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体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
621a4085
编写于
5月 13, 2020
作者:
Z
zhang wenhui
提交者:
GitHub
5月 13, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
enhance cvm bpr_loss adam adagrad adamax ftrl error message, test=develop (#24452)
上级
e5861929
变更
12
隐藏空白更改
内联
并排
Showing
12 changed file
with
228 addition
and
179 deletion
+228
-179
paddle/fluid/operators/bpr_loss_op.cc
paddle/fluid/operators/bpr_loss_op.cc
+37
-25
paddle/fluid/operators/bpr_loss_op.h
paddle/fluid/operators/bpr_loss_op.h
+5
-3
paddle/fluid/operators/cvm_op.cc
paddle/fluid/operators/cvm_op.cc
+37
-25
paddle/fluid/operators/optimizers/adagrad_op.cc
paddle/fluid/operators/optimizers/adagrad_op.cc
+19
-20
paddle/fluid/operators/optimizers/adagrad_op.h
paddle/fluid/operators/optimizers/adagrad_op.h
+14
-8
paddle/fluid/operators/optimizers/adam_op.h
paddle/fluid/operators/optimizers/adam_op.h
+8
-6
paddle/fluid/operators/optimizers/adamax_op.cc
paddle/fluid/operators/optimizers/adamax_op.cc
+42
-38
paddle/fluid/operators/optimizers/adamax_op.h
paddle/fluid/operators/optimizers/adamax_op.h
+12
-10
paddle/fluid/operators/optimizers/ftrl_op.cc
paddle/fluid/operators/optimizers/ftrl_op.cc
+38
-34
paddle/fluid/operators/optimizers/ftrl_op.h
paddle/fluid/operators/optimizers/ftrl_op.h
+12
-10
python/paddle/fluid/layers/loss.py
python/paddle/fluid/layers/loss.py
+2
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+2
-0
未找到文件。
paddle/fluid/operators/bpr_loss_op.cc
浏览文件 @
621a4085
...
...
@@ -23,22 +23,26 @@ class BprLossOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should be not null.
"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Label"
),
"Input(Label) should be not null.
"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Y"
),
"Output(Y) should be not null.
"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"X"
),
"Input"
,
"X"
,
"BprLoss
"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Label"
),
"Input"
,
"Label"
,
"BprLoss
"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"Y"
),
"Output"
,
"Y"
,
"BprLoss
"
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
label_dims
=
ctx
->
GetInputDim
(
"Label"
);
int
rank
=
x_dims
.
size
();
PADDLE_ENFORCE_EQ
(
rank
,
label_dims
.
size
(),
"Input(X) and Input(Label) shall have the same rank."
);
PADDLE_ENFORCE_EQ
(
rank
,
label_dims
.
size
(),
platform
::
errors
::
InvalidArgument
(
"Input(X) and Input(Label) shall have the same rank."
));
if
(
ctx
->
IsRuntime
()
||
(
framework
::
product
(
x_dims
)
>
0
&&
framework
::
product
(
label_dims
)
>
0
))
{
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
0
,
rank
-
1
),
framework
::
slice_ddim
(
label_dims
,
0
,
rank
-
1
),
"Input(X) and Input(Label) shall have the same shape "
"except the last dimension."
);
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
0
,
rank
-
1
),
framework
::
slice_ddim
(
label_dims
,
0
,
rank
-
1
),
platform
::
errors
::
InvalidArgument
(
"Input(X) and Input(Label) shall have the same shape "
"except the last dimension."
));
}
auto
y_dims
=
x_dims
;
...
...
@@ -63,33 +67,41 @@ class BprLossGradientOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should be not null.
"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Label"
),
"Input(Label) should be not null.
"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Y"
))
,
"Input(Y@GRAD) shoudl be not null.
"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
))
,
"Output(X@GRAD) should be not null.
