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Merge branch 'master' into gguf

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Georgi Gerganov 2023-08-14 10:14:05 +03:00
commit 56a1f32072
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25 changed files with 2465 additions and 556 deletions
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3
.gitignore vendored
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@ -2,6 +2,7 @@
*.a
*.so
*.gguf
*.bin
.DS_Store
.build/
.cache/
@ -40,6 +41,7 @@ models-mnt
/perplexity
/embedding
/train-text-from-scratch
/convert-llama2c-to-ggml
/simple
/benchmark-matmult
/vdot
@ -71,6 +73,7 @@ poetry.lock
poetry.toml
# Test binaries
tests/test-grammar-parser
tests/test-double-float
tests/test-grad0
tests/test-opt

2
CMakeLists.txt
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@ -69,7 +69,6 @@ option(LLAMA_BLAS "llama: use BLAS"
set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
option(LLAMA_CUBLAS "llama: use CUDA" OFF)
#option(LLAMA_CUDA_CUBLAS "llama: use cuBLAS for prompt processing" OFF)
set(LLAMA_CUDA_MMQ_Y "64" CACHE STRING "llama: y tile size for mmq CUDA kernels")
option(LLAMA_CUDA_FORCE_DMMV "llama: use dmmv instead of mmvq CUDA kernels" OFF)
set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
set(LLAMA_CUDA_MMV_Y "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
@ -256,7 +255,6 @@ if (LLAMA_CUBLAS)
# if (LLAMA_CUDA_CUBLAS)
# add_compile_definitions(GGML_CUDA_CUBLAS)
# endif()
add_compile_definitions(GGML_CUDA_MMQ_Y=${LLAMA_CUDA_MMQ_Y})
if (LLAMA_CUDA_FORCE_DMMV)
add_compile_definitions(GGML_CUDA_FORCE_DMMV)
endif()

19
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 simple server embd-input-test gguf gguf-llama-simple 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 gguf-llama-simple gptneox-main
# Binaries only useful for tests
TEST_TARGETS = 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-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: $(BUILD_TARGETS)
@ -253,11 +253,6 @@ ifdef LLAMA_CUDA_KQUANTS_ITER
else
NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
endif
ifdef LLAMA_CUDA_MMQ_Y
NVCCFLAGS += -DGGML_CUDA_MMQ_Y=$(LLAMA_CUDA_MMQ_Y)
else
NVCCFLAGS += -DGGML_CUDA_MMQ_Y=64
endif # LLAMA_CUDA_MMQ_Y
#ifdef LLAMA_CUDA_CUBLAS
# NVCCFLAGS += -DGGML_CUDA_CUBLAS
#endif # LLAMA_CUDA_CUBLAS
@ -353,7 +348,7 @@ libllama.so: llama.o ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
clean:
rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch embd-input-test gguf build-info.h $(TEST_TARGETS)
rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch convert-llama2c-to-ggml embd-input-test gguf build-info.h $(TEST_TARGETS)
#
# Examples
@ -383,7 +378,7 @@ embedding: examples/embedding/embedding.cpp build-info.h ggml.
save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.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 examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp build-info.h ggml.o llama.o common.o $(OBJS)
server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2)
$(LIB_PRE)embdinput$(DSO_EXT): examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o common.o $(OBJS)
@ -405,6 +400,9 @@ gptneox-main: gptneox-main.cpp ggml.o $(OBJS)
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)
convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
build-info.h: $(wildcard .git/index) scripts/build-info.sh
@sh scripts/build-info.sh > $@.tmp
@if ! cmp -s $@.tmp $@; then \
@ -426,6 +424,9 @@ 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-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)
tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)

1
README.md
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@ -406,7 +406,6 @@ Building the program with BLAS support may lead to some performance improvements
--->
| Option | Legal values | Default | Description |
|-------------------------|------------------------|---------|-------------|
| LLAMA_CUDA_MMQ_Y | Positive integer >= 32 | 64 | Tile size in y direction when using the custom CUDA kernels for prompt processing. Higher values can be faster depending on the amount of shared memory available. Power of 2 heavily recommended. |
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |

1
examples/CMakeLists.txt
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@ -42,6 +42,7 @@ else()
add_subdirectory(benchmark)
add_subdirectory(baby-llama)
add_subdirectory(train-text-from-scratch)
add_subdirectory(convert-llama2c-to-ggml)
add_subdirectory(simple)
add_subdirectory(embd-input)
if (LLAMA_METAL)

13
examples/common.cpp
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@ -194,6 +194,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.rope_freq_scale = std::stof(argv[i]);
} else if (arg == "--rope-scale") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.rope_freq_scale = 1.0f/std::stof(argv[i]);
} else if (arg == "--memory-f32") {
params.memory_f16 = false;
} else if (arg == "--top-p") {
@ -537,7 +543,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " --in-suffix STRING string to suffix after user inputs with (default: empty)\n");
fprintf(stdout, " -f FNAME, --file FNAME\n");
fprintf(stdout, " prompt file to start generation.\n");
fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict);
fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stdout, " -gqa N, --gqa N grouped-query attention factor (TEMP!!! use 8 for LLaMAv2 70B) (default: %d)\n", params.n_gqa);
@ -564,8 +570,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " --cfg-negative-prompt PROMPT \n");
fprintf(stdout, " negative prompt 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-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
fprintf(stdout, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_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);
fprintf(stdout, " --rope-freq-scale N RoPE frequency linear scaling factor, inverse of --rope-scale (default: %g)\n", params.rope_freq_scale);
fprintf(stdout, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
fprintf(stdout, " --no-penalize-nl do not penalize newline token\n");
fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");

10
examples/console.cpp
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@ -10,6 +10,9 @@
#include <windows.h>
#include <fcntl.h>
#include <io.h>
#ifndef ENABLE_VIRTUAL_TERMINAL_PROCESSING
#define ENABLE_VIRTUAL_TERMINAL_PROCESSING 0x0004
#endif
#else
#include <climits>
#include <sys/ioctl.h>
@ -68,9 +71,10 @@ namespace console {
}
}
if (hConsole) {
// Enable ANSI colors on Windows 10+
if (advanced_display && !(dwMode & ENABLE_VIRTUAL_TERMINAL_PROCESSING)) {
SetConsoleMode(hConsole, dwMode | ENABLE_VIRTUAL_TERMINAL_PROCESSING);
// Check conditions combined to reduce nesting
if (advanced_display && !(dwMode & ENABLE_VIRTUAL_TERMINAL_PROCESSING) &&
!SetConsoleMode(hConsole, dwMode | ENABLE_VIRTUAL_TERMINAL_PROCESSING)) {
advanced_display = false;
}
// Set console output codepage to UTF8
SetConsoleOutputCP(CP_UTF8);

5
examples/convert-llama2c-to-ggml/CMakeLists.txt Normal file
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@ -0,0 +1,5 @@
set(TARGET convert-llama2c-to-ggml)
add_executable(${TARGET} convert-llama2c-to-ggml.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

26
examples/convert-llama2c-to-ggml/README.md Normal file
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@ -0,0 +1,26 @@
## Convert llama2.c model to ggml
This example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default.
To convert the model first download the models from the [llma2.c](https://github.com/karpathy/llama2.c) repository:
`$ make -j`
After successful compilation, following usage options are available:
```
usage: ./convert-llama2c-to-ggml [options]
options:
-h, --help show this help message and exit
--copy-vocab-from-model FNAME model path from which to copy vocab (default 'models/ggml-vocab.bin')
--llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model
--llama2c-output-model FNAME model path to save the converted llama2.c model (default ak_llama_model.bin')
```
An example command is as follows:
`$ ./convert-llama2c-to-ggml --copy-vocab-from-model <ggml-vocab.bin> --llama2c-model <llama2.c model path> --llama2c-output-model <ggml output model path>`
Now you can use the model with command like:
`$ ./main -m <ggml output model path> -p "One day, Lily met a Shoggoth" -n 500 -c 256 -eps 1e-5`

