PyTorch Lightning adapter fails to log hyperparams when a git repo is initialized

🐛 Bug

Starting from 3.6, Pytorch Lightning adapter fails to log hyper-parameters when a git repo is initialized

To reproduce

1 - git init 2 - Run any PL script with Aim logging hyperparameters.

For example

import os
import torch
from torch import nn
import torch.nn.functional as F
from torchvision import transforms
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader, random_split
import pytorch_lightning as pl
from aim.pytorch_lightning import AimLogger
from aim import Run, Figure
from typing import Optional, Any
from pytorch_lightning.callbacks import RichProgressBar, ModelCheckpoint

class MNISTDataModule(pl.LightningDataModule):
    def __init__(self, data_dir: str = "./"):
        super().__init__()
        self.data_dir = data_dir
        self.transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])

        # Setting default dims here because we know them.
        # Could optionally be assigned dynamically in dm.setup()
        self.dims = (1, 28, 28)

    def prepare_data(self):
        # download
        MNIST(self.data_dir, train=True, download=True)
        MNIST(self.data_dir, train=False, download=True)

    def setup(self, stage: Optional[str] = None):

        # Assign train/val datasets for use in dataloaders
        if stage == "fit" or stage is None:
            mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
            self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])

            # Optionally...
            # self.dims = tuple(self.mnist_train[0][0].shape)

        # Assign test dataset for use in dataloader(s)
        if stage == "test" or stage is None:
            self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)

            # Optionally...
            # self.dims = tuple(self.mnist_test[0][0].shape)

    def train_dataloader(self):
        return DataLoader(self.mnist_train, batch_size=32, num_workers = 32)

    def val_dataloader(self):
        return DataLoader(self.mnist_val, batch_size=32, num_workers = 32)

    def test_dataloader(self):
        return DataLoader(self.mnist_test, batch_size=32, num_workers = 32)

class LitAutoEncoder(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(nn.Linear(28 * 28, 64), nn.ReLU(), nn.Linear(64, 3))
        self.decoder = nn.Sequential(nn.Linear(3, 64), nn.ReLU(), nn.Linear(64, 28 * 28))

    def forward(self, x):
        # in lightning, forward defines the prediction/inference actions
        embedding = self.encoder(x)
        return embedding

    def training_step(self, batch, batch_idx):
        # training_step defined the train loop.
        # It is independent of forward
        x, y = batch
        x = x.view(x.size(0), -1)
        z = self.encoder(x)
        x_hat = self.decoder(z)
        loss = F.mse_loss(x_hat, x)
        # Logging to TensorBoard by default
        self.log("train_loss", loss)
        return loss
    
    def validation_step(self, batch, batch_idx):
        # training_step defined the train loop.
        # It is independent of forward
        x, y = batch
        x = x.view(x.size(0), -1)
        z = self.encoder(x)
        x_hat = self.decoder(z)
        loss = F.mse_loss(x_hat, x)
        

        self.log("val_loss", loss)
        return {"val_loss" : loss}
    
    def test_step(self, batch, batch_idx):
        # training_step defined the train loop.
        # It is independent of forward
        x, y = batch
        x = x.view(x.size(0), -1)
        z = self.encoder(x)
        x_hat = self.decoder(z)
        loss = F.mse_loss(x_hat, x)
        # Logging to TensorBoard by default
        self.log("test_loss", loss)
        return {"test_loss" : loss}

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
        return optimizer



datamodule = MNISTDataModule(os.getcwd() + "/mnist_data/")

# init model
autoencoder = LitAutoEncoder()

aim_logger = AimLogger(experiment="test")
aim_logger.log_hyperparams({
        'learning_rate': 0.001,
        'batch_size': 64
})

ckpt =  ModelCheckpoint(monitor = 'val_loss', dirpath = "checkpoint/", filename = "mycheck")
ckpt.test_score = 26.51
trainer = pl.Trainer(callbacks = [RichProgressBar()], logger = aim_logger,
            check_val_every_n_epoch = 1, gpus = 1, max_epochs = 2)

trainer.fit(autoencoder, datamodule)

Error when calling `get_git_info`

File "/home/rdeffaye/workspace/playground/pl_test.py", line 118, in <module>
    aim_logger.log_hyperparams({
  File "/home/rdeffaye/.rl4rec39/lib/python3.9/site-packages/pytorch_lightning/utilities/distributed.py", line 50, in wrapped_fn
    return fn(*args, **kwargs)
  File "/home/rdeffaye/.rl4rec39/lib/python3.9/site-packages/aim/sdk/adapters/pytorch_lightning.py", line 74, in log_hyperparams
    self.experiment.set(('hparams', key), value, strict=False)
  File "/home/rdeffaye/.rl4rec39/lib/python3.9/site-packages/pytorch_lightning/loggers/base.py", line 43, in experiment
    return get_experiment() or DummyExperiment()
  File "/home/rdeffaye/.rl4rec39/lib/python3.9/site-packages/pytorch_lightning/utilities/distributed.py", line 50, in wrapped_fn
    return fn(*args, **kwargs)
  File "/home/rdeffaye/.rl4rec39/lib/python3.9/site-packages/pytorch_lightning/loggers/base.py", line 41, in get_experiment
    return fn(self)
  File "/home/rdeffaye/.rl4rec39/lib/python3.9/site-packages/aim/sdk/adapters/pytorch_lightning.py", line 51, in experiment
    self._run = Run(
  File "/home/rdeffaye/.rl4rec39/lib/python3.9/site-packages/aim/sdk/run.py", line 287, in __init__
    'git_info': get_git_info(),
  File "/home/rdeffaye/.rl4rec39/lib/python3.9/site-packages/aim/ext/utils.py", line 41, in get_git_info
    commit_hash, commit_timestamp, commit_author = results[1].split('/')
ValueError: not enough values to unpack (expected 3, got 1)

Environment

• Aim Version >= 3.6
• Python version 3.9
• pip version 22.0.4
• OS : RedHat 4.8.5
• git version 2.26

[alberttorosyan]

Hey @RomDeffayet ! Thanks for reporting this. @devfox-se please look into this.

[devfox-se]

Closing due to inactivity, feel free to reopen in case this still persists.