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体验新版 GitCode,发现更多精彩内容 >>
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提交
0c0c5df4
编写于
6月 01, 2018
作者:
Y
Yan Chunwei
提交者:
GitHub
6月 01, 2018
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
feature/add TRT fc converter (#11043)
上级
18d64025
变更
12
隐藏空白更改
内联
并排
Showing
12 changed file
with
240 addition
and
35 deletion
+240
-35
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
+2
-0
paddle/fluid/inference/tensorrt/convert/conv2d_op.cc
paddle/fluid/inference/tensorrt/convert/conv2d_op.cc
+2
-1
paddle/fluid/inference/tensorrt/convert/fc_op.cc
paddle/fluid/inference/tensorrt/convert/fc_op.cc
+119
-0
paddle/fluid/inference/tensorrt/convert/mul_op.cc
paddle/fluid/inference/tensorrt/convert/mul_op.cc
+3
-2
paddle/fluid/inference/tensorrt/convert/op_converter.h
paddle/fluid/inference/tensorrt/convert/op_converter.h
+28
-13
paddle/fluid/inference/tensorrt/convert/test_fc_op.cc
paddle/fluid/inference/tensorrt/convert/test_fc_op.cc
+46
-0
paddle/fluid/inference/tensorrt/convert/test_mul_op.cc
paddle/fluid/inference/tensorrt/convert/test_mul_op.cc
+3
-1
paddle/fluid/inference/tensorrt/convert/test_op_converter.cc
paddle/fluid/inference/tensorrt/convert/test_op_converter.cc
+5
-2
paddle/fluid/inference/tensorrt/convert/ut_helper.h
paddle/fluid/inference/tensorrt/convert/ut_helper.h
+26
-14
paddle/fluid/inference/tensorrt/engine.cc
paddle/fluid/inference/tensorrt/engine.cc
+1
-0
paddle/fluid/inference/tensorrt/engine.h
paddle/fluid/inference/tensorrt/engine.h
+3
-1
paddle/fluid/operators/tensorrt_engine_op.cc
paddle/fluid/operators/tensorrt_engine_op.cc
+2
-1
未找到文件。
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @
0c0c5df4
...
...
@@ -8,3 +8,5 @@ nv_test(test_op_converter SRCS test_op_converter.cc mul_op.cc conv2d_op.cc DEPS
nv_test
(
test_io_converter SRCS test_io_converter.cc io_converter.cc DEPS dynload_cuda dynamic_loader lod_tensor
)
nv_test
(
test_trt_mul_op SRCS test_mul_op.cc mul_op.cc
DEPS
${
FLUID_CORE_MODULES
}
tensorrt_engine mul_op SERIAL
)
nv_test
(
test_trt_fc_op SRCS test_fc_op.cc fc_op.cc
DEPS
${
FLUID_CORE_MODULES
}
tensorrt_engine mul_op SERIAL
)
paddle/fluid/inference/tensorrt/convert/conv2d_op.cc
浏览文件 @
0c0c5df4
...
...
@@ -21,7 +21,8 @@ namespace tensorrt {
class
Conv2dOpConverter
:
public
OpConverter
{
public:
Conv2dOpConverter
()
{}
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
)
override
{
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
)
override
{
LOG
(
INFO
)
<<
"convert a fluid conv2d op to tensorrt conv layer without bias"
;
}
...
...
paddle/fluid/inference/tensorrt/convert/fc_op.cc
0 → 100644
浏览文件 @
0c0c5df4
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
#include "paddle/fluid/platform/place.h"
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
// Reorder the elements from istrides to ostrides, borrowed from TRT convert in
// tensorflow.
// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/tensorrt/convert/convert_nodes.cc#L318
template
<
typename
T
>
void
Reorder2
(
nvinfer1
::
DimsHW
shape
,
const
T
*
idata
,
nvinfer1
::
DimsHW
istrides
,
T
*
odata
,
nvinfer1
::
DimsHW
ostrides
)
{
for
(
int
h
=
0
;
h
<
shape
.
h
();
++
h
)
{
for
(
int
w
=
0
;
w
<
shape
.
w
();
++
w
)
{
odata
[
h
*
ostrides
.
h
()
+
w
*
ostrides
.
w
()]
=
idata
[
h
*
ostrides
.
h
()
+
w
*
ostrides
.
w
()];
}
}
}
// Reorder the data layout from CK to KC.
void
ReorderCKtoKC
(
TensorRTEngine
::
Weight
&
iweights
,
TensorRTEngine
::
Weight
*
oweights
)
{
int
c
=
iweights
.
dims
[
0
];
int
k
=
iweights
.
dims
[
1
];
oweights
->
dims
.
assign
({
k
,
c
});
nvinfer1
::
DimsHW
istrides
=
{
1
,
k
};
nvinfer1
::
DimsHW
ostrides
=
{
c
,
1
};
Reorder2
({
k
,
c
},
static_cast
<
float
const
*>
(
iweights
.
get
().
values
),
istrides
,
static_cast
<
float
*>
(
const_cast
<
void
*>
(
oweights
->
get
().
values
)),
ostrides
);
}
/*
* FC converter convert a MUL op in Fluid to a FC layer in TRT.
*/
class
FcOpConverter
:
public
OpConverter
{
public:
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
)
override
{
VLOG
(
4
)
<<
"convert a fluid fc op to tensorrt fc layer without bias"
;
framework
::
OpDesc
op_desc
(
op
,
nullptr
,
nullptr
);
PADDLE_ENFORCE_EQ
(
op_desc
.
Input
(
"X"
).
size
(),
1
);
PADDLE_ENFORCE_EQ
(
op_desc
.
Input
(
"Y"
).
size
(),
1
);
// Y is a weight
PADDLE_ENFORCE_EQ
(
op_desc
.
Output
(
"Out"
).
size
(),
1
);
// Declare inputs
auto
*
X
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
).
front
());
// Declare weights
auto
*
Y_v
=
scope
.
FindVar
(
op_desc
.
Input
(
"Y"
).
front
());
PADDLE_ENFORCE_NOT_NULL
(
Y_v
);
auto
*
Y_t
=
Y_v
->
GetMutable
<
framework
::
LoDTensor
>
();
// This may trigger a GPU->CPU copy, because TRT's weight can only be
// assigned from CPU memory, that can't be avoided.
auto
*
weight_data
=
Y_t
->
mutable_data
<
float
>
(
platform
::
CPUPlace
());
PADDLE_ENFORCE_EQ
(
Y_t
->
dims
().
size
(),
2UL
);
// a matrix
size_t
n_output
=
Y_t
->
dims
()[
1
];
framework
::
LoDTensor
tmp
;
tmp
.
Resize
(
Y_t
->
dims
());
memcpy
(
tmp
.
mutable_data
<
float
>
(
platform
::
CPUPlace
()),
Y_t
->
data
<
float
>
(),
Y_t
->
dims
()[
0
]
*
Y_t
->
dims
()[
1
]);
TensorRTEngine
::
Weight
weight
{
nvinfer1
::
DataType
::
kFLOAT
,
static_cast
<
void
*>
(
weight_data
),
Y_t
->
memory_size
()
/
sizeof
(
float
)};
TensorRTEngine
::
Weight
tmp_weight
(
nvinfer1
::
DataType
::
kFLOAT
,
static_cast
<
void
*>
(
tmp
.
data
<
float
>
()),
Y_t
->
memory_size
()
/
sizeof
(
float
));
weight
.
dims
.
assign
({
Y_t
->
dims
()[
0
],
Y_t
->
dims
()[
1
]});
tmp_weight
.
dims
=
weight
.
dims
;
// The data layout of TRT FC layer's weight is different from fluid's FC,
// need to reorder the elements.
