enable group_offloading and PipelineDeviceAndDtypeStabilityTests on XPU, all passed (#11620)

* enable group_offloading and PipelineDeviceAndDtypeStabilityTests on XPU,
all passed

Signed-off-by: Matrix YAO <[email protected]>

* fix style

Signed-off-by: Matrix YAO <[email protected]>

* fix

Signed-off-by: Matrix YAO <[email protected]>

---------

Signed-off-by: Matrix YAO <[email protected]>
Co-authored-by: Aryan <[email protected]>

Author: yao-matrix

tests/hooks/test_group_offloading.py

@@ -22,7 +22,13 @@
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.utils import get_logger
 from diffusers.utils.import_utils import compare_versions
-from diffusers.utils.testing_utils import require_torch_gpu, torch_device
+from diffusers.utils.testing_utils import (
+    backend_empty_cache,
+    backend_max_memory_allocated,
+    backend_reset_peak_memory_stats,
+    require_torch_accelerator,
+    torch_device,
+)
 
 
 class DummyBlock(torch.nn.Module):
@@ -107,7 +113,7 @@ def __call__(self, x: torch.Tensor) -> torch.Tensor:
         return x
 
 
-@require_torch_gpu
+@require_torch_accelerator
 class GroupOffloadTests(unittest.TestCase):
     in_features = 64
     hidden_features = 256
@@ -125,8 +131,8 @@ def tearDown(self):
         del self.model
         del self.input
         gc.collect()
-        torch.cuda.empty_cache()
-        torch.cuda.reset_peak_memory_stats()
+        backend_empty_cache(torch_device)
+        backend_reset_peak_memory_stats(torch_device)
 
     def get_model(self):
         torch.manual_seed(0)
@@ -141,8 +147,8 @@ def test_offloading_forward_pass(self):
         @torch.no_grad()
         def run_forward(model):
             gc.collect()
-            torch.cuda.empty_cache()
-            torch.cuda.reset_peak_memory_stats()
+            backend_empty_cache(torch_device)
+            backend_reset_peak_memory_stats(torch_device)
             self.assertTrue(
                 all(
                     module._diffusers_hook.get_hook("group_offloading") is not None
@@ -152,7 +158,7 @@ def run_forward(model):
             )
             model.eval()
             output = model(self.input)[0].cpu()
-            max_memory_allocated = torch.cuda.max_memory_allocated()
+            max_memory_allocated = backend_max_memory_allocated(torch_device)
             return output, max_memory_allocated
 
         self.model.to(torch_device)
@@ -187,10 +193,10 @@ def run_forward(model):
         self.assertTrue(torch.allclose(output_without_group_offloading, output_with_group_offloading5, atol=1e-5))
 
         # Memory assertions - offloading should reduce memory usage
-        self.assertTrue(mem4 <= mem5 < mem2 < mem3 < mem1 < mem_baseline)
+        self.assertTrue(mem4 <= mem5 < mem2 <= mem3 < mem1 < mem_baseline)
 
-    def test_warning_logged_if_group_offloaded_module_moved_to_cuda(self):
-        if torch.device(torch_device).type != "cuda":
+    def test_warning_logged_if_group_offloaded_module_moved_to_accelerator(self):
+        if torch.device(torch_device).type not in ["cuda", "xpu"]:
             return
         self.model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=3)
         logger = get_logger("diffusers.models.modeling_utils")
@@ -199,8 +205,8 @@ def test_warning_logged_if_group_offloaded_module_moved_to_cuda(self):
             self.model.to(torch_device)
         self.assertIn(f"The module '{self.model.__class__.__name__}' is group offloaded", cm.output[0])
 
-    def test_warning_logged_if_group_offloaded_pipe_moved_to_cuda(self):
-        if torch.device(torch_device).type != "cuda":
+    def test_warning_logged_if_group_offloaded_pipe_moved_to_accelerator(self):
+        if torch.device(torch_device).type not in ["cuda", "xpu"]:
             return
         pipe = DummyPipeline(self.model)
         self.model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=3)
@@ -210,19 +216,20 @@ def test_warning_logged_if_group_offloaded_pipe_moved_to_cuda(self):
             pipe.to(torch_device)
         self.assertIn(f"The module '{self.model.__class__.__name__}' is group offloaded", cm.output[0])
 
-    def test_error_raised_if_streams_used_and_no_cuda_device(self):
-        original_is_available = torch.cuda.is_available
-        torch.cuda.is_available = lambda: False
+    def test_error_raised_if_streams_used_and_no_accelerator_device(self):
+        torch_accelerator_module = getattr(torch, torch_device, torch.cuda)
+        original_is_available = torch_accelerator_module.is_available
+        torch_accelerator_module.is_available = lambda: False
         with self.assertRaises(ValueError):
             self.model.enable_group_offload(
-                onload_device=torch.device("cuda"), offload_type="leaf_level", use_stream=True
+                onload_device=torch.device(torch_device), offload_type="leaf_level", use_stream=True
             )
-        torch.cuda.is_available = original_is_available
+        torch_accelerator_module.is_available = original_is_available
 
     def test_error_raised_if_supports_group_offloading_false(self):
         self.model._supports_group_offloading = False
         with self.assertRaisesRegex(ValueError, "does not support group offloading"):
-            self.model.enable_group_offload(onload_device=torch.device("cuda"))
+            self.model.enable_group_offload(onload_device=torch.device(torch_device))
 
     def test_error_raised_if_model_offloading_applied_on_group_offloaded_module(self):
         pipe = DummyPipeline(self.model)
@@ -249,7 +256,7 @@ def test_error_raised_if_group_offloading_applied_on_sequential_offloaded_module
             pipe.model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=3)
 
     def test_block_level_stream_with_invocation_order_different_from_initialization_order(self):
-        if torch.device(torch_device).type != "cuda":
+        if torch.device(torch_device).type not in ["cuda", "xpu"]:
             return
         model = DummyModelWithMultipleBlocks(
             in_features=self.in_features,

tests/pipelines/test_pipeline_utils.py

@@ -19,7 +19,7 @@
     UNet2DConditionModel,
 )
 from diffusers.pipelines.pipeline_loading_utils import is_safetensors_compatible, variant_compatible_siblings
-from diffusers.utils.testing_utils import require_torch_gpu, torch_device
+from diffusers.utils.testing_utils import require_torch_accelerator, torch_device
 
 
 class IsSafetensorsCompatibleTests(unittest.TestCase):
@@ -850,9 +850,9 @@ def test_video_to_video(self):
             self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled")
 
 
-@require_torch_gpu
+@require_torch_accelerator
 class PipelineDeviceAndDtypeStabilityTests(unittest.TestCase):
-    expected_pipe_device = torch.device("cuda:0")
+    expected_pipe_device = torch.device(f"{torch_device}:0")
     expected_pipe_dtype = torch.float64
 
     def get_dummy_components_image_generation(self):
@@ -921,8 +921,8 @@ def test_deterministic_device(self):
         pipe.to(device=torch_device, dtype=torch.float32)
 
         pipe.unet.to(device="cpu")
-        pipe.vae.to(device="cuda")
-        pipe.text_encoder.to(device="cuda:0")
+        pipe.vae.to(device=torch_device)
+        pipe.text_encoder.to(device=f"{torch_device}:0")
 
         pipe_device = pipe.device