[DOC] Adding documentation to xlnet scripts

scripts/language_model/transformer/transformer.py

@@ -486,6 +486,45 @@ def hybrid_forward(self, F, inputs, pos_emb, mem_value, mask, segments):
 
 
 class _BaseXLNet(mx.gluon.HybridBlock):
+    """
+    Parameters
+    ----------
+    vocab_size : int
+        The size of the vocabulary.
+    num_layers : int
+    units : int
+    hidden_size : int
+        number of units in the hidden layer of position-wise feed-forward networks
+    num_heads : int
+        Number of heads in multi-head attention
+    activation
+        Activation function used for the position-wise feed-forward networks
+    two_stream
+        If True, use Two-Stream Self-Attention. Typically set to True for
+        pre-training and False during finetuning.
+    scaled : bool
+        Whether to scale the softmax input by the sqrt of the input dimension
+        in multi-head attention
+    dropout : float
+    attention_dropout : float
+    use_residual : bool
+    clamp_len : int
+        Clamp all relative distances larger than clamp_len
+    use_decoder : bool, default True
+        Whether to include the decoder for language model prediction.
+    tie_decoder_weight : bool, default True
+        Whether to tie the decoder weight with the input embeddings
+    weight_initializer : str or Initializer
+        Initializer for the input weights matrix, used for the linear
+        transformation of the inputs.
+    bias_initializer : str or Initializer
+        Initializer for the bias vector.
+    prefix : str, default 'rnn_'
+        Prefix for name of `Block`s (and name of weight if params is `None`).
+    params : Parameter or None
+        Container for weight sharing between cells. Created if `None`.
+
+    """
     def __init__(self, vocab_size, num_layers=2, units=128, hidden_size=2048, num_heads=4,
                  activation='gelu', two_stream: bool = False, scaled=True, dropout=0.0,
                  attention_dropout=0.0, use_residual=True, clamp_len: typing.Optional[int] = None,
@@ -529,6 +568,32 @@ def __init__(self, vocab_size, num_layers=2, units=128, hidden_size=2048, num_he
                     params=self.word_embed.params if tie_decoder_weight else None)
 
     def hybrid_forward(self, F, step_input, segments, mask, pos_seq, mems, mask_embed):  #pylint: disable=arguments-differ
+        """
+        Parameters
+        ----------
+        step_input : Symbol or NDArray
+            Input of shape [batch_size, query_length]
+        segments : Symbol or NDArray
+            One-hot vector indicating if a query-key pair is in the same
+            segment or not. Shape [batch_size, query_length, query_length +
+            memory_length, 2]. `1` indicates that the pair is not in the same
+            segment.
+        mask : Symbol or NDArray
+            Attention mask of shape (batch_size, length, length + mem_length)
+        pos_seq : Symbol or NDArray
+            Relative distances
+        mems : List of NDArray or Symbol, optional
+            Memory from previous forward passes containing
+            `num_layers` `NDArray`s or `Symbol`s each of shape [batch_size,
+            memory_length, units].
+
+        Returns
+        -------
+        core_out : NDArray or Symbol
+            For use_decoder=True, logits. Otherwise output of last layer.
+        hids : List of NDArray or Symbol
+            Stacking the output of each layer
+        """
         if self._clamp_len:
             pos_seq = F.clip(pos_seq, a_min=0, a_max=self._clamp_len)
 
@@ -635,6 +700,8 @@ def forward(self, step_input, token_types, mems=None, mask=None):  # pylint: dis
             Optional memory from previous forward passes containing
             `num_layers` `NDArray`s or `Symbol`s each of shape [batch_size,
             memory_length, units].
+        mask : Symbol or NDArray
+            Attention mask of shape (batch_size, length, length + mem_length)
 
         Returns
         -------