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a948eea3
编写于
1月 04, 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
clean unused code.
上级
58827e3e
变更
13
隐藏空白更改
内联
并排
Showing
13 changed file
with
64 addition
and
557 deletion
+64
-557
paddle/cuda/include/hl_matrix.h
paddle/cuda/include/hl_matrix.h
+0
-42
paddle/cuda/include/stub/hl_matrix_stub.h
paddle/cuda/include/stub/hl_matrix_stub.h
+0
-19
paddle/cuda/src/hl_cuda_matrix.cu
paddle/cuda/src/hl_cuda_matrix.cu
+0
-171
paddle/function/CosSimOp.cpp
paddle/function/CosSimOp.cpp
+27
-25
paddle/function/CosSimOp.h
paddle/function/CosSimOp.h
+10
-10
paddle/function/CosSimOpTest.cpp
paddle/function/CosSimOpTest.cpp
+12
-12
paddle/gserver/layers/CosSimLayer.cpp
paddle/gserver/layers/CosSimLayer.cpp
+6
-6
paddle/gserver/layers/CosSimVecMatLayer.cpp
paddle/gserver/layers/CosSimVecMatLayer.cpp
+7
-7
paddle/gserver/layers/CosSimVecMatLayer.h
paddle/gserver/layers/CosSimVecMatLayer.h
+2
-0
paddle/math/Matrix.cpp
paddle/math/Matrix.cpp
+0
-152
paddle/math/Matrix.h
paddle/math/Matrix.h
+0
-36
paddle/math/tests/test_Matrix.cpp
paddle/math/tests/test_Matrix.cpp
+0
-22
paddle/math/tests/test_matrixCompare.cpp
paddle/math/tests/test_matrixCompare.cpp
+0
-55
未找到文件。
paddle/cuda/include/hl_matrix.h
浏览文件 @
a948eea3
...
...
@@ -188,48 +188,6 @@ extern void hl_param_relu_backward_diff(real* grad_o,
int
width
,
int
height
,
int
partial_sum
);
/**
* @brief cos sim forward
*
* @param[out] output output data
* @param[in] input1 input1 data(matrix)
* @param[in] input2 input2 data(matrix or vector)
* @param[in] width matrix width
* @param[in] input1_height input1_height
* @param[in] input2_height input2_height
* @param[in] scale scale factor
*/
extern
void
hl_cossim
(
real
*
output
,
real
*
input1
,
real
*
input2
,
int
width
,
int
input1_height
,
int
input2_height
,
real
scale
);
/**
* @brief cos sim derivate
*
* @param[in] grad output grad
* @param[in] output output data
* @param[in] prevOutX input1 data
* @param[in] prevOutY input2 data
* @param[out] prevGradX input1 grad
* @param[out] prevGradY input2 grad
* @param[in] width matrix width
* @param[in] input1_height input1 height
* @param[in] input2_height input2 height
* @param[in] scale scale factor
*/
extern
void
hl_cossim_derivative
(
real
*
grad
,
real
*
output
,
real
*
prevOutX
,
real
*
prevOutY
,
real
*
prevGradX
,
real
*
prevGradY
,
int
width
,
int
input1_height
,
int
input2_height
,
real
scale
);
/**
* @brief Matrix addition: A_d[i][j] += scale * B_d[j/channel].
...
...
paddle/cuda/include/stub/hl_matrix_stub.h
浏览文件 @
a948eea3
...
...
@@ -74,25 +74,6 @@ inline void hl_param_relu_backward_diff(real* grad_o,
int
height
,
int
partial_sum
)
{}
inline
void
hl_cossim
(
real
*
output
,
real
*
input1
,
real
*
input2
,
int
width
,
int
input1_height
,
int
input2_height
,
real
scale
)
{}
inline
void
hl_cossim_derivative
(
real
*
grad
,
real
*
output
,
real
*
prevOutX
,
real
*
prevOutY
,
real
*
prevGradX
,
real
*
prevGradY
,
int
width
,
int
input1_height
,
int
input2_height
,
real
scale
)
{}
inline
void
hl_matrix_add_shared_bias
(
real
*
A_d
,
real
*
B_d
,
const
int
channel
,
...
...
paddle/cuda/src/hl_cuda_matrix.cu
浏览文件 @
a948eea3
...
...
@@ -584,177 +584,6 @@ void hl_param_relu_backward_diff(real* grad_o,
CHECK_SYNC
(
"hl_param_relu_backward_diff failed"
);
}
template
<
int
blockSize
>
__global__
void
KeCosSim
(
real
*
output
,
real
*
input1
,
real
*
input2
,
int
width
,
int
input1_height
,
int
input2_height
,
real
scale
)
{
const
int
ty
=
blockIdx
.
y
;
int
tid
=
threadIdx
.
