# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import os
from itertools import zip_longest
from typing import Any, Iterable, List, Optional, Set, Tuple, Union
import torch
from ..common import get_operator, register_operator
from .attn_bias import (
AttentionBias,
BlockDiagonalCausalFromBottomRightMask,
BlockDiagonalCausalLocalAttentionFromBottomRightMask,
BlockDiagonalCausalLocalAttentionMask,
BlockDiagonalCausalMask,
BlockDiagonalCausalWithOffsetGappyKeysMask,
BlockDiagonalCausalWithOffsetPaddedKeysMask,
BlockDiagonalGappyKeysMask,
BlockDiagonalMask,
BlockDiagonalPaddedKeysMask,
LocalAttentionFromBottomRightMask,
LowerTriangularFromBottomRightLocalAttentionMask,
LowerTriangularFromBottomRightMask,
LowerTriangularMask,
)
from .common import (
AttentionBwOpBase,
AttentionFwOpBase,
Context,
Gradients,
Inputs,
check_lastdim_alignment_stride1,
)
FLASH_VERSION = "0.0.0"
try:
try:
from ... import _C_flashattention # type: ignore[attr-defined]
from ..._cpp_lib import _build_metadata
if _build_metadata is not None:
FLASH_VERSION = _build_metadata.flash_version
except ImportError:
import flash_attn
from flash_attn.flash_attn_interface import flash_attn_cuda as _C_flashattention
FLASH_VERSION = flash_attn.__version__
FLASH_VER_MIN = (2, 5, 2)
FLASH_VER_LAST = (2, 5, 6) # last supported, inclusive
flash_ver_parsed = tuple(int(s) for s in FLASH_VERSION.split(".")[:3])
if (
flash_ver_parsed < FLASH_VER_MIN or flash_ver_parsed > FLASH_VER_LAST
) and os.environ.get("XFORMERS_IGNORE_FLASH_VERSION_CHECK", "0") != "1":
raise ImportError(
f"Requires Flash-Attention version >={'.'.join([str(i) for i in FLASH_VER_MIN])},"
f"<={'.'.join([str(i) for i in FLASH_VER_LAST])} "
f"but got {FLASH_VERSION}."
)
# create library so that flash-attn goes through the PyTorch Dispatcher
torch.library.define(
"xformers_flash::flash_fwd",
"(Tensor query, Tensor key, Tensor value, "
"Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, Tensor? seqused_k, "
"int max_seqlen_q, int max_seqlen_k, "
"float p, float softmax_scale, "
"bool is_causal, int window_left, "
"int window_right, bool return_softmax) -> (Tensor, Tensor, Tensor)",
)
torch.library.define(
"xformers_flash::flash_bwd",
"(bool grads_share_storage, Tensor dout, Tensor query, Tensor key, Tensor value, "
"Tensor out, Tensor softmax_lse_, "
"Tensor cu_seqlens_q, Tensor cu_seqlens_k, "
"int max_seqlen_q, int max_seqlen_k, "
"float p, float softmax_scale, bool is_causal, "
"int window_left, int window_right, Tensor rng_state) -> (Tensor dq, Tensor dk, Tensor dv)",
)
@torch.library.impl("xformers_flash::flash_fwd", "default")
def _flash_fwd(
query,
key,
value,
cu_seq_lens_q,
cu_seq_lens_k,
seqused_k,
max_seq_len_q,
max_seq_len_k,
p,
softmax_scale,
is_causal,
window_left,
window_right,
return_softmax,
):
if query.__class__.__name__ == "FakeTensor":
breakpoint()
if cu_seq_lens_q is None:
assert cu_seq_lens_k is None
assert seqused_k is None
(
out,
q_padded,
k_padded,
v_padded,
out_padded,
softmax_lse,
p,
rng_state,
) = _C_flashattention.fwd(
query,
key,
value,
None, # out
None, # alibi_slopes
p,
softmax_scale,
is_causal,
