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470335e8
编写于
8月 21, 2018
作者:
Z
Zhaolong Xing
提交者:
GitHub
8月 21, 2018
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Merge pull request #12786 from NHZlX/add_batch_norm_trt_converter
Add batch norm trt converter
上级
3d11d018
ff052c0e
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
225 addition
and
2 deletion
+225
-2
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
+4
-1
paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc
paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc
+136
-0
paddle/fluid/inference/tensorrt/convert/test_batch_norm_op.cc
...le/fluid/inference/tensorrt/convert/test_batch_norm_op.cc
+71
-0
paddle/fluid/inference/tensorrt/convert/ut_helper.h
paddle/fluid/inference/tensorrt/convert/ut_helper.h
+14
-1
未找到文件。
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @
470335e8
# Add TRT tests
nv_library
(
tensorrt_converter
SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc
activation_op.cc softmax_op.cc
batch_norm_op.cc activation_op.cc softmax_op.cc
DEPS tensorrt_engine operator scope framework_proto op_registry
)
nv_test
(
test_op_converter SRCS test_op_converter.cc DEPS
...
...
@@ -24,3 +24,6 @@ nv_test(test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc
nv_test
(
test_trt_softmax_op SRCS test_softmax_op.cc softmax_op.cc
DEPS
${
FLUID_CORE_MODULES
}
tensorrt_engine softmax_op SERIAL
)
nv_test
(
test_trt_batch_norm_op SRCS test_batch_norm_op.cc batch_norm_op.cc
DEPS
${
FLUID_CORE_MODULES
}
tensorrt_engine batch_norm_op SERIAL
)
paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc
0 → 100644
浏览文件 @
470335e8
/* 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 <math.h>
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
class
BatchNormOpConverter
:
public
OpConverter
{
public:
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
,
bool
test_mode
)
override
{
LOG
(
INFO
)
<<
"convert a fluid batch norm op to tensorrt batch_norm"
;
framework
::
OpDesc
op_desc
(
op
,
nullptr
);
PADDLE_ENFORCE_EQ
(
op_desc
.
Input
(
"X"
).
size
(),
1
);
PADDLE_ENFORCE_EQ
(
op_desc
.
Input
(
"Bias"
).
size
(),
1
);
// Bias is a weight
PADDLE_ENFORCE_EQ
(
op_desc
.
Input
(
"Mean"
).
size
(),
1
);
// Mean is a weight
PADDLE_ENFORCE_EQ
(
op_desc
.
Input
(
"Scale"
).
size
(),
1
);
// Scale is a weight
PADDLE_ENFORCE_EQ
(
op_desc
.
Input
(
"Variance"
).
size
(),
1
);
// Variance is a weight
PADDLE_ENFORCE_EQ
(
op_desc
.
Output
(
"Y"
).
size
(),
1
);
auto
*
X
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
).
front
());
// Declare weights
auto
*
Bias_v
=
scope
.
FindVar
(
op_desc
.
Input
(
"Bias"
).
front
());
auto
*
Mean_v
=
scope
.
FindVar
(
op_desc
.
Input
(
"Mean"
).
front
());
auto
*
Scale_v
=
scope
.
FindVar
(
op_desc
.
Input
(
"Scale"
).
front
());
auto
*
Variance_v
=
scope
.
FindVar
(
op_desc
.
Input
(
"Variance"
).
front
());
const
float
eps
=
boost
::
get
<
float
>
(
op_desc
.
GetAttr
(
"epsilon"
));
PADDLE_ENFORCE_NOT_NULL
(
Bias_v
);
PADDLE_ENFORCE_NOT_NULL
(
Mean_v
);
PADDLE_ENFORCE_NOT_NULL
(
Scale_v
);
PADDLE_ENFORCE_NOT_NULL
(
Variance_v
);
// get tensor
auto
*
Bias_t
=
Bias_v
->
GetMutable
<
framework
::
LoDTensor
>
();
auto
*
Mean_t
=
Mean_v
->
GetMutable
<
framework
::
LoDTensor
>
();
auto
*
Scale_t
=
Scale_v
->
GetMutable
<
framework
::
LoDTensor
>
();
auto
*
Variance_t
=
Variance_v
->
GetMutable
<
framework
::
LoDTensor
>
();
// create temp tensor for weights
framework
::
LoDTensor
bias_tensor
;
framework
::
LoDTensor
mean_tensor
;
framework
::
LoDTensor
scale_tensor
;
framework
::
LoDTensor
variance_tensor
;
bias_tensor
.
Resize
(
Bias_t
->
dims
());
mean_tensor
.
Resize
(
Mean_t
->
dims
());
scale_tensor
.
Resize
(
Scale_t
->
dims
());
variance_tensor
.
Resize
(
Variance_t
->
dims
());
platform
::
CPUPlace
cpu_place
;
// copy data from gpu to cpu
TensorCopySync
((
*
Bias_t
),
cpu_place
,
&
bias_tensor
);
TensorCopySync
((
*
Mean_t
),
cpu_place
,
&
mean_tensor
);
TensorCopySync
((
*
Scale_t
),
cpu_place
,
&
scale_tensor
);
TensorCopySync
((
*
Variance_t
),
cpu_place
,
&
variance_tensor
);
auto
*
bias_data
=
bias_tensor
.
mutable_data
<
float
>
(
platform
::
CPUPlace
());
auto
*
mean_data
=
mean_tensor
.
mutable_data
<
float
>
(
platform
::
CPUPlace
());
auto
*
scale_data
=
scale_tensor
.
mutable_data
<
float
>
(
platform
::
CPUPlace
());
auto
*
variance_data
=
variance_tensor
.
mutable_data
<
float
>
(
platform
::
CPUPlace
());
std
::
unique_ptr
<
framework
::
LoDTensor
>
combile_scale_tensor
(
new
framework
::
LoDTensor
());
std
::
unique_ptr
<
framework
::
LoDTensor
>
combile_bias_tensor
(
new
framework
::
LoDTensor
());
combile_scale_tensor
->
Resize
(
scale_tensor
.
dims
());
combile_bias_tensor
->
Resize
(
bias_tensor
.
dims
());
auto
*
combile_scale_data
=
combile_scale_tensor
->
mutable_data
<
float
>
(
platform
::
CPUPlace
());
auto
*
combile_bias_data
=
combile_bias_tensor
->
mutable_data
<
float
>
(
platform
::
CPUPlace
());
size_t
ele_num
=
combile_scale_tensor
->
memory_size
()
/
sizeof
(
float
);
for
(
size_t
i
=
0
;
i
<
ele_num
;
i
++
)
{
float
scale
=
scale_data
[
i
];
float
bias
=
bias_data
[
i
];
float
mean
=
mean_data
[
i
];
float
variance
=
variance_data
[
i
];
combile_scale_data
[
i
]
=
scale
/
sqrtf
(
variance
+
eps
);
combile_bias_data
[
i
]
=
bias
-
mean
*
combile_scale_data
[
i
];
}
TensorRTEngine
::
Weight
scale_weights
{
nvinfer1
::
DataType
::
kFLOAT
,
static_cast
<
void
*>
(
combile_scale_data
),
combile_scale_tensor
->
memory_size
()
/
sizeof
(
float
)};
TensorRTEngine
::
Weight
shift_weights
{
nvinfer1
::
DataType
::
kFLOAT
,
static_cast
<
void
*>
(
combile_bias_data
),
combile_bias_tensor
->
memory_size
()
/
sizeof
(
float
)};
TensorRTEngine
::
Weight
power_weights
{
nvinfer1
::
DataType
::
kFLOAT
,
nullptr
,
0
};
nvinfer1
::
IScaleLayer
*
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Scale
,
*
const_cast
<
nvinfer1
::
ITensor
*>
(
X
),
nvinfer1
::
ScaleMode
::
kCHANNEL
,
shift_weights
.
get
(),
scale_weights
.
get
(),
power_weights
.
get
());
auto
output_name
=
op_desc
.
Output
(
"Y"
).
front
();
engine_
->
weight_map
[
op_desc
.
Input
(
"Bias"
).
front
()]
=
std
::
move
(
combile_bias_tensor
);
engine_
->
weight_map
[
op_desc
.
Input
(
"Scale"
).
front
()]
=
std
::
move
(
combile_scale_tensor
);
engine_
->
SetITensor
(
output_name
,
layer
->
getOutput
(
0
));
if
(
test_mode
)
{
engine_
->
DeclareOutput
(
output_name
);
}
}
};
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
REGISTER_TRT_OP_CONVERTER
(
batch_norm
,
BatchNormOpConverter
);
paddle/fluid/inference/tensorrt/convert/test_batch_norm_op.cc
0 → 100644
浏览文件 @
470335e8
/* 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
(
batch_norm_op
,
test
)
{
std
::
unordered_set
<
std
::
string
>
parameters
(
{
"batch_norm_scale"
,
"batch_norm_bias"
,
"batch_norm_mean"
,
"batch_norm_variance"
});
framework
::
Scope
scope
;
TRTConvertValidation
validator
(
5
,
parameters
,
scope
,
1
<<
15
);
std
::
vector
<
int
>
param_shape
{
2
};
validator
.
DeclInputVar
(
"batch_norm_X"
,
nvinfer1
::
DimsCHW
(
2
,
5
,
5
));
validator
.
DeclParamVar
(
"batch_norm_scale"
,
param_shape
);
validator
.
DeclParamVar
(
"batch_norm_bias"
,
param_shape
);
validator
.
DeclParamVar
(
"batch_norm_mean"
,
param_shape
);
validator
.
DeclParamVar
(
"batch_norm_variance"
,
param_shape
);
validator
.
DeclOutputVar
(
"batch_norm_Y"
,
nvinfer1
::
DimsCHW
(
2
,
5
,
5
));
validator
.
DeclOutputVar
(
"batch_norm_save_mean"
,
param_shape
);
validator
.
DeclOutputVar
(
"batch_norm_save_variance"
,
param_shape
);
// Prepare Op description
framework
::
OpDesc
desc
;
desc
.
SetType
(
"batch_norm"
);
desc
.
SetInput
(
"X"
,
{
"batch_norm_X"
});
desc
.
SetInput
(
"Scale"
,
{
"batch_norm_scale"
});
desc
.
SetInput
(
"Bias"
,
{
"batch_norm_bias"
});
desc
.
SetInput
(
"Mean"
,
{
"batch_norm_mean"
});
desc
.
SetInput
(
"Variance"
,
{
"batch_norm_variance"
});
desc
.
SetOutput
(
"Y"
,
{
"batch_norm_Y"
});
desc
.
SetOutput
(
"MeanOut"
,
{
"batch_norm_mean"
});
desc
.
SetOutput
(
"VarianceOut"
,
{
"batch_norm_variance"
});
desc
.
SetOutput
(
"SavedMean"
,
{
"batch_norm_save_mean"
});
desc
.
SetOutput
(
"SavedVariance"
,
{
"batch_norm_save_variance"
});
float
eps
=
1e-5
f
;
bool
is_test
=
true
;
desc
.
SetAttr
(
"epsilon"
,
eps
);
desc
.
SetAttr
(
"is_test"
,
is_test
);
validator
.
SetOp
(
*
desc
.
Proto
());
std
::
unordered_set
<
std
::
string
>
neglected_output
=
{
"batch_norm_save_mean"
,
"batch_norm_save_variance"
,
"batch_norm_mean"
,
"batch_norm_variance"
};
validator
.
Execute
(
3
,
neglected_output
);
}
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
USE_OP
(
batch_norm
);
paddle/fluid/inference/tensorrt/convert/ut_helper.h
浏览文件 @
470335e8
...
...
@@ -98,11 +98,19 @@ class TRTConvertValidation {
engine_
->
DeclareInput
(
name
,
nvinfer1
::
DataType
::
kFLOAT
,
dims
);
}
void
DeclParamVar
(
const
std
::
string
&
name
,
const
std
::
vector
<
int
>
dim_vec
)
{
DeclVar
(
name
,
dim_vec
);
}
// Declare a parameter varaible in the scope.
void
DeclParamVar
(
const
std
::
string
&
name
,
const
nvinfer1
::
Dims
&
dims
)
{
DeclVar
(
name
,
dims
,
true
);
}
void
DeclOutputVar
(
const
std
::
string
&
name
,
const
std
::
vector
<
int
>
dim_vec
)
{
DeclVar
(
name
,
dim_vec
);
}
void
DeclOutputVar
(
const
std
::
string
&
name
,
const
nvinfer1
::
Dims
&
dims
)
{
DeclVar
(
name
,
dims
);
}
...
...
@@ -155,7 +163,11 @@ class TRTConvertValidation {
}
}
void
Execute
(
int
batch_size
)
{
// We use the set 'neglected_output' here, because some Ops like batch norm,
// the outputs specified in the op des are only used during training,
// so we should neglect those output during inference.
void
Execute
(
int
batch_size
,
std
::
unordered_set
<
std
::
string
>
neglected_output
=
{})
{
// Execute Fluid Op
PADDLE_ENFORCE_LE
(
batch_size
,
max_batch_size_
);
platform
::
CUDAPlace
place
;
...
...
@@ -168,6 +180,7 @@ class TRTConvertValidation {
ASSERT_FALSE
(
op_desc_
->
OutputArgumentNames
().
empty
());
const
size_t
output_space_size
=
3000
;
for
(
const
auto
&
output
:
op_desc_
->
OutputArgumentNames
())
{
if
(
neglected_output
.
count
(
output
))
continue
;
std
::
vector
<
float
>
fluid_out
;
std
::
vector
<
float
>
trt_out
(
output_space_size
);
engine_
->
GetOutputInCPU
(
output
,
&
trt_out
[
0
],
output_space_size
);
...
...
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