File size: 6,949 Bytes
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 | import copy
import hashlib
import json
from pathlib import Path
from types import SimpleNamespace
from unittest import mock
import pytest
import torch
from safetensors.torch import save_file
from utils.triple_video_feature_bank import (
BANK_KIND,
BANK_SCHEMA_VERSION,
TripleVideoFeatureBank,
canonical_sha256,
file_sha256,
)
from trainer.distillation import Trainer
VJEPA_SHA = "1" * 64
VIDEOMAE_REVISION = "9981a9c8f77118c421e5228e1b219468a4b0238d"
VIDEOMAE_DOWNLOAD_HASH = "2" * 64
SOURCE_SHA = "3" * 64
DINOV3_SHA = "4" * 64
DINOV3_COMMIT = "6876159a11b4df116f30f667f8c9888617df0751"
def _write_bank(root: Path):
samples = [
{"index": index, "sample_id": f"sample-{index}", "prompt": f"p{index}"}
for index in range(3)
]
branch_metadata = {
"dinov3": {
"sample_shape": [1024, 1024],
"dtype": "float16",
"feature_contract": {"name": "dinov3-test"},
"model_identity": {
"checkpoint_sha256": DINOV3_SHA,
"source_commit": DINOV3_COMMIT,
},
},
"vjepa2": {
"sample_shape": [1024, 1024],
"dtype": "float16",
"feature_contract": {"name": "vjepa2-test"},
"model_identity": {"sha256": VJEPA_SHA},
},
"videomae_v2": {
"sample_shape": [1280, 1024],
"dtype": "float16",
"feature_contract": {"name": "videomae-test"},
"model_identity": {
"revision": VIDEOMAE_REVISION,
"download_manifest_content_hash": VIDEOMAE_DOWNLOAD_HASH,
},
},
}
shard_dir = root / "shards"
shard_dir.mkdir(parents=True)
shards = []
for shard_index, (start, end) in enumerate(((0, 2), (2, 3))):
shard_content_hash = canonical_sha256({
"index": shard_index,
"sample_start": start,
"sample_end": end,
})
path = shard_dir / f"{shard_index:05d}.safetensors"
save_file(
{
"dinov3": torch.stack([
torch.full((1024, 1024), float(row + 20), dtype=torch.float16)
for row in range(start, end)
]),
"vjepa2": torch.stack([
torch.full((1024, 1024), float(row), dtype=torch.float16)
for row in range(start, end)
]),
"videomae_v2": torch.stack([
torch.full((1280, 1024), float(row + 10), dtype=torch.float16)
for row in range(start, end)
]),
},
path,
metadata={"content_hash": shard_content_hash},
)
shards.append({
"index": shard_index,
"path": str(path.relative_to(root)),
"sample_start": start,
"sample_end": end,
"sample_ids_sha256": canonical_sha256([
item["sample_id"] for item in samples[start:end]
]),
"sha256": file_sha256(path),
"content_hash": shard_content_hash,
})
payload = {
"schema_version": BANK_SCHEMA_VERSION,
"kind": BANK_KIND,
"sample_count": len(samples),
"source_manifest": {"sha256": SOURCE_SHA},
"branches": branch_metadata,
"samples": samples,
"shards": shards,
}
manifest = {**payload, "content_hash": canonical_sha256(payload)}
manifest_path = root / "manifest.json"
manifest_path.write_text(
json.dumps(manifest, sort_keys=True) + "\n", encoding="utf-8")
return manifest_path, manifest
def _open(manifest_path: Path, manifest: dict, **overrides):
kwargs = {
"expected_manifest_sha256": file_sha256(manifest_path),
"expected_content_hash": manifest["content_hash"],
"expected_source_manifest_sha256": SOURCE_SHA,
"expected_sample_count": 3,
"expected_videomae_revision": VIDEOMAE_REVISION,
"expected_videomae_download_manifest_content_hash": (
VIDEOMAE_DOWNLOAD_HASH),
"expected_vjepa_model_sha256": VJEPA_SHA,
"expected_dinov3_checkpoint_sha256": DINOV3_SHA,
"expected_dinov3_source_commit": DINOV3_COMMIT,
"expected_shard_size": 2,
"validate_all_shards": True,
}
kwargs.update(overrides)
return TripleVideoFeatureBank(manifest_path, **kwargs)
def test_random_access_preserves_order_and_duplicate_sample_ids(tmp_path):
manifest_path, manifest = _write_bank(tmp_path)
bank = _open(manifest_path, manifest)
assert [bank[index]["sample_ids"] for index in (2, 0, 2)] == [
"sample-2", "sample-0", "sample-2"]
loaded = bank.load_rows(["sample-2", "sample-0", "sample-2"])
assert tuple(loaded["dinov3"].shape) == (3, 1024, 1024)
assert tuple(loaded["vjepa2"].shape) == (3, 1024, 1024)
assert tuple(loaded["videomae_v2"].shape) == (3, 1280, 1024)
torch.testing.assert_close(
loaded["dinov3"][:, 0, 0], torch.tensor([22, 20, 22]).half())
torch.testing.assert_close(
loaded["vjepa2"][:, 0, 0], torch.tensor([2, 0, 2]).half())
torch.testing.assert_close(
loaded["videomae_v2"][:, 0, 0],
torch.tensor([12, 10, 12]).half(),
)
def test_counter_sampler_is_rank_major_ordered_and_with_replacement():
class Dataset:
def __len__(self):
return 3
def __getitem__(self, index):
return {"sample_ids": f"sample-{index}", "prompts": f"p{index}"}
@staticmethod
def collate_fn(items):
return {
"sample_ids": [item["sample_ids"] for item in items],
"prompts": [item["prompts"] for item in items],
}
trainer = Trainer.__new__(Trainer)
trainer.matched_counter_sampling_enabled = True
trainer.dataset = Dataset()
trainer.config = SimpleNamespace(seed=7, batch_size=1)
trainer.step = 11
trainer.data_counter = 0
trainer.fresh_video_total_samples = 8
trainer.fresh_video_local_samples = 2
expected = tuple(
int.from_bytes(hashlib.sha256(
f"sf-matched-counter-v1\0{trainer.config.seed}\0{trainer.step}\0{position}"
.encode("ascii")
).digest()[:8], "little") % len(trainer.dataset)
for position in range(trainer.fresh_video_total_samples)
)
assert trainer._matched_global_indices_for_step() == expected
# Eight with-replacement draws from a three-row bank must contain repeats.
assert len(set(expected)) < len(expected)
with mock.patch("trainer.distillation.dist.get_rank", return_value=2):
first = trainer._matched_batch_for_microbatch(0)
second = trainer._matched_batch_for_microbatch(1)
assert first["sample_ids"] == [f"sample-{expected[4]}"]
assert second["sample_ids"] == [f"sample-{expected[5]}"]
assert trainer.data_counter == 2
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