File size: 16,043 Bytes
74da989 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 | import torch
import torch.distributed as dist
import math
import time
class ModulePlugin:
def __init__(self, module, module_id, global_state=None):
self.module = module
self.module_id = module_id
self.global_state = global_state
self.enable = True
self.implement_forward()
@property
def is_log_node(self):
return (
self.global_state.get("dist_controller").rank == 0
and self.module_id[1] == 0
)
@property
def t(self):
return self.global_state.get("timestep")
@property
def p(self):
return self.t / 1000
def implement_forward(self):
module = self.module
if not hasattr(module, "old_forward"):
module.old_forward = module.forward
self.new_forward = self.get_new_forward()
def forward(*args, **kwargs):
self.update_config() # update config
return (
self.new_forward(*args, **kwargs)
if self.enable
else self.old_forward(*args, **kwargs)
)
module.forward = forward
def set_enable(self, enable=True):
self.enable = enable
def get_new_forward(self):
raise NotImplementedError
def update_config(self, config: dict = None):
if config is None:
config = self.global_state.get("plugin_configs", {}).get(
self.module_id[0], {}
)
for key, value in config.items():
setattr(self, key, value)
def _calculate_padding(self, kernel_size, chunk_dim):
"""Calculate padding parameters for convolution layers.
Args:
kernel_size: kernel size tuple or int
chunk_dim: dimension to chunk/pad along
Returns:
tuple: (padding_left, padding_right, padding_flag)
"""
if isinstance(kernel_size, int):
kernel_size = tuple([kernel_size] * (chunk_dim - 1))
# For 3D conv, use width dimension (index 2)
# For 2D conv, calculate based on chunk_dim
if len(kernel_size) == 3:
kernel_width = kernel_size[2]
else:
pad_kernel_dim = chunk_dim - 3
kernel_width = kernel_size[pad_kernel_dim]
d = kernel_width - 1
padding_left = d // 2
padding_right = d - padding_left
padding_flag = padding_left if d > 0 else 0
return padding_left, padding_right, padding_flag
def _pad_context_bilateral_impl(
self, h, padding_left, padding_right, slice_dim, cat_dim
):
"""Core implementation for bilateral padding with context from adjacent ranks.
Args:
h: input tensor to pad
padding_left: number of elements to pad on left
padding_right: number of elements to pad on right
slice_dim: dimension to slice from (3 for 3D, 3 for 2D height/width)
cat_dim: dimension to concatenate along
Returns:
padded tensor with context from adjacent ranks
"""
if padding_left == 0:
return h
# Extract slices to share with neighbors
if slice_dim == 3:
share_to_left = h[:, :, :, :padding_left].contiguous()
share_to_right = h[:, :, :, -padding_right:].contiguous()
elif slice_dim == 4:
share_to_left = h[:, :, :, :, :padding_left].contiguous()
share_to_right = h[:, :, :, :, -padding_right:].contiguous()
elif slice_dim == 2:
share_to_left = h[:, :, :padding_left].contiguous()
share_to_right = h[:, :, -padding_right:].contiguous()
else: # slice_dim == 1
share_to_left = h[:, :, :, :padding_left].contiguous()
share_to_right = h[:, :, :, -padding_right:].contiguous()
rank = dist.get_rank()
world_size = dist.get_world_size()
adj_groups = self.global_state.get("dist_controller").adj_groups
if rank % 2:
# Odd rank: pad left first, then right
if rank:
padding_list = [torch.zeros_like(share_to_left) for _ in range(2)]
dist.all_gather(padding_list, share_to_left, group=adj_groups[rank - 1])
left_context = padding_list[0].to(h.device, non_blocking=True)
else:
left_context = torch.zeros_like(share_to_left).to(
h.device, non_blocking=True
)
if rank != world_size - 1:
padding_list = [torch.zeros_like(share_to_right) for _ in range(2)]
dist.all_gather(padding_list, share_to_right, group=adj_groups[rank])
right_context = padding_list[1].to(h.device, non_blocking=True)
else:
right_context = torch.zeros_like(share_to_right).to(
h.device, non_blocking=True
)
else:
# Even rank: pad right first, then left
if rank != world_size - 1:
padding_list = [torch.zeros_like(share_to_right) for _ in range(2)]
dist.all_gather(padding_list, share_to_right, group=adj_groups[rank])
right_context = padding_list[1].to(h.device, non_blocking=True)
else:
right_context = torch.zeros_like(share_to_right).to(
h.device, non_blocking=True
)
if rank:
padding_list = [torch.zeros_like(share_to_left) for _ in range(2)]
dist.all_gather(padding_list, share_to_left, group=adj_groups[rank - 1])
left_context = padding_list[0].to(h.device, non_blocking=True)
else:
left_context = torch.zeros_like(share_to_left).to(
h.device, non_blocking=True
)
h_with_context = torch.cat([left_context, h, right_context], dim=cat_dim)
return h_with_context
def _pad_context_bilateral(self, h, padding_left, padding_right, chunk_dim):
"""Pad tensor with context from adjacent ranks using bilateral communication.
