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体验新版 GitCode,发现更多精彩内容 >>
提交
6117af64
编写于
9月 21, 2017
作者:
Q
qingqing01
提交者:
GitHub
9月 21, 2017
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #4284 from qingqing01/lod_share
Pass LoD from input to output in the operators.
上级
fbc0db4a
bdd1404e
变更
44
隐藏空白更改
内联
并排
Showing
44 changed file
with
219 addition
and
187 deletion
+219
-187
paddle/framework/backward.cc
paddle/framework/backward.cc
+2
-3
paddle/framework/backward_test.cc
paddle/framework/backward_test.cc
+6
-6
paddle/framework/operator.cc
paddle/framework/operator.cc
+7
-8
paddle/framework/operator.h
paddle/framework/operator.h
+23
-30
paddle/operators/accuracy_op.cc
paddle/operators/accuracy_op.cc
+9
-4
paddle/operators/activation_op.cc
paddle/operators/activation_op.cc
+3
-2
paddle/operators/add_op.cc
paddle/operators/add_op.cc
+1
-1
paddle/operators/clip_op.cc
paddle/operators/clip_op.cc
+5
-6
paddle/operators/concat_op.cc
paddle/operators/concat_op.cc
+1
-1
paddle/operators/conv2d_op.cc
paddle/operators/conv2d_op.cc
+3
-4
paddle/operators/cos_sim_op.cc
paddle/operators/cos_sim_op.cc
+12
-10
paddle/operators/crop_op.cc
paddle/operators/crop_op.cc
+5
-6
paddle/operators/cross_entropy_op.cc
paddle/operators/cross_entropy_op.cc
+6
-4
paddle/operators/dropout_op.cc
paddle/operators/dropout_op.cc
+4
-4
paddle/operators/elementwise_mul_op.cc
paddle/operators/elementwise_mul_op.cc
+8
-5
paddle/operators/fc_op.cc
paddle/operators/fc_op.cc
+3
-0
paddle/operators/fill_zeros_like_op.cc
paddle/operators/fill_zeros_like_op.cc
+10
-11
paddle/operators/fill_zeros_like_op.h
paddle/operators/fill_zeros_like_op.h
+1
-1
paddle/operators/gather_op.cc
paddle/operators/gather_op.cc
+2
-2
paddle/operators/gaussian_random_op.cc
paddle/operators/gaussian_random_op.cc
+1
-1
paddle/operators/lookup_table_op.cc
paddle/operators/lookup_table_op.cc
+10
-5
paddle/operators/mean_op.cc
paddle/operators/mean_op.cc
+4
-3
paddle/operators/minus_op.cc
paddle/operators/minus_op.cc
+8
-2
paddle/operators/modified_huber_loss_op.cc
paddle/operators/modified_huber_loss_op.cc
+3
-3
paddle/operators/modified_huber_loss_op.h
paddle/operators/modified_huber_loss_op.h
+5
-7
paddle/operators/mul_op.cc
paddle/operators/mul_op.cc
+11
-8
paddle/operators/pad_op.cc
paddle/operators/pad_op.cc
+7
-2
paddle/operators/prelu_op.cc
paddle/operators/prelu_op.cc
+6
-3
paddle/operators/rank_loss_op.cc
paddle/operators/rank_loss_op.cc
+3
-3
paddle/operators/rank_loss_op.h
paddle/operators/rank_loss_op.h
+3
-3
paddle/operators/reshape_op.cc
paddle/operators/reshape_op.cc
+7
-2
paddle/operators/rowwise_add_op.cc
paddle/operators/rowwise_add_op.cc
+4
-3
paddle/operators/scale_op.cc
paddle/operators/scale_op.cc
+2
-1
paddle/operators/scatter_op.cc
paddle/operators/scatter_op.cc
+3
-4
paddle/operators/sgd_op.cc
paddle/operators/sgd_op.cc
+1
-1
paddle/operators/smooth_l1_loss_op.cc
paddle/operators/smooth_l1_loss_op.cc
+4
-6
paddle/operators/softmax_op.cc
paddle/operators/softmax_op.cc
+2
-3
paddle/operators/split_op.cc
paddle/operators/split_op.cc
+1
-1
paddle/operators/squared_l2_distance_op.cc
paddle/operators/squared_l2_distance_op.cc
+8
-6
paddle/operators/sum_op.cc
paddle/operators/sum_op.cc
+8
-4
paddle/operators/top_k_op.cc
paddle/operators/top_k_op.cc
+2
-2
paddle/operators/transpose_op.cc
paddle/operators/transpose_op.cc
+2
-3
paddle/operators/uniform_random_op.cc
paddle/operators/uniform_random_op.cc
+1
-1
python/paddle/v2/framework/tests/test_fill_zeros_like_op.py
python/paddle/v2/framework/tests/test_fill_zeros_like_op.py
+2
-2
未找到文件。
paddle/framework/backward.cc
浏览文件 @
6117af64
...
...
@@ -166,9 +166,8 @@ static std::unique_ptr<OperatorBase> BackwardRecursive(
// If part of input gradient of that operator is not calculated, fill
// zero variables to that input gradient.
net
->
AppendOp
(
OpRegistry
::
CreateOp
(
"fill_zeros_like"
,
{{
"Src"
,
{
prefix
}}},
{{
"Dst"
,
{
grad_input
}}},
{}));
net
->
AppendOp
(
OpRegistry
::
CreateOp
(
"fill_zeros_like"
,
{{
"X"
,
{
prefix
}}},
{{
"Y"
,
{
grad_input
}}},
{}));
}
return
false
;
});
...
...
paddle/framework/backward_test.cc
浏览文件 @
6117af64
...
...
@@ -127,8 +127,8 @@ class FillZeroOpMaker : public OpProtoAndCheckerMaker {
public:
FillZeroOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"
Src
"
,
"x"
);
AddOutput
(
"
Dst
"
,
"out"
);
AddInput
(
"
X
"
,
"x"
);
AddOutput
(
"
Y
"
,
"out"
);
AddComment
(
""
);
}
};
...
...
@@ -325,10 +325,10 @@ TEST(Backward, op_part_of_output_are_not_need) {
auto
&
fill_zero
=
*
net
->
ops_
[
0
];
ASSERT_EQ
(
"fill_zeros_like"
,
fill_zero
.
