[WIP] Support tuning_fused_moe for MLLMs

[JustinTong0323]

Motivation

blocks by #11199

Modifications

Accuracy Tests

Benchmarking and Profiling

Checklist

• Format your code according to the Format code with pre-commit.
• Add unit tests according to the Run and add unit tests.
• Update documentation according to Write documentations.
• Provide accuracy and speed benchmark results according to Test the accuracy and Benchmark the speed.

[gemini-code-assist]

Summary of Changes

Hello @JustinTong0323, 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 'tuning_fused_moe_triton' script to broaden its compatibility with various Mixture-of-Experts (MoE) model architectures, particularly focusing on Multimodal Large Language Models (MLLMs). It streamlines the process of extracting essential MoE configuration parameters from different model types, ensuring the tuning process can be applied to a wider range of models.

Highlights

• Expanded MoE Model Support: The tuning script now includes support for 'Qwen3VLMoeForConditionalGeneration' and improves handling for encoder-decoder models by checking for 'text_config' to correctly extract MoE parameters.
• Refactored Architecture Detection: The logic for identifying model architectures and extracting MoE-related parameters (number of experts, top-k, intermediate size) has been refactored for clarity and maintainability, introducing an 'architecture' variable to simplify conditional checks.
• Temporary Parameter Exclusion: The 'per_channel_quant' parameter has been temporarily commented out in the 'save_configs' function, likely for debugging or a temporary disablement.

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