Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
PaddleDetection
提交
c1bf06f9
P
PaddleDetection
项目概览
PaddlePaddle
/
PaddleDetection
12 个月 前同步成功
通知
692
Star
11112
Fork
2696
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
184
列表
看板
标记
里程碑
合并请求
40
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
PaddleDetection
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
184
Issue
184
列表
看板
标记
里程碑
合并请求
40
合并请求
40
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
c1bf06f9
编写于
4月 13, 2018
作者:
F
fengjiayi
提交者:
GitHub
4月 13, 2018
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #9289 from dzhwinter/speed/sequence_expand
Speed/sequence expand
上级
925c17ab
62d1f9a7
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
260 addition
and
72 deletion
+260
-72
paddle/fluid/operators/sequence_expand_op.cc
paddle/fluid/operators/sequence_expand_op.cc
+2
-3
paddle/fluid/operators/sequence_expand_op.cu
paddle/fluid/operators/sequence_expand_op.cu
+128
-1
paddle/fluid/operators/sequence_expand_op.h
paddle/fluid/operators/sequence_expand_op.h
+119
-63
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+4
-0
python/paddle/fluid/tests/unittests/test_sequence_expand.py
python/paddle/fluid/tests/unittests/test_sequence_expand.py
+7
-5
未找到文件。
paddle/fluid/operators/sequence_expand_op.cc
浏览文件 @
c1bf06f9
...
@@ -84,12 +84,11 @@ class SequenceExpandOp : public framework::OperatorWithKernel {
...
@@ -84,12 +84,11 @@ class SequenceExpandOp : public framework::OperatorWithKernel {
}
}
}
}
out_dims
[
0
]
=
out_first_dim
;
out_dims
[
0
]
=
out_first_dim
;
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
}
else
{
}
else
{
out_dims
[
0
]
=
-
1
;
out_dims
[
0
]
=
-
1
;
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
}
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
}
};
};
...
...
paddle/fluid/operators/sequence_expand_op.cu
浏览文件 @
c1bf06f9
...
@@ -12,8 +12,135 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
...
@@ -12,8 +12,135 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
See the License for the specific language governing permissions and
limitations under the License. */
limitations under the License. */
#
define EIGEN_USE_GPU
#
include <algorithm>
#include "paddle/fluid/operators/sequence_expand_op.h"
#include "paddle/fluid/operators/sequence_expand_op.h"
#include "paddle/fluid/platform/cuda_helper.h"
namespace
paddle
{
namespace
operators
{
using
LoDTensor
=
framework
::
LoDTensor
;
template
<
typename
T
>
__global__
void
sequence_expand_kernel
(
const
T
*
x_data
,
const
size_t
*
x_lod
,
const
size_t
*
ref_lod
,
const
size_t
*
offset
,
const
size_t
lod_size
,
/* default=1,
the instance length*/
const
int
x_item_length
,
T
*
out_data
)
{
int
bid
=
blockIdx
.
x
;
if
(
bid
>=
lod_size
-
1
)
return
;
int
x_item_count
=
x_lod
[
bid
+
1
]
-
x_lod
[
bid
];
int
repeats
=
ref_lod
[
bid
+
1
]
-
ref_lod
[
bid
];
int
out_offset
=
static_cast
<
int
>
(
offset
[
bid
]);
int
x_offset
=
x_lod
[
bid
];
for
(
int
tid_z
=
threadIdx
.
z
;
tid_z
<
repeats
;
tid_z
+=
blockDim
.
z
)
{
for
(
int
tid_y
=
threadIdx
.
y
;
tid_y
<
x_item_count
;
tid_y
+=
blockDim
.
y
)
{
for
(
int
tid_x
=
threadIdx
.
x
;
tid_x
<
x_item_length
;
tid_x
+=
blockDim
.
