[Tests] Skip layerwise casting tests on devices without float8_e4m3fn support

tests/lora/utils.py

@@ -2106,6 +2106,11 @@ def test_correct_lora_configs_with_different_ranks(self):
         self.assertTrue(not np.allclose(lora_output_diff_alpha, lora_output_same_rank, atol=1e-3, rtol=1e-3))
 
     def test_layerwise_casting_inference_denoiser(self):
+        try:
+            torch.zeros(1, device=torch_device).to(dtype=torch.float8_e4m3fn)
+        except TypeError:
+            self.skipTest(f"Device {torch_device} does not support float8 storage dtype.")
+
         from diffusers.hooks._common import _GO_LC_SUPPORTED_PYTORCH_LAYERS
         from diffusers.hooks.layerwise_casting import DEFAULT_SKIP_MODULES_PATTERN
 
@@ -2164,6 +2169,10 @@ def test_layerwise_casting_peft_input_autocast_denoiser(self):
 
         See the docstring of [`hooks.layerwise_casting.PeftInputAutocastDisableHook`] for more details.
         """
+        try:
+            torch.zeros(1, device=torch_device).to(dtype=torch.float8_e4m3fn)
+        except TypeError:
+            self.skipTest(f"Device {torch_device} does not support float8 storage dtype.")
 
         from diffusers.hooks._common import _GO_LC_SUPPORTED_PYTORCH_LAYERS
         from diffusers.hooks.layerwise_casting import (

tests/models/testing_utils/memory.py

@@ -385,6 +385,11 @@ class LayerwiseCastingTesterMixin:
 
     @torch.no_grad()
     def test_layerwise_casting_memory(self):
+        try:
+            torch.zeros(1, device=torch_device).to(dtype=torch.float8_e4m3fn)
+        except TypeError:
+            pytest.skip(f"Device {torch_device} does not support float8 storage dtype.")
+
         MB_TOLERANCE = 0.2
         LEAST_COMPUTE_CAPABILITY = 8.0
 
@@ -437,6 +442,11 @@ def get_memory_usage(storage_dtype, compute_dtype):
         ), "Peak memory should be lower or within tolerance with fp8 storage"
 
     def test_layerwise_casting_training(self):
+        try:
+            torch.zeros(1, device=torch_device).to(dtype=torch.float8_e4m3fn)
+        except TypeError:
+            pytest.skip(f"Device {torch_device} does not support float8 storage dtype.")
+
         def test_fn(storage_dtype, compute_dtype):
             if torch.device(torch_device).type == "cpu" and compute_dtype == torch.bfloat16:
                 pytest.skip("Skipping test because CPU doesn't go well with bfloat16.")

tests/pipelines/test_pipelines_common.py

@@ -2296,6 +2296,11 @@ def test_layerwise_casting_inference(self):
         if not self.test_layerwise_casting:
             return
 
+        try:
+            torch.zeros(1, device=torch_device).to(dtype=torch.float8_e4m3fn)
+        except TypeError:
+            self.skipTest(f"Device {torch_device} does not support float8 storage dtype.")
+
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         pipe.to(torch_device, dtype=torch.bfloat16)