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体验新版 GitCode,发现更多精彩内容 >>
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8bd3514c
编写于
6月 08, 2022
作者:
F
fwenguang
提交者:
GitHub
6月 08, 2022
浏览文件
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电子邮件补丁
差异文件
[MLU] add logical ops (#43286)
上级
b056c9cb
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
338 addition
and
17 deletion
+338
-17
paddle/fluid/operators/controlflow/logical_op_mlu.cc
paddle/fluid/operators/controlflow/logical_op_mlu.cc
+77
-0
paddle/fluid/operators/mlu/mlu_baseop.cc
paddle/fluid/operators/mlu/mlu_baseop.cc
+4
-4
paddle/fluid/operators/mlu/mlu_baseop.h
paddle/fluid/operators/mlu/mlu_baseop.h
+1
-13
python/paddle/fluid/tests/unittests/mlu/test_logical_op_mlu.py
...n/paddle/fluid/tests/unittests/mlu/test_logical_op_mlu.py
+256
-0
未找到文件。
paddle/fluid/operators/controlflow/logical_op_mlu.cc
0 → 100644
浏览文件 @
8bd3514c
/* 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. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
cnnlLogicOp_t
log_method
>
class
LogicalMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
if
(
log_method
==
CNNL_LOGIC_OP_NOT
)
{
y
=
x
;
}
MLUCnnlTensorDesc
x_desc
(
*
x
);
MLUCnnlTensorDesc
y_desc
(
*
y
);
MLUCnnlTensorDesc
out_desc
(
*
out
);
MLUCnnl
::
Logic
(
ctx
,
log_method
,
x_desc
.
get
(),
GetBasePtr
(
x
),
y_desc
.
get
(),
GetBasePtr
(
y
),
out_desc
.
get
(),
GetBasePtr
(
out
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_MLU_KERNEL
(
logical_not
,
ops
::
LogicalMLUKernel
<
bool
,
CNNL_LOGIC_OP_NOT
>
,
ops
::
LogicalMLUKernel
<
int8_t
,
CNNL_LOGIC_OP_NOT
>
,
ops
::
LogicalMLUKernel
<
int16_t
,
CNNL_LOGIC_OP_NOT
>
,
ops
::
LogicalMLUKernel
<
int
,
CNNL_LOGIC_OP_NOT
>
,
ops
::
LogicalMLUKernel
<
float
,
CNNL_LOGIC_OP_NOT
>
);
REGISTER_OP_MLU_KERNEL
(
logical_and
,
ops
::
LogicalMLUKernel
<
bool
,
CNNL_LOGIC_OP_AND
>
,
ops
::
LogicalMLUKernel
<
int8_t
,
CNNL_LOGIC_OP_AND
>
,
ops
::
LogicalMLUKernel
<
int16_t
,
CNNL_LOGIC_OP_AND
>
,
ops
::
LogicalMLUKernel
<
int
,
CNNL_LOGIC_OP_AND
>
,
ops
::
LogicalMLUKernel
<
float
,
CNNL_LOGIC_OP_AND
>
);
REGISTER_OP_MLU_KERNEL
(
logical_or
,
ops
::
LogicalMLUKernel
<
bool
,
CNNL_LOGIC_OP_OR
>
,
ops
::
LogicalMLUKernel
<
int8_t
,
CNNL_LOGIC_OP_OR
>
,
ops
::
LogicalMLUKernel
<
int16_t
,
CNNL_LOGIC_OP_OR
>
,
ops
::
LogicalMLUKernel
<
int
,
CNNL_LOGIC_OP_OR
>
,
ops
::
LogicalMLUKernel
<
float
,
CNNL_LOGIC_OP_OR
>
);
REGISTER_OP_MLU_KERNEL
(
logical_xor
,
ops
::
LogicalMLUKernel
<
bool
,
CNNL_LOGIC_OP_XOR
>
,
ops
::
LogicalMLUKernel
<
int8_t
,
CNNL_LOGIC_OP_XOR
>
,
ops
::
LogicalMLUKernel
<
int16_t
,
CNNL_LOGIC_OP_XOR
>
,
ops
::
LogicalMLUKernel
<
int
,
CNNL_LOGIC_OP_XOR
>
,
ops
::
LogicalMLUKernel
<
float
,
CNNL_LOGIC_OP_XOR
>
);
paddle/fluid/operators/mlu/mlu_baseop.cc
浏览文件 @
8bd3514c
...
...
