Adding dtype casting for direct state transfer.

[NicoGrande]

This PR adds support for dtype casting in the direct_state_transfer method if we have different dtypes for identical keys in source and target pytrees. This is useful for the case where we want to train a model with a certain dtype precision and then serve the same model with lower precision.

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 @NicoGrande, 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 state transfer utility by introducing automatic data type casting. This improvement allows models with different precision requirements (e.g., training in float32 and deploying in bfloat16) to seamlessly transfer weights, significantly improving flexibility and reducing manual conversion steps. The changes ensure that parameters are correctly cast to the target data type during the transfer process, maintaining numerical accuracy where possible.

Highlights

• Data Type Casting in State Transfer: Implemented automatic data type casting during direct state transfer operations, allowing source parameters (e.g., float32) to be converted to the target's data type (e.g., bfloat16) when their shapes align.
• Support for Scanned Layers: Extended the data type casting functionality to correctly handle parameters within scanned layers, ensuring proper slicing and casting when transferring state from scanned to unrolled layer structures.
• New Unit Tests: Added comprehensive unit tests to validate the new data type casting behavior for both direct parameter mapping and scanned layer scenarios, confirming correct conversion and value preservation.

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Changelog

• tests/generate/utils_test.py
  • Added test_transfer_state_directly_with_dtype_casting to verify direct and scanned layer state transfer with dtype casting from float32 to bfloat16.
  • Added test_transfer_state_directly_scanned_layers_casting to specifically test dtype casting during transfer from scanned layers to unrolled layers.
• tunix/generate/utils.py
  • Modified intersect_trees to apply _apply_dtype_cast to source values when a direct key match is found, ensuring the source data type matches the target.
  • Modified intersect_trees to apply _apply_dtype_cast to sliced source values when transferring state from scanned layers, ensuring correct data type conversion for unrolled layers.

Activity

• The pull request author, NicoGrande, has provided a checklist indicating that unit tests have been added and verified, existing code is not broken, documentation is appropriate, the PR is based on the latest main branch, the Contributor License Agreement has been signed, and contribution guidelines have been followed. However, the checkboxes are currently unchecked.

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Code Review

This pull request introduces dtype casting during direct state transfer, which is a valuable feature for handling weights of different precisions. The implementation correctly covers both direct parameter matches and mappings from scanned layers. The new tests are comprehensive and effectively validate this functionality. I've included two suggestions to enhance the robustness of the code by adding checks to prevent potential crashes when encountering non-tensor values within state dictionaries.