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# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import functools
import inspect
import math
from enum import Enum
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Tuple, Union
import torch
if torch.distributed.is_available():
import torch.distributed._functional_collectives as funcol
from ..utils import (
get_logger,
is_flash_attn_3_available,
is_flash_attn_available,
is_flash_attn_version,
is_kernels_available,
is_sageattention_available,
is_sageattention_version,
is_torch_npu_available,
is_torch_version,
is_torch_xla_available,
is_torch_xla_version,
is_xformers_available,
is_xformers_version,
)
from ..utils.constants import DIFFUSERS_ATTN_BACKEND, DIFFUSERS_ATTN_CHECKS, DIFFUSERS_ENABLE_HUB_KERNELS
if TYPE_CHECKING:
from ._modeling_parallel import ParallelConfig
_REQUIRED_FLASH_VERSION = "2.6.3"
_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_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 DIFFUSERS_ENABLE_HUB_KERNELS:
if not is_kernels_available():
raise ImportError(
"To use FA3 kernel for your hardware from the Hub, the `kernels` library must be installed. Install with `pip install kernels`."
)
from ..utils.kernels_utils import _get_fa3_from_hub
flash_attn_interface_hub = _get_fa3_from_hub()
flash_attn_3_func_hub = flash_attn_interface_hub.flash_attn_func
else:
flash_attn_3_func_hub = 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
# pytorch documentation) that the user may compile it. If we import directly, we will not have access to the
# compiled function.
import torch.nn.attention.flex_attention as flex_attention
if _CAN_USE_NPU_ATTN:
from torch_npu import npu_fusion_attention
else:
npu_fusion_attention = None
if _CAN_USE_XLA_ATTN:
from torch_xla.experimental.custom_kernel import flash_attention as xla_flash_attention
else:
xla_flash_attention = None
if _CAN_USE_XFORMERS_ATTN:
import xformers.ops as xops
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
# TODO(aryan): Add support for the following:
# - Sage Attention++
# - block sparse, radial and other attention methods
# - 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_VARLEN = "flash_varlen"
_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.
# PyTorch native
FLEX = "flex"
NATIVE = "native"
_NATIVE_CUDNN = "_native_cudnn"
_NATIVE_EFFICIENT = "_native_efficient"
_NATIVE_FLASH = "_native_flash"
_NATIVE_MATH = "_native_math"
_NATIVE_NPU = "_native_npu"
_NATIVE_XLA = "_native_xla"
# `sageattention`
SAGE = "sage"
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"
_SAGE_QK_INT8_PV_FP16_CUDA = "_sage_qk_int8_pv_fp16_cuda"
_SAGE_QK_INT8_PV_FP16_TRITON = "_sage_qk_int8_pv_fp16_triton"
# TODO: let's not add support for Sparge Attention now because it requires tuning per model
# We can look into supporting something "autotune"-ing in the future
# SPARGE = "sparge"
# `xformers`
XFORMERS = "xformers"
class _AttentionBackendRegistry:
_backends = {}
_constraints = {}
_supported_arg_names = {}
_supports_context_parallel = {}
_active_backend = AttentionBackendName(DIFFUSERS_ATTN_BACKEND)
_checks_enabled = DIFFUSERS_ATTN_CHECKS
@classmethod
def register(
cls,
backend: AttentionBackendName,
constraints: Optional[List[Callable]] = None,
supports_context_parallel: bool = False,
):
logger.debug(f"Registering attention backend: {backend} with constraints: {constraints}")
def decorator(func):
cls._backends[backend] = func
cls._constraints[backend] = constraints or []
cls._supported_arg_names[backend] = set(inspect.signature(func).parameters.keys())
cls._supports_context_parallel[backend] = supports_context_parallel
return func
return decorator
@classmethod
def get_active_backend(cls):
return cls._active_backend, cls._backends[cls._active_backend]
@classmethod
def list_backends(cls):
return list(cls._backends.keys())
@classmethod
def _is_context_parallel_enabled(
cls, backend: AttentionBackendName, parallel_config: Optional["ParallelConfig"]
) -> bool:
supports_context_parallel = backend in cls._supports_context_parallel
is_degree_greater_than_1 = parallel_config is not None and (
parallel_config.context_parallel_config.ring_degree > 1
or parallel_config.context_parallel_config.ulysses_degree > 1
)
return supports_context_parallel and is_degree_greater_than_1
@contextlib.contextmanager
def attention_backend(backend: Union[str, AttentionBackendName] = AttentionBackendName.NATIVE):
"""
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)
