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f3f27d25
编写于
3月 13, 2022
作者:
Z
zyfncg
提交者:
GitHub
3月 13, 2022
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差异文件
[PHI] Refactor infermeta files (Part2) (#40367)
* refactor infermeta files * update
上级
080024f0
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
731 addition
and
728 deletion
+731
-728
paddle/fluid/operators/gather_nd_op.cc
paddle/fluid/operators/gather_nd_op.cc
+0
-1
paddle/phi/infermeta/backward.cc
paddle/phi/infermeta/backward.cc
+15
-14
paddle/phi/infermeta/backward.h
paddle/phi/infermeta/backward.h
+6
-1
paddle/phi/infermeta/binary.cc
paddle/phi/infermeta/binary.cc
+452
-451
paddle/phi/infermeta/binary.h
paddle/phi/infermeta/binary.h
+57
-57
paddle/phi/infermeta/nullary.cc
paddle/phi/infermeta/nullary.cc
+18
-18
paddle/phi/infermeta/nullary.h
paddle/phi/infermeta/nullary.h
+9
-9
paddle/phi/infermeta/ternary.cc
paddle/phi/infermeta/ternary.cc
+153
-152
paddle/phi/infermeta/ternary.h
paddle/phi/infermeta/ternary.h
+21
-25
未找到文件。
paddle/fluid/operators/gather_nd_op.cc
浏览文件 @
f3f27d25
...
@@ -16,7 +16,6 @@ limitations under the License. */
...
@@ -16,7 +16,6 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/phi/infermeta/backward.h"
#include "paddle/phi/infermeta/backward.h"
#include "paddle/phi/infermeta/binary.h"
#include "paddle/phi/infermeta/binary.h"
#include "paddle/phi/infermeta/ternary.h"
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
...
...
paddle/phi/infermeta/backward.cc
浏览文件 @
f3f27d25
...
@@ -64,10 +64,14 @@ void BilinearTensorProductGradInferMeta(const MetaTensor& x,
...
@@ -64,10 +64,14 @@ void BilinearTensorProductGradInferMeta(const MetaTensor& x,
}
}
}
}
void
GeneralUnaryGradInferMeta
(
const
MetaTensor
&
x
,
MetaTensor
*
dx
)
{
void
GatherNdGradInferMeta
(
const
MetaTensor
&
x
,
if
(
dx
)
{
const
MetaTensor
&
index
,
dx
->
share_meta
(
x
);
const
MetaTensor
&
out_grad
,
}
MetaTensor
*
x_grad
)
{
const
auto
&
dtype
=
out_grad
.
dtype
();
x_grad
->
set_dims
(
x
.
dims
());
x_grad
->
share_lod
(
x
);
x_grad
->
set_dtype
(
dtype
);
}
}
void
GeneralBinaryGradInferMeta
(
const
MetaTensor
&
x
,
void
GeneralBinaryGradInferMeta
(
const
MetaTensor
&
x
,
...
@@ -99,6 +103,12 @@ void GeneralTernaryGradInferMeta(const MetaTensor& x,
...
@@ -99,6 +103,12 @@ void GeneralTernaryGradInferMeta(const MetaTensor& x,
}
}
}
}
void
GeneralUnaryGradInferMeta
(
const
MetaTensor
&
x
,
MetaTensor
*
dx
)
{
if
(
dx
)
{
dx
->
share_meta
(
x
);
}
}
void
GumbelSoftmaxGradInferMeta
(
const
MetaTensor
&
out
,
void
GumbelSoftmaxGradInferMeta
(
const
MetaTensor
&
out
,
const
MetaTensor
&
dout
,
const
MetaTensor
&
dout
,
int
axis
,
int
axis
,
...
@@ -108,17 +118,8 @@ void GumbelSoftmaxGradInferMeta(const MetaTensor& out,
...
@@ -108,17 +118,8 @@ void GumbelSoftmaxGradInferMeta(const MetaTensor& out,
dout
.
dims
(),
dout
.
dims
(),
errors
::
InvalidArgument
(
errors
::
InvalidArgument
(
"Input(Out) and its gradients should have the same shape."
));
"Input(Out) and its gradients should have the same shape."
));
dx
->
share_meta
(
dout
);
}
void
GatherNdGradInferMeta
(
const
MetaTensor
&
x
,
dx
->
share_meta
(
dout
);
const
MetaTensor
&
index
,
const
MetaTensor
&
out_grad
,
MetaTensor
*
x_grad
)
{
const
auto
&
dtype
=
out_grad
.
dtype
();
x_grad
->
set_dims
(
x
.
dims
());
x_grad
->
share_lod
(
x
);
x_grad
->
set_dtype
(
dtype
);
}
}
void
PsroiPoolGradInferMeta
(
const
MetaTensor
&
x
,
void
PsroiPoolGradInferMeta
(
const
MetaTensor
&
x
,
...
...
paddle/phi/infermeta/backward.h
浏览文件 @
f3f27d25
...
@@ -30,7 +30,10 @@ void BilinearTensorProductGradInferMeta(const MetaTensor& x,
...
@@ -30,7 +30,10 @@ void BilinearTensorProductGradInferMeta(const MetaTensor& x,
MetaTensor
*
dweight
,
MetaTensor
*
dweight
,
MetaTensor
*
dbias
);
MetaTensor
*
dbias
);
void
GeneralUnaryGradInferMeta
(
const
MetaTensor
&
x
,
MetaTensor
*
dx
);
void
GatherNdGradInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
index
,
const
MetaTensor
&
out_grad
,
MetaTensor
*
x_grad
);
void
GeneralBinaryGradInferMeta
(
const
MetaTensor
&
x
,
void
GeneralBinaryGradInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
const
MetaTensor
&
y
,
...
@@ -44,6 +47,8 @@ void GeneralTernaryGradInferMeta(const MetaTensor& x,
...
