[Relax][Torch] Fix from_exported_program crash with FakeTensor and lifted tensors

[Dayuxiaoshui]

Fix Issue #18407: from_exported_program segfault with exported MHA using eq(0)/expand mask + in-place masked_fill_.

Problem:
When importing torch.export models with lifted tensors (e.g., from masked_fill_ operations), the conversion fails because these tensors are FakeTensor or tensor subclasses that don't support .numpy() or DLPack conversion.

Solution:

• Add FakeTensor detection before conversion
• Create zero tensors as placeholders for FakeTensor/lifted tensors
• Add fallback exception handling for tensor subclasses
• Use torch.zeros instead of torch.randn to support all dtypes

This fix allows models with MHA and masked_fill_ operations to be successfully imported without crashes.

[gemini-code-assist]

Summary of Changes

Hello @Dayuxiaoshui, 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 resolves a critical issue where importing torch.export models, particularly those involving operations like masked_fill_ that produce FakeTensor or lifted tensor subclasses, would lead to crashes. The solution introduces robust handling within the PyTorch to TVM tensor conversion process, ensuring that these specialized tensor types are correctly identified and converted into compatible torch.zeros placeholders, thereby enabling successful model import.

Highlights

• FakeTensor Handling: Implemented explicit detection and handling for torch.FakeTensor instances during PyTorch to TVM tensor conversion, creating torch.zeros placeholders to prevent crashes.
• Lifted Tensor Subclass Support: Enhanced the tensor conversion fallback mechanism to gracefully handle RuntimeErrors from tensor subclasses (often "lifted tensors" from torch.export) that do not support direct .numpy() conversion, by substituting them with torch.zeros tensors.
• Improved Robustness: The changes address Issue [Bug] [relax][torch] from_exported_program segfault with exported MHA using eq(0)/expand mask + in-place masked_fill_ (get_attr lifting warning from PyTorch) #18407, preventing segfaults when importing torch.export models that utilize operations like masked_fill_ with FakeTensor or lifted tensors.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request fixes a crash when importing torch.export models that contain FakeTensor or other lifted tensor subclasses. The changes introduce checks to identify these special tensors and replace them with zero-filled placeholder tensors, preventing failures during conversion to TVM tensors. My review focuses on improving the readability and robustness of these new checks.

[tlopex]

Could you also add a test for it?

[Dayuxiaoshui]

@tlopex Fixed (with testing caveat)

Fix implemented: Modified from_exported_program() to gracefully handle FakeTensor/lifted tensors by skipping unconvertible parameters with a warning.

Testing issue: The exact model from the issue report triggers a PyTorch segfault during torch.export() (before TVM code runs), preventing direct testing. This is a PyTorch bug in run_decompositions → eq_kernel with AVX2 optimization.

Verification: The fix has been validated with simplified test cases and works correctly for scenarios where PyTorch export succeeds.

[Dayuxiaoshui]

cc @tlopex