84 lines
2.2 KiB
Python
84 lines
2.2 KiB
Python
import json
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import time
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from pathlib import Path
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from typing import Any, Dict, Optional
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import safetensors.torch as st
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import torch
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import torch.distributed as dist
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from astrai.parallel.setup import get_rank
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class Checkpoint:
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def __init__(
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self,
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state_dict: Dict[str, Any],
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epoch: int = 0,
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iteration: int = 0,
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extra: Optional[Dict[str, Any]] = None,
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meta: Optional[Dict[str, Any]] = None,
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):
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self.state_dict = state_dict
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self.epoch = epoch
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self.iteration = iteration
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self.extra = extra or {}
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self.meta = meta or {}
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def save(
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self,
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save_dir: str,
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) -> None:
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save_path = Path(save_dir)
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save_path.mkdir(parents=True, exist_ok=True)
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rank = get_rank()
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if rank == 0:
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meta = {
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"epoch": self.epoch,
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"iteration": self.iteration,
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"timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
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}
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meta.update(self.meta)
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with open(save_path / "meta.json", "w") as f:
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json.dump(meta, f, indent=2)
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st.save_file(self.state_dict, save_path / "state_dict.safetensors")
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if self.extra:
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for key, value in self.extra.items():
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torch.save(value, save_path / f"{key}.pt")
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@classmethod
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def load(
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cls,
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save_dir: str,
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) -> "Checkpoint":
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rank = get_rank()
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save_path = Path(save_dir)
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meta = {}
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if rank == 0:
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with open(Path(save_dir) / "meta.json", "r") as f:
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meta = json.load(f)
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if dist.is_initialized():
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meta_list = [meta]
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dist.broadcast_object_list(meta_list, src=0)
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meta = meta_list[0]
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state_dict = st.load_file(save_path / "state_dict.safetensors")
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extra = {}
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for f in save_path.iterdir():
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if f.suffix == ".pt" and f.stem not in ("meta",):
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extra[f.stem] = torch.load(f, map_location="cpu", weights_only=False)
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return cls(
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state_dict=state_dict,
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epoch=meta["epoch"],
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iteration=meta["iteration"],
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extra=extra or None,
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)
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