[Relax] Refactoring Duplicate cuBLAS/hipBLAS Tests

[guan404ming]

How

• Created test_codegen_blas_common.py with shared test code
• Removed duplicate code from both test files

[gemini-code-assist]

Summary of Changes

Hello @guan404ming, 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 refactors the testing infrastructure for cuBLAS and hipBLAS codegen by extracting common testing logic into a new shared utility file. This change significantly reduces code duplication, improves the maintainability of the test suite, and streamlines the process for verifying BLAS offload functionalities across different backends.

Highlights

• Shared Test Utilities: A new file, test_codegen_blas_common.py, has been introduced to centralize common utility functions used across cuBLAS and hipBLAS codegen tests.
• Code Duplication Reduction: Duplicate implementations of build_and_run, to_concrete_shape, and run_matmul_offload_test have been removed from test_codegen_cublas.py and test_codegen_hipblas.py.
• Improved Test Structure: Existing matmul offload tests in both cuBLAS and hipBLAS now leverage the newly shared run_matmul_offload_test function, enhancing consistency and maintainability.

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[gemini-code-assist]

Code Review

This pull request successfully refactors duplicated test code from cuBLAS and hipBLAS tests into a shared utility file. This is a good improvement for code reuse and maintainability. The logic appears to be preserved correctly. I have one suggestion in the new common file to improve the clarity of a piece of logic that is a bit confusing, which could help future maintenance. Overall, this is a solid refactoring.

[gemini-code-assist]

The logic for handling transpose_y is correct but confusing. The y_shape variable is used to define the shape of the y operand for matmul, but then it's reassigned to represent the shape of the y argument for the Relax function, which is different when transpose_y is true. This dual-purpose use of y_shape makes the code hard to reason about.

A refactor using separate variables (e.g., y_matmul_shape and y_arg_shape) would greatly improve readability and maintainability.