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586a6dd3
编写于
8月 18, 2020
作者:
Z
zhupengyang
提交者:
GitHub
8月 18, 2020
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差异文件
log_softmax and LogSoftmax: impl kernel and refind docs (#26088)
上级
23261ff4
变更
6
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Showing
6 changed file
with
546 addition
and
153 deletion
+546
-153
paddle/fluid/operators/log_softmax_op.cc
paddle/fluid/operators/log_softmax_op.cc
+128
-0
paddle/fluid/operators/log_softmax_op.cu
paddle/fluid/operators/log_softmax_op.cu
+26
-0
paddle/fluid/operators/log_softmax_op.h
paddle/fluid/operators/log_softmax_op.h
+192
-0
python/paddle/fluid/tests/unittests/test_log_softmax.py
python/paddle/fluid/tests/unittests/test_log_softmax.py
+104
-61
python/paddle/nn/functional/activation.py
python/paddle/nn/functional/activation.py
+64
-59
python/paddle/nn/layer/activation.py
python/paddle/nn/layer/activation.py
+32
-33
未找到文件。
paddle/fluid/operators/log_softmax_op.cc
0 → 100644
浏览文件 @
586a6dd3
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/log_softmax_op.h"
#include <string>
#include <unordered_map>
#include "paddle/fluid/operators/common_infer_shape_functions.h"
namespace
paddle
{
namespace
operators
{
class
LogSoftmaxOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
return
UnaryOpUnchangedInferShapeCheckAxis
(
ctx
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
"X"
),
ctx
.
device_context
());
}
};
class
LogSoftmaxOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"The input tensor of softmax, "
"whose dimension :attr:`axis` is the input_feature_dimensions."
);
AddOutput
(
"Out"
,
"The normalized values with the same shape as X."
);
AddAttr
<
int
>
(
"axis"
,
"The dimension index of Input(x) to perform log_softmax,"
"default -1 for last dimension"
)
.
SetDefault
(
-
1
);
AddComment
(
R"DOC(
LogSoftmax Operator.
)DOC"
);
}
};
class
LogSoftmaxOpInferVarType
:
public
framework
::
PassInDtypeAndVarTypeToOutput
{
protected:
std
::
unordered_map
<
std
::
string
,
std
::
string
>&
GetInputOutputWithSameType
()
const
override
{
static
std
::
unordered_map
<
std
::
string
,
std
::
string
>
m
{{
"X"
,
/*->*/
"Out"
}};
return
m
;
}
};
class
LogSoftmaxGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Out"
),
"Input"
,
"Out"
,
"log_softmax_grad"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input"
,
"Out@grad"
,
"log_softmax_grad"
);
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputDim
(
"Out"
),
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
)),
platform
::
errors
::
InvalidArgument
(
"Input(Out) and its gradients "
"should have the same shape."
));
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
)));
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
framework
::
GradVarName
(
"Out"
)),
ctx
.
device_context
());
}
};
template
<
typename
T
>
class
LogSoftmaxGradOpMaker
:
public
framework
::
SingleGradOpMaker
<
T
>
{
public:
using
framework
::
SingleGradOpMaker
<
T
>::
SingleGradOpMaker
;
protected:
void
Apply
(
GradOpPtr
<
T
>
op
)
const
override
{
op
->
SetType
(
"log_softmax_grad"
);
op
->
SetInput
(
"Out"
,
this
->
Output
(
"Out"
));
op
->
SetInput
(
framework
::
GradVarName
(
"Out"
),
this
->
OutputGrad
(
"Out"
));
op
->
SetOutput
(
framework
::
GradVarName
(
"X"
),
this
->
InputGrad
(
"X"
));
op
->
SetAttrMap
(
this
->
Attrs
());
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
log_softmax
,
ops
::
LogSoftmaxOp
,
ops
::
