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838ef366
编写于
12月 24, 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
add first paddle function example for ContextProjectionForward operator,
by going through Daoyuan's excellent paddle function design.
上级
54a2b1f6
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
479 addition
and
16 deletion
+479
-16
paddle/function/CMakeLists.txt
paddle/function/CMakeLists.txt
+4
-0
paddle/function/Function.cpp
paddle/function/Function.cpp
+28
-0
paddle/function/Function.h
paddle/function/Function.h
+2
-0
paddle/function/FunctionTest.h
paddle/function/FunctionTest.h
+28
-16
paddle/function/context_projection_op.cpp
paddle/function/context_projection_op.cpp
+136
-0
paddle/function/context_projection_op.h
paddle/function/context_projection_op.h
+43
-0
paddle/function/context_projection_op_gpu.cu
paddle/function/context_projection_op_gpu.cu
+137
-0
paddle/function/context_projection_op_test.cpp
paddle/function/context_projection_op_test.cpp
+101
-0
未找到文件。
paddle/function/CMakeLists.txt
浏览文件 @
838ef366
...
...
@@ -17,6 +17,10 @@ if(WITH_TESTING)
# file(GLOB test_files . *OpTest.cpp)
# add_executable(${test_bin} EXCLUDE_FROM_ALL ${test_files})
add_simple_unittest
(
CrossMapNormalOpTest
)
add_unittest
(
ContextProjectionOpTest
ContextProjectionOpTest.cpp
ContextProjectionOpGpu.cu
../gserver/tests/TestUtil.cpp
)
endif
()
endif
()
...
...
paddle/function/Function.cpp
浏览文件 @
838ef366
...
...
@@ -30,6 +30,20 @@ real FuncConfig::get<real>(const std::string& key) const {
return
it
->
second
.
r
;
}
template
<
>
int
FuncConfig
::
get
<
int
>
(
const
std
::
string
&
key
)
const
{
auto
it
=
valueMap_
.
find
(
key
);
CHECK
(
it
!=
valueMap_
.
end
())
<<
"Cannot find value: '"
<<
key
<<
"'"
;
return
it
->
second
.
i
;
}
template
<
>
bool
FuncConfig
::
get
<
bool
>
(
const
std
::
string
&
key
)
const
{
auto
it
=
valueMap_
.
find
(
key
);
CHECK
(
it
!=
valueMap_
.
end
())
<<
"Cannot find value: '"
<<
key
<<
"'"
;
return
it
->
second
.
b
;
}
template
<
>
FuncConfig
&
FuncConfig
::
set
<
size_t
>
(
const
std
::
string
&
key
,
size_t
v
)
{
CHECK_EQ
(
valueMap_
.
count
(
key
),
0
)
<<
"Duplicated value: "
<<
key
;
...
...
@@ -44,6 +58,20 @@ FuncConfig& FuncConfig::set<real>(const std::string& key, real v) {
return
*
this
;
}
template
<
>
FuncConfig
&
FuncConfig
::
set
<
int
>
(
const
std
::
string
&
key
,
int
v
)
{
CHECK
(
valueMap_
.
count
(
key
)
==
0
)
<<
"Duplicated value: "
<<
key
;
valueMap_
[
key
].
i
=
v
;
return
*
this
;
}
template
<
>
FuncConfig
&
FuncConfig
::
set
<
bool
>
(
const
std
::
string
&
key
,
bool
v
)
{
CHECK
(
valueMap_
.
count
(
key
)
==
0
)
<<
"Duplicated value: "
<<
key
;
valueMap_
[
key
].
b
=
v
;
return
*
this
;
}
ClassRegistrar
<
FunctionBase
>
FunctionBase
::
funcRegistrar_
;
}
// namespace paddle
paddle/function/Function.h
浏览文件 @
838ef366
...
...
@@ -59,6 +59,8 @@ public:
union
value
{
size_t
s
;
real
r
;
int
i
;
bool
b
;
};
template
<
typename
T
>
...
...
paddle/function/FunctionTest.h
浏览文件 @
838ef366
...