Type
());
ASSERT_EQ
(
1UL
,
fill_zero
.
Inputs
(
"
Src
"
).
size
());
ASSERT_EQ
(
"Z"
,
fill_zero
.
Input
(
"
Src
"
));
ASSERT_EQ
(
1UL
,
fill_zero
.
Outputs
(
"
Dst
"
).
size
());
ASSERT_EQ
(
std
::
string
(
"Z"
)
+
f
::
kZeroVarSuffix
,
fill_zero
.
Output
(
"
Dst
"
));
ASSERT_EQ
(
1UL
,
fill_zero
.
Inputs
(
"
X
"
).
size
());
ASSERT_EQ
(
"Z"
,
fill_zero
.
Input
(
"
X
"
));
ASSERT_EQ
(
1UL
,
fill_zero
.
Outputs
(
"
Y
"
).
size
());
ASSERT_EQ
(
std
::
string
(
"Z"
)
+
f
::
kZeroVarSuffix
,
fill_zero
.
Output
(
"
Y
"
));
auto
&
d_many_out
=
*
net
->
ops_
[
1
];
ASSERT_EQ
(
"many_output_op_grad"
,
d_many_out
.
Type
());
...
...
paddle/framework/operator.cc
浏览文件 @
6117af64
...
...
@@ -207,23 +207,22 @@ const std::vector<const Tensor*> InferShapeContext::MultiInput<Tensor>(
}
template
<
>
Tensor
*
Execution
Context
::
Output
<
Tensor
>
(
const
std
::
string
&
name
)
const
{
auto
*
var
=
OutputVar
(
name
);
return
var
==
nullptr
?
nullptr
:
const_cast
<
Tensor
*>
(
GetTensorFromVar
(
var
)
);
Tensor
*
InferShape
Context
::
Output
<
Tensor
>
(
const
std
::
string
&
name
)
const
{
auto
var
=
OutputVar
(
name
);
return
var
==
nullptr
?
nullptr
:
var
->
GetMutable
<
LoDTensor
>
(
);
}
template
<
>
std
::
vector
<
Tensor
*>
Execution
Context
::
MultiOutput
<
Tensor
>
(
std
::
vector
<
Tensor
*>
InferShape
Context
::
MultiOutput
<
Tensor
>
(
const
std
::
string
&
name
)
const
{
auto
names
=
op
().
Outputs
(
name
);
std
::
vector
<
Tensor
*>
res
;
res
.
reserve
(
names
.
size
());
std
::
transform
(
names
.
begin
(),
names
.
end
(),
std
::
back_inserter
(
res
),
[
&
](
const
std
::
string
&
sub_name
)
{
auto
var
=
scope
().
FindVar
(
sub_name
);
return
var
==
nullptr
?
nullptr
:
const_cast
<
Tensor
*>
(
GetTensorFromVar
(
var
));
auto
var
=
scope_
.
FindVar
(
sub_name
);
return
var
==
nullptr
?
nullptr
:
var
->
GetMutable
<
LoDTensor
>
();
});
return
res
;
}
...
...
paddle/framework/operator.h
浏览文件 @
6117af64
...
...
@@ -212,9 +212,9 @@ class InferShapeContext {
return
res
;
}
std
::
vector
<
const
Variable
*>
MultiOutputVar
(
const
std
::
string
&
name
)
const
{
std
::
vector
<
Variable
*>
MultiOutputVar
(
const
std
::
string
&
name
)
const
{
auto
names
=
op_
.
Outputs
(
name
);
std
::
vector
<
const
Variable
*>
res
;
std
::
vector
<
Variable
*>
res
;
res
.
reserve
(
names
.
size
());
std
::
transform
(
names
.
begin
(),
names
.
end
(),
std
::
back_inserter
(
res
),
[
this
](
const
std
::
string
&
name
)
{
...
...
@@ -271,6 +271,20 @@ class InferShapeContext {
return
&
var
->
Get
<
Tensor
>
();
}
void
ShareLoD
(
const
std
::
string
&
in
,
const
std
::
string
&
out
,
size_t
i
=
0
,
size_t
j
=
0
)
const
{
PADDLE_ENFORCE_LT
(
i
,
InputSize
(
in
));
PADDLE_ENFORCE_LT
(
j
,
OutputSize
(
out
));
auto
*
in_var
=
MultiInputVar
(
in
)[
i
];
auto
*
out_var
=
MultiOutputVar
(
out
)[
j
];
if
(
!
in_var
->
IsType
<
LoDTensor
>
())
return
;
PADDLE_ENFORCE
(
out_var
->
IsType
<
LoDTensor
>
(),
"The %d-th output of Output(%s) must be LoDTensor."
,
j
,
out
);
auto
in_tensor
=
in_var
->
Get
<
LoDTensor
>
();
auto
*
out_tensor
=
out_var
->
GetMutable
<
LoDTensor
>
();
out_tensor
->
set_lod
(
in_tensor
.
lod
());
}
private:
const
OperatorBase
&
op_
;
const
Scope
&
scope_
;
...
...
@@ -283,6 +297,13 @@ template <>
const
std
::
vector
<
const
Tensor
*>
InferShapeContext
::
MultiInput
<
Tensor
>
(
const
std
::
string
&
name
)
const
;
template
<>
Tensor
*
InferShapeContext
::
Output
<
Tensor
>
(
const
std
::
string
&
name
)
const
;
template
<>
std
::
vector
<
Tensor
*>
InferShapeContext
::
MultiOutput
<
Tensor
>
(
const
std
::
string
&
name
)
const
;
template
<
typename
T
>
struct
EigenDeviceConverter
;
...
...
@@ -315,38 +336,10 @@ class ExecutionContext : public InferShapeContext {
return
device_context_
;
}
// redefine Output function,
// use Variable::Get instead of Variable::GetMutable
template
<
typename
T
>
T
*
Output
(
const
std
::
string
&
name
)
const
{
auto
var
=
OutputVar
(
name
);
return
var
==
nullptr
?
nullptr
:
const_cast
<
T
*>
(
&
var
->
Get
<
T
>
());
}
// redefine MultiOutput function.
// use Variable::Get instead of Variable::GetMutable
template
<
typename
T
>
std
::
vector
<
T
*>
MultiOutput
(
const
std
::
string
&
name
)
const
{
auto
names
=
op
().
