vbatts/llama.cpp
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Add support for quantizing already quantized models

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This commit is contained in:
KerfuffleV2 2023-06-04 11:33:18 -06:00
parent f1465624c2
commit 5c7b0e79a7
3 changed files with 85 additions and 38 deletions
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57
examples/quantize/quantize.cpp
View file

@ -3,6 +3,7 @@
#include "llama.h"
#include <cstdio>
#include <cstring>
#include <map>
#include <string>
@ -53,27 +54,49 @@ bool try_parse_ftype(const std::string & ftype_str, llama_ftype & ftype, std::st
// usage:
// ./quantize models/llama/ggml-model.bin [models/llama/ggml-model-quant.bin] type [nthreads]
//
void usage(const char * executable) {
fprintf(stderr, "usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.bin [model-quant.bin] type [nthreads]\n", executable);
fprintf(stderr, " --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
fprintf(stderr, " --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
fprintf(stderr, "Allowed quantization types:\n");
for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
fprintf(stderr, " type = \"%s\" or %d\n", it->first.c_str(), it->second);
}
exit(1);
}
int main(int argc, char ** argv) {
if (argc < 3) {
fprintf(stderr, "usage: %s model-f32.bin [model-quant.bin] type [nthreads]\n", argv[0]);
for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
fprintf(stderr, " type = \"%s\" or %d\n", it->first.c_str(), it->second);
usage(argv[0]);
}
llama_model_quantize_params params = llama_model_quantize_default_params();
int arg_idx = 1;
for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) {
params.quantize_output_tensor = false;
} else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) {
params.allow_requantize = true;
} else {
usage(argv[0]);
}
return 1;
}
if (argc - arg_idx < 3) {
usage(argv[0]);
}
llama_init_backend();
// parse command line arguments
const std::string fname_inp = argv[1];
const std::string fname_inp = argv[arg_idx];
arg_idx++;
std::string fname_out;
int nthread;
llama_ftype ftype;
int arg_idx = 2;
std::string ftype_str;
if (try_parse_ftype(argv[arg_idx], ftype, ftype_str)) {
// argv[2] is the ftype
if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
std::string fpath;
const size_t pos = fname_inp.find_last_of('/');
if (pos != std::string::npos) {
@ -84,7 +107,6 @@ int main(int argc, char ** argv) {
arg_idx++;
}
else {
// argv[2] is the output path
fname_out = argv[arg_idx];
arg_idx++;
@ -92,8 +114,7 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: missing ftype\n", __func__);
return 1;
}
// argv[3] is the ftype
if (!try_parse_ftype(argv[arg_idx], ftype, ftype_str)) {
if (!try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]);
return 1;
}
@ -103,21 +124,19 @@ int main(int argc, char ** argv) {
// parse nthreads
if (argc > arg_idx) {
try {
nthread = std::stoi(argv[arg_idx]);
params.nthread = std::stoi(argv[arg_idx]);
}
catch (const std::exception & e) {
fprintf(stderr, "%s: invalid nthread '%s' (%s)\n", __func__, argv[arg_idx], e.what());
return 1;
}
} else {
nthread = 0;
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
fprintf(stderr, "%s: quantizing '%s' to '%s' as %s", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str());
if (nthread > 0) {
fprintf(stderr, " using %d threads", nthread);
if (params.nthread > 0) {
fprintf(stderr, " using %d threads", params.nthread);
}
fprintf(stderr, "\n");
@ -129,7 +148,7 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_us = llama_time_us();
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype, nthread)) {
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), &params)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}

52
llama.cpp
View file

@ -865,6 +865,17 @@ struct llama_context_params llama_context_default_params() {
return result;
}
struct llama_model_quantize_params llama_model_quantize_default_params() {
struct llama_model_quantize_params result = {
/*.nthread =*/ 0,
/*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
/*.allow_requantize =*/ false,
/*.quantize_output_tensor =*/ true,
};
return result;
}
bool llama_mmap_supported() {
return llama_mmap::SUPPORTED;
}
@ -2112,9 +2123,12 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra
// quantization
//
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, enum llama_ftype ftype, int nthread) {
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
ggml_type quantized_type;
switch (ftype) {
llama_ftype ftype = params->ftype;
int nthread = params->nthread;
switch (params->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;
case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
@ -2140,7 +2154,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false,
/*vocab_only*/ false));
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), ftype);
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), params->ftype);
int n_attention_wv = 0;
int n_feed_forward_w2 = 0;
@ -2182,9 +2196,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
quantize &= (tensor.ne.size() == 2);
// uncomment this to keep the output layer in FP16
//if (tensor.name == "output.weight") {
// quantize = false;
//}
if (!params->quantize_output_tensor && tensor.name == "output.weight") {
quantize = false;
}
enum ggml_type new_type;
void * new_data;
@ -2222,17 +2236,26 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
float * f32_data;
size_t nelements = tensor.ne.at(0) * tensor.ne.at(1);
llama_buffer f32_conv_buf;
if (tensor.type == GGML_TYPE_F32) {
f32_data = (float *) tensor.data;
} else if (tensor.type == GGML_TYPE_F16) {
} else {
f32_conv_buf.resize(nelements * sizeof(float));
f32_data = (float *) f32_conv_buf.addr;
const auto * f16_data = (const ggml_fp16_t *) tensor.data;
for (size_t i = 0; i < nelements; i++) {
f32_data[i] = ggml_fp16_to_fp32(f16_data[i]);
if (tensor.type == GGML_TYPE_F16) {
const auto * f16_data = (const ggml_fp16_t *) tensor.data;
for (size_t i = 0; i < nelements; i++) {
f32_data[i] = ggml_fp16_to_fp32(f16_data[i]);
}
} else if (!params->allow_requantize) {
throw format("requantizing from type %s is disabled", ggml_type_name(tensor.type));
} else {
quantize_fns_t qtype = ggml_internal_get_quantize_fn(tensor.type);
if (qtype.dequantize_row_q == NULL) {
throw format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor.type));
}
qtype.dequantize_row_q((void *)tensor.data, f32_data, nelements);
}
} else {
throw std::runtime_error(format("type %s unsupported for integer quantization", ggml_type_name(tensor.type)));
}
printf("quantizing .. ");
@ -2429,10 +2452,9 @@ void llama_free(struct llama_context * ctx) {
int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
enum llama_ftype ftype,
int nthread) {
const llama_model_quantize_params *params) {
try {
llama_model_quantize_internal(fname_inp, fname_out, ftype, nthread);
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());

14
llama.h
View file

@ -105,7 +105,16 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_Q6_K = 18,// except 1d tensors
};
// model quantization parameters
typedef struct llama_model_quantize_params {
int nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
enum llama_ftype ftype; // quantize to this llama_ftype
bool allow_requantize; // allow quantizing non-f32/f16 tensors
bool quantize_output_tensor; // quantize output.weight
} llama_model_quantize_params;
LLAMA_API struct llama_context_params llama_context_default_params();
LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params();
LLAMA_API bool llama_mmap_supported();
LLAMA_API bool llama_mlock_supported();
@ -127,14 +136,11 @@ extern "C" {
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
// TODO: not great API - very likely to change
// Returns 0 on success
// nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
LLAMA_API int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
enum llama_ftype ftype,
int nthread);
const llama_model_quantize_params * params);
// Apply a LoRA adapter to a loaded model
// path_base_model is the path to a higher quality model to use as a base for