old_backend = _AttentionBackendRegistry._active_backend
_AttentionBackendRegistry._active_backend = backend
try:
yield
finally:
_AttentionBackendRegistry._active_backend = old_backend
def dispatch_attention_fn(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
dropout_p: float = 0.0,
is_causal: bool = False,
scale: Optional[float] = None,
enable_gqa: bool = False,
attention_kwargs: Optional[Dict[str, Any]] = None,
*,
backend: Optional[AttentionBackendName] = None,
parallel_config: Optional["ParallelConfig"] = None,
) -> torch.Tensor:
attention_kwargs = attention_kwargs or {}
if backend is None:
# If no backend is specified, we either use the default backend (set via the DIFFUSERS_ATTN_BACKEND environment
# variable), or we use a custom backend based on whether user is using the `attention_backend` context manager
backend_name, backend_fn = _AttentionBackendRegistry.get_active_backend()
else:
backend_name = AttentionBackendName(backend)
backend_fn = _AttentionBackendRegistry._backends.get(backend_name)
if parallel_config is not None and not _AttentionBackendRegistry._is_context_parallel_enabled(
backend_name, parallel_config
):
raise ValueError(
f"Backend {backend_name} either does not support context parallelism or context parallelism "
f"was enabled with a world size of 1."
)
kwargs = {
"query": query,
"key": key,
"value": value,
"attn_mask": attn_mask,
"dropout_p": dropout_p,
"is_causal": is_causal,
"scale": scale,
**attention_kwargs,
"_parallel_config": parallel_config,
}
if is_torch_version(">=", "2.5.0"):
kwargs["enable_gqa"] = enable_gqa
if _AttentionBackendRegistry._checks_enabled:
removed_kwargs = set(kwargs) - set(_AttentionBackendRegistry._supported_arg_names[backend_name])
if removed_kwargs:
logger.warning(f"Removing unsupported arguments for attention backend {backend_name}: {removed_kwargs}.")
for check in _AttentionBackendRegistry._constraints.get(backend_name):
check(**kwargs)
kwargs = {k: v for k, v in kwargs.items() if k in _AttentionBackendRegistry._supported_arg_names[backend_name]}
return backend_fn(**kwargs)
# ===== Checks =====
# A list of very simple functions to catch common errors quickly when debugging.
def _check_attn_mask_or_causal(attn_mask: Optional[torch.Tensor], is_causal: bool, **kwargs) -> None:
if attn_mask is not None and is_causal:
raise ValueError("`is_causal` cannot be True when `attn_mask` is not None.")
def _check_device(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, **kwargs) -> None:
if query.device != key.device or query.device != value.device:
raise ValueError("Query, key, and value must be on the same device.")
if query.dtype != key.dtype or query.dtype != value.dtype:
raise ValueError("Query, key, and value must have the same dtype.")
def _check_device_cuda(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, **kwargs) -> None:
_check_device(query, key, value)
if query.device.type != "cuda":
raise ValueError("Query, key, and value must be on a CUDA device.")
def _check_device_cuda_atleast_smXY(major: int, minor: int) -> Callable:
def check_device_cuda(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, **kwargs) -> None:
_check_device_cuda(query, key, value)
if torch.cuda.get_device_capability(query.device) < (major, minor):
raise ValueError(
f"Query, key, and value must be on a CUDA device with compute capability >= {major}.{minor}."
)
return check_device_cuda
def _check_qkv_dtype_match(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, **kwargs) -> None:
if query.dtype != key.dtype:
raise ValueError("Query and key must have the same dtype.")
if query.dtype != value.dtype:
raise ValueError("Query and value must have the same dtype.")
def _check_qkv_dtype_bf16_or_fp16(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, **kwargs) -> None:
_check_qkv_dtype_match(query, key, value)
if query.dtype not in (torch.bfloat16, torch.float16):
raise ValueError("Query, key, and value must be either bfloat16 or float16.")
def _check_shape(
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
**kwargs,
) -> None:
if query.shape[-1] != key.shape[-1]:
raise ValueError("Query and key must have the same last dimension.")
if query.shape[-2] != value.shape[-2]:
raise ValueError("Query and value must have the same second to last dimension.")
if attn_mask is not None and attn_mask.shape[-1] != key.shape[-2]:
raise ValueError("Attention mask must match the key's second to last dimension.")
