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体验新版 GitCode,发现更多精彩内容 >>
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e31f6e98
编写于
3月 11, 2019
作者:
T
Tao Luo
提交者:
GitHub
3月 11, 2019
浏览文件
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差异文件
Merge pull request #16146 from luotao1/zero_copy
unify ZeroCopy in analysis_test
上级
ad80bde8
12838333
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
213 addition
and
326 deletion
+213
-326
paddle/fluid/inference/api/details/zero_copy_tensor.cc
paddle/fluid/inference/api/details/zero_copy_tensor.cc
+5
-0
paddle/fluid/inference/api/helper.h
paddle/fluid/inference/api/helper.h
+11
-4
paddle/fluid/inference/tests/api/analyzer_pyramid_dnn_tester.cc
.../fluid/inference/tests/api/analyzer_pyramid_dnn_tester.cc
+17
-1
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
+13
-126
paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
...le/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
+13
-130
paddle/fluid/inference/tests/api/tester_helper.h
paddle/fluid/inference/tests/api/tester_helper.h
+154
-65
未找到文件。
paddle/fluid/inference/api/details/zero_copy_tensor.cc
浏览文件 @
e31f6e98
...
...
@@ -126,15 +126,20 @@ void ZeroCopyTensor::copy_to_cpu(T *data) {
}
template
void
ZeroCopyTensor
::
copy_from_cpu
<
float
>(
const
float
*
data
);
template
void
ZeroCopyTensor
::
copy_from_cpu
<
int64_t
>(
const
int64_t
*
data
);
template
void
ZeroCopyTensor
::
copy_from_cpu
<
int32_t
>(
const
int32_t
*
data
);
template
void
ZeroCopyTensor
::
copy_to_cpu
<
float
>(
float
*
data
);
template
void
ZeroCopyTensor
::
copy_to_cpu
<
int64_t
>(
int64_t
*
data
);
template
void
ZeroCopyTensor
::
copy_to_cpu
<
int32_t
>(
int32_t
*
data
);
template
float
*
ZeroCopyTensor
::
data
<
float
>(
PaddlePlace
*
place
,
int
*
size
)
const
;
template
int64_t
*
ZeroCopyTensor
::
data
<
int64_t
>(
PaddlePlace
*
place
,
int
*
size
)
const
;
template
int32_t
*
ZeroCopyTensor
::
data
<
int32_t
>(
PaddlePlace
*
place
,
int
*
size
)
const
;
template
float
*
ZeroCopyTensor
::
mutable_data
<
float
>(
PaddlePlace
place
);
template
int64_t
*
ZeroCopyTensor
::
mutable_data
<
int64_t
>(
PaddlePlace
place
);
template
int32_t
*
ZeroCopyTensor
::
mutable_data
<
int32_t
>(
PaddlePlace
place
);
void
*
ZeroCopyTensor
::
FindTensor
()
const
{
PADDLE_ENFORCE
(
!
name_
.
empty
(),
...
...
paddle/fluid/inference/api/helper.h
浏览文件 @
e31f6e98
...
...
@@ -139,9 +139,8 @@ static void TensorAssignData(PaddleTensor *tensor,
}
template
<
typename
T
>
static
int
ZeroCopyTensorAssignData
(
ZeroCopyTensor
*
tensor
,
const
std
::
vector
<
std
::
vector
<
T
>>
&
data
)
{
int
size
{
0
};
static
void
ZeroCopyTensorAssignData
(
ZeroCopyTensor
*
tensor
,
const
std
::
vector
<
std
::
vector
<
T
>>
&
data
)
{
auto
*
ptr
=
tensor
->
mutable_data
<
T
>
(
PaddlePlace
::
kCPU
);
int
c
=
0
;
for
(
const
auto
&
f
:
data
)
{
...
...
@@ -149,7 +148,15 @@ static int ZeroCopyTensorAssignData(ZeroCopyTensor *tensor,
ptr
[
c
++
]
=
v
;
}
}
return
size
;
}
template
<
typename
T
>
static
void
ZeroCopyTensorAssignData
(
ZeroCopyTensor
*
tensor
,
const
PaddleBuf
&
data
)
{
auto
*
ptr
=
tensor
->
mutable_data
<
T
>
(
PaddlePlace
::
kCPU
);
for
(
size_t
i
=
0
;
i
<
data
.
length
()
/
sizeof
(
T
);
i
++
)
{
ptr
[
i
]
=
*
(
reinterpret_cast
<
T
*>
(
data
.
data
())
+
i
);
}
}
static
bool
CompareTensor
(
const
PaddleTensor
&
a
,
const
PaddleTensor
&
b
)
{
...
