add current changes

src/transformers/modeling_utils.py

@@ -614,6 +614,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
         max_length=None,
         min_length=None,
         do_sample=True,
+        early_stopping=False,
         num_beams=None,
         temperature=None,
         top_k=None,
@@ -720,7 +721,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
 
         max_length = max_length if max_length is not None else self.config.max_length
         min_length = min_length if min_length is not None else self.config.min_length
-        do_sample = do_sample if do_sample is not None else self.config.do_sample
+        early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
         num_beams = num_beams if num_beams is not None else self.config.num_beams
         temperature = temperature if temperature is not None else self.config.temperature
         top_k = top_k if top_k is not None else self.config.top_k
@@ -747,6 +748,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
         assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer."
         assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
         assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
+        assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
         assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer."
         assert temperature > 0, "`temperature` should be strictly positive."
         assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
@@ -841,8 +843,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             encoder_inputs = input_ids
             input_ids = torch.full(
                 (effective_batch_size * num_beams, 1),
-#                eos_token_id,
-                bos_token_id,
+                eos_token_id,
+#                bos_token_id,
                 #                eos_token_id, # Why eos_token_id here? bos_token_id seems to work as well ... to see if it works as well with hard summarization case
                 dtype=torch.long,
                 device=next(self.parameters()).device,
@@ -860,6 +862,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                 max_length,
                 min_length,
                 do_sample,
+                early_stopping,
                 temperature,
                 top_k,
                 top_p,
@@ -1012,6 +1015,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
         max_length,
         min_length,
         do_sample,
+        early_stopping,
         temperature,
         top_k,
         top_p,
@@ -1033,7 +1037,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
 
         # generated hypotheses
         generated_hyps = [
-            BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=False) for _ in range(batch_size)
+            BeamHypotheses(num_beams, max_length - 1, length_penalty, early_stopping=early_stopping) for _ in range(batch_size)
         ]
 
         # scores for each sentence in the beam
@@ -1080,11 +1084,11 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             # force eos to be chosen at end of generation for encoder-decoder models
             # TODO (PVP): both these things are very hacky see whether it might be possible to solve this differently
             if self.config.is_encoder_decoder:
-#                self.prepare_logits_for_softmax(next_token_logits, cur_len, max_length)
                 if cur_len == 1:
                     self._force_token_ids_generation(next_token_logits, bos_token_id)
                 if cur_len == max_length - 1:
                     self._force_token_ids_generation(next_token_logits, eos_token_ids)
+#                self.prepare_logits_for_softmax(next_token_logits, cur_len, max_length)
 
             if do_sample:
                 # Temperature (higher temperature => more likely to sample low probability tokens)