up

src/diffusers/models/attention_dispatch.py

@@ -17,6 +17,7 @@ import functools
 import inspect
 import math
 from enum import Enum
+from functools import lru_cache
 from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
 
 import torch
@@ -39,8 +40,6 @@ from ..utils import (
 from ..utils.constants import DIFFUSERS_ATTN_BACKEND, DIFFUSERS_ATTN_CHECKS
 
 
-logger = get_logger(__name__)  # pylint: disable=invalid-name
-
 _REQUIRED_FLASH_VERSION = "2.6.3"
 _REQUIRED_SAGE_VERSION = "2.1.1"
 _REQUIRED_FLEX_VERSION = "2.5.0"
@@ -70,20 +69,6 @@ else:
     flash_attn_3_func = None
     flash_attn_3_varlen_func = None
 
-if is_kernels_available():
-    from ..utils.kernels_utils import _get_fa3_from_hub
-
-    flash_attn_interface_hub = _get_fa3_from_hub()
-    if flash_attn_interface_hub is not None:
-        flash_attn_3_hub_func = flash_attn_interface_hub.flash_attn_func
-        flash_attn_3_varlen_hub_func = flash_attn_interface_hub.flash_attn_varlen_func
-    else:
-        flash_attn_3_hub_func = None
-        flash_attn_3_varlen_hub_func = None
-else:
-    flash_attn_3_hub_func = None
-    flash_attn_3_varlen_hub_func = None
-
 
 if _CAN_USE_SAGE_ATTN:
     from sageattention import (
@@ -148,6 +133,7 @@ else:
     _custom_op = custom_op_no_op
     _register_fake = register_fake_no_op
 
+logger = get_logger(__name__)  # pylint: disable=invalid-name
 
 # TODO(aryan): Add support for the following:
 # - Sage Attention++
@@ -169,7 +155,7 @@ class AttentionBackendName(str, Enum):
     _FLASH_3 = "_flash_3"
     _FLASH_VARLEN_3 = "_flash_varlen_3"
     _FLASH_3_HUB = "_flash_3_hub"
-    _FLASH_VARLEN_3_HUB = "_flash_varlen_3_hub"  # not supported yet.
+    # _FLASH_VARLEN_3_HUB = "_flash_varlen_3_hub"  # not supported yet.
 
     # PyTorch native
     FLEX = "flex"
@@ -224,6 +210,22 @@ class _AttentionBackendRegistry:
         return list(cls._backends.keys())
 
 
+@lru_cache(maxsize=None)
+def _load_fa3_hub():
+    from ..utils.kernels_utils import _get_fa3_from_hub
+
+    fa3_hub = _get_fa3_from_hub()  # won't re-download if already present
+    if fa3_hub is None:
+        raise RuntimeError(
+            "Failed to load FlashAttention-3 kernels from the Hub. Please ensure the wheel is available for your platform."
+        )
+    return fa3_hub
+
+
+def flash_attn_3_hub_func(*args, **kwargs):
+    return _load_fa3_hub().flash_attn_func(*args, **kwargs)
+
+
 @contextlib.contextmanager
 def attention_backend(backend: Union[str, AttentionBackendName] = AttentionBackendName.NATIVE):
     """
@@ -374,12 +376,6 @@ def _check_attention_backend_requirements(backend: AttentionBackendName) -> None
             raise RuntimeError(
                 f"Flash Attention 3 Hub backend '{backend.value}' is not usable because the `kernels` package isn't available. Please install it with `pip install kernels`."
             )
-        if flash_attn_3_hub_func is None:
-            raise RuntimeError(
-                "`flash_attn_3_hub_func` wasn't available. Please double if `kernels` was able to successfully pull the FA3 kernel from kernels-community/vllm-flash-attn3."
-            )
-    elif backend in [AttentionBackendName._FLASH_VARLEN_3_HUB]:
-        raise NotImplementedError
 
     elif backend in [
         AttentionBackendName.SAGE,
@@ -544,7 +540,7 @@ def _(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> Tuple[torc
     return torch.empty_like(query), query.new_empty(lse_shape)
 
 
-@_custom_op("vllm_flash_attn3::_flash_attn_forward", mutates_args=(), device_types="cuda")
+@_custom_op("vllm_flash_attn3::flash_attn", mutates_args=(), device_types="cuda")
 def _wrapped_flash_attn_3_hub(
     query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
 ) -> Tuple[torch.Tensor, torch.Tensor]:
@@ -553,7 +549,7 @@ def _wrapped_flash_attn_3_hub(
     return out, lse
 
 
-@_register_fake("vllm_flash_attn3::_flash_attn_forward")
+@_register_fake("vllm_flash_attn3::flash_attn")
 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)

src/diffusers/utils/kernels_utils.py

@@ -1,6 +1,10 @@
+from ..utils import get_logger
 from .import_utils import is_kernels_available
 
 
+logger = get_logger(__name__)
+
+
 _DEFAULT_HUB_ID_FA3 = "kernels-community/vllm-flash-attn3"
 
 
@@ -13,5 +17,6 @@ def _get_fa3_from_hub():
         try:
             flash_attn_3_hub = get_kernel(_DEFAULT_HUB_ID_FA3)
             return flash_attn_3_hub
-        except Exception:
-            return None
+        except Exception as e:
+            logger.error(f"An error occurred while fetching kernel '{_DEFAULT_HUB_ID_FA3}' from the Hub: {e}")
+            raise