remove non-hub attention backends.

peft (fal) lora format

src/diffusers/loaders/lora_pipeline.py

@@ -5472,6 +5472,10 @@ class Flux2LoraLoaderMixin(LoraBaseMixin):
             logger.warning(warn_msg)
             state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k}
 
+        is_peft_format = any(k.startswith("base_model.model.") for k in state_dict)
+        if is_peft_format:
+            state_dict = {k.replace("base_model.model.", "diffusion_model."): v for k, v in state_dict.items()}
+
         is_ai_toolkit = any(k.startswith("diffusion_model.") for k in state_dict)
         if is_ai_toolkit:
             state_dict = _convert_non_diffusers_flux2_lora_to_diffusers(state_dict)

src/diffusers/models/attention_dispatch.py

@@ -34,13 +34,7 @@ from ..utils import (
     get_logger,
     is_aiter_available,
     is_aiter_version,
-    is_flash_attn_3_available,
-    is_flash_attn_available,
-    is_flash_attn_version,
     is_kernels_available,
-    is_kernels_version,
-    is_sageattention_available,
-    is_sageattention_version,
     is_torch_npu_available,
     is_torch_version,
     is_torch_xla_available,
@@ -56,62 +50,23 @@ from ._modeling_parallel import gather_size_by_comm
 if TYPE_CHECKING:
     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()
 _CAN_USE_XLA_ATTN = is_torch_xla_available() and is_torch_xla_version(">=", _REQUIRED_XLA_VERSION)
 _CAN_USE_XFORMERS_ATTN = is_xformers_available() and is_xformers_version(">=", _REQUIRED_XFORMERS_VERSION)
 
 
-if _CAN_USE_FLASH_ATTN:
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.flash_attn_interface import _wrapped_flash_attn_backward, _wrapped_flash_attn_forward
-else:
-    flash_attn_func = None
-    flash_attn_varlen_func = None
-    _wrapped_flash_attn_backward = None
-    _wrapped_flash_attn_forward = None
-
-
-if _CAN_USE_FLASH_ATTN_3:
-    from flash_attn_interface import flash_attn_func as flash_attn_3_func
-    from flash_attn_interface import flash_attn_varlen_func as flash_attn_3_varlen_func
-else:
-    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 _CAN_USE_SAGE_ATTN:
-    from sageattention import (
-        sageattn,
-        sageattn_qk_int8_pv_fp8_cuda,
-        sageattn_qk_int8_pv_fp8_cuda_sm90,
-        sageattn_qk_int8_pv_fp16_cuda,
-        sageattn_qk_int8_pv_fp16_triton,
-        sageattn_varlen,
-    )
-else:
-    sageattn = None
-    sageattn_qk_int8_pv_fp16_cuda = None
-    sageattn_qk_int8_pv_fp16_triton = None
-    sageattn_qk_int8_pv_fp8_cuda = None
-    sageattn_qk_int8_pv_fp8_cuda_sm90 = None
-    sageattn_varlen = None
-
 
 if _CAN_USE_FLEX_ATTN:
     # We cannot import the flex_attention function from the package directly because it is expected (from the
@@ -137,27 +92,6 @@ 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
 
@@ -266,7 +200,6 @@ class _HubKernelConfig:
     repo_id: str
     function_attr: str
     revision: str | None = None
-    version: int | None = None
     kernel_fn: Callable | None = None
     wrapped_forward_attr: str | None = None
     wrapped_backward_attr: str | None = None
@@ -276,31 +209,27 @@ class _HubKernelConfig:
 
 # Registry for hub-based attention kernels
 _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", version=1
+        repo_id="kernels-community/flash-attn3", function_attr="flash_attn_func", revision="fake-ops-return-probs"
     ),
     AttentionBackendName._FLASH_3_VARLEN_HUB: _HubKernelConfig(
         repo_id="kernels-community/flash-attn3",
         function_attr="flash_attn_varlen_func",
-        version=1,
+        # revision="fake-ops-return-probs",
     ),
     AttentionBackendName.FLASH_HUB: _HubKernelConfig(
         repo_id="kernels-community/flash-attn2",
         function_attr="flash_attn_func",
-        version=1,
         revision=None,
         wrapped_forward_attr="flash_attn_interface._wrapped_flash_attn_forward",
         wrapped_backward_attr="flash_attn_interface._wrapped_flash_attn_backward",
     ),
     AttentionBackendName.FLASH_VARLEN_HUB: _HubKernelConfig(
-        repo_id="kernels-community/flash-attn2",
-        function_attr="flash_attn_varlen_func",
-        version=1,
+        repo_id="kernels-community/flash-attn2", function_attr="flash_attn_varlen_func", revision=None
     ),
     AttentionBackendName.SAGE_HUB: _HubKernelConfig(
-        repo_id="kernels-community/sage-attention",
-        function_attr="sageattn",
-        version=1,
+        repo_id="kernels-community/sage_attention", function_attr="sageattn", revision=None
     ),
 }
 
