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819b9589
编写于
11月 09, 2021
作者:
T
TTerror
提交者:
GitHub
11月 09, 2021
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差异文件
add gather_nd/tile op for kunlun (#37029)
上级
655f4e3f
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
746 addition
and
4 deletion
+746
-4
cmake/external/xpu.cmake
cmake/external/xpu.cmake
+1
-2
paddle/fluid/operators/gather_nd_op_xpu.cc
paddle/fluid/operators/gather_nd_op_xpu.cc
+79
-0
paddle/fluid/operators/tile_op.h
paddle/fluid/operators/tile_op.h
+2
-0
paddle/fluid/operators/tile_op_xpu.cc
paddle/fluid/operators/tile_op_xpu.cc
+119
-0
paddle/fluid/platform/xpu/xpu2_op_list.h
paddle/fluid/platform/xpu/xpu2_op_list.h
+10
-2
python/paddle/fluid/tests/unittests/xpu/test_gather_nd_op_xpu.py
...paddle/fluid/tests/unittests/xpu/test_gather_nd_op_xpu.py
+268
-0
python/paddle/fluid/tests/unittests/xpu/test_tile_op_xpu.py
python/paddle/fluid/tests/unittests/xpu/test_tile_op_xpu.py
+267
-0
未找到文件。
cmake/external/xpu.cmake
浏览文件 @
819b9589
...
...
@@ -35,8 +35,7 @@ ELSE ()
ENDIF
()
SET
(
XPU_BASE_URL_WITHOUT_DATE
"https://baidu-kunlun-product.cdn.bcebos.com/KL-SDK/klsdk-dev"
)
SET
(
XPU_BASE_URL
"
${
XPU_BASE_URL_WITHOUT_DATE
}
/20211029"
)
#SET(XPU_BASE_URL "${XPU_BASE_URL_WITHOUT_DATE}/20211020")
SET
(
XPU_BASE_URL
"
${
XPU_BASE_URL_WITHOUT_DATE
}
/20211107"
)
SET
(
XPU_XRE_URL
"
${
XPU_BASE_URL
}
/
${
XPU_XRE_DIR_NAME
}
.tar.gz"
CACHE STRING
""
FORCE
)
SET
(
XPU_XDNN_URL
"
${
XPU_BASE_URL
}
/
${
XPU_XDNN_DIR_NAME
}
.tar.gz"
CACHE STRING
""
FORCE
)
SET
(
XPU_XCCL_URL
"
${
XPU_BASE_URL_WITHOUT_DATE
}
/20210623/
${
XPU_XCCL_DIR_NAME
}
.tar.gz"
CACHE STRING
""
FORCE
)
...
...
paddle/fluid/operators/gather_nd_op_xpu.cc
0 → 100644
浏览文件 @
819b9589
/* 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/gather_nd_op.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
GatherNdXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
index
=
ctx
.
Input
<
Tensor
>
(
"Index"
);
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
out
->
template
mutable_data
<
T
>(
ctx
.
GetPlace
());
if
(
x
->
numel
()
==
0
)
return
;
if
(
index
->
numel
()
==
0
)
{
framework
::
TensorCopy
(
*
x
,
ctx
.
GetPlace
(),
ctx
.
device_context
(),
out
);
return
;
}
const
auto
&
index_type
=
index
->
type
();
bool
index_type_match
=
index_type
==
framework
::
proto
::
VarType
::
INT32
||
index_type
==
framework
::
proto
::
VarType
::
INT64
;
PADDLE_ENFORCE_EQ
(
index_type_match
,
true
,
platform
::
errors
::
InvalidArgument
(
"Index holds the wrong type, it holds [%s],"
"but desires to be [%s] or [%s]"
,
paddle
::
framework
::
DataTypeToString
(
index_type
),
paddle
::
framework
::
DataTypeToString
(
framework
::
proto
::
VarType
::
INT32
),
paddle
::
framework
::
DataTypeToString
(
framework
::
proto
::
VarType
::
INT64
)));
auto
x_shape
=
paddle
::
framework
::
vectorize
<
int
>
(
x
->
dims
());
auto
index_shape
=
paddle
::
framework
::
vectorize
<
int
>
(
index
->
dims
());
xpu
::
VectorParam
<
int
>
x_vec
=
{
x_shape
.