LogSoftmaxOpMaker
,
ops
::
LogSoftmaxOpInferVarType
,
ops
::
LogSoftmaxGradOpMaker
<
paddle
::
framework
::
OpDesc
>
,
ops
::
LogSoftmaxGradOpMaker
<
paddle
::
imperative
::
OpBase
>
);
REGISTER_OPERATOR
(
log_softmax_grad
,
ops
::
LogSoftmaxGradOp
);
REGISTER_OP_CPU_KERNEL
(
log_softmax
,
ops
::
LogSoftmaxKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
LogSoftmaxKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
log_softmax_grad
,
ops
::
LogSoftmaxGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
LogSoftmaxGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/log_softmax_op.cu
0 → 100644
浏览文件 @
586a6dd3
// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/log_softmax_op.h"
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_CUDA_KERNEL
(
log_softmax
,
ops
::
LogSoftmaxKernel
<
plat
::
CUDADeviceContext
,
float
>
,
ops
::
LogSoftmaxKernel
<
plat
::
CUDADeviceContext
,
double
>
,
ops
::
LogSoftmaxKernel
<
plat
::
CUDADeviceContext
,
plat
::
float16
>
);
REGISTER_OP_CUDA_KERNEL
(
log_softmax_grad
,
ops
::
LogSoftmaxGradKernel
<
plat
::
CUDADeviceContext
,
float
>
,
ops
::
LogSoftmaxGradKernel
<
plat
::
CUDADeviceContext
,
double
>
,
ops
::
LogSoftmaxGradKernel
<
plat
::
CUDADeviceContext
,
plat
::
float16
>
);
paddle/fluid/operators/log_softmax_op.h
0 → 100644
浏览文件 @
586a6dd3
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
static
inline
int
CanonicalAxis
(
const
int
axis
,
const
int
rank
)
{
if
(
axis
<
0
)
{
return
axis
+
rank
;
}
return
axis
;
}
static
inline
int
SizeToAxis
(
const
int
axis
,
const
framework
::
DDim
dims
)
{
int
size
=
1
;
for
(
int
i
=
0
;
i
<
axis
;
i
++
)
{
size
*=
dims
[
i
];
}
return
size
;
}
static
inline
int
SizeFromAxis
(
const
int
axis
,
const
framework
::
DDim
dims
)
{
int
size
=
1
;
for
(
int
i
=
axis
;
i
<
dims
.
size
();
i
++
)
{
size
*=
dims
[
i
];
}
return
size
;
}
template
<
typename
T
>
struct
ValueClip
{
HOSTDEVICE
T
operator
()(
const
T
&
x
)
const
{
const
T
kThreshold
=
static_cast
<
T
>
(
-
64.
);
return
x
<
kThreshold
?
kThreshold
:
x
;
}
};
template
<
typename
DeviceContext
,
typename
T
>
struct
LogSoftmaxFunctor
{
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
*
X
,
framework
::
Tensor
*
Y
,
const
int
axis
)
{
constexpr
int
kBatchDim
=
0
;
constexpr
int
kClassDim
=
1
;
constexpr
int
kAxisDim
=
1
;
int
axis_dim
=
X
->
dims
()[
axis
];
const
int
n
=
SizeToAxis
(
axis
,
X
->
dims
());
const
int
d
=
SizeFromAxis
(
axis
,
X
->
dims
());
framework
::
DDim
dim_2d
{
n
,
d
};
auto
logits
=
EigenMatrix
<
T
>::
From
(
*
X
,
dim_2d
);
auto
log_softmax
=
EigenMatrix
<
T
>::
From
(
*
Y
,
dim_2d
);
const
int
batch_size
=
logits
.
dimension
(
kBatchDim
);
const
int
num_classes
=
logits
.
dimension
(
kClassDim
);
const
int
num_remain
=
num_classes
/
axis_dim
;
Eigen
::
DSizes
<
int
,
1
>
along_axis
(
kAxisDim
);
Eigen
::
DSizes
<
int
,
2
>
batch_classes
(
batch_size
,
num_classes
);
Eigen
::
DSizes
<
int
,
2
>
batch_by_one
(
batch_size
,
1
);
Eigen
::
DSizes
<
int
,
2
>
one_by_class
(
1
,
num_classes
);
Eigen
::
DSizes
<
int
,
3
>
batch_one_remain
(
batch_size
,
1
,
num_remain
);
Eigen
::
DSizes
<
int
,
3
>
one_axis_one
(
1
,
axis_dim
,
1
);
Eigen
::
DSizes
<
int
,
2
>
one_axis
(
1
,
axis_dim
);
Eigen
::
DSizes
<
int
,
3
>
batch_axis_remain
(
batch_size
,
axis_dim
,
num_remain
);
// For numerical stability, logits should be shifted by maximum number along
// axis, calculate shifted_logits into log_softmax tensor for memory reuse.