window_left, # window_size_left
window_right, # window_size_right
return_softmax,
None, # rng
)
else:
(
out,
q_padded,
k_padded,
v_padded,
out_padded,
softmax_lse,
p,
rng_state,
) = _C_flashattention.varlen_fwd(
query,
key,
value,
None, # out
cu_seq_lens_q,
cu_seq_lens_k,
seqused_k,
None, # alibi_slopes
max_seq_len_q,
max_seq_len_k,
p,
softmax_scale,
False,
is_causal,
window_left,
window_right,
return_softmax,
None,
)
return out, softmax_lse, rng_state
@torch.library.impl_abstract("xformers_flash::flash_fwd")
def _flash_fwd_abstract(
query,
key,
value,
*args,
**kwargs,
):
B, M, H, K = query.shape
out = torch.empty_like(query)
softmax_lse = torch.empty([B, H, M], device=query.device, dtype=torch.float32)
rng_state = torch.empty([2], device=query.device, dtype=torch.int64)
return out, softmax_lse, rng_state
@torch.library.impl("xformers_flash::flash_bwd", "default")
def _flash_bwd(
grads_share_storage,
grad,
query,
key,
value,
out,
lse,
cu_seq_lens_q,
cu_seq_lens_k,
max_seq_len_q,
max_seq_len_k,
p,
softmax_scale,
is_causal,
window_left,
window_right,
rng_state,
):
dq, dk, dv = _create_dq_dk_dv(grads_share_storage, query, key, value)
if cu_seq_lens_k is None:
assert cu_seq_lens_q is None
_C_flashattention.bwd(
grad,
query,
key,
value,
out,
lse,
dq,
dk,
dv,
None, # alibi_slopes
p,
softmax_scale,
is_causal,
window_left,
window_right,
False, # deterministic
None,
rng_state,
)
else:
_C_flashattention.varlen_bwd(
grad,
query,
key,
value,
out,
lse,
dq,
dk,
dv,
cu_seq_lens_q,
cu_seq_lens_k,
None, # alibi_slopes
max_seq_len_q,
max_seq_len_k,
p,
softmax_scale,
False, # zero_tensors
is_causal,
window_left,
window_right,
False, # deterministic
None,
rng_state,
)
return dq, dk, dv
@torch.library.impl_abstract("xformers_flash::flash_bwd")
def _flash_bwd_abstract(
grads_share_storage,
grad,
query,
key,
value,
*args,
**kwargs,
):
return _create_dq_dk_dv(grads_share_storage, query, key, value)
def _create_dq_dk_dv(
grads_share_storage: bool, query, key, value
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
# Create dq,dk,dv
# If Q/K/V come from a single QKV tensor, let's put the gradient in the
# right strides, so we can avoid a `cat`
if grads_share_storage:
chunk = torch.empty(
(*query.shape[0:-2], 3, query.shape[-2], query.shape[-1]),
dtype=query.dtype,
device=query.device,
)
return chunk.select(-3, 0), chunk.select(-3, 1), chunk.select(-3, 2)
return torch.empty_like(query), torch.empty_like(key), torch.empty_like(value)
except ImportError:
pass
def _convert_input_format(
inp: Inputs,
supports_mqa: bool,
) -> Tuple[
Inputs,
Optional[torch.Tensor],
int,
Optional[torch.Tensor],
int,
Optional[torch.Tensor],
]:
assert inp.query.ndim in [4, 5]
query, key, value = inp.query, inp.key, inp.value
batch = query.shape[0]
seqlen_q = query.shape[1]
seqlen_kv = key.shape[1]
head_dim_q = query.shape[-1]
head_dim_v = value.shape[-1]
attn_bias = inp.attn_bias
if isinstance(attn_bias, BlockDiagonalMask):
# BlockDiagonalMask or BlockDiagonalCausalMask
attn_bias.k_seqinfo.seqstart = attn_bias.k_seqinfo.seqstart.to(
inp.query.device, non_blocking=True
)
