llama

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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 <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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}
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int main(int argc, char ** argv) {
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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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    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 * 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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    // 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 * 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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    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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    }
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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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    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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    // 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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    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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            // 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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                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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            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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            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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    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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    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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