if
(
num_remain
==
1
)
{
// axis == -1, axis and class in same dimension, calculate along
// class dimension directly for higher performance
log_softmax
.
device
(
*
context
.
eigen_device
())
=
(
logits
-
logits
.
maximum
(
along_axis
)
.
eval
()
.
reshape
(
batch_by_one
)
.
broadcast
(
one_by_class
))
.
unaryExpr
(
ValueClip
<
T
>
());
}
else
{
// axis != -1, class dimension split into (axis, remain), max and sum
// should be calculated along axis dimension
log_softmax
.
device
(
*
context
.
eigen_device
())
=
(
logits
.
reshape
(
batch_axis_remain
)
-
logits
.
reshape
(
batch_axis_remain
)
.
maximum
(
along_axis
)
.
eval
()
.
reshape
(
batch_one_remain
)
.
broadcast
(
one_axis_one
)
.
reshape
(
batch_classes
))
.
unaryExpr
(
ValueClip
<
T
>
());
}
log_softmax
.
device
(
*
context
.
eigen_device
())
=
log_softmax
-
log_softmax
.
exp
()
.
eval
()
.
reshape
(
batch_axis_remain
)
.
sum
(
along_axis
)
.
log
()
.
broadcast
(
one_axis
);
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
LogSoftmaxKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
Out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
const
int
rank
=
X
->
dims
().
size
();
const
int
axis
=
CanonicalAxis
(
context
.
Attr
<
int
>
(
"axis"
),
rank
);
// allocate memory on device.
Out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
LogSoftmaxFunctor
<
DeviceContext
,
T
>
()(
context
.
template
device_context
<
DeviceContext
>(),
X
,
Out
,
axis
);
}
};
template
<
typename
DeviceContext
,
typename
T
>
struct
LogSoftmaxGradFunctor
{
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
*
Y
,
const
framework
::
Tensor
*
dY
,
framework
::
Tensor
*
dX
,
const
int
axis
)
{
constexpr
int
kBatchDim
=
0
;
constexpr
int
kClassDim
=
1
;
const
int
n
=
SizeToAxis
(
axis
,
Y
->
dims
());
const
int
d
=
SizeFromAxis
(
axis
,
Y
->
dims
());
framework
::
DDim
dim_2d
{
n
,
d
};
auto
y
=
EigenMatrix
<
T
>::
From
(
*
Y
,
dim_2d
);
auto
dy
=
EigenMatrix
<
T
>::
From
(
*
dY
,
dim_2d
);
auto
dx
=
EigenMatrix
<
T
>::
From
(
*
dX
,
dim_2d
);
const
int
axis_dim
=
Y
->
dims
()[
axis
];
const
int
batch_size
=
y
.
dimension
(
kBatchDim
);
const
int
num_classes
=
y
.
dimension
(
kClassDim
);
const
int
num_remain
=
num_classes
/
axis_dim
;
Eigen
::
DSizes
<
int
,
1
>
along_class
(
kClassDim
);
Eigen
::
DSizes
<
int
,
3
>
batch_axis_remain
(
batch_size
,
axis_dim
,
num_remain
);
Eigen
::
DSizes
<
int
,
2
>
one_axis
(
1
,
axis_dim
);
dx
.
device
(
*
context
.
eigen_device
())
=
dy
-
(
y
.
exp
())
*
(
dy
.
reshape
(
batch_axis_remain
)
.
sum
(
along_class
)
.
broadcast
(
one_axis
));
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
LogSoftmaxGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
Out
=
context
.
Input
<
framework
::
Tensor
>
(
"Out"
);
auto
*
dOut
=
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
dX
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
const
int
rank
=
Out
->
dims
().
size
();
const
int
axis
=
CanonicalAxis
(
context
.
