66c48046f1
This is mostly the result of an internal cleanup and formatting pass. PiperOrigin-RevId: 286318018 Change-Id: I8f9e2f7519070035da73f9f24d2fc90864abc51b
88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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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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"""Local CPU benchmarks for collective ops."""
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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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import time
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import numpy as np
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from tensorflow.core.protobuf import config_pb2
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from tensorflow.python.client import session
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import collective_ops
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from tensorflow.python.platform import test
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class CollectiveOpBenchmark(test.Benchmark):
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"""Benchmarks for local CPU collective op execution."""
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def benchmark_collective(self):
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"""Measures the performance of local CPU collective execution."""
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shapes = [(10,), (1000,), (1000000,)]
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devices = [2, 4, 8]
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collective_key_counter = 0
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for group_size in devices:
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group_key = collective_key_counter
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instance_key = collective_key_counter
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collective_key_counter += 1
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for shape in shapes:
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config = config_pb2.ConfigProto(device_count={"CPU": group_size})
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with session.Session(config=config) as sess:
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# Use a C++ callable to minimize the Python overhead in the benchmark.
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callable_opts = config_pb2.CallableOptions()
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reduce_ops = []
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for device in range(group_size):
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with ops.device("CPU:{}".format(device)):
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t = constant_op.constant(np.multiply(range(shape[0]), 1.0))
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r = collective_ops.all_reduce(t, group_size, group_key,
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instance_key, "Add", "Div")
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reduce_ops.append(r)
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callable_opts.target.append(r.name)
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op_callable = sess._make_callable_from_options(callable_opts) # pylint: disable=protected-access
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# Run five steps to warm up the session caches and do collective param
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# resolution before taking the first measurement.
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for _ in range(5):
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op_callable()
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deltas = []
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overall_start = time.time()
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# Run at least five repetitions and for at least five seconds.
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while len(deltas) < 5 or time.time() - overall_start < 5.0:
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start = time.time()
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for _ in range(100):
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op_callable()
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end = time.time()
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deltas.append(end - start)
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del op_callable
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median_wall_time = np.median(deltas) / 100.0
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iters = len(deltas) * 100
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self.report_benchmark(
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iters=iters, wall_time=median_wall_time,
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name="num_elements_{}_num_devices_{}".format(np.prod(shape),
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group_size))
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if __name__ == "__main__":
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test.main()
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