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Merge branch 'gguf' into gguf-write-single-pass

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Georgi Gerganov 2023-08-17 21:51:15 +03:00 committed by GitHub
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1
.gitignore vendored
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@ -50,7 +50,6 @@ models-mnt
/embd-input-test
/gguf
/gguf-llama-simple
/gptneox-main
/libllama.so
build-info.h
arm_neon.h

12
CMakeLists.txt
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@ -296,7 +296,6 @@ if (LLAMA_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
find_library(METALPERFORMANCE_FRAMEWORK MetalPerformanceShaders REQUIRED)
set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
@ -313,7 +312,6 @@ if (LLAMA_METAL)
${FOUNDATION_LIBRARY}
${METAL_FRAMEWORK}
${METALKIT_FRAMEWORK}
${METALPERFORMANCE_FRAMEWORK}
)
endif()
@ -570,6 +568,16 @@ install(
WORLD_READ
WORLD_EXECUTE
DESTINATION ${CMAKE_INSTALL_BINDIR})
if (LLAMA_METAL)
install(
FILES ggml-metal.metal
PERMISSIONS
OWNER_READ
OWNER_WRITE
GROUP_READ
WORLD_READ
DESTINATION ${CMAKE_INSTALL_BINDIR})
endif()
#
# programs, examples and tests

12
Makefile
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@ -1,8 +1,8 @@
# Define the default target now so that it is always the first target
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test gguf gptneox-main
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test gguf
# Binaries only useful for tests
TEST_TARGETS = tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0
TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0
default: $(BUILD_TARGETS)
@ -283,7 +283,7 @@ endif # LLAMA_CLBLAST
ifdef LLAMA_METAL
CFLAGS += -DGGML_USE_METAL -DGGML_METAL_NDEBUG
CXXFLAGS += -DGGML_USE_METAL
LDFLAGS += -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
LDFLAGS += -framework Foundation -framework Metal -framework MetalKit
OBJS += ggml-metal.o
endif # LLAMA_METAL
@ -388,9 +388,6 @@ embd-input-test: $(LIB_PRE)embdinput$(DSO_EXT) examples/embd-input/embd-input-te
gguf: examples/gguf/gguf.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
gptneox-main: gptneox-main.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
@ -418,6 +415,9 @@ benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o
vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
tests/test-grammar-parser: tests/test-grammar-parser.cpp examples/grammar-parser.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)

22
README.md
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@ -284,7 +284,7 @@ When built with Metal support, you can enable GPU inference with the `--gpu-laye
Any value larger than 0 will offload the computation to the GPU. For example:
```bash
./main -m ./models/7B/ggml-model-q4_0.bin -n 128 -ngl 1
./main -m ./models/7B/ggml-model-q4_0.gguf -n 128 -ngl 1
```
### MPI Build
@ -323,7 +323,7 @@ The above will distribute the computation across 2 processes on the first host a
Finally, you're ready to run a computation using `mpirun`:
```bash
mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.bin -n 128
mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128
```
### BLAS Build
@ -506,10 +506,10 @@ python3 convert.py models/7B/
python convert.py models/7B/ --vocabtype bpe
# quantize the model to 4-bits (using q4_0 method)
./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin q4_0
./quantize ./models/7B/ggml-model-f16.gguf ./models/7B/ggml-model-q4_0.gguf q4_0
# run the inference
./main -m ./models/7B/ggml-model-q4_0.bin -n 128
./main -m ./models/7B/ggml-model-q4_0.gguf -n 128
```
When running the larger models, make sure you have enough disk space to store all the intermediate files.
@ -565,7 +565,7 @@ Here is an example of a few-shot interaction, invoked with the command
./examples/chat-13B.sh
# custom arguments using a 13B model
./main -m ./models/13B/ggml-model-q4_0.bin -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
```
Note the use of `--color` to distinguish between user input and generated text. Other parameters are explained in more detail in the [README](examples/main/README.md) for the `main` example program.
@ -628,6 +628,8 @@ OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. It
### Using [GPT4All](https://github.com/nomic-ai/gpt4all)
*Note: these instructions are likely obsoleted by the GGUF update*
- Obtain the `tokenizer.model` file from LLaMA model and put it to `models`
- Obtain the `added_tokens.json` file from Alpaca model and put it to `models`
- Obtain the `gpt4all-lora-quantized.bin` file from GPT4All model and put it to `models/gpt4all-7B`
@ -703,7 +705,7 @@ If your issue is with model generation quality, then please at least scan the fo
#### How to run
1. Download/extract: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
2. Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
2. Run `./perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw`
3. Output:
```
perplexity : calculating perplexity over 655 chunks
@ -802,13 +804,13 @@ docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-
On completion, you are ready to play!
```bash
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
```
or with a light image:
```bash
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
```
### Docker With CUDA
@ -839,8 +841,8 @@ The resulting images, are essentially the same as the non-CUDA images:
After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag.
```bash
docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
```
### Contributing

44
ci/run.sh
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@ -159,17 +159,17 @@ function gg_run_open_llama_3b_v2 {
python3 ../convert.py ${path_models}
model_f16="${path_models}/ggml-model-f16.bin"
model_q8_0="${path_models}/ggml-model-q8_0.bin"
model_q4_0="${path_models}/ggml-model-q4_0.bin"
model_q4_1="${path_models}/ggml-model-q4_1.bin"
model_q5_0="${path_models}/ggml-model-q5_0.bin"
model_q5_1="${path_models}/ggml-model-q5_1.bin"
model_q2_k="${path_models}/ggml-model-q2_k.bin"
model_q3_k="${path_models}/ggml-model-q3_k.bin"
model_q4_k="${path_models}/ggml-model-q4_k.bin"
model_q5_k="${path_models}/ggml-model-q5_k.bin"
model_q6_k="${path_models}/ggml-model-q6_k.bin"
model_f16="${path_models}/ggml-model-f16.gguf"
model_q8_0="${path_models}/ggml-model-q8_0.gguf"
model_q4_0="${path_models}/ggml-model-q4_0.gguf"
model_q4_1="${path_models}/ggml-model-q4_1.gguf"
model_q5_0="${path_models}/ggml-model-q5_0.gguf"
model_q5_1="${path_models}/ggml-model-q5_1.gguf"
model_q2_k="${path_models}/ggml-model-q2_k.gguf"
model_q3_k="${path_models}/ggml-model-q3_k.gguf"
model_q4_k="${path_models}/ggml-model-q4_k.gguf"
model_q5_k="${path_models}/ggml-model-q5_k.gguf"
model_q6_k="${path_models}/ggml-model-q6_k.gguf"
wiki_test_60="${path_wiki}/wiki.test-60.raw"
@ -285,17 +285,17 @@ function gg_run_open_llama_7b_v2 {
python3 ../convert.py ${path_models}
model_f16="${path_models}/ggml-model-f16.bin"
model_q8_0="${path_models}/ggml-model-q8_0.bin"
model_q4_0="${path_models}/ggml-model-q4_0.bin"
model_q4_1="${path_models}/ggml-model-q4_1.bin"
model_q5_0="${path_models}/ggml-model-q5_0.bin"
model_q5_1="${path_models}/ggml-model-q5_1.bin"
model_q2_k="${path_models}/ggml-model-q2_k.bin"
model_q3_k="${path_models}/ggml-model-q3_k.bin"
model_q4_k="${path_models}/ggml-model-q4_k.bin"
model_q5_k="${path_models}/ggml-model-q5_k.bin"
model_q6_k="${path_models}/ggml-model-q6_k.bin"
model_f16="${path_models}/ggml-model-f16.gguf"
model_q8_0="${path_models}/ggml-model-q8_0.gguf"
model_q4_0="${path_models}/ggml-model-q4_0.gguf"
model_q4_1="${path_models}/ggml-model-q4_1.gguf"
model_q5_0="${path_models}/ggml-model-q5_0.gguf"
model_q5_1="${path_models}/ggml-model-q5_1.gguf"
model_q2_k="${path_models}/ggml-model-q2_k.gguf"
model_q3_k="${path_models}/ggml-model-q3_k.gguf"
model_q4_k="${path_models}/ggml-model-q4_k.gguf"
model_q5_k="${path_models}/ggml-model-q5_k.gguf"
model_q6_k="${path_models}/ggml-model-q6_k.gguf"
wiki_test="${path_wiki}/wiki.test.raw"

29
convert.py
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@ -155,12 +155,7 @@ class Params:
n_layer = config["num_hidden_layers"]
n_ff = config["intermediate_size"]
n_head = config["num_attention_heads"]
if "num_key_value_heads" in config:
n_head_kv = config["num_key_value_heads"]
else:
n_head_kv = None
n_head_kv = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head
f_norm_eps = config["rms_norm_eps"]
n_mult = Params.find_n_mult(n_ff, n_embd)
@ -719,7 +714,7 @@ class OutputFile:
self.gguf.add_feed_forward_length (params.n_ff)
self.gguf.add_rope_dimension_count(params.n_embd // params.n_head)
self.gguf.add_head_count (params.n_head)
if params.n_head_kv is not None: self.gguf.add_head_count_kv(params.n_head_kv)
self.gguf.add_head_count_kv (params.n_head_kv)
self.gguf.add_layer_norm_rms_eps (params.f_norm_eps)
def add_meta_vocab(self, vocab: Vocab) -> None:
@ -817,6 +812,23 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM
def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
tmap = gguf.get_tensor_name_map(ARCH, params.n_layer)
tmp = model
# HF models permut or pack some of the tensors, so we need to undo that
for i in itertools.count():
if f"model.layers.{i}.self_attn.q_proj.weight" in model:
print(f"Permuting layer {i}")
tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head, params.n_head_kv)
tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_head_kv)
#tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
print(f"Unpacking and permuting layer {i}")
tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head_kv)
tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv)
tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2)
else:
break
out: LazyModel = {}
for name, lazy_tensor in model.items():
name_new = name
@ -830,8 +842,9 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
else:
raise Exception(f"Unexpected tensor name: {name}")
if gguf.should_skip_tensor(ARCH, params.n_layer, name_new):
if gguf.should_skip_tensor_TMP(ARCH, params.n_layer, name_new):
print(f"skipping tensor {name_new}")
continue
else:
print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type} | {lazy_tensor.shape}")
out[name_new] = lazy_tensor

6
docs/token_generation_performance_tips.md
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@ -3,7 +3,7 @@
## 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 "
./main -m "path/to/model.gguf" -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:
@ -25,9 +25,9 @@ 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)
Model: `TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.q4_0.gguf` (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]`
Run command: `./main -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]`
Result:

19
examples/common.cpp
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@ -262,6 +262,21 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.cfg_negative_prompt = argv[i];
} else if (arg == "--cfg-negative-prompt-file") {
if (++i >= argc) {
invalid_param = true;
break;
}
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
break;
}
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.cfg_negative_prompt));
if (params.cfg_negative_prompt.back() == '\n') {
params.cfg_negative_prompt.pop_back();
}
} else if (arg == "--cfg-scale") {
if (++i >= argc) {
invalid_param = true;
