mirror of
https://github.com/ggerganov/llama.cpp.git
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2891c8aa9a
* BERT model graph construction (build_bert) * WordPiece tokenizer (llm_tokenize_wpm) * Add flag for non-causal attention models * Allow for models that only output embeddings * Support conversion of BERT models to GGUF * Based on prior work by @xyzhang626 and @skeskinen --------- Co-authored-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
115 lines
3.0 KiB
C++
115 lines
3.0 KiB
C++
#include "common.h"
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#include "llama.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)) {
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return 1;
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}
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params.embedding = true;
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print_build_info();
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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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const int n_ctx_train = llama_n_ctx_train(model);
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const int n_ctx = llama_n_ctx(ctx);
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if (n_ctx > n_ctx_train) {
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fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
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__func__, n_ctx_train, n_ctx);
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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, "%s\n", get_system_info(params).c_str());
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}
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int n_past = 0;
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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_piece(ctx, embd_inp[i]).c_str());
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}
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fprintf(stderr, "\n");
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}
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if (embd_inp.size() > (size_t)n_ctx) {
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fprintf(stderr, "%s: error: prompt is longer than the context window (%zu tokens, n_ctx = %d)\n",
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__func__, embd_inp.size(), n_ctx);
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return 1;
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}
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while (!embd_inp.empty()) {
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int n_tokens = std::min(params.n_batch, (int) embd_inp.size());
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if (llama_decode(ctx, llama_batch_get_one(embd_inp.data(), n_tokens, n_past, 0))) {
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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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n_past += n_tokens;
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embd_inp.erase(embd_inp.begin(), embd_inp.begin() + n_tokens);
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}
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const int n_embd = llama_n_embd(model);
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auto * embeddings = llama_get_embeddings(ctx);
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// l2-normalize embeddings
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float norm = 0;
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for (int i = 0; i < n_embd; i++) {
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norm += embeddings[i] * embeddings[i];
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}
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norm = sqrt(norm);
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for (int i = 0; i < n_embd; i++) {
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embeddings[i] /= norm;
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}
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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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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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