# 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.VariableV1( random_ops.truncated_normal(input_shape, dtype=dtype)) filt = variables.VariableV1( 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()