Merge branch 'ggerganov:master' into master
This commit is contained in:
qingfengfenga
GitHub
commit
994ebe1218
27 changed files with 4626 additions and 675 deletions
2
.github/workflows/tidy-post.yml
vendored
2
.github/workflows/tidy-post.yml
vendored
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@ -1,7 +1,7 @@
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|||
name: clang-tidy review post comments
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||||
|
||||
on:
|
||||
workflow_run:
|
||||
workflow_dispatch:
|
||||
workflows: ["clang-tidy-review"]
|
||||
types:
|
||||
- completed
|
||||
|
|
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|||
2
.gitignore
vendored
2
.gitignore
vendored
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|
@ -7,6 +7,7 @@
|
|||
.envrc
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||||
.swiftpm
|
||||
.venv
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||||
.clang-tidy
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||||
.vs/
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.vscode/
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||||
|
||||
|
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@ -34,6 +35,7 @@ models/*
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|||
/benchmark-matmult
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/vdot
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/Pipfile
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||||
/libllama.so
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||||
|
||||
build-info.h
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arm_neon.h
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|
|
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|||
|
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@ -396,6 +396,8 @@ endif()
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|||
add_library(ggml OBJECT
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||||
ggml.c
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||||
ggml.h
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||||
ggml-quants-k.h
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||||
ggml-quants-k.c
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${GGML_SOURCES_CUDA}
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||||
${GGML_SOURCES_OPENCL}
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${GGML_SOURCES_METAL}
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||||
|
|
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|||
32
Makefile
32
Makefile
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|
@ -40,8 +40,11 @@ endif
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|||
#
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||||
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||||
# keep standard at C11 and C++11
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||||
CFLAGS = -I. -O3 -std=c11 -fPIC
|
||||
CXXFLAGS = -I. -I./examples -O3 -std=c++11 -fPIC
|
||||
# -Ofast tends to produce faster code, but may not be available for some compilers.
|
||||
#OPT = -Ofast
|
||||
OPT = -O3
|
||||
CFLAGS = -I. $(OPT) -std=c11 -fPIC
|
||||
CXXFLAGS = -I. -I./examples $(OPT) -std=c++11 -fPIC
|
||||
LDFLAGS =
|
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|
||||
ifdef LLAMA_DEBUG
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|
|
@ -228,7 +231,10 @@ $(info )
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|||
# Build library
|
||||
#
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ggml.o: ggml.c ggml.h ggml-cuda.h
|
||||
ggml.o: ggml.c ggml.h ggml-cuda.h ggml-quants-k.h
|
||||
$(CC) $(CFLAGS) -c $< -o $@
|
||||
|
||||
ggml-quants-k.o: ggml-quants-k.c ggml-quants-k.h ggml.h ggml-cuda.h
|
||||
$(CC) $(CFLAGS) -c $< -o $@
|
||||
|
||||
llama.o: llama.cpp ggml.h ggml-cuda.h llama.h llama-util.h
|
||||
|
|
@ -237,7 +243,7 @@ llama.o: llama.cpp ggml.h ggml-cuda.h llama.h llama-util.h
|
|||
common.o: examples/common.cpp examples/common.h
|
||||
$(CXX) $(CXXFLAGS) -c $< -o $@
|
||||
|
||||
libllama.so: llama.o ggml.o $(OBJS)
|
||||
libllama.so: llama.o ggml.o ggml-quants-k.o $(OBJS)
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||||
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
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||||
|
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clean:
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|
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@ -247,28 +253,28 @@ clean:
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|||
# Examples
|
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#
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main: examples/main/main.cpp build-info.h ggml.o llama.o common.o $(OBJS)
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main: examples/main/main.cpp build-info.h ggml.o ggml-quants-k.o llama.o common.o $(OBJS)
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||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
@echo
|
||||
@echo '==== Run ./main -h for help. ===='
|
||||
@echo
|
||||
|
||||
quantize: examples/quantize/quantize.cpp build-info.h ggml.o llama.o $(OBJS)
|
||||
quantize: examples/quantize/quantize.cpp build-info.h ggml.o ggml-quants-k.o llama.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.h ggml.o llama.o $(OBJS)
|
||||
quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.h ggml.o ggml-quants-k.o llama.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
perplexity: examples/perplexity/perplexity.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
perplexity: examples/perplexity/perplexity.cpp build-info.h ggml.o ggml-quants-k.o llama.o common.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
embedding: examples/embedding/embedding.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
embedding: examples/embedding/embedding.cpp build-info.h ggml.o ggml-quants-k.o llama.o common.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o ggml-quants-k.o llama.o common.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
|
||||
server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp build-info.h ggml.o llama.o common.o $(OBJS)
|
||||
server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp build-info.h ggml.o ggml-quants-k.o llama.o common.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
|
||||
|
||||
build-info.h: $(wildcard .git/index) scripts/build-info.sh
|
||||
|
|
@ -283,11 +289,11 @@ build-info.h: $(wildcard .git/index) scripts/build-info.sh
|
|||
# Tests
|
||||
#
|
||||
|
||||
benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o $(OBJS)
|
||||
benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o ggml-quants-k.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
|
||||
./$@
|
||||
|
||||
vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
|
||||
vdot: pocs/vdot/vdot.cpp ggml.o ggml-quants-k.o $(OBJS)
|
||||
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
|
||||
|
||||
.PHONY: tests clean
|
||||
|
|
|
|||
15
README.md
15
README.md
|
|
@ -267,11 +267,11 @@ Any value larger than 0 will offload the computation to the GPU. For example:
|
|||
|
||||
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:
|
||||
|
||||
- **Accelerate Framework**:
|
||||
- #### Accelerate Framework:
|
||||
|
||||
This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
|
||||
|
||||
- **OpenBLAS**:
|
||||
- #### OpenBLAS:
|
||||
|
||||
This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
|
||||
|
||||
|
|
@ -305,11 +305,11 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
cmake --build . --config Release
|
||||
```
|
||||
|
||||
- **BLIS**
|
||||
- #### BLIS
|
||||
|
||||
Check [BLIS.md](BLIS.md) for more information.
|
||||
|
||||
- **Intel MKL**
|
||||
- #### Intel MKL
|
||||
|
||||
By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. You may also specify it by:
|
||||
|
||||
|
|
@ -317,10 +317,10 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
mkdir build
|
||||
cd build
|
||||
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
|
||||
cmake --build . -config Release
|
||||
cmake --build . --config Release
|
||||
```
|
||||
|
||||
- **cuBLAS**
|
||||
- #### cuBLAS
|
||||
|
||||
This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
|
||||
- Using `make`:
|
||||
|
|
@ -339,7 +339,7 @@ Building the program with BLAS support may lead to some performance improvements
|
|||
|
||||
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used.
|
||||
|
||||
- **CLBlast**
|
||||
- #### CLBlast
|
||||
|
||||
OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.
|
||||
|
||||
|
|
@ -689,3 +689,4 @@ docker run -v /path/to/models:/models -e LANG=C.utf8 ghcr.io/ggerganov/llama.cpp
|
|||
### Docs
|
||||
|
||||
- [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)
|
||||
- [Performance troubleshooting](./docs/token_generation_performance_tips.md)
|
||||
|
|
|
|||
40
docs/token_generation_performance_tips.md
Normal file
40
docs/token_generation_performance_tips.md
Normal file
|
|
@ -0,0 +1,40 @@
|
|||
# Token generation performance troubleshooting
|
||||
|
||||
## Verifying that the model is running on the GPU with cuBLAS
|
||||
Make sure you compiled llama with the correct env variables according to [this guide](../README.md#cublas), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
|
||||
```shell
|
||||
./main -m "path/to/model.bin" -ngl 200000 -p "Please sir, may I have some "
|
||||
```
|
||||
|
||||
When running llama, before it starts the inference work, it will output diagnostic information that shows whether cuBLAS is offloading work to the GPU. Look for these lines:
|
||||
```shell
|
||||
llama_model_load_internal: [cublas] offloading 60 layers to GPU
|
||||
llama_model_load_internal: [cublas] offloading output layer to GPU
|
||||
llama_model_load_internal: [cublas] total VRAM used: 17223 MB
|
||||
... rest of inference
|
||||
```
|
||||
|
||||
If you see these lines, then the GPU is being used.
|
||||
|
||||
## Verifying that the CPU is not oversaturated
|
||||
llama accepts a `-t N` (or `--threads N`) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physicial CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down.
|
||||
|
||||
# Example of runtime flags effect on inference speed benchmark
|
||||
These runs were tested on the following machine:
|
||||
GPU: A6000 (48GB VRAM)
|
||||
CPU: 7 physical cores
|
||||
RAM: 32GB
|
||||
|
||||
Model: `TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.ggmlv3.q4_0.bin` (30B parameters, 4bit quantization, GGML)
|
||||
|
||||
Run command: `./main -m "path/to/model.bin" -p "-p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]`
|
||||
|
||||
Result:
|
||||
|
||||
| command | tokens/second (higher is better) |
|
||||
| - | - |
|
||||
| -ngl 2000000 | N/A (less than 0.1) |
|
||||
| -t 7 | 1.7 |
|
||||
| -t 1 -ngl 2000000 | 5.5 |
|
||||
| -t 7 -ngl 2000000 | 8.7 |
|
||||
| -t 4 -ngl 2000000 | 9.1 |
|
||||
|
|
@ -9,6 +9,7 @@
|
|||
#include <algorithm>
|
||||
#include <sstream>
|
||||
#include <unordered_set>
|
||||
#include <regex>
|
||||
|
||||
#if defined(__APPLE__) && defined(__MACH__)
|
||||
#include <sys/types.h>
|
||||
|
|
@ -295,6 +296,40 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
|
||||
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
|
||||
#endif
|
||||
} else if (arg == "--main-gpu" || arg == "-mg") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
params.main_gpu = std::stoi(argv[i]);
|
||||
#else
|
||||
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
|
||||
#endif
|
||||
} else if (arg == "--tensor-split" || arg == "-ts") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
std::string arg_next = argv[i];
|
||||
|
||||
// split string by , and /
|
||||
const std::regex regex{R"([,/]+)"};
|
||||
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
|
||||
std::vector<std::string> split_arg{it, {}};
|
||||
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
|
||||
|
||||
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
|
||||
if (i < split_arg.size()) {
|
||||
params.tensor_split[i] = std::stof(split_arg[i]);
|
||||
} else {
|
||||
params.tensor_split[i] = 0.0f;
|
||||
}
|
||||
}
|
||||
#else
|
||||
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
|
||||
#endif // GGML_USE_CUBLAS
|
||||
} else if (arg == "--no-mmap") {
|
||||
params.use_mmap = false;
|
||||
} else if (arg == "--mtest") {
|
||||
|
|
@ -438,6 +473,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
|||
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
||||
fprintf(stderr, " -ngl N, --n-gpu-layers N\n");
|
||||
fprintf(stderr, " number of layers to store in VRAM\n");
|
||||
fprintf(stderr, " -ts SPLIT --tensor-split SPLIT\n");
|
||||
fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stderr, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" );
|
||||
#endif
|
||||
fprintf(stderr, " --mtest compute maximum memory usage\n");
|
||||
fprintf(stderr, " --export export the computation graph to 'llama.ggml'\n");
|
||||
|
|
@ -483,7 +521,10 @@ struct llama_context * llama_init_from_gpt_params(const gpt_params & params) {
|
|||
auto lparams = llama_context_default_params();
|
||||
|
||||
lparams.n_ctx = params.n_ctx;
|
||||
lparams.n_batch = params.n_batch;
|
||||
lparams.n_gpu_layers = params.n_gpu_layers;
|
||||
lparams.main_gpu = params.main_gpu;
|
||||
memcpy(lparams.tensor_split, params.tensor_split, LLAMA_MAX_DEVICES*sizeof(float));
|
||||
lparams.seed = params.seed;
|
||||
lparams.f16_kv = params.memory_f16;
|
||||
lparams.use_mmap = params.use_mmap;
|
||||
|
|
|
|||
|
|
@ -28,6 +28,8 @@ struct gpt_params {
|
|||
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
|
||||
int32_t n_keep = 0; // number of tokens to keep from initial prompt
|
||||
int32_t n_gpu_layers = 0; // number of layers to store in VRAM
|
||||
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
|
||||
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
|
||||
|
||||
// sampling parameters
|
||||
std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
|
||||
|
|
|
|||
|
|
@ -286,5 +286,7 @@ These options provide extra functionality and customization when running the LLa
|
|||
- `--verbose-prompt`: Print the prompt before generating text.
