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体验新版 GitCode,发现更多精彩内容 >>
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32f8ac7d
编写于
3月 30, 2018
作者:
M
mozga-intel
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34a80843
变更
3
隐藏空白更改
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Showing
3 changed file
with
21 addition
and
42 deletion
+21
-42
paddle/fluid/operators/fc_mkldnn_op.cc
paddle/fluid/operators/fc_mkldnn_op.cc
+15
-26
paddle/fluid/operators/fc_op.cc
paddle/fluid/operators/fc_op.cc
+3
-14
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+3
-2
未找到文件。
paddle/fluid/operators/fc_mkldnn_op.cc
浏览文件 @
32f8ac7d
...
...
@@ -23,67 +23,56 @@ namespace operators {
using
paddle
::
framework
::
Tensor
;
using
paddle
::
platform
::
MKLDNNDeviceContext
;
struct
MKLDNNMatrixSize
final
{
explicit
MKLDNNMatrixSize
(
const
std
::
vector
<
int
>&
in
,
const
std
::
vector
<
int
>&
w
)
:
mb
{
in
[
0
]},
ic
{
in
[
1
]},
oc
{
w
[
1
]},
h
{
in
[
2
]},
w
{
in
[
3
]}
{}
bool
is_spatial
()
const
{
return
h
>
2
&&
w
>
2
;
}
const
int
mb
;
const
int
ic
;
const
int
oc
;
const
int
h
,
w
;
};
template
<
typename
T
>
class
MKLDNNMD
{
public:
explicit
MKLDNNMD
(
const
T
*
in
,
const
T
*
w
,
bool
bias
)
:
sz_
(
std
::
unique_ptr
<
MKLDNNMatrixSize
>
(
new
MKLDNNMatrixSize
(
paddle
::
framework
::
vectorize2int
(
in
->
dims
()),
paddle
::
framework
::
vectorize2int
(
w
->
dims
()))))
{
:
in
{
paddle
::
framework
::
vectorize2int
(
in
->
dims
())},
w
{
paddle
::
framework
::
vectorize2int
(
w
->
dims
())}
{
with_bias_
=
bias
;
}
mkldnn
::
memory
::
desc
dst
()
const
{
return
platform
::
MKLDNNMemDesc
({
sz_
->
mb
,
sz_
->
oc
},
return
platform
::
MKLDNNMemDesc
({
in
[
0
],
w
[
1
]
},
mkldnn
::
memory
::
data_type
::
f32
,
mkldnn
::
memory
::
format
::
nc
);
}
mkldnn
::
memory
::
desc
src
()
const
{
return
sz_
->
is_spatial
()
?
platform
::
MKLDNNMemDesc
({
sz_
->
mb
,
sz_
->
ic
,
sz_
->
h
,
sz_
->
w
},
return
is_spatial
()
?
platform
::
MKLDNNMemDesc
({
in
[
0
],
in
[
1
],
in
[
2
],
in
[
3
]
},
mkldnn
::
memory
::
data_type
::
f32
,
mkldnn
::
memory
::
format
::
nchw
)
:
platform
::
MKLDNNMemDesc
({
sz_
->
mb
,
sz_
->
ic
},
:
platform
::
MKLDNNMemDesc
({
in
[
0
],
in
[
1
]
},
mkldnn
::
memory
::
data_type
::
f32
,
mkldnn
::
memory
::
format
::
nc
);
}
mkldnn
::
memory
::
desc
weights
()
const
{
return
sz_
->
is_spatial
()
?
platform
::
MKLDNNMemDesc
({
sz_
->
oc
,
sz_
->
ic
,
sz_
->
h
,
sz_
->
w
},
return
is_spatial
()
?
platform
::
MKLDNNMemDesc
({
w
[
1
],
in
[
1
],
in
[
2
],
in
[
3
]
},
mkldnn
::
memory
::
data_type
::
f32
,
mkldnn
::
memory
::
format
::
oihw
)
:
platform
::
MKLDNNMemDesc
({
sz_
->
oc
,
sz_
->
ic
},
:
platform
::
MKLDNNMemDesc
({
w
[
1
],
in
[
1
]
},
mkldnn
::
memory
::
data_type
::
f32
,
mkldnn
::
memory
::
format
::
oi
);
}
mkldnn
::
memory
::
desc
bias
()
const
{
return
with_bias_
?
platform
::
MKLDNNMemDesc
({
sz_
->
oc
},
mkldnn
::
memory
::
data_type
::
f32
,
?
platform
::
MKLDNNMemDesc
({
w
[
1
]},
mkldnn
::
memory
::
data_type
::
f32
,
mkldnn
::
memory
::
format
::
format_undef
)
:
platform
::
MKLDNNMemDesc
({},
mkldnn
::
memory
::
data_type
::
f32
,
mkldnn
::
memory
::
format
::
format_undef
);
}
private:
std
::
unique_ptr
<
MKLDNNMatrixSize
>
sz_
;
bool
is_spatial
()
const
{
return
in
.
size
()
>
1
&&
w
.
size
()
>
1
;
}
std
::
vector
<
int
>
in
;
std
::
vector
<
int
>
w
;
bool
with_bias_
;
bool
is_spatial_
;
};
class
MKLDNNMemory
{
...
...
paddle/fluid/operators/fc_op.cc
浏览文件 @
32f8ac7d
...
...
@@ -29,8 +29,8 @@ void FCOp::InferShape(framework::InferShapeContext* ctx) const {
auto
w_dims
=
ctx
->
GetInputDim
(
"W"
);
std
::
vector
<
int64_t
>
output_shape
({
in_dims
[
0
],
w_dims
[
1
]});
PADDLE_ENFORCE
(
in_dims
.
size
()
==
4
,
"Fully Connected input should be 4-D tensor."
);
PADDLE_ENFORCE
(
in_dims
.
size
()
==
4
||
in_dims
.
size
()
==
2
,
"Fully Connected input should be
2-D or
4-D tensor."
);
PADDLE_ENFORCE
(
w_dims
.
size
()
==
2
,
"Fully Connected input should be 2-D tensor."
);
...
...
@@ -96,22 +96,11 @@ FCOpMaker::FCOpMaker(OpProto* proto, OpAttrChecker* op_checker)
The fully connected operation calculates the output based on the input, weights and bias attribute.
The size of each dimension of the parameters checked in the infer-shape.
Input(Input) is NCHW or NC format. Where N is batch size, C is the number of channels,
H is the height of the feature, and W is the width of the feature.
Weights(W) is OIHW or OI format. Where H is the height of the feature, W is the width of the feature,
O is the height of output, and I is the number of channels.
Output(Out) is NC format. Where N is batch size, and C is the number of channels.
The matrix of bias is generated by the mkldnn framework, when the bias_attr is True.
Additional parametrs are use_mkldnn and bias_attr.
The input(X) size and output(Out) size may be diffrent.
Example:
Input:
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Weight shape: $(O_{out}, I_{in}, H_{in}, W_{in})$
Bias shape: $(O_{out})$
Output:
Output shape: $(N, C_{out})$
The fully connected layer only supports MKLDNN version
)DOC"
);
}
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
32f8ac7d
...
...
@@ -167,8 +167,9 @@ def fc(input,
shape
=
param_shape
,
dtype
=
dtype
,
is_bias
=
False
)
bias_attr
=
False
if
bias_attr
is
not
None
:
if
bias_attr
is
None
or
bias_attr
is
False
:
bias_attr
=
False
else
:
bias_attr
=
True
helper
.
append_op
(
type
=
"fc"
,
...
...
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