Fix mps not functional

tests/ignite/__init__.py

@@ -3,3 +3,14 @@
 
 def cpu_and_maybe_cuda():
     return ("cpu",) + (("cuda",) if torch.cuda.is_available() else ())
+
+
+def is_mps_available_and_functional():
+    if not torch.backends.mps.is_available():
+        return False
+    try:
+        # Try to allocate a small tensor on the MPS device
+        torch.tensor([1.0], device="mps")
+        return True
+    except RuntimeError:
+        return False

tests/ignite/distributed/test_auto.py

@@ -12,6 +12,7 @@
 
 import ignite.distributed as idist
 from ignite.distributed.auto import auto_dataloader, auto_model, auto_optim, DistributedProxySampler
+from tests.ignite import is_mps_available_and_functional
 
 
 class DummyDS(Dataset):
@@ -179,6 +180,9 @@ def _test_auto_model_optimizer(ws, device):
         assert optimizer.backward_passes_per_step == backward_passes_per_step
 
 
+@pytest.mark.skipif(
+    torch.backends.mps.is_available() and not is_mps_available_and_functional(), reason="Skip if MPS not functional"
+)
 def test_auto_methods_no_dist():
     _test_auto_dataloader(1, 1, batch_size=1)
     _test_auto_dataloader(1, 1, batch_size=10, num_workers=2)

tests/ignite/distributed/test_launcher.py

@@ -9,6 +9,7 @@
 
 import ignite.distributed as idist
 from ignite.distributed.utils import has_hvd_support, has_native_dist_support, has_xla_support
+from tests.ignite import is_mps_available_and_functional
 
 
 def test_parallel_wrong_inputs():
@@ -54,6 +55,9 @@ def execute(cmd, env=None):
     return str(process.stdout.read()) + str(process.stderr.read())
 
 
+@pytest.mark.skipif(
+    torch.backends.mps.is_available() and not is_mps_available_and_functional(), reason="Skip if MPS not functional"
+)
 def test_check_idist_parallel_no_dist(exec_filepath):
     cmd = [sys.executable, "-u", exec_filepath]
     out = execute(cmd)

tests/ignite/engine/test_create_supervised.py

@@ -25,6 +25,8 @@
 )
 from ignite.metrics import MeanSquaredError
 
+from tests.ignite import is_mps_available_and_functional
+
 
 class DummyModel(torch.nn.Module):
     def __init__(self, output_as_list=False):
@@ -485,7 +487,7 @@ def test_create_supervised_trainer_on_cuda():
     _test_create_mocked_supervised_trainer(model_device=model_device, trainer_device=trainer_device)
 
 
-@pytest.mark.skipif(not (_torch_version_le_112 and torch.backends.mps.is_available()), reason="Skip if no MPS")
+@pytest.mark.skipif(not (_torch_version_le_112 and is_mps_available_and_functional()), reason="Skip if no MPS")
 def test_create_supervised_trainer_on_mps():
     model_device = trainer_device = "mps"
     _test_create_supervised_trainer_wrong_accumulation(model_device=model_device, trainer_device=trainer_device)
@@ -666,14 +668,14 @@ def test_create_supervised_evaluator_on_cuda_with_model_on_cpu():
     _test_mocked_supervised_evaluator(evaluator_device="cuda")
 
 
-@pytest.mark.skipif(not (_torch_version_le_112 and torch.backends.mps.is_available()), reason="Skip if no MPS")
+@pytest.mark.skipif(not (_torch_version_le_112 and is_mps_available_and_functional()), reason="Skip if no MPS")
 def test_create_supervised_evaluator_on_mps():
     model_device = evaluator_device = "mps"
     _test_create_supervised_evaluator(model_device=model_device, evaluator_device=evaluator_device)
     _test_mocked_supervised_evaluator(model_device=model_device, evaluator_device=evaluator_device)
 
 
-@pytest.mark.skipif(not (_torch_version_le_112 and torch.backends.mps.is_available()), reason="Skip if no MPS")
+@pytest.mark.skipif(not (_torch_version_le_112 and is_mps_available_and_functional()), reason="Skip if no MPS")
 def test_create_supervised_evaluator_on_mps_with_model_on_cpu():
     _test_create_supervised_evaluator(evaluator_device="mps")
     _test_mocked_supervised_evaluator(evaluator_device="mps")