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import numpy as np
import pytest
import torch
from diffusers.models.autoencoders.vae import DecoderOutput
from diffusers.utils.torch_utils import torch_device
class AutoencoderTesterMixin:
"""
Test mixin class specific to VAEs to test for slicing and tiling. Diffusion networks
usually don't do slicing and tiling.
"""
@staticmethod
def _accepts_generator(model):
model_sig = inspect.signature(model.forward)
accepts_generator = "generator" in model_sig.parameters
return accepts_generator
@staticmethod
def _accepts_norm_num_groups(model_class):
model_sig = inspect.signature(model_class.__init__)
accepts_norm_groups = "norm_num_groups" in model_sig.parameters
return accepts_norm_groups
def test_forward_with_norm_groups(self):
if not self._accepts_norm_num_groups(self.model_class):
pytest.skip(f"Test not supported for {self.model_class.__name__}")
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
init_dict["norm_num_groups"] = 16
init_dict["block_out_channels"] = (16, 32)
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
if isinstance(output, dict):
output = output.to_tuple()[0]
assert output is not None
expected_shape = inputs_dict["sample"].shape
assert output.shape == expected_shape, "Input and output shapes do not match"
def test_enable_disable_tiling(self):
if not hasattr(self.model_class, "enable_tiling"):
pytest.skip(f"Skipping test as {self.model_class.__name__} doesn't support tiling.")
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)
if not hasattr(model, "use_tiling"):
pytest.skip(f"Skipping test as {self.model_class.__name__} doesn't support tiling.")
inputs_dict.update({"return_dict": False})
_ = inputs_dict.pop("generator", None)
accepts_generator = self._accepts_generator(model)
torch.manual_seed(0)
if accepts_generator:
inputs_dict["generator"] = torch.manual_seed(0)
output_without_tiling = model(**inputs_dict)[0]
# Mochi-1
if isinstance(output_without_tiling, DecoderOutput):
output_without_tiling = output_without_tiling.sample
torch.manual_seed(0)
model.enable_tiling()
if accepts_generator:
inputs_dict["generator"] = torch.manual_seed(0)
output_with_tiling = model(**inputs_dict)[0]
if isinstance(output_with_tiling, DecoderOutput):
output_with_tiling = output_with_tiling.sample
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()
if accepts_generator:
inputs_dict["generator"] = torch.manual_seed(0)
output_without_tiling_2 = model(**inputs_dict)[0]
if isinstance(output_without_tiling_2, DecoderOutput):
output_without_tiling_2 = output_without_tiling_2.sample
assert np.allclose(
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):
if not hasattr(self.model_class, "enable_slicing"):
pytest.skip(f"Skipping test as {self.model_class.__name__} doesn't support slicing.")
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)
if not hasattr(model, "use_slicing"):
pytest.skip(f"Skipping test as {self.model_class.__name__} doesn't support tiling.")
inputs_dict.update({"return_dict": False})
_ = inputs_dict.pop("generator", None)
accepts_generator = self._accepts_generator(model)
if accepts_generator:
inputs_dict["generator"] = torch.manual_seed(0)
torch.manual_seed(0)
output_without_slicing = model(**inputs_dict)[0]
# Mochi-1
if isinstance(output_without_slicing, DecoderOutput):
output_without_slicing = output_without_slicing.sample
torch.manual_seed(0)
model.enable_slicing()
if accepts_generator:
inputs_dict["generator"] = torch.manual_seed(0)
output_with_slicing = model(**inputs_dict)[0]
if isinstance(output_with_slicing, DecoderOutput):
output_with_slicing = output_with_slicing.sample
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()
if accepts_generator:
inputs_dict["generator"] = torch.manual_seed(0)
output_without_slicing_2 = model(**inputs_dict)[0]
if isinstance(output_without_slicing_2, DecoderOutput):
output_without_slicing_2 = output_without_slicing_2.sample
assert np.allclose(
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."
