add a first, super simple logger

train_gpt2.cu

@@ -1798,16 +1798,48 @@ void tokenizer_free(Tokenizer *tokenizer) {
     }
 }
 
+// ----------------------------------------------------------------------------
+// Logger lite, will probably grow/change some over time
+
+typedef struct {
+    FILE *logfile;
+    int flush_every; // every how many steps to flush the log
+} Logger;
+
+void logger_init(Logger *logger, const char *filename) {
+    logger->flush_every = 20;
+    logger->logfile = NULL;
+    if (filename != NULL) { logger->logfile = fopenCheck(filename, "w"); }
+}
+
+void logger_log_val(Logger *logger, int step, float val_loss) {
+    if (logger->logfile != NULL) {
+        fprintf(logger->logfile, "s:%d tel:%.4f\n", step, val_loss);
+    }
+}
+
+void logger_log_train(Logger *logger, int step, float train_loss) {
+    if (logger->logfile != NULL) {
+        fprintf(logger->logfile, "s:%d trl:%.4f\n", step, train_loss);
+        if (step % 10 == 0) { fflush(logger->logfile); }
+    }
+}
+
+void logger_free(Logger *logger) {
+    if (logger->logfile != NULL) { fclose(logger->logfile); }
+}
+
 // ----------------------------------------------------------------------------
 // CLI, poor man's argparse
 
 void error_usage() {
     // default run = debugging run with TinyShakespeare
-    // bigger run = train on TinyStories! e.g. val/sample less often, but sample more tokens
+    // bigger run = train on TinyStories! e.g. val/sample less often, but sample more tokens, write to logfile
     fprintf(stderr, "Usage:   ./train_gpt2cu [options]\n");
-    fprintf(stderr, "Example: ./train_gpt2cu -i data/TinyStories -v 100 -s 100 -g 144\n");
+    fprintf(stderr, "Example: ./train_gpt2cu -i data/TinyStories -v 100 -s 100 -g 144 -o stories.log\n");
     fprintf(stderr, "Options:\n");
     fprintf(stderr, "  -i <string> input dataset prefix (default = data/tiny_shakespeare)\n");
+    fprintf(stderr, "  -o <string> output log file (default = NULL)\n");
     fprintf(stderr, "  -b <int>    batch size B (default = 4)\n");
     fprintf(stderr, "  -t <int>    sequence length T (default = 1024)\n");
     fprintf(stderr, "  -l <float>  learning rate (default = 1e-4f)\n");
@@ -1824,6 +1856,7 @@ int main(int argc, char *argv[]) {
 
     // read in the (optional) command line arguments
     const char* input_dataset_prefix = "data/tiny_shakespeare"; // or e.g. data/TinyStories
+    const char* output_log_file = NULL;
     int B = 4; // batch size
     int T = 1024; // sequence length max
     float learning_rate = 1e-4f;
@@ -1837,6 +1870,7 @@ int main(int argc, char *argv[]) {
         if (strlen(argv[i]) != 2) { error_usage(); } // must be -x (one dash, one letter)
         // read in the args
         if (argv[i][1] == 'i') { input_dataset_prefix = argv[i+1]; }
+        else if (argv[i][1] == 'o') { output_log_file = argv[i+1]; }
         else if (argv[i][1] == 'b') { B = atoi(argv[i+1]); }
         else if (argv[i][1] == 't') { T = atoi(argv[i+1]); }
         else if (argv[i][1] == 'l') { learning_rate = atof(argv[i+1]); }
@@ -1847,6 +1881,7 @@ int main(int argc, char *argv[]) {
         else { error_usage(); }
     }
     printf("input dataset prefix: %s\n", input_dataset_prefix);
+    printf("output log file: %s\n", output_log_file == NULL ? "NULL" : output_log_file);
     printf("batch size B: %d\n", B);
     printf("sequence length T: %d\n", T);
     printf("learning rate: %f\n", learning_rate);
@@ -1895,6 +1930,10 @@ int main(int argc, char *argv[]) {
     printf("train dataset num_batches: %d\n", train_loader.num_batches);
     printf("val dataset num_batches: %d\n", val_loader.num_batches);
 
+    // set up the logfile
+    Logger logger;
+    logger_init(&logger, output_log_file);
+
     // build the Tokenizer
     Tokenizer tokenizer;
     tokenizer_init(&tokenizer, "gpt2_tokenizer.bin");
@@ -1921,6 +1960,7 @@ int main(int argc, char *argv[]) {
             }
             val_loss /= val_num_batches;
             printf("val loss %f\n", val_loss);
+            logger_log_val(&logger, step, val_loss);
         }
 
         // once in a while do model inference to print generated text
@@ -1978,6 +2018,7 @@ int main(int argc, char *argv[]) {
         double time_elapsed_s = (end.tv_sec - start.tv_sec) + (end.tv_nsec - start.tv_nsec) / 1e9;
         total_sum_iteration_time_s += time_elapsed_s;
         printf("step %d/%d: train loss %f (%f ms)\n", step + 1, train_num_batches, model.mean_loss, time_elapsed_s * 1000);
+        logger_log_train(&logger, step, model.mean_loss);
     }
     // add a total average, for optimizations that are only mild improvements
     printf("total average iteration time: %f ms\n", total_sum_iteration_time_s / train_num_batches * 1000);
@@ -1992,6 +2033,7 @@ int main(int argc, char *argv[]) {
     cudaCheck(cudaFree(cublaslt_workspace));
     cublasCheck(cublasDestroy(cublas_handle));
     cublasCheck(cublasLtDestroy(cublaslt_handle));
+    logger_free(&logger);
 
     return 0;
 }