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体验新版 GitCode,发现更多精彩内容 >>
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7dd8adb5
编写于
9月 06, 2018
作者:
T
Tao Luo
提交者:
GitHub
9月 06, 2018
浏览文件
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差异文件
Merge pull request #13211 from luotao1/multi-thread2
add multi-thread ut for ditu-rnn
上级
f143c9a9
f615ba2f
变更
6
显示空白变更内容
内联
并排
Showing
6 changed file
with
126 addition
and
86 deletion
+126
-86
paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
+0
-3
paddle/fluid/framework/ir/graph_pattern_detector.cc
paddle/fluid/framework/ir/graph_pattern_detector.cc
+1
-1
paddle/fluid/inference/analysis/analyzer.cc
paddle/fluid/inference/analysis/analyzer.cc
+0
-1
paddle/fluid/inference/analysis/analyzer_tester.cc
paddle/fluid/inference/analysis/analyzer_tester.cc
+84
-80
paddle/fluid/inference/api/analysis_predictor.cc
paddle/fluid/inference/api/analysis_predictor.cc
+0
-1
paddle/fluid/inference/api/helper.h
paddle/fluid/inference/api/helper.h
+41
-0
未找到文件。
paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
浏览文件 @
7dd8adb5
...
...
@@ -13,13 +13,10 @@
// limitations under the License.
#include "paddle/fluid/framework/ir/attention_lstm_fuse_pass.h"
#include <string>
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/inference/api/helper.h"
namespace
paddle
{
namespace
framework
{
...
...
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @
7dd8adb5
...
...
@@ -85,7 +85,7 @@ void GraphPatternDetector::operator()(Graph* graph,
LOG
(
INFO
)
<<
"detect "
<<
subgraphs
.
size
()
<<
" subgraph matches the pattern"
;
int
id
=
0
;
for
(
auto
&
g
:
subgraphs
)
{
LOG
(
INFO
)
<<
"optimizing #"
<<
id
++
<<
" subgraph"
;
VLOG
(
3
)
<<
"optimizing #"
<<
id
++
<<
" subgraph"
;
handler
(
g
,
graph
);
}
}
...
...
paddle/fluid/inference/analysis/analyzer.cc
浏览文件 @
7dd8adb5
...
...
@@ -106,7 +106,6 @@ void Analyzer::Run(Argument* argument) {
}
}
passes
.
push_back
(
"graph_viz_pass"
);
// Ugly support fluid-to-ir-pass
argument
->
Set
(
kFluidToIrPassesAttr
,
new
std
::
vector
<
std
::
string
>
(
passes
));
for
(
auto
&
x
:
data_
)
{
...
...
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @
7dd8adb5
...
...
@@ -16,6 +16,7 @@
#include <google/protobuf/text_format.h>
#include <gtest/gtest.h>
#include <thread> // NOLINT
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
...
...
@@ -24,12 +25,12 @@
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_string
(
infer_ditu_rnn_model
,
""
,
"model path for ditu RNN"
);
DEFINE_string
(
infer_ditu_rnn_data
,
""
,
"data path for ditu RNN"
);
DEFINE_int32
(
batch_size
,
10
,
"batch size."
);
DEFINE_int32
(
repeat
,
1
,
"Running the inference program repeat times."
);
DEFINE_int32
(
num_threads
,
1
,
"Running the inference program in multi-threads."
);
namespace
paddle
{
namespace
inference
{
...
...
@@ -220,39 +221,6 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
}
std
::
string
DescribeTensor
(
const
PaddleTensor
&
tensor
)
{
std
::
stringstream
os
;
os
<<
"Tensor ["
<<
tensor
.
name
<<
"]
\n
"
;
os
<<
" - type: "
;
switch
(
tensor
.
dtype
)
{
case
PaddleDType
::
FLOAT32
:
os
<<
"float32"
;
break
;
case
PaddleDType
::
INT64
:
os
<<
"int64"
;
break
;
default:
os
<<
"unset"
;
}
os
<<
'\n'
;
os
<<
" - shape: "
<<
to_string
(
tensor
.
