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体验新版 GitCode,发现更多精彩内容 >>
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c8d6c146
编写于
2月 22, 2022
作者:
Z
zhangxiaoci
提交者:
GitHub
2月 22, 2022
浏览文件
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电子邮件补丁
差异文件
refactor reshape2/shape unittest for kunlun (#39665)
*test=kunlun
上级
0efa64c8
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
245 addition
and
250 deletion
+245
-250
python/paddle/fluid/tests/unittests/xpu/test_reshape2_op_xpu.py
.../paddle/fluid/tests/unittests/xpu/test_reshape2_op_xpu.py
+156
-183
python/paddle/fluid/tests/unittests/xpu/test_shape_op_xpu.py
python/paddle/fluid/tests/unittests/xpu/test_shape_op_xpu.py
+89
-67
未找到文件。
python/paddle/fluid/tests/unittests/xpu/test_reshape2_op_xpu.py
浏览文件 @
c8d6c146
...
...
@@ -14,194 +14,167 @@
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
sys
import
unittest
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid
import
compiler
,
Program
,
program_guard
# situation 1: have shape( list, no tensor), no actual shape(Tensor)
class
TestReshapeOp
(
OpTest
):
def
setUp
(
self
):
self
.
init_data
()
self
.
op_type
=
"reshape2"
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)}
self
.
attrs
=
{
"shape"
:
self
.
new_shape
,
"use_xpu"
:
True
}
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
infered_shape
),
'XShape'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)
}
def
init_data
(
self
):
self
.
ori_shape
=
(
2
,
60
)
self
.
new_shape
=
(
12
,
10
)
self
.
infered_shape
=
(
12
,
10
)
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
no_check_set
=
[
'XShape'
])
def
test_check_grad
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
"X"
],
"Out"
)
class
TestReshapeOpDimInfer1
(
TestReshapeOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
5
,
25
)
self
.
new_shape
=
(
5
,
-
1
,
5
)
self
.
infered_shape
=
(
5
,
-
1
,
5
)
class
TestReshapeOpDimInfer2
(
TestReshapeOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
10
,
2
,
6
)
self
.
new_shape
=
(
10
,
0
,
3
,
-
1
)
self
.
infered_shape
=
(
10
,
2
,
3
,
-
1
)
# situation 2: have shape(list, no tensor), have actual shape(Tensor)
class
TestReshapeOpWithInputShape
(
OpTest
):
def
setUp
(
self
):
self
.
init_data
()
self
.
op_type
=
"reshape2"
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
),
"Shape"
:
np
.
array
(
self
.
actual_shape
,
dtype
=
"int32"
)
}
self
.
attrs
=
{
"shape"
:
self
.
new_shape
,
"use_xpu"
:
True
}
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
actual_shape
),
'XShape'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)
}
def
init_data
(
self
):
self
.
ori_shape
=
(
6
,
20
)
self
.
new_shape
=
(
0
,
-
1
,
20
)
self
.
actual_shape
=
(
2
,
3
,
20
)
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
no_check_set
=
[
'XShape'
])
def
test_check_grad
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
"X"
],
"Out"
)
# Situation 3: have shape(list, have tensor), no actual shape(Tensor)
class
TestReshapeOp_attr_ShapeTensor
(
OpTest
):
def
setUp
(
self
):
self
.
init_data
()
self
.
op_type
=
"reshape2"
shape_tensor
=
[]
for
index
,
ele
in
enumerate
(
self
.
new_shape
):
shape_tensor
.
append
((
"x"
+
str
(
index
),
np
.
ones
(
(
1
)).
astype
(
'int32'
)
*
ele
))
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
),
'ShapeTensor'
:
shape_tensor
}
self
.
attrs
=
{
'shape'
:
self
.
shape
,
"use_xpu"
:
True
}
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
infered_shape
),
'XShape'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)
}
def
init_data
(
self
):
self
.
ori_shape
=
(
4
,
25
)
self
.
new_shape
=
(
10
,
10
)
self
.
infered_shape
=
(
10
,
10
)
self
.
shape
=
(
-
1
,
-
1
)
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
no_check_set
=
[
'XShape'
])
def
test_check_grad
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
"X"
],
"Out"
)
class
TestReshapeOpDimInfer1_attr_ShapeTensor
(
TestReshapeOp_attr_ShapeTensor
):
def
init_data
(
self
):
self
.
ori_shape
=
(
5
,
20
)
self
.
