mirror of https://github.com/alibaba/MNN.git
88 lines
3.2 KiB
C++
88 lines
3.2 KiB
C++
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// CPUDequantizeLinear.cpp
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// MNN
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//
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// Created by MNN on 2018/07/15.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/cpu/CPUBackend.hpp"
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#include "core/Concurrency.h"
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#include "backend/cpu/CPUDequantizeLinear.hpp"
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#include "core/TensorUtils.hpp"
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#include "compute/CommonOptFunction.h"
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namespace MNN {
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CPUDequantizeLinear::CPUDequantizeLinear(Backend *b, float* scale, int8_t* zeroPoints, int size, int axis, int inputBits) : MNN::Execution(b){
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mSize = size;
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mAxis = axis;
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mInputBits = inputBits;
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}
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ErrorCode CPUDequantizeLinear::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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if (mInputBits == 8) {
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mFunc = dequantizeFunc<int8_t>;
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} else if (mInputBits == 16) {
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mFunc = dequantizeFunc<int16_t>;
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} else {
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mFunc = dequantizeFunc<int32_t>;
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}
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float *scale = inputs[1]->host<float>();
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int8_t *zero = nullptr;
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if (inputs.size() > 2) {
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zero = inputs[2]->host<int8_t>();;
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}
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if (mSize == 1) {
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mQuantScales.resize(4, *scale);
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if (nullptr != zero) {
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mQuantZeroPoints.resize(4, *zero);
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} else {
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mQuantZeroPoints.resize(4, 0);
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}
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} else {
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mQuantScales.resize(mSize);
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::memcpy(mQuantScales.data(), scale, sizeof(float) * mSize);
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if (nullptr != zero) {
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mQuantZeroPoints.resize(mSize);
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::memcpy(mQuantZeroPoints.data(), zero, mSize);
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} else {
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mQuantZeroPoints.resize(mSize);
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}
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}
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return NO_ERROR;
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}
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ErrorCode CPUDequantizeLinear::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto input = inputs[0];
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int N = input->length(0);
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ssize_t size = N;
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auto core = static_cast<CPUBackend*>(backend())->int8Functions();
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int UNIT, SRC_UNIT, DST_XUNIT;
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core->MNNGetGemmUnit(&UNIT, &SRC_UNIT, &DST_XUNIT);
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auto dst = outputs[0]->host<float>();
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auto src = input->host<int8_t>();
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mFunc(dst, src, input->dimensions(), input->size(), mSize, UNIT, mQuantScales.data(), mQuantZeroPoints.data(), core);
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return NO_ERROR;
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}
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class CPUDequantizeLinearCreator : 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 override {
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auto dataType = inputs[0]->getType();
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if (dataType.bits != 8 && dataType.bits != 16 && dataType.bits != 32) {
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MNN_ERROR("Input of Dequantize must be int8/uint8/fp16/int32\n");
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return nullptr;
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}
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int inputBits = dataType.bits;
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int size = op->main_as_DequantizeLinear()->scaleSize();
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int axis = op->main_as_DequantizeLinear()->scaleAxis();
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if (inputs.size() > 2) {
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return new CPUDequantizeLinear(backend, inputs[1]->host<float>(), inputs[2]->host<int8_t>(), size, axis, inputBits);
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}
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return new CPUDequantizeLinear(backend, inputs[1]->host<float>(), nullptr, size, axis, inputBits);
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUDequantizeLinearCreator, OpType_DequantizeLinear);
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} // namespace MNN
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