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9297f49e
编写于
4月 06, 2020
作者:
C
cc
提交者:
GitHub
4月 06, 2020
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差异文件
[OP] Add randperm op (#23292)
上级
08e3d9c0
变更
7
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7 changed file
with
456 addition
and
3 deletion
+456
-3
paddle/fluid/operators/randperm_op.cc
paddle/fluid/operators/randperm_op.cc
+96
-0
paddle/fluid/operators/randperm_op.cu
paddle/fluid/operators/randperm_op.cu
+23
-0
paddle/fluid/operators/randperm_op.h
paddle/fluid/operators/randperm_op.h
+65
-0
python/paddle/__init__.py
python/paddle/__init__.py
+1
-1
python/paddle/fluid/tests/unittests/test_randperm_op.py
python/paddle/fluid/tests/unittests/test_randperm_op.py
+175
-0
python/paddle/tensor/__init__.py
python/paddle/tensor/__init__.py
+1
-1
python/paddle/tensor/random.py
python/paddle/tensor/random.py
+95
-1
未找到文件。
paddle/fluid/operators/randperm_op.cc
0 → 100644
浏览文件 @
9297f49e
/* 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/randperm_op.h"
#include <string>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
namespace
paddle
{
namespace
operators
{
class
RandpermOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Out"
),
true
,
platform
::
errors
::
NotFound
(
"The output(Out) of randperm op must not be null."
));
int
n
=
ctx
->
Attrs
().
Get
<
int
>
(
"n"
);
PADDLE_ENFORCE_GT
(
n
,
0
,
platform
::
errors
::
InvalidArgument
(
"The input(n) of randperm op must be greater than 0."
));
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
({
n
}));
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
data_type
=
static_cast
<
framework
::
proto
::
VarType
::
Type
>
(
ctx
.
Attr
<
int
>
(
"dtype"
));
return
framework
::
OpKernelType
(
data_type
,
ctx
.
GetPlace
());
}
};
class
RandpermOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddOutput
(
"Out"
,
"The output tensor of randperm op."
);
AddAttr
<
int
>
(
"n"
,
"The upper bound (exclusive), and it should be greater than 0."
);
AddAttr
<
int
>
(
"dtype"
,
"The data type of output tensor. "
"Default: 3[int64]."
)
.
SetDefault
(
framework
::
proto
::
VarType
::
INT64
);
AddAttr
<
int
>
(
"seed"
,
"Random seed used for permute samples. "
"0 means use a seed generated by the system."
"Note that if seed is not 0, this operator will always "
"generate the same random permutation every time. "
"Default: 0."
)
.
SetDefault
(
0
);
AddComment
(
R"DOC(
This operator returns a random permutation of integers from 0 to n-1.
)DOC"
);
}
};
class
RandpermOpVarTypeInference
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
framework
::
InferVarTypeContext
*
ctx
)
const
override
{
auto
var_data_type
=
static_cast
<
framework
::
proto
::
VarType
::
Type
>
(
boost
::
get
<
int
>
(
ctx
->
GetAttr
(
"dtype"
)));
auto
out_var_name
=
ctx
->
Output
(
"Out"
).
front
();
ctx
->
SetDataType
(
out_var_name
,
var_data_type
);
}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OPERATOR
(
randperm
,
paddle
::
operators
::
RandpermOp
,
paddle
::
operators
::
RandpermOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
<
paddle
::
framework
::
OpDesc
>
,
paddle
::
framework
::
EmptyGradOpMaker
<
paddle
::
imperative
::
OpBase
>
,
paddle
::
operators
::
RandpermOpVarTypeInference
);
template
<
typename
T
>
using
kernel
=
paddle
::
operators
::
RandpermKernel
<
paddle
::
platform
::
CPUDeviceContext
,
T
>
;
REGISTER_OP_CPU_KERNEL
(
randperm
,
kernel
<
int64_t
>
,
kernel
<
int
>
);
paddle/fluid/operators/randperm_op.cu
0 → 100644
浏览文件 @
9297f49e
/* 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/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/randperm_op.h"
template
<
typename
T
>
using
kernel
=
paddle
::
operators
::
RandpermKernel
<
paddle
::
platform
::
CUDADeviceContext
,
T
>
;
REGISTER_OP_CUDA_KERNEL
(
randperm
,
kernel
<
int64_t
>
,
kernel
<
int
>
);
paddle/fluid/operators/randperm_op.h
0 → 100644
浏览文件 @
9297f49e
/* 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 <algorithm>
#include <cstdlib>
#include <ctime>
#include <string>
#include <vector>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/platform/place.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
static
inline
void
random_permate
(
T
*
data_ptr
,
int
num
,
unsigned
int
seed
)
{
for
(
int
i
=
0
;
i
<
num
;
++
i
)
{
data_ptr
[
i
]
=
static_cast
<
T
>
(
i
);
}
if
(
seed
==
0
)
{
seed
=
std
::
random_device
()();
}
std
::
srand
(
seed
);
std
::
random_shuffle
(
data_ptr
,
data_ptr
+
num
);
}
template
<
typename
DeviceContext
,
typename
T
>
class
RandpermKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
int
n
=
ctx
.