Outputs
(
name
);
std
::
vector
<
T
*>
res
;
res
.
reserve
(
names
.
size
());
std
::
transform
(
names
.
begin
(),
names
.
end
(),
std
::
back_inserter
(
res
),
[
&
](
const
std
::
string
&
sub_name
)
{
return
Output
<
T
>
(
sub_name
);
});
return
res
;
}
private:
const
platform
::
DeviceContext
&
device_context_
;
};
template
<>
Tensor
*
ExecutionContext
::
Output
<
Tensor
>
(
const
std
::
string
&
name
)
const
;
template
<>
std
::
vector
<
Tensor
*>
ExecutionContext
::
MultiOutput
<
Tensor
>
(
const
std
::
string
&
name
)
const
;
class
OpKernel
{
public:
/**
...
...
paddle/operators/accuracy_op.cc
浏览文件 @
6117af64
...
...
@@ -39,7 +39,8 @@ class AccuracyOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
inference
->
dims
()[
0
],
label
->
dims
()[
0
],
"inference size must be the same as label size"
);
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Accuracy"
)
->
Resize
({
1
});
ctx
.
Output
<
framework
::
Tensor
>
(
"Accuracy"
)
->
Resize
({
1
});
ctx
.
ShareLoD
(
"Inference"
,
/*->*/
"Accuracy"
);
}
};
...
...
@@ -54,11 +55,15 @@ class AccuracyOpMaker : public framework::OpProtoAndCheckerMaker {
// TODO(typhoonzero): AddInput("Weight", ...
AddOutput
(
"Accuracy"
,
"The accuracy of current batch"
);
AddComment
(
R"DOC(
Accuracy. It will print accuracy rate for classification.
AddComment
(
R"DOC(
Accuracy. It will print accuracy rate for classification.
The accuracy is:
.. math::
accuracy = \\frac{NumOfCorrectPredicts}{NumOfAllSamples})DOC"
);
accuracy = \\frac{NumOfCorrectPredicts}{NumOfAllSamples})
Both the input `Inference` and `Label` can carry the LoD (Level of Details)
information, or not. But the output only shares the LoD with input `Inference`.
)DOC"
);
}
};
...
...
paddle/operators/activation_op.cc
浏览文件 @
6117af64
...
...
@@ -23,8 +23,9 @@ class ActivationOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Y"
)
->
Resize
(
ctx
.
Output
<
framework
::
Tensor
>
(
"Y"
)
->
Resize
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
dims
());
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Y"
);
}
};
...
...
@@ -34,7 +35,7 @@ class ActivationOpGrad : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
))
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
))
->
Resize
(
ctx
.
Input
<
framework
::
Tensor
>
(
"Y"
)
->
dims
());
}
};
...
...
paddle/operators/add_op.cc
浏览文件 @
6117af64
...
...
@@ -33,7 +33,7 @@ class AddOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
(),
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
(),
"Two input of Add Op's dimension must be same."
);
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
)
->
Resize
(
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
());
}
};
...
...
paddle/operators/clip_op.cc
浏览文件 @
6117af64
...
...
@@ -17,8 +17,6 @@
namespace
paddle
{
namespace
operators
{
using
framework
::
LoDTensor
;
class
ClipOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
@@ -29,11 +27,12 @@ class ClipOp : public framework::OperatorWithKernel {
"Input(X) of ClipOp should not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Out"
),
"Output(Out) of ClipOp should not be null."
);
auto
x_dims
=
ctx
.
Input
<
LoD
Tensor
>
(
"X"
)
->
dims
();
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
max
=
Attr
<
float
>
(
"max"
);
auto
min
=
Attr
<
float
>
(
"min"
);
PADDLE_ENFORCE_LT
(
min
,
max
,
"max should be greater than min."
);
ctx
.
Output
<
LoDTensor
>
(
"Out"
)
->
Resize
(
x_dims
);
ctx
.
Output
<
Tensor
>
(
"Out"
)
->
Resize
(
x_dims
);
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
...
...
@@ -66,8 +65,8 @@ class ClipOpGrad : public framework::OperatorWithKernel {
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) should not be null"
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
auto
x_dims
=
ctx
.
Input
<
LoD
Tensor
>
(
"X"
)
->
dims
();
auto
*
x_grad
=
ctx
.
Output
<
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
*
x_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
if
(
x_grad
!=
nullptr
)
{
x_grad
->
Resize
(
x_dims
);
}
...
...
paddle/operators/concat_op.cc
浏览文件 @
6117af64
...
...
@@ -29,7 +29,7 @@ class ConcatOp : public framework::OperatorWithKernel {
"Output(Out) of ConcatOp should not be null."
);
auto
ins
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
);
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
size_t
axis
=
static_cast
<
size_t
>
(
ctx
.
Attr
<
int
>
(
"axis"
));
size_t
n
=
ins
.
size
();
...
...
paddle/operators/conv2d_op.cc
浏览文件 @
6117af64
...
...
@@ -37,7 +37,7 @@ class Conv2DOp : public framework::OperatorWithKernel {
auto
in
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
auto
filter
=
ctx
.
Input
<
Tensor
>
(
"Filter"
);
auto
out
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Output"
);
auto
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Output"
);
std
::
vector
<
int
>
strides
=
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
int
groups
=
Attr
<
int
>
(
"groups"
);
...
...
@@ -111,10 +111,9 @@ class Conv2DOpGrad : public framework::OperatorWithKernel {
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
auto
in
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
auto
filter
=
ctx
.
Input
<
Tensor
>
(
"Filter"
);
auto
d_in
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Input"
));
auto
d_in
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
auto
d_filter
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
if
(
d_in
)
d_in
->
Resize
(
in
->
dims
());
if
(
d_filter
)
d_filter
->
Resize
(
filter
->
dims
());
}
...
...
paddle/operators/cos_sim_op.cc
浏览文件 @
6117af64
...
...
@@ -54,9 +54,10 @@ class CosSimOp : public framework::OperatorWithKernel {
" just 1 (which will be broadcasted to match Input(X))."
);
// resize tensor
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
)
->
Resize
({
x_dims
[
0
],
1
});
ctx
.
Output
<
framework
::
LoDTensor
>
(
"XNorm"
)
->
Resize
({
x_dims
[
0
],
1
});
ctx
.
