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7620efdf
编写于
9月 23, 2017
作者:
Y
Yibing Liu
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
combine gpu&cpu code in multiplex_op
上级
85a5d384
变更
3
显示空白变更内容
内联
并排
Showing
3 changed file
with
81 addition
and
96 deletion
+81
-96
paddle/operators/multiplex_op.cc
paddle/operators/multiplex_op.cc
+11
-15
paddle/operators/multiplex_op.cu
paddle/operators/multiplex_op.cu
+6
-64
paddle/operators/multiplex_op.h
paddle/operators/multiplex_op.h
+64
-17
未找到文件。
paddle/operators/multiplex_op.cc
浏览文件 @
7620efdf
...
@@ -22,10 +22,7 @@ using LoDTensor = framework::LoDTensor;
...
@@ -22,10 +22,7 @@ using LoDTensor = framework::LoDTensor;
class
MultiplexOp
:
public
framework
::
OperatorWithKernel
{
class
MultiplexOp
:
public
framework
::
OperatorWithKernel
{
public:
public:
MultiplexOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorWithKernel
(
type
,
inputs
,
outputs
,
attrs
)
{}
protected:
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
...
@@ -64,12 +61,12 @@ class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -64,12 +61,12 @@ class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
Multiplex multiple tensors according to the index provided by the first
Multiplex multiple tensors according to the index provided by the first
input tensor.
input tensor.
ins[0]: the index
of the tensor to output of size batchSize
.
ins[0]: the index
tensor
.
ins[1:N]: the candidate output tensor.
ins[1:N]: the candidate output tensor
s
.
For each index i from 0 to batchSize - 1, the output is the i-th row of the
For each index i from 0 to batchSize - 1, the output is the i-th row of the
the (index[i] + 1)-th tensor.
the (index[i] + 1)-th tensor.
For
each i-th row of output
:
For
i-th row of the output tensor
:
y[i][j] = x_{k}[i][j], j = 0,1, ... , (x_{1}.width - 1)
y[i][j] = x_{k}[i][j], j = 0,1, ... , (x_{1}.width - 1)
...
@@ -82,11 +79,7 @@ and `k = x{0}[i] + 1`.
...
@@ -82,11 +79,7 @@ and `k = x{0}[i] + 1`.
class
MultiplexGradOp
:
public
framework
::
OperatorWithKernel
{
class
MultiplexGradOp
:
public
framework
::
OperatorWithKernel
{
public:
public:
MultiplexGradOp
(
const
std
::
string
&
type
,
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorWithKernel
(
type
,
inputs
,
outputs
,
attrs
)
{}
protected:
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
...
@@ -98,7 +91,7 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
...
@@ -98,7 +91,7 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
"Input(Out@GRAD) shouldn't be null."
);
"Input(Out@GRAD) shouldn't be null."
);
auto
d_ins
=
ctx
.
MultiOutput
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
d_ins
=
ctx
.
MultiOutput
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
ins
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
auto
ins
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
// don
;t compute gradient for index
// don
't compute gradient for index (ins[0])
for
(
size_t
i
=
1
;
i
<
ins
.
size
();
i
++
)
{
for
(
size_t
i
=
1
;
i
<
ins
.
size
();
i
++
)
{
if
(
d_ins
[
i
])
{
if
(
d_ins
[
i
])
{
d_ins
[
i
]
->
Resize
(
ins
[
i
]
->
dims
());
d_ins
[
i
]
->
Resize
(
ins
[
i
]
->
dims
());
...
@@ -113,5 +106,8 @@ namespace ops = paddle::operators;
...
@@ -113,5 +106,8 @@ namespace ops = paddle::operators;
REGISTER_OP
(
multiplex
,
ops
::
MultiplexOp
,
ops
::
MultiplexOpMaker
,
multiplex_grad
,
REGISTER_OP
(
multiplex
,
ops
::
MultiplexOp
,
ops
::
MultiplexOpMaker
,
multiplex_grad
,
ops
::
MultiplexGradOp
);
ops
::
MultiplexGradOp
);
REGISTER_OP_CPU_KERNEL
(
multiplex
,
ops
::
MultiplexCPUKernel
<
float
>
);
REGISTER_OP_CPU_KERNEL
(
multiplex
,
REGISTER_OP_CPU_KERNEL
(
multiplex_grad
,
ops
::
MultiplexGradCPUKernel
<
float
>
);
ops
::
MultiplexKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
multiplex_grad
,
ops
::
MultiplexGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/multiplex_op.cu
浏览文件 @
7620efdf
...
