1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
|
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/core/grappler/costs/utils.h"
#include <stddef.h>
#include <utility>
#include "third_party/eigen3/Eigen/Core"
#if GOOGLE_CUDA
#include "cuda/include/cuda.h"
#include "cuda/include/cuda_runtime_api.h"
#include "cuda/include/cudnn.h"
#endif
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_def.pb.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/tensor_id.h"
#include "tensorflow/core/grappler/clusters/utils.h"
#include "tensorflow/core/lib/strings/numbers.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/cpu_info.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/util/device_name_utils.h"
namespace tensorflow {
namespace grappler {
static OpInfo::TensorProperties UnknownInput() {
OpInfo::TensorProperties input;
input.set_dtype(DataType::DT_INVALID);
input.mutable_shape()->set_unknown_rank(true);
return input;
}
static std::vector<TensorProto> ExtractTensors(const AttrValue& attr_value) {
std::vector<TensorProto> tensors;
switch (attr_value.value_case()) {
case AttrValue::kTensor: {
tensors.push_back(attr_value.tensor());
break;
}
case AttrValue::kList: {
for (const auto& tensor_proto : attr_value.list().tensor()) {
tensors.push_back(tensor_proto);
}
break;
}
default: {}
}
return tensors;
}
static void ExtractExtraProperties(
const NodeDef& node,
const std::unordered_map<string, const NodeDef*>& name_to_node,
std::vector<OpInfo::TensorProperties>* extra_inputs,
protobuf::Map<string, AttrValue>* attr_map) {
OpRegistry* op_registry = OpRegistry::Global();
const OpDef* op_def = nullptr;
auto s = op_registry->LookUpOpDef(node.op(), &op_def);
if (!s.ok()) {
op_def = nullptr;
}
for (int i = 0; i < node.input_size(); ++i) {
const string input_name = node.input(i);
CHECK(!input_name.empty());
TensorId input_tensor_id = ParseTensorName(input_name);
const string input_node_name = input_tensor_id.first.ToString();
auto iter = name_to_node.find(input_node_name);
if (iter == name_to_node.end()) continue;
const NodeDef* input_node = iter->second;
// The value attribute in Const input is useful for cost prediction.
if (input_node->op() == "Const") {
auto it = input_node->attr().find("value");
if (it == input_node->attr().end()) continue;
const AttrValue& attr_value = it->second;
std::vector<TensorProto> tensors = ExtractTensors(attr_value);
if (tensors.empty()) continue;
const TensorProto& t = tensors[0];
OpInfo::TensorProperties input;
input.set_dtype(t.dtype());
*(input.mutable_shape()) = t.tensor_shape();
*(input.mutable_value()) = t;
extra_inputs->push_back(input);
// For filename input, the file size can also be useful.
if (op_def && i < op_def->input_arg_size() &&
op_def->input_arg(i).name().find("filename") != std::string::npos) {
Tensor tensor;
if (!tensor.FromProto(t)) {
continue;
}
if (tensor.NumElements() != 1) {
continue;
}
const string filename = tensor.scalar<string>()();
Env* env = Env::Default();
FileStatistics stat;
Status s = env->Stat(filename, &stat);
if (!s.ok()) {
continue;
}
AttrValue attr;
attr.set_i(stat.length);
string attr_key = strings::StrCat("input_", i, "_filesize");
(*attr_map)[attr_key] = attr;
}
}
// When the input is a handle (e.g. look up table handle), the information
// in the op itself is not sufficient to predict the op memory.
if (op_def && i < op_def->input_arg_size() &&
op_def->input_arg(i).name().find("handle") != std::string::npos) {
string new_key = strings::StrCat("parent_", i, "_op");
AttrValue attr;
attr.set_s(input_node->op());
(*attr_map)[new_key] = attr;
// TODO(yuefengz): Only parent node's op name is copied. Copy inputs
// and attributes when necessary.
}
}
}
std::vector<OpInfo::TensorProperties> FindInputFeatures(
const NodeDef& node,
const std::unordered_map<string, const CostGraphDef::Node*>& name_to_cost,
const std::unordered_map<string, const NodeDef*>& name_to_node) {
std::vector<OpInfo::TensorProperties> inputs;
for (const auto& input_name : node.input()) {
CHECK(!input_name.empty());
TensorId input_tensor_id = ParseTensorName(input_name);
const string input_node_name = input_tensor_id.first.ToString();
const int output_index = input_tensor_id.second;
// Skip control inputs.
if (output_index == Graph::kControlSlot) {
continue;
}
auto it = name_to_cost.find(input_node_name);
if (it == name_to_cost.end() || output_index < 0) {
inputs.push_back(UnknownInput());
} else {
const CostGraphDef::Node* input_cost = it->second;
const CostGraphDef::Node::OutputInfo& output =
input_cost->output_info(output_index);
OpInfo::TensorProperties input;
input.set_dtype(output.dtype());
*input.mutable_shape() = output.shape();
inputs.push_back(input);
}
}
return inputs;
}
DeviceProperties GetDeviceInfo(const string& device_str) {
DeviceNameUtils::ParsedName parsed;
if (DeviceNameUtils::ParseFullName(device_str, &parsed)) {
if (parsed.type == "GPU") {
return GetLocalGPUInfo(parsed.id);
} else if (parsed.type == "CPU") {
return GetLocalCPUInfo();
}
}
DeviceProperties device;
device.set_type("UNKNOWN");
return device;
}
DeviceProperties GetDeviceInfo(const CostGraphDef::Node& node) {
return GetDeviceInfo(node.device());
}
OpInfo BuildOpInfo(
const NodeDef& node, const string& device_str,
const std::unordered_map<string, const NodeDef*>& name_to_node,
const std::vector<OpInfo::TensorProperties>& inputs) {
OpInfo op_info;
op_info.set_op(node.op());
*op_info.mutable_attr() = node.attr();
*op_info.mutable_device() = GetDeviceInfo(device_str);
for (auto& input : inputs) {
*op_info.add_inputs() = input;
}
std::vector<OpInfo::TensorProperties> extra_inputs;
ExtractExtraProperties(node, name_to_node, &extra_inputs,
op_info.mutable_attr());
for (auto& input : extra_inputs) {
*op_info.add_inputs() = input;
}
return op_info;
}
string GetOpDescription(const OpInfo& op_info) {
string description = "[";
description += "Op=" + op_info.op() + ", ";
description += "input_shapes=[";
for (auto const& input : op_info.inputs()) {
description += PartialTensorShape::DebugString(input.shape());
}
description += "]";
return description;
}
} // end namespace grappler
} // end namespace tensorflow
|
