-
Notifications
You must be signed in to change notification settings - Fork 74.6k
/
Copy pathfunctions.cc
624 lines (519 loc) · 22.7 KB
/
functions.cc
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
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
/* Copyright 2018 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/utils/functions.h"
#include "absl/container/flat_hash_map.h"
#include "absl/container/flat_hash_set.h"
#include "absl/status/status.h"
#include "absl/strings/str_cat.h"
#include "absl/strings/str_replace.h"
#include "absl/strings/substitute.h"
#include "tensorflow/core/common_runtime/function.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/framework/graph_def_util.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/framework/versions.pb.h"
#include "tensorflow/core/grappler/op_types.h"
#include "tensorflow/core/grappler/utils.h"
#include "tensorflow/core/lib/strings/scanner.h"
namespace tensorflow {
namespace grappler {
GrapplerFunctionItem::GrapplerFunctionItem(
string func_name, string description, AttrSlice func_attr,
std::vector<const FunctionDef::ArgAttrs*> arg_attr,
std::vector<InputArgInstantiation> input_args,
std::vector<OutputArgInstantiation> output_args,
std::vector<ControlOutput> control_outputs, const int graph_def_version,
const bool is_stateful, GraphDef&& function_body)
: description_(std::move(description)),
func_attr_(func_attr),
arg_attr_(std::move(arg_attr)),
input_args_(std::move(input_args)),
output_args_(std::move(output_args)),
control_outputs_(std::move(control_outputs)),
is_stateful_(is_stateful) {
id = std::move(func_name);
graph = std::move(function_body);
graph.mutable_versions()->set_producer(graph_def_version);
// Fill the feed nodes with function input arguments.
for (const InputArgInstantiation& input_arg : input_args_) {
feed.push_back({input_arg.node_name, Tensor()});
}
// Fill the fetch nodes with outputs.
for (const OutputArgInstantiation& output_arg : output_args_) {
fetch.push_back(output_arg.node_name);
}
// We must keep all control output nodes.
for (const ControlOutput& control_output : control_outputs_) {
keep_ops.push_back(control_output.node_name);
}
// Tensorflow functions execution semantics is different from the main graph,
// and we need to preserve it when we do graph optimizations.
optimization_options().allow_pruning_stateful_and_dataset_ops = false;
}
const string& GrapplerFunctionItem::description() const { return description_; }
const std::vector<InputArgInstantiation>& GrapplerFunctionItem::inputs() const {
return input_args_;
}
const InputArgInstantiation& GrapplerFunctionItem::input(int i) const {
return input_args_[i];
}
const std::size_t GrapplerFunctionItem::input_size() const {
return input_args_.size();
}
const std::vector<OutputArgInstantiation>& GrapplerFunctionItem::outputs()
const {
return output_args_;
}
const OutputArgInstantiation& GrapplerFunctionItem::output(int i) const {
return output_args_[i];
}
const std::size_t GrapplerFunctionItem::output_size() const {
return output_args_.size();
}
const std::vector<ControlOutput>& GrapplerFunctionItem::control_outputs()
const {
return control_outputs_;
}
const std::size_t GrapplerFunctionItem::control_output_size() const {
return control_outputs_.size();
}
const AttrSlice& GrapplerFunctionItem::func_attr() const { return func_attr_; }
const std::vector<const FunctionDef::ArgAttrs*>&
GrapplerFunctionItem::arg_attr() const {
return arg_attr_;
}
const GraphDef& GrapplerFunctionItem::function_body() const { return graph; }
GraphDef& GrapplerFunctionItem::mutable_function_body() { return graph; }
bool GrapplerFunctionItem::is_stateful() const { return is_stateful_; }
GrapplerFunctionItem& GrapplerFunctionItem::SwapFunctionBody(GraphDef&& other) {
graph = std::move(other);
return *this;
}
bool HasParametrizedType(const FunctionDef& func) {