ReorderCKtoKC
(
tmp_weight
,
&
weight
);
// Currently, the framework can only handle one fluid op -> one TRT layer,
// but fc fuses `mul` and `bias` (2 fluid ops), so here is a trick, just
// handle `mul`, leave `add` as another layer.
// DEBUG
TensorRTEngine
::
Weight
bias
{
nvinfer1
::
DataType
::
kFLOAT
,
nullptr
,
0
};
auto
*
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
FullyConnected
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
X
),
n_output
,
weight
.
get
(),
bias
.
get
());
auto
output_name
=
op_desc
.
Output
(
"Out"
).
front
();
engine_
->
DeclareOutput
(
layer
,
0
,
output_name
);
}
};
REGISTER_TRT_OP_CONVERTER
(
fc
,
FcOpConverter
);
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
USE_OP
(
mul
);
paddle/fluid/inference/tensorrt/convert/mul_op.cc
浏览文件 @
0c0c5df4
...
...
@@ -24,8 +24,9 @@ namespace tensorrt {
class
MulOpConverter
:
public
OpConverter
{
public:
MulOpConverter
()
{}
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
)
override
{
VLOG
(
4
)
<<
"convert a fluid mul op to tensorrt fc layer without bias"
;
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
)
override
{
VLOG
(
4
)
<<
"convert a fluid mul op to tensorrt mul layer without bias"
;
framework
::
OpDesc
op_desc
(
op
,
nullptr
);
// Declare inputs
...
...
paddle/fluid/inference/tensorrt/convert/op_converter.h
浏览文件 @
0c0c5df4
...
...
@@ -31,27 +31,42 @@ namespace tensorrt {
class
OpConverter
{
public:
OpConverter
()
{}
virtual
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
)
{}
void
Run
(
const
framework
::
proto
::
OpDesc
&
op
,
TensorRTEngine
*
engine
)
{
std
::
string
type
=
op
.
type
();
auto
*
it
=
Registry
<
OpConverter
>::
Lookup
(
type
);
PADDLE_ENFORCE_NOT_NULL
(
it
,
"no OpConverter for optype [%s]"
,
type
);
it
->
SetEngine
(
engine
);
(
*
it
)(
op
);
}
// Converter logic for an op.
virtual
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
)
{}
// Convert a single fluid operaotr and add the corresponding layer to TRT.
void
ConvertOp
(
const
framework
::
proto
::
OpDesc
&
op
,
const
std
::
unordered_set
<
std
::
string
>&
parameters
,
const
framework
::
Scope
&
scope
,
TensorRTEngine
*
engine
)
{
framework
::
OpDesc
op_desc
(
op
,
nullptr
,
nullptr
);
OpConverter
*
it
{
nullptr
};
// convert fluid op to tensorrt layer
void
ConvertOp
(
const
framework
::
proto
::
OpDesc
&
op
,
TensorRTEngine
*
engine
)
{
OpConverter
::
Run
(
op
,
engine
);
if
(
op_desc
.
Type
()
==
"mul"
)
{
PADDLE_ENFORCE_EQ
(
op_desc
.
Input
(
"Y"
).
size
(),
1UL
);
std
::
string
Y
=
op_desc
.
Input
(
"Y"
)[
0
];
if
(
parameters
.
count
(
Y
))
{
it
=
Registry
<
OpConverter
>::
Lookup
(
"fc"
);
}
}
if
(
!
it
)
{
it
=
Registry
<
OpConverter
>::
Lookup
(
op_desc
.
Type
());
}
PADDLE_ENFORCE_NOT_NULL
(
it
,
"no OpConverter for optype [%s]"
,
op_desc
.