# ===== Helper functions =====
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}`."
)
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."
)
# TODO: add support Hub variant of FA3 varlen later
elif backend in [AttentionBackendName._FLASH_3_HUB]:
if not DIFFUSERS_ENABLE_HUB_KERNELS:
raise RuntimeError(
f"Flash Attention 3 Hub backend '{backend.value}' is not usable because the `DIFFUSERS_ENABLE_HUB_KERNELS` env var isn't set. Please set it like `export DIFFUSERS_ENABLE_HUB_KERNELS=yes`."
)
if not is_kernels_available():
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`."
)
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(
f"Flex Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `torch>=2.5.0`."
)
elif backend == AttentionBackendName._NATIVE_NPU:
if not _CAN_USE_NPU_ATTN:
raise RuntimeError(
f"NPU Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `torch_npu`."
)
elif backend == AttentionBackendName._NATIVE_XLA:
if not _CAN_USE_XLA_ATTN:
raise RuntimeError(
f"XLA Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `torch_xla>={_REQUIRED_XLA_VERSION}`."
)
elif backend == AttentionBackendName.XFORMERS:
if not _CAN_USE_XFORMERS_ATTN:
raise RuntimeError(
f"Xformers Attention backend '{backend.value}' is not usable because of missing package or the version is too old. Please install `xformers>={_REQUIRED_XFORMERS_VERSION}`."
)
@functools.lru_cache(maxsize=128)
def _prepare_for_flash_attn_or_sage_varlen_without_mask(
batch_size: int,
seq_len_q: int,
seq_len_kv: int,
device: Optional[torch.device] = None,
):
seqlens_q = torch.full((batch_size,), seq_len_q, dtype=torch.int32, device=device)
seqlens_k = torch.full((batch_size,), seq_len_kv, dtype=torch.int32, device=device)
cu_seqlens_q = torch.zeros(batch_size + 1, dtype=torch.int32, device=device)
cu_seqlens_k = torch.zeros(batch_size + 1, dtype=torch.int32, device=device)
cu_seqlens_q[1:] = torch.cumsum(seqlens_q, dim=0)
cu_seqlens_k[1:] = torch.cumsum(seqlens_k, dim=0)
max_seqlen_q = seqlens_q.max().item()
max_seqlen_k = seqlens_k.max().item()
return (seqlens_q, seqlens_k), (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k)
def _prepare_for_flash_attn_or_sage_varlen_with_mask(
batch_size: int,
seq_len_q: int,
attn_mask: torch.Tensor,
device: Optional[torch.device] = None,
):
seqlens_q = torch.full((batch_size,), seq_len_q, dtype=torch.int32, device=device)
seqlens_k = attn_mask.sum(dim=1, dtype=torch.int32)
cu_seqlens_q = torch.zeros(batch_size + 1, dtype=torch.int32, device=device)
cu_seqlens_k = torch.zeros(batch_size + 1, dtype=torch.int32, device=device)
cu_seqlens_q[1:] = torch.cumsum(seqlens_q, dim=0)
cu_seqlens_k[1:] = torch.cumsum(seqlens_k, dim=0)
max_seqlen_q = seqlens_q.max().item()
max_seqlen_k = seqlens_k.max().item()
return (seqlens_q, seqlens_k), (cu_seqlens_q, cu_seqlens_k), (max_seqlen_q, max_seqlen_k)
def _prepare_for_flash_attn_or_sage_varlen(
batch_size: int,
seq_len_q: int,
seq_len_kv: int,
attn_mask: Optional[torch.Tensor] = None,
device: Optional[torch.device] = None,
) -> None:
if attn_mask is None:
return _prepare_for_flash_attn_or_sage_varlen_without_mask(batch_size, seq_len_q, seq_len_kv, device)
return _prepare_for_flash_attn_or_sage_varlen_with_mask(batch_size, seq_len_q, attn_mask, device)
def _normalize_attn_mask(attn_mask: torch.Tensor, batch_size: int, seq_len_k: int) -> torch.Tensor:
"""
Normalize an attention mask to shape [batch_size, seq_len_k] (bool) suitable for inferring seqlens_[q|k] in
FlashAttention/Sage varlen.
