[Idefics2] - Fix FA2 call for Perceiver layer (#32275)

* Fix FA2 call for Perciever layer

* [run_slow] idefics2

* [run_slow] idefics2

* [run_slow] idefics2

* Fix up

* [run_slow] idefics2

* [run_slow] idefics2

* [run_slow] idefics2

src/transformers/models/idefics2/modeling_idefics2.py

@@ -894,7 +894,7 @@ class Idefics2PerceiverFlashAttention2(Idefics2PerceiverAttention):
             attention_mask,
             q_len,
             dropout=dropout_rate,
-            sliding_window=False,
+            sliding_window=None,
             is_causal=self.is_causal,
             use_top_left_mask=self._flash_attn_uses_top_left_mask,
         )

tests/models/idefics2/test_modeling_idefics2.py

@@ -29,7 +29,14 @@ from transformers import (
     is_torch_available,
     is_vision_available,
 )
-from transformers.testing_utils import require_bitsandbytes, require_torch, slow, torch_device
+from transformers.testing_utils import (
+    require_bitsandbytes,
+    require_flash_attn,
+    require_torch,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
 
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
@@ -491,13 +498,13 @@ class Idefics2ForConditionalGenerationIntegrationTest(unittest.TestCase):
         torch.cuda.empty_cache()
 
     @slow
+    @unittest.skip("Test hits OOM on CI - https://github.com/huggingface/transformers/issues/32288")
     def test_integration_test(self):
         model = Idefics2ForConditionalGeneration.from_pretrained(
             "HuggingFaceM4/idefics2-8b-base",
             torch_dtype=torch.bfloat16,
             device_map="auto",
         )
-        model.to(torch_device)
 
         # Create inputs
         text = "<image>In this image, we see"
@@ -517,7 +524,8 @@ class Idefics2ForConditionalGenerationIntegrationTest(unittest.TestCase):
     def test_integration_test_4bit(self):
         # Let' s make sure we test the preprocessing to replace what is used
         model = Idefics2ForConditionalGeneration.from_pretrained(
-            "HuggingFaceM4/idefics2-8b-base", load_in_4bit=True, device_map="auto"
+            "HuggingFaceM4/idefics2-8b-base",
+            load_in_4bit=True,
         )
 
         # Create pixel inputs
@@ -530,3 +538,37 @@ class Idefics2ForConditionalGenerationIntegrationTest(unittest.TestCase):
 
         expected_generated_text = "In this image, we see the Statue of Liberty, the Hudson River,"
         self.assertEqual(generated_texts[0], expected_generated_text)
+
+    @require_flash_attn
+    @require_torch_gpu
+    @require_bitsandbytes
+    def test_flash_attn_2_eager_equivalence(self):
+        # Create inputs
+        text = "<image>In this image, we see"
+        images = self.image1
+        inputs = self.processor(text=text, images=images, return_tensors="pt", padding=True)
+        inputs.to(torch_device)
+
+        # Eager model
+        model_eager = Idefics2ForConditionalGeneration.from_pretrained(
+            "HuggingFaceM4/idefics2-8b-base",
+            attn_implementation="eager",
+            load_in_4bit=True,
+        )
+        generated_ids_eager = model_eager.generate(**inputs, max_new_tokens=10)
+        generated_texts_eager = self.processor.batch_decode(generated_ids_eager, skip_special_tokens=True)
+
+        del model_eager
+
+        # Flash Attention 2 model
+        model_flash_attention_2 = Idefics2ForConditionalGeneration.from_pretrained(
+            "HuggingFaceM4/idefics2-8b-base",
+            attn_implementation="flash_attention_2",
+            load_in_4bit=True,
+        )
+        generated_ids_flash_attention_2 = model_flash_attention_2.generate(**inputs, max_new_tokens=10)
+        generated_texts_flash_attention_2 = self.processor.batch_decode(
+            generated_ids_flash_attention_2, skip_special_tokens=True
+        )
+
+        self.assertEqual(generated_texts_eager[0], generated_texts_flash_attention_2[0])