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c96f7a29
编写于
6月 09, 2022
作者:
C
cambriconhsq
提交者:
GitHub
6月 09, 2022
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差异文件
[MLU]add mlu kernel for conv2dtransposed op (#43233)
上级
c49f35cf
变更
3
展开全部
隐藏空白更改
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并排
Showing
3 changed file
with
928 addition
and
1 deletion
+928
-1
paddle/fluid/operators/conv_transpose_op_mlu.cc
paddle/fluid/operators/conv_transpose_op_mlu.cc
+266
-0
paddle/fluid/operators/mlu/mlu_baseop.h
paddle/fluid/operators/mlu/mlu_baseop.h
+1
-1
python/paddle/fluid/tests/unittests/mlu/test_conv2d_transposed_op_mlu.py
...luid/tests/unittests/mlu/test_conv2d_transposed_op_mlu.py
+661
-0
未找到文件。
paddle/fluid/operators/conv_transpose_op_mlu.cc
0 → 100644
浏览文件 @
c96f7a29
/* 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/conv_transpose_op.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
#include "paddle/phi/kernels/cpu/conv_util.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
DataLayout
=
framework
::
DataLayout
;
template
<
typename
T
>
class
Conv2DTransposeMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
Tensor
*
input
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
const
Tensor
*
filter
=
ctx
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
*
output
=
ctx
.
Output
<
Tensor
>
(
"Output"
);
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
std
::
vector
<
int
>
output_padding
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"output_padding"
);
const
std
::
vector
<
int
>
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
dilations
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
const
std
::
string
data_format
=
ctx
.
Attr
<
std
::
string
>
(
"data_format"
);
int
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
const
std
::
string
padding_algorithm
=
ctx
.
Attr
<
std
::
string
>
(
"padding_algorithm"
);
// check dimension
const
bool
channel_last
=
data_format
==
"NHWC"
;
auto
in_dims
=
input
->
dims
();
auto
filter_dims
=
filter
->
dims
();
auto
in_dims_size
=
in_dims
.
size
();
framework
::
DDim
in_data_dims
;
framework
::
DDim
filter_data_dims
;
if
(
channel_last
)
{
in_data_dims
=
phi
::
slice_ddim
(
in_dims
,
1
,
in_dims
.
size
()
-
1
);
}
else
{
in_data_dims
=
phi
::
slice_ddim
(
in_dims
,
2
,
in_dims
.
size
());
}
filter_data_dims
=
phi
::
slice_ddim
(
filter_dims
,
2
,
in_dims
.
size
());
std
::
vector
<
int
>
ksize
=
phi
::
vectorize
<
int
>
(
filter_data_dims
);
phi
::
UpdatePaddingAndDilation
(
&
paddings
,
&
dilations
,
padding_algorithm
,
in_data_dims
,
strides
,
ksize
);
Tensor
input_tensor
(
input
->
type
());
Tensor
output_tensor
(
output
->
type
());
input_tensor
.
set_layout
(
DataLayout
::
kNHWC
);
output_tensor
.
set_layout
(
DataLayout
::
kNHWC
);
const
std
::
vector
<
int
>
perm_to_nhwc
=
{
0
,
2
,
3
,
1
};
if
(
channel_last
)
{
input_tensor
.
ShareDataWith
(
*
input
);
output_tensor
.
ShareDataWith
(
*
output
);
}
else
{
// transpose input from NCHW to NHWC
TransposeFromMLUTensor
<
T
>
(
ctx
,
perm_to_nhwc
,
input
,
&
input_tensor
,
true
/*need_reshape_or_alloc*/
);
auto
output_dims
=
output
->
dims
();
output_tensor
.
mutable_data
<
T
>
(
{
output_dims
[
0
],
output_dims
[
2
],
output_dims
[
3
],
output_dims
[
1
]},
ctx
.
GetPlace
());
}
// transpose filter from MCHW to MHWC
Tensor
trans_filter
(
filter
->
type
());
TransposeFromMLUTensor
<
T
>
(
ctx
,
perm_to_nhwc
,
filter
,
&
trans_filter
,
true
/*need_reshape_or_alloc*/
);
// construct MLU attr
cnnlTensorLayout_t
data_layout
=
CNNL_LAYOUT_NHWC
;
MLUCnnlTensorDesc
input_desc
(
input_tensor
,
data_layout
,
ToCnnlDataType
(
input_tensor
.
dtype
()));
MLUCnnlTensorDesc
filter_desc
(
trans_filter
,
data_layout
,
ToCnnlDataType
(
trans_filter
.
type
()));
MLUCnnlTensorDesc
output_desc
(
output_tensor
,
data_layout
,
ToCnnlDataType
(
output_tensor
.
dtype
()));
MLUCnnlConvolutionDesc
conv_desc
(
in_dims_size
,
paddings
.
data
(),
strides
.
data
(),
dilations
.
data
(),
groups
,
ToCnnlDataType
<
T
>
());
MLUCnnl
::
ConvBackpropInput
(
ctx
,
conv_desc
.
get
(),
filter_desc
.
get
(),
GetBasePtr
(
&
trans_filter
),
input_desc
.
get
(),
GetBasePtr
(
&
input_tensor
),
output_desc
.
get
(),
GetBasePtr
(
&
output_tensor
));
if
(
!
channel_last
)
{
// transpose output from NHWC to NCHW
const
std
::
vector
<
int
>
perm_to_nchw
=
{
0
,
3
,
1
,
2
};
TransposeFromMLUTensor
<
T
>
(
ctx
,
perm_to_nchw
,
&
output_tensor
,
output
,
false
/*need_reshape_or_alloc*/
);
}
}
};
template
<
typename
T
>
class
Conv2DTransposeGradMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
Tensor
*
input
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
const
Tensor
*
filter
=
ctx
.
Input
<
Tensor
>
(
"Filter"
);
const
Tensor
*
output_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Output"
));
Tensor
*
input_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
Tensor
*
filter_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
if
((
!
input_grad
)
&&
(
!
filter_grad
))
return
;
std
::
vector
<
int
>
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
dilations
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
const
int
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
std
::
string
padding_algorithm
=
ctx
.
Attr
<
std
::
string
>
(
"padding_algorithm"
);
const
std
::
string
data_format
=
ctx
.
Attr
<
std
::
string
>
(
"data_format"
);
const
framework
::
DataLayout
data_layout
=
framework
::
StringToDataLayout
(
data_format
);
auto
in_dims
=
input
->
dims
();
auto
filter_dims
=
filter
->
dims
();
auto
in_dims_size
=
in_dims
.
size
();
const
bool
channel_last
=
(
data_layout
==
framework
::
DataLayout
::
kNHWC
);
framework
::
DDim
in_data_dims
;
if
(
channel_last
)
{
in_data_dims
=
phi
::
slice_ddim
(
in_dims
,
1
,
in_dims
.
size
()
-
1
);
}
else
{
in_data_dims
=
phi
::
slice_ddim
(
in_dims
,
2
,
in_dims
.
size
());
}
framework
::
DDim
filter_data_dims
=
phi
::
slice_ddim
(
filter_dims
,
2
,
filter_dims
.
size
());
std
::
vector
<
int
>
ksize
=
phi
::
vectorize
<
int
>
(
filter_data_dims
);
phi
::
UpdatePaddingAndDilation
(
&
paddings
,
&
dilations
,
padding_algorithm
,
in_data_dims
,
strides
,
ksize
);
Tensor
input_tensor
(
input
->
type
());
Tensor
output_grad_tensor
(
output_grad
->
type
());
output_grad_tensor
.
set_layout
(
DataLayout
::
kNHWC
);
const
std
::
vector
<
int
>
perm_to_nhwc
=
{
0
,
2
,
3
,
1
};
if
(
channel_last
)
{
input_tensor
.
ShareDataWith
(
*
input
);
output_grad_tensor
.
ShareDataWith
(
*
output_grad
);
}
else
{
// transpose input from NCHW to NHWC
TransposeFromMLUTensor
<
T
>
(
ctx
,
perm_to_nhwc
,
input
,
&
input_tensor
,
true
/*need_reshape_or_alloc*/
);
TransposeFromMLUTensor
<
T
>
(
ctx
,
perm_to_nhwc
,
output_grad
,
&
output_grad_tensor
,
true
/*need_reshape_or_alloc*/
);
}
// transpose filter from MCHW to MHWC
Tensor
trans_filter
(
filter
->
type
());
TransposeFromMLUTensor
<
T
>
(
ctx
,
perm_to_nhwc
,
filter
,
&
trans_filter
,
true
/*need_reshape_or_alloc*/
);
// MLU descs
cnnlTensorLayout_t
data_layout_mlu
=
CNNL_LAYOUT_NHWC
;
MLUCnnlTensorDesc
input_desc
(
input_tensor
,
data_layout_mlu
,
ToCnnlDataType
(
input_tensor
.
dtype
()));
MLUCnnlTensorDesc
trans_filter_desc
(
trans_filter
,
data_layout_mlu
,
ToCnnlDataType
(
trans_filter
.
type
()));
MLUCnnlTensorDesc
output_grad_desc
(
output_grad_tensor
,
data_layout_mlu
,
ToCnnlDataType
(
output_grad_tensor
.
dtype
()));
MLUCnnlConvolutionDesc
conv_desc
(
in_dims_size
,
paddings
.
data
(),
strides
.
data
(),
dilations
.
data
(),
groups
,
ToCnnlDataType
<
T
>
());
if
(
filter_grad
)
{
filter_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
Tensor
filter_grad_tensor
(
filter_grad
->
type
());
// filter_grad always MCHW
// filter_grad_tensor always MHWC
auto
filter_grad_dims
=
filter_grad
->
dims
();
filter_grad_tensor
.
mutable_data
<
T
>
(
{
filter_grad_dims
[
0
],
filter_grad_dims
[
2
],
filter_grad_dims
[
3
],
filter_grad_dims
[
1
]},
ctx
.
GetPlace
());
//}
filter_grad_tensor
.
set_layout
(
DataLayout
::
kNHWC
);
MLUCnnlTensorDesc
filter_grad_desc
(
filter_grad_tensor
,
data_layout_mlu
,
ToCnnlDataType
(
filter_grad_tensor
.
dtype
()));
MLUCnnl
::
ConvBackpropFilter
(
ctx
,
conv_desc
.
get
(),
output_grad_desc
.
get
(),
GetBasePtr
(
output_grad
),
input_desc
.
get
(),
GetBasePtr
(
&
input_tensor
),
filter_grad_desc
.
get
(),
GetBasePtr
(
&
filter_grad_tensor
));
// transpose output from MHWC to MCHW
const
std
::
vector
<
int
>
perm_to_mchw
=
{
0
,
3
,
1
,
2
};
TransposeFromMLUTensor
<
T
>
(
ctx
,
perm_to_mchw
,
&
filter_grad_tensor
,
filter_grad
,
false
/*need_reshape_or_alloc*/
);
}
if
(
input_grad
)
{
input_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
Tensor
input_grad_tensor
(
input_grad
->
type
());
input_tensor
.
set_layout
(
DataLayout
::
kNHWC
);
if
(
channel_last
)
{
input_grad_tensor
.
ShareDataWith
(
*
input_grad
);
}
else
{
auto
input_grad_dims
=
input_grad
->
dims
();
input_grad_tensor
.
mutable_data
<
T
>
(
{
input_grad_dims
[
0
],
input_grad_dims
[
2
],
input_grad_dims
[
3
],
input_grad_dims
[
1
]},
ctx
.
GetPlace
());
}
MLUCnnlTensorDesc
input_grad_desc
(
input_grad_tensor
,
data_layout_mlu
,
ToCnnlDataType
(
input_grad_tensor
.
dtype
()));
cnnlDataType_t
tensor_dtype
=
ToCnnlDataType
<
T
>
();
cnnlDataType_t
dt_onchip
=
ToCnnlDataType
<
T
>
();
MLUCnnl
::
Conv2D
(
ctx
,
conv_desc
.
get
(),
tensor_dtype
,
dt_onchip
,
nullptr
/* input_position */
,
nullptr
/* input_scale */
,
nullptr
/* input_offset */
,
nullptr
/* filter_position */
,
nullptr
/* filter_scale */
,
nullptr
/* filter_offset */
,
output_grad_desc
.
get
(),
GetBasePtr
(
&
output_grad_tensor
),
trans_filter_desc
.
get
(),
GetBasePtr
(
&
trans_filter
),
nullptr
/* bias_desc*/
,
nullptr
/* bias */
,
input_grad_desc
.
get
(),
GetBasePtr
(
&
input_grad_tensor
));
if
(
!
channel_last
)
{
// transpose output from NHWC to NCHW
const
std
::
vector
<
int
>
perm_to_nchw
=
{
0
,
3
,
1
,
2
};
TransposeFromMLUTensor
<
T
>
(
ctx
,
perm_to_nchw
,
&
input_grad_tensor
,
input_grad
,
false
/*need_reshape_or_alloc*/
);
}
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_MLU_KERNEL
(
conv2d_transpose
,
ops
::
Conv2DTransposeMLUKernel
<
float
>
,
ops
::
Conv2DTransposeMLUKernel
<
plat
::
float16
>
);
REGISTER_OP_MLU_KERNEL
(
conv2d_transpose_grad
,
ops
::
Conv2DTransposeGradMLUKernel
<
float
>
,
ops
::
Conv2DTransposeGradMLUKernel
<
plat
::
float16
>
);
paddle/fluid/operators/mlu/mlu_baseop.h
浏览文件 @
c96f7a29
...
...
@@ -1159,7 +1159,7 @@ class MLUCnnl {
static
void
ConvBackpropInput
(
const
ExecutionContext
&
ctx
,
const
cnnlConvolutionDescriptor_t
conv_desc
,
const
cnnlTensorDescriptor_t
input
_desc
,
const
void
*
filter
,
const
cnnlTensorDescriptor_t
filter
_desc
,
const
void
*
filter
,
const
cnnlTensorDescriptor_t
out_backprop_desc
,
const
void
*
out_backprop
,
const
cnnlTensorDescriptor_t
in_backprop_desc
,
void
*
in_backprop
);
...
...
python/paddle/fluid/tests/unittests/mlu/test_conv2d_transposed_op_mlu.py
0 → 100644
浏览文件 @
c96f7a29
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