[bugfix] fix multimodal peft_model megatron

[Jintao-Huang]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @Jintao-Huang, 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 addresses a bug in the Megatron framework concerning multimodal PEFT models. It ensures that when a multimodal setup is active, the trainable parameters of the visual model correctly average gradients across tensor parallel domains, which is crucial for proper distributed training and preventing potential issues.

Highlights

• Multimodal PEFT Gradient Averaging: Ensures that trainable parameters within the visual component of multimodal PEFT models in Megatron have average_gradients_across_tp_domain set to True to correctly handle distributed training.

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[gemini-code-assist]

Code Review

This pull request introduces a crucial fix for multimodal PEFT models within the Megatron framework. It correctly ensures that gradients for trainable parameters in the visual component of multimodal models are averaged across the tensor parallelism domain, preventing potential issues during distributed training. The change is well-placed within the prepare_adapter function, aligning with the overall model preparation logic.

[gemini-code-assist]

The code directly accesses model.visual without checking if the attribute exists. While args.is_multimodal implies its presence, explicitly checking with hasattr(model, 'visual') would make the code more robust against unexpected model structures or future changes, preventing a potential AttributeError.

Suggested change

	visual_model = model.visual
	if hasattr(model, 'visual'):
	visual_model = model.visual