@@ -310,11 +239,11 @@ def attention_backend(backend: str | AttentionBackendName = AttentionBackendName
     """
     Context manager to set the active attention backend.
     """
-    if backend not in _AttentionBackendRegistry._backends:
-        raise ValueError(f"Backend {backend} is not registered.")
-
     backend = AttentionBackendName(backend)
     _check_attention_backend_requirements(backend)
+
+    if backend not in _AttentionBackendRegistry._backends:
+        raise ValueError(f"Backend {backend} is not registered.")
     _maybe_download_kernel_for_backend(backend)
 
     old_backend = _AttentionBackendRegistry._active_backend
@@ -448,16 +377,32 @@ def _check_shape(
 
 def _check_attention_backend_requirements(backend: AttentionBackendName) -> None:
     if backend in [AttentionBackendName.FLASH, AttentionBackendName.FLASH_VARLEN]:
-        if not _CAN_USE_FLASH_ATTN:
-            raise RuntimeError(
-                f"Flash Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `flash-attn>={_REQUIRED_FLASH_VERSION}`."
-            )
+        raise RuntimeError(
+            f"The '{backend.value}' attention backend has been removed. "
+            f"Please use 'flash_hub' or 'flash_varlen_hub' instead, which load the flash-attn kernel from the Hub. "
+            f"Install the required package with `pip install kernels`."
+        )
 
     elif backend in [AttentionBackendName._FLASH_3, AttentionBackendName._FLASH_VARLEN_3]:
-        if not _CAN_USE_FLASH_ATTN_3:
-            raise RuntimeError(
-                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."
-            )
+        raise RuntimeError(
+            f"The '{backend.value}' attention backend has been removed. "
+            f"Please use '_flash_3_hub' or '_flash_3_varlen_hub' instead, which load the flash-attn-3 kernel from the Hub. "
+            f"Install the required package with `pip install kernels`."
+        )
+
+    elif backend in [
+        AttentionBackendName.SAGE,
+        AttentionBackendName.SAGE_VARLEN,
+        AttentionBackendName._SAGE_QK_INT8_PV_FP8_CUDA,
+        AttentionBackendName._SAGE_QK_INT8_PV_FP8_CUDA_SM90,
+        AttentionBackendName._SAGE_QK_INT8_PV_FP16_CUDA,
+        AttentionBackendName._SAGE_QK_INT8_PV_FP16_TRITON,
+    ]:
+        raise RuntimeError(
+            f"The '{backend.value}' attention backend has been removed. "
+            f"Please use 'sage_hub' instead, which loads the SageAttention kernel from the Hub. "
+            f"Install the required package with `pip install kernels`."
+        )
 
     elif backend in [
         AttentionBackendName.FLASH_HUB,
@@ -470,10 +415,6 @@ def _check_attention_backend_requirements(backend: AttentionBackendName) -> None
             raise RuntimeError(
                 f"Backend '{backend.value}' is not usable because the `kernels` package isn't available. Please install it with `pip install kernels`."
             )
-        if not is_kernels_version(">=", "0.12"):
-            raise RuntimeError(
-                f"Backend '{backend.value}' needs to be used with a `kernels` version of at least 0.12. Please update with `pip install -U kernels`."
-            )
 
     elif backend == AttentionBackendName.AITER:
         if not _CAN_USE_AITER_ATTN:
@@ -481,19 +422,6 @@ def _check_attention_backend_requirements(backend: AttentionBackendName) -> None
                 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,
-        AttentionBackendName._SAGE_QK_INT8_PV_FP8_CUDA,
-        AttentionBackendName._SAGE_QK_INT8_PV_FP8_CUDA_SM90,
-        AttentionBackendName._SAGE_QK_INT8_PV_FP16_CUDA,
-        AttentionBackendName._SAGE_QK_INT8_PV_FP16_TRITON,
-    ]:
-        if not _CAN_USE_SAGE_ATTN:
-            raise RuntimeError(
-                f"Sage Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `sageattention>={_REQUIRED_SAGE_VERSION}`."
-            )
-
     elif backend == AttentionBackendName.FLEX:
         if not _CAN_USE_FLEX_ATTN:
             raise RuntimeError(
@@ -662,78 +590,6 @@ def _maybe_download_kernel_for_backend(backend: AttentionBackendName) -> None:
         raise
 
