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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/buffer/ReductionBufExecution.cpp
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//
// ReductionBufExecution.cpp
// MNN
//
// Created by MNN on 2019/10/25.
// Copyright © 2018, Alibaba Group Holding Limited
//
#ifndef MNN_OPENCL_BUFFER_CLOSED
#include "backend/opencl/execution/buffer/ReductionBufExecution.hpp"
#include "core/Macro.h"
#include "core/TensorUtils.hpp"
namespace MNN {
namespace OpenCL {
ReductionBufExecution::ReductionBufExecution(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 ReductionBufExecution init !\n");
#endif
mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
mAxis = op->main_as_ReductionParam()->dim()->data()[0];
switch (op->main_as_ReductionParam()->operation()) {
case ReductionType_MEAN:
mBuildOptions.emplace("-DOPERATE(a,b)=(a+b)");
mBuildOptions.emplace("-DGET_AVG");
mBuildOptions.emplace("-DVALUE=0");
break;
case ReductionType_MAXIMUM:
mBuildOptions.emplace("-DOPERATE(a,b)=max(a,b)");
mBuildOptions.emplace("-DVALUE=-FLT_MAX");
break;
case ReductionType_MINIMUM:
mBuildOptions.emplace("-DOPERATE(a,b)=min(a,b)");
mBuildOptions.emplace("-DVALUE=FLT_MAX");
break;
case ReductionType_PROD:
mBuildOptions.emplace("-DOPERATE(a,b)=(a*b)");
mBuildOptions.emplace("-DVALUE=1");
break;
case ReductionType_SUM:
mBuildOptions.emplace("-DOPERATE(a,b)=(a+b)");
mBuildOptions.emplace("-DVALUE=0");
break;
default:
MNN_ASSERT(false);
break;
}
auto kernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("reduction_buf", "reduct_buf", {"-DOPERATE(a,b)=(a+b)","-DVALUE=0","-DLOCAL_SIZE=512"}, mOpenCLBackend->getPrecision(), inputs[0], outputs[0]);
OPENCL_CHECK_KERNEL_CTOR(kernel);
mMaxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(kernel));
#ifdef LOG_VERBOSE
MNN_PRINT("end ReductionBufExecution init !\n");
#endif
}
int ReductionBufExecution::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 ReductionBufExecution::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
mUnits.resize(1);
auto &unit = mUnits[0];
auto openCLBackend = static_cast<OpenCLBackend*>(backend());
auto runtime = openCLBackend->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 localSize = getLocalSize(dim, MaxLocalSize);
if(localSize < 4){
localSize = 1;
}
std::set<std::string> buildOptions = mBuildOptions;
buildOptions.emplace("-DREDUCT_LOCAL_SIZE=" + std::to_string(localSize));
std::string kernelName;
if(inside % 4 == 0){
unit.kernel = runtime->buildKernel("reduction_buf", "reduct_v4_buf", buildOptions, mOpenCLBackend->getPrecision(), input, output);
mGlobalWorkSize = {static_cast<uint32_t>(localSize), static_cast<uint32_t>(UP_DIV(inside, 4)), static_cast<uint32_t>(outside)};
}else {
unit.kernel = runtime->buildKernel("reduction_buf", "reduct_buf", buildOptions, mOpenCLBackend->getPrecision(), input, output);
mGlobalWorkSize = {static_cast<uint32_t>(localSize), static_cast<uint32_t>(inside), static_cast<uint32_t>(outside)};
}
mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
mLocalWorkSize = {(uint32_t)(localSize), 1, 1};
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++, openCLBuffer(input));
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
ret |= unit.kernel->get().setArg(idx++, inside);
ret |= unit.kernel->get().setArg(idx++, outside);
ret |= unit.kernel->get().setArg(idx++, dim);
MNN_CHECK_CL_SUCCESS(ret, "setArg ReductionBufExecution");
if(localSize == 1){
mMaxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
std::string kernelName = "reduct_buf";
mLocalWorkSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, openCLBackend->getOpenCLRuntime(), kernelName, unit.kernel, openCLBackend->getCLTuneLevel(), "reduction_buf").first;
}
openCLBackend->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 ReductionBufCreator : public OpenCLBackend::Creator {
public:
virtual ~ReductionBufCreator() = default;
virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
const MNN::Op *op, Backend *backend) const override {
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);
}
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;
}
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 ReductionBufExecution(inputs, outputs, op, backend));
}
};
REGISTER_OPENCL_OP_CREATOR(ReductionBufCreator, OpType_Reduction, BUFFER);
} // namespace OpenCL
} // namespace MNN
#endif /* MNN_OPENCL_BUFFER_CLOSED */