Fix issue where LoRa loads on a device different from Dit

[YZBPXX]

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[gemini-code-assist]

Summary of Changes

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This pull request resolves an issue where LoRa models could inadvertently load onto an incorrect device, leading to potential operational problems. The change ensures that a specific tensor (mlp) is explicitly placed on the intended device during the model loading process, thereby enhancing the reliability and correctness of LoRa model initialization.

Highlights

• LoRa Device Placement Fix: Explicitly moves the mlp tensor to the correct device during the LoRa state dictionary processing, resolving an issue where LoRa models might load on an unintended device.

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[gemini-code-assist]

Code Review

This pull request correctly fixes a device mismatch issue when loading LoRA weights, preventing potential runtime errors during tensor operations. The added line ensures that the mlp tensor is on the same device as other tensors before concatenation. I've added one comment suggesting a minor performance optimization to avoid an unnecessary data copy. Overall, this is a good fix that improves the robustness of the LoRA loader.

[gemini-code-assist]

This change introduces a small inefficiency. When mlp is created with torch.zeros (in the if mlp is None branch), it's created on the CPU and then moved to the target device here. This causes an unnecessary data copy. For better performance, it would be ideal to create the tensor directly on the target device by adding device=state_dict_[name].device to the torch.zeros call on line 147. This .to() call would then only be needed for the else branch.