mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-28 12:24:35 +00:00
simple : minor style changes
This commit is contained in:
Georgi Gerganov
parent
5c5a95ba2d
commit
0c19ae70d5
@ -2,17 +2,18 @@
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import gguf
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import gguf_namemap as tmap
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import os
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import sys
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import struct
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import json
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import numpy as np
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import torch
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from typing import Any, List
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from pathlib import Path
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import torch
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from sentencepiece import SentencePieceProcessor
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#NDArray = np.ndarray[Any, Any]
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# compatible with python < 3.9
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NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
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@ -268,7 +269,6 @@ for part_name in part_names:
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for name in model_part.keys():
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data = model_part[name]
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old_dtype = data.dtype
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# we don't need these
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@ -6,65 +6,32 @@
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#include "gguf-llama.h"
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#include "build-info.h"
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#include <cassert>
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#include <cinttypes>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <ctime>
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#include <fstream>
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#include <iostream>
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#include <string>
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#include <vector>
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#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
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#include <signal.h>
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#include <unistd.h>
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#elif defined (_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#define NOMINMAX
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#include <windows.h>
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#include <signal.h>
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#endif
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int main(int argc, char ** argv)
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{
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int main(int argc, char ** argv) {
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gpt_params params;
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//---------------------------------
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// Print help :
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//---------------------------------
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if ( argc == 1 || argv[1][0] == '-' )
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{
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printf( "usage: %s MODEL_PATH [PROMPT]\n" , argv[0] );
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if (argc == 1 || argv[1][0] == '-') {
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printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]);
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return 1 ;
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}
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//---------------------------------
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// Load parameters :
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//---------------------------------
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if ( argc >= 2 )
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{
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if (argc >= 2) {
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params.model = argv[1];
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}
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if ( argc >= 3 )
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{
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if (argc >= 3) {
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params.prompt = argv[2];
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}
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if ( params.prompt.empty() )
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{
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if (params.prompt.empty()) {
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params.prompt = "Hello my name is";
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}
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//---------------------------------
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// Init LLM :
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//---------------------------------
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// init LLM
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llama_backend_init(params.numa);
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@ -72,111 +39,88 @@ int main(int argc, char ** argv)
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llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params);
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if ( model == NULL )
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{
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fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
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if (model == NULL) {
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fprintf(stderr , "%s: error: unable to load model\n" , __func__);
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return 1;
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}
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llama_context * ctx = llama_new_context_with_model(model, ctx_params);
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//---------------------------------
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// Tokenize the prompt :
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//---------------------------------
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// tokenize the prompt
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std::vector<llama_token> tokens_list;
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tokens_list = ::llama_tokenize( ctx , params.prompt , true );
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tokens_list = ::llama_tokenize(ctx, params.prompt, true);
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const int max_context_size = llama_n_ctx( ctx );
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const int max_tokens_list_size = max_context_size - 4 ;
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const int max_context_size = llama_n_ctx(ctx);
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const int max_tokens_list_size = max_context_size - 4;
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if ( (int)tokens_list.size() > max_tokens_list_size )
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{
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fprintf( stderr , "%s: error: prompt too long (%d tokens, max %d)\n" ,
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__func__ , (int)tokens_list.size() , max_tokens_list_size );
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if ((int)tokens_list.size() > max_tokens_list_size) {
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fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) tokens_list.size(), max_tokens_list_size);
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return 1;
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}
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fprintf( stderr, "\n\n" );
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fprintf(stderr, "\n\n");
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// Print the tokens from the prompt :
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for( auto id : tokens_list )
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{
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printf( "%s" , llama_token_to_str( ctx , id ) );
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for (auto id : tokens_list) {
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fprintf(stderr, "%s", llama_token_to_str(ctx, id));
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}
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fflush(stdout);
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fflush(stderr);
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//---------------------------------
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// Main prediction loop :
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//---------------------------------
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// main loop
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// The LLM keeps a contextual cache memory of previous token evaluation.
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// Usually, once this cache is full, it is required to recompute a compressed context based on previous
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// tokens (see "infinite text generation via context swapping" in the main example), but in this minimalist
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// example, we will just stop the loop once this cache is full or once an end of stream is detected.
