Merge 'origin/master' into hipblas
This commit is contained in:
Henri Vasserman
commit
6f7c15637a
42 changed files with 3008 additions and 1732 deletions
2
.flake8
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2
.flake8
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@ -0,0 +1,2 @@
|
|||
[flake8]
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max-line-length = 125
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4
.gitignore
vendored
4
.gitignore
vendored
|
|
@ -22,6 +22,7 @@ build-metal/
|
|||
build-no-accel/
|
||||
build-sanitize-addr/
|
||||
build-sanitize-thread/
|
||||
out/
|
||||
|
||||
models/*
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||||
*.bin
|
||||
|
|
@ -32,14 +33,17 @@ models/*
|
|||
/result
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||||
/perplexity
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||||
/embedding
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||||
/train-text-from-scratch
|
||||
/benchmark-matmult
|
||||
/vdot
|
||||
/server
|
||||
/Pipfile
|
||||
/libllama.so
|
||||
|
||||
build-info.h
|
||||
arm_neon.h
|
||||
compile_commands.json
|
||||
CMakeSettings.json
|
||||
|
||||
__pycache__
|
||||
|
||||
|
|
|
|||
15
.pre-commit-config.yaml
Normal file
15
.pre-commit-config.yaml
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
# See https://pre-commit.com for more information
|
||||
# See https://pre-commit.com/hooks.html for more hooks
|
||||
exclude: prompts/.*.txt
|
||||
repos:
|
||||
- repo: https://github.com/pre-commit/pre-commit-hooks
|
||||
rev: v3.2.0
|
||||
hooks:
|
||||
- id: trailing-whitespace
|
||||
- id: end-of-file-fixer
|
||||
- id: check-yaml
|
||||
- id: check-added-large-files
|
||||
- repo: https://github.com/PyCQA/flake8
|
||||
rev: 6.0.0
|
||||
hooks:
|
||||
- id: flake8
|
||||
|
|
@ -70,6 +70,7 @@ set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
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|||
option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
|
||||
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")
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||||
set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
|
||||
option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF)
|
||||
option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
|
||||
option(LLAMA_METAL "llama: use Metal" OFF)
|
||||
|
|
@ -159,17 +160,64 @@ if (LLAMA_BLAS)
|
|||
if ($(CMAKE_VERSION) VERSION_GREATER_EQUAL 3.22)
|
||||
set(BLA_SIZEOF_INTEGER 8)
|
||||
endif()
|
||||
|
||||
set(BLA_VENDOR ${LLAMA_BLAS_VENDOR})
|
||||
find_package(BLAS)
|
||||
|
||||
if (BLAS_FOUND)
|
||||
message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}")
|
||||
|
||||
if ("${BLAS_INCLUDE_DIRS}" STREQUAL "")
|
||||
# BLAS_INCLUDE_DIRS is missing in FindBLAS.cmake.
|
||||
# see https://gitlab.kitware.com/cmake/cmake/-/issues/20268
|
||||
find_package(PkgConfig REQUIRED)
|
||||
if (${LLAMA_BLAS_VENDOR} MATCHES "Generic")
|
||||
pkg_check_modules(DepBLAS REQUIRED blas)
|
||||
elseif (${LLAMA_BLAS_VENDOR} MATCHES "OpenBLAS")
|
||||
pkg_check_modules(DepBLAS REQUIRED openblas)
|
||||
elseif (${LLAMA_BLAS_VENDOR} MATCHES "FLAME")
|
||||
pkg_check_modules(DepBLAS REQUIRED blis)
|
||||
elseif (${LLAMA_BLAS_VENDOR} MATCHES "ATLAS")
|
||||
pkg_check_modules(DepBLAS REQUIRED blas-atlas)
|
||||
elseif (${LLAMA_BLAS_VENDOR} MATCHES "FlexiBLAS")
|
||||
pkg_check_modules(DepBLAS REQUIRED flexiblas_api)
|
||||
elseif (${LLAMA_BLAS_VENDOR} MATCHES "Intel")
|
||||
# all Intel* libraries share the same include path
|
||||
pkg_check_modules(DepBLAS REQUIRED mkl-sdl)
|
||||
elseif (${LLAMA_BLAS_VENDOR} MATCHES "NVHPC")
|
||||
# this doesn't provide pkg-config
|
||||
# suggest to assign BLAS_INCLUDE_DIRS on your own
|
||||
if ("${NVHPC_VERSION}" STREQUAL "")
|
||||
message(WARNING "Better to set NVHPC_VERSION")
|
||||
else()
|
||||
set(DepBLAS_FOUND ON)
|
||||
set(DepBLAS_INCLUDE_DIRS "/opt/nvidia/hpc_sdk/${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR}/${NVHPC_VERSION}/math_libs/include")
|
||||
endif()
|
||||
endif()
|
||||
if (DepBLAS_FOUND)
|
||||
set(BLAS_INCLUDE_DIRS ${DepBLAS_INCLUDE_DIRS})
|
||||
else()
|
||||
message(WARNING "BLAS_INCLUDE_DIRS neither been provided nor been automatically"
|
||||
" detected by pkgconfig, trying to find cblas.h from possible paths...")
|
||||
find_path(BLAS_INCLUDE_DIRS
|
||||
NAMES cblas.h
|
||||
HINTS
|
||||
/usr/include
|
||||
/usr/local/include
|
||||
/usr/include/openblas
|
||||
/opt/homebrew/opt/openblas/include
|
||||
/usr/local/opt/openblas/include
|
||||
/usr/include/x86_64-linux-gnu/openblas/include
|
||||
)
|
||||
endif()
|
||||
endif()
|
||||
|
||||
message(STATUS "BLAS found, Includes: ${BLAS_INCLUDE_DIRS}")
|
||||
add_compile_options(${BLAS_LINKER_FLAGS})
|
||||
add_compile_definitions(GGML_USE_OPENBLAS)
|
||||
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${BLAS_LIBRARIES})
|
||||
set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS})
|
||||
|
||||
message("${BLAS_LIBRARIES} ${BLAS_INCLUDE_DIRS}")
|
||||
include_directories(${BLAS_INCLUDE_DIRS})
|
||||
else()
|
||||
message(WARNING "BLAS not found, please refer to "
|
||||
"https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
|
||||
|
|
@ -191,6 +239,7 @@ if (LLAMA_CUBLAS)
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|||
add_compile_definitions(GGML_USE_CUBLAS)
|
||||
add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
|
||||
add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y})
|
||||
add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
|
||||
|
||||
if (LLAMA_STATIC)
|
||||
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
|
||||
|
|
@ -440,12 +489,14 @@ add_library(ggml OBJECT
|
|||
${GGML_SOURCES_EXTRA}
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||||
)
|
||||
|
||||
target_include_directories(ggml PUBLIC .)
|
||||
target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES})
|
||||
target_compile_features(ggml PUBLIC c_std_11) # don't bump
|
||||
target_link_libraries(ggml PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS})
|
||||
|
||||
add_library(ggml_static STATIC $<TARGET_OBJECTS:ggml>)
|
||||
if (BUILD_SHARED_LIBS)
|
||||
set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON)
|
||||
add_library(ggml_shared SHARED $<TARGET_OBJECTS:ggml>)
|
||||
endif()
|
||||
|
||||
add_library(llama
|
||||
|
|
|
|||
20
Makefile
20
Makefile
|
|
@ -1,8 +1,10 @@
|
|||
# Define the default target now so that it is always the first target
|
||||
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot
|
||||
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple
|
||||
|
||||
ifdef LLAMA_BUILD_SERVER
|
||||
BUILD_TARGETS += server
|
||||
LLAMA_SERVER_VERBOSE ?= 1
|
||||
server: private CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
|
||||
endif
|
||||
|
||||
default: $(BUILD_TARGETS)
|
||||
|
|
@ -171,6 +173,11 @@ ifdef LLAMA_CUDA_DMMV_Y
|
|||
else
|
||||
NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1
|
||||
endif # LLAMA_CUDA_DMMV_Y
|
||||
ifdef LLAMA_CUDA_KQUANTS_ITER
|
||||
NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
|
||||
else
|
||||
NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
|
||||
endif
|
||||
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
|
||||
$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
|
||||
endif # LLAMA_CUBLAS
|
||||
|
|
@ -277,7 +284,7 @@ libllama.so: llama.o ggml.o $(OBJS)
|
|||
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
|
||||
|
||||
clean:
|
||||
rm -vf *.o main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot build-info.h
|
||||
rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot train-text-from-scratch build-info.h
|
||||
|
||||
#
|
||||
# Examples
|
||||
|
|
@ -289,6 +296,12 @@ main: examples/main/main.cpp build-info.h ggml.
|
|||
@echo '==== Run ./main -h for help. ===='
|
||||
@echo
|
||||
|
||||
simple: examples/simple/simple.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
@echo
|
||||
@echo '==== Run ./simple -h for help. ===='
|
||||
@echo
|
||||
|
||||
quantize: examples/quantize/quantize.cpp build-info.h ggml.o llama.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
|
|
@ -307,6 +320,9 @@ save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.
|
|||
server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
|
||||
|
||||
train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp build-info.h ggml.o llama.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
build-info.h: $(wildcard .git/index) scripts/build-info.sh
|
||||
@sh scripts/build-info.sh > $@.tmp
|
||||
@if ! cmp -s $@.tmp $@; then \
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@ let package = Package(
|
|||
.target(
|
||||
name: "llama",
|
||||
path: ".",
|
||||
exclude: ["ggml-metal.metal"],
|
||||
sources: ["ggml.c", "llama.cpp"],
|
||||
publicHeadersPath: "spm-headers",
|
||||
cSettings: [.unsafeFlags(["-Wno-shorten-64-to-32"]), .define("GGML_USE_ACCELERATE")],
|
||||
|
|
|
|||
41
README.md
41
README.md
|
|
@ -616,6 +616,7 @@ And after 4.45 hours, you will have the final perplexity.
|
|||
|
||||
### Android
|
||||
|
||||
#### Building the Project using Android NDK
|
||||
You can easily run `llama.cpp` on Android device with [termux](https://termux.dev/).
|
||||
First, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake:
|
||||
```
|
||||
|
|
@ -630,6 +631,46 @@ Finally, copy the `llama` binary and the model files to your device storage. Her
|
|||
|
||||
https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
|
||||
|
||||
#### Building the Project using Termux (F-Droid)
|
||||
Termux from F-Droid offers an alternative route to execute the project on an Android device. This method empowers you to construct the project right from within the terminal, negating the requirement for a rooted device or SD Card.
|
||||
|
||||
Outlined below are the directives for installing the project using OpenBLAS and CLBlast. This combination is specifically designed to deliver peak performance on recent devices that feature a GPU.
|
||||
|
||||
If you opt to utilize OpenBLAS, you'll need to install the corresponding package.
|
||||
```
|
||||
apt install libopenblas
|
||||
```
|
||||
|
||||
Subsequently, if you decide to incorporate CLBlast, you'll first need to install the requisite OpenCL packages:
|
||||
```
|
||||
apt install ocl-icd opencl-headers opencl-clhpp clinfo
|
||||
```
|
||||
|
||||
In order to compile CLBlast, you'll need to first clone the respective Git repository, which can be found at this URL: https://github.com/CNugteren/CLBlast. Alongside this, clone this repository into your home directory. Once this is done, navigate to the CLBlast folder and execute the commands detailed below:
|
||||
```
|
||||
cmake .
|
||||
make
|
||||
cp libclblast.so* $PREFIX/lib
|
||||
cp ./include/clblast.h ../llama.cpp
|
||||
```
|
||||
|
||||
Following the previous steps, navigate to the LlamaCpp directory. To compile it with OpenBLAS and CLBlast, execute the command provided below:
|
||||
```
|
||||
cp /data/data/com.termux/files/usr/include/openblas/cblas.h .
|
||||
cp /data/data/com.termux/files/usr/include/openblas/openblas_config.h .
|
||||
make LLAMA_CLBLAST=1 //(sometimes you need to run this command twice)
|
||||
```
|
||||
|
||||
Upon completion of the aforementioned steps, you will have successfully compiled the project. To run it using CLBlast, a slight adjustment is required: a command must be issued to direct the operations towards your device's physical GPU, rather than the virtual one. The necessary command is detailed below:
|
||||
```
|
||||
GGML_OPENCL_PLATFORM=0
|
||||
GGML_OPENCL_DEVICE=0
|
||||
export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH
|
||||
./main (...)
|
||||
```
|
||||
|
||||
For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle.
