wehub-resource-sync
82 lines
2.9 KiB
C++
82 lines
2.9 KiB
C++
//
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// QNNReduce.cpp
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// MNN
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//
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// Created by MNN on b'2025/04/10'.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "QNNReduce.hpp"
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namespace MNN {
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namespace QNN {
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#ifdef ENABLE_QNN_ONLINE_FINALIZE
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ErrorCode QNNReduce::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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MNN_ASSERT(inputs.size() == 2 || inputs.size() == 1);
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std::map<ReductionType, std::string> reduceMap {
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{ReductionType_SUM, "ReduceSum"},
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{ReductionType_MEAN, "ReduceMean"},
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{ReductionType_MAXIMUM, "ReduceMax"},
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{ReductionType_MINIMUM, "ReduceMin"},
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{ReductionType_PROD, "ReduceProd"},
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};
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auto param = mOp->main_as_ReductionParam();
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auto operation = param->operation();
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bool keepDims = param->keepDims();
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auto iter = reduceMap.find(operation);
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if (iter == reduceMap.end()) {
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MNN_QNN_NOT_SUPPORT_SPECIAL_CASE;
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}
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mNodeType = iter->second;
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std::vector<uint32_t> axesData;
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int inputDim = inputs[0]->dimensions();
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int positiveAxis;
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Tensor::DimensionType inputDimType = inputs[0]->getDimensionType();
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if (inputs.size() == 2) {
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int32_t * reduceAxes = inputs[1]->host<int32_t>();
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for (int i = 0; i < inputs[1]->elementSize(); ++i) {
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positiveAxis = (reduceAxes[i] < 0) ? (inputDim + reduceAxes[i]) : (reduceAxes[i]);
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axesData.push_back((uint32_t) positiveAxis);
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}
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} else {
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MNN_ASSERT(param->dim() != nullptr);
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const int32_t * reduceAxes = param->dim()->data();
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for (int i = 0; i < param->dim()->size(); i++) {
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positiveAxis = (reduceAxes[i] < 0) ? (inputDim + reduceAxes[i]) : (reduceAxes[i]);
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axesData.push_back((uint32_t) positiveAxis);
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}
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}
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this->createParamTensor("axes", QNN_DATATYPE_UINT_32, {(uint32_t) axesData.size()}, (void *) axesData.data());
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this->createParamScalar("keep_dims", keepDims);
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if (inputs.size() == 2) {
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mParams.push_back(*(mParamTensorWrappers.back()->getNativeParam()));
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mParams.push_back(*(mParamScalarWrappers.back()->getNativeParam()));
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mInputs.push_back(*(mBackend->getNativeTensor(inputs[0])));
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mOutputs.push_back(*(mBackend->getNativeTensor(outputs[0])));
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mBackend->addNodeToGraph(mOpConfigVersion, mNodeName.c_str(), mPackageName.c_str(), mNodeType.c_str(), mParams, mInputs, mOutputs);
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} else {
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this->addNodeCommon(inputs, outputs);
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}
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return NO_ERROR;
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}
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class QNNReduceCreator : public QnnBackend::Creator {
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public:
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virtual QNNCommonExecution * onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs, const MNN::Op* op,
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Backend* backend) const override {
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return new QNNReduce(backend, op);
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}
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};
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REGISTER_QNN_OP_CREATOR(QNNReduceCreator, OpType_Reduction)
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#endif
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} // end namespace QNN
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} // end namespace MNN
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