Args:
h: input tensor to pad
padding_left: number of elements to pad on left
padding_right: number of elements to pad on right
chunk_dim: dimension to pad along
Returns:
padded tensor with context from adjacent ranks
"""
slice_dim = 3 if chunk_dim == 3 else 4
return self._pad_context_bilateral_impl(
h, padding_left, padding_right, slice_dim, chunk_dim
)
def _pad_context_bilateral_2d(self, h, padding_left, padding_right, chunk_dim):
"""Pad 2D tensor with context from adjacent ranks using bilateral communication.
Similar to _pad_context_bilateral but pads on chunk_dim-1 dimension for 2D convolutions.
Args:
h: input tensor to pad
padding_left: number of elements to pad on left
padding_right: number of elements to pad on right
chunk_dim: base dimension reference (actual padding on chunk_dim-1)
Returns:
padded tensor with context from adjacent ranks
"""
slice_dim = 2 if chunk_dim == 3 else 1
return self._pad_context_bilateral_impl(
h, padding_left, padding_right, slice_dim, chunk_dim - 1
)
class GroupNormPlugin(ModulePlugin):
def __init__(self, module, module_id, global_state=None):
super().__init__(module, module_id, global_state)
def get_new_forward(self):
module = self.module
def new_forward(x):
shape = x.shape
N, C, G = shape[0], shape[1], module.num_groups
assert C % G == 0
x = x.reshape(N, G, -1)
mean = x.mean(-1, keepdim=True).to(torch.float32)
dist.all_reduce(mean)
mean = mean / dist.get_world_size()
var = ((x - mean.to(x.dtype)) ** 2).mean(-1, keepdim=True).to(torch.float32)
dist.all_reduce(var)
var = var / dist.get_world_size()
x = (x - mean.to(x.dtype)) / (var.to(x.dtype) + module.eps).sqrt()
x = x.view(shape)
new_shape = [1 for _ in shape]
new_shape[1] = -1
return x * module.weight.view(new_shape) + module.bias.view(new_shape)
return new_forward
class Conv3DSafeNewPligin(ModulePlugin):
def __init__(self, module, module_id, global_state=None):
super().__init__(module, module_id, global_state)
self.kernel_size = getattr(module, "kernel_size", (1, 1, 1))
if isinstance(self.kernel_size, int):
self.kernel_size = (self.kernel_size, self.kernel_size, self.kernel_size)
self.chunk_dim = global_state.get("chunk_dim")
self.padding_left, self.padding_right, self.padding_flag = (
self._calculate_padding(self.kernel_size, self.chunk_dim)
)
def pad_context(self, h):
return self._pad_context_bilateral(
h, self.padding_left, self.padding_right, self.chunk_dim
)
def get_new_forward(self):
module = self.module
def new_forward(hidden_states, cache_x=None, *args, **kwargs):
if self.padding_flag == 0:
# print(f"padding=0, return old_forward")
return module.old_forward(hidden_states, cache_x, *args, **kwargs)
hidden_states = self.pad_context(hidden_states)
if cache_x is not None:
cache_x = self.pad_context(cache_x)
result = module.old_forward(hidden_states, cache_x, *args, **kwargs)
if self.chunk_dim == 3:
result = result[
:,
:,
:,
self.padding_left : (
-self.padding_right if self.padding_right > 0 else None
),
]
else:
result = result[
:,
:,
:,
:,
self.padding_left : (
-self.padding_right if self.padding_right > 0 else None
),
]
return result
return new_forward
class Conv2DSafeNewPligin(ModulePlugin):
def __init__(self, module, module_id, global_state=None):
super().__init__(module, module_id, global_state)
self.kernel_size = getattr(module, "kernel_size", (1, 1))
self.stride = getattr(module, "stride", (1, 1))
if isinstance(self.kernel_size, int):
self.kernel_size = (self.kernel_size, self.kernel_size)
self.chunk_dim = global_state.get("chunk_dim")
pad_kernel_dim = self.chunk_dim - 3
kernel_height = self.kernel_size[pad_kernel_dim]
d = kernel_height - 1
self.padding_left = d // 2
self.padding_right = d - self.padding_left
self.padding = self.padding_left if d > 0 else 0