|
||||
|
||||
### Docker
|
||||
|
||||
#### Prerequisites
|
||||
|
|
|
|||
22
convert.py
22
convert.py
|
|
@ -512,7 +512,11 @@ class LazyTensor:
|
|||
if not isinstance(self.data_type, QuantizedDataType):
|
||||
raise Exception(f"Can't turn an unquantized tensor into a quantized type ({data_type})")
|
||||
if self.data_type.have_g_idx:
|
||||
sys.stderr.write("Error: Input uses the newer GPTQ-for-LLaMa format (using g_idx), which is not yet natively supported by GGML. For now you can still convert this model by passing `--outtype f16` to dequantize, but that will result in a much larger output file for no quality benefit.\n")
|
||||
sys.stderr.write(
|
||||
"Error: Input uses the newer GPTQ-for-LLaMa format (using g_idx), "
|
||||
"which is not yet natively supported by GGML. "
|
||||
"For now you can still convert this model by passing `--outtype f16` to dequantize, "
|
||||
"but that will result in a much larger output file for no quality benefit.\n")
|
||||
sys.exit(1)
|
||||
assert not data_type.have_g_idx and self.data_type.have_addends and data_type.have_addends
|
||||
|
||||
|
|
@ -695,7 +699,8 @@ class LazyUnpickler(pickle.Unpickler):
|
|||
return LazyStorage(load=load, kind=pid[1], description=description)
|
||||
|
||||
# @staticmethod
|
||||
def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any, # pyright: ignore[reportSelfClsParameterName]
|
||||
def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any,
|
||||
# pyright: ignore[reportSelfClsParameterName]
|
||||
requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor:
|
||||
assert isinstance(storage, LazyStorage)
|
||||
|
||||
|
|
@ -1054,7 +1059,7 @@ def load_some_model(path: Path) -> ModelPlus:
|
|||
files = list(path.glob("model-00001-of-*.safetensors"))
|
||||
if not files:
|
||||
# Try the PyTorch patterns too, with lower priority
|
||||
globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin" ]
|
||||
globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"]
|
||||
files = [file for glob in globs for file in path.glob(glob)]
|
||||
if not files:
|
||||
# Try GGML too, but with lower priority, since if both a non-GGML
|
||||
|
|
@ -1094,7 +1099,9 @@ def load_vocab(path: Path) -> SentencePieceVocab:
|
|||
elif path3.exists():
|
||||
path = path3
|
||||
else:
|
||||
raise FileNotFoundError(f"Could not find tokenizer.model in {path} or its parent; if it's in another directory, pass the directory as --vocab-dir")
|
||||
raise FileNotFoundError(
|
||||
f"Could not find tokenizer.model in {path} or its parent; "
|
||||
"if it's in another directory, pass the directory as --vocab-dir")
|
||||
added_tokens_path = path.parent / "added_tokens.json"
|
||||
print(f"Loading vocab file {path}")
|
||||
return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None)
|
||||
|
|
@ -1110,7 +1117,9 @@ def default_outfile(model_paths: List[Path], params: Params) -> Path:
|
|||
}[params.file_type]
|
||||
ret = model_paths[0].parent / f"ggml-model-{namestr}.bin"
|
||||
if ret in model_paths:
|
||||
sys.stderr.write(f"Error: Default output path ({ret}) would overwrite the input. Please explicitly specify a path using --outfile.\n")
|
||||
sys.stderr.write(
|
||||
f"Error: Default output path ({ret}) would overwrite the input. "
|
||||
"Please explicitly specify a path using --outfile.\n")
|
||||
sys.exit(1)
|
||||
return ret
|
||||
|
||||
|
|
@ -1131,7 +1140,8 @@ def main(args_in: Optional[List[str]] = None) -> None:
|
|||
parser.add_argument("--outtype", choices=["f32", "f16", "q4_1", "q4_0"], help="output format (default: based on input)")
|
||||
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
|
||||
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
|
||||
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
|
||||
parser.add_argument("model", type=Path,
|
||||
help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
|
||||
args = parser.parse_args(args_in)
|
||||
|
||||
vocab: Vocab
|
||||
|
|
|
|||
|
|
@ -4,6 +4,10 @@
|
|||
#include <random>
|
||||
#include <cstring>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
float frand() {
|
||||
return (float)rand()/(float)RAND_MAX;
|
||||
}
|
||||
|
|
@ -1470,7 +1474,7 @@ struct ggml_tensor * square_error_loss(struct ggml_context * ctx, struct ggml_te
|
|||
}
|
||||
|
||||
struct ggml_tensor * cross_entropy_loss(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
|
||||
const float eps = 1e-3;
|
||||
const float eps = 1e-3f;
|
||||
return
|
||||
ggml_sum(ctx,
|
||||
ggml_neg(ctx,
|
||||
|
|
|
|||
|
|
@ -16,6 +16,10 @@
|
|||
#include <iterator>
|
||||
#include <algorithm>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
float tensor_sum_elements(const ggml_tensor * tensor) {
|
||||
float sum = 0;
|
||||
if (tensor->type==GGML_TYPE_F32) {
|
||||
|
|
@ -29,9 +33,9 @@ float tensor_sum_elements(const ggml_tensor * tensor) {
|
|||
}
|
||||
|
||||
void tensor_dump(const ggml_tensor * tensor, const char * name) {
|
||||
printf("%15s: type = %i (%5s) ne = %5d x %5d x %5d, nb = (%5li, %5li, %5li) - ", name,
|
||||
printf("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi) - ", name,
|
||||
tensor->type, ggml_type_name(tensor->type),
|
||||
(int) tensor->ne[0], (int) tensor->ne[1], (int) tensor->ne[2], tensor->nb[0], tensor->nb[1], tensor->nb[2]);
|
||||
tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->nb[0], tensor->nb[1], tensor->nb[2]);
|
||||
float sum = tensor_sum_elements(tensor);
|
||||
printf("Sum of tensor %s is %6.2f\n", name, sum);
|
||||
}
|
||||
|
|
@ -120,7 +124,7 @@ int main(int argc, char ** argv) {
|
|||
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS
|
||||
ctx_size += 1024*1024*16;
|
||||
|
||||
printf("Allocating Memory of size %li bytes, %li MB\n",ctx_size, (ctx_size/1024/1024));
|
||||
printf("Allocating Memory of size %zi bytes, %zi MB\n",ctx_size, (ctx_size/1024/1024));
|
||||
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ ctx_size,
|
||||
|
|
|
|||
41
examples/chat-vicuna.sh
Executable file
41
examples/chat-vicuna.sh
Executable file
|
|
@ -0,0 +1,41 @@
|
|||
#!/bin/bash
|
||||
|
||||
set -e
|
||||
|
||||
cd "$(dirname "$0")/.." || exit
|
||||
|
||||
MODEL="${MODEL:-./models/ggml-vic13b-uncensored-q5_0.bin}"
|
||||
PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat.txt}
|
||||
USER_NAME="### Human"
|
||||
AI_NAME="### Assistant"
|
||||
|
||||
# Adjust to the number of CPU cores you want to use.
|
||||
N_THREAD="${N_THREAD:-8}"
|
||||
# Number of tokens to predict (made it larger than default because we want a long interaction)
|
||||
N_PREDICTS="${N_PREDICTS:-2048}"
|
||||
|
||||
# Note: you can also override the generation options by specifying them on the command line:
|
||||
# For example, override the context size by doing: ./chatLLaMa --ctx_size 1024
|
||||
GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647}"
|
||||
|
||||
DATE_TIME=$(date +%H:%M)
|
||||
DATE_YEAR=$(date +%Y)
|
||||
|
||||
PROMPT_FILE=$(mktemp -t llamacpp_prompt.XXXXXXX.txt)
|
||||
|
||||
sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \
|
||||
-e "s/\[\[AI_NAME\]\]/$AI_NAME/g" \
|
||||
-e "s/\[\[DATE_TIME\]\]/$DATE_TIME/g" \
|
||||
-e "s/\[\[DATE_YEAR\]\]/$DATE_YEAR/g" \
|
||||
$PROMPT_TEMPLATE > $PROMPT_FILE
|
||||
|
||||
# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
|
||||
./bin/main $GEN_OPTIONS \
|
||||
--model "$MODEL" \
|
||||
--threads "$N_THREAD" \
|
||||
--n_predict "$N_PREDICTS" \
|
||||
--color --interactive \
|
||||
--file ${PROMPT_FILE} \
|
||||
--reverse-prompt "### Human:" \
|
||||
--in-prefix ' ' \
|
||||
"$@"
|
||||
|
|
@ -28,6 +28,10 @@
|
|||
#include <wchar.h>
|
||||
#endif
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
int32_t get_num_physical_cores() {
|
||||
#ifdef __linux__
|
||||
// enumerate the set of thread siblings, num entries is num cores
|
||||
|
|
@ -373,7 +377,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
} else {
|
||||
throw std::exception();
|
||||
}
|
||||
} catch (const std::exception &e) {
|
||||
} catch (const std::exception&) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
|
|
@ -412,6 +416,14 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
gpt_print_usage(argc, argv, default_params);
|
||||
exit(1);
|
||||
}
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
if (!params.lora_adapter.empty() && params.n_gpu_layers > 0) {
|
||||
fprintf(stderr, "%s: error: the simultaneous use of LoRAs and GPU acceleration is not supported", __func__);
|
||||
exit(1);
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
|
||||
if (escape_prompt) {
|
||||
process_escapes(params.prompt);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -4,6 +4,10 @@
|
|||
|
||||
#include <ctime>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
int main(int argc, char ** argv) {
|
||||
gpt_params params;
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import matplotlib.pyplot as plt
|
||||
import sys, os
|
||||
import os
|
||||
import csv
|
||||
|
||||
labels = []
|
||||
|
|
@ -8,6 +8,7 @@ numEntries = 1
|
|||
|
||||
rows = []
|
||||
|
||||
|
||||
def bar_chart(numbers, labels, pos):
|
||||
plt.bar(pos, numbers, color='blue')
|
||||
plt.xticks(ticks=pos, labels=labels)
|
||||
|
|
@ -16,6 +17,7 @@ def bar_chart(numbers, labels, pos):
|
|||
plt.ylabel("Questions Correct")
|
||||
plt.show()
|
||||
|
||||
|
||||
def calculatecorrect():
|
||||
directory = os.fsencode("./examples/jeopardy/results/")
|
||||
csv_reader = csv.reader(open("./examples/jeopardy/qasheet.csv", 'rt'), delimiter=',')
|
||||
|
|
@ -38,14 +40,13 @@ def calculatecorrect():
|
|||
print(line)
|
||||
else:
|
||||
print("Correct answer: " + rows[i][2] + "\n")
|
||||
i+=1
|
||||
i += 1
|
||||
print("Did the AI get the question right? (y/n)")
|
||||
if input() == "y":
|
||||
totalcorrect += 1
|
||||
numbers.append(totalcorrect)
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
calculatecorrect()
|
||||
pos = list(range(numEntries))
|
||||
|
|
|
|||
|
|
@ -23,11 +23,17 @@
|
|||
#include <unistd.h>
|
||||
#elif defined (_WIN32)
|
||||
#define WIN32_LEAN_AND_MEAN
|
||||
#ifndef NOMINMAX
|
||||
#define NOMINMAX
|
||||
#endif
|
||||
#include <windows.h>
|
||||
#include <signal.h>
|
||||
#endif
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
static console_state con_st;
|
||||
static llama_context ** g_ctx;
|
||||
|
||||
|
|
@ -348,7 +354,7 @@ int main(int argc, char ** argv) {
|
|||
if ((int)embd.size() > max_embd_size) {
|
||||
auto skipped_tokens = embd.size() - max_embd_size;
|
||||
console_set_color(con_st, CONSOLE_COLOR_ERROR);
|
||||
printf("<<input too long: skipped %ld token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
|
||||
printf("<<input too long: skipped %" PRIu64 " token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
|
||||
console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
|
||||
fflush(stdout);
|
||||
embd.resize(max_embd_size);
|
||||
|
|
|
|||
|
|
@ -5,6 +5,10 @@
|
|||
#include <cmath>
|
||||
#include <ctime>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
std::vector<float> softmax(const std::vector<float>& logits) {
|
||||
std::vector<float> probs(logits.size());
|
||||
float max_logit = logits[0];
|
||||
|
|
|
|||
|
|
@ -19,6 +19,10 @@
|
|||
#include <thread>
|
||||
#include <mutex>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
struct quantize_stats_params {
|
||||
std::string model = "models/7B/ggml-model-f16.bin";
|
||||
bool verbose = false;
|
||||
|
|
|
|||
|
|
@ -37,7 +37,7 @@ int main(int argc, char ** argv) {
|
|||
// init
|
||||
auto ctx = llama_init_from_file(params.model.c_str(), lparams);
|
||||
auto tokens = std::vector<llama_token>(params.n_ctx);
|
||||
auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), tokens.size(), true);
|
||||
auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), int(tokens.size()), true);
|
||||
|
||||
if (n_prompt_tokens < 1) {
|
||||
fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
|
||||
|
|
|
|||
|
|
@ -1,6 +1,10 @@
|
|||
set(TARGET server)
|
||||
option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
|
||||
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
|
||||
add_executable(${TARGET} server.cpp json.hpp httplib.h)
|
||||
target_compile_definitions(${TARGET} PRIVATE
|
||||
SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
|
||||
)
|
||||
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
|
||||
target_compile_features(${TARGET} PRIVATE cxx_std_11)
|
||||
if(TARGET BUILD_INFO)
|
||||
|
|
|
|||
|
|
@ -1,33 +1,74 @@
|
|||
# llama.cpp/example/server
|
||||
|
||||
This example allow you to have a llama.cpp http server to interact from a web page or consume the API.
|
||||
This example demonstrates a simple HTTP API server to interact with llama.cpp.
|
||||
|
||||
## Table of Contents
|
||||
Command line options:
|
||||
|
||||
1. [Quick Start](#quick-start)
|
||||
2. [Node JS Test](#node-js-test)
|
||||
3. [API Endpoints](#api-endpoints)
|
||||
4. [More examples](#more-examples)
|
||||
5. [Common Options](#common-options)
|
||||
6. [Performance Tuning and Memory Options](#performance-tuning-and-memory-options)
|
||||
- `--threads N`, `-t N`: Set the number of threads to use during computation.
|
||||
- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
|
||||
- `-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.
|
||||
- `-lv, --low-vram`: Do not allocate a VRAM scratch buffer for holding temporary results. Reduces VRAM usage at the cost of performance, particularly prompt processing speed. 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.
|
||||
- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
|
||||
- `--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.
|
||||
- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
|
||||
- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
|
||||
- `--port`: Set the port to listen. Default: `8080`.
|
||||
|
||||
## Build
|
||||
|
||||
Build llama.cpp with server from repository root with either make or CMake.
|
||||
|
||||
- Using `make`:
|
||||
|
||||
```bash
|
||||
LLAMA_BUILD_SERVER=1 make
|
||||
```
|
||||
|
||||
- Using `CMake`:
|
||||
|
||||
```bash
|
||||
mkdir build-server
|
||||
cd build-server
|
||||
cmake -DLLAMA_BUILD_SERVER=ON ..
|
||||
cmake --build . --config Release
|
||||
```
|
||||
|
||||
## Quick Start
|
||||
|
||||
To get started right away, run the following command, making sure to use the correct path for the model you have:
|
||||
|
||||
#### Unix-based systems (Linux, macOS, etc.):
|
||||
### Unix-based systems (Linux, macOS, etc.):
|
||||
|
||||
```bash
|
||||
./server -m models/7B/ggml-model.bin --ctx_size 2048
|
||||
./server -m models/7B/ggml-model.bin -c 2048
|
||||
```
|
||||
|
||||
#### Windows:
|
||||
### Windows:
|
||||
|
||||
```powershell
|
||||
server.exe -m models\7B\ggml-model.bin --ctx_size 2048
|
||||
server.exe -m models\7B\ggml-model.bin -c 2048
|
||||
```
|
||||
|
||||
That will start a server that by default listens on `127.0.0.1:8080`. You can consume the endpoints with Postman or NodeJS with axios library.
|
||||
The above command will start a server that by default listens on `127.0.0.1:8080`.
|
||||
You can consume the endpoints with Postman or NodeJS with axios library.
|
||||
|
||||
## Testing with CURL
|
||||
|
||||
Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the base OS.
|
||||
|
||||
```sh
|
||||
curl --request POST \
|
||||
--url http://localhost:8080/completion \
|
||||
--data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}'
|
||||
```
|
||||
|
||||
## Node JS Test
|
||||
|
||||
|
|
@ -50,7 +91,6 @@ const prompt = `Building a website can be done in 10 simple steps:`;
|
|||
async function Test() {
|
||||
let result = await axios.post("http://127.0.0.1:8080/completion", {
|
||||
prompt,
|
||||
batch_size: 128,
|
||||
n_predict: 512,
|
||||
});
|
||||
|
||||
|
|
@ -69,247 +109,75 @@ node .
|
|||
|
||||
## API Endpoints
|
||||
|
||||
You can interact with this API Endpoints. This implementations just support chat style interaction.
|
||||
- **POST** `/completion`: Given a prompt, it returns the predicted completion.
|
||||
|
||||
- **POST** `hostname:port/completion`: Setting up the Llama Context to begin the completions tasks.
|
||||
*Options:*
|
||||
|
||||
*Options:*
|
||||
`temperature`: Adjust the randomness of the generated text (default: 0.8).
|
||||
|
||||
`batch_size`: Set the batch size for prompt processing (default: 512).
|
||||
`top_k`: Limit the next token selection to the K most probable tokens (default: 40).
|
||||
|
||||
`temperature`: Adjust the randomness of the generated text (default: 0.8).
|
||||
`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9).
|
||||
|
||||
`top_k`: Limit the next token selection to the K most probable tokens (default: 40).
|
||||
`n_predict`: Set the number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. (default: 128, -1 = infinity).
|
||||
|
||||
`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9).
|
||||
`n_keep`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context.
|
||||
By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt.
|
||||
|
||||
`n_predict`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity).
|
||||
`stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
|
||||
|
||||
`threads`: Set the number of threads to use during computation.
|
||||
`prompt`: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate.
|
||||
|
||||
`n_keep`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context. By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt.
|
||||
`stop`: Specify a JSON array of stopping strings.
|
||||
These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []).
|
||||
|
||||
`as_loop`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
|
||||
`tfs_z`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
|
||||
|
||||
`interactive`: It allows interacting with the completion, and the completion stops as soon as it encounters a `stop word`. To enable this, set to `true`.
|
||||
`typical_p`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled).
|
||||
|
||||
`prompt`: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate.
|
||||
`repeat_penalty`: Control the repetition of token sequences in the generated text (default: 1.1).