data
(),
static_cast
<
int
>
(
x_shape
.
size
()),
nullptr
};
auto
&
dev_ctx
=
ctx
.
template
device_context
<
paddle
::
platform
::
XPUDeviceContext
>();
int
ret
=
XPU_SUCCESS
;
if
(
index_type
==
framework
::
proto
::
VarType
::
INT32
)
{
ret
=
xpu
::
gather_nd
<
T
,
int
>
(
dev_ctx
.
x_context
(),
x
->
data
<
T
>
(),
index
->
data
<
int
>
(),
out
->
data
<
T
>
(),
x_vec
,
index_shape
);
}
else
{
ret
=
xpu
::
gather_nd
<
T
,
int64_t
>
(
dev_ctx
.
x_context
(),
x
->
data
<
T
>
(),
index
->
data
<
int64_t
>
(),
out
->
data
<
T
>
(),
x_vec
,
index_shape
);
}
PADDLE_ENFORCE_EQ
(
ret
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU gather_nd kernel return wrong value[%d %s]"
,
ret
,
XPUAPIErrorMsg
[
ret
]));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
gather_nd
,
ops
::
GatherNdXPUKernel
<
int
>
,
ops
::
GatherNdXPUKernel
<
int64_t
>
,
ops
::
GatherNdXPUKernel
<
float
>
);
#endif
paddle/fluid/operators/tile_op.h
浏览文件 @
819b9589
...
...
@@ -33,6 +33,7 @@ inline std::vector<int> get_repeat_times(
auto
*
repeat_data
=
repeat_tensor
->
data
<
int
>
();
framework
::
Tensor
cpu_repeat_tensor
;
if
(
platform
::
is_gpu_place
(
repeat_tensor
->
place
())
||
platform
::
is_xpu_place
(
repeat_tensor
->
place
())
||
platform
::
is_npu_place
(
repeat_tensor
->
place
()))
{
TensorCopySync
(
*
repeat_tensor
,
platform
::
CPUPlace
(),
&
cpu_repeat_tensor
);
repeat_data
=
cpu_repeat_tensor
.
data
<
int
>
();
...
...
@@ -50,6 +51,7 @@ inline std::vector<int> get_repeat_times(
for
(
size_t
i
=
0
;
i
<
list_repeat_times_tensor
.
size
();
++
i
)
{
auto
tensor
=
list_repeat_times_tensor
[
i
];
if
(
platform
::
is_gpu_place
(
tensor
->
place
())
||
platform
::
is_xpu_place
(
tensor
->
place
())
||
platform
::
is_npu_place
(
tensor
->
place
()))
{
framework
::
Tensor
temp
;
TensorCopySync
(
*
tensor
,
platform
::
CPUPlace
(),
&
temp
);
...
...
paddle/fluid/operators/tile_op_xpu.cc
0 → 100644
浏览文件 @
819b9589
/* 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/tile_op.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
TileXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
rank
=
context
.
Input
<
Tensor
>
(
"X"
)
->
dims
().
size
();
PADDLE_ENFORCE_GE
(
rank
,
1
,
platform
::
errors
::
InvalidArgument
(
"The rank of the input 'x' for tile op must be a positive "
"integer, but the value received is %d."
,
rank
));
PADDLE_ENFORCE_LE
(
rank
,
MAX_RANK_SUPPORTED
,
platform
::
errors
::
InvalidArgument
(
"The rank of the input 'x' for tile op "
"must be less than or equal to %d, but the value received is %d."
,
MAX_RANK_SUPPORTED
,
rank
));
auto
repeat_times
=
get_repeat_times
(
context
);
int
repeat_times_size
=
repeat_times
.
size
();
PADDLE_ENFORCE_GE
(
repeat_times_size
,
1
,
platform
::
errors
::
InvalidArgument
(
"The number of elements of the input 'repeat_times' for tile "
"op must be positive, but the value received is %d."
