root
/
MNN
mirror of https://github.com/alibaba/MNN.git
1
0
Fork 0
Code Issues Actions 6 Packages Projects Releases Wiki Activity
MNN/source/backend/opencl/core/OpenCLBackend.cpp

756 lines
29 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 "shape/SizeComputer.hpp"
#include <map>
#include <mutex>
#include <thread>
#include "core/Macro.h"
namespace MNN {
namespace OpenCL {
CLRuntime::CLRuntime(const Backend::Info& info){
mInfo = info;
BackendConfig::PrecisionMode precision = BackendConfig::Precision_Normal;
BackendConfig::PowerMode power = BackendConfig::Power_Normal;
if (nullptr != mInfo.user) {
precision = mInfo.user->precision;
power = mInfo.user->power;
}
// Shader precision
mOpenCLRuntime.reset(new OpenCLRuntime(precision, mInfo.gpuMode));
mPrecision = precision;
}
CLRuntime::~CLRuntime() {
mOpenCLRuntime = nullptr;
}
bool CLRuntime::onSetCache(const void* buffer, size_t size) {
mOpenCLRuntime->setCache(std::make_pair(buffer, size));
return true;
}
std::pair<const void*, size_t> CLRuntime::onGetCache() {
return mOpenCLRuntime->makeCache();
}
Backend* CLRuntime::onCreate(const BackendConfig* config) const {
// FIXME: Use config info
return new OpenCLBackend(this);
}
void CLRuntime::onGabageCollect(int level) {
//nothing now
}
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(const CLRuntime *runtime)
: Backend(MNN_FORWARD_OPENCL) {
mCLRuntime = runtime;
mOpenCLRuntime = mCLRuntime->mOpenCLRuntime;
mPrecision = mCLRuntime->mPrecision;
if(mOpenCLRuntime.get()){
if(mOpenCLRuntime->isCreateError() == true) {
mIsCreateError = true;
}
mStaticImagePool.reset(new ImagePool(mOpenCLRuntime->context()));
mStaticBufferPool.reset(new BufferPool(mOpenCLRuntime->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR));
mImagePool.reset(new ImagePool(mOpenCLRuntime->context()));
mBufferPool.reset(new BufferPool(mOpenCLRuntime->context(), CL_MEM_READ_WRITE | CL_MEM_ALLOC_HOST_PTR));
}
}
OpenCLBackend::~OpenCLBackend() {
#ifdef LOG_VERBOSE
MNN_PRINT("enter OpenCLBackend::~OpenCLBackend \n");
#endif
mImagePool = nullptr;
mBufferPool = nullptr;
mStaticImagePool = nullptr;
mStaticBufferPool = nullptr;
}
OpenCLRuntime* OpenCLBackend::getOpenCLRuntime() {
return mOpenCLRuntime.get();
}
bool OpenCLBackend::onAcquireBuffer(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: [%d, %d, %d, %d], [%d, %d]\n", N, H, W, C, (int)imageWidth,
(int)imageHeight);
#endif
#ifndef MNN_OPENCL_BUFFER_CLOSED
if(mOpenCLRuntime->getGpuMemType() == BUFFER)
{
size_t imageWidth = (size_t) ROUND_UP(UP_DIV(C, 4), 2) * ROUND_UP(W, 4);//C-round to 8,W-round to 4, for memory alloc
size_t imageHeight = (size_t)N * H;
cl_channel_type dataType = CL_FLOAT;
//when support and want fp16, use half datatype
if (getOpenCLRuntime()->isSupportedFP16()) {
dataType = CL_HALF_FLOAT;
}
if (storageType == DYNAMIC_SEPERATE) {
auto buffer = mBufferPool->alloc(imageWidth*imageHeight*4*
(dataType==CL_HALF_FLOAT?sizeof(half_float::half):sizeof(float)), true);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)buffer;
return true;
}
if (storageType == DYNAMIC) {
auto buffer = mBufferPool->alloc(imageWidth*imageHeight*4*
(dataType==CL_HALF_FLOAT?sizeof(half_float::half):sizeof(float)));
((Tensor*)nativeTensor)->buffer().device = (uint64_t)buffer;
return true;
}
MNN_ASSERT(storageType == STATIC);
auto buffer = mStaticBufferPool->alloc(imageWidth*imageHeight*4*
(dataType==CL_HALF_FLOAT?sizeof(half_float::half):sizeof(float)));
((Tensor*)nativeTensor)->buffer().device = (uint64_t)buffer; // fix
return true;
}
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;
//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 true;
}
if (storageType == DYNAMIC) {
auto image = mImagePool->alloc(imageWidth, imageHeight, dataType);
((Tensor*)nativeTensor)->buffer().device = (uint64_t)image; // fix
return true;
}
MNN_ASSERT(storageType == STATIC);
auto image = mStaticImagePool->alloc(imageWidth, imageHeight, dataType);
((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;
}
if(mOpenCLRuntime->getGpuMemType() == BUFFER) {
auto buffer = (cl::Buffer*)nativeTensor->deviceId();
if (storageType == DYNAMIC) {
mBufferPool->recycle(buffer);
return true;
}
if (storageType == STATIC) {
mStaticBufferPool->recycle(buffer, true);
}
return true;
} else {
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::onClearBuffer() {
mImagePool->clear();
mBufferPool->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(std::make_pair(op->type(), mOpenCLRuntime->getGpuMemType()));
if (iter == creators->end()) {
return std::make_pair(0.0f, false);
}
const float defaultScheduleTime = 0.05f;
// FIXME: Compute in future
auto flops = 0.0f;
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(std::make_pair(op->type(), mOpenCLRuntime->getGpuMemType()));
if (iter == creators->end()) {
#if 0//close 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
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;
}
//input in raster not used, origin instead
auto des = TensorUtils::getDescribe(t)->regions;
for(auto region : des)
{
auto tensor = region.origin;
auto tensorShape = OpenCL::tensorShapeFormat(tensor);
int originHeight = tensorShape[0] * tensorShape[1];
int originWidth = tensorShape[2] * UP_DIV(tensorShape[3], 4);
if (originHeight > maxImageSize.at(0) || originWidth > 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) {
#if 0//close 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
return NULL;
}
}
auto exe = iter->second->onCreate(inputs, outputs, op, this);
if (NULL == exe) {
#if 0//close 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
return NULL;
}
#ifdef LOG_VERBOSE
MNN_PRINT("End OpenCLBackend::onCreate \n");
#endif
return exe;
}
void OpenCLBackend::onResizeBegin() {
#ifndef MNN_OPENCL_BUFFER_CLOSED
if(mOpenCLRuntime->getGpuMemType() == BUFFER)
{
std::set<std::string> buildOptions;
//when input or output need buffer2image transformation, open macro BUFFER_IMAGE_IO_TRANS
//because cpu input and output are fp32
buildOptions.emplace("-DBUFFER_FORMAT_INP_TRANS");
mNCHWBufferToNC4HW4BufferInp = mOpenCLRuntime->buildKernel("buffer_convert_buf", "nchw_buffer_to_nc4hw4_buffer", buildOptions);
mNHWCBufferToNC4HW4BufferInp = mOpenCLRuntime->buildKernel("buffer_convert_buf", "nhwc_buffer_to_nc4hw4_buffer", buildOptions);
mNC4HW4BufferToNC4HW4BufferInp = mOpenCLRuntime->buildKernel("buffer_convert_buf", "nc4hw4_buffer_to_nc4hw4_buffer", buildOptions);
buildOptions.clear();
buildOptions.emplace("-DBUFFER_FORMAT_OUT_TRANS");
mNC4HW4BufferToNHWCBufferOut = mOpenCLRuntime->buildKernel("buffer_convert_buf", "nc4hw4_buffer_to_nhwc_buffer", buildOptions);
mNC4HW4BufferToNCHWBufferOut = mOpenCLRuntime->buildKernel("buffer_convert_buf", "nc4hw4_buffer_to_nchw_buffer", buildOptions);
mNC4HW4BufferToNC4HW4BufferOut = mOpenCLRuntime->buildKernel("buffer_convert_buf", "nc4hw4_buffer_to_nc4hw4_buffer", buildOptions);
}
else
#endif /* MNN_OPENCL_BUFFER_CLOSED */
{
std::set<std::string> buildOptions;
//when input or output need buffer2image transformation, open macro BUFFER_IMAGE_IO_TRANS
//because cpu input and output are fp32
buildOptions.emplace("-DBUFFER_IMAGE_IO_TRANS");
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);
}
mOpenCLRuntime->setCommandQueueProfileEnable();
}
void OpenCLBackend::onResizeEnd() {
#ifndef ENABLE_OPENCL_TIME_PROFILER
mOpenCLRuntime->setCommandQueueProfileDisable();
#endif
}
void OpenCLBackend::onExecuteBegin() const {
mOpenCLRuntime->mQueueCount = 0;
mOpenCLRuntime->mKernelTime = 0;
}
void OpenCLBackend::onExecuteEnd() const {
mOpenCLRuntime->mQueueCount = 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{
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);
}
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();
void* hostPtr;
void* tmpPtr;
if(dstTensor->getType().code == halide_type_int) {
if(dstTensor->getType().bits == 8){
needSize *= 4;
hostPtr = malloc(needSize);
} else if(dstTensor->getType().bits == 32){
hostPtr = malloc(needSize);
} else {
MNN_PRINT("opencl input datatype not support, bit:%d\n", dstTensor->getType().bits);
MNN_ASSERT(false);
}
} else if(dstTensor->getType().code == halide_type_uint){
if(dstTensor->getType().bits == 8){
needSize *= 4;
hostPtr = malloc(needSize);
} else if(dstTensor->getType().bits == 32){
hostPtr = malloc(needSize);
} else {
MNN_PRINT("opencl input datatype not support, bit:%d\n", dstTensor->getType().bits);
MNN_ASSERT(false);
}
} else {
hostPtr = dstTensor->host<float>();
}
_allocHostBuffer(needSize);
interBuffer.buffer().device = (uint64_t)mHostBuffer.second.get();
#ifndef MNN_OPENCL_BUFFER_CLOSED
if(mOpenCLRuntime->getGpuMemType() == BUFFER)
{
MNN_DATA_FORMAT data_format = TensorUtils::getDescribe(dstTensor)->dimensionFormat;
switch (data_format) {
case MNN_DATA_FORMAT_NHWC:
OpenCL::convertNC4HW4BufferToNHWCBuffer(srcTensor, &interBuffer,
*const_cast<cl::Kernel*>(&mNC4HW4BufferToNHWCBufferOut), mOpenCLRuntime.get(), true);
break;
case MNN_DATA_FORMAT_NCHW:
OpenCL::convertNC4HW4BufferToNCHWBuffer(srcTensor, &interBuffer,
*const_cast<cl::Kernel*>(&mNC4HW4BufferToNCHWBufferOut), mOpenCLRuntime.get(), true);
break;
case MNN_DATA_FORMAT_NC4HW4:
OpenCL::convertNC4HW4BufferToNC4HW4Buffer(srcTensor, &interBuffer,
*const_cast<cl::Kernel*>(&mNC4HW4BufferToNC4HW4BufferOut), mOpenCLRuntime.get(), true);
break;
default:
MNN_PRINT("output data format not support!\n");
break;
}
#ifdef ENABLE_OPENCL_TIME_PROFILER
mOpenCLRuntime->commandQueue().finish();
{
AUTOTIME;
mOpenCLRuntime->commandQueue().enqueueReadBuffer(*mHostBuffer.second, CL_TRUE, 0, needSize, hostPtr);
}
#else
mOpenCLRuntime->commandQueue().enqueueReadBuffer(*mHostBuffer.second, CL_TRUE, 0, needSize, hostPtr);
#endif
}
else
#endif /* MNN_OPENCL_BUFFER_CLOSED */
{
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;
}
cl_int error = CL_SUCCESS;
#ifdef ENABLE_OPENCL_TIME_PROFILER
mOpenCLRuntime->commandQueue().finish();
{
AUTOTIME;
mOpenCLRuntime->commandQueue().enqueueReadBuffer(*mHostBuffer.second, CL_TRUE, 0, needSize, hostPtr);
}
#else
mOpenCLRuntime->commandQueue().enqueueReadBuffer(*mHostBuffer.second, CL_TRUE, 0, needSize, hostPtr);
#endif
}
if(dstTensor->getType().code == halide_type_int) {
if(dstTensor->getType().bits == 8){
tmpPtr = dstTensor->host<int8_t>();
for(int i=0; i<needSize/4; i++) {
((int8_t*)tmpPtr)[i] = (int8_t)((float*)hostPtr)[i];
}
} else if(dstTensor->getType().bits == 32){
tmpPtr = dstTensor->host<int32_t>();
for(int i=0; i<needSize/4; i++) {
((int32_t*)tmpPtr)[i] = (int32_t)((float*)hostPtr)[i];
}
}
if(hostPtr != nullptr) {
free(hostPtr);
hostPtr = nullptr;
}
} else if(dstTensor->getType().code == halide_type_uint){
if(dstTensor->getType().bits == 8){
tmpPtr = dstTensor->host<uint8_t>();
for(int i=0; i<needSize/4; i++) {
((uint8_t*)tmpPtr)[i] = (uint8_t)((float*)hostPtr)[i];
}
} else if(dstTensor->getType().bits == 32){
tmpPtr = dstTensor->host<uint32_t>();
for(int i=0; i<needSize/4; i++) {
((uint32_t*)tmpPtr)[i] = (uint32_t)((float*)hostPtr)[i];
}
}
if(hostPtr != nullptr) {
free(hostPtr);
hostPtr = nullptr;
}
}
#ifdef ENABLE_OPENCL_TIME_PROFILER
MNN_PRINT("total kernel time:%d us\n", (int)mOpenCLRuntime->mKernelTime);
#endif
}
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();
void* hostPtr;
void* tmpPtr;
if(srcTensor->getType().code == halide_type_int) {
//Copy maybe slow, TODO
if(srcTensor->getType().bits == 8){
tmpPtr = srcTensor->host<int8_t>();
needSize *= 4;
hostPtr = malloc(needSize);
for(int i=0; i<needSize/4; i++) {
((float*)hostPtr)[i] = (float)((int8_t*)tmpPtr)[i];
}
} else if(srcTensor->getType().bits == 32){
tmpPtr = srcTensor->host<int32_t>();
hostPtr = malloc(needSize);
for(int i=0; i<needSize/4; i++) {
((float*)hostPtr)[i] = (float)((int32_t*)tmpPtr)[i];
}
}
} else if(srcTensor->getType().code == halide_type_uint){
//Copy maybe slow, TODO
if(srcTensor->getType().bits == 8){
tmpPtr = srcTensor->host<uint8_t>();
needSize *= 4;
hostPtr = malloc(needSize);
for(int i=0; i<needSize/4; i++) {
((float*)hostPtr)[i] = (float)((uint8_t*)tmpPtr)[i];
}
} else if(srcTensor->getType().bits == 32){
tmpPtr = srcTensor->host<uint32_t>();
hostPtr = malloc(needSize);
for(int i=0; i<needSize/4; i++) {
((float*)hostPtr)[i] = (float)((uint32_t*)tmpPtr)[i];
}
}
} else {
hostPtr = srcTensor->host<float>();
}
_allocHostBuffer(needSize);
interBuffer.buffer().device = (uint64_t)mHostBuffer.second.get();
cl_int error = CL_SUCCESS;
#ifdef ENABLE_OPENCL_TIME_PROFILER
mOpenCLRuntime->commandQueue().finish();
{
AUTOTIME;
mOpenCLRuntime->commandQueue().enqueueWriteBuffer(*mHostBuffer.second, CL_TRUE, 0, srcTensor->elementSize()*sizeof(float), hostPtr);
}
#else
mOpenCLRuntime->commandQueue().enqueueWriteBuffer(*mHostBuffer.second, CL_TRUE, 0, srcTensor->elementSize()*sizeof(float), hostPtr);
#endif
// Host -> OpenCL
MNN_DATA_FORMAT data_format = TensorUtils::getDescribe(srcTensor)->dimensionFormat;
#ifndef MNN_OPENCL_BUFFER_CLOSED
if(mOpenCLRuntime->getGpuMemType() == BUFFER)
{
if (MNN_DATA_FORMAT_NHWC == data_format) {
OpenCL::convertNHWCBufferToNC4HW4Buffer(&interBuffer, const_cast<Tensor*>(dstTensor),
*const_cast<cl::Kernel*>(&mNHWCBufferToNC4HW4BufferInp), mOpenCLRuntime.get(), true);
} else if (MNN_DATA_FORMAT_NCHW == data_format) {
OpenCL::convertNCHWBufferToNC4HW4Buffer(&interBuffer, const_cast<Tensor*>(dstTensor),
*const_cast<cl::Kernel*>(&mNCHWBufferToNC4HW4BufferInp), mOpenCLRuntime.get(), true);
} else if (MNN_DATA_FORMAT_NC4HW4 == data_format) {
OpenCL::convertNC4HW4BufferToNC4HW4Buffer(&interBuffer, const_cast<Tensor*>(dstTensor),
*const_cast<cl::Kernel*>(&mNC4HW4BufferToNC4HW4BufferInp), mOpenCLRuntime.get());
} else {
MNN_PRINT("input data format not support\n");
MNN_ASSERT(false);
}
}
else
#endif /* MNN_OPENCL_BUFFER_CLOSED */
{
if (MNN_DATA_FORMAT_NHWC == data_format) {
OpenCL::convertNHWCBufferToImage(&interBuffer, const_cast<Tensor*>(dstTensor),
*const_cast<cl::Kernel*>(&mNHWCBufferToImageFloat), mOpenCLRuntime.get());
} else if (MNN_DATA_FORMAT_NCHW == data_format) {
OpenCL::convertNCHWBufferToImage(&interBuffer, const_cast<Tensor*>(dstTensor),
*const_cast<cl::Kernel*>(&mNCHWBufferToImageFloat), mOpenCLRuntime.get());
} else if (MNN_DATA_FORMAT_NC4HW4 == data_format) {
OpenCL::convertNC4HW4BufferToImage(&interBuffer, const_cast<Tensor*>(dstTensor),
*const_cast<cl::Kernel*>(&mNC4HW4BufferToImageFloat),
mOpenCLRuntime.get());
} else {
MNN_PRINT("data format not support\n");
MNN_ASSERT(false);
}
}
if(srcTensor->getType().code == halide_type_uint || srcTensor->getType().code == halide_type_int){
mOpenCLRuntime.get()->commandQueue().finish();
if(nullptr != hostPtr){
free(hostPtr);
hostPtr = nullptr;
}
}
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(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
auto rt = new CLRuntime(info);
if(rt->isCLRuntimeError() == true) {
delete rt;
return nullptr;
}
return rt;
}
virtual bool onValid(Backend::Info& info) const {
return true;
}
};
static bool gResistor = []() {
MNNInsertExtraRuntimeCreator(MNN_FORWARD_OPENCL, new CLRuntimeCreator, true);
return false;
}();
} // namespace OpenCL
} // namespace MNN
Reference in New Issue View Git Blame Copy Permalink