|
||||
|
||||
`stop`: Specify the words or characters that indicate a stop. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration.
|
||||
`repeat_last_n`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
|
||||
|
||||
`exclude`: Specify the words or characters you do not want to appear in the completion. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration.
|
||||
`penalize_nl`: Penalize newline tokens when applying the repeat penalty (default: true).
|
||||
|
||||
- **POST** `hostname:port/embedding`: Generate embedding of a given text
|
||||
`presence_penalty`: Repeat alpha presence penalty (default: 0.0, 0.0 = disabled).
|
||||
|
||||
*Options:*
|
||||
`frequency_penalty`: Repeat alpha frequency penalty (default: 0.0, 0.0 = disabled);
|
||||
|
||||
`content`: Set the text to get generate the embedding.
|
||||
`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).
|
||||
|
||||
`threads`: Set the number of threads to use during computation.
|
||||
`mirostat_tau`: Set the Mirostat target entropy, parameter tau (default: 5.0).
|
||||
|
||||
To use this endpoint, you need to start the server with the `--embedding` option added.
|
||||
`mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1).
|
||||
|
||||
- **POST** `hostname:port/tokenize`: Tokenize a given text
|
||||
`seed`: Set the random number generator (RNG) seed (default: -1, < 0 = random seed).
|
||||
|
||||
*Options:*
|
||||
`ignore_eos`: Ignore end of stream token and continue generating (default: false).
|
||||
|
||||
`content`: Set the text to tokenize.
|
||||
`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced (default: []).
|
||||
|
||||
- **GET** `hostname:port/next-token`: Receive the next token predicted, execute this request in a loop. Make sure set `as_loop` as `true` in the completion request.
|
||||
- **POST** `/tokenize`: Tokenize a given text.
|
||||
|
||||
*Options:*
|
||||
*Options:*
|
||||
|
||||
`stop`: Set `hostname:port/next-token?stop=true` to stop the token generation.
|
||||
`content`: Set the text to tokenize.
|
||||
|
||||
## More examples
|
||||
|
||||
### Interactive mode
|
||||
|
||||
This mode allows interacting in a chat-like manner. It is recommended for models designed as assistants such as `Vicuna`, `WizardLM`, `Koala`, among others. Make sure to add the correct stop word for the corresponding model.
|
||||
Check the sample in [chat.mjs](chat.mjs).
|
||||
Run with NodeJS version 16 or later:
|
||||
|
||||
The prompt should be generated by you, according to the model's guidelines. You should keep adding the model's completions to the context as well.
|
||||
|
||||
This example works well for `Vicuna - version 1`.
|
||||
|
||||
```javascript
|
||||
const axios = require("axios");
|
||||
|
||||
let prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.
|
||||
### Human: Hello, Assistant.
|
||||
### Assistant: Hello. How may I help you today?
|
||||
### Human: Please tell me the largest city in Europe.
|
||||
### Assistant: Sure. The largest city in Europe is Moscow, the capital of Russia.`;
|
||||
|
||||
async function ChatCompletion(answer) {
|
||||
// the user's next question to the prompt
|
||||
prompt += `\n### Human: ${answer}\n`
|
||||
|
||||
result = await axios.post("http://127.0.0.1:8080/completion", {
|
||||
prompt,
|
||||
batch_size: 128,
|
||||
temperature: 0.2,
|
||||
top_k: 40,
|
||||
top_p: 0.9,
|
||||
n_keep: -1,
|
||||
n_predict: 2048,
|
||||
stop: ["\n### Human:"], // when detect this, stop completion
|
||||
exclude: ["### Assistant:"], // no show in the completion
|
||||
threads: 8,
|
||||
as_loop: true, // use this to request the completion token by token
|
||||
interactive: true, // enable the detection of a stop word
|
||||
});
|
||||
|
||||
// create a loop to receive every token predicted
|
||||
// note: this operation is blocking, avoid use this in a ui thread
|
||||
|
||||
let message = "";
|
||||
while (true) {
|
||||
// you can stop the inference adding '?stop=true' like this http://127.0.0.1:8080/next-token?stop=true
|
||||
result = await axios.get("http://127.0.0.1:8080/next-token");
|
||||
process.stdout.write(result.data.content);
|
||||
message += result.data.content;
|
||||
|
||||
// to avoid an infinite loop
|
||||
if (result.data.stop) {
|
||||
console.log("Completed");
|
||||
// make sure to add the completion to the prompt.
|
||||
prompt += `### Assistant: ${message}`;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// This function should be called every time a question to the model is needed.
|
||||
async function Test() {
|
||||
// the server can't inference in paralell
|
||||
await ChatCompletion("Write a long story about a time magician in a fantasy world");
|
||||
await ChatCompletion("Summary the story");
|
||||
}
|
||||
|
||||
Test();
|
||||
```sh
|
||||
node chat.mjs
|
||||
```
|
||||
|
||||
### Alpaca example
|
||||
Another sample in [chat.sh](chat.sh).
|
||||
Requires [bash](https://www.gnu.org/software/bash/), [curl](https://curl.se) and [jq](https://jqlang.github.io/jq/).
|
||||
Run with bash:
|
||||
|
||||
**Temporaly note:** no tested, if you have the model, please test it and report me some issue
|
||||
|
||||
```javascript
|
||||
const axios = require("axios");
|
||||
|
||||
let prompt = `Below is an instruction that describes a task. Write a response that appropriately completes the request.
|
||||
`;
|
||||
|
||||
async function DoInstruction(instruction) {
|
||||
prompt += `\n\n### Instruction:\n\n${instruction}\n\n### Response:\n\n`;
|
||||
result = await axios.post("http://127.0.0.1:8080/completion", {
|
||||
prompt,
|
||||
batch_size: 128,
|
||||
temperature: 0.2,
|
||||
top_k: 40,
|
||||
top_p: 0.9,
|
||||
n_keep: -1,
|
||||
n_predict: 2048,
|
||||
stop: ["### Instruction:\n\n"], // when detect this, stop completion
|
||||
exclude: [], // no show in the completion
|
||||
threads: 8,
|
||||
as_loop: true, // use this to request the completion token by token
|
||||
interactive: true, // enable the detection of a stop word
|
||||
});
|
||||
|
||||
// create a loop to receive every token predicted
|
||||
// note: this operation is blocking, avoid use this in a ui thread
|
||||
|
||||
let message = "";
|
||||
while (true) {
|
||||
result = await axios.get("http://127.0.0.1:8080/next-token");
|
||||
process.stdout.write(result.data.content);
|
||||
message += result.data.content;
|
||||
|
||||
// to avoid an infinite loop
|
||||
if (result.data.stop) {
|
||||
console.log("Completed");
|
||||
// make sure to add the completion and the user's next question to the prompt.
|
||||
prompt += message;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// This function should be called every time a instruction to the model is needed.
|
||||
DoInstruction("Destroy the world"); // as joke
|
||||
```sh
|
||||
bash chat.sh
|
||||
```
|
||||
|
||||
### Embeddings
|
||||
|
||||
First, run the server with `--embedding` option:
|
||||
|
||||
```bash
|
||||
server -m models/7B/ggml-model.bin --ctx_size 2048 --embedding
|
||||
```
|
||||
|
||||
Run this code in NodeJS:
|
||||
|
||||
```javascript
|
||||
const axios = require('axios');
|
||||
|
||||
async function Test() {
|
||||
let result = await axios.post("http://127.0.0.1:8080/embedding", {
|
||||
content: `Hello`,
|
||||
threads: 5
|
||||
});
|
||||
// print the embedding array
|
||||
console.log(result.data.embedding);
|
||||
}
|
||||
|
||||
Test();
|
||||
```
|
||||
|
||||
### Tokenize
|
||||
|
||||
Run this code in NodeJS:
|
||||
|
||||
```javascript
|
||||
const axios = require('axios');
|
||||
|
||||
async function Test() {
|
||||
let result = await axios.post("http://127.0.0.1:8080/tokenize", {
|
||||
content: `Hello`
|
||||
});
|
||||
// print the embedding array
|
||||
console.log(result.data.tokens);
|
||||
}
|
||||
|
||||
Test();
|
||||
```
|
||||
|
||||
## Common Options
|
||||
|
||||
- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
|
||||
- `-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.
|
||||
- `-lv, --low-vram`: Do not allocate a VRAM scratch buffer for holding temporary results. Reduces VRAM usage at the cost of performance, particularly prompt processing speed. Requires cuBLAS.
|
||||
- `--embedding`: Enable the embedding mode. **Completion function doesn't work in this mode**.
|
||||
- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`;
|
||||
- `--port`: Set the port to listen. Default: `8080`.
|
||||
|
||||
### RNG Seed
|
||||
|
||||
- `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, < 0 = random seed).
|
||||
|
||||
The RNG seed is used to initialize the random number generator that influences the text generation process. By setting a specific seed value, you can obtain consistent and reproducible results across multiple runs with the same input and settings. This can be helpful for testing, debugging, or comparing the effects of different options on the generated text to see when they diverge. If the seed is set to a value less than 0, a random seed will be used, which will result in different outputs on each run.
|
||||
|
||||
## Performance Tuning and Memory Options
|
||||
|
||||
### No Memory Mapping
|
||||
|
||||
- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. However, if the model is larger than your total amount of RAM or if your system is low on available memory, using mmap might increase the risk of pageouts, negatively impacting performance.
|
||||
|
||||
### Memory Float 32
|
||||
|
||||
- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement but does not appear to increase generation quality in a measurable way. Not recommended.
|
||||
|
||||
## Limitations:
|
||||
|
||||
- The actual implementation of llama.cpp need a `llama-state` for handle multiple contexts and clients, but this could require more powerful hardware.
|
||||
|
|
|
|||
89
examples/server/chat.mjs
Normal file
89
examples/server/chat.mjs
Normal file
|
|
@ -0,0 +1,89 @@
|
|||
import * as readline from 'node:readline'
|
||||
import { stdin, stdout } from 'node:process'
|
||||
|
||||
const API_URL = 'http://127.0.0.1:8080'
|
||||
|
||||
const chat = [
|
||||
{
|
||||
human: "Hello, Assistant.",
|
||||
assistant: "Hello. How may I help you today?"
|
||||
},
|
||||
{
|
||||
human: "Please tell me the largest city in Europe.",
|
||||
assistant: "Sure. The largest city in Europe is Moscow, the capital of Russia."
|
||||
},
|
||||
]
|
||||
|
||||
const instruction = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.`
|
||||
|
||||
function format_prompt(question) {
|
||||
return `${instruction}\n${
|
||||
chat.map(m =>`### Human: ${m.human}\n### Assistant: ${m.assistant}`).join("\n")
|
||||
}\n### Human: ${question}\n### Assistant:`
|
||||
}
|
||||
|
||||
async function tokenize(content) {
|
||||
const result = await fetch(`${API_URL}/tokenize`, {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({ content })
|
||||
})
|
||||
|
||||
if (!result.ok) {
|
||||
return []
|
||||
}
|
||||
|
||||
return await result.json().tokens
|
||||
}
|
||||
|
||||
const n_keep = await tokenize(instruction).length
|
||||
|
||||
async function chat_completion(question) {
|
||||
const result = await fetch(`${API_URL}/completion`, {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({
|
||||
prompt: format_prompt(question),
|
||||
temperature: 0.2,
|
||||
top_k: 40,
|
||||
top_p: 0.9,
|
||||
n_keep: n_keep,
|
||||
n_predict: 256,
|
||||
stop: ["\n### Human:"], // stop completion after generating this
|
||||
stream: true,
|
||||
})
|
||||
})
|
||||
|
||||
if (!result.ok) {
|
||||
return
|
||||
}
|
||||
|
||||
let answer = ''
|
||||
|
||||
for await (var chunk of result.body) {
|
||||
const t = Buffer.from(chunk).toString('utf8')
|
||||
if (t.startsWith('data: ')) {
|
||||
const message = JSON.parse(t.substring(6))
|
||||
answer += message.content
|
||||
process.stdout.write(message.content)
|
||||
if (message.stop) {
|
||||
if (message.truncated) {
|
||||
chat.shift()
|
||||
}
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
process.stdout.write('\n')
|
||||
chat.push({ human: question, assistant: answer.trimStart() })
|
||||
}
|
||||
|
||||
const rl = readline.createInterface({ input: stdin, output: stdout });
|
||||
|
||||
const readlineQuestion = (rl, query, options) => new Promise((resolve, reject) => {
|
||||
rl.question(query, options, resolve)
|
||||
});
|
||||
|
||||
while(true) {
|
||||
const question = await readlineQuestion(rl, '> ')
|
||||
await chat_completion(question)
|
||||
}
|
||||
77
examples/server/chat.sh
Normal file
77
examples/server/chat.sh
Normal file
|
|
@ -0,0 +1,77 @@
|
|||
#!/bin/bash
|
||||
|
||||
API_URL="${API_URL:-http://127.0.0.1:8080}"
|
||||
|
||||
CHAT=(
|
||||
"Hello, Assistant."
|
||||
"Hello. How may I help you today?"
|
||||
"Please tell me the largest city in Europe."
|
||||
"Sure. The largest city in Europe is Moscow, the capital of Russia."
