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ec6b13db
编写于
12月 29, 2016
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
clean up unused code.
上级
ea4d08da
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
53 addition
and
747 deletion
+53
-747
paddle/cuda/include/hl_sequence.h
paddle/cuda/include/hl_sequence.h
+0
-72
paddle/cuda/include/stub/hl_sequence_stub.h
paddle/cuda/include/stub/hl_sequence_stub.h
+0
-29
paddle/cuda/src/hl_cuda_sequence.cu
paddle/cuda/src/hl_cuda_sequence.cu
+0
-252
paddle/function/ContextProjectionOpGpu.cu
paddle/function/ContextProjectionOpGpu.cu
+53
-11
paddle/math/Matrix.cpp
paddle/math/Matrix.cpp
+0
-169
paddle/math/Matrix.h
paddle/math/Matrix.h
+0
-72
paddle/math/tests/test_matrixCompare.cpp
paddle/math/tests/test_matrixCompare.cpp
+0
-142
未找到文件。
paddle/cuda/include/hl_sequence.h
浏览文件 @
ec6b13db
...
@@ -48,78 +48,6 @@ extern void hl_max_sequence_forward(real* input,
...
@@ -48,78 +48,6 @@ extern void hl_max_sequence_forward(real* input,
extern
void
hl_max_sequence_backward
(
extern
void
hl_max_sequence_backward
(
real
*
outputGrad
,
int
*
index
,
real
*
inputGrad
,
int
numSequences
,
int
dim
);
real
*
outputGrad
,
int
*
index
,
real
*
inputGrad
,
int
numSequences
,
int
dim
);
/**
* @brief Context projection forward.
*
* @param[in] input input sequence.
* @param[in] sequence sequence index.
* @param[in] weightData padding data.
* @param[out] output output sequence.
* @param[in] numSequences number of sequences.
* @param[in] inputDim input sequence dimension.
* @param[in] contextLength context length.
* @param[in] contextStart context start.
* @param[in] beginPad number of extra timesteps added at the
* beginning.
* @param[in] isPadding trainable padding.
*
*/
extern
void
hl_context_projection_forward
(
real
*
input
,
const
int
*
sequence
,
real
*
weightData
,
real
*
output
,
int
numSequences
,
int
inputDim
,
int
contextLength
,
int
contextStart
,
int
beginPad
,
bool
isPadding
);
/**
* @brief Context projection backward data.
*
* @param[in] outputGrad output gradient.
* @param[in] sequence sequence index.
* @param[out] inputGrad input gradient.
* @param[in] numSequences number of sequences.
* @param[in] inputDim input sequence dimension.
* @param[in] contextLength context length.
* @param[in] contextStart context start.
*
*/
extern
void
hl_context_projection_backward_data
(
real
*
outputGrad
,
const
int
*
sequence
,
real
*
inputGrad
,
int
numSequences
,
int
inputDim
,
int
contextLength
,
int
contextStart
);
/**
* @brief Context projection backward weight.
*
* @param[in] outputGrad output gradient.
* @param[in] sequence sequence index.
* @param[out] weightGrad weight gradient.
* @param[in] numSequences number of sequences.
* @param[in] weightDim input sequence dimension.
* @param[in] totalPad number of extra timesteps.
* @param[in] contextLength context length.
* @param[in] contextStart context start.
* @param[in] beginPad number of extra timesteps added at the
* beginning.
*
*/
extern
void
hl_context_projection_backward_weight
(
real
*
outputGrad
,
const
int
*
sequence
,
real
*
weightGrad
,
int
numSequences
,
int
weightDim
,
int
totalPad
,
int
contextLength
,
int
contextStart
,
int
beginPad
);
/**
/**
* @brief Memory copy from sequence to batch.
* @brief Memory copy from sequence to batch.
*
*
...
...
paddle/cuda/include/stub/hl_sequence_stub.h
浏览文件 @
ec6b13db
...
@@ -27,35 +27,6 @@ inline void hl_max_sequence_forward(real* input,
...
@@ -27,35 +27,6 @@ inline void hl_max_sequence_forward(real* input,
inline
void
hl_max_sequence_backward
(
inline
void
hl_max_sequence_backward
(
real
*
outputGrad
,
int
*
index
,
real
*
inputGrad
,
int
numSequences
,
int
dim
)
{}
real
*
outputGrad
,
int
*
index
,
real
*
inputGrad
,
int
numSequences
,
int
dim
)
{}
inline
void
hl_context_projection_forward
(
real
*
input
,
const
int
*
sequence
,
real
*
weightData
,
real
*
output
,
int
numSequences
,
int
inputDim
,
int
contextLength
,
int
contextStart
,
int
beginPad
,
bool
isPadding
)
{}
inline
void
hl_context_projection_backward_data
(
real
*
outputGrad
,
const
int
*
sequence
,
real
*
inputGrad
,
int
numSequences
,
int
inputDim
,
int
contextLength
,
int
contextStart
)
{}
inline
void
hl_context_projection_backward_weight
(
real
*
outputGrad
,
const
int
*
sequence
,
real
*
weightGrad
,
int
numSequences
,
int
weightDim
,
int
totalPad
,
int
contextLength
,
int
contextStart
,
int
beginPad
)
{}
inline
void
hl_sequence2batch_copy
(
real
*
batch
,
inline
void
hl_sequence2batch_copy
(
real
*
batch
,
real
*
sequence
,
real
*
sequence
,
const
int
*
batchIndex
,
const
int
*
batchIndex
,
...
