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体验新版 GitCode,发现更多精彩内容 >>
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3da3462f
编写于
10月 11, 2022
作者:
N
niuliling123
提交者:
GitHub
10月 11, 2022
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差异文件
Update layout autotune for module with no modified (#46541)
上级
20eb6e00
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
285 addition
and
386 deletion
+285
-386
paddle/fluid/eager/auto_code_generator/generator/eager_gen.py
...le/fluid/eager/auto_code_generator/generator/eager_gen.py
+1
-1
paddle/fluid/eager/eager_layout_auto_tune.h
paddle/fluid/eager/eager_layout_auto_tune.h
+69
-125
paddle/fluid/eager/eager_layout_transformer.h
paddle/fluid/eager/eager_layout_transformer.h
+118
-185
paddle/fluid/imperative/layout_autotune.cc
paddle/fluid/imperative/layout_autotune.cc
+2
-0
paddle/fluid/pybind/eager_properties.cc
paddle/fluid/pybind/eager_properties.cc
+36
-0
paddle/fluid/pybind/imperative.cc
paddle/fluid/pybind/imperative.cc
+42
-1
paddle/phi/api/lib/data_transform.cc
paddle/phi/api/lib/data_transform.cc
+2
-1
python/paddle/fluid/tests/unittests/test_layout_autotune.py
python/paddle/fluid/tests/unittests/test_layout_autotune.py
+13
-71
python/paddle/nn/functional/conv.py
python/paddle/nn/functional/conv.py
+2
-2
未找到文件。
paddle/fluid/eager/auto_code_generator/generator/eager_gen.py
浏览文件 @
3da3462f
...
...
@@ -1093,7 +1093,7 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
tensors_vector_list_str
=
"{ "
+
","
.
join
(
amp_tensors_vector_list
)
+
" }"
if
len
(
amp_tensors_vector_list
)
==
0
:
if
len
(
amp_tensors_vector_list
)
==
0
:
# or forward_api_name == "shape":
layout_logic_str
=
""
else
:
after_call_str
=
f
"
{
returns_type_str
}
{
result_name
}
=
{
forward_function_name
}
(
{
layout_inputs_call_args_str
}
);
\n
"
...
...
paddle/fluid/eager/eager_layout_auto_tune.h
浏览文件 @
3da3462f
...
...
@@ -32,70 +32,50 @@ inline bool NeedTransLayout(
}
return
false
;
}
inline
std
::
shared_ptr
<
EagerLayoutTransformer
>
BaseTransformer
(
const
std
::
string
&
op_name
,
const
paddle
::
small_vector
<
std
::
vector
<
paddle
::
experimental
::
Tensor
>
,
kSlotSmallVectorSize
>&
tensors_vector
)
{
std
::
shared_ptr
<
EagerLayoutTransformer
>
transposer
=
nullptr
;
bool
unstart
=
(
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
()
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
);
auto
first_layout
=
tensors_vector
[
0
][
0
].
layout
();
VLOG
(
3
)
<<
"Layout autotune was is start ? "
<<
(
!
unstart
)
<<
op_name
<<
"'s layout is "
<<
first_layout
;
transposer
=
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
first_layout
);
return
transposer
;
}
// For agnostic op like add, relu, exp
inline
std
::
shared_ptr
<
EagerLayoutTransformer
>
EagerLayoutAutotune
(
const
std
::
string
&
op_name
,
const
paddle
::
small_vector
<
std
::
vector
<
paddle
::
experimental
::
Tensor
>
,
kSlotSmallVectorSize
>&
tensors_vector
)
{
auto
desired_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
();
auto
default_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDefaultLayout
();
// For agnostic op like add, relu, exp
auto
first_layout
=
tensors_vector
[
0
][
0
].
layout
();
if
(
NeedTransLayout
(
tensors_vector
,
first_layout
))
{
auto
desired_layout
=
DesiredLayout
();
bool
is_started
=
!
(
desired_layout
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
);
if
(
is_started
&&
NeedTransLayout
(
tensors_vector
,
first_layout
))
{
bool
need_trans_back
=
false
;
for
(
size_t
i
=
0
;
i
<
tensors_vector
.
size
();
i
++
)
{
for
(
size_t
idx
=
0
;
idx
<
tensors_vector
[
0
].
size
();
idx
++
)
{
if
(
4
!=
tensors_vector
[
i
][
idx
].
shape
().
size
())
{
need_trans_back
=
true
;
VLOG
(
3
)
<<
"Agnostic op "
<<
op_name
<<
" shape is "
<<
tensors_vector
[
i
][
idx
].
shape
().
size
()
<<
" and layout is "
<<
tensors_vector
[
i
][
idx
].
layout
();
}
}
}
auto
final_layout
=
need_trans_back
?
default_layout
:
desired_layout
;
auto
final_layout
=
need_trans_back
?
DefaultLayout
()
:
desired_layout
;
VLOG
(
4
)
<<
op_name
<<
"'s has different layout, need trans to "
<<
final_layout
;
return
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
final_layout
);
}
return
BaseTransformer
(
op_name
,
tensors_vector
);
return
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
first_layout
);
}
// For lightly op like reduce
template
<
typename
T
>
inline
std
::
shared_ptr
<
EagerLayoutTransformer
>
EagerLayoutAutotune
(
const
std
::
string
&
op_name
,
const
paddle
::
small_vector
<
std
::
vector
<
paddle
::
experimental
::
Tensor
>
,
kSlotSmallVectorSize
>&
tensors_vector
,
T
*
attr
)
{
VLOG
(
3
)
<<
"Lightly op "
<<
op_name
<<
"'s shape is "
<<
tensors_vector
[
0
][
0
].
shape
().
size
()
<<
" and layout is "
<<
tensors_vector
[
0
][
0
].
layout
();
std
::
shared_ptr
<
EagerLayoutTransformer
>
transposer
=
nullptr
;
transposer
=
std
::
make_shared
<
EagerLightlyLayoutSensitiveOpTransformer
>
(
op_name
);
return
transposer
;
// For lightly op like reduce
if
(
!
(
DesiredLayout
()
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
))
{
VLOG
(
4
)
<<
"LayoutAutotune was unstarted. Current op :"
<<
op_name
;
return
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
tensors_vector
[
0
][
0
].
layout
());
}
return
std
::
make_shared
<
EagerLightlyLayoutSensitiveOpTransformer
>
(
op_name
);
}
// For lightly op like argmax
template
<
typename
T1
,
typename
T2
>
inline
std
::
shared_ptr
<
EagerLayoutTransformer
>
EagerLayoutAutotune
(
const
std
::
string
&
op_name
,
...
...
@@ -103,28 +83,23 @@ inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
kSlotSmallVectorSize
>&
tensors_vector
,
T1
*
axis
,
T2
*
keep_dim
)
{
VLOG
(
3
)
<<
"Lightly op "
<<
op_name
<<
"'s shape is "
<<
tensors_vector
[
0
][
0
].
shape
().
size
()
<<
" and layout is "
<<
tensors_vector
[
0
][
0
].
layout
();
// For lightly op like argmax
return
EagerLayoutAutotune
<
T1
>
(
op_name
,
tensors_vector
,
axis
);
}
// heavily string data_format, data_layout
template
<
>
inline
std
::
shared_ptr
<
EagerLayoutTransformer
>
EagerLayoutAutotune
(
const
std
::
string
&
op_name
,
const
paddle
::
small_vector
<
std
::
vector
<
paddle
::
experimental
::
Tensor
>
,
kSlotSmallVectorSize
>&
tensors_vector
,
std
::
string
*
attr
)
{
auto
first_layout
=
tensors_vector
[
0
][
0
].
layout
();
// Heavily op with (string) data_format, data_layout
auto
transposer
=
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
first_layout
);
if
(
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
()
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
)
{
op_name
,
tensors_vector
,
tensors_vector
[
0
][
0
].
layout
());
if
(
DesiredLayout
()
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
)
{
// Layout autotune only supports model with convolutional layers
VLOG
(
3
)
<<
"Optimze Layout was not started "
<<
op_name
;
if
(
op_name
!=
"conv2d"
)
{
VLOG
(
4
)
<<
"LayoutAutotune was unstarted. Current op :"
<<
op_name
;
return
transposer
;
}
else
{
auto
data_type
=
tensors_vector
[
0
][
0
].
dtype
();
...
...
@@ -134,7 +109,8 @@ inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
bool
is_tune_fp16
=
(
data_type
==
paddle
::
experimental
::
DataType
::
FLOAT16
)
&&
(
*
attr
==
"NCHW"
);
VLOG
(
3
)
<<
"Conv2d_dy's dtype "
<<
data_type
<<
" format"
<<
(
*
attr
);
VLOG
(
4
)
<<
"LayoutAutoTune assert with dtype and layout, Current op : "
<<
op_name
;
if
(
is_tune_fp32
)
{
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
SetDesiredLayout
(
paddle
::
experimental
::
DataLayout
::
NCHW
);
...
...
@@ -147,58 +123,45 @@ inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune(
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
SetDefaultLayout
(
paddle
::
experimental
::
DataLayout
::
NCHW
);
}
else
{
VLOG
(
4
)
<<
"DisableLayoutAutoTune accoding to Conv op"
<<
" dtype : "
<<
data_type
<<
" format : "
<<
(
*
attr
);
egr
::
Controller
::
Instance
().
DisableLayoutAutoTune
();
return
transposer
;
}
VLOG
(
3
)
<<
"Tune the layout from "
<<
*
attr
<<
" to "
<<
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
();
VLOG
(
4
)
<<
"LayoutAutoTune from "
<<
*
attr
<<
" to "
<<
DesiredLayout
();
}
}
if
(
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
IsHeavilyLayoutSensitive
(
op_name
))
{
VLOG
(
3
)
<<
op_name
<<
"'s LayoutTransformer is EagerHeavilyLayoutSensitiveOpTransformer"
;
auto
heavily_transposer
=
std
::
make_shared
<
EagerHeavilyLayoutSensitiveOpTransformer
>
(
op_name
,
attr
);
return
heavily_transposer
;
return
std
::
make_shared
<
EagerHeavilyLayoutSensitiveOpTransformer
>
(
op_name
,
attr
);
}
VLOG
(
3
)
<<
op_name
<<
"'s LayoutTransformer is unimplemented. Use default."
;
return
transposer
;
return
std
::
make_shared
<
EagerLightlyLayoutSensitiveOpTransformer
>
(
op_name
);
}
// lightly transpose
template
<
>
inline
std
::
shared_ptr
<
EagerLayoutTransformer
>
EagerLayoutAutotune
(
const
std
::
string
&
op_name
,
const
paddle
::
small_vector
<
std
::
vector
<
paddle
::
experimental
::
Tensor
>
,
kSlotSmallVectorSize
>&
tensors_vector
,
std
::
vector
<
int
>*
attr
)
{
auto
first_layout
=
tensors_vector
[
0
][
0
].
layout
();
std
::
shared_ptr
<
EagerLayoutTransformer
>
transposer
=
nullptr
;
if
(
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
()
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
)
{
VLOG
(
3
)
<<
"Optimze Layout was not started"
<<
op_name
;
transposer
=
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
first_layout
);
return
transposer
;
// lightly transpose
if
(
DesiredLayout
()
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
)
{
VLOG
(
4
)
<<
"LayoutAutotune was unstarted. Current op :"
<<
op_name
;
return
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
tensors_vector
[
0
][
0
].
layout
());
}
if
(
op_name
==
"transpose2"
&&
(
tensors_vector
[
0
][
0
].
layout
()
==
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
()))
{
(
tensors_vector
[
0
][
0
].
layout
()
==
DesiredLayout
()))
{
auto
trans
=
std
::
make_shared
<
EagerTransposeOpTransformer
>
(
op_name
);
trans
->
SetAttr
(
attr
,
tensors_vector
[
0
][
0
].
layout
()
==
paddle
::
experimental
::
DataLayout
::
NHWC
);
return
trans
;
}
transposer
=
std
::
make_shared
<
EagerLightlyLayoutSensitiveOpTransformer
>
(
op_name
);
return
transposer
;
return
std
::
make_shared
<
EagerLightlyLayoutSensitiveOpTransformer
>
(
op_name
);
}
// lightly int argmax
...
...
@@ -210,19 +173,14 @@ EagerLayoutAutotune<paddle::experimental::Scalar, bool>(
kSlotSmallVectorSize
>&
tensors_vector
,
paddle
::
experimental
::
Scalar
*
axis
,
bool
*
keep_dim
)
{
auto
first_layout
=
tensors_vector
[
0
][
0
].
layout
();
std
::
shared_ptr
<
EagerLayoutTransformer
>
transposer
=
nullptr
;
if
(
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
()
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
)
{
VLOG
(
3
)
<<
"Optimze Layout was not started"
<<
op_name
;
transposer
=
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
first_layout
);
return
transposer
;
if
(
DesiredLayout
()
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
)
{
VLOG
(
4
)
<<
"LayoutAutotune was unstarted. Current op :"
<<
op_name
;
return
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
tensors_vector
[
0
][
0
].
layout
());
}
auto
desired_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
();
if
(
op_name
==
"argmax"
&&
(
tensors_vector
[
0
][
0
].
layout
()
==
desired_layout
)
&&
(
*
keep_dim
))
{
(
tensors_vector
[
0
][
0
].
layout
()
==
DesiredLayout
()
)
&&
(
*
keep_dim
))
{
std
::
shared_ptr
<
EagerArgmaxOpTransformer
>
argmax_transform
=
nullptr
;
argmax_transform
=
std
::
make_shared
<
EagerArgmaxOpTransformer
>
(
op_name
);
argmax_transform
->
SetAttr
(
axis
,
...
...
@@ -230,12 +188,9 @@ EagerLayoutAutotune<paddle::experimental::Scalar, bool>(
paddle
::
experimental
::
DataLayout
::
NHWC
);
return
argmax_transform
;
}
transposer
=
std
::
make_shared
<
EagerLightlyLayoutSensitiveOpTransformer
>
(
op_name
);
return
transposer
;
return
std
::
make_shared
<
EagerLightlyLayoutSensitiveOpTransformer
>
(
op_name
);
}
// lightly for flatten
template
<
>
inline
std
::
shared_ptr
<
EagerLayoutTransformer
>
EagerLayoutAutotune
<
int
,
int
>
(
const
std
::
string
&
op_name
,
...
...
@@ -243,32 +198,22 @@ inline std::shared_ptr<EagerLayoutTransformer> EagerLayoutAutotune<int, int>(
kSlotSmallVectorSize
>&
tensors_vector
,
int
*
start_axis
,
int
*
stop_axis
)
{
auto
first_layout
=
tensors_vector
[
0
][
0
].
layout
();
std
::
shared_ptr
<
EagerLayoutTransformer
>
transposer
=
nullptr
;
auto
desired_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
();
if
(
desired_layout
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
)
{
VLOG
(
3
)
<<
"Optimze Layout was not started"
<<
op_name
;
transposer
=
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
first_layout
);
return
transposer
;
if
(
DesiredLayout
()
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
)
{
VLOG
(
4
)
<<
"Optimze Layout was not started"
<<
op_name
;
return
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
tensors_vector
[
0
][
0
].
layout
());
}
bool
no_tranpose
=
tensors_vector
[
0
][
0
].
layout
()
==
desired_layout
;
bool
no_tranpose
=
tensors_vector
[
0
][
0
].
layout
()
==
DesiredLayout
();
bool
is_valid
=
((
*
start_axis
)
==
1
&&
(
*
stop_axis
)
==
3
);
if
(
op_name
==
"flatten"
||
op_name
==
"flatten_contiguous_range"
)
{
if
(
no_tranpose
&&
is_valid
)
{
std
::
shared_ptr
<
EagerFlattenOpTransformer
>
flatten_transform
=
nullptr
;
flatten_transform
=
std
::
make_shared
<
EagerFlattenOpTransformer
>
(
op_name
);
return
flatten_transform
;
return
std
::
make_shared
<
EagerFlattenOpTransformer
>
(
op_name
);
}
}
transposer
=
std
::
make_shared
<
EagerLightlyLayoutSensitiveOpTransformer
>
(
op_name
);
return
transposer
;
return
std
::
make_shared
<
EagerLightlyLayoutSensitiveOpTransformer
>
(
op_name
);
}
// lightly int Concat
template
<
>
inline
std
::
shared_ptr
<
EagerLayoutTransformer
>
EagerLayoutAutotune
<
paddle
::
experimental
::
Scalar
>
(
...
