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2ea2835 | 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 | """Five-segment VideoMAE-v2 encoder for an 81-frame Wan video.
Frame zero is intentionally excluded. The remaining 80 frames are split into
five fixed, adjacent 16-frame segments. Production training may reuse one
frozen checkpoint instance for every segment; the legacy five-replica layout
remains available for compatibility. Selected dense tokens are concatenated
in chronological segment order.
"""
from __future__ import annotations
from typing import Callable, Dict, Optional, Sequence
import torch
import torch.nn as nn
from video_fd.encoders.videomae_v2_adapter import VideoMAEv2Adapter
class SegmentedVideoMAEv2Ensemble(nn.Module):
"""Five logical same-checkpoint segments with a V-JEPA-compatible API."""
def __init__(
self,
config,
*,
adapter_factory: Optional[Callable[[dict], nn.Module]] = None,
) -> None:
super().__init__()
self.config = dict(config)
self.encoder_count = int(self.config.get("encoder_count", 5))
self.share_encoder_weights = bool(
self.config.get("share_encoder_weights", False)
)
self.skip_leading_frames = int(
self.config.get("skip_leading_frames", 1))
self.segment_num_frames = int(
self.config.get("segment_num_frames", 16))
self.source_num_frames = int(
self.config.get("source_num_frames", 81))
if self.encoder_count != 5:
raise ValueError(
"the VideoMAE-v2 whole-video contract requires exactly five "
f"encoders, got {self.encoder_count}")
if self.skip_leading_frames != 1:
raise ValueError(
"the VideoMAE-v2 whole-video contract skips exactly one "
f"leading frame, got {self.skip_leading_frames}")
if self.segment_num_frames != 16:
raise ValueError(
"each VideoMAE-v2 segment must contain 16 frames, got "
f"{self.segment_num_frames}")
expected_source = (
self.skip_leading_frames
+ self.encoder_count * self.segment_num_frames
)
if self.source_num_frames != expected_source:
raise ValueError(
"source_num_frames must exactly cover the skipped frame and "
f"five segments: {self.source_num_frames} != {expected_source}")
factory = adapter_factory or VideoMAEv2Adapter
adapter_config = dict(self.config)
adapter_config.update({
"input_frames": self.segment_num_frames,
"dense_num_frames": self.segment_num_frames,
})
physical_encoder_count = (
1 if self.share_encoder_weights else self.encoder_count
)
self.encoders = nn.ModuleList([
factory(dict(adapter_config))
for _ in range(physical_encoder_count)
])
self._validate_replicas()
def _validate_replicas(self) -> None:
expected_physical = 1 if self.share_encoder_weights else self.encoder_count
if len(self.encoders) != expected_physical:
raise RuntimeError("VideoMAE-v2 replica construction was incomplete")
if len({id(encoder) for encoder in self.encoders}) != expected_physical:
raise RuntimeError(
"VideoMAE-v2 physical encoder instances are not distinct")
first = self.encoders[0]
geometry = (
int(first.dense_layer_index),
int(first.dense_num_frames),
int(first.resize_short_side),
int(first.input_size),
int(first.retained_dense_blocks),
int(first.dense_raw_tokens),
int(first.dense_feature_dim),
)
for index, encoder in enumerate(self.encoders[1:], start=1):
candidate = (
int(encoder.dense_layer_index),
int(encoder.dense_num_frames),
int(encoder.resize_short_side),
int(encoder.input_size),
int(encoder.retained_dense_blocks),
int(encoder.dense_raw_tokens),
int(encoder.dense_feature_dim),
)
if candidate != geometry:
raise RuntimeError(
"VideoMAE-v2 replica geometry differs at index "
f"{index}: {candidate} != {geometry}")
storage_sets = []
for encoder in self.encoders:
storage_sets.append({
parameter.untyped_storage().data_ptr()
for parameter in encoder.parameters()
if parameter.numel() > 0
})
for left in range(len(storage_sets)):
for right in range(left + 1, len(storage_sets)):
if storage_sets[left].intersection(storage_sets[right]):
raise RuntimeError(
"VideoMAE-v2 replicas unexpectedly share parameter "
f"storage between segments {left} and {right}")
def _encoder_for_segment(self, index: int) -> nn.Module:
if index < 0 or index >= self.encoder_count:
raise IndexError(f"segment index outside [0,{self.encoder_count})")
return self.encoders[0 if self.share_encoder_weights else index]
@property
def dense_layer_index(self) -> int:
return int(self.encoders[0].dense_layer_index)
@property
def dense_num_frames(self) -> int:
return self.segment_num_frames
@property
def resize_short_side(self) -> int:
return int(self.encoders[0].resize_short_side)
@property
def input_size(self) -> int:
return int(self.encoders[0].input_size)
@property
def retained_dense_blocks(self) -> int:
return int(self.encoders[0].retained_dense_blocks)
@property
def dense_raw_tokens(self) -> int:
"""Raw tokens per 16-frame segment, not the five-segment total."""
