125 lines
3.4 KiB
C++
125 lines
3.4 KiB
C++
#include "common.h"
|
|
#include "llama.h"
|
|
|
|
#include <cmath>
|
|
#include <cstdio>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
int main(int argc, char ** argv) {
|
|
gpt_params params;
|
|
|
|
if (argc == 1 || argv[1][0] == '-') {
|
|
printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]);
|
|
return 1 ;
|
|
}
|
|
|
|
if (argc >= 2) {
|
|
params.model = argv[1];
|
|
}
|
|
|
|
if (argc >= 3) {
|
|
params.prompt = argv[2];
|
|
}
|
|
|
|
if (params.prompt.empty()) {
|
|
params.prompt = "Hello my name is";
|
|
}
|
|
|
|
// init LLM
|
|
|
|
llama_backend_init(params.numa);
|
|
|
|
llama_context_params ctx_params = llama_context_default_params();
|
|
|
|
llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params);
|
|
|
|
if (model == NULL) {
|
|
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
|
|
return 1;
|
|
}
|
|
|
|
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
|
|
|
|
// tokenize the prompt
|
|
|
|
std::vector<llama_token> tokens_list;
|
|
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;
|
|
|
|
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");
|
|
|
|
for (auto id : tokens_list) {
|
|
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
|
|
}
|
|
|
|
fflush(stderr);
|
|
|
|
// 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.
|
|
|
|
const int n_gen = std::min(32, max_context_size);
|
|
|
|
while (llama_get_kv_cache_token_count(ctx) < n_gen) {
|
|
// 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__);
|
|
return 1;
|
|
}
|
|
|
|
tokens_list.clear();
|
|
|
|
// sample the next token
|
|
|
|
llama_token new_token_id = 0;
|
|
|
|
auto logits = llama_get_logits(ctx);
|
|
auto n_vocab = llama_n_vocab(ctx);
|
|
|
|
std::vector<llama_token_data> candidates;
|
|
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 });
|
|
}
|
|
|
|
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
|
|
|
new_token_id = llama_sample_token_greedy(ctx , &candidates_p);
|
|
|
|
// is it an end of stream ?
|
|
if (new_token_id == llama_token_eos(ctx)) {
|
|
fprintf(stderr, " [end of text]\n");
|
|
break;
|
|
}
|
|
|
|
// print the new token :
|
|
printf("%s", llama_token_to_piece(ctx, new_token_id).c_str());
|
|
fflush(stdout);
|
|
|
|
// push this new token for next evaluation
|
|
tokens_list.push_back(new_token_id);
|
|
}
|
|
|
|
llama_free(ctx);
|
|
llama_free_model(model);
|
|
|
|
llama_backend_free();
|
|
|
|
fprintf(stderr, "\n\n");
|
|
|
|
return 0;
|
|
}
|