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体验新版 GitCode,发现更多精彩内容 >>
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5de576b0
编写于
8月 17, 2021
作者:
Z
zhiboniu
提交者:
GitHub
8月 17, 2021
浏览文件
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电子邮件补丁
差异文件
add api fill_diagonal_inplace (#34460)
上级
16146088
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
586 addition
and
0 deletion
+586
-0
paddle/fluid/operators/fill_diagonal_op.cc
paddle/fluid/operators/fill_diagonal_op.cc
+217
-0
paddle/fluid/operators/fill_diagonal_op.cu
paddle/fluid/operators/fill_diagonal_op.cu
+122
-0
paddle/fluid/operators/fill_diagonal_op.h
paddle/fluid/operators/fill_diagonal_op.h
+25
-0
python/paddle/fluid/tests/unittests/test_tensor_fill_diagonal_.py
...addle/fluid/tests/unittests/test_tensor_fill_diagonal_.py
+173
-0
python/paddle/tensor/manipulation.py
python/paddle/tensor/manipulation.py
+49
-0
未找到文件。
paddle/fluid/operators/fill_diagonal_op.cc
0 → 100644
浏览文件 @
5de576b0
/* Copyright (c) 2021 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/fill_diagonal_op.h"
namespace
paddle
{
namespace
operators
{
int64_t
CalStride
(
framework
::
DDim
dim
)
{
int
rank
=
dim
.
size
();
int64_t
dimsum
=
1
;
int64_t
strides
=
0
;
for
(
int
i
=
rank
-
1
;
i
>=
0
;
i
--
)
{
strides
+=
dimsum
;
dimsum
*=
dim
[
i
];
}
return
strides
;
}
class
FillIDiagonalOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddComment
(
R"DOC(Fill replace operator
Fill the diagonal of an tensor with 'value'.
)DOC"
);
AddInput
(
"X"
,
"(Tensor) The input tensor."
);
AddOutput
(
"Out"
,
"Tensor, the output tensor, with the same shape and data type "
"as input(x)"
);
AddAttr
<
float
>
(
"value"
,
"The float values of tensor, whose dim is one, and no need of grad"
)
.
SetDefault
(
0
);
AddAttr
<
bool
>
(
"wrap"
,
"the diagonal 'wrapped' after N columns for tall matrices"
)
.
SetDefault
(
false
);
AddAttr
<
int
>
(
"offset"
,
"offset of diagonal, zero means no offset, positive means "
"offset to up-right corner; negtive means offset to "
"bottom-left corner"
)
.
SetDefault
(
0
);
}
};
class
FillIDiagonalOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
context
)
const
override
{
OP_INOUT_CHECK
(
context
->
HasInput
(
"X"
),
"Input"
,
"X"
,
"FillIDiagonal"
);
OP_INOUT_CHECK
(
context
->
HasOutput
(
"Out"
),
"Output"
,
"Out"
,
"FillIDiagonal"
);
auto
x_dims
=
context
->
GetInputDim
(
"X"
);
context
->
SetOutputDim
(
"Out"
,
x_dims
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
"X"
),
ctx
.
GetPlace
());
}
};
class
FillIDiagonalOpVarTypeInference
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
framework
::
InferVarTypeContext
*
ctx
)
const
override
{
auto
var_type
=
ctx
->
GetInputType
(
"X"
,
0
);
auto
data_type
=
ctx
->
GetInputDataType
(
"X"
,
0
);
ctx
->
SetOutputType
(
"Out"
,
var_type
,
framework
::
ALL_ELEMENTS
);
ctx
->
SetOutputDataType
(
"Out"
,
data_type
,
framework
::
ALL_ELEMENTS
);
}
};
template
<
typename
T
>
class
FillIDiagonalKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
fill_val
=
ctx
.
template
Attr
<
float
>(
"value"
);
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
offset
=
ctx
.
Attr
<
int
>
(
"offset"
);
auto
wrap
=
ctx
.
Attr
<
bool
>
(
"wrap"
);
auto
*
xin
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
T
temp_var
=
static_cast
<
T
>
(
fill_val
);
T
*
out_data
=
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
framework
::
TensorCopy
(
*
xin
,
ctx
.
