[Relax][PyTorch] Unify dtype used in conv2d tests

[guan404ming]

Why

• resolve todo in test_op_gradient_numeric.py by unifying the dtype used in conv2d related test
• use float32 with reduced range [0, 3] to maintain numerical precision for gradient checking

[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 refines the gradient numeric tests for conv2d, max_pool2d, and avg_pool2d operations by standardizing the input data type to float32. This change addresses a previous TODO comment, aiming to prevent potential precision issues during gradient checks and ensure consistency across related tests. The update also includes a minor adjustment to the range of randomly generated test data.

Highlights

• Data Type Unification: Updated the gradient numeric tests for conv2d, max_pool2d, and avg_pool2d to consistently use float32 for input data, addressing potential precision issues.
• TODO Resolution: Resolved a long-standing TODO comment in test_op_gradient_numeric.py that called for unifying the data types used in conv2d related tests.
• Random Data Range Adjustment: Modified the np.random.uniform range for generating test data from (0, 16) to (0, 3) across the affected convolutional and pooling tests.

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[gemini-code-assist]

Code Review

This pull request is a nice improvement that resolves a TODO by updating several neural network operator gradient tests (conv2d, max_pool2d, avg_pool2d) to use float32 instead of float64. This change enhances consistency across the test suite and is better for checking correctness, avoiding potential precision issues with float64. The accompanying change to unify the random data generation range is also a good cleanup. The changes are correct and well-contained.

[guan404ming]

cc @tlopex @mshr-h

[guan404ming]

Thanks!