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MNN/source/backend/cpu/CPUDeconvolution.cpp

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//
// CPUDeconvolution.cpp
// MNN
//
// Created by MNN on 2018/07/20.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "CPUDeconvolution.hpp"
#include "core/BufferAllocator.hpp"
#include "CPUBackend.hpp"
#include "core/Concurrency.h"
#include "core/Macro.h"
#include "core/OpCommonUtils.hpp"
#include "core/AutoStorage.h"
#include "math/Matrix.hpp"
#include "core/TensorUtils.hpp"
#include "core/ConvolutionCommon.hpp"
#include "compute/CommonOptFunction.h"
#include "compute/ConvOpt.h"
#include "compute/DeconvolutionWithStride.hpp"
//#define MNN_OPEN_TIME_TRACE
#include <MNN/AutoTime.hpp>
namespace MNN {
CPUDeconvolutionBasic::CPUDeconvolutionBasic(const Tensor* input, const Op* convOp, Backend* b)
: CPUConvolution(convOp->main_as_Convolution2D()->common(), b) {
mSrcCount = input->channel();
mPostParameters = getPostParameters();
}
ErrorCode CPUDeconvolutionBasic::onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
auto input = inputs[0];
auto output = outputs[0];
auto pad = ConvolutionCommon::convolutionTransposePad(input, output, mCommon);
mPadY = pad.second;
mPadX = pad.first;
return NO_ERROR;
}
CPUDeconvolutionCommon::CPUDeconvolutionCommon(const Tensor* input, const Op* convOp, Backend* b, bool dynamicWeight)
: CPUDeconvolutionBasic(input, convOp, b) {
auto conv2D = convOp->main_as_Convolution2D();
int outputCount = mCommon->outputCount();
auto core = static_cast<CPUBackend*>(b)->functions();
mDynamicWeight = dynamicWeight;
mBias.reset(Tensor::createDevice<float>(std::vector<int>{UP_DIV(outputCount, core->pack) * core->pack}));
if (dynamicWeight) {
return;
}
bool success = b->onAcquireBuffer(mBias.get(), Backend::STATIC);
if (!success) {
mValid = false;
return;
}
::memset(mBias->host<float>(), 0, mBias->length(0) * core->bytes);
if (core->bytes == 4) {
::memcpy(mBias->host<float>(), conv2D->bias()->data(), conv2D->bias()->size() * sizeof(float));
} else {
core->MNNFp32ToLowp(conv2D->bias()->data(), mBias->host<int16_t>(), conv2D->bias()->size());
}
}
CPUDeconvolutionCommon::~CPUDeconvolutionCommon() {
// Do nothing
}
// Float Weight.
static void _transformWeight(const uint8_t* tempWeight, uint8_t* dest, int outputCount, int srcCount, int fh, int fw,
uint8_t* cache, const CoreFunctions* core) {
auto outputC4 = UP_DIV(outputCount, core->pack);
int offset[] = {
(int)(fw * fh),
(int)(fw * fh),
};
// c, n, h, w-> c, n/4 * 4, h, w
for (int c=0; c<srcCount; ++c) {
auto dst = cache + c * outputC4 * fw * fh * core->pack * core->bytes;
auto src = tempWeight + c * outputCount * fw * fh * core->bytes;
core->MNNPackCUnit((float*)dst, (const float*)src, fw*fh, outputCount, offset);
}
//printf("%d - %d - %d - %d\n", outputCount, srcCount, fh, fw);
core->MNNPackForMatMul_B((float*)dest, (const float*)cache, outputC4 * fw * fh * core->pack, srcCount, false);
}
// Int8 Weight.
static void _reorderWeightInt8(Backend* bn, const Convolution2DCommon* common, const int8_t* srcPtr,
std::shared_ptr<Tensor>& weight) {
auto core = static_cast<CPUBackend*>(bn)->int8Functions();
auto gcore = static_cast<CPUBackend*>(bn)->functions();
int UNIT, SRC_UNIT, DST_XUNIT;
core->MNNGetGemmUnit(&UNIT, &SRC_UNIT, &DST_XUNIT);
UNIT = gcore->pack;
int oc = common->outputCount(), ic = common->inputCount(), kernelCount = common->kernelX() * common->kernelY();
std::vector<int> shape = {UP_DIV(oc, UNIT), UP_DIV(ic, SRC_UNIT) * kernelCount, UNIT, SRC_UNIT};
weight.reset(Tensor::createDevice<int8_t>(shape));
bool succ = bn->onAcquireBuffer(weight.get(), Backend::STATIC);
if (!succ) {
MNN_ERROR("Memory not enough");
return;
}
auto dstPtr = weight->host<int8_t>();
::memset(dstPtr, 0, weight->size());
int icDiv = UP_DIV(ic, SRC_UNIT);
for (int k = 0; k < kernelCount; ++k) {
auto srcK = srcPtr + k;
auto dstK = dstPtr + k * SRC_UNIT * UNIT * icDiv;
for (int x = 0; x < oc; ++x) {
int xout = x / UNIT;
int xin = x % UNIT;
auto srcY = srcK + x * kernelCount;
auto dstY = dstK + xout * SRC_UNIT * UNIT * icDiv * kernelCount + xin * SRC_UNIT;
for (int y = 0; y < ic; ++y) {
int yout = y / SRC_UNIT;
int yin = y % SRC_UNIT;
const int dstIndex = yout * SRC_UNIT * UNIT + yin;
const int srcIndex = y * oc * kernelCount;
dstY[dstIndex] = srcY[srcIndex];
}
}
}
}
CPUDeconvolution::CPUDeconvolution(const Tensor* input, const Op* convOp, Backend* backend, bool dynamicWeight)
: MNN::CPUDeconvolutionCommon(input, convOp, backend, dynamicWeight) {
auto core = static_cast<CPUBackend*>(backend)->functions();
auto coreInt8 = static_cast<CPUBackend*>(backend)->int8Functions();
int eP, lP, hP;
core->MNNGetMatMulPackMode(&eP, &lP, &hP);
int UNIT, SRC_UNIT, DST_XUNIT;
coreInt8->MNNGetGemmUnit(&UNIT, &SRC_UNIT, &DST_XUNIT);
bool ModeInt8 = false;
if (CPUBackend::getDataType(input) == DataType_DT_INT8 || input->getType().bytes() == 1) {
eP = DST_XUNIT;
lP = SRC_UNIT;
hP = UNIT;
ModeInt8 = true;
}
auto conv2d = convOp->main_as_Convolution2D();
auto layer = conv2d->common();
int outputCount = layer->outputCount();
const auto outputChannleUp4 = UP_DIV(outputCount, hP) * hP;
int fw = layer->kernelX();
int fh = layer->kernelY();
int srcCount = mSrcCount;
mParam.fh = fh;
mParam.fw = fw;
mParam.srcCount = srcCount;
mParam.outputCount = outputCount;
auto outputAlign = UP_DIV(layer->outputCount(), core->pack) * core->pack * fw * fh;
mWeight.reset(Tensor::createDevice<float>(std::vector<int>{UP_DIV(outputAlign, hP), UP_DIV(srcCount, lP) * lP, hP}));
std::shared_ptr<Tensor> cache(Tensor::createDevice<float>({outputAlign * srcCount}));
if (dynamicWeight) {
mOrigin.reset(new CPUDeconvolutionOrigin(input, mWeight.get(), convOp, backend, ModeInt8));
mWeightTransformCache = cache;
return;
}
const float* tempWeight = nullptr;
const int8_t* quanWeightInt8 = nullptr;
int tempWeightSize = 0;
std::unique_ptr<Tensor> externalWeightTensor;
std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon;
std::vector<int32_t> _bias(outputChannleUp4, 0);
std::vector<float> _scale(outputChannleUp4, 0);
std::vector<int32_t> _beta(outputChannleUp4, 0);
auto biasPtr = _bias.data();
auto scalePtr = _scale.data();
auto betaPtr = _beta.data();
if (ModeInt8) {
ConvolutionCommon::getConvInt8Parameters(convOp, quanCommon, backend, quanWeightInt8, tempWeightSize, scalePtr, biasPtr, betaPtr);
} else {
ConvolutionCommon::getConvParameters(&quanCommon, backend, convOp, &tempWeight, &tempWeightSize);
}
bool success = backend->onAcquireBuffer(mWeight.get(), Backend::STATIC) &&
backend->onAcquireBuffer(cache.get(), Backend::STATIC);
if (!success) {
mValid = false;
return;
}
AutoStorage<uint8_t> lowpWeight;
if (core->bytes < 4) {
lowpWeight.reset(outputCount * srcCount * fh * fw * core->bytes);
if (lowpWeight.get() == nullptr) {
mValid = false;
return;
}
core->MNNFp32ToLowp(tempWeight, (int16_t*)lowpWeight.get(), outputCount * srcCount * fh * fw);
tempWeight = (float*)lowpWeight.get();
}
if (!ModeInt8) {
mWeight.reset(Tensor::createDevice<float>(std::vector<int>{UP_DIV(outputAlign, hP), UP_DIV(srcCount, lP) * lP, hP}));
success = backend->onAcquireBuffer(mWeight.get(), Backend::STATIC);
if (!success) {
mValid = false;
return;
}
auto dest = mWeight->host<uint8_t>();
_transformWeight((uint8_t*)tempWeight, dest, outputCount, srcCount, fh, fw, cache->host<uint8_t>(), core);
} else {
mWeight.reset(Tensor::createDevice<int8_t>(std::vector<int>{UP_DIV(outputAlign, hP), UP_DIV(srcCount, lP) * lP, hP}));
success = backend->onAcquireBuffer(mWeight.get(), Backend::STATIC);
if (!success) {
mValid = false;
return;
}
_reorderWeightInt8(backend, layer, quanWeightInt8, mWeight);
}
backend->onReleaseBuffer(cache.get(), Backend::STATIC);
mOrigin.reset(new CPUDeconvolutionOrigin(input, mWeight.get(), convOp, backend, ModeInt8));
}
CPUDeconvolution::~CPUDeconvolution() {
// Do nothing
}
ErrorCode CPUDeconvolution::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
if (mDynamicWeight) {
auto core = static_cast<CPUBackend*>(backend())->functions();
_transformWeight(inputs[1]->host<uint8_t>(), mWeight->host<uint8_t>(), mParam.outputCount, mParam.srcCount, mParam.fh, mParam.fw, mWeightTransformCache->host<uint8_t>(), core);
::memset(mBias->host<uint8_t>(), 0, mBias->length(0) * core->bytes);
if (inputs.size() >= 3) {
::memcpy(mBias->host<uint8_t>(), inputs[2]->host<uint8_t>(), TensorUtils::getRawSize(inputs[2]) * core->bytes);
}
}
return mOrigin->onExecute(mTempInputs, outputs);
}
ErrorCode CPUDeconvolution::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
if (mDynamicWeight) {
bool res = backend()->onAcquireBuffer(mWeight.get(), Backend::DYNAMIC);
if (!res) {
return OUT_OF_MEMORY;
}
res = backend()->onAcquireBuffer(mWeightTransformCache.get(), Backend::DYNAMIC);
if (!res) {
return OUT_OF_MEMORY;
}
res = backend()->onAcquireBuffer(mBias.get(), Backend::DYNAMIC);
if (!res) {
return OUT_OF_MEMORY;
}
}
mTempInputs = {inputs[0], mWeight.get(), mBias.get()};
auto code = mOrigin->onResize(mTempInputs, outputs);
if (NO_ERROR != code) {
return code;
}
if (mDynamicWeight) {
backend()->onReleaseBuffer(mWeight.get(), Backend::DYNAMIC);
backend()->onReleaseBuffer(mWeightTransformCache.get(), Backend::DYNAMIC);
backend()->onReleaseBuffer(mBias.get(), Backend::DYNAMIC);
}
return NO_ERROR;
}
ErrorCode CPUDeconvolutionOrigin::onResize(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
CPUDeconvolutionBasic::onResize(inputs, outputs);
auto core = static_cast<CPUBackend*>(backend())->functions();
auto gcore = static_cast<CPUBackend*>(backend())->int8Functions();
int bytes = core->bytes;
auto input = inputs[0];
auto output = outputs[0];
auto oc = output->channel();
int UNIT, SRC_UNIT, DST_XUNIT;
gcore->MNNGetGemmUnit(&UNIT, &SRC_UNIT, &DST_XUNIT);
if (UP_DIV(oc, core->pack) * core->pack != inputs[2]->length(0)) {
return INPUT_DATA_ERROR;
}
auto ocC4 = UP_DIV(output->channel(), core->pack);
auto icC4 = UP_DIV(input->channel(), core->pack);
auto kw = mCommon->kernelX();
auto kh = mCommon->kernelY();
auto dilateX = mCommon->dilateX();
auto dilateY = mCommon->dilateY();
auto strideX = mCommon->strideX();
auto strideY = mCommon->strideY();
auto padX = mPadX;
auto padY = mPadY;
auto width = input->width();
auto height = input->height();
auto src_height = output->height();
auto src_width = output->width();
auto batch = output->batch();
auto kernelCount = ocC4 * mCommon->kernelX() * mCommon->kernelY();
mPostFunctions.clear();
auto plane = width * height * batch;
const int maxDepth = 5;
auto allocator = static_cast<CPUBackend*>(backend())->getBufferAllocator();
//int zeroPoint = 0;
auto biasTensor = inputs[2];
// prepare for float2int8 if necessary.
auto outputQuant = TensorUtils::getQuantInfo(outputs[0]);
float scale = outputQuant[0];
scale = (scale == 0.f ? 0.f : 1.f / scale);
auto maxValue = outputQuant[3];
auto minValue = outputQuant[2];
auto zeroPoint = outputQuant[1];
AutoRelease<Tensor> tempInput(Tensor::createDevice<float>({icC4, plane, core->pack}));
bool needReleaseTempInput = true;
int outi8 = 0;
if (CPUBackend::getDataType(output) == DataType_DT_INT8 || output->getType().bytes() == 1) {
outi8 = 1;
}
if (CPUBackend::getDataType(inputs[0]) == DataType_DT_INT8 || inputs[0]->getType().bytes() == 1) {
mTempOutput.reset(Tensor::createDevice<float>({batch, height, width, ocC4 * kw * kh * core->pack}));
auto res = backend()->onAcquireBuffer(mTempOutput.get(), Backend::DYNAMIC);
if (!res) {
return OUT_OF_MEMORY;
}
mDeconvInt8Exe->onResize({input}, {mTempOutput.get()});
if (mResource->mRelu) {
minValue = outputQuant[1];
}
}
else {
mTempOutput.reset(Tensor::createDevice<float>({kernelCount, plane, core->pack}));
auto res = backend()->onAcquireBuffer(mTempOutput.get(), Backend::DYNAMIC);
if (!res) {
return OUT_OF_MEMORY;
}
mMatMul.reset(new StrassenMatrixComputor(backend(), true, maxDepth));
// tempInput->buffer().host = (uint8_t*)inputPtr;
needReleaseTempInput = false;
TensorUtils::getDescribeOrigin(tempInput.get())->mem = new CPUMemObj(nullptr, TensorUtils::getDescribeOrigin(input)->mem->chunk(), 0);
mMatMul->onEncode({tempInput.get(), inputs[1]}, {mTempOutput.get()});
}
auto threadNumber = ((CPUBackend*)backend())->threadNumber();
std::vector<float> scales(core->pack * src_height * src_width * batch, scale);
auto outputFp32Ptr = allocator->alloc(batch * src_height * src_width * ocC4 * core->pack * bytes);
if (outputFp32Ptr.invalid()) {
return OUT_OF_MEMORY;
}
mPostFunctions.emplace_back(std::make_pair([ocC4, width, height, kh, kw, padY, padX, dilateY, dilateX, strideY,
strideX, threadNumber, src_width, src_height, plane, input, biasTensor, this, core, gcore, batch, outi8, scale,
minValue, maxValue, zeroPoint, outputFp32Ptr](uint8_t* outputPtr, int tId) {
auto colBufferPtr = mTempOutput->host<uint8_t>();
auto biasPtr = biasTensor->host<float>();
auto inputPtr = input->host<float>();
auto unitBytes = core->pack * core->bytes;
auto tempOutPtr = outputPtr;
auto float2Int8_step = src_height * src_width * batch;
if (outi8) {
tempOutPtr = outputFp32Ptr.ptr();
}
for (int z = (tId); z < ocC4; z += threadNumber) {
auto dstZ = tempOutPtr + z * src_height * src_width * batch * unitBytes;
auto srcZ = colBufferPtr + kw * kh * plane * z * unitBytes;
::memset(dstZ, 0, src_width * src_height * batch * unitBytes);
for (int b = 0; b < batch; ++b) {
auto dstB = dstZ + b * src_width * src_height * unitBytes;
auto srcB = srcZ + b * width * height * unitBytes;
for (int oy = 0; oy < height; ++oy) {
for (int ox = 0; ox < width; ++ox) {
int srcStartX = ox * strideX - padX;
int srcStartY = oy * strideY - padY;
int sfy = ALIMAX(0, (UP_DIV(-srcStartY, dilateY)));
int efy = ALIMIN(kh, UP_DIV(src_height - srcStartY, dilateY));
int sfx = ALIMAX(0, (UP_DIV(-srcStartX, dilateX)));
int efx = ALIMIN(kw, UP_DIV(src_width - srcStartX, dilateX));
auto dstStart = dstB + srcStartX * unitBytes + srcStartY * src_width * unitBytes;
auto srcStart = srcB + unitBytes * (ox + oy * width);
if (sfy >= efy || sfx >= efx) {
continue;
}
for (int fy = sfy; fy < efy; ++fy) {
auto dstY = dstStart + fy * unitBytes * dilateY * src_width;
auto srcY = srcStart + fy * kw * plane * unitBytes;
core->MNNAddC4WithStride((const float*)(srcY + sfx * plane * unitBytes), (float*)(dstY + sfx * dilateX * unitBytes), plane * core->pack, dilateX * core->pack, efx - sfx);
}
}
}
}
core->MNNAxByClampBroadcastUnit((float*)dstZ, (float*)dstZ, (const float*)((uint8_t*)biasPtr + unitBytes * z), src_height * src_width * batch, 0, 0, 1, mPostParameters.data());
if (outi8) {
float scaleOne = scale;
float zeroOne = zeroPoint;
gcore->MNNFloat2Int8((float*)dstZ, (int8_t*)(outputPtr + z * float2Int8_step * core->pack), float2Int8_step, &scaleOne, minValue, maxValue, &zeroOne, 0);
}
}
}, threadNumber));
/*
if (TensorUtils::getDescribe(tempInput.get())->mem->chunk().offset() != TensorUtils::getDescribe(input)->mem->chunk().offset()) {
backend()->onReleaseBuffer(tempInput.get(), Backend::DYNAMIC);
}
if (tempInput->host<float>() != inputPtr) {
backend()->onReleaseBuffer(tempInput.get(), Backend::DYNAMIC);
}
*/
allocator->free(outputFp32Ptr);
if (needReleaseTempInput) {
backend()->onReleaseBuffer(tempInput.get(), Backend::DYNAMIC);
}
backend()->onReleaseBuffer(mTempOutput.get(), Backend::DYNAMIC);
return NO_ERROR;
}
ErrorCode CPUDeconvolutionOrigin::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
auto inputPtr = inputs[0]->host<uint8_t>();
auto outputPtr = outputs[0]->host<uint8_t>();
if (CPUBackend::getDataType(inputs[0]) == DataType_DT_INT8 || inputs[0]->getType().bytes() == 1) {
mDeconvInt8Exe->onExecute({inputs[0], inputs[1]}, {mTempOutput.get()});
}
else {
mMatMul->onExecute();
}
for (auto& unit : mPostFunctions) {
MNN_CONCURRENCY_BEGIN(tId, unit.second) {
unit.first(outputPtr, (int)tId);
}
MNN_CONCURRENCY_END();
}
return NO_ERROR;
}
class CPUDeconvolutionCreator : public CPUBackend::Creator {
public:
virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
const MNN::Op* op, Backend* backend) const {
auto convOp = op->main_as_Convolution2D();
auto common = convOp->common();
if (backend->type() == MNN_FORWARD_CPU && inputs.size() == 1) {
if (common->strideY() > 1 || common->strideX() > 1) {
if (common->dilateX() == 1 && common->dilateY() == 1) {
if (common->kernelX() / common->strideX() > 2 || common->kernelY() / common->strideY() > 2) {
return new DeconvolutionWithStride(inputs[0], op, backend);
}
}
}
}
return new CPUDeconvolution(inputs[0], op, backend, inputs.size() > 1);
}
};
REGISTER_CPU_OP_CREATOR(CPUDeconvolutionCreator, OpType_Deconvolution);
} // namespace MNN
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