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https://github.com/zebrajr/pytorch.git
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0aaff5eaf9
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/13275 This resulted in a bunch of knock-on changes, which I will now describe: - s/original_index/original_device/ - s/last_index/last_device/ - A bunch of places that used set_index, now use CUDAGuard (which does have set_index) because they were CUDA-specific code. Major caveat: DeviceGuard doesn't *actually* work non-CUDA/CPU devices, To make that happen, I plan on totally replacing the implementation of DeviceGuard; what I mostly care about here is wrangling the API into an acceptable state. Reviewed By: gchanan Differential Revision: D12832080 fbshipit-source-id: 7de068c7cec35663dc8a533026a626331336e61d
109 lines
3.2 KiB
C++
109 lines
3.2 KiB
C++
#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/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 <vector>
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#include <utility>
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#include <memory>
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namespace at {
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struct DeviceGuard;
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} // namespace at
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namespace torch { namespace jit { struct Graph; }}
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namespace torch { namespace autograd {
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struct VariableInfo {
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explicit VariableInfo(const Variable& var);
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Variable zeros(at::DeviceGuard& device_guard) const;
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at::Type* type;
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at::Device device = at::kCPU;
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std::vector<int64_t> size;
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bool requires_grad;
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};
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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 PyFunction : public Function {
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PyFunction(PyObject* obj) : obj(obj) {}
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virtual variable_list apply(variable_list&& inputs) override;
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variable_list legacy_apply(const variable_list& inputs);
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virtual void release_variables() override;
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virtual std::string name() const override;
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virtual std::shared_ptr<Function> get_shared_ptr() override;
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virtual bool is_traceable() override;
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// THPFunction this Function is wrapping.
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PyObject* obj;
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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 C++ wrapper for this Python function.
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// See a comment in THPFunction_asFunction for details about this field.
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torch::autograd::PyFunction 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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// XXX: this function requires the GIL (it can have side effects).
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std::shared_ptr<torch::autograd::PyFunction> THPFunction_asFunction(THPFunction* self);
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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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