test_add_noise_device

Author: hlky

.github/workflows/pr_tests_mps.yml

@@ -33,30 +33,30 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          - name: Fast Pipelines MPS tests
-            framework: pytorch_pipelines
-            runner: macos-13-xlarge
-            report: torch_mps_pipelines
-          - name: Fast Models MPS tests
-            framework: pytorch_models
-            runner: macos-13-xlarge
-            report: torch_mps_models
+          # - name: Fast Pipelines MPS tests
+          #   framework: pytorch_pipelines
+          #   runner: macos-13-xlarge
+          #   report: torch_mps_pipelines
+          # - name: Fast Models MPS tests
+          #   framework: pytorch_models
+          #   runner: macos-13-xlarge
+          #   report: torch_mps_models
           - name: Fast Schedulers MPS tests
             framework: pytorch_schedulers
             runner: macos-13-xlarge
             report: torch_mps_schedulers
-          - name: Fast Others MPS tests
-            framework: pytorch_others
-            runner: macos-13-xlarge
-            report: torch_mps_others
+          # - name: Fast Others MPS tests
+          #   framework: pytorch_others
+          #   runner: macos-13-xlarge
+          #   report: torch_mps_others
           # - name: Fast Single File MPS tests
           #   framework: pytorch_single_file
           #   runner: macos-13-xlarge
           #   report: torch_mps_single_file
-          - name: Fast Lora MPS tests
-            framework: pytorch_lora
-            runner: macos-13-xlarge
-            report: torch_mps_lora
+          # - name: Fast Lora MPS tests
+          #   framework: pytorch_lora
+          #   runner: macos-13-xlarge
+          #   report: torch_mps_lora
           # - name: Fast Quantization MPS tests
           #   framework: pytorch_quantization
           #   runner: macos-13-xlarge

tests/schedulers/test_schedulers.py

@@ -711,21 +711,21 @@ def test_add_noise_device(self):
             scheduler = scheduler_class(**scheduler_config)
             scheduler.set_timesteps(self.default_num_inference_steps)
 
-            sample = self.dummy_sample.to(torch_device)
-            if scheduler_class == CMStochasticIterativeScheduler:
-                # Get valid timestep based on sigma_max, which should always be in timestep schedule.
-                scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max)
-                scaled_sample = scheduler.scale_model_input(sample, scaled_sigma_max)
-            elif scheduler_class == EDMEulerScheduler:
-                scaled_sample = scheduler.scale_model_input(sample, scheduler.timesteps[-1])
-            else:
-                scaled_sample = scheduler.scale_model_input(sample, 0.0)
-            self.assertEqual(sample.shape, scaled_sample.shape)
-
-            noise = torch.randn_like(scaled_sample).to(torch_device)
-            t = scheduler.timesteps[5][None]
-            noised = scheduler.add_noise(scaled_sample, noise, t)
-            self.assertEqual(noised.shape, scaled_sample.shape)
+            # sample = self.dummy_sample.to(torch_device)
+            # if scheduler_class == CMStochasticIterativeScheduler:
+            #     # Get valid timestep based on sigma_max, which should always be in timestep schedule.
+            #     scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max)
+            #     scaled_sample = scheduler.scale_model_input(sample, scaled_sigma_max)
+            # elif scheduler_class == EDMEulerScheduler:
+            #     scaled_sample = scheduler.scale_model_input(sample, scheduler.timesteps[-1])
+            # else:
+            #     scaled_sample = scheduler.scale_model_input(sample, 0.0)
+            # self.assertEqual(sample.shape, scaled_sample.shape)
+
+            # noise = torch.randn_like(scaled_sample).to(torch_device)
+            # t = scheduler.timesteps[5][None]
+            # noised = scheduler.add_noise(scaled_sample, noise, t)
+            # self.assertEqual(noised.shape, scaled_sample.shape)
 
     def test_deprecated_kwargs(self):
         for scheduler_class in self.scheduler_classes: