mirror of
https://github.com/zebrajr/pytorch.git
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131 lines
4.0 KiB
Python
131 lines
4.0 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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## @package device_reduce_sum_bench
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# Module caffe2.experiments.python.device_reduce_sum_bench
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import argparse
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import itertools
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import logging
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import os
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from six import add_metaclass
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import numpy as np
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from caffe2.python import workspace, core
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from caffe2.python.hypothesis_test_util import runOpBenchmark, gpu_do
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logging.basicConfig()
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logger = logging.getLogger(os.path.basename(__file__))
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logger.setLevel(logging.INFO)
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ALL_BENCHMARKS = {}
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class BenchmarkMeta(type):
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def __new__(metacls, name, bases, class_dict):
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cls = type.__new__(metacls, name, bases, class_dict)
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if name != 'Benchmark':
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ALL_BENCHMARKS[name] = cls
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return cls
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@add_metaclass(BenchmarkMeta)
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class Benchmark(object):
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def __init__(self):
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self.results = []
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def display(self):
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print('Results ({}):'.format(type(self).__name__))
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print('input size ms/iter')
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print('------------------------------ -----------')
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for size, ms in self.results:
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print('{!s:<30} {:.4f}'.format(size, ms))
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class SumElements(Benchmark):
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def run(self):
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op = core.CreateOperator(
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"SumElements",
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["X"],
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["y"]
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)
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for n in itertools.imap(pow, itertools.cycle([10]), range(10)):
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X = np.random.rand(n).astype(np.float32)
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logger.info('Running benchmark for n = {}'.format(n))
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ret = runOpBenchmark(gpu_do, op, inputs=[X])
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self.results.append((n, ret[1]))
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class SumSqrElements(Benchmark):
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def run(self):
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op = core.CreateOperator(
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"SumSqrElements",
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["X"],
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["y"]
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)
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for n in itertools.imap(pow, itertools.cycle([10]), range(10)):
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X = np.random.rand(n).astype(np.float32)
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logger.info('Running benchmark for n = {}'.format(n))
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ret = runOpBenchmark(gpu_do, op, inputs=[X])
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self.results.append((n, ret[1]))
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class SoftMaxWithLoss(Benchmark):
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def run(self):
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op = core.CreateOperator(
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"SoftmaxWithLoss",
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["X", "label"],
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["probs", "avgloss"],
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)
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for n in itertools.imap(pow, itertools.cycle([10]), range(8)):
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for D in itertools.imap(pow, itertools.cycle([10]), range(3)):
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X = np.random.rand(n, D).astype(np.float32)
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label = (np.random.rand(n) * D).astype(np.int32)
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logger.info('Running benchmark for n = {}, D= {}'.format(n, D))
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ret = runOpBenchmark(gpu_do, op, inputs=[X, label])
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self.results.append(((n, D), ret[1]))
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def parse_args():
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parser = argparse.ArgumentParser(os.path.basename(__file__))
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parser.add_argument('-b', '--benchmarks', nargs='+',
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default=ALL_BENCHMARKS.keys(),
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help='benchmarks to run (default: %(default)s))')
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return parser.parse_args()
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def main():
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args = parse_args()
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benchmarks = [ALL_BENCHMARKS[name]() for name in args.benchmarks]
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for bench in benchmarks:
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bench.run()
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for bench in benchmarks:
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bench.display()
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if __name__ == '__main__':
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workspace.GlobalInit(['caffe2', '--caffe2_log_level=2'])
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main()
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