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d22f92ad
编写于
12月 29, 2021
作者:
Y
ykkk2333
提交者:
GitHub
12月 29, 2021
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差异文件
add top k v2 operator, test=kunlun (#38434)
上级
995332ef
变更
3
隐藏空白更改
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并排
Showing
3 changed file
with
489 addition
and
0 deletion
+489
-0
paddle/fluid/operators/top_k_v2_op_xpu.cc
paddle/fluid/operators/top_k_v2_op_xpu.cc
+198
-0
paddle/fluid/platform/device/xpu/xpu2_op_list.h
paddle/fluid/platform/device/xpu/xpu2_op_list.h
+2
-0
python/paddle/fluid/tests/unittests/xpu/test_top_k_v2_op_xpu.py
.../paddle/fluid/tests/unittests/xpu/test_top_k_v2_op_xpu.py
+289
-0
未找到文件。
paddle/fluid/operators/top_k_v2_op_xpu.cc
0 → 100644
浏览文件 @
d22f92ad
/* 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 <memory>
#include "paddle/fluid/operators/top_k_op.h"
#include "paddle/fluid/operators/transpose_op.h"
#include "xpu/refactor/math.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
class
TopkV2XPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
output
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
auto
*
indices
=
ctx
.
Output
<
Tensor
>
(
"Indices"
);
const
auto
&
in_dims
=
input
->
dims
();
const
T
*
in_data
=
input
->
data
<
T
>
();
int64_t
*
indices_data
=
indices
->
mutable_data
<
int64_t
>
(
ctx
.
GetPlace
());
T
*
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
auto
&
out_dims
=
output
->
dims
();
const
auto
&
sorted
=
static_cast
<
bool
>
(
ctx
.
Attr
<
bool
>
(
"sorted"
));
const
auto
&
largest
=
static_cast
<
bool
>
(
ctx
.
Attr
<
bool
>
(
"largest"
));
PADDLE_ENFORCE_EQ
(
sorted
,
true
,
platform
::
errors
::
External
(
"XPU API does not support unsorted topk operation currently."
" Operator will be supported in future update."
));
PADDLE_ENFORCE_EQ
(
largest
,
true
,
platform
::
errors
::
External
(
"XPU API does not support smallest topk operation currently."
" Operator will be supported in future update."
));
int
axis
=
static_cast
<
int
>
(
ctx
.
Attr
<
int
>
(
"axis"
));
if
(
axis
<
0
)
axis
+=
in_dims
.
size
();
size_t
k
=
static_cast
<
int
>
(
ctx
.
Attr
<
int
>
(
"k"
));
auto
*
k_t
=
ctx
.
Input
<
Tensor
>
(
"K"
);
if
(
k_t
)
{
k
=
k_t
->
data
<
int
>
()[
0
];
framework
::
DDim
output_dims
=
output
->
dims
();
output_dims
[
axis
]
=
k
;
output
->
Resize
(
output_dims
);
indices
->
Resize
(
output_dims
);
}
if
(
axis
+
1
==
in_dims
.
size
())
{
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
XPUDeviceContext
>();
xpu
::
ctx_guard
RAII_GUARD
(
dev_ctx
.
x_context
());
int32_t
*
indices_int_data
=
RAII_GUARD
.
alloc_l3_or_gm
<
int32_t
>
(
indices
->
numel
());
const
size_t
row
=
framework
::
product
(
framework
::
slice_ddim
(
in_dims
,
0
,
in_dims
.
size
()
-
1
));
const
size_t
col
=
in_dims
[
in_dims
.
size
()
-
1
];
int
r
=
xpu
::
sorted_topk
<
T
>
(
dev_ctx
.
x_context
(),
in_data
,
output_data
,
indices_int_data
,
row
,
col
,
k
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API return wrong value[%d %s] in call kernel name "
"[%s], please check "
"where Baidu Kunlun Card is properly installed."
,
r
,
XPUAPIErrorMsg
[
r
],
"sorted_topk"
));
r
=
xpu
::
cast_v2
<
int32_t
,
int64_t
>
(
dev_ctx
.
x_context
(),
(
const
int32_t
*
)
indices_int_data
,
indices_data
,
indices
->
numel
());
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API return wrong value[%d %s] in call kernel name "
"[%s], please check "
"where Baidu Kunlun Card is properly installed."
