fix hypothesis strategy that skips entire test without CUDA (#2690)

Summary:
Pull Request resolved: #2690

# context
* original implementation will skip the entire test set if there is no cuda available
* the actual intention is to loop all devices (cpu, meta, cuda) and only skip cuda if not available.

Reviewed By: dstaay-fb

Differential Revision: D68373224

fbshipit-source-id: 28c8b12a61213ebfc794b07f51e5eff77f13938a

Author: TroyGarden

torchrec/sparse/tests/test_tensor_dict.py

@@ -17,14 +17,14 @@
 from torchrec.sparse.tensor_dict import maybe_td_to_kjt
 
 
-class TestTensorDIct(unittest.TestCase):
-    @given(device_str=st.sampled_from(["cpu", "cuda", "meta"]))
-    @settings(verbosity=Verbosity.verbose, max_examples=5, deadline=None)
+class TestTensorDict(unittest.TestCase):
     # pyre-ignore[56]
-    @unittest.skipIf(
-        torch.cuda.device_count() <= 0,
-        "CUDA is not available",
+    @given(
+        device_str=st.sampled_from(
+            ["cpu", "meta"] + (["cuda"] if torch.cuda.device_count() > 0 else [])
+        )
     )
+    @settings(verbosity=Verbosity.verbose, max_examples=5, deadline=None)
     def test_kjt_input(self, device_str: str) -> None:
         device = torch.device(device_str)
         values = torch.tensor([0, 1, 2, 3, 2, 3, 4], device=device)
@@ -36,13 +36,13 @@ def test_kjt_input(self, device_str: str) -> None:
         features = maybe_td_to_kjt(kjt)
         self.assertEqual(features, kjt)
 
-    @given(device_str=st.sampled_from(["cpu", "cuda", "meta"]))
-    @settings(verbosity=Verbosity.verbose, max_examples=5, deadline=None)
     # pyre-ignore[56]
-    @unittest.skipIf(
-        torch.cuda.device_count() <= 0,
-        "CUDA is not available",
+    @given(
+        device_str=st.sampled_from(
+            ["cpu", "meta"] + (["cuda"] if torch.cuda.device_count() > 0 else [])
+        )
     )
+    @settings(verbosity=Verbosity.verbose, max_examples=5, deadline=None)
     def test_td_kjt(self, device_str: str) -> None:
         device = torch.device(device_str)
         values = torch.tensor([0, 1, 2, 3, 2, 3, 4], device=device)