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89 lines
2.6 KiB
89 lines
2.6 KiB
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Helper functions for IO
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"""
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import json
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import os
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from typing import Any, Dict, Tuple, Union
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import torch
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from torch.optim.lr_scheduler import _LRScheduler
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from torch.optim.optimizer import Optimizer
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from colossalai.booster import Booster
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from colossalai.cluster import DistCoordinator
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def load_json(file_path: Union[str, os.PathLike]) -> Dict[str, Any]:
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"""
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Load file in JSON format
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"""
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with open(file=file_path, mode="r", encoding="utf-8") as fp:
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return json.load(fp)
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def save_json(data: Dict[str, Any], file_path: Union[str, os.PathLike]) -> None:
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"""
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Save as JSON format
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"""
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with open(file=file_path, mode="w", encoding="utf-8") as fp:
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json.dump(data, fp=fp, ensure_ascii=False, indent=4)
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def save_checkpoint(
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save_dir: Union[str, os.PathLike],
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booster: Booster,
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model: torch.nn.Module,
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optimizer: Optimizer,
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lr_scheduler: _LRScheduler,
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epoch: int,
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step: int,
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batch_size: int,
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coordinator: DistCoordinator,
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) -> None:
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"""
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Save model checkpoint, optimizer, LR scheduler and intermedidate running states.
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"""
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save_dir = os.path.join(save_dir, f"epoch-{epoch}_step-{step}")
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os.makedirs(os.path.join(save_dir, "modeling"), exist_ok=True)
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booster.save_model(model, os.path.join(save_dir, "modeling"), shard=True)
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booster.save_optimizer(optimizer, os.path.join(save_dir, "optimizer"), shard=True)
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booster.save_lr_scheduler(lr_scheduler, os.path.join(save_dir, "lr_scheduler"))
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running_states = {
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"epoch": epoch,
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"step": step,
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"sample_start_index": step * batch_size,
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}
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if coordinator.is_master():
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save_json(running_states, os.path.join(save_dir, "running_states.json"))
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def load_checkpoint(
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load_dir: Union[str, os.PathLike],
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booster: Booster,
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model: torch.nn.Module,
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optimizer: Optimizer,
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lr_scheduler: _LRScheduler,
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) -> Tuple[int, int, int]:
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"""
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Load model checkpoint, optimizer, LR scheduler and intermedidate running states.
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"""
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# Update booster params states.
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booster.load_model(model=model, checkpoint=os.path.join(load_dir, "modeling"))
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booster.load_optimizer(optimizer=optimizer, checkpoint=os.path.join(load_dir, "optimizer"))
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booster.load_lr_scheduler(lr_scheduler=lr_scheduler, checkpoint=os.path.join(load_dir, "lr_scheduler"))
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running_states = load_json(file_path=os.path.join(load_dir, "running_states.json"))
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return (
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running_states["epoch"],
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running_states["step"],
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running_states["sample_start_index"],
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)
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