[tests] make TestDeepSpeedModelZoo device-agnostic (#31402)

* fix

* use accelerator device count

* ci fix

src/transformers/testing_utils.py

@@ -2432,6 +2432,10 @@ if is_torch_available():
     BACKEND_MANUAL_SEED = {"cuda": torch.cuda.manual_seed, "cpu": torch.manual_seed, "default": torch.manual_seed}
     BACKEND_EMPTY_CACHE = {"cuda": torch.cuda.empty_cache, "cpu": None, "default": None}
     BACKEND_DEVICE_COUNT = {"cuda": torch.cuda.device_count, "cpu": lambda: 0, "default": lambda: 1}
+else:
+    BACKEND_MANUAL_SEED = {"default": None}
+    BACKEND_EMPTY_CACHE = {"default": None}
+    BACKEND_DEVICE_COUNT = {"default": lambda: 0}
 
 
 def backend_manual_seed(device: str, seed: int):

tests/deepspeed/test_model_zoo.py

@@ -23,12 +23,13 @@ from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
 from transformers import is_torch_available
 from transformers.testing_utils import (
     TestCasePlus,
+    backend_device_count,
     execute_subprocess_async,
-    get_gpu_count,
     get_tests_dir,
     require_deepspeed,
-    require_torch_gpu,
+    require_torch_accelerator,
     slow,
+    torch_device,
 )
 from transformers.trainer_utils import set_seed
 
@@ -143,7 +144,7 @@ def get_launcher(distributed=False):
     # - it won't be able to handle that
     # 2. for now testing with just 2 gpus max (since some quality tests may give different
     # results with mode gpus because we use very little data)
-    num_gpus = min(2, get_gpu_count()) if distributed else 1
+    num_gpus = min(2, backend_device_count(torch_device)) if distributed else 1
     master_port = os.environ.get("DS_TEST_PORT", DEFAULT_MASTER_PORT)
     return f"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()
 
@@ -326,7 +327,7 @@ params = list(itertools.product(stages, task_cmds.keys()))
 
 @slow
 @require_deepspeed
-@require_torch_gpu
+@require_torch_accelerator
 class TestDeepSpeedModelZoo(TestCasePlus):
     """This class is for testing via an external script - can do multiple gpus"""