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236af566
编写于
9月 25, 2017
作者:
Y
Yibing Liu
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差异文件
separate index tensor from candidate tensors in multiplex_op
上级
f94109d4
e114aad8
变更
11
隐藏空白更改
内联
并排
Showing
11 changed file
with
427 addition
and
51 deletion
+427
-51
doc/faq/index_cn.rst
doc/faq/index_cn.rst
+122
-1
paddle/operators/crop_op.h
paddle/operators/crop_op.h
+3
-3
paddle/operators/math/math_function.cc
paddle/operators/math/math_function.cc
+26
-0
paddle/operators/math/math_function.cu
paddle/operators/math/math_function.cu
+36
-0
paddle/operators/math/math_function.h
paddle/operators/math/math_function.h
+7
-0
paddle/operators/math/math_function_test.cc
paddle/operators/math/math_function_test.cc
+170
-0
paddle/operators/multiplex_op.cc
paddle/operators/multiplex_op.cc
+25
-18
paddle/operators/multiplex_op.cu
paddle/operators/multiplex_op.cu
+14
-11
paddle/operators/multiplex_op.h
paddle/operators/multiplex_op.h
+15
-12
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+2
-1
python/paddle/v2/framework/tests/test_multiplex_op.py
python/paddle/v2/framework/tests/test_multiplex_op.py
+7
-5
未找到文件。
doc/faq/index_cn.rst
浏览文件 @
236af566
...
@@ -390,4 +390,125 @@ PaddlePaddle保存的模型参数文件内容由16字节头信息和网络参数
...
@@ -390,4 +390,125 @@ PaddlePaddle保存的模型参数文件内容由16字节头信息和网络参数
* 如果发现最早的报错就是网络通信的问题,很有可能是非独占方式执行导致的端口冲突,可以联系OP,看当前MPI集群是否支持resource=full参数提交,如果支持增加此参数提交,并更换job 端口。
* 如果发现最早的报错就是网络通信的问题,很有可能是非独占方式执行导致的端口冲突,可以联系OP,看当前MPI集群是否支持resource=full参数提交,如果支持增加此参数提交,并更换job 端口。
* 如果当前MPI集群并不支持任务独占模式,可以联系OP是否可以更换集群或升级当前集群。
* 如果当前MPI集群并不支持任务独占模式,可以联系OP是否可以更换集群或升级当前集群。
\ No newline at end of file
19. PaddlePaddle如何输出多个层
------------------------------
* 将需要输出的层作为 :code:`paddle.inference.Inference()` 接口的 :code:`output_layer` 参数输入,代码如下:
.. code-block:: python
inferer = paddle.inference.Inference(output_layer=[layer1, layer2], parameters=parameters)
* 指定要输出的字段进行输出。以输出 :code:`value` 字段为例,代码如下:
.. code-block:: python
out = inferer.infer(input=data_batch, flatten_result=False, field=["value"])
这里设置 :code:`flatten_result=False`,得到的输出结果是元素个数等于输出字段数的 :code:`list`,该 :code:`list` 的每个元素是由所有输出层相应字段结果组成的 :code:`list`,每个字段结果的类型是 :code:`numpy.array`。:code:`flatten_result` 的默认值为 :code:`True`,该情况下,PaddlePaddle会分别对每个字段将所有输出层的结果按行进行拼接,如果各输出层该字段 :code:`numpy.array` 结果的相应维数不匹配,程序将不能正常运行。
20. :code:`paddle.layer.memory` 的参数 :code:`name` 如何使用
-------------------------------------------------------------
* :code:`paddle.layer.memory` 用于获取特定layer上一时间步的输出,该layer是通过参数 :code:`name` 指定,即,:code:`paddle.layer.memory` 会关联参数 :code:`name` 取值相同的layer,并将该layer上一时间步的输出作为自身当前时间步的输出。
* PaddlePaddle的所有layer都有唯一的name,用户通过参数 :code:`name` 设定,当用户没有显式设定时,PaddlePaddle会自动设定。而 :code:`paddle.layer.memory` 不是真正的layer,其name由参数 :code:`memory_name` 设定,当用户没有显式设定时,PaddlePaddle会自动设定。:code:`paddle.layer.memory` 的参数 :code:`name` 用于指定其要关联的layer,需要用户显式设定。
21. dropout 使用
-----------------
* 在PaddlePaddle中使用dropout有两种方式
* 在相应layer的 :code:`layer_atter` 设置 :code:`drop_rate`,以 :code:`paddle.layer.fc` 为例,代码如下:
.. code-block:: python
fc = paddle.layer.fc(input=input, layer_attr=paddle.attr.ExtraLayerAttribute(drop_rate=0.5))
* 使用 :code:`paddle.layer.dropout`,以 :code:`paddle.layer.fc` 为例,代码如下:
.. code-block:: python
fc = paddle.layer.fc(input=input)
drop_fc = paddle.layer.dropout(input=fc, dropout_rate=0.5)
* :code:`paddle.layer.dropout` 实际上使用了 :code:`paddle.layer.add_to`,并在该layer里采用第一种方式设置 :code:`drop_rate` 来使用dropout的。这种方式对内存消耗较大。
* PaddlePaddle在激活函数里实现dropout,而不是在layer里实现。
* :code:`paddle.layer.lstmemory`、:code:`paddle.layer.grumemory`、:code:`paddle.layer.recurrent` 不是通过一般的方式来实现对输出的激活,所以不能采用第一种方式在这几个layer里设置 :code:`drop_rate` 来使用dropout。若要对这几个layer使用dropout,可采用第二种方式,即使用 :code:`paddle.layer.dropout`。
22. 如何设置学习率退火(learning rate annealing)
------------------------------------------------
在相应的优化算法里设置learning_rate_schedule及相关参数,以使用Adam算法为例,代码如下:
.. code-block:: python
optimizer = paddle.optimizer.Adam(
learning_rate=1e-3,
learning_rate_decay_a=0.5,
learning_rate_decay_b=0.75,
learning_rate_schedule="poly",)
