add args check for learning rate scheduler API (#34394)

python/paddle/optimizer/lr.py

@@ -570,7 +570,7 @@ class PolynomialDecay(LRScheduler):
 
     Args:
         learning_rate (float): The initial learning rate. It is a python float number.
-        decay_steps(int): The decay step size. It determines the decay cycle.
+        decay_steps(int): The decay step size. It determines the decay cycle. It must be a positive integer.
         end_lr(float, optional): The minimum final learning rate. Default: 0.0001.
         power(float, optional): Power of polynomial. Default: 1.0.
         cycle(bool, optional): Whether the learning rate rises again. If True, then the learning rate will rise when it decrease 
@@ -639,6 +639,8 @@ class PolynomialDecay(LRScheduler):
                  cycle=False,
                  last_epoch=-1,
                  verbose=False):
+        assert decay_steps > 0 and isinstance(
+            decay_steps, int), " 'decay_steps' must be a positive integer."
         self.decay_steps = decay_steps
         self.end_lr = end_lr
         self.power = power
@@ -688,7 +690,7 @@ class LinearWarmup(LRScheduler):
 
     Args:
         learning_rate (float|LRScheduler): The learning rate after warm-up. It is a python float number or any subclass of ``LRScheduler`` .
-        warmup_steps (int): total steps of warm up.
+        warmup_steps (int): total steps of warm up. It must be a positive integer.
         start_lr (float): Initial learning rate of warm up.
         end_lr (float): Final learning rate of warm up.
         last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
@@ -763,6 +765,8 @@ class LinearWarmup(LRScheduler):
                 "the type of learning_rate should be [int, float or LRScheduler], the current type is {}".
                 format(learning_rate))
         self.learning_rate = learning_rate
+        assert warmup_steps > 0 and isinstance(
+            warmup_steps, int), " 'warmup_steps' must be a positive integer."
         self.warmup_steps = warmup_steps
         self.start_lr = start_lr
         self.end_lr = end_lr
@@ -1010,7 +1014,7 @@ class StepDecay(LRScheduler):
 
     Args:
         learning_rate (float): The initial learning rate. It is a python float number.
-        step_size (int): the interval to update.
+        step_size (int): the interval to update. It must be a positive integer.
         gamma (float, optional): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` . 
             It should be less than 1.0. Default: 0.1.
         last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
@@ -1083,6 +1087,8 @@ class StepDecay(LRScheduler):
         if gamma >= 1.0:
             raise ValueError('gamma should be < 1.0.')
 
+        assert step_size > 0 and isinstance(
+            step_size, int), " 'step_size' must be a positive integer."
         self.step_size = step_size
         self.gamma = gamma
         super(StepDecay, self).__init__(learning_rate, last_epoch, verbose)
@@ -1415,7 +1421,7 @@ class CosineAnnealingDecay(LRScheduler):
     
     Args:
         learning_rate (float): The initial learning rate, that is :math:`\eta_{max}` . It can be set to python float or int number.
-        T_max (int): Maximum number of iterations. It is half of the decay cycle of learning rate.
+        T_max (int): Maximum number of iterations. It is half of the decay cycle of learning rate. It must be a positive integer.
         eta_min (float|int, optional): Minimum learning rate, that is :math:`\eta_{min}` . Default: 0.
         last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
         verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
@@ -1487,6 +1493,8 @@ class CosineAnnealingDecay(LRScheduler):
             raise TypeError(
                 "The type of 'eta_min' in 'CosineAnnealingDecay' must be 'float, int', but received %s."
                 % type(eta_min))
+        assert T_max > 0 and isinstance(
+            T_max, int), " 'T_max' must be a positive integer."
         self.T_max = T_max
         self.eta_min = float(eta_min)
         super(CosineAnnealingDecay, self).__init__(learning_rate, last_epoch,