torch/csrc/autograd/python_function.h

#pragma once

#include <torch/csrc/python_headers.h>

#include <torch/csrc/Exceptions.h>
#include <torch/csrc/autograd/custom_function.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/saved_variable.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/utils/object_ptr.h>

#include <c10/core/DeviceGuard.h>
#include <c10/util/Optional.h>

#include <memory>
#include <utility>
#include <vector>

namespace torch {
namespace jit {
struct Graph;
}
} // namespace torch
namespace torch {
namespace autograd {

// A Function which is implemented by a Python object (i.e., a THPFunction).
// Calls to 'apply' are forwarded to the Python method implementation.
struct PyNode : public Node {
  PyNode(THPObjectPtr obj) : obj(obj.release()) {}

  variable_list apply(variable_list&& inputs) override;

  void release_variables() override;
  std::string name() const override;
  bool is_traceable() override;

  // THPFunction this Function is wrapping.  Owning!
  PyObject* obj;

  ~PyNode() override {
    // Can't use THPObjectPtr as a field in this class; destructor won't take
    // out GIL!  When I forgot to do this by hand
    // TestAutograd.test_inplace_view_python called me out about it.
    // If python is already dead, leak the wrapped python objects
    if (Py_IsInitialized()) {
      pybind11::gil_scoped_acquire gil;
      Py_DECREF(obj);
    }
  }
};

/**
 * Cast an object into a tuple, if it is not a tuple already. Returns true
 * if the original object was not a tuple.
 */
inline bool ensure_tuple(THPObjectPtr& obj) {
  if (PyTuple_Check(obj.get()))
    return false;

  PyObject* tuple = PyTuple_New(1);
  if (!tuple)
    throw python_error();
  PyTuple_SET_ITEM(tuple, 0, obj.release());
  obj = tuple;
  return true;
}

} // namespace autograd
} // namespace torch

// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
struct THPFunction {
  PyObject_HEAD

      PyObject* needs_input_grad;

  // Python tuple of tensors whose variables we should save.  Set
  // by Python with 'save_for_backward'.  If nullptr, no tensors were
  // saved.
  PyObject* to_save;
  // Python tuple of tensors which are not differentiable.  Set by
  // Python with 'mark_non_differentiable'.  If nullptr, no tensors were
  // non-differentiable.
  PyObject* non_differentiable;
  // Python tuple of tensors which had inplace updates in the forward()
  // pass.  Set by Python with 'mark_dirty'.  If nullptr, no tensors were
  // modified inplace.
  PyObject* dirty_tensors;

  // boolean indicating whether to materialize undefined output grad tensors
  // into tensors full of zeros. Set by Python with 'set_materialize_grads'.
  // Default is true.
  bool materialize_grads;

  std::vector<torch::autograd::VariableInfo> output_info;
  std::vector<torch::autograd::VariableInfo> input_info;
  std::vector<torch::autograd::SavedVariable> saved_variables;
  // For each input, true if the input is a THPVariable
  std::vector<bool> is_variable_input;
  char has_freed_buffers;

  PyObject* saved_for_forward;
  // The actual PyNode (in the autograd graph) that this data was
  // saved for.  This field may be NULL (because a user can construct
  // a THPFunction directly from Python), but when this field is non-NULL,
  // it is guaranteed that cdata.lock()->obj == this
  //
  // In most ordinary use, this field should always be non-NULL; e.g.,
  // when we allocate a THPFunction because we are running Node.apply,
  // after constructing a THPFunction, we immediately allocate a PyNode
  // for it.  We can't enforce this directly in the constructor of
  // THPFunction though, because there's no way to keep it live long enough
  // to save an owning reference to PyNode into the grad_fn of a Variable.
  std::weak_ptr<torch::autograd::PyNode> cdata;
};

bool THPFunction_initModule(PyObject* module);
extern PyTypeObject THPFunctionType;
extern PyObject* THPFunctionClass;

inline bool THPFunction_Check(PyObject* obj) {
  return PyObject_IsInstance(obj, (PyObject*)&THPFunctionType);
}