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MTP Multimodal compatible#8303

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jhvmhg wants to merge 3 commits intomodelscope:mainfrom
jhvmhg:fetaure-MTP-Multimodal-compatible
Open

MTP Multimodal compatible#8303
jhvmhg wants to merge 3 commits intomodelscope:mainfrom
jhvmhg:fetaure-MTP-Multimodal-compatible

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@jhvmhg jhvmhg commented Mar 12, 2026

PR type

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

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Summary of Changes

Hello, 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 enhances the multimodal compatibility within the Megatron framework by introducing more flexible handling of rotary positional embeddings for packed sequences. It specifically addresses scenarios where position IDs might not conform to a single-element shape, which is crucial for supporting diverse input modalities. Additionally, a minor refactoring improves the readability of the recomputation logic.

Highlights

  • Multimodal Compatibility: The strict assertion requiring position_ids.shape[0] to be 1 for packed sequences was removed, and new logic was added to handle cases where position_ids.shape[0] is not 1 by rolling the rotary_pos_emb.
  • Code Readability: The condition for full recomputation during training was refactored into a dedicated boolean variable _do_recompute for improved clarity.
  • TODO Removal: A TODO comment related to multimodal compatibility in the forward method was removed, indicating progress or completion of the related task.

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Changelog
  • swift/megatron/init.py
    • Removed a TODO comment related to multimodal compatibility.
    • Updated the logic for applying rotary positional embeddings to packed sequences, allowing for position_ids with shapes other than [1, ...].
    • Refactored the recomputation condition into a new variable _do_recompute for better code clarity.
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Code Review

This pull request makes Multi-Token Prediction (MTP) compatible with multimodal models. The core change in swift/megatron/init.py replaces a restrictive assertion on position_ids shape with more flexible logic. This new logic uses torch.roll to correctly align rotary position embeddings for next-token prediction when dealing with multimodal inputs in packed sequences. The changes appear to correctly implement the intended feature. I have added one suggestion to improve code clarity by adding a comment to an otherwise obscure part of the logic.

jhvmhg and others added 2 commits March 12, 2026 16:03
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
@Jintao-Huang
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Thank you for your suggestion. The changes from this PR have been integrated here:
#8313

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