mirror of https://github.com/ggerganov/llama.cpp
263 lines
7.6 KiB
C++
263 lines
7.6 KiB
C++
#include "common.h"
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#include "llama.h"
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#include <algorithm>
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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] [PARALLEL] [LEN] [NGL]\n" , argv[0]);
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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 = 1;
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// total length of the sequences including the prompt
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int n_len = 32;
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// number of layers to offload to the GPU
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int n_gpu_layers = 0;
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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 (argc >= 4) {
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n_parallel = std::atoi(argv[3]);
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}
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if (argc >= 5) {
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n_len = std::atoi(argv[4]);
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}
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if (argc >= 6) {
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n_gpu_layers = std::atoi(argv[5]);
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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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process_escapes(params.prompt);
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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_default_params();
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model_params.n_gpu_layers = n_gpu_layers;
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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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fprintf(stderr , "%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_len - tokens_list.size())*n_parallel;
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// initialize the context
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llama_context_params ctx_params = llama_context_default_params();
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ctx_params.seed = 1234;
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ctx_params.n_ctx = n_kv_req;
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ctx_params.n_batch = std::max(n_len, n_parallel);
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ctx_params.n_seq_max = n_parallel;
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ctx_params.n_threads = params.n_threads;
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ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
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llama_context * ctx = llama_new_context_with_model(model, ctx_params);
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if (ctx == NULL) {
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fprintf(stderr , "%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_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, 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_TEE("%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_TEE("%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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fprintf(stderr, "\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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// 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, 1);
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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, { 0 }, false);
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}
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GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());
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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_TEE("%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_TEE("\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_len) {
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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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auto n_vocab = llama_n_vocab(model);
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auto * logits = llama_get_logits_ith(ctx, i_batch[i]);
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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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const int top_k = 40;
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const float top_p = 0.9f;
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const float temp = 0.4f;
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llama_sample_top_k(ctx, &candidates_p, top_k, 1);
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llama_sample_top_p(ctx, &candidates_p, top_p, 1);
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llama_sample_temp (ctx, &candidates_p, temp);
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const llama_token new_token_id = llama_sample_token(ctx, &candidates_p);
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//const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
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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_len) {
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i_batch[i] = -1;
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LOG_TEE("\n");
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if (n_parallel > 1) {
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LOG_TEE("%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_TEE("%s", llama_token_to_piece(ctx, new_token_id).c_str());
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fflush(stdout);
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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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fprintf(stderr, "%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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LOG_TEE("\n");
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if (n_parallel > 1) {
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LOG_TEE("\n");
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for (int32_t i = 0; i < n_parallel; ++i) {
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LOG_TEE("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_TEE("%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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llama_print_timings(ctx);
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fprintf(stderr, "\n");
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llama_batch_free(batch);
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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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