825
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp Normal file
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@ -0,0 +1,825 @@
#include "ggml.h"
#include "llama.h"
#include <unordered_map>
#include <vector>
#include <cassert>
#include <climits>
#include <cstring>
#include <cstdarg>
#include <ctime>
#include <random>
#include <stdexcept>
#include <algorithm>
#include <string>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
//////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc.
typedef struct {
int dim; // transformer dimension
int hidden_dim; // for ffn layers
int n_layers; // number of layers
int n_heads; // number of query heads
int n_kv_heads; // number of key/value heads (can be < query heads because of multiquery)
int vocab_size; // vocabulary size, usually 256 (byte-level)
int seq_len; // max sequence length
} Config;
typedef struct {
// token embedding table
float* token_embedding_table; // (vocab_size, dim)
// weights for rmsnorms
float* rms_att_weight; // (layer, dim) rmsnorm weights
float* rms_ffn_weight; // (layer, dim)
// weights for matmuls
float* wq; // (layer, dim, dim)
float* wk; // (layer, dim, dim)
float* wv; // (layer, dim, dim)
float* wo; // (layer, dim, dim)
// weights for ffn
float* w1; // (layer, hidden_dim, dim)
float* w2; // (layer, dim, hidden_dim)
float* w3; // (layer, hidden_dim, dim)
// final rmsnorm
float* rms_final_weight; // (dim,)
// freq_cis for RoPE relatively positional embeddings
// float* freq_cis_real; // (seq_len, dim/2)
// float* freq_cis_imag; // (seq_len, dim/2)
// (optional) classifier weights for the logits, on the last layer
//float* wcls;
} TransformerWeights;
void malloc_weights(TransformerWeights* w, Config* p) {
// we calloc instead of malloc to keep valgrind happy
w->token_embedding_table = new float[p->vocab_size * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
w->rms_att_weight = new float[p->n_layers * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim);
w->rms_ffn_weight = new float[p->n_layers * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim);
w->wq = new float[p->n_layers * p->dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->wk = new float[p->n_layers * p->dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->wv = new float[p->n_layers * p->dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->wo = new float[p->n_layers * p->dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->w1 = new float[p->n_layers * p->hidden_dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
w->w2 = new float[p->n_layers * p->hidden_dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim);
w->w3 = new float[p->n_layers * p->hidden_dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
w->rms_final_weight = new float[p->dim]();
printf("[%s:AK] Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim);
}
int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f) {
if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
if (fread(w->wk, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
if (fread(w->wv, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
if (fread(w->wo, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
if (fread(w->rms_ffn_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
if (fread(w->w1, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
if (fread(w->w2, sizeof(float), p->n_layers * p->hidden_dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->hidden_dim * p->dim)) return 1;
if (fread(w->w3, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
if (fread(w->rms_final_weight, sizeof(float), p->dim, f) != static_cast<size_t>(p->dim)) return 1;
return 0;
}
void free_weights(TransformerWeights* w) {
delete w->token_embedding_table;
delete w->rms_att_weight;
delete w->rms_ffn_weight;
delete w->wq;
delete w->wk;
delete w->wv;
delete w->wo;
delete w->w1;
delete w->w2;
delete w->w3;
delete w->rms_final_weight;
}
void print_sample_weights(TransformerWeights *w){
printf("----- Quick print of first of the weight vales of all the variables\n");
printf("%f\n", w->token_embedding_table[0]);
printf("%f\n", w->rms_att_weight[0]);
printf("%f\n", w->rms_ffn_weight[0]);
printf("%f\n", w->wq[0]);
printf("%f\n", w->wk[0]);
printf("%f\n", w->wv[0]);
printf("%f\n", w->wo[0]);
printf("%f\n", w->w1[0]);
printf("%f\n", w->w2[0]);
printf("%f\n", w->w3[0]);
printf("%f\n", w->rms_att_weight[0]);
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////// ggml structs and functions required to load models, configs and save the model.
struct llama_vocab {
using id = int32_t;
using token = std::string;
struct token_score {
token tok;
float score;
};
std::unordered_map<token, id> token_to_id;
std::vector<token_score> id_to_token;
};
struct my_llama_hparams {
uint32_t n_vocab = 32000;
uint32_t n_ctx = 512; // this is provided as user input?
uint32_t n_embd = 4096;
uint32_t n_mult = 4;
uint32_t n_head = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
bool operator!=(const my_llama_hparams& other) const {
return memcmp(this, &other, sizeof(my_llama_hparams));
}
};
struct my_llama_layer {
// normalization
struct ggml_tensor * attention_norm;
// attention
struct ggml_tensor * wq;
struct ggml_tensor * wk;
struct ggml_tensor * wv;
struct ggml_tensor * wo;
// normalization
struct ggml_tensor * ffn_norm;
// ff
struct ggml_tensor * w1;
struct ggml_tensor * w2;
struct ggml_tensor * w3;
};
struct my_llama_model {
struct ggml_context * ctx = NULL;
my_llama_hparams hparams;
struct ggml_tensor * tok_embeddings;
struct ggml_tensor * norm;
struct ggml_tensor * output;
std::vector<my_llama_layer> layers;
uint32_t train_its = 0;
uint32_t train_samples = 0;
uint32_t train_tokens = 0;
};
struct train_params {
const char * fn_vocab_model;
const char * fn_llama2c_model;
const char * fn_llama2c_output_model;
const char * fn_train_data;
const char * fn_checkpoint_in;
const char * fn_checkpoint_out;
const char * fn_model_out;
uint32_t seed;
int n_ctx;
int n_embd;
int n_mult;
int n_head;
int n_layer;
int n_rotmax;
int n_threads;
int n_batch;
int n_examples;
int n_predict;
int print_info_interval;
int print_details_interval;
bool samples_start_after_nl;
bool use_adam;
bool use_flash;
bool use_scratch;
// only adam
int warmup;
int cos_decay_steps;
float cos_decay_restart;
float cos_decay_alpha;
int lbfgs_n_iter;
int adam_n_iter;
float adam_alpha;
float adam_decay;
int mem_model_gb;
int mem_compute_gb;
int mem_compute0_gb;
int mem_compute1_gb;
};
uint32_t get_n_ff(const struct my_llama_hparams* hparams) {
const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult;
return n_ff;
}
void print_params(struct my_llama_hparams * params) {
printf("%s: n_vocab: %d\n", __func__, params->n_vocab);
printf("%s: n_ctx: %d\n", __func__, params->n_ctx);
printf("%s: n_embd: %d\n", __func__, params->n_embd);
printf("%s: n_mult: %d\n", __func__, params->n_mult);
printf("%s: n_head: %d\n", __func__, params->n_head);
printf("%s: n_ff: %d\n", __func__, get_n_ff(params));
printf("%s: n_layer: %d\n", __func__, params->n_layer);
printf("%s: n_rot: %d\n", __func__, params->n_rot);
}
void init_model(struct my_llama_model * model) {
const auto & hparams = model->hparams;
const uint32_t n_embd = hparams.n_embd;
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_vocab = hparams.n_vocab;
const uint32_t n_ff = get_n_ff(&hparams);
struct ggml_context * ctx = model->ctx;
model->train_its = 0;
model->train_samples = 0;
model->train_tokens = 0;
model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
printf("[%s:GG] Allocating [%d] x [%d] = [%d] float space for model->tok_embeddings\n",__func__,n_embd , n_vocab, n_embd * n_vocab);
model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
printf("[%s:GG] Allocating [%d] float space for model->norm\n",__func__,n_embd);
model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for model->output\n",__func__,n_embd, n_vocab, n_embd * n_vocab);
// printing the per-layer allocations here so we dont print in the for loop.
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wq for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wk for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wv for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.wo for [%d] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
printf("[%s:GG] Allocating [%d] float space for layer.ffn_norm for [%d] layers\n",__func__,n_embd, n_layer);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w1 for [%d] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w2 for [%d] layers\n",__func__, n_embd, n_ff, n_ff * n_embd, n_layer);
printf("[%s:GG] Allocating [%d] x[%d] = [%d] float space for layer.w3 for [%d] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);
ggml_set_name(model->tok_embeddings, "tok_embeddings.weight");
ggml_set_name(model->norm, "norm.weight");
ggml_set_name(model->output, "output.weight");
model->layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = model->layers[i];
std::string layers_i = "layers." + std::to_string(i);
layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd);
layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff);
ggml_set_name(layer.attention_norm, (layers_i + ".attention_norm.weight").c_str());
ggml_set_name(layer.wq, (layers_i + ".attention.wq.weight").c_str());
ggml_set_name(layer.wk, (layers_i + ".attention.wk.weight").c_str());
ggml_set_name(layer.wv, (layers_i + ".attention.wv.weight").c_str());
ggml_set_name(layer.wo, (layers_i + ".attention.wo.weight").c_str());
ggml_set_name(layer.ffn_norm, (layers_i + ".ffn_norm.weight").c_str());
ggml_format_name(layer.w1, "%s.feed_forward.w1.weight", layers_i.c_str());
ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str());
ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str());
}
}
float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
float * ptr = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
return *ptr;
}
int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
int32_t * ptr = (int32_t *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]);
return *ptr;
}
void print_row(struct ggml_tensor * probs, int i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = get_f32_2d(probs, k, i);
printf(" %f", p);
}
printf("\n");
}
void print_matrix(struct ggml_tensor * probs) {
assert(probs->n_dims == 2);
for (int i = 0; i < probs->ne[1]; ++i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = get_f32_2d(probs, k, i);
printf(" %.2f", p);
}
printf("\n");
}
}
#ifdef __GNUC__
#ifdef __MINGW32__
__attribute__((format(gnu_printf, 1, 2)))
#else
__attribute__((format(printf, 1, 2)))
#endif
#endif
static std::string format(const char * fmt, ...) {
va_list ap, ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX);
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
struct llama_file {
// use FILE * so we don't have to re-open the file to mmap
FILE * fp;
size_t size;
llama_file(const char * fname, const char * mode) {
fp = std::fopen(fname, mode);
if (fp == NULL) {
size = 0;
} else {
seek(0, SEEK_END);
size = tell();
seek(0, SEEK_SET);
}
}
size_t tell() const {
#ifdef _WIN32
__int64 ret = _ftelli64(fp);
#else
long ret = std::ftell(fp);
#endif
GGML_ASSERT(ret != -1); // this really shouldn't fail
return (size_t) ret;
}
void seek(size_t offset, int whence) {
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, whence);
#else
int ret = std::fseek(fp, (long) offset, whence);
#endif
GGML_ASSERT(ret == 0); // same
}
void read_raw(void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
std::size_t ret = std::fread(ptr, size, 1, fp);
if (ferror(fp)) {
throw std::runtime_error(format("read error: %s", strerror(errno)));
}
if (ret != 1) {
throw std::runtime_error(std::string("unexpectedly reached end of file"));
}
}
std::uint32_t read_u32() {
std::uint32_t ret;
read_raw(&ret, sizeof(ret));
return ret;
}
std::float_t read_f32() {
std::float_t ret;
read_raw(&ret, sizeof(ret));
return ret;
}
std::string read_string(std::uint32_t len) {
std::vector<char> chars(len);
read_raw(chars.data(), len);
return std::string(chars.data(), len);
}
void write_raw(const void * ptr, size_t size) {
if (size == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, size, 1, fp);
if (ret != 1) {
throw std::runtime_error(format("write error: %s", strerror(errno)));
}
}
void write_u32(std::uint32_t val) {
write_raw(&val, sizeof(val));
}
~llama_file() {
if (fp) {
std::fclose(fp);
}
}
};
void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
if (tensor == NULL) {
file->write_u32(0);
file->write_u32(0);
file->write_u32(GGML_TYPE_F32);
file->seek((0-file->tell()) & 31, SEEK_CUR);
return;
}
const char * name = ggml_get_name(tensor);
uint32_t name_len = strlen(name);
uint32_t nd = tensor->n_dims;
uint32_t ne[4] = { (uint32_t)tensor->ne[0],
(uint32_t)tensor->ne[1],
(uint32_t)tensor->ne[2],
(uint32_t)tensor->ne[3] };
file->write_u32(nd);
file->write_u32(name_len);
file->write_u32(tensor->type);
file->write_raw(ne, sizeof(ne[0]) * nd);
file->write_raw(name, name_len);
file->seek((0-file->tell()) & 31, SEEK_CUR);
file->write_raw(tensor->data, ggml_nbytes(tensor));
}
bool is_ggml_file(const char *filename) {
llama_file file(filename, "rb");
if (file.size < 4) {
return false;
}
uint32_t magic = file.read_u32();
return magic == LLAMA_FILE_MAGIC;
}
void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
// heuristic to infer whether vocab is from ggml or from llama2.c vocabulary
if (is_ggml_file(filename)) {
struct llama_context_params llama_params = llama_context_default_params();
llama_params.vocab_only = true;
struct llama_model * lmodel = llama_load_model_from_file(filename, llama_params);
struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params);
std::vector<const char *> strings;
std::vector<float> scores;
int n_vocab = llama_n_vocab(lctx);
strings.resize(n_vocab, NULL);
scores.resize(n_vocab, 0);
n_vocab = llama_get_vocab(lctx, strings.data(), scores.data(), n_vocab);
GGML_ASSERT(n_vocab == llama_n_vocab(lctx));
vocab->id_to_token.resize(n_vocab);
for (int i=0; i<n_vocab; ++i) {
std::string tok = std::string(strings[i]);
float score = scores[i];
vocab->id_to_token[i].tok = tok;
vocab->id_to_token[i].score = score;
vocab->token_to_id.emplace(tok, i);
}
llama_free(lctx);
llama_free_model(lmodel);
} else { // assume llama2.c vocabulary
printf("Assuming llama2.c vocabulary since %s is not a ggml file\n", filename);
llama_file file(filename, "rb");
uint32_t n_vocab = config->vocab_size;
/* uint32_t max_token_length = */ file.read_u32(); // unused
vocab->id_to_token.resize(n_vocab);
for (uint32_t i=0; i<n_vocab; ++i) {
float_t score = file.read_f32();
uint32_t len = file.read_u32();
std::string tok = file.read_string(len);
vocab->id_to_token[i].tok = tok;
vocab->id_to_token[i].score = score;
vocab->token_to_id.emplace(tok, i);
}
}
}
void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, float * karpathy_weights){
int ct;
switch (gg_weights->n_dims){
case 1:
ct = 0;
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++){
float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0]);
*ptr = karpathy_weights[ct];
ct++;
}
break;
case 2:
ct = 0;
for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {
float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1]);
*ptr = karpathy_weights[ct];
ct++;
}
}
break;
case 3:
ct = 0;
for (int i2 = 0; i2 < gg_weights->ne[2]; i2++) {
for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {
float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1] + i2*gg_weights->nb[2]);
*ptr = karpathy_weights[ct];
ct++;
}
}
}
break;
}
}
void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename) {
struct llama_file file(filename, "wb");
if (file.fp == NULL) {
return;
}
// write_magic
file.write_u32(LLAMA_FILE_MAGIC); // magic
file.write_u32(LLAMA_FILE_VERSION); // version
// write_hparams
file.write_u32(model->hparams.n_vocab);
file.write_u32(model->hparams.n_embd);
file.write_u32(model->hparams.n_mult);
file.write_u32(model->hparams.n_head);
file.write_u32(model->hparams.n_layer);
file.write_u32(model->hparams.n_rot);
file.write_u32(LLAMA_FTYPE_ALL_F32);
// write_vocab - for now we are just writing the existing BPE voc. assuming karpathy's vocabulary is the same. idk.
uint32_t n_vocab = model->hparams.n_vocab;
for (uint32_t i = 0; i < n_vocab; i++) {
const auto & token_score = vocab->id_to_token.at(i);
file.write_u32((uint32_t) token_score.tok.size());
file.write_raw(token_score.tok.data(), token_score.tok.size());
file.write_raw(&token_score.score, sizeof(token_score.score));
}
// stuff AK weights into GG weights one by one.
// w->token_embedding_table -> model->tok_embeddings
// float* -> struct ggml_tensor
stuff_karpathy_weights_into_gg(model->tok_embeddings, w->token_embedding_table);
stuff_karpathy_weights_into_gg(model->output, w->token_embedding_table);
stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight);
//print_row(model->norm, 0);
// for rms-att-weight
int row_length = model->hparams.n_embd;
const auto & hparams = model->hparams;
//int n_ff = model->hparams.n_embd;
int n_ff = get_n_ff(&hparams);
for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
auto & layer = model->layers[i];
// 1d
stuff_karpathy_weights_into_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]);
stuff_karpathy_weights_into_gg(layer.ffn_norm , &w->rms_ffn_weight[i*row_length]);
// from 3d matrix layer x dim x dim to 2d matrix dim x dim
stuff_karpathy_weights_into_gg(layer.wq , &w->wq[i*row_length*row_length]);
stuff_karpathy_weights_into_gg(layer.wk , &w->wk[i*row_length*row_length]);
stuff_karpathy_weights_into_gg(layer.wv , &w->wv[i*row_length*row_length]);
stuff_karpathy_weights_into_gg(layer.wo , &w->wo[i*row_length*row_length]);
stuff_karpathy_weights_into_gg(layer.w1 , &w->w1[i*row_length*n_ff]);
stuff_karpathy_weights_into_gg(layer.w2 , &w->w2[i*n_ff*row_length]);
stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]);
}
// write tensors
write_tensor(&file, model->tok_embeddings);
write_tensor(&file, model->norm);
write_tensor(&file, model->output); // ?
for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
auto & layer = model->layers[i];
write_tensor(&file, layer.attention_norm);
write_tensor(&file, layer.wq);
write_tensor(&file, layer.wk);
write_tensor(&file, layer.wv);
write_tensor(&file, layer.wo);
write_tensor(&file, layer.ffn_norm);
write_tensor(&file, layer.w1);
write_tensor(&file, layer.w2);
write_tensor(&file, layer.w3);
}
}
struct train_params get_default_train_params() {
struct train_params params;
params.fn_vocab_model = "models/ggml-vocab.bin";
params.fn_llama2c_output_model = "ak_llama_model.bin";
params.fn_train_data = "shakespeare.txt";
params.fn_checkpoint_in = "checkpoint.bin";
params.fn_checkpoint_out = "checkpoint.bin";
params.fn_model_out = "ggml-checkpoint-f32.bin";
params.seed = -1;
params.n_ctx = 128;
params.n_embd = 256;
params.n_mult = 256;
params.n_head = 8;
params.n_layer = 16;
params.n_rotmax = 64;
params.n_threads = 6;
params.n_batch = 8;
params.n_examples = 8;
params.n_predict = 1024;
params.print_info_interval = 1;
params.print_details_interval = 2;
params.samples_start_after_nl = false;
params.use_adam = true;
params.use_flash = true;
params.use_scratch = true;
// only adam
params.warmup = 100;
params.cos_decay_steps = 1000;
params.cos_decay_restart = 1.1f;
params.cos_decay_alpha = 0.0f;
params.lbfgs_n_iter = 16;
params.adam_n_iter = 16;
params.adam_alpha = 1e-3f;
params.adam_decay = 1e-3f;
params.mem_model_gb = 2;
params.mem_compute_gb = 24;
params.mem_compute0_gb = 8;
params.mem_compute1_gb = 2;
return params;
}
void print_usage(int /*argc*/, char ** argv, const struct train_params * params) {
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " --copy-vocab-from-model FNAME llama2.c vocabulary or ggml model path from which to copy vocab (default '%s')\n", params->fn_vocab_model);
fprintf(stderr, " --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model\n");
fprintf(stderr, " --llama2c-output-model FNAME model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model);
fprintf(stderr, "\n");
}
bool params_parse(int argc, char ** argv, struct train_params * params) {
bool invalid_param = false;
bool reqd_param_found = false;
std::string arg;
struct train_params default_params = get_default_train_params();
const std::string arg_prefix = "--";
for (int i = 1; i < argc; i++) {
arg = argv[i];
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
if (arg == "--copy-vocab-from-model") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_vocab_model = argv[i];
} else if (arg == "--llama2c-model") {
if (++i >= argc) {
invalid_param = true;
break;
}
reqd_param_found = true;
params->fn_llama2c_model = argv[i];
} else if (arg == "--llama2c-output-model") {
if (++i >= argc) {
invalid_param = true;
break;
}
params->fn_llama2c_output_model = argv[i];
} else if (arg == "-h" || arg == "--help") {
print_usage(argc, argv, &default_params);
exit(0);
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
print_usage(argc, argv, &default_params);
exit(1);
}
}
if (invalid_param) {
fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
print_usage(argc, argv, &default_params);
exit(1);
}
if (!reqd_param_found){
fprintf(stderr, "error: please specify a llama2.c .bin file to be converted with argument --llama2c-model\n");
print_usage(argc, argv, &default_params);
exit(1);
}
return true;
}
int main(int argc, char ** argv) {
struct train_params params = get_default_train_params();
if (!params_parse(argc, argv, &params)) {
return 1;
}
Config config;
TransformerWeights weights;
{
FILE *file = fopen(params.fn_llama2c_model, "rb");
if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; }
// read in the config header
if(fread(&config, sizeof(Config), 1, file) != 1) { return 1; }
// read in the Transformer weights
malloc_weights(&weights, &config);
if(checkpoint_init_weights(&weights, &config, file)) { return 1; }
fclose(file);
}
struct llama_vocab vocab;
load_vocab(params.fn_vocab_model, &config, &vocab);
struct my_llama_model model;
model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
model.hparams.n_ctx = params.n_ctx;
model.hparams.n_embd = config.dim; //params.n_embd;
model.hparams.n_mult = 32;//params.n_mult;
model.hparams.n_head = config.n_heads; //params.n_head;
model.hparams.n_layer = config.n_layers; //params.n_layer;
model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
print_params(&model.hparams);
struct ggml_init_params lcparams;
lcparams.mem_size = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb);
lcparams.mem_buffer = NULL;
lcparams.no_alloc = false;
model.ctx = ggml_init(lcparams);
init_model(&model);
save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model);
printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model);
ggml_free(model.ctx);
free_weights(&weights);
return 0;
}

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" Requires an already running llama.cpp server
" To install either copy or symlink to ~/.vim/autoload/llama.vim
" Then start with either :call llama#doLlamaGen(),
" or add a keybind to your vimrc such as
" nnoremap Z :call llama#doLlamaGen()<CR>
" Similarly, you could add an insert mode keybind with
" inoremap <C-B> <Cmd>call llama#doLlamaGen()<CR>
"
" g:llama_api_url and g:llama_overrides can be configured in your .vimrc
" let g:llama_api_url = "192.168.1.10:8080"
" llama_overrides can also be set through buffer/window scopes. For instance
" autocmd filetype python let b:llama_overrides = {"temp": 0.2}
" Could be added to your .vimrc to automatically set a lower temperature when
" editing a python script
" Additionally, an override dict can be stored at the top of a file
" !*{"stop": ["User:"]}
" Could be added to the start of your chatlog.txt to set the stopping token
" These parameter dicts are merged together from lowest to highest priority:
" server default -> g:llama_overrides -> w:llama_overrides ->
" b:llama_overrides -> in file (!*) overrides
"
" Sublists (like logit_bias and stop) are overridden, not merged
" Example override:
" !*{"logit_bias": [[13, -5], [2, false]], "temperature": 1, "top_k": 5, "top_p": 0.5, "n_predict": 256, "repeat_last_n": 256, "repeat_penalty": 1.17647}
if !exists("g:llama_api_url")
let g:llama_api_url= "127.0.0.1:8080"
endif
if !exists("g:llama_overrides")
let g:llama_overrides = {}
endif
const s:querydata = {"n_predict": 256, "stop": [ "\n" ], "stream": v:true }
const s:curlcommand = ['curl','--data-raw', "{\"prompt\":\"### System:\"}", '--silent', '--no-buffer', '--request', 'POST', '--url', g:llama_api_url .. '/completion', '--header', "Content-Type: application/json"]
let s:linedict = {}
func s:callbackHandler(bufn, channel, msg)
if len(a:msg) < 3
return
elseif a:msg[0] == "d"
let l:msg = a:msg[6:-1]
else
let l:msg = a:msg
endif
let l:decoded_msg = json_decode(l:msg)
let l:newtext = split(l:decoded_msg['content'], "\n", 1)
if len(l:newtext) > 0
call setbufline(a:bufn, s:linedict[a:bufn], getbufline(a:bufn, s:linedict[a:bufn])[0] .. newtext[0])
else
echo "nothing genned"
endif
if len(newtext) > 1
let l:failed = appendbufline(a:bufn, s:linedict[a:bufn], newtext[1:-1])
let s:linedict[a:bufn] = s:linedict[a:bufn] + len(newtext)-1
endif
if has_key(l:decoded_msg, "stop") && l:decoded_msg.stop
echo "Finished generation"
endif
endfunction
func llama#doLlamaGen()
if exists("b:job")
if job_status(b:job) == "run"
call job_stop(b:job)
return
endif
endif
let l:cbuffer = bufnr("%")
let s:linedict[l:cbuffer] = line('$')
let l:buflines = getbufline(l:cbuffer, 1, 1000)
let l:querydata = copy(s:querydata)
call extend(l:querydata, g:llama_overrides)
if exists("w:llama_overrides")
call extend(l:querydata, w:llama_overrides)
endif
if exists("b:llama_overrides")
call extend(l:querydata, b:llama_overrides)
endif
if l:buflines[0][0:1] == '!*'
let l:userdata = json_decode(l:buflines[0][2:-1])
call extend(l:querydata, l:userdata)
let l:buflines = l:buflines[1:-1]
endif
let l:querydata.prompt = join(l:buflines, "\n")
let l:curlcommand = copy(s:curlcommand)
let l:curlcommand[2] = json_encode(l:querydata)
let b:job = job_start(l:curlcommand, {"callback": function("s:callbackHandler", [l:cbuffer])})
endfunction
" Echos the tokkenization of the provided string , or cursor to end of word
" Onus is placed on the user to include the preceding space
func llama#tokenizeWord(...)
if (a:0 > 0)
let l:input = a:1
else
exe "normal \"*ye"
let l:input = @*
endif
let l:querydata = {"content": l:input}
let l:curlcommand = copy(s:curlcommand)
let l:curlcommand[2] = json_encode(l:querydata)
let l:curlcommand[8] = g:llama_api_url .. "/tokenize"
let s:token_job = job_start(l:curlcommand, {"callback": function("s:tokenizeWordCallback", [l:input])})
endfunction
func s:tokenizeWordCallback(plaintext, channel, msg)
echo '"' .. a:plaintext ..'" - ' .. string(json_decode(a:msg).tokens)
endfunction
" Echos the token count of the entire buffer (or provided string)
" Example usage :echo llama#tokenCount()
func llama#tokenCount(...)
if (a:0 > 0)
let l:buflines = a:1
else
let l:buflines = getline(1,1000)
if l:buflines[0][0:1] == '!*'
let l:buflines = l:buflines[1:-1]
endif
let l:buflines = join(l:buflines, "\n")
endif
let l:querydata = {"content": l:buflines}
let l:curlcommand = copy(s:curlcommand)
let l:curlcommand[2] = json_encode(l:querydata)
let l:curlcommand[8] = g:llama_api_url .. "/tokenize"
let s:token_job = job_start(l:curlcommand, {"callback": "s:tokenCountCallback"})
endfunction
func s:tokenCountCallback(channel, msg)
let resp = json_decode(a:msg)
echo len(resp.tokens)
endfunction