const auto is_type_parametrized = [](const OpDef::ArgDef& arg) {
return !arg.type_attr().empty() || !arg.number_attr().empty() ||
!arg.type_list_attr().empty();
};
const auto& input = func.signature().input_arg();
const auto& output = func.signature().output_arg();
return std::any_of(input.begin(), input.end(), is_type_parametrized) ||
std::any_of(output.begin(), output.end(), is_type_parametrized);
}
bool HasParametrizedBody(const FunctionDef& func) {
const auto is_parametrized = [&](const NodeDef& node) {
for (const auto& attr : node.attr()) {
if (!attr.second.placeholder().empty()) return true;
}
return false;
};
return std::any_of(func.node_def().begin(), func.node_def().end(),
is_parametrized);
}
bool IsParametrized(const FunctionDef& func) {
return HasParametrizedType(func) || HasParametrizedBody(func);
}
absl::Status InstantiationTypeParameters(
const FunctionDef& func, const AttrSlice& func_instantiation_attr,
absl::flat_hash_map<string, DataType>* type_parameters) {
if (!type_parameters->empty()) {
return absl::InvalidArgumentError(
"Type parameters output map must be empty");
}
const auto resolve_type_attr = [&](const OpDef::ArgDef& arg) -> absl::Status {
if (!arg.type_attr().empty()) {
DataType dtype;
TF_RETURN_IF_ERROR(
GetNodeAttr(func_instantiation_attr, arg.type_attr(), &dtype));
type_parameters->emplace(arg.type_attr(), dtype);
} else if (!arg.type_list_attr().empty()) {
std::vector<DataType> dtypes;
TF_RETURN_IF_ERROR(
GetNodeAttr(func_instantiation_attr, arg.type_list_attr(), &dtypes));
int index = 0;
for (const DataType& dtype : dtypes) {
type_parameters->emplace(absl::StrCat(arg.type_list_attr(), ":", index),
dtype);
++index;
}
}
return absl::OkStatus();
};
for (const auto& input : func.signature().input_arg())
TF_RETURN_IF_ERROR(resolve_type_attr(input));
for (const auto& output : func.signature().output_arg())
TF_RETURN_IF_ERROR(resolve_type_attr(output));
return absl::OkStatus();
}
absl::Status InstantiationBodyParameters(
const FunctionDef& func, const AttrSlice& func_instantiation_attr,
absl::flat_hash_map<string, AttrValue>* body_parameters) {
if (!body_parameters->empty()) {
return absl::InvalidArgumentError(
"Body parameters output map must be empty");
}
for (const NodeDef& func_body_node : func.node_def()) {
for (auto& attr : func_body_node.attr()) {
const string& placeholder = attr.second.placeholder();
if (placeholder.empty() || body_parameters->contains(placeholder)) {
continue;
}
const AttrValue* placeholder_value =
func_instantiation_attr.Find(placeholder);
if (placeholder_value) {
body_parameters->insert({placeholder, *placeholder_value});
} else {
return absl::InvalidArgumentError(
absl::StrCat("Can't resolve placeholder: ", placeholder));
}
}
}
return absl::OkStatus();
}
absl::Status MakeGrapplerFunctionItem(const FunctionDef& func,
const AttrSlice& func_instantiation_attr,
const FunctionLibraryDefinition& flib,
const int graph_def_version,
GrapplerFunctionItem* item) {
const OpDef& signature = func.signature();
if (signature.name().empty()) {
return absl::InvalidArgumentError("Function name must be specified");
}
// Function types will be resolved from function instantiation attributes. All
// other attributes will be lost during conversion to FunctionDef.
for (const OpDef::AttrDef& attr : signature.attr()) {
if (attr.type() != "type") {
return absl::InvalidArgumentError(
"Function signature must have only type attributes");
}
}
// Instantiate function into a statically defined FunctionBody Graph.
std::unique_ptr<FunctionBody> fbody;
TF_RETURN_IF_ERROR(
FunctionDefToBodyHelper(func, func_instantiation_attr, &flib, &fbody));
GraphDef function_body;
fbody->graph->ToGraphDef(&function_body);
// Function body shares the library with the graph that instantiated it. We do
// not need a full copy of the function library, just the reachable subset.
