Add neural transformer leanring rate decay function. (#9951)

Add neural transformer leanring rate decay function

python/paddle/fluid/layers/learning_rate_scheduler.py

@@ -20,7 +20,7 @@ from ..initializer import init_on_cpu
 
 __all__ = [
     'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
-    'polynomial_decay', 'piecewise_decay'
+    'polynomial_decay', 'piecewise_decay', 'noam_decay'
 ]
 """
 When training a model, it's often useful to decay the
@@ -32,14 +32,41 @@ strategy according to this module.
 """
 
 
-def _decay_step_counter():
+def _decay_step_counter(begin=0):
     # the first global step is zero in learning rate decay
     global_step = nn.autoincreased_step_counter(
-        counter_name='@LR_DECAY_COUNTER@', begin=0, step=1)
+        counter_name='@LR_DECAY_COUNTER@', begin=begin, step=1)
     global_step = tensor.cast(global_step, 'float32')
     return global_step
 
 
+def noam_decay(d_model, warmup_steps):
+    """Apply decay to learning rate.
+    ```python
+    lr_value = np.power(d_model, -0.5) * np.min([
+            np.power(current_steps, -0.5),
+            np.power(warmup_steps, -1.5) * current_steps
+        ])
+    ```
+
+    Args:
+        d_model(Variable): The dimensionality of input and output of model.
+            Reference: attention is all you need
+                https://arxiv.org/pdf/1706.03762.pdf
+        warmup_steps(Variable): A super parameter.
+
+    Returns:
+        The decayed learning rate.
+    """
+    global_step = _decay_step_counter(1)
+    with init_on_cpu():
+        a = global_step**-0.5
+        b = (warmup_steps**-1.5) * global_step
+        lr_value = (d_model**-0.5) * ops.elementwise_min(a, b)
+
+    return lr_value
+
+
 def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
     """Applies exponential decay to the learning rate.