Add more cuda tests

[ahmadsharif1]

This addresses comments:

#319 (comment)
#319 (comment)

1. Add a mark in conftest.py
2. Add a decorator that passes in cpu and cuda devices
3. Adds more tests for cuda, especially batch frame extraction tests
4. Update the tensor_close function to be more tolerant of differences. The default is now 90% of the values should be within 20 of each other.

I was not able to reuse the logic in the needs_cuda() decorator so I just duplicated that logic. If someone has better ideas let me know and I can incorporate them.

[NicolasHug]

Since the default tolerance has changed, did that implicitly changed the tolerance of the existing calls as well? E.g. the ones introduced in https://github.com/pytorch/torchcodec/pull/319/files has a stricter tolerance IIUC - we may need to update those?

[ahmadsharif1]

I am fine using higher tolerances for those too -- as they may have different values for different GPU models anyway

[NicolasHug]

Thanks @ahmadsharif1

IIUC this PR mostly tests get_frames_at_indices, get_frames_displayed_at and get_frames_in_range.

But for coverage we probably want to also parametrize the existing tests for get_frame_at, get_frame_displayed_at, get_next_frame(), etc.

And we'll also want to add tests to the public decoder class.

I can address those in a follow-up if you'd like

[NicolasHug]

Nit: here and everywhere below, call this frame_compare_function

[ahmadsharif1]

Thanks. Actually I just realized that in doing all the test refactors I missed a very important check -- we don't actually check that the frame is on the correct device.

@NicolasHug do you have any opinions on how it should be done.

I think we can even have a function like test_frame_against_reference(frame1, frame2, expected_device)

Within that function we can 1) assert frame is on device and 2) use the correct comparison function -- i.e. approximately equal for cuda and exactly equal for CPU

Thoughts?

As far as more tests are concerned, you (or I) can address those later. But let's align on the overall testing flow for cuda tests including testing the device and tensor values both.