
||||
|
||||
struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
||||
// offload_func(tmpk);
|
||||
ggml_set_name(tmpk, "tmpk");
|
||||
|
||||
struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0);
|
||||
ggml_set_name(Kcur, "Kcur");
|
||||
|
||||
struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0);
|
||||
ggml_set_name(Qcur, "Qcur");
|
||||
|
||||
// store key and value to memory
|
||||
{
|
||||
// compute the transposed [N, n_embd] V matrix
|
||||
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), n_embd, N));
|
||||
ggml_set_name(Vcur, "Vcur");
|
||||
|
||||
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
|
||||
ggml_set_name(k, "k");
|
||||
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
|
||||
( n_ctx)*ggml_element_size(kv_self.v),
|
||||
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
|
||||
ggml_set_name(v, "v");
|
||||
|
||||
// important: storing RoPE-ed version of K in the KV cache!
|
||||
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
|
||||
|
|
@ -1326,7 +1429,6 @@ static bool llama_eval_internal(
|
|||
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
|
||||
ggml_set_name(KQ_soft_max, "KQ_soft_max");
|
||||
|
||||
|
||||
// split cached V into n_head heads
|
||||
struct ggml_tensor * V =
|
||||
ggml_view_3d(ctx0, kv_self.v,
|
||||
|
|
@ -1361,73 +1463,143 @@ static bool llama_eval_internal(
|
|||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].wo,
|
||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_wo");
|
||||
}
|
||||
|
||||
lctx.use_buf(ctx0, 1);
|
||||
//ggml_cuda_set_scratch(1);
|
||||
|
||||
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
||||
offload_func(inpFF);
|
||||
ggml_set_name(inpFF, "inpFF");
|
||||
|
||||
// feed-forward network
|
||||
{
|
||||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, inpFF);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "rms_norm_1");
|
||||
|
||||
// cur = cur*ffn_norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "ffn_norm");
|
||||
}
|
||||
|
||||
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w3,
|
||||
cur);
|
||||
offload_func(tmp);
|
||||
ggml_set_name(tmp, "result_w3");
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w1,
|
||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_w2");
|
||||
|
||||
// SILU activation
|
||||
cur = ggml_silu(ctx0, cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "silu");
|
||||
|
||||
cur = ggml_mul(ctx0, cur, tmp);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "silu_x_result_w3");
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w2,
|
||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_w2");
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, inpFF);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "inpFF_+_result_w2");
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
|
||||
}
|
||||
|
||||
lctx.use_buf(ctx0, 0);
|
||||
//ggml_cuda_set_scratch(0);
|
||||
|
||||
// used at the end to optionally extract the embeddings
|
||||
struct ggml_tensor * embeddings = NULL;
|
||||
|
||||
offload_func_t offload_func = llama_nop;
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
if (n_gpu_layers > n_layer) {
|
||||
offload_func = ggml_cuda_assign_buffers; // sets the output backend to GPU
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
|
||||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, inpL);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "rms_norm_inpL");
|
||||
|
||||
inpL = ggml_rms_norm(ctx0, inpL);
|
||||
cur = ggml_rms_norm(ctx0, cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "rms_norm_after");
|
||||
|
||||
// inpL = inpL*norm(broadcasted)
|
||||
inpL = ggml_mul(ctx0, inpL, model.norm);
|
||||
// cur = cur*norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.norm);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_norm");
|
||||
|
||||
embeddings = inpL;
|
||||
embeddings = cur;
|
||||
}
|
||||
|
||||
|
||||
// lm_head
|
||||
inpL = ggml_mul_mat(ctx0, model.output, inpL);
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
ggml_set_name(cur, "result_output");
|
||||
|
||||
lctx.use_buf(ctx0, -1);
|
||||
|
||||
// logits -> probs
|
||||
//inpL = ggml_soft_max_inplace(ctx0, inpL);
|
||||
//cur = ggml_soft_max_inplace(ctx0, cur);
|
||||
|
||||
// run the computation
|
||||
ggml_build_forward_expand(&gf, inpL);
|
||||
ggml_graph_compute (ctx0, &gf);
|
||||
ggml_build_forward_expand(&gf, cur);
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
if (lctx.ctx_metal && N == 1) {
|
||||
ggml_metal_graph_compute(lctx.ctx_metal, &gf);
|
||||
ggml_metal_get_tensor (lctx.ctx_metal, cur);
|
||||
} else {
|
||||
// IMPORTANT:
|
||||
// Since we don't have efficient Matrix x Matrix Metal multiplication yet, we fallback to vanilla
|
||||
// ggml_graph_compute(). It uses Apple's Accelerate CBLAS API which takes advantage of the ANE or the AMX
|
||||
// coprocessor.
