[fix](kt-kernel): Drop Python-loaded weights after C++ weight loading in LlamafileMoEWrapper

[SCDESPERTATE]

What does this PR do?

In the LlamafileMoEWrapper's load_weights method, weights loaded by the Python loader are retained in weights_to_keep for TP-splitting and copying to the C++ backend. However, these weights are no longer needed after the C++ backend completes the loading process.
This PR ensures that the Python-loaded weights are explicitly dropped after the C++ backend finishes its operation, freeing up memory and preventing unnecessary retention of large weight tensors.

Fixes # (issue)
reset the self.weights_to_keep at the end of LlamafileMoEWrapper.load_weights

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• Did you write any new necessary tests?

[gemini-code-assist]

Summary of Changes

Hello @SCDESPERTATE, 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 introduces a memory optimization within the LlamafileMoEWrapper's weight loading mechanism. It addresses the issue where Python-loaded weight tensors were retained in memory even after the C++ backend had finished processing them. By explicitly setting self.weights_to_keep to None post-loading, the change ensures that these large tensors are promptly released, leading to improved memory efficiency during the weight loading phase.

Highlights

• Memory Optimization: Explicitly drops Python-loaded weights in LlamafileMoEWrapper after the C++ backend completes its loading process, preventing unnecessary memory retention.
• Resource Management: Resets self.weights_to_keep to None at the end of the LlamafileMoEWrapper.load_weights method, ensuring that large weight tensors are released.

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[gemini-code-assist]

Code Review

This pull request correctly addresses a memory management issue in LlamafileMoEWrapper by releasing references to large weight tensors after they have been loaded into the C++ backend. This prevents unnecessary memory retention. My review includes a suggestion to further simplify the code by removing the member variable used for lifetime management, which would make the implementation cleaner and more idiomatic.

[gemini-code-assist]

While setting self.weights_to_keep = None correctly frees up the memory, a cleaner approach would be to remove the self.weights_to_keep member variable entirely.

The tensors (gate_data, up_data, down_data) are local to the load_weights method and will remain in scope until the method returns. Since self.cpu_infer.sync() blocks until the C++ copy is complete, the lifetime of these local variables is sufficient to ensure the memory is valid for the C++ backend.

This makes self.weights_to_keep redundant. You could consider a refactoring to simplify the code:

1. Remove self.weights_to_keep = None from the __init__ method (line 135).
2. Remove the assignment self.weights_to_keep = (gate_data, up_data, down_data) on line 182.
3. Remove this newly added code block.

This would make the lifetime management implicit and rely on standard Python garbage collection, resulting in cleaner, more idiomatic code.