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体验新版 GitCode,发现更多精彩内容 >>
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0bc5a122
编写于
10月 12, 2017
作者:
G
guosheng
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Refine gru_unit_op by optional bias
上级
1cabdb87
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
69 addition
and
35 deletion
+69
-35
paddle/operators/gru_unit_op.cc
paddle/operators/gru_unit_op.cc
+23
-21
paddle/operators/gru_unit_op.h
paddle/operators/gru_unit_op.h
+13
-7
python/paddle/v2/framework/tests/test_gru_unit_op.py
python/paddle/v2/framework/tests/test_gru_unit_op.py
+33
-7
未找到文件。
paddle/operators/gru_unit_op.cc
浏览文件 @
0bc5a122
...
...
@@ -31,8 +31,6 @@ class GRUUnitOp : public framework::OperatorWithKernel {
"Input(%s) of GRUUnitOp should not be null."
,
"HiddenPrev"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Weight"
),
"Input(%s) of GRUUnitOp should not be null."
,
"Weight"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Bias"
),
"Input(%s) of GRUUnitOp should not be null."
,
"Bias"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Gate"
),
"Output(%s) of GRUUnitOp should not be null."
,
"Gate"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"ResetHiddenPrev"
),
...
...
@@ -43,14 +41,11 @@ class GRUUnitOp : public framework::OperatorWithKernel {
auto
input_dims
=
ctx
->
GetInputDim
(
"Input"
);
auto
hidden_prev_dims
=
ctx
->
GetInputDim
(
"HiddenPrev"
);
auto
weight_dims
=
ctx
->
GetInputDim
(
"Weight"
);
auto
bias_dims
=
ctx
->
GetInputDim
(
"Bias"
);
int
batch_size
=
input_dims
[
0
];
int
input_size
=
input_dims
[
1
];
int
frame_size
=
hidden_prev_dims
[
1
];
int
weight_height
=
weight_dims
[
0
];
int
weight_width
=
weight_dims
[
1
];
int
bias_height
=
bias_dims
[
0
];
int
bias_width
=
bias_dims
[
1
];
PADDLE_ENFORCE_EQ
(
input_size
,
frame_size
*
3
,
"The input_size must be 3 times of frame_size in GRUUnitOp."
);
...
...
@@ -60,10 +55,16 @@ class GRUUnitOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
weight_width
,
frame_size
*
3
,
"The shape of Weight matrix must be [frame_size, frame_size * 3]."
);
PADDLE_ENFORCE_EQ
(
bias_height
,
1
,
"The shape of Bias must be [1, frame_size * 3]."
);
PADDLE_ENFORCE_EQ
(
bias_width
,
frame_size
*
3
,
"The shape of Bias must be [1, frame_size * 3]."
);
auto
bias
=
Input
(
"Bias"
);
if
(
bias
!=
framework
::
kEmptyVarName
)
{
auto
bias_dims
=
ctx
->
GetInputDim
(
"Bias"
);
int
bias_height
=
bias_dims
[
0
];
int
bias_width
=
bias_dims
[
1
];
PADDLE_ENFORCE_EQ
(
bias_height
,
1
,
"The shape of Bias must be [1, frame_size * 3]."
);
PADDLE_ENFORCE_EQ
(
bias_width
,
frame_size
*
3
,
"The shape of Bias must be [1, frame_size * 3]."
);
}
ctx
->
SetOutputDim
(
"Gate"
,
{
batch_size
,
frame_size
*
3
});
ctx
->
SetOutputDim
(
"ResetHiddenPrev"
,
{
batch_size
,
frame_size
});
ctx
->
SetOutputDim
(
"Hidden"
,
{
batch_size
,
frame_size
});
...
...
@@ -139,8 +140,6 @@ class GRUUnitGradOp : public framework::OperatorWithKernel {
"HiddenPrev"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Weight"
),
"Input(%s) of GRUUnitGradOp should not be null."
,
"Weight"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Bias"
),
"Input(%s) of GRUUnitGradOp should not be null."
,
"Bias"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Gate"
),
"Input(%s) of GRUUnitGradOp should not be null."
,
"Gate"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"ResetHiddenPrev"
),
...
...
@@ -160,14 +159,11 @@ class GRUUnitGradOp : public framework::OperatorWithKernel {
auto
input_dims
=
ctx
->
GetInputDim
(
"Input"
);
auto
hidden_prev_dims
=
ctx
->
GetInputDim
(
"HiddenPrev"
);
auto
weight_dims
=
ctx
->
GetInputDim
(
"Weight"
);
auto
bias_dims
=
ctx
->
GetInputDim
(
"Bias"
);
// int batch_size = input_dims[0];
int
input_size
=
input_dims
[
1
];
int
frame_size
=
hidden_prev_dims
[
1
];
int
weight_height
=
weight_dims
[
0
];
int
weight_width
=
weight_dims
[
1
];
int
bias_height
=
bias_dims
[
0
];
int
bias_width
=
bias_dims
[
1
];
PADDLE_ENFORCE_EQ
(
input_size
,
frame_size
*
3
,
"The input_size must be 3 times of frame_size in GRUUnitOp."
);
...
...
@@ -177,10 +173,19 @@ class GRUUnitGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
weight_width
,
frame_size
*
3
,
"The shape of Weight matrix must be [frame_size, frame_size * 3]."
);
PADDLE_ENFORCE_EQ
(
bias_height
,
1
,
"The shape of Bias must be [1, frame_size * 3]."
);
PADDLE_ENFORCE_EQ
(
bias_width
,
frame_size
*
3
,
"The shape of Bias must be [1, frame_size * 3]."
);
auto
bias
=
Input
(
"Bias"
);
if
(
bias
!=
framework
::
kEmptyVarName
)
{
auto
bias_dims
=
ctx
->
GetInputDim
(
"Bias"
);
int
bias_height
=
bias_dims
[
0
];
int
bias_width
=
bias_dims
[
1
];
PADDLE_ENFORCE_EQ
(
bias_height
,
1
,
"The shape of Bias must be [1, frame_size * 3]."
);
PADDLE_ENFORCE_EQ
(
bias_width
,
frame_size
*
3
,
"The shape of Bias must be [1, frame_size * 3]."
);
auto
bias_grad_name
=
framework
::
GradVarName
(
"Bias"
);
if
(
ctx
->
HasOutput
(
bias_grad_name
))
ctx
->
SetOutputDim
(
bias_grad_name
,
bias_dims
);
}
auto
input_grad_name
=
framework
::
GradVarName
(
"Input"
);
if
(
ctx
->
HasOutput
(
input_grad_name
))
ctx
->
SetOutputDim
(
input_grad_name
,
input_dims
);
...
...
@@ -190,9 +195,6 @@ class GRUUnitGradOp : public framework::OperatorWithKernel {
auto
weight_grad_name
=
framework
::
GradVarName
(
"Weight"
);
if
(
ctx
->
HasOutput
(
weight_grad_name
))
ctx
->
SetOutputDim
(
weight_grad_name
,
weight_dims
);
auto
bias_grad_name
=
framework
::
GradVarName
(
"Bias"
);
if
(
ctx
->
HasOutput
(
bias_grad_name
))
ctx
->
SetOutputDim
(
bias_grad_name
,
bias_dims
);
}
};
...
...
paddle/operators/gru_unit_op.h
浏览文件 @
0bc5a122
...
...
@@ -64,16 +64,20 @@ class GRUUnitKernel : public framework::OpKernel<T> {
auto
x
=
EigenMatrix
<
T
>::
From
(
*
input
);
auto
h_p
=
EigenMatrix
<
T
>::
From
(
*
hidden_prev
);
auto
b
=
EigenMatrix
<
T
>::
From
(
*
bias
);
auto
g
=
EigenMatrix
<
T
>::
From
(
*
gate
);
auto
r_h_p
=
EigenMatrix
<
T
>::
From
(
*
reset_hidden_prev
);
auto
h
=
EigenMatrix
<
T
>::
From
(
*
hidden
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
// calculate unactivated gate outputs
g
.
device
(
place
)
=
x
+
b
.
reshape
(
Eigen
::
array
<
int
,
2
>
({{
1
,
frame_size
*
3
}}))
.
broadcast
(
Eigen
::
array
<
int
,
2
>
({{
batch_size
,
1
}}));
if
(
bias
)
{
auto
b
=
EigenMatrix
<
T
>::
From
(
*
bias
);
g
.
device
(
place
)
=
x
+
b
.
reshape
(
Eigen
::
array
<
int
,
2
>
({{
1
,
frame_size
*
3
}}))
.
broadcast
(
Eigen
::
array
<
int
,
2
>
({{
batch_size
,
1
}}));
}
else
{
g
.
device
(
place
)
=
x
;
}
const
T
*
hidden_prev_data
=
hidden_prev
->
data
<
T
>
();
const
T
*
weight_data
=
weight
->
data
<
T
>
();
T
*
gate_data
=
gate
->
data
<
T
>
();
...
...
@@ -145,7 +149,6 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
hidden_prev_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
weight_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
bias_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
Tensor
gate_grad
;
gate_grad
.
mutable_data
<
T
>
(
input
->
dims
(),
context
.
GetPlace
());
Tensor
reset_hidden_prev_grad
;
...
...
