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ee5a9489
编写于
9月 02, 2021
作者:
S
ShiningZhang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
server support dtype uint8&int8
上级
52f2c635
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
306 addition
and
71 deletion
+306
-71
core/general-client/src/client.cpp
core/general-client/src/client.cpp
+17
-1
core/general-client/src/general_model.cpp
core/general-client/src/general_model.cpp
+17
-1
core/general-server/op/general_reader_op.cpp
core/general-server/op/general_reader_op.cpp
+59
-40
core/general-server/op/general_response_op.cpp
core/general-server/op/general_response_op.cpp
+18
-1
core/general-server/proto/general_model_service.proto
core/general-server/proto/general_model_service.proto
+69
-14
core/predictor/framework/infer.h
core/predictor/framework/infer.h
+57
-0
core/sdk-cpp/proto/general_model_service.proto
core/sdk-cpp/proto/general_model_service.proto
+69
-14
未找到文件。
core/general-client/src/client.cpp
浏览文件 @
ee5a9489
...
...
@@ -23,7 +23,23 @@ using configure::GeneralModelConfig;
using
baidu
::
paddle_serving
::
predictor
::
general_model
::
Request
;
using
baidu
::
paddle_serving
::
predictor
::
general_model
::
Response
;
using
baidu
::
paddle_serving
::
predictor
::
general_model
::
Tensor
;
enum
ProtoDataType
{
P_INT64
,
P_FLOAT32
,
P_INT32
,
P_STRING
};
// paddle inference 2.1 support: FLOAT32, INT64, INT32, UINT8
// will support: INT8, FLOAT16
enum
ProtoDataType
{
P_INT64
=
0
,
P_FLOAT32
,
P_INT32
,
P_FP64
,
P_INT16
,
P_FP16
,
P_BF16
,
P_UINT8
,
P_INT8
,
P_BOOL
,
P_COMPLEX64
,
P_COMPLEX128
,
P_STRING
,
};
int
ServingClient
::
init
(
const
std
::
vector
<
std
::
string
>&
client_conf
,
const
std
::
string
server_port
)
{
...
...
core/general-client/src/general_model.cpp
浏览文件 @
ee5a9489
...
...
@@ -25,7 +25,23 @@ using baidu::paddle_serving::Timer;
using
baidu
::
paddle_serving
::
predictor
::
general_model
::
Request
;
using
baidu
::
paddle_serving
::
predictor
::
general_model
::
Response
;
using
baidu
::
paddle_serving
::
predictor
::
general_model
::
Tensor
;
enum
ProtoDataType
{
P_INT64
,
P_FLOAT32
,
P_INT32
,
P_STRING
};
// paddle inference 2.1 support: FLOAT32, INT64, INT32, UINT8
// will support: INT8, FLOAT16
enum
ProtoDataType
{
P_INT64
=
0
,
P_FLOAT32
,
P_INT32
,
P_FP64
,
P_INT16
,
P_FP16
,
P_BF16
,
P_UINT8
,
P_INT8
,
P_BOOL
,
P_COMPLEX64
,
P_COMPLEX128
,
P_STRING
,
};
std
::
once_flag
gflags_init_flag
;
namespace
py
=
pybind11
;
...
...
core/general-server/op/general_reader_op.cpp
浏览文件 @
ee5a9489
...
...
@@ -31,7 +31,23 @@ using baidu::paddle_serving::predictor::MempoolWrapper;
using
baidu
::
paddle_serving
::
predictor
::
general_model
::
Tensor
;
using
baidu
::
paddle_serving
::
predictor
::
general_model
::
Request
;
using
baidu
::
paddle_serving
::
predictor
::
PaddleGeneralModelConfig
;
enum
ProtoDataType
{
P_INT64
,
P_FLOAT32
,
P_INT32
,
P_STRING
};
// paddle inference 2.1 support: FLOAT32, INT64, INT32, UINT8, INT8
// will support: FLOAT16
enum
ProtoDataType
{
P_INT64
=
0
,
P_FLOAT32
,
P_INT32
,
P_FP64
,
P_INT16
,
P_FP16
,
P_BF16
,
P_UINT8
,
P_INT8
,
P_BOOL
,
P_COMPLEX64
,
P_COMPLEX128
,
P_STRING
=
20
,
};
int
GeneralReaderOp
::
inference
()
{
// read request from client
...
