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MNN/source/backend/opencl/execution/image/ConvExecution.cpp

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//
// ConvExecution.cpp
// MNN
//
// Created by MNN on 2019/02/28.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "ConvExecution.hpp"
#include "ConvWinograd.hpp"
#include "core/ConvolutionCommon.hpp"
#include "core/Macro.h"
#include "core/TensorUtils.hpp"
#include "backend/opencl/core/OpenCLBackend.hpp"
#include "backend/opencl/core/OpenCLRunningUtils.hpp"
#define UNIT 4
namespace MNN {
namespace OpenCL {
ConvCommonExecution::ConvCommonExecution(const Convolution2D *conv2dParams, Backend *backend) : Execution(backend) {
auto openclBackend = (OpenCLBackend *)backend;
int biasSize = conv2dParams->bias()->size();
const float *biasDataPtr = conv2dParams->bias()->data();
int buffer_size = ALIGN_UP4(biasSize);
if(openclBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) {
buffer_size *= sizeof(half_float::half);
} else {
buffer_size *= sizeof(float);
}
cl::Buffer biasBuffer(openclBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size);
cl_int error;
auto biasPtrCL = openclBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(
biasBuffer, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error);
if(biasPtrCL != nullptr && error == CL_SUCCESS){
if(openclBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){
for(int i=0; i<biasSize; i++) {
((half_float::half*)biasPtrCL)[i] = (half_float::half)(biasDataPtr[i]);
}
for(int i=biasSize; i<ALIGN_UP4(biasSize); i++) {
((half_float::half*)biasPtrCL)[i] = (half_float::half)(0.0f);
}
}else{
::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);
mBias.reset(Tensor::createDevice<float>({1, 1, 1, biasSize}));
backend->onAcquireBuffer(mBias.get(), Backend::STATIC);
copyBufferToImage(openclBackend->getOpenCLRuntime(), biasBuffer, openCLImage(mBias.get()), UP_DIV(biasSize, 4), 1);
}
ConvCommonExecution::~ConvCommonExecution() {
MNN_ASSERT(nullptr != mBias);
backend()->onReleaseBuffer(mBias.get(), Backend::STATIC);
}
ConvExecution::ConvExecution(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs, 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()};
auto pad = ConvolutionCommon::convolutionPad(inputs[0], outputs[0], mConv2dCommonParams);
mPaddings[0] = pad.second;
mPaddings[1] = pad.first;
int kernelWidth = conv2dCommonParams->kernelX();
int kernelHeight = conv2dCommonParams->kernelY();
int outputChannel = conv2dCommonParams->outputCount();
auto gpuType = mOpenCLBackend->getOpenCLRuntime()->getGpuType();
mWeightUseBuffer = gpuType == GpuType::MALI;
int weightSize = 0;
const float *filterDataPtr = nullptr;
std::shared_ptr<MNN::ConvolutionCommon::Int8Common> quanCommon;
if (nullptr != conv2dParams->quanParameter()) {
quanCommon = ConvolutionCommon::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();
} else if (nullptr == conv2dParams->weight() || nullptr == conv2dParams->bias()) {
MNN_ERROR("%s has no weight or bias. The model may be benchmark model, please revert the weight/bias firstly\n", op->name()->c_str());
}
if (nullptr == filterDataPtr) {
weightSize = conv2dParams->weight()->size();
filterDataPtr = conv2dParams->weight()->data();
}
int inputChannel = weightSize / (kernelWidth * kernelHeight * outputChannel);
//select opt conv method
std::string kernelName = "conv_2d_c4h1w4";
if (kernelHeight == kernelWidth && kernelHeight == 1 && mPaddings[0] == 0 &&
mPaddings[1] == 0) {
mConv1x1Opt = (mStrides[0] == 1 && mStrides[1] == 1 && gpuType == GpuType::MALI && !mWeightUseBuffer);
#if 0
if((gpuType == GpuType::ADRENO)){
uint64_t useLocalSize = UNIT*UNIT*4*sizeof(float)*4;
if(useLocalSize >= mOpenCLBackend->getOpenCLRuntime()->getMaxLocalMem()){
mUseLocalMem = false;
}else{
kernelName = "conv_2d_1x1_local";
mUseLocalMem=true;
}
}
#endif
if(!mUseLocalMem){
if(mConv1x1Opt){
kernelName = "conv_2d_1x1_mali";
}else{
kernelName = "conv_2d_1x1";
}
}
}
if(mConv1x1Opt && !mUseLocalMem){
cl_int error;
std::shared_ptr<Tensor> filterBuffer(Tensor::createDevice<float>({UP_DIV(outputChannel, 4)*4, UP_DIV(inputChannel, 4)*4, kernelWidth, kernelHeight}));
int buffer_size = filterBuffer->elementSize();
if(mOpenCLBackend->getOpenCLRuntime()->isSupportedFP16()) {
buffer_size *= sizeof(half_float::half);
} else {
buffer_size *= sizeof(float);
}
mKernelBuffer.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size));
auto kernelBufferPtr = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(*(mKernelBuffer.get()), true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error);
if(kernelBufferPtr != nullptr && error == CL_SUCCESS){
::memset(kernelBufferPtr, 0, buffer_size);
for(int o = 0; o < outputChannel; o++){
for(int i = 0 ; i < inputChannel; i++){
int bufferIdx = (o/4) * ROUND_UP(inputChannel, 4)*4 + (i/4)*16 + (o%4)*4 + (i%4);
int filterIdx = o*inputChannel + i;
if(mOpenCLBackend->getOpenCLRuntime()->isSupportedFP16()){
((half_float::half*)kernelBufferPtr)[bufferIdx] = (half_float::half)(filterDataPtr[filterIdx]);
}else{
((float*)kernelBufferPtr)[bufferIdx] = (float)(filterDataPtr[filterIdx]);
}
}
}
}else{
MNN_ERROR("Map error ptrCL == nullptr \n");
}
mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(*(mKernelBuffer.get()), kernelBufferPtr);
//bias
int biasSize = conv2dParams->bias()->size();
const float *biasDataPtr = conv2dParams->bias()->data();
buffer_size = ALIGN_UP4(biasSize);
if(mOpenCLBackend->getOpenCLRuntime()->isSupportedFP16()) {
buffer_size *= sizeof(half_float::half);
} else {
buffer_size *= sizeof(float);
}
mBiasBuffer.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size));
auto biasPtrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(
*(mBiasBuffer.get()), true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error);
if(biasPtrCL != nullptr && error == CL_SUCCESS){
if(mOpenCLBackend->getOpenCLRuntime()->isSupportedFP16()){
for (int i = 0; i < biasSize; i++)
{
((half_float::half*)biasPtrCL)[i] = (half_float::half)(biasDataPtr[i]);
}
for(int i=biasSize; i<ALIGN_UP4(biasSize); i++) {
((half_float::half*)biasPtrCL)[i] = (half_float::half)(0.0f);
}
}else{
::memset(biasPtrCL, 0, ALIGN_UP4(biasSize) * sizeof(float));
::memcpy(biasPtrCL, biasDataPtr, biasSize * sizeof(float));
}
}else{
MNN_ERROR("Map error biasPtrCL == nullptr \n");
}
mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(*(mBiasBuffer.get()), biasPtrCL);
}else if(kernelHeight == kernelWidth && kernelHeight == 1 && mPaddings[0] == 0 && mPaddings[1] == 0 && mWeightUseBuffer){
cl_int error;
std::shared_ptr<Tensor> filterBuffer(Tensor::createDevice<float>({UP_DIV(outputChannel, 4), ROUND_UP(inputChannel, 4), 4}));
int buffer_size = filterBuffer->elementSize();
if(mOpenCLBackend->getOpenCLRuntime()->isSupportedFP16()) {
buffer_size *= sizeof(half_float::half);
} else {
buffer_size *= sizeof(float);
}
mKernelBuffer.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size));
auto kernelBufferPtr = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(*(mKernelBuffer.get()), true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error);
if(kernelBufferPtr != nullptr && error == CL_SUCCESS){
::memset(kernelBufferPtr, 0, buffer_size);
for(int o = 0; o < outputChannel; o++){
for(int i = 0 ; i < inputChannel; i++){
int bufferIdx = (o/4) * ROUND_UP(inputChannel, 4)*4 + i*4 + (o%4);
int filterIdx = o*inputChannel + i;
if(mOpenCLBackend->getOpenCLRuntime()->isSupportedFP16()){
((half_float::half*)kernelBufferPtr)[bufferIdx] = (half_float::half)(filterDataPtr[filterIdx]);
}else{
((float*)kernelBufferPtr)[bufferIdx] = (float)(filterDataPtr[filterIdx]);
}
}
}
}else{
MNN_ERROR("Map error ptrCL == nullptr \n");
}
mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(*(mKernelBuffer.get()), kernelBufferPtr);
}else{
std::vector<int> filterImageShape{(int)inputChannel, (int)(UP_DIV(outputChannel, 4) * kernelWidth * kernelHeight)};
std::shared_ptr<Tensor> filterBuffer(
Tensor::createDevice<float>({outputChannel, inputChannel, kernelWidth, kernelHeight}));
int buffer_size = filterBuffer->elementSize();
if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()) {
buffer_size *= sizeof(half_float::half);
} else {
buffer_size *= sizeof(float);
}
cl::Buffer filterBufferCL(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, buffer_size);
filterBuffer->buffer().device = (uint64_t)(&filterBufferCL);
cl_int error;
auto ptrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(filterBufferCL, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &error);
if(ptrCL != nullptr && error == CL_SUCCESS) {
::memset(ptrCL, 0, buffer_size);
if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){
for(int i = 0 ; i < filterBuffer->elementSize(); i++){
((half_float::half*)ptrCL)[i] = (half_float::half)(filterDataPtr[i]);
}
}else{
::memcpy(ptrCL, filterDataPtr, filterBuffer->size());
}
}else{
MNN_ERROR("Map error ptrCL == nullptr \n");
}
mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(filterBufferCL, ptrCL);
if(mWeightUseBuffer){
mFilter.reset(Tensor::createDevice<float>({UP_DIV(inputChannel, 4)*4, UP_DIV(outputChannel, 4), kernelWidth * kernelHeight, 4}));
int kernel_buffer_size = UP_DIV(outputChannel, 4)*4* UP_DIV(inputChannel, 4)*4* kernelWidth* kernelHeight;
if(mOpenCLBackend->getOpenCLRuntime()->isSupportedFP16()) {
kernel_buffer_size *= sizeof(half_float::half);
} else {
kernel_buffer_size *= sizeof(float);
}
mKernelBuffer.reset(new cl::Buffer(mOpenCLBackend->getOpenCLRuntime()->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, kernel_buffer_size));
mFilter.get()->buffer().device = (uint64_t)mKernelBuffer.get();
MNN::OpenCL::BufferConvertor bufferConvertor{mOpenCLBackend->getOpenCLRuntime()};
bool needTrans = false;
if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf() == false){
needTrans = true;
}
bufferConvertor.convertToNC4HW4Buffer(filterBuffer.get(), MNN::OpenCL::CONV2D_FILTER, mFilter.get(), needTrans);
} else{
mFilter.reset(Tensor::createDevice<float>({1, filterImageShape[1], 1, 4 * filterImageShape[0]}));
mOpenCLBackend->onAcquireBuffer(mFilter.get(), Backend::STATIC);
MNN::OpenCL::ImageBufferConvertor imageBufferConvertor{mOpenCLBackend->getOpenCLRuntime()};
std::string buildOption = "";
if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf() == false){
buildOption = "-DBUFFER_INP_FP32";
}
imageBufferConvertor.convertBufferToImage(filterBuffer.get(), MNN::OpenCL::CONV2D_FILTER, mFilter.get(), false, buildOption);
}
}
// Create Kernel
if (mStrides[0] == 1 && mStrides[1] == 1 && mDilations[0] == 1 && mDilations[1] == 1) {
mBuildOptions.emplace("-DMNN_CONV_S1D1");
}
mBuildOptions.emplace("-DBIAS");
if (mConv2dCommonParams->relu()) {
mBuildOptions.emplace("-DRELU");
} else if (mConv2dCommonParams->relu6()) {
mBuildOptions.emplace("-DRELU6");
}
if(mWeightUseBuffer){
mBuildOptions.emplace("-DUSE_BUFFER");
}
mKernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("conv_2d", kernelName, mBuildOptions);
mMaxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(mKernel));
#ifdef LOG_VERBOSE
MNN_PRINT("end ConvExecution init !\n");
#endif
}
ConvExecution::~ConvExecution() {
if((mUseLocalMem || !mConv1x1Opt) && !mWeightUseBuffer){
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
startRecord(mOpenCLBackend->getOpenCLRuntime(), mRecording);
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);
int kernelHeight = mConv2dCommonParams->kernelY();
int kernelWidth = mConv2dCommonParams->kernelX();
auto pad = ConvolutionCommon::convolutionPad(input, output, mConv2dCommonParams);
mPaddings[0] = pad.second;
mPaddings[1] = pad.first;
std::string info = std::to_string(inputChannels) + "_" + std::to_string(kernelHeight) + "_" + std::to_string(kernelWidth) + "_" + std::to_string(mStrides[0]) + "_" + std::to_string(mStrides[1]) + "_" + std::to_string(mDilations[0]) + "_" + std::to_string(mDilations[1]);
if (kernelHeight == kernelWidth && kernelHeight == 1 && mPaddings[0] == 0 && mPaddings[1] == 0) {
if(mConv1x1Opt){
auto kernel = &mKernel;
uint32_t idx = 0;
if(mUseLocalMem){
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))};
std::vector<uint32_t> lws{UNIT, UNIT, 1};
mLocalWorkSize = lws;
kernel->setArg(idx++, mGlobalWorkSize[0]);
kernel->setArg(idx++, mGlobalWorkSize[1]);
kernel->setArg(idx++, mGlobalWorkSize[2]);
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++, static_cast<int>(inputChannelBlocks));
kernel->setArg(idx++, height);
kernel->setArg(idx++, width);
recordKernel3d(mKernel, mGlobalWorkSize, mLocalWorkSize, mOpenCLBackend->getOpenCLRuntime());
}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))};
kernel->setArg(idx++, mGlobalWorkSize[0]);
kernel->setArg(idx++, mGlobalWorkSize[1]);
kernel->setArg(idx++, UP_DIV(width, 4));
kernel->setArg(idx++, openCLImage(input));
kernel->setArg(idx++, *mKernelBuffer.get());
kernel->setArg(idx++, *mBiasBuffer.get());
kernel->setArg(idx++, openCLImage(output));
kernel->setArg(idx++, static_cast<int>(inputChannelBlocks));
kernel->setArg(idx++, height);
kernel->setArg(idx++, width);
std::string kernelName = "conv_2d_1x1_mali";
mLocalWorkSize = localWS2DDefault(mGlobalWorkSize, mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), kernelName, mKernel).first;
recordKernel2d(mKernel, mGlobalWorkSize, mLocalWorkSize, mOpenCLBackend->getOpenCLRuntime());
}
}else{
int inputImageShape[2] = {inputHeight, inputWidth};
int outputImageShape[2] = {height, width};
int stideShape[2] = {mStrides[0], mStrides[1]};
const int total_kernel = 2;
std::string kernelName[total_kernel] = {"conv_2d_1x1", "conv_2d_1x1_c8h1w4"};
int itemC[total_kernel] = {4, 8};
int itemH[total_kernel] = {1, 1};
int itemW[total_kernel] = {4, 4};
int actual_kernel = total_kernel;
cl::Kernel kernel[total_kernel];
std::vector<uint32_t> globalWorkSize[total_kernel];
std::vector<uint32_t> localWorkSize[total_kernel];
std::pair<int, int> min_cost(INT_MAX, 0);//(min_time, min_index)
for(int knl_idx = 0; knl_idx < total_kernel; knl_idx++) {
kernel[knl_idx] = mOpenCLBackend->getOpenCLRuntime()->buildKernel("conv_2d", kernelName[knl_idx], mBuildOptions);
uint32_t maxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(kernel[knl_idx]));
globalWorkSize[knl_idx] = {static_cast<uint32_t>(UP_DIV(outputShape.at(3), itemC[knl_idx]) * UP_DIV(outputShape.at(2), itemW[knl_idx])), static_cast<uint32_t>(outputShape.at(0) * UP_DIV(outputShape.at(1), itemH[knl_idx]))};
uint32_t idx = 0;
kernel[knl_idx].setArg(idx++, globalWorkSize[knl_idx][0]);
kernel[knl_idx].setArg(idx++, globalWorkSize[knl_idx][1]);
kernel[knl_idx].setArg(idx++, openCLImage(input));
if(mWeightUseBuffer){
kernel[knl_idx].setArg(idx++, *mKernelBuffer.get());
}else{
kernel[knl_idx].setArg(idx++, openCLImage(mFilter.get()));
}
kernel[knl_idx].setArg(idx++, openCLImage(mBias.get()));
kernel[knl_idx].setArg(idx++, openCLImage(output));
kernel[knl_idx].setArg(idx++, sizeof(inputImageShape), inputImageShape);
kernel[knl_idx].setArg(idx++, static_cast<int>(inputChannelBlocks));
kernel[knl_idx].setArg(idx++, sizeof(outputImageShape), outputImageShape);
kernel[knl_idx].setArg(idx++, sizeof(stideShape), stideShape);
kernel[knl_idx].setArg(idx++, UP_DIV(width, 4));
kernel[knl_idx].setArg(idx++, UP_DIV(outputShape.at(3), 4));
std::pair<std::vector<uint32_t>, uint32_t> retTune;
retTune = localWS2DDefault(globalWorkSize[knl_idx], mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), kernelName[knl_idx] + info, kernel[knl_idx]);
//printf("conv1x1 kernel_%d = %d [%d, %d]\n", knl_idx, retTune.second, retTune.first[0], retTune.first[1]);
if(min_cost.first > retTune.second) {
min_cost.first = retTune.second;
min_cost.second = knl_idx;
mLocalWorkSize = {retTune.first[0], retTune.first[1]};
}
}
int min_index = min_cost.second;
//printf("min_index = %d %d\n", min_index, min_cost.first);
mGlobalWorkSize = {globalWorkSize[min_index][0], globalWorkSize[min_index][1]};
mKernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("conv_2d", kernelName[min_index], mBuildOptions);
uint32_t idx = 0;
mKernel.setArg(idx++, mGlobalWorkSize[0]);
mKernel.setArg(idx++, mGlobalWorkSize[1]);
mKernel.setArg(idx++, openCLImage(input));
if(mWeightUseBuffer){
mKernel.setArg(idx++, *mKernelBuffer.get());
}else{
mKernel.setArg(idx++, openCLImage(mFilter.get()));
}
mKernel.setArg(idx++, openCLImage(mBias.get()));
mKernel.setArg(idx++, openCLImage(output));
mKernel.setArg(idx++, sizeof(inputImageShape), inputImageShape);
mKernel.setArg(idx++, static_cast<int>(inputChannelBlocks));
mKernel.setArg(idx++, sizeof(outputImageShape), outputImageShape);
mKernel.setArg(idx++, sizeof(stideShape), stideShape);
mKernel.setArg(idx++, UP_DIV(width, 4));
mKernel.setArg(idx++, UP_DIV(outputShape.at(3), 4));
recordKernel2d(mKernel, mGlobalWorkSize, mLocalWorkSize, mOpenCLBackend->getOpenCLRuntime());
}
}else {
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], mPaddings[1]};
int dilationShape[2] = {mDilations[0], mDilations[1]};
const int total_kernel = 3;
std::string kernelName[total_kernel] = {"conv_2d_c4h1w4", "conv_2d_c4h4w1", "conv_2d_c8h4w1" };
int itemC[total_kernel] = {4, 4, 8};
int itemH[total_kernel] = {1, 4, 4};
int itemW[total_kernel] = {4, 1, 1};
int actual_kernel = total_kernel;
cl::Kernel kernel[total_kernel];
std::vector<uint32_t> globalWorkSize[total_kernel];
std::vector<uint32_t> localWorkSize[total_kernel];
std::pair<int, int> min_cost(INT_MAX, 0);//(min_time, min_index)
for(int knl_idx = 0; knl_idx < total_kernel; knl_idx++) {
kernel[knl_idx] = mOpenCLBackend->getOpenCLRuntime()->buildKernel("conv_2d", kernelName[knl_idx], mBuildOptions);
uint32_t maxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(kernel[knl_idx]));
globalWorkSize[knl_idx] = {static_cast<uint32_t>(UP_DIV(outputShape.at(3), itemC[knl_idx]) * UP_DIV(outputShape.at(2), itemW[knl_idx])), static_cast<uint32_t>(outputShape.at(0) * UP_DIV(outputShape.at(1), itemH[knl_idx]))};
uint32_t idx = 0;
cl_int ret = CL_SUCCESS;
ret |= kernel[knl_idx].setArg(idx++, globalWorkSize[knl_idx][0]);
ret |= kernel[knl_idx].setArg(idx++, globalWorkSize[knl_idx][1]);
ret |= kernel[knl_idx].setArg(idx++, openCLImage(input));
if(mWeightUseBuffer){
ret |= kernel[knl_idx].setArg(idx++, openCLBuffer(mFilter.get()));
}else{
ret |= kernel[knl_idx].setArg(idx++, openCLImage(mFilter.get()));
}
ret |= kernel[knl_idx].setArg(idx++, openCLImage(mBias.get()));
ret |= kernel[knl_idx].setArg(idx++, openCLImage(output));
ret |= kernel[knl_idx].setArg(idx++, sizeof(inputImageShape), inputImageShape);
ret |= kernel[knl_idx].setArg(idx++, inputChannelBlocks);
ret |= kernel[knl_idx].setArg(idx++, sizeof(outputImageShape), outputImageShape);
ret |= kernel[knl_idx].setArg(idx++, sizeof(kernelShape), kernelShape);
ret |= kernel[knl_idx].setArg(idx++, sizeof(strideShape), strideShape);
ret |= kernel[knl_idx].setArg(idx++, sizeof(paddingShape), paddingShape);
ret |= kernel[knl_idx].setArg(idx++, sizeof(dilationShape), dilationShape);
ret |= kernel[knl_idx].setArg(idx++, UP_DIV(width, itemW[knl_idx]));
ret |= kernel[knl_idx].setArg(idx++, UP_DIV(outputShape.at(3), 4));
ret |= kernel[knl_idx].setArg(idx++, UP_DIV(height, itemH[knl_idx]));
MNN_CHECK_CL_SUCCESS(ret, "setArg ConvExecution Kernel Select");
std::pair<std::vector<uint32_t>, uint32_t> retTune;
retTune = localWS2DDefault(globalWorkSize[knl_idx], mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), kernelName[knl_idx] + info, kernel[knl_idx]);
if(min_cost.first > retTune.second) {
min_cost.first = retTune.second;
min_cost.second = knl_idx;
mLocalWorkSize = {retTune.first[0], retTune.first[1]};
}
}
int min_index = min_cost.second;
mGlobalWorkSize = {globalWorkSize[min_index][0], globalWorkSize[min_index][1]};
mKernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("conv_2d", kernelName[min_index], mBuildOptions);
uint32_t idx = 0;
cl_int ret = CL_SUCCESS;
ret |= mKernel.setArg(idx++, mGlobalWorkSize[0]);
ret |= mKernel.setArg(idx++, mGlobalWorkSize[1]);
ret |= mKernel.setArg(idx++, openCLImage(input));
if(mWeightUseBuffer){
ret |= mKernel.setArg(idx++, openCLBuffer(mFilter.get()));
}else{
ret |= mKernel.setArg(idx++, openCLImage(mFilter.get()));
}
ret |= mKernel.setArg(idx++, openCLImage(mBias.get()));
ret |= mKernel.setArg(idx++, openCLImage(output));
ret |= mKernel.setArg(idx++, sizeof(inputImageShape), inputImageShape);
ret |= mKernel.setArg(idx++, inputChannelBlocks);
ret |= mKernel.setArg(idx++, sizeof(outputImageShape), outputImageShape);
ret |= mKernel.setArg(idx++, sizeof(kernelShape), kernelShape);
ret |= mKernel.setArg(idx++, sizeof(strideShape), strideShape);
ret |= mKernel.setArg(idx++, sizeof(paddingShape), paddingShape);
ret |= mKernel.setArg(idx++, sizeof(dilationShape), dilationShape);
ret |= mKernel.setArg(idx++, UP_DIV(width, itemW[min_index]));
ret |= mKernel.setArg(idx++, UP_DIV(outputShape.at(3), 4));
ret |= mKernel.setArg(idx++, UP_DIV(height, itemH[min_index]));
MNN_CHECK_CL_SUCCESS(ret, "setArg ConvExecution");
recordKernel2d(mKernel, mGlobalWorkSize, mLocalWorkSize, mOpenCLBackend->getOpenCLRuntime());
}
endRecord(mOpenCLBackend->getOpenCLRuntime(), mRecording);
#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
if(mUseLocalMem){
#ifdef ENABLE_OPENCL_TIME_PROFILER
cl::Event event;
run3DKernelDefault(mKernel, mGlobalWorkSize, mLocalWorkSize,
mOpenCLBackend->getOpenCLRuntime(), &event);
float costTime = mOpenCLBackend->getOpenCLRuntime()->getCostTime(&event);
MNN_PRINT("kernel cost:%f us Conv UseLocalMem\n",costTime);
#else
if(mOpenCLBackend->getOpenCLRuntime()->isUseRecordQueue()){
if(mOpenCLBackend->getOpenCLRuntime()->isDevideOpRecord())
mOpenCLBackend->getOpenCLRuntime()->getRecordings()->emplace_back(mRecording);
#ifdef LOG_VERBOSE
MNN_PRINT("end ConvExecution onExecute !\n");
#endif
return NO_ERROR;
}
run3DKernelDefault(mKernel, mGlobalWorkSize, mLocalWorkSize,
mOpenCLBackend->getOpenCLRuntime());
#endif
}
#ifdef ENABLE_OPENCL_TIME_PROFILER
cl::Event event;
runKernel2D(mKernel, mGlobalWorkSize, mLocalWorkSize,
mOpenCLBackend->getOpenCLRuntime(), &event);
int costTime = (int)mOpenCLBackend->getOpenCLRuntime()->getCostTime(&event);
MNN_PRINT("kernel cost:%d us Conv2D\n",costTime);
#else
if(mOpenCLBackend->getOpenCLRuntime()->isUseRecordQueue()){
if(mOpenCLBackend->getOpenCLRuntime()->isDevideOpRecord())
mOpenCLBackend->getOpenCLRuntime()->getRecordings()->emplace_back(mRecording);
#ifdef LOG_VERBOSE
MNN_PRINT("end ConvExecution onExecute !\n");
#endif
return NO_ERROR;
}
runKernel2D(mKernel, mGlobalWorkSize, mLocalWorkSize,
mOpenCLBackend->getOpenCLRuntime());
#endif
#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 {
if (inputs.size() > 1) {
return nullptr;
}
if (nullptr != op->main_as_Convolution2D()->quanParameter()) {
auto quan = op->main_as_Convolution2D()->quanParameter();
if (1 == quan->type() || 2 == quan->type()) {
if (quan->has_scaleInt()) {
// Don't support IDST-int8 because of error
return nullptr;
}
}
}
auto conv2D = op->main_as_Convolution2D();
int maxWidth = static_cast<OpenCLBackend *>(backend)->getOpenCLRuntime()->getMaxImage2DSize()[0];
int maxHeight = static_cast<OpenCLBackend *>(backend)->getOpenCLRuntime()->getMaxImage2DSize()[1];
if (ConvWinograd::valid(conv2D->common(), inputs[0], outputs[0], maxWidth, maxHeight)) {
return new ConvWinograd(conv2D, backend);
}
return new ConvExecution(inputs, outputs, op, backend);
}
};
OpenCLCreatorRegister<ConvolutionCreator> __conv_op(OpType_Convolution, IMAGE);
} // namespace OpenCL
} // namespace MNN
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