...
paddle/fluid/inference/tests/api/analyzer_pyramid_dnn_tester.cc
浏览文件 @
e31f6e98
...
...
@@ -107,6 +107,9 @@ void SetConfig(AnalysisConfig *cfg) {
cfg
->
DisableGpu
();
cfg
->
SwitchSpecifyInputNames
();
cfg
->
SwitchIrOptim
();
if
(
FLAGS_zero_copy
)
{
cfg
->
SwitchUseFeedFetchOps
(
false
);
}
}
void
SetInput
(
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
*
inputs
)
{
...
...
@@ -131,7 +134,7 @@ TEST(Analyzer_Pyramid_DNN, profile) {
TestPrediction
(
reinterpret_cast
<
const
PaddlePredictor
::
Config
*>
(
&
cfg
),
input_slots_all
,
&
outputs
,
FLAGS_num_threads
);
if
(
FLAGS_num_threads
==
1
&&
!
FLAGS_test_all_data
)
{
if
(
FLAGS_num_threads
==
1
&&
!
FLAGS_test_all_data
&&
!
FLAGS_zero_copy
)
{
PADDLE_ENFORCE_EQ
(
outputs
.
size
(),
1UL
);
size_t
size
=
GetSize
(
outputs
[
0
]);
PADDLE_ENFORCE_GT
(
size
,
0
);
...
...
@@ -166,6 +169,19 @@ TEST(Analyzer_Pyramid_DNN, compare) {
reinterpret_cast
<
const
PaddlePredictor
::
Config
*>
(
&
cfg
),
input_slots_all
);
}
// Compare result of AnalysisConfig and AnalysisConfig + ZeroCopy
TEST
(
Analyzer_Pyramid_DNN
,
compare_zero_copy
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
std
::
vector
<
std
::
string
>
outputs_name
;
outputs_name
.
emplace_back
(
"cos_sim_2.tmp_0"
);
CompareAnalysisAndZeroCopy
(
reinterpret_cast
<
PaddlePredictor
::
Config
*>
(
&
cfg
),
input_slots_all
,
outputs_name
);
}
// Compare Deterministic result
TEST
(
Analyzer_Pyramid_DNN
,
compare_determine
)
{
AnalysisConfig
cfg
;
...
...
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
浏览文件 @
e31f6e98
...
...
@@ -207,6 +207,9 @@ void SetConfig(AnalysisConfig *cfg) {
cfg
->
DisableGpu
();
cfg
->
SwitchSpecifyInputNames
();
cfg
->
SwitchIrOptim
();
if
(
FLAGS_zero_copy
)
{
cfg
->
SwitchUseFeedFetchOps
(
false
);
}
}
void
SetInput
(
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
*
inputs
)
{
...
...
@@ -285,133 +288,17 @@ TEST(Analyzer_rnn1, multi_thread) {
input_slots_all
,
&
outputs
,
2
/* multi_thread */
);
}
// Validate that the AnalysisPredictor + ZeroCopyTensor really works by testing
// on the complex RNN1 model.
TEST
(
Analyzer_rnn1
,
ZeroCopy
)
{
AnalysisConfig
config
;
SetConfig
(
&
config
);
config
.
SwitchUseFeedFetchOps
(
false
);
PaddlePlace
place
;
auto
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
>
(
config
);
config
.
SwitchUseFeedFetchOps
(
true
);
auto
native_predictor
=
CreatePaddlePredictor
<
NativeConfig
>
(
config
.
ToNativeConfig
());
config
.
SwitchUseFeedFetchOps
(
true
);
// the analysis predictor needs feed/fetch.
auto
analysis_predictor
=
CreatePaddlePredictor
<
AnalysisConfig
>
(
config
);
#define NEW_TENSOR(name__) \
auto name__##_tensor = predictor->GetInputTensor(#name__);
NEW_TENSOR
(
data_lod_attention
);
NEW_TENSOR
(
cell_init
);
NEW_TENSOR
(
data
);
NEW_TENSOR
(
week
);
NEW_TENSOR
(
minute
);
NEW_TENSOR
(
hidden_init
);
// Prepare data for AnalysisPredictor
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
PrepareZeroCopyInputs
(
data_lod_attention_tensor
.
get
(),
cell_init_tensor
.
get
(),
data_tensor
.
get
(),
hidden_init_tensor
.
get
(),
week_tensor
.
get
(),
minute_tensor
.
get
(),
&
data
,
FLAGS_batch_size
);
// Prepare data for NativePredictor
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
native_inputs
;
SetInput
(
&
native_inputs
);
std
::
vector
<
PaddleTensor
>
native_outputs
;
std
::
vector
<
PaddleTensor
>
analysis_outputs
;
auto
output_tensor
=
predictor
->
GetOutputTensor
(
"final_output.tmp_1"
);
// Run analysis predictor
int
num_ops
;
auto
fuse_statis
=
GetFuseStatis
(
predictor
.
get
(),
&
num_ops
);
ASSERT_TRUE
(
fuse_statis
.
count
(
"fc_fuse"
));
ASSERT_EQ
(
fuse_statis
.
at
(
"fc_fuse"
),
1
);
ASSERT_EQ
(
fuse_statis
.
at
(
"fc_nobias_lstm_fuse"
),
2
);
// bi-directional LSTM
ASSERT_EQ
(
fuse_statis
.
at
(
"seq_concat_fc_fuse"
),
1
);
ASSERT_EQ
(
num_ops
,
13
);
// After graph optimization, only 13 operators exists.
Timer
timer
;
double
total_time
{
0
};
for
(
int
i
=
0
;
i
<
FLAGS_repeat
;
i
++
)
{
timer
.
tic
();
predictor
->
ZeroCopyRun
();
total_time
+=
timer
.
toc
();
}
LOG
(
INFO
)
<<
"ZeroCopy output: "
<<
DescribeZeroCopyTensor
(
*
output_tensor
);
ASSERT_TRUE
(
native_predictor
->
Run
(
native_inputs
.
front
(),
&
native_outputs
));
LOG
(
INFO
)
<<
"native output "
<<
DescribeTensor
(
native_outputs
.
front
());
int
output_size
{
0
};
// this is the number of elements not memory size
auto
*
zero_copy_data
=
output_tensor
->
data
<
float
>
(
&
place
,
&
output_size
);
auto
*
native_data
=
static_cast
<
float
*>
(
native_outputs
.
front
().
data
.
data
());
for
(
int
i
=
0
;
i
<
output_size
;
i
++
)
{
EXPECT_NEAR
(
zero_copy_data
[
i
],
native_data
[
i
],
1e-3
);
}
}
TEST
(
Analyzer_rnn1
,
ZeroCopyMultiThread
)
{
AnalysisConfig
config
;
SetConfig
(
&
config
);
config
.
SwitchUseFeedFetchOps
(
false
);
#define NEW_TENSOR(name__) \
auto name__##_tensor = predictor->GetInputTensor(#name__);
std
::
vector
<
std
::
unique_ptr
<
PaddlePredictor
>>
predictors
;
predictors
.
emplace_back
(
CreatePaddlePredictor
<
AnalysisConfig
>
(
config
));
for
(
int
tid
=
1
;
tid
<
FLAGS_num_threads
;
tid
++
)
{
predictors
.
emplace_back
(
predictors
.
front
()
->
Clone
());
}
double
total_time_of_threads
{
0
};
std
::
vector
<
std
::
thread
>
threads
;
for
(
int
tid
=
0
;
tid
<
FLAGS_num_threads
;
tid
++
)
{
threads
.
emplace_back
([
&
,
tid
]
{
auto
&
predictor
=
predictors
[
tid
];
NEW_TENSOR
(
data_lod_attention
);
NEW_TENSOR
(
cell_init
);
NEW_TENSOR
(
data
);
NEW_TENSOR
(
week
);
NEW_TENSOR
(
minute
);
NEW_TENSOR
(
hidden_init
);
// Prepare data for AnalysisPredictor
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
Timer
timer
;
double
total_time
{
0
};
for
(
int
i
=
0
;
i
<
FLAGS_repeat
;
i
++
)
{
PrepareZeroCopyInputs
(
data_lod_attention_tensor
.
get
(),
cell_init_tensor
.
get
(),
data_tensor
.
get
(),
hidden_init_tensor
.
get
(),
week_tensor
.
get
(),
minute_tensor
.
get
(),
&
data
,
FLAGS_batch_size
);
timer
.
tic
();
predictor
->
ZeroCopyRun
();
total_time
+=
timer
.
toc
();
}
total_time_of_threads
+=
total_time
;
LOG
(
INFO
)
<<
"thread time: "
<<
total_time
/
FLAGS_repeat
;
});
}
for
(
auto
&
t
:
threads
)
{
t
.
join
();
}
// Compare result of AnalysisConfig and AnalysisConfig + ZeroCopy
TEST
(
Analyzer_rnn1
,
compare_zero_copy
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
LOG
(
INFO
)
<<
"average time: "
<<
total_time_of_threads
/
FLAGS_num_threads
/
FLAGS_repeat
;
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
std
::
vector
<
std
::
string
>
outputs_name
;
outputs_name
.
emplace_back
(
"final_output.tmp_1"
);
CompareAnalysisAndZeroCopy
(
reinterpret_cast
<
PaddlePredictor
::
Config
*>
(
&
cfg
),
input_slots_all
,
outputs_name
);
}
}
// namespace inference
...
...
paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
浏览文件 @
e31f6e98
...
...
@@ -144,6 +144,9 @@ void SetConfig(AnalysisConfig *cfg, bool use_mkldnn = false) {
cfg
->
SwitchSpecifyInputNames
();
cfg
->
SwitchIrDebug
();
cfg
->
SetCpuMathLibraryNumThreads
(
FLAGS_paddle_num_threads
);
if
(
FLAGS_zero_copy
)
{
cfg
->
SwitchUseFeedFetchOps
(
false
);
}
if
(
use_mkldnn
)
{
cfg
->
EnableMKLDNN
();
}
...
...
@@ -184,10 +187,10 @@ TEST(Analyzer_seq_pool1, compare_determine) {
input_slots_all
);
}
void
analysis_fuse_statis
(
bool
use_zerocopy
)
{
// Check the fuse status
TEST
(
Analyzer_seq_pool1
,
fuse_statis
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
cfg
.
SwitchUseFeedFetchOps
(
!
use_zerocopy
);
int
num_ops
;
auto
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
>
(
cfg
);
auto
fuse_statis
=
GetFuseStatis
(
predictor
.
get
(),
&
num_ops
);
...
...
@@ -203,137 +206,17 @@ void analysis_fuse_statis(bool use_zerocopy) {
EXPECT_EQ
(
num_ops
,
171
);
}
// Check the fuse status
TEST
(
Analyzer_seq_pool1
,
fuse_statis
)
{
analysis_fuse_statis
(
false
);
}
void
PrepareZeroCopyInputs
(
const
std
::
unique_ptr
<
PaddlePredictor
>
&
predictor
,
std
::
vector
<
std
::
unique_ptr
<
ZeroCopyTensor
>>
*
inputs
)
{
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
// only feed one batch
const
auto
&
one_batch
=
data
.
NextBatch
();
inputs
->
clear
();
for
(
size_t
i
=
0
;
i
<
one_batch
.
size
();
++
i
)
{
auto
&
slot
=
one_batch
[
i
];
auto
tensor
=
predictor
->
GetInputTensor
(
slot
.
name
+
"_embed"
);
tensor
->
Reshape
(
slot
.
shape
);
tensor
->
SetLoD
({
slot
.
lod
});
ZeroCopyTensorAssignData
<
float
>
(
tensor
.
get
(),
slot
.
data
);
inputs
->
emplace_back
(
std
::
move
(
tensor
));
}
}
// return the output values
std
::
vector
<
float
>
zerocopy_profile
(
int
repeat_times
)
{
AnalysisConfig
config
;
SetConfig
(
&
config
);
config
.
SwitchUseFeedFetchOps
(
false
);
auto
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
>
(
config
);
std
::
vector
<
std
::
unique_ptr
<
ZeroCopyTensor
>>
inputs
;
PrepareZeroCopyInputs
(
predictor
,
&
inputs
);
auto
output_tensor
=
predictor
->
GetOutputTensor
(
out_var_name
);
Timer
timer
;
LOG
(
INFO
)
<<
"Warm up run..."
;
timer
.
tic
();
predictor
->
ZeroCopyRun
();
PrintTime
(
FLAGS_batch_size
,
1
,
1
,
0
,
timer
.
toc
(),
1
);
if
(
FLAGS_profile
)
{
paddle
::
platform
::
ResetProfiler
();
}
LOG
(
INFO
)
<<
"Run "
<<
repeat_times
<<
" times..."
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
repeat_times
;
i
++
)
{
predictor
->
ZeroCopyRun
();
}
PrintTime
(
FLAGS_batch_size
,
repeat_times
,
1
,
0
,
timer
.
toc
()
/
repeat_times
,
1
);
LOG
(
INFO
)
<<
"ZeroCopy output: "
<<
DescribeZeroCopyTensor
(
*
output_tensor
);
PaddlePlace
place
;
int
output_size
{
0
};
auto
*
pdata
=
output_tensor
->
data
<
float
>
(
&
place
,
&
output_size
);
std
::
vector
<
float
>
res
(
output_size
);
for
(
int
i
=
0
;
i
<
output_size
;
++
i
)
{
res
[
i
]
=
pdata
[
i
];
}
return
res
;
}
TEST
(
Analyzer_seq_pool1
,
zerocopy_profile
)
{
zerocopy_profile
(
FLAGS_repeat
);
}
TEST
(
Analyzer_seq_pool1
,
zerocopy_profile_threads
)
{
AnalysisConfig
config
;
SetConfig
(
&
config
);
config
.
SwitchUseFeedFetchOps
(
false
);
std
::
vector
<
std
::
unique_ptr
<
PaddlePredictor
>>
predictors
;
predictors
.
emplace_back
(
CreatePaddlePredictor
<
AnalysisConfig
>
(
config
));
for
(
int
tid
=
1
;
tid
<
FLAGS_num_threads
;
tid
++
)
{
predictors
.
emplace_back
(
predictors
.
front
()
->
Clone
());
}
double
total_time_of_threads
{
0
};
std
::
vector
<
std
::
thread
>
threads
;
for
(
int
tid
=
0
;
tid
<
FLAGS_num_threads
;
tid
++
)
{
threads
.
emplace_back
([
&
,
tid
]
{
auto
&
predictor
=
predictors
[
tid
];
std
::
vector
<
std
::
unique_ptr
<
ZeroCopyTensor
>>
inputs
;
PrepareZeroCopyInputs
(
predictor
,
&
inputs
);
auto
output_tensor
=
predictor
->
GetOutputTensor
(
out_var_name
);
Timer
timer
;
double
total_time
{
0
};
LOG
(
INFO
)
<<
"Warm up run..."
;
timer
.
tic
();
predictor
->
ZeroCopyRun
();
PrintTime
(
FLAGS_batch_size
,
1
,
FLAGS_num_threads
,
tid
,
timer
.
toc
(),
1
);
if
(
FLAGS_profile
)
{
paddle
::
platform
::
ResetProfiler
();
}
int
repeat_times
=
FLAGS_repeat
;
LOG
(
INFO
)
<<
"Run "
<<
repeat_times
<<
" times..."
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
repeat_times
;
i
++
)
{
predictor
->
ZeroCopyRun
();
}
total_time
+=
timer
.
toc
();
total_time_of_threads
+=
total_time
;
LOG
(
INFO
)
<<
"thread time: "
<<
total_time
/
repeat_times
;
});
}
for
(
auto
&
t
:
threads
)
{
t
.
join
();
}
LOG
(
INFO
)
<<
"average time: "
<<
total_time_of_threads
/
FLAGS_num_threads
/
FLAGS_repeat
;
}
TEST
(
Analyzer_seq_pool1
,
zerocopy_fuse_statis
)
{
analysis_fuse_statis
(
true
);
}
// Compare result of AnalysisConfig and AnalysisConfig + ZeroCopy
TEST
(
Analyzer_seq_pool1
,
compare_zero_copy
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
TEST
(
Analyzer_seq_pool1
,
zerocopy_compare_native
)
{
AnalysisConfig
config
;
SetConfig
(
&
config
);
config
.
SwitchUseFeedFetchOps
(
true
);
auto
predictor
=
CreatePaddlePredictor
<
NativeConfig
>
(
config
.
ToNativeConfig
());
std
::
vector
<
PaddleTensor
>
native_outputs
;
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
ASSERT_TRUE
(
predictor
->
Run
(
input_slots_all
[
0
],
&
native_outputs
));
EXPECT_EQ
(
native_outputs
.
size
(),
1UL
);
auto
zerocopy_output
=
zerocopy_profile
(
1
);
EXPECT_EQ
(
zerocopy_output
.
size
()
*
sizeof
(
float
),
native_outputs
.
front
().
data
.
length
());
auto
*
native_data
=
static_cast
<
float
*>
(
native_outputs
.
front
().
data
.
data
());
for
(
size_t
i
=
0
;
i
<
zerocopy_output
.
size
();
++
i
)
{
EXPECT_LT
(
std
::
fabs
((
zerocopy_output
[
i
]
-
native_data
[
i
])
/
zerocopy_output
[
i
]),
1e-3
);
}
std
::
vector
<
std
::
string
>
outputs_name
;
outputs_name
.
emplace_back
(
out_var_name
);
CompareAnalysisAndZeroCopy
(
reinterpret_cast
<
PaddlePredictor
::
Config
*>
(
&
cfg
),
input_slots_all
,
outputs_name
);
}
}
// namespace analysis
...
...
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @
e31f6e98
...
...
@@ -50,6 +50,7 @@ DEFINE_bool(use_analysis, true,
DEFINE_bool
(
record_benchmark
,
false
,
"Record benchmark after profiling the model"
);
DEFINE_double
(
accuracy
,
1e-3
,
"Result Accuracy."
);
DEFINE_bool
(
zero_copy
,
false
,
"Use ZeroCopy to speedup Feed/Fetch."
);
DECLARE_bool
(
profile
);
DECLARE_int32
(
paddle_num_threads
);
...
...