|
||||
- `--mtest`: Test the model's functionality by running a series of tests to ensure it's working properly.
|
||||
- `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
|
||||
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
|
||||
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
|
||||
- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
|
||||
- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
|
||||
|
|
|
|||
|
|
@ -282,8 +282,9 @@ int main(int argc, char ** argv) {
|
|||
break;
|
||||
}
|
||||
int j;
|
||||
for (j = 0; j < GGML_TYPE_COUNT && strcmp(argv[i], ggml_type_name((ggml_type) j)) != 0; j++) {
|
||||
// find match
|
||||
for (j = 0; j < GGML_TYPE_COUNT; ++j) {
|
||||
const auto * name = ggml_type_name((ggml_type) j);
|
||||
if (name && strcmp(argv[i], name) == 0) break;
|
||||
}
|
||||
if (j < GGML_TYPE_COUNT) {
|
||||
params.include_types.push_back((ggml_type) j);
|
||||
|
|
|
|||
|
|
@ -12,6 +12,18 @@ static const std::map<std::string, llama_ftype> LLAMA_FTYPE_MAP = {
|
|||
{"q5_0", LLAMA_FTYPE_MOSTLY_Q5_0},
|
||||
{"q5_1", LLAMA_FTYPE_MOSTLY_Q5_1},
|
||||
{"q8_0", LLAMA_FTYPE_MOSTLY_Q8_0},
|
||||
{"q2_K", LLAMA_FTYPE_MOSTLY_Q2_K},
|
||||
{"q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M},
|
||||
{"q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S},
|
||||
{"q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M},
|
||||
{"q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L},
|
||||
{"q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M},
|
||||
{"q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S},
|
||||
{"q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M},
|
||||
{"q5_K", LLAMA_FTYPE_MOSTLY_Q5_K_M},
|
||||
{"q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S},
|
||||
{"q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M},
|
||||
{"q6_K", LLAMA_FTYPE_MOSTLY_Q6_K},
|
||||
};
|
||||
|
||||
bool try_parse_ftype(const std::string & ftype_str, llama_ftype & ftype, std::string & ftype_str_out) {
|
||||
|
|
|
|||
|
|
@ -287,6 +287,8 @@ Test();
|
|||
- `-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.
|
||||
- `--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`.
|
||||
|
|
|
|||
|
|
@ -401,6 +401,10 @@ void server_print_usage(int /*argc*/, char **argv, const gpt_params ¶ms)
|
|||
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
|
||||
fprintf(stderr, " -ngl N, --n-gpu-layers N\n");
|
||||
fprintf(stderr, " number of layers to store in VRAM\n");
|
||||
fprintf(stderr, " -ts SPLIT --tensor-split SPLIT\n");
|
||||
fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
|
||||
fprintf(stderr, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" );
|
||||
#endif
|
||||
fprintf(stderr, " -m FNAME, --model FNAME\n");
|
||||
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
|
||||
|
|
@ -502,6 +506,50 @@ bool server_params_parse(int argc, char **argv, server_params &sparams, gpt_para
|
|||
#else
|
||||
fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n");
|
||||
fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
|
||||
#endif
|
||||
}
|
||||
else if (arg == "--tensor-split" || arg == "-ts")
|
||||
{
|
||||
if (++i >= argc)
|
||||
{
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
std::string arg_next = argv[i];
|
||||
|
||||
// split string by , and /
|
||||
const std::regex regex{R"([,/]+)"};
|
||||
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
|
||||
std::vector<std::string> split_arg{it, {}};
|
||||
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
|
||||
|
||||
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i)
|
||||
{
|
||||
if (i < split_arg.size())
|
||||
{
|
||||
params.tensor_split[i] = std::stof(split_arg[i]);
|
||||
}
|
||||
else
|
||||
{
|
||||
params.tensor_split[i] = 0.0f;
|
||||
}
|
||||
}
|
||||
#else
|
||||
fprintf(stderr, "WARNING: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
|
||||
#endif // GGML_USE_CUBLAS
|
||||
}
|
||||
else if (arg == "--main-gpu" || arg == "-mg")
|
||||
{
|
||||
if (++i >= argc)
|
||||
{
|
||||
invalid_param = true;
|
||||
break;
|
||||
}
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
params.main_gpu = std::stoi(argv[i]);
|
||||
#else
|
||||
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.\n");
|
||||
#endif
|
||||
}
|
||||
else
|
||||
|
|
|
|||
1726
ggml-cuda.cu
1726
ggml-cuda.cu
File diff suppressed because it is too large
Load diff
15
ggml-cuda.h
15
ggml-cuda.h
|
|
@ -1,10 +1,19 @@
|
|||
#pragma once
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
#define GGML_CUDA_MAX_DEVICES 16
|
||||
|
||||
struct ggml_tensor_extra_gpu {
|
||||
void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
|
||||
};
|
||||
|
||||
void ggml_init_cublas(void);
|
||||
void ggml_cuda_set_tensor_split(const float * tensor_split);
|
||||
|
||||
void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
|
|
@ -15,8 +24,12 @@ void ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens
|
|||
void * ggml_cuda_host_malloc(size_t size);
|
||||
void ggml_cuda_host_free(void * ptr);
|
||||
|
||||
void ggml_cuda_transform_tensor(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensors, size_t offset);
|
||||
void ggml_cuda_free_data(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_set_main_device(int main_device);
|
||||
void ggml_cuda_set_scratch_size(size_t scratch_size);
|
||||
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
|
|
|||
45
ggml-metal.m
45
ggml-metal.m
|
|
@ -47,10 +47,11 @@ struct ggml_metal_context {
|
|||
GGML_METAL_DECL_KERNEL(relu);
|
||||
GGML_METAL_DECL_KERNEL(soft_max);
|
||||
GGML_METAL_DECL_KERNEL(diag_mask_inf);
|
||||
GGML_METAL_DECL_KERNEL(get_rows_f16);
|
||||
GGML_METAL_DECL_KERNEL(get_rows_q4_0);
|
||||
GGML_METAL_DECL_KERNEL(rms_norm);
|
||||
GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
|
||||
GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
|
||||
GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
|
||||
GGML_METAL_DECL_KERNEL(rope);
|
||||
GGML_METAL_DECL_KERNEL(cpy_f32_f16);
|
||||
GGML_METAL_DECL_KERNEL(cpy_f32_f32);
|
||||
|
|
@ -130,10 +131,11 @@ struct ggml_metal_context * ggml_metal_init(void) {
|
|||
GGML_METAL_ADD_KERNEL(relu);
|
||||
GGML_METAL_ADD_KERNEL(soft_max);
|
||||
GGML_METAL_ADD_KERNEL(diag_mask_inf);
|
||||
GGML_METAL_ADD_KERNEL(get_rows_f16);
|
||||
GGML_METAL_ADD_KERNEL(get_rows_q4_0);
|
||||
GGML_METAL_ADD_KERNEL(rms_norm);
|
||||
GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
|
||||
GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
|
||||
GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
|
||||
GGML_METAL_ADD_KERNEL(rope);
|
||||
GGML_METAL_ADD_KERNEL(cpy_f32_f16);
|
||||
GGML_METAL_ADD_KERNEL(cpy_f32_f32);
|
||||
|
|
@ -195,14 +197,30 @@ bool ggml_metal_add_buffer(
|
|||
}
|
||||
}
|
||||
|
||||
size_t page_size = getpagesize();
|
||||
size_t aligned_size = size;
|
||||
if ((aligned_size % page_size) != 0) {
|
||||
aligned_size += (page_size - (aligned_size % page_size));
|
||||
}
|
||||
|
||||
ctx->buffers[ctx->n_buffers].name = name;
|
||||
ctx->buffers[ctx->n_buffers].data = data;
|
||||
ctx->buffers[ctx->n_buffers].size = size;
|
||||
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytes:data length:size options:MTLResourceStorageModeShared];
|
||||
|
||||
if (ctx->device.maxBufferLength < aligned_size) {
|
||||
fprintf(stderr, "%s: buffer '%s' size %zu is larger than buffer maximum of %zu\n", __func__, name, aligned_size, ctx->device.maxBufferLength);
|
||||
return false;
|
||||
}
|
||||
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:aligned_size options:MTLResourceStorageModeShared deallocator:nil];
|
||||
|
||||
if (ctx->buffers[ctx->n_buffers].metal == nil) {
|
||||
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0);
|
||||
return false;
|
||||
} else {
|
||||
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0);
|
||||
}
|
||||
|
||||
++ctx->n_buffers;
|
||||
|
||||
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB\n", __func__, name, size / 1024.0 / 1024.0);
|
||||
}
|
||||
|
||||
return true;
|
||||
|
|
@ -482,6 +500,14 @@ void ggml_metal_graph_compute(
|
|||
|
||||
// use custom matrix x vector kernel
|
||||
switch (src0t) {
|
||||
case GGML_TYPE_F16:
|
||||
{
|
||||
GGML_ASSERT(ne02 == ne12);
|
||||
|
||||
nth0 = 64;
|
||||
nth1 = 1;
|
||||
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
|
||||
} break;
|
||||
case GGML_TYPE_Q4_0:
|
||||
{
|
||||
GGML_ASSERT(ne02 == 1);
|
||||
|
|
@ -491,14 +517,6 @@ void ggml_metal_graph_compute(
|
|||
nth1 = 4;
|
||||
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32];
|
||||
} break;
|
||||
case GGML_TYPE_F16:
|
||||
{
|
||||
GGML_ASSERT(ne02 == ne12);
|
||||
|
||||
nth0 = 32;
|
||||
nth1 = 1;
|
||||
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32];
|
||||
} break;
|
||||
default: GGML_ASSERT(false && "not implemented");
|
||||
};
|
||||
|
||||
|
|
@ -535,6 +553,7 @@ void ggml_metal_graph_compute(
|
|||
}
|
||||
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break;
|
||||
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
|
||||
default: GGML_ASSERT(false && "not implemented");
|
||||
}
|
||||
|
|
|
|||
|
|
@ -169,6 +169,22 @@ kernel void kernel_diag_mask_inf(
|
|||
}
|
||||
}
|
||||
|
||||
kernel void kernel_get_rows_f16(
|
||||
device const void * src0,
|
||||
device const int * src1,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb1,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
const int i = tpig;
|
||||
const int r = ((device int32_t *) src1)[i];
|
||||
|
||||
for (int j = 0; j < ne00; j++) {
|
||||
dst[i*nb1 + j] = ((device half *) ((device char *) src0 + r*nb01))[j];
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_get_rows_q4_0(
|
||||
device const void * src0,
|
||||
device const int * src1,
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@
|
|||
#include <atomic>
|
||||
#include <sstream>
|
||||
#include <vector>
|
||||
#include <limits>
|
||||
|
||||
#define CL_TARGET_OPENCL_VERSION 110
|
||||
#include <clblast.h>
|
||||
|
|
@ -604,21 +605,44 @@ struct cl_buffer {
|
|||
static cl_buffer g_cl_buffer_pool[MAX_CL_BUFFERS];
|
||||
static std::atomic_flag g_cl_pool_lock = ATOMIC_FLAG_INIT;
|
||||
|
||||
static cl_mem ggml_cl_pool_malloc(size_t size, size_t * actual_size, cl_mem_flags flags) {
|
||||
static cl_mem ggml_cl_pool_malloc(size_t size, size_t * actual_size) {
|
||||
scoped_spin_lock lock(g_cl_pool_lock);
|
||||
cl_int err;
|
||||
|
||||
int best_i = -1;
|
||||
size_t best_size = std::numeric_limits<size_t>::max(); //smallest unused buffer that fits our needs
|
||||
int worst_i = -1;
|
||||
size_t worst_size = 0; //largest unused buffer seen so far
|
||||
for (int i = 0; i < MAX_CL_BUFFERS; ++i) {
|
||||
cl_buffer& b = g_cl_buffer_pool[i];
|
||||
if (b.size > 0 && b.size >= size) {
|
||||
cl_buffer &b = g_cl_buffer_pool[i];
|
||||
if (b.size > 0 && b.size >= size && b.size < best_size)
|
||||
{
|
||||
best_i = i;
|
||||
best_size = b.size;
|
||||
}
|
||||
if (b.size > 0 && b.size > worst_size)
|
||||
{
|
||||
worst_i = i;
|
||||
worst_size = b.size;
|
||||
}
|
||||
}
|
||||
if(best_i!=-1) //found the smallest buffer that fits our needs
|
||||
{
|
||||
cl_buffer& b = g_cl_buffer_pool[best_i];
|
||||
cl_mem mem = b.mem;
|
||||
*actual_size = b.size;
|
||||
b.size = 0;
|
||||
return mem;
|
||||
}
|
||||
if(worst_i!=-1) //no buffer that fits our needs, resize largest one to save memory
|
||||
{
|
||||
cl_buffer& b = g_cl_buffer_pool[worst_i];
|
||||
cl_mem mem = b.mem;
|
||||
b.size = 0;
|
||||
clReleaseMemObject(mem);
|
||||
}
|
||||
cl_mem mem;
|
||||
CL_CHECK((mem = clCreateBuffer(context, flags, size, NULL, &err), err));
|
||||
CL_CHECK((mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err), err));
|
||||
*actual_size = size;
|
||||
return mem;
|
||||
}
|
||||
|
|
@ -676,7 +700,7 @@ static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t o
|
|||
}
|
||||
|
||||
static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
||||
GGML_ASSERT(src1->backend == GGML_BACKEND_CL);
|
||||
GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
|
||||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t ne01 = src0->ne[1];
|
||||
const int64_t ne02 = src0->ne[2];
|
||||
|
|
@ -692,9 +716,10 @@ static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1,
|
|||
size_t x_size;
|
||||
size_t d_size;
|
||||
|
||||
cl_mem d_X = ggml_cl_pool_malloc(ne0 * sizeof(float), &x_size, CL_MEM_READ_ONLY); // src0
|
||||
cl_mem d_X = ggml_cl_pool_malloc(ne0 * sizeof(float), &x_size); // src0
|
||||
cl_mem d_Y = (cl_mem) src1->data; // src1 is already on device, broadcasted.