x
;
__shared__
real
xx
[
blockSize
];
__shared__
real
yy
[
blockSize
];
__shared__
real
xy
[
blockSize
];
xx
[
tid
]
=
0.0
;
yy
[
tid
]
=
0.0
;
xy
[
tid
]
=
0.0
;
__syncthreads
();
input1
+=
ty
*
width
;
if
(
input2_height
>
1
)
{
input2
+=
ty
*
width
;
}
for
(
int
index
=
tid
;
index
<
width
;
index
+=
blockSize
)
{
real
x
=
input1
[
index
];
real
y
=
input2
[
index
];
xx
[
tid
]
+=
x
*
x
;
yy
[
tid
]
+=
y
*
y
;
xy
[
tid
]
+=
x
*
y
;
}
__syncthreads
();
for
(
int
s
=
blockSize
/
2
;
s
>
0
;
s
>>=
1
)
{
if
(
tid
<
s
)
{
xx
[
tid
]
+=
xx
[
tid
+
s
];
yy
[
tid
]
+=
yy
[
tid
+
s
];
xy
[
tid
]
+=
xy
[
tid
+
s
];
}
__syncthreads
();
}
if
(
tid
==
0
)
{
output
[
ty
]
=
scale
*
xy
[
0
]
/
(
sqrt
(
xx
[
0
])
*
sqrt
(
yy
[
0
]));
}
}
void
hl_cossim
(
real
*
output
,
real
*
input1
,
real
*
input2
,
int
width
,
int
input1_height
,
int
input2_height
,
real
scale
)
{
CHECK_NOTNULL
(
output
);
CHECK_NOTNULL
(
input1
);
CHECK_NOTNULL
(
input2
);
const
int
blockSize
=
256
;
dim3
threads
(
blockSize
,
1
);
dim3
grid
(
1
,
input1_height
);
KeCosSim
<
blockSize
><<<
grid
,
threads
,
0
,
STREAM_DEFAULT
>>>
(
output
,
input1
,
input2
,
width
,
input1_height
,
input2_height
,
scale
);
CHECK_SYNC
(
"hl_cossim failed"
);
}
template
<
int
blockSize
>
__global__
void
KeCosSimDerivative
(
real
*
grad
,
real
*
output
,
real
*
prevOutX
,
real
*
prevOutY
,
real
*
prevGradX
,
real
*
prevGradY
,
int
width
,
int
input1_height
,
int
input2_height
,
real
scale
)
{
const
int
ty
=
blockIdx
.
y
;
int
tid
=
threadIdx
.
x
;
__shared__
real
xx
[
blockSize
];
__shared__
real
yy
[
blockSize
];
__shared__
real
xy
[
blockSize
];
xx
[
tid
]
=
0.0
;
yy
[
tid
]
=
0.0
;
xy
[
tid
]
=
0.0
;
__syncthreads
();
prevOutX
+=
ty
*
width
;
prevGradX
+=
ty
*
width
;
if
(
input2_height
>
1
)
{
prevOutY
+=
ty
*
width
;
prevGradY
+=
ty
*
width
;
}
for
(
int
index
=
tid
;
index
<
width
;
index
+=
blockSize
)
{
real
x
=
prevOutX
[
index
];
real
y
=
prevOutY
[
index
];
xx
[
tid
]
+=
x
*
x
;
yy
[
tid
]
+=
y
*
y
;
xy
[
tid
]
+=
x
*
y
;
}
__syncthreads
();
for
(
int
s
=
blockSize
/
2
;
s
>
0
;
s
>>=
1
)
{
if
(
tid
<
s
)
{
xx
[
tid
]
+=
xx
[
tid
+
s
];
yy
[
tid
]
+=
yy
[
tid
+
s
];
xy
[
tid
]
+=
xy
[
tid
+
s
];
}
__syncthreads
();
}
if
(
xy
[
0
]
==
0
)
{
real
reciprocal
=
1.0
/
(
sqrt
(
xx
[
0
])
*
sqrt
(
yy
[
0
]));
for
(
int
index
=
tid
;
index
<
width
;
index
+=
blockSize
)
{
prevGradX
[
index
]
+=
scale
*
grad
[
ty
]
*
prevOutY
[
index
]
*
reciprocal
;
if
(
input2_height
>
1
)
{
prevGradY
[
index
]
+=
scale
*
grad
[
ty
]
*
prevOutX
[
index
]
*
reciprocal
;
}
else
{
paddle
::
paddleAtomicAdd
(
prevGradY
+
index
,
scale
*
grad
[
ty
]
*
prevOutX
[
index
]
*
reciprocal
);
}
}
}
else
{
real
reciprocalXY
=
1.0
/
xy
[
0
];
real
reciprocalSquareSumX
=
1.0
/
xx
[
0
];
real
reciprocalSquareSumY
=
1.0
/
yy
[
0
];
for
(
int
index
=
tid
;
index
<
width
;
index
+=
blockSize
)
{
prevGradX
[
index
]
+=
output
[
ty
]
*
grad
[
ty
]
*
(
prevOutY
[
index
]
*
reciprocalXY
-
prevOutX
[
index
]
*
reciprocalSquareSumX
);
if
(
input2_height
>
1
)
{
prevGradY
[
index
]
+=
output
[
ty
]
*
grad
[
ty
]
*
(
prevOutX
[
index
]
*
reciprocalXY
-
prevOutY
[
index
]
*
reciprocalSquareSumY
);
}
else
{
paddle
::
paddleAtomicAdd
(
prevGradY
+
index
,
output
[
ty
]
*
grad
[
ty
]
*
(
prevOutX
[
index
]
*
reciprocalXY
-
prevOutY
[
index
]
*
reciprocalSquareSumY
));
}
}
}
}
void
hl_cossim_derivative
(
real
*
grad
,
real
*
output
,
real
*
prevOutX
,
real
*
prevOutY
,
real
*
prevGradX
,
real
*
prevGradY
,
int
width
,
int
input1_height
,
int
input2_height
,
real
scale
)
{
CHECK_NOTNULL
(
grad
);
CHECK_NOTNULL
(
output
);
CHECK_NOTNULL
(
prevOutX
);
CHECK_NOTNULL
(
prevOutY
);
CHECK_NOTNULL
(
prevGradX
);
CHECK_NOTNULL
(
prevGradY
);
const
int
blockSize
=
256
;
dim3
threads
(
blockSize
,
1
);
dim3
grid
(
1
,
input1_height
);
KeCosSimDerivative
<
blockSize
><<<
grid
,
threads
,
0
,
STREAM_DEFAULT
>>>
(
grad
,
output
,
prevOutX
,
prevOutY
,
prevGradX
,
prevGradY
,
width
,
input1_height
,
input2_height
,
scale
);
CHECK_SYNC
(
"hl_cossim_derivate failed"
);
}
__global__
void
KeMatrixAddSharedBias
(
real
*
A
,
real
*
B
,
const
int
channel
,
...
