mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 14:20:31 +01:00
582 lines
21 KiB
C++
582 lines
21 KiB
C++
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#include "arg.h"
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#include "base64.hpp"
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#include "log.h"
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#include "common.h"
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#include "sampling.h"
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#include "clip.h"
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#include "llava.h"
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#include "llama.h"
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#include "ggml.h"
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#ifdef GGML_USE_CUDA
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#include "ggml-cuda.h"
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#endif
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#ifdef NDEBUG
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#include "ggml-alloc.h"
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#include "ggml-backend.h"
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#endif
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#include <cstdio>
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#include <cstdlib>
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#include <cstring>
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#include <vector>
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#include <algorithm>
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#include <iostream>
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#include <fstream>
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static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed,
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int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) {
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int n_embd = llama_n_embd(llama_get_model(ctx_llama));
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const int patch_size = 14 * 2;
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const int ph = image_size->height / patch_size + (image_size->height % patch_size > 0);
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const int pw = image_size->width / patch_size + (image_size->width % patch_size > 0);
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auto img_tokens = image_embed->n_image_pos;
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// llama_pos mrope_pos[img_tokens * 4];
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std::vector<llama_pos> mrope_pos;
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mrope_pos.resize(img_tokens * 4);
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for (int y = 0; y < ph; y++)
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{
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for (int x = 0; x < pw; x++)
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{
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int i = y * pw + x;
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mrope_pos[i] = *st_pos_id;
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mrope_pos[i + img_tokens] = *st_pos_id + y;
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mrope_pos[i + img_tokens * 2] = *st_pos_id + x;
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mrope_pos[i + img_tokens * 3] = 0;
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}
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}
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*st_pos_id += std::max(pw, ph);
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int processed = 0;
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std::vector<llama_pos> batch_mrope_pos;
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batch_mrope_pos.resize(img_tokens * 4);
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for (int i = 0; i < img_tokens; i += n_batch) {
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int n_eval = img_tokens - 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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// llama_pos batch_mrope_pos[n_eval * 4];
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std::fill(batch_mrope_pos.begin(), batch_mrope_pos.end(), 0);
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memcpy(batch_mrope_pos.data(), &mrope_pos[processed], n_eval * sizeof(llama_pos));
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memcpy(&batch_mrope_pos[n_eval * 1], &mrope_pos[img_tokens * 1 + processed], n_eval * sizeof(llama_pos));
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memcpy(&batch_mrope_pos[n_eval * 2], &mrope_pos[img_tokens * 2 + processed], n_eval * sizeof(llama_pos));
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memcpy(&batch_mrope_pos[n_eval * 3], &mrope_pos[img_tokens * 3 + processed], n_eval * sizeof(llama_pos));
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llama_batch batch = {
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int32_t(n_eval), // n_tokens
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nullptr, // token
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(image_embed->embed+i*n_embd), // embed
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batch_mrope_pos.data(), // pos
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nullptr, // n_seq_id
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nullptr, // seq_id
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nullptr, // logits
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};
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if (llama_decode(ctx_llama, batch)) {
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LOG_ERR("%s : failed to eval\n", __func__);
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return false;
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}
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*n_past += n_eval;
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processed += n_eval;
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}
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return true;
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}
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static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past, int * st_pos_id) {
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int N = (int) tokens.size();
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std::vector<llama_pos> pos;
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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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auto batch = llama_batch_get_one(&tokens[i], n_eval);
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// TODO: add mrope pos ids somewhere else
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pos.resize(batch.n_tokens * 4);
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std::fill(pos.begin(), pos.end(), 0);
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for (int j = 0; j < batch.n_tokens * 3; j ++) {
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pos[j] = *st_pos_id + (j % batch.n_tokens);
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}
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batch.pos = pos.data();
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if (llama_decode(ctx_llama, batch)) {
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LOG_ERR("%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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*st_pos_id += n_eval;
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}
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return true;
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}
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static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past, int * st_pos_id) {
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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, st_pos_id);
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}
