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//
// CPUQuanConvolutionDepthwise.cpp
// MNN
//
// Created by MNN on 2018/10/23.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "backend/cpu/CPUBackend.hpp"
#ifdef MNN_SUPPORT_DEPRECATED_OP
#include "backend/cpu/CPUQuanConvolutionDepthwise.hpp"
#include "backend/cpu/CPUFixedPoint.hpp"
#include "backend/cpu/CPUQuantizationUtils.hpp"
#include "backend/cpu/compute/CommonOptFunction.h"
#include "core/Concurrency.h"
#include "core/Macro.h"
#include "core/TensorUtils.hpp"
#define UNIT 4
extern "C" {
void MNNConvRunForUnitDepthWiseUint8(uint8_t* dst, const int16_t* src, const int16_t* weight, size_t fw, size_t fh,
const MNN::ConstConvolutionParameter* parameter, const int32_t* biasData);
void MNNConvRunForLineDepthWiseUint8(uint8_t* dst, const int16_t* src, const int16_t* weight, size_t width,
MNN::ConstConvolutionParameter* parameters, const int32_t* biasData);
}
struct MNN::ConstConvolutionParameter {
size_t kw;
size_t kh;
size_t weightYStep;
size_t dilateXStep;
size_t dilateYStep;
size_t strideXStep;
int32_t outputMultiplier;
int32_t outputShiftBefore;
int32_t outputShiftAfter;
int32_t outputOffset;
int32_t outputActivationMin;
int32_t outputActivationMax;
};
#ifndef MNN_USE_NEON
void MNNConvRunForUnitDepthWiseUint8(uint8_t* dst, const int16_t* src, const int16_t* weight, size_t fw, size_t fh,
const MNN::ConstConvolutionParameter* parameter, const int32_t* biasData) {
int fx, fy;
int dstTemp[UNIT];
for (int i = 0; i < UNIT; ++i) {
dstTemp[i] = 0;
}
auto dilateYStep = parameter->dilateYStep / sizeof(int16_t);
auto dilateXStep = parameter->dilateXStep / sizeof(int16_t);
auto weightYStep = parameter->weightYStep / sizeof(int16_t);
const int16_t* srcZ = src;
const int16_t* weightZ = weight;
for (fy = 0; fy < fh; ++fy) {
const int16_t* srcY = srcZ + fy * dilateYStep;
const int16_t* weightY = weightZ + fy * weightYStep;
for (fx = 0; fx < fw; ++fx) {
const int16_t* weightX = weightY + UNIT * fx;
const int16_t* srcX = srcY + fx * dilateXStep;
for (int j = 0; j < UNIT; ++j) {
dstTemp[j] += ((int32_t)srcX[j]) * ((int32_t)weightX[j]);
}
}
}
for (int i = 0; i < UNIT; i++) {
int acc = dstTemp[i] + biasData[i];
acc = MNN::SaturatingRoundingDoublingHighMul(acc * (1 << parameter->outputShiftBefore),
parameter->outputMultiplier);
acc = MNN::RoundingDivideByPOT(acc, -parameter->outputShiftAfter);
acc += parameter->outputOffset;
acc = std::max(acc, parameter->outputActivationMin);
acc = std::min(acc, parameter->outputActivationMax);
dst[i] = static_cast<uint8_t>(acc);
}
}
void MNNConvRunForLineDepthWiseUint8(uint8_t* dst, const int16_t* src, const int16_t* weight, size_t width,
MNN::ConstConvolutionParameter* parameters, const int32_t* biasData) {
int dx;
for (dx = 0; dx < width; ++dx) {
uint8_t* dstX = dst + dx * UNIT;
auto srcX = src + dx * parameters->strideXStep / sizeof(int16_t);
MNNConvRunForUnitDepthWiseUint8(dstX, srcX, weight, parameters->kw, parameters->kh, parameters, biasData);
}
}
#endif
namespace MNN {
CPUQuanConvolutionDepthwise::CPUQuanConvolutionDepthwise(Backend* backend, const Op* CPUDepthwiseOp)
: Execution(backend) {
mLayerParam = CPUDepthwiseOp->main_as_TfQuantizedConv2D();
auto commonParam = mLayerParam->common();
mPadMode = commonParam->padMode();
mStrideH = commonParam->strideY();
mStrideW = commonParam->strideX();
mDepthMultiplier = mLayerParam->depthMultiplier();
mFusedActivationFunction = mLayerParam->activationType();
auto layer = mLayerParam->common();
int kw = layer->kernelX();
int kh = layer->kernelY();
int outputCount = commonParam->outputCount();
int depthQuad = UP_DIV(outputCount, UNIT);
int planeStride = kw * kh * UNIT;
const uint8_t* tempWeight = mLayerParam->weight()->data();
int kernelSize = depthQuad * UNIT * kw * kh;
mBias.reset(ALIGN_UP4(mLayerParam->bias()->size()));
mBias.clear();
::memcpy(mBias.get(), mLayerParam->bias()->data(), mLayerParam->bias()->size() * sizeof(int32_t));
mWeight.reset(kernelSize);
mWeight.clear();
auto weight = mWeight.get();
auto filterOffset = mLayerParam->filterQuantizedParam()->zeroPoint();
for (int c = 0; c < outputCount; c++) {
int plane = c / UNIT;
int offset = c % UNIT;
for (int i = 0; i < kh * kw; i++) {
int16_t* dst = weight + plane * planeStride + offset + i * UNIT;
*dst = (int16_t)((int32_t)tempWeight[i * outputCount + c] - filterOffset);
}
}
mConstParameter = new ConstConvolutionParameter;
}
CPUQuanConvolutionDepthwise::~CPUQuanConvolutionDepthwise() {
delete mConstParameter;
}
inline int ComputePadding(int stride, int dilationRate, int inSize, int filterSize, int outSize) {
int effectiveFilterSize = (filterSize - 1) * dilationRate + 1;
int padding = ((outSize - 1) * stride + effectiveFilterSize - inSize) / 2;
return padding > 0 ? padding : 0;
}
ErrorCode CPUQuanConvolutionDepthwise::onResize(const std::vector<Tensor*>& inputs,
const std::vector<Tensor*>& outputs) {
auto input = inputs[0];
auto inputWidth = input->width();
auto inputHeight = input->height();
auto common = mLayerParam->common();
mFusedActivationFunction = mLayerParam->activationType();
int threadNumber = std::max(((CPUBackend*)backend())->threadNumber(), 1);
mTempBuffer.buffer().type = halide_type_of<int16_t>();
mTempBuffer.buffer().dimensions = 4;
mTempBuffer.setLength(0, threadNumber);
mTempBuffer.setLength(1, inputHeight);
mTempBuffer.setLength(2, inputWidth);
mTempBuffer.setLength(3, UNIT);
TensorUtils::setLinearLayout(&mTempBuffer);
bool res = backend()->onAcquireBuffer(&mTempBuffer, Backend::DYNAMIC);
if (!res) {
return OUT_OF_MEMORY;
}
backend()->onReleaseBuffer(&mTempBuffer, Backend::DYNAMIC);
mConstParameter->dilateXStep = common->dilateX() * UNIT * sizeof(int16_t);
mConstParameter->dilateYStep = common->dilateY() * inputWidth * UNIT * sizeof(int16_t);
mConstParameter->strideXStep = common->strideX() * UNIT * sizeof(int16_t);
mConstParameter->kh = common->kernelY();
mConstParameter->kw = common->kernelX();
mConstParameter->weightYStep = sizeof(int16_t) * common->kernelX() * UNIT;
float inputScale = mLayerParam->inputQuantizedParam()->scale();
float filterScale = mLayerParam->filterQuantizedParam()->scale();
{
double realMultiplier = 0.0;
const double inputProductScale = inputScale * filterScale;
const double outputScale = mLayerParam->outputQuantizedParam()->scale();
realMultiplier = inputProductScale / outputScale;
int exponent;
QuantizeMultiplier(realMultiplier, &mConstParameter->outputMultiplier, &exponent);
if (exponent < 0) {
mConstParameter->outputShiftBefore = 0;
mConstParameter->outputShiftAfter = exponent;
} else {
mConstParameter->outputShiftBefore = exponent;
mConstParameter->outputShiftAfter = 0;
}
CalculateActivationRangeUint8(mFusedActivationFunction, mLayerParam->outputQuantizedParam()->zeroPoint(),
mLayerParam->outputQuantizedParam()->scale(),
&mConstParameter->outputActivationMin, &mConstParameter->outputActivationMax);
mConstParameter->outputOffset = mLayerParam->outputQuantizedParam()->zeroPoint();
}
mDilateX = mLayerParam->common()->dilateX();
mDilateY = mLayerParam->common()->dilateY();
mZeroPoint = mLayerParam->inputQuantizedParam()->zeroPoint();
const int outputWidth = outputs[0]->width();
const int outputHeight = outputs[0]->height();
int filterHeight = (int)mConstParameter->kh;
int filterWidth = (int)mConstParameter->kw;
mPaddingHeight = ComputePadding(mStrideH, 1, inputHeight, filterHeight, outputHeight);
mPaddingWidth = ComputePadding(mStrideW, 1, inputWidth, filterWidth, outputWidth);
// Compute Mid Rect
ml = 0; mt = 0; mr = outputWidth; mb = outputHeight;
for (; ml * mStrideW - mPaddingWidth < 0 && ml < outputWidth; ml++) {
// do nothing
}
for (; mt * mStrideH - mPaddingHeight < 0 && mt < outputHeight; mt++) {
// do nothing
}
for (; (mr - 1) * mStrideW - mPaddingWidth + (filterWidth - 1) * mDilateX >= inputWidth && mr > ml; mr--) {
// do nothing
}
for (; (mb - 1) * mStrideH - mPaddingHeight + (filterHeight - 1) * mDilateY >= inputHeight && mb > mt; mb--) {
// do nothing
}
mDstYStep = outputWidth * UNIT;
mSrcYStep = inputWidth * UNIT;
mWeightZStep = filterHeight * filterWidth * UNIT;
return NO_ERROR;
}
ErrorCode CPUQuanConvolutionDepthwise::onExecute(const std::vector<Tensor*>& inputs,
const std::vector<Tensor*>& outputs) {
const Tensor* input = inputs[0];
Tensor* output = outputs[0];
const int outputBatch = outputs[0]->batch();
const int outputWidth = outputs[0]->width();
const int outputHeight = outputs[0]->height();
const int inputHeight = inputs[0]->height();
const int inputWidth = inputs[0]->width();
const int inputChannel = inputs[0]->channel();
int filterHeight = (int)mConstParameter->kh;
int filterWidth = (int)mConstParameter->kw;
auto bias = mBias.get();
auto runBasic = [&](uint8_t* dstZ, const int16_t* srcZ, const int16_t* weightDZ, int L, int T, int R, int B,
const int32_t* biasData) {
for (int dy = T; dy < B; ++dy) {
uint8_t* dstY = dstZ + dy * mDstYStep;
int srcStartY = dy * mStrideH - mPaddingHeight;
int sfy = ALIMAX(0, (UP_DIV(-srcStartY, mDilateY)));
int efy = ALIMIN(filterHeight, UP_DIV(inputHeight - srcStartY, mDilateY));
auto srcDY = srcZ + (srcStartY + sfy * mDilateY) * mSrcYStep;
auto weightDY = weightDZ + sfy * filterWidth * UNIT;
for (int dx = L; dx < R; ++dx) {
uint8_t* dstX = dstY + UNIT * dx;
int srcStartX = dx * mStrideW - mPaddingWidth;
auto srcDX = srcDY + srcStartX * UNIT;
int sfx = ALIMAX(0, (UP_DIV(-srcStartX, mDilateX)));
int efx = ALIMIN(filterWidth, UP_DIV(inputWidth - srcStartX, mDilateX));
MNNConvRunForUnitDepthWiseUint8(dstX, srcDX + (sfx * mDilateX) * UNIT, weightDY + UNIT * sfx,
efx - sfx, efy - sfy, mConstParameter, biasData);
}
}
};
int icDiv4 = UP_DIV(inputChannel, 4);
int threadNumber = std::max(((CPUBackend*)backend())->threadNumber(), 1);
threadNumber = std::min(threadNumber, icDiv4);
for (int batchIndex = 0; batchIndex < outputBatch; ++batchIndex) {
const uint8_t* srcOrigin = input->host<uint8_t>() + batchIndex * input->stride(0);
auto dstOrigin = output->host<uint8_t>() + batchIndex * output->stride(0);
MNN_CONCURRENCY_BEGIN(tId, threadNumber) {
auto colBuffer = mTempBuffer.host<int16_t>() + mTempBuffer.stride(0) * tId;
for (int z = (int)tId; z < icDiv4; z += threadNumber) {
auto srcZ = srcOrigin + z * inputWidth * inputHeight * UNIT;
MNNUInt8ToInt16WithOffsetC4Fast(colBuffer, srcZ, mZeroPoint, inputHeight * inputWidth, 1, 0, 0);
const int32_t* curBiasPtr = bias + z * UNIT;
uint8_t* dstZ = dstOrigin + z * outputWidth * outputHeight * UNIT;
const int16_t* weightDZ = mWeight.get() + z * mWeightZStep;
runBasic(dstZ, colBuffer, weightDZ, 0, 0, outputWidth, mt, curBiasPtr);
runBasic(dstZ, colBuffer, weightDZ, 0, mb, outputWidth, outputHeight, curBiasPtr);
runBasic(dstZ, colBuffer, weightDZ, 0, mt, ml, mb, curBiasPtr);
runBasic(dstZ, colBuffer, weightDZ, mr, mt, outputWidth, mb, curBiasPtr);
if (mr > ml) {
for (int dy = mt; dy < mb; ++dy) {
uint8_t* dstY = dstZ + dy * mDstYStep;
int srcStartY = dy * mStrideH - mPaddingHeight;
const int16_t* srcDY = colBuffer + srcStartY * mSrcYStep;
MNNConvRunForLineDepthWiseUint8(dstY + ml * UNIT, srcDY + (ml * mStrideW - mPaddingWidth) * UNIT,
weightDZ, mr - ml, mConstParameter, curBiasPtr);
}
}
}
}
MNN_CONCURRENCY_END();
}
return NO_ERROR;
}
class CPUDepthwiseCreator : public CPUBackend::Creator {
public:
virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
const MNN::Op* op, Backend* backend) const {
return new CPUQuanConvolutionDepthwise(backend, op);
}
};
} // namespace MNN
#endif
namespace MNN {
REGISTER_CPU_OP_CREATOR_OLD(CPUDepthwiseCreator, OpType_QuantizedDepthwiseConv2D);
};
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