src/operator/contrib/multi_sum_sq.cc

/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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.
 */

/*!
 * \file multi_sum_sq.cc
 * \brief vectorized sum or squared over multiple arrays operators
 * \author Clement Fuji Tsang, Andrei Ivanov, Moises Hernandez, Shuai Zheng
 */

#include "./multi_sum_sq-inl.h"

namespace mxnet {
namespace op {

DMLC_REGISTER_PARAMETER(MultiSumSqParam);

NNVM_REGISTER_OP(multi_sum_sq)
    .describe(R"code(Compute the sums of squares of multiple arrays
)code" ADD_FILELINE)
    .set_num_inputs([](const nnvm::NodeAttrs& attrs) {
      return static_cast<uint32_t>(dmlc::get<MultiSumSqParam>(attrs.parsed).num_arrays);
    })
    .set_num_outputs(1)
    .set_attr_parser(ParamParser<MultiSumSqParam>)
    .set_attr<mxnet::FInferShape>("FInferShape", MultiSumSqShape)
    .set_attr<nnvm::FInferType>("FInferType", MultiSumSqType)
    .set_attr<nnvm::FListInputNames>("FListInputNames",
                                     [](const NodeAttrs& attrs) {
                                       const auto& param = dmlc::get<MultiSumSqParam>(attrs.parsed);
                                       const uint32_t num_args = param.num_arrays;
                                       std::vector<std::string> ret;
                                       for (uint32_t i = 0; i < num_args; ++i) {
                                         ret.push_back(std::string("array_") + std::to_string(i));
                                       }
                                       return ret;
                                     })
    .set_attr<FCompute>("FCompute<cpu>", MultiSumSq<cpu>)
    .set_attr<FResourceRequest>("FResourceRequest",
                                [](const NodeAttrs& attrs) {
                                  return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
                                })
    .add_argument("data", "NDArray-or-Symbol[]", "Arrays")
    .add_arguments(MultiSumSqParam::__FIELDS__());

template <>
size_t GetRequiredStorageMultiSumSq<cpu>(const std::vector<TBlob>& inputs,
                                         int* param_max_chunks_per_tensor) {
  return 0;
}

template <typename DType>
inline void CalcSumSq(const std::vector<TBlob>& inputs,
                      int n_inputs,
                      float* out_ptr,
                      mshadow::Stream<cpu>* s,
                      float scale) {
  int i;
  size_t j;
#pragma omp parallel for private(i, j)
  for (i = 0; i < n_inputs; ++i) {  // array index in inputs
    float sum          = 0;
    const auto address = inputs[i].FlatTo2D<cpu, DType>(s).dptr_;
    const auto j_max   = inputs[i].shape_.Size();
    for (j = 0; j < j_max; ++j) {
      auto val = static_cast<float>(address[j]);
      if (scale != 1.0f) {
        val *= scale;
      }
      sum += val * val;
    }
    out_ptr[i] = sum;
  }
}

template <>
void MultiSumSqRun<cpu>(const std::vector<TBlob>& inputs,
                        int n_inputs,
                        float* out_ptr,
                        const OpContext& ctx,
                        float scale) {
  MSHADOW_REAL_TYPE_SWITCH(
      inputs[0].type_flag_,
      DType,
      CalcSumSq<DType>(inputs, n_inputs, out_ptr, ctx.get_stream<cpu>(), scale);)
}

}  // namespace op
}  // namespace mxnet