|
||||
)
|
||||
|
||||
INSTRUCTION="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
|
||||
|
||||
trim() {
|
||||
shopt -s extglob
|
||||
set -- "${1##+([[:space:]])}"
|
||||
printf "%s" "${1%%+([[:space:]])}"
|
||||
}
|
||||
|
||||
trim_trailing() {
|
||||
shopt -s extglob
|
||||
printf "%s" "${1%%+([[:space:]])}"
|
||||
}
|
||||
|
||||
format_prompt() {
|
||||
echo -n "${INSTRUCTION}"
|
||||
printf "\n### Human: %s\n### Assistant: %s" "${CHAT[@]}" "$1"
|
||||
}
|
||||
|
||||
tokenize() {
|
||||
curl \
|
||||
--silent \
|
||||
--request POST \
|
||||
--url "${API_URL}/tokenize" \
|
||||
--data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \
|
||||
| jq '.tokens[]'
|
||||
}
|
||||
|
||||
N_KEEP=$(tokenize "${INSTRUCTION}" | wc -l)
|
||||
|
||||
chat_completion() {
|
||||
PROMPT="$(trim_trailing "$(format_prompt "$1")")"
|
||||
DATA="$(echo -n "$PROMPT" | jq -Rs --argjson n_keep $N_KEEP '{
|
||||
prompt: .,
|
||||
temperature: 0.2,
|
||||
top_k: 40,
|
||||
top_p: 0.9,
|
||||
n_keep: $n_keep,
|
||||
n_predict: 256,
|
||||
stop: ["\n### Human:"],
|
||||
stream: true
|
||||
}')"
|
||||
|
||||
ANSWER=''
|
||||
|
||||
while IFS= read -r LINE; do
|
||||
if [[ $LINE = data:* ]]; then
|
||||
CONTENT="$(echo "${LINE:5}" | jq -r '.content')"
|
||||
printf "%s" "${CONTENT}"
|
||||
ANSWER+="${CONTENT}"
|
||||
fi
|
||||
done < <(curl \
|
||||
--silent \
|
||||
--no-buffer \
|
||||
--request POST \
|
||||
--url "${API_URL}/completion" \
|
||||
--data-raw "${DATA}")
|
||||
|
||||
printf "\n"
|
||||
|
||||
CHAT+=("$1" "$(trim "$ANSWER")")
|
||||
}
|
||||
|
||||
while true; do
|
||||
read -r -e -p "> " QUESTION
|
||||
chat_completion "${QUESTION}"
|
||||
done
|
||||
File diff suppressed because it is too large
Load diff
7
examples/simple/CMakeLists.txt
Normal file
7
examples/simple/CMakeLists.txt
Normal file
|
|
@ -0,0 +1,7 @@
|
|||
set(TARGET simple)
|
||||
add_executable(${TARGET} simple.cpp)
|
||||
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
|
||||
target_compile_features(${TARGET} PRIVATE cxx_std_11)
|
||||
if(TARGET BUILD_INFO)
|
||||
add_dependencies(${TARGET} BUILD_INFO)
|
||||
endif()
|
||||
177
examples/simple/simple.cpp
Normal file
177
examples/simple/simple.cpp
Normal file
|
|
@ -0,0 +1,177 @@
|
|||
#ifndef _GNU_SOURCE
|
||||
#define _GNU_SOURCE
|
||||
#endif
|
||||
|
||||
#include "common.h"
|
||||
#include "llama.h"
|
||||
#include "build-info.h"
|
||||
|
||||
#include <cassert>
|
||||
#include <cinttypes>
|
||||
#include <cmath>
|
||||
#include <cstdio>
|
||||
#include <cstring>
|
||||
#include <ctime>
|
||||
#include <fstream>
|
||||
#include <iostream>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
|
||||
#include <signal.h>
|
||||
#include <unistd.h>
|
||||
#elif defined (_WIN32)
|
||||
#define WIN32_LEAN_AND_MEAN
|
||||
#define NOMINMAX
|
||||
#include <windows.h>
|
||||
#include <signal.h>
|
||||
#endif
|
||||
|
||||
|
||||
|
||||
int main(int argc, char ** argv)
|
||||
{
|
||||
gpt_params params;
|
||||
|
||||
//---------------------------------
|
||||
// Print help :
|
||||
//---------------------------------
|
||||
|
||||
if ( argc == 1 || argv[1][0] == '-' )
|
||||
{
|
||||
printf( "usage: %s MODEL_PATH [PROMPT]\n" , argv[0] );
|
||||
return 1 ;
|
||||
}
|
||||
|
||||
//---------------------------------
|
||||
// Load parameters :
|
||||
//---------------------------------
|
||||
|
||||
if ( argc >= 2 )
|
||||
{
|
||||
params.model = argv[1];
|
||||
}
|
||||
|
||||
if ( argc >= 3 )
|
||||
{
|
||||
params.prompt = argv[2];
|
||||
}
|
||||
|
||||
if ( params.prompt.empty() )
|
||||
{
|
||||
params.prompt = "Hello my name is";
|
||||
}
|
||||
|
||||
//---------------------------------
|
||||
// Init LLM :
|
||||
//---------------------------------
|
||||
|
||||
llama_init_backend();
|
||||
|
||||
llama_context * ctx ;
|
||||
|
||||
ctx = llama_init_from_gpt_params( params );
|
||||
|
||||
if ( ctx == NULL )
|
||||
{
|
||||
fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
|
||||
return 1;
|
||||
}
|
||||
|
||||
//---------------------------------
|
||||
// Tokenize the prompt :
|
||||
//---------------------------------
|
||||
|
||||
std::vector<llama_token> tokens_list;
|
||||
tokens_list = ::llama_tokenize( ctx , params.prompt , true );
|
||||
|
||||
const int max_context_size = llama_n_ctx( ctx );
|
||||
const int max_tokens_list_size = max_context_size - 4 ;
|
||||
|
||||
if ( (int)tokens_list.size() > max_tokens_list_size )
|
||||
{
|
||||
fprintf( stderr , "%s: error: prompt too long (%d tokens, max %d)\n" ,
|
||||
__func__ , (int)tokens_list.size() , max_tokens_list_size );
|
||||
return 1;
|
||||
}
|
||||
|
||||
fprintf( stderr, "\n\n" );
|
||||
|
||||
// Print the tokens from the prompt :
|
||||
|
||||
for( auto id : tokens_list )
|
||||
{
|
||||
printf( "%s" , llama_token_to_str( ctx , id ) );
|
||||
}
|
||||
|
||||
fflush(stdout);
|
||||
|
||||
|
||||
//---------------------------------
|
||||
// Main prediction loop :
|
||||
//---------------------------------
|
||||
|
||||
// The LLM keeps a contextual cache memory of previous token evaluation.
|
||||
// Usually, once this cache is full, it is required to recompute a compressed context based on previous
|
||||
// tokens (see "infinite text generation via context swapping" in the main example), but in this minimalist
|
||||
// example, we will just stop the loop once this cache is full or once an end of stream is detected.
|
||||
|
||||
while ( llama_get_kv_cache_token_count( ctx ) < max_context_size )
|
||||
{
|
||||
//---------------------------------
|
||||
// Evaluate the tokens :
|
||||
//---------------------------------
|
||||
|
||||
if ( llama_eval( ctx , tokens_list.data() , tokens_list.size() , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
|
||||
{
|
||||
fprintf( stderr, "%s : failed to eval\n" , __func__ );
|
||||
return 1;
|
||||
}
|
||||
|
||||
tokens_list.clear();
|
||||
|
||||
//---------------------------------
|
||||
// Select the best prediction :
|
||||
//---------------------------------
|
||||
|
||||
llama_token new_token_id = 0;
|
||||
|
||||
auto logits = llama_get_logits( ctx );
|
||||
auto n_vocab = llama_n_vocab( ctx ); // the size of the LLM vocabulary (in tokens)
|
||||
|
||||
std::vector<llama_token_data> candidates;
|
||||
candidates.reserve( n_vocab );
|
||||
|
||||
for( llama_token token_id = 0 ; token_id < n_vocab ; token_id++ )
|
||||
{
|
||||
candidates.emplace_back( llama_token_data{ token_id , logits[ token_id ] , 0.0f } );
|
||||
}
|
||||
|
||||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||||
|
||||
// Select it using the "Greedy sampling" method :
|
||||
new_token_id = llama_sample_token_greedy( ctx , &candidates_p );
|
||||
|
||||
|
||||
// is it an end of stream ?
|
||||
if ( new_token_id == llama_token_eos() )
|
||||
{
|
||||
fprintf(stderr, " [end of text]\n");
|
||||
break;
|
||||
}
|
||||
|
||||
// Print the new token :
|
||||
printf( "%s" , llama_token_to_str( ctx , new_token_id ) );
|
||||
fflush( stdout );
|
||||
|
||||
// Push this new token for next evaluation :
|
||||
tokens_list.push_back( new_token_id );
|
||||
|
||||
} // wend of main loop
|
||||
|
||||
llama_free( ctx );
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
// EOF
|
||||
|
|
@ -4,7 +4,7 @@ Basic usage instructions:
|
|||
|
||||
```bash
|
||||
# get training data
|
||||
wget https://github.com/brunoklein99/deep-learning-notes/blob/master/shakespeare.txt
|
||||
wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt
|
||||
|
||||
# train
|
||||
./bin/train-text-from-scratch \
|
||||
|
|
|
|||
|
|
@ -12,6 +12,9 @@
|
|||
#include <algorithm>
|
||||
#include <string>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
struct random_normal_distribution {
|
||||
std::mt19937 gen;
|
||||
|
|
@ -20,7 +23,6 @@ struct random_normal_distribution {
|
|||
float max;
|
||||
};
|
||||
|
||||
|
||||
struct random_uniform_distribution {
|
||||
std::mt19937 gen;
|
||||
std::uniform_real_distribution<float> rd;
|
||||
|
|
@ -2366,7 +2368,7 @@ void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
|
|||
file->write_u32(0);
|
||||
file->write_u32(0);
|
||||
file->write_u32(GGML_TYPE_F32);
|
||||
file->seek(-file->tell() & 31, SEEK_CUR);
|
||||
file->seek(0-file->tell() & 31, SEEK_CUR);
|
||||
return;
|
||||
}
|
||||
const char * name = ggml_get_name(tensor);
|
||||
|
|
@ -2381,7 +2383,7 @@ void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
|
|||
file->write_u32(tensor->type);
|
||||
file->write_raw(ne, sizeof(ne[0]) * nd);
|
||||
file->write_raw(name, name_len);
|
||||
file->seek(-file->tell() & 31, SEEK_CUR);
|
||||
file->seek(0-file->tell() & 31, SEEK_CUR);
|
||||
file->write_raw(tensor->data, ggml_nbytes(tensor));
|
||||
}
|
||||
|
||||
|
|
@ -2402,7 +2404,7 @@ void read_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
|
|||
std::string name = file->read_string(name_len);
|
||||
GGML_ASSERT(strncmp(ggml_get_name(tensor), name.c_str(), sizeof(tensor->name)-1) == 0);
|
||||
|
||||
file->seek(-file->tell() & 31, SEEK_CUR);
|
||||
file->seek(0-file->tell() & 31, SEEK_CUR);
|
||||
file->read_raw(tensor->data, ggml_nbytes(tensor));
|
||||
}
|
||||
|
||||
|
|
@ -2756,8 +2758,8 @@ struct train_params get_default_train_params() {
|
|||
|
||||
params.lbfgs_n_iter = 16;
|
||||
params.adam_n_iter = 16;
|
||||
params.adam_alpha = 1e-3;
|
||||
params.adam_decay = 1e-3;
|
||||
params.adam_alpha = 1e-3f;
|
||||
params.adam_decay = 1e-3f;
|
||||
|
||||
params.mem_model_gb = 2;
|
||||
params.mem_compute_gb = 24;
|
||||
|
|
@ -3331,8 +3333,8 @@ int main(int argc, char ** argv) {
|
|||
int n_gen = params.n_predict;
|
||||
int sample_ctx = n_tokens - n_tokens/8;
|
||||
|
||||
sampler.params.temp = 0.2;
|
||||
sampler.params.repeat_penalty = 1.1;
|
||||
sampler.params.temp = 0.2f;
|
||||
sampler.params.repeat_penalty = 1.1f;
|
||||
sampler.params.mirostat = 2;
|
||||
init_sampler(&sampler, lctx);
|
||||
|
||||
|
|
|
|||
13
flake.nix
13
flake.nix
|
|
@ -48,6 +48,19 @@
|
|||
'';
|
||||
meta.mainProgram = "llama";
|
||||
};
|
||||
apps.llama-server = {
|
||||
type = "app";
|
||||
program = "${self.packages.${system}.default}/bin/llama-server";
|
||||
};
|
||||
apps.llama-embedding = {
|
||||
type = "app";
|
||||
program = "${self.packages.${system}.default}/bin/embedding";
|
||||
};
|
||||
apps.llama = {
|
||||
type = "app";
|
||||
program = "${self.packages.${system}.default}/bin/llama";
|
||||
};
|
||||
apps.default = self.apps.${system}.llama;
|
||||
devShells.default = pkgs.mkShell {
|
||||
packages = with pkgs; [
|
||||
cmake
|
||||
|
|
|
|||
603
ggml-cuda.cu
603
ggml-cuda.cu
|
|
@ -80,7 +80,7 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
|
|||
} \
|
||||
} while (0)
|
||||
|
||||
#if CUDART_VERSION >= 12
|
||||
#if CUDART_VERSION >= 12000
|
||||
#define CUBLAS_CHECK(err) \
|
||||
do { \
|
||||
cublasStatus_t err_ = (err); \
|
||||
|
|
@ -222,6 +222,12 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
|
|||
#define GGML_CUDA_DMMV_Y 1
|
||||
#endif
|
||||
|
||||
#ifndef K_QUANTS_PER_ITERATION
|
||||
#define K_QUANTS_PER_ITERATION 2
|
||||
#else
|
||||
static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
|
||||
#endif
|
||||
|
||||
static __global__ void add_f32(const float * x, const float * y, float * dst, const int k) {
|
||||
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
||||
|
|
@ -381,37 +387,6 @@ static __global__ void dequantize_block_q2_K(const void * vx, float * yy) {
|
|||
|
||||
}
|
||||
|
||||
static __device__ void vec_dot_q2_K(const void * vx, const int ib, const int iqs, const float * yy, float & result) {
|
||||
|
||||
const block_q2_K * x = (const block_q2_K *) vx;
|
||||
|
||||
// if n is 0, we want to do the lower 128, else the upper 128,
|
||||
// covering y[l+0], y[l+32], y[l+64], y[l+96] and
|
||||
// y[l+16], y[l+48], y[l+80], y[l+112]
|
||||
int n = iqs/128; // 0 or 1
|
||||
int r = iqs - 128*n; // 0...120 in steps of 8
|
||||
int l = r/8; // 0...15 in steps of 1
|
||||
|
||||
const float * y = yy + 128*n + l;
|
||||
const uint8_t * q = x[ib].qs + 32*n + l;
|
||||
const uint8_t * s = x[ib].scales + 8*n;
|
||||
|
||||
const float dall = x[ib].d;
|
||||
const float dmin = x[ib].dmin;
|
||||
|
||||
float sum = y[ 0] * (dall * ((s[0] & 0xF) * ((q[ 0] >> 0) & 3)) - dmin * (s[0] >> 4))
|
||||
+ y[ 32] * (dall * ((s[2] & 0xF) * ((q[ 0] >> 2) & 3)) - dmin * (s[2] >> 4))
|
||||
+ y[ 64] * (dall * ((s[4] & 0xF) * ((q[ 0] >> 4) & 3)) - dmin * (s[4] >> 4))
|
||||
+ y[ 96] * (dall * ((s[6] & 0xF) * ((q[ 0] >> 6) & 3)) - dmin * (s[6] >> 4))
|
||||
+ y[ 16] * (dall * ((s[1] & 0xF) * ((q[16] >> 0) & 3)) - dmin * (s[1] >> 4))
|
||||
+ y[ 48] * (dall * ((s[3] & 0xF) * ((q[16] >> 2) & 3)) - dmin * (s[3] >> 4))
|
||||
+ y[ 80] * (dall * ((s[5] & 0xF) * ((q[16] >> 4) & 3)) - dmin * (s[5] >> 4))
|
||||
+ y[112] * (dall * ((s[7] & 0xF) * ((q[16] >> 6) & 3)) - dmin * (s[7] >> 4));
|
||||
|
||||
result = sum;
|
||||
|
||||
}
|
||||
|
||||
static __global__ void dequantize_block_q3_K(const void * vx, float * yy) {
|
||||
|
||||
int r = threadIdx.x/4;
|
||||
|
|
@ -443,51 +418,6 @@ static __global__ void dequantize_block_q3_K(const void * vx, float * yy) {
|
|||
|
||||
}
|
||||
|
||||
static __device__ void vec_dot_q3_K(const void * vx, const int ib, const int iqs, const float * yy, float & result) {
|
||||
|
||||
const block_q3_K * x = (const block_q3_K *) vx;
|
||||
|
||||
const uint32_t kmask1 = 0x03030303;
|
||||
const uint32_t kmask2 = 0x0f0f0f0f;
|
||||
|
||||
uint32_t aux[3];
|
||||
uint32_t utmp[4];
|
||||
|
||||