@@ -44,6 +47,8 @@ void GeneralTernaryGradInferMeta(const MetaTensor& x,
MetaTensor
*
dy
,
MetaTensor
*
dy
,
MetaTensor
*
dz
);
MetaTensor
*
dz
);
void
GeneralUnaryGradInferMeta
(
const
MetaTensor
&
x
,
MetaTensor
*
dx
);
void
GumbelSoftmaxGradInferMeta
(
const
MetaTensor
&
out
,
void
GumbelSoftmaxGradInferMeta
(
const
MetaTensor
&
out
,
const
MetaTensor
&
dout
,
const
MetaTensor
&
dout
,
int
axis
,
int
axis
,
...
...
paddle/phi/infermeta/binary.cc
浏览文件 @
f3f27d25
...
@@ -22,6 +22,153 @@ limitations under the License. */
...
@@ -22,6 +22,153 @@ limitations under the License. */
namespace
phi
{
namespace
phi
{
void
Atan2InferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
MetaTensor
*
out
)
{
out
->
share_meta
(
x
);
}
void
BCELossInferMeta
(
const
MetaTensor
&
input
,
const
MetaTensor
&
label
,
MetaTensor
*
out
,
MetaConfig
config
)
{
auto
input_dims
=
input
.
dims
();
auto
label_dims
=
label
.
dims
();
int
rank
=
input_dims
.
size
();
PADDLE_ENFORCE_EQ
(
rank
,
label_dims
.
size
(),
phi
::
errors
::
InvalidArgument
(
"Input(X) and Input(Label) shall have the same rank."
"But received: the rank of Input(X) is [%d], "
"the rank of Input(Label) is [%d]."
,
rank
,
label_dims
.
size
()));
bool
check
=
true
;
if
((
!
config
.
is_runtime
)
&&
(
phi
::
product
(
input_dims
)
<=
0
||
phi
::
product
(
label_dims
)
<=
0
))
{
check
=
false
;
}
if
(
check
)
{
PADDLE_ENFORCE_EQ
(
input_dims
,
label_dims
,
phi
::
errors
::
InvalidArgument
(
"Input(X) and Input(Label) shall have the same "
"shape. But received: the shape of Input(X) is "
"[%s], the shape of Input(Label) is [%s]."
,
input_dims
,
label_dims
));
}
out
->
set_dims
(
input_dims
);
out
->
set_dtype
(
input
.
dtype
());
out
->
share_lod
(
input
);
}
void
BincountInferMeta
(
const
MetaTensor
&
x
,
const
paddle
::
optional
<
const
MetaTensor
&>
weights
,
int
minlength
,
MetaTensor
*
out
)
{
auto
input_dim
=
x
.
dims
();
PADDLE_ENFORCE_GE
(
minlength
,
0
,
phi
::
errors
::
InvalidArgument
(
"The minlength should be greater than or equal to 0."
"But received minlength is %d"
,
minlength
));
PADDLE_ENFORCE_EQ
(
input_dim
.
size
(),
1
,
phi
::
errors
::
InvalidArgument
(
"The 'shape' of Input(X) must be 1-D tensor."
"But the dimension of Input(X) is [%d]"
,
input_dim
.
size
()));
if
(
weights
.
is_initialized
())
{
auto
weights_dim
=
weights
->
dims
();
PADDLE_ENFORCE_EQ
(
weights_dim
.
size
(),
1
,
phi
::
errors
::
InvalidArgument
(
"The 'shape' of Input(Weights) must be 1-D tensor."
"But the dimension of Input(Weights) is [%d]"
,
weights_dim
.
size
()));
PADDLE_ENFORCE_EQ
(
weights_dim
[
0
],
input_dim
[
0
],
phi
::
errors
::
InvalidArgument
(
"The 'shape' of Input(Weights) must be equal to the 'shape' of "
"Input(X)."
"But received: the 'shape' of Input(Weights) is [%s],"
"the 'shape' of Input(X) is [%s]"
,
weights_dim
,
input_dim
));
}
out
->
set_dims
(
phi
::
make_ddim
({
-
1
}));
if
(
weights
.
is_initialized
())
{
out
->
set_dtype
(
weights
->
dtype
());
}
else
{
out
->
set_dtype
(
x
.
dtype
());
}
out
->
share_lod
(
x
);
}
void
CholeskySolveInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
bool
upper
,
MetaTensor
*
out
)
{
auto
x_dims
=
x
.
dims
();
auto
y_dims
=
y
.
dims
();
auto
x_dims_n
=
x_dims
.
size
();
auto
y_dims_n
=
y_dims
.
size
();
PADDLE_ENFORCE_GE
(
x_dims_n
,
2
,
phi
::
errors
::
InvalidArgument
(
"the rank of input Y must greater or equal to 2"
));
PADDLE_ENFORCE_GE
(
y_dims_n
,
2
,
phi
::
errors
::
InvalidArgument
(
"the rank of input X must greater or equal to 2"
));
PADDLE_ENFORCE_EQ
(
y_dims
[
y_dims_n
-
1
],
y_dims
[
y_dims_n
-
2
],
phi
::
errors
::
InvalidArgument
(
"input Matrix Y should be square matrix,"
"But Got last shape of %ld x %ld"
,
y_dims
[
y_dims_n
-
1
],
y_dims
[
y_dims_n
-
2
]));
PADDLE_ENFORCE_EQ
(
x_dims
[
x_dims_n
-
2
],
y_dims
[
y_dims_n
-
2
],
phi
::
errors
::
InvalidArgument
(
"the first dim of Matrix X must be equal to "
"the fisrt dim of Matrix Y,"
"But Got %ld and %ld"
,
x_dims
[
x_dims_n
-
2
],
y_dims
[
y_dims_n
-
2
]));
std
::
vector
<
int64_t
>
x_dims_vec
=
phi
::
vectorize
(
x_dims
);
std
::
vector
<
int64_t
>
y_dims_vec
=
phi
::
vectorize
(
y_dims
);
std
::
vector
<
int64_t
>
x_dims_vec_cut
(
x_dims_vec
.