Attr
<
int
>
(
"axis"
),
rank
);
// allocate memory on device.
dX
->
mutable_data
<
T
>
(
context
.
GetPlace
());
LogSoftmaxGradFunctor
<
DeviceContext
,
T
>
()(
context
.
template
device_context
<
DeviceContext
>(),
Out
,
dOut
,
dX
,
axis
);
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/tests/unittests/test_log_softmax.py
浏览文件 @
586a6dd3
...
...
@@ -14,93 +14,136 @@
import
unittest
import
numpy
as
np
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
paddle.nn
as
nn
import
paddle.nn.functional
as
functional
from
op_test
import
OpTest
import
paddle
import
paddle.nn.functional
as
F
np
.
random
.
seed
(
10
)
def
stable_softmax
(
x
):
def
ref_log_softmax
(
x
):
shiftx
=
(
x
-
np
.
max
(
x
))
exps
=
np
.
exp
(
shiftx
)
return
exps
/
np
.
sum
(
exps
)
out
=
shiftx
-
np
.
log
(
np
.
exp
(
shiftx
).
sum
()
)
return
out
def
ref_log_softmax
(
x
,
axis
=
None
,
dtype
=
None
):
x_t
=
x
.
copy
()
if
dtype
is
not
None
:
x_t
=
x_t
.
astype
(
dtype
)
if
axis
is
None
:
axis
=
-
1
out
=
np
.
apply_along_axis
(
stable_softmax
,
axis
,
x_t
)
return
np
.
log
(
out
)
def
ref_log_softmax_grad
(
x
,
axis
):
if
axis
<
0
:
axis
+=
len
(
x
.
shape
)
out
=
np
.
apply_along_axis
(
ref_log_softmax
,
axis
,
x
)
axis_dim
=
x
.
shape
[
axis
]
dout
=
np
.
full_like
(
x
,
fill_value
=
1.
/
x
.
size
)
dx
=
dout
-
np
.
exp
(
out
)
*
dout
.
copy
().
sum
(
axis
=
axis
,
keepdims
=
True
).
repeat
(
axis_dim
,
axis
=
axis
)
return
dx
class
Test
NNLogSoftmaxAPI
(
unittest
.
TestCase
):
class
Test
LogSoftmaxOp
(
OpTest
):
def
setUp
(
self
):
self
.
init_data
()
self
.
op_type
=
'log_softmax'
self
.
dtype
=
'float64'
self
.
shape
=
[
2
,
3
,
4
,
5
]
self
.
axis
=
-
1
self
.
set_attrs
()
def
init_data
(
self
):
self
.
x_shape
=
[
2
,
3
,
4
,
5
]
self
.
x
=
np
.
random
.
uniform
(
-
1
,
1
,
self
.
x_shape
).
astype
(
np
.
float32
)
x
=
np
.
random
.
uniform
(
0.1
,
1.
,
self
.
shape
).
astype
(
self
.
dtype
)
out
=
np
.
apply_along_axis
(
ref_log_softmax
,
self
.
axis
,
x
)
self
.
x_grad
=
ref_log_softmax_grad
(
x
,
self
.
axis
)
self
.
inputs
=
{
'X'
:
x
}
self
.
outputs
=
{
'Out'
:
out
}
self
.
attrs
=
{
'axis'
:
self
.
axis
}
def
set_attrs
(
self
):
pass
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
[
'Out'
],
user_defined_grads
=
[
self
.
x_grad
])
class
TestLogSoftmaxShape
(
TestLogSoftmaxOp
):
def
set_attrs
(
self
):
self
.
shape
=
[
12
,
10
]
def
check_api
(
self
,
place
=
fluid
.
CPUPlace
(),
axis
=
None
):
ref_out
=
ref_log_softmax
(
self
.
x
,
axis
)
main_program
=
fluid
.
Program
()
mylogsoftmax
=
nn
.
LogSoftmax
(
axis
)
with
fluid
.
program_guard
(
main_program
):
x
=
fluid
.
data
(
name
=
'x'
,
shape
=
self
.
x_shape
)
y
=
mylogsoftmax
(
x
)
exe
=
fluid
.
