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ddb3868e
编写于
5月 12, 2022
作者:
F
fwenguang
提交者:
GitHub
5月 12, 2022
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浏览文件
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电子邮件补丁
差异文件
[MLU] add slice kernel (#42245)
上级
bf44034c
变更
3
展开全部
隐藏空白更改
内联
并排
Showing
3 changed file
with
830 addition
and
2 deletion
+830
-2
paddle/fluid/operators/mlu/mlu_baseop.cc
paddle/fluid/operators/mlu/mlu_baseop.cc
+3
-2
paddle/fluid/operators/slice_op_mlu.cc
paddle/fluid/operators/slice_op_mlu.cc
+196
-0
python/paddle/fluid/tests/unittests/mlu/test_slice_op_mlu.py
python/paddle/fluid/tests/unittests/mlu/test_slice_op_mlu.py
+631
-0
未找到文件。
paddle/fluid/operators/mlu/mlu_baseop.cc
浏览文件 @
ddb3868e
...
...
@@ -688,8 +688,9 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
const
cnnlTensorDescriptor_t
diff_y_desc
,
void
*
back_out
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlSparseSoftmaxCrossEntropyWithLogits
(
handle
,
mode
,
x_desc
,
input
,
label_desc
,
label
,
y_desc
,
output
,
const
cnnlComputationPreference_t
prefer
=
CNNL_COMPUTATION_HIGH_PRECISION
;
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlSparseSoftmaxCrossEntropyWithLogits_v2
(
handle
,
prefer
,
mode
,
x_desc
,
input
,
label_desc
,
label
,
y_desc
,
output
,
diff_y_desc
,
back_out
));
}
...
...
paddle/fluid/operators/slice_op_mlu.cc
0 → 100644
浏览文件 @
ddb3868e
/* 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/operators/slice_op.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
#include "paddle/phi/kernels/funcs/slice_utils.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
class
SliceMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
auto
axes
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"axes"
);
auto
starts
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"starts"
);
auto
ends
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"ends"
);
auto
decrease_axis
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"decrease_axis"
);
auto
infer_flags
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"infer_flags"
);
// Get the accurate attribute value of starts and ends
auto
starts_tensor_list
=
ctx
.
MultiInput
<
Tensor
>
(
"StartsTensorList"
);
if
(
ctx
.
HasInput
(
"StartsTensor"
))
{
starts
=
GetDataFromTensor
<
int
>
(
ctx
.
Input
<
Tensor
>
(
"StartsTensor"
));
}
else
if
(
starts_tensor_list
.
size
()
>
0
)
{
starts
=
GetDataFromTensorList
<
int
>
(
starts_tensor_list
);
}
auto
ends_tensor_list
=
ctx
.
MultiInput
<
Tensor
>
(
"EndsTensorList"
);
if
(
ctx
.
HasInput
(
"EndsTensor"
))
{
ends
=
GetDataFromTensor
<
int
>
(
ctx
.
Input
<
Tensor
>
(
"EndsTensor"
));
}
else
if
(
ends_tensor_list
.
size
()
>
0
)
{
ends
=
GetDataFromTensorList
<
int
>
(
ends_tensor_list
);
}
PADDLE_ENFORCE_EQ
(
starts
.
size
(),
axes
.
size
(),
platform
::
errors
::
InvalidArgument
(
"The size of starts must be equal to the size of axes."
));
PADDLE_ENFORCE_EQ
(
ends
.
size
(),
axes
.
size
(),
platform
::
errors
::
InvalidArgument
(
"The size of ends must be equal to the size of axes."
));
const
auto
&
in_dims
=
input
->
dims
();
auto
slice_dims
=
out
->
dims
();
bool
reset_slice_dims
=
false
;
if
(
ctx
.
HasInput
(
"StartsTensor"
)
||
ctx
.
HasInput
(
"EndsTensor"
)
||
starts_tensor_list
.
size
()
>
0
||
ends_tensor_list
.
size
()
>
0
)
{
// Infer output dims
for
(
size_t
i
=
0
;
i
<
axes
.
size
();
++
i
)
{
// when start == -1 && end == start+1
if
(
starts
[
i
]
==
-
1
&&
ends
[
i
]
==
0
&&
infer_flags
[
i
]
==
-
1
)
{
auto
ret
=
std
::
find
(
decrease_axis
.
begin
(),
decrease_axis
.
end
(),
axes
[
i
]);
if
(
ret
!=
decrease_axis
.
end
())
{
ends
[
i
]
=
in_dims
[
axes
[
i
]];
}
}
}
phi
::
funcs
::
CheckAndUpdateSliceAttrs
(
in_dims
,
axes
,
&
starts
,
&
ends
);
slice_dims
=
phi
::
funcs
::
GetSliceDims
<
int
>
(
in_dims
,
axes
,
starts
,
ends
,
nullptr
,
nullptr
);
reset_slice_dims
=
true
;
auto
out_dims
=
phi
::
funcs
::
GetDecreasedDims
(
slice_dims
,
decrease_axis
);
out
->
Resize
(
out_dims
);
}
if
(
slice_dims
.
size
()
!=
in_dims
.
size
()
&&
!
reset_slice_dims
)
{
phi
::
funcs
::
CheckAndUpdateSliceAttrs
(
in_dims
,
axes
,
&
starts
,
&
ends
);
slice_dims
=
phi
::
funcs
::
GetSliceDims
<
int
>
(
in_dims
,
axes
,
starts
,
ends
,
nullptr
,
nullptr
);
}
int
in_dim_size
=
input
->
dims
().
size
();
if
(
static_cast
<
int
>
(
axes
.
size
())
!=
in_dim_size
)
{
std
::
vector
<
int
>
tmp_starts
(
in_dim_size
,
0
);
const
auto
&
in_dims_vec
=
phi
::
vectorize
(
input
->
dims
());
std
::
vector
<
int
>
tmp_ends
(
in_dims_vec
.
begin
(),
in_dims_vec
.
end
());
for
(
size_t
i
=
0
;
i
<
axes
.
size
();
++
i
)
{
tmp_starts
[
axes
[
i
]]
=
starts
[
i
];
tmp_ends
[
axes
[
i
]]
=
ends
[
i
];
}
starts
.
swap
(
tmp_starts
);
ends
.
swap
(
tmp_ends
);
}
std
::
vector
<
int
>
strides
(
in_dim_size
,
1
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
MLUCnnlTensorDesc
input_desc
(
*
input
);
MLUCnnlTensorDesc
out_desc
(
slice_dims
.
size
(),
phi
::
vectorize
(
slice_dims
).
data
(),
ToCnnlDataType
<
T
>
());
MLUCnnl
::
StridedSlice
(
ctx
,
starts
.
data
(),
ends
.
data
(),
strides
.
data
(),
input_desc
.
get
(),
GetBasePtr
(
input
),
out_desc
.
get
(),
GetBasePtr
(
out
));
}
};
template
<
typename
T
>
class
SliceGradMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
auto
*
dout
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
dinput
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
auto
axes
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"axes"
);
auto
starts
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"starts"
);
auto
ends
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"ends"
);
// Get the accurate attribute value of starts and ends
auto
starts_tensor_list
=
ctx
.
MultiInput
<
Tensor
>
(
"StartsTensorList"
);
if
(
ctx
.
HasInput
(
"StartsTensor"
))
{
starts
=
GetDataFromTensor
<
int
>
(
ctx
.
Input
<
Tensor
>
(
"StartsTensor"
));
}
else
if
(
starts_tensor_list
.
size
()
>
0
)
{
starts
=
GetDataFromTensorList
<
int
>
(
starts_tensor_list
);
}
auto
ends_tensor_list
=
ctx
.
MultiInput
<
Tensor
>
(
"EndsTensorList"
);
if
(
ctx
.
HasInput
(
"EndsTensor"
))
{
ends
=
GetDataFromTensor
<
int
>
(
ctx
.
Input
<
Tensor
>
(
"EndsTensor"
));
}
else
if
(
ends_tensor_list
.
size
()
>
0
)
{
ends
=
GetDataFromTensorList
<
int
>
(
ends_tensor_list
);
}
const
auto
&
in_dims
=
input
->
dims
();
auto
slice_dims
=
dout
->
dims
();
if
(
slice_dims
.
size
()
!=
in_dims
.
size
())
{
phi
::
funcs
::
CheckAndUpdateSliceAttrs
(
in_dims
,
axes
,
&
starts
,
&
ends
);
slice_dims
=
phi
::
funcs
::
GetSliceDims
<
int
>
(
in_dims
,
axes
,
starts
,
ends
,
nullptr
,
nullptr
);
}
int
in_dim_size
=
input
->
dims
().
size
();
if
(
static_cast
<
int
>
(
axes
.
size
())
!=
in_dim_size
)
{
std
::
vector
<
int
>
tmp_starts
(
in_dim_size
,
0
);
const
auto
&
in_dims_vec
=
phi
::
vectorize
(
input
->
dims
());
std
::
vector
<
int
>
tmp_ends
(
in_dims_vec
.
begin
(),
in_dims_vec
.
end
());
for
(
size_t
i
=
0
;
i
<
axes
.
size
();
++
i
)
{
tmp_starts
[
axes
[
i
]]
=
starts
[
i
];
tmp_ends
[
axes
[
i
]]
=
ends
[
i
];
}
starts
.
swap
(
tmp_starts
);
ends
.
swap
(
tmp_ends
);
}
std
::
vector
<
int
>
strides
(
in_dim_size
,
1
);
dinput
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
MLUCnnlTensorDesc
dout_desc
(
slice_dims
.
size
(),
phi
::
vectorize
(
slice_dims
).
data
(),
ToCnnlDataType
<
T
>
());
MLUCnnlTensorDesc
dinput_desc
(
*
dinput
);
MLUCnnl
::
StridedSliceGrad
(
ctx
,
starts
.
data
(),
ends
.
data
(),
strides
.
data
(),
dout_desc
.
get
(),
GetBasePtr
(
dout
),
dinput_desc
.
get
(),
GetBasePtr
(
dinput
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_MLU_KERNEL
(
slice
,
ops
::
SliceMLUKernel
<
float
>
,
ops
::
SliceMLUKernel
<
int
>
,
ops
::
SliceMLUKernel
<
bool
>
,
ops
::
SliceMLUKernel
<
int64_t
>
,
ops
::
SliceMLUKernel
<
double
>
,
ops
::
SliceMLUKernel
<
paddle
::
platform
::
float16
>
);
REGISTER_OP_MLU_KERNEL
(
slice_grad
,
ops
::
SliceGradMLUKernel
<
float
>
,
ops
::
SliceGradMLUKernel
<
int
>
,
ops
::
SliceGradMLUKernel
<
bool
>
,
ops
::
SliceGradMLUKernel
<
int64_t
>
,
ops
::
SliceGradMLUKernel
<
paddle
::
platform
::
float16
>
);
python/paddle/fluid/tests/unittests/mlu/test_slice_op_mlu.py
0 → 100644
浏览文件 @
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