// if n is 0, we want to do the lower 128, else the upper 128,
|
||||
// covering y[l+0], y[l+32], y[l+64], y[l+96] and
|
||||
// y[l+16], y[l+48], y[l+80], y[l+112]
|
||||
int n = iqs/128; // 0 or 1
|
||||
int r = iqs - 128*n; // 0...120 in steps of 8
|
||||
int l = r/8; // 0...15 in steps of 1
|
||||
|
||||
const float * y = yy + 128*n + l;
|
||||
const uint8_t * q = x[ib].qs + 32*n + l;
|
||||
const uint8_t * hm = x[ib].hmask + l;
|
||||
const int8_t * s = (const int8_t *)utmp + 8*n;
|
||||
|
||||
memcpy(aux, x[ib].scales, 12);
|
||||
utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4);
|
||||
utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4);
|
||||
utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4);
|
||||
utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4);
|
||||
|
||||
const float dall = x[ib].d;
|
||||
|
||||
const uint8_t m = 1 << (4*n);
|
||||
|
||||
float sum = y[ 0] * (s[0] - 32) * (((q[ 0] >> 0) & 3) - (hm[ 0] & (m << 0) ? 0 : 4))
|
||||
+ y[ 32] * (s[2] - 32) * (((q[ 0] >> 2) & 3) - (hm[ 0] & (m << 1) ? 0 : 4))
|
||||
+ y[ 64] * (s[4] - 32) * (((q[ 0] >> 4) & 3) - (hm[ 0] & (m << 2) ? 0 : 4))
|
||||
+ y[ 96] * (s[6] - 32) * (((q[ 0] >> 6) & 3) - (hm[ 0] & (m << 3) ? 0 : 4))
|
||||
+ y[ 16] * (s[1] - 32) * (((q[16] >> 0) & 3) - (hm[16] & (m << 0) ? 0 : 4))
|
||||
+ y[ 48] * (s[3] - 32) * (((q[16] >> 2) & 3) - (hm[16] & (m << 1) ? 0 : 4))
|
||||
+ y[ 80] * (s[5] - 32) * (((q[16] >> 4) & 3) - (hm[16] & (m << 2) ? 0 : 4))
|
||||
+ y[112] * (s[7] - 32) * (((q[16] >> 6) & 3) - (hm[16] & (m << 3) ? 0 : 4));
|
||||
|
||||
result = sum * dall;
|
||||
|
||||
}
|
||||
|
||||
static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
|
||||
if (j < 4) {
|
||||
d = q[j] & 63; m = q[j + 4] & 63;
|
||||
|
|
@ -534,38 +464,6 @@ static __global__ void dequantize_block_q4_K(const void * vx, float * yy) {
|
|||
}
|
||||
}
|
||||
|
||||
static __device__ void vec_dot_q4_K(const void * vx, const int ib, const int iqs, const float * yy, float & result) {
|
||||
|
||||
const block_q4_K * x = (const block_q4_K *) vx;
|
||||
|
||||
// iqs is in 0...248 in steps of 8 =>
|
||||
const int j = iqs / 64; // j is in 0...3
|
||||
const int ir = (iqs - 64*j)/2; // ir is in 0...28 in steps of 4
|
||||
const int is = 2*j; // is is in 0...6 in steps of 2
|
||||
|
||||
const float * y = yy + 64*j + ir;
|
||||
const uint8_t * q = x[ib].qs + 32*j + ir;
|
||||
|
||||
const float dall = x[ib].d;
|
||||
const float dmin = x[ib].dmin;
|
||||
|
||||
uint8_t sc, m;
|
||||
get_scale_min_k4(is + 0, x[ib].scales, sc, m);
|
||||
const float d1 = dall * sc;
|
||||
const float m1 = dmin * m;
|
||||
get_scale_min_k4(is + 1, x[ib].scales, sc, m);
|
||||
const float d2 = dall * sc;
|
||||
const float m2 = dmin * m;
|
||||
|
||||
float sum = 0;
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
sum += y[k + 0] * (d1 * (q[k] & 0xF) - m1);
|
||||
sum += y[k + 32] * (d2 * (q[k] >> 4) - m2);
|
||||
}
|
||||
result = sum;
|
||||
|
||||
}
|
||||
|
||||
static __global__ void dequantize_block_q5_K(const void * vx, float * yy) {
|
||||
const block_q5_K * x = (const block_q5_K *) vx;
|
||||
|
||||
|
|
@ -599,43 +497,6 @@ static __global__ void dequantize_block_q5_K(const void * vx, float * yy) {
|
|||
y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2;
|
||||
}
|
||||
|
||||
static __device__ void vec_dot_q5_K(const void * vx, const int ib, const int iqs, const float * yy, float & result) {
|
||||
|
||||
const block_q5_K * x = (const block_q5_K *) vx;
|
||||
|
||||
// iqs is in 0...248 in steps of 8 =>
|
||||
const int j = iqs / 64; // j is in 0...3
|
||||
const int ir = (iqs - 64*j)/2; // ir is in 0...28 in steps of 4
|
||||
const int is = 2*j; // is is in 0...6 in steps of 2
|
||||
|
||||
const float * y = yy + 64*j + ir;
|
||||
const uint8_t * ql = x[ib].qs + 32*j + ir;
|
||||
const uint8_t * qh = x[ib].qh + ir;
|
||||
|
||||
const float dall = x[ib].d;
|
||||
const float dmin = x[ib].dmin;
|
||||
|
||||
uint8_t sc, m;
|
||||
get_scale_min_k4(is + 0, x[ib].scales, sc, m);
|
||||
const float d1 = dall * sc;
|
||||
const float m1 = dmin * m;
|
||||
get_scale_min_k4(is + 1, x[ib].scales, sc, m);
|
||||
const float d2 = dall * sc;
|
||||
const float m2 = dmin * m;
|
||||
|
||||
uint8_t hm = 1 << is;
|
||||
float sum = 0;
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
sum += y[k + 0] * (d1 * ((ql[k] & 0xF) + (qh[k] & hm ? 16 : 0)) - m1);
|
||||
}
|
||||
hm <<= 1;
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
sum += y[k + 32] * (d2 * ((ql[k] >> 4) + (qh[k] & hm ? 16 : 0)) - m2);
|
||||
}
|
||||
result = sum;
|
||||
|
||||
}
|
||||
|
||||
static __global__ void dequantize_block_q6_K(const void * vx, float * yy) {
|
||||
const block_q6_K * x = (const block_q6_K *) vx;
|
||||
|
||||
|
|
@ -661,31 +522,376 @@ static __global__ void dequantize_block_q6_K(const void * vx, float * yy) {
|
|||
y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32);
|
||||
}
|
||||
|
||||
static __device__ void vec_dot_q6_K(const void * vx, const int ib, const int iqs, const float * yy, float & result) {
|
||||
static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) {
|
||||
|
||||
const block_q6_K * x = (const block_q6_K *) vx;
|
||||
static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
|
||||
|
||||
const int ip = iqs / 128; // 0 or 1
|
||||
const int il = (iqs - 128*ip)/8; // 0...15
|
||||
const int is = 8*ip;
|
||||
const int row = blockIdx.y*blockDim.y + threadIdx.y;
|
||||
if (row > nrows) return;
|
||||
|
||||
const float * y = yy + 128*ip + il;
|
||||
const int num_blocks_per_row = ncols / QK_K;
|
||||
const int ib0 = row*num_blocks_per_row;
|
||||
|
||||
const float d = x[ib].d;
|
||||
const block_q2_K * x = (const block_q2_K *)vx + ib0;
|
||||
|
||||
const uint8_t * ql = x[ib].ql + 64*ip + il;
|
||||
const uint8_t * qh = x[ib].qh + 32*ip + il;
|
||||
const int8_t * sc = x[ib].scales + is;
|
||||
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31
|
||||
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0
|
||||
|
||||
result = y[ 0] * d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh[ 0] >> 0) & 3) << 4)) - 32)
|
||||
+ y[ 32] * d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh[ 0] >> 2) & 3) << 4)) - 32)
|
||||
+ y[ 64] * d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh[ 0] >> 4) & 3) << 4)) - 32)
|
||||
+ y[ 96] * d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh[ 0] >> 6) & 3) << 4)) - 32)
|
||||
+ y[ 16] * d * sc[1] * ((int8_t)((ql[16] & 0xF) | (((qh[16] >> 0) & 3) << 4)) - 32)
|
||||
+ y[ 48] * d * sc[3] * ((int8_t)((ql[48] & 0xF) | (((qh[16] >> 2) & 3) << 4)) - 32)
|
||||
+ y[ 80] * d * sc[5] * ((int8_t)((ql[16] >> 4) | (((qh[16] >> 4) & 3) << 4)) - 32)
|
||||
+ y[112] * d * sc[7] * ((int8_t)((ql[48] >> 4) | (((qh[16] >> 6) & 3) << 4)) - 32);
|
||||
const int step = 16/K_QUANTS_PER_ITERATION;
|
||||
|
||||
const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
|
||||
const int in = tid - step*im; // 0...7
|
||||
|
||||
const int l0 = K_QUANTS_PER_ITERATION*in; // 0...14 in steps of 4
|
||||
const int q_offset = 32*im + l0;
|
||||
const int s_offset = 8*im;
|
||||
const int y_offset = 128*im + l0;
|
||||
|
||||
float tmp = 0; // partial sum for thread in warp
|
||||
|
||||
uint32_t aux[4];
|
||||
const uint8_t * d = (const uint8_t *)aux;
|
||||
const uint8_t * m = (const uint8_t *)(aux + 2);
|
||||
|
||||
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
|
||||
|
||||
const float * y = yy + i * QK_K + y_offset;
|
||||
const uint8_t * q = x[i].qs + q_offset;
|
||||
|
||||
const float dall = x[i].d;
|
||||
const float dmin = x[i].dmin;
|
||||
|
||||
const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
|
||||
aux[0] = a[0] & 0x0f0f0f0f;
|
||||
aux[1] = a[1] & 0x0f0f0f0f;
|
||||
aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
|
||||
aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
|
||||
|
||||
float sum1 = 0, sum2 = 0;
|
||||
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
|
||||
sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
|
||||
+ y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
|
||||
+ y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
|
||||
+ y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
|
||||
+ y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
|
||||
+ y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
|
||||
+ y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
|
||||
+y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
|
||||
sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
|
||||
+ y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
|
||||
|
||||
}
|
||||
tmp += dall * sum1 - dmin * sum2;
|
||||
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
__syncthreads();
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float * yy, float * dst, const int ncols) {
|
||||
|
||||
const uint16_t kmask1 = 0x0303;
|
||||
const uint16_t kmask2 = 0x0f0f;
|
||||
|
||||
const int row = blockIdx.x;
|
||||
const int num_blocks_per_row = ncols / QK_K;
|
||||
const int ib0 = row*num_blocks_per_row;
|
||||
|
||||
const block_q3_K * x = (const block_q3_K *)vx + ib0;
|
||||
|
||||
const int tid = threadIdx.x/2; // 0...15
|
||||
const int ix = threadIdx.x%2; // 0, 1
|
||||
|
||||
const int n = 2; // iterations in the inner loop
|
||||
const int im = tid/8; // 0 or 1. 0 computes 0..., 1 computes 128...
|
||||
const int in = tid - 8*im; // 0...7
|
||||
|
||||
const uint8_t m = 1 << (4*im);
|
||||
|
||||
const int l0 = n*in; // 0...28 in steps of 4
|
||||
const int q_offset = 32*im + l0;
|
||||
const int y_offset = 128*im + l0;
|
||||
|
||||
uint16_t utmp[4];
|
||||
const int8_t * s = (const int8_t *)utmp;
|
||||
|
||||
const uint16_t s_shift = 4*im;
|
||||
|
||||
float tmp = 0; // partial sum for thread in warp
|
||||
|
||||
for (int i = ix; i < num_blocks_per_row; i += 2) {
|
||||
|
||||
const float * y = yy + i * QK_K + y_offset;
|
||||
const uint8_t * q = x[i].qs + q_offset;
|
||||
const uint8_t * h = x[i].hmask + l0;
|
||||
|
||||
const uint16_t * a = (const uint16_t *)x[i].scales;
|
||||
utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
|
||||
utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
|
||||
utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
|
||||
utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
|
||||
|
||||
const float d = x[i].d;
|
||||
|
||||
float sum = 0;
|
||||
for (int l = 0; l < n; ++l) {
|
||||
sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
|
||||
+ y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
|
||||
+ y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
|
||||
+ y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
|
||||
sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
|
||||
+ y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
|
||||
+ y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
|
||||
+ y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
|
||||
}
|
||||
tmp += d * sum;
|
||||
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
__syncthreads();
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float * yy, float * dst, const int ncols) {
|
||||
|
||||
const uint16_t kmask1 = 0x3f3f;
|
||||
const uint16_t kmask2 = 0x0f0f;
|
||||
const uint16_t kmask3 = 0xc0c0;
|
||||
|
||||
const int row = blockIdx.x;
|
||||
const int num_blocks_per_row = ncols / QK_K;
|
||||
const int ib0 = row*num_blocks_per_row;
|
||||
|
||||
const int tid = threadIdx.x/2; // 0...15
|
||||
const int ix = threadIdx.x%2;
|
||||
|
||||
const int il = tid/4; // 0...3
|
||||
const int ir = tid - 4*il;// 0...3
|
||||
const int n = 4;
|
||||
|
||||
const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
|
||||
const int in = il%2;
|
||||
|
||||
const int l0 = n*(2*ir + in);
|
||||
const int q_offset = 32*im + l0;
|
||||
const int y_offset = 64*im + l0;
|
||||
|
||||
uint16_t aux[4];
|
||||
const uint8_t * sc = (const uint8_t *)aux;
|
||||
|
||||
const block_q4_K * x = (const block_q4_K *)vx + ib0;
|
||||
|
||||
float tmp = 0; // partial sum for thread in warp
|
||||
|
||||
for (int i = ix; i < num_blocks_per_row; i += 2) {
|
||||
|
||||
const uint8_t * q1 = x[i].qs + q_offset;
|
||||
const uint8_t * q2 = q1 + 64;
|
||||
const float * y1 = yy + i*QK_K + y_offset;
|
||||
const float * y2 = y1 + 128;
|
||||
|
||||
const float dall = x[i].d;
|
||||
const float dmin = x[i].dmin;
|
||||
|
||||
const uint16_t * a = (const uint16_t *)x[i].scales;
|
||||
aux[0] = a[im+0] & kmask1;
|
||||
aux[1] = a[im+2] & kmask1;
|
||||
aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
|
||||
aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
|
||||
|
||||
float4 s = {0.f, 0.f, 0.f, 0.f};
|
||||
float smin = 0;
|
||||
for (int l = 0; l < n; ++l) {
|
||||
s.x += y1[l] * (q1[l] & 0xF); s.y += y1[l+32] * (q1[l] >> 4);
|
||||
s.z += y2[l] * (q2[l] & 0xF); s.w += y2[l+32] * (q2[l] >> 4);
|
||||
smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
|
||||
}
|
||||
tmp += dall * (s.x * sc[0] + s.y * sc[1] + s.z * sc[4] + s.w * sc[5]) - dmin * smin;
|
||||
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
__syncthreads();
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float * yy, float * dst, const int ncols) {
|
||||
|
||||
const uint16_t kmask1 = 0x3f3f;
|
||||
const uint16_t kmask2 = 0x0f0f;
|
||||
const uint16_t kmask3 = 0xc0c0;
|
||||
|
||||
//const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
||||
const int row = blockIdx.x;
|
||||
const int num_blocks_per_row = ncols / QK_K;
|
||||
const int ib0 = row*num_blocks_per_row;
|
||||
|
||||
