Fix PyTorch 2.3.1 compatibility: add version guard for torch.library.… (#12206)

* Fix PyTorch 2.3.1 compatibility: add version guard for torch.library.custom_op

- Add hasattr() check for torch.library.custom_op and register_fake
- These functions were added in PyTorch 2.4, causing import failures in 2.3.1
- Both decorators and functions are now properly guarded with version checks
- Maintains backward compatibility while preserving functionality

Fixes #12195

* Use dummy decorators approach for PyTorch version compatibility

- Replace hasattr check with version string comparison
- Add no-op decorator functions for PyTorch < 2.4.0
- Follows pattern from #11941 as suggested by reviewer
- Maintains cleaner code structure without indentation changes

* Update src/diffusers/models/attention_dispatch.py

Update all the decorator usages
Co-authored-by: Aryan <contact.aryanvs@gmail.com>

* Update src/diffusers/models/attention_dispatch.py
Co-authored-by: Aryan <contact.aryanvs@gmail.com>

* Update src/diffusers/models/attention_dispatch.py
Co-authored-by: Aryan <contact.aryanvs@gmail.com>

* Update src/diffusers/models/attention_dispatch.py
Co-authored-by: Aryan <contact.aryanvs@gmail.com>

* Move version check to top of file and use private naming as requested

* Apply style fixes

---------
Co-authored-by: Aryan <contact.aryanvs@gmail.com>
Co-authored-by: Aryan <aryan@huggingface.co>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

src/diffusers/models/attention_dispatch.py

@@ -110,6 +110,27 @@ if _CAN_USE_XFORMERS_ATTN:
 else:
     xops = None
 
+# Version guard for PyTorch compatibility - custom_op was added in PyTorch 2.4
+if torch.__version__ >= "2.4.0":
+    _custom_op = torch.library.custom_op
+    _register_fake = torch.library.register_fake
+else:
+
+    def custom_op_no_op(name, fn=None, /, *, mutates_args, device_types=None, schema=None):
+        def wrap(func):
+            return func
+
+        return wrap if fn is None else fn
+
+    def register_fake_no_op(op, fn=None, /, *, lib=None, _stacklevel=1):
+        def wrap(func):
+            return func
+
+        return wrap if fn is None else fn
+
+    _custom_op = custom_op_no_op
+    _register_fake = register_fake_no_op
+
 
 logger = get_logger(__name__)  # pylint: disable=invalid-name
 
@@ -473,12 +494,11 @@ def _flex_attention_causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
 
 # ===== torch op registrations =====
 # Registrations are required for fullgraph tracing compatibility
-
-
-# TODO: library.custom_op and register_fake probably need version guards?
 # TODO: this is only required because the beta release FA3 does not have it. There is a PR adding
 # this but it was never merged: https://github.com/Dao-AILab/flash-attention/pull/1590
-@torch.library.custom_op("flash_attn_3::_flash_attn_forward", mutates_args=(), device_types="cuda")
+
+
+@_custom_op("flash_attn_3::_flash_attn_forward", mutates_args=(), device_types="cuda")
 def _wrapped_flash_attn_3_original(
     query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
 ) -> Tuple[torch.Tensor, torch.Tensor]:
@@ -487,7 +507,7 @@ def _wrapped_flash_attn_3_original(
     return out, lse
 
 
-@torch.library.register_fake("flash_attn_3::_flash_attn_forward")
+@_register_fake("flash_attn_3::_flash_attn_forward")
 def _(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
     batch_size, seq_len, num_heads, head_dim = query.shape
     lse_shape = (batch_size, seq_len, num_heads)