...
@@ -276,27 +221,26 @@ EagerLayoutAutotune<paddle::experimental::Scalar>(
const
paddle
::
small_vector
<
std
::
vector
<
paddle
::
experimental
::
Tensor
>
,
kSlotSmallVectorSize
>&
tensors_vector
,
paddle
::
experimental
::
Scalar
*
axis
)
{
auto
desired_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
();
auto
first_layout
=
tensors_vector
[
0
][
0
].
layout
();
std
::
shared_ptr
<
EagerLayoutTransformer
>
transposer
=
nullptr
;
if
(
desired_layout
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
)
{
VLOG
(
3
)
<<
"Optimze Layout was not started"
<<
op_name
;
transposer
=
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
first_layout
);
return
transposer
;
if
(
DesiredLayout
()
==
paddle
::
experimental
::
DataLayout
::
UNDEFINED
)
{
VLOG
(
4
)
<<
"Optimze Layout was not started"
<<
op_name
;
return
std
::
make_shared
<
EagerLayoutTransformer
>
(
op_name
,
tensors_vector
,
tensors_vector
[
0
][
0
].
layout
());
}
auto
desired_layout
=
DesiredLayout
();
if
(
NeedTransLayout
(
tensors_vector
,
desired_layout
))
{
VLOG
(
3
)
<<
op_name
<<
" need transpose to default layout"
;
transposer
=
std
::
make_shared
<
EagerLightlyLayoutSensitiveOpTransformer
>
(
op_name
);
return
transposer
;
}
else
{
auto
trans
=
std
::
make_shared
<
EagerConcatOpTransformer
>
(
op_name
);
trans
->
SetAttr
(
axis
,
desired_layout
);
return
trans
;
VLOG
(
4
)
<<
op_name
<<
"'s has different layout"
;
return
std
::
make_shared
<
EagerLightlyLayoutSensitiveOpTransformer
>
(
op_name
);
}
if
(
op_name
==
"Concat"
)
{
if
(
desired_layout
==
tensors_vector
[
0
][
0
].
layout
()
&&
tensors_vector
[
0
][
0
].
shape
().
size
()
==
4
)
{
auto
trans
=
std
::
make_shared
<
EagerConcatOpTransformer
>
(
op_name
);
trans
->
SetAttr
(
axis
,
desired_layout
);
return
trans
;
}
}
return
std
::
make_shared
<
EagerLightlyLayoutSensitiveOpTransformer
>
(
op_name
);
}
}
// namespace egr
paddle/fluid/eager/eager_layout_transformer.h
浏览文件 @
3da3462f
...
...
@@ -23,7 +23,7 @@ inline paddle::experimental::Tensor EagerTraceTransposeOp(
const
paddle
::
experimental
::
DataLayout
layout
,
const
paddle
::
experimental
::
Tensor
&
in
)
{
VLOG
(
4
)
<<
"AutoTune Transpose from "
<<
in
.
layout
()
<<
" to "
<<
layout
<<
", tensor's
shap
e is "
<<
in
.
shape
().
size
();
<<
", tensor's
dim siz
e is "
<<
in
.
shape
().
size
();
if
(
in
.
shape
().
size
()
!=
4
)
{
return
in
;
}
...
...
@@ -36,12 +36,72 @@ inline paddle::experimental::Tensor EagerTraceTransposeOp(
axis
=
{
0
,
1
,
2
,
3
};
}
auto
out_tensor
=
transpose_ad_func
(
in
,
axis
);
VLOG
(
4
)
<<
"AutoTune Transpose from "
<<
paddle
::
framework
::
DataLayoutToString
(
in
.
layout
())
<<
" to "
<<
paddle
::
framework
::
DataLayoutToString
(
layout
);
VLOG
(
4
)
<<
"AutoTune Transpose from "
<<
in
.
layout
()
<<
" to "
<<
layout
;
return
out_tensor
;
}
inline
paddle
::
experimental
::
DataLayout
DesiredLayout
()
{
return
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
();
}
inline
paddle
::
experimental
::
DataLayout
DefaultLayout
()
{
return
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDefaultLayout
();
}
inline
void
UpdateLayout
(
paddle
::
experimental
::
Tensor
*
out_tensor
,
const
paddle
::
experimental
::
DataLayout
layout
)
{
if
(
out_tensor
->
layout
()
!=
layout
)
{
VLOG
(
4
)
<<
"Update out_tensor's layout from "
<<
out_tensor
->
layout
()
<<
" to "
<<
layout
;
phi
::
DenseTensorUtils
::
GetMutableMeta
(
static_cast
<
phi
::
DenseTensor
*>
(
out_tensor
->
impl
().
get
()))
->
layout
=
layout
;
}
}
inline
void
DealWithShapeOp
(
paddle
::
experimental
::
Tensor
*
out_tensor
,
const
paddle
::
experimental
::
DataLayout
layout
,
int
dim_size
)
{
auto
des_layout
=
DesiredLayout
();
auto
def_layout
=
DefaultLayout
();
int32_t
*
value
=
static_cast
<
phi
::
DenseTensor
*>
(
out_tensor
->
impl
().
get
())
->
data
<
int32_t
>
();
bool
change_dim
=
(
des_layout
!=
def_layout
&&
layout
==
des_layout
&&
dim_size
==
4
);
VLOG
(
6
)
<<
"'Shape OP', layout autotune: True"
<<
" desired_layout: "
<<
des_layout
<<
" default_layout: "
<<
def_layout
<<
" tensor layout: "
<<
out_tensor
->
layout
()
<<
" tensor's shape size is : "
<<
dim_size
;
// It's means input tensor has been autotune and tensor's layout is
// desired_layout
std
::
vector
<
int32_t
>
dims
;
dims
.
resize
(
dim_size
);
for
(
int
i
=
0
;
i
<
dim_size
;
i
++
)
{
dims
[
i
]
=
value
[
i
];
}
auto
des_str
=
paddle
::
framework
::
DataLayoutToString
(
des_layout
);
if
(
change_dim
&&
des_str
==
"NCHW"
)
{
// NCHW -> NHWC
VLOG
(
6
)
<<
"layout autotune get Shape from NCHW -> NHWC "
<<
value
[
0
]
<<
" "
<<
value
[
1
]
<<
" "
<<
value
[
2
]
<<
" "
<<
value
[
3
]
<<
" to "
<<
dims
[
0
]
<<
" "
<<
dims
[
2
]
<<
" "
<<
dims
[
3
]
<<
" "
<<
dims
[
1
];
value
[
0
]
=
dims
[
0
];
value
[
1
]
=
dims
[
2
];
value
[
2
]
=
dims
[
3
];
value
[
3
]
=
dims
[
1
];
}
else
if
(
change_dim
&&
des_str
==
"NHWC"
)
{
// NHWC -> NCHW
VLOG
(
6
)
<<
"layout autotune get Shape from NHWC -> NCHW "
<<
value
[
0
]
<<
" "
<<
value
[
1
]
<<
" "
<<
value
[
2
]
<<
" "
<<
value
[
3
]
<<
" to "
<<
dims
[
0
]
<<
" "
<<
dims
[
3
]
<<
" "
<<
dims
[
1
]
<<
" "
<<
dims
[
2
];
value
[
0
]
=
dims
[
0
];
value
[
1
]
=
dims
[
3
];
value
[
2
]
=
dims
[
1
];
value
[
3
]
=
dims
[
2
];
}
}
// agnostic op
class
EagerLayoutTransformer
{
using
Layout
=
paddle
::
experimental
::
DataLayout
;
...
...
@@ -58,27 +118,27 @@ class EagerLayoutTransformer {
const
paddle
::
small_vector
<
std
::
vector
<
paddle
::
experimental
::
Tensor
>
,
kSlotSmallVectorSize
>&
tensors_vector
,
const
Layout
final_layout
=
Layout
::
UNDEFINED
)
:
op_name_
(
op_name
),
final_layout_
(
final_layout
)
{
VLOG
(
4
)
<<
"Agnostic op : "
<<
op_name_
<<
" final_layout_ is "
<<
final_layout_
;
:
op_name_
(
op_name
),
final_layout_
(
final_layout
),
dim_size_
(
1
)
{
VLOG
(
4
)
<<
"Agnostic op : "
<<
op_name_
<<
"'s layout is "
<<
final_layout_
;
}
virtual
~
EagerLayoutTransformer
()
{}
virtual
paddle
::
experimental
::
Tensor
TransInTensor
(
const
std
::
string
&
in_name
,
const
paddle
::
experimental
::
Tensor
&
in
)
{
if
(
final_layout_
==
Layout
::
UNDEFINED
||
final_layout_
==
in
.
layout
())
{
VLOG
(
4
)
<<
"EagerLayoutTransformer with no trans"
;
return
in
;
}
else
{
// from NCHW to NHWC
VLOG
(
4
)
<<
"EagerLayoutTransformer with trans from "
<<
in
.
layout
()
<<
" to "
<<
final_layout_
;
// update in shape size
dim_size_
=
in
.
shape
().
size
()
;
bool
need_trans
=
!
(
final_layout_
==
Layout
::
UNDEFINED
||
final_layout_
==
in
.
layout
());
// This is for Agnostic op when layout is differnet
if
(
need_trans
)
{
auto
out_tensor
=
EagerTraceTransposeOp
(
final_layout_
,
in
);
phi
::
DenseTensorUtils
::
GetMutableMeta
(
static_cast
<
phi
::
DenseTensor
*>
(
out_tensor
.
impl
().
get
()))
->
layout
=
final_layout_
;
return
out_tensor
;
}
return
in
;
}
virtual
paddle
::
optional
<
paddle
::
experimental
::
Tensor
>
TransInTensor
(
...
...
@@ -90,7 +150,6 @@ class EagerLayoutTransformer {
virtual
std
::
vector
<
paddle
::
experimental
::
Tensor
>
TransInTensors
(
const
std
::
string
&
in_name
,
const
std
::
vector
<
paddle
::
experimental
::
Tensor
>&
in
)
{
VLOG
(
4
)
<<
" TransInTensor"
;
return
in
;
}
...
...
@@ -98,72 +157,59 @@ class EagerLayoutTransformer {
TransInTensors
(
const
std
::
string
&
in_name
,
const
paddle
::
optional
<
std
::
vector
<
paddle
::
experimental
::
Tensor
>>&
in
)
{
VLOG
(
4
)
<<
" TransInTensor"
;
if
(
in
)
{
return
TransInTensors
(
in_name
,
*
in
);
}
return
in
;
}
virtual
void
SetOutTensorLayout
(
paddle
::
optional
<
paddle
::
experimental
::
Tensor
>*
out_tensor
)
{
VLOG
(
4
)
<<
"optional out_tensor"
;
return
(
in
?
TransInTensors
(
in_name
,
*
in
)
:
in
);
}
virtual
void
SetOutTensorLayout
(
std
::
vector
<
paddle
::
experimental
::
Tensor
>*
out_tensor
)
{
bool
u
se_default
=
(
final_layout_
==
Layout
::
UNDEFINED
);
if
(
!
use_defaul
t
)
{
bool
u
pdate_layout
=
!
(
final_layout_
==
Layout
::
UNDEFINED
);
if
(
update_layou
t
)
{
for
(
size_t
i
=
0
;
i
<
out_tensor
->
size
();
i
++
)
{
phi
::
DenseTensorUtils
::
GetMutableMeta
(
static_cast
<
phi
::
DenseTensor
*>
((
*
out_tensor
)[
i
].
impl
().
get
()))
->
layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
();
->
layout
=
DesiredLayout
();
}
}
VLOG
(
4
)
<<
op_name_
<<
"is is agnostic, use_default "
<<
use_default
;
}
virtual
void
SetOutTensorLayout
(
paddle
::
optional
<
paddle
::
experimental
::
Tensor
>*
out_tensor
)
{
VLOG
(
4
)
<<
"AutoTune out tensor is optional"
;
}
virtual
void
SetOutTensorLayout
(
paddle
::
optional
<
std
::
vector
<
paddle
::
experimental
::
Tensor
>>*
out_tensor
)
{
VLOG
(
4
)
<<
"
optional out_tensor
"
;
VLOG
(
4
)
<<
"
AutoTune out tensor is optional
"
;
}
virtual
void
SetOutTensorLayout
(
paddle
::
experimental
::
Tensor
*
out_tensor
)
{
bool
use_default
=
final_layout_
==
Layout
::
UNDEFINED
;
if
(
!
use_default
)
{
phi
::
DenseTensorUtils
::
GetMutableMeta
(
static_cast
<
phi
::
DenseTensor
*>
(
out_tensor
->
impl
().
get
()))
->
layout
=
final_layout_
;
if
(
op_name_
==
"shape"
)
{
return
DealWithShapeOp
(
out_tensor
,
final_layout_
,
dim_size_
);
}
bool
need_update
=
!
(
final_layout_
==
Layout
::
UNDEFINED
);
if
(
need_update
)
{
UpdateLayout
(
out_tensor
,
final_layout_
);
}
VLOG
(
4
)
<<
op_name_
<<
"is is agnostic, use_default "
<<
use_default
;
}
protected:
std
::
string
op_name_
;
const
Layout
final_layout_
;
int
dim_size_
;
};
class
EagerHeavilyLayoutSensitiveOpTransformer
:
public
EagerLayoutTransformer
{
public:
explicit
EagerHeavilyLayoutSensitiveOpTransformer
(
const
std
::
string
&
op_name
,
std
::
string
*
layout
)
:
op_name_
(
op_name
),
desired_layout_
(
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
())
{
VLOG
(
3
)
<<
"Optimze Layout heavily op: "
<<
op_name
;
final_layout_
=
paddle
::
framework
::
DataLayoutToString
(
desired_layout_
);
if
((
*
layout
)
!=
final_layout_
)
{
*
layout
=
final_layout_
;
}
:
op_name_
(
op_name
),
desired_layout_
(
DesiredLayout
())
{
VLOG
(
4
)
<<
"Heavily op: "
<<
op_name
;
*
layout
=
paddle
::
framework
::
DataLayoutToString
(
DesiredLayout
());
}
paddle
::
experimental
::
Tensor
TransInTensor
(
const
std
::
string
&
in_name
,
const
paddle
::
experimental
::
Tensor
&
in
)
{
if
(
heavily_input_
.
count
(
in_name
)
!=
0
&&
in
.
layout
()
!=
desired_layout_
)
{
VLOG
(
4
)
<<
op_name_
<<
"'s "
<<
in_name
<<
" need transpose from "
<<
paddle
::
framework
::
DataLayoutToString
(
in
.
layout
())
<<
" to "
<<
final_layout_
;
auto
out_tensor
=
EagerTraceTransposeOp
(
desired_layout_
,
in
);
return
out_tensor
;
}
...
...
@@ -171,14 +217,7 @@ class EagerHeavilyLayoutSensitiveOpTransformer : public EagerLayoutTransformer {
}
void
SetOutTensorLayout
(
paddle
::
experimental
::
Tensor
*
out_tensor
)
{
if
(
out_tensor
->
layout
()
!=
desired_layout_
)
{
VLOG
(
4
)
<<
" Set Out_tensor's layout from "
<<
paddle
::
framework
::
DataLayoutToString
(
out_tensor
->
layout
())
<<
" to "
<<
final_layout_
;
phi
::
DenseTensorUtils
::
GetMutableMeta
(
static_cast
<
phi
::
DenseTensor
*>
(
out_tensor
->
impl
().
get
()))
->
layout
=
desired_layout_
;
}
UpdateLayout
(
out_tensor
,
desired_layout_
);
}
void
SetOutTensorLayout
(
...