 
-# ===== torch op registrations =====
-# Registrations are required for fullgraph tracing compatibility
-# 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
-@_custom_op("_diffusers_flash_attn_3::_flash_attn_forward", mutates_args=(), device_types="cuda")
-def _wrapped_flash_attn_3(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    softmax_scale: float | None = None,
-    causal: bool = False,
-    qv: torch.Tensor | None = None,
-    q_descale: torch.Tensor | None = None,
-    k_descale: torch.Tensor | None = None,
-    v_descale: torch.Tensor | None = None,
-    attention_chunk: int = 0,
-    softcap: float = 0.0,
-    num_splits: int = 1,
-    pack_gqa: bool | None = None,
-    deterministic: bool = False,
-    sm_margin: int = 0,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    # Hardcoded for now because pytorch does not support tuple/int type hints
-    window_size = (-1, -1)
-    out, lse, *_ = flash_attn_3_func(
-        q=q,
-        k=k,
-        v=v,
-        softmax_scale=softmax_scale,
-        causal=causal,
-        qv=qv,
-        q_descale=q_descale,
-        k_descale=k_descale,
-        v_descale=v_descale,
-        window_size=window_size,
-        attention_chunk=attention_chunk,
-        softcap=softcap,
-        num_splits=num_splits,
-        pack_gqa=pack_gqa,
-        deterministic=deterministic,
-        sm_margin=sm_margin,
-    )
-    lse = lse.permute(0, 2, 1)
-    return out, lse
-
-
-@_register_fake("_diffusers_flash_attn_3::_flash_attn_forward")
-def _(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    softmax_scale: float | None = None,
-    causal: bool = False,
-    qv: torch.Tensor | None = None,
-    q_descale: torch.Tensor | None = None,
-    k_descale: torch.Tensor | None = None,
-    v_descale: torch.Tensor | None = None,
-    attention_chunk: int = 0,
-    softcap: float = 0.0,
-    num_splits: int = 1,
-    pack_gqa: bool | None = None,
-    deterministic: bool = False,
-    sm_margin: int = 0,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    window_size = (-1, -1)  # noqa: F841
-    # A lot of the parameters here are not yet used in any way within diffusers.
-    # We can safely ignore for now and keep the fake op shape propagation simple.
-    batch_size, seq_len, num_heads, head_dim = q.shape
-    lse_shape = (batch_size, seq_len, num_heads)
-    return torch.empty_like(q), q.new_empty(lse_shape)
-
-
 # ===== Helper functions to use attention backends with templated CP autograd functions =====
 
 
@@ -1005,107 +861,6 @@ def _native_flash_attention_backward_op(
     return grad_query, grad_key, grad_value
 