const int tid = threadIdx.x/2; // 0...15
|
||||
const int ix = threadIdx.x%2;
|
||||
|
||||
const int il = tid/4; // 0...3
|
||||
const int ir = tid - 4*il;// 0...3
|
||||
const int n = 4;
|
||||
|
||||
const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
|
||||
const int in = il%2;
|
||||
|
||||
const int l0 = n*(2*ir + in);
|
||||
const int q_offset = 32*im + l0;
|
||||
const int y_offset = 64*im + l0;
|
||||
|
||||
const uint8_t hm1 = 1 << (2*im);
|
||||
const uint8_t hm2 = hm1 << 4;
|
||||
|
||||
uint16_t aux[4];
|
||||
const uint8_t * sc = (const uint8_t *)aux;
|
||||
|
||||
const block_q5_K * x = (const block_q5_K *)vx + ib0;
|
||||
|
||||
float tmp = 0; // partial sum for thread in warp
|
||||
|
||||
for (int i = ix; i < num_blocks_per_row; i += 2) {
|
||||
|
||||
const uint8_t * ql1 = x[i].qs + q_offset;
|
||||
const uint8_t * ql2 = ql1 + 64;
|
||||
const uint8_t * qh = x[i].qh + l0;
|
||||
const float * y1 = yy + i*QK_K + y_offset;
|
||||
const float * y2 = y1 + 128;
|
||||
|
||||
const float dall = x[i].d;
|
||||
const float dmin = x[i].dmin;
|
||||
|
||||
const uint16_t * a = (const uint16_t *)x[i].scales;
|
||||
aux[0] = a[im+0] & kmask1;
|
||||
aux[1] = a[im+2] & kmask1;
|
||||
aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
|
||||
aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
|
||||
|
||||
float4 sum = {0.f, 0.f, 0.f, 0.f};
|
||||
float smin = 0;
|
||||
for (int l = 0; l < n; ++l) {
|
||||
sum.x += y1[l+ 0] * ((ql1[l] & 0xF) + (qh[l] & (hm1 << 0) ? 16 : 0));
|
||||
sum.y += y1[l+32] * ((ql1[l] >> 4) + (qh[l] & (hm1 << 1) ? 16 : 0));
|
||||
sum.z += y2[l+ 0] * ((ql2[l] & 0xF) + (qh[l] & (hm2 << 0) ? 16 : 0));
|
||||
sum.w += y2[l+32] * ((ql2[l] >> 4) + (qh[l] & (hm2 << 1) ? 16 : 0));
|
||||
smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
|
||||
}
|
||||
tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
|
||||
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
__syncthreads();
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
static __global__ void dequantize_mul_mat_vec_q6_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) {
|
||||
|
||||
static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
|
||||
|
||||
const int row = blockIdx.y*blockDim.y + threadIdx.y;
|
||||
if (row > nrows) return;
|
||||
|
||||
const int num_blocks_per_row = ncols / QK_K;
|
||||
const int ib0 = row*num_blocks_per_row;
|
||||
|
||||
const block_q6_K * x = (const block_q6_K *)vx + ib0;
|
||||
|
||||
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
|
||||
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1
|
||||
|
||||
const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8
|
||||
|
||||
const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
|
||||
const int in = tid - step*im; // 0...15 or 0...7
|
||||
|
||||
#if K_QUANTS_PER_ITERATION == 1
|
||||
const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15
|
||||
const int is = 0;
|
||||
#else
|
||||
const int l0 = 4 * in; // 0, 4, 8, ..., 28
|
||||
const int is = in / 4;
|
||||
#endif
|
||||
const int ql_offset = 64*im + l0;
|
||||
const int qh_offset = 32*im + l0;
|
||||
const int s_offset = 8*im + is;
|
||||
const int y_offset = 128*im + l0;
|
||||
|
||||
float tmp = 0; // partial sum for thread in warp
|
||||
|
||||
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
|
||||
|
||||
const float * y = yy + i * QK_K + y_offset;
|
||||
const uint8_t * ql = x[i].ql + ql_offset;
|
||||
const uint8_t * qh = x[i].qh + qh_offset;
|
||||
const int8_t * s = x[i].scales + s_offset;
|
||||
|
||||
const float d = x[i].d;
|
||||
|
||||
#if K_QUANTS_PER_ITERATION == 1
|
||||
float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
|
||||
+ y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
|
||||
+ y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
|
||||
+ y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
|
||||
+ y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
|
||||
+ y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
|
||||
+ y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
|
||||
+y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
|
||||
tmp += sum;
|
||||
#else
|
||||
float sum = 0;
|
||||
for (int l = 0; l < 4; ++l) {
|
||||
sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
|
||||
+ y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
|
||||
+ y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
|
||||
+ y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
|
||||
}
|
||||
tmp += sum;
|
||||
#endif
|
||||
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
__syncthreads();
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
static __device__ void convert_f16(const void * vx, const int ib, const int iqs, float & v0, float & v1){
|
||||
|
|
@ -767,46 +973,6 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y,
|
|||
}
|
||||
}
|
||||
|
||||
template <int n_thread, dot_kernel_k_t dot_kernel>
|
||||
static __global__ void dequantize_mul_mat_vec_k(const void * vx, const float * y, float * dst, const int ncols, const int nrows) {
|
||||
const int row = blockIdx.y*blockDim.y + threadIdx.y;
|
||||
|
||||
if (row >= nrows) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int tid = threadIdx.x;
|
||||
|
||||
const int iter_stride = QK_K;
|
||||
const int vals_per_iter = iter_stride / n_thread;
|
||||
const int num_blocks_per_row = ncols / QK_K;
|
||||
const int ib0 = row*num_blocks_per_row;
|
||||
|
||||
float tmp = 0; // partial sum for thread in warp
|
||||
|
||||
for (int i = 0; i < ncols; i += iter_stride) {
|
||||
const int col = i + vals_per_iter*tid;
|
||||
const int ib = ib0 + col/QK_K; // x block index
|
||||
const int iqs = col%QK_K; // x quant index
|
||||
const int iybs = col - col%QK_K; // y block start index
|
||||
|
||||
float v;
|
||||
dot_kernel(vx, ib, iqs, y + iybs, v);
|
||||
tmp += v;
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
__syncthreads();
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
||||
}
|
||||
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp;
|
||||
}
|
||||
}
|
||||
|
||||
static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nchannels_x) {
|
||||
const half * x = (half *) vx;
|
||||
|
||||
|
|
@ -1149,43 +1315,34 @@ static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, f
|
|||
const int block_num_y = (nrows + ny - 1) / ny;
|
||||
const dim3 block_nums(1, block_num_y, 1);
|
||||
const dim3 block_dims(32, ny, 1);
|
||||
dequantize_mul_mat_vec_k<32, vec_dot_q2_K><<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
dequantize_mul_mat_vec_q2_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % QK_K == 0);
|
||||
const int ny = 2;
|
||||
const int block_num_y = (nrows + ny - 1) / ny;
|
||||
const dim3 block_nums(1, block_num_y, 1);
|
||||
const dim3 block_dims(32, ny, 1);
|
||||
dequantize_mul_mat_vec_k<32, vec_dot_q3_K><<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
const dim3 block_dims(32, 1, 1);
|
||||
dequantize_mul_mat_vec_q3_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
||||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % QK_K == 0);
|
||||
const int ny = 2;
|
||||
const int block_num_y = (nrows + ny - 1) / ny;
|
||||
const dim3 block_nums(1, block_num_y, 1);
|
||||
const dim3 block_dims(32, ny, 1);
|
||||
dequantize_mul_mat_vec_k<32, vec_dot_q4_K><<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
const dim3 block_dims(32, 1, 1);
|
||||
dequantize_mul_mat_vec_q4_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
||||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % QK_K == 0);
|
||||
const int ny = 2;
|
||||
const int block_num_y = (nrows + ny - 1) / ny;
|
||||
const dim3 block_nums(1, block_num_y, 1);
|
||||
const dim3 block_dims(32, ny, 1);
|
||||
dequantize_mul_mat_vec_k<32, vec_dot_q5_K><<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
const dim3 block_dims(32, 1, 1);
|
||||
dequantize_mul_mat_vec_q5_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
||||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
||||
GGML_ASSERT(ncols % QK_K == 0);
|
||||
const int ny = 2;
|
||||
const int ny = 2 / K_QUANTS_PER_ITERATION;
|
||||
const int block_num_y = (nrows + ny - 1) / ny;
|
||||
const dim3 block_nums(1, block_num_y, 1);
|
||||
const dim3 block_dims(32, ny, 1);
|
||||
dequantize_mul_mat_vec_k<32, vec_dot_q6_K><<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
||||
}
|
||||
|
||||
static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
|
||||
|
|
@ -2421,7 +2578,7 @@ void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) {
|
|||
}
|
||||
|
||||
void ggml_cuda_set_main_device(int main_device) {
|
||||
if (main_device > g_device_count) {
|
||||
if (main_device >= g_device_count) {
|
||||
fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n",
|
||||
main_device, g_device_count, g_main_device);
|
||||
return;
|
||||
|
|
|
|||
|
|
@ -55,6 +55,7 @@ void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor *
|
|||
void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
|
||||
|
||||
// same as ggml_graph_compute but uses Metal
|
||||
// creates gf->n_threads command buffers in parallel
|
||||
void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
|
||||
|
||||
#ifdef __cplusplus
|
||||
|
|
|
|||
45
ggml-metal.m
45
ggml-metal.m
|
|
@ -287,15 +287,40 @@ void ggml_metal_graph_compute(
|
|||
struct ggml_cgraph * gf) {
|
||||
metal_printf("%s: evaluating graph\n", __func__);
|
||||
|
||||
// create multiple command buffers and enqueue them
|
||||
// then, we encode the graph into the command buffers in parallel
|
||||
|
||||
const int n_cb = gf->n_threads;
|
||||
|
||||
NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb];
|
||||
|
||||
for (int i = 0; i < n_cb; ++i) {
|
||||
command_buffers[i] = [ctx->queue commandBuffer];
|
||||
|
||||
// enqueue the command buffers in order to specify their execution order
|
||||
[command_buffers[i] enqueue];
|
||||
}
|
||||
|
||||
// TODO: is this the best way to start threads?
|
||||
dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
|
||||
|
||||
for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
|
||||
const int n_nodes_per_cb = (gf->n_nodes + n_cb - 1) / n_cb;
|
||||
|
||||
dispatch_async(queue, ^{
|
||||
size_t offs_src0 = 0;
|
||||
size_t offs_src1 = 0;
|
||||
size_t offs_dst = 0;
|
||||
|
||||
id<MTLCommandBuffer> command_buffer = [ctx->queue commandBuffer];
|
||||
id<MTLCommandBuffer> command_buffer = command_buffers[cb_idx];
|
||||
|
||||
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));
|
||||
const int node_start = (cb_idx + 0) * n_nodes_per_cb;
|
||||
const int node_end = (cb_idx == n_cb - 1) ? gf->n_nodes : (cb_idx + 1) * n_nodes_per_cb;
|
||||
|
||||
for (int i = node_start; i < node_end; ++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;
|
||||
|
|
@ -626,7 +651,6 @@ void ggml_metal_graph_compute(
|
|||
}
|
||||
};
|
||||
|
||||
|
||||
[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];
|
||||
|
|
@ -800,12 +824,11 @@ void ggml_metal_graph_compute(
|
|||
}
|
||||
|
||||
[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);
|
||||
});
|
||||
}
|
||||
|
||||
// wait for all threads to finish
|
||||
dispatch_barrier_sync(queue, ^{});
|
||||
|
||||
[command_buffers[n_cb - 1] waitUntilCompleted];
|
||||
}
|
||||
|
|
|
|||
493
ggml-opencl.cpp
493
ggml-opencl.cpp
|
|
@ -15,7 +15,7 @@
|
|||
|
||||
#include "ggml.h"
|
||||
|
||||
#define CL_DMMV_BLOCK_SIZE 32;
|
||||
#define CL_DMMV_BLOCK_SIZE 32
|
||||
|
||||
#define MULTILINE_QUOTE(...) #__VA_ARGS__
|
||||
static std::string program_source = MULTILINE_QUOTE(
|
||||
|
|
@ -59,6 +59,46 @@ struct __attribute__ ((packed)) block_q8_0
|
|||
int8_t qs[QK8_0];
|
||||
};
|
||||
|
||||
struct __attribute__((packed)) block_q2_K
|
||||
{
|
||||
uint8_t scales[16];
|
||||
uint8_t qs[64];
|
||||
half d;
|
||||
half dmin;
|
||||
};
|
||||
|
||||
struct __attribute__((packed)) block_q3_K
|
||||
{
|
||||
uint8_t hmask[32];
|
||||
uint8_t qs[64];
|
||||
uint8_t scales[12];
|
||||
half d;
|
||||
};
|
||||
|
||||
struct __attribute__((packed)) block_q4_K
|
||||
{
|
||||
half d;
|
||||
half dmin;
|
||||
uint8_t scales[12];
|
||||
uint8_t qs[128];
|
||||
};
|
||||
|
||||
struct __attribute__((packed)) block_q5_K
|
||||
{
|
||||
half d;
|
||||
half dmin;
|
||||
uint8_t scales[12];
|
||||
uint8_t qh[32];
|
||||
uint8_t qs[128];
|
||||
};
|
||||
|
||||
struct __attribute__((packed)) block_q6_K
|
||||
{
|
||||
uint8_t ql[128];
|
||||
uint8_t qh[64];
|
||||
int8_t scales[16];
|
||||
half d;
|
||||
};
|
||||
|
||||
__kernel void convert_fp16_to_fp32(__global half* x, __global float* y) {
|
||||
const uint i = get_global_id(0);
|
||||
|
|
@ -131,8 +171,314 @@ void convert_f16(__global half* x, const int ib, const int iqs, float* v0, float
|
|||
*v0 = vload_half(0, &x[ib + 0]);
|
||||
*v1 = vload_half(0, &x[ib + 1]);
|
||||
}
|
||||
|
||||
inline void get_scale_min_k4(int j, const __global uint8_t *q, uint8_t *d, uint8_t *m)
|
||||
{
|
||||
if (j < 4)
|
||||
{
|
||||
*d = q[j] & 63;
|
||||
*m = q[j + 4] & 63;
|
||||
}
|
||||
else
|
||||
{
|
||||
*d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4);
|
||||
*m = (q[j + 4] >> 4) | ((q[j - 0] >> 6) << 4);
|
||||
}
|
||||
}
|
||||
|
||||
__kernel void dequantize_block_q2_K(__global const struct block_q2_K *x, __global float *yy)
|
||||
{
|
||||
const int i = get_group_id(0);
|
||||
const int tid = get_local_id(0);
|
||||
const int n = tid / 32;
|
||||
const int l = tid - 32 * n;
|
||||
const int is = 8 * n + l / 16;
|
||||
|
||||
const uint8_t q = x[i].qs[32 * n + l];
|
||||
