new_contents: # 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.
  """Should be run with valgrind to get memory consumption
     for sparse format storage and dot operators. This script can be
     used for memory benchmarking on CPU only"""
  import ctypes
  import sys
  import argparse
  import mxnet as mx
  from mxnet.test_utils import rand_ndarray
  from mxnet.base import check_call, _LIB
  
  
  def parse_args():
      """ Function to parse arguments
      """
      parser = argparse.ArgumentParser()
      parser.add_argument("--lhs-row-dim",
                          required=True,
                          help="Provide batch_size")
      parser.add_argument("--lhs-col-dim",
                          required=True,
                          help="Provide feature_dim")
      parser.add_argument("--rhs-col-dim",
                          required=True,
                          help="Provide output_dim")
      parser.add_argument("--density",
                          required=True,
                          help="Density for lhs")
      parser.add_argument("--num-omp-threads", type=int,
                          default=1, help="number of omp threads to set in MXNet")
      parser.add_argument("--lhs-stype", default="csr",
                          choices=["csr", "default", "row_sparse"],
                          help="stype for lhs",
                          required=True)
      parser.add_argument("--rhs-stype", default="default",
                          choices=["default", "row_sparse"],
                          help="rhs stype",
                          required=True)
      parser.add_argument("--only-storage",
                          action="store_true",
                          help="only storage")
      parser.add_argument("--rhs-density",
                          help="rhs_density")
      return parser.parse_args()
  
  
  def main():
      args = parse_args()
      lhs_row_dim = int(args.lhs_row_dim)
      lhs_col_dim = int(args.lhs_col_dim)
      rhs_col_dim = int(args.rhs_col_dim)
      density = float(args.density)
      lhs_stype = args.lhs_stype
      rhs_stype = args.rhs_stype
      if args.rhs_density:
          rhs_density = float(args.rhs_density)
      else:
          rhs_density = density
      dot_func = mx.nd.sparse.dot if lhs_stype == "csr" else mx.nd.dot
      check_call(_LIB.MXSetNumOMPThreads(ctypes.c_int(args.num_omp_threads)))
      bench_dot(lhs_row_dim, lhs_col_dim, rhs_col_dim, density,
                rhs_density, dot_func, False, lhs_stype, rhs_stype, args.only_storage)
  
  def bench_dot(lhs_row_dim, lhs_col_dim, rhs_col_dim, density,
                rhs_density, dot_func, trans_lhs, lhs_stype,
                rhs_stype, only_storage, distribution="uniform"):
      """ Benchmarking both storage and dot
      """
      lhs_nd = rand_ndarray((lhs_row_dim, lhs_col_dim), lhs_stype, density, distribution=distribution)
      if not only_storage:
          rhs_nd = rand_ndarray((lhs_col_dim, rhs_col_dim), rhs_stype,
                                density=rhs_density, distribution=distribution)
          out = dot_func(lhs_nd, rhs_nd, trans_lhs)
      mx.nd.waitall()
  
  
  if __name__ == '__main__':
      sys.exit(main())
  
subject: Add sparse memory benchmark script (#7800)