ViperEkura
553a42702d
- StreamStore/RecordStore → Streamable/Recordable (stateless mixins) - Store is sole base class, no MRO ambiguity - H5Store/MmapStore/JsonlStore mix in both traits explicitly - segments_are_records declared per-subclass (H5/Jsonl=True, bin=False) - Add tests for dpo_tokenize, lazy jsonl, dual-mode H5, stream-only bin - Remove unused _to_tensor helper
491 lines
17 KiB
Python
491 lines
17 KiB
Python
"""Storage backends for different data formats.
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Architecture (mixin composition, no diamond inheritance):
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Store (ABC) — shared _data/_cum/_offsets bookkeeping
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+ _normalize() for registering segments
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Streamable (mixin) — fetch(begin, end, key) for stream access
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Recordable (mixin) — fetch_record(i, key) for record access
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H5Store(Store, Streamable, Recordable)
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MmapStore(Store, Streamable, Recordable)
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JsonlStore(Store, Streamable, Recordable)
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Each mixin is a stateless trait that relies on ``self._data`` etc.
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provided by :class:`Store`. Concrete stores mix in whichever access
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modes they support — ``Store`` is the sole base class, so there is no
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diamond inheritance or MRO ambiguity.
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Access-mode semantics:
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- **Stream** (SEQ/SFT): ``fetch(begin, end, key)`` slices across
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concatenated segments. ``len(store)`` returns the total token count.
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- **Record** (DPO/GRPO): ``fetch_record(i, key)`` returns the *i*-th
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record without cross-record concatenation. ``num_records`` returns
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the record count.
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``segments_are_records`` (class attribute on each Store subclass)
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tells ``_normalize`` whether segments are inherently per-record (H5/
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JSONL) or opaque shards (bin). Record access for bin relies on
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``_offsets`` instead.
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:class:`JsonlStore` supports a lazy mode (``processor=fn``) that keeps
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raw records and defers tokenisation to ``fetch_record`` — used by DPO
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to train directly from a ``.jsonl`` file without a pre-tokenised copy.
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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 Callable, Dict, List, Optional, Union
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import torch
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from torch import Tensor
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from astrai.factory import BaseFactory
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from astrai.preprocessing.transform import TokenizeTransform
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from astrai.serialization import (
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load_bin,
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load_bin_offsets,
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load_h5,
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)
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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", "jsonl", or "processed")
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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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"""Common base for all storage backends.
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Owns the shared ``_data`` / ``_cum`` / ``_offsets`` bookkeeping and
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the ``_normalize`` entry point used by tensor-backed subclasses.
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Does **not** expose an access API — that is the job of
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:class:`StreamStore` and :class:`RecordStore`.
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"""
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segments_are_records: bool = False
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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._offsets: Dict[str, List[int]] = {}
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self._length: int = 0
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self._num_records: int = 0
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@abstractmethod
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def load(self, path: str, **kwargs) -> 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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"""Default: token count (stream semantics).
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Subclasses that are record-only (e.g. lazy JsonlStore) override
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to return ``self._num_records``.
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"""
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return self._length
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def _normalize(
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self,
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raw: Dict[str, list],
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offsets: Optional[Dict[str, List[int]]] = None,
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):
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"""Register segments and pre-compute indices for both access modes.
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Stream mode: ``_cum[key]`` accumulates per-segment lengths so
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``StreamStore._fetch_key`` can bisect across segments without
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concatenation.
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Record mode: if *offsets* is provided (bin layout),
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``_offsets[key]`` stores cumulative per-record offsets into the
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single concatenated segment. Otherwise, when
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``segments_are_records`` is True (H5/JSONL), ``_data[key]`` is a
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per-record list and ``fetch_record`` indexes it directly.
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Nested keys (GRPO ``responses``/``masks`` as
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``List[List[Tensor]]``) are stored as-is and excluded from both
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cumulative bookkeepings — they are only accessed record-by-record.
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"""
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flat_lengths = []
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for key, tensors in raw.items():
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self._data[key] = tensors
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if not tensors:
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self._cum[key] = []
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flat_lengths.append(0)
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continue
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if isinstance(tensors[0], list):
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self._cum[key] = []
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continue
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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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flat_lengths.append(cum[-1] if cum else 0)
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self._length = min(flat_lengths) if flat_lengths else 0
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valid_offsets: Dict[str, List[int]] = {}
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if offsets:
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for key, off in offsets.items():
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segs = self._data.get(key, [])
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if len(segs) == 1 and len(off) > 1:
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valid_offsets[key] = off
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elif len(segs) > 1:
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logger.warning(
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"Key '%s' has %d segments with offsets — record mode "
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"disabled for this key (multi-shard bin+offsets not "
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"supported). Merge shards or use H5/JSONL.",
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key,
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len(segs),
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)
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self._offsets = valid_offsets
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if valid_offsets:
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record_counts = [len(v) - 1 for v in valid_offsets.values()]
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self._num_records = min(record_counts) if record_counts else 0
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elif self.segments_are_records:
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per_record_counts = []
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for key, tensors in self._data.items():
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if not tensors or isinstance(tensors[0], list):
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continue
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per_record_counts.append(len(tensors))
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self._num_records = min(per_record_counts) if per_record_counts else 0
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else:
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self._num_records = 0
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class Streamable:
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"""Mixin: stream access ``fetch(begin, end, key)``.
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No base class — relies on ``self._data``, ``self._cum``,
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``self._length`` provided by :class:`Store`. Used by SEQ/SFT
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where data is a long token stream.
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"""
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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_stream_key(keys, begin, end)
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return {k: self._fetch_stream_key(k, begin, end) for k in keys}
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def _fetch_stream_key(self, key: str, begin: int, end: int) -> Tensor:
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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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class Recordable:
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"""Mixin: record access ``fetch_record(i, key)``.
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No base class — relies on ``self._data``, ``self._offsets``,
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``self._num_records`` provided by :class:`Store`. Used by
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DPO/GRPO where each record is an independent training unit.
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"""
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segments_are_records = True
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@property
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def num_records(self) -> int:
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return self._num_records
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def fetch_record(
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self,
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index: int,
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keys: Union[str, List[str]],
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):
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if not self._data and self._num_records == 0:
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raise RuntimeError("Store not loaded")
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if not 0 <= index < self._num_records:
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raise ValueError(
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f"Record index out of bounds: {index}, num_records={self._num_records}"
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)
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if isinstance(keys, str):
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return self._fetch_record_key(keys, index)
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return {k: self._fetch_record_key(k, index) for k in keys}
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def _fetch_record_key(self, key: str, index: int):
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offsets = self._offsets.get(key)
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if offsets:
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start = offsets[index]
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end = (
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offsets[index + 1]
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if index + 1 < len(offsets)
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else self._data[key][0].shape[0]
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)
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return self._data[key][0][start:end]
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return self._data[key][index]
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class StoreFactory(BaseFactory["Store"]):
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"""Factory for creating Store instances by type name."""
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@StoreFactory.register("h5")
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class H5Store(Store, Streamable, Recordable):
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"""HDF5-based storage backend (pre-tokenized data).
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Each key is stored as a group of per-record datasets (``data_0``,
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``data_1``, …). Supports both access modes:
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- **Stream** (``fetch(begin, end, key)``): concatenates across
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records via ``_cum`` — used by SEQ/SFT where data is a token stream.
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- **Record** (``fetch_record(i, key)``): indexes ``_data[key]``
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directly — used by DPO/GRPO where each record is independent.
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``len(store)`` returns the **token count** (stream semantics) so
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SEQ/SFT windowing works. Record-only code uses
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``store.num_records`` instead.
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"""
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segments_are_records = True
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def load(self, path: str, **kwargs):
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self._normalize(load_h5(path))
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@StoreFactory.register("bin")
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class MmapStore(Store, Streamable, Recordable):
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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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Supports both access modes:
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- **Stream** (``fetch(begin, end, key)``): always available.
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- **Record** (``fetch_record(i, key)``): only when ``meta.json``
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contains per-record ``offsets`` (written via
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``save_bin(..., record_keys=...)``). Legacy bin files without
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offsets have ``num_records == 0``.
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``segments_are_records`` is ``False`` here (bin segments are
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contiguous streams, not per-record) — record access is driven
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purely by ``_offsets``.
