Remove max length beam scorer (#11378)

* removed max_len

* removed max_length from BeamSearchScorer

* correct max length

* finish

* del vim

* finish & add test

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

tests/test_generation_beam_search.py

@@ -59,7 +59,6 @@ class BeamSearchTester:
     def prepare_beam_scorer(self, **kwargs):
         return BeamSearchScorer(
             batch_size=kwargs.get("batch_size", self.batch_size),
-            max_length=kwargs.get("max_length", self.max_length),
             num_beams=kwargs.get("num_beams", self.num_beams),
             device=torch_device,
             length_penalty=kwargs.get("length_penalty", self.length_penalty),
@@ -170,9 +169,7 @@ class BeamSearchTester:
     def check_beam_scores_finalize(self, input_ids, next_tokens, next_indices, next_scores):
         # max_length should be only one more than current input_ids to check that eos is correctly appended
         max_length = self.sequence_length + 1
-        beam_scorer = self.prepare_beam_scorer(
-            num_beam_hyps_to_keep=1, max_length=max_length, length_penalty=1.0, do_early_stopping=False
-        )
+        beam_scorer = self.prepare_beam_scorer(num_beam_hyps_to_keep=1, length_penalty=1.0, do_early_stopping=False)
 
         # update beams and append to input_ids
         tokens = next_tokens.clone()
@@ -197,6 +194,7 @@ class BeamSearchTester:
             output_indices,
             pad_token_id=self.pad_token_id,
             eos_token_id=self.eos_token_id,
+            max_length=max_length,
         )
 
         sequences = sequence_output["sequences"]
@@ -225,6 +223,7 @@ class BeamSearchTester:
             output_indices,
             pad_token_id=self.pad_token_id,
             eos_token_id=self.eos_token_id,
+            max_length=max_length,
         )
         sequences = sequence_output["sequences"]
         sequence_scores = sequence_output["sequence_scores"]

tests/test_generation_utils.py

@@ -148,7 +148,6 @@ class GenerationTesterMixin:
         }
         beam_scorer = BeamSearchScorer(
             batch_size=batch_size,
-            max_length=max_length,
             num_beams=beam_kwargs["num_beams"],
             device=torch_device,
             length_penalty=beam_kwargs["length_penalty"],
@@ -169,7 +168,6 @@ class GenerationTesterMixin:
         }
         beam_scorer = BeamSearchScorer(
             batch_size=batch_size,
-            max_length=max_length,
             num_beams=beam_kwargs["num_beams"],
             device=torch_device,
             length_penalty=beam_kwargs["length_penalty"],
@@ -1411,7 +1409,6 @@ class GenerationIntegrationTests(unittest.TestCase):
 
         beam_scorer = BeamSearchScorer(
             batch_size=batch_size,
-            max_length=max_length,
             num_beams=num_beams,
             device=torch_device,
         )
@@ -1442,7 +1439,6 @@ class GenerationIntegrationTests(unittest.TestCase):
 
         diverse_beam_scorer = BeamSearchScorer(
             batch_size=batch_size,
-            max_length=max_length,
             num_beams=num_beams,
             device=torch_device,
             num_beam_hyps_to_keep=num_return_sequences,
@@ -1502,7 +1498,6 @@ class GenerationIntegrationTests(unittest.TestCase):
         # Beam
         beam_scorer = BeamSearchScorer(
             batch_size=batch_size,
-            max_length=max_length,
             num_beams=num_beams,
             device=torch_device,
         )
@@ -1520,7 +1515,6 @@ class GenerationIntegrationTests(unittest.TestCase):
         # Grouped beam search
         diverse_beam_scorer = BeamSearchScorer(
             batch_size=batch_size,
-            max_length=max_length,
             num_beams=num_beams,
             device=torch_device,
             num_beam_hyps_to_keep=num_return_sequences,
@@ -1535,3 +1529,51 @@ class GenerationIntegrationTests(unittest.TestCase):
                 max_length=max_length,
                 **model_kwargs,
             )
+
+    def test_beam_search_warning_if_max_length_is_passed(self):
+        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
+        bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
+        bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
+
+        batch_size = 1
+        num_beams = 3
+
+        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
+        input_ids = input_ids.expand(num_beams, -1)
+        model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
+
+        stopping_criteria_max_length = 18
+        stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=stopping_criteria_max_length)])
+
+        with self.assertWarns(UserWarning):
+            beam_scorer = BeamSearchScorer(
+                batch_size=batch_size,
+                num_beams=num_beams,
+                device=torch_device,
+                max_length=10,
+            )
+
+        generated_ids = bart_model.beam_search(
+            input_ids,
+            num_beams=num_beams,
+            stopping_criteria=stopping_criteria,
+            beam_scorer=beam_scorer,
+            **model_kwargs,
+        )
+
+        beam_scorer_no_max_len = BeamSearchScorer(
+            batch_size=batch_size,
+            num_beams=num_beams,
+            device=torch_device,
+        )
+
+        generated_ids_no_max_len = bart_model.beam_search(
+            input_ids,
+            num_beams=num_beams,
+            stopping_criteria=stopping_criteria,
+            beam_scorer=beam_scorer_no_max_len,
+            **model_kwargs,
+        )
+
+        # BeamSearchScorer max_length should not influence "real" max_length
+        self.assertEqual(generated_ids.tolist(), generated_ids_no_max_len.tolist())