Update rnn_layer.py

python/mxnet/gluon/rnn/rnn_layer.py

@@ -285,10 +285,10 @@ class RNN(_RNNLayer):
 
     Inputs:
         - **data**: input tensor with shape `(sequence_length, batch_size, input_size)`
-          when `layout` is "TNC". For other layouts dimensions are permuted accordingly.
-          Be aware that a `transpose` operation with a ndarray results in a new allocation of
-          memory. For optimal performance and when applicable, consider transposing
-          your layout to "TNC" before loading your data into a ndarray.
+          when `layout` is "TNC". For other layouts, dimensions are permuted accordingly
+          using transpose() operator which adds performance overhead. Consider creating
+          batches in TNC layout during data batching step.
+
         - **states**: initial recurrent state tensor with shape
           `(num_layers, batch_size, num_hidden)`. If `bidirectional` is True,
           shape will instead be `(2*num_layers, batch_size, num_hidden)`. If
@@ -388,10 +388,9 @@ class LSTM(_RNNLayer):
 
     Inputs:
         - **data**: input tensor with shape `(sequence_length, batch_size, input_size)`
-          when `layout` is "TNC". For other layouts dimensions are permuted accordingly.
-          Be aware that a `transpose` operation with a ndarray results in a new allocation of
-          memory. For optimal performance and when applicable, consider transposing
-          your layout to "TNC" before loading your data into a ndarray.
+          when `layout` is "TNC". For other layouts, dimensions are permuted accordingly
+          using transpose() operator which adds performance overhead. Consider creating
+          batches in TNC layout during data batching step.
         - **states**: a list of two initial recurrent state tensors. Each has shape
           `(num_layers, batch_size, num_hidden)`. If `bidirectional` is True,
           shape will instead be `(2*num_layers, batch_size, num_hidden)`. If
@@ -488,10 +487,9 @@ class GRU(_RNNLayer):
 
     Inputs:
         - **data**: input tensor with shape `(sequence_length, batch_size, input_size)`
-          when `layout` is "TNC". For other layouts dimensions are permuted accordingly.
-          Be aware that a `transpose` operation with a ndarray results in a new allocation of
-          memory. For optimal performance and when applicable, consider transposing
-          your layout to "TNC" before loading your data into a ndarray.
+          when `layout` is "TNC". For other layouts, dimensions are permuted accordingly
+          using transpose() operator which adds performance overhead. Consider creating
+          batches in TNC layout during data batching step.
         - **states**: initial recurrent state tensor with shape
           `(num_layers, batch_size, num_hidden)`. If `bidirectional` is True,
           shape will instead be `(2*num_layers, batch_size, num_hidden)`. If