# 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.
from dataclasses import replace
from enum import Enum
from functools import partial
from typing import Any, Iterable, List, Optional, Set, Tuple, Union
import torch
from ..common import get_xformers_operator, register_operator
from . import attn_bias
from .attn_bias import (
AttentionBias,
BlockDiagonalCausalLocalAttentionFromBottomRightMask,
BlockDiagonalCausalLocalAttentionMask,
BlockDiagonalCausalMask,
BlockDiagonalCausalWithOffsetPaddedKeysMask,
BlockDiagonalMask,
LowerTriangularFromBottomRightLocalAttentionMask,
LowerTriangularFromBottomRightMask,
LowerTriangularMask,
LowerTriangularMaskWithTensorBias,
)
from .common import (
AttentionBwOpBase,
AttentionFwOpBase,
Context,
Gradients,
Inputs,
_attn_bias_apply,
check_lastdim_alignment_stride1,
)
def _uses_tensorcores(sm: int, is_half: bool) -> bool:
if sm >= 80:
return True
if sm >= 70:
return is_half
return False
def _minimum_gemm_alignment(inp: Inputs) -> int:
if inp.device.type != "cuda":
return 1
cap = torch.cuda.get_device_capability(inp.device)
sm = cap[0] * 10 + cap[1]
bits_per_scalar = {torch.float: 32, torch.half: 16, torch.bfloat16: 16}[
inp.query.dtype
]
uses_tensorcores = _uses_tensorcores(sm, bits_per_scalar == 16)
matmul_alignment_mn = 1
if sm >= 80:
matmul_alignment_mn = 4
if uses_tensorcores:
matmul_alignment_mn = max(matmul_alignment_mn, 128 // bits_per_scalar)
return matmul_alignment_mn
def _get_seqlen_info(
inp: Inputs,
) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], int, int]:
attn_bias = inp.attn_bias
if isinstance(
attn_bias, (BlockDiagonalMask, BlockDiagonalCausalWithOffsetPaddedKeysMask)
):
attn_bias.k_seqinfo.to(inp.query.device)
attn_bias.q_seqinfo.to(inp.query.device)
seqstart_k = attn_bias.k_seqinfo.seqstart
seqstart_q = attn_bias.q_seqinfo.seqstart
max_seqlen_q = attn_bias.q_seqinfo.max_seqlen
max_seqlen_k = attn_bias.k_seqinfo.max_seqlen
else:
seqstart_k = None
seqstart_q = None
max_seqlen_q = -1
max_seqlen_k = -1
return seqstart_k, seqstart_q, max_seqlen_q, max_seqlen_k
def _get_tensor_bias(
attn_bias: Optional[Union[torch.Tensor, AttentionBias]]
) -> Optional[torch.Tensor]:
if isinstance(attn_bias, torch.Tensor):
return attn_bias
elif isinstance(attn_bias, LowerTriangularMaskWithTensorBias):
return attn_bias._bias
return None
def _check_bias_alignment(
reasons: List[str], attn_bias: Optional[Union[torch.Tensor, AttentionBias]]
) -> None:
attn_bias_tensor = _get_tensor_bias(attn_bias)
if attn_bias_tensor is not None:
alignment = 128 // torch.finfo(attn_bias_tensor.dtype).bits
show_padding_hint = False
for d in range(attn_bias_tensor.ndim - 1):
if attn_bias_tensor.stride(d) % alignment != 0:
reasons.append(
f"attn_bias.stride(-2) % {alignment} != 0 (attn_bias.stride() = {attn_bias_tensor.stride()})"
)
show_padding_hint = True
if show_padding_hint:
reasons.append(
"""\
HINT: To use an `attn_bias` with a sequence length that is not a multiple of 8, \
you need to ensure memory is aligned by slicing a bigger tensor. \
Example: use `attn_bias = torch.zeros([1, 1, 5, 8])[:,:,:,:5]` instead of `torch.zeros([1, 1, 5, 5])`"""
)
# We can have stride=0 sometimes if dimension=1
if attn_bias_tensor.stride(-1) > 1:
reasons.append(
f"attn_bias.stride(-1) > 1 (attn_bias.stride() = {attn_bias_tensor.stride()}) - "
"you should call `.contiguous()` on the bias"
)
class _CustomMaskType(int, Enum):
"""
(Matches CustomMaskType in C++.)
