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e9794214
编写于
1月 12, 2017
作者:
X
xutianbing
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Address further comments.
上级
8560ce69
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
126 addition
and
90 deletion
+126
-90
paddle/function/BufferArg.cpp
paddle/function/BufferArg.cpp
+8
-4
paddle/function/BufferArg.h
paddle/function/BufferArg.h
+22
-8
paddle/function/ContextProjectionOp.cpp
paddle/function/ContextProjectionOp.cpp
+93
-76
paddle/function/ContextProjectionOpTest.cpp
paddle/function/ContextProjectionOpTest.cpp
+2
-2
paddle/gserver/layers/ContextProjection.cpp
paddle/gserver/layers/ContextProjection.cpp
+1
-0
未找到文件。
paddle/function/BufferArg.cpp
浏览文件 @
e9794214
...
...
@@ -20,23 +20,27 @@ limitations under the License. */
namespace
paddle
{
const
SequenceArg
&
BufferArg
::
sequence
()
const
{
//
CHECK_EQ(bufferType_, TENSOR_SEQUENCE_DATA);
CHECK_EQ
(
bufferType_
,
TENSOR_SEQUENCE_DATA
);
return
dynamic_cast
<
const
SequenceArg
&>
(
*
this
);
}
const
SparseMatrixArg
&
BufferArg
::
sparse
()
const
{
//
CHECK_EQ(bufferType_, TENSOR_SPARSE);
CHECK_EQ
(
bufferType_
,
TENSOR_SPARSE
);
return
dynamic_cast
<
const
SparseMatrixArg
&>
(
*
this
);
}
SparseMatrixArg
::
SparseMatrixArg
(
const
CpuSparseMatrix
&
sparse
,
ArgType
argType
)
:
BufferArg
(
sparse
,
argType
),
row_
(
reinterpret_cast
<
void
*>
(
sparse
.
getRows
()),
VALUE_TYPE_INT32
),
col_
(
reinterpret_cast
<
void
*>
(
sparse
.
getCols
()),
VALUE_TYPE_INT32
)
{}
col_
(
reinterpret_cast
<
void
*>
(
sparse
.
getCols
()),
VALUE_TYPE_INT32
)
{
bufferType_
=
TENSOR_SPARSE
;
}
SparseMatrixArg
::
SparseMatrixArg
(
const
GpuSparseMatrix
&
sparse
,
ArgType
argType
)
:
BufferArg
(
sparse
,
argType
),
row_
(
reinterpret_cast
<
void
*>
(
sparse
.
getRows
()),
VALUE_TYPE_INT32
),
col_
(
reinterpret_cast
<
void
*>
(
sparse
.
getCols
()),
VALUE_TYPE_INT32
)
{}
col_
(
reinterpret_cast
<
void
*>
(
sparse
.
getCols
()),
VALUE_TYPE_INT32
)
{
bufferType_
=
TENSOR_SPARSE
;
}
}
// namespace paddle
paddle/function/BufferArg.h
浏览文件 @
e9794214
...
...
@@ -23,10 +23,11 @@ limitations under the License. */
namespace
paddle
{
enum
BufferType
{
TENSOR_NORMAL
=
0
,
TENSOR_SEQUENCE_ID
=
1
,
TENSOR_SEQUENCE_DATA
=
2
,
TENSOR_SPARSE
=
3
TENSOR_UNKNOWN
=
0
,
TENSOR_NORMAL
=
1
,
TENSOR_SEQUENCE_ID
=
2
,
TENSOR_SEQUENCE_DATA
=
3
,
TENSOR_SPARSE
=
4
};
enum
SparseDataType
{
...
...
@@ -86,6 +87,7 @@ public:
valueType_
(
DataType
<
real
>::
value
),
shape_
(
2
),
argType_
(
argType
)
{
bufferType_
=
TENSOR_NORMAL
;
shape_
.
setDim
(
0
,
matrix
.
getHeight
());
shape_
.
setDim
(
1
,
matrix
.
getWidth
());
}
...
...
