4af4801566
This change makes it possible to use flags like `--grammar` when using the `llava-cli` program. The rest is just code cleanup deleting a long standing TODO comment. This change also ensures that logging information is emitted to stderr which helps the `llava-cli` command be more friendly to shell scripts. See Mozilla-Ocho/llamafile@1cd334f
256 lines
9.3 KiB
C++
256 lines
9.3 KiB
C++
#include "ggml.h"
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#include "common.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 "base64.hpp"
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#include <cstdio>
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#include <cstdlib>
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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, *n_past, 0))) {
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fprintf(stderr, "%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 = ::llama_tokenize(ctx_llama, str2, add_bos);
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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 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 (id == llama_token_eos(llama_get_model(ctx_llama))) {
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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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}
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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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fprintf(stderr, "%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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fprintf(stderr, "%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 show_additional_info(int /*argc*/, char ** argv) {
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fprintf(stderr, "\n example usage: %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> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
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fprintf(stderr, " 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, gpt_params * params) {
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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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fprintf(stderr, "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->n_threads, prompt);
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if (!embed) {
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fprintf(stderr, "%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->n_threads, params->image.c_str());
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if (!embed) {
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fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.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, gpt_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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const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx_llava->ctx_llama));
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// llava chat format is "<system_prompt>\nUSER:<image_embeddings>\n<textual_prompt>\nASSISTANT:"
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eval_string(ctx_llava->ctx_llama, "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:", params->n_batch, &n_past, add_bos);
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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, (prompt + "\nASSISTANT:").c_str(), params->n_batch, &n_past, false);
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// generate the response
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fprintf(stderr, "\n");
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struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
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for (int i = 0; i < max_tgt_len; i++) {
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const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past);
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if (strcmp(tmp, "</s>") == 0) break;
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printf("%s", tmp);
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fflush(stdout);
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}
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llama_sampling_free(ctx_sampling);
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printf("\n");
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}
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static struct llava_context * llava_init(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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auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
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llama_backend_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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fprintf(stderr , "%s: error: unable to load model\n" , __func__);
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return NULL;
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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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fprintf(stderr , "%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 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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gpt_params params;
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if (!gpt_params_parse(argc, argv, params)) {
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show_additional_info(argc, argv);
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return 1;
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}
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if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
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gpt_print_usage(argc, argv, params);
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show_additional_info(argc, argv);
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return 1;
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}
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auto ctx_llava = llava_init(¶ms);
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if (ctx_llava == NULL) {
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fprintf(stderr, "%s: error: failed to init llava\n", __func__);
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return 1;
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}
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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_print_timings(ctx_llava->ctx_llama);
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llava_image_embed_free(image_embed);
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llava_free(ctx_llava);
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return 0;
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}
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