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 | |
16 | #ifndef TENSORFLOW_CORE_GRAPH_WHILE_CONTEXT_H_ |
17 | #define TENSORFLOW_CORE_GRAPH_WHILE_CONTEXT_H_ |
18 | |
19 | #include "tensorflow/core/graph/graph.h" |
20 | |
21 | namespace tensorflow { |
22 | |
23 | // Information about a while loop. Every user-defined while loop has an |
24 | // associated WhileContext, i.e., there is a WhileContext for every execution |
25 | // frame. Created with the while loop and used during gradient |
26 | // construction. Note that the gradient graph of while loop contains while loops |
27 | // itself, but these do not generate separate WhileContexts. |
28 | // |
29 | // TODO(skyewm): this is currently insufficient to handle nested loops and |
30 | // conditionals (and possibly other requirements). This may change a lot in the |
31 | // future to support these features. |
32 | // |
33 | // TODO(skyewm): de/serialize in MetaGraphDef so imported while loops will be |
34 | // differentiable. Figure out backwards compatibility story. |
35 | class WhileContext { |
36 | public: |
37 | WhileContext(StringPiece frame_name, std::vector<Node*> enter_nodes, |
38 | std::vector<Node*> exit_nodes, OutputTensor cond_output, |
39 | std::vector<OutputTensor> body_inputs, |
40 | std::vector<OutputTensor> body_outputs); |
41 | |
42 | const string& frame_name() const { return frame_name_; } |
43 | const std::vector<Node*>& enter_nodes() const { return enter_nodes_; } |
44 | const std::vector<Node*>& exit_nodes() const { return exit_nodes_; } |
45 | const OutputTensor& cond_output() const { return cond_output_; } |
46 | const std::vector<OutputTensor>& body_inputs() const { return body_inputs_; } |
47 | const std::vector<OutputTensor>& body_outputs() const { |
48 | return body_outputs_; |
49 | } |
50 | |
51 | private: |
52 | // Each user-defined while loop defines a new execution frame, which is |
53 | // uniquely identified by its frame name. Frames are used by the executor to |
54 | // manage the iterations of a loop. See the FrameState comment in |
55 | // core/common_runtime/executor.cc for more details. |
56 | const string frame_name_; |
57 | |
58 | // The enter nodes defining the input loop variables to the while loop. This |
59 | // vector defines the order of the loop variables. |
60 | const std::vector<Node*> enter_nodes_; |
61 | |
62 | // The exit nodes defining the outputs of the while loop. These are in loop |
63 | // variable order. |
64 | const std::vector<Node*> exit_nodes_; |
65 | |
66 | // The boolean output of the loop predicate. |
67 | const OutputTensor cond_output_; |
68 | |
69 | // The inputs and outputs to the loop body. |
70 | const std::vector<OutputTensor> body_inputs_; |
71 | const std::vector<OutputTensor> body_outputs_; |
72 | }; |
73 | |
74 | } // namespace tensorflow |
75 | |
76 | #endif // TENSORFLOW_CORE_GRAPH_WHILE_CONTEXT_H_ |
77 | |