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体验新版 GitCode,发现更多精彩内容 >>
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提交
5fd142c3
编写于
6月 15, 2018
作者:
Y
Yan Chunwei
提交者:
GitHub
6月 15, 2018
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
bugfix/trt engine op (#11487)
上级
34ac0eb8
变更
5
显示空白变更内容
内联
并排
Showing
5 changed file
with
158 addition
and
37 deletion
+158
-37
paddle/fluid/inference/tensorrt/convert/op_converter.h
paddle/fluid/inference/tensorrt/convert/op_converter.h
+2
-1
paddle/fluid/inference/tensorrt/engine.h
paddle/fluid/inference/tensorrt/engine.h
+23
-9
paddle/fluid/operators/tensorrt_engine_op.cc
paddle/fluid/operators/tensorrt_engine_op.cc
+18
-10
paddle/fluid/operators/tensorrt_engine_op.h
paddle/fluid/operators/tensorrt_engine_op.h
+17
-16
paddle/fluid/operators/tensorrt_engine_op_test.cc
paddle/fluid/operators/tensorrt_engine_op_test.cc
+98
-1
未找到文件。
paddle/fluid/inference/tensorrt/convert/op_converter.h
浏览文件 @
5fd142c3
...
...
@@ -64,7 +64,8 @@ class OpConverter {
(
*
it
)(
op
,
scope
,
test_mode
);
}
// convert fluid block to tensorrt network
// Convert a fluid block to tensorrt network, NOTE it just convert operators,
// the INetwork's inputs and outputs should specified in some other modules.
void
ConvertBlock
(
const
framework
::
proto
::
BlockDesc
&
block
,
const
std
::
unordered_set
<
std
::
string
>&
parameters
,
const
framework
::
Scope
&
scope
,
TensorRTEngine
*
engine
)
{
...
...
paddle/fluid/inference/tensorrt/engine.h
浏览文件 @
5fd142c3
...
...
@@ -51,11 +51,12 @@ class TensorRTEngine : public EngineBase {
nvinfer1
::
Weights
w_
;
};
TensorRTEngine
(
int
max_batch
,
int
max_workspace
,
cudaStream_t
*
stream
,
TensorRTEngine
(
int
max_batch
,
int
max_workspace
,
cudaStream_t
*
stream
=
nullptr
,
nvinfer1
::
ILogger
&
logger
=
NaiveLogger
::
Global
())
:
max_batch_
(
max_batch
),
max_workspace_
(
max_workspace
),
stream_
(
stream
),
stream_
(
stream
?
stream
:
&
default_stream_
),
logger_
(
logger
)
{}
virtual
~
TensorRTEngine
();
...
...
@@ -121,6 +122,8 @@ class TensorRTEngine : public EngineBase {
// the max memory size the engine uses
int
max_workspace_
;
cudaStream_t
*
stream_
;
// If stream_ is not set from outside, hold its own stream.
cudaStream_t
default_stream_
;
nvinfer1
::
ILogger
&
logger_
;
std
::
vector
<
Buffer
>
buffers_
;
...
...
@@ -165,20 +168,31 @@ class TensorRTEngine : public EngineBase {
*/
class
TRT_EngineManager
{
public:
TensorRTEngine
*
Create
(
int
max_batch
,
int
max_workspace
,
cudaStream_t
*
stream
)
{
engines_
.
emplace_back
(
new
TensorRTEngine
(
max_batch
,
max_workspace
,
stream
));
return
engines_
.
back
().
get
();
bool
HasEngine
(
const
std
::
string
&
name
)
const
{
return
engines_
.
count
(
name
)
!=
0
;
}
// Get an engine called `name`.
TensorRTEngine
*
Get
(
const
std
::
string
&
name
)
const
{
return
engines_
.
at
(
name
).
get
();
}
// Create or get an engine called `name`
TensorRTEngine
*
Create
(
int
max_batch
,
int
max_workspace
,
cudaStream_t
*
stream
,
const
std
::
string
&
name
)
{
auto
*
p
=
new
TensorRTEngine
(
max_batch
,
max_workspace
,
stream
);
engines_
[
name
].
reset
(
p
);
return
p
;
}
void
DeleteALl
()
{
for
(
auto
&
ptr
:
engines_
)
{
ptr
.
reset
(
nullptr
);
for
(
auto
&
item
:
engines_
)
{
item
.
second
.
reset
(
nullptr
);
}
}
private:
std
::
vector
<
std
::
unique_ptr
<
TensorRTEngine
>>
engines_
;
std
::
unordered_map
<
std
::
string
,
std
::
unique_ptr
<
TensorRTEngine
>>
engines_
;
};
}
// namespace tensorrt
...
...
paddle/fluid/operators/tensorrt_engine_op.cc
浏览文件 @
5fd142c3
...
...
@@ -66,17 +66,25 @@ nvinfer1::Dims Vec2TRT_Dims(const std::vector<int64_t> &shape) {
}
// namespace
template
<
typename
DeviceContext
,
typename
T
>
void
paddle
::
operators
::
TensorRTEngineKernel
<
DeviceContext
,
T
>::
Prepare
(
void
TensorRTEngineKernel
<
DeviceContext
,
T
>::
Prepare
(
const
framework
::
ExecutionContext
&
context
)
const
{
VLOG
(
4
)
<<
"Prepare engine"
;
// Get the ProgramDesc and pass to convert.
framework
::
proto
::
BlockDesc
block_desc
;
block_desc
.
ParseFromString
(
context
.
Attr
<
std
::
string
>
(
"subgraph"
));
max_batch_
=
context
.
Attr
<
int
>
(
"max_batch"
);
int
max_batch
=
context
.
Attr
<
int
>
(
"max_batch"
);
auto
max_workspace
=
context
.
Attr
<
int
>
(
"max_workspace"
);
engine_
=
Singleton
<
TRT_EngineManager
>::
Global
().
Create
(
max_batch_
,
max_workspace
,
&
stream_
);
engine_
->
InitNetwork
();
auto
params
=
context
.
Attr
<
std
::
vector
<
std
::
string
>>
(
"parameters"
);
std
::
unordered_set
<
std
::
string
>
parameters
;
for
(
const
auto
&
param
:
params
)
{
parameters
.
insert
(
param
);
}
// TODO(Superjomn) replace this with a different stream
auto
*
engine
=
Singleton
<
TRT_EngineManager
>::
Global
().
Create
(
max_batch
,
max_workspace
,
nullptr
/*engine hold its own stream*/
,
context
.
Attr
<
std
::
string
>
(
"engine_uniq_key"
));
engine
->
InitNetwork
();
framework
::
BlockDesc
block
(
nullptr
/*programdesc*/
,
&
block_desc
);
// Add inputs
...
...
@@ -87,24 +95,23 @@ void paddle::operators::TensorRTEngineKernel<DeviceContext, T>::Prepare(
PADDLE_ENFORCE_EQ
(
var
->
GetType
(),
FluidDT
::
VarType_Type_LOD_TENSOR
,
"TensorRT engine only takes LoDTensor as input"
);
auto
shape
=
var
->
GetShape
();
engine
_
->
DeclareInput
(
engine
->
DeclareInput
(
input
,
FluidDataType2TRT
(
var
->
Proto
()
->
type
().
lod_tensor
().
tensor
().
data_type
()),
Vec2TRT_Dims
(
var
->
GetShape
()));
}
// TODO(Superjomn) parameters should be passed after analysised from outside.
inference
::
Singleton
<
inference
::
tensorrt
::
OpConverter
>::
Global
().
ConvertBlock
(
block_desc
,
{},
context
.
scope
(),
engine_
);
block_desc
,
parameters
,
context
.
scope
(),
engine
);
// Add outputs
VLOG
(
4
)
<<
"declare outputs"
;
for
(
auto
&
output
:
context
.
Outputs
(
"Ys"
))
{
VLOG
(
4
)
<<
"declare output "
<<
output
;
engine
_
->
DeclareOutput
(
output
);
engine
->
DeclareOutput
(
output
);
}
engine
_
->
FreezeNetwork
();
engine
->
FreezeNetwork
();
}
class
TensorRTEngineOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
...
...
@@ -113,6 +120,7 @@ class TensorRTEngineOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"Xs"
,
"A list of inputs."
).
AsDuplicable
();
AddOutput
(
"Ys"
,
"A list of outputs"
).
