fix OCR template error.

examples/omni-vlm/omni-vlm-cli.cpp

@@ -149,7 +149,7 @@ static void process_prompt(struct omnivlm_context * ctx_omnivlm, struct omni_ima
             LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_omnivlm->ctx_llama, tmp[i]).c_str());
         }
     }
-    LOG_TEE("user_prompt: %s\n", user_prompt.c_str());
+    // LOG_TEE("user_prompt: %s\n", user_prompt.c_str());
     if (params->verbose_prompt) {
         auto tmp = ::llama_tokenize(ctx_omnivlm->ctx_llama, user_prompt, true, true);
         for (int i = 0; i < (int) tmp.size(); i++) {
@@ -165,6 +165,9 @@ static void process_prompt(struct omnivlm_context * ctx_omnivlm, struct omni_ima
 
     LOG("\n");
 
+    params->sparams.temp = 0.0f;
+    params->sparams.top_k = 1;
+    params->sparams.top_p = 1.0f;
     struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
     if (!ctx_sampling) {
         LOG_TEE("%s: failed to initialize sampling subsystem\n", __func__);
@@ -177,8 +180,8 @@ static void process_prompt(struct omnivlm_context * ctx_omnivlm, struct omni_ima
         response += tmp;
         if (strcmp(tmp, "<|im_end|>") == 0) break;
         if (strcmp(tmp, "</s>") == 0) break;
-        // if (strstr(tmp, "###")) break; // Yi-VL behavior
         printf("%s", tmp);
+        // LOG("%s", tmp);
         // 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)
         // if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6
         // if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6
@@ -265,7 +268,7 @@ int main(int argc, char ** argv) {
     }
 
     if (params.omni_vlm_version == "vlm-81-ocr") {
-        params.prompt = "<|im_start|>system\nYou are Nano-Omni-VLM, created by Nexa AI. You are a helpful assistant.<|im_end|>\n<|im_start|>user\n <|ocr_start|><|vision_start|><|image_pad|><|vision_end|><|ocr_end|><|im_end|>";
+        params.prompt = "<|im_start|>system\nYou are Nano-Omni-VLM, created by Nexa AI. You are a helpful assistant.<|im_end|>\n<|im_start|>user\n <|vision_start|><|image_pad|><|vision_end|><|im_end|>";
     } else if (params.omni_vlm_version == "vlm-81-instruct" || params.omni_vlm_version == "nano-vlm-instruct") {
         params.prompt = "<|im_start|>system\nYou are Nano-Omni-VLM, created by Nexa AI. You are a helpful assistant.<|im_end|>\n<|im_start|>user\n" + params.prompt + "\n<|vision_start|><|image_pad|><|vision_end|><|im_end|>";
     } else {
@@ -282,10 +285,6 @@ int main(int argc, char ** argv) {
 
     auto * ctx_omnivlm = omnivlm_init_context(&params, model);
 
-    // temporarily set to greedy decoding.
-    params.sparams.top_k = 1;
-    params.sparams.top_p = 1.0f;
-
     for (auto & image : params.image) {
         auto * image_embed = load_image(ctx_omnivlm, &params, image);
         if (!image_embed) {

examples/omni-vlm/omni-vlm-wrapper.cpp

@@ -222,8 +222,6 @@ static void print_usage(int argc, char ** argv, const gpt_params & params) {
 
 // inference interface definition
 void omnivlm_init(const char* llm_model_path, const char* projector_model_path, const char* omni_vlm_version) {
-    std::cout << "debug0 " << llm_model_path << std::endl;
-    std::cout << "debug1 " << omni_vlm_version << std::endl;
     const char* argv = "omni-wrapper-py";
     char* nc_argv = const_cast<char*>(argv);
     if (!gpt_params_parse(1, &nc_argv, params)) {
@@ -235,8 +233,6 @@ void omnivlm_init(const char* llm_model_path, const char* projector_model_path,
     params.omni_vlm_version = omni_vlm_version;
 
     std::string omni_vlm_ver = params.omni_vlm_version;
-    std::cout << "\t\t DEBUG omni_ver" << std::endl;
-    std::cout << params.omni_vlm_version << std::endl;
     if(omni_vlm_ver != "vlm-81-ocr" && omni_vlm_ver != "vlm-81-instruct" && omni_vlm_ver != "nano-vlm-instruct") {
         fprintf(stderr, "%s: error: you set wrong omni_vlm_string: %s\n", __func__, omni_vlm_version);
         fprintf(stderr, "%s: Valid omni_vlm_version set is ('vlm-81-ocr', 'vlm-81-instruct', 'nano-vlm-instruct')\n", __func__);

