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体验新版 GitCode,发现更多精彩内容 >>
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182ce51c
编写于
10月 17, 2017
作者:
Q
qijun
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add sparse kernel of sgd operator
上级
3ae9aa93
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
130 addition
and
17 deletion
+130
-17
paddle/operators/sgd_op.cc
paddle/operators/sgd_op.cc
+36
-4
paddle/operators/sgd_op.cu
paddle/operators/sgd_op.cu
+60
-0
paddle/operators/sgd_op.h
paddle/operators/sgd_op.h
+34
-13
未找到文件。
paddle/operators/sgd_op.cc
浏览文件 @
182ce51c
...
...
@@ -21,7 +21,7 @@ class SGDOp : public framework::OperatorWithKernel {
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Param"
),
"Input(Param) of SGDOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Grad"
),
...
...
@@ -35,15 +35,15 @@ class SGDOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
framework
::
product
(
lr_dims
),
1
,
"Learning rate should have 1 element"
);
auto
param_dim
=
ctx
->
GetInputDim
(
"Param"
);
PADDLE_ENFORCE_EQ
(
param_dim
,
ctx
->
GetInputDim
(
"Grad"
),
"Two input of SGD Op's dimension must be same."
);
// TODO(qijun): check dimensions of Param and Grad at complie
// and run time.
ctx
->
SetOutputDim
(
"ParamOut"
,
param_dim
);
}
};
class
SGDOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SGDOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
SGDOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Param"
,
"Input parameter"
);
AddInput
(
"LearningRate"
,
"Learning rate of SGD"
);
...
...
@@ -58,6 +58,38 @@ param_out = param - learning_rate * grad;
)DOC"
);
}
};
template
<
typename
T
>
struct
SparseSGDFunctor
<
platform
::
CPUPlace
,
T
>
{
void
operator
()(
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
SelectedRows
&
input
,
const
framework
::
Tensor
&
learning_rate
,
framework
::
Tensor
*
output
)
{
auto
in_height
=
input
.
height
();
auto
out_dims
=
output
->
dims
();
PADDLE_ENFORCE_EQ
(
in_height
,
out_dims
[
0
]);
auto
&
in_value
=
input
.
value
();
auto
&
in_rows
=
input
.
rows
();
int64_t
in_row_numel
=
in_value
.
numel
()
/
in_rows
.
size
();
PADDLE_ENFORCE_EQ
(
in_row_numel
,
output
->
numel
()
/
in_height
);
auto
*
in_data
=
in_value
.
data
<
T
>
();
auto
*
out_data
=
output
->
data
<
T
>
();
auto
*
lr
=
learning_rate
.
data
<
T
>
();
for
(
size_t
i
=
0
;
i
<
in_rows
.
size
();
i
++
)
{
for
(
int64_t
j
=
0
;
j
<
in_row_numel
;
j
++
)
{
out_data
[
in_rows
[
i
]
*
in_row_numel
+
j
]
-=
lr
[
0
]
*
in_data
[
i
*
in_row_numel
+
j
];
}
}
}
};
template
struct
SparseSGDFunctor
<
platform
::
CPUPlace
,
float
>;
}
// namespace operators
}
// namespace paddle
...
...
paddle/operators/sgd_op.cu
浏览文件 @
182ce51c
...
...
@@ -14,6 +14,66 @@
#define EIGEN_USE_GPU
#include "paddle/operators/sgd_op.h"
#include "paddle/platform/cuda_helper.h"
namespace
paddle
{
namespace
operators
{
namespace
{
template
<
typename
T
>
__global__
void
SparseSGDFunctorKernel
(
const
T
*
selected_rows
,
const
int64_t
*
rows
,
const
T
*
learning_rate
,
T
*
tensor_out
,
int64_t
row_numel
,
int
block_size
)
{
const
int
ty
=
blockIdx
.
y
;
int
tid
=
threadIdx
.
x
;
selected_rows
+=
ty
*
row_numel
;
tensor_out
+=
rows
[
ty
]
*
row_numel
;
for
(
int
index
=
tid
;
index
<
row_numel
;
index
+=
block_size
)
{
// Since index in rows of SelectedRows can be duplicate, we have to use
// Atomic Operation to avoid concurrent write error.
