tests: fix CUDA OOM in VidTok slicing/tiling tests - #14554
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TestAutoencoderVidTokSlicingTiling overrode test_enable_disable_tiling and test_enable_disable_slicing with copies that ran their three forward passes outside torch.no_grad(). The retained autograd graphs pushed each test to a 10.9 GiB peak on a 14.74 GiB T4, so with any memory left over from earlier files in the same xdist worker the class OOM'd -- taking test_forward_with_norm_groups down with it. The mixin already implements both tests correctly (no_grad, plus torch.allclose for the disable-comparison instead of the `.all() == .all()` idiom the copies used, which compares two booleans and always passes). Drop the overrides and inherit them. Peak memory on a T4, measured per test: before 10937.3 MiB after 1666.5 MiB Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
sayakpaul
commented
Aug 21, 2026
| class TestAutoencoderVidTokSlicingTiling(AutoencoderVidTokTesterConfig, NewAutoencoderTesterMixin): | ||
| """Slicing and tiling tests for AutoencoderVidTok.""" | ||
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| def test_enable_disable_tiling(self): |
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Already tested in the base mixin and since these were not wrapped in torch.no_grad() these were causing OOMs.
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Fixes the three
TestAutoencoderVidTokSlicingTilingfailures in Torch CUDA Tests (models):