...
@@ -78,6 +94,7 @@ int GeneralReaderOp::inference() {
int64_t
elem_type
=
0
;
int64_t
elem_size
=
0
;
int64_t
databuf_size
=
0
;
const
void
*
src_ptr
=
nullptr
;
for
(
int
i
=
0
;
i
<
var_num
;
++
i
)
{
paddle
::
PaddleTensor
paddleTensor
;
const
Tensor
&
tensor
=
req
->
tensor
(
i
);
...
...
@@ -86,19 +103,38 @@ int GeneralReaderOp::inference() {
elem_size
=
0
;
databuf_size
=
0
;
elem_type
=
tensor
.
elem_type
();
VLOG
(
2
)
<<
"var["
<<
i
<<
"] has elem type: "
<<
elem_type
;
src_ptr
=
nullptr
;
if
(
elem_type
==
P_INT64
)
{
// int64
elem_size
=
sizeof
(
int64_t
);
paddleTensor
.
dtype
=
paddle
::
PaddleDType
::
INT64
;
data_len
=
tensor
.
int64_data_size
();
src_ptr
=
tensor
.
int64_data
().
data
();
}
else
if
(
elem_type
==
P_FLOAT32
)
{
elem_size
=
sizeof
(
float
);
paddleTensor
.
dtype
=
paddle
::
PaddleDType
::
FLOAT32
;
data_len
=
tensor
.
float_data_size
();
src_ptr
=
tensor
.
float_data
().
data
();
}
else
if
(
elem_type
==
P_INT32
)
{
elem_size
=
sizeof
(
int32_t
);
paddleTensor
.
dtype
=
paddle
::
PaddleDType
::
INT32
;
data_len
=
tensor
.
int_data_size
();
src_ptr
=
tensor
.
int_data
().
data
();
}
else
if
(
elem_type
==
P_UINT8
)
{
elem_size
=
sizeof
(
uint8_t
);
paddleTensor
.
dtype
=
paddle
::
PaddleDType
::
UINT8
;
data_len
=
tensor
.
tensor_content
().
size
();
src_ptr
=
tensor
.
tensor_content
().
data
();
}
else
if
(
elem_type
==
P_INT8
)
{
elem_size
=
sizeof
(
int8_t
);
paddleTensor
.
dtype
=
paddle
::
PaddleDType
::
INT8
;
data_len
=
tensor
.
tensor_content
().
size
();
src_ptr
=
tensor
.
tensor_content
().
data
();
}
else
if
(
elem_type
==
P_FP16
)
{
// paddle inference will support FLOAT16
// elem_size = 1;
// paddleTensor.dtype = paddle::PaddleDType::FLOAT16;
// data_len = tensor.tensor_content().size();
// src_ptr = tensor.tensor_content().data();
}
else
if
(
elem_type
==
P_STRING
)
{
// use paddle::PaddleDType::UINT8 as for String.
elem_size
=
sizeof
(
char
);
...
...
@@ -109,8 +145,18 @@ int GeneralReaderOp::inference() {
// now only support single string
for
(
int
idx
=
0
;
idx
<
tensor
.
data_size
();
idx
++
)
{
data_len
+=
tensor
.
data
()[
idx
].
length
()
+
1
;
src_ptr
=
tensor
.
data
()[
idx
].
data
();
}
}
VLOG
(
2
)
<<
"var["
<<
i
<<
"] has elem type: "
<<
elem_type
<<
";"
<<
"elem_size="
<<
elem_size
<<
";"
<<
"dtype="
<<
paddleTensor
.
dtype
<<
";"
<<
"data_len="
<<
data_len
;
if
(
src_ptr
==
nullptr
)
{
LOG
(
ERROR
)
<<
"Not support var["
<<
i
<<
"] with elem_type["
<<
elem_type
<<
"]"
;
continue
;
}
// implement lod tensor here
// only support 1-D lod
// TODO(HexToString): support 2-D lod
...
...
