vbatts/llama.cpp
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Concedo 2023-04-11 23:38:15 +08:00
commit 9245c7d7d0
7 changed files with 96 additions and 61 deletions
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5
examples/main/main.cpp
View file

@ -1,3 +1,8 @@
// Defines sigaction on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif
#include "common.h"
#include "llama.h"

10
examples/quantize/quantize.cpp
View file

@ -5,15 +5,15 @@
#include <string>
// usage:
// ./llama-quantize models/llama/ggml-model.bin models/llama/ggml-model-quant.bin type
// ./quantize models/llama/ggml-model.bin models/llama/ggml-model-quant.bin type
//
int main(int argc, char ** argv) {
ggml_time_init();
if (argc != 4) {
fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]);
fprintf(stderr, " type = 2 - q4_0\n");
fprintf(stderr, " type = 3 - q4_1\n");
fprintf(stderr, " type = %d - q4_0\n", LLAMA_FTYPE_MOSTLY_Q4_0);
fprintf(stderr, " type = %d - q4_1\n", LLAMA_FTYPE_MOSTLY_Q4_1);
return 1;
}
@ -27,7 +27,7 @@ int main(int argc, char ** argv) {
const std::string fname_inp = argv[1];
const std::string fname_out = argv[2];
const int itype = atoi(argv[3]);
const enum llama_ftype ftype = (enum llama_ftype)atoi(argv[3]);
const int64_t t_main_start_us = ggml_time_us();
@ -37,7 +37,7 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_us = ggml_time_us();
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype)) {
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}

39
ggml.c
View file

@ -1,4 +1,4 @@
// Defines CLOCK_MONOTONIC and asprintf on Linux
// Defines CLOCK_MONOTONIC on Linux
#define _GNU_SOURCE
#include "ggml.h"
@ -50,6 +50,7 @@ typedef HANDLE pthread_t;
typedef DWORD thread_ret_t;
static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void*), void* arg) {
(void) unused;
HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL);
if (handle == NULL)
{
@ -61,6 +62,7 @@ static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void
}
static int pthread_join(pthread_t thread, void* unused) {
(void) unused;
return (int) WaitForSingleObject(thread, INFINITE);
}
@ -2558,29 +2560,26 @@ inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x
//
static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = {
QK,
QK,
1,
1,
1,
1,
1,
[GGML_TYPE_F32] = 1,
[GGML_TYPE_F16] = 1,
[GGML_TYPE_Q4_0] = QK,
[GGML_TYPE_Q4_1] = QK,
[GGML_TYPE_I8] = 1,
[GGML_TYPE_I16] = 1,
[GGML_TYPE_I32] = 1,
};
static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_COUNT != 5");
static_assert(GGML_TYPE_COUNT == 7, "GGML_BLCK_SIZE is outdated");
static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
sizeof(block_q4_0),
sizeof(block_q4_1),
sizeof(int8_t ),
sizeof(int16_t),
sizeof(int32_t),
sizeof(ggml_fp16_t),
sizeof(float ),
[GGML_TYPE_F32] = sizeof(float),
[GGML_TYPE_F16] = sizeof(ggml_fp16_t),
[GGML_TYPE_Q4_0] = sizeof(block_q4_0),
[GGML_TYPE_Q4_1] = sizeof(block_q4_1),
[GGML_TYPE_I8] = sizeof(int8_t),
[GGML_TYPE_I16] = sizeof(int16_t),
[GGML_TYPE_I32] = sizeof(int32_t),
};
// don't forget to update the array above when adding new types
static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_COUNT != 5");
static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_SIZE is outdated");
static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"NONE",

9
ggml.h
View file

@ -198,13 +198,14 @@ struct ggml_object;
struct ggml_context;
enum ggml_type {
GGML_TYPE_Q4_0,
GGML_TYPE_Q4_1,
// explicitly numbered values are used in llama.cpp files
GGML_TYPE_F32 = 0,
GGML_TYPE_F16 = 1,
GGML_TYPE_Q4_0 = 2,
GGML_TYPE_Q4_1 = 3,
GGML_TYPE_I8,
GGML_TYPE_I16,
GGML_TYPE_I32,
GGML_TYPE_F16,
GGML_TYPE_F32,
GGML_TYPE_COUNT,
};