GetPlace
(),
out
);
auto
out_dims
=
out
->
dims
();
auto
strides
=
CalStride
(
out_dims
);
auto
size
=
out
->
numel
();
// The wrap mode supported only the dims equels to 2; In wrap mode, the
// value will be filled in cycles
if
(
!
wrap
)
{
size
=
std
::
min
(
size
,
out_dims
[
1
]
*
out_dims
[
1
]);
}
for
(
int64_t
i
=
offset
;
i
<
size
;
i
+=
strides
)
{
out_data
[
i
]
=
temp_var
;
}
}
};
class
FillIDiagonalGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
OP_INOUT_CHECK
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input"
,
"Out@GRAD"
,
"mul"
);
auto
x_dims
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
));
auto
x_grad_name
=
framework
::
GradVarName
(
"X"
);
if
(
ctx
->
HasOutput
(
x_grad_name
))
{
ctx
->
SetOutputDim
(
x_grad_name
,
x_dims
);
}
}
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
// Note: don't get data type from ctx.Input<framework::Tensor>("Input");
auto
dtype
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
type
();
return
framework
::
OpKernelType
(
dtype
,
ctx
.
GetPlace
());
}
};
template
<
typename
T
>
class
FillIDiagonalGradOpMaker
:
public
framework
::
SingleGradOpMaker
<
T
>
{
public:
using
framework
::
SingleGradOpMaker
<
T
>::
SingleGradOpMaker
;
protected:
void
Apply
(
GradOpPtr
<
T
>
retv
)
const
override
{
retv
->
SetType
(
"fill_diagonal_grad"
);
retv
->
SetInput
(
framework
::
GradVarName
(
"Out"
),
this
->
OutputGrad
(
"Out"
));
retv
->
SetOutput
(
framework
::
GradVarName
(
"X"
),
this
->
InputGrad
(
"X"
));
retv
->
SetAttrMap
(
this
->
Attrs
());
}
};
template
<
typename
T
>
class
FillIDiagonalGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
dx
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dout
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
offset
=
ctx
.
Attr
<
int
>
(
"offset"
);
auto
wrap
=
ctx
.
Attr
<
bool
>
(
"wrap"
);
if
(
dx
)
{
auto
*
data
=
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
framework
::
TensorCopy
(
*
dout
,
ctx
.
GetPlace
(),
dx
);
auto
dx_dims
=
dx
->
dims
();
auto
strides
=
CalStride
(
dx_dims
);
auto
size
=
dx
->
numel
();
auto
wrapsize
=
std
::
min
(
size
,
dx_dims
[
1
]
*
dx_dims
[
1
]);
// The wrap mode supported only the dims equels to 2; In wrap mode, the
// value will be filled in cycles
if
(
wrap
)
{
wrapsize
=
size
;
}
for
(
int64_t
i
=
offset
;
i
<
wrapsize
;
i
+=
strides
)
{
data
[
i
]
=
T
(
0
);
}
}
}
};
DECLARE_INPLACE_OP_INFERER
(
FillIDiagonalOpInplaceInferer
,
{
"X"
,
"Out"
});
DECLARE_INPLACE_OP_INFERER
(
FillIDiagonalGradOpInplaceInferer
,
{
framework
::
GradVarName
(
"Out"
),
framework
::
GradVarName
(
"X"
)});
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
fill_diagonal
,
ops
::
FillIDiagonalOp
,
ops
::
FillIDiagonalOpMaker
,
ops
::
FillIDiagonalOpVarTypeInference
,
ops
::
FillIDiagonalGradOpMaker
<
paddle
::
framework
::
OpDesc
>
,
ops
::
FillIDiagonalGradOpMaker
<
paddle
::
imperative
::
OpBase
>
,
ops
::
FillIDiagonalOpInplaceInferer
);
REGISTER_OPERATOR
(
fill_diagonal_grad
