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MNN/source/backend/opencl/execution/buffer/ArgMaxBufExecution.cpp

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//
// ArgMaxBufExecution.cpp
// MNN
//
// Created by MNN on 2023/08/11.
// Copyright © 2018, Alibaba Group Holding Limited
//
#ifndef MNN_OPENCL_BUFFER_CLOSED
#include "backend/opencl/execution/buffer/ArgMaxBufExecution.hpp"
namespace MNN {
namespace OpenCL {
ArgMaxBufExecution::ArgMaxBufExecution(const std::string &compute, const MNN::Op* op, Backend* backend, const int axis) : CommonExecution(backend, op) {
mBuildOptions.emplace(compute);
mAxis = axis;
// Do nothing
mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
std::set<std::string> buildOptions = mBuildOptions;
buildOptions.emplace("-DARGMAX_LOCAL_SIZE=512");
auto kernel = mOpenCLBackend->getOpenCLRuntime()->buildKernel("argmax_buf", "argmax_buf", buildOptions, mOpenCLBackend->getPrecision());
mMaxWorkGroupSize = static_cast<uint32_t>(mOpenCLBackend->getOpenCLRuntime()->getMaxWorkGroupSize(kernel));
}
int ArgMaxBufExecution::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 ArgMaxBufExecution::onEncode(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
mUnits.clear();
auto runtime = mOpenCLBackend->getOpenCLRuntime();
auto MaxLocalSize = std::min(runtime->getMaxWorkItemSizes()[0], mMaxWorkGroupSize);
auto input = inputs[0];
auto output = outputs[0];
const auto layout = TensorUtils::getDescribe(input)->dimensionFormat;
mNeedUnpackC4 = layout == MNN_DATA_FORMAT_NC4HW4;
if (mNeedUnpackC4) {
int inputTotalSize = 1, outputTotalSize = 1;
for (int i = 1; i < input->dimensions(); ++i) {
inputTotalSize *= input->length(i);
}
for (int i = 1; i < output->dimensions(); ++i) {
outputTotalSize *= output->length(i);
}
mTempInputTensor.reset(Tensor::createDevice<float>({inputTotalSize}));
mTempOutputTensor.reset(Tensor::createDevice<float>({outputTotalSize}));
mOpenCLBackend->onAcquireBuffer(mTempInputTensor.get(), Backend::DYNAMIC);
mOpenCLBackend->onAcquireBuffer(mTempOutputTensor.get(), Backend::DYNAMIC);
mOpenCLBackend->onReleaseBuffer(mTempInputTensor.get(), Backend::DYNAMIC);
mOpenCLBackend->onReleaseBuffer(mTempOutputTensor.get(), Backend::DYNAMIC);
}
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);
// NC4HW4 -> NCHW
if(mNeedUnpackC4){
Unit unit;
std::vector<int> outputShape = tensorShapeFormat(input);
int shape[4] = {outputShape[0], outputShape[3], outputShape[1], outputShape[2]};//N C H W
std::set<std::string> buildOptions;
buildOptions.emplace("-DINPUT_FORMAT=MNN_DATA_FORMAT_NC4HW4");
buildOptions.emplace("-DOUTPUT_FORMAT=MNN_DATA_FORMAT_NCHW");
unit.kernel = runtime->buildKernel("buffer_convert_buf", "buffer_convert_to_buffer", buildOptions, mOpenCLBackend->getPrecision(), input, output);
mGlobalWorkSize = {static_cast<uint32_t>(shape[2] * shape[3]), static_cast<uint32_t>(shape[1]), static_cast<uint32_t>(shape[0])};
cl_int ret = CL_SUCCESS;
uint32_t idx = 0;
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++, sizeof(shape), shape);
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mTempInputTensor.get()));
MNN_CHECK_CL_SUCCESS(ret, "setArg buffer_convert_to_buffer");
const uint32_t maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
mLocalSize = {16, std::max((uint32_t)1, maxWorkGroupSize / 16), 1};
mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalSize);
unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
unit.localWorkSize = {mLocalSize[0], mLocalSize[1], mLocalSize[2]};
mUnits.emplace_back(unit);
}
// Argmax
{
Unit unit;
int localSize = getLocalSize(dim, MaxLocalSize);
if(localSize < 4){
localSize = 1;
}
std::set<std::string> buildOptions = mBuildOptions;
buildOptions.emplace("-DARGMAX_LOCAL_SIZE=" + std::to_string(localSize));
std::string kernelName;
if(inside % 4 == 0){
kernelName = "argmax_v4_buf";
unit.kernel = runtime->buildKernel("argmax_buf", kernelName, buildOptions, mOpenCLBackend->getPrecision());
mGlobalWorkSize = {static_cast<uint32_t>(localSize), static_cast<uint32_t>(UP_DIV(inside, 4)), static_cast<uint32_t>(outside)};
}else {
kernelName = "argmax_buf";
unit.kernel = runtime->buildKernel("argmax_buf", kernelName, buildOptions, mOpenCLBackend->getPrecision());
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));
mLocalSize = {(uint32_t)(localSize), 1, 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]);
if(mNeedUnpackC4){
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mTempInputTensor.get()));
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mTempOutputTensor.get()));
}else{
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 ArgMaxBufExecution");
if(localSize == 1){
mLocalSize = localWS3DDefault(mGlobalWorkSize, mMaxWorkGroupSize, mOpenCLBackend->getOpenCLRuntime(), kernelName, unit.kernel, mOpenCLBackend->getCLTuneLevel(), "argmax_buf").first;
}
mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalSize);
unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
unit.localWorkSize = {mLocalSize[0], mLocalSize[1], mLocalSize[2]};
mUnits.emplace_back(unit);
}
// NCHW -> NC4HW4
if(mNeedUnpackC4){
Unit unit;
std::vector<int> outputShape = tensorShapeFormat(output);
int shape[4] = {outputShape[0], outputShape[3], outputShape[1], outputShape[2]};//N C H W
std::set<std::string> buildOptions;
buildOptions.emplace("-DINPUT_FORMAT=MNN_DATA_FORMAT_NCHW");
buildOptions.emplace("-DOUTPUT_FORMAT=MNN_DATA_FORMAT_NC4HW4");
unit.kernel = runtime->buildKernel("buffer_convert_buf", "buffer_convert_to_buffer", buildOptions, mOpenCLBackend->getPrecision(), input, output);
mGlobalWorkSize = {static_cast<uint32_t>(shape[2] * shape[3]), static_cast<uint32_t>(shape[1]), static_cast<uint32_t>(shape[0])};
cl_int ret = CL_SUCCESS;
uint32_t idx = 0;
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(mTempOutputTensor.get()));
ret |= unit.kernel->get().setArg(idx++, sizeof(shape), shape);
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
MNN_CHECK_CL_SUCCESS(ret, "setArg buffer_convert_to_buffer");
const uint32_t maxWorkGroupSize = static_cast<uint32_t>(runtime->getMaxWorkGroupSize(unit.kernel));
mLocalSize = {16, std::max((uint32_t)1, maxWorkGroupSize / 16), 1};
mOpenCLBackend->recordKernel3d(unit.kernel, mGlobalWorkSize, mLocalSize);
unit.globalWorkSize = {mGlobalWorkSize[0], mGlobalWorkSize[1], mGlobalWorkSize[2]};
unit.localWorkSize = {mLocalSize[0], mLocalSize[1], mLocalSize[2]};
mUnits.emplace_back(unit);
}
return NO_ERROR;
}
class ArgMaxBufCreator : public OpenCLBackend::Creator {
public:
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 inputDimensionFromat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
if(inputDimensionFromat == MNN_DATA_FORMAT_NC4HW4){
return nullptr;
}
int axis = op->main_as_ArgMax()->axis();
if (op->type() == OpType_ArgMax) {
return new ArgMaxBufExecution("-DARGMAX", op, backend, axis);
}else{
return new ArgMaxBufExecution("", op, backend, axis);
}
}
};
REGISTER_OPENCL_OP_CREATOR(ArgMaxBufCreator, OpType_ArgMax, BUFFER);
REGISTER_OPENCL_OP_CREATOR(ArgMaxBufCreator, OpType_ArgMin, BUFFER);
} // namespace OpenCL
} // namespace MNN
#endif /* MNN_OPENCL_BUFFER_CLOSED */
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