mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 11:24:35 +00:00
b9fd7eee57
* ggml : remove Q4_0 bit shufling (ARM NEON)
* ggml : remove Q4_1 bit shuffling (ARM NEON + reference)
* ggml : nibbles_from_floats() + bytes_from_nibbles() (ARM NEON)
* ggml : remove Q4_2 bit shuffling (WIP, BROKEN)
* ggml : remove Q5_0 bit shuffling (ARM NEON)
* ggml : 2x faster scalar implementations
* ggml : remove Q5_1 bit shuffling (ARM NEON + scalar)
* ggml : simplify scalar dot
* ggml : remove WASM SIMD bit shuffling + remove vzip for ARM 32-bit
* ggml : fix Q4_1 quantization
* ggml : update cuBLAS + normalize variable names
* ggml : remove Q4_2 mode
* ggml : minor formatting
* ggml : fix Q5_0 quantization
* scripts : add script for measuring the time per token
* AVX implementations (#1370)
* ggml : uniform 5th bit extraction
* llama : produce error upon loading old model files
* llama : fix model magic/version write
* ggml : speed-up Q5_0 + Q5_1 at 4 threads
* ggml : preserve old Q4 and Q5 formats
* ggml : simplify Q8_1 - no need for low / high sums anymore
* ggml : fix Q8_0 and Q8_1 rounding
* Revert "AVX implementations (#1370)"
This reverts commit 948d124837
.
* ggml : fix AVX2 implementation
* sha : update hashes for 7B and 13B
* readme : update timings + remove warning banner
* llama : update v2 PR number to 1405
* ggml : fix WASM comments
* ggml : back to original bit order
* readme : add note that Q4 and Q5 have been changed
* llama : fix return for unknown version
---------
Co-authored-by: Stephan Walter <stephan@walter.name>
146 lines
4.1 KiB
C++
146 lines
4.1 KiB
C++
#include "ggml.h"
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#include "llama.h"
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#include "build-info.h"
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#include <cstdio>
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#include <map>
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#include <string>
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static const std::map<std::string, llama_ftype> LLAMA_FTYPE_MAP = {
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{"q4_0", LLAMA_FTYPE_MOSTLY_Q4_0},
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{"q4_1", LLAMA_FTYPE_MOSTLY_Q4_1},
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{"q5_0", LLAMA_FTYPE_MOSTLY_Q5_0},
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{"q5_1", LLAMA_FTYPE_MOSTLY_Q5_1},
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{"q8_0", LLAMA_FTYPE_MOSTLY_Q8_0},
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};
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bool try_parse_ftype(const std::string & ftype_str, llama_ftype & ftype, std::string & ftype_str_out) {
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auto it = LLAMA_FTYPE_MAP.find(ftype_str);
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if (it != LLAMA_FTYPE_MAP.end()) {
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ftype = it->second;
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ftype_str_out = it->first;
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return true;
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}
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// try to parse as an integer
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try {
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int ftype_int = std::stoi(ftype_str);
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for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
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if (it->second == ftype_int) {
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ftype = it->second;
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ftype_str_out = it->first;
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return true;
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}
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}
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}
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catch (...) {
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// stoi failed
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}
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return false;
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}
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// usage:
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// ./quantize models/llama/ggml-model.bin [models/llama/ggml-model-quant.bin] type [nthreads]
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//
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int main(int argc, char ** argv) {
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ggml_time_init();
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if (argc < 3) {
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fprintf(stderr, "usage: %s model-f32.bin [model-quant.bin] type [nthreads]\n", argv[0]);
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for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
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fprintf(stderr, " type = \"%s\" or %d\n", it->first.c_str(), it->second);
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}
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return 1;
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}
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// needed to initialize f16 tables
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{
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struct ggml_init_params params = { 0, NULL, false };
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struct ggml_context * ctx = ggml_init(params);
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ggml_free(ctx);
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}
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// parse command line arguments
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const std::string fname_inp = argv[1];
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std::string fname_out;
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int nthread;
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llama_ftype ftype;
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int arg_idx = 2;
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std::string ftype_str;
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if (try_parse_ftype(argv[arg_idx], ftype, ftype_str)) {
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// argv[2] is the ftype
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std::string fpath;
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const size_t pos = fname_inp.find_last_of('/');
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if (pos != std::string::npos) {
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fpath = fname_inp.substr(0, pos + 1);
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}
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// export as [inp path]/ggml-model-[ftype].bin
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fname_out = fpath + "ggml-model-" + ftype_str + ".bin";
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arg_idx++;
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}
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else {
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// argv[2] is the output path
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fname_out = argv[arg_idx];
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arg_idx++;
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if (argc <= arg_idx) {
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fprintf(stderr, "%s: missing ftype\n", __func__);
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return 1;
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}
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// argv[3] is the ftype
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if (!try_parse_ftype(argv[arg_idx], ftype, ftype_str)) {
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fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]);
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return 1;
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}
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arg_idx++;
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}
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// parse nthreads
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if (argc > arg_idx) {
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try {
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nthread = std::stoi(argv[arg_idx]);
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}
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catch (const std::exception & e) {
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fprintf(stderr, "%s: invalid nthread '%s' (%s)\n", __func__, argv[arg_idx], e.what());
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return 1;
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}
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} else {
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nthread = 0;
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}
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fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
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fprintf(stderr, "%s: quantizing '%s' to '%s' as %s", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str());
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if (nthread > 0) {
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fprintf(stderr, " using %d threads", nthread);
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}
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fprintf(stderr, "\n");
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const int64_t t_main_start_us = ggml_time_us();
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int64_t t_quantize_us = 0;
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// load the model
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{
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const int64_t t_start_us = ggml_time_us();
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if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype, nthread)) {
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fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
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return 1;
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}
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t_quantize_us = ggml_time_us() - t_start_us;
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}
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// report timing
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{
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const int64_t t_main_end_us = ggml_time_us();
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printf("\n");
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printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0);
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printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0);
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}
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return 0;
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}
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