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import os
import torch
from torch.nn.parallel.data_parallel import DataParallel
from torch.nn.parallel.distributed import DistributedDataParallel
import gc
import DeDoDe
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 DeDoDe.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")
print(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 DeDoDe.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
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