"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"X"
),
"Input"
,
"X"
,
"BprLossGradient
"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Label"
),
"Input"
,
"Label"
,
"BprLossGradient
"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Y"
)),
"Input"
,
framework
::
GradVarName
(
"Y"
),
"BprLossGradient
"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Output"
,
framework
::
GradVarName
(
"X"
),
"BprLossGradient
"
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
label_dims
=
ctx
->
GetInputDim
(
"Label"
);
auto
dy_dims
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Y"
));
int
rank
=
x_dims
.
size
();
PADDLE_ENFORCE_EQ
(
dy_dims
.
size
(),
rank
,
"Input(Y@Grad) and Input(X) should have the same rank."
);
PADDLE_ENFORCE_EQ
(
label_dims
.
size
(),
rank
,
"Input(Label) and Input(X) should have the same rank."
);
PADDLE_ENFORCE_EQ
(
dy_dims
.
size
(),
rank
,
platform
::
errors
::
InvalidArgument
(
"Input(Y@Grad) and Input(X) should have the same rank."
));
PADDLE_ENFORCE_EQ
(
label_dims
.
size
(),
rank
,
platform
::
errors
::
InvalidArgument
(
"Input(Label) and Input(X) should have the same rank."
));
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
0
,
rank
-
1
),
framework
::
slice_ddim
(
label_dims
,
0
,
rank
-
1
),
"The Input(X) and Input(Label) should have the same "
"shape except the last dimension."
);
platform
::
errors
::
InvalidArgument
(
"The Input(X) and Input(Label) should have the same "
"shape except the last dimension."
));
PADDLE_ENFORCE_EQ
(
framework
::
slice_ddim
(
x_dims
,
0
,
rank
-
1
),
framework
::
slice_ddim
(
dy_dims
,
0
,
rank
-
1
),
"The Input(X) and Input(Y@Grad) should have the same "
"shape except the last dimension."
);
platform
::
errors
::
InvalidArgument
(
"The Input(X) and Input(Y@Grad) should have the same "
"shape except the last dimension."
));
PADDLE_ENFORCE_EQ
(
dy_dims
[
rank
-
1
],
1
,
"The last dimension of Input(Y@Grad) should be 1."
);
platform
::
errors
::
InvalidArgument
(
"The last dimension of Input(Y@Grad) should be 1."
));
PADDLE_ENFORCE_EQ
(
label_dims
[
rank
-
1
],
1
,
" the last dimension of Input(Label) should be 1."
);
platform
::
errors
::
InvalidArgument
(
" the last dimension of Input(Label) should be 1."
));
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
x_dims
);
ctx
->
ShareLoD
(
"X"
,
framework
::
GradVarName
(
"X"
));
}
...
...
paddle/fluid/operators/bpr_loss_op.h
浏览文件 @
621a4085
...
...
@@ -28,7 +28,6 @@ using Tensor = framework::Tensor;
template
<
typename
T
>
struct
TolerableValue
{
HOSTDEVICE
T
operator
()(
const
T
&
x
)
const
{
PADDLE_ENFORCE_EQ
(
std
::
is_floating_point
<
T
>::
value
,
true
);
const
T
kApproInf
=
1e20
;
if
(
x
==
INFINITY
)
return
kApproInf
;
if
(
x
==
-
INFINITY
)
return
-
kApproInf
;
...
...
@@ -62,8 +61,11 @@ class BprLossOpKernel : public framework::OpKernel<T> {
const
int64_t
*
label_data
=
labels
->
data
<
int64_t
>
();
for
(
int
i
=
0
;
i
<
step_size
;
++
i
)
{
int
lbl_pos
=
label_data
[
i
];
PADDLE_ENFORCE_GE
(
lbl_pos
,
0
);
PADDLE_ENFORCE_LT
(
lbl_pos
,
class_num
);
PADDLE_ENFORCE_GE
(
lbl_pos
,
0
,
platform
::
errors
::
InvalidArgument
(
"label data %d is illegal."
,
lbl_pos
));
PADDLE_ENFORCE_LT
(
lbl_pos
,
class_num
,
platform
::
errors
::
InvalidArgument
(
"label data %d is illegal."