new_shape
=
(
5
,
-
1
,
20
)
self
.
infered_shape
=
(
5
,
-
1
,
20
)
self
.
shape
=
(
5
,
-
1
,
-
1
)
class
TestReshapeOpDimInfer2_attr_ShapeTensor
(
TestReshapeOp_attr_ShapeTensor
):
def
init_data
(
self
):
self
.
ori_shape
=
(
10
,
2
,
6
)
self
.
new_shape
=
(
10
,
0
,
3
,
-
1
)
self
.
infered_shape
=
(
10
,
2
,
3
,
-
1
)
self
.
shape
=
(
10
,
0
,
3
,
-
1
)
# Situation 4: have shape(Tensor), no actual shape(Tensor)
class
TestReshapeOp_attr_OnlyShape
(
OpTest
):
def
setUp
(
self
):
self
.
init_data
()
self
.
op_type
=
"reshape2"
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
),
"Shape"
:
np
.
array
(
self
.
new_shape
,
dtype
=
"int32"
)
}
self
.
attrs
=
{
"use_xpu"
:
True
}
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
infered_shape
),
'XShape'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)
}
def
init_data
(
self
):
self
.
ori_shape
=
(
4
,
25
)
self
.
new_shape
=
(
10
,
10
)
self
.
infered_shape
=
(
10
,
10
)
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
no_check_set
=
[
'XShape'
])
def
test_check_grad
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
"X"
],
"Out"
)
class
TestReshapeOpDimInfer1_attr_OnlyShape
(
TestReshapeOp_attr_OnlyShape
):
def
init_data
(
self
):
self
.
ori_shape
=
(
5
,
20
)
self
.
new_shape
=
(
5
,
-
1
,
10
)
self
.
infered_shape
=
(
5
,
-
1
,
10
)
self
.
shape
=
(
5
,
-
1
,
-
1
)
class
TestReshapeOpDimInfer2_attr_OnlyShape
(
TestReshapeOp_attr_OnlyShape
):
def
init_data
(
self
):
self
.
ori_shape
=
(
10
,
2
,
6
)
self
.
new_shape
=
(
10
,
0
,
3
,
-
1
)
self
.
infered_shape
=
(
10
,
2
,
3
,
-
1
)
self
.
shape
=
(
10
,
0
,
3
,
-
1
)
from
op_test_xpu
import
XPUOpTest
from
xpu.get_test_cover_info
import
create_test_class
,
get_xpu_op_support_types
,
XPUOpTestWrapper
paddle
.
enable_static
()
class
XPUTestReshapeOp
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
"reshape2"
self
.
use_dynamic_create_class
=
False
# situation 1: have shape( list, no tensor), no actual shape(Tensor)
class
TestReshapeOp
(
XPUOpTest
):
def
setUp
(
self
):
self
.
init_data
()
self
.
op_type
=
"reshape2"
self
.
init_test_input
()
self
.
init_test_output
()
self
.
init_attrs
()
def
init_data
(
self
):
self
.
ori_shape
=
(
2
,
60
)
self
.
new_shape
=
(
12
,
10
)
self
.
infered_shape
=
(
12
,
10
)
def
init_test_input
(
self
):
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
self
.
dtype
)
}
def
init_test_output
(
self
):
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
infered_shape
),
'XShape'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
self
.
dtype
)
}
def
init_attrs
(
self
):
self
.
attrs
=
{
"shape"
:
self
.
new_shape
,
"use_xpu"
:
True
}
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
no_check_set
=
[
'XShape'
])
def
test_check_grad
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
"X"
],
"Out"
)
class
TestReshapeOpDimInfer1
(
TestReshapeOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
5
,
25
)
self
.
new_shape
=
(
5
,
-
1
,
5
)
self
.
infered_shape
=
(
5
,
-
1
,
5
)
class
TestReshapeOpDimInfer2
(
TestReshapeOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
10
,
2
,
6
)
self
.
new_shape
=
(
10
,
0
,
3
,
-
1
)
self
.
infered_shape
=
(
10
,
2
,
3
,
-
1
)
# situation 2: have shape(list, no tensor), have actual shape(Tensor)
class
TestReshapeOpWithInputShape
(
TestReshapeOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
6
,
20
)
self
.
new_shape
=
(
0
,
-
1
,
20
)
self
.
actual_shape
=
(
2
,
3
,
20
)
def
init_test_input
(
self
):
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
self
.