"""
NoCustomMask = 0
CausalFromTopLeft = 1
CausalFromBottomRight = 2
def _custom_mask_type(bias: Optional[Union[torch.Tensor, AttentionBias]]) -> int:
if isinstance(
bias,
(
LowerTriangularMask,
BlockDiagonalCausalMask,
BlockDiagonalCausalLocalAttentionMask,
),
):
return int(_CustomMaskType.CausalFromTopLeft)
if isinstance(
bias,
(
LowerTriangularFromBottomRightMask,
LowerTriangularFromBottomRightLocalAttentionMask,
attn_bias.BlockDiagonalCausalFromBottomRightMask,
BlockDiagonalCausalWithOffsetPaddedKeysMask,
BlockDiagonalCausalLocalAttentionFromBottomRightMask,
),
):
return int(_CustomMaskType.CausalFromBottomRight)
return int(_CustomMaskType.NoCustomMask)
[docs]@register_operator
class FwOp(AttentionFwOpBase):
"""xFormers' MHA kernel based on CUTLASS.
Supports a large number of settings (including without TensorCores, f32 ...)
and GPUs as old as P100 (Sm60)
"""
OPERATOR = get_xformers_operator("efficient_attention_forward_cutlass")
SUPPORTED_DEVICES: Set[str] = {"cuda"}
SUPPORTED_DTYPES: Set[torch.dtype] = {torch.float, torch.half, torch.bfloat16}
SUPPORTED_MAX_K = 65536
SUPPORTED_ATTN_BIAS_TYPES: Iterable[Any] = (
type(None),
torch.Tensor,
LowerTriangularMask,
LowerTriangularFromBottomRightMask,
LowerTriangularFromBottomRightLocalAttentionMask,
LowerTriangularMaskWithTensorBias,
BlockDiagonalMask,
BlockDiagonalCausalMask,
BlockDiagonalCausalWithOffsetPaddedKeysMask,
attn_bias.BlockDiagonalCausalFromBottomRightMask,
attn_bias.BlockDiagonalCausalLocalAttentionMask,
BlockDiagonalCausalLocalAttentionFromBottomRightMask,
)
SUPPORTS_DROPOUT = True
SUPPORTS_CUSTOM_SCALE = True
SUPPORTS_DIFFERENT_VALUE_EMBED = True
SUPPORTS_BMGHK = True
NAME = "cutlassF"
_TEST_K: List[int] = [
32, # 64x64 kernel
128, # 64x128 kernel
256, # 64x128 with accumulation in gmem
]
@classmethod
def apply(
cls, inp: Inputs, needs_gradient: bool
) -> Tuple[torch.Tensor, Optional[Context]]:
if type(inp.attn_bias) not in FwOp.SUPPORTED_ATTN_BIAS_TYPES:
raise NotImplementedError("Unsupported attn_bias type")
if inp.query.ndim in [3, 4]:
return cls.apply_bmhk(inp, needs_gradient=needs_gradient)
assert inp.query.ndim == 5, f"query has shape {inp.query.shape}"
ctx: Optional[Context] = None
# XXX: Hackfix for BMGHK with H=1
# In that case we don't want to run G different streams because it adds
# some overhead
if inp.query.ndim == 5 and inp.query.shape[3] == 1:
slice_op = partial(torch.squeeze, dim=3)
inp = replace(
inp,
query=slice_op(inp.query),
key=slice_op(inp.key),
value=slice_op(inp.value),
attn_bias=_attn_bias_apply(
inp.attn_bias, partial(torch.squeeze, dim=2)
),
)
out, ctx = cls.apply_bmhk(inp, needs_gradient=needs_gradient)
out = out.unsqueeze(3)
if ctx is not None:
ctx = replace(ctx, lse=ctx.lse.unsqueeze(1), out=out)
return out, ctx
# Workaround until this is properly implemented in C++
# run each head group in a different stream
n_groups = inp.key.shape[2]
main_stream = torch.cuda.current_stream()
streams = [main_stream] + [
torch.cuda.Stream(device=inp.query.device) for _ in range(n_groups - 1)
]
outs = []
for group, stream in enumerate(streams):
stream.wait_stream(main_stream)
with torch.cuda.stream(stream):
query = inp.query[:, :, group]
key = inp.key[:, :, group]
value = inp.value[:, :, group]
bias = _attn_bias_apply(
inp.attn_bias, partial(torch.select, dim=1, index=group)
)
outs.append(
cls.apply_bmhk(
replace(inp, query=query, key=key, value=value, attn_bias=bias),
needs_gradient=needs_gradient,
)
)
for s in streams[1:]:
main_stream.wait_stream(s)
out = torch.stack([o[0] for o in outs], dim=2)
if needs_gradient:
ctx = Context(
out=out,
lse=torch.stack([o[1].lse for o in outs], dim=1), # type: ignore
op_bw=outs[0][1].op_bw, # type: ignore
)
return out, ctx
@classmethod
def apply_bmhk(