Output
<
framework
::
LoDTensor
>
(
"YNorm"
)
->
Resize
({
y_dims
[
0
],
1
});
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
({
x_dims
[
0
],
1
});
ctx
.
Output
<
framework
::
Tensor
>
(
"XNorm"
)
->
Resize
({
x_dims
[
0
],
1
});
ctx
.
Output
<
framework
::
Tensor
>
(
"YNorm"
)
->
Resize
({
y_dims
[
0
],
1
});
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
...
...
@@ -81,10 +82,13 @@ Cosine Similarity Operator.
The equation is: Out = X^T * Y / (sqrt(X^T * X) * sqrt(Y^T * Y)).
Input(X) and Input(Y)
must have the same shape, except that the 1st dimension
of
Input(Y) could be just 1 (different from Input(X)
), which will be
broadcasted to match the shape of
Input(X)
before computing their cosine
The input `X` and `Y`
must have the same shape, except that the 1st dimension
of
input `Y` could be just 1 (different from input `X`
), which will be
broadcasted to match the shape of
input `X`
before computing their cosine
similarity.
Both the input `X` and `Y` can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD with input `X`.
)DOC"
);
}
};
...
...
@@ -139,10 +143,8 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
"Shape of Input(Out@Grad) must be [X.Dim(0), 1]."
);
// resize tensor
auto
*
x_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
x_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
if
(
x_grad
)
x_grad
->
Resize
(
x_dims
);
if
(
y_grad
)
y_grad
->
Resize
(
y_dims
);
}
...
...
paddle/operators/crop_op.cc
浏览文件 @
6117af64
...
...
@@ -19,7 +19,6 @@ namespace paddle {
namespace
operators
{
using
framework
::
Tensor
;
using
framework
::
LoDTensor
;
class
CropOp
:
public
framework
::
OperatorWithKernel
{
public:
...
...
@@ -31,9 +30,9 @@ class CropOp : public framework::OperatorWithKernel {
"Input(X) of CropOp should not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Out"
),
"Output(Out) of CropOp should not be null."
);
auto
x_dim
=
ctx
.
Input
<
LoD
Tensor
>
(
"X"
)
->
dims
();
auto
*
y
=
ctx
.
Input
<
LoD
Tensor
>
(
"Y"
);
auto
*
out
=
ctx
.
Output
<
LoD
Tensor
>
(
"Out"
);
auto
x_dim
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
*
y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
if
(
y
==
nullptr
)
{
auto
shape
=
Attr
<
std
::
vector
<
int
>>
(
"shape"
);
PADDLE_ENFORCE_EQ
(
...
...
@@ -121,8 +120,8 @@ class CropOpGrad : public framework::OperatorWithKernel {
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) should not be null"
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
auto
x_dims
=
ctx
.
Input
<
LoD
Tensor
>
(
"X"
)
->
dims
();
auto
*
x_grad
=
ctx
.
Output
<
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
*
x_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
if
(
x_grad
!=
nullptr
)
{
x_grad
->
Resize
(
x_dims
);
}
...
...
paddle/operators/cross_entropy_op.cc
浏览文件 @
6117af64
...
...
@@ -17,8 +17,6 @@ limitations under the License. */
namespace
paddle
{
namespace
operators
{
using
framework
::
LoDTensor
;
class
CrossEntropyOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
@@ -51,7 +49,8 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
"Input(Label) must be 1."
);
}
ctx
.
Output
<
LoDTensor
>
(
"Y"
)
->
Resize
({
x
->
dims
()[
0
],
1
});
ctx
.
Output
<
Tensor
>
(
"Y"
)
->
Resize
({
x
->
dims
()[
0
],
1
});
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Y"
);
}
};
...
...
@@ -95,7 +94,7 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
"Input(Label) must be 1."
);
}
auto
dx
=
ctx
.
Output
<
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
dx
->
Resize
(
x
->
dims
());
}
};
...
...
@@ -133,6 +132,9 @@ computation.
As a special case of 2), when each row of Input(Label) has only one
non-zero element (equals 1), soft-label cross-entropy degenerates to a
one-hot cross-entropy with one-hot label representation.
Both the input `X` and `Label` can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD with input `X`.
)DOC"
);
}
};
...
...
paddle/operators/dropout_op.cc
浏览文件 @
6117af64
...
...
@@ -18,7 +18,6 @@ namespace paddle {
namespace
operators
{
using
framework
::
Tensor
;
using
framework
::
LoDTensor
;
class
DropoutOp
:
public
framework
::
OperatorWithKernel
{
public:
...
...
@@ -34,10 +33,11 @@ class DropoutOp : public framework::OperatorWithKernel {
ctx
.
Attr
<
int
>
(
"is_training"
)
==
1
);
auto
dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
ctx
.
Output
<
LoD
Tensor
>
(
"Out"
)
->
Resize
(
dims
);
ctx
.
Output
<
Tensor
>
(
"Out"
)
->
Resize
(
dims
);
if
(
ctx
.
Attr
<
int
>
(
"is_training"
)
==
1
)
{
ctx
.
Output
<
LoD
Tensor
>
(
"Mask"
)
->
Resize
(
dims
);
ctx
.
Output
<
Tensor
>
(
"Mask"
)
->
Resize
(
dims
);
}
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
...
...
@@ -96,7 +96,7 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
x_dims
,
mask_dims
,
"Dimensions of Input(X) and Mask must be the same."
);
auto
*
x_grad
=
ctx
.
Output
<
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
x_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
x_grad
->
Resize
(
x_dims
);
}
};
...
...
paddle/operators/elementwise_mul_op.cc
浏览文件 @
6117af64
...
...
@@ -37,7 +37,8 @@ class ElementWiseMulOp : public framework::OperatorWithKernel {
auto
y_dim
=
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
();
PADDLE_ENFORCE_GE
(
x_dim
.
size
(),
y_dim
.
size
(),
"Rank of first input must >= rank of second input."
)
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
)
->
Resize
(
x_dim
);
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
(
x_dim
);
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
...
...
@@ -63,11 +64,15 @@ Limited elementwise multiple operator.The equation is: Out = X ⊙ Y.
2. Y's shape is a subset of X.
Y will be broadcasted to match the shape of X and axis should be dimension index Y in X.
example:
shape(X) = (2, 3, 4, 5), shape(Y) = (,)
shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
Both the input X and Y can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD with input X.
