330 lines
14 KiB
Python
330 lines
14 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for the swig wrapper tf_optimizer."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.core.framework import attr_value_pb2
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from tensorflow.core.protobuf import config_pb2
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from tensorflow.core.protobuf import rewriter_config_pb2
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from tensorflow.python.client import session
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from tensorflow.python.framework import meta_graph
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import random_seed
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from tensorflow.python.framework import test_util
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from tensorflow.python.grappler import tf_optimizer
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn
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from tensorflow.python.ops import variable_scope
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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from tensorflow.python.training import training as train
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class MemoryOptimizerSwapTest(test.TestCase):
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"""Tests the Grappler memory optimizer."""
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@test_util.run_deprecated_v1
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def testNoSwapping(self):
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"""Make sure the graph is preserved when there is nothing to swap."""
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a = variables.VariableV1(10, name='a')
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b = variables.VariableV1(20, name='b')
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c = math_ops.add_n([a, b], name='c')
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d = math_ops.add_n([b, c], name='d')
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(d)
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mg = meta_graph.create_meta_graph_def(graph=ops.get_default_graph())
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graph_size = len(mg.graph_def.node)
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nodes = [node.name for node in mg.graph_def.node]
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config = config_pb2.ConfigProto()
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config.graph_options.rewrite_options.CopyFrom(
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rewriter_config_pb2.RewriterConfig(
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disable_model_pruning=True,
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constant_folding=rewriter_config_pb2.RewriterConfig.OFF,
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dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF,
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memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL))
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graph = tf_optimizer.OptimizeGraph(config, mg)
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self.assertEqual(len(graph.node), graph_size)
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self.assertItemsEqual([node.name for node in graph.node], nodes)
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@test_util.run_v1_only('b/120545219')
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def testSimpleSwap(self):
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"""Check that the swap annotations are followed."""
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with ops.device('/gpu:0'):
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a = variables.VariableV1(10, name='a')
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b = variables.VariableV1(20, name='b')
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c = math_ops.add_n([a, b], name='c')
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d = math_ops.add_n([b, c], name='d')
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(d)
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d.op._set_attr('_swap_to_host', attr_value_pb2.AttrValue(i=0))
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mg = meta_graph.create_meta_graph_def(graph=ops.get_default_graph())
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graph_size = len(mg.graph_def.node)
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config = config_pb2.ConfigProto()
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config.graph_options.rewrite_options.CopyFrom(
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rewriter_config_pb2.RewriterConfig(
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disable_model_pruning=True,
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meta_optimizer_iterations=rewriter_config_pb2.RewriterConfig.ONE,
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constant_folding=rewriter_config_pb2.RewriterConfig.OFF,
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memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL,
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min_graph_nodes=-1))
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graph = tf_optimizer.OptimizeGraph(config, mg)
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self.assertEqual(len(graph.node), graph_size + 2)
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self.assertTrue(
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set(node.name for node in graph.node) > set(
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['a', 'b', 'c', 'd', 'swap_in_d_0', 'swap_out_d_0']))
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for node in graph.node:
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if node.name == 'swap_in_d_0':
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self.assertEqual('swap_out_d_0', node.input[0])
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self.assertEqual('^b/read', node.input[1])
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elif node.name == 'swap_out_d_0':
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self.assertEqual('b/read', node.input[0])
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elif node.name == 'd':
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self.assertEqual('swap_in_d_0', node.input[0])
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self.assertEqual('c', node.input[1])
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class MemoryOptimizerRecomputeTest(test.TestCase):
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"""Tests the Python interface to recomputation rewrites.
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See core/grappler/optimizers/memory_optimizer_test.cc for functional tests.
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"""
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def _GetMetaGraph(self, batch_size=14, image_dim=12, optimizer_scope_name=''):
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"""A simple layered graph with conv, an intermediate op, and a ReLU."""
