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DataModules: add configurable args to dataloader #2333

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@anton-emanuel anton-emanuel commented Oct 4, 2024

Closes #2332.

@github-actions github-actions bot added the datamodules PyTorch Lightning datamodules label Oct 4, 2024
@anton-emanuel anton-emanuel marked this pull request as draft October 4, 2024 08:09
@adamjstewart
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How much of a speed difference do you notice?

At the moment, the datamodules are all designed to pass additional kwargs to the dataset class. So we should technically add these new args to every datamodule, not just to the base class. We could design things such that all kwargs are either passed to the data loader or the dataset class, but I'm worried users might get confused by this. Of course, they don't need to understand the feature if they don't want to use it.

I guess my concern is, why stop at only these variables?

@adamjstewart adamjstewart added this to the 0.7.0 milestone Oct 4, 2024
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anton-emanuel commented Oct 4, 2024

Varying all of those three parameters I was able to at least half the time for [_TrainingEpochLoop].train_dataloader_next (probably even less). But I am reading from COGs over internet, so I guess it's very different from having the raster data on the same device.

I agree, it would be better to solve for all arguments, and that there is a trade off between full configurability and simplicity. I just added those variables since those were the ones I wanted to vary.

Could it be an idea to do something like what is done in BaseDataModule._valid_attribute, but just taking kwargs starting with dataloader_ and put in dataloader (I feel like I have seen this solution somewhere in TorchGeo, but might have been another library)?

# ...
DATALOADER_KWARG_PREFIX = "dataloader_"

    # ...
    dataloader_kwargs = {
        k.replace(DATALOADER_KWARG_PREFIX, ""): v
        for k, v in kwargs.items()
        if k.startswith(DATALOADER_KWARG_PREFIX)
    }
    
    # ...
    DataLoader(
        dataset=dataset,
        batch_size=batch_size,
        sampler=sampler,
        batch_sampler=batch_sampler,
        num_workers=self.num_workers,
        collate_fn=self.collate_fn,
        persistent_workers=self.num_workers > 0,
        **dataloader_kwargs
    )

@anton-emanuel anton-emanuel force-pushed the feat/datamodules-configurable-dataloader branch from 1af0682 to 1f56161 Compare October 8, 2024 11:38
@anton-emanuel anton-emanuel force-pushed the feat/datamodules-configurable-dataloader branch from 1f56161 to 8257b8e Compare October 8, 2024 11:38
@anton-emanuel
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anton-emanuel commented Oct 8, 2024

I changed it to use kwargs now

        self.dataloader_kwargs, self.kwargs = split_prefixed_kwargs(
            'dataloader_', **kwargs
        )

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@anton-emanuel anton-emanuel marked this pull request as ready for review October 8, 2024 12:19
@DimitrisMantas
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DimitrisMantas commented Oct 8, 2024

I think it woul be better for kwargs to be a JSON-like dict, something like {"dataset": ..., "dataloader": ...}. We could even turn this into a typed dict or a dataclass so that it can be clearly documented and tested. I think the transformers library likes to use these config structures a lot

@anton-emanuel anton-emanuel force-pushed the feat/datamodules-configurable-dataloader branch from 8fc54ef to b5291a6 Compare October 8, 2024 17:07
@anton-emanuel anton-emanuel force-pushed the feat/datamodules-configurable-dataloader branch from 9a78733 to 88cedbc Compare October 8, 2024 17:23
@github-actions github-actions bot added the testing Continuous integration testing label Oct 9, 2024
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Expose more arguments to dataloader
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