Supports 1D to 4D shapes and common broadcasting patterns.
"""
if attn_mask.dtype != torch.bool:
raise ValueError(f"Attention mask must be of type bool, got {attn_mask.dtype}.")
if attn_mask.ndim == 1:
# [seq_len_k] -> broadcast across batch
attn_mask = attn_mask.unsqueeze(0).expand(batch_size, seq_len_k)
elif attn_mask.ndim == 2:
# [batch_size, seq_len_k]. Maybe broadcast across batch
if attn_mask.size(0) not in [1, batch_size]:
raise ValueError(
f"attn_mask.shape[0] ({attn_mask.shape[0]}) must be 1 or {batch_size} for 2D attention mask."
)
attn_mask = attn_mask.expand(batch_size, seq_len_k)
elif attn_mask.ndim == 3:
# [batch_size, seq_len_q, seq_len_k] -> reduce over query dimension
# We do this reduction because we know that arbitrary QK masks is not supported in Flash/Sage varlen.
if attn_mask.size(0) not in [1, batch_size]:
raise ValueError(
f"attn_mask.shape[0] ({attn_mask.shape[0]}) must be 1 or {batch_size} for 3D attention mask."
)
attn_mask = attn_mask.any(dim=1)
attn_mask = attn_mask.expand(batch_size, seq_len_k)
elif attn_mask.ndim == 4:
# [batch_size, num_heads, seq_len_q, seq_len_k] or broadcastable versions
if attn_mask.size(0) not in [1, batch_size]:
raise ValueError(
f"attn_mask.shape[0] ({attn_mask.shape[0]}) must be 1 or {batch_size} for 4D attention mask."
)
attn_mask = attn_mask.expand(batch_size, -1, -1, seq_len_k) # [B, H, Q, K]
attn_mask = attn_mask.any(dim=(1, 2)) # [B, K]
else:
raise ValueError(f"Unsupported attention mask shape: {attn_mask.shape}")
if attn_mask.shape != (batch_size, seq_len_k):
raise ValueError(
f"Normalized attention mask shape mismatch: got {attn_mask.shape}, expected ({batch_size}, {seq_len_k})"
)
return attn_mask
def _flex_attention_causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
return q_idx >= kv_idx
# ===== 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: Optional[float] = None,
causal: bool = False,
qv: Optional[torch.Tensor] = None,
q_descale: Optional[torch.Tensor] = None,
k_descale: Optional[torch.Tensor] = None,
v_descale: Optional[torch.Tensor] = None,
attention_chunk: int = 0,
softcap: float = 0.0,
num_splits: int = 1,
pack_gqa: Optional[bool] = 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: Optional[float] = None,
causal: bool = False,
qv: Optional[torch.Tensor] = None,
q_descale: Optional[torch.Tensor] = None,
k_descale: Optional[torch.Tensor] = None,
v_descale: Optional[torch.Tensor] = None,
attention_chunk: int = 0,
softcap: float = 0.0,
num_splits: int = 1,
pack_gqa: Optional[bool] = 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 =====
# https://github.com/pytorch/pytorch/blob/8904ba638726f8c9a5aff5977c4aa76c9d2edfa6/aten/src/ATen/native/native_functions.yaml#L14958
# forward declaration:
# aten::_scaled_dot_product_cudnn_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, float dropout_p=0., bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask)
def _cudnn_attention_forward_op(
ctx: torch.autograd.function.FunctionCtx,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
dropout_p: float = 0.0,
is_causal: bool = False,
scale: Optional[float] = None,
enable_gqa: bool = False,
return_lse: bool = False,
_save_ctx: bool = True,
_parallel_config: Optional["ParallelConfig"] = None,
):
if enable_gqa:
raise ValueError("`enable_gqa` is not yet supported for cuDNN attention.")
