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585d12a3
编写于
9月 19, 2017
作者:
X
Xinghai Sun
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add is_training attr and testing phrase compuation to dropout operator.
Change type of dropout_prob to template typename.
上级
32645b52
变更
4
显示空白变更内容
内联
并排
Showing
4 changed file
with
103 addition
and
56 deletion
+103
-56
paddle/operators/dropout_op.cc
paddle/operators/dropout_op.cc
+17
-6
paddle/operators/dropout_op.cu
paddle/operators/dropout_op.cu
+24
-22
paddle/operators/dropout_op.h
paddle/operators/dropout_op.h
+31
-25
python/paddle/v2/framework/tests/test_dropout_op.py
python/paddle/v2/framework/tests/test_dropout_op.py
+31
-3
未找到文件。
paddle/operators/dropout_op.cc
浏览文件 @
585d12a3
...
...
@@ -30,6 +30,10 @@ class DropoutOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE_GE
(
ctx
.
Attr
<
float
>
(
"dropout_prob"
),
0
);
PADDLE_ENFORCE_LE
(
ctx
.
Attr
<
float
>
(
"dropout_prob"
),
1
);
// TODO(xinghai-sun): remove this check after swtiching to bool
PADDLE_ENFORCE
(
ctx
.
Attr
<
int
>
(
"is_training"
)
==
0
||
ctx
.
Attr
<
int
>
(
"is_training"
)
==
1
);
// resize
auto
dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
ctx
.
Output
<
LoDTensor
>
(
"Out"
)
->
Resize
(
dims
);
...
...
@@ -37,13 +41,16 @@ class DropoutOp : public framework::OperatorWithKernel {
}
};
template
<
typename
AttrType
>
class
DropoutOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
DropoutOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddAttr
<
float
>
(
"dropout_prob"
,
"Probability for dropping out units
."
)
AddAttr
<
AttrType
>
(
"dropout_prob"
,
"Probability of setting units to zero
."
)
.
SetDefault
(
.5
f
);
// TODO(xinghai-sun): use bool for is_training after bool is supported.
AddAttr
<
int
>
(
"is_training"
,
"Whether in training phase."
).
SetDefault
(
1
);
AddAttr
<
int
>
(
"seed"
,
"Dropout random seed."
).
SetDefault
(
0
);
AddInput
(
"X"
,
"The input of dropout op."
);
AddOutput
(
"Out"
,
"The output of dropout op."
);
...
...
@@ -61,6 +68,7 @@ being set to their inputs.
}
};
template
<
typename
AttrType
>
class
DropoutOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
@@ -72,8 +80,11 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Mask"
),
"Mask must not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) must not be null."
);
PADDLE_ENFORCE_GE
(
ctx
.
Attr
<
float
>
(
"dropout_prob"
),
0
);
PADDLE_ENFORCE_LE
(
ctx
.
Attr
<
float
>
(
"dropout_prob"
),
1
);
PADDLE_ENFORCE_GE
(
ctx
.
Attr
<
AttrType
>
(
"dropout_prob"
),
0
);
PADDLE_ENFORCE_LE
(
ctx
.
Attr
<
AttrType
>
(
"dropout_prob"
),
1
);
// TODO(xinghai-sun): remove this check after swtiching to bool
PADDLE_ENFORCE
(
ctx
.
Attr
<
int
>
(
"is_training"
)
==
0
||
ctx
.
Attr
<
int
>
(
"is_training"
)
==
1
);
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
mask_dims
=
ctx
.
Input
<
Tensor
>
(
"Mask"
)
->
dims
();
auto
out_dims
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
dims
();
...
...
@@ -91,9 +102,9 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
dropout
,
ops
::
DropoutOp
,
ops
::
DropoutOpMaker
,
dropout_grad
,
ops
::
DropoutOpGrad
);
REGISTER_OP
(
dropout
,
ops
::
DropoutOp
,
ops
::
DropoutOpMaker
<
float
>
,
dropout_grad
,
ops
::
DropoutOpGrad
<
float
>
);
REGISTER_OP_CPU_KERNEL
(
dropout
,
ops
::
CPUDropoutKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
dropout
,
ops
::
CPUDropoutKernel
<
paddle
::
platform
::
CPUPlace
,
float
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
dropout_grad
,
ops
::
DropoutGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/dropout_op.cu
浏览文件 @
585d12a3
...
...
