remove uhd_image_embed
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parent
72b962925b
commit
f3d400dac0
6 changed files with 291 additions and 365 deletions
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@ -9,10 +9,6 @@
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#include <cstdlib>
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#include <vector>
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struct uhd_image_embed {
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std::vector<std::vector<struct llava_image_embed *>> image_embeds;
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};
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struct llava_context {
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struct clip_ctx * ctx_clip = NULL;
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struct llama_context * ctx_llama = NULL;
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@ -30,7 +26,7 @@ static void llama_log_callback_logTee(ggml_log_level level, const char * text, v
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LOG_TEE("%s", text);
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}
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struct llama_model * llava_init(gpt_params * params) {
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static 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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@ -44,7 +40,7 @@ struct llama_model * llava_init(gpt_params * params) {
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return model;
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}
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struct llava_context * llava_init_context(gpt_params * params, llama_model * model) {
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static struct llava_context * llava_init_context(gpt_params * params, llama_model * model) {
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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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@ -73,13 +69,18 @@ struct llava_context * llava_init_context(gpt_params * params, llama_model * mod
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return ctx_llava;
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}
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void llava_free(struct llava_context * ctx_llava) {
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static void llava_free(struct llava_context * ctx_llava) {
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if (ctx_llava->ctx_clip) {
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clip_free(ctx_llava->ctx_clip);
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ctx_llava->ctx_clip = NULL;
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}
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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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static 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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@ -90,18 +91,7 @@ struct clip_ctx * clip_init_context(gpt_params * params) {
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return ctx_clip;
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}
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struct uhd_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_uhd(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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static 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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@ -117,45 +107,57 @@ bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> toke
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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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static 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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static 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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return eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
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}
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void process_image(struct llava_context * ctx_llava, struct uhd_image_embed * image_embed_slices, gpt_params * params, int &n_past) {
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std::string system_prompt;
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static void process_eval_image_embed(struct llava_context * ctx_llava, const struct llava_image_embed * embeds, int n_batch, int * n_past, int idx) {
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float * image_embed = (float *)malloc(clip_embd_nbytes(ctx_llava->ctx_clip));
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std::memcpy(image_embed, embeds->embed + idx * clip_n_patches(ctx_llava->ctx_clip) * clip_n_mmproj_embd(ctx_llava->ctx_clip), clip_embd_nbytes(ctx_llava->ctx_clip));
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auto slice_embed = (llava_image_embed*)malloc(sizeof(llava_image_embed));
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slice_embed->embed = image_embed;
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slice_embed->n_image_pos = clip_n_patches(ctx_llava->ctx_clip);
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llava_eval_image_embed(ctx_llava->ctx_llama, slice_embed, n_batch, n_past);
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llava_image_embed_free(slice_embed);
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}
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static void process_image(struct llava_context * ctx_llava, struct llava_image_embed * embeds, gpt_params * params, int &n_past) {
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std::string system_prompt;
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int idx = 0;
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int num_image_embeds = embeds->n_image_pos / clip_n_patches(ctx_llava->ctx_clip);
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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, false);
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llava_eval_image_embed(ctx_llava->ctx_llama, image_embed_slices->image_embeds[0][0], params->n_batch, &n_past);
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process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
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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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if (image_embed_slices->image_embeds.size() > 1) {
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if (num_image_embeds > 1) {
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size_t num_image_embeds_col = clip_uhd_num_image_embeds_col(ctx_llava->ctx_clip);
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eval_string(ctx_llava->ctx_llama, std::string("<slice>").c_str(), params->n_batch, &n_past, false);
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for (size_t i = 1; i < image_embed_slices->image_embeds.size(); ++i) {
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for (size_t j = 0; j < image_embed_slices->image_embeds[i].size(); ++j) {
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for (size_t i = 0; i < (num_image_embeds-1)/num_image_embeds_col; ++i) {
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for (size_t j = 0; j < num_image_embeds_col; ++j) {
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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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llava_eval_image_embed(ctx_llava->ctx_llama, image_embed_slices->image_embeds[i][j], params->n_batch, &n_past);
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process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
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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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if (j == image_embed_slices->image_embeds[i].size() - 1) {
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if (j == num_image_embeds_col - 1) {
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eval_string(ctx_llava->ctx_llama, std::string("\n").c_str(), params->n_batch, &n_past, false);
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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>").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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static 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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@ -171,9 +173,9 @@ const char * sample(struct llama_sampling_context * ctx_sampling,
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}
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static struct llava_context * minicpmv_init(gpt_params * params, const std::string & fname, int &n_past){
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auto embeds = minicpmv_image_embed(params, fname);
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auto image_embed_slices = embeds->image_embeds;
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if (!image_embed_slices[0][0]) {
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auto ctx_clip = clip_init_context(params);
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auto embeds = llava_image_embed_make_with_filename(ctx_clip, params->n_threads, fname.c_str());
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if (!embeds) {
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std::cerr << "error: failed to load image " << fname << ". Terminating\n\n";
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return NULL;
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}
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@ -191,7 +193,7 @@ static struct llava_context * minicpmv_init(gpt_params * params, const std::stri
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}
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const int64_t t_llava_init_start_us = ggml_time_us();
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auto ctx_llava = llava_init_context(params, model);
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ctx_llava->ctx_clip = ctx_clip;
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const int64_t t_llava_init_end_us = ggml_time_us();
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float t_llava_init_ms = (t_llava_init_end_us - t_llava_init_start_us) / 1000.0;
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LOG_TEE("\n%s: llava init in %8.2f ms.\n", __func__, t_llava_init_ms);
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@ -202,7 +204,7 @@ static struct llava_context * minicpmv_init(gpt_params * params, const std::stri
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float t_process_image_ms = (t_process_image_end_us - t_process_image_start_us) / 1000.0;
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LOG_TEE("\n%s: llama process image in %8.2f ms.\n", __func__, t_process_image_ms);
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llava_image_embed_free_uhd(embeds);
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llava_image_embed_free(embeds);
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return ctx_llava;
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}
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@ -220,7 +222,7 @@ static struct llama_sampling_context * llama_init(struct llava_context * ctx_lla
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return ctx_sampling;
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}
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static const char * llama_loop(struct minicpmv_context * ctx_llava,struct llama_sampling_context * ctx_sampling, int &n_past){
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static const char * llama_loop(struct llava_context * ctx_llava,struct llama_sampling_context * ctx_sampling, int &n_past){
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const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past);
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return tmp;
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