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graph = ops.Graph()
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with graph.as_default():
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random_seed.set_random_seed(1)
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current_activation = variable_scope.get_variable(
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name='start', shape=[batch_size, image_dim, image_dim, 5])
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conv_filter = variable_scope.get_variable(
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name='filter', shape=[5, 5, 5, 5])
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for layer_number in range(10):
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with variable_scope.variable_scope('layer_{}'.format(layer_number)):
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after_conv = nn.conv2d(current_activation, conv_filter, [1, 1, 1, 1],
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'SAME')
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current_activation = 2. * after_conv
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current_activation = nn.relu(current_activation)
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loss = math_ops.reduce_mean(current_activation)
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with ops.name_scope(optimizer_scope_name):
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optimizer = train.AdamOptimizer(0.001)
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train_op = optimizer.minimize(loss)
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init_op = variables.global_variables_initializer()
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metagraph = train.export_meta_graph()
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return (metagraph, init_op.name, train_op.name, loss.name)
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def testRewritingDefaultGradientNames(self):
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"""Tests that rewriting occurs with default gradient names."""
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(original_metagraph, _, _, _) = self._GetMetaGraph()
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config = config_pb2.ConfigProto()
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config.graph_options.rewrite_options.CopyFrom(
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rewriter_config_pb2.RewriterConfig(
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disable_model_pruning=True,
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constant_folding=rewriter_config_pb2.RewriterConfig.OFF,
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dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF,
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layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF,
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arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF,
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min_graph_nodes=-1,
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memory_optimization=(
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rewriter_config_pb2.RewriterConfig.RECOMPUTATION_HEURISTICS)))
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rewritten_graph_def = tf_optimizer.OptimizeGraph(config, original_metagraph)
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self.assertGreater(
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len(rewritten_graph_def.node),
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len(original_metagraph.graph_def.node))
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self.assertEqual(
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0,
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len([node for node in original_metagraph.graph_def.node
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if 'Recomputed/' in node.name]))
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self.assertEqual(
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20, # Two per layer
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len([node for node in rewritten_graph_def.node
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if 'Recomputed/' in node.name]))
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def testRewritingNameScopedGradientNames(self):
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"""Tests that rewriting occurs with non-standard gradient names."""
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(original_metagraph, _, _, _) = self._GetMetaGraph(
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optimizer_scope_name='optimizer')
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config = config_pb2.ConfigProto()
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config.graph_options.rewrite_options.CopyFrom(
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rewriter_config_pb2.RewriterConfig(
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disable_model_pruning=True,
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constant_folding=rewriter_config_pb2.RewriterConfig.OFF,
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dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF,
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layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF,
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arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF,
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min_graph_nodes=-1,
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memory_optimization=rewriter_config_pb2.RewriterConfig
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.RECOMPUTATION_HEURISTICS,
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# Checks that name scope "gradients/" also match sub-scope.
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memory_optimizer_target_node_name_scope='gradients/'))
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rewritten_graph_def = tf_optimizer.OptimizeGraph(config, original_metagraph)
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self.assertGreater(
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len(rewritten_graph_def.node),
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len(original_metagraph.graph_def.node))
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self.assertEqual(
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0,
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len([node for node in original_metagraph.graph_def.node
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if 'Recomputed/' in node.name]))
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self.assertEqual(
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20, # Two per layer
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len([node for node in rewritten_graph_def.node
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if 'Recomputed/' in node.name]))
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def testRewritingNameScopedGradientNamesScope(self):
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"""Tests that rewriting occurs with non-standard gradient names."""
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(original_metagraph, _, _,
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_) = self._GetMetaGraph(optimizer_scope_name='foo/bar')
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config = config_pb2.ConfigProto()
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config.graph_options.rewrite_options.CopyFrom(
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rewriter_config_pb2.RewriterConfig(
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disable_model_pruning=True,
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constant_folding=rewriter_config_pb2.RewriterConfig.OFF,
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dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF,
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layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF,
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arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF,
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memory_optimization=rewriter_config_pb2.RewriterConfig
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.RECOMPUTATION_HEURISTICS,
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# This should not match anything.