...
@@ -33,25 +33,33 @@ public:
// init cpu and gpu arguments
auto
initArgs
=
[
=
](
Arguments
&
cpuArgs
,
Arguments
&
gpuArgs
,
const
Arguments
&
inArgs
)
{
for
(
auto
arg
:
inArgs
)
{
for
(
const
auto
arg
:
inArgs
)
{
size_t
size
=
sizeof
(
real
);
for
(
auto
dim
:
arg
.
dims_
)
{
for
(
const
auto
dim
:
arg
.
dims_
)
{
size
*=
dim
;
}
cpuMemory
.
emplace_back
(
std
::
make_shared
<
CpuMemoryHandle
>
(
size
));
gpuMemory
.
emplace_back
(
std
::
make_shared
<
GpuMemoryHandle
>
(
size
));
cpuArgs
.
emplace_back
(
Tensor
((
real
*
)
cpuMemory
.
back
()
->
getBuf
(),
arg
.
dims_
));
gpuArgs
.
emplace_back
(
Tensor
((
real
*
)
gpuMemory
.
back
()
->
getBuf
(),
arg
.
dims_
));
// will use an api to refactor this code.
CpuVector
cpuVector
(
size
/
sizeof
(
real
),
(
real
*
)
cpuArgs
.
back
().
getData
());
GpuVector
gpuVector
(
size
/
sizeof
(
real
),
(
real
*
)
gpuArgs
.
back
().
getData
());
cpuVector
.
uniform
(
0.001
,
1
);
gpuVector
.
copyFrom
(
cpuVector
);
if
(
arg
.
getData
())
{
// todo(tianbing), waste unnecessary mem here
cpuMemory
.
emplace_back
(
std
::
make_shared
<
CpuMemoryHandle
>
(
size
));
gpuMemory
.
emplace_back
(
std
::
make_shared
<
GpuMemoryHandle
>
(
size
));
cpuArgs
.
emplace_back
(
Tensor
((
real
*
)
arg
.
getData
(),
arg
.
dims_
));
gpuArgs
.
emplace_back
(
Tensor
((
real
*
)
arg
.
getData
(),
arg
.
dims_
));
// already init outside
}
else
{
cpuMemory
.
emplace_back
(
std
::
make_shared
<
CpuMemoryHandle
>
(
size
));
gpuMemory
.
emplace_back
(
std
::
make_shared
<
GpuMemoryHandle
>
(
size
));
cpuArgs
.
emplace_back
(
Tensor
((
real
*
)
cpuMemory
.
back
()
->
getBuf
(),
arg
.
dims_
));
gpuArgs
.
emplace_back
(
Tensor
((
real
*
)
gpuMemory
.
back
()
->
getBuf
(),
arg
.
dims_
));
// will use an api to refactor this code.
CpuVector
cpuVector
(
size
/
sizeof
(
real
),
(
real
*
)
cpuArgs
.
back
().
getData
());
GpuVector
gpuVector
(
size
/
sizeof
(
real
),
(
real
*
)
gpuArgs
.
back
().
getData
());
cpuVector
.
uniform
(
0.001
,
1
);
gpuVector
.
copyFrom
(
cpuVector
);
}
}
};
initArgs
(
cpuInputs
,
gpuInputs
,
inputs
);
...
...
@@ -81,6 +89,10 @@ public:
checkArgs
(
cpuInouts
,
gpuInouts
);
}
std
::
shared_ptr
<
FunctionBase
>
getCpuFunction
()
const
{
return
cpu
;
}
std
::
shared_ptr
<
FunctionBase
>
getGpuFunction
()
const
{
return
gpu
;
}
protected:
std
::
shared_ptr
<
FunctionBase
>
cpu
;
std
::
shared_ptr
<
FunctionBase
>
gpu
;
...
