Add start_lr() method to BaseLR for better encapsulation

- Add start_lr() getter method to BaseLR class
- Replace direct attribute access lr_exp.start_lr with method call lr_exp.start_lr() in pt and pd training modules
- Align API consistency with existing TF implementation

This addresses the code review comment about providing a method
for accessing start_lr rather than directly reading object data.

Author: OutisLi

deepmd/dpmodel/utils/learning_rate.py

@@ -123,6 +123,17 @@ def __init__(
         # Decay phase covers (num_steps - warmup_steps) steps
         self.decay_num_steps = num_steps - self.warmup_steps
 
+    def start_lr(self) -> float:
+        """
+        Get the starting learning rate.
+
+        Returns
+        -------
+        float
+            The starting learning rate.
+        """
+        return self.start_lr
+
     @abstractmethod
     def _decay_value(self, step: int | Array) -> Array:
         """

deepmd/pd/train/training.py

@@ -580,9 +580,9 @@ def single_model_finetune(
         # author: iProzd
         if self.opt_type == "Adam":
             self.scheduler = paddle.optimizer.lr.LambdaDecay(
-                learning_rate=self.lr_exp.start_lr,
+                learning_rate=self.lr_exp.start_lr(),
                 lr_lambda=lambda step: self.lr_exp.value(step + self.start_step)
-                / self.lr_exp.start_lr,
+                / self.lr_exp.start_lr(),
             )
             self.optimizer = paddle.optimizer.Adam(
                 learning_rate=self.scheduler, parameters=self.wrapper.parameters()

deepmd/pt/train/training.py

@@ -688,13 +688,13 @@ def single_model_finetune(
             if self.opt_type == "Adam":
                 self.optimizer = torch.optim.Adam(
                     self.wrapper.parameters(),
-                    lr=self.lr_exp.start_lr,
+                    lr=self.lr_exp.start_lr(),
                     fused=False if DEVICE.type == "cpu" else True,
                 )
             else:
                 self.optimizer = torch.optim.AdamW(
                     self.wrapper.parameters(),
-                    lr=self.lr_exp.start_lr,
+                    lr=self.lr_exp.start_lr(),
                     weight_decay=float(self.opt_param["weight_decay"]),
                     fused=False if DEVICE.type == "cpu" else True,
                 )
@@ -703,7 +703,7 @@ def single_model_finetune(
             self.scheduler = torch.optim.lr_scheduler.LambdaLR(
                 self.optimizer,
                 lambda step: self.lr_exp.value(step + self.start_step)
-                / self.lr_exp.start_lr,
+                / self.lr_exp.start_lr(),
             )
         elif self.opt_type == "LKF":
             self.optimizer = LKFOptimizer(
@@ -712,7 +712,7 @@ def single_model_finetune(
         elif self.opt_type == "AdaMuon":
             self.optimizer = AdaMuonOptimizer(
                 self.wrapper.parameters(),
-                lr=self.lr_exp.start_lr,
+                lr=self.lr_exp.start_lr(),
                 momentum=float(self.opt_param["momentum"]),
                 weight_decay=float(self.opt_param["weight_decay"]),
                 adam_betas=(
@@ -725,7 +725,7 @@ def single_model_finetune(
         elif self.opt_type == "HybridMuon":
             self.optimizer = HybridMuonOptimizer(
                 self.wrapper.parameters(),
-                lr=self.lr_exp.start_lr,
+                lr=self.lr_exp.start_lr(),
                 momentum=float(self.opt_param["momentum"]),
                 weight_decay=float(self.opt_param["weight_decay"]),
                 adam_betas=(
@@ -742,7 +742,7 @@ def single_model_finetune(
             self.scheduler = torch.optim.lr_scheduler.LambdaLR(
                 self.optimizer,
                 lambda step: self.lr_exp.value(step + self.start_step)
-                / self.lr_exp.start_lr,
+                / self.lr_exp.start_lr(),
             )
         else:
             raise ValueError(f"Not supported optimizer type '{self.opt_type}'")