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体验新版 GitCode,发现更多精彩内容 >>
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6af480ca
编写于
3月 26, 2020
作者:
L
liym27
提交者:
GitHub
3月 26, 2020
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电子邮件补丁
差异文件
Support int64 for op assign_value. test=develop (#23179)
上级
d6f72c4f
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
77 addition
and
31 deletion
+77
-31
paddle/fluid/operators/assign_value_op.cc
paddle/fluid/operators/assign_value_op.cc
+8
-4
paddle/fluid/operators/assign_value_op.cu.cc
paddle/fluid/operators/assign_value_op.cu.cc
+2
-1
paddle/fluid/operators/assign_value_op.h
paddle/fluid/operators/assign_value_op.h
+3
-0
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+4
-1
python/paddle/fluid/tests/unittests/test_assign_op.py
python/paddle/fluid/tests/unittests/test_assign_op.py
+1
-3
python/paddle/fluid/tests/unittests/test_assign_value_op.py
python/paddle/fluid/tests/unittests/test_assign_value_op.py
+59
-22
未找到文件。
paddle/fluid/operators/assign_value_op.cc
浏览文件 @
6af480ca
...
...
@@ -52,10 +52,13 @@ class AssignValueOpMaker : public framework::OpProtoAndCheckerMaker {
"Shape of values."
);
AddAttr
<
int
>
(
"dtype"
,
"data type of values"
)
.
InEnum
({
framework
::
proto
::
VarType
::
INT32
,
framework
::
proto
::
VarType
::
FP32
});
AddAttr
<
std
::
vector
<
float
>>
(
"fp32_values"
,
"store the float values"
)
framework
::
proto
::
VarType
::
FP32
,
framework
::
proto
::
VarType
::
INT64
});
AddAttr
<
std
::
vector
<
float
>>
(
"fp32_values"
,
"store the float32 values"
)
.
SetDefault
({});
AddAttr
<
std
::
vector
<
int
>>
(
"int32_values"
,
"store the int values"
)
AddAttr
<
std
::
vector
<
int
>>
(
"int32_values"
,
"store the int32 values"
)
.
SetDefault
({});
AddAttr
<
std
::
vector
<
int64_t
>>
(
"int64_values"
,
"store the int64 values"
)
.
SetDefault
({});
AddComment
(
R"DOC(
AssignValue operator
...
...
@@ -75,4 +78,5 @@ REGISTER_OPERATOR(
paddle
::
framework
::
EmptyGradOpMaker
<
paddle
::
framework
::
OpDesc
>
,
paddle
::
framework
::
EmptyGradOpMaker
<
paddle
::
imperative
::
OpBase
>
);
REGISTER_OP_CPU_KERNEL
(
assign_value
,
ops
::
AssignValueKernel
<
int
>
,
ops
::
AssignValueKernel
<
float
>
);
ops
::
AssignValueKernel
<
float
>
,
ops
::
AssignValueKernel
<
int64_t
>
);
paddle/fluid/operators/assign_value_op.cu.cc
浏览文件 @
6af480ca
...
...
@@ -16,4 +16,5 @@ limitations under the License. */
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
assign_value
,
ops
::
AssignValueKernel
<
int
>
,
ops
::
AssignValueKernel
<
float
>
);
ops
::
AssignValueKernel
<
float
>
,
ops
::
AssignValueKernel
<
int64_t
>
);
paddle/fluid/operators/assign_value_op.h
浏览文件 @
6af480ca
...
...
@@ -37,6 +37,9 @@ class AssignValueKernel : public framework::OpKernel<T> {
case
framework
::
proto
::
VarType
::
FP32
:
value_name
=
"fp32_values"
;
break
;
case
framework
::
proto
::
VarType
::
INT64
:
value_name
=
"int64_values"
;
break
;
default:
PADDLE_THROW
(
"Unsupported dtype for assign_value_op: %d"
,
dtype
);
break
;
...
...
python/paddle/fluid/layers/tensor.py
浏览文件 @
6af480ca
...
...
@@ -502,10 +502,13 @@ def assign(input, output=None):
elif
dtype
==
VarDesc
.
VarType
.
INT32
:
value_name
=
"int32_values"
values
=
[
int
(
v
)
for
v
in
input
.
flat
]
elif
dtype
==
VarDesc
.
VarType
.
INT64
:
value_name
=
"int64_values"
values
=
[
int
(
v
)
for
v
in
input
.
flat
]
else
:
raise
TypeError
(
"When the type of 'input' in assign is numpy.ndarray, "
"the data type of 'input' must be float32
or int32
, but "
"the data type of 'input' must be float32
, int32 or int64
, but "
"received %s."
%
convert_dtype
(
dtype
))
if
input
.
size
>
1024
*
1024
:
raise
ValueError
(
"The size of input is too big. Please consider "
...
...
python/paddle/fluid/tests/unittests/test_assign_op.py
浏览文件 @
6af480ca
...
...
