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90780e22
编写于
6月 21, 2018
作者:
T
tensor-tang
提交者:
GitHub
6月 21, 2018
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Revert "MKLDNN layout: Support for sum operator"
上级
0151e4eb
变更
12
显示空白变更内容
内联
并排
Showing
12 changed file
with
100 addition
and
409 deletion
+100
-409
paddle/fluid/operators/parallel_do_op.cc
paddle/fluid/operators/parallel_do_op.cc
+1
-1
paddle/fluid/operators/recurrent_op.cc
paddle/fluid/operators/recurrent_op.cc
+1
-2
paddle/fluid/operators/sum_mkldnn_op.cc
paddle/fluid/operators/sum_mkldnn_op.cc
+0
-240
paddle/fluid/operators/sum_op.cc
paddle/fluid/operators/sum_op.cc
+6
-26
paddle/fluid/operators/while_op.cc
paddle/fluid/operators/while_op.cc
+2
-2
paddle/fluid/platform/mkldnn_helper.h
paddle/fluid/platform/mkldnn_helper.h
+0
-6
python/paddle/fluid/backward.py
python/paddle/fluid/backward.py
+5
-6
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+70
-73
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+13
-17
python/paddle/fluid/tests/unittests/test_sum_mkldnn_op.py
python/paddle/fluid/tests/unittests/test_sum_mkldnn_op.py
+0
-26
python/paddle/fluid/tests/unittests/test_sum_op.py
python/paddle/fluid/tests/unittests/test_sum_op.py
+0
-6
python/paddle/fluid/transpiler/distribute_transpiler.py
python/paddle/fluid/transpiler/distribute_transpiler.py
+2
-4
未找到文件。
paddle/fluid/operators/parallel_do_op.cc
浏览文件 @
90780e22
...
...
@@ -295,7 +295,7 @@ class ParallelDoGradOp : public framework::OperatorBase {
auto
sum_op
=
framework
::
OpRegistry
::
CreateOp
(
"sum"
,
{{
"X"
,
{
s
,
tmp_name
}}},
{{
"Out"
,
{
s
}}},
framework
::
AttributeMap
{
{
"use_mkldnn"
,
{
false
}}
});
framework
::
AttributeMap
{});
VLOG
(
10
)
<<
sum_op
->
DebugStringEx
(
sub_scopes
[
0
]);
sum_op
->
Run
(
*
sub_scopes
[
0
],
places
[
0
]);
WaitOnPlace
(
places
[
0
]);
...
...
paddle/fluid/operators/recurrent_op.cc
浏览文件 @
90780e22
...
...
@@ -429,8 +429,7 @@ class RecurrentGradOp : public RecurrentBase {
auto
sum_op
=
framework
::
OpRegistry
::
CreateOp
(
"sum"
,
{{
"X"
,
{
pg_names
[
param_id
],
new_inside_name
}}},
{{
"Out"
,
{
pg_names
[
param_id
]}}},
framework
::
AttributeMap
{{
"use_mkldnn"
,
{
false
}}});
{{
"Out"
,
{
pg_names
[
param_id
]}}},
framework
::
AttributeMap
{});
sum_op
->
Run
(
cur_scope
,
place
);
cur_scope
.
Rename
(
new_inside_name
,
inside_grad_name
);
...
...
paddle/fluid/operators/sum_mkldnn_op.cc
已删除
100644 → 0
浏览文件 @
0151e4eb
// 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.
/*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 "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
#include "paddle/fluid/operators/sum_op.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace
paddle
{
namespace
operators
{
using
paddle
::
framework
::
Tensor
;
using
paddle
::
platform
::
MKLDNNDeviceContext
;
using
paddle
::
platform
::
CPUDeviceContext
;
using
framework
::
DataLayout
;
using
mkldnn
::
memory
;
using
mkldnn
::
primitive
;
using
mkldnn
::
stream
;
using
mkldnn
::
sum
;
using
mkldnn
::
reorder
;
using
platform
::
to_void_cast
;
template
<
typename
T
>
class
SumMKLDNNOpKernel
:
public
paddle
::
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
const
override
{
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
();
auto
in_vars
=
ctx
.
