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