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[Inference] support deepseek_vl2 #10182

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@cheng221 cheng221 commented Mar 18, 2025

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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paddle-bot bot commented Mar 18, 2025

Thanks for your contribution!

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codecov bot commented Mar 18, 2025

Codecov Report

Attention: Patch coverage is 0% with 16 lines in your changes missing coverage. Please review.

Project coverage is 49.69%. Comparing base (3399827) to head (fff5783).
Report is 17 commits behind head on develop.

Files with missing lines Patch % Lines
.../experimental/transformers/deepseek_v2/modeling.py 0.00% 16 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 (49.69%) 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   #10182      +/-   ##
===========================================
- Coverage    49.86%   49.69%   -0.17%     
===========================================
  Files          757      761       +4     
  Lines       122792   124232    +1440     
===========================================
+ Hits         61233    61742     +509     
- Misses       61559    62490     +931     

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@DrownFish19 DrownFish19 changed the title support deepseek_vl2 inference [Inference] support deepseek_vl2 Mar 24, 2025
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LGTM,辛苦推理同学确认

inputs_embeds = self.embed_tokens(ids_remove_padding)
else:
assert len(inputs_embeds.shape) == 3
# This is the case in the image-to-text model such as qwen2-vl,
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这里注释改一下,代码里qwen2-vl全部去掉

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done了

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5 participants