@@ -1142,7 +1142,7 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
}
/* static */
void
MLUCnnl
::
Logic
(
const
ExecutionContext
&
ctx
,
const
MLULogicMethod
log_method
,
const
ExecutionContext
&
ctx
,
const
cnnlLogicOp_t
log_method
,
const
cnnlTensorDescriptor_t
input1_desc
,
const
void
*
input1
,
const
cnnlTensorDescriptor_t
input2_desc
,
const
void
*
input2
,
const
cnnlTensorDescriptor_t
output_desc
,
void
*
output
)
{
...
...
@@ -1157,9 +1157,9 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
{
static_cast
<
int64_t
>
(
workspace_size
)},
dev_ctx
);
void
*
workspace_ptr
=
workspace
.
mutable_data
(
ctx
.
GetPlace
());
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlLogicOp
(
handle
,
cnnlLogicOp_t
(
log_method
),
input1_desc
,
input1
,
input2_desc
,
input2
,
workspace_ptr
,
workspace_size
,
output_desc
,
output
));
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlLogicOp
(
handle
,
log_method
,
input1_desc
,
input1
,
input2_desc
,
input2
,
workspace_ptr
,
workspace_size
,
output_desc
,
output
));
}
/* static */
void
MLUCnnl
::
Select
(
...
...
paddle/fluid/operators/mlu/mlu_baseop.h
浏览文件 @
8bd3514c
...
...
@@ -34,17 +34,6 @@ using ExecutionContext = framework::ExecutionContext;
using
DeviceContextPool
=
platform
::
DeviceContextPool
;
using
MLUDeviceContext
=
platform
::
MLUDeviceContext
;
enum
MLULogicMethod
{
CNNL_LOGIC_OP_EQ
=
0
,
CNNL_LOGIC_OP_NE
=
1
,
CNNL_LOGIC_OP_GT
=
2
,
CNNL_LOGIC_OP_GE
=
3
,
CNNL_LOGIC_OP_LT
=
4
,
CNNL_LOGIC_OP_LE
=
5
,
CNNL_LOGIC_OP_AND
=
6
,
CNNL_LOGIC_OP_OR
=
7
,
};
const
std
::
map
<
std
::
string
,
cnnlReduceOp_t
>
MLUReduceOpMap
=
{
{
"reduce_all"
,
CNNL_REDUCE_AND
},
{
"reduce_any"
,
CNNL_REDUCE_OR
},
{
"reduce_max"
,
CNNL_REDUCE_MAX
},
{
"reduce_mean"
,
CNNL_REDUCE_AVG
},
...
...
@@ -645,8 +634,7 @@ class MLUCnnl {
const
cnnlTensorDescriptor_t
output_desc
,
void
*
output
);
static
void
Logic
(
const
ExecutionContext
&
ctx
,
const
MLULogicMethod
log_method
,
static
void
Logic
(
const
ExecutionContext
&
ctx
,
const
cnnlLogicOp_t
log_method
,
const
cnnlTensorDescriptor_t
input1_desc
,
const
void
*
input1
,
const
cnnlTensorDescriptor_t
input2_desc
,
...
...
python/paddle/fluid/tests/unittests/mlu/test_logical_op_mlu.py
0 → 100755
浏览文件 @
8bd3514c
# 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
sys
sys
.
path
.
append
(
'..'
)
import
op_test
import
unittest
import
numpy
as
np
import
paddle
from
paddle.static
import
Program
,
program_guard
,
Executor
from
paddle.framework
import
_non_static_mode
paddle
.
enable_static
()
SUPPORTED_DTYPES
=
[
bool
,
np
.
int8
,
np
.
int16
,
np
.
int32
,
np
.
float32
]
TEST_META_OP_DATA
=
[{
'op_str'
:
'logical_and'
,
'binary_op'
:
True
},
{
'op_str'
:
'logical_or'
,
'binary_op'
:
True
},
{
'op_str'
:
'logical_xor'
,
'binary_op'
:
True
},
{
'op_str'
:
'logical_not'
,
'binary_op'
:
False
}]
TEST_META_SHAPE_DATA
=
{
'XDimLargerThanYDim1'
:
{
'x_shape'
:
[
2
,
3
,
4
,
5
],
'y_shape'
:
[
4
,
5
]
},
'XDimLargerThanYDim2'
:
{
'x_shape'
:
[
2
,
3
,
4
,
5
],
'y_shape'
:
[
4
,
1
]
},
'XDimLargerThanYDim3'
:
{
'x_shape'
:
[
2
,
3
,
4
,
5
],
'y_shape'
:
[
1
,
4
,
1
]
},
'XDimLargerThanYDim4'
:
{
'x_shape'
:
[
2
,
3
,
4
,
5
],
'y_shape'
:
[
3
,
4
,
1
]
},
'XDimLargerThanYDim5'
:
{
'x_shape'
:
[
2
,
3
,
1
,
5
],
'y_shape'
:
[
3
,
1
,
1
]
},
'XDimLessThanYDim1'
:
{
'x_shape'
:
[
4
,
1
],
'y_shape'
:
[
2
,
3
,
4
,
5
]
},
'XDimLessThanYDim2'
:
{
'x_shape'
:
[
1
,
4
,
1
],
'y_shape'
:
[
2
,
3
,
4
,
5
]
},
'XDimLessThanYDim3'
:
{
'x_shape'
:
[
3
,
4
,
1
],
'y_shape'
:
[
2
,
3
,
4
,
5
]
},
'XDimLessThanYDim4'
:
{
'x_shape'
:
[
3
,
1
,
1
],
'y_shape'
:
[
2
,
3
,
1
,
5
]
},
'XDimLessThanYDim5'
:
{
'x_shape'
:
[
4
,
5
],
'y_shape'
:
[
2
,
3
,
4
,
5
]
},
'Axis1InLargerDim'
:
{
'x_shape'
:
[
1
,
4
,
5
],
'y_shape'
:
[
2
,
3
,
1
,
5
]
},
'EqualDim1'
:
{
'x_shape'
:
[
10
,
7
],
'y_shape'
:
[
10
,
7
]
},
'EqualDim2'
:
{
'x_shape'
:
[
1
,
1
,
4
,
5
],
'y_shape'
:
[
2
,
3
,
1
,
5
]
}
}
TEST_META_WRONG_SHAPE_DATA
=
{
'ErrorDim1'
:
{
'x_shape'
:
[
2
,
3
,
4
,
5
],
'y_shape'
:
[
3
,
4
]
},
'ErrorDim2'
:
{
'x_shape'
:
[
2
,
3
,
4
,
5
],
'y_shape'
:
[
4
,
3
]
}
}
def
run_static
(
x_np
,
y_np
,
op_str
,
use_mlu
=
False
,
binary_op
=
True
):
paddle
.
enable_static
()
startup_program
=
Program
()
main_program
=
Program
()
place
=
paddle
.
CPUPlace
()
if
use_mlu
and
paddle
.
is_compiled_with_mlu
():
place
=
paddle
.
MLUPlace
(
0
)
exe
=
Executor
(
place
)
with
program_guard
(
main_program
,
startup_program
):
x
=
paddle
.
static
.
data
(
name
=
'x'
,
shape
=
x_np
.
shape
,
dtype
=
x_np
.
dtype
)
op
=
getattr
(
paddle
,
op_str
)
feed_list
=
{
'x'
:
x_np
}
if
not
binary_op
:
res
=
op
(
x
)
else
:
y
=
paddle
.
static
.
data
(
name
=
'y'
,
shape
=
y_np
.
shape
,
dtype
=
y_np
.
dtype
)
feed_list
[
'y'
]
=
y_np
res
=
op
(
x
,
y
)
exe
.
run
(
startup_program
)
static_result
=
exe
.
run
(
main_program
,
feed
=
feed_list
,
fetch_list
=
[
res
])
return
static_result
def
run_dygraph
(
x_np
,
y_np
,
op_str
,
use_mlu
=
False
,
binary_op
=
True
):
place
=
paddle
.
CPUPlace
()
if
use_mlu
and
paddle
.
is_compiled_with_mlu
():
place
=
paddle
.
MLUPlace
(
0
)
paddle
.
disable_static
(
place
)
op
=
getattr
(
paddle
,
op_str
)
x
=
paddle
.
to_tensor
(
x_np
,
dtype
=
x_np
.
dtype
)
if
not
binary_op
:
dygraph_result
=
op
(
x
)
else
:
y
=
paddle
.
to_tensor
(
y_np
,
dtype
=
y_np
.
dtype
)
dygraph_result
=
op
(
x
,
y
)
return
dygraph_result
def
np_data_generator
(
np_shape
,
dtype
,
*
args
,
**
kwargs
):
if
dtype
==
bool
:
return
np
.
random
.
choice
(
a
=
[
True
,
False
],
size
=
np_shape
).
astype
(
bool
)
else
:
return
np
.
random
.
randn
(
*
np_shape
).
astype
(
dtype
)
def
test
(
unit_test
,
use_mlu
=
False
,
test_error
=
False
):
for
op_data
in
TEST_META_OP_DATA
:
meta_data
=
dict
(
op_data
)
meta_data
[
'use_mlu'
]
=
use_mlu
np_op
=
getattr
(
np
,
meta_data
[
'op_str'
])
META_DATA
=
dict
(
TEST_META_SHAPE_DATA
)
if
test_error
:
META_DATA
=
dict
(
TEST_META_WRONG_SHAPE_DATA
)
for
shape_data
in
META_DATA
.
values
():
for
data_type
in
SUPPORTED_DTYPES
:
meta_data
[
'x_np'
]
=
np_data_generator
(
shape_data
[
'x_shape'
],
dtype
=
data_type
)
meta_data
[
'y_np'
]
=
np_data_generator
(
shape_data
[
'y_shape'
],
dtype
=
data_type
)
if
meta_data
[
'binary_op'
]
and
test_error
:
# catch C++ Exception
unit_test
.
assertRaises
(
BaseException
,
run_static
,
**
meta_data
)
unit_test
.
assertRaises
(
BaseException
,
run_dygraph
,
**
meta_data
)
continue
static_result
=
run_static
(
**
meta_data
)
dygraph_result
=
run_dygraph
(
**
meta_data
)
if
meta_data
[
'binary_op'
]:
np_result
=
np_op
(
meta_data
[
'x_np'
],
meta_data
[
'y_np'
])
else
:
np_result
=
np_op
(
meta_data
[
'x_np'
])
unit_test
.
assertTrue
((
static_result
==
np_result
).
all
())
unit_test
.
assertTrue
(
(
dygraph_result
.
numpy
()
==
np_result
).
all
())
def
test_type_error
(
unit_test
,
use_mlu
,
type_str_map
):
def
check_type
(
op_str
,
x
,
y
,
binary_op
):
op
=
getattr
(
paddle
,
op_str
)
error_type
=
ValueError
if
isinstance
(
x
,
np
.
ndarray
):
x
=
paddle
.
to_tensor
(
x
)
y
=
paddle
.
to_tensor
(
y
)
error_type
=
BaseException
if
binary_op
:
if
type_str_map
[
'x'
]
!=
type_str_map
[
'y'
]:
unit_test
.
assertRaises
(
error_type
,
op
,
x
=
x
,
y
=
y
)
if
not
_non_static_mode
():
error_type
=
TypeError
unit_test
.
assertRaises
(
error_type
,
op
,
x
=
x
,
y
=
y
,
out
=
1
)
else
:
if
not
_non_static_mode
():
error_type
=
TypeError
unit_test
.
assertRaises
(
error_type
,
op
,
x
=
x
,
out
=
1
)
place
=
paddle
.
CPUPlace
()
if
use_mlu
and
paddle
.
is_compiled_with_mlu
():
place
=
paddle
.
MLUPlace
(
0
)
for
op_data
in
TEST_META_OP_DATA
:
meta_data
=
dict
(
op_data
)
binary_op
=
meta_data
[
'binary_op'
]
paddle
.
disable_static
(
place
)
x
=
np
.
random
.
choice
(
a
=
[
0
,
1
],
size
=
[
10
]).
astype
(
type_str_map
[
'x'
])
y
=
np
.
random
.
choice
(
a
=
[
0
,
1
],
size
=
[
10
]).
astype
(
type_str_map
[
'y'
])
check_type
(
meta_data
[
'op_str'
],
x
,
y
,
binary_op
)
paddle
.
enable_static
()
startup_program
=
paddle
.
static
.
Program
()
main_program
=
paddle
.
static
.
Program
()
with
paddle
.
static
.
program_guard
(
main_program
,
startup_program
):
x
=
paddle
.
static
.
data
(
name
=
'x'
,
shape
=
[
10
],
dtype
=
type_str_map
[
'x'
])
y
=
paddle
.
static
.
data
(
name
=
'y'
,
shape
=
[
10
],
dtype
=
type_str_map
[
'y'
])
check_type
(
meta_data
[
'op_str'
],
x
,
y
,
binary_op
)
def
type_map_factory
():
return
[{
'x'
:
x_type
,
'y'
:
y_type
}
for
x_type
in
SUPPORTED_DTYPES
for
y_type
in
SUPPORTED_DTYPES
]
class
TestMLU
(
unittest
.
TestCase
):
def
test
(
self
):
test
(
self
,
True
)
def
test_error
(
self
):
test
(
self
,
True
,
True
)
def
test_type_error
(
self
):
type_map_list
=
type_map_factory
()
for
type_map
in
type_map_list
:
test_type_error
(
self
,
True
,
type_map
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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