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047971f0
编写于
11月 08, 2022
作者:
Z
zhangyikun02
提交者:
GitHub
11月 08, 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
add adadelta op for xpu, test=kunlun (#47661)
上级
6a6a3ff1
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
348 addition
and
41 deletion
+348
-41
paddle/fluid/platform/device/xpu/xpu2_op_list.h
paddle/fluid/platform/device/xpu/xpu2_op_list.h
+3
-0
paddle/phi/kernels/xpu/adadelta_kernel.cc
paddle/phi/kernels/xpu/adadelta_kernel.cc
+53
-0
python/paddle/fluid/tests/unittests/xpu/test_adadelta_op_xpu.py
.../paddle/fluid/tests/unittests/xpu/test_adadelta_op_xpu.py
+239
-0
python/paddle/fluid/tests/unittests/xpu/test_clip_by_norm_op_xpu.py
...dle/fluid/tests/unittests/xpu/test_clip_by_norm_op_xpu.py
+53
-41
未找到文件。
paddle/fluid/platform/device/xpu/xpu2_op_list.h
浏览文件 @
047971f0
...
@@ -33,6 +33,7 @@ XPUOpMap& get_kl2_ops() {
...
@@ -33,6 +33,7 @@ XPUOpMap& get_kl2_ops() {
{
"abs_grad"
,
{
"abs_grad"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
{
"adadelta"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"adamw"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"adamw"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"adam"
,
{
"adam"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
...
@@ -109,6 +110,8 @@ XPUOpMap& get_kl2_ops() {
...
@@ -109,6 +110,8 @@ XPUOpMap& get_kl2_ops() {
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
{
"clip"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"clip"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"clip_by_norm"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"coalesce_tensor"
,
{
"coalesce_tensor"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"concat_grad"
,
{
"concat_grad"
,
...
...
paddle/phi/kernels/xpu/adadelta_kernel.cc
0 → 100644
浏览文件 @
047971f0
// 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/adadelta_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
AdadeltaKernel
(
const
Context
&
dev_ctx
,
const
DenseTensor
&
param
,
const
DenseTensor
&
grad
,
const
DenseTensor
&
avg_squared_grad
,
const
DenseTensor
&
avg_squared_update
,
float
rho
,
float
epsilon
,
DenseTensor
*
param_out
,
DenseTensor
*
avg_squared_grad_out
,
DenseTensor
*
avg_squared_update_out
)
{
dev_ctx
.
template
Alloc
<
T
>(
param_out
);
dev_ctx
.
template
Alloc
<
T
>(
avg_squared_grad_out
);
dev_ctx
.
template
Alloc
<
T
>(
avg_squared_update_out
);
int
r
=
xpu
::
adadelta
<
T
,
T
>
(
dev_ctx
.
x_context
(),
param
.
data
<
T
>
(),
grad
.
data
<
T
>
(),
avg_squared_grad
.
data
<
T
>
(),
avg_squared_update
.
data
<
T
>
(),
param_out
->
data
<
T
>
(),
avg_squared_grad_out
->
data
<
T
>
(),
avg_squared_update_out
->
data
<
T
>
(),
param
.
numel
(),
rho
,
epsilon
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"adadelta"
);
}
}
// namespace phi
PD_REGISTER_KERNEL
(
adadelta
,
XPU
,
ALL_LAYOUT
,
phi
::
AdadeltaKernel
,
float
)
{}
python/paddle/fluid/tests/unittests/xpu/test_adadelta_op_xpu.py
0 → 100644
浏览文件 @
047971f0
# 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.
import
unittest
import
numpy
as
np
import
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
import
paddle
import
paddle.fluid
as
fluid
from
op_test_xpu
import
XPUOpTest
from
xpu.get_test_cover_info
import
(
create_test_class
,
get_xpu_op_support_types
,
XPUOpTestWrapper
,
)
paddle
.
enable_static
()
class
XPUTestAdadelta
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
'adadelta'
class
TestAdadeltaOp1
(
XPUOpTest
):
def
setUp
(
self
):
self
.
op_type
=
"adadelta"
self
.
dtype
=
self
.
in_type
self
.
place
=
paddle
.
XPUPlace
(
0
)
param
=
np
.
random
.
uniform
(
-
1
,
1
,
(
102
,
105
)).
astype
(
self
.
dtype
)
grad
=
np
.
random
.
uniform
(
-
1
,
1
,
(
102
,
105
)).
astype
(
self
.
dtype
)
# The squared gradient is positive
avg_squared_grad
=
np
.
random
.
random
((
102
,
105
)).
astype
(
self
.
dtype
)
# The squared update is positive
avg_squared_update
=
np
.
random
.
random
((
102
,
105
)).
astype
(
self
.
dtype
)
rho
=
0.95
epsilon
=
1e-6
self
.
inputs
=
{
'Param'
:
param
,
'Grad'
:
grad
,
'AvgSquaredGrad'
:
avg_squared_grad
,
'AvgSquaredUpdate'
:
avg_squared_update
,
}
self
.
attrs
=
{
'rho'
:
rho
,
'epsilon'
:
epsilon
}
avg_squared_grad_out
=
rho
*
avg_squared_grad
+
(
1
-
rho
)
*
np
.
square
(
grad
)
update
=
-
np
.
multiply
(
np
.
sqrt
(
np
.
divide
(
avg_squared_update
+
epsilon
,
avg_squared_grad_out
+
epsilon
,
)
),
grad
,
)
avg_squared_update_out
=
rho
*
avg_squared_update
+
(
1
-
rho
)
*
np
.
square
(
update
)
param_out
=
param
+
update
self
.
outputs
=
{
'ParamOut'
:
param_out
,
'AvgSquaredGradOut'
:
avg_squared_grad_out
,
'AvgSquaredUpdateOut'
:
avg_squared_update_out
,
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestAdadeltaOp2
(
OpTest
):
'''Test Adadelta op with default attribute values'''
def
setUp
(
self
):
self
.
op_type
=
"adadelta"
self
.
dtype
=
self
.
in_type
self
.
place
=
paddle
.
XPUPlace
(
0
)
param
=
np
.
random
.
uniform
(
-
1
,
1
,
(
102
,
105
)).
astype
(
self
.
dtype
)
grad
=
np
.
random
.
uniform
(
-
1
,
1
,
(
102
,
105
)).
astype
(
self
.
dtype
)
# The squared gradient is positive
avg_squared_grad
=
np
.
random
.
random
((
102
,
105
)).
astype
(
self
.
dtype
)
# The squared update is positive
avg_squared_update
=
np
.
random
.
random
((
102
,
105
)).
astype
(
self
.
dtype
)
rho
=
0.95
epsilon
=
1e-6
self
.
inputs
=
{
'Param'
:
param
,
'Grad'
:
grad
,
'AvgSquaredGrad'
:
avg_squared_grad
,
'AvgSquaredUpdate'
:
avg_squared_update
,
}
avg_squared_grad_out
=
rho
*
avg_squared_grad
+
(
1
-
rho
)
*
np
.
square
(
grad
)
update
=
-
np
.
multiply
(
np
.
sqrt
(
np
.
divide
(
avg_squared_update
+
epsilon
,
avg_squared_grad_out
+
epsilon
,
)
),
grad
,
)
avg_squared_update_out
=
rho
*
avg_squared_update
+
(
1
-
rho
)
*
np
.
square
(
update
)
param_out
=
param
+
update
self
.
outputs
=
{
'ParamOut'
:
param_out
,
'AvgSquaredGradOut'
:
avg_squared_grad_out
,
'AvgSquaredUpdateOut'
:
avg_squared_update_out
,
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestAdadeltaV2
(
unittest
.
TestCase
):
def
test_adadelta_dygraph
(
self
):
self
.
dtype
=
self
.
in_type
self
.
place
=
paddle
.
XPUPlace
(
0
)
paddle
.
disable_static
(
self
.
place
)
value
=
np
.
arange
(
26
).
reshape
(
2
,
13
).
astype
(
self
.
dtype
)
a
=
paddle
.
to_tensor
(
value
)
linear
=
paddle
.
nn
.
Linear
(
13
,
5
)
# This can be any optimizer supported by dygraph.
adam
=
paddle
.
optimizer
.
Adadelta
(
learning_rate
=
0.01
,
parameters
=
linear
.
parameters
(),
weight_decay
=
0.01
,
)
out
=
linear
(
a
)
out
.
backward
()
adam
.
step
()
adam
.
clear_gradients
()
def
test_adadelta
(
self
):
self
.
dtype
=
self
.
in_type
paddle
.
enable_static
()
place
=
fluid
.
XPUPlace
(
0
)
main
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
):
x
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
13
],
dtype
=
self
.
dtype
)
y
=
fluid
.
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
dtype
=
self
.
dtype
)
y_predict
=
fluid
.
layers
.
fc
(
input
=
x
,
size
=
1
,
act
=
None
)
cost
=
fluid
.
layers
.
square_error_cost
(
input
=
y_predict
,
label
=
y
)
avg_cost
=
paddle
.
mean
(
cost
)
rms_optimizer
=
paddle
.
optimizer
.
Adadelta
(
learning_rate
=
0.1
)
rms_optimizer
.
minimize
(
avg_cost
)
fetch_list
=
[
avg_cost
]
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
uci_housing
.
train
(),
batch_size
=
1
)
feeder
=
fluid
.
DataFeeder
(
place
=
place
,
feed_list
=
[
x
,
y
])
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
fluid
.
default_startup_program
())
for
data
in
train_reader
():
exe
.
run
(
main
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
fetch_list
)
def
test_raise_error
(
self
):
self
.
assertRaises
(
ValueError
,
paddle
.
optimizer
.
Adadelta
,
None
)
self
.
assertRaises
(
ValueError
,
paddle
.
optimizer
.
Adadelta
,
learning_rate
=
0.1
,
rho
=
None
,
)
self
.
assertRaises
(
ValueError
,
paddle
.
optimizer
.
Adadelta
,
learning_rate
=
0.1
,
epsilon
=
None
,
)
class
TestAdadeltaV2Group
(
TestAdadeltaV2
):
def
test_adadelta_dygraph
(
self
):
self
.
dtype
=
self
.
in_type
self
.
place
=
paddle
.
XPUPlace
(
0
)
paddle
.
disable_static
(
self
.
place
)
value
=
np
.
arange
(
26
).
reshape
(
2
,
13
).
astype
(
self
.
dtype
)
a
=
paddle
.
to_tensor
(
value
)
linear_1
=
paddle
.
nn
.
Linear
(
13
,
5
)
linear_2
=
paddle
.
nn
.
Linear
(
5
,
5
)
# This can be any optimizer supported by dygraph.
adam
=
paddle
.
optimizer
.
Adadelta
(
learning_rate
=
0.01
,
parameters
=
[
{
'params'
:
linear_1
.
parameters
()},
{
'params'
:
linear_2
.
parameters
(),
'weight_decay'
:
0.001
,
},
],
weight_decay
=
0.1
,
)
out
=
linear_1
(
a
)
out
=
linear_2
(
out
)
out
.
backward
()
adam
.
step
()
adam
.
clear_gradients
()
support_types
=
get_xpu_op_support_types
(
'adadelta'
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestAdadelta
,
stype
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/xpu/test_clip_by_norm_op_xpu.py
浏览文件 @
047971f0
# Copyright (c) 202
0
PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 202
2
PaddlePaddle Authors. All Rights Reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# you may not use this file except in compliance with the License.
...
@@ -19,56 +19,68 @@ import unittest
...
@@ -19,56 +19,68 @@ import unittest
import
numpy
as
np
import
numpy
as
np
from
op_test_xpu
import
XPUOpTest
from
op_test_xpu
import
XPUOpTest
import
paddle
import
paddle
from
xpu.get_test_cover_info
import
(
create_test_class
,
get_xpu_op_support_types
,
XPUOpTestWrapper
,
)
class
TestXPUClipByNormOp
(
XPUOpTest
):
class
XPUTestClipByNormOp
(
XPUOpTestWrapper
):
def
setUp
(
self
):
def
__init__
(
self
):
self
.
op_type
=
"clip_by_norm"
self
.
op_name
=
'clip_by_norm'
self
.
dtype
=
np
.
float32
self
.
use_dynamic_create_class
=
False
self
.
use_xpu
=
True
self
.
max_relative_error
=
0.006
self
.
initTestCase
()
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
"float32"
)
input
[
np
.
abs
(
input
)
<
self
.
max_relative_error
]
=
0.5
self
.
inputs
=
{
'X'
:
input
,
}
self
.
attrs
=
{}
self
.
attrs
[
'max_norm'
]
=
self
.
max_norm
norm
=
np
.
sqrt
(
np
.
sum
(
np
.
square
(
input
)))
if
norm
>
self
.
max_norm
:
output
=
self
.
max_norm
*
input
/
norm
else
:
output
=
input
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
class
TestClipByNormOp
(
XPUOpTest
):
if
paddle
.
is_compiled_with_xpu
():
def
setUp
(
self
):
paddle
.
enable_static
()
self
.
op_type
=
"clip_by_norm"
place
=
paddle
.
XPUPlace
(
0
)
self
.
dtype
=
self
.
in_type
self
.
check_output_with_place
(
place
)
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
use_xpu
=
True
self
.
max_relative_error
=
0.006
self
.
initTestCase
()
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
input
[
np
.
abs
(
input
)
<
self
.
max_relative_error
]
=
0.5
self
.
inputs
=
{
'X'
:
input
,
}
self
.
attrs
=
{}
self
.
attrs
[
'max_norm'
]
=
self
.
max_norm
norm
=
np
.
sqrt
(
np
.
sum
(
np
.
square
(
input
)))
if
norm
>
self
.
max_norm
:
output
=
self
.
max_norm
*
input
/
norm
else
:
output
=
input
self
.
outputs
=
{
'Out'
:
output
}
def
initTestCase
(
self
):
def
test_check_output
(
self
):
self
.
shape
=
(
100
,)
if
paddle
.
is_compiled_with_xpu
():
self
.
max_norm
=
1.0
paddle
.
enable_static
()
self
.
check_output_with_place
(
self
.
place
)
def
initTestCase
(
self
):
self
.
shape
=
(
100
,)
self
.
max_norm
=
1.0
class
TestCase1
(
TestXPU
ClipByNormOp
):
class
TestCase1
(
Test
ClipByNormOp
):
def
initTestCase
(
self
):
def
initTestCase
(
self
):
self
.
shape
=
(
100
,)
self
.
shape
=
(
100
,)
self
.
max_norm
=
1e20
self
.
max_norm
=
1e20
class
TestCase2
(
TestClipByNormOp
):
def
initTestCase
(
self
):
self
.
shape
=
(
16
,
16
)
self
.
max_norm
=
0.1
class
TestCase2
(
TestXPU
ClipByNormOp
):
class
TestCase3
(
Test
ClipByNormOp
):
def
initTestCase
(
self
):
def
initTestCase
(
self
):
self
.
shape
=
(
16
,
16
)
self
.
shape
=
(
4
,
8
,
16
)
self
.
max_norm
=
0.1
self
.
max_norm
=
1.0
class
TestCase3
(
TestXPUClipByNormOp
):
support_types
=
get_xpu_op_support_types
(
'clip_by_norm'
)
def
initTestCase
(
self
):
for
stype
in
support_types
:
self
.
shape
=
(
4
,
8
,
16
)
create_test_class
(
globals
(),
XPUTestClipByNormOp
,
stype
)
self
.
max_norm
=
1.0
if
__name__
==
"__main__"
:
if
__name__
==
"__main__"
:
...
...
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