PaddlePaddle目前支持8种learning_rate_schedule,这8种learning_rate_schedule及其对应学习率计算方式如下:
* "constant"
lr = learning_rate
* "poly"
lr = learning_rate * pow(1 + learning_rate_decay_a * num_samples_processed, -learning_rate_decay_b)
其中,num_samples_processed为已训练样本数,下同。
* "caffe_poly"
lr = learning_rate * pow(1.0 - num_samples_processed / learning_rate_decay_a, learning_rate_decay_b)
* "exp"
lr = learning_rate * pow(learning_rate_decay_a, num_samples_processed / learning_rate_decay_b)
* "discexp"
lr = learning_rate * pow(learning_rate_decay_a, floor(num_samples_processed / learning_rate_decay_b))
* "linear"
lr = max(learning_rate - learning_rate_decay_a * num_samples_processed, learning_rate_decay_b)
* "manual"
这是一种按已训练样本数分段取值的学习率退火方法。使用该learning_rate_schedule时,用户通过参数 :code:`learning_rate_args` 设置学习率衰减因子分段函数,当前的学习率为所设置 :code:`learning_rate` 与当前的衰减因子的乘积。以使用Adam算法为例,代码如下:
.. code-block:: python
optimizer = paddle.optimizer.Adam(
learning_rate=1e-3,
learning_rate_schedule="manual",
learning_rate_args="1000:1.0,2000:0.9,3000:0.8",)
在该示例中,当已训练样本数小于等于1000时,学习率为 :code:`1e-3 * 1.0`;当已训练样本数大于1000小于等于2000时,学习率为 :code:`1e-3 * 0.9`;当已训练样本数大于2000时,学习率为 :code:`1e-3 * 0.8`。
* "pass_manual"
这是一种按已训练pass数分段取值的学习率退火方法。使用该learning_rate_schedule时,用户通过参数 :code:`learning_rate_args` 设置学习率衰减因子分段函数,当前的学习率为所设置 :code:`learning_rate` 与当前的衰减因子的乘积。以使用Adam算法为例,代码如下:
.. code-block:: python
optimizer = paddle.optimizer.Adam(
learning_rate=1e-3,
learning_rate_schedule="manual",
learning_rate_args="1:1.0,2:0.9,3:0.8",)
在该示例中,当已训练pass数小于等于1时,学习率为 :code:`1e-3 * 1.0`;当已训练pass数大于1小于等于2时,学习率为 :code:`1e-3 * 0.9`;当已训练pass数大于2时,学习率为 :code:`1e-3 * 0.8`。
23. 出现 :code:`Duplicated layer name` 错误怎么办
--------------------------------------------------
出现该错误的原因一般是用户对不同layer的参数 :code:`name` 设置了相同的取值。遇到该错误时,先找出参数 :code:`name` 取值相同的layer,然后将这些layer的参数 :code:`name` 设置为不同的值。
paddle/operators/crop_op.h
浏览文件 @
236af566
...
@@ -38,10 +38,10 @@ class CropKernel : public framework::OpKernel {
...
@@ -38,10 +38,10 @@ class CropKernel : public framework::OpKernel {
auto
out_stride
=
framework
::
stride
(
out
->
dims
());
auto
out_stride
=
framework
::
stride
(
out
->
dims
());
auto
offsets
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"offsets"
);
auto
offsets
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"offsets"
);
PADDLE_ENFORCE_EQ
(
PADDLE_ENFORCE_EQ
(
x
->
dims
().
size
(),
offsets
.
size
(
),
x
->
dims
().
size
(),
static_cast
<
int64_t
>
(
offsets
.
size
()
),
"Offsets size should be equal to dimension size of input tensor."
);
"Offsets size should be equal to dimension size of input tensor."
);
int64_t
offset
=
0
;
int64_t
offset
=
0
;
for
(
in
t
i
=
0
;
i
<
offsets
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
offsets
.
size
();
++
i
)
{
offset
+=
(
x_stride
[
i
]
*
offsets
[
i
]);
offset
+=
(
x_stride
[
i
]
*
offsets
[
i
]);
}
}
StridedMemcpy
<
T
>
(
context
.
device_context
(),
x_data
+
offset
,
x_stride
,
StridedMemcpy
<
T
>
(
context
.
device_context
(),
x_data
+
offset
,
x_stride
,
...
@@ -57,7 +57,7 @@ void CropGradFunction(const framework::ExecutionContext& context) {
...
@@ -57,7 +57,7 @@ void CropGradFunction(const framework::ExecutionContext& context) {
d_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
d_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
offsets
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"offsets"
);
auto
offsets
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"offsets"
);
Eigen
::
array
<
std
::
pair
<
int
,
int
>
,
D
>
paddings
;
Eigen
::
array
<
std
::
pair
<
int
,
int
>
,
D
>
paddings
;
for
(
in
t
i
=
0
;
i
<
D
;
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
D
;
++
i
)
{
paddings
[
i
].
first
=
offsets
[
i
];
paddings
[
i
].
first
=
offsets
[
i
];
paddings
[
i
].
second
=
d_x
->
dims
()[
i
]
-
d_out
->
dims
()[
i
]
-
offsets
[
i
];
paddings
[
i
].
second
=
d_x
->
dims
()[
i
]
-
d_out
->
dims
()[
i
]
-
offsets
[
i
];
}
}
...
...
paddle/operators/math/math_function.cc
浏览文件 @
236af566
...
@@ -48,6 +48,32 @@ void gemm<platform::CPUPlace, double>(const platform::DeviceContext& context,
...
@@ -48,6 +48,32 @@ void gemm<platform::CPUPlace, double>(const platform::DeviceContext& context,
beta
,
C
,
ldc
);
beta
,
C
,
ldc
);
}
}
template
<
>
void
gemm
<
platform
::
CPUPlace
,
float
>
(
const
platform
::
DeviceContext
&
context
,
const
bool
transA
,
const
bool
transB
,
const
int
M
,
const
int
N
,
const
int
K
,
const
float
alpha
,
const
float
*
A
,
const
int
lda
,
const
float
*
B
,
const
int
ldb
,
const
float
beta
,
float
*
C
,
const
int
ldc
)
{
cblas_sgemm
(
CblasRowMajor
,
transA
==
false
?
CblasNoTrans
:
CblasTrans
,
transB
==
false
?
CblasNoTrans
:
CblasTrans
,
M
,
N
,
K
,
alpha
,
A
,
lda
,
B
,
ldb
,
beta
,
C
,
ldc
);
}
template
<
>
void
gemm
<
platform
::
CPUPlace
,
double
>
(
const
platform
::
DeviceContext
&
context
,
const
bool
transA
,
const
bool
transB
,
const
int
M
,
const
int
N
,
const
int
K
,
const
double
alpha
,
const
double
*
A
,
const
int
lda
,
const
double
*
B
,
const
int
ldb
,
const
double
beta
,
double
*
C
,
const
int
ldc
)
{
cblas_dgemm
(
CblasRowMajor
,
transA
==
false
?
CblasNoTrans
:
CblasTrans
,
transB
==
false
?
CblasNoTrans
:
CblasTrans
,
M
,
N
,
K
,
alpha
,
A
,
lda
,
B
,
ldb
,
beta
,
C
,
ldc
);
}
template
<
>
template
<
>
void
matmul
<
platform
::
CPUPlace
,
float
>
(
void
matmul
<
platform
::
CPUPlace
,
float
>
(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
matrix_a
,
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
matrix_a
,
...
...
paddle/operators/math/math_function.cu
浏览文件 @
236af566
...
@@ -63,6 +63,42 @@ void gemm<platform::GPUPlace, double>(const platform::DeviceContext& context,
...
@@ -63,6 +63,42 @@ void gemm<platform::GPUPlace, double>(const platform::DeviceContext& context,
cuTransB
,
cuTransA
,
N
,
M
,
K
,
&
alpha
,
B
,
ldb
,
A
,
lda
,
&
beta
,
C
,
N
));
cuTransB
,
cuTransA
,
N
,
M
,
K
,
&
alpha
,
B
,
ldb
,
A
,
lda
,
&
beta
,
C
,
N
));
}
}
template
<
>
void
gemm
<
platform
::
GPUPlace
,
float
>
(
const
platform
::
DeviceContext
&
context
,
const
bool
transA
,
const
bool
transB
,
const
int
M
,
const
int
N
,
const
int
K
,
const
float
alpha
,
const
float
*
A
,
const
int
lda
,
const
float
*
B
,
const
int
ldb
,
const
float
beta
,
float
*
C
,
const
int
ldc
)
{
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
cublasOperation_t
cuTransA
=
transA
==
false
?
CUBLAS_OP_N
:
CUBLAS_OP_T
;
cublasOperation_t
cuTransB
=
transB
==
false
?
CUBLAS_OP_N
:
CUBLAS_OP_T
;
PADDLE_ENFORCE
(
platform
::
dynload
::
cublasSgemm
(
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
cublas_handle
(),
cuTransB
,
cuTransA
,
N
,
M
,
K
,
&
alpha
,
B
,
ldb
,
A
,
lda
,
&
beta
,
C
,
ldc
));
}
template
<
>
void
gemm
<
platform
::
GPUPlace
,
double
>
(
const
platform
::
DeviceContext
&
context
,
const
bool
transA
,
const
bool
transB
,
const
int
M
,
const
int
N
,
const
int
K
,
const
double
alpha
,
const
double
*
A
,
const
int
lda
,
const
double
*
B
,
const
int
ldb
,
const
double
beta
,
double
*
C
,
const
int
ldc
)
{
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
cublasOperation_t
cuTransA
=
transA
==
false
?
CUBLAS_OP_N
:
CUBLAS_OP_T
;
cublasOperation_t
cuTransB
=
transB
==
false
?
CUBLAS_OP_N
:
CUBLAS_OP_T
;
PADDLE_ENFORCE
(
platform
::
dynload
::
cublasDgemm
(
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
cublas_handle
(),
cuTransB
,
cuTransA
,
N
,
M
,
K
,
&
alpha
,
B
,
ldb
,
A
,
lda
,
&
beta
,
C
,
ldc
));
}
template
<
>
template
<
>
void
matmul
<
platform
::
GPUPlace
,
float
>
(
void
matmul
<
platform
::
GPUPlace
,
float
>
(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
matrix_a
,
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
matrix_a
,
...
...
paddle/operators/math/math_function.h
浏览文件 @
236af566
...
@@ -70,6 +70,13 @@ void gemm(const platform::DeviceContext& context, const CBLAS_TRANSPOSE transA,
...
@@ -70,6 +70,13 @@ void gemm(const platform::DeviceContext& context, const CBLAS_TRANSPOSE transA,
const
CBLAS_TRANSPOSE
transB
,
const
int
M
,
const
int
N
,
const
int
K
,
const
CBLAS_TRANSPOSE
transB
,
const
int
M
,
const
int
N
,
const
int
K
,
const
T
alpha
,
const
T
*
A
,
const
T
*
B
,
const
T
beta
,
T
*
C
);
const
T
alpha
,
const
T
*
A
,
const
T
*
B
,
const
T
beta
,
T
*
C
);
// gemm wrapper with stride args for matrix uncontinuous in memory
template
<
typename
Place
,
typename
T
>
void
gemm
(
const
platform
::
DeviceContext
&
context
,
const
bool
transA
,
const
bool
transB
,
const
int
M
,
const
int
N
,
const
int
K
,
const
T
alpha
,
const
T
*
A
,
const
int
lda
,
const
T
*
B
,
const
int
ldb
,
const
T
beta
,
T
*
C
,
const
int
ldc
);
// matrix multiply with continuous memory
// matrix multiply with continuous memory
template
<
typename
Place
,
typename
T
>
template
<
typename
Place
,
typename
T
>
void
matmul
(
const
platform
::
DeviceContext
&
context
,
void
matmul
(
const
platform
::
DeviceContext
&
context
,
...