examples/omni-vlm/omni-vlm.cpp

@@ -258,111 +258,6 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
 
     *n_img_pos = clip_n_patches(ctx_clip);
     bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd);
-    // cout << "\t\t A NICE START" << endl;
-    // cout << "\t\t" <<  *n_img_pos << endl;
-    /*
-    if (clip_is_minicpmv(ctx_clip)) {
-        std::vector<float *> image_embd_v;
-        image_embd_v.resize(img_res_v.size);
-        struct clip_image_size * load_image_size = clip_image_size_init();
-        for (size_t i = 0; i < img_res_v.size; i++) {
-            const int64_t t_img_enc_step_start_us = ggml_time_us();
-            image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip));
-            int patch_size=14;
-            load_image_size->width = img_res_v.data[i].nx;
-            load_image_size->height = img_res_v.data[i].ny;
-            clip_add_load_image_size(ctx_clip, load_image_size);
-            bool encoded = false;
-            int has_minicpmv_projector = clip_is_minicpmv(ctx_clip);
-            if (has_minicpmv_projector == 2) {
-                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]);
-            }
-            else if (has_minicpmv_projector == 3) {
-                encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
-            }
-            if (!encoded) {
-                LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
-                return false;
-            }
-            const int64_t t_img_enc_steop_batch_us = ggml_time_us();
-            LOG_INF("%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);
-        }
-        const int64_t t_img_enc_batch_us = ggml_time_us();
-        LOG_INF("%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);
-
-        int n_img_pos_out = 0;
-        for (size_t i = 0; i < image_embd_v.size(); i++) {
-            std::memcpy(image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip), image_embd_v[i], clip_embd_nbytes(ctx_clip));
-            n_img_pos_out += clip_n_patches(ctx_clip);
-        }
-        *n_img_pos = n_img_pos_out;
-        for (size_t i = 0; i < image_embd_v.size(); i++) {
-            free(image_embd_v[i]);
-        }
-        image_embd_v.clear();
-        load_image_size->width = img->nx;
-        load_image_size->height = img->ny;
-        clip_add_load_image_size(ctx_clip, load_image_size);
-        LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size->width, load_image_size->height);
-    }
-    else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
-        // flat / default llava-1.5 type embedding
-        *n_img_pos = clip_n_patches(ctx_clip);
-        bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
-        delete[] img_res_v.data;
-        if (!encoded) {
-            LOG_ERR("Unable to encode image\n");
-
-            return false;
-        }
-    }
-    else {
-        // spatial_unpad llava-1.6 type embedding
-        // TODO: CLIP needs batching support - in HF the llm projection is separate after encoding, which might be a solution to quickly get batching working
-        std::vector<float *> image_embd_v;
-        image_embd_v.resize(img_res_v.size);
-        for (size_t i = 0; i < img_res_v.size; i++) {
-            image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
-            const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
-            if (!encoded) {
-                LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
-                return false;
-            }
-        }
-        const int64_t t_img_enc_batch_us = ggml_time_us();
-        LOG_INF("%s: %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);
-
-        const int32_t * image_grid = clip_image_grid(ctx_clip);
-
-        std::vector<std::pair<int, int>> grid_pinpoints;
-        for (int i = 0; i < 32 && image_grid[i] != 0; i += 2) {
-            grid_pinpoints.push_back({image_grid[i], image_grid[i+1]});
-        }
-
-        // free all img_res_v - not needed anymore
-        delete[] img_res_v.data;
-        img_res_v.size = 0;
-        img_res_v.data = nullptr;
-
-        const int32_t image_size = clip_image_size(ctx_clip);
-
-        struct clip_image_grid_shape grid_shape = get_anyres_image_grid_shape({img->nx,img->ny}, grid_pinpoints, image_size);
-
-        int n_img_pos_out;
-        clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out);
-        *n_img_pos = n_img_pos_out;
-
-        for (size_t i = 0; i < image_embd_v.size(); i++) {
-            free(image_embd_v[i]);
-        }
-        image_embd_v.clear();
-
-        // debug image/segment/normalization content:
-        // clip_image_u8 * tmp = clip_image_u8_init();
-        // clip_image_convert_f32_to_u8(*image_feature, *tmp);
-        // clip_image_save_to_bmp(*tmp, "image_feature.bmp");
-    }
-    */
 
     LOG("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);