6
examples/llm.vim
View File

@ -1,3 +1,5 @@
" Basic plugin example
function! Llm()
let url = "http://127.0.0.1:8080/completion"
@ -16,8 +18,10 @@ function! Llm()
" Extract the content field from the response
let content = json_decode(response).content
let split_newlines = split(content, '\n', 1)
" Insert the content at the cursor position
call setline(line('.'), getline('.') . content)
call setline(line('.'), [ getline('.') . split_newlines[0] ] + split_newlines[1:])
endfunction
command! Llm call Llm()

14
examples/main/README.md
View File

@ -140,6 +140,12 @@ The `--ctx-size` option allows you to set the size of the prompt context used by
- `-c N, --ctx-size N`: Set the size of the prompt context (default: 512). The LLaMA models were built with a context of 2048, which will yield the best results on longer input/inference. However, increasing the context size beyond 2048 may lead to unpredictable results.
### Extended Context Size
Some fine-tuned models have extened the context length by scaling RoPE. For example, if the original pretrained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8.
- `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model.
### Keep Prompt
The `--keep` option allows users to retain the original prompt when the model runs out of context, ensuring a connection to the initial instruction or conversation topic is maintained.
@ -154,9 +160,13 @@ The following options allow you to control the text generation process and fine-
### Number of Tokens to Predict
- `-n N, --n-predict N`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity).
- `-n N, --n-predict N`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity, -2 = until context filled)
The `--n-predict` option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text. A value of -1 will cause text to be generated without limit.
The `--n-predict` option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text.
A value of -1 will enable infinite text generation, even though we have a finite context window. When the context window is full, some of the earlier tokens (half of the tokens after `--n-keep`) will be discarded. The context must then be re-evaluated before generation can resume. On large models and/or large context windows, this will result in significant pause in output.
If the pause is undesirable, a value of -2 will stop generation immediately when the context is filled.
It is important to note that the generated text may be shorter than the specified number of tokens if an End-of-Sequence (EOS) token or a reverse prompt is encountered. In interactive mode text generation will pause and control will be returned to the user. In non-interactive mode, the program will end. In both cases, the text generation may stop before reaching the specified `n-predict` value. If you want the model to keep going without ever producing End-of-Sequence on its own, you can use the `--ignore-eos` parameter.

6
examples/main/main.cpp
View File

@ -431,8 +431,12 @@ int main(int argc, char ** argv) {
// - take the n_keep first tokens from the original prompt (via n_past)
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
const int n_left = n_past - params.n_keep;
if (params.n_predict == -2) {
fprintf(stderr, "\n\n%s: context full, stopping generation\n", __func__);
break;
}
const int n_left = n_past - params.n_keep;
// always keep the first token - BOS
n_past = std::max(1, params.n_keep);
n_past_guidance = std::max(1, params.n_keep + guidance_offset);

2
examples/server/README.md
View File

@ -151,6 +151,8 @@ node .
`mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1).
`grammar`: Set grammar for grammar-based sampling (default: no grammar)
`seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
`ignore_eos`: Ignore end of stream token and continue generating (default: false).

67
examples/server/server.cpp
View File

@ -1,6 +1,7 @@
#include "common.h"
#include "llama.h"
#include "build-info.h"
#include "grammar-parser.h"
#ifndef NDEBUG
// crash the server in debug mode, otherwise send an http 500 error
@ -195,6 +196,9 @@ struct llama_server_context
llama_context *ctx = nullptr;
gpt_params params;
grammar_parser::parse_state parsed_grammar;
llama_grammar *grammar = nullptr;
bool truncated = false;
bool stopped_eos = false;
bool stopped_word = false;
@ -226,6 +230,7 @@ struct llama_server_context
void rewind()
{
params.antiprompt.clear();
params.grammar.clear();
num_prompt_tokens = 0;
num_tokens_predicted = 0;
generated_text = "";
@ -237,9 +242,13 @@ struct llama_server_context
stopped_limit = false;
stopping_word = "";
multibyte_pending = 0;
n_remain = 0;
n_past = 0;
if (grammar != nullptr) {
llama_grammar_free(grammar);
grammar = nullptr;
}
}
bool loadModel(const gpt_params &params_)
@ -257,6 +266,31 @@ struct llama_server_context
return true;
}
bool loadGrammar()
{
if (!params.grammar.empty()) {
parsed_grammar = grammar_parser::parse(params.grammar.c_str());
// will be empty (default) if there are parse errors
if (parsed_grammar.rules.empty()) {
LOG_ERROR("grammar parse error", {{"grammar", params.grammar}});
return false;
}
grammar_parser::print_grammar(stderr, parsed_grammar);
{
auto it = params.logit_bias.find(llama_token_eos());
if (it != params.logit_bias.end() && it->second == -INFINITY) {
LOG_WARNING("EOS token is disabled, which will cause most grammars to fail", {});
}
}
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"));
}
return true;
}
void loadPrompt()
{
params.prompt.insert(0, 1, ' '); // always add a first space
@ -420,6 +454,10 @@ struct llama_server_context
logits[llama_token_nl()] = nl_logit;
}
if (grammar != nullptr) {
llama_sample_grammar(ctx, &candidates_p, grammar);
}
if (temp <= 0)
{
// Greedy sampling
@ -457,10 +495,15 @@ struct llama_server_context
}
}
if (grammar != nullptr) {
llama_grammar_accept_token(ctx, grammar, result.tok);
}
for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i)
{
result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p});
}
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(result.tok);
num_tokens_predicted++;
@ -947,6 +990,7 @@ static json format_generation_settings(llama_server_context &llama)
{"stream", llama.stream},
{"logit_bias", llama.params.logit_bias},
{"n_probs", llama.params.n_probs},
{"grammar", llama.params.grammar},
};
}
@ -964,7 +1008,7 @@ static json format_timings(llama_server_context &llama)
assert(timings.n_eval == llama.num_tokens_predicted);
return json{
{"prompt_n", timings.n_eval},
{"prompt_n", timings.n_p_eval},
{"prompt_ms", timings.t_p_eval_ms},
{"prompt_per_token_ms", timings.t_p_eval_ms / timings.n_p_eval},
{"prompt_per_second", 1e3 / timings.t_p_eval_ms * timings.n_p_eval},
@ -993,7 +1037,6 @@ static json format_final_response(llama_server_context &llama, const std::string
{"stopped_limit", llama.stopped_limit},
{"stopping_word", llama.stopping_word},
{"tokens_cached", llama.n_past},
{"tokens_predicted", llama.num_tokens_predicted},
{"timings", format_timings(llama)},
};
@ -1048,6 +1091,7 @@ static void parse_options_completion(const json &body, llama_server_context &lla
llama.params.n_keep = body.value("n_keep", default_params.n_keep);
llama.params.seed = body.value("seed", default_params.seed);
llama.params.prompt = body.value("prompt", default_params.prompt);
llama.params.grammar = body.value("grammar", default_params.grammar);
llama.params.n_probs = body.value("n_probs", default_params.n_probs);
llama.params.logit_bias.clear();
@ -1179,6 +1223,12 @@ int main(int argc, char **argv)
parse_options_completion(json::parse(req.body), llama);
if (!llama.loadGrammar())
{
res.status = 400;
return;
}
llama.loadPrompt();
llama.beginCompletion();
@ -1334,8 +1384,12 @@ int main(int argc, char **argv)
svr.set_error_handler([](const Request &, Response &res)
{
res.set_content("File Not Found", "text/plain");
res.status = 404; });
if (res.status == 400) {
res.set_content("Invalid request", "text/plain");
} else {
res.set_content("File Not Found", "text/plain");
res.status = 404;
} });
// set timeouts and change hostname and port
svr.set_read_timeout(sparams.read_timeout);
@ -1363,6 +1417,9 @@ int main(int argc, char **argv)
return 1;
}
if (llama.grammar != nullptr) {
llama_grammar_free(llama.grammar);
}
llama_backend_free();
return 0;

