Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/tensorflow/python/grappler/tf_optimizer.py: 44%

1# Copyright 2017 The TensorFlow Authors. All Rights Reserved. 

2# 

3# Licensed under the Apache License, Version 2.0 (the "License"); 

4# you may not use this file except in compliance with the License. 

5# You may obtain a copy of the License at 

6# 

7# http://www.apache.org/licenses/LICENSE-2.0 

8# 

9# Unless required by applicable law or agreed to in writing, software 

10# distributed under the License is distributed on an "AS IS" BASIS, 

11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 

12# See the License for the specific language governing permissions and 

13# limitations under the License. 

14# ============================================================================= 

15"""Provides a proper python API for the symbols exported through swig.""" 

16 

17import threading 

18 

19from tensorflow.core.framework import graph_pb2 

20from tensorflow.core.protobuf import config_pb2 

21from tensorflow.python.grappler import _pywrap_tf_optimizer as tf_opt 

22from tensorflow.python.grappler import cluster as gcluster 

23 

24_OPTIMIZE_GRAPH_CLUSTER_LOCK = threading.Lock() 

25 

26 

27def OptimizeGraph(config_proto, 

28 metagraph, 

29 verbose=True, 

30 graph_id=b'graph_to_optimize', 

31 cluster=None, 

32 strip_default_attributes=False): 

33 """Optimize the provided metagraph. 

34 

35 For best results, the signature_def field in `metagraph` should be populated 

36 with information about input (feed) and output (fetch) tensors. 

37 

38 Args: 

39 config_proto: a ConfigProto protobuf. 

40 metagraph: a MetagraphDef protobuf. 

41 verbose: whether to log optimization results. 

42 graph_id: a string identifying this graph. 

43 cluster: a grappler cluster object representing hardware resources 

44 available to run this graph. 

45 strip_default_attributes: whether graph node attributes having default 

46 values should be removed after all the optimization passes. This 

47 option is useful if the resulting graph will be executed by an older 

48 process that might not know some of the recently added attributes. 

49 """ 

50 if not isinstance(config_proto, config_pb2.ConfigProto): 

51 raise TypeError('Argument `config_proto` should be a tf.ConfigProto, ' 

52 f'received type: {type(config_proto).__name__}') 

53 if cluster is not None: 

54 out_graph = tf_opt.TF_OptimizeGraph(cluster.tf_cluster, 

55 config_proto.SerializeToString(), 

56 metagraph.SerializeToString(), verbose, 

57 graph_id, strip_default_attributes) 

58 else: 

59 # Currently Grappler assumes no more than 1 sessions alive globally. 

60 # See comments on SingleMachine::Provision(), hence we use the following 

61 # lock to prevent concurrent access to the following code. 

62 with _OPTIMIZE_GRAPH_CLUSTER_LOCK: 

63 cluster = gcluster.Cluster() 

64 try: 

65 out_graph = tf_opt.TF_OptimizeGraph(cluster.tf_cluster, 

66 config_proto.SerializeToString(), 

67 metagraph.SerializeToString(), 

68 verbose, graph_id, 

69 strip_default_attributes) 

70 finally: 

71 # Force the cleanup instead of waiting on python GC to cleanup the 

72 # temporary cluster we've created. Otherwise subsequent calls might 

73 # not have a clean slate because GC may not have run yet. 

74 cluster.Shutdown() 

75 return graph_pb2.GraphDef().FromString(out_graph)