...
paddle/function/CosSimOp.cpp
浏览文件 @
a948eea3
...
...
@@ -34,7 +34,6 @@ void CosSimForward<DEVICE_TYPE_CPU>(CpuMatrix* out_mat,
CHECK
(
in2_mat
->
getHeight
()
==
1LU
||
in2_mat
->
getHeight
()
==
num_samples
);
size_t
inc
=
(
in2_mat
->
getHeight
()
==
1LU
)
?
0
:
dim
;
for
(
size_t
i
=
0
;
i
<
num_samples
;
++
i
,
x
+=
dim
,
y
+=
inc
)
{
/// for each row, todo(tianbing), use TensorExpression square2 ?
real
square_sum_x
=
0
;
real
square_sum_y
=
0
;
real
xy
=
0
;
...
...
@@ -147,12 +146,15 @@ void CosSimBackward<DEVICE_TYPE_CPU>(const CpuMatrix* out_grad,
}
/**
* \param inputs[0] output value 1, size: nSamples * 1.
* \param inputs[1] input value 1, size: nSamples * dim.
* \param inputs[2] input value 2, size: n2 * dim (n2 == 1 or n2 == nSamples).
* \param inputs[3] input grad 1, size: nSamples * dim.
* \param inputs[4] input grad 2, size: n2 * dim (n2 == 1 or n2 == nSamples).
* \param outputs[0] output grad, size : nSamples * 1.
* \param inouts[0] forward input grad 1, size: nSamples * dim.
* \param inouts[1] forward input grad 2,
* size: n2 * dim (n2 == 1 or n2 == nSamples).
*
* \param inputs[0] backward loss output grad, size : nSamples * 1.
* \param inputs[1] forward output value, size: nSamples * 1.
* \param inputs[2] forward input value 1, size: nSamples * dim.
* \param inputs[3] forward input value 2,
* size: n2 * dim (n2 == 1 or n2 == nSamples).
*/
template
<
DeviceType
Device
>
class
CosSimBackwardFunc
:
public
FunctionBase
{
...
...
@@ -163,35 +165,35 @@ class CosSimBackwardFunc : public FunctionBase {
void
calc
(
const
Arguments
&
inputs
,
const
Arguments
&
outputs
,
const
Arguments
&
inouts
)
override
{
CHECK_EQ
(
inputs
.
size
(),
5
);
CHECK_EQ
(
outputs
.
size
(),
1
);
CHECK_EQ
(
inouts
.
size
(),
0
);
CHECK_EQ
(
inputs
.
size
(),
4
);
CHECK_EQ
(
outputs
.
size
(),
0
);
CHECK_EQ
(
inouts
.
size
(),
2
);
/// dim of out_grad and out_val == 1, column vector
CHECK_EQ
(
outputs
[
0
].
dims_
[
1
],
1UL
);
CHECK_EQ
(
inputs
[
0
].
dims_
[
1
],
1UL
);
CHECK_EQ
(
inputs
[
1
].
dims_
[
1
],
1UL
);
/// nSamples of out_grad == out_val == in_val1 == in_grad1
CHECK_EQ
(
inputs
[
0
].
dims_
[
0
],
out
puts
[
0
].
dims_
[
0
]);
CHECK_EQ
(
inputs
[
1
].
dims_
[
0
],
out
puts
[
0
].
dims_
[
0
]);
CHECK_EQ
(
in
puts
[
3
].
dims_
[
0
],
out
puts
[
0
].
dims_
[
0
]);
CHECK_EQ
(
inputs
[
1
].
dims_
[
0
],
in
puts
[
0
].
dims_
[
0
]);
CHECK_EQ
(
inputs
[
0
].
dims_
[
0
],
in
puts
[
0
].
dims_
[
0
]);
CHECK_EQ
(
in
outs
[
0
].
dims_
[
0
],
in
puts
[
0
].
dims_
[
0
]);
/// dim of in1_val1 == in_val2 == in_grad1 == in_grad2
CHECK_EQ
(
inputs
[
2
].
dims_
[
1
],
inputs
[
1
].
dims_
[
1
]);
CHECK_EQ
(
in
puts
[
3
].
dims_
[
1
],
inputs
[
1
].
dims_
[
1
]);
CHECK_EQ
(
in
puts
[
4
].
dims_
[
1
],
inputs
[
1
].
dims_
[
1
]);
CHECK_EQ
(
inputs
[
3
].
dims_
[
1
],
inputs
[
2
].
dims_
[
1
]);
CHECK_EQ
(
in
outs
[
0
].
dims_
[
1
],
inputs
[
2
].
dims_
[
1
]);
CHECK_EQ
(
in
outs
[
1
].
dims_
[
1
],
inputs
[
2
].
dims_
[
1
]);
CHECK
(
outputs
[
0
].
getData
()
&&
inputs
[
0
].
getData
()
&&
inputs
[
1
].
getData
()
&&
inputs
[
2
].
getData
()
&&
inputs
[
3
].
getData
()
&&
inputs
[
4
].