,
lbl_pos
));
int
index_pos
=
i
*
class_num
+
lbl_pos
;
T
sum
=
static_cast
<
T
>
(
0
);
for
(
int
j
=
0
;
j
<
class_num
;
j
++
)
{
...
...
paddle/fluid/operators/cvm_op.cc
浏览文件 @
621a4085
...
...
@@ -26,17 +26,20 @@ class CVMOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should be not null.
"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"CVM"
),
"Input(CVM) should be not null.
"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Y"
),
"Output(Y) should be not null.
"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"X"
),
"Input"
,
"X"
,
"CVM
"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"CVM"
),
"Input"
,
"CVM"
,
"CVM
"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"Y"
),
"Output"
,
"Y"
,
"CVM
"
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
cvm_dims
=
ctx
->
GetInputDim
(
"CVM"
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2UL
,
"Input(X)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
cvm_dims
.
size
(),
2UL
,
"Input(CVM)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
cvm_dims
[
1
],
2UL
,
"The 2nd dimension of "
"Input(CVM) should be 2."
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2UL
,
platform
::
errors
::
InvalidArgument
(
"Input(X)'s rank should be 2."
));
PADDLE_ENFORCE_EQ
(
cvm_dims
.
size
(),
2UL
,
platform
::
errors
::
InvalidArgument
(
"Input(CVM)'s rank should be 2."
));
PADDLE_ENFORCE_EQ
(
cvm_dims
[
1
],
2UL
,
platform
::
errors
::
InvalidArgument
(
"The 2nd dimension of "
"Input(CVM) should be 2."
));
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"use_cvm"
))
{
ctx
->
SetOutputDim
(
"Y"
,
{
x_dims
[
0
],
x_dims
[
1
]});
...
...
@@ -63,27 +66,36 @@ class CVMGradientOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should be not null.
"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"CVM"
),
"Input(CVM) should be not null.
"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Y"
))
,
"Input(Y@GRAD) should be not null.
"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
))
,
"Output(X@GRAD) should be not null.
"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"X"
),
"Input"
,
"X"
,
"CVMGradient
"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"CVM"
),
"Input"
,
"CVM"
,
"CVMGradient
"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Y"
)),
"Input"
,
framework
::
GradVarName
(
"Y"
),
"CVMGradient
"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Output"
,
framework
::
GradVarName
(
"X"
),
"CVMGradient
"
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
cvm_dims
=
ctx
->
GetInputDim
(
"CVM"
);
auto
dy_dims
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Y"
));
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2
,
"Input(X)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
dy_dims
.
size
(),
2
,
"Input(Y@Grad)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
cvm_dims
.
size
(),
2
,
"Input(CVM)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
dy_dims
[
0
],
"The 1st dimension of Input(X) and Input(Y@Grad) should "
"be equal."
);
PADDLE_ENFORCE_EQ
(
cvm_dims
[
1
],
2
,
"When Attr(soft_label) == false, the 2nd dimension of "
"Input(CVM) should be 2."
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
"Input(X)'s rank should be 2."
));
PADDLE_ENFORCE_EQ
(
dy_dims
.
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
"Input(Y@Grad)'s rank should be 2."
));
PADDLE_ENFORCE_EQ
(
cvm_dims
.
size
(),
2
,
platform
::
errors
::
InvalidArgument
(
"Input(CVM)'s rank should be 2."
));
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
dy_dims
[
0
],
platform
::
errors
::
InvalidArgument
(
"The 1st dimension of Input(X) and Input(Y@Grad) should "
"be equal."
));
PADDLE_ENFORCE_EQ
(
cvm_dims
[
1
],
2
,
platform
::
errors
::
InvalidArgument
(
"When Attr(soft_label) == false, the 2nd dimension of "
"Input(CVM) should be 2."
));
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
x_dims
);
ctx
->
ShareLoD
(
"X"
,
framework
::
GradVarName
(
"X"
));
}
...
...
paddle/fluid/operators/optimizers/adagrad_op.cc
浏览文件 @
621a4085
...
...
