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e19323e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | # Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# For a list of all contributors, visit:
# https://github.com/fla-org/flash-linear-attention/graphs/contributors
import torch
import triton
import triton.language as tl
from ...ops.utils.index import prepare_chunk_indices
from ...utils import autotune_cache_kwargs
@triton.heuristics({
'HAS_SCALE': lambda args: args['scale'] is not None,
'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps)
for num_warps in [1, 2, 4, 8]
],
key=['B', 'H', 'BT', 'IS_VARLEN'],
**autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def chunk_comba_cumsum_scalar_fwd_kernel(
g,
g0,
g1,
scale,
cu_seqlens,
chunk_indices,
T,
B: tl.constexpr,
H: tl.constexpr,
BT: tl.constexpr,
HAS_SCALE: tl.constexpr,
IS_VARLEN: tl.constexpr,
):
i_t, i_bh = tl.program_id(0), tl.program_id(1)
i_b, i_h = i_bh // H, i_bh % H
if IS_VARLEN:
i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32)
bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
T = eos - bos
else:
bos, eos = i_b * T, i_b * T + T
p_g = tl.make_block_ptr(g + bos*H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
p_g0 = tl.make_block_ptr(g0 + bos*H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
p_g1 = tl.make_block_ptr(g1 + bos*H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
# [BT]
b_g = tl.load(p_g, boundary_check=(0,)).to(tl.float32)
if HAS_SCALE:
b_g = b_g * scale
b_g1 = tl.cumsum(b_g, axis=0)
b_g0 = b_g1 - b_g
tl.store(p_g0, b_g0.to(p_g0.dtype.element_ty), boundary_check=(0,))
tl.store(p_g1, b_g1.to(p_g1.dtype.element_ty), boundary_check=(0,))
def chunk_comba_cumsum_scalar_fwd(
g: torch.Tensor,
chunk_size: int,
cu_seqlens: torch.Tensor | None = None,
output_dtype: torch.dtype | None = torch.float,
chunk_indices: torch.LongTensor | None = None,
scale: float | None = None,
) -> torch.Tensor:
B, T, H = g.shape
assert chunk_size == 2**(chunk_size.bit_length()-1), "chunk_size must be a power of 2"
BT = chunk_size
if chunk_indices is None and cu_seqlens is not None:
chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
g0, g1 = torch.empty_like(g, dtype=output_dtype or g.dtype), torch.empty_like(g, dtype=output_dtype or g.dtype)
grid = (NT, B * H)
chunk_comba_cumsum_scalar_fwd_kernel[grid](
g,
g0,
g1,
scale,
cu_seqlens,
chunk_indices,
T=T,
B=B,
H=H,
BT=BT,
)
return g0, g1
@triton.heuristics({
'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps)
for num_warps in [1, 2, 4, 8]
],
key=['B', 'H', 'BT', 'IS_VARLEN'],
**autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def chunk_comba_cumsum_scalar_bwd_kernel(
dg0,
dgr,
cu_seqlens,
chunk_indices,
T,
B: tl.constexpr,
H: tl.constexpr,
BT: tl.constexpr,
IS_VARLEN: tl.constexpr,
):
i_t, i_bh = tl.program_id(0), tl.program_id(1)
i_b, i_h = i_bh // H, i_bh % H
if IS_VARLEN:
i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32)
bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
T = eos - bos
else:
bos, eos = i_b * T, i_b * T + T
p_dg0 = tl.make_block_ptr(dg0 + bos*H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
p_dgr = tl.make_block_ptr(dgr + bos*H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
# [BT]
"""
b_dg: 1,2,3,4
b_dg0: 0,1,2,3
b_temp: 0,1,3,6
b_dz: 6
b_dgr: 6,5,3,0
"""
b_dg0 = tl.load(p_dg0, boundary_check=(0,)).to(tl.float32)
b_temp = tl.cumsum(b_dg0, axis=0)
b_dz = tl.sum(b_dg0, axis=0)
b_dgr = -b_temp + b_dz[None]
tl.store(p_dgr, b_dgr.to(p_dgr.dtype.element_ty), boundary_check=(0,))
def chunk_comba_cumsum_scalar_bwd(
dg0: torch.Tensor,
chunk_size: int,
cu_seqlens: torch.Tensor | None = None,
output_dtype: torch.dtype | None = torch.float,
chunk_indices: torch.LongTensor | None = None,
) -> torch.Tensor:
B, T, H = dg0.shape
assert chunk_size == 2**(chunk_size.bit_length()-1), "chunk_size must be a power of 2"
BT = chunk_size
if chunk_indices is None and cu_seqlens is not None:
chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
dg = torch.empty_like(dg0, dtype=output_dtype or dg0.dtype)
grid = (NT, B * H)
chunk_comba_cumsum_scalar_bwd_kernel[grid](
dg0,
dg,
cu_seqlens,
chunk_indices,
T=T,
B=B,
H=H,
BT=BT,
)
return dg
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