tensors_to_save = ()
# Contiguous is a must here! Calling cuDNN backend with aten ops produces incorrect results
# if the input tensors are not contiguous.
query = query.transpose(1, 2).contiguous()
key = key.transpose(1, 2).contiguous()
value = value.transpose(1, 2).contiguous()
tensors_to_save += (query, key, value)
out, lse, cum_seq_q, cum_seq_k, max_q, max_k, philox_seed, philox_offset, debug_attn_mask = (
torch.ops.aten._scaled_dot_product_cudnn_attention(
query=query,
key=key,
value=value,
attn_bias=attn_mask,
compute_log_sumexp=return_lse,
dropout_p=dropout_p,
is_causal=is_causal,
return_debug_mask=False,
scale=scale,
)
)
tensors_to_save += (out, lse, cum_seq_q, cum_seq_k, philox_seed, philox_offset)
if _save_ctx:
ctx.save_for_backward(*tensors_to_save)
ctx.dropout_p = dropout_p
ctx.is_causal = is_causal
ctx.scale = scale
ctx.attn_mask = attn_mask
ctx.max_q = max_q
ctx.max_k = max_k
out = out.transpose(1, 2).contiguous()
if lse is not None:
lse = lse.transpose(1, 2).contiguous()
return (out, lse) if return_lse else out
# backward declaration:
# aten::_scaled_dot_product_cudnn_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, Tensor attn_bias, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, *, float? scale=None) -> (Tensor, Tensor, Tensor)
def _cudnn_attention_backward_op(
ctx: torch.autograd.function.FunctionCtx,
grad_out: torch.Tensor,
*args,
**kwargs,
):
query, key, value, out, lse, cum_seq_q, cum_seq_k, philox_seed, philox_offset = ctx.saved_tensors
grad_out = grad_out.transpose(1, 2).contiguous()
key = key.transpose(1, 2).contiguous()
value = value.transpose(1, 2).contiguous()
# Cannot pass first 5 arguments as kwargs because: https://github.com/pytorch/pytorch/blob/d26ca5de058dbcf56ac52bb43e84dd98df2ace97/torch/_dynamo/variables/torch.py#L1341
grad_query, grad_key, grad_value = torch.ops.aten._scaled_dot_product_cudnn_attention_backward(
grad_out,
query,
key,
value,
out,
logsumexp=lse,
philox_seed=philox_seed,
philox_offset=philox_offset,
attn_bias=ctx.attn_mask,
cum_seq_q=cum_seq_q,
cum_seq_k=cum_seq_k,
max_q=ctx.max_q,
max_k=ctx.max_k,
dropout_p=ctx.dropout_p,
is_causal=ctx.is_causal,
scale=ctx.scale,
)
grad_query, grad_key, grad_value = (x.transpose(1, 2).contiguous() for x in (grad_query, grad_key, grad_value))
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: Optional[torch.Tensor] = None,
dropout_p: float = 0.0,
is_causal: bool = False,
scale: Optional[float] = None,
enable_gqa: bool = False,
return_lse: bool = False,
_save_ctx: bool = True,
_parallel_config: Optional["ParallelConfig"] = 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 _sage_attention_forward_op(
ctx: torch.autograd.function.FunctionCtx,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
dropout_p: float = 0.0,
is_causal: bool = False,
scale: Optional[float] = None,
enable_gqa: bool = False,
return_lse: bool = False,
_save_ctx: bool = True,
_parallel_config: Optional["ParallelConfig"] = 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_backward_op(
ctx: torch.autograd.function.FunctionCtx,
grad_out: torch.Tensor,
*args,
):
raise NotImplementedError("Backward pass is not implemented for Sage attention.")