__global float *y = yy + i * 256 + 128 * n;
|
||||
|
||||
const float dall = vload_half(0, &x[i].d);
|
||||
const float dmin = vload_half(0, &x[i].dmin);
|
||||
|
||||
y[l + 0] = dall * (x[i].scales[is + 0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is + 0] >> 4);
|
||||
y[l + 32] = dall * (x[i].scales[is + 2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is + 2] >> 4);
|
||||
y[l + 64] = dall * (x[i].scales[is + 4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is + 4] >> 4);
|
||||
y[l + 96] = dall * (x[i].scales[is + 6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is + 6] >> 4);
|
||||
}
|
||||
|
||||
__kernel void dequantize_block_q3_K(__global const struct block_q3_K *x, __global float *yy)
|
||||
{
|
||||
int r = get_local_id(0) / 4;
|
||||
int i = get_group_id(0);
|
||||
int tid = r / 2;
|
||||
int is0 = r % 2;
|
||||
int l0 = 16 * is0 + 4 * (get_local_id(0) % 4);
|
||||
int n = tid / 4;
|
||||
int j = tid - 4 * n;
|
||||
|
||||
uint8_t m = 1 << (4 * n + j);
|
||||
int is = 8 * n + 2 * j + is0;
|
||||
int shift = 2 * j;
|
||||
|
||||
int8_t us = is < 4 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 8] >> 0) & 3) << 4)
|
||||
: is < 8 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 4] >> 2) & 3) << 4)
|
||||
: is < 12 ? (x[i].scales[is - 8] >> 4) | (((x[i].scales[is + 0] >> 4) & 3) << 4)
|
||||
: (x[i].scales[is - 8] >> 4) | (((x[i].scales[is - 4] >> 6) & 3) << 4);
|
||||
float d_all = vload_half(0, &x[i].d);
|
||||
float dl = d_all * (us - 32);
|
||||
|
||||
__global float *y = yy + i * 256 + 128 * n + 32 * j;
|
||||
const __global uint8_t *q = x[i].qs + 32 * n;
|
||||
const __global uint8_t *hm = x[i].hmask;
|
||||
|
||||
for (int l = l0; l < l0 + 4; ++l)
|
||||
y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
|
||||
}
|
||||
|
||||
__kernel void dequantize_block_q4_K(__global const struct block_q4_K *x, __global float *yy)
|
||||
{
|
||||
const int i = get_group_id(0);
|
||||
const int tid = get_local_id(0);
|
||||
const int il = tid / 8;
|
||||
const int ir = tid % 8;
|
||||
const int is = 2 * il;
|
||||
const int n = 4;
|
||||
|
||||
__global float *y = yy + i * 256 + 64 * il + n * ir;
|
||||
|
||||
const float dall = vload_half(0, &x[i].d);
|
||||
const float dmin = vload_half(0, &x[i].dmin);
|
||||
|
||||
__global const uint8_t *q = x[i].qs + 32 * il + n * ir;
|
||||
|
||||
uint8_t sc, m;
|
||||
get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
|
||||
float d1 = dall * sc;
|
||||
float m1 = dmin * m;
|
||||
get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
|
||||
float d2 = dall * sc;
|
||||
float m2 = dmin * m;
|
||||
for (int l = 0; l < n; ++l)
|
||||
{
|
||||
y[l + 0] = d1 * (q[l] & 0xF) - m1;
|
||||
y[l + 32] = d2 * (q[l] >> 4) - m2;
|
||||
}
|
||||
}
|
||||
|
||||
__kernel void dequantize_block_q5_K(__global const struct block_q5_K *x, __global float *yy)
|
||||
{
|
||||
const int i = get_group_id(0);
|
||||
const int tid = get_local_id(0);
|
||||
const int il = tid / 16;
|
||||
const int ir = tid % 16;
|
||||
const int is = 2 * il;
|
||||
|
||||
__global float *y = yy + i * 256 + 64 * il + 2 * ir;
|
||||
|
||||
const float dall = vload_half(0, &x[i].d);
|
||||
const float dmin = vload_half(0, &x[i].dmin);
|
||||
|
||||
__global const uint8_t *ql = x[i].qs + 32 * il + 2 * ir;
|
||||
__global const uint8_t *qh = x[i].qh + 2 * ir;
|
||||
|
||||
uint8_t sc, m;
|
||||
get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
|
||||
const float d1 = dall * sc;
|
||||
const float m1 = dmin * m;
|
||||
get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
|
||||
const float d2 = dall * sc;
|
||||
const float m2 = dmin * m;
|
||||
|
||||
uint8_t hm = 1 << (2 * il);
|
||||
y[0] = d1 * ((ql[0] & 0xF) + (qh[0] & hm ? 16 : 0)) - m1;
|
||||
y[1] = d1 * ((ql[1] & 0xF) + (qh[1] & hm ? 16 : 0)) - m1;
|
||||
hm <<= 1;
|
||||
y[32] = d2 * ((ql[0] >> 4) + (qh[0] & hm ? 16 : 0)) - m2;
|
||||
y[33] = d2 * ((ql[1] >> 4) + (qh[1] & hm ? 16 : 0)) - m2;
|
||||
}
|
||||
|
||||
__kernel void dequantize_block_q6_K(__global const struct block_q6_K *x, __global float *yy)
|
||||
{
|
||||
const int i = get_group_id(0);
|
||||
const int tid = get_local_id(0);
|
||||
const int ip = tid / 32;
|
||||
const int il = tid - 32 * ip;
|
||||
const int is = 8 * ip + il / 16;
|
||||
|
||||
__global float *y = yy + i * 256 + 128 * ip + il;
|
||||
|
||||
const float d = vload_half(0, &x[i].d);
|
||||
|
||||
__global const uint8_t *ql = x[i].ql + 64 * ip + il;
|
||||
const uint8_t qh = x[i].qh[32 * ip + il];
|
||||
__global const int8_t *sc = x[i].scales + is;
|
||||
|
||||
y[0] = d * sc[0] * ((int8_t)((ql[0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
|
||||
y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
|
||||
y[64] = d * sc[4] * ((int8_t)((ql[0] >> 4) | (((qh >> 4) & 3) << 4)) - 32);
|
||||
y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32);
|
||||
}
|
||||
|
||||
|
||||
void vec_dot_q2_K(__global const struct block_q2_K* x, const int ib, const int iqs, const __global float *yy, float *result) {
|
||||
|
||||
int n = iqs / 128;
|
||||
int r = iqs - 128 * n;
|
||||
int l = r / 8;
|
||||
|
||||
__global const float *y = yy + 128 * n + l;
|
||||
__global const uint8_t *q = x[ib].qs + 32 * n + l;
|
||||
__global const uint8_t *s = x[ib].scales + 8 * n;
|
||||
|
||||
const float dall = vload_half(0, &x[ib].d);
|
||||
const float dmin = vload_half(0, &x[ib].dmin);
|
||||
|
||||
float sum = y[ 0] * (dall * ((s[0] & 0xF) * ((q[ 0] >> 0) & 3)) - dmin * (s[0] >> 4))
|
||||
+ y[ 32] * (dall * ((s[2] & 0xF) * ((q[ 0] >> 2) & 3)) - dmin * (s[2] >> 4))
|
||||
+ y[ 64] * (dall * ((s[4] & 0xF) * ((q[ 0] >> 4) & 3)) - dmin * (s[4] >> 4))
|
||||
+ y[ 96] * (dall * ((s[6] & 0xF) * ((q[ 0] >> 6) & 3)) - dmin * (s[6] >> 4))
|
||||
+ y[ 16] * (dall * ((s[1] & 0xF) * ((q[16] >> 0) & 3)) - dmin * (s[1] >> 4))
|
||||
+ y[ 48] * (dall * ((s[3] & 0xF) * ((q[16] >> 2) & 3)) - dmin * (s[3] >> 4))
|
||||
+ y[ 80] * (dall * ((s[5] & 0xF) * ((q[16] >> 4) & 3)) - dmin * (s[5] >> 4))
|
||||
+ y[112] * (dall * ((s[7] & 0xF) * ((q[16] >> 6) & 3)) - dmin * (s[7] >> 4));
|
||||
|
||||
*result = sum;
|
||||
}
|
||||
|
||||
void vec_dot_q3_K(__global const struct block_q3_K* x, const int ib, const int iqs, const __global float *yy, float *result) {
|
||||
|
||||
const uint32_t kmask1 = 0x03030303;
|
||||
const uint32_t kmask2 = 0x0f0f0f0f;
|
||||
|
||||
uint32_t aux[3];
|
||||
uint32_t utmp[4];
|
||||
|
||||
int n = iqs/128;
|
||||
int r = iqs - 128*n;
|
||||
int l = r/8;
|
||||
|
||||
__global const float * y = yy + 128*n + l;
|
||||
__global const uint8_t * q = x[ib].qs + 32*n + l;
|
||||
__global const uint8_t * hm = x[ib].hmask + l;
|
||||
const int8_t * s = (const int8_t *)utmp + 8*n;
|
||||
|
||||
aux[0] = x[ib].scales[0] | x[ib].scales[1] << 8 | x[ib].scales[2] << 16 | x[ib].scales[3] << 24;
|
||||
aux[1] = x[ib].scales[4] | x[ib].scales[5] << 8 | x[ib].scales[6] << 16 | x[ib].scales[7] << 24;
|
||||
aux[2] = x[ib].scales[8] | x[ib].scales[9] << 8 | x[ib].scales[10] << 16 | x[ib].scales[11] << 24;
|
||||
|
||||
utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4);
|
||||
utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4);
|
||||
utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4);
|
||||
utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4);
|
||||
|
||||
const float dall = vload_half(0, &x[ib].d);
|
||||
const uint8_t m = 1 << (4*n);
|
||||
|
||||
float sum = y[ 0] * (s[0] - 32) * (((q[ 0] >> 0) & 3) - (hm[ 0] & (m << 0) ? 0 : 4))
|
||||
+ y[ 32] * (s[2] - 32) * (((q[ 0] >> 2) & 3) - (hm[ 0] & (m << 1) ? 0 : 4))
|
||||
+ y[ 64] * (s[4] - 32) * (((q[ 0] >> 4) & 3) - (hm[ 0] & (m << 2) ? 0 : 4))
|
||||
+ y[ 96] * (s[6] - 32) * (((q[ 0] >> 6) & 3) - (hm[ 0] & (m << 3) ? 0 : 4))
|
||||
+ y[ 16] * (s[1] - 32) * (((q[16] >> 0) & 3) - (hm[16] & (m << 0) ? 0 : 4))
|
||||
+ y[ 48] * (s[3] - 32) * (((q[16] >> 2) & 3) - (hm[16] & (m << 1) ? 0 : 4))
|
||||
+ y[ 80] * (s[5] - 32) * (((q[16] >> 4) & 3) - (hm[16] & (m << 2) ? 0 : 4))
|
||||
+ y[112] * (s[7] - 32) * (((q[16] >> 6) & 3) - (hm[16] & (m << 3) ? 0 : 4));
|
||||
|
||||
*result = sum * dall;
|
||||
|
||||
}
|
||||
|
||||
void vec_dot_q4_K(__global const struct block_q4_K* x, const int ib, const int iqs, const __global float *yy, float *result) {
|
||||
|
||||
const int j = iqs / 64; // j is in 0...3
|
||||
const int ir = (iqs - 64*j)/2; // ir is in 0...28 in steps of 4
|
||||
const int is = 2*j; // is is in 0...6 in steps of 2
|
||||
|
||||
__global const float * y = yy + 64*j + ir;
|
||||
__global const uint8_t * q = x[ib].qs + 32*j + ir;
|
||||
|
||||
const float dall = vload_half(0, &x[ib].d);
|
||||
const float dmin = vload_half(0, &x[ib].dmin);
|
||||
|
||||
uint8_t sc, m;
|
||||
get_scale_min_k4(is + 0, x[ib].scales, &sc, &m);
|
||||
const float d1 = dall * sc;
|
||||
const float m1 = dmin * m;
|
||||
get_scale_min_k4(is + 1, x[ib].scales, &sc, &m);
|
||||
const float d2 = dall * sc;
|
||||
const float m2 = dmin * m;
|
||||
|
||||
float sum = 0;
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
sum += y[k + 0] * (d1 * (q[k] & 0xF) - m1);
|
||||
sum += y[k + 32] * (d2 * (q[k] >> 4) - m2);
|
||||
}
|
||||
|
||||
*result = sum;
|
||||
}
|
||||
|
||||
void vec_dot_q5_K(__global const struct block_q5_K* x, const int ib, const int iqs, const __global float *yy, float *result) {
|
||||
|
||||
const int j = iqs / 64;
|
||||
const int ir = (iqs - 64*j)/2;
|
||||
const int is = 2*j;
|
||||
|
||||
__global const float * y = yy + 64*j + ir;
|
||||
__global const uint8_t * ql = x[ib].qs + 32*j + ir;
|
||||
__global const uint8_t * qh = x[ib].qh + ir;
|
||||
|
||||
const float dall = vload_half(0, &x[ib].d);
|
||||
const float dmin = vload_half(0, &x[ib].dmin);
|
||||
|
||||
uint8_t sc, m;
|
||||
get_scale_min_k4(is + 0, x[ib].scales, &sc, &m);
|
||||
const float d1 = dall * sc;
|
||||
const float m1 = dmin * m;
|
||||
get_scale_min_k4(is + 1, x[ib].scales, &sc, &m);
|
||||
const float d2 = dall * sc;
|
||||
const float m2 = dmin * m;
|
||||
|
||||
uint8_t hm = 1 << is;
|
||||
float sum = 0;
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
sum += y[k + 0] * (d1 * ((ql[k] & 0xF) + (qh[k] & hm ? 16 : 0)) - m1);
|
||||
}
|
||||
hm <<= 1;
|
||||
for (int k = 0; k < 4; ++k) {
|
||||
sum += y[k + 32] * (d2 * ((ql[k] >> 4) + (qh[k] & hm ? 16 : 0)) - m2);
|
||||
}
|
||||
*result = sum;
|
||||
|
||||
}
|
||||
|
||||
void vec_dot_q6_K(__global const struct block_q6_K* x, const int ib, const int iqs, const __global float *yy, float *result) {
|
||||
|
||||
|
||||
const int ip = iqs / 128; // 0 or 1
|
||||
const int il = (iqs - 128*ip)/8; // 0...15
|
||||
const int is = 8*ip;
|
||||
|
||||
__global const float * y = yy + 128*ip + il;
|
||||
|
||||
const float d = vload_half(0, &x[ib].d);
|
||||
|
||||
__global const uint8_t * ql = x[ib].ql + 64*ip + il;
|
||||
__global const uint8_t * qh = x[ib].qh + 32*ip + il;
|
||||
__global const int8_t * sc = x[ib].scales + is;
|
||||
|
||||
*result = y[ 0] * d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh[ 0] >> 0) & 3) << 4)) - 32)
|
||||
+ y[ 32] * d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh[ 0] >> 2) & 3) << 4)) - 32)
|
||||
+ y[ 64] * d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh[ 0] >> 4) & 3) << 4)) - 32)
|
||||
+ y[ 96] * d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh[ 0] >> 6) & 3) << 4)) - 32)
|
||||
+ y[ 16] * d * sc[1] * ((int8_t)((ql[16] & 0xF) | (((qh[16] >> 0) & 3) << 4)) - 32)
|
||||
+ y[ 48] * d * sc[3] * ((int8_t)((ql[48] & 0xF) | (((qh[16] >> 2) & 3) << 4)) - 32)
|
||||
+ y[ 80] * d * sc[5] * ((int8_t)((ql[16] >> 4) | (((qh[16] >> 4) & 3) << 4)) - 32)
|
||||
+ y[112] * d * sc[7] * ((int8_t)((ql[48] >> 4) | (((qh[16] >> 6) & 3) << 4)) - 32);
|
||||
|
||||
}
|
||||
|
||||
);
|
||||
|
||||
|
||||
std::string dequant_template = MULTILINE_QUOTE(
|
||||
__kernel void KERNEL_NAME(__global X_TYPE* x, __global float* y) {
|
||||
const int i = get_group_id(0)*get_local_size(0) + get_local_id(0)*2;
|
||||
|
|
@ -160,7 +506,7 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __global float* y) {
|
|||
std::string dequant_mul_mat_vec_template = MULTILINE_QUOTE(
|
||||
__kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float* y, __global float* dst, const int ncols) {
|
||||
const int block_size = get_local_size(0);
|
||||
const int row = get_global_id(0) / block_size;
|
||||
const int row = get_group_id(0);
|
||||
const int tid = get_local_id(0);
|
||||
|
||||
const uint qk = QUANT_K;
|
||||
|
|
@ -199,6 +545,45 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float
|
|||
}
|
||||
);
|
||||
|
||||
std::string dequant_mul_mat_vec_k_template = MULTILINE_QUOTE(
|
||||
__kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float* y, __global float* dst, const int ncols) {
|
||||
const int block_size = get_local_size(0);
|
||||
const int row = get_group_id(0);
|
||||
const int tid = get_local_id(0);
|
||||
|
||||
const int iter_stride = 256;
|
||||
const int vals_per_iter = iter_stride / block_size;
|
||||
const int num_blocks_per_row = ncols / 256;
|
||||
const int ib0 = row*num_blocks_per_row;
|
||||
|
||||
tmp[tid] = 0;
|
||||
|
||||
for (int i = 0; i < ncols; i += iter_stride) {
|
||||
const int col = i + vals_per_iter*tid;
|
||||
const int ib = ib0 + col/256; // x block index
|
||||
const int iqs = col%256; // x quant index
|
||||
const int iybs = col - col%256; // y block start index
|
||||
|
||||
// dequantize
|
||||
float v;
|
||||
DOT_KERNEL(x, ib, iqs, y + iybs, &v);
|
||||
tmp[tid] += v;
|
||||
}
|
||||
