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8fc1064b7f
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/61494 Creating a constexpr array and then looping over it is much cheaper than emitting a function call per item. ghstack-source-id: 136639302 Test Plan: fitsships Buildsizebot some mobile apps to check size impact. Reviewed By: dhruvbird, iseeyuan Differential Revision: D29646977 fbshipit-source-id: 6144999f6acfc4e5dcd659845859702051344d88
33 lines
1.1 KiB
C++
33 lines
1.1 KiB
C++
#pragma once
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#include <ATen/core/op_registration/op_registration.h>
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#include <ATen/core/stack.h>
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#include <torch/csrc/jit/runtime/operator.h>
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namespace torch {
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namespace jit {
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/// Registration class for new operators. Effectively calls
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/// `torch::jit::registerOperator` for every supplied operator, but allows doing
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/// so in the global scope when a `RegisterOperators` object is assigned to a
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/// static variable.
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/// Note: This is *not* the custom operator API. If you want to register custom
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/// operators, take a look at torch::RegisterOperators.
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struct TORCH_API RegisterOperators {
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RegisterOperators() = default;
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/// Registers a vector of already created `Operator`s.
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/// The operator element is now optional to filter null ops. It's backward
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/// compatible and works for selective operator registration.
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explicit RegisterOperators(std::vector<c10::optional<Operator>> operators) {
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for (c10::optional<Operator>& o : operators) {
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if (o) {
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registerOperator(std::move(o.value()));
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}
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}
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}
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};
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} // namespace jit
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} // namespace torch
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