# ===== Context parallel =====
# Reference:
# - https://github.com/pytorch/pytorch/blob/f58a680d09e13658a52c6ba05c63c15759846bcc/torch/distributed/_functional_collectives.py#L827
# - https://github.com/pytorch/pytorch/blob/f58a680d09e13658a52c6ba05c63c15759846bcc/torch/distributed/_functional_collectives.py#L246
# For fullgraph=True tracing compatibility (since FakeTensor does not have a `wait` method):
def _wait_tensor(tensor):
if isinstance(tensor, funcol.AsyncCollectiveTensor):
tensor = tensor.wait()
return tensor
def _all_to_all_single(x: torch.Tensor, group) -> torch.Tensor:
shape = x.shape
# HACK: We need to flatten because despite making tensors contiguous, torch single-file-ization
# to benchmark triton codegen fails somewhere:
# buf25 = torch.ops._c10d_functional.all_to_all_single.default(buf24, [1, 1], [1, 1], '3')
# ValueError: Tensors must be contiguous
x = x.flatten()
x = funcol.all_to_all_single(x, None, None, group)
x = x.reshape(shape)
x = _wait_tensor(x)
return x
class TemplatedRingAttention(torch.autograd.Function):
@staticmethod
def forward(
ctx: torch.autograd.function.FunctionCtx,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attn_mask: Optional[torch.Tensor],
dropout_p: float,
is_causal: bool,
scale: Optional[float],
enable_gqa: bool,
return_lse: bool,
forward_op,
backward_op,
_parallel_config: Optional["ParallelConfig"] = None,
):
ring_mesh = _parallel_config.context_parallel_config._ring_mesh
rank = _parallel_config.context_parallel_config._ring_local_rank
world_size = _parallel_config.context_parallel_config.ring_degree
next_rank = (rank + 1) % world_size
prev_out = prev_lse = None
ctx.forward_op = forward_op
ctx.backward_op = backward_op
ctx.q_shape = query.shape
ctx.kv_shape = key.shape
ctx._parallel_config = _parallel_config
kv_buffer = torch.cat([key.flatten(), value.flatten()]).contiguous()
kv_buffer = funcol.all_gather_tensor(kv_buffer, gather_dim=0, group=ring_mesh.get_group())
kv_buffer = kv_buffer.chunk(world_size)
for i in range(world_size):
if i > 0:
kv = kv_buffer[next_rank]
key_numel = key.numel()
key = kv[:key_numel].reshape_as(key)
value = kv[key_numel:].reshape_as(value)
next_rank = (next_rank + 1) % world_size
out, lse = forward_op(
ctx,
query,
key,
value,
attn_mask,
dropout_p,
is_causal,
scale,
enable_gqa,
True,
_save_ctx=i == 0,
_parallel_config=_parallel_config,
)
if _parallel_config.context_parallel_config.convert_to_fp32:
out = out.to(torch.float32)
lse = lse.to(torch.float32)
lse = lse.unsqueeze(-1)
if prev_out is not None:
out = prev_out - torch.nn.functional.sigmoid(lse - prev_lse) * (prev_out - out)
lse = prev_lse - torch.nn.functional.logsigmoid(prev_lse - lse)
prev_out = out
prev_lse = lse
out = out.to(query.dtype)
lse = lse.squeeze(-1)
return (out, lse) if return_lse else out
@staticmethod
def backward(
ctx: torch.autograd.function.FunctionCtx,
grad_out: torch.Tensor,
*args,
):
ring_mesh = ctx._parallel_config.context_parallel_config._ring_mesh
rank = ctx._parallel_config.context_parallel_config._ring_local_rank
world_size = ctx._parallel_config.context_parallel_config.ring_degree
next_rank = (rank + 1) % world_size
next_ranks = list(range(1, world_size)) + [0]
accum_dtype = torch.float32 if ctx._parallel_config.context_parallel_config.convert_to_fp32 else grad_out.dtype
grad_query = torch.zeros(ctx.q_shape, dtype=accum_dtype, device=grad_out.device)
grad_key = torch.zeros(ctx.kv_shape, dtype=accum_dtype, device=grad_out.device)
grad_value = torch.zeros(ctx.kv_shape, dtype=accum_dtype, device=grad_out.device)
next_grad_kv = None
query, key, value, *_ = ctx.saved_tensors
kv_buffer = torch.cat([key.flatten(), value.flatten()]).contiguous()
kv_buffer = funcol.all_gather_tensor(kv_buffer, gather_dim=0, group=ring_mesh.get_group())
kv_buffer = kv_buffer.chunk(world_size)
for i in range(world_size):
if i > 0:
kv = kv_buffer[next_rank]