@@ -141,44 +187,17 @@ int GeneralReaderOp::inference() {
VLOG
(
2
)
<<
"(logid="
<<
log_id
<<
") var["
<<
i
<<
"] has lod_tensor and len="
<<
out
->
at
(
i
).
lod
[
0
].
back
();
}
if
(
elem_type
==
P_INT64
)
{
int64_t
*
dst_ptr
=
static_cast
<
int64_t
*>
(
out
->
at
(
i
).
data
.
data
());
VLOG
(
2
)
<<
"(logid="
<<
log_id
<<
") first element data in var["
<<
i
<<
"] is "
<<
tensor
.
int64_data
(
0
);
if
(
!
dst_ptr
)
{
LOG
(
ERROR
)
<<
"dst_ptr is nullptr"
;
return
-
1
;
}
memcpy
(
dst_ptr
,
tensor
.
int64_data
().
data
(),
databuf_size
);
/*
int elem_num = tensor.int64_data_size();
for (int k = 0; k < elem_num; ++k) {
dst_ptr[k] = tensor.int64_data(k);
}
*/
}
else
if
(
elem_type
==
P_FLOAT32
)
{
float
*
dst_ptr
=
static_cast
<
float
*>
(
out
->
at
(
i
).
data
.
data
());
VLOG
(
2
)
<<
"(logid="
<<
log_id
<<
") first element data in var["
<<
i
<<
"] is "
<<
tensor
.
float_data
(
0
);
if
(
!
dst_ptr
)
{
LOG
(
ERROR
)
<<
"dst_ptr is nullptr"
;
return
-
1
;
}
memcpy
(
dst_ptr
,
tensor
.
float_data
().
data
(),
databuf_size
);
/*int elem_num = tensor.float_data_size();
for (int k = 0; k < elem_num; ++k) {
dst_ptr[k] = tensor.float_data(k);
}*/
}
else
if
(
elem_type
==
P_INT32
)
{
int32_t
*
dst_ptr
=
static_cast
<
int32_t
*>
(
out
->
at
(
i
).
data
.
data
());
VLOG
(
2
)
<<
"(logid="
<<
log_id
<<
") first element data in var["
<<
i
<<
"] is "
<<
tensor
.
int_data
(
0
);
if
(
!
dst_ptr
)
{
LOG
(
ERROR
)
<<
"dst_ptr is nullptr"
;
return
-
1
;
}
memcpy
(
dst_ptr
,
tensor
.
int_data
().
data
(),
databuf_size
);
}
else
if
(
elem_type
==
P_STRING
)
{
void
*
dst_ptr
=
out
->
at
(
i
).
data
.
data
();
if
(
!
dst_ptr
)
{
LOG
(
ERROR
)
<<
"dst_ptr is nullptr"
;
return
-
1
;
}
// For common data, we just copy from src to dst
// For string data, we need to iterate through all str
if
(
elem_type
!=
P_STRING
)
{
memcpy
(
dst_ptr
,
src_ptr
,
databuf_size
);
}
else
{
char
*
dst_ptr
=
static_cast
<
char
*>
(
out
->
at
(
i
).
data
.
data
());
VLOG
(
2
)
<<
"(logid="
<<
log_id
<<
") first element data in var["
<<
i
<<
"] is "
<<
tensor
.
data
(
0
);
...
...
core/general-server/op/general_response_op.cpp
浏览文件 @
ee5a9489
...
...
@@ -168,7 +168,24 @@ int GeneralResponseOp::inference() {
google
::
protobuf
::
RepeatedField
<
int32_t
>
tmp_data
(
data_ptr
,
data_ptr
+
cap
);
output
->
mutable_tensor
(
var_idx
)
->
mutable_int_data
()
->
Swap
(
&
tmp_data
);
}
}
else
if
(
dtype
==
paddle
::
PaddleDType
::
UINT8
)
{
tensor
->
set_elem_type
(
7
);
VLOG
(
2
)
<<
"(logid="
<<
log_id
<<
")Prepare uint8 var ["
<<
model_config
->
_fetch_name
[
idx
]
<<
"]."
