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chore: import upstream snapshot with attribution

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wehub-resource-sync
2026-07-13 13:33:03 +08:00
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source/backend/opencl/execution/image/ReductionExecution.cpp
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//
// ReductionExecution.cpp
// MNN
//
// Created by MNN on 2019/10/25.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include "backend/opencl/execution/image/ReductionExecution.hpp"
#include "core/Macro.h"
#include "core/TensorUtils.hpp"
namespace MNN {
namespace OpenCL {
ReductionExecution::ReductionExecution(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs, const MNN::Op* op, Backend* backend) : CommonExecution(backend, op) {
#ifdef LOG_VERBOSE
MNN_PRINT("start ReductionExecution init !\n");
#endif
mUnits.resize(1);
auto &unit = mUnits[0];
mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
mAxis = op->main_as_ReductionParam()->dim()->data()[0];
switch (op->main_as_ReductionParam()->operation()) {
case ReductionType_MEAN:
mReductType = 0;
break;
case ReductionType_MAXIMUM:
mReductType = 1;
break;
case ReductionType_MINIMUM:
mReductType = 2;
break;
case ReductionType_PROD:
mReductType = 3;
break;
case ReductionType_SUM:
mReductType = 4;
break;
default:
MNN_ASSERT(false);
break;
}
unit.kernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("reduction", "reduct_width", {"-DOPERATE(a,b)=(a+b)","-DVALUE=0","-DLOCAL_SIZE=512"}, mOpenCLBackend->getPrecision(), inputs[0], outputs[0]);
OPENCL_CHECK_KERNEL_CTOR(unit.kernel);
mMaxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(unit.kernel));
#ifdef LOG_VERBOSE
MNN_PRINT("end ReductionExecution init !\n");
#endif
}
int ReductionExecution::getLocalSize(int size, int maxGroupSize){
int local_size = 1;
while(local_size * 2 <= maxGroupSize && local_size * 2 <= size){
local_size *= 2;
}
return local_size;
}
ErrorCode ReductionExecution::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
auto &unit = mUnits[0];
auto runtime = mOpenCLBackend->getOpenCLRuntime();
auto MaxLocalSize = std::min(runtime->getMaxWorkItemSizes()[0], mMaxWorkGroupSize);
auto input = inputs[0];
auto output = outputs[0];
if(mAxis < 0){
mAxis = input->dimensions() + mAxis;
}
int inside = 1;
int outside = 1;
for(int i = 0; i < mAxis; ++i){
outside *= input->length(i);
}
for(int i = mAxis + 1; i < input->dimensions(); ++i){
inside *= input->length(i);
}
int dim = input->length(mAxis);
int local_size = 0;
if(dim >= 16){
mUseLocal = true;
}
std::vector<int> inputShape = tensorShapeFormat(input);
std::vector<int> outputShape = tensorShapeFormat(output);
int batch = inputShape.at(0);
int inputHeight = inputShape.at(1);
int inputWidth = inputShape.at(2);
int inputChannels = inputShape.at(3);
int inputChannelBlocks = (inputChannels + 3) / 4;
int outputBatch = outputShape.at(0);
int outputHeight = outputShape.at(1);
int outputWidth = outputShape.at(2);
int outputChannels = outputShape.at(3);
int outputChannelBlocks = (outputChannels + 3) / 4;
std::set<std::string> buildOption;
switch (mReductType) {
case 0:
buildOption.emplace("-DOPERATE(a,b)=(a+b)");
buildOption.emplace("-DGET_AVG");
buildOption.emplace("-DVALUE=0");
break;
case 1:
buildOption.emplace("-DOPERATE(a,b)=max(a,b)");
buildOption.emplace("-DVALUE=-FLT_MAX");
break;
case 2:
buildOption.emplace("-DOPERATE(a,b)=min(a,b)");
buildOption.emplace("-DVALUE=FLT_MAX");
break;
case 3:
buildOption.emplace("-DOPERATE(a,b)=(a*b)");
buildOption.emplace("-DVALUE=1");
break;
case 4:
buildOption.emplace("-DOPERATE(a,b)=(a+b)");
buildOption.emplace("-DVALUE=0");
break;
default:
MNN_ASSERT(false);
break;
}
std::vector<uint32_t> mGlobalWorkSize = {1, 1, 1};
std::vector<uint32_t> mLocalWorkSize{1, 1, 1};
mGlobalWorkSize = {
static_cast<uint32_t>(outputWidth),
static_cast<uint32_t>(outputHeight),
static_cast<uint32_t>(outputBatch * outputChannelBlocks)
};
if(mUseLocal){
if(batch * inputHeight * inputChannels == outside && 1 == inside && dim == inputWidth){
local_size = getLocalSize(inputWidth, MaxLocalSize);
buildOption.emplace("-DLOCAL_SIZE=" + std::to_string(local_size));
unit.kernel = runtime->buildKernel("reduction", "reduct_width", buildOption, mOpenCLBackend->getPrecision(), input, output);
}else if(batch * inputChannels == outside && inputWidth == inside && dim == inputHeight){
local_size = getLocalSize(inputHeight, MaxLocalSize);
buildOption.emplace("-DLOCAL_SIZE=" + std::to_string(local_size));
unit.kernel = runtime->buildKernel("reduction", "reduct_height", buildOption, mOpenCLBackend->getPrecision(), input, output);
}else if(batch == outside && inputWidth * inputHeight == inside && dim == inputChannels){
local_size = getLocalSize(inputChannelBlocks - 1, MaxLocalSize);
buildOption.emplace("-DLOCAL_SIZE=" + std::to_string(local_size));
unit.kernel = runtime->buildKernel("reduction", "reduct_channel", buildOption, mOpenCLBackend->getPrecision(), input, output);
mGlobalWorkSize[2] = static_cast<uint32_t>(outputBatch * outputChannels);
}else if(1 == outside && inputWidth * inputHeight * inputChannels == inside && dim == batch){
local_size = getLocalSize(batch, MaxLocalSize);
buildOption.emplace("-DLOCAL_SIZE=" + std::to_string(local_size));
unit.kernel = runtime->buildKernel("reduction", "reduct_batch", buildOption, mOpenCLBackend->getPrecision(), input, output);
}
mGlobalWorkSize[0] *= local_size;
}else{
buildOption.emplace("-DLOCAL_SIZE=0");
if(batch * inputHeight * inputChannels == outside && 1 == inside && dim == inputWidth){
unit.kernel = runtime->buildKernel("reduction", "reduct_width", buildOption, mOpenCLBackend->getPrecision(), input, output);
}else if(batch * inputChannels == outside && inputWidth == inside && dim == inputHeight){
unit.kernel = runtime->buildKernel("reduction", "reduct_height", buildOption, mOpenCLBackend->getPrecision(), input, output);
}else if(batch == outside && inputWidth * inputHeight == inside && dim == inputChannels){
unit.kernel = runtime->buildKernel("reduction", "reduct_channel", buildOption, mOpenCLBackend->getPrecision(), input, output);
mGlobalWorkSize[2] = static_cast<uint32_t>(outputBatch * outputChannels);
}else if(1 == outside && inputWidth * inputHeight * inputChannels == inside && dim == batch){
unit.kernel = runtime->buildKernel("reduction", "reduct_batch", buildOption, mOpenCLBackend->getPrecision(), input, output);
}
}
mUnits.resize(1);
uint32_t idx = 0;
cl_int ret = CL_SUCCESS;
ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[0]);
ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[1]);
ret |= unit.kernel->get().setArg(idx++, mGlobalWorkSize[2]);
ret |= unit.kernel->get().setArg(idx++, openCLImage(input));
ret |= unit.kernel->get().setArg(idx++, openCLImage(output));
ret |= unit.kernel->get().setArg(idx++, inputWidth);
ret |= unit.kernel->get().setArg(idx++, inputHeight);
ret |= unit.kernel->get().setArg(idx++, inputChannels);
ret |= unit.kernel->get().setArg(idx++, batch);
ret |= unit.kernel->get().setArg(idx++, inputChannelBlocks);
ret |= unit.kernel->get().setArg(idx++, outputWidth);
ret |= unit.kernel->get().setArg(idx++, outputHeight);
ret |= unit.kernel->get().setArg(idx++, outputChannels);
ret |= unit.kernel->get().setArg(idx++, outputChannelBlocks);
MNN_CHECK_CL_SUCCESS(ret, "setArg ReductionExecution");
if(mUseLocal){
mLocalWorkSize = {static_cast<uint32_t>(local_size), 1, 1};
}else{
mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
std::string kernelName = "reduct";
mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, runtime, kernelName, unit.kernel, mOpenCLBackend->getCLTuneLevel(), "reduction").first;
}
mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalWorkSize);
unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
unit.localWorkSize = {mLocalWorkSize[0], mLocalWorkSize[1], mLocalWorkSize[2]};
return NO_ERROR;
}
class ReductionCreator : public OpenCLBackend::Creator {
public:
virtual ~ReductionCreator() = default;
virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
const MNN::Op *op, Backend *backend) const override {
auto openCLBackend = static_cast<OpenCLBackend *>(backend);
auto reduct = op->main_as_ReductionParam();
if (nullptr == reduct->dim()) {
return NULL;
}
if(reduct->dim()->size() != 1) {
return NULL;
}
auto axis = reduct->dim()->data()[0];
int dim = inputs[0]->length(axis);
std::vector<int> inputShape = tensorShapeFormat(inputs[0]);
if(dim == inputShape.at(3) && outputs[0]->buffer().dimensions == 1){
return NULL;
}
switch (op->main_as_ReductionParam()->operation()) {
case ReductionType_MEAN:
break;
case ReductionType_MAXIMUM:
break;
case ReductionType_MINIMUM:
break;
case ReductionType_PROD:
break;
case ReductionType_SUM:
break;
default:
return NULL;
break;
}
OPENCL_CREATOR_CHECK(new ReductionExecution(inputs, outputs, op, backend));
return NULL;
}
};
REGISTER_OPENCL_OP_CREATOR(ReductionCreator, OpType_Reduction, IMAGE);
} // namespace OpenCL
} // namespace MNN