mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-30 08:42:00 +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>
106 lines
3.1 KiB
C++
106 lines
3.1 KiB
C++
#include "llama.h"
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#include <cstdio>
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#include <string>
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#include <map>
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#include <vector>
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static const std::map<std::string, std::vector<llama_token>> & k_tests()
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{
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static std::map<std::string, std::vector<llama_token>> _k_tests = {
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{ "Hello World", { 1, 10994, 2787, }, },
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{ " Hello World", { 1, 15043, 2787, }, },
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{ " Hello World!", { 1, 15043, 2787, 29991, }, },
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{ " this is 🦙.cpp", { 1, 445, 338, 29871, 243, 162, 169, 156, 29889, 8223, }, },
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{ "w048 7tuijk dsdfhu", { 1, 29893, 29900, 29946, 29947, 29871, 29955, 9161, 13535, 18031, 2176, 6905, }, },
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{ "нещо на Български", { 1, 821, 4851, 665, 1386, 29713, 1305, }, },
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};
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return _k_tests;
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};
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int main(int argc, char **argv) {
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if (argc < 2) {
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fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
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return 1;
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}
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const std::string fname = argv[1];
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fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
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llama_model * model;
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llama_context * ctx;
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llama_backend_init(false);
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// load the vocab
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{
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auto lparams = llama_context_default_params();
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lparams.vocab_only = true;
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model = llama_load_model_from_file(fname.c_str(), lparams);
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if (model == NULL) {
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fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
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return 1;
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}
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ctx = llama_new_context_with_model(model, lparams);
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if (ctx == NULL) {
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fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
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llama_free_model(model);
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return 1;
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}
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}
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const int n_vocab = llama_n_vocab(ctx);
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if (n_vocab != 32000) {
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fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab);
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llama_free_model(model);
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llama_free(ctx);
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return 2;
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}
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for (const auto & test_kv : k_tests()) {
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std::vector<llama_token> res(test_kv.first.size());
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const int n = llama_tokenize(ctx, test_kv.first.c_str(), res.data(), int(res.size()), true);
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res.resize(n);
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bool correct = res.size() == test_kv.second.size();
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for (int i = 0; i < (int) res.size() && correct; ++i) {
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if (res[i] != test_kv.second[i]) {
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correct = false;
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}
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}
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if (!correct) {
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fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
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fprintf(stderr, "%s : expected tokens: ", __func__);
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for (const auto & t : test_kv.second) {
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fprintf(stderr, "%6d, ", t);
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}
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fprintf(stderr, "\n");
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fprintf(stderr, "%s : got tokens: ", __func__);
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for (const auto & t : res) {
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fprintf(stderr, "%6d, ", t);
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}
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fprintf(stderr, "\n");
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llama_free_model(model);
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llama_free(ctx);
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return 3;
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}
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}
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llama_free_model(model);
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llama_free(ctx);
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llama_backend_free();
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return 0;
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}
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