| |
| |
| """CLI helpers shared across experiment scripts.""" |
| from __future__ import annotations |
|
|
| import argparse |
| import logging |
|
|
| from .io_utils import DEFAULT_MANIFEST_FILE, DEFAULT_OUTPUT_ROOT |
|
|
|
|
| def add_base_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser: |
| """Base runtime arguments applicable to every experiment.""" |
| parser.add_argument("--run_name", required=True, help="Name of this run (used for output directory).") |
| parser.add_argument("--output_root", default=str(DEFAULT_OUTPUT_ROOT), help="Root directory for all run outputs.") |
| parser.add_argument("--manifest_file", default=str(DEFAULT_MANIFEST_FILE), help="Path to dataset_manifests.json.") |
| parser.add_argument("--smoke", action="store_true", help="Run in smoke-test mode (small sample).") |
| parser.add_argument("--seed", type=int, default=42, help="Random seed.") |
| parser.add_argument("--device", default="auto", help="Device for torch models (auto/cpu/cuda).") |
| parser.add_argument("--log_level", default="INFO", choices=["DEBUG", "INFO", "WARNING", "ERROR"], |
| help="Console log level.") |
| return parser |
|
|
|
|
| def add_train_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser: |
| """Training hyper-parameters commonly overridden across experiments.""" |
| parser.add_argument("--model_id", default=None, help="HuggingFace model identifier (e.g. hfl/chinese-bert-wwm-ext).") |
| parser.add_argument("--epochs", type=int, default=None, help="Number of training epochs.") |
| parser.add_argument("--batch_size", type=int, default=None, help="Training batch size.") |
| parser.add_argument("--eval_batch_size", type=int, default=None, help="Evaluation batch size.") |
| parser.add_argument("--max_len", type=int, default=None, help="Maximum sequence length (tokenizer).") |
| parser.add_argument("--lr", type=float, default=None, help="Learning rate.") |
| parser.add_argument("--weight_decay", type=float, default=None, help="AdamW weight decay.") |
| parser.add_argument("--warmup_ratio", type=float, default=None, help="Warmup ratio for linear scheduler.") |
| parser.add_argument("--grad_acc", type=int, default=None, help="Gradient accumulation steps.") |
| parser.add_argument("--early_stopping_patience", type=int, default=None, help="Early stopping patience (epochs).") |
| parser.add_argument("--use_amp", action="store_true", default=None, help="Enable automatic mixed precision (AMP).") |
| parser.add_argument("--no_amp", action="store_true", default=None, help="Disable automatic mixed precision (AMP).") |
| return parser |
|
|
|
|
| def setup_logging(level: str | int = logging.INFO) -> None: |
| """Configure root logger with a consistent format.""" |
| if isinstance(level, str): |
| level = getattr(logging, level.upper(), logging.INFO) |
| logging.basicConfig( |
| level=level, |
| format="%(asctime)s | %(levelname)-8s | %(message)s", |
| datefmt="%Y-%m-%d %H:%M:%S", |
| ) |
|
|
|
|
| def resolve_arg(val, fallback): |
| """Return CLI value if explicitly provided, otherwise fallback.""" |
| return val if val is not None else fallback |
|
|
