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体验新版 GitCode,发现更多精彩内容 >>
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cb3bbbd5
编写于
3月 26, 2018
作者:
T
Tao Luo
提交者:
GitHub
3月 26, 2018
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差异文件
Merge pull request #9081 from kbinias/kbinias/mkldnn-activations
MKLDNN Relu Tanh Sqrt Abs activations added
上级
8ccc61f3
6461e800
变更
7
显示空白变更内容
内联
并排
Showing
7 changed file
with
387 addition
and
10 deletion
+387
-10
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+5
-0
paddle/fluid/operators/activation_mkldnn_op.cc
paddle/fluid/operators/activation_mkldnn_op.cc
+193
-0
paddle/fluid/operators/activation_op.cc
paddle/fluid/operators/activation_op.cc
+22
-9
paddle/fluid/operators/activation_op.h
paddle/fluid/operators/activation_op.h
+5
-1
paddle/fluid/operators/mkldnn_activation_op.h
paddle/fluid/operators/mkldnn_activation_op.h
+111
-0
python/paddle/fluid/layer_helper.py
python/paddle/fluid/layer_helper.py
+2
-0
python/paddle/fluid/tests/unittests/test_activation_op.py
python/paddle/fluid/tests/unittests/test_activation_op.py
+49
-0
未找到文件。
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
cb3bbbd5
...
...
@@ -153,8 +153,13 @@ function(op_library TARGET)
# pybind USE_OP_DEVICE_KERNEL for MKLDNN
if
(
WITH_MKLDNN AND
${
mkldnn_cc_srcs_len
}
GREATER 0
)
# Append first implemented MKLDNN activation operator
if
(
${
MKLDNN_FILE
}
STREQUAL
"activation_mkldnn_op"
)
file
(
APPEND
${
pybind_file
}
"USE_OP_DEVICE_KERNEL(relu, MKLDNN);
\n
"
)
else
()
file
(
APPEND
${
pybind_file
}
"USE_OP_DEVICE_KERNEL(
${
TARGET
}
, MKLDNN);
\n
"
)
endif
()
endif
()
# pybind USE_OP
if
(
${
pybind_flag
}
EQUAL 0
)
...
...
paddle/fluid/operators/activation_mkldnn_op.cc
0 → 100644
浏览文件 @
cb3bbbd5
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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 "mkldnn.hpp"
#include "mkldnn_activation_op.h"
#include "paddle/fluid/operators/activation_op.h"
namespace
paddle
{
namespace
operators
{
using
paddle
::
framework
::
Tensor
;
using
paddle
::
platform
::
MKLDNNDeviceContext
;
namespace
{
template
<
typename
T
,
typename
ExecContext
>
void
eltwise_forward
(
const
ExecContext
&
ctx
,
mkldnn
::
algorithm
algorithm
,
const
T
alpha
=
0
,
const
T
beta
=
0
)
{
PADDLE_ENFORCE
(
paddle
::
platform
::
is_cpu_place
(
ctx
.
GetPlace
()),
"It must use CPUPlace."
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
MKLDNNDeviceContext
>();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
// get buffers
const
auto
*
src
=
ctx
.
template
Input
<
Tensor
>(
"X"
);
const
auto
*
src_data
=
src
->
template
data
<
T
>();
auto
*
dst
=
ctx
.
template
Output
<
Tensor
>(
"Out"
);
const
T
*
dst_data
=
dst
->
template
mutable_data
<
T
>(
ctx
.
GetPlace
());
// get memory dim
PADDLE_ENFORCE
(
src
->
dims
().
size
()
==
4
,
"Input dim must be with 4, i.e. NCHW"
);
std
::
vector
<
int
>
src_tz
=
framework
::
vectorize2int
(
src
->
dims
());
// create memory description
// TODO(kbinias-intel): support more formats
auto
data_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
mkldnn
::
memory
::
f32
,
mkldnn
::
memory
::
format
::
nchw
);
// create memory primitives
auto
src_memory
=
mkldnn
::
memory
({
data_md
,
mkldnn_engine
},
(
void
*
)
src_data
);
auto
dst_memory
=
mkldnn
::
memory
({
data_md
,
mkldnn_engine
},
(
void
*
)
dst_data
);
auto
forward_desc
=
mkldnn
::
eltwise_forward
::
desc
(
mkldnn
::
prop_kind
::
forward_training
,
algorithm
,
data_md
,
alpha
,
beta
);
// save prim desc into global device context to be referred in backward path
const
std
::
string
key
=
ctx
.
op
().
Output
(
"Out"
);
const
std
::
string
key_eltwise_pd
=
key
+
"@eltwise_pd"
;
auto
forward_pd
=
std
::
make_shared
<
mkldnn
::
eltwise_forward
::
primitive_desc
>
(
forward_desc
,
mkldnn_engine
);
dev_ctx
.
SetBlob
(
key_eltwise_pd
,
forward_pd
);
auto
eltwise
=
mkldnn
::
eltwise_forward
(
*
forward_pd
,
src_memory
,
dst_memory
);
// push primitive to stream and wait until it's executed
std
::
vector
<
mkldnn
::
primitive
>
pipeline
=
{
eltwise
};
mkldnn
::
stream
(
mkldnn
::
stream
::
kind
::
eager
).
submit
(
pipeline
).
wait
();
}
template
<
typename
T
,
typename
ExecContext
>
void
eltwise_grad
(
const
ExecContext
&
ctx
,
mkldnn
::
algorithm
algorithm
,
const
T
alpha
=
0
,
const
T
beta
=
0
)
{
auto
&
dev_ctx
=
ctx
.
template
device_context
<
MKLDNNDeviceContext
>();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
// get buffers
const
auto
*
x
=
ctx
.
template
Input
<
Tensor
>(
"X"
);
const
auto
*
src
=
x
->
template
data
<
T
>();
auto
*
dout
=
ctx
.
template
Input
<
Tensor
>(
framework
::
GradVarName
(
"Out"
));
const
auto
*
diff_dst
=
dout
->
template
data
<
T
>();
auto
*
dx
=
ctx
.
template
Output
<
framework
::
Tensor
>(
framework
::
GradVarName
(
"X"
));
const
T
*
diff_src
=
dx
->
template
mutable_data
<
T
>(
ctx
.
GetPlace
());
// get memory dim
std
::
vector
<
int
>
src_tz
=
framework
::
vectorize2int
(
x
->
dims
());
// create memory description
auto
data_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
mkldnn
::
memory
::
f32
,
mkldnn
::
memory
::
format
::
nchw
);
// create memory primitives
auto
src_memory
=
mkldnn
::
memory
({
data_md
,
mkldnn_engine
},
(
void
*
)
src
);
auto
diff_src_memory
=
mkldnn
::
memory
({
data_md
,
mkldnn_engine
},
(
void
*
)
diff_src
);
auto
diff_dst_memory
=
mkldnn
::
memory
({
data_md
,
mkldnn_engine
},
(
void
*
)
diff_dst
);
auto
backward_desc
=
mkldnn
::
eltwise_backward
::
desc
(
algorithm
,
data_md
,
data_md
,
alpha
,
beta
);
// retrieve eltwise primitive desc from device context
const
std
::
string
key
=
ctx
.
op
().
Input
(
"Out"
);
const
std
::
string
key_eltwise_pd
=
key
+
"@eltwise_pd"
;
const
std
::
shared_ptr
<
void
>
forward_pd
=
dev_ctx
.
GetBlob
(
key_eltwise_pd
);
PADDLE_ENFORCE
(
forward_pd
!=
nullptr
,
"Fail to find eltwise_pd in device context"
);
auto
*
p_forward_pd
=
static_cast
<
mkldnn
::
eltwise_forward
::
primitive_desc
*>
(
forward_pd
.
get
());
auto
eltwise_bwd_prim_desc
=
mkldnn
::
eltwise_backward
::
primitive_desc
(
backward_desc
,
mkldnn_engine
,
*
p_forward_pd
);
auto
eltwise_bwd
=
mkldnn
::
eltwise_backward
(
eltwise_bwd_prim_desc
,
src_memory
,
diff_dst_memory
,
diff_src_memory
);
// push primitive to stream and wait until it's executed
std
::
vector
<
mkldnn
::
primitive
>
pipeline
=
{
eltwise_bwd
};
mkldnn
::
stream
(
mkldnn
::
stream
::
kind
::
eager
).
submit
(
pipeline
).
wait
();
}
}
// anonymous namespace
template
<
typename
T
,
mkldnn
::
algorithm
algorithm
>
struct
MKLDNNActivationFunc
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
ExecContext
>
void
operator
()(
const
ExecContext
&
ctx
)
const
{
eltwise_forward
<
T
>
(
ctx
,
algorithm
);
}
};
template
<
typename
T
,
mkldnn
::
algorithm
algorithm
>
struct
MKLDNNActivationGradFunc
:
public
BaseActivationFunctor
<
T
>
{
template
<
typename
ExecContext
>
void
operator
()(
const
ExecContext
&
ctx
)
const
{
eltwise_grad
<
T
>
(
ctx
,
algorithm
);
}
};
template
<
typename
T
>
using
ReluMkldnnFunctor
=
MKLDNNActivationFunc
<
T
,
mkldnn
::
algorithm
::
eltwise_relu
>
;
template
<
typename
T
>
using
TanhMkldnnFunctor
=
MKLDNNActivationFunc
<
T
,
mkldnn
::
algorithm
::
eltwise_tanh
>
;
template
<
typename
T
>
using
SqrtMkldnnFunctor
=
MKLDNNActivationFunc
<
T
,
mkldnn
::
algorithm
::
eltwise_sqrt
>
;
template
<
typename
T
>
using
AbsMkldnnFunctor
=
MKLDNNActivationFunc
<
T
,
mkldnn
::
algorithm
::
eltwise_abs
>
;
template
<
typename
T
>
using
ReluMkldnnGradFunctor
=
MKLDNNActivationGradFunc
<
T
,
mkldnn
::
algorithm
::
eltwise_relu
>
;
template
<
typename
T
>
using
TanhMkldnnGradFunctor
=
MKLDNNActivationGradFunc
<
T
,
mkldnn
::
algorithm
::
eltwise_tanh
>
;
template
<
typename
T
>
using
SqrtMkldnnGradFunctor
=
MKLDNNActivationGradFunc
<
T
,
mkldnn
::
algorithm
::
eltwise_sqrt
>
;
template
<
typename
T
>
using
AbsMkldnnGradFunctor
=
MKLDNNActivationGradFunc
<
T
,
mkldnn
::
algorithm
::
eltwise_abs
>
;
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
#define REGISTER_ACTIVATION_MKLDNN_KERNEL(act_type, functor, grad_functor) \
REGISTER_OP_KERNEL(act_type, MKLDNN, ::paddle::platform::CPUPlace, \
ops::MKLDNNActivationKernel<ops::functor<float>>); \
REGISTER_OP_KERNEL( \
act_type##_grad, MKLDNN, ::paddle::platform::CPUPlace, \
ops::MKLDNNActivationGradKernel<ops::grad_functor<float>>);
#define FOR_EACH_MKLDNN_KERNEL_FUNCTOR(__macro) \
__macro(relu, ReluMkldnnFunctor, ReluMkldnnGradFunctor); \
__macro(tanh, TanhMkldnnFunctor, TanhMkldnnGradFunctor); \
__macro(sqrt, SqrtMkldnnFunctor, SqrtMkldnnGradFunctor); \
__macro(abs, AbsMkldnnFunctor, AbsMkldnnGradFunctor);
FOR_EACH_MKLDNN_KERNEL_FUNCTOR
(
REGISTER_ACTIVATION_MKLDNN_KERNEL
);
paddle/fluid/operators/activation_op.cc
浏览文件 @
cb3bbbd5
/* Copyright (c) 201
6
PaddlePaddle Authors. All Rights Reserved.
/* Copyright (c) 201
8
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.
...
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/mkldnn_activation_op.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -87,6 +88,9 @@ class ReluOpMaker : public framework::OpProtoAndCheckerMaker {
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Relu operator"
);
AddOutput
(
"Out"
,
"Output of Relu operator"
);
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
Relu Activation Operator.
...
...
@@ -140,6 +144,9 @@ class TanhOpMaker : public framework::OpProtoAndCheckerMaker {
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Tanh operator"
);
AddOutput
(
"Out"
,
"Output of Tanh operator"
);
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
Tanh Activation Operator.
...
...
@@ -193,6 +200,9 @@ class SqrtOpMaker : public framework::OpProtoAndCheckerMaker {
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Sqrt operator"
);
AddOutput
(
"Out"
,
"Output of Sqrt operator"
);
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
Sqrt Activation Operator.
...
...
@@ -208,6 +218,9 @@ class AbsOpMaker : public framework::OpProtoAndCheckerMaker {
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Abs operator"
);
AddOutput
(
"Out"
,
"Output of Abs operator"
);
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
Abs Activation Operator.
...
...
@@ -524,11 +537,11 @@ REGISTER_OP(logsigmoid, ops::ActivationOp, ops::LogSigmoidOpMaker,
REGISTER_OP
(
exp
,
ops
::
ActivationOp
,
ops
::
ExpOpMaker
,
exp_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
relu
,
ops
::
ActivationOp
,
ops
::
ReluOpMaker
,
relu_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
relu
,
ops
::
Activation
WithMKLDNN
Op
,
ops
::
ReluOpMaker
,
relu_grad
,
ops
::
Activation
WithMKLDNN
OpGrad
);
REGISTER_OP
(
tanh
,
ops
::
ActivationOp
,
ops
::
TanhOpMaker
,
tanh_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
tanh
,
ops
::
Activation
WithMKLDNN
Op
,
ops
::
TanhOpMaker
,
tanh_grad
,
ops
::
Activation
WithMKLDNN
OpGrad
);
REGISTER_OP
(
tanh_shrink
,
ops
::
ActivationOp
,
ops
::
TanhShrinkOpMaker
,
tanh_shrink_grad
,
ops
::
ActivationOpGrad
);
...
...
@@ -536,11 +549,11 @@ REGISTER_OP(tanh_shrink, ops::ActivationOp, ops::TanhShrinkOpMaker,
REGISTER_OP
(
softshrink
,
ops
::
ActivationOp
,
ops
::
SoftShrinkOpMaker
,
softshrink_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
sqrt
,
ops
::
ActivationOp
,
ops
::
SqrtOpMaker
,
sqrt_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
sqrt
,
ops
::
Activation
WithMKLDNN
Op
,
ops
::
SqrtOpMaker
,
sqrt_grad
,
ops
::
Activation
WithMKLDNN
OpGrad
);
REGISTER_OP
(
abs
,
ops
::
ActivationOp
,
ops
::
AbsOpMaker
,
abs_grad
,
ops
::
ActivationOpGrad
);
REGISTER_OP
(
abs
,
ops
::
Activation
WithMKLDNN
Op
,
ops
::
AbsOpMaker
,
abs_grad
,
ops
::
Activation
WithMKLDNN
OpGrad
);
REGISTER_OP
(
ceil
,
ops
::
ActivationOp
,
ops
::
CeilOpMaker
,
ceil_grad
,
ops
::
ActivationOpGrad
);
...
...
paddle/fluid/operators/activation_op.h
浏览文件 @
cb3bbbd5
/* Copyright (c) 201
6
PaddlePaddle Authors. All Rights Reserved.
/* Copyright (c) 201
8
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.
...
...
@@ -17,6 +17,10 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace
paddle
{
namespace
operators
{
...
...
paddle/fluid/operators/mkldnn_activation_op.h
0 → 100644
浏览文件 @
cb3bbbd5
/* Copyright (c) 2018 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. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace
paddle
{
namespace
operators
{
template
<
typename
Functor
>
class
MKLDNNActivationKernel
:
public
framework
::
OpKernel
<
typename
Functor
::
ELEMENT_TYPE
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
PADDLE_ENFORCE
(
context
.
Input
<
framework
::
Tensor
>
(
"X"
)
!=
nullptr
,
"Cannot get input tensor X, variable name = %s"
,
context
.
op
().
Input
(
"X"
));
PADDLE_ENFORCE
(
context
.
Output
<
framework
::
Tensor
>
(
"Out"
)
!=
nullptr
,
"Cannot find output tensor Out, variable name = %s"
,
context
.
op
().
Output
(
"Out"
));
Functor
functor
;
auto
attrs
=
functor
.
GetAttrs
();
for
(
auto
&
attr
:
attrs
)
{
*
attr
.
second
=
context
.
Attr
<
float
>
(
attr
.
first
);
}
functor
(
context
);
}
};
template
<
typename
Functor
>
class
MKLDNNActivationGradKernel
:
public
framework
::
OpKernel
<
typename
Functor
::
ELEMENT_TYPE
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
Functor
functor
;
auto
attrs
=
functor
.
GetAttrs
();
for
(
auto
&
attr
:
attrs
)
{
*
attr
.
second
=
context
.
Attr
<
float
>
(
attr
.
first
);
}
functor
(
context
);
}
};
namespace
{
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
,
const
framework
::
OperatorWithKernel
&
oper
)
{
framework
::
LibraryType
library
{
framework
::
LibraryType
::
kPlain
};
#ifdef PADDLE_WITH_MKLDNN
if
(
library
==
framework
::
LibraryType
::
kPlain
&&
platform
::
CanMKLDNNBeUsed
(
ctx
))
{
library
=
framework
::
LibraryType
::
kMKLDNN
;
}
#endif
framework
::
DataLayout
layout
=
framework
::
DataLayout
::
kAnyLayout
;
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
type
()),
ctx
.
GetPlace
(),
layout
,
library
);
}
}
// anonymous namespace
class
ActivationWithMKLDNNOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
ctx
->
SetOutputDim
(
"Out"
,
ctx
->
GetInputDim
(
"X"
));
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
GetKernelType
(
ctx
,
*
this
);
}
};
class
ActivationWithMKLDNNOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"Out"
));
}
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
GetKernelType
(
ctx
,
*
this
);
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/layer_helper.py
浏览文件 @
cb3bbbd5
...
...
@@ -403,6 +403,8 @@ class LayerHelper(object):
if
'use_mkldnn'
in
self
.
kwargs
:
act
[
'use_mkldnn'
]
=
self
.
kwargs
.
get
(
'use_mkldnn'
)
act_type
=
act
.
pop
(
'type'
)
if
'use_mkldnn'
in
self
.
kwargs
:
act
[
'use_mkldnn'
]
=
self
.
kwargs
.
get
(
'use_mkldnn'
)
self
.
append_op
(
type
=
act_type
,
inputs
=
{
"X"
:
[
input_var
]},
...
...
python/paddle/fluid/tests/unittests/test_activation_op.py
浏览文件 @
cb3bbbd5
...
...
@@ -506,5 +506,54 @@ class TestSwish(OpTest):
self
.
check_grad
([
'X'
],
'Out'
,
max_relative_error
=
0.008
)
#--------------------test MKLDNN--------------------
class
TestMKLDNNRelu
(
TestRelu
):
def
setUp
(
self
):
super
(
TestMKLDNNRelu
,
self
).
setUp
()
x
=
np
.
random
.
uniform
(
-
1
,
1
,
[
2
,
4
,
3
,
5
]).
astype
(
"float32"
)
# The same reason with TestAbs
x
[
np
.
abs
(
x
)
<
0.005
]
=
0.02
out
=
np
.
maximum
(
x
,
0
)
self
.
inputs
=
{
'X'
:
OpTest
.
np_dtype_to_fluid_dtype
(
x
)}
self
.
outputs
=
{
'Out'
:
out
}
self
.
attrs
=
{
"use_mkldnn"
:
True
}
class
TestMKLDNNTanh
(
TestTanh
):
def
setUp
(
self
):
super
(
TestMKLDNNTanh
,
self
).
setUp
()
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
2
,
4
,
3
,
5
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'Out'
:
np
.
tanh
(
self
.
inputs
[
'X'
])}
self
.
attrs
=
{
"use_mkldnn"
:
True
}
class
TestMKLDNNSqrt
(
TestSqrt
):
def
setUp
(
self
):
super
(
TestMKLDNNSqrt
,
self
).
setUp
()
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
2
,
4
,
3
,
5
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'Out'
:
np
.
sqrt
(
self
.
inputs
[
'X'
])}
self
.
attrs
=
{
"use_mkldnn"
:
True
}
class
TestMKLDNNAbs
(
TestAbs
):
def
setUp
(
self
):
super
(
TestMKLDNNAbs
,
self
).
setUp
()
x
=
np
.
random
.
uniform
(
-
1
,
1
,
[
2
,
4
,
3
,
5
]).
astype
(
"float32"
)
# The same reason with TestAbs
x
[
np
.
abs
(
x
)
<
0.005
]
=
0.02
self
.
inputs
=
{
'X'
:
x
}
self
.
outputs
=
{
'Out'
:
np
.
abs
(
self
.
inputs
[
'X'
])}
self
.
attrs
=
{
"use_mkldnn"
:
True
}
if
__name__
==
"__main__"
:
unittest
.
main
()
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