chore: move LearningRateExp to deepmd.utils.learning_rate (#4219)

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- Introduced a new exponential decay learning rate scheduler to enhance
training efficiency.
- Added functionality to compute learning rates at specific training
steps.

- **Bug Fixes**
- Removed the outdated `LearningRateExp` class from the previous module
to avoid confusion.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jinzhe Zeng <[email protected]>

deepmd/dpmodel/utils/learning_rate.py

@@ -0,0 +1,53 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+import numpy as np
+
+
+class LearningRateExp:
+    def __init__(
+        self,
+        start_lr,
+        stop_lr,
+        decay_steps,
+        stop_steps,
+        decay_rate=None,
+        **kwargs,
+    ):
+        """
+        Construct an exponential-decayed learning rate.
+
+        Parameters
+        ----------
+        start_lr
+            The learning rate at the start of the training.
+        stop_lr
+            The desired learning rate at the end of the training.
+            When decay_rate is explicitly set, this value will serve as
+            the minimum learning rate during training. In other words,
+            if the learning rate decays below stop_lr, stop_lr will be applied instead.
+        decay_steps
+            The learning rate is decaying every this number of training steps.
+        stop_steps
+            The total training steps for learning rate scheduler.
+        decay_rate
+            The decay rate for the learning rate.
+            If provided, the decay rate will be set instead of
+            calculating it through interpolation between start_lr and stop_lr.
+        """
+        self.start_lr = start_lr
+        default_ds = 100 if stop_steps // 10 > 100 else stop_steps // 100 + 1
+        self.decay_steps = decay_steps
+        if self.decay_steps >= stop_steps:
+            self.decay_steps = default_ds
+        self.decay_rate = np.exp(
+            np.log(stop_lr / self.start_lr) / (stop_steps / self.decay_steps)
+        )
+        if decay_rate is not None:
+            self.decay_rate = decay_rate
+        self.min_lr = stop_lr
+
+    def value(self, step) -> np.float64:
+        """Get the learning rate at the given step."""
+        step_lr = self.start_lr * np.power(self.decay_rate, step // self.decay_steps)
+        if step_lr < self.min_lr:
+            step_lr = self.min_lr
+        return step_lr

deepmd/pt/utils/learning_rate.py

@@ -1,53 +1,8 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
-import numpy as np
+from deepmd.dpmodel.utils.learning_rate import (
+    LearningRateExp,
+)
 
-
-class LearningRateExp:
-    def __init__(
-        self,
-        start_lr,
-        stop_lr,
-        decay_steps,
-        stop_steps,
-        decay_rate=None,
-        **kwargs,
-    ):
-        """
-        Construct an exponential-decayed learning rate.
-
-        Parameters
-        ----------
-        start_lr
-            The learning rate at the start of the training.
-        stop_lr
-            The desired learning rate at the end of the training.
-            When decay_rate is explicitly set, this value will serve as
-            the minimum learning rate during training. In other words,
-            if the learning rate decays below stop_lr, stop_lr will be applied instead.
-        decay_steps
-            The learning rate is decaying every this number of training steps.
-        stop_steps
-            The total training steps for learning rate scheduler.
-        decay_rate
-            The decay rate for the learning rate.
-            If provided, the decay rate will be set instead of
-            calculating it through interpolation between start_lr and stop_lr.
-        """
-        self.start_lr = start_lr
-        default_ds = 100 if stop_steps // 10 > 100 else stop_steps // 100 + 1
-        self.decay_steps = decay_steps
-        if self.decay_steps >= stop_steps:
-            self.decay_steps = default_ds
-        self.decay_rate = np.exp(
-            np.log(stop_lr / self.start_lr) / (stop_steps / self.decay_steps)
-        )
-        if decay_rate is not None:
-            self.decay_rate = decay_rate
-        self.min_lr = stop_lr
-
-    def value(self, step):
-        """Get the learning rate at the given step."""
-        step_lr = self.start_lr * np.power(self.decay_rate, step // self.decay_steps)
-        if step_lr < self.min_lr:
-            step_lr = self.min_lr
-        return step_lr
+__all__ = [
+    "LearningRateExp",
+]