@@ -13,70 +13,12 @@
...
@@ -13,70 +13,12 @@
limitations under the License. */
limitations under the License. */
#include "paddle/framework/op_registry.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/multiplex_op.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
LoDTensor
=
framework
::
LoDTensor
;
template
<
typename
T
>
class
MultiplexGPUKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
ins
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
rows
=
ins
[
1
]
->
dims
()[
0
];
auto
cols
=
ins
[
1
]
->
dims
()[
1
];
// copy index to cpu
Tensor
index_t_cpu
;
index_t_cpu
.
CopyFrom
<
T
>
(
*
(
ins
[
0
]),
paddle
::
platform
::
CPUPlace
());
auto
index
=
index_t_cpu
.
data
<
T
>
();
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
int
k
=
(
int
)
index
[
i
]
+
1
;
cudaMemcpy
(
out
->
data
<
T
>
()
+
i
*
cols
,
ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
cols
*
sizeof
(
T
),
cudaMemcpyDeviceToDevice
);
}
}
};
template
<
typename
T
>
class
MultiplexGradGPUKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
*
d_out
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
ins
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
auto
d_ins
=
ctx
.
MultiOutput
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
for
(
size_t
i
=
1
;
i
<
d_ins
.
size
();
++
i
)
{
if
(
d_ins
[
i
])
{
d_ins
[
i
]
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
dims
=
d_ins
[
i
]
->
dims
();
cudaMemset
(
d_ins
[
i
]
->
data
<
T
>
(),
0
,
framework
::
product
(
dims
)
*
sizeof
(
T
));
}
}
auto
rows
=
ins
[
1
]
->
dims
()[
0
];
auto
cols
=
ins
[
1
]
->
dims
()[
1
];
// copy index to cpu
Tensor
index_t_cpu
;
index_t_cpu
.
CopyFrom
<
T
>
(
*
(
ins
[
0
]),
paddle
::
platform
::
CPUPlace
());
auto
index
=
index_t_cpu
.
data
<
T
>
();
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
int
k
=
(
int
)
index
[
i
]
+
1
;
if
(
d_ins
[
k
])
{
cudaMemcpy
(
d_ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
d_out
->
data
<
T
>
()
+
i
*
cols
,
cols
*
sizeof
(
T
),
cudaMemcpyDeviceToDevice
);
}
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
multiplex
,
ops
::
MultiplexGPUKernel
<
float
>
);
REGISTER_OP_GPU_KERNEL
(
multiplex
,
REGISTER_OP_GPU_KERNEL
(
multiplex_grad
,
ops
::
MultiplexGradGPUKernel
<
float
>
);
ops
::
MultiplexKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
multiplex_grad
,
ops
::
MultiplexGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/multiplex_op.h
浏览文件 @
7620efdf
...
@@ -17,31 +17,56 @@
...
@@ -17,31 +17,56 @@
#include "paddle/framework/eigen.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/op_registry.h"
#include "paddle/memory/memcpy.h"
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
template
<
typename
T
>
template
<
typename
Place
,
typename
T
>
class
Multiplex
CPU
Kernel
:
public
framework
::
OpKernel
{
class
MultiplexKernel
:
public
framework
::
OpKernel
{
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
ins
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"X"
);
auto
ins
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
auto
*
out
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
index
=
ins
[
0
]
->
data
<
T
>
();
auto
rows
=
ins
[
1
]
->
dims
()[
0
];
auto
rows
=
ins
[
1
]
->
dims
()[
0
];
auto
cols
=
ins
[
1
]
->
dims
()[
1
];
auto
cols
=
ins
[
1
]
->
dims
()[
1
];
if
(
platform
::
is_cpu_place
(
ctx
.
GetPlace
()))
{
auto
*
index
=
ins
[
0
]
->
data
<
T
>
();
platform
::
CPUPlace
place
=
boost
::
get
<
platform
::
CPUPlace
>
(
ctx
.
GetPlace
());
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
int
k
=
(
int
)
index
[
i
]
+
1
;
PADDLE_ENFORCE_LT
(
k
,
ins
.
size
(),
"index exceeds the number of candidate tensors."
);
memory
::
Copy
(
place
,
out
->
data
<
T
>
()
+
i
*
cols
,
place
,
ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
cols
*
sizeof
(
T
));
}
}
else
{
#ifndef PADDLE_ONLY_CPU
// copy index to cpu
framework
::
Tensor
index_t_cpu
;
index_t_cpu
.
CopyFrom
<
T
>
(
*
(
ins
[
0
]),
platform
::
CPUPlace
());
auto
*
index
=
index_t_cpu
.
data
<
T
>
();
auto
stream
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
();
platform
::
GPUPlace
place
=
boost
::
get
<
platform
::
GPUPlace
>
(
ctx
.
GetPlace
());
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
int
k
=
(
int
)
index
[
i
]
+
1
;
int
k
=
(
int
)
index
[
i
]
+
1
;
memcpy
(
out
->
data
<
T
>
()
+
i
*
cols
,
ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
PADDLE_ENFORCE_LT
(
k
,
ins
.
size
(),
cols
*
sizeof
(
T
));
"index exceeds the number of candidate tensors."
);
memory
::
Copy
(
place
,
out
->
data
<
T
>
()
+
i
*
cols
,
place
,
ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
cols
*
sizeof
(
T
),
stream
);
}
#endif
}
}
}
}
};
};
template
<
typename
T
>
template
<
typename
Place
,
typename
T
>
class
MultiplexGrad
CPU
Kernel
:
public
framework
::
OpKernel
{
class
MultiplexGradKernel
:
public
framework
::
OpKernel
{
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
*
d_out
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
d_out
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
...
@@ -51,20 +76,42 @@ class MultiplexGradCPUKernel : public framework::OpKernel {
...
@@ -51,20 +76,42 @@ class MultiplexGradCPUKernel : public framework::OpKernel {
for
(
size_t
i
=
1
;
i
<
d_ins
.
size
();
i
++
)
{
for
(
size_t
i
=
1
;
i
<
d_ins
.
size
();
i
++
)
{
if
(
d_ins
[
i
])
{
if
(
d_ins
[
i
])
{
d_ins
[
i
]
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
d_ins
[
i
]
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
dims
=
d_ins
[
i
]
->
dims
(
);
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
d_ins
[
i
]
);
memset
(
d_ins
[
i
]
->
data
<
T
>
(),
0
,
framework
::
product
(
dims
)
*
sizeof
(
T
));
t
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
}
}
}
}
auto
index
=
ins
[
0
]
->
data
<
T
>
();
auto
rows
=
ins
[
1
]
->
dims
()[
0
];
auto
rows
=
ins
[
1
]
->
dims
()[
0
];
auto
cols
=
ins
[
1
]
->
dims
()[
1
];
auto
cols
=
ins
[
1
]
->
dims
()[
1
];
if
(
platform
::
is_cpu_place
(
ctx
.
GetPlace
()))
{
auto
*
index
=
ins
[
0
]
->
data
<
T
>
();
platform
::
CPUPlace
place
=
boost
::
get
<
platform
::
CPUPlace
>
(
ctx
.
GetPlace
());
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
int
k
=
(
int
)
index
[
i
]
+
1
;
if
(
d_ins
[
k
])
{
memory
::
Copy
(
place
,
d_ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
place
,
d_out
->
data
<
T
>
()
+
i
*
cols
,
cols
*
sizeof
(
T
));
}
}
}
else
{
#ifndef PADDLE_ONLY_CPU
// copy index to cpu
framework
::
Tensor
index_t_cpu
;
index_t_cpu
.
CopyFrom
<
T
>
(
*
(
ins
[
0
]),
platform
::
CPUPlace
());
auto
*
index
=
index_t_cpu
.
data
<
T
>
();
auto
stream
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
();
platform
::
GPUPlace
place
=
boost
::
get
<
platform
::
GPUPlace
>
(
ctx
.
GetPlace
());
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
int
k
=
(
int
)
index
[
i
]
+
1
;
int
k
=
(
int
)
index
[
i
]
+
1
;
if
(
d_ins
[
k
])
{
if
(
d_ins
[
k
])
{
memcpy
(
d_ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
d_out
->
data
<
T
>
()
+
i
*
cols
,
memory
::
Copy
(
place
,
d_ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
place
,
cols
*
sizeof
(
T
));
d_out
->
data
<
T
>
()
+
i
*
cols
,
cols
*
sizeof
(
T
),
stream
);
}
}
}
#endif
}
}
}
}
};
};
...
...
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