/*
 * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
 * Copyright (C) 2017 The Android Open Source Project
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include "FullyConnectedLayer.h"

#include <cker/operation/FullyConnected.h>

#include "OperationUtils.h"

namespace neurun
{
namespace backend
{
namespace cpu
{
namespace kernel
{

FullyConnectedLayer::FullyConnectedLayer()
    : _inputData(), _weightsData(), _biasData(), _outputData(), _inputDescr(), _weightsDescr(),
      _biasDescr(), _outputDescr(), _activation(ir::Activation::NONE),
      _inputType(OperandType::FLOAT32)
{
  // DO NOTHING
}

void FullyConnectedLayer::fullyConnectedFloat32()
{
  float output_activation_min, output_activation_max;
  CalculateActivationRangeFloat(_activation, &output_activation_min, &output_activation_max);

  nnfw::cker::FullyConnectedParams op_params;
  op_params.float_activation_min = output_activation_min;
  op_params.float_activation_max = output_activation_max;

  nnfw::cker::FullyConnected(op_params, convertToExtendedCkerShape(_inputDescr), _inputData.f,
                             convertToExtendedCkerShape(_weightsDescr), _weightsData.f,
                             convertToExtendedCkerShape(_biasDescr), _biasData.f,
                             convertToExtendedCkerShape(_outputDescr), _outputData.f);
}

// executionMutex is used to protect concurrent access of non-threadsafe resources
// like gemmlowp::GemmContext.
void FullyConnectedLayer::fullyConnectedQuant8()
{
  float real_multiplier = 0.0;
  int32_t output_multiplier = 0;
  int32_t output_shift = 0;
  int32_t output_activation_min = 0;
  int32_t output_activation_max = 0;
  GetQuantizedConvolutionMultiplier(_inputDescr, _weightsDescr, _biasDescr, _outputDescr,
                                    &real_multiplier);
  QuantizeMultiplier(real_multiplier, &output_multiplier, &output_shift);
  CalculateActivationRangeUint8(_activation, _outputDescr, &output_activation_min,
                                &output_activation_max);

  nnfw::cker::FullyConnectedParams op_params;
  op_params.input_offset = -_inputDescr.offset;
  op_params.weights_offset = -_weightsDescr.offset;
  op_params.output_offset = _outputDescr.offset;
  op_params.output_multiplier = output_multiplier;
  op_params.output_shift = output_shift;
  op_params.quantized_activation_min = output_activation_min;
  op_params.quantized_activation_max = output_activation_max;

  nnfw::cker::FullyConnected(op_params, convertToExtendedCkerShape(_inputDescr), _inputData.u8,
                             convertToExtendedCkerShape(_weightsDescr), _weightsData.u8,
                             convertToExtendedCkerShape(_biasDescr), _biasData.i32,
                             convertToExtendedCkerShape(_outputDescr), _outputData.u8);
}

void FullyConnectedLayer::configure(uint8_t *inputData, const TensorDescriptor inputDescr,
                                    uint8_t *weightsData, const TensorDescriptor weightsDescr,
                                    uint8_t *biasData, const TensorDescriptor biasDescr,
                                    ir::Activation activation, uint8_t *outputData,
                                    const TensorDescriptor outputDescr)
{
  _inputData.u8 = inputData;
  _inputDescr = inputDescr;
  _inputType = inputDescr.type;
  _weightsData.u8 = weightsData;
  _weightsDescr = weightsDescr;
  _biasData.u8 = biasData;
  _biasDescr = biasDescr;
  _activation = activation;
  _outputData.u8 = outputData;
  _outputDescr = outputDescr;
}

void FullyConnectedLayer::run()
{
  if (_inputType == OperandType::FLOAT32)
  {
    fullyConnectedFloat32();
  }
  else if (_inputType == OperandType::QUANT8_ASYMM)
  {
    fullyConnectedQuant8();
  }
}

} // namespace kernel
} // namespace cpu
} // namespace backend
} // namespace neurun