attn_bias.q_seqinfo.seqstart = attn_bias.q_seqinfo.seqstart.to(
inp.query.device, non_blocking=True
)
cu_seqlen_k = attn_bias.k_seqinfo.seqstart
cu_seqlen_q = attn_bias.q_seqinfo.seqstart
max_seqlen_q = attn_bias.q_seqinfo.max_seqlen
max_seqlen_k = attn_bias.k_seqinfo.max_seqlen
seqused_k = None
elif isinstance(
attn_bias,
(
BlockDiagonalGappyKeysMask,
BlockDiagonalPaddedKeysMask,
),
):
attn_bias.k_seqinfo.seqstart = attn_bias.k_seqinfo.seqstart.to(
inp.query.device, non_blocking=True
)
attn_bias.q_seqinfo.seqstart = attn_bias.q_seqinfo.seqstart.to(
inp.query.device, non_blocking=True
)
attn_bias.k_seqinfo.seqlen = attn_bias.k_seqinfo.seqlen.to(
inp.query.device, non_blocking=True
)
cu_seqlen_k = attn_bias.k_seqinfo.seqstart
cu_seqlen_q = attn_bias.q_seqinfo.seqstart
max_seqlen_q = attn_bias.q_seqinfo.max_seqlen
max_seqlen_k = attn_bias.k_seqinfo.max_seqlen
seqused_k = attn_bias.k_seqinfo.seqlen
else:
cu_seqlen_k = None
cu_seqlen_q = None
seqused_k = None
max_seqlen_q = inp.query.shape[1]
max_seqlen_k = inp.key.shape[1]
if query.ndim == 5: # GQA
assert supports_mqa
# Fold the group/head_in_group dimensions together
def fold(x):
# Either the head is replicated
if x.stride(3) == 0:
return x[:, :, :, 0]
# Or we reshape
return x.reshape(
[
x.shape[0],
x.shape[1],
-1,
x.shape[4],
]
)
query = fold(query)
key = fold(key)
value = fold(value)
# Optimize for MHA
if supports_mqa and key.ndim == 4 and key.stride(2) == 0 and value.stride(2) == 0:
key = key[:, :, :1]
value = value[:, :, :1]
# Initially we have `query.shape = [batch, seqlen, num_heads, head_dim_q]`
# We want format `[batch * seqlen, num_heads, head_dim_q]`
if cu_seqlen_k is not None:
query = query.reshape([batch * seqlen_q, -1, head_dim_q])
key = key.reshape([batch * seqlen_kv, -1, head_dim_q])
value = value.reshape([batch * seqlen_kv, -1, head_dim_v])
new_inp = Inputs(
query=query,
key=key,
value=value,
attn_bias=inp.attn_bias,
p=inp.p,
scale=inp.scale,
output_dtype=inp.output_dtype,
is_partial=inp.is_partial,
)
return new_inp, cu_seqlen_q, max_seqlen_q, cu_seqlen_k, max_seqlen_k, seqused_k
def _is_causal(attn_bias: Optional[Union[torch.Tensor, AttentionBias]]) -> bool:
return isinstance(
attn_bias,
(
LowerTriangularMask,
LowerTriangularFromBottomRightMask,
LowerTriangularFromBottomRightLocalAttentionMask,
BlockDiagonalCausalMask,
BlockDiagonalCausalLocalAttentionMask,
BlockDiagonalCausalFromBottomRightMask,
BlockDiagonalCausalLocalAttentionFromBottomRightMask,
BlockDiagonalCausalWithOffsetGappyKeysMask,
BlockDiagonalCausalWithOffsetPaddedKeysMask,
),
)
def _window_size(
attn_bias: Optional[Union[torch.Tensor, AttentionBias]]
) -> Tuple[int, int]:
win_left = -1
win_right = -1
if isinstance(
attn_bias,
(
BlockDiagonalCausalLocalAttentionMask,
BlockDiagonalCausalLocalAttentionFromBottomRightMask,
LowerTriangularFromBottomRightLocalAttentionMask,
),
):
win_left = attn_bias._window_size - 1
if isinstance(attn_bias, LocalAttentionFromBottomRightMask):
win_left = attn_bias.window_left
win_right = attn_bias.window_right
return (win_left, win_right)
def _check_needs_no_topleft(d: Inputs, reasons: List[str]) -> None:
# Flash does not support TopLeft, so only allow causal masks with TopLeft
# if each batch element has equal number of queries and keys.
if isinstance(d.attn_bias, BlockDiagonalCausalMask):
# Flash does not support TopLeft, so only allow BlockDiagonalCausalMask
# if each batch element has equal number of queries and keys.
for k_start, q_start in zip_longest(
d.attn_bias.k_seqinfo.seqstart_py, d.attn_bias.q_seqinfo.seqstart_py
):
if k_start != q_start:
reasons.append(
"Only support BlockDiagonalCausalMask if equal"
" numbers of keys and queries"
)
break
elif isinstance(d.attn_bias, LowerTriangularMask):
if d.query.shape[1] != d.key.shape[1]:
reasons.append(
"Only support LowerTriangularMask if equal number of" "keys and queries"
)
def _check_strides_for_bmghk(x: torch.Tensor, name: str, reasons: List[str]) -> None:
"""
We want to be able to collapse the G/H dimensions together
"""
if x.ndim == 5:
stride_g, stride_h = x.stride(2), x.stride(3)
if x.shape[2] == 1:
return
if x.shape[3] == 1 or stride_h == 0:
return
if stride_g != stride_h * x.shape[-2]:
reasons.append(
f"GQA is only supported when the G/H dimensions are contiguous\n"
f" {name}.stride: {x.stride()}\n"
f" {name}.shape : {list(x.shape)}"
)
def _post_process_lse(
lse: torch.Tensor, inp: Inputs, original_query_shape: Tuple[int, ...]
) -> torch.Tensor:
if not isinstance(
inp.attn_bias,
(
BlockDiagonalGappyKeysMask,
BlockDiagonalPaddedKeysMask,
),
):
if inp.is_partial and len(original_query_shape) == 5:
# [B, GH, M] => [B, G, H, M]
return lse.unflatten(1, original_query_shape[2:4])
return lse
q_seqinfo = inp.attn_bias.q_seqinfo
B = len(q_seqinfo.seqstart_py) - 1
if q_seqinfo.max_seqlen * B != original_query_shape[1]:
# Heterogeneous batch. We can't fix it.
return lse
# reshape from (B, G*H, max_seqlen) to (1, G*H, B*max_seqlen)
# Unfortunately this flatten is not just a view.
lse_hkm = lse.permute(1, 0, 2).flatten(start_dim=1)[None]
if len(original_query_shape) == 5:
return lse_hkm.unflatten(1, original_query_shape[2:4])
return lse_hkm
[docs]@register_operator
class FwOp(AttentionFwOpBase):
"""Operator that computes memory-efficient attention using \
`Flash-Attention <https://github.com/HazyResearch/flash-attention>`_ \
implementation.
"""
OPERATOR = get_operator("xformers_flash", "flash_fwd")
SUPPORTED_DEVICES: Set[str] = {"cuda"}
CUDA_MINIMUM_COMPUTE_CAPABILITY = (8, 0)
SUPPORTED_DTYPES: Set[torch.dtype] = {torch.half, torch.bfloat16}
SUPPORTED_MAX_K = 256
SUPPORTED_ATTN_BIAS_TYPES: Iterable[Any] = (
type(None),
LowerTriangularMask,
LowerTriangularFromBottomRightMask,
LowerTriangularFromBottomRightLocalAttentionMask,
BlockDiagonalMask,
BlockDiagonalCausalMask,
BlockDiagonalCausalLocalAttentionMask,
BlockDiagonalCausalLocalAttentionFromBottomRightMask,
BlockDiagonalCausalFromBottomRightMask,
BlockDiagonalCausalWithOffsetGappyKeysMask,
BlockDiagonalCausalWithOffsetPaddedKeysMask,
BlockDiagonalGappyKeysMask,
BlockDiagonalPaddedKeysMask,
LocalAttentionFromBottomRightMask,
)
SUPPORTS_DROPOUT = True
SUPPORTS_CUSTOM_SCALE = True
SUPPORTS_DIFFERENT_VALUE_EMBED = False
SUPPORTS_BMGHK = True
SUPPORTS_PARTIAL = True
NAME = f"flshattF@{FLASH_VERSION}"
VERSION = FLASH_VERSION
@classmethod
def not_supported_reasons(cls, d: Inputs) -> List[str]:
reasons = super(FwOp, cls).not_supported_reasons(d)
check_lastdim_alignment_stride1(reasons, "query", d.query, 8)
_check_needs_no_topleft(d, reasons)
_check_strides_for_bmghk(d.query, "query", reasons)
_check_strides_for_bmghk(d.key, "key", reasons)
_check_strides_for_bmghk(d.value, "value", reasons)
if d.is_partial and isinstance(
d.attn_bias,
(
BlockDiagonalGappyKeysMask,
BlockDiagonalPaddedKeysMask,
),
):
q_seqinfo = d.attn_bias.q_seqinfo
if q_seqinfo.min_seqlen != q_seqinfo.max_seqlen:
# Flash provides padded LSE which we don't handle.
reasons.append("partial attention with heterogeneous queries")
return reasons
@classmethod
def apply(
cls, inp: Inputs, needs_gradient: bool
) -> Tuple[torch.Tensor, Optional[Context]]:
return_softmax = False
original_query_shape = inp.query.shape
out_shape = [
*inp.query.shape[:-1],
inp.value.shape[-1],
]
# no cumulative seqlen
(
inp,
cu_seqlens_q,
max_seqlen_q,
cu_seqlens_k,
max_seqlen_k,
seqused_k,
) = _convert_input_format(inp, supports_mqa=True)
if inp.query.numel() > 0 and inp.key.numel() > 0:
win_left, win_right = _window_size(inp.attn_bias)
out, softmax_lse, rng_state = cls.OPERATOR(
inp.query,
inp.key,
inp.value,
cu_seqlens_q,
cu_seqlens_k,
seqused_k,
max_seqlen_q,
max_seqlen_k,
inp.p,
inp.scale_float,
_is_causal(inp.attn_bias),
window_left=win_left,
window_right=win_right,
return_softmax=return_softmax,
)
out = out.reshape(out_shape)
else:
out = torch.zeros(out_shape, device=inp.query.device, dtype=inp.query.dtype)
rng_state = None
softmax_lse = torch.empty(
[inp.query.shape[0], inp.query.shape[2], inp.query.shape[1]],
device=inp.query.device,
dtype=torch.float32,
)
if not needs_gradient:
return out, None
ctx = Context(
out=out, lse=_post_process_lse(softmax_lse, inp, original_query_shape)
)
if inp.p != 0.0:
ctx.op_bw = BwOp
ctx.rng_state = rng_state
return (out, ctx)
@classmethod
# type: ignore
def operator_flop(
cls,
query,
key,
value,
cu_seq_lens_q,
cu_seq_lens_k,
max_seq_len_q,
max_seq_len_k,
p,
softmax_scale,
causal,
return_softmax,
) -> int:
return cls.attn_operator_flop(
query.unsqueeze(0),
key.unsqueeze(0),
value.unsqueeze(0),
causal=causal,
seqstart_k=cu_seq_lens_k,
seqstart_q=cu_seq_lens_q,
)
[docs]@register_operator
class BwOp(AttentionBwOpBase):
__doc__ = FwOp.__doc__
OPERATOR = get_operator("xformers_flash", "flash_bwd")
SUPPORTED_DEVICES = FwOp.SUPPORTED_DEVICES
CUDA_MINIMUM_COMPUTE_CAPABILITY = FwOp.CUDA_MINIMUM_COMPUTE_CAPABILITY
SUPPORTED_DTYPES = FwOp.SUPPORTED_DTYPES
SUPPORTED_MAX_K = FwOp.SUPPORTED_MAX_K
SUPPORTED_ATTN_BIAS_TYPES: Iterable[Any] = tuple(
set(FwOp.SUPPORTED_ATTN_BIAS_TYPES).difference(
{
BlockDiagonalCausalWithOffsetGappyKeysMask,
BlockDiagonalCausalWithOffsetPaddedKeysMask,
BlockDiagonalGappyKeysMask,
BlockDiagonalPaddedKeysMask,
}
)
)
SUPPORTS_DROPOUT = FwOp.SUPPORTS_DROPOUT
SUPPORTS_CUSTOM_SCALE = FwOp.SUPPORTS_CUSTOM_SCALE
SUPPORTS_DIFFERENT_VALUE_EMBED = FwOp.SUPPORTS_DIFFERENT_VALUE_EMBED
IS_DETERMINISTIC = False
SUPPORTS_BMGHK = False # NOTE: Don't forget to update fmha doc when changing this!
