Line data    Source code

       1              : // SPDX-License-Identifier: Apache-2.0
       2              : /**
       3              :  * Copyright (C) 2025 Sumon Nath <sumon.nath@samsung.com>
       4              :  *
       5              :  * @file   reduce_sum_layer.cpp
       6              :  * @date   24 July 2025
       7              :  * @see    https://github.com/nnstreamer/nntrainer
       8              :  * @author Sumon Nath <sumon.nath@samsung.com>
       9              :  * @bug    No known bugs except for NYI items
      10              :  * @brief  This is Reduce Sum Layer Class for Neural Network
      11              :  */
      12              : 
      13              : #include <layer_context.h>
      14              : #include <nntrainer_error.h>
      15              : #include <nntrainer_log.h>
      16              : #include <node_exporter.h>
      17              : #include <reduce_sum_layer.h>
      18              : 
      19              : namespace nntrainer {
      20              : 
      21              : static constexpr size_t SINGLE_INOUT_IDX = 0;
      22              : 
      23            8 : void ReduceSumLayer::finalize(InitLayerContext &context) {
      24            8 :   NNTR_THROW_IF(context.getNumInputs() != 1, std::invalid_argument)
      25              :     << "Reduce sum only supports 1 input for now";
      26              : 
      27              :   const TensorDim &in_dim = context.getInputDimensions()[0];
      28            8 :   TensorDim out_dim = in_dim;
      29              : 
      30              :   /**
      31              :    * if reduce axis is not provided, reduction is performed across all the
      32              :    * dimensions except the batch
      33              :    */
      34              :   auto &reduce_axis = std::get<props::ReduceDimension>(reduce_sum_props);
      35            8 :   out_dim.setTensorDim(reduce_axis.get(), 1);
      36            8 :   context.setOutputDimensions({out_dim});
      37            8 : }
      38              : 
      39            6 : void ReduceSumLayer::forwarding(RunLayerContext &context, bool training) {
      40              :   auto &reduce_axis = std::get<props::ReduceDimension>(reduce_sum_props);
      41            6 :   context.getInput(SINGLE_INOUT_IDX)
      42            6 :     .sum(reduce_axis, context.getOutput(SINGLE_INOUT_IDX));
      43            6 : }
      44              : 
      45            3 : void ReduceSumLayer::calcDerivative(RunLayerContext &context) {
      46            3 :   auto &deriv = context.getIncomingDerivative(SINGLE_INOUT_IDX);
      47            3 :   auto &ret_deriv = context.getOutgoingDerivative(SINGLE_INOUT_IDX);
      48              : 
      49            3 :   ret_deriv.setZero();
      50            3 :   ret_deriv.add_i(deriv);
      51            3 : }
      52              : 
      53           55 : void ReduceSumLayer::setProperty(const std::vector<std::string> &values) {
      54           55 :   auto remain_props = loadProperties(values, reduce_sum_props);
      55           54 :   if (!remain_props.empty()) {
      56              :     std::string msg = "[ReduceSumLayer] Unknown Layer Properties count " +
      57            2 :                       std::to_string(remain_props.size());
      58            4 :     throw exception::not_supported(msg);
      59              :   }
      60           54 : }
      61              : 
      62            2 : void ReduceSumLayer::exportTo(Exporter &exporter,
      63              :                               const ml::train::ExportMethods &method) const {
      64            2 :   exporter.saveResult(reduce_sum_props, method, this);
      65            2 : }
      66              : 
      67              : } /* namespace nntrainer */