begin
(),
x_dims_vec
.
end
()
-
2
);
std
::
vector
<
int64_t
>
y_dims_vec_cut
(
y_dims_vec
.
begin
(),
y_dims_vec
.
end
()
-
2
);
std
::
vector
<
int64_t
>
expand_batch_portion
=
funcs
::
MatrixGetBroadcastBatchPortion
(
x_dims_vec_cut
,
y_dims_vec_cut
);
std
::
vector
<
int64_t
>
x_broadcast_dims
({
expand_batch_portion
});
x_broadcast_dims
.
insert
(
x_broadcast_dims
.
end
(),
{
x_dims_vec
[
x_dims_n
-
2
],
x_dims_vec
[
x_dims_n
-
1
]});
// dim of 'out' is the same with 'X' after broadcast
out
->
set_dims
(
phi
::
make_ddim
(
x_broadcast_dims
));
out
->
set_dtype
(
x
.
dtype
());
out
->
set_layout
(
x
.
layout
());
out
->
share_lod
(
x
);
}
void
CompareInferMeta
(
const
MetaTensor
&
x
,
void
CompareInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
const
MetaTensor
&
y
,
int
axis
,
int
axis
,
...
@@ -67,6 +214,74 @@ void CompareAllInferMeta(const MetaTensor& x,
...
@@ -67,6 +214,74 @@ void CompareAllInferMeta(const MetaTensor& x,
out
->
set_dtype
(
DataType
::
BOOL
);
out
->
set_dtype
(
DataType
::
BOOL
);
}
}
void
CrossInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
int
axis
,
MetaTensor
*
out
)
{
auto
x_dim
=
x
.
dims
();
auto
y_dim
=
y
.
dims
();
auto
dim
=
axis
;
bool
dims_match
=
phi
::
funcs
::
CheckDims
(
x_dim
,
y_dim
);
PADDLE_ENFORCE_EQ
(
dims_match
,
true
,
phi
::
errors
::
InvalidArgument
(
"The 'shape' of Input(X) should be equal to "
"the 'shape' of Input(Y). But received "
"Input(X).dimensions = [%s], "
"Input(Y).dimensions = [%s]"
,
x_dim
,
y_dim
));
if
(
dim
!=
DDim
::
kMaxRank
)
{
PADDLE_ENFORCE_EQ
(
dim
<
x_dim
.
size
()
&&
dim
>=
(
0
-
x_dim
.
size
()),
true
,
phi
::
errors
::
OutOfRange
(
"Attr(dim) is out of range, It's expected "
"to be in range of [-%d, %d]. But received Attr(dim) = %d."
,
x_dim
.
size
(),
x_dim
.
size
()
-
1
,
dim
));
if
(
dim
<
0
)
{
dim
+=
x_dim
.
size
();
}
PADDLE_ENFORCE_EQ
(
x_dim
[
dim
]
==
3
&&
y_dim
[
dim
]
==
3
,
true
,
phi
::
errors
::
InvalidArgument
(
"Input(X/Y).dims()[dim] should be equal to 3."
"But received Input(X/Y).dims()[dim] = %d."
,
x_dim
[
dim
]));
}
out
->
set_dims
(
x_dim
);
out
->
set_dtype
(
x
.
dtype
());
out
->
set_layout
(
x
.
layout
());
out
->
share_lod
(
x
);
}
void
DistInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
float
p
,
MetaTensor
*
out
)
{
auto
x_dims
=
x
.
dims
();
auto
y_dims
=
y
.
dims
();
PADDLE_ENFORCE_NE
(
phi
::
product
(
x_dims
),
0
,
phi
::
errors
::
InvalidArgument
(
"The Input(X) has not been initialized properly. The "
"shape of Input(X) = [%s]."
,
x_dims
));
PADDLE_ENFORCE_NE
(
phi
::
product
(
y_dims
),
0
,
phi
::
errors
::
InvalidArgument
(
"The Input(Y) has not been initialized properly. The "
"shape of Input(Y) = [%s]."
,
y_dims
));
out
->
set_dims
({
1
});
out
->
set_dtype
(
x
.
dtype
());
}
void
DotInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
MetaTensor
*
out
)
{
void
DotInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
MetaTensor
*
out
)
{
auto
x_dims
=
x
.
dims
();
auto
x_dims
=
x
.
dims
();
auto
x_rank
=
static_cast
<
size_t
>
(
x_dims
.
size
());
auto
x_rank
=
static_cast
<
size_t
>
(
x_dims
.
size
());
...
@@ -109,84 +324,11 @@ void DotInferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
...
@@ -109,84 +324,11 @@ void DotInferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
out
->
set_layout
(
x
.
layout
());
out
->
set_layout
(
x
.
layout
());
}
}
void
MatmulInferMeta
(
const
MetaTensor
&
x
,
void
ElementwiseInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
const
MetaTensor
&
y
,
bool
trans_x
,
MetaTensor
*
out
)
{
bool
trans_y
,
return
ElementwiseRawInferMeta
(
x
,
y
,
-
1
,
std
::
move
(
out
));
MetaTensor
*
out
)
{
}
std
::
vector
<
int64_t
>
dims_x
=
phi
::
vectorize
(
x
.
dims
());
std
::
vector
<
int64_t
>
dims_y
=
phi
::
vectorize
(
y
.
dims
());
auto
ndims_x
=
dims_x
.
size
();
auto
ndims_y
=
dims_y
.
size
();
PADDLE_ENFORCE_GT
(
ndims_x
,
0UL
,
phi
::
errors
::
InvalidArgument
(
"The Input(x) dims size must be greater than 0,"
" but reviced dims size is 0. "
));
PADDLE_ENFORCE_GT
(
ndims_y
,
0UL
,
phi
::
errors
::
InvalidArgument
(
"The Input(y) dims size must be greater than 0,"
" but reviced dims size is 0. "
));
bool
x_broadcasted
=
false
,
y_broadcasted
=
false
;
if
(
ndims_x
==
1
)
{
dims_x
.
insert
(
dims_x
.
begin
(),
1
);
ndims_x
=
2
;
x_broadcasted
=
true
;
}
if
(
ndims_y
==
1
)
{
dims_y
.
push_back
(
1
);
ndims_y
=
2
;
y_broadcasted
=
true
;
}
size_t
M
,
N
;
if
(
trans_x
)
{
M
=
dims_x
[
ndims_x
-
1
];
}
else
{
M
=
dims_x
[
ndims_x
-
2
];
}
if
(
trans_y
)
{
N
=
dims_y
[
ndims_y
-
2
];
}
else
{
N
=
dims_y
[
ndims_y
-
1
];
}
std
::
vector
<
int64_t
>
new_dims
;
if
(
ndims_x
>
ndims_y
)
{
new_dims
.
assign
(
dims_x
.
begin
(),
dims_x
.
end
()
-
2
);
}
else
if
(
ndims_x
<
ndims_y
)
{
new_dims
.
assign
(
dims_y
.
begin
(),
dims_y
.
end
()
-
2
);
}
else
{
new_dims
.
reserve
(
ndims_x
);
for
(
size_t
i
=
0
;
i
<
ndims_x
-
2
;
++
i
)
{
new_dims
.
push_back
(
std
::
max
(
dims_x
[
i
],
dims_y
[
i
]));
}
}
if
(
!
x_broadcasted
)
{
new_dims
.
push_back
(
M
);
}
if
(
!
y_broadcasted
)
{
new_dims
.
push_back
(
N
);
}
if
(
x_broadcasted
&&
y_broadcasted
)
{
new_dims
.
push_back
(
1
);
}
auto
ddim_out
=
phi
::
make_ddim
(
new_dims
);
out
->
set_dims
(
ddim_out
);
out
->
set_dtype
(
x
.
dtype
());
out
->
set_layout
(
x
.
layout
());
}
void
ElementwiseInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
MetaTensor
*
out
)
{
return
ElementwiseRawInferMeta
(
x
,
y
,
-
1
,
std
::
move
(
out
));
}
void
ElementwiseRawInferMeta
(
const
MetaTensor
&
x
,
void
ElementwiseRawInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
const
MetaTensor
&
y
,
...
@@ -223,383 +365,19 @@ void ElementwiseRawInferMeta(const MetaTensor& x,
...
@@ -223,383 +365,19 @@ void ElementwiseRawInferMeta(const MetaTensor& x,
funcs
::
GetBroadcastDimsArrays
(
x_dims
,
funcs
::
GetBroadcastDimsArrays
(
x_dims
,
y_dims
,
y_dims
,
x_dims_array
.
data
(),
x_dims_array
.
data
(),
y_dims_array
.
data
(),
y_dims_array
.
data
(),
out_dims_array
.
data
(),
out_dims_array
.
data
(),
max_dim
,
max_dim
,
axis
);
axis
);
auto
out_dims
=
phi
::
make_ddim
(
out_dims_array
);
auto
out_dims
=
phi
::
make_ddim
(
out_dims_array
);
out
->
set_dims
(
out_dims
);
out
->
set_dims
(
out_dims
);
}
else
{
out
->
set_dims
(
x
.
dims
());
}
out
->
set_dtype
(
x
.
dtype
());
out
->
set_layout
(
x
.
layout
());
out
->
share_lod
(
x
);
}
void
HuberLossInferMeta
(
const
MetaTensor
&
input
,
const
MetaTensor
&
label
,
float
delta
,
MetaTensor
*
out
,
MetaTensor
*
residual
,
MetaConfig
config
)
{
auto
input_dims
=
input
.
dims
();
auto
label_dims
=
label
.
dims
();
PADDLE_ENFORCE_EQ
(
input_dims
.
size
(),
label_dims
.
size
(),
phi
::
errors
::
InvalidArgument
(
"Input(input) rank and Input(label) rank should be "
"same, but received input rank(%d) != label rank(%d)"
,
input_dims
.
size
(),
label_dims
.
size
()));
bool
contain_unknown_dim
=
phi
::
contain_unknown_dim
(
input_dims
)
||
phi
::
contain_unknown_dim
(
label_dims
);
if
(
config
.
is_runtime
||
!
contain_unknown_dim
)
{
PADDLE_ENFORCE_EQ
(
input_dims
,
label_dims
,
phi
::
errors
::
InvalidArgument
(
"The Input(input) and Input(label) should have the same "
"shape, but received input shape [%s] != label shape [%s]"
,
input_dims
,
label_dims
));
}
auto
out_dims
=
label_dims
;
residual
->
set_dims
(
out_dims
);
out
->
set_dims
(
out_dims
);
out
->
share_lod
(
input
);
}
void
CholeskySolveInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
bool
upper
,
MetaTensor
*
out
)
{
auto
x_dims
=
x
.
dims
();
auto
y_dims
=
y
.
dims
();
auto
x_dims_n
=
x_dims
.
size
();
auto
y_dims_n
=
y_dims
.
size
();
PADDLE_ENFORCE_GE
(
x_dims_n
,
2
,
phi
::
errors
::
InvalidArgument
(
"the rank of input Y must greater or equal to 2"
));
PADDLE_ENFORCE_GE
(
y_dims_n
,
2
,
phi
::
errors
::
InvalidArgument
(
"the rank of input X must greater or equal to 2"
));
PADDLE_ENFORCE_EQ
(
y_dims
[
y_dims_n
-
1
],
y_dims
[
y_dims_n
-
2
],
phi
::
errors
::
InvalidArgument
(
"input Matrix Y should be square matrix,"
"But Got last shape of %ld x %ld"
,
y_dims
[
y_dims_n
-
1
],
y_dims
[
y_dims_n
-
2
]));
PADDLE_ENFORCE_EQ
(
x_dims
[
x_dims_n
-
2
],
y_dims
[
y_dims_n
-
2
],
phi
::
errors
::
InvalidArgument
(
"the first dim of Matrix X must be equal to "
"the fisrt dim of Matrix Y,"
"But Got %ld and %ld"
,
x_dims
[
x_dims_n
-
2
],
y_dims
[
y_dims_n
-
2
]));
std
::
vector
<
int64_t
>
x_dims_vec
=
phi
::
vectorize
(
x_dims
);
std
::
vector
<
int64_t
>
y_dims_vec
=
phi
::
vectorize
(
y_dims
);
std
::
vector
<
int64_t
>
x_dims_vec_cut
(
x_dims_vec
.
begin
(),
x_dims_vec
.
end
()
-
2
);
std
::
vector
<
int64_t
>
y_dims_vec_cut
(
y_dims_vec
.
begin
(),
y_dims_vec
.
end
()
-
2
);
std
::
vector
<
int64_t
>
expand_batch_portion
=
funcs
::
MatrixGetBroadcastBatchPortion
(
x_dims_vec_cut
,
y_dims_vec_cut
);
std
::
vector
<
int64_t
>
x_broadcast_dims
({
expand_batch_portion
});
x_broadcast_dims
.
insert
(
x_broadcast_dims
.
end
(),
{
x_dims_vec
[
x_dims_n
-
2
],
x_dims_vec
[
x_dims_n
-
1
]});
// dim of 'out' is the same with 'X' after broadcast
out
->
set_dims
(
phi
::
make_ddim
(
x_broadcast_dims
));
out
->
set_dtype
(
x
.
dtype
());
out
->
set_layout
(
x
.
layout
());
out
->
share_lod
(
x
);
}
void
TriangularSolveInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
bool
upper
,
bool
transpose
,
bool
unitriangular
,
MetaTensor
*
out
)
{
auto
x_dims
=
x
.
dims
();
auto
y_dims
=
y
.
dims
();
auto
x_dims_n
=
x_dims
.
size
();
auto
y_dims_n
=
y_dims
.
size
();
PADDLE_ENFORCE_GE
(
x_dims_n
,
2
,
phi
::
errors
::
InvalidArgument
(
"The input tensor X's dimensions of TriangularSolveOp "
"should be >= 2. But received X's "
"dimensions = %d, X's shape = [%s]"
,
x_dims
.
size
(),
x_dims
));
PADDLE_ENFORCE_GE
(
y_dims_n
,
2
,
phi
::
errors
::
InvalidArgument
(
"The input tensor Y's dimensions of TriangularSolveOp "
"should be >=2. But received Y's "
"dimensions = %d, Y's shape = [%s]"
,
y_dims
.
size
(),
y_dims
));
PADDLE_ENFORCE_EQ
(
x_dims
[
x_dims_n
-
2
],
x_dims
[
x_dims_n
-
1
],
phi
::
errors
::
InvalidArgument
(
"The inner-most 2 dimensions of Input(X) all should "
"be square matrices "
"But received X's shape[-2] = %d and shape[-1] = %d."
,
x_dims
[
x_dims_n
-
2
],
x_dims
[
x_dims_n
-
1
]));
std
::
vector
<
int64_t
>
x_dims_vec
=
phi
::
vectorize
(
x_dims
);
std
::
vector
<
int64_t
>
y_dims_vec
=
phi
::
vectorize
(
y_dims
);
std
::
vector
<
int64_t
>
x_dims_vec_cut
(
x_dims_vec
.
begin
(),
x_dims_vec
.
end
()
-
2
);
std
::
vector
<
int64_t
>
y_dims_vec_cut
(
y_dims_vec
.
begin
(),
y_dims_vec
.
end
()
-
2
);
std
::
vector
<
int64_t
>
expand_batch_portion
=
funcs
::
MatrixGetBroadcastBatchPortion
(
x_dims_vec_cut
,
y_dims_vec_cut
);
std
::
vector
<
int64_t
>
y_broadcast_dims
({
expand_batch_portion
});
y_broadcast_dims
.
insert
(
y_broadcast_dims
.
end
(),
{
y_dims_vec
[
y_dims_n
-
2
],
y_dims_vec
[
y_dims_n
-
1
]});
// dim of 'out' is the same with 'Y' after broadcast
out
->
set_dims
(
phi
::
make_ddim
(
y_broadcast_dims
));
out
->
set_dtype
(
y
.
dtype
());
out
->
set_layout
(
y
.
layout
());
out
->
share_lod
(
y
);
}
void
IndexSampleInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
MetaTensor
*
out
,
MetaConfig
config
)
{
auto
input_dims
=
x
.
dims
();
PADDLE_ENFORCE_EQ
(
input_dims
.
size
(),
2
,
errors
::
InvalidArgument
(
"Inputs(X) shape of IndexSample op should be 2-D, but "
"got X's shape = [%s], please check X shape."
,
input_dims
));
auto
index_dims
=
y
.
dims
();
PADDLE_ENFORCE_EQ
(
index_dims
.
size
(),
2
,
errors
::
InvalidArgument
(
"Inputs(Index) shape of IndexSample op should be 2-D, but "
"got Index's shape [%s] , please check index shape."
,
input_dims
));
if
(
config
.
is_runtime
)
{
PADDLE_ENFORCE_EQ
(
input_dims
[
0
],
index_dims
[
0
],
errors
::
InvalidArgument
(
"Inputs(X)'s value of dimension 0 must same with "
"Inputs(Index)'s value of dimension 0, but "
"got %d of Inputs(X), and got %d of Inputs(Index), "
"please check Inputs shape."
