Fix failing mps tests

tests/ignite/distributed/comp_models/test_base.py

@@ -2,11 +2,9 @@
 import torch
 
 from ignite.distributed.comp_models.base import _SerialModel, _torch_version_le_112, ComputationModel
+from ignite.distributed.comp_models.base import _torch_version_le_112
 
 
-@pytest.mark.skipif(
-    _torch_version_le_112 and torch.backends.mps.is_available(), reason="Temporary skip if MPS is available"
-)
 def test_serial_model():
     _SerialModel.create_from_backend()
     model = _SerialModel.create_from_context()
@@ -19,6 +17,8 @@ def test_serial_model():
     assert model.get_node_rank() == 0
     if torch.cuda.is_available():
         assert model.device().type == "cuda"
+    elif _torch_version_le_112 and torch.backends.mps.is_available():
+            assert model.device().type == "mps"
     else:
         assert model.device().type == "cpu"
     assert model.backend() is None

tests/ignite/distributed/test_auto.py

@@ -180,16 +180,13 @@ def _test_auto_model_optimizer(ws, device):
         assert optimizer.backward_passes_per_step == backward_passes_per_step
 
 
-@pytest.mark.skipif(
-    _torch_version_le_112 and torch.backends.mps.is_available(), reason="Temporary skip if MPS is available"
-)
 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)
     _test_auto_dataloader(1, 1, batch_size=10, sampler_name="WeightedRandomSampler")
     _test_auto_dataloader(1, 1, batch_size=10, sampler_name="DistributedSampler")
-
-    _test_auto_model_optimizer(1, "cuda" if torch.cuda.is_available() else "cpu")
+    device = idist.device()
+    _test_auto_model_optimizer(1, device)
 
 
 @pytest.mark.distributed

tests/ignite/distributed/test_launcher.py

@@ -258,11 +258,8 @@ def test_idist_parallel_n_procs_native(init_method, backend, get_fixed_dirname,
 
 
 @pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc")
-@pytest.mark.skipif(
-    _torch_version_le_112 and torch.backends.mps.is_available(), reason="Temporary skip if MPS is available"
-)
 def test_idist_parallel_no_dist():
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = idist.device()
     with idist.Parallel(backend=None) as parallel:
         parallel.run(_test_func, ws=1, device=device, backend=None, true_init_method=None)
 

tests/ignite/distributed/utils/test_serial.py

@@ -15,13 +15,12 @@
 )
 
 
-@pytest.mark.skipif(
-    _torch_version_le_112 and torch.backends.mps.is_available(), reason="Temporary skip if MPS is available"
-)
 def test_no_distrib(capsys):
     assert idist.backend() is None
     if torch.cuda.is_available():
         assert idist.device().type == "cuda"
+    elif _torch_version_le_112 and torch.backends.mps.is_available():
+        assert idist.device().type == "mps"
     else:
         assert idist.device().type == "cpu"
     assert idist.get_rank() == 0
@@ -43,6 +42,9 @@ def test_no_distrib(capsys):
     assert "ignite.distributed.utils INFO: backend: None" in out[-1]
     if torch.cuda.is_available():
         assert "ignite.distributed.utils INFO: device: cuda" in out[-1]
+    elif _torch_version_le_112:
+        if torch.backends.mps.is_available():
+            assert "ignite.distributed.utils INFO: device: mps" in out[-1]
     else:
         assert "ignite.distributed.utils INFO: device: cpu" in out[-1]
     assert "ignite.distributed.utils INFO: rank: 0" in out[-1]

tests/ignite/engine/test_create_supervised.py

@@ -286,12 +286,15 @@ def _test_create_supervised_evaluator(
         with_model_fn=with_model_fn,
     )
     x = torch.tensor([[1.0], [2.0]])
+    x.to(evaluator_device)
     y = torch.tensor([[3.0], [5.0]])
+    y.to(evaluator_device)
     if with_model_fn:
         y += 0.01
     data = [(x, y)]
 
     if model_device == evaluator_device or ((model_device == "cpu") ^ (evaluator_device == "cpu")):
+        print("inside test")
         state = evaluator.run(data)
 
         y_pred, y = state.output