Check for released variables in SavedVariable::unpack() (pytorch#1648)

Fixes pytorch#1288

Author: colesbury

test/test_nn.py

@@ -1321,6 +1321,14 @@ def test_Conv2d_missing_argument(self):
  c = nn.Conv2d(3, 3, 3)
  self.assertRaises(RuntimeError, lambda: c(None))
 
+ def test_Conv2d_backward_twice(self):
+ input = Variable(torch.randn(2, 3, 5, 5))
+ c = nn.Conv2d(3, 3, 3)
+ o1 = c(input)
+ o1.sum().backward()
+ self.assertRaisesRegex(RuntimeError, 'Specify retain_variables=True',
+ lambda: o1.sum().backward())
+
  @unittest.skipIf(not TEST_CUDA, 'CUDA not available')
  def test_Conv2d_large_workspace(self):
  # These sizes require huge cuDNN workspaces. Make sure we choose a

torch/csrc/autograd/functions/convolution.cpp

@@ -204,9 +204,9 @@ auto ConvBackward::apply(const variable_list& grad_outputs) -> variable_list {
  if (is_padding_neg()) throw std::runtime_error("negative padding is not supported");
  if (is_output_padding_neg()) throw std::runtime_error("negative output_padding is not supported");
 
- AutoGPU guard(input_.data->getDevice());
-
- auto input = input_.unpack_data()->contiguous();
+ auto input = input_.unpack_data();
+ AutoGPU guard(input->getDevice());
+ input = input->contiguous();
  std::unique_ptr<Tensor> weight(weight_.unpack_data()->clone_shallow());
  auto bias = bias_.unpack_data();
  auto grad_output = grad_outputs[0]->data->contiguous();

torch/csrc/autograd/python_function.cpp

@@ -849,9 +849,7 @@ PyObject* THPFunction_register_hook(THPFunction *self, PyObject *hook)
 
 PyObject *THPFunction_saved_tensors(THPFunction *self, void *_unused)
 {
- THPUtils_assert(!self->has_freed_buffers, "Trying to backward through the "
- "graph second time, but the buffers have already been freed. Please "
- "specify retain_variables=True when calling backward for the first time.");
+ THPUtils_assert(!self->has_freed_buffers, ERR_BACKWARD_TWICE);
  if (!self->saved_variables)
  return PyTuple_New(0);
 
@@ -876,9 +874,7 @@ PyObject *THPFunction_saved_tensors(THPFunction *self, void *_unused)
 
 PyObject *THPFunction_saved_variables(THPFunction *self, void *_unused)
 {
- THPUtils_assert(!self->has_freed_buffers, "Trying to backward through the "
- "graph second time, but the buffers have already been freed. Please "
- "specify retain_variables=True when calling backward for the first time.");
+ THPUtils_assert(!self->has_freed_buffers, ERR_BACKWARD_TWICE);
  if (!self->saved_variables)
  return PyTuple_New(0);
 

torch/csrc/autograd/variable.cpp

@@ -60,7 +60,12 @@ auto Variable::get_grad_accumulator() -> std::shared_ptr<Function> {
 }
 
 auto SavedVariable::unpack() -> std::shared_ptr<Variable> {
- if (!data) return nullptr;
+ if (!data) {
+ if (version) {
+ throw std::runtime_error(ERR_BACKWARD_TWICE);
+ }
+ return nullptr;
+ }
 
  int current_version = **version;
  if (expected_version != current_version) {
@@ -91,4 +96,9 @@ auto SavedVariable::unpack() -> std::shared_ptr<Variable> {
  return new_var;
 }
 
+const char* ERR_BACKWARD_TWICE =
+ "Trying to backward through the graph a second time, but the buffers have "
+ "already been freed. Specify retain_variables=True when calling backward "
+ "the first time.";
+
 }} // namespace torch::autograd

torch/csrc/autograd/variable.h

@@ -11,6 +11,8 @@
 
 namespace torch { namespace autograd {
 
+extern const char* ERR_BACKWARD_TWICE;
+
 struct Variable : std::enable_shared_from_this<Variable> {
 
  struct SavedVariable {