text: # 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.framework import test_util
  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."""
  
    @test_util.run_deprecated_v1
    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]
  
      config = config_pb2.ConfigProto()
      config.graph_options.rewrite_options.CopyFrom(
          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(config, mg)
  
      self.assertEqual(len(graph.node), graph_size)
      self.assertItemsEqual([node.name for node in graph.node], nodes)
  
    @test_util.run_v1_only('b/120545219')
    def testSimpleSwap(self):
      """Check that the swap annotations are followed."""
      with ops.device('/gpu:0'):
        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)
  
        config = config_pb2.ConfigProto()
        config.graph_options.rewrite_options.CopyFrom(
            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(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()
      config = config_pb2.ConfigProto()
      config.graph_options.rewrite_options.CopyFrom(
          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)))
      rewritten_graph_def = tf_optimizer.OptimizeGraph(config, 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')
      config = config_pb2.ConfigProto()
      config.graph_options.rewrite_options.CopyFrom(
          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/'))
      rewritten_graph_def = tf_optimizer.OptimizeGraph(config, 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')
      config = config_pb2.ConfigProto()
      config.graph_options.rewrite_options.CopyFrom(
          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/'))
      rewritten_graph_def = tf_optimizer.OptimizeGraph(config, 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:
          self.evaluate(init_op)
          self.evaluate(train_op)
          self.evaluate(train_op)
          return self.evaluate(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:
          self.evaluate(init_op)
          self.evaluate(train_op)
  
    @test_util.run_v1_only('b/120545219')
    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]))
      config = config_pb2.ConfigProto()
      config.graph_options.rewrite_options.CopyFrom(
          rewriter_config_pb2.RewriterConfig(
              min_graph_nodes=-1,
              memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL))
      rewritten_graph_def = tf_optimizer.OptimizeGraph(config, metagraph)
      self.assertEqual(
          9,
          len([
              node for node in rewritten_graph_def.node
              if 'Recomputed/' in node.name
          ]))
  
  if __name__ == '__main__':
    test.main()
  