dtype
),
"Shape"
:
np
.
array
(
self
.
actual_shape
,
dtype
=
"int32"
)
}
def
init_test_output
(
self
):
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
actual_shape
),
'XShape'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
self
.
dtype
)
}
# Situation 3: have shape(list, have tensor), no actual shape(Tensor)
class
TestReshapeOp_attr_ShapeTensor
(
TestReshapeOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
4
,
25
)
self
.
new_shape
=
(
10
,
10
)
self
.
infered_shape
=
(
10
,
10
)
self
.
shape
=
(
-
1
,
-
1
)
def
init_test_input
(
self
):
shape_tensor
=
[]
for
index
,
ele
in
enumerate
(
self
.
new_shape
):
shape_tensor
.
append
((
"x"
+
str
(
index
),
np
.
ones
(
(
1
)).
astype
(
'int32'
)
*
ele
))
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
self
.
dtype
),
'ShapeTensor'
:
shape_tensor
}
def
init_attrs
(
self
):
self
.
attrs
=
{
'shape'
:
self
.
shape
,
"use_xpu"
:
True
}
class
TestReshapeOpDimInfer1_attr_ShapeTensor
(
TestReshapeOp_attr_ShapeTensor
):
def
init_data
(
self
):
self
.
ori_shape
=
(
5
,
20
)
self
.
new_shape
=
(
5
,
-
1
,
20
)
self
.
infered_shape
=
(
5
,
-
1
,
20
)
self
.
shape
=
(
5
,
-
1
,
-
1
)
class
TestReshapeOpDimInfer2_attr_ShapeTensor
(
TestReshapeOp_attr_ShapeTensor
):
def
init_data
(
self
):
self
.
ori_shape
=
(
10
,
2
,
6
)
self
.
new_shape
=
(
10
,
0
,
3
,
-
1
)
self
.
infered_shape
=
(
10
,
2
,
3
,
-
1
)
self
.
shape
=
(
10
,
0
,
3
,
-
1
)
# Situation 4: have shape(Tensor), no actual shape(Tensor)
class
TestReshapeOp_attr_OnlyShape
(
TestReshapeOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
4
,
25
)
self
.
new_shape
=
(
10
,
10
)
self
.
infered_shape
=
(
10
,
10
)
def
init_test_input
(
self
):
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
self
.
dtype
),
"Shape"
:
np
.
array
(
self
.
new_shape
,
dtype
=
"int32"
)
}
def
init_attrs
(
self
):
self
.
attrs
=
{
"use_xpu"
:
True
}
class
TestReshapeOpDimInfer1_attr_OnlyShape
(
TestReshapeOp_attr_OnlyShape
):
def
init_data
(
self
):
self
.
ori_shape
=
(
5
,
20
)
self
.
new_shape
=
(
5
,
-
1
,
10
)
self
.
infered_shape
=
(
5
,
-
1
,
10
)
self
.
shape
=
(
5
,
-
1
,
-
1
)
class
TestReshapeOpDimInfer2_attr_OnlyShape
(
TestReshapeOp_attr_OnlyShape
):
def
init_data
(
self
):
self
.
ori_shape
=
(
10
,
2
,
6
)
self
.
new_shape
=
(
10
,
0
,
3
,
-
1
)
self
.
infered_shape
=
(
10
,
2
,
3
,
-
1
)
self
.
shape
=
(
10
,
0
,
3
,
-
1
)
support_types
=
get_xpu_op_support_types
(
"reshape2"
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestReshapeOp
,
stype
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/xpu/test_shape_op_xpu.py
浏览文件 @
c8d6c146
...
...
@@ -18,77 +18,99 @@ import unittest
import
numpy
as
np
import
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
from
op_test_xpu
import
XPUOpTest
from
xpu.get_test_cover_info
import
create_test_class
,
get_xpu_op_support_types
,
XPUOpTestWrapper
import
paddle
from
paddle.fluid
import
core
from
paddle.fluid.op
import
Operator
class
TestShapeOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"shape"
self
.
config
()
self
.
shape
=
[
2
,
3
]
input
=
np
.
zeros
(
self
.
shape
)
self
.
inputs
=
{
'Input'
:
input
}
self
.
outputs
=
{
'Out'
:
np
.
array
(
self
.
shape
)}
def
config
(
self
):
self
.
shape
=
[
2
,
3
]
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
class
case1
(
TestShapeOp
):
def
config
(
self
):
self
.
shape
=
[
2
]
class
case2
(
TestShapeOp
):
def
config
(
self
):
self
.
shape
=
[
1
,
2
,
3
]
class
TestShapeWithSelectedRows
(
unittest
.