...
paddle/function/context_projection_op.cpp
0 → 100644
浏览文件 @
838ef366
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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. */
#include "context_projection_op.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/Vector.h"
namespace
paddle
{
template
<
>
void
ContextProjectionForward
<
DEVICE_TYPE_CPU
>
(
Tensor
&
output
,
const
Tensor
&
input
,
const
Tensor
&
weight
,
const
Tensor
&
sequence
,
size_t
context_length
,
int
context_start
,
size_t
begin_pad
,
bool
is_padding
)
{
CHECK
(
output
.
getData
()
&&
input
.
getData
()
&&
sequence
.
getData
());
CHECK_EQ
(
output
.
dims_
.
size
(),
2
);
CHECK_EQ
(
input
.
dims_
.
size
(),
2
);
CHECK_EQ
(
weight
.
dims_
.
size
(),
2
);
CHECK_EQ
(
sequence
.
dims_
.
size
(),
1
);
auto
out_mat
=
std
::
make_shared
<
CpuMatrix
>
(
output
.
getData
(),
output
.
dims_
[
0
],
output
.
dims_
[
1
]);
const
auto
in_mat
=
std
::
make_shared
<
CpuMatrix
>
(
input
.
getData
(),
input
.
dims_
[
0
],
input
.
dims_
[
1
]);
const
auto
weight_mat
=
!
weight
.
getData
()
?
nullptr
:
std
::
make_shared
<
CpuMatrix
>
(
weight
.
getData
(),
weight
.
dims_
[
0
],
input
.
dims_
[
1
]);
CpuIVector
seq_vec
(
sequence
.
dims_
[
0
],
reinterpret_cast
<
int
*>
(
sequence
.
getData
()));
CHECK_EQ
(
out_mat
->
getWidth
(),
in_mat
->
getWidth
()
*
context_length
);
const
int
*
starts
=
seq_vec
.
getData
();
const
size_t
num_sequences
=
seq_vec
.
getSize
()
-
1
;
for
(
size_t
i
=
0
;
i
<
num_sequences
;
++
i
)
{
for
(
size_t
j
=
0
;
j
<
context_length
;
++
j
)
{
int
begin
=
starts
[
i
]
+
context_start
+
j
;
int
end
=
starts
[
i
+
1
]
+
context_start
+
j
;
int
dst_begin
=
starts
[
i
];
int
dst_end
=
starts
[
i
+
1
];
if
(
begin
<
starts
[
i
])
{
int64_t
pad_size
=
std
::
min
(
starts
[
i
]
-
begin
,
starts
[
i
+
1
]
-
starts
[
i
]);
MatrixPtr
mat
=
out_mat
->
subMatrix
(
starts
[
i
],
pad_size
);
if
(
is_padding
&&
weight_mat
)
{
MatrixPtr
sub
=
weight_mat
->
subMatrix
(
j
,
pad_size
);
mat
->
addAtOffset
(
*
sub
,
j
*
in_mat
->
getWidth
());
}
dst_begin
=
starts
[
i
]
+
pad_size
;
begin
=
starts
[
i
];
}
if
(
end
>
starts
[
i
+
1
])
{
int64_t
pad_size
=
std
::
min
(
end
-
starts
[
i
+
1
],
starts
[
i
+
1
]
-
starts
[
i
]);
MatrixPtr
mat
=
out_mat
->
subMatrix
(
starts
[
i
+
1
]
-
pad_size
,
pad_size
);
if
(
is_padding
&&
weight_mat
)
{
MatrixPtr
sub
=
weight_mat
->
subMatrix
(
begin_pad
+
context_start
+
j
-
pad_size
,
pad_size
);
mat
->
addAtOffset
(
*
sub
,
j
*
in_mat
->
getWidth
());
}
dst_end
=
starts
[
i
+
1
]
-
pad_size
;
end
=
starts
[
i
+
1
];
}
if
(
end
<=
begin
)
continue
;
MatrixPtr
src
=
in_mat
->
subMatrix
(
begin
,
end
-
begin
);
MatrixPtr
dst
=
out_mat
->
subMatrix
(
dst_begin
,
dst_end
-
dst_begin
);
dst
->
addAtOffset
(
*
src
,
j
*
in_mat
->
getWidth
());
}
}
}
/**
* \param inputs[0] input value.