x
)
{
out_data
[(
out_offset
+
tid_z
*
x_item_count
+
tid_y
)
*
x_item_length
+
tid_x
]
=
x_data
[(
x_offset
+
tid_y
)
*
x_item_length
+
tid_x
];
}
}
}
}
template
<
typename
T
>
__global__
void
sequence_expand_grad_kernel
(
const
T
*
dout_data
,
const
size_t
*
ref_lod
,
const
size_t
*
dx_lod
,
const
size_t
*
offset
,
const
size_t
lod_size
,
/* default=1,
the instance length*/
const
int
x_item_length
,
T
*
dx_data
)
{
int
bid
=
blockIdx
.
x
;
if
(
bid
>=
lod_size
-
1
)
return
;
int
x_item_count
=
dx_lod
[
bid
+
1
]
-
dx_lod
[
bid
];
int
repeats
=
ref_lod
[
bid
+
1
]
-
ref_lod
[
bid
];
int
out_offset
=
static_cast
<
int
>
(
offset
[
bid
]);
int
x_offset
=
dx_lod
[
bid
];
for
(
int
tid_z
=
threadIdx
.
z
;
tid_z
<
repeats
;
tid_z
+=
blockDim
.
z
)
{
for
(
int
tid_y
=
threadIdx
.
y
;
tid_y
<
x_item_count
;
tid_y
+=
blockDim
.
y
)
{
for
(
int
tid_x
=
threadIdx
.
x
;
tid_x
<
x_item_length
;
tid_x
+=
blockDim
.
x
)
{
platform
::
CudaAtomicAdd
(
&
dx_data
[(
x_offset
+
tid_y
)
*
x_item_length
+
tid_x
],
dout_data
[(
out_offset
+
tid_z
*
x_item_count
+
tid_y
)
*
x_item_length
+
tid_x
]);
}
}
}
}
void
GetOutputOffset
(
const
framework
::
Vector
<
size_t
>&
x_lod
,
const
framework
::
Vector
<
size_t
>&
ref_lod
,
framework
::
Vector
<
size_t
>*
out_offset
)
{
size_t
offset
=
0
;
int
lod_size
=
static_cast
<
int
>
(
x_lod
.
size
());
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
x_lod
.
size
());
++
i
)
{
(
*
out_offset
)[
i
]
=
offset
;
if
(
i
<
lod_size
-
1
)
{
offset
+=
(
ref_lod
[
i
+
1
]
-
ref_lod
[
i
])
*
(
x_lod
[
i
+
1
]
-
x_lod
[
i
]);
}
}
}
template
<
typename
T
>
struct
SequenceExpandFunctor
<
platform
::
CUDADeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CUDADeviceContext
&
context
,
const
LoDTensor
&
x
,
const
framework
::
Vector
<
size_t
>&
x_lod
,
/*expand source lod*/
const
framework
::
Vector
<
size_t
>&
ref_lod
,
/*expand referenced lod*/
LoDTensor
*
out
)
{
int
x_item_length
=
x
.
numel
()
/
x
.
dims
()[
0
];
framework
::
Vector
<
size_t
>
out_offset
(
x_lod
.
size
());
GetOutputOffset
(
x_lod
,
ref_lod
,
&
out_offset
);
int
thread_x
=
std
::
min
(
32
,
std
::
max
(
static_cast
<
int
>
(
ref_lod
.
size
()),
16
));
int
thread_y
=
16
;
int
thread_z
=
1024
/
thread_x
/
thread_y
;
int
block_x
=
static_cast
<
int
>
(
ref_lod
.
size
());
dim3
block_size
(
thread_x
,
thread_y
,
thread_z
);
dim3
grid_size
(
block_x
,
1
);
sequence_expand_kernel
<<<
grid_size
,
block_size
,
0
,
context
.
stream
()
>>>
(
x
.
data
<
T
>
(),
x_lod
.
CUDAData
(
context
.
GetPlace
()),
ref_lod
.
CUDAData
(
context
.
GetPlace
()),
out_offset
.
CUDAData
(
context
.
GetPlace
()),
x_lod
.
size
(),
x_item_length
,
out
->
mutable_data
<
T
>
(
context
.