,
input_dims
[
0
],
index_dims
[
0
]));
}
out
->
set_dtype
(
x
.
dtype
());
out
->
set_dims
(
index_dims
);
out
->
share_lod
(
y
);
}
void
CrossInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
int
axis
,
MetaTensor
*
out
)
{
auto
x_dim
=
x
.
dims
();
auto
y_dim
=
y
.
dims
();
auto
dim
=
axis
;
bool
dims_match
=
phi
::
funcs
::
CheckDims
(
x_dim
,
y_dim
);
PADDLE_ENFORCE_EQ
(
dims_match
,
true
,
phi
::
errors
::
InvalidArgument
(
"The 'shape' of Input(X) should be equal to "
"the 'shape' of Input(Y). But received "
"Input(X).dimensions = [%s], "
"Input(Y).dimensions = [%s]"
,
x_dim
,
y_dim
));
if
(
dim
!=
DDim
::
kMaxRank
)
{
PADDLE_ENFORCE_EQ
(
dim
<
x_dim
.
size
()
&&
dim
>=
(
0
-
x_dim
.
size
()),
true
,
phi
::
errors
::
OutOfRange
(
"Attr(dim) is out of range, It's expected "
"to be in range of [-%d, %d]. But received Attr(dim) = %d."
,
x_dim
.
size
(),
x_dim
.
size
()
-
1
,
dim
));
if
(
dim
<
0
)
{
dim
+=
x_dim
.
size
();
}
PADDLE_ENFORCE_EQ
(
x_dim
[
dim
]
==
3
&&
y_dim
[
dim
]
==
3
,
true
,
phi
::
errors
::
InvalidArgument
(
"Input(X/Y).dims()[dim] should be equal to 3."
"But received Input(X/Y).dims()[dim] = %d."
,
x_dim
[
dim
]));
}
out
->
set_dims
(
x_dim
);
out
->
set_dtype
(
x
.
dtype
());
out
->
set_layout
(
x
.
layout
());
out
->
share_lod
(
x
);
}
void
Atan2InferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
MetaTensor
*
out
)
{
out
->
share_meta
(
x
);
}
void
SegmentPoolInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
segment_ids
,
const
std
::
string
&
pooltype
,
MetaTensor
*
out
,
MetaTensor
*
summed_ids
,
MetaConfig
config
)
{
auto
dims
=
x
.
dims
();
dims
[
0
]
=
-
1
;
out
->
set_dims
(
dims
);
out
->
set_dtype
(
x
.
dtype
());
out
->
set_layout
(
x
.
layout
());
if
(
pooltype
==
"MEAN"
)
{
summed_ids
->
set_dims
({
-
1
,
1
});
summed_ids
->
set_dtype
(
x
.
dtype
());
summed_ids
->
set_layout
(
x
.
layout
());
}
}
void
BCELossInferMeta
(
const
MetaTensor
&
input
,
const
MetaTensor
&
label
,
MetaTensor
*
out
,
MetaConfig
config
)
{
auto
input_dims
=
input
.
dims
();
auto
label_dims
=
label
.
dims
();
int
rank
=
input_dims
.
size
();
PADDLE_ENFORCE_EQ
(
rank
,
label_dims
.
size
(),
phi
::
errors
::
InvalidArgument
(
"Input(X) and Input(Label) shall have the same rank."
"But received: the rank of Input(X) is [%d], "
"the rank of Input(Label) is [%d]."
,
rank
,
label_dims
.
size
()));
bool
check
=
true
;
if
((
!
config
.
is_runtime
)
&&
(
phi
::
product
(
input_dims
)
<=
0
||
phi
::
product
(
label_dims
)
<=
0
))
{
check
=
false
;
}
if
(
check
)
{
PADDLE_ENFORCE_EQ
(
input_dims
,
label_dims
,
phi
::
errors
::
InvalidArgument
(
"Input(X) and Input(Label) shall have the same "
"shape. But received: the shape of Input(X) is "
"[%s], the shape of Input(Label) is [%s]."
,
input_dims
,
label_dims
));
}
out
->
set_dims
(
input_dims
);
out
->
set_dtype
(
input
.
dtype
());
out
->
share_lod
(
input
);
}
void
BincountInferMeta
(
const
MetaTensor
&
x
,
const
paddle
::
optional
<
const
MetaTensor
&>
weights
,
int
minlength
,
MetaTensor
*
out
)
{
auto
input_dim
=
x
.
dims
();
PADDLE_ENFORCE_GE
(
minlength
,
0
,
phi
::
errors
::
InvalidArgument
(
"The minlength should be greater than or equal to 0."
"But received minlength is %d"
,
minlength
));
PADDLE_ENFORCE_EQ
(
input_dim
.
size
(),
1
,
phi
::
errors
::
InvalidArgument
(
"The 'shape' of Input(X) must be 1-D tensor."
"But the dimension of Input(X) is [%d]"
,
input_dim
.
size
()));
if
(
weights
.
is_initialized
())
{
auto
weights_dim
=
weights
->
dims
();
PADDLE_ENFORCE_EQ
(
weights_dim
.
size
(),
1
,
phi
::
errors
::
InvalidArgument
(
"The 'shape' of Input(Weights) must be 1-D tensor."
"But the dimension of Input(Weights) is [%d]"
,
weights_dim
.
size
()));
PADDLE_ENFORCE_EQ
(
weights_dim
[
0
],
input_dim
[
0
],
phi
::
errors
::
InvalidArgument
(
"The 'shape' of Input(Weights) must be equal to the 'shape' of "
"Input(X)."
