mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-29 08:41:22 +00:00
5656d10599
* MPI support, first cut * fix warnings, update README * fixes * wrap includes * PR comments * Update CMakeLists.txt * Add GH workflow, fix test * Add info to README * mpi : trying to move more MPI stuff into ggml-mpi (WIP) (#2099) * mpi : add names for layer inputs + prep ggml_mpi_graph_compute() * mpi : move all MPI logic into ggml-mpi Not tested yet * mpi : various fixes - communication now works but results are wrong * mpi : fix output tensor after MPI compute (still not working) * mpi : fix inference * mpi : minor * Add OpenMPI to GH action * [mpi] continue-on-error: true * mpi : fix after master merge * [mpi] Link MPI C++ libraries to fix OpenMPI * tests : fix new llama_backend API * [mpi] use MPI_INT32_T * mpi : factor out recv / send in functions and reuse * mpi : extend API to allow usage with outer backends (e.g. Metal) --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
100 lines
2.7 KiB
C++
100 lines
2.7 KiB
C++
#include "common.h"
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#include "llama.h"
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#include "build-info.h"
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#include <ctime>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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int main(int argc, char ** argv) {
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gpt_params params;
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if (gpt_params_parse(argc, argv, params) == false) {
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return 1;
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}
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params.embedding = true;
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if (params.n_ctx > 2048) {
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fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
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"expect poor results\n", __func__, params.n_ctx);
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}
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fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
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if (params.seed == LLAMA_DEFAULT_SEED) {
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params.seed = time(NULL);
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}
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fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
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std::mt19937 rng(params.seed);
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if (params.random_prompt) {
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params.prompt = gpt_random_prompt(rng);
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}
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llama_backend_init(params.numa);
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llama_model * model;
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llama_context * ctx;
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// load the model
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std::tie(model, ctx) = llama_init_from_gpt_params(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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// print system information
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{
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fprintf(stderr, "\n");
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fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
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params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
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}
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int n_past = 0;
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// Add a space in front of the first character to match OG llama tokenizer behavior
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params.prompt.insert(0, 1, ' ');
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// tokenize the prompt
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auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
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if (params.verbose_prompt) {
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fprintf(stderr, "\n");
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fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
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fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
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for (int i = 0; i < (int) embd_inp.size(); i++) {
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fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]));
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}
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fprintf(stderr, "\n");
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}
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if (params.embedding){
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if (embd_inp.size() > 0) {
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if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past, params.n_threads)) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
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}
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}
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const int n_embd = llama_n_embd(ctx);
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const auto embeddings = llama_get_embeddings(ctx);
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for (int i = 0; i < n_embd; i++) {
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printf("%f ", embeddings[i]);
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}
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printf("\n");
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}
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llama_print_timings(ctx);
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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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