[core] support sage attention + FA2 through `kernels` (#12439)

* up

* support automatic dispatch.

* disable compile support for now./

* up

* flash too.

* document.

* up

* up

* up

* up

docs/source/en/optimization/attention_backends.md

@@ -139,12 +139,14 @@ Refer to the table below for a complete list of available attention backends and
 | `_native_npu` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | NPU-optimized attention |
 | `_native_xla` | [PyTorch native](https://docs.pytorch.org/docs/stable/generated/torch.nn.attention.SDPBackend.html#torch.nn.attention.SDPBackend) | XLA-optimized attention |
 | `flash` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-2 |
+| `flash_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-2 from kernels |
 | `flash_varlen` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention |
 | `aiter` | [AI Tensor Engine for ROCm](https://github.com/ROCm/aiter) | FlashAttention for AMD ROCm |
 | `_flash_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 |
 | `_flash_varlen_3` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | Variable length FlashAttention-3 |
 | `_flash_3_hub` | [FlashAttention](https://github.com/Dao-AILab/flash-attention) | FlashAttention-3 from kernels |
 | `sage` | [SageAttention](https://github.com/thu-ml/SageAttention) | Quantized attention (INT8 QK) |
+| `sage_hub` | [SageAttention](https://github.com/thu-ml/SageAttention) | Quantized attention (INT8 QK) from kernels |
 | `sage_varlen` | [SageAttention](https://github.com/thu-ml/SageAttention) | Variable length SageAttention |
 | `_sage_qk_int8_pv_fp8_cuda` | [SageAttention](https://github.com/thu-ml/SageAttention) | INT8 QK + FP8 PV (CUDA) |
 | `_sage_qk_int8_pv_fp8_cuda_sm90` | [SageAttention](https://github.com/thu-ml/SageAttention) | INT8 QK + FP8 PV (SM90) |

src/diffusers/models/attention_dispatch.py

@@ -18,7 +18,7 @@ import inspect
 import math
 from dataclasses import dataclass
 from enum import Enum
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 
@@ -160,16 +160,13 @@ logger = get_logger(__name__)  # pylint: disable=invalid-name
 # - CP with sage attention, flex, xformers, other missing backends
 # - Add support for normal and CP training with backends that don't support it yet
 
-_SAGE_ATTENTION_PV_ACCUM_DTYPE = Literal["fp32", "fp32+fp32"]
-_SAGE_ATTENTION_QK_QUANT_GRAN = Literal["per_thread", "per_warp"]
-_SAGE_ATTENTION_QUANTIZATION_BACKEND = Literal["cuda", "triton"]
-
 
 class AttentionBackendName(str, Enum):
     # EAGER = "eager"
 
     # `flash-attn`
     FLASH = "flash"
+    FLASH_HUB = "flash_hub"
     FLASH_VARLEN = "flash_varlen"
     _FLASH_3 = "_flash_3"
     _FLASH_VARLEN_3 = "_flash_varlen_3"
@@ -191,6 +188,7 @@ class AttentionBackendName(str, Enum):
 
     # `sageattention`
     SAGE = "sage"
+    SAGE_HUB = "sage_hub"
     SAGE_VARLEN = "sage_varlen"
     _SAGE_QK_INT8_PV_FP8_CUDA = "_sage_qk_int8_pv_fp8_cuda"
     _SAGE_QK_INT8_PV_FP8_CUDA_SM90 = "_sage_qk_int8_pv_fp8_cuda_sm90"
@@ -264,7 +262,13 @@ _HUB_KERNELS_REGISTRY: Dict["AttentionBackendName", _HubKernelConfig] = {
     # TODO: temporary revision for now. Remove when merged upstream into `main`.
     AttentionBackendName._FLASH_3_HUB: _HubKernelConfig(
         repo_id="kernels-community/flash-attn3", function_attr="flash_attn_func", revision="fake-ops-return-probs"
-    )
+    ),
+    AttentionBackendName.FLASH_HUB: _HubKernelConfig(
+        repo_id="kernels-community/flash-attn2", function_attr="flash_attn_func", revision=None
+    ),
+    AttentionBackendName.SAGE_HUB: _HubKernelConfig(
+        repo_id="kernels-community/sage_attention", function_attr="sageattn", revision=None
+    ),
 }
 
 
@@ -420,8 +424,8 @@ def _check_attention_backend_requirements(backend: AttentionBackendName) -> None
                 f"Flash Attention 3 backend '{backend.value}' is not usable because of missing package or the version is too old. Please build FA3 beta release from source."
             )
 
-    # TODO: add support Hub variant of FA3 varlen later
-    elif backend in [AttentionBackendName._FLASH_3_HUB]:
+    # TODO: add support Hub variant of varlen later
+    elif backend in [AttentionBackendName._FLASH_3_HUB, AttentionBackendName.FLASH_HUB, AttentionBackendName.SAGE_HUB]:
         if not is_kernels_available():
             raise RuntimeError(
                 f"Backend '{backend.value}' is not usable because the `kernels` package isn't available. Please install it with `pip install kernels`."
@@ -1350,6 +1354,38 @@ def _flash_attention(
     return (out, lse) if return_lse else out
 
 
+@_AttentionBackendRegistry.register(
+    AttentionBackendName.FLASH_HUB,
+    constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
+    supports_context_parallel=False,
+)
+def _flash_attention_hub(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    dropout_p: float = 0.0,
+    is_causal: bool = False,
+    scale: Optional[float] = None,
+    return_lse: bool = False,
+    _parallel_config: Optional["ParallelConfig"] = None,
+) -> torch.Tensor:
+    lse = None
+    func = _HUB_KERNELS_REGISTRY[AttentionBackendName.FLASH_HUB].kernel_fn
+    out = func(
+        q=query,
+        k=key,
+        v=value,
+        dropout_p=dropout_p,
+        softmax_scale=scale,
+        causal=is_causal,
+        return_attn_probs=return_lse,
+    )
+    if return_lse:
+        out, lse, *_ = out
+
+    return (out, lse) if return_lse else out
+
+
 @_AttentionBackendRegistry.register(
     AttentionBackendName.FLASH_VARLEN,
     constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
@@ -1431,6 +1467,7 @@ def _flash_attention_3(
 @_AttentionBackendRegistry.register(
     AttentionBackendName._FLASH_3_HUB,
     constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
+    supports_context_parallel=False,
 )
 def _flash_attention_3_hub(
     query: torch.Tensor,
@@ -1444,6 +1481,9 @@ def _flash_attention_3_hub(
     return_attn_probs: bool = False,
     _parallel_config: Optional["ParallelConfig"] = None,
 ) -> torch.Tensor:
+    if _parallel_config:
+        raise NotImplementedError(f"{AttentionBackendName._FLASH_3_HUB.value} is not implemented for parallelism yet.")
+
     func = _HUB_KERNELS_REGISTRY[AttentionBackendName._FLASH_3_HUB].kernel_fn
     out = func(
         q=query,
@@ -1938,6 +1978,38 @@ def _sage_attention(
     return (out, lse) if return_lse else out
 
 
+@_AttentionBackendRegistry.register(
+    AttentionBackendName.SAGE_HUB,
+    constraints=[_check_device_cuda, _check_qkv_dtype_bf16_or_fp16, _check_shape],
+    supports_context_parallel=False,
+)
+def _sage_attention_hub(
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    is_causal: bool = False,
+    scale: Optional[float] = None,
+    return_lse: bool = False,
+    _parallel_config: Optional["ParallelConfig"] = None,
+) -> torch.Tensor:
+    lse = None
+    func = _HUB_KERNELS_REGISTRY[AttentionBackendName.SAGE_HUB].kernel_fn
+    if _parallel_config is None:
+        out = func(
+            q=query,
+            k=key,
+            v=value,
+            tensor_layout="NHD",
+            is_causal=is_causal,
+            sm_scale=scale,
+            return_lse=return_lse,
+        )
+        if return_lse:
+            out, lse, *_ = out
+
+    return (out, lse) if return_lse else out
+
+
 @_AttentionBackendRegistry.register(
     AttentionBackendName.SAGE_VARLEN,
     constraints=[_check_device_cuda, _check_qkv_dtype_bf16_or_fp16, _check_shape],

tests/others/test_attention_backends.py

@@ -34,7 +34,10 @@ from diffusers.utils import is_torch_version  # noqa: E402
 
 # fmt: off
 FORWARD_CASES = [
-    ("flash_hub", None),
+    (
+        "flash_hub",
+        torch.tensor([0.0820, 0.0859, 0.0918, 0.1016, 0.0957, 0.0996, 0.0996, 0.1016, 0.2188, 0.2266, 0.2363, 0.2500, 0.2539, 0.2461, 0.2422, 0.2695], dtype=torch.bfloat16)
+    ),
     (
         "_flash_3_hub",
         torch.tensor([0.0820, 0.0859, 0.0938, 0.1016, 0.0977, 0.0996, 0.1016, 0.1016, 0.2188, 0.2246, 0.2344, 0.2480, 0.2539, 0.2480, 0.2441, 0.2715], dtype=torch.bfloat16),
@@ -54,7 +57,11 @@ FORWARD_CASES = [
 ]
 
 COMPILE_CASES = [
-    ("flash_hub", None, True),
+    (
+        "flash_hub",
+        torch.tensor([0.0410, 0.0410, 0.0449, 0.0508, 0.0488, 0.0586, 0.0605, 0.0586, 0.2324, 0.2422, 0.2539, 0.2734, 0.2832, 0.2812, 0.2773, 0.3047], dtype=torch.bfloat16),
+        True
+    ),
     (
         "_flash_3_hub",
         torch.tensor([0.0410, 0.0410, 0.0449, 0.0508, 0.0508, 0.0605, 0.0625, 0.0605, 0.2344, 0.2461, 0.2578, 0.2734, 0.2852, 0.2812, 0.2773, 0.3047], dtype=torch.bfloat16),