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")