[tests] make tests device-agnostic (part 4)

tests/lora/test_lora_layers_sd.py

@@ -33,11 +33,12 @@
 )
 from diffusers.utils.import_utils import is_accelerate_available
 from diffusers.utils.testing_utils import (
+    backend_empty_cache,
     load_image,
     nightly,
     numpy_cosine_similarity_distance,
     require_peft_backend,
-    require_torch_gpu,
+    require_torch_accelerator,
     slow,
     torch_device,
 )
@@ -101,7 +102,7 @@ def tearDown(self):
     # Keeping this test here makes sense because it doesn't look any integration
     # (value assertions on logits).
     @slow
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_integration_move_lora_cpu(self):
         path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
         lora_id = "takuma104/lora-test-text-encoder-lora-target"
@@ -158,7 +159,7 @@ def test_integration_move_lora_cpu(self):
                 self.assertTrue(m.weight.device != torch.device("cpu"))
 
     @slow
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_integration_move_lora_dora_cpu(self):
         from peft import LoraConfig
 
@@ -209,18 +210,18 @@ def test_integration_move_lora_dora_cpu(self):
 
 @slow
 @nightly
-@require_torch_gpu
+@require_torch_accelerator
 @require_peft_backend
 class LoraIntegrationTests(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_integration_logits_with_scale(self):
         path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
@@ -378,7 +379,7 @@ def test_a1111_with_model_cpu_offload(self):
         generator = torch.Generator().manual_seed(0)
 
         pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/Counterfeit-V2.5", safety_checker=None)
-        pipe.enable_model_cpu_offload()
+        pipe.enable_model_cpu_offload(device=torch_device)
         lora_model_id = "hf-internal-testing/civitai-light-shadow-lora"
         lora_filename = "light_and_shadow.safetensors"
         pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
@@ -400,7 +401,7 @@ def test_a1111_with_sequential_cpu_offload(self):
         generator = torch.Generator().manual_seed(0)
 
         pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/Counterfeit-V2.5", safety_checker=None)
-        pipe.enable_sequential_cpu_offload()
+        pipe.enable_sequential_cpu_offload(device=torch_device)
         lora_model_id = "hf-internal-testing/civitai-light-shadow-lora"
         lora_filename = "light_and_shadow.safetensors"
         pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
@@ -656,7 +657,7 @@ def test_sd_load_civitai_empty_network_alpha(self):
         See: https://github.com/huggingface/diffusers/issues/5606
         """
         pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
-        pipeline.enable_sequential_cpu_offload()
+        pipeline.enable_sequential_cpu_offload(device=torch_device)
         civitai_path = hf_hub_download("ybelkada/test-ahi-civitai", "ahi_lora_weights.safetensors")
         pipeline.load_lora_weights(civitai_path, adapter_name="ahri")
 

tests/lora/test_lora_layers_sd3.py

@@ -30,12 +30,13 @@
 from diffusers.utils import load_image
 from diffusers.utils.import_utils import is_accelerate_available
 from diffusers.utils.testing_utils import (
+    backend_empty_cache,
     is_flaky,
     nightly,
     numpy_cosine_similarity_distance,
     require_big_gpu_with_torch_cuda,
     require_peft_backend,
-    require_torch_gpu,
+    require_torch_accelerator,
     torch_device,
 )
 
@@ -93,7 +94,7 @@ class SD3LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
     def output_shape(self):
         return (1, 32, 32, 3)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_sd3_lora(self):
         """
         Test loading the loras that are saved with the diffusers and peft formats.
@@ -135,7 +136,7 @@ def test_multiple_wrong_adapter_name_raises_error(self):
 
