# 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. # ============================================================================== """Tests for the swig wrapper tf_optimizer.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.core.framework import attr_value_pb2 from tensorflow.core.protobuf import config_pb2 from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.client import session from tensorflow.python.framework import meta_graph from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.grappler import tf_optimizer from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import test from tensorflow.python.training import training as train class MemoryOptimizerSwapTest(test.TestCase): """Tests the Grappler memory optimizer.""" def testNoSwapping(self): """Make sure the graph is preserved when there is nothing to swap.""" a = variables.VariableV1(10, name='a') b = variables.VariableV1(20, name='b') c = math_ops.add_n([a, b], name='c') d = math_ops.add_n([b, c], name='d') train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP) train_op.append(d) mg = meta_graph.create_meta_graph_def(graph=ops.get_default_graph()) graph_size = len(mg.graph_def.node) nodes = [node.name for node in mg.graph_def.node] rewriter_config = rewriter_config_pb2.RewriterConfig( disable_model_pruning=True, constant_folding=rewriter_config_pb2.RewriterConfig.OFF, memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL) graph = tf_optimizer.OptimizeGraph(rewriter_config, mg) self.assertEqual(len(graph.node), graph_size) self.assertItemsEqual([node.name for node in graph.node], nodes) def testSimpleSwap(self): """Check that the swap annotations are followed.""" a = variables.VariableV1(10, name='a') b = variables.VariableV1(20, name='b') c = math_ops.add_n([a, b], name='c') d = math_ops.add_n([b, c], name='d') train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP) train_op.append(d) d.op._set_attr('_swap_to_host', attr_value_pb2.AttrValue(i=0)) mg = meta_graph.create_meta_graph_def(graph=ops.get_default_graph()) graph_size = len(mg.graph_def.node) rewriter_config = rewriter_config_pb2.RewriterConfig( disable_model_pruning=True, meta_optimizer_iterations=rewriter_config_pb2.RewriterConfig.ONE, constant_folding=rewriter_config_pb2.RewriterConfig.OFF, memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL, min_graph_nodes=-1) graph = tf_optimizer.OptimizeGraph(rewriter_config, mg) self.assertEqual(len(graph.node), graph_size + 2) self.assertTrue( set([node.name for node in graph.node]) > set( ['a', 'b', 'c', 'd', 'swap_in_d_0', 'swap_out_d_0'])) for node in graph.node: if node.name == 'swap_in_d_0': self.assertEqual('swap_out_d_0', node.input[0]) self.assertEqual('^b/read', node.input[1]) elif node.name == 'swap_out_d_0': self.assertEqual('b/read', node.input[0]) elif node.name == 'd': self.assertEqual('swap_in_d_0', node.input[0]) self.assertEqual('c', node.input[1]) class MemoryOptimizerRecomputeTest(test.TestCase): """Tests the Python interface to recomputation rewrites. See core/grappler/optimizers/memory_optimizer_test.cc for functional tests. """ def _GetMetaGraph(self, batch_size=14, image_dim=12, optimizer_scope_name=''): """A simple layered graph with conv, an intermediate op, and a ReLU.""" graph = ops.Graph() with graph.as_default(): random_seed.set_random_seed(1) current_activation = variable_scope.get_variable( name='start', shape=[batch_size, image_dim, image_dim, 5]) conv_filter = variable_scope.get_variable( name='filter', shape=[5, 5, 5, 5]) for layer_number in range(10): with variable_scope.variable_scope('layer_{}'.format(layer_number)): after_conv = nn.conv2d(current_activation, conv_filter, [1, 1, 1, 1], 'SAME') current_activation = 2. * after_conv current_activation = nn.relu(current_activation) loss = math_ops.reduce_mean(current_activation) with ops.name_scope(optimizer_scope_name): optimizer = train.AdamOptimizer(0.001) train_op = optimizer.minimize(loss) init_op = variables.global_variables_initializer() metagraph = train.export_meta_graph() return (metagraph, init_op.name, train_op.name, loss.name) def testRewritingDefaultGradientNames(self): """Tests that rewriting occurs with default gradient names.""" (original_metagraph, _, _, _) = self._GetMetaGraph() rewritten_graph_def = tf_optimizer.OptimizeGraph( rewriter_config_pb2.RewriterConfig( disable_model_pruning=True, constant_folding=rewriter_config_pb2.RewriterConfig.OFF, dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF, layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF, arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, min_graph_nodes=-1, memory_optimization=rewriter_config_pb2.RewriterConfig. RECOMPUTATION_HEURISTICS), original_metagraph) self.assertGreater( len(rewritten_graph_def.node), len(original_metagraph.graph_def.node)) self.assertEqual( 0, len([node for node in original_metagraph.graph_def.node if 'Recomputed/' in node.name])) self.assertEqual( 20, # Two per layer len([node for node in rewritten_graph_def.node if 'Recomputed/' in node.name])) def testRewritingNameScopedGradientNames(self): """Tests that rewriting occurs with non-standard gradient names.""" (original_metagraph, _, _, _) = self._GetMetaGraph( optimizer_scope_name='optimizer') rewritten_graph_def = tf_optimizer.OptimizeGraph( rewriter_config_pb2.RewriterConfig( disable_model_pruning=True, constant_folding=rewriter_config_pb2.RewriterConfig.OFF, dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF, layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF, arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, min_graph_nodes=-1, memory_optimization=rewriter_config_pb2.RewriterConfig. RECOMPUTATION_HEURISTICS, # Checks that name scope "gradients/" also match sub-scope. memory_optimizer_target_node_name_scope='gradients/'), original_metagraph) self.assertGreater( len(rewritten_graph_def.node), len(original_metagraph.graph_def.node)) self.assertEqual( 0, len([node for node in original_metagraph.graph_def.node if 'Recomputed/' in node.name])) self.assertEqual( 20, # Two per layer len([node for node in rewritten_graph_def.node if 'Recomputed/' in node.name])) def testRewritingNameScopedGradientNamesScope(self): """Tests that rewriting occurs with non-standard gradient names.""" (original_metagraph, _, _, _) = self._GetMetaGraph(optimizer_scope_name='foo/bar') rewritten_graph_def = tf_optimizer.OptimizeGraph( rewriter_config_pb2.RewriterConfig( disable_model_pruning=True, constant_folding=rewriter_config_pb2.RewriterConfig.OFF, dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF, layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF, arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF, memory_optimization=rewriter_config_pb2.RewriterConfig. RECOMPUTATION_HEURISTICS, # This should not match anything. memory_optimizer_target_node_name_scope='r/gradients/'), original_metagraph) self.assertEqual( len(rewritten_graph_def.node), len(original_metagraph.graph_def.node)) self.assertEqual(0, len([ node for node in original_metagraph.graph_def.node if 'Recomputed/' in node.name ])) self.assertEqual(0, len([ node for node in rewritten_graph_def.node if 'Recomputed/' in node.name ])) def _GetMemoryOptimizerSessionConfig(self): rewrite_options = rewriter_config_pb2.RewriterConfig( disable_model_pruning=True, memory_optimization=rewriter_config_pb2.RewriterConfig.HEURISTICS) graph_options = config_pb2.GraphOptions(rewrite_options=rewrite_options) return config_pb2.ConfigProto(graph_options=graph_options) def _RunMetaGraphWithConfig( self, config, metagraph, init_op_name, train_op_name, loss_op_name): graph = ops.Graph() with graph.as_default(): train.import_meta_graph(metagraph) init_op = graph.get_operation_by_name(init_op_name) train_op = graph.get_operation_by_name(train_op_name) loss_op = graph.get_tensor_by_name(loss_op_name) with session.Session(config=config, graph=graph) as sess: sess.run(init_op) sess.run(train_op) sess.run(train_op) return sess.run(loss_op) def testRecomputationRewritingNoErrors(self): """Tests that graph output is not significantly different with rewriting.""" (original_metagraph, init_op_name, train_op_name, loss_op_name ) = self._GetMetaGraph() original_loss = self._RunMetaGraphWithConfig( config=config_pb2.ConfigProto(), metagraph=original_metagraph, init_op_name=init_op_name, train_op_name=train_op_name, loss_op_name=loss_op_name) memory_optimized_loss = self._RunMetaGraphWithConfig( config=self._GetMemoryOptimizerSessionConfig(), metagraph=original_metagraph, init_op_name=init_op_name, train_op_name=train_op_name, loss_op_name=loss_op_name) self.assertAllClose(original_loss, memory_optimized_loss, rtol=1e-2) def _annotated_graph(self): graph = ops.Graph() with graph.as_default(): random_seed.set_random_seed(2) current_activation = variable_scope.get_variable( name='start', shape=[1, 2, 2, 5]) conv_filter = variable_scope.get_variable( name='filter', shape=[5, 5, 5, 5]) for layer_number in range(3): with variable_scope.variable_scope('layer_{}'.format(layer_number)): after_conv = nn.conv2d(current_activation, conv_filter, [1, 1, 1, 1], 'SAME') current_activation = 2. * after_conv current_activation.op._set_attr( '_recompute_hint', # The value of the attribute does not matter; just that the key # exists in the op's attributes. attr_value_pb2.AttrValue(i=1)) current_activation += 5. current_activation.op._set_attr( '_recompute_hint', attr_value_pb2.AttrValue(i=0)) current_activation = nn.relu(current_activation) current_activation.op._set_attr( '_recompute_hint', attr_value_pb2.AttrValue(i=1)) loss = math_ops.reduce_mean(current_activation) optimizer = train.AdamOptimizer(0.001) train_op = optimizer.minimize(loss) init_op = variables.global_variables_initializer() return graph, init_op, train_op def testHintNoMetaGraph(self): # Closer to expected usage, but does not check that a re-write actually # happens; see testHintDoesRewrite. graph, init_op, train_op = self._annotated_graph() with graph.as_default(): manual_memory_config = rewriter_config_pb2.RewriterConfig( memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL) graph_options = config_pb2.GraphOptions( rewrite_options=manual_memory_config) session_config = config_pb2.ConfigProto(graph_options=graph_options) with session.Session(config=session_config) as sess: sess.run(init_op) sess.run(train_op) def testHintDoesRewrite(self): graph = self._annotated_graph()[0] with graph.as_default(): metagraph = train.export_meta_graph() self.assertEqual( 0, len([node for node in metagraph.graph_def.node if 'Recomputed/' in node.name])) rewritten_graph_def = tf_optimizer.OptimizeGraph( rewriter_config_pb2.RewriterConfig( min_graph_nodes=-1, memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL), metagraph) self.assertEqual( 9, len([node for node in rewritten_graph_def.node if 'Recomputed/' in node.name])) if __name__ == '__main__': test.main()