72
llama.cpp
View file

@ -1,3 +1,8 @@
// Defines fileno on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif
#include "llama_util.h"
#include "llama.h"
#include "llama_internal.h"
@ -77,7 +82,7 @@ struct llama_hparams {
uint32_t n_head = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
uint32_t f16 = 1;
enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
bool operator!=(const llama_hparams & other) const {
return memcmp(this, &other, sizeof(llama_hparams));
@ -427,7 +432,7 @@ struct llama_file_loader {
hparams.n_head = file.read_u32();
hparams.n_layer = file.read_u32();
hparams.n_rot = file.read_u32();
hparams.f16 = file.read_u32();
hparams.ftype = (enum llama_ftype) file.read_u32();
}
void read_vocab() {
vocab.id_to_token.resize(hparams.n_vocab);
@ -460,20 +465,21 @@ struct llama_file_loader {
llama_load_tensor_shard shard;
uint32_t n_dims = file.read_u32();
uint32_t name_len = file.read_u32();
uint32_t ftype = file.read_u32();
shard.type = (enum ggml_type) file.read_u32();
shard.ne.resize(n_dims);
file.read_raw(shard.ne.data(), sizeof(shard.ne[0]) * n_dims);
std::string name = file.read_string(name_len);
if (n_dims < 1 || n_dims > 2) {
throw format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims);
}
switch (ftype) {
case 0: shard.type = GGML_TYPE_F32; break;
case 1: shard.type = GGML_TYPE_F16; break;
case 2: shard.type = GGML_TYPE_Q4_0; break;
case 3: shard.type = GGML_TYPE_Q4_1; break;
switch (shard.type) {
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
break;
default: {
throw format("unrecognized ftype %u\n", ftype);
throw format("unrecognized tensor type %u\n", shard.type);
}
}
@ -504,18 +510,18 @@ struct llama_file_loader {
struct llama_file_saver {
llama_file file;
llama_file_loader * any_file_loader;
llama_file_saver(const char * fname, llama_file_loader * any_file_loader, uint32_t new_f16)
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);
write_magic();
write_hparams(new_f16);
write_hparams(new_ftype);
write_vocab();
}
void write_magic() {
file.write_u32('ggjt'); // magic
file.write_u32(1); // version
}
void write_hparams(uint32_t new_f16) {
void write_hparams(enum llama_ftype new_ftype) {
const llama_hparams & hparams = any_file_loader->hparams;
file.write_u32(hparams.n_vocab);
file.write_u32(hparams.n_embd);
@ -523,7 +529,7 @@ struct llama_file_saver {
file.write_u32(hparams.n_head);
file.write_u32(hparams.n_layer);
file.write_u32(hparams.n_rot);
file.write_u32(new_f16);
file.write_u32(new_ftype);
}
void write_vocab() {
if (any_file_loader->file_version == LLAMA_FILE_VERSION_GGML) {
@ -538,17 +544,17 @@ struct llama_file_saver {
}
}
void write_tensor(llama_load_tensor & tensor, enum ggml_type new_type, const void * new_data, size_t new_size) {
uint32_t ftype;
switch (new_type) {
case GGML_TYPE_F32: ftype = 0; break;
case GGML_TYPE_F16: ftype = 1; break;
case GGML_TYPE_Q4_0: ftype = 2; break;
case GGML_TYPE_Q4_1: ftype = 3; break;
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
break;
default: LLAMA_ASSERT(false);
}
file.write_u32((uint32_t) tensor.ne.size());
file.write_u32((uint32_t) tensor.name.size());
file.write_u32(ftype);
file.write_u32(new_type);
file.write_raw(tensor.ne.data(), sizeof(tensor.ne[0]) * tensor.ne.size());
file.write_raw(tensor.name.data(), tensor.name.size());
file.seek(-file.tell() & 31, SEEK_CUR);
@ -822,6 +828,16 @@ static const char *llama_file_version_name(llama_file_version version) {
}
}
static const char *llama_ftype_name(enum llama_ftype ftype) {
switch (ftype) {
case LLAMA_FTYPE_ALL_F32: return "all F32";
case LLAMA_FTYPE_MOSTLY_F16: return "mostly F16";
case LLAMA_FTYPE_MOSTLY_Q4_0: return "mostly Q4_0";
case LLAMA_FTYPE_MOSTLY_Q4_1: return "mostly Q4_1";
default: LLAMA_ASSERT(false);
}
}
static const char *llama_model_type_name(e_model type) {
switch (type) {
case MODEL_7B: return "7B";
@ -874,7 +890,7 @@ static void llama_model_load_internal(
fprintf(stderr, "%s: n_head = %u\n", __func__, hparams.n_head);
fprintf(stderr, "%s: n_layer = %u\n", __func__, hparams.n_layer);
fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_rot);
fprintf(stderr, "%s: f16 = %u\n", __func__, hparams.f16);
fprintf(stderr, "%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff);
fprintf(stderr, "%s: n_parts = %zu\n", __func__, ml->file_loaders.size());
fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type));
@ -1546,17 +1562,17 @@ static llama_vocab::id llama_sample_top_p_top_k(
// quantization
//
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, int itype) {
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, enum llama_ftype ftype) {
ggml_type quantized_type;
switch (itype) {
case 2: quantized_type = GGML_TYPE_Q4_0; break;
case 3: quantized_type = GGML_TYPE_Q4_1; break;
default: throw format("invalid quantization type %d\n", itype);
switch (ftype) {
case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
default: throw format("invalid output file type %d\n", ftype);
};
std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp.c_str(), /*use_mmap*/ false,
/*vocab_only*/ false));
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), (uint32_t) itype);
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), ftype);
size_t total_size_org = 0;
size_t total_size_new = 0;
@ -1747,9 +1763,9 @@ void llama_free(struct llama_context * ctx) {
int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
int itype) {
enum llama_ftype ftype) {
try {
llama_model_quantize_internal(fname_inp, fname_out, itype);
llama_model_quantize_internal(fname_inp, fname_out, ftype);
return 0;
} catch (const std::string & err) {
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.c_str());

