mirror of https://github.com/alibaba/MNN.git
109 lines
3.4 KiB
C++
109 lines
3.4 KiB
C++
//
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// CPUEltwise.cpp
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// MNN
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//
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// Created by MNN on 2018/07/19.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/cpu/CPUEltwise.hpp"
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#include <math.h>
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#include <string.h>
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#include "core/Concurrency.h"
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#include <algorithm>
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#include "backend/cpu/CPUBackend.hpp"
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#include "backend/cpu/compute/CommonOptFunction.h"
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#include "backend/cpu/compute/ConvOpt.h"
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#include "core/Macro.h"
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#ifdef MNN_USE_NEON
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#include <arm_neon.h>
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#endif
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namespace MNN {
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CPUEltwise::CPUEltwise(Backend *b, EltwiseType type, std::vector<float> coef) : Execution(b) {
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mType = type;
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mCoeff = coef;
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}
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ErrorCode CPUEltwise::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto inputTensor = inputs[0];
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const int size = inputTensor->elementSize();
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MNN_ASSERT(outputs[0]->elementSize() == size);
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auto outputTensor = outputs[0];
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auto outputHost = outputTensor->host<float>();
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const auto input0Ptr = inputs[0]->host<float>();
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auto coeffSize = mCoeff.size();
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bool isIdentity = coeffSize >= 2;
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if (isIdentity) {
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// when Eltwise has coeff
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if (mCoeff[0] == 1.0f && mCoeff[1] == 0.0f) {
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memcpy(outputHost, input0Ptr, inputs[0]->size());
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return NO_ERROR;
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} else {
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return NOT_SUPPORT;
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}
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}
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auto proc = MNNMatrixProdCommon;
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switch (mType) {
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case EltwiseType_PROD:
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proc = MNNMatrixProdCommon;
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break;
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case EltwiseType_SUM:
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proc = MNNMatrixAddCommon;
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break;
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case EltwiseType_MAXIMUM:
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proc = MNNMatrixMaxCommon;
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break;
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case EltwiseType_SUB:
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proc = MNNMatrixSubCommon;
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break;
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default:
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MNN_ERROR("Don't support %d type for eltwise", mType);
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return INPUT_DATA_ERROR;
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}
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auto schedule = ((CPUBackend*)backend())->multiThreadDivide(size);
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int sizeDivide = schedule.first;
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int scheduleNumber = schedule.second;
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MNN_CONCURRENCY_BEGIN(tId, scheduleNumber) {
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int start = sizeDivide * (int)tId;
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int realSize = sizeDivide;
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if (tId == scheduleNumber -1 ) {
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realSize = size - start;
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}
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if (realSize > 0) {
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auto inputT1 = inputs[1];
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proc(outputHost + start, input0Ptr + start, inputT1->host<float>() + start, realSize, 0, 0, 0, 1);
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for (int i = 2; i < inputs.size(); ++i) {
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proc(outputHost + start, outputHost + start, inputs[i]->host<float>() + start, realSize, 0, 0, 0, 1);
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}
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}
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}
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MNN_CONCURRENCY_END();
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return NO_ERROR;
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}
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class CPUEltwiseCreator : public CPUBackend::Creator {
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public:
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virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
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const MNN::Op *op, Backend *backend) const {
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auto eltwiseParam = op->main_as_Eltwise();
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auto type = eltwiseParam->type();
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std::vector<float> coeff;
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// keep compatible with old model
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if (eltwiseParam->coeff()) {
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const int size = eltwiseParam->coeff()->size();
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coeff.resize(size);
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memcpy(coeff.data(), eltwiseParam->coeff()->data(), size * sizeof(float));
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}
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return new CPUEltwise(backend, type, coeff);
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUEltwiseCreator, OpType_Eltwise);
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} // namespace MNN
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