...
paddle/cuda/src/hl_cuda_sequence.cu
浏览文件 @
ec6b13db
...
@@ -90,258 +90,6 @@ void hl_max_sequence_backward(real* outputGrad,
...
@@ -90,258 +90,6 @@ void hl_max_sequence_backward(real* outputGrad,
CHECK_SYNC
(
"hl_max_sequence_backward failed"
);
CHECK_SYNC
(
"hl_max_sequence_backward failed"
);
}
}
template
<
bool
padding
>
__global__
void
KeContextProjectionForward
(
real
*
input
,
const
int
*
sequence
,
real
*
weightData
,
real
*
output
,
int
inputDim
,
int
contextLength
,
int
contextStart
,
int
beginPad
)
{
int
idx
=
threadIdx
.
x
;
int
blockSize
=
blockDim
.
x
;
int
sequenceId
=
blockIdx
.
x
;
int
seqStart
=
sequence
[
sequenceId
];
int
seqEnd
=
sequence
[
sequenceId
+
1
];
real
value
=
0
;
int
instances
=
seqEnd
-
seqStart
+
contextLength
-
1
;
output
+=
seqStart
*
inputDim
*
contextLength
;
input
+=
seqStart
*
inputDim
;
for
(
int
k
=
0
;
k
<=
inputDim
/
blockSize
;
k
++
)
{
if
(
idx
<
inputDim
)
{
for
(
int
i
=
0
;
i
<
instances
;
i
++
)
{
// i + contextStart;
if
((
i
+
contextStart
)
<
0
)
{
if
(
padding
)
{
value
=
weightData
[
i
*
inputDim
+
idx
];
}
else
{
continue
;
}
}
else
if
((
i
+
contextStart
)
>=
(
seqEnd
-
seqStart
))
{
if
(
padding
)
{
value
=
weightData
[(
beginPad
+
i
+
contextStart
-
(
seqEnd
-
seqStart
))
*
inputDim
+
idx
];
}
else
{
continue
;
}
}
else
{
value
=
input
[(
i
+
contextStart
)
*
inputDim
+
idx
];
}
int
outx
=
(
i
-
contextLength
)
<
0
?
i
:
(
contextLength
-
1
);
int
outy
=
(
i
-
contextLength
)
<
0
?
0
:
(
i
-
(
contextLength
-
1
));
real
*
output_r
=
output
+
outy
*
inputDim
*
contextLength
+
outx
*
inputDim
;
for
(
int
j
=
outy
;
j
<
seqEnd
-
seqStart
;
j
++
)
{
output_r
[
idx
]
+=
value
;
if
(
j
-
outy
==
outx
)
break
;
output_r
+=
(
contextLength
-
1
)
*
inputDim
;
}
}
}
idx
+=
blockSize
;
}
}
void
hl_context_projection_forward
(
real
*
input
,
const
int
*
sequence
,
real
*
weightData
,
real
*
output
,
int
numSequences
,
int
inputDim
,
int
contextLength
,
int
contextStart
,
int
beginPad
,
bool
isPadding
)
{
CHECK_NOTNULL
(
input
);
CHECK_NOTNULL
(
sequence
);
CHECK_NOTNULL
(
output
);
CHECK
(
!
isPadding
||
weightData
);
int
blockSize
=
128
;
int
blocksX
=
numSequences
;
int
blocksY
=
1
;
dim3
threads
(
blockSize
,
1
);
dim3
grid
(
blocksX
,
blocksY
);
if
(
isPadding
)
{
KeContextProjectionForward
<
true
><<<
grid
,
threads
,
0
,
STREAM_DEFAULT
>>>
(
input
,
sequence
,
weightData
,
output
,
inputDim
,
contextLength
,
contextStart
,
beginPad
);
}
else
{
KeContextProjectionForward
<
false
><<<
grid
,
threads
,
0
,
STREAM_DEFAULT
>>>
(
input
,
sequence
,
weightData
,
output
,
inputDim
,
contextLength
,
contextStart
,
beginPad
);
}
CHECK_SYNC
(
"hl_context_projection_forward failed"
);
}
__global__
void
KeContextProjectionBackwardData
(
real
*
outputGrad
,
const
int
*
sequence
,
real
*
inputGrad
,
int
inputDim
,
int
contextLength
,
int
contextStart
)
{
int
idx
=
threadIdx
.
x
;
int
blockSize
=
blockDim
.
x
;
int
sequenceId
=
blockIdx
.
x
;
int
seqStart
=
sequence
[
sequenceId
];
int
seqEnd
=
sequence
[
sequenceId
+
1
];
real
value
=
0
;
int
instances
=
seqEnd
-
seqStart
+
contextLength
-
1
;
outputGrad
+=
seqStart
*
inputDim
*
contextLength
;
inputGrad
+=
seqStart
*
inputDim
;
for
(
int
k
=
0
;
k
<=
inputDim
/
blockSize
;
k
++
)
{
if
(
idx
<
inputDim
)
{
for
(
int
i
=
0
;
i
<
instances
;
i
++
)
{
if
((
i
+
contextStart
)
<
0
)
{
continue
;
}
else
if
((
i
+
contextStart
)
>=
(
seqEnd
-
seqStart
))
{
continue
;
}
else
{
// value = 0;
value
=
inputGrad
[(
i
+
contextStart
)
*
inputDim
+
idx
];
}
int
outx
=
(
i
-
contextLength
)
<
0
?
i
:
(
contextLength
-
1
);
int
outy
=
(
i
-
contextLength
)
<
0
?
0
:
(
i
-
(
contextLength
-
1
));
real
*
output_r
=
outputGrad
+
outy
*
inputDim
*
contextLength
+
outx
*
inputDim
;
for
(
int
j
=
outy
;
j
<
seqEnd
-
seqStart
;
j
++
)
{
value
+=
output_r
[
idx
];
if
(
j
-
outy
==
outx
)
break
;
output_r
+=
(
contextLength
-
1
)
*
inputDim
;
}
inputGrad
[(
i
+
contextStart
)
*
inputDim
+
idx
]
=
value
;
}
}
idx
+=
blockSize
;
}
}
void
hl_context_projection_backward_data
(
real
*
outputGrad
,
const
int
*
sequence
,
real
*
inputGrad
,
int
numSequences
,
int
inputDim
,
int
contextLength
,
int
contextStart
)
{
CHECK_NOTNULL
(
outputGrad
);
CHECK_NOTNULL
(
sequence
);
CHECK_NOTNULL
(
inputGrad
);
int
blockSize
=
128
;
int
blocksX
=
numSequences
;
int
blocksY
=
1
;
dim3
threads
(
blockSize
,
1
);
dim3
grid
(
blocksX
,
blocksY
);
KeContextProjectionBackwardData
<<<
grid
,
threads
,
0
,
STREAM_DEFAULT
>>>
(
outputGrad
,
sequence
,
inputGrad
,
inputDim
,
contextLength
,
contextStart
);
CHECK_SYNC
(
"hl_context_projection_backward_data failed"
);
}
template
<
int
THREADS_X
,
int
THREADS_Y
>
__global__
void
KeContextProjectionBackwardWeight
(
real
*
outputGrad
,
const
int
*
sequence
,
real
*
weightGrad
,
int
numSequences
,
int
weightDim
,
int
contextLength
,
int
contextStart
,
int
beginPad
)
{
__shared__
real
sum_s
[
THREADS_Y
][
THREADS_X
];
int
padOfBlock
=
(
weightDim
+
THREADS_X
-
1
)
/
THREADS_X
;
const
int
idx
=
threadIdx
.
x
;
const
int
idy
=
threadIdx
.
y
;
int
padId
=
blockIdx
.
x
/
padOfBlock
;
int
weightIdx
=
idx
+
THREADS_X
*
(
blockIdx
.
x
%
padOfBlock
);
int
instanceId
;
real
value
=
0
;
real
*
output_r
;
sum_s
[
idy
][
idx
]
=
0.0
f
;
if
(
weightIdx
<
weightDim
)
{
for
(
int
seqId
=
idy
;
seqId
<
numSequences
;
seqId
+=
THREADS_Y
)
{
int
seqStart
=
sequence
[
seqId
];
int
seqEnd
=
sequence
[
seqId
+
1
];
output_r
=
outputGrad
+
seqStart
*
weightDim
*
contextLength
;
if
(
contextStart
<
0
)
{
if
(
padId
+
contextStart
<
0
)
{
instanceId
=
padId
;
}
else
{
// beginPad > 0;
instanceId
=
(
padId
-
beginPad
)
+
(
seqEnd
-
seqStart
)
-
contextStart
;
}
}
else
{
if
(
padId
+
(
seqEnd
-
seqStart
)
<
contextStart
)
{
continue
;
}
else
{
// beginPad == 0;
instanceId
=
padId
+
(
seqEnd
-
seqStart
)
-
contextStart
;
}
}
int
outx
=
(
instanceId
-
contextLength
)
<
0
?
instanceId
:
(
contextLength
-
1
);
int
outy
=
(
instanceId
-
contextLength
)
<
0
?