MultiInputVar
(
"X"
);
const
int
N
=
in_vars
.
size
();
auto
out_var
=
ctx
.
OutputVar
(
"Out"
);
bool
in_place
=
out_var
==
in_vars
[
0
];
if
(
out_var
->
IsType
<
framework
::
LoDTensor
>
())
{
LoDTensor
*
output
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
T
*
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
std
::
vector
<
int
>
dst_tz
=
framework
::
vectorize2int
(
output
->
dims
());
auto
src_tz
=
dst_tz
;
memory
::
format
output_format
{
memory
::
format
::
format_undef
};
std
::
vector
<
float
>
scales
;
std
::
vector
<
memory
::
primitive_desc
>
srcs_mpd
;
std
::
vector
<
mkldnn
::
memory
>
srcs_mem
;
PADDLE_ENFORCE
(
in_vars
[
0
]
->
IsType
<
LoDTensor
>
(),
"Input[0] must be LoDTensors"
);
auto
&
input0
=
in_vars
[
0
]
->
Get
<
LoDTensor
>
();
PADDLE_ENFORCE
(
input0
.
layout
()
==
DataLayout
::
kMKLDNN
&&
input0
.
format
()
!=
memory
::
format
::
format_undef
,
"Wrong layout/format for inputs[0]"
);
memory
::
format
input_format
=
input0
.
format
();
if
(
src_tz
.
size
()
==
1
&&
(
input_format
==
memory
::
format
::
nchw
||
input_format
==
memory
::
format
::
nhwc
))
{
input_format
=
memory
::
format
::
x
;
}
if
(
src_tz
.
size
()
==
2
&&
(
input_format
==
memory
::
format
::
nchw
||
input_format
==
memory
::
format
::
nhwc
))
{
input_format
=
memory
::
format
::
nc
;
}
for
(
int
i
=
in_place
?
1
:
0
;
i
<
N
;
i
++
)
{
PADDLE_ENFORCE
(
in_vars
[
i
]
->
IsType
<
LoDTensor
>
(),
"all inputs must be all LoDTensors"
);
auto
&
input
=
in_vars
[
i
]
->
Get
<
LoDTensor
>
();
PADDLE_ENFORCE
(
input
.
layout
()
==
DataLayout
::
kMKLDNN
&&
input
.
format
()
!=
memory
::
format
::
format_undef
,
"Wrong layout/format for inputs"
);
if
(
input
.
numel
()
==
0
)
{
continue
;
}
const
T
*
input_data
=
input
.
data
<
T
>
();
auto
src_md
=
memory
::
desc
(
src_tz
,
memory
::
data_type
::
f32
,
input_format
);
auto
src_mpd
=
memory
::
primitive_desc
(
src_md
,
mkldnn_engine
);
auto
src_mem
=
memory
(
src_mpd
,
to_void_cast
(
input_data
));
srcs_mpd
.
push_back
(
src_mpd
);
srcs_mem
.
push_back
(
src_mem
);
scales
.
push_back
(
1.0
);
}
auto
dst_md
=
memory
::
desc
(
dst_tz
,
memory
::
data_type
::
f32
,
memory
::
format
::
any
);
auto
sum_pd
=
sum
::
primitive_desc
(
dst_md
,
scales
,
srcs_mpd
);
std
::
shared_ptr
<
memory
>
dst_mem
;
if
(
in_place
)
{
dst_mem
.
reset
(
new
memory
(
sum_pd
.
dst_primitive_desc
()));
}
else
{
dst_mem
.
reset
(
new
memory
(
sum_pd
.
dst_primitive_desc
(),
output_data
));
}
std
::
vector
<
mkldnn
::
primitive
::
at
>
inputs
;
for
(
size_t
i
=
0
;
i
<
srcs_mem
.
size
();
++
i
)
{
inputs
.
push_back
(
srcs_mem
[
i
]);
}
auto
sum_prim
=
mkldnn
::
sum
(
sum_pd
,
inputs
,
*
dst_mem
);
output_format
=
(
memory
::
format
)
platform
::
GetMKLDNNFormat
(
sum_pd
);
primitive
reorder_prim
;
std
::
shared_ptr
<
memory
>
target_mem
;
if
(
in_place
)
{
output_format
=
input_format
;
target_mem
.
reset
(
new
memory
(
{{{
src_tz
},
memory
::
data_type
::
f32
,
output_format
},
mkldnn_engine
},
output_data
));
reorder_prim
=
reorder
(
*
dst_mem
,
*
target_mem
);
}
std
::
vector
<
primitive
>
pipeline
;
pipeline
.
push_back
(
sum_prim
);
if
(
in_place
)
pipeline
.
push_back
(
reorder_prim
);
stream
(
stream
::
kind
::
eager
).
submit
(
pipeline
).
wait
();
output
->
set_layout
(
DataLayout
::
kMKLDNN
);
output
->
set_format
(
output_format
);
}
else
if
(
out_var
->
IsType
<
framework
::
SelectedRows
>
())
{
// TODO(@mozga-intel) Add MKLDNN SelectedRows support
std
::
unique_ptr
<
framework
::
SelectedRows
>
in0
;
if
(
in_place
)
{
// If is in_place, we store the input[0] to in0
auto
&
in_sel0
=
in_vars
[
0
]
->
Get
<
SelectedRows
>
();
auto
&
rows
=
in_sel0
.
rows
();
in0
.
reset
(
new
framework
::
SelectedRows
(
rows
,
in_sel0
.
height
()));
in0
->
mutable_value
()
->
ShareDataWith
(
in_sel0
.
value
());
}
auto
get_selected_row
=
[
&
](
size_t
i
)
->
const
SelectedRows
&
{
if
(
i
==
0
&&
in0
)
{
return
*
in0
.
get
();
}
else
{
return
in_vars
[
i
]
->
Get
<
SelectedRows
>
();
}
};
auto
*
out
=
ctx
.
Output
<
SelectedRows
>
(
"Out"
);
out
->
mutable_rows
()
->
clear
();
auto
*
out_value
=
out
->
mutable_value
();
// Runtime InferShape
size_t
first_dim
=
0
;
for
(
int
i
=
0
;
i
<
N
;
i
++
)
{
auto
&
sel_row
=
get_selected_row
(
i
);
first_dim
+=
sel_row
.
rows
().
size
();
}
auto
in_dim
=
framework
::
vectorize
(
get_selected_row
(
N
-
1
).
value
().
dims
());
in_dim
[
0
]
=
static_cast
<
int64_t
>
(
first_dim
);
out_value
->
Resize
(
framework
::
make_ddim
(
in_dim
));
// if all the input sparse vars are empty, no need to
// merge these vars.
if
(
first_dim
==
0UL
)
{
return
;
}
out_value
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
math
::
SelectedRowsAddTo
<
CPUDeviceContext
,
T
>
functor
;
int64_t
offset
=
0
;
for
(
int
i
=
0
;
i
<
N
;
i
++
)
{
auto
&
sel_row
=
get_selected_row
(
i
);
if
(
sel_row
.
rows
().
size
()
==
0
)
{
continue
;
}
PADDLE_ENFORCE_EQ
(
out
->
height
(),
sel_row
.
height
());
functor
(
ctx
.
template
device_context
<
CPUDeviceContext
>(),
sel_row
,
offset
,
out
);
offset
+=
sel_row
.
value
().
numel
();
}
}
else
if
(
out_var
->
IsType
<
framework
::
LoDTensorArray
>
())
{
// TODO(@mozga-intel) Add MKLDNN LoDTensorArray support
auto
&
out_array
=
*
out_var
->
GetMutable
<
framework
::
LoDTensorArray
>
();
for
(
size_t
i
=
in_place
?
1
:
0
;
i
<
in_vars
.
size
();
++
i
)
{
PADDLE_ENFORCE
(
in_vars
[
i
]
->
IsType
<
framework
::
LoDTensorArray
>
(),
"Only support all inputs are TensorArray"
);
auto
&
in_array
=
in_vars
[
i
]
->
Get
<
framework
::
LoDTensorArray
>
();
for
(
size_t
i
=
0
;
i
<
in_array
.
size
();
++
i
)
{
if
(
in_array
[
i
].
numel
()
!=
0
)
{
if
(
i
>=
out_array
.
size
())
{
out_array
.
resize
(
i
+
1
);
}
if
(
out_array
[
i
].
numel
()
==
0
)
{
framework
::
TensorCopy
(
in_array
[
i
],
in_array
[
i
].
place
(),
ctx
.
device_context
(),
&
out_array
[
i
]);
out_array
[
i
].
set_lod
(
in_array
[
i
].
lod
());
}
else
{
PADDLE_ENFORCE
(
out_array
[
i
].
lod
()
==
in_array
[
i
].
lod
());
auto
in
=
EigenVector
<
T
>::
Flatten
(
in_array
[
i
]);
auto
result
=
EigenVector
<
T
>::
Flatten
(
out_array
[
i
]);
result
.
device
(
*
ctx
.
template
device_context
<
MKLDNNDeviceContext
>()
.
eigen_device
())
=
result
+
in
;
}
}
}
}
}
else
{
PADDLE_THROW
(
"Unexpected branch, output variable type is %s"
,
out_var
->
Type
().
name
());
}
}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OP_KERNEL
(
sum
,
MKLDNN
,
::
paddle
::
platform
::
CPUPlace
,
paddle
::
operators
::
SumMKLDNNOpKernel
<
float
>
);
paddle/fluid/operators/sum_op.cc
浏览文件 @
90780e22
...
...
@@ -18,10 +18,6 @@ limitations under the License. */
#include "paddle/fluid/framework/var_type_inference.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
{
using
framework
::
Tensor
;
...
...
@@ -67,18 +63,6 @@ class SumOp : public framework::OperatorWithKernel {
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
x_vars
=
ctx
.
MultiInputVar
(
"X"
);
framework
::
LibraryType
library
{
framework
::
LibraryType
::
kPlain
};
framework
::
DataLayout
layout
{
framework
::
DataLayout
::
kAnyLayout
};
#ifdef PADDLE_WITH_MKLDNN
if
(
library
==
framework
::
LibraryType
::
kPlain
&&
platform
::
CanMKLDNNBeUsed
(
ctx
))
{
library
=
framework
::
LibraryType
::
kMKLDNN
;
layout
=
framework
::
DataLayout
::
kMKLDNN
;
}
#endif
if
(
x_vars
[
0
]
->
IsType
<
framework
::
LoDTensor
>
())
{
int
dtype
=
-
1
;
for
(
auto
&
x_var
:
x_vars
)
{
...
...
@@ -96,27 +80,26 @@ class SumOp : public framework::OperatorWithKernel {
"Sum operator should have at least one tensor"
);
return
framework
::
OpKernelType
(
static_cast
<
framework
::
proto
::
VarType
::
Type
>
(
dtype
),
ctx
.
GetPlace
(),
layout
,
library
);
static_cast
<
framework
::
proto
::
VarType
::
Type
>
(
dtype
),
ctx
.
device_context
()
);
}
else
if
(
x_vars
[
0
]
->
IsType
<
framework
::
SelectedRows
>
())
{
for
(
auto
&
var
:
x_vars
)
{
auto
&
value
=
var
->
Get
<
framework
::
SelectedRows
>
().
value
();
if
(
value
.
IsInitialized
())
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
value
.
type
()),
ctx
.
device_context
()
,
layout
,
library
);
ctx
.
device_context
());
}
}
// if input sparse vars are not initialized, use an default kernel type.
return
framework
::
OpKernelType
(
framework
::
proto
::
VarType
::
FP32
,
ctx
.
device_context
()
,
layout
,
library
);
ctx
.
device_context
());
}
else
if
(
x_vars
[
0
]
->
IsType
<
framework
::
LoDTensorArray
>
())
{
for
(
auto
&
x_var
:
x_vars
)
{
auto
&
array
=
x_var
->
Get
<
framework
::
LoDTensorArray
>
();
for
(
auto
&
each
:
array
)
{
if
(
each
.
numel
()
!=
0
)
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
each
.
type
()),
ctx
.
device_context
(),
layout
,
library
);
ctx
.
device_context
());
}
}
}
...
...
@@ -133,9 +116,6 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"X"
,
"(vector<Tensor>) The input tensors of sum operator."
)
.
AsDuplicable
();
AddOutput
(
"Out"
,
"(Tensor) The output tensor of sum operator."
).
Reuse
(
"X"
);
AddAttr
<
bool
>
(
"use_mkldnn"
,
"(bool, default false) Only used in mkldnn kernel"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
Sum operator.
...
...
@@ -152,6 +132,7 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
framework
::
BlockDesc
*
block
)
const
override
{
auto
&
inputs
=
op_desc
.
Input
(
"X"
);
auto
var_type
=
framework
::
proto
::
VarType
::
SELECTED_ROWS
;
for
(
auto
&
name
:
op_desc
.
Input
(
"X"
))
{
VLOG
(
10
)
<<
name
<<
" "
<<
block
->
FindRecursiveOrCreateVar
(
name
).
GetType
();
...
...
@@ -225,7 +206,6 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR
(
sum
,
ops
::
SumOp
,
ops
::
SumOpMaker
,
ops
::
SumGradMaker
,
ops
::
SumOpVarTypeInference
);
REGISTER_OP_CPU_KERNEL
(
sum
,
ops
::
SumKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SumKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
...
...
paddle/fluid/operators/while_op.cc
浏览文件 @
90780e22
...
...
@@ -203,11 +203,11 @@ class WhileGradOp : public framework::OperatorBase {
->
set_lod
(
inside_tensor
.
lod
());
}
}
auto
new_inside_name
=
cur_scope
.
Rename
(
inside_grad_name
);
auto
sum_op
=
framework
::
OpRegistry
::
CreateOp
(
"sum"
,
{{
"X"
,
{
pg_names
[
param_id
],
new_inside_name
}}},
{{
"Out"
,
{
pg_names
[
param_id
]}}},
framework
::
AttributeMap
{{
"use_mkldnn"
,
{
false
}}});
{{
"Out"
,
{
pg_names
[
param_id
]}}},
framework
::
AttributeMap
{});
sum_op
->
Run
(
cur_scope
,
dev_place
);
cur_scope
.
Rename
(
new_inside_name
,
inside_grad_name
);
}
...
...
paddle/fluid/platform/mkldnn_helper.h
浏览文件 @
90780e22
...
...
@@ -99,11 +99,5 @@ inline mkldnn::memory::format GetMKLDNNFormat(const mkldnn::memory memory) {
memory
.
get_primitive_desc
().
desc
().
data
.
format
);
}
inline
mkldnn
::
memory
::
format
GetMKLDNNFormat
(
const
mkldnn
::
sum
::
primitive_desc
&
memory
)
{
return
static_cast
<
mkldnn
::
memory
::
format
>
(
memory
.
dst_primitive_desc
().
desc
().
data
.
format
);
}
}
// namespace platform
}
// namespace paddle
python/paddle/fluid/backward.py
浏览文件 @
90780e22
...
...
@@ -132,9 +132,9 @@ def _addup_repetitive_outputs_(op_descs):
for
idx
,
op_desc
in
enumerate
(
op_descs
):
for
var_name
in
op_desc
.
input_arg_names
():
if
len
(
renamed_vars
[
var_name
])
>
1
:
pending_sum_ops
.
append
(
(
_create_op_desc_
(
"sum"
,
{
"X"
:
renamed_vars
[
var_name
]},
{
"Out"
:
[
var_name
]},
{
"use_mkldnn"
:
False
}),
idx
))
pending_sum_ops
.
append
(
(
_create_op_desc_
(
"sum"
,
{
"X"
:
renamed_vars
[
var_name
]},
{
"Out"
:
[
var_name
]},
{
}),
idx
))
renamed_vars
[
var_name
]
=
[
var_name
]
for
var_name
in
op_desc
.
output_arg_names
():
if
var_name
==
core
.
empty_var_name
(
...
...
@@ -161,9 +161,8 @@ def _addup_repetitive_outputs_(op_descs):
renamed_vars
[
var_name
].
append
(
new_name
)
for
var_name
,
inputs
in
renamed_vars
.
iteritems
():
if
len
(
inputs
)
>
1
:
pending_sum_ops
.
append
(
(
_create_op_desc_
(
"sum"
,
{
"X"
:
inputs
},
{
"Out"
:
[
var_name
]},
{
"use_mkldnn"
:
False
}),
len
(
op_descs
)))
pending_sum_ops
.
append
((
_create_op_desc_
(
"sum"
,
{
"X"
:
inputs
},
{
"Out"
:
[
var_name
]},
{}),
len
(
op_descs
)))
# sum_op descs are sorted according to their insert position
for
p
in
reversed
(
pending_sum_ops
):
op_descs
.
insert
(
p
[
1
],
p
[
0
])
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
90780e22
...
...
@@ -198,10 +198,7 @@ def fc(input,
else
:
pre_bias
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
"sum"
,
inputs
=
{
"X"
:
mul_results
},
outputs
=
{
"Out"
:
pre_bias
},
attrs
=
{
"use_mkldnn"
:
use_mkldnn
})
type
=
"sum"
,
inputs
=
{
"X"
:
mul_results
},
outputs
=
{
"Out"
:
pre_bias
})
# add bias
pre_activation
=
helper
.
append_bias_op
(
pre_bias
,
dim_start
=
num_flatten_dims
)
# add activation
...
...
python/paddle/fluid/layers/tensor.py
浏览文件 @
90780e22
...
...
@@ -230,11 +230,7 @@ def sums(input, out=None):
helper
=
LayerHelper
(
'sum'
,
**
locals
())
if
out
is
None
:
out
=
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
())
helper
.
append_op
(
type
=
'sum'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'use_mkldnn'
:
False
})
helper
.
append_op
(
type
=
'sum'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
out
})
return
out
...
...
python/paddle/fluid/tests/unittests/test_sum_mkldnn_op.py
已删除
100644 → 0
浏览文件 @
0151e4eb
# 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.
import
unittest
from
test_sum_op
import
TestSumOp
class
TestMKLDNN
(
TestSumOp
):
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_sum_op.py
浏览文件 @
90780e22
...
...
@@ -20,15 +20,12 @@ from op_test import OpTest
class
TestSumOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"sum"
self
.
use_mkldnn
=
False
self
.
init_kernel_type
()
x0
=
np
.
random
.
random
((
3
,
4
)).
astype
(
'float32'
)
x1
=
np
.
random
.
random
((
3
,
4
)).
astype
(
'float32'
)
x2
=
np
.
random
.
random
((
3
,
4
)).
astype
(
'float32'
)
self
.
inputs
=
{
"X"
:
[(
"x0"
,
x0
),
(
"x1"
,
x1
),
(
"x2"
,
x2
)]}
y
=
x0
+
x1
+
x2
self
.
outputs
=
{
'Out'
:
y
}
self
.
attrs
=
{
'use_mkldnn'
:
self
.
use_mkldnn
}
def
test_check_output
(
self
):
self
.
check_output
()
...
...
@@ -36,9 +33,6 @@ class TestSumOp(OpTest):
def
test_check_grad
(
self
):
self
.
check_grad
([
'x0'
],
'Out'
)
def
init_kernel_type
(
self
):
pass
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @
90780e22
...
...
@@ -872,8 +872,7 @@ class DistributeTranspiler(object):
table_opt_block
.
append_op
(
type
=
"sum"
,
inputs
=
{
"X"
:
pserver_side_table_grad_list
},
outputs
=
{
"Out"
:
[
grad_var
]},
attrs
=
{
"use_mkldnn"
:
False
})
outputs
=
{
"Out"
:
[
grad_var
]})
else
:
# in async_mode, for table gradient, it also need to be splited to each parameter server
origin_grad_name
=
grad_var
.
name
...
...
@@ -1105,8 +1104,7 @@ class DistributeTranspiler(object):
optimize_block
.
append_op
(
type
=
"sum"
,
inputs
=
{
"X"
:
vars2merge
},
outputs
=
{
"Out"
:
merged_var
},
attrs
=
{
"use_mkldnn"
:
False
})
outputs
=
{
"Out"
:
merged_var
})
# TODO(panyx0718): What if it's SELECTED_ROWS.
if
not
merged_var
.
type
==
core
.
VarDesc
.
VarType
.
SELECTED_ROWS
:
optimize_block
.
append_op
(
...
...
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