@@ -67,6 +68,7 @@ void PrintConfig(const PaddlePredictor::Config *config, bool use_analysis) {
LOG
(
INFO
)
<<
analysis_config
->
ToNativeConfig
();
}
// Compare result between two PaddleTensor
void
CompareResult
(
const
std
::
vector
<
PaddleTensor
>
&
outputs
,
const
std
::
vector
<
PaddleTensor
>
&
ref_outputs
)
{
EXPECT_GT
(
outputs
.
size
(),
0UL
);
...
...
@@ -108,6 +110,50 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
}
}
// Compare result between a PaddleTensor and a ZeroCopyTensor
void
CompareResult
(
const
std
::
vector
<
PaddleTensor
>
&
outputs
,
const
std
::
vector
<
ZeroCopyTensor
>
&
ref_outputs
)
{
EXPECT_GT
(
outputs
.
size
(),
0UL
);
EXPECT_EQ
(
outputs
.
size
(),
ref_outputs
.
size
());
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
i
++
)
{
auto
&
out
=
outputs
[
i
];
auto
&
ref_out
=
ref_outputs
[
i
];
size_t
size
=
VecReduceToInt
(
out
.
shape
);
EXPECT_GT
(
size
,
0UL
);
int
ref_size
=
0
;
// this is the number of elements not memory size
PaddlePlace
place
;
switch
(
out
.
dtype
)
{
case
PaddleDType
::
INT64
:
{
int64_t
*
pdata
=
static_cast
<
int64_t
*>
(
out
.
data
.
data
());
int64_t
*
pdata_ref
=
ref_out
.
data
<
int64_t
>
(
&
place
,
&
ref_size
);
EXPECT_EQ
(
size
,
ref_size
);
for
(
size_t
j
=
0
;
j
<
size
;
++
j
)
{
EXPECT_EQ
(
pdata_ref
[
j
],
pdata
[
j
]);
}
break
;
}
case
PaddleDType
::
FLOAT32
:
{
float
*
pdata
=
static_cast
<
float
*>
(
out
.
data
.
data
());
float
*
pdata_ref
=
ref_out
.
data
<
float
>
(
&
place
,
&
ref_size
);
EXPECT_EQ
(
size
,
ref_size
);
for
(
size_t
j
=
0
;
j
<
size
;
++
j
)
{
CHECK_LE
(
std
::
abs
(
pdata_ref
[
j
]
-
pdata
[
j
]),
FLAGS_accuracy
);
}
break
;
}
case
PaddleDType
::
INT32
:
{
int32_t
*
pdata
=
static_cast
<
int32_t
*>
(
out
.
data
.
data
());
int32_t
*
pdata_ref
=
ref_out
.
data
<
int32_t
>
(
&
place
,
&
ref_size
);
EXPECT_EQ
(
size
,
ref_size
);
for
(
size_t
j
=
0
;
j
<
size
;
++
j
)
{
EXPECT_EQ
(
pdata_ref
[
j
],
pdata
[
j
]);
}
break
;
}
}
}
}
std
::
unique_ptr
<
PaddlePredictor
>
CreateTestPredictor
(
const
PaddlePredictor
::
Config
*
config
,
bool
use_analysis
=
true
)
{
const
auto
*
analysis_config
=
...
...
@@ -205,61 +251,106 @@ void GetInputPerBatch(const std::vector<std::vector<int64_t>> &in,
}
}
void
TestOneThreadPrediction
(
const
PaddlePredictor
::
Config
*
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
,
bool
use_analysis
=
true
)
{
int
batch_size
=
FLAGS_batch_size
;
int
num_times
=
FLAGS_repeat
;
auto
predictor
=
CreateTestPredictor
(
config
,
use_analysis
);
void
ConvertPaddleTensorToZeroCopyTensor
(
PaddlePredictor
*
predictor
,
const
std
::
vector
<
PaddleTensor
>
&
inputs
)
{
for
(
size_t
i
=
0
;
i
<
inputs
.
size
();
i
++
)
{
auto
input
=
inputs
[
i
];
auto
tensor
=
predictor
->
GetInputTensor
(
input
.
name
);
tensor
->
Reshape
(
input
.
shape
);
tensor
->
SetLoD
({
input
.
lod
});
if
(
input
.
dtype
==
PaddleDType
::
INT64
)
{
ZeroCopyTensorAssignData
<
int64_t
>
(
tensor
.
get
(),
input
.
data
);
}
else
if
(
input
.
dtype
==
PaddleDType
::
FLOAT32
)
{
ZeroCopyTensorAssignData
<
float
>
(
tensor
.
get
(),
input
.
data
);
}
else
if
(
input
.
dtype
==
PaddleDType
::
INT32
)
{
ZeroCopyTensorAssignData
<
int32_t
>
(
tensor
.
get
(),
input
.
data
);
}
else
{
LOG
(
ERROR
)
<<
"unsupported feed type "
<<
input
.
dtype
;
}
}
}
// warmup run
LOG
(
INFO
)
<<
"Warm up run..."
