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[AutoParallel] Fix qwen2vl image shape #10086

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Before submitting

  • Lint code. If there are lint issues, please format the code first.
# Install and register `pre-commit` in the project folder
pip install pre-commit && pre-commit install

# Process previous code files separately
pre-commit run --file XXXX.py
  • Add test cases into tests folder. If there are codecov issues, please add tests cases first.

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Description

Fix qwen2vl image shape

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paddle-bot bot commented Mar 11, 2025

Thanks for your contribution!

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codecov bot commented Mar 11, 2025

Codecov Report

Attention: Patch coverage is 0% with 50 lines in your changes missing coverage. Please review.

Project coverage is 49.95%. Comparing base (f85f1e9) to head (8997484).
Report is 10 commits behind head on develop.

Files with missing lines Patch % Lines
paddlenlp/trainer/auto_trainer.py 0.00% 50 Missing ⚠️

❌ Your patch check has failed because the patch coverage (0.00%) is below the target coverage (80.00%). You can increase the patch coverage or adjust the target coverage.
❌ Your project check has failed because the head coverage (49.95%) is below the target coverage (58.00%). You can increase the head coverage or adjust the target coverage.

Additional details and impacted files
@@             Coverage Diff             @@
##           develop   #10086      +/-   ##
===========================================
- Coverage    50.00%   49.95%   -0.05%     
===========================================
  Files          757      757              
  Lines       122442   122540      +98     
===========================================
- Hits         61225    61218       -7     
- Misses       61217    61322     +105     

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if isinstance(dtensors, paddle.Tensor):
if self.dense_tensor_idx is not None and self.dense_tensor_idx[i] != []:
global_datas = dtensors.split(self.args.gradient_accumulation_steps, axis=0)
next_dtensor = dtensors[i + 1]
next_dtensor_list = (
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shall we refer to num_or_sections parameter in split, user can pass a number(even segmentation) or sections(uneven segmentation)

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Thx, num_or_sections support done

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LGTM

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2 participants