开源大模型框架llama.cpp使用C++ api开发入门

llama.cpp是一个C++编写的轻量级开源类AIGC大模型框架，可以支持在消费级普通设备上本地部署运行大模型，以及作为依赖库集成的到应用程序中提供类GPT的功能。

以下基于llama.cpp的源码利用C++ api来开发实例demo演示加载本地模型文件并提供GPT文本生成。

项目结构

llamacpp_starter- llama.cpp-b1547- src|- main.cpp- CMakeLists.txt

CMakeLists.txt

cmake_minimum_required(VERSION 3.15)# this only works for unix, xapian source code not support compile in windows yetproject(llamacpp_starter)set(CMAKE_CXX_STANDARD 14)
set(CMAKE_CXX_STANDARD_REQUIRED ON)add_subdirectory(llama.cpp-b1547)include_directories(${CMAKE_CURRENT_SOURCE_DIR}/llama.cpp-b1547${CMAKE_CURRENT_SOURCE_DIR}/llama.cpp-b1547/common
)file(GLOB SRCsrc/*.hsrc/*.cpp
)add_executable(${PROJECT_NAME} ${SRC})target_link_libraries(${PROJECT_NAME}commonllama
)

main.cpp

#include <iostream>
#include <string>
#include <vector>
#include "common.h"
#include "llama.h"int main(int argc, char** argv)
{bool numa_support = false;const std::string model_file_path = "./llama-ggml.gguf";const std::string prompt = "once upon a time"; // input wordsconst int n_len = 32; 	// total length of the sequence including the prompt// set gpt paramsgpt_params params;params.model = model_file_path;params.prompt = prompt;// init LLMllama_backend_init(false);// load modelllama_model_params model_params = llama_model_default_params();//model_params.n_gpu_layers = 99; // offload all layers to the GPUllama_model* model = llama_load_model_from_file(model_file_path.c_str(), model_params);if (model == NULL){std::cerr << __func__ << " load model file error" << std::endl;return 1;}// init contextllama_context_params ctx_params = llama_context_default_params();ctx_params.seed = 1234;ctx_params.n_ctx = 2048;ctx_params.n_threads = params.n_threads;ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;llama_context* ctx = llama_new_context_with_model(model, ctx_params);if (ctx == NULL){std::cerr << __func__ << " failed to create the llama_context" << std::endl;return 1;}// tokenize the promptstd::vector<llama_token> tokens_list = llama_tokenize(ctx, params.prompt, true);const int n_ctx = llama_n_ctx(ctx);const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size());// make sure the KV cache is big enough to hold all the prompt and generated tokensif (n_kv_req > n_ctx){std::cerr << __func__ << " error: n_kv_req > n_ctx, the required KV cache size is not big enough" << std::endl;std::cerr << __func__ << " either reduce n_parallel or increase n_ctx" << std::endl;return 1;}// print the prompt token-by-tokenfor (auto id : tokens_list)std::cout << llama_token_to_piece(ctx, id) << " ";std::cout << std::endl;// create a llama_batch with size 512// we use this object to submit token data for decodingllama_batch batch = llama_batch_init(512, 0, 1);// evaluate the initial promptfor (size_t i = 0; i < tokens_list.size(); i++)llama_batch_add(batch, tokens_list[i], i, { 0 }, false);// llama_decode will output logits only for the last token of the promptbatch.logits[batch.n_tokens - 1] = true;if (llama_decode(ctx, batch) != 0){std::cerr << __func__ << " llama_decode failed" << std::endl;return 1;}// main loop to generate wordsint n_cur = batch.n_tokens;int n_decode = 0;const auto t_main_start = ggml_time_us();while (n_cur <= n_len){// sample the next tokenauto n_vocab = llama_n_vocab(model);auto* logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);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 };// sample the most likely tokenconst llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);// is it an end of stream?if (new_token_id == llama_token_eos(model) || n_cur == n_len){std::cout << std::endl;break;}std::cout << llama_token_to_piece(ctx, new_token_id) << " ";// prepare the next batchllama_batch_clear(batch);// push this new token for next evaluationllama_batch_add(batch, new_token_id, n_cur, { 0 }, true);n_decode += 1;n_cur += 1;// evaluate the current batch with the transformer modelif (llama_decode(ctx, batch)){std::cerr << __func__ << " failed to eval" << std::endl;return 1;}}std::cout << std::endl;const auto t_main_end = ggml_time_us();std::cout << __func__ << " decoded " << n_decode << " tokens in " << (t_main_end - t_main_start) / 1000000.0f << " s, speed: " << n_decode / ((t_main_end - t_main_start) / 1000000.0f) << " t / s" << std::endl;llama_print_timings(ctx);llama_batch_free(batch);// free contextllama_free(ctx);llama_free_model(model);// free LLMllama_backend_free();return 0;
}

注：

• llama支持的模型文件需要自己去下载，推荐到huggingface官网下载转换好的gguf格式文件
• llama.cpp编译可以配置多种类型的增强选项，比如支持CPU/GPU加速，数据计算加速库

源码

llamacpp_starter

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