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tensorflow/tensorflow/cc/profiler/profiler_test.cc at master · codeplaysoftware/tensorflow · GitHub
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/*
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/platform/test.h
"
#
include
"
tensorflow/cc/ops/standard_ops.h
"
#
include
"
tensorflow/cc/profiler/profiler.h
"
#
include
"
tensorflow/core/framework/graph.pb.h
"
#
include
"
tensorflow/core/framework/tensor.h
"
#
include
"
tensorflow/core/graph/default_device.h
"
#
include
"
tensorflow/core/lib/io/path.h
"
#
include
"
tensorflow/core/platform/env.h
"
#
include
"
tensorflow/core/public/session.h
"
namespace
tensorflow
{
namespace
tfprof
{
class
ProfilerTest
: public ::testing::Test {
protected:
ProfilerTest
() {}
};
GraphDef
CreateGraphDef
() {
Scope root =
Scope::NewRootScope
();
auto
a = ops::Const<
float
>(root, {{
3
,
2
}, {-
1
,
0
}});
auto
x =
ops::Const
(root.
WithOpName
(
"
x
"
), {{
1
.
f
}, {
1
.
f
}});
auto
y =
ops::MatMul
(root.
WithOpName
(
"
y
"
), a, x);
auto
y2 =
ops::Square
(root, y);
auto
y2_sum =
ops::Sum
(root, y2,
0
);
auto
y_norm =
ops::Sqrt
(root, y2_sum);
auto
y_div =
ops::Div
(root.
WithOpName
(
"
y_normalized
"
), y, y_norm);
GraphDef def;
TF_CHECK_OK
(root.
ToGraphDef
(&def));
return
def;
}
Options
Default
() {
Options
opts
(
1000
,
/*
max_depth
*/
0
,
/*
min_bytes
*/
0
,
/*
min_peak_bytes
*/
0
,
/*
min_residual_bytes
*/
0
,
/*
min_output_bytes
*/
0
,
/*
min_micros
*/
0
,
/*
min_accelerator_micros
*/
0
,
/*
min_cpu_micros
*/
0
,
/*
min_params
*/
0
,
/*
min_float_ops
*/
0
,
/*
min_occurrence
*/
0
,
/*
step
*/
"
name
"
,
/*
order_by
*/
{
"
.*
"
},
/*
account_type_regexes
*/
{
"
.*
"
},
/*
start_name_regexes
*/
{},
/*
trim_name_regexes
*/
{
"
.*
"
}, {},
/*
hide_name_regexes
*/
false
,
/*
account_displayed_op_only
*/
{
"
micros
"
},
/*
select
*/
{
"
none
"
},
/*
output_type
*/
{});
return
opts;
}
template
<
typename
T>
const
T*
ExtractNode
(
const
T& pb,
const
string& name) {
if
(pb.
name
() == name) {
return
&pb;
}
for
(
const
T& c : pb.
children
()) {
const
T* ret =
ExtractNode
(c, name);
if
(ret)
return
ret;
}
return
nullptr
;
}
TEST_F
(ProfilerTest, Basics) {
SessionOptions options;
options.
config
.
set_allow_soft_placement
(
true
);
std::unique_ptr<Session>
session
(
NewSession
(options));
GraphDef def =
CreateGraphDef
();
if
(options.
target
.
empty
()) {
graph::SetDefaultDevice
(
"
/gpu:0
"
, &def);
}
TF_CHECK_OK
(session->
Create
(def));
Tensor
x
(
DT_FLOAT
,
TensorShape
({
2
,
1
}));
auto
x_flat = x.
flat
<
float
>();
x_flat.
setRandom
();
Eigen::Tensor<
float
,
0
, Eigen::RowMajor> inv_norm =
x_flat.
square
().
sum
().
sqrt
().
inverse
();
x_flat = x_flat *
inv_norm
();
std::vector<Tensor> outputs;
RunOptions run_options;
run_options.
set_trace_level
(RunOptions::
FULL_TRACE
);
RunMetadata run_metadata;
outputs.
clear
();
Profiler
profiler
(def);
for
(
int
i =
0
; i <
2
; ++i) {
TF_CHECK_OK
(session->
Run
(run_options, {{
"
x
"
, x}}, {
"
y:0
"
,
"
y_normalized:0
"
},
{}, &outputs, &run_metadata));
profiler.
AddStep
(i, run_metadata);
CHECK_EQ
(
size_t
{
2
}, outputs.
size
());
}
std::vector<DeviceAttributes> resp;
TF_CHECK_OK
(session->
ListDevices
(&resp));
bool
has_gpu =
false
;
for
(
const
auto
& dev : resp) {
if
(dev.
device_type
() ==
"
GPU
"
) {
has_gpu =
true
;
}
}
GraphNodeProto ret = profiler.
ProfileNameScope
(
Default
());
const
GraphNodeProto* matmul =
ExtractNode
(ret,
"
y
"
);
EXPECT_TRUE
(matmul);
EXPECT_GT
(matmul->
exec_micros
(),
0
);
if
(has_gpu) {
EXPECT_GT
(matmul->
accelerator_exec_micros
(),
0
);
}
else
{
EXPECT_EQ
(matmul->
accelerator_exec_micros
(),
0
);
}
const
GraphNodeProto* square =
ExtractNode
(ret,
"
Square
"
);
EXPECT_TRUE
(square);
EXPECT_GT
(square->
exec_micros
(),
0
);
if
(has_gpu) {
EXPECT_GT
(square->
accelerator_exec_micros
(),
0
);
}
else
{
EXPECT_EQ
(square->
accelerator_exec_micros
(),
0
);
}
Options opts2 =
Default
();
opts2.
output_type
=
"
timeline
"
;
string timeline_file =
io::JoinPath
(
testing::TmpDir
(),
"
timeline
"
);
opts2.
output_options
[
"
outfile
"
] = timeline_file;
GraphNodeProto ret2 = profiler.
ProfileGraph
(opts2);
string s;
TF_CHECK_OK
(
ReadFileToString
(
Env::Default
(), timeline_file +
"
_0
"
, &s));
EXPECT_TRUE
(s.
find
(
"
Square
"
) != s.
npos
);
MultiGraphNodeProto ret3 = profiler.
ProfileOperations
(
Default
());
const
MultiGraphNodeProto* matmul2 =
ExtractNode
(ret3,
"
MatMul
"
);
EXPECT_TRUE
(matmul2);
EXPECT_GT
(matmul2->
exec_micros
(),
0
);
if
(has_gpu) {
EXPECT_GT
(matmul2->
accelerator_exec_micros
(),
0
);
}
else
{
EXPECT_EQ
(matmul2->
accelerator_exec_micros
(),
0
);
}
TF_CHECK_OK
(session->
Close
());
}
}
//
namespace tfprof
}
//
namespace tensorflow
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