Super tiny add UT for copy_to_gpu_no_ce

[fzyzcjy]

Motivation

suggested by ke in #10007

Modifications

Accuracy Tests

Benchmarking and Profiling

Checklist

• Format your code according to the Format code with pre-commit.
• Add unit tests according to the Run and add unit tests.
• Update documentation according to Write documentations.
• Provide accuracy and speed benchmark results according to Test the accuracy and Benchmark the speed.

[gemini-code-assist]

Summary of Changes

Hello @fzyzcjy, 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 primarily focuses on enhancing code reliability by introducing a dedicated unit test for the copy_to_gpu_no_ce function, as suggested in a related pull request. Additionally, it includes a minor but important correction to the type hint for the input parameter of this function, aligning it with its actual usage of torch.Tensor.

Highlights

• Type Hint Correction: The type hint for the input parameter of the copy_to_gpu_no_ce function in sgl-kernel/python/sgl_kernel/elementwise.py has been updated from List[int] to torch.Tensor.
• Unit Test Addition: A new unit test file, sgl-kernel/tests/test_copy.py, has been added to verify the functionality of the copy_to_gpu_no_ce function.

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[gemini-code-assist]

Code Review

This pull request adds a unit test for the copy_to_gpu_no_ce function and correctly updates its type hint. The changes are a good addition for ensuring correctness. I have a couple of suggestions for the new test file to improve its implementation and clarity.

[gemini-code-assist]

This import of sgl_kernel is not used in the file and can be removed to keep the code clean.

[gemini-code-assist]

For a clearer and more efficient assertion, it's better to move the GPU tensor back to the CPU for comparison against the original CPU tensor. This avoids an unnecessary cuda() transfer within the assertion.

Suggested change

	assert torch.all(tensor_cpu.cuda() == tensor_gpu)
	assert torch.all(tensor_cpu == tensor_gpu.cpu())