class NewAutoencoderTesterMixin:
@staticmethod
def _accepts_generator(model):
model_sig = inspect.signature(model.forward)
accepts_generator = "generator" in model_sig.parameters
return accepts_generator
@staticmethod
def _accepts_norm_num_groups(model_class):
model_sig = inspect.signature(model_class.__init__)
accepts_norm_groups = "norm_num_groups" in model_sig.parameters
return accepts_norm_groups
def test_forward_with_norm_groups(self):
if not self._accepts_norm_num_groups(self.model_class):
pytest.skip(f"Test not supported for {self.model_class.__name__}")
init_dict = self.get_init_dict()
inputs_dict = self.get_dummy_inputs()
init_dict["norm_num_groups"] = 16
init_dict["block_out_channels"] = (16, 32)
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
if isinstance(output, dict):
output = output.to_tuple()[0]
assert output is not None
expected_shape = inputs_dict["sample"].shape
assert output.shape == expected_shape, "Input and output shapes do not match"
def test_enable_disable_tiling(self):
if not hasattr(self.model_class, "enable_tiling"):
pytest.skip(f"Skipping test as {self.model_class.__name__} doesn't support tiling.")
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)
if not hasattr(model, "use_tiling"):
pytest.skip(f"Skipping test as {self.model_class.__name__} doesn't support tiling.")
inputs_dict.update({"return_dict": False})
_ = inputs_dict.pop("generator", None)
accepts_generator = self._accepts_generator(model)
with torch.no_grad():
torch.manual_seed(0)
if accepts_generator:
inputs_dict["generator"] = torch.manual_seed(0)
output_without_tiling = model(**inputs_dict)[0]
if isinstance(output_without_tiling, DecoderOutput):
output_without_tiling = output_without_tiling.sample
torch.manual_seed(0)
model.enable_tiling()
if accepts_generator:
inputs_dict["generator"] = torch.manual_seed(0)
output_with_tiling = model(**inputs_dict)[0]
if isinstance(output_with_tiling, DecoderOutput):
output_with_tiling = output_with_tiling.sample
assert (output_without_tiling.cpu() - output_with_tiling.cpu()).max() < 0.5, (
"VAE tiling should not affect the inference results"
)
torch.manual_seed(0)
model.disable_tiling()
if accepts_generator:
inputs_dict["generator"] = torch.manual_seed(0)
output_without_tiling_2 = model(**inputs_dict)[0]
if isinstance(output_without_tiling_2, DecoderOutput):
output_without_tiling_2 = output_without_tiling_2.sample
assert torch.allclose(output_without_tiling.cpu(), output_without_tiling_2.cpu()), (
"Without tiling outputs should match with the outputs when tiling is manually disabled."
)
def test_enable_disable_slicing(self):
if not hasattr(self.model_class, "enable_slicing"):
pytest.skip(f"Skipping test as {self.model_class.__name__} doesn't support slicing.")
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)
if not hasattr(model, "use_slicing"):
pytest.skip(f"Skipping test as {self.model_class.__name__} doesn't support tiling.")
inputs_dict.update({"return_dict": False})
_ = inputs_dict.pop("generator", None)
accepts_generator = self._accepts_generator(model)
with torch.no_grad():
if accepts_generator:
inputs_dict["generator"] = torch.manual_seed(0)
torch.manual_seed(0)
output_without_slicing = model(**inputs_dict)[0]
if isinstance(output_without_slicing, DecoderOutput):
output_without_slicing = output_without_slicing.sample
torch.manual_seed(0)
model.enable_slicing()
if accepts_generator:
inputs_dict["generator"] = torch.manual_seed(0)
output_with_slicing = model(**inputs_dict)[0]
if isinstance(output_with_slicing, DecoderOutput):
output_with_slicing = output_with_slicing.sample
assert (output_without_slicing.cpu() - output_with_slicing.cpu()).max() < 0.5, (
"VAE slicing should not affect the inference results"
)
torch.manual_seed(0)
model.disable_slicing()
if accepts_generator:
inputs_dict["generator"] = torch.manual_seed(0)
output_without_slicing_2 = model(**inputs_dict)[0]
if isinstance(output_without_slicing_2, DecoderOutput):
output_without_slicing_2 = output_without_slicing_2.sample
assert torch.allclose(output_without_slicing.cpu(), output_without_slicing_2.cpu()), (
"Without slicing outputs should match with the outputs when slicing is manually disabled."
)
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