add sa solver test file

tests/schedulers/test_scheduler_sasolver.py

@@ -0,0 +1,166 @@
+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,)
+    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_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)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            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 ["mps"]:
+            assert abs(result_sum.item() - 167.47821044921875) < 1e-2
+            assert abs(result_mean.item() - 0.2178705964565277) < 1e-3
+        elif torch_device in ["cuda"]:
+            assert abs(result_sum.item() - 171.59352111816406) < 1e-2
+            assert abs(result_mean.item() - 0.22342906892299652) < 1e-3
+        else:
+            assert abs(result_sum.item() - 162.52383422851562) < 1e-2
+            assert abs(result_mean.item() - 0.211619570851326) < 1e-3
+
+    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)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            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 ["mps"]:
+            assert abs(result_sum.item() - 124.77149200439453) < 1e-2
+            assert abs(result_mean.item() - 0.16226289014816284) < 1e-3
+        elif torch_device in ["cuda"]:
+            assert abs(result_sum.item() - 128.1663360595703) < 1e-2
+            assert abs(result_mean.item() - 0.16688326001167297) < 1e-3
+        else:
+            assert abs(result_sum.item() - 119.8487548828125) < 1e-2
+            assert abs(result_mean.item() - 0.1560530662536621) < 1e-3
+
+    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
+
+        for t in scheduler.timesteps:
+            sample = scheduler.scale_model_input(sample, t)
+
+            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 ["mps"]:
+            assert abs(result_sum.item() - 167.46957397460938) < 1e-2
+            assert abs(result_mean.item() - 0.21805934607982635) < 1e-3
+        elif torch_device in ["cuda"]:
+            assert abs(result_sum.item() - 171.59353637695312) < 1e-2
+            assert abs(result_mean.item() - 0.22342908382415771) < 1e-3
+        else:
+            assert abs(result_sum.item() - 162.52383422851562) < 1e-2
+            assert abs(result_mean.item() - 0.211619570851326) < 1e-3
+
+    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)
+
+        for t in scheduler.timesteps:
+            sample = scheduler.scale_model_input(sample, t)
+
+            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 ["mps"]:
+            assert abs(result_sum.item() - 176.66974135742188) < 1e-2
+            assert abs(result_mean.item() - 0.23003872730981811) < 1e-2
+        elif torch_device in ["cuda"]:
+            assert abs(result_sum.item() - 177.63653564453125) < 1e-2
+            assert abs(result_mean.item() - 0.23003872730981811) < 1e-2
+        else:
+            assert abs(result_sum.item() - 170.3135223388672) < 1e-2
+            assert abs(result_mean.item() - 0.23003872730981811) < 1e-2