benchmark/python/sparse/memory_benchmark.py - master - githubmirror/incubator-mxnet

benchmark/python/sparse/memory_benchmark.py
93 строки4 KB
Anirudh Subramanian
Add sparse memory benchmark script (#7800)
10 сен 2017, 19:48
10 сен 2017, 19:48a537257
# Licensed to the Apache Software Foundation (ASF) under one
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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
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# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# 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())