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static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, int * st_pos_id, bool add_bos){
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std::string str2 = str;
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std::vector<llama_token> embd_inp = common_tokenize(ctx_llama, str2, add_bos, true);
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eval_tokens(ctx_llama, embd_inp, n_batch, n_past, st_pos_id);
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return true;
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}
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static const char * sample(struct common_sampler * smpl,
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struct llama_context * ctx_llama,
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int * n_past, int * st_pos_id) {
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const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
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common_sampler_accept(smpl, 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 = common_token_to_piece(ctx_llama, id);
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}
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eval_id(ctx_llama, id, n_past, st_pos_id);
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return ret.c_str();
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}
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static const char* IMG_BASE64_TAG_BEGIN = "<img src=\"data:image/jpeg;base64,";
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static const char* IMG_BASE64_TAG_END = "\">";
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static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) {
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begin_out = prompt.find(IMG_BASE64_TAG_BEGIN);
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end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out);
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}
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static bool prompt_contains_image(const std::string& prompt) {
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size_t begin, end;
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find_image_tag_in_prompt(prompt, begin, end);
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return (begin != std::string::npos);
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}
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// replaces the base64 image tag in the prompt with `replacement`
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static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) {
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size_t img_base64_str_start, img_base64_str_end;
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find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
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if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
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LOG_ERR("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
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return NULL;
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}
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auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN);
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auto base64_bytes_count = img_base64_str_end - base64_bytes_start;
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auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count );
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auto required_bytes = base64::required_encode_size(base64_str.size());
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auto img_bytes = std::vector<unsigned char>(required_bytes);
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base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin());
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auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
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if (!embed) {
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LOG_ERR("%s: could not load image from base64 string.\n", __func__);
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return NULL;
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}
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return embed;
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}
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static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") {
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size_t begin, end;
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find_image_tag_in_prompt(prompt, begin, end);
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if (begin == std::string::npos || end == std::string::npos) {
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return prompt;
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}
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auto pre = prompt.substr(0, begin);
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auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END));
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return pre + replacement + post;
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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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struct llama_model * model = NULL;
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};
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static void print_usage(int, char ** argv) {
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LOG("\n example usage:\n");
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LOG("\n %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
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LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
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}
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static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) {
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// load and preprocess the image
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llava_image_embed * embed = NULL;
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auto prompt = params->prompt;
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if (prompt_contains_image(prompt)) {
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if (!params->image.empty()) {
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LOG_INF("using base64 encoded image instead of command line image path\n");
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}
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embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt);
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if (!embed) {
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LOG_ERR("%s: can't load image from prompt\n", __func__);
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return NULL;
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}
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params->prompt = remove_image_from_prompt(prompt);
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} else {
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embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str());
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if (!embed) {
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fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
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return NULL;
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}
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}
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return embed;
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}
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static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) {
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int n_past = 0;
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int cur_pos_id = 0;
