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f786fcf9
编写于
9月 27, 2022
作者:
C
Chenxiao Niu
提交者:
GitHub
9月 27, 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
[MLU] add huber_loss kernel. (#46455)
上级
c82d1020
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
409 addition
and
0 deletion
+409
-0
paddle/fluid/operators/huber_loss_op_mlu.cc
paddle/fluid/operators/huber_loss_op_mlu.cc
+187
-0
paddle/fluid/operators/mlu/mlu_baseop.cc
paddle/fluid/operators/mlu/mlu_baseop.cc
+72
-0
paddle/fluid/operators/mlu/mlu_baseop.h
paddle/fluid/operators/mlu/mlu_baseop.h
+22
-0
python/paddle/fluid/tests/unittests/mlu/test_huber_loss_op_mlu.py
...addle/fluid/tests/unittests/mlu/test_huber_loss_op_mlu.py
+128
-0
未找到文件。
paddle/fluid/operators/huber_loss_op_mlu.cc
0 → 100644
浏览文件 @
f786fcf9
/* 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
=
phi
::
DenseTensor
;
template
<
typename
T
>
class
HuberLossMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
&
dev_ctx
=
GetDevCtxFromCTX
(
ctx
);
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
residual
=
ctx
.
Output
<
Tensor
>
(
"Residual"
);
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
auto
delta
=
ctx
.
Attr
<
float
>
(
"delta"
);
auto
place
=
ctx
.
GetPlace
();
// compute y-x
cnnlDataType_t
data_type
=
ToCnnlDataType
<
T
>
();
residual
->
mutable_data
<
T
>
(
x
->
dims
(),
place
);
MLUCnnlTensorDesc
x_desc
(
*
x
);
MLUCnnlOpTensorDesc
sub_op_desc
(
CNNL_OP_TENSOR_SUB
,
data_type
,
CNNL_NOT_PROPAGATE_NAN
);
MLUCnnl
::
OpTensor
(
ctx
,
sub_op_desc
.
get
(),
x_desc
.
get
(),
GetBasePtr
(
y
),
x_desc
.
get
(),
GetBasePtr
(
x
),
x_desc
.
get
(),
GetBasePtr
(
residual
),
data_type
);
// compute smoothl1loss
out
->
mutable_data
<
T
>
(
x
->
dims
(),
place
);
cnnlSmoothL1LossAlgorithm_t
smoothl1_algo
=
CNNL_SMOOTHL1LOSS_REDUCTION_NONE
;
// defines whether to do reduction
// here
MLUCnnl
::
SmoothL1LossForward
(
ctx
,
x_desc
.
get
(),
GetBasePtr
(
x
),
x_desc
.
get
(),
/* target has same shape as x */
GetBasePtr
(
y
),
static_cast
<
float
>
(
delta
),
smoothl1_algo
,
x_desc
.
get
(),
/* out has same shape as x */
GetBasePtr
(
out
));
// compute multiply by delta
framework
::
Tensor
scale_tensor
,
bias_tensor
;
scale_tensor
=
ctx
.
AllocateTmpTensor
<
T
,
MLUDeviceContext
>
({
1
},
dev_ctx
);
bias_tensor
=
ctx
.
AllocateTmpTensor
<
T
,
MLUDeviceContext
>
({
1
},
dev_ctx
);
FillMLUTensorWithHostValue
(
ctx
,
static_cast
<
T
>
(
delta
),
&
scale_tensor
);
FillMLUTensorWithHostValue
(
ctx
,
static_cast
<
T
>
(
0.
f
),
&
bias_tensor
);
const
int
axis
=
std
::
max
(
out
->
dims
().
size
()
-
1
,
0
);
MLUCnnlTensorDesc
scale_desc
(
scale_tensor
);
MLUCnnlTensorDesc
bias_desc
(
bias_tensor
);
MLUCnnlTensorDesc
out_desc
(
*
out
);
MLUCnnl
::
Scale
(
ctx
,
axis
,
out_desc
.
get
(),
GetBasePtr
(
out
),
scale_desc
.
get
(),
GetBasePtr
(
&
scale_tensor
),
bias_desc
.
get
(),
GetBasePtr
(
&
bias_tensor
),
out_desc
.
get
(),
GetBasePtr
(
out
));
}
};
template
<
typename
T
>
class
HuberLossGradMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
&
dev_ctx
=
GetDevCtxFromCTX
(
ctx
);
auto
*
residual
=
ctx
.
Input
<
Tensor
>
(
"Residual"
);
auto
*
dout
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dy
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
delta
=
ctx
.
Attr
<
float
>
(
"delta"
);
auto
place
=
ctx
.
GetPlace
();
Tensor
t_grad_rd
;
t_grad_rd
=
ctx
.
AllocateTmpTensor
<
T
,
MLUDeviceContext
>
(
residual
->
dims
(),
dev_ctx
);
MLUCnnlTensorDesc
t_grad_rd_desc
(
t_grad_rd
);
if
(
dx
||
dy
)
{
Tensor
t_zero
;
t_zero
=
ctx
.
AllocateTmpTensor
<
T
,
MLUDeviceContext
>
(
residual
->
dims
(),
dev_ctx
);
FillMLUTensorWithHostValue
(
ctx
,
static_cast
<
T
>
(
0.
f
),
&
t_zero
);
MLUCnnlTensorDesc
residual_desc
(
*
residual
);
MLUCnnlTensorDesc
dout_desc
(
*
dout
);
cnnlSmoothL1LossAlgorithm_t
smoothl1_algo
=
CNNL_SMOOTHL1LOSS_REDUCTION_NONE
;
// defines whether to do reduction
// here
MLUCnnl
::
SmoothL1LossBackward
(
ctx
,
residual_desc
.
get
(),
GetBasePtr
(
residual
),
residual_desc
.
get
(),
GetBasePtr
(
&
t_zero
),
dout_desc
.
get
(),
GetBasePtr
(
dout
),
static_cast
<
float
>
(
delta
),
smoothl1_algo
,
t_grad_rd_desc
.
get
(),
GetBasePtr
(
&
t_grad_rd
));
}
// compute multiply by delta
framework
::
Tensor
scale_tensor
,
bias_tensor
;
scale_tensor
=
ctx
.
AllocateTmpTensor
<
T
,
MLUDeviceContext
>
({
1
},
dev_ctx
);
bias_tensor
=
ctx
.
AllocateTmpTensor
<
T
,
MLUDeviceContext
>
({
1
},
dev_ctx
);
FillMLUTensorWithHostValue
(
ctx
,
static_cast
<
T
>
(
0.
f
),
&
bias_tensor
);
const
int
axis
=
std
::
max
(
t_grad_rd
.
dims
().
size
()
-
1
,
0
);
MLUCnnlTensorDesc
scale_desc
(
scale_tensor
);
MLUCnnlTensorDesc
bias_desc
(
bias_tensor
);
if
(
dx
)
{
dx
->
mutable_data
<
T
>
(
place
);
FillMLUTensorWithHostValue
(
ctx
,
static_cast
<
T
>
(
-
delta
),
&
scale_tensor
);
MLUCnnlTensorDesc
out_desc
(
*
dx
);
MLUCnnl
::
Scale
(
ctx
,
axis
,
t_grad_rd_desc
.
get
(),
GetBasePtr
(
&
t_grad_rd
),
scale_desc
.
get
(),
GetBasePtr
(
&
scale_tensor
),
bias_desc
.
get
(),
GetBasePtr
(
&
bias_tensor
),
out_desc
.
get
(),
GetBasePtr
(
dx
));
}
if
(
dy
)
{
dy
->
mutable_data
<
T
>
(
place
);
FillMLUTensorWithHostValue
(
ctx
,
static_cast
<
T
>
(
delta
),
&
scale_tensor
);
MLUCnnlTensorDesc
out_desc
(
*
dy
);
MLUCnnl
::
Scale
(
ctx
,
axis
,
t_grad_rd_desc
.
get
(),
GetBasePtr
(
&
t_grad_rd
),
scale_desc
.
get
(),
GetBasePtr
(
&
scale_tensor
),
bias_desc
.
get
(),
GetBasePtr
(
&
bias_tensor
),
out_desc
.
get
(),
GetBasePtr
(
dy
));
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_MLU_KERNEL
(
huber_loss
,
ops
::
HuberLossMLUKernel
<
float
>
,
ops
::
HuberLossMLUKernel
<
plat
::
float16
>
);
REGISTER_OP_MLU_KERNEL
(
huber_loss_grad
,
ops
::
HuberLossGradMLUKernel
<
float
>
,
ops
::
HuberLossGradMLUKernel
<
plat
::
float16
>
);
paddle/fluid/operators/mlu/mlu_baseop.cc
浏览文件 @
f786fcf9
...
...
@@ -4725,6 +4725,78 @@ MLURNNDesc::~MLURNNDesc() {
output
));
}
/* static */
void
MLUCnnl
::
SmoothL1LossForward
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
t_desc
,
const
void
*
target
,
const
float
beta
,
const
cnnlSmoothL1LossAlgorithm_t
algorithm
,
const
cnnlTensorDescriptor_t
y_desc
,
void
*
y
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
size_t
workspace_size
;
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlGetSmoothL1LossForwardWorkspaceSize
(
handle
,
x_desc
,
algorithm
,
&
workspace_size
));
auto
&
dev_ctx
=
GetDevCtxFromCTX
(
ctx
);
Tensor
workspace
=
ctx
.
AllocateTmpTensor
<
int8_t
,
MLUDeviceContext
>
(
{
static_cast
<
int64_t
>
(
workspace_size
)},
dev_ctx
);
void
*
workspace_ptr
=
workspace
.
mutable_data
(
ctx
.
GetPlace
());
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlSmoothL1LossForward_v2
(
handle
,
x_desc
,
x
,
t_desc
,
target
,
beta
,
algorithm
,
workspace_ptr
,
workspace_size
,
y_desc
,
y
));
}
/* static */
void
MLUCnnl
::
SmoothL1LossBackward
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
target_desc
,
const
void
*
target
,
const
cnnlTensorDescriptor_t
dy_desc
,
const
void
*
dy
,
const
float
beta
,
const
cnnlSmoothL1LossAlgorithm_t
algorithm
,
const
cnnlTensorDescriptor_t
dx_desc
,
void
*
dx
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
size_t
workspace_size
;
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlGetSmoothL1LossBackwardWorkspaceSize
(
handle
,
x_desc
,
algorithm
,
&
workspace_size
));
auto
&
dev_ctx
=
GetDevCtxFromCTX
(
ctx
);
Tensor
workspace
=
ctx
.
AllocateTmpTensor
<
int8_t
,
MLUDeviceContext
>
(
{
static_cast
<
int64_t
>
(
workspace_size
)},
dev_ctx
);
void
*
workspace_ptr
=
workspace
.
mutable_data
(
ctx
.
GetPlace
());
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlSmoothL1LossBackward_v2
(
handle
,
x_desc
,
x
,
target_desc
,
target
,
dy_desc
,
dy
,
beta
,
algorithm
,
workspace_ptr
,
workspace_size
,
dx_desc
,
dx
));
}
/* static */
void
MLUCnnl
::
EmbeddingForward
(
const
ExecutionContext
&
ctx
,
const
int
padding_idx
,
...
...
paddle/fluid/operators/mlu/mlu_baseop.h
浏览文件 @
f786fcf9
...
...
@@ -2042,6 +2042,28 @@ class MLUCnnl {
const
cnnlTensorDescriptor_t
output_desc
,
void
*
output
);
static
void
SmoothL1LossForward
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
t_desc
,
const
void
*
target
,
const
float
beta
,
const
cnnlSmoothL1LossAlgorithm_t
algorithm
,
const
cnnlTensorDescriptor_t
y_desc
,
void
*
y
);
static
void
SmoothL1LossBackward
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
x_desc
,
const
void
*
x
,
const
cnnlTensorDescriptor_t
target_desc
,
const
void
*
target
,
const
cnnlTensorDescriptor_t
dy_desc
,
const
void
*
dy
,
const
float
beta
,
const
cnnlSmoothL1LossAlgorithm_t
algorithm
,
const
cnnlTensorDescriptor_t
dx_desc
,
void
*
dx
);
static
void
EmbeddingForward
(
const
ExecutionContext
&
ctx
,
const
int
padding_idx
,
const
cnnlTensorDescriptor_t
weight_desc
,
...
...
python/paddle/fluid/tests/unittests/mlu/test_huber_loss_op_mlu.py
0 → 100644
浏览文件 @
f786fcf9
# 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
unittest
import
numpy
as
np
import
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid
import
compiler
,
Program
,
program_guard
paddle
.
enable_static
()
def
huber_loss_forward
(
val
,
delta
):
abs_val
=
abs
(
val
)
if
abs_val
<=
delta
:
return
0.5
*
val
*
val
else
:
return
delta
*
(
abs_val
-
0.5
*
delta
)
class
TestHuberLossOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
'huber_loss'
self
.
set_mlu
()
self
.
python_api
=
paddle
.
fluid
.
layers
.
huber_loss
self
.
python_out_sig
=
[
"Out"
]
self
.
delta
=
1.0
self
.
init_input
()
shape
=
self
.
set_shape
()
residual
=
self
.
inputs
[
'Y'
]
-
self
.
inputs
[
'X'
]
loss
=
np
.
vectorize
(
huber_loss_forward
)(
residual
,
self
.
delta
).
astype
(
'float32'
)
self
.
attrs
=
{
'delta'
:
self
.
delta
}
self
.
outputs
=
{
'Residual'
:
residual
,
'Out'
:
loss
.
reshape
(
shape
)}
def
init_input
(
self
):
shape
=
self
.
set_shape
()
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0
,
1.
,
shape
).
astype
(
'float32'
),
'Y'
:
np
.
random
.
uniform
(
0
,
1.
,
shape
).
astype
(
'float32'
),
}
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
MLUPlace
(
0
)
def
set_shape
(
self
):
return
(
100
,
1
)
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
,
atol
=
1e-3
)
def
test_check_grad_normal
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'X'
,
'Y'
],
'Out'
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'Y'
],
'Out'
,
max_relative_error
=
0.008
,
no_grad_set
=
set
(
"residual"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'X'
],
'Out'
,
max_relative_error
=
0.008
,
no_grad_set
=
set
(
'residual'
))
def
TestHuberLossOp1
(
TestHuberLossOp
):
def
set_shape
(
self
):
return
(
64
)
def
TestHuberLossOp2
(
TestHuberLossOp
):
def
set_shape
(
self
):
return
(
6
,
6
)
def
TestHuberLossOp3
(
TestHuberLossOp
):
def
set_shape
(
self
):
return
(
6
,
6
,
1
)
class
TestHuberLossOpError
(
unittest
.
TestCase
):
def
test_errors
(
self
):
with
program_guard
(
Program
(),
Program
()):
# the input and label must be Variable
xw
=
np
.
random
.
random
((
6
,
6
)).
astype
(
"float32"
)
xr
=
fluid
.
data
(
name
=
'xr'
,
shape
=
[
None
,
6
],
dtype
=
"float32"
)
lw
=
np
.
random
.
random
((
6
,
6
)).
astype
(
"float32"
)
lr
=
fluid
.
data
(
name
=
'lr'
,
shape
=
[
None
,
6
],
dtype
=
"float32"
)
delta
=
1.0
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
huber_loss
,
xr
,
lw
,
delta
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
huber_loss
,
xw
,
lr
,
delta
)
# the dtype of input and label must be float32 or float64
xw2
=
fluid
.
data
(
name
=
'xw2'
,
shape
=
[
None
,
6
],
dtype
=
"int32"
)
lw2
=
fluid
.
data
(
name
=
'lw2'
,
shape
=
[
None
,
6
],
dtype
=
"int32"
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
huber_loss
,
xw2
,
lr
,
delta
)
self
.
assertRaises
(
TypeError
,
fluid
.
layers
.
huber_loss
,
xr
,
lw2
,
delta
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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