add aiter attention backend

Author: lauri9

docs/source/en/optimization/attention_backends.md

@@ -21,6 +21,7 @@ Refer to the table below for an overview of the available attention families and
 | attention family | main feature |
 |---|---|
 | FlashAttention | minimizes memory reads/writes through tiling and recomputation |
+| AI Tensor Engine for ROCm | FlashAttention implementation optimized for AMD ROCm accelerators |
 | SageAttention | quantizes attention to int8 |
 | PyTorch native | built-in PyTorch implementation using [scaled_dot_product_attention](./fp16#scaled-dot-product-attention) |
 | xFormers | memory-efficient attention with support for various attention kernels |
@@ -139,6 +140,7 @@ Refer to the table below for a complete list of available attention backends and
 | `_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_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 |

src/diffusers/models/attention_dispatch.py

@@ -30,6 +30,8 @@
     is_flash_attn_3_available,
     is_flash_attn_available,
     is_flash_attn_version,
+    is_aiter_available,
+    is_aiter_version,
     is_kernels_available,
     is_sageattention_available,
     is_sageattention_version,
@@ -47,13 +49,15 @@
     from ._modeling_parallel import ParallelConfig
 
 _REQUIRED_FLASH_VERSION = "2.6.3"
+_REQUIRED_AITER_VERSION = "0.1.5"
 _REQUIRED_SAGE_VERSION = "2.1.1"
 _REQUIRED_FLEX_VERSION = "2.5.0"
 _REQUIRED_XLA_VERSION = "2.2"
 _REQUIRED_XFORMERS_VERSION = "0.0.29"
 
 _CAN_USE_FLASH_ATTN = is_flash_attn_available() and is_flash_attn_version(">=", _REQUIRED_FLASH_VERSION)
 _CAN_USE_FLASH_ATTN_3 = is_flash_attn_3_available()
+_CAN_USE_AITER_ATTN = is_aiter_available() and is_aiter_version(">=", _REQUIRED_AITER_VERSION)
 _CAN_USE_SAGE_ATTN = is_sageattention_available() and is_sageattention_version(">=", _REQUIRED_SAGE_VERSION)
 _CAN_USE_FLEX_ATTN = is_torch_version(">=", _REQUIRED_FLEX_VERSION)
 _CAN_USE_NPU_ATTN = is_torch_npu_available()
@@ -78,6 +82,12 @@
     flash_attn_3_func = None
     flash_attn_3_varlen_func = None
 
+
+if _CAN_USE_AITER_ATTN:
+    from aiter import flash_attn_func as aiter_flash_attn_func
+else:
+    aiter_flash_attn_func = None
+
 if DIFFUSERS_ENABLE_HUB_KERNELS:
     if not is_kernels_available():
         raise ImportError(
@@ -178,6 +188,9 @@ class AttentionBackendName(str, Enum):
     _FLASH_3_HUB = "_flash_3_hub"
     # _FLASH_VARLEN_3_HUB = "_flash_varlen_3_hub"  # not supported yet.
 
+    # `aiter`
+    AITER = "aiter"
+
     # PyTorch native
     FLEX = "flex"
     NATIVE = "native"
@@ -414,6 +427,12 @@ def _check_attention_backend_requirements(backend: AttentionBackendName) -> None
                 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`."
             )
 
+    elif backend == AttentionBackendName.AITER:
+        if not _CAN_USE_AITER_ATTN:
+            raise RuntimeError(
+                f"Aiter Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `aiter>={_REQUIRED_AITER_VERSION}`."
+            )
+
     elif backend in [
         AttentionBackendName.SAGE,
         AttentionBackendName.SAGE_VARLEN,
@@ -1397,6 +1416,47 @@ def _flash_varlen_attention_3(
     return (out, lse) if return_lse else out
 
 
+@_AttentionBackendRegistry.register(
+    AttentionBackendName.AITER,
+    constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
+)
+def _aiter_flash_attention(
+    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:
+    if not return_lse and torch.is_grad_enabled():
+        # aiter requires return_lse=True by assertion when gradients are enabled.
+        out, lse, *_ = aiter_flash_attn_func(
+            q=query,
+            k=key,
+            v=value,
+            dropout_p=dropout_p,
+            softmax_scale=scale,
+            causal=is_causal,
+            return_lse=True,
+        )
+    else:
+        out = aiter_flash_attn_func(
+            q=query,
+            k=key,
+            v=value,
+            dropout_p=dropout_p,
+            softmax_scale=scale,
+            causal=is_causal,
+            return_lse=return_lse,
+        )
+        if return_lse:
+            out, lse, *_ = out
+
+    return (out, lse) if return_lse else out
+
+
 @_AttentionBackendRegistry.register(
     AttentionBackendName.FLEX,
     constraints=[_check_attn_mask_or_causal, _check_device, _check_shape],

src/diffusers/utils/__init__.py

@@ -72,6 +72,8 @@
     is_flash_attn_3_available,
     is_flash_attn_available,
     is_flash_attn_version,
+    is_aiter_available,
+    is_aiter_version,
     is_flax_available,
     is_ftfy_available,
     is_gguf_available,

src/diffusers/utils/import_utils.py

@@ -226,6 +226,7 @@ def _is_package_available(pkg_name: str, get_dist_name: bool = False) -> Tuple[b
 _sageattention_available, _sageattention_version = _is_package_available("sageattention")
 _flash_attn_available, _flash_attn_version = _is_package_available("flash_attn")
 _flash_attn_3_available, _flash_attn_3_version = _is_package_available("flash_attn_3")
+_aiter_available, _aiter_version = _is_package_available("aiter")
 _kornia_available, _kornia_version = _is_package_available("kornia")
 _nvidia_modelopt_available, _nvidia_modelopt_version = _is_package_available("modelopt", get_dist_name=True)
 
@@ -405,6 +406,8 @@ def is_flash_attn_available():
 def is_flash_attn_3_available():
     return _flash_attn_3_available
 
+def is_aiter_available():
+    return _aiter_available
 
 def is_kornia_available():
     return _kornia_available
@@ -910,6 +913,21 @@ def is_flash_attn_version(operation: str, version: str):
         return False
     return compare_versions(parse(_flash_attn_version), operation, version)
 
+@cache
+def is_aiter_version(operation: str, version: str):
+    """
+    Compares the current aiter version to a given reference with an operation.
+
+    Args:
+        operation (`str`):
+            A string representation of an operator, such as `">"` or `"<="`
+        version (`str`):
+            A version string
+    """
+    if not _aiter_available:
+        return False
+    return compare_versions(parse(_aiter_version), operation, version)
+
 
 def get_objects_from_module(module):
     """

tests/others/test_attention_backends.py

@@ -14,6 +14,10 @@
 
 Tests were conducted on an H100 with PyTorch 2.8.0 (CUDA 12.9). Slices for the compilation tests in
 "native" variants were obtained with a torch nightly version (2.10.0.dev20250924+cu128).
+
+Tests for aiter backend were conducted and slices for the aiter backend tests collected on a MI355X
+with torch 2025-09-25 nightly version (ad2f7315ca66b42497047bb7951f696b50f1e81b) and
+aiter 0.1.5.post4.dev20+ga25e55e79.
 """
 
 import os
@@ -44,6 +48,10 @@
         "_native_cudnn",
         torch.tensor([0.0781, 0.0840, 0.0879, 0.0957, 0.0898, 0.0957, 0.0957, 0.0977, 0.2168, 0.2246, 0.2324, 0.2500, 0.2539, 0.2480, 0.2441, 0.2695], dtype=torch.bfloat16),
     ),
+    (
+        "aiter",
+        torch.tensor([0.0781, 0.0820, 0.0879, 0.0957, 0.0898, 0.0938, 0.0957, 0.0957, 0.2285, 0.2363, 0.2461, 0.2637, 0.2695, 0.2617, 0.2617, 0.2891], dtype=torch.bfloat16),
+    )
 ]
 
 COMPILE_CASES = [
@@ -63,6 +71,11 @@
         torch.tensor([0.0410, 0.0410, 0.0430, 0.0508, 0.0488, 0.0586, 0.0605, 0.0586, 0.2344, 0.2461, 0.2578, 0.2773, 0.2871, 0.2832, 0.2793, 0.3086], dtype=torch.bfloat16),
         True,
     ),
+    (
+        "aiter",
+        torch.tensor([0.0391, 0.0391, 0.0430, 0.0488, 0.0469, 0.0566, 0.0586, 0.0566, 0.2402, 0.2539, 0.2637, 0.2812, 0.2930, 0.2910, 0.2891, 0.3164], dtype=torch.bfloat16),
+        True,
+    )
 ]
 # fmt: on