...
paddle/operators/math/math_function_test.cc
浏览文件 @
236af566
...
@@ -72,4 +72,174 @@ TEST(math_function, trans_mul_notrans) {
...
@@ -72,4 +72,174 @@ TEST(math_function, trans_mul_notrans) {
EXPECT_EQ
(
out_ptr
[
8
],
29
);
EXPECT_EQ
(
out_ptr
[
8
],
29
);
delete
gpu_place
;
delete
gpu_place
;
}
}
TEST
(
math_function
,
gemm_notrans_cublas
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input2
;
paddle
::
framework
::
Tensor
input3
;
paddle
::
framework
::
Tensor
input1_gpu
;
paddle
::
framework
::
Tensor
input2_gpu
;
paddle
::
framework
::
Tensor
input3_gpu
;
int
m
=
2
;
int
n
=
3
;
int
k
=
3
;
auto
*
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
float
*
input1_ptr
=
input1
.
mutable_data
<
float
>
({
2
,
3
},
*
cpu_place
);
float
arr1
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
memcpy
(
input1_ptr
,
arr1
,
6
*
sizeof
(
float
));
float
*
input2_ptr
=
input2
.
mutable_data
<
float
>
({
3
,
4
},
*
cpu_place
);
float
arr2
[
12
]
=
{
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
,
8
,
9
,
10
,
11
};
memcpy
(
input2_ptr
,
arr2
,
12
*
sizeof
(
float
));
float
*
input3_ptr
=
input3
.
mutable_data
<
float
>
({
2
,
4
},
*
cpu_place
);
float
arr3
[
8
]
=
{
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
};
memcpy
(
input3_ptr
,
arr3
,
8
*
sizeof
(
float
));
auto
*
gpu_place
=
new
paddle
::
platform
::
GPUPlace
(
0
);
paddle
::
platform
::
CUDADeviceContext
context
(
*
gpu_place
);
input1_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
);
input2_gpu
.
CopyFrom
<
float
>
(
input2
,
*
gpu_place
);
input3_gpu
.
CopyFrom
<
float
>
(
input3
,
*
gpu_place
);
float
*
a
=
input1_gpu
.
data
<
float
>
();
float
*
b
=
input2_gpu
.
data
<
float
>
();
float
*
c
=
input3_gpu
.
mutable_data
<
float
>
(
*
gpu_place
);
paddle
::
operators
::
math
::
gemm
<
paddle
::
platform
::
GPUPlace
,
float
>
(
context
,
false
,
false
,
m
,
n
,
k
,
1
,
a
,
3
,
b
+
1
,
4
,
1
,
c
+
1
,
4
);
input3
.
CopyFrom
<
float
>
(
input3_gpu
,
*
cpu_place
);
// numpy code:
// a = np.arange(6).reshape(2, 3)
// b = np.arange(12).reshape(3, 4)[:, 1:]
// c = np.arange(8).reshape(2, 4)[:, 1:]
// out = np.arange(8).reshape(2, 4)
// out[:, 1:] = np.dot(a, b) + c
EXPECT_EQ
(
input3_ptr
[
0
],
0
);
EXPECT_EQ
(
input3_ptr
[
1
],
24
);
EXPECT_EQ
(
input3_ptr
[
2
],
28
);
EXPECT_EQ
(
input3_ptr
[
3
],
32
);
EXPECT_EQ
(
input3_ptr
[
4
],
4
);
EXPECT_EQ
(
input3_ptr
[
5
],
73
);
EXPECT_EQ
(
input3_ptr
[
6
],
86
);
EXPECT_EQ
(
input3_ptr
[
7
],
99
);
delete
gpu_place
;
}
TEST
(
math_function
,
gemm_trans_cublas
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input2
;
paddle
::
framework
::
Tensor
input3
;
paddle
::
framework
::
Tensor
input1_gpu
;
paddle
::
framework
::
Tensor
input2_gpu
;
paddle
::
framework
::
Tensor
input3_gpu
;
int
m
=
2
;
int
n
=
3
;
int
k
=
3
;
auto
*
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
float
*
input1_ptr
=
input1
.
mutable_data
<
float
>
({
2
,
3
},
*
cpu_place
);
float
arr1
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
memcpy
(
input1_ptr
,
arr1
,
6
*
sizeof
(
float
));
float
*
input2_ptr
=
input2
.
mutable_data
<
float
>
({
4
,
3
},
*
cpu_place
);
float
arr2
[
12
]
=
{
0
,
4
,
8
,
1
,
5
,
9
,
2
,
6
,
10
,
3
,
7
,
11
};
memcpy
(
input2_ptr
,
arr2
,
12
*
sizeof
(
float
));
float
*
input3_ptr
=
input3
.
mutable_data
<
float
>
({
2
,
4
},
*
cpu_place
);
float
arr3
[
8
]
=
{
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
};
memcpy
(
input3_ptr
,
arr3
,
8
*
sizeof
(
float
));
auto
*
gpu_place
=
new
paddle
::
platform
::
GPUPlace
(
0
);
paddle
::
platform
::
CUDADeviceContext
context
(
*
gpu_place
);
input1_gpu
.
CopyFrom
<
float
>
(
input1
,
*
gpu_place
);
input2_gpu
.
CopyFrom
<
float
>
(
input2
,
*
gpu_place
);
input3_gpu
.
CopyFrom
<
float
>
(
input3
,
*
gpu_place
);
float
*
a
=
input1_gpu
.
data
<
float
>
();
float
*
b
=
input2_gpu
.
data
<
float
>
();
float
*
c
=
input3_gpu
.
mutable_data
<
float
>
(
*
gpu_place
);
paddle
::
operators
::
math
::
gemm
<
paddle
::
platform
::
GPUPlace
,
float
>
(
context
,
false
,
true
,
m
,
n
,
k
,
1
,
a
,
3
,
b
+
3
,
3
,
1
,
c
+
1
,
4
);
input3
.
CopyFrom
<
float
>
(
input3_gpu
,
*
cpu_place
);
EXPECT_EQ
(
input3_ptr
[
0
],
0
);
EXPECT_EQ
(
input3_ptr
[
1
],
24
);
EXPECT_EQ
(
input3_ptr
[
2
],
28
);
EXPECT_EQ
(
input3_ptr
[
3
],
32
);
EXPECT_EQ
(
input3_ptr
[
4
],
4
);
EXPECT_EQ
(
input3_ptr
[
5
],
73
);
EXPECT_EQ
(
input3_ptr
[
6
],
86
);
EXPECT_EQ
(
input3_ptr
[
7
],
99
);
delete
gpu_place
;
}
#endif
#endif
TEST
(
math_function
,
gemm_notrans_cblas
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input2
;
paddle
::
framework
::
Tensor
input3
;
int
m
=
2
;
int
n
=
3
;
int
k
=
3
;
auto
*
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
float
*
input1_ptr
=
input1
.
mutable_data
<
float
>
({
2
,
3
},
*
cpu_place
);
float
arr1
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
memcpy
(
input1_ptr
,
arr1
,
6
*
sizeof
(
float
));
float
*
input2_ptr
=
input2
.
mutable_data
<
float
>
({
3
,
4
},
*
cpu_place
);
float
arr2
[
12
]
=
{
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
,
8
,
9
,
10
,
11
};
memcpy
(
input2_ptr
,
arr2
,
12
*
sizeof
(
float
));
float
*
input3_ptr
=
input3
.
mutable_data
<
float
>
({
2
,
4
},
*
cpu_place
);
float
arr3
[
8
]
=
{
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
};
memcpy
(
input3_ptr
,
arr3
,
8
*
sizeof
(
float
));
paddle
::
platform
::
CPUDeviceContext
context
(
*
cpu_place
);
paddle
::
operators
::
math
::
gemm
<
paddle
::
platform
::
CPUPlace
,
float
>
(
context
,
false
,
false
,
m
,
n
,
k
,
1
,
input1_ptr
,
3
,
input2_ptr
+
1
,
4
,
1
,
input3_ptr
+
1
,
4
);
EXPECT_EQ
(
input3_ptr
[
0
],
0
);
EXPECT_EQ
(
input3_ptr
[
1
],
24
);
EXPECT_EQ
(
input3_ptr
[
2
],
28
);
EXPECT_EQ
(
input3_ptr
[
3
],
32
);
EXPECT_EQ
(
input3_ptr
[
4
],
4
);
EXPECT_EQ
(
input3_ptr
[
5
],
73
);
EXPECT_EQ
(
input3_ptr
[
6
],
86
);
EXPECT_EQ
(
input3_ptr
[
7
],
99
);
}
TEST
(
math_function
,
gemm_trans_clbas
)
{
paddle
::
framework
::
Tensor
input1
;
paddle
::
framework
::
Tensor
input2
;
paddle
::
framework
::
Tensor
input3
;
int
m
=
2
;
int
n
=
3
;
int
k
=
3
;
auto
*
cpu_place
=
new
paddle
::
platform
::
CPUPlace
();
float
*
input1_ptr
=
input1
.
mutable_data
<
float
>
({
2
,
3
},
*
cpu_place
);
float
arr1
[
6
]
=
{
0
,
1
,
2
,
3
,
4
,
5
};
memcpy
(
input1_ptr
,
arr1
,
6
*
sizeof
(
float
));
float
*
input2_ptr
=
input2
.
mutable_data
<
float
>
({
4
,
3
},
*
cpu_place
);
float
arr2
[
12
]
=
{
0
,
4
,
8
,
1
,
5
,
9
,
2
,
6
,
10
,
3
,
7
,
11
};
memcpy
(
input2_ptr
,
arr2
,
12
*
sizeof
(
float
));
float
*
input3_ptr
=
input3
.
mutable_data
<
float
>
({
2
,
4
},
*
cpu_place
);
float
arr3
[
8
]
=
{
0
,
1
,
2
,
3
,
4
,
5
,
6
,
7
};
memcpy
(
input3_ptr
,
arr3
,
8
*
sizeof
(
float
));
paddle
::
platform
::
CPUDeviceContext
context
(
*
cpu_place
);
paddle
::
operators
::
math
::
gemm
<
paddle
::
platform
::
CPUPlace
,
float
>
(
context
,
false
,
true
,
m
,
n
,
k
,
1
,
input1_ptr
,
3
,
input2_ptr
+
3
,
3
,
1
,
input3_ptr
+
1
,
4
);
EXPECT_EQ
(
input3_ptr
[
0
],
0
);
EXPECT_EQ
(
input3_ptr
[
1
],
24
);
EXPECT_EQ
(
input3_ptr
[
2
],
28
);
EXPECT_EQ
(
input3_ptr
[
3
],
32
);
EXPECT_EQ
(
input3_ptr
[
4
],
4
);
EXPECT_EQ
(
input3_ptr
[
5
],
73
);
EXPECT_EQ
(
input3_ptr
[
6
],
86
);
EXPECT_EQ
(
input3_ptr
[
7
],
99
);
}
paddle/operators/multiplex_op.cc
浏览文件 @
236af566
...
