Extended api of LSTM encoder. (#2030)

Summary:
# Before submitting

- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
- [x] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)?
- [x] Did you make sure to update the docs?
- [ ] Did you write any new necessary tests?

## What does this PR do?
Fixes #1862.
Nobody responded to my issue. Nevertheless change is very small, therefore i think is doesn't need much discussion.

## PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.

## Did you have fun?
Make sure you had fun coding �
Pull Request resolved: #2030

Reviewed By: joshim5, ngoyal2707

Differential Revision: D21584250

Pulled By: myleott

fbshipit-source-id: 28f0ccaca0df2860806178dbce02bcc12d7115d4

fairseq/models/lstm.py

@@ -230,7 +230,23 @@ def __init__(
         if bidirectional:
             self.output_units *= 2
 
-    def forward(self, src_tokens, src_lengths: Tensor):
+    def forward(
+        self,
+        src_tokens: Tensor,
+        src_lengths: Tensor,
+        enforce_sorted: bool = True,
+    ):
+        """
+        Args:
+            src_tokens (LongTensor): tokens in the source language of
+                shape `(batch, src_len)`
+            src_lengths (LongTensor): lengths of each source sentence of
+                shape `(batch)`
+            enforce_sorted (bool, optional): if True, `src_tokens` is
+                expected to contain sequences sorted by length in a
+                decreasing order. If False, this condition is not
+                required. Default: True.
+        """
         if self.left_pad:
             # nn.utils.rnn.pack_padded_sequence requires right-padding;
             # convert left-padding to right-padding
@@ -250,7 +266,9 @@ def forward(self, src_tokens, src_lengths: Tensor):
         x = x.transpose(0, 1)
 
         # pack embedded source tokens into a PackedSequence
-        packed_x = nn.utils.rnn.pack_padded_sequence(x, src_lengths.data)
+        packed_x = nn.utils.rnn.pack_padded_sequence(
+            x, src_lengths.data, enforce_sorted=enforce_sorted
+        )
 
         # apply LSTM
         if self.bidirectional: