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# Copyright 2016 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 accumulate_n() in math_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import random
import time
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.client import session
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import gen_control_flow_ops
from tensorflow.python.ops import gen_state_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.platform import test
class AccumulateNBenchmark(test.Benchmark):
def _AccumulateNTemplate(self, inputs, init, shape, validate_shape):
var = gen_state_ops.temporary_variable(
shape=shape, dtype=inputs[0].dtype.base_dtype)
ref = state_ops.assign(var, init, validate_shape=validate_shape)
update_ops = [
state_ops.assign_add(
ref, tensor, use_locking=True).op for tensor in inputs
]
with ops.control_dependencies(update_ops):
return gen_state_ops.destroy_temporary_variable(ref, var_name=var.op.name)
def _AccumulateNInitializedWithFirst(self, inputs):
return self._AccumulateNTemplate(
inputs,
init=array_ops.zeros_like(inputs[0]),
shape=inputs[0].get_shape(),
validate_shape=True)
def _AccumulateNInitializedWithMerge(self, inputs):
return self._AccumulateNTemplate(
inputs,
init=array_ops.zeros_like(gen_control_flow_ops.merge(inputs)[0]),
shape=tensor_shape.vector(0),
validate_shape=False)
def _AccumulateNInitializedWithShape(self, inputs):
return self._AccumulateNTemplate(
inputs,
init=array_ops.zeros(
shape=inputs[0].get_shape(), dtype=inputs[0].dtype.base_dtype),
shape=inputs[0].get_shape(),
validate_shape=True)
def _GenerateUnorderedInputs(self, size, n):
inputs = [random_ops.random_uniform(shape=[size]) for _ in xrange(n)]
random.shuffle(inputs)
return inputs
def _GenerateReplicatedInputs(self, size, n):
return n * self._GenerateUnorderedInputs(size, 1)
def _GenerateOrderedInputs(self, size, n):
inputs = self._GenerateUnorderedInputs(size, 1)
queue = data_flow_ops.FIFOQueue(
capacity=1, dtypes=[inputs[0].dtype], shapes=[inputs[0].get_shape()])
for _ in xrange(n - 1):
op = queue.enqueue(inputs[-1])
with ops.control_dependencies([op]):
inputs.append(math_ops.tanh(1.0 + queue.dequeue()))
return inputs
def _GenerateReversedInputs(self, size, n):
inputs = self._GenerateOrderedInputs(size, n)
inputs.reverse()
return inputs
def _SetupAndRunBenchmark(self, graph, inputs, repeats, format_args):
with graph.as_default():
add_n = math_ops.add_n(inputs)
acc_n_first = self._AccumulateNInitializedWithFirst(inputs)
acc_n_merge = self._AccumulateNInitializedWithMerge(inputs)
acc_n_shape = self._AccumulateNInitializedWithShape(inputs)
test_ops = (("AddN", add_n.op),
("AccNFirst", acc_n_first.op),
("AccNMerge", acc_n_merge.op),
("AccNShape", acc_n_shape.op))
with session.Session(graph=graph):
for tag, op in test_ops:
for _ in xrange(100):
op.run() # Run for warm up.
start = time.time()
for _ in xrange(repeats):
op.run()
duration = time.time() - start
args = format_args + (tag, duration)
print(self._template.format(*args))
def _RunBenchmark(self, tag, input_fn, sizes, ninputs, repeats):
for size in sizes:
for ninput in ninputs:
graph = ops.Graph()
with graph.as_default():
inputs = input_fn(size, ninput)
format_args = (tag, size, ninput, repeats)
self._SetupAndRunBenchmark(graph, inputs, repeats, format_args)
def benchmarkAccumulateN(self):
self._template = "{:<15}" * 6
args = {
"sizes": (128, 128**2),
"ninputs": (1, 10, 100, 300),
"repeats": 100
}
benchmarks = (("Replicated", self._GenerateReplicatedInputs),
("Unordered", self._GenerateUnorderedInputs),
("Ordered", self._GenerateOrderedInputs),
("Reversed", self._GenerateReversedInputs))
print(self._template.format("", "Size", "#Inputs", "#Repeat", "Method",
"Duration"))
print("-" * 90)
for benchmark in benchmarks:
self._RunBenchmark(*benchmark, **args)
if __name__ == "__main__":
test.main()
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