Executor
(
place
)
out
=
exe
.
run
(
main_program
,
feed
=
{
'x'
:
self
.
x
},
fetch_list
=
[
y
])
class
TestLogSoftmaxAxis
(
TestLogSoftmaxOp
):
def
set_attrs
(
self
):
self
.
axis
=
1
class
TestNNLogSoftmaxAPI
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
x_shape
=
[
2
,
3
,
4
,
5
]
self
.
x
=
np
.
random
.
uniform
(
-
1.
,
1.
,
self
.
x_shape
).
astype
(
np
.
float32
)
self
.
place
=
paddle
.
CUDAPlace
(
0
)
\
if
paddle
.
fluid
.
core
.
is_compiled_with_cuda
()
\
else
paddle
.
CPUPlace
()
def
check_api
(
self
,
axis
=-
1
):
ref_out
=
np
.
apply_along_axis
(
ref_log_softmax
,
axis
,
self
.
x
)
logsoftmax
=
paddle
.
nn
.
LogSoftmax
(
axis
)
# test static api
with
paddle
.
static
.
program_guard
(
paddle
.
static
.
Program
()):
x
=
paddle
.
data
(
name
=
'x'
,
shape
=
self
.
x_shape
)
y
=
logsoftmax
(
x
)
exe
=
paddle
.
static
.
Executor
(
self
.
place
)
out
=
exe
.
run
(
feed
=
{
'x'
:
self
.
x
},
fetch_list
=
[
y
])
self
.
assertTrue
(
np
.
allclose
(
out
[
0
],
ref_out
))
with
fluid
.
dygraph
.
guard
(
place
):
x
=
fluid
.
dygraph
.
to_variable
(
self
.
x
)
y
=
mylogsoftmax
(
x
)
# test dygrapg api
paddle
.
disable_static
()
x
=
paddle
.
to_variable
(
self
.
x
)
y
=
logsoftmax
(
x
)
self
.
assertTrue
(
np
.
allclose
(
y
.
numpy
(),
ref_out
))
paddle
.
enable_static
()
def
test_check_api
(
self
):
places
=
[
fluid
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
places
.
append
(
fluid
.
CUDAPlace
(
0
))
for
place
in
places
:
for
axis
in
[
None
,
2
]:
self
.
check_api
(
place
,
axis
)
for
axis
in
[
-
1
,
1
]:
self
.
check_api
(
axis
)
class
TestNNFunctionalLogSoftmaxAPI
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
init_data
()
def
init_data
(
self
):
self
.
x_shape
=
[
2
,
3
,
4
,
5
]
self
.
x
=
np
.
random
.
uniform
(
-
1
,
1
,
self
.
x_shape
).
astype
(
np
.
float32
)
self
.
place
=
paddle
.
CUDAPlace
(
0
)
\
if
paddle
.
fluid
.
core
.
is_compiled_with_cuda
()
\
else
paddle
.
CPUPlace
()
def
check_api
(
self
,
place
=
fluid
.
CPUPlace
(),
axis
=
None
,
dtype
=
None
):
ref_out
=
ref_log_softmax
(
self
.
x
,
axis
,
dtype
)
main_program
=
fluid
.
Program
()
mylogsoftmax
=
nn
.
LogSoftmax
(
axis
)
with
fluid
.
program_guard
(
main_program
):
x
=
fluid
.
data
(
name
=
'x'
,
shape
=
self
.
x_shape
)
y
=
functional
.
log_softmax
(
x
,
axis
,
dtype
)
exe
=
fluid
.
Executor
(
place
)
out
=
exe
.
run
(
main_program
,
feed
=
{
'x'
:
self
.
x
},
fetch_list
=
[
y
])
def
check_api
(
self
,
axis
=-
1
,
dtype
=
None
):
x
=
self
.
x
.
copy
()
if
dtype
is
not
None
:
x
=
x
.
astype
(
dtype
)
ref_out
=
np
.
apply_along_axis
(
ref_log_softmax
,
axis
,
x
)
with
paddle
.
static
.
program_guard
(
paddle
.
static
.
Program
()):
x
=
paddle
.
data
(
name
=
'x'
,
shape
=
self
.
x_shape
)
y
=
F
.
log_softmax
(
x
,
axis
,
dtype
)
exe
=
paddle
.
static
.