|
||||
cl_mem d_D = ggml_cl_pool_malloc(ne0 * sizeof(float), &d_size, CL_MEM_WRITE_ONLY); // dst
|
||||
cl_mem d_D = ggml_cl_pool_malloc(ne0 * sizeof(float), &d_size); // dst
|
||||
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
|
|
@ -789,18 +814,18 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
size_t y_size;
|
||||
size_t d_size;
|
||||
cl_mem d_X;
|
||||
if (src0->backend == GGML_BACKEND_CL) {
|
||||
if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
|
||||
d_X = (cl_mem) src0->data;
|
||||
} else {
|
||||
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size, CL_MEM_READ_ONLY);
|
||||
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
|
||||
}
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size, CL_MEM_READ_ONLY);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size, CL_MEM_WRITE_ONLY);
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
// copy data to device
|
||||
if (src0->backend != GGML_BACKEND_CL) {
|
||||
if (src0->backend != GGML_BACKEND_GPU) {
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
|
||||
}
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, NULL));
|
||||
|
|
@ -829,7 +854,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
}
|
||||
}
|
||||
|
||||
if (src0->backend != GGML_BACKEND_CL) {
|
||||
if (src0->backend != GGML_BACKEND_GPU) {
|
||||
ggml_cl_pool_free(d_X, x_size);
|
||||
}
|
||||
ggml_cl_pool_free(d_Y, y_size);
|
||||
|
|
@ -865,13 +890,13 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
size_t y_size;
|
||||
size_t d_size;
|
||||
cl_mem d_X;
|
||||
if (src0->backend == GGML_BACKEND_CL) {
|
||||
if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
|
||||
d_X = (cl_mem) src0->data;
|
||||
} else {
|
||||
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size, CL_MEM_READ_ONLY);
|
||||
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
|
||||
}
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * y_ne, &y_size, CL_MEM_READ_ONLY);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * d_ne, &d_size, CL_MEM_WRITE_ONLY);
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * y_ne, &y_size);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * d_ne, &d_size);
|
||||
|
||||
bool src1_cont_rows = nb10 == sizeof(float);
|
||||
bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
|
||||
|
|
@ -879,7 +904,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
// copy src0 to device
|
||||
if (src0->backend != GGML_BACKEND_CL) {
|
||||
if (src0->backend != GGML_BACKEND_GPU) {
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
|
||||
}
|
||||
|
||||
|
|
@ -936,7 +961,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
|
|||
}
|
||||
}
|
||||
|
||||
if (src0->backend != GGML_BACKEND_CL) {
|
||||
if (src0->backend != GGML_BACKEND_GPU) {
|
||||
ggml_cl_pool_free(d_X, x_size);
|
||||
}
|
||||
ggml_cl_pool_free(d_Y, y_size);
|
||||
|
|
@ -970,13 +995,13 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
|
|||
size_t q_size;
|
||||
cl_mem d_X;
|
||||
if (!mul_mat_vec) {
|
||||
d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size, CL_MEM_READ_WRITE);
|
||||
d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
|
||||
}
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size, CL_MEM_READ_ONLY);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size, CL_MEM_WRITE_ONLY);
|
||||
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
|
||||
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
|
||||
cl_mem d_Q;
|
||||
if (src0->backend == GGML_BACKEND_CPU) {
|
||||
d_Q = ggml_cl_pool_malloc(q_sz, &q_size, CL_MEM_READ_ONLY);
|
||||
d_Q = ggml_cl_pool_malloc(q_sz, &q_size);
|
||||
}
|
||||
|
||||
cl_kernel* to_fp32_cl = ggml_get_to_fp32_cl(type);
|
||||
|
|
@ -992,7 +1017,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
|
|||
if (src0->backend == GGML_BACKEND_CPU) {
|
||||
events.emplace_back();
|
||||
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
|
||||
} else if (src0->backend == GGML_BACKEND_CL) {
|
||||
} else if (src0->backend == GGML_BACKEND_GPU) {
|
||||
d_Q = (cl_mem) src0->data;
|
||||
} else {
|
||||
GGML_ASSERT(false);
|
||||
|
|
@ -1077,7 +1102,7 @@ bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens
|
|||
if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
|
||||
src1->type == GGML_TYPE_F32 &&
|
||||
dst->type == GGML_TYPE_F32 &&
|
||||
((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_CL)) {
|
||||
((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU)) {
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
@ -1143,7 +1168,7 @@ void ggml_cl_transform_tensor(ggml_tensor * tensor) {
|
|||
const size_t q_sz = ggml_type_size(type) * ne0 * ne1 * ne2 * ne3 / ggml_blck_size(type);
|
||||
|
||||
size_t q_size;
|
||||
cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size, CL_MEM_READ_ONLY);
|
||||
cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size);
|
||||
|
||||
// copy tensor to device
|
||||
for (int64_t i3 = 0; i3 < ne3; i3++) {
|
||||
|
|
@ -1156,7 +1181,7 @@ void ggml_cl_transform_tensor(ggml_tensor * tensor) {
|
|||
CL_CHECK(clFinish(queue));
|
||||
|
||||
tensor->data = dst;
|
||||
tensor->backend = GGML_BACKEND_CL;
|
||||
tensor->backend = GGML_BACKEND_GPU;
|
||||
}
|
||||
|
||||
void ggml_cl_load_data(const char * fname, struct ggml_tensor * tensor, const size_t offset) {
|
||||
|
|
|
|||
2246
ggml-quants-k.c
Normal file
2246
ggml-quants-k.c
Normal file
File diff suppressed because it is too large
Load diff
122
ggml-quants-k.h
Normal file
122
ggml-quants-k.h
Normal file
|
|
@ -0,0 +1,122 @@
|
|||
#pragma once
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#include <stdint.h>
|
||||
#include <assert.h>
|
||||
#include <stddef.h>
|
||||
|
||||
// Super-block size
|
||||
#define QK_K 256
|
||||
|
||||
//
|
||||
// Super-block quantization structures
|
||||
//
|
||||
|
||||
// 2-bit quantization
|
||||
// weight is represented as x = a * q + b
|
||||
// 16 blocks of 16 elemenets each
|
||||
// Effectively 2.5625 bits per weight
|
||||
typedef struct {
|
||||
uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
|
||||
uint8_t qs[QK_K/4]; // quants
|
||||
ggml_fp16_t d; // super-block scale for quantized scales
|
||||
ggml_fp16_t dmin; // super-block scale for quantized mins
|
||||
} block_q2_k;
|
||||
static_assert(sizeof(block_q2_k) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_k block size/padding");
|
||||
|
||||
// 3-bit quantization
|
||||
// weight is represented as x = a * q
|
||||
// 16 blocks of 16 elemenets each
|
||||
// Effectively 3.4375 bits per weight
|
||||
typedef struct {
|
||||
uint8_t hmask[QK_K/8]; // quants - high bit
|
||||
uint8_t qs[QK_K/4]; // quants - low 2 bits
|
||||
uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
|
||||
ggml_fp16_t d; // super-block scale
|
||||
} block_q3_k;
|
||||
static_assert(sizeof(block_q3_k) == sizeof(ggml_fp16_t) + QK_K / 4 + 11 * QK_K / 64, "wrong q3_k block size/padding");
|
||||
|
||||
// 4-bit quantization
|
||||
// 16 blocks of 32 elements each
|
||||
// weight is represented as x = a * q + b
|
||||
// Effectively 4.5 bits per weight
|
||||
typedef struct {
|
||||
ggml_fp16_t d; // super-block scale for quantized scales
|
||||
ggml_fp16_t dmin; // super-block scale for quantized mins
|
||||
uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits
|
||||
uint8_t qs[QK_K/2]; // 4--bit quants
|
||||
} block_q4_k;
|
||||
static_assert(sizeof(block_q4_k) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_k block size/padding");
|
||||
|
||||
// 5-bit quantization
|
||||
// 16 blocks of 32 elements each
|
||||
// weight is represented as x = a * q + b
|
||||
// Effectively 5.5 bits per weight
|
||||
typedef struct {
|
||||
ggml_fp16_t d; // super-block scale for quantized scales
|
||||
ggml_fp16_t dmin; // super-block scale for quantized mins
|
||||
uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits
|
||||
uint8_t qh[QK_K/8]; // quants, high bit
|
||||
uint8_t qs[QK_K/2]; // quants, low 4 bits
|
||||
} block_q5_k;
|
||||
static_assert(sizeof(block_q5_k) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2 + QK_K/8, "wrong q5_k block size/padding");
|
||||
|
||||
// 6-bit quantization
|
||||
// weight is represented as x = a * q
|
||||
// 16 blocks of 16 elemenets each
|
||||
// Effectively 6.5625 bits per weight
|
||||
typedef struct {
|
||||
uint8_t ql[QK_K/2]; // quants, lower 4 bits
|
||||
uint8_t qh[QK_K/4]; // quants, upper 2 bits
|
||||
int8_t scales[QK_K/16]; // scales, quantized with 8 bits
|
||||
ggml_fp16_t d; // super-block scale
|
||||
} block_q6_k;
|
||||
static_assert(sizeof(block_q6_k) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_k block size/padding");
|
||||
|
||||
// This is only used for intermediate quantization and dot products
|
||||
typedef struct {
|
||||
float d; // delta
|
||||
int8_t qs[QK_K]; // quants
|
||||
int16_t bsums[QK_K/16]; // sum of quants in groups of 16
|
||||
} block_q8_k;
|
||||
static_assert(sizeof(block_q8_k) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_k block size/padding");
|
||||
|
||||
|
||||
// Quantization
|
||||
void quantize_row_q2_k_reference(const float * restrict x, block_q2_k * restrict y, int k);
|
||||
void quantize_row_q3_k_reference(const float * restrict x, block_q3_k * restrict y, int k);
|
||||
void quantize_row_q4_k_reference(const float * restrict x, block_q4_k * restrict y, int k);
|
||||
void quantize_row_q5_k_reference(const float * restrict x, block_q5_k * restrict y, int k);
|
||||
void quantize_row_q6_k_reference(const float * restrict x, block_q6_k * restrict y, int k);
|
||||
void quantize_row_q8_k_reference(const float * restrict x, block_q8_k * restrict y, int k);
|
||||
|
||||
void quantize_row_q2_k(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q3_k(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q4_k(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q5_k(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q6_k(const float * restrict x, void * restrict y, int k);
|
||||
void quantize_row_q8_k(const float * restrict x, void * restrict y, int k);
|
||||
|
||||
// Dequantization
|
||||
void dequantize_row_q2_k(const block_q2_k * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_q3_k(const block_q3_k * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_q4_k(const block_q4_k * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_q5_k(const block_q5_k * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_q6_k(const block_q6_k * restrict x, float * restrict y, int k);
|
||||
void dequantize_row_q8_k(const block_q8_k * restrict x, float * restrict y, int k);
|
||||
|
||||
// Dot product
|
||||
void ggml_vec_dot_q2_k_q8_k(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q3_k_q8_k(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q4_k_q8_k(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q5_k_q8_k(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
void ggml_vec_dot_q6_k_q8_k(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
|
||||
|
||||
// Quantization with histogram collection
|
||||
size_t ggml_quantize_q2_k(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q3_k(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q4_k(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q5_k(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q6_k(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
|
||||
267
ggml.c
267
ggml.c
|
|
@ -2,6 +2,7 @@
|
|||
#define _GNU_SOURCE
|
||||
|
||||
#include "ggml.h"
|
||||
#include "ggml-quants-k.h"
|
||||
|
||||
#if defined(_MSC_VER) || defined(__MINGW32__)
|
||||
#include <malloc.h> // using malloc.h with MSC/MINGW
|
||||
|
|
@ -21,6 +22,10 @@
|
|||
#include <float.h>
|
||||
#include <limits.h>
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
#include <unistd.h>
|
||||
#endif
|
||||
|
||||
// if C99 - static_assert is noop
|
||||
// ref: https://stackoverflow.com/a/53923785/4039976
|
||||
#ifndef static_assert
|
||||
|
|
@ -121,7 +126,11 @@ typedef void* thread_ret_t;
|
|||
#else
|
||||
inline static void* ggml_aligned_malloc(size_t size) {
|
||||
void* aligned_memory = NULL;
|
||||
#ifdef GGML_USE_METAL
|
||||
int result = posix_memalign(&aligned_memory, getpagesize(), size);
|
||||
#else
|
||||
int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size);
|
||||
#endif
|
||||
if (result != 0) {
|
||||
// Handle allocation failure
|
||||
return NULL;
|
||||
|
|
@ -403,21 +412,27 @@ void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n) {
|
|||
//
|
||||
|
||||
#if defined(_MSC_VER) || defined(__MINGW32__)
|
||||
static int64_t timer_freq;
|
||||
static int64_t timer_freq, timer_start;
|
||||
void ggml_time_init(void) {
|
||||
LARGE_INTEGER frequency;
|
||||
QueryPerformanceFrequency(&frequency);
|
||||
timer_freq = frequency.QuadPart;
|
||||
LARGE_INTEGER t;
|
||||
QueryPerformanceFrequency(&t);
|
||||
timer_freq = t.QuadPart;
|
||||
|
||||
// The multiplication by 1000 or 1000000 below can cause an overflow if timer_freq
|
||||
// and the uptime is high enough.