paddle
::
platform
::
CudaAtomicSub
(
tensor_out
+
index
,
learning_rate
[
0
]
*
selected_rows
[
index
]);
}
}
}
// namespace
template
<
typename
T
>
struct
SparseSGDFunctor
<
platform
::
GPUPlace
,
T
>
{
void
operator
()(
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
SelectedRows
&
input
,
const
framework
::
Tensor
&
learning_rate
,
framework
::
Tensor
*
output
)
{
auto
in_height
=
input
.
height
();
auto
out_dims
=
output
->
dims
();
PADDLE_ENFORCE_EQ
(
in_height
,
out_dims
[
0
]);
auto
&
in_value
=
input
.
value
();
auto
&
in_rows
=
input
.
rows
();
int64_t
in_row_numel
=
in_value
.
numel
()
/
in_rows
.
size
();
PADDLE_ENFORCE_EQ
(
in_row_numel
,
output
->
numel
()
/
in_height
);
auto
*
in_data
=
in_value
.
data
<
T
>
();
auto
*
out_data
=
output
->
data
<
T
>
();
int
block_size
=
256
;
dim3
threads
(
block_size
,
1
);
dim3
grid
(
1
,
in_rows
.
size
());
SparseSGDFunctorKernel
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
in_data
,
in_rows
.
data
(),
learning_rate
.
data
<
T
>
(),
out_data
,
in_row_numel
,
block_size
);
}
};
template
struct
SparseSGDFunctor
<
platform
::
GPUPlace
,
float
>;
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
sgd
,
...
...
paddle/operators/sgd_op.h
浏览文件 @
182ce51c
...
...
@@ -15,31 +15,52 @@ limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/selected_rows.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
Place
,
typename
T
>
struct
SparseSGDFunctor
{
void
operator
()(
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
SelectedRows
&
input
,
const
framework
::
Tensor
&
learning_rate
,
framework
::
Tensor
*
output
);
};
template
<
typename
Place
,
typename
T
>
class
SGDOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
param
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Param"
);
auto
grad
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Grad"
);
auto
param_out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"ParamOut"
);
auto
learning_rate
=
ctx
.
Input
<
framework
::
Tensor
>
(
"LearningRate"
);
auto
*
param
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Param"
);
auto
*
param_out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"ParamOut"
);
auto
*
learning_rate
=
ctx
.
Input
<
framework
::
Tensor
>
(
"LearningRate"
);
param_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
*
grad_var
=
ctx
.
InputVar
(
"Grad"
);
if
(
grad_var
->
IsType
<
framework
::
Tensor
>
())
{
param_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
*
grad
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Grad"
);
auto
p
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
param
);
auto
g
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
grad
);
auto
o
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
param_out
);
auto
lr
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
learning_rate
);
auto
place
=
ctx
.
GetEigenDevice
<
Place
>
();
auto
p
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
param
);
auto
g
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
grad
);
auto
o
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
param_out
);
auto
lr
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
learning_rate
);
auto
place
=
ctx
.
GetEigenDevice
<
Place
>
();
Eigen
::
DSizes
<
int
,
1
>
grad_dsize
(
grad
->
numel
());
o
.
device
(
place
)
=
p
-
lr
.
broadcast
(
grad_dsize
)
*
g
;
Eigen
::
DSizes
<
int
,
1
>
grad_dsize
(
grad
->
numel
());
o
.
device
(
place
)
=
p
-
lr
.
broadcast
(
grad_dsize
)
*
g
;
}
else
if
(
grad_var
->
IsType
<
framework
::
SelectedRows
>
())
{
// TODO(qijun): In Sparse SGD operator, in-place update is enforced.
// This manual optimization brings difficulty to track data dependency.
// It's better to find a more elegant solution.
PADDLE_ENFORCE_EQ
(
param
,
param_out
);
auto
*
grad
=
ctx
.
Input
<
framework
::
SelectedRows
>
(
"Grad"
);
SparseSGDFunctor
<
Place
,
T
>
functor
;
functor
(
ctx
.
device_context
(),
*
grad
,
*
learning_rate
,
param_out
);
}
else
{
PADDLE_THROW
(
"Unsupported Variable Type of Grad"
);
}
}
};
}
// namespace operators
}
// namespace paddle
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