Type
());
it
->
SetEngine
(
engine
);
(
*
it
)(
op
,
scope
);
}
// convert fluid block to tensorrt network
void
ConvertBlock
(
const
framework
::
proto
::
BlockDesc
&
block
,
TensorRTEngine
*
engine
)
{
const
std
::
unordered_set
<
std
::
string
>&
parameters
,
const
framework
::
Scope
&
scope
,
TensorRTEngine
*
engine
)
{
for
(
int
i
=
0
;
i
<
block
.
ops_size
();
i
++
)
{
const
auto
&
op
=
block
.
ops
(
i
);
OpConverter
::
Run
(
op
,
engine
);
ConvertOp
(
op
,
parameters
,
scope
,
engine
);
}
}
...
...
paddle/fluid/inference/tensorrt/convert/test_fc_op.cc
0 → 100644
浏览文件 @
0c0c5df4
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
TEST
(
fc_op
,
test
)
{
std
::
unordered_set
<
std
::
string
>
parameters
({
"mul-Y"
});
framework
::
Scope
scope
;
TRTConvertValidation
validator
(
20
,
parameters
,
scope
,
1000
);
validator
.
DeclInputVar
(
"mul-X"
,
nvinfer1
::
Dims4
(
8
,
3
,
1
,
1
));
validator
.
DeclParamVar
(
"mul-Y"
,
nvinfer1
::
Dims2
(
3
,
2
));
validator
.
DeclOutputVar
(
"mul-Out"
,
nvinfer1
::
Dims2
(
8
,
2
));
// Prepare Op description
framework
::
OpDesc
desc
;
desc
.
SetType
(
"mul"
);
desc
.
SetInput
(
"X"
,
{
"mul-X"
});
desc
.
SetInput
(
"Y"
,
{
"mul-Y"
});
desc
.
SetOutput
(
"Out"
,
{
"mul-Out"
});
validator
.
SetOp
(
*
desc
.
Proto
());
validator
.
Execute
(
10
);
}
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
paddle/fluid/inference/tensorrt/convert/test_mul_op.cc
浏览文件 @
0c0c5df4
...
...
@@ -21,7 +21,9 @@ namespace inference {
namespace
tensorrt
{
TEST
(
MulOpConverter
,
main
)
{
TRTConvertValidation
validator
(
10
,
1000
);
framework
::
Scope
scope
;
std
::
unordered_set
<
std
::
string
>
parameters
;
TRTConvertValidation
validator
(
10
,
parameters
,
scope
,
1000
);
validator
.
DeclInputVar
(
"mul-X"
,
nvinfer1
::
Dims2
(
10
,
6
));
validator
.
DeclInputVar
(
"mul-Y"
,
nvinfer1
::
Dims2
(
6
,
10
));
validator
.
DeclOutputVar
(
"mul-Out"
,
nvinfer1
::
Dims2
(
10
,
10
));
...
...
paddle/fluid/inference/tensorrt/convert/test_op_converter.cc
浏览文件 @
0c0c5df4
...
...
@@ -12,9 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
namespace
paddle
{
namespace
inference
{
...
...
@@ -27,7 +28,9 @@ TEST(OpConverter, ConvertBlock) {
conv2d_op
->
SetType
(
"conv2d"
);
OpConverter
converter
;
converter
.
ConvertBlock
(
*
block
->
Proto
(),
nullptr
/*TensorRTEngine*/
);
framework
::
Scope
scope
;
converter
.
ConvertBlock
(
*
block
->
Proto
(),
{},
scope
,
nullptr
/*TensorRTEngine*/
);
}
}
// namespace tensorrt
...
...
paddle/fluid/inference/tensorrt/convert/ut_helper.h
浏览文件 @
0c0c5df4
...
...
@@ -61,7 +61,10 @@ class TRTConvertValidation {
public:
TRTConvertValidation
()
=
delete
;
explicit
TRTConvertValidation
(
int
batch_size
,
int
workspace_size
=
1024
)
{
TRTConvertValidation
(
int
batch_size
,
const
std
::
unordered_set
<
std
::
string
>&
parameters
,
framework
::
Scope
&
scope
,
int
workspace_size
=
1
<<
10
)
:
parameters_
(
parameters
),
scope_
(
scope
)
{
// create engine.
engine_
.
reset
(
new
TensorRTEngine
(
10
,
1
<<
10
,
&
stream_
));
engine_
->
InitNetwork
();
...