,
repeat_times_size
));
PADDLE_ENFORCE_LE
(
repeat_times_size
,
MAX_RANK_SUPPORTED
,
platform
::
errors
::
InvalidArgument
(
"The number of elements of the input 'repeat_times' for tile op "
"must be less than or equal to %d, but the value received is %d."
,
MAX_RANK_SUPPORTED
,
repeat_times_size
));
auto
*
in0
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
in_dims
=
in0
->
dims
();
for
(
size_t
i
=
0
;
i
<
repeat_times
.
size
();
++
i
)
{
PADDLE_ENFORCE_GT
(
repeat_times
[
i
],
0
,
platform
::
errors
::
InvalidArgument
(
"All elements of the input 'repeat_times' for tile op must "
"be positive integers, but the value received is %d."
,
repeat_times
[
i
]));
}
auto
vec_in_dims
=
framework
::
vectorize
<
int
>
(
in_dims
);
if
(
repeat_times
.
size
()
<
vec_in_dims
.
size
())
{
int
diff
=
vec_in_dims
.
size
()
-
repeat_times
.
size
();
repeat_times
.
insert
(
repeat_times
.
begin
(),
diff
,
1
);
}
else
{
int
diff
=
repeat_times
.
size
()
-
vec_in_dims
.
size
();
vec_in_dims
.
insert
(
vec_in_dims
.
begin
(),
diff
,
1
);
}
PADDLE_ENFORCE_EQ
(
repeat_times
.
size
(),
vec_in_dims
.
size
(),
platform
::
errors
::
InvalidArgument
(
"The rank (%d) of the input 'x' and the rank (%d) of the input "
"'repeat_times' for tile op must match after promotion."
,
vec_in_dims
.
size
(),
repeat_times
.
size
()));
auto
*
out0
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
framework
::
DDim
new_in_dims
=
framework
::
make_ddim
(
vec_in_dims
);
framework
::
DDim
out_dims
(
new_in_dims
);
for
(
size_t
i
=
0
;
i
<
repeat_times
.
size
();
++
i
)
{
out_dims
[
i
]
*=
repeat_times
[
i
];
}
auto
vec_out_dims
=
framework
::
vectorize
<
int
>
(
out_dims
);
out0
->
Resize
(
out_dims
);
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
&
dev_ctx
=
context
.
template
device_context
<
paddle
::
platform
::
XPUDeviceContext
>();
std
::
vector
<
int
>
temp
(
repeat_times
.
size
(),
1
);
if
(
repeat_times
==
temp
)
{
framework
::
TensorCopy
(
*
in0
,
context
.
GetPlace
(),
dev_ctx
,
out0
);
return
;
}
int
ret
=
XPU_SUCCESS
;
if
(
std
::
is_same
<
T
,
bool
>::
value
)
{
ret
=
xpu
::
broadcast
<
int8_t
>
(
dev_ctx
.
x_context
(),
reinterpret_cast
<
const
int8_t
*>
(
in0
->
data
<
T
>
()),
reinterpret_cast
<
int8_t
*>
(
out0
->
data
<
T
>
()),
vec_in_dims
,
vec_out_dims
);
}
else
{
ret
=
xpu
::
broadcast
<
T
>
(
dev_ctx
.
x_context
(),
in0
->
data
<
T
>
(),
out0
->
data
<
T
>
(),
vec_in_dims
,
vec_out_dims
);
}
PADDLE_ENFORCE_EQ
(
ret
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU tile kernel return wrong value[%d %s]"
,
ret
,
XPUAPIErrorMsg
[
ret
]));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
tile
,
ops
::
TileXPUKernel
<
bool
>
,
ops
::
TileXPUKernel
<
int
>
,
ops
::
TileXPUKernel
<
int64_t
>
,
ops
::
TileXPUKernel
<
float
>
);
#endif
paddle/fluid/platform/xpu/xpu2_op_list.h
浏览文件 @
819b9589
...
...
@@ -252,8 +252,16 @@ XPUOpMap& get_kl2_ops() {
pOpKernelType
(
vartype
::
COMPLEX128
,
XPUPlace
())})},
{
"softmax"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
{
"softmax_grad"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})}
{
"softmax_grad"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
{
"gather_nd"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
INT32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
INT64
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"tile"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
INT32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
INT64
,
XPUPlace
()),
pOpKernelType
(
vartype
::
BOOL
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})}
// AddMore
};
...