|
||||
// We subtract the program start time to reduce the likelihood of that happening.
|
||||
QueryPerformanceCounter(&t);
|
||||
timer_start = t.QuadPart;
|
||||
}
|
||||
int64_t ggml_time_ms(void) {
|
||||
LARGE_INTEGER t;
|
||||
QueryPerformanceCounter(&t);
|
||||
return (t.QuadPart * 1000) / timer_freq;
|
||||
return ((t.QuadPart-timer_start) * 1000) / timer_freq;
|
||||
}
|
||||
int64_t ggml_time_us(void) {
|
||||
LARGE_INTEGER t;
|
||||
QueryPerformanceCounter(&t);
|
||||
return (t.QuadPart * 1000000) / timer_freq;
|
||||
return ((t.QuadPart-timer_start) * 1000000) / timer_freq;
|
||||
}
|
||||
#else
|
||||
void ggml_time_init(void) {}
|
||||
|
|
@ -1565,6 +1580,46 @@ static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
|
|||
.vec_dot_q = NULL, // TODO
|
||||
.vec_dot_type = GGML_TYPE_Q8_1,
|
||||
},
|
||||
[GGML_TYPE_Q2_K] = {
|
||||
.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q2_k,
|
||||
.quantize_row_q = quantize_row_q2_k,
|
||||
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q2_k_reference,
|
||||
.quantize_row_q_dot = quantize_row_q8_k,
|
||||
.vec_dot_q = ggml_vec_dot_q2_k_q8_k,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
},
|
||||
[GGML_TYPE_Q3_K] = {
|
||||
.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q3_k,
|
||||
.quantize_row_q = quantize_row_q3_k,
|
||||
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q3_k_reference,
|
||||
.quantize_row_q_dot = quantize_row_q8_k,
|
||||
.vec_dot_q = ggml_vec_dot_q3_k_q8_k,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
},
|
||||
[GGML_TYPE_Q4_K] = {
|
||||
.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q4_k,
|
||||
.quantize_row_q = quantize_row_q4_k,
|
||||
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_k_reference,
|
||||
.quantize_row_q_dot = quantize_row_q8_k,
|
||||
.vec_dot_q = ggml_vec_dot_q4_k_q8_k,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
},
|
||||
[GGML_TYPE_Q5_K] = {
|
||||
.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q5_k,
|
||||
.quantize_row_q = quantize_row_q5_k,
|
||||
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_k_reference,
|
||||
.quantize_row_q_dot = quantize_row_q8_k,
|
||||
.vec_dot_q = ggml_vec_dot_q5_k_q8_k,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
},
|
||||
[GGML_TYPE_Q6_K] = {
|
||||
.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q6_k,
|
||||
.quantize_row_q = quantize_row_q6_k,
|
||||
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q6_k_reference,
|
||||
.quantize_row_q_dot = quantize_row_q8_k,
|
||||
.vec_dot_q = ggml_vec_dot_q6_k_q8_k,
|
||||
.vec_dot_type = GGML_TYPE_Q8_K,
|
||||
},
|
||||
};
|
||||
|
||||
// For internal test use
|
||||
|
|
@ -3444,11 +3499,17 @@ static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = {
|
|||
[GGML_TYPE_Q5_1] = QK5_1,
|
||||
[GGML_TYPE_Q8_0] = QK8_0,
|
||||
[GGML_TYPE_Q8_1] = QK8_1,
|
||||
[GGML_TYPE_Q2_K] = QK_K,
|
||||
[GGML_TYPE_Q3_K] = QK_K,
|
||||
[GGML_TYPE_Q4_K] = QK_K,
|
||||
[GGML_TYPE_Q5_K] = QK_K,
|
||||
[GGML_TYPE_Q6_K] = QK_K,
|
||||
[GGML_TYPE_Q8_K] = QK_K,
|
||||
[GGML_TYPE_I8] = 1,
|
||||
[GGML_TYPE_I16] = 1,
|
||||
[GGML_TYPE_I32] = 1,
|
||||
};
|
||||
static_assert(GGML_TYPE_COUNT == 13, "GGML_BLCK_SIZE is outdated");
|
||||
static_assert(GGML_TYPE_COUNT == 19, "GGML_BLCK_SIZE is outdated");
|
||||
|
||||
static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
|
||||
[GGML_TYPE_F32] = sizeof(float),
|
||||
|
|
@ -3459,11 +3520,17 @@ static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
|
|||
[GGML_TYPE_Q5_1] = sizeof(block_q5_1),
|
||||
[GGML_TYPE_Q8_0] = sizeof(block_q8_0),
|
||||
[GGML_TYPE_Q8_1] = sizeof(block_q8_1),
|
||||
[GGML_TYPE_Q2_K] = sizeof(block_q2_k),
|
||||
[GGML_TYPE_Q3_K] = sizeof(block_q3_k),
|
||||
[GGML_TYPE_Q4_K] = sizeof(block_q4_k),
|
||||
[GGML_TYPE_Q5_K] = sizeof(block_q5_k),
|
||||
[GGML_TYPE_Q6_K] = sizeof(block_q6_k),
|
||||
[GGML_TYPE_Q8_K] = sizeof(block_q8_k),
|
||||
[GGML_TYPE_I8] = sizeof(int8_t),
|
||||
[GGML_TYPE_I16] = sizeof(int16_t),
|
||||
[GGML_TYPE_I32] = sizeof(int32_t),
|
||||
};
|
||||
static_assert(GGML_TYPE_COUNT == 13, "GGML_TYPE_SIZE is outdated");
|
||||
static_assert(GGML_TYPE_COUNT == 19, "GGML_TYPE_SIZE is outdated");
|
||||
|
||||
|
||||
static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
|
||||
|
|
@ -3475,11 +3542,17 @@ static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
|
|||
[GGML_TYPE_Q5_1] = "q5_1",
|
||||
[GGML_TYPE_Q8_0] = "q8_0",
|
||||
[GGML_TYPE_Q8_1] = "q8_1",
|
||||
[GGML_TYPE_Q2_K] = "q2_k",
|
||||
[GGML_TYPE_Q3_K] = "q3_k",
|
||||
[GGML_TYPE_Q4_K] = "q4_k",
|
||||
[GGML_TYPE_Q5_K] = "q5_k",
|
||||
[GGML_TYPE_Q6_K] = "q6_k",
|
||||
[GGML_TYPE_Q8_K] = "q8_k",
|
||||
[GGML_TYPE_I8] = "i8",
|
||||
[GGML_TYPE_I16] = "i16",
|
||||
[GGML_TYPE_I32] = "i32",
|
||||
};
|
||||
static_assert(GGML_TYPE_COUNT == 13, "GGML_TYPE_NAME is outdated");
|
||||
static_assert(GGML_TYPE_COUNT == 19, "GGML_TYPE_NAME is outdated");
|
||||
|
||||
static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
|
||||
[GGML_TYPE_F32] = false,
|
||||
|
|
@ -3490,11 +3563,17 @@ static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
|
|||
[GGML_TYPE_Q5_1] = true,
|
||||
[GGML_TYPE_Q8_0] = true,
|
||||
[GGML_TYPE_Q8_1] = true,
|
||||
[GGML_TYPE_Q2_K] = true,
|
||||
[GGML_TYPE_Q3_K] = true,
|
||||
[GGML_TYPE_Q4_K] = true,
|
||||
[GGML_TYPE_Q5_K] = true,
|
||||
[GGML_TYPE_Q6_K] = true,
|
||||
[GGML_TYPE_Q8_K] = true,
|
||||
[GGML_TYPE_I8] = false,
|
||||
[GGML_TYPE_I16] = false,
|
||||
[GGML_TYPE_I32] = false,
|
||||
};
|
||||
static_assert(GGML_TYPE_COUNT == 13, "GGML_IS_QUANTIZED is outdated");
|
||||
static_assert(GGML_TYPE_COUNT == 19, "GGML_IS_QUANTIZED is outdated");
|
||||
|
||||
static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
|
||||
"NONE",
|
||||
|
|
@ -3647,26 +3726,6 @@ struct ggml_context_container {
|
|||
struct ggml_context context;
|
||||
};
|
||||
|
||||
//
|
||||
// compute types
|
||||
//
|
||||
|
||||
enum ggml_task_type {
|
||||
GGML_TASK_INIT = 0,
|
||||
GGML_TASK_COMPUTE,
|
||||
GGML_TASK_FINALIZE,
|
||||
};
|
||||
|
||||
struct ggml_compute_params {
|
||||
enum ggml_task_type type;
|
||||
|
||||
int ith, nth;
|
||||
|
||||
// work buffer for all threads
|
||||
size_t wsize;
|
||||
void * wdata;
|
||||
};
|
||||
|
||||
//
|
||||
// ggml state
|
||||
//
|
||||
|
|
@ -3742,6 +3801,12 @@ size_t ggml_nbytes(const struct ggml_tensor * tensor) {
|
|||
return MAX(tensor->ne[3]*tensor->nb[3], (ggml_nelements(tensor)*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type]);
|
||||
}
|
||||
|
||||
size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
|
||||
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
||||
|
||||
return (nrows_split*tensor->ne[0]*GGML_TYPE_SIZE[tensor->type])/GGML_BLCK_SIZE[tensor->type];
|
||||
}
|
||||
|
||||
int ggml_blck_size(enum ggml_type type) {
|
||||
return GGML_BLCK_SIZE[type];
|
||||
}
|
||||
|
|
@ -3808,6 +3873,11 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
|
|||
case GGML_FTYPE_MOSTLY_Q5_0: wtype = GGML_TYPE_Q5_0; break;
|
||||
case GGML_FTYPE_MOSTLY_Q5_1: wtype = GGML_TYPE_Q5_1; break;
|
||||
case GGML_FTYPE_MOSTLY_Q8_0: wtype = GGML_TYPE_Q8_0; break;
|
||||
case GGML_FTYPE_MOSTLY_Q2_K: wtype = GGML_TYPE_Q2_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q3_K: wtype = GGML_TYPE_Q3_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q4_K: wtype = GGML_TYPE_Q4_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q5_K: wtype = GGML_TYPE_Q5_K; break;
|
||||
case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break;
|
||||
case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break;
|
||||
case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
|
||||
}
|
||||
|
|
@ -4164,6 +4234,7 @@ struct ggml_tensor * ggml_new_tensor_impl(
|
|||
/*.perf_time_us =*/ 0,
|
||||
/*.data =*/ (data == NULL && !ctx->no_alloc) ? (void *)(result + 1) : data,
|
||||
/*.name =*/ { 0 },
|
||||
/*.extra =*/ NULL,
|
||||
/*.pad =*/ { 0 },
|
||||
};
|
||||
|
||||
|
|
@ -7623,6 +7694,11 @@ static void ggml_compute_forward_add(
|
|||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
{
|
||||
ggml_compute_forward_add_q_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
|
|
@ -7926,6 +8002,11 @@ static void ggml_compute_forward_add1(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
{
|
||||
ggml_compute_forward_add1_q_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
|
|
@ -8048,6 +8129,11 @@ static void ggml_compute_forward_acc(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
|
|
@ -8166,15 +8252,8 @@ static void ggml_compute_forward_mul_f32(
|
|||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
if (src1->backend == GGML_BACKEND_CUDA) {
|
||||
if (ith == 0) {
|
||||
ggml_cuda_mul(src0, src1, dst);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
if (src1->backend == GGML_BACKEND_CL) {
|
||||
#ifdef GGML_USE_CLBLAST
|
||||
if (src1->backend == GGML_BACKEND_GPU) {
|
||||
if (ith == 0) {
|
||||
ggml_cl_mul(src0, src1, dst);
|
||||
}
|
||||
|
|
@ -9614,14 +9693,7 @@ static void ggml_compute_forward_mul_mat_f32(
|
|||
// nb01 >= nb00 - src0 is not transposed
|
||||
// compute by src0 rows
|
||||
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
if (ggml_cuda_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
|
|
@ -9786,14 +9858,7 @@ static void ggml_compute_forward_mul_mat_f16_f32(
|
|||
// nb01 >= nb00 - src0 is not transposed
|
||||
// compute by src0 rows
|
||||
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
if (ggml_cuda_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
|
|
@ -9998,14 +10063,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
// nb01 >= nb00 - src0 is not transposed
|
||||
// compute by src0 rows
|
||||
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
if (ggml_cuda_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
|
|
@ -10148,6 +10206,11 @@ static void ggml_compute_forward_mul_mat(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
{
|
