llava : expose as a shared library for downstream projects (#3613)
* wip llava python bindings compatibility * add external llava API * add base64 in-prompt image support * wip refactor image loading * refactor image load out of llava init * cleanup * further cleanup; move llava-cli into its own file and rename * move base64.hpp into common/ * collapse clip and llava libraries * move llava into its own subdir * wip * fix bug where base64 string was not removed from the prompt * get libllava to output in the right place * expose llava methods in libllama.dylib * cleanup memory usage around clip_image_* * cleanup and refactor *again* * update headerdoc * build with cmake, not tested (WIP) * Editorconfig * Editorconfig * Build with make * Build with make * Fix cyclical depts on Windows * attempt to fix build on Windows * attempt to fix build on Windows * Upd TODOs * attempt to fix build on Windows+CUDA * Revert changes in cmake * Fix according to review comments * Support building as a shared library * address review comments --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> Co-authored-by: Jared Van Bortel <jared@nomic.ai>
This commit is contained in:
Damian Stewart
GitHub
parent
2833a6f63c
commit
381efbf480
13 changed files with 1022 additions and 354 deletions
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@ -1,164 +1,156 @@
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#include "clip.h"
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#include "llava-utils.h"
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#include "common.h"
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#include "llama.h"
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#include "llava.h"
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#include <cstdio>
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#include <cstdlib>
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#include <vector>
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static void show_additional_info(int /*argc*/, char ** argv) {
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printf("\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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printf(" note: a lower temperature value like 0.1 is recommended for better quality.\n");
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}
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#include "base64.hpp"
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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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static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
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clip_image_f32 * img_res = make_clip_image_f32();
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if (!clip_image_preprocess(ctx_clip, img, img_res, /*pad2square =*/ true)) {
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fprintf(stderr, "%s: unable to preprocess image\n", __func__);
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clip_image_f32_free(img_res);
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return false;
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}
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if (params.mmproj.empty() || params.image.empty()) {
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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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const char * clip_path = params.mmproj.c_str();
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const char * img_path = params.image.c_str();
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if (params.prompt.empty()) {
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params.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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// load and preprocess the image
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clip_image_u8 img;
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clip_image_f32 img_res;
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if (!clip_image_load_from_file(img_path, &img)) {
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fprintf(stderr, "%s: is %s really an image file?\n", __func__, img_path);
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clip_free(ctx_clip);
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return 1;
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}
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if (!clip_image_preprocess(ctx_clip, &img, &img_res, /*pad2square =*/ true)) {
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fprintf(stderr, "%s: unable to preprocess %s\n", __func__, img_path);
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clip_free(ctx_clip);
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return 1;
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}
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int n_img_pos = clip_n_patches(ctx_clip);
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int n_img_embd = clip_n_mmproj_embd(ctx_clip);
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float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip));
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if (!image_embd) {
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fprintf(stderr, "Unable to allocate memory for image embeddings\n");
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return 1;
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}
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*n_img_pos = clip_n_patches(ctx_clip);
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const int64_t t_img_enc_start_us = ggml_time_us();
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if (!clip_image_encode(ctx_clip, params.n_threads, &img_res, image_embd)) {
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bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd);
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clip_image_f32_free(img_res);
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if (!encoded) {
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fprintf(stderr, "Unable to encode image\n");
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return 1;
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return false;
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}
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const int64_t t_img_enc_end_us = ggml_time_us();
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float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
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// we get the embeddings, free up the memory required for CLIP
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clip_free(ctx_clip);
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printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
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llama_backend_init(params.numa);
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llama_model_params model_params = llama_model_default_params();
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model_params.n_gpu_layers = params.n_gpu_layers;
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model_params.main_gpu = params.main_gpu;
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model_params.tensor_split = params.tensor_split;
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model_params.use_mmap = params.use_mmap;
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model_params.use_mlock = params.use_mlock;
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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 1;
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}
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llama_context_params ctx_params = llama_context_default_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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ctx_params.n_threads = params.n_threads;
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ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
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ctx_params.seed = params.seed;
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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 1;
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}
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// make sure that the correct mmproj was used, i.e., compare apples to apples
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const int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
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if (n_img_embd != n_llama_embd) {
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printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_img_embd, n_llama_embd);
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llama_free(ctx_llama);
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llama_free_model(model);
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llama_backend_free();
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free(image_embd);
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return 1;
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}
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// process the prompt
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// llava chat format is "<system_prompt>USER: <image_embeddings>\n<textual_prompt>\nASSISTANT:"
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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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eval_string(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, true);
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eval_image_embd(ctx_llama, image_embd, n_img_pos, params.n_batch, &n_past);
