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
|
# Copyright 2016 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.
# ==============================================================================
r"""Computes a header file to be used with SELECTIVE_REGISTRATION.
See the executable wrapper, print_selective_registration_header.py, for more
information.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
from google.protobuf import text_format
from tensorflow.core.framework import graph_pb2
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.platform import gfile
from tensorflow.python.platform import tf_logging
# Usually, we use each graph node to induce registration of an op and
# corresponding kernel; nodes without a corresponding kernel (perhaps due to
# attr types) generate a warning but are otherwise ignored. Ops in this set are
# registered even if there's no corresponding kernel.
OPS_WITHOUT_KERNEL_WHITELIST = frozenset([
# AccumulateNV2 is rewritten away by AccumulateNV2RemovePass; see
# core/common_runtime/accumulate_n_optimizer.cc.
'AccumulateNV2'
])
def get_ops_and_kernels(proto_fileformat, proto_files, default_ops_str):
"""Gets the ops and kernels needed from the model files."""
ops = set()
for proto_file in proto_files:
tf_logging.info('Loading proto file %s', proto_file)
# Load GraphDef.
file_data = gfile.GFile(proto_file, 'rb').read()
if proto_fileformat == 'rawproto':
graph_def = graph_pb2.GraphDef.FromString(file_data)
else:
assert proto_fileformat == 'textproto'
graph_def = text_format.Parse(file_data, graph_pb2.GraphDef())
# Find all ops and kernels used by the graph.
for node_def in graph_def.node:
if not node_def.device:
node_def.device = '/cpu:0'
kernel_class = pywrap_tensorflow.TryFindKernelClass(
node_def.SerializeToString())
op = str(node_def.op)
if kernel_class or op in OPS_WITHOUT_KERNEL_WHITELIST:
op_and_kernel = (op, str(kernel_class.decode('utf-8'))
if kernel_class else None)
if op_and_kernel not in ops:
ops.add(op_and_kernel)
else:
print(
'Warning: no kernel found for op %s' % node_def.op, file=sys.stderr)
# Add default ops.
if default_ops_str and default_ops_str != 'all':
for s in default_ops_str.split(','):
op, kernel = s.split(':')
op_and_kernel = (op, kernel)
if op_and_kernel not in ops:
ops.add(op_and_kernel)
return list(sorted(ops))
def get_header_from_ops_and_kernels(ops_and_kernels,
include_all_ops_and_kernels):
"""Returns a header for use with tensorflow SELECTIVE_REGISTRATION.
Args:
ops_and_kernels: a set of (op_name, kernel_class_name) pairs to include.
include_all_ops_and_kernels: if True, ops_and_kernels is ignored and all op
kernels are included.
Returns:
the string of the header that should be written as ops_to_register.h.
"""
ops = set([op for op, _ in ops_and_kernels])
result_list = []
def append(s):
result_list.append(s)
_, script_name = os.path.split(sys.argv[0])
append('// This file was autogenerated by %s' % script_name)
append('#ifndef OPS_TO_REGISTER')
append('#define OPS_TO_REGISTER')
if include_all_ops_and_kernels:
append('#define SHOULD_REGISTER_OP(op) true')
append('#define SHOULD_REGISTER_OP_KERNEL(clz) true')
append('#define SHOULD_REGISTER_OP_GRADIENT true')
else:
line = '''
namespace {
constexpr const char* skip(const char* x) {
return (*x) ? (*x == ' ' ? skip(x + 1) : x) : x;
}
constexpr bool isequal(const char* x, const char* y) {
return (*skip(x) && *skip(y))
? (*skip(x) == *skip(y) && isequal(skip(x) + 1, skip(y) + 1))
: (!*skip(x) && !*skip(y));
}
template<int N>
struct find_in {
static constexpr bool f(const char* x, const char* const y[N]) {
return isequal(x, y[0]) || find_in<N - 1>::f(x, y + 1);
}
};
template<>
struct find_in<0> {
static constexpr bool f(const char* x, const char* const y[]) {
return false;
}
};
} // end namespace
'''
line += 'constexpr const char* kNecessaryOpKernelClasses[] = {\n'
for _, kernel_class in ops_and_kernels:
if kernel_class is None: continue
line += '"%s",\n' % kernel_class
line += '};'
append(line)
append('#define SHOULD_REGISTER_OP_KERNEL(clz) '
'(find_in<sizeof(kNecessaryOpKernelClasses) '
'/ sizeof(*kNecessaryOpKernelClasses)>::f(clz, '
'kNecessaryOpKernelClasses))')
append('')
append('constexpr inline bool ShouldRegisterOp(const char op[]) {')
append(' return false')
for op in sorted(ops):
append(' || isequal(op, "%s")' % op)
append(' ;')
append('}')
append('#define SHOULD_REGISTER_OP(op) ShouldRegisterOp(op)')
append('')
append('#define SHOULD_REGISTER_OP_GRADIENT ' + (
'true' if 'SymbolicGradient' in ops else 'false'))
append('#endif')
return '\n'.join(result_list)
def get_header(graphs,
proto_fileformat='rawproto',
default_ops='NoOp:NoOp,_Recv:RecvOp,_Send:SendOp'):
"""Computes a header for use with tensorflow SELECTIVE_REGISTRATION.
Args:
graphs: a list of paths to GraphDef files to include.
proto_fileformat: optional format of proto file, either 'textproto' or
'rawproto' (default).
default_ops: optional comma-separated string of operator:kernel pairs to
always include implementation for. Pass 'all' to have all operators and
kernels included. Default: 'NoOp:NoOp,_Recv:RecvOp,_Send:SendOp'.
Returns:
the string of the header that should be written as ops_to_register.h.
"""
ops_and_kernels = get_ops_and_kernels(proto_fileformat, graphs, default_ops)
if not ops_and_kernels:
print('Error reading graph!')
return 1
return get_header_from_ops_and_kernels(ops_and_kernels, default_ops == 'all')
|
