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_GRAPPLER_UTILS_FRAME_H_ 17 #define TENSORFLOW_CORE_GRAPPLER_UTILS_FRAME_H_ 18 19 #include <unordered_map> 20 #include "absl/container/flat_hash_map.h" 21 #include "tensorflow/core/framework/graph.pb.h" 22 #include "tensorflow/core/grappler/graph_view.h" 23 #include "tensorflow/core/lib/core/status.h" 24 25 namespace tensorflow { 26 namespace grappler { 27 28 // FrameView is a helper class that allows to find in what execution frames (if 29 // any) the given node can be running in. It's constructed from an immutable 30 // GraphView, and any modification of the underlying graph might invalidate it. 31 // 32 // All execution frames assigned an unique integer id, but they do not have any 33 // meaning whatsoever, it's just a sequence number. 34 // 35 // See the paper "Dynamic Control Flow in Large-Scale Machine Learning" for 36 // detailed explanation of execution frames (https://arxiv.org/abs/1805.01772). 37 class FrameView { 38 public: FrameView()39 FrameView() : is_inferred_(false), num_frames_(0) {} 40 41 // Infers nodes execution frames from the GraphView. Returns an error if 42 // called multiple times. 43 Status InferFromGraphView(const GraphView& graph_view); 44 // Infers nodes execution by constructing temporary GraphView and passing it 45 // to InferFromGraphView. 46 Status InferFromGraph(const GraphDef& graph); 47 48 // Returns all frames of the given node (denoted by their frame ids) in 49 // outermost-to-innermost order. 50 const std::vector<int>& Frames(const NodeDef& node) const; 51 52 // Returns true iff the node is at least in one execution frame. 53 bool IsInFrame(const NodeDef& node) const; 54 num_frames()55 int num_frames() const { return num_frames_; } is_inferred()56 bool is_inferred() const { return is_inferred_; } 57 58 private: 59 bool is_inferred_; // true if it was inferred from the graph 60 int num_frames_; // number of frames present in a graph 61 absl::flat_hash_map<const NodeDef*, std::vector<int>> node_to_frames_; 62 63 // We return a reference to this vector if node has no frames. 64 const std::vector<int> node_has_no_frames_; 65 }; 66 67 } // namespace grappler 68 } // namespace tensorflow 69 70 #endif // TENSORFLOW_CORE_GRAPPLER_UTILS_FRAME_H_ 71