Attr
<
int
>
(
"n"
);
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
ctx
.
Attr
<
int
>
(
"seed"
));
framework
::
Variable
*
out_var
=
ctx
.
OutputVar
(
"Out"
);
framework
::
Tensor
*
out_tensor
=
framework
::
GetMutableLoDTensorOrSelectedRowsValueFromVar
(
out_var
);
if
(
platform
::
is_cpu_place
(
ctx
.
GetPlace
()))
{
T
*
out_data
=
out_tensor
->
mutable_data
<
T
>
(
platform
::
CPUPlace
());
random_permate
<
T
>
(
out_data
,
n
,
seed
);
}
else
{
framework
::
Tensor
tmp_tensor
;
tmp_tensor
.
Resize
(
framework
::
make_ddim
({
n
}));
T
*
tmp_data
=
tmp_tensor
.
mutable_data
<
T
>
(
platform
::
CPUPlace
());
random_permate
<
T
>
(
tmp_data
,
n
,
seed
);
framework
::
TensorCopy
(
tmp_tensor
,
platform
::
CUDAPlace
(),
out_tensor
);
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/__init__.py
浏览文件 @
9297f49e
...
@@ -87,7 +87,7 @@ from .tensor.logic import elementwise_equal #DEFINE_ALIAS
...
@@ -87,7 +87,7 @@ from .tensor.logic import elementwise_equal #DEFINE_ALIAS
# from .tensor.random import uniform #DEFINE_ALIAS
# from .tensor.random import uniform #DEFINE_ALIAS
# from .tensor.random import shuffle #DEFINE_ALIAS
# from .tensor.random import shuffle #DEFINE_ALIAS
# from .tensor.random import randn #DEFINE_ALIAS
# from .tensor.random import randn #DEFINE_ALIAS
# from .tensor.random import randperm #DEFINE_ALIAS
from
.tensor.random
import
randperm
# from .tensor.random import rand #DEFINE_ALIAS
# from .tensor.random import rand #DEFINE_ALIAS
# from .tensor.random import randint #DEFINE_ALIAS
# from .tensor.random import randint #DEFINE_ALIAS
# from .tensor.math import abs #DEFINE_ALIAS
# from .tensor.math import abs #DEFINE_ALIAS
...
...
python/paddle/fluid/tests/unittests/test_randperm_op.py
0 → 100644
浏览文件 @
9297f49e
# 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.
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
from
paddle.fluid
import
Program
,
program_guard
def
check_randperm_out
(
n
,
data_np
):
assert
isinstance
(
data_np
,
np
.
ndarray
),
\
"The input data_np should be np.ndarray."
gt_sorted
=
np
.
arange
(
n
)
out_sorted
=
np
.
sort
(
data_np
)
return
list
(
gt_sorted
==
out_sorted
)
def
error_msg
(
data_np
):
return
"The sorted ground truth and sorted out should "
+
\
"be equal, out = "
+
str
(
data_np
)
def
convert_dtype
(
dtype_str
):
dtype_str_list
=
[
"int32"
,
"int64"
]
dtype_num_list
=
[
2
,
3
]
assert
dtype_str
in
dtype_str_list
,
dtype_str
+
\
" should in "
+
str
(
dtype_str_list
)
return
dtype_num_list
[
dtype_str_list
.
index
(
dtype_str
)]
class
TestRandpermOp
(
OpTest
):
""" Test randperm op."""
def
setUp
(
self
):
self
.
op_type
=
"randperm"
self
.
n
=
200
self
.
dtype
=
"int64"
self
.
device
=
None
self
.
seed
=
0
self
.
inputs
=
{}
self
.
