File size: 8,415 Bytes
510ab6b | 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 | """Crash-safe writer for Predictor v4 Full-DiT teacher trajectories."""
from __future__ import annotations
import json
import os
from collections.abc import Mapping
from pathlib import Path
from typing import Any
import torch
from safetensors.torch import save_file
from .schema import (
CANDIDATE_BLOCK_IDS,
SCHEMA_VERSION,
validate_case_tensors,
validate_clean_prefeature,
validate_step_tensors,
)
def _bf16_cpu(tensor: torch.Tensor) -> torch.Tensor:
return tensor.detach().to(device="cpu", dtype=torch.bfloat16).contiguous()
def atomic_write_text(path: Path, text: str) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
temporary = path.with_name(f".{path.name}.tmp.{os.getpid()}")
with temporary.open("w", encoding="utf-8") as handle:
handle.write(text)
handle.flush()
os.fsync(handle.fileno())
os.replace(temporary, path)
def atomic_save_safetensors(
path: Path,
tensors: Mapping[str, torch.Tensor],
) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
temporary = path.with_name(f".{path.name}.tmp.{os.getpid()}")
try:
save_file(dict(tensors), str(temporary))
os.replace(temporary, path)
finally:
if temporary.exists():
temporary.unlink()
class PredictorV4DatasetWriter:
"""Write one worker shard and fsync every committed manifest record."""
def __init__(
self,
root: str | os.PathLike[str],
*,
worker_id: int,
block_ids: tuple[int, ...] = CANDIDATE_BLOCK_IDS,
) -> None:
self.root = Path(root).resolve()
self.worker_id = int(worker_id)
self.block_ids = tuple(int(value) for value in block_ids)
self.case_dir = self.root / "cases"
self.step_dir = self.root / "steps"
self.clean_dir = self.root / "clean_prefeature"
self.manifest_dir = self.root / "manifests"
self.log_dir = self.root / "logs"
self.manifest_path = self.manifest_dir / f"worker_{self.worker_id:02d}.jsonl"
for path in (
self.case_dir,
self.step_dir,
self.clean_dir,
self.manifest_dir,
self.log_dir,
):
path.mkdir(parents=True, exist_ok=True)
self._records = self._read_committed_records()
def _read_committed_records(self) -> dict[tuple[int, int], dict[str, Any]]:
records: dict[tuple[int, int], dict[str, Any]] = {}
if not self.manifest_path.is_file():
return records
text = self.manifest_path.read_text(encoding="utf-8")
lines = text.splitlines(keepends=True)
committed_lines: list[str] = []
for line_number, line in enumerate(lines, start=1):
if not line.strip():
committed_lines.append(line)
continue
try:
item = json.loads(line)
except json.JSONDecodeError as exc:
is_truncated_tail = (
line_number == len(lines) and not line.endswith(("\n", "\r"))
)
if not is_truncated_tail:
raise ValueError(
f"invalid JSON at {self.manifest_path}:{line_number}"
) from exc
# A process may be killed between write(2) and fsync(2). Repair
# only an unterminated final record; corruption elsewhere is
# never silently ignored.
atomic_write_text(self.manifest_path, "".join(committed_lines))
break
key = (int(item["case_id"]), int(item["chunk_id"]))
if key in records and records[key] != item:
raise ValueError(f"conflicting duplicate worker manifest record {key}")
records[key] = item
committed_lines.append(line)
return records
def case_path(self, case_id: int) -> Path:
return self.case_dir / f"case_{int(case_id):06d}.safetensors"
def step_path(self, case_id: int, chunk_id: int) -> Path:
return (
self.step_dir
/ f"case_{int(case_id):06d}"
/ f"chunk_{int(chunk_id):02d}.safetensors"
)
def clean_path(self, block_id: int, case_id: int, chunk_id: int) -> Path:
return (
self.clean_dir
/ f"block_{int(block_id):02d}"
/ f"case_{int(case_id):06d}"
/ f"chunk_{int(chunk_id):02d}.safetensors"
)
def _record_files_exist(self, record: Mapping[str, Any]) -> bool:
paths = [
self.root / str(record["case_tensor_file"]),
self.root / str(record["step_tensor_file"]),
]
paths.extend(
self.root / str(value)
for value in record["clean_prefeature_files"].values()
)
return all(path.is_file() for path in paths)
def is_chunk_complete(self, case_id: int, chunk_id: int) -> bool:
record = self._records.get((int(case_id), int(chunk_id)))
return record is not None and self._record_files_exist(record)
def save_case(
self,
case_id: int,
tensors: Mapping[str, torch.Tensor],
) -> Path:
converted = {name: _bf16_cpu(value) for name, value in tensors.items()}
validate_case_tensors(converted, self.block_ids)
path = self.case_path(case_id)
if not path.is_file():
atomic_save_safetensors(path, converted)
return path
def save_chunk(
self,
*,
case_id: int,
chunk_id: int,
step_tensors: Mapping[str, torch.Tensor],
clean_features: Mapping[int, torch.Tensor],
start_frame: int,
metadata: Mapping[str, Any],
) -> Path:
key = (int(case_id), int(chunk_id))
if self.is_chunk_complete(*key):
return self.step_path(*key)
if not self.case_path(case_id).is_file():
raise RuntimeError(f"case tensor file must be saved before chunk {key}")
converted_steps: dict[str, torch.Tensor] = {}
for name, value in step_tensors.items():
if name.endswith("_timestep"):
converted_steps[name] = value.detach().to(
device="cpu", dtype=torch.int64
).contiguous()
else:
converted_steps[name] = _bf16_cpu(value)
validate_step_tensors(converted_steps)
converted_clean: dict[int, dict[str, torch.Tensor]] = {}
for block_id in self.block_ids:
if block_id not in clean_features:
raise ValueError(f"missing clean prefeature for block {block_id}")
values = {
"self_attn_input": _bf16_cpu(clean_features[block_id]),
"start_frame": torch.tensor([int(start_frame)], dtype=torch.int64),
"num_frames": torch.tensor([3], dtype=torch.int64),
}
validate_clean_prefeature(block_id, values)
converted_clean[block_id] = values
step_path = self.step_path(*key)
atomic_save_safetensors(step_path, converted_steps)
clean_files: dict[str, str] = {}
for block_id, values in converted_clean.items():
path = self.clean_path(block_id, *key)
atomic_save_safetensors(path, values)
clean_files[str(block_id)] = str(path.relative_to(self.root))
record = {
"schema_version": SCHEMA_VERSION,
"case_id": key[0],
"chunk_id": key[1],
"case_tensor_file": str(self.case_path(case_id).relative_to(self.root)),
"step_tensor_file": str(step_path.relative_to(self.root)),
"clean_prefeature_files": clean_files,
"candidate_block_ids": list(self.block_ids),
"context_frames": int(start_frame),
**dict(metadata),
}
existing = self._records.get(key)
if existing is None:
line = json.dumps(record, ensure_ascii=False, sort_keys=True) + "\n"
with self.manifest_path.open("a", encoding="utf-8") as handle:
handle.write(line)
handle.flush()
os.fsync(handle.fileno())
self._records[key] = record
elif existing != record:
raise ValueError(f"recomputed metadata differs for manifest record {key}")
return step_path
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