313 lines
10 KiB
Python
313 lines
10 KiB
Python
"""Storage backends for different data formats.
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Layers:
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- I/O layer: save_* / load_* functions, read/write raw files (HDF5/bin)
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return Dict[str, List[Tensor]] — format-specific, no state
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- Store (ABC): central abstraction, normalizes multi-segment into
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Dict[str, List[Tensor]] per key via _normalize(),
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fetch() uses bisect across segments — no forced concat
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- Dataset layer: BaseDataset owns a Store, only calls store.fetch(begin, end, key)
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Key properties:
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- Multi-segment: segments kept as-is, no forced concatenation — safe for
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datasets larger than RAM
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- Explicit length: _length = min(total elements across keys), set at load,
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__len__ returns O(1)
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- Zero-copy mmap: MmapStore wraps np.memmap(mode="r"), all DataLoader
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workers share OS page-cache pages
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"""
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import bisect
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import glob
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import json
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import logging
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from abc import ABC, abstractmethod
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from pathlib import Path
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from typing import Dict, List, Union
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import torch
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from torch import Tensor
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from astrai.config.preprocess_config import PipelineConfig
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from astrai.factory import BaseFactory
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from astrai.preprocessing.builder import MaskBuilderFactory
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from astrai.preprocessing.position_id import PositionIdStrategyFactory
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from astrai.serialization import (
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load_bin,
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load_h5,
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)
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from astrai.tokenize import AutoTokenizer
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logger = logging.getLogger(__name__)
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def detect_format(load_path: str) -> str:
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"""Auto-detect storage format from files in the directory.
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Args:
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load_path: Directory or file path
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Returns:
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Format string ("h5", "bin", or "jsonl")
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Raises:
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FileNotFoundError: If no supported data files are found
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"""
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root = Path(load_path)
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if root.is_file():
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suffix = root.suffix.lower()
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if suffix in (".h5", ".hdf5"):
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return "h5"
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if suffix == ".jsonl":
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return "jsonl"
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raise ValueError(f"Unsupported file format: {suffix}")
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h5_files = [
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Path(p)
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for pattern in ("*.h5", "*.hdf5")
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for p in glob.glob(str(root / "**" / pattern), recursive=True)
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]
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if h5_files:
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return "h5"
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bin_files = [Path(p) for p in glob.glob(str(root / "**" / "*.bin"), recursive=True)]
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if bin_files:
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has_meta = (root / "meta.json").exists() or len(
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[Path(p) for p in glob.glob(str(root / "**" / "meta.json"), recursive=True)]
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) > 0
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if has_meta:
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return "bin"
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jsonl_files = [
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Path(p) for p in glob.glob(str(root / "**" / "*.jsonl"), recursive=True)
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]
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if jsonl_files:
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return "jsonl"
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raise FileNotFoundError(f"No supported data files found at {load_path}")
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class Store(ABC):
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"""String keys -> segmented tensors with ``fetch(begin, end, keys)``.
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Each key maps to one or more tensor segments (no forced concatenation).
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``len(store)`` returns ``self._length`` (explicit, O(1)), the minimum
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total element count across all keys.
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Subclasses fill ``self._data`` and ``self._cum`` during ``load()``
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via ``_normalize()``.
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"""
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def __init__(self):
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self._data: Dict[str, List[Tensor]] = {}
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self._cum: Dict[str, List[int]] = {}
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self._length: int = 0
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@abstractmethod
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def load(self, path: str) -> None:
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raise NotImplementedError
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@property
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def keys(self) -> List[str]:
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return list(self._data.keys())
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def __len__(self) -> int:
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return self._length
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def fetch(
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self,
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begin: int,
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end: int,
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keys: Union[str, List[str]],
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):
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if not self._data:
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raise RuntimeError("Store not loaded")
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if not (0 <= begin < self._length and 0 <= end <= self._length):
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raise ValueError(
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f"Index out of bounds: begin={begin}, end={end}, length={self._length}"
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)
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if isinstance(keys, str):
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return self._fetch_key(keys, begin, end)
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return {k: self._fetch_key(k, begin, end) for k in keys}
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def _fetch_key(self, key: str, begin: int, end: int) -> Tensor:
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"""Fetch slice [begin, end) across potentially multiple segments."""
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segments = self._data[key]
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cum = self._cum[key]
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seg_start = bisect.bisect_right(cum, begin)
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seg_end = bisect.bisect_left(cum, end)
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results = []
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for i in range(seg_start, seg_end + 1):
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prev = cum[i - 1] if i > 0 else 0
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s = max(begin - prev, 0)
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e = min(end - prev, segments[i].shape[0])
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results.append(segments[i][s:e])
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return results[0] if len(results) == 1 else torch.cat(results, dim=0)
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def _normalize(self, raw: Dict[str, List[Tensor]]):
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"""Register segments and pre-compute cumulative lengths.