getData
());
CHECK
(
inputs
[
0
].
getData
()
&&
inputs
[
1
].
getData
()
&&
inputs
[
2
].
getData
()
&&
inputs
[
3
].
getData
()
&&
inouts
[
0
].
getData
()
&&
inouts
[
1
].
getData
());
const
auto
out_grad
=
std
::
make_shared
<
typename
MatrixT
<
Device
>::
type
>
(
outputs
[
0
].
getData
(),
outputs
[
0
].
dims_
[
0
],
outputs
[
0
].
dims_
[
1
]);
const
auto
out_val
=
std
::
make_shared
<
typename
MatrixT
<
Device
>::
type
>
(
inputs
[
0
].
getData
(),
inputs
[
0
].
dims_
[
0
],
inputs
[
0
].
dims_
[
1
]);
const
auto
in1
_val
=
std
::
make_shared
<
typename
MatrixT
<
Device
>::
type
>
(
const
auto
out
_val
=
std
::
make_shared
<
typename
MatrixT
<
Device
>::
type
>
(
inputs
[
1
].
getData
(),
inputs
[
1
].
dims_
[
0
],
inputs
[
1
].
dims_
[
1
]);
const
auto
in
2
_val
=
std
::
make_shared
<
typename
MatrixT
<
Device
>::
type
>
(
const
auto
in
1
_val
=
std
::
make_shared
<
typename
MatrixT
<
Device
>::
type
>
(
inputs
[
2
].
getData
(),
inputs
[
2
].
dims_
[
0
],
inputs
[
2
].
dims_
[
1
]);
auto
in1_grad
=
std
::
make_shared
<
typename
MatrixT
<
Device
>::
type
>
(
const
auto
in2_val
=
std
::
make_shared
<
typename
MatrixT
<
Device
>::
type
>
(
inputs
[
3
].
getData
(),
inputs
[
3
].
dims_
[
0
],
inputs
[
3
].
dims_
[
1
]);
auto
in1_grad
=
std
::
make_shared
<
typename
MatrixT
<
Device
>::
type
>
(
inouts
[
0
].
getData
(),
inouts
[
0
].
dims_
[
0
],
inouts
[
0
].
dims_
[
1
]);
auto
in2_grad
=
std
::
make_shared
<
typename
MatrixT
<
Device
>::
type
>
(
in
puts
[
4
].
getData
(),
inputs
[
4
].
dims_
[
0
],
inputs
[
4
].
dims_
[
1
]);
in
outs
[
1
].
getData
(),
inouts
[
1
].
dims_
[
0
],
inouts
[
1
].
dims_
[
1
]);
CosSimBackward
<
Device
>
(
out_grad
.
get
(),
out_val
.
get
(),
...
...
paddle/function/CosSimOp.h
浏览文件 @
a948eea3
...
...
@@ -25,9 +25,9 @@ namespace paddle {
* = scale * \sum_j (in1[i][j] * in2[i][j]) /
* sqrt(sum_j (in1[i][j]^2) * sum_j (in2[i][j])^2)
*
* \param[out] output output
data
.
* \param[in] intput1 input
data
.
* \param[in] intput2 input
data
.
* \param[out] output output
value
.
* \param[in] intput1 input
value
.
* \param[in] intput2 input
value
.
* \param[in] scale default 1.0.
*
*/
...
...
@@ -40,13 +40,13 @@ void CosSimForward(typename MatrixT<Device>::type* output,
/**
* \brief Cosine Similarity BackWard for Derivative.
*
* \param[
out] output1
backward loss output grad.
* \param[in]
input1
forward-output value.
* \param[in]
input2
forward input value 1.
* \param[in]
input3
forward input value 2.
* \param[in
] input4
forward input grad 1.
* \param[in
] input5
forward input grad 2.
* \param[in]
scale
default 1.0.
* \param[
in] output grad
backward loss output grad.
* \param[in]
output val
forward-output value.
* \param[in]
input val1
forward input value 1.
* \param[in]
input val2
forward input value 2.
* \param[in
/out] input grad
forward input grad 1.
* \param[in
/out] input grad
forward input grad 2.
* \param[in]
scale
default 1.0.
*
*/
template
<
DeviceType
Device
>
...
...
paddle/function/CosSimOpTest.cpp
浏览文件 @
a948eea3
...
...
@@ -97,22 +97,22 @@ void testCosSimBackward(size_t height_x,
gpu_in2_grad
.
copyFrom
(
cpu_in2_grad
);
compare
.
getCpuFunction
()
->
calc
(
{
Tensor
(
cpu_out_val
.
getData
(),
Dims
{
height_x
,
1
}),
{
Tensor
(
cpu_out_grad
.
getData
(),
Dims
{
height_x
,
1
}),
Tensor
(
cpu_out_val
.
getData
(),
Dims
{
height_x
,
1
}),
Tensor
(
cpu_in1_val
.
getData
(),
Dims
{
height_x
,
width
}),
Tensor
(
cpu_in2_val
.
getData
(),
Dims
{
height_x
,
width
}),
Tensor
(
cpu_in1_grad
.
getData
(),
Dims
{
height_x
,
width
}),
Tensor
(
cpu_in2_grad
.
getData
(),
Dims
{
height_x
,
width
})},
{
Tensor
(
cpu_out_grad
.
getData
(),
Dims
{
height_x
,
1
})},
{});
Tensor
(
cpu_in2_val
.
getData
(),
Dims
{
height_x
,
width
})},
{},
{
Tensor
(
cpu_in1_grad
.
getData
(),
Dims
{
height_x
,
width
}),
Tensor
(
cpu_in2_grad
.