* \param inputs[1] input weight.
* \param inputs[2] input sequence.
* \param outputs[0] output value.
*/
template
<
DeviceType
Device
>
class
ContextProjectionForwardFunc
:
public
FunctionBase
{
public:
void
init
(
const
FuncConfig
&
config
)
override
{
context_length_
=
config
.
get
<
size_t
>
(
"context_length"
);
context_start_
=
config
.
get
<
int
>
(
"context_start"
);
begin_pad_
=
config
.
get
<
size_t
>
(
"begin_pad"
);
is_padding_
=
config
.
get
<
bool
>
(
"is_padding"
);
}
void
calc
(
const
Arguments
&
inputs
,
const
Arguments
&
outputs
,
const
Arguments
&
inouts
)
override
{
CHECK_EQ
(
3
,
inputs
.
size
());
CHECK_EQ
(
1
,
outputs
.
size
());
CHECK_EQ
(
0
,
inouts
.
size
());
ContextProjectionForward
<
Device
>
((
Tensor
&
)
outputs
[
0
],
inputs
[
0
],
inputs
[
1
],
inputs
[
2
],
context_length_
,
context_start_
,
begin_pad_
,
is_padding_
);
}
private:
size_t
context_length_
;
int
context_start_
;
size_t
begin_pad_
;
bool
is_padding_
;
};
REGISTER_TYPED_FUNC
(
ContextProjectionForward
,
CPU
,
ContextProjectionForwardFunc
);
#ifndef PADDLE_ONLY_CPU
REGISTER_TYPED_FUNC
(
ContextProjectionForward
,
GPU
,
ContextProjectionForwardFunc
);
#endif
}
// namespace paddle
paddle/function/context_projection_op.h
0 → 100644
浏览文件 @
838ef366
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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 "Function.h"
namespace
paddle
{
/**
* \brief Context Projection Forward.
*
* \param[out] outputs output data.
* \param[in] input input data.
* \param[in] weight input weight.
* \param[in] sequence input data.
* \param[in] context_length consecutive rows for concatenation.
* \param[in] begin_pad context start position.
* \param[in] is_padding whether padding 0 or not.
*
*/
template
<
DeviceType
Device
>
void
ContextProjectionForward
(
Tensor
&
output
,
const
Tensor
&
input
,
const
Tensor
&
weight
,
const
Tensor
&
sequence
,
size_t
context_length
,
int
context_start
,
size_t
begin_pad
,
bool
is_padding
);
}
// namespace paddle
paddle/function/context_projection_op_gpu.cu
0 → 100644
浏览文件 @
838ef366
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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. */
#include "hl_base.h"
#include "context_projection_op.h"
namespace
paddle
{
template
<
bool
padding
>
__global__
void
KeContextProjectionForward
(
const
real
*
input
,
const
int
*
sequence
,
const
real
*
weight
,
real
*
output
,
int
input_dim
,
int
context_length
,
int
context_start
,
int
begin_pad
)
{
int
idx
=
threadIdx
.
x
;
int
block_size
=
blockDim
.
x
;
int
sequenceId
=
blockIdx
.
x
;
int
seq_start
=
sequence
[
sequenceId
];
int
seq_end
=
sequence
[
sequenceId
+
1
];
real
value
=
0
;
int
instances
=
seq_end
-
seq_start
+
context_length
-
1
;
output
+=
seq_start
*
input_dim
*
context_length
;
input
+=
seq_start
*
input_dim
;
for
(
int
k
=
0
;
k
<=
input_dim
/
block_size
;
k
++
)
{
if
(
idx
<
input_dim
)
{
for
(
int
i
=
0
;
i
<
instances
;
i
++
)
{
// i + context_start;
if
((
i
+
context_start
)
<
0
)
{
if
(
padding
)
{
value
=
weight
[
i
*
input_dim
+
idx
];
}
else
{
continue
;
}
}
else
if
((
i
+
context_start
)
>=
(
seq_end
-
seq_start
))
{
if
(
padding
)
{
value
=
weight
[(
begin_pad
+
i
+
context_start
-
(
seq_end
-
seq_start
))
*
input_dim
+
idx
];
}
else
{
continue
;
}
}
else
{
value
=
input
[(
i
+
context_start
)
*
input_dim
+
idx
];
}
int
outx
=
(
i
-
context_length
)
<
0
?
i
:
(
context_length
-
1
);
int
outy
=
(
i
-
context_length
)
<
0
?