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"""
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segments_are_records = False
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def load(self, path: str, **kwargs):
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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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all_offsets: Dict[str, List[int]] = {}
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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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off = load_bin_offsets(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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for key, o in off.items():
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if key not in all_offsets:
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all_offsets[key] = []
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all_offsets[key].extend(o)
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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, offsets=all_offsets or None)
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for tensors in self._data.values():
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self._mmap_refs.extend(tensors)
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class JsonlSource:
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"""Read raw JSON records from a ``.jsonl`` file or directory.
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A thin reader used by :class:`JsonlStore` in processor mode — holds
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no tokenizer, performs no tokenisation, just yields dicts.
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"""
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def __init__(self, path: str):
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self.path = Path(path)
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self._records: Optional[List[dict]] = None
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def load(self) -> List[dict]:
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if self._records is None:
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self._records = self._read(self.path)
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return self._records
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@staticmethod
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def _read(root: Path) -> List[dict]:
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if root.is_file():
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return JsonlSource._read_file(root)
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return JsonlSource._read_dir(root)
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@staticmethod
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def _read_file(path: Path) -> List[dict]:
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records: List[dict] = []
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with open(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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records.append(json.loads(line))
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except json.JSONDecodeError:
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logger.warning("Failed to parse JSON line in %s, skipping", path)
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return records
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@staticmethod
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def _read_dir(root: Path) -> List[dict]:
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records: List[dict] = []
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for jsonl_path in sorted(root.glob("*.jsonl")):
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records.extend(JsonlSource._read_file(jsonl_path))
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return records
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@StoreFactory.register("jsonl")
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class JsonlStore(Store, Streamable, Recordable):
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"""JSONL reader with two tokenisation modes.
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A JSONL dataset is a ``.jsonl`` file or a directory of ``*.jsonl``
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files plus (optionally) a ``dataset_config.json`` describing the
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tokenization pipeline.
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Two modes, selected at :meth:`load` time:
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- **Eager** (default): applies a :class:`TokenizeTransform` to every
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record at load time and registers per-key tensors via
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``_normalize``. Both stream (``fetch``) and record
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(``fetch_record``) access work — stream concatenates across
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records via ``_cum``, record indexes directly.
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- **Lazy** (``processor=fn`` given): keeps raw records and defers
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tokenisation to ``fetch_record``. Only record access works.
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Used by DPO/GRPO where each record is independent.
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``len(store)`` returns the **token count** (stream semantics) in
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eager mode so SEQ/SFT windowing works; returns the **record count**
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in lazy mode where stream access is unavailable.
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"""
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CONFIG_NAME = "dataset_config.json"
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segments_are_records = True
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def __init__(self):
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super().__init__()
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self._source: Optional[JsonlSource] = None
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self._processor: Optional[Callable[[dict], Dict[str, Tensor]]] = None
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self._keys_cache: Optional[List[str]] = None
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def __len__(self) -> int:
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if self._processor is not None:
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return self._num_records
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return self._length
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def load(self, path: str, transform=None, processor=None, **kwargs):
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self._source = JsonlSource(path)
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records = self._source.load()
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if processor is not None:
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self._processor = processor
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self._num_records = len(records)
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return
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if transform is None:
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root = Path(path)
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config_path = root / self.CONFIG_NAME if root.is_dir() else None
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if config_path is None or not config_path.exists():
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raise FileNotFoundError(
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f"JSONL dataset config not found. Expected "
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f"{self.CONFIG_NAME} alongside *.jsonl files, pass an "
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f"explicit transform, or pass processor= for lazy "
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f"on-the-fly tokenisation."
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)
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transform = TokenizeTransform.from_config_file(str(config_path))
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transformed = transform.apply(records)
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self._normalize(transformed)
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@property
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def keys(self) -> List[str]:
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if self._processor is not None:
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if self._keys_cache is None and self._num_records > 0:
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sample = self._processor(self._source.load()[0])
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self._keys_cache = list(sample.keys())
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return self._keys_cache or []
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return list(self._data.keys())
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def fetch_record(self, index: int, keys: Union[str, List[str]]):
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if self._processor is not None:
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if not 0 <= index < self._num_records:
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raise ValueError(
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f"Record index out of bounds: {index}, "
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f"num_records={self._num_records}"
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)
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record = self._source.load()[index]
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data = self._processor(record)
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if isinstance(keys, str):
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return data[keys]
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return {k: data[k] for k in keys}
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return super().fetch_record(index, keys)
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