...
python/paddle/fluid/tests/unittests/xpu/test_gather_nd_op_xpu.py
0 → 100644
浏览文件 @
819b9589
# 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
from
op_test_xpu
import
XPUOpTest
import
paddle.fluid
as
fluid
import
paddle
def
gather_nd_grad
(
x
,
index
):
dout_shape
=
index
.
shape
[:
-
1
]
+
x
.
shape
[
index
.
shape
[
-
1
]:]
numel
=
1
for
i
in
dout_shape
:
numel
=
numel
*
i
dout
=
np
.
full
(
dout_shape
,
1.
/
numel
)
dx
=
np
.
full_like
(
x
,
0
)
index
=
tuple
(
index
.
reshape
(
-
1
,
index
.
shape
[
-
1
]).
T
)
np
.
add
.
at
(
dx
,
index
,
dout
)
return
dx
def
test_class1
(
op_type
,
typename
):
class
TestGatherNdOpWithEmptyIndex
(
XPUOpTest
):
#Index has empty element, which means copy entire tensor
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"gather_nd"
xnp
=
np
.
random
.
random
((
5
,
20
)).
astype
(
typename
)
self
.
inputs
=
{
'X'
:
xnp
,
'Index'
:
np
.
array
([[],
[]]).
astype
(
"int32"
)
}
self
.
outputs
=
{
'Out'
:
np
.
vstack
((
xnp
[
np
.
newaxis
,
:],
xnp
[
np
.
newaxis
,
:]))
}
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
pass
cls_name
=
"{0}_{1}_1"
.
format
(
op_type
,
typename
)
TestGatherNdOpWithEmptyIndex
.
__name__
=
cls_name
globals
()[
cls_name
]
=
TestGatherNdOpWithEmptyIndex
def
test_class2
(
op_type
,
typename
):
class
TestGatherNdOpWithIndex1
(
OpTest
):
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"gather_nd"
xnp
=
np
.
random
.
random
((
5
,
20
)).
astype
(
typename
)
self
.
inputs
=
{
'X'
:
xnp
,
'Index'
:
np
.
array
([
1
]).
astype
(
"int32"
)}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
"X"
][
self
.
inputs
[
"Index"
]]}
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
pass
cls_name
=
"{0}_{1}_2"
.
format
(
op_type
,
typename
)
TestGatherNdOpWithIndex1
.
__name__
=
cls_name
globals
()[
cls_name
]
=
TestGatherNdOpWithIndex1
def
test_class3
(
op_type
,
typename
):
class
TestGatherNdOpWithLowIndex
(
OpTest
):
#Index has low rank, X has high rank
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"gather_nd"
xnp
=
np
.
random
.
uniform
(
0
,
100
,
(
10
,
10
)).
astype
(
typename
)
index
=
np
.
array
([[
1
],
[
2
]]).
astype
(
"int64"
)
self
.
inputs
=
{
'X'
:
xnp
,
'Index'
:
index
}
self
.
outputs
=
{
'Out'
:
xnp
[
tuple
(
index
.
T
)]}
self
.
x_grad
=
gather_nd_grad
(
xnp
,
index
)
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
pass
cls_name
=
"{0}_{1}_3"
.
format
(
op_type
,
typename
)
TestGatherNdOpWithLowIndex
.
__name__
=
cls_name
globals
()[
cls_name
]
=
TestGatherNdOpWithLowIndex
def
test_class4
(
op_type
,
typename
):
class
TestGatherNdOpIndex1
(
OpTest
):
#Index has low rank, X has high rank
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"gather_nd"
xnp
=
np
.
random
.
uniform
(
0
,
100
,
(
10
,
10
)).
astype
(
typename
)
index
=
np
.
array
([
1
,
2
]).
astype
(
"int64"
)
self
.
inputs
=
{
'X'
:
xnp
,
'Index'
:
index
}
self
.
outputs
=
{
'Out'
:
xnp
[
tuple
(
index
.
T
)]}
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
pass
cls_name
=
"{0}_{1}_4"
.
format
(
op_type
,
typename
)
TestGatherNdOpIndex1
.
__name__
=
cls_name
globals
()[
cls_name
]
=
TestGatherNdOpIndex1
def
test_class5
(
op_type
,
typename
):
class
TestGatherNdOpWithSameIndexAsX
(
OpTest
):
#Index has same rank as X's rank
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"gather_nd"
xnp
=
np
.
random
.
uniform
(
0
,
100
,
(
10
,
10
)).
astype
(
typename
)
index
=
np
.
array
([[
1
,
1
],
[
2
,
1
]]).
astype
(
"int64"
)
self
.
inputs
=
{
'X'
:
xnp
,
'Index'
:
index
}
self
.
outputs
=
{
'Out'
:
xnp
[
tuple
(
index
.
T
)]}
#[25, 22]
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
pass
cls_name
=
"{0}_{1}_5"
.
format
(
op_type
,
typename
)
TestGatherNdOpWithSameIndexAsX
.
__name__
=
cls_name
globals
()[
cls_name
]
=
TestGatherNdOpWithSameIndexAsX
def
test_class6
(
op_type
,
typename
):
class
TestGatherNdOpWithHighRankSame
(
OpTest
):
#Both Index and X have high rank, and Rank(Index) = Rank(X)
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"gather_nd"
shape
=
(
5
,
2
,
3
,
1
,
10
)
xnp
=
np
.
random
.
rand
(
*
shape
).
astype
(
typename
)
index
=
np
.
vstack
([
np
.
random
.
randint
(
0
,
s
,
size
=
2
)
for
s
in
shape
]).
T
self
.
inputs
=
{
'X'
:
xnp
,
'Index'
:
index
.
astype
(
"int32"
)}
self
.
outputs
=
{
'Out'
:
xnp
[
tuple
(
index
.
T
)]}
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
pass
cls_name
=
"{0}_{1}_6"
.
format
(
op_type
,
typename
)
TestGatherNdOpWithHighRankSame
.
__name__
=
cls_name
globals
()[
cls_name
]
=
TestGatherNdOpWithHighRankSame
def
test_class7
(
op_type
,
typename
):
class
TestGatherNdOpWithHighRankDiff
(
OpTest
):
#Both Index and X have high rank, Rank(Index) < Rank(X)
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"gather_nd"
shape
=
(
2
,
3
,
4
,
1
,
10
)
xnp
=
np
.
random
.
rand
(
*
shape
).
astype
(
typename
)
index
=
np
.
vstack
(
[
np
.
random
.
randint
(
0
,
s
,
size
=
200
)
for
s
in
shape
]).
T
index_re
=
index
.
reshape
([
20
,
5
,
2
,
5
])
self
.
inputs
=
{
'X'
:
xnp
,
'Index'
:
index_re
.
astype
(
"int32"
)}
self
.
outputs
=
{
'Out'
:
xnp
[
tuple
(
index
.
T
)].
reshape
([
20
,
5
,
2
])}
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
pass
cls_name
=
"{0}_{1}_7"
.
format
(
op_type
,
typename
)
TestGatherNdOpWithHighRankDiff
.
__name__
=
cls_name
globals
()[
cls_name
]
=
TestGatherNdOpWithHighRankDiff
class
TestGatherNdAPI
(
unittest
.
TestCase
):
def
test_imperative
(
self
):
paddle
.
disable_static
()
input_1
=
np
.
array
([[
1
,
2
],
[
3
,
4
],
[
5
,
6
]])
index_1
=
np
.
array
([[
1
]])
input
=
fluid
.
dygraph
.
to_variable
(
input_1
)
index
=
fluid
.
dygraph
.
to_variable
(
index_1
)
output
=
paddle
.
fluid
.
layers
.
gather
(
input
,
index
)
output_np
=
output
.
numpy
()
expected_output
=
np
.
array
([
3
,
4
])
self
.
assertTrue
(
np
.
allclose
(
output_np
,
expected_output
))
paddle
.
enable_static
()
for
_typename
in
{
'float32'
,
'int'
,
'int64'
}:
test_class1
(
'gather_nd'
,
_typename
)
test_class2
(
'gather_nd'
,
_typename
)
test_class3
(
'gather_nd'
,
_typename
)
test_class4
(
'gather_nd'
,
_typename
)
test_class5
(
'gather_nd'
,
_typename
)
test_class6
(
'gather_nd'
,
_typename
)
test_class7
(
'gather_nd'
,
_typename
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/xpu/test_tile_op_xpu.py
0 → 100644
浏览文件 @
819b9589
# Copyright (c) 2018 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
from
op_test_xpu
import
XPUOpTest
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid
import
compiler
,
Program
,
program_guard
from
paddle.fluid
import
core
paddle
.
enable_static
()
np
.
random
.
seed
(
10
)
#Situation 1: repeat_times is a list (without tensor)
class
TestTileOpRank1
(
XPUOpTest
):
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"tile"
self
.
init_data
()
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)}
self
.
attrs
=
{
'repeat_times'
:
self
.
repeat_times
}
output
=
np
.
tile
(
self
.
inputs
[
'X'
],
self
.
repeat_times
)
self
.
outputs
=
{
'Out'
:
output
}
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
init_data
(
self
):
self
.
ori_shape
=
[
100
]
self
.
repeat_times
=
[
2
]
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
pass
#with dimension expanding
class
TestTileOpRank2Expanding
(
TestTileOpRank1
):
def
init_data
(
self
):
self
.
ori_shape
=
[
120
]
self
.
repeat_times
=
[
2
,
2
]
class
TestTileOpRank2
(
TestTileOpRank1
):
def
init_data
(
self
):
self
.
ori_shape
=
[
12
,
14
]
self
.
repeat_times
=
[
2
,
3
]
class
TestTileOpRank3_Corner
(
TestTileOpRank1
):
def
init_data
(
self
):
self
.
ori_shape
=
(
2
,
10
,
5
)
self
.
repeat_times
=
(
1
,
1
,
1
)
class
TestTileOpRank3_Corner2
(
TestTileOpRank1
):
def
init_data
(
self
):
self
.
ori_shape
=
(
2
,
10
,
5
)
self
.
repeat_times
=
(
2
,
2
)
class
TestTileOpRank3
(
TestTileOpRank1
):
def
init_data
(
self
):
self
.
ori_shape
=
(
2
,
4
,
15
)
self
.
repeat_times
=
(
2
,
1
,
4
)
class
TestTileOpRank4
(
TestTileOpRank1
):
def
init_data
(
self
):
self
.
ori_shape
=
(
2
,
4
,
5
,
7
)
self
.
repeat_times
=
(
3
,
2
,
1
,
2
)
# Situation 2: repeat_times is a list (with tensor)
class
TestTileOpRank1_tensor_attr
(
XPUOpTest
):
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"tile"
self
.
init_data
()
repeat_times_tensor
=
[]
for
index
,
ele
in
enumerate
(
self
.
repeat_times
):
repeat_times_tensor
.
append
((
"x"
+
str
(
index
),
np
.
ones
(
(
1
)).
astype
(
'int32'
)
*
ele
))
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
),
'repeat_times_tensor'
:
repeat_times_tensor
,
}
self
.
attrs
=
{
"repeat_times"
:
self
.
infer_repeat_times
}
output
=
np
.
tile
(
self
.
inputs
[
'X'
],
self
.
repeat_times
)
self
.
outputs
=
{
'Out'
:
output
}
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
init_data
(
self
):
self
.
ori_shape
=
[
100
]
self
.
repeat_times
=
[
2
]
self
.
infer_repeat_times
=
[
-
1
]
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
pass
class
TestTileOpRank2_Corner_tensor_attr
(
TestTileOpRank1_tensor_attr
):
def
init_data
(
self
):
self
.
ori_shape
=
[
12
,
14
]
self
.
repeat_times
=
[
1
,
1
]
self
.
infer_repeat_times
=
[
1
,
-
1
]
class
TestTileOpRank2_attr_tensor
(
TestTileOpRank1_tensor_attr
):
def
init_data
(
self
):
self
.
ori_shape
=
[
12
,
14
]
self
.
repeat_times
=
[
2
,
3
]
self
.
