Fix graph break in torch.compile when using FA2 with attention_mask=None and batch size > 1 (#37332)

* Fix graph break in torch.compile when using FA2 with attention_mask=None and batch size > 1

* fix code format

* add test; replace position_ids with query_states becasue position_ids.shape[0] is always 1

* add assert loss is not nan

src/transformers/modeling_flash_attention_utils.py

@@ -385,8 +385,10 @@ def _flash_attention_forward(
     # If position_ids is provided and check all examples do not contain only 1 sequence, If tensor in increasing
     # then we probably have one sequence, otherwise it is packed. Additionally check we are in pre-fill/training stage.
     # Use `flash_attn_varlen_func` to prevent cross-example attention and also allow padding free approach
-    elif position_ids is not None and (
+    elif (
-        max_length_q is not None or (query_length != 1 and not (torch.diff(position_ids, dim=-1) >= 0).all())
+        position_ids is not None
+        and query_states.shape[0] == 1
+        and (max_length_q is not None or (query_length != 1 and not (torch.diff(position_ids, dim=-1) >= 0).all()))
     ):
         batch_size = query_states.size(0)
 

tests/test_modeling_common.py

@@ -4082,6 +4082,45 @@ class ModelTesterMixin:
                     # with attention mask
                     _ = model(dummy_input, attention_mask=dummy_attention_mask)
 
+    @require_flash_attn
+    @require_torch_gpu
+    @mark.flash_attn_test
+    @slow
+    def test_flash_attn_2_can_compile_with_attention_mask_None_without_graph_break(self):
+        if version.parse(torch.__version__) < version.parse("2.3"):
+            self.skipTest(reason="This test requires torch >= 2.3 to run.")
+
+        if not hasattr(self, "_torch_compile_train_cls"):
+            self.skipTest(f"{self.__class__.__name__} doesn't have the attribute `_torch_compile_train_cls`.")
+
+        if not self.has_attentions:
+            self.skipTest(reason="Model architecture does not support attentions")
+
+        if not is_torch_fp16_available_on_device(torch_device):
+            self.skipTest(f"float16 not supported on {torch_device} (on the specific device currently used)")
+
+        torch.compiler.reset()
+        torch_dtype = torch.float16
+
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+        config._attn_implementation = "flash_attention_2"
+        cls = self._torch_compile_train_cls
+        model = cls(config).to(device=torch_device, dtype=torch_dtype)
+
+        inputs = {
+            "input_ids": torch.randint(low=1, high=model.config.vocab_size, size=(2, 10), device=torch_device),
+            "labels": torch.randint(low=1, high=model.config.vocab_size, size=(2, 10), device=torch_device),
+        }
+
+        model = torch.compile(model, fullgraph=True)
+        # forward compilation
+        set_seed(42)
+        loss = model(**inputs).loss
+        # backward compilation
+        loss.backward()
+
+        assert not loss.isnan().any()
+
     @require_flash_attn
     @require_torch_gpu
     @mark.flash_attn_test