,
r
,
XPUAPIErrorMsg
[
r
],
"cast_v2"
));
}
else
{
// do transpose if axis is not the last dim of input
std
::
vector
<
int
>
trans_axes
;
for
(
int
i
=
0
;
i
<
axis
;
i
++
)
{
trans_axes
.
emplace_back
(
i
);
}
for
(
int
i
=
axis
+
1
;
i
<
in_dims
.
size
();
i
++
)
{
trans_axes
.
emplace_back
(
i
);
}
trans_axes
.
emplace_back
(
axis
);
// Get input and output dims for transpose
framework
::
DDim
trans_dims
(
in_dims
);
framework
::
DDim
trans_out_dims
(
output
->
dims
());
for
(
size_t
i
=
0
;
i
<
trans_axes
.
size
();
i
++
)
{
trans_dims
[
i
]
=
in_dims
[
trans_axes
[
i
]];
trans_out_dims
[
i
]
=
out_dims
[
trans_axes
[
i
]];
}
std
::
vector
<
int
>
x_shape_host
(
in_dims
.
size
(),
0
);
for
(
int
i
=
0
;
i
<
in_dims
.
size
();
++
i
)
{
x_shape_host
[
i
]
=
in_dims
[
i
];
}
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
XPUDeviceContext
>();
xpu
::
ctx_guard
RAII_GUARD
(
dev_ctx
.
x_context
());
T
*
trans_in_data
=
RAII_GUARD
.
alloc_l3_or_gm
<
T
>
(
input
->
numel
());
// Transpose and save interval output to trans_in
int
r
=
xpu
::
transpose
<
T
>
(
dev_ctx
.
x_context
(),
in_data
,
trans_in_data
,
x_shape_host
,
trans_axes
);
PADDLE_ENFORCE_EQ
(
r
,
xpu
::
Error_t
::
SUCCESS
,
platform
::
errors
::
External
(
"XPU API 1st Transpose kernel"
" returns wrong value[%d %s]!"
,
r
,
XPUAPIErrorMsg
[
r
]));
T
*
trans_out_data
=
RAII_GUARD
.
alloc_l3_or_gm
<
T
>
(
output
->
numel
());
int64_t
*
trans_idx_data
=
RAII_GUARD
.
alloc_l3_or_gm
<
int64_t
>
(
output
->
numel
());
int32_t
*
trans_idx_int32_data
=
RAII_GUARD
.
alloc_l3_or_gm
<
int32_t
>
(
output
->
numel
());
const
size_t
row
=
framework
::
product
(
framework
::
slice_ddim
(
trans_dims
,
0
,
trans_dims
.
size
()
-
1
));
const
size_t
col
=
trans_dims
[
trans_dims
.
size
()
-
1
];
// Do top k on transposed input
r
=
xpu
::
sorted_topk
<
T
>
(
dev_ctx
.
x_context
(),
trans_in_data
,
trans_out_data
,
trans_idx_int32_data
,
row
,
col
,
k
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API return wrong value[%d %s] in call kernel name "
"[%s], please check "
"where Baidu Kunlun Card is properly installed."
,
r
,
XPUAPIErrorMsg
[
r
],
"sorted_topk"
));
r
=
xpu
::
cast_v2
<
int32_t
,
int64_t
>
(
dev_ctx
.
x_context
(),
(
const
int32_t
*
)
trans_idx_int32_data
,
trans_idx_data
,
indices
->
numel
());
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU API return wrong value[%d %s in call kernel name "
"[%s], please check "
"where Baidu Kunlun Card is properly installed."
,
r
,
XPUAPIErrorMsg
[
r
],
"cast_v2"
));
// Transpose back to original dims
std
::
vector
<
int
>
trans_back_axes
;
for
(
int
i
=
0
;
i
<
axis
;
i
++
)
{
trans_axes
.
emplace_back
(
i
);
}
trans_axes
.
emplace_back
(
trans_out_dims
.
size
()
-
1
);
for
(
int
i
=
axis
;
i
<
trans_out_dims
.
size
()
-
1
;
i
++
)
{
trans_axes
.
emplace_back
(
i
);
}
std
::
vector
<
int
>
trans_out_shape_host
(
trans_back_axes
.
size
(),
0
);
for
(
size_t
i
=
0
;
i
<
trans_back_axes
.
size
();
++
i
)
{
trans_out_shape_host
[
i
]
=
trans_out_dims
[
i
];
}
r
=
xpu
::
transpose
<
T
>
(
dev_ctx
.
x_context
(),
trans_out_data
,
output_data
,
trans_out_shape_host
,
trans_back_axes
);
PADDLE_ENFORCE_EQ
(
r
,
xpu
::
Error_t
::
SUCCESS
,
platform
::
errors
::
External
(
"XPU API 2nd Transpose kernel"
" returns wrong value[%d %s]"
,
r
,
XPUAPIErrorMsg
[
r
]));
r
=
xpu
::
transpose
<
int64_t
>
(
dev_ctx
.
x_context
(),
trans_idx_data
,
indices_data
,
trans_out_shape_host
,
trans_back_axes
);
PADDLE_ENFORCE_EQ
(
r
,
xpu
::
Error_t
::
SUCCESS
,
platform
::
errors
::
External
(
"XPU API 3rd Transpose kernel"
" returns wrong value[%d %s]"
,
r
,
XPUAPIErrorMsg
[
r
]));
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
top_k_v2
,
ops
::
TopkV2XPUKernel
<
float
>
);
#endif
paddle/fluid/platform/device/xpu/xpu2_op_list.h
浏览文件 @
d22f92ad
...