0
:
(
instanceId
-
(
contextLength
-
1
));
output_r
+=
outy
*
weightDim
*
contextLength
+
outx
*
weightDim
;
for
(
int
j
=
outy
;
j
<
seqEnd
-
seqStart
;
j
++
)
{
value
+=
output_r
[
weightIdx
];
if
(
j
-
outy
==
outx
)
break
;
output_r
+=
(
contextLength
-
1
)
*
weightDim
;
}
}
sum_s
[
idy
][
idx
]
=
value
;
}
__syncthreads
();
for
(
int
stride
=
THREADS_Y
/
2
;
stride
>
0
;
stride
=
stride
/
2
)
{
if
(
idy
<
stride
)
{
sum_s
[
idy
][
idx
]
+=
sum_s
[
idy
+
stride
][
idx
];
}
__syncthreads
();
}
__syncthreads
();
if
(
weightIdx
<
weightDim
)
{
if
(
idy
==
0
)
{
weightGrad
[
padId
*
weightDim
+
weightIdx
]
+=
sum_s
[
0
][
idx
];
}
}
}
void
hl_context_projection_backward_weight
(
real
*
outputGrad
,
const
int
*
sequence
,
real
*
weightGrad
,
int
numSequences
,
int
weightDim
,
int
totalPad
,
int
contextLength
,
int
contextStart
,
int
beginPad
)
{
CHECK_NOTNULL
(
outputGrad
);
CHECK_NOTNULL
(
sequence
);
CHECK_NOTNULL
(
weightGrad
);
int
threadsX
=
32
;
int
threadsY
=
32
;
int
blocksX
=
totalPad
*
((
weightDim
+
threadsX
-
1
)
/
threadsX
);
dim3
threads
(
threadsX
,
threadsY
);
dim3
grid
(
blocksX
,
1
);
KeContextProjectionBackwardWeight
<
32
,
32
>
<<<
grid
,
threads
,
0
,
STREAM_DEFAULT
>>>
(
outputGrad
,
sequence
,
weightGrad
,
numSequences
,
weightDim
,
contextLength
,
contextStart
,
beginPad
);
CHECK_SYNC
(
"hl_context_projection_backward_weight failed"
);
}
template
<
int
blockDimX
,
int
blockDimY
,
int
gridDimX
,
bool
AddRow
>
template
<
int
blockDimX
,
int
blockDimY
,
int
gridDimX
,
bool
AddRow
>
__global__
void
KeMatrixAddRows
(
real
*
output
,
__global__
void
KeMatrixAddRows
(
real
*
output
,
real
*
table
,
real
*
table
,
...
...
paddle/function/ContextProjectionOpGpu.cu
浏览文件 @
ec6b13db
...
@@ -73,15 +73,30 @@ __global__ void KeContextProjectionForward(const real* input,
...
@@ -73,15 +73,30 @@ __global__ void KeContextProjectionForward(const real* input,
}
}
}
}
/**
* @brief Context projection forward.
*
* @param[in] input input sequence.
* @param[in] sequence sequence index.
* @param[in] weight padding data.
* @param[out] output output sequence.
* @param[in] num_sequences number of sequences.
* @param[in] input_dim input sequence dimension.
* @param[in] context_length context length.
* @param[in] context_start context start.
* @param[in] begin_pad number of extra timesteps added at the
* beginning.
*
*/
void
hl_context_projection_forward
(
const
real
*
input
,
void
hl_context_projection_forward
(
const
real
*
input
,
const
int
*
sequence
,
const
int
*
sequence
,
const
real
*
weight
,
const
real
*
weight
,
real
*
output
,
real
*
output
,
in
t
num_sequences
,
size_
t
num_sequences
,
in
t
input_dim
,
size_
t
input_dim
,
in
t
context_length
,
size_
t
context_length
,
int
context_start
,
int
context_start
,
in
t
begin_pad
)
{
size_
t
begin_pad
)
{
CHECK_NOTNULL
(
input
);
CHECK_NOTNULL
(
input
);
CHECK_NOTNULL
(
sequence
);
CHECK_NOTNULL
(
sequence
);
CHECK_NOTNULL
(
output
);
CHECK_NOTNULL
(
output
);
...
@@ -168,12 +183,24 @@ __global__ void KeContextProjectionBackwardData(real* out_grad,
...
@@ -168,12 +183,24 @@ __global__ void KeContextProjectionBackwardData(real* out_grad,
}
}
}
}
/**
* @brief Context projection backward data.
*
* @param[in] out_grad output gradient.
* @param[in] sequence sequence index.
* @param[out] input_grad input gradient.
* @param[in] num_sequences number of sequences.
* @param[in] input_dim input sequence dimension.
* @param[in] context_length context length.
* @param[in] context_start context start.
*
*/
void
hl_context_projection_backward_data
(
real
*
out_grad
,
void
hl_context_projection_backward_data
(
real
*
out_grad
,
const
int
*
sequence
,
const
int
*
sequence
,
real
*
input_grad
,
real
*
input_grad
,
in
t
num_sequences
,
size_
t
num_sequences
,
in
t
input_dim
,
size_
t
input_dim
,
in
t
context_length
,
size_
t
context_length
,
int
context_start
)
{
int
context_start
)
{
CHECK_NOTNULL
(
out_grad
);
CHECK_NOTNULL
(
out_grad
);
CHECK_NOTNULL
(
sequence
);
CHECK_NOTNULL
(
sequence
);
...
@@ -278,15 +305,30 @@ __global__ void KeContextProjectionBackwardWeight(real* out_grad,
...
@@ -278,15 +305,30 @@ __global__ void KeContextProjectionBackwardWeight(real* out_grad,
}
}
}
}
/**
* @brief Context projection backward weight.
*
* @param[in] out_grad output gradient.
* @param[in] sequence sequence index.
* @param[out] w_grad weight gradient.
* @param[in] num_sequences number of sequences.
* @param[in] w_dim input sequence dimension.
* @param[in] total_pad number of extra timesteps.
* @param[in] context_length context length.
* @param[in] context_start context start.
* @param[in] begin_pad number of extra timesteps added at the
* beginning.