@ -553,8 +568,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
fprintf(stdout, " --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n");
fprintf(stdout, " --grammar-file FNAME file to read grammar from\n");
fprintf(stdout, " --cfg-negative-prompt PROMPT \n");
fprintf(stdout, " --cfg-negative-prompt PROMPT\n");
fprintf(stdout, " negative prompt to use for guidance. (default: empty)\n");
fprintf(stdout, " --cfg-negative-prompt-file FNAME\n");
fprintf(stdout, " negative prompt file to use for guidance. (default: empty)\n");
fprintf(stdout, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
fprintf(stdout, " --rope-scale N RoPE context linear scaling factor, inverse of --rope-freq-scale (default: %g)\n", 1.0f/params.rope_freq_scale);
fprintf(stdout, " --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: %.1f)\n", params.rope_freq_base);

2
examples/common.h
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@ -52,7 +52,7 @@ struct gpt_params {
std::string cfg_negative_prompt; // string to help guidance
float cfg_scale = 1.f; // How strong is guidance
std::string model = "models/7B/ggml-model.bin"; // model path
std::string model = "models/7B/ggml-model-f16.gguf"; // model path
std::string model_alias = "unknown"; // model alias
std::string prompt = "";
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state

0
cmpnct_gpt2bpe.hpp → examples/gptneox-wip/cmpnct_gpt2bpe.hpp
View File

0
gptneox-main.cpp → examples/gptneox-wip/gptneox-main.cpp
View File

2
examples/metal/metal.cpp
View File

@ -2,7 +2,7 @@
//
// - First, export a LLaMA graph:
//
// $ ./bin/main -m ../models/7B/ggml-model-q4_0.bin --export
// $ ./bin/main -m ../models/7B/ggml-model-q4_0.gguf --export
//
// - Run this tool to evaluate the exported graph:
//

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

@ -25,7 +25,7 @@
#endif
struct quantize_stats_params {
std::string model = "models/7B/ggml-model-f16.bin";
std::string model = "models/7B/ggml-model-f16.gguf";
bool verbose = false;
bool per_layer_stats = false;
bool print_histogram = false;

6
examples/server/README.md
View File

@ -5,7 +5,7 @@ This example demonstrates a simple HTTP API server and a simple web front end to
Command line options:
- `--threads N`, `-t N`: Set the number of threads to use during computation.
- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
- `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
- `-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.
@ -48,14 +48,12 @@ To get started right away, run the following command, making sure to use the cor
### Unix-based systems (Linux, macOS, etc.):
```bash
./server -m models/7B/ggml-model.bin -c 2048
./server -m models/7B/ggml-model.gguf -c 2048
```
### Windows:
```powershell
server.exe -m models\7B\ggml-model.bin -c 2048
```
The above command will start a server that by default listens on `127.0.0.1:8080`.
You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url.

7
examples/server/server.cpp
View File

@ -15,6 +15,7 @@
#include "index.html.hpp"
#include "index.js.hpp"
#include "completion.js.hpp"
#include "json-schema-to-grammar.mjs.hpp"
#ifndef SERVER_VERBOSE
#define SERVER_VERBOSE 1
@ -1199,6 +1200,12 @@ int main(int argc, char **argv)
res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript");
return false; });
// this is only called if no index.html is found in the public --path
svr.Get("/json-schema-to-grammar.mjs", [](const Request &, Response &res)
{
res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript");
return false; });
svr.Post("/completion", [&llama](const Request &req, Response &res)
{
auto lock = llama.lock();

2
flake.nix
View File

@ -14,8 +14,6 @@
with pkgs.darwin.apple_sdk_11_0.frameworks; [
Accelerate
MetalKit
MetalPerformanceShaders
MetalPerformanceShadersGraph
]
else if isAarch32 && isDarwin then
with pkgs.darwin.apple_sdk.frameworks; [

42
ggml-alloc.c
View File

@ -67,6 +67,8 @@ struct ggml_allocr {
struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE];
size_t max_size;
bool measure;
int parse_seq[GGML_MAX_NODES];
bool has_parse_seq;
#ifdef GGML_ALLOCATOR_DEBUG
struct ggml_tensor * allocated_tensors[1024];
@ -111,10 +113,10 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor)
size_t max_avail = 0;
// find the best fitting free block
// find the best fitting free block besides the last block
int best_fit_block = -1;
size_t best_fit_size = SIZE_MAX;
for (int i = 0; i < alloc->n_free_blocks; i++) {
for (int i = 0; i < alloc->n_free_blocks - 1; i++) {
struct free_block * block = &alloc->free_blocks[i];
max_avail = MAX(max_avail, block->size);
if (block->size >= size && block->size <= best_fit_size) {
@ -126,10 +128,17 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor)
AT_PRINTF("block %d\n", best_fit_block);
if (best_fit_block == -1) {
fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
__func__, size, max_avail);
GGML_ASSERT(!"not enough space in the buffer");
// the last block is our last resort
struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1];
if (block->size >= size) {
best_fit_block = alloc->n_free_blocks - 1;
max_avail = MAX(max_avail, block->size);
} else {
fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
__func__, size, max_avail);
GGML_ASSERT(!"not enough space in the buffer");
return;
}
}
struct free_block * block = &alloc->free_blocks[best_fit_block];
void * addr = block->addr;
@ -229,6 +238,17 @@ static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_t
alloc->n_free_blocks++;
}
void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, int * list, int n) {
int pos = 0;
for (int i = 0; i < n; i++) {
if (list[i] != -1) {
alloc->parse_seq[pos] = list[i];
pos++;
}
}
alloc->has_parse_seq = true;
}
void ggml_allocr_reset(struct ggml_allocr * alloc) {
alloc->n_free_blocks = 1;
size_t align_offset = aligned_offset(alloc->data, 0, alloc->alignment);
@ -248,6 +268,8 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment)