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while ( llama_get_kv_cache_token_count( ctx ) < max_context_size )
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{
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//---------------------------------
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// Evaluate the tokens :
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//---------------------------------
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while (llama_get_kv_cache_token_count(ctx) < max_context_size) {
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// evaluate the transformer
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if ( llama_eval( ctx , tokens_list.data() , int(tokens_list.size()) , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
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{
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fprintf( stderr, "%s : failed to eval\n" , __func__ );
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if (llama_eval(ctx, tokens_list.data(), int(tokens_list.size()), llama_get_kv_cache_token_count(ctx), params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
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}
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tokens_list.clear();
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//---------------------------------
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// Select the best prediction :
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//---------------------------------
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// sample the next token
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llama_token new_token_id = 0;
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auto logits = llama_get_logits( ctx );
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auto n_vocab = llama_n_vocab( ctx ); // the size of the LLM vocabulary (in tokens)
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auto logits = llama_get_logits(ctx);
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auto n_vocab = llama_n_vocab(ctx);
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std::vector<llama_token_data> candidates;
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candidates.reserve( n_vocab );
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candidates.reserve(n_vocab);
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for( llama_token token_id = 0 ; token_id < n_vocab ; token_id++ )
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{
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candidates.emplace_back( llama_token_data{ token_id , logits[ token_id ] , 0.0f } );
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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// Select it using the "Greedy sampling" method :
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new_token_id = llama_sample_token_greedy( ctx , &candidates_p );
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new_token_id = llama_sample_token_greedy(ctx , &candidates_p);
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// is it an end of stream ?
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if ( new_token_id == llama_token_eos() )
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{
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if (new_token_id == llama_token_eos()) {
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fprintf(stderr, " [end of text]\n");
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break;
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}
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// Print the new token :
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printf( "%s" , llama_token_to_str( ctx , new_token_id ) );
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fflush( stdout );
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// print the new token :
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printf("%s", llama_token_to_str(ctx, new_token_id));
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fflush(stdout);
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// Push this new token for next evaluation :
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tokens_list.push_back( new_token_id );
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// push this new token for next evaluation
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tokens_list.push_back(new_token_id);
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} // wend of main loop
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}
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llama_free( ctx );
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llama_free_model( model );
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llama_free(ctx);
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llama_free_model(model);
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llama_backend_free();
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return 0;
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}
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// EOF
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@ -2,180 +2,125 @@
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#define _GNU_SOURCE
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#endif
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#include "common.h"
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#include "llama.h"
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#include "build-info.h"
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#include <cassert>
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#include <cinttypes>
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#include "common.h"
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#include "llama.h"
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <ctime>
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#include <fstream>
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#include <iostream>
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#include <string>
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#include <vector>
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#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
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#include <signal.h>
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#include <unistd.h>
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#elif defined (_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#define NOMINMAX
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#include <windows.h>
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#include <signal.h>
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#endif
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int main(int argc, char ** argv)
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{
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int main(int argc, char ** argv) {
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gpt_params params;
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//---------------------------------
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// Print help :
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//---------------------------------
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if ( argc == 1 || argv[1][0] == '-' )
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{
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printf( "usage: %s MODEL_PATH [PROMPT]\n" , argv[0] );
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if (argc == 1 || argv[1][0] == '-') {
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printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]);
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return 1 ;
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}
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//---------------------------------
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// Load parameters :
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//---------------------------------
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if ( argc >= 2 )
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{
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if (argc >= 2) {
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params.model = argv[1];
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}
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if ( argc >= 3 )
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{
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if (argc >= 3) {
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params.prompt = argv[2];
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}
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if ( params.prompt.empty() )
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{
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if (params.prompt.empty()) {
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params.prompt = "Hello my name is";
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}
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//---------------------------------
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// Init LLM :
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//---------------------------------
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// init LLM
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llama_backend_init(params.numa);
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llama_model * model;
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llama_context * ctx;
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std::tie(model, ctx) = llama_init_from_gpt_params( params );
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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if ( model == NULL )
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{
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fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
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if (model == NULL) {
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fprintf(stderr, "%s: error: unable to load model\n", __func__);
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return 1;
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}
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//---------------------------------
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// Tokenize the prompt :
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//---------------------------------
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// tokenize the prompt
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std::vector<llama_token> tokens_list;
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tokens_list = ::llama_tokenize( ctx , params.prompt , true );
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tokens_list = ::llama_tokenize(ctx, params.prompt, true);
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const int max_context_size = llama_n_ctx( ctx );
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const int max_tokens_list_size = max_context_size - 4 ;
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const int max_context_size = llama_n_ctx(ctx);
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const int max_tokens_list_size = max_context_size - 4;
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if ( (int)tokens_list.size() > max_tokens_list_size )
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{
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fprintf( stderr , "%s: error: prompt too long (%d tokens, max %d)\n" ,
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__func__ , (int)tokens_list.size() , max_tokens_list_size );
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if ((int)tokens_list.size() > max_tokens_list_size) {
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fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) tokens_list.size(), max_tokens_list_size);
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return 1;
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}
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fprintf( stderr, "\n\n" );
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fprintf(stderr, "\n\n");
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// Print the tokens from the prompt :
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for( auto id : tokens_list )
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{
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printf( "%s" , llama_token_to_str( ctx , id ) );
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for (auto id : tokens_list) {
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fprintf(stderr, "%s", llama_token_to_str(ctx, id));
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}
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fflush(stdout);
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fflush(stderr);
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//---------------------------------
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// Main prediction loop :
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//---------------------------------
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// main loop
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// The LLM keeps a contextual cache memory of previous token evaluation.
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// Usually, once this cache is full, it is required to recompute a compressed context based on previous
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// tokens (see "infinite text generation via context swapping" in the main example), but in this minimalist
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// example, we will just stop the loop once this cache is full or once an end of stream is detected.
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while ( llama_get_kv_cache_token_count( ctx ) < max_context_size )
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{
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//---------------------------------
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// Evaluate the tokens :
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//---------------------------------
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while (llama_get_kv_cache_token_count( ctx ) < max_context_size) {
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// evaluate the transformer
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if ( llama_eval( ctx , tokens_list.data() , int(tokens_list.size()) , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
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{
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fprintf( stderr, "%s : failed to eval\n" , __func__ );
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if (llama_eval(ctx, tokens_list.data(), int(tokens_list.size()), llama_get_kv_cache_token_count(ctx), params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
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}
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tokens_list.clear();
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//---------------------------------
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// Select the best prediction :
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//---------------------------------
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// sample the next token
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llama_token new_token_id = 0;
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auto logits = llama_get_logits( ctx );
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auto n_vocab = llama_n_vocab( ctx ); // the size of the LLM vocabulary (in tokens)
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auto logits = llama_get_logits(ctx);
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auto n_vocab = llama_n_vocab(ctx);
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std::vector<llama_token_data> candidates;
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candidates.reserve( n_vocab );
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candidates.reserve(n_vocab);
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for( llama_token token_id = 0 ; token_id < n_vocab ; token_id++ )
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{
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candidates.emplace_back( llama_token_data{ token_id , logits[ token_id ] , 0.0f } );
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for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
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candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
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}
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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// Select it using the "Greedy sampling" method :
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new_token_id = llama_sample_token_greedy( ctx , &candidates_p );
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new_token_id = llama_sample_token_greedy(ctx , &candidates_p);
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// is it an end of stream ?
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if ( new_token_id == llama_token_eos() )
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{
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if (new_token_id == llama_token_eos()) {
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fprintf(stderr, " [end of text]\n");
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break;
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}
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// Print the new token :
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printf( "%s" , llama_token_to_str( ctx , new_token_id ) );
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fflush( stdout );
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// print the new token :
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printf("%s", llama_token_to_str(ctx, new_token_id));
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fflush(stdout);
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// Push this new token for next evaluation :
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tokens_list.push_back( new_token_id );
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// push this new token for next evaluation
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tokens_list.push_back(new_token_id);
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} // wend of main loop
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}
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llama_free( ctx );
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llama_free_model( model );
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llama_free(ctx);
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llama_free_model(model);
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llama_backend_free();
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return 0;
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}
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// EOF
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@ -5,7 +5,9 @@
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#ifndef GGUF_UTIL_H
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#define GGUF_UTIL_H
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#include "ggml.h"
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#include <cstdio>
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#include <cstdint>
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#include <cerrno>
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@ -62,7 +64,6 @@ static std::string format(const char * fmt, ...) {
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return std::string(buf.data(), size);
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}
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template<typename T>
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static std::string to_string(const T & val) {
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std::stringstream ss;
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