diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 5954bf6cd..e431c7f70 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -3,6 +3,7 @@ // I'll gradually clean and extend it // Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch #include "clip.h" +#include "log.h" #include "ggml.h" #include "ggml-alloc.h" #include "ggml-backend.h" @@ -23,7 +24,6 @@ #include #include #include -#include #include #include #include @@ -145,7 +145,7 @@ static std::map PROJECTOR_TYPE_NAMES = { static int get_key_idx(const gguf_context * ctx, const char * key) { int i = gguf_find_key(ctx, key); if (i == -1) { - fprintf(stderr, "key %s not found in file\n", key); + LOG_TEE("key %s not found in file\n", key); throw std::runtime_error(format("Missing required key: %s", key)); } @@ -247,7 +247,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") { size_t tensor_size = ggml_nbytes(tensor); - printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n", + LOG_TEE("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n", prefix, ggml_n_dims(tensor), tensor->name, tensor_size, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type)); } @@ -265,7 +265,7 @@ static projector_type clip_projector_type_from_string(const std::string & name) static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) { std::ofstream file(filename, std::ios::binary); if (!file.is_open()) { - std::cerr << "Failed to open file for writing: " << filename << std::endl; + LOG_TEE("Failed to open file for writing: %s\n", filename.c_str()); return; } @@ -284,7 +284,7 @@ static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::s static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) { std::ofstream file(filename, std::ios::binary); if (!file.is_open()) { - std::cerr << "Failed to open file for writing: " << filename << std::endl; + LOG_TEE("Failed to open file for writing: %s\n", filename.c_str()); return; } @@ -515,7 +515,7 @@ struct clip_ctx { static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) { if (!ctx->has_vision_encoder) { - printf("This gguf file seems to have no vision encoder\n"); + LOG_TEE("This gguf file seems to have no vision encoder\n"); return nullptr; } @@ -879,21 +879,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { const int idx_name = gguf_find_key(ctx, KEY_NAME); if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug const std::string name = gguf_get_val_str(ctx, idx_name); - printf("%s: model name: %s\n", __func__, name.c_str()); + LOG_TEE("%s: model name: %s\n", __func__, name.c_str()); } - printf("%s: description: %s\n", __func__, description.c_str()); - printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx)); - printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); - printf("%s: n_tensors: %d\n", __func__, n_tensors); - printf("%s: n_kv: %d\n", __func__, n_kv); - printf("%s: ftype: %s\n", __func__, ftype_str.c_str()); - printf("\n"); + LOG_TEE("%s: description: %s\n", __func__, description.c_str()); + LOG_TEE("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx)); + LOG_TEE("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx)); + LOG_TEE("%s: n_tensors: %d\n", __func__, n_tensors); + LOG_TEE("%s: n_kv: %d\n", __func__, n_kv); + LOG_TEE("%s: ftype: %s\n", __func__, ftype_str.c_str()); + LOG_TEE("\n"); } const int n_tensors = gguf_get_n_tensors(ctx); // kv const int n_kv = gguf_get_n_kv(ctx); - printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n", + LOG_TEE("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n", __func__, n_kv, n_tensors, fname); { std::map n_type; @@ -904,7 +904,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { n_type[type]++; } - printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__); + LOG_TEE("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__); for (int i = 0; i < n_kv; i++) { const char * name = gguf_get_key(ctx, i); const enum gguf_type type = gguf_get_kv_type(ctx, i); @@ -920,7 +920,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } replace_all(value, "\n", "\\n"); - printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str()); + LOG_TEE("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str()); } // print type counts @@ -929,7 +929,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { continue; } - printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second); + LOG_TEE("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second); } } @@ -944,7 +944,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { size_t tensor_size = ggml_nbytes(cur); model_size += tensor_size; if (verbosity >= 3) { - printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n", + LOG_TEE("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n", __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type)); } } @@ -971,18 +971,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { #ifdef GGML_USE_CUDA new_clip->backend = ggml_backend_cuda_init(0); - printf("%s: CLIP using CUDA backend\n", __func__); + LOG_TEE("%s: CLIP using CUDA backend\n", __func__); #endif #ifdef GGML_USE_METAL new_clip->backend = ggml_backend_metal_init(); - printf("%s: CLIP using Metal backend\n", __func__); + LOG_TEE("%s: CLIP using Metal backend\n", __func__); #endif if (!new_clip->backend) { new_clip->backend = ggml_backend_cpu_init(); - printf("%s: CLIP using CPU backend\n", __func__); + LOG_TEE("%s: CLIP using CPU backend\n", __func__); } // model size and capabilities @@ -1006,15 +1006,15 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { new_clip->use_gelu = gguf_get_val_bool(ctx, idx); if (verbosity >= 1) { - printf("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder); - printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder); - printf("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector); - printf("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0); - printf("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0); + LOG_TEE("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder); + LOG_TEE("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder); + LOG_TEE("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector); + LOG_TEE("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0); + LOG_TEE("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0); } } - printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors); + LOG_TEE("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors); // load