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test_scheduler_ipndm.py
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importtempfile
importunittest
importtorch
fromdiffusersimportIPNDMScheduler
from .test_schedulersimportSchedulerCommonTest
classIPNDMSchedulerTest(SchedulerCommonTest):
scheduler_classes= (IPNDMScheduler,)
forward_default_kwargs= (("num_inference_steps", 50),)
defget_scheduler_config(self, **kwargs):
config= {"num_train_timesteps": 1000}
config.update(**kwargs)
returnconfig
defcheck_over_configs(self, time_step=0, **config):
kwargs=dict(self.forward_default_kwargs)
num_inference_steps=kwargs.pop("num_inference_steps", None)
sample=self.dummy_sample
residual=0.1*sample
dummy_past_residuals= [residual+0.2, residual+0.15, residual+0.1, residual+0.05]
forscheduler_classinself.scheduler_classes:
scheduler_config=self.get_scheduler_config(**config)
scheduler=scheduler_class(**scheduler_config)
scheduler.set_timesteps(num_inference_steps)
# copy over dummy past residuals
scheduler.ets=dummy_past_residuals[:]
iftime_stepisNone:
time_step=scheduler.timesteps[len(scheduler.timesteps) //2]
withtempfile.TemporaryDirectory() astmpdirname:
scheduler.save_config(tmpdirname)
new_scheduler=scheduler_class.from_pretrained(tmpdirname)
new_scheduler.set_timesteps(num_inference_steps)
# copy over dummy past residuals
new_scheduler.ets=dummy_past_residuals[:]
output=scheduler.step(residual, time_step, sample, **kwargs).prev_sample
new_output=new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
asserttorch.sum(torch.abs(output-new_output)) <1e-5, "Scheduler outputs are not identical"
output=scheduler.step(residual, time_step, sample, **kwargs).prev_sample
new_output=new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
asserttorch.sum(torch.abs(output-new_output)) <1e-5, "Scheduler outputs are not identical"
@unittest.skip("Test not supported.")
deftest_from_save_pretrained(self):
pass
defcheck_over_forward(self, time_step=0, **forward_kwargs):
kwargs=dict(self.forward_default_kwargs)
num_inference_steps=kwargs.pop("num_inference_steps", None)
sample=self.dummy_sample
residual=0.1*sample
dummy_past_residuals= [residual+0.2, residual+0.15, residual+0.1, residual+0.05]
forscheduler_classinself.scheduler_classes:
scheduler_config=self.get_scheduler_config()
scheduler=scheduler_class(**scheduler_config)
scheduler.set_timesteps(num_inference_steps)
# copy over dummy past residuals (must be after setting timesteps)
scheduler.ets=dummy_past_residuals[:]
iftime_stepisNone:
time_step=scheduler.timesteps[len(scheduler.timesteps) //2]
withtempfile.TemporaryDirectory() astmpdirname:
scheduler.save_config(tmpdirname)
new_scheduler=scheduler_class.from_pretrained(tmpdirname)
# copy over dummy past residuals
new_scheduler.set_timesteps(num_inference_steps)
# copy over dummy past residual (must be after setting timesteps)
new_scheduler.ets=dummy_past_residuals[:]
output=scheduler.step(residual, time_step, sample, **kwargs).prev_sample
new_output=new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
asserttorch.sum(torch.abs(output-new_output)) <1e-5, "Scheduler outputs are not identical"
output=scheduler.step(residual, time_step, sample, **kwargs).prev_sample
new_output=new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
asserttorch.sum(torch.abs(output-new_output)) <1e-5, "Scheduler outputs are not identical"
deffull_loop(self, **config):
scheduler_class=self.scheduler_classes[0]
scheduler_config=self.get_scheduler_config(**config)
scheduler=scheduler_class(**scheduler_config)
num_inference_steps=10
model=self.dummy_model()
sample=self.dummy_sample_deter
scheduler.set_timesteps(num_inference_steps)
fori, tinenumerate(scheduler.timesteps):
residual=model(sample, t)
sample=scheduler.step(residual, t, sample).prev_sample
scheduler._step_index=None
fori, tinenumerate(scheduler.timesteps):
residual=model(sample, t)
sample=scheduler.step(residual, t, sample).prev_sample
returnsample
deftest_step_shape(self):
kwargs=dict(self.forward_default_kwargs)
num_inference_steps=kwargs.pop("num_inference_steps", None)
forscheduler_classinself.scheduler_classes:
scheduler_config=self.get_scheduler_config()
scheduler=scheduler_class(**scheduler_config)
sample=self.dummy_sample
residual=0.1*sample
ifnum_inference_stepsisnotNoneandhasattr(scheduler, "set_timesteps"):
scheduler.set_timesteps(num_inference_steps)
elifnum_inference_stepsisnotNoneandnothasattr(scheduler, "set_timesteps"):
kwargs["num_inference_steps"] =num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
dummy_past_residuals= [residual+0.2, residual+0.15, residual+0.1, residual+0.05]
scheduler.ets=dummy_past_residuals[:]
time_step_0=scheduler.timesteps[5]
time_step_1=scheduler.timesteps[6]
output_0=scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample
output_1=scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample
self.assertEqual(output_0.shape, sample.shape)
self.assertEqual(output_0.shape, output_1.shape)
output_0=scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample
output_1=scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample
self.assertEqual(output_0.shape, sample.shape)
self.assertEqual(output_0.shape, output_1.shape)
deftest_timesteps(self):
fortimestepsin [100, 1000]:
self.check_over_configs(num_train_timesteps=timesteps, time_step=None)
deftest_inference_steps(self):
fort, num_inference_stepsinzip([1, 5, 10], [10, 50, 100]):
self.check_over_forward(num_inference_steps=num_inference_steps, time_step=None)
deftest_full_loop_no_noise(self):
sample=self.full_loop()
result_mean=torch.mean(torch.abs(sample))
assertabs(result_mean.item() -2540529) <10