;
tensor
->
set_tensor_content
(
in
->
at
(
idx
).
data
.
data
(),
in
->
at
(
idx
).
data
.
length
());
}
else
if
(
dtype
==
paddle
::
PaddleDType
::
INT8
)
{
tensor
->
set_elem_type
(
8
);
VLOG
(
2
)
<<
"(logid="
<<
log_id
<<
")Prepare int8 var ["
<<
model_config
->
_fetch_name
[
idx
]
<<
"]."
;
tensor
->
set_tensor_content
(
in
->
at
(
idx
).
data
.
data
(),
in
->
at
(
idx
).
data
.
length
());
}
// inference will support fp16
// else if (dtype == paddle::PaddleDType::FLOAT16) {
// tensor->set_elem_type(5);
// VLOG(2) << "(logid=" << log_id << ")Prepare float16 var ["
// << model_config->_fetch_name[idx] << "].";
// tensor->set_tensor_content(in->at(idx).data.data(), in->at(idx).data.length());
// }
VLOG
(
2
)
<<
"(logid="
<<
log_id
<<
") fetch var ["
<<
model_config
->
_fetch_name
[
idx
]
<<
"] ready"
;
...
...
core/general-server/proto/general_model_service.proto
浏览文件 @
ee5a9489
...
...
@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
syntax
=
"proto
2
"
;
syntax
=
"proto
3
"
;
import
"pds_option.proto"
;
import
"builtin_format.proto"
;
package
baidu
.
paddle_serving.predictor.general_model
;
...
...
@@ -20,33 +20,88 @@ package baidu.paddle_serving.predictor.general_model;
option
cc_generic_services
=
true
;
message
Tensor
{
repeated
string
data
=
1
;
repeated
int32
int_data
=
2
;
repeated
int64
int64_data
=
3
;
repeated
float
float_data
=
4
;
optional
int32
elem_type
=
5
;
// 0 means int64, 1 means float32, 2 means int32, 3 means string
repeated
int32
shape
=
6
;
// shape should include batch
repeated
int32
lod
=
7
;
// only for fetch tensor currently
optional
string
name
=
8
;
// get from the Model prototxt
optional
string
alias_name
=
9
;
// get from the Model prototxt
// VarType: INT64
repeated
int64
int64_data
=
1
;
// VarType: FP32
repeated
float
float_data
=
2
;
// VarType: INT32
repeated
int32
int_data
=
3
;
// VarType: FP64
repeated
double
float64_data
=
4
;
// VarType: UINT32
repeated
uint32
uint32_data
=
5
;
// VarType: BOOL
repeated
bool
bool_data
=
6
;
// (No support)VarType: COMPLEX64, 2x represents the real part, 2x+1
// represents the imaginary part
repeated
float
complex64_data
=
7
;
// (No support)VarType: COMPLEX128, 2x represents the real part, 2x+1
// represents the imaginary part
repeated
double
complex128_data
=
8
;
// VarType: STRING
repeated
string
data
=
9
;
// Element types:
// 0 => INT64
// 1 => FP32
// 2 => INT32
// 3 => FP64
// 4 => INT16
// 5 => FP16
// 6 => BF16
// 7 => UINT8
// 8 => INT8
// 9 => BOOL
// 10 => COMPLEX64
// 11 => COMPLEX128
// 12 => STRING
int32
elem_type
=
10
;
// Shape of the tensor, including batch dimensions.
repeated
int32
shape
=
11
;
// Level of data(LOD), support variable length data, only for fetch tensor
// currently.
repeated
int32
lod
=
12
;
// Correspond to the variable 'name' in the model description prototxt.
string
name
=
13
;
// Correspond to the variable 'alias_name' in the model description prototxt.
string
alias_name
=
14
;
// get from the Model prototxt
// VarType: FP16, INT16, INT8, BF16, UINT8
bytes
tensor_content
=
15
;
};
message
Request
{
repeated
Tensor
tensor
=
1
;
repeated
string
fetch_var_names
=
2
;
optional
bool
profile_server
=
3
[
default
=
false
]
;
required
uint64
log_id
=
4
[
default
=
0
]
;
bool
profile_server
=
3
;
uint64
log_id
=
4
;
};
message
Response
{
repeated
ModelOutput
outputs
=
1
;
repeated
int64
profile_time
=
2
;
// Error code
int32
err_no
=
3
;
// Error messages
string
err_msg
=
4
;
};
message
ModelOutput
{
repeated
Tensor
tensor
=
1
;
optional
string
engine_name
=
2
;
string
engine_name
=
2
;
}
service
GeneralModelService
{
...
