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# 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.
# ==============================================================================
"""Tests for print_selective_registration_header."""
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.framework import test_util
from tensorflow.python.platform import gfile
from tensorflow.python.platform import test
from tensorflow.python.tools import selective_registration_header_lib
# Note that this graph def is not valid to be loaded - its inputs are not
# assigned correctly in all cases.
GRAPH_DEF_TXT = """
node: {
name: "node_1"
op: "Reshape"
input: [ "none", "none" ]
device: "/cpu:0"
attr: { key: "T" value: { type: DT_FLOAT } }
}
node: {
name: "node_2"
op: "MatMul"
input: [ "none", "none" ]
device: "/cpu:0"
attr: { key: "T" value: { type: DT_FLOAT } }
attr: { key: "transpose_a" value: { b: false } }
attr: { key: "transpose_b" value: { b: false } }
}
node: {
name: "node_3"
op: "MatMul"
input: [ "none", "none" ]
device: "/cpu:0"
attr: { key: "T" value: { type: DT_DOUBLE } }
attr: { key: "transpose_a" value: { b: false } }
attr: { key: "transpose_b" value: { b: false } }
}
"""
GRAPH_DEF_TXT_2 = """
node: {
name: "node_4"
op: "BiasAdd"
input: [ "none", "none" ]
device: "/cpu:0"
attr: { key: "T" value: { type: DT_FLOAT } }
}
"""
class PrintOpFilegroupTest(test.TestCase):
def setUp(self):
_, self.script_name = os.path.split(sys.argv[0])
def WriteGraphFiles(self, graphs):
fnames = []
for i, graph in enumerate(graphs):
fname = os.path.join(self.get_temp_dir(), 'graph%s.pb' % i)
with gfile.GFile(fname, 'wb') as f:
f.write(graph.SerializeToString())
fnames.append(fname)
return fnames
def testGetOps(self):
default_ops = 'NoOp:NoOp,_Recv:RecvOp,_Send:SendOp'
graphs = [
text_format.Parse(d, graph_pb2.GraphDef())
for d in [GRAPH_DEF_TXT, GRAPH_DEF_TXT_2]
]
ops_and_kernels = selective_registration_header_lib.get_ops_and_kernels(
'rawproto', self.WriteGraphFiles(graphs), default_ops)
matmul_prefix = ''
if test_util.IsMklEnabled():
matmul_prefix = 'Mkl'
self.assertListEqual(
[
('BiasAdd', 'BiasOp<CPUDevice, float>'), #
('MatMul',
matmul_prefix + 'MatMulOp<CPUDevice, double, false >'), #
('MatMul', matmul_prefix + 'MatMulOp<CPUDevice, float, false >'), #
('NoOp', 'NoOp'), #
('Reshape', 'ReshapeOp'), #
('_Recv', 'RecvOp'), #
('_Send', 'SendOp'), #
],
ops_and_kernels)
graphs[0].node[0].ClearField('device')
graphs[0].node[2].ClearField('device')
ops_and_kernels = selective_registration_header_lib.get_ops_and_kernels(
'rawproto', self.WriteGraphFiles(graphs), default_ops)
self.assertListEqual(
[
('BiasAdd', 'BiasOp<CPUDevice, float>'), #
('MatMul',
matmul_prefix + 'MatMulOp<CPUDevice, double, false >'), #
('MatMul', matmul_prefix + 'MatMulOp<CPUDevice, float, false >'), #
('NoOp', 'NoOp'), #
('Reshape', 'ReshapeOp'), #
('_Recv', 'RecvOp'), #
('_Send', 'SendOp'), #
],
ops_and_kernels)
def testAll(self):
default_ops = 'all'
graphs = [
text_format.Parse(d, graph_pb2.GraphDef())
for d in [GRAPH_DEF_TXT, GRAPH_DEF_TXT_2]
]
ops_and_kernels = selective_registration_header_lib.get_ops_and_kernels(
'rawproto', self.WriteGraphFiles(graphs), default_ops)
header = selective_registration_header_lib.get_header_from_ops_and_kernels(
ops_and_kernels, include_all_ops_and_kernels=True)
self.assertListEqual(
[
'// This file was autogenerated by %s' % self.script_name,
'#ifndef OPS_TO_REGISTER', #
'#define OPS_TO_REGISTER', #
'#define SHOULD_REGISTER_OP(op) true', #
'#define SHOULD_REGISTER_OP_KERNEL(clz) true', #
'#define SHOULD_REGISTER_OP_GRADIENT true', #
'#endif'
],
header.split('\n'))
self.assertListEqual(
header.split('\n'),
selective_registration_header_lib.get_header(
self.WriteGraphFiles(graphs), 'rawproto', default_ops).split('\n'))
def testGetSelectiveHeader(self):
default_ops = ''
graphs = [text_format.Parse(GRAPH_DEF_TXT_2, graph_pb2.GraphDef())]
expected = '''// This file was autogenerated by %s
#ifndef OPS_TO_REGISTER
#define OPS_TO_REGISTER
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
constexpr const char* kNecessaryOpKernelClasses[] = {
"BiasOp<CPUDevice, float>",
};
#define SHOULD_REGISTER_OP_KERNEL(clz) (find_in<sizeof(kNecessaryOpKernelClasses) / sizeof(*kNecessaryOpKernelClasses)>::f(clz, kNecessaryOpKernelClasses))
constexpr inline bool ShouldRegisterOp(const char op[]) {
return false
|| isequal(op, "BiasAdd")
;
}
#define SHOULD_REGISTER_OP(op) ShouldRegisterOp(op)
#define SHOULD_REGISTER_OP_GRADIENT false
#endif''' % self.script_name
header = selective_registration_header_lib.get_header(
self.WriteGraphFiles(graphs), 'rawproto', default_ops)
print(header)
self.assertListEqual(expected.split('\n'), header.split('\n'))
if __name__ == '__main__':
test.main()
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