@@ -29,35 +29,34 @@ class AdagradOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Param"
),
"Input(Param) of AdagradOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Grad"
),
"Input(Grad) of AdagradOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Moment"
),
"Input(Moment) of AdagradOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"LearningRate"
),
"Input(LearningRate) of AdagradOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"ParamOut"
),
"Output(ParamOut) of AdagradOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"MomentOut"
),
"Output(MomentOut) of AdagradOp should not be null."
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Param"
),
"Input"
,
"Param"
,
"Adagrad"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Grad"
),
"Input"
,
"Grad"
,
"Adagrad"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Moment"
),
"Input"
,
"Moment"
,
"Adagrad"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"LearningRate"
),
"Input"
,
"LearningRate"
,
"Adagrad"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"ParamOut"
),
"Output"
,
"ParamOut"
,
"Adagrad"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"MomentOut"
),
"Output"
,
"MomentOut"
,
"Adagrad"
);
auto
lr_dims
=
ctx
->
GetInputDim
(
"LearningRate"
);
PADDLE_ENFORCE_NE
(
framework
::
product
(
lr_dims
),
0
,
"Maybe the Input variable LearningRate has not "
"been initialized. You may need to confirm "
"if you put exe.run(startup_program) "
"after optimizer.minimize function."
);
platform
::
errors
::
InvalidArgument
(
"Maybe the Input variable LearningRate has not "
"been initialized. You may need to confirm "
"if you put exe.run(startup_program) "
"after optimizer.minimize function."
));
PADDLE_ENFORCE_EQ
(
framework
::
product
(
lr_dims
),
1
,
"LearningRate should have one element"
);
platform
::
errors
::
InvalidArgument
(
"LearningRate should have one element"
));
auto
param_dims
=
ctx
->
GetInputDim
(
"Param"
);
PADDLE_ENFORCE_EQ
(
param_dims
,
ctx
->
GetInputDim
(
"Grad"
),
"Param and Grad input of AdagradOp should have the same dimension."
);
platform
::
errors
::
InvalidArgument
(
"Param and Grad input of AdagradOp "
"should have the same dimension."
));
PADDLE_ENFORCE_EQ
(
param_dims
,
ctx
->
GetInputDim
(
"Moment"
),
"Param and Moment input of AdagradOp should have the same dimension."
);
platform
::
errors
::
InvalidArgument
(
"Param and Moment input of AdagradOp "
"should have the same dimension."
));
ctx
->
SetOutputDim
(
"ParamOut"
,
param_dims
);
ctx
->
SetOutputDim
(
"MomentOut"
,
param_dims
);
...
...
paddle/fluid/operators/optimizers/adagrad_op.h
浏览文件 @
621a4085
...
...
@@ -47,11 +47,12 @@ class AdagradOpKernel : public framework::OpKernel<T> {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
*
param_var
=
ctx
.
InputVar
(
"Param"
);
PADDLE_ENFORCE
(
param_var
->
IsType
<
framework
::
LoDTensor
>
(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Param"
).
front
(),
framework
::
ToTypeName
(
param_var
->
Type
()));
PADDLE_ENFORCE_EQ
(
param_var
->
IsType
<
framework
::
LoDTensor
>
(),
true
,
platform
::
errors
::
InvalidArgument
(
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Param"
).
front
(),
framework
::
ToTypeName
(
param_var
->
Type
())));
auto
*
param_out_tensor
=
ctx
.
Output
<
framework
::
Tensor
>
(
"ParamOut"
);
auto
*
moment_out_tensor
=
ctx
.
Output
<
framework
::
Tensor
>
(
"MomentOut"
);
...
...
@@ -89,10 +90,14 @@ class AdagradOpKernel : public framework::OpKernel<T> {
}
}
else
if
(
grad_var
->
IsType
<
framework
::
SelectedRows
>
())
{
auto
*
param_tensor
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Param"
);
PADDLE_ENFORCE_EQ
(
param_tensor
,
param_out_tensor
);
PADDLE_ENFORCE_EQ
(
param_tensor
,
param_out_tensor
,
platform
::
errors
::
InvalidArgument
(
"the input tensor not euqal with output tensor"
));
auto
*
moment_tensor
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Moment"
);
PADDLE_ENFORCE_EQ
(
moment_tensor
,
moment_out_tensor
);
PADDLE_ENFORCE_EQ
(
moment_tensor
,
moment_out_tensor
,
platform
::
errors
::
InvalidArgument
(
"the input moment not eual with output moment"
));
SparseAdagradFunctor
<
DeviceContext
,
T
>
functor
;
functor
(
ctx
.
template
device_context
<
DeviceContext
>(),
...
...
@@ -100,7 +105,8 @@ class AdagradOpKernel : public framework::OpKernel<T> {
*
ctx
.
Input
<
framework
::
Tensor
>
(
"LearningRate"
),
epsilon
,
moment_out_tensor
,
param_out_tensor
);
}
else
{
PADDLE_THROW
(
"Unsupported Variable Type of Grad"
);
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"Unsupported Variable Type of Grad"
));
}
}
};
...
...
paddle/fluid/operators/optimizers/adam_op.h
浏览文件 @
621a4085
...
...
@@ -376,11 +376,12 @@ class AdamOpKernel : public framework::OpKernel<T> {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
*
param_var
=
ctx
.
InputVar
(
"Param"
);
PADDLE_ENFORCE
(
param_var
->
IsType
<
framework
::
LoDTensor
>
(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Param"
).
front
(),
framework
::
ToTypeName
(
param_var
->
Type
()));
PADDLE_ENFORCE_EQ
(
param_var
->
IsType
<
framework
::
LoDTensor
>
(),
true
,
platform
::
errors
::
InvalidArgument
(
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Param"
).
front
(),
framework
::
ToTypeName
(
param_var
->
Type
())));
using
paddle
::
framework
::
LoDTensor
;
...
...
@@ -572,7 +573,8 @@ class AdamOpKernel : public framework::OpKernel<T> {
functor
(
param
->
numel
());
}
}
else
{
PADDLE_THROW
(
"Variable type not supported by adam_op"
);
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"Variable type not supported by adam_op"
));
}
}
};
...
...
paddle/fluid/operators/optimizers/adamax_op.cc
浏览文件 @
621a4085
...
...
@@ -23,57 +23,61 @@ class AdamaxOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Param"
),
"Input(Param) of AdamaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Grad"
),
"Input(Grad) of AdamaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Moment"
),
"Input(Moment) of AdamaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"InfNorm"
),
"Input(InfNorm) of AdamaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"LearningRate"
),
"Input(LearningRate) of AdamaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Beta1Pow"
),
"Input(Beta1Pow) of AdamaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
GetInputsVarType
(
"Param"
).
front
()
==
framework
::
proto
::
VarType
::
LOD_TENSOR
,
"The input var's type should be LoDTensor, but the received is %s"
,
ctx
->
Inputs
(
"Param"
).
front
(),
ctx
->
GetInputsVarType
(
"Param"
).
front
());
PADDLE_ENFORCE
(
ctx
->
GetInputsVarType
(
"Grad"
).
front
()
==
framework
::
proto
::
VarType
::
LOD_TENSOR
,
"The input var's type should be LoDTensor, but the received is %s"
,
ctx
->
Inputs
(
"Grad"
).
front
(),
ctx
->
GetInputsVarType
(
"Grad"
).
front
());
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"ParamOut"
),
"Output(ParamOut) of AdamaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"MomentOut"
),
"Output(MomentOut) of AdamaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"InfNormOut"
),
"Output(InfNormOut) of AdamaxOp should not be null."