,
ops
::
FillIDiagonalGradOp
,
ops
::
FillIDiagonalGradOpInplaceInferer
);
REGISTER_OP_CPU_KERNEL
(
fill_diagonal
,
ops
::
FillIDiagonalKernel
<
float
>
,
ops
::
FillIDiagonalKernel
<
double
>
,
ops
::
FillIDiagonalKernel
<
int64_t
>
,
ops
::
FillIDiagonalKernel
<
int
>
,
ops
::
FillIDiagonalKernel
<
paddle
::
platform
::
float16
>
,
ops
::
FillIDiagonalKernel
<
bool
>
);
REGISTER_OP_CPU_KERNEL
(
fill_diagonal_grad
,
ops
::
FillIDiagonalGradKernel
<
float
>
,
ops
::
FillIDiagonalGradKernel
<
double
>
,
ops
::
FillIDiagonalGradKernel
<
int64_t
>
,
ops
::
FillIDiagonalGradKernel
<
int
>
,
ops
::
FillIDiagonalGradKernel
<
paddle
::
platform
::
float16
>
,
ops
::
FillIDiagonalGradKernel
<
bool
>
);
paddle/fluid/operators/fill_diagonal_op.cu
0 → 100644
浏览文件 @
5de576b0
/* Copyright (c) 2021 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/fill_diagonal_op.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
CUDADeviceContext
=
paddle
::
platform
::
CUDADeviceContext
;
template
<
typename
T
>
__global__
void
fill_constant_kernel
(
const
int64_t
featuresize
,
T
*
in_data
,
int64_t
strides
,
int
offset
,
T
fillvar
)
{
for
(
int64_t
idx
=
blockIdx
.
x
*
featuresize
+
threadIdx
.
x
;
idx
*
strides
+
offset
<
(
blockIdx
.
x
+
1
)
*
featuresize
;
idx
+=
blockDim
.
x
)
{
in_data
[
idx
*
strides
+
offset
]
=
fillvar
;
}
}
template
<
typename
T
>
class
FillIDiagonalCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
#ifdef __HIPCC__
const
int64_t
kMaxBlockDim
=
256
;
#else
const
int64_t
kMaxBlockDim
=
512
;
#endif
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
auto
offset
=
ctx
.
Attr
<
int
>
(
"offset"
);
auto
wrap
=
ctx
.
Attr
<
bool
>
(
"wrap"
);
auto
*
xin
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
framework
::
TensorCopy
(
*
xin
,
ctx
.
GetPlace
(),
out
);
T
*
out_data
=
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
fill_val
=
static_cast
<
T
>
(
ctx
.
template
Attr
<
float
>(
"value"
));
T
temp_var
=
static_cast
<
T
>
(
fill_val
);
auto
size
=
out
->
numel
();
auto
out_dims
=
out
->
dims
();
auto
strides
=
CalStride
(
out_dims
);
// The wrap mode supported only the dims equels to 2; In wrap mode, the
// value will be filled in cycles
if
(
!
wrap
)
{
size
=
std
::
min
(
size
,
out_dims
[
1
]
*
out_dims
[
1
]);
}
int64_t
kBlockDim
=
std
::
min
(
int64_t
(
size
/
strides
),
kMaxBlockDim
);
fill_constant_kernel
<
T
><<<
1
,
kBlockDim
,
0
>>>
(
size
,
out_data
,
strides
,
offset
,
temp_var
);
}
};
template
<
typename
T
>
class
FillIDiagonalGradCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
#ifdef __HIPCC__
const
int64_t
kMaxBlockDim
=
256
;
#else
const
int64_t
kMaxBlockDim
=
512
;
#endif
auto
*
dx
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
in_data
=
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
*
dout
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
offset
=
ctx
.
Attr
<
int
>
(
"offset"
);
auto
wrap
=
ctx
.
Attr
<
bool
>
(
"wrap"
);
framework
::
TensorCopy
(
*
dout
,
ctx
.
