963674de71
Add argv to benchmark/main function so they can handle passing command line arguments. Change: 144254260
148 lines
5.1 KiB
Python
148 lines
5.1 KiB
Python
# Copyright 2015 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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"""Benchmark for split and grad of split."""
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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 itertools
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import random
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import time
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from tensorflow.core.protobuf import config_pb2
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from tensorflow.python.client import session as session_lib
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import gradients_impl
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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def build_graph(device, input_shape, variable, num_inputs, axis, grad):
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"""Build a graph containing a sequence of concat operations.
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Args:
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device: string, the device to run on.
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input_shape: shape of the input tensors.
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variable: whether or not to randomize the input shape
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num_inputs: the number of inputs to concat
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axis: axis to be concat'ed
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grad: if True compute the gradient
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Returns:
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An array of tensors to run()
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"""
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with ops.device("/%s:0" % device):
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if not variable:
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inputs = [array_ops.zeros(input_shape) for _ in range(num_inputs)]
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else:
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if axis == 1:
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inputs = [
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array_ops.zeros([
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input_shape[0],
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random.randint(max(1, input_shape[1] - 5), input_shape[1] + 5)
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]) for _ in range(num_inputs)
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]
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else:
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inputs = [
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array_ops.zeros([
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random.randint(max(1, input_shape[0] - 5), input_shape[0] + 5),
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input_shape[1]
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]) for _ in range(num_inputs)
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]
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outputs = [array_ops.concat(inputs, axis) for _ in range(100)]
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if grad:
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return control_flow_ops.group(*list(
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itertools.chain.from_iterable([
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gradients_impl.gradients(output, inputs) for output in outputs
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])))
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else:
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return control_flow_ops.group(*outputs)
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class ConcatBenchmark(test.Benchmark):
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"""Benchmark concat."""
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def _run_graph(self, device, input_shape, variable, num_inputs, axis, grad,
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num_iters):
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"""Run the graph and print its execution time.
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Args:
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device: string, the device to run on.
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input_shape: shape of the input tensors.
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variable: whether or not the input shape should be fixed
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num_inputs: the number of inputs to concat
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axis: axis to be concat'ed
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grad: if True compute the gradient
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num_iters: number of steps to run.
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Returns:
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The duration of the run in seconds.
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"""
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graph = ops.Graph()
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with graph.as_default():
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outputs = build_graph(device, input_shape, variable, num_inputs, axis,
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grad)
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config = config_pb2.ConfigProto(graph_options=config_pb2.GraphOptions(
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optimizer_options=config_pb2.OptimizerOptions(
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opt_level=config_pb2.OptimizerOptions.L0)))
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with session_lib.Session(graph=graph, config=config) as session:
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variables.global_variables_initializer().run()
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_ = session.run(outputs) # warm up.
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start_time = time.time()
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for _ in range(num_iters):
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_ = session.run(outputs)
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duration = time.time() - start_time
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print("%s shape:%d/%d var: %r #inputs:%d axis:%d grad:%r - %f secs - %f "
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"GB/sec" % (device, input_shape[0], input_shape[1], variable,
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num_inputs, axis, grad, duration / num_iters,
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num_inputs * input_shape[0] * input_shape[1] * 4 * 2 *
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100 / (duration / num_iters) / 1e9))
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name_template = (
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"concat_bench_{device}_input_shape_{shape}_variable_{variable}"
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"_num_inputs_{num_inputs}_axis_{axis}_grad_{grad}")
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self.report_benchmark(name=name_template.format(
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device=device,
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num_inputs=num_inputs,
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variable=variable,
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grad=grad,
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shape=str(input_shape).replace(" ", ""),
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axis=str(axis),
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iters=num_iters))
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return duration
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def benchmark_concat(self):
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print("Forward vs backward concat")
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shapes = [[2000, 8], [8, 2000], [100, 18], [1000, 18], [100, 97],
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[1000, 97], [10000, 1], [1, 10000]]
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axis_ = [0, 1]
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num_inputs = 20
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num_iters = [10] * len(shapes)
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variable = [False, True] # fixed input size or not
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for shape, iters in zip(shapes, num_iters):
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for axis in axis_:
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for v in variable:
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self._run_graph("cpu", shape, v, num_inputs, axis, True, iters)
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if __name__ == "__main__":
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test.main()
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