cls, inp: Inputs, needs_gradient: bool
) -> Tuple[torch.Tensor, Optional[Context]]:
if type(inp.attn_bias) not in FwOp.SUPPORTED_ATTN_BIAS_TYPES:
raise NotImplementedError("Unsupported attn_bias type")
seqstart_k, seqstart_q, max_seqlen_q, max_seqlen_k = _get_seqlen_info(inp)
out, lse, rng_seed, rng_offset, _, _ = cls.OPERATOR(
query=inp.query,
key=inp.key,
value=inp.value,
bias=_get_tensor_bias(inp.attn_bias),
cu_seqlens_q=seqstart_q,
cu_seqlens_k=seqstart_k,
max_seqlen_q=max_seqlen_q,
max_seqlen_k=max_seqlen_k,
dropout_p=inp.p,
compute_log_sumexp=needs_gradient,
custom_mask_type=_custom_mask_type(inp.attn_bias),
scale=inp.scale,
seqlen_k=inp.attn_bias.k_seqinfo.seqlen
if isinstance(inp.attn_bias, BlockDiagonalCausalWithOffsetPaddedKeysMask)
else None,
window_size=inp.attn_bias._window_size
if isinstance(
inp.attn_bias,
(
BlockDiagonalCausalLocalAttentionMask,
BlockDiagonalCausalLocalAttentionFromBottomRightMask,
LowerTriangularFromBottomRightLocalAttentionMask,
),
)
else None,
)
ctx: Optional[Context] = None
if needs_gradient:
ctx = Context(out=out, lse=lse)
if inp.p != 0:
# cutlass forward is only compatible with cutlass backward if
# dropout is used (because of the way RNG states are passed and the
# way random numbers are generated during backward)
ctx.rng_state = (rng_seed, rng_offset)
ctx.op_bw = BwOp
return out, ctx
@classmethod
def not_supported_reasons(cls, d: Inputs) -> List[str]:
reasons = super(FwOp, cls).not_supported_reasons(d)
matmul_alignment_mn = _minimum_gemm_alignment(d)
check_lastdim_alignment_stride1(reasons, "query", d.query, matmul_alignment_mn)
check_lastdim_alignment_stride1(reasons, "value", d.value, matmul_alignment_mn)
_check_bias_alignment(reasons, d.attn_bias)
return reasons
@classmethod
# type: ignore
def operator_flop(
cls,
q,
k,
v,
b,
seqstart_q,
seqstart_k,
max_seqlen_q_,
compute_lse,
custom_mask_type,
*a,
) -> int:
return cls.attn_operator_flop(
q,
k,
v,
causal=custom_mask_type > 0,
seqstart_k=seqstart_k,
seqstart_q=seqstart_q,
)
[docs]@register_operator
class BwOp(AttentionBwOpBase):
__doc__ = FwOp.__doc__
OPERATOR = get_xformers_operator("efficient_attention_backward_cutlass")
SUPPORTED_DEVICES = FwOp.SUPPORTED_DEVICES
SUPPORTED_DTYPES = FwOp.SUPPORTED_DTYPES
SUPPORTED_MAX_K = FwOp.SUPPORTED_MAX_K
SUPPORTED_ATTN_BIAS_TYPES: Iterable[Any] = (
type(None),
torch.Tensor,
LowerTriangularMask,
LowerTriangularFromBottomRightMask,
# TODO: Still some infs/nans in the BW pass for
# local + causal
# LowerTriangularFromBottomRightLocalAttentionMask,
# TODO: Fix handling of gradient through the fMHA autograd function
# LowerTriangularMaskWithTensorBias,
BlockDiagonalMask,
BlockDiagonalCausalMask,
attn_bias.BlockDiagonalCausalFromBottomRightMask,
attn_bias.BlockDiagonalCausalLocalAttentionMask,
)
SUPPORTS_ATTN_BIAS_GRAD = True
SUPPORTS_DROPOUT = FwOp.SUPPORTS_DROPOUT
SUPPORTS_CUSTOM_SCALE = FwOp.SUPPORTS_CUSTOM_SCALE
SUPPORTS_DIFFERENT_VALUE_EMBED = FwOp.SUPPORTS_DIFFERENT_VALUE_EMBED
NAME = "cutlassB"
_TEST_K: List[int] = [
32, # 64x64 kernel
128, # 64x128/128x128 kernel
256, # 64x128 with accumulation in gmem
]
@classmethod
def not_supported_reasons(cls, d: Inputs) -> List[str]:
reasons = super(BwOp, cls).not_supported_reasons(d)
matmul_alignment_mn = _minimum_gemm_alignment(d)
check_lastdim_alignment_stride1(reasons, "query", d.query, matmul_alignment_mn)
check_lastdim_alignment_stride1(reasons, "key", d.key, matmul_alignment_mn)
check_lastdim_alignment_stride1(reasons, "value", d.value, matmul_alignment_mn)
_check_bias_alignment(reasons, d.attn_bias)
attn_bias_tensor = _get_tensor_bias(d.attn_bias)
# Backprop of gradient through broadcasted bias is not supported
if attn_bias_tensor is not None and attn_bias_tensor.requires_grad:
# Don't forget that inputs are either in BMK or BMHK!
if d.query.ndim == 3 and attn_bias_tensor.ndim == 3:
expected_bias_shape = (*d.query.shape[:2], d.key.shape[1])
else:
# bias is B H Mq Mk
expected_bias_shape = (
d.query.shape[0],
d.query.shape[2] if d.query.ndim == 4 else 1,
d.query.shape[1],
d.key.shape[1],
)
if tuple(attn_bias_tensor.shape) != expected_bias_shape:
reasons.append(
"Broadcasting the `attn_bias` tensor is not supported "
f"(shape: {tuple(attn_bias_tensor.shape)}"
f"/ expected: {expected_bias_shape})"
)
return reasons
@classmethod
def apply(cls, ctx: Context, inp: Inputs, grad: torch.Tensor) -> Gradients:
if type(inp.attn_bias) not in BwOp.SUPPORTED_ATTN_BIAS_TYPES:
raise NotImplementedError("Unsupported attn_bias type")
seqstart_k, seqstart_q, max_seqlen_q, max_seqlen_k = _get_seqlen_info(inp)
dtype = inp.query.dtype
rng_seed = rng_offset = torch.Tensor()
if inp.p != 0.0:
assert ctx.rng_state is not None
rng_seed, rng_offset = ctx.rng_state
tensor_bias = _get_tensor_bias(inp.attn_bias)
force_pad_inf = torch.cuda.get_device_capability(inp.query.device) == (7, 5)
(grad_q, grad_k, grad_v, grad_bias) = cls.OPERATOR(
grad.to(dtype),
inp.query,
inp.key,
inp.value,
bias=tensor_bias,
bias_requires_grad=tensor_bias.requires_grad
if tensor_bias is not None
else False,
cu_seqlens_q=seqstart_q,
cu_seqlens_k=seqstart_k,
max_seqlen_q=max_seqlen_q,
max_seqlen_k=max_seqlen_k,
logsumexp=ctx.get_padded_lse(32, force_pad_inf=force_pad_inf),
out=ctx.out.to(dtype),
dropout_p=inp.p,
# if not using dropout, seed and offset are irrelevant but still expected
# in function signature so just pass 0
# seed and offset could be None if a different FW op other than cutlass
# was used.
philox_seed=rng_seed,
philox_offset=rng_offset,
custom_mask_type=_custom_mask_type(inp.attn_bias),
scale=inp.scale,
num_splits_key=-1, # Let C++ determine it
window_size=inp.attn_bias._window_size
if isinstance(
inp.attn_bias,
(
BlockDiagonalCausalLocalAttentionMask,
BlockDiagonalCausalLocalAttentionFromBottomRightMask,
LowerTriangularFromBottomRightLocalAttentionMask,
),
)
else None,
)
# c++/CUDA implementation returns an uninitialized tensor if bias doesn't
# require grad
if not (
isinstance(inp.attn_bias, torch.Tensor) and inp.attn_bias.requires_grad
):
grad_bias = None
return Gradients(dq=grad_q, dk=grad_k, dv=grad_v, db=grad_bias)
@classmethod
# type: ignore
def operator_flop(
cls,
dO,
q,
k,
v,
b,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
logsumexp,
output,
dropout_p,
rng_seed,
rng_offset,
custom_mask_type,
scale,
) -> int:
return cls.attn_operator_flop(
q,
k,
v,
seqstart_q=cu_seqlens_q,
seqstart_k=cu_seqlens_k,
causal=custom_mask_type > 0,
)