@@ -22,18 +22,18 @@
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
template
<
typename
T
,
typename
AttrType
>
struct
MaskGenerator
{
float
dropout_prob
;
AttrType
dropout_prob
;
int
seed
;
__host__
__device__
MaskGenerator
(
float
dropout_prob
,
int
seed
)
__host__
__device__
MaskGenerator
(
AttrType
dropout_prob
,
int
seed
)
:
dropout_prob
(
dropout_prob
),
seed
(
seed
)
{}
__host__
__device__
T
operator
()(
const
unsigned
int
n
)
const
{
thrust
::
minstd_rand
rng
;
rng
.
seed
(
seed
);
thrust
::
uniform_real_distribution
<
T
>
dist
(
0
,
1
);
thrust
::
uniform_real_distribution
<
AttrType
>
dist
(
0
,
1
);
rng
.
discard
(
n
);
if
(
dist
(
rng
)
<
dropout_prob
)
{
return
static_cast
<
T
>
(
0
);
...
...
@@ -46,33 +46,35 @@ struct MaskGenerator {
// It seems that Eigen::Tensor::setRandom in GPU will SEGFAULT.
// Use std::random and thrust::random(thrust is a std library in CUDA) to
// implement uniform random.
template
<
typename
Place
,
typename
T
>
template
<
typename
Place
,
typename
T
,
typename
AttrType
>
class
GPUDropoutKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
context
.
Output
<
Tensor
>
(
"Out"
);
auto
*
mask
=
context
.
Output
<
Tensor
>
(
"Mask"
);
y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
*
mask
=
context
.
Output
<
Tensor
>
(
"Mask"
);
auto
*
mask_data
=
mask
->
mutable_data
<
T
>
(
context
.
GetPlace
());
AttrType
dropout_prob
=
context
.
Attr
<
AttrType
>
(
"dropout_prob"
);
float
dropout_prob
=
context
.
Attr
<
float
>
(
"dropout_prob"
);
auto
X
=
EigenMatrix
<
T
>::
Reshape
(
*
x
,
1
);
auto
Y
=
EigenMatrix
<
T
>::
Reshape
(
*
y
,
1
);
auto
M
=
EigenMatrix
<
T
>::
Reshape
(
*
mask
,
1
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
int
size
=
framework
::
product
(
mask
->
dims
());
if
(
context
.
Attr
<
int
>
(
"is_training"
)
==
1
)
{
int
seed
=
context
.
Attr
<
int
>
(
"seed"
);
thrust
::
counting_iterator
<
unsigned
int
>
index_sequence_begin
(
0
);
int
size
=
framework
::
product
(
mask
->
dims
());
T
*
mask_data
=
mask
->
mutable_data
<
T
>
(
context
.
GetPlace
());
thrust
::
transform
(
index_sequence_begin
,
index_sequence_begin
+
size
,
thrust
::
device_ptr
<
T
>
(
mask_data
),
MaskGenerator
<
T
>
(
dropout_prob
,
seed
));
auto
dims
=
x
->
dims
();
auto
new_dims
=
framework
::
make_ddim
({
dims
[
0
],
size
/
dims
[
0
]});
auto
X
=
EigenMatrix
<
T
>::
From
(
*
x
,
new_dims
);
auto
Y
=
EigenMatrix
<
T
>::
From
(
*
y
,
new_dims
);
auto
M
=
EigenMatrix
<
T
>::
From
(
*
mask
,
new_dims
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
MaskGenerator
<
T
,
AttrType
>
(
dropout_prob
,
seed
));
Y
.
device
(
place
)
=
X
*
M
;
// TODO(xinghai-sun): add test time logits.
}
else
{
cudaMemset
(
mask_data
,
0
,
sizeof
(
T
)
*
size
);
Y
.
device
(
place
)
=
X
*
dropout_prob
;
}
}
};
...
...
@@ -81,6 +83,6 @@ class GPUDropoutKernel : public framework::OpKernel {
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
dropout
,
ops
::
GPUDropoutKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
dropout
,
ops
::
GPUDropoutKernel
<
paddle
::
platform
::
GPUPlace
,
float
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
dropout_grad
,
ops
::
DropoutGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/dropout_op.h
浏览文件 @
585d12a3
...
...
@@ -25,23 +25,24 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
Place
,
typename
T
>
template
<
typename
Place
,
typename
T
,
typename
AttrType
>
class
CPUDropoutKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
context
.
Output
<
Tensor
>
(
"Out"
);
auto
*
mask
=
context
.
Output
<
Tensor
>
(
"Mask"
);
T
*
mask_data
=
mask
->
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
y_data
=
y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
T
*
x_data
=
x
->
data
<
T
>
();
auto
*
mask_data
=
mask
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
*
y_data
=
y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
auto
*
x_data
=
x
->
data
<
T
>
();
float
dropout_prob
=
context
.
Attr
<
float
>
(
"dropout_prob"
);
int
seed
=
context
.
Attr
<
int
>
(
"seed"
);
AttrType
dropout_prob
=
context
.
Attr
<
AttrType
>
(
"dropout_prob"
);
if
(
context
.
Attr
<
int
>
(
"is_training"
)
==
1
)
{
int
seed
=
context
.
Attr
<
int
>
(
"seed"
);
std
::
minstd_rand
engine
;
engine
.
seed
(
seed
);
std
::
uniform_real_distribution
<
T
>
dist
(
0
,
1
);
std
::
uniform_real_distribution
<
AttrType
>
dist
(
0
,
1
);
size_t
size
=
framework
::
product
(
mask
->
dims
());
for
(
size_t
i
=
0
;
i
<
size
;
++
i
)
{
if
(
dist
(
engine
)
<
dropout_prob
)
{
...
...
@@ -52,7 +53,14 @@ class CPUDropoutKernel : public framework::OpKernel {
y_data
[
i
]
=
x_data
[
i
];
}
}
// TODO: add test phase logits.
}
else
{
size_t
size
=
framework
::
product
(
mask
->
dims
());
memset
(
mask_data
,
0
,
sizeof
(
T
)
*
size
);
auto
X
=
EigenMatrix
<
T
>::
Reshape
(
*
x
,
1
);
auto
Y
=
EigenMatrix
<
T
>::
Reshape
(
*
y
,
1
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
Y
.
device
(
place
)
=
X
*
dropout_prob
;
}
}
};
...
...
@@ -60,21 +68,19 @@ template <typename Place, typename T>
class
DropoutGradKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
PADDLE_ENFORCE_EQ
(
context
.
Attr
<
int
>
(
"is_training"
),
1
,
"Only callable when is_training is true"
);
auto
*
grad_x
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
grad_y
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
mask
=
context
.
Input
<
Tensor
>
(
"Mask"
);
grad_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
dims
=
grad_x
->
dims
();
int
size
=
static_cast
<
int
>
(
framework
::
product
(
dims
));
auto
new_dims
=
framework
::
make_ddim
({
dims
[
0
],
size
/
dims
[
0
]});
auto
M
=
EigenMatrix
<
T
>::
From
(
*
mask
,
new_dims
);
auto
dX
=
EigenMatrix
<
T
>::
From
(
*
grad_x
,
new_dims
);
auto
dY
=
EigenMatrix
<
T
>::
From
(
*
grad_y
,
new_dims
);
auto
M
=
EigenMatrix
<
T
>::
Reshape
(
*
mask
,
1
);
auto
dX
=
EigenMatrix
<
T
>::
Reshape
(
*
grad_x
,
1
);
auto
dY
=
EigenMatrix
<
T
>::
Reshape
(
*
grad_y
,
1
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
dX
.
device
(
place
)
=
dY
*
M
;
// TODO: add test time logits.
}
};
...
...
python/paddle/v2/framework/tests/test_dropout_op.py
浏览文件 @
585d12a3
...
...
@@ -7,7 +7,7 @@ class TestDropoutOp(OpTest):
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
64
)).
astype
(
"float32"
)}
self
.
attrs
=
{
'dropout_prob'
:
0.0
}
self
.
attrs
=
{
'dropout_prob'
:
0.0
,
'is_training'
:
1
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
],
'Mask'
:
np
.
ones
((
32
,
64
))}
def
test_check_output
(
self
):
...
...
@@ -21,7 +21,7 @@ class TestDropoutOp2(TestDropoutOp):
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
64
)).
astype
(
"float32"
)}
self
.
attrs
=
{
'dropout_prob'
:
1.0
}
self
.
attrs
=
{
'dropout_prob'
:
1.0
,
'is_training'
:
1
}
self
.
outputs
=
{
'Out'
:
np
.
zeros
((
32
,
64
)),
'Mask'
:
np
.
zeros
((
32
,
64
))}
...
...
@@ -29,9 +29,37 @@ class TestDropoutOp3(TestDropoutOp):
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
64
,
2
)).
astype
(
"float32"
)}
self
.
attrs
=
{
'dropout_prob'
:
0.0
}
self
.
attrs
=
{
'dropout_prob'
:
0.0
,
'is_training'
:
1
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
],
'Mask'
:
np
.
ones
((
32
,
64
,
2
))}
class
TestDropoutOp4
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
64
)).
astype
(
"float32"
)}
self
.
attrs
=
{
'dropout_prob'
:
0.35
,
'is_training'
:
0
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
]
*
self
.
attrs
[
'dropout_prob'
],
'Mask'
:
np
.
zeros
((
32
,
64
))
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestDropoutOp5
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
64
,
3
)).
astype
(
"float32"
)}
self
.
attrs
=
{
'dropout_prob'
:
0.75
,
'is_training'
:
0
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
]
*
self
.
attrs
[
'dropout_prob'
],
'Mask'
:
np
.
zeros
((
32
,
64
,
3
))
}
def
test_check_output
(
self
):
self
.
check_output
()
if
__name__
==
'__main__'
:
unittest
.
main
()
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