IBM experimental dataloaders

[daviswer]

This PR introduces an experimental PyTorch-native dataloader from IBM that is distributed, stateful, checkpointable, composable and rescalable. It is intended for use in large-scale model pretraining, particularly in research settings where rapid iteration between datasets may be required. It automatically and invisibly handles data sharding, shuffling, subdataset weighting, checkpoint saving and loading, and more, with minimal overhead and high throughput.

• Add experimental dataset source file
• Add experimental dataloader builder, hooked into torchtitan cfg
• Update torchtitan cfg with additional dataset arg fields
• Update train script to build experimental dataloader instead of hf depending on cfg flags
• Replace the existing C4-mini example dataset with one that matches the expected formatting for the experimental dataloader
• TODO: port over unit tests as well
• TODO: preprocessing script(s) for the new dataset format
• TODO: further cleanup/iteration

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[gokulavasan]

Thanks for the PR! Reviewing it now

[daviswer]

Thanks for taking a look! Left a bunch of responses, hopefully this brings clarity

[daviswer]

Updating the dataloader to match the latest version in our public repo. Re-syncs to latest main, and incorporates many previously discussed features:

• Remove dependence on the separate metadata "count file" in the dataset directory
• Support n_workers > 1. This is accomplished by shunting all path/rank-dependent setup out of initialization and into a new setup() method, which runs after init but before any other op
• Support HF-style parquet raw text datasets, with tokenization on the fly (for reasonably sized documents/shardfiles)
• Support non-flat data directories: all legal files under the specified location will be included, regardless of depth or location. Enables simple weight-free dataset mixing via a single StreamingDocDataset on the parent directory
• SamplingDataset and ScalableShardDatasetare now implemented as proper _WrapperDatasets, reflecting the intended modular usage
• Fix Weird_Separated_Camel_Case naming convention in favor of ProperClassNaming
• Allow PreloadBufferDataset to shrink back down to the desired size after rescaling to a smaller number of workers