outputs
=
{
"Out"
:
np
.
zeros
((
self
.
n
)).
astype
(
self
.
dtype
)}
self
.
init_attrs
()
self
.
attrs
=
{
"n"
:
self
.
n
,
"dtype"
:
convert_dtype
(
self
.
dtype
),
"device"
:
self
.
device
,
"seed"
:
self
.
seed
,
}
def
init_attrs
(
self
):
pass
def
test_check_output
(
self
):
self
.
check_output_customized
(
self
.
verify_output
)
def
verify_output
(
self
,
outs
):
out_np
=
np
.
array
(
outs
[
0
])
self
.
assertTrue
(
check_randperm_out
(
self
.
n
,
out_np
),
msg
=
error_msg
(
out_np
))
class
TestRandpermOp_attr_n
(
TestRandpermOp
):
""" Test randperm op for attr n. """
def
init_attrs
(
self
):
self
.
n
=
10000
class
TestRandpermOp_attr_int32
(
TestRandpermOp
):
""" Test randperm op for attr int32 dtype. """
def
init_attrs
(
self
):
self
.
dtype
=
"int32"
class
TestRandpermOp_attr_device_cpu
(
TestRandpermOp
):
""" Test randperm op for cpu device. """
def
init_attrs
(
self
):
self
.
device
=
"cpu"
class
TestRandpermOp_attr_device_gpu
(
TestRandpermOp
):
""" Test randperm op for gpu device. """
def
init_attrs
(
self
):
self
.
device
=
"gpu"
class
TestRandpermOp_attr_seed
(
TestRandpermOp
):
""" Test randperm op for attr seed. """
def
init_attrs
(
self
):
self
.
seed
=
10
class
TestRandpermOpError
(
unittest
.
TestCase
):
""" Test randperm op for raise error. """
def
test_errors
(
self
):
main_prog
=
Program
()
start_prog
=
Program
()
with
program_guard
(
main_prog
,
start_prog
):
def
test_Variable
():
out
=
np
.
arange
(
10
)
paddle
.
randperm
(
n
=
10
,
out
=
out
)
self
.
assertRaises
(
TypeError
,
test_Variable
)
def
test_value
():
paddle
.
randperm
(
n
=-
3
)
self
.
assertRaises
(
ValueError
,
test_value
)
class
TestRandpermOp_attr_out
(
unittest
.
TestCase
):
""" Test randperm op for attr out. """
def
test_attr_tensor_API
(
self
):
startup_program
=
fluid
.
Program
()
train_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
train_program
,
startup_program
):
n
=
10
data_1
=
fluid
.
layers
.
fill_constant
([
n
],
"int64"
,
3
)
paddle
.
randperm
(
n
=
n
,
out
=
data_1
)
data_2
=
paddle
.
randperm
(
n
=
n
,
dtype
=
"int32"
,
device
=
"cpu"
)
place
=
fluid
.
CPUPlace
()
if
fluid
.
core
.
is_compiled_with_cuda
():
place
=
fluid
.
CUDAPlace
(
0
)
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup_program
)
outs
=
exe
.
run
(
train_program
,
fetch_list
=
[
data_1
,
data_2
])
out_np
=
np
.
array
(
outs
[
0
])
self
.
assertTrue
(
check_randperm_out
(
n
,
out_np
),
msg
=
error_msg
(
out_np
))
class
TestRandpermDygraphMode
(
unittest
.
TestCase
):
def
test_check_output
(
self
):
with
fluid
.
dygraph
.
guard
():
n
=
10
data_1
=
paddle
.
randperm
(
n
,
dtype
=
"int64"
)
data_1_np
=
data_1
.
numpy
()
self
.
assertTrue
(
check_randperm_out
(
n
,
data_1_np
),
msg
=
error_msg
(
data_1_np
))
data_2
=
paddle
.
randperm
(
n
,
dtype
=
"int32"
,
device
=
"cpu"
)
data_2_np
=
data_2
.
numpy
()
self
.
assertTrue
(
check_randperm_out
(
n
,
data_2_np
),
msg
=
error_msg
(
data_2_np
))
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/tensor/__init__.py
浏览文件 @
9297f49e
...
@@ -63,9 +63,9 @@ from .logic import elementwise_equal #DEFINE_ALIAS
...
