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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
108 lines
3.6 KiB
C++
108 lines
3.6 KiB
C++
#ifndef CAFFE2_OPERATORS_TENSOR_PROTOS_DB_INPUT_H_
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#define CAFFE2_OPERATORS_TENSOR_PROTOS_DB_INPUT_H_
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#include "caffe2/core/db.h"
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#include "caffe2/operators/prefetch_op.h"
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#include "c10/util/irange.h"
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#include <iostream>
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#include <mutex>
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namespace caffe2 {
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template <class Context>
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class TensorProtosDBInput final : public PrefetchOperator<Context> {
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public:
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using OperatorBase::OutputSize;
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using PrefetchOperator<Context>::prefetch_thread_;
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explicit TensorProtosDBInput(const OperatorDef& operator_def, Workspace* ws);
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~TensorProtosDBInput() override {
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PrefetchOperator<Context>::Finalize();
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}
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bool Prefetch() override;
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bool CopyPrefetched() override;
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private:
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// Prefetch will always just happen on the CPU side.
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vector<Blob> prefetched_blobs_;
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int batch_size_;
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bool shape_inferred_ = false;
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string key_;
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string value_;
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};
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template <class Context>
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TensorProtosDBInput<Context>::TensorProtosDBInput(
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const OperatorDef& operator_def,
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Workspace* ws)
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: PrefetchOperator<Context>(operator_def, ws),
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prefetched_blobs_(operator_def.output_size()),
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batch_size_(
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this->template GetSingleArgument<int>("batch_size", 0)) {}
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template <class Context>
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bool TensorProtosDBInput<Context>::Prefetch() {
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const db::DBReader& reader = this->template Input<db::DBReader>(0);
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TensorDeserializer deserializer;
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if (batch_size_ == 0) {
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// We do not need to construct a batch. As a result, we will simply
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// deserialize everything into the target prefetched blob.
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reader.Read(&key_, &value_);
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TensorProtos protos;
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CAFFE_ENFORCE(protos.ParseFromString(value_));
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CAFFE_ENFORCE(protos.protos_size() == OutputSize());
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for (const auto i : c10::irange(protos.protos_size())) {
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if (protos.protos(i).has_device_detail()) {
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protos.mutable_protos(i)->clear_device_detail();
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}
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BlobSetTensor(
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&prefetched_blobs_[i], deserializer.Deserialize(protos.protos(i)));
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// deserializer.Deserialize(
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// protos.protos(i), BlobGetMutableTensor(&prefetched_blobs_[i],
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// CPU));
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}
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} else {
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for (const auto item_id : c10::irange(batch_size_)) {
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reader.Read(&key_, &value_);
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TensorProtos protos;
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CAFFE_ENFORCE(protos.ParseFromString(value_));
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CAFFE_ENFORCE(protos.protos_size() == OutputSize());
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// Note: shape_inferred_ is ignored, we'll always get dimensions from
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// proto
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for (const auto i : c10::irange(protos.protos_size())) {
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vector<int64_t> dims(
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protos.protos(i).dims().begin(), protos.protos(i).dims().end());
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dims.insert(dims.begin(), batch_size_);
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if (protos.protos(i).has_device_detail()) {
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protos.mutable_protos(i)->clear_device_detail();
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}
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Tensor src = deserializer.Deserialize(protos.protos(i));
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Tensor* dst = BlobGetMutableTensor(
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&prefetched_blobs_[i], dims, at::dtype(src.dtype()).device(CPU));
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TORCH_DCHECK_EQ(src.numel() * batch_size_, dst->numel());
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this->context_.CopyItemsSameDevice(
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src.dtype(),
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src.numel(),
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src.raw_data(),
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static_cast<char*>(dst->raw_mutable_data(src.dtype())) +
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src.nbytes() * item_id);
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}
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}
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}
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return true;
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}
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template <class Context>
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bool TensorProtosDBInput<Context>::CopyPrefetched() {
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for (const auto i : c10::irange(OutputSize())) {
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OperatorBase::template Output<Tensor>(i, Context::GetDeviceType())
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->CopyFrom(
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prefetched_blobs_[i].template Get<TensorCPU>(), /* async */ true);
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}
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return true;
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}
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} // namespace caffe2
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#endif // CAFFE2_OPERATORS_TENSOR_PROTOS_DB_INPUT_H_
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