AsDuplicable
();
AddAttr
<
std
::
string
>
(
"subgraph"
,
"the subgraph."
);
AddAttr
<
std
::
string
>
(
"engine_uniq_key"
,
"unique key for the TRT engine."
);
AddAttr
<
int
>
(
"max_batch"
,
"the maximum batch size."
);
AddAttr
<
int
>
(
"max_workspace"
,
"the maximum batch size."
);
AddComment
(
"TensorRT engine operator."
);
...
...
paddle/fluid/operators/tensorrt_engine_op.h
浏览文件 @
5fd142c3
...
...
@@ -19,10 +19,14 @@
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/inference/analysis/helper.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
namespace
paddle
{
namespace
operators
{
using
inference
::
Singleton
;
using
inference
::
tensorrt
::
TRT_EngineManager
;
class
TensorRTEngineOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
@@ -47,16 +51,18 @@ template <typename DeviceContext, typename T>
class
TensorRTEngineKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
if
(
!
engine_
)
{
auto
engine_name
=
context
.
Attr
<
std
::
string
>
(
"engine_uniq_key"
);
if
(
!
Singleton
<
TRT_EngineManager
>::
Global
().
HasEngine
(
engine_name
))
{
Prepare
(
context
);
}
auto
*
engine
=
Singleton
<
TRT_EngineManager
>::
Global
().
Get
(
engine_name
);
auto
input_names
=
context
.
op
().
Inputs
(
"Xs"
);
PADDLE_ENFORCE
(
!
input_names
.
empty
(),
"should pass more than one inputs"
);
// Try to determine a batch_size
auto
&
tensor0
=
inference
::
analysis
::
GetFromScope
<
framework
::
LoDTensor
>
(
context
.
scope
(),
input_names
.
front
());
int
batch_size
=
tensor0
.
dims
()[
0
];
PADDLE_ENFORCE_LE
(
batch_size
,
max_batch_
);
PADDLE_ENFORCE_LE
(
batch_size
,
context
.
Attr
<
int
>
(
"max_batch"
)
);
// Convert input tensor from fluid to engine.
for
(
const
auto
&
x
:
context
.
Inputs
(
"Xs"
))
{
...
...
@@ -64,20 +70,20 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
auto
&
t
=
inference
::
analysis
::
GetFromScope
<
framework
::
LoDTensor
>
(
context
.
scope
(),
x
);
if
(
platform
::
is_cpu_place
(
t
.
place
()))
{
engine
_
->
SetInputFromCPU
(
x
,
static_cast
<
const
void
*>
(
t
.
data
<
void
>
()),
engine
->
SetInputFromCPU
(
x
,
static_cast
<
const
void
*>
(
t
.
data
<
void
>
()),
t
.
memory_size
());
}
else
{
engine
_
->
SetInputFromGPU
(
x
,
static_cast
<
const
void
*>
(
t
.
data
<
void
>
()),
engine
->
SetInputFromGPU
(
x
,
static_cast
<
const
void
*>
(
t
.
data
<
void
>
()),
t
.
memory_size
());
}
}
// Execute the engine.
PADDLE_ENFORCE_GT
(
batch_size
,
0
);
engine
_
->
Execute
(
batch_size
);
engine
->
Execute
(
batch_size
);
// Convert output tensor from engine to fluid
for
(
const
auto
&
y
:
context
.
Outputs
(
"Ys"
))
{
// convert output and copy to fluid.
nvinfer1
::
ITensor
*
trt_t
=
engine
_
->
GetITensor
(
y
);
nvinfer1
::
ITensor
*
trt_t
=
engine
->
GetITensor
(
y
);
auto
dims
=
trt_t
->
getDimensions
();
// Use the output ITensor's dims to reshape the Fluid Tensor.
std
::
vector
<
int
>
ddim
(
dims
.
d
,
dims
.
d
+
dims
.
nbDims
);
...
...