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Does NOT concatenate — segments are kept as-is to avoid OOM on
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large datasets. Sets ``self._length`` to the minimum total
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element count across all keys.
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"""
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for key, tensors in raw.items():
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self._data[key] = tensors
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cum = []
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total = 0
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for t in tensors:
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total += t.shape[0]
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cum.append(total)
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self._cum[key] = cum
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self._length = (
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min((cum[-1] if cum else 0) for cum in self._cum.values())
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if self._cum
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else 0
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)
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class StoreFactory(BaseFactory["Store"]):
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"""Factory for creating Store instances by type name.
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Example::
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@StoreFactory.register("custom")
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class CustomStore(Store):
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...
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"""
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@StoreFactory.register("h5")
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class H5Store(Store):
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"""HDF5-based storage backend (pre-tokenized data)."""
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def load(self, path: str):
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self._normalize(load_h5(path))
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@StoreFactory.register("bin")
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class MmapStore(Store):
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"""Memory-mapped binary storage backend.
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Each key is a single .bin file backed by ``np.memmap(mode="r")``.
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No per-process memory duplication — all DataLoader workers share the
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same OS page-cache pages.
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Format on disk::
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data_root/
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meta.json # {key: {shape, dtype}, ...}
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<key>.bin # raw numpy array, one per key
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"""
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def load(self, path: str):
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self._mmap_refs = []
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root = Path(path)
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all_raw: Dict[str, List[Tensor]] = {}
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meta_paths = [
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Path(p) for p in glob.glob(str(root / "**" / "meta.json"), recursive=True)
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]
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for meta_path in meta_paths:
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raw = load_bin(str(meta_path.parent))
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for key, tensors in raw.items():
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if key not in all_raw:
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all_raw[key] = []
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all_raw[key].extend(tensors)
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if not meta_paths:
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raise FileNotFoundError(f"No meta.json found under {path}")
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self._normalize(all_raw)
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for tensors in self._data.values():
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self._mmap_refs.extend(tensors)
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@StoreFactory.register("jsonl")
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class JsonlStore(Store):
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"""On-the-fly tokenization store for raw JSONL files.
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A JSONL dataset directory contains ``*.jsonl`` files plus a
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``dataset_config.json`` file that follows the same schema as
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:class:`PipelineConfig` with an additional ``tokenizer_path`` field.
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Records are tokenized when the store is loaded and concatenated into
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segmented tensors matching the key layout expected by the dataset
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classes (``sequence``, ``loss_mask``, ``position_ids``, ...).
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"""
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CONFIG_NAME = "dataset_config.json"
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def load(self, path: str):
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root = Path(path)
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config_path = root / self.CONFIG_NAME
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if not config_path.exists():
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raise FileNotFoundError(
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f"JSONL dataset config not found: {config_path}. "
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f"Expected {self.CONFIG_NAME} alongside *.jsonl files."
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)
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with open(config_path, "r", encoding="utf-8") as f:
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raw_config = json.load(f)
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tokenizer_path = raw_config.pop("tokenizer_path", None)
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if tokenizer_path is None:
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raise ValueError(
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f"JSONL dataset config must specify 'tokenizer_path': {config_path}"
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)
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self.config = PipelineConfig.from_dict(raw_config)
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
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mask_builder = MaskBuilderFactory.create("sectioned")
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position_strategy = PositionIdStrategyFactory.create(
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self.config.output.position_ids_mode
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)
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raw: Dict[str, List[Tensor]] = {}
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doc_sequences: List[List[int]] = []
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for jsonl_path in sorted(root.glob("*.jsonl")):
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with open(jsonl_path, "r", encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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try:
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item = json.loads(line)
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except json.JSONDecodeError:
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logger.warning(
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"Failed to parse JSON line in %s, skipping", jsonl_path
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)
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continue
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result = mask_builder.build(item, self.config, tokenizer)
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if result is None:
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continue
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result.pop("domain", None)
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primary_ids = self._primary_ids(result)
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if not primary_ids:
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continue
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doc_sequences.append(primary_ids)
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for key, ids in result.items():
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if key not in raw:
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raw[key] = []
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raw[key].append(torch.tensor(ids, dtype=self._infer_dtype(ids)))
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pos_ids = position_strategy.generate(doc_sequences)
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if pos_ids:
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raw["position_ids"] = [torch.tensor(pos_ids, dtype=torch.int32)]
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self._normalize(raw)
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@staticmethod
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def _primary_ids(result: dict) -> List[int]:
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"""Return the first integer list in *result* as the primary id sequence."""
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for val in result.values():
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if isinstance(val, list) and val and isinstance(val[0], int):
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return val
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return []
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@staticmethod
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def _infer_dtype(ids: List) -> torch.dtype:
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"""Infer tensor dtype from the first element of a token/value list."""
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if ids and isinstance(ids[0], float):
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return torch.float32
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return torch.int32
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