mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2025-01-12 21:37:19 +01:00
153 lines
6.0 KiB
C++
153 lines
6.0 KiB
C++
#include "ggml.h"
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#include "common.h"
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#include "clip.h"
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#include "minicpmv.h"
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#include "minicpmv_io.h"
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#include "llama.h"
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#include <cstdio>
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#include <cstdlib>
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#include <vector>
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struct llama_model * llava_init(gpt_params * params) {
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llama_backend_init();
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llama_numa_init(params->numa);
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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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LOG_TEE("%s: error: unable to load model\n" , __func__);
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return NULL;
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}
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return model;
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}
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struct minicpmv_context * llava_init_context(gpt_params * params, llama_model * model) {
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const char * clip_path = params->mmproj.c_str();
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auto prompt = params->prompt;
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if (prompt.empty()) {
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prompt = "describe the image in detail.";
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}
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llama_context_params ctx_params = llama_context_params_from_gpt_params(*params);
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ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
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llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
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if (ctx_llama == NULL) {
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LOG_TEE("%s: error: failed to create the llama_context\n" , __func__);
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return NULL;
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}
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auto ctx_llava = (struct minicpmv_context *)malloc(sizeof(minicpmv_context));
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ctx_llava->ctx_llama = ctx_llama;
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ctx_llava->model = model;
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return ctx_llava;
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}
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void llava_free(struct minicpmv_context * ctx_llava) {
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llama_free(ctx_llava->ctx_llama);
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llama_free_model(ctx_llava->model);
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llama_backend_free();
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}
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struct clip_ctx * clip_init_context(gpt_params * params) {
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const char * clip_path = params->mmproj.c_str();
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auto prompt = params->prompt;
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if (prompt.empty()) {
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prompt = "describe the image in detail.";
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}
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std::pair<int, int> load_image_size = std::make_pair(70, 70);
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auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1, load_image_size);
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return ctx_clip;
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}
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std::vector<std::vector<struct llava_image_embed *>> minicpmv_image_embed(gpt_params * params, const std::string & fname){
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auto ctx_clip = clip_init_context(params);
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auto image_embed_and_slices = llava_image_embed_make_with_filename_slice(ctx_clip, params->n_threads, fname.c_str());
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if (ctx_clip) {
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clip_free(ctx_clip);
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ctx_clip = NULL;
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}
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return image_embed_and_slices;
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}
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bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past) {
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int N = (int) tokens.size();
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for (int i = 0; i < N; i += n_batch) {
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int n_eval = (int) tokens.size() - i;
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if (n_eval > n_batch) {
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n_eval = n_batch;
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}
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if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
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LOG_TEE("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
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return false;
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}
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*n_past += n_eval;
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}
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return true;
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}
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bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
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std::vector<llama_token> tokens;
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tokens.push_back(id);
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return eval_tokens(ctx_llama, tokens, 1, n_past);
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}
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bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
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std::string str2 = str;
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std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true);
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eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
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return true;
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}
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void process_image(struct minicpmv_context * ctx_llava, std::vector<std::vector<struct llava_image_embed *>> image_embed_slices, gpt_params * params, int &n_past) {
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std::string system_prompt;
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system_prompt = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n";
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LOG_TEE("%s: image token past: %d\n", __func__, n_past);
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eval_string(ctx_llava->ctx_llama, (system_prompt+"<image>").c_str(), params->n_batch, &n_past, true);
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llava_eval_image_embed(ctx_llava->ctx_llama, image_embed_slices[0][0], params->n_batch, &n_past);
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if (image_embed_slices.size() > 1) {
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eval_string(ctx_llava->ctx_llama, std::string("</image><slice>").c_str(), params->n_batch, &n_past, false);
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for (size_t i = 1; i < image_embed_slices.size(); ++i) {
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eval_string(ctx_llava->ctx_llama, std::string("<image>").c_str(), params->n_batch, &n_past, false);
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for (size_t j = 0; j < image_embed_slices[i].size(); ++j) {
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llava_eval_image_embed(ctx_llava->ctx_llama, image_embed_slices[i][j], params->n_batch, &n_past);
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if (j != image_embed_slices[i].size() - 1) {
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eval_string(ctx_llava->ctx_llama, std::string("</image><image>").c_str(), params->n_batch, &n_past, false);
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} else {
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if (i != image_embed_slices.size() - 1) {
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eval_string(ctx_llava->ctx_llama, std::string("</image>\n").c_str(), params->n_batch, &n_past, false);
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} else {
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eval_string(ctx_llava->ctx_llama, std::string("</image>").c_str(), params->n_batch, &n_past, false);
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}
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}
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}
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}
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eval_string(ctx_llava->ctx_llama, std::string("</slice>\n").c_str(), params->n_batch, &n_past, false);
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} else {
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eval_string(ctx_llava->ctx_llama, std::string("</image>\n").c_str(), params->n_batch, &n_past, false);
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}
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LOG_TEE("%s: image token past: %d\n", __func__, n_past);
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}
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const char * sample(struct llama_sampling_context * ctx_sampling,
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struct llama_context * ctx_llama,
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int * n_past) {
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const llama_token id = llama_sampling_sample(ctx_sampling, ctx_llama, NULL);
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llama_sampling_accept(ctx_sampling, ctx_llama, id, true);
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static std::string ret;
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if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
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ret = "</s>";
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} else {
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ret = llama_token_to_piece(ctx_llama, id);
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}
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eval_id(ctx_llama, id, n_past);
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return ret.c_str();
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} |