GetPlace
(),
dx
);
auto
size
=
dx
->
numel
();
auto
out_dims
=
dx
->
dims
();
auto
strides
=
CalStride
(
out_dims
);
auto
wrapsize
=
std
::
min
(
size
,
out_dims
[
1
]
*
out_dims
[
1
]);
// The wrap mode supported only the dims equels to 2; In wrap mode, the
// value will be filled in cycles
if
(
wrap
)
{
wrapsize
=
size
;
}
int64_t
kBlockDim
=
std
::
min
(
int64_t
(
size
),
kMaxBlockDim
);
fill_constant_kernel
<
T
><<<
1
,
kBlockDim
,
0
>>>
(
wrapsize
,
in_data
,
strides
,
offset
,
T
(
0
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_CUDA_KERNEL
(
fill_diagonal
,
ops
::
FillIDiagonalCUDAKernel
<
float
>
,
ops
::
FillIDiagonalCUDAKernel
<
double
>
,
ops
::
FillIDiagonalCUDAKernel
<
plat
::
float16
>
,
ops
::
FillIDiagonalCUDAKernel
<
int
>
,
ops
::
FillIDiagonalCUDAKernel
<
int64_t
>
,
ops
::
FillIDiagonalCUDAKernel
<
bool
>
);
REGISTER_OP_CUDA_KERNEL
(
fill_diagonal_grad
,
ops
::
FillIDiagonalGradCUDAKernel
<
float
>
,
ops
::
FillIDiagonalGradCUDAKernel
<
double
>
,
ops
::
FillIDiagonalGradCUDAKernel
<
int
>
,
ops
::
FillIDiagonalGradCUDAKernel
<
int64_t
>
,
ops
::
FillIDiagonalGradCUDAKernel
<
plat
::
float16
>
,
ops
::
FillIDiagonalGradCUDAKernel
<
bool
>
);
paddle/fluid/operators/fill_diagonal_op.h
0 → 100644
浏览文件 @
5de576b0
/* Copyright (c) 2021 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
{
int64_t
CalStride
(
framework
::
DDim
dim
);
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/tests/unittests/test_tensor_fill_diagonal_.py
0 → 100644
浏览文件 @
5de576b0
# Copyright (c) 2019 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.
import
paddle.fluid
as
fluid
import
unittest
import
numpy
as
np
import
six
import
paddle
class
TensorFillDiagonal_Test
(
unittest
.
TestCase
):
def
test_dim2_normal
(
self
):
expected_np
=
np
.
array
(
[[
1
,
2
,
2
],
[
2
,
1
,
2
],
[
2
,
2
,
1
]]).
astype
(
'float32'
)
expected_grad
=
np
.
array
(
[[
0
,
1
,
1
],
[
1
,
0
,
1
],
[
1
,
1
,
0
]]).
astype
(
'float32'
)
typelist
=
[
'float32'
,
'float64'
,
'int32'
,
'int64'
]
places
=
[
fluid
.
CPUPlace
()]
if
fluid
.
core
.
is_compiled_with_cuda
():
places
.
append
(
fluid
.
CUDAPlace
(
0
))
for
idx
,
p
in
enumerate
(
places
):
if
idx
==
0
:
paddle
.
set_device
(
'cpu'
)
else
:
paddle
.
set_device
(
'gpu'
)
for
dtype
in
typelist
:
x
=
paddle
.
ones
((
3
,
3
),
dtype
=
dtype
)
x
.
stop_gradient
=
False
y
=
x
*
2
y
.
fill_diagonal_
(
1
,
offset
=
0
,
wrap
=
True
)
loss
=
y
.
sum
()
loss
.
backward
()
self
.
assertEqual
(
(
y
.
numpy
().
astype
(
'float32'
)
==
expected_np
).
all
(),
True
)
self
.
assertEqual
(
(
y
.
grad
.
numpy
().
astype
(
'float32'
)
==
expected_grad
).
all
(),
True
)
def
test_bool
(
self
):
expected_np
=
np
.
array
(
[[
False
,
True
,
True
],
[
True
,
False
,
True
],
[
True
,
True
,
False
]])
typelist
=
[
'bool'
]
places
=
[
fluid
.
CPUPlace
()]
if
fluid
.
core
.
is_compiled_with_cuda
():
places
.
append
(
fluid
.