*function_body.mutable_library() = flib.ReachableDefinitions(func).ToProto();
VLOG(3) << absl::Substitute(
"Deleted $0 unreachable functions from the Grappler function item "
"instantiation of $1 (library size = $2)",
flib.num_functions() - function_body.library().function_size(),
signature.name(), function_body.library().function_size());
const int num_instantiated_inputs = fbody->arg_types.size();
const int num_instantiated_outputs = fbody->ret_types.size();
std::vector<InputArgInstantiation> inputs;
inputs.reserve(num_instantiated_inputs);
for (int in_id = 0; in_id < num_instantiated_inputs; ++in_id) {
const Node* node = fbody->arg_nodes[in_id];
const DataType& dtype = fbody->arg_types[in_id];
inputs.emplace_back(node->name(), dtype);
}
std::vector<OutputArgInstantiation> outputs;
outputs.reserve(num_instantiated_outputs);
for (int out_id = 0; out_id < num_instantiated_outputs; ++out_id) {
const Node* node = fbody->ret_nodes[out_id];
const DataType& dtype = fbody->ret_types[out_id];
outputs.emplace_back(node->name(), dtype);
}
// Control outputs ensure that all side-effectful nodes in the function body
// will execute, even if they are not required to compute regular output args.
std::vector<ControlOutput> control_outputs;
control_outputs.reserve(func.control_ret_size());
for (const auto& control_ret : func.control_ret()) {
control_outputs.push_back({control_ret.first, control_ret.second});
}
// Sort control outputs to keep FunctionDef output stable. The sort order of
// map entries in func.control_ret() are not stable.
// See b/174715578 for context on why stability is desired.
std::sort(control_outputs.begin(), control_outputs.end());
std::vector<const FunctionDef::ArgAttrs*> arg_attr(inputs.size(), nullptr);
for (const auto& attr : func.arg_attr()) {
if (attr.first >= inputs.size()) {
return absl::InvalidArgumentError(
absl::StrCat("Invalid attribute index, got ", attr.first,
" but expected less than ", inputs.size()));
}
arg_attr.at(attr.first) = &attr.second;
}
*item = GrapplerFunctionItem(
/*func_name=*/signature.name(),
/*description=*/signature.description(),
/*func_attr=*/AttrSlice(&func.attr()), std::move(arg_attr),
std::move(inputs), std::move(outputs), std::move(control_outputs),
graph_def_version, signature.is_stateful(), std::move(function_body));
return absl::OkStatus();
}
absl::Status MakeGrapplerFunctionItem(const FunctionDef& func,
const FunctionLibraryDefinition& flib,
const int graph_def_version,
GrapplerFunctionItem* item) {
return MakeGrapplerFunctionItem(func, AttrSlice(), flib, graph_def_version,
item);
}
absl::Status ReplaceInputWithConst(const NodeDef& input_const, int input_index,
GrapplerFunctionItem* item) {
if (!IsConstant(input_const)) {
return absl::InvalidArgumentError(absl::StrCat(
"Input node is not a constant: ", SummarizeNodeDef(input_const)));
}
const int item_input_size = item->input_size();
if (input_index < 0 || input_index >= item_input_size) {
return absl::InvalidArgumentError(absl::StrCat(
"Function input index is out of bound: index=", input_index,
" input_size=", item->input_size()));
}
const InputArgInstantiation& input_arg = item->input(input_index);
for (NodeDef& node : *item->graph.mutable_node()) {
// Replace '_Arg' node in the function body with a 'Const' node.
if (node.name() == input_arg.node_name) {
node = input_const;
node.set_name(input_arg.node_name);
node.clear_input();
node.clear_device(); // device placement is defined by instantiating node
}
// Update index in all inputs after the removed const input.
if (IsArg(node)) {
auto attrs = AttrSlice(node);
int index;
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "index", &index));
if (index >= input_index) {
(*node.mutable_attr())["index"].set_i(index - 1);
}
}
}
item->input_args_.erase(item->input_args_.begin() + input_index);
item->arg_attr_.erase(item->arg_attr_.begin() + input_index);
return absl::OkStatus();
}
absl::Status RemoveFunctionOutputs(
const absl::flat_hash_set<int>& remove_outputs, GrapplerFunctionItem* item,
std::vector<std::pair<int, int>>* output_mapping) {
DCHECK(output_mapping->empty());
// Do some sanity checking of the removed outputs positions.