@@ -25,24 +25,30 @@ class MultiplexOp : public framework::OperatorWithKernel {
...
@@ -25,24 +25,30 @@ class MultiplexOp : public framework::OperatorWithKernel {
protected:
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Ids"
),
"Input(Ids) shouldn't be null."
);
PADDLE_ENFORCE
(
!
ctx
.
MultiInputVar
(
"X"
).
empty
(),
PADDLE_ENFORCE
(
!
ctx
.
MultiInputVar
(
"X"
).
empty
(),
"
Input(X) should not be null
."
);
"
MultiInput(X) shouldn't be empty
."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Out"
),
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Out"
),
"Output(Out) shouldn't be null."
);
"Output(Out) shouldn't be null."
);
auto
ids_dim
=
ctx
.
Input
<
Tensor
>
(
"Ids"
)
->
dims
();
PADDLE_ENFORCE
(
ids_dim
.
size
()
==
2
&&
ids_dim
[
1
]
==
1
,
"The index tensor must be a vector with size batchSize x 1."
);
auto
ins
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
auto
ins
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
auto
num_ins
=
ins
.
size
();
auto
num_ins
=
ins
.
size
();
PADDLE_ENFORCE
(
num_ins
>
2
,
PADDLE_ENFORCE
(
num_ins
>
1
,
"multiplex operator should have more than 2 inputs."
);
"multiplex operator should have more than "
PADDLE_ENFORCE_EQ
(
ins
[
0
]
->
dims
().
size
(),
1
,
"one candidate input tensors."
);
"The first input must be a index vector."
);
auto
in_dim
=
ins
[
1
]
->
dims
();
for
(
size_t
i
=
2
;
i
<
num_ins
;
i
++
)
{
auto
in_dim
=
ins
[
0
]
->
dims
();
PADDLE_ENFORCE
(
in_dim
.
size
()
==
2
,
"Candidate tensors must be matrix."
);
for
(
size_t
i
=
1
;
i
<
num_ins
;
i
++
)
{
auto
dim
=
ins
[
i
]
->
dims
();
auto
dim
=
ins
[
i
]
->
dims
();
PADDLE_ENFORCE
(
in_dim
==
dim
,
PADDLE_ENFORCE
(
in_dim
==
dim
,
"All the input tensors except the first one must have the "
"All the candidate tensors must have the same size."
);
"same size."
);
}
}
out
->
Resize
(
in_dim
);
out
->
Resize
(
in_dim
);
}
}
...
@@ -53,25 +59,26 @@ class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -53,25 +59,26 @@ class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
MultiplexOpMaker
(
framework
::
OpProto
*
proto
,
MultiplexOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The input tensors of multiplex operator."
).
AsDuplicable
();
AddInput
(
"Ids"
,
"The index tensor of multiplex operator."
);
AddInput
(
"X"
,
"The candidate tensors of multiplex operator."
)
.
AsDuplicable
();
AddOutput
(
"Out"
,
"The output tensor of multiplex operator."
);
AddOutput
(
"Out"
,
"The output tensor of multiplex operator."
);
AddComment
(
R"DOC(Multiplex operator
AddComment
(
R"DOC(Multiplex operator
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 tensor.
Ids
: the index tensor.
ins[1:N]: the candidate output tensors
.
X[0 : N - 1]: the candidate tensors for output (N >= 2)
.
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 (
Ids[i]
)-th tensor.
For i-th row of the output tensor:
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_{
0
}.width - 1)
where y is the output tensor. `x_{k}` is the k-th input tensor
where y is the output tensor. `x_{k}` is the k-th input tensor
and `k = x{0}[i] + 1`.
and `k = Ids[i]`.
)DOC"
);
)DOC"
);
}
}
};
};
...
@@ -90,8 +97,8 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
...
@@ -90,8 +97,8 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
"Input(Out@GRAD) shouldn't be null."
);
"Input(Out@GRAD) shouldn't be null."
);
auto
d_ins
=
ctx
.
MultiOutput
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
d_ins
=
ctx
.
MultiOutput
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
ins
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
auto
ins
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
//
don't compute gradient for index (ins[0]
)
//
No need to compute gradient for Input(Ids
)
for
(
size_t
i
=
1
;
i
<
ins
.
size
();
i
++
)
{
for
(
size_t
i
=
0
;
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
());
}
}
...
...
paddle/operators/multiplex_op.cu
浏览文件 @
236af566
...
@@ -25,21 +25,23 @@ class MultiplexGPUKernel : public framework::OpKernel {
...
@@ -25,21 +25,23 @@ class MultiplexGPUKernel : public framework::OpKernel {
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
ins
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
auto
ins
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
auto
*
ids
=
ctx
.
Input
<
Tensor
>
(
"Ids"
);
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
rows
=
ins
[
1
]
->
dims
()[
0
];
auto
rows
=
ins
[
0
]
->
dims
()[
0
];
auto
cols
=
ins
[
1
]
->
dims
()[
1
];
auto
cols
=
ins
[
0
]
->
dims
()[
1
];
// copy index to cpu
// copy index to cpu
Tensor
index_t_cpu
;
Tensor
index_t_cpu
;
index_t_cpu
.
CopyFrom
<
T
>
(
*
(
ins
[
0
])
,
platform
::
CPUPlace
());
index_t_cpu
.
CopyFrom
<
int32_t
>
(
*
ids
,
platform
::
CPUPlace
());
auto
*
index
=
index_t_cpu
.
data
<
T
>
();
auto
*
index
=
index_t_cpu
.
data
<
int32_t
>
();
auto
stream
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
auto
stream
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
ctx
.
device_context
())
.
stream
();
.
stream
();
Place
place
=
boost
::
get
<
Place
>
(
ctx
.
GetPlace
());
Place
place
=
boost
::
get
<
Place
>
(
ctx
.
GetPlace
());
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
size_t
k
=
(
size_t
)
index
[
i
]
+
1
;
int32_t
k
=
index
[
i
];
PADDLE_ENFORCE_GE
(
k
,
0
,
"index must be nonnegative."
);
PADDLE_ENFORCE_LT
(
k
,
ins
.
size
(),
PADDLE_ENFORCE_LT
(
k
,
ins
.
size
(),
"index exceeds the number of candidate tensors."
);
"index exceeds the number of candidate tensors."
);
memory
::
Copy
(
place
,
out
->
data
<
T
>
()
+
i
*
cols
,
place
,
memory
::
Copy
(
place
,
out
->
data
<
T
>
()
+
i
*
cols
,
place
,
...
@@ -54,8 +56,9 @@ class MultiplexGradGPUKernel : public framework::OpKernel {
...
@@ -54,8 +56,9 @@ class MultiplexGradGPUKernel : public framework::OpKernel {
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
*
d_out
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
d_out
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
ins
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
auto
ins
=
ctx
.
MultiInput
<
Tensor
>
(
"X"
);
auto
*
ids
=
ctx
.
Input
<
Tensor
>
(
"Ids"
);
auto
d_ins
=
ctx
.
MultiOutput
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
d_ins
=
ctx
.
MultiOutput
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
for
(
size_t
i
=
1
;
i
<
d_ins
.
size
();
i
++
)
{
for
(
size_t
i
=
0
;
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
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
d_ins
[
i
]);
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
d_ins
[
i
]);
...
@@ -63,19 +66,19 @@ class MultiplexGradGPUKernel : public framework::OpKernel {
...
@@ -63,19 +66,19 @@ class MultiplexGradGPUKernel : public framework::OpKernel {
}
}
}
}
auto
rows
=
ins
[
1
]
->
dims
()[
0
];
auto
rows
=
ins
[
0
]
->
dims
()[
0
];
auto
cols
=
ins
[
1
]
->
dims
()[
1
];
auto
cols
=
ins
[
0
]
->
dims
()[
1
];
// copy index to cpu
// copy index to cpu
Tensor
index_t_cpu
;
Tensor
index_t_cpu
;
index_t_cpu
.
CopyFrom
<
T
>
(
*
(
ins
[
0
])
,
platform
::
CPUPlace
());
index_t_cpu
.
CopyFrom
<
int32_t
>
(
*
ids
,
platform
::
CPUPlace
());
auto
*
index
=
index_t_cpu
.
data
<
T
>
();
auto
*
index
=
index_t_cpu
.
data
<
int32_t
>
();
auto
stream
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
auto
stream
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
ctx
.
device_context
())
.
stream
();
.
stream
();
Place
place
=
boost
::
get
<
Place
>
(
ctx
.
GetPlace
());
Place
place
=
boost
::
get
<
Place
>
(
ctx
.
GetPlace
());
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
size_t
k
=
(
size_t
)
index
[
i
]
+
1
;
size_t
k
=
static_cast
<
size_t
>
(
index
[
i
])
;
if
(
d_ins
[
k
])
{
if
(
d_ins
[
k
])
{
memory
::
Copy
(
place
,
d_ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
place
,
memory
::
Copy
(
place
,
d_ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
place
,
d_out
->
data
<
T
>
()
+
i
*
cols
,
cols
*
sizeof
(
T
),
stream
);
d_out
->
data
<
T
>
()
+
i
*
cols
,
cols
*
sizeof
(
T
),
stream
);
...
...
paddle/operators/multiplex_op.h
浏览文件 @
236af566
...
@@ -27,17 +27,19 @@ class MultiplexCPUKernel : public framework::OpKernel {
...
@@ -27,17 +27,19 @@ class MultiplexCPUKernel : 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
ids
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Ids"
);
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
rows
=
ins
[
1
]
->
dims
()[
0
];
auto
rows
=
ins
[
0
]
->
dims
()[
0
];
auto
cols
=
ins
[
1
]
->
dims
()[
1
];
auto
cols
=
ins
[
0
]
->
dims
()[
1
];
auto
*
index
=
ins
[
0
]
->
data
<
T
>
();
auto
index
=
ids
->
data
<
int32_t
>
();
Place
place
=
boost
::
get
<
Place
>
(
ctx
.
GetPlace
());
Place
place
=
boost
::
get
<
Place
>
(
ctx
.
GetPlace
());
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
size_t
k
=
(
size_t
)
index
[
i
]
+
1
;
int32_t
k
=
index
[
i
];
PADDLE_ENFORCE_LT
(
k
,
ins
.
size
(),
PADDLE_ENFORCE_GE
(
k
,
0
,
"index must be nonnegative."
);
PADDLE_ENFORCE_LT
(
static_cast
<
size_t
>
(
k
),
ins
.
size
(),
"index exceeds the number of candidate tensors."
);
"index exceeds the number of candidate tensors."
);
memory
::
Copy
(
place
,
out
->
data
<
T
>
()
+
i
*
cols
,
place
,
memory
::
Copy
(
place
,
out
->
data
<
T
>
()
+
i
*
cols
,
place
,
ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
cols
*
sizeof
(
T
));
ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
cols
*
sizeof
(
T
));
...
@@ -50,10 +52,11 @@ class MultiplexGradCPUKernel : public framework::OpKernel {
...
@@ -50,10 +52,11 @@ class MultiplexGradCPUKernel : 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"
));
auto
*
ids
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Ids"
);
auto
ins
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"X"
);
auto
ins
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"X"
);
auto
d_ins
=
auto
d_ins
=
ctx
.
MultiOutput
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
ctx
.