|
||||
//
|
||||
// When we implement Matrix x Matrix Metal multiplication, we can avoid this branch.
|
||||
// But for now, we have focused only on Matrix x Vector Metal multiplication.
|
||||
//
|
||||
// TODO: avoid these syncs via shared memory (ref #1696)
|
||||
//
|
||||
if (lctx.ctx_metal) {
|
||||
// We need to sync the GPU KV cache with the CPU KV cache
|
||||
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.k);
|
||||
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.v);
|
||||
}
|
||||
|
||||
ggml_graph_compute(ctx0, &gf);
|
||||
}
|
||||
#else
|
||||
ggml_graph_compute(ctx0, &gf);
|
||||
#endif
|
||||
|
||||
if (cgraph_fname) {
|
||||
ggml_graph_export(&gf, cgraph_fname);
|
||||
}
|
||||
|
||||
#ifdef GGML_PERF
|
||||
// print timing information per ggml operation (for debugging purposes)
|
||||
|
|
@ -1441,7 +1613,7 @@ static bool llama_eval_internal(
|
|||
//}
|
||||
|
||||
//embd_w.resize(n_vocab*N);
|
||||
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
|
||||
//memcpy(embd_w.data(), ggml_get_data(cur), sizeof(float)*n_vocab*N);
|
||||
|
||||
// update kv token count
|
||||
lctx.model.kv_self.n = n_past + N;
|
||||
|
|
@ -1452,11 +1624,11 @@ static bool llama_eval_internal(
|
|||
|
||||
if (lctx.logits_all) {
|
||||
logits_out.resize(n_vocab * N);
|
||||
memcpy(logits_out.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N);
|
||||
memcpy(logits_out.data(), (float *) ggml_get_data(cur), sizeof(float)*n_vocab*N);
|
||||
} else {
|
||||
// return result for just the last token
|
||||
logits_out.resize(n_vocab);
|
||||
memcpy(logits_out.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
|
||||
memcpy(logits_out.data(), (float *) ggml_get_data(cur) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -2063,8 +2235,19 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
|
||||
default: throw format("invalid output file type %d\n", ftype);
|
||||
};
|
||||
|
||||
// K-quants
|
||||
case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_M:
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_S:
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_S:
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break;
|
||||
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
|
||||
}
|
||||
|
||||
if (nthread <= 0) {
|
||||
nthread = std::thread::hardware_concurrency();
|
||||
|
|
@ -2074,6 +2257,20 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
/*vocab_only*/ false));
|
||||
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), ftype);
|
||||
|
||||
int n_attention_wv = 0;
|
||||
int n_feed_forward_w2 = 0;
|
||||
for (auto& tensor : model_loader->tensors_map.tensors) {
|
||||
if (tensor.name.find("attention.wv.weight") != std::string::npos) {
|
||||
++n_attention_wv;
|
||||
}
|
||||
else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
|
||||
++n_feed_forward_w2;
|
||||
}
|
||||
}
|
||||
|
||||
int i_attention_wv = 0;
|
||||
int i_feed_forward_w2 = 0;
|
||||
|
||||
size_t total_size_org = 0;
|
||||
size_t total_size_new = 0;
|
||||
std::vector<int64_t> hist_all(1 << 4, 0);
|
||||
|
|
@ -2116,6 +2313,32 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
printf("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
|
||||
} else {
|
||||
new_type = quantized_type;
|
||||
// TODO: temporary disabled until Metal / OpenCL support is available
|
||||
// ref: https://github.com/ggerganov/llama.cpp/issues/1711
|
||||
//if (tensor.name == "output.weight") {
|
||||
// new_type = GGML_TYPE_Q6_K;
|
||||
//}
|
||||
if (tensor.name.find("attention.wv.weight") != std::string::npos) {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
|
||||
(i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8 ||
|
||||
(i_attention_wv - n_attention_wv/8)%3 == 2)) new_type = GGML_TYPE_Q6_K;
|
||||
++i_attention_wv;
|
||||
}
|
||||
if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
|
||||
(i_feed_forward_w2 < n_feed_forward_w2/8 || i_feed_forward_w2 >= 7*n_feed_forward_w2/8 ||
|
||||
(i_feed_forward_w2 - n_feed_forward_w2/8)%3 == 2)) new_type = GGML_TYPE_Q6_K;
|
||||
++i_feed_forward_w2;
|
||||
}
|
||||
if (tensor.name.find("attention.wo.weight") != std::string::npos) {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
|
||||
float * f32_data;
|
||||
size_t nelements = tensor.ne.at(0) * tensor.ne.at(1);
|
||||
llama_buffer f32_conv_buf;
|
||||
|
|
@ -2129,7 +2352,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
f32_data[i] = ggml_fp16_to_fp32(f16_data[i]);
|
