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体验新版 GitCode,发现更多精彩内容 >>
提交
df1e4e2f
编写于
12月 20, 2018
作者:
T
Tao Luo
提交者:
sneaxiy
12月 21, 2018
浏览文件
操作
浏览文件
下载
差异文件
fix check_lod
test=develop
上级
2537ac51
709d9e3c
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
150 addition
and
64 deletion
+150
-64
paddle/fluid/framework/lod_tensor.cc
paddle/fluid/framework/lod_tensor.cc
+8
-5
paddle/fluid/framework/lod_tensor_test.cc
paddle/fluid/framework/lod_tensor_test.cc
+4
-0
paddle/fluid/operators/transpose_mkldnn_op.cc
paddle/fluid/operators/transpose_mkldnn_op.cc
+14
-59
paddle/fluid/platform/mkldnn_reuse.h
paddle/fluid/platform/mkldnn_reuse.h
+124
-0
未找到文件。
paddle/fluid/framework/lod_tensor.cc
浏览文件 @
df1e4e2f
...
@@ -159,12 +159,15 @@ bool CheckLoD(const LoD &in, int tensor_height) {
...
@@ -159,12 +159,15 @@ bool CheckLoD(const LoD &in, int tensor_height) {
if
(
level
.
front
()
!=
0
)
return
false
;
if
(
level
.
front
()
!=
0
)
return
false
;
// check: all the offsets in a level should be ascending(no same items
// check: all the offsets in a level should be ascending(no same items
// allows).
// allows).
if
(
!
std
::
is_sorted
(
level
.
begin
(),
level
.
begin
(),
[](
size_t
a
,
size_t
b
)
{
auto
beg
=
level
.
begin
();
if
(
a
<
b
)
return
true
;
auto
end
=
level
.
end
();
// Do not use std::is_sorted, because we need strictly sorted lod
if
(
beg
!=
end
)
{
for
(
auto
it
=
beg
+
1
;
it
!=
end
;
++
it
)
{
if
(
*
(
it
-
1
)
>=
*
it
)
{
return
false
;
return
false
;
}))
{
}
LOG
(
INFO
)
<<
"ascending error"
;
}
return
false
;
}
}
}
}
// check: the lowest level's last offset should equals `tensor_height` if
// check: the lowest level's last offset should equals `tensor_height` if
...
...
paddle/fluid/framework/lod_tensor_test.cc
浏览文件 @
df1e4e2f
...
@@ -217,6 +217,10 @@ TEST(LoD, CheckLoD) {
...
@@ -217,6 +217,10 @@ TEST(LoD, CheckLoD) {
// check with underlying tensor storage.
// check with underlying tensor storage.
ASSERT_TRUE
(
CheckLoD
(
relative_lod
,
5
));
ASSERT_TRUE
(
CheckLoD
(
relative_lod
,
5
));
ASSERT_FALSE
(
CheckLoD
(
relative_lod
,
9
));
ASSERT_FALSE
(
CheckLoD
(
relative_lod
,
9
));
// check strictly sorted lod
ASSERT_TRUE
(
CheckLoD
({{
0
,
1
,
2
,
3
,
4
,
5
}},
5
));
ASSERT_FALSE
(
CheckLoD
({{
0
,
1
,
3
,
3
,
4
,
5
}},
5
));
}
}
TEST
(
LoD
,
CheckAbsLoD
)
{
TEST
(
LoD
,
CheckAbsLoD
)
{
...
...
paddle/fluid/operators/transpose_mkldnn_op.cc
浏览文件 @
df1e4e2f
...
@@ -32,7 +32,7 @@ class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
...
@@ -32,7 +32,7 @@ class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const
bool
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
const
bool
is_test
=
ctx
.
Attr
<
bool
>
(
"is_test"
);
PADDLE_ENFORCE
(
PADDLE_ENFORCE
(
is_test
==
true
,
is_test
==
true
,
"
Conv
TransposeMKLDNN works only for inference!. Set is_test = True"
);
"TransposeMKLDNN works only for inference!. Set is_test = True"
);
auto
&
dev_ctx
=
auto
&
dev_ctx
=
ctx
.
template
device_context
<
paddle
::
platform
::
MKLDNNDeviceContext
>();
ctx
.
template
device_context
<
paddle
::
platform
::
MKLDNNDeviceContext
>();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
...
@@ -47,69 +47,24 @@ class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
...
@@ -47,69 +47,24 @@ class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
return
;
return
;
}
}
std
::
vector
<
int
>
nchw_axis
(
ndims
,
0
);
for
(
size_t
i
=
0
;
i
<
nchw_axis
.
size
();
++
i
)
{
nchw_axis
[
i
]
=
i
;
}
std
::
vector
<
int
>
nchw_tz
=
paddle
::
framework
::
vectorize2int
(
input
->
dims
());
std
::
vector
<
int
>
nchw_tz
=
paddle
::
framework
::
vectorize2int
(
input
->
dims
());
std
::
string
data_format
=
ctx
.
Attr
<
std
::
string
>
(
"data_format"
);
auto
src_md
=
input
->
format
()
!=
mkldnn
::
memory
::
format
::
nchw
?
platform
::
MKLDNNMemDesc
(
nchw_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
input
->
format
())
:
Axis2MemoryDesc
(
nchw_tz
,
nchw_axis
);
this
->
TransposeKernel
(
ctx
.
GetPlace
(),
Axis2MemoryDesc
(
nchw_tz
,
axis
),
src_md
,
output
,
input_data
,
nchw_tz
,
mkldnn_engine
);
}
protected:
mkldnn
::
memory
::
desc
Axis2MemoryDesc
(
std
::
vector
<
int
>&
nchw_tz
,
std
::
vector
<
int
>&
axis
)
const
{
mkldnn_memory_desc_t
mem_fmt
;
mem_fmt
.
primitive_kind
=
mkldnn_memory
;
mem_fmt
.
ndims
=
axis
.
size
();
for
(
unsigned
int
i
=
0
;
i
<
nchw_tz
.
size
();
++
i
)
{
mem_fmt
.
dims
[
i
]
=
nchw_tz
[
i
];
// logical dimensions (nchw format,
// regardless physical layout)
}
mem_fmt
.
data_type
=
mkldnn_f32
;
mem_fmt
.
format
=
mkldnn_blocked
;
unsigned
int
total_stride
=
1
;
for
(
int
i
=
nchw_tz
.
size
()
-
1
;
i
>=
0
;
--
i
)
{
mem_fmt
.
layout_desc
.
blocking
.
padding_dims
[
i
]
=
nchw_tz
[
i
];
// logical dimensions (nchw format, regardless physical
// layout)
mem_fmt
.
layout_desc
.
blocking
.
block_dims
[
i
]
=
1
;
mem_fmt
.
layout_desc
.
blocking
.
offset_padding_to_data
[
i
]
=
0
;
// no offset
mem_fmt
.
layout_desc
.
blocking
.
strides
[
0
][
axis
[
i
]]
=
total_stride
;
mem_fmt
.
layout_desc
.
blocking
.
strides
[
1
][
axis
[
i
]]
=
1
;
total_stride
*=
nchw_tz
[
axis
[
i
]];
}
mem_fmt
.
layout_desc
.
blocking
.
offset_padding
=
0
;
// no initial offset
return
mem_fmt
;
}
void
TransposeKernel
(
platform
::
Place
place
,
mkldnn
::
memory
::
desc
md_o
,
const
std
::
string
key
=
platform
::
TransposeMKLDNNHandler
::
GetHash
(
mkldnn
::
memory
::
desc
md_i
,
Tensor
*
output
,
nchw_tz
,
axis
,
ctx
.
op
().
Output
(
"Out"
));
const
T
*
data_i
,
std
::
vector
<
int
>&
nchw_dims
,
const
mkldnn
::
engine
&
eng
)
const
{
// Make Memory primitive descriptors
auto
mpd_o
=
mkldnn
::
memory
::
primitive_desc
(
md_o
,
eng
);
auto
mpd_i
=
mkldnn
::
memory
::
primitive_desc
(
md_i
,
eng
);
auto
data_o
=
output
->
mutable_data
<
T
>
(
platform
::
TransposeMKLDNNHandler
handler
(
nchw_tz
,
axis
,
dev_ctx
,
place
,
paddle
::
memory
::
Allocator
::
kDefault
,
mpd_o
.
get_size
()
);
mkldnn_engine
,
key
);
auto
src
=
mkldnn
::
memory
(
mpd_i
,
(
T
*
)(
data_i
));
auto
transpose_src_memory_p
=
handler
.
AcquireSrcMemory
(
auto
dst
=
mkldnn
::
memory
(
mpd_o
,
data_o
);
input
->
format
(),
platform
::
to_void_cast
<
T
>
(
input_data
));
auto
transpose_dst_memory_p
=
handler
.
AcquireDstMemory
(
output
,
ctx
.
GetPlace
());
auto
transpose_p
=
handler
.
AcquireTranspose
(
transpose_dst_memory_p
,
transpose_src_memory_p
);
auto
r
=
mkldnn
::
reorder
(
src
,
dst
);
std
::
vector
<
mkldnn
::
primitive
>
pipeline
;
mkldnn
::
stream
(
mkldnn
::
stream
::
kind
::
eager
).
submit
({
r
}).
wait
();
pipeline
.
push_back
(
*
transpose_p
);
mkldnn
::
stream
(
mkldnn
::
stream
::
kind
::
eager
).
submit
(
pipeline
).
wait
();
}
}
};
};
...
...
paddle/fluid/platform/mkldnn_reuse.h
浏览文件 @
df1e4e2f
...
@@ -197,6 +197,130 @@ class MKLDNNHandler {
...
@@ -197,6 +197,130 @@ class MKLDNNHandler {
bool
is_reusing_
;
bool
is_reusing_
;
};
};
class
TransposeMKLDNNHandler
:
public
MKLDNNHandler
{
public:
TransposeMKLDNNHandler
(
std
::
vector
<
int
>&
dims
,
std
::
vector
<
int
>&
axis
,
const
platform
::
MKLDNNDeviceContext
&
dev_ctx
,
mkldnn
::
engine
engine
,
const
std
::
string
&
base_key
)
:
platform
::
MKLDNNHandler
(
dev_ctx
,
engine
,
base_key
),
dims_
(
dims
),
axis_
(
axis
),
logical_axis_
(
dims
.
size
(),
0
)
{}
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireSrcMemory
(
const
mkldnn
::
memory
::
format
&
fmt
,
void
*
ptr
)
{
auto
local_key
=
key_
+
"@user_src_mem_p"
;
auto
mem_p
=
std
::
static_pointer_cast
<
mkldnn
::
memory
>
(
dev_ctx_
.
GetBlob
(
local_key
));
PADDLE_ENFORCE
((
mem_p
!=
nullptr
)
||
(
is_reusing_
==
false
),
" find mem primitive in device context"
);
if
(
mem_p
==
nullptr
)
{
// Make memory descriptor using input format, unless it
// cannot be trusted (nchw) then make up memory fmt manually
for
(
size_t
i
=
0
;
i
<
logical_axis_
.
size
();
++
i
)
{
logical_axis_
[
i
]
=
i
;
}
auto
src_md
=
fmt
!=
mkldnn
::
memory
::
format
::
nchw
?
platform
::
MKLDNNMemDesc
(
dims_
,
platform
::
MKLDNNGetDataType
<
float
>
(),
fmt
)
:
Axis2MemoryDesc
(
dims_
,
logical_axis_
);
mem_p
=
std
::
make_shared
<
mkldnn
::
memory
>
(
mkldnn
::
memory
::
primitive_desc
{
src_md
,
engine_
},
ptr
);
dev_ctx_
.
SetBlob
(
local_key
,
mem_p
);
}
else
{
mem_p
->
set_data_handle
(
ptr
);
// Mark that reusing happenned. All primitives from operator instance
// should be reused or none of them. So we check consistency
is_reusing_
=
true
;
}
return
mem_p
;
}
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireDstMemory
(
framework
::
Tensor
*
output
,
platform
::
Place
place
)
{
auto
local_key
=
key_
+
"@user_dst_mem_p"
;
auto
mem_p
=
std
::
static_pointer_cast
<
mkldnn
::
memory
>
(
dev_ctx_
.
GetBlob
(
local_key
));
PADDLE_ENFORCE
((
mem_p
!=
nullptr
)
||
(
is_reusing_
==
false
),
" find mem primitive in device context"
);
if
(
mem_p
==
nullptr
)
{
auto
dst_mdp
=
mkldnn
::
memory
::
primitive_desc
{
Axis2MemoryDesc
(
dims_
,
axis_
),
engine_
};
auto
dst_data
=
output
->
mutable_data
<
float
>
(
place
,
paddle
::
memory
::
Allocator
::
kDefault
,
dst_mdp
.
get_size
());
mem_p
=
std
::
make_shared
<
mkldnn
::
memory
>
(
dst_mdp
,
dst_data
);
dev_ctx_
.
SetBlob
(
local_key
,
mem_p
);
}
else
{
auto
dst_data
=
output
->
mutable_data
<
float
>
(
place
);
mem_p
->
set_data_handle
(
dst_data
);
// Mark that reusing happenned. All primitives from operator instance
// should be reused or none of them. So we check consistency
is_reusing_
=
true
;
}
return
mem_p
;
}
std
::
shared_ptr
<
mkldnn
::
reorder
>
AcquireTranspose
(
std
::
shared_ptr
<
mkldnn
::
memory
>
dst_memory_p
,
std
::
shared_ptr
<
mkldnn
::
memory
>
src_memory_p
)
{
auto
prim_key
=
key_
+
"@transpose_p"
;
auto
transpose_p
=
std
::
static_pointer_cast
<
mkldnn
::
reorder
>
(
dev_ctx_
.
GetBlob
(
prim_key
));
PADDLE_ENFORCE
((
transpose_p
!=
nullptr
)
||
(
is_reusing_
==
false
),
"Fail to find convolution primitive in device context"
);
if
(
transpose_p
==
nullptr
)
{
transpose_p
=
std
::
make_shared
<
mkldnn
::
reorder
>
(
*
(
src_memory_p
),
*
(
dst_memory_p
));
dev_ctx_
.
SetBlob
(
prim_key
,
transpose_p
);
}
else
{
is_reusing_
=
true
;
}
return
transpose_p
;
}
static
std
::
string
GetHash
(
std
::
vector
<
int
>&
shape
,
// NOLINT
std
::
vector
<
int
>&
axis
,
// NOLINT
const
std
::
string
&
suffix
)
{
return
dims2str
(
shape
)
+
dims2str
(
axis
)
+
suffix
;
}
protected:
mkldnn_memory_desc_t
Axis2MemoryDesc
(
std
::
vector
<
int
>&
nchw_tz
,
std
::
vector
<
int
>&
axis
)
{
mkldnn_memory_desc_t
mem_fmt
;
mem_fmt
.
primitive_kind
=
mkldnn_memory
;
mem_fmt
.
ndims
=
axis
.
size
();
for
(
unsigned
int
i
=
0
;
i
<
nchw_tz
.
size
();
++
i
)
{
mem_fmt
.
dims
[
i
]
=
nchw_tz
[
i
];
// logical dimensions (nchw format,
// regardless physical layout)
}
mem_fmt
.
data_type
=
mkldnn_f32
;
mem_fmt
.
format
=
mkldnn_blocked
;
unsigned
int
total_stride
=
1
;
for
(
int
i
=
nchw_tz
.
size
()
-
1
;
i
>=
0
;
--
i
)
{
mem_fmt
.
layout_desc
.
blocking
.
padding_dims
[
i
]
=
nchw_tz
[
i
];
// logical dimensions (nchw format, regardless physical
// layout)
mem_fmt
.
layout_desc
.
blocking
.
block_dims
[
i
]
=
1
;
mem_fmt
.
layout_desc
.
blocking
.
offset_padding_to_data
[
i
]
=
0
;
// no offset
mem_fmt
.
layout_desc
.
blocking
.
strides
[
0
][
axis
[
i
]]
=
total_stride
;
mem_fmt
.
layout_desc
.
blocking
.
strides
[
1
][
axis
[
i
]]
=
1
;
total_stride
*=
nchw_tz
[
axis
[
i
]];
}
mem_fmt
.
layout_desc
.
blocking
.
offset_padding
=
0
;
// no initial offset
return
mem_fmt
;
}
private:
std
::
vector
<
int
>
dims_
;
std
::
vector
<
int
>
axis_
;
std
::
vector
<
int
>
logical_axis_
;
};
template
<
class
forward_t
,
class
backward_data_t
,
class
backward_weights_t
>
template
<
class
forward_t
,
class
backward_data_t
,
class
backward_weights_t
>
class
ConvMKLDNNTemplateHandler
:
public
MKLDNNHandler
{
class
ConvMKLDNNTemplateHandler
:
public
MKLDNNHandler
{
public:
public:
...
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