Fix: Bad autograd side effects from printing

test/test_autograd.py

@@ -7355,6 +7355,19 @@ def test_inplace_view_of_view(self, device):
         x.sum().backward()
         self.assertEqual(root.grad.tolist(), [[1, 2], [1, 1]])
 
+    def test_inplace_view_then_no_grad(self, device):
+        # Perform an in-place operation on a view of a non-leaf variable.
+        a = torch.ones(3, 1, device=device, requires_grad=True)
+        b = a * 2
+        c = b.view_as(b)
+        c[0][0] = 3
+
+        # Force a graph update with grad disabled.
+        with torch.no_grad():
+            c.grad_fn
+
+        c.sum().backward()
+
     def test_inplace_view_gradcheck(self, device):
         # gradcheck modifications to views
         a = torch.randn(4, 4, device=device, requires_grad=True)

torch/csrc/autograd/custom_function.h

@@ -212,7 +212,7 @@ auto Function<T>::apply(Args&&... args) -> std::enable_if_t<std::is_same<X,T>::v
   extract_vars(node->is_variable_input_, input_vars, args...);
 
   bool is_executable =  GradMode::is_enabled() && any_variable_requires_grad(input_vars);
-  auto next_edges = collect_next_edges(input_vars);
+  auto next_edges = (is_executable ? collect_next_edges(input_vars) : edge_list());
   node->set_ctx_grad_fn(node);
   node->set_next_edges(std::move(next_edges));
   node->clear_input_metadata();

torch/csrc/autograd/function.h

@@ -504,8 +504,6 @@ inline bool any_variable_requires_grad(const variable_list& variables) {
 /// Return the next edges of all the given variables, or tuples of variables.
 template <typename... Variables>
 edge_list collect_next_edges(Variables&&... variables) {
-  if (!GradMode::is_enabled())
-    return {};
   detail::MakeNextFunctionList make;
   make.apply(std::forward<Variables>(variables)...);
   return std::move(make.next_edges);

torch/csrc/autograd/functions/utils.cpp

@@ -22,7 +22,7 @@ variable_list wrap_outputs(const variable_list& inputs, tensor_list&& outputs,
       }
     }
   } else {
-    auto grad_fn = ctr(collect_next_edges(inputs));
+    auto grad_fn = ctr(GradMode::is_enabled() ? collect_next_edges(inputs) : edge_list());
     for (auto& output : outputs) {
       if (output.defined()) {
         auto variable = autograd::make_variable(output, /*requires_grad=*/false);

torch/csrc/autograd/python_function.cpp

@@ -487,7 +487,7 @@ std::pair<UnpackedInput, InputFlags> unpack_input(PyObject *args) {
   }
 
   flags.is_executable = GradMode::is_enabled() && any_variable_requires_grad(unpacked.input_vars);
-  flags.next_edges = collect_next_edges(unpacked.input_vars);
+  flags.next_edges = (flags.is_executable ? collect_next_edges(unpacked.input_vars) : edge_list());
   return std::make_pair(std::move(unpacked), std::move(flags));
 }