enable lora cases on XPU

tests/lora/test_lora_layers_flux.py

@@ -31,13 +31,14 @@
 from diffusers.utils import load_image, logging
 from diffusers.utils.testing_utils import (
     CaptureLogger,
+    backend_empty_cache,
     floats_tensor,
     is_peft_available,
     nightly,
     numpy_cosine_similarity_distance,
-    require_big_gpu_with_torch_cuda,
+    require_big_accelerator,
     require_peft_backend,
-    require_torch_gpu,
+    require_torch_accelerator,
     slow,
     torch_device,
 )
@@ -809,10 +810,10 @@ def test_simple_inference_with_text_denoiser_multi_adapter_block_lora(self):
 
 @slow
 @nightly
-@require_torch_gpu
+@require_torch_accelerator
 @require_peft_backend
-@require_big_gpu_with_torch_cuda
-@pytest.mark.big_gpu_with_torch_cuda
+@require_big_accelerator
+@pytest.mark.big_accelerator
 class FluxLoRAIntegrationTests(unittest.TestCase):
     """internal note: The integration slices were obtained on audace.
 
@@ -827,7 +828,7 @@ def setUp(self):
         super().setUp()
 
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
         self.pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
 
@@ -836,13 +837,13 @@ def tearDown(self):
 
         del self.pipeline
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def test_flux_the_last_ben(self):
         self.pipeline.load_lora_weights("TheLastBen/Jon_Snow_Flux_LoRA", weight_name="jon_snow.safetensors")
         self.pipeline.fuse_lora()
         self.pipeline.unload_lora_weights()
-        # Instead of calling `enable_model_cpu_offload()`, we do a cuda placement here because the CI
+        # Instead of calling `enable_model_cpu_offload()`, we do a accelerator placement here because the CI
         # run supports it. We have about 34GB RAM in the CI runner which kills the test when run with
         # `enable_model_cpu_offload()`. We repeat this for the other tests, too.
         self.pipeline = self.pipeline.to(torch_device)
@@ -956,10 +957,10 @@ def test_flux_xlabs_load_lora_with_single_blocks(self):
 
 
 @nightly
-@require_torch_gpu
+@require_torch_accelerator
 @require_peft_backend
-@require_big_gpu_with_torch_cuda
-@pytest.mark.big_gpu_with_torch_cuda
+@require_big_accelerator
+@pytest.mark.big_accelerator
 class FluxControlLoRAIntegrationTests(unittest.TestCase):
     num_inference_steps = 10
     seed = 0
@@ -969,17 +970,17 @@ def setUp(self):
         super().setUp()
 
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
         self.pipeline = FluxControlPipeline.from_pretrained(
             "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
-        ).to("cuda")
+        ).to(torch_device)
 
     def tearDown(self):
         super().tearDown()
 
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     @parameterized.expand(["black-forest-labs/FLUX.1-Canny-dev-lora", "black-forest-labs/FLUX.1-Depth-dev-lora"])
     def test_lora(self, lora_ckpt_id):

tests/lora/test_lora_layers_hunyuanvideo.py

@@ -28,13 +28,16 @@
     HunyuanVideoTransformer3DModel,
 )
 from diffusers.utils.testing_utils import (
+    Expectations,
+    backend_empty_cache,
     floats_tensor,
     nightly,
     numpy_cosine_similarity_distance,
-    require_big_gpu_with_torch_cuda,
+    require_big_accelerator,
     require_peft_backend,
-    require_torch_gpu,
+    require_torch_accelerator,
     skip_mps,
+    torch_device,
 )
 
 
@@ -192,10 +195,10 @@ def test_simple_inference_with_text_lora_save_load(self):
 
 
 @nightly
-@require_torch_gpu
+@require_torch_accelerator
 @require_peft_backend
-@require_big_gpu_with_torch_cuda
-@pytest.mark.big_gpu_with_torch_cuda
+@require_big_accelerator
+@pytest.mark.big_accelerator
 class HunyuanVideoLoRAIntegrationTests(unittest.TestCase):
     """internal note: The integration slices were obtained on DGX.
 