"But received: the 'shape' of Input(Weights) is [%s],"
"the 'shape' of Input(X) is [%s]"
,
weights_dim
,
input_dim
));
}
out
->
set_dims
(
phi
::
make_ddim
({
-
1
}));
if
(
weights
.
is_initialized
())
{
out
->
set_dtype
(
weights
->
dtype
());
}
else
{
}
else
{
out
->
set_d
type
(
x
.
dtype
());
out
->
set_d
ims
(
x
.
dims
());
}
}
out
->
share_lod
(
x
);
}
void
DistInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
float
p
,
MetaTensor
*
out
)
{
auto
x_dims
=
x
.
dims
();
auto
y_dims
=
y
.
dims
();
PADDLE_ENFORCE_NE
(
phi
::
product
(
x_dims
),
0
,
phi
::
errors
::
InvalidArgument
(
"The Input(X) has not been initialized properly. The "
"shape of Input(X) = [%s]."
,
x_dims
));
PADDLE_ENFORCE_NE
(
phi
::
product
(
y_dims
),
0
,
phi
::
errors
::
InvalidArgument
(
"The Input(Y) has not been initialized properly. The "
"shape of Input(Y) = [%s]."
,
y_dims
));
out
->
set_dims
({
1
});
out
->
set_dtype
(
x
.
dtype
());
out
->
set_dtype
(
x
.
dtype
());
out
->
set_layout
(
x
.
layout
());
out
->
share_lod
(
x
);
}
}
void
GatherNdInferMeta
(
const
MetaTensor
&
x
,
void
GatherNdInferMeta
(
const
MetaTensor
&
x
,
...
@@ -648,6 +426,78 @@ void GatherTreeMeta(const MetaTensor& ids,
...
@@ -648,6 +426,78 @@ void GatherTreeMeta(const MetaTensor& ids,
out
->
set_dims
(
ids_dims
);
out
->
set_dims
(
ids_dims
);
}
}
void
HuberLossInferMeta
(
const
MetaTensor
&
input
,
const
MetaTensor
&
label
,
float
delta
,
MetaTensor
*
out
,
MetaTensor
*
residual
,
MetaConfig
config
)
{
auto
input_dims
=
input
.
dims
();
auto
label_dims
=
label
.
dims
();
PADDLE_ENFORCE_EQ
(
input_dims
.
size
(),
label_dims
.
size
(),
phi
::
errors
::
InvalidArgument
(
"Input(input) rank and Input(label) rank should be "
"same, but received input rank(%d) != label rank(%d)"
,
input_dims
.
size
(),
label_dims
.
size
()));
bool
contain_unknown_dim
=
phi
::
contain_unknown_dim
(
input_dims
)
||
phi
::
contain_unknown_dim
(
label_dims
);
if
(
config
.
is_runtime
||
!
contain_unknown_dim
)
{
PADDLE_ENFORCE_EQ
(
input_dims
,
label_dims
,
phi
::
errors
::
InvalidArgument
(
"The Input(input) and Input(label) should have the same "
"shape, but received input shape [%s] != label shape [%s]"
,
input_dims
,
label_dims
));
}
auto
out_dims
=
label_dims
;
residual
->
set_dims
(
out_dims
);
out
->
set_dims
(
out_dims
);
out
->
share_lod
(
input
);
}
void
IndexSampleInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
MetaTensor
*
out
,
MetaConfig
config
)
{
auto
input_dims
=
x
.
dims
();
PADDLE_ENFORCE_EQ
(
input_dims
.
size
(),
2
,
errors
::
InvalidArgument
(
"Inputs(X) shape of IndexSample op should be 2-D, but "
"got X's shape = [%s], please check X shape."
,
input_dims
));
auto
index_dims
=
y
.
dims
();
PADDLE_ENFORCE_EQ
(
index_dims
.
size
(),
2
,
errors
::
InvalidArgument
(
"Inputs(Index) shape of IndexSample op should be 2-D, but "
"got Index's shape [%s] , please check index shape."
,
input_dims
));
if
(
config
.
is_runtime
)
{
PADDLE_ENFORCE_EQ
(
input_dims
[
0
],
index_dims
[
0
],
errors
::
InvalidArgument
(
"Inputs(X)'s value of dimension 0 must same with "
"Inputs(Index)'s value of dimension 0, but "
"got %d of Inputs(X), and got %d of Inputs(Index), "
"please check Inputs shape."
,
input_dims
[
0
],
index_dims
[
0
]));
}
out
->
set_dtype
(
x
.
dtype
());
out
->
set_dims
(
index_dims
);
out
->
share_lod
(
y
);
}
void
LogLossInferMeta
(
const
MetaTensor
&
input
,
void
LogLossInferMeta
(
const
MetaTensor
&
input
,
const
MetaTensor
&
label
,
const
MetaTensor
&
label
,
float
epsilon
,
float
epsilon
,
...
@@ -690,6 +540,79 @@ void LogLossInferMeta(const MetaTensor& input,
...
@@ -690,6 +540,79 @@ void LogLossInferMeta(const MetaTensor& input,
out
->
share_lod
(
input
);
out
->
share_lod
(
input
);
}
}
void
MatmulInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
bool
trans_x
,
bool
trans_y
,
MetaTensor
*
out
)
{
std
::
vector
<
int64_t
>
dims_x
=
phi
::
vectorize
(
x
.
dims
());
std
::
vector
<
int64_t
>
dims_y
=
phi
::
vectorize
(
y
.
dims
());
auto
ndims_x
=
dims_x
.
size
();
auto
ndims_y
=
dims_y
.
size
();
PADDLE_ENFORCE_GT
(
ndims_x
,
0UL
,
phi
::
errors
::
InvalidArgument
(
"The Input(x) dims size must be greater than 0,"
" but reviced dims size is 0. "
));
PADDLE_ENFORCE_GT
(
ndims_y
,
0UL
,
phi
::
errors
::
InvalidArgument
(
"The Input(y) dims size must be greater than 0,"
" but reviced dims size is 0. "
));
bool
x_broadcasted
=
false
,
y_broadcasted
=
false
;
if
(
ndims_x
==
1
)
{
dims_x
.
