Use fprintf for diagnostic output (#48)

keep printf only for printing model output

one can now use ./main ... 2>dev/null to suppress any diagnostic output

diff --git a/main.cpp b/main.cpp
index ee0952f74483f9a74c30da02a6401adc604fa7b3..c96f9edc915d9178456a994bf7b5cb5af3a1dec0 100644 (file)
--- a/main.cpp
+++ b/main.cpp
@@ -85,7 +85,7 @@ struct llama_model {
 
 // load the model's weights from a file
 bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx) {
-    printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
+    fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
 
     std::vector<char> f_buf(1024*1024);
 
@@ -127,16 +127,16 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
         n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
         n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
 
-        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
-        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
-        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
-        printf("%s: n_mult  = %d\n", __func__, hparams.n_mult);
-        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
-        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
-        printf("%s: n_rot   = %d\n", __func__, hparams.n_rot);
-        printf("%s: f16     = %d\n", __func__, hparams.f16);
-        printf("%s: n_ff    = %d\n", __func__, n_ff);
-        printf("%s: n_parts = %d\n", __func__, n_parts);
+        fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
+        fprintf(stderr, "%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
+        fprintf(stderr, "%s: n_embd  = %d\n", __func__, hparams.n_embd);
+        fprintf(stderr, "%s: n_mult  = %d\n", __func__, hparams.n_mult);
+        fprintf(stderr, "%s: n_head  = %d\n", __func__, hparams.n_head);
+        fprintf(stderr, "%s: n_layer = %d\n", __func__, hparams.n_layer);
+        fprintf(stderr, "%s: n_rot   = %d\n", __func__, hparams.n_rot);
+        fprintf(stderr, "%s: f16     = %d\n", __func__, hparams.f16);
+        fprintf(stderr, "%s: n_ff    = %d\n", __func__, n_ff);
+        fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts);
     }
 
     // load vocab
@@ -161,7 +161,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
             vocab.id_to_token[i] = word;
 
             //if (i < 30000) {
-            //    printf("%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
+            //    fprintf(stderr, "%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
             //}
         }
     }
@@ -220,7 +220,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
 
         ctx_size += (5 + 10*n_layer)*256; // object overhead
 
-        printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
+        fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
     }
 
     // create the ggml context
@@ -307,7 +307,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
 
         const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
 
-        printf("%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
+        fprintf(stderr, "%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
     }
 
     const size_t file_offset = fin.tellg();
@@ -325,7 +325,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
             fname_part += "." + std::to_string(i);
         }
 
-        printf("%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
+        fprintf(stderr, "%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
 
         fin = std::ifstream(fname_part, std::ios::binary);
         fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
@@ -336,7 +336,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
             int n_tensors = 0;
             size_t total_size = 0;
 
-            printf("%s: ", __func__);
+            fprintf(stderr, "%s: ", __func__);
 
             while (true) {
                 int32_t n_dims;
@@ -436,7 +436,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
 
                 if (0) {
                     static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
-                    printf("%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
+                    fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
                 }
 
                 size_t bpe = 0;
@@ -499,16 +499,16 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
                     total_size += ggml_nbytes(tensor)/n_parts;
                 }
 
-                //printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
+                //fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
                 if (++n_tensors % 8 == 0) {
-                    printf(".");
-                    fflush(stdout);
+                    fprintf(stderr, ".");
+                    fflush(stderr);
                 }
             }
 
-            printf(" done\n");
+            fprintf(stderr, " done\n");
 
-            printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
+            fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
         }
 
         fin.close();
@@ -552,7 +552,7 @@ bool llama_eval(
 
     if (mem_per_token > 0 && mem_per_token*N > buf_size) {
         const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
-        //printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
+        //fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
 
         // reallocate
         buf_size = buf_size_new;
@@ -744,7 +744,7 @@ bool llama_eval(
     if (mem_per_token == 0) {
         mem_per_token = ggml_used_mem(ctx0)/N;
     }
-    //printf("used_mem = %zu\n", ggml_used_mem(ctx0));
+    //fprintf(stderr, "used_mem = %zu\n", ggml_used_mem(ctx0));
 
     ggml_free(ctx0);
 
@@ -780,7 +780,7 @@ int main(int argc, char ** argv) {
         params.seed = time(NULL);
     }
 
-    printf("%s: seed = %d\n", __func__, params.seed);
+    fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
 
     std::mt19937 rng(params.seed);
     if (params.prompt.empty()) {
@@ -822,13 +822,13 @@ int main(int argc, char ** argv) {
     // tokenize the reverse prompt
     std::vector<gpt_vocab::id> antiprompt_inp = ::llama_tokenize(vocab, params.antiprompt, false);
 
-    printf("\n");
-    printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
-    printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
+    fprintf(stderr, "\n");
+    fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
+    fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
     for (int i = 0; i < (int) embd_inp.size(); i++) {
-        printf("%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
+        fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
     }
-    printf("\n");
+    fprintf(stderr, "\n");
     if (params.interactive) {
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
         struct sigaction sigint_action;
@@ -838,19 +838,19 @@ int main(int argc, char ** argv) {
         sigaction(SIGINT, &sigint_action, NULL);
 #endif
 
-        printf("%s: interactive mode on.\n", __func__);
+        fprintf(stderr, "%s: interactive mode on.\n", __func__);
 
         if(antiprompt_inp.size()) {
-            printf("%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
-            printf("%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
+            fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
+            fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
             for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
-                printf("%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
+                fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
             }
-            printf("\n");
+            fprintf(stderr, "\n");
         }
     }
-    printf("sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
-    printf("\n\n");
+    fprintf(stderr, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
+    fprintf(stderr, "\n\n");
 
     std::vector<gpt_vocab::id> embd;
 
@@ -864,7 +864,7 @@ int main(int argc, char ** argv) {
 
 
     if (params.interactive) {
-        printf("== Running in interactive mode. ==\n"
+        fprintf(stderr, "== Running in interactive mode. ==\n"
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
                " - Press Ctrl+C to interject at any time.\n"
 #endif
@@ -892,7 +892,7 @@ int main(int argc, char ** argv) {
             const int64_t t_start_us = ggml_time_us();
 
             if (!llama_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
-                printf("Failed to predict\n");
+                fprintf(stderr, "Failed to predict\n");
                 return 1;
             }
 
@@ -1005,7 +1005,7 @@ int main(int argc, char ** argv) {
 
         // end of text token
         if (embd.back() == 2) {
-            printf(" [end of text]\n");
+            fprintf(stderr, " [end of text]\n");
             break;
         }
     }
@@ -1015,12 +1015,12 @@ int main(int argc, char ** argv) {
     {
         const int64_t t_main_end_us = ggml_time_us();
 
-        printf("\n\n");
-        printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
-        printf("%s:     load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
-        printf("%s:   sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
-        printf("%s:  predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
-        printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
+        fprintf(stderr, "\n\n");
+        fprintf(stderr, "%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
+        fprintf(stderr, "%s:     load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
+        fprintf(stderr, "%s:   sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
+        fprintf(stderr, "%s:  predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
+        fprintf(stderr, "%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
     }
 
     ggml_free(model.ctx);