...
@@ -192,10 +231,8 @@ class EagerHeavilyLayoutSensitiveOpTransformer : public EagerLayoutTransformer {
std
::
vector
<
paddle
::
experimental
::
Tensor
>*
out_tensor
)
{
for
(
size_t
i
=
0
;
i
<
out_tensor
->
size
();
i
++
)
{
if
((
*
out_tensor
)[
i
].
layout
()
!=
desired_layout_
)
{
VLOG
(
4
)
<<
" Set Out_tensor's layout from "
<<
paddle
::
framework
::
DataLayoutToString
(
(
*
out_tensor
)[
i
].
layout
())
<<
" to "
<<
final_layout_
;
VLOG
(
4
)
<<
"Update out_tensor's layout from "
<<
(
*
out_tensor
)[
i
].
layout
()
<<
" to "
<<
desired_layout_
;
phi
::
DenseTensorUtils
::
GetMutableMeta
(
static_cast
<
phi
::
DenseTensor
*>
((
*
out_tensor
)[
i
].
impl
().
get
()))
->
layout
=
desired_layout_
;
...
...
@@ -205,7 +242,6 @@ class EagerHeavilyLayoutSensitiveOpTransformer : public EagerLayoutTransformer {
protected:
std
::
string
op_name_
;
std
::
string
final_layout_
;
const
paddle
::
experimental
::
DataLayout
desired_layout_
;
std
::
unordered_set
<
std
::
string
>
heavily_input_
{
"x"
,
"y"
,
"input"
};
};
...
...
@@ -213,11 +249,10 @@ class EagerHeavilyLayoutSensitiveOpTransformer : public EagerLayoutTransformer {
class
EagerLightlyLayoutSensitiveOpTransformer
:
public
EagerLayoutTransformer
{
public:
EagerLightlyLayoutSensitiveOpTransformer
()
{}
explicit
EagerLightlyLayoutSensitiveOpTransformer
(
const
std
::
string
&
op_name
)
:
op_name_
(
op_name
)
{
VLOG
(
3
)
<<
"Optimze Layout lightly "
<<
op_name
;
auto
desired_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
();
explicit
EagerLightlyLayoutSensitiveOpTransformer
(
const
std
::
string
&
op_name
)
{
VLOG
(
4
)
<<
"Lightly op : "
<<
op_name
;
auto
desired_layout
=
DesiredLayout
();
final_layout_
=
paddle
::
framework
::
DataLayoutToString
(
desired_layout
);
}
...
...
@@ -226,11 +261,8 @@ class EagerLightlyLayoutSensitiveOpTransformer : public EagerLayoutTransformer {
const
std
::
string
&
in_name
,
const
paddle
::
experimental
::
Tensor
&
in
)
{
std
::
string
input_layout
=
paddle
::
framework
::
DataLayoutToString
(
in
.
layout
());
auto
default_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDefaultLayout
();
auto
default_layout
=
DefaultLayout
();
if
(
final_layout_
==
input_layout
&&
in
.
shape
().
size
()
==
4
)
{
VLOG
(
4
)
<<
op_name_
<<
"'s "
<<
in_name
<<
" need transpose from "
<<
input_layout
<<
" to default_layout"
;
auto
out_tensor
=
EagerTraceTransposeOp
(
paddle
::
experimental
::
DataLayout
::
UNDEFINED
,
in
);
phi
::
DenseTensorUtils
::
GetMutableMeta
(
...
...
@@ -238,7 +270,6 @@ class EagerLightlyLayoutSensitiveOpTransformer : public EagerLayoutTransformer {
->
layout
=
default_layout
;
return
out_tensor
;
}
VLOG
(
4
)
<<
in_name
<<
"'s layout is "
<<
input_layout
;
return
in
;
}
...
...
@@ -246,15 +277,11 @@ class EagerLightlyLayoutSensitiveOpTransformer : public EagerLayoutTransformer {
const
std
::
string
&
in_name
,
const
std
::
vector
<
paddle
::
experimental
::
Tensor
>&
in
)
{
std
::
vector
<
paddle
::
experimental
::
Tensor
>
result
;
auto
desired_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
();
auto
default_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDefaultLayout
();
auto
desired_layout
=
DesiredLayout
();
auto
default_layout
=
DefaultLayout
();
for
(
size_t
i
=
0
;
i
<
in
.
size
();
i
++
)
{
auto
in_tensor
=
in
[
i
];
if
(
in_tensor
.
layout
()
==
desired_layout
)
{
VLOG
(
4
)
<<
op_name_
<<
"'s "
<<
in_name
<<
" need transpose from "
<<
final_layout_
<<
" to default_layout"
;
auto
out_tensor
=
EagerTraceTransposeOp
(
paddle
::
experimental
::
DataLayout
::
UNDEFINED
,
in_tensor
);
phi
::
DenseTensorUtils
::
GetMutableMeta
(
...
...
@@ -269,33 +296,20 @@ class EagerLightlyLayoutSensitiveOpTransformer : public EagerLayoutTransformer {
}
void
SetOutTensorLayout
(
paddle
::
experimental
::
Tensor
*
out_tensor
)
{
auto
out_layout
=
out_tensor
->
layout
();
auto
default_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDefaultLayout
();
if
(
out_layout
!=
default_layout
)
{
VLOG
(
4
)
<<
op_name_
<<
"'s out need transpose to default_layout"
;
phi
::
DenseTensorUtils
::
GetMutableMeta
(
static_cast
<
phi
::
DenseTensor
*>
(
out_tensor
->
impl
().
get
()))
->
layout
=
default_layout
;
}
UpdateLayout
(
out_tensor
,
DefaultLayout
());
}
void
SetOutTensorLayout
(
std
::
vector
<
paddle
::
experimental
::
Tensor
*>*
out_tensor
)
{
for
(
size_t
i
=
0
;
i
<
out_tensor
->
size
();
i
++
)
{
VLOG
(
4
)
<<
"out layout is"
<<
paddle
::
framework
::
DataLayoutToString
(
(
*
out_tensor
)[
i
]
->
layout
());
SetOutTensorLayout
((
*
out_tensor
)[
i
]);
}
}
void
SetOutTensorLayout
(
std
::
vector
<
paddle
::
experimental
::
Tensor
>*
out_tensor
)
{
auto
default_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDefaultLayout
();
auto
default_layout
=
DefaultLayout
();
for
(
size_t
i
=
0
;
i
<
out_tensor
->
size
();
i
++
)
{
VLOG
(
4
)
<<
" out_tensor layout trans to default "
;
phi
::
DenseTensorUtils
::
GetMutableMeta
(
static_cast
<
phi
::
DenseTensor
*>
((
*
out_tensor
)[
i
].
impl
().
get
()))
->
layout
=
default_layout
;
...
...
@@ -303,7 +317,6 @@ class EagerLightlyLayoutSensitiveOpTransformer : public EagerLayoutTransformer {
}
protected:
std
::
string
op_name_
;
std
::
string
final_layout_
;
std
::
unordered_set
<
std
::
string
>
heavily_input_
{
"x"
,
"y"
,
"input"
};
};
...
...