tensors { @@ -1027,7 +1027,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { new_clip->ctx_data = ggml_init(params); if (!new_clip->ctx_data) { - fprintf(stderr, "%s: ggml_init() failed\n", __func__); + LOG_TEE("%s: ggml_init() failed\n", __func__); clip_free(new_clip); gguf_free(ctx); return nullptr; @@ -1035,7 +1035,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { auto fin = std::ifstream(fname, std::ios::binary); if (!fin) { - printf("cannot open model file for loading tensors\n"); + LOG_TEE("cannot open model file for loading tensors\n"); clip_free(new_clip); gguf_free(ctx); return nullptr; @@ -1057,7 +1057,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i); fin.seekg(offset, std::ios::beg); if (!fin) { - printf("%s: failed to seek for tensor %s\n", __func__, name); + LOG_TEE("%s: failed to seek for tensor %s\n", __func__, name); clip_free(new_clip); gguf_free(ctx); return nullptr; @@ -1128,23 +1128,23 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } if (verbosity >= 2) { - printf("\n%s: vision model hparams\n", __func__); - printf("image_size %d\n", hparams.image_size); - printf("patch_size %d\n", hparams.patch_size); - printf("v_hidden_size %d\n", hparams.hidden_size); - printf("v_n_intermediate %d\n", hparams.n_intermediate); - printf("v_projection_dim %d\n", hparams.projection_dim); - printf("v_n_head %d\n", hparams.n_head); - printf("v_n_layer %d\n", hparams.n_layer); - printf("v_eps %f\n", hparams.eps); - printf("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]); - printf("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]); - printf("v_image_grid_pinpoints: "); + LOG_TEE("\n%s: vision model hparams\n", __func__); + LOG_TEE("image_size %d\n", hparams.image_size); + LOG_TEE("patch_size %d\n", hparams.patch_size); + LOG_TEE("v_hidden_size %d\n", hparams.hidden_size); + LOG_TEE("v_n_intermediate %d\n", hparams.n_intermediate); + LOG_TEE("v_projection_dim %d\n", hparams.projection_dim); + LOG_TEE("v_n_head %d\n", hparams.n_head); + LOG_TEE("v_n_layer %d\n", hparams.n_layer); + LOG_TEE("v_eps %f\n", hparams.eps); + LOG_TEE("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]); + LOG_TEE("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]); + LOG_TEE("v_image_grid_pinpoints: "); for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) { - printf("%d ", hparams.image_grid_pinpoints[i]); + LOG_TEE("%d ", hparams.image_grid_pinpoints[i]); } - printf("\n"); - printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type); + LOG_TEE("\n"); + LOG_TEE("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type); } @@ -1155,7 +1155,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); } catch(const std::exception& e) { - fprintf(stderr, "%s: failed to load vision model tensors\n", __func__); + LOG_TEE("%s: failed to load vision model tensors\n", __func__); } // LLaVA projection @@ -1184,7 +1184,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } catch (std::runtime_error & e) { } try { vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE); - // fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__); + // LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__); } catch (std::runtime_error & e) { } } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) { // MobileVLM projection @@ -1264,7 +1264,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch); ggml_gallocr_reserve(new_clip->compute_alloc, gf); size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0); - printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0); + LOG_TEE("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0); } return new_clip; @@ -1304,7 +1304,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) { int nx, ny, nc; auto * data = stbi_load(fname, &nx, &ny, &nc, 3); if (!data) { - fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname); + LOG_TEE("%s: failed to load image '%s'\n", __func__, fname); return false; } build_clip_img_from_data(data, nx, ny, img); @@ -1316,7 +1316,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length int nx, ny, nc; auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3); if (!data) { - fprintf(stderr, "%s: failed to decode image bytes\n", __func__); + LOG_TEE("%s: failed to decode image bytes\n", __func__); return false; } build_clip_img_from_data(data, nx, ny, img); @@ -1506,7 +1506,7 @@ static std::pair select_best_resolution(const std::pair & or int downscaled_height = static_cast(original_height * scale); int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height); int wasted_resolution = (width * height) - effective_resolution; - // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); + // LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) { max_effective_resolution = effective_resolution; min_wasted_resolution = wasted_resolution; @@ -1545,7 +1545,7 @@ static std::vector divide_to_patches_u8(const clip_image_u8 & im bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) { bool pad_to_square = true; if (!ctx->has_vision_encoder) { - printf("This gguf file seems to have no vision encoder\n"); + LOG_TEE("This gguf file seems to have no vision encoder\n"); return false; } auto & params = ctx->vision_model.hparams; @@ -1622,7 +1622,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli } for (size_t i = 0; i < patches.size(); i++) { - // printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny); + // LOG_TEE("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny); clip_image_u8_free(patches[i]); } @@ -1765,7 +1765,7 @@ int clip_n_patches(const struct clip_ctx * ctx) { bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) { if (!ctx->has_vision_encoder) { - printf("This gguf file seems to have no vision encoder\n"); + LOG_TEE("This gguf file seems to have no vision encoder\n"); return false; } @@ -1777,7 +1777,7 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3 bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) { if (!ctx->has_vision_encoder) { - printf("This gguf file seems to have no vision encoder\n"); + LOG_TEE("This gguf file seems to have no vision encoder\n"); return false; } @@ -1939,7 +1939,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i new_type = type; if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) { new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type - // fprintf(stderr, "%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type)); + // LOG_TEE("%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type)); } const size_t n_elms = ggml_nelements(cur); float * f32_data; @@ -1958,7 +1958,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i f32_data = (float *)conv_buf.data(); break; default: - printf("Please use an input file in f32 or f16\n"); + LOG_TEE("Please use an input file in f32 or f16\n"); gguf_free(ctx_out); return false; } @@ -1985,7 +1985,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i fout.put(0); } - printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize, + LOG_TEE("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize, orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); } @@ -2001,8 +2001,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i gguf_free(ctx_out); { - printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); - printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); + LOG_TEE("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0); + LOG_TEE("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0); } return true; diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 50dac4cae..a44c6cd76 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -1,4 +1,5 @@ #include "ggml.h" +#include "log.h" #include "common.h" #include "clip.h" #include "llava.h" @@ -18,7 +19,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END); + LOG_TEE("%s: invalid base64 image tag. must be %s%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END); return NULL; } @@ -87,7 +88,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size()); if (!embed) { - fprintf(stderr, "%s: could not load image from base64 string.\n", __func__); + LOG_TEE("%s: could not load image from base64 string.\n", __func__); return NULL; } @@ -112,8 +113,8 @@ struct llava_context { }; static void show_additional_info(int /*argc*/, char ** argv) { - fprintf(stderr, "\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); - fprintf(stderr, " note: a lower temperature value like 0.1 is recommended for better quality.\n"); + LOG_TEE("\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + LOG_TEE(" note: a lower temperature value like 0.1 is recommended for better quality.\n"); } static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) { @@ -123,18 +124,18 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para auto prompt = params->prompt; if (prompt_contains_image(prompt)) { if (!params->image.empty()) { - fprintf(stderr, "using base64 encoded image instead of command line image path\n"); + LOG_TEE("using base64 encoded image instead of command line image path\n"); } embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt); if (!embed) { - fprintf(stderr, "%s: can't load image from prompt\n", __func__); + LOG_TEE("%s: can't load image from prompt\n", __func__); return NULL; } params->prompt = remove_image_from_prompt(prompt); } else { embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str()); if (!embed) { - fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str()); + LOG_TEE("%s: is %s really an image file?\n", __func__, params->image.c_str()); return NULL; } } @@ -153,18 +154,18 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ // new templating mode: Provide the full prompt including system message and use as a placeholder for the image system_prompt = prompt.substr(0, image_pos); user_prompt = prompt.substr(image_pos + std::string("").length()); - printf("system_prompt: %s\n", system_prompt.c_str()); + LOG_TEE("system_prompt: %s\n", system_prompt.c_str()); if (params->verbose_prompt) { auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true); for (int i = 0; i < (int) tmp.size(); i++) { - printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); } } - printf("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_llava->ctx_llama, user_prompt, true, true); for (int i = 0; i < (int) tmp.size(); i++) { - printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); } } } else { @@ -174,7 +175,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ if (params->verbose_prompt) { auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true); for (int i = 0; i < (int) tmp.size(); i++) { - printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); + LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str()); } } } @@ -185,7 +186,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ // generate the response - fprintf(stderr, "\n"); + LOG_TEE("\n"); struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams); std::string response = ""; @@ -224,7 +225,7 @@ static struct llava_context * llava_init(gpt_params * params) { llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params); if (model == NULL) { - fprintf(stderr , "%s: error: unable to load model\n" , __func__); + LOG_TEE("%s: error: unable to load model\n" , __func__); return NULL; } @@ -234,7 +235,7 @@ static struct llava_context * llava_init(gpt_params * params) { llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params); if (ctx_llama == NULL) { - fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + LOG_TEE("%s: error: failed to create the llama_context\n" , __func__); return NULL; } @@ -257,6 +258,12 @@ static void llava_free(struct llava_context * ctx_llava) { llama_backend_free(); } +static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) { + (void) level; + (void) user_data; + LOG_TEE("%s", text); +} + int main(int argc, char ** argv) { ggml_time_init(); @@ -266,6 +273,14 @@ int main(int argc, char ** argv) { show_additional_info(argc, argv); return 1; } + +#ifndef LOG_DISABLE_LOGS + log_set_target(log_filename_generator("llava", "log")); + LOG_TEE("Log start\n"); + log_dump_cmdline(argc, argv); + llama_log_set(llama_log_callback_logTee, nullptr); +#endif // LOG_DISABLE_LOGS + if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { gpt_print_usage(argc, argv, params); show_additional_info(argc, argv); @@ -274,7 +289,7 @@ int main(int argc, char ** argv) { auto ctx_llava = llava_init(¶ms); if (ctx_llava == NULL) { - fprintf(stderr, "%s: error: failed to init llava\n", __func__); + LOG_TEE("%s: error: failed to init llava\n", __func__); return 1; } diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 29764757a..9a990bb18 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -54,7 +54,7 @@ static std::pair select_best_resolution(const std::pair& ori int downscaled_height = static_cast(original_height * scale); int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height); int wasted_resolution = (width * height) - effective_resolution; - // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); + // LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution); if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) { max_effective_resolution = effective_resolution; min_wasted_resolution = wasted_resolution; @@ -154,13 +154,13 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]); if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) { if (newline_tmp->buffer == NULL) { - printf("newline_tmp tensor buffer is NULL\n"); + LOG_TEE("newline_tmp tensor buffer is NULL\n"); } ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp)); } else { model.newline->data = newline_tmp->data; if (model.newline->data == NULL) { - printf("newline_tmp tensor data is NULL\n"); + LOG_TEE("newline_tmp tensor data is NULL\n"); } } @@ -224,7 +224,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli img_res_v.size = 0; img_res_v.data = nullptr; if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) { - fprintf(stderr, "%s: unable to preprocess image\n", __func__); + LOG_TEE("%s: unable to preprocess image\n", __func__); delete[] img_res_v.data; return false; } @@ -239,7 +239,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli 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) { - fprintf(stderr, "Unable to encode image\n"); + LOG_TEE("Unable to encode image\n"); return false; } @@ -252,12 +252,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli 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) { - fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size); + LOG_TEE("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(); - printf("%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); + LOG_TEE("%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); @@ -290,12 +290,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli // clip_image_save_to_bmp(*tmp, "image_feature.bmp"); } - printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos); + LOG_TEE("%s: image embedding created: %d tokens\n", __func__, *n_img_pos); const int64_t t_img_enc_end_us = ggml_time_us(); float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0; - 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); + LOG_TEE("\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); return true; } @@ -305,7 +305,7 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama)); auto n_image_embd = clip_n_mmproj_embd(ctx_clip); if (n_image_embd != n_llama_embd) { - 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); + LOG_TEE("%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); return false; } return true; @@ -314,13 +314,13 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * 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) { float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model if (!image_embd) { - fprintf(stderr, "Unable to allocate memory for image embeddings\n"); + LOG_TEE("Unable to allocate memory for image embeddings\n"); return false; } int n_img_pos; if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) { - fprintf(stderr, "%s: cannot encode image, aborting\n", __func__); + LOG_TEE("%s: cannot encode image, aborting\n", __func__); free(image_embd); return false; } @@ -340,7 +340,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_ } llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, }; if (llama_decode(ctx_llama, batch)) { - fprintf(stderr, "%s : failed to eval\n", __func__); + LOG_TEE("%s : failed to eval\n", __func__); return false; } *n_past += n_eval; @@ -352,7 +352,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c clip_image_u8 * img = clip_image_u8_init(); if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) { clip_image_u8_free(img); - fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__); + LOG_TEE("%s: can't load image from bytes, is it a valid image?", __func__); return NULL; } @@ -361,7 +361,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos); if (!image_embed_result) { clip_image_u8_free(img); - fprintf(stderr, "%s: coulnd't embed the image\n", __func__); + LOG_TEE("%s: coulnd't embed the image\n", __func__); return NULL; } @@ -375,7 +375,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) { auto file = fopen(path, "rb"); if (file == NULL) { - fprintf(stderr, "%s: can't read file %s\n", __func__, path); + LOG_TEE("%s: can't read file %s\n", __func__, path); return false; } @@ -385,7 +385,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data if (buffer == NULL) { - fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path); + LOG_TEE("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path); perror("Memory allocation error"); fclose(file); return false; @@ -410,7 +410,7 @@ struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx long image_bytes_length; auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length); if (!loaded) { - fprintf(stderr, "%s: failed to load %s\n", __func__, image_path); + LOG_TEE("%s: failed to load %s\n", __func__, image_path); return NULL; }