diff --git a/llama.cpp b/llama.cpp index 1f7ac60e6..25b7554a1 100644 --- a/llama.cpp +++ b/llama.cpp @@ -519,7 +519,7 @@ struct llama_file_loader { llama_file_loader(const char * fname, llama_load_tensors_map & tensors_map) : file(fname, "rb") { - LLAMA_LOG_INFO("llama.cpp: loading model from %s", fname); + LLAMA_LOG_INFO("llama.cpp: loading model from %s\n", fname); read_magic(); read_hparams(); read_vocab(); @@ -634,7 +634,7 @@ struct llama_file_saver { llama_file_loader * any_file_loader; llama_file_saver(const char * fname, llama_file_loader * any_file_loader, enum llama_ftype new_ftype) : file(fname, "wb"), any_file_loader(any_file_loader) { - LLAMA_LOG_INFO("llama.cpp: saving model to %s", fname); + LLAMA_LOG_INFO("llama.cpp: saving model to %s\n", fname); write_magic(); write_hparams(new_ftype); write_vocab(); @@ -655,7 +655,7 @@ struct llama_file_saver { } void write_vocab() { if (any_file_loader->file_version == LLAMA_FILE_VERSION_GGML) { - LLAMA_LOG_WARN("llama.cpp: WARNING: input is an old file that doesn't have scores; will add dummy scores"); + LLAMA_LOG_WARN("llama.cpp: WARNING: input is an old file that doesn't have scores; will add dummy scores\n"); } uint32_t n_vocab = any_file_loader->hparams.n_vocab; for (uint32_t i = 0; i < n_vocab; i++) { @@ -846,7 +846,7 @@ struct llama_model_loader { uint8_t byte = lt.data[i]; sum = byte + (sum << 6) + (sum << 16) - sum; // sdbm hash } - LLAMA_LOG_INFO("%s checksum: %#08x (%s, size %zu)", lt.name.c_str(), sum, + LLAMA_LOG_INFO("%s checksum: %#08x (%s, size %zu)\n", lt.name.c_str(), sum, llama_format_tensor_shape(lt.ne).c_str(), lt.size); } @@ -879,7 +879,7 @@ static bool kv_cache_init( cache.ctx = ggml_init(params); if (!cache.ctx) { - LLAMA_LOG_ERROR("%s: failed to allocate memory for kv cache", __func__); + LLAMA_LOG_ERROR("%s: failed to allocate memory for kv cache\n", __func__); return false; } @@ -1091,7 +1091,7 @@ static void llama_model_load_internal( LLAMA_ASSERT(hparams.n_head % n_gqa == 0); hparams.n_head_kv = hparams.n_head / n_gqa; if (model.type == e_model::MODEL_65B && n_gqa == 8) { - LLAMA_LOG_WARN("%s: warning: assuming 70B model based on GQA == %d", __func__, n_gqa); + LLAMA_LOG_WARN("%s: warning: assuming 70B model based on GQA == %d\n", __func__, n_gqa); model.type = e_model::MODEL_70B; hparams.f_ffn_mult = 1.3f; // from the params.json of the 70B model } @@ -1150,7 +1150,7 @@ static void llama_model_load_internal( size_t ctx_size; size_t mmapped_size; ml->calc_sizes(&ctx_size, &mmapped_size); - LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MB", __func__, ctx_size/1024.0/1024.0); + LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0); // create the ggml context { @@ -1175,13 +1175,13 @@ static void llama_model_load_internal( (void) main_gpu; (void) mul_mat_q; #if defined(GGML_USE_CUBLAS) - LLAMA_LOG_INFO("%s: using CUDA for GPU acceleration", __func__); + LLAMA_LOG_INFO("%s: using CUDA for GPU acceleration\n", __func__); ggml_cuda_set_main_device(main_gpu); ggml_cuda_set_mul_mat_q(mul_mat_q); #define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU #define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT #elif defined(GGML_USE_CLBLAST) - LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration", __func__); + LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__); #define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU #define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU #else @@ -1286,14 +1286,14 @@ static void llama_model_load_internal( const size_t mem_required_state = scale*hparams.kv_size(); - LLAMA_LOG_INFO("%s: mem required = %7.2f MB (+ %7.2f MB per state)", __func__, + LLAMA_LOG_INFO("%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__, mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0); (void) vram_scratch; (void) n_batch; #ifdef GGML_USE_CUBLAS if (low_vram) { - LLAMA_LOG_INFO("%s: not allocating a VRAM scratch buffer due to low VRAM option", __func__); + LLAMA_LOG_INFO("%s: not allocating a VRAM scratch buffer due to low VRAM option\n", __func__); ggml_cuda_set_scratch_size(0); // disable scratch } else { const size_t vram_scratch_base = VRAM_REQ_SCRATCH_BASE().at(model.type); @@ -1301,7 +1301,7 @@ static void llama_model_load_internal( vram_scratch = n_batch * (vram_scratch_base + n_ctx * vram_scratch_per_context); ggml_cuda_set_scratch_size(vram_scratch); if (n_gpu_layers > 0) { - LLAMA_LOG_INFO("%s: allocating batch_size x (%zd kB + n_ctx x %zd B) = %zd MB VRAM for the scratch buffer", + LLAMA_LOG_INFO("%s: allocating batch_size x (%zd kB + n_ctx x %zd B) = %zd MB VRAM for the scratch buffer\n", __func__, vram_scratch_base / kB, vram_scratch_per_context, (vram_scratch + MB - 1) / MB); // round up } @@ -1311,9 +1311,9 @@ static void llama_model_load_internal( #if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); - LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU", __func__, n_gpu); + LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu); if (n_gpu_layers > (int) hparams.n_layer) { - LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU", __func__); + LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__); } size_t vram_kv_cache = 0; @@ -1322,17 +1322,17 @@ static void llama_model_load_internal( const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3; if (n_gpu_layers > (int) hparams.n_layer + 1) { if (low_vram) { - LLAMA_LOG_INFO("%s: cannot offload v cache to GPU due to low VRAM option", __func__); + LLAMA_LOG_INFO("%s: cannot offload v cache to GPU due to low VRAM option\n", __func__); } else { - LLAMA_LOG_INFO("%s: offloading v cache to GPU", __func__); + LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__); vram_kv_cache += hparams.kv_size() / 2; } } if (n_gpu_layers > (int) hparams.n_layer + 2) { if (low_vram) { - LLAMA_LOG_WARN("%s: cannot offload k cache to GPU due to low VRAM option", __func__); + LLAMA_LOG_WARN("%s: cannot offload k cache to GPU due to low VRAM option\n", __func__); } else { - LLAMA_LOG_INFO("%s: offloading k cache to GPU", __func__); + LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__); vram_kv_cache += hparams.kv_size() / 2; } } @@ -1341,9 +1341,9 @@ static void llama_model_load_internal( const int max_offloadable_layers = hparams.n_layer + 1; #endif // GGML_USE_CUBLAS - LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU", + LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers); - LLAMA_LOG_INFO("%s: total VRAM used: %zu MB", + LLAMA_LOG_INFO("%s: total VRAM used: %zu MB\n", __func__, (vram_weights + vram_scratch + vram_kv_cache + MB - 1) / MB); // round up #else (void) n_gpu_layers; @@ -1402,7 +1402,7 @@ static bool llama_model_load( use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data); return true; } catch (const std::exception & err) { - LLAMA_LOG_ERROR("error loading model: %s", err.what()); + LLAMA_LOG_ERROR("error loading model: %s\n", err.what()); return false; } } @@ -3079,7 +3079,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s int nx = tensor.ne.at(0); int ny = tensor.ne.at(1); if (nx % QK_K != 0 || ny % QK_K != 0) { - LLAMA_LOG_INFO("\n\nTensor sizes %d x %d are not divisible by %d, required for k-quants.",nx,ny,QK_K); + LLAMA_LOG_INFO("\n\nTensor sizes %d x %d are not divisible by %d, required for k-quants.\n",nx,ny,QK_K); convert_incompatible_tensor = true; } } @@ -3216,7 +3216,7 @@ struct llama_model * llama_load_model_from_file( params.main_gpu, params.tensor_split, params.mul_mat_q, params.rope_freq_base, params.rope_freq_scale,params.low_vram, memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback, params.progress_callback_user_data)) { - LLAMA_LOG_ERROR("%s: failed to load model", __func__); + LLAMA_LOG_ERROR("%s: failed to load model\n", __func__); delete model; return nullptr; } @@ -3267,14 +3267,14 @@ struct llama_context * llama_new_context_with_model( // reserve memory for context buffers if (!params.vocab_only) { if (!kv_cache_init(ctx->model.hparams, ctx->kv_self, memory_type, ctx->model.hparams.n_ctx, params.n_gpu_layers)) { - LLAMA_LOG_ERROR("%s: kv_cache_init() failed for self-attention cache", __func__); + LLAMA_LOG_ERROR("%s: kv_cache_init() failed for self-attention cache\n", __func__); llama_free(ctx); return nullptr; } { const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v); - LLAMA_LOG_INFO("%s: kv self size = %7.2f MB", __func__, memory_size / 1024.0 / 1024.0); + LLAMA_LOG_INFO("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0); } const auto & hparams = ctx->model.hparams; @@ -3353,11 +3353,11 @@ struct llama_context * llama_new_context_with_model( LLAMA_LOG_INFO("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0); -#define LLAMA_METAL_CHECK_BUF(result) \ - if (!(result)) { \ - LLAMA_LOG_ERROR("%s: failed to add buffer", __func__); \ - llama_free(ctx); \ - return NULL; \ +#define LLAMA_METAL_CHECK_BUF(result) \ + if (!(result)) { \ + LLAMA_LOG_ERROR("%s: failed to add buffer\n", __func__); \ + llama_free(ctx); \ + return NULL; \ } LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size)); @@ -3411,19 +3411,19 @@ int llama_model_quantize( llama_model_quantize_internal(fname_inp, fname_out, params); return 0; } catch (const std::exception & err) { - LLAMA_LOG_ERROR("%s: failed to quantize: %s", __func__, err.what()); + LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what()); return 1; } } int llama_apply_lora_from_file_internal(const struct llama_model & model, const char * path_lora, const char * path_base_model, int n_threads) { - LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...", __func__, path_lora); + LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora); const int64_t t_start_lora_us = ggml_time_us(); auto fin = std::ifstream(path_lora, std::ios::binary); if (!fin) { - LLAMA_LOG_ERROR("%s: failed to open '%s'", __func__, path_lora); + LLAMA_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_lora); return 1; } @@ -3432,14 +3432,14 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const uint32_t magic; fin.read((char *) &magic, sizeof(magic)); if (magic != LLAMA_FILE_MAGIC_GGLA) { - LLAMA_LOG_ERROR("%s: bad file magic", __func__); + LLAMA_LOG_ERROR("%s: bad file magic\n", __func__); return 1; } uint32_t format_version; fin.read((char *) &format_version, sizeof(format_version)); if (format_version != 1) { - LLAMA_LOG_ERROR("%s: unsupported file version", __func__ ); + LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ ); return 1; } } @@ -3450,7 +3450,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const fin.read((char *) &lora_alpha, sizeof(lora_alpha)); float scaling = (float)lora_alpha / (float)lora_r; - LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f", __func__, lora_r, lora_alpha, scaling); + LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling); // create a temporary ggml context to store the lora tensors @@ -3476,7 +3476,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const ggml_context * base_ctx = NULL; llama_buffer base_buf; if (path_base_model) { - LLAMA_LOG_INFO("%s: loading base model from '%s'", __func__, path_base_model); + LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model); model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true)); size_t ctx_size; @@ -3533,17 +3533,17 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const const std::string lora_suffix = ".lora"; size_t pos = name.rfind(lora_suffix); if (pos == std::string::npos) { - LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor", __func__, name.c_str()); + LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str()); return 1; } std::string lora_type = name.substr(pos + lora_suffix.length()); std::string base_name = name; base_name.erase(pos); - // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) ", __func__, name.c_str(),base_name.c_str(), lora_type.c_str()); + // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(),base_name.c_str(), lora_type.c_str()); if (model_tensors.find(base_name) == model_tensors.end()) { - LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter", __func__, name.data()); + LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data()); return 1; } @@ -3554,7 +3554,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const case 1: wtype = GGML_TYPE_F16; break; default: { - LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'", + LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n", __func__, ftype); return false; } @@ -3564,7 +3564,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]); } else { - LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d", __func__, n_dims); + LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims); return 1; } ggml_set_name(lora_tensor, "lora_tensor"); @@ -3602,7 +3602,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const if (model_loader) { // load from base model if (model_loader->tensors_map.name_to_idx.find(base_name) == model_loader->tensors_map.name_to_idx.end()) { - LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model", __func__, base_name.c_str()); + LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str()); return 1; } size_t idx = model_loader->tensors_map.name_to_idx[base_name]; @@ -3619,7 +3619,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const if (ggml_is_quantized(base_t->type)) { if (!warned) { LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, " - "use a f16 or f32 base model with --lora-base", __func__); + "use a f16 or f32 base model with --lora-base\n", __func__); warned = true; } } @@ -3634,7 +3634,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) { LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");" - " are you sure that this adapter is for this model?", __func__, base_t->ne[0], loraA->ne[1]); + " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]); return 1; } @@ -3691,7 +3691,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const } const int64_t t_lora_us = ggml_time_us() - t_start_lora_us; - LLAMA_LOG_INFO(" done (%.2f ms)", t_lora_us / 1000.0); + LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0); return 0; } @@ -3700,7 +3700,7 @@ int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lor try { return llama_apply_lora_from_file_internal(ctx->model, path_lora, path_base_model, n_threads); } catch (const std::exception & err) { - LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s", __func__, err.what()); + LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what()); return 1; } } @@ -3709,7 +3709,7 @@ int llama_model_apply_lora_from_file(const struct llama_model * model, const cha try { return llama_apply_lora_from_file_internal(*model, path_lora, path_base_model, n_threads); } catch (const std::exception & err) { - LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s", __func__, err.what()); + LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what()); return 1; } } @@ -3972,7 +3972,7 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c const uint32_t version = file.read_u32(); if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) { - LLAMA_LOG_ERROR("%s : unknown (magic, version) for session file: %08x, %08x", __func__, magic, version); + LLAMA_LOG_ERROR("%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version); return false; } @@ -3980,7 +3980,7 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c file.read_raw(&session_hparams, sizeof(llama_hparams)); if (session_hparams != ctx->model.hparams) { - LLAMA_LOG_INFO("%s : model hparams didn't match from session file!", __func__); + LLAMA_LOG_INFO("%s : model hparams didn't match from session file!\n", __func__); return false; } } @@ -3990,7 +3990,7 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c const uint32_t n_token_count = file.read_u32(); if (n_token_count > n_token_capacity) { - LLAMA_LOG_ERROR("%s : token count in session file exceeded capacity! %u > %zu", __func__, n_token_count, n_token_capacity); + LLAMA_LOG_ERROR("%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity); return false; } @@ -4004,7 +4004,7 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c const size_t n_state_size_max = llama_get_state_size(ctx); if (n_state_size_cur > n_state_size_max) { - LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu", __func__, n_state_size_max, n_state_size_cur); + LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur); return false; } @@ -4021,7 +4021,7 @@ bool llama_load_session_file(struct llama_context * ctx, const char * path_sessi try { return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out); } catch (const std::exception & err) { - LLAMA_LOG_ERROR("error loading session file: %s", err.what()); + LLAMA_LOG_ERROR("error loading session file: %s\n", err.what()); return false; } } @@ -4058,7 +4058,7 @@ int llama_eval( int n_past, int n_threads) { if (!llama_eval_internal(*ctx, tokens, nullptr, n_tokens, n_past, n_threads, nullptr)) { - LLAMA_LOG_ERROR("%s: failed to eval", __func__); + LLAMA_LOG_ERROR("%s: failed to eval\n", __func__); return 1; } @@ -4080,7 +4080,7 @@ int llama_eval_embd( int n_past, int n_threads) { if (!llama_eval_internal(*ctx, nullptr, embd, n_tokens, n_past, n_threads, nullptr)) { - LLAMA_LOG_ERROR("%s: failed to eval", __func__); + LLAMA_LOG_ERROR("%s: failed to eval\n", __func__); return 1; } @@ -4101,7 +4101,7 @@ int llama_eval_export(struct llama_context * ctx, const char * fname) { const std::vector tmp(n_batch, llama_token_bos()); if (!llama_eval_internal(*ctx, tmp.data(), nullptr, tmp.size(), n_ctx, 1, fname)) { - LLAMA_LOG_ERROR("%s: failed to eval", __func__); + LLAMA_LOG_ERROR("%s: failed to eval\n", __func__); return 1; } @@ -4117,7 +4117,7 @@ int llama_tokenize_with_model( auto res = llama_tokenize(model->vocab, text, add_bos); if (n_max_tokens < (int) res.size()) { - LLAMA_LOG_ERROR("%s: too many tokens", __func__); + LLAMA_LOG_ERROR("%s: too many tokens\n", __func__); return -((int) res.size()); } @@ -4234,15 +4234,15 @@ struct llama_timings llama_get_timings(struct llama_context * ctx) { void llama_print_timings(struct llama_context * ctx) { const llama_timings timings = llama_get_timings(ctx); - LLAMA_LOG_INFO(""); - LLAMA_LOG_INFO("%s: load time = %8.2f ms", __func__, timings.t_load_ms); - LLAMA_LOG_INFO("%s: sample time = %8.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)", + LLAMA_LOG_INFO("\n"); + LLAMA_LOG_INFO("%s: load time = %8.2f ms\n", __func__, timings.t_load_ms); + LLAMA_LOG_INFO("%s: sample time = %8.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n", __func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample); - LLAMA_LOG_INFO("%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)", + LLAMA_LOG_INFO("%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n", __func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval); - LLAMA_LOG_INFO("%s: eval time = %8.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)", + LLAMA_LOG_INFO("%s: eval time = %8.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n", __func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval); - LLAMA_LOG_INFO("%s: total time = %8.2f ms", __func__, (timings.t_end_ms - timings.t_start_ms)); + LLAMA_LOG_INFO("%s: total time = %8.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms)); } void llama_reset_timings(struct llama_context * ctx) { @@ -4316,5 +4316,5 @@ static void llama_log_internal(llama_log_level level, const char * format, ...) static void llama_log_callback_default(llama_log_level level, const char * text, void * user_data) { (void) level; (void) user_data; - fprintf(stderr, "%s\n", text); + fprintf(stderr, "%s", text); }