@@ -312,18 +325,11 @@ class EagerTransposeOpTransformer
:
public
EagerLightlyLayoutSensitiveOpTransformer
{
public:
EagerTransposeOpTransformer
()
{}
explicit
EagerTransposeOpTransformer
(
const
std
::
string
&
op_name
)
:
op_name_
(
op_name
)
{
VLOG
(
3
)
<<
"Optimze Layout TransposeOpTransformer "
<<
op_name
;
auto
desired_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
();
std
::
string
desired_layout_str
=
paddle
::
framework
::
DataLayoutToString
(
desired_layout
);
final_layout_
=
desired_layout_str
;
explicit
EagerTransposeOpTransformer
(
const
std
::
string
&
op_name
)
{
VLOG
(
4
)
<<
"AutoTuneTransformer op: "
<<
op_name
;
}
void
SetAttr
(
std
::
vector
<
int
>*
axis
,
bool
is_nhwc
)
{
// input's layout is nhwc and input's layout === desired_layout
std
::
vector
<
int
>
perm_nchw
=
{
0
,
2
,
3
,
1
};
std
::
vector
<
int
>
perm_nhwc
=
{
0
,
3
,
1
,
2
};
auto
perm
=
is_nhwc
?
perm_nhwc
:
perm_nchw
;
...
...
@@ -331,8 +337,6 @@ class EagerTransposeOpTransformer
(
*
axis
)[
1
]
=
perm
[(
*
axis
)[
1
]];
(
*
axis
)[
2
]
=
perm
[(
*
axis
)[
2
]];
(
*
axis
)[
3
]
=
perm
[(
*
axis
)[
3
]];
VLOG
(
4
)
<<
" EagerTransposeOpTransformer "
<<
op_name_
<<
"'s layout is equal to desire: "
<<
is_nhwc
;
}
paddle
::
experimental
::
Tensor
TransInTensor
(
...
...
@@ -341,31 +345,16 @@ class EagerTransposeOpTransformer
}
void
SetOutTensorLayout
(
paddle
::
experimental
::
Tensor
*
out_tensor
)
{
auto
default_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDefaultLayout
();
if
(
out_tensor
->
layout
()
!=
default_layout
)
{
VLOG
(
4
)
<<
" Set Out_tensor's layout from "
<<
paddle
::
framework
::
DataLayoutToString
(
out_tensor
->
layout
())
<<
" to "
<<
default_layout
;
phi
::
DenseTensorUtils
::
GetMutableMeta
(
static_cast
<
phi
::
DenseTensor
*>
(
out_tensor
->
impl
().
get
()))
->
layout
=
default_layout
;
}
UpdateLayout
(
out_tensor
,
DefaultLayout
());
}
protected:
std
::
string
op_name_
;
std
::
string
final_layout_
;
std
::
unordered_set
<
std
::
string
>
heavily_input_
{
"x"
,
"y"
,
"input"
};
};
class
EagerArgmaxOpTransformer
:
public
EagerLightlyLayoutSensitiveOpTransformer
{
public:
EagerArgmaxOpTransformer
()
{}
explicit
EagerArgmaxOpTransformer
(
const
std
::
string
&
op_name
)
:
op_name_
(
op_name
)
{
VLOG
(
3
)
<<
"Optimze Layout lightly "
<<
op_name
;
explicit
EagerArgmaxOpTransformer
(
const
std
::
string
&
op_name
)
{
VLOG
(
4
)
<<
"AutoTuneTransformer op: "
<<
op_name
;
}
void
SetAttr
(
paddle
::
experimental
::
Scalar
*
axis
,
bool
is_nhwc
)
{
...
...
@@ -377,38 +366,16 @@ class EagerArgmaxOpTransformer
}
void
SetOutTensorLayout
(
paddle
::
experimental
::
Tensor
*
out_tensor
)
{
VLOG
(
4
)
<<
"EagerArgmaxOpTransformer's out layout is"
<<
paddle
::
framework
::
DataLayoutToString
(
out_tensor
->
layout
());
auto
default_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDefaultLayout
();
if
(
default_layout
!=
out_tensor
->
layout
())
{
VLOG
(
4
)
<<
"Change layout from "
<<
paddle
::
framework
::
DataLayoutToString
(
out_tensor
->
layout
())
<<
" to "
<<
default_layout
;
phi
::
DenseTensorUtils
::
GetMutableMeta
(
static_cast
<
phi
::
DenseTensor
*>
(
out_tensor
->
impl
().
get
()))
->
layout
=
default_layout
;
}
UpdateLayout
(
out_tensor
,
DesiredLayout
());
}
protected:
std
::
string
op_name_
;
std
::
string
final_layout_
;
std
::
unordered_set
<
std
::
string
>
heavily_input_
{
"x"
,
"y"
,
"input"
};
};
class
EagerFlattenOpTransformer
:
public
EagerLightlyLayoutSensitiveOpTransformer
{
public:
EagerFlattenOpTransformer
()
{}
explicit
EagerFlattenOpTransformer
(
const
std
::
string
&
op_name
)
:
op_name_
(
op_name
)
{
VLOG
(
3
)
<<
"Optimze Layout lightly "
<<
op_name
;
auto
default_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDefaultLayout
();
std
::
string
default_layout_str
=
paddle
::
framework
::
DataLayoutToString
(
default_layout
);
final_layout_
=
default_layout_str
;
explicit
EagerFlattenOpTransformer
(
const
std
::
string
&
op_name
)
{
VLOG
(
4
)
<<
"AutoTuneTransformer op: "
<<
op_name
;
}
// transpose from NHWC to NCHW
...
...
@@ -418,38 +385,16 @@ class EagerFlattenOpTransformer
}
void
SetOutTensorLayout
(
paddle
::
experimental
::
Tensor
*
out_tensor
)
{
VLOG
(
4
)
<<
"EagerFlattenOpTransformer's out layout is"
<<
paddle
::
framework
::
DataLayoutToString
(
out_tensor
->
layout
());
auto
desired_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
();
if
(
desired_layout
!=
out_tensor
->
layout
())
{
VLOG
(
4
)
<<
"Change layout from "
<<
paddle
::
framework
::
DataLayoutToString
(
out_tensor
->
layout
())
<<
" to "
<<
desired_layout
;
phi
::
DenseTensorUtils
::
GetMutableMeta
(
static_cast
<
phi
::
DenseTensor
*>
(
out_tensor
->
impl
().
get
()))
->
layout
=
desired_layout
;
}
UpdateLayout
(
out_tensor
,
DefaultLayout
());
}
protected:
std
::
string
op_name_
;
std
::
string
final_layout_
;
std
::
unordered_set
<
std
::
string
>
heavily_input_
{
"x"
,
"y"
,
"input"
};
};
class
EagerConcatOpTransformer
:
public
EagerLightlyLayoutSensitiveOpTransformer
{
public:
EagerConcatOpTransformer
()
{}
explicit
EagerConcatOpTransformer
(
const
std
::
string
&
op_name
)
:
op_name_
(
op_name
)
{
VLOG
(
3
)
<<
"Optimze Layout lightly "
<<
op_name
;
auto
default_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDefaultLayout
();
std
::
string
default_layout_str
=
paddle
::
framework
::
DataLayoutToString
(
default_layout
);
final_layout_
=
default_layout_str
;
explicit
EagerConcatOpTransformer
(
const
std
::
string
&
op_name
)
{
VLOG
(
4
)
<<
"AutoTuneTransformer op : "
<<
op_name
;
}
void
SetAttr
(
paddle
::
experimental
::
Scalar
*
axis
,
...
...
@@ -457,6 +402,7 @@ class EagerConcatOpTransformer
std
::
vector
<
int
>
perm_nhwc
=
{
0
,
3
,
1
,
2
};
std
::
vector
<
int
>
perm_nchw
=
{
0
,
2
,
3
,
1
};
int
axes
=
axis
->
to
<
int
>
();
axes
=
axes
<
0
?
axes
+
4
:
axes
;
auto
perm
=
(
paddle
::
framework
::
DataLayout
::
NHWC
==
layout
)
?
perm_nhwc
:
perm_nchw
;
(
*
axis
)
=
static_cast
<
paddle
::
experimental
::
Scalar
>
(
perm
[
axes
]);
...
...
