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体验新版 GitCode,发现更多精彩内容 >>
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提交
5b573c58
编写于
7月 10, 2020
作者:
Z
zhupengyang
提交者:
GitHub
7月 10, 2020
浏览文件
操作
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电子邮件补丁
差异文件
randperm API: remove out, devive, stop_gradient; add name (#25410)
上级
ccb98cde
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
66 addition
and
141 deletion
+66
-141
paddle/fluid/operators/randperm_op.cc
paddle/fluid/operators/randperm_op.cc
+2
-1
paddle/fluid/operators/randperm_op.cu
paddle/fluid/operators/randperm_op.cu
+2
-1
python/paddle/fluid/tests/unittests/test_randperm_op.py
python/paddle/fluid/tests/unittests/test_randperm_op.py
+37
-80
python/paddle/tensor/random.py
python/paddle/tensor/random.py
+25
-59
未找到文件。
paddle/fluid/operators/randperm_op.cc
浏览文件 @
5b573c58
...
...
@@ -92,4 +92,5 @@ template <typename T>
using
kernel
=
paddle
::
operators
::
RandpermKernel
<
paddle
::
platform
::
CPUDeviceContext
,
T
>
;
REGISTER_OP_CPU_KERNEL
(
randperm
,
kernel
<
int64_t
>
,
kernel
<
int
>
);
REGISTER_OP_CPU_KERNEL
(
randperm
,
kernel
<
int64_t
>
,
kernel
<
int
>
,
kernel
<
float
>
,
kernel
<
double
>
);
paddle/fluid/operators/randperm_op.cu
浏览文件 @
5b573c58
...
...
@@ -20,4 +20,5 @@ template <typename T>
using
kernel
=
paddle
::
operators
::
RandpermKernel
<
paddle
::
platform
::
CUDADeviceContext
,
T
>
;
REGISTER_OP_CUDA_KERNEL
(
randperm
,
kernel
<
int64_t
>
,
kernel
<
int
>
);
REGISTER_OP_CUDA_KERNEL
(
randperm
,
kernel
<
int64_t
>
,
kernel
<
int
>
,
kernel
<
float
>
,
kernel
<
double
>
);
python/paddle/fluid/tests/unittests/test_randperm_op.py
浏览文件 @
5b573c58
...
...
@@ -16,10 +16,8 @@ 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
from
paddle
import
Program
,
program_guard
def
check_randperm_out
(
n
,
data_np
):
...
...
@@ -36,8 +34,11 @@ def error_msg(data_np):
def
convert_dtype
(
dtype_str
):
dtype_str_list
=
[
"int32"
,
"int64"
]
dtype_num_list
=
[
2
,
3
]
dtype_str_list
=
[
"int32"
,
"int64"
,
"float32"
,
"float64"
]
dtype_num_list
=
[
core
.
VarDesc
.
VarType
.
INT32
,
core
.
VarDesc
.
VarType
.
INT64
,
core
.
VarDesc
.
VarType
.
FP32
,
core
.
VarDesc
.
VarType
.
FP64
]
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
)]
...
...
@@ -50,8 +51,6 @@ class TestRandpermOp(OpTest):
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
)}
...
...
@@ -59,8 +58,6 @@ class TestRandpermOp(OpTest):
self
.
attrs
=
{
"n"
:
self
.
n
,
"dtype"
:
convert_dtype
(
self
.
dtype
),
"device"
:
self
.
device
,
"seed"
:
self
.
seed
,
}
def
init_attrs
(
self
):
...
...
@@ -75,100 +72,60 @@ class TestRandpermOp(OpTest):
check_randperm_out
(
self
.
n
,
out_np
),
msg
=
error_msg
(
out_np
))
class
TestRandpermOp_attr_n
(
TestRandpermOp
):
""" Test randperm op for attr n. """
class
TestRandpermOpN
(
TestRandpermOp
):
def
init_attrs
(
self
):
self
.
n
=
10000
class
TestRandpermOp_attr_int32
(
TestRandpermOp
):
""" Test randperm op for attr int32 dtype. """
class
TestRandpermOpInt32
(
TestRandpermOp
):
def
init_attrs
(
self
):
self
.
dtype
=
"int32"
class
TestRandpermOp_attr_device_cpu
(
TestRandpermOp
):
""" Test randperm op for cpu device. """
class
TestRandpermOpFloat32
(
TestRandpermOp
):
def
init_attrs
(
self
):
self
.
d
evice
=
"cpu
"
self
.
