[Generate] Correct input_ids detection (#14815)

* [Generate] Correct input_ids detection

* correct

src/transformers/generation_utils.py

@@ -457,7 +457,7 @@ class GenerationMixin:
         pad_token_id: int,
         eos_token_id: int,
     ) -> torch.LongTensor:
-        is_input_ids = isinstance(inputs, torch.LongTensor) and len(inputs.shape) == 2
+        is_input_ids = len(inputs.shape) == 2 and inputs.dtype in [torch.int, torch.long]
         is_pad_token_in_inputs = (pad_token_id is not None) and (pad_token_id in inputs)
         is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or (
             (eos_token_id is not None) and (pad_token_id != eos_token_id)

tests/test_generation_utils.py

@@ -1719,6 +1719,31 @@ class GenerationIntegrationTests(unittest.TestCase):
         # make sure model generated correctly until `max_length`
         self.assertEqual(output_sequences.shape, (1, 5))
 
+    def test_encoder_decoder_generate_attention_mask(self):
+        articles = ["Timberlake", "Jessica Biel, welcome to parenthood among other things"]
+        tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
+        # need extrem generation values here to force this test
+        # to fail when `attention_mask` is not correctly treated in generate
+        model = BartForConditionalGeneration.from_pretrained(
+            "hf-internal-testing/tiny-random-bart", max_length=50, num_beams=5, num_return_sequences=5
+        ).to(torch_device)
+
+        model.config.eos_token_id = None
+        input_ids = tokenizer(articles[0], return_tensors="pt").input_ids.to(torch_device)
+        input_ids_batched = tokenizer(articles, padding=True, return_tensors="pt").input_ids.to(torch_device)
+
+        output_sequences_batched = model.generate(
+            input_ids=input_ids_batched, return_dict_in_generate=True, output_scores=True
+        )
+        output_sequences = model.generate(input_ids=input_ids, return_dict_in_generate=True, output_scores=True)
+
+        batched_out = output_sequences_batched.sequences_scores
+        out = output_sequences.sequences_scores
+
+        diff = (batched_out[:5].sum() - out.sum()).abs()
+
+        self.assertTrue(diff < 1e-4)
+
     def test_decoder_generate_with_inputs_embeds(self):
         article = """I need input_ids to generate"""
         tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")