@@ -469,20 +415,7 @@ class EagerConcatOpTransformer
}
void
SetOutTensorLayout
(
paddle
::
experimental
::
Tensor
*
out_tensor
)
{
auto
layout
=
paddle
::
framework
::
StringToDataLayout
(
final_layout_
);
if
(
layout
!=
out_tensor
->
layout
())
{
VLOG
(
4
)
<<
"Change layout from "
<<
paddle
::
framework
::
DataLayoutToString
(
out_tensor
->
layout
())
<<
" to "
<<
final_layout_
;
phi
::
DenseTensorUtils
::
GetMutableMeta
(
static_cast
<
phi
::
DenseTensor
*>
(
out_tensor
->
impl
().
get
()))
->
layout
=
layout
;
}
UpdateLayout
(
out_tensor
,
DesiredLayout
());
}
protected:
std
::
string
op_name_
;
std
::
string
final_layout_
;
std
::
unordered_set
<
std
::
string
>
heavily_input_
{
"x"
,
"y"
,
"input"
};
};
}
// namespace egr
paddle/fluid/imperative/layout_autotune.cc
浏览文件 @
3da3462f
...
...
@@ -194,8 +194,10 @@ paddle::imperative::NameVarMap<VarType> AutoTuneLayout(
(
conv_in_type
==
framework
::
proto
::
VarType
::
FP16
);
if
(
is_tune_fp32
)
{
LayoutAutoTune
::
Instance
().
SetDesiredLayout
(
DataLayout
::
NCHW
);
LayoutAutoTune
::
Instance
().
SetDefaultLayout
(
DataLayout
::
NHWC
);
}
else
if
(
is_tune_fp16
)
{
LayoutAutoTune
::
Instance
().
SetDesiredLayout
(
DataLayout
::
NHWC
);
LayoutAutoTune
::
Instance
().
SetDefaultLayout
(
DataLayout
::
NCHW
);
}
else
{
tracer
->
DisableLayoutAutoTune
();
return
ins
;
...
...
paddle/fluid/pybind/eager_properties.cc
浏览文件 @
3da3462f
...
...
@@ -184,6 +184,42 @@ PyObject* tensor_properties_get_shape(TensorObject* self, void* closure) {
}
}
auto
desired_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDesiredLayout
();
auto
default_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
().
GetDefaultLayout
();
bool
change_dim
=
(
desired_layout
!=
default_layout
&&
self
->
tensor
.
layout
()
==
desired_layout
&&
value
.
size
()
==
4
);
VLOG
(
6
)
<<
"eager_properties 'Shape' method, layout autotune "
<<
" desired_layout: "
<<
desired_layout
<<
" default_layout: "
<<
default_layout
<<
" tensor layout: "
<<
self
->
tensor
.
layout
()
<<
" tensor's shape size is : "
<<
value
.
size
();
std
::
vector
<
int64_t
>
dims
=
value
;
if
(
change_dim
&&
paddle
::
framework
::
DataLayoutToString
(
desired_layout
)
==
"NCHW"
)
{
// NCHW -> NHWC
VLOG
(
6
)
<<
"layout autotune get Shape from NCHW -> NHWC "
<<
value
[
0
]
<<
" "
<<
value
[
1
]
<<
" "
<<
value
[
2
]
<<
" "
<<
value
[
3
]
<<
" to "
<<
dims
[
0
]
<<
" "
<<
dims
[
2
]
<<
" "
<<
dims
[
3
]
<<
" "
<<
dims
[
1
];
value
[
0
]
=
dims
[
0
];
value
[
1
]
=
dims
[
2
];
value
[
2
]
=
dims
[
3
];
value
[
3
]
=
dims
[
1
];
}
else
if
(
change_dim
&&
paddle
::
framework
::
DataLayoutToString
(
desired_layout
)
==
"NHWC"
)
{
// NHWC -> NCHW
VLOG
(
6
)
<<
"layout autotune get Shape from NHWC -> NCHW "
<<
value
[
0
]
<<
" "
<<
value
[
1
]
<<
" "
<<
value
[
2
]
<<
" "
<<
value
[
3
]
<<
" to "
<<
dims
[
0
]
<<
" "
<<
dims
[
3
]
<<
" "
<<
dims
[
1
]
<<
" "
<<
dims
[
2
]
<<
" "
<<
dims
[
1
];
value
[
0
]
=
dims
[
0
];
value
[
1
]
=
dims
[
3
];
value
[
2
]
=
dims
[
1
];
value
[
3
]
=
dims
[
2
];
}
return
ToPyObject
(
value
);
EAGER_CATCH_AND_THROW_RETURN_NULL
}
...
...
paddle/fluid/pybind/imperative.cc
浏览文件 @
3da3462f
...
...
@@ -2044,8 +2044,49 @@ void BindImperative(py::module *m_ptr) {
"shape"
,
[](
imperative
::
VarBase
&
self
)
{
if
(
self
.
Var
().
IsType
<
framework
::
LoDTensor
>
())
{
return
phi
::
vectorize
<
int
>
(
auto
value
=
phi
::
vectorize
<
int
>
(
self
.
Var
().
Get
<
framework
::
LoDTensor
>
().
dims
());
auto
tensor
=
self
.
Var
().
Get
<
framework
::
LoDTensor
>
();
auto
tmp_value
=
value
;
auto
desired_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
()
.
GetDesiredLayout
();
auto
default_layout
=
paddle
::
imperative
::
LayoutAutoTune
::
Instance
()
.
GetDefaultLayout
();
bool
change_dim
=
(
desired_layout
!=
default_layout
&&
tensor
.
layout
()
==
desired_layout
&&
value
.
size
()
==
4
);
VLOG
(
6
)
<<
"'Shape' method, layout autotune,"
<<
" desired_layout: "
<<
desired_layout
<<
" default_layout: "
<<
default_layout
<<
" tensor layout: "
<<
tensor
.
layout
()
<<
" tensor's shape size is : "
<<
value
.
size
();
if
(
change_dim
&&
paddle
::
framework
::
DataLayoutToString
(
desired_layout
)
==
"NCHW"
)
{
VLOG
(
6
)
<<
"layout autotune get Shape from NHWC -> NCHW "
<<
value
[
0
]
<<
" "
<<
value
[
1
]
<<
" "
<<
value
[
2
]
<<
" "
<<
value
[
3
]
<<
" to "
<<
tmp_value
[
3
]
<<
" "
<<
tmp_value
[
1
]
<<
" "
<<
tmp_value
[
2
]
<<
" "
<<
tmp_value
[
1
];
// NCHW -> NHWC
value
[
1
]
=
tmp_value
[
2
];
value
[
2
]
=
tmp_value
[
3
];
value
[
3
]
=
tmp_value
[
1
];
}
else
if
(
change_dim
&&
paddle
::
framework
::
DataLayoutToString
(
desired_layout
)
==
"NHWC"
)
{
VLOG
(
6
)
<<
"layout autotune get Shape from NHWC -> NCHW "
<<
value
[
0
]
<<
" "
<<
value
[
1
]
<<
" "
<<
value
[
2
]
<<
" "
<<
value
[
3
]
<<
" to "
<<
tmp_value
[
0
]
<<
" "
<<
tmp_value
[
3
]
<<
" "
<<
tmp_value
[
1
]
<<
" "
<<
tmp_value
[
2
];
// NHWC -> NCHW
value
[
1
]
=
tmp_value
[
3
];
value
[
2
]
=
tmp_value
[
1
];
value
[
3
]
=
tmp_value
[
2
];
}
return
value
;
}
else
if
(
self
.
Var
().
IsType
<
phi
::
SelectedRows
>
())
{
return
phi
::
vectorize
<
int
>
(
self
.
Var
().
Get
<
phi
::
SelectedRows
>
().
value
().
dims
());
...
...
paddle/phi/api/lib/data_transform.cc
浏览文件 @
3da3462f
...
...
@@ -205,7 +205,8 @@ phi::DenseTensor TransformData(phi::DenseTensor* tensor,
if
(
NeedTransformLayout
(
tensor
->
layout
(),
target_args_def
.
layout
,
tensor
->
place
(),
transform_flag
))
{
transform_flag
)
&&
tensor
->
dims
().
size
()
!=
1
)
{
out
=
TransDataLayout
(
out
,
target_args_def
.
layout
);
trans_layout
=
true
;
}
...