@@ -98,6 +100,7 @@ public:
valueType_
(
DataType
<
real
>::
value
),
shape_
(
shape
),
argType_
(
argType
)
{
bufferType_
=
TENSOR_NORMAL
;
CHECK_EQ
(
matrix
.
getElementCnt
(),
shape
.
getElements
());
}
...
...
@@ -107,6 +110,7 @@ public:
valueType_
(
DataType
<
real
>::
value
),
shape_
(
1
),
argType_
(
argType
)
{
bufferType_
=
TENSOR_NORMAL
;
shape_
.
setDim
(
0
,
vector
.
getSize
());
}
...
...
@@ -116,6 +120,7 @@ public:
valueType_
(
VALUE_TYPE_INT32
),
shape_
(
1
),
argType_
(
argType
)
{
bufferType_
=
TENSOR_NORMAL
;
shape_
.
setDim
(
0
,
vector
.
getSize
());
}
...
...
@@ -150,6 +155,8 @@ public:
ValueType
valueType
()
const
{
return
valueType_
;
}
BufferType
bufferType
()
const
{
return
bufferType_
;
}
const
TensorShape
&
shape
()
const
{
return
shape_
;
}
bool
isSparse
()
const
{
return
(
TENSOR_SPARSE
==
bufferType_
);
}
bool
isSequenceArg
()
const
{
return
TENSOR_SEQUENCE_DATA
==
bufferType_
;
}
const
SequenceArg
&
sequence
()
const
;
const
SparseMatrixArg
&
sparse
()
const
;
...
...
@@ -158,8 +165,8 @@ protected:
void
*
buf_
;
ValueType
valueType_
;
TensorShape
shape_
;
BufferType
bufferType_
;
ArgType
argType_
=
UNSPECIFIED
;
BufferType
bufferType_
{
TENSOR_UNKNOWN
}
;
ArgType
argType_
{
UNSPECIFIED
}
;
// leading dimensions. The size is dims_.size()
// Dims lds_;
};
...
...
@@ -174,11 +181,13 @@ public:
const
TensorShape
&
shape
,
ArgType
argType
=
UNSPECIFIED
)
:
BufferArg
(
buf
,
VALUE_TYPE_INT32
,
shape
,
argType
)
{
bufferType_
=
TENSOR_SEQUENCE_ID
;
CHECK_EQ
(
shape_
.
ndims
(),
(
size_t
)
1
);
numSeqs_
=
shape_
[
0
]
-
1
;
}
SequenceIdArg
(
const
IVector
&
vector
)
:
BufferArg
(
vector
)
{
bufferType_
=
TENSOR_SEQUENCE_ID
;
numSeqs_
=
shape_
[
0
]
-
1
;
}
...
...
@@ -199,12 +208,16 @@ public:
const
SequenceIdArg
&
startPositions
,
ArgType
argType
=
UNSPECIFIED
)
:
BufferArg
(
buf
,
valueType
,
shape
,
argType
),
startPositions_
(
startPositions
)
{}
startPositions_
(
startPositions
)
{
bufferType_
=
TENSOR_SEQUENCE_DATA
;
}
SequenceArg
(
const
Matrix
&
matrix
,
const
IVector
&
vector
,
ArgType
argType
=
UNSPECIFIED
)
:
BufferArg
(
matrix
,
argType
),
startPositions_
(
vector
)
{}
:
BufferArg
(
matrix
,
argType
),
startPositions_
(
vector
)
{
bufferType_
=
TENSOR_SEQUENCE_DATA
;
}
~
SequenceArg
()
{}
...
...
@@ -236,6 +249,7 @@ public:
nnz_
(
nnz
),
format_
(
format
),
type_
(
type
)
{
bufferType_
=
TENSOR_SPARSE
;
CHECK
((
valueType
==
VALUE_TYPE_FLOAT
)
||
(
valueType
==
VALUE_TYPE_DOUBLE
));
CHECK_EQ
(
shape_
.
ndims
(),
(
size_t
)
2
);
CHECK_EQ
(
row_
.
shape
().
ndims
(),
(
size_t
)
1
);
...
...
paddle/function/ContextProjectionOp.cpp
浏览文件 @
e9794214
...
...
@@ -74,9 +74,9 @@ void ContextProjectionForward<DEVICE_TYPE_CPU>(CpuMatrix& out_mat,
/**
* Paddle Function for Context Projection Forward.
