Sanbao/gpt

[susanbao]

Resolves #<issue_number_goes_here>

It's a good idea to open an issue first for discussion.

Reference

Colab Notebook

Checklist

• I have added all the necessary unit tests for my change.
• I have verified that my change does not break existing code and all unit tests pass.
• I have added all appropriate doc-strings/documentation.
• My PR is based on the latest changes of the main branch (if unsure, rebase the code).
• I have signed the Contributor License Agreement.
• I have followed Contribution Guidelines.

[gemini-code-assist]

Summary of Changes

Hello @susanbao, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request enhances the model generation and sharding utilities. It introduces support for complex model architectures that utilize interleaved layers, ensuring that parameters are correctly processed during tree intersection. Additionally, it improves compatibility with JAX's sharding mechanisms by explicitly recognizing GSPMDSharding, which is crucial for distributed training setups.

Highlights

• Inhomogeneous Scan Support: Implemented logic within the intersect_trees function to support inhomogeneous scanning, specifically for models with interleaved layers (e.g., GPT-OSS 120B). This allows for correct parameter slicing when layers follow a cyclical pattern.
• JAX Sharding Compatibility: Extended the reshard_pytree utility to recognize and correctly handle GSPMDSharding objects from JAX, ensuring broader compatibility with different sharding configurations.

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Changelog

• tunix/generate/utils.py
  • Added logic to intersect_trees to detect and handle inhomogeneous scanning patterns, such as those found in models with interleaved layers (e.g., 'layers_0', 'layers_1').
  • Introduced a scan_idx variable to correctly index parameters based on the detected layer cycle.
  • Updated the call to _slice_scanned_param to use the new scan_idx.
• tunix/rl/reshard.py
  • Imported GSPMDSharding from jax._src.sharding_impls.
  • Modified the _get_dst_sharding helper function to include GSPMDSharding in its type check, allowing it to correctly identify this sharding type.

Activity

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[gemini-code-assist]

Code Review

This pull request adds support for loading models with inhomogeneous scanned layers and updates the JAX resharding utility. The new layer mapping logic in tunix/generate/utils.py seems correct for its purpose.

However, I've found a high-severity maintainability issue in tunix/rl/reshard.py. The code now imports GSPMDSharding from a private jax._src module, which is an unstable API that could break in future JAX updates. I've provided a suggestion to use the public jax.sharding.Sharding base class instead, which is a more robust and future-proof solution.

[gemini-code-assist]

Using GSPMDSharding requires importing from a private JAX _src module (line 31), which is risky as these internal APIs can change without warning. This violates the maintainability principle from the style guide (line 12).

A more robust and future-proof approach is to check against the public abstract base class jax.sharding.Sharding. This will cover all sharding types (NamedSharding, SingleDeviceSharding, GSPMDSharding, etc.) and allows you to remove the import from jax._src.sharding_impls.

    if isinstance(x, jax.sharding.Sharding):

References

1. The style guide states that code should be maintainable and easy to modify and extend. Relying on private, unstable APIs from dependencies makes the code harder to maintain as it can break with dependency updates. ^(link)