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体验新版 GitCode,发现更多精彩内容 >>
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提交
7f3c7aeb
编写于
9月 06, 2022
作者:
Y
ykkk2333
提交者:
GitHub
9月 06, 2022
浏览文件
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浏览文件
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电子邮件补丁
差异文件
migrate deformable_conv and merged momentum kernels to phi, test=kunlun (#45691)
上级
d8a09e25
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
447 addition
and
482 deletion
+447
-482
paddle/fluid/operators/deformable_conv_op_xpu.cc
paddle/fluid/operators/deformable_conv_op_xpu.cc
+0
-338
paddle/fluid/operators/optimizers/merged_momentum_op_xpu.cc
paddle/fluid/operators/optimizers/merged_momentum_op_xpu.cc
+0
-144
paddle/phi/kernels/xpu/deformable_conv_grad_kernel.cc
paddle/phi/kernels/xpu/deformable_conv_grad_kernel.cc
+199
-0
paddle/phi/kernels/xpu/deformable_conv_kernel.cc
paddle/phi/kernels/xpu/deformable_conv_kernel.cc
+108
-0
paddle/phi/kernels/xpu/merged_momentum_kernel.cc
paddle/phi/kernels/xpu/merged_momentum_kernel.cc
+140
-0
未找到文件。
paddle/fluid/operators/deformable_conv_op_xpu.cc
已删除
100644 → 0
浏览文件 @
d8a09e25
/* Copyright (c) 2020 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. */
#ifdef PADDLE_WITH_XPU
#include <algorithm>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device/xpu/xpu_header.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
DeviceContext
,
typename
T
>
class
DeformableConvXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
auto
*
offset
=
ctx
.
Input
<
Tensor
>
(
"Offset"
);
auto
*
mask
=
ctx
.
Input
<
Tensor
>
(
"Mask"
);
Tensor
filter
=
*
ctx
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
*
output
=
ctx
.
Output
<
Tensor
>
(
"Output"
);
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
const
int
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
const
int
deformable_groups
=
ctx
.
Attr
<
int
>
(
"deformable_groups"
);
const
int
im2col_step
=
ctx
.
Attr
<
int
>
(
"im2col_step"
);
const
std
::
vector
<
int
>
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
const
std
::
vector
<
int
>
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
const
std
::
vector
<
int
>
dilations
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
PADDLE_ENFORCE_EQ
(
deformable_groups
==
1
,
true
,
platform
::
errors
::
InvalidArgument
((
"XPU only support deformable_groups == 1 in deformable_conv op."
)));
PADDLE_ENFORCE_EQ
(
groups
==
1
,
true
,
platform
::
errors
::
InvalidArgument
(
(
"XPU only support groups == 1 in deformable_conv op."
)));
PADDLE_ENFORCE_EQ
(
filter
.
dims
()[
2
]
<=
8
&&
filter
.
dims
()[
3
]
<=
8
,
true
,
platform
::
errors
::
InvalidArgument
(
"Filter high and weight should less than 8 on xpu "
"in deformable_conv op."
));
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
std
::
vector
<
int64_t
>
output_shape_vec
(
phi
::
vectorize
(
output
->
dims
()));
const
T
*
input_ptr
=
input
->
data
<
T
>
();
const
T
*
filter_ptr
=
filter
.
data
<
T
>
();
const
float
*
offset_ptr
=
offset
->
data
<
T
>
();
const
float
*
mask_ptr
=
mask
->
data
<
T
>
();
T
*
output_prt
=
output
->
data
<
T
>
();
// set zeros for d_table_data
const
int
zero
=
0
;
int
r
=
xpu
::
constant
<
T
>
(
dev_ctx
.
x_context
(),
output_prt
,
output
->
numel
(),
zero
);
PADDLE_ENFORCE_EQ
(
r
==
xpu
::
Error_t
::
SUCCESS
,
true
,
platform
::
errors
::
External
(
"XPU API return wrong value[%d], please check where "
"Baidu Kunlun Card is properly installed."
,
r
));
int
input_dim
=
input
->
numel
()
/
input
->
dims
()[
0
];
int
input_offset_dim
=
offset
->
numel
()
/
offset
->
dims
()[
0
];
int
input_mask_dim
=
mask
->
numel
()
/
mask
->
dims
()[
0
];
int
output_dim
=
output_shape_vec
[
1
]
*
output_shape_vec
[
2
]
*
output_shape_vec
[
3
];
std
::
vector
<
int
>
ksize
{
static_cast
<
int
>
(
filter
.
dims
()[
2
]),
static_cast
<
int
>
(
filter
.
dims
()[
3
])};
int
n
=
im2col_step
;
int
c
=
input
->
dims
()[
1
];
int
h
=
input
->
dims
()[
2
];
int
w
=
input
->
dims
()[
3
];
int
f
=
filter
.
dims
()[
0
];
for
(
int
i
=
0
;
i
<
batch_size
/
im2col_step
;
++
i
)
{
int
r
=
xpu
::
deformable_conv
<
float
,
float
,
float
,
int
>
(
dev_ctx
.
x_context
(),
input_ptr
+
i
*
im2col_step
*
input_dim
,
filter_ptr
,
offset_ptr
+
i
*
im2col_step
*
input_offset_dim
,
mask_ptr
+
i
*
im2col_step
*
input_mask_dim
,
output_prt
+
i
*
im2col_step
*
output_dim
,
n
,
c
,
h
,
w
,
f
,
ksize
,
strides
,
paddings
,
dilations
,
groups
,
deformable_groups
,
nullptr
,
nullptr
,
nullptr
,
true
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU deformable_conv kernel return wrong value[%d]."
,
r
));
}
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
DeformableConvGradXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
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"
));
Tensor
*
offset_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Offset"
));
Tensor
*
mask_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Mask"
));
T
*
dx_data
=
nullptr
;
T
*
dw_data
=
nullptr
;
T
*
dmask_data
=
nullptr
;
T
*
doffset_data
=
nullptr
;
if
(
input_grad
!=
nullptr
)
{
input_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
dx_data
=
input_grad
->
data
<
T
>
();
}
if
(
filter_grad
!=
nullptr
)
{
filter_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
dw_data
=
filter_grad
->
data
<
T
>
();
}
if
(
offset_grad
!=
nullptr
)
{
offset_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
doffset_data
=
offset_grad
->
data
<
T
>
();
}
if
(
mask_grad
!=
nullptr
)
{
mask_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
dmask_data
=
mask_grad
->
data
<
T
>
();
}
const
Tensor
*
input
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
Tensor
offset
=
*
ctx
.
Input
<
Tensor
>
(
"Offset"
);
Tensor
mask
=
*
ctx
.
Input
<
Tensor
>
(
"Mask"
);
Tensor
filter
=
*
ctx
.
Input
<
Tensor
>
(
"Filter"
);
int
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
int
deformable_groups
=
ctx
.
Attr
<
int
>
(
"deformable_groups"
);
int
im2col_step
=
ctx
.
Attr
<
int
>
(
"im2col_step"
);
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"
);
PADDLE_ENFORCE_EQ
(
deformable_groups
==
1
,
true
,
platform
::
errors
::
InvalidArgument
((
"XPU only support deformable_groups == 1 in deformable_conv op."
)));
PADDLE_ENFORCE_EQ
(
groups
==
1
,
true
,
platform
::
errors
::
InvalidArgument
(
(
"XPU only support groups == 1 in deformable_conv op."
)));
PADDLE_ENFORCE_EQ
(
filter
.
dims
()[
2
]
<=
8
&&
filter
.
dims
()[
3
]
<=
8
,
true
,
platform
::
errors
::
InvalidArgument
(
"Filter high and weight should less than 8 on xpu "
"in deformable_conv op."
));
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
std
::
vector
<
int64_t
>
output_shape_vec
(
phi
::
vectorize
(
output_grad
->
dims
()));
const
T
*
output_grad_ptr
=
output_grad
->
data
<
T
>
();
const
T
*
input_ptr
=
input
->
data
<
T
>
();
const
T
*
filter_ptr
=
filter
.
data
<
T
>
();
const
float
*
offset_ptr
=
offset
.
data
<
float
>
();
const
float
*
mask_ptr
=
mask
.
data
<
float
>
();
if
(
dx_data
==
nullptr
)
{
PADDLE_ENFORCE_EQ
(
xpu_malloc
(
reinterpret_cast
<
void
**>
(
&
dx_data
),
input
->
numel
()
*
sizeof
(
T
)),
XPU_SUCCESS
,
platform
::
errors
::
ResourceExhausted
(
"XPU has no enough memory"
));
}
if
(
dw_data
==
nullptr
)
{
PADDLE_ENFORCE_EQ
(
xpu_malloc
(
reinterpret_cast
<
void
**>
(
&
dw_data
),
filter
.
numel
()
*
sizeof
(
T
)),
XPU_SUCCESS
,
platform
::
errors
::
ResourceExhausted
(
"XPU has no enough memory"
));
}
if
(
doffset_data
==
nullptr
)
{
PADDLE_ENFORCE_EQ
(
xpu_malloc
(
reinterpret_cast
<
void
**>
(
&
doffset_data
),
offset
.
numel
()
*
sizeof
(
T
)),
XPU_SUCCESS
,
platform
::
errors
::
ResourceExhausted
(
"XPU has no enough memory"
));
}
if
(
dmask_data
==
nullptr
)
{
PADDLE_ENFORCE_EQ
(
xpu_malloc
(
reinterpret_cast
<
void
**>
(
&
dmask_data
),
mask
.
numel
()
*
sizeof
(
T
)),
XPU_SUCCESS
,
platform
::
errors
::
ResourceExhausted
(
"XPU has no enough memory"
));
}
int
input_dim
=
input
->
numel
()
/
input
->
dims
()[
0
];
int
input_offset_dim
=
offset
.
numel
()
/
offset
.
dims
()[
0
];
int
input_mask_dim
=
mask
.
numel
()
/
mask
.
dims
()[
0
];
int
output_dim
=
output_shape_vec
[
1
]
*
output_shape_vec
[
2
]
*
output_shape_vec
[
3
];
std
::
vector
<
int
>
ksize
{
static_cast
<
int
>
(
filter
.
dims
()[
2
]),
static_cast
<
int
>
(
filter
.
dims
()[
3
])};
int
n
=
im2col_step
;
int
c
=
input
->
dims
()[
1
];
int
h
=
input
->
dims
()[
2
];
int
w
=
input
->
dims
()[
3
];
int
f
=
filter
.
dims
()[
0
];
T
*
filter_grad_tmp
=
nullptr
;
PADDLE_ENFORCE_EQ
(
xpu_malloc
(
reinterpret_cast
<
void
**>
(
&
filter_grad_tmp
),
filter_grad
->
numel
()
*
sizeof
(
T
)),
XPU_SUCCESS
,
platform
::
errors
::
ResourceExhausted
(
"XPU has no enough memory"
));
// set zeros for d_table_data
const
int
zero
=
0
;
int
r_dx
=
xpu
::
constant
<
T
>
(
dev_ctx
.
x_context
(),
dx_data
,
input
->
numel
(),
zero
);
int
r_dw
=
xpu
::
constant
<
T
>
(
dev_ctx
.
x_context
(),
dw_data
,
filter
.
numel
(),
zero
);
int
r_doffset
=
xpu
::
constant
<
T
>
(
dev_ctx
.
x_context
(),
doffset_data
,
offset
.
numel
(),
zero
);
int
r_dmask
=
xpu
::
constant
<
T
>
(
dev_ctx
.
x_context
(),
dmask_data
,
mask
.
numel
(),
zero
);
int
r_filter
=
xpu
::
constant
<
T
>
(
dev_ctx
.
x_context
(),
filter_grad_tmp
,
filter
.
numel
(),
zero
);
auto
ret
=
(
r_dx
==
xpu
::
Error_t
::
SUCCESS
)
&&
(
r_dx
==
r_dw
)
&&
(
r_dx
==
r_doffset
)
&&
(
r_dx
==
r_dmask
)
&&
(
r_dx
==
r_filter
);
PADDLE_ENFORCE_EQ
(
ret
,
true
,
platform
::
errors
::
External
(
"XPU API return wrong value, please check where "
"Baidu Kunlun Card is properly installed."
));
for
(
int
i
=
0
;
i
<
batch_size
/
im2col_step
;
++
i
)
{
int
r
=
xpu
::
deformable_conv_grad
<
float
,
float
,
float
,
int
>
(
dev_ctx
.
x_context
(),
input_ptr
+
i
*
im2col_step
*
input_dim
,
filter_ptr
,
offset_ptr
+
i
*
im2col_step
*
input_offset_dim
,
mask_ptr
+
i
*
im2col_step
*
input_mask_dim
,
output_grad_ptr
+
i
*
im2col_step
*
output_dim
,
dx_data
+
i
*
im2col_step
*
input_dim
,
filter_grad_tmp
,
doffset_data
+
i
*
im2col_step
*
input_offset_dim
,
dmask_data
+
i
*
im2col_step
*
input_mask_dim
,
n
,
c
,
h
,
w
,
f
,
ksize
,
strides
,
paddings
,
dilations
,
groups
,
deformable_groups
,
nullptr
,
nullptr
,
nullptr
,
nullptr
,
nullptr
,
true
);
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU deformable_conv_grad kernel return wrong value[%d]."
,
r
));
r
=
baidu
::
xpu
::
api
::
add
<
T
>
(
dev_ctx
.
x_context
(),
filter_grad_tmp
,
dw_data
,
dw_data
,
filter
.
numel
());
PADDLE_ENFORCE_EQ
(
r
,
XPU_SUCCESS
,
platform
::
errors
::
External
(
"XPU add kernel return wrong value[%d]."
,
r
));
}
dev_ctx
.
Wait
();
xpu_free
(
filter_grad_tmp
);
if
(
input_grad
==
nullptr
)
{
xpu_free
(
dx_data
);
}
if
(
filter_grad
==
nullptr
)
{
xpu_free
(
dw_data
);
}
if
(
offset_grad
==
nullptr
)
{
xpu_free
(
doffset_data
);
}
if
(
mask_grad
==
nullptr
)
{
xpu_free
(
dmask_data
);
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
using
XPUDeviceContext
=
paddle
::
platform
::
XPUDeviceContext
;
REGISTER_OP_XPU_KERNEL
(
deformable_conv
,
ops
::
DeformableConvXPUKernel
<
XPUDeviceContext
,
float
>
);
REGISTER_OP_XPU_KERNEL
(
deformable_conv_grad
,
ops
::
DeformableConvGradXPUKernel
<
XPUDeviceContext
,
float
>
);
#endif
paddle/fluid/operators/optimizers/merged_momentum_op_xpu.cc
已删除
100644 → 0
浏览文件 @
d8a09e25
// 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.
#ifdef PADDLE_WITH_XPU
#include <sys/syscall.h>
#include <unistd.h>
#include <iostream>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device/device_wrapper.h"
#include "paddle/fluid/platform/device/xpu/xpu_header.h"
#include "paddle/phi/kernels/impl/momentum_kernel_impl.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
DeviceContext
,
typename
T
>
class
MergedMomentumOpXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
T
mu
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"mu"
));
auto
params
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"Param"
);
auto
params_out
=
ctx
.
MultiOutput
<
framework
::
Tensor
>
(
"ParamOut"
);
auto
lr
=
ctx
.
Input
<
framework
::
Tensor
>
(
"LearningRate"
);
int
op_num
=
params
.
size
();
auto
velocity
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"Velocity"
);
auto
grad
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"Grad"
);
auto
velocity_out
=
ctx
.
MultiOutput
<
framework
::
Tensor
>
(
"VelocityOut"
);
auto
use_nesterov
=
ctx
.
Attr
<
bool
>
(
"use_nesterov"
);
auto
regularization_method
=
ctx
.
Attr
<
std
::
vector
<
std
::
string
>>
(
"regularization_method"
);
auto
regularization_coeff
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"regularization_coeff"
);
PADDLE_ENFORCE_EQ
(
op_num
,
params_out
.
size
(),
platform
::
errors
::
InvalidArgument
(
"The size of Output(ParamOut) must be equal to "
"Input(Param), but got the size of Output(ParamOut) "
"is %d, the size of Input(Param) is %d."
,
params_out
.
size
(),
op_num
));
PADDLE_ENFORCE_EQ
(
op_num
,
velocity
.
size
(),
platform
::
errors
::
InvalidArgument
(
"The size of Output(Velocity) must be equal to "
"Input(Param), but got the size of Output(Velocity) "
"is %d, the size of Input(Param) is %d."
,
velocity
.
size
(),
op_num
));
PADDLE_ENFORCE_EQ
(
op_num
,
velocity_out
.
size
(),
platform
::
errors
::
InvalidArgument
(
"The size of Output(VelocityOut) must be equal to "
"Input(Param), but got the size of Output(VelocityOut) "
"is %d, the size of Input(Param) is %d."
,
velocity_out
.
size
(),
op_num
));
PADDLE_ENFORCE_EQ
(
op_num
,
grad
.
size
(),
platform
::
errors
::
InvalidArgument
(
"The size of Input(Grad) must be equal to Input(Param), but got "
"the size of Input(Grad) is %d, the size of Input(Param) is %d."
,
grad
.
size
(),
op_num
));
if
(
regularization_method
.
size
()
==
0
)
{
regularization_method
.
resize
(
op_num
);
}
std
::
vector
<
XPUType
*>
param_list
(
op_num
);
std
::
vector
<
XPUType
*>
velocity_list
(
op_num
);
std
::
vector
<
XPUType
*>
grad_list
(
op_num
);
std
::
vector
<
XPUType
*>
velocity_out_list
(
op_num
);
std
::
vector
<
XPUType
*>
param_out_list
(
op_num
);
std
::
vector
<
int
>
sizes
(
op_num
);
std
::
vector
<
float
>
l2_weight_decay
(
op_num
);
if
(
op_num
>
0
)
{
for
(
int
j
=
0
;
j
<
op_num
;
j
++
)
{
param_list
[
j
]
=
reinterpret_cast
<
XPUType
*>
(
const_cast
<
T
*>
(
params
[
j
]
->
data
<
T
>
()));
velocity_list
[
j
]
=
reinterpret_cast
<
XPUType
*>
(
const_cast
<
T
*>
(
velocity
[
j
]
->
data
<
T
>
()));
grad_list
[
j
]
=
reinterpret_cast
<
XPUType
*>
(
const_cast
<
T
*>
(
grad
[
j
]
->
data
<
T
>
()));
param_out_list
[
j
]
=
reinterpret_cast
<
XPUType
*>
(
params_out
[
j
]
->
data
<
T
>
());
velocity_out_list
[
j
]
=
reinterpret_cast
<
XPUType
*>
(
velocity_out
[
j
]
->
data
<
T
>
());
sizes
[
j
]
=
static_cast
<
int
>
(
params
[
j
]
->
numel
());
if
(
regularization_method
[
j
]
!=
"l2_decay"
)
{
l2_weight_decay
[
j
]
=
0.0
f
;
}
else
{
l2_weight_decay
[
j
]
=
static_cast
<
float
>
(
regularization_coeff
[
j
]);
}
PADDLE_ENFORCE_EQ
(
params
[
j
],
params_out
[
j
],
platform
::
errors
::
InvalidArgument
(
"The size of Input(Param) and Output(ParamOut) "
"must be the same Tensors."
));
PADDLE_ENFORCE_EQ
(
velocity
[
j
],
velocity_out
[
j
],
platform
::
errors
::
InvalidArgument
(
"The size of Input(velocity) and Output(velocity) "
"must be the same Tensors."
));
}
}
else
{
return
;
}
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
int
r
=
xpu
::
merged_momentum
(
dev_ctx
.
x_context
(),
param_list
,
velocity_list
,
grad_list
,
param_out_list
,
velocity_out_list
,
l2_weight_decay
,
sizes
,
lr
->
data
<
float
>
(),
mu
,
use_nesterov
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"merged_momentum"
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
merged_momentum
,
ops
::
MergedMomentumOpXPUKernel
<
paddle
::
platform
::
XPUDeviceContext
,
float
>
,
ops
::
MergedMomentumOpXPUKernel
<
paddle
::
platform
::
XPUDeviceContext
,
paddle
::
platform
::
float16
>
);
#endif
paddle/phi/kernels/xpu/deformable_conv_grad_kernel.cc
0 → 100644
浏览文件 @
7f3c7aeb
// 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/phi/kernels/deformable_conv_grad_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
DeformableConvGradKernel
(
const
Context
&
dev_ctx
,
const
DenseTensor
&
x
,
const
DenseTensor
&
offset
,
const
DenseTensor
&
filter
,
const
paddle
::
optional
<
DenseTensor
>&
mask
,
const
DenseTensor
&
out_grad
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
dilations
,
int
deformable_groups
,
int
groups
,
int
im2col_step
,
DenseTensor
*
dx
,
DenseTensor
*
offset_grad
,
DenseTensor
*
filter_grad
,
DenseTensor
*
mask_grad
)
{
T
*
dx_data
=
nullptr
;
T
*
dw_data
=
nullptr
;
T
*
dmask_data
=
nullptr
;
T
*
doffset_data
=
nullptr
;
if
(
dx
!=
nullptr
)
{
dx_data
=
dev_ctx
.
template
Alloc
<
T
>(
dx
);
}
if
(
filter_grad
!=
nullptr
)
{
dw_data
=
dev_ctx
.
template
Alloc
<
T
>(
filter_grad
);
}
if
(
offset_grad
!=
nullptr
)
{
doffset_data
=
dev_ctx
.
template
Alloc
<
T
>(
offset_grad
);
}
if
(
mask_grad
!=
nullptr
)
{
dmask_data
=
dev_ctx
.
template
Alloc
<
T
>(
mask_grad
);
}
PADDLE_ENFORCE_EQ
(
deformable_groups
==
1
,
true
,
errors
::
InvalidArgument
(
(
"XPU only support deformable_groups == 1 in deformable_conv op."
)));
PADDLE_ENFORCE_EQ
(
groups
==
1
,
true
,
errors
::
InvalidArgument
(
(
"XPU only support groups == 1 in deformable_conv op."
)));
PADDLE_ENFORCE_EQ
(
filter
.
dims
()[
2
]
<=
8
&&
filter
.
dims
()[
3
]
<=
8
,
true
,
errors
::
InvalidArgument
(
"Filter high and weight should less than 8 on xpu "
"in deformable_conv op."
));
const
int
batch_size
=
static_cast
<
int
>
(
x
.
dims
()[
0
]);
std
::
vector
<
int64_t
>
output_shape_vec
(
phi
::
vectorize
(
out_grad
.
dims
()));
const
T
*
output_grad_ptr
=
out_grad
.
data
<
T
>
();
const
T
*
input_ptr
=
x
.
data
<
T
>
();
const
T
*
filter_ptr
=
filter
.
data
<
T
>
();
const
float
*
offset_ptr
=
offset
.
data
<
float
>
();
const
float
*
mask_ptr
=
mask
->
data
<
float
>
();
if
(
dx_data
==
nullptr
)
{
PADDLE_ENFORCE_EQ
(
xpu_malloc
(
reinterpret_cast
<
void
**>
(
&
dx_data
),
x
.
numel
()
*
sizeof
(
T
)),
XPU_SUCCESS
,
errors
::
ResourceExhausted
(
"XPU has no enough memory"
));
}
if
(
dw_data
==
nullptr
)
{
PADDLE_ENFORCE_EQ
(
xpu_malloc
(
reinterpret_cast
<
void
**>
(
&
dw_data
),
filter
.
numel
()
*
sizeof
(
T
)),
XPU_SUCCESS
,
errors
::
ResourceExhausted
(
"XPU has no enough memory"
));
}
if
(
doffset_data
==
nullptr
)
{
PADDLE_ENFORCE_EQ
(
xpu_malloc
(
reinterpret_cast
<
void
**>
(
&
doffset_data
),
offset
.
numel
()
*
sizeof
(
T
)),
XPU_SUCCESS
,
errors
::
ResourceExhausted
(
"XPU has no enough memory"
));
}
if
(
dmask_data
==
nullptr
)
{
PADDLE_ENFORCE_EQ
(
xpu_malloc
(
reinterpret_cast
<
void
**>
(
&
dmask_data
),
mask
->
numel
()
*
sizeof
(
T
)),
XPU_SUCCESS
,
errors
::
ResourceExhausted
(
"XPU has no enough memory"
));
}
int
input_dim
=
x
.
numel
()
/
x
.
dims
()[
0
];
int
input_offset_dim
=
offset
.
numel
()
/
offset
.
dims
()[
0
];
int
input_mask_dim
=
mask
->
numel
()
/
mask
->
dims
()[
0
];
int
output_dim
=
output_shape_vec
[
1
]
*
output_shape_vec
[
2
]
*
output_shape_vec
[
3
];
std
::
vector
<
int
>
ksize
{
static_cast
<
int
>
(
filter
.
dims
()[
2
]),
static_cast
<
int
>
(
filter
.
dims
()[
3
])};
int
n
=
im2col_step
;
int
c
=
x
.
dims
()[
1
];
int
h
=
x
.
dims
()[
2
];
int
w
=
x
.
dims
()[
3
];
int
f
=
filter
.
dims
()[
0
];
T
*
filter_grad_tmp
=
nullptr
;
PADDLE_ENFORCE_EQ
(
xpu_malloc
(
reinterpret_cast
<
void
**>
(
&
filter_grad_tmp
),
filter_grad
->
numel
()
*
sizeof
(
T
)),
XPU_SUCCESS
,
errors
::
ResourceExhausted
(
"XPU has no enough memory"
));
// set zeros for d_table_data
const
int
zero
=
0
;
int
r_dx
=
xpu
::
constant
<
T
>
(
dev_ctx
.
x_context
(),
dx_data
,
x
.
numel
(),
zero
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r_dx
,
"constant"
);
int
r_dw
=
xpu
::
constant
<
T
>
(
dev_ctx
.
x_context
(),
dw_data
,
filter
.
numel
(),
zero
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r_dw
,
"constant"
);
int
r_doffset
=
xpu
::
constant
<
T
>
(
dev_ctx
.
x_context
(),
doffset_data
,
offset
.
numel
(),
zero
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r_doffset
,
"constant"
);
int
r_dmask
=
xpu
::
constant
<
T
>
(
dev_ctx
.
x_context
(),
dmask_data
,
mask
->
numel
(),
zero
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r_dmask
,
"constant"
);
int
r_filter
=
xpu
::
constant
<
T
>
(
dev_ctx
.
x_context
(),
filter_grad_tmp
,
filter
.
numel
(),
zero
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r_filter
,
"constant"
);
for
(
int
i
=
0
;
i
<
batch_size
/
im2col_step
;
++
i
)
{
int
r
=
xpu
::
deformable_conv_grad
<
float
,
float
,
float
,
int
>
(
dev_ctx
.
x_context
(),
input_ptr
+
i
*
im2col_step
*
input_dim
,
filter_ptr
,
offset_ptr
+
i
*
im2col_step
*
input_offset_dim
,
mask_ptr
+
i
*
im2col_step
*
input_mask_dim
,
output_grad_ptr
+
i
*
im2col_step
*
output_dim
,
dx_data
+
i
*
im2col_step
*
input_dim
,
filter_grad_tmp
,
doffset_data
+
i
*
im2col_step
*
input_offset_dim
,
dmask_data
+
i
*
im2col_step
*
input_mask_dim
,
n
,
c
,
h
,
w
,
f
,
ksize
,
strides
,
paddings
,
dilations
,
groups
,
deformable_groups
,
nullptr
,
nullptr
,
nullptr
,
nullptr
,
nullptr
,
true
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"deformable_conv_grad"
);
r
=
baidu
::
xpu
::
api
::
add
<
T
>
(
dev_ctx
.
x_context
(),
filter_grad_tmp
,
dw_data
,
dw_data
,
filter
.
numel
());
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"add"
);
}
dev_ctx
.
Wait
();
xpu_free
(
filter_grad_tmp
);
if
(
dx
==
nullptr
)
{
xpu_free
(
dx_data
);
}
if
(
filter_grad
==
nullptr
)
{
xpu_free
(
dw_data
);
}
if
(
offset_grad
==
nullptr
)
{
xpu_free
(
doffset_data
);
}
if
(
mask_grad
==
nullptr
)
{
xpu_free
(
dmask_data
);
}
}
}
// namespace phi
PD_REGISTER_KERNEL
(
deformable_conv_grad
,
XPU
,
ALL_LAYOUT
,
phi
::
DeformableConvGradKernel
,
float
)
{}
paddle/phi/kernels/xpu/deformable_conv_kernel.cc
0 → 100644
浏览文件 @
7f3c7aeb
// 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/phi/kernels/deformable_conv_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
DeformableConvKernel
(
const
Context
&
dev_ctx
,
const
DenseTensor
&
x
,
const
DenseTensor
&
offset
,
const
DenseTensor
&
filter
,
const
paddle
::
optional
<
DenseTensor
>&
mask
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
int
>&
paddings
,
const
std
::
vector
<
int
>&
dilations
,
int
deformable_groups
,
int
groups
,
int
im2col_step
,
DenseTensor
*
out
)
{
dev_ctx
.
template
Alloc
<
T
>(
out
);
PADDLE_ENFORCE_EQ
(
deformable_groups
==
1
,
true
,
errors
::
InvalidArgument
(
(
"XPU only support deformable_groups == 1 in deformable_conv op."
)));
PADDLE_ENFORCE_EQ
(
groups
==
1
,
true
,
errors
::
InvalidArgument
(
(
"XPU only support groups == 1 in deformable_conv op."
)));
PADDLE_ENFORCE_EQ
(
filter
.
dims
()[
2
]
<=
8
&&
filter
.
dims
()[
3
]
<=
8
,
true
,
errors
::
InvalidArgument
(
"Filter high and weight should less than 8 on xpu "
"in deformable_conv op."
));
const
int
batch_size
=
static_cast
<
int
>
(
x
.
dims
()[
0
]);
std
::
vector
<
int64_t
>
output_shape_vec
(
phi
::
vectorize
(
out
->
dims
()));
const
T
*
input_ptr
=
x
.
data
<
T
>
();
const
T
*
filter_ptr
=
filter
.
data
<
T
>
();
const
float
*
offset_ptr
=
offset
.
data
<
T
>
();
const
float
*
mask_ptr
=
mask
->
data
<
T
>
();
T
*
output_prt
=
out
->
data
<
T
>
();
// set zeros for d_table_data
const
int
zero
=
0
;
int
r
=
xpu
::
constant
<
T
>
(
dev_ctx
.
x_context
(),
output_prt
,
out
->
numel
(),
zero
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"constant"
);
int
input_dim
=
x
.
numel
()
/
x
.
dims
()[
0
];
int
input_offset_dim
=
offset
.
numel
()
/
offset
.
dims
()[
0
];
int
input_mask_dim
=
mask
->
numel
()
/
mask
->
dims
()[
0
];
int
output_dim
=
output_shape_vec
[
1
]
*
output_shape_vec
[
2
]
*
output_shape_vec
[
3
];
std
::
vector
<
int
>
ksize
{
static_cast
<
int
>
(
filter
.
dims
()[
2
]),
static_cast
<
int
>
(
filter
.
dims
()[
3
])};
int
n
=
im2col_step
;
int
c
=
x
.
dims
()[
1
];
int
h
=
x
.
dims
()[
2
];
int
w
=
x
.
dims
()[
3
];
int
f
=
filter
.
dims
()[
0
];
for
(
int
i
=
0
;
i
<
batch_size
/
im2col_step
;
++
i
)
{
int
r
=
xpu
::
deformable_conv
<
float
,
float
,
float
,
int
>
(
dev_ctx
.
x_context
(),
input_ptr
+
i
*
im2col_step
*
input_dim
,
filter_ptr
,
offset_ptr
+
i
*
im2col_step
*
input_offset_dim
,
mask_ptr
+
i
*
im2col_step
*
input_mask_dim
,
output_prt
+
i
*
im2col_step
*
output_dim
,
n
,
c
,
h
,
w
,
f
,
ksize
,
strides
,
paddings
,
dilations
,
groups
,
deformable_groups
,
nullptr
,
nullptr
,
nullptr
,
true
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"deformable_conv"
);
}
}
}
// namespace phi
PD_REGISTER_KERNEL
(
deformable_conv
,
XPU
,
ALL_LAYOUT
,
phi
::
DeformableConvKernel
,
float
)
{}
paddle/phi/kernels/xpu/merged_momentum_kernel.cc
0 → 100644
浏览文件 @
7f3c7aeb
// 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 <sys/syscall.h>
#include <unistd.h>
#include <iostream>
#include <string>
#include <vector>
#include "paddle/phi/kernels/merged_momentum_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
MergedMomentumKernel
(
const
Context
&
dev_ctx
,
const
std
::
vector
<
const
DenseTensor
*>&
params
,
const
std
::
vector
<
const
DenseTensor
*>&
grad
,
const
std
::
vector
<
const
DenseTensor
*>&
velocity
,
const
std
::
vector
<
const
DenseTensor
*>&
learning_rate
,
const
paddle
::
optional
<
std
::
vector
<
const
DenseTensor
*>>&
master_param
,
float
mu_in
,
bool
use_nesterov
,
const
std
::
vector
<
std
::
string
>&
regularization_method
,
const
std
::
vector
<
float
>&
regularization_coeff
,
bool
multi_precision
,
float
rescale_grad
,
std
::
vector
<
DenseTensor
*>
params_out
,
std
::
vector
<
DenseTensor
*>
velocity_out
,
std
::
vector
<
DenseTensor
*>
master_param_out
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
auto
lr
=
learning_rate
[
0
];
T
mu
=
static_cast
<
T
>
(
mu_in
);
int
op_num
=
params
.
size
();
PADDLE_ENFORCE_EQ
(
op_num
,
params_out
.
size
(),
errors
::
InvalidArgument
(
"The size of Output(ParamOut) must be equal to "
"Input(Param), but got the size of Output(ParamOut) "
"is %d, the size of Input(Param) is %d."
,
params_out
.
size
(),
op_num
));
PADDLE_ENFORCE_EQ
(
op_num
,
velocity
.
size
(),
errors
::
InvalidArgument
(
"The size of Output(Velocity) must be equal to "
"Input(Param), but got the size of Output(Velocity) "
"is %d, the size of Input(Param) is %d."
,
velocity
.
size
(),
op_num
));
PADDLE_ENFORCE_EQ
(
op_num
,
velocity_out
.
size
(),
errors
::
InvalidArgument
(
"The size of Output(VelocityOut) must be equal to "
"Input(Param), but got the size of Output(VelocityOut) "
"is %d, the size of Input(Param) is %d."
,
velocity_out
.
size
(),
op_num
));
PADDLE_ENFORCE_EQ
(
op_num
,
grad
.
size
(),
errors
::
InvalidArgument
(
"The size of Input(Grad) must be equal to Input(Param), but got "
"the size of Input(Grad) is %d, the size of Input(Param) is %d."
,
grad
.
size
(),
op_num
));
std
::
vector
<
XPUType
*>
param_list
(
op_num
);
std
::
vector
<
XPUType
*>
velocity_list
(
op_num
);
std
::
vector
<
XPUType
*>
grad_list
(
op_num
);
std
::
vector
<
XPUType
*>
velocity_out_list
(
op_num
);
std
::
vector
<
XPUType
*>
param_out_list
(
op_num
);
std
::
vector
<
int
>
sizes
(
op_num
);
std
::
vector
<
float
>
l2_weight_decay
(
op_num
);
if
(
op_num
>
0
)
{
for
(
int
j
=
0
;
j
<
op_num
;
j
++
)
{
param_list
[
j
]
=
reinterpret_cast
<
XPUType
*>
(
const_cast
<
T
*>
(
params
[
j
]
->
data
<
T
>
()));
velocity_list
[
j
]
=
reinterpret_cast
<
XPUType
*>
(
const_cast
<
T
*>
(
velocity
[
j
]
->
data
<
T
>
()));
grad_list
[
j
]
=
reinterpret_cast
<
XPUType
*>
(
const_cast
<
T
*>
(
grad
[
j
]
->
data
<
T
>
()));
param_out_list
[
j
]
=
reinterpret_cast
<
XPUType
*>
(
params_out
[
j
]
->
data
<
T
>
());
velocity_out_list
[
j
]
=
reinterpret_cast
<
XPUType
*>
(
velocity_out
[
j
]
->
data
<
T
>
());
sizes
[
j
]
=
static_cast
<
int
>
(
params
[
j
]
->
numel
());
if
(
regularization_method
[
j
]
!=
"l2_decay"
)
{
l2_weight_decay
[
j
]
=
0.0
f
;
}
else
{
l2_weight_decay
[
j
]
=
static_cast
<
float
>
(
regularization_coeff
[
j
]);
}
PADDLE_ENFORCE_EQ
(
params
[
j
],
params_out
[
j
],
errors
::
InvalidArgument
(
"The size of Input(Param) and Output(ParamOut) "
"must be the same Tensors."
));
PADDLE_ENFORCE_EQ
(
velocity
[
j
],
velocity_out
[
j
],
errors
::
InvalidArgument
(
"The size of Input(velocity) and Output(velocity) "
"must be the same Tensors."
));
}
}
else
{
return
;
}
int
r
=
xpu
::
merged_momentum
(
dev_ctx
.
x_context
(),
param_list
,
velocity_list
,
grad_list
,
param_out_list
,
velocity_out_list
,
l2_weight_decay
,
sizes
,
lr
->
data
<
float
>
(),
mu
,
use_nesterov
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"merged_momentum"
);
}
}
// namespace phi
PD_REGISTER_KERNEL
(
merged_momentum
,
XPU
,
ALL_LAYOUT
,
phi
::
MergedMomentumKernel
,
float
,
phi
::
dtype
::
float16
)
{}
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