MultiOutput
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
for
(
size_t
i
=
1
;
i
<
d_ins
.
size
();
i
++
)
{
for
(
size_t
i
=
0
;
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
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
d_ins
[
i
]);
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
d_ins
[
i
]);
...
@@ -61,12 +64,12 @@ class MultiplexGradCPUKernel : public framework::OpKernel {
...
@@ -61,12 +64,12 @@ class MultiplexGradCPUKernel : public framework::OpKernel {
}
}
}
}
auto
rows
=
ins
[
1
]
->
dims
()[
0
];
auto
rows
=
ins
[
0
]
->
dims
()[
0
];
auto
cols
=
ins
[
1
]
->
dims
()[
1
];
auto
cols
=
ins
[
0
]
->
dims
()[
1
];
auto
*
index
=
i
ns
[
0
]
->
data
<
T
>
();
auto
*
index
=
i
ds
->
data
<
int32_t
>
();
Place
place
=
boost
::
get
<
Place
>
(
ctx
.
GetPlace
());
Place
place
=
boost
::
get
<
Place
>
(
ctx
.
GetPlace
());
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
for
(
auto
i
=
0
;
i
<
rows
;
i
++
)
{
size_t
k
=
(
size_t
)
index
[
i
]
+
1
;
size_t
k
=
static_cast
<
size_t
>
(
index
[
i
])
;
if
(
d_ins
[
k
])
{
if
(
d_ins
[
k
])
{
memory
::
Copy
(
place
,
d_ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
place
,
memory
::
Copy
(
place
,
d_ins
[
k
]
->
data
<
T
>
()
+
i
*
cols
,
place
,
d_out
->
data
<
T
>
()
+
i
*
cols
,
cols
*
sizeof
(
T
));
d_out
->
data
<
T
>
()
+
i
*
cols
,
cols
*
sizeof
(
T
));
...
@@ -74,5 +77,5 @@ class MultiplexGradCPUKernel : public framework::OpKernel {
...
@@ -74,5 +77,5 @@ class MultiplexGradCPUKernel : public framework::OpKernel {
}
}
}
}
};
};
}
}
// namespace operators
}
}
// namespace paddle
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
236af566
...
@@ -921,7 +921,7 @@ def data_layer(name, size, depth=None, height=None, width=None,
...
@@ -921,7 +921,7 @@ def data_layer(name, size, depth=None, height=None, width=None,
data = data_layer(name="input", size=1000)
data = data_layer(name="input", size=1000)
:param name: The name of this layer.
It is optional.
:param name: The name of this layer.
:type name: basestring
:type name: basestring
:param size: Size of this data layer.
:param size: Size of this data layer.
:type size: int
:type size: int
...
@@ -3668,6 +3668,7 @@ def gru_step_naive_layer(input,
...
@@ -3668,6 +3668,7 @@ def gru_step_naive_layer(input,
:param param_attr:
:param param_attr:
:param layer_attr:
:param layer_attr:
:return:
:return:
:rtype: LayerOutput
"""
"""
if
input
.
size
%
3
!=
0
:
if
input
.
size
%
3
!=
0
:
raise
ValueError
(
"GruStep input size must be divided by 3"
)
raise
ValueError
(
"GruStep input size must be divided by 3"
)
...
...
python/paddle/v2/framework/tests/test_multiplex_op.py
浏览文件 @
236af566
...
@@ -6,20 +6,22 @@ from op_test import OpTest
...
@@ -6,20 +6,22 @@ from op_test import OpTest
class
TestMultiplexOp
(
OpTest
):
class
TestMultiplexOp
(
OpTest
):
def
setUp
(
self
):
def
setUp
(
self
):
self
.
op_type
=
"multiplex"
self
.
op_type
=
"multiplex"
rows
=
3
rows
=
4
index
=
np
.
array
([
3
,
1
,
0
])
index
=
np
.
arange
(
0
,
rows
).
astype
(
'int32'
)
np
.
random
.
shuffle
(
index
)
index
=
np
.
reshape
(
index
,
(
rows
,
1
))
ins1
=
np
.
random
.
random
((
rows
,
10
)).
astype
(
"float32"
)
ins1
=
np
.
random
.
random
((
rows
,
10
)).
astype
(
"float32"
)
ins2
=
np
.
random
.
random
((
rows
,
10
)).
astype
(
"float32"
)
ins2
=
np
.
random
.
random
((
rows
,
10
)).
astype
(
"float32"
)
ins3
=
np
.
random
.
random
((
rows
,
10
)).
astype
(
"float32"
)
ins3
=
np
.
random
.
random
((
rows
,
10
)).
astype
(
"float32"
)
ins4
=
np
.
random
.
random
((
rows
,
10
)).
astype
(
"float32"
)
ins4
=
np
.
random
.
random
((
rows
,
10
)).
astype
(
"float32"
)
self
.
inputs
=
{
self
.
inputs
=
{
'
X'
:
[(
'index'
,
index
),
(
'x1'
,
ins1
),
(
'x2'
,
ins2
),
(
'x3'
,
ins3
)
,
'
Ids'
:
index
,
(
'x4'
,
ins4
)]
'X'
:
[(
'x1'
,
ins1
),
(
'x2'
,
ins2
),
(
'x3'
,
ins3
),
(
'x4'
,
ins4
)]
}
}
# multiplex output
# multiplex output
output
=
np
.
zeros_like
(
ins1
)
output
=
np
.
zeros_like
(
ins1
)
for
i
in
range
(
0
,
rows
):
for
i
in
range
(
0
,
rows
):
k
=
index
[
i
]
+
1
k
=
index
[
i
]
[
0
]
output
[
i
]
=
self
.
inputs
[
'X'
][
k
][
1
][
i
]
output
[
i
]
=
self
.
inputs
[
'X'
][
k
][
1
][
i
]
self
.
outputs
=
{
'Out'
:
output
}
self
.
outputs
=
{
'Out'
:
output
}
...
...
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