File size: 5,047 Bytes
ccefec1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | # -*- coding: utf-8 -*-
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
# Code adapted from https://github.com/mayank31398/cute-kernels
from typing import Optional
import torch
import triton
import triton.language as tl
from ...ops.utils.index import prepare_lens
from ...utils import input_guard
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps)
for num_warps in [4, 8, 16, 32]
],
key=['D', 'PADDING_SIDE', 'PACK']
)
@triton.jit
def packunpack_sequence_kernel(
x,
y,
cu_seqlens,
S,
D,
BD: tl.constexpr,
PADDING_SIDE: tl.constexpr,
PACK: tl.constexpr,
):
i_d, i_s, i_b = tl.program_id(0), tl.program_id(1), tl.program_id(2)
bos, eos = tl.load(cu_seqlens + i_b), tl.load(cu_seqlens + i_b + 1)
T = eos - bos
if PADDING_SIDE == 'left':
NP = S - T
if i_s < NP:
return
i_t = bos + (i_s - NP)
else:
if i_s >= T:
return
i_t = bos + i_s
o_d = i_d * BD + tl.arange(0, BD)
mask = o_d < D
if PACK:
b_x = tl.load(x + (i_b * S + i_s) * D + o_d, mask=mask)
tl.store(y + i_t * D + o_d, b_x, mask=mask)
else:
b_x = tl.load(x + i_t * D + o_d, mask=mask)
tl.store(y + (i_b * S + i_s) * D + o_d, b_x, mask=mask)
def pack_sequence_fwdbwd(
x: torch.Tensor,
cu_seqlens: torch.Tensor,
padding_side: str,
) -> torch.Tensor:
B, S = x.shape[:2]
D = x.numel() // (B * S)
BD = min(triton.next_power_of_2(D), 4096)
ND = triton.cdiv(D, BD)
y = torch.empty(cu_seqlens[-1].item(), *x.shape[2:], device=x.device, dtype=x.dtype)
packunpack_sequence_kernel[ND, S, B](
x=x,
y=y,
cu_seqlens=cu_seqlens,
S=S,
D=D,
BD=BD,
PADDING_SIDE=padding_side,
PACK=True,
)
return y
def unpack_sequence_fwdbwd(
x: torch.Tensor,
cu_seqlens: torch.Tensor,
padding_side: str,
desired_shape: torch.Size,
) -> torch.Tensor:
if desired_shape is None:
desired_shape = (len(cu_seqlens) - 1, prepare_lens(cu_seqlens).max().item(), *x.shape[1:])
y = torch.zeros(desired_shape, device=x.device, dtype=x.dtype)
B, S = y.shape[:2]
D = y.numel() // (B * S)
BD = min(triton.next_power_of_2(D), 4096)
ND = triton.cdiv(D, BD)
packunpack_sequence_kernel[ND, S, B](
x=x,
y=y,
cu_seqlens=cu_seqlens,
S=S,
D=D,
BD=BD,
PADDING_SIDE=padding_side,
PACK=False,
)
return y
class PackSequenceFunction(torch.autograd.Function):
@staticmethod
@input_guard
def forward(
ctx,
x: torch.Tensor,
cu_seqlens: torch.Tensor,
padding_side: str,
) -> torch.Tensor:
assert padding_side in ['left', 'right']
assert x.ndim >= 2
ctx.cu_seqlens = cu_seqlens
ctx.padding_side = padding_side
ctx.desired_shape = x.shape
y = pack_sequence_fwdbwd(
x=x,
cu_seqlens=cu_seqlens,
padding_side=padding_side,
)
return y
@staticmethod
@input_guard
def backward(ctx, dy: torch.Tensor) -> tuple[torch.Tensor | None]:
dx = unpack_sequence_fwdbwd(
x=dy,
cu_seqlens=ctx.cu_seqlens,
padding_side=ctx.padding_side,
desired_shape=ctx.desired_shape,
)
return dx, *[None] * 10
class UnpackSequenceFunction(torch.autograd.Function):
@staticmethod
@input_guard
def forward(
ctx,
x: torch.Tensor,
cu_seqlens: torch.Tensor,
padding_side: str,
desired_shape: Optional[torch.Size] = None,
) -> torch.Tensor:
assert padding_side in ['left', 'right']
assert x.ndim >= 2
if desired_shape is not None:
assert desired_shape[0] == cu_seqlens.shape[0] - 1
assert desired_shape[2:] == x.shape[1:]
ctx.cu_seqlens = cu_seqlens
ctx.padding_side = padding_side
y = unpack_sequence_fwdbwd(
x=x,
cu_seqlens=cu_seqlens,
padding_side=padding_side,
desired_shape=desired_shape,
)
return y
@staticmethod
@input_guard
def backward(ctx, dy: torch.Tensor) -> tuple[torch.Tensor | None]:
dx = pack_sequence_fwdbwd(
x=dy,
cu_seqlens=ctx.cu_seqlens,
padding_side=ctx.padding_side,
)
return dx, None, None, None
def pack_sequence(
x: torch.Tensor,
cu_seqlens: torch.Tensor,
padding_side: str = 'left'
) -> torch.Tensor:
return PackSequenceFunction.apply(
x,
cu_seqlens,
padding_side,
)
def unpack_sequence(
x: torch.Tensor,
cu_seqlens: torch.Tensor,
padding_side: str = 'left',
desired_shape: Optional[torch.Size] = None,
) -> torch.Tensor:
return UnpackSequenceFunction.apply(
x,
cu_seqlens,
padding_side,
desired_shape,
)
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