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f615ba2f
编写于
9月 05, 2018
作者:
L
luotao1
浏览文件
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电子邮件补丁
差异文件
update the multi-thread unit-tests
上级
35cff5e0
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
23 addition
and
12 deletion
+23
-12
paddle/fluid/inference/analysis/analyzer_tester.cc
paddle/fluid/inference/analysis/analyzer_tester.cc
+23
-12
未找到文件。
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @
f615ba2f
...
@@ -255,8 +255,8 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
...
@@ -255,8 +255,8 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
}
}
}
}
// Test with a really complicate model.
// Test with a really complicate model.
void
TestDituRNNPrediction
(
bool
use_analysis
_and_activate_ir
=
false
,
void
TestDituRNNPrediction
(
bool
use_analysis
,
bool
activate_ir
,
int
num_threads
=
FLAGS_num_threads
)
{
int
num_threads
)
{
AnalysisConfig
config
;
AnalysisConfig
config
;
config
.
prog_file
=
FLAGS_infer_ditu_rnn_model
+
"/__model__"
;
config
.
prog_file
=
FLAGS_infer_ditu_rnn_model
+
"/__model__"
;
config
.
param_file
=
FLAGS_infer_ditu_rnn_model
+
"/param"
;
config
.
param_file
=
FLAGS_infer_ditu_rnn_model
+
"/param"
;
...
@@ -300,7 +300,7 @@ void TestDituRNNPrediction(bool use_analysis_and_activate_ir = false,
...
@@ -300,7 +300,7 @@ void TestDituRNNPrediction(bool use_analysis_and_activate_ir = false,
// because AttentionLSTM's hard code nodeid will be damanged.
// because AttentionLSTM's hard code nodeid will be damanged.
for
(
int
tid
=
0
;
tid
<
num_threads
;
++
tid
)
{
for
(
int
tid
=
0
;
tid
<
num_threads
;
++
tid
)
{
predictors
.
emplace_back
(
predictors
.
emplace_back
(
CreatePaddlePredictor
<
Native
Config
,
PaddleEngineKind
::
kAnalysis
>
(
CreatePaddlePredictor
<
Analysis
Config
,
PaddleEngineKind
::
kAnalysis
>
(
config
));
config
));
}
}
for
(
int
tid
=
0
;
tid
<
num_threads
;
++
tid
)
{
for
(
int
tid
=
0
;
tid
<
num_threads
;
++
tid
)
{
...
@@ -326,7 +326,7 @@ void TestDituRNNPrediction(bool use_analysis_and_activate_ir = false,
...
@@ -326,7 +326,7 @@ void TestDituRNNPrediction(bool use_analysis_and_activate_ir = false,
}
}
LOG
(
INFO
)
<<
"====================================="
;
LOG
(
INFO
)
<<
"====================================="
;
if
(
use_analysis
_and_
activate_ir
)
{
if
(
use_analysis
&&
activate_ir
)
{
AnalysisPredictor
*
analysis_predictor
=
AnalysisPredictor
*
analysis_predictor
=
dynamic_cast
<
AnalysisPredictor
*>
(
predictor
.
get
());
dynamic_cast
<
AnalysisPredictor
*>
(
predictor
.
get
());
auto
&
fuse_statis
=
analysis_predictor
->
analysis_argument
()
auto
&
fuse_statis
=
analysis_predictor
->
analysis_argument
()
...
@@ -353,15 +353,26 @@ void TestDituRNNPrediction(bool use_analysis_and_activate_ir = false,
...
@@ -353,15 +353,26 @@ void TestDituRNNPrediction(bool use_analysis_and_activate_ir = false,
}
}
}
}
// basic unit-test of DituRNN, easy for profiling independently.
// Inference with analysis and IR, easy for profiling independently.
TEST
(
Analyzer
,
DituRNN
)
{
TestDituRNNPrediction
(
false
,
FLAGS_num_threads
);
}
TEST
(
Analyzer
,
DituRNN
)
{
TestDituRNNPrediction
(
true
,
true
,
FLAGS_num_threads
);
}
// advance unit-test of DituRNN, test use_analysis_and_activate_ir and
// Other unit-tests of DituRNN, test different options of use_analysis,
// multi-threads.
// activate_ir and multi-threads.
TEST
(
Analyzer
,
DituRNN_multi_thread
)
{
TEST
(
Analyzer
,
DituRNN_tests
)
{
TestDituRNNPrediction
(
true
,
1
);
int
num_threads
[
2
]
=
{
1
,
4
};
TestDituRNNPrediction
(
false
,
4
);
for
(
auto
i
:
num_threads
)
{
TestDituRNNPrediction
(
true
,
4
);
// Directly infer with the original model.
TestDituRNNPrediction
(
false
,
false
,
i
);
// Inference with the original model with the analysis turned on, the
// analysis
// module will transform the program to a data flow graph.
TestDituRNNPrediction
(
true
,
false
,
i
);
// Inference with analysis and IR. The IR module will fuse some large
// kernels.
TestDituRNNPrediction
(
true
,
true
,
i
);
}
}
}
}
// namespace analysis
}
// namespace analysis
...
...
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