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483f748dd5
This PR enables `-Winconsistent-missing-destructor-override` and `-Winconsistent-missing-override` and fixes violations. <!-- copilot:summary --> ### <samp>🤖 Generated by Copilot at 47e904e</samp> This pull request updates the code of various classes and operators in the `caffe2` and `aten` subdirectories to use the `override` specifier instead of the `virtual` keyword for destructors and other virtual functions that override a base class function. This improves the code readability, quality, and consistency with C++ best practices. It also modifies the `./CMakeLists.txt` file to enable warnings for these specifiers, but disable errors. Pull Request resolved: https://github.com/pytorch/pytorch/pull/104032 Approved by: https://github.com/malfet
80 lines
2.1 KiB
C++
80 lines
2.1 KiB
C++
#ifndef CAFFE2_OPERATORS_FIND_OP_H_
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#define CAFFE2_OPERATORS_FIND_OP_H_
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#include "caffe2/core/context.h"
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#include "caffe2/core/logging.h"
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#include "caffe2/core/operator.h"
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#include "c10/util/irange.h"
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#include <unordered_map>
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namespace caffe2 {
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template <class Context>
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class FindOp final : public Operator<Context> {
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public:
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template <class... Args>
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explicit FindOp(Args&&... args)
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: Operator<Context>(std::forward<Args>(args)...),
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missing_value_(
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this->template GetSingleArgument<int>("missing_value", -1)) {}
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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USE_DISPATCH_HELPER;
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bool RunOnDevice() override {
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return DispatchHelper<TensorTypes<int, long>>::call(this, Input(0));
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}
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protected:
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template <typename T>
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bool DoRunWithType() {
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auto& idx = Input(0);
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auto& needles = Input(1);
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auto* res_indices = Output(0, needles.sizes(), at::dtype<T>());
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const T* idx_data = idx.template data<T>();
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const T* needles_data = needles.template data<T>();
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T* res_data = res_indices->template mutable_data<T>();
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auto idx_size = idx.numel();
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// Use an arbitrary cut-off for when to use brute-force
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// search. For larger needle sizes we first put the
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// index into a map
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if (needles.numel() < 16) {
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// Brute force O(nm)
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for (const auto i : c10::irange(needles.numel())) {
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T x = needles_data[i];
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T res = static_cast<T>(missing_value_);
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for (int j = idx_size - 1; j >= 0; j--) {
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if (idx_data[j] == x) {
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res = j;
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break;
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}
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}
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res_data[i] = res;
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}
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} else {
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// O(n + m)
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std::unordered_map<T, int> idx_map;
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for (const auto j : c10::irange(idx_size)) {
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idx_map[idx_data[j]] = j;
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}
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for (const auto i : c10::irange(needles.numel())) {
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T x = needles_data[i];
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auto it = idx_map.find(x);
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res_data[i] = (it == idx_map.end() ? missing_value_ : it->second);
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}
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}
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return true;
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}
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protected:
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int missing_value_;
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};
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_FIND_OP_H_
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