return int(self.encoders[0].dense_raw_tokens)
@property
def aggregate_dense_raw_tokens(self) -> int:
return self.encoder_count * self.dense_raw_tokens
@property
def dense_feature_dim(self) -> int:
return int(self.encoders[0].dense_feature_dim)
def _validate_video(self, video_clip_01: torch.Tensor) -> None:
if video_clip_01.ndim != 5 or int(video_clip_01.shape[2]) != 3:
raise ValueError(
"video_clip_01 must have shape [B,81,3,H,W], got "
f"{tuple(video_clip_01.shape)}")
if int(video_clip_01.shape[1]) != self.source_num_frames:
raise ValueError(
"segmented VideoMAE-v2 requires exactly "
f"{self.source_num_frames} decoded frames, got "
f"{int(video_clip_01.shape[1])}")
def segment(self, video_clip_01: torch.Tensor, index: int) -> torch.Tensor:
self._validate_video(video_clip_01)
index = int(index)
if index < 0 or index >= self.encoder_count:
raise IndexError(f"segment index outside [0,{self.encoder_count})")
start = self.skip_leading_frames + index * self.segment_num_frames
return video_clip_01.narrow(1, start, self.segment_num_frames)
def forward_dense(
self,
video_clip_01: torch.Tensor,
layer_index: Optional[int] = None,
token_indices: Optional[torch.Tensor] = None,
gradient_checkpointing: bool = False,
temporal_window_starts: Optional[torch.Tensor] = None,
temporal_window_indices: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if temporal_window_starts is not None or temporal_window_indices is not None:
raise ValueError(
"fixed five-segment VideoMAE-v2 does not accept temporal windows")
outputs = [
self._encoder_for_segment(index).forward_dense(
self.segment(video_clip_01, index),
layer_index=layer_index,
token_indices=token_indices,
gradient_checkpointing=gradient_checkpointing,
)
for index in range(self.encoder_count)
]
return torch.cat(outputs, dim=1)
def forward_dense_layers(
self,
video_clip_01: torch.Tensor,
layer_indices: Sequence[int],
token_indices: Optional[torch.Tensor] = None,
gradient_checkpointing: bool = False,
temporal_window_starts: Optional[torch.Tensor] = None,
temporal_window_indices: Optional[torch.Tensor] = None,
) -> Dict[int, torch.Tensor]:
if temporal_window_starts is not None or temporal_window_indices is not None:
raise ValueError(
"fixed five-segment VideoMAE-v2 does not accept temporal windows")
layers = tuple(int(layer) for layer in layer_indices)
by_segment = [
self._encoder_for_segment(index).forward_dense_layers(
self.segment(video_clip_01, index),
layer_indices=layers,
token_indices=token_indices,
gradient_checkpointing=gradient_checkpointing,
)
for index in range(self.encoder_count)
]
return {
layer: torch.cat(
[segment_features[layer] for segment_features in by_segment],
dim=1,
)
for layer in layers
}
def replay_dense_vjp(
self,
video_clip_01: torch.Tensor,
*,
layer_indices: Sequence[int],
token_indices: torch.Tensor,
expected_features: torch.Tensor,
feature_cotangent: torch.Tensor,
gradient_checkpointing: bool,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Replay five encoder VJPs sequentially to cap activation memory.
Feature transport is layer-major and, within a layer, segment-major.
The returned pixel cotangent has a zero first frame by construction.
"""
self._validate_video(video_clip_01)
layers = tuple(int(layer) for layer in layer_indices)
if not layers:
raise ValueError("layer_indices must not be empty")
tokens_per_segment = int(token_indices.numel())
tokens_per_layer = self.encoder_count * tokens_per_segment
expected_shape = (
int(video_clip_01.shape[0]),
len(layers) * tokens_per_layer,
self.dense_feature_dim,
)
if tuple(expected_features.shape) != expected_shape:
raise ValueError(
"expected_features has the wrong segmented shape: "
f"{tuple(expected_features.shape)} != {expected_shape}")
if tuple(feature_cotangent.shape) != expected_shape:
raise ValueError(
"feature_cotangent has the wrong segmented shape: "
f"{tuple(feature_cotangent.shape)} != {expected_shape}")
expected_by_layer = expected_features.split(tokens_per_layer, dim=1)
cotangent_by_layer = feature_cotangent.split(tokens_per_layer, dim=1)
replay_by_layer = [[] for _ in layers]
video_cotangent = torch.zeros_like(video_clip_01, dtype=torch.float32)
for segment_index in range(self.encoder_count):
encoder = self._encoder_for_segment(segment_index)
segment_leaf = self.segment(
video_clip_01, segment_index).detach().requires_grad_(True)
if len(layers) == 1:
outputs = {
layers[0]: encoder.forward_dense(
segment_leaf,
layer_index=layers[0],
token_indices=token_indices,
gradient_checkpointing=gradient_checkpointing,
).float()
}
else:
outputs = encoder.forward_dense_layers(
segment_leaf,
layer_indices=layers,
token_indices=token_indices,
gradient_checkpointing=gradient_checkpointing,
)
outputs = {layer: value.float() for layer, value in outputs.items()}
surrogate = segment_leaf.new_zeros((), dtype=torch.float32)
for layer_offset, layer in enumerate(layers):
start = segment_index * tokens_per_segment
expected_part = expected_by_layer[layer_offset].narrow(
1, start, tokens_per_segment)
cotangent_part = cotangent_by_layer[layer_offset].narrow(
1, start, tokens_per_segment)
output = outputs[layer]
if tuple(output.shape) != tuple(expected_part.shape):
raise RuntimeError(
"VideoMAE-v2 segment replay shape changed for "
f"segment={segment_index}, layer={layer}")
replay_by_layer[layer_offset].append(output.detach())
surrogate = surrogate + (output * cotangent_part).sum()
(segment_cotangent,) = torch.autograd.grad(
surrogate,
segment_leaf,
retain_graph=False,
create_graph=False,
)
start_frame = (
self.skip_leading_frames
+ segment_index * self.segment_num_frames
)
video_cotangent[:, start_frame:start_frame + self.segment_num_frames] = (
segment_cotangent.detach().float())
del surrogate, segment_leaf, segment_cotangent, outputs
replay_features = torch.cat([
torch.cat(parts, dim=1)
for parts in replay_by_layer
], dim=1)
return replay_features, video_cotangent
__all__ = ["SegmentedVideoMAEv2Ensemble"]
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