def pad_context(self, h):
return self._pad_context_bilateral_2d(
h, self.padding_left, self.padding_right, self.chunk_dim
)
def get_new_forward(self):
module = self.module
def new_forward(hidden_states: torch.Tensor) -> torch.Tensor:
if self.padding == 0:
return module.old_forward(hidden_states)
hidden_states = self.pad_context(hidden_states)
if self.chunk_dim == 3:
hidden_states = module.old_forward(hidden_states)[
:,
:,
self.padding_left : (
-self.padding_right if self.padding_right > 0 else None
),
]
else:
hidden_states = module.old_forward(hidden_states)[
:,
:,
:,
self.padding_left : (
-self.padding_right if self.padding_right > 0 else None
),
]
return hidden_states
return new_forward
class Conv2DSafeNewPliginStride2(ModulePlugin):
def __init__(self, module, module_id, global_state=None):
super().__init__(module, module_id, global_state)
self.kernel_size = getattr(module, "kernel_size", (1, 1))
self.stride = getattr(module, "stride", (1, 1))
if isinstance(self.kernel_size, int):
self.kernel_size = (self.kernel_size, self.kernel_size)
self.chunk_dim = global_state.get("chunk_dim")
assert self.kernel_size[0] == self.kernel_size[1]
kernel_height = self.kernel_size[0]
d = kernel_height - 1
self.padding_left = d // 2
self.padding_right = d - self.padding_left
self.padding = self.padding_left if d > 0 else 0
def pad_context(self, h):
if self.padding == 0:
return h
rank = dist.get_rank()
world_size = dist.get_world_size()
if self.chunk_dim == 3:
share_to_left = h[:, :, : self.padding_left].contiguous()
else:
share_to_left = h[:, :, :, : self.padding_left].contiguous()
if rank < world_size - 1:
right_context = torch.zeros_like(share_to_left)
dist.recv(right_context, src=rank + 1)
if rank > 0:
dist.send(share_to_left, dst=rank - 1)
# torch.cuda.synchronize()
if rank < world_size - 1:
h_with_context = torch.cat([h, right_context], dim=self.chunk_dim - 1)
else:
h_with_context = h
return h_with_context
def get_new_forward(self):
module = self.module
def new_forward(hidden_states: torch.Tensor) -> torch.Tensor:
# assert self.global_state["chunk_dim"] == 3 # only support chunk height now
if self.padding == 0:
return module.old_forward(hidden_states)
if self.chunk_dim == 3:
hidden_states = hidden_states[:, :, :-1, :]
else:
hidden_states = hidden_states[:, :, :, :-1]
hidden_states = self.pad_context(hidden_states)
if self.chunk_dim == 3:
hidden_states = torch.nn.functional.pad(hidden_states, (0, 0, 0, 1))
else:
hidden_states = torch.nn.functional.pad(hidden_states, (0, 1))
hidden_states = module.old_forward(
hidden_states
) # [:,:,self.padding_left:-self.padding_right if self.padding_right > 0 else None]
return hidden_states
return new_forward
class WanAttentionPlugin(ModulePlugin):
def __init__(self, module, module_id, global_state=None):
self.rank = dist.get_rank()
self.world_size = dist.get_world_size()
self.gather_dim = global_state.get("chunk_dim")
super().__init__(module, module_id, global_state)
def get_new_forward(self):
module = self.module
rank = self.rank
world_size = self.world_size
def new_forward(hidden_states: torch.Tensor) -> torch.Tensor:
gathered_tensors = [
torch.zeros_like(hidden_states) for _ in range(world_size)
]
dist.all_gather(gathered_tensors, hidden_states)
combined_tensor = torch.cat(gathered_tensors, dim=self.gather_dim)
forward_output = module.old_forward(combined_tensor)
# chunk_sizes = [t.size(self.gather_dim) for t in gathered_tensors]
# start_idx = sum(chunk_sizes[:rank])
# end_idx = start_idx + chunk_sizes[rank]
# local_output = forward_output[:, :, :, start_idx:end_idx].contiguous()
local_output = forward_output.chunk(chunks=world_size, dim=self.gather_dim)[
rank
]
return local_output
return new_forward
|