 
 @nightly
-@require_torch_gpu
+@require_torch_accelerator
 @require_peft_backend
 @require_big_gpu_with_torch_cuda
 @pytest.mark.big_gpu_with_torch_cuda
@@ -146,12 +147,12 @@ class SD3LoraIntegrationTests(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 get_inputs(self, device, seed=0):
         init_image = load_image(

tests/models/unets/test_models_unet_2d_condition.py

@@ -36,6 +36,9 @@
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import (
     backend_empty_cache,
+    backend_max_memory_allocated,
+    backend_reset_max_memory_allocated,
+    backend_reset_peak_memory_stats,
     enable_full_determinism,
     floats_tensor,
     is_peft_available,
@@ -1002,7 +1005,7 @@ def test_load_sharded_checkpoint_from_hub_subfolder(self, repo_id, variant):
         assert loaded_model
         assert new_output.sample.shape == (4, 4, 16, 16)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_load_sharded_checkpoint_from_hub_local(self):
         _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
         ckpt_path = snapshot_download("hf-internal-testing/unet2d-sharded-dummy")
@@ -1013,7 +1016,7 @@ def test_load_sharded_checkpoint_from_hub_local(self):
         assert loaded_model
         assert new_output.sample.shape == (4, 4, 16, 16)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_load_sharded_checkpoint_from_hub_local_subfolder(self):
         _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
         ckpt_path = snapshot_download("hf-internal-testing/unet2d-sharded-dummy-subfolder")
@@ -1024,7 +1027,7 @@ def test_load_sharded_checkpoint_from_hub_local_subfolder(self):
         assert loaded_model
         assert new_output.sample.shape == (4, 4, 16, 16)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     @parameterized.expand(
         [
             ("hf-internal-testing/unet2d-sharded-dummy", None),
@@ -1039,7 +1042,7 @@ def test_load_sharded_checkpoint_device_map_from_hub(self, repo_id, variant):
         assert loaded_model
         assert new_output.sample.shape == (4, 4, 16, 16)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     @parameterized.expand(
         [
             ("hf-internal-testing/unet2d-sharded-dummy-subfolder", None),
@@ -1054,7 +1057,7 @@ def test_load_sharded_checkpoint_device_map_from_hub_subfolder(self, repo_id, va
         assert loaded_model
         assert new_output.sample.shape == (4, 4, 16, 16)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_load_sharded_checkpoint_device_map_from_hub_local(self):
         _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
         ckpt_path = snapshot_download("hf-internal-testing/unet2d-sharded-dummy")
@@ -1064,7 +1067,7 @@ def test_load_sharded_checkpoint_device_map_from_hub_local(self):
         assert loaded_model
         assert new_output.sample.shape == (4, 4, 16, 16)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_load_sharded_checkpoint_device_map_from_hub_local_subfolder(self):
         _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
         ckpt_path = snapshot_download("hf-internal-testing/unet2d-sharded-dummy-subfolder")
@@ -1164,11 +1167,11 @@ def get_unet_model(self, fp16=False, model_id="CompVis/stable-diffusion-v1-4"):
 
         return model
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_set_attention_slice_auto(self):
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
+        backend_empty_cache(torch_device)
+        backend_reset_max_memory_allocated(torch_device)
+        backend_reset_peak_memory_stats(torch_device)
 
         unet = self.get_unet_model()
         unet.set_attention_slice("auto")
@@ -1180,15 +1183,15 @@ def test_set_attention_slice_auto(self):
         with torch.no_grad():
             _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
 
-        mem_bytes = torch.cuda.max_memory_allocated()
+        mem_bytes = backend_max_memory_allocated(torch_device)
 
         assert mem_bytes < 5 * 10**9
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_set_attention_slice_max(self):
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
+        backend_empty_cache(torch_device)
+        backend_reset_max_memory_allocated(torch_device)
+        backend_reset_peak_memory_stats(torch_device)
 
         unet = self.get_unet_model()
         unet.set_attention_slice("max")
@@ -1200,15 +1203,15 @@ def test_set_attention_slice_max(self):
         with torch.no_grad():
             _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
 
-        mem_bytes = torch.cuda.max_memory_allocated()
+        mem_bytes = backend_max_memory_allocated(torch_device)
 
         assert mem_bytes < 5 * 10**9
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_set_attention_slice_int(self):
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
+        backend_empty_cache(torch_device)
+        backend_reset_max_memory_allocated(torch_device)
+        backend_reset_peak_memory_stats(torch_device)
 
         unet = self.get_unet_model()
         unet.set_attention_slice(2)
@@ -1220,15 +1223,15 @@ def test_set_attention_slice_int(self):
         with torch.no_grad():
             _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
 
-        mem_bytes = torch.cuda.max_memory_allocated()
+        mem_bytes = backend_max_memory_allocated(torch_device)
 
         assert mem_bytes < 5 * 10**9
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_set_attention_slice_list(self):
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
+        backend_empty_cache(torch_device)
+        backend_reset_max_memory_allocated(torch_device)
+        backend_reset_peak_memory_stats(torch_device)
 
         # there are 32 sliceable layers
         slice_list = 16 * [2, 3]
@@ -1242,7 +1245,7 @@ def test_set_attention_slice_list(self):
         with torch.no_grad():
             _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
 
-        mem_bytes = torch.cuda.max_memory_allocated()
+        mem_bytes = backend_max_memory_allocated(torch_device)
 
         assert mem_bytes < 5 * 10**9
 

tests/pipelines/controlnet/test_controlnet.py

@@ -79,7 +79,7 @@ def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
         pipe = StableDiffusionControlNetPipeline.from_pretrained(
             "stable-diffusion-v1-5/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
         )
-        pipe.to("cuda")
+        pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 
         pipe.unet.to(memory_format=torch.channels_last)

tests/pipelines/controlnet/test_controlnet_inpaint_sdxl.py

@@ -40,7 +40,7 @@
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
     floats_tensor,
-    require_torch_gpu,
+    require_torch_accelerator,
     torch_device,
 )
 
@@ -245,7 +245,7 @@ def test_xformers_attention_forwardGenerator_pass(self):
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
-    @require_torch_gpu
+    @require_torch_accelerator
     def test_stable_diffusion_xl_offloads(self):
         pipes = []
         components = self.get_dummy_components()
@@ -254,12 +254,12 @@ def test_stable_diffusion_xl_offloads(self):
 
         components = self.get_dummy_components()
         sd_pipe = self.pipeline_class(**components)
-        sd_pipe.enable_model_cpu_offload()
+        sd_pipe.enable_model_cpu_offload(device=torch_device)
         pipes.append(sd_pipe)
 
         components = self.get_dummy_components()
         sd_pipe = self.pipeline_class(**components)
-        sd_pipe.enable_sequential_cpu_offload()
+        sd_pipe.enable_sequential_cpu_offload(device=torch_device)
         pipes.append(sd_pipe)
 
         image_slices = []

tests/pipelines/controlnet/test_controlnet_sdxl.py

@@ -223,12 +223,12 @@ def test_stable_diffusion_xl_offloads(self):
 
         components = self.get_dummy_components()
         sd_pipe = self.pipeline_class(**components)
-        sd_pipe.enable_model_cpu_offload()
+        sd_pipe.enable_model_cpu_offload(device=torch_device)
         pipes.append(sd_pipe)
 
         components = self.get_dummy_components()
         sd_pipe = self.pipeline_class(**components)
-        sd_pipe.enable_sequential_cpu_offload()
+        sd_pipe.enable_sequential_cpu_offload(device=torch_device)
         pipes.append(sd_pipe)
 
         image_slices = []

tests/pipelines/controlnet_flux/test_controlnet_flux.py

@@ -31,6 +31,7 @@
 from diffusers.models import FluxControlNetModel
 from diffusers.utils import load_image
 from diffusers.utils.testing_utils import (
+    backend_empty_cache,
     enable_full_determinism,
     nightly,
     numpy_cosine_similarity_distance,
@@ -215,12 +216,12 @@ class FluxControlNetPipelineSlowTests(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_canny(self):
         controlnet = FluxControlNetModel.from_pretrained(

tests/pipelines/controlnet_sd3/test_controlnet_sd3.py

@@ -239,7 +239,7 @@ def test_canny(self):
         pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
             "stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16
         )
-        pipe.enable_model_cpu_offload()
+        pipe.enable_model_cpu_offload(device=torch_device)
         pipe.set_progress_bar_config(disable=None)
 
         generator = torch.Generator(device="cpu").manual_seed(0)

tests/pipelines/flux/test_pipeline_flux.py

@@ -9,6 +9,7 @@
 
 from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, FluxPipeline, FluxTransformer2DModel
 from diffusers.utils.testing_utils import (
+    backend_empty_cache,
     nightly,
     numpy_cosine_similarity_distance,
     require_big_gpu_with_torch_cuda,
@@ -236,12 +237,12 @@ class FluxPipelineSlowTests(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 get_inputs(self, device, seed=0):
         generator = torch.Generator(device="cpu").manual_seed(seed)