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4fb63838
编写于
7月 29, 2019
作者:
W
wangguibao
浏览文件
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电子邮件补丁
差异文件
CTR prediction serving
上级
d8b05642
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
110 addition
and
19 deletion
+110
-19
demo-serving/op/ctr_prediction_op.cpp
demo-serving/op/ctr_prediction_op.cpp
+104
-18
demo-serving/proto/CMakeLists.txt
demo-serving/proto/CMakeLists.txt
+1
-0
demo-serving/proto/ctr_prediction.proto
demo-serving/proto/ctr_prediction.proto
+5
-1
未找到文件。
demo-serving/op/ctr_prediction_op.cpp
浏览文件 @
4fb63838
...
...
@@ -14,6 +14,7 @@
#include "demo-serving/op/ctr_prediction_op.h"
#include <algorithm>
#include <string>
#include "predictor/framework/infer.h"
#include "predictor/framework/memory.h"
...
...
@@ -27,31 +28,94 @@ using baidu::paddle_serving::predictor::ctr_prediction::Response;
using
baidu
::
paddle_serving
::
predictor
::
ctr_prediction
::
CTRReqInstance
;
using
baidu
::
paddle_serving
::
predictor
::
ctr_prediction
::
Request
;
const
int
CTR_PREDICTION_INPUT_SLOTS
=
27
;
// Total 26 sparse input + 1 dense input
const
int
CTR_PREDICTION_SPARSE_SLOTS
=
26
;
// First 26: sparse input
const
int
CTR_PREDICTION_DENSE_SLOT
=
26
;
// Last 1: dense input
// Total 26 sparse input + 1 dense input
const
int
CTR_PREDICTION_INPUT_SLOTS
=
27
;
// First 26: sparse input
const
int
CTR_PREDICTION_SPARSE_SLOTS
=
26
;
// Last 1: dense input
const
int
CTR_PREDICTION_DENSE_SLOT_ID
=
26
;
const
int
CTR_PREDICTION_DENSE_DIM
=
13
;
const
int
CTR_PREDICTION_EMBEDDING_SIZE
=
10
;
#if 1
struct
CubeValue
{
int
error
;
std
::
string
buff
;
};
#endif
void
fill_response_with_message
(
Response
*
response
,
int
err_code
,
std
::
string
err_msg
)
{
if
(
response
==
NULL
)
{
LOG
(
ERROR
)
<<
"response is NULL"
;
return
;
}
response
->
set_err_code
(
err_code
);
response
->
set_err_msg
(
err_msg
);
return
;
}
int
CTRPredictionOp
::
inference
()
{
const
Request
*
req
=
dynamic_cast
<
const
Request
*>
(
get_request_message
());
TensorVector
*
in
=
butil
::
get_object
<
TensorVector
>
();
Response
*
res
=
mutable_data
<
Response
>
();
uint32_t
sample_size
=
req
->
instances_size
();
if
(
sample_size
<=
0
)
{
LOG
(
WARNING
)
<<
"No instances need to inference!"
;
fill_response_with_message
(
res
,
-
1
,
"Sample size invalid"
);
return
-
1
;
}
paddle
::
PaddleTensor
lod_tensors
[
CTR_PREDICTION_INPUT_SLOTS
];
for
(
int
i
=
0
;
i
<
CTR_PREDICTION_
INPUT
_SLOTS
;
++
i
)
{
for
(
int
i
=
0
;
i
<
CTR_PREDICTION_
SPARSE
_SLOTS
;
++
i
)
{
lod_tensors
[
i
].
dtype
=
paddle
::
PaddleDType
::
FLOAT32
;
std
::
vector
<
std
::
vector
<
size_t
>>
&
lod
=
lod_tensors
[
i
].
lod
;
lod
.
resize
(
1
);
lod
[
0
].
push_back
(
0
);
}
lot_tensors
[
CTR_PREDICTION_SPARSE_SLOTS
].
dtype
=
paddle
::
PaddleDType
::
INT64
;
// Query cube API for sparse embeddings
std
::
vector
<
int64_t
>
keys
;
std
::
vector
<
CubeValue
>
values
;
for
(
uint32_t
si
=
0
;
si
<
sample_size
;
++
si
)
{
const
CTRReqInstance
&
req_instance
=
req
->
instances
(
si
);
if
(
req_instance
.
sparse_ids_size
()
!=
CTR_PREDICTION_DENSE_DIM
)
{
std
::
ostringstream
iss
;
iss
<<
"dense input size != "
<<
CTR_PREDICTION_DENSE_DIM
;
fill_response_with_message
(
res
,
-
1
,
iss
.
str
());
return
-
1
;
}
for
(
int
i
=
0
;
i
<
req_instance
.
sparse_ids_size
();
++
i
)
{
keys
.
push_back
(
req_instance
.
sparse_ids
(
i
));
}
}
#if 0
mCube::CubeAPI* cube = CubeAPI::instance();
int ret = cube->seek(keys, values);
if (ret != 0) {
fill_response_with_message(res, -1, "Query cube for embeddings error");
LOG(ERROR) << "Query cube for embeddings error";
return -1;
}
#else
float
buff
[
CTR_PREDICTION_EMBEDDING_SIZE
]
=
{
0.01
,
0.02
,
0.03
,
0.04
,
0.05
,
0.06
,
0.07
,
0.08
,
0.09
,
0.00
};
for
(
int
i
=
0
;
i
<
keys
.
size
();
++
i
)
{
values
[
i
].
error
=
0
;
values
[
i
].
buff
=
std
::
string
(
reinterpret_cast
<
char
*>
(
buff
),
sizeof
(
buff
));
}
#endif
// Sparse embeddings
for
(
int
i
=
0
;
i
<
CTR_PREDICTION_SPARSE_SLOTS
;
++
i
)
{
paddle
::
PaddleTensor
lod_tensor
=
lod_tensors
[
i
];
std
::
vector
<
std
::
vector
<
size_t
>>
&
lod
=
lod_tensor
.
lod
;
...
...
@@ -61,31 +125,48 @@ int CTRPredictionOp::inference() {
lod
[
0
].
push_back
(
lod
[
0
].
back
()
+
1
);
}
lod_tensor
.
shape
=
{
lod
[
0
].
back
(),
1
};
lod_tensor
.
data
.
Resize
(
lod
[
0
].
back
()
*
sizeof
(
int64_t
));
lod_tensor
.
shape
=
{
lod
[
0
].
back
(),
CTR_PREDICTION_EMBEDDING_SIZE
};
lod_tensor
.
data
.
Resize
(
lod
[
0
].
back
()
*
sizeof
(
float
)
*
CTR_PREDICTION_EMBEDDING_SIZE
);
int
offset
=
0
;
for
(
uint32_t
si
=
0
;
si
<
sample_size
;
++
si
)
{
int64_t
*
data_ptr
=
static_cast
<
int64_t
*>
(
lod_tensor
.
data
.
data
())
+
offset
;
float
*
data_ptr
=
static_cast
<
float
*>
(
lod_tensor
.
data
.
data
())
+
offset
;
const
CTRReqInstance
&
req_instance
=
req
->
instances
(
si
);
int
id_count
=
1
;
memcpy
(
data_ptr
,
&
req_instance
.
sparse_ids
().
data
()[
i
],
sizeof
(
int64_t
));
offset
+=
1
;
int
idx
=
si
*
CTR_PREDICTION_SPARSE_SLOTS
+
i
;
if
(
values
[
idx
].
buff
.
size
()
!=
sizeof
(
float
)
*
CTR_PREDICTION_EMBEDDING_SIZE
)
{
LOG
(
ERROR
)
<<
"Embedding vector size not expected"
;
fill_response_with_message
(
res
,
-
1
,
"Embedding vector size not expected"
);
return
-
1
;
}
memcpy
(
data_ptr
,
values
[
idx
].
buff
.
data
(),
values
[
idx
].
buff
.
size
());
offset
+=
CTR_PREDICTION_EMBEDDING_SIZE
;
}
in
->
push_back
(
lod_tensor
);
}
paddle
::
PaddleTensor
lod_tensor
=
lod_tensors
[
CTR_PREDICTION_DENSE_SLOT
];
// Dense features
paddle
::
PaddleTensor
lod_tensor
=
lod_tensors
[
CTR_PREDICTION_DENSE_SLOT_ID
];
lod_tensor
.
dtype
=
paddle
::
PaddleDType
::
INT64
;
std
::
vector
<
std
::
vector
<
size_t
>>
&
lod
=
lod_tensor
.
lod
;
for
(
uint32_t
si
=
0
;
si
<
sample_size
;
++
si
)
{
const
CTRReqInstance
&
req_instance
=
req
->
instances
(
si
);
if
(
req_instance
.
dense_ids_size
()
!=
CTR_PREDICTION_DENSE_DIM
)
{
std
::
ostringstream
iss
;
iss
<<
"dense input size != "
<<
CTR_PREDICTION_DENSE_DIM
;
fill_response_with_message
(
res
,
-
1
,
iss
.
str
());
return
-
1
;
}
lod
[
0
].
push_back
(
lod
[
0
].
back
()
+
req_instance
.
dense_ids_size
());
}
lod_tensor
.
shape
=
{
lod
[
0
].
back
(),
1
};
lod_tensor
.
shape
=
{
lod
[
0
].
back
(),
CTR_PREDICTION_DENSE_DIM
};
lod_tensor
.
data
.
Resize
(
lod
[
0
].
back
()
*
sizeof
(
int64_t
));
int
offset
=
0
;
...
...
@@ -94,7 +175,7 @@ int CTRPredictionOp::inference() {
const
CTRReqInstance
&
req_instance
=
req
->
instances
(
si
);
int
id_count
=
req_instance
.
dense_ids_size
();
memcpy
(
data_ptr
,
req_instance
.
ids
().
data
(),
req_instance
.
dense_
ids
().
data
(),
sizeof
(
int64_t
)
*
req_instance
.
dense_ids_size
());
offset
+=
req_instance
.
dense_ids_size
();
}
...
...
@@ -104,6 +185,7 @@ int CTRPredictionOp::inference() {
TensorVector
*
out
=
butil
::
get_object
<
TensorVector
>
();
if
(
!
out
)
{
LOG
(
ERROR
)
<<
"Failed get tls output object"
;
fill_response_with_message
(
res
,
-
1
,
"Failed get thread local resource"
);
return
-
1
;
}
...
...
@@ -112,22 +194,23 @@ int CTRPredictionOp::inference() {
CTR_PREDICTION_MODEL_NAME
,
in
,
out
,
sample_size
))
{
LOG
(
ERROR
)
<<
"Failed do infer in fluid model: "
<<
CTR_PREDICTION_MODEL_NAME
;
fill_response_with_message
(
res
,
-
1
,
"Failed do infer in fluid model"
);
return
-
1
;
}
if
(
out
->
size
()
!=
in
->
size
())
{
LOG
(
ERROR
)
<<
"Output tensor size not equal that of input"
;
fill_response_with_message
(
res
,
-
1
,
"Output size != input size"
);
return
-
1
;
}
Response
*
res
=
mutable_data
<
Response
>
();
for
(
size_t
i
=
0
;
i
<
out
->
size
();
++
i
)
{
int
dim1
=
out
->
at
(
i
).
shape
[
0
];
int
dim2
=
out
->
at
(
i
).
shape
[
1
];
if
(
out
->
at
(
i
).
dtype
!=
paddle
::
PaddleDType
::
FLOAT32
)
{
LOG
(
ERROR
)
<<
"Expected data type float"
;
fill_response_with_message
(
res
,
-
1
,
"Expected data type float"
);
return
-
1
;
}
...
...
@@ -150,6 +233,9 @@ int CTRPredictionOp::inference() {
}
out
->
clear
();
butil
::
return_object
<
TensorVector
>
(
out
);
res
->
set_err_code
(
0
);
res
->
set_err_msg
(
std
::
string
(
""
));
return
0
;
}
...
...
demo-serving/proto/CMakeLists.txt
浏览文件 @
4fb63838
...
...
@@ -6,6 +6,7 @@ LIST(APPEND protofiles
${
CMAKE_CURRENT_LIST_DIR
}
/echo_kvdb_service.proto
${
CMAKE_CURRENT_LIST_DIR
}
/int64tensor_service.proto
${
CMAKE_CURRENT_LIST_DIR
}
/text_classification.proto
${
CMAKE_CURRENT_LIST_DIR
}
/ctr_prediction.proto
)
PROTOBUF_GENERATE_SERVING_CPP
(
TRUE PROTO_SRCS PROTO_HDRS
${
protofiles
}
)
...
...
demo-serving/proto/ctr_prediction.proto
浏览文件 @
4fb63838
...
...
@@ -31,7 +31,11 @@ message CTRResInstance {
required
float
prob1
=
2
;
};
message
Response
{
repeated
CTRResInstance
predictions
=
1
;
};
message
Response
{
repeated
CTRResInstance
predictions
=
1
;
required
int64
err_code
=
2
;
optional
string
err_msg
=
3
;
};
service
CTRPredictionService
{
rpc
inference
(
Request
)
returns
(
Response
);
...
...
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