1 # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 # 3 # Licensed under the Apache License, Version 2.0 (the "License"); 4 # you may not use this file except in compliance with the License. 5 # You may obtain a copy of the License at 6 # 7 # http://www.apache.org/licenses/LICENSE-2.0 8 # 9 # Unless required by applicable law or agreed to in writing, software 10 # distributed under the License is distributed on an "AS IS" BASIS, 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 # See the License for the specific language governing permissions and 13 # limitations under the License. 14 # ============================================================================== 15 """Benchmark for fused conv2d bias and activation op.""" 16 17 from __future__ import absolute_import 18 from __future__ import division 19 from __future__ import print_function 20 import time 21 22 from tensorflow.contrib.fused_conv.python.ops import fused_conv2d_bias_activation_op 23 from tensorflow.python.client import session as session_lib 24 from tensorflow.python.framework import ops 25 from tensorflow.python.ops import control_flow_ops 26 from tensorflow.python.ops import nn_ops 27 from tensorflow.python.ops import random_ops 28 from tensorflow.python.ops import variables 29 from tensorflow.python.platform import test 30 31 32 def build_conv_bias_relu_graph(device, input_shape, filter_shape, strides, 33 padding, num_iters, data_format): 34 """builds a graph containing a sequence of conv2d operations. 35 36 Args: 37 device: String, the device to run on. 38 input_shape: Shape of the input tensor. 39 filter_shape: Shape of the filter tensor. 40 strides: A list of ints. 1-D of length 4. The stride of sliding 41 window for each dimension of input. 42 padding: A string from: "SAME", "VALID". The type of padding 43 algorithm to use. 44 num_iters: number of iterations to run conv2d. 45 data_format: data format string of input, 'NHWC' and 'NCHW' are 46 supported. 47 48 Returns: 49 An array of tensors to run() 50 """ 51 if data_format == "NCHW": 52 input_shape = [ 53 input_shape[0], input_shape[3], input_shape[1], input_shape[2] 54 ] 55 with ops.device("/%s:0" % device): 56 inp = variables.Variable(random_ops.truncated_normal(input_shape)) 57 filt = variables.Variable(random_ops.truncated_normal(filter_shape)) 58 bias_shape = [filter_shape[-1]] 59 bias = variables.Variable(random_ops.truncated_normal(bias_shape)) 60 61 outputs = [] 62 conv2d_out = nn_ops.conv2d( 63 inp, filt, strides, padding, data_format=data_format) 64 bias_out = nn_ops.bias_add(conv2d_out, bias, data_format=data_format) 65 relu_out = nn_ops.relu(bias_out) 66 outputs.append(relu_out) 67 for _ in range(1, num_iters): 68 with ops.control_dependencies([relu_out]): 69 conv2d_out = nn_ops.conv2d( 70 inp, filt, strides, padding, data_format=data_format) 71 bias_out = nn_ops.bias_add(conv2d_out, bias, data_format=data_format) 72 relu_out = nn_ops.relu(bias_out) 73 outputs.append(relu_out) 74 return control_flow_ops.group(*outputs) 75 76 77 def build_fused_conv_bias_relu_graph(device, input_shape, filter_shape, strides, 78 padding, num_iters, data_format): 79 """builds a graph containing a sequence of conv2d operations. 80 81 Args: 82 device: String, the device to run on. 83 input_shape: Shape of the input tensor. 84 filter_shape: Shape of the filter tensor. 85 strides: A list of ints. 1-D of length 4. The stride of sliding 86 window for each dimension of input. 87 padding: A string from: "SAME", "VALID". The type of padding 88 algorithm to use. 89 num_iters: number of iterations to run conv2d. 90 data_format: data format string of input, 'NHWC' and 'NCHW' are 91 supported. 92 93 Returns: 94 An array of tensors to run() 95 """ 96 if data_format == "NCHW": 97 input_shape = [ 98 input_shape[0], input_shape[3], input_shape[1], input_shape[2] 99 ] 100 with ops.device("/%s:0" % device): 101 inp = variables.Variable(random_ops.truncated_normal(input_shape)) 102 filt = variables.Variable(random_ops.truncated_normal(filter_shape)) 103 bias_shape = [filter_shape[-1]] 104 bias = variables.Variable(random_ops.truncated_normal(bias_shape)) 105 106 outputs = [] 107 fused_out = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation( 108 inp, 109 filt, 110 bias, 111 strides, 112 padding, 113 data_format=data_format, 114 activation_mode="Relu") 115 outputs.append(fused_out) 116 for _ in range(1, num_iters): 117 with ops.control_dependencies([fused_out]): 118 # pylint: disable=g-line-too-long 119 fused_out = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation( # pylint: disable=line-too-long 120 inp, 121 filt, 122 bias, 123 strides, 124 padding, 125 data_format=data_format, 126 activation_mode="Relu") 127 outputs.append(fused_out) 128 return control_flow_ops.group(*outputs) 129 130 131 class FusedConv2DBiasActivationBenchmark(test.Benchmark): 132 """Benchmark conv2d!""" 133 134 def _run_graph(self, device, input_shape, filter_shape, strides, padding, 135 num_iters, data_format): 136 """runs the graph and print its execution time. 137 138 Args: 139 device: String, the device to run on. 140 input_shape: Shape of the input tensor. 