 
-# Adapted from: https://github.com/Dao-AILab/flash-attention/blob/fd2fc9d85c8e54e5c20436465bca709bc1a6c5a1/flash_attn/flash_attn_interface.py#L807
-def _flash_attention_forward_op(
-    ctx: torch.autograd.function.FunctionCtx,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    dropout_p: float = 0.0,
-    is_causal: bool = False,
-    scale: float | None = None,
-    enable_gqa: bool = False,
-    return_lse: bool = False,
-    _save_ctx: bool = True,
-    _parallel_config: "ParallelConfig" | None = None,
-):
-    if attn_mask is not None:
-        raise ValueError("`attn_mask` is not yet supported for flash-attn 2.")
-    if enable_gqa:
-        raise ValueError("`enable_gqa` is not yet supported for flash-attn 2.")
-
-    # Hardcoded for now
-    window_size = (-1, -1)
-    softcap = 0.0
-    alibi_slopes = None
-    deterministic = False
-    grad_enabled = any(x.requires_grad for x in (query, key, value))
-
-    if scale is None:
-        scale = query.shape[-1] ** (-0.5)
-
-    # flash-attn only returns LSE if dropout_p > 0. So, we need to workaround.
-    if grad_enabled or (_parallel_config is not None and _parallel_config.context_parallel_config._world_size > 1):
-        dropout_p = dropout_p if dropout_p > 0 else 1e-30
-
-    with torch.set_grad_enabled(grad_enabled):
-        out, lse, S_dmask, rng_state = _wrapped_flash_attn_forward(
-            query,
-            key,
-            value,
-            dropout_p,
-            scale,
-            is_causal,
-            window_size[0],
-            window_size[1],
-            softcap,
-            alibi_slopes,
-            return_lse,
-        )
-        lse = lse.permute(0, 2, 1)
-
-    if _save_ctx:
-        ctx.save_for_backward(query, key, value, out, lse, rng_state)
-        ctx.dropout_p = dropout_p
-        ctx.scale = scale
-        ctx.is_causal = is_causal
-        ctx.window_size = window_size
-        ctx.softcap = softcap
-        ctx.alibi_slopes = alibi_slopes
-        ctx.deterministic = deterministic
-
-    return (out, lse) if return_lse else out
-
-
-def _flash_attention_backward_op(
-    ctx: torch.autograd.function.FunctionCtx,
-    grad_out: torch.Tensor,
-    *args,
-    **kwargs,
-):
-    query, key, value, out, lse, rng_state = ctx.saved_tensors
-    grad_query, grad_key, grad_value = torch.empty_like(query), torch.empty_like(key), torch.empty_like(value)
-
-    lse_d = _wrapped_flash_attn_backward(  # noqa: F841
-        grad_out,
-        query,
-        key,
-        value,
-        out,
-        lse,
-        grad_query,
-        grad_key,
-        grad_value,
-        ctx.dropout_p,
-        ctx.scale,
-        ctx.is_causal,
-        ctx.window_size[0],
-        ctx.window_size[1],
-        ctx.softcap,
-        ctx.alibi_slopes,
-        ctx.deterministic,
-        rng_state,
-    )
-
-    # Head dimension may have been padded
-    grad_query = grad_query[..., : grad_out.shape[-1]]
-    grad_key = grad_key[..., : grad_out.shape[-1]]
-    grad_value = grad_value[..., : grad_out.shape[-1]]
-
-    return grad_query, grad_key, grad_value
-
-
 def _flash_attention_hub_forward_op(
     ctx: torch.autograd.function.FunctionCtx,
     query: torch.Tensor,
@@ -1337,44 +1092,6 @@ def _flash_attention_3_hub_backward_op(
     return grad_query, grad_key, grad_value
 
 
-def _sage_attention_forward_op(
-    ctx: torch.autograd.function.FunctionCtx,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    dropout_p: float = 0.0,
-    is_causal: bool = False,
-    scale: float | None = None,
-    enable_gqa: bool = False,
-    return_lse: bool = False,
-    _save_ctx: bool = True,
-    _parallel_config: "ParallelConfig" | None = None,
-):
-    if attn_mask is not None:
-        raise ValueError("`attn_mask` is not yet supported for Sage attention.")
-    if dropout_p > 0.0:
-        raise ValueError("`dropout_p` is not yet supported for Sage attention.")
-    if enable_gqa:
-        raise ValueError("`enable_gqa` is not yet supported for Sage attention.")
-
-    out = sageattn(
-        q=query,
-        k=key,
-        v=value,
-        tensor_layout="NHD",
-        is_causal=is_causal,
-        sm_scale=scale,
-        return_lse=return_lse,
-    )
-    lse = None
-    if return_lse:
-        out, lse, *_ = out
-        lse = lse.permute(0, 2, 1)
-
-    return (out, lse) if return_lse else out
-
-
 def _sage_attention_hub_forward_op(
     ctx: torch.autograd.function.FunctionCtx,
     query: torch.Tensor,
@@ -2215,59 +1932,6 @@ def _templated_context_parallel_attention(
 # ===== Attention backends =====
 