getData
(),
Dims
{
height_x
,
width
})});
compare
.
getGpuFunction
()
->
calc
(
{
Tensor
(
gpu_out_val
.
getData
(),
Dims
{
height_x
,
1
}),
{
Tensor
(
gpu_out_grad
.
getData
(),
Dims
{
height_x
,
1
}),
Tensor
(
gpu_out_val
.
getData
(),
Dims
{
height_x
,
1
}),
Tensor
(
gpu_in1_val
.
getData
(),
Dims
{
height_x
,
width
}),
Tensor
(
gpu_in2_val
.
getData
(),
Dims
{
height_x
,
width
}),
Tensor
(
gpu_in1_grad
.
getData
(),
Dims
{
height_x
,
width
}),
Tensor
(
gpu_in2_grad
.
getData
(),
Dims
{
height_x
,
width
})},
{
Tensor
(
gpu_out_grad
.
getData
(),
Dims
{
height_x
,
1
})},
{});
Tensor
(
gpu_in2_val
.
getData
(),
Dims
{
height_x
,
width
})},
{},
{
Tensor
(
gpu_in1_grad
.
getData
(),
Dims
{
height_x
,
width
}),
Tensor
(
gpu_in2_grad
.
getData
(),
Dims
{
height_x
,
width
})});
autotest
::
TensorCheckErr
(
cpu_in1_grad
,
gpu_in1_grad
);
autotest
::
TensorCheckErr
(
cpu_in2_grad
,
gpu_in2_grad
);
...
...
paddle/gserver/layers/CosSimLayer.cpp
浏览文件 @
a948eea3
...
...
@@ -79,13 +79,13 @@ void CosSimLayer::backward(const UpdateCallback& callback) {
auto
inG2
=
this
->
getInputGrad
(
1
);
CHECK
(
outG
&&
outV
&&
inV1
&&
inV2
&&
inG1
&&
inG2
);
backward_
[
0
]
->
calc
(
{
Tensor
(
outV
->
getData
(),
Dims
{
outV
->
getHeight
(),
outV
->
getWidth
()}),
{
Tensor
(
outG
->
getData
(),
Dims
{
outG
->
getHeight
(),
outG
->
getWidth
()}),
Tensor
(
outV
->
getData
(),
Dims
{
outV
->
getHeight
(),
outV
->
getWidth
()}),
Tensor
(
inV1
->
getData
(),
Dims
{
inV1
->
getHeight
(),
inV1
->
getWidth
()}),
Tensor
(
inV2
->
getData
(),
Dims
{
inV2
->
getHeight
(),
inV2
->
getWidth
()}),
Tensor
(
inG1
->
getData
(),
Dims
{
inG1
->
getHeight
(),
inG1
->
getWidth
()}),
Tensor
(
inG2
->
getData
(),
Dims
{
inG2
->
getHeight
(),
inG2
->
getWidth
()})},
{
Tensor
(
outG
->
getData
(),
Dims
{
outG
->
getHeight
(),
outG
->
getWidth
()})},
{});
Tensor
(
inV2
->
getData
(),
Dims
{
inV2
->
getHeight
(),
inV2
->
getWidth
()})},
{},
{
Tensor
(
inG1
->
getData
(),
Dims
{
inG1
->
getHeight
(),
inG1
->
getWidth
()}),
Tensor
(
inG2
->
getData
(),
Dims
{
inG2
->
getHeight
(),
inG2
->
getWidth
()})});
}
}
...
...
paddle/gserver/layers/CosSimVecMatLayer.cpp
浏览文件 @
a948eea3
...
...
@@ -169,19 +169,19 @@ void CosSimVecMatLayer::backward(const UpdateCallback& callback) {
tmpRow3
->
setData
(
outG
->
rowBuf
(
i
));
backward_
[
0
]
->
calc
(
{
Tensor
(
tmpRow2
->
getData
(),
{
Tensor
(
tmpRow3
->
getData
(),
Dims
{
tmpRow3
->
getHeight
(),
tmpRow3
->
getWidth
()}),
Tensor
(
tmpRow2
->
getData
(),
Dims
{
tmpRow2
->
getHeight
(),
tmpRow2
->
getWidth
()}),
Tensor
(
tmpMtx0
->
getData
(),
Dims
{
tmpMtx0
->
getHeight
(),
tmpMtx0
->
getWidth
()}),
Tensor
(
tmpRow0
->
getData
(),
Dims
{
tmpRow0
->
getHeight
(),
tmpRow0
->
getWidth
()}),
Tensor
(
tmpMtx1
->
getData
(),
Dims
{
tmpRow0
->
getHeight
(),
tmpRow0
->
getWidth
()})},
{},
{
Tensor
(
tmpMtx1
->
getData
(),
Dims
{
tmpMtx1
->
getHeight
(),
tmpMtx1
->
getWidth
()}),
Tensor
(
tmpRow1
->
getData
(),
Dims
{
tmpRow1
->
getHeight
(),
tmpRow1
->
getWidth
()})},
{
Tensor
(
tmpRow3
->
getData
(),
Dims
{
tmpRow3
->
getHeight
(),
tmpRow3
->
getWidth
()})},
{});
Dims
{
tmpRow1
->
getHeight
(),
tmpRow1
->
getWidth
()})});
}
}
...
...
paddle/gserver/layers/CosSimVecMatLayer.h
浏览文件 @
a948eea3
...
...
@@ -12,6 +12,8 @@ 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. */
#pragma once
#include "Layer.h"
#include "paddle/math/Matrix.h"
...