d
type
=
"float32
"
class
TestRandpermOp_attr_device_gpu
(
TestRandpermOp
):
""" Test randperm op for gpu device. """
class
TestRandpermOpFloat64
(
TestRandpermOp
):
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
self
.
dtype
=
"float64"
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
):
with
program_guard
(
Program
(),
Program
()):
self
.
assertRaises
(
ValueError
,
paddle
.
randperm
,
-
3
)
self
.
assertRaises
(
TypeError
,
paddle
.
randperm
,
10
,
'int8'
)
def
test_Variable
():
out
=
np
.
arange
(
10
)
paddle
.
randperm
(
n
=
10
,
out
=
out
)
self
.
assertRaises
(
TypeError
,
test_Variable
)
class
TestRandpermAPI
(
unittest
.
TestCase
):
def
test_out
(
self
):
n
=
10
place
=
paddle
.
CUDAPlace
(
0
)
if
core
.
is_compiled_with_cuda
(
)
else
paddle
.
CPUPlace
()
with
program_guard
(
Program
(),
Program
()):
x1
=
paddle
.
randperm
(
n
)
x2
=
paddle
.
randperm
(
n
,
'float32'
)
def
test_value
():
paddle
.
randperm
(
n
=-
3
)
exe
=
paddle
.
Executor
(
place
)
res
=
exe
.
run
(
fetch_list
=
[
x1
,
x2
]
)
self
.
assertRaises
(
ValueError
,
test_value
)
self
.
assertEqual
(
res
[
0
].
dtype
,
np
.
int64
)
self
.
assertEqual
(
res
[
1
].
dtype
,
np
.
float32
)
self
.
assertTrue
(
check_randperm_out
(
n
,
res
[
0
]))
self
.
assertTrue
(
check_randperm_out
(
n
,
res
[
1
]))
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
():
class
TestRandpermImperative
(
unittest
.
TestCase
):
def
test_out
(
self
):
with
paddle
.
imperative
.
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
))
for
dtype
in
[
'int32'
,
np
.
int64
,
'float32'
,
'float64'
]:
data_p
=
paddle
.
randperm
(
n
,
dtype
)
data_np
=
data_p
.
numpy
()
self
.
assertTrue
(
check_randperm_out
(
n
,
data_np
),
msg
=
error_msg
(
data_np
))
if
__name__
==
"__main__"
:
...
...
python/paddle/tensor/random.py
浏览文件 @
5b573c58
...
...
@@ -317,12 +317,7 @@ def randn(shape,
@
templatedoc
()
def
randperm
(
n
,
out
=
None
,
dtype
=
"int64"
,
device
=
None
,
stop_gradient
=
True
,
seed
=
0
):
def
randperm
(
n
,
dtype
=
"int64"
,
name
=
None
):
"""
:alias_main: paddle.randperm
:alias: paddle.randperm,paddle.tensor.randperm,paddle.tensor.random.randperm
...
...
@@ -330,23 +325,13 @@ def randperm(n,
${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.
n(int): The upper bound (exclusive), and it should be greater than 0.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the
output Tensor. Supported data types: int32, int64, float32, float64.
Default: int32.
name(str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
Default is None.
Returns:
${out_comment}.
...
...
@@ -357,52 +342,33 @@ def randperm(n,
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)
import paddle
data_3 = paddle.randperm(num, stop_gradient=False, device="cpu")
fluid.layers.Print(data_3)
paddle.enable_imperative()
paddle.randperm(num, out=data_3
)
fluid.layers.Print(data_3)
result_1 = paddle.randperm(5
)
# [4 1 2 3 0]
place = fluid.CUDAPlace(0) if is_use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
exe.run()
result_2 = paddle.randperm(7, 'int32')
# [1 6 2 0 4 3 5]
"""
if
not
isinstance
(
dtype
,
core
.
VarDesc
.
VarType
):
dtype
=
convert_np_dtype_to_dtype_
(
dtype
)
if
in_dygraph_mode
():
return
core
.
ops
.
randperm
(
'n'
,
n
,
'seed'
,
0
,
'dtype'
,
dtype
)
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'
)
check_dtype
(
dtype
,
'dtype'
,
[
'int64'
,
'int32'
,
'float32'
,
'float64'
],
'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
)
out
=
helper
.
create_variable_for_type_inference
(
dtype
)
attrs
=
{
'n'
:
n
,
'dtype'
:
dtype
,
'seed'
:
0
}
helper
.
append_op
(
type
=
'randperm'
,
inputs
=
{},
outputs
=
{
'Out'
:
out
},
attrs
=
attrs
)
return
out
...
...
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