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体验新版 GitCode,发现更多精彩内容 >>
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13a21cf7
编写于
5月 30, 2022
作者:
C
Chenxiao Niu
提交者:
GitHub
5月 30, 2022
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差异文件
[mlu] add one_hot_v2 mlu kernel (#43025)
上级
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变更
2
隐藏空白更改
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Showing
2 changed file
with
321 addition
and
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+321
-0
paddle/fluid/operators/one_hot_v2_op_mlu.cc
paddle/fluid/operators/one_hot_v2_op_mlu.cc
+86
-0
python/paddle/fluid/tests/unittests/mlu/test_one_hot_v2_op_mlu.py
...addle/fluid/tests/unittests/mlu/test_one_hot_v2_op_mlu.py
+235
-0
未找到文件。
paddle/fluid/operators/one_hot_v2_op_mlu.cc
0 → 100644
浏览文件 @
13a21cf7
/* 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. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
#include "paddle/fluid/operators/utils.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
LoDTensor
=
framework
::
LoDTensor
;
template
<
typename
T
>
class
OneHotV2MLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
&
dev_ctx
=
ctx
.
template
device_context
<
paddle
::
platform
::
MLUDeviceContext
>();
auto
*
in
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
int
depth
=
ctx
.
Attr
<
int
>
(
"depth"
);
if
(
ctx
.
HasInput
(
"depth_tensor"
))
{
std
::
vector
<
int32_t
>
depth_data
;
depth_data
=
GetDataFromTensor
<
int
>
(
ctx
.
Input
<
Tensor
>
(
"depth_tensor"
));
depth
=
depth_data
[
0
];
auto
out_dims
=
out
->
dims
();
out_dims
[
out_dims
.
size
()
-
1
]
=
depth
;
out
->
Resize
(
out_dims
);
}
out
->
mutable_data
<
float
>
(
ctx
.
GetPlace
());
float
on_value
=
1.0
f
,
off_value
=
0.0
f
;
const
int
in_off_dim
[
1
]
=
{
1
};
Tensor
on_value_tensor
=
ctx
.
AllocateTmpTensor
<
float
,
MLUDeviceContext
>
(
framework
::
DDim
(
in_off_dim
,
1
),
dev_ctx
);
Tensor
off_value_tensor
=
ctx
.
AllocateTmpTensor
<
float
,
MLUDeviceContext
>
(
framework
::
DDim
(
in_off_dim
,
1
),
dev_ctx
);
FillMLUTensorWithHostValue
(
ctx
,
on_value
,
&
on_value_tensor
);
FillMLUTensorWithHostValue
(
ctx
,
off_value
,
&
off_value_tensor
);
if
(
framework
::
TransToProtoVarType
(
in
->
dtype
())
==
framework
::
proto
::
VarType
::
INT32
)
{
MLUCnnlTensorDesc
desc_indices
(
*
in
);
MLUCnnl
::
OneHot
(
ctx
,
desc_indices
.
get
(),
GetBasePtr
(
in
),
depth
,
GetBasePtr
(
&
on_value_tensor
),
GetBasePtr
(
&
off_value_tensor
),
-
1
,
ToCnnlDataType
(
out
->
dtype
()),
GetBasePtr
(
out
));
}
else
{
Tensor
transformed_in
;
transformed_in
.
mutable_data
<
int32_t
>
(
in
->
dims
(),
dev_ctx
.
GetPlace
());
// use cnnlCast to cast int64_t to int32_t then do one_hot
MLUCnnlTensorDesc
in_desc
(
*
in
);
MLUCnnlTensorDesc
transformed_in_desc
(
transformed_in
);
cnnlCastDataType_t
cast_type
=
GetCastDataType
(
framework
::
TransToProtoVarType
(
in
->
dtype
()),
framework
::
TransToProtoVarType
(
transformed_in
.
dtype
()));
MLUCnnl
::
Cast
(
ctx
,
cast_type
,
in_desc
.
get
(),
GetBasePtr
(
in
),
transformed_in_desc
.
get
(),
GetBasePtr
(
&
transformed_in
));
MLUCnnl
::
OneHot
(
ctx
,
transformed_in_desc
.
get
(),
GetBasePtr
(
&
transformed_in
),
depth
,
GetBasePtr
(
&
on_value_tensor
),
GetBasePtr
(
&
off_value_tensor
),
-
1
,
ToCnnlDataType
(
out
->
dtype
()),
GetBasePtr
(
out
));
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_MLU_KERNEL
(
one_hot_v2
,
ops
::
OneHotV2MLUKernel
<
int32_t
>
);
python/paddle/fluid/tests/unittests/mlu/test_one_hot_v2_op_mlu.py
0 → 100644
浏览文件 @
13a21cf7
# Copyright (c) 2022 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
math
import
sys
sys
.
path
.
append
(
'..'