...
@@ -76,19 +79,22 @@ class TRTConvertValidation {
engine_
->
DeclareInput
(
name
,
nvinfer1
::
DataType
::
kFLOAT
,
dims
);
}
// Declare a parameter varaible in the scope.
void
DeclParamVar
(
const
std
::
string
&
name
,
const
nvinfer1
::
Dims
&
dims
)
{
DeclVar
(
name
,
dims
);
}
void
DeclOutputVar
(
const
std
::
string
&
name
,
const
nvinfer1
::
Dims
&
dims
)
{
DeclVar
(
name
,
dims
);
}
// Declare a variable in a fluid Scope.
void
DeclVar
(
const
std
::
string
&
name
,
const
nvinfer1
::
Dims
&
dims
)
{
platform
::
CPUPlace
place
;
platform
::
CPUDeviceContext
ctx
(
place
);
// Init Fluid tensor.
std
::
vector
<
int
>
dim_vec
(
dims
.
nbDims
);
for
(
int
i
=
0
;
i
<
dims
.
nbDims
;
i
++
)
{
dim_vec
[
i
]
=
dims
.
d
[
i
];
}
std
::
vector
<
int
>
dim_vec
(
dims
.
d
,
dims
.
d
+
dims
.
nbDims
);
auto
*
x
=
scope_
.
Var
(
name
);
auto
*
x_tensor
=
x
->
GetMutable
<
framework
::
LoDTensor
>
();
x_tensor
->
Resize
(
framework
::
make_ddim
(
dim_vec
));
...
...
@@ -99,7 +105,7 @@ class TRTConvertValidation {
op_
=
framework
::
OpRegistry
::
CreateOp
(
desc
);
OpConverter
op_converter
;
op_converter
.
ConvertOp
(
desc
,
engine_
.
get
());
op_converter
.
ConvertOp
(
desc
,
parameters_
,
scope_
,
engine_
.
get
());
engine_
->
FreezeNetwork
();
...
...
@@ -108,11 +114,13 @@ class TRTConvertValidation {
// Set Inputs.
for
(
const
auto
&
input
:
op_desc_
->
InputArgumentNames
())
{
if
(
parameters_
.
count
(
input
))
continue
;
auto
*
var
=
scope_
.
FindVar
(
input
);
PADDLE_ENFORCE
(
var
);
auto
tensor
=
var
->
GetMutable
<
framework
::
LoDTensor
>
();
engine_
->
SetInputFromCPU
(
input
,
static_cast
<
void
*>
(
tensor
->
data
<
float
>
()),
input
,
static_cast
<
void
*>
(
tensor
->
data
<
void
>
()),
sizeof
(
float
)
*
analysis
::
AccuDims
(
tensor
->
dims
(),
tensor
->
dims
().
size
()));
}
...
...
@@ -120,18 +128,21 @@ class TRTConvertValidation {
void
Execute
(
int
batch_size
)
{
// Execute Fluid Op
// Execute TRT
platform
::
CPUPlace
place
;
platform
::
CPUDeviceContext
ctx
(
place
);
engine_
->
Execute
(
batch_size
);
op_
->
Run
(
scope_
,
place
);
// Execute TRT.
engine_
->
Execute
(
batch_size
);
cudaStreamSynchronize
(
*
engine_
->
stream
());
ASSERT_FALSE
(
op_desc_
->
OutputArgumentNames
().
empty
());
const
size_t
output_space_size
=
200
;
for
(
const
auto
&
output
:
op_desc_
->
OutputArgumentNames
())
{
std
::
vector
<
float
>
fluid_out
;
std
::
vector
<
float
>
trt_out
(
200
);
engine_
->
GetOutputInCPU
(
output
,
&
trt_out
[
0
],
200
*
sizeof
(
float
));
std
::
vector
<
float
>
trt_out
(
output_space_size
);
engine_
->
GetOutputInCPU
(
output
,
&
trt_out
[
0
],
output_space_size
*
sizeof
(
float
));
cudaStreamSynchronize
(
*
engine_
->
stream
());
auto
*
var
=
scope_
.
