mirror of https://github.com/alibaba/MNN.git
101 lines
3.6 KiB
C++
101 lines
3.6 KiB
C++
//
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// CPUFloatToInt8.cpp
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// MNN
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//
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// Created by MNN on 2019/5/22.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/cpu/CPUFloatToInt8.hpp"
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#include "backend/cpu/CPUBackend.hpp"
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#include "core/Concurrency.h"
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#include "backend/cpu/compute/Int8FunctionsOpt.h"
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#include "core/Macro.h"
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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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CPUFloatToInt8::CPUFloatToInt8(Backend* backend, const MNN::Op* param) : Execution(backend) {
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auto scale = param->main_as_QuantizedFloatParam();
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const int scaleLen = scale->tensorScale()->size();
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mClipBits = scale->nbits();
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auto pack = static_cast<CPUBackend*>(backend)->functions()->pack;
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mScales.reset(Tensor::createDevice<float>({UP_DIV(scaleLen, pack) * pack}));
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mValid = backend->onAcquireBuffer(mScales.get(), Backend::STATIC);
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if (!mValid) {
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return;
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}
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if (1 == scaleLen) {
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mSingle = true;
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for (int i = 0; i < pack; ++i) {
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mScales->host<float>()[i] = scale->tensorScale()->data()[0];
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}
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} else {
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memset(mScales->host<float>(), 0, UP_DIV(scaleLen, pack) * pack * sizeof(float));
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memcpy(mScales->host<float>(), scale->tensorScale()->data(), scaleLen * sizeof(float));
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}
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mZeroPoint = static_cast<float>(scale->zeroPoint());
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mClampMin = scale->clampMin();
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mClampMax = scale->clampMax();
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}
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CPUFloatToInt8::~CPUFloatToInt8() {
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backend()->onReleaseBuffer(mScales.get(), Backend::STATIC);
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}
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ErrorCode CPUFloatToInt8::onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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return NO_ERROR;
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}
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ErrorCode CPUFloatToInt8::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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const auto input = inputs[0];
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auto output = outputs[0];
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auto pack = static_cast<CPUBackend*>(backend())->functions()->pack;
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auto int8F = static_cast<CPUBackend*>(backend())->int8Functions();
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const auto inputDataPtr = input->host<float>();
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auto outputDataPtr = output->host<int8_t>();
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const auto scaleDataPtr = mScales->host<float>();
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const int channels = input->channel();
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int icDiv4 = UP_DIV(channels, pack);
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const int batch = input->batch();
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const int batchStride = input->stride(0);
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int oc4Stride = 1;
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for (int i = 2; i < input->dimensions(); ++i) {
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oc4Stride *= input->length(i);
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}
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if (mSingle) {
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oc4Stride = icDiv4 * oc4Stride;
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icDiv4 = 1;
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}
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int total = batch * icDiv4;
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auto numberThread = std::min(icDiv4, ((CPUBackend*)backend())->threadNumber());
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MNN_CONCURRENCY_BEGIN(tId, total) {
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int bIndex = tId / icDiv4;
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int z = tId % icDiv4;
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const auto srcChannelPtr = inputDataPtr + tId * oc4Stride * pack;
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const auto scaleChannelPtr = scaleDataPtr + z * pack;
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auto dstChannlePtr = outputDataPtr + tId * oc4Stride * pack;
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int8F->MNNFloat2Int8(srcChannelPtr, dstChannlePtr, oc4Stride, scaleChannelPtr, mClampMin, mClampMax, &mZeroPoint, 1);
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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 CPUFloatToInt8Creator : 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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if (nullptr == op->main_as_QuantizedFloatParam()) {
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return new CastWrapExecution(backend, DataType_DT_INT8);
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}
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return new CPUFloatToInt8(backend, op);
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUFloatToInt8Creator, OpType_FloatToInt8);
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} // namespace MNN
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