* Calculate the output sequence after context projection.
* Calculate the output
layer value
sequence after context projection.
*
* What is Context Projection?
* What is Context Projection
for a sequence
?
* For example, assumed input (x) has 4 words and the dimension of each word
* representation is 2. If we use zero to pad instead of learned weight to pad,
* and the context_lenth is 3, the output (y) is:
...
...
@@ -92,12 +92,11 @@ void ContextProjectionForward<DEVICE_TYPE_CPU>(CpuMatrix& out_mat,
* c1, c2, d1, d2, 0, 0]
* @endcode
*
* \param outputs[0].matrix output value, n * (d * l)
* \param outputs[0].vector input sequence, n * 1
* \param inputs[0].matrix input value, n * d
* \param inputs[0].vector input sequence, n * 1
* \param inputs[1].matrix input weight, pad * d
* \param inputs[1].vector input sequence, n * 1
* \param outputs[0].matrix output layer value, n * (d * l)
* \param outputs[0].vector start position sequence, n * 1
* \param inputs[0].matrix input layer value, n * d
* \param inputs[0].vector start position sequence, n * 1
* \param inputs[1].matrix input layer weight, pad * d
*/
template
<
DeviceType
Device
>
class
ContextProjectionForwardFunc
:
public
FunctionBase
{
...
...
@@ -111,37 +110,35 @@ public:
void
calc
(
const
BufferArgs
&
inputs
,
const
BufferArgs
&
outputs
)
override
{
CHECK
(
1
==
inputs
.
size
()
||
2
==
inputs
.
size
());
CHECK_EQ
((
size_t
)
1
,
outputs
.
size
());
CHECK
(
inputs
[
0
].
isSequenceArg
()
&&
outputs
[
0
].
isSequenceArg
())
<<
"SequenceArg required here"
;
const
auto
val_seqs
=
dynamic_cast
<
const
SequenceArg
&>
(
inputs
[
0
]);
const
auto
w_seqs
=
inputs
.
size
()
<=
1
?
nullptr
:
dynamic_cast
<
const
SequenceArg
*>
(
&
inputs
[
1
]);
auto
out_seqs
=
dynamic_cast
<
const
SequenceArg
&>
(
outputs
[
0
]);
auto
out_seq
=
dynamic_cast
<
const
SequenceArg
&>
(
outputs
[
0
]);
CHECK
(
out_seq
s
.
data
()
&&
val_seqs
.
data
()
&&
CHECK
(
out_seq
.
data
()
&&
val_seqs
.
data
()
&&
val_seqs
.
getSequenceIds
().
data
());
CHECK_EQ
(
out_seq
s
.
shape
().
ndims
(),
(
size_t
)
2
);
CHECK_EQ
(
out_seq
.
shape
().
ndims
(),
(
size_t
)
2
);
CHECK_EQ
(
val_seqs
.
shape
().
ndims
(),
(
size_t
)
2
);
CHECK_EQ
(
val_seqs
.
getSequenceIds
().
shape
().
ndims
(),
(
size_t
)
1
);
if
(
w_seqs
)
{
CHECK_EQ
(
w_seqs
->
shape
().
ndims
(),
(
size_t
)
2
);
CHECK_EQ
(
w_seqs
->
getSequenceIds
().
shape
().
ndims
(),
(
size_t
)
1
);
if
(
2
==
inputs
.
size
())
{
CHECK_EQ
(
inputs
[
1
].
shape
().
ndims
(),
(
size_t
)
2
);
}
/// dim of output = dim of input * context_length
CHECK_EQ
(
out_seq
s
.
shape
()[
1
],
val_seqs
.
shape
()[
1
]
*
context_length_
);
CHECK_EQ
(
out_seq
.
shape
()[
1
],
val_seqs
.
shape
()[
1
]
*
context_length_
);
/// input and output has the same batch_size
CHECK_EQ
(
val_seqs
.
shape
()[
0
],
out_seq
s
.
shape
()[
0
]);
CHECK_EQ
(
val_seqs
.
shape
()[
0
],
out_seq
.