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memory_optimizer_target_node_name_scope='r/gradients/'))
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rewritten_graph_def = tf_optimizer.OptimizeGraph(config, original_metagraph)
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self.assertEqual(
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len(rewritten_graph_def.node), len(original_metagraph.graph_def.node))
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self.assertEqual(0,
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len([
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node for node in original_metagraph.graph_def.node
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if 'Recomputed/' in node.name
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]))
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self.assertEqual(0,
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len([
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node for node in rewritten_graph_def.node
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if 'Recomputed/' in node.name
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]))
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def _GetMemoryOptimizerSessionConfig(self):
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rewrite_options = rewriter_config_pb2.RewriterConfig(
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disable_model_pruning=True,
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memory_optimization=rewriter_config_pb2.RewriterConfig.HEURISTICS)
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graph_options = config_pb2.GraphOptions(rewrite_options=rewrite_options)
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return config_pb2.ConfigProto(graph_options=graph_options)
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def _RunMetaGraphWithConfig(
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self, config, metagraph, init_op_name, train_op_name, loss_op_name):
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graph = ops.Graph()
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with graph.as_default():
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train.import_meta_graph(metagraph)
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init_op = graph.get_operation_by_name(init_op_name)
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train_op = graph.get_operation_by_name(train_op_name)
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loss_op = graph.get_tensor_by_name(loss_op_name)
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with session.Session(config=config, graph=graph) as sess:
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self.evaluate(init_op)
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self.evaluate(train_op)
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self.evaluate(train_op)
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return self.evaluate(loss_op)
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def testRecomputationRewritingNoErrors(self):
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"""Tests that graph output is not significantly different with rewriting."""
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(original_metagraph, init_op_name, train_op_name, loss_op_name
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) = self._GetMetaGraph()
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original_loss = self._RunMetaGraphWithConfig(
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config=config_pb2.ConfigProto(),
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metagraph=original_metagraph,
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init_op_name=init_op_name,
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train_op_name=train_op_name,
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loss_op_name=loss_op_name)
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memory_optimized_loss = self._RunMetaGraphWithConfig(
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config=self._GetMemoryOptimizerSessionConfig(),
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metagraph=original_metagraph,
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init_op_name=init_op_name,
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train_op_name=train_op_name,
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loss_op_name=loss_op_name)
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self.assertAllClose(original_loss, memory_optimized_loss, rtol=1e-2)
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def _annotated_graph(self):
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graph = ops.Graph()
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with graph.as_default():
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random_seed.set_random_seed(2)
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current_activation = variable_scope.get_variable(
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name='start', shape=[1, 2, 2, 5])
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conv_filter = variable_scope.get_variable(
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name='filter', shape=[5, 5, 5, 5])
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for layer_number in range(3):
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with variable_scope.variable_scope('layer_{}'.format(layer_number)):
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after_conv = nn.conv2d(current_activation, conv_filter, [1, 1, 1, 1],
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'SAME')
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current_activation = 2. * after_conv
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current_activation.op._set_attr(
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'_recompute_hint',
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# The value of the attribute does not matter; just that the key
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# exists in the op's attributes.
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attr_value_pb2.AttrValue(i=1))
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current_activation += 5.
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current_activation.op._set_attr(
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'_recompute_hint', attr_value_pb2.AttrValue(i=0))
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current_activation = nn.relu(current_activation)
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current_activation.op._set_attr(
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'_recompute_hint', attr_value_pb2.AttrValue(i=1))
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loss = math_ops.reduce_mean(current_activation)
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optimizer = train.AdamOptimizer(0.001)
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train_op = optimizer.minimize(loss)
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init_op = variables.global_variables_initializer()
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return graph, init_op, train_op
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def testHintNoMetaGraph(self):
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# Closer to expected usage, but does not check that a re-write actually
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# happens; see testHintDoesRewrite.
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graph, init_op, train_op = self._annotated_graph()
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with graph.as_default():
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manual_memory_config = rewriter_config_pb2.RewriterConfig(
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memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL)
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graph_options = config_pb2.GraphOptions(
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rewrite_options=manual_memory_config)
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session_config = config_pb2.ConfigProto(graph_options=graph_options)
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with session.Session(config=session_config) as sess:
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self.evaluate(init_op)
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self.evaluate(train_op)
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@test_util.run_v1_only('b/120545219')
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def testHintDoesRewrite(self):
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graph = self._annotated_graph()[0]
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with graph.as_default():
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metagraph = train.export_meta_graph()
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self.assertEqual(
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0,
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len([node for node in metagraph.graph_def.node
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if 'Recomputed/' in node.name]))
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config = config_pb2.ConfigProto()
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config.graph_options.rewrite_options.CopyFrom(
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rewriter_config_pb2.RewriterConfig(
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min_graph_nodes=-1,
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memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL))
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rewritten_graph_def = tf_optimizer.OptimizeGraph(config, metagraph)
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self.assertEqual(
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9,
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len([
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node for node in rewritten_graph_def.node
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if 'Recomputed/' in node.name
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]))
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if __name__ == '__main__':
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test.main()
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