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c0bba28 | 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 | import torch
import torch.nn as nn
import torch.distributed as dist
from torch import Tensor
from torch.nn import functional as F
from torch.nn.modules.utils import _pair
from torch.nn.common_types import _size_2_t
from typing import Optional, List, Tuple, Union
from distvae.utils import DistributedEnv
class PatchConv2d(nn.Conv2d):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: _size_2_t,
stride: _size_2_t = 1,
padding: Union[str, _size_2_t] = 0,
dilation: _size_2_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros', # TODO: refine this type
device=None,
dtype=None,
block_size: Union[int, Tuple[int, int]] = 0
) -> None:
if isinstance(dilation, int):
assert dilation == 1, "dilation is not supported in PatchConv2d"
else:
for i in dilation:
assert i == 1, "dilation is not supported in PatchConv2d"
self.block_size = block_size
super().__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
groups, bias, padding_mode, device, dtype)
def _get_world_size_and_rank(self):
group_world_size = DistributedEnv.get_group_world_size()
global_rank = DistributedEnv.get_global_rank()
rank_in_group = DistributedEnv.get_rank_in_vae_group()
local_rank = DistributedEnv.get_local_rank()
return group_world_size, global_rank, rank_in_group, local_rank
def _calc_patch_height_index(self, patch_height_list: List[Tensor]):
height_index = []
cur = 0
for t in patch_height_list:
height_index.append(cur)
cur += t.item()
height_index.append(cur)
return height_index
def _calc_bottom_halo_width(self, rank, height_index, kernel_size, padding = 0, stride = 1):
assert rank >= 0, "rank should not be smaller than 0"
assert rank < len(height_index) - 1, "rank should be smaller than the length of height_index - 1"
assert padding >= 0, "padding should not smaller than 0"
assert stride > 0, "stride should be larger than 0"
if rank == DistributedEnv.get_group_world_size() - 1:
return 0
nstep_before_bottom = (height_index[rank + 1] + padding - (kernel_size - 1) // 2 + stride - 1) // stride
assert nstep_before_bottom > 0, "nstep_before_bottom should be larger than 0"
bottom_halo_width = (nstep_before_bottom - 1) * stride + kernel_size - padding - height_index[rank + 1]
return max(0, bottom_halo_width)
def _calc_top_halo_width(self, rank, height_index, kernel_size, padding = 0, stride = 1):
assert rank >= 0, "rank should not be smaller than 0"
assert rank < len(height_index) - 1, "rank should be smaller than the length of height_index - 1"
assert padding >= 0, "padding should not smaller than 0"
assert stride > 0, "stride should be larger than 0"
if rank == 0:
return 0
nstep_before_top = (height_index[rank] + padding - (kernel_size - 1) // 2 + stride - 1) // stride
top_halo_width = height_index[rank] - (nstep_before_top * stride - padding)
return top_halo_width
def _calc_halo_width_in_h_dim(self, rank, height_index, kernel_size, padding = 0, stride = 1):
'''
Calculate the width of halo region in height dimension.
The halo region is the region that is used for convolution but not included in the output.
return value: (top_halo_width, bottom_halo_width)
'''
halo_width = [
self._calc_top_halo_width(rank, height_index, kernel_size, padding, stride),
self._calc_bottom_halo_width(rank, height_index, kernel_size, padding, stride)
]
if rank == 0:
halo_width[0] = 0
elif rank == DistributedEnv.get_group_world_size() - 1:
halo_width[1] = 0
return tuple(halo_width)
# in 2d case, padding is a tuple of 4 integers: [left_pad, right_pad, top_pad, bottom_pad]
def _adjust_padding_for_patch(self, padding, rank, world_size):
if isinstance(padding, tuple):
padding = list(padding)
elif isinstance(padding, int):
padding = [padding] * 4
if rank == 0:
padding[-1] = 0
elif rank == world_size - 1:
padding[-2] = 0
else:
padding[-2:] = [0, 0]
return tuple(padding)
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
bs, channels, h, w = input.shape
group_world_size, global_rank, rank_in_group, local_rank = self._get_world_size_and_rank()
if (group_world_size == 1):
if self.padding_mode != 'zeros':
return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
weight, bias, self.stride,
_pair(0), self.dilation, self.groups)
return F.conv2d(input, weight, bias, self.stride,
self.padding, self.dilation, self.groups)
else:
# 1. get the meta data of input tensor and conv operation
patch_height_list = [torch.zeros(1, dtype=torch.int64, device=f"cuda:{local_rank}") for _ in range(group_world_size)]
dist.all_gather(patch_height_list, torch.tensor([h], dtype=torch.int64, device=f"cuda:{local_rank}"), group=DistributedEnv.get_vae_group())
patch_height_index = self._calc_patch_height_index(patch_height_list)
halo_width = self._calc_halo_width_in_h_dim(rank_in_group, patch_height_index, self.kernel_size[0], self.padding[0], self.stride[0])
prev_bottom_halo_width: int = 0
next_top_halo_width: int = 0
if rank_in_group != 0:
prev_bottom_halo_width = self._calc_bottom_halo_width(rank_in_group - 1, patch_height_index, self.kernel_size[0], self.padding[0], self.stride[0])
if rank_in_group != group_world_size - 1:
next_top_halo_width = self._calc_top_halo_width(rank_in_group + 1, patch_height_index, self.kernel_size[0], self.padding[0], self.stride[0])
next_top_halo_width = max(0, next_top_halo_width)
assert halo_width[0] <= h and halo_width[1] <= h, "halo width is larger than the height of input tensor"
# 2. get the halo region from other ranks
# up to down
to_next = None
to_prev = None
top_halo_recv = None
bottom_halo_recv = None
global_rank_of_next, global_rank_of_prev = None, None
if next_top_halo_width > 0:
# isend to next
bottom_halo_send = input[:, :, -next_top_halo_width:, :].contiguous()
global_rank_of_next = DistributedEnv.get_global_rank_from_group_rank(rank_in_group + 1)
to_next = dist.isend(bottom_halo_send, global_rank_of_next, group=DistributedEnv.get_vae_group())
if halo_width[0] > 0:
# recv from prev
assert patch_height_index[rank_in_group] - halo_width[0] >= patch_height_index[rank_in_group-1], \
"width of top halo region is larger than the height of input tensor of last rank"
top_halo_recv = torch.empty([bs, channels, halo_width[0], w], dtype=input.dtype, device=f"cuda:{local_rank}")
global_rank_of_prev = DistributedEnv.get_global_rank_from_group_rank(rank_in_group - 1)
dist.recv(top_halo_recv, global_rank_of_prev, group=DistributedEnv.get_vae_group())
# down to up
if prev_bottom_halo_width > 0:
# isend to prev
top_halo_send = input[:, :, :prev_bottom_halo_width, :].contiguous()
if global_rank_of_prev is None:
global_rank_of_prev = DistributedEnv.get_global_rank_from_group_rank(rank_in_group - 1)
to_prev = dist.isend(top_halo_send, global_rank_of_prev, group=DistributedEnv.get_vae_group())
if halo_width[1] > 0:
# recv from next
assert patch_height_index[rank_in_group+1] + halo_width[1] < patch_height_index[rank_in_group+2], \
"width of bottom halo region is larger than the height of input tensor of next rank"
bottom_halo_recv = torch.empty([bs, channels, halo_width[1], w], dtype=input.dtype, device=f"cuda:{local_rank}")