||||
}
|
||||
} else {
|
||||
throw format("type %s unsupported for integer quantization", ggml_type_name(tensor.type));
|
||||
throw std::runtime_error(format("type %s unsupported for integer quantization", ggml_type_name(tensor.type)));
|
||||
}
|
||||
|
||||
printf("quantizing .. ");
|
||||
|
|
@ -2183,13 +2406,17 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
}
|
||||
|
||||
printf("size = %8.2f MB -> %8.2f MB | hist: ", tensor.size/1024.0/1024.0, new_size/1024.0/1024.0);
|
||||
int64_t tot_count = 0;
|
||||
for (size_t i = 0; i < hist_cur.size(); i++) {
|
||||
hist_all[i] += hist_cur[i];
|
||||
tot_count += hist_cur[i];
|
||||
}
|
||||
|
||||
if (tot_count > 0) {
|
||||
for (size_t i = 0; i < hist_cur.size(); i++) {
|
||||
printf("%5.3f ", hist_cur[i] / float(nelements));
|
||||
}
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
total_size_org += tensor.size;
|
||||
|
|
@ -2206,12 +2433,14 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
sum_all += hist_all[i];
|
||||
}
|
||||
|
||||
if (sum_all > 0) {
|
||||
printf("%s: hist: ", __func__);
|
||||
for (size_t i = 0; i < hist_all.size(); i++) {
|
||||
printf("%5.3f ", hist_all[i] / float(sum_all));
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
//
|
||||
|
|
@ -2251,9 +2480,9 @@ struct llama_context * llama_init_from_file(
|
|||
|
||||
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
||||
|
||||
if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_gpu_layers, memory_type,
|
||||
params.use_mmap, params.use_mlock, params.vocab_only,
|
||||
params.progress_callback, params.progress_callback_user_data)) {
|
||||
if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_batch, params.n_gpu_layers,
|
||||
params.main_gpu, params.tensor_split, memory_type, params.use_mmap, params.use_mlock,
|
||||
params.vocab_only, params.progress_callback, params.progress_callback_user_data)) {
|
||||
fprintf(stderr, "%s: failed to load model\n", __func__);
|
||||
llama_free(ctx);
|
||||
return nullptr;
|
||||
|
|
@ -2291,6 +2520,38 @@ struct llama_context * llama_init_from_file(
|
|||
ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
|
||||
}
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
if (params.n_gpu_layers > 0) {
|
||||
// this allocates all Metal resources and memory buffers
|
||||
ctx->ctx_metal = ggml_metal_init();
|
||||
|
||||
void *data_ptr = NULL;
|
||||
size_t data_size = 0;
|
||||
if (params.use_mmap) {
|
||||
data_ptr = ctx->model.mapping->addr;
|
||||
data_size= ctx->model.mapping->size;
|
||||
} else {
|
||||
data_ptr = ggml_get_mem_buffer(ctx->model.ctx);
|
||||
data_size= ggml_get_mem_size(ctx->model.ctx);
|
||||
}
|
||||
|
||||
#define LLAMA_METAL_CHECK_BUF(result) \
|
||||
if (!(result)) { \
|
||||
fprintf(stderr, "%s: failed to add buffer\n", __func__); \
|
||||
llama_free(ctx); \
|
||||
return NULL; \
|
||||
}
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size));
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->model.kv_self.buf.addr, ctx->model.kv_self.buf.size));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size));
|
||||
#undef LLAMA_METAL_CHECK_BUF
|
||||
}
|
||||
#endif
|
||||
|
||||
return ctx;
|
||||
}
|
||||
|
||||
|
|
@ -2306,8 +2567,8 @@ int llama_model_quantize(
|
|||
try {
|
||||
llama_model_quantize_internal(fname_inp, fname_out, ftype, nthread);
|
||||
return 0;
|
||||
} catch (const std::string & err) {
|
||||
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.c_str());
|
||||
} catch (const std::exception & err) {
|
||||
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.what());
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
|
@ -2560,8 +2821,8 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
|
|||
int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) {
|
||||
try {
|
||||
return llama_apply_lora_from_file_internal(ctx, path_lora, path_base_model, n_threads);
|
||||
} catch (const std::string & err) {
|
||||
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.c_str());
|
||||
} catch (const std::exception & err) {
|
||||
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what());
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
|
@ -2906,7 +3167,7 @@ int llama_eval(
|
|||
int n_tokens,
|
||||
int n_past,
|
||||
int n_threads) {
|
||||
if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads)) {
|
||||
if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads, nullptr)) {
|
||||