shape
)
<<
'\n'
;
os
<<
" - lod: "
;
for
(
auto
&
l
:
tensor
.
lod
)
{
os
<<
to_string
(
l
)
<<
"; "
;
}
os
<<
"
\n
"
;
os
<<
" - data: "
;
int
dim
=
std
::
accumulate
(
tensor
.
shape
.
begin
(),
tensor
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
for
(
int
i
=
0
;
i
<
dim
;
i
++
)
{
os
<<
static_cast
<
float
*>
(
tensor
.
data
.
data
())[
i
]
<<
" "
;
}
os
<<
'\n'
;
return
os
.
str
();
}
}
// namespace
const
float
ditu_rnn_target_data
[]
=
{
...
...
@@ -266,11 +234,29 @@ const float ditu_rnn_target_data[] = {
10.7286
,
12.0595
,
10.6672
,
0
,
0
,
0
,
0
,
0
,
93.5771
,
3.84641
,
0
,
0
,
0
,
0
,
0
,
0
,
169.426
,
0
,
0
,
0
,
0
,
0
,
0
,
0
};
void
CompareResult
(
const
std
::
vector
<
PaddleTensor
>
&
outputs
,
const
std
::
vector
<
PaddleTensor
>
&
base_outputs
)
{
PADDLE_ENFORCE_GT
(
outputs
.
size
(),
0
);
PADDLE_ENFORCE_EQ
(
outputs
.
size
(),
base_outputs
.
size
());
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
i
++
)
{
auto
&
out
=
outputs
[
i
];
auto
&
base_out
=
base_outputs
[
i
];
size_t
size
=
std
::
accumulate
(
out
.
shape
.
begin
(),
out
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
size_t
size1
=
std
::
accumulate
(
base_out
.
shape
.
begin
(),
base_out
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
PADDLE_ENFORCE_EQ
(
size
,
size1
);
PADDLE_ENFORCE_GT
(
size
,
0
);
float
*
data
=
static_cast
<
float
*>
(
out
.
data
.
data
());
float
*
base_data
=
static_cast
<
float
*>
(
base_out
.
data
.
data
());
for
(
size_t
i
=
0
;
i
<
size
;
i
++
)
{
EXPECT_NEAR
(
data
[
i
],
base_data
[
i
],
1e-3
);
}
}
}
// Test with a really complicate model.
void
TestDituRNNPrediction
(
const
std
::
string
&
model_path
,
const
std
::
string
&
data_path
,
int
batch_size
,
bool
use_analysis
,
bool
activate_ir
,
int
num_times
=
1
)
{
void
TestDituRNNPrediction
(
bool
use_analysis
,
bool
activate_ir
,
int
num_threads
)
{
AnalysisConfig
config
;
config
.
prog_file
=
FLAGS_infer_ditu_rnn_model
+
"/__model__"
;
config
.
param_file
=
FLAGS_infer_ditu_rnn_model
+
"/param"
;
...
...
@@ -281,6 +267,8 @@ void TestDituRNNPrediction(const std::string &model_path,
PADDLE_ENFORCE
(
config
.
ir_mode
==
AnalysisConfig
::
IrPassMode
::
kExclude
);
// default
config
.
ir_passes
.
clear
();
// Do not exclude any pass.
int
batch_size
=
FLAGS_batch_size
;
int
num_times
=
FLAGS_repeat
;
auto
base_predictor
=
CreatePaddlePredictor
<
NativeConfig
,
PaddleEngineKind
::
kNative
>
(
config
);
...
...
@@ -288,40 +276,55 @@ void TestDituRNNPrediction(const std::string &model_path,
CreatePaddlePredictor
<
AnalysisConfig
,
PaddleEngineKind
::
kAnalysis
>
(
config
);
std
::
vector
<
PaddleTensor
>
input_slots
;
DataRecord
data
(
data_path
,
batch_size
);
DataRecord
data
(
FLAGS_infer_ditu_rnn_data
,
batch_size
);
// Prepare inputs.