if global_rank_of_next is None:
global_rank_of_next = DistributedEnv.get_global_rank_from_group_rank(rank_in_group + 1)
dist.recv(bottom_halo_recv, global_rank_of_next, group=DistributedEnv.get_vae_group())
# Remove redundancy at the top of the input
if halo_width[0] < 0:
input = input[:, :, -halo_width[0]:, :]
# concat the halo region to the input tensor
if top_halo_recv is not None:
input = torch.cat([top_halo_recv, input], dim=-2)
if bottom_halo_recv is not None:
input = torch.cat([input, bottom_halo_recv], dim=-2)
# wait for the communication to finish
if to_next is not None:
to_next.wait()
if to_prev is not None:
to_prev.wait()
# 3. do convolution and postprocess
conv_res: Tensor
padding = self._adjust_padding_for_patch(self._reversed_padding_repeated_twice, rank=rank_in_group, world_size=group_world_size)
bs, channels, h, w = input.shape
if self.block_size == 0 or (h <= self.block_size and w <= self.block_size):
if self.padding_mode != 'zeros':
conv_res = F.conv2d(F.pad(input, padding, mode=self.padding_mode),
weight, bias, self.stride,
_pair(0), self.dilation, self.groups)
else:
if self.stride[0] == 1 and self.padding[0] == 1 and self.kernel_size[0] == 3:
conv_res = F.conv2d(input, weight, bias, self.stride,
self.padding, self.dilation, self.groups)
if halo_width[1] == 0:
conv_res = conv_res[:, :, halo_width[0]:, :].contiguous()
else:
conv_res = conv_res[:, :, halo_width[0]:-halo_width[1], :]
# print(rank, conv_res.shape, flush=True)
else:
conv_res = F.conv2d(F.pad(input, padding, "constant", 0.0),
weight, bias, self.stride,
_pair(0), self.dilation, self.groups)
return conv_res
# 3.1. if block_size is not 0, split patch to block and do convolution to
# reduce memory spike
else:
if self.padding_mode != "zeros":
input = F.pad(input, padding, mode=self.padding_mode)
elif self.padding != 0:
input = F.pad(input, padding, mode="constant")
_, _, h, w = input.shape
num_chunks_in_h = 0
num_chunks_in_w = 0
if isinstance(self.block_size, int):
num_chunks_in_h = (h + self.block_size - 1) // self.block_size
num_chunks_in_w = (w + self.block_size - 1) // self.block_size
elif isinstance(self.block_size, tuple):
num_chunks_in_h = (h + self.block_size[0] - 1) // self.block_size[0]
num_chunks_in_w = (w + self.block_size[1] - 1) // self.block_size[1]
unit_chunk_size_h = h // num_chunks_in_h
unit_chunk_size_w = w // num_chunks_in_w
if isinstance(self.kernel_size, int):
kernel_size_h, kernel_size_w = self.kernel_size, self.kernel_size
elif isinstance(self.kernel_size, tuple):
kernel_size_h, kernel_size_w = self.kernel_size
else:
raise ValueError(
f"kernel_size should be int or tuple, type:{type(self.kernel_size)}"
)
if isinstance(self.stride, int):
stride_h, stride_w = self.stride, self.stride
elif isinstance(self.stride, tuple):
stride_h, stride_w = self.stride
else:
raise ValueError(
f"stride should be int or tuple, type: {type(self.stride)}"
)
def correct_end(end, kernel_size, stride):
return ((end + stride - 1) // stride - 1) * stride + kernel_size
def correct_start(start, stride):
return ((start + stride - 1) // stride) * stride
outputs = []
for idx_h in range(num_chunks_in_h):
inner_output = []
for idx_w in range(num_chunks_in_w):
start_w = idx_w * unit_chunk_size_w
start_h = idx_h * unit_chunk_size_h
end_w = (idx_w + 1) * unit_chunk_size_w
end_h = (idx_h + 1) * unit_chunk_size_h
if idx_w + 1 < num_chunks_in_w:
end_w = correct_end(end_w, kernel_size_w, stride_w)
else:
end_w = w
if idx_h + 1 < num_chunks_in_h:
end_h = correct_end(end_h, kernel_size_h, stride_h)
else:
end_h = h
if idx_w > 0:
start_w = correct_start(start_w, stride_w)
if idx_h > 0:
start_h = correct_start(start_h, stride_h)
inner_output.append(
F.conv2d(
input[:, :, start_h:end_h, start_w:end_w],
weight,
bias,
self.stride,
0,
self.dilation,
self.groups,
)
)
outputs.append(torch.cat(inner_output, dim=-1))
return torch.cat(outputs, dim=-2) |