[feat]: kt-kernel: Add resume arg to CPU weight conversion

[DocShotgun]

What does this PR do?

Adds an arg to scripts/convert_cpu_weights.py named --resume-layer to allow resuming conversion on a specific layer - useful if conversion fails mid-run due to running out of memory despite enabling --no-merge-safetensor.

Before submitting

• Did you read the contributor guideline?
• Did you write any new necessary tests? N/A

[gemini-code-assist]

Summary of Changes

Hello @DocShotgun, 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 CPU weight conversion script by adding a resume functionality. This improvement allows the conversion process to be restarted from a specific layer, significantly increasing the robustness and user-friendliness of the script, especially when dealing with large models or resource constraints that might cause interruptions.

Highlights

• New Feature: Resume Conversion: Introduced a new command-line argument --resume-layer to scripts/convert_cpu_weights.py, enabling users to restart the weight conversion process from a specified layer index.
• Improved Robustness: This feature is particularly useful for handling mid-run failures, such as out-of-memory errors during large model conversions, allowing for efficient recovery without re-processing already completed layers.

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[gemini-code-assist]

Code Review

This pull request introduces a --resume-layer argument to allow resuming the weight conversion process from a specific layer. The implementation is straightforward and correct. My feedback focuses on improving the logging messages for better clarity and reducing verbosity, and on updating the documentation to reflect the new functionality. These changes will enhance the user experience and maintainability of the script.

[gemini-code-assist]

This print statement is executed for every skipped layer. If resuming from a high layer number (e.g., 100), this will produce 100 lines of logs, which can be noisy. Additionally, the resume_layer is already printed at the start, making it redundant here. Consider simplifying the message to reduce log verbosity. For a future improvement, you might consider printing a single summary message before the loop instead of logging each skipped layer.

                print(f"Skipping layer {layer_idx}")