Loading a checkpoint that was saved in PL < 1.2 still breaks #7400
Description
🐛 Bug
Related Issue/PRs.
- Loading a model from PL 1.2 that was saved in PL 1.1 breaks #6549
- fix gpus default for Trainer.add_argparse_args #6898
- Can't get attribute '_gpus_arg_default' #7031
- Fix attribute error for _gpus_arg_default loading checkpoint prior to 1.2.8 #7043
A checkpoint that was saved in PL < 1.2 with Trainer.add_argparse_args still breaks, when loaded in PL >= 1.2
AttributeError: Can't get attribute '_gpus_arg_default' on <module 'pytorch_lightning.utilities.argparse_utils' from Please reproduce using the BoringModel
To Reproduce
Use following BoringModel and post here
import argparse
import torch
from torch.utils.data import DataLoader, Dataset
from pytorch_lightning import Trainer, LightningModule
class RandomDataset(Dataset):
def __init__(self, size, length):
self.len = length
self.data = torch.randn(length, size)
def __getitem__(self, index):
return self.data[index]
def __len__(self):
return self.len
class BoringModel(LightningModule):
def __init__(self, **kwargs):
super().__init__()
self.save_hyperparameters()
self.layer = torch.nn.Linear(32, 2)
def forward(self, x):
return self.layer(x)
def loss(self, batch, prediction):
# An arbitrary loss to have a loss that updates the model weights during `Trainer.fit` calls
return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))
def step(self, x):
x = self(x)
out = torch.nn.functional.mse_loss(x, torch.ones_like(x))
return out
def training_step(self, batch, batch_idx):
output = self(batch)
loss = self.loss(batch, output)
return {"loss": loss}
def validation_step(self, batch, batch_idx):
output = self(batch)
loss = self.loss(batch, output)
return {"x": loss}
def test_step(self, batch, batch_idx):
output = self(batch)
loss = self.loss(batch, output)
return {"y": loss}
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
return [optimizer], [lr_scheduler]
def train_dataloader(self):
return DataLoader(RandomDataset(32, 64))
def val_dataloader(self):
return DataLoader(RandomDataset(32, 64))
def test_dataloader(self):
return DataLoader(RandomDataset(32, 64))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser = Trainer.add_argparse_args(parser)
args = parser.parse_args()
model = BoringModel(**vars(args))
trainer = Trainer.from_argparse_args(args, fast_dev_run=True)
trainer.fit(model)
# comment the line on >= 1.2.0
trainer.save_checkpoint('model.pt')
# comment the line below on < 1.2.0
BoringModel.load_from_checkpoint('model.pt')Expected behavior
The model should load without any error.
Environment
Note: Bugs with code are solved faster ! Colab Notebook should be made public !
-
IDE: Please, use our python bug_report_model.py template. -
Colab Notebook: Please copy and paste the output from our environment collection script (or fill out the checklist below manually).
You can get the script and run it with:
wget https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/tests/collect_env_details.py
# For security purposes, please check the contents of collect_env_details.py before running it.
python collect_env_details.py
- PyTorch Version (e.g., 1.0): 1.8.1
- OS (e.g., Linux): Windows10
- How you installed PyTorch (
conda,pip, source): pip - Build command you used (if compiling from source):
- Python version: 3.8.6
- CUDA/cuDNN version: 11.0 / 8.0.2
- GPU models and configuration: RTX 3070
- Any other relevant information: