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体验新版 GitCode,发现更多精彩内容 >>
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c1bf06f9
编写于
4月 13, 2018
作者:
F
fengjiayi
提交者:
GitHub
4月 13, 2018
浏览文件
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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 {
}
}
out_dims
[
0
]
=
out_first_dim
;
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
}
else
{
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.
See the License for the specific language governing permissions and
limitations under the License. */
#
define EIGEN_USE_GPU
#
include <algorithm>
#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
;
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
limitations under the License. */
#pragma once
#include <numeric> // std::iota
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memcpy.h"
...
...
@@ -26,6 +27,57 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename
IndexType
=
Eigen
::
DenseIndex
>
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
>
class
SequenceExpandKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -47,45 +99,36 @@ class SequenceExpandKernel : public framework::OpKernel<T> {
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
)
{
out_lod
.
resize
(
1
);
out_lod
[
0
]
=
{
0
};
}
int
out_offset
=
0
;
auto
&
eigen_place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
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
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
;
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_lod
.
push_back
(
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
x_start
=
x_lod
[
0
][
i
-
1
];
int
x_end
=
x_lod
[
0
][
i
];
int
x_seq_len
=
x_end
-
x_start
;
for
(
int
j
=
0
;
j
<
repeat_num
;
++
j
)
{
out_lod
.
push_back
(
out_lod
.
back
()
+
x_seq_len
);
out_offset
++
;
}
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> {
* 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
>
class
SequenceExpandGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -114,43 +187,26 @@ class SequenceExpandGradKernel : public framework::OpKernel<T> {
g_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
g_x
->
set_lod
(
x
->
lod
());
auto
&
x_lod
=
x
->
lod
();
auto
&
y_lod
=
y
->
lod
();
if
(
ref_level
==
-
1
)
ref_level
=
y_lod
.
size
()
-
1
;
// just copy the gradient
if
(
y_lod
[
ref_level
].
size
()
<=
1
)
{
framework
::
TensorCopy
(
*
g_out
,
context
.
GetPlace
(),
g_x
);
return
;
}
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
math
::
SetConstant
<
DeviceContext
,
T
>
set_zero
;
set_zero
(
dev_ctx
,
g_x
,
static_cast
<
T
>
(
0
));
int
g_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
];
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
;
}
framework
::
Vector
<
size_t
>
ref_x_lod
;
framework
::
Vector
<
size_t
>
ref_lod
=
y_lod
[
ref_level
];
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
);
}
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)
endif
()
endfunction
()
list
(
REMOVE_ITEM TEST_OPS test_sequence_expand
)
# 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_warpctc_op
)
...
...
@@ -70,6 +72,8 @@ else()
endforeach
(
TEST_OP
)
endif
(
WITH_FAST_BUNDLE_TEST
)
#
py_test_modules
(
test_sequence_expand MODULES test_sequence_expand
)
# tests with high overhead
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
}
)
...
...
python/paddle/fluid/tests/unittests/test_sequence_expand.py
浏览文件 @
c1bf06f9
...
...
@@ -47,8 +47,10 @@ class TestSequenceExpand(OpTest):
x_len
=
x_idx
[
i
]
-
x_idx
[
i
-
1
]
if
repeat_num
>
0
:
x_sub
=
x_data
[
x_idx
[
i
-
1
]:
x_idx
[
i
],
:]
x_sub
=
np
.
repeat
(
x_sub
,
repeat_num
,
axis
=
0
)
out
=
np
.
vstack
((
out
,
x_sub
))
stacked_x_sub
=
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
:
for
j
in
xrange
(
repeat_num
):
out_lod
[
0
].
append
(
out_lod
[
0
][
-
1
]
+
x_len
)
...
...
@@ -101,11 +103,11 @@ class TestSequenceExpandCase3(TestSequenceExpand):
class
TestSequenceExpandCase4
(
TestSequenceExpand
):
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_lod
=
[[
0
,
2
,
5
]]
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
2
,
1
]).
astype
(
'float32'
)
y_lod
=
[[
0
,
1
,
2
],
[
0
,
1
,
2
]]
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
3
,
1
]).
astype
(
'float32'
)
y_lod
=
[[
0
,
1
,
3
],
[
0
,
1
,
3
]]
self
.
inputs
=
{
'X'
:
(
x_data
,
x_lod
),
'Y'
:
(
y_data
,
y_lod
)}
...
...
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