*
*/
void
hl_context_projection_backward_weight
(
real
*
out_grad
,
void
hl_context_projection_backward_weight
(
real
*
out_grad
,
const
int
*
sequence
,
const
int
*
sequence
,
real
*
w_grad
,
real
*
w_grad
,
in
t
num_sequences
,
size_
t
num_sequences
,
in
t
w_dim
,
size_
t
w_dim
,
size_t
total_pad
,
size_t
total_pad
,
in
t
context_length
,
size_
t
context_length
,
int
context_start
,
int
context_start
,
in
t
begin_pad
)
{
size_
t
begin_pad
)
{
CHECK_NOTNULL
(
out_grad
);
CHECK_NOTNULL
(
out_grad
);
CHECK_NOTNULL
(
sequence
);
CHECK_NOTNULL
(
sequence
);
CHECK_NOTNULL
(
w_grad
);
CHECK_NOTNULL
(
w_grad
);
...
...
paddle/math/Matrix.cpp
浏览文件 @
ec6b13db
...
@@ -1304,68 +1304,6 @@ void GpuMatrix::maxSequenceBackward(Matrix& outputGrad,
...
@@ -1304,68 +1304,6 @@ void GpuMatrix::maxSequenceBackward(Matrix& outputGrad,
hl_max_sequence_backward
(
outGrad
,
maxIndex
,
inputGrad
,
numSequences
,
dim
);
hl_max_sequence_backward
(
outGrad
,
maxIndex
,
inputGrad
,
numSequences
,
dim
);
}
}
void
GpuMatrix
::
contextProjectionForward
(
Matrix
&
input
,
Matrix
*
weight
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
)
{
CHECK
(
dynamic_cast
<
GpuMatrix
*>
(
&
input
));
CHECK
(
dynamic_cast
<
const
GpuIVector
*>
(
&
sequence
));
if
(
weight
)
CHECK
(
dynamic_cast
<
GpuMatrix
*>
(
weight
));
CHECK_EQ
(
getWidth
(),
input
.
getWidth
()
*
contextLength
);
hl_context_projection_forward
(
input
.
getData
(),
sequence
.
getData
(),
isPadding
?
weight
->
getData
()
:
NULL
,
getData
(),
sequence
.
getSize
()
-
1
,
input
.
getWidth
(),
contextLength
,
contextStart
,
beginPad
,
isPadding
);
}
void
GpuMatrix
::
contextProjectionBackwardData
(
Matrix
&
inputGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
)
{
CHECK
(
dynamic_cast
<
GpuMatrix
*>
(
&
inputGrad
));
CHECK
(
dynamic_cast
<
const
GpuIVector
*>
(
&
sequence
));
CHECK_EQ
(
getWidth
(),
inputGrad
.
getWidth
()
*
contextLength
);
hl_context_projection_backward_data
(
getData
(),
sequence
.
getData
(),
inputGrad
.
getData
(),
sequence
.
getSize
()
-
1
,
inputGrad
.
getWidth
(),
contextLength
,
contextStart
);
}
void
GpuMatrix
::
contextProjectionBackwardWeight
(
Matrix
&
weightGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
int
totalPad
,
size_t
beginPad
)
{
CHECK
(
dynamic_cast
<
GpuMatrix
*>
(
&
weightGrad
));
CHECK
(
dynamic_cast
<
const
GpuIVector
*>
(
&
sequence
));
CHECK_EQ
(
getWidth
(),
weightGrad
.
getWidth
()
*
contextLength
);
hl_context_projection_backward_weight
(
getData
(),
sequence
.
getData
(),
weightGrad
.
getData
(),
sequence
.
getSize
()
-
1
,
weightGrad
.
getWidth
(),
totalPad
,
contextLength
,
contextStart
,
beginPad
);
}
void
GpuMatrix
::
paramReluForward
(
Matrix
&
data
,
Matrix
&
W
)
{
void
GpuMatrix
::
paramReluForward
(
Matrix
&
data
,
Matrix
&
W
)
{
CHECK
(
data
.
useGpu_
==
true
&&
W
.
useGpu_
==
true
)
CHECK
(
data
.
useGpu_
==
true
&&
W
.
useGpu_
==
true
)
<<
"Matrix type are not equal"
;
<<
"Matrix type are not equal"
;
...
@@ -2203,113 +2141,6 @@ void CpuMatrix::maxSequenceBackward(Matrix& outputGrad,
...