infer_repeat_times
=
[
-
1
,
3
]
# Situation 3: repeat_times is a tensor
class
TestTileOpRank1_tensor
(
XPUOpTest
):
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"tile"
self
.
init_data
()
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
),
'RepeatTimes'
:
np
.
array
(
self
.
repeat_times
).
astype
(
"int32"
),
}
self
.
attrs
=
{}
output
=
np
.
tile
(
self
.
inputs
[
'X'
],
self
.
repeat_times
)
self
.
outputs
=
{
'Out'
:
output
}
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
init_data
(
self
):
self
.
ori_shape
=
[
100
]
self
.
repeat_times
=
[
2
]
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
pass
class
TestTileOpRank2_tensor
(
TestTileOpRank1_tensor
):
def
init_data
(
self
):
self
.
ori_shape
=
[
12
,
14
]
self
.
repeat_times
=
[
2
,
3
]
# Situation 4: input x is Integer
class
TestTileOpInteger
(
XPUOpTest
):
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"tile"
self
.
inputs
=
{
'X'
:
np
.
random
.
randint
(
10
,
size
=
(
4
,
4
,
5
)).
astype
(
"int32"
)
}
self
.
attrs
=
{
'repeat_times'
:
[
2
,
1
,
4
]}
output
=
np
.
tile
(
self
.
inputs
[
'X'
],
(
2
,
1
,
4
))
self
.
outputs
=
{
'Out'
:
output
}
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
# Situation 5: input x is Integer
class
TestTileOpInt64_t
(
XPUOpTest
):
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"tile"
self
.
inputs
=
{
'X'
:
np
.
random
.
randint
(
10
,
size
=
(
2
,
4
,
5
)).
astype
(
"int64"
)
}
self
.
attrs
=
{
'repeat_times'
:
[
2
,
1
,
4
]}
output
=
np
.
tile
(
self
.
inputs
[
'X'
],
(
2
,
1
,
4
))
self
.
outputs
=
{
'Out'
:
output
}
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
# Situation 6: input x is Bool
class
TestTileOpBool
(
XPUOpTest
):
def
setUp
(
self
):
self
.
set_xpu
()
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"tile"
self
.
inputs
=
{
'X'
:
np
.
random
.
randint
(
10
,
size
=
(
2
,
4
,
5
)).
astype
(
"bool"
)
}
self
.
attrs
=
{
'repeat_times'
:
[
2
,
1
,
4
]}
output
=
np
.
tile
(
self
.
inputs
[
'X'
],
(
2
,
1
,
4
))
self
.
outputs
=
{
'Out'
:
output
}
def
set_xpu
(
self
):
self
.
__class__
.
use_xpu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
# Test python API
class
TestTileAPI
(
unittest
.
TestCase
):
def
test_api
(
self
):
with
fluid
.
dygraph
.
guard
(
paddle
.
XPUPlace
(
0
)):
np_x
=
np
.
random
.
random
([
12
,
14
]).
astype
(
"float32"
)
x
=
paddle
.
to_tensor
(
np_x
)
positive_2
=
np
.
array
([
2
]).
astype
(
"int32"
)
positive_2
=
paddle
.
to_tensor
(
positive_2
)
repeat_times
=
np
.
array
([
2
,
3
]).
astype
(
"int32"
)
repeat_times
=
paddle
.
to_tensor
(
repeat_times
)
out_1
=
paddle
.
tile
(
x
,
repeat_times
=
[
2
,
3
])
out_2
=
paddle
.
tile
(
x
,
repeat_times
=
[
positive_2
,
3
])
out_3
=
paddle
.
tile
(
x
,
repeat_times
=
repeat_times
)
assert
np
.
array_equal
(
out_1
.
numpy
(),
np
.
tile
(
np_x
,
(
2
,
3
)))
assert
np
.
array_equal
(
out_2
.
numpy
(),
np
.
tile
(
np_x
,
(
2
,
3
)))
assert
np
.
array_equal
(
out_3
.
numpy
(),
np
.
tile
(
np_x
,
(
2
,
3
)))
if
__name__
==
"__main__"
:
unittest
.
main
()
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