File size: 5,238 Bytes
61449ba | 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 | from __future__ import annotations
import hashlib
import importlib.util
import json
from pathlib import Path
from typing import Any, Iterable, Sequence
CACHE_VERSION = 1
def _json_hash(value: Any) -> str:
encoded = json.dumps(value, ensure_ascii=False, sort_keys=True).encode("utf-8")
return hashlib.sha256(encoded).hexdigest()
def _hash_text(value: Any) -> str:
return hashlib.sha256(str(value or "").encode("utf-8")).hexdigest()
def parquet_reader_available() -> bool:
return any(
importlib.util.find_spec(package) is not None
for package in ("pyarrow", "polars", "pandas")
)
def dataset_source_files(dataset_name: str, data_dir: str | Path) -> list[Path]:
root = Path(data_dir)
if dataset_name == "multimodal":
return sorted((root / "MultimodalReasoning").glob("*.jsonl"))
if dataset_name == "reasoning_dpo":
return sorted((root / "ReasoningDPO").glob("*.jsonl"))
if dataset_name == "limo":
return [root / "limo.jsonl"]
if dataset_name == "complex_bespoke":
return [root / "ComplexReasoningBespoke.parquet"]
if dataset_name == "all":
files: list[Path] = []
files.extend(dataset_source_files("multimodal", root))
files.extend(dataset_source_files("reasoning_dpo", root))
files.extend(dataset_source_files("limo", root))
files.extend(dataset_source_files("complex_bespoke", root))
return files
return []
def file_metadata(path: Path) -> dict[str, Any]:
if not path.exists():
return {
"path": str(path),
"exists": False,
}
stat = path.stat()
return {
"path": str(path),
"exists": True,
"size": stat.st_size,
"mtime_ns": stat.st_mtime_ns,
}
def data_digest(records: Sequence[dict[str, str]]) -> dict[str, Any]:
digest = hashlib.sha256()
for record in records:
digest.update(str(record.get("user", "")).encode("utf-8"))
digest.update(b"\0")
digest.update(str(record.get("assistant", "")).encode("utf-8"))
digest.update(b"\0\0")
return {
"rows": len(records),
"sha256": digest.hexdigest(),
}
def tokenizer_metadata(tokenizer: Any) -> dict[str, Any]:
return {
"name_or_path": str(getattr(tokenizer, "name_or_path", "")),
"class": tokenizer.__class__.__name__,
"eos_token": str(getattr(tokenizer, "eos_token", "")),
"pad_token": str(getattr(tokenizer, "pad_token", "")),
"padding_side": str(getattr(tokenizer, "padding_side", "")),
"chat_template_sha256": _hash_text(getattr(tokenizer, "chat_template", "")),
"vocab_size": len(tokenizer) if hasattr(tokenizer, "__len__") else None,
}
def build_cache_metadata(
*,
config: dict[str, Any],
tokenizer: Any,
provided_records: Sequence[dict[str, str]] | None = None,
) -> dict[str, Any]:
relevant_config = {
key: config.get(key)
for key in (
"model_name",
"data_dir",
"dataset_name",
"parquet_limit",
"system_prompt",
"dataset_format",
"append_eos_to_completion",
"max_samples",
"eval_split_size",
"max_eval_samples",
"shuffle_data",
"seed",
"max_length",
"completion_only_loss",
"assistant_only_loss",
)
}
metadata: dict[str, Any] = {
"cache_version": CACHE_VERSION,
"config": relevant_config,
"tokenizer": tokenizer_metadata(tokenizer),
"parquet_reader_available": parquet_reader_available(),
}
if provided_records is not None:
metadata["provided_data"] = data_digest(provided_records)
else:
metadata["source_files"] = [
file_metadata(path)
for path in dataset_source_files(
str(config.get("dataset_name", "all")),
str(config.get("data_dir", "data")),
)
]
metadata["cache_key"] = _json_hash(metadata)[:24]
return metadata
def cache_path(cache_root: str | Path, metadata: dict[str, Any]) -> Path:
dataset_name = str(metadata["config"].get("dataset_name") or "dataset")
safe_name = "".join(
char if char.isalnum() or char in ("-", "_") else "_"
for char in dataset_name
)
return Path(cache_root) / f"{safe_name}_{metadata['cache_key']}"
def metadata_path(path: str | Path) -> Path:
return Path(path) / "metadata.json"
def read_metadata(path: str | Path) -> dict[str, Any] | None:
meta_path = metadata_path(path)
if not meta_path.exists():
return None
with meta_path.open("r", encoding="utf-8") as f:
return json.load(f)
def write_metadata(path: str | Path, metadata: dict[str, Any]) -> None:
meta_path = metadata_path(path)
meta_path.parent.mkdir(parents=True, exist_ok=True)
with meta_path.open("w", encoding="utf-8") as f:
json.dump(metadata, f, ensure_ascii=False, indent=2, sort_keys=True)
def metadata_matches(path: str | Path, metadata: dict[str, Any]) -> bool:
cached = read_metadata(path)
return cached == metadata
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