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[tests] introduce VAETesterMixin to consolidate tests for slicing and tiling #12374

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[tests] introduce VAETesterMixin to consolidate tests for slicing and tiling #12374

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sayakpaul Sep 23, 2025
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Merge branch 'main' into vae-tests-mixin
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Merge branch 'main' into vae-tests-mixin
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66 changes: 2 additions & 64 deletions tests/models/autoencoders/test_models_autoencoder_hunyuan_video.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,13 +25,13 @@
floats_tensor,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin, VAETestMixin


enable_full_determinism()


class AutoencoderKLHunyuanVideoTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
class AutoencoderKLHunyuanVideoTests(ModelTesterMixin, VAETestMixin, UNetTesterMixin, unittest.TestCase):
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Nit. Let's call it AutoencoderTesterMixin to be consistent. Also it looks like UNetTesterMixin is used in all the autoencoder tests? It only has a single test method. Let's add it to the AutoencoderTesterMixin and just have these classes inherit from their specific Mixin

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Done!

model_class = AutoencoderKLHunyuanVideo
main_input_name = "sample"
base_precision = 1e-2
Expand Down Expand Up @@ -87,68 +87,6 @@ def prepare_init_args_and_inputs_for_common(self):
inputs_dict = self.dummy_input
return init_dict, inputs_dict

def test_enable_disable_tiling(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

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_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]

self.assertLess(
(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]

self.assertEqual(
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, inputs_dict = self.prepare_init_args_and_inputs_for_common()

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]

self.assertLess(
(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]

self.assertEqual(
output_without_slicing.detach().cpu().numpy().all(),
output_without_slicing_2.detach().cpu().numpy().all(),
"Without slicing outputs should match with the outputs when slicing is manually disabled.",
)

def test_gradient_checkpointing_is_applied(self):
expected_set = {
"HunyuanVideoDecoder3D",
Expand Down
66 changes: 2 additions & 64 deletions tests/models/autoencoders/test_models_autoencoder_kl.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -35,13 +35,13 @@
torch_all_close,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin, VAETestMixin


enable_full_determinism()


class AutoencoderKLTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
class AutoencoderKLTests(ModelTesterMixin, UNetTesterMixin, VAETestMixin, unittest.TestCase):
model_class = AutoencoderKL
main_input_name = "sample"
base_precision = 1e-2
Expand Down Expand Up @@ -83,68 +83,6 @@ def prepare_init_args_and_inputs_for_common(self):
inputs_dict = self.dummy_input
return init_dict, inputs_dict

def test_enable_disable_tiling(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

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_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]

self.assertLess(
(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]

self.assertEqual(
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, inputs_dict = self.prepare_init_args_and_inputs_for_common()

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]

self.assertLess(
(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]

self.assertEqual(
output_without_slicing.detach().cpu().numpy().all(),
output_without_slicing_2.detach().cpu().numpy().all(),
"Without slicing outputs should match with the outputs when slicing is manually disabled.",
)

def test_gradient_checkpointing_is_applied(self):
expected_set = {"Decoder", "Encoder", "UNetMidBlock2D"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
Expand Down
66 changes: 2 additions & 64 deletions tests/models/autoencoders/test_models_autoencoder_kl_cogvideox.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -24,13 +24,13 @@
floats_tensor,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin, VAETestMixin


enable_full_determinism()


class AutoencoderKLCogVideoXTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
class AutoencoderKLCogVideoXTests(ModelTesterMixin, VAETestMixin, UNetTesterMixin, unittest.TestCase):
model_class = AutoencoderKLCogVideoX
main_input_name = "sample"
base_precision = 1e-2
Expand Down Expand Up @@ -82,68 +82,6 @@ def prepare_init_args_and_inputs_for_common(self):
inputs_dict = self.dummy_input
return init_dict, inputs_dict

def test_enable_disable_tiling(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

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_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]

self.assertLess(
(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]

self.assertEqual(
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, inputs_dict = self.prepare_init_args_and_inputs_for_common()

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]

self.assertLess(
(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]

self.assertEqual(
output_without_slicing.detach().cpu().numpy().all(),
output_without_slicing_2.detach().cpu().numpy().all(),
"Without slicing outputs should match with the outputs when slicing is manually disabled.",
)

def test_gradient_checkpointing_is_applied(self):
expected_set = {
"CogVideoXDownBlock3D",
Expand Down
35 changes: 2 additions & 33 deletions tests/models/autoencoders/test_models_autoencoder_ltx_video.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -24,13 +24,13 @@
floats_tensor,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin, VAETestMixin


enable_full_determinism()


class AutoencoderKLLTXVideo090Tests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
class AutoencoderKLLTXVideo090Tests(ModelTesterMixin, VAETestMixin, UNetTesterMixin, unittest.TestCase):
model_class = AutoencoderKLLTXVideo
main_input_name = "sample"
base_precision = 1e-2
Expand Down Expand Up @@ -167,34 +167,3 @@ def test_outputs_equivalence(self):
@unittest.skip("AutoencoderKLLTXVideo does not support `norm_num_groups` because it does not use GroupNorm.")
def test_forward_with_norm_groups(self):
pass

def test_enable_disable_tiling(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

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_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]

self.assertLess(
(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]

self.assertEqual(
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.",
)
35 changes: 2 additions & 33 deletions tests/models/autoencoders/test_models_autoencoder_tiny.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -31,13 +31,13 @@
torch_all_close,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin, VAETestMixin


enable_full_determinism()


class AutoencoderTinyTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
class AutoencoderTinyTests(ModelTesterMixin, VAETestMixin, UNetTesterMixin, unittest.TestCase):
model_class = AutoencoderTiny
main_input_name = "sample"
base_precision = 1e-2
Expand Down Expand Up @@ -81,37 +81,6 @@ def prepare_init_args_and_inputs_for_common(self):
def test_enable_disable_tiling(self):
pass

def test_enable_disable_slicing(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

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)[0]

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

self.assertLess(
(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)[0]

self.assertEqual(
output_without_slicing.detach().cpu().numpy().all(),
output_without_slicing_2.detach().cpu().numpy().all(),
"Without slicing outputs should match with the outputs when slicing is manually disabled.",
)

@unittest.skip("Test not supported.")
def test_outputs_equivalence(self):
pass
Expand Down
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