||||
ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
|
|
@ -10331,6 +10394,11 @@ static void ggml_compute_forward_set(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
|
|
@ -10496,6 +10564,11 @@ static void ggml_compute_forward_get_rows(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
{
|
||||
ggml_compute_forward_get_rows_q(params, src0, src1, dst);
|
||||
} break;
|
||||
|
|
@ -11042,6 +11115,12 @@ static void ggml_compute_forward_alibi(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_Q8_K:
|
||||
case GGML_TYPE_I8:
|
||||
case GGML_TYPE_I16:
|
||||
case GGML_TYPE_I32:
|
||||
|
|
@ -11113,6 +11192,12 @@ static void ggml_compute_forward_clamp(
|
|||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_Q8_K:
|
||||
case GGML_TYPE_I8:
|
||||
case GGML_TYPE_I16:
|
||||
case GGML_TYPE_I32:
|
||||
|
|
@ -12931,6 +13016,15 @@ static void ggml_compute_forward_map_binary(
|
|||
static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
|
||||
GGML_ASSERT(params);
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
bool skip_cpu = ggml_cuda_compute_forward(params, tensor);
|
||||
if (skip_cpu) {
|
||||
return;
|
||||
}
|
||||
GGML_ASSERT(tensor->src0->backend == GGML_BACKEND_CPU);
|
||||
GGML_ASSERT(tensor->src1 == NULL || tensor->src1->backend == GGML_BACKEND_CPU);
|
||||
#endif // GGML_USE_CUBLAS
|
||||
|
||||
switch (tensor->op) {
|
||||
case GGML_OP_DUP:
|
||||
{
|
||||
|
|
@ -14237,7 +14331,6 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
|
|||
if (ggml_cuda_can_mul_mat(node->src0, node->src1, node)) {
|
||||
node->n_tasks = 1; // TODO: this actually is doing nothing
|
||||
// the threads are still spinning
|
||||
cur = ggml_cuda_mul_mat_get_wsize(node->src0, node->src1, node);
|
||||
}
|
||||
else
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
|
|
@ -14627,11 +14720,11 @@ static void ggml_graph_export_leaf(const struct ggml_tensor * tensor, FILE * fou
|
|||
const int64_t * ne = tensor->ne;
|
||||
const size_t * nb = tensor->nb;
|
||||
|
||||
fprintf(fout, "%-6s %-12s %8d %8lld %8lld %8lld %8lld %16zu %16zu %16zu %16zu %16p %32s\n",
|
||||
fprintf(fout, "%-6s %-12s %8d %8d %d %d %d %16zu %16zu %16zu %16zu %16p %32s\n",
|
||||
ggml_type_name(tensor->type),
|
||||
ggml_op_name (tensor->op),
|
||||
tensor->n_dims,
|
||||
ne[0], ne[1], ne[2], ne[3],
|
||||
(int) ne[0], (int) ne[1], (int) ne[2], (int) ne[3],
|
||||
nb[0], nb[1], nb[2], nb[3],
|
||||
tensor->data,
|
||||
tensor->name);
|
||||
|
|
@ -14641,12 +14734,12 @@ static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char
|
|||
const int64_t * ne = tensor->ne;
|
||||
const size_t * nb = tensor->nb;
|
||||
|
||||
fprintf(fout, "%-6s %-6s %-12s %8d %8lld %8lld %8lld %8lld %16zu %16zu %16zu %16zu %8d %16p %32s\n",
|
||||
fprintf(fout, "%-6s %-6s %-12s %8d %d %d %d %d %16zu %16zu %16zu %16zu %8d %16p %32s\n",
|
||||
arg,
|
||||
ggml_type_name(tensor->type),
|
||||
ggml_op_name (tensor->op),
|
||||
tensor->n_dims,
|
||||
ne[0], ne[1], ne[2], ne[3],
|
||||
(int) ne[0], (int) ne[1], (int) ne[2], (int) ne[3],
|
||||
nb[0], nb[1], nb[2], nb[3],
|
||||
tensor->n_tasks,
|
||||
tensor->data,
|
||||
|
|
@ -14674,7 +14767,7 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
|
|||
fprintf(fout, "%-16s %8d\n", "version", GGML_FILE_VERSION);
|
||||
fprintf(fout, "%-16s %8d\n", "leafs", cgraph->n_leafs);
|
||||
fprintf(fout, "%-16s %8d\n", "nodes", cgraph->n_nodes);
|
||||
fprintf(fout, "%-16s %8llu\n", "eval", size_eval);
|
||||
fprintf(fout, "%-16s %8d\n", "eval", (int) size_eval);
|
||||
|
||||
// header
|
||||
fprintf(fout, "\n");
|
||||
|
|
@ -14907,7 +15000,11 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
|
|||
|
||||
data = ggml_new_tensor_1d(*ctx_data, GGML_TYPE_I8, fsize);
|
||||
|
||||
fread(data->data, sizeof(char), fsize, fin);
|
||||
const size_t ret = fread(data->data, sizeof(char), fsize, fin);
|
||||
if (ret != fsize) {
|
||||
fprintf(stderr, "%s: failed to read %s\n", __func__, fname);
|
||||
return result;
|
||||
}
|
||||
|
||||
fclose(fin);
|
||||
}
|
||||
|
|
@ -16152,6 +16249,36 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
|
|||
block_q8_0 * block = (block_q8_0*)dst + start / QK8_0;
|
||||
result = ggml_quantize_q8_0(src + start, block, n, n, hist);
|
||||
} break;
|
||||
case GGML_TYPE_Q2_K:
|
||||
{
|
||||
GGML_ASSERT(start % QK_K == 0);
|
||||
block_q2_k * block = (block_q2_k*)dst + start / QK_K;
|
||||
result = ggml_quantize_q2_k(src + start, block, n, n, hist);
|
||||
} break;
|
||||
case GGML_TYPE_Q3_K:
|
||||
{
|
||||
GGML_ASSERT(start % QK_K == 0);
|
||||
block_q3_k * block = (block_q3_k*)dst + start / QK_K;
|
||||
result = ggml_quantize_q3_k(src + start, block, n, n, hist);
|
||||
} break;
|
||||
case GGML_TYPE_Q4_K:
|
||||
{
|
||||
GGML_ASSERT(start % QK_K == 0);
|
||||
block_q4_k * block = (block_q4_k*)dst + start / QK_K;
|
||||
result = ggml_quantize_q4_k(src + start, block, n, n, hist);
|
||||
} break;
|
||||
case GGML_TYPE_Q5_K:
|
||||
{
|
||||
GGML_ASSERT(start % QK_K == 0);
|
||||
block_q5_k * block = (block_q5_k*)dst + start / QK_K;
|
||||
result = ggml_quantize_q5_k(src + start, block, n, n, hist);
|
||||
} break;
|
||||
case GGML_TYPE_Q6_K:
|
||||
{
|
||||
GGML_ASSERT(start % QK_K == 0);
|
||||
block_q6_k * block = (block_q6_k*)dst + start / QK_K;
|
||||
result = ggml_quantize_q6_k(src + start, block, n, n, hist);
|
||||
} break;
|
||||
default:
|
||||
assert(false);
|
||||
}
|
||||
|
|
|
|||
42
ggml.h
42
ggml.h
|
|
@ -241,6 +241,13 @@ extern "C" {
|
|||
GGML_TYPE_Q5_1 = 7,
|
||||
GGML_TYPE_Q8_0 = 8,
|
||||
GGML_TYPE_Q8_1 = 9,
|
||||
// k-quantizations
|
||||
GGML_TYPE_Q2_K = 10,
|
||||
GGML_TYPE_Q3_K = 11,
|
||||
GGML_TYPE_Q4_K = 12,
|
||||
GGML_TYPE_Q5_K = 13,
|
||||
GGML_TYPE_Q6_K = 14,
|
||||
GGML_TYPE_Q8_K = 15,
|
||||
GGML_TYPE_I8,
|
||||
GGML_TYPE_I16,
|
||||
GGML_TYPE_I32,
|
||||
|
|
@ -249,8 +256,8 @@ extern "C" {
|
|||
|
||||
enum ggml_backend {
|
||||
GGML_BACKEND_CPU = 0,
|
||||
GGML_BACKEND_CUDA = 1,
|
||||
GGML_BACKEND_CL = 2,
|
||||
GGML_BACKEND_GPU = 10,
|
||||
GGML_BACKEND_GPU_SPLIT = 20,
|
||||
};
|
||||
|
||||
// model file types
|
||||
|
|
@ -264,6 +271,11 @@ extern "C" {
|
|||
GGML_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors
|
||||
GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
|
||||
};
|
||||
|
||||
// available tensor operations:
|
||||
|
|
@ -375,7 +387,9 @@ extern "C" {
|
|||
|
||||
char name[GGML_MAX_NAME];
|
||||
|
||||
char padding[16];
|
||||
void * extra; // extra things e.g. for ggml-cuda.cu
|
||||
|
||||
char padding[4];
|
||||
};
|
||||
|
||||
static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
|
||||
|
|
@ -413,6 +427,25 @@ extern "C" {
|
|||
bool no_alloc; // don't allocate memory for the tensor data
|
||||
};
|
||||
|
||||
|
||||
// compute types
|
||||
enum ggml_task_type {
|
||||
GGML_TASK_INIT = 0,
|
||||
GGML_TASK_COMPUTE,
|
||||
GGML_TASK_FINALIZE,
|
||||
};
|
||||
|
||||
struct ggml_compute_params {
|
||||
enum ggml_task_type type;
|
||||
|
||||
// ith = thread index, nth = number of threads
|
||||
int ith, nth;
|
||||
|
||||
// work buffer for all threads
|
||||
size_t wsize;
|
||||
void * wdata;
|
||||
};
|
||||
|
||||
// misc
|
||||
|
||||
GGML_API void ggml_time_init(void); // call this once at the beginning of the program
|
||||
|
|
@ -424,9 +457,10 @@ extern "C" {
|
|||
GGML_API void ggml_print_object (const struct ggml_object * obj);
|
||||
GGML_API void ggml_print_objects(const struct ggml_context * ctx);
|
||||
|
||||
GGML_API int64_t ggml_nelements(const struct ggml_tensor * tensor);
|
||||
GGML_API int64_t ggml_nelements (const struct ggml_tensor * tensor);
|
||||
GGML_API int64_t ggml_nrows (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split);
|
||||
|
||||
GGML_API int ggml_blck_size (enum ggml_type type);
|
||||
GGML_API size_t ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block
|
||||
|
|
|
|||
16
llama-util.h
16
llama-util.h
|
|
@ -405,13 +405,29 @@ struct llama_buffer {
|
|||
llama_buffer() = default;
|
||||
|
||||
void resize(size_t len) {
|
||||
#ifdef GGML_USE_METAL
|
||||
free(addr);
|
||||
int result = posix_memalign((void **) &addr, getpagesize(), len);
|
||||
if (result == 0) {
|
||||
memset(addr, 0, len);
|
||||
}
|
||||
else {
|
||||
addr = NULL;
|
||||
}
|
||||
#else
|
||||
delete[] addr;
|
||||
addr = new uint8_t[len];
|
||||
#endif
|
||||
size = len;
|
||||
}
|
||||
|
||||
~llama_buffer() {
|
||||
#ifdef GGML_USE_METAL
|
||||
free(addr);
|
||||
#else
|
||||
delete[] addr;
|
||||
#endif
|
||||
addr = NULL;
|
||||
}
|
||||
|
||||
// disable copy and move
|
||||
|
|
|
|||
354
llama.cpp
354
llama.cpp
|
|
@ -53,17 +53,22 @@ enum e_model {
|
|||
MODEL_65B,
|
||||
};
|
||||
|
||||
|
||||
static const size_t MB = 1024*1024;
|
||||
|
||||
// computed for n_ctx == 2048
|
||||
// TODO: dynamically determine these sizes
|
||||
// needs modifications in ggml
|
||||
|
||||
typedef void (*offload_func_t)(struct ggml_tensor * tensor);
|
||||
|
||||
void llama_nop(struct ggml_tensor * tensor) { // don't offload by default
|
||||
(void) tensor;
|
||||
}
|
||||
|
||||
static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0()
|
||||
{
|
||||
static std::map<e_model, size_t> k_sizes = {
|
||||
{ MODEL_3B, 128ull * MB },
|
||||
{ MODEL_3B, 256ull * MB },
|
||||
{ MODEL_7B, 512ull * MB },
|
||||
{ MODEL_13B, 512ull * MB },
|
||||
{ MODEL_30B, 512ull * MB },
|
||||
|
|
@ -75,7 +80,7 @@ static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0()
|
|||
static const std::map<e_model, size_t> & MEM_REQ_SCRATCH1()
|
||||
{
|
||||
static std::map<e_model, size_t> k_sizes = {
|
||||
{ MODEL_3B, 128ull * MB },
|
||||
{ MODEL_3B, 256ull * MB },
|
||||
{ MODEL_7B, 512ull * MB },
|
||||
{ MODEL_13B, 512ull * MB },
|
||||
{ MODEL_30B, 512ull * MB },
|
||||
|
|
@ -174,6 +179,7 @@ struct llama_model {
|
|||
struct ggml_tensor * output;
|
||||
|
||||
std::vector<llama_layer> layers;
|
||||
int n_gpu_layers;
|
||||
|
||||
// context
|
||||
struct ggml_context * ctx = NULL;
|