FindVar
(
output
);
auto
tensor
=
var
->
GetMutable
<
framework
::
LoDTensor
>
();
...
...
@@ -139,7 +150,7 @@ class TRTConvertValidation {
// Compare two output
ASSERT_FALSE
(
fluid_out
.
empty
());
for
(
size_t
i
=
0
;
i
<
fluid_out
.
size
();
i
++
)
{
EXPECT_LT
(
std
::
abs
(
fluid_out
[
i
]
-
trt_out
[
i
]),
0.001
);
EXPECT_LT
(
std
::
abs
(
fluid_out
[
i
]
-
trt_out
[
i
]),
1e-6
);
}
}
}
...
...
@@ -149,9 +160,10 @@ class TRTConvertValidation {
private:
std
::
unique_ptr
<
TensorRTEngine
>
engine_
;
cudaStream_t
stream_
;
framework
::
Scope
scope_
;
std
::
unique_ptr
<
framework
::
OperatorBase
>
op_
;
std
::
unique_ptr
<
framework
::
OpDesc
>
op_desc_
;
const
std
::
unordered_set
<
std
::
string
>&
parameters_
;
framework
::
Scope
&
scope_
;
};
}
// namespace tensorrt
...
...
paddle/fluid/inference/tensorrt/engine.cc
浏览文件 @
0c0c5df4
...
...
@@ -106,6 +106,7 @@ void TensorRTEngine::DeclareOutput(const nvinfer1::ILayer* layer, int offset,
name
);
auto
*
output
=
layer
->
getOutput
(
offset
);
SetITensor
(
name
,
output
);
PADDLE_ENFORCE
(
output
!=
nullptr
);
output
->
setName
(
name
.
c_str
());
infer_network_
->
markOutput
(
*
output
);
...
...
paddle/fluid/inference/tensorrt/engine.h
浏览文件 @
0c0c5df4
...
...
@@ -37,13 +37,15 @@ class TensorRTEngine : public EngineBase {
// Weight is model parameter.
class
Weight
{
public:
Weight
(
nvinfer1
::
DataType
dtype
,
void
*
value
,
in
t
num_elem
)
{
Weight
(
nvinfer1
::
DataType
dtype
,
void
*
value
,
size_
t
num_elem
)
{
w_
.
type
=
dtype
;
w_
.
values
=
value
;
w_
.
count
=
num_elem
;
}
const
nvinfer1
::
Weights
&
get
()
{
return
w_
;
}
std
::
vector
<
int64_t
>
dims
;
private:
nvinfer1
::
Weights
w_
;
};
...
...
paddle/fluid/operators/tensorrt_engine_op.cc
浏览文件 @
0c0c5df4
...
...
@@ -31,8 +31,9 @@ void paddle::operators::TensorRTEngineKernel<DeviceContext, T>::Prepare(
auto
max_workspace
=
context
.
Attr
<
int
>
(
"max_workspace"
);
engine_
.
reset
(
new
inference
::
tensorrt
::
TensorRTEngine
(
max_batch_
,
max_workspace
,
nullptr
));
// TODO(Superjomn) parameters should be passed after analysised from outside.
inference
::
Singleton
<
inference
::
tensorrt
::
OpConverter
>::
Global
().
ConvertBlock
(
block
,
engine_
.
get
());
block
,
{},
context
.
scope
(),
engine_
.
get
());
engine_
->
FreezeNetwork
();
}
...
...
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