10
llama.h
View file

@ -65,6 +65,14 @@ extern "C" {
void * progress_callback_user_data;
};
// model file types
enum llama_ftype {
LLAMA_FTYPE_ALL_F32 = 0,
LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
};
LLAMA_API struct llama_context_params llama_context_default_params();
LLAMA_API bool llama_mmap_supported();
@ -85,7 +93,7 @@ extern "C" {
LLAMA_API int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
int itype);
enum llama_ftype ftype);
// Returns the KV cache that will contain the context for the
// ongoing prediction with the model.

12
llama_util.h
View file

@ -26,7 +26,9 @@
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#define NOMINMAX
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <io.h>
#include <stdio.h> // for _fseeki64
@ -209,6 +211,7 @@ struct llama_mmap {
throw format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str());
}
#if _WIN32_WINNT >= _WIN32_WINNT_WIN8
// Advise the kernel to preload the mapped memory
WIN32_MEMORY_RANGE_ENTRY range;
range.VirtualAddress = addr;
@ -217,6 +220,9 @@ struct llama_mmap {
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() {
@ -338,8 +344,8 @@ struct llama_mlock {
// Hopefully a megabyte is enough overhead:
size_t increment = size + 1048576;
// The minimum must be <= the maximum, so we need to increase both:
min_ws_size += size;
max_ws_size += size;
min_ws_size += increment;
max_ws_size += increment;
if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
llama_format_win_err(GetLastError()).c_str());