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
|
/* 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/optimizers/remapper.h"
#include "tensorflow/core/framework/versions.pb.h"
#include "tensorflow/core/grappler/costs/graph_properties.h"
#include "tensorflow/core/grappler/graph_view.h"
#include "tensorflow/core/grappler/grappler_item.h"
#include "tensorflow/core/grappler/op_types.h"
#include "tensorflow/core/grappler/optimizers/constant_folding.h"
#include "tensorflow/core/grappler/utils.h"
#include "tensorflow/core/platform/logging.h"
namespace tensorflow {
namespace grappler {
void AddBatchNormNodes(GraphDef* optimized_graph, const NodeDef& fused_node) {
const string& x = fused_node.input(0);
string scale = fused_node.input(1);
string offset = fused_node.input(2);
string mean = fused_node.input(3);
string variance = fused_node.input(4);
if (fused_node.attr().at("data_format").s() == "NCHW") {
// Need to reshape the last 4 inputs
NodeDef* new_shape = optimized_graph->add_node();
new_shape->set_name(AddPrefixToNodeName("NCHWShape", fused_node.name()));
new_shape->set_op("Const");
new_shape->set_device(fused_node.device());
*new_shape->add_input() = AsControlDependency(scale);
(*new_shape->mutable_attr())["dtype"].set_type(DT_INT32);
Tensor t(DT_INT32, {4});
t.flat<int32>()(0) = 1;
t.flat<int32>()(1) = -1;
t.flat<int32>()(2) = 1;
t.flat<int32>()(3) = 1;
t.AsProtoTensorContent(
(*new_shape->mutable_attr())["value"].mutable_tensor());
NodeDef* reshaped_scale = optimized_graph->add_node();
reshaped_scale->set_name(
AddPrefixToNodeName("NCHWShapedScale", fused_node.name()));
reshaped_scale->set_op("Reshape");
reshaped_scale->set_device(fused_node.device());
*reshaped_scale->add_input() = scale;
*reshaped_scale->add_input() = new_shape->name();
(*reshaped_scale->mutable_attr())["T"] = fused_node.attr().at("T");
(*reshaped_scale->mutable_attr())["Tshape"].set_type(DT_INT32);
scale = reshaped_scale->name();
NodeDef* reshaped_offset = optimized_graph->add_node();
reshaped_offset->set_name(
AddPrefixToNodeName("NCHWShapedOffset", fused_node.name()));
reshaped_offset->set_op("Reshape");
reshaped_offset->set_device(fused_node.device());
*reshaped_offset->add_input() = offset;
*reshaped_offset->add_input() = new_shape->name();
(*reshaped_offset->mutable_attr())["T"] = fused_node.attr().at("T");
(*reshaped_offset->mutable_attr())["Tshape"].set_type(DT_INT32);
offset = reshaped_offset->name();
NodeDef* reshaped_mean = optimized_graph->add_node();
reshaped_mean->set_name(
AddPrefixToNodeName("NCHWShapedMean", fused_node.name()));
reshaped_mean->set_op("Reshape");
reshaped_mean->set_device(fused_node.device());
*reshaped_mean->add_input() = mean;
*reshaped_mean->add_input() = new_shape->name();
(*reshaped_mean->mutable_attr())["T"] = fused_node.attr().at("T");
(*reshaped_mean->mutable_attr())["Tshape"].set_type(DT_INT32);
mean = reshaped_mean->name();
NodeDef* reshaped_variance = optimized_graph->add_node();
reshaped_variance->set_name(
AddPrefixToNodeName("NCHWShapedVariance", fused_node.name()));
reshaped_variance->set_op("Reshape");
reshaped_variance->set_device(fused_node.device());
*reshaped_variance->add_input() = variance;
*reshaped_variance->add_input() = new_shape->name();
(*reshaped_variance->mutable_attr())["T"] = fused_node.attr().at("T");
(*reshaped_variance->mutable_attr())["Tshape"].set_type(DT_INT32);
variance = reshaped_variance->name();
}
float epsilon = 0.0f;
if (fused_node.attr().count("epsilon")) {
epsilon = fused_node.attr().at("epsilon").f();
}
DataType dtype = fused_node.attr().at("T").type();
Tensor value(dtype, TensorShape());
value.scalar<float>()() = epsilon;
NodeDef* variance_epsilon = optimized_graph->add_node();
TF_CHECK_OK(ConstantFolding::CreateNodeDef(
AddPrefixToNodeName("Const", fused_node.name()), &value,
variance_epsilon));
variance_epsilon->set_device(fused_node.device());
NodeDef* variance_plus_epsilon = optimized_graph->add_node();
variance_plus_epsilon->set_name(
AddPrefixToNodeName("VarPlusEpsilon", fused_node.name()));
variance_plus_epsilon->set_op("Add");
(*variance_plus_epsilon->mutable_attr())["T"].set_type(dtype);
variance_plus_epsilon->set_device(fused_node.device());
*variance_plus_epsilon->add_input() = variance;
*variance_plus_epsilon->add_input() = variance_epsilon->name();
NodeDef* inv = optimized_graph->add_node();
inv->set_name(AddPrefixToNodeName("Inv", fused_node.name()));
inv->set_op("Rsqrt");
inv->set_device(fused_node.device());
(*inv->mutable_attr())["T"].set_type(dtype);
*inv->add_input() = variance_plus_epsilon->name();
NodeDef* scaled = optimized_graph->add_node();
scaled->set_name(AddPrefixToNodeName("Scaled", fused_node.name()));
scaled->set_op("Mul");
scaled->set_device(fused_node.device());
(*scaled->mutable_attr())["T"].set_type(dtype);
*scaled->add_input() = inv->name();
*scaled->add_input() = scale;
NodeDef* a = optimized_graph->add_node();
a->set_name(AddPrefixToNodeName("Mul", fused_node.name()));
a->set_op("Mul");
a->set_device(fused_node.device());
(*a->mutable_attr())["T"].set_type(dtype);
*a->add_input() = x;
*a->add_input() = scaled->name();
NodeDef* b = optimized_graph->add_node();
b->set_name(AddPrefixToNodeName("Mul2", fused_node.name()));
b->set_op("Mul");
b->set_device(fused_node.device());
(*b->mutable_attr())["T"].set_type(dtype);
*b->add_input() = mean;
*b->add_input() = scaled->name();
NodeDef* c = optimized_graph->add_node();
c->set_name(AddPrefixToNodeName("Offset", fused_node.name()));
c->set_op("Sub");
c->set_device(fused_node.device());
(*c->mutable_attr())["T"].set_type(dtype);
*c->add_input() = offset;
*c->add_input() = b->name();
NodeDef* r = optimized_graph->add_node();
r->set_name(fused_node.name());
r->set_op("Add");
r->set_device(fused_node.device());
(*r->mutable_attr())["T"].set_type(dtype);
*r->add_input() = a->name();
*r->add_input() = c->name();
}
Status Remapper::Optimize(Cluster* /*cluster*/, const GrapplerItem& item,
GraphDef* optimized_graph) {
GraphProperties properties(item);
bool inferred_properties = false;
GraphView graph(const_cast<GraphDef*>(&item.graph));
// During inference, most of the inputs to FusedBatchNorm are constant, and we
// can therefore replace the op with a much cheaper set of primitives.
optimized_graph->mutable_node()->Reserve(item.graph.node_size());
for (const NodeDef& node : item.graph.node()) {
if (node.op() == "FusedBatchNorm" || node.op() == "FusedBatchNormV2") {
bool optimizable = (node.attr().count("T") == 0 ||
node.attr().at("T").type() == DT_FLOAT);
optimizable &= (node.attr().count("is_training") == 0 ||
!node.attr().at("is_training").b());
if (optimizable) {
int const_inputs = 0;
if (!inferred_properties) {
// Infer properties lazily in case they are not needed.
TF_RETURN_IF_ERROR(properties.InferStatically(false));
inferred_properties = true;
}
const auto& props = properties.GetInputProperties(node.name());
for (const auto& prop : props) {
if (prop.has_value()) {
const_inputs += 1;
}
}
// TODO(bsteiner): use the cost model to compare the cost of fused batch
// norm against that of the optimized form.
optimizable = (const_inputs >= 4);
}
if (optimizable) {
for (GraphView::Edge edge : graph.GetFanoutEdges(node, false)) {
if (edge.src.port_id != 0) {
// The optimized version only generates the first output.
optimizable = false;
break;
}
}
}
if (optimizable) {
VLOG(1) << "Optimizing fused batch norm node " << node.DebugString();
AddBatchNormNodes(optimized_graph, node);
continue;
}
}
*optimized_graph->add_node() = node;
}
*optimized_graph->mutable_library() = item.graph.library();
*optimized_graph->mutable_versions() = item.graph.versions();
return Status::OK();
}
void Remapper::Feedback(Cluster* /*cluster*/, const GrapplerItem& /*item*/,
const GraphDef& /*optimized_graph*/,
double /*result*/) {
// Nothing to do for RemapperOptimizer.
}
} // namespace grappler
} // namespace tensorflow
|
