mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-31 08:51:55 +00:00
d9d54e498d
* speculative : refactor and add a simpler example ggml-ci * speculative : clean-up and add comments and TODOs [no ci] * speculative : manage context in common_speculative ggml-ci * speculative : simplify ggml-ci * speculative : simplify (cont) ggml-ci * speculative : add --draft-min CLI arg * speculative : minor fixup * make : build fixes * speculative : do not redraft previous drafts ggml-ci * speculative : fix the draft sampling ggml-ci * speculative : fix compile warning * common : refactor args ggml-ci * common : change defaults [no ci] * common : final touches ggml-ci
330 lines
12 KiB
C++
330 lines
12 KiB
C++
#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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#include <cstdio>
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#include <cstdlib>
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#include <cstring>
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#include <vector>
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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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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))) {
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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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}
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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) {
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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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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 = common_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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static const char * sample(struct common_sampler * smpl,
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struct llama_context * ctx_llama,
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int * n_past) {
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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);
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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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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("<image>");
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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("<image>").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 = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:";
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user_prompt = prompt + "\nASSISTANT:";
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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, true);
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llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past);
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eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, 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);
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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);
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llama_model_params model_params = common_model_params_to_llama(*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_ERR("%s: 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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static struct llava_context * llava_init_context(common_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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auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
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llama_context_params ctx_params = common_context_params_to_llama(*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_ERR("%s: 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 llava_context *)malloc(sizeof(llava_context));
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ctx_llava->ctx_llama = ctx_llama;
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ctx_llava->ctx_clip = ctx_clip;
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ctx_llava->model = model;
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return ctx_llava;
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}
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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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int main(int argc, char ** argv) {
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ggml_time_init();
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common_params params;
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if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
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return 1;
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}
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common_init();
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if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
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print_usage(argc, argv);
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return 1;
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}
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auto * model = llava_init(¶ms);
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if (model == NULL) {
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fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
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return 1;
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}
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if (prompt_contains_image(params.prompt)) {
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auto * ctx_llava = llava_init_context(¶ms, model);
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auto * image_embed = load_image(ctx_llava, ¶ms, "");
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// process the prompt
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process_prompt(ctx_llava, image_embed, ¶ms, params.prompt);
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llama_perf_context_print(ctx_llava->ctx_llama);
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llava_image_embed_free(image_embed);
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ctx_llava->model = NULL;
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llava_free(ctx_llava);
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} else {
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for (auto & image : params.image) {
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auto * ctx_llava = llava_init_context(¶ms, model);
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auto * image_embed = load_image(ctx_llava, ¶ms, image);
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if (!image_embed) {
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LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str());
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return 1;
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}
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// process the prompt
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process_prompt(ctx_llava, image_embed, ¶ms, params.prompt);
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llama_perf_context_print(ctx_llava->ctx_llama);
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llava_image_embed_free(image_embed);
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ctx_llava->model = NULL;
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llava_free(ctx_llava);
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}
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}
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llama_free_model(model);
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return 0;
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}
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