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eval_string(ctx_llama, (params.prompt + "\nASSISTANT:").c_str(), params.n_batch, &n_past, false);
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// generate the response
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printf("\n");
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printf("prompt: '%s'\n", params.prompt.c_str());
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printf("\n");
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for (int i = 0; i < max_tgt_len; i++) {
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const char * tmp = sample(ctx_llama, params, &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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printf("\n");
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{
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const float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
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printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / n_img_pos);
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}
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llama_print_timings(ctx_llama);
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llama_free(ctx_llama);
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llama_free_model(model);
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llama_backend_free();
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free(image_embd);
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return 0;
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return true;
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}
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bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) {
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// make sure that the correct mmproj was used, i.e., compare apples to apples
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int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
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auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
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if (n_image_embd != n_llama_embd) {
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printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
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return false;
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}
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return true;
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}
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static bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
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float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip));
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if (!image_embd) {
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fprintf(stderr, "Unable to allocate memory for image embeddings\n");
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free(image_embd);
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return false;
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}
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int n_img_pos;
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if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
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fprintf(stderr, "%s: cannot encode image, aborting\n", __func__);
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free(image_embd);
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return false;
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}
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*image_embd_out = image_embd;
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*n_img_pos_out = n_img_pos;
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return true;
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}
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bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) {
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int n_embd = llama_n_embd(llama_get_model(ctx_llama));
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for (int i = 0; i < image_embed->n_image_pos; i += n_batch) {
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int n_eval = image_embed->n_image_pos - 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_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
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if (llama_decode(ctx_llama, batch)) {
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fprintf(stderr, "%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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}
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return true;
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}
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LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {
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clip_image_u8 * img = make_clip_image_u8();
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if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
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clip_image_u8_free(img);
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fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__);
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return NULL;
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}
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float* image_embed = NULL;
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int n_image_pos = 0;
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bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
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if (!image_embed_result) {
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clip_image_u8_free(img);
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fprintf(stderr, "%s: coulnd't embed the image\n", __func__);
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return NULL;
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}
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clip_image_u8_free(img);
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auto result = (llava_image_embed*)malloc(sizeof(llava_image_embed));
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result->embed = image_embed;
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result->n_image_pos = n_image_pos;
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return result;
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}
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static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {
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auto file = fopen(path, "rb");
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if (file == NULL) {
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fprintf(stderr, "%s: can't read file %s\n", __func__, path);
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return false;
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}
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fseek(file, 0, SEEK_END);
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auto fileSize = ftell(file);
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fseek(file, 0, SEEK_SET);
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auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data
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if (buffer == NULL) {
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fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
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perror("Memory allocation error");
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fclose(file);
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return false;
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}
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fread(buffer, 1, fileSize, file); // Read the file into the buffer
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fclose(file); // Close the file
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*bytesOut = buffer;
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*sizeOut = fileSize;
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return true;
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}
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LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) {
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unsigned char* image_bytes;
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long image_bytes_length;
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auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
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if (!loaded) {
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fprintf(stderr, "%s: failed to load %s\n", __func__, image_path);
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return NULL;
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}
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auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length);
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free(image_bytes);
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return embed;
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}
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LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed) {
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free(embed->embed);
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free(embed);
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}
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