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体验新版 GitCode,发现更多精彩内容 >>
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9c7fde45
编写于
8月 23, 2018
作者:
L
luotao1
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
enhance test_analyzer to profile ditu inference demo
上级
decda738
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
32 addition
and
28 deletion
+32
-28
paddle/fluid/framework/ir/graph_pattern_detecter_tester.cc
paddle/fluid/framework/ir/graph_pattern_detecter_tester.cc
+2
-2
paddle/fluid/framework/selected_rows.cc
paddle/fluid/framework/selected_rows.cc
+2
-2
paddle/fluid/inference/analysis/analyzer_tester.cc
paddle/fluid/inference/analysis/analyzer_tester.cc
+27
-21
paddle/fluid/operators/sampling_id_op.h
paddle/fluid/operators/sampling_id_op.h
+1
-1
paddle/scripts/paddle_build.sh
paddle/scripts/paddle_build.sh
+0
-2
未找到文件。
paddle/fluid/framework/ir/graph_pattern_detecter_tester.cc
浏览文件 @
9c7fde45
...
...
@@ -163,8 +163,8 @@ TEST(GraphPatternDetecter, MultiSubgraph) {
// 3. Detect op2 -> var2 -> op4
// 4. Detect op2 -> var3 -> op5
// But 2 and 3 and 4 overlapped, so keep 2, so the final choices are 1 and 2
ASSERT_GE
(
count
,
1
UL
);
ASSERT_LE
(
count
,
2
UL
);
ASSERT_GE
(
count
,
1
);
ASSERT_LE
(
count
,
2
);
}
}
// namespace ir
...
...
paddle/fluid/framework/selected_rows.cc
浏览文件 @
9c7fde45
...
...
@@ -139,7 +139,7 @@ int64_t SelectedRows::AutoGrownIndex(int64_t key, bool auto_grown) {
}
auto
write_iter
=
id_to_index_
.
find
(
key
);
if
(
write_iter
==
id_to_index_
.
end
())
{
size_
t
row_num
=
rows_
.
size
();
in
t
row_num
=
rows_
.
size
();
if
(
row_num
==
value_
->
dims
()[
0
])
{
rwlock_
->
UNLock
();
PADDLE_THROW
(
"selected rows is full, then length exceed %d"
,
row_num
);
...
...
@@ -182,7 +182,7 @@ void SelectedRows::Get(const framework::Tensor& ids, framework::Tensor* value,
PADDLE_ENFORCE_EQ
(
value_width
,
value
->
numel
()
/
value
->
dims
()[
0
],
"output tensor should have the same shape with table "
"except the dims[0]."
);
for
(
size_
t
i
=
0
;
i
<
ids
.
numel
();
++
i
)
{
for
(
in
t
i
=
0
;
i
<
ids
.
numel
();
++
i
)
{
int64_t
index
=
AutoGrownIndex
(
ids
.
data
<
int64_t
>
()[
i
],
auto_grown
);
framework
::
VisitDataType
(
framework
::
ToDataType
(
value_
->
type
()),
...
...
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @
9c7fde45
...
...
@@ -23,6 +23,8 @@
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."
);
namespace
paddle
{
namespace
inference
{
...
...
@@ -92,7 +94,7 @@ struct DataRecord {
size_t
batch_iter
{
0
};
size_t
batch_size
{
1
};
DataRecord
()
=
default
;
DataRecord
(
const
std
::
string
&
path
,
int
batch_size
=
1
)
explicit
DataRecord
(
const
std
::
string
&
path
,
int
batch_size
=
1
)
:
batch_size
(
batch_size
)
{
Load
(
path
);
}
...
...
@@ -165,7 +167,6 @@ struct DataRecord {
};
void
PrepareInputs
(
std
::
vector
<
PaddleTensor
>
*
input_slots
,
DataRecord
*
data
,
int
batch_size
)
{
// DataRecord data(FLAGS_datapath, batch_size);
PaddleTensor
lod_attention_tensor
,
init_zero_tensor
,
lod_tensor_tensor
,
week_tensor
,
minute_tensor
;
lod_attention_tensor
.
name
=
"data_lod_attention"
;
...
...
@@ -174,28 +175,33 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
week_tensor
.
name
=
"week"
;
minute_tensor
.
name
=
"minute"
;
auto
one_batch
=
data
->
NextBatch
();
// clang-format off
std
::
vector
<
int
>
rnn_link_data_shape
({
static_cast
<
int
>
(
one_batch
.
rnn_link_data
.
size
()),
static_cast
<
int
>
(
one_batch
.
rnn_link_data
.
front
().
size
())});
std
::
vector
<
int
>
rnn_link_data_shape
(
{
static_cast
<
int
>
(
one_batch
.
rnn_link_data
.
size
()),
static_cast
<
int
>
(
one_batch
.
rnn_link_data
.
front
().
size
())});
lod_attention_tensor
.
shape
.
assign
({
1
,
2
});
lod_attention_tensor
.
lod
.
assign
({
one_batch
.
lod1
,
one_batch
.
lod2
});
init_zero_tensor
.
shape
.
assign
({
batch_size
,
15
});
init_zero_tensor
.
lod
.
assign
({
one_batch
.
lod3
});
lod_tensor_tensor
.
shape
=
rnn_link_data_shape
;
lod_tensor_tensor
.
lod
.
assign
({
one_batch
.
lod1
});
week_tensor
.
shape
.
assign
({(
int
)
one_batch
.
rnn_week_datas
.
size
(),
(
int
)
one_batch
.
rnn_week_datas
.
front
().
size
()});
// clang-format off
week_tensor
.
shape
.
assign
(
{
static_cast
<
int
>
(
one_batch
.
rnn_week_datas
.
size
()),
static_cast
<
int
>
(
one_batch
.
rnn_week_datas
.
front
().
size
())});
week_tensor
.
lod
.
assign
({
one_batch
.
lod3
});
minute_tensor
.
shape
.
assign
({(
int
)
one_batch
.
rnn_minute_datas
.
size
(),
(
int
)
one_batch
.
rnn_minute_datas
.
front
().
size
()});
minute_tensor
.
shape
.
assign
(
{
static_cast
<
int
>
(
one_batch
.
rnn_minute_datas
.
size
()),
static_cast
<
int
>
(
one_batch
.
rnn_minute_datas
.
front
().
size
())});
minute_tensor
.
lod
.
assign
({
one_batch
.
lod3
});
// clang-format on
// assign data
TensorAssignData
(
&
lod_attention_tensor
,
std
::
vector
<
std
::
vector
<
float
>>
({{
0
,
0
}}));
TensorAssignData
(
&
lod_attention_tensor
,
std
::
vector
<
std
::
vector
<
float
>>
({{
0
,
0
}}));
std
::
vector
<
float
>
tmp_zeros
(
batch_size
*
15
,
0.
);
TensorAssignData
(
&
init_zero_tensor
,
{
tmp_zeros
});
TensorAssignData
(
&
lod_tensor_tensor
,
one_batch
.
rnn_link_data
);
TensorAssignData
(
&
week_tensor
,
one_batch
.
rnn_week_datas
);
TensorAssignData
(
&
minute_tensor
,
one_batch
.
rnn_minute_datas
);
// clang-format on
// Set inputs.
auto
init_zero_tensor1
=
init_zero_tensor
;
init_zero_tensor1
.
name
=
"hidden_init"
;
...
...
@@ -231,12 +237,9 @@ std::string DescribeTensor(const PaddleTensor &tensor) {
os
<<
"
\n
"
;
os
<<
" - data: "
;
// clang-format off
int
dim
=
std
::
accumulate
(
tensor
.
shape
.
begin
(),
tensor
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
// clang-format on
for
(
size_t
i
=
0
;
i
<
dim
;
i
++
)
{
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'
;
...
...
@@ -300,13 +303,16 @@ void TestDituRNNPrediction(const std::string &model_path,
for
(
int
i
=
0
;
i
<
num_times
;
i
++
)
{
predictor
->
Run
(
input_slots
,
&
outputs
);
}
LOG
(
INFO
)
<<
"time/batch: "
<<
timer
.
toc
()
/
num_times
;
LOG
(
INFO
)
<<
"===========profile result==========="
;
LOG
(
INFO
)
<<
"batch_size: "
<<
batch_size
<<
", repeat: "
<<
num_times
<<
", latency: "
<<
timer
.
toc
()
/
num_times
<<
"ms"
;
LOG
(
INFO
)
<<
"====================================="
;
for
(
auto
&
out
:
outputs
)
{
size_t
size
=
std
::
accumulate
(
out
.
shape
.
begin
(),
out
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
float
*
data
=
static_cast
<
float
*>
(
out
.
data
.
data
());
for
(
in
t
i
=
0
;
for
(
size_
t
i
=
0
;
i
<
std
::
min
(
sizeof
(
ditu_rnn_target_data
)
/
sizeof
(
float
),
size
);
i
++
)
{
EXPECT_NEAR
(
data
[
i
],
ditu_rnn_target_data
[
i
],
1e-3
);
...
...
@@ -336,7 +342,7 @@ TEST(Analyzer, SupportIRPass) {
// Directly infer with the original model.
TEST
(
Analyzer
,
DituRNN_without_analysis
)
{
TestDituRNNPrediction
(
FLAGS_infer_ditu_rnn_model
,
FLAGS_infer_ditu_rnn_data
,
10
,
false
,
false
);
FLAGS_batch_size
,
false
,
false
,
FLAGS_repeat
);
}
// Inference with the original model with the analysis turned on, the analysis
...
...
@@ -344,14 +350,14 @@ TEST(Analyzer, DituRNN_without_analysis) {
TEST
(
Analyzer
,
DituRNN_with_analysis
)
{
LOG
(
INFO
)
<<
"ditu rnn with analysis"
;
TestDituRNNPrediction
(
FLAGS_infer_ditu_rnn_model
,
FLAGS_infer_ditu_rnn_data
,
10
,
true
,
false
,
1
);
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
,
10
,
true
,
true
,
1
);
FLAGS_batch_size
,
true
,
true
,
FLAGS_repeat
);
}
}
// namespace analysis
...
...
paddle/fluid/operators/sampling_id_op.h
浏览文件 @
9c7fde45
...
...
@@ -54,7 +54,7 @@ class SamplingIdKernel : public framework::OpKernel<T> {
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"max"
)));
std
::
vector
<
T
>
ids
(
batch_size
);
for
(
size_
t
i
=
0
;
i
<
batch_size
;
++
i
)
{
for
(
in
t
i
=
0
;
i
<
batch_size
;
++
i
)
{
T
r
=
dist
(
engine
);
int
idx
=
width
-
1
;
for
(
int
j
=
0
;
j
<
width
;
++
j
)
{
...
...
paddle/scripts/paddle_build.sh
浏览文件 @
9c7fde45
...
...
@@ -116,7 +116,6 @@ function cmake_gen() {
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
-DWITH_CONTRIB=
${
WITH_CONTRIB
:-
ON
}
-DWITH_ANAKIN=
${
WITH_ANAKIN
:-
OFF
}
-DWITH_INFERENCE_DEMO=
${
WITH_INFERENCE_DEMO
:-
ON
}
-DPY_VERSION=
${
PY_VERSION
:-
2
.7
}
========================================
EOF
...
...
@@ -146,7 +145,6 @@ EOF
-DCMAKE_EXPORT_COMPILE_COMMANDS
=
ON
\
-DWITH_CONTRIB
=
${
WITH_CONTRIB
:-
ON
}
\
-DWITH_ANAKIN
=
${
WITH_ANAKIN
:-
OFF
}
\
-DWITH_INFERENCE_DEMO
=
${
WITH_INFERENCE_DEMO
:-
ON
}
\
-DPY_VERSION
=
${
PY_VERSION
:-
2
.7
}
}
...
...
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