@@ -63,9 +63,9 @@ from .logic import elementwise_equal #DEFINE_ALIAS
# from .random import uniform #DEFINE_ALIAS
# from .random import uniform #DEFINE_ALIAS
# from .random import shuffle #DEFINE_ALIAS
# from .random import shuffle #DEFINE_ALIAS
# from .random import randn #DEFINE_ALIAS
# from .random import randn #DEFINE_ALIAS
# from .random import randperm #DEFINE_ALIAS
# from .random import rand #DEFINE_ALIAS
# from .random import rand #DEFINE_ALIAS
# from .random import randint #DEFINE_ALIAS
# from .random import randint #DEFINE_ALIAS
from
.random
import
randperm
# from .math import abs #DEFINE_ALIAS
# from .math import abs #DEFINE_ALIAS
# from .math import acos #DEFINE_ALIAS
# from .math import acos #DEFINE_ALIAS
# from .math import asin #DEFINE_ALIAS
# from .math import asin #DEFINE_ALIAS
...
...
python/paddle/tensor/random.py
浏览文件 @
9297f49e
...
@@ -17,6 +17,100 @@
...
@@ -17,6 +17,100 @@
# 'uniform',
# 'uniform',
# 'shuffle',
# 'shuffle',
# 'randn',
# 'randn',
# 'randperm',
# 'rand',
# 'rand',
# 'randint']
# 'randint']
from
..fluid
import
core
from
..fluid.framework
import
device_guard
,
in_dygraph_mode
,
_varbase_creator
from
..fluid.layers.layer_function_generator
import
templatedoc
from
..fluid.layer_helper
import
LayerHelper
from
..fluid.data_feeder
import
convert_dtype
,
check_variable_and_dtype
,
check_type
,
check_dtype
__all__
=
[
'randperm'
]
@
templatedoc
()
def
randperm
(
n
,
out
=
None
,
dtype
=
"int64"
,
device
=
None
,
stop_gradient
=
True
,
seed
=
0
):
"""
${comment}
Args:
n (int): The upper bound (exclusive), and it should be greater than 0.
out (Variable, optional): Optional output which can be any created
Variable that meets the requirements to store the result of operation.
If out is None, a new Varibale will be create to store the result.
Default: None.
dtype (np.dtype|core.VarDesc.VarType|str, optional): The type of the
output Tensor. Supported data types: int64, int32. Default: int32.
device (str, optional): Specific the output variable to be saved in cpu
or gpu memory. Supported None, 'cpu', 'gpu'. If it is None, the output
variable will be automatically assigned devices.
Default: None.
stop_gradient (bool, optional): Whether grad should record operations
on the returned tensor. Default: True.
seed (int, optional): Random seed used for permute samples. If seed is
equal to 0, it means use a seed generated by the system. Note that
if seed is not 0, this operator will always generate the same random
permutation every time. Default: 0.
Returns:
${out_comment}.
Return Type:
${out_type}
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
num = 6
is_use_gpu = False
data_1 = paddle.randperm(num)
fluid.layers.Print(data_1)
data_2 = paddle.randperm(num, dtype="int32", seed=1)
fluid.layers.Print(data_2)
data_3 = paddle.randperm(num, stop_gradient=False, device="cpu")
fluid.layers.Print(data_3)
paddle.randperm(num, out=data_3)
fluid.layers.Print(data_3)
place = fluid.CUDAPlace(0) if is_use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
exe.run()
"""
if
n
<
1
:
raise
ValueError
(
"The input n should be greater than 0 in randperm op."
)
check_dtype
(
dtype
,
'dtype'
,
[
'int64'
,
'int32'
],
'randperm'
)
dtype
=
convert_dtype
(
dtype
)
if
device
not
in
[
None
,
'cpu'
,
'gpu'
]:
raise
ValueError
(
"The input device should in [None, 'cpu', 'gpu']."
)
check_type
(
stop_gradient
,
'stop_gradient'
,
bool
,
'randperm'
)
helper
=
LayerHelper
(
"randperm"
,
**
locals
())
if
out
is
None
:
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
else
:
check_variable_and_dtype
(
out
,
'out'
,
[
dtype
],
'randperm'
)
if
stop_gradient
:
out
.
stop_gradient
=
True
inputs
=
dict
()
outputs
=
{
'Out'
:
[
out
]}
attrs
=
{
'n'
:
n
,
'dtype'
:
out
.
dtype
,
'seed'
:
seed
}
with
device_guard
(
device
):
helper
.
append_op
(
type
=
'randperm'
,
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
attrs
)
return
out
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