TestCase
):
def
get_places
(
self
):
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
places
.
append
(
core
.
CUDAPlace
(
0
))
if
core
.
is_compiled_with_xpu
():
places
.
append
(
core
.
XPUPlace
(
0
))
return
places
def
check_with_place
(
self
,
place
):
scope
=
core
.
Scope
()
x_rows
=
[
0
,
1
,
5
,
4
,
19
]
height
=
20
row_numel
=
2
np_array
=
np
.
ones
((
len
(
x_rows
),
row_numel
)).
astype
(
"float32"
)
# initialize input variable X
x
=
scope
.
var
(
'X'
).
get_selected_rows
()
x
.
set_rows
(
x_rows
)
x
.
set_height
(
height
)
x_tensor
=
x
.
get_tensor
()
x_tensor
.
set
(
np_array
,
place
)
# initialize input variable Out
out_shape
=
scope
.
var
(
"Out"
).
get_tensor
()
op
=
Operator
(
"shape"
,
Input
=
"X"
,
Out
=
"Out"
)
op
.
run
(
scope
,
place
)
out_shape
=
np
.
array
(
out_shape
).
tolist
()
self
.
assertListEqual
([
5
,
2
],
out_shape
)
def
test_check_output
(
self
):
for
place
in
self
.
get_places
():
self
.
check_with_place
(
place
)
paddle
.
enable_static
()
class
XPUTestShapeOp
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
"shape"
self
.
use_dynamic_create_class
=
False
class
TestShapeOp
(
XPUOpTest
):
def
setUp
(
self
):
self
.
dtype
=
self
.
in_type
self
.
op_type
=
"shape"
self
.
config
()
input
=
np
.
zeros
(
self
.
shape
)
self
.
inputs
=
{
'Input'
:
input
.
astype
(
self
.
dtype
)}
self
.
outputs
=
{
'Out'
:
np
.
array
(
self
.
shape
)}
def
config
(
self
):
self
.
shape
=
[
2
,
3
]
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
class
TestShapeOp1
(
TestShapeOp
):
def
config
(
self
):
self
.
shape
=
[
2
]
class
TestShapeOp2
(
TestShapeOp
):
def
config
(
self
):
self
.
shape
=
[
1
,
2
,
3
]
class
TestShapeOp3
(
TestShapeOp
):
def
config
(
self
):
self
.
shape
=
[
1
,
2
,
3
,
4
]
class
TestShapeOp4
(
TestShapeOp
):
def
config
(
self
):
self
.
shape
=
[
1
,
2
,
3
,
4
,
1024
]
class
TestShapeOp5
(
TestShapeOp
):
def
config
(
self
):
self
.
shape
=
[
1
,
2
,
3
,
4
,
1
,
201
]
class
TestShapeWithSelectedRows
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
dtype
=
self
.
in_type
def
get_places
(
self
):
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
places
.
append
(
core
.
CUDAPlace
(
0
))
if
core
.
is_compiled_with_xpu
():
places
.
append
(
core
.
XPUPlace
(
0
))
return
places
def
check_with_place
(
self
,
place
):
scope
=
core
.
Scope
()
x_rows
=
[
0
,
1
,
5
,
4
,
19
]
height
=
20
row_numel
=
2
np_array
=
np
.
ones
((
len
(
x_rows
),
row_numel
)).
astype
(
self
.
dtype
)
# initialize input variable X
x
=
scope
.
var
(
'X'
).
get_selected_rows
()
x
.
set_rows
(
x_rows
)
x
.
set_height
(
height
)
x_tensor
=
x
.
get_tensor
()
x_tensor
.
set
(
np_array
,
place
)
out_shape
=
scope
.
var
(
"Out"
).
get_tensor
()
op
=
Operator
(
"shape"
,
Input
=
"X"
,
Out
=
"Out"
)
op
.
run
(
scope
,
place
)
out_shape
=
np
.
array
(
out_shape
).
tolist
()
self
.
assertListEqual
([
5
,
2
],
out_shape
)
def
test_check_output
(
self
):
for
place
in
self
.
get_places
():
self
.
check_with_place
(
place
)
support_types
=
get_xpu_op_support_types
(
"shape"
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestShapeOp
,
stype
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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