8
ggml-alloc.c
View File

@ -394,6 +394,14 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
if (parent == NULL) {
break;
}
// if the node's data is external, then we cannot re-use it
if ((char *) parent->data < (char *) alloc->data ||
(char *) parent->data >= ((char *) alloc->data + alloc->size)) {
AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data);
continue;
}
struct hash_node * p_hn = hash_get(ht, parent);
if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
if (ggml_is_view(parent)) {

1266
ggml-cuda.cu
View File

File diff suppressed because it is too large Load Diff

30
gguf-llama.cpp
View File

@ -470,7 +470,7 @@ struct gguf_load_tensors_map {
enum gguf_file_version {
GGUF_FILE_VERSION_V1 = 1,
};
@ -485,7 +485,7 @@ struct ggml_context * ctx_data = NULL;
gguf_file_loader(const char * fname, gguf_load_tensors_map & tensors_map)
: file(fname, "rb") {
fprintf(stderr, "llama.cpp: loading model from %s\n", fname);
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx_data,
@ -530,7 +530,7 @@ struct ggml_context * ctx_data = NULL;
// TODO define keys as constants in header
// TODO: read all hparams from file
hparams.n_vocab = read_n_vocab();
hparams.n_ctx = read_u32("llama.context_length");
hparams.n_embd = read_u32("llama.embedding_length");
@ -539,7 +539,7 @@ struct ggml_context * ctx_data = NULL;
hparams.n_layer = read_u32("llama.layer_count");
hparams.n_rot = read_u32("llama.rope.dimension_count");
hparams.f_rms_norm_eps = read_f32("llama.attention.layer_norm_rms_epsilon");
// LLaMAv2
// hparams.n_head_kv = read_u32("llama.attention.head_count_kv");
}
@ -559,7 +559,7 @@ struct ggml_context * ctx_data = NULL;
for (uint32_t i = 0; i < hparams.n_vocab; i++) {
std::string word = gguf_get_arr_str(gguf_ctx, token_idx, i);
vocab.token_to_id[word] = i;
auto & tok_score = vocab.id_to_token[i];
@ -607,10 +607,10 @@ struct ggml_context * ctx_data = NULL;
tensor.file_off = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, i);
tensor.name = name;
tensor.size = llama_calc_tensor_size(tensor.ne, tensor.type);
tensors_map.tensors.push_back(tensor);
tensors_map.name_to_idx[name] = tensors_map.tensors.size() - 1;
}
@ -624,7 +624,7 @@ struct gguf_file_saver {
// this may not be true when we add quantization version and change ftype description (currently it's string according to the specs,
// but better to have it as uint32).
// we need to calculate the delta in number of bytes written with a counter as a struct member.
gguf_file file;
gguf_file_loader * fl;
size_t info_offset;
@ -640,7 +640,7 @@ struct gguf_file_saver {
void write_header() {
const int32_t magic = GGUF_MAGIC;
file.write_i32(magic);
const int32_t version = GGUF_VERSION;
file.write_i32(version);
@ -658,7 +658,7 @@ struct gguf_file_saver {
std::string val = gguf_get_arr_str(fl->gguf_ctx, i, j);
data[j] = val;
}
file.write_arr<std::string>(key, type, data);
}
@ -669,7 +669,7 @@ struct gguf_file_saver {
float val = gguf_get_arr_f32(fl->gguf_ctx, i, j);
data[j] = val;
}
file.write_arr<float>(key, type, data);
}
@ -772,7 +772,7 @@ struct gguf_file_saver {
info_offset += total_written; // position to write info of the next tensor
file.seek(0, SEEK_END);
return total_written;
}
@ -793,7 +793,7 @@ struct gguf_file_saver {
break;
default: GGML_ASSERT(false);
}
write_tensor_info(tensor, new_type);
file.write_raw(new_data, new_size);
size_t padded_size = GGML_PAD(new_size, GGUF_DEFAULT_ALIGNMENT); // TODO: handle custom alignment
@ -1200,7 +1200,7 @@ static void llama_model_load_internal(
}
const uint32_t n_ff = hparams.n_ff;
{
fprintf(stderr, "%s: format = %s\n", __func__, gguf_file_version_name(file_version));
fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab);
@ -1224,7 +1224,7 @@ static void llama_model_load_internal(
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q8_0) {
throw std::runtime_error(format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1508)"));
}
if (vocab_only) {
return;
}

10
gguf-util.h
View File

@ -146,9 +146,8 @@ struct gguf_file {
fwrite((const char *) &n, sizeof(n), 1, fp);
fwrite(val.data(), sizeof(T), n, fp);
}
template<>
void write_val<std::string>(const std::string & key, enum gguf_type type, const std::string & val) {
void write_str(const std::string & key, enum gguf_type type, const std::string & val) {
write_str(key);
fwrite((const char *) &type, sizeof(type), 1, fp);
@ -157,8 +156,7 @@ struct gguf_file {
fwrite(val.c_str(), n, 1, fp);
}
template<>
void write_arr<std::string>(const std::string & key, enum gguf_type type, const std::vector<std::string> & val) {
void write_str(const std::string & key, enum gguf_type type, const std::vector<std::string> & val) {
write_str(key);
{
const enum gguf_type tarr = GGUF_TYPE_ARRAY;
@ -180,7 +178,7 @@ struct gguf_file {
fputc(0, fp);
}
}
void read_raw(void * ptr, size_t len) const {
if (len == 0) {
return;

31
llama-util.h
View File

@ -271,20 +271,29 @@ struct llama_mmap {
throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
}
#if _WIN32_WINNT >= _WIN32_WINNT_WIN8
if (prefetch) {
// Advise the kernel to preload the mapped memory
WIN32_MEMORY_RANGE_ENTRY range;
range.VirtualAddress = addr;
range.NumberOfBytes = (SIZE_T)size;
if (!PrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
// The PrefetchVirtualMemory API is only present on Windows 8 and above, so we
// will dynamically load it using GetProcAddress.
BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
HMODULE hKernel32;
// This call is guaranteed to succeed.
hKernel32 = GetModuleHandleW(L"kernel32.dll");
// This call may fail if on a pre-Win8 system.
pPrefetchVirtualMemory = reinterpret_cast<decltype(pPrefetchVirtualMemory)> (GetProcAddress(hKernel32, "PrefetchVirtualMemory"));
if (pPrefetchVirtualMemory) {
// Advise the kernel to preload the mapped memory.
WIN32_MEMORY_RANGE_ENTRY range;
range.VirtualAddress = addr;
range.NumberOfBytes = (SIZE_T)size;
if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
}
}
#else
#pragma message("warning: You are building for pre-Windows 8; prefetch not supported")
#endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8
}
~llama_mmap() {

