diff --git a/test/test_autograd.py b/test/test_autograd.py
index 937dd391ede9..0bc86915d2d5 100644
--- a/test/test_autograd.py
+++ b/test/test_autograd.py
@@ -5624,7 +5624,7 @@ def test_ctc_loss_cudnn(self, device):
         self.assertEqual(grad_cudnn, grad_native, atol=1e-4)
 
     @skipCUDAIfRocm
-    def test_leaky_relu_inplace_with_zero_or_neg_slope(self, device):
+    def test_leaky_relu_inplace_with_neg_slope(self, device):
         a = torch.tensor([-1., 1.], device=device, requires_grad=True)
         b = torch.nn.functional.leaky_relu_(a.clone(), -2)
         with self.assertRaisesRegex(RuntimeError, "call out-of-place version"):
@@ -5635,10 +5635,13 @@ def test_leaky_relu_inplace_with_zero_or_neg_slope(self, device):
         with self.assertRaisesRegex(RuntimeError, "call out-of-place version"):
             b.backward(torch.ones(2, device=device))
 
+    @skipCUDAIfRocm
+    def test_leaky_relu_inplace_with_zero_slope(self, device):
         a = torch.tensor([-2., 0., 2.], device=device, requires_grad=True)
         b = torch.nn.functional.leaky_relu_(a.clone(), 0.0)
         b.backward(torch.ones(3, device=device))
-        self.assertEqual(a.grad, torch.tensor([0., 0., 1.], device=device))
+        expected = torch.tensor([0., 0., 1.], device=device)
+        self.assertEqual(a.grad, expected)
 
     @onlyCUDA
     def test_free_unneeded_tensor(self, device):
diff --git a/torch/testing/_internal/common_nn.py b/torch/testing/_internal/common_nn.py
index 59fc6506eb45..b1f66a717b29 100644
--- a/torch/testing/_internal/common_nn.py
+++ b/torch/testing/_internal/common_nn.py
@@ -225,6 +225,14 @@ def get_weight(m):
         check_inplace=True,
         desc='with_negval'
     ),
+    dict(
+        module_name='LeakyReLU',
+        constructor_args=(0.0,),
+        cpp_constructor_args='torch::nn::LeakyReLUOptions().negative_slope(0.0)',
+        input_fn=lambda: torch.randn(10, 10),
+        check_inplace=True,
+        desc='with_zero_negval'
+    ),
     dict(
         module_name='LogSigmoid',
         input_size=(2, 3, 4),