wehub-resource-sync
213 lines
9.6 KiB
C++
213 lines
9.6 KiB
C++
//
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// NPUBinary.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 "NPUBinary.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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template<class T>
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void NPUBinary::BinaryCastIR(string opName, hiai::Operator& input0, hiai::Operator& input1,
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const std::vector<Tensor *>& outputs, int activationType, shared_ptr<T> binary) {
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shared_ptr<hiai::op::CastT> castTOp(new hiai::op::CastT(opName + "castTOp"));
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shared_ptr<hiai::op::CastT> castTOp1(new hiai::op::CastT(opName + "castTOp1"));
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shared_ptr<hiai::op::CastT> castTOpAfter(new hiai::op::CastT(opName + "castTOpAfter"));
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auto binaryParam = mOp->main_as_BinaryOp();
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auto t = binaryParam->T();
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if (flag0) {
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(*castTOp)
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.set_input_x(input0.GetOutput(mNpuBackend->mSclipMap[inputIndex0]))
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.set_attr_dst_dtype(0);
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(*binary).set_input_x1(*castTOp.get());
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} else {
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(*castTOp)
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.set_input_x(input0)
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.set_attr_dst_dtype(0);
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(*binary).set_input_x1(*castTOp.get());
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}
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if (flag1) {
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(*castTOp1)
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.set_input_x(input1.GetOutput(mNpuBackend->mSclipMap[inputIndex1]))
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.set_attr_dst_dtype(0);
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(*binary).set_input_x2(*castTOp1.get());
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} else {
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(*castTOp1)
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.set_input_x(input1)
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.set_attr_dst_dtype(0);
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(*binary).set_input_x2(*castTOp1.get());
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}
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(*castTOpAfter)
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.set_input_x(*binary.get())
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.set_attr_dst_dtype(mapDataType(t));
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if(activationType == 1) {
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shared_ptr<hiai::op::Activation> binary_activation(new hiai::op::Activation(opName + "_Relu"));
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(*binary_activation)
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.set_input_x(*castTOpAfter.get())
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.set_attr_mode(1);
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mNpuBackend->setOutputOps(mOp, {castTOp, castTOp1, binary, castTOpAfter, binary_activation}, outputs);
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} else {
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mNpuBackend->setOutputOps(mOp, {castTOp, castTOp1, binary, castTOpAfter}, outputs);
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}
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}
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template<class T>
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void NPUBinary::BinaryIR(string opName, hiai::Operator& input0, hiai::Operator& input1,
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const std::vector<Tensor *>& outputs, int activationType, shared_ptr<T> binary) {
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if (flag0) {
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(*binary).set_input_x1(input0.GetOutput(mNpuBackend->mSclipMap[inputIndex0]));
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} else {
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(*binary).set_input_x1(input0);
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}
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if (flag1) {
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(*binary).set_input_x2(input1.GetOutput(mNpuBackend->mSclipMap[inputIndex1]));
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} else {
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(*binary).set_input_x2(input1);
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}
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if(activationType == 1) {
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shared_ptr<hiai::op::Activation> binary_activation(new hiai::op::Activation(opName + "_Relu"));
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(*binary_activation)
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.set_input_x(*binary.get())
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.set_attr_mode(1);
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mNpuBackend->setOutputOps(mOp, {binary, binary_activation}, outputs);
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} else {
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mNpuBackend->setOutputOps(mOp, {binary}, outputs);
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}
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}
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void NPUBinary::OpInsert(int binary_type, string opName,
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hiai::Operator& input0, hiai::Operator& input1,
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const std::vector<Tensor *> &outputs, int activationType){
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if (binary_type == BinaryOpOperation_ADD) {
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shared_ptr<hiai::op::Add> binary(new hiai::op::Add(opName));
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BinaryCastIR(opName, input0, input1, outputs, activationType, binary);
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} else if (binary_type == BinaryOpOperation_MUL) {
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shared_ptr<hiai::op::Mul> binary(new hiai::op::Mul(opName));
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BinaryCastIR(opName, input0, input1, outputs, activationType, binary);
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} else if (binary_type == BinaryOpOperation_REALDIV) {
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shared_ptr<hiai::op::RealDiv> binary(new hiai::op::RealDiv(opName));
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BinaryCastIR(opName, input0, input1, outputs, activationType, binary);
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} else if (binary_type == BinaryOpOperation_SUB) {
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shared_ptr<hiai::op::Sub> binary(new hiai::op::Sub(opName));
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BinaryCastIR(opName, input0, input1, outputs, activationType, binary);
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} else if (binary_type == BinaryOpOperation_MINIMUM) {
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shared_ptr<hiai::op::Minimum> binary(new hiai::op::Minimum(opName));
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BinaryIR(opName, input0, input1, outputs, activationType, binary);
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} else if (binary_type == BinaryOpOperation_MAXIMUM) {
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shared_ptr<hiai::op::Maximum> binary(new hiai::op::Maximum(opName));
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BinaryIR(opName, input0, input1, outputs, activationType, binary);
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} else if (binary_type == BinaryOpOperation_EQUAL) {
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shared_ptr<hiai::op::Equal> binary(new hiai::op::Equal(opName));
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BinaryIR(opName, input0, input1, outputs, activationType, binary);
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} else if (binary_type == BinaryOpOperation_LESS_EQUAL) {
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shared_ptr<hiai::op::LessEqual> binary(new hiai::op::LessEqual(opName));
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BinaryIR(opName, input0, input1, outputs, activationType, binary);
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} else if (binary_type == BinaryOpOperation_POW) {
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shared_ptr<hiai::op::Pow> binary(new hiai::op::Pow(opName));
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BinaryIR(opName, input0, input1, outputs, activationType, binary);
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} else if (binary_type == BinaryOpOperation_LESS) {
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shared_ptr<hiai::op::Less> binary(new hiai::op::Less(opName));
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BinaryIR(opName, input0, input1, outputs, activationType, binary);
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} else if (binary_type == BinaryOpOperation_MOD) {
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shared_ptr<hiai::op::FloorMod> binary(new hiai::op::FloorMod(opName));
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BinaryCastIR(opName, input0, input1, outputs, activationType, binary);
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} else if (binary_type == BinaryOpOperation_SquaredDifference) {
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shared_ptr<hiai::op::SquaredDifference> binary(new hiai::op::SquaredDifference(opName));
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BinaryCastIR(opName, input0, input1, outputs, activationType, binary);
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} else if (binary_type == BinaryOpOperation_GREATER) {
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shared_ptr<hiai::op::Greater> binary(new hiai::op::Greater(opName));
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BinaryIR(opName, input0, input1, outputs, activationType, binary);
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} else {
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MNN_ERROR("npu binary not support type : %d \n", binary_type);
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MNN_ASSERT(false);
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}
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}
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NPUBinary::NPUBinary(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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auto opName = mOp->name()->str();
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bool isConst0 = TensorUtils::getDescribe(inputs[0])->usage==Tensor::InsideDescribe::Usage::CONSTANT;
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bool isConst1 = TensorUtils::getDescribe(inputs[1])->usage==Tensor::InsideDescribe::Usage::CONSTANT;
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auto binary_type = mOp->main_as_BinaryOp()->opType();
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auto len = mOp->inputIndexes()->size();
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Tensor* input = nullptr;
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if(isConst0 && !isConst1) {
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input = inputs[0];
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} else if (!isConst0 && isConst1) {
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input = inputs[1];
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}
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mConst = hiai::op::Const(opName + "_w_const");
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if(input != nullptr) {
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ge::TensorPtr filter = std::make_shared<ge::Tensor>();
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vector<int64_t> dims;
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for (int32_t i = 0; i < input->buffer().dimensions; i++) {
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dims.push_back(input->buffer().dim[i].extent);
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}
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ge::TensorDesc fdesc(ge::Shape(dims), ge::FORMAT_NCHW, ge::DT_FLOAT);
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if (input->getType().code == halide_type_float) {
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filter->SetData((uint8_t *)input->host<float>(), input->elementSize() * sizeof(float));
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}
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if (input->getType().code == halide_type_int && input->getType().bits == 32) {
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fdesc.SetDataType(ge::DT_INT32);
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filter->SetData((uint8_t *)input->host<int32_t>(), input->elementSize() * sizeof(int32_t));
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}
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filter->SetTensorDesc(fdesc);
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mConst.set_attr_value(filter);
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}
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}
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ErrorCode NPUBinary::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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bool isConst0 = TensorUtils::getDescribe(inputs[0])->usage==Tensor::InsideDescribe::Usage::CONSTANT;
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bool isConst1 = TensorUtils::getDescribe(inputs[1])->usage==Tensor::InsideDescribe::Usage::CONSTANT;
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auto binary_type = mOp->main_as_BinaryOp()->opType();
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int activationType = mOp->main_as_BinaryOp()->activationType();
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flag0 = false;
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flag1 = false;
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if (!isConst0 && isConst1) {
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inputIndex0 = mOp->inputIndexes()->data()[0];
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auto iops0 = mNpuBackend->mGrapMap[inputIndex0]; // x
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auto xOp0 = iops0.back().first;
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if (mNpuBackend->mSclipMap.find(inputIndex0) != mNpuBackend->mSclipMap.end()) {
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flag0 = true;
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}
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inputIndex1 = -1;
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OpInsert(binary_type, opName, *xOp0.get(), mConst, outputs, activationType);
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} else if(isConst0 && !isConst1) {
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inputIndex1 = mOp->inputIndexes()->data()[1];
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auto iops1 = mNpuBackend->mGrapMap[inputIndex1]; // x
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auto xOp1 = iops1.back().first;
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if (mNpuBackend->mSclipMap.find(inputIndex1) != mNpuBackend->mSclipMap.end()) {
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flag1 = true;
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}
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inputIndex0 = -1;
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OpInsert(binary_type, opName, mConst, *xOp1.get(), outputs, activationType);
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} else {
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inputIndex0 = mOp->inputIndexes()->data()[0];
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auto iops0 = mNpuBackend->mGrapMap[inputIndex0]; // x
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auto xOp0 = iops0.back().first;
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inputIndex1 = mOp->inputIndexes()->data()[1];
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auto iops1 = mNpuBackend->mGrapMap[inputIndex1]; // x
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auto xOp1 = iops1.back().first;
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if (mNpuBackend->mSclipMap.find(inputIndex0) != mNpuBackend->mSclipMap.end()) {
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flag0 = true;
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}
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if (mNpuBackend->mSclipMap.find(inputIndex1) != mNpuBackend->mSclipMap.end()) {
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flag1 = true;
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}
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OpInsert(binary_type, opName, *xOp0.get(), *xOp1.get(), outputs, activationType);
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}
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return NO_ERROR;
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}
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NPUCreatorRegister<TypedCreator<NPUBinary>> __bianry_op(OpType_BinaryOp);
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} // namespace MNN
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