shape
()[
0
]);
/// dim of input == dim of weight
if
(
w_seqs
)
{
CHECK_EQ
(
val_seqs
.
shape
()[
1
],
w_seqs
->
shape
()[
1
]);
if
(
2
==
inputs
.
size
()
)
{
CHECK_EQ
(
val_seqs
.
shape
()[
1
],
inputs
[
1
].
shape
()[
1
]);
}
CHECK_EQ
(
out_seq
s
.
getArgType
(),
ADD_TO
);
auto
out_mat
=
out_seq
s
.
matrix
<
Device
>
();
CHECK_EQ
(
out_seq
.
getArgType
(),
ADD_TO
);
auto
out_mat
=
out_seq
.
matrix
<
Device
>
();
const
auto
in_mat
=
val_seqs
.
matrix
<
Device
>
();
const
auto
w_mat
=
w_seqs
?
w_seqs
->
matrix
<
Device
>
()
:
typename
Tensor
<
real
,
Device
>::
Matrix
(
nullptr
,
0
,
0
);
(
2
==
inputs
.
size
())
?
inputs
[
1
].
matrix
<
Device
>
()
:
typename
Tensor
<
real
,
Device
>::
Matrix
(
nullptr
,
0
,
0
);
const
auto
seq_vec
=
val_seqs
.
getSequenceIds
().
vector
<
int
,
Device
>
();
ContextProjectionForward
<
Device
>
(
out_mat
,
in_mat
,
...
...
@@ -221,10 +218,11 @@ void ContextProjectionBackward<DEVICE_TYPE_CPU>(const CpuMatrix& out_grad_mat,
* Context Projection Backward Function.
* Update the weight gradient and input layer gradient with backprop
*
* \param inputs[0].seq input sequence.
* \param inputs[0].matrix output layer grad.
* \param outputs[0] input layer grad.
* \param outputs[1] weight grad.
* \param inputs[0].matrix output layer grad, n * (d * l)
* \param inputs[0].vector start position sequence, n * 1
* \param outputs[0].matrix input layer grad, n * d
* \param outputs[0].vector start position sequence, n * 1
* \param outputs[1] weight grad, pad * d
*/
template
<
DeviceType
Device
>
class
ContextProjectionBackwardFunc
:
public
FunctionBase
{
...
...
@@ -240,30 +238,31 @@ public:
void
calc
(
const
BufferArgs
&
inputs
,
const
BufferArgs
&
outputs
)
override
{
CHECK_EQ
((
size_t
)
1
,
inputs
.
size
());
CHECK_EQ
((
size_t
)
2
,
outputs
.
size
());
const
auto
seq_arg
=
dynamic_cast
<
const
SequenceArg
&>
(
inputs
[
0
]);
CHECK
(
seq_arg
.
data
()
&&
inputs
[
0
].
data
());
CHECK_EQ
(
seq_arg
.
shape
().
ndims
(),
(
size_t
)
2
);
CHECK_EQ
(
seq_arg
.
getSequenceIds
().
shape
().
ndims
(),
(
size_t
)
1
);
CHECK_EQ
(
outputs
[
0
].
shape
().
ndims
(),
(
size_t
)
2
);
CHECK
(
inputs
[
0
].
isSequenceArg
()
&&
outputs
[
0
].
isSequenceArg
())
<<
"SequenceArg required here"
;
const
auto
in_seq
=
dynamic_cast
<
const
SequenceArg
&>
(
inputs
[
0
]);
auto
out_seq
=
dynamic_cast
<
const
SequenceArg
&>
(
outputs
[
0
]);
CHECK
(
in_seq
.
data
()
&&
in_seq
.
getSequenceIds
().
data
());
CHECK_EQ
(
in_seq
.
shape
().
ndims
(),
(
size_t
)
2
);
CHECK_EQ
(
in_seq
.
getSequenceIds
().
shape
().
ndims
(),
(
size_t
)
1
);
CHECK_EQ
(
out_seq
.
shape
().
ndims
(),
(
size_t
)
2
);
CHECK_EQ
(
out_seq
.
getSequenceIds
().
shape
().
ndims
(),
(
size_t
)
1
);
CHECK_EQ
(
outputs
[
1
].