@@ -2203,113 +2141,6 @@ void CpuMatrix::maxSequenceBackward(Matrix& outputGrad,
}
}
}
}
void
CpuMatrix
::
contextProjectionForward
(
Matrix
&
input
,
Matrix
*
weight
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
)
{
auto
input_ptr
=
dynamic_cast
<
CpuMatrix
*>
(
&
input
);
auto
seq_ptr
=
dynamic_cast
<
const
CpuIVector
*>
(
&
sequence
);
CHECK
(
input_ptr
&&
seq_ptr
);
if
(
weight
)
CHECK
(
dynamic_cast
<
CpuMatrix
*>
(
weight
));
CHECK_EQ
(
getWidth
(),
input_ptr
->
getWidth
()
*
contextLength
);
const
int
*
starts
=
seq_ptr
->
getData
();
size_t
numSequences
=
seq_ptr
->
getSize
()
-
1
;
for
(
size_t
i
=
0
;
i
<
numSequences
;
++
i
)
{
for
(
int
j
=
0
;
j
<
contextLength
;
++
j
)
{
int
begin
=
starts
[
i
]
+
contextStart
+
j
;
int
end
=
starts
[
i
+
1
]
+
contextStart
+
j
;
int
dstBegin
=
starts
[
i
];
int
dstEnd
=
starts
[
i
+
1
];
if
(
begin
<
starts
[
i
])
{
int64_t
padSize
=
std
::
min
(
starts
[
i
]
-
begin
,
starts
[
i
+
1
]
-
starts
[
i
]);
MatrixPtr
mat
=
this
->
subMatrix
(
starts
[
i
],
padSize
);
if
(
isPadding
)
{
MatrixPtr
sub
=
weight
->
subMatrix
(
j
,
padSize
);
mat
->
addAtOffset
(
*
sub
,
j
*
input_ptr
->
getWidth
());
}
dstBegin
=
starts
[
i
]
+
padSize
;
begin
=
starts
[
i
];
}
if
(
end
>
starts
[
i
+
1
])
{
int64_t
padSize
=
std
::
min
(
end
-
starts
[
i
+
1
],
starts
[
i
+
1
]
-
starts
[
i
]);
MatrixPtr
mat
=
this
->
subMatrix
(
starts
[
i
+
1
]
-
padSize
,
padSize
);
if
(
isPadding
)
{
MatrixPtr
sub
=
weight
->
subMatrix
(
beginPad
+
contextStart
+
j
-
padSize
,
padSize
);
mat
->
addAtOffset
(
*
sub
,
j
*
input_ptr
->
getWidth
());
}
dstEnd
=
starts
[
i
+
1
]
-
padSize
;
end
=
starts
[
i
+
1
];
}
if
(
end
<=
begin
)
continue
;
MatrixPtr
src
=
input_ptr
->
subMatrix
(
begin
,
end
-
begin
);
MatrixPtr
dst
=
this
->
subMatrix
(
dstBegin
,
dstEnd
-
dstBegin
);
dst
->
addAtOffset
(
*
src
,
j
*
input_ptr
->
getWidth
());
}
}
}
void
CpuMatrix
::
contextProjectionBackward
(
Matrix
*
inputGrad
,
Matrix
*
weightGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
)
{
if
(
inputGrad
)
CHECK
(
dynamic_cast
<
CpuMatrix
*>
(
inputGrad
));
if
(
weightGrad
)
CHECK
(
dynamic_cast
<
CpuMatrix
*>
(
weightGrad
));
CHECK
(
dynamic_cast
<
const
CpuIVector
*>
(
&
sequence
));
int64_t
inputDim
=
inputGrad
?
inputGrad
->
getWidth
()
:
weightGrad
?
weightGrad
->
getWidth
()
:
0
;
CHECK_EQ
(
getWidth
(),
inputDim
*
contextLength
);
const
int
*
starts
=
sequence
.
getData
();
size_t
numSequences
=
sequence
.
getSize
()
-
1
;
for
(
size_t
i
=
0
;
i
<
numSequences
;
++
i
)
{
for
(
int
j
=
0
;
j
<
contextLength
;
++
j
)
{
int
begin
=
starts
[
i
]
+
contextStart
+
j
;
int
end
=
starts
[
i
+
1
]
+
contextStart
+
j
;
int
dstBegin
=
starts
[
i
];
int
dstEnd
=
starts
[
i
+
1
];
if
(
begin
<
starts
[
i
])
{
int64_t
padSize
=
std
::
min
(
starts
[
i
]
-
begin
,
starts
[
i
+
1
]
-
starts
[
i
]);
if
(
isPadding
&&
weightGrad
)
{
MatrixPtr
mat
=
this
->
subMatrix
(
starts
[
i
],
padSize
);
MatrixPtr
sub
=
weightGrad
->
subMatrix
(
j
,
padSize
);
sub
->
addAtOffset
(
*
mat
,
j
*
inputDim
);
}
dstBegin
=
starts
[
i
]
+
padSize
;
begin
=
starts
[
i
];
}
if
(
end
>
starts
[
i
+
1
])
{
int64_t
padSize
=
std
::
min
(
end
-
starts
[
i
+
1
],
starts
[
i
+
1
]
-
starts
[
i
]);
if
(
isPadding
&&
weightGrad
)
{
MatrixPtr
mat
=
this
->
subMatrix
(
starts
[
i
+
1
]
-
padSize
,
padSize
);
MatrixPtr
sub
=
weightGrad
->
subMatrix
(
beginPad
+
contextStart
+
j
-
padSize
,
padSize
);
sub
->
addAtOffset
(
*
mat
,
j
*
inputDim
);
}
dstEnd
=
starts
[
i
+
1
]
-
padSize
;
end
=
starts
[
i
+
1
];
}
if
(
end
<=
begin
)
continue
;
if
(
!
inputGrad
)
continue
;
MatrixPtr
src
=
inputGrad
->
subMatrix
(
begin
,
end
-
begin
);
MatrixPtr
dst
=
this
->
subMatrix
(
dstBegin
,
dstEnd
-
dstBegin
);
src
->
addAtOffset
(
*
dst
,
j
*
inputDim
);
}
}
}
inline
void
vecAddTo
(
real
*
a
,
const
real
*
b
,
size_t
len
)
{
inline
void
vecAddTo
(
real
*
a
,
const
real
*
b
,
size_t
len
)
{
for
(
unsigned
int
i
=
0
;
i
<
len
;
++
i
)
{
for
(
unsigned
int
i
=
0
;
i
<
len
;
++
i
)
{
a
[
i
]
+=
b
[
i
];
a
[
i
]
+=
b
[
i
];
...