CUDAPlace
(
0
))
for
idx
,
p
in
enumerate
(
places
):
if
idx
==
0
:
paddle
.
set_device
(
'cpu'
)
else
:
paddle
.
set_device
(
'gpu'
)
for
dtype
in
typelist
:
x
=
paddle
.
ones
((
3
,
3
),
dtype
=
dtype
)
x
.
stop_gradient
=
True
x
.
fill_diagonal_
(
0
,
offset
=
0
,
wrap
=
True
)
self
.
assertEqual
((
x
.
numpy
()
==
expected_np
).
all
(),
True
)
def
test_dim2_unnormal_wrap
(
self
):
expected_np
=
np
.
array
([[
1
,
2
,
2
],
[
2
,
1
,
2
],
[
2
,
2
,
1
],
[
2
,
2
,
2
],
[
1
,
2
,
2
],
[
2
,
1
,
2
],
[
2
,
2
,
1
]]).
astype
(
'float32'
)
expected_grad
=
np
.
array
([[
0
,
1
,
1
],
[
1
,
0
,
1
],
[
1
,
1
,
0
],
[
1
,
1
,
1
],
[
0
,
1
,
1
],
[
1
,
0
,
1
],
[
1
,
1
,
0
]]).
astype
(
'float32'
)
typelist
=
[
'float32'
,
'float64'
,
'int32'
,
'int64'
]
places
=
[
fluid
.
CPUPlace
()]
if
fluid
.
core
.
is_compiled_with_cuda
():
places
.
append
(
fluid
.
CUDAPlace
(
0
))
for
idx
,
p
in
enumerate
(
places
):
if
idx
==
0
:
paddle
.
set_device
(
'cpu'
)
else
:
paddle
.
set_device
(
'gpu'
)
for
dtype
in
typelist
:
x
=
paddle
.
ones
((
7
,
3
),
dtype
=
dtype
)
x
.
stop_gradient
=
False
y
=
x
*
2
y
.
fill_diagonal_
(
1
,
offset
=
0
,
wrap
=
True
)
loss
=
y
.
sum
()
loss
.
backward
()
self
.
assertEqual
(
(
y
.
numpy
().
astype
(
'float32'
)
==
expected_np
).
all
(),
True
)
self
.
assertEqual
(
(
y
.
grad
.
numpy
().
astype
(
'float32'
)
==
expected_grad
).
all
(),
True
)
def
test_dim2_unnormal_unwrap
(
self
):
expected_np
=
np
.
array
([[
1
,
2
,
2
],
[
2
,
1
,
2
],
[
2
,
2
,
1
],
[
2
,
2
,
2
],
[
2
,
2
,
2
],
[
2
,
2
,
2
],
[
2
,
2
,
2
]]).
astype
(
'float32'
)
expected_grad
=
np
.
array
([[
0
,
1
,
1
],
[
1
,
0
,
1
],
[
1
,
1
,
0
],
[
1
,
1
,
1
],
[
1
,
1
,
1
],
[
1
,
1
,
1
],
[
1
,
1
,
1
]]).
astype
(
'float32'
)
typelist
=
[
'float32'
,
'float64'
,
'int32'
,
'int64'
]
places
=
[
fluid
.
CPUPlace
()]
if
fluid
.
core
.
is_compiled_with_cuda
():
places
.
append
(
fluid
.
CUDAPlace
(
0
))
for
idx
,
p
in
enumerate
(
places
):
if
idx
==
0
:
paddle
.
set_device
(
'cpu'
)
else
:
paddle
.
set_device
(
'gpu'
)
for
dtype
in
typelist
:
x
=
paddle
.
ones
((
7
,
3
),
dtype
=
dtype
)
x
.
stop_gradient
=
False
y
=
x
*
2
y
.
fill_diagonal_
(
1
,
offset
=
0
,
wrap
=
False
)
loss
=
y
.
sum
()
loss
.
backward
()
self
.
assertEqual
(
(
y
.
numpy
().
astype
(
'float32'
)
==
expected_np
).
all
(),
True
)
self
.
assertEqual
(
(
y
.
grad
.
numpy
().
astype
(
'float32'
)
==
expected_grad
).
all
(),
True
)
def
test_dim_larger2_normal
(
self
):
expected_np
=
np
.
array
([[[
1
,
2
,
2
],
[
2
,
2
,
2
],
[
2
,
2
,
2
]],
[[
2
,
2
,
2
],
[
2
,
1
,
2
],
[
2
,
2
,
2
]],
[[
2
,
2
,
2
],
[
2
,
2
,
2
],
[
2
,
2
,
1
]]]).
astype
(
'float32'
)
expected_grad
=
np
.
array
(
[[[
0
,
1
,
1
],
[
1
,
1
,
1
],
[
1
,
1
,
1
]],
[[
1
,
1
,
1
],
[
1
,
0
,
1
],
[
1
,
1
,
1
]],
[[
1
,
1
,
1
],
[
1
,
1
,
1
],
[
1
,
1
,
0
]]]).
astype
(
'float32'
)
typelist
=
[
'float32'
,
'float64'
,
'int32'
,
'int64'
]
places
=
[
fluid
.
CPUPlace
()]
if
fluid
.
core
.
is_compiled_with_cuda
():
places
.
append
(
fluid
.
CUDAPlace
(
0
))
for
idx
,
p
in
enumerate
(
places
):
if
idx
==
0
:
paddle
.
set_device
(
'cpu'
)
else
:
paddle
.
set_device
(
'gpu'
)
for
dtype
in
typelist
:
x
=
paddle
.
ones
((
3
,
3
,
3
),
dtype
=
dtype
)
x
.
stop_gradient
=
False
y
=
x
*
2
y
.
fill_diagonal_
(
1
,
offset
=
0
,
wrap
=
True
)
loss
=
y
.
sum
()
loss
.
backward
()
self
.
assertEqual
(
(
y
.
numpy
().
astype
(
'float32'
)
==
expected_np
).
all
(),
True
)
self
.
assertEqual
(
(
y
.
grad
.
numpy
().
astype
(
'float32'
)
==
expected_grad
).
all
(),
True
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/tensor/manipulation.py
浏览文件 @
5de576b0
...
...
@@ -37,6 +37,55 @@ from paddle import _C_ops
__all__
=
[]
@
dygraph_only
def
fill_diagonal_
(
x
,
value
,
offset
=
0
,
wrap
=
False
,
name
=
None
):
"""
**Notes**:
**This API is ONLY available in Dygraph mode**
This function fill the value into the x Tensor's diagonal inplace.
Args:
x(Tensor): ``x`` is the original Tensor
value(Scale): ``value`` is the value to filled in x
offset(int,optional): the offset to the main diagonal. Default: 0 (main diagonal).
wrap(bool,optional): the diagonal 'wrapped' after N columns for tall matrices.
name(str,optional): Name for the operation (optional, default is None)
Returns:
Tensor: Tensor with diagonal filled with value.
Returns type:
dtype is same as x Tensor
Examples:
.. code-block:: python
import paddle
x = paddle.ones((4, 3)) * 2
x.fill_diagonal_(1.0)
print(x.tolist()) #[[1.0, 2.0, 2.0], [2.0, 1.0, 2.0], [2.0, 2.0, 1.0], [2.0, 2.0, 2.0]]
"""
helper
=
LayerHelper
(
"fill_diagonal_"
,
**
locals
())
check_type
(
x
,
'X'
,
(
Variable
),
'fill_diagonal_'
)
dtype
=
helper
.
input_dtype
(
'x'
)
check_dtype
(
dtype
,
'X'
,
[
'bool'
,
'float16'
,
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'fill_diagonal_'
)
check_type
(
value
,
'value'
,
(
bool
,
int
,
float
),
'fill_diagonal_'
)
check_type
(
wrap
,
'wrap'
,
(
bool
),
'fill_diagonal_'
)
inshape
=
x
.
shape
inshapeset
=
set
(
inshape
)
assert
len
(
inshape
)
>=
2
,
(
'Tensor dims should >= 2 in fill_diagonal_ API'
)
if
len
(
inshape
)
>
2
:
assert
len
(
inshapeset
)
==
1
,
(
'Tensor dims should be equal while input dims > 2 in fill_diagonal_ API'
)
if
len
(
inshape
)
==
2
:
return
core
.
ops
.
fill_diagonal_
(
x
,
'value'
,
value
,
'offset'
,
offset
,
'wrap'
,
wrap
)
return
core
.
ops
.
fill_diagonal_
(
x
,
'value'
,
value
,
'offset'
,
offset
,
'wrap'
,
True
)
setattr
(
core
.
VarBase
,
'fill_diagonal_'
,
fill_diagonal_
)
@
dygraph_only
def
tolist
(
x
):
"""
...
...
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