parameterize nms gpu test

test/test_ops.py

@@ -751,47 +751,36 @@ def test_qnms(self, iou, scale, zero_point):
 
         torch.testing.assert_close(qkeep, keep, msg=err_msg.format(iou))
 
-    @needs_cuda
+    @pytest.mark.parametrize(
+        "device",
+        (
+            pytest.param("cuda", marks=pytest.mark.needs_cuda),
+            pytest.param("mps", marks=pytest.mark.needs_mps),
+        ),
+    )
     @pytest.mark.parametrize("iou", (0.2, 0.5, 0.8))
-    def test_nms_cuda(self, iou, dtype=torch.float64):
+    def test_nms_gpu(self, iou, device, dtype=torch.float64):
+        dtype = torch.float32 if device == "mps" else dtype
         tol = 1e-3 if dtype is torch.half else 1e-5
         err_msg = "NMS incompatible between CPU and CUDA for IoU={}"
 
         boxes, scores = self._create_tensors_with_iou(1000, iou)
         r_cpu = ops.nms(boxes, scores, iou)
-        r_cuda = ops.nms(boxes.cuda(), scores.cuda(), iou)
-
-        is_eq = torch.allclose(r_cpu, r_cuda.cpu())
-        if not is_eq:
-            # if the indices are not the same, ensure that it's because the scores
-            # are duplicate
-            is_eq = torch.allclose(scores[r_cpu], scores[r_cuda.cpu()], rtol=tol, atol=tol)
-        assert is_eq, err_msg.format(iou)
-
-    @needs_mps
-    @pytest.mark.parametrize("iou", (0.2, 0.5, 0.8))
-    def test_nms_mps(self, iou, dtype=torch.float32):
-        tol = 1e-3 if dtype is torch.half else 1e-5
-        err_msg = "NMS incompatible between CPU and MPS for IoU={}"
-
-        boxes, scores = self._create_tensors_with_iou(1000, iou)
-        r_cpu = ops.nms(boxes, scores, iou)
-        r_mps = ops.nms(boxes.to("mps"), scores.to("mps"), iou)
+        r_gpu = ops.nms(boxes.to(device), scores.to(device), iou)
 
-        print(r_cpu.size(), r_mps.size())
-        is_eq = torch.allclose(r_cpu, r_mps.cpu())
+        is_eq = torch.allclose(r_cpu, r_gpu.cpu())
         if not is_eq:
             # if the indices are not the same, ensure that it's because the scores
             # are duplicate
-            is_eq = torch.allclose(scores[r_cpu], scores[r_mps.cpu()], rtol=tol, atol=tol)
+            is_eq = torch.allclose(scores[r_cpu], scores[r_gpu.cpu()], rtol=tol, atol=tol)
         assert is_eq, err_msg.format(iou)
 
     @needs_cuda
     @pytest.mark.parametrize("iou", (0.2, 0.5, 0.8))
     @pytest.mark.parametrize("dtype", (torch.float, torch.half))
     def test_autocast(self, iou, dtype):
         with torch.cuda.amp.autocast():
-            self.test_nms_cuda(iou=iou, dtype=dtype)
+            self.test_nms_gpu(iou=iou, dtype=dtype, device="cuda")
 
     @pytest.mark.parametrize(
         "device",