feat(pt): add parameter numbers output

deepmd/pt/train/training.py

@@ -761,6 +761,47 @@ def warm_up_linear(step: int, warmup_steps: int) -> float:
         self.profiling = training_params.get("profiling", False)
         self.profiling_file = training_params.get("profiling_file", "timeline.json")
 
+        # Log model parameter count
+        if self.rank == 0:
+            self._log_parameter_count()
+
+    @staticmethod
+    def _count_parameters(model: torch.nn.Module) -> tuple[int, int]:
+        """
+        Count model parameters.
+
+        Parameters
+        ----------
+        model : torch.nn.Module
+            The model to count parameters for.
+
+        Returns
+        -------
+        tuple[int, int]
+            A tuple of (trainable, total) parameter counts.
+        """
+        trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
+        total = sum(p.numel() for p in model.parameters())
+        return trainable, total
+
+    def _log_parameter_count(self) -> None:
+        """Log model parameter count."""
+        if not self.multi_task:
+            trainable, total = self._count_parameters(self.model)
+            log.info(
+                f"Model Params:  {total / 1e6:.3f} M   (Trainable: {trainable / 1e6:.3f} M)"
+            )
+        else:
+            log.warning(
+                "In multitask mode, parameters may be shared across tasks. "
+                "The following per-task counts may include duplicates."
+            )
+            for model_key in self.model_keys:
+                trainable, total = self._count_parameters(self.model[model_key])
+                log.info(
+                    f"Model Params [{model_key}]: {total / 1e6:.3f} M   (Trainable: {trainable / 1e6:.3f} M)"
+                )
+
     def run(self) -> None:
         fout = (
             open(