Spaces:
Running
on
A100
Running
on
A100
File size: 9,155 Bytes
174ae06 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 |
# Copyright (c) 2025 NVIDIA CORPORATION.
# Licensed under the MIT license.
# Adapted from https://github.com/NVlabs/VILA/tree/main under the Apache 2.0 license.
# LICENSE is in incl_licenses directory.
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
# Adopted from https://github.com/zhuzilin/ring-flash-attention.
# Implementation refers to Ring Attention Paper: https://arxiv.org/abs/2310.01889
import torch
from flash_attn.flash_attn_interface import _flash_attn_varlen_backward, _flash_attn_varlen_forward
from .utils import RingComm, update_out_and_lse
try:
from .triton_utils import flatten_varlen_lse, unflatten_varlen_lse
except:
from .utils import flatten_varlen_lse, unflatten_varlen_lse
def ring_flash_attn_varlen_forward(
process_group,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
cu_seqlens,
max_seqlen,
softmax_scale,
dropout_p=0,
causal=True,
window_size=(-1, -1),
alibi_slopes=None,
deterministic=False,
):
comm = RingComm(process_group)
out = None
lse = None
next_k, next_v = None, None
for step in range(comm.world_size):
if step + 1 != comm.world_size:
next_k: torch.Tensor = comm.send_recv(k)
next_v: torch.Tensor = comm.send_recv(v)
comm.commit()
if not causal or step <= comm.rank:
block_out, _, _, _, _, block_lse, _, _ = _flash_attn_varlen_forward(
q,
k,
v,
cu_seqlens,
cu_seqlens,
max_seqlen,
max_seqlen,
dropout_p,
softmax_scale,
causal=causal and step == 0,
window_size=window_size,
alibi_slopes=alibi_slopes,
return_softmax=True and dropout_p > 0,
block_table=None,
)
block_lse = flatten_varlen_lse(block_lse, cu_seqlens=cu_seqlens)
out, lse = update_out_and_lse(out, lse, block_out, block_lse)
if step + 1 != comm.world_size:
comm.wait()
k = next_k
v = next_v
out = out.to(q.dtype)
lse = unflatten_varlen_lse(lse, cu_seqlens, max_seqlen)
return out, lse
def ring_flash_attn_varlen_backward(
process_group,
dout,
q,
k,
v,
out,
softmax_lse,
cu_seqlens,
max_seqlen,
softmax_scale,
dropout_p=0,
causal=True,
window_size=(-1, -1),
alibi_slopes=None,
deterministic=False,
):
kv_comm = RingComm(process_group)
d_kv_comm = RingComm(process_group)
dq, dk, dv = None, None, None
next_dk, next_dv = None, None
block_dq_buffer = torch.empty(q.shape, dtype=q.dtype, device=q.device)
block_dk_buffer = torch.empty(k.shape, dtype=k.dtype, device=k.device)
block_dv_buffer = torch.empty(v.shape, dtype=v.dtype, device=v.device)
next_dk, next_dv = None, None
next_k, next_v = None, None
for step in range(kv_comm.world_size):
if step + 1 != kv_comm.world_size:
next_k = kv_comm.send_recv(k)
next_v = kv_comm.send_recv(v)
kv_comm.commit()
if step <= kv_comm.rank or not causal:
bwd_causal = causal and step == 0
_flash_attn_varlen_backward(
dout,
q,
k,
v,
out,
softmax_lse,
block_dq_buffer,
block_dk_buffer,
block_dv_buffer,
cu_seqlens,
cu_seqlens,
max_seqlen,
max_seqlen,
dropout_p,
softmax_scale,
bwd_causal,
window_size,
alibi_slopes,
deterministic,
rng_state=None,
)
if dq is None:
dq = block_dq_buffer.to(torch.float32)
dk = block_dk_buffer.to(torch.float32)
dv = block_dv_buffer.to(torch.float32)
else:
dq += block_dq_buffer
d_kv_comm.wait()
dk = block_dk_buffer + next_dk
dv = block_dv_buffer + next_dv
elif step != 0:
d_kv_comm.wait()
dk = next_dk
dv = next_dv
if step + 1 != kv_comm.world_size:
kv_comm.wait()
k = next_k
v = next_v
next_dk = d_kv_comm.send_recv(dk)
next_dv = d_kv_comm.send_recv(dv)
d_kv_comm.commit()
d_kv_comm.wait()
return dq.to(torch.bfloat16), next_dk.to(q.dtype), next_dv.to(q.dtype)
class RingFlashAttnVarlenFunc(torch.autograd.Function):
@staticmethod
def forward(
ctx,
q,
k,
v,
cu_seqlens,
max_seqlen,
dropout_p,
softmax_scale,
causal,
window_size,
alibi_slopes,
deterministic,
return_softmax,
group,
):
if softmax_scale is None:
softmax_scale = q.shape[-1] ** (-0.5)
assert alibi_slopes is None
k = k.contiguous()
v = v.contiguous()
out, softmax_lse = ring_flash_attn_varlen_forward(
group,
q,
k,
v,
cu_seqlens,
max_seqlen,
softmax_scale=softmax_scale,
dropout_p=dropout_p,
causal=causal,
window_size=window_size,
alibi_slopes=alibi_slopes,
deterministic=False,
)
# this should be out_padded
ctx.save_for_backward(q, k, v, out, softmax_lse, cu_seqlens)
ctx.max_seqlen = max_seqlen
ctx.dropout_p = dropout_p
ctx.softmax_scale = softmax_scale
ctx.causal = causal
ctx.window_size = window_size
ctx.alibi_slopes = alibi_slopes
ctx.deterministic = deterministic
ctx.group = group
return out if not return_softmax else (out, softmax_lse, None)
@staticmethod
def backward(ctx, dout, *args):
q, k, v, out, softmax_lse, cu_seqlens = ctx.saved_tensors
dq, dk, dv = ring_flash_attn_varlen_backward(
ctx.group,
dout,
q,
k,
v,
out,
softmax_lse,
cu_seqlens,
ctx.max_seqlen,
softmax_scale=ctx.softmax_scale,
dropout_p=ctx.dropout_p,
causal=ctx.causal,
window_size=ctx.window_size,
alibi_slopes=ctx.alibi_slopes,
deterministic=ctx.deterministic,
)
return dq, dk, dv, None, None, None, None, None, None, None, None, None, None
def ring_flash_attn_varlen_qkvpacked_func(
qkv,
cu_seqlens,
max_seqlen,
dropout_p=0.0,
softmax_scale=None,
causal=False,
window_size=(-1, -1), # -1 means infinite context window
alibi_slopes=None,
deterministic=False,
return_attn_probs=False,
group=None,
):
return RingFlashAttnVarlenFunc.apply(
qkv[:, 0],
qkv[:, 1],
qkv[:, 2],
cu_seqlens,
max_seqlen,
dropout_p,
softmax_scale,
causal,
window_size,
alibi_slopes,
deterministic,
return_attn_probs,
group,
)
def ring_flash_attn_varlen_kvpacked_func(
q,
kv,
cu_seqlens,
max_seqlen,
dropout_p=0.0,
softmax_scale=None,
causal=False,
window_size=(-1, -1), # -1 means infinite context window
alibi_slopes=None,
deterministic=False,
return_attn_probs=False,
group=None,
):
return RingFlashAttnVarlenFunc.apply(
q,
kv[:, 0],
kv[:, 1],
cu_seqlens,
max_seqlen,
dropout_p,
softmax_scale,
causal,
window_size,
alibi_slopes,
deterministic,
return_attn_probs,
group,
)
def ring_flash_attn_varlen_func(
q,
k,
v,
cu_seqlens,
max_seqlen,
dropout_p=0.0,
softmax_scale=None,
causal=False,
window_size=(-1, -1), # -1 means infinite context window
alibi_slopes=None,
deterministic=False,
return_attn_probs=False,
group=None,
):
return RingFlashAttnVarlenFunc.apply(
q,
k,
v,
cu_seqlens,
max_seqlen,
dropout_p,
softmax_scale,
causal,
window_size,
alibi_slopes,
deterministic,
return_attn_probs,
group,
)
|