52 lines
1.5 KiB
C
52 lines
1.5 KiB
C
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#pragma once
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// ${generated_comment}
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#include <ATen/ATen.h>
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#include <ATen/core/functional.h>
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#include <ATen/TensorGeometry.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/Export.h>
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#include <c10/core/SymIntArrayRef.h>
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namespace torch { namespace autograd { namespace generated {
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using at::Scalar;
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using at::Tensor;
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using at::IntArrayRef;
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using at::ArrayRef;
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using at::Type;
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using at::TensorGeometry;
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using at::ScalarType;
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using c10::optional;
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using c10::fmap;
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inline std::vector<Tensor> unpack_list(at::ArrayRef<SavedVariable> xs, std::shared_ptr<Node> saved_for = nullptr) {
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// NB: we must explicitly do the conversion in the lambda, otherwise template
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// deduction will give a Tensor of Variable which is not convertible
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return fmap(xs, [&saved_for](const SavedVariable& x) {
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// TODO(crcrpar): Use `std::move(saved_for)` to avoid incrementing refcount, which would need refactoring.
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return static_cast<Tensor>(x.unpack(saved_for));
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});
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}
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inline c10::List<c10::optional<Tensor>> unpack_opt_list(at::ArrayRef<SavedVariable> xs, std::shared_ptr<Node> saved_for = nullptr) {
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torch::List<c10::optional<Tensor>> result;
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result.reserve(xs.size());
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for (const SavedVariable& v : xs) {
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auto var = v.unpack(saved_for);
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result.push_back(var.defined() ? c10::optional<Tensor>(var) : c10::nullopt);
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}
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return result;
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}
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using torch::autograd::TypeAndSize;
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${autograd_function_declarations}
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}}} // namespace torch::autograd::generated
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