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# Copyright 2017 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 Conv2D op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import time
from tensorflow.core.protobuf import config_pb2
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python.client import session as session_lib
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import flags
from tensorflow.python.platform import test
FLAGS = flags.FLAGS
flags.DEFINE_boolean(
"enable_layout_optimizer", False,
"If true, enables layout optimizer to update input data format for faster "
"execution of convolution ops.")
def build_graph(device, dtype, data_format, input_shape, filter_shape, strides,
padding, num_iters, warmup_iters):
"""builds a graph containing a sequence of conv2d operations.
Args:
device: String, the device to run on.
dtype: Data type for the convolution.
data_format: A string from: "NHWC" or "NCHW". Data format for input and
output data.
input_shape: Shape of the input tensor.
filter_shape: Shape of the filter tensor.
strides: A list of ints. 1-D of length 4. The stride of sliding
window for each dimension of input.
padding: A string from: "SAME", "VALID". The type of padding
algorithm to use.
num_iters: number of iterations to run conv2d.
warmup_iters: number of iterations for warmup runs.
Returns:
An array of tensors to run()
"""
with ops.device("/%s:0" % device):
inp = variables.Variable(
random_ops.truncated_normal(input_shape, dtype=dtype))
filt = variables.Variable(
random_ops.truncated_normal(filter_shape, dtype=dtype))
outputs = []
conv2d_op = nn_ops.conv2d(
inp, filt, strides, padding, data_format=data_format)
outputs.append(conv2d_op)
for _ in range(1, num_iters):
with ops.control_dependencies([conv2d_op]):
conv2d_op = nn_ops.conv2d(
inp, filt, strides, padding, data_format=data_format)
outputs.append(conv2d_op)
warmup_groups = []
warmup_conv2d_op = nn_ops.conv2d(
inp, filt, strides, padding, data_format=data_format)
warmup_groups.append(warmup_conv2d_op)
for _ in range(1, warmup_iters):
with ops.control_dependencies([warmup_conv2d_op]):
warmup_conv2d_op = nn_ops.conv2d(
inp, filt, strides, padding, data_format=data_format)
warmup_groups.append(warmup_conv2d_op)
return control_flow_ops.group(*warmup_groups), control_flow_ops.group(
*outputs)
class Conv2DBenchmark(test.Benchmark):
"""Benchmark conv2d!"""
def _run_graph(self, device, dtype, data_format, input_shape, filter_shape,
strides, padding, num_iters, warmup_iters):
"""runs the graph and print its execution time.
Args:
device: String, the device to run on.
dtype: Data type for the convolution.
data_format: A string from: "NHWC" or "NCHW". Data format for input and
output data.
input_shape: Shape of the input tensor.
filter_shape: Shape of the filter tensor.
strides: A list of ints. 1-D of length 4. The stride of sliding
window for each dimension of input.
padding: A string from: "SAME", "VALID". The type of padding
algorithm to use. num_iters: Number of iterations to run the
benchmark.
num_iters: number of iterations to run conv2d.
warmup_iters: number of iterations for warmup runs.
Returns:
The duration of the run in seconds.
"""
graph = ops.Graph()
with graph.as_default():
warmup_outputs, outputs = build_graph(device, dtype, data_format,
input_shape, filter_shape, strides,
padding, num_iters, warmup_iters)
config = config_pb2.ConfigProto()
config.graph_options.optimizer_options.opt_level = -1
rewrite_options = config.graph_options.rewrite_options
# Disable layout optimizer to not change input data_format.
rewrite_options.layout_optimizer = (
rewriter_config_pb2.RewriterConfig.ON if FLAGS.enable_layout_optimizer
else rewriter_config_pb2.RewriterConfig.OFF)
# Convolution ops are effectively noop in the test graph as we are not
# fetching the convolution outputs. Disable dependency optimizer to not
# remove the conv ops.
rewrite_options.dependency_optimization = (
rewriter_config_pb2.RewriterConfig.OFF)
with session_lib.Session(graph=graph, config=config) as session:
# TODO(hinsu): Use run_op_benchmark method from test.Benchmark to run
# benchmark along with warmup.
variables.global_variables_initializer().run()
# warmup runs
session.run(warmup_outputs)
start_time = time.time()
session.run(outputs)
duration = (time.time() - start_time) / num_iters
print("%s %s %s inputshape:%s filtershape:%s strides:%s padding:%s "
"%d iters: %.8f sec" %
(device, str(dtype), data_format, str(input_shape).replace(
" ", ""), str(filter_shape).replace(" ", ""),
str(strides).replace(" ", ""), padding, num_iters, duration))
name_template = (
"conv2d_{device}_{datatype}_{data_format}_input_shape_{inputshape}_"
"filter_shape_{filtershape}_strides_{strides}_padding_{padding}")
self.report_benchmark(
name=name_template.format(
device=device,
datatype=str(dtype),
data_format=str(data_format),
inputshape=str(input_shape).replace(" ", ""),
filtershape=str(filter_shape).replace(" ", ""),
strides=str(strides).replace(" ", ""),
padding=padding).replace(" ", ""),
iters=num_iters,
wall_time=duration)
return duration
def benchmark_conv2d(self):
print("conv2d benchmark:")
data_types = [dtypes.float32, dtypes.float16]
data_formats = ["NHWC", "NCHW"]
in_channels = list(range(1, 10)) + list(range(10, 20, 2)) + list(
range(20, 33, 4))
out_channels = [4, 16, 32]
hw_strides = [[2, 2]]
paddings = ["VALID", "SAME"]
args_lists = [
data_types, data_formats, in_channels, out_channels, hw_strides,
paddings
]
for args in itertools.product(*args_lists):
dtype, data_format, in_channel, out_channel, hw_stride, padding = args
# Keep batch size same as out channels just to reduce the number of
# different configurations to benchmark.
batch_size = out_channel
h, w, fh, fw = 500, 500, 3, 3
if data_format == "NHWC":
ishape = [batch_size, h, w, in_channel]
stride = [1] + hw_stride + [1]
elif data_format == "NCHW":
ishape = [batch_size, in_channel, h, w]
stride = [1, 1] + hw_stride
else:
raise ValueError("Unknown data_format: " + str(data_format))
fshape = [fh, fw, in_channel, out_channel]
num_iters = 80
warmup_iters = 2
self._run_graph("gpu", dtype, data_format, ishape, fshape, stride,
padding, num_iters, warmup_iters)
if __name__ == "__main__":
test.main()
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