Fix emb loss scale

[tastelikefeet]

PR type

• Bug Fix
• New Feature
• Document Updates
• More Models or Datasets Support

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[gemini-code-assist]

Summary of Changes

Hello @tastelikefeet, 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 critical bug in the infonce_loss function by correcting the loss scaling mechanism, which previously involved an unnecessary division by world_size. This ensures accurate loss computation, particularly in distributed training setups. Additionally, it includes a minor update to the command-line parameters documentation, clarifying the acceptable values for the SWIFT_SINGLE_DEVICE_MODE parameter.

Highlights

• Loss Calculation Correction: Fixed an issue in the infonce_loss function where the loss was incorrectly scaled by world_size, leading to an erroneous calculation in distributed training environments. The division by world_size was removed from two separate loss calculation paths.
• Documentation Update: Clarified the valid input values for the SWIFT_SINGLE_DEVICE_MODE parameter in both the Chinese and English versions of the command-line parameters documentation, specifying that '0' (default) and '1' are the acceptable values.

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[gemini-code-assist]

Code Review

This pull request fixes a bug in the loss scaling for infonce_loss by removing a redundant division by world_size. This ensures the loss is correctly scaled, especially in distributed training scenarios. The changes in swift/plugin/loss.py are correct and address the issue described. The documentation updates are also clear and accurate.