mirror of https://github.com/alibaba/MNN.git
75 lines
3.0 KiB
C++
75 lines
3.0 KiB
C++
//
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// NPULayerNorm.cpp
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// MNN
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//
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// Created by MNN on b'2020/10/15'.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "NPULayerNorm.hpp"
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#include "NPUBackend.hpp"
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using namespace std;
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namespace MNN {
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NPULayerNorm::NPULayerNorm(MNN::Backend *b, const MNN::Op *op, const std::vector<Tensor *> &inputs, const std::vector<MNN::Tensor *> &outputs) : NPUCommonExecution(b, op) {}
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ErrorCode NPULayerNorm::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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mNpuBackend->setNetworkInput(inputs, mOp);
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auto opName = mOp->name()->str();
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auto param = mOp->main_as_LayerNorm();
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auto xOp = mNpuBackend->getInputOps(mOp);
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shared_ptr<hiai::op::LayerNorm> layerNorm(new hiai::op::LayerNorm(opName));
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auto inputIndex = mOp->inputIndexes()->data()[0];
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auto iops = mNpuBackend->mGrapMap[inputIndex]; // x
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xOp = iops.back().first;
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constw = hiai::op::Const(opName + "_w_const");
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constb = hiai::op::Const(opName + "_b_const");
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if (param->gamma() == nullptr && param->beta() == nullptr) {
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auto shape = inputs[0]->shape();
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int32_t size = shape[shape.size()-1];
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vector<float> data(size, 1);
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vector<float> data1(size, 0);
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vector<int64_t> shape1{static_cast<int64_t>(size)};
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ge::TensorDesc fdesc(ge::Shape(shape1), ge::FORMAT_NCHW, ge::DT_FLOAT);
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ge::TensorPtr filter = std::make_shared<ge::Tensor>();
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filter->SetTensorDesc(fdesc);
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filter->SetData((uint8_t *)data.data(), size * sizeof(float));
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constw.set_attr_value(filter);
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ge::TensorDesc fdesc1(ge::Shape(shape1), ge::FORMAT_NCHW, ge::DT_FLOAT);
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ge::TensorPtr filter1 = std::make_shared<ge::Tensor>();
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filter1->SetTensorDesc(fdesc1);
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filter1->SetData((uint8_t *)data1.data(), size * sizeof(float));
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constb.set_attr_value(filter1);
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} else {
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uint32_t size = param->gamma()->size();
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vector<int64_t> shape1{size};
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ge::TensorDesc fdesc(ge::Shape(shape1), ge::FORMAT_NCHW, ge::DT_FLOAT);
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ge::TensorPtr filter = std::make_shared<ge::Tensor>();
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filter->SetTensorDesc(fdesc);
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filter->SetData((uint8_t *)param->gamma()->Data(), size * sizeof(float));
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constw.set_attr_value(filter);
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size = param->beta()->size();
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vector<int64_t> shape2{size};
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ge::TensorDesc fdesc1(ge::Shape(shape2), ge::FORMAT_NCHW, ge::DT_FLOAT);
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ge::TensorPtr filter1 = std::make_shared<ge::Tensor>();
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filter1->SetTensorDesc(fdesc1);
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filter1->SetData((uint8_t *)param->beta()->Data(), size * sizeof(float));
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constb.set_attr_value(filter1);
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}
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float eps = param->epsilon();
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(*layerNorm).set_input_x(*xOp.get())
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.set_input_gamma(constw)
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.set_input_beta(constb)
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.set_attr_epsilon(eps);
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mNpuBackend->setOutputOps(mOp, {layerNorm}, outputs);
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return NO_ERROR;
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}
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NPUCreatorRegister<TypedCreator<NPULayerNorm>> __LayerNorm_op(OpType_LayerNorm);
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} // namespace MNN
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