;
{
Timer
warmup_timer
;
warmup_timer
.
tic
();
void
PredictionWarmUp
(
PaddlePredictor
*
predictor
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
,
int
num_threads
,
int
tid
)
{
int
batch_size
=
FLAGS_batch_size
;
LOG
(
INFO
)
<<
"Running thread "
<<
tid
<<
", warm up run..."
;
if
(
FLAGS_zero_copy
)
{
ConvertPaddleTensorToZeroCopyTensor
(
predictor
,
inputs
[
0
]);
}
Timer
warmup_timer
;
warmup_timer
.
tic
();
if
(
!
FLAGS_zero_copy
)
{
predictor
->
Run
(
inputs
[
0
],
outputs
,
batch_size
);
PrintTime
(
batch_size
,
1
,
1
,
0
,
warmup_timer
.
toc
(),
1
);
if
(
FLAGS_profile
)
{
paddle
::
platform
::
ResetProfiler
();
}
}
else
{
predictor
->
ZeroCopyRun
();
}
PrintTime
(
batch_size
,
1
,
num_threads
,
tid
,
warmup_timer
.
toc
(),
1
);
if
(
FLAGS_profile
)
{
paddle
::
platform
::
ResetProfiler
();
}
}
LOG
(
INFO
)
<<
"Run "
<<
num_times
<<
" times..."
;
{
Timer
run_timer
;
run_timer
.
tic
();
void
PredictionRun
(
PaddlePredictor
*
predictor
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
,
int
num_threads
,
int
tid
)
{
int
batch_size
=
FLAGS_batch_size
;
int
num_times
=
FLAGS_repeat
;
LOG
(
INFO
)
<<
"Thread "
<<
tid
<<
" run "
<<
num_times
<<
" times..."
;
Timer
run_timer
;
double
elapsed_time
=
0
;
#ifdef WITH_GPERFTOOLS
ProfilerStart
(
"paddle_inference.prof"
);
ProfilerStart
(
"paddle_inference.prof"
);
#endif
for
(
int
i
=
0
;
i
<
num_times
;
i
++
)
{
for
(
size_t
j
=
0
;
j
<
inputs
.
size
();
j
++
)
{
predictor
->
Run
(
inputs
[
j
],
outputs
,
batch_size
);
if
(
!
FLAGS_zero_copy
)
{
run_timer
.
tic
();
for
(
size_t
i
=
0
;
i
<
inputs
.
size
();
i
++
)
{
for
(
int
j
=
0
;
j
<
num_times
;
j
++
)
{
predictor
->
Run
(
inputs
[
i
],
outputs
,
batch_size
);
}
}
elapsed_time
=
run_timer
.
toc
();
}
else
{
for
(
size_t
i
=
0
;
i
<
inputs
.
size
();
i
++
)
{
ConvertPaddleTensorToZeroCopyTensor
(
predictor
,
inputs
[
i
]);
run_timer
.
tic
();
for
(
int
j
=
0
;
j
<
num_times
;
j
++
)
{
predictor
->
ZeroCopyRun
();
}
elapsed_time
+=
run_timer
.
toc
();
}
}
#ifdef WITH_GPERFTOOLS
ProfilerStop
();
ProfilerStop
();
#endif
double
latency
=
run_timer
.
toc
()
/
(
num_times
>
1
?
num_times
:
1
);
PrintTime
(
batch_size
,
num_times
,
1
,
0
,
latency
,
inputs
.
size
());
if
(
FLAGS_record_benchmark
)
{
Benchmark
benchmark
;
benchmark
.
SetName
(
FLAGS_model_name
);
benchmark
.
SetBatchSize
(
batch_size
);
benchmark
.
SetLatency
(
latency
);
benchmark
.
PersistToFile
(
"benchmark_record.txt"
);
}
PrintTime
(
batch_size
,
num_times
,
num_threads
,
tid
,
elapsed_time
/
num_times
,
inputs
.
size
());
if
(
FLAGS_record_benchmark
)
{
Benchmark
benchmark
;
benchmark
.
SetName
(
FLAGS_model_name
);
benchmark
.
SetBatchSize
(
batch_size
);
benchmark
.
SetLatency
(
elapsed_time
/
num_times
);
benchmark
.
PersistToFile
(
"benchmark_record.txt"
);
}
}
void
TestOneThreadPrediction
(
const
PaddlePredictor
::
Config
*
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
,
bool
use_analysis
=
true
)
{
auto
predictor
=
CreateTestPredictor
(
config
,
use_analysis
);
PredictionWarmUp
(
predictor
.
get
(),
inputs
,
outputs
,
1
,
0
);
PredictionRun
(
predictor
.
get
(),
inputs
,
outputs
,
1
,
0
);
}
void
TestMultiThreadPrediction
(
const
PaddlePredictor
::
Config
*
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
,
int
num_threads
,
bool
use_analysis
=
true
)
{
int
batch_size
=
FLAGS_batch_size
;
int
num_times
=
FLAGS_repeat
;
std
::
vector
<
std
::
thread
>
threads
;
std
::
vector
<
std
::
unique_ptr
<
PaddlePredictor
>>
predictors
;
predictors
.
emplace_back
(
CreateTestPredictor
(
config
,
use_analysis
));
...
...
@@ -267,7 +358,6 @@ void TestMultiThreadPrediction(
predictors
.
emplace_back
(
predictors
.
front
()
->
Clone
());
}
size_t
total_time
{
0
};
for
(
int
tid
=
0
;
tid
<
num_threads
;
++
tid
)
{
threads
.
emplace_back
([
&
,
tid
]()
{
// Each thread should have local inputs and outputs.
...
...
@@ -280,34 +370,8 @@ void TestMultiThreadPrediction(
->
SetMkldnnThreadID
(
static_cast
<
int
>
(
tid
)
+
1
);
}
#endif
// warmup run
LOG
(
INFO
)
<<
"Running thread "
<<
tid
<<
", warm up run..."
;
{
Timer
warmup_timer
;
warmup_timer
.
tic
();
predictor
->
Run
(
inputs
[
0
],
outputs
,
batch_size
);
PrintTime
(
batch_size
,
1
,
num_threads
,
tid
,
warmup_timer
.
toc
(),
1
);
if
(
FLAGS_profile
)
{
paddle
::
platform
::
ResetProfiler
();
}
}
LOG
(
INFO
)
<<
"Thread "
<<
tid
<<
" run "
<<
num_times
<<
" times..."
;
{
Timer
timer
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
num_times
;
i
++
)
{
for
(
const
auto
&
input
:
inputs
)
{
ASSERT_TRUE
(
predictor
->
Run
(
input
,
&
outputs_tid
));
}
}
auto
time
=
timer
.
toc
();
total_time
+=
time
;
PrintTime
(
batch_size
,
num_times
,
num_threads
,
tid
,
time
/
num_times
,
inputs
.
size
());
}
PredictionWarmUp
(
predictor
.
get
(),
inputs
,
outputs
,
num_threads
,
tid
);
PredictionRun
(
predictor
.
get
(),
inputs
,
outputs
,
num_threads
,
tid
);
});
}
for
(
int
i
=
0
;
i
<
num_threads
;
++
i
)
{
...
...
@@ -367,6 +431,31 @@ void CompareNativeAndAnalysis(
CompareResult
(
analysis_outputs
,
native_outputs
);
}
void
CompareAnalysisAndZeroCopy
(
PaddlePredictor
::
Config
*
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
const
std
::
vector
<
std
::
string
>
&
outputs_name
)
{
int
batch_size
=
FLAGS_batch_size
;
// analysis
std
::
vector
<
PaddleTensor
>
analysis_outputs
;
auto
predictor
=
CreateTestPredictor
(
config
,
true
);
predictor
->
Run
(
inputs
[
0
],
&
analysis_outputs
,
batch_size
);
// analysis + zero_copy
std
::
vector
<
ZeroCopyTensor
>
zerocopy_outputs
;
reinterpret_cast
<
AnalysisConfig
*>
(
config
)
->
SwitchUseFeedFetchOps
(
false
);
predictor
=
CreateTestPredictor
(
config
,
true
);
ConvertPaddleTensorToZeroCopyTensor
(
predictor
.
get
(),
inputs
[
0
]);
predictor
->
ZeroCopyRun
();
for
(
size_t
i
=
0
;
i
<
outputs_name
.
size
();
i
++
)
{
ZeroCopyTensor
zerocopy_output
=
*
predictor
->
GetOutputTensor
(
outputs_name
[
i
]).
get
();
zerocopy_outputs
.
emplace_back
(
zerocopy_output
);
LOG
(
INFO
)
<<
"ZeroCopy output: "
<<
DescribeZeroCopyTensor
(
zerocopy_output
);
}
// compare
CompareResult
(
analysis_outputs
,
zerocopy_outputs
);
}
template
<
typename
T
>
std
::
string
LoDTensorSummary
(
const
framework
::
LoDTensor
&
tensor
)
{
std
::
stringstream
ss
;
...
...
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