mirror of
https://github.com/ggerganov/llama.cpp.git
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edd4c14817
* tests : write a Python tokenizer test (wip) * llama : prefix input text for tokenization with whitespace * llama : distinguish pieces from decoded text + fix detokenization * common : add comments * examples : no longer manually add leading space when tokenizing * tests : use Python to generate tokenizer tests for C++ * tests : add option to tokenize text files ggml-ci * tests : add test-tokenizer-1.py * llama.cpp : fix LF token * hellaswag : move the concat space for clarity * tests : add falcon tests (py + cpp, currently do not pass Unicode) ggml-ci * common : temporary separate llama_detokenize calls for SPM and BPE --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
131 lines
3.5 KiB
C++
131 lines
3.5 KiB
C++
#ifndef _GNU_SOURCE
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#define _GNU_SOURCE
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#endif
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#include "build-info.h"
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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 <string>
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#include <vector>
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int main(int argc, char ** argv) {
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gpt_params params;
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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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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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params.prompt = argv[2];
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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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// init LLM
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llama_backend_init(params.numa);
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llama_context_params ctx_params = llama_context_default_params();
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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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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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// 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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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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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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for (auto id : tokens_list) {
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fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
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}
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fflush(stderr);
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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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const int n_gen = std::min(32, max_context_size);
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while (llama_get_kv_cache_token_count(ctx) < n_gen) {
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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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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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// 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);
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std::vector<llama_token_data> candidates;
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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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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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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(ctx)) {
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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_piece(ctx, new_token_id).c_str());
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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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}
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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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fprintf(stderr, "\n\n");
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return 0;
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}
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