return empty tensor instead of None

pytorch_translate/ensemble_export.py

@@ -256,25 +256,12 @@ def forward(self, src_tokens, src_lengths):
             # evaluation mode
             model.eval()
 
-            # TODO(jamesreed): transformer encodder returns a None output, and
-            # the fork/join API doesn't handle that well. We should figure out
-            # a way to annotate outputs as Optional and record that in fork/join
-            # traces.
-            if isinstance(model.encoder, TransformerEncoder):
-                futures.append(model.encoder(src_tokens_seq_first, src_lengths))
-            else:
-                futures.append(
-                    torch.jit._fork(model.encoder, src_tokens_seq_first, src_lengths)
-                )
-
-            # evaluation mode
-            model.eval()
+            futures.append(
+                torch.jit._fork(model.encoder, src_tokens_seq_first, src_lengths)
+            )
 
         for i, (model, future) in enumerate(zip(self.models, futures)):
-            if isinstance(model.encoder, TransformerEncoder):
-                encoder_out = future
-            else:
-                encoder_out = torch.jit._wait(future)
+            encoder_out = torch.jit._wait(future)
             # "primary" encoder output (vector representations per source token)
             encoder_outputs = encoder_out[0]
             outputs.append(encoder_outputs)
@@ -1361,9 +1348,6 @@ def forward(self, src_tokens, src_lengths, char_inds, word_lengths):
                 src_tokens_seq_first, src_lengths, char_inds, word_lengths
             )
 
-            # evaluation mode
-            model.eval()
-
             # "primary" encoder output (vector representations per source token)
             encoder_outputs = encoder_out[0]
             outputs.append(encoder_outputs)

pytorch_translate/hybrid_transformer_rnn.py

@@ -247,6 +247,9 @@ def forward(
     ):
         (encoder_x, src_tokens, encoder_padding_mask) = encoder_out
 
+        if encoder_padding_mask is not None and encoder_padding_mask.numel() == 0:
+            encoder_padding_mask = None
+
         bsz, seqlen = prev_output_tokens.size()
         if incremental_state is not None:
             prev_output_tokens = prev_output_tokens[:, -1:]

pytorch_translate/transformer.py

@@ -277,6 +277,10 @@ def forward(self, src_tokens, src_lengths):
             x=x, positions=positions, encoder_padding_mask=encoder_padding_mask
         )
 
+        if encoder_padding_mask is None:
+            # using an empty tensor instead of None for PyTorch native export
+            encoder_padding_mask = torch.Tensor().type_as(src_tokens)
+
         return x, src_tokens, encoder_padding_mask
 
     def reorder_encoder_out(self, encoder_out, new_order):
@@ -285,7 +289,7 @@ def reorder_encoder_out(self, encoder_out, new_order):
             x = x.index_select(1, new_order)
         if src_tokens is not None:
             src_tokens = src_tokens.index_select(0, new_order)
-        if encoder_padding_mask is not None:
+        if encoder_padding_mask is not None and encoder_padding_mask.numel() != 0:
             encoder_padding_mask = encoder_padding_mask.index_select(0, new_order)
         return (x, src_tokens, encoder_padding_mask)
 
@@ -382,6 +386,9 @@ def forward(
     ):
         (encoder_x, src_tokens, encoder_padding_mask) = encoder_out
 
+        if encoder_padding_mask is not None and encoder_padding_mask.numel() == 0:
+            encoder_padding_mask = None
+
         # embed positions
         positions = self.embed_positions(
             prev_output_tokens, incremental_state=incremental_state, timestep=timestep