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ec422ea5
编写于
9月 15, 2021
作者:
R
ronnywang
提交者:
GitHub
9月 15, 2021
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差异文件
[NPU] add masked_select_op_npu (#35649)
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变更
2
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Showing
2 changed file
with
361 addition
and
0 deletion
+361
-0
paddle/fluid/operators/masked_select_op_npu.cc
paddle/fluid/operators/masked_select_op_npu.cc
+195
-0
python/paddle/fluid/tests/unittests/npu/test_masked_select_op_npu.py
...le/fluid/tests/unittests/npu/test_masked_select_op_npu.py
+166
-0
未找到文件。
paddle/fluid/operators/masked_select_op_npu.cc
0 → 100644
浏览文件 @
ec422ea5
/* 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/operators/masked_select_op.h"
#include "paddle/fluid/operators/npu_op_runner.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
MaskedSelectedNPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
input
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
mask
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Mask"
);
auto
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Y"
);
auto
input_dim
=
input
->
dims
();
auto
mask_dim
=
mask
->
dims
();
PADDLE_ENFORCE_EQ
(
input_dim
,
mask_dim
,
platform
::
errors
::
InvalidArgument
(
"The dim size of input and mask in OP(masked_selected) "
"must be equal, but got input dim:(%ld), mask dim: "
"(%ld). Please check input "
"value."
,
input_dim
,
mask_dim
));
auto
&
dev_ctx
=
ctx
.
template
device_context
<
paddle
::
platform
::
NPUDeviceContext
>();
auto
stream
=
dev_ctx
.
stream
();
Tensor
mask_int32
,
out_size
;
std
::
vector
<
int32_t
>
out_size_vec
;
mask_int32
.
mutable_data
<
int32_t
>
(
mask
->
dims
(),
ctx
.
GetPlace
());
out_size
.
mutable_data
<
int32_t
>
({
1
},
ctx
.
GetPlace
());
{
const
auto
&
cast_runner
=
NpuOpRunner
(
"Cast"
,
{
*
mask
},
{
mask_int32
},
{{
"dst_type"
,
static_cast
<
int32_t
>
(
ConvertToNpuDtype
(
framework
::
proto
::
VarType
::
INT32
))}});
cast_runner
.
Run
(
stream
);
mask_int32
.
Resize
({
mask_int32
.
numel
()});
NpuOpRunner
sum_runner
;
sum_runner
.
SetType
(
"ReduceSum"
);
sum_runner
.
AddInput
(
mask_int32
);
sum_runner
.
AddInput
(
std
::
vector
<
int32_t
>
({
0
}));
sum_runner
.
AddOutput
(
out_size
);
sum_runner
.
AddAttr
(
"keep_dims"
,
false
);
sum_runner
.
Run
(
stream
);
TensorToVector
(
out_size
,
dev_ctx
,
&
out_size_vec
);
}
out
->
Resize
({
out_size_vec
[
0
]});
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
Tensor
topkv2_out
,
indices
;
topkv2_out
.
mutable_data
<
int32_t
>
({
out_size_vec
[
0
]},
ctx
.
GetPlace
());
indices
.
mutable_data
<
int32_t
>
({
out_size_vec
[
0
]},
ctx
.
GetPlace
());
{
NpuOpRunner
topkv2_runner
;
topkv2_runner
.
SetType
(
"TopKV2"
)
.
AddInput
(
mask_int32
)
.
AddInput
(
out_size
)
.
AddOutput
(
topkv2_out
)
.
AddOutput
(
indices
)
.
AddAttr
(
"sorted"
,
false
)
.
AddAttr
(
"dim"
,
0
)
.
AddAttr
(
"largest"
,
true
)
.
Run
(
stream
);
// TopKV2 may be unstable
NpuOpRunner
topkv2_runner2
;
topkv2_runner2
.
SetType
(
"TopKV2"
)
.
AddInput
(
indices
)
.
AddInput
(
out_size
)
.
AddOutput
(
topkv2_out
)
.
AddOutput
(
indices
)
.
AddAttr
(
"sorted"
,
true
)
.
AddAttr
(
"dim"
,
0
)
.
AddAttr
(
"largest"
,
false
)
.
Run
(
stream
);
Tensor
input_tmp
;
input_tmp
.
ShareDataWith
(
*
input
);
input_tmp
.
Resize
({
input
->
numel
()});
const
auto
&
gather_runner
=
NpuOpRunner
(
"GatherV2D"
,
{
input_tmp
,
topkv2_out
},
{
*
out
},
{{
"axis"
,
0
}});
gather_runner
.
Run
(
stream
);
}
}
};
template
<
typename
T
>
class
MaskedSelectedGradNPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
mask
=
ctx
.
Input
<
framework
::
Tensor
>
(
"Mask"
);
auto
y_grad
=
ctx
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
x_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
x_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
&
dev_ctx
=
ctx
.
template
device_context
<
paddle
::
platform
::
NPUDeviceContext
>();
auto
stream
=
dev_ctx
.
stream
();
Tensor
mask_int32
,
out_size
;
std
::
vector
<
int32_t
>
out_size_vec
;
mask_int32
.
mutable_data
<
int32_t
>
(
mask
->
dims
(),
ctx
.
GetPlace
());
out_size
.
mutable_data
<
int32_t
>
({
1
},
ctx
.
GetPlace
());
{
const
auto
&
cast_runner
=
NpuOpRunner
(
"Cast"
,
{
*
mask
},
{
mask_int32
},
{{
"dst_type"
,
static_cast
<
int32_t
>
(
ConvertToNpuDtype
(
framework
::
proto
::
VarType
::
INT32
))}});
cast_runner
.
Run
(
stream
);
mask_int32
.
Resize
({
mask_int32
.
numel
()});
NpuOpRunner
sum_runner
;
sum_runner
.
SetType
(
"ReduceSum"
);
sum_runner
.
AddInput
(
mask_int32
);
sum_runner
.
AddInput
(
std
::
vector
<
int32_t
>
({
0
}));
sum_runner
.
AddOutput
(
out_size
);
sum_runner
.
AddAttr
(
"keep_dims"
,
false
);
sum_runner
.
Run
(
stream
);
TensorToVector
(
out_size
,
dev_ctx
,
&
out_size_vec
);
}
Tensor
topkv2_out
,
indices
;
topkv2_out
.
mutable_data
<
int32_t
>
({
out_size_vec
[
0
]},
ctx
.
GetPlace
());
indices
.
mutable_data
<
int32_t
>
({
out_size_vec
[
0
]},
ctx
.
GetPlace
());
{
NpuOpRunner
topkv2_runner
;
topkv2_runner
.
SetType
(
"TopKV2"
)
.
AddInput
(
mask_int32
)
.
AddInput
(
out_size
)
.
AddOutput
(
topkv2_out
)
.
AddOutput
(
indices
)
.
AddAttr
(
"sorted"
,
false
)
.
AddAttr
(
"dim"
,
0
)
.
AddAttr
(
"largest"
,
true
)
.
Run
(
stream
);
NpuOpRunner
topkv2_runner2
;
topkv2_runner2
.
SetType
(
"TopKV2"
)
.
AddInput
(
indices
)
.
AddInput
(
out_size
)
.
AddOutput
(
topkv2_out
)
.
AddOutput
(
indices
)
.
AddAttr
(
"sorted"
,
true
)
.
AddAttr
(
"dim"
,
0
)
.
AddAttr
(
"largest"
,
false
)
.
Run
(
stream
);
topkv2_out
.
Resize
({
out_size_vec
[
0
],
1
});
x_grad
->
Resize
({
x_grad
->
numel
()});
NpuOpRunner
scatter_runner
;
scatter_runner
.
SetType
(
"ScatterNd"
);
scatter_runner
.
AddInput
(
topkv2_out
);
scatter_runner
.
AddInput
(
*
y_grad
);
scatter_runner
.
AddInput
(
std
::
vector
<
int32_t
>
({
static_cast
<
int32_t
>
(
x_grad
->
numel
())}));
scatter_runner
.
AddOutput
(
*
x_grad
);
scatter_runner
.
Run
(
stream
);
x_grad
->
Resize
(
mask
->
dims
());
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_NPU_KERNEL
(
masked_select
,
ops
::
MaskedSelectedNPUKernel
<
plat
::
float16
>
,
ops
::
MaskedSelectedNPUKernel
<
float
>
,
ops
::
MaskedSelectedNPUKernel
<
int
>
,
ops
::
MaskedSelectedNPUKernel
<
int64_t
>
);
REGISTER_OP_NPU_KERNEL
(
masked_select_grad
,
ops
::
MaskedSelectedGradNPUKernel
<
plat
::
float16
>
,
ops
::
MaskedSelectedGradNPUKernel
<
float
>
,
ops
::
MaskedSelectedGradNPUKernel
<
int
>
,
ops
::
MaskedSelectedGradNPUKernel
<
int64_t
>
);
python/paddle/fluid/tests/unittests/npu/test_masked_select_op_npu.py
0 → 100644
浏览文件 @
ec422ea5
# 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.
from
__future__
import
print_function
import
numpy
as
np
import
unittest
import
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
,
skip_check_grad_ci
import
paddle
import
paddle.fluid
as
fluid
paddle
.
enable_static
()
def
np_masked_select
(
x
,
mask
):
result
=
np
.
empty
(
shape
=
(
0
),
dtype
=
x
.
dtype
)
for
ele
,
ma
in
zip
(
np
.
nditer
(
x
),
np
.
nditer
(
mask
)):
if
ma
:
result
=
np
.
append
(
result
,
ele
)
return
result
.
flatten
()
class
TestMaskedSelectOp
(
OpTest
):
def
set_npu
(
self
):
self
.
__class__
.
use_npu
=
True
def
setUp
(
self
):
self
.
set_npu
()
self
.
init
()
self
.
init_dtype
()
self
.
place
=
paddle
.
NPUPlace
(
0
)
self
.
op_type
=
"masked_select"
x
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
mask
=
np
.
array
(
np
.
random
.
randint
(
2
,
size
=
self
.
shape
,
dtype
=
bool
))
out
=
np_masked_select
(
x
,
mask
)
self
.
inputs
=
{
'X'
:
x
,
'Mask'
:
mask
}
self
.
outputs
=
{
'Y'
:
out
}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'X'
],
'Y'
)
def
init
(
self
):
self
.
shape
=
(
50
,
3
)
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float32
class
TestMaskedSelectOp1
(
TestMaskedSelectOp
):
def
init
(
self
):
self
.
shape
=
(
6
,
8
,
9
,
18
)
class
TestMaskedSelectOp2
(
TestMaskedSelectOp
):
def
init
(
self
):
self
.
shape
=
(
168
,
)
class
TestMaskedSelectOpFp16
(
TestMaskedSelectOp
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float16
def
test_check_grad
(
self
):
x_grad
=
self
.
inputs
[
'Mask'
].
astype
(
self
.
dtype
)
x_grad
=
x_grad
*
(
1
/
x_grad
.
sum
())
self
.
check_grad_with_place
(
self
.
place
,
[
'X'
],
'Y'
,
user_defined_grads
=
[
x_grad
])
@
skip_check_grad_ci
(
reason
=
"get_numeric_gradient not support int32"
)
class
TestMaskedSelectOpInt32
(
TestMaskedSelectOp
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
int32
def
test_check_grad
(
self
):
pass
@
skip_check_grad_ci
(
reason
=
"get_numeric_gradient not support int64"
)
class
TestMaskedSelectOpInt64
(
TestMaskedSelectOp
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
int64
def
test_check_grad
(
self
):
pass
class
TestMaskedSelectAPI
(
unittest
.
TestCase
):
def
test_imperative_mode
(
self
):
paddle
.
disable_static
(
paddle
.
NPUPlace
(
0
))
shape
=
(
88
,
6
,
8
)
np_x
=
np
.
random
.
random
(
shape
).
astype
(
'float32'
)
np_mask
=
np
.
array
(
np
.
random
.
randint
(
2
,
size
=
shape
,
dtype
=
bool
))
x
=
paddle
.
to_tensor
(
np_x
)
mask
=
paddle
.
to_tensor
(
np_mask
)
out
=
paddle
.
masked_select
(
x
,
mask
)
np_out
=
np_masked_select
(
np_x
,
np_mask
)
self
.
assertEqual
(
np
.
allclose
(
out
.
numpy
(),
np_out
),
True
)
paddle
.
enable_static
()
def
test_static_mode
(
self
):
shape
=
[
8
,
9
,
6
]
x
=
paddle
.
fluid
.
data
(
shape
=
shape
,
dtype
=
'float32'
,
name
=
'x'
)
mask
=
paddle
.
fluid
.
data
(
shape
=
shape
,
dtype
=
'bool'
,
name
=
'mask'
)
np_x
=
np
.
random
.
random
(
shape
).
astype
(
'float32'
)
np_mask
=
np
.
array
(
np
.
random
.
randint
(
2
,
size
=
shape
,
dtype
=
bool
))
out
=
paddle
.
masked_select
(
x
,
mask
)
np_out
=
np_masked_select
(
np_x
,
np_mask
)
exe
=
paddle
.
static
.
Executor
(
place
=
paddle
.
NPUPlace
(
0
))
res
=
exe
.
run
(
paddle
.
static
.
default_main_program
(),
feed
=
{
"x"
:
np_x
,
"mask"
:
np_mask
},
fetch_list
=
[
out
])
self
.
assertEqual
(
np
.
allclose
(
res
,
np_out
),
True
)
class
TestMaskedSelectError
(
unittest
.
TestCase
):
def
test_error
(
self
):
with
paddle
.
static
.
program_guard
(
paddle
.
static
.
Program
(),
paddle
.
static
.
Program
()):
shape
=
[
8
,
9
,
6
]
x
=
paddle
.
fluid
.
data
(
shape
=
shape
,
dtype
=
'float32'
,
name
=
'x'
)
mask
=
paddle
.
fluid
.
data
(
shape
=
shape
,
dtype
=
'bool'
,
name
=
'mask'
)
mask_float
=
paddle
.
fluid
.
data
(
shape
=
shape
,
dtype
=
'float32'
,
name
=
'mask_float'
)
np_x
=
np
.
random
.
random
(
shape
).
astype
(
'float32'
)
np_mask
=
np
.
array
(
np
.
random
.
randint
(
2
,
size
=
shape
,
dtype
=
bool
))
def
test_x_type
():
paddle
.
masked_select
(
np_x
,
mask
)
self
.
assertRaises
(
TypeError
,
test_x_type
)
def
test_mask_type
():
paddle
.
masked_select
(
x
,
np_mask
)
self
.
assertRaises
(
TypeError
,
test_mask_type
)
def
test_mask_dtype
():
paddle
.
masked_select
(
x
,
mask_float
)
self
.
assertRaises
(
TypeError
,
test_mask_dtype
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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