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acca0352
编写于
4月 27, 2022
作者:
Q
qipengh
提交者:
GitHub
4月 27, 2022
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
[MLU]add dropout op (#42274)
上级
89951472
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
535 addition
and
16 deletion
+535
-16
paddle/fluid/operators/dropout_op_mlu.cc
paddle/fluid/operators/dropout_op_mlu.cc
+165
-0
paddle/fluid/operators/mlu/mlu_baseop.cc
paddle/fluid/operators/mlu/mlu_baseop.cc
+80
-14
paddle/fluid/operators/mlu/mlu_baseop.h
paddle/fluid/operators/mlu/mlu_baseop.h
+17
-2
python/paddle/fluid/tests/unittests/mlu/test_dropout_op_mlu.py
...n/paddle/fluid/tests/unittests/mlu/test_dropout_op_mlu.py
+273
-0
未找到文件。
paddle/fluid/operators/dropout_op_mlu.cc
0 → 100644
浏览文件 @
acca0352
/* 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
=
framework
::
Tensor
;
template
<
typename
T
>
class
DropoutMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
auto
dropout_prob
=
ctx
.
Attr
<
float
>
(
"dropout_prob"
);
auto
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
auto
*
seed_tensor
=
ctx
.
HasInput
(
"Seed"
)
?
ctx
.
Input
<
Tensor
>
(
"Seed"
)
:
nullptr
;
auto
dropout_implementation
=
ctx
.
Attr
<
std
::
string
>
(
"dropout_implementation"
);
const
bool
is_upscale
=
(
dropout_implementation
==
"upscale_in_train"
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
MLUCnnlTensorDesc
x_desc
(
*
x
);
MLUCnnlTensorDesc
out_desc
(
*
out
);
if
(
!
is_test
)
{
// exec dropout op for training only.
int
seed_data
=
0
;
if
(
seed_tensor
)
{
if
(
platform
::
is_mlu_place
(
seed_tensor
->
place
()))
{
memory
::
Copy
(
platform
::
CPUPlace
(),
&
seed_data
,
seed_tensor
->
place
(),
seed_tensor
->
data
<
int
>
(),
sizeof
(
int
));
}
else
{
seed_data
=
*
(
seed_tensor
->
data
<
int
>
());
}
}
else
{
seed_data
=
ctx
.
Attr
<
bool
>
(
"fix_seed"
)
?
ctx
.
Attr
<
int
>
(
"seed"
)
:
0
;
}
auto
*
mask
=
ctx
.
Output
<
Tensor
>
(
"Mask"
);
mask
->
mutable_data
<
uint8_t
>
(
ctx
.
GetPlace
());
MLUCnnlTensorDesc
mask_desc
(
*
mask
);
// Special case when dropout_prob is 1.0
if
(
dropout_prob
==
1.0
f
)
{
auto
value_t
=
static_cast
<
T
>
(
0.0
f
);
MLUCnnl
::
Fill
(
ctx
,
CNNL_POINTER_MODE_HOST
,
&
value_t
,
out_desc
.
get
(),
GetBasePtr
(
out
));
MLUCnnl
::
Fill
(
ctx
,
CNNL_POINTER_MODE_HOST
,
&
value_t
,
mask_desc
.
get
(),
GetBasePtr
(
mask
));
return
;
}
// create mlu random generator
const
int
device_id
=
ctx
.
GetPlace
().
GetDeviceId
();
auto
mlu_gen_random
=
GetMLURandomGenerator
(
ctx
,
device_id
,
seed_data
);
const
float
prob
=
is_upscale
?
dropout_prob
:
0.0
f
;
MLUCnnl
::
FusedDropout
(
ctx
,
mlu_gen_random
->
get
(),
x_desc
.
get
(),
GetBasePtr
(
x
),
prob
,
GetBasePtr
(
&
(
mlu_gen_random
->
get_state
())),
mask_desc
.
get
(),
GetBasePtr
(
mask
),
out_desc
.
get
(),
GetBasePtr
(
out
));
}
else
{
// exec dropout op for inference only.
if
(
is_upscale
)
{
framework
::
TensorCopy
(
*
x
,
ctx
.
GetPlace
(),
ctx
.
template
device_context
<
platform
::
MLUDeviceContext
>(),
out
);
}
else
{
float
scale
=
static_cast
<
T
>
(
1.0
f
-
dropout_prob
);
Tensor
scale_tensor
(
x
->
dtype
());
scale_tensor
.
mutable_data
<
T
>
({
1
},
ctx
.
GetPlace
());
MLUCnnlTensorDesc
scale_desc
(
scale_tensor
);
MLUCnnl
::
Fill
(
ctx
,
CNNL_POINTER_MODE_HOST
,
&
scale
,
scale_desc
.
get
(),
GetBasePtr
(
&
scale_tensor
));
auto
data_type
=
ToCnnlDataType
<
T
>
();
MLUCnnlOpTensorDesc
op_tensor_desc
(
CNNL_OP_TENSOR_MUL
,
data_type
,
CNNL_NOT_PROPAGATE_NAN
);
MLUCnnl
::
OpTensor
(
ctx
,
op_tensor_desc
.
get
(),
x_desc
.
get
(),
GetBasePtr
(
x
),
scale_desc
.
get
(),
GetBasePtr
(
&
scale_tensor
),
out_desc
.
get
(),
GetBasePtr
(
out
),
data_type
);
}
}
}
};
template
<
typename
T
>
class
DropoutGradMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
!
ctx
.
Attr
<
bool
>
(
"is_test"
),
true
,
platform
::
errors
::
InvalidArgument
(
"GradOp is only callable when is_test is false"
));
auto
*
grad_x
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
grad_out
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
mask
=
ctx
.
Input
<
Tensor
>
(
"Mask"
);
auto
dropout_prob
=
ctx
.
Attr
<
float
>
(
"dropout_prob"
);
auto
dropout_impl
=
ctx
.
Attr
<
std
::
string
>
(
"dropout_implementation"
);
grad_x
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
MLUCnnlTensorDesc
grad_x_desc
(
*
grad_x
);
if
(
dropout_prob
==
1.
)
{
auto
value_t
=
static_cast
<
T
>
(
0.0
f
);
MLUCnnl
::
Fill
(
ctx
,
CNNL_POINTER_MODE_HOST
,
&
value_t
,
grad_x_desc
.
get
(),
GetBasePtr
(
grad_x
));
return
;
}
// cast mask from uint8 to float32/float16
Tensor
cast_mask
(
grad_x
->
dtype
());
cast_mask
.
Resize
(
mask
->
dims
());
cast_mask
.
mutable_data
<
T
>
(
ctx
.
GetPlace
());
MLUCnnlTensorDesc
mask_desc
(
*
mask
);
MLUCnnlTensorDesc
cast_mask_desc
(
cast_mask
);
cnnlCastDataType_t
cast_type
=
GetCastDataType
(
framework
::
TransToProtoVarType
(
mask
->
dtype
()),
framework
::
TransToProtoVarType
(
cast_mask
.
dtype
()));
MLUCnnl
::
Cast
(
ctx
,
cast_type
,
mask_desc
.
get
(),
GetBasePtr
(
mask
),
cast_mask_desc
.
get
(),
GetBasePtr
(
&
cast_mask
));
const
bool
is_upscale
=
(
dropout_impl
==
"upscale_in_train"
);
const
float
scale
=
is_upscale
?
(
1.0
f
/
(
1.0
f
-
dropout_prob
))
:
(
1.0
f
);
auto
data_type
=
ToCnnlDataType
<
T
>
();
MLUCnnlTensorDesc
grad_out_desc
(
*
grad_out
);
MLUCnnlOpTensorDesc
op_tensor_desc
(
CNNL_OP_TENSOR_MUL
,
data_type
,
CNNL_NOT_PROPAGATE_NAN
);
MLUCnnl
::
OpTensor
(
ctx
,
op_tensor_desc
.
get
(),
cast_mask_desc
.
get
(),
GetBasePtr
(
&
cast_mask
),
grad_out_desc
.
get
(),
GetBasePtr
(
grad_out
),
grad_x_desc
.
get
(),
GetBasePtr
(
grad_x
),
data_type
,
scale
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_MLU_KERNEL
(
dropout
,
ops
::
DropoutMLUKernel
<
float
>
,
ops
::
DropoutMLUKernel
<
plat
::
float16
>
);
REGISTER_OP_MLU_KERNEL
(
dropout_grad
,
ops
::
DropoutGradMLUKernel
<
float
>
,
ops
::
DropoutGradMLUKernel
<
plat
::
float16
>
);
paddle/fluid/operators/mlu/mlu_baseop.cc
浏览文件 @
acca0352
...
@@ -44,6 +44,32 @@ bool MLUSupportsCast(const VT::Type& src_type, const VT::Type& dst_type) {
...
@@ -44,6 +44,32 @@ bool MLUSupportsCast(const VT::Type& src_type, const VT::Type& dst_type) {
return
false
;
return
false
;
}
}
const
std
::
shared_ptr
<
MLUCnnlRandomGeneratorDesc
>&
GetMLURandomGenerator
(
const
ExecutionContext
&
ctx
,
const
int64_t
device_id
,
const
int
seed
)
{
static
int64_t
num_mlu_devices
=
-
1
;
static
std
::
once_flag
num_devices_init_flag
;
static
std
::
deque
<
std
::
once_flag
>
mlu_device_flags
;
static
std
::
vector
<
std
::
shared_ptr
<
MLUCnnlRandomGeneratorDesc
>>
mlu_rand_generators
;
std
::
call_once
(
num_devices_init_flag
,
[]()
{
num_mlu_devices
=
paddle
::
platform
::
GetMLUDeviceCount
();
mlu_device_flags
.
resize
(
num_mlu_devices
);
mlu_rand_generators
.
resize
(
num_mlu_devices
);
});
if
(
device_id
<
0
)
{
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"mlu device id shoule be greater than 0"
));
}
std
::
call_once
(
mlu_device_flags
[
device_id
],
[
&
]()
{
mlu_rand_generators
[
device_id
].
reset
(
new
MLUCnnlRandomGeneratorDesc
(
ctx
,
seed
));
VLOG
(
4
)
<<
"device_id: "
<<
device_id
<<
", initial seed: "
<<
seed
;
});
return
mlu_rand_generators
[
device_id
];
}
class
MLUCnnlTensorDescPool
{
class
MLUCnnlTensorDescPool
{
public:
public:
cnnlTensorDescriptor_t
Pop
()
{
cnnlTensorDescriptor_t
Pop
()
{
...
@@ -266,23 +292,32 @@ MLUCnnlPoolingDesc::~MLUCnnlPoolingDesc() {
...
@@ -266,23 +292,32 @@ MLUCnnlPoolingDesc::~MLUCnnlPoolingDesc() {
}
}
}
}
MLUCnnlRandomGeneratorDesc
::
MLUCnnlRandomGeneratorDesc
(
const
bool
is_mlu200
,
MLUCnnlRandomGeneratorDesc
::
MLUCnnlRandomGeneratorDesc
(
const
int
seed
)
{
const
ExecutionContext
&
ctx
,
const
int
seed
)
{
if
(
is_mlu200
)
{
PADDLE_ENFORCE_MLU_SUCCESS
(
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlRandCreateGenerator
(
&
mlu_generator
,
CNNL_RAND_RNG_MTGP32
));
cnnlRandCreateGenerator
(
&
mlu_generator
,
CNNL_RAND_RNG_FAST
));
PADDLE_ENFORCE_MLU_SUCCESS
(
}
else
{
cnnlRandSetPseudoRandomGeneratorSeed
(
mlu_generator
,
seed
));
PADDLE_ENFORCE_MLU_SUCCESS
(
size_t
workspace_size
;
cnnlRandCreateGenerator
(
&
mlu_generator
,
CNNL_RAND_RNG_MTGP32
));
PADDLE_ENFORCE_MLU_SUCCESS
(
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlRandGetMTGP32StateSize
(
mlu_generator
,
&
workspace_size
));
cnnlRandSetPseudoRandomGeneratorSeed
(
mlu_generator
,
seed
));
}
auto
&
dev_ctx
=
GetDevCtxFromCTX
(
ctx
);
mlu_state
=
ctx
.
AllocateTmpTensor
<
int8_t
,
MLUDeviceContext
>
(
{
static_cast
<
int64_t
>
(
workspace_size
)},
dev_ctx
);
void
*
mlu_state_ptr
=
mlu_state
.
mutable_data
(
ctx
.
GetPlace
());
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlRandMakeMTGP32KernelState
(
handle
,
mlu_state_ptr
,
nullptr
,
nullptr
,
seed
));
}
}
const
cnnlRandGenerator_t
MLUCnnlRandomGeneratorDesc
::
get
()
const
{
const
cnnlRandGenerator_t
MLUCnnlRandomGeneratorDesc
::
get
()
const
{
return
mlu_generator
;
return
mlu_generator
;
}
}
Tensor
&
MLUCnnlRandomGeneratorDesc
::
get_state
()
{
return
mlu_state
;
}
MLUCnnlRandomGeneratorDesc
::~
MLUCnnlRandomGeneratorDesc
()
{
MLUCnnlRandomGeneratorDesc
::~
MLUCnnlRandomGeneratorDesc
()
{
if
(
mlu_generator
)
{
if
(
mlu_generator
)
{
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlRandDestroyGenerator
(
mlu_generator
));
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlRandDestroyGenerator
(
mlu_generator
));
...
@@ -947,6 +982,26 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
...
@@ -947,6 +982,26 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
workspace_ptr
,
workspace_size
,
beta_ptr
,
output_desc
,
output
));
workspace_ptr
,
workspace_size
,
beta_ptr
,
output_desc
,
output
));
}
}
/* static */
void
MLUCnnl
::
MulAx
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
alpha_desc
,
const
void
*
alpha
,
const
cnnlTensorDescriptor_t
output_desc
,
void
*
output
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
size_t
workspace_size
;
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlGetAxWorkspaceSize
(
handle
,
alpha_desc
,
output_desc
,
&
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
(
cnnlAx_v2
(
handle
,
alpha_desc
,
alpha
,
output_desc
,
output
,
workspace_ptr
,
workspace_size
));
}
/* static */
void
MLUCnnl
::
BiasAddGrad
(
/* static */
void
MLUCnnl
::
BiasAddGrad
(
const
ExecutionContext
&
ctx
,
const
int
axis
,
const
ExecutionContext
&
ctx
,
const
int
axis
,
const
cnnlTensorDescriptor_t
out_backprop_desc
,
const
void
*
out_backprop
,
const
cnnlTensorDescriptor_t
out_backprop_desc
,
const
void
*
out_backprop
,
...
@@ -959,12 +1014,23 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
...
@@ -959,12 +1014,23 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
/* static */
void
MLUCnnl
::
RandomUniform
(
/* static */
void
MLUCnnl
::
RandomUniform
(
const
ExecutionContext
&
ctx
,
const
int
num
,
const
cnnlDataType_t
data_type
,
const
ExecutionContext
&
ctx
,
const
int
num
,
const
cnnlDataType_t
data_type
,
const
cnnlRandGenerator_t
mlu_generator
,
const
float
min
,
const
float
max
,
const
cnnlRandGenerator_t
mlu_generator
,
void
*
mlu_state
,
void
*
output
)
{
void
*
output
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlRandGenerateUniform
(
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlRandGenerateUniform
(
handle
,
mlu_generator
,
data_type
,
nullptr
,
num
,
min
,
max
,
output
));
handle
,
mlu_generator
,
data_type
,
mlu_state
,
num
,
0
,
1
,
output
));
}
/* static */
void
MLUCnnl
::
FusedDropout
(
const
ExecutionContext
&
ctx
,
const
cnnlRandGenerator_t
generator
,
const
cnnlTensorDescriptor_t
input_desc
,
const
void
*
input
,
const
float
p
,
void
*
state
,
const
cnnlTensorDescriptor_t
mask_desc
,
const
void
*
mask
,
const
cnnlTensorDescriptor_t
output_desc
,
void
*
output
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlFusedDropout_v2
(
handle
,
generator
,
input_desc
,
input
,
p
,
state
,
mask_desc
,
mask
,
output_desc
,
output
));
}
}
/* static */
void
MLUCnnl
::
TopK
(
/* static */
void
MLUCnnl
::
TopK
(
...
...
paddle/fluid/operators/mlu/mlu_baseop.h
浏览文件 @
acca0352
...
@@ -273,14 +273,19 @@ class MLUCnnlPoolingDesc {
...
@@ -273,14 +273,19 @@ class MLUCnnlPoolingDesc {
class
MLUCnnlRandomGeneratorDesc
{
class
MLUCnnlRandomGeneratorDesc
{
public:
public:
MLUCnnlRandomGeneratorDesc
(
const
bool
is_mlu200
,
const
int
seed
);
MLUCnnlRandomGeneratorDesc
(
const
ExecutionContext
&
ctx
,
const
int
seed
);
const
cnnlRandGenerator_t
get
()
const
;
const
cnnlRandGenerator_t
get
()
const
;
Tensor
&
get_state
();
~
MLUCnnlRandomGeneratorDesc
();
~
MLUCnnlRandomGeneratorDesc
();
private:
private:
Tensor
mlu_state
;
cnnlRandGenerator_t
mlu_generator
=
nullptr
;
cnnlRandGenerator_t
mlu_generator
=
nullptr
;
};
};
const
std
::
shared_ptr
<
MLUCnnlRandomGeneratorDesc
>&
GetMLURandomGenerator
(
const
ExecutionContext
&
ctx
,
const
int64_t
device_id
,
const
int
seed
);
class
MLUCnnlReduceDesc
{
class
MLUCnnlReduceDesc
{
public:
public:
MLUCnnlReduceDesc
(
const
MLUCnnlReduceDesc
&
desc
)
=
delete
;
MLUCnnlReduceDesc
(
const
MLUCnnlReduceDesc
&
desc
)
=
delete
;
...
@@ -537,7 +542,13 @@ class MLUCnnl {
...
@@ -537,7 +542,13 @@ class MLUCnnl {
static
void
RandomUniform
(
const
ExecutionContext
&
ctx
,
const
int
num
,
static
void
RandomUniform
(
const
ExecutionContext
&
ctx
,
const
int
num
,
const
cnnlDataType_t
data_type
,
const
cnnlDataType_t
data_type
,
const
cnnlRandGenerator_t
mlu_generator
,
const
cnnlRandGenerator_t
mlu_generator
,
const
float
min
,
const
float
max
,
void
*
output
);
void
*
mlu_state
,
void
*
output
);
static
void
FusedDropout
(
const
ExecutionContext
&
ctx
,
const
cnnlRandGenerator_t
generator
,
const
cnnlTensorDescriptor_t
input_desc
,
const
void
*
input
,
const
float
p
,
void
*
state
,
const
cnnlTensorDescriptor_t
mask_desc
,
const
void
*
mask
,
const
cnnlTensorDescriptor_t
output_desc
,
void
*
output
);
static
void
Cumsum
(
const
ExecutionContext
&
ctx
,
const
int
axis
,
static
void
Cumsum
(
const
ExecutionContext
&
ctx
,
const
int
axis
,
const
bool
exclusive
,
const
bool
reverse
,
const
bool
exclusive
,
const
bool
reverse
,
...
@@ -709,6 +720,10 @@ class MLUCnnl {
...
@@ -709,6 +720,10 @@ class MLUCnnl {
const
void
*
in0
,
const
cnnlTensorDescriptor_t
in1_desc
,
const
void
*
in1
,
const
void
*
in0
,
const
cnnlTensorDescriptor_t
in1_desc
,
const
void
*
in1
,
const
cnnlTensorDescriptor_t
output_desc
,
void
*
output
);
const
cnnlTensorDescriptor_t
output_desc
,
void
*
output
);
static
void
MulAx
(
const
ExecutionContext
&
ctx
,
const
cnnlTensorDescriptor_t
alpha_desc
,
const
void
*
alpha
,
const
cnnlTensorDescriptor_t
output_desc
,
void
*
output
);
static
void
OpTensor
(
const
ExecutionContext
&
ctx
,
static
void
OpTensor
(
const
ExecutionContext
&
ctx
,
const
cnnlOpTensorDescriptor_t
op_tensor_desc
,
const
cnnlOpTensorDescriptor_t
op_tensor_desc
,
const
cnnlTensorDescriptor_t
a_desc
,
const
void
*
a
,
const
cnnlTensorDescriptor_t
a_desc
,
const
void
*
a
,
...
...
python/paddle/fluid/tests/unittests/mlu/test_dropout_op_mlu.py
0 → 100644
浏览文件 @
acca0352
# 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
numpy
as
np
import
unittest
import
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
,
skip_check_grad_ci
import
paddle
import
paddle.fluid
as
fluid
paddle
.
enable_static
()
SEED
=
2022
class
TestDropoutOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
set_mlu
()
self
.
init_dtype
()
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
64
)).
astype
(
self
.
dtype
)}
self
.
attrs
=
{
'dropout_prob'
:
0.0
,
'fix_seed'
:
True
,
'is_test'
:
False
,
'dropout_implementation'
:
'upscale_in_train'
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
],
'Mask'
:
np
.
ones
((
32
,
64
)).
astype
(
'uint8'
)
}
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float32
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad_normal
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'X'
],
'Out'
)
class
TestDropoutOpInput1d
(
TestDropoutOp
):
# change input shape
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
set_mlu
()
self
.
init_dtype
()
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
3
,
62
)).
astype
(
self
.
dtype
)}
self
.
attrs
=
{
'dropout_prob'
:
0.0
,
'fix_seed'
:
True
,
'is_test'
:
False
,
'dropout_implementation'
:
'upscale_in_train'
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
],
'Mask'
:
np
.
ones
((
3
,
62
)).
astype
(
'uint8'
)
}
class
TestDropoutOpInput1d_1
(
TestDropoutOp
):
# the input is 1-D
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
set_mlu
()
self
.
init_dtype
()
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
2000
)).
astype
(
self
.
dtype
)}
self
.
attrs
=
{
'dropout_prob'
:
0.0
,
'fix_seed'
:
True
,
'is_test'
:
False
,
'dropout_implementation'
:
'upscale_in_train'
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
],
'Mask'
:
np
.
ones
((
2000
)).
astype
(
'uint8'
)
}
class
TestDropoutOp2
(
TestDropoutOp
):
# the dropout_prob is 1.0
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
set_mlu
()
self
.
init_dtype
()
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
64
)).
astype
(
self
.
dtype
)}
self
.
attrs
=
{
'dropout_prob'
:
1.0
,
'fix_seed'
:
True
,
'is_test'
:
False
,
'dropout_implementation'
:
'upscale_in_train'
}
self
.
outputs
=
{
'Out'
:
np
.
zeros
((
32
,
64
)).
astype
(
'float32'
),
'Mask'
:
np
.
zeros
((
32
,
64
)).
astype
(
'uint8'
)
}
class
TestDropoutOp3
(
TestDropoutOp
):
# the input dim is 3
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
set_mlu
()
self
.
init_dtype
()
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
64
,
2
)).
astype
(
self
.
dtype
)}
self
.
attrs
=
{
'dropout_prob'
:
0.0
,
'fix_seed'
:
True
,
'is_test'
:
False
,
'dropout_implementation'
:
'upscale_in_train'
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
],
'Mask'
:
np
.
ones
((
32
,
64
,
2
)).
astype
(
'uint8'
)
}
@
skip_check_grad_ci
(
reason
=
"For inference, check_grad is not required."
)
class
TestDropoutOpInference
(
OpTest
):
# is_test = True
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
set_mlu
()
self
.
init_dtype
()
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
64
)).
astype
(
self
.
dtype
)}
self
.
attrs
=
{
'dropout_prob'
:
0.35
,
'fix_seed'
:
True
,
'is_test'
:
True
,
'dropout_implementation'
:
'upscale_in_train'
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
]}
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float32
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
@
skip_check_grad_ci
(
reason
=
"For inference, check_grad is not required."
)
class
TestDropoutOpInference2
(
TestDropoutOpInference
):
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
set_mlu
()
self
.
init_dtype
()
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
64
,
3
)).
astype
(
self
.
dtype
)}
self
.
attrs
=
{
'dropout_prob'
:
0.75
,
'is_test'
:
True
,
'dropout_implementation'
:
'upscale_in_train'
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
]}
class
TestDropoutOpWithSeed
(
TestDropoutOp
):
# the seed is a Tensor
def
setUp
(
self
):
self
.
op_type
=
"dropout"
self
.
set_mlu
()
self
.
init_dtype
()
self
.
inputs
=
{
"X"
:
np
.
random
.
random
((
32
,
64
)).
astype
(
self
.
dtype
),
"Seed"
:
np
.
asarray
(
[
125
],
dtype
=
"int32"
)
}
self
.
attrs
=
{
'dropout_prob'
:
0.0
,
'is_test'
:
False
,
'dropout_implementation'
:
'upscale_in_train'
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
],
'Mask'
:
np
.
ones
((
32
,
64
)).
astype
(
'uint8'
)
}
class
TestDropoutOpFp16
(
TestDropoutOp
):
# float16
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float16
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
no_need_check_grad
=
True
class
TestDropoutAPI
(
unittest
.
TestCase
):
def
setUp
(
self
):
np
.
random
.
seed
(
123
)
self
.
places
=
[
fluid
.
CPUPlace
(),
paddle
.
device
.
MLUPlace
(
0
)]
def
check_static_result
(
self
,
place
):
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
input
=
fluid
.
data
(
name
=
"input"
,
shape
=
[
40
,
40
],
dtype
=
"float32"
)
res1
=
paddle
.
nn
.
functional
.
dropout
(
x
=
input
,
p
=
0.
,
training
=
False
,
mode
=
'upscale_in_train'
)
res2
=
paddle
.
nn
.
functional
.
dropout
(
x
=
input
,
p
=
0.
,
axis
=
0
,
training
=
True
,
mode
=
'upscale_in_train'
)
res3
=
paddle
.
nn
.
functional
.
dropout
(
x
=
input
,
p
=
0.
,
axis
=
0
,
training
=
False
,
mode
=
'upscale_in_train'
)
res4
=
paddle
.
nn
.
functional
.
dropout
(
x
=
input
,
p
=
0.
,
axis
=
[
0
,
1
],
training
=
True
,
mode
=
'upscale_in_train'
)
res5
=
paddle
.
nn
.
functional
.
dropout
(
x
=
input
,
p
=
0.
,
axis
=
[
0
,
1
],
training
=
False
,
mode
=
'upscale_in_train'
)
res6
=
paddle
.
nn
.
functional
.
dropout
(
x
=
input
,
p
=
1.
,
training
=
True
,
mode
=
'upscale_in_train'
)
res7
=
paddle
.
fluid
.
layers
.
dropout
(
x
=
input
,
dropout_prob
=
0.
,
dropout_implementation
=
'upscale_in_train'
)
res8
=
paddle
.
nn
.
functional
.
dropout
(
x
=
input
,
p
=
0.
,
axis
=
(
0
,
1
),
training
=
False
,
mode
=
'upscale_in_train'
)
in_np
=
np
.
random
.
random
([
40
,
40
]).
astype
(
"float32"
)
res_np
=
in_np
res_np2
=
np
.
zeros_like
(
in_np
)
exe
=
fluid
.
Executor
(
place
)
res_list
=
[
res1
,
res2
,
res3
,
res4
,
res5
,
res7
,
res8
]
for
res
in
res_list
:
fetches
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"input"
:
in_np
},
fetch_list
=
[
res
])
self
.
assertTrue
(
np
.
allclose
(
fetches
[
0
],
res_np
))
fetches2
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"input"
:
in_np
},
fetch_list
=
[
res6
])
self
.
assertTrue
(
np
.
allclose
(
fetches2
[
0
],
res_np2
))
def
test_static
(
self
):
for
place
in
self
.
places
:
self
.
check_static_result
(
place
=
place
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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