 
-@_AttentionBackendRegistry.register(
-    AttentionBackendName.FLASH,
-    constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
-    supports_context_parallel=True,
-)
-def _flash_attention(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    dropout_p: float = 0.0,
-    is_causal: bool = False,
-    scale: float | None = None,
-    return_lse: bool = False,
-    _parallel_config: "ParallelConfig" | None = None,
-) -> torch.Tensor:
-    lse = None
-    if attn_mask is not None:
-        raise ValueError("`attn_mask` is not supported for flash-attn 2.")
-
-    if _parallel_config is None:
-        out = flash_attn_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
-    else:
-        out = _templated_context_parallel_attention(
-            query,
-            key,
-            value,
-            None,
-            dropout_p,
-            is_causal,
-            scale,
-            False,
-            return_lse,
-            forward_op=_flash_attention_forward_op,
-            backward_op=_flash_attention_backward_op,
-            _parallel_config=_parallel_config,
-        )
-        if return_lse:
-            out, lse = out
-
-    return (out, lse) if return_lse else out
-
-
 @_AttentionBackendRegistry.register(
     AttentionBackendName.FLASH_HUB,
     constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
@@ -2379,88 +2043,6 @@ def _flash_varlen_attention_hub(
     return out
 
 
-@_AttentionBackendRegistry.register(
-    AttentionBackendName.FLASH_VARLEN,
-    constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
-)
-def _flash_varlen_attention(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    dropout_p: float = 0.0,
-    scale: float | None = None,
-    is_causal: bool = False,
-    return_lse: bool = False,
-    _parallel_config: "ParallelConfig" | None = None,
-) -> torch.Tensor:
-    batch_size, seq_len_q, _, _ = query.shape
-    _, seq_len_kv, _, _ = key.shape
-
-    if attn_mask is not None:
-        attn_mask = _normalize_attn_mask(attn_mask, batch_size, seq_len_kv)
-
-    (_, seqlens_k), (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k) = (
-        _prepare_for_flash_attn_or_sage_varlen(
-            batch_size, seq_len_q, seq_len_kv, attn_mask=attn_mask, device=query.device
-        )
-    )
-
-    key_valid, value_valid = [], []
-    for b in range(batch_size):
-        valid_len = seqlens_k[b]
-        key_valid.append(key[b, :valid_len])
-        value_valid.append(value[b, :valid_len])
-
-    query_packed = query.flatten(0, 1)
-    key_packed = torch.cat(key_valid, dim=0)
-    value_packed = torch.cat(value_valid, dim=0)
-
-    out = flash_attn_varlen_func(
-        q=query_packed,
-        k=key_packed,
-        v=value_packed,
-        cu_seqlens_q=cu_seqlens_q,
-        cu_seqlens_k=cu_seqlens_k,
-        max_seqlen_q=max_seqlen_q,
-        max_seqlen_k=max_seqlen_k,
-        dropout_p=dropout_p,
-        softmax_scale=scale,
-        causal=is_causal,
-        return_attn_probs=return_lse,
-    )
-    out = out.unflatten(0, (batch_size, -1))
-
-    return out
-
-
-@_AttentionBackendRegistry.register(
-    AttentionBackendName._FLASH_3,
-    constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
-)
-def _flash_attention_3(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    scale: float | None = None,
-    is_causal: bool = False,
-    return_lse: bool = False,
-    _parallel_config: "ParallelConfig" | None = None,
-) -> torch.Tensor:
-    if attn_mask is not None:
-        raise ValueError("`attn_mask` is not supported for flash-attn 3.")
-
-    out, lse = _wrapped_flash_attn_3(
-        q=query,
-        k=key,
-        v=value,
-        softmax_scale=scale,
-        causal=is_causal,
-    )
-    return (out, lse) if return_lse else out
-
-
 @_AttentionBackendRegistry.register(
     AttentionBackendName._FLASH_3_HUB,
     constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
@@ -2597,58 +2179,6 @@ def _flash_attention_3_varlen_hub(
     return (out, lse) if return_lse else out
 
