1/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
2
3Licensed under the Apache License, Version 2.0 (the "License");
4you may not use this file except in compliance with the License.
5You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9Unless required by applicable law or agreed to in writing, software
10distributed under the License is distributed on an "AS IS" BASIS,
11WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12See the License for the specific language governing permissions and
13limitations under the License.
14==============================================================================*/
15
16#include "tensorflow/python/grappler/model_analyzer.h"
17
18#include <iomanip>
19#include "tensorflow/core/framework/op.h"
20#include "tensorflow/core/framework/tensor_shape.pb.h"
21#include "tensorflow/core/grappler/costs/graph_properties.h"
22#include "tensorflow/core/grappler/grappler_item.h"
23
24namespace tensorflow {
25namespace grappler {
26
27ModelAnalyzer::ModelAnalyzer(const GrapplerItem& item) : item_(item) {}
28
29Status ModelAnalyzer::GenerateReport(bool debug, bool assume_valid_feeds,
30 std::ostream& os) {
31 GraphProperties properties(item_);
32 TF_RETURN_IF_ERROR(properties.InferStatically(assume_valid_feeds));
33
34 for (const auto& node : item_.MainOpsFanin()) {
35 PrintNodeInfo(node, properties, debug, os);
36 }
37 for (const auto& node : item_.EnqueueOpsFanin()) {
38 PrintNodeInfo(node, properties, debug, os);
39 }
40
41 return OkStatus();
42}
43
44void ModelAnalyzer::PrintNodeInfo(const NodeDef* node,
45 const GraphProperties& properties, bool debug,
46 std::ostream& os) const {
47 os << node->name() << " [" << node->op() << "]" << std::endl;
48 if (properties.HasOutputProperties(node->name())) {
49 const std::vector<OpInfo::TensorProperties>& props =
50 properties.GetOutputProperties(node->name());
51 for (int i = 0, props_size = props.size(); i < props_size; ++i) {
52 const OpInfo::TensorProperties& prop = props[i];
53 os << "\t"
54 << "output " << i << " (" << DataTypeString(prop.dtype())
55 << ") has shape ";
56 if (prop.shape().unknown_rank()) {
57 os << "?";
58 } else {
59 os << "[";
60 for (int i = 0; i < prop.shape().dim_size(); ++i) {
61 if (i > 0) {
62 os << ", ";
63 }
64 if (prop.shape().dim(i).size() >= 0) {
65 // Print the actual dimension.
66 os << prop.shape().dim(i).size();
67 } else if (prop.shape().dim(i).size() == -1) {
68 // We don't know anything about the dimension.
69 os << "?";
70 } else {
71 // Symbolic dimension.
72 os << "x" << -prop.shape().dim(i).size();
73 }
74 }
75 os << "]";
76 }
77 os << std::endl;
78 }
79 }
80
81 if (debug) {
82 const OpRegistrationData* op_reg_data;
83 Status status = OpRegistry::Global()->LookUp(node->op(), &op_reg_data);
84 if (!status.ok()) {
85 os << "\tCouldn't find op registration for " << node->op() << std::endl;
86 } else if (!op_reg_data->shape_inference_fn) {
87 os << "\tCouldn't find shape function for op " << node->op() << std::endl;
88 } else if (properties.HasInputProperties(node->name())) {
89 const std::vector<OpInfo::TensorProperties>& props =
90 properties.GetInputProperties(node->name());
91 for (int i = 0, props_size = props.size(); i < props_size; ++i) {
92 const OpInfo::TensorProperties& prop = props[i];
93 if (prop.has_value()) {
94 os << "\t"
95 << "input " << i << " (" << DataTypeString(prop.dtype())
96 << ") has known value" << std::endl;
97 }
98 }
99 }
100 }
101}
102
103} // end namespace grappler
104} // end namespace tensorflow
105