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const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
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std::string system_prompt, user_prompt;
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size_t image_pos = prompt.find("<|vision_start|>");
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if (image_pos != std::string::npos) {
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// new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
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system_prompt = prompt.substr(0, image_pos);
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user_prompt = prompt.substr(image_pos + std::string("<|vision_pad|>").length());
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LOG_INF("system_prompt: %s\n", system_prompt.c_str());
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if (params->verbose_prompt) {
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auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
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for (int i = 0; i < (int) tmp.size(); i++) {
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LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
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}
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}
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LOG_INF("user_prompt: %s\n", user_prompt.c_str());
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if (params->verbose_prompt) {
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auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
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for (int i = 0; i < (int) tmp.size(); i++) {
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LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
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}
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}
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} else {
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// llava-1.5 native mode
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system_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|>";
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user_prompt = "<|vision_end|>" + prompt + "<|im_end|>\n<|im_start|>assistant\n";
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if (params->verbose_prompt) {
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auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
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for (int i = 0; i < (int) tmp.size(); i++) {
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LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
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}
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}
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}
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eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, true);
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if (image_embed != nullptr) {
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auto image_size = clip_get_load_image_size(ctx_llava->ctx_clip);
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qwen2vl_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past, &cur_pos_id, image_size);
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}
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eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, false);
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// generate the response
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LOG("\n");
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struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
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if (!smpl) {
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LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
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exit(1);
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}
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std::string response = "";
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for (int i = 0; i < max_tgt_len; i++) {
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const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past, &cur_pos_id);
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response += tmp;
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if (strcmp(tmp, "</s>") == 0) break;
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if (strstr(tmp, "###")) break; // Yi-VL behavior
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LOG("%s", tmp);
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if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works)
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if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6
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if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6
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fflush(stdout);
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}
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common_sampler_free(smpl);
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LOG("\n");
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}
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static struct llama_model * llava_init(common_params * params) {
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llama_backend_init();
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||
|
|
llama_numa_init(params->numa);
|
||
|
|
|
||
|
|
llama_model_params model_params = common_model_params_to_llama(*params);
|
||
|
|
|
||
|
|
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
|
||
|
|
if (model == NULL) {
|
||
|
|
LOG_ERR("%s: unable to load model\n" , __func__);
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
return model;
|
||
|
|
}
|
||
|
|
|
||
|
|
static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
|
||
|
|
const char * clip_path = params->mmproj.c_str();
|
||
|
|
|
||
|
|
auto prompt = params->prompt;
|
||
|
|
if (prompt.empty()) {
|
||
|
|
prompt = "describe the image in detail.";
|
||
|
|
}
|
||
|
|
|
||
|
|
auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
|
||
|
|
|
||
|
|
|
||
|
|
llama_context_params ctx_params = common_context_params_to_llama(*params);
|
||
|
|
ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
|
||
|
|
|
||
|
|
llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
|
||
|
|
|
||
|
|
if (ctx_llama == NULL) {
|
||
|
|
LOG_ERR("%s: failed to create the llama_context\n" , __func__);
|
||
|
|
return NULL;
|
||
|
|
}
|
||
|
|
|
||
|
|
auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));
|
||
|
|
|
||
|
|
ctx_llava->ctx_llama = ctx_llama;
|
||
|
|
ctx_llava->ctx_clip = ctx_clip;
|
||
|
|
ctx_llava->model = model;
|
||
|
|
return ctx_llava;
|
||
|
|
}
|
||
|
|
|
||
|
|
static void llava_free(struct llava_context * ctx_llava) {
|
||
|
|
if (ctx_llava->ctx_clip) {
|
||
|
|
clip_free(ctx_llava->ctx_clip);
|
||
|
|
ctx_llava->ctx_clip = NULL;
|
||
|
|
}
|
||
|
|
|
||
|
|
llama_free(ctx_llava->ctx_llama);
|
||
|
|
llama_free_model(ctx_llava->model);
|
||
|
|
llama_backend_free();
|
||
|
|
}
|
||
|
|
|
||
|
|
#ifndef NDEBUG
|
||
|
|
|
||
|
|
static void debug_test_mrope_2d() {
|
||
|
|
// 1. Initialize backend
|
||
|
|
ggml_backend_t backend = NULL;
|
||
|
|
std::string backend_name = "";
|
||
|
|
#ifdef GGML_USE_CUDA
|
||
|
|
fprintf(stderr, "%s: using CUDA backend\n", __func__);
|
||
|
|
backend = ggml_backend_cuda_init(0); // init device 0
|
||
|
|
backend_name = "cuda";
|
||
|
|
if (!backend) {
|
||
|
|
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
|
||
|
|
}
|
||
|
|
#endif
|
||
|
|
// if there aren't GPU Backends fallback to CPU backend
|
||
|
|
if (!backend) {
|
||
|
|
backend = ggml_backend_cpu_init();
|
||
|
|
backend_name = "cpu";
|
||
|
|
}
|
||
|
|
|
||
|
|
// Calculate the size needed to allocate
|
||
|
|
size_t ctx_size = 0;
|
||
|
|
ctx_size += 2 * ggml_tensor_overhead(); // tensors
|
||
|
|
// no need to allocate anything else!