||||
|
|
@ -199,6 +205,12 @@ struct llama_model {
|
|||
if (ctx) {
|
||||
ggml_free(ctx);
|
||||
}
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
|
||||
ggml_cuda_free_data(tensors_by_name[i].second);
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
}
|
||||
};
|
||||
|
||||
|
|
@ -290,15 +302,15 @@ template <typename T>
|
|||
static T checked_mul(T a, T b) {
|
||||
T ret = a * b;
|
||||
if (a != 0 && ret / a != b) {
|
||||
throw format("overflow multiplying %llu * %llu",
|
||||
(unsigned long long) a, (unsigned long long) b);
|
||||
throw std::runtime_error(format("overflow multiplying %llu * %llu",
|
||||
(unsigned long long) a, (unsigned long long) b));
|
||||
}
|
||||
return ret;
|
||||
}
|
||||
|
||||
static size_t checked_div(size_t a, size_t b) {
|
||||
if (b == 0 || a % b != 0) {
|
||||
throw format("error dividing %zu / %zu", a, b);
|
||||
throw std::runtime_error(format("error dividing %zu / %zu", a, b));
|
||||
}
|
||||
return a / b;
|
||||
}
|
||||
|
|
@ -362,7 +374,7 @@ struct llama_load_tensor {
|
|||
const auto & first_shard = shards.at(0);
|
||||
for (const auto & shard : shards) {
|
||||
if (shard.type != first_shard.type) {
|
||||
throw format("inconsistent tensor shard type in '%s'", name.c_str());
|
||||
throw std::runtime_error(format("inconsistent tensor shard type in '%s'", name.c_str()));
|
||||
}
|
||||
}
|
||||
type = first_shard.type;
|
||||
|
|
@ -385,8 +397,8 @@ struct llama_load_tensor {
|
|||
const auto & first_shard = shards.at(0);
|
||||
for (const auto & shard : shards) {
|
||||
if (shard.ne != first_shard.ne) {
|
||||
throw format("inconsistent tensor shard shape in '%s': first was %s, other was %s",
|
||||
name.c_str(), llama_format_tensor_shape(first_shard.ne).c_str(), llama_format_tensor_shape(shard.ne).c_str());
|
||||
throw std::runtime_error(format("inconsistent tensor shard shape in '%s': first was %s, other was %s",
|
||||
name.c_str(), llama_format_tensor_shape(first_shard.ne).c_str(), llama_format_tensor_shape(shard.ne).c_str()));
|
||||
}
|
||||
}
|
||||
ne = first_shard.ne;
|
||||
|
|
@ -464,8 +476,8 @@ struct llama_file_loader {
|
|||
}
|
||||
}
|
||||
|
||||
throw format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?",
|
||||
magic, version);
|
||||
throw std::runtime_error(format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?",
|
||||
magic, version));
|
||||
}
|
||||
void read_hparams() {
|
||||
hparams.n_vocab = file.read_u32();
|
||||
|
|
@ -505,7 +517,7 @@ struct llama_file_loader {
|
|||
file.read_raw(shard.ne.data(), sizeof(shard.ne[0]) * n_dims);
|
||||
std::string name = file.read_string(name_len);
|
||||
if (n_dims < 1 || n_dims > 2) {
|
||||
throw format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims);
|
||||
throw std::runtime_error(format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims));
|
||||
}
|
||||
switch (shard.type) {
|
||||
case GGML_TYPE_F32:
|
||||
|
|
@ -515,9 +527,14 @@ struct llama_file_loader {
|
|||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
break;
|
||||
default: {
|
||||
throw format("unrecognized tensor type %u\n", shard.type);
|
||||
throw std::runtime_error(format("unrecognized tensor type %u\n", shard.type));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -590,6 +607,11 @@ struct llama_file_saver {
|
|||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
break;
|
||||
default: LLAMA_ASSERT(false);
|
||||
}
|
||||
|
|
@ -621,7 +643,7 @@ struct llama_model_loader {
|
|||
auto * ith_file = new llama_file_loader(fname.c_str(), i, tensors_map);
|
||||
file_loaders.emplace_back(ith_file);
|
||||
if (ith_file->hparams != first_file->hparams) {
|
||||
throw format("llama.cpp: hparams inconsistent between files");
|
||||
throw std::runtime_error(format("llama.cpp: hparams inconsistent between files"));
|
||||
}
|
||||
}
|
||||
if (!llama_mmap::SUPPORTED) {
|
||||
|
|
@ -651,7 +673,7 @@ struct llama_model_loader {
|
|||
uint32_t guess_n_parts() const {
|
||||
auto it = tensors_map.name_to_idx.find("tok_embeddings.weight");
|
||||
if (it == tensors_map.name_to_idx.end()) {
|
||||
throw std::string("missing tok_embeddings.weight");
|
||||
throw std::runtime_error(std::string("missing tok_embeddings.weight"));
|
||||
}
|
||||
const llama_load_tensor & lt = tensors_map.tensors.at(it->second);
|
||||
return file_loaders.at(0)->hparams.n_embd / lt.shards.at(0).ne.at(0);
|
||||
|
|
@ -668,12 +690,12 @@ struct llama_model_loader {
|
|||
struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) {
|
||||
auto it = tensors_map.name_to_idx.find(name);
|
||||
if (it == tensors_map.name_to_idx.end()) {
|
||||
throw format("llama.cpp: tensor '%s' is missing from model", name.c_str());
|
||||
throw std::runtime_error(std::runtime_error(format("llama.cpp: tensor '%s' is missing from model", name.c_str())));
|
||||
}
|
||||
llama_load_tensor & lt = tensors_map.tensors.at(it->second);
|
||||
if (lt.ne != ne) {
|
||||
throw format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s",
|
||||
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str());
|
||||
throw std::runtime_error(format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s",
|
||||
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str()));
|
||||
}
|
||||
|
||||
return get_tensor_for(lt, backend);
|
||||
|
|
@ -689,6 +711,7 @@ struct llama_model_loader {
|
|||
}
|
||||
ggml_set_name(tensor, lt.name.c_str());
|
||||
LLAMA_ASSERT(lt.ggml_tensor == NULL); // if this fails, we called get_tensor twice on the same tensor
|
||||
|
||||
tensor->backend = backend;
|
||||
lt.ggml_tensor = tensor;
|
||||
num_ggml_tensors_created++;
|
||||
|
|
@ -697,7 +720,7 @@ struct llama_model_loader {
|
|||
|
||||
void done_getting_tensors() const {
|
||||
if (num_ggml_tensors_created != tensors_map.tensors.size()) {
|
||||
throw std::string("llama.cpp: file contained more tensors than expected");
|
||||
throw std::runtime_error(std::string("llama.cpp: file contained more tensors than expected"));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -841,7 +864,10 @@ static bool kv_cache_init(
|
|||
struct llama_context_params llama_context_default_params() {
|
||||
struct llama_context_params result = {
|
||||
/*.n_ctx =*/ 512,
|
||||
/*.n_batch =*/ 512,
|
||||
/*.gpu_layers =*/ 0,
|
||||
/*.main_gpu =*/ 0,
|
||||
/*.tensor_split =*/ {0},
|
||||
/*.seed =*/ -1,
|
||||
/*.f16_kv =*/ true,
|
||||
/*.logits_all =*/ false,
|
||||
|
|
@ -906,6 +932,16 @@ static const char *llama_ftype_name(enum llama_ftype ftype) {
|
|||
case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
|
||||
case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";
|
||||
// K-quants
|
||||
case LLAMA_FTYPE_MOSTLY_Q2_K: return "mostly Q2_K";
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small";
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium";
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large";
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small";
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium";
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small";
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium";
|
||||
case LLAMA_FTYPE_MOSTLY_Q6_K: return "mostly Q6_K";
|
||||
default: return "unknown, may not work";
|
||||
}
|
||||
}
|
||||
|
|
@ -925,7 +961,10 @@ static void llama_model_load_internal(
|
|||
const std::string & fname,
|
||||
llama_context & lctx,
|
||||
int n_ctx,
|
||||
int n_batch,
|
||||
int n_gpu_layers,
|
||||
int main_gpu,
|
||||
const float * tensor_split,
|
||||
ggml_type memory_type,
|
||||
bool use_mmap,
|
||||
bool use_mlock,
|
||||
|
|
@ -940,9 +979,9 @@ static void llama_model_load_internal(
|
|||
lctx.vocab = std::move(ml->file_loaders.at(0)->vocab);
|
||||
auto & model = lctx.model;
|
||||
model.hparams = ml->file_loaders.at(0)->hparams;
|
||||
model.n_gpu_layers = n_gpu_layers;
|
||||
llama_file_version file_version = ml->file_loaders.at(0)->file_version;
|
||||
auto & hparams = model.hparams;
|
||||
uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
|
||||
|
||||
{
|
||||
switch (hparams.n_layer) {
|
||||
|
|
@ -956,6 +995,8 @@ static void llama_model_load_internal(
|
|||
hparams.n_ctx = n_ctx;
|
||||
}
|
||||
|
||||
const uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
|
||||
|
||||
{
|
||||
fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version));
|
||||
fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab);
|
||||
|
|
@ -975,7 +1016,7 @@ static void llama_model_load_internal(
|
|||
if (hparams.ftype != LLAMA_FTYPE_ALL_F32 &&
|
||||
hparams.ftype != LLAMA_FTYPE_MOSTLY_F16 &&
|
||||
hparams.ftype != LLAMA_FTYPE_MOSTLY_Q8_0) {
|
||||
throw format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1405)");
|
||||
throw std::runtime_error(format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1405)"));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -983,7 +1024,7 @@ static void llama_model_load_internal(
|
|||
if (hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
|
||||
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_1 ||
|
||||
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q8_0) {
|
||||
throw format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1508)");
|
||||
throw std::runtime_error(format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1508)"));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -1014,22 +1055,28 @@ static void llama_model_load_internal(
|
|||
|
||||
model.ctx = ggml_init(params);
|
||||
if (!model.ctx) {
|
||||
throw format("ggml_init() failed");
|
||||
throw std::runtime_error(format("ggml_init() failed"));
|
||||
}
|
||||
}
|
||||
|
||||
(void) main_gpu;
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CUDA
|
||||
fprintf(stderr, "%s: using CUDA for GPU acceleration\n", __func__);
|
||||
ggml_cuda_set_main_device(main_gpu);
|
||||
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
|
||||
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CL
|
||||
fprintf(stderr, "%s: using OpenCL for GPU acceleration\n", __func__);
