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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 | #!/usr/bin/env python3
"""Build the two new Nyström references without changing either estimator."""
from __future__ import annotations
import argparse
import json
import os
from pathlib import Path
import torch
from safetensors import safe_open
from losses.video_nystrom_mmd import (
build_video_nystrom_reference,
compute_reference_pack_hash,
load_reference_pack,
)
from utils.triple_video_feature_bank import (
TripleVideoFeatureBank,
file_sha256,
)
OUTPUT_NAMES = {
"vjepa2": "vjepa2_l15_last64_k1024_m1024_vbench8852.pt",
"videomae_v2": "videomae_v2_l15_fixed5x16_k256_m256_vbench8852.pt",
}
LANDMARKS = {"dinov3": 256, "vjepa2": 1024, "videomae_v2": 256}
def atomic_torch_save(value, path: Path) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
temporary = path.with_name(f".{path.name}.tmp")
torch.save(value, temporary)
with temporary.open('rb') as handle:
os.fsync(handle.fileno())
os.replace(temporary, path)
def load_branch(bank: TripleVideoFeatureBank, branch: str) -> torch.Tensor:
parts = []
for shard in bank.shards:
with safe_open(
shard["resolved_path"], framework="pt", device="cpu"
) as handle:
parts.append(handle.get_tensor(branch))
value = torch.cat(parts, dim=0)
expected = (len(bank), *bank.manifest["branches"][branch]["sample_shape"])
if value.dtype != torch.float16 or tuple(value.shape) != expected:
raise ValueError(f"bank branch {branch} geometry/dtype mismatch")
return value
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--bank-manifest", required=True)
parser.add_argument("--bank-manifest-sha256", required=True)
parser.add_argument("--bank-content-hash", required=True)
parser.add_argument("--source-manifest-sha256", required=True)
parser.add_argument("--videomae-revision", default=(
"9981a9c8f77118c421e5228e1b219468a4b0238d"))
parser.add_argument(
"--videomae-download-manifest-content-hash", required=True)
parser.add_argument("--vjepa-model-sha256", required=True)
parser.add_argument("--output-dir", required=True)
parser.add_argument("--device", default="cuda:0")
parser.add_argument("--kmeans-iterations", type=int, default=20)
parser.add_argument("--chunk-size", type=int, default=32)
parser.add_argument("--ridge", type=float, default=1.0e-6)
parser.add_argument("--expected-sample-count", type=int, default=5000)
parser.add_argument("--expected-shard-size", type=int, default=32)
parser.add_argument("--dataset-name", default="fdwan13b_vidprom5k_matched")
parser.add_argument("--skip-bank-shard-hashes", action="store_true")
args = parser.parse_args()
bank = TripleVideoFeatureBank(
args.bank_manifest,
expected_manifest_sha256=args.bank_manifest_sha256,
expected_content_hash=args.bank_content_hash,
expected_source_manifest_sha256=args.source_manifest_sha256,
expected_sample_count=args.expected_sample_count,
expected_videomae_revision=args.videomae_revision,
expected_videomae_download_manifest_content_hash=(
args.videomae_download_manifest_content_hash),
expected_vjepa_model_sha256=args.vjepa_model_sha256,
active_branches=("vjepa2", "videomae_v2"),
expected_shard_size=args.expected_shard_size,
validate_all_shards=not args.skip_bank_shard_hashes,
)
output_dir = Path(args.output_dir).expanduser().resolve()
output_dir.mkdir(parents=True, exist_ok=True)
build_request = {
"chunk_size": int(args.chunk_size),
"kmeans_iterations": int(args.kmeans_iterations),
"ridge": float(args.ridge),
"seed": 0,
}
summaries = {}
for branch in ("vjepa2", "videomae_v2"):
print(json.dumps({'stage': 'reference_start', 'branch': branch, 'samples': len(bank)}), flush=True)
output = output_dir / OUTPUT_NAMES[branch]
if output.is_file():
existing = load_reference_pack(output, map_location="cpu")
reference_metadata = existing["metadata"].get("reference", {})
source = reference_metadata.get("source", {})
nystrom = existing["metadata"].get("nystrom", {})
if (
source.get("triple_teacher_bank_content_hash")
!= bank.content_hash
or int(nystrom.get("num_landmarks", -1)) != LANDMARKS[branch]
or int(nystrom.get("kmeans_iterations", -1))
!= int(args.kmeans_iterations)
or float(nystrom.get("ridge", float("nan")))
!= float(args.ridge)
or int(nystrom.get("seed", -1)) != 0
or reference_metadata.get("build_request") != build_request
):
raise ValueError(
f"existing reference has another identity: {output}")
summaries[branch] = {
"path": str(output),
"sha256": file_sha256(output),
"content_hash": existing["content_hash"],
"resumed": True,
}
continue
cpu_reference = load_branch(bank, branch)
print(json.dumps({'stage': 'reference_rows_loaded', 'branch': branch}), flush=True)
reference = cpu_reference.to(
device=torch.device(args.device), dtype=torch.float32)
del cpu_reference
branch_manifest = bank.manifest["branches"][branch]
metadata = {
"feature": branch_manifest["feature_contract"],
"reference": {
"dataset": args.dataset_name,
"build_request": build_request,
"source": {
"triple_teacher_bank_content_hash": bank.content_hash,
"branch_name": branch,
"triple_teacher_bank_manifest_sha256": (
bank.manifest_sha256),
"source_manifest_sha256": (
args.source_manifest_sha256),
"selected_samples": len(bank),
"sample_order": "frozen_manifest_order",
"feature_bank_fp16_sha256": branch_manifest[
"feature_bank_fp16_sha256"],
"model_identity": branch_manifest["model_identity"],
},
},
}
pack = build_video_nystrom_reference(
reference,
num_landmarks=LANDMARKS[branch],
metadata=metadata,
kmeans_iterations=args.kmeans_iterations,
ridge=args.ridge,
chunk_size=args.chunk_size,
seed=0,
)
print(json.dumps({'stage': 'reference_computed', 'branch': branch}), flush=True)
del reference
cpu_pack = {
key: (value.detach().cpu() if isinstance(value, torch.Tensor) else value)
for key, value in pack.items()
}
if compute_reference_pack_hash(cpu_pack) != cpu_pack["content_hash"]:
raise RuntimeError("reference hash changed during CPU transfer")
atomic_torch_save(cpu_pack, output)
validated = load_reference_pack(output, map_location="cpu")
summaries[branch] = {
"path": str(output),
"sha256": file_sha256(output),
"content_hash": validated["content_hash"],
"resumed": False,
}
del pack, cpu_pack, validated
if torch.cuda.is_available():
torch.cuda.empty_cache()
manifest = {
"schema_version": 1,
"bank_manifest": str(Path(args.bank_manifest).resolve()),
"bank_manifest_sha256": args.bank_manifest_sha256,
"bank_content_hash": bank.content_hash,
"build_request": build_request,
"references": summaries,
}
path = output_dir / "no_dinov3_reference_manifest.json"
temporary = path.with_name(f".{path.name}.tmp")
temporary.write_text(
json.dumps(manifest, sort_keys=True, indent=2) + "\n",
encoding="utf-8",
)
os.replace(temporary, path)
print(json.dumps(manifest, sort_keys=True), flush=True)
if __name__ == "__main__":
main()
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