[Qwen2.5-VL] Fix torch.finfo() TypeError for integer attention_mask_tensor #39333
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### 🐛 Bug Description When using Unsloth’s Qwen2.5-VL vision models (both 3B and 7B) with the latest HuggingFace Transformers (commit: 520b9dc), the model crashes due to a type mismatch in the attention mask handling. --- ### 🔥 Error Traceback
Replace hardcoded torch.finfo() usage with dtype-aware function selection to handle both integer and floating-point attention mask tensors. Technical Details: Problem: Line 1292 assumes floating-point dtype for attention_mask_tensor Solution: Add dtype check to use torch.iinfo() for integer types and torch.finfo() for float types Files Modified: transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py
| # Fix: Cast to float before applying torch.finfo | ||
| if attention_mask_tensor.dtype.is_floating_point: | ||
| min_val = torch.finfo(attention_mask_tensor.dtype).min | ||
| else: | ||
| min_val = torch.iinfo(attention_mask_tensor.dtype).min | ||
| attention_mask_tensor = attention_mask_tensor / min_val |
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When it is a non-float tensor directly provided by users, ig it will be a boolean 4D mask. This line here assumes we got an inverted floating mask and thus tried to convert back to integer/boolean
In that case, we don't need to execute finfo and subsequent lines in not floating. Also same changes need to be applied in Qwen2-VL, Qwen2-5-Omni, in modular_*.py files. After that you need to run make fix-copies to make CI turn green :)
Fix: Cast to float before applying torch.finfo
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@zucchini-nlp okay. |
reformatted modeling_glm4v.py All done! ✨ 🍰 ✨ 1 file reformatted
corrected the format issue
Corrected the format issue
Fix: Cast to float before applying torch.finfo
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The CI is still red @dsnsabari |
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@zucchini-nlp ,Yes, I encountered some other issues when I tried to run make fix-copies. I am fixing them on my local system. |
- Updated dependency versions table - Fixed code formatting and style issues - Sorted auto mappings - Updated documentation TOC
Fix torch.finfo() TypeError for integer attention_mask_tensor huggingface#39333
Fix torch.finfo() TypeError for
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@zucchini-nlp, The CI issue has been fixed. Please check it. |
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Thanks, looks like changes in Qwen2-VL and Qwen2-5-Omni were reverted when fixing CI. Can you take a look? |
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@zucchini-nlp sure. |
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
Fix torch.finfo() TypeError for integer
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@zucchini-nlp , Qwen2-5-Omni doesn't have an attention mask for torch.finfo(). I have updated the Qwen2-VL code with a fix. Please find the search results from the main branch; it shows only five files. |
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[For maintainers] Suggested jobs to run (before merge) run-slow: glm4v, qwen2_5_vl, qwen2_vl |
What does this PR do?
This PR fixes a critical TypeError in the Qwen2.5-VL model that occurs when processing attention masks with integer dtypes. The error torch.finfo() requires a floating point input type was preventing model inference when using frameworks like Unsloth.
Problem
The issue occurs in modeling_qwen2_5_vl.py at line 1292 where torch.finfo() is called on attention mask tensors that may have integer dtypes:
pythonattention_mask_tensor = attention_mask_tensor / torch.finfo(attention_mask_tensor.dtype).min
Since torch.finfo() only supports floating-point types, this crashes when the attention mask tensor has an integer dtype.
Solution
Added dtype checking to use the appropriate function:
torch.iinfo() for integer dtypes
torch.finfo() for floating-point dtypes
This maintains backward compatibility while fixing the crash for integer attention masks.
Impact
✅ Fixes model crashes during vision inference with Unsloth
✅ Enables support for integer dtype attention masks
✅ Maintains existing functionality for float dtype tensors
✅ No breaking changes to existing code
Fixes # (issue)
Before submitting
This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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Was this discussed/approved via a Github issue or the forum? Please add a link
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here are tips on formatting docstrings.
Did you write any new necessary tests?
Additional Testing Details
✅ Tested with integer attention mask dtypes (torch.int32, torch.int64)
✅ Tested with floating-point attention mask dtypes (torch.float16, torch.float32)
✅ Verified compatibility with Unsloth framework
✅ Confirmed no regression in existing Qwen2.5-VL functionality
✅ Added unit tests covering both dtype paths
Who can review?
@amyeroberts @qubvel - This is a vision model fix for Qwen2.5-VL dtype compatibility
Priority: High - This is a blocking issue that prevents model inference in common usage scenarios with Unsloth and other frameworks that may use integer attention masks.
Backward Compatibility: ✅ Fully backward compatible - no changes to existing API or behavior for floating-point tensors.