275
llama.cpp
View File

@ -56,6 +56,13 @@
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
static void llama_log_internal(llama_log_level level, const char* format, ...);
static void llama_log_callback_default(llama_log_level level, const char * text, void * user_data);
#define LLAMA_LOG_INFO(...) llama_log_internal(LLAMA_LOG_LEVEL_INFO , __VA_ARGS__)
#define LLAMA_LOG_WARN(...) llama_log_internal(LLAMA_LOG_LEVEL_WARN , __VA_ARGS__)
#define LLAMA_LOG_ERROR(...) llama_log_internal(LLAMA_LOG_LEVEL_ERROR, __VA_ARGS__)
#if !defined(GGML_USE_CUBLAS) && !defined(GGML_USE_METAL)
#include "ggml-alloc.h"
#define LLAMA_USE_ALLOCATOR
@ -149,7 +156,7 @@ static const std::map<e_model, size_t> & MEM_REQ_EVAL()
}
// amount of VRAM needed per batch size to hold temporary results
// the values for 3b and 65b are not derived from testing but instead chosen conservatively
// the values for 3b are not derived from testing but instead chosen conservatively
static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_BASE()
{
static std::map<e_model, size_t> k_sizes = {
@ -157,14 +164,14 @@ static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_BASE()
{ MODEL_7B, 512ull * kB },
{ MODEL_13B, 640ull * kB },
{ MODEL_30B, 768ull * kB },
{ MODEL_65B, 1536ull * kB },
{ MODEL_70B, 1536ull * kB }, // TODO (likely can be reduced)
{ MODEL_65B, 1280ull * kB },
{ MODEL_70B, 1280ull * kB },
};
return k_sizes;
}
// amount of VRAM needed per batch size and context to hold temporary results
// the values for 3b and 65b are not derived from testing but instead chosen conservatively
// the values for 3b are not derived from testing but instead chosen conservatively
static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_PER_CONTEXT()
{
static std::map<e_model, size_t> k_sizes = {
@ -172,8 +179,8 @@ static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_PER_CONTEXT()
{ MODEL_7B, 128ull },
{ MODEL_13B, 160ull },
{ MODEL_30B, 208ull },
{ MODEL_65B, 416ull },
{ MODEL_70B, 416ull }, // TODO (likely can be reduced)
{ MODEL_65B, 256ull },
{ MODEL_70B, 256ull },
};
return k_sizes;
}
@ -438,6 +445,14 @@ struct llama_context {
}
};
struct llama_state {
// We save the log callback globally
llama_log_callback log_callback = llama_log_callback_default;
void * log_callback_user_data = nullptr;
};
// global state
static llama_state g_state;
template <typename T>
static T checked_mul(T a, T b) {
T ret = a * b;
@ -504,7 +519,7 @@ struct llama_file_loader {
llama_file_loader(const char * fname, llama_load_tensors_map & tensors_map)
: file(fname, "rb") {
fprintf(stderr, "llama.cpp: loading model from %s\n", fname);
LLAMA_LOG_INFO("llama.cpp: loading model from %s\n", fname);
read_magic();
read_hparams();
read_vocab();
@ -619,7 +634,7 @@ struct llama_file_saver {
llama_file_loader * any_file_loader;
llama_file_saver(const char * fname, llama_file_loader * any_file_loader, enum llama_ftype new_ftype)
: file(fname, "wb"), any_file_loader(any_file_loader) {
fprintf(stderr, "llama.cpp: saving model to %s\n", fname);
LLAMA_LOG_INFO("llama.cpp: saving model to %s\n", fname);
write_magic();
write_hparams(new_ftype);
write_vocab();
@ -640,7 +655,7 @@ struct llama_file_saver {
}
void write_vocab() {
if (any_file_loader->file_version == LLAMA_FILE_VERSION_GGML) {
fprintf(stderr, "llama.cpp: WARNING: input is an old file that doesn't have scores; will add dummy scores\n");
LLAMA_LOG_WARN("llama.cpp: WARNING: input is an old file that doesn't have scores; will add dummy scores\n");
}
uint32_t n_vocab = any_file_loader->hparams.n_vocab;
for (uint32_t i = 0; i < n_vocab; i++) {
@ -831,7 +846,7 @@ struct llama_model_loader {
uint8_t byte = lt.data[i];
sum = byte + (sum << 6) + (sum << 16) - sum; // sdbm hash
}
fprintf(stderr, "%s checksum: %#08x (%s, size %zu)\n", lt.name.c_str(), sum,
LLAMA_LOG_INFO("%s checksum: %#08x (%s, size %zu)\n", lt.name.c_str(), sum,
llama_format_tensor_shape(lt.ne).c_str(), lt.size);
}
@ -864,7 +879,7 @@ static bool kv_cache_init(
cache.ctx = ggml_init(params);
if (!cache.ctx) {
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
LLAMA_LOG_ERROR("%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
@ -1076,7 +1091,7 @@ static void llama_model_load_internal(
LLAMA_ASSERT(hparams.n_head % n_gqa == 0);
hparams.n_head_kv = hparams.n_head / n_gqa;
if (model.type == e_model::MODEL_65B && n_gqa == 8) {
fprintf(stderr, "%s: warning: assuming 70B model based on GQA == %d\n", __func__, n_gqa);
LLAMA_LOG_WARN("%s: warning: assuming 70B model based on GQA == %d\n", __func__, n_gqa);
model.type = e_model::MODEL_70B;
hparams.f_ffn_mult = 1.3f; // from the params.json of the 70B model
}
@ -1092,22 +1107,22 @@ static void llama_model_load_internal(
//const uint32_t n_ff = 28672;
{
fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version));
fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab);
fprintf(stderr, "%s: n_ctx = %u\n", __func__, hparams.n_ctx);
fprintf(stderr, "%s: n_embd = %u\n", __func__, hparams.n_embd);
fprintf(stderr, "%s: n_mult = %u\n", __func__, hparams.n_mult);
fprintf(stderr, "%s: n_head = %u\n", __func__, hparams.n_head);
fprintf(stderr, "%s: n_head_kv = %u\n", __func__, hparams.n_head_kv);
fprintf(stderr, "%s: n_layer = %u\n", __func__, hparams.n_layer);
fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
fprintf(stderr, "%s: n_gqa = %u\n", __func__, hparams.n_gqa());
fprintf(stderr, "%s: rnorm_eps = %.1e\n", __func__, hparams.f_rms_norm_eps);
fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff);
fprintf(stderr, "%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base);
fprintf(stderr, "%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
fprintf(stderr, "%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type));
LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(file_version));
LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab);
LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, hparams.n_ctx);
LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd);
LLAMA_LOG_INFO("%s: n_mult = %u\n", __func__, hparams.n_mult);
LLAMA_LOG_INFO("%s: n_head = %u\n", __func__, hparams.n_head);
LLAMA_LOG_INFO("%s: n_head_kv = %u\n", __func__, hparams.n_head_kv);
LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_gqa());
LLAMA_LOG_INFO("%s: rnorm_eps = %.1e\n", __func__, hparams.f_rms_norm_eps);
LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, n_ff);
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
LLAMA_LOG_INFO("%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
LLAMA_LOG_INFO("%s: model size = %s\n", __func__, llama_model_type_name(model.type));
}
if (file_version < LLAMA_FILE_VERSION_GGJT_V2) {
@ -1135,7 +1150,7 @@ static void llama_model_load_internal(
size_t ctx_size;
size_t mmapped_size;
ml->calc_sizes(&ctx_size, &mmapped_size);
fprintf(stderr, "%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0);
LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0);
// create the ggml context
{
@ -1160,13 +1175,13 @@ static void llama_model_load_internal(
(void) main_gpu;
(void) mul_mat_q;
#if defined(GGML_USE_CUBLAS)
fprintf(stderr, "%s: using CUDA for GPU acceleration\n", __func__);
LLAMA_LOG_INFO("%s: using CUDA for GPU acceleration\n", __func__);
ggml_cuda_set_main_device(main_gpu);
ggml_cuda_set_mul_mat_q(mul_mat_q);
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
#elif defined(GGML_USE_CLBLAST)
fprintf(stderr, "%s: using OpenCL for GPU acceleration\n", __func__);
LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__);
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU
#else
@ -1271,14 +1286,14 @@ static void llama_model_load_internal(
const size_t mem_required_state =
scale*hparams.kv_size();
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
LLAMA_LOG_INFO("%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;
(void) n_batch;
#ifdef GGML_USE_CUBLAS
if (low_vram) {
fprintf(stderr, "%s: not allocating a VRAM scratch buffer due to low VRAM option\n", __func__);
LLAMA_LOG_INFO("%s: not allocating a VRAM scratch buffer due to low VRAM option\n", __func__);
ggml_cuda_set_scratch_size(0); // disable scratch
} else {
const size_t vram_scratch_base = VRAM_REQ_SCRATCH_BASE().at(model.type);
@ -1286,7 +1301,7 @@ static void llama_model_load_internal(
vram_scratch = n_batch * (vram_scratch_base + n_ctx * vram_scratch_per_context);
ggml_cuda_set_scratch_size(vram_scratch);
if (n_gpu_layers > 0) {
fprintf(stderr, "%s: allocating batch_size x (%zd kB + n_ctx x %zd B) = %zd MB VRAM for the scratch buffer\n",
LLAMA_LOG_INFO("%s: allocating batch_size x (%zd kB + n_ctx x %zd B) = %zd MB VRAM for the scratch buffer\n",
__func__, vram_scratch_base / kB, vram_scratch_per_context,
(vram_scratch + MB - 1) / MB); // round up
}
@ -1296,9 +1311,9 @@ static void llama_model_load_internal(
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
fprintf(stderr, "%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
if (n_gpu_layers > (int) hparams.n_layer) {
fprintf(stderr, "%s: offloading non-repeating layers to GPU\n", __func__);
LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__);
}
size_t vram_kv_cache = 0;
@ -1307,17 +1322,17 @@ static void llama_model_load_internal(
const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3;
if (n_gpu_layers > (int) hparams.n_layer + 1) {
if (low_vram) {
fprintf(stderr, "%s: cannot offload v cache to GPU due to low VRAM option\n", __func__);
LLAMA_LOG_INFO("%s: cannot offload v cache to GPU due to low VRAM option\n", __func__);
} else {
fprintf(stderr, "%s: offloading v cache to GPU\n", __func__);
LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__);
vram_kv_cache += hparams.kv_size() / 2;
}
}
if (n_gpu_layers > (int) hparams.n_layer + 2) {
if (low_vram) {
fprintf(stderr, "%s: cannot offload k cache to GPU due to low VRAM option\n", __func__);
LLAMA_LOG_WARN("%s: cannot offload k cache to GPU due to low VRAM option\n", __func__);
} else {
fprintf(stderr, "%s: offloading k cache to GPU\n", __func__);
LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__);
vram_kv_cache += hparams.kv_size() / 2;
}
}
@ -1326,9 +1341,9 @@ static void llama_model_load_internal(
const int max_offloadable_layers = hparams.n_layer + 1;
#endif // GGML_USE_CUBLAS
fprintf(stderr, "%s: offloaded %d/%d layers to GPU\n",
LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n",
__func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
fprintf(stderr, "%s: total VRAM used: %zu MB\n",
LLAMA_LOG_INFO("%s: total VRAM used: %zu MB\n",
__func__, (vram_weights + vram_scratch + vram_kv_cache + MB - 1) / MB); // round up
#else
(void) n_gpu_layers;
@ -1387,7 +1402,7 @@ static bool llama_model_load(
use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
return true;
} catch (const std::exception & err) {
fprintf(stderr, "error loading model: %s\n", err.what());
LLAMA_LOG_ERROR("error loading model: %s\n", err.what());
return false;
}
}
@ -1751,7 +1766,7 @@ static struct ggml_cgraph * llama_build_graph(
}
#if 0
printf("\n%s: used_mem: eval ctx %.3f MB, scratch %.3f MB %.3f MB, work buf %.3f MB, n_past = %d, N = %d\n", __func__,
LLAMA_LOG_INFO("\n%s: used_mem: eval ctx %.3f MB, scratch %.3f MB %.3f MB, work buf %.3f MB, n_past = %d, N = %d\n", __func__,
ggml_used_mem(ctx0)/1024.0/1024.0,
lctx.get_buf_max_mem(0)/1024.0/1024.0,
lctx.get_buf_max_mem(1)/1024.0/1024.0,
@ -1812,7 +1827,7 @@ static bool llama_eval_internal(
ggml_allocr_alloc_graph(lctx.alloc, gf);
#endif
// fprintf(stderr, "graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
// LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
// for big prompts, if BLAS is enabled, it is better to use only one thread
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
@ -1999,7 +2014,7 @@ struct llama_tokenizer {
left_sym.n += right_sym.n;
right_sym.n = 0;
//printf("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
//LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
// remove the right sym from the chain
left_sym.next = right_sym.next;
@ -3007,7 +3022,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
tensor.data = read_data.addr;
model_loader->load_data_for(tensor);
printf("[%4zu/%4zu] %36s - %16s, type = %6s, ",
LLAMA_LOG_INFO("[%4zu/%4zu] %36s - %16s, type = %6s, ",
++idx, model_loader->tensors_map.tensors.size(),
tensor.name.c_str(), llama_format_tensor_shape(tensor.ne).c_str(),
ggml_type_name(tensor.type));
@ -3029,7 +3044,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_type = tensor.type;
new_data = tensor.data;
new_size = tensor.size;
printf("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
LLAMA_LOG_INFO("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
} else {
new_type = quantized_type;
#ifdef GGML_USE_K_QUANTS
@ -3064,17 +3079,17 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
int nx = tensor.ne.at(0);
int ny = tensor.ne.at(1);
if (nx % QK_K != 0 || ny % QK_K != 0) {
fprintf(stderr, "\n\nTensor sizes %d x %d are not divisible by %d, required for k-quants.\n",nx,ny,QK_K);
LLAMA_LOG_INFO("\n\nTensor sizes %d x %d are not divisible by %d, required for k-quants.\n",nx,ny,QK_K);
convert_incompatible_tensor = true;
}
}
if (convert_incompatible_tensor) {
if (tensor.name == "output.weight") {
new_type = GGML_TYPE_F16; //fall back to F16 instead of just failing.
fprintf(stderr, "F16 will be used for this tensor instead.\n");
LLAMA_LOG_WARN("F16 will be used for this tensor instead.\n");
} else if (tensor.name == "tok_embeddings.weight") {
new_type = GGML_TYPE_Q4_0; //fall back to Q4_0 instead of just failing.
fprintf(stderr, "Q4_0 will be used for this tensor instead.\n");
LLAMA_LOG_WARN("Q4_0 will be used for this tensor instead.\n");
} else {
throw std::runtime_error("Unsupported tensor size encountered\n");
}
@ -3094,7 +3109,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
f32_data = (float *) f32_conv_buf.addr;
}
printf("quantizing to %s .. ", ggml_type_name(new_type));
LLAMA_LOG_INFO("quantizing to %s .. ", ggml_type_name(new_type));
fflush(stdout);
work.resize(nelements * 4); // upper bound on size
@ -3144,7 +3159,7 @@ 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);
LLAMA_LOG_INFO("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];
@ -3153,18 +3168,18 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (tot_count > 0) {