)DOC"
);
}
};
...
...
@@ -86,10 +91,8 @@ class ElementWiseMulOpGrad : public framework::OperatorWithKernel {
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
y_dims
=
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
();
auto
out_dims
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
dims
();
auto
*
x_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
x_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
y_dims
.
size
(),
"Rank of first input must >= rank of second input."
)
...
...
paddle/operators/fc_op.cc
浏览文件 @
6117af64
...
...
@@ -186,6 +186,9 @@ W_i is a 2-D matrix of size (K x N), where N means the number of neurons
in the fully connected layer. B is a 1-D vector of size N.
Thus, the output Out is a 2-D matrix of size (M x N).
Activation type can be set to `identity` (default), `sigmoid` or `softmax`.
All the inputs can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD with first input (`X[0]`).
)DOC"
);
}
};
...
...
paddle/operators/fill_zeros_like_op.cc
浏览文件 @
6117af64
...
...
@@ -23,15 +23,14 @@ class FillZerosLikeOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Src"
),
"Input(Src) of FillZerosLikeOp should not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Dst"
),
"Output(Dst) of FillZerosLikeOp should not be null."
);
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Dst"
)
->
Resize
(
ctx
.
Input
<
framework
::
Tensor
>
(
"Src"
)
->
dims
());
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) of FillZerosLikeOp should not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Y"
),
"Output(Y) of FillZerosLikeOp should not be null."
);
ctx
.
Output
<
framework
::
Tensor
>
(
"Y"
)
->
Resize
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
dims
());
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Y"
);
}
};
...
...
@@ -40,8 +39,8 @@ class FillZerosLikeOpMaker : public framework::OpProtoAndCheckerMaker {
FillZerosLikeOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"
Src
"
,
"The input of fill-zeros-like op."
);
AddOutput
(
"
Dst
"
,
"The varibale will be filled up with zeros."
);
AddInput
(
"
X
"
,
"The input of fill-zeros-like op."
);
AddOutput
(
"
Y
"
,
"The varibale will be filled up with zeros."
);
AddComment
(
R"DOC(
Fill up a vriable with zeros.
...
...
paddle/operators/fill_zeros_like_op.h
浏览文件 @
6117af64
...
...
@@ -23,7 +23,7 @@ template <typename Place, typename T>
class
FillZerosLikeKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
output
=
context
.
Output
<
framework
::
Tensor
>
(
"
Dst
"
);
auto
*
output
=
context
.
Output
<
framework
::
Tensor
>
(
"
Y
"
);
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
output
);
t
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
...
...
paddle/operators/gather_op.cc
浏览文件 @
6117af64
...
...
@@ -35,7 +35,7 @@ class GatherOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_GE
(
batch_size
,
0
,
"Batch size must be >0"
);
framework
::
DDim
output_dims
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
());
output_dims
[
0
]
=
batch_size
;
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
)
->
Resize
(
output_dims
);
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
(
output_dims
);
}
};
...
...
@@ -45,7 +45,7 @@ class GatherGradOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
auto
X_grad
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
X_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
X
=
ctx
.
Input
<
Tensor
>
(
"X"
);
X_grad
->
Resize
(
X
->
dims
());
...
...
paddle/operators/gaussian_random_op.cc
浏览文件 @
6117af64
...
...
@@ -48,7 +48,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
ctx
.
OutputVar
(
"Out"
),
"Output(Out) of GaussianRandomOp should not be null."
);
auto
*
tensor
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
);
auto
*
tensor
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
dims
=
Attr
<
std
::
vector
<
int
>>
(
"dims"
);
std
::
vector
<
int64_t
>
temp
;
temp
.
reserve
(
dims
.
size
());
...
...
paddle/operators/lookup_table_op.cc
浏览文件 @
6117af64
...
...
@@ -32,9 +32,10 @@ class LookupTableOp : public framework::OperatorWithKernel {
auto
table_t
=
ctx
.
Input
<
Tensor
>
(
"W"
);
auto
ids_t
=
ctx
.
Input
<
Tensor
>
(
"Ids"
);
auto
output_t
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
);
auto
output_t
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
output_t
->
Resize
({
ids_t
->
dims
()[
0
],
table_t
->
dims
()[
1
]});
ctx
.
ShareLoD
(
"Ids"
,
/*->*/
"Out"
);
}
};
...
...
@@ -50,9 +51,13 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
"An input with type int32 or int64"
"contains the ids to be looked up in W."
);
AddOutput
(
"Out"
,
"The lookup results, which have the same type with W."
);
AddComment
(
"This operator is used to perform lookups on the parameter W,"
"then concatenated into a dense tensor."
);
AddComment
(
R"DOC(
This operator is used to perform lookups on the parameter W,
then concatenated into a dense tensor.
The input `Ids` can carry the LoD (Level of Details) information,
or not. And the output only shares the LoD with input `Ids`.
)DOC"
);
}
};
...
...
@@ -64,7 +69,7 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
void
InferShape
(
const
framework
::
InferShapeContext
&
context
)
const
override
{
auto
table
=
context
.
Input
<
Tensor
>
(
"W"
);
auto
d_table
=
context
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"W"
));
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"W"
));
d_table
->
Resize
(
table
->
dims
());
}
};
...
...
paddle/operators/mean_op.cc
浏览文件 @
6117af64
...
...
@@ -27,7 +27,7 @@ class MeanOp : public framework::OperatorWithKernel {
"Input(X) of MeanOp should not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Out"
),
"Output(Out) of MeanOp should not be null."
);
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
)
->
Resize
({
1
});
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
({
1
});
}
};
...
...
@@ -37,7 +37,8 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The input of mean op"
);
AddOutput
(
"Out"
,
"The output of mean op"
).
NotInGradient
();
AddComment
(
"Mean Operator"
);
AddComment
(
R"DOC( Mean Operator
)DOC"
);
}
};
...
...
@@ -47,7 +48,7 @@ class MeanGradOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
))
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
))
->
Resize
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
());
}
};
...
...
paddle/operators/minus_op.cc
浏览文件 @
6117af64
...
...
@@ -40,7 +40,8 @@ class MinusOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
left_tensor
->
numel
(),
right_tensor
->
numel
(),
"Minus operator must take two tensor with same num of elements"
);
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
)
->
Resize
(
left_tensor
->
dims
());
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
(
left_tensor
->
dims
());
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
...