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Param"
),
"Input"
,
"Param"
,
"Adamax"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Grad"
),
"Input"
,
"Grad"
,
"Adamax"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Moment"
),
"Input"
,
"Moment"
,
"Adamax"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"InfNorm"
),
"Input"
,
"InfNorm"
,
"Adamax"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"LearningRate"
),
"Input"
,
"LearningRate"
,
"Adamax"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Beta1Pow"
),
"Input"
,
"Beta1Pow"
,
"Adamax"
);
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputsVarType
(
"Param"
).
front
(),
framework
::
proto
::
VarType
::
LOD_TENSOR
,
platform
::
errors
::
InvalidArgument
(
"The input var's type should be LoDTensor, but the received is %s"
,
ctx
->
Inputs
(
"Param"
).
front
(),
ctx
->
GetInputsVarType
(
"Param"
).
front
()));
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputsVarType
(
"Grad"
).
front
(),
framework
::
proto
::
VarType
::
LOD_TENSOR
,
platform
::
errors
::
InvalidArgument
(
"The input var's type should be LoDTensor, but the received is %s"
,
ctx
->
Inputs
(
"Grad"
).
front
(),
ctx
->
GetInputsVarType
(
"Grad"
).
front
()));
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"ParamOut"
),
"Output"
,
"ParamOut"
,
"Adamax"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"MomentOut"
),
"Output"
,
"MomentOut"
,
"Adamax"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"InfNormOut"
),
"Output"
,
"InfNormOut"
,
"Adamax"
);
auto
lr_dims
=
ctx
->
GetInputDim
(
"LearningRate"
);
PADDLE_ENFORCE_NE
(
framework
::
product
(
lr_dims
),
0
,
"Maybe the Input variable LearningRate has not "
"been initialized. You may need to confirm "
"if you put exe.run(startup_program) "
"after optimizer.minimize function."
);
platform
::
errors
::
InvalidArgument
(
"Maybe the Input variable LearningRate has not "
"been initialized. You may need to confirm "
"if you put exe.run(startup_program) "
"after optimizer.minimize function."
));
PADDLE_ENFORCE_EQ
(
framework
::
product
(
lr_dims
),
1
,
"Learning rate should have 1 dimension"
);
platform
::
errors
::
InvalidArgument
(
"Learning rate should have 1 dimension"
));
auto
beta1_pow_dims
=
ctx
->
GetInputDim
(
"Beta1Pow"
);
PADDLE_ENFORCE_EQ
(
framework
::
product
(
beta1_pow_dims
),
1
,
"Beta1 power accumulator should have 1 dimension"
);
platform
::
errors
::
InvalidArgument
(
"Beta1 power accumulator should have 1 dimension"
));
auto
param_dims
=
ctx
->
GetInputDim
(
"Param"
);
PADDLE_ENFORCE_EQ
(
param_dims
,
ctx
->
GetInputDim
(
"Grad"
),
"Param and Grad input of AdamaxOp should have same dimension"
);
platform
::
errors
::
InvalidArgument
(
"Param and Grad input of AdamaxOp should have same dimension"
));
PADDLE_ENFORCE_EQ
(
param_dims
,
ctx
->
GetInputDim
(
"Moment"
),
"Param and Moment input of AdamaxOp should have same dimension"
);
platform
::
errors
::
InvalidArgument
(
"Param and Moment input of AdamaxOp should have same dimension"
));
PADDLE_ENFORCE_EQ
(
param_dims
,
ctx
->
GetInputDim
(
"InfNorm"
),
"Param and InfNorm input of AdamaxOp should have same dimension"
);
platform
::
errors
::
InvalidArgument
(
"Param and InfNorm input of AdamaxOp should have same dimension"
));
ctx
->
SetOutputDim
(
"ParamOut"
,
param_dims
);
ctx
->
SetOutputDim
(
"MomentOut"
,
param_dims
);
...
...
paddle/fluid/operators/optimizers/adamax_op.h
浏览文件 @
621a4085
...
...
@@ -24,17 +24,19 @@ class AdamaxOpKernel : public framework::OpKernel<T> {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
*
param_var
=
ctx
.
InputVar
(
"Param"
);
PADDLE_ENFORCE
(
param_var
->
IsType
<
framework
::
LoDTensor
>
(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Param"
).
front
(),
framework
::
ToTypeName
(
param_var
->
Type
()));
PADDLE_ENFORCE_EQ
(
param_var
->
IsType
<
framework
::
LoDTensor
>
(),
true
,
platform
::
errors
::
InvalidArgument
(
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Param"
).
front
(),
framework
::
ToTypeName
(
param_var
->
Type
())));
const
auto
*
grad_var
=
ctx
.
