koboldcpp/otherarch/gptj_v1_main.cpp

#include "gptj_v1.cpp"

int main(int argc, char ** argv) {
    ggml_v1_time_init();
    const int64_t t_main_start_us = ggml_v1_time_us();

    gpt_params params;
    params.model = "models/gpt-j-6B/ggml-model.bin";

    if (utils_gpt_params_parse(argc, argv, params) == false) {
        return 1;
    }

    if (params.seed < 0) {
        params.seed = time(NULL);
    }

    printf("%s: seed = %d\n", __func__, params.seed);

    std::mt19937 rng(params.seed);
    if (params.prompt.empty()) {
        if( !isatty(STDIN_FILENO) ){
            std::string line;
            while( std::getline(std::cin, line) ){
                params.prompt = params.prompt + "\n" + line;
            }
        } else {
            params.prompt = utils_gpt_random_prompt(rng);
        }
    }

    int64_t t_load_us = 0;

    gpt_vocab vocab;
    gptj_model_v1 model;
    FileFormat file_format = FileFormat::GPTJ_2;

    // load the model
    {
        const int64_t t_start_us = ggml_v1_time_us();

        if (legacy_gptj_model_load(params.model, model, vocab, file_format)!=ModelLoadResult::SUCCESS) {
            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
            return 1;
        }

        t_load_us = ggml_v1_time_us() - t_start_us;
    }

    int n_past = 0;

    int64_t t_sample_us  = 0;
    int64_t t_predict_us = 0;

    std::vector<float> logits;

    // tokenize the prompt
    std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);

    params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());

    printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
    printf("\n");

    std::vector<gpt_vocab::id> embd;

    // determine the required inference memory per token:
    size_t mem_per_token = 0;
    legacy_gptj_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token, file_format);

    for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
        // predict
        if (embd.size() > 0) {
            const int64_t t_start_us = ggml_v1_time_us();

            if (!legacy_gptj_eval(model, params.n_threads, n_past, embd, logits, mem_per_token,file_format)) {
                printf("Failed to predict\n");
                return 1;
            }

            t_predict_us += ggml_v1_time_us() - t_start_us;
        }

        n_past += embd.size();
        embd.clear();

        if (i >= embd_inp.size()) {
            // sample next token
            const int   top_k = params.top_k;
            const float top_p = params.top_p;
            const float temp  = params.temp;

            const int n_vocab = model.hparams.n_vocab;

            gpt_vocab::id id = 0;

            {
                const int64_t t_start_sample_us = ggml_v1_time_us();

                id = gpt_sample_top_k_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);

                t_sample_us += ggml_v1_time_us() - t_start_sample_us;
            }

            // add it to the context
            embd.push_back(id);
        } else {
            // if here, it means we are still processing the input prompt
            for (int k = i; k < embd_inp.size(); k++) {
                embd.push_back(embd_inp[k]);
                if (embd.size() > params.n_batch) {
                    break;
                }
            }
            i += embd.size() - 1;
        }

        // display text
        for (auto id : embd) {
            printf("%s", vocab.id_to_token[id].c_str());
        }
        fflush(stdout);

        // end of text token
        if (embd.back() == 50256) {
            break;
        }
    }

    // report timing
    {
        const int64_t t_main_end_us = ggml_v1_time_us();

        printf("\n\n");
        printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
        printf("%s:     load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
        printf("%s:   sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
        printf("%s:  predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
        printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
    }

    ggml_v1_free(model.ctx);

    return 0;
}