Revert preix beamsearch fix (#2763)

Summary:
# Before submitting

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

## What does this PR do?
reverting to fix issue mentioned [here](https://github.com/pytorch/fairseq/issues/3913). Having another PR for fixing the original issue later.

## 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: https://github.com/fairinternal/fairseq-py/pull/2763

Reviewed By: myleott

Differential Revision: D33000411

Pulled By: jingfeidu

fbshipit-source-id: 95a54cbdc612129a0eab4b5e6aa576a5bcf00588

fairseq/sequence_generator.py

@@ -350,6 +350,17 @@ class SequenceGenerator(nn.Module):
                 )
                 probs = probs[:, -1, :] * self.lm_weight
                 lprobs += probs
+
+            lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs)
+
+            lprobs[:, self.pad] = -math.inf  # never select pad
+            lprobs[:, self.unk] -= self.unk_penalty  # apply unk penalty
+
+            # handle max length constraint
+            if step >= max_len:
+                lprobs[:, : self.eos] = -math.inf
+                lprobs[:, self.eos + 1 :] = -math.inf
+
             # handle prefix tokens (possibly with different lengths)
             if (
                 prefix_tokens is not None
@@ -363,16 +374,6 @@ class SequenceGenerator(nn.Module):
                 # minimum length constraint (does not apply if using prefix_tokens)
                 lprobs[:, self.eos] = -math.inf
 
-            lprobs[lprobs != lprobs] = torch.tensor(-math.inf).to(lprobs)
-
-            lprobs[:, self.pad] = -math.inf  # never select pad
-            lprobs[:, self.unk] -= self.unk_penalty  # apply unk penalty
-
-            # handle max length constraint
-            if step >= max_len:
-                lprobs[:, : self.eos] = -math.inf
-                lprobs[:, self.eos + 1 :] = -math.inf
-
             # Record attention scores, only support avg_attn_scores is a Tensor
             if avg_attn_scores is not None:
                 if attn is None:
@@ -574,7 +575,7 @@ class SequenceGenerator(nn.Module):
         prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1)
         prefix_lprobs = lprobs.gather(-1, prefix_toks.unsqueeze(-1))
         prefix_mask = prefix_toks.ne(self.pad)
-        lprobs[prefix_mask] = torch.min(prefix_lprobs) - 1
+        lprobs[prefix_mask] = torch.tensor(-math.inf).to(lprobs)
         lprobs[prefix_mask] = lprobs[prefix_mask].scatter(
             -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lprobs[prefix_mask]
         )

tests/test_sequence_generator.py

@@ -562,62 +562,6 @@ class TestDiverseSiblingsSearch(TestDiverseBeamSearch):
         self.assertHypoScore(hypos[1][1], [0.7, 0.35, 0.9], [0, 2, 1], 0.5)
 
 
-class TestPrefixBeamSearch(TestSequenceGeneratorBase):
-    def setUp(self):
-        # construct dummy dictionary
-        vocab_size = 10
-        d = test_utils.dummy_dictionary(vocab_size=vocab_size)
-        self.assertEqual(d.pad(), 1)
-        self.assertEqual(d.eos(), 2)
-        self.assertEqual(d.unk(), 3)
-        self.eos = d.eos()
-        self.w1 = 4
-        self.w2 = 5
-        self.beam_size = 3
-
-        # construct prefix data
-        self.tokens = torch.LongTensor(
-            [
-                [self.w1, self.w2, self.eos],
-            ]
-        )
-        self.token_lengths = torch.LongTensor([2])
-
-        args = argparse.Namespace()
-        unk = 0.0
-        args.beam_probs = [
-            # prefix step 0:
-            torch.FloatTensor(
-                [
-                    # eos
-                    [0.0, unk]
-                    + [1.0 / vocab_size] * vocab_size  # beam 1
-                ]
-                * self.beam_size
-            ),
-        ] * vocab_size
-
-        task = test_utils.TestTranslationTask.setup_task(args, d, d)
-        self.model = task.build_model(args)
-        self.tgt_dict = task.target_dictionary
-
-    def test_prefix_beam_search(self):
-        search_strategy = search.BeamSearch(self.tgt_dict)
-        generator = SequenceGenerator(
-            [self.model],
-            self.tgt_dict,
-            beam_size=self.beam_size,
-            search_strategy=search_strategy,
-        )
-        sample = {
-            "net_input": {
-                "src_tokens": self.tokens,
-                "src_lengths": self.token_lengths,
-            }
-        }
-        # make sure test sample doesn't break any assertion
-        generator.forward(sample, prefix_tokens=self.tokens[:, :-1])
-
 
 class TestTopPSamplingSearch(TestSequenceGeneratorBase):
     def setUp(self):