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

433 lines
16 KiB
C++
Raw Blame History

//
// OpenCLBackend.cpp
// MNN
//
// Created by MNN on 2019/02/28.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "backend/opencl/core/OpenCLBackend.hpp"
#include "MNN_generated.h"
#include "core/TensorUtils.hpp"
#include "core/SizeComputer.hpp"
#include <map>
#include <mutex>
#include <thread>
#include "core/Macro.h"
namespace MNN {
namespace OpenCL {
std::map<OpType, OpenCLBackend::Creator*>* gCreator() {
static std::once_flag once;
static std::map<OpType, OpenCLBackend::Creator*>* creators = nullptr;
std::call_once(once, [&]() { creators = new std::map<OpType, OpenCLBackend::Creator*>; });
return creators;
};
OpenCLBackend::OpenCLBackend(BackendConfig::PrecisionMode precision, BackendConfig::PowerMode power)
: Backend(MNN_FORWARD_OPENCL) {
mPrecision = precision;
// Shader precision
if (precision == BackendConfig::Precision_Low) {
mOpenCLRuntime.reset(new OpenCLRuntime(true));
} else {
mOpenCLRuntime.reset(new OpenCLRuntime(false));
}
if(mOpenCLRuntime.get()){
if(mOpenCLRuntime->isCreateError() == true){
mIsCreateError = true;
}
// Mid memory precision
cl_channel_type dataType = CL_HALF_FLOAT;
if (precision == BackendConfig::Precision_High) {
dataType = CL_FLOAT;
}
mImagePool.reset(new ImagePool(mOpenCLRuntime->context(), dataType));
mStaticImagePool.reset(new ImagePool(mOpenCLRuntime->context(), dataType));
mBufferPool.reset(new BufferPool(mOpenCLRuntime->context(), CL_MEM_READ_WRITE));
mBufferPoolInt8.reset(new BufferPoolInt8(mOpenCLRuntime->context(), CL_MEM_READ_WRITE));
std::set<std::string> buildOptions;
mNC4HW4BufferToImageFloat = mOpenCLRuntime->buildKernel("buffer_to_image", "nc4hw4_buffer_to_image", buildOptions);
mNCHWBufferToImageFloat = mOpenCLRuntime->buildKernel("buffer_to_image", "nchw_buffer_to_image", buildOptions);
mNHWCBufferToImageFloat = mOpenCLRuntime->buildKernel("buffer_to_image", "nhwc_buffer_to_image", buildOptions);
mImageToNC4HW4BufferFloat = mOpenCLRuntime->buildKernel("buffer_to_image", "image_to_nc4hw4_buffer", buildOptions);
mImageToNHWCBufferFloat = mOpenCLRuntime->buildKernel("buffer_to_image", "image_to_nhwc_buffer", buildOptions);
mImageToNCHWBufferFloat = mOpenCLRuntime->buildKernel("buffer_to_image", "image_to_nchw_buffer", buildOptions);
}
}
OpenCLBackend::~OpenCLBackend() {
#ifdef LOG_VERBOSE
MNN_PRINT("enter OpenCLBackend::~OpenCLBackend \n");
#endif
}
OpenCLRuntime* OpenCLBackend::getOpenCLRuntime() {
return mOpenCLRuntime.get();
}
bool OpenCLBackend::onAcquireBuffer(const Tensor* nativeTensor, StorageType storageType) {
#ifdef LOG_VERBOSE
MNN_PRINT("Start OpenCLBackend::onAcquireBuffer !\n");
#endif
//int8
if(nativeTensor->getType().code == halide_type_int && nativeTensor->getType().bits == 8){
unsigned int size = nativeTensor->size();
#ifdef LOG_VERBOSE
MNN_PRINT("enter int8 alloc ! size : %d \n", size);
#endif
if (storageType == DYNAMIC_SEPERATE || storageType == STATIC) {
auto buffer = mBufferPoolInt8->alloc(size, true);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)buffer; // fix
return true;
}
if (storageType == DYNAMIC) {
auto buffer = mBufferPoolInt8->alloc(size);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)buffer; // fix
return true;
}
return false;
}
auto tensorShape = OpenCL::tensorShapeFormat(nativeTensor);
int N = tensorShape.at(0);
int H = tensorShape.at(1);
int W = tensorShape.at(2);
int C = tensorShape.at(3);
size_t imageWidth = (size_t)UP_DIV(C, 4) * W;
size_t imageHeight = (size_t)N * H;
const std::vector<size_t> requestShape{imageWidth, imageHeight};
#ifdef LOG_VERBOSE
MNN_PRINT("OpenCLBackend::onAcquireBuffer: [%d, %d, %d, %d], [%d, %d]\n", N, H, W, C, (int)imageWidth,
(int)imageHeight);
#endif
if (storageType == DYNAMIC_SEPERATE) {
auto image = mImagePool->alloc(imageWidth, imageHeight, true);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)image; // fix
return true;
}
if (storageType == DYNAMIC) {
auto image = mImagePool->alloc(imageWidth, imageHeight);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)image; // fix
return true;
}
MNN_ASSERT(storageType == STATIC);
auto image = mStaticImagePool->alloc(imageWidth, imageHeight);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)image; // fix
return true;
}
bool OpenCLBackend::onReleaseBuffer(const Tensor* nativeTensor, StorageType storageType) {
if(nativeTensor->getType().code == halide_type_int && nativeTensor->getType().bits == 8){
return true;
}
if (storageType == DYNAMIC_SEPERATE) {
return true;
}
auto image = (cl::Image*)nativeTensor->deviceId();
if (storageType == DYNAMIC) {
mImagePool->recycle(image);
return true;
}
if (storageType == STATIC) {
mStaticImagePool->recycle(image, true);
}
return true;
}
bool OpenCLBackend::onAllocateBuffer() {
return true;
}
bool OpenCLBackend::onClearBuffer() {
mImagePool->clear();
mBufferPool->clear();
mBufferPoolInt8->clear();
return true;
}
std::pair<float, bool> OpenCLBackend::onMeasure(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs, const MNN::Op* op) {
auto creators = gCreator();
auto iter = creators->find(op->type());
if (iter == creators->end()) {
return std::make_pair(0.0f, false);
}
const float defaultScheduleTime = 0.05f;
auto flops = SizeComputer::computeFlops(op, inputs, outputs);
auto computeFlops = mOpenCLRuntime->flops();
return std::make_pair(defaultScheduleTime + flops / 1024.0f / computeFlops * 1000.0f, true);
}
Execution* OpenCLBackend::onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
const MNN::Op* op) {
#ifdef LOG_VERBOSE
MNN_PRINT("Start OpenCLBackend::onCreate \n");
#endif
auto creators = gCreator();
auto iter = creators->find(op->type());
#if 0
bool res = false;
#define PERMIT(t) if (op->type() == t) res = true
PERMIT(OpType_Convolution);
PERMIT(OpType_Deconvolution);
PERMIT(OpType_Pooling);
PERMIT(OpType_ReLU);
//PERMIT(OpType_Softmax);
PERMIT(OpType_UnaryOp);
//PERMIT(OpType_SoftmaxGrad);
PERMIT(OpType_Conv2DBackPropFilter);
#undef PERMIT
if (!res) {
return nullptr;
}
#endif
if (iter == creators->end()) {
if (nullptr != op->name()) {
MNN_PRINT("Don't support type %s, %s\n", EnumNameOpType(op->type()), op->name()->c_str());
} else {
MNN_PRINT("Don't support type %s\n", EnumNameOpType(op->type()));
}
return NULL;
}
auto maxImageSize = mOpenCLRuntime->getMaxImage2DSize();
bool valid = true;
for (auto t : inputs) {
int imageHeight = t->batch() * t->height();
int imageWidth = t->width() * UP_DIV(t->channel(), 4);
if (imageHeight > maxImageSize.at(0) || imageWidth > maxImageSize.at(1)) {
valid = false;
break;
}
}
for (auto t : outputs) {
int imageHeight = t->batch() * t->height();
int imageWidth = t->width() * UP_DIV(t->channel(), 4);
if (imageHeight > maxImageSize.at(0) || imageWidth > maxImageSize.at(1)) {
valid = false;
break;
}
}
if (!valid) {
MNN_PRINT("beyond cl_image creat size! fallback to cpu backend\n");
return NULL;
}
auto exe = iter->second->onCreate(inputs, outputs, op, this);
if (NULL == exe) {
if (nullptr != op->name()) {
MNN_PRINT("The Creator Don't support type %d, %s\n", op->type(), op->name()->c_str());
} else {
// MNN_PRINT("The Creator Don't support type %s\n", EnumNameOpType(op->type()));
}
return NULL;
}
#ifdef LOG_VERBOSE
MNN_PRINT("End OpenCLBackend::onCreate \n");
#endif
return exe;
}
void OpenCLBackend::onResizeBegin() {
mOpenCLRuntime->setCommandQueueProfileEnable();
}
void OpenCLBackend::onResizeEnd() {
mOpenCLRuntime->setCommandQueueProfileDisable();
}
void OpenCLBackend::onExecuteBegin() const {
mOpenCLRuntime->mQueueCount = 0;
}
void OpenCLBackend::onExecuteEnd() const {
mOpenCLRuntime->mQueueCount = 0;
}
bool OpenCLBackend::onWaitFinish() {
int rc = mOpenCLRuntime.get()->commandQueue().finish();
return rc == 0;
}
bool OpenCLBackend::isCreateError() const {
return mIsCreateError;
}
void OpenCLBackend::_allocHostBuffer(int length) const {
MNN_ASSERT(length > 0);
if (nullptr != mHostBuffer.second && length <= mHostBuffer.first) {
return;
}
mHostBuffer.first = length;
mHostBuffer.second.reset(
new cl::Buffer(mOpenCLRuntime->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR, length));
}
void OpenCLBackend::copyFromDeviceInt8(const Tensor* srcTensor, const Tensor* dstTensor) const{
auto needSize = dstTensor->size();
auto hostPtr = dstTensor->host<float>();
cl_int error = CL_SUCCESS;
auto DeviceBuffer = (cl::Buffer*)srcTensor->deviceId();
mOpenCLRuntime->commandQueue().enqueueReadBuffer(*DeviceBuffer, CL_TRUE, 0, needSize, hostPtr);
}
void OpenCLBackend::copyToDeviceInt8(const Tensor* srcTensor, const Tensor* dstTensor) const{
auto needSize = srcTensor->size();
auto hostPtr = srcTensor->host<int8_t>();
cl_int error = CL_SUCCESS;
auto DeviceBuffer = (cl::Buffer*)dstTensor->deviceId();
mOpenCLRuntime->commandQueue().enqueueWriteBuffer(*DeviceBuffer, CL_TRUE, 0, needSize, hostPtr);
}
void OpenCLBackend::copyFromDevice(const Tensor* srcTensor, const Tensor* dstTensor) const{
std::vector<int> bufferShape = MNN::OpenCL::tensorShapeFormat(srcTensor);
MNN::Tensor interBuffer(0, Tensor::TENSORFLOW);
interBuffer.buffer().dimensions = bufferShape.size();
for (int i = 0; i < bufferShape.size(); i++) {
interBuffer.buffer().dim[i].extent = bufferShape.at(i);
}
auto needSize = dstTensor->size();
_allocHostBuffer(needSize);
interBuffer.buffer().device = (uint64_t)mHostBuffer.second.get();
MNN_DATA_FORMAT data_format = TensorUtils::getDescribe(dstTensor)->dimensionFormat;
switch (data_format) {
case MNN_DATA_FORMAT_NHWC:
OpenCL::convertImageToNHWCBuffer(srcTensor, &interBuffer,
*const_cast<cl::Kernel*>(&mImageToNHWCBufferFloat), mOpenCLRuntime.get());
break;
case MNN_DATA_FORMAT_NCHW:
OpenCL::convertImageToNCHWBuffer(srcTensor, &interBuffer,
*const_cast<cl::Kernel*>(&mImageToNCHWBufferFloat), mOpenCLRuntime.get());
break;
case MNN_DATA_FORMAT_NC4HW4:
OpenCL::convertImageToNC4HW4Buffer(
srcTensor, &interBuffer, *const_cast<cl::Kernel*>(&mImageToNC4HW4BufferFloat), mOpenCLRuntime.get());
break;
default:
break;
}
auto hostPtr = dstTensor->host<float>();
cl_int error = CL_SUCCESS;
mOpenCLRuntime->commandQueue().enqueueReadBuffer(*mHostBuffer.second, CL_TRUE, 0, needSize, hostPtr);
}
void OpenCLBackend::copyToDevice(const Tensor* srcTensor, const Tensor* dstTensor) const{
std::vector<int> bufferShape = MNN::OpenCL::tensorShapeFormat(srcTensor);
MNN::Tensor interBuffer(0, Tensor::TENSORFLOW);
interBuffer.buffer().dimensions = bufferShape.size();
for (int i = 0; i < bufferShape.size(); i++) {
interBuffer.buffer().dim[i].extent = bufferShape.at(i);
}
auto needSize = srcTensor->size();
_allocHostBuffer(needSize);
interBuffer.buffer().device = (uint64_t)mHostBuffer.second.get();
auto hostPtr = srcTensor->host<float>();
cl_int error = CL_SUCCESS;
mOpenCLRuntime->commandQueue().enqueueWriteBuffer(*mHostBuffer.second, CL_FALSE, 0, needSize, hostPtr);
// Host -> OpenCL
MNN_DATA_FORMAT data_format = TensorUtils::getDescribe(srcTensor)->dimensionFormat;
if (MNN_DATA_FORMAT_NHWC == data_format) {
OpenCL::convertNHWCBufferToImage(&interBuffer, const_cast<Tensor*>(dstTensor),
*const_cast<cl::Kernel*>(&mNHWCBufferToImageFloat), mOpenCLRuntime.get());
return;
}
if (MNN_DATA_FORMAT_NCHW == data_format) {
OpenCL::convertNCHWBufferToImage(&interBuffer, const_cast<Tensor*>(dstTensor),
*const_cast<cl::Kernel*>(&mNCHWBufferToImageFloat), mOpenCLRuntime.get());
return;
}
if (MNN_DATA_FORMAT_NC4HW4 == data_format) {
OpenCL::convertNC4HW4BufferToImage(&interBuffer, const_cast<Tensor*>(dstTensor),
*const_cast<cl::Kernel*>(&mNC4HW4BufferToImageFloat),
mOpenCLRuntime.get());
return;
}
MNN_ASSERT(false);
return;
}
void OpenCLBackend::onCopyBuffer(const Tensor* srcTensor, const Tensor* dstTensor) const {
#ifdef LOG_VERBOSE
MNN_PRINT("Start onCopyBuffer !\n");
#endif
//int8
if(srcTensor->getType().code == halide_type_int && srcTensor->getType().bits == 8){
if (srcTensor->deviceId() == 0 && dstTensor->deviceId() != 0) {
copyToDeviceInt8(srcTensor, dstTensor);
}else if(srcTensor->deviceId() != 0 && dstTensor->deviceId() == 0){
copyFromDeviceInt8(srcTensor, dstTensor);
}else{
MNN_PRINT("onCopyBuffer int8 error !!! \n");
}
}else{
if (srcTensor->deviceId() == 0 && dstTensor->deviceId() != 0) {
copyToDevice(srcTensor, dstTensor);
}else if(srcTensor->deviceId() != 0 && dstTensor->deviceId() == 0){
copyFromDevice(srcTensor, dstTensor);
}else{
MNN_PRINT("onCopyBuffer float error !!! \n");
}
}
#ifdef LOG_VERBOSE
MNN_PRINT("end onCopyBuffer !\n");
#endif
}
bool OpenCLBackend::addCreator(OpType t, Creator* c) {
auto map = gCreator();
if (map->find(t) != map->end()) {
MNN_PRINT("Error: %d type has be added\n", t);
return false;
}
map->insert(std::make_pair(t, c));
return true;
}
class CLBackendCreator : public BackendCreator {
public:
virtual Backend* onCreate(const Backend::Info& info) const override {
#ifdef MNN_USE_LIB_WRAPPER
OpenCLSymbolsOperator::createOpenCLSymbolsOperatorSingleInstance();
if (nullptr == OpenCLSymbolsOperator::getOpenclSymbolsPtr()) {
MNN_PRINT("OpenCL init error , callback ... \n");
return nullptr;
}
if (true == OpenCLSymbolsOperator::getOpenclSymbolsPtr()->isError()) {
MNN_PRINT("parsing symbols error !!! \n");
return nullptr;
}
#endif
BackendConfig::PrecisionMode precision = BackendConfig::Precision_Normal;
BackendConfig::PowerMode power = BackendConfig::Power_Normal;
if (nullptr != info.user) {
precision = info.user->precision;
power = info.user->power;
}
auto backend = new OpenCLBackend(precision, power);
if(backend != nullptr){
if(!backend->isCreateError()){
return backend;
}else{
delete backend;
}
}
return nullptr;
}
};
static const auto __opencl_global_initializer = []() {
MNNInsertExtraBackendCreator(MNN_FORWARD_OPENCL, new CLBackendCreator, true);
return true;
}();
} // namespace OpenCL
} // namespace MNN
Reference in New Issue View Git Blame Copy Permalink