runtime/onert/backend/cpu/ops/SubLayer.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 "SubLayer.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 SubLayer::subFloat32()
  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::SUB>(
  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::SUB>(
  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 SubLayer::subInt32()
  56 {
  57   int32_t output_activation_min = 0, output_activation_max = 0;
  58   CalculateActivationRange(_activation, &output_activation_min, &output_activation_max);
  59   nnfw::cker::BinaryArithmeticOpParam op_params;
  60   op_params.quantized_activation_max = output_activation_max;
  61   op_params.quantized_activation_min = output_activation_min;
  62
  63   const bool need_broadcast =
  64       nnfw::cker::ProcessBroadcastShapes(getTensorShape(_lhs), getTensorShape(_rhs), &op_params);
  65   if (need_broadcast)
  66   {
  67     nnfw::cker::BroadcastBinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::SUB>(
  68         op_params, getTensorShape(_lhs), reinterpret_cast<const int32_t *>(_lhs->buffer()),
  69         getTensorShape(_rhs), reinterpret_cast<const int32_t *>(_rhs->buffer()),
  70         getTensorShape(_output), reinterpret_cast<int32_t *>(_output->buffer()));
  71     return;
  72   }
  73
  74   nnfw::cker::BinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::SUB>(
  75       op_params, getTensorShape(_lhs), reinterpret_cast<const int32_t *>(_lhs->buffer()),
  76       getTensorShape(_rhs), reinterpret_cast<const int32_t *>(_rhs->buffer()),
  77       getTensorShape(_output), reinterpret_cast<int32_t *>(_output->buffer()));
  78 }
  79
  80 void SubLayer::subQuant8()
  81 {
  82   int32_t output_activation_min, output_activation_max;
  83   CalculateActivationRangeUint8(_activation, _output, &output_activation_min,
  84                                 &output_activation_max);
  85   nnfw::cker::BinaryArithmeticOpParam op_params;
  86   op_params.quantized_activation_max = output_activation_max;
  87   op_params.quantized_activation_min = output_activation_min;
  88   // Parameters for scaled quantized computation
  89   op_params.left_shift = 20;
  90   // Zero-points of input and output tensors
  91   op_params.input1_offset = -_lhs->data_offset();
  92   op_params.input2_offset = -_rhs->data_offset();
  93   op_params.output_offset = _output->data_offset();
  94   assert((op_params.input1_offset >= 0) && (op_params.input1_offset <= 255));
  95   assert((op_params.input2_offset >= 0) && (op_params.input2_offset <= 255));
  96   assert((op_params.output_offset >= 0) && (op_params.output_offset <= 255));
  97
  98   // Compute normalized scale for _lhs and _rhs values,
  99   // and represent in 32-bit fixed point
 100   const double norm_max_scale = 2 * std::max(_lhs->data_scale(), _rhs->data_scale());
 101   const double real_lhs_scale = _lhs->data_scale() / norm_max_scale;
 102   const double real_rhs_scale = _rhs->data_scale() / norm_max_scale;
 103   // output scale is used to normalize final result, so we invert the scale here
 104   const double real_output_scale =
 105       norm_max_scale / (_output->data_scale() * (1 << op_params.left_shift));
 106
 107   // Represent the scales as fixed int32_t multipliers, and int32_t shifts
 108   QuantizeMultiplier(real_lhs_scale, &op_params.input1_multiplier, &op_params.input1_shift);
 109   QuantizeMultiplier(real_rhs_scale, &op_params.input2_multiplier, &op_params.input2_shift);
 110   op_params.input2_multiplier *= -1;
 111   QuantizeMultiplier(real_output_scale, &op_params.output_multiplier, &op_params.output_shift);
 112
 113   const bool need_broadcast =
 114       nnfw::cker::ProcessBroadcastShapes(getTensorShape(_lhs), getTensorShape(_rhs), &op_params);
 115   if (need_broadcast)
 116   {
 117     nnfw::cker::BroadcastBinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::SUB>(
 118         op_params, getTensorShape(_lhs), reinterpret_cast<const uint8_t *>(_lhs->buffer()),
 119         getTensorShape(_rhs), reinterpret_cast<const uint8_t *>(_rhs->buffer()),
 120         getTensorShape(_output), reinterpret_cast<uint8_t *>(_output->buffer()));
 121     return;
 122   }
 123
 124   nnfw::cker::BinaryArithmeticOp<nnfw::cker::BinaryArithmeticOpType::SUB>(
 125       op_params, getTensorShape(_lhs), reinterpret_cast<const uint8_t *>(_lhs->buffer()),
 126       getTensorShape(_rhs), reinterpret_cast<const uint8_t *>(_rhs->buffer()),
 127       getTensorShape(_output), reinterpret_cast<uint8_t *>(_output->buffer()));
 128 }
 129
 130 void SubLayer::configure(const IPortableTensor *lhs, const IPortableTensor *rhs,
 131                          const ir::Activation activation, IPortableTensor *output)
 132 {
 133   _lhs = lhs;
 134   _rhs = rhs;
 135   _activation = activation;
 136   _output = output;
 137 }
 138
 139 void SubLayer::run()
 140 {
 141   if (_output->data_type() == OperandType::FLOAT32)
 142   {
 143     subFloat32();
 144   }
 145   else if (_output->data_type() == OperandType::QUANT_UINT8_ASYMM)
 146   {
 147     subQuant8();
 148   }
 149   else if (_output->data_type() == OperandType::INT32)
 150   {
 151     subInt32();
 152   }
 153   else
 154   {
 155     throw std::runtime_error{"Sub: unsupported data type"};
 156   }
 157 }
 158
 159 } // namespace ops
 160 } // namespace cpu
 161 } // namespace backend
 162 } // namespace onert