runtime/onert/backend/cpu/ops/DivLayer.cc

   1 /*
   2  * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
   3  *
   4  * Licensed under the Apache License, Version 2.0 (the "License");
   5  * you may not use this file except in compliance with the License.
   6  * You may obtain a copy of the License at
   7  *
   8  *      http://www.apache.org/licenses/LICENSE-2.0
   9  *
  10  * Unless required by applicable law or agreed to in writing, software
  11  * distributed under the License is distributed on an "AS IS" BASIS,
  12  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13  * See the License for the specific language governing permissions and
  14  * limitations under the License.
  15  */
  16
  17 #include "DivLayer.h"
  18
  19 #include <cker/operation/BinaryArithmeticOps.h>
  20
  21 namespace onert
  22 {
  23 namespace backend
  24 {
  25 namespace cpu
  26 {
  27 namespace ops
  28 {
  29
  30 void DivLayer::divFloat32()
  31 {
  32   float output_activation_min = 0, output_activation_max = 0;
  33   CalculateActivationRange(_activation, &output_activation_min, &output_activation_max);
  34   nnfw::cker::BinaryArithmeticOpParam op_params;
  35   op_params.float_activation_max = output_activation_max;
  36   op_params.float_activation_min = output_activation_min;
  37
  38   const bool requires_broadcast = !HaveSameShapes(_lhs, _rhs);
  39   if (requires_broadcast)
  40   {
  41     nnfw::cker::BroadcastBinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::DIV>(
  42         op_params, getTensorShape(_lhs), reinterpret_cast<const float *>(_lhs->buffer()),
  43         getTensorShape(_rhs), reinterpret_cast<const float *>(_rhs->buffer()),
  44         getTensorShape(_output), reinterpret_cast<float *>(_output->buffer()));
  45   }
  46   else
  47   {
  48     nnfw::cker::BinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::DIV>(
  49         op_params, getTensorShape(_lhs), reinterpret_cast<const float *>(_lhs->buffer()),
  50         getTensorShape(_rhs), reinterpret_cast<const float *>(_rhs->buffer()),
  51         getTensorShape(_output), reinterpret_cast<float *>(_output->buffer()));
  52   }
  53 }
  54
  55 void DivLayer::divQuant8()
  56 {
  57   int32_t output_activation_min, output_activation_max;
  58   CalculateActivationRangeUint8(_activation, _output, &output_activation_min,
  59                                 &output_activation_max);
  60   // op_params.quantized_activation_max = output_activation_max;
  61   // op_params.quantized_activation_min = output_activation_min;
  62
  63   // cker quant8 div is not implemented yet
  64   throw std::runtime_error{"Div NYI for quantized"};
  65 }
  66
  67 void DivLayer::configure(const IPortableTensor *lhs, const IPortableTensor *rhs,
  68                          const ir::Activation activation, IPortableTensor *output)
  69 {
  70   _lhs = lhs;
  71   _rhs = rhs;
  72   _activation = activation;
  73   _output = output;
  74 }
  75
  76 void DivLayer::run()
  77 {
  78   if (_output->data_type() == OperandType::FLOAT32)
  79   {
  80     divFloat32();
  81   }
  82   else if (_output->data_type() == OperandType::QUANT_UINT8_ASYMM)
  83   {
  84     divQuant8();
  85   }
  86   else
  87   {
  88     throw std::runtime_error{"Div: unsupported data type"};
  89   }
  90 }
  91
  92 } // namespace ops
  93 } // namespace cpu
  94 } // namespace backend
  95 } // namespace onert