Grain vs. `tf.data` Input Pipeline

[Leandro Lacerda Campos]

Hello MaxText Team,

I'm in the process of deciding between Grain and tf.data for the input pipeline of my next research project, that will be based on Flax. While I recognize the potential benefits of both, I'm particularly interested in understanding the advantages of using Grain over tf.data from your experience, especially regarding runtime performance aspects.

Could you share any benchmarks or insights on the runtime performance differences you observed between the two input pipelines? Additionally, any details on scenarios where Grain particularly outshines tf.data or vice versa would be incredibly helpful for my decision-making process.

Thank you very much for your time and for any information you can provide.

All the best,

[Rafi Witten]

@aireenmei to give context.

Mostly runtime performance is that both are fast enough to not be your bottleneck but Grain gives determinism guarantees!

[aireenmei]

Hi @leandrolcampos,

The biggest benefit of grain is determinism. No matter how your run gets interrupted, you can always recover the same data order from your checkpoint, so your loss curve is reproducible, debuggable.
Other benefits includes:

• minimal dependency, especially if you don't want to depends on TensorFlow
• Close to a global shuffle, while the shuffle in tf.data is limited by memory
• For some transformations, it may be easier to implement in numpy

And like I mentioned with you in another thread under grain repo, I just put up some grain docs: https://github.com/google/grain/tree/main/docs

[Leandro Lacerda Campos]

Hi @rwitten and @aireenmei,

Thanks for your answers. They helped me to better understand the PyGrain value proposition. I'm looking forward to use it in my next research projects.

All the best,