...
@@ -327,6 +327,7 @@ XPUOpMap& get_kl2_ops() {
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
{
"transpose"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
{
"top_k_v2"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"unsqueeze2_grad"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP64
,
XPUPlace
()),
pOpKernelType
(
vartype
::
INT64
,
XPUPlace
()),
...
...
@@ -348,6 +349,7 @@ XPUOpMap& get_kl2_ops() {
{
"where"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
INT32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
INT64
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
// AddMore
};
...
...
python/paddle/fluid/tests/unittests/xpu/test_top_k_v2_op_xpu.py
0 → 100644
浏览文件 @
d22f92ad
# 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
import
paddle
import
paddle.fluid.core
as
core
paddle
.
enable_static
()
def
numpy_topk
(
x
,
k
=
1
,
axis
=-
1
,
largest
=
True
):
if
axis
<
0
:
axis
=
len
(
x
.
shape
)
+
axis
if
largest
:
indices
=
np
.
argsort
(
-
x
,
axis
=
axis
)
else
:
indices
=
np
.
argsort
(
x
,
axis
=
axis
)
if
largest
:
value
=
-
np
.
sort
(
-
x
,
axis
=
axis
)
else
:
value
=
np
.
sort
(
x
,
axis
=
axis
)
indices
=
indices
.
take
(
indices
=
range
(
0
,
k
),
axis
=
axis
)
value
=
value
.
take
(
indices
=
range
(
0
,
k
),
axis
=
axis
)
return
value
,
indices
class
TestTopkOp
(
OpTest
):
def
init_args
(
self
):
self
.
k
=
3
self
.
axis
=
1
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
10
,
20
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
def
test_check_grad
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad
(
set
([
'X'
]),
'Out'
)
class
TestTopkOp1
(
TestTopkOp
):
def
init_args
(
self
):
self
.
k
=
3
self
.
axis
=
1
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
10
,
10
,
5
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
class
TestTopkOp2
(
TestTopkOp
):
def
init_args
(
self
):
self
.
k
=
3
self
.
axis
=
1
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
10
,
10
,
5
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
class
TestTopkOp3
(
TestTopkOp
):
def
init_args
(
self
):
self
.
k
=
5
self
.
axis
=
1
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
10
,
10
,
5
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
class
TestTopkOp4
(
TestTopkOp
):
def
init_args
(
self
):
self
.
k
=
1
self
.
axis
=
1
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
10
,
10
,
5
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
class
TestTopkOp5
(
TestTopkOp
):
def
init_args
(
self
):
self
.
k
=
3
self
.
axis
=
2
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
10
,
10
,
5
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
class
TestTopkOp6
(
TestTopkOp
):
def
init_args
(
self
):
self
.
k
=
5
self
.
axis
=
1
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
8
,
32
,
64
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
class
TestTopkOp7
(
TestTopkOp
):
def
init_args
(
self
):
self
.
k
=
10
self
.
axis
=
2
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
8
,
5
,
10
,
16
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
class
TestTopkOp8
(
TestTopkOp
):
def
init_args
(
self
):
self
.
k
=
1
self
.
axis
=
1
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
8
,
32
,
64
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
class
TestTopkOp9
(
TestTopkOp
):
def
init_args
(
self
):
self
.
k
=
3
self
.
axis
=
1
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
10
,
10
,
5
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
class
TestTopkOp10
(
TestTopkOp
):
def
init_args
(
self
):
self
.
k
=
3
self
.
axis
=
1
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
10
,
10
,
5
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
class
TestTopkOp11
(
TestTopkOp
):
def
init_args
(
self
):
self
.
k
=
5
self
.
axis
=
1
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
10
,
10
,
5
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
class
TestTopkOp12
(
TestTopkOp
):
def
init_args
(
self
):
self
.
k
=
1
self
.
axis
=
1
self
.
largest
=
True
def
setUp
(
self
):
self
.
op_type
=
"top_k_v2"
self
.
dtype
=
np
.
float32
self
.
input_data
=
np
.
random
.
rand
(
10
,
10
,
5
)
self
.
init_args
()
self
.
inputs
=
{
'X'
:
self
.
input_data
}
self
.
attrs
=
{
'k'
:
self
.
k
,
'axis'
:
self
.
axis
,
'largest'
:
self
.
largest
}
output
,
indices
=
numpy_topk
(
self
.
input_data
,
axis
=
self
.
axis
,
k
=
self
.
k
,
largest
=
self
.
largest
)
self
.
outputs
=
{
'Out'
:
output
,
'Indices'
:
indices
}
if
__name__
==
"__main__"
:
unittest
.
main
()
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