 
-@_AttentionBackendRegistry.register(
-    AttentionBackendName._FLASH_VARLEN_3,
-    constraints=[_check_device, _check_qkv_dtype_bf16_or_fp16, _check_shape],
-)
-def _flash_varlen_attention_3(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    scale: float | None = None,
-    is_causal: bool = False,
-    return_lse: bool = False,
-    _parallel_config: "ParallelConfig" | None = None,
-) -> torch.Tensor:
-    batch_size, seq_len_q, _, _ = query.shape
-    _, seq_len_kv, _, _ = key.shape
-
-    if attn_mask is not None:
-        attn_mask = _normalize_attn_mask(attn_mask, batch_size, seq_len_kv)
-
-    (_, seqlens_k), (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k) = (
-        _prepare_for_flash_attn_or_sage_varlen(
-            batch_size, seq_len_q, seq_len_kv, attn_mask=attn_mask, device=query.device
-        )
-    )
-
-    key_valid, value_valid = [], []
-    for b in range(batch_size):
-        valid_len = seqlens_k[b]
-        key_valid.append(key[b, :valid_len])
-        value_valid.append(value[b, :valid_len])
-
-    query_packed = query.flatten(0, 1)
-    key_packed = torch.cat(key_valid, dim=0)
-    value_packed = torch.cat(value_valid, dim=0)
-
-    out, lse, *_ = flash_attn_3_varlen_func(
-        q=query_packed,
-        k=key_packed,
-        v=value_packed,
-        cu_seqlens_q=cu_seqlens_q,
-        cu_seqlens_k=cu_seqlens_k,
-        max_seqlen_q=max_seqlen_q,
-        max_seqlen_k=max_seqlen_k,
-        softmax_scale=scale,
-        causal=is_causal,
-    )
-    out = out.unflatten(0, (batch_size, -1))
-
-    return (out, lse) if return_lse else out
-
-
 @_AttentionBackendRegistry.register(
     AttentionBackendName.AITER,
     constraints=[_check_device_cuda, _check_qkv_dtype_bf16_or_fp16, _check_shape],
@@ -3118,57 +2648,6 @@ def _native_xla_attention(
     return out
 
