[NFC] polish colossalai/nn/init.py code style (#1292)

colossalai/nn/init.py

@@ -7,6 +7,7 @@ import torch.nn as nn
 
 def zeros_():
     """Return the initializer filling the input Tensor with the scalar zeros"""
+
     def initializer(tensor: Tensor, fan_in: int = None, fan_out: int = None):
         return nn.init.zeros_(tensor)
 
@@ -15,6 +16,7 @@ def zeros_():
 
 def ones_():
     """Return the initializer filling the input Tensor with the scalar ones"""
+
     def initializer(tensor: Tensor, fan_in: int = None, fan_out: int = None):
         return nn.init.ones_(tensor)
 
@@ -46,6 +48,7 @@ def normal_(mean: float = 0., std: float = 1.):
         mean (float): the mean of the normal distribution. Defaults 0.0.
         std (float): the standard deviation of the normal distribution. Defaults 1.0.
      """
+
     def initializer(tensor: Tensor, fan_in: int = None, fan_out: int = None):
         return nn.init.normal_(tensor, mean, std)
 
@@ -66,6 +69,7 @@ def trunc_normal_(mean: float = 0., std: float = 1., a: float = -2., b: float =
         a (float): the minimum cutoff value. Defaults -2.0.
         b (float): the maximum cutoff value. Defaults 2.0.
     """
+
     def initializer(tensor: Tensor, fan_in: int = None, fan_out: int = None):
         return nn.init.trunc_normal_(tensor, mean, std, a, b)
 
@@ -93,6 +97,7 @@ def kaiming_uniform_(a=0, mode='fan_in', nonlinearity='leaky_relu'):
         nonlinearity (str, optional): the non-linear function (`nn.functional` name),
                         recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).
     """
+
     # adapted from torch.nn.init
     def initializer(tensor: Tensor, fan_in: int = None, fan_out: int = None):
         if 0 in tensor.shape:
@@ -136,6 +141,7 @@ def kaiming_normal_(a=0, mode='fan_in', nonlinearity='leaky_relu'):
         nonlinearity (str, optional): the non-linear function (`nn.functional` name),
                         recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).
     """
+
     # adapted from torch.nn.init
     def initializer(tensor: Tensor, fan_in: int = None, fan_out: int = None):
         if 0 in tensor.shape:
@@ -175,6 +181,7 @@ def xavier_uniform_(a: float = math.sqrt(3.), scale: float = 2., gain: float = 1
         scale (float, optional): an optional scaling factor used to calculate standard deviation. Defaults 2.0.
         gain (float, optional): an optional scaling factor. Defaults 1.0.
     """
+
     # adapted from torch.nn.init
     def initializer(tensor: Tensor, fan_in: int = None, fan_out: int = None):
         assert fan_in is not None, 'Fan_in is not provided.'
@@ -206,6 +213,7 @@ def xavier_normal_(scale: float = 2., gain: float = 1.):
         scale (float, optional): an optional scaling factor used to calculate standard deviation. Defaults 2.0.
         gain (float, optional): an optional scaling factor. Defaults 1.0.
     """
+
     # adapted from torch.nn.init
     def initializer(tensor: Tensor, fan_in: int = None, fan_out: int = None):
         assert fan_in is not None, 'Fan_in is not provided.'
@@ -241,4 +249,4 @@ def lecun_normal_():
         std = math.sqrt(1.0 / fan_in)
         return nn.init.trunc_normal_(tensor, std=std / .87962566103423978)
 
-    return initializer
+    return initializer