@@ -168,7 +171,6 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
auto
d_h
=
EigenMatrix
<
T
>::
From
(
*
hidden_grad
);
auto
d_x
=
EigenMatrix
<
T
>::
From
(
*
input_grad
);
auto
d_h_p
=
EigenMatrix
<
T
>::
From
(
*
hidden_prev_grad
);
auto
d_b
=
EigenMatrix
<
T
>::
From
(
*
bias_grad
);
auto
d_g
=
EigenMatrix
<
T
>::
From
(
gate_grad
);
auto
d_r_h_p
=
EigenMatrix
<
T
>::
From
(
reset_hidden_prev_grad
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
...
...
@@ -216,7 +218,11 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
// backward for input
d_x
.
device
(
place
)
=
d_g
;
// backward for bias
d_b
.
device
(
place
)
=
d_g
.
sum
(
Eigen
::
array
<
int
,
1
>
({{
0
}}));
if
(
bias_grad
)
{
bias_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
d_b
=
EigenMatrix
<
T
>::
From
(
*
bias_grad
);
d_b
.
device
(
place
)
=
d_g
.
sum
(
Eigen
::
array
<
int
,
1
>
({{
0
}}));
}
}
};
...
...
python/paddle/v2/framework/tests/test_gru_unit_op.py
浏览文件 @
0bc5a122
...
...
@@ -28,6 +28,8 @@ def relu(x):
class
TestGRUUnitOp
(
OpTest
):
batch_size
=
3
frame_size
=
5
activate
=
{
GRUActivationType
.
identity
:
identity
,
GRUActivationType
.
sigmoid
:
sigmoid
,
...
...
@@ -35,9 +37,9 @@ class TestGRUUnitOp(OpTest):
GRUActivationType
.
relu
:
relu
,
}
def
set
Up
(
self
):
batch_size
=
3
frame_size
=
5
def
set
_inputs
(
self
):
batch_size
=
self
.
batch_size
frame_size
=
self
.
frame_size
self
.
op_type
=
'gru_unit'
self
.
inputs
=
{
'Input'
:
np
.
random
.
uniform
(
...
...
@@ -47,18 +49,21 @@ class TestGRUUnitOp(OpTest):
'Weight'
:
np
.
random
.
uniform
(
-
1.
/
math
.
sqrt
(
frame_size
),
1.
/
math
.
sqrt
(
frame_size
),
(
frame_size
,
frame_size
*
3
)).
astype
(
'float32'
),
'Bias'
:
np
.
random
.
uniform
(
-
0.1
,
0.1
,
(
1
,
frame_size
*
3
)).
astype
(
'float32'
)
}
self
.
attrs
=
{
'activation'
:
GRUActivationType
.
tanh
,
'gate_activation'
:
GRUActivationType
.
sigmoid
}
def
set_outputs
(
self
):
# GRU calculations
batch_size
=
self
.
batch_size
frame_size
=
self
.
frame_size
x
=
self
.
inputs
[
'Input'
]
h_p
=
self
.
inputs
[
'HiddenPrev'
]
w
=
self
.
inputs
[
'Weight'
]
b
=
self
.
inputs
[
'Bias'
]
b
=
self
.
inputs
[
'Bias'
]
if
self
.
inputs
.
has_key
(
'Bias'
)
else
np
.
zeros
(
(
1
,
frame_size
*
3
))
g
=
x
+
np
.
tile
(
b
,
(
batch_size
,
1
))
w_u_r
=
w
.
flatten
()[:
frame_size
*
frame_size
*
2
].
reshape
(
(
frame_size
,
frame_size
*
2
))
...
...
@@ -73,12 +78,33 @@ class TestGRUUnitOp(OpTest):
g
[:,
frame_size
*
2
:])
g
=
np
.
hstack
((
u_r
,
c
))
h
=
u
*
h_p
+
(
1
-
u
)
*
c
self
.
outputs
=
{
'Gate'
:
g
,
'ResetHiddenPrev'
:
r_h_p
,
'Hidden'
:
h
}
def
setUp
(
self
):
self
.
set_inputs
()
self
.
set_outputs
()
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
(
[
'Input'
,
'HiddenPrev'
,
'Weight'
],
[
'Hidden'
],
max_relative_error
=
0.007
)
class
TestGRUUnitOpWithBias
(
TestGRUUnitOp
):
def
set_inputs
(
self
):
batch_size
=
self
.
batch_size
frame_size
=
self
.
frame_size
super
(
TestGRUUnitOpWithBias
,
self
).
set_inputs
()
self
.
inputs
[
'Bias'
]
=
np
.
random
.
uniform
(
-
0.1
,
0.1
,
(
1
,
frame_size
*
3
)).
astype
(
'float32'
)
self
.
attrs
=
{
'activation'
:
GRUActivationType
.
identity
,
'gate_activation'
:
GRUActivationType
.
sigmoid
}
def
test_check_grad
(
self
):
self
.
check_grad
(
[
'Input'
,
'HiddenPrev'
,
'Weight'
,
'Bias'
],
[
'Hidden'
],
...
...
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