runtime/onert/backend/cpu/ops/MulLayer.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 "MulLayer.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 MulLayer::mulFloat32()
  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 need_broadcast =
  39       nnfw::cker::ProcessBroadcastShapes(getTensorShape(_lhs), getTensorShape(_rhs), &op_params);
  40   if (need_broadcast)
  41   {
  42     nnfw::cker::BroadcastBinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>(
  43         op_params, getTensorShape(_lhs), reinterpret_cast<const float *>(_lhs->buffer()),
  44         getTensorShape(_rhs), reinterpret_cast<const float *>(_rhs->buffer()),
  45         getTensorShape(_output), reinterpret_cast<float *>(_output->buffer()));
  46     return;
  47   }
  48
  49   nnfw::cker::BinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>(
  50       op_params, getTensorShape(_lhs), reinterpret_cast<const float *>(_lhs->buffer()),
  51       getTensorShape(_rhs), reinterpret_cast<const float *>(_rhs->buffer()),
  52       getTensorShape(_output), reinterpret_cast<float *>(_output->buffer()));
  53 }
  54
  55 void MulLayer::mulQuant8()
  56 {
  57   int32_t output_activation_min, output_activation_max;
  58   CalculateActivationRangeUint8(_activation, _output, &output_activation_min,
  59                                 &output_activation_max);
  60   nnfw::cker::BinaryArithmeticOpParam op_params;
  61
  62   op_params.quantized_activation_max = output_activation_max;
  63   op_params.quantized_activation_min = output_activation_min;
  64   op_params.input1_offset = -_lhs->data_offset();
  65   op_params.input2_offset = -_rhs->data_offset();
  66   op_params.output_offset = _output->data_offset();
  67
  68   double real_multiplier = _lhs->data_scale() * _rhs->data_scale() / _output->data_scale();
  69   QuantizeMultiplier(real_multiplier, &op_params.output_multiplier, &op_params.output_shift);
  70
  71   const bool need_broadcast =
  72       nnfw::cker::ProcessBroadcastShapes(getTensorShape(_lhs), getTensorShape(_rhs), &op_params);
  73   if (need_broadcast)
  74   {
  75     nnfw::cker::BroadcastBinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>(
  76         op_params, getTensorShape(_lhs), reinterpret_cast<const uint8_t *>(_lhs->buffer()),
  77         getTensorShape(_rhs), reinterpret_cast<const uint8_t *>(_rhs->buffer()),
  78         getTensorShape(_output), reinterpret_cast<uint8_t *>(_output->buffer()));
  79     return;
  80   }
  81
  82   nnfw::cker::BinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::MUL>(
  83       op_params, getTensorShape(_lhs), reinterpret_cast<const uint8_t *>(_lhs->buffer()),
  84       getTensorShape(_rhs), reinterpret_cast<const uint8_t *>(_rhs->buffer()),
  85       getTensorShape(_output), reinterpret_cast<uint8_t *>(_output->buffer()));
  86 }
  87
  88 void MulLayer::configure(const IPortableTensor *lhs, const IPortableTensor *rhs,
  89                          const ir::Activation activation, IPortableTensor *output)
  90 {
  91   _lhs = lhs;
  92   _rhs = rhs;
  93   _activation = activation;
  94   _output = output;
  95 }
  96
  97 void MulLayer::run()
  98 {
  99   if (_output->data_type() == OperandType::FLOAT32)
 100   {
 101     mulFloat32();
 102   }
 103   else if (_output->data_type() == OperandType::QUANT_UINT8_ASYMM)
 104   {
 105     mulQuant8();
 106   }
 107   else
 108   {
 109     throw std::runtime_error{"Mul: unsupported data type"};
 110   }
 111 }
 112
 113 } // namespace ops
 114 } // namespace cpu
 115 } // namespace backend
 116 } // namespace onert