Executor
(
self
.
place
)
out
=
exe
.
run
(
feed
=
{
'x'
:
self
.
x
},
fetch_list
=
[
y
])
self
.
assertTrue
(
np
.
allclose
(
out
[
0
],
ref_out
))
with
fluid
.
dygraph
.
guard
(
place
):
x
=
fluid
.
dygraph
.
to_variable
(
self
.
x
)
y
=
functional
.
log_softmax
(
x
,
axis
,
dtype
)
self
.
assertTrue
(
np
.
allclose
(
y
.
numpy
(),
ref_out
))
paddle
.
disable_static
()
x
=
paddle
.
to_variable
(
self
.
x
)
y
=
F
.
log_softmax
(
x
,
axis
,
dtype
)
self
.
assertTrue
(
np
.
allclose
(
y
.
numpy
(),
ref_out
),
True
)
paddle
.
enable_static
()
def
test_check_api
(
self
):
places
=
[
fluid
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
places
.
append
(
fluid
.
CUDAPlace
(
0
))
for
place
in
places
:
self
.
check_api
(
place
,
None
,
None
)
self
.
check_api
(
place
,
None
,
np
.
float64
)
for
axis
in
[
-
1
,
1
]:
self
.
check_api
(
axis
)
self
.
check_api
(
-
1
,
'float64'
)
def
test_errors
(
self
):
with
paddle
.
static
.
program_guard
(
paddle
.
static
.
Program
()):
x
=
paddle
.
data
(
name
=
'X1'
,
shape
=
[
100
],
dtype
=
'int32'
)
self
.
assertRaises
(
TypeError
,
F
.
log_softmax
,
x
)
x
=
paddle
.
data
(
name
=
'X2'
,
shape
=
[
100
],
dtype
=
'float32'
)
self
.
assertRaises
(
TypeError
,
F
.
log_softmax
,
x
,
dtype
=
'int32'
)
if
__name__
==
"__main__"
:
...
...
python/paddle/nn/functional/activation.py
浏览文件 @
586a6dd3
...
...
@@ -65,7 +65,7 @@ import warnings
from
...fluid.layer_helper
import
LayerHelper
from
...fluid.framework
import
in_dygraph_mode
,
convert_np_dtype_to_dtype_
from
...fluid
import
core
from
...fluid.data_feeder
import
check_variable_and_dtype
from
...fluid.data_feeder
import
check_variable_and_dtype
,
check_dtype
import
paddle
...
...
@@ -413,12 +413,10 @@ def softmax(x, axis=-1, name=None):
return
paddle
.
fluid
.
layers
.
softmax
(
input
=
x
,
axis
=
axis
,
name
=
name
)
def
log_softmax
(
input
,
axis
=
None
,
dtype
=
None
,
name
=
None
):
def
log_softmax
(
x
,
axis
=-
1
,
dtype
=
None
,
name
=
None
):
"""
:alias_main: paddle.nn.functional.log_softmax
:alias: paddle.nn.functional.log_softmax,paddle.nn.functional.activation.log_softmax
This operator implements the log_softmax layer. The calculation process is as follows:
This operator implements the log_softmax layer. The calculation process is
as follows:
.. math::
...
...
@@ -426,35 +424,44 @@ def log_softmax(input, axis=None, dtype=None, name=None):
= log(
\\
frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])})
Parameters:
input (Variable): The input variable. A multi-dimension Tensor with type float32, or float64.
axis (int, optional): The index of dimension to perform softmax calculations, it should be in
range :math:`[-1, rank-1]`, while :math:`rank` is the rank of input variable. Default: None.
None and -1 means the last dimension.
dtype (np.dtype|core.VarDesc.VarType|str): The desired data type of returned tensor. If specified,
the input tensor is casted to dtype before the operation is performed. This is useful for
preventing data type overflows. Default: None. Supported dtype: float32 or float64
name (str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
x (Tensor): The input Tensor with data type float32, float64.
axis (int, optional): The axis along which to perform log_softmax
calculations. It should be in range [-D, D), where D is the
dimensions of ``x`` . If ``axis`` < 0, it works the same way as
:math:`axis + D` . Default is -1.
dtype (str|np.dtype|core.VarDesc.VarType, optional): The desired data
type of the output tensor. If dtype is specified, ``x`` is casted
to ``dtype`` before the operation is performed. This is useful for
preventing data type overflows. Supported dtype: float32, float64.
