# 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. # ============================================================================== """End-to-end benchmark for batch normalization.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys import time from tensorflow.python.client import session as session_lib from tensorflow.python.framework import constant_op from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_impl from tensorflow.python.ops import random_ops from tensorflow.python.ops import variables import tensorflow.python.ops.nn_grad # pylint: disable=unused-import from tensorflow.python.platform import test def batch_norm_op(tensor, mean, variance, beta, gamma, scale): """Fused kernel for batch normalization.""" # _batch_norm_with_global_normalization is deprecated in v9 test_util.set_producer_version(ops.get_default_graph(), 8) # pylint: disable=protected-access return gen_nn_ops._batch_norm_with_global_normalization( tensor, mean, variance, beta, gamma, 0.001, scale) # pylint: enable=protected-access # Note that the naive implementation is much slower: # batch_norm = (tensor - mean) * tf.rsqrt(variance + 0.001) # if scale: # batch_norm *= gamma # return batch_norm + beta def batch_norm_py(tensor, mean, variance, beta, gamma, scale): """Python implementation of batch normalization.""" return nn_impl.batch_normalization(tensor, mean, variance, beta, gamma if scale else None, 0.001) def batch_norm_slow(tensor, mean, variance, beta, gamma, scale): batch_norm = (tensor - mean) * math_ops.rsqrt(variance + 0.001) if scale: batch_norm *= gamma return batch_norm + beta def build_graph(device, input_shape, axes, num_layers, mode, scale, train): """Build a graph containing a sequence of batch normalizations. Args: device: string, the device to run on. input_shape: shape of the input tensor. axes: axes that are to be normalized across. num_layers: number of batch normalization layers in the graph. mode: "op", "py" or "slow" depending on the implementation. scale: scale after normalization. train: if true, also run backprop. Returns: An array of tensors to run() """ moment_shape = [] keep_dims = mode == "py" or mode == "slow" if keep_dims: for axis in range(len(input_shape)): if axis in axes: moment_shape.append(1) else: moment_shape.append(input_shape[axis]) else: for axis in range(len(input_shape)): if axis not in axes: moment_shape.append(input_shape[axis]) with ops.device("/%s:0" % device): tensor = variables.Variable(random_ops.truncated_normal(input_shape)) for _ in range(num_layers): if train: mean, variance = nn_impl.moments(tensor, axes, keep_dims=keep_dims) else: mean = array_ops.zeros(moment_shape) variance = array_ops.ones(moment_shape) beta = variables.Variable(array_ops.zeros(moment_shape)) gamma = variables.Variable(constant_op.constant(1.0, shape=moment_shape)) if mode == "py": tensor = batch_norm_py(tensor, mean, variance, beta, gamma, scale) elif mode == "op": tensor = batch_norm_op(tensor, mean, variance, beta, gamma, scale) elif mode == "slow": tensor = batch_norm_slow(tensor, mean, variance, beta, gamma, scale) if train: return gradients_impl.gradients([tensor], variables.trainable_variables()) else: return [tensor] def print_difference(mode, t1, t2): """Print the difference in timing between two runs.""" difference = (t2 - t1) / t1 * 100.0 print("=== %s: %.1f%% ===" % (mode, difference)) class BatchNormBenchmark(test.Benchmark): """Benchmark batch normalization.""" def _run_graph(self, device, input_shape, axes, num_layers, mode, scale, train, num_iters): """Run the graph and print its execution time. Args: device: string, the device to run on. input_shape: shape of the input tensor. axes: axes that are to be normalized across. num_layers: number of batch normalization layers in the graph. mode: "op", "py" or "slow" depending on the implementation. scale: scale after normalization. train: if true, also run backprop. 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, axes, num_layers, mode, scale, train) with session_lib.Session(graph=graph) as session: variables.global_variables_initializer().run() _ = session.run([out.op for out in outputs]) # warm up. start_time = time.time() for _ in range(num_iters): _ = session.run([out.op for out in outputs]) duration = time.time() - start_time print("%s shape:%d/%d #layers:%d mode:%s scale:%r train:%r - %f secs" % (device, len(input_shape), len(axes), num_layers, mode, scale, train, duration / num_iters)) name_template = ( "batch_norm_{device}_input_shape_{shape}_axes_{axes}_mode_{mode}_" "layers_{num_layers}_scale_{scale}_" "train_{train}") self.report_benchmark( name=name_template.format( device=device, mode=mode, num_layers=num_layers, scale=scale, train=train, shape=str(input_shape).replace(" ", ""), axes=str(axes)).replace(" ", ""), iters=num_iters, wall_time=duration / num_iters) return duration def benchmark_batch_norm(self): print("Forward convolution (lower layers).") shape = [8, 128, 128, 32] axes = [0, 1, 2] t1 = self._run_graph("cpu", shape, axes, 10, "op", True, False, 5) t2 = self._run_graph("cpu", shape, axes, 10, "py", True, False, 5) t3 = self._run_graph("cpu", shape, axes, 10, "slow", True, False, 5) print_difference("op vs py", t1, t2) print_difference("py vs slow", t2, t3) if FLAGS.use_gpu: t1 = self._run_graph("gpu", shape, axes, 10, "op", True, False, 50) t2 = self._run_graph("gpu", shape, axes, 10, "py", True, False, 50) t3 = self._run_graph("gpu", shape, axes, 10, "slow", True, False, 50) print_difference("op vs py", t1, t2) print_difference("py vs slow", t2, t3) print("Forward/backward convolution (lower layers).") t1 = self._run_graph("cpu", shape, axes, 10, "op", True, True, 5) t2 = self._run_graph("cpu", shape, axes, 10, "py", True, True, 5) t3 = self._run_graph("cpu", shape, axes, 10, "slow", True, True, 5) print_difference("op vs py", t1, t2) print_difference("py vs slow", t2, t3) if FLAGS.use_gpu: t1 = self._run_graph("gpu", shape, axes, 10, "op", True, True, 50) t2 = self._run_graph("gpu", shape, axes, 10, "py", True, True, 50) t3 = self._run_graph("gpu", shape, axes, 10, "slow", True, True, 50) print_difference("op vs py", t1, t2) print_difference("py vs slow", t2, t3) print("Forward convolution (higher layers).") shape = [256, 17, 17, 32] axes = [0, 1, 2] t1 = self._run_graph("cpu", shape, axes, 10, "op", True, False, 5) t2 = self._run_graph("cpu", shape, axes, 10, "py", True, False, 5) t3 = self._run_graph("cpu", shape, axes, 10, "slow", True, False, 5) print_difference("op vs py", t1, t2) print_difference("py vs slow", t2, t3) if FLAGS.use_gpu: t1 = self._run_graph("gpu", shape, axes, 10, "op", True, False, 50) t2 = self._run_graph("gpu", shape, axes, 10, "py", True, False, 50) t3 = self._run_graph("gpu", shape, axes, 10, "slow", True, False, 50) print_difference("op vs py", t1, t2) print_difference("py vs slow", t2, t3) print("Forward/backward convolution (higher layers).") t1 = self._run_graph("cpu", shape, axes, 10, "op", True, True, 5) t2 = self._run_graph("cpu", shape, axes, 10, "py", True, True, 5) t3 = self._run_graph("cpu", shape, axes, 10, "slow", True, True, 5) print_difference("op vs py", t1, t2) print_difference("py vs slow", t2, t3) if FLAGS.use_gpu: t1 = self._run_graph("gpu", shape, axes, 10, "op", True, True, 50) t2 = self._run_graph("gpu", shape, axes, 10, "py", True, True, 50) t3 = self._run_graph("gpu", shape, axes, 10, "slow", True, True, 50) print_difference("op vs py", t1, t2) print_difference("py vs slow", t2, t3) print("Forward fully-connected.") shape = [1024, 32] axes = [0] t1 = self._run_graph("cpu", shape, axes, 10, "py", True, False, 5) t2 = self._run_graph("cpu", shape, axes, 10, "slow", True, False, 5) print_difference("py vs slow", t1, t2) if FLAGS.use_gpu: t1 = self._run_graph("gpu", shape, axes, 10, "py", True, False, 50) t2 = self._run_graph("gpu", shape, axes, 10, "slow", True, False, 50) print_difference("py vs slow", t1, t2) print("Forward/backward fully-connected.") t1 = self._run_graph("cpu", shape, axes, 10, "py", True, True, 50) t2 = self._run_graph("cpu", shape, axes, 10, "slow", True, True, 50) print_difference("py vs slow", t1, t2) if FLAGS.use_gpu: t1 = self._run_graph("gpu", shape, axes, 10, "py", True, True, 5) t2 = self._run_graph("gpu", shape, axes, 10, "slow", True, True, 5) print_difference("py vs slow", t1, t2) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.register("type", "bool", lambda v: v.lower() == "true") parser.add_argument( "--use_gpu", type="bool", nargs="?", const=True, default=True, help="Run GPU benchmarks." ) global FLAGS # pylint:disable=global-at-module-level FLAGS, unparsed = parser.parse_known_args() test.main(argv=[sys.argv[0]] + unparsed)