vbatts/llama.cpp
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llama : support requantizing models instead of only allowing quantization from 16/32bit (#1691)

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* Add support for quantizing already quantized models

* Threaded dequantizing and f16 to f32 conversion

* Clean up thread blocks with spares calculation a bit

* Use std::runtime_error exceptions.
This commit is contained in:
Kerfuffle 2023-06-10 01:59:17 -06:00 • committed by GitHub
parent ef3171d162
commit 4f0154b0ba
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3 changed files with 134 additions and 40 deletions
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57
examples/quantize/quantize.cpp
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@ -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;
}