shape
().
ndims
(),
(
size_t
)
2
);
/// dim of input grad == dim of weight
CHECK_EQ
(
out
puts
[
0
]
.
shape
()[
1
],
outputs
[
1
].
shape
()[
1
]);
CHECK_EQ
(
out
_seq
.
shape
()[
1
],
outputs
[
1
].
shape
()[
1
]);
/// input and output grad has the same batch_size
CHECK_EQ
(
outputs
[
0
].
shape
()[
0
],
seq_arg
.
shape
()[
0
]);
/// dim of output val = dim of input grad * context_length
CHECK_EQ
(
seq_arg
.
shape
()[
1
],
outputs
[
0
].
shape
()[
1
]
*
context_length_
);
CHECK_EQ
(
outputs
[
0
].
getArgType
(),
ADD_TO
);
CHECK_EQ
(
out_seq
.
shape
()[
0
],
in_seq
.
shape
()[
0
]);
/// dim of output grad = dim of input grad * context_length
CHECK_EQ
(
in_seq
.
shape
()[
1
],
out_seq
.
shape
()[
1
]
*
context_length_
);
CHECK_EQ
(
out_seq
.
getArgType
(),
ADD_TO
);
CHECK_EQ
(
outputs
[
1
].
getArgType
(),
ADD_TO
);
const
auto
seq_vec
=
seq_arg
.
getSequenceIds
().
vector
<
int
,
Device
>
();
const
auto
out_grad_mat
=
seq_arg
.
matrix
<
Device
>
();
const
auto
seq_vec
=
in_seq
.
getSequenceIds
().
vector
<
int
,
Device
>
();
const
auto
out_grad_mat
=
in_seq
.
matrix
<
Device
>
();
auto
in_grad_mat
=
!
outputs
[
0
].
data
()
?
typename
Tensor
<
real
,
Device
>::
Matrix
(
nullptr
,
0
,
0
)
:
outputs
[
0
].
matrix
<
Device
>
();
!
out_seq
.
data
()
?
typename
Tensor
<
real
,
Device
>::
Matrix
(
nullptr
,
0
,
0
)
:
out_seq
.
matrix
<
Device
>
();
auto
w_grad_mat
=
!
outputs
[
1
].
data
()
?
typename
Tensor
<
real
,
Device
>::
Matrix
(
nullptr
,
0
,
0
)
:
outputs
[
1
].
matrix
<
Device
>
();
...
...
@@ -287,9 +286,15 @@ private:
};
/**
* \param inputs[0].matrix input grad, n*d
* \param inputs[0].vector input sequence, n*1
* \param outputs[0] output grad, n*(d*l)
* Context Projection Backward Data Function
* Update input layer grad
* input: sequence of output layer grad
* output: sequence of input layer grad
*
* \param outputs[0].matrix input layer grad, n * d
* \param outputs[0].vector start position sequence, n * 1
* \param inputs[0].matrix output layer grad, n * (d * l)
* \param inputs[0].vector start positon sequence, n * 1
*/
template
<
DeviceType
Device
>
class
ContextProjectionBackwardDataFunc
:
public
FunctionBase
{
...
...
@@ -302,19 +307,24 @@ public:
void
calc
(
const
BufferArgs
&
inputs
,
const
BufferArgs
&
outputs
)
override
{
CHECK_EQ
(
1
,
static_cast
<
int
>
(
inputs
.
size
()));
CHECK_EQ
(
1
,
static_cast
<
int
>
(
outputs
.
size
()));
const
auto
in_seqs
=
dynamic_cast
<
const
SequenceArg
&>
(
inputs
[
0
]);
CHECK
(
in_seqs
.
data
()
&&
outputs
[
0
].
data
()
&&
in_seqs
.
getSequenceIds
().
data
());
CHECK_EQ
(
static_cast
<
int
>
(
outputs
[
0
].
shape
().
ndims
()),
2
);
CHECK_EQ
(
static_cast
<
int
>
(
in_seqs
.
shape
().
ndims
()),
2
);
CHECK_EQ
(
static_cast
<
int
>
(
in_seqs
.
getSequenceIds
().
shape
().
