Change torch.jit.trace to no longer be a decorator (#11069)

Summary:
This was done because it surprising for a decorator to run a function
rather than wrap it, and not simplify the syntax for tracing modules.
Pull Request resolved: pytorch/pytorch#11069

Reviewed By: jamesr66a

Differential Revision: D9583192

Pulled By: zdevito

fbshipit-source-id: b914b7ab4c73c255086465a6576eef3a22de1e13

Author: zdevito

pytorch_translate/ensemble_export.py

@@ -512,7 +512,7 @@ def __init__(
 
         encoder_ens = EncoderEnsemble(self.models)
         example_encoder_outs = encoder_ens(src_tokens, src_lengths)
-        self.encoder_ens = torch.jit.trace(src_tokens, src_lengths)(encoder_ens)
+        self.encoder_ens = torch.jit.trace(encoder_ens, (src_tokens, src_lengths))
         decoder_ens = DecoderBatchedStepEnsemble(
             self.models,
             tgt_dict,
@@ -536,14 +536,17 @@ def __init__(
             prev_token, prev_scores, ts, *example_encoder_outs
         )
         self.decoder_ens_tile = torch.jit.trace(
-            prev_token, prev_scores, ts, *example_encoder_outs
-        )(decoder_ens_tile)
+            decoder_ens_tile, (prev_token, prev_scores, ts, *example_encoder_outs)
+        )
         self.decoder_ens = torch.jit.trace(
-            prev_token.repeat(self.beam_size),
-            prev_scores.repeat(self.beam_size),
-            ts,
-            *tiled_states,
-        )(decoder_ens)
+            decoder_ens,
+            (
+                prev_token.repeat(self.beam_size),
+                prev_scores.repeat(self.beam_size),
+                ts,
+                *tiled_states,
+            ),
+        )
 
         self.input_names = [
             "src_tokens",
@@ -858,7 +861,7 @@ def __init__(self, model_list, tgt_dict, word_reward=0, unk_reward=0):
 
         encoder_ens = EncoderEnsemble(self.models)
         example_encoder_outs = encoder_ens(source_tokens, source_length)
-        self.encoder_ens = torch.jit.trace(source_tokens, source_length)(encoder_ens)
+        self.encoder_ens = torch.jit.trace(encoder_ens, (source_tokens, source_length))
         decoder_ens = KnownOutputDecoderStepEnsemble(
             self.models, tgt_dict, word_reward, unk_reward
         )
@@ -867,8 +870,8 @@ def __init__(self, model_list, tgt_dict, word_reward=0, unk_reward=0):
         ts = torch.LongTensor([0])
         _, *states = decoder_ens(prev_token, target_token, ts, *example_encoder_outs)
         self.decoder_ens = torch.jit.trace(
-            prev_token, target_token, ts, *example_encoder_outs
-        )(decoder_ens)
+            decoder_ens, (prev_token, target_token, ts, *example_encoder_outs)
+        )
 
         self.input_names = [
             "source_tokens",