 
-@_AttentionBackendRegistry.register(
-    AttentionBackendName.SAGE,
-    constraints=[_check_device_cuda, _check_qkv_dtype_bf16_or_fp16, _check_shape],
-    supports_context_parallel=True,
-)
-def _sage_attention(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    is_causal: bool = False,
-    scale: float | None = None,
-    return_lse: bool = False,
-    _parallel_config: "ParallelConfig" | None = None,
-) -> torch.Tensor:
-    if attn_mask is not None:
-        raise ValueError("`attn_mask` is not supported for sage attention")
-    lse = None
-    if _parallel_config is None:
-        out = sageattn(
-            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
-    else:
-        out = _templated_context_parallel_attention(
-            query,
-            key,
-            value,
-            None,
-            0.0,
-            is_causal,
-            scale,
-            False,
-            return_lse,
-            forward_op=_sage_attention_forward_op,
-            backward_op=_sage_attention_backward_op,
-            _parallel_config=_parallel_config,
-        )
-        if return_lse:
-            out, lse = out
-
-    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],
@@ -3221,169 +2700,6 @@ def _sage_attention_hub(
     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],
-)
-def _sage_varlen_attention(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    is_causal: bool = False,
-    scale: float | None = None,
-    return_lse: bool = False,
-    _parallel_config: "ParallelConfig" | None = None,
-) -> torch.Tensor:
-    if return_lse:
-        raise ValueError("Sage varlen backend does not support setting `return_lse=True`.")
-
-    batch_size, seq_len_q, _, _ = query.shape
-    _, seq_len_kv, _, _ = key.shape
-
-    if attn_mask is not None:
-        attn_mask = _normalize_attn_mask(attn_mask, batch_size, seq_len_kv)
-
-    (_, seqlens_k), (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k) = (
-        _prepare_for_flash_attn_or_sage_varlen(
-            batch_size, seq_len_q, seq_len_kv, attn_mask=attn_mask, device=query.device
-        )
-    )
-
-    key_valid, value_valid = [], []
-    for b in range(batch_size):
-        valid_len = seqlens_k[b]
-        key_valid.append(key[b, :valid_len])
-        value_valid.append(value[b, :valid_len])
-
-    query_packed = query.flatten(0, 1)
-    key_packed = torch.cat(key_valid, dim=0)
-    value_packed = torch.cat(value_valid, dim=0)
-
-    out = sageattn_varlen(
-        q=query_packed,
-        k=key_packed,
-        v=value_packed,
-        cu_seqlens_q=cu_seqlens_q,
-        cu_seqlens_k=cu_seqlens_k,
-        max_seqlen_q=max_seqlen_q,
-        max_seqlen_k=max_seqlen_k,
-        is_causal=is_causal,
-        sm_scale=scale,
-    )
-    out = out.unflatten(0, (batch_size, -1))
-
-    return out
-
-
-@_AttentionBackendRegistry.register(
-    AttentionBackendName._SAGE_QK_INT8_PV_FP8_CUDA,
-    constraints=[_check_device_cuda_atleast_smXY(9, 0), _check_shape],
-)
-def _sage_qk_int8_pv_fp8_cuda_attention(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    is_causal: bool = False,
-    scale: float | None = None,
-    return_lse: bool = False,
-    _parallel_config: "ParallelConfig" | None = None,
-) -> torch.Tensor:
-    if attn_mask is not None:
-        raise ValueError("`attn_mask` is not supported for sage attention")
-    return sageattn_qk_int8_pv_fp8_cuda(
-        q=query,
-        k=key,
-        v=value,
-        tensor_layout="NHD",
-        is_causal=is_causal,
-        sm_scale=scale,
-        return_lse=return_lse,
-    )
-
-
-@_AttentionBackendRegistry.register(
-    AttentionBackendName._SAGE_QK_INT8_PV_FP8_CUDA_SM90,
-    constraints=[_check_device_cuda_atleast_smXY(9, 0), _check_shape],
-)
-def _sage_qk_int8_pv_fp8_cuda_sm90_attention(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    is_causal: bool = False,
-    scale: float | None = None,
-    return_lse: bool = False,
-    _parallel_config: "ParallelConfig" | None = None,
-) -> torch.Tensor:
-    if attn_mask is not None:
-        raise ValueError("`attn_mask` is not supported for sage attention")
-    return sageattn_qk_int8_pv_fp8_cuda_sm90(
-        q=query,
-        k=key,
-        v=value,
-        tensor_layout="NHD",
-        is_causal=is_causal,
-        sm_scale=scale,
-        return_lse=return_lse,
-    )
-
-
-@_AttentionBackendRegistry.register(
-    AttentionBackendName._SAGE_QK_INT8_PV_FP16_CUDA,
-    constraints=[_check_device_cuda_atleast_smXY(8, 0), _check_shape],
-)
-def _sage_qk_int8_pv_fp16_cuda_attention(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    is_causal: bool = False,
-    scale: float | None = None,
-    return_lse: bool = False,
-    _parallel_config: "ParallelConfig" | None = None,
-) -> torch.Tensor:
-    if attn_mask is not None:
-        raise ValueError("`attn_mask` is not supported for sage attention")
-    return sageattn_qk_int8_pv_fp16_cuda(
-        q=query,
-        k=key,
-        v=value,
-        tensor_layout="NHD",
-        is_causal=is_causal,
-        sm_scale=scale,
-        return_lse=return_lse,
-    )
-
-
-@_AttentionBackendRegistry.register(
-    AttentionBackendName._SAGE_QK_INT8_PV_FP16_TRITON,
-    constraints=[_check_device_cuda_atleast_smXY(8, 0), _check_shape],
-)
-def _sage_qk_int8_pv_fp16_triton_attention(
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attn_mask: torch.Tensor | None = None,
-    is_causal: bool = False,
-    scale: float | None = None,
-    return_lse: bool = False,
-    _parallel_config: "ParallelConfig" | None = None,
-) -> torch.Tensor:
-    if attn_mask is not None:
-        raise ValueError("`attn_mask` is not supported for sage attention")
-    return sageattn_qk_int8_pv_fp16_triton(
-        q=query,
-        k=key,
-        v=value,
-        tensor_layout="NHD",
-        is_causal=is_causal,
-        sm_scale=scale,
-        return_lse=return_lse,
-    )
-
-
 @_AttentionBackendRegistry.register(
     AttentionBackendName.XFORMERS,
     constraints=[_check_attn_mask_or_causal, _check_device, _check_shape],

src/diffusers/utils/__init__.py

@@ -86,7 +86,6 @@ from .import_utils import (
     is_inflect_available,
     is_invisible_watermark_available,
     is_kernels_available,
-    is_kernels_version,
     is_kornia_available,
     is_librosa_available,
     is_matplotlib_available,

src/diffusers/utils/import_utils.py

@@ -724,22 +724,6 @@ def is_transformers_version(operation: str, version: str):
     return compare_versions(parse(_transformers_version), operation, version)
 
 
-@cache
-def is_kernels_version(operation: str, version: str):
-    """
-    Compares the current Kernels 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 _kernels_available:
-        return False
-    return compare_versions(parse(_kernels_version), operation, version)
-
-
 @cache
 def is_hf_hub_version(operation: str, version: str):
     """