ndims
()),
1
);
CHECK_EQ
(
outputs
[
0
].
shape
().
ndims
(),
in_seqs
.
shape
().
ndims
()
*
context_length_
);
CHECK
(
inputs
[
0
].
isSequenceArg
()
&&
outputs
[
0
].
isSequenceArg
())
<<
"SequenceArg required here"
;
const
auto
in_seq
=
dynamic_cast
<
const
SequenceArg
&>
(
inputs
[
0
]);
const
auto
out_seq
=
dynamic_cast
<
const
SequenceArg
&>
(
outputs
[
0
]);
CHECK
(
in_seq
.
data
()
&&
out_seq
.
data
()
&&
in_seq
.
getSequenceIds
().
data
());
CHECK_EQ
(
static_cast
<
int
>
(
out_seq
.
shape
().
ndims
()),
2
);
CHECK_EQ
(
static_cast
<
int
>
(
in_seq
.
shape
().
ndims
()),
2
);
CHECK_EQ
(
static_cast
<
int
>
(
in_seq
.
getSequenceIds
().
shape
().
ndims
()),
1
);
/// output layer grad dim == input layer grad dim * context_length_
CHECK_EQ
(
in_seq
.
shape
().
ndims
(),
out_seq
.
shape
().
ndims
()
*
context_length_
);
/// input and output has the same batch_size
CHECK_EQ
(
in_seqs
.
shape
()[
0
],
outputs
[
0
].
shape
()[
0
]);
const
auto
out_grad_mat
=
outputs
[
0
].
matrix
<
Device
>
();
auto
in_grad_mat
=
in_seqs
.
matrix
<
Device
>
();
const
auto
seq_vec
=
in_seqs
.
getSequenceIds
().
vector
<
int
,
Device
>
();
CHECK_EQ
(
in_seq
.
shape
()[
0
],
out_seq
.
shape
()[
0
]);
CHECK_EQ
(
outputs
[
0
].
getArgType
(),
ASSIGN_TO
);
const
auto
out_grad_mat
=
in_seq
.
matrix
<
Device
>
();
const
auto
seq_vec
=
in_seq
.
getSequenceIds
().
vector
<
int
,
Device
>
();
auto
in_grad_mat
=
out_seq
.
matrix
<
Device
>
();
ContextProjectionBackwardData
<
Device
>
(
out_grad_mat
,
in_grad_mat
,
seq_vec
,
context_length_
,
context_start_
);
...
...
@@ -326,9 +336,14 @@ private:
};
/**
* \param inputs[0].matrix weight grad, pad * d
* \param inputs[0].vecotr input sequence, n * 1
* \param outputs[0] output grad, n * (d * l)
* Context Projection Backward Weight Function
* Update weight grad by backprop
* input: sequence of output layer grad
* output: weight grad
*
* \param outputs[0] weight grad, pad * d
* \param inputs[0].matrix output layer grad, n * (d * l)
* \param inputs[0].vecotr start positon sequence, n * 1
*/
template
<
DeviceType
Device
>
class
ContextProjectionBackwardWeightFunc
:
public
FunctionBase
{
...
...
@@ -343,18 +358,20 @@ public:
void
calc
(
const
BufferArgs
&
inputs
,
const
BufferArgs
&
outputs
)
override
{
CHECK_EQ
(
1
,
static_cast
<
int
>
(
inputs
.
size
()));
CHECK_EQ
(
1
,
static_cast
<
int
>
(
outputs
.
size
()));
const
auto
in_seqs
=
dynamic_cast
<
const
SequenceArg
&>
(
inputs
[
0
]);
CHECK
(
in_seqs
.
data
()
&&
in_seqs
.
getSequenceIds
().
data
()
&&
outputs
[
0
].
data
());
CHECK
(
inputs
[
0
].
isSequenceArg
())
<<
"SequenceArg required here"
;
const
auto
in_seq
=
dynamic_cast
<
const
SequenceArg
&>
(
inputs
[
0
]);
CHECK
(
in_seq
.
data
()
&&
in_seq
.
getSequenceIds
().
data
()
&&
outputs
[
0
].
data
());
CHECK_EQ
(
static_cast
<
int
>
(
outputs
[
0
].
