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| import os | |
| import torch | |
| from torch.nn.parallel.data_parallel import DataParallel | |
| from torch.nn.parallel.distributed import DistributedDataParallel | |
| from loguru import logger | |
| import gc | |
| import romatch | |
| class CheckPoint: | |
| def __init__(self, dir=None, name="tmp"): | |
| self.name = name | |
| self.dir = dir | |
| os.makedirs(self.dir, exist_ok=True) | |
| def save( | |
| self, | |
| model, | |
| optimizer, | |
| lr_scheduler, | |
| n, | |
| ): | |
| if romatch.RANK == 0: | |
| assert model is not None | |
| if isinstance(model, (DataParallel, DistributedDataParallel)): | |
| model = model.module | |
| states = { | |
| "model": model.state_dict(), | |
| "n": n, | |
| "optimizer": optimizer.state_dict(), | |
| "lr_scheduler": lr_scheduler.state_dict(), | |
| } | |
| torch.save(states, self.dir + self.name + f"_latest.pth") | |
| logger.info(f"Saved states {list(states.keys())}, at step {n}") | |
| def load( | |
| self, | |
| model, | |
| optimizer, | |
| lr_scheduler, | |
| n, | |
| ): | |
| if os.path.exists(self.dir + self.name + f"_latest.pth") and romatch.RANK == 0: | |
| states = torch.load(self.dir + self.name + f"_latest.pth") | |
| if "model" in states: | |
| model.load_state_dict(states["model"]) | |
| if "n" in states: | |
| n = states["n"] if states["n"] else n | |
| if "optimizer" in states: | |
| try: | |
| optimizer.load_state_dict(states["optimizer"]) | |
| except Exception as e: | |
| print(f"Failed to load states for optimizer, with error {e}") | |
| if "lr_scheduler" in states: | |
| lr_scheduler.load_state_dict(states["lr_scheduler"]) | |
| print(f"Loaded states {list(states.keys())}, at step {n}") | |
| del states | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| return model, optimizer, lr_scheduler, n |