[Quantization] add BNB for MixtralForCausalLM

[jeejeelee]

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Purpose

FIX #20061 (comment)

Test Plan

Test Result

(Optional) Documentation Update

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @jeejeelee, 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 primarily introduces BitsAndBytes (BNB) quantization support for Mixtral models. To achieve this, I've undertaken significant refactoring to decouple the weight loading mechanisms of GraniteMoe from Mixtral, allowing for independent support of quantization methods. Additionally, I've improved the modularity of Mixtral's weight loading and applied minor optimizations to other MoE models' weight loading processes.

Highlights

• Quantization Support: Added explicit support for 'bitsandbytes' (BNB) quantization for Mixtral models by including it in the mixtral_supported list within vllm/model_executor/model_loader/utils.py.
• Model Decoupling and Refactoring: Decoupled the weight loading logic for GraniteMoeModel from MixtralModel. The _load_weights method, which was previously shared, has been copied and adapted into GraniteMoeModel in vllm/model_executor/models/granitemoe.py. This allows GraniteMoe to support new quantization methods like BNB independently without relying on Mixtral's implementation.
• Modularity Improvements: Refactored MixtralModel in vllm/model_executor/models/mixtral.py by extracting the expert mapping logic into a new get_expert_mapping method. This improves code organization and reusability within the load_weights method.
• Weight Loading Optimization: Applied a minor optimization to the load_weights methods in olmoe.py, qwen2_moe.py, and qwen3_moe.py by caching the result of self.get_expert_mapping() to avoid redundant calls within the loading loop.

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[gemini-code-assist]

Code Review

This pull request adds BNB quantization support for Mixtral models by decoupling GraniteMoe from Mixtral, duplicating weight loading logic. While functional, this introduces a maintainability concern due to code duplication. Refactoring the shared logic into a common utility or mixin is suggested to address this.

[gemini-code-assist]

This function is copied from MixtralModel.load_weights to decouple from mixtral, but this introduces code duplication. Consider refactoring the weight loading logic into a shared utility function or a mixin to reduce duplication and improve maintainability.

[gemini-code-assist]

Consider calling self._load_weights instead of mixtral.MixtralModel.load_weights to avoid direct dependency on the mixtral module.

[gemini-code-assist]

Consider calling self._load_weights instead of GraniteMoeModel._load_weights to avoid direct dependency on the GraniteMoeModel module.

[gemini-code-assist]

Consider adding bnb to mixtral_supported for consistency and clarity.

        "quark",
        "bnb",

[Isotr0py]

LGTM!