/*.hash_table = */ {{0}},
/*.max_size = */ 0,
/*.measure = */ false,
/*.parse_seq = */ {0},
/*.has_parse_seq = */ false,
#ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ = {0},
#endif
@ -275,6 +297,8 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
/*.hash_table = */ {{0}},
/*.max_size = */ 0,
/*.measure = */ true,
/*.parse_seq = */ {0},
/*.has_parse_seq = */ false,
#ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ = {0},
#endif
@ -473,7 +497,13 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
allocate_node(alloc, input);
}
}
for (int i = 0; i < gf->n_nodes; i++) {
for (int ind = 0; ind < gf->n_nodes; ind++) {
int i;
if (alloc->has_parse_seq) {
i = alloc->parse_seq[ind];
} else {
i = ind;
}
struct ggml_tensor * node = gf->nodes[i];
// allocate parents (leafs)

4
ggml-alloc.h
View File

@ -10,6 +10,10 @@ extern "C" {
GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
// tell the allocator to parse nodes following the order described in the list
// you should call this if your graph are optimized to execute out-of-order
GGML_API void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, int * list, int n);
GGML_API void ggml_allocr_free(struct ggml_allocr * alloc);
GGML_API bool ggml_allocr_is_measure(struct ggml_allocr * alloc);
GGML_API void ggml_allocr_reset(struct ggml_allocr * alloc);

9
ggml-metal.h
View File

@ -66,10 +66,13 @@ void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor *
// try to find operations that can be run concurrently in the graph
// you should run it again if the topology of your graph changes
void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf, bool check_mem);
// if the graph has been optimized for concurrently dispatch
bool ggml_metal_if_optimized(struct ggml_metal_context * ctx);
// if the graph has been optimized for concurrently dispatch, return length of the concur_list if optimized
int ggml_metal_if_optimized(struct ggml_metal_context * ctx);
// output the concur_list for ggml_alloc
int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
// same as ggml_graph_compute but uses Metal
// creates gf->n_threads command buffers in parallel

195
ggml-metal.m
View File

@ -5,7 +5,6 @@
#import <Foundation/Foundation.h>
#import <Metal/Metal.h>
#import <MetalPerformanceShaders/MetalPerformanceShaders.h>
#undef MIN
#undef MAX
@ -79,6 +78,14 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(mul_mat_q4_K_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q5_K_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q6_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_f16_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q4_1_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q2_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q3_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q4_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q5_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q6_K_f32);
GGML_METAL_DECL_KERNEL(rope);
GGML_METAL_DECL_KERNEL(alibi_f32);
GGML_METAL_DECL_KERNEL(cpy_f32_f16);
@ -110,13 +117,6 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
ctx->n_buffers = 0;
ctx->concur_list_len = 0;
// determine if we can use MPS
if (MPSSupportsMTLDevice(ctx->device)) {
fprintf(stderr, "%s: using MPS\n", __func__);
} else {
fprintf(stderr, "%s: not using MPS\n", __func__);
GGML_ASSERT(false && "MPS not supported");
}
#if 0
// compile from source string and show compile log
@ -163,10 +163,15 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
// load kernels
{
NSError * error = nil;
#define GGML_METAL_ADD_KERNEL(name) \
ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \
ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:nil]; \
fprintf(stderr, "%s: loaded %-32s %16p\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name);
ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \
fprintf(stderr, "%s: loaded %-32s %16p\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name); \
if (error) { \
fprintf(stderr, "%s: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
return NULL; \
}
GGML_METAL_ADD_KERNEL(add);
GGML_METAL_ADD_KERNEL(add_row);
@ -196,6 +201,14 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(mul_mat_q4_K_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q5_K_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q6_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_f16_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q4_0_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q4_1_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q2_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q3_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q4_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q5_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q6_K_f32);
GGML_METAL_ADD_KERNEL(rope);
GGML_METAL_ADD_KERNEL(alibi_f32);
GGML_METAL_ADD_KERNEL(cpy_f32_f16);
@ -243,11 +256,12 @@ void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
ctx->n_cb = n_cb;
}
bool ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
if (ctx->concur_list_len) {
return true;
}
return false;
int ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
return ctx->concur_list_len;
}
int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) {
return ctx->concur_list;
}
// finds the Metal buffer that contains the tensor data on the GPU device
@ -390,7 +404,7 @@ void ggml_metal_get_tensor(
void ggml_metal_graph_find_concurrency(
struct ggml_metal_context * ctx,
struct ggml_cgraph * gf) {
struct ggml_cgraph * gf, bool check_mem) {
int search_depth = gf->n_nodes; //we only find concurrency in this range to avoid wasting too much time
int nodes_unused[GGML_MAX_CONCUR];
@ -437,7 +451,7 @@ void ggml_metal_graph_find_concurrency(
}
}
}
if (exe_flag) {
if (exe_flag && check_mem) {
// check if nodes[i]'s data will be overwritten by a node before nodes[i].
