parameterize tests for cuda devices

currently failing a few of them, some are expected failures.
funkelab · Dec 19, 2023 · b6c425f · b6c425f
1 parent 076661f
commit b6c425f
Showing 1 changed file with 72 additions and 5 deletions.
diff --git a/tests/cases/torch_train.py b/tests/cases/torch_train.py
@@ -68,8 +68,20 @@ def forward(self, a, b):
             return d_pred
 
 
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param(
+            "cuda:0",
+            marks=pytest.mark.skipif(
+                not torch.cuda.is_available(), reason="CUDA not available"
+            ),
+        ),
+    ],
+)
 @skipIf(isinstance(torch, NoSuchModule), "torch is not installed")
-def test_loss_drops(tmpdir):
+def test_loss_drops(tmpdir, device):
     checkpoint_basename = str(tmpdir / "model")
 
     a_key = ArrayKey("A")
@@ -80,7 +92,7 @@ def test_loss_drops(tmpdir):
 
     model = ExampleLinearModel()
     loss = torch.nn.MSELoss()
-    optimizer = torch.optim.SGD(model.parameters(), lr=1e-7, momentum=0.999)
+    optimizer = torch.optim.SGD(model.parameters(), lr=1e-8, momentum=0.999)
 
     source = example_train_source(a_key, b_key, c_key)
     train = Train(
@@ -98,6 +110,7 @@ def test_loss_drops(tmpdir):
         checkpoint_basename=checkpoint_basename,
         save_every=100,
         spawn_subprocess=False,
+        device=device,
     )
     pipeline = source + train
 
@@ -130,8 +143,25 @@ def test_loss_drops(tmpdir):
             assert loss2 < loss1
 
 
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param(
+            "cuda:0",
+            marks=[
+                pytest.mark.skipif(
+                    not torch.cuda.is_available(), reason="CUDA not available"
+                ),
+                pytest.mark.xfail(
+                    reason="failing to move model to device when using a subprocess"
+                ),
+            ],
+        ),
+    ],
+)
 @skipIf(isinstance(torch, NoSuchModule), "torch is not installed")
-def test_output():
+def test_output(device):
     logging.getLogger("gunpowder.torch.nodes.predict").setLevel(logging.INFO)
 
     a_key = ArrayKey("A")
@@ -153,6 +183,7 @@ def test_output():
             d_pred: ArraySpec(nonspatial=True),
         },
         spawn_subprocess=True,
+        device=device,
     )
     pipeline = source + predict
 
@@ -191,8 +222,25 @@ def forward(self, a):
             return pred
 
 
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param(
+            "cuda:0",
+            marks=[
+                pytest.mark.skipif(
+                    not torch.cuda.is_available(), reason="CUDA not available"
+                ),
+                pytest.mark.xfail(
+                    reason="failing to move model to device in multiprocessing context"
+                ),
+            ],
+        ),
+    ],
+)
 @skipIf(isinstance(torch, NoSuchModule), "torch is not installed")
-def test_scan():
+def test_scan(device):
     logging.getLogger("gunpowder.torch.nodes.predict").setLevel(logging.INFO)
 
     a_key = ArrayKey("A")
@@ -210,6 +258,7 @@ def test_scan():
         inputs={"a": a_key},
         outputs={0: pred},
         array_specs={pred: ArraySpec()},
+        device=device,
     )
     pipeline = source + predict + Scan(reference_request, num_workers=2)
 
@@ -226,8 +275,25 @@ def test_scan():
         assert pred in batch
 
 
+@pytest.mark.parametrize(
+    "device",
+    [
+        "cpu",
+        pytest.param(
+            "cuda:0",
+            marks=[
+                pytest.mark.skipif(
+                    not torch.cuda.is_available(), reason="CUDA not available"
+                ),
+                pytest.mark.xfail(
+                    reason="failing to move model to device in multiprocessing context"
+                ),
+            ],
+        ),
+    ],
+)
 @skipIf(isinstance(torch, NoSuchModule), "torch is not installed")
-def test_precache():
+def test_precache(device):
     logging.getLogger("gunpowder.torch.nodes.predict").setLevel(logging.INFO)
 
     a_key = ArrayKey("A")
@@ -245,6 +311,7 @@ def test_precache():
         inputs={"a": a_key},
         outputs={0: pred},
         array_specs={pred: ArraySpec()},
+        device=device,
     )
     pipeline = source + predict + PreCache(cache_size=3, num_workers=2)