141 filter_shape: Shape of the filter tensor. 142 strides: A list of ints. 1-D of length 4. The stride of sliding 143 window for each dimension of input. 144 padding: A string from: "SAME", "VALID". The type of padding 145 algorithm to use. num_iters: Number of iterations to run the 146 benchmark. 147 num_iters: number of iterations to run conv2d. 148 data_format: data format string of input, 'NHWC' and 'NCHW' are 149 supported. 150 151 Returns: 152 The duration of the run in seconds. 153 """ 154 graph = ops.Graph() 155 with graph.as_default(): 156 outputs = build_fused_conv_bias_relu_graph(device, input_shape, 157 filter_shape, strides, padding, 158 num_iters, data_format) 159 with session_lib.Session(graph=graph) as session: 160 variables.global_variables_initializer().run() 161 # warmup runs 162 session.run(outputs) 163 164 start_time = time.time() 165 session.run(outputs) 166 duration = (time.time() - start_time) / num_iters 167 168 print("%s inputshape:%s filtershape:%s strides:%s padding:%s " 169 "%d iters: %.8f sec" % (device, str(input_shape).replace(" ", ""), 170 str(filter_shape).replace(" ", ""), 171 str(strides).replace(" ", ""), padding, 172 num_iters, duration)) 173 name_template = ( 174 "conv2d_{device}_input_shape_{inputshape}_filter_shape_{filtershape}_" 175 "strides_{strides}_padding_{padding}") 176 177 self.report_benchmark( 178 name=name_template.format( 179 device=device, 180 inputshape=str(input_shape).replace(" ", ""), 181 filtershape=str(filter_shape).replace(" ", ""), 182 strides=str(strides).replace(" ", ""), 183 padding=padding).replace(" ", ""), 184 iters=num_iters, 185 wall_time=duration) 186 187 return duration 188 189 def benchmark_fused_conv2d_bias_activation(self): 190 191 stride = [1, 1, 1, 1] 192 paddings = ["VALID", "SAME"] 193 data_formats = ["NHWC", "NCHW"] 194 195 resnet50_input_shapes = [[64, 14, 14, 256], [64, 14, 14, 256], [ 196 64, 14, 14, 1024 197 ], [64, 55, 55, 64], [64, 28, 28, 128], [64, 28, 28, 128], [64, 55, 55, 64], 198 [64, 7, 7, 512], [64, 7, 7, 512], 199 [64, 28, 28, 512], [64, 55, 55, 200 256], [64, 7, 7, 2048]] 201 202 resnet50_filter_shapes = [[1, 1, 256, 1024], [3, 3, 256, 256], [ 203 1, 1, 1024, 256 204 ], [1, 1, 64, 256], [1, 1, 128, 512], [3, 3, 128, 128], [3, 3, 64, 64], [ 205 3, 3, 512, 512 206 ], [1, 1, 512, 2048], [1, 1, 512, 128], [1, 1, 256, 64], [1, 1, 2048, 512]] 207 208 inception3_input_shapes = [[64, 17, 17, 768], [64, 35, 35, 96], [ 209 64, 35, 35, 288 210 ], [64, 8, 8, 384], [64, 8, 8, 384], [64, 17, 17, 192], [64, 35, 35, 64], [ 211 64, 17, 17, 192 212 ], [64, 17, 17, 160], [64, 17, 17, 160], [64, 17, 17, 768], [ 213 64, 35, 35, 256 214 ], [64, 35, 35, 48], [64, 35, 35, 192], [64, 17, 17, 128], [ 215 64, 17, 17, 160 216 ], [64, 8, 8, 448], [64, 17, 17, 128], [64, 17, 17, 768], [64, 17, 17, 160]] 217 inception3_filter_shapes = [[1, 1, 768, 192], [3, 3, 96, 96], [ 218 1, 1, 288, 64 219 ], [1, 3, 384, 384], [3, 1, 384, 384], [7, 1, 192, 192], [3, 3, 64, 96], [ 220 1, 7, 192, 192 221 ], [7, 1, 160, 160], [1, 7, 160, 160], [1, 1, 768, 160], [1, 1, 256, 64], [ 222 5, 5, 48, 64 223 ], [1, 1, 192, 64], [1, 7, 128, 128], [1, 7, 160, 192], [3, 3, 448, 384], 224 [7, 1, 128, 128], [1, 1, 768, 225 128], [7, 1, 160, 192]] 226 227 print("fused conv2d bias activation benchmark using resnet50's shapes:") 228 for ishape, fshape in zip(resnet50_input_shapes, resnet50_filter_shapes): 229 for padding in paddings: 230 for data_format in data_formats: 231 self._run_graph("gpu", ishape, fshape, stride, padding, 80, 232 data_format) 233 print("fused conv2d bias activation benchmark using inception3's shapes:") 234 for ishape, fshape in zip(inception3_input_shapes, 235 inception3_filter_shapes): 236 for padding in paddings: 237 for data_format in data_formats: 238 self._run_graph("gpu", ishape, fshape, stride, padding, 80, 239 data_format) 240 241 242 if __name__ == "__main__": 243 test.main() 244
