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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 | """Five-replica 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. Each segment is processed by its own
frozen VideoMAE-v2 module instance loaded from the same checkpoint. 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 physical same-checkpoint encoders 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.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))
expected_source = (
self.skip_leading_frames
+ self.encoder_count * self.segment_num_frames
)
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,
})
# These are deliberately five independent Module objects with distinct
# parameter storages. The checkpoint identity is nevertheless shared.
self.encoders = nn.ModuleList([
factory(dict(adapter_config))
for _ in range(self.encoder_count)
])
self._validate_replicas()
def _validate_replicas(self) -> None:
return None
@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:
return None
def segment(self, video_clip_01: torch.Tensor, index: int) -> torch.Tensor:
self._validate_video(video_clip_01)
index = int(index)
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:
outputs = [
encoder.forward_dense(
self.segment(video_clip_01, index),
layer_index=layer_index,
token_indices=token_indices,
gradient_checkpointing=gradient_checkpointing,
)
for index, encoder in enumerate(self.encoders)
]
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]:
layers = tuple(int(layer) for layer in layer_indices)
by_segment = [
encoder.forward_dense_layers(
self.segment(video_clip_01, index),
layer_indices=layers,
token_indices=token_indices,
gradient_checkpointing=gradient_checkpointing,
)
for index, encoder in enumerate(self.encoders)
]
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)
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,
)
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, encoder in enumerate(self.encoders):
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]
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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