...
@@ -54,7 +55,12 @@ class MinusOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(Minus Operator
Equation: Out = X - Y
Equation:
Out = X - Y
Both the input `X` and `Y` can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD with input `X`.
)DOC"
);
}
};
...
...
paddle/operators/modified_huber_loss_op.cc
浏览文件 @
6117af64
...
...
@@ -34,8 +34,8 @@ class ModifiedHuberLossOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
x
->
dims
().
size
(),
2
,
"The tensor rank of X must be 2."
);
PADDLE_ENFORCE_EQ
(
x
->
dims
()[
1
],
1
,
"The 2nd dimension of X must be 1."
);
context
.
Output
<
framework
::
LoD
Tensor
>
(
"IntermediateVal"
)
->
Resize
(
x
->
dims
());
context
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
)
->
Resize
({
x
->
dims
()[
0
],
1
});
context
.
Output
<
framework
::
Tensor
>
(
"IntermediateVal"
)
->
Resize
(
x
->
dims
());
context
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
({
x
->
dims
()[
0
],
1
});
}
};
...
...
@@ -81,7 +81,7 @@ class ModifiedHuberLossGradOp : public framework::OperatorWithKernel {
auto
*
intermediate_val
=
context
.
Input
<
Tensor
>
(
"IntermediateVal"
);
auto
*
out_grad
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
x_grad
=
context
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
));
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
PADDLE_ENFORCE_NOT_NULL
(
x
,
"X must be initialized."
);
PADDLE_ENFORCE_NOT_NULL
(
y
,
"Y must be initialized."
);
...
...
paddle/operators/modified_huber_loss_op.h
浏览文件 @
6117af64
...
...
@@ -52,8 +52,8 @@ class ModifiedHuberLossKernel : public framework::OpKernel {
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in0
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
in1
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
out0
=
context
.
Output
<
framework
::
LoD
Tensor
>
(
"IntermediateVal"
);
auto
*
out1
=
context
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
);
auto
*
out0
=
context
.
Output
<
framework
::
Tensor
>
(
"IntermediateVal"
);
auto
*
out1
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
());
out1
->
mutable_data
<
T
>
(
context
.
GetPlace
());
...
...
@@ -77,11 +77,9 @@ class ModifiedHuberLossGradCPUKernel : public framework::OpKernel {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in0
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
in1
=
context
.
Input
<
framework
::
LoDTensor
>
(
"IntermediateVal"
);
auto
*
in2
=
context
.
Input
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
out0
=
context
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
in1
=
context
.
Input
<
framework
::
Tensor
>
(
"IntermediateVal"
);
auto
*
in2
=
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
out0
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
if
(
out0
)
{
const
T
*
y_ptr
=
in0
->
data
<
T
>
();
...
...
paddle/operators/mul_op.cc
浏览文件 @
6117af64
...
...
@@ -18,7 +18,6 @@ namespace paddle {
namespace
operators
{
using
framework
::
Tensor
;
using
framework
::
LoDTensor
;
class
MulOp
:
public
framework
::
OperatorWithKernel
{
public:
...
...
@@ -53,8 +52,9 @@ class MulOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
x_mat_dims
[
1
],
y_mat_dims
[
0
],
"First matrix's width must be equal with second matrix's height."
);
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
)
->
Resize
(
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
(
{
x_mat_dims
[
0
],
y_mat_dims
[
1
]});
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
...
...
@@ -83,9 +83,14 @@ class MulOpMaker : public framework::OpProtoAndCheckerMaker {
.
SetDefault
(
1
)
.
EqualGreaterThan
(
1
);
AddComment
(
R"DOC(
Two Element Mul Operator
.
Mul operator is used to perform matrix multiplication for input X and Y
.
The equation is: Out = X * Y
The equation is:
Out = X * Y
Both the input `X` and `Y` can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD with input `X`.
)DOC"
);
}
};
...
...
@@ -103,10 +108,8 @@ class MulOpGrad : public framework::OperatorWithKernel {
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
y_dims
=
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
();
auto
out_dims
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
dims
();
auto
*
x_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
x_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
x_mat_dims
=
framework
::
flatten_to_2d
(
x_dims
,
Attr
<
int
>
(
"x_num_col_dims"
));
...
...
paddle/operators/pad_op.cc
浏览文件 @
6117af64
...
...
@@ -39,8 +39,13 @@ class PadOp : public framework::OperatorWithKernel {
for
(
int
i
=
0
;
i
<
x_dim
.
size
();
++
i
)
{
out_dims
[
i
]
=
x_dim
[
i
]
+
paddings
[
i
*
2
]
+
paddings
[
i
*
2
+
1
];
}
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
)
->
Resize
(
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
(
framework
::
make_ddim
(
out_dims
));
if
(
out_dims
[
0
]
==
x_dim
[
0
])
{
// Only pass LoD when the first dimension is equal between
// output and input.
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
}
};
...
...
@@ -101,7 +106,7 @@ class PadOpGrad : public framework::OperatorWithKernel {
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
*
x_g
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
x_g
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
if
(
x_g
!=
nullptr
)
{
x_g
->
Resize
(
x_dims
);
}
...
...
paddle/operators/prelu_op.cc
浏览文件 @
6117af64
...
...
@@ -36,8 +36,9 @@ class PReluOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Out"
),
"Output(Out) should not be null"
);
auto
*
out
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
);
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
out
->
Resize
(
in
->
dims
());
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
...
...
@@ -55,6 +56,8 @@ The equation is:
f(x) = alpha * x , for x < 0
f(x) = x , for x >= 0
The input `X` can carry the LoD (Level of Details) information,
or not. And the output shares the LoD with input `X`.
)DOC"
);
}
};
...
...
@@ -69,11 +72,11 @@ class PReluGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
auto
*
dx
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dx
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
x
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
dalpha
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"Alpha"
));
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Alpha"
));
auto
*
alpha
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Alpha"
);
dx
->
Resize
(
x
->
dims
());
...
...
paddle/operators/rank_loss_op.cc
浏览文件 @
6117af64
...
...
@@ -40,7 +40,7 @@ class RankLossOp : public framework::OperatorWithKernel {
"All inputs must have the same size"
);
PADDLE_ENFORCE
((
label_dims
.
size
()
==
2
)
&&
(
label_dims
[
1
]
==
1
),
"All inputs must be row vector with size batch_size x 1."