|
||||
// sum up partial sums and write back result
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
for (int s=block_size/2; s>0; s>>=1) {
|
||||
if (tid < s) {
|
||||
tmp[tid] += tmp[tid + s];
|
||||
}
|
||||
barrier(CLK_LOCAL_MEM_FENCE);
|
||||
}
|
||||
if (tid == 0) {
|
||||
dst[row] = tmp[0];
|
||||
}
|
||||
}
|
||||
);
|
||||
|
||||
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);
|
||||
|
|
@ -260,6 +645,18 @@ std::array<std::string, 2> mul_str_values = {
|
|||
"mul_f32", "float"
|
||||
};
|
||||
|
||||
std::array<std::string, 3> dmmv_k_str_keys = {
|
||||
"KERNEL_NAME", "X_TYPE", "DOT_KERNEL"
|
||||
};
|
||||
|
||||
std::array<std::string, 15> dmmv_k_str_values = {
|
||||
"dequantize_mul_mat_vec_q2_K", "struct block_q2_K", "vec_dot_q2_K",
|
||||
"dequantize_mul_mat_vec_q3_K", "struct block_q3_K", "vec_dot_q3_K",
|
||||
"dequantize_mul_mat_vec_q4_K", "struct block_q4_K", "vec_dot_q4_K",
|
||||
"dequantize_mul_mat_vec_q5_K", "struct block_q5_K", "vec_dot_q5_K",
|
||||
"dequantize_mul_mat_vec_q6_K", "struct block_q6_K", "vec_dot_q6_K",
|
||||
};
|
||||
|
||||
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) {
|
||||
|
|
@ -289,6 +686,14 @@ std::string generate_kernels() {
|
|||
}
|
||||
src << mul_kernel << '\n';
|
||||
}
|
||||
for (size_t i = 0; i < dmmv_k_str_values.size(); i += dmmv_k_str_keys.size()) {
|
||||
std::string dmmv_k_kernel = dequant_mul_mat_vec_k_template;
|
||||
for (size_t j = 0; j < dmmv_k_str_keys.size(); j++) {
|
||||
replace(dmmv_k_kernel, dmmv_k_str_keys[j], dmmv_k_str_values[i + j]);
|
||||
}
|
||||
src << dmmv_k_kernel << '\n';
|
||||
}
|
||||
|
||||
return src.str();
|
||||
}
|
||||
|
||||
|
|
@ -300,6 +705,8 @@ 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 dequantize_block_q2_k_cl, dequantize_block_q3_k_cl, dequantize_block_q4_k_cl, dequantize_block_q5_k_cl, dequantize_block_q6_k_cl;
|
||||
static cl_kernel dequantize_mul_mat_vec_q2_K_cl, dequantize_mul_mat_vec_q3_K_cl, dequantize_mul_mat_vec_q4_K_cl, dequantize_mul_mat_vec_q5_K_cl, dequantize_mul_mat_vec_q6_K_cl;
|
||||
static cl_kernel mul_f32_cl;
|
||||
static bool fp16_support;
|
||||
|
||||
|
|
@ -529,6 +936,12 @@ void ggml_cl_init(void) {
|
|||
CL_CHECK((dequantize_row_q5_0_cl = clCreateKernel(program, "dequantize_row_q5_0", &err), err));
|
||||
CL_CHECK((dequantize_row_q5_1_cl = clCreateKernel(program, "dequantize_row_q5_1", &err), err));
|
||||
CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
|
||||
CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
|
||||
CL_CHECK((dequantize_block_q2_k_cl = clCreateKernel(program, "dequantize_block_q2_K", &err), err));
|
||||
CL_CHECK((dequantize_block_q3_k_cl = clCreateKernel(program, "dequantize_block_q3_K", &err), err));
|
||||
CL_CHECK((dequantize_block_q4_k_cl = clCreateKernel(program, "dequantize_block_q4_K", &err), err));
|
||||
CL_CHECK((dequantize_block_q5_k_cl = clCreateKernel(program, "dequantize_block_q5_K", &err), err));
|
||||
CL_CHECK((dequantize_block_q6_k_cl = clCreateKernel(program, "dequantize_block_q6_K", &err), err));
|
||||
|
||||
// dequant mul mat kernel
|
||||
CL_CHECK((dequantize_mul_mat_vec_q4_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_0", &err), err));
|
||||
|
|
@ -537,6 +950,11 @@ 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));
|
||||
CL_CHECK((dequantize_mul_mat_vec_q2_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q2_K", &err), err));
|
||||
CL_CHECK((dequantize_mul_mat_vec_q3_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q3_K", &err), err));
|
||||
CL_CHECK((dequantize_mul_mat_vec_q4_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_K", &err), err));
|
||||
CL_CHECK((dequantize_mul_mat_vec_q5_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_K", &err), err));
|
||||
CL_CHECK((dequantize_mul_mat_vec_q6_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q6_K", &err), err));
|
||||
|
||||
// mul kernel
|
||||
CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err));
|
||||
|
|
@ -554,6 +972,16 @@ static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
|
|||
return &dequantize_row_q5_1_cl;
|
||||
case GGML_TYPE_Q8_0:
|
||||
return &dequantize_row_q8_0_cl;
|
||||
case GGML_TYPE_Q2_K:
|
||||
return &dequantize_block_q2_k_cl;
|
||||
case GGML_TYPE_Q3_K:
|
||||
return &dequantize_block_q3_k_cl;
|
||||
case GGML_TYPE_Q4_K:
|
||||
return &dequantize_block_q4_k_cl;
|
||||
case GGML_TYPE_Q5_K:
|
||||
return &dequantize_block_q5_k_cl;
|
||||
case GGML_TYPE_Q6_K:
|
||||
return &dequantize_block_q6_k_cl;
|
||||
case GGML_TYPE_F16:
|
||||
return &convert_row_f16_cl;
|
||||
default:
|
||||
|
|
@ -561,6 +989,50 @@ static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
|
|||
}
|
||||
}
|
||||
|
||||
static size_t ggml_cl_global_denom(ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
return 1;
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
return 4;
|
||||
case GGML_TYPE_Q4_K:
|
||||
return 8;
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
return 4;
|
||||
case GGML_TYPE_F16:
|
||||
default:
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
static size_t ggml_cl_local_size(ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
return 0;
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
return 64;
|
||||
case GGML_TYPE_Q4_K:
|
||||
return 32;
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
return 64;
|
||||
case GGML_TYPE_F16:
|
||||
default:
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
static cl_kernel* ggml_get_dequantize_mul_mat_vec_cl(ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q4_0:
|
||||
|
|
@ -575,6 +1047,16 @@ static cl_kernel* ggml_get_dequantize_mul_mat_vec_cl(ggml_type type) {
|
|||
return &dequantize_mul_mat_vec_q8_0_cl;
|
||||
case GGML_TYPE_F16:
|
||||
return &convert_mul_mat_vec_f16_cl;
|
||||
case GGML_TYPE_Q2_K:
|
||||
return &dequantize_mul_mat_vec_q2_K_cl;
|
||||
case GGML_TYPE_Q3_K:
|
||||
return &dequantize_mul_mat_vec_q3_K_cl;
|
||||
case GGML_TYPE_Q4_K:
|
||||
return &dequantize_mul_mat_vec_q4_K_cl;
|
||||
case GGML_TYPE_Q5_K:
|
||||
return &dequantize_mul_mat_vec_q5_K_cl;
|
||||
case GGML_TYPE_Q6_K:
|
||||
return &dequantize_mul_mat_vec_q6_K_cl;
|
||||
default:
|
||||
return nullptr;
|
||||
}
|
||||
|
|
@ -1017,6 +1499,9 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
|
|||
cl_kernel* dmmv = ggml_get_dequantize_mul_mat_vec_cl(type);
|
||||
GGML_ASSERT(to_fp32_cl != nullptr);
|
||||
|
||||
const size_t global_denom = ggml_cl_global_denom(type);
|
||||
const size_t local = ggml_cl_local_size(type);
|
||||
|
||||
size_t ev_idx = 0;
|
||||
std::vector<cl_event> events;
|
||||
|
||||
|
|
@ -1049,10 +1534,10 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
|
|||
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;
|
||||
const size_t global = x_ne / global_denom;
|
||||
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(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, NULL, &global, NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
|
||||
CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, NULL, &global, local > 0 ? &local : 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));
|
||||
|
|
|
|||
6
ggml.c
6
ggml.c
|
|
@ -35,6 +35,12 @@
|
|||
#define static_assert(cond, msg) struct global_scope_noop_trick
|
||||
#endif
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
// disable "possible loss of data" to avoid hundreds of casts
|
||||
// we should just be careful :)
|
||||
#pragma warning(disable: 4244 4267)
|
||||
#endif
|
||||
|
||||
#if defined(_WIN32)
|
||||
|
||||
#include <windows.h>
|
||||
|
|
|
|||
|
|
@ -40,6 +40,10 @@
|
|||
#include <sstream>
|
||||
#include <numeric>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
#define LLAMA_USE_SCRATCH
|
||||
#define LLAMA_MAX_SCRATCH_BUFFERS 16
|
||||
|
||||
|
|
@ -1654,7 +1658,7 @@ static bool llama_eval_internal(
|
|||
|
||||
// cur = cur*norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.norm);
|
||||
offload_func_nr(cur);
|
||||
// offload_func_nr(cur); // TODO CPU + GPU mirrored backend
|
||||
ggml_set_name(cur, "result_norm");
|
||||
|
||||
embeddings = cur;
|
||||
|
|
|
|||
6
llama.h
6
llama.h
|
|
@ -244,9 +244,9 @@ extern "C" {
|
|||
LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
|
||||
|
||||
// Special tokens
|
||||
LLAMA_API llama_token llama_token_bos();
|
||||
LLAMA_API llama_token llama_token_eos();
|
||||
LLAMA_API llama_token llama_token_nl();
|
||||
LLAMA_API llama_token llama_token_bos(); // beginning-of-sentence
|
||||
LLAMA_API llama_token llama_token_eos(); // end-of-sentence
|
||||
LLAMA_API llama_token llama_token_nl(); // next-line
|
||||
|
||||
// Sampling functions
|
||||
|
||||
|
|
|
|||
|
|
@ -10,6 +10,10 @@
|
|||
|
||||
#include <ggml.h>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
constexpr int kVecSize = 1 << 18;
|
||||
|
||||
float drawFromGaussianPdf(std::mt19937& rndm) {
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import os
|
||||
import hashlib
|
||||
|
||||
|
||||
def sha256sum(file):
|
||||
block_size = 16 * 1024 * 1024 # 16 MB block size
|
||||
b = bytearray(block_size)
|
||||
|
|
@ -15,6 +16,7 @@ def sha256sum(file):
|
|||
|
||||
return file_hash.hexdigest()
|
||||
|
||||
|
||||
# Define the path to the llama directory (parent folder of script directory)
|
||||
llama_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))
|
||||
|
||||
|
|
|
|||
1
spm-headers/ggml.h
Symbolic link
1
spm-headers/ggml.h
Symbolic link
|
|
@ -0,0 +1 @@
|
|||
../ggml.h
|
||||
|
|
@ -9,12 +9,15 @@
|
|||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
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 float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001f;
|
||||
const float MAX_QUANTIZATION_TOTAL_ERROR = 0.002f;
|
||||
const float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f;
|
||||
const float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f;
|
||||
const float MAX_DOT_PRODUCT_ERROR = 0.02f;
|
||||
|
||||
const char* RESULT_STR[] = {"ok", "FAILED"};
|
||||
|
||||
|
|
|
|||
|
|
@ -13,6 +13,10 @@
|
|||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
#define MAX_ALIGNMENT 64
|
||||
#define QK 32
|
||||
#define WARMUP 5
|
||||
|
|
|
|||
|
|
@ -176,27 +176,27 @@ void test_frequency_presence_penalty(
|
|||
int main(void) {
|
||||
ggml_time_init();
|
||||
|
||||
test_top_k({0.1, 0.2, 0.3, 0.4}, {0.4}, 1);
|
||||
test_top_k({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3, 0.2}, 3);
|
||||
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 1);
|
||||
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 3);
|
||||
|
||||
test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4}, 0);
|
||||
test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3}, 0.7);
|
||||
test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3, 0.2, 0.1}, 1);
|
||||
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0);
|
||||
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f}, 0.7f);
|
||||
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1);
|
||||
|
||||
test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3}, 0.25);
|
||||
test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3, 0.25}, 0.75);
|
||||
test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3, 0.25}, 0.99);
|
||||
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f);
|
||||
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f);
|
||||
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f);
|
||||
|
||||
test_typical({0.97, 0.01, 0.01, 0.01}, {0.97}, 0.5);
|
||||
test_typical({0.4, 0.2, 0.2, 0.2}, {0.2, 0.2, 0.2}, 0.5);
|
||||
test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
|
||||
test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
|
||||
|
||||
test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0}, {0.25, 0.25, 0.25, 0.25, 0}, 50.0);
|
||||
test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2}, {0.5, 0.5, 0, 0, 0}, 50.0);
|
||||
test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2, 0, 0}, {0.5, 0.5, 0, 0, 0}, 50.0);
|
||||
test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.25f, 0.25f, 0.25f, 0.25f, 0}, 50.0f);
|
||||
test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0}, 50.0f);
|
||||
test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.5f, 0.5f, 0, 0, 0}, 50.0f);
|
||||
|
||||
test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0}, {0.249997, 0.249997, 0.249997, 0.249997, 0.000011}, 5.0, 5.0);
|
||||
test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2}, {0.499966, 0.499966, 0.000023, 0.000023, 0.000023}, 5.0, 5.0);
|
||||
test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2, 0, 0}, {0.499977, 0.499977, 0.000023, 0.000023, 0.000000}, 5.0, 5.0);
|
||||
test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.249997f, 0.249997f, 0.249997f, 0.249997f, 0.000011f}, 5.0f, 5.0f);
|
||||
test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 5.0f, 5.0f);
|
||||
test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 5.0f, 5.0f);
|
||||
|
||||
printf("OK\n");
|
||||
}
|
||||
|
|
|
|||
|
|
@ -53,7 +53,7 @@ int main(int argc, char **argv) {
|
|||
|
||||
for (const auto & test_kv : k_tests()) {
|
||||
std::vector<llama_token> res(test_kv.first.size());
|
||||
const int n = llama_tokenize(ctx, test_kv.first.c_str(), res.data(), res.size(), true);
|
||||
const int n = llama_tokenize(ctx, test_kv.first.c_str(), res.data(), int(res.size()), true);
|
||||
res.resize(n);
|
||||
|
||||
bool correct = res.size() == test_kv.second.size();
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue