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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/GroupNormBufExecution.cpp
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//
// GroupNormBufExecution.cpp
// MNN
//
// Created by MNN on 2024/06/24.
// Copyright © 2018, Alibaba Group Holding Limited
//
#ifdef MNN_SUPPORT_TRANSFORMER_FUSE
#include "backend/opencl/execution/buffer/GroupNormBufExecution.hpp"
namespace MNN {
namespace OpenCL {
GroupNormBufExecution::GroupNormBufExecution(const MNN::Op* op, Backend* backend) : CommonExecution(backend, op) {
auto group_norm_param = op->main_as_GroupNorm();
mOpenCLBackend = static_cast<OpenCLBackend *>(backend);
auto runtime = mOpenCLBackend->getOpenCLRuntime();
mEpsilon = group_norm_param->epsilon();
mBSwish = group_norm_param->bSwish();
mGroup = group_norm_param->group();
if (group_norm_param->gamma() && group_norm_param->beta()) {
auto bufferUnitSize = mOpenCLBackend->getPrecision() != BackendConfig::Precision_High ? sizeof(half_float::half) : sizeof(float);
mHasGammaBeta = true;
int size = group_norm_param->gamma()->size();
mGammaTensor.reset(Tensor::createDevice<float>({ALIGN_UP4(size)}));
auto status = backend->onAcquireBuffer(mGammaTensor.get(), Backend::STATIC);
if (!status) {
MNN_ERROR("Out of memory when gamma is acquired in GroupNorm.\n");
}
if (group_norm_param->beta()->size() != size) {
MNN_ERROR("Size of gamma and beta are not match in GroupNorm.\n");
}
mBetaTensor.reset(Tensor::createDevice<float>({ALIGN_UP4(size)}));
status = backend->onAcquireBuffer(mBetaTensor.get(), Backend::STATIC);
if (!status) {
mValid = false;
MNN_ERROR("Out of memory when beta is acquired in GroupNorm.\n");
return;
}
if (mOpenCLBackend->getRuntime()->hint().useCachedMmap <= 1){
cl_int res;
cl::Buffer &gammaBuffer = openCLBuffer(mGammaTensor.get());
auto GammaPtrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(gammaBuffer, true, CL_MAP_WRITE, 0, ALIGN_UP4(size) * bufferUnitSize, nullptr, nullptr, &res);
if(GammaPtrCL != nullptr && res == CL_SUCCESS){
if(mOpenCLBackend->getPrecision() != BackendConfig::Precision_High){
for (int i = 0; i < size; i++) {
((half_float::half*)GammaPtrCL)[i] = (half_float::half)(group_norm_param->gamma()->data()[i]);
}
for(int i=size; i<ALIGN_UP4(size); i++) {
((half_float::half*)GammaPtrCL)[i] = (half_float::half)(0.0f);
}
}else{
::memset(GammaPtrCL, 0, ALIGN_UP4(size) * sizeof(float));
::memcpy(GammaPtrCL, group_norm_param->gamma()->data(), size * sizeof(float));
}
} else {
MNN_ERROR("GroupNorm Gamma map error:%d\n", res);
}
cl::Buffer &betaBuffer = openCLBuffer(mBetaTensor.get());
auto BetaPtrCL = mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueMapBuffer(betaBuffer, true, CL_MAP_WRITE, 0, ALIGN_UP4(size) * bufferUnitSize, nullptr, nullptr, &res);
if(BetaPtrCL != nullptr && res == CL_SUCCESS){
if(mOpenCLBackend->getPrecision() != BackendConfig::Precision_High){
for (int i = 0; i < size; i++) {
((half_float::half*)BetaPtrCL)[i] = (half_float::half)(group_norm_param->beta()->data()[i]);
}
for(int i=size; i<ALIGN_UP4(size); i++) {
((half_float::half*)BetaPtrCL)[i] = (half_float::half)(0.0f);
}
}else{
::memset(BetaPtrCL, 0, ALIGN_UP4(size) * sizeof(float));
::memcpy(BetaPtrCL, group_norm_param->beta()->data(), size * sizeof(float));
}
} else {
MNN_ERROR("GroupNorm Beta map error:%d\n", res);
}
mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(gammaBuffer, GammaPtrCL);
mOpenCLBackend->getOpenCLRuntime()->commandQueue().enqueueUnmapMemObject(betaBuffer, BetaPtrCL);
}
}
}
int GroupNormBufExecution::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 GroupNormBufExecution::onEncode(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
auto runtime = static_cast<OpenCLBackend*>(backend())->getOpenCLRuntime();
MNN_ASSERT(outputs.size() == 1);
auto input = inputs[0];
auto output = outputs[0];
mBatch = input->length(0);
if(inputs.size() > 1) {
MNN_ASSERT(inputs[1]->dimensions() == 2);
MNN_ASSERT(inputs[1]->length(0) == inputs[0]->length(0));
MNN_ASSERT(inputs[1]->length(1) == inputs[0]->length(1));
}
size_t outter_size = mBatch * mGroup;
size_t inner_size = 1;
for (int i = 1; i < input->dimensions(); i++) {
inner_size *= inputs[0]->length(i);
}
inner_size /= mGroup;
mUnits.clear();
mUnits.resize(1);
std::vector<int> inputShape = tensorShapeFormat(inputs[0]);
int inputWH[] = {inputShape[2], inputShape[1]};
int region[] = {inputShape[0], UP_DIV(inputShape[3], 4), inputShape[1], inputShape[2]};
std::set<std::string> buildOptions;
// do groupnorm
{
int area = inputWH[1] * inputWH[0];
if(mHasGammaBeta){
buildOptions.emplace("-DGAMMA_BETA");
}
if(mBSwish) {
buildOptions.emplace("-DSWISH");
}
if(area % 4 == 0) {
buildOptions.emplace("-DWH_4");
}
if(inputs.size() > 1) {
buildOptions.emplace("-DDOUBLE_INPUTS");
}
auto MaxLocalSize = std::min(runtime->getMaxWorkItemSizes()[0], (uint32_t)256);
auto &unit = mUnits[0];
std::string kernelName = "groupnorm_plain_buf";
int local_size = getLocalSize(UP_DIV(inner_size, 4), MaxLocalSize);
buildOptions.emplace("-DLOCAL_SIZE=" + std::to_string(local_size));
unit.kernel = runtime->buildKernel("groupnorm_buf", kernelName, buildOptions, mOpenCLBackend->getPrecision());
mGWS = {static_cast<uint32_t>(local_size),
static_cast<uint32_t>(1),
static_cast<uint32_t>(outter_size)};
mLWS = {static_cast<uint32_t>(local_size), 1, 1};
unit.globalWorkSize = {mGWS[0], mGWS[1], mGWS[2]};
unit.localWorkSize = {mLWS[0], mLWS[1], mLWS[2]};
uint32_t idx = 0;
cl_int ret = CL_SUCCESS;
ret |= unit.kernel->get().setArg(idx++, mGWS[0]);
ret |= unit.kernel->get().setArg(idx++, mGWS[1]);
ret |= unit.kernel->get().setArg(idx++, mGWS[2]);
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(input));
if(inputs.size() > 1) {
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(inputs[1]));
}
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(output));
ret |= unit.kernel->get().setArg(idx++, static_cast<int32_t>(area));
ret |= unit.kernel->get().setArg(idx++, static_cast<int32_t>(mGroup));
ret |= unit.kernel->get().setArg(idx++, static_cast<int32_t>(inner_size));
ret |= unit.kernel->get().setArg(idx++, static_cast<int32_t>(outter_size));
if(mHasGammaBeta){
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mGammaTensor.get()));
ret |= unit.kernel->get().setArg(idx++, openCLBuffer(mBetaTensor.get()));
}
ret |= unit.kernel->get().setArg(idx++, mEpsilon);
MNN_CHECK_CL_SUCCESS(ret, "setArg GroupNormBufExecution with group, do group layernorm");
mOpenCLBackend->recordKernel3d(unit.kernel, mGWS, mLWS);
}
mOpenCLBackend->endRecord(mRecording);
return NO_ERROR;
}
class GroupNormBufCreator : public OpenCLBackend::Creator {
public:
virtual ~GroupNormBufCreator() = 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);
}
OPENCL_CREATOR_CHECK(new GroupNormBufExecution(op, backend));
}
};
REGISTER_OPENCL_OP_CREATOR_TRANSFORMER(GroupNormBufCreator, OpType_GroupNorm, BUFFER);
} // namespace OpenCL
} // namespace MNN
#endif/* MNN_SUPPORT_TRANSFORMER_FUSE */