Log and save model parameter counts

TrajMixer_设计方案.md

@@ -319,7 +319,7 @@ output_init_std: 0.001
 group_wise_layer_norm: false
 ```
 
-训练时必须将 `model_architecture: traj_mixer_v1` 写入 `train_config.json`。本分支的评估和导出入口只接受带有该标识、且 checkpoint 中包含 TrajMixer 参数张量的模型；其他分支生成的模型应直接拒绝加载。
+训练时必须将 `model_architecture: traj_mixer_v1`、`model_parameter_count` 和 `trainable_parameter_count` 写入 `train_config.json`，并在训练日志中显式打印总参数量与可训练参数量。本分支的评估和导出入口只接受带有该架构标识、且 checkpoint 中包含 TrajMixer 参数张量的模型；其他分支生成的模型应直接拒绝加载。
 
 必须满足：
 

test_traj_mixer.py

@@ -8,6 +8,7 @@ from models import (
     validate_traj_mixer_config,
     validate_traj_mixer_state_dict,
 )
+from train_util import get_model_parameter_counts
 
 
 class TrajMixerTest(unittest.TestCase):
@@ -91,6 +92,16 @@ class TrajMixerTest(unittest.TestCase):
         with self.assertRaisesRegex(ValueError, "divisible"):
             TrajMixer(n_embd=121, n_head=10, hidden_group=20)
 
+    def test_parameter_counts_match_traj_mixer_parameters(self) -> None:
+        mixer = TrajMixer(n_embd=120, n_head=10, hidden_group=20)
+        self.assertEqual(
+            get_model_parameter_counts(mixer),
+            {
+                "model_parameter_count": 8_640,
+                "trainable_parameter_count": 8_640,
+            },
+        )
+
 
 if __name__ == "__main__":
     unittest.main()

train_all_future.py

@@ -33,6 +33,7 @@ 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,
@@ -437,6 +438,12 @@ def main() -> None:
     )
 
     model = build_model(args, train_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']:,}"
+    )
     optimizer = AdamW(
         model.parameters(),
         lr=args.base_lr,
@@ -446,10 +453,14 @@ def main() -> None:
     criterion = build_criterion(args, train_dataset)
     adaptive_lr = args.base_lr * math.sqrt(args.batch_size / 128)
 
+    train_metadata = build_metadata(
+        args, train_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, train_dataset, run_name, train_subset, val_subset, test_subset),
+        extra=train_metadata,
     )
 
     best_val = float("inf")

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")

train_util.py

@@ -300,6 +300,20 @@ def build_optimizer(args: Any, model: DeepHealth) -> AdamW:
     )
 
 
+def get_model_parameter_counts(model: torch.nn.Module) -> Dict[str, int]:
+    """Return stable parameter-count fields for logs and train_config.json."""
+    return {
+        "model_parameter_count": sum(
+            parameter.numel() for parameter in model.parameters()
+        ),
+        "trainable_parameter_count": sum(
+            parameter.numel()
+            for parameter in model.parameters()
+            if parameter.requires_grad
+        ),
+    }
+
+
 def set_optimizer_lr(optimizer: AdamW, lr: float) -> None:
     for param_group in optimizer.param_groups:
         param_group["lr"] = lr