Write a comparator for PyTorch

[misrasaurabh1]

PR Type

• Enhancement
• Tests

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Description

• Add torch dependency check and flag.

• Implement torch.Tensor equality verification.

• Include comprehensive torch tensor tests.

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Changes walkthrough 📝

	Relevant files
Enhancement	comparator.py Integrate torch.Tensor comparisons.                                            codeflash/verification/comparator.py Introduced torch import within try/except. Defined HAS_TORCH flag for dependency handling. Added torch.Tensor specific comparison logic. +12/-0
Tests	test_comparator.py Add torch tensor comparison tests.                                              tests/test_comparator.py Added test_torch function to verify tensor behavior. Tested equality, shape, dtype, and NaN handling. Ensured skipping tests when torch is unavailable. +51/-0

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[github-actions]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 PR contains tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Type Safety The torch tensor comparison logic only checks if the original object is a torch.Tensor and then compares dtypes, shapes, and values without confirming that the new object is also a torch.Tensor. It would be safer to ensure type consistency between both objects. if HAS_TORCH and isinstance(orig, torch.Tensor): if orig.dtype != new.dtype: return False if orig.shape != new.shape: return False return torch.allclose(orig, new, equal_nan=True)

[github-actions]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
Possible issue	Ensure tensor type consistency Insert a type-check for new to confirm it is a torch.Tensor before accessing its attributes. codeflash/verification/comparator.py [171-176] if HAS_TORCH and isinstance(orig, torch.Tensor): + if not isinstance(new, torch.Tensor): + return False if orig.dtype != new.dtype: return False if orig.shape != new.shape: return False return torch.allclose(orig, new, equal_nan=True) Suggestion importance[1-10]: 7 __ Why: The suggestion correctly adds a type-check for new as a torch.Tensor, preventing potential attribute errors, and is an appropriate enhancement to the robustness of the code.	Medium

[aseembits93]

this looks good, torch.allclose will throw exceptions for all the edge cases (except requires_grad) you are checking for, you could simply do try, except for conciseness. Not too sure if we want to check for requires_grad

[dasarchan]

torch.allclose with default params doesn't work as expected for tensors with low magnitude - might want to default to just using rtol?

[aseembits93]

import torch
def torch_comparator(a,b):
    try:
        ans = torch.allclose(a,b,equal_nan=True)
    except RuntimeError:
        return False
    return ans
a = torch.tensor([1,2,3],dtype=torch.float32)
b = torch.tensor([1,2,3],dtype=torch.int64)
print(torch_comparator(a,b))
a = torch.tensor([1,2,3,4],dtype=torch.float32)
b = torch.tensor([1,2,3],dtype=torch.float32)
print(torch_comparator(a,b))
a = torch.tensor([1,2,3],dtype=torch.float32).to(torch.device('mps'))
b = torch.tensor([1,2,3],dtype=torch.float32)
print(torch_comparator(a,b))
a = torch.tensor([1,2,3],dtype=torch.float32)
b = torch.tensor([1,2,3],dtype=torch.float32)
print(torch_comparator(a,b))
#wont work with requires_grad
a = torch.tensor([1,2,3],dtype=torch.float32,requires_grad=True)
b = torch.tensor([1,2,3],dtype=torch.float32,requires_grad=False)
print(torch_comparator(a,b))

@misrasaurabh1 try running this. here's the output

False
False
False
True
True