@@ -89,27 +95,22 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
auto
size
=
inference
::
analysis
::
AccuDims
(
dims
.
d
,
dims
.
nbDims
);
if
(
platform
::
is_cpu_place
(
fluid_t
->
place
()))
{
// TODO(Superjomn) change this float to dtype size.
engine
_
->
GetOutputInCPU
(
engine
->
GetOutputInCPU
(
y
,
fluid_t
->
mutable_data
<
float
>
(
platform
::
CPUPlace
()),
size
*
sizeof
(
float
));
}
else
{
engine
_
->
GetOutputInGPU
(
engine
->
GetOutputInGPU
(
y
,
fluid_t
->
mutable_data
<
float
>
(
platform
::
CUDAPlace
()),
size
*
sizeof
(
float
));
}
}
cudaStreamSynchronize
(
stream_
);
cudaStreamSynchronize
(
*
engine
->
stream
()
);
}
protected:
// Build the engine.
void
Prepare
(
const
framework
::
ExecutionContext
&
context
)
const
;
private:
mutable
cudaStream_t
stream_
;
mutable
inference
::
tensorrt
::
TensorRTEngine
*
engine_
{
nullptr
};
mutable
int
max_batch_
{
0
};
};
}
// namespace operators
...
...
paddle/fluid/operators/tensorrt_engine_op_test.cc
浏览文件 @
5fd142c3
...
...
@@ -79,6 +79,17 @@ void SetAttr<int64_t>(framework::proto::OpDesc* op, const std::string& name,
attr
->
set_type
(
paddle
::
framework
::
proto
::
AttrType
::
LONG
);
attr
->
set_l
(
data
);
}
template
<
>
void
SetAttr
<
std
::
vector
<
std
::
string
>>
(
framework
::
proto
::
OpDesc
*
op
,
const
std
::
string
&
name
,
const
std
::
vector
<
std
::
string
>&
data
)
{
auto
*
attr
=
op
->
add_attrs
();
attr
->
set_name
(
name
);
attr
->
set_type
(
paddle
::
framework
::
proto
::
AttrType
::
STRINGS
);
for
(
const
auto
&
s
:
data
)
{
attr
->
add_strings
(
s
.
c_str
());
}
}
}
// namespace
...
...
@@ -123,11 +134,15 @@ TEST(TensorRTEngineOp, manual) {
engine_op_desc
.
SetOutput
(
"Ys"
,
std
::
vector
<
std
::
string
>
({
"z0"
}));
SetAttr
<
std
::
string
>
(
engine_op_desc
.
Proto
(),
"subgraph"
,
block_
->
SerializeAsString
());
SetAttr
<
int
>
(
engine_op_desc
.
Proto
(),
"max_batch"
,
3
0
);
SetAttr
<
int
>
(
engine_op_desc
.
Proto
(),
"max_batch"
,
10
0
);
SetAttr
<
int
>
(
engine_op_desc
.
Proto
(),
"max_workspace"
,
1
<<
10
);
SetAttr
<
std
::
string
>
(
engine_op_desc
.
Proto
(),
"engine_uniq_key"
,
"a_engine"
);
SetAttr
<
std
::
vector
<
std
::
string
>>
(
engine_op_desc
.
Proto
(),
"parameters"
,
std
::
vector
<
std
::
string
>
({}));
LOG
(
INFO
)
<<
"create engine op"
;
auto
engine_op
=
framework
::
OpRegistry
::
CreateOp
(
*
engine_op_desc
.
Proto
());
LOG
(
INFO
)
<<
"engine_op "
<<
engine_op
.
get
();
framework
::
Scope
scope
;
platform
::
CPUPlace
place
;
...
...
@@ -145,6 +160,88 @@ TEST(TensorRTEngineOp, manual) {
engine_op
->
Run
(
scope
,
place
);
}
void
Execute
(
int
batch_size
,
int
input_dim
,
int
output_dim
,
int
nlayers
=
1
)
{
framework
::
ProgramDesc
program
;
framework
::
Scope
scope
;
platform
::
CPUPlace
place
;
platform
::
CPUDeviceContext
ctx
(
place
);
auto
*
block_
=
program
.
Proto
()
->
add_blocks
();
block_
->
set_idx
(
0
);
block_
->
set_parent_idx
(
-
1
);
using
shape_t
=
std
::
vector
<
int64_t
>
;
LOG
(
INFO
)
<<
"create block desc"
;
framework
::
BlockDesc
block_desc
(
&
program
,
block_
);
auto
AddFCLayer
=
[
&
](
const
std
::
string
&
x_name
,
const
std
::
string
&
y_name
,
const
std
::
string
&
z_name
,
bool
x_created
,
const
shape_t
&
x_shape
,
const
shape_t
&
y_shape
,
const
shape_t
&
z_shape
)
{
LOG
(
INFO
)
<<
"create fc op"
;
auto
*
fc
=
block_desc
.
