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MNN/source/backend/opencl/execution/buffer/ConvBufExecution.cpp

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//
// ConvBufExecution.cpp
// MNN
//
// Created by MNN on 2019/02/28.
// Copyright © 2018, Alibaba Group Holding Limited
//
#ifndef MNN_OPENCL_BUFFER_CLOSED
#include "ConvBufExecution.hpp"
#include "ConvBufWinograd.hpp"
#include "ConvSubgroupBufExecution.hpp"
#include "core/ConvolutionCommon.hpp"
#include "core/Backend.hpp"
#include "RasterBufExecution.hpp"
namespace MNN {
namespace OpenCL {
std::pair<std::vector<uint32_t>, uint32_t> ConvBufCommonExecution::gws2dLwsTune(const cl::Kernel &kernel, const std::vector<uint32_t> &gws, const std::string &kernelName, const uint32_t maxWorkGroupSize) {
MNN_ASSERT(gws.size() == 2);
auto runtime = mOpenCLBackend->getOpenCLRuntime();
auto maxWorkItemSizes = runtime->getMaxWorkItemSizes();
MNN_ASSERT(maxWorkItemSizes.size() >= 2);
auto& tunedLws = runtime->tunedLwsMap();
std::pair<std::string, std::vector<uint32_t>> info = std::make_pair(kernelName, gws);
if (tunedLws.find(info) != tunedLws.end()) {
//printf("ConvBuf2dGeneralLocalWS Found! gws:%d %d lws:%d %d\n", gws[0], gws[1], tunedLws[info][0], tunedLws[info][1]);
return tunedLws[info];
}
std::vector<uint32_t> lws(3, 1);
std::vector<uint32_t> lws_prefer(3, 1);
uint32_t min_cost = UINT_MAX;
if(runtime->getCLTuneLevel() == Heavy) {
while(lws[1] <= gws[1] || lws[1] <= 6) {
lws[0] = 1;
while(lws[0] <= gws[0] || lws[0] <= 6) {
if(lws[0] <= maxWorkItemSizes[0] && lws[1] <= maxWorkItemSizes[1] && lws[0]*lws[1] <= maxWorkGroupSize) {
cl::Event event;
std::vector<uint32_t> internalGlobalWS(2, 1);
for (size_t i = 0; i < gws.size(); ++i) {
internalGlobalWS[i] = ROUND_UP(gws[i], std::max((uint32_t)1, lws[i]));
}
cl_int res = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueNDRangeKernel(
kernel, cl::NullRange,
cl::NDRange(internalGlobalWS[0], internalGlobalWS[1]),
cl::NDRange(lws[0], lws[1]),
nullptr, &event);
MNN_CHECK_CL_SUCCESS(res, kernelName.c_str());
int cost_time = (int)mOpenCLBackend->getOpenCLRuntime()->getCostTime(&event);
if(cost_time < min_cost) {
min_cost = cost_time;
lws_prefer[0] = lws[0];
lws_prefer[1] = lws[1];
}
}
lws[0]<<=1;
}
lws[1]<<=1;
}
} else if(runtime->getCLTuneLevel() == Wide) {
while(lws[1] <= gws[1] || lws[1] <= 6) {
lws[0] = 1;
while(lws[0] <= gws[0] || lws[0] <= 6) {
if(lws[0] <= maxWorkItemSizes[0] && lws[1] <= maxWorkItemSizes[1] && lws[0]*lws[1] <= maxWorkGroupSize) {
cl::Event event;
std::vector<uint32_t> internalGlobalWS(2, 1);
for (size_t i = 0; i < gws.size(); ++i) {
internalGlobalWS[i] = ROUND_UP(gws[i], std::max((uint32_t)1, lws[i]));
}
cl_int res = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueNDRangeKernel(
kernel, cl::NullRange,
cl::NDRange(internalGlobalWS[0], internalGlobalWS[1]),
cl::NDRange(lws[0], lws[1]),
nullptr, &event);
MNN_CHECK_CL_SUCCESS(res, kernelName.c_str());
int cost_time = (int)mOpenCLBackend->getOpenCLRuntime()->getCostTime(&event);
if(cost_time < min_cost) {
min_cost = cost_time;
lws_prefer[0] = lws[0];
lws_prefer[1] = lws[1];
}
}
do {
lws[0]<<=1;
}
while(((2*gws[0])%lws[0] > 1) && (lws[0] & (lws[0] - 1)) != 0 && (lws[0] <= gws[0]) && (lws[0] > 6));//divisible powOfTwo lessThanSix
}
do {
lws[1]<<=1;
}
while(((2*gws[1])%lws[1] > 1) && (lws[1] & (lws[1] - 1)) != 0 && (lws[1] <= gws[1]) && (lws[1] > 6));//divisible powOfTwo lessThanSix
}
} else if(runtime->getCLTuneLevel() == Normal) {
while(lws[1] <= gws[1] && lws[1] <= 6) {
lws[0] = 1;
while(lws[0] <= gws[0] || lws[0] <= 6) {
if(lws[0] <= maxWorkItemSizes[0] && lws[1] <= maxWorkItemSizes[1] && lws[0]*lws[1] <= maxWorkGroupSize) {
cl::Event event;
std::vector<uint32_t> internalGlobalWS(2, 1);
for (size_t i = 0; i < gws.size(); ++i) {
internalGlobalWS[i] = ROUND_UP(gws[i], std::max((uint32_t)1, lws[i]));
}
cl_int res = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueNDRangeKernel(
kernel, cl::NullRange,
cl::NDRange(internalGlobalWS[0], internalGlobalWS[1]),
cl::NDRange(lws[0], lws[1]),
nullptr, &event);
MNN_CHECK_CL_SUCCESS(res, kernelName.c_str());
int cost_time = (int)mOpenCLBackend->getOpenCLRuntime()->getCostTime(&event);
if(cost_time < min_cost) {
min_cost = cost_time;
lws_prefer[0] = lws[0];
lws_prefer[1] = lws[1];
}
}
do {
lws[0]<<=1;
}
while(((2*gws[0])%lws[0] > 1) && (lws[0] & (lws[0] - 1)) != 0 && (lws[0] <= gws[0]) && (lws[0] > 6));//divisible powOfTwo lessThanSix
}
do {
lws[1]<<=1;
}
while(((2*gws[1])%lws[1] > 1) && (lws[1] & (lws[1] - 1)) != 0 && (lws[1] <= gws[1]) && (lws[1] <= 6));//divisible powOfTwo lessThanSix
}
} else if(runtime->getCLTuneLevel() == Fast) {
while(lws[1] <= gws[1] && lws[1] <= 6) {
lws[0] = 1;
while(lws[0] <= gws[0] && lws[0] <= 6) {
if(lws[0] <= maxWorkItemSizes[0] && lws[1] <= maxWorkItemSizes[1] && lws[0]*lws[1] <= maxWorkGroupSize) {
cl::Event event;
std::vector<uint32_t> internalGlobalWS(2, 1);
for (size_t i = 0; i < gws.size(); ++i) {
internalGlobalWS[i] = ROUND_UP(gws[i], std::max((uint32_t)1, lws[i]));
}
cl_int res = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueNDRangeKernel(
kernel, cl::NullRange,
cl::NDRange(internalGlobalWS[0], internalGlobalWS[1]),
cl::NDRange(lws[0], lws[1]),
nullptr, &event);
MNN_CHECK_CL_SUCCESS(res, kernelName.c_str());
int cost_time = (int)mOpenCLBackend->getOpenCLRuntime()->getCostTime(&event);
if(cost_time < min_cost) {
min_cost = cost_time;
lws_prefer[0] = lws[0];
lws_prefer[1] = lws[1];
}
}
do {
lws[0]<<=1;
}
while(((2*gws[0])%lws[0] > 1) && (lws[0] & (lws[0] - 1)) != 0 && (lws[0] <= gws[0]) && (lws[0] <= 6));//divisible powOfTwo lessThanSix
}
do {
lws[1]<<=1;
}
while(((2*gws[1])%lws[1] > 1) && (lws[1] & (lws[1] - 1)) != 0 && (lws[1] <= gws[1]) && (lws[1] <= 6));//divisible powOfTwo lessThanSix
}
} else if(runtime->getCLTuneLevel() == None) {
// define not tune method to choose lws
if(runtime->getGpuMemType() == GpuMemObject::IMAGE) {
lws_prefer[0] = 8;
lws_prefer[1] = 4;
} else {
lws_prefer[0] = 0;
lws_prefer[1] = 0;
}
cl::Event event;
std::vector<uint32_t> internalGlobalWS(2, 1);
for (size_t i = 0; i < gws.size(); ++i) {
internalGlobalWS[i] = ROUND_UP(gws[i], std::max((uint32_t)1, lws_prefer[i]));
}
cl_int res = CL_SUCCESS;
if(lws_prefer[0] == 0 || lws_prefer[1] == 0){
res = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueNDRangeKernel(
kernel, cl::NullRange, cl::NDRange(internalGlobalWS[0], internalGlobalWS[1]), cl::NullRange, nullptr, &event);
}else{
res = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueNDRangeKernel(
kernel, cl::NullRange, cl::NDRange(internalGlobalWS[0], internalGlobalWS[1]), cl::NDRange(lws_prefer[0], lws_prefer[1]), nullptr, &event);
}
MNN_CHECK_CL_SUCCESS(res, kernelName.c_str());
min_cost = (int)mOpenCLBackend->getOpenCLRuntime()->getCostTime(&event);
}
if (tunedLws.find(info) == tunedLws.end()) {
//printf("ConvBuf2dGeneralLocalWS %d Insert! gws:%d %d, lws:%d %d, time:%dus\n", (int)tunedLws.size(), gws[0], gws[1], lws_prefer[0], lws_prefer[1], min_cost);
tunedLws.insert(std::make_pair(info, std::make_pair(lws_prefer, min_cost)));
}
return std::make_pair(lws_prefer, min_cost);
}
ConvBufCommonExecution::ConvBufCommonExecution(const Convolution2D *conv2dParams, Backend *backend) : Execution(backend) {
auto openclBackend = (OpenCLBackend *)backend;
int biasSize = conv2dParams->common()->outputCount();
int buffer_size = ROUND_UP(biasSize, 16);//pack to 16
if(openclBackend->getOpenCLRuntime()->isSupportedFP16()) {
buffer_size *= sizeof(half_float::half);
} else {
buffer_size *= sizeof(float);
}
mBias.reset(Tensor::createDevice<float>({1, 1, 1, ROUND_UP(biasSize, 16)}));
backend->onAcquireBuffer(mBias.get(), Backend::STATIC);
cl::Buffer &biasBuffer = openCLBuffer(mBias.get());
cl_int res;
auto biasPtrCL = openclBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(
biasBuffer, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &res);
if(biasPtrCL != nullptr && res == CL_SUCCESS){
::memset(biasPtrCL, 0, buffer_size);
if (nullptr != conv2dParams->bias()) {
const float *biasDataPtr = conv2dParams->bias()->data();
if(openclBackend->getOpenCLRuntime()->isSupportedFP16()){
for(int i=0; i<biasSize; i++) {
((half_float::half*)biasPtrCL)[i] = (half_float::half)(biasDataPtr[i]);
}
}else{
::memcpy(biasPtrCL, biasDataPtr, biasSize * sizeof(float));
}
}
}else{
MNN_ERROR("Map error biasPtrCL == nullptr \n");
}
openclBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(biasBuffer, biasPtrCL);
}
ConvBufCommonExecution::~ConvBufCommonExecution() {
MNN_ASSERT(nullptr != mBias);
backend()->onReleaseBuffer(mBias.get(), Backend::STATIC);
}
void ConvBufExecution::setConv1x1WeightBuffer(int packCout, int packCin, const float* filterDataPtr) {
cl_int res;
std::shared_ptr<Tensor> filterBuffer(Tensor::createDevice<float>({ROUND_UP(mOutputChannel, 8)/*Cout pack set to max 8*/, ROUND_UP(mInputChannel, packCin), mKernelWidth, mKernelHeight}));
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, &res);
if(kernelBufferPtr != nullptr && res == CL_SUCCESS){
::memset(kernelBufferPtr, 0, buffer_size);
for(int o = 0; o < mOutputChannel; o++){
for(int i = 0 ; i < mInputChannel; i++){
int bufferIdx = (o/packCout) * ROUND_UP(mInputChannel, packCin)*packCout + (i/packCin)*packCin*packCout + (o%packCout)*packCin + (i%packCin);//(Co/packCout, Ci/packCin, packCout, packCin)
int filterIdx = o*mInputChannel + 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");
MNN_ASSERT(false);
}
mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(*(mKernelBuffer.get()), kernelBufferPtr);
}
void ConvBufExecution::_generateFilterConvertRegion(Tensor* virtualFilter, Tensor* originBuffer) const {
auto filterDes = TensorUtils::getDescribe(virtualFilter);
filterDes->regions.clear();
for (int so=0; so<4; ++so) {
int oSize = (mOutputChannel - so + 3) / 4;
if (oSize <= 0) {
continue;
}
Tensor::InsideDescribe::Region slice;
slice.origin = originBuffer;
slice.size[0] = oSize;
slice.size[1] = mInputChannel;
slice.size[2] = mKernelWidth * mKernelHeight;
slice.src.stride[0] = mInputChannel * mKernelWidth * mKernelHeight * 4;
slice.src.stride[1] = mKernelWidth * mKernelHeight;
slice.src.stride[2] = 1;
slice.src.offset = so * mInputChannel * mKernelWidth * mKernelHeight;
slice.dst.stride[0] = mKernelWidth * mKernelHeight * 4;
slice.dst.stride[1] = mKernelWidth * mKernelHeight * UP_DIV(mOutputChannel, 4) * 4;
slice.dst.stride[2] = 4;
slice.dst.offset = so;
filterDes->regions.emplace_back(std::move(slice));
}
}
ConvBufExecution::ConvBufExecution(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs, const MNN::Op *op, Backend *backend)
: ConvBufCommonExecution(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();
mConv2dParams = conv2dParams;
mConv2dCommonParams = conv2dCommonParams;
mStrides = {conv2dCommonParams->strideY(), conv2dCommonParams->strideX()};
mDilations = {conv2dCommonParams->dilateY(), conv2dCommonParams->dilateX()};
auto padding = ConvolutionCommon::convolutionPad(inputs[0], outputs[0], mConv2dCommonParams);
mPaddings[0] = padding.second;//padY
mPaddings[1] = padding.first;//padX
mKernelWidth = conv2dCommonParams->kernelX();
mKernelHeight = conv2dCommonParams->kernelY();
mOutputChannel = conv2dCommonParams->outputCount();
std::string kernelName = "conv_2d_c4h1w4";
mInputChannel = inputs[0]->channel();
std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon;
if (inputs.size() != 1) {
// Multi - Input
mConv1x1Opt = false;
mRasterExe.reset(new RasterBufExecution({mFilter.get()}, op, mOpenCLBackend));
} else {
int weightSize = 0;
ConvolutionCommon::getConvParameters(&quanCommon, conv2dParams, &mFilterDataPtr, &weightSize);
//select opt conv method
mConv1x1Opt = (mKernelHeight == mKernelWidth && mKernelHeight == 1 && mPaddings[0] == 0 &&
mPaddings[1] == 0 && mStrides[0] == 1 && mStrides[1] == 1 && inputs[0]->width() >= 4);
}
if (mConv1x1Opt) {
//At first, set packCout equal to 4
setConv1x1WeightBuffer(4, 4, mFilterDataPtr);
kernelName = "conv_2d_1x1_c4h1w4";
} else {
mFilter.reset(
Tensor::createDevice<float>({ROUND_UP(mOutputChannel, 4) * ROUND_UP(mInputChannel, 4) * mKernelWidth * mKernelHeight}));
if (mFilterDataPtr != nullptr) {
std::vector<int> filterImageShape{ROUND_UP(mInputChannel, 4), (UP_DIV(mOutputChannel, 4) * mKernelWidth * mKernelHeight)};
std::shared_ptr<Tensor> filterBuffer(
Tensor::createDevice<float>({mOutputChannel, ROUND_UP(mInputChannel, 4), mKernelWidth, mKernelHeight}));
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 res;
auto ptrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(filterBufferCL, true, CL_MAP_WRITE, 0, buffer_size, nullptr, nullptr, &res);
if(ptrCL != nullptr && res == CL_SUCCESS) {
::memset(ptrCL, 0, buffer_size);
if(mOpenCLBackend->getOpenCLRuntime()->isWeightCpuTransHalf()){
for(int oc=0; oc<mOutputChannel; oc++) {
for(int ic=0; ic<mInputChannel; ic++) {
for(int kh=0; kh<mKernelHeight; kh++) {
for(int kw=0; kw<mKernelWidth; kw++) {
int dst_idx = ((oc * ROUND_UP(mInputChannel, 4) + ic) * mKernelHeight + kh)* mKernelWidth + kw;
int src_idx = ((oc * mInputChannel + ic) * mKernelHeight + kh)* mKernelWidth + kw;
((half_float::half*)ptrCL)[dst_idx] = (half_float::half)(mFilterDataPtr[src_idx]);
}
}
}
}
}else{
const int copy_size = mKernelWidth * mKernelHeight * sizeof(float);
for(int oc=0; oc<mOutputChannel; oc++) {
for(int ic=0; ic<mInputChannel; ic++) {
::memcpy((float *)ptrCL + (oc * ROUND_UP(mInputChannel, 4) + ic) * mKernelWidth * mKernelHeight, mFilterDataPtr + (oc * mInputChannel + ic) * mKernelWidth * mKernelHeight, copy_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::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);
}
}
// Create Kernel
if (mConv2dCommonParams->relu()) {
mBuildOptions.emplace("-DRELU");
} else if (mConv2dCommonParams->relu6()) {
mBuildOptions.emplace("-DRELU6");
}
mKernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("conv_2d_buf", kernelName, mBuildOptions);
mMaxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(mKernel));
#ifdef LOG_VERBOSE
MNN_PRINT("end ConvExecution init !\n");
#endif
}
ConvBufExecution::~ConvBufExecution() {
// Do nothing
}
ErrorCode ConvBufExecution::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];
if (inputs.size() > 1) {
// Multi Input, need pretreat
_generateFilterConvertRegion(mFilter.get(), inputs[1]);
bool res = backend()->onAcquireBuffer(mFilter.get(), Backend::DYNAMIC);
if (!res) {
return OUT_OF_MEMORY;
}
mRasterExe->onResize({}, {mFilter.get()});
}
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 outChannel = outputShape.at(3);
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);
auto padding = ConvolutionCommon::convolutionPad(input, output, mConv2dCommonParams);
mPaddings[0] = padding.second;//padY
mPaddings[1] = padding.first;//padX
std::string info = std::to_string(inputChannels) + "_" + std::to_string(mKernelHeight) + "_" + std::to_string(mKernelWidth) + "_" + std::to_string(mStrides[0]) + "_" + std::to_string(mStrides[1]) + "_" + std::to_string(mDilations[0]) + "_" + std::to_string(mDilations[1]);
if (mConv1x1Opt) {
// {"conv_2d_1x1_c4h1w4", "conv_2d_1x1_c4h1w2", "conv_2d_1x1_c4h1w1", "conv_2d_1x1_c8h1w4"};
const int total_kernel = 5;
std::string kernelName[total_kernel] = {"conv_2d_1x1_c4h1w4", "conv_2d_1x1_c4h1w2", "conv_2d_1x1_c4h1w1", "conv_2d_1x1_c8h1w4", "conv_2d_1x1_c8h1w2"};
int itemC[total_kernel] = {4, 4, 4, 8, 8};
int itemW[total_kernel] = {4, 2, 1, 4, 2};
int c8_index_start = 3;
int actual_kernel = total_kernel;
if(mOpenCLBackend->getOpenCLRuntime()->getCLTuneLevel() == Normal) {
actual_kernel = 2;
kernelName[0] = "conv_2d_1x1_c4h1w1";
itemC[0] = 4;
itemW[0] = 1;
kernelName[1] = "conv_2d_1x1_c8h1w2";
itemC[1] = 8;
itemW[1] = 2;
c8_index_start = 1;
} else if(mOpenCLBackend->getOpenCLRuntime()->getCLTuneLevel() == Fast || mOpenCLBackend->getOpenCLRuntime()->getCLTuneLevel() == None) {
actual_kernel = 1;
kernelName[0] = "conv_2d_1x1_c4h1w1";
itemC[0] = 4;
itemW[0] = 1;
}
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 < actual_kernel; knl_idx++) {
std::set<std::string> buildOption = mBuildOptions;
if(outputShape.at(3) % itemC[knl_idx] != 0){
buildOption.emplace("-DCHANNEL_LEAVE");
}
if((outputShape.at(2) % itemW[knl_idx]) != 0){
buildOption.emplace("-DBLOCK_LEAVE");
}
kernel[knl_idx] = mOpenCLBackend->getOpenCLRuntime()->buildKernel("conv_2d_buf", kernelName[knl_idx], buildOption);
uint32_t maxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(kernel[knl_idx]));
uint32_t idx = 0;
cl_int ret = CL_SUCCESS;
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) * outputShape.at(1))};
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++, UP_DIV(width, itemW[knl_idx]));
ret |= kernel[knl_idx].setArg(idx++, openCLBuffer(input));
ret |= kernel[knl_idx].setArg(idx++, *mKernelBuffer.get());
ret |= kernel[knl_idx].setArg(idx++, openCLBuffer(mBias.get()));
ret |= kernel[knl_idx].setArg(idx++, openCLBuffer(output));
ret |= kernel[knl_idx].setArg(idx++, static_cast<int>(inputChannelBlocks));
ret |= kernel[knl_idx].setArg(idx++, height);
ret |= kernel[knl_idx].setArg(idx++, width);
ret |= kernel[knl_idx].setArg(idx++, UP_DIV(outChannel, 4));
MNN_CHECK_CL_SUCCESS(ret, "setArg Conv1x1Buf Kernel Select");
std::pair<std::vector<uint32_t>, int> retTune;
retTune = gws2dLwsTune(kernel[knl_idx], globalWorkSize[knl_idx], kernelName[knl_idx], maxWorkGroupSize);
//printf("cov1x1 %d, %d\n", knl_idx, retTune.second);
if(min_cost.first > retTune.second) {
min_cost.first = retTune.second;
min_cost.second = knl_idx;
mLocalWorkSize = {retTune.first[0], retTune.first[1]};
}
}
std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon;
int min_index = min_cost.second;
if(min_index >= c8_index_start) {//if best kernel is "conv_2d_1x1_c8h1w4", set weight packCout to 8
int weightSize = 0;
ConvolutionCommon::getConvParameters(&quanCommon, mConv2dParams, &mFilterDataPtr, &weightSize);
setConv1x1WeightBuffer(8, 4, mFilterDataPtr);
}
mGlobalWorkSize = {globalWorkSize[min_index][0], globalWorkSize[min_index][1]};
std::set<std::string> buildOption = mBuildOptions;
if(outputShape.at(3) % itemC[min_index] != 0){
buildOption.emplace("-DCHANNEL_LEAVE");
}
if((outputShape.at(2) % itemW[min_index]) != 0){
buildOption.emplace("-DBLOCK_LEAVE");
}
mKernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("conv_2d_buf", kernelName[min_index], buildOption);
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++, UP_DIV(width, itemW[min_index]));
ret |= mKernel.setArg(idx++, openCLBuffer(input));
ret |= mKernel.setArg(idx++, *mKernelBuffer.get());
ret |= mKernel.setArg(idx++, openCLBuffer(mBias.get()));
ret |= mKernel.setArg(idx++, openCLBuffer(output));
ret |= mKernel.setArg(idx++, static_cast<int>(inputChannelBlocks));
ret |= mKernel.setArg(idx++, height);
ret |= mKernel.setArg(idx++, width);
ret |= mKernel.setArg(idx++, UP_DIV(outChannel, 4));
MNN_CHECK_CL_SUCCESS(ret, "setArg Conv1x1Buf");
//printf("conv1x1 %d, %d %d, %d %d, %d %d\n", min_index, mGlobalWorkSize[0], mGlobalWorkSize[1], mLocalWorkSize[0], mLocalWorkSize[1], outChannel, width);
} else {
int inputImageShape[2] = {inputHeight, inputWidth};
int outputImageShape[2] = {height, width};
int kernelShape[2] = {mKernelHeight, mKernelWidth};
int strideShape[2] = {mStrides[0], mStrides[1]};
int paddingShape[2] = {mPaddings[0], mPaddings[1]};
int dilationShape[2] = {mDilations[0], mDilations[1]};
// {"conv_2d_c4h1w2", "conv_2d_c4h1w1", "conv_2d_c8h1w1", "conv_2d_c4h1w4", "conv_2d_c8h2w1", "conv_2d_c4h4w1"};
const int total_kernel = 7;
std::string kernelName[total_kernel] = {"conv_2d_c4h1w1", "conv_2d_c4h1w2", "conv_2d_c4h4w1", "conv_2d_c8h2w1", "conv_2d_c8h4w1", "conv_2d_c4h1w4", "conv_2d_c8h1w4"};
int itemC[total_kernel] = {4, 4, 4, 8, 8, 4, 8};
int itemH[total_kernel] = {1, 1, 4, 2, 4, 1, 1};
int itemW[total_kernel] = {1, 2, 1, 1, 1, 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 < actual_kernel; knl_idx++) {
std::set<std::string> buildOption = mBuildOptions;
if(outputShape.at(3) % itemC[knl_idx] != 0){
buildOption.emplace("-DCHANNEL_LEAVE");
}
if((outputShape.at(2) % itemW[knl_idx]) != 0 || (outputShape.at(1) % itemH[knl_idx]) != 0){
buildOption.emplace("-DBLOCK_LEAVE");
}
kernel[knl_idx] = mOpenCLBackend->getOpenCLRuntime()->buildKernel("conv_2d_buf", kernelName[knl_idx], buildOption);
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++, openCLBuffer(input));
ret |= kernel[knl_idx].setArg(idx++, openCLBuffer(mFilter.get()));
ret |= kernel[knl_idx].setArg(idx++, openCLBuffer(mBias.get()));
ret |= kernel[knl_idx].setArg(idx++, openCLBuffer(output));
ret |= kernel[knl_idx].setArg(idx++, sizeof(inputImageShape), inputImageShape);
ret |= kernel[knl_idx].setArg(idx++, inputChannels);
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(outChannel, 4));
ret |= kernel[knl_idx].setArg(idx++, UP_DIV(height, itemH[knl_idx]));
MNN_CHECK_CL_SUCCESS(ret, "setArg ConvBuf Kernel Select");
std::pair<std::vector<uint32_t>, int> retTune;
retTune = gws2dLwsTune(kernel[knl_idx], globalWorkSize[knl_idx], kernelName[knl_idx] + info, maxWorkGroupSize);
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]};
std::set<std::string> buildOption = mBuildOptions;
if(outputShape.at(3) % itemC[min_index] != 0){
buildOption.emplace("-DCHANNEL_LEAVE");
}
if((outputShape.at(2) % itemW[min_index]) != 0 || (outputShape.at(1) % itemH[min_index]) != 0){
buildOption.emplace("-DBLOCK_LEAVE");
}
mKernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("conv_2d_buf", kernelName[min_index], buildOption);
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++, openCLBuffer(input));
ret |= mKernel.setArg(idx++, openCLBuffer(mFilter.get()));
ret |= mKernel.setArg(idx++, openCLBuffer(mBias.get()));
ret |= mKernel.setArg(idx++, openCLBuffer(output));
ret |= mKernel.setArg(idx++, sizeof(inputImageShape), inputImageShape);
ret |= mKernel.setArg(idx++, inputChannels);
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(outChannel, 4));
ret |= mKernel.setArg(idx++, UP_DIV(height, itemH[min_index]));
MNN_CHECK_CL_SUCCESS(ret, "setArg ConvBuf");
}
if (inputs.size() > 1) {
backend()->onReleaseBuffer(mFilter.get(), Backend::DYNAMIC);
}
#ifdef LOG_VERBOSE
MNN_PRINT("end ConvExecution onResize !\n");
#endif
return NO_ERROR;
}
ErrorCode ConvBufExecution::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
#ifdef LOG_VERBOSE
MNN_PRINT("Start ConvExecution onExecute !\n");
#endif
if (inputs.size() > 1) {
mRasterExe->onExecute({}, {mFilter.get()});
if (inputs.size() > 2) {
auto buffer_size = inputs[2]->elementSize();
if(mOpenCLBackend->getOpenCLRuntime()->isSupportedFP16()) {
buffer_size *= sizeof(half_float::half);
} else {
buffer_size *= sizeof(float);
}
mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueCopyBuffer(openCLBuffer(inputs[2]), openCLBuffer(mBias.get()), 0, 0, buffer_size);
}
}
#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 ConvBuf2D\n",costTime);
#else
runKernel2D(mKernel, mGlobalWorkSize, mLocalWorkSize, mOpenCLBackend->getOpenCLRuntime());
#endif
#ifdef LOG_VERBOSE
MNN_PRINT("end ConvExecution onExecute !\n");
#endif
return NO_ERROR;
}
class ConvolutionBufCreator : public OpenCLBackend::Creator {
public:
virtual ~ConvolutionBufCreator() = default;
virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
const MNN::Op *op, Backend *backend) const override {
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;
}
}
}
if (inputs.size() > 1) {
// Multi inputs
for (int i = 0; i < inputs.size(); ++i) {
TensorUtils::setTensorSupportPack(inputs[i], false);
}
for (int i = 0; i < outputs.size(); ++i) {
TensorUtils::setTensorSupportPack(outputs[i], false);
}
return new ConvBufExecution(inputs, outputs, op, backend);
}
auto conv2D = op->main_as_Convolution2D();
auto input = inputs[0];
auto output = outputs[0];
auto padding = ConvolutionCommon::convolutionPad(inputs[0], outputs[0], conv2D->common());
std::vector<int> inputShape = tensorShapeFormat(input);
std::vector<int> outputShape = tensorShapeFormat(output);
const int outputChannel = outputShape.at(3);
const int inputChannels = inputShape.at(3);
if (ConvBufWinograd::valid(conv2D->common(), inputs[0], outputs[0], static_cast<OpenCLBackend *>(backend)->getOpenCLRuntime()->getGpuType() == INTEL)) {
std::vector<int> inputShape = tensorShapeFormat(input);
std::vector<int> outputShape = tensorShapeFormat(output);
const int src_width = inputShape.at(2);
const int dst_width = outputShape.at(2);
int pad_right = (UP_DIV(dst_width, 2) - 1) * 2 + 3 - padding.first - src_width + 1;
TensorUtils::setTensorPad(input, padding.first, pad_right, 0, 0);
TensorUtils::setTensorChannelPack(input, 16);
return new ConvBufWinograd(conv2D, backend);
}
#ifdef MNN_SUPPORT_INTEL_SUBGROUP
if (static_cast<OpenCLBackend *>(backend)->getOpenCLRuntime()->isSupportedIntelSubgroup() && outputChannel >= 16) {
if (inputChannels >= 16) {
auto pads = ConvolutionCommon::convolutionPadFull(inputs[0], outputs[0], conv2D->common());
TensorUtils::setTensorPad(inputs[0], std::get<0>(pads), std::get<2>(pads), 0, 0);
TensorUtils::setTensorChannelPack(inputs[0], 16);
}
return new ConvSubgroupBuf(inputs, outputs, op, backend);
}
#endif /* MNN_SUPPORT_INTEL_SUBGROUP */
for (int i = 0; i < inputs.size(); ++i) {
TensorUtils::setTensorSupportPack(inputs[i], false);
}
for (int i = 0; i < outputs.size(); ++i) {
TensorUtils::setTensorSupportPack(outputs[i], false);
}
return new ConvBufExecution(inputs, outputs, op, backend);
}
};
OpenCLCreatorRegister<ConvolutionBufCreator> __convBuf_op(OpType_Convolution, BUFFER);
} // namespace OpenCL
} // namespace MNN
#endif /* MNN_OPENCL_BUFFER_CLOSED */
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