PrepareInputs
(
&
input_slots
,
&
data
,
batch_size
);
std
::
vector
<
PaddleTensor
>
outputs
,
base_outputs
;
base_predictor
->
Run
(
input_slots
,
&
base_outputs
);
LOG
(
INFO
)
<<
"===========profile result==========="
;
if
(
num_threads
==
1
)
{
// Prepare inputs.
Timer
timer
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
num_times
;
i
++
)
{
predictor
->
Run
(
input_slots
,
&
outputs
);
}
LOG
(
INFO
)
<<
"===========profile result==========="
;
LOG
(
INFO
)
<<
"batch_size: "
<<
batch_size
<<
", repeat: "
<<
num_times
<<
", latency: "
<<
timer
.
toc
()
/
num_times
<<
"ms"
;
LOG
(
INFO
)
<<
"====================================="
;
PADDLE_ENFORCE_GT
(
outputs
.
size
(),
0
);
PADDLE_ENFORCE_EQ
(
outputs
.
size
(),
base_outputs
.
size
());
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
i
++
)
{
auto
&
out
=
outputs
[
i
];
auto
&
base_out
=
base_outputs
[
i
];
size_t
size
=
std
::
accumulate
(
out
.
shape
.
begin
(),
out
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
size_t
size1
=
std
::
accumulate
(
base_out
.
shape
.
begin
(),
base_out
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
PADDLE_ENFORCE_EQ
(
size
,
size1
);
PADDLE_ENFORCE_GT
(
size
,
0
);
float
*
data
=
static_cast
<
float
*>
(
out
.
data
.
data
());
float
*
base_data
=
static_cast
<
float
*>
(
base_out
.
data
.
data
());
for
(
size_t
j
=
0
;
j
<
size
;
j
++
)
{
EXPECT_NEAR
(
data
[
j
],
base_data
[
j
],
1e-3
);
PrintTime
(
batch_size
,
num_times
,
1
,
0
,
timer
.
toc
()
/
num_times
);
CompareResult
(
outputs
,
base_outputs
);
}
else
{
std
::
vector
<
std
::
thread
>
threads
;
std
::
vector
<
std
::
unique_ptr
<
PaddlePredictor
>>
predictors
;
// TODO(yanchunwei): Bug here, the analyzer phase can't be parallelled
// because AttentionLSTM's hard code nodeid will be damanged.
for
(
int
tid
=
0
;
tid
<
num_threads
;
++
tid
)
{
predictors
.
emplace_back
(
CreatePaddlePredictor
<
AnalysisConfig
,
PaddleEngineKind
::
kAnalysis
>
(
config
));
}
for
(
int
tid
=
0
;
tid
<
num_threads
;
++
tid
)
{
threads
.
emplace_back
([
&
,
tid
]()
{
// Each thread should have local input_slots and outputs.
std
::
vector
<
PaddleTensor
>
input_slots
;
DataRecord
data
(
FLAGS_infer_ditu_rnn_data
,
batch_size
);
PrepareInputs
(
&
input_slots
,
&
data
,
batch_size
);
std
::
vector
<
PaddleTensor
>
outputs
;
Timer
timer
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
num_times
;
i
++
)
{
predictors
[
tid
]
->
Run
(
input_slots
,
&
outputs
);
}
PrintTime
(
batch_size
,
num_times
,
num_threads
,
tid
,
timer
.
toc
()
/
num_times
);
CompareResult
(
outputs
,
base_outputs
);
});
}
for
(
int
i
=
0
;
i
<
num_threads
;
++
i
)
{
threads
[
i
].
join
();
}
}
LOG
(
INFO
)
<<
"====================================="
;
if
(
use_analysis
&&
activate_ir
)
{
AnalysisPredictor
*
analysis_predictor
=
...
...
@@ -350,25 +353,26 @@ void TestDituRNNPrediction(const std::string &model_path,
}
}
// Directly infer with the original model.
TEST
(
Analyzer
,
DituRNN_without_analysis
)
{
TestDituRNNPrediction
(
FLAGS_infer_ditu_rnn_model
,
FLAGS_infer_ditu_rnn_data
,
FLAGS_batch_size
,
false
,
false
,
FLAGS_repeat
);
// Inference with analysis and IR, easy for profiling independently.