);
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
)
->
Resize
(
label_dims
);
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
(
label_dims
);
}
};
...
...
@@ -102,9 +102,9 @@ class RankLossGradOp : public framework::OperatorWithKernel {
"Input(Out@GRAD) shouldn't be null."
);
auto
dims
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Left"
)
->
dims
();
auto
*
left_grad
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"Left"
));
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Left"
));
auto
*
right_grad
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"Right"
));
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Right"
));
if
(
left_grad
)
{
left_grad
->
Resize
(
dims
);
}
...
...
paddle/operators/rank_loss_op.h
浏览文件 @
6117af64
...
...
@@ -24,7 +24,7 @@ template <typename Place, typename T>
class
RankLossKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
*
out_t
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
);
auto
*
out_t
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
label_t
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Label"
);
auto
*
left_t
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Left"
);
auto
*
right_t
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Right"
);
...
...
@@ -46,9 +46,9 @@ class RankLossGradKernel : public framework::OpKernel {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
*
d_left_t
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"Left"
));
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Left"
));
auto
*
d_right_t
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"Right"
));
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Right"
));
auto
*
d_out_t
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
label_t
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Label"
);
...
...
paddle/operators/reshape_op.cc
浏览文件 @
6117af64
...
...
@@ -50,7 +50,12 @@ class ReshapeOp : public framework::OperatorWithKernel {
std
::
transform
(
shape
.
begin
(),
shape
.
end
(),
shape_int64
.
begin
(),
[](
int
a
)
{
return
static_cast
<
int64_t
>
(
a
);
});
auto
out_dims
=
framework
::
make_ddim
(
shape_int64
);
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
)
->
Resize
(
out_dims
);
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
(
out_dims
);
if
(
shape
[
0
]
==
in
->
dims
()[
0
])
{
// Only pass LoD when the first dimension is equal between
// output and input.
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
}
};
...
...
@@ -94,7 +99,7 @@ class ReshapeGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) shouldn't be null."
);
auto
dims
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
dims
();
auto
*
d_in
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
d_in
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
d_in
->
Resize
(
dims
);
}
};
...
...
paddle/operators/rowwise_add_op.cc
浏览文件 @
6117af64
...
...
@@ -44,7 +44,8 @@ class RowwiseAddOp : public framework::OperatorWithKernel {
framework
::
slice_ddim
(
x_dims
,
num_col_dims
,
x_dims
.
size
()),
b_dims
,
"The width of two operands must be same"
);
PADDLE_ENFORCE_EQ
(
ctx
.
OutputSize
(
"Out"
),
1
,
"The output size must be 1"
);
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
)
->
Resize
(
x_dims
);
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
(
x_dims
);
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
...
...
@@ -83,8 +84,8 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
num_col_dims
,
x_dims
.
size
()),
b_dims
,
"The width of two operands must be same"
);
auto
*
dx
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
db
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"b"
));
auto
*
dx
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
db
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"b"
));
if
(
dx
)
dx
->
Resize
(
x_dims
);
if
(
db
)
db
->
Resize
(
b_dims
);
}
...
...
paddle/operators/scale_op.cc
浏览文件 @
6117af64
...
...
@@ -33,8 +33,9 @@ class ScaleOp : public framework::OperatorWithKernel {
"Output(Out) of ScaleOp should not be null."
);
auto
*
in
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
);
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
out
->
Resize
(
in
->
dims
());
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
...
...
paddle/operators/scatter_op.cc
浏览文件 @
6117af64
...
...
@@ -44,7 +44,7 @@ class ScatterOp : public framework::OperatorWithKernel {
framework
::
DDim
data_dim
(
ctx
.
Input
<
Tensor
>
(
"Updates"
)
->
dims
());
for
(
int
i
=
1
;
i
<
data_dim
.
size
();
++
i
)
PADDLE_ENFORCE_EQ
(
data_dim
[
i
],
ctx
.
Input
<
Tensor
>
(
"Updates"
)
->
dims
()[
i
]);
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
)
->
Resize
(
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
(
ctx
.
Input
<
Tensor
>
(
"Ref"
)
->
dims
());
}
};
...
...
@@ -56,10 +56,9 @@ class ScatterGradOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
auto
*
dUpdates
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"Updates"
));
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Updates"
));
auto
*
Updates
=
ctx
.
Input
<
Tensor
>
(
"Updates"
);
auto
*
dRef
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Ref"
));
auto
*
dRef
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Ref"
));
auto
*
Ref
=
ctx
.
Input
<
Tensor
>
(
"Ref"
);
dRef
->
Resize
(
Ref
->
dims
());
...
...
paddle/operators/sgd_op.cc
浏览文件 @
6117af64
...
...
@@ -33,7 +33,7 @@ class SGDOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
ctx
.
Input
<
Tensor
>
(
"param"
)
->
dims
(),
ctx
.
Input
<
Tensor
>
(
"grad"
)
->
dims
(),
"Two input of SGD Op's dimension must be same."
);
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"param_out"
)
ctx
.
Output
<
framework
::
Tensor
>
(
"param_out"
)
->
Resize
(
ctx
.
Input
<
Tensor
>
(
"param"
)
->
dims
());
}
};
...
...
paddle/operators/smooth_l1_loss_op.cc
浏览文件 @
6117af64
...
...
@@ -44,8 +44,8 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
"The shape of OutsideWeight must be same as X."
);
}
auto
*
diff
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Diff"
);
auto
*
out
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
);
auto
*
diff
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Diff"
);
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
diff
->
Resize
(
x
->
dims
());
// loss is a two-rank tensor
out
->
Resize
({
x
->
dims
()[
0
],
1
});
...
...
@@ -103,10 +103,8 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
auto
in_dims
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
dims
();
auto
out_dims
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
dims
();
auto
*
x_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
x_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
PADDLE_ENFORCE_GE
(
out_dims
.
size
(),
2
,
"The tensor rank of Input(Out@Grad) should be 2."
);
...
...
paddle/operators/softmax_op.cc
浏览文件 @
6117af64
...
...
@@ -30,8 +30,7 @@ class SoftmaxOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
().
size
()
==
2UL
,
"The input of softmax op must be a matrix."
);
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Y"
)
->
Resize
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
());
ctx
.