InputVar
(
"Grad"
);
PADDLE_ENFORCE
(
grad_var
->
IsType
<
framework
::
LoDTensor
>
(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Grad"
).
front
(),
framework
::
ToTypeName
(
grad_var
->
Type
()));
PADDLE_ENFORCE_EQ
(
grad_var
->
IsType
<
framework
::
LoDTensor
>
(),
true
,
platform
::
errors
::
InvalidArgument
(
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Grad"
).
front
(),
framework
::
ToTypeName
(
grad_var
->
Type
())));
auto
param_out_tensor
=
ctx
.
Output
<
framework
::
Tensor
>
(
"ParamOut"
);
auto
moment_out_tensor
=
ctx
.
Output
<
framework
::
Tensor
>
(
"MomentOut"
);
...
...
paddle/fluid/operators/optimizers/ftrl_op.cc
浏览文件 @
621a4085
...
...
@@ -24,46 +24,50 @@ class FTRLOp : public framework::OperatorWithKernel {
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Param"
),
"Input(Param) of FTRL should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"SquaredAccumulator"
),
"Input(SquaredAccumulator) of FTRL should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"LinearAccumulator"
),
"Input(LinearAccumulator) of FTRL should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Grad"
),
"Input(Grad) of FTRL should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"LearningRate"
),
"Input(LearningRate) of FTRL should not be null."
);
PADDLE_ENFORCE
(
ctx
->
GetInputsVarType
(
"Param"
).
front
()
==
framework
::
proto
::
VarType
::
LOD_TENSOR
,
"The input var's type should be LoDTensor, but the received is %s"
,
ctx
->
Inputs
(
"Param"
).
front
(),
ctx
->
GetInputsVarType
(
"Param"
).
front
());
PADDLE_ENFORCE
(
ctx
->
GetInputsVarType
(
"Grad"
).
front
()
==
framework
::
proto
::
VarType
::
LOD_TENSOR
,
"The input var's type should be LoDTensor, but the received is %s"
,
ctx
->
Inputs
(
"Grad"
).
front
(),
ctx
->
GetInputsVarType
(
"Grad"
).
front
());
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"ParamOut"
),
"Output(ParamOut) of FTRL should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"SquaredAccumOut"
),
"Output(SquaredAccumOut) of FTRL should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"LinearAccumOut"
),
"Output(LinearAccumOut) of FTRL should not be null."
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Param"
),
"Input"
,
"Param"
,
"FTRL"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"SquaredAccumulator"
),
"Input"
,
"SquaredAccumulator"
,
"FTRL"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"LinearAccumulator"
),
"Input"
,
"LinearAccumulator"
,
"FTRL"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Grad"
),
"Input"
,
"Grad"
,
"FTRL"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"LearningRate"
),
"Input"
,
"LearningRate"
,
"FTRL"
);
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputsVarType
(
"Param"
).
front
(),
framework
::
proto
::
VarType
::
LOD_TENSOR
,
platform
::
errors
::
InvalidArgument
(
"The input var's type should be LoDTensor, but the received is %s"
,
ctx
->
Inputs
(
"Param"
).
front
(),
ctx
->
GetInputsVarType
(
"Param"
).
front
()));
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputsVarType
(
"Grad"
).
front
(),
framework
::
proto
::
VarType
::
LOD_TENSOR
,
platform
::
errors
::
InvalidArgument
(
"The input var's type should be LoDTensor, but the received is %s"
,
ctx
->
Inputs
(
"Grad"
).
front
(),
ctx
->
GetInputsVarType
(
"Grad"
).
front
()));
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"ParamOut"
),
"Output"
,
"ParamOut"
,
"FTRL"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"SquaredAccumOut"
),
"Output"
,
"SquaredAccumOut"
,
"FTRL"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"LinearAccumOut"
),
"Output"
,
"LinearAccumOut"
,
"FTRL"
);
auto
param_dim
=
ctx
->
GetInputDim
(
"Param"
);
PADDLE_ENFORCE_EQ
(
param_dim
,
ctx
->
GetInputDim
(
"Grad"
),
"Two input of FTRL Op's dimension must be same."
