File size: 10,410 Bytes
d02d576 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 | from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
def maybe_contiguous(x):
return x.contiguous() if x is not None and x.stride(-1) != 1 else x
# Sparse attention utils
def convert_vertical_slash_indexes(
q_seqlens: torch.Tensor, # [BATCH, ]
kv_seqlens: torch.Tensor, # [BATCH, ]
vertical_indexes: torch.Tensor, # [BATCH, N_HEADS, NNZ_V]
slash_indexes: torch.Tensor, # [BATCH, N_HEADS, NNZ_S]
context_size: int,
block_size_M: int,
block_size_N: int,
causal: bool = True,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
batch_size = slash_indexes.size(0)
num_heads = slash_indexes.size(1)
nnz_slash = slash_indexes.size(2)
nnz_vertical = vertical_indexes.size(2)
num_rows = (context_size + block_size_M - 1) // block_size_M
block_count = torch.zeros(
batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
)
block_offset = torch.zeros(
batch_size,
num_heads,
num_rows,
nnz_slash,
dtype=q_seqlens.dtype,
device=q_seqlens.device,
)
column_count = torch.zeros(
batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
)
column_index = torch.zeros(
batch_size,
num_heads,
num_rows,
nnz_vertical,
dtype=q_seqlens.dtype,
device=q_seqlens.device,
)
torch.ops.sgl_kernel.convert_vertical_slash_indexes.default(
block_count,
block_offset,
column_count,
column_index,
q_seqlens,
kv_seqlens,
vertical_indexes,
slash_indexes,
context_size,
block_size_M,
block_size_N,
causal,
)
return block_count, block_offset, column_count, column_index
def convert_vertical_slash_indexes_mergehead(
q_seqlens: torch.Tensor, # [BATCH, ]
kv_seqlens: torch.Tensor, # [BATCH, ]
vertical_indexes: torch.Tensor, # [BATCH, N_HEADS, NNZ_V]
slash_indexes: torch.Tensor, # [BATCH, N_HEADS, NNZ_S]
# [N_HEADS] : different head use different number of indices
vertical_indices_count: torch.Tensor,
slash_indices_count: torch.Tensor,
context_size: int,
block_size_M: int,
block_size_N: int,
causal: bool = True,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
batch_size = slash_indexes.size(0)
num_heads = slash_indexes.size(1)
nnz_slash = slash_indexes.size(2)
nnz_vertical = vertical_indexes.size(2)
num_rows = (context_size + block_size_M - 1) // block_size_M
block_count = torch.empty(
batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
)
block_offset = torch.empty(
batch_size,
num_heads,
num_rows,
nnz_slash,
dtype=q_seqlens.dtype,
device=q_seqlens.device,
)
column_count = torch.empty(
batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
)
column_index = torch.empty(
batch_size,
num_heads,
num_rows,
nnz_vertical,
dtype=q_seqlens.dtype,
device=q_seqlens.device,
)
torch.ops.sgl_kernel.convert_vertical_slash_indexes_mergehead.default(
block_count,
block_offset,
column_count,
column_index,
q_seqlens,
kv_seqlens,
vertical_indexes,
slash_indexes,
vertical_indices_count,
slash_indices_count,
context_size,
block_size_M,
block_size_N,
causal,
)
return block_count, block_offset, column_count, column_index
def sparse_attn_func(
q,
k,
v,
block_count,
block_offset,
column_count,
column_index,
dropout_p=0.0,
softmax_scale=None,
causal=False,
softcap=0.0, # 0.0 means deactivated
alibi_slopes=None,
deterministic=False,
return_attn_probs=False,
*,
return_softmax_lse=False,
out=None,
):
"""Compute attention with vertical and slash sparsity patterns.
Most Arguments are the same with the flash_attn_func interface, except for 4 extra args:
block_count and block_offset for slash sparsity patterns, and
column_count and column_index for vertical sparsity patterns.
For more details please refer to Appendix C.4.2 of paper https://arxiv.org/abs/2407.02490.
Arguments:
q: (batch_size, seqlen, nheads, headdim)
k: (batch_size, seqlen, nheads_k, headdim)
v: (batch_size, seqlen, nheads_k, headdim)
block_count: (batch_size, nheads, cdiv(seqlen, BLOCK_M))
block_offset: (batch_size, nheads, cdiv(seqlen, BLOCK_M), NNZ_S)
column_count: (batch_size, nheads, cdiv(seqlen, BLOCK_M))
column_index: (batch_size, nheads, cdiv(seqlen, BLOCK_M), NNZ_V)
dropout_p: float. Dropout probability.
softmax_scale: float. The scaling of QK^T before applying softmax.
Default to 1 / sqrt(headdim).
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
(-alibi_slope * |i + seqlen_k - seqlen_q - j|)
is added to the attention score of query i and key j.
deterministic: bool. Whether to use the deterministic implementation of the backward pass,
which is slightly slower and uses more memory. The forward pass is always deterministic.
return_attn_probs: bool. Whether to return the attention probabilities. This option is for
testing only. The returned probabilities are not guaranteed to be correct
(they might not have the right scaling).
Return:
out: (batch_size, seqlen, nheads, headdim).
softmax_lse [optional, if return_softmax_lse=True]: (batch_size, nheads, seqlen). The
logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
normalization factor).
"""
if softmax_scale is None:
softmax_scale = q.shape[-1] ** (-0.5)
q, k, v = [maybe_contiguous(x) for x in (q, k, v)]
out, softmax_lse = torch.ops.sgl_kernel.fwd_sparse.default(
q,
k,
v,
block_count,
block_offset,
column_count,
column_index,
out,
alibi_slopes,
dropout_p,
softmax_scale,
causal,
softcap,
return_attn_probs and dropout_p > 0,
None,
)
return (out, softmax_lse) if return_softmax_lse else out
def sparse_attn_varlen_func(
q,
k,
v,
block_count,
block_offset,
column_count,
column_index,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
dropout_p=0.0,
softmax_scale=None,
causal=False,
softcap=0.0, # 0.0 means deactivated
alibi_slopes=None,
deterministic=False,
return_attn_probs=False,
*,
return_softmax_lse=False,
out=None,
):
"""Compute attention with vertical and slash sparsity patterns.
Most Arguments are the same with the flash_attn_varlen_func interface, except for 4 extra args:
block_count and block_offset for slash sparsity patterns, and
column_count and column_index for vertical sparsity patterns.
For more details please refer to Appendix C.4.2 of paper https://arxiv.org/abs/2407.02490.
Arguments:
q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.
k: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.
v: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.
block_count: (batch_size, nheads, cdiv(seqlen, BLOCK_M))
block_offset: (batch_size, nheads, cdiv(seqlen, BLOCK_M), NNZ_S)
column_count: (batch_size, nheads, cdiv(seqlen, BLOCK_M))
column_index: (batch_size, nheads, cdiv(seqlen, BLOCK_M), NNZ_V)
cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
of the sequences in the batch, used to index into q.
cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
of the sequences in the batch, used to index into kv.
max_seqlen_q: int. Maximum query sequence length in the batch.
max_seqlen_k: int. Maximum key sequence length in the batch.
dropout_p: float. Dropout probability.
softmax_scale: float. The scaling of QK^T before applying softmax.
Default to 1 / sqrt(headdim).
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
softcap: float. Anything > 0 activates softcapping attention.
alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
(-alibi_slope * |i + seqlen_k - seqlen_q - j|)
is added to the attention score of query i and key j.
deterministic: bool. Whether to use the deterministic implementation of the backward pass,
which is slightly slower and uses more memory. The forward pass is always deterministic.
return_attn_probs: bool. Whether to return the attention probabilities. This option is for
testing only. The returned probabilities are not guaranteed to be correct
(they might not have the right scaling).
Return:
out: (total, nheads, headdim).
softmax_lse [optional, if return_softmax_lse=True]: (nheads, total_q_seqlen). The
logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
normalization factor).
"""
if softmax_scale is None:
softmax_scale = q.shape[-1] ** (-0.5)
q, k, v = [maybe_contiguous(x) for x in (q, k, v)]
out, softmax_lse = torch.ops.sgl_kernel.varlen_fwd_sparse.default(
q,
k,
v,
block_count,
block_offset,
column_count,
column_index,
out,
cu_seqlens_q,
cu_seqlens_k,
None,
alibi_slopes,
max_seqlen_q,
max_seqlen_k,
dropout_p,
softmax_scale,
False,
causal,
softcap,
return_attn_probs and dropout_p > 0,
None,
)
return (out, softmax_lse) if return_softmax_lse else out
|