GetPlace
()));
}
};
template
<
typename
T
>
struct
SequenceExpandGradFunctor
<
platform
::
CUDADeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CUDADeviceContext
&
context
,
const
LoDTensor
&
dout
,
const
framework
::
Vector
<
size_t
>&
x_lod
,
/*expand source lod*/
const
framework
::
Vector
<
size_t
>&
ref_lod
,
/*expand based lod*/
LoDTensor
*
dx
)
{
int
x_item_length
=
framework
::
product
(
dx
->
dims
())
/
dx
->
dims
()[
0
];
framework
::
Vector
<
size_t
>
out_offset
(
x_lod
.
size
());
GetOutputOffset
(
x_lod
,
ref_lod
,
&
out_offset
);
int
thread_x
=
std
::
min
(
32
,
std
::
max
(
static_cast
<
int
>
(
ref_lod
.
size
()),
16
));
int
thread_y
=
16
;
int
thread_z
=
1024
/
thread_x
/
thread_y
;
int
block_x
=
static_cast
<
int
>
(
ref_lod
.
size
());
dim3
block_size
(
thread_x
,
thread_y
,
thread_z
);
dim3
grid_size
(
block_x
,
1
);
sequence_expand_grad_kernel
<<<
grid_size
,
block_size
,
0
,
context
.
stream
()
>>>
(
dout
.
data
<
T
>
(),
ref_lod
.
CUDAData
(
context
.
GetPlace
()),
x_lod
.
CUDAData
(
context
.
GetPlace
()),
out_offset
.
CUDAData
(
context
.
GetPlace
()),
ref_lod
.
size
(),
x_item_length
,
dx
->
mutable_data
<
T
>
(
context
.
GetPlace
()));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
REGISTER_OP_CUDA_KERNEL
(
...
...
paddle/fluid/operators/sequence_expand_op.h
浏览文件 @
c1bf06f9
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
limitations under the License. */
#pragma once
#pragma once
#include <numeric> // std::iota
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/memory/memcpy.h"
...
@@ -26,6 +27,57 @@ template <typename T, int MajorType = Eigen::RowMajor,
...
@@ -26,6 +27,57 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename
IndexType
=
Eigen
::
DenseIndex
>
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
DeviceContext
,
typename
T
>
struct
SequenceExpandFunctor
{
void
operator
()(
const
DeviceContext
&
ctx
,
const
LoDTensor
&
x
,
const
framework
::
Vector
<
size_t
>&
x_lod
,
/*expand source lod*/
const
framework
::
Vector
<
size_t
>&
ref_lod
,
/*expand referenced lod*/
LoDTensor
*
out
);
};
template
<
typename
DeviceContext
,
typename
T
>
struct
SequenceExpandGradFunctor
{
void
operator
()(
const
DeviceContext
&
ctx
,
const
LoDTensor
&
dout
,
const
framework
::
Vector
<
size_t
>&
x_lod
,
/*expand source lod*/
const
framework
::
Vector
<
size_t
>&
ref_lod
,
/*expand referenced lod*/
LoDTensor
*
dx
);
};
template
<
typename
T
>
struct
SequenceExpandFunctor
<
platform
::
CPUDeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
LoDTensor
&
x
,
const
framework
::
Vector
<
size_t
>&
x_lod
,
/*expand source lod*/
const
framework
::
Vector
<
size_t
>&
ref_lod
,
/*expand referenced lod*/
LoDTensor
*
out
)
{
int
out_offset
=
0
;
auto
&
eigen_place
=
*
context
.
eigen_device
();
for
(
size_t
i
=
1
;
i
<
ref_lod
.
size
();
++
i
)
{
int
repeat_num
=
ref_lod
[
i
]
-
ref_lod
[
i
-
1
];
int
x_start
=
x_lod
[
i
-
1
];
int
x_end
=
x_lod
[
i
];
int
x_seq_len
=
x_end
-
x_start
;
if
(
repeat_num
>
0
)
{
auto
x_sub_tensor
=
x
.
Slice
(
x_start
,
x_end
);
x_sub_tensor
.
Resize
({
1
,
x_sub_tensor
.
numel
()});
int
out_start
=
out_offset
;
if
(
out
->
lod
().
size
()
==
1
)
{
out_start
=
out
->
lod
()[
0
][
out_offset
];
}
auto
out_sub_tensor
=
out
->
Slice
(
out_start
,
out_start
+
x_seq_len
*
repeat_num
);
out_sub_tensor
.
Resize
({
repeat_num
,
x_sub_tensor
.
dims
()[
1
]});
EigenMatrix
<
T
>::
From
(
out_sub_tensor
).
device
(
eigen_place
)
=
EigenMatrix
<
T
>::
From
(
x_sub_tensor
)
.
broadcast
(
Eigen
::
array
<
int
,
2
>
({{
repeat_num
,
1
}}));
}
out_offset
+=
repeat_num
;
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
template
<
typename
DeviceContext
,
typename
T
>
class
SequenceExpandKernel
:
public
framework
::
OpKernel
<
T
>
{
class
SequenceExpandKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
public:
...
@@ -47,45 +99,36 @@ class SequenceExpandKernel : public framework::OpKernel<T> {
...
@@ -47,45 +99,36 @@ class SequenceExpandKernel : public framework::OpKernel<T> {
return
;
return
;
}
}
auto
&
out_lod
=
*
out
->
mutable_lod
();
// x lod level is at most 1.
framework
::
Vector
<
size_t
>
out_lod
;
if
(
x_lod
.
size
()
==
1
)
{
if
(
x_lod
.
size
()
==
1
)
{
out_lod
.
resize
(
1
);
out_lod
.
push_back
(
0
);
out_lod
[
0
]
=
{
0
};
int
out_offset
=
0
;
}
for
(
size_t
i
=
1
;
i
<
y_lod
[
ref_level
].
size
();
++
i
)
{
int
repeat_num
=
y_lod
[
ref_level
][
i
]
-
y_lod
[
ref_level
][
i
-
1
];
int
out_offset
=
0
;
int
x_start
=
x_lod
[
0
][
i
-
1
];
auto
&
eigen_place
=
int
x_end
=
x_lod
[
0
][
i
];
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
int
x_seq_len
=
x_end
-
x_start
;
for
(
size_t
i
=
1
;
i
<
y_lod
[
ref_level
].
size
();
++
i
)
{
for
(
int
j
=
0
;
j
<
repeat_num
;
++
j
)
{
int
repeat_num
=
y_lod
[
ref_level
][
i
]
-
y_lod
[
ref_level
][
i
-
1
];
out_lod
.
push_back
(
out_lod
.
back
()
+
x_seq_len
);
int
x_start
=
i
-
1
;
out_offset
++
;
int
x_end
=
i
;
if
(
x_lod
.
size
()
==
1
)
{
x_start
=
x_lod
[
0
][
i
-
1
];
x_end
=
x_lod
[
0
][
i
];
}
int
x_seq_len
=
x_end
-
x_start
;
if
(
repeat_num
>
0
)
{
auto
x_sub_tensor
=
x
->
Slice
(
x_start
,
x_end
);
x_sub_tensor
.
Resize
({
1
,
x_sub_tensor
.
numel
()});
int
out_start
=
out_offset
;
if
(
x_lod
.
size
()
==
1
)
{
out_start
=
out_lod
[
0
][
out_offset
];
}
auto
out_sub_tensor
=
out
->
Slice
(
out_start
,
out_start
+
x_seq_len
*
repeat_num
);
out_sub_tensor
.
Resize
({
repeat_num
,
x_sub_tensor
.
dims
()[
1
]});
EigenMatrix
<
T
>::
From
(
out_sub_tensor
).
device
(
eigen_place
)
=
EigenMatrix
<
T
>::
From
(
x_sub_tensor
)
.
broadcast
(
Eigen
::
array
<
int
,
2
>
({{
repeat_num
,
1
}}));
}
for
(
int
j
=
0
;
j
<
repeat_num
;
++
j
)
{
if
(
x_lod
.
size
()
==
1
)
{
out_lod
[
0
].
push_back
(
out_lod
[
0
].
back
()
+
x_seq_len
);
}
}
out_offset
++
;
}
}
// write lod to out if x has lod
auto
&
ref_lod
=
*
out
->
mutable_lod
();
ref_lod
[
0
]
=
out_lod
;
}
}
framework
::
Vector
<
size_t
>
ref_x_lod
;
if
(
x
->
lod
().
size
()
==
1
)
{
ref_x_lod
=
x
->
lod
()[
0
];
}
else
{
// x_lod doesn't has lod, use fake x lod, level = 0
ref_x_lod
.
resize
(
x
->
dims
()[
0
]
+
1
);
std
::
iota
(
ref_x_lod
.
begin
(),
ref_x_lod
.
end
(),
0
);
}
SequenceExpandFunctor
<
DeviceContext
,
T
>
functor
;
functor
(
context
.
template
device_context
<
DeviceContext
>(),
*
x
,
ref_x_lod
,
y_lod
[
ref_level
],
out
);
}
}
};
};
...
@@ -101,6 +144,36 @@ class SequenceExpandKernel : public framework::OpKernel<T> {
...
@@ -101,6 +144,36 @@ class SequenceExpandKernel : public framework::OpKernel<T> {
* Grad(X).lod = Input(X).lod
* Grad(X).lod = Input(X).lod
*
*
* */
* */
template
<
typename
T
>
struct
SequenceExpandGradFunctor
<
platform
::
CPUDeviceContext
,
T
>
{
void
operator
()(
const
platform
::
CPUDeviceContext
&
context
,
const
LoDTensor
&
dout
,
const
framework
::
Vector
<
size_t
>&
x_lod
,
/*expand source lod*/
const
framework
::
Vector
<
size_t
>&
ref_lod
,
/*expand referenced lod*/
LoDTensor
*
dx
)
{
math
::
SetConstant
<
platform
::
CPUDeviceContext
,
T
>
set_zero
;
set_zero
(
context
,
dx
,
static_cast
<
T
>
(
0
));
int
dout_offset
=
0
;
for
(
size_t
i
=
1
;
i
<
ref_lod
.
size
();
++
i
)
{
int
repeat_num
=
ref_lod
[
i
]
-
ref_lod
[
i
-
1
];
if
(
repeat_num
>
0
)
{
int
x_start
=
x_lod
[
i
-
1
];
int
x_end
=
x_lod
[
i
];
int
x_seq_len
=
x_end
-
x_start
;
auto
dx_sub
=
dx
->
Slice
(
x_start
,
x_end
);
dx_sub
.
Resize
(
flatten_to_1d
(
dx_sub
.
dims
()));
int
dout_end
=
dout_offset
+
repeat_num
*
x_seq_len
;
auto
dout_sub
=
dout
.
Slice
(
dout_offset
,
dout_end
);
dout_sub
.
Resize
({
repeat_num
,
dx_sub
.
dims
()[
0
]});
math
::
ColwiseSum
<
platform
::
CPUDeviceContext
,
T
>
col_sum
;
col_sum
(
context
,
dout_sub
,
&
dx_sub
);
dout_offset
+=
repeat_num
*
x_seq_len
;
}
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
template
<
typename
DeviceContext
,
typename
T
>
class
SequenceExpandGradKernel
:
public
framework
::
OpKernel
<
T
>
{
class
SequenceExpandGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
public:
...
@@ -114,43 +187,26 @@ class SequenceExpandGradKernel : public framework::OpKernel<T> {
...
@@ -114,43 +187,26 @@ class SequenceExpandGradKernel : public framework::OpKernel<T> {
g_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
g_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
g_x
->
set_lod
(
x
->
lod
());
g_x
->
set_lod
(
x
->
lod
());
auto
&
x_lod
=
x
->
lod
();
auto
&
y_lod
=
y
->
lod
();
auto
&
y_lod
=
y
->
lod
();
if
(
ref_level
==
-
1
)
ref_level
=
y_lod
.
size
()
-
1
;
if
(
ref_level
==
-
1
)
ref_level
=
y_lod
.
size
()
-
1
;
// just copy the gradient
// just copy the gradient
if
(
y_lod
[
ref_level
].
size
()
<=
1
)
{
if
(
y_lod
[
ref_level
].
size
()
<=
1
)
{
framework
::
TensorCopy
(
*
g_out
,
context
.
GetPlace
(),
g_x
);
framework
::
TensorCopy
(
*
g_out
,
context
.
GetPlace
(),
g_x
);
return
;
return
;
}
}
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
framework
::
Vector
<
size_t
>
ref_x_lod
;
framework
::
Vector
<
size_t
>
ref_lod
=
y_lod
[
ref_level
];
math
::
SetConstant
<
DeviceContext
,
T
>
set_zero
;
if
(
x
->
lod
().
size
()
==
1
)
{
set_zero
(
dev_ctx
,
g_x
,
static_cast
<
T
>
(
0
));
ref_x_lod
=
x
->
lod
()[
0
];
}
else
{
int
g_out_offset
=
0
;
// x_lod doesn't has lod, use fake x lod, level = 0
for
(
size_t
i
=
1
;
i
<
y_lod
[
ref_level
].
size
();
++
i
)
{
ref_x_lod
.
resize
(
x
->
dims
()[
0
]
+
1
);
int
repeat_num
=
y_lod
[
ref_level
][
i
]
-
y_lod
[
ref_level
][
i
-
1
];
std
::
iota
(
ref_x_lod
.
begin
(),
ref_x_lod
.
end
(),
0
);
if
(
repeat_num
>
0
)
{
int
x_start
=
i
-
1
;
int
x_end
=
i
;
if
(
x_lod
.
size
()
==
1
)
{
x_start
=
x_lod
[
0
][
i
-
1
];
x_end
=
x_lod
[
0
][
i
];
}
int
x_seq_len
=
x_end
-
x_start
;
auto
g_x_sub
=
g_x
->
Slice
(
x_start
,
x_end
);
g_x_sub
.
Resize
(
flatten_to_1d
(
g_x_sub
.
dims
()));
int
g_out_end
=
g_out_offset
+
repeat_num
*
x_seq_len
;
auto
g_out_sub
=
g_out
->
Slice
(
g_out_offset
,
g_out_end
);
g_out_sub
.
Resize
({
repeat_num
,
g_x_sub
.
dims
()[
0
]});
math
::
ColwiseSum
<
DeviceContext
,
T
>
col_sum
;
col_sum
(
dev_ctx
,
g_out_sub
,
&
g_x_sub
);
g_out_offset
+=
repeat_num
*
x_seq_len
;
}
}
}
SequenceExpandGradFunctor
<
DeviceContext
,
T
>
functor
;
functor
(
context
.
template
device_context
<
DeviceContext
>(),
*
g_out
,
ref_x_lod
,
ref_lod
,
g_x
);
}
}
};
};
...
...
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
c1bf06f9
...
@@ -34,6 +34,8 @@ function(py_test_modules TARGET_NAME)
...
@@ -34,6 +34,8 @@ function(py_test_modules TARGET_NAME)
endif
()
endif
()
endfunction
()
endfunction
()
list
(
REMOVE_ITEM TEST_OPS test_sequence_expand
)
# test time consuming OPs in a separate process for expliot parallism
# test time consuming OPs in a separate process for expliot parallism
list
(
REMOVE_ITEM TEST_OPS test_parallel_executor
)
list
(
REMOVE_ITEM TEST_OPS test_parallel_executor
)
list
(
REMOVE_ITEM TEST_OPS test_warpctc_op
)
list
(
REMOVE_ITEM TEST_OPS test_warpctc_op
)
...
@@ -70,6 +72,8 @@ else()
...
@@ -70,6 +72,8 @@ else()
endforeach
(
TEST_OP
)
endforeach
(
TEST_OP
)
endif
(
WITH_FAST_BUNDLE_TEST
)
endif
(
WITH_FAST_BUNDLE_TEST
)
#
py_test_modules
(
test_sequence_expand MODULES test_sequence_expand
)
# tests with high overhead
# tests with high overhead
py_test_modules
(
test_parallel_executor MODULES test_parallel_executor
)
py_test_modules
(
test_parallel_executor MODULES test_parallel_executor
)
py_test_modules
(
test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=
${
WARPCTC_LIB_DIR
}
)
py_test_modules
(
test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=
${
WARPCTC_LIB_DIR
}
)
...
...
python/paddle/fluid/tests/unittests/test_sequence_expand.py
浏览文件 @
c1bf06f9
...
@@ -47,8 +47,10 @@ class TestSequenceExpand(OpTest):
...
@@ -47,8 +47,10 @@ class TestSequenceExpand(OpTest):
x_len
=
x_idx
[
i
]
-
x_idx
[
i
-
1
]
x_len
=
x_idx
[
i
]
-
x_idx
[
i
-
1
]
if
repeat_num
>
0
:
if
repeat_num
>
0
:
x_sub
=
x_data
[
x_idx
[
i
-
1
]:
x_idx
[
i
],
:]
x_sub
=
x_data
[
x_idx
[
i
-
1
]:
x_idx
[
i
],
:]
x_sub
=
np
.
repeat
(
x_sub
,
repeat_num
,
axis
=
0
)
stacked_x_sub
=
x_sub
out
=
np
.
vstack
((
out
,
x_sub
))
for
r
in
range
(
repeat_num
-
1
):
stacked_x_sub
=
np
.
vstack
((
stacked_x_sub
,
x_sub
))
out
=
np
.
vstack
((
out
,
stacked_x_sub
))
if
x_lod
is
not
None
:
if
x_lod
is
not
None
:
for
j
in
xrange
(
repeat_num
):
for
j
in
xrange
(
repeat_num
):
out_lod
[
0
].
append
(
out_lod
[
0
][
-
1
]
+
x_len
)
out_lod
[
0
].
append
(
out_lod
[
0
][
-
1
]
+
x_len
)
...
@@ -101,11 +103,11 @@ class TestSequenceExpandCase3(TestSequenceExpand):
...
@@ -101,11 +103,11 @@ class TestSequenceExpandCase3(TestSequenceExpand):
class
TestSequenceExpandCase4
(
TestSequenceExpand
):
class
TestSequenceExpandCase4
(
TestSequenceExpand
):
def
set_data
(
self
):
def
set_data
(
self
):
data
=
[
0.1
,
0.3
,
0.2
,
0.15
,
0.25
,
0.2
,
0.15
,
0.25
,
0.1
,
0.3
]
data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
5
*
2
,
1
])
x_data
=
np
.
array
(
data
).
reshape
([
5
,
2
]).
astype
(
'float32'
)
x_data
=
np
.
array
(
data
).
reshape
([
5
,
2
]).
astype
(
'float32'
)
x_lod
=
[[
0
,
2
,
5
]]
x_lod
=
[[
0
,
2
,
5
]]
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
2
,
1
]).
astype
(
'float32'
)
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
3
,
1
]).
astype
(
'float32'
)
y_lod
=
[[
0
,
1
,
2
],
[
0
,
1
,
2
]]
y_lod
=
[[
0
,
1
,
3
],
[
0
,
1
,
3
]]
self
.
inputs
=
{
'X'
:
(
x_data
,
x_lod
),
'Y'
:
(
y_data
,
y_lod
)}
self
.
inputs
=
{
'X'
:
(
x_data
,
x_lod
),
'Y'
:
(
y_data
,
y_lod
)}
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录