for (int remove_output : remove_outputs) {
const int item_output_size = item->output_size();
if (remove_output < 0 || remove_output >= item_output_size) {
return absl::InvalidArgumentError(absl::StrCat(
"Function output index is out of bound: index=", remove_output,
" output_size=", item->output_size()));
}
}
absl::flat_hash_set<const OutputArgInstantiation*> remove_output_args;
const auto is_remove_output_arg = [&](const OutputArgInstantiation& output) {
return remove_output_args.find(&output) != remove_output_args.end();
};
for (int i = 0, end = item->output_size(); i < end; ++i) {
const OutputArgInstantiation& output = item->output(i);
if (remove_outputs.contains(i)) {
VLOG(3) << "Remove functions output: name=" << output.node_name
<< "(index = " << i << ")";
remove_output_args.insert(&output);
} else if (!remove_output_args.empty()) {
// Add output mapping only if output position changed.
output_mapping->push_back({i, i - remove_output_args.size()});
}
}
// Update 'index' attribute in all '_Retval' nodes that are in output mapping.
for (NodeDef& node : *item->graph.mutable_node()) {
if (IsRetval(node)) {
auto attrs = AttrSlice(node);
int index;
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "index", &index));
for (const auto& mapping : *output_mapping) {
const int from = mapping.first;
const int to = mapping.second;
if (index == from) {
(*node.mutable_attr())["index"].set_i(to);
}
}
}
}
auto& o = item->output_args_;
o.erase(std::remove_if(o.begin(), o.end(), is_remove_output_arg), o.end());
return absl::OkStatus();
}
namespace {
// FunctionDef uses different connectivity encoding for the function body nodes,
// than a GraphDef (see function.proto for details). This is a helper class that
// converts inputs in GraphDef format (node[:position]) to the FunctionDef
// format (node:output[:position]).
class MakeFunctionDefHelper {
public:
MakeFunctionDefHelper() = default;
absl::Status Initialize(const GrapplerFunctionItem& item,
const FunctionLibraryDefinition& flib);
// Converts input name from GraphDef format (name[:position]) to the
// FunctionDef input format (name[:output][:position]) using registered input
// arg instantiations and function body outputs.
absl::Status AsFunctionDefInput(const string& graph_def_input,
string* func_def_input) const;
// Updates Node inputs from GraphDef to FunctionDef format.
absl::Status AsFunctionDefNode(NodeDef* function_body_node) const;
bool IsInputNode(const NodeDef& node) const {
return input_nodes_.contains(node.name());
}
bool IsOutputNode(const NodeDef& node) const {
return output_nodes_.contains(node.name());
}
private:
absl::flat_hash_set<absl::string_view> input_nodes_;
absl::flat_hash_set<absl::string_view> output_nodes_;
// Mapping from function body node name to output names range map.
absl::flat_hash_map<string, tensorflow::NameRangeMap> function_body_outputs_;
};
absl::Status MakeFunctionDefHelper::Initialize(
const GrapplerFunctionItem& item, const FunctionLibraryDefinition& flib) {
for (const InputArgInstantiation& input_arg : item.inputs()) {
input_nodes_.insert(input_arg.node_name);
}
for (const OutputArgInstantiation& output_arg : item.outputs()) {
output_nodes_.insert(output_arg.node_name);
}
for (const NodeDef& node : item.function_body().node()) {
const OpRegistrationData* registration;
TF_RETURN_IF_ERROR(flib.LookUp(node.op(), ®istration));
tensorflow::NameRangeMap outputs_range_map;
TF_RETURN_IF_ERROR(tensorflow::NameRangesForNode(
node, registration->op_def, nullptr, &outputs_range_map));
function_body_outputs_.emplace(node.name(), std::move(outputs_range_map));
}
return absl::OkStatus();
}
absl::Status MakeFunctionDefHelper::AsFunctionDefInput(
const string& graph_def_input, string* func_def_input) const {
if (IsControlInput(graph_def_input)) {
*func_def_input = graph_def_input;
return absl::OkStatus();
}
const SafeTensorId tensor = ParseTensorName(graph_def_input);
DCHECK_GE(tensor.index(), 0);
// Graph def input corresponds to one of the function inputs.
const auto is_input = input_nodes_.find(tensor.node());
if (is_input != input_nodes_.end()) {
DCHECK_EQ(tensor.index(), 0);
*func_def_input = tensor.node();
return absl::OkStatus();
}
// Or it must be output from one of the function body nodes
const auto is_body_output = function_body_outputs_.find(tensor.node());
if (is_body_output != function_body_outputs_.end()) {
const tensorflow::NameRangeMap& outputs_range_map = is_body_output->second;
for (const auto& el : outputs_range_map) {
const auto& output_name = el.first;
const auto& output_range = el.second;
if (tensor.index() >= output_range.first &&
tensor.index() < output_range.second) {
*func_def_input = absl::StrCat(tensor.node(), ":", output_name, ":",
tensor.index() - output_range.first);
return absl::OkStatus();
}
}
}
return absl::InvalidArgumentError(
absl::StrCat("Unknown graph def input: ", graph_def_input));
}
absl::Status MakeFunctionDefHelper::AsFunctionDefNode(
NodeDef* function_body_node) const {
string func_def_input;
for (int i = 0; i < function_body_node->input_size(); ++i) {
TF_RETURN_IF_ERROR(
AsFunctionDefInput(function_body_node->input(i), &func_def_input));
function_body_node->set_input(i, func_def_input);
}
return absl::OkStatus();
}
} // namespace
absl::Status MakeFunctionDef(const GrapplerFunctionItem& item,
const FunctionLibraryDefinition& flib,
FunctionDef* func) {
func->mutable_signature()->set_name(item.id);
func->mutable_signature()->set_description(item.description());
func->mutable_signature()->set_is_stateful(item.is_stateful());
MakeFunctionDefHelper helper;
TF_RETURN_IF_ERROR(helper.Initialize(item, flib));
// Mapping from the '_Retval' node name to the output tensor.
absl::flat_hash_map<absl::string_view, string> output_tensors;
for (const NodeDef& func_body_node : item.function_body().node()) {
if (!helper.IsOutputNode(func_body_node)) continue;
if (func_body_node.input_size() != 1) {
return absl::InternalError(
absl::StrCat("_Retval node must have single input: ",
SummarizeNodeDef(func_body_node)));
}
output_tensors.emplace(func_body_node.name(), func_body_node.input(0));
}
for (const InputArgInstantiation& input_arg : item.inputs()) {
OpDef::ArgDef arg_def;
arg_def.set_name(input_arg.node_name);
arg_def.set_type(input_arg.data_type);
arg_def.set_is_ref(IsRefType(input_arg.data_type));
*func->mutable_signature()->add_input_arg() = arg_def;
}
// Add function output arguments.
for (const OutputArgInstantiation& output_arg : item.outputs()) {
const string output_name =
absl::StrReplaceAll(output_arg.node_name, {{"_RetVal", ""}});
OpDef::ArgDef arg_def;
arg_def.set_name(output_name);
arg_def.set_type(output_arg.data_type);
arg_def.set_is_ref(IsRefType(output_arg.data_type));
*func->mutable_signature()->add_output_arg() = arg_def;
auto it = output_tensors.find(output_arg.node_name);
if (it == output_tensors.end()) {
return absl::InternalError(
absl::StrCat("Can't find an output tensor for the output node: ",
output_arg.node_name));
}
TF_RETURN_IF_ERROR(helper.AsFunctionDefInput(
it->second, &(*func->mutable_ret())[output_name]));
}
// Add function control outputs.
for (const ControlOutput& control_out : item.control_outputs()) {
func->mutable_control_ret()->insert(
{control_out.output_name, control_out.node_name});
*func->mutable_signature()->add_control_output() = control_out.output_name;
}
// Copy function definition specific attributes.
for (const auto& attr : item.func_attr()) {
const auto& attr_name = attr.first;
const auto& attr_value = attr.second;
(*func->mutable_attr())[attr_name] = attr_value;
}
// Copy function arg attributes.
for (int i = 0, end = item.arg_attr().size(); i < end; ++i) {
const auto* attr = item.arg_attr().at(i);
if (attr != nullptr) {
(*func->mutable_arg_attr())[i] = *attr;
}
}
// Copy function body nodes to the FunctionDef and update input format
for (const NodeDef& func_node : item.function_body().node()) {
// Skip original `_Arg` and `_Retval` nodes. If node was converted to some
// other type (e.g. inputs converted to placeholders), we need to check that
// it's not registered as function input or output node.
if (IsArg(func_node) || IsRetval(func_node) ||
helper.IsInputNode(func_node) || helper.IsOutputNode(func_node))
continue;
NodeDef* func_def_node = func->add_node_def();
*func_def_node = func_node;
TF_RETURN_IF_ERROR(helper.AsFunctionDefNode(func_def_node));
}
return absl::OkStatus();
}
} // end namespace grappler
} // end namespace tensorflow