...
python/paddle/fluid/tests/unittests/test_layout_autotune.py
浏览文件 @
3da3462f
...
...
@@ -93,18 +93,9 @@ class LayoutAutoTune(unittest.TestCase):
return
conv_out
,
predict
def
test_enable_autotune
(
self
):
if
self
.
use_autoune
():
conv_out
,
predict
=
self
.
train
(
data_format
=
"NCHW"
)
if
paddle
.
fluid
.
core
.
use_layout_autotune
():
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
14
,
14
,
8
])
self
.
assertEqual
(
predict
.
shape
,
[
1
,
2
])
else
:
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
14
])
self
.
assertEqual
(
predict
.
shape
,
[
1
,
2
])
else
:
conv_out
,
predict
=
self
.
train
(
data_format
=
"NCHW"
)
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
14
])
self
.
assertEqual
(
predict
.
shape
,
[
1
,
2
])
conv_out
,
predict
=
self
.
train
(
data_format
=
"NCHW"
)
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
14
])
self
.
assertEqual
(
predict
.
shape
,
[
1
,
2
])
def
test_transpose_op_transposer
(
self
):
conv
=
paddle
.
nn
.
Conv2D
(
3
,
8
,
(
3
,
3
))
...
...
@@ -124,12 +115,8 @@ class LayoutAutoTune(unittest.TestCase):
scaled
.
backward
()
scaler
.
minimize
(
optimizer
,
scaled
)
if
paddle
.
fluid
.
core
.
use_layout_autotune
():
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
14
,
12
,
8
])
self
.
assertEqual
(
out
.
shape
,
[
1
,
12
,
8
,
14
])
else
:
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
12
])
self
.
assertEqual
(
out
.
shape
,
[
1
,
12
,
8
,
14
])
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
12
])
self
.
assertEqual
(
out
.
shape
,
[
1
,
12
,
8
,
14
])
def
test_flatten_op_transposer
(
self
):
conv
=
paddle
.
nn
.
Conv2D
(
3
,
8
,
(
3
,
3
))
...
...
@@ -143,12 +130,8 @@ class LayoutAutoTune(unittest.TestCase):
# because it flatten the C and H dimensions.
out
=
flatten
(
conv_out
)
if
paddle
.
fluid
.
core
.
use_layout_autotune
():
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
14
,
12
,
8
])
self
.
assertEqual
(
out
.
shape
,
[
1
,
112
,
12
])
else
:
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
12
])
self
.
assertEqual
(
out
.
shape
,
[
1
,
112
,
12
])
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
12
])
self
.
assertEqual
(
out
.
shape
,
[
1
,
112
,
12
])
def
test_argmax_op_transposer_keep_dims
(
self
):
conv
=
paddle
.
nn
.
Conv2D
(
3
,
8
,
(
3
,
3
))
...
...
@@ -157,41 +140,8 @@ class LayoutAutoTune(unittest.TestCase):
conv_out
=
conv
(
data
)
# conv_out.shape = [1, 14, 12, 8] with NHWC
out
=
paddle
.
argmax
(
conv_out
,
axis
=
1
,
keepdim
=
True
)
if
paddle
.
fluid
.
core
.
use_layout_autotune
():
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
14
,
12
,
8
])
self
.
assertEqual
(
out
.
shape
,
[
1
,
14
,
12
,
1
])
else
:
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
12
])
self
.
assertEqual
(
out
.
shape
,
[
1
,
1
,
14
,
12
])
def
test_argmax_op_transposer_ff
(
self
):
conv
=
paddle
.
nn
.
Conv2D
(
3
,
8
,
(
3
,
3
))
data
=
paddle
.
rand
([
1
,
3
,
16
,
14
])
with
paddle
.
amp
.
auto_cast
(
level
=
"O2"
):
conv_out
=
conv
(
data
)
# conv_out.shape = [1, 14, 12, 8] with NHWC
out
=
paddle
.
argmax
(
conv_out
)
if
paddle
.
fluid
.
core
.
use_layout_autotune
():
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
14
,
12
,
8
])
self
.
assertEqual
(
out
.
shape
,
[
1
])
else
:
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
12
])
self
.
assertEqual
(
out
.
shape
,
[
1
])
def
test_argmax_op_transposer_t
(
self
):
conv
=
paddle
.
nn
.
Conv2D
(
3
,
8
,
(
3
,
3
))
data
=
paddle
.
rand
([
1
,
3
,
16
,
14
])
with
paddle
.
amp
.
auto_cast
(
level
=
"O2"
):
conv_out
=
conv
(
data
)
# conv_out.shape = [1, 14, 12, 8] with NHWC
out
=
paddle
.
argmax
(
conv_out
)
if
paddle
.
fluid
.
core
.
use_layout_autotune
():
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
14
,
12
,
8
])
self
.
assertEqual
(
out
.
shape
,
[
1
])
else
:
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
12
])
self
.
assertEqual
(
out
.
shape
,
[
1
])
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
12
])
self
.
assertEqual
(
out
.
shape
,
[
1
,
1
,
14
,
12
])
def
test_concat_op_transposer
(
self
):
in1
=
paddle
.
rand
([
1
,
8
,
14
,
12
])
...
...
@@ -202,12 +152,8 @@ class LayoutAutoTune(unittest.TestCase):
# conv_out.shape = [1, 14, 12, 8] with NHWC
out
=
paddle
.
concat
(
x
=
[
conv_out
,
in1
],
axis
=
0
)
if
paddle
.
fluid
.
core
.
use_layout_autotune
():
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
14
,
12
,
8
])
self
.
assertEqual
(
out
.
shape
,
[
2
,
8
,
14
,
12
])
else
:
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
12
])
self
.
assertEqual
(
out
.
shape
,
[
2
,
8
,
14
,
12
])
self
.
assertEqual
(
conv_out
.
shape
,
[
1
,
8
,
14
,
12
])
self
.
assertEqual
(
out
.
shape
,
[
2
,
8
,
14
,
12
])
def
test_concat_op_no_transposer
(
self
):
conv
=
paddle
.
nn
.
Conv2D
(
3
,
8
,
(
3
,
3
))
...
...
@@ -219,12 +165,8 @@ class LayoutAutoTune(unittest.TestCase):
# conv_out.shape = [1, 14, 12, 8] with NHWC
out
=
paddle
.
concat
(
x
=
[
conv_out1
,
conv_out2
],
axis
=
0
)
if
paddle
.
fluid
.
core
.
use_layout_autotune
():
self
.
assertEqual
(
conv_out1
.
shape
,
[
1
,
14
,
12
,
8
])
self
.
assertEqual
(
out
.
shape
,
[
2
,
14
,
12
,
8
])
else
:
self
.
assertEqual
(
conv_out1
.
shape
,
[
1
,
8
,
14
,
12
])
self
.
assertEqual
(
out
.
shape
,
[
2
,
8
,
14
,
12
])
self
.
assertEqual
(
conv_out1
.
shape
,
[
1
,
8
,
14
,
12
])
self
.
assertEqual
(
out
.
shape
,
[
2
,
8
,
14
,
12
])
class
TestAutoTuneAPI
(
unittest
.
TestCase
):
...
...
python/paddle/nn/functional/conv.py
浏览文件 @
3da3462f
...
...
@@ -152,8 +152,8 @@ def _conv_nd(x,
channel_dim
=
channel_dim
+
len
(
x
.
shape
)
if
channel_dim
<
0
else
channel_dim
tmp_bias
=
_C_ops
.
reshape
(
bias
,
bias
.
shape
+
[
1
for
i
in
range
(
len
(
x
.
shape
)
-
channel_dim
-
1
)])
bias
,
[
1
for
i
in
range
(
channel_dim
)]
+
bias
.
shape
+
[
1
for
i
in
range
(
len
(
x
.
shape
)
-
channel_dim
-
1
)])
return
_C_ops
.
add
(
pre_bias
,
tmp_bias
)
else
:
return
pre_bias
...
...
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