...
core/predictor/framework/infer.h
浏览文件 @
ee5a9489
...
...
@@ -443,7 +443,30 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<EngineCore> {
paddle
::
PaddleDType
::
INT32
)
{
int32_t
*
data
=
static_cast
<
int32_t
*>
(
origin_data
);
lod_tensor_in
->
CopyFromCpu
(
data
);
}
else
if
((
*
tensorVector_in_pointer
)[
i
].
dtype
==
paddle
::
PaddleDType
::
UINT8
)
{
uint8_t
*
data
=
static_cast
<
uint8_t
*>
(
origin_data
);
lod_tensor_in
->
CopyFromCpu
(
data
);
}
else
if
((
*
tensorVector_in_pointer
)[
i
].
dtype
==
paddle
::
PaddleDType
::
INT8
)
{
int8_t
*
data
=
static_cast
<
int8_t
*>
(
origin_data
);
lod_tensor_in
->
CopyFromCpu
(
data
);
}
else
{
LOG
(
ERROR
)
<<
"Inference not support type["
<<
(
*
tensorVector_in_pointer
)[
i
].
dtype
<<
"],name["
<<
(
*
tensorVector_in_pointer
)[
i
].
name
<<
"]"
<<
" copy into core failed!"
;
}
// Paddle inference will support FP16 in next version.
// else if ((*tensorVector_in_pointer)[i].dtype ==
// paddle::PaddleDType::FLOAT16) {
// paddle::platform::float16* data =
// static_cast<paddle::platform::float16*>(origin_data);
// lod_tensor_in->CopyFromCpu(data);
// }
VLOG
(
2
)
<<
"Tensor:name="
<<
(
*
tensorVector_in_pointer
)[
i
].
name
<<
";in_dtype="
<<
(
*
tensorVector_in_pointer
)[
i
].
dtype
<<
";tensor_dtype="
<<
lod_tensor_in
->
type
();
}
// After the input data is passed in,
// call 'core->Run()' perform the prediction process.
...
...
@@ -508,7 +531,41 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<EngineCore> {
int32_t
*
data_out
=
reinterpret_cast
<
int32_t
*>
(
databuf_data
);
lod_tensor_out
->
CopyToCpu
(
data_out
);
databuf_char
=
reinterpret_cast
<
char
*>
(
data_out
);
}
else
if
(
dataType
==
paddle
::
PaddleDType
::
UINT8
)
{
databuf_size
=
out_num
*
sizeof
(
uint8_t
);
databuf_data
=
MempoolWrapper
::
instance
().
malloc
(
databuf_size
);
if
(
!
databuf_data
)
{
LOG
(
ERROR
)
<<
"Malloc failed, size: "
<<
databuf_size
;
return
-
1
;
}
uint8_t
*
data_out
=
reinterpret_cast
<
uint8_t
*>
(
databuf_data
);
lod_tensor_out
->
CopyToCpu
(
data_out
);
databuf_char
=
reinterpret_cast
<
char
*>
(
data_out
);
}
else
if
(
dataType
==
paddle
::
PaddleDType
::
INT8
)
{
databuf_size
=
out_num
*
sizeof
(
int8_t
);
databuf_data
=
MempoolWrapper
::
instance
().
malloc
(
databuf_size
);
if
(
!
