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# Copyright (c) 2016-present, Facebook, Inc.
#
# 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.
##############################################################################
## @package device_reduce_sum_bench
# Module caffe2.experiments.python.device_reduce_sum_bench
import argparse
import itertools
import logging
import os
from six import add_metaclass
import numpy as np
from caffe2.python import workspace, core
from caffe2.python.hypothesis_test_util import runOpBenchmark, gpu_do
logging.basicConfig()
logger = logging.getLogger(os.path.basename(__file__))
logger.setLevel(logging.INFO)
ALL_BENCHMARKS = {}
class BenchmarkMeta(type):
def __new__(metacls, name, bases, class_dict):
cls = type.__new__(metacls, name, bases, class_dict)
if name != 'Benchmark':
ALL_BENCHMARKS[name] = cls
return cls
@add_metaclass(BenchmarkMeta)
class Benchmark(object):
def __init__(self):
self.results = []
def display(self):
print('Results ({}):'.format(type(self).__name__))
print('input size ms/iter')
print('------------------------------ -----------')
for size, ms in self.results:
print('{!s:<30} {:.4f}'.format(size, ms))
class SumElements(Benchmark):
def run(self):
op = core.CreateOperator(
"SumElements",
["X"],
["y"]
)
for n in itertools.imap(pow, itertools.cycle([10]), range(10)):
X = np.random.rand(n).astype(np.float32)
logger.info('Running benchmark for n = {}'.format(n))
ret = runOpBenchmark(gpu_do, op, inputs=[X])
self.results.append((n, ret[1]))
class SumSqrElements(Benchmark):
def run(self):
op = core.CreateOperator(
"SumSqrElements",
["X"],
["y"]
)
for n in itertools.imap(pow, itertools.cycle([10]), range(10)):
X = np.random.rand(n).astype(np.float32)
logger.info('Running benchmark for n = {}'.format(n))
ret = runOpBenchmark(gpu_do, op, inputs=[X])
self.results.append((n, ret[1]))
class SoftMaxWithLoss(Benchmark):
def run(self):
op = core.CreateOperator(
"SoftmaxWithLoss",
["X", "label"],
["probs", "avgloss"],
)
for n in itertools.imap(pow, itertools.cycle([10]), range(8)):
for D in itertools.imap(pow, itertools.cycle([10]), range(3)):
X = np.random.rand(n, D).astype(np.float32)
label = (np.random.rand(n) * D).astype(np.int32)
logger.info('Running benchmark for n = {}, D= {}'.format(n, D))
ret = runOpBenchmark(gpu_do, op, inputs=[X, label])
self.results.append(((n, D), ret[1]))
def parse_args():
parser = argparse.ArgumentParser(os.path.basename(__file__))
parser.add_argument('-b', '--benchmarks', nargs='+',
default=ALL_BENCHMARKS.keys(),
help='benchmarks to run (default: %(default)s))')
return parser.parse_args()
def main():
args = parse_args()
benchmarks = [ALL_BENCHMARKS[name]() for name in args.benchmarks]
for bench in benchmarks:
bench.run()
for bench in benchmarks:
bench.display()
if __name__ == '__main__':
workspace.GlobalInit(['caffe2', '--caffe2_log_level=2'])
main()
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