for (size_t i = 0; i < hist_cur.size(); i++) {
printf("%5.3f ", hist_cur[i] / float(nelements));
LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements));
}
}
printf("\n");
LLAMA_LOG_INFO("\n");
}
total_size_org += tensor.size;
total_size_new += new_size;
file_saver.write_tensor(tensor, new_type, new_data, new_size);
}
printf("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
printf("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
{
int64_t sum_all = 0;
@ -3173,11 +3188,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
}
if (sum_all > 0) {
printf("%s: hist: ", __func__);
LLAMA_LOG_INFO("%s: hist: ", __func__);
for (size_t i = 0; i < hist_all.size(); i++) {
printf("%5.3f ", hist_all[i] / float(sum_all));
LLAMA_LOG_INFO("%5.3f ", hist_all[i] / float(sum_all));
}
printf("\n");
LLAMA_LOG_INFO("\n");
}
}
}
@ -3201,8 +3216,8 @@ struct llama_model * llama_load_model_from_file(
params.main_gpu, params.tensor_split, params.mul_mat_q, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback,
params.progress_callback_user_data)) {
LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
delete model;
fprintf(stderr, "%s: failed to load model\n", __func__);
return nullptr;
}
@ -3235,10 +3250,9 @@ struct llama_context * llama_new_context_with_model(
unsigned percentage = (unsigned) (100 * progress);
while (percentage > *cur_percentage_p) {
*cur_percentage_p = percentage;
fprintf(stderr, ".");
fflush(stderr);
LLAMA_LOG_INFO(".");
if (percentage >= 100) {
fprintf(stderr, "\n");
LLAMA_LOG_INFO("\n");
}
}
};
@ -3252,14 +3266,14 @@ struct llama_context * llama_new_context_with_model(
// reserve memory for context buffers
if (!params.vocab_only) {
if (!kv_cache_init(ctx->model.hparams, ctx->kv_self, memory_type, ctx->model.hparams.n_ctx, params.n_gpu_layers)) {
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
LLAMA_LOG_ERROR("%s: kv_cache_init() failed for self-attention cache\n", __func__);
llama_free(ctx);
return nullptr;
}
{
const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v);
fprintf(stderr, "%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
LLAMA_LOG_INFO("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
}
const auto & hparams = ctx->model.hparams;
@ -3293,14 +3307,14 @@ struct llama_context * llama_new_context_with_model(
// measure memory requirements for the graph
size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment;
fprintf(stderr, "%s: compute buffer total size = %7.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);
LLAMA_LOG_INFO("%s: compute buffer total size = %7.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);
// debug - for comparison with scratch buffer
//size_t prev_req =
// MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type) +
// MEM_REQ_SCRATCH1().at(ctx->model.type) +
// MEM_REQ_EVAL().at(ctx->model.type);
//fprintf(stderr, "%s: (debug) equivalent with scratch buffer = %7.2f MB\n", __func__, prev_req / 1024.0 / 1024.0);
//LLAMA_LOG_INFO("%s: (debug) equivalent with scratch buffer = %7.2f MB\n", __func__, prev_req / 1024.0 / 1024.0);
// recreate allocator with exact memory requirements
ggml_allocr_free(ctx->alloc);
@ -3336,13 +3350,13 @@ struct llama_context * llama_new_context_with_model(
const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx);
fprintf(stderr, "%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0);
LLAMA_LOG_INFO("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0);
#define LLAMA_METAL_CHECK_BUF(result) \
if (!(result)) { \
fprintf(stderr, "%s: failed to add buffer\n", __func__); \
llama_free(ctx); \
return NULL; \
#define LLAMA_METAL_CHECK_BUF(result) \
if (!(result)) { \
LLAMA_LOG_ERROR("%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, max_size));
@ -3396,19 +3410,19 @@ int llama_model_quantize(
llama_model_quantize_internal(fname_inp, fname_out, params);
return 0;
} catch (const std::exception & err) {
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.what());
LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
return 1;
}
}
int llama_apply_lora_from_file_internal(const struct llama_model & model, const char * path_lora, const char * path_base_model, int n_threads) {
fprintf(stderr, "%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
const int64_t t_start_lora_us = ggml_time_us();
auto fin = std::ifstream(path_lora, std::ios::binary);
if (!fin) {
fprintf(stderr, "%s: failed to open '%s'\n", __func__, path_lora);
LLAMA_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_lora);
return 1;
}
@ -3417,14 +3431,14 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
uint32_t magic;
fin.read((char *) &magic, sizeof(magic));
if (magic != LLAMA_FILE_MAGIC_GGLA) {
fprintf(stderr, "%s: bad file magic\n", __func__);
LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
return 1;
}
uint32_t format_version;
fin.read((char *) &format_version, sizeof(format_version));
if (format_version != 1) {
fprintf(stderr, "%s: unsupported file version\n", __func__ );
LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
return 1;
}
}
@ -3435,7 +3449,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
fin.read((char *) &lora_alpha, sizeof(lora_alpha));
float scaling = (float)lora_alpha / (float)lora_r;
fprintf(stderr, "%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
// create a temporary ggml context to store the lora tensors
@ -3461,7 +3475,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
ggml_context * base_ctx = NULL;
llama_buffer base_buf;
if (path_base_model) {
fprintf(stderr, "%s: loading base model from '%s'\n", __func__, path_base_model);
LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true));
size_t ctx_size;
@ -3518,17 +3532,17 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
const std::string lora_suffix = ".lora";
size_t pos = name.rfind(lora_suffix);
if (pos == std::string::npos) {
fprintf(stderr, "%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
return 1;
}
std::string lora_type = name.substr(pos + lora_suffix.length());
std::string base_name = name;
base_name.erase(pos);
// fprintf(stderr, "%s: %s => %s (lora type %s) ", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
// LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
if (model_tensors.find(base_name) == model_tensors.end()) {
fprintf(stderr, "%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
return 1;
}
@ -3539,7 +3553,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
case 1: wtype = GGML_TYPE_F16; break;
default:
{
fprintf(stderr, "%s: invalid tensor data type '%d'\n",
LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
__func__, ftype);
return false;
}
@ -3549,7 +3563,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
}
else {
fprintf(stderr, "%s: unsupported tensor dimension %d\n", __func__, n_dims);
LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
return 1;
}
ggml_set_name(lora_tensor, "lora_tensor");
@ -3587,7 +3601,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
if (model_loader) {
// load from base model
if (model_loader->tensors_map.name_to_idx.find(base_name) == model_loader->tensors_map.name_to_idx.end()) {
fprintf(stderr, "%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
return 1;
}
size_t idx = model_loader->tensors_map.name_to_idx[base_name];
@ -3603,8 +3617,8 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
if (ggml_is_quantized(base_t->type)) {
if (!warned) {
fprintf(stderr, "%s: warning: using a lora adapter with a quantized model may result in poor quality, "
"use a f16 or f32 base model with --lora-base\n", __func__);
LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
"use a f16 or f32 base model with --lora-base\n", __func__);
warned = true;
}
}
@ -3618,8 +3632,8 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
ggml_set_name(loraB, "loraB");
if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
fprintf(stderr, "%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
" are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
" are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
return 1;
}
@ -3664,7 +3678,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
n_tensors++;
if (n_tensors % 4 == 0) {
fprintf(stderr, ".");
LLAMA_LOG_INFO(".");
}
}
}
@ -3676,7 +3690,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
}
const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
fprintf(stderr, " done (%.2f ms)\n", t_lora_us / 1000.0);
LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
return 0;
}
@ -3685,7 +3699,7 @@ int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lor
try {
return llama_apply_lora_from_file_internal(ctx->model, path_lora, path_base_model, n_threads);
} catch (const std::exception & err) {
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what());
LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
return 1;
}
}
@ -3694,7 +3708,7 @@ int llama_model_apply_lora_from_file(const struct llama_model * model, const cha
try {
return llama_apply_lora_from_file_internal(*model, path_lora, path_base_model, n_threads);
} catch (const std::exception & err) {
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what());
LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
return 1;
}
}
@ -3976,7 +3990,7 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c
const uint32_t version = file.read_u32();
if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
fprintf(stderr, "%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
LLAMA_LOG_ERROR("%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
return false;
}
@ -3984,7 +3998,7 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c
file.read_raw(&session_hparams, sizeof(llama_hparams));
if (session_hparams != ctx->model.hparams) {
fprintf(stderr, "%s : model hparams didn't match from session file!\n", __func__);
LLAMA_LOG_INFO("%s : model hparams didn't match from session file!\n", __func__);
return false;
}
}
@ -3994,7 +4008,7 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c
const uint32_t n_token_count = file.read_u32();
if (n_token_count > n_token_capacity) {
fprintf(stderr, "%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
LLAMA_LOG_ERROR("%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
return false;
}
@ -4008,7 +4022,7 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c
const size_t n_state_size_max = llama_get_state_size(ctx);
if (n_state_size_cur > n_state_size_max) {
fprintf(stderr, "%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
return false;
}
@ -4025,7 +4039,7 @@ bool llama_load_session_file(struct llama_context * ctx, const char * path_sessi
try {
return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
} catch (const std::exception & err) {
fprintf(stderr, "error loading session file: %s\n", err.what());
LLAMA_LOG_ERROR("error loading session file: %s\n", err.what());
return false;
}
}
@ -4056,7 +4070,7 @@ int llama_eval(
int n_past,
int n_threads) {
if (!llama_eval_internal(*ctx, tokens, nullptr, n_tokens, n_past, n_threads, nullptr)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
LLAMA_LOG_ERROR("%s: failed to eval\n", __func__);
return 1;
}
@ -4078,7 +4092,7 @@ int llama_eval_embd(
int n_past,
int n_threads) {
if (!llama_eval_internal(*ctx, nullptr, embd, n_tokens, n_past, n_threads, nullptr)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
LLAMA_LOG_ERROR("%s: failed to eval\n", __func__);
return 1;
}
@ -4099,7 +4113,7 @@ int llama_eval_export(struct llama_context * ctx, const char * fname) {
const std::vector<llama_token> tmp(n_batch, llama_token_bos());
if (!llama_eval_internal(*ctx, tmp.data(), nullptr, tmp.size(), n_ctx, 1, fname)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
LLAMA_LOG_ERROR("%s: failed to eval\n", __func__);
return 1;
}
@ -4115,7 +4129,7 @@ int llama_tokenize_with_model(
auto res = llama_tokenize(model->vocab, text, add_bos);
if (n_max_tokens < (int) res.size()) {
fprintf(stderr, "%s: too many tokens\n", __func__);
LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
return -((int) res.size());
}
@ -4232,15 +4246,15 @@ struct llama_timings llama_get_timings(struct llama_context * ctx) {
void llama_print_timings(struct llama_context * ctx) {
const llama_timings timings = llama_get_timings(ctx);
fprintf(stderr, "\n");
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, timings.t_load_ms);
fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
LLAMA_LOG_INFO("\n");
LLAMA_LOG_INFO("%s: load time = %8.2f ms\n", __func__, timings.t_load_ms);
LLAMA_LOG_INFO("%s: sample time = %8.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample);
fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
LLAMA_LOG_INFO("%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
LLAMA_LOG_INFO("%s: eval time = %8.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms));
LLAMA_LOG_INFO("%s: total time = %8.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms));
}
void llama_reset_timings(struct llama_context * ctx) {
@ -4276,3 +4290,44 @@ const char * llama_print_system_info(void) {
const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) {
return ctx->model.tensors_by_name;
}
void llama_log_set(llama_log_callback log_callback, void * user_data) {
g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
g_state.log_callback_user_data = user_data;
}
#if defined(_MSC_VER) && !defined(vsnprintf)
#define vsnprintf _vsnprintf
#endif
static void llama_log_internal_v(llama_log_level level, const char * format, va_list args) {
va_list args_copy;
va_copy(args_copy, args);
char buffer[128];
int len = vsnprintf(buffer, 128, format, args);
if (len < 128) {
g_state.log_callback(level, buffer, g_state.log_callback_user_data);
} else {
char* buffer2 = new char[len+1];
vsnprintf(buffer2, len+1, format, args_copy);
buffer2[len] = 0;
g_state.log_callback(level, buffer2, g_state.log_callback_user_data);
delete[] buffer2;
}
va_end(args_copy);
}
static void llama_log_internal(llama_log_level level, const char * format, ...) {
va_list args;
va_start(args, format);
llama_log_internal_v(level, format, args);
va_end(args);
}
static void llama_log_callback_default(llama_log_level level, const char * text, void * user_data) {
(void) level;
(void) user_data;
fputs(text, stderr);
fflush(stderr);
}