0
:
(
i
-
(
context_length
-
1
));
real
*
output_r
=
output
+
outy
*
input_dim
*
context_length
+
outx
*
input_dim
;
for
(
int
j
=
outy
;
j
<
seq_end
-
seq_start
;
j
++
)
{
output_r
[
idx
]
+=
value
;
if
(
j
-
outy
==
outx
)
break
;
output_r
+=
(
context_length
-
1
)
*
input_dim
;
}
}
}
idx
+=
block_size
;
}
}
void
hl_context_projection_forward
(
const
real
*
input
,
const
int
*
sequence
,
real
*
weight
,
real
*
output
,
int
num_sequences
,
int
input_dim
,
int
context_length
,
int
context_start
,
int
begin_pad
,
bool
is_padding
)
{
CHECK_NOTNULL
(
input
);
CHECK_NOTNULL
(
sequence
);
CHECK_NOTNULL
(
output
);
CHECK
(
!
is_padding
||
weight
);
int
block_size
=
128
;
int
blocks_x
=
num_sequences
;
int
blocks_y
=
1
;
dim3
threads
(
block_size
,
1
);
dim3
grid
(
blocks_x
,
blocks_y
);
if
(
is_padding
)
{
KeContextProjectionForward
<
true
><<<
grid
,
threads
,
0
,
STREAM_DEFAULT
>>>
(
input
,
sequence
,
weight
,
output
,
input_dim
,
context_length
,
context_start
,
begin_pad
);
}
else
{
KeContextProjectionForward
<
false
><<<
grid
,
threads
,
0
,
STREAM_DEFAULT
>>>
(
input
,
sequence
,
weight
,
output
,
input_dim
,
context_length
,
context_start
,
begin_pad
);
}
CHECK_SYNC
(
"hl_context_projection_forward failed"
);
}
template
<
>
void
ContextProjectionForward
<
DEVICE_TYPE_GPU
>
(
Tensor
&
output
,
const
Tensor
&
input
,
const
Tensor
&
weight
,
const
Tensor
&
sequence
,
size_t
context_length
,
int
context_start
,
size_t
begin_pad
,
bool
is_padding
)
{
CHECK
(
output
.
getData
()
&&
input
.
getData
()
&&
sequence
.
getData
());
CHECK_EQ
(
output
.
dims_
.
size
(),
2
);
CHECK_EQ
(
input
.
dims_
.
size
(),
2
);
CHECK_EQ
(
weight
.
dims_
.
size
(),
2
);
CHECK_EQ
(
sequence
.
dims_
.
size
(),
1
);
CHECK_EQ
(
output
.
dims_
[
1
],
input
.
dims_
[
1
]
*
context_length
);
hl_context_projection_forward
(
input
.
getData
(),
reinterpret_cast
<
int
*>
(
sequence
.
getData
()),
weight
.
getData
(),
output
.
getData
(),
sequence
.
dims_
[
0
]
-
1
,
input
.
dims_
[
1
],
context_length
,
context_start
,
begin_pad
,
is_padding
);
}
}
// namespace paddle
paddle/function/context_projection_op_test.cpp
0 → 100644
浏览文件 @
838ef366
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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. */
#include <gtest/gtest.h>
#include "FunctionTest.h"
#include "paddle/gserver/tests/TestUtil.h"
#include "paddle/math/Matrix.h"
using
namespace
paddle
;
// NOLINT
void
testMatrixProjectionForward
(
int
context_start
,
size_t
context_length
,
bool
is_padding
,
size_t
batch_size
,
size_t
input_dim
)
{
size_t
pad
=
std
::
max
(
0
,
-
context_start
)
+
std
::
max
(
0
,
(
int
)(
context_start
+
context_length
-
1
));
if
(
pad
==
0
)
is_padding
=
false
;
FunctionCompare
compare
(
"ContextProjectionForward"
,
FuncConfig
()
.
set
(
"context_length"
,
context_length
)
.
set
(
"context_start"
,
context_start
)
.
set
(
"begin_pad"
,
std
::
max
(
0
,
-
context_start
))
.
set
(
"is_padding"
,
is_padding
));
CpuMatrix
cpu_in
(
batch_size
,
input_dim
);
cpu_in
.
randomizeUniform
();
GpuMatrix
gpu_in
(
batch_size
,
input_dim
);
gpu_in
.
copyFrom
(
cpu_in
);
auto
cpu_weight
=
is_padding
?
std
::
make_shared
<
CpuMatrix
>
(
pad
,
input_dim
)
:
nullptr
;
auto
gpu_weight
=
is_padding
?
std
::
make_shared
<
GpuMatrix
>
(
pad
,
input_dim
)
:
nullptr
;
if
(
is_padding
)
{
cpu_weight
->
randomizeUniform
();
gpu_weight
->
copyFrom
(
*
cpu_weight
);
}
IVectorPtr
cpu_seq
;
generateSequenceStartPositions
(
batch_size
,
cpu_seq
);
IVectorPtr
gpu_seq
=
IVector
::
create
(
cpu_seq
->
getSize
(),
true
);
gpu_seq
->
copyFrom
(
*
cpu_seq
);
CpuMatrix
cpu_out
(
batch_size
,
input_dim
*
context_length
);
GpuMatrix
gpu_out
(
batch_size
,
input_dim
*
context_length
);
cpu_out
.
randomizeUniform
();
gpu_out
.
copyFrom
(
cpu_out
);
compare
.
getCpuFunction
()
->
calc
(
{
Tensor
(
cpu_in
.
getData
(),
Dims
{
batch_size
,
input_dim
}),
Tensor
(
cpu_weight
?
cpu_weight
->
getData
()
:
nullptr
,
Dims
{
pad
,
input_dim
}),
Tensor
(
reinterpret_cast
<
real
*>
(
cpu_seq
->
getData
()),
Dims
{
cpu_seq
->
getSize
()})},
{
Tensor
(
cpu_out
.
getData
(),
Dims
{
batch_size
,
input_dim
*
context_length
})},
{});
compare
.
getGpuFunction
()
->
calc
(
{
Tensor
(
gpu_in
.
getData
(),
Dims
{
batch_size
,
input_dim
}),
Tensor
(
gpu_weight
?
gpu_weight
->
getData
()
:
nullptr
,
Dims
{
pad
,
input_dim
}),
Tensor
(
reinterpret_cast
<
real
*>
(
gpu_seq
->
getData
()),
Dims
{
gpu_seq
->
getSize
()})},
{
Tensor
(
gpu_out
.
getData
(),
Dims
{
batch_size
,
input_dim
*
context_length
})},
{});
autotest
::
TensorCheckEqual
(
cpu_out
,
gpu_out
);
}
TEST
(
ContextProjectionForward
,
projection
)
{
for
(
auto
context_start
:
{
-
5
,
-
3
,
-
1
,
0
,
3
})
{
for
(
auto
context_length
:
{
1
,
2
,
5
,
7
})
{
for
(
auto
trainable_padding
:
{
false
,
true
})
{
for
(
auto
batch_size
:
{
1
,
2
,
5
,
20
,
100
})
{
for
(
auto
input_dim
:
{
15
,
32
,
63
,
128
,
200
})
{
VLOG
(
3
)
<<
" context_start="
<<
context_start
<<
" context_length="
<<
context_length
<<
" trainable_padding="
<<
trainable_padding
<<
" batch_size="
<<
batch_size
<<
" input_dim="
<<
input_dim
;
testMatrixProjectionForward
(
context_start
,
context_length
,
trainable_padding
,
batch_size
,
input_dim
);
}
}
}
}
}
}
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