...
paddle/math/Matrix.cpp
浏览文件 @
a948eea3
...
...
@@ -941,59 +941,6 @@ void GpuMatrix::softreluDerivative(Matrix& output) {
void
GpuMatrix
::
scaledTanh
(
Matrix
&
output
,
real
p1
,
real
p2
)
{
BaseMatrix
::
scaledTanh
(
output
,
p1
,
p2
);
}
void
GpuMatrix
::
cosSim
(
Matrix
&
output1
,
Matrix
&
output2
,
real
scale
)
{
CHECK
(
output1
.
useGpu_
==
true
&&
output2
.
useGpu_
==
true
)
<<
"Matrix type are not equal"
;
size_t
numSamples
=
getHeight
();
size_t
dim
=
output1
.
getWidth
();
CHECK_EQ
(
getWidth
(),
1UL
);
CHECK_EQ
(
output1
.
getHeight
(),
numSamples
);
CHECK_EQ
(
output1
.
getWidth
(),
output2
.
getWidth
());
real
*
out
=
getData
();
real
*
x
=
output1
.
getData
();
real
*
y
=
output2
.
getData
();
hl_cossim
(
out
,
x
,
y
,
dim
,
output1
.
getHeight
(),
output2
.
getHeight
(),
scale
);
}
void
GpuMatrix
::
cosSimDerivative
(
Matrix
&
output
,
Matrix
&
prevOut1
,
Matrix
&
prevOut2
,
Matrix
&
prevGrad1
,
Matrix
&
prevGrad2
,
real
scale
)
{
CHECK
(
output
.
useGpu_
==
true
&&
prevOut1
.
useGpu_
==
true
&&
prevOut2
.
useGpu_
==
true
&&
prevGrad1
.
useGpu_
==
true
&&
prevGrad2
.
useGpu_
==
true
)
<<
"Matrix type are not equal"
;
CHECK_EQ
(
getWidth
(),
1UL
);
CHECK_EQ
(
output
.
getWidth
(),
1UL
);
size_t
numSamples
=
getHeight
();
CHECK_EQ
(
output
.
getHeight
(),
numSamples
);
CHECK_EQ
(
prevOut1
.
getHeight
(),
numSamples
);
CHECK_EQ
(
prevGrad1
.
getHeight
(),
numSamples
);
size_t
dim
=
prevOut1
.
getWidth
();
CHECK_EQ
(
prevOut2
.
getWidth
(),
dim
);
CHECK_EQ
(
prevGrad1
.
getWidth
(),
dim
);
CHECK_EQ
(
prevGrad2
.
getWidth
(),
dim
);
real
*
grad
=
getData
();
real
*
out
=
output
.
getData
();
real
*
prevOutX
=
prevOut1
.
getData
();
real
*
prevOutY
=
prevOut2
.
getData
();
real
*
prevGradX
=
prevGrad1
.
getData
();
real
*
prevGradY
=
prevGrad2
.
getData
();
hl_cossim_derivative
(
grad
,
out
,
prevOutX
,
prevOutY
,
prevGradX
,
prevGradY
,
dim
,
prevOut1
.
getHeight
(),
prevOut2
.
getHeight
(),
scale
);
}
void
GpuMatrix
::
randomizeUniform
()
{
CHECK
(
isContiguous
());
...
...
@@ -3470,105 +3417,6 @@ void CpuMatrix::softmaxDerivative(Matrix& output, Matrix& sftmaxSum) {
}
}
void
CpuMatrix
::
cosSim
(
Matrix
&
output1
,
Matrix
&
output2
,
real
scale
)
{
size_t
numSamples
=
getHeight
();
size_t
dim
=
output1
.
getWidth
();
CHECK_EQ
(
getWidth
(),
1UL
);
CHECK_EQ
(
output1
.
getHeight
(),
numSamples
);
CHECK_EQ
(
output1
.
getWidth
(),
output2
.
getWidth
());
real
*
out
=
getData
();
const
real
*
x
=
output1
.
getData
();
const
real
*
y
=
output2
.
getData
();
size_t
yInc
=
dim
;
if
(
output2
.
getHeight
()
==
1LU
)
{
yInc
=
0
;
}
else
{
CHECK_EQ
(
output2
.
getHeight
(),
numSamples
);
}
for
(
size_t
i
=
0
;
i
<
numSamples
;
++
i
,
x
+=
dim
,
y
+=
yInc
)
{
real
squareSumX
=
0
;
real
squareSumY
=
0
;
real
xy
=
0
;
for
(
size_t
j
=
0
;
j
<
dim
;
++
j
)
{
squareSumX
+=
_square
(
x
[
j
]);
squareSumY
+=
_square
(
y
[
j
]);
xy
+=
x
[
j
]
*
y
[
j
];
}
CHECK
(
squareSumX
>
0
&&
squareSumY
>
0
);
out
[
i
]
=
scale
*
xy
/
(
std
::
sqrt
(
squareSumX
)
*
std
::
sqrt
(
squareSumY
));
}
}
void
CpuMatrix
::
cosSimDerivative
(
Matrix
&
output
,
Matrix
&
prevOut1
,
Matrix
&
prevOut2
,
Matrix
&
prevGrad1
,
Matrix
&
prevGrad2
,
real
scale
)
{
CHECK
(
output
.
useGpu_
==
false
)
<<
"Matrix type are not equal"
;
CHECK_EQ
(
getWidth
(),
1UL
);
CHECK_EQ
(
output
.
getWidth
(),
1UL
);
size_t
numSamples
=
getHeight
();
CHECK_EQ
(
output
.
