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)

project(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 SRC
    src/*.h
    src/*.cpp
)

add_executable(${PROJECT_NAME} ${SRC})

target_link_libraries(${PROJECT_NAME}
    common
    llama
)

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 words
	const int n_len = 32; 	// total length of the sequence including the prompt

	// set gpt params
	gpt_params params;
	params.model = model_file_path;
	params.prompt = prompt;


	// init LLM
	llama_backend_init(false);

	// load model
	llama_model_params model_params = llama_model_default_params();
	//model_params.n_gpu_layers = 99; // offload all layers to the GPU

	llama_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 context
	llama_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 prompt
	std::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 tokens
	if (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-token
	for (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 decoding
	llama_batch batch = llama_batch_init(512, 0, 1);

	// evaluate the initial prompt
	for (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 prompt
	batch.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 words
	int 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 token
		auto 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 token
		const 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 batch
		llama_batch_clear(batch);

		// push this new token for next evaluation
		llama_batch_add(batch, new_token_id, n_cur, { 0 }, true);

		n_decode += 1;

		n_cur += 1;

		// evaluate the current batch with the transformer model
		if (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 context
	llama_free(ctx);
	llama_free_model(model);

	// free LLM
	llama_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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