);
platform
::
errors
::
InvalidArgument
(
"Two input of FTRL Op's dimension must be same."
));
auto
lr_dim
=
ctx
->
GetInputDim
(
"LearningRate"
);
PADDLE_ENFORCE_NE
(
framework
::
product
(
lr_dim
),
0
,
"Maybe the Input variable LearningRate has not "
"been initialized. You may need to confirm "
"if you put exe.run(startup_program) "
"after optimizer.minimize function."
);
PADDLE_ENFORCE_EQ
(
framework
::
product
(
lr_dim
),
1
,
"Learning Rate should be a scalar."
);
platform
::
errors
::
InvalidArgument
(
"Maybe the Input variable LearningRate has not "
"been initialized. You may need to confirm "
"if you put exe.run(startup_program) "
"after optimizer.minimize function."
));
PADDLE_ENFORCE_EQ
(
framework
::
product
(
lr_dim
),
1
,
platform
::
errors
::
InvalidArgument
(
"Learning Rate should be a scalar."
));
ctx
->
SetOutputDim
(
"ParamOut"
,
param_dim
);
ctx
->
SetOutputDim
(
"SquaredAccumOut"
,
param_dim
);
...
...
paddle/fluid/operators/optimizers/ftrl_op.h
浏览文件 @
621a4085
...
...
@@ -29,17 +29,19 @@ class FTRLOpKernel : public framework::OpKernel<T> {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
auto
*
param_var
=
ctx
.
InputVar
(
"Param"
);
PADDLE_ENFORCE
(
param_var
->
IsType
<
framework
::
LoDTensor
>
(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Param"
).
front
(),
framework
::
ToTypeName
(
param_var
->
Type
()));
PADDLE_ENFORCE_EQ
(
param_var
->
IsType
<
framework
::
LoDTensor
>
(),
true
,
platform
::
errors
::
InvalidArgument
(
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Param"
).
front
(),
framework
::
ToTypeName
(
param_var
->
Type
())));
const
auto
*
grad_var
=
ctx
.
InputVar
(
"Grad"
);
PADDLE_ENFORCE
(
grad_var
->
IsType
<
framework
::
LoDTensor
>
(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Grad"
).
front
(),
framework
::
ToTypeName
(
grad_var
->
Type
()));
PADDLE_ENFORCE_EQ
(
grad_var
->
IsType
<
framework
::
LoDTensor
>
(),
true
,
platform
::
errors
::
InvalidArgument
(
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Grad"
).
front
(),
framework
::
ToTypeName
(
grad_var
->
Type
())));
auto
*
param_out
=
ctx
.
Output
<
Tensor
>
(
"ParamOut"
);
auto
*
sq_accum_out
=
ctx
.
Output
<
Tensor
>
(
"SquaredAccumOut"
);
...
...
python/paddle/fluid/layers/loss.py
浏览文件 @
621a4085
...
...
@@ -183,6 +183,8 @@ def bpr_loss(input, label, name=None):
"""
helper
=
LayerHelper
(
'bpr_loss'
,
**
locals
())
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
input
.
dtype
)
check_variable_and_dtype
(
input
,
'input'
,
[
'float16'
,
'float32'
,
'float64'
],
'bpr_loss'
)
helper
.
append_op
(
type
=
'bpr_loss'
,
inputs
=
{
'X'
:
[
input
],
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
621a4085
...
...
@@ -13490,6 +13490,8 @@ def continuous_value_model(input, cvm, use_cvm=True):
"""
helper = LayerHelper('cvm', **locals())
out = helper.create_variable(dtype=input.dtype)
check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'],
'cvm')
helper.append_op(
type='cvm',
inputs={'X': [input],
...
...
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