vbatts/llama.cpp
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simple : minor style changes

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Georgi Gerganov 2023-08-14 12:56:48 +03:00
parent 5c5a95ba2d
commit 0c19ae70d5
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4 changed files with 92 additions and 202 deletions
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examples/simple/simple.cpp
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@ -2,180 +2,125 @@
#define _GNU_SOURCE
#endif
#include "common.h"
#include "llama.h"
#include "build-info.h"
#include <cassert>
#include <cinttypes>
#include "common.h"
#include "llama.h"
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined (_WIN32)
#define WIN32_LEAN_AND_MEAN
#define NOMINMAX
#include <windows.h>
#include <signal.h>
#endif
int main(int argc, char ** argv)
{
int main(int argc, char ** argv) {
gpt_params params;
//---------------------------------
// Print help :
//---------------------------------
if ( argc == 1 || argv[1][0] == '-' )
{
printf( "usage: %s MODEL_PATH [PROMPT]\n" , argv[0] );
if (argc == 1 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]);
return 1 ;
}
//---------------------------------
// Load parameters :
//---------------------------------
if ( argc >= 2 )
{
if (argc >= 2) {
params.model = argv[1];
}
if ( argc >= 3 )
{
if (argc >= 3) {
params.prompt = argv[2];
}
if ( params.prompt.empty() )
{
if (params.prompt.empty()) {
params.prompt = "Hello my name is";
}
//---------------------------------
// Init LLM :
//---------------------------------
// init LLM
llama_backend_init(params.numa);
llama_model * model;
llama_context * ctx;
std::tie(model, ctx) = llama_init_from_gpt_params( params );
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if ( model == NULL )
{
fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
if (model == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
return 1;
}
//---------------------------------
// Tokenize the prompt :
//---------------------------------
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize( ctx , params.prompt , true );
tokens_list = ::llama_tokenize(ctx, params.prompt, true);
const int max_context_size = llama_n_ctx( ctx );
const int max_tokens_list_size = max_context_size - 4 ;
const int max_context_size = llama_n_ctx(ctx);
const int max_tokens_list_size = max_context_size - 4;
if ( (int)tokens_list.size() > max_tokens_list_size )
{
fprintf( stderr , "%s: error: prompt too long (%d tokens, max %d)\n" ,
__func__ , (int)tokens_list.size() , max_tokens_list_size );
if ((int)tokens_list.size() > max_tokens_list_size) {
fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) tokens_list.size(), max_tokens_list_size);
return 1;
}
fprintf( stderr, "\n\n" );
fprintf(stderr, "\n\n");
// Print the tokens from the prompt :
for( auto id : tokens_list )
{
printf( "%s" , llama_token_to_str( ctx , id ) );
for (auto id : tokens_list) {
fprintf(stderr, "%s", llama_token_to_str(ctx, id));
}
fflush(stdout);
fflush(stderr);
//---------------------------------
// Main prediction loop :
//---------------------------------
// main loop
// The LLM keeps a contextual cache memory of previous token evaluation.
// Usually, once this cache is full, it is required to recompute a compressed context based on previous
// tokens (see "infinite text generation via context swapping" in the main example), but in this minimalist
// example, we will just stop the loop once this cache is full or once an end of stream is detected.
while ( llama_get_kv_cache_token_count( ctx ) < max_context_size )
{
//---------------------------------
// Evaluate the tokens :
//---------------------------------
while (llama_get_kv_cache_token_count( ctx ) < max_context_size) {
// evaluate the transformer
if ( llama_eval( ctx , tokens_list.data() , int(tokens_list.size()) , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
{
fprintf( stderr, "%s : failed to eval\n" , __func__ );
if (llama_eval(ctx, tokens_list.data(), int(tokens_list.size()), llama_get_kv_cache_token_count(ctx), params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
tokens_list.clear();
//---------------------------------
// Select the best prediction :
//---------------------------------
// sample the next token
llama_token new_token_id = 0;
auto logits = llama_get_logits( ctx );
auto n_vocab = llama_n_vocab( ctx ); // the size of the LLM vocabulary (in tokens)
auto logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
std::vector<llama_token_data> candidates;
candidates.reserve( n_vocab );
candidates.reserve(n_vocab);
for( llama_token token_id = 0 ; token_id < n_vocab ; token_id++ )
{
candidates.emplace_back( llama_token_data{ token_id , logits[ token_id ] , 0.0f } );
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
// Select it using the "Greedy sampling" method :
new_token_id = llama_sample_token_greedy( ctx , &candidates_p );
new_token_id = llama_sample_token_greedy(ctx , &candidates_p);
// is it an end of stream ?
if ( new_token_id == llama_token_eos() )
{
if (new_token_id == llama_token_eos()) {
fprintf(stderr, " [end of text]\n");
break;
}
// Print the new token :
printf( "%s" , llama_token_to_str( ctx , new_token_id ) );
fflush( stdout );
// print the new token :
printf("%s", llama_token_to_str(ctx, new_token_id));
fflush(stdout);
// Push this new token for next evaluation :
tokens_list.push_back( new_token_id );
// push this new token for next evaluation
tokens_list.push_back(new_token_id);
} // wend of main loop
}
llama_free( ctx );
llama_free_model( model );
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}
// EOF