NAME = f"flshattB@{FLASH_VERSION}"
VERSION = FLASH_VERSION
MAX_HEADDIM_DROPOUT_SM8x = 224
@classmethod
def not_supported_reasons(cls, d: Inputs) -> List[str]:
reasons = super(BwOp, cls).not_supported_reasons(d)
check_lastdim_alignment_stride1(reasons, "query", d.query, 8)
_check_needs_no_topleft(d, reasons)
if d.device.type == "cuda":
# Due to limited shared-memory, some GPUs are limited in head dimension
device_capability = torch.cuda.get_device_capability(d.device)
is_sm80_or_sm90 = device_capability in [(8, 0), (9, 0)]
if (
max(d.key.shape[-1], d.query.shape[-1]) > cls.MAX_HEADDIM_DROPOUT_SM8x
and not is_sm80_or_sm90
and d.p != 0.0
):
reasons.append(
"requires a GPU with compute capability 8.0 "
f"(A100) or 9.0 (H100) for dropout when 'query.shape[-1] > {cls.MAX_HEADDIM_DROPOUT_SM8x}'"
)
return reasons
@classmethod
def apply(cls, ctx: Context, inp: Inputs, grad: torch.Tensor) -> Gradients:
dq_shape, dk_shape, dv_shape = inp.query.shape, inp.key.shape, inp.value.shape
(
inp,
cu_seqlens_q,
max_seqlen_q,
cu_seqlens_k,
max_seqlen_k,
seqused_k,
) = _convert_input_format(inp, supports_mqa=False)
# assert ctx.lse.is_contiguous()
assert seqused_k is None
ctx_lse = ctx.lse
assert ctx_lse.shape[2] >= max_seqlen_q
if max_seqlen_q != ctx_lse.shape[2]:
ctx_lse = ctx_lse[:, :, :max_seqlen_q].contiguous()
kernel_out_shape = [
*inp.query.shape[:-1],
inp.value.shape[-1],
]
assert grad.dtype in cls.SUPPORTED_DTYPES
if inp.query.numel() and inp.key.numel():
win_left, win_right = _window_size(inp.attn_bias)
grads = Gradients(
*cls.OPERATOR(
ctx.qkv_share_storage,
grad.reshape(kernel_out_shape).contiguous(),
inp.query,
inp.key,
inp.value,
ctx.out.reshape(kernel_out_shape),
ctx_lse,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
inp.p,
inp.scale_float,
_is_causal(inp.attn_bias),
window_left=win_left,
window_right=win_right,
rng_state=ctx.rng_state if inp.p > 0.0 else None,
)
)
else:
grads = Gradients(
dq=torch.zeros_like(inp.query),
dk=torch.zeros_like(inp.key),
dv=torch.zeros_like(inp.value),
)
if grads.dq.numel() == 0:
grads.dk.zero_()
grads.dv.zero_()
if grads.dv.numel() == 0:
grads.dq.zero_()
grads.dq = grads.dq.reshape(dq_shape)
grads.dk = grads.dk.reshape(dk_shape)
grads.dv = grads.dv.reshape(dv_shape)
return grads
@classmethod
# type: ignore
def operator_flop(
cls,
grad,
query,
key,
value,
out,
lse,
dq,
dk,
dv,
cu_seq_lens_q,
cu_seq_lens_k,
max_seq_len_q,
max_seq_len_k,
p,
softmax_scale,
causal,
) -> int:
return cls.attn_operator_flop(
query.unsqueeze(0),
key.unsqueeze(0),
value.unsqueeze(0),
causal=causal,
seqstart_k=cu_seq_lens_k,
seqstart_q=cu_seq_lens_q,
)
