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https://github.com/ggerganov/llama.cpp.git
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examples : do not use common library in simple example (#9803)
* examples : do not use common library in simple example * add command line parser, simplify code
This commit is contained in:
Diego Devesa
GitHub
parent
c81f3bbb05
commit
c7499c557c
@ -1,5 +1,5 @@
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set(TARGET llama-simple)
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add_executable(${TARGET} simple.cpp)
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install(TARGETS ${TARGET} RUNTIME)
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target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
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target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
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target_compile_features(${TARGET} PRIVATE cxx_std_11)
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@ -1,50 +1,112 @@
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#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 <cstdio>
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#include <cstring>
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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\n", argv[0]);
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LOG("\n");
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printf("\nexample usage:\n");
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printf("\n %s -m model.gguf [-n n_predict] [-ngl n_gpu_layers] [prompt]\n", argv[0]);
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printf("\n");
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}
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int main(int argc, char ** argv) {
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gpt_params params;
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// path to the model gguf file
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std::string model_path;
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// prompt to generate text from
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std::string prompt = "Hello my name is";
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// number of layers to offload to the GPU
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int ngl = 99;
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// number of tokens to predict
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int n_predict = 32;
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params.prompt = "Hello my name is";
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params.n_predict = 32;
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// parse command line arguments
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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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int i = 1;
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for (; i < argc; i++) {
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if (strcmp(argv[i], "-m") == 0) {
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if (i + 1 < argc) {
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model_path = argv[++i];
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} else {
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print_usage(argc, argv);
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return 1;
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}
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} else if (strcmp(argv[i], "-n") == 0) {
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if (i + 1 < argc) {
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try {
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n_predict = std::stoi(argv[++i]);
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} catch (...) {
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print_usage(argc, argv);
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return 1;
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}
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} else {
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print_usage(argc, argv);
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return 1;
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}
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} else if (strcmp(argv[i], "-ngl") == 0) {
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if (i + 1 < argc) {
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try {
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ngl = std::stoi(argv[++i]);
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} catch (...) {
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print_usage(argc, argv);
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return 1;
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}
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} else {
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print_usage(argc, argv);
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return 1;
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}
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} else {
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// prompt starts here
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break;
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}
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}
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if (model_path.empty()) {
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print_usage(argc, argv);
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return 1;
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}
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if (i < argc) {
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prompt = argv[i++];
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for (; i < argc; i++) {
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prompt += " ";
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prompt += argv[i];
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}
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}
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}
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gpt_init();
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// total length of the sequence including the prompt
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const 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_params model_params = llama_model_default_params();
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model_params.n_gpu_layers = ngl;
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llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
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llama_model * model = llama_load_model_from_file(model_path.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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// find the number of tokens in the prompt
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const int n_prompt = -llama_tokenize(model, prompt.c_str(), prompt.size(), NULL, 0, true, true);
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// allocate space for the tokens and tokenize the prompt
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std::vector<llama_token> prompt_tokens(n_prompt);
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if (llama_tokenize(model, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true, true) < 0) {
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fprintf(stderr, "%s: error: failed to tokenize the prompt\n", __func__);
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return 1;
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}
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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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llama_context_params ctx_params = llama_context_default_params();
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// n_ctx is the context size
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ctx_params.n_ctx = n_prompt + n_predict - 1;
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// n_batch is the maximum number of tokens that can be processed in a single call to llama_decode
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ctx_params.n_batch = n_prompt;
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// enable performance counters
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ctx_params.no_perf = false;
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llama_context * ctx = llama_new_context_with_model(model, ctx_params);
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@ -53,117 +115,87 @@ int main(int argc, char ** argv) {
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return 1;
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}
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// initialize the sampler
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auto sparams = llama_sampler_chain_default_params();
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sparams.no_perf = false;
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llama_sampler * smpl = llama_sampler_chain_init(sparams);
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llama_sampler_chain_add(smpl, llama_sampler_init_greedy());
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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 n_ctx = llama_n_ctx(ctx);
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const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size());
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LOG("\n");
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LOG_INF("%s: n_predict = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, 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 > n_ctx, the required KV cache size is not big enough\n", __func__);
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LOG_ERR("%s: either reduce n_predict 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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for (auto id : prompt_tokens) {
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char buf[128];
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int n = llama_token_to_piece(model, id, buf, sizeof(buf), 0, true);
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if (n < 0) {
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fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__);
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return 1;
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}
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std::string s(buf, n);
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printf("%s", s.c_str());
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}
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// create a llama_batch with size 512
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// we use this object to submit token data for decoding
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// prepare a batch for the prompt
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llama_batch batch = llama_batch_init(512, 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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// 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("%s: llama_decode() failed\n", __func__);
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return 1;
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}
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llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size(), 0, 0);
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// main loop
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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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int n_decode = 0;
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llama_token new_token_id;
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for (int n_pos = 0; n_pos + batch.n_tokens < n_prompt + n_predict; ) {
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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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n_pos += batch.n_tokens;
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while (n_cur <= n_predict) {
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// sample the next token
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{
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const llama_token new_token_id = llama_sampler_sample(smpl, ctx, -1);
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new_token_id = llama_sampler_sample(smpl, ctx, -1);
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// is it an end of generation?
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if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
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LOG("\n");
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if (llama_token_is_eog(model, new_token_id)) {
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break;
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}
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LOG("%s", llama_token_to_piece(ctx, new_token_id).c_str());
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char buf[128];
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int n = llama_token_to_piece(model, new_token_id, buf, sizeof(buf), 0, true);
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if (n < 0) {
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fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__);
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return 1;
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}
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std::string s(buf, n);
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printf("%s", s.c_str());
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fflush(stdout);
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// prepare the next batch
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llama_batch_clear(batch);
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// push this new token for next evaluation
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llama_batch_add(batch, new_token_id, n_cur, { 0 }, true);
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// prepare the next batch with the sampled token
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batch = llama_batch_get_one(&new_token_id, 1, n_pos, 0);
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n_decode += 1;
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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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LOG("\n");
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printf("\n");
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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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fprintf(stderr, "%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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fprintf(stderr, "\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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LOG("\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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