[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[bot]]

Summary of Changes

Hello @Dayuxiaoshui, I'm Gemini Code Assist[^1]! 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 #18407, preventing segfaults when importing torch.export models that utilize operations like masked_fill_ with FakeTensor or lifted tensors.

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[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]

@tlopex