getHeight
(),
numSamples
);
CHECK_EQ
(
prevOut1
.
getHeight
(),
numSamples
);
CHECK_EQ
(
prevGrad1
.
getHeight
(),
numSamples
);
size_t
dim
=
prevOut1
.
getWidth
();
CHECK_EQ
(
prevOut2
.
getWidth
(),
dim
);
CHECK_EQ
(
prevGrad1
.
getWidth
(),
dim
);
CHECK_EQ
(
prevGrad2
.
getWidth
(),
dim
);
const
real
*
grad
=
getData
();
const
real
*
out
=
output
.
getData
();
const
real
*
prevOutX
=
prevOut1
.
getData
();
const
real
*
prevOutY
=
prevOut2
.
getData
();
real
*
prevGradX
=
prevGrad1
.
getData
();
real
*
prevGradY
=
prevGrad2
.
getData
();
size_t
yInc
=
dim
;
if
(
prevOut2
.
getHeight
()
==
1LU
)
{
yInc
=
0
;
CHECK_EQ
(
prevGrad2
.
getHeight
(),
1LU
);
}
else
{
CHECK_EQ
(
prevOut2
.
getHeight
(),
numSamples
);
CHECK_EQ
(
prevGrad2
.
getHeight
(),
numSamples
);
}
for
(
size_t
i
=
0
;
i
<
numSamples
;
++
i
,
prevOutX
+=
dim
,
prevOutY
+=
yInc
,
prevGradX
+=
dim
,
prevGradY
+=
yInc
)
{
real
squareSumX
=
0
;
real
squareSumY
=
0
;
real
xy
=
0
;
for
(
size_t
j
=
0
;
j
<
dim
;
++
j
)
{
squareSumX
+=
_square
(
prevOutX
[
j
]);
squareSumY
+=
_square
(
prevOutY
[
j
]);
xy
+=
prevOutX
[
j
]
*
prevOutY
[
j
];
}
CHECK
(
squareSumX
>
0
&&
squareSumY
>
0
);
if
(
xy
==
0
)
{
real
reciprocal
=
1.0
f
/
(
std
::
sqrt
(
squareSumX
)
*
std
::
sqrt
(
squareSumY
));
for
(
size_t
j
=
0
;
j
<
dim
;
++
j
)
{
prevGradX
[
j
]
+=
scale
*
grad
[
i
]
*
prevOutY
[
j
]
*
reciprocal
;
prevGradY
[
j
]
+=
scale
*
grad
[
i
]
*
prevOutX
[
j
]
*
reciprocal
;
}
}
else
{
real
reciprocalXY
=
1.0
f
/
xy
;
real
reciprocalSquareSumX
=
1.0
f
/
squareSumX
;
real
reciprocalSquareSumY
=
1.0
f
/
squareSumY
;
for
(
size_t
j
=
0
;
j
<
dim
;
++
j
)
{
prevGradX
[
j
]
+=
out
[
i
]
*
grad
[
i
]
*
(
prevOutY
[
j
]
*
reciprocalXY
-
prevOutX
[
j
]
*
reciprocalSquareSumX
);
prevGradY
[
j
]
+=
out
[
i
]
*
grad
[
i
]
*
(
prevOutX
[
j
]
*
reciprocalXY
-
prevOutY
[
j
]
*
reciprocalSquareSumY
);
}
}
}
}
void
CpuMatrix
::
sumOfSquares
(
Matrix
&
output
,
Matrix
&
label
)
{
CHECK
(
output
.
useGpu_
==
false
&&
label
.
useGpu_
==
false
)
<<
"Matrix type are not equal"
;
...
...
paddle/math/Matrix.h
浏览文件 @
a948eea3
...
...
@@ -799,26 +799,6 @@ public:
LOG
(
FATAL
)
<<
"Not implemented"
;
}
/**
* cosine similarity, for each row i,
* this[i] = cos(output1[i], output2[i])
*
* output2 can only have one row, then for each row i,
* this[i] = cos(output1[i], output2[0])
*/
virtual
void
cosSim
(
Matrix
&
output1
,
Matrix
&
output2
,
real
scale
=
1.0
f
)
{
LOG
(
FATAL
)
<<
"Not implemented"
;
}
virtual
void
cosSimDerivative
(
Matrix
&
output
,
Matrix
&
prevOut1
,
Matrix
&
prevOut2
,
Matrix
&
prevGrad1
,
Matrix
&
prevGrad2
,
real
scale
=
1.0
f
)
{
LOG
(
FATAL
)
<<
"Not implemented"
;
}
/// print out the values of elements to os
virtual
void
print
(
std
::
ostream
&
os
)
const
{
LOG
(
FATAL
)
<<
"Not implemented"
;
...
...
@@ -1324,14 +1304,6 @@ public:
void
softreluDerivative
(
Matrix
&
output
);
void
scaledTanh
(
Matrix
&
output
,
real
p1
,
real
p2
);
void
cosSim
(
Matrix
&
output1
,
Matrix
&
output2
,
real
scale
);
void
cosSimDerivative
(
Matrix
&
output
,
Matrix
&
prevOut1
,
Matrix
&
prevOut2
,
Matrix
&
prevGrad1
,
Matrix
&
prevGrad2
,
real
scale
);
virtual
void
print
(
std
::
ostream
&
os
)
const
;
virtual
void
print
(
std
::
ostream
&
os
,
size_t
height
,
size_t
width
)
const
;
...
...