// if node[5] and node[3] write to the same memory region, then we can't issue node[5] before node[3]
int64_t data_start = (int64_t) gf->nodes[i]->data;
@ -521,7 +535,7 @@ void ggml_metal_graph_compute(
id<MTLCommandBuffer> command_buffer = command_buffers[cb_idx];
id<MTLComputeCommandEncoder> encoder = nil;
id<MTLComputeCommandEncoder> encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
const int node_start = (cb_idx + 0) * n_nodes_per_cb;
const int node_end = (cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb;
@ -530,10 +544,6 @@ void ggml_metal_graph_compute(
const int i = has_concur ? ctx->concur_list[ind] : ind;
if (i == -1) {
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
continue;
}
[encoder memoryBarrierWithScope:MTLBarrierScopeBuffers];
continue;
}
@ -607,10 +617,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_ADD:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
if (ggml_nelements(src1) == ne10) {
// src1 is a row
[encoder setComputePipelineState:ctx->pipeline_add_row];
@ -628,10 +634,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_MUL:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
if (ggml_nelements(src1) == ne10) {
// src1 is a row
[encoder setComputePipelineState:ctx->pipeline_mul_row];
@ -649,10 +651,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_SCALE:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
const float scale = *(const float *) src1->data;
[encoder setComputePipelineState:ctx->pipeline_scale];
@ -668,10 +666,6 @@ void ggml_metal_graph_compute(
switch (ggml_get_unary_op(gf->nodes[i])) {
case GGML_UNARY_OP_SILU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
[encoder setComputePipelineState:ctx->pipeline_silu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
@ -682,10 +676,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_UNARY_OP_RELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
[encoder setComputePipelineState:ctx->pipeline_relu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
@ -696,10 +686,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_UNARY_OP_GELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
[encoder setComputePipelineState:ctx->pipeline_gelu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
@ -716,10 +702,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_SOFT_MAX:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
const int nth = 32;
[encoder setComputePipelineState:ctx->pipeline_soft_max];
@ -734,10 +716,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_DIAG_MASK_INF:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
const int n_past = ((int32_t *)(dst->op_params))[0];
[encoder setComputePipelineState:ctx->pipeline_diag_mask_inf];
@ -755,53 +733,43 @@ void ggml_metal_graph_compute(
GGML_ASSERT(ne00 == ne10);
// GGML_ASSERT(ne02 == ne12); // Should be checked on individual data types until broadcast is implemented everywhere
uint gqa = ne12/ne02;
GGML_ASSERT(ne03 == ne13);
// for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
// AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
if (ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
(src0t == GGML_TYPE_F32 || src0t == GGML_TYPE_F16) && ne11 > 1) {
if (encoder != nil) {
[encoder endEncoding];
encoder = nil;
src1t == GGML_TYPE_F32 &&
[ctx->device supportsFamily:MTLGPUFamilyApple7] &&
ne00%32 == 0 &&
ne11 > 1) {
switch (src0->type) {
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32]; break;
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_0_f32]; break;
case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_1_f32]; break;
case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q2_K_f32]; break;
case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q3_K_f32]; break;
case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_K_f32]; break;
case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_K_f32]; break;
case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q6_K_f32]; break;
default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
}
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:8];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:9];
[encoder setBytes:&gqa length:sizeof(gqa) atIndex:10];
[encoder setThreadgroupMemoryLength:8192 atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake( (ne11+31)/32, (ne01+63) / 64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
}
MPSDataType src0dt = src0t == GGML_TYPE_F32 ? MPSDataTypeFloat32 : MPSDataTypeFloat16;
MPSDataType src1dt = src1t == GGML_TYPE_F32 ? MPSDataTypeFloat32 : MPSDataTypeFloat16;
// for F32 x F32 we use MPS
MPSMatrixDescriptor * desc0 = [MPSMatrixDescriptor
matrixDescriptorWithRows:ne01 columns:ne00 rowBytes:src0->nb[1] dataType:src0dt];
MPSMatrixDescriptor * desc1 = [MPSMatrixDescriptor
matrixDescriptorWithRows:ne11 columns:ne10 rowBytes:src1->nb[1] dataType:src1dt];
MPSMatrixDescriptor * desc = [MPSMatrixDescriptor
matrixDescriptorWithRows:ne1 columns:ne0 rowBytes:dst->nb[1] dataType:MPSDataTypeFloat32];
MPSMatrixMultiplication * mul = [[MPSMatrixMultiplication alloc]
initWithDevice:ctx->device transposeLeft:false transposeRight:true
resultRows:ne11 resultColumns:ne01 interiorColumns:ne00 alpha:1.0 beta:0.0];
// we need to do ne12 multiplications
// TODO: is there a way to do this in parallel - currently very slow ..
// TODO: might be possible to offload part of the computation to ANE using Accelerate's CBLAS
for (int64_t i02 = 0; i02 < ne12; ++i02) {
size_t offs_src0_cur = offs_src0 + i02/(ne12/ne02)*nb02; // gqa not used for now
size_t offs_src1_cur = offs_src1 + i02*nb12;
size_t offs_dst_cur = offs_dst + i02*nb2;
MPSMatrix * mat_src0 = [[MPSMatrix alloc] initWithBuffer:id_src0 offset:offs_src0_cur descriptor:desc0];
MPSMatrix * mat_src1 = [[MPSMatrix alloc] initWithBuffer:id_src1 offset:offs_src1_cur descriptor:desc1];
MPSMatrix * mat_dst = [[MPSMatrix alloc] initWithBuffer:id_dst offset:offs_dst_cur descriptor:desc ];
[mul encodeToCommandBuffer:command_buffer leftMatrix:mat_src1 rightMatrix:mat_src0 resultMatrix:mat_dst];
}
} else {
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
else {
int nth0 = 32;
int nth1 = 1;
@ -900,23 +868,24 @@ void ggml_metal_graph_compute(
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16];
[encoder setBytes:&gqa length:sizeof(gqa) atIndex:17];
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7) / 8, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7) / 8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_Q3_K) {
#ifdef GGML_QKK_64
[encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
#else
[encoder dispatchThreadgroups:MTLSizeMake((ne01+3)/4, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01+3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
#endif
}
else if (src0t == GGML_TYPE_Q5_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3) / 4, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3) / 4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_Q6_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else {
[encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@ -925,10 +894,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_GET_ROWS:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
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;
@ -954,10 +919,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_RMS_NORM:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
@ -977,10 +938,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_NORM:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
const float eps = 1e-5f;
const int nth = 256;
@ -999,10 +956,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_ALIBI:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
GGML_ASSERT((src0t == GGML_TYPE_F32));
const int n_past = ((int32_t *) dst->op_params)[0]; UNUSED(n_past);
@ -1042,10 +995,6 @@ void ggml_metal_graph_compute(
} break;
case GGML_OP_ROPE:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
@ -1086,10 +1035,6 @@ void ggml_metal_graph_compute(
case GGML_OP_CPY:
case GGML_OP_CONT:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
}
const int nth = 32;
switch (src0t) {

969
ggml-metal.metal
View File

File diff suppressed because it is too large Load Diff

2
ggml.c
View File

@ -19290,7 +19290,7 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
}
gguf_set_arr_str(ctx, src->kv[i].key.data, data, src->kv[i].value.arr.n);
free(data);
} if (src->kv[i].value.arr.type == GGUF_TYPE_ARRAY) {
} else if (src->kv[i].value.arr.type == GGUF_TYPE_ARRAY) {
GGML_ASSERT(false && "nested arrays not supported");
} else {
gguf_set_arr_data(ctx, src->kv[i].key.data, src->kv[i].value.arr.type, src->kv[i].value.arr.data, src->kv[i].value.arr.n);

6
gguf.py
View File

@ -147,7 +147,11 @@ MODEL_TENSOR_SKIP = {
}
def should_skip_tensor(arch: MODEL_ARCH, n_blocks: int, name: str) -> bool:
# TODO: the following helper functions should be removed
# instead, get_tensor_name_map should return tuples of (name, MODEL_TENSOR)
# however, my Python is very bad, and I couldn't figure out how to do this, hence these functions
# REMOVE
def should_skip_tensor_TMP(arch: MODEL_ARCH, n_blocks: int, name: str) -> bool:
for skip in MODEL_TENSOR_SKIP.get(arch, []):
for i in range(n_blocks):
if name == MODEL_TENSOR_NAMES[arch][skip].format(bid=i):

90
llama.cpp
View File

@ -11,7 +11,7 @@
#include "ggml.h"
#if !defined(GGML_USE_CUBLAS) && !defined(GGML_USE_METAL)
#if !defined(GGML_USE_CUBLAS)
# include "ggml-alloc.h"
# define LLAMA_USE_ALLOCATOR
#else
@ -1042,6 +1042,9 @@ struct llama_model_loader {
};
ctx_gguf = gguf_init_from_file(fname.c_str(), params);
if (!ctx_gguf) {
throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
}
n_kv = gguf_get_n_kv(ctx_gguf);
n_tensors = gguf_get_n_tensors(ctx_gguf);
@ -1057,7 +1060,7 @@ struct llama_model_loader {
// print meta data
// TODO: make optional
{
LLAMA_LOG_INFO("%s: loaded meta data with %d key-value paris and %d tensors from %s (version %s)\n",
LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
__func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(file_version));
for (int i = 0; i < n_tensors; i++) {
@ -1083,6 +1086,15 @@ struct llama_model_loader {
this->use_mmap = use_mmap;
}
~llama_model_loader() {
if (ctx_gguf) {
gguf_free(ctx_gguf);
}
if (ctx_meta) {
ggml_free(ctx_meta);
}
}
const char * get_tensor_name(int i) const {
return gguf_get_tensor_name(ctx_gguf, i);
}
@ -1895,11 +1907,11 @@ static struct ggml_cgraph * llama_build_graph(
ggml_set_name(Q, "Q");
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_embd_gqa, il*n_ctx*ggml_element_size(kv_self.k)*n_embd_gqa),
n_embd_head, n_head_kv, n_past + N),
0, 2, 1, 3);
ggml_view_3d(ctx0, kv_self.k,
n_embd_head, n_past + N, n_head_kv,
ggml_element_size(kv_self.k)*n_embd_gqa,
ggml_element_size(kv_self.k)*n_embd_head,
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
offload_func_kq(K);
ggml_set_name(K, "K");
@ -1928,9 +1940,9 @@ static struct ggml_cgraph * llama_build_graph(
struct ggml_tensor * V =
ggml_view_3d(ctx0, kv_self.v,
n_past + N, n_embd_head, n_head_kv,
n_ctx*ggml_element_size(kv_self.v),
n_ctx*ggml_element_size(kv_self.v)*n_embd_head,
n_ctx*ggml_element_size(kv_self.v)*n_embd_gqa*il);
ggml_element_size(kv_self.v)*n_ctx,
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
offload_func_v(V);
ggml_set_name(V, "V");
@ -2131,11 +2143,7 @@ static bool llama_eval_internal(
#endif
#ifdef GGML_USE_METAL
if (lctx.ctx_metal && N == 1) {
// TODO: disabled until #2413 is resolved
//if (!ggml_metal_if_optimized(lctx.ctx_metal)) {
// ggml_metal_graph_find_concurrency(lctx.ctx_metal, gf);
//}
if (lctx.ctx_metal) {
ggml_metal_set_n_cb (lctx.ctx_metal, n_threads);
ggml_metal_graph_compute(lctx.ctx_metal, gf);
ggml_metal_get_tensor (lctx.ctx_metal, res);
@ -2143,22 +2151,6 @@ static bool llama_eval_internal(
ggml_metal_get_tensor(lctx.ctx_metal, embeddings);
}
} else {
// IMPORTANT:
// Since we don't have efficient Matrix x Matrix Metal multiplication yet, we fallback to vanilla
// ggml_graph_compute(). It uses Apple's Accelerate CBLAS API which takes advantage of the ANE or the AMX
// coprocessor.
//
// When we implement Matrix x Matrix Metal multiplication, we can avoid this branch.
// But for now, we have focused only on Matrix x Vector Metal multiplication.