fprintf(stderr, "%s: failed to eval\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
|
@ -2921,6 +3182,20 @@ int llama_eval(
|
|||
return 0;
|
||||
}
|
||||
|
||||
int llama_eval_export(struct llama_context * ctx, const char * fname) {
|
||||
const int n_batch = 1;
|
||||
const int n_ctx = 512 - n_batch;
|
||||
|
||||
const std::vector<llama_token> tmp(n_batch, llama_token_bos());
|
||||
|
||||
if (!llama_eval_internal(*ctx, tmp.data(), tmp.size(), n_ctx, 1, fname)) {
|
||||
fprintf(stderr, "%s: failed to eval\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
int llama_tokenize(
|
||||
struct llama_context * ctx,
|
||||
const char * text,
|
||||
|
|
|
|||
27
llama.h
27
llama.h
|
|
@ -1,6 +1,13 @@
|
|||
#ifndef LLAMA_H
|
||||
#define LLAMA_H
|
||||
|
||||
#include "ggml.h"
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
#include "ggml-cuda.h"
|
||||
#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES
|
||||
#else
|
||||
#define LLAMA_MAX_DEVICES 1
|
||||
#endif // GGML_USE_CUBLAS
|
||||
#include <stddef.h>
|
||||
#include <stdint.h>
|
||||
#include <stdbool.h>
|
||||
|
|
@ -31,7 +38,7 @@
|
|||
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
|
||||
#define LLAMA_SESSION_VERSION 1
|
||||
|
||||
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
|
||||
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
|
||||
#define LLAMA_SUPPORTS_GPU_OFFLOAD
|
||||
#endif
|
||||
|
|
@ -66,7 +73,10 @@ extern "C" {
|
|||
|
||||
struct llama_context_params {
|
||||
int n_ctx; // text context
|
||||
int n_batch; // prompt processing batch size
|
||||
int n_gpu_layers; // number of layers to store in VRAM
|
||||
int main_gpu; // the GPU that is used for scratch and small tensors
|
||||
float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
|
||||
int seed; // RNG seed, -1 for random
|
||||
|
||||
bool f16_kv; // use fp16 for KV cache
|
||||
|
|
@ -94,6 +104,15 @@ extern "C" {
|
|||
LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q2_K = 10,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q3_K_S = 11,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q3_K_M = 12,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q3_K_L = 13,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q4_K_S = 14,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q4_K_M = 15,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_K_S = 16,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_K_M = 17,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q6_K = 18,// except 1d tensors
|
||||
};
|
||||
|
||||
LLAMA_API struct llama_context_params llama_context_default_params();
|
||||
|
|
@ -173,6 +192,12 @@ extern "C" {
|
|||
int n_past,
|
||||
int n_threads);
|
||||
|
||||
// Export a static computation graph for context of 511 and batch size of 1
|
||||
// NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
|
||||
// parameters here to keep things simple
|
||||
// IMPORTANT: do not use for anything else other than debugging and testing!
|
||||
LLAMA_API int llama_eval_export(struct llama_context * ctx, const char * fname);
|
||||
|
||||
// Convert the provided text into tokens.
|
||||
// The tokens pointer must be large enough to hold the resulting tokens.
|
||||
// Returns the number of tokens on success, no more than n_max_tokens
|
||||
|
|
|
|||
|
|
@ -12,6 +12,8 @@
|
|||
|
||||
const float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001;
|
||||
const float MAX_QUANTIZATION_TOTAL_ERROR = 0.002;
|
||||
const float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075;
|
||||
const float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040;
|
||||
const float MAX_DOT_PRODUCT_ERROR = 0.02;
|
||||
|
||||
const char* RESULT_STR[] = {"ok", "FAILED"};
|
||||
|
|
@ -122,7 +124,10 @@ int main(int argc, char * argv[]) {
|
|||
|
||||
if (qfns.quantize_row_q && qfns.dequantize_row_q) {
|
||||
const float total_error = total_quantization_error(qfns, test_size, test_data.data());
|
||||
failed = !(total_error < MAX_QUANTIZATION_TOTAL_ERROR);
|
||||
const float max_quantization_error =
|
||||
type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
|
||||
type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS : MAX_QUANTIZATION_TOTAL_ERROR;
|
||||
failed = !(total_error < max_quantization_error);
|
||||
num_failed += failed;
|
||||
if (failed || verbose) {
|
||||
printf("%5s absolute quantization error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], total_error);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue