#pragma once
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#include <torch/csrc/python_headers.h>
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#include <torch/csrc/Exceptions.h>
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#include <torch/csrc/autograd/custom_function.h>
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#include <torch/csrc/autograd/function.h>
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#include <torch/csrc/autograd/variable.h>
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#include <torch/csrc/autograd/saved_variable.h>
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#include <torch/csrc/utils/object_ptr.h>
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#include <c10/util/Optional.h>
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#include <c10/core/DeviceGuard.h>
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#include <vector>
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#include <utility>
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#include <memory>
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namespace torch { namespace jit { struct Graph; }}
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namespace torch { namespace autograd {
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// A Function which is implemented by a Python object (i.e., a THPFunction).
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// Calls to 'apply' are forwarded to the Python method implementation.
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struct PyNode : public Node {
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PyNode(THPObjectPtr obj) : obj(obj.release()) {}
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variable_list apply(variable_list&& inputs) override;
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variable_list legacy_apply(const variable_list& inputs);
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void release_variables() override;
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std::string name() const override;
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bool is_traceable() override;
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// THPFunction this Function is wrapping. Owning!
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PyObject* obj;
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~PyNode() {
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// Can't use THPObjectPtr as a field in this class; destructor won't take
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// out GIL! When I forgot to do this by hand
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// TestAutograd.test_inplace_view_python called me out about it.
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AutoGIL g;
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Py_DECREF(obj);
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}
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};
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/**
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* Cast an object into a tuple, if it is not a tuple already. Returns true
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* if the original object was not a tuple.
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*/
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inline bool ensure_tuple(THPObjectPtr& obj) {
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if (PyTuple_Check(obj.get()))
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return false;
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PyObject *tuple = PyTuple_New(1);
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if (!tuple) throw python_error();
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PyTuple_SET_ITEM(tuple, 0, obj.release());
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obj = tuple;
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return true;
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}
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}} // namespace torch::autograd
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struct THPFunction {
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PyObject_HEAD
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PyObject *needs_input_grad;
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// Python tuple of tensors whose variables we should save. Set
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// by Python with 'save_for_backward'. If nullptr, no tensors were
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// saved.
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PyObject *to_save;
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// Python tuple of tensors which are not differentiable. Set by
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// Python with 'mark_non_differentiable'. If nullptr, no tensors were
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// non-differentiable.
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PyObject *non_differentiable;
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// Python tuple of tensors which had inplace updates in the forward()
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// pass. Set by Python with 'mark_dirty'. If nullptr, no tensors were
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// modified inplace.
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PyObject *dirty_tensors;
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std::vector<torch::autograd::VariableInfo> output_info;
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std::vector<torch::autograd::VariableInfo> input_info;
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std::vector<torch::autograd::SavedVariable> saved_variables;
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// For each input, true if the input is a THPVariable
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std::vector<bool> is_variable_input;
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char has_freed_buffers;
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// The actual PyNode (in the autograd graph) that this data was
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// saved for. This field may be NULL (because a user can construct
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// a THPFunction directly from Python), but when this field is non-NULL,
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// it is guaranteed that cdata.lock()->obj == this
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//
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// In most ordinary use, this field should always be non-NULL; e.g.,
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// when we allocate a THPFunction because we are running Node.apply,
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// after constructing a THPFunction, we immediately allocate a PyNode
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// for it. We can't enforce this directly in the constructor of
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// THPFunction though, because there's no way to keep it live long enough
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// to save an owning reference to PyNode into the grad_fn of a Variable.
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std::weak_ptr<torch::autograd::PyNode> cdata;
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};
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bool THPFunction_initModule(PyObject *module);
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extern PyTypeObject THPFunctionType;
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extern PyObject *THPFunctionClass;
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inline bool THPFunction_Check(PyObject* obj) {
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return PyObject_IsInstance(obj, (PyObject*)&THPFunctionType);
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}
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