|
||||
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
|
||||
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU
|
||||
#else
|
||||
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU
|
||||
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_CPU
|
||||
#endif
|
||||
|
||||
// prepare memory for the weights
|
||||
size_t vram_total = 0;
|
||||
size_t vram_weights = 0;
|
||||
size_t vram_scratch = 0;
|
||||
{
|
||||
const uint32_t n_embd = hparams.n_embd;
|
||||
const uint32_t n_layer = hparams.n_layer;
|
||||
|
|
@ -1044,7 +1091,7 @@ static void llama_model_load_internal(
|
|||
{
|
||||
ggml_backend backend_output;
|
||||
if (n_gpu_layers > int(n_layer)) { // NOLINT
|
||||
backend_output = LLAMA_BACKEND_OFFLOAD;
|
||||
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
|
||||
} else {
|
||||
backend_output = GGML_BACKEND_CPU;
|
||||
}
|
||||
|
|
@ -1056,7 +1103,8 @@ static void llama_model_load_internal(
|
|||
|
||||
model.layers.resize(n_layer);
|
||||
for (uint32_t i = 0; i < n_layer; ++i) {
|
||||
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
|
||||
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
|
||||
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
|
||||
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
|
|
@ -1064,19 +1112,19 @@ static void llama_model_load_internal(
|
|||
|
||||
layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd}, backend);
|
||||
|
||||
layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd}, backend);
|
||||
layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd}, backend);
|
||||
layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd}, backend);
|
||||
layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd}, backend);
|
||||
layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd}, backend_split);
|
||||
layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd}, backend_split);
|
||||
layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd}, backend_split);
|
||||
layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd}, backend_split);
|
||||
|
||||
layer.ffn_norm = ml->get_tensor(layers_i + ".ffn_norm.weight", {n_embd}, backend);
|
||||
|
||||
layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}, backend);
|
||||
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}, backend);
|
||||
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend);
|
||||
layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}, backend_split);
|
||||
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}, backend_split);
|
||||
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend_split);
|
||||
|
||||
if (backend == LLAMA_BACKEND_OFFLOAD) {
|
||||
vram_total +=
|
||||
if (backend == GGML_BACKEND_GPU) {
|
||||
vram_weights +=
|
||||
ggml_nbytes(layer.attention_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
|
||||
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.attention_norm) +
|
||||
ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
|
||||
|
|
@ -1093,7 +1141,7 @@ static void llama_model_load_internal(
|
|||
// this is the total memory required to run the inference
|
||||
const size_t mem_required =
|
||||
ctx_size +
|
||||
mmapped_size - vram_total + // weights in VRAM not in memory
|
||||
mmapped_size - vram_weights + // weights in VRAM not in memory
|
||||
MEM_REQ_SCRATCH0().at(model.type) +
|
||||
MEM_REQ_SCRATCH1().at(model.type) +
|
||||
MEM_REQ_EVAL().at (model.type);
|
||||
|
|
@ -1105,14 +1153,24 @@ static void llama_model_load_internal(
|
|||
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
|
||||
mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
|
||||
|
||||
(void) vram_scratch;
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
vram_scratch = n_batch * MB;
|
||||
ggml_cuda_set_scratch_size(vram_scratch);
|
||||
if (n_gpu_layers > 0) {
|
||||
fprintf(stderr, "%s: allocating batch_size x 1 MB = %ld MB VRAM for the scratch buffer\n",
|
||||
__func__, vram_scratch / MB);
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
|
||||
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
|
||||
|
||||
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
|
||||
fprintf(stderr, "%s: offloading %d layers to GPU\n", __func__, n_gpu);
|
||||
if (n_gpu_layers > (int) hparams.n_layer) {
|
||||
fprintf(stderr, "%s: offloading output layer to GPU\n", __func__);
|
||||
}
|
||||
fprintf(stderr, "%s: total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
|
||||
fprintf(stderr, "%s: total VRAM used: %zu MB\n",
|
||||
__func__, (vram_weights + vram_scratch + MB - 1) / MB); // round up
|
||||
#else
|
||||
(void) n_gpu_layers;
|
||||
#endif
|
||||
|
|
@ -1127,6 +1185,8 @@ static void llama_model_load_internal(
|
|||
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
{
|
||||
ggml_cuda_set_tensor_split(tensor_split);
|
||||
|
||||
size_t done_size = 0;
|
||||
size_t data_size = 0;
|
||||
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
|
||||
|
|
@ -1136,7 +1196,8 @@ static void llama_model_load_internal(
|
|||
}
|
||||
}
|
||||
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
|
||||
if (lt.ggml_tensor->backend != GGML_BACKEND_CUDA) {
|
||||
ggml_backend backend = lt.ggml_tensor->backend;
|
||||
if (backend != GGML_BACKEND_GPU && backend != GGML_BACKEND_GPU_SPLIT) {
|
||||
continue;
|
||||
}
|
||||
if (progress_callback) {
|
||||
|
|
@ -1157,7 +1218,7 @@ static void llama_model_load_internal(
|
|||
}
|
||||
}
|
||||
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
|
||||
if (lt.ggml_tensor->backend != GGML_BACKEND_CL) {
|
||||
if (lt.ggml_tensor->backend != GGML_BACKEND_GPU) {
|
||||
continue;
|
||||
}
|
||||
if (progress_callback) {
|
||||
|
|
@ -1167,6 +1228,9 @@ static void llama_model_load_internal(
|
|||
done_size += lt.size;
|
||||
}
|
||||
}
|
||||
#else
|
||||
(void) n_batch;
|
||||
(void) tensor_split;
|
||||
#endif
|
||||
|
||||
if (progress_callback) {
|
||||
|
|
@ -1184,7 +1248,10 @@ static bool llama_model_load(
|
|||
const std::string & fname,
|
||||
llama_context & lctx,
|
||||
int n_ctx,
|
||||
int n_batch,
|
||||
int n_gpu_layers,
|
||||
int main_gpu,
|
||||
float * tensor_split,
|
||||
ggml_type memory_type,
|
||||
bool use_mmap,
|
||||
bool use_mlock,
|
||||
|
|
@ -1192,11 +1259,11 @@ static bool llama_model_load(
|
|||
llama_progress_callback progress_callback,
|
||||
void *progress_callback_user_data) {
|
||||
try {
|
||||
llama_model_load_internal(fname, lctx, n_ctx, n_gpu_layers, memory_type, use_mmap, use_mlock,
|
||||
vocab_only, progress_callback, progress_callback_user_data);
|
||||
llama_model_load_internal(fname, lctx, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, memory_type,
|
||||
use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
|
||||
return true;
|
||||
} catch (const std::string & err) {
|
||||
fprintf(stderr, "error loading model: %s\n", err.c_str());
|
||||
} catch (const std::exception & err) {
|
||||
fprintf(stderr, "error loading model: %s\n", err.what());
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
|
@ -1240,6 +1307,7 @@ static bool llama_eval_internal(
|
|||
const int n_head = hparams.n_head;
|
||||
const int n_vocab = hparams.n_vocab;
|
||||
const int n_rot = hparams.n_embd/hparams.n_head;
|
||||
const int n_gpu_layers = model.n_gpu_layers;
|
||||
|
||||
auto & mem_per_token = lctx.mem_per_token;
|
||||
auto & buf_compute = lctx.buf_compute;
|
||||
|
|
@ -1261,16 +1329,21 @@ static bool llama_eval_internal(
|
|||
ggml_set_name(embd, "embd");
|
||||
memcpy(embd->data, tokens, N*ggml_element_size(embd));
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
if (lctx.ctx_metal && N == 1) {
|
||||
ggml_metal_set_tensor(lctx.ctx_metal, embd);
|
||||
}
|
||||
#endif
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
|
||||
|
||||
const int i_gpu_start = n_layer - n_gpu_layers;
|
||||
(void) i_gpu_start;
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
offload_func_t offload_func = llama_nop;
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
if (il >= i_gpu_start) {
|
||||
offload_func = ggml_cuda_assign_buffers; // sets the output backend to GPU
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
|
||||
struct ggml_tensor * inpSA = inpL;
|
||||
|
||||
lctx.use_buf(ctx0, 0);
|
||||
|
|
@ -1278,20 +1351,32 @@ static bool llama_eval_internal(
|
|||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, inpL);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "rms_norm_0");
|
||||
|
||||
// cur = cur*attention_norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.layers[il].attention_norm);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "attention_norm_0");
|
||||
}
|
||||
|
||||
// self-attention
|
||||
{
|
||||
// compute Q and K and RoPE them
|
||||
struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
||||
// offload_func(tmpq);
|
||||
ggml_set_name(tmpq, "tmpq");
|
||||
|
||||
struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
|
||||
struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
|
||||
ggml_set_name(Qcur, "Qcur");
|
||||
struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
||||
// offload_func(tmpk);
|
||||
ggml_set_name(tmpk, "tmpk");
|
||||
|
||||
struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0);
|
||||
ggml_set_name(Kcur, "Kcur");
|
||||
|
||||
struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0);
|
||||
ggml_set_name(Qcur, "Qcur");
|
||||
|
||||
// store key and value to memory
|
||||
{
|
||||
// compute the transposed [N, n_embd] V matrix
|
||||
|
|
@ -1299,9 +1384,11 @@ static bool llama_eval_internal(
|
|||
ggml_set_name(Vcur, "Vcur");
|
||||
|
||||
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
|
||||
ggml_set_name(k, "k");
|
||||
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
|
||||
( n_ctx)*ggml_element_size(kv_self.v),
|
||||
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
|
||||
ggml_set_name(v, "v");
|
||||
|
||||
// important: storing RoPE-ed version of K in the KV cache!