@@ -1752,14 +1724,6 @@ public:
void
softreluDerivative
(
Matrix
&
output
);
void
scaledTanh
(
Matrix
&
output
,
real
p1
,
real
p2
);
void
cosSim
(
Matrix
&
output1
,
Matrix
&
output2
,
real
scale
);
void
cosSimDerivative
(
Matrix
&
output
,
Matrix
&
prevOut1
,
Matrix
&
prevOut2
,
Matrix
&
prevGrad1
,
Matrix
&
prevGrad2
,
real
scale
);
void
print
(
std
::
ostream
&
os
)
const
;
void
print
(
std
::
ostream
&
os
,
size_t
height
,
size_t
width
)
const
;
void
printOneRow
(
std
::
ostream
&
os
,
size_t
idx
)
const
;
...
...
paddle/math/tests/test_Matrix.cpp
浏览文件 @
a948eea3
...
...
@@ -181,28 +181,6 @@ TEST(Matrix, copyByRowIndex) {
}
}
void
testCosSim
(
int
heightX
,
int
heightY
,
int
width
,
real
scale
)
{
AutoCompare
test
(
heightX
,
1
);
CpuMatrix
arg1
(
heightX
,
width
);
CpuMatrix
arg2
(
heightY
,
width
);
arg1
.
randomizeUniform
();
arg2
.
randomizeUniform
();
arg2
.
add
(
-
0.5
);
test
.
cmpWithArg
(
&
Matrix
::
cosSim
,
arg1
,
arg2
,
scale
);
}
TEST
(
Matrix
,
cosSim
)
{
for
(
auto
heightX
:
{
10
,
100
,
1000
})
{
for
(
auto
heightY
:
{
1
,
heightX
})
{
for
(
auto
width
:
{
10
,
100
,
1000
})
{
for
(
auto
scale
:
{
1.0
,
2.0
})
{
testCosSim
(
heightX
,
heightY
,
width
,
scale
);
}
}
}
}
}
void
testParamReluForward
(
int
height
,
int
width
,
int
w_height
,
int
w_width
)
{
AutoCompare
test
(
height
,
width
);
CpuMatrix
arg1
(
height
,
width
);
...
...
paddle/math/tests/test_matrixCompare.cpp
浏览文件 @
a948eea3
...
...
@@ -720,61 +720,6 @@ TEST(Matrix, sequenceAvgForward) {
}
}
void
testCosSimDerivate
(
int
heightX
,
int
heightY
,
int
width
,
real
scale
)
{
MatrixPtr
prevOutX
=
CpuMatrix
::
create
(
heightX
,
width
,
false
,
false
);
MatrixPtr
prevOutY
=
CpuMatrix
::
create
(
heightY
,
width
,
false
,
false
);
MatrixPtr
grad
=
CpuMatrix
::
create
(
heightX
,
1
,
false
,
false
);
MatrixPtr
output
=
CpuMatrix
::
create
(
heightX
,
1
,
false
,
false
);
MatrixPtr
prevGradX
=
CpuMatrix
::
create
(
heightX
,
width
,
false
,
false
);
MatrixPtr
prevGradY
=
CpuMatrix
::
create
(
heightY
,
width
,
false
,
false
);
prevOutX
->
randomizeUniform
();
prevOutY
->
randomizeUniform
();
grad
->
randomizeUniform
();
output
->
randomizeUniform
();
prevGradX
->
randomizeUniform
();
prevGradY
->
randomizeUniform
();
MatrixPtr
prevOutXGpu
=
GpuMatrix
::
create
(
heightX
,
width
,
false
,
true
);
MatrixPtr
prevOutYGpu
=
GpuMatrix
::
create
(
heightY
,
width
,
false
,
true
);
MatrixPtr
gradGpu
=
GpuMatrix
::
create
(
heightX
,
1
,
false
,
true
);
MatrixPtr
outputGpu
=
GpuMatrix
::
create
(
heightX
,
1
,
false
,
true
);
MatrixPtr
prevGradXGpu
=
GpuMatrix
::
create
(
heightX
,
width
,
false
,
true
);
MatrixPtr
prevGradYGpu
=
GpuMatrix
::
create
(
heightY
,
width
,
false
,
true
);
prevOutXGpu
->
copyFrom
(
*
prevOutX
);
prevOutYGpu
->
copyFrom
(
*
prevOutY
);
gradGpu
->
copyFrom
(
*
grad
);
outputGpu
->
copyFrom
(
*
output
);
prevGradXGpu
->
copyFrom
(
*
prevGradX
);
prevGradYGpu
->
copyFrom
(
*
prevGradY
);
grad
->
cosSimDerivative
(
*
output
,
*
prevOutX
,
*
prevOutY
,
*
prevGradX
,
*
prevGradY
,
scale
);
gradGpu
->
cosSimDerivative
(
*
outputGpu
,
*
prevOutXGpu
,
*
prevOutYGpu
,
*
prevGradXGpu
,
*
prevGradYGpu
,
scale
);
TensorCheckErr
(
*
prevGradX
,
*
prevGradXGpu
);
TensorCheckErr
(
*
prevGradY
,
*
prevGradYGpu
);
}
TEST
(
Matrix
,
cosSimDerivate
)
{
for
(
auto
heightX
:
{
1
,
10
,
100
})
{
for
(
auto
heightY
:
{
1
,
heightX
})
{
for
(
auto
width
:
{
1
,
10
,
100
})
{
for
(
auto
scale
:
{
1.0
,
2.0
})
{
testCosSimDerivate
(
heightX
,
heightY
,
width
,
scale
);
}
}
}
}
}
void
testParamReluBackwardDiff
(
int
height
,
int
width
,
int
w_height
,
...
...
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