databuf_data
)
{
LOG
(
ERROR
)
<<
"Malloc failed, size: "
<<
databuf_size
;
return
-
1
;
}
int8_t
*
data_out
=
reinterpret_cast
<
int8_t
*>
(
databuf_data
);
lod_tensor_out
->
CopyToCpu
(
data_out
);
databuf_char
=
reinterpret_cast
<
char
*>
(
data_out
);
}
// Inference will support FP16 in next version
// else if (dataType == paddle::PaddleDType::FLOAT16) {
// using float16 = paddle::platform::float16;
// databuf_size = out_num * sizeof(float16);
// databuf_data = MempoolWrapper::instance().malloc(databuf_size);
// if (!databuf_data) {
// LOG(ERROR) << "Malloc failed, size: " << databuf_size;
// return -1;
// }
// float16* data_out = reinterpret_cast<float16*>(databuf_data);
// lod_tensor_out->CopyToCpu(data_out);
// databuf_char = reinterpret_cast<char*>(data_out);
// }
// Because task scheduling requires OPs to use 'Channel'
// (which is a data structure) to transfer data between OPs.
// We need to copy the processed data to the 'Channel' for the next OP.
...
...
core/sdk-cpp/proto/general_model_service.proto
浏览文件 @
ee5a9489
...
...
@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
syntax
=
"proto
2
"
;
syntax
=
"proto
3
"
;
import
"pds_option.proto"
;
import
"builtin_format.proto"
;
package
baidu
.
paddle_serving.predictor.general_model
;
...
...
@@ -20,33 +20,88 @@ package baidu.paddle_serving.predictor.general_model;
option
cc_generic_services
=
true
;
message
Tensor
{
repeated
string
data
=
1
;
repeated
int32
int_data
=
2
;
repeated
int64
int64_data
=
3
;
repeated
float
float_data
=
4
;
optional
int32
elem_type
=
5
;
// 0 means int64, 1 means float32, 2 means int32, 3 means string
repeated
int32
shape
=
6
;
// shape should include batch
repeated
int32
lod
=
7
;
// only for fetch tensor currently
optional
string
name
=
8
;
// get from the Model prototxt
optional
string
alias_name
=
9
;
// get from the Model prototxt
// VarType: INT64
repeated
int64
int64_data
=
1
;
// VarType: FP32
repeated
float
float_data
=
2
;
// VarType: INT32
repeated
int32
int_data
=
3
;
// VarType: FP64
repeated
double
float64_data
=
4
;
// VarType: UINT32
repeated
uint32
uint32_data
=
5
;
// VarType: BOOL
repeated
bool
bool_data
=
6
;
// (No support)VarType: COMPLEX64, 2x represents the real part, 2x+1
// represents the imaginary part
repeated
float
complex64_data
=
7
;
// (No support)VarType: COMPLEX128, 2x represents the real part, 2x+1
// represents the imaginary part
repeated
double
complex128_data
=
8
;
// VarType: STRING
repeated
string
data
=
9
;
// Element types:
// 0 => INT64
// 1 => FP32
// 2 => INT32
// 3 => FP64
// 4 => INT16
// 5 => FP16
// 6 => BF16
// 7 => UINT8
// 8 => INT8
// 9 => BOOL
// 10 => COMPLEX64
// 11 => COMPLEX128
// 20 => STRING
int32
elem_type
=
10
;
// Shape of the tensor, including batch dimensions.
repeated
int32
shape
=
11
;
// Level of data(LOD), support variable length data, only for fetch tensor
// currently.
repeated
int32
lod
=
12
;
// Correspond to the variable 'name' in the model description prototxt.
string
name
=
13
;
// Correspond to the variable 'alias_name' in the model description prototxt.
string
alias_name
=
14
;
// get from the Model prototxt
// VarType: FP16, INT16, INT8, BF16, UINT8
bytes
tensor_content
=
15
;
};
message
Request
{
repeated
Tensor
tensor
=
1
;
repeated
string
fetch_var_names
=
2
;
optional
bool
profile_server
=
3
[
default
=
false
]
;
required
uint64
log_id
=
4
[
default
=
0
]
;
bool
profile_server
=
3
;
uint64
log_id
=
4
;
};
message
Response
{
repeated
ModelOutput
outputs
=
1
;
repeated
int64
profile_time
=
2
;
// Error code
int32
err_no
=
3
;
// Error messages
string
err_msg
=
4
;
};
message
ModelOutput
{
repeated
Tensor
tensor
=
1
;
optional
string
engine_name
=
2
;
string
engine_name
=
2
;
}
service
GeneralModelService
{
...
...
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