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体验新版 GitCode,发现更多精彩内容 >>
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641b4c0f
编写于
12月 29, 2017
作者:
T
typhoonzero
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
wip
上级
74b12288
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
251 addition
and
129 deletion
+251
-129
paddle/operators/adagrad_op.cc
paddle/operators/adagrad_op.cc
+7
-37
paddle/operators/adagrad_op.cu
paddle/operators/adagrad_op.cu
+7
-41
paddle/operators/adam_op.h
paddle/operators/adam_op.h
+11
-6
paddle/operators/math/selected_rows_functor.cc
paddle/operators/math/selected_rows_functor.cc
+79
-11
paddle/operators/math/selected_rows_functor.cu
paddle/operators/math/selected_rows_functor.cu
+95
-11
paddle/operators/math/selected_rows_functor.h
paddle/operators/math/selected_rows_functor.h
+52
-22
python/paddle/v2/fluid/tests/test_adam_op.py
python/paddle/v2/fluid/tests/test_adam_op.py
+0
-1
未找到文件。
paddle/operators/adagrad_op.cc
浏览文件 @
641b4c0f
...
...
@@ -105,48 +105,18 @@ struct SparseAdagradFunctor<platform::CPUDeviceContext, T> {
const
framework
::
Tensor
&
learning_rate
,
T
epsilon
,
framework
::
Tensor
*
moment
,
framework
::
Tensor
*
param
)
{
// 1. g_m.rows = set(g.rows)
auto
grad_rows
=
grad
.
rows
();
std
::
set
<
int64_t
>
row_set
(
grad_rows
.
begin
(),
grad_rows
.
end
());
std
::
vector
<
int64_t
>
merge_rows
(
row_set
.
begin
(),
row_set
.
end
());
auto
grad_width
=
grad
.
value
().
dims
()[
1
];
std
::
unique_ptr
<
framework
::
SelectedRows
>
grad_merge
{
new
framework
::
SelectedRows
()};
grad_merge
->
set_rows
(
merge_rows
);
grad_merge
->
set_height
(
grad
.
height
());
grad_merge
->
mutable_value
()
->
mutable_data
<
T
>
(
framework
::
make_ddim
(
{
static_cast
<
int64_t
>
(
merge_rows
.
size
()),
grad_width
}),
context
.
GetPlace
());
math
::
SetConstant
<
platform
::
CPUDeviceContext
,
T
>
constant_functor
;
constant_functor
(
context
,
grad_merge
->
mutable_value
(),
0.0
);
auto
*
grad_merge_data
=
grad_merge
->
mutable_value
()
->
data
<
T
>
();
auto
*
grad_data
=
grad
.
value
().
data
<
T
>
();
for
(
size_t
i
=
0
;
i
<
grad_rows
.
size
();
i
++
)
{
size_t
grad_merge_i
=
FindPos
(
merge_rows
,
grad_rows
[
i
]);
for
(
int64_t
j
=
0
;
j
<
grad_width
;
j
++
)
{
grad_merge_data
[
grad_merge_i
*
grad_width
+
j
]
+=
grad_data
[
i
*
grad_width
+
j
];
}
}
math
::
scatter
::
MergeAdd
<
platform
::
CPUDeviceContext
,
T
>
merge_func
;
auto
grad_merge
=
merge_func
(
context
,
grad
);
auto
&
merge_rows
=
grad_merge
.
rows
();
auto
*
grad_merge_data
=
grad_merge
.
mutable_value
()
->
template
data
<
T
>();
// 2. m += g_m * g_m
std
::
unique_ptr
<
framework
::
SelectedRows
>
grad_square
{
new
framework
::
SelectedRows
()};
grad_square
->
set_rows
(
grad_merge
->
rows
());
grad_square
->
set_height
(
grad_merge
->
height
());
grad_square
->
mutable_value
()
->
mutable_data
<
T
>
(
grad_merge
->
value
().
dims
(),
context
.
GetPlace
());
auto
gs
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
(
grad_square
->
mutable_value
()));
auto
gm
=
framework
::
EigenVector
<
T
>::
Flatten
(
grad_merge
->
value
());
gs
.
device
(
*
context
.
eigen_device
())
=
gm
*
gm
;
math
::
scatter
::
Mul
<
platform
::
CPUDeviceContext
,
T
>
sqare_func
;
auto
grad_square
=
sqare_func
(
context
,
grad_merge
,
grad_merge
);
math
::
SelectedRowsAddToTensor
<
platform
::
CPUDeviceContext
,
T
>
functor
;
functor
(
context
,
*
grad_square
,
moment
);
functor
(
context
,
grad_square
,
moment
);
// 3. update parameter
auto
*
lr
=
learning_rate
.
data
<
T
>
();
...
...
paddle/operators/adagrad_op.cu
浏览文件 @
641b4c0f
...
...
@@ -78,51 +78,17 @@ struct SparseAdagradFunctor<platform::CUDADeviceContext, T> {
const
framework
::
Tensor
&
learning_rate
,
T
epsilon
,
framework
::
Tensor
*
moment
,
framework
::
Tensor
*
param
)
{
// 1. g_m.rows = set(g.rows)
auto
grad_rows
=
grad
.
rows
();
std
::
set
<
int64_t
>
row_set
(
grad_rows
.
begin
(),
grad_rows
.
end
());
std
::
vector
<
int64_t
>
merge_rows
(
row_set
.
begin
(),
row_set
.
end
());
auto
grad_width
=
grad
.
value
().
dims
()[
1
];
std
::
unique_ptr
<
framework
::
SelectedRows
>
grad_merge
{
new
framework
::
SelectedRows
()};
grad_merge
->
set_rows
(
merge_rows
);
grad_merge
->
set_height
(
grad
.
height
());
grad_merge
->
mutable_value
()
->
mutable_data
<
T
>
(
framework
::
make_ddim
(
{
static_cast
<
int64_t
>
(
merge_rows
.
size
()),
grad_width
}),
context
.
GetPlace
());
math
::
SetConstant
<
platform
::
CUDADeviceContext
,
T
>
constant_functor
;
constant_functor
(
context
,
grad_merge
->
mutable_value
(),
0.0
);
auto
*
grad_merge_data
=
grad_merge
->
mutable_value
()
->
data
<
T
>
();
auto
*
grad_data
=
grad
.
value
().
data
<
T
>
();
const
int
block_size
=
256
;
dim3
threads
(
block_size
,
1
);
dim3
grid1
(
1
,
grad_rows
.
size
());
MergeGradKernel
<
T
,
256
><<<
grid1
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
grad_data
,
grad
.
rows
().
data
(),
grad_merge_data
,
grad_merge
->
rows
().
data
(),
grad_merge
->
rows
().
size
(),
grad_width
);
math
::
scatter
::
MergeAdd
<
platform
::
CPUDeviceContext
,
T
>
merge_func
;
auto
grad_merge
=
merge_func
(
context
,
grad
);
auto
*
grad_merge_data
=
grad_merge
.
mutable_value
()
->
template
data
<
T
>();
auto
&
merge_rows
=
grad_merge
.
rows
;
// 2. m += g_m * g_m
std
::
unique_ptr
<
framework
::
SelectedRows
>
grad_square
{
new
framework
::
SelectedRows
()};
grad_square
->
set_rows
(
grad_merge
->
rows
());
grad_square
->
set_height
(
grad_merge
->
height
());
grad_square
->
mutable_value
()
->
mutable_data
<
T
>
(
grad_merge
->
value
().
dims
(),
context
.
GetPlace
());
auto
gs
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
(
grad_square
->
mutable_value
()));
auto
gm
=
framework
::
EigenVector
<
T
>::
Flatten
(
grad_merge
->
value
());
gs
.
device
(
*
context
.
eigen_device
())
=
gm
*
gm
;
math
::
scatter
::
Mul
<
platform
::
CPUDeviceContext
,
T
>
sqare_func
;
auto
grad_square
=
sqare_func
(
context
,
grad_merge
,
grad_merge
);
math
::
SelectedRowsAddToTensor
<
platform
::
CUDADeviceContext
,
T
>
functor
;
functor
(
context
,
*
grad_square
,
moment
);
functor
(
context
,
grad_square
,
moment
);
// 3. update parameter
auto
*
lr
=
learning_rate
.
data
<
T
>
();
...
...
paddle/operators/adam_op.h
浏览文件 @
641b4c0f
...
...
@@ -16,11 +16,14 @@ limitations under the License. */
#include <math.h> // for sqrt in CPU and CUDA
#include "paddle/framework/op_registry.h"
#include "paddle/operators/detail/safe_ref.h"
#include "paddle/operators/math/selected_rows_functor.h"
#include "paddle/platform/for_range.h"
namespace
paddle
{
namespace
operators
{
namespace
scatter
=
paddle
::
operators
::
math
::
scatter
;
template
<
typename
T
>
struct
AdamFunctor
{
T
beta1_
;
...
...
@@ -134,8 +137,6 @@ struct SparseAdamFunctor {
mom1
=
beta1_
*
mom1
+
(
1
-
beta1_
)
*
g
;
mom2
=
beta2_
*
mom2
+
(
1
-
beta2_
)
*
g
*
g
;
p
-=
lr
*
(
mom1
/
(
sqrt
(
mom2
)
+
epsilon_
));
// IMPORTANT:
// FIXME(typhoonzero): row id may be duplicate
moment1_out_
[
rows_
[
i
]
*
row_numel_
+
j
]
=
mom1
;
moment2_out_
[
rows_
[
i
]
*
row_numel_
+
j
]
=
mom2
;
param_out_
[
rows_
[
i
]
*
row_numel_
+
j
]
=
p
;
...
...
@@ -191,10 +192,14 @@ class AdamOpKernel : public framework::OpKernel<T> {
}
else
if
(
grad_var
->
IsType
<
framework
::
SelectedRows
>
())
{
auto
&
grad
=
Ref
(
ctx
.
Input
<
framework
::
SelectedRows
>
(
"Grad"
),
"Must set Grad"
);
auto
&
grad_tensor
=
grad
.
value
();
// merge duplicated rows if any.
scatter
::
MergeAdd
<
DeviceContext
,
T
>
merge_func
;
auto
grad_merge
=
merge_func
(
ctx
.
template
device_context
<
DeviceContext
>(),
grad
);
auto
&
grad_tensor
=
grad_merge
.
value
();
const
T
*
grad_data
=
grad_tensor
.
template
data
<
T
>();
auto
*
rows
=
grad
.
rows
().
data
();
auto
row_numel
=
grad_tensor
.
numel
()
/
grad
.
rows
().
size
();
auto
*
rows
=
grad
_merge
.
rows
().
data
();
auto
row_numel
=
grad_tensor
.
numel
()
/
grad
_merge
.
rows
().
size
();
SparseAdamFunctor
<
T
>
functor
(
beta1
,
beta2
,
epsilon
,
beta1_pow
.
template
data
<
T
>(),
...
...
@@ -206,7 +211,7 @@ class AdamOpKernel : public framework::OpKernel<T> {
param_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
rows
,
row_numel
);
platform
::
ForRange
<
DeviceContext
>
for_range
(
static_cast
<
const
DeviceContext
&>
(
ctx
.
device_context
()),
grad
.
rows
().
size
());
grad
_merge
.
rows
().
size
());
for_range
(
functor
);
}
else
{
PADDLE_THROW
(
"Variable type not supported by adam_op"
);
...
...
paddle/operators/math/selected_rows_functor.cc
浏览文件 @
641b4c0f
...
...
@@ -12,8 +12,10 @@ 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 "paddle/operators/math/selected_rows_functor.h"
#include <set>
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/selected_rows_functor.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -193,27 +195,25 @@ size_t FindPos(const std::vector<int64_t>& rows, int64_t value) {
template
<
typename
T
>
struct
MergeAdd
<
platform
::
CPUDeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
SelectedRows
&
input
,
framework
::
SelectedRows
*
out
)
{
framework
::
SelectedRows
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
framework
::
SelectedRows
&
input
)
{
framework
::
SelectedRows
out
;
auto
input_rows
=
input
.
rows
();
std
::
set
<
int64_t
>
row_set
(
input_rows
.
begin
(),
input_rows
.
end
());
std
::
vector
<
int64_t
>
merge_rows
(
row_set
.
begin
(),
row_set
.
end
());
auto
input_width
=
input
.
value
().
dims
()[
1
];
// std::unique_ptr<framework::SelectedRows> out{
// new framework::SelectedRows()};
out
->
set_rows
(
merge_rows
);
out
->
set_height
(
input
.
height
());
out
->
mutable_value
()
->
mutable_data
<
T
>
(
out
.
set_rows
(
merge_rows
);
out
.
set_height
(
input
.
height
());
out
.
mutable_value
()
->
mutable_data
<
T
>
(
framework
::
make_ddim
(
{
static_cast
<
int64_t
>
(
merge_rows
.
size
()),
input_width
}),
context
.
GetPlace
());
math
::
SetConstant
<
platform
::
CPUDeviceContext
,
T
>
constant_functor
;
constant_functor
(
context
,
out
->
mutable_value
(),
0.0
);
constant_functor
(
context
,
out
.
mutable_value
(),
0.0
);
auto
*
out_data
=
out
->
mutable_value
()
->
data
<
T
>
();
auto
*
out_data
=
out
.
mutable_value
()
->
data
<
T
>
();
auto
*
input_data
=
input
.
value
().
data
<
T
>
();
for
(
size_t
i
=
0
;
i
<
input_rows
.
size
();
i
++
)
{
...
...
@@ -222,6 +222,74 @@ struct MergeAdd<platform::CPUDeviceContext, T> {
out_data
[
out_i
*
input_width
+
j
]
+=
input_data
[
i
*
input_width
+
j
];
}
}
return
out
;
}
};
template
struct
MergeAdd
<
platform
::
CPUDeviceContext
,
float
>;
template
struct
MergeAdd
<
platform
::
CPUDeviceContext
,
double
>;
template
struct
MergeAdd
<
platform
::
CPUDeviceContext
,
int
>;
template
struct
MergeAdd
<
platform
::
CPUDeviceContext
,
int64_t
>;
template
<
typename
T
>
struct
UpdateToTensor
<
platform
::
CPUDeviceContext
,
T
>
{
framework
::
Tensor
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
ScatterOps
&
op
,
const
framework
::
SelectedRows
&
input1
,
framework
::
Tensor
*
input2
)
{
auto
in1_height
=
input1
.
height
();
auto
in2_dims
=
input2
->
dims
();
PADDLE_ENFORCE_EQ
(
in1_height
,
in2_dims
[
0
]);
auto
&
in1_value
=
input1
.
value
();
auto
&
in1_rows
=
input1
.
rows
();
int64_t
in1_row_numel
=
in1_value
.
numel
()
/
in1_rows
.
size
();
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
input2
->
numel
()
/
in1_height
);
auto
*
in1_data
=
in1_value
.
data
<
T
>
();
auto
*
input2_data
=
input2
->
data
<
T
>
();
// FIXME(typhoonzero): use macro fix the below messy code.
switch
(
op
)
{
case
ScatterOps
::
ASSIGN
:
INLINE_FOR2
(
in1_rows
.
size
(),
in1_row_numel
)
input2_data
[
in1_rows
[
i
]
*
in1_row_numel
+
j
]
=
in1_data
[
i
*
in1_row_numel
+
j
];
break
;
case
ScatterOps
::
ADD
:
INLINE_FOR2
(
in1_rows
.
size
(),
in1_row_numel
)
input2_data
[
in1_rows
[
i
]
*
in1_row_numel
+
j
]
+=
in1_data
[
i
*
in1_row_numel
+
j
];
break
;
case
ScatterOps
::
SUB
:
INLINE_FOR2
(
in1_rows
.
size
(),
in1_row_numel
)
input2_data
[
in1_rows
[
i
]
*
in1_row_numel
+
j
]
-=
in1_data
[
i
*
in1_row_numel
+
j
];
break
;
case
ScatterOps
::
SUBBY
:
INLINE_FOR2
(
in1_rows
.
size
(),
in1_row_numel
)
input2_data
[
in1_rows
[
i
]
*
in1_row_numel
+
j
]
=
in1_data
[
i
*
in1_row_numel
+
j
]
-
input2_data
[
in1_rows
[
i
]
*
in1_row_numel
+
j
];
break
;
case
ScatterOps
::
MUL
:
INLINE_FOR2
(
in1_rows
.
size
(),
in1_row_numel
)
input2_data
[
in1_rows
[
i
]
*
in1_row_numel
+
j
]
*=
in1_data
[
i
*
in1_row_numel
+
j
];
break
;
case
ScatterOps
::
DIV
:
INLINE_FOR2
(
in1_rows
.
size
(),
in1_row_numel
)
input2_data
[
in1_rows
[
i
]
*
in1_row_numel
+
j
]
/=
in1_data
[
i
*
in1_row_numel
+
j
];
break
;
case
ScatterOps
::
DIVBY
:
INLINE_FOR2
(
in1_rows
.
size
(),
in1_row_numel
)
input2_data
[
in1_rows
[
i
]
*
in1_row_numel
+
j
]
=
in1_data
[
i
*
in1_row_numel
+
j
]
/
input2_data
[
in1_rows
[
i
]
*
in1_row_numel
+
j
];
break
;
}
}
};
...
...
paddle/operators/math/selected_rows_functor.cu
浏览文件 @
641b4c0f
...
...
@@ -252,27 +252,26 @@ __global__ void MergeAddKernel(const T* input, const int64_t* input_rows,
template
<
typename
T
>
struct
MergeAdd
<
platform
::
GPUDeviceContext
,
T
>
{
void
operator
()(
const
platform
::
GPUDeviceContext
&
context
,
const
framework
::
SelectedRows
&
input
,
framework
::
SelectedRows
*
out
)
{
framework
::
SelectedRows
operator
()(
const
platform
::
GPUDeviceContext
&
context
,
const
framework
::
SelectedRows
&
input
)
{
framework
::
SelectedRows
out
;
auto
input_rows
=
input
.
rows
();
std
::
set
<
int64_t
>
row_set
(
input_rows
.
begin
(),
input_rows
.
end
());
std
::
vector
<
int64_t
>
merge_rows
(
row_set
.
begin
(),
row_set
.
end
());
auto
input_width
=
input
.
value
().
dims
()[
1
];
// std::unique_ptr<framework::SelectedRows> out{
// new framework::SelectedRows()};
out
->
set_rows
(
merge_rows
);
out
->
set_height
(
input
.
height
());
out
->
mutable_value
()
->
mutable_data
<
T
>
(
out
.
set_rows
(
merge_rows
);
out
.
set_height
(
input
.
height
());
out
.
mutable_value
()
->
mutable_data
<
T
>
(
framework
::
make_ddim
(
{
static_cast
<
int64_t
>
(
merge_rows
.
size
()),
input_width
}),
context
.
GetPlace
());
math
::
SetConstant
<
platform
::
CUDADeviceContext
,
T
>
constant_functor
;
constant_functor
(
context
,
out
->
mutable_value
(),
0.0
);
constant_functor
(
context
,
out
.
mutable_value
(),
0.0
);
auto
*
out_data
=
out
->
mutable_value
()
->
data
<
T
>
();
auto
*
out_data
=
out
.
mutable_value
()
->
data
<
T
>
();
auto
*
input_data
=
input
.
value
().
data
<
T
>
();
const
int
block_size
=
256
;
...
...
@@ -283,11 +282,96 @@ struct MergeAdd<platform::GPUDeviceContext, T> {
T
,
256
><<<
grid1
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
input_data
,
input
.
rows
().
data
(),
out_data
,
out
->
rows
().
data
(),
out
->
rows
().
size
(),
out
.
rows
().
data
(),
out
.
rows
().
size
(),
input_width
);
return
out
;
}
};
template
struct
MergeAdd
<
platform
::
GPUDeviceContext
,
float
>;
template
struct
MergeAdd
<
platform
::
GPUDeviceContext
,
double
>;
template
struct
MergeAdd
<
platform
::
GPUDeviceContext
,
int
>;
template
struct
MergeAdd
<
platform
::
GPUDeviceContext
,
int64_t
>;
template
<
typename
T
,
int
block_size
>
__global__
void
UpdateToTensorKernel
(
const
T
*
selected_rows
,
const
int64_t
*
rows
,
const
ScatterOps
&
op
,
T
*
tensor_out
,
int64_t
row_numel
)
{
const
int
ty
=
blockIdx
.
y
;
int
tid
=
threadIdx
.
x
;
selected_rows
+=
ty
*
row_numel
;
tensor_out
+=
rows
[
ty
]
*
row_numel
;
// FIXME(typhoonzero): use macro fix the below messy code.
switch
(
op
)
{
case
ScatterOps
::
ASSIGN
:
for
(
int
index
=
tid
;
index
<
row_numel
;
index
+=
block_size
)
{
tensor_out
[
index
]
=
selected_rows
[
index
];
}
break
;
case
ScatterOps
::
ADD
:
for
(
int
index
=
tid
;
index
<
row_numel
;
index
+=
block_size
)
{
tensor_out
[
index
]
+=
selected_rows
[
index
];
}
break
;
case
ScatterOps
::
SUB
:
for
(
int
index
=
tid
;
index
<
row_numel
;
index
+=
block_size
)
{
tensor_out
[
index
]
-=
selected_rows
[
index
];
}
break
;
case
ScatterOps
::
SUBBY
:
for
(
int
index
=
tid
;
index
<
row_numel
;
index
+=
block_size
)
{
tensor_out
[
index
]
=
selected_rows
[
index
]
-
tensor_out
[
index
];
}
break
;
case
ScatterOps
::
MUL
:
for
(
int
index
=
tid
;
index
<
row_numel
;
index
+=
block_size
)
{
tensor_out
[
index
]
*=
selected_rows
[
index
];
}
break
;
case
ScatterOps
::
DIV
:
for
(
int
index
=
tid
;
index
<
row_numel
;
index
+=
block_size
)
{
tensor_out
[
index
]
/=
selected_rows
[
index
];
}
break
;
case
ScatterOps
::
DIVBY
:
for
(
int
index
=
tid
;
index
<
row_numel
;
index
+=
block_size
)
{
tensor_out
[
index
]
=
selected_rows
[
index
]
/
tensor_out
[
index
];
}
break
;
}
}
template
<
typename
T
>
struct
UpdateToTensor
<
platform
::
GPUDeviceContext
,
T
>
{
framework
::
Tensor
operator
()(
const
platform
::
GPUDeviceContext
&
context
,
const
ScatterOps
&
op
,
const
framework
::
SelectedRows
&
input1
,
framework
::
Tensor
*
input2
)
{
// NOTE: Use SelectedRowsAddToTensor for better performance
// no additional MergeAdd called.
auto
merged_in1
=
MergeAdd
()(
context
,
input1
);
auto
in1_height
=
merged_in1
.
height
();
auto
in2_dims
=
input2
->
dims
();
PADDLE_ENFORCE_EQ
(
in1_height
,
in2_dims
[
0
]);
auto
&
in1_value
=
merged_in1
.
value
();
auto
&
in1_rows
=
merged_in1
.
rows
();
int64_t
in1_row_numel
=
in1_value
.
numel
()
/
in1_rows
.
size
();
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
input2
->
numel
()
/
in1_height
);
auto
*
in1_data
=
in1_value
.
data
<
T
>
();
auto
*
input2_data
=
input2
->
data
<
T
>
();
dim3
threads
(
PADDLE_CUDA_NUM_THREADS
,
1
);
dim3
grid
(
1
,
in1_rows
.
size
());
UpdateToTensorKernel
<
T
,
PADDLE_CUDA_NUM_THREADS
><<<
grid
,
threads
,
0
,
context
.
stream
()
>>>
(
in1_data
,
in1_rows
.
data
(),
op
,
in2_data
,
in1_row_numel
);
}
};
}
// namespace scatter
}
// namespace math
}
// namespace operators
...
...
paddle/operators/math/selected_rows_functor.h
浏览文件 @
641b4c0f
...
...
@@ -16,6 +16,10 @@ limitations under the License. */
#include "paddle/framework/selected_rows.h"
#include "paddle/platform/device_context.h"
#define INLINE_FOR2(sizei, sizej) \
for (int64_t i = 0; i < sizei; i++) \
for (int64_t j = 0; j < sizej; j++)
namespace
paddle
{
namespace
operators
{
namespace
math
{
...
...
@@ -55,50 +59,76 @@ struct SelectedRowsAddToTensor {
namespace
scatter
{
// functors for manuplating SelectedRows data
template
<
typename
DeviceContext
,
typename
T
>
struct
MergeAdd
{
// unary functor, merge by adding duplicated rows in
// the input SelectedRows object.
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
SelectedRows
&
input
,
framework
::
SelectedRows
*
out
);
framework
::
SelectedRows
operator
()(
const
DeviceContext
&
context
,
const
framework
::
SelectedRows
&
input
);
};
template
<
typename
DeviceContext
,
typename
T
>
struct
Add
{
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
SelectedRows
&
input1
,
const
framework
::
SelectedRows
&
input2
,
framework
::
SelectedRows
*
out
)
{
out
->
set_rows
(
input1
.
rows
());
out
->
set_height
(
input1
.
height
());
out
->
mutable_value
()
->
mutable_data
<
T
>
(
input1
.
value
().
dims
(),
context
.
GetPlace
());
auto
e_out
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
(
out
->
mutable_value
()));
framework
::
SelectedRows
operator
()(
const
DeviceContext
&
context
,
const
framework
::
SelectedRows
&
input1
,
const
framework
::
SelectedRows
&
input2
)
{
framework
::
SelectedRows
out
;
out
.
set_rows
(
input1
.
rows
());
out
.
set_height
(
input1
.
height
());
out
.
mutable_value
()
->
mutable_data
<
T
>
(
input1
.
value
().
dims
(),
context
.
GetPlace
());
auto
e_out
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
(
out
.
mutable_value
()));
auto
e_in1
=
framework
::
EigenVector
<
T
>::
Flatten
(
input1
.
value
());
auto
e_in2
=
framework
::
EigenVector
<
T
>::
Flatten
(
input2
.
value
());
e_out
.
device
(
*
context
.
eigen_device
())
=
e_in1
+
e_in2
;
return
out
;
}
};
template
<
typename
DeviceContext
,
typename
T
>
struct
Mul
{
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
SelectedRows
&
input1
,
const
framework
::
SelectedRows
&
input2
,
framework
::
SelectedRows
*
out
)
{
out
->
set_rows
(
input1
.
rows
());
out
->
set_height
(
input1
.
height
());
out
->
mutable_value
()
->
mutable_data
<
T
>
(
input1
.
value
().
dims
(),
context
.
GetPlace
());
auto
e_out
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
(
out
->
mutable_value
()));
// multiply two SelectedRows
framework
::
SelectedRows
operator
()(
const
DeviceContext
&
context
,
const
framework
::
SelectedRows
&
input1
,
const
framework
::
SelectedRows
&
input2
)
{
framework
::
SelectedRows
out
;
out
.
set_rows
(
input1
.
rows
());
out
.
set_height
(
input1
.
height
());
out
.
mutable_value
()
->
mutable_data
<
T
>
(
input1
.
value
().
dims
(),
context
.
GetPlace
());
auto
e_out
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
(
out
.
mutable_value
()));
auto
e_in1
=
framework
::
EigenVector
<
T
>::
Flatten
(
input1
.
value
());
auto
e_in2
=
framework
::
EigenVector
<
T
>::
Flatten
(
input2
.
value
());
e_out
.
device
(
*
context
.
eigen_device
())
=
e_in1
*
e_in2
;
return
out
;
}
// multiply scalar to SelectedRows
framework
::
SelectedRows
operator
()(
const
DeviceContext
&
context
,
const
framework
::
SelectedRows
&
input1
,
const
T
input2
)
{
framework
::
SelectedRows
out
;
out
.
set_rows
(
input1
.
rows
());
out
.
set_height
(
input1
.
height
());
out
.
mutable_value
()
->
mutable_data
<
T
>
(
input1
.
value
().
dims
(),
context
.
GetPlace
());
auto
e_out
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
(
out
.
mutable_value
()));
auto
e_in1
=
framework
::
EigenVector
<
T
>::
Flatten
(
input1
.
value
());
e_out
.
device
(
*
context
.
eigen_device
())
=
input2
*
e_in1
;
return
out
;
}
};
enum
class
ScatterOps
{
ASSIGN
,
ADD
,
SUB
,
SUBBY
,
MUL
,
DIV
,
DIVBY
};
// out = seleted_rows_in / tensor
template
<
typename
DeviceContext
,
typename
T
>
struct
UpdateToTensor
{
framework
::
Tensor
operator
()(
const
DeviceContext
&
context
,
const
ScatterOps
&
op
,
const
framework
::
SelectedRows
&
input1
,
framework
::
Tensor
*
input2
);
};
}
// namespace scatter
}
// namespace math
}
// namespace operators
...
...
python/paddle/v2/fluid/tests/test_adam_op.py
浏览文件 @
641b4c0f
...
...
@@ -285,7 +285,6 @@ class TestSparseAdamOp(unittest.TestCase):
j
=
0
while
j
<
self
.
row_numel
:
pos
=
row_id
*
self
.
row_numel
+
j
print
(
actual
[
pos
]
-
np_array
[
pos
])
/
actual
[
pos
]
self
.
assertLess
((
actual
[
pos
]
-
np_array
[
pos
])
/
actual
[
pos
],
0.00001
)
j
+=
1
...
...
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