If ``dtype`` is None, the output Tensor has the same dtype as x.
Default is None.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Variable: ``Tensor`` indicates the output of softmax. The data type and shape are the same as ``input``.
A Tensor with the same shape and data type (use ``dtype`` if it is
specified) as x.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
import paddle.nn.functional as F
import numpy as np
data = np.array([[[-2.0, 3.0, -4.0, 5.0],
paddle.disable_static()
x = np.array([[[-2.0, 3.0, -4.0, 5.0],
[3.0, -4.0, 5.0, -6.0],
[-7.0, -8.0, 8.0, 9.0]],
[[1.0, -2.0, -3.0, 4.0],
[-5.0, 6.0, 7.0, -8.0],
[6.0, 7.0, 8.0, 9.0]]]).astype('float32')
with fluid.dygraph.guard():
data = fluid.dygraph.to_variable(data)
res = F.log_softmax(data, -1)
[6.0, 7.0, 8.0, 9.0]]], 'float32')
x = paddle.to_tensor(x)
out1 = F.log_softmax(x)
out2 = F.log_softmax(x, dtype='float64')
# out1's data type is float32; out2's data type is float64
# out1 and out2's value is as follows:
# [[[ -7.1278396 -2.1278396 -9.127839 -0.12783948]
# [ -2.1270514 -9.127051 -0.12705144 -11.127051 ]
# [-16.313261 -17.313261 -1.3132617 -0.31326184]]
...
...
@@ -463,41 +470,39 @@ def log_softmax(input, axis=None, dtype=None, name=None):
# [ -3.4401896 -2.4401896 -1.4401896 -0.44018966]]]
"""
axis
=
-
1
if
axis
is
None
else
axis
dtype
=
convert_np_dtype_to_dtype_
(
dtype
)
if
dtype
is
not
None
else
dtype
if
axis
is
None
:
axis
=
-
1
if
(
dtype
is
not
None
)
and
(
not
isinstance
(
dtype
,
core
.
VarDesc
.
VarType
)):
dtype
=
convert_np_dtype_to_dtype_
(
dtype
)
if
in_dygraph_mode
():
outs_cast
=
input
if
dtype
is
None
\
else
core
.
ops
.
cast
(
input
,
'in_dtype'
,
input
.
dtype
,
'out_dtype'
,
dtype
)
outs_softmax
=
core
.
ops
.
softmax
(
outs_cast
,
'axis'
,
axis
,
'use_cudnn'
,
False
)
return
core
.
ops
.
log
(
outs_softmax
)
if
dtype
is
not
None
:
x
=
core
.
ops
.
cast
(
x
,
'in_dtype'
,
x
.
dtype
,
'out_dtype'
,
dtype
)
return
core
.
ops
.
log_softmax
(
x
,
'axis'
,
axis
)
if
dtype
is
None
:
check_variable_and_dtype
(
input
,
'input'
,
[
'float16'
,
'float32'
,
'float64'
],
'log_softmax'
)
check_variable_and_dtype
(
x
,
'x'
,
[
'float16'
,
'float32'
,
'float64'
],
'log_softmax'
)
else
:
check_dtype
(
dtype
,
'dtype'
,
[
'float32'
,
'float64'
],
'log_softmax'
,
'If dtype is not None, it only support float32 or float64.'