19
llama.h
View File

@ -86,7 +86,20 @@ extern "C" {
typedef void (*llama_progress_callback)(float progress, void *ctx);
struct llama_context_params {
enum llama_log_level {
LLAMA_LOG_LEVEL_ERROR = 2,
LLAMA_LOG_LEVEL_WARN = 3,
LLAMA_LOG_LEVEL_INFO = 4
};
// Signature for logging events
// Note that text includes the new line character at the end for most events.
// If your logging mechanism cannot handle that, check if the last character is '\n' and strip it
// if it exists.
// It might not exist for progress report where '.' is output repeatedly.
typedef void (*llama_log_callback)(llama_log_level level, const char * text, void * user_data);
struct llama_context_params {
uint32_t seed; // RNG seed, -1 for random
int32_t n_ctx; // text context
int32_t n_batch; // prompt processing batch size
@ -195,6 +208,10 @@ extern "C" {
int32_t n_eval;
};
// Set callback for all future logging events.
// If this is not called, or NULL is supplied, everything is output on stderr.
LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
LLAMA_API int llama_max_devices();
LLAMA_API struct llama_context_params llama_context_default_params();

1
tests/CMakeLists.txt
View File

@ -11,5 +11,6 @@ llama_add_test(test-quantize-fns.cpp)
llama_add_test(test-quantize-perf.cpp)
llama_add_test(test-sampling.cpp)
llama_add_test(test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab.bin)
llama_add_test(test-grammar-parser.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/grammar-parser.cpp)
llama_add_test(test-grad0.cpp) # SLOW
# llama_add_test(test-opt.cpp) # SLOW

249
tests/test-grammar-parser.cpp Normal file
View File

@ -0,0 +1,249 @@
#ifdef NDEBUG
#undef NDEBUG
#endif
#include "llama.h"
#include "examples/grammar-parser.cpp"
#include <cassert>
int main()
{
grammar_parser::parse_state parsed_grammar;
const char *grammar_bytes = R"""(root ::= (expr "=" term "\n")+
expr ::= term ([-+*/] term)*
term ::= [0-9]+)""";
parsed_grammar = grammar_parser::parse(grammar_bytes);
std::vector<std::pair<std::string, uint32_t>> expected = {
{"expr", 2},
{"expr_5", 5},
{"expr_6", 6},
{"root", 0},
{"root_1", 1},
{"root_4", 4},
{"term", 3},
{"term_7", 7},
};
uint32_t index = 0;
for (auto it = parsed_grammar.symbol_ids.begin(); it != parsed_grammar.symbol_ids.end(); ++it)
{
std::string key = it->first;
uint32_t value = it->second;
std::pair<std::string, uint32_t> expected_pair = expected[index];
// pretty print error message before asserting
if (expected_pair.first != key || expected_pair.second != value)
{
fprintf(stderr, "expected_pair: %s, %d\n", expected_pair.first.c_str(), expected_pair.second);
fprintf(stderr, "actual_pair: %s, %d\n", key.c_str(), value);
fprintf(stderr, "expected_pair != actual_pair\n");
}
assert(expected_pair.first == key && expected_pair.second == value);
index++;
}
std::vector<llama_grammar_element> expected_rules = {
{LLAMA_GRETYPE_RULE_REF, 4},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 2},
{LLAMA_GRETYPE_CHAR, 61},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_CHAR, 10},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 3},
{LLAMA_GRETYPE_RULE_REF, 6},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 1},
{LLAMA_GRETYPE_RULE_REF, 4},
{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, 3},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_RULE_REF, 5},
{LLAMA_GRETYPE_RULE_REF, 6},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_END, 0},
{LLAMA_GRETYPE_CHAR, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_RULE_REF, 7},
{LLAMA_GRETYPE_ALT, 0},
{LLAMA_GRETYPE_CHAR, 48},
{LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
{LLAMA_GRETYPE_END, 0},
};
index = 0;
for (auto rule : parsed_grammar.rules)
{
// compare rule to expected rule
for (uint32_t i = 0; i < rule.size(); i++)
{
llama_grammar_element element = rule[i];
llama_grammar_element expected_element = expected_rules[index];
// 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++;
}
}
const char *longer_grammar_bytes = R"""(
root ::= (expr "=" ws term "\n")+
expr ::= term ([-+*/] term)*
term ::= ident | num | "(" ws expr ")" ws
ident ::= [a-z] [a-z0-9_]* ws
num ::= [0-9]+ ws
ws ::= [ \t\n]*
)""";
parsed_grammar = grammar_parser::parse(longer_grammar_bytes);
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},
};
index = 0;
for (auto it = parsed_grammar.symbol_ids.begin(); it != parsed_grammar.symbol_ids.end(); ++it)
{
std::string key = it->first;
uint32_t value = it->second;
std::pair<std::string, uint32_t> expected_pair = expected[index];
// pretty print error message before asserting
if (expected_pair.first != key || expected_pair.second != value)
{
fprintf(stderr, "expected_pair: %s, %d\n", expected_pair.first.c_str(), expected_pair.second);
fprintf(stderr, "actual_pair: %s, %d\n", key.c_str(), value);
fprintf(stderr, "expected_pair != actual_pair\n");
}
assert(expected_pair.first == key && expected_pair.second == value);
index++;
}
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},
};
index = 0;
for (auto rule : parsed_grammar.rules)
{
// compare rule to expected rule
for (uint32_t i = 0; i < rule.size(); i++)
{
llama_grammar_element element = rule[i];
llama_grammar_element expected_element = expected_rules[index];
// 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++;
}
}
return 0;
}