llava : support MiniCPM-V-2.5 (#7599)
* init * rename * add run android for termux in readme * add android readme * add instructions in readme * change name in readme * Update README.md * fixed line * add result in readme * random pos_embed * add positions index * change for ollama * change for ollama * better pos_embed in clip * support ollama * updata cmakelist * updata cmakelist * rename wrapper * clear code * replace and organize code * add link * sync master * fix warnings * fix warnings * fix bug in bicubic resize when need resize iamge smaller * receive review comments and modify * receive review comments and modify * put all code into llava dir * fix quality problem in pr code * change n_layer * add space in "-1" * imitate reshape bug of python code * fix bug in clip * fix issues for merging * fix llama-minicpmv-cli in cmake file * change pr readme * fix code review * remove in line 33 directory in the /cmakelists.txt (not in example, in the main dir * fix cmakefile * add warn * fix KEY_HAS_MINICPMV_PROJ * remove load_image_size into clip_ctx * remove the extern "C", MINICPMV_API * fix uhd code for review comment * delete minicpmv-wrapper in pr * remove uhd_image_embed * Modify 2 notes * clip : style changes * del common.h in clip * fix Type-Check error * fix Type-Check error * fix Type-Check error * fix Type-Check error * fix makefile error * fix ubuntu-make error * try fix clip * try fix 1 --------- Co-authored-by: Hongji Zhu <fireyoucan@gmail.com> Co-authored-by: harvestingmoon <leewenyeong@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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12 changed files with 1488 additions and 79 deletions
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@ -202,6 +202,33 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
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return true;
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}
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static clip_image_f32 * only_v2_5_reshape_by_patch(clip_image_f32 * image, int patch_size) {
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int width = image->nx;
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int height = image->ny;
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int num_patches = (height / patch_size) * (width / patch_size);
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clip_image_f32 * patch = clip_image_f32_init();
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patch->nx = patch_size * num_patches;
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patch->ny = patch_size;
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patch->buf.resize(3 * patch->nx * patch->ny);
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int patch_index = 0;
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for (int i = 0; i < height; i += patch_size) {
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for (int j = 0; j < width; j += patch_size) {
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for (int pi = 0; pi < patch_size; ++pi) {
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for (int pj = 0; pj < patch_size; ++pj) {
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int input_index = ((i + pi) * width + (j + pj)) * 3;
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int output_index = (pi * patch_size * num_patches + patch_index * patch_size + pj) * 3;
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patch->buf[output_index] = image->buf[input_index];
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patch->buf[output_index+1] = image->buf[input_index+1];
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patch->buf[output_index+2] = image->buf[input_index+2];
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}
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}
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patch_index++;
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}
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}
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return patch;
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}
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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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// std::vector<clip_image_f32*> img_res_v; // format VectN x H x W x RGB (N x 336 x 336 x 3), so interleaved RGB - different to the python implementation which is N x 3 x 336 x 336
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@ -218,7 +245,44 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
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const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip);
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if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
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if (clip_is_minicpmv(ctx_clip)) {
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std::vector<float *> image_embd_v;
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image_embd_v.resize(img_res_v.size);
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struct clip_image_size * load_image_size = clip_image_size_init();
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for (size_t i = 0; i < img_res_v.size; i++) {
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const int64_t t_img_enc_step_start_us = ggml_time_us();
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image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip));
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int patch_size=14;
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load_image_size->width = img_res_v.data[i].nx;
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load_image_size->height = img_res_v.data[i].ny;
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clip_add_load_image_size(ctx_clip, load_image_size);
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const bool encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
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if (!encoded) {
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LOG_TEE("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
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return false;
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}
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const int64_t t_img_enc_steop_batch_us = ggml_time_us();
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LOG_TEE("%s: step %d of %d encoded in %8.2f ms\n", __func__, (int)i+1, (int)img_res_v.size, (t_img_enc_steop_batch_us - t_img_enc_step_start_us) / 1000.0);
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}
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const int64_t t_img_enc_batch_us = ggml_time_us();
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LOG_TEE("%s: all %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
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int n_img_pos_out = 0;
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for (size_t i = 0; i < image_embd_v.size(); i++) {
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std::memcpy(image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip), image_embd_v[i], clip_embd_nbytes(ctx_clip));
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n_img_pos_out += clip_n_patches(ctx_clip);
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}
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*n_img_pos = n_img_pos_out;
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for (size_t i = 0; i < image_embd_v.size(); i++) {
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free(image_embd_v[i]);
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}
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image_embd_v.clear();
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load_image_size->width = img->nx;
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load_image_size->height = img->ny;
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clip_add_load_image_size(ctx_clip, load_image_size);
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LOG_TEE("%s: load_image_size %d %d\n", __func__, load_image_size->width, load_image_size->height);
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}
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else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
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// flat / default llava-1.5 type embedding
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*n_img_pos = clip_n_patches(ctx_clip);
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bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
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@ -228,7 +292,8 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
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return false;
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}
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} else {
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}
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else {
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// spatial_unpad llava-1.6 type embedding
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// TODO: CLIP needs batching support - in HF the llm projection is separate after encoding, which might be a solution to quickly get batching working
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std::vector<float *> image_embd_v;
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@ -297,7 +362,11 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
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}
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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)*6); // TODO: base on gridsize/llava model
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int num_max_patches = 6;
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if (clip_is_minicpmv(ctx_clip)) {
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num_max_patches = 10;
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}
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float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model
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if (!image_embd) {
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LOG_TEE("Unable to allocate memory for image embeddings\n");
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return false;
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