aligned sample_beam output selection with beam_search (huggingface#25375

) * aligned sample_beam specs with beam_search * pull origin main * Revert "pull origin main" This reverts commit 06d356f. * update test_utils.py * fix format * remove comment --------- Co-authored-by: Shogo Fujita <[email protected]>
EduardoPach · Aug 9, 2023 · 018061a · 018061a
1 parent c66415a
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Showing 2 changed files with 11 additions and 28 deletions.
diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
@@ -1691,18 +1691,19 @@ def generate(
 
             # 12. prepare beam search scorer
             beam_scorer = BeamSearchScorer(
-                batch_size=batch_size * generation_config.num_return_sequences,
+                batch_size=batch_size,
                 num_beams=generation_config.num_beams,
                 device=inputs_tensor.device,
                 length_penalty=generation_config.length_penalty,
                 do_early_stopping=generation_config.early_stopping,
+                num_beam_hyps_to_keep=generation_config.num_return_sequences,
                 max_length=generation_config.max_length,
             )
 
             # 13. interleave input_ids with `num_beams` additional sequences per batch
             input_ids, model_kwargs = self._expand_inputs_for_generation(
                 input_ids=input_ids,
-                expand_size=generation_config.num_beams * generation_config.num_return_sequences,
+                expand_size=generation_config.num_beams,
                 is_encoder_decoder=self.config.is_encoder_decoder,
                 **model_kwargs,
             )

diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
@@ -438,7 +438,6 @@ def _beam_sample_generate(
         input_ids,
         attention_mask,
         max_length,
-        num_return_sequences,
         beam_scorer,
         beam_kwargs,
         logits_warper,
@@ -463,21 +462,21 @@ def _beam_sample_generate(
             **logits_warper_kwargs,
             **model_kwargs,
         )
-        # beam_search does not automatically interleave `batch_size` dim for `num_beams * num_return_sequences`
+        # beam_search does not automatically interleave `batch_size` dim for `num_beams`
         torch.manual_seed(0)
         kwargs = {}
         if model.config.is_encoder_decoder:
             encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
                 model,
                 input_ids,
                 attention_mask,
-                num_interleave=beam_scorer.num_beams * num_return_sequences,
+                num_interleave=beam_scorer.num_beams,
                 output_attentions=output_attentions,
                 output_hidden_states=output_hidden_states,
             )
             kwargs["encoder_outputs"] = encoder_outputs
         elif attention_mask is not None:
-            attention_mask = attention_mask.repeat_interleave(beam_scorer.num_beams * num_return_sequences, dim=0)
+            attention_mask = attention_mask.repeat_interleave(beam_scorer.num_beams, dim=0)
 
         # prevent flaky generation test failures
         logits_processor = LogitsProcessorList()
@@ -486,7 +485,7 @@ def _beam_sample_generate(
         with torch.no_grad():
             model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
             output_beam_sample = model.beam_sample(
-                input_ids.repeat_interleave(beam_scorer.num_beams * num_return_sequences, dim=0),
+                input_ids.repeat_interleave(beam_scorer.num_beams, dim=0),
                 beam_scorer,
                 max_length=max_length,
                 logits_warper=logits_warper,
@@ -891,13 +890,9 @@ def test_beam_search_generate(self):
 
             self.assertListEqual(output_generate.tolist(), output_beam_search.tolist())
 
-            # check `generate()` and `beam_search()` are equal for `num_return_sequences`
-            num_return_sequences = 2
             if model.config.is_encoder_decoder:
                 max_length = 4
-            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(
-                input_ids.shape[0], max_length, num_return_sequences=num_return_sequences
-            )
+            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
 
             output_generate, output_beam_search = self._beam_search_generate(
                 model=model,
@@ -1036,21 +1031,15 @@ def test_beam_sample_generate(self):
             model = model_class(config).to(torch_device).eval()
 
             # check `generate()` and `beam_search()` are equal
-            # change `num_return_sequences = 2` but not for `beam_scorer`
-            num_return_sequences = 2
             if model.config.is_encoder_decoder:
                 max_length = 4
-            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(
-                input_ids.shape[0] * num_return_sequences, max_length
-            )
-            beam_kwargs["num_return_sequences"] = num_return_sequences
+            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
 
             output_generate, output_beam_sample = self._beam_sample_generate(
                 model=model,
                 input_ids=input_ids,
                 attention_mask=attention_mask,
                 max_length=max_length,
-                num_return_sequences=num_return_sequences,
                 beam_scorer=beam_scorer,
                 beam_kwargs=beam_kwargs,
                 logits_warper=logits_warper,
@@ -1074,20 +1063,15 @@ def test_beam_sample_generate_dict_output(self):
             model = model_class(config).to(torch_device).eval()
             logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)
 
-            num_return_sequences = 2
             if model.config.is_encoder_decoder:
                 max_length = 4
-            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(
-                input_ids.shape[0] * num_return_sequences, max_length
-            )
-            beam_kwargs["num_return_sequences"] = num_return_sequences
+            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
 
             output_beam_sample, output_generate = self._beam_sample_generate(
                 model=model,
                 input_ids=input_ids,
                 attention_mask=attention_mask,
                 max_length=max_length,
-                num_return_sequences=num_return_sequences,
                 beam_scorer=beam_scorer,
                 beam_kwargs=beam_kwargs,
                 logits_warper=logits_warper,
@@ -1113,9 +1097,7 @@ def test_beam_sample_generate_dict_output(self):
             self.assertTrue((output_generate["sequences_scores"] < 0).all().item())
 
             for output in (output_beam_sample, output_generate):
-                self._check_outputs(
-                    output, input_ids, model.config, num_return_sequences=num_return_sequences * beam_scorer.num_beams
-                )
+                self._check_outputs(output, input_ids, model.config, num_return_sequences=beam_scorer.num_beams)
 
     def test_generate_without_input_ids(self):
         config, _, _, max_length = self._get_input_ids_and_config()