Support for Flash Attention 3 (#38972)

* Support `flash_attn_3`
Implements fwd and tests for Flash Attention 3 https://github.com/Dao-AILab/flash-attention/commits/main/hopper

- Includes checks for dropout>0 and ALiBi in `modeling_utils.PreTrainedModel._check_and_enable_flash_attn_3` (Dropout will likely be supported soon, so this will need to be updated and `modeling_flash_attention_utils._flash_attention_forward` at the `if _IS_FLASH_ATTN_3_AVAILABLE: ...`

An example Llama implementation is included in `modeling_llama.py` but other models would still need to be updated

Based on https://github.com/huggingface/transformers/pull/36190 which has model implementations and examples which could be merged

* Add tests for Flash Attention 2 and 3 parity

* ci fix

* FA2 compatibiity
- `_prepare_flash_attention_from_position_ids` ->`prepare_fa2_from_position_ids`
- Remove bettertransformer check in Flash Attention 3
- Merge tests
- Add licensing

* ci fix

* Test naming consistency

* ci fix

* Deprecation warning for `prepare_fa2_from_position_ids`

* ci fix

tests/generation/test_utils.py

@@ -34,6 +34,7 @@ from transformers.testing_utils import (
     is_flaky,
     require_accelerate,
     require_flash_attn,
+    require_flash_attn_3,
     require_optimum_quanto,
     require_read_token,
     require_torch,
@@ -2292,6 +2293,7 @@ class GenerationTesterMixin:
         support_flag = {
             "sdpa": "_supports_sdpa",
             "flash_attention_2": "_supports_flash_attn_2",
+            "flash_attention_3": "_supports_flash_attn_3",
         }
 
         for model_class in self.all_generative_model_classes:
@@ -2369,6 +2371,14 @@ class GenerationTesterMixin:
         """Tests that generate has equivalent outputs with FA2 and eager attention implementations."""
         self._test_attention_implementation("flash_attention_2")
 
+    @pytest.mark.flash_attn_3_test
+    @require_flash_attn_3
+    @require_torch_gpu
+    @slow
+    def test_eager_matches_fa3_generate(self):
+        """Tests that generate has equivalent outputs with FA3 and eager attention implementations."""
+        self._test_attention_implementation("flash_attention_3")
+
     def _check_generate_outputs(self, output, config, use_cache=False, num_return_sequences=1, num_beams=1):
         input_batch_size = int(output.sequences.shape[0] / num_return_sequences)
         internal_batch_size = (