@@ -97,10 +97,8 @@ class TestAssignOpError(unittest.TestCase):
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
assign
,
x4
)
x5
=
np
.
array
([[
2.5
,
2.5
]],
dtype
=
'float64'
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
assign
,
x5
)
x6
=
np
.
array
([[
2.5
,
2.5
]],
dtype
=
'
int64
'
)
x6
=
np
.
array
([[
2.5
,
2.5
]],
dtype
=
'
uint8
'
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
assign
,
x6
)
x7
=
np
.
array
([[
2.5
,
2.5
]],
dtype
=
'uint8'
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
assign
,
x7
)
if
__name__
==
'__main__'
:
...
...
python/paddle/fluid/tests/unittests/test_assign_value_op.py
浏览文件 @
6af480ca
...
...
@@ -14,42 +14,79 @@
from
__future__
import
print_function
import
paddle.fluid
as
fluid
import
paddle.fluid.layers
as
layers
import
op_test
import
numpy
import
unittest
import
numpy
import
op_test
import
paddle.fluid
as
fluid
import
paddle.fluid.framework
as
framework
import
paddle.fluid.layers
as
layers
class
TestAssignValueOp
(
op_test
.
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"assign_value"
x
=
numpy
.
random
.
random
(
size
=
(
2
,
5
)).
astype
(
numpy
.
float32
)
self
.
inputs
=
{}
self
.
outputs
=
{
'Out'
:
x
}
self
.
attrs
=
{
'shape'
:
x
.
shape
,
'dtype'
:
framework
.
convert_np_dtype_to_dtype_
(
x
.
dtype
),
'fp32_values'
:
[
float
(
v
)
for
v
in
x
.
flat
]
}
self
.
attrs
=
{}
self
.
init_data
()
self
.
attrs
[
"shape"
]
=
self
.
value
.
shape
self
.
attrs
[
"dtype"
]
=
framework
.
convert_np_dtype_to_dtype_
(
self
.
value
.
dtype
)
self
.
outputs
=
{
"Out"
:
self
.
value
}
def
init_data
(
self
):
self
.
value
=
numpy
.
random
.
random
(
size
=
(
2
,
5
)).
astype
(
numpy
.
float32
)
self
.
attrs
[
"fp32_values"
]
=
[
float
(
v
)
for
v
in
self
.
value
.
flat
]
def
test_forward
(
self
):
self
.
check_output
()
class
TestAssignValueOp2
(
TestAssignValueOp
):
def
init_data
(
self
):
self
.
value
=
numpy
.
random
.
random
(
size
=
(
2
,
5
)).
astype
(
numpy
.
int32
)
self
.
attrs
[
"int32_values"
]
=
[
int
(
v
)
for
v
in
self
.
value
.
flat
]
class
TestAssignValueOp3
(
TestAssignValueOp
):
def
init_data
(
self
):
self
.
value
=
numpy
.
random
.
random
(
size
=
(
2
,
5
)).
astype
(
numpy
.
int64
)
self
.
attrs
[
"int64_values"
]
=
[
int
(
v
)
for
v
in
self
.
value
.
flat
]
class
TestAssignApi
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
init_dtype
()
self
.
value
=
(
-
100
+
200
*
numpy
.
random
.
random
(
size
=
(
2
,
5
))).
astype
(
self
.
dtype
)
self
.
place
=
fluid
.
CUDAPlace
(
0
)
if
fluid
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
def
init_dtype
(
self
):
self
.
dtype
=
"float32"
def
test_assign
(
self
):
val
=
(
-
100
+
200
*
numpy
.
random
.
random
(
size
=
(
2
,
5
))).
astype
(
numpy
.
int32
)
x
=
layers
.
create_tensor
(
dtype
=
"float32"
)
layers
.
assign
(
input
=
val
,
output
=
x
)
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
fetched_x
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{},
fetch_list
=
[
x
])[
0
]
main_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main_program
):
x
=
layers
.
create_tensor
(
dtype
=
self
.
dtype
)
layers
.
assign
(
input
=
self
.
value
,
output
=
x
)
exe
=
fluid
.
Executor
(
self
.
place
)
[
fetched_x
]
=
exe
.
run
(
main_program
,
feed
=
{},
fetch_list
=
[
x
])
self
.
assertTrue
(
numpy
.
array_equal
(
fetched_x
,
val
),
"fetch_x=%s val=%s"
%
(
fetched_x
,
val
))
self
.
assertEqual
(
fetched_x
.
dtype
,
val
.
dtype
)
numpy
.
array_equal
(
fetched_x
,
self
.
value
),
"fetch_x=%s val=%s"
%
(
fetched_x
,
self
.
value
))
self
.
assertEqual
(
fetched_x
.
dtype
,
self
.
value
.
dtype
)
class
TestAssignApi2
(
TestAssignApi
):
def
init_dtype
(
self
):
self
.
dtype
=
"int32"
class
TestAssignApi3
(
TestAssignApi
):
def
init_dtype
(
self
):
self
.
dtype
=
"int64"
if
__name__
==
'__main__'
:
...
...
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