Output
<
framework
::
Tensor
>
(
"Y"
)
->
Resize
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
());
}
};
...
...
@@ -77,7 +76,7 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
))
->
dims
(),
"Input(Y) and its gradients should have a same shape."
);
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
))
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
))
->
Resize
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
());
}
};
...
...
paddle/operators/split_op.cc
浏览文件 @
6117af64
...
...
@@ -27,7 +27,7 @@ class SplitOp : public framework::OperatorWithKernel {
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
// infershape
auto
*
in
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
outs
=
ctx
.
MultiOutput
<
framework
::
LoD
Tensor
>
(
"Out"
);
auto
outs
=
ctx
.
MultiOutput
<
framework
::
Tensor
>
(
"Out"
);
size_t
axis
=
static_cast
<
size_t
>
(
ctx
.
Attr
<
int
>
(
"axis"
));
size_t
num
=
static_cast
<
size_t
>
(
ctx
.
Attr
<
int
>
(
"num"
));
std
::
vector
<
int
>
sections
=
...
...
paddle/operators/squared_l2_distance_op.cc
浏览文件 @
6117af64
...
...
@@ -54,9 +54,10 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
"First dimension of target must be equal to input "
"or to 1."
);
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"sub_result"
)
ctx
.
Output
<
framework
::
Tensor
>
(
"sub_result"
)
->
Resize
({
x_dims
[
0
],
x
->
numel
()
/
x_dims
[
0
]});
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
)
->
Resize
({
x_dims
[
0
],
1
});
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
({
x_dims
[
0
],
1
});
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
...
...
@@ -79,6 +80,9 @@ class SquaredL2DistanceOpMaker : public framework::OpProtoAndCheckerMaker {
input or to 1. If the first dimension of target is 1, SquaredL2DistanceOp
will broadcast target's first dimension to input's first dimension.
You can decide whether calculate the gradient of input and target.
Both the input X and Y can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD with input X.
)DOC"
);
}
};
...
...
@@ -100,10 +104,8 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
out_dims
[
1
],
1
,
"Second dimension of output gradient "
"must be 1."
);
auto
*
x_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
*
x_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
if
(
x_grad
)
x_grad
->
Resize
(
x_dims
);
if
(
y_grad
)
y_grad
->
Resize
(
y_dims
);
}
...
...
paddle/operators/sum_op.cc
浏览文件 @
6117af64
...
...
@@ -28,7 +28,7 @@ class SumOp : public framework::OperatorWithKernel {
"Output(Out) of SumOp should not be null."
);
auto
ins
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
);
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
int
N
=
ins
.
size
();
auto
in_dim
=
ins
[
0
]
->
dims
();
...
...
@@ -39,6 +39,7 @@ class SumOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE
(
in_dim
==
dim
,
"Input tensors must have same shape"
);
}
out
->
Resize
(
in_dim
);
ctx
.
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
...
...
@@ -49,8 +50,11 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"X"
,
"the input tensors of sum operator."
).
AsDuplicable
();
AddOutput
(
"Out"
,
"the output tensor of sum operator."
);
AddComment
(
R"DOC(
Sum the input tensors.
)DOC"
);
Sum the input tensors.
All the inputs can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD with the first input.
)DOC"
);
}
};
...
...
@@ -61,7 +65,7 @@ class SumGradOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
auto
outputs
=
ctx
.
MultiOutput
<
framework
::
LoD
Tensor
>
(
framework
::
GradVarName
(
"X"
));
ctx
.
MultiOutput
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
dims
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
dims
();
for
(
auto
output
:
outputs
)
{
output
->
Resize
(
dims
);
...
...
paddle/operators/top_k_op.cc
浏览文件 @
6117af64
...
...
@@ -40,8 +40,8 @@ class TopkOp : public framework::OperatorWithKernel {
framework
::
DDim
dims
=
input
->
dims
();
dims
[
dims
.
size
()
-
1
]
=
k
;
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
)
->
Resize
(
dims
);
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Indices"
)
->
Resize
(
dims
);
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
(
dims
);
ctx
.
Output
<
framework
::
Tensor
>
(
"Indices"
)
->
Resize
(
dims
);
}
};
...
...
paddle/operators/transpose_op.cc
浏览文件 @
6117af64
...
...
@@ -51,7 +51,7 @@ class TransposeOp : public framework::OperatorWithKernel {
for
(
size_t
i
=
0
;
i
<
axis_size
;
i
++
)
{
out_dims
[
i
]
=
x_dims
[
axis
[
i
]];
}
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
)
->
Resize
(
out_dims
);
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
)
->
Resize
(
out_dims
);
}
};
...
...
@@ -99,8 +99,7 @@ class TransposeOpGrad : public framework::OperatorWithKernel {
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
*
x_grad
=
ctx
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
x_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
if
(
x_grad
)
x_grad
->
Resize
(
x_dims
);
}
...
...
paddle/operators/uniform_random_op.cc
浏览文件 @
6117af64
...
...
@@ -54,7 +54,7 @@ class UniformRandomOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE
(
Attr
<
float
>
(
"min"
)
<
Attr
<
float
>
(
"max"
),
"uniform_random's min must less then max"
);
auto
*
tensor
=
ctx
.
Output
<
framework
::
LoD
Tensor
>
(
"Out"
);
auto
*
tensor
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
dims
=
Attr
<
std
::
vector
<
int
>>
(
"dims"
);
std
::
vector
<
int64_t
>
temp
;
temp
.
reserve
(
dims
.
size
());
...
...
python/paddle/v2/framework/tests/test_fill_zeros_like_op.py
浏览文件 @
6117af64
...
...
@@ -6,8 +6,8 @@ from op_test import OpTest
class
TestFillZerosLikeOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"fill_zeros_like"
self
.
inputs
=
{
'
Src
'
:
np
.
random
.
random
((
219
,
232
)).
astype
(
"float32"
)}
self
.
outputs
=
{
'
Dst'
:
np
.
zeros_like
(
self
.
inputs
[
"Src
"
])}
self
.
inputs
=
{
'
X
'
:
np
.
random
.
random
((
219
,
232
)).
astype
(
"float32"
)}
self
.
outputs
=
{
'
Y'
:
np
.
zeros_like
(
self
.
inputs
[
"X
"
])}
def
test_check_output
(
self
):
self
.
check_output
()
...
...
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