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import torch
from diffusers import SASolverScheduler
from diffusers.utils.testing_utils import require_torchsde, torch_device
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class SASolverSchedulerTest(SchedulerCommonTest):
scheduler_classes = (SASolverScheduler,)
forward_default_kwargs = (("num_inference_steps", 10),)
num_inference_steps = 10
def get_scheduler_config(self, **kwargs):
config = {
"num_train_timesteps": 1100,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**kwargs)
return config
def test_step_shape(self):
kwargs = dict(self.forward_default_kwargs)
num_inference_steps = kwargs.pop("num_inference_steps", None)
for scheduler_class in self.scheduler_classes:
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
sample = self.dummy_sample
residual = 0.1 * sample
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
scheduler.set_timesteps(num_inference_steps)
elif num_inference_steps is not None and not hasattr(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 + 0.10]
scheduler.model_outputs = dummy_past_residuals[
: max(
scheduler.config.predictor_order,
scheduler.config.corrector_order - 1,
)
]
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)
def test_timesteps(self):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=timesteps)
def test_betas(self):
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
def test_schedules(self):
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=schedule)
def test_prediction_type(self):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=prediction_type)
def test_full_loop_no_noise(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(self.num_inference_steps)
model = self.dummy_model()
sample = self.dummy_sample_deter * scheduler.init_noise_sigma
sample = sample.to(torch_device)
generator = torch.manual_seed(0)
for i, t in enumerate(scheduler.timesteps):
sample = scheduler.scale_model_input(sample, t, generator=generator)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample)
sample = output.prev_sample
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
if torch_device in ["cpu"]:
assert abs(result_sum.item() - 337.394287109375) < 1e-2
assert abs(result_mean.item() - 0.43931546807289124) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 329.1999816894531) < 1e-2
assert abs(result_mean.item() - 0.4286458194255829) < 1e-3
else:
print("None")
def test_full_loop_with_v_prediction(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config(prediction_type="v_prediction")
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(self.num_inference_steps)
model = self.dummy_model()
sample = self.dummy_sample_deter * scheduler.init_noise_sigma
sample = sample.to(torch_device)
generator = torch.manual_seed(0)
for i, t in enumerate(scheduler.timesteps):
sample = scheduler.scale_model_input(sample, t, generator=generator)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample)
sample = output.prev_sample
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
if torch_device in ["cpu"]:
assert abs(result_sum.item() - 193.1467742919922) < 1e-2
assert abs(result_mean.item() - 0.2514931857585907) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 193.4154052734375) < 1e-2
assert abs(result_mean.item() - 0.2518429756164551) < 1e-3
else:
print("None")
def test_full_loop_device(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config)
scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
model = self.dummy_model()
sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma
generator = torch.manual_seed(0)
for t in scheduler.timesteps:
sample = scheduler.scale_model_input(sample, t)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample, generator=generator)
sample = output.prev_sample
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
if torch_device in ["cpu"]:
assert abs(result_sum.item() - 337.394287109375) < 1e-2
assert abs(result_mean.item() - 0.43931546807289124) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 337.394287109375) < 1e-2
assert abs(result_mean.item() - 0.4393154978752136) < 1e-3
else:
print("None")
def test_full_loop_device_karras_sigmas(self):
scheduler_class = self.scheduler_classes[0]
scheduler_config = self.get_scheduler_config()
scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True)
scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
model = self.dummy_model()
sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma
sample = sample.to(torch_device)
generator = torch.manual_seed(0)
for t in scheduler.timesteps:
sample = scheduler.scale_model_input(sample, t)
model_output = model(sample, t)
output = scheduler.step(model_output, t, sample, generator=generator)
sample = output.prev_sample
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
if torch_device in ["cpu"]:
assert abs(result_sum.item() - 837.2554931640625) < 1e-2
assert abs(result_mean.item() - 1.0901764631271362) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 837.25537109375) < 1e-2
assert abs(result_mean.item() - 1.0901763439178467) < 1e-2
else:
print("None")
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