shape
().
ndims
()),
2
);
CHECK_EQ
(
static_cast
<
int
>
(
in_seqs
.
shape
().
ndims
()),
2
);
CHECK_EQ
(
static_cast
<
int
>
(
in_seqs
.
getSequenceIds
().
shape
().
ndims
()),
1
);
CHECK_EQ
(
in_seqs
.
shape
()[
0
],
outputs
[
0
].
shape
()[
0
]);
CHECK_EQ
(
outputs
[
0
].
shape
()[
1
],
in_seqs
.
shape
()[
1
]
*
context_length_
);
const
auto
out_grad_mat
=
outputs
[
0
].
matrix
<
Device
>
();
auto
w_grad_mat
=
inputs
[
0
].
matrix
<
Device
>
();
const
auto
seq_vec
=
in_seqs
.
getSequenceIds
().
vector
<
int
,
Device
>
();
CHECK_EQ
(
static_cast
<
int
>
(
in_seq
.
shape
().
ndims
()),
2
);
CHECK_EQ
(
static_cast
<
int
>
(
in_seq
.
getSequenceIds
().
shape
().
ndims
()),
1
);
CHECK_EQ
(
in_seq
.
shape
()[
0
],
outputs
[
0
].
shape
()[
0
]);
/// output layer grad dim == weight dim * context_length_
CHECK_EQ
(
in_seq
.
shape
()[
1
],
outputs
[
0
].
shape
()[
1
]
*
context_length_
);
CHECK_EQ
(
outputs
[
0
].
getArgType
(),
ADD_TO
);
const
auto
seq_vec
=
in_seq
.
getSequenceIds
().
vector
<
int
,
Device
>
();
const
auto
out_grad_mat
=
in_seq
.
matrix
<
Device
>
();
auto
w_grad_mat
=
outputs
[
0
].
matrix
<
Device
>
();
ContextProjectionBackwardWeight
<
Device
>
(
out_grad_mat
,
w_grad_mat
,
seq_vec
,
...
...
paddle/function/ContextProjectionOpTest.cpp
浏览文件 @
e9794214
...
...
@@ -123,7 +123,7 @@ void testMatrixProjectionBackward(int context_start,
BufferArgs
cpu_inputs
;
BufferArgs
cpu_outputs
;
cpu_inputs
.
addArg
(
cpu_out_grad
,
*
cpu_seq
);
cpu_outputs
.
addArg
(
cpu_in_grad
,
ADD_TO
);
cpu_outputs
.
addArg
(
cpu_in_grad
,
*
cpu_seq
,
ADD_TO
);
cpu_outputs
.
addArg
(
cpu_w_grad
?
*
cpu_w_grad
:
CpuMatrix
(
nullptr
,
0
,
input_dim
),
ADD_TO
);
...
...
@@ -132,7 +132,7 @@ void testMatrixProjectionBackward(int context_start,
BufferArgs
gpu_inputs
;
BufferArgs
gpu_outputs
;
gpu_inputs
.
addArg
(
gpu_out_grad
,
*
gpu_seq
);
gpu_outputs
.
addArg
(
gpu_in_grad
,
ADD_TO
);
gpu_outputs
.
addArg
(
gpu_in_grad
,
*
gpu_seq
,
ADD_TO
);
gpu_outputs
.
addArg
(
gpu_w_grad
?
*
gpu_w_grad
:
GpuMatrix
(
nullptr
,
0
,
input_dim
),
ADD_TO
);
...
...
paddle/gserver/layers/ContextProjection.cpp
浏览文件 @
e9794214
...
...
@@ -169,6 +169,7 @@ void ContextProjection::backward(const UpdateCallback& callback) {
outputs
.
addArg
(
CpuMatrix
(
in_
->
grad
?
in_
->
grad
->
getData
()
:
nullptr
,
batch_size
,
input_dim
),
*
in_
->
sequenceStartPositions
->
getVector
(
useGpu_
),
ADD_TO
);
outputs
.
addArg
(
CpuMatrix
(
w_ptr
?
w_ptr
->
getData
()
:
nullptr
,
w_ptr
?
w_ptr
->
getHeight
()
:
0
,
...
...
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