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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 | #!/usr/bin/env python3
"""Validate the active three-encoder MMD composition and gradients on CPU."""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
import torch
sys.dont_write_bytecode = True
def assert_same(left: torch.Tensor, right: torch.Tensor, label: str) -> None:
torch.testing.assert_close(left, right, rtol=0, atol=0, msg=label)
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--release", choices=("1.3B", "14B"), required=True)
args = parser.parse_args()
root = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(root / args.release / "code"))
from losses.multi_encoder_mmd import (
MultiEncoderMMD,
normalize_mmd_with_detached_scale,
)
from losses.video_nystrom_mmd import (
VideoNystromMMD,
build_video_nystrom_reference,
)
shapes = {
"dinov3": (1, 2),
"vjepa2": (2, 3),
"videomae_v2": (1, 4),
}
def objective() -> MultiEncoderMMD:
branches = {}
for index, (name, shape) in enumerate(shapes.items()):
generator = torch.Generator().manual_seed(9000 + index)
reference = torch.randn(12, *shape, generator=generator)
pack = build_video_nystrom_reference(
reference,
num_landmarks=5,
metadata={
"feature": {"name": name, "matrix_shape": list(shape)},
"reference": {"dataset": "active-mmd-check"},
},
bandwidth=1.7,
kmeans_iterations=3,
ridge=1.0e-5,
chunk_size=16,
)
branches[name] = VideoNystromMMD(
pack,
kernel_chunk_size=3,
attraction_weight=2.0,
current_teacher_attraction_enabled=True,
current_teacher_attraction_weight=1.0,
)
return MultiEncoderMMD(
branches,
weights={"dinov3": 10.0, "vjepa2": 1.0, "videomae_v2": 1.0},
normalization_eps=1.0e-4,
aggregation_divisor=3.0,
)
generator = torch.Generator().manual_seed(1862643)
for mode in ("full", "partial"):
combined = objective()
generated = {
name: torch.randn(4, *shape, generator=generator)
for name, shape in shapes.items()
}
teacher = {
name: torch.randn(
4 if mode == "full" else 2, *shape, generator=generator)
for name, shape in shapes.items()
}
if mode == "full":
leaves = {
name: value.clone().requires_grad_()
for name, value in generated.items()
}
loss, metadata = combined(
leaves, current_teacher=teacher, stage=False)
else:
indices = torch.tensor([0, 2])
leaves = {
name: value.index_select(0, indices).clone().requires_grad_()
for name, value in generated.items()
}
loss, metadata = combined(
gradient_students=leaves,
student_reference=generated,
current_teacher=teacher,
gradient_student_indices=indices,
stage=False,
)
expected_terms = []
for name, branch in combined.branches.items():
if mode == "full":
raw, _ = branch(
leaves[name], current_teacher=teacher[name], stage=False)
else:
raw, _ = branch(
gradient_students=leaves[name],
student_reference=generated[name],
current_teacher=teacher[name],
gradient_student_indices=indices,
stage=False,
)
normalized, denominator = normalize_mmd_with_detached_scale(
raw, combined.normalization_eps)
expected_terms.append(combined.branch_weights[name] * normalized)
assert_same(
metadata["branches"][name]["raw_mmd"],
raw.detach(),
f"{mode} {name} raw MMD",
)
assert_same(
metadata["branches"][name]["normalization_denominator"],
denominator.detach(),
f"{mode} {name} normalization denominator",
)
expected = torch.stack(expected_terms).sum() / 3.0
assert_same(loss, expected, f"{mode} combined MMD")
observed_gradients = torch.autograd.grad(
loss, tuple(leaves.values()), retain_graph=True)
expected_gradients = torch.autograd.grad(
expected, tuple(leaves.values()))
for name, observed, reference in zip(
leaves, observed_gradients, expected_gradients):
assert_same(observed, reference, f"{mode} {name} feature gradient")
assert torch.isfinite(observed).all() and observed.norm() > 0
print(
f"PASS {args.release} {mode}: weighted three-encoder MMD, "
"metadata and feature gradients")
eps = 1.0e-4
for value in (-2.0e-4, 0.0, 1.0e-8, 0.1, 7.0):
observed_input = torch.tensor(
value, dtype=torch.float64, requires_grad=True)
expected_input = observed_input.detach().clone().requires_grad_(True)
observed, observed_denominator = normalize_mmd_with_detached_scale(
observed_input, eps)
expected_denominator = expected_input.detach() + eps
expected = expected_input / expected_denominator
assert_same(observed.detach(), expected.detach(), "normalization value")
assert_same(
observed_denominator, expected_denominator,
"normalization denominator")
observed_gradient, = torch.autograd.grad(observed, observed_input)
expected_gradient, = torch.autograd.grad(expected, expected_input)
assert_same(
observed_gradient, expected_gradient, "normalization gradient")
print(f"PASS {args.release}: detached MMD normalization behavior")
if __name__ == "__main__":
main()
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