wehub-resource-sync
145 lines
5.9 KiB
Python
145 lines
5.9 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 pybind11 wrapper of Grappler items."""
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import errors_impl
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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 tensor_shape
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from tensorflow.python.framework import test_util
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from tensorflow.python.grappler import item
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import gen_array_ops
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from tensorflow.python.ops import state_ops
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from tensorflow.python.ops import variable_v1
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from tensorflow.python.platform import test
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class ItemTest(test.TestCase):
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"""Unit tests for Grappler Item pybind11 wrapper functionality."""
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def _create_sample_metagraph(self, include_train_op=True):
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with ops.Graph().as_default() as g:
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a = constant_op.constant(10)
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b = constant_op.constant(20)
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c = a + b
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z = control_flow_ops.no_op()
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if include_train_op:
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(c)
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return meta_graph.create_meta_graph_def(graph=g), z
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def test_invalid_item_missing_train_op_raises(self):
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"""Verifies that Item raises InvalidArgumentError when train_op is missing."""
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mg, _ = self._create_sample_metagraph(include_train_op=False)
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with self.assertRaisesRegex(
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errors_impl.InvalidArgumentError,
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'train_op not specified in the metagraph'):
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item.Item(mg)
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def test_important_ops_identification(self):
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"""Verifies important ops are correctly identified from the metagraph."""
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mg, _ = self._create_sample_metagraph(include_train_op=True)
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grappler_item = item.Item(mg)
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op_list = grappler_item.IdentifyImportantOps()
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self.assertCountEqual(['Const', 'Const_1', 'add'], op_list)
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def test_op_properties_extraction(self):
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"""Verifies op properties are correctly extracted for graph nodes."""
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mg, z = self._create_sample_metagraph(include_train_op=True)
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grappler_item = item.Item(mg)
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op_properties = grappler_item.GetOpProperties()
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z_prop = op_properties[z.name]
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self.assertEmpty(z_prop)
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const_prop = op_properties['Const']
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self.assertLen(const_prop, 1)
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self.assertEqual(dtypes.int32, const_prop[0].dtype)
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self.assertEqual(tensor_shape.TensorShape([]), const_prop[0].shape)
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def test_tf_item_initial_properties_equal(self):
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"""Verifies initial tf_item properties are consistent and equal."""
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mg, _ = self._create_sample_metagraph(include_train_op=True)
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grappler_item = item.Item(mg)
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initial_tf_item = grappler_item.tf_item
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no_change_tf_item = grappler_item.tf_item
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self.assertEqual(initial_tf_item, no_change_tf_item)
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def test_tf_item_device_modification_updates_wrapper(self):
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"""Verifies modifying node placement creates a new underlying tf_item."""
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mg, _ = self._create_sample_metagraph(include_train_op=True)
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grappler_item = item.Item(mg)
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initial_tf_item = grappler_item.tf_item
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for node in grappler_item.metagraph.graph_def.node:
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node.device = '/cpu:0'
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new_tf_item = grappler_item.tf_item
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self.assertNotEqual(initial_tf_item, new_tf_item)
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def test_tf_item_identical_device_reassignment_unchanged(self):
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"""Verifies re-assigning identical placement keeps tf_item unchanged."""
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mg, _ = self._create_sample_metagraph(include_train_op=True)
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grappler_item = item.Item(mg)
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for node in grappler_item.metagraph.graph_def.node:
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node.device = '/cpu:0'
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new_tf_item = grappler_item.tf_item
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for node in grappler_item.metagraph.graph_def.node:
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node.device = '/cpu:0'
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newest_tf_item = grappler_item.tf_item
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self.assertEqual(new_tf_item, newest_tf_item)
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@test_util.run_v1_only('b/120545219')
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def test_colocation_constraints(self):
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"""Verifies colocation constraints are correctly grouped."""
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with ops.Graph().as_default() as g:
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c = constant_op.constant([10])
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v = variable_v1.VariableV1([3], dtype=dtypes.int32)
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i = gen_array_ops.ref_identity(v)
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a = state_ops.assign(i, c)
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(a)
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mg = meta_graph.create_meta_graph_def(graph=g)
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grappler_item = item.Item(mg)
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groups = grappler_item.GetColocationGroups()
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self.assertLen(groups, 1)
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self.assertCountEqual(
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groups[0], ['Assign', 'RefIdentity', 'Variable', 'Variable/Assign'])
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@test_util.run_v1_only('b/120545219')
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def test_colocation_constraints_missing_input(self):
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"""Verifies standalone nodes with missing inputs are correctly grouped."""
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with ops.Graph().as_default() as g:
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c = constant_op.constant([10])
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v = variable_v1.VariableV1([3], dtype=dtypes.int32)
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i = gen_array_ops.ref_identity(v)
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a = state_ops.assign(i, c)
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train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
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train_op.append(a)
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mg = meta_graph.create_meta_graph_def(graph=g)
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for node in mg.graph_def.node:
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if node.op == 'Assign':
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del node.input[:]
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grappler_item = item.Item(mg)
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groups = grappler_item.GetColocationGroups()
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self.assertLen(groups, 1)
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self.assertCountEqual(
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groups[0], ['RefIdentity', 'Variable'])
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if __name__ == '__main__':
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test.main()
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