|
||
|
|
|
||
|
|
// 2. Allocate `ggml_context` to store tensor data
|
||
|
|
struct ggml_init_params params = {
|
||
|
|
/*.mem_size =*/ ctx_size,
|
||
|
|
/*.mem_buffer =*/ NULL,
|
||
|
|
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors()
|
||
|
|
};
|
||
|
|
struct ggml_context * ctx = ggml_init(params);
|
||
|
|
|
||
|
|
struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 128, 12, 30);
|
||
|
|
ggml_set_name(inp_raw, "inp_raw");
|
||
|
|
ggml_set_input(inp_raw);
|
||
|
|
|
||
|
|
struct ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 30 * 4);
|
||
|
|
ggml_set_name(pos, "pos");
|
||
|
|
ggml_set_input(pos);
|
||
|
|
|
||
|
|
std::vector<float> dummy_q;
|
||
|
|
dummy_q.resize(128 * 12 * 30);
|
||
|
|
std::fill(dummy_q.begin(), dummy_q.end(), 0.1);
|
||
|
|
// memcpy(inp_raw->data, dummy_q.data(), 128 * 12 * 30 * ggml_element_size(inp_raw));
|
||
|
|
|
||
|
|
std::vector<int> pos_id;
|
||
|
|
pos_id.resize(30 * 4);
|
||
|
|
for (int i = 0; i < 30; i ++) {
|
||
|
|
pos_id[i] = i;
|
||
|
|
pos_id[i + 30] = i + 10;
|
||
|
|
pos_id[i + 60] = i + 20;
|
||
|
|
pos_id[i + 90] = i + 30;
|
||
|
|
}
|
||
|
|
int sections[4] = {32, 32, 0, 0};
|
||
|
|
|
||
|
|
// 4. Allocate a `ggml_backend_buffer` to store all tensors
|
||
|
|
ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
|
||
|
|
|
||
|
|
// 5. Copy tensor data from main memory (RAM) to backend buffer
|
||
|
|
ggml_backend_tensor_set(inp_raw, dummy_q.data(), 0, ggml_nbytes(inp_raw));
|
||
|
|
ggml_backend_tensor_set(pos, pos_id.data(), 0, ggml_nbytes(pos));
|
||
|
|
|
||
|
|
// 6. Create a `ggml_cgraph` for mul_mat operation
|
||
|
|
struct ggml_cgraph * gf = NULL;
|
||
|
|
struct ggml_context * ctx_cgraph = NULL;
|
||
|
|
|
||
|
|
// create a temporally context to build the graph
|
||
|
|
struct ggml_init_params params0 = {
|
||
|
|
/*.mem_size =*/ ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(),
|
||
|
|
/*.mem_buffer =*/ NULL,
|
||
|
|
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
|
||
|
|
};
|
||
|
|
ctx_cgraph = ggml_init(params0);
|
||
|
|
gf = ggml_new_graph(ctx_cgraph);
|
||
|
|
|
||
|
|
struct ggml_tensor * result0 = ggml_rope_multi(
|
||
|
|
ctx_cgraph, inp_raw, pos, nullptr,
|
||
|
|
128/2, sections, LLAMA_ROPE_TYPE_VISION, 32768, 1000000, 1,
|
||
|
|
0, 1, 32, 1);
|
||
|
|
|
||
|
|
// Add "result" tensor and all of its dependencies to the cgraph
|
||
|
|
ggml_build_forward_expand(gf, result0);
|
||
|
|
|
||
|
|
// 7. Create a `ggml_gallocr` for cgraph computation
|
||
|
|
ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
|
||
|
|
ggml_gallocr_alloc_graph(allocr, gf);
|
||
|
|
|
||
|
|
// 9. Run the computation
|
||
|
|
int n_threads = 1; // Optional: number of threads to perform some operations with multi-threading
|
||
|
|
if (ggml_backend_is_cpu(backend)) {
|
||
|
|
ggml_backend_cpu_set_n_threads(backend, n_threads);
|
||
|
|
}
|
||
|
|
ggml_backend_graph_compute(backend, gf);
|
||
|
|
|
||
|
|
// 10. Retrieve results (output tensors)
|
||
|
|
// in this example, output tensor is always the last tensor in the graph
|
||
|
|
struct ggml_tensor * result = result0;
|
||
|
|
// struct ggml_tensor * result = gf->nodes[gf->n_nodes - 1];
|
||
|
|
float * result_data = (float *)malloc(ggml_nbytes(result));
|
||
|
|
// because the tensor data is stored in device buffer, we need to copy it back to RAM
|
||
|
|
ggml_backend_tensor_get(result, result_data, 0, ggml_nbytes(result));
|
||
|
|
const std::string bin_file = "mrope_2d_" + backend_name +".bin";
|
||
|
|
std::ofstream outFile(bin_file, std::ios::binary);
|
||
|
|
|
||
|
|
if (outFile.is_open()) {
|
||
|
|
