[FIX] Update BERTLayerNorm Implementation (#485)

* fix layer norm

* fix indentation

* fix lint

src/gluonnlp/model/bert.py

@@ -22,7 +22,7 @@
            'BERTLayerNorm', 'bert_12_768_12', 'bert_24_1024_16']
 
 import os
-from mxnet.gluon import Block, HybridBlock
+from mxnet.gluon import Block
 from mxnet.gluon import nn
 from mxnet.gluon.model_zoo import model_store
 import mxnet as mx
@@ -34,35 +34,18 @@
 #                              COMPONENTS                                     #
 ###############################################################################
 
-class BERTLayerNorm(HybridBlock):
-    """BERT style Layer Normalization.
-
-    Epsilon is added inside the square root.
+class BERTLayerNorm(nn.LayerNorm):
+    """BERT style Layer Normalization, where epsilon is added inside the square
+    root and set to 1e-12 by default.
 
     Inputs:
         - **data**: input tensor with arbitrary shape.
-    Outputs:
         - **out**: output tensor with the same shape as `data`.
     """
     def __init__(self, epsilon=1e-12, in_channels=0, prefix=None, params=None):
-        super(BERTLayerNorm, self).__init__(prefix=prefix, params=params)
-        self.gamma = self.params.get('gamma', shape=(in_channels,),
-                                     allow_deferred_init=True)
-        self.beta = self.params.get('beta', shape=(in_channels,),
-                                    allow_deferred_init=True)
-        self._eps = epsilon
-
-    def hybrid_forward(self, F, x, gamma, beta): # pylint: disable=arguments-differ
-        u = F.mean(x, -1, keepdims=True)
-        s = F.mean(F.broadcast_sub(x, u) ** 2, -1, keepdims=True) + self._eps
-        x = F.broadcast_div(F.broadcast_sub(x, u), s.sqrt())
-        return F.broadcast_add(F.broadcast_mul(gamma, x), beta)
-
-    def __repr__(self):
-        s = '{name}('
-        in_channels = self.gamma.shape[0]
-        s += 'in_channels={0}, epsilon={1})'.format(in_channels, self._eps)
-        return s.format(name=self.__class__.__name__)
+        super(BERTLayerNorm, self).__init__(epsilon=epsilon, in_channels=in_channels,
+                                            prefix=prefix, params=params)
+
 
 class BERTPositionwiseFFN(BasePositionwiseFFN):
     """Structure of the Positionwise Feed-Forward Neural Network for