//
// TODO: avoid these syncs via shared memory (ref #1696)
//
if (lctx.ctx_metal) {
// We need to sync the GPU KV cache with the CPU KV cache
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.k);
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.v);
}
ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
}
#else
@ -3440,6 +3432,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
const std::string name = ggml_get_name(meta);
// TODO: avoid hardcoded tensor names - use the TN_* constants
if (name.find("attn_v.weight") != std::string::npos) {
++n_attention_wv;
}
@ -3518,6 +3511,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
} else {
new_type = quantized_type;
#ifdef GGML_USE_K_QUANTS
// TODO: avoid hardcoded tensor names - use the TN_* constants
if (name == TN_OUTPUT) {
int nx = tensor->ne[0];
int ny = tensor->ne[1];
@ -3532,7 +3526,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
(i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
++i_attention_wv;
} else if (name.find("feed_forward.w2.weight") != std::string::npos) {
} else if (name.find("ffn_down.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) &&
@ -3587,7 +3581,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_data = work.data();
std::vector<int64_t> hist_cur(1 << 4, 0);
const int chunk_size = 32 * 512;
static const int chunk_size = 32 * 512;
const int nchunk = (nelements + chunk_size - 1)/chunk_size;
const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
if (nthread_use < 2) {
@ -3595,7 +3589,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
} else {
size_t counter = 0;
new_size = 0;
auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements] () {
auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements]() {
std::vector<int64_t> local_hist;
size_t local_size = 0;
while (true) {
@ -4141,7 +4135,18 @@ struct llama_context * llama_new_context_with_model(
int n_past = hparams.n_ctx - n_tokens;
llama_token token = llama_token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
ggml_cgraph * gf = llama_build_graph(*ctx, &token, NULL, n_tokens, n_past);
#ifdef GGML_USE_METAL
if (params.n_gpu_layers > 0) {
ctx->ctx_metal = ggml_metal_init(1);
if (!ctx->ctx_metal) {
LLAMA_LOG_ERROR("%s: ggml_metal_init() failed\n", __func__);
llama_free(ctx);
return NULL;
}
ggml_metal_graph_find_concurrency(ctx->ctx_metal, gf, false);
ggml_allocr_set_parse_seq(ctx->alloc, ggml_metal_get_concur_list(ctx->ctx_metal), ggml_metal_if_optimized(ctx->ctx_metal));
}
#endif
// measure memory requirements for the graph
size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment;
@ -4159,6 +4164,11 @@ struct llama_context * llama_new_context_with_model(
ctx->buf_alloc.resize(alloc_size);
ctx->alloc = ggml_allocr_new(ctx->buf_alloc.data, ctx->buf_alloc.size, tensor_alignment);
#ifdef GGML_USE_METAL
if (ctx->ctx_metal) {
ggml_allocr_set_parse_seq(ctx->alloc, ggml_metal_get_concur_list(ctx->ctx_metal), ggml_metal_if_optimized(ctx->ctx_metal));
}
#endif
}
#else
ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type) + ggml_graph_overhead());
@ -4173,13 +4183,6 @@ struct llama_context * llama_new_context_with_model(
#ifdef GGML_USE_METAL
if (params.n_gpu_layers > 0) {
// this allocates all Metal resources and memory buffers
ctx->ctx_metal = ggml_metal_init(1);
if (!ctx->ctx_metal) {
LLAMA_LOG_ERROR("%s: ggml_metal_init() failed\n", __func__);
llama_free(ctx);
return NULL;
}
void * data_ptr = NULL;
size_t data_size = 0;
@ -4208,8 +4211,7 @@ struct llama_context * llama_new_context_with_model(
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.data, ctx->buf_compute.size, 0));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->kv_self.buf.data, ctx->kv_self.buf.size, 0));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].data, ctx->buf_scratch[0].size, 0));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].data, ctx->buf_scratch[1].size, 0));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "alloc", ctx->buf_alloc.data, ctx->buf_alloc.size, 0));
#undef LLAMA_METAL_CHECK_BUF
}
#endif

1
models/.editorconfig Normal file
View File

@ -0,0 +1 @@
root = true

3
scripts/get-wikitext-2.sh Normal file
View File

@ -0,0 +1,3 @@
#!/bin/bash
wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip

1
tests/CMakeLists.txt
View File

@ -31,5 +31,6 @@ llama_build_executable(test-tokenizer-1.cpp)
llama_test_executable(test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
#llama_test_executable(test-tokenizer-1.aquila test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
llama_build_and_test_executable(test-grammar-parser.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/grammar-parser.cpp)
llama_build_and_test_executable(test-llama-grammar.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/grammar-parser.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/common.cpp)
llama_build_and_test_executable(test-grad0.cpp) # SLOW
# llama_build_and_test_executable(test-opt.cpp) # SLOW

403
tests/test-llama-grammar.cpp Normal file
View File

@ -0,0 +1,403 @@
#ifdef NDEBUG
#undef NDEBUG
#endif
#include "llama.cpp"
#include "examples/common.cpp"
#include "examples/grammar-parser.cpp"
#include <cassert>
int main()
{
grammar_parser::parse_state parsed_grammar;
std::vector<std::pair<std::string, uint32_t>> expected = {
{"expr", 2},
{"expr_6", 6},
{"expr_7", 7},
{"ident", 8},
{"ident_10", 10},
{"num", 9},
{"num_11", 11},
{"root", 0},
{"root_1", 1},
{"root_5", 5},
{"term", 4},
{"ws", 3},
{"ws_12", 12},
};
std::vector<std::vector<llama_grammar_element>> expected_rules = {
{{LLAMA_GRETYPE_RULE_REF, 5}, {LLAMA_GRETYPE_END, 0}},
{
{LLAMA_GRETYPE_RULE_REF, 2},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_RULE_REF, 4},
{LLAMA_GRETYPE_CHAR, 10},
{LLAMA_GRETYPE_END, 0},
},
{{LLAMA_GRETYPE_RULE_REF, 4}, {LLAMA_GRETYPE_RULE_REF, 7}, {LLAMA_GRETYPE_END, 0}},
