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MNN/source/backend/opencl/core/OpenCLBackend.cpp

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//
// 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/BufferAllocator.hpp"
#include "core/TensorUtils.hpp"
#include "shape/SizeComputer.hpp"
#include <map>
#include <mutex>
#include <thread>
#include "core/Macro.h"
#include "runtime/OpenCLTuneInfo.hpp"
#ifdef __ANDROID__
#include <GLES2/gl2.h>
#endif
//#define OPENCL_FALLBACK_LOG
namespace MNN {
namespace OpenCL {
#ifndef MNN_OPENCL_SEP_BUILD
void registerOpenCLOps();
#endif
CLRuntime::CLRuntime(const Backend::Info& info, int platformSize, int platformId, int deviceId, void *contextPtr, void* glshared){
mInfo = info;
BackendConfig::PrecisionMode precision = BackendConfig::Precision_Normal;
BackendConfig::PowerMode power = BackendConfig::Power_Normal;
BackendConfig::MemoryMode memory = BackendConfig::Memory_Normal;
if (nullptr != mInfo.user) {
precision = mInfo.user->precision;
power = mInfo.user->power;
memory = mInfo.user->memory;
}
// Shader precision
mOpenCLRuntime.reset(new OpenCLRuntime(precision, mInfo.gpuMode, platformSize, platformId, deviceId, contextPtr, glshared, hint()));
//Whether runtimeError
mCLRuntimeError = mOpenCLRuntime->isCreateError();
mPrecision = precision;
mMemory = memory;
mTunedInfo = new TuneInfo;
mImagePool.reset(new ImagePool(mOpenCLRuntime->context()));
mBufferPool.reset(new BufferPool(mOpenCLRuntime->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR));
}
CLRuntime::~CLRuntime() {
mImagePool = nullptr;
mBufferPool = nullptr;
mOpenCLRuntime = nullptr;
delete mTunedInfo;
}
static bool _checkTensorInfo(const CLCache::TensorInfoT* dst, const Tensor* src) {
if (dst->shape.size() != src->dimensions()) {
return false;
}
for (int j=0; j<dst->shape.size(); ++j) {
if (dst->shape[j] != src->length(j)) {
return false;
}
}
return true;
}
bool CLRuntime::onMeasure(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
const MNN::Op* op, Runtime::OpInfo& dstInfo) const {
dstInfo.initCostLong = true;
if (nullptr == op->name()) {
dstInfo.initCostLong = false;
return true;
}
for(auto& info : mTunedInfo->mInfos) {
if (info->type != op->type()) {
continue;
}
if (info->name != op->name()->str()) {
continue;
}
if (info->inputs.size() != inputs.size() || info->outputs.size() != outputs.size()) {
continue;
}
bool match = true;
for (int i=0; i<inputs.size(); ++i) {
auto& dst = info->inputs[i];
auto src = inputs[i];
if (!_checkTensorInfo(dst.get(), src)) {
match = false;
break;
}
}
if (!match) {
continue;
}
for (int i=0; i<outputs.size(); ++i) {
auto& dst = info->outputs[i];
auto src = outputs[i];
if (!_checkTensorInfo(dst.get(), src)) {
match = false;
break;
}
}
if (match) {
// All Info is match
dstInfo.initCostLong = false;
break;
}
}
return true;
}
int CLRuntime::onGetRuntimeStatus(RuntimeStatus statusEnum) const {
switch (statusEnum) {
case STATUS_SUPPORT_FP16: {
return mOpenCLRuntime->isDeviceSupportedFP16();
break;
}
case STATUS_SUPPORT_DOT_PRODUCT: {
return 0;
break;
}
case STATUS_SUPPORT_POWER_LOW: {
return mOpenCLRuntime->isDeviceSupportedLowPower();
break;
}
default: {
MNN_ERROR("unsupported interface");
break;
}
}
return 0;
}
void CLRuntime::onMaskOpReady(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
const MNN::Op* op) {
if (nullptr != op->name()) {
auto dstInfo = mTunedInfo;
std::unique_ptr<CLCache::OpInfoT> opInfo(new CLCache::OpInfoT);;
opInfo->type = op->type();
opInfo->name = op->name()->str();
opInfo->inputs.resize(inputs.size());
for (int v=0; v<opInfo->inputs.size(); ++v) {
opInfo->inputs[v].reset(new CLCache::TensorInfoT);
opInfo->inputs[v]->shape.resize(inputs[v]->dimensions());
for (int u=0; u<opInfo->inputs[v]->shape.size(); ++u) {
opInfo->inputs[v]->shape[u] = inputs[v]->length(u);
}
}
opInfo->outputs.resize(outputs.size());
for (int v=0; v<opInfo->outputs.size(); ++v) {
opInfo->outputs[v].reset(new CLCache::TensorInfoT);
opInfo->outputs[v]->shape.resize(outputs[v]->dimensions());
for (int u=0; u<opInfo->outputs[v]->shape.size(); ++u) {
opInfo->outputs[v]->shape[u] = outputs[v]->length(u);
}
}
dstInfo->mInfos.emplace_back(std::move(opInfo));
}
}
void CLRuntime::onReset(int numberThread, const BackendConfig* config, bool full) {
mOpenCLRuntime->setGpuMode(numberThread);
}
bool CLRuntime::onSetCache(const void* buffer, size_t size) {
if (nullptr == buffer) {
return false;
}
auto cacheBuffer = CLCache::GetCache(buffer);
flatbuffers::Verifier verify((const uint8_t*)buffer, size);
if (false == CLCache::VerifyCacheBuffer(verify)) {
return false;
}
if(nullptr != cacheBuffer->tuned()) {
for (int i=0; i<cacheBuffer->tuned()->size(); ++i) {
auto srcInfo = cacheBuffer->tuned()->GetAs<CLCache::OpInfo>(i);
std::unique_ptr<CLCache::OpInfoT> dst(srcInfo->UnPack());
mTunedInfo->mInfos.emplace_back(std::move(dst));
}
}
bool res = mOpenCLRuntime->setCache(std::make_pair(buffer, size));
return res;
}
std::pair<const void*, size_t> CLRuntime::onGetCache() {
return mOpenCLRuntime->makeCache(mTunedInfo);
}
Backend* CLRuntime::onCreate(const BackendConfig* config, Backend* origin) const {
auto precision = mPrecision;
auto memory = mMemory;
if (nullptr != config) {
precision = config->precision;
memory = config->memory;
}
return new OpenCLBackend(precision, memory, mImagePool, mBufferPool, this);
}
void CLRuntime::onGabageCollect(int level) {
mImagePool->releaseFreeList();
mBufferPool->releaseFreeList();
}
float CLRuntime::onGetMemoryInMB() {
auto staticMemoryInMB = mBufferPool->totalSize() / 1024.0f / 1024.0f;
return staticMemoryInMB;
}
bool CLRuntime::isCLRuntimeError() {
return mCLRuntimeError;
}
std::map<std::pair<OpType, GpuMemObject>, OpenCLBackend::Creator*>* gCreator() {
static std::once_flag once;
static std::map<std::pair<OpType, GpuMemObject>, OpenCLBackend::Creator*>* creators = nullptr;
std::call_once(once, [&]() { creators = new std::map<std::pair<OpType, GpuMemObject>, OpenCLBackend::Creator*>; });
return creators;
};
OpenCLBackend::OpenCLBackend(BackendConfig::PrecisionMode precision, BackendConfig::MemoryMode memory, std::shared_ptr<ImagePool>imgPool, std::shared_ptr<BufferPool> bufPool, const CLRuntime *runtime)
: Backend(MNN_FORWARD_OPENCL) {
mCLRuntime = runtime;
mOpenCLRuntime = mCLRuntime->mOpenCLRuntime;
mPrecision = precision;
mMemory = memory;
mOpenCLRuntime->setPrecision(precision);
mStaticImagePool = imgPool;
mStaticBufferPool = bufPool;
if(mOpenCLRuntime.get()){
if(mOpenCLRuntime->isCreateError() == true) {
mIsCreateError = true;
}
mImagePoolFirst.reset(new ImagePool(mOpenCLRuntime->context()));
mBufferPoolFirst.reset(new BufferPool(mOpenCLRuntime->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR));
mExecutionBufferPool.reset(new BufferExecutionPool(mOpenCLRuntime->context(), mOpenCLRuntime->commandQueue(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR));
mImagePool = mImagePoolFirst.get();
mBufferPool = mBufferPoolFirst.get();
}
mMapMem = std::make_pair(0, nullptr);
mUseRecordQueue = mOpenCLRuntime->isSupportRecordQueue();
mDevideOpRecord = mOpenCLRuntime->isDevideOpRecord();
mUseRecordableQueueSize = mOpenCLRuntime->getUseRecordableQueueSize();
}
OpenCLBackend::~OpenCLBackend() {
#ifdef LOG_VERBOSE
MNN_PRINT("enter OpenCLBackend::~OpenCLBackend \n");
#endif
releaseRecord();
mRecordings.clear();
mImagePool = nullptr;
mBufferPool = nullptr;
mExecutionBufferPool->clear();
if(mMapMem.second != nullptr) {
#ifdef MNN_OPENCL_SVM_ENABLE
if(mUseSvm)
{
clSVMFree(mOpenCLRuntime->context().get(), mMapMem.second);
}
else
#endif
{
free(mMapMem.second);
mMapMem.second = nullptr;
}
}
}
OpenCLRuntime* OpenCLBackend::getOpenCLRuntime() {
return mOpenCLRuntime.get();
}
class CLReleaseExecutionBuffer : public Backend::MemObj {
public:
CLReleaseExecutionBuffer(std::shared_ptr<OpenCLBufferNode> node, BufferExecutionPool* bufferPool) {
mNode = node;
mBufferPool = bufferPool;
}
virtual ~ CLReleaseExecutionBuffer() {
mBufferPool->recycle(mNode);
}
private:
std::shared_ptr<OpenCLBufferNode> mNode;
BufferExecutionPool* mBufferPool;
};
class CLMemReleaseBuffer : public Backend::MemObj {
public:
CLMemReleaseBuffer(cl::Buffer* bId, BufferPool* bufferPool) {
mBuffer = bId;
mBufferPool = bufferPool;
}
virtual ~ CLMemReleaseBuffer() {
mBufferPool->recycle(mBuffer);
}
private:
cl::Buffer* mBuffer;
BufferPool* mBufferPool;
};
class CLMemReleaseImage : public Backend::MemObj {
public:
CLMemReleaseImage(cl::Image* bId, ImagePool* bufferPool) {
mBuffer = bId;
mBufferPool = bufferPool;
}
virtual ~ CLMemReleaseImage() {
mBufferPool->recycle(mBuffer);
}
private:
cl::Image* mBuffer;
ImagePool* mBufferPool;
};
float OpenCLBackend::getBytes(const Tensor* tensor) {
float bytes = (float)tensor->getType().bytes();
if (getOpenCLRuntime()->isSupportedFP16()) {// Fp16
if (halide_type_float == tensor->getType().code) {
bytes = 2.0;
}
}
auto quant = TensorUtils::getDescribe(tensor)->quantAttr.get();
if (nullptr != quant && TensorUtils::getDescribe(tensor)->type == DataType_DT_INT8) {
bytes = 1.0;
}
if(tensor->getType().bits == 4) {
bytes = 0.5;
}
return bytes;
}
Backend::MemObj* OpenCLBackend::onAcquire(const Tensor* nativeTensor, StorageType storageType) {
#ifdef LOG_VERBOSE
MNN_PRINT("Start OpenCLBackend::onAcquireBuffer !\n");
#endif
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);
#ifdef LOG_VERBOSE
MNN_PRINT("OpenCLBackend::onAcquireBuffer: NHWC:[%d, %d, %d, %d]\n", N, H, W, C);
#endif
#ifndef MNN_OPENCL_BUFFER_CLOSED
if(mOpenCLRuntime->getGpuMemType() == BUFFER) {
size_t size;
float typeSize = getBytes(nativeTensor);
if (MNN_DATA_FORMAT_NC4HW4 == TensorUtils::getDescribe(nativeTensor)->dimensionFormat && nativeTensor->dimensions() >= 2) {
auto alignC = ROUND_UP(C, 4);
// increment of height and width
auto hR = ROUND_UP(H + 3, 4) - H;
auto wR = ROUND_UP(W + 3, 4) - W;
size = N * alignC * W * H;
size = size + hR * W * 4 + wR * 4;
} else {
size = N * H * W * C;
size = ROUND_UP(size, 4);
}
if (mOpenCLRuntime->isSupportedIntelSubgroup()) {
int cPack = TensorUtils::getTensorChannelPack(nativeTensor);
auto pads = TensorUtils::getDescribe(nativeTensor)->mPads;
size_t imageWidth = (size_t) ROUND_UP(UP_DIV(C, cPack), 2) * ROUND_UP(pads.left + W + pads.right, 4);//C-round to 8,W-round to 4, for memory alloc
size_t imageHeight = (size_t)N * H;
size = imageWidth*imageHeight*cPack;
}
// Align when int4 memory
size = ROUND_UP(size, 2);
if (storageType == DYNAMIC_SEPERATE) {
auto buffer = mBufferPool->alloc(size*typeSize, true);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)buffer;
return new CLMemReleaseBuffer(buffer, mBufferPool);
}
if (storageType == DYNAMIC) {
auto buffer = mBufferPool->alloc(size*typeSize);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)buffer;
return new CLMemReleaseBuffer(buffer, mBufferPool);
}
if (storageType == DYNAMIC_IN_EXECUTION){
auto node = mExecutionBufferPool->alloc(size*typeSize);
((Tensor*)nativeTensor)->buffer().device = reinterpret_cast<uint64_t>(node.get());
return new CLReleaseExecutionBuffer(node, mExecutionBufferPool.get());
}
MNN_ASSERT(storageType == STATIC);
auto buffer = mStaticBufferPool->alloc(size*typeSize);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)buffer; // fix
return new CLMemReleaseBuffer(buffer, mStaticBufferPool.get());
}
else
#endif /* MNN_OPENCL_BUFFER_CLOSED */
{
size_t imageWidth = (size_t) (UP_DIV(C, 4) * W);//image mode only C pack to 4
size_t imageHeight = (size_t)N * H;
cl_channel_type dataType = CL_HALF_FLOAT;
if(nativeTensor->getType().code == halide_type_int) {
if(nativeTensor->getType().bits == 8){
dataType = CL_SIGNED_INT8;
} else if(nativeTensor->getType().bits == 32){
dataType = CL_SIGNED_INT32;
}
} else if(nativeTensor->getType().code == halide_type_uint){
if(nativeTensor->getType().bits == 8){
dataType = CL_UNSIGNED_INT8;
} else if(nativeTensor->getType().bits == 32){
dataType = CL_UNSIGNED_INT32;
}
} else {
//when user want high precision, use float datatype
if (mPrecision == BackendConfig::Precision_High) {
dataType = CL_FLOAT;
}
}
if (storageType == DYNAMIC_SEPERATE) {
auto image = mImagePool->alloc(imageWidth, imageHeight, dataType, true);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)image; // fix
return new CLMemReleaseImage(image, mImagePool);
}
if (storageType == DYNAMIC) {
auto image = mImagePool->alloc(imageWidth, imageHeight, dataType);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)image; // fix
return new CLMemReleaseImage(image, mImagePool);
}
MNN_ASSERT(storageType == STATIC);
auto image = mStaticImagePool->alloc(imageWidth, imageHeight, dataType);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)image; // fix
return new CLMemReleaseImage(image, mStaticImagePool.get());
}
}
bool OpenCLBackend::onSelectDynamicAllocator(int index, int maxIndex) {
if (mUseRecordQueue && false == mDevideOpRecord){
return false;
}
if (maxIndex > 2) {
return false;
}
if (maxIndex > 1 && mImagePoolSecond.get() == nullptr) {
mImagePoolSecond.reset(new ImagePool(mOpenCLRuntime->context()));
mBufferPoolSecond.reset(new BufferPool(mOpenCLRuntime->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR));
}
if (index == 0) {
mImagePool = mImagePoolFirst.get();
mBufferPool = mBufferPoolFirst.get();
} else if (index == 1) {
mImagePool = mImagePoolSecond.get();
mBufferPool = mBufferPoolSecond.get();
}
return true;
}
bool OpenCLBackend::onClearBuffer() {
mImagePool->clear();
mBufferPool->clear();
if(mMapMem.second != nullptr) {
#ifdef MNN_OPENCL_SVM_ENABLE
if(mUseSvm)
{
clSVMFree(mOpenCLRuntime->context().get(), mMapMem.second);
}
else
#endif
{
free(mMapMem.second);
mMapMem.second = nullptr;
}
}
return 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(std::make_pair(op->type(), mOpenCLRuntime->getGpuMemType()));
if (0 != inputs.size() && (getDataType(inputs[0]) == DataType_DT_INT8 || inputs[0]->getType().bytes() == 1)) {
#ifdef OPENCL_FALLBACK_LOG
MNN_PRINT("Don't support type %s for int8 input\n", EnumNameOpType(op->type()));
#endif
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 NULL;
}
if (iter == creators->end()) {
mDevideOpRecord = true;
#ifdef OPENCL_FALLBACK_LOG
if (nullptr != op->name()) {
MNN_PRINT("Don't support type %s memObject:%d, %s\n", EnumNameOpType(op->type()), mOpenCLRuntime->getGpuMemType(), op->name()->c_str());
} else {
MNN_PRINT("Don't support type %s memObject:%d\n", EnumNameOpType(op->type()), mOpenCLRuntime->getGpuMemType());
}
#endif
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 NULL;
}
if(mOpenCLRuntime->getGpuMemType() == IMAGE) {
auto maxImageSize = mOpenCLRuntime->getMaxImage2DSize();
bool valid = true;
for (auto t : inputs) {
auto tensorShape = OpenCL::tensorShapeFormat(t);
int imageHeight = tensorShape[0] * tensorShape[1];
int imageWidth = tensorShape[2] * UP_DIV(tensorShape[3], 4);
if (imageHeight > maxImageSize.at(0) || imageWidth > maxImageSize.at(1)) {
valid = false;
break;
}
}
for (auto t : outputs) {
auto tensorShape = OpenCL::tensorShapeFormat(t);
int imageHeight = tensorShape[0] * tensorShape[1];
int imageWidth = tensorShape[2] * UP_DIV(tensorShape[3], 4);
if (imageHeight > maxImageSize.at(0) || imageWidth > maxImageSize.at(1)) {
valid = false;
break;
}
}
if (!valid) {
mDevideOpRecord = true;
#ifdef OPENCL_FALLBACK_LOG
for (auto t : inputs) {
auto tensorShape = OpenCL::tensorShapeFormat(t);
MNN_PRINT("input n:%d, h:%d, w:%d, c:%d\n", tensorShape[0], tensorShape[1], tensorShape[2], tensorShape[3]);
}
for (auto t : outputs) {
auto tensorShape = OpenCL::tensorShapeFormat(t);
MNN_PRINT("output n:%d, h:%d, w:%d, c:%d\n", tensorShape[0], tensorShape[1], tensorShape[2], tensorShape[3]);
}
MNN_PRINT("beyond cl_image creat size! fallback to cpu backend\n");
#endif
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 NULL;
}
}
auto exe = iter->second->onCreate(inputs, outputs, op, this);
if (NULL == exe) {
mDevideOpRecord = true;
#ifdef OPENCL_FALLBACK_LOG
if (nullptr != op->name()) {
MNN_PRINT("The Creator Don't support type %s, memObject:%d, %s\n", MNN::EnumNameOpType(op->type()), mOpenCLRuntime->getGpuMemType(), op->name()->c_str());
} else {
MNN_PRINT("The Creator Don't support type %s, memObject:%d,\n", EnumNameOpType(op->type()), mOpenCLRuntime->getGpuMemType());
}
#endif
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 NULL;
}
#ifdef LOG_VERBOSE
MNN_PRINT("End OpenCLBackend::onCreate \n");
#endif
return exe;
}
void OpenCLBackend::onResizeBegin() {
#ifndef ENABLE_OPENCL_TIME_PROFILER
mOpenCLRuntime->setCommandQueueProfileEnable();
#endif
// update mUseRecordableQueueSize if hint has changed
mUseRecordableQueueSize = mCLRuntime->hint().encorderNumForCommit <= mUseRecordableQueueSize ? mCLRuntime->hint().encorderNumForCommit : mUseRecordableQueueSize;
releaseRecord();
}
ErrorCode OpenCLBackend::onResizeEnd() {
#ifndef ENABLE_OPENCL_TIME_PROFILER
mOpenCLRuntime->setCommandQueueProfileDisable();
#endif
if(!mRecordings.empty()){
endRecord(mRecordings.back().record, true);
}
return NO_ERROR;
}
void OpenCLBackend::onExecuteBegin() const {
mOpenCLRuntime->mQueueCount = 0;
clearRecord();
mOpenCLRuntime->clearEvent();
}
void OpenCLBackend::onExecuteEnd() const {
mOpenCLRuntime->mQueueCount = 0;
clearRecord();
enqeueRecord();
mOpenCLRuntime->printEventTime();
}
bool OpenCLBackend::isCreateError() const {
return mIsCreateError;
}
bool OpenCLBackend::_allocHostBuffer(int length, const Tensor* srcTensor) const {
auto memType = srcTensor->buffer().flags;
if (nullptr != mHostBuffer.second && length <= mHostBuffer.first && memType != MNN_MEMORY_AHARDWAREBUFFER) {
return true;
}
cl_int error;
#ifdef __ANDROID__
if(MNN_MEMORY_AHARDWAREBUFFER == memType){
if (mOpenCLRuntime->isSupportAHD()){
CLSharedMemReleaseBuffer *sharedMem = (CLSharedMemReleaseBuffer*)TensorUtils::getSharedMem(srcTensor);
if(sharedMem == nullptr || (sharedMem != nullptr && srcTensor->buffer().device != sharedMem->getSharedId())){
if(mOpenCLRuntime->getGpuType() == MALI){
const cl_import_properties_arm properties[] = {CL_IMPORT_TYPE_ARM, CL_IMPORT_TYPE_ANDROID_HARDWARE_BUFFER_ARM, 0};
Backend::MemObj* SharedTmp = new CLSharedMemReleaseBuffer(srcTensor->buffer().device, new cl::Buffer(mOpenCLRuntime->context(), (cl_mem_flags)CL_MEM_READ_WRITE, properties, (void*)srcTensor->buffer().device, CL_IMPORT_MEMORY_WHOLE_ALLOCATION_ARM, &error));
TensorUtils::setSharedMem(srcTensor, SharedTmp);
}else if(mOpenCLRuntime->getGpuType() == ADRENO){
cl_mem_ahardwarebuffer_host_ptr myAHBmem = {0};
myAHBmem.ext_host_ptr.allocation_type = CL_MEM_ANDROID_AHARDWAREBUFFER_HOST_PTR_QCOM;
myAHBmem.ext_host_ptr.host_cache_policy = CL_MEM_HOST_WRITEBACK_QCOM;
myAHBmem.ahb_ptr = (AHardwareBuffer*)srcTensor->buffer().device;
Backend::MemObj* SharedTmp = new CLSharedMemReleaseBuffer(srcTensor->buffer().device, new cl::Buffer(mOpenCLRuntime->context(), (cl_mem_flags)(CL_MEM_USE_HOST_PTR | CL_MEM_EXT_HOST_PTR_QCOM), 0, &myAHBmem, &error));
TensorUtils::setSharedMem(srcTensor, SharedTmp);
} else{
MNN_ERROR("This device not support AHardWareBuffer\n");
return false;
}
if (error != CL_SUCCESS) {
MNN_ERROR("Alloc mAHardWareBuffer error, code:%d \n", error);
return false;
}
}
} else{
MNN_ERROR("This device not support AHardWareBuffer\n");
return false;
}
} else
#endif
{
MNN_ASSERT(length > 0);
mHostBuffer.first = length;
mHostBuffer.second.reset(new cl::Buffer(mOpenCLRuntime->context(), (cl_mem_flags)(CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR), (size_t)length, NULL, &error));
if (nullptr == mHostBuffer.second.get() || error != CL_SUCCESS) {
MNN_ERROR("Alloc mHostBuffer %d error, code:%d \n", length, error);
return false;
}
}
return true;
}
void OpenCLBackend::copyFromDeviceInt8(const Tensor* srcTensor, const Tensor* dstTensor) const{
std::vector<int> bufferShape = MNN::OpenCL::tensorShapeFormat(dstTensor);
auto needSize = dstTensor->size();
auto hostPtr = dstTensor->host<int8_t>();
auto DeviceBuffer = (cl::Buffer*)srcTensor->deviceId();
cl_int error = CL_SUCCESS;
#ifndef MNN_OCL_QUANT_DUMP
error = mOpenCLRuntime->commandQueue().enqueueReadBuffer(*DeviceBuffer, CL_TRUE, 0, needSize, hostPtr);
MNN_ASSERT(error == 0);
#else//for dump test
int8_t* tmpPtr = (int8_t *)malloc(needSize);
error = mOpenCLRuntime->commandQueue().enqueueReadBuffer(*DeviceBuffer, CL_TRUE, 0, needSize, tmpPtr);
MNN_ASSERT(error == 0);
int C_4 = (bufferShape[3]+3)/4;
for(int n=0; n<bufferShape[0]; n++) {
for(int c=0; c<bufferShape[3]; c++) {
for(int h=0; h<bufferShape[1]; h++) {
for(int w=0; w<bufferShape[2]; w++) {
hostPtr[n*bufferShape[3]*bufferShape[1]*bufferShape[2] + c*bufferShape[1]*bufferShape[2] + h*bufferShape[2] + w] =
tmpPtr[n*C_4*bufferShape[1]*bufferShape[2]*4 + (c/4)*bufferShape[1]*bufferShape[2]*4 + h*bufferShape[2]*4 + w*4 + c%4];
}
}
}
}
if(tmpPtr != nullptr) {
free(tmpPtr);
tmpPtr = nullptr;
}
#endif
#ifdef ENABLE_OPENCL_TIME_PROFILER
MNN_PRINT("total kernel time:%d us\n", (int)mOpenCLRuntime->mKernelTime);
#endif
}
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);
}
int OpenCLBackend::onSync(Tensor::MapType mtype, bool toCpu, const Tensor* dstTensor) {
if (toCpu) {
mOpenCLRuntime->commandQueue().finish();
}
return 0;
}
void CLRuntime::convertFromDevice(const Tensor* srcTensor, const Tensor* dstTensor, MNN_DATA_FORMAT data_format, bool svmFlag, int memtype) const {
#ifdef __ANDROID__
if(MNN_MEMORY_AHARDWAREBUFFER == memtype){
convertBetweenAHDandCLmem(const_cast<Tensor*>(srcTensor), const_cast<Tensor*>(dstTensor), mOpenCLRuntime.get(), memtype, false, true);
return;
}
#endif
#ifndef MNN_OPENCL_BUFFER_CLOSED
if(mOpenCLRuntime->getGpuMemType() == BUFFER)
{
#ifdef MNN_SUPPORT_INTEL_SUBGROUP
int cPack = TensorUtils::getTensorChannelPack(srcTensor);
if (cPack == 16 && mOpenCLRuntime->isSupportedIntelSubgroup()) {
switch (data_format) {
case MNN_DATA_FORMAT_NHWC:
OpenCL::convertNC4HW4OrNC16HW16BufferToNCHWOrNHWCBuffer(srcTensor, const_cast<Tensor*>(dstTensor),
"nc16hw16_buffer_to_nhwc_buffer", mOpenCLRuntime.get(), true, false, svmFlag);
break;
case MNN_DATA_FORMAT_NCHW:
OpenCL::convertNC4HW4OrNC16HW16BufferToNCHWOrNHWCBuffer(srcTensor, const_cast<Tensor*>(dstTensor),
"nc16hw16_buffer_to_nchw_buffer", mOpenCLRuntime.get(), true, false, svmFlag);
break;
case MNN_DATA_FORMAT_NC4HW4:
OpenCL::convertNC4HW4BufferBetweenNC16HW16Buffer(srcTensor, const_cast<Tensor*>(dstTensor),
"nc16hw16_buffer_to_nc4hw4_buffer", mOpenCLRuntime.get(), OutTrans, false, svmFlag, false, true);
break;
default:
MNN_PRINT("output data format not support for subgroup!\n");
break;
}
} else
#endif
OpenCL::convertBufferToBuffer(const_cast<Tensor*>(srcTensor), const_cast<Tensor*>(dstTensor), mOpenCLRuntime.get(), false, true, true, svmFlag);
}
else
#endif /* MNN_OPENCL_BUFFER_CLOSED */
{
switch (data_format) {
case MNN_DATA_FORMAT_NHWC:
OpenCL::convertImageToNHWCBuffer(srcTensor, const_cast<Tensor*>(dstTensor), mOpenCLRuntime.get(), false, svmFlag);
break;
case MNN_DATA_FORMAT_NCHW:
OpenCL::convertImageToNCHWBuffer(srcTensor, const_cast<Tensor*>(dstTensor), mOpenCLRuntime.get(), false, svmFlag);
break;
case MNN_DATA_FORMAT_NC4HW4:
OpenCL::convertImageToNC4HW4Buffer(srcTensor, const_cast<Tensor*>(dstTensor),
mOpenCLRuntime.get(), false, svmFlag);
break;
default:
break;
}
}
}
void OpenCLBackend::copyFromDevice(const Tensor* srcTensor, const Tensor* dstTensor) const{
auto needSize = dstTensor->size();
auto shape = tensorShapeFormat(srcTensor);
auto srcDimensionFormat = TensorUtils::getDescribe(srcTensor)->dimensionFormat;
auto dstDimensionFormat = TensorUtils::getDescribe(dstTensor)->dimensionFormat;
auto memType = dstTensor->buffer().flags;
bool directCopy = BUFFER == mOpenCLRuntime->getGpuMemType()
&& (srcDimensionFormat == dstDimensionFormat || srcTensor->dimensions() <= 1)
&& MNN::MNN_DATA_FORMAT_NC4HW4 != dstDimensionFormat && MNN_DATA_FORMAT_NC4HW4 != srcDimensionFormat
&& (getDataType(srcTensor) == getDataType(dstTensor))
&& memType != MNN_MEMORY_AHARDWAREBUFFER;
if (mOpenCLRuntime->isSupportedFP16()) { // Fp16
if (dstTensor->getType().code == halide_type_float) {
directCopy = false;
}
}
if(mOpenCLRuntime->isSupportedIntelSubgroup()){
int cPack = TensorUtils::getTensorChannelPack(srcTensor);
if (cPack == 16){
directCopy = false;
}
}
void* hostPtr = dstTensor->host<float>();
if(directCopy){
mOpenCLRuntime->commandQueue().enqueueReadBuffer(openCLBuffer(srcTensor), CL_TRUE, 0, needSize, hostPtr);
return;
}
_allocHostBuffer(needSize, dstTensor);
MNN::Tensor interTensor(dstTensor, dstTensor->getDimensionType(), false);
interTensor.buffer().device = (uint64_t)mHostBuffer.second.get();
TensorUtils::getDescribe(&interTensor)->dimensionFormat = dstDimensionFormat;
//Convert format
mCLRuntime->convertFromDevice(srcTensor, (const Tensor*)&interTensor, dstDimensionFormat, false);
mOpenCLRuntime->printEventTime();
cl_int res;
#ifdef ENABLE_OPENCL_TIME_PROFILER
mOpenCLRuntime->commandQueue().finish();
{
AUTOTIME;
res = mOpenCLRuntime->commandQueue().enqueueReadBuffer(*mHostBuffer.second, CL_TRUE, 0, needSize, hostPtr);
}
#else
res = mOpenCLRuntime->commandQueue().enqueueReadBuffer(*mHostBuffer.second, CL_TRUE, 0, needSize, hostPtr);
#endif
}
void CLRuntime::convertToDevice(const Tensor* srcTensor, const Tensor* dstTensor, MNN_DATA_FORMAT data_format, bool svmFlag, int memtype) const {
// Format: Host -> OpenCL
#ifdef __ANDROID__
if(MNN_MEMORY_AHARDWAREBUFFER == memtype){
convertBetweenAHDandCLmem(const_cast<Tensor*>(srcTensor), const_cast<Tensor*>(dstTensor), mOpenCLRuntime.get(), memtype, true, false);
return;
}
#endif
#ifndef MNN_OPENCL_BUFFER_CLOSED
if(mOpenCLRuntime->getGpuMemType() == BUFFER)
{
#ifdef MNN_SUPPORT_INTEL_SUBGROUP
int cPack = TensorUtils::getTensorChannelPack(dstTensor);
if (cPack == 16 && mOpenCLRuntime->isSupportedIntelSubgroup()) {
if (MNN_DATA_FORMAT_NHWC == data_format) {
OpenCL::converNCHWOrNHWCBufferToNC4HW4OrNC16HW16Buffer(srcTensor, const_cast<Tensor*>(dstTensor), "nhwc_buffer_to_nc16hw16_buffer", mOpenCLRuntime.get(), true, false, svmFlag);
} else if (MNN_DATA_FORMAT_NCHW == data_format) {
OpenCL::converNCHWOrNHWCBufferToNC4HW4OrNC16HW16Buffer(srcTensor, const_cast<Tensor*>(dstTensor), "nchw_buffer_to_nc16hw16_buffer", mOpenCLRuntime.get(), true, false, svmFlag);
} else if (MNN_DATA_FORMAT_NC4HW4 == data_format) {
OpenCL::convertNC4HW4BufferBetweenNC16HW16Buffer(srcTensor, const_cast<Tensor*>(dstTensor), "nc4hw4_buffer_to_nc16hw16_buffer", mOpenCLRuntime.get(), InpTrans, false, svmFlag, true, false);
} else {
MNN_PRINT("input data format not support or subgroup\n");
MNN_ASSERT(false);
}
}else
#endif
OpenCL::convertBufferToBuffer(const_cast<Tensor*>(srcTensor), const_cast<Tensor*>(dstTensor), mOpenCLRuntime.get(), true, false, false, svmFlag);
}
else
#endif /* MNN_OPENCL_BUFFER_CLOSED */
{
if (MNN_DATA_FORMAT_NHWC == data_format) {
OpenCL::convertNHWCBufferToImage(srcTensor, const_cast<Tensor*>(dstTensor), mOpenCLRuntime.get(), false, svmFlag);
} else if (MNN_DATA_FORMAT_NCHW == data_format) {
OpenCL::convertNCHWBufferToImage(srcTensor, const_cast<Tensor*>(dstTensor), mOpenCLRuntime.get(), false, svmFlag);
} else if (MNN_DATA_FORMAT_NC4HW4 == data_format) {
OpenCL::convertNC4HW4BufferToImage(srcTensor, const_cast<Tensor*>(dstTensor),
mOpenCLRuntime.get(), false, svmFlag);
} else {
MNN_PRINT("data format not support\n");
MNN_ASSERT(false);
}
}
}
void OpenCLBackend::copyToDevice(const Tensor* srcTensor, const Tensor* dstTensor) const{
auto needSize = srcTensor->size();
auto shape = tensorShapeFormat(srcTensor);
auto srcDimensionFormat = TensorUtils::getDescribe(srcTensor)->dimensionFormat;
auto dstDimensionFormat = TensorUtils::getDescribe(dstTensor)->dimensionFormat;
auto memType = srcTensor->buffer().flags;
void* hostPtr = srcTensor->host<float>();
// 1*1*1*1 don't need convert
if(BUFFER == mOpenCLRuntime->getGpuMemType() && srcTensor->getType().code == halide_type_float && mOpenCLRuntime->isSupportedFP16() && 1 == shape[0] * shape[1] * shape[2] * shape[3]){
needSize /= 2;
void *tmpPtr = malloc(needSize);
((half_float::half*)tmpPtr)[0] = (half_float::half)(((float*)hostPtr)[0]);
mOpenCLRuntime->commandQueue().enqueueWriteBuffer(openCLBuffer(dstTensor), CL_TRUE, 0, needSize, tmpPtr);
free(tmpPtr);
return;
}
bool directCopy = BUFFER == mOpenCLRuntime->getGpuMemType()
&& (srcDimensionFormat == dstDimensionFormat || srcTensor->dimensions() <= 1)
&& MNN_DATA_FORMAT_NC4HW4 != dstDimensionFormat && MNN_DATA_FORMAT_NC4HW4 != srcDimensionFormat
&& (getDataType(srcTensor) == getDataType(dstTensor))
&& memType != MNN_MEMORY_AHARDWAREBUFFER;
if (mOpenCLRuntime->isSupportedFP16()) { // Fp16
if (dstTensor->getType().code == halide_type_float) {
directCopy = false;
}
}
if(mOpenCLRuntime->isSupportedIntelSubgroup()){
int cPack = TensorUtils::getTensorChannelPack(dstTensor);
if (cPack == 16){
directCopy = false;
}
}
if(directCopy){
mOpenCLRuntime->commandQueue().enqueueWriteBuffer(openCLBuffer(dstTensor), CL_TRUE, 0, needSize, hostPtr);
return;
}
_allocHostBuffer(needSize, srcTensor);
MNN::Tensor interTensor(srcTensor, srcTensor->getDimensionType(), false);
interTensor.buffer().device = (uint64_t)mHostBuffer.second.get();
TensorUtils::getDescribe(&interTensor)->dimensionFormat = srcDimensionFormat;
#ifdef ENABLE_OPENCL_TIME_PROFILER
mOpenCLRuntime->commandQueue().finish();
{
AUTOTIME;
mOpenCLRuntime->commandQueue().enqueueWriteBuffer(*mHostBuffer.second, CL_TRUE, 0, needSize, hostPtr);
}
#else
auto res = mOpenCLRuntime->commandQueue().enqueueWriteBuffer(*mHostBuffer.second, CL_TRUE, 0, needSize, hostPtr);
if(res != CL_SUCCESS) {
MNN_ERROR("OpenCL enqueue write error:%d\n", res);
return;
}
#endif
//Covert format
mCLRuntime->convertToDevice((const Tensor*)&interTensor, dstTensor, srcDimensionFormat, false);
}
void OpenCLBackend::copyBetweenDevice(const Tensor* srcTensor, const Tensor* dstTensor) const{
int srcMemtype = srcTensor->buffer().flags;
int dstMemtype = dstTensor->buffer().flags;
if(MNN_FORWARD_CPU == srcMemtype && MNN_FORWARD_CPU == dstMemtype){
mCLRuntime->copyBetweenDevice(srcTensor, dstTensor);
} else {
const Tensor* hostTensor = MNN_FORWARD_CPU != srcMemtype ? srcTensor : dstTensor;
const Tensor* deviceTensor = MNN_FORWARD_CPU == srcMemtype ? srcTensor : dstTensor;
MNN_DATA_FORMAT data_format = TensorUtils::getDescribe(deviceTensor)->dimensionFormat;
bool alloc_error = _allocHostBuffer(0, hostTensor);
if(false == alloc_error){
MNN_ERROR("Alloc _allocHostBuffer error\n");
return;
}
//Covert format
if(MNN_FORWARD_CPU != srcMemtype){
mCLRuntime->convertToDevice(hostTensor, deviceTensor, data_format, false, srcMemtype);
}else{
mCLRuntime->convertFromDevice(deviceTensor, hostTensor, data_format, false, dstMemtype);
}
}
}
void CLRuntime::copyBetweenDevice(const Tensor* srcTensor, const Tensor* dstTensor) const{
#ifndef MNN_OPENCL_BUFFER_CLOSED
if(mOpenCLRuntime->getGpuMemType() == BUFFER)
{
OpenCL::convertBufferToBuffer(const_cast<Tensor*>(srcTensor), const_cast<Tensor*>(dstTensor), mOpenCLRuntime.get(), true, true);
}
else
#endif /* MNN_OPENCL_BUFFER_CLOSED */
{
std::vector<int> bufferShape = MNN::OpenCL::tensorShapeFormat(srcTensor);
mOpenCLRuntime.get()->commandQueue().enqueueCopyImage(
openCLImage(srcTensor), openCLImage(dstTensor),
{0, 0, 0}, {0, 0, 0},
{(size_t)bufferShape[2]* UP_DIV(bufferShape[3], 4), (size_t)bufferShape[0]*bufferShape[1], 1});
}
return;
}
void OpenCLBackend::onCopyBuffer(const Tensor* srcTensor, const Tensor* dstTensor) const {
#ifdef LOG_VERBOSE
MNN_PRINT("Start onCopyBuffer !\n");
#endif
clearRecord();
if (srcTensor->host<float>() != nullptr) {
copyToDevice(srcTensor, dstTensor);
}else if(dstTensor->host<void>() != nullptr){
copyFromDevice(srcTensor, dstTensor);
}else{
copyBetweenDevice(srcTensor, dstTensor);
}
#ifdef LOG_VERBOSE
MNN_PRINT("end onCopyBuffer !\n");
#endif
}
void* OpenCLBackend::allocMapTensorMemory(int length, bool svmFlag, cl_device_svm_capabilities svm_cap_) {
if(length <= mMapMem.first) {
return mMapMem.second;
}
#ifdef MNN_OPENCL_SVM_ENABLE
if(svmFlag)
{
if(mMapMem.first != 0) {
//Release small SVM Memory
clSVMFree(mOpenCLRuntime->context().get(), mMapMem.second);
}
//Alloc proper SVM Memory
cl_svm_mem_flags flags = CL_MEM_READ_WRITE;
flags |= (svm_cap_ & CL_DEVICE_SVM_FINE_GRAIN_BUFFER) ? CL_MEM_SVM_FINE_GRAIN_BUFFER : 0;
flags |= ((svm_cap_ & CL_DEVICE_SVM_FINE_GRAIN_BUFFER) && (svm_cap_ & CL_DEVICE_SVM_ATOMICS)) ? CL_MEM_SVM_ATOMICS : 0;
mMapMem.second = clSVMAlloc(mOpenCLRuntime->context().get(), flags, length, 0);
if(mMapMem.second == nullptr) {
MNN_PRINT("SVM Alloc Failed\n");
}
}
else
#endif
{
if(mMapMem.first != 0) {
free(mMapMem.second);
mMapMem.second = nullptr;
}
mMapMem.second = malloc(length);
}
mMapMem.first = length;
return mMapMem.second;
}
void* OpenCLBackend::onMapTensor(Tensor::MapType mtype, Tensor::DimensionType dtype, const Tensor* srcTensor) {
auto needSize = srcTensor->size();
clearRecord();
#ifdef MNN_OPENCL_SVM_ENABLE
auto svm_cap_ = mOpenCLRuntime->getSvmCapabilities();
bool use_svm = (svm_cap_ & CL_DEVICE_SVM_FINE_GRAIN_BUFFER);//support fine grain svm
use_svm |= ((svm_cap_ & CL_DEVICE_SVM_COARSE_GRAIN_BUFFER) && mOpenCLRuntime->getGpuType() == ADRENO);//support coarse grain svm and adreno gpu
mUseSvm = (mOpenCLRuntime->getCLVersion() > 1.99f && use_svm);
if(mUseSvm) {// CL version beyond 2.0 & support svm
svmPtr = allocMapTensorMemory(needSize, true, svm_cap_);
if(mtype == Tensor::MAP_TENSOR_READ) {
//tmpTensor alloc
MNN::Tensor tmpTensor(srcTensor, dtype, false);
tmpTensor.buffer().device = (uint64_t)svmPtr;
//Convert format
MNN_DATA_FORMAT format_type = MNN_DATA_FORMAT_NCHW;
if(dtype == MNN::Tensor::TENSORFLOW) {
format_type = MNN_DATA_FORMAT_NHWC;
} else if(dtype == MNN::Tensor::CAFFE_C4) {
format_type = MNN_DATA_FORMAT_NC4HW4;
}
mCLRuntime->convertFromDevice(srcTensor, &tmpTensor, format_type, true);
}
if(svm_cap_ & CL_DEVICE_SVM_FINE_GRAIN_BUFFER) {
//Make sure command finished
mOpenCLRuntime->commandQueue().finish();
return svmPtr;
}
auto map_flag = CL_MAP_WRITE;
if(mtype == Tensor::MAP_TENSOR_READ) {
map_flag = CL_MAP_READ;
}
cl_int res = clEnqueueSVMMap(mOpenCLRuntime->commandQueue().get(), true, map_flag, svmPtr, needSize, 0, nullptr, nullptr);
MNN_CHECK_CL_SUCCESS(res, "svm_map")
return svmPtr;
}
#endif
/**
Not Support Svm, Use onopyBuffer
*/
svmPtr = allocMapTensorMemory(needSize, false);
if(mtype == Tensor::MAP_TENSOR_READ) {
//tmpTensor alloc
MNN::Tensor tmpTensor(srcTensor, dtype, false);
tmpTensor.buffer().host = (uint8_t *)svmPtr;
//use onCopyBuffer
onCopyBuffer(srcTensor, &tmpTensor);
}
return svmPtr;
}
bool OpenCLBackend::onUnmapTensor(Tensor::MapType mtype, Tensor::DimensionType dtype, const Tensor* dstTensor, void* mapPtr) {
#ifdef MNN_OPENCL_SVM_ENABLE
auto svm_cap_ = mOpenCLRuntime->getSvmCapabilities();
if(mUseSvm) {// CL version beyond 2.0 & support svm
//If COARSE_SVM, Unmap first
if(!(svm_cap_ & CL_DEVICE_SVM_FINE_GRAIN_BUFFER)) {
cl_int res = clEnqueueSVMUnmap(mOpenCLRuntime->commandQueue().get(), svmPtr, 0, nullptr, nullptr);
MNN_CHECK_CL_SUCCESS(res, "svm_unmap")
}
if(mtype == Tensor::MAP_TENSOR_WRITE) {
//interTensor alloc
MNN::Tensor interTensor(dstTensor, dtype, false);
interTensor.buffer().device = (uint64_t)svmPtr;
//Convert format
MNN_DATA_FORMAT format_type = MNN_DATA_FORMAT_NCHW;
if(dtype == MNN::Tensor::TENSORFLOW) {
format_type = MNN_DATA_FORMAT_NHWC;
} else if(dtype == MNN::Tensor::CAFFE_C4) {
format_type = MNN_DATA_FORMAT_NC4HW4;
}
mCLRuntime->convertToDevice(&interTensor, dstTensor, format_type, true);
}
mOpenCLRuntime->commandQueue().finish();
return true;
}
#endif
/**
Not Support Svm, Use onopyBuffer
*/
if(mtype == Tensor::MAP_TENSOR_WRITE) {
//srcTensor alloc
MNN::Tensor srcTensor(dstTensor, dtype, false);
srcTensor.buffer().host = (uint8_t *)svmPtr;
//use onCopyBuffer
onCopyBuffer(&srcTensor, dstTensor);
}
return true;
}
bool OpenCLBackend::addCreator(std::pair<OpType, GpuMemObject> t, Creator* c) {
auto map = gCreator();
if (map->find(t) != map->end()) {
MNN_PRINT("Error: %d type, %d GpuMemObject has be added\n", t.first, t.second);
return false;
}
map->insert(std::make_pair(t, c));
return true;
}
// -----------------------------------------------------------------------------
// Runtime Register
// -----------------------------------------------------------------------------
class CLRuntimeCreator : public RuntimeCreator {
virtual Runtime* onCreate(const Backend::Info& info) const {
#ifdef MNN_USE_LIB_WRAPPER
OpenCLSymbolsOperator::createOpenCLSymbolsOperatorSingleInstance();
if (nullptr == OpenCLSymbolsOperator::getOpenclSymbolsPtr()) {
MNN_PRINT("OpenCL init error, fallback ... \n");
return nullptr;
}
if (true == OpenCLSymbolsOperator::getOpenclSymbolsPtr()->isError()) {
MNN_PRINT("Parsing OpenCL symbols error !!! \n");
return nullptr;
}
#endif
int platform_id = 0;
int device_id = 0;
int platform_size = 0;
void *context_ptr = nullptr;
void *glShared = nullptr;
if (nullptr != info.user) {
if (info.user->sharedContext != nullptr) {
platform_id = ((MNNDeviceContext*)info.user->sharedContext)->platformId;
device_id = ((MNNDeviceContext*)info.user->sharedContext)->deviceId;
platform_size = ((MNNDeviceContext*)info.user->sharedContext)->platformSize;
context_ptr = (((MNNDeviceContext*)info.user->sharedContext)->contextPtr);
glShared = (((MNNDeviceContext*)info.user->sharedContext)->glShared);
}
}
auto rt = new CLRuntime(info, platform_size, platform_id, device_id, context_ptr, glShared);
if(rt->isCLRuntimeError() == true) {
delete rt;
return nullptr;
}
return rt;
}
virtual bool onValid(Backend::Info& info) const {
return true;
}
};
DataType OpenCLBackend::getDataType(const Tensor* tensor) const{
auto des = TensorUtils::getDescribe(tensor);
if (nullptr == des->quantAttr.get()) {
return DataType_DT_FLOAT;
}
return des->type;
}
cl_channel_type OpenCLBackend::fpType() {
if (getOpenCLRuntime()->isSupportedFP16() &&
mPrecision != BackendConfig::Precision_High) {
return CL_HALF_FLOAT;
}
return CL_FLOAT;
}
int OpenCLBackend::fpBytes() {
return (fpType() == CL_FLOAT ? sizeof(float) : sizeof(half_float::half));
}
void OpenCLBackend::clearRecord() const{
#if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER)
if(mUseRecordQueue && mDevideOpRecord){
for(int i = 0; i < mRecordings.size(); ++i){
std::vector<cl_array_arg_qcom> update_kernel_args;
std::vector<cl_workgroup_qcom> update_global_size;
std::vector<cl_workgroup_qcom> update_local_size;
for (int j = 0; j < mRecordings[i].updateInfo.size(); ++j){
for(int k = 0; k < mRecordings[i].updateInfo[j]->update_kernel_args.size(); ++k){
update_kernel_args.emplace_back(mRecordings[i].updateInfo[j]->update_kernel_args[k]);
update_kernel_args.back().dispatch_index = j;
}
for(int k = 0; k < mRecordings[i].updateInfo[j]->update_global_size.size(); ++k){
update_global_size.emplace_back(mRecordings[i].updateInfo[j]->update_global_size[k]);
update_global_size.back().dispatch_index = j;
}
for(int k = 0; k < mRecordings[i].updateInfo[j]->update_local_size.size(); ++k){
update_local_size.emplace_back(mRecordings[i].updateInfo[j]->update_local_size[k]);
update_local_size.back().dispatch_index = j;
}
}
cl_int res = mOpenCLRuntime->commandQueue().EnqueueRecordingQCOM(mRecordings[i].record, update_kernel_args.size(), update_kernel_args.data(), 0, nullptr,
update_global_size.size(), update_global_size.data(), update_local_size.size(), update_local_size.data(), 0, nullptr, nullptr);
MNN_CHECK_CL_SUCCESS(res, "EnqueueRecordingQCOM");
}
mOpenCLRuntime->commandQueue().finish();
mRecordings.clear();
}
#endif
}
void OpenCLBackend::enqeueRecord() const{
#if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER)
if(mUseRecordQueue && !mDevideOpRecord){
for(int i = 0; i < mRecordings.size(); ++i){
std::vector<cl_array_arg_qcom> update_kernel_args;
std::vector<cl_workgroup_qcom> update_global_size;
std::vector<cl_workgroup_qcom> update_local_size;
for (int j = 0; j < mRecordings[i].updateInfo.size(); ++j){
for(int k = 0; k < mRecordings[i].updateInfo[j]->update_kernel_args.size(); ++k){
update_kernel_args.emplace_back(mRecordings[i].updateInfo[j]->update_kernel_args[k]);
}
for(int k = 0; k < mRecordings[i].updateInfo[j]->update_global_size.size(); ++k){
update_global_size.emplace_back(mRecordings[i].updateInfo[j]->update_global_size[k]);
}
for(int k = 0; k < mRecordings[i].updateInfo[j]->update_local_size.size(); ++k){
update_local_size.emplace_back(mRecordings[i].updateInfo[j]->update_local_size[k]);
}
}
cl_int res = mOpenCLRuntime->commandQueue().EnqueueRecordingQCOM(mRecordings[i].record, update_kernel_args.size(), update_kernel_args.data(), 0, nullptr,
update_global_size.size(), update_global_size.data(), update_local_size.size(), update_local_size.data(), 0, nullptr, nullptr);
MNN_CHECK_CL_SUCCESS(res, "EnqueueRecordingQCOM");
}
mOpenCLRuntime->commandQueue().finish();
}
#endif
}
void OpenCLBackend::releaseRecord(){
#if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER)
if(mUseRecordQueue && !mDevideOpRecord){
for(int i = 0; i < mRecordings.size(); ++i){
cl_int res = clReleaseRecordingQCOM(mRecordings[i].record);
MNN_CHECK_CL_SUCCESS(res, "clReleaseRecordingQCOM");
}
mRecordings.clear();
}
#endif
}
void OpenCLBackend::startRecord(cl_recording_qcom &recording){
#if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER)
if(!mUseRecordQueue){
return;
}
#ifdef LOG_VERBOSE
MNN_PRINT("start startRecord !\n");
#endif
cl_int res = CL_SUCCESS;
if(mDevideOpRecord){
if(recording != NULL){
clReleaseRecordingQCOM(recording);
}
recording = mOpenCLRuntime->recordableQueue().NewRecordingQCOM(&res);
MNN_CHECK_CL_SUCCESS(res, "clNewRecordingQCOM");
}
#ifdef LOG_VERBOSE
MNN_PRINT("end startRecord !\n");
#endif
#endif //ENABLE_OPENCL_TIME_PROFILER
}
void OpenCLBackend::endRecord(cl_recording_qcom &recording, bool flag){
#if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER)
if(!mUseRecordQueue){
return;
}
#ifdef LOG_VERBOSE
MNN_PRINT("start endRecord !\n");
#endif
if(mDevideOpRecord){
cl_int res = CL_SUCCESS;
res = clEndRecordingQCOM(recording);
MNN_CHECK_CL_SUCCESS(res, "clEndRecordingQCOM");
} else if(flag) {
// endRecord for last kernel be recorded when record mode is MNN_GPU_RECORD_BATCH
if(!mRecordings.empty()){
cl_int res = clEndRecordingQCOM(mRecordings.back().record);
mRecordNums = 0;
MNN_CHECK_CL_SUCCESS(res, "clEndRecordingQCOM");
}
}
#ifdef LOG_VERBOSE
MNN_PRINT("end endRecord !\n");
#endif
#endif //ENABLE_OPENCL_TIME_PROFILER
}
void OpenCLBackend::addRecord(cl_recording_qcom &record, std::vector<RecordUpdateInfo *>updateInfo){
if(mDevideOpRecord){
RecordInfo info;
info.record = record;
for(int i = 0; i < updateInfo.size(); ++i) {
info.updateInfo.emplace_back(updateInfo[i]);
}
mRecordings.emplace_back(info);
}
}
void OpenCLBackend::recordKernel2d(const std::shared_ptr<KernelWrap> &kernelW, const std::vector<uint32_t> &gws, const std::vector<uint32_t> &lws, RecordUpdateInfo *updateInfo) {
#if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER)
if(!mUseRecordQueue){
return;
}
auto kernel = kernelW->get();
#ifdef LOG_VERBOSE
MNN_PRINT("start record2dKernel !\n");
#endif
cl_int res = CL_SUCCESS;
if(!mDevideOpRecord){
RecordInfo info;
int recordNum = mRecordNums == mUseRecordableQueueSize ? 0 : mRecordNums;
if(updateInfo != nullptr){
for(int i = 0; i < updateInfo->update_kernel_args.size(); ++i){
updateInfo->update_kernel_args[i].dispatch_index = recordNum;
}
for(int i = 0; i < updateInfo->update_global_size.size(); ++i){
updateInfo->update_global_size[i].dispatch_index = recordNum;
}
for(int i = 0; i < updateInfo->update_local_size.size(); ++i){
updateInfo->update_local_size[i].dispatch_index = recordNum;
}
info.updateInfo.emplace_back(updateInfo);
}
if(mRecordNums == 0){
cl_recording_qcom recording = mOpenCLRuntime->recordableQueue().NewRecordingQCOM(&res);
MNN_CHECK_CL_SUCCESS(res, "clNewRecordingQCOM");
info.record = recording;
mRecordings.emplace_back(info);
}else if(mRecordNums == mUseRecordableQueueSize){
res = clEndRecordingQCOM(mRecordings.back().record);
MNN_CHECK_CL_SUCCESS(res, "clEndRecordingQCOM");
cl_recording_qcom recording = mOpenCLRuntime->recordableQueue().NewRecordingQCOM(&res);
MNN_CHECK_CL_SUCCESS(res, "clNewRecordingQCOM");
info.record = recording;
mRecordings.emplace_back(info);
mRecordNums = 0;
} else if(updateInfo != nullptr){
auto &lastInfo = mRecordings.back();
lastInfo.updateInfo.emplace_back(updateInfo);
}
mRecordNums++;
}
std::vector<uint32_t> internalGlobalWS = gws;
for (size_t i = 0; i < 2; ++i) {
internalGlobalWS[i] = ROUND_UP(gws[i], std::max((uint32_t)1, lws[i]));
}
if(lws[0]==0 || lws[1]==0){
res = mOpenCLRuntime->recordableQueue().enqueueNDRangeKernel(
kernel, cl::NullRange, cl::NDRange(internalGlobalWS[0], internalGlobalWS[1]), cl::NullRange, nullptr, nullptr);
}else{
res = mOpenCLRuntime->recordableQueue().enqueueNDRangeKernel(
kernel, cl::NullRange, cl::NDRange(internalGlobalWS[0], internalGlobalWS[1]), cl::NDRange(lws[0], lws[1]), nullptr, nullptr);
}
MNN_CHECK_CL_SUCCESS(res, "recordKernel2d");
#ifdef LOG_VERBOSE
MNN_PRINT("end record2dKernel !\n");
#endif
#endif //ENABLE_OPENCL_TIME_PROFILER
}
void OpenCLBackend::recordKernel3d(const std::shared_ptr<KernelWrap> &kernelW, const std::vector<uint32_t> &gws, const std::vector<uint32_t> &lws, RecordUpdateInfo *updateInfo) {
#if !defined(ENABLE_OPENCL_TIME_PROFILER) && defined(MNN_USE_LIB_WRAPPER)
if(!mUseRecordQueue){
return;
}
auto kernel = kernelW->get();
#ifdef LOG_VERBOSE
MNN_PRINT("start record3dKernel !\n");
#endif
cl_int res = CL_SUCCESS;
std::vector<uint32_t> internalGlobalWS = gws;
for (size_t i = 0; i < 3; ++i) {
internalGlobalWS[i] = ROUND_UP(gws[i], std::max((uint32_t)1, lws[i]));
}
if(!mDevideOpRecord){
RecordInfo info;
int recordNum = mRecordNums == mUseRecordableQueueSize ? 0 : mRecordNums;
if(updateInfo != nullptr){
for(int i = 0; i < updateInfo->update_kernel_args.size(); ++i){
updateInfo->update_kernel_args[i].dispatch_index = recordNum;
}
for(int i = 0; i < updateInfo->update_global_size.size(); ++i){
updateInfo->update_global_size[i].dispatch_index = recordNum;
}
for(int i = 0; i < updateInfo->update_local_size.size(); ++i){
updateInfo->update_local_size[i].dispatch_index = recordNum;
}
info.updateInfo.emplace_back(updateInfo);
}
if(mRecordNums == 0){
cl_recording_qcom recording = mOpenCLRuntime->recordableQueue().NewRecordingQCOM(&res);
MNN_CHECK_CL_SUCCESS(res, "clNewRecordingQCOM");
info.record = recording;
mRecordings.emplace_back(info);
}else if(mRecordNums == mUseRecordableQueueSize){
res = clEndRecordingQCOM(mRecordings.back().record);
MNN_CHECK_CL_SUCCESS(res, "clEndRecordingQCOM");
cl_recording_qcom recording = mOpenCLRuntime->recordableQueue().NewRecordingQCOM(&res);
MNN_CHECK_CL_SUCCESS(res, "clNewRecordingQCOM");
info.record = recording;
mRecordings.emplace_back(info);
mRecordNums = 0;
} else if(updateInfo != nullptr){
auto &lastInfo = mRecordings.back();
lastInfo.updateInfo.emplace_back(updateInfo);
}
mRecordNums++;
}
if(lws[0]==0 || lws[1]==0 || lws[2]==0){
res = mOpenCLRuntime->recordableQueue().enqueueNDRangeKernel(
kernel, cl::NullRange, cl::NDRange(internalGlobalWS[0], internalGlobalWS[1], internalGlobalWS[2]), cl::NullRange, nullptr, nullptr);
}else{
res = mOpenCLRuntime->recordableQueue().enqueueNDRangeKernel(
kernel, cl::NullRange, cl::NDRange(internalGlobalWS[0], internalGlobalWS[1], internalGlobalWS[2]), cl::NDRange(lws[0], lws[1], lws[2]), nullptr, nullptr);
}
MNN_CHECK_CL_SUCCESS(res, "recordKernel3d");
#ifdef LOG_VERBOSE
MNN_PRINT("end record3dKernel !\n");
#endif
#endif //ENABLE_OPENCL_TIME_PROFILER
}
#ifdef MNN_OPENCL_SEP_BUILD
bool placeholder = []() {
static std::once_flag createOnce;
std::call_once(createOnce, []() {
MNNInsertExtraRuntimeCreator(MNN_FORWARD_OPENCL, new CLRuntimeCreator, true);
});
return true;
}();
#else
void registerOpenCLRuntimeCreator() {
registerOpenCLOps();
MNNInsertExtraRuntimeCreator(MNN_FORWARD_OPENCL, new CLRuntimeCreator, true);
}
#endif
} // namespace OpenCL
} // namespace MNN
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