|
||||
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
|
||||
|
|
@ -1376,63 +1463,104 @@ static bool llama_eval_internal(
|
|||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].wo,
|
||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_wo");
|
||||
}
|
||||
|
||||
lctx.use_buf(ctx0, 1);
|
||||
//ggml_cuda_set_scratch(1);
|
||||
|
||||
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
||||
offload_func(inpFF);
|
||||
ggml_set_name(inpFF, "inpFF");
|
||||
|
||||
// feed-forward network
|
||||
{
|
||||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, inpFF);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "rms_norm_1");
|
||||
|
||||
// cur = cur*ffn_norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "ffn_norm");
|
||||
}
|
||||
|
||||
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w3,
|
||||
cur);
|
||||
offload_func(tmp);
|
||||
ggml_set_name(tmp, "result_w3");
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w1,
|
||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_w2");
|
||||
|
||||
// SILU activation
|
||||
cur = ggml_silu(ctx0, cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "silu");
|
||||
|
||||
cur = ggml_mul(ctx0, cur, tmp);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "silu_x_result_w3");
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w2,
|
||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_w2");
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, inpFF);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "inpFF_+_result_w2");
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
|
||||
}
|
||||
|
||||
lctx.use_buf(ctx0, 0);
|
||||
//ggml_cuda_set_scratch(0);
|
||||
|
||||
// used at the end to optionally extract the embeddings
|
||||
struct ggml_tensor * embeddings = NULL;
|
||||
|
||||
offload_func_t offload_func = llama_nop;
|
||||
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
if (n_gpu_layers > n_layer) {
|
||||
offload_func = ggml_cuda_assign_buffers; // sets the output backend to GPU
|
||||
}
|
||||
#endif // GGML_USE_CUBLAS
|
||||
|
||||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, inpL);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "rms_norm_inpL");
|
||||
|
||||
cur = ggml_rms_norm(ctx0, cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "rms_norm_after");
|
||||
|
||||
// cur = cur*norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.norm);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_norm");
|
||||
|
||||
embeddings = cur;
|
||||
}
|
||||
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
ggml_set_name(cur, "result_output");
|
||||
|
||||
lctx.use_buf(ctx0, -1);
|
||||
|
||||
|
|
@ -1455,13 +1583,15 @@ static bool llama_eval_internal(
|
|||
// When we implement Matrix x Matrix Metal multiplication, we can avoid this branch.
|
||||
// But for now, we have focused only on Matrix x Vector Metal multiplication.
|
||||
//
|
||||
ggml_graph_compute(ctx0, &gf);
|
||||
|
||||
// TODO: avoid these syncs via shared memory (ref #1696)
|
||||
//
|
||||
if (lctx.ctx_metal) {
|
||||
// We need to sync the CPU KV cache with the GPU KV cache
|
||||
ggml_metal_set_tensor(lctx.ctx_metal, kv_self.k);
|
||||
ggml_metal_set_tensor(lctx.ctx_metal, kv_self.v);
|
||||
// We need to sync the GPU KV cache with the CPU KV cache
|
||||
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.k);
|
||||
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.v);
|
||||
}
|
||||
|
||||
ggml_graph_compute(ctx0, &gf);
|
||||
}
|
||||
#else
|
||||
ggml_graph_compute(ctx0, &gf);
|
||||
|
|
@ -2105,8 +2235,19 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
|
||||
default: throw format("invalid output file type %d\n", ftype);
|
||||
};
|
||||
|
||||
// K-quants
|
||||
case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_M:
|
||||
case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_S:
|
||||
case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_S:
|
||||
case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
|
||||
case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break;
|
||||
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
|
||||
}
|
||||
|
||||
if (nthread <= 0) {
|
||||
nthread = std::thread::hardware_concurrency();
|
||||
|
|
@ -2116,6 +2257,20 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
/*vocab_only*/ false));
|
||||
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), ftype);
|
||||
|
||||
int n_attention_wv = 0;
|
||||
int n_feed_forward_w2 = 0;
|
||||
for (auto& tensor : model_loader->tensors_map.tensors) {
|
||||
if (tensor.name.find("attention.wv.weight") != std::string::npos) {
|
||||
++n_attention_wv;
|
||||
}
|
||||
else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
|
||||
++n_feed_forward_w2;
|
||||
}
|
||||
}
|
||||
|
||||
int i_attention_wv = 0;
|
||||
int i_feed_forward_w2 = 0;
|
||||
|
||||
size_t total_size_org = 0;
|
||||
size_t total_size_new = 0;
|
||||
std::vector<int64_t> hist_all(1 << 4, 0);
|
||||
|
|
@ -2158,6 +2313,32 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
printf("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
|
||||
} else {
|
||||
new_type = quantized_type;
|
||||
// TODO: temporary disabled until Metal / OpenCL support is available
|
||||
// ref: https://github.com/ggerganov/llama.cpp/issues/1711
|
||||
//if (tensor.name == "output.weight") {
|
||||
// new_type = GGML_TYPE_Q6_K;
|
||||
//}
|
||||
if (tensor.name.find("attention.wv.weight") != std::string::npos) {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
|
||||
(i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8 ||
|
||||
(i_attention_wv - n_attention_wv/8)%3 == 2)) new_type = GGML_TYPE_Q6_K;
|
||||
++i_attention_wv;
|
||||
}
|
||||
if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
||||
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
|
||||
(i_feed_forward_w2 < n_feed_forward_w2/8 || i_feed_forward_w2 >= 7*n_feed_forward_w2/8 ||
|
||||
(i_feed_forward_w2 - n_feed_forward_w2/8)%3 == 2)) new_type = GGML_TYPE_Q6_K;
|
||||
++i_feed_forward_w2;
|
||||
}
|
||||
if (tensor.name.find("attention.wo.weight") != std::string::npos) {
|
||||
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
|
||||
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
||||
}
|
||||
|
||||
float * f32_data;
|
||||
size_t nelements = tensor.ne.at(0) * tensor.ne.at(1);
|
||||
llama_buffer f32_conv_buf;
|
||||
|
|
@ -2171,7 +2352,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
f32_data[i] = ggml_fp16_to_fp32(f16_data[i]);
|
||||
}
|
||||
} else {
|
||||
throw format("type %s unsupported for integer quantization", ggml_type_name(tensor.type));
|
||||
throw std::runtime_error(format("type %s unsupported for integer quantization", ggml_type_name(tensor.type)));
|
||||
}
|
||||
|
||||
printf("quantizing .. ");
|
||||
|
|
@ -2225,13 +2406,17 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
}
|
||||
|
||||
printf("size = %8.2f MB -> %8.2f MB | hist: ", tensor.size/1024.0/1024.0, new_size/1024.0/1024.0);
|
||||
int64_t tot_count = 0;
|
||||
for (size_t i = 0; i < hist_cur.size(); i++) {
|
||||
hist_all[i] += hist_cur[i];
|
||||
tot_count += hist_cur[i];
|
||||
}
|
||||
|
||||
if (tot_count > 0) {
|
||||
for (size_t i = 0; i < hist_cur.size(); i++) {
|
||||
printf("%5.3f ", hist_cur[i] / float(nelements));
|
||||
}
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
total_size_org += tensor.size;
|
||||
|
|
@ -2248,12 +2433,14 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
sum_all += hist_all[i];
|
||||
}
|
||||
|
||||
if (sum_all > 0) {
|
||||
printf("%s: hist: ", __func__);
|
||||
for (size_t i = 0; i < hist_all.size(); i++) {
|
||||
printf("%5.3f ", hist_all[i] / float(sum_all));
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
//
|
||||
|
|
@ -2293,9 +2480,9 @@ struct llama_context * llama_init_from_file(
|
|||
|
||||
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
||||
|
||||
if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_gpu_layers, memory_type,
|
||||
params.use_mmap, params.use_mlock, params.vocab_only,
|
||||
params.progress_callback, params.progress_callback_user_data)) {
|
||||
if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_batch, params.n_gpu_layers,
|
||||
params.main_gpu, params.tensor_split, memory_type, params.use_mmap, params.use_mlock,
|
||||
params.vocab_only, params.progress_callback, params.progress_callback_user_data)) {
|
||||
fprintf(stderr, "%s: failed to load model\n", __func__);
|
||||
llama_free(ctx);
|
||||
return nullptr;
|
||||
|
|
@ -2338,17 +2525,30 @@ struct llama_context * llama_init_from_file(
|
|||
// this allocates all Metal resources and memory buffers
|
||||
ctx->ctx_metal = ggml_metal_init();
|
||||
|
||||
void *data_ptr = NULL;
|
||||
size_t data_size = 0;
|
||||
if (params.use_mmap) {
|
||||
ggml_metal_add_buffer(ctx->ctx_metal, "data", ctx->model.mapping->addr, ctx->model.mapping->size);
|
||||
ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size);
|
||||
data_ptr = ctx->model.mapping->addr;
|
||||
data_size= ctx->model.mapping->size;
|
||||
} else {
|
||||
ggml_metal_add_buffer(ctx->ctx_metal, "data", ggml_get_mem_buffer(ctx->model.ctx), ggml_get_mem_size(ctx->model.ctx));
|
||||
ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size);
|
||||
data_ptr = ggml_get_mem_buffer(ctx->model.ctx);
|
||||
data_size= ggml_get_mem_size(ctx->model.ctx);
|
||||
}
|
||||
|
||||
ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->model.kv_self.buf.addr, ctx->model.kv_self.buf.size);
|
||||
ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size);
|
||||
ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size);
|
||||
#define LLAMA_METAL_CHECK_BUF(result) \
|
||||
if (!(result)) { \
|
||||
fprintf(stderr, "%s: failed to add buffer\n", __func__); \
|
||||
llama_free(ctx); \
|
||||
return NULL; \
|
||||
}
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size));
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->model.kv_self.buf.addr, ctx->model.kv_self.buf.size));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size));
|
||||
#undef LLAMA_METAL_CHECK_BUF
|
||||
}
|
||||
#endif
|
||||
|
||||
|
|
@ -2367,8 +2567,8 @@ int llama_model_quantize(
|
|||
try {
|
||||
llama_model_quantize_internal(fname_inp, fname_out, ftype, nthread);
|
||||
return 0;
|
||||
} catch (const std::string & err) {
|
||||
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.c_str());
|
||||
} catch (const std::exception & err) {
|
||||
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.what());
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
|
@ -2621,8 +2821,8 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
|
|||
int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) {
|
||||
try {
|
||||
return llama_apply_lora_from_file_internal(ctx, path_lora, path_base_model, n_threads);
|
||||
} catch (const std::string & err) {
|
||||
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.c_str());
|
||||
} catch (const std::exception & err) {
|
||||
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what());
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
19
llama.h
19
llama.h
|
|
@ -1,6 +1,13 @@
|
|||
#ifndef LLAMA_H
|
||||
#define LLAMA_H
|
||||
|
||||
#include "ggml.h"
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
#include "ggml-cuda.h"
|
||||
#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES
|
||||
#else
|
||||
#define LLAMA_MAX_DEVICES 1
|
||||
#endif // GGML_USE_CUBLAS
|
||||
#include <stddef.h>
|
||||
#include <stdint.h>
|
||||
#include <stdbool.h>
|
||||
|
|
@ -66,7 +73,10 @@ extern "C" {
|
|||
|
||||
struct llama_context_params {
|
||||
int n_ctx; // text context
|
||||
int n_batch; // prompt processing batch size
|
||||
int n_gpu_layers; // number of layers to store in VRAM
|
||||
int main_gpu; // the GPU that is used for scratch and small tensors
|
||||
float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
|
||||
int seed; // RNG seed, -1 for random
|
||||
|
||||
bool f16_kv; // use fp16 for KV cache
|
||||
|
|
@ -94,6 +104,15 @@ extern "C" {
|
|||
LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q2_K = 10,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q3_K_S = 11,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q3_K_M = 12,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q3_K_L = 13,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q4_K_S = 14,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q4_K_M = 15,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_K_S = 16,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q5_K_M = 17,// except 1d tensors
|
||||
LLAMA_FTYPE_MOSTLY_Q6_K = 18,// except 1d tensors
|
||||
};
|
||||
|
||||
LLAMA_API struct llama_context_params llama_context_default_params();
|
||||
|
|
|
|||
|
|
@ -12,6 +12,8 @@
|
|||
|
||||
const float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001;
|
||||
const float MAX_QUANTIZATION_TOTAL_ERROR = 0.002;
|
||||
const float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075;
|
||||
const float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040;
|
||||
const float MAX_DOT_PRODUCT_ERROR = 0.02;
|
||||
|
||||
const char* RESULT_STR[] = {"ok", "FAILED"};
|
||||
|
|
@ -122,7 +124,10 @@ int main(int argc, char * argv[]) {
|
|||
|
||||
if (qfns.quantize_row_q && qfns.dequantize_row_q) {
|
||||
const float total_error = total_quantization_error(qfns, test_size, test_data.data());
|
||||
failed = !(total_error < MAX_QUANTIZATION_TOTAL_ERROR);
|
||||
const float max_quantization_error =
|
||||
type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
|
||||
type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS : MAX_QUANTIZATION_TOTAL_ERROR;
|
||||
failed = !(total_error < max_quantization_error);
|
||||
num_failed += failed;
|
||||
if (failed || verbose) {
|
||||
printf("%5s absolute quantization error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], total_error);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue