root
/
MNN
mirror of https://github.com/alibaba/MNN.git
1
0
Fork 0
Code Issues Actions 8 Packages Projects Releases Wiki Activity
MNN/source/backend/opencl/execution/ConvExecution.cpp

363 lines
15 KiB
C++
Raw Blame History

//
// ConvExecution.cpp
// MNN
//
// Created by MNN on 2019/02/28.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "execution/ConvExecution.hpp"
#include "ConvWinograd.hpp"
#include "ConvolutionIntFactory.hpp"
#include "Macro.h"
#include "TensorUtils.hpp"
#include "core/OpenCLBackend.hpp"
#include "core/OpenCLRunningUtils.hpp"
namespace MNN {
namespace OpenCL {
std::vector<uint32_t> ConvExecution::conv2d1x1LocalWS(std::vector<uint32_t> &gws, const uint32_t maxWorkGroupSize) {
uint32_t deviceComputeUnits = mOpenCLBackend->getOpenCLRuntime()->deviceComputeUnits();
std::vector<uint32_t> lws(4, 0);
int coreNum = deviceComputeUnits * 2;
int remain = gws[0] % coreNum;
int groupSize = gws[0] / coreNum;
if (remain == 0) {
lws[0] = groupSize;
} else {
while (groupSize) {
int remain = gws[0] % groupSize;
if (remain == 0 && groupSize <= maxWorkGroupSize) {
lws[0] = groupSize;
break;
}
groupSize--;
}
}
lws[0] = std::max<uint32_t>(std::min<uint32_t>(maxWorkGroupSize, lws[0]), 1);
remain = gws[1] % coreNum;
groupSize = gws[1] / coreNum;
if (remain == 0) {
lws[1] = groupSize;
} else {
while (groupSize) {
int remain = gws[1] % groupSize;
if (remain == 0) {
lws[1] = groupSize;
break;
}
groupSize--;
}
}
lws[1] = std::max<uint32_t>(std::min<uint32_t>(maxWorkGroupSize / lws[0], lws[1]), 1);
// MNN_PRINT("deviceComputeUnits : %d , maxWorkGroupSize : %d\n", deviceComputeUnits, maxWorkGroupSize);
// MNN_PRINT("[%d, %d, %d] -- [%d, %d, %d] \n", gws[0], gws[1], gws[2], lws[0], lws[1], lws[2]);
return lws;
}
std::vector<uint32_t> ConvExecution::conv2dGeneralLocalWS(const std::vector<uint32_t> &gws, const uint32_t kernelSize,
const uint32_t maxWorkGroupSize) {
uint32_t deviceComputeUnits = mOpenCLBackend->getOpenCLRuntime()->deviceComputeUnits();
GpuType gpuType = mOpenCLBackend->getOpenCLRuntime()->getGpuType();
std::vector<uint32_t> lws(4, 0);
if (gpuType == GpuType::ADRENO) {
int coreNum = deviceComputeUnits;
int remain = gws[0] % coreNum;
int groupSize = gws[0] / coreNum;
if (remain == 0) {
lws[0] = groupSize;
} else {
while (groupSize) {
int remain = gws[0] % groupSize;
if (remain == 0 && groupSize <= maxWorkGroupSize) {
lws[0] = groupSize;
break;
}
groupSize--;
}
}
lws[0] = std::max<uint32_t>(std::min<uint32_t>(maxWorkGroupSize, lws[0]), 1);
remain = gws[1] % coreNum;
groupSize = gws[1] / coreNum;
if (remain == 0) {
lws[1] = groupSize;
} else {
while (groupSize) {
int remain = gws[1] % groupSize;
if (remain == 0) {
lws[1] = groupSize;
break;
}
groupSize--;
}
}
lws[1] = std::max<uint32_t>(std::min<uint32_t>(maxWorkGroupSize / lws[0], lws[1]), 1);
remain = gws[2] % coreNum;
groupSize = gws[2] / coreNum;
if (remain == 0) {
lws[2] = groupSize;
} else {
while (groupSize) {
int remain = gws[2] % groupSize;
if (remain == 0) {
lws[2] = groupSize;
break;
}
groupSize--;
}
}
lws[2] = std::max<uint32_t>(std::min<uint32_t>(maxWorkGroupSize / (lws[0] * lws[1]), lws[2]), 1);
} else {
lws[0] = deviceComputeUnits * 2;
lws[1] = 4;
lws[2] = 1;
}
return lws;
}
ConvCommonExecution::ConvCommonExecution(const Convolution2D *conv2dParams, Backend *backend) : Execution(backend) {
auto openclBackend = (OpenCLBackend *)backend;
int biasSize = conv2dParams->bias()->size();
const float *biasDataPtr = conv2dParams->bias()->data();
cl::Buffer biasBuffer(openclBackend->getOpenCLRuntime()->context(), CL_MEM_READ_ONLY | CL_MEM_ALLOC_HOST_PTR,
UP_DIV(biasSize, 4) * 4 * sizeof(float));
auto biasPtrCL = openclBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(
biasBuffer, true, CL_MAP_WRITE, 0, ALIGN_UP4(biasSize) * sizeof(float));
if(biasPtrCL != nullptr){
::memset(biasPtrCL, 0, ALIGN_UP4(biasSize) * sizeof(float));
::memcpy(biasPtrCL, biasDataPtr, biasSize * sizeof(float));
}else{
MNN_ERROR("Map error biasPtrCL == nullptr \n");
}
openclBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(biasBuffer, biasPtrCL);
std::shared_ptr<Tensor> bias;
bias.reset(Tensor::createDevice<float>({1, 1, 1, biasSize}));
backend->onAcquireBuffer(bias.get(), Backend::STATIC);
copyBufferToImage(openclBackend->getOpenCLRuntime(), biasBuffer, openCLImage(bias.get()), UP_DIV(biasSize, 4), 1);
mBias = bias;
}
ConvCommonExecution::~ConvCommonExecution() {
MNN_ASSERT(nullptr != mBias);
backend()->onReleaseBuffer(mBias.get(), Backend::STATIC);
}
ConvExecution::ConvExecution(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend)
: ConvCommonExecution(op->main_as_Convolution2D(), backend) {
#ifdef LOG_VERBOSE
MNN_PRINT("Start ConvExecution init !\n");
#endif
mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
const auto *conv2dParams = op->main_as_Convolution2D();
const auto *conv2dCommonParams = conv2dParams->common();
mConv2dCommonParams = conv2dCommonParams;
mStrides = {conv2dCommonParams->strideY(), conv2dCommonParams->strideX()};
mDilations = {conv2dCommonParams->dilateY(), conv2dCommonParams->dilateX()};
mPaddings[0] = conv2dCommonParams->padY() * 2;
mPaddings[1] = conv2dCommonParams->padX() * 2;
PadMode padMode = conv2dCommonParams->padMode();
if (padMode == PadMode_VALID) {
mPaddings[0] = 0;
mPaddings[1] = 0;
}
int kernelWidth = conv2dCommonParams->kernelX();
int kernelHeight = conv2dCommonParams->kernelY();
int outputChannel = conv2dCommonParams->outputCount();
int weightSize = 0;
const float *filterDataPtr = nullptr;
std::shared_ptr<MNN::ConvolutionIntFactory::Int8Common> quanCommon;
if (nullptr != conv2dParams->quanParameter()) {
quanCommon = ConvolutionIntFactory::load(conv2dParams->quanParameter(), true);
if (nullptr == quanCommon) {
MNN_ERROR("Memory not Enough, can't extract IDST Convolution: %s \n", op->name()->c_str());
}
if (quanCommon->weightFloat.get() == nullptr) {
MNN_PRINT("quanCommon->weightFloat.get() == nullptr \n");
}
// Back to float
filterDataPtr = quanCommon->weightFloat.get();
weightSize = quanCommon->weightFloat.size();
}
if (nullptr == filterDataPtr) {
weightSize = conv2dParams->weight()->size();
filterDataPtr = conv2dParams->weight()->data();
}
int inputChannel = weightSize / (kernelWidth * kernelHeight * outputChannel);
std::vector<int> filter_shape{outputChannel, inputChannel, kernelHeight, kernelWidth};
std::vector<int> filterImageShape{(int)inputChannel, (int)(UP_DIV(outputChannel, 4) * kernelWidth * kernelHeight)};
std::shared_ptr<Tensor> filterBuffer(
Tensor::createDevice<float>({outputChannel, inputChannel, kernelHeight, kernelWidth}));
cl::Buffer filterBufferCL(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_ONLY | CL_MEM_ALLOC_HOST_PTR,
filterBuffer->size());
filterBuffer->buffer().device = (uint64_t)(&filterBufferCL);
auto ptrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(filterBufferCL, true, CL_MAP_WRITE,
0, filterBuffer->size());
if(ptrCL != nullptr){
::memcpy(ptrCL, filterDataPtr, filterBuffer->size());
}else{
MNN_ERROR("Map error ptrCL == nullptr \n");
}
mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(filterBufferCL, ptrCL);
mFilter.reset(Tensor::createDevice<float>({1, filterImageShape[1], 1, 4 * filterImageShape[0]}));
mOpenCLBackend->onAcquireBuffer(mFilter.get(), Backend::STATIC);
MNN::OpenCL::ImageBufferConvertor imageBufferConvertor{mOpenCLBackend->getOpenCLRuntime()};
imageBufferConvertor.convertBufferToImage(filterBuffer.get(), MNN::OpenCL::CONV2D_FILTER, mFilter.get());
// Create Kernel
std::set<std::string> buildOptions;
if (mConv2dCommonParams->relu()) {
buildOptions.emplace("-DRELU");
} else if (mConv2dCommonParams->relu6()) {
buildOptions.emplace("-DRELU6");
}
std::string kernelName = "conv_2d";
if (kernelHeight == kernelWidth && kernelHeight == 1 && mConv2dCommonParams->padX() == 0 &&
mConv2dCommonParams->padY() == 0) {
kernelName = "conv_2d_1x1";
}
mKernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("conv_2d", kernelName, buildOptions);
mMaxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(mKernel));
#ifdef LOG_VERBOSE
MNN_PRINT("end ConvExecution init !\n");
#endif
}
ConvExecution::~ConvExecution() {
mOpenCLBackend->onReleaseBuffer(mFilter.get(), Backend::STATIC);
}
ErrorCode ConvExecution::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
#ifdef LOG_VERBOSE
MNN_PRINT("Start ConvExecution onResize !\n");
#endif
auto input = inputs[0];
auto output = outputs[0];
std::vector<int> inputShape = tensorShapeFormat(input);
std::vector<int> outputShape = tensorShapeFormat(output);
const int height = outputShape.at(1);
const int width = outputShape.at(2);
const int inputHeight = inputShape.at(1);
const int inputWidth = inputShape.at(2);
const int inputChannels = inputShape.at(3);
const int inputChannelBlocks = UP_DIV(inputChannels, 4);
if (mConv2dCommonParams->padMode() == PadMode_SAME) {
int kernelHeightSize = (mConv2dCommonParams->kernelY() - 1) * mConv2dCommonParams->dilateY() + 1;
int padNeededHeight = (outputShape.at(1) - 1) * mConv2dCommonParams->strideY() +
kernelHeightSize - inputShape.at(1);
int kernelWidthSize = (mConv2dCommonParams->kernelX() - 1) * mConv2dCommonParams->dilateX() + 1;
int padNeededWidth = (outputShape.at(2) - 1) * mConv2dCommonParams->strideX() + kernelWidthSize -
inputShape.at(2);
mPaddings[0] = padNeededHeight;
mPaddings[1] = padNeededWidth;
}
int kernelHeight = mConv2dCommonParams->kernelY();
int kernelWidth = mConv2dCommonParams->kernelX();
if (kernelHeight == kernelWidth && kernelHeight == 1 && mPaddings[0] == 0 && mPaddings[1] == 0) {
mGlobalWorkSize = {
static_cast<uint32_t>(UP_DIV(outputShape.at(3), 4) * static_cast<uint32_t>(UP_DIV(outputShape.at(2), 4))),
static_cast<uint32_t>(outputShape.at(0) * outputShape.at(1))};
mLocalWorkSize = conv2d1x1LocalWS(mGlobalWorkSize, mMaxWorkGroupSize);
auto kernel = &mKernel;
uint32_t idx = 0;
int inputImageShape[2] = {inputHeight, inputWidth};
int outputImageShape[2] = {height, width};
int stideShape[2] = {mStrides[0], mStrides[1]};
kernel->setArg(idx++, mGlobalWorkSize[0]);
kernel->setArg(idx++, mGlobalWorkSize[1]);
kernel->setArg(idx++, openCLImage(input));
kernel->setArg(idx++, openCLImage(mFilter.get()));
kernel->setArg(idx++, openCLImage(mBias.get()));
kernel->setArg(idx++, openCLImage(output));
kernel->setArg(idx++, sizeof(inputImageShape), inputImageShape);
kernel->setArg(idx++, static_cast<int>(inputChannelBlocks));
kernel->setArg(idx++, sizeof(outputImageShape), outputImageShape);
kernel->setArg(idx++, sizeof(stideShape), stideShape);
kernel->setArg(idx++, UP_DIV(width, 4));
} else {
mGlobalWorkSize = {static_cast<uint32_t>(UP_DIV(outputShape.at(3), 4) * UP_DIV(outputShape.at(2), 4)),
static_cast<uint32_t>(outputShape.at(0) * outputShape.at(1))};
mLocalWorkSize = conv2dGeneralLocalWS(mGlobalWorkSize, kernelHeight * kernelWidth, mMaxWorkGroupSize);
int inputImageShape[2] = {inputHeight, inputWidth};
int outputImageShape[2] = {height, width};
int kernelShape[2] = {kernelHeight, kernelWidth};
int strideShape[2] = {mStrides[0], mStrides[1]};
int paddingShape[2] = {mPaddings[0] / 2, mPaddings[1] / 2};
int dilationShape[2] = {mDilations[0], mDilations[1]};
uint32_t idx = 0;
auto kernel = &mKernel;
kernel->setArg(idx++, mGlobalWorkSize[0]);
kernel->setArg(idx++, mGlobalWorkSize[1]);
kernel->setArg(idx++, openCLImage(input));
kernel->setArg(idx++, openCLImage(mFilter.get()));
kernel->setArg(idx++, openCLImage(mBias.get()));
kernel->setArg(idx++, openCLImage(output));
kernel->setArg(idx++, sizeof(inputImageShape), inputImageShape);
kernel->setArg(idx++, inputChannelBlocks);
kernel->setArg(idx++, sizeof(outputImageShape), outputImageShape);
kernel->setArg(idx++, sizeof(kernelShape), kernelShape);
kernel->setArg(idx++, sizeof(strideShape), strideShape);
kernel->setArg(idx++, sizeof(paddingShape), paddingShape);
kernel->setArg(idx++, sizeof(dilationShape), dilationShape);
kernel->setArg(idx++, UP_DIV(width, 4));
}
#ifdef LOG_VERBOSE
MNN_PRINT("end ConvExecution onResize !\n");
#endif
return NO_ERROR;
}
ErrorCode ConvExecution::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
#ifdef LOG_VERBOSE
MNN_PRINT("Start ConvExecution onExecute !\n");
#endif
runKernel2D(mKernel, mGlobalWorkSize, mLocalWorkSize, mOpenCLBackend->getOpenCLRuntime());
#ifdef LOG_VERBOSE
MNN_PRINT("end ConvExecution onExecute !\n");
#endif
return NO_ERROR;
}
class ConvolutionCreator : public OpenCLBackend::Creator {
public:
virtual ~ConvolutionCreator() = default;
virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
const MNN::Op *op, Backend *backend) const override {
auto conv2D = op->main_as_Convolution2D();
if (ConvWinograd::valid(conv2D->common(), inputs[0])) {
return new ConvWinograd(conv2D, backend);
}
return new ConvExecution(inputs, op, backend);
}
};
OpenCLCreatorRegister<ConvolutionCreator> __conv_op(OpType_Convolution);
} // namespace OpenCL
} // namespace MNN
Reference in New Issue View Git Blame Copy Permalink