diff --git a/torch/autograd/gradcheck.py b/torch/autograd/gradcheck.py
index 086c2b470ef0f1..dd56ff517b61a7 100644
--- a/torch/autograd/gradcheck.py
+++ b/torch/autograd/gradcheck.py
@@ -725,10 +725,11 @@ def _check_outputs(outputs) -> None:
                          'Please call to_dense() on the output of fn for gradcheck.')
 
 
-def _check_no_differentiable_outputs(func, inputs, func_out, eps) -> bool:
+def _check_no_differentiable_outputs(func, inputs, func_out, eps, *, is_forward_ad) -> bool:
     # When there are no differentiable outputs, numerical gradient for a function is
     # expected to be zero.
-    jacobians_all_inputs_outputs = _get_numerical_jacobian(func, inputs, func_out, eps=eps)
+    jacobians_all_inputs_outputs = _get_numerical_jacobian(func, inputs, func_out,
+                                                           eps=eps, is_forward_ad=is_forward_ad)
     for jacobians_all_outputs_and_fixed_input in jacobians_all_inputs_outputs:
         for jacobian in jacobians_all_outputs_and_fixed_input:
             if torch.ne(jacobian, 0).sum() > 0:
@@ -1143,7 +1144,8 @@ def _slow_gradcheck(func, func_out, tupled_inputs, outputs, eps, rtol, atol, che
                     nondet_tol, *, use_forward_ad=False, complex_indices=None, test_imag=False):
     func_out = _as_tuple(func_out)
     if not outputs:
-        return _check_no_differentiable_outputs(func, tupled_inputs, func_out, eps)
+        return _check_no_differentiable_outputs(func, tupled_inputs, func_out,
+                                                eps=eps, is_forward_ad=use_forward_ad)
 
     numerical = _transpose(_get_numerical_jacobian(func, tupled_inputs, func_out, eps=eps, is_forward_ad=use_forward_ad))
     # Note: [numerical vs analytical output length]