Log and save model parameter counts

train_next_step.py

@@ -31,6 +31,7 @@ from train_util import (
     configure_torch_for_training,
     create_unique_run_dir,
     format_extra_info_types,
+    get_model_parameter_counts,
     load_extra_info_types_file,
     resolve_device,
     save_checkpoint,
@@ -599,6 +600,12 @@ def main() -> None:
     )
 
     model = build_model(args, dataset).to(device)
+    parameter_counts = get_model_parameter_counts(model)
+    logger.info(
+        "Model parameters: "
+        f"total={parameter_counts['model_parameter_count']:,}, "
+        f"trainable={parameter_counts['trainable_parameter_count']:,}"
+    )
     readout = build_next_step_readout(args).to(device)
     criterion = build_next_step_loss(args)
     optimizer = AdamW(
@@ -609,10 +616,14 @@ def main() -> None:
     )
     adaptive_lr = args.base_lr * math.sqrt(args.batch_size / 128)
 
+    train_metadata = build_metadata(
+        args, dataset, run_name, train_subset, val_subset, test_subset
+    )
+    train_metadata.update(parameter_counts)
     save_config(
         args,
         run_dir / "train_config.json",
-        extra=build_metadata(args, dataset, run_name, train_subset, val_subset, test_subset),
+        extra=train_metadata,
     )
 
     best_val = float("inf")