...
paddle/math/Matrix.h
浏览文件 @
ec6b13db
...
@@ -972,42 +972,6 @@ public:
...
@@ -972,42 +972,6 @@ public:
LOG
(
FATAL
)
<<
"Not implemeted"
;
LOG
(
FATAL
)
<<
"Not implemeted"
;
}
}
virtual
void
contextProjectionForward
(
Matrix
&
input
,
Matrix
*
weight
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
)
{
LOG
(
FATAL
)
<<
"Not implemeted"
;
}
virtual
void
contextProjectionBackward
(
Matrix
*
inputGrad
,
Matrix
*
weightGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
)
{
LOG
(
FATAL
)
<<
"Not implemeted"
;
}
virtual
void
contextProjectionBackwardData
(
Matrix
&
inputGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
)
{
LOG
(
FATAL
)
<<
"Not implemeted"
;
}
virtual
void
contextProjectionBackwardWeight
(
Matrix
&
weightGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
int
totalPad
,
size_t
beginPad
)
{
LOG
(
FATAL
)
<<
"Not implemeted"
;
}
/**
/**
* @code
* @code
* this.row[i] += table.row[ids[i]]
* this.row[i] += table.row[ids[i]]
...
@@ -1442,26 +1406,6 @@ public:
...
@@ -1442,26 +1406,6 @@ public:
const
IVector
&
sequence
,
const
IVector
&
sequence
,
IVector
&
index
);
IVector
&
index
);
void
contextProjectionForward
(
Matrix
&
input
,
Matrix
*
weight
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
);
void
contextProjectionBackwardData
(
Matrix
&
inputGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
);
void
contextProjectionBackwardWeight
(
Matrix
&
weightGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
int
totalPad
,
size_t
beginPad
);
void
bilinearForward
(
const
Matrix
&
in
,
void
bilinearForward
(
const
Matrix
&
in
,
const
size_t
inImgH
,
const
size_t
inImgH
,
const
size_t
inImgW
,
const
size_t
inImgW
,
...
@@ -1648,22 +1592,6 @@ public:
...
@@ -1648,22 +1592,6 @@ public:
const
IVector
&
sequence
,
const
IVector
&
sequence
,
IVector
&
index
);
IVector
&
index
);
void
contextProjectionForward
(
Matrix
&
input
,
Matrix
*
weight
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
);
void
contextProjectionBackward
(
Matrix
*
inputGrad
,
Matrix
*
weightGrad
,
const
IVector
&
sequence
,
int
contextLength
,
int
contextStart
,
size_t
beginPad
,
bool
isPadding
);
real
*
getRow
(
size_t
row
)
{
return
BaseMatrix
::
rowBuf
(
row
);
}
real
*
getRow
(
size_t
row
)
{
return
BaseMatrix
::
rowBuf
(
row
);
}
virtual
real
*
getRowBuf
(
size_t
row
)
{
return
getRow
(
row
);
}
virtual
real
*
getRowBuf
(
size_t
row
)
{
return
getRow
(
row
);
}
...
...
paddle/math/tests/test_matrixCompare.cpp
浏览文件 @
ec6b13db
...
@@ -29,148 +29,6 @@ using namespace std; // NOLINT
...
@@ -29,148 +29,6 @@ using namespace std; // NOLINT
using
autotest
::
TensorCheckEqual
;
using
autotest
::
TensorCheckEqual
;
using
autotest
::
TensorCheckErr
;
using
autotest
::
TensorCheckErr
;
void
testMatrixProjectionForward
(
int
contextStart
,
int
contextLength
,
bool
padding
,
int
batchSize
,
int
inputDim
)
{
MatrixPtr
cpuInput
=
std
::
make_shared
<
CpuMatrix
>
(
batchSize
,
inputDim
);
MatrixPtr
gpuInput
=
std
::
make_shared
<
GpuMatrix
>
(
batchSize
,
inputDim
);
cpuInput
->
randomizeUniform
();
gpuInput
->
copyFrom
(
*
cpuInput
);
int
pad
=
std
::
max
(
0
,
-
contextStart
)
+
std
::
max
(
0
,
contextStart
+
contextLength
-
1
);
if
(
pad
==
0
)
padding
=
false
;
MatrixPtr
cpuWeight
=
nullptr
;
MatrixPtr
gpuWeight
=
nullptr
;
if
(
padding
)
{
cpuWeight
=
std
::
make_shared
<
CpuMatrix
>
(
pad
,
inputDim
);
gpuWeight
=
std
::
make_shared
<
GpuMatrix
>
(
pad
,
inputDim
);
cpuWeight
->
randomizeUniform
();
gpuWeight
->
copyFrom
(
*
cpuWeight
);
}
IVectorPtr
cpuSequence
;
generateSequenceStartPositions
(
batchSize
,
cpuSequence
);
IVectorPtr
gpuSequence
=
IVector
::
create
(
cpuSequence
->
getSize
(),
true
);
gpuSequence
->
copyFrom
(
*
cpuSequence
);
MatrixPtr
cpuOutput
=
std
::
make_shared
<
CpuMatrix
>
(
batchSize
,
inputDim
*
contextLength
);
MatrixPtr
gpuOutput
=
std
::
make_shared
<
GpuMatrix
>
(
batchSize
,
inputDim
*
contextLength
);
cpuOutput
->
randomizeUniform
();
gpuOutput
->
copyFrom
(
*
cpuOutput
);
// calculate
int
beginPad
=
std
::
max
(
0
,
-
contextStart
);
cpuOutput
->
contextProjectionForward
(
*
cpuInput
,
cpuWeight
.