)
helper
=
LayerHelper
(
"log_softmax"
,
**
locals
())
out
s_cast
=
input
out
_cast
=
x
if
dtype
is
not
None
:
out
s
_cast
=
helper
.
create_variable_for_type_inference
(
dtype
)
out_cast
=
helper
.
create_variable_for_type_inference
(
dtype
)
helper
.
append_op
(
type
=
'cast'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
out
s
_cast
},
attrs
=
{
'in_dtype'
:
input
.
dtype
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out_cast
},
attrs
=
{
'in_dtype'
:
x
.
dtype
,
'out_dtype'
:
dtype
})
outs_softmax
=
helper
.
create_variable_for_type_inference
(
outs_cast
.
dtype
)
helper
.
append_op
(
type
=
'softmax'
,
inputs
=
{
'X'
:
outs_cast
},
outputs
=
{
'Out'
:
outs_softmax
},
attrs
=
{
'axis'
:
axis
,
'use_cudnn'
:
False
})
outs_log
=
helper
.
create_variable_for_type_inference
(
outs_softmax
.
dtype
)
out
=
helper
.
create_variable_for_type_inference
(
out_cast
.
dtype
)
helper
.
append_op
(
type
=
'log'
,
inputs
=
{
'X'
:
outs_softmax
},
outputs
=
{
'Out'
:
outs_log
})
type
=
'log_softmax'
,
inputs
=
{
'X'
:
out_cast
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'axis'
:
axis
})
return
out
s_log
return
out
python/paddle/nn/layer/activation.py
浏览文件 @
586a6dd3
...
...
@@ -338,9 +338,6 @@ class Sigmoid(layers.Layer):
class
LogSoftmax
(
layers
.
Layer
):
"""
:alias_main: paddle.nn.LogSoftmax
:alias: paddle.nn.LogSoftmax,paddle.nn.layer.LogSoftmax,paddle.nn.layer.activation.LogSoftmax
This operator implements the log_softmax layer. The calculation process is as follows:
.. math::
...
...
@@ -349,33 +346,34 @@ class LogSoftmax(layers.Layer):
= log(
\\
frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])})
Parameters:
axis (int, optional): The
index of dimension to perform softmax calculations, it should be in
range :math:`[-1, rank-1]`, while :math:`rank` is the rank of input variable. Default: None.
None and -1 means the last dimension.
dtype (np.dtype|core.VarDesc.VarType|str): The desired data type of returned tensor. If specified,
the input tensor is casted to dtype before the operation is performed. This is useful for
preventing data type overflows. Default: None. Supported dtype: float32 or float64
axis (int, optional): The
axis along which to perform log_softmax
calculations. It should be in range [-D, D), where D is the
dimensions of the input Tensor . If ``axis`` < 0, it works the
same way as :math:`axis + D` . Default is -1.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
None
Shape:
- input: Tensor with any shape.
- output: Tensor with the same shape as input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.nn as nn
import paddle
import numpy as np
data = np.array([[[-2.0, 3.0, -4.0, 5.0],
paddle.disable_static()
x = np.array([[[-2.0, 3.0, -4.0, 5.0],
[3.0, -4.0, 5.0, -6.0],
[-7.0, -8.0, 8.0, 9.0]],
[[1.0, -2.0, -3.0, 4.0],
[-5.0, 6.0, 7.0, -8.0],
[6.0, 7.0, 8.0, 9.0]]]).astype('float32')
my_log_softnmax = nn.LogSoftmax()
with fluid.dygraph.guard():
data = fluid.dygraph.to_variable(data)
res = my_log_softnmax(data)
[6.0, 7.0, 8.0, 9.0]]])
m = paddle.nn.LogSoftmax()
x = paddle.to_tensor(x)
out = m(x)
# [[[ -7.1278396 -2.1278396 -9.127839 -0.12783948]
# [ -2.1270514 -9.127051 -0.12705144 -11.127051 ]
# [-16.313261 -17.313261 -1.3132617 -0.31326184]]
...
...
@@ -384,9 +382,10 @@ class LogSoftmax(layers.Layer):
# [ -3.4401896 -2.4401896 -1.4401896 -0.44018966]]]
"""
def
__init__
(
self
,
axis
=
None
):
def
__init__
(
self
,
axis
=
-
1
,
name
=
None
):
super
(
LogSoftmax
,
self
).
__init__
()
self
.
_axis
=
axis
self
.
_name
=
name
def
forward
(
self
,
input
):
return
F
.
log_softmax
(
input
,
self
.
_axis
)
def
forward
(
self
,
x
):
return
F
.
log_softmax
(
x
,
self
.
_axis
)
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