outFile.write(reinterpret_cast<const char*>(result_data), ggml_nbytes(result));
|
||
|
|
outFile.close();
|
||
|
|
std::cout << "Data successfully written to " + bin_file << std::endl;
|
||
|
|
} else {
|
||
|
|
std::cerr << "Error opening file!" << std::endl;
|
||
|
|
}
|
||
|
|
|
||
|
|
free(result_data);
|
||
|
|
// 11. Free memory and exit
|
||
|
|
ggml_free(ctx_cgraph);
|
||
|
|
ggml_gallocr_free(allocr);
|
||
|
|
ggml_free(ctx);
|
||
|
|
ggml_backend_buffer_free(buffer);
|
||
|
|
ggml_backend_free(backend);
|
||
|
|
}
|
||
|
|
|
||
|
|
static void debug_dump_img_embed(struct llava_context * ctx_llava) {
|
||
|
|
int n_embd = llama_n_embd(llama_get_model(ctx_llava->ctx_llama));
|
||
|
|
int ne = n_embd * 4;
|
||
|
|
float vals[56 * 56 * 3];
|
||
|
|
// float embd[ne];
|
||
|
|
std::vector<float> embd;
|
||
|
|
embd.resize(ne);
|
||
|
|
|
||
|
|
for (int i = 0; i < 56*56; i++)
|
||
|
|
{
|
||
|
|
for (int c = 0; c < 3; c++)
|
||
|
|
vals[i * 3 + c] = (float)(i % (56 * 56)) / (56*56);
|
||
|
|
}
|
||
|
|
|
||
|
|
clip_encode_float_image(ctx_llava->ctx_clip, 16, vals, 56, 56, embd.data());
|
||
|
|
|
||
|
|
std::ofstream outFile("img_embed.bin", std::ios::binary);
|
||
|
|
if (outFile.is_open()) {
|
||
|
|
outFile.write(reinterpret_cast<const char*>(embd.data()), ne * sizeof(float));
|
||
|
|
|
||
|
|
outFile.close();
|
||
|
|
std::cout << "Data successfully written to mrope.bin" << std::endl;
|
||
|
|
} else {
|
||
|
|
std::cerr << "Error opening file!" << std::endl;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
#endif
|
||
|
|
|
||
|
|
|
||
|
|
int main(int argc, char ** argv) {
|
||
|
|
ggml_time_init();
|
||
|
|
|
||
|
|
common_params params;
|
||
|
|
|
||
|
|
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
|
||
|
|
return 1;
|
||
|
|
}
|
||
|
|
|
||
|
|
common_init();
|
||
|
|
|
||
|
|
if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
|
||
|
|
print_usage(argc, argv);
|
||
|
|
return 1;
|
||
|
|
}
|
||
|
|
|
||
|
|
auto * model = llava_init(¶ms);
|
||
|
|
if (model == NULL) {
|
||
|
|
fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
|
||
|
|
return 1;
|
||
|
|
}
|
||
|
|
|
||
|
|
if (prompt_contains_image(params.prompt)) {
|
||
|
|
auto * ctx_llava = llava_init_context(¶ms, model);
|
||
|
|
|
||
|
|
auto * image_embed = load_image(ctx_llava, ¶ms, "");
|
||
|
|
|
||
|
|
// process the prompt
|
||
|
|
process_prompt(ctx_llava, image_embed, ¶ms, params.prompt);
|
||
|
|
|
||
|
|
llama_perf_context_print(ctx_llava->ctx_llama);
|
||
|
|
llava_image_embed_free(image_embed);
|
||
|
|
ctx_llava->model = NULL;
|
||
|
|
llava_free(ctx_llava);
|
||
|
|
#ifndef NDEBUG
|
||
|
|
} else if (params.image[0].empty()) {
|
||
|
|
auto ctx_llava = llava_init_context(¶ms, model);
|
||
|
|
|
||
|
|
debug_test_mrope_2d();
|
||
|
|
debug_dump_img_embed(ctx_llava);
|
||
|
|
|
||
|
|
llama_perf_context_print(ctx_llava->ctx_llama);
|
||
|
|
ctx_llava->model = NULL;
|
||
|
|
llava_free(ctx_llava);
|
||
|
|
#endif
|
||
|
|
} else {
|
||
|
|
for (auto & image : params.image) {
|
||
|
|
auto * ctx_llava = llava_init_context(¶ms, model);
|
||
|
|
|
||
|
|
auto * image_embed = load_image(ctx_llava, ¶ms, image);
|
||
|
|
if (!image_embed) {
|
||
|
|
LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str());
|
||
|
|
return 1;
|
||
|
|
}
|
||
|
|
|
||
|
|
// process the prompt
|
||
|
|
process_prompt(ctx_llava, image_embed, ¶ms, params.prompt);
|
||
|
|
|
||
|
|
llama_perf_context_print(ctx_llava->ctx_llama);
|
||
|
|
llava_image_embed_free(image_embed);
|
||
|
|
ctx_llava->model = NULL;
|
||
|
|
llava_free(ctx_llava);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
llama_free_model(model);
|
||
|
|
|
||
|
|
return 0;
|
||
|
|
}
|