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# 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.protobuf import rewriter_config_pb2
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import meta_graph
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.grappler import item as gitem
from tensorflow.python.grappler import tf_optimizer
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
class PyWrapOptimizeGraphTest(test.TestCase):
def testBasic(self):
"""Make sure arguments can be passed correctly."""
a = constant_op.constant(10, name='a')
b = constant_op.constant(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)
# Being a train_op will make 'd' to be added as a fetch node.
train_op.append(d)
mg = meta_graph.create_meta_graph_def(graph=ops.get_default_graph())
rewriter_config = rewriter_config_pb2.RewriterConfig()
rewriter_config.optimizers.append('constfold')
rewriter_config.min_graph_nodes = -1
graph = tf_optimizer.OptimizeGraph(rewriter_config, mg)
self.assertEqual(len(graph.node), 1)
self.assertItemsEqual([node.name for node in graph.node], ['d'])
def testKeepNodes(self):
g = ops.Graph()
with g.as_default():
a1 = variables.VariableV1(
1.0) # Must be preserved since it's in the collection 'variables'.
a2 = constant_op.constant(0, shape=[50, 50], name='keep')
ops.add_to_collection('a2', a2) # Explicitly add to collection.
b = constant_op.constant(1, shape=[100, 10])
c = constant_op.constant(0, shape=[10, 30])
d = math_ops.matmul(b, c)
ops.add_to_collection('train_op', d) # d is the fetch node.
# Optimize the graph.
mg = meta_graph.create_meta_graph_def(graph=g)
rewriter_config = rewriter_config_pb2.RewriterConfig()
rewriter_config.min_graph_nodes = -1
optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, mg)
# Check that the nodes referenced in various collections have been preserved
self.assertEqual(len(optimized_graph.node), 5)
self.assertEqual(d.op.name, optimized_graph.node[0].name)
self.assertEqual(a1.op.name, optimized_graph.node[1].name)
self.assertEqual('Variable/initial_value', optimized_graph.node[2].name)
self.assertEqual(a2.op.name, optimized_graph.node[3].name)
self.assertEqual('Variable/Assign', optimized_graph.node[4].name)
def testLoops(self):
g = ops.Graph()
with g.as_default():
def _Cond(_, counter):
return counter < end
def _Body(buf, counter):
buf = array_ops.concat([buf, [counter]], 0)
counter += 1
return [buf, counter]
start = array_ops.placeholder(shape=[], dtype=dtypes.int32)
end = array_ops.placeholder(shape=[], dtype=dtypes.int32)
init_buf = array_ops.zeros(shape=[0], dtype=dtypes.int32)
loop_vars = [init_buf, start]
shape_inv = [
tensor_shape.TensorShape([None]),
tensor_shape.TensorShape([])
]
buf, _ = control_flow_ops.while_loop(_Cond, _Body, loop_vars, shape_inv)
f = -array_ops.ones_like(buf, optimize=False)
buf_shape = array_ops.shape(buf)
f_shape = array_ops.shape(f)
ops.add_to_collection('train_op', buf_shape)
ops.add_to_collection('train_op', f_shape)
# Optimize the graph.
mg = meta_graph.create_meta_graph_def(graph=g)
rewriter_config = rewriter_config_pb2.RewriterConfig()
rewriter_config.min_graph_nodes = -1
optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, mg)
mg.graph_def.CopyFrom(optimized_graph)
# Check that the nodes referenced in various collections have been preserved
item = gitem.Item(mg)
props = item.GetOpProperties()
buf_prop = props[buf.op.name]
f_prop = props[f.op.name]
self.assertEqual(buf_prop, f_prop)
if __name__ == '__main__':
test.main()
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