[bugfix] fix infer_backend lmdeploy

[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 compatibility bug within the inference backend selection logic. Previously, a specific argument, reranker_use_activation, was unconditionally passed to all inference engines. This fix modifies the system to only include this argument when the selected backend is either PyTorch or vLLM, thereby preventing issues with other backends like lmdeploy that do not recognize or utilize this parameter.

Highlights

• Conditional Argument Passing: The reranker_use_activation argument is now conditionally passed to the inference engine. It will only be included if the infer_backend is explicitly set to 'pt' (PyTorch) or 'vllm'.
• Bug Fix for lmdeploy Compatibility: This change resolves a bug where reranker_use_activation was being passed to the lmdeploy backend, which does not support this argument, leading to potential errors or unexpected behavior.

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[gemini-code-assist]

Code Review

This pull request addresses a bug where the reranker_use_activation parameter was incorrectly passed to all inference backends, leading to errors with unsupported backends like lmdeploy. The fix correctly isolates this parameter, ensuring it is only passed to the pt and vllm backends that utilize it. The change is well-targeted and effectively resolves the issue. The implementation is clean and I have no further suggestions.