Hydra + DDP Fails for NeMo after Hydra refactor in 1.8

Bug description

Related #15545

NeMo PR where regression was spotted https://github.com/NVIDIA/NeMo/pull/5353

After https://github.com/Lightning-AI/lightning/pull/11617 was merged and included in 1.8, this has caused NeMo to break with DDP (as NeMo uses Hydra internally). I’m going to cross-paste the explanation from the above PR:

In this PR https://github.com/Lightning-AI/lightning/pull/11617 Lightning changed the way in which sub-processes are started in DDP. Instead of re-running the command (and passing env variables to set the rank), sub-processes now use a saved config yaml file auto-generated by Hydra, default stored to .hydra to pass the arguments via config.

In NeMo, we disable the creation of this output subdirectory, and we always set the run directory to the current working directory. This lets the experiment manager handle everything regarding checkpoints/logging directory instead of hydra.

The issue is that at running of sub-processes, the hydra runner is not aware of the experiment manager’s directory choices. If we allow the sub directory to be created in the default .hydra, with the DDP Lightning code we’d start processes in the current working directory, each with a new folder (`.pl_hydra_local_rank_{rank}).

This issue is if you have multiple runs in the same repo, each of them will overwrite each other and it would be a mess.

I have been unable to come up with an elegant solution between NeMo and Lightning.

• The easiest option would be to monkey-patch the class to the old behaviour with argv, however this doesn’t future proof at all, if anything goes the opposite direction.

I may have missed something however, so if anyone has any other suggestions on how we can fix this please let me know!

cc @tchaton @justusschock @awaelchli @akihironitta @borda @titu1994 @ericharper

How to reproduce the bug

Requires 2 devices (not sure if it has to be on the GPU though).

# install NeMo with Lightning 1.8 (WIP) support
pip install https://github.com/NVIDIA/NeMo/archive/refs/heads/feat/lightning_1.8_support.zip

Create config in conf/config.yaml:

name: "QuartzNet15x5"
exp_manager:
  exp_dir: null
  name: ${name}
  create_tensorboard_logger: False
  create_checkpoint_callback: False
  create_wandb_logger: False

Create file with this code:

import os

import torch
from nemo.core.config import hydra_runner
from nemo.utils.exp_manager import exp_manager
from pytorch_lightning import LightningModule, Trainer
from torch.utils.data import DataLoader, Dataset


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):
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

    def forward(self, x):
        return self.layer(x)

    def training_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("train_loss", loss)
        return {"loss": loss}

    def validation_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("valid_loss", loss)

    def test_step(self, batch, batch_idx):
        loss = self(batch).sum()
        self.log("test_loss", loss)

    def configure_optimizers(self):
        return torch.optim.SGD(self.layer.parameters(), lr=0.1)


@hydra_runner(config_path="conf", config_name="config")
def run(cfg):
    train_data = DataLoader(RandomDataset(32, 64), batch_size=2)
    val_data = DataLoader(RandomDataset(32, 64), batch_size=2)

    model = BoringModel()
    trainer = Trainer(
        default_root_dir=os.getcwd(),
        max_epochs=1,
        devices=2,
        accelerator='gpu',
        logger=False,
        enable_checkpointing=False,
        strategy='ddp',
    )
    exp_manager(trainer=trainer, cfg=cfg.exp_manager)
    trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)


if __name__ == "__main__":
    run()

Run file.

python report.py
[NeMo W 2022-11-15 05:08:44 nemo_logging:349] /home/snarenthiran/anaconda3/lib/python3.9/site-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.
    See https://hydra.cc/docs/next/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.
      ret = run_job(

GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
[NeMo I 2022-11-15 05:08:44 exp_manager:343] Experiments will be logged at /home/snarenthiran/nemo_experiments/QuartzNet15x5/2022-11-15_05-08-44
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/2
Cannot find primary config 'config.yaml'. Check that it's in your config search path.

Config search path:
	provider=hydra, path=pkg://hydra.conf
	provider=main, path=file:///home/snarenthiran
	provider=schema, path=structured://

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

[awaelchli]

Brief update: We decided to revert #11617 in #15727 to restore the previous behavior. We believe that this issue with Nemo is affecting more users than the original #11300, but we will revisit the original fix. https://github.com/Lightning-AI/lightning/pull/15727 is implementing the revert and I’m considering breaking out a test case based on what @SeanNaren has shared here. However, I need to study the behavior of the exp manager first.

[justusschock]

@titu1994 that’s fair. We’ll definitely be looking for other options! Just wanted to know if we need to look into this direction or rather another