@@ -210,21 +213,21 @@ def setUp(self):
         super().setUp()
 
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
         model_id = "hunyuanvideo-community/HunyuanVideo"
         transformer = HunyuanVideoTransformer3DModel.from_pretrained(
             model_id, subfolder="transformer", torch_dtype=torch.bfloat16
         )
         self.pipeline = HunyuanVideoPipeline.from_pretrained(
             model_id, transformer=transformer, torch_dtype=torch.float16
-        ).to("cuda")
+        ).to(torch_device)
 
     def tearDown(self):
         super().tearDown()
 
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def test_original_format_cseti(self):
         self.pipeline.load_lora_weights(
@@ -249,8 +252,13 @@ def test_original_format_cseti(self):
         out_slice = np.concatenate((out[:8], out[-8:]))
 
         # fmt: off
-        expected_slice = np.array([0.1013, 0.1924, 0.0078, 0.1021, 0.1929, 0.0078, 0.1023, 0.1919, 0.7402, 0.104, 0.4482, 0.7354, 0.0925, 0.4382, 0.7275, 0.0815])
+        expected_slices = Expectations(
+            {
+                ("cuda", 7): np.array([0.1013, 0.1924, 0.0078, 0.1021, 0.1929, 0.0078, 0.1023, 0.1919, 0.7402, 0.104, 0.4482, 0.7354, 0.0925, 0.4382, 0.7275, 0.0815]),
+            }
+        )
         # fmt: on
+        expected_slice = expected_slices.get_expectation()
 
         max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), out_slice)
 

tests/lora/test_lora_layers_sd.py

@@ -93,12 +93,12 @@ def output_shape(self):
     def setUp(self):
         super().setUp()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def tearDown(self):
         super().tearDown()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     # Keeping this test here makes sense because it doesn't look any integration
     # (value assertions on logits).

tests/lora/test_lora_layers_sd3.py

@@ -34,7 +34,7 @@
     is_flaky,
     nightly,
     numpy_cosine_similarity_distance,
-    require_big_gpu_with_torch_cuda,
+    require_big_accelerator,
     require_peft_backend,
     require_torch_accelerator,
     torch_device,
@@ -138,8 +138,8 @@ def test_multiple_wrong_adapter_name_raises_error(self):
 @nightly
 @require_torch_accelerator
 @require_peft_backend
-@require_big_gpu_with_torch_cuda
-@pytest.mark.big_gpu_with_torch_cuda
+@require_big_accelerator
+@pytest.mark.big_accelerator
 class SD3LoraIntegrationTests(unittest.TestCase):
     pipeline_class = StableDiffusion3Img2ImgPipeline
     repo_id = "stabilityai/stable-diffusion-3-medium-diffusers"

tests/lora/test_lora_layers_sdxl.py

@@ -37,12 +37,13 @@
 from diffusers.utils.import_utils import is_accelerate_available
 from diffusers.utils.testing_utils import (
     CaptureLogger,
+    backend_empty_cache,
     is_flaky,
     load_image,
     nightly,
     numpy_cosine_similarity_distance,
     require_peft_backend,
-    require_torch_gpu,
+    require_torch_accelerator,
     slow,
     torch_device,
 )
@@ -105,12 +106,12 @@ def output_shape(self):
     def setUp(self):
         super().setUp()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def tearDown(self):
         super().tearDown()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     @is_flaky
     def test_multiple_wrong_adapter_name_raises_error(self):
@@ -119,18 +120,18 @@ def test_multiple_wrong_adapter_name_raises_error(self):
 
 @slow
 @nightly
-@require_torch_gpu
+@require_torch_accelerator
 @require_peft_backend
 class LoraSDXLIntegrationTests(unittest.TestCase):
     def setUp(self):
         super().setUp()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def tearDown(self):
         super().tearDown()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def test_sdxl_1_0_lora(self):
         generator = torch.Generator("cpu").manual_seed(0)