mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-15 07:19:53 +00:00
245 lines
7.3 KiB
C++
245 lines
7.3 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "log.h"
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#include "llama.h"
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#include <algorithm>
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#include <cstdio>
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#include <string>
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#include <vector>
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static void print_usage(int, char ** argv) {
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LOG("\nexample usage:\n");
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LOG("\n %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]);
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LOG("\n");
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}
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int main(int argc, char ** argv) {
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gpt_log_init();
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gpt_params params;
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params.prompt = "Hello my name is";
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params.n_predict = 32;
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if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
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return 1;
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}
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// number of parallel batches
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int n_parallel = params.n_parallel;
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// total length of the sequences including the prompt
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int n_predict = params.n_predict;
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// init LLM
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llama_backend_init();
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llama_numa_init(params.numa);
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// initialize the model
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llama_model_params model_params = llama_model_params_from_gpt_params(params);
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llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
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if (model == NULL) {
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LOG_ERR("%s: error: unable to load model\n" , __func__);
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return 1;
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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(model, params.prompt, true);
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const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
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// initialize the context
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llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
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ctx_params.n_ctx = n_kv_req;
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ctx_params.n_batch = std::max(n_predict, n_parallel);
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llama_context * ctx = llama_new_context_with_model(model, ctx_params);
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auto sparams = llama_sampler_chain_default_params();
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llama_sampler * smpl = llama_sampler_chain_init(sparams);
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llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sparams.top_k));
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llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sparams.top_p, params.sparams.min_keep));
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llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sparams.temp));
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llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sparams.seed));
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if (ctx == NULL) {
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LOG_ERR("%s: error: failed to create the llama_context\n" , __func__);
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return 1;
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}
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const int n_ctx = llama_n_ctx(ctx);
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LOG_INF("\n%s: n_predict = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
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// make sure the KV cache is big enough to hold all the prompt and generated tokens
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if (n_kv_req > n_ctx) {
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LOG_ERR("%s: error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", __func__, n_kv_req);
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LOG_ERR("%s: either reduce n_parallel or increase n_ctx\n", __func__);
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return 1;
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}
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// print the prompt token-by-token
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LOG("\n");
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for (auto id : tokens_list) {
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LOG("%s", llama_token_to_piece(ctx, id).c_str());
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}
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// create a llama_batch
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// we use this object to submit token data for decoding
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llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t) n_parallel), 0, n_parallel);
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std::vector<llama_seq_id> seq_ids(n_parallel, 0);
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for (int32_t i = 0; i < n_parallel; ++i) {
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seq_ids[i] = i;
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}
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// evaluate the initial prompt
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for (size_t i = 0; i < tokens_list.size(); ++i) {
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llama_batch_add(batch, tokens_list[i], i, seq_ids, false);
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}
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GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());
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if (llama_model_has_encoder(model)) {
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if (llama_encode(ctx, batch)) {
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LOG_ERR("%s : failed to eval\n", __func__);
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return 1;
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}
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llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
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if (decoder_start_token_id == -1) {
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decoder_start_token_id = llama_token_bos(model);
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}
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llama_batch_clear(batch);
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llama_batch_add(batch, decoder_start_token_id, 0, seq_ids, false);
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}
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// llama_decode will output logits only for the last token of the prompt
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batch.logits[batch.n_tokens - 1] = true;
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if (llama_decode(ctx, batch) != 0) {
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LOG_ERR("%s: llama_decode() failed\n", __func__);
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return 1;
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}
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//// assign the system KV cache to all parallel sequences
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//// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
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//for (int32_t i = 1; i < n_parallel; ++i) {
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// llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
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//}
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if (n_parallel > 1) {
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LOG("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
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}
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// main loop
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// we will store the parallel decoded sequences in this vector
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std::vector<std::string> streams(n_parallel);
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// remember the batch index of the last token for each parallel sequence
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// we need this to determine which logits to sample from
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std::vector<int32_t> i_batch(n_parallel, batch.n_tokens - 1);
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int n_cur = batch.n_tokens;
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int n_decode = 0;
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const auto t_main_start = ggml_time_us();
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while (n_cur <= n_predict) {
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// prepare the next batch
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llama_batch_clear(batch);
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// sample the next token for each parallel sequence / stream
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for (int32_t i = 0; i < n_parallel; ++i) {
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if (i_batch[i] < 0) {
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// the stream has already finished
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continue;
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}
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const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]);
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// is it an end of generation? -> mark the stream as finished
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if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
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i_batch[i] = -1;
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LOG("\n");
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if (n_parallel > 1) {
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LOG_INF("%s: stream %d finished at n_cur = %d", __func__, i, n_cur);
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}
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continue;
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}
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// if there is only one stream, we print immediately to stdout
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if (n_parallel == 1) {
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LOG("%s", llama_token_to_piece(ctx, new_token_id).c_str());
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}
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streams[i] += llama_token_to_piece(ctx, new_token_id);
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i_batch[i] = batch.n_tokens;
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// push this new token for next evaluation
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llama_batch_add(batch, new_token_id, n_cur, { i }, true);
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n_decode += 1;
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}
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// all streams are finished
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if (batch.n_tokens == 0) {
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break;
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}
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n_cur += 1;
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// evaluate the current batch with the transformer model
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if (llama_decode(ctx, batch)) {
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LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1);
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return 1;
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}
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}
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if (n_parallel > 1) {
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LOG("\n");
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for (int32_t i = 0; i < n_parallel; ++i) {
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LOG("sequence %d:\n\n%s%s\n\n", i, params.prompt.c_str(), streams[i].c_str());
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}
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}
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const auto t_main_end = ggml_time_us();
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LOG_INF("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
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__func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
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LOG("\n");
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llama_perf_sampler_print(smpl);
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llama_perf_context_print(ctx);
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fprintf(stderr, "\n");
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llama_batch_free(batch);
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llama_sampler_free(smpl);
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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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