{{LLAMA_GRETYPE_RULE_REF, 12}, {LLAMA_GRETYPE_END, 0}},
{
{LLAMA_GRETYPE_RULE_REF, 8},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_RULE_REF, 9},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_CHAR, 40},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_RULE_REF, 2},
{LLAMA_GRETYPE_CHAR, 41},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_END, 0},
},
{{LLAMA_GRETYPE_RULE_REF, 1}, {LLAMA_GRETYPE_RULE_REF, 5}, {LLAMA_GRETYPE_ALT, 0}, {LLAMA_GRETYPE_RULE_REF, 1}, {LLAMA_GRETYPE_END, 0}},
{
{LLAMA_GRETYPE_CHAR, 45},
{LLAMA_GRETYPE_CHAR_ALT, 43},
{LLAMA_GRETYPE_CHAR_ALT, 42},
{LLAMA_GRETYPE_CHAR_ALT, 47},
{LLAMA_GRETYPE_RULE_REF, 4},
{LLAMA_GRETYPE_END, 0},
},
{{LLAMA_GRETYPE_RULE_REF, 6}, {LLAMA_GRETYPE_RULE_REF, 7}, {LLAMA_GRETYPE_ALT, 0}, {LLAMA_GRETYPE_END, 0}},
{
{LLAMA_GRETYPE_CHAR, 97},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 122},
{LLAMA_GRETYPE_RULE_REF, 10},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_END, 0},
},
{{LLAMA_GRETYPE_RULE_REF, 11}, {LLAMA_GRETYPE_RULE_REF, 3}, {LLAMA_GRETYPE_END, 0}},
{
{LLAMA_GRETYPE_CHAR, 97},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 122},
{LLAMA_GRETYPE_CHAR_ALT, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_CHAR_ALT, 95},
{LLAMA_GRETYPE_RULE_REF, 10},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_END, 0},
},
{
{LLAMA_GRETYPE_CHAR, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_RULE_REF, 11},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_CHAR, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_END, 0},
},
{
{LLAMA_GRETYPE_CHAR, 32},
{LLAMA_GRETYPE_CHAR_ALT, 9},
{LLAMA_GRETYPE_CHAR_ALT, 10},
{LLAMA_GRETYPE_RULE_REF, 12},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_END, 0},
},
};
for (auto pair : expected)
{
parsed_grammar.symbol_ids[pair.first] = pair.second;
}
for (auto rule : expected_rules)
{
parsed_grammar.rules.push_back({});
for (auto element : rule)
{
parsed_grammar.rules.back().push_back(element);
}
}
llama_grammar *grammar = NULL;
std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
std::vector<std::vector<llama_grammar_element>> expected_stacks = {
{
{LLAMA_GRETYPE_RULE_REF, 5},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_CHAR, 97},
},
{
{LLAMA_GRETYPE_RULE_REF, 5},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_CHAR, 48},
},
{
{LLAMA_GRETYPE_RULE_REF, 5},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_CHAR, 48},
},
{
{LLAMA_GRETYPE_RULE_REF, 5},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_CHAR, 40},
},
{
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_CHAR, 97},
},
{
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_CHAR, 48},
},
{
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_CHAR, 48},
},
{
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_CHAR, 40},
}};
auto index = 0;
for (auto stack : grammar->stacks)
{
// compare stack to expected_stack
for (uint32_t i = 0; i < stack.size(); i++)
{
auto element = stack[i];
auto expected_element = expected_stacks[index][i];
// pretty print error message before asserting
if (expected_element.type != element->type || expected_element.value != element->value)
{
fprintf(stderr, "index: %d\n", index);
fprintf(stderr, "expected_element: %d, %d\n", expected_element.type, expected_element.value);
fprintf(stderr, "actual_element: %d, %d\n", element->type, element->value);
fprintf(stderr, "expected_element != actual_element\n");
}
assert(expected_element.type == element->type && expected_element.value == element->value);
}
index++;
}
std::vector<std::vector<const llama_grammar_element *>> next_stacks;
std::vector<llama_grammar_candidate> next_candidates;
next_candidates.resize(24);
for (size_t i = 0; i < 24; ++i)
{
uint32_t *cp = new uint32_t[2]; // dynamically allocate memory for code_point
cp[0] = 37 + i;
cp[1] = 0;
next_candidates[i] = {i, cp};
}
std::vector<std::vector<std::pair<uint32_t, uint16_t>>> expected_reject = {
{
{0, 37},
{1, 38},
{2, 39},
{3, 40},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{11, 48},
{12, 49},
{13, 50},
{14, 51},
{15, 52},
{16, 53},
{17, 54},
{18, 55},
{19, 56},
{20, 57},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{3, 40},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{3, 40},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{11, 48},
{12, 49},
{13, 50},
{14, 51},
{15, 52},
{16, 53},
{17, 54},
{18, 55},
{19, 56},
{20, 57},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{3, 40},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{11, 48},
{12, 49},
{13, 50},
{14, 51},
{15, 52},
{16, 53},
{17, 54},
{18, 55},
{19, 56},
{20, 57},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{3, 40},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{3, 40},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{21, 58},
{22, 59},
{23, 60},
},
{
{0, 37},
{1, 38},
{2, 39},
{4, 41},
{5, 42},
{6, 43},
{7, 44},
{8, 45},
{9, 46},
{10, 47},
{11, 48},
{12, 49},
{13, 50},
{14, 51},
{15, 52},
{16, 53},
{17, 54},
{18, 55},
{19, 56},
{20, 57},
{21, 58},
{22, 59},
{23, 60},
},
};
std::vector<llama_grammar_candidate> rejects = llama_grammar_reject_candidates_for_stack(grammar->rules, grammar->stacks[0], next_candidates);
std::vector<std::vector<llama_grammar_candidate>> all_rejects;
for (std::size_t count = 0; count < grammar->stacks.size(); ++count)
{
rejects = llama_grammar_reject_candidates_for_stack(grammar->rules, grammar->stacks[count], next_candidates);
all_rejects.push_back(rejects);
}
index = 0;
for (auto rej : all_rejects)
{
for (uint32_t i = 0; i < rej.size(); i++)
{
auto element = rej[i];
auto expected_element = expected_reject[index][i];
assert(element.index == expected_element.first && *element.code_points == expected_element.second);
}
index++;
}
for (auto &candidate : next_candidates)
{
delete[] candidate.code_points;
candidate.code_points = nullptr;
}
delete grammar;
return 0;
}