AppendOp
();
fc
->
SetType
(
"mul"
);
fc
->
SetInput
(
"X"
,
std
::
vector
<
std
::
string
>
({
x_name
}));
fc
->
SetInput
(
"Y"
,
std
::
vector
<
std
::
string
>
({
y_name
}));
fc
->
SetOutput
(
"Out"
,
std
::
vector
<
std
::
string
>
({
z_name
}));
// Set inputs' variable shape in BlockDesc
if
(
!
x_created
)
{
AddTensorToBlockDesc
(
block_
,
x_name
,
std
::
vector
<
int64_t
>
({
batch_size
,
input_dim
,
1
,
1
}));
}
AddTensorToBlockDesc
(
block_
,
y_name
,
std
::
vector
<
int64_t
>
({
input_dim
,
output_dim
}));
AddTensorToBlockDesc
(
block_
,
z_name
,
std
::
vector
<
int64_t
>
({
batch_size
,
output_dim
}));
// Prepare variables.
if
(
!
x_created
)
{
CreateCPUTensor
(
&
scope
,
x_name
,
std
::
vector
<
int64_t
>
(
x_shape
));
}
CreateCPUTensor
(
&
scope
,
y_name
,
std
::
vector
<
int64_t
>
(
y_shape
));
CreateCPUTensor
(
&
scope
,
z_name
,
std
::
vector
<
int64_t
>
(
z_shape
));
// It is wired, need to copy manually.
*
block_
->
add_ops
()
=
*
fc
->
Proto
();
};
// Test with 4 layer FC
AddFCLayer
(
"x0"
,
"y0"
,
"z0"
,
false
,
{
batch_size
,
input_dim
},
{
input_dim
,
output_dim
},
{
batch_size
,
output_dim
});
AddFCLayer
(
"z0"
,
"y1"
,
"z1"
,
true
,
{},
{
output_dim
,
output_dim
},
{
batch_size
,
output_dim
});
AddFCLayer
(
"z1"
,
"y2"
,
"z2"
,
true
,
{},
{
output_dim
,
output_dim
},
{
batch_size
,
output_dim
});
AddFCLayer
(
"z2"
,
"y3"
,
"z3"
,
true
,
{},
{
output_dim
,
output_dim
},
{
batch_size
,
output_dim
});
LOG
(
INFO
)
<<
"create tensorrt desc"
;
framework
::
OpDesc
engine_op_desc
(
nullptr
);
engine_op_desc
.
SetType
(
"tensorrt_engine"
);
engine_op_desc
.
SetInput
(
"Xs"
,
std
::
vector
<
std
::
string
>
({
"x0"
}));
engine_op_desc
.
SetOutput
(
"Ys"
,
std
::
vector
<
std
::
string
>
({
"z3"
}));
SetAttr
<
std
::
string
>
(
engine_op_desc
.
Proto
(),
"subgraph"
,
block_
->
SerializeAsString
());
SetAttr
<
int
>
(
engine_op_desc
.
Proto
(),
"max_batch"
,
batch_size
);
SetAttr
<
int
>
(
engine_op_desc
.
Proto
(),
"max_workspace"
,
2
<<
10
);
SetAttr
<
std
::
vector
<
std
::
string
>>
(
engine_op_desc
.
Proto
(),
"parameters"
,
std
::
vector
<
std
::
string
>
({
"y0"
,
"y1"
,
"y2"
,
"y3"
}));
SetAttr
<
std
::
string
>
(
engine_op_desc
.
Proto
(),
"engine_uniq_key"
,
"b_engine"
);
auto
engine_op
=
framework
::
OpRegistry
::
CreateOp
(
*
engine_op_desc
.
Proto
());
// Execute them.
engine_op
->
Run
(
scope
,
place
);
}
// Test with a larger FC layer.
TEST
(
TensorRTEngineOp
,
fc
)
{
Execute
(
40
,
256
,
256
);
}
}
// namespace operators
}
// namespace paddle
...
...
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