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tests: fix CUDA OOM in VidTok slicing/tiling tests #14554

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sayakpaul merged 2 commits into from
Aug 22, 2026
+0 −54
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tests: fix CUDA OOM in VidTok slicing/tiling tests #14554

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tests: fix CUDA OOM in VidTok slicing/tiling tests
sayakpaul Aug 21, 2026
148bc93
Merge branch 'main' into fix-vidtok-slicing-tiling-oom
sayakpaul Aug 22, 2026
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54 changes: 0 additions & 54 deletions tests/models/autoencoders/test_models_autoencoder_vidtok.py
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Expand Up @@ -113,60 +113,6 @@ def test_layerwise_casting_training(self):
class TestAutoencoderVidTokSlicingTiling(AutoencoderVidTokTesterConfig, AutoencoderTesterMixin):
"""Slicing and tiling tests for AutoencoderVidTok."""

def test_enable_disable_tiling(self):

@sayakpaul sayakpaul Aug 21, 2026 •
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Already tested in the base mixin and since these were not wrapped in torch.no_grad() these were causing OOMs.

init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()

torch.manual_seed(0)
model = self.model_class(**init_dict).to(torch_device)

torch.manual_seed(0)
output_without_tiling = model(**inputs_dict, generator=torch.manual_seed(0))[0]

torch.manual_seed(0)
model.enable_tiling()
output_with_tiling = model(**inputs_dict, generator=torch.manual_seed(0))[0]

assert (
output_without_tiling.detach().cpu().numpy() - output_with_tiling.detach().cpu().numpy()
).max() < 0.5, "VAE tiling should not affect the inference results"

torch.manual_seed(0)
model.disable_tiling()
output_without_tiling_2 = model(**inputs_dict, generator=torch.manual_seed(0))[0]

assert (
output_without_tiling.detach().cpu().numpy().all() == output_without_tiling_2.detach().cpu().numpy().all()
), "Without tiling outputs should match with the outputs when tiling is manually disabled."

def test_enable_disable_slicing(self):
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()

torch.manual_seed(0)
model = self.model_class(**init_dict).to(torch_device)
inputs_dict.update({"return_dict": False})

torch.manual_seed(0)
output_without_slicing = model(**inputs_dict, generator=torch.manual_seed(0))[0]

torch.manual_seed(0)
model.enable_slicing()
output_with_slicing = model(**inputs_dict, generator=torch.manual_seed(0))[0]

assert (
output_without_slicing.detach().cpu().numpy() - output_with_slicing.detach().cpu().numpy()
).max() < 0.5, "VAE slicing should not affect the inference results"

torch.manual_seed(0)
model.disable_slicing()
output_without_slicing_2 = model(**inputs_dict, generator=torch.manual_seed(0))[0]

assert (
output_without_slicing.detach().cpu().numpy().all()
== output_without_slicing_2.detach().cpu().numpy().all()
), "Without slicing outputs should match when slicing is manually disabled."

def test_forward_with_norm_groups(self):
"""VidTok uses layernorm instead of groupnorm."""
init_dict = self.get_init_dict()
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