Files
AstrAI/astrai/config/preprocess_config.py
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ViperEkura 164be9708b refactor: migrate config system to Pydantic dataclasses
- Replace hand-rolled BaseConfig (from_dict/to_dict/_coerce/_unwrap_optional) with pydantic.dataclasses
- from_dict now uses cls(**d), to_dict uses dataclasses.asdict + json.dumps filter
- TrainConfig: required fields are now truly required (no default=None), delete manual validate()/__post_init__
- Remove dead required() helper and metadata={'help': ...} annotations
- Fix gradient_checkpointing_modules type from List[str] to List[type]
- Add pydantic>=2.0 as direct dependency in pyproject.toml
- Add numpy-style Parameters docstrings to all config classes
- Enable use_attribute_docstrings in BaseConfig for schema generation
- LoRAConfig also migrated to pydantic dataclass
2026-07-30 08:25:32 +08:00

103 lines
3.8 KiB
Python

"""Pipeline configuration for JSONL preprocessing.
Supports single-sequence (SFT/pretrain) and multi-output (DPO/GRPO)
modes, both driven declaratively through ``input.sections`` or
``input.sources``.
"""
from dataclasses import field
from typing import Dict, List, Optional
from pydantic.dataclasses import dataclass
from astrai.config.base import BaseConfig
@dataclass
class InputConfig(BaseConfig):
"""Declarative input mapping.
Single-output mode (backward-compatible)::
{"input": {"sections": [{"field": "messages", ...}]}}
Multi-output mode (DPO / GRPO)::
{"input": {"sources": {
"chosen": {"sections": [{"field": "chosen", ...}]},
"rejected": {"sections": [{"field": "rejected", ...}]},
}}}
Args:
sections (Optional[List[Dict]]): Section list for single-output mode. Defaults to None.
sources (Optional[Dict[str, Dict]]): Source map for multi-output mode, DPO/GRPO. Defaults to None.
"""
sections: Optional[List[Dict]] = None
sources: Optional[Dict[str, Dict]] = None
@dataclass
class ProcessingConfig(BaseConfig):
"""Processing configuration for tokenization and packing.
Args:
max_seq_len (int): Maximum sequence length. Defaults to 2048.
min_chars (int): Minimum number of characters to keep. Defaults to 50.
max_chars (int): Maximum number of characters to keep. Defaults to 2_000_000.
max_items (Optional[int]): Maximum number of items to process, None=unlimited. Defaults to None.
batch_size (int): Number of records tokenized together. Defaults to 256.
packing_strategy (str): How to pack sequences: 'simple', 'bfd', or 'bfd_split'. Defaults to "simple".
max_packed_len (int): Maximum length of a packed bin. Defaults to 8192.
truncation_mode (str): How to truncate over-length sequences: 'keep_start' or 'keep_end'. Defaults to "keep_start".
"""
max_seq_len: int = 2048
min_chars: int = 50
max_chars: int = 2_000_000
max_items: Optional[int] = None
batch_size: int = 256
packing_strategy: str = "simple"
max_packed_len: int = 8192
truncation_mode: str = "keep_start"
@dataclass
class OutputConfig(BaseConfig):
"""Output configuration for storage.
Args:
domain_key (Optional[str]): Domain key for the output store. Defaults to None.
storage_format (str): Storage format: 'bin' or 'jsonl'. Defaults to "bin".
max_tokens_per_shard (int): Maximum tokens per shard before splitting. Defaults to 100_000_000.
dtype (Dict[str, str]): Per-key dtype overrides, e.g. {"input_ids": "int32"}. Defaults to {}.
position_ids_mode (str): Position ids mode: 'none', 'doc_reset', or 'continuous'. Defaults to "doc_reset".
"""
domain_key: Optional[str] = None
storage_format: str = "bin"
max_tokens_per_shard: int = 100_000_000
dtype: Dict[str, str] = field(default_factory=dict)
position_ids_mode: str = "doc_reset"
@dataclass
class PipelineConfig(BaseConfig):
"""Top-level preprocessing pipeline config.
Args:
version (int): Config schema version. Defaults to 1.
input (InputConfig): Input mapping config.
mask (Dict[str, str]): Per-field mask labels, e.g. {"system": "mask", "assistant": "train"}. Defaults to {}.
mask_default (str): Default mask label for unlisted fields. Defaults to "mask".
preprocessing (ProcessingConfig): Processing config.
output (OutputConfig): Output config.
"""
version: int = 1
input: InputConfig = field(default_factory=InputConfig)
mask: Dict[str, str] = field(default_factory=dict)
mask_default: str = "mask"
preprocessing: ProcessingConfig = field(default_factory=ProcessingConfig)
output: OutputConfig = field(default_factory=OutputConfig)