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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 | """Combine distribution-matching losses across frozen video encoders.
Each encoder defines its own video feature space and Gaussian kernel. Compute
one MMD per encoder, apply the release's detached per-encoder loss scaling,
then apply the configured weights and aggregation divisor. ``VideoNystromMMD``
implements generated repulsion and persistent/minibatch reference attraction.
"""
from __future__ import annotations
import math
from collections import OrderedDict
from typing import Any, Mapping, Optional, Sequence
import torch
from torch import nn
from losses.video_nystrom_mmd import VideoNystromMMD
def normalize_mmd_with_detached_scale(
raw_mmd: torch.Tensor,
normalization_eps: float = 1.0e-4,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Return M / (stop_gradient(M) + eps) and its detached scale.
Detaching only the denominator preserves gradients through M while
balancing the different numerical scales of the encoder objectives.
This release-specific scaling is additional to the paper's MMD objective;
the expression deliberately has no absolute value or clamping.
"""
denominator = raw_mmd.detach() + float(normalization_eps)
return raw_mmd / denominator, denominator
class MultiEncoderMMD(nn.Module):
"""Weighted distribution matching in multiple frozen feature spaces.
A branch is one encoder, its kernel bandwidth, and its persistent Nyström
reference. For raw branch losses M_e, the combined objective is
sum_e weight_e * M_e / (stop_gradient(M_e) + eps), divided by
``aggregation_divisor``. That divisor is explicit; it need not equal the
number of branches or the sum of weights.
"""
def __init__(
self,
branches: Mapping[str, VideoNystromMMD],
*,
weights: Optional[Mapping[str, float]] = None,
normalization_eps: float = 1.0e-4,
aggregation_divisor: float = 1.0,
) -> None:
super().__init__()
ordered = OrderedDict()
for name, objective in branches.items():
ordered[name] = objective
self.branches = nn.ModuleDict(ordered)
configured_weights = weights or {name: 1.0 for name in ordered}
self.branch_weights = {}
for name in ordered:
weight = float(configured_weights[name])
self.branch_weights[name] = weight
self.normalization_eps = float(normalization_eps)
self.aggregation_divisor = float(aggregation_divisor)
self.current_teacher_attraction_enabled = True
self.attraction_ema_enabled = False
self.repulsion_fifo_enabled = False
@property
def branch_names(self) -> tuple[str, ...]:
return tuple(self.branches.keys())
@property
def sample_shapes(self) -> dict[str, tuple[int, int]]:
return {
name: objective.sample_shape
for name, objective in self.branches.items()
}
@property
def landmarks(self) -> torch.Tensor:
"""Legacy diagnostics use the first branch; branch logs stay exact."""
return self.branches[self.branch_names[0]].landmarks
@property
def branch_registry(self) -> dict[str, dict[str, Any]]:
return {
name: {
"sample_shape": objective.sample_shape,
"reference_hash": objective.reference_hash,
"weight": self.branch_weights[name],
}
for name, objective in self.branches.items()
}
def _validate_mapping(
self,
value: Optional[Mapping[str, torch.Tensor]],
label: str,
*,
required: bool,
) -> Optional[Mapping[str, torch.Tensor]]:
if value is None:
return None
return value
def forward(
self,
generated: Optional[Mapping[str, torch.Tensor]] = None,
current_teacher: Optional[Mapping[str, torch.Tensor]] = None,
*,
gradient_students: Optional[Mapping[str, torch.Tensor]] = None,
student_reference: Optional[Mapping[str, torch.Tensor]] = None,
gradient_student_indices: Optional[
torch.Tensor | Sequence[int]
] = None,
stage: bool = True,
) -> tuple[torch.Tensor, dict[str, Any]]:
"""Evaluate each encoder loss, then balance and combine their scales.
``gradient_students`` selects generated samples that retain gradients;
``student_reference`` holds the full generated feature population.
``current_teacher`` is the current frozen reference minibatch.
These input names are retained for existing callers and checkpoints.
"""
# Select full-gradient or cached partial-gradient inputs. Every encoder
# receives the same sample selection.
partial = gradient_students is not None or student_reference is not None
generated = self._validate_mapping(
generated, "generated", required=not partial)
gradient_students = self._validate_mapping(
gradient_students, "gradient_students", required=partial)
student_reference = self._validate_mapping(
student_reference, "student_reference", required=partial)
current_teacher = self._validate_mapping(
current_teacher, "current_teacher", required=True)
# Each feature space has its own kernel objective; encoder features
# and persistent reference landmarks remain separate.
normalized_terms = []
branch_metadata: dict[str, dict[str, Any]] = {}
for name, objective in self.branches.items():
if partial:
raw, metadata = objective(
gradient_students=gradient_students[name],
student_reference=student_reference[name],
current_teacher=current_teacher[name],
gradient_student_indices=gradient_student_indices,
stage=stage,
)
else:
raw, metadata = objective(
generated[name],
current_teacher=current_teacher[name],
stage=stage,
)
# Balance the branch scale before applying its configured weight.
normalized, denominator = normalize_mmd_with_detached_scale(
raw, self.normalization_eps)
weighted = self.branch_weights[name] * normalized
normalized_terms.append(weighted)
branch_metadata[name] = {
**metadata,
"raw_mmd": raw.detach(),
"normalization_denominator": denominator.detach(),
"normalized_mmd": normalized.detach(),
"branch_weight": raw.new_tensor(self.branch_weights[name]),
"weighted_normalized_mmd": weighted.detach(),
"sample_shape": objective.sample_shape,
"reference_hash": objective.reference_hash,
}
# Preserve the explicit reduction order and configured divisor.
total = (
torch.stack(normalized_terms).sum() / self.aggregation_divisor
)
# Detached values below are diagnostics; ``total`` retains the graph.
first = branch_metadata[self.branch_names[0]]
metadata = {
"branches": branch_metadata,
"branch_names": self.branch_names,
"raw_mmd_sum": torch.stack([
branch_metadata[name]["raw_mmd"] for name in self.branch_names
]).sum(),
"normalized_mmd_sum": total.detach(),
"normalization_eps": total.new_tensor(self.normalization_eps),
"normalization": "raw_div_stop_gradient_raw_plus_eps",
"aggregation": "weighted_sum_then_divide",
"aggregation_divisor": total.new_tensor(
self.aggregation_divisor),
# Shared batch counts are used by feature-gradient replay;
# per-encoder diagnostics remain available under ``branches``.
"global_samples": first["global_samples"],
"gradient_student_samples": first.get(
"gradient_student_samples", first["global_samples"]),
"repulsion_denominator": first.get("repulsion_denominator"),
"repulsion": torch.stack([
branch_metadata[name]["repulsion"]
for name in self.branch_names
]).sum(),
"attraction": torch.stack([
branch_metadata[name]["attraction"]
for name in self.branch_names
]).sum(),
"attraction_weight": total.new_ones(()),
"reference_constant": torch.stack([
branch_metadata[name]["reference_constant"]
for name in self.branch_names
]).sum(),
}
return total, metadata
def prepare_commit(self) -> None:
for objective in self.branches.values():
objective.prepare_commit()
def commit(self) -> None:
for objective in self.branches.values():
objective.commit()
def discard(self) -> None:
for objective in self.branches.values():
objective.discard()
# Historical imports and serialized class references use these aliases.
MultiRepresentationVideoNystromMMD = MultiEncoderMMD
fd_normalize_raw_mmd = normalize_mmd_with_detached_scale
__all__ = [
"MultiEncoderMMD",
"normalize_mmd_with_detached_scale",
"MultiRepresentationVideoNystromMMD",
"fd_normalize_raw_mmd",
]
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