Generation should stop after two new lines if that is the stop criteria

Summary: This addresses Issue 642. When the stop token is \n\n the generation
should stop after generation two new lines.

metaseq/hub_utils.py

@@ -82,6 +82,15 @@ def decode(self, sentence: str) -> str:
         return self.tokenizer.decode(sentence)
 
 
+class RecurringPunctuation(object):
+    """Class for groping tokens of similar type. For example \n and \n\n"""
+
+    def __init__(self, single_token, multiple_token):
+        super().__init__()
+        self.single_token = single_token
+        self.multiple_token = multiple_token
+
+
 class GeneratorInterface:
     """
     PyTorch Hub interface for generating sequences from a pre-trained
@@ -323,14 +332,21 @@ def generate(
                 self.cfg.generation,
                 extra_gen_cls_kwargs=extra_gen_cls_kwargs,
             )
-
             # okay actually generate
             logger.info(f"Executing generation on input tensor size {src_tokens.shape}")
             if use_cuda:
                 batch = utils.move_to_cuda(batch)
 
             translate_start_time = time.time()
-            translations = self.task.inference_step(generator, self.models, batch)
+            recurring_punctuation = RecurringPunctuation(
+                self.bpe.bpe.encode("\n").ids[0], self.bpe.bpe.encode("\n\n").ids[0]
+            )
+            translations = self.task.inference_step(
+                generator,
+                self.models,
+                batch,
+                recurring_punctuation=recurring_punctuation,
+            )
             translate_time = time.time() - translate_start_time
             total_generation_time += translate_time
 

metaseq/sequence_generator.py

@@ -19,6 +19,7 @@
 from metaseq import utils
 from metaseq.data import data_utils
 from metaseq.models import BaseDecoder
+from metaseq.metaseq.hub_utils import RecurringPunctuation
 
 logger = logging.getLogger(__name__)
 
@@ -130,6 +131,7 @@ def forward(
         sample: Dict[str, Dict[str, Tensor]],
         prefix_tokens: Optional[Tensor] = None,
         bos_token: Optional[int] = None,
+        recurring_punctuation: Optional[RecurringPunctuation] = None,
     ):
         """Generate a batch of translations."""
         return self._generate(sample, prefix_tokens, bos_token=bos_token)
@@ -144,6 +146,7 @@ def _generate(
         sample: Dict[str, Dict[str, Tensor]],
         prefix_tokens: Optional[Tensor] = None,
         bos_token: Optional[int] = None,
+        recurring_punctuation: Optional[RecurringPunctuation] = None,
     ):
         """
         Args:
@@ -268,6 +271,7 @@ def _generate(
 
         eos_mask = torch.zeros(lprobs.size(0), dtype=torch.bool, device=lprobs.device)
 
+        prev_token = None
         for step in range(start_step, max_len):
             if step < min_len:
                 # minimum length constraint (does not apply if using prefix_tokens)
@@ -303,13 +307,20 @@ def _generate(
                     all_lprobs[:, step] = lprobs
 
             eos_mask |= next_toks == self.eos
+
             for stop_token in self.stop:
                 # if there are other early stopping tokens, allow those to trigger stop
                 eos_mask |= next_toks == stop_token
+                eos_mask |= (
+                    recurring_punctuation
+                    and recurring_punctuation.multiple_token == stop_token
+                    and recurring_punctuation.single_token == next_toks == prev_token
+                )
 
             if torch.all(eos_mask):
                 break
 
+            prev_token = next_toks
             # forward through the next pass
             model_out = self.model.decoder(
                 tokens[:, : step + 1],

metaseq/tasks/base_task.py

@@ -420,9 +420,16 @@ def build_dataset_for_inference(
     ) -> torch.utils.data.Dataset:
         raise NotImplementedError
 
-    def inference_step(self, generator, models, sample, prefix_tokens=None):
+    def inference_step(
+        self, generator, models, sample, prefix_tokens=None, recurring_punctuation=None
+    ):
         with torch.no_grad():
-            return generator.generate(models, sample, prefix_tokens=prefix_tokens)
+            return generator.generate(
+                models,
+                sample,
+                prefix_tokens=prefix_tokens,
+                recurring_punctuation=recurring_punctuation,
+            )
 
     def begin_epoch(self, epoch, model):
         """Hook function called before the start of each epoch."""