get
(),
*
cpuSequence
,
contextLength
,
contextStart
,
beginPad
,
padding
);
gpuOutput
->
contextProjectionForward
(
*
gpuInput
,
gpuWeight
.
get
(),
*
gpuSequence
,
contextLength
,
contextStart
,
beginPad
,
padding
);
TensorCheckEqual
(
*
cpuOutput
,
*
gpuOutput
);
}
void
testMatrixProjectionBackward
(
int
contextStart
,
int
contextLength
,
bool
padding
,
int
batchSize
,
int
inputDim
)
{
MatrixPtr
cpuOutputGrad
=
std
::
make_shared
<
CpuMatrix
>
(
batchSize
,
inputDim
*
contextLength
);
MatrixPtr
gpuOutputGrad
=
std
::
make_shared
<
GpuMatrix
>
(
batchSize
,
inputDim
*
contextLength
);
cpuOutputGrad
->
randomizeUniform
();
gpuOutputGrad
->
copyFrom
(
*
cpuOutputGrad
);
IVectorPtr
cpuSequence
;
generateSequenceStartPositions
(
batchSize
,
cpuSequence
);
IVectorPtr
gpuSequence
=
IVector
::
create
(
cpuSequence
->
getSize
(),
true
);
gpuSequence
->
copyFrom
(
*
cpuSequence
);
MatrixPtr
cpuInputGrad
=
std
::
make_shared
<
CpuMatrix
>
(
batchSize
,
inputDim
);
MatrixPtr
gpuInputGrad
=
std
::
make_shared
<
GpuMatrix
>
(
batchSize
,
inputDim
);
cpuInputGrad
->
randomizeUniform
();
gpuInputGrad
->
copyFrom
(
*
cpuInputGrad
);
int
pad
=
std
::
max
(
0
,
-
contextStart
)
+
std
::
max
(
0
,
contextStart
+
contextLength
-
1
);
if
(
pad
==
0
)
padding
=
false
;
MatrixPtr
cpuWeightGrad
=
nullptr
;
MatrixPtr
gpuWeightGrad
=
nullptr
;
if
(
padding
)
{
cpuWeightGrad
=
std
::
make_shared
<
CpuMatrix
>
(
pad
,
inputDim
);
gpuWeightGrad
=
std
::
make_shared
<
GpuMatrix
>
(
pad
,
inputDim
);
cpuWeightGrad
->
randomizeUniform
();
gpuWeightGrad
->
copyFrom
(
*
cpuWeightGrad
);
}
// calculate
int
beginPad
=
std
::
max
(
0
,
-
contextStart
);
cpuOutputGrad
->
contextProjectionBackward
(
cpuInputGrad
.
get
(),
cpuWeightGrad
.
get
(),
*
cpuSequence
,
contextLength
,
contextStart
,
beginPad
,
padding
);
gpuOutputGrad
->
contextProjectionBackwardData
(
*
gpuInputGrad
,
*
gpuSequence
,
contextLength
,
contextStart
);
if
(
padding
)
{
gpuOutputGrad
->
contextProjectionBackwardWeight
(
*
gpuWeightGrad
,
*
gpuSequence
,
contextLength
,
contextStart
,
pad
,
beginPad
);
}
TensorCheckErr
(
*
cpuInputGrad
,
*
gpuInputGrad
);
if
(
padding
)
{
TensorCheckErr
(
*
cpuWeightGrad
,
*
gpuWeightGrad
);
}
}
TEST
(
Matrix
,
projection
)
{
for
(
auto
contextStart
:
{
-
5
,
-
3
,
-
1
,
0
,
3
})
{
for
(
auto
contextLength
:
{
1
,
2
,
5
,
7
})
{
for
(
auto
trainablePadding
:
{
false
,
true
})
{
for
(
auto
batchSize
:
{
1
,
2
,
5
,
20
,
100
})
{
for
(
auto
inputDim
:
{
15
,
32
,
63
,
128
,
200
})
{
VLOG
(
3
)
<<
" contextStart="
<<
contextStart
<<
" contextLength="
<<
contextLength
<<
" trainablePadding="
<<
trainablePadding
<<
" batchSize="
<<
batchSize
<<
" inputDim="
<<
inputDim
;
testMatrixProjectionForward
(
contextStart
,
contextLength
,
trainablePadding
,
batchSize
,
inputDim
);
testMatrixProjectionBackward
(
contextStart
,
contextLength
,
trainablePadding
,
batchSize
,
inputDim
);
}
}
}
}
}
}
void
testMatrixMaxSequence
(
int
batchSize
,
int
inputDim
)
{
void
testMatrixMaxSequence
(
int
batchSize
,
int
inputDim
)
{
// forward
// forward
MatrixPtr
cpuInput
=
std
::
make_shared
<
CpuMatrix
>
(
batchSize
,
inputDim
);
MatrixPtr
cpuInput
=
std
::
make_shared
<
CpuMatrix
>
(
batchSize
,
inputDim
);
...
...
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