TEST
(
Analyzer
,
DituRNN
)
{
TestDituRNNPrediction
(
true
,
true
,
FLAGS_num_threads
);
}
// Inference with the original model with the analysis turned on, the analysis
// module will transform the program to a data flow graph.
TEST
(
Analyzer
,
DituRNN_with_analysis
)
{
LOG
(
INFO
)
<<
"ditu rnn with analysis"
;
TestDituRNNPrediction
(
FLAGS_infer_ditu_rnn_model
,
FLAGS_infer_ditu_rnn_data
,
FLAGS_batch_size
,
true
,
false
,
FLAGS_repeat
);
}
// Inference with analysis and IR. The IR module will fuse some large kernels.
TEST
(
Analyzer
,
DituRNN_with_analysis_with_IR
)
{
LOG
(
INFO
)
<<
"ditu rnn with analysis and IR fuse"
;
TestDituRNNPrediction
(
FLAGS_infer_ditu_rnn_model
,
FLAGS_infer_ditu_rnn_data
,
FLAGS_batch_size
,
true
,
true
,
FLAGS_repeat
);
// Other unit-tests of DituRNN, test different options of use_analysis,
// activate_ir and multi-threads.
TEST
(
Analyzer
,
DituRNN_tests
)
{
int
num_threads
[
2
]
=
{
1
,
4
};
for
(
auto
i
:
num_threads
)
{
// 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
...
...
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @
7dd8adb5
...
...
@@ -35,7 +35,6 @@ bool AnalysisPredictor::Init(
}
else
{
place_
=
paddle
::
platform
::
CPUPlace
();
}
PADDLE_ENFORCE
(
!
parent_scope
);
if
(
parent_scope
)
{
scope_
=
parent_scope
;
sub_scope_
=
&
(
parent_scope
->
NewScope
());
...
...
paddle/fluid/inference/api/helper.h
浏览文件 @
7dd8adb5
...
...
@@ -14,6 +14,7 @@
#pragma once
#include <glog/logging.h>
#include <sys/time.h>
#include <algorithm>
#include <numeric>
...
...
@@ -88,5 +89,45 @@ static void TensorAssignData(PaddleTensor *tensor,
}
}
std
::
string
DescribeTensor
(
const
PaddleTensor
&
tensor
)
{
std
::
stringstream
os
;
os
<<
"Tensor ["
<<
tensor
.
name
<<
"]
\n
"
;
os
<<
" - type: "
;
switch
(
tensor
.
dtype
)
{
case
PaddleDType
::
FLOAT32
:
os
<<
"float32"
;
break
;
case
PaddleDType
::
INT64
:
os
<<
"int64"
;
break
;
default:
os
<<
"unset"
;
}
os
<<
'\n'
;
os
<<
" - shape: "
<<
to_string
(
tensor
.
shape
)
<<
'\n'
;
os
<<
" - lod: "
;
for
(
auto
&
l
:
tensor
.
lod
)
{
os
<<
to_string
(
l
)
<<
"; "
;
}
os
<<
"
\n
"
;
os
<<
" - data: "
;
int
dim
=
std
::
accumulate
(
tensor
.
shape
.
begin
(),
tensor
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
for
(
int
i
=
0
;
i
<
dim
;
i
++
)
{
os
<<
static_cast
<
float
*>
(
tensor
.
data
.
data
())[
i
]
<<
" "
;
}
os
<<
'\n'
;
return
os
.
str
();
}
void
PrintTime
(
int
batch_size
,
int
repeat
,
int
num_threads
,
int
tid
,
double
latency
)
{
LOG
(
INFO
)
<<
"batch_size: "
<<
batch_size
<<
", repeat: "
<<
repeat
<<
", threads: "
<<
num_threads
<<
", thread id: "
<<
tid
<<
", latency: "
<<
latency
<<
"ms"
;
}
}
// namespace inference
}
// namespace paddle
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