diff --git a/test/nn/test_module_hooks.py b/test/nn/test_module_hooks.py
index c9c29f0ba4a..72e3665cfdd 100644
--- a/test/nn/test_module_hooks.py
+++ b/test/nn/test_module_hooks.py
@@ -1445,7 +1445,14 @@ class TestModuleHookNN(NNTestCase):
         mod.register_full_backward_hook(hook)
 
         # This should run and trigger the hook properly
-        mod(inp).sum().backward()
+        with self.assertWarnsRegex(
+            UserWarning,
+            (
+                "Full backward hook is firing when gradients are computed with "
+                "respect to module outputs since no inputs require gradients"
+            ),
+        ):
+            mod(inp).sum().backward()
         self.assertEqual(hook_called[0], 1)
 
         return_val = "grad_input"
diff --git a/torch/utils/hooks.py b/torch/utils/hooks.py
index c41add0fcbe..e6e93966afd 100644
--- a/torch/utils/hooks.py
+++ b/torch/utils/hooks.py
@@ -223,6 +223,11 @@ class BackwardHook:
                 # Special case if no input required gradients, this hook should call the user
                 # hook directly
                 if self.input_tensors_index is None:
+                    warnings.warn("Full backward hook is firing when gradients are computed "
+                                  "with respect to module outputs since no inputs require gradients. See "
+                                  "https://docs.pytorch.org/docs/main/generated/torch.nn.Module.html#torch.nn.Module.register_full_backward_hook "  # noqa: B950
+                                  "for more details.",
+                                  stacklevel=5)
                     grad_inputs = self._pack_with_none([], [], self.n_inputs)
                     for user_hook in self.user_hooks:
                         res = user_hook(self.module, grad_inputs, self.grad_outputs)