)
from
op_test
import
OpTest
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
paddle.fluid.framework
as
framework
from
paddle.fluid.framework
import
Program
,
program_guard
,
_test_eager_guard
paddle
.
enable_static
()
class
TestOneHotOp
(
OpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
self
.
op_type
=
'one_hot_v2'
depth
=
10
depth_np
=
np
.
array
(
10
).
astype
(
'int32'
)
dimension
=
12
x_lod
=
[[
4
,
1
,
3
,
3
]]
x
=
[
np
.
random
.
randint
(
0
,
depth
-
1
)
for
i
in
range
(
sum
(
x_lod
[
0
]))]
x
=
np
.
array
(
x
).
astype
(
'int32'
).
reshape
([
sum
(
x_lod
[
0
])])
out
=
np
.
zeros
(
shape
=
(
np
.
product
(
x
.
shape
),
depth
)).
astype
(
'float32'
)
for
i
in
range
(
np
.
product
(
x
.
shape
)):
out
[
i
,
x
[
i
]]
=
1.0
self
.
inputs
=
{
'X'
:
(
x
,
x_lod
),
'depth_tensor'
:
depth_np
}
self
.
attrs
=
{
'dtype'
:
int
(
core
.
VarDesc
.
VarType
.
FP32
)}
self
.
outputs
=
{
'Out'
:
(
out
,
x_lod
)}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
class
TestOneHotOp_attr
(
OpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
self
.
op_type
=
'one_hot_v2'
depth
=
10
dimension
=
12
x_lod
=
[[
4
,
1
,
3
,
3
]]
x
=
[
np
.
random
.
randint
(
0
,
depth
-
1
)
for
i
in
range
(
sum
(
x_lod
[
0
]))]
x
=
np
.
array
(
x
).
astype
(
'int32'
).
reshape
([
sum
(
x_lod
[
0
]),
1
])
out
=
np
.
zeros
(
shape
=
(
np
.
product
(
x
.
shape
[:
-
1
]),
1
,
depth
)).
astype
(
'float32'
)
for
i
in
range
(
np
.
product
(
x
.
shape
)):
out
[
i
,
0
,
x
[
i
]]
=
1.0
self
.
inputs
=
{
'X'
:
(
x
,
x_lod
)}
self
.
attrs
=
{
'dtype'
:
int
(
core
.
VarDesc
.
VarType
.
FP32
),
'depth'
:
depth
}
self
.
outputs
=
{
'Out'
:
(
out
,
x_lod
)}
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
class
TestOneHotOp_default_dtype
(
OpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
self
.
op_type
=
'one_hot_v2'
depth
=
10
depth_np
=
np
.
array
(
10
).
astype
(
'int32'
)
dimension
=
12
x_lod
=
[[
4
,
1
,
3
,
3
]]
x
=
[
np
.
random
.
randint
(
0
,
depth
-
1
)
for
i
in
range
(
sum
(
x_lod
[
0
]))]
x
=
np
.
array
(
x
).
astype
(
'int32'
).
reshape
([
sum
(
x_lod
[
0
])])
out
=
np
.
zeros
(
shape
=
(
np
.
product
(
x
.
shape
),
depth
)).
astype
(
'float32'
)
for
i
in
range
(
np
.
product
(
x
.
shape
)):
out
[
i
,
x
[
i
]]
=
1.0
self
.
inputs
=
{
'X'
:
(
x
,
x_lod
),
'depth_tensor'
:
depth_np
}
self
.
attrs
=
{}
self
.
outputs
=
{
'Out'
:
(
out
,
x_lod
)}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
class
TestOneHotOp_default_dtype_attr
(
OpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
self
.
op_type
=
'one_hot_v2'
depth
=
10
dimension
=
12
x_lod
=
[[
4
,
1
,
3
,
3
]]
x
=
[
np
.
random
.
randint
(
0
,
depth
-
1
)
for
i
in
range
(
sum
(
x_lod
[
0
]))]
x
=
np
.
array
(
x
).
astype
(
'int32'
).
reshape
([
sum
(
x_lod
[
0
]),
1
])
out
=
np
.
zeros
(
shape
=
(
np
.
product
(
x
.
shape
[:
-
1
]),
1
,
depth
)).
astype
(
'float32'
)
for
i
in
range
(
np
.
product
(
x
.
shape
)):
out
[
i
,
0
,
x
[
i
]]
=
1.0
self
.
inputs
=
{
'X'
:
(
x
,
x_lod
)}
self
.
attrs
=
{
'depth'
:
depth
}
self
.
outputs
=
{
'Out'
:
(
out
,
x_lod
)}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
class
TestOneHotOp_exception
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
self
.
op_type
=
'one_hot_v2'
self
.
depth
=
10
self
.
place
=
core
.
CPUPlace
()
self
.
dimension
=
12
self
.
x
=
core
.
LoDTensor
()
x_lod
=
[[
4
,
1
,
3
,
3
]]
data
=
[
np
.
random
.
randint
(
11
,
20
)
for
i
in
range
(
sum
(
x_lod
[
0
]))]
data
=
np
.
array
(
data
).
astype
(
'int'
).
reshape
([
sum
(
x_lod
[
0
]),
1
])
self
.
x
.
set
(
data
,
self
.
place
)
self
.
x
.
set_recursive_sequence_lengths
(
x_lod
)
def
test_check_output
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
self
.
dimension
],
dtype
=
'float32'
,
lod_level
=
1
)
block
=
program
.
current_block
()
one_hot_out
=
block
.
create_var
(
name
=
"one_hot_out"
,
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
dtype
=
'float32'
)
block
.
append_op
(
type
=
'one_hot'
,
inputs
=
{
'X'
:
x
},
attrs
=
{
'depth'
:
self
.
depth
},
outputs
=
{
'Out'
:
one_hot_out
})
exe
=
fluid
.
Executor
(
self
.
place
)
def
run
():
exe
.
run
(
feed
=
{
'x'
:
self
.
x
},
fetch_list
=
[
one_hot_out
],
return_numpy
=
False
)
self
.
assertRaises
(
ValueError
,
run
)
class
TestOneHotOpApi
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
def
test_api
(
self
):
depth
=
10
self
.
_run
(
depth
)
def
test_api_with_depthTensor
(
self
):
depth
=
fluid
.
layers
.
assign
(
input
=
np
.
array
([
10
],
dtype
=
np
.
int32
))
self
.
_run
(
depth
)
def
test_api_with_dygraph
(
self
):
depth
=
10
label
=
np
.
array
([
np
.
random
.
randint
(
0
,
depth
-
1
)
for
i
in
range
(
6
)]).
reshape
([
6
,
1
])
with
fluid
.
dygraph
.
guard
():
one_hot_label
=
fluid
.
one_hot
(
input
=
fluid
.
dygraph
.
to_variable
(
label
),
depth
=
depth
)
one_hot_label
=
paddle
.
nn
.
functional
.
one_hot
(
fluid
.
dygraph
.
to_variable
(
label
),
depth
)
# with _test_eager_guard():
# one_hot_label = paddle.nn.functional.one_hot(
# paddle.to_tensor(label), depth)
def
_run
(
self
,
depth
):
label
=
fluid
.
layers
.
data
(
name
=
"label"
,
shape
=
[
1
],
dtype
=
"int64"
)
one_hot_label
=
fluid
.
one_hot
(
input
=
label
,
depth
=
depth
)
label_data
=
np
.
array
([
np
.
random
.
randint
(
0
,
10
-
1
)
for
i
in
range
(
6
)]).
reshape
([
6
,
1
])
exe
=
fluid
.
Executor
(
self
.
place
)
exe
.
run
(
fluid
.
default_startup_program
())
ret
=
exe
.
run
(
feed
=
{
'label'
:
label_data
,
},
fetch_list
=
[
one_hot_label
],
return_numpy
=
False
)
class
BadInputTestOnehotV2
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
def
test_error
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
()):
def
test_bad_x
():
label
=
fluid
.
layers
.
data
(
name
=
"label"
,
shape
=
[
4
],
append_batch_size
=
False
,
dtype
=
"float32"
)
one_hot_label
=
fluid
.
one_hot
(
input
=
label
,
depth
=
4
)
self
.
assertRaises
(
TypeError
,
test_bad_x
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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