[Bug Fix] Beam search example in docs fails & a fix (integrating `max_length`...

[Bug Fix] Beam search example in docs fails & a fix (integrating `max_length` in `BeamScorer.finalize()`) (#15555)

* added the test and fix

* had left out a comment

src/transformers/generation_beam_search.py

@@ -332,7 +332,8 @@ class BeamSearchScorer(BeamScorer):
                 best_scores[i * self.num_beam_hyps_to_keep + j] = best_score
 
         # prepare for adding eos
-        sent_max_len = min(sent_lengths.max().item() + 1, max_length)
+        sent_lengths_max = sent_lengths.max().item() + 1
+        sent_max_len = min(sent_lengths_max, max_length) if max_length is not None else sent_lengths_max
         decoded: torch.LongTensor = input_ids.new(batch_size * self.num_beam_hyps_to_keep, sent_max_len)
         # shorter batches are padded if needed
         if sent_lengths.min().item() != sent_lengths.max().item():
@@ -341,7 +342,7 @@ class BeamSearchScorer(BeamScorer):
         # fill with hypotheses and eos_token_id if the latter fits in
         for i, hypo in enumerate(best):
             decoded[i, : sent_lengths[i]] = hypo
-            if sent_lengths[i] < max_length:
+            if sent_lengths[i] < sent_max_len:
                 decoded[i, sent_lengths[i]] = eos_token_id
 
         return UserDict(

tests/generation/test_generation_utils.py

@@ -2315,6 +2315,48 @@ class GenerationIntegrationTests(unittest.TestCase):
 
         self.assertTrue(torch.allclose(transition_scores_sum, outputs.sequences_scores, atol=1e-3))
 
+    @slow
+    def test_beam_search_example_integration(self):
+        # exactly the example provided in the docstrings of beam search, which previously
+        # failed after directly copying from it. Refer to PR #15555
+        tokenizer = AutoTokenizer.from_pretrained("t5-base")
+        model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+
+        encoder_input_str = "translate English to German: How old are you?"
+        encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
+
+        # lets run beam search using 3 beams
+        num_beams = 3
+        # define decoder start token ids
+        input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
+        input_ids = input_ids * model.config.decoder_start_token_id
+
+        # add encoder_outputs to model keyword arguments
+        model_kwargs = {
+            "encoder_outputs": model.get_encoder()(
+                encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
+            )
+        }
+
+        # instantiate beam scorer
+        beam_scorer = BeamSearchScorer(
+            batch_size=1,
+            num_beams=num_beams,
+            device=model.device,
+        )
+
+        # instantiate logits processors
+        logits_processor = LogitsProcessorList(
+            [
+                MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
+            ]
+        )
+
+        outputs = model.beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)
+        outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+        self.assertListEqual(outputs, ["Wie alt bist du?"])
+
     @slow
     def test_constrained_beam_search(self):
         model = GPT2LMHeadModel.from_pretrained("../gpt2").to(torch_device)