use gt rather than ge

ignite/distributed/comp_models/base.py

@@ -5,7 +5,7 @@
 import torch
 from packaging.version import Version
 
-_torch_version_ge_112 = Version(torch.__version__) > Version("1.12.0")
+_torch_version_gt_112 = Version(torch.__version__) > Version("1.12.0")
 
 
 class ComputationModel(metaclass=ABCMeta):
@@ -329,7 +329,7 @@ def get_node_rank(self) -> int:
     def device(self) -> torch.device:
         if torch.cuda.is_available():
             return torch.device("cuda")
-        if _torch_version_ge_112 and torch.backends.mps.is_available():
+        if _torch_version_gt_112 and torch.backends.mps.is_available():
             return torch.device("mps")
         return torch.device("cpu")
 

tests/ignite/distributed/comp_models/test_base.py

@@ -1,7 +1,7 @@
 import pytest
 import torch
 
-from ignite.distributed.comp_models.base import _SerialModel, _torch_version_ge_112, ComputationModel
+from ignite.distributed.comp_models.base import _SerialModel, _torch_version_gt_112, ComputationModel
 
 
 def test_serial_model():
@@ -16,7 +16,7 @@ def test_serial_model():
     assert model.get_node_rank() == 0
     if torch.cuda.is_available():
         assert model.device().type == "cuda"
-    elif _torch_version_ge_112 and torch.backends.mps.is_available():
+    elif _torch_version_gt_112 and torch.backends.mps.is_available():
         assert model.device().type == "mps"
     else:
         assert model.device().type == "cpu"

tests/ignite/distributed/test_auto.py

@@ -12,7 +12,7 @@
 
 import ignite.distributed as idist
 from ignite.distributed.auto import auto_dataloader, auto_model, auto_optim, DistributedProxySampler
-from ignite.distributed.comp_models.base import _torch_version_ge_112
+from ignite.distributed.comp_models.base import _torch_version_gt_112
 from tests.ignite import is_mps_available_and_functional
 
 
@@ -182,7 +182,7 @@ def _test_auto_model_optimizer(ws, device):
 
 
 @pytest.mark.skipif(
-    (not _torch_version_ge_112) or (torch.backends.mps.is_available() and not is_mps_available_and_functional()),
+    (not _torch_version_gt_112) or (torch.backends.mps.is_available() and not is_mps_available_and_functional()),
     reason="Skip if MPS not functional",
 )
 def test_auto_methods_no_dist():

tests/ignite/distributed/test_launcher.py

@@ -8,7 +8,7 @@
 from packaging.version import Version
 
 import ignite.distributed as idist
-from ignite.distributed.comp_models.base import _torch_version_ge_112
+from ignite.distributed.comp_models.base import _torch_version_gt_112
 from ignite.distributed.utils import has_hvd_support, has_native_dist_support, has_xla_support
 from tests.ignite import is_mps_available_and_functional
 
@@ -57,7 +57,7 @@ def execute(cmd, env=None):
 
 
 @pytest.mark.skipif(
-    (not _torch_version_ge_112) or (torch.backends.mps.is_available() and not is_mps_available_and_functional()),
+    (not _torch_version_gt_112) or (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):

tests/ignite/distributed/utils/test_serial.py

@@ -1,7 +1,7 @@
 import torch
 
 import ignite.distributed as idist
-from ignite.distributed.comp_models.base import _torch_version_ge_112
+from ignite.distributed.comp_models.base import _torch_version_gt_112
 from tests.ignite.distributed.utils import (
     _sanity_check,
     _test_distrib__get_max_length,
@@ -18,7 +18,7 @@ def test_no_distrib(capsys):
     assert idist.backend() is None
     if torch.cuda.is_available():
         assert idist.device().type == "cuda"
-    elif _torch_version_ge_112 and torch.backends.mps.is_available():
+    elif _torch_version_gt_112 and torch.backends.mps.is_available():
         assert idist.device().type == "mps"
     else:
         assert idist.device().type == "cpu"
@@ -41,7 +41,7 @@ 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_ge_112 and torch.backends.mps.is_available():
+    elif _torch_version_gt_112 and 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]

tests/ignite/engine/test_create_supervised.py

@@ -12,7 +12,7 @@
 from torch.optim import SGD
 
 import ignite.distributed as idist
-from ignite.distributed.comp_models.base import _torch_version_ge_112
+from ignite.distributed.comp_models.base import _torch_version_gt_112
 from ignite.engine import (
     _check_arg,
     create_supervised_evaluator,
@@ -487,7 +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_ge_112 and is_mps_available_and_functional()), reason="Skip if no MPS")
+@pytest.mark.skipif(not (_torch_version_gt_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)
@@ -668,14 +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_ge_112 and is_mps_available_and_functional()), reason="Skip if no MPS")
+@pytest.mark.skipif(not (_torch_version_gt_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_ge_112 and is_mps_available_and_functional()), reason="Skip if no MPS")
+@pytest.mark.skipif(not (_torch_version_gt_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")