refactor

tests/unit/test_common_pytorch_utils.py

@@ -4,8 +4,18 @@
 import pytest
 import torch
 import torch.nn as nn
+from torch.nn.parallel.data_parallel import DataParallel
+from torch.nn.modules.container import Sequential
+
 from utils_nlp.common.pytorch_utils import get_device, move_to_device
         
+
+@pytest.fixture
+def model():
+    return nn.Sequential(
+        nn.Linear(24, 8), nn.ReLU(), nn.Linear(8, 2), nn.Sigmoid()
+    )
+
         
 def test_get_device_cpu():
     device = get_device("cpu")
@@ -19,7 +29,7 @@ def test_get_device_exception():
 
 
 @pytest.mark.gpu
-def test_gpu_machine():
+def test_machine_is_gpu_machine():
     assert torch.cuda.is_available() is True
     
     
@@ -29,12 +39,14 @@ def test_get_device_gpu():
     assert isinstance(device, torch.device)
     assert device.type == "cuda"
     
-    
-def test_move_to_device():
-    model = nn.Sequential(
-        nn.Linear(24, 8), nn.ReLU(), nn.Linear(8, 2), nn.Sigmoid()
-    )
 
+def test_move_to_device_cpu(model):
+    # test when device.type="cpu"
+    model_cpu = move_to_device(model, torch.device("cpu"))
+    assert isinstance(model_cpu, nn.modules.container.Sequential)
+    
+
+def test_move_to_device_cpu_parallelized(model):
     # test when input model is parallelized
     model_parallelized = nn.DataParallel(model)
     model_parallelized_output = move_to_device(
@@ -43,63 +55,62 @@ def test_move_to_device():
     assert isinstance(
         model_parallelized_output, nn.modules.container.Sequential
     )
-
+    
+    
+def test_move_to_device_exception_not_torch_device(model):
     # test when device is not torch.device
     with pytest.raises(ValueError):
         move_to_device(model, "abc")
-
+        
-    if torch.cuda.is_available():
+        
-        # test when device.type="cuda"
+def test_move_to_device_exception_wrong_type(model):
-        model_cuda = move_to_device(model, torch.device("cuda"))
-        num_cuda_devices = torch.cuda.device_count()
-
-        if num_cuda_devices > 1:
-            assert isinstance(
-                model_cuda, nn.parallel.data_parallel.DataParallel
-            )
-        else:
-            assert isinstance(model_cuda, nn.modules.container.Sequential)
-
-        model_cuda_1_gpu = move_to_device(
-            model, torch.device("cuda"), num_gpus=1
-        )
-        assert isinstance(model_cuda_1_gpu, nn.modules.container.Sequential)
-
-        model_cuda_1_more_gpu = move_to_device(
-            model, torch.device("cuda"), num_gpus=num_cuda_devices + 1
-        )
-        if num_cuda_devices > 1:
-            assert isinstance(
-                model_cuda_1_more_gpu, nn.parallel.data_parallel.DataParallel
-            )
-        else:
-            assert isinstance(
-                model_cuda_1_more_gpu, nn.modules.container.Sequential
-            )
-
-        model_cuda_same_gpu = move_to_device(
-            model, torch.device("cuda"), num_gpus=num_cuda_devices
-        )
-        if num_cuda_devices > 1:
-            assert isinstance(
-                model_cuda_same_gpu, nn.parallel.data_parallel.DataParallel
-            )
-        else:
-            assert isinstance(
-                model_cuda_same_gpu, nn.modules.container.Sequential
-            )
-
-        # test when device.type is cuda, but num_gpus is 0
-        with pytest.raises(ValueError):
-            move_to_device(model, torch.device("cuda"), num_gpus=0)
-    else:
-        with pytest.raises(Exception):
-            move_to_device(model, torch.device("cuda"))
-
-    # test when device.type="cpu"
-    model_cpu = move_to_device(model, torch.device("cpu"))
-    assert isinstance(model_cpu, nn.modules.container.Sequential)
-
     # test when device.type is not "cuda" or "cpu"
     with pytest.raises(Exception):
         move_to_device(model, torch.device("opengl"))
+
+        
+def test_move_to_device_exception_gpu_model_on_cpu_machine(model):
+    # test when the model is moved to a gpu but it is a cpu machine
+    with pytest.raises(Exception):
+        move_to_device(model, torch.device("cuda"))
+        
+        
+@pytest.mark.gpu        
+def test_move_to_device_exception_cuda_zero_gpus(model):
+    # test when device.type is cuda, but num_gpus is 0
+    with pytest.raises(ValueError):
+        move_to_device(model, torch.device("cuda"), num_gpus=0)
+    
+    
+@pytest.mark.gpu
+def test_move_to_device_gpu(model):
+    # test when device.type="cuda"
+    model_cuda = move_to_device(model, torch.device("cuda"))
+    num_cuda_devices = torch.cuda.device_count()
+
+    if num_cuda_devices > 1:
+        assert isinstance(model_cuda, DataParallel)
+    else:
+        assert isinstance(model_cuda, Sequential)
+
+    model_cuda_1_gpu = move_to_device(
+        model, torch.device("cuda"), num_gpus=1
+    )
+    assert isinstance(model_cuda_1_gpu, Sequential)
+
+    model_cuda_1_more_gpu = move_to_device(
+        model, torch.device("cuda"), num_gpus=num_cuda_devices + 1
+    )
+    if num_cuda_devices > 1:
+        assert isinstance(model_cuda_1_more_gpu, DataParallel)
+    else:
+        assert isinstance(model_cuda_1_more_gpu, Sequential)
+
+    model_cuda_same_gpu = move_to_device(
+        model, torch.device("cuda"), num_gpus=num_cuda_devices
+    )
+    if num_cuda_devices > 1:
+        assert isinstance(model_cuda_same_gpu, DataParallel)
+    else:
+        assert isinstance(model_cuda_same_gpu, Sequential)
+