insert
(
dims_x
.
begin
(),
1
);
ndims_x
=
2
;
x_broadcasted
=
true
;
}
if
(
ndims_y
==
1
)
{
dims_y
.
push_back
(
1
);
ndims_y
=
2
;
y_broadcasted
=
true
;
}
size_t
M
,
N
;
if
(
trans_x
)
{
M
=
dims_x
[
ndims_x
-
1
];
}
else
{
M
=
dims_x
[
ndims_x
-
2
];
}
if
(
trans_y
)
{
N
=
dims_y
[
ndims_y
-
2
];
}
else
{
N
=
dims_y
[
ndims_y
-
1
];
}
std
::
vector
<
int64_t
>
new_dims
;
if
(
ndims_x
>
ndims_y
)
{
new_dims
.
assign
(
dims_x
.
begin
(),
dims_x
.
end
()
-
2
);
}
else
if
(
ndims_x
<
ndims_y
)
{
new_dims
.
assign
(
dims_y
.
begin
(),
dims_y
.
end
()
-
2
);
}
else
{
new_dims
.
reserve
(
ndims_x
);
for
(
size_t
i
=
0
;
i
<
ndims_x
-
2
;
++
i
)
{
new_dims
.
push_back
(
std
::
max
(
dims_x
[
i
],
dims_y
[
i
]));
}
}
if
(
!
x_broadcasted
)
{
new_dims
.
push_back
(
M
);
}
if
(
!
y_broadcasted
)
{
new_dims
.
push_back
(
N
);
}
if
(
x_broadcasted
&&
y_broadcasted
)
{
new_dims
.
push_back
(
1
);
}
auto
ddim_out
=
phi
::
make_ddim
(
new_dims
);
out
->
set_dims
(
ddim_out
);
out
->
set_dtype
(
x
.
dtype
());
out
->
set_layout
(
x
.
layout
());
}
void
MvInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
vec
,
MetaTensor
*
out
)
{
void
MvInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
vec
,
MetaTensor
*
out
)
{
auto
dim_x
=
x
.
dims
();
auto
dim_x
=
x
.
dims
();
auto
dim_vec
=
vec
.
dims
();
auto
dim_vec
=
vec
.
dims
();
...
@@ -720,6 +643,25 @@ void MvInferMeta(const MetaTensor& x, const MetaTensor& vec, MetaTensor* out) {
...
@@ -720,6 +643,25 @@ void MvInferMeta(const MetaTensor& x, const MetaTensor& vec, MetaTensor* out) {
out
->
share_lod
(
x
);
out
->
share_lod
(
x
);
}
}
void
SegmentPoolInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
segment_ids
,
const
std
::
string
&
pooltype
,
MetaTensor
*
out
,
MetaTensor
*
summed_ids
,
MetaConfig
config
)
{
auto
dims
=
x
.
dims
();
dims
[
0
]
=
-
1
;
out
->
set_dims
(
dims
);
out
->
set_dtype
(
x
.
dtype
());
out
->
set_layout
(
x
.
layout
());
if
(
pooltype
==
"MEAN"
)
{
summed_ids
->
set_dims
({
-
1
,
1
});
summed_ids
->
set_dtype
(
x
.
dtype
());
summed_ids
->
set_layout
(
x
.
layout
());
}
}
void
SigmoidCrossEntropyWithLogitsInferMeta
(
const
MetaTensor
&
x
,
void
SigmoidCrossEntropyWithLogitsInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
label
,
const
MetaTensor
&
label
,
bool
normalize
,
bool
normalize
,
...
@@ -761,4 +703,63 @@ void SigmoidCrossEntropyWithLogitsInferMeta(const MetaTensor& x,
...
@@ -761,4 +703,63 @@ void SigmoidCrossEntropyWithLogitsInferMeta(const MetaTensor& x,
out
->
share_lod
(
x
);
out
->
share_lod
(
x
);
}
}
void
TriangularSolveInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
bool
upper
,
bool
transpose
,
bool
unitriangular
,
MetaTensor
*
out
)
{
auto
x_dims
=
x
.
dims
();
auto
y_dims
=
y
.
dims
();
auto
x_dims_n
=
x_dims
.
size
();
auto
y_dims_n
=
y_dims
.
size
();
PADDLE_ENFORCE_GE
(
x_dims_n
,
2
,
phi
::
errors
::
InvalidArgument
(
"The input tensor X's dimensions of TriangularSolveOp "
"should be >= 2. But received X's "
"dimensions = %d, X's shape = [%s]"
,
x_dims
.
size
(),
x_dims
));
PADDLE_ENFORCE_GE
(
y_dims_n
,
2
,
phi
::
errors
::
InvalidArgument
(
"The input tensor Y's dimensions of TriangularSolveOp "
"should be >=2. But received Y's "
"dimensions = %d, Y's shape = [%s]"
,
y_dims
.
size
(),
y_dims
));
PADDLE_ENFORCE_EQ
(
x_dims
[
x_dims_n
-
2
],
x_dims
[
x_dims_n
-
1
],
phi
::
errors
::
InvalidArgument
(
"The inner-most 2 dimensions of Input(X) all should "
"be square matrices "
"But received X's shape[-2] = %d and shape[-1] = %d."
,
x_dims
[
x_dims_n
-
2
],
x_dims
[
x_dims_n
-
1
]));
std
::
vector
<
int64_t
>
x_dims_vec
=
phi
::
vectorize
(
x_dims
);
std
::
vector
<
int64_t
>
y_dims_vec
=
phi
::
vectorize
(
y_dims
);
std
::
vector
<
int64_t
>
x_dims_vec_cut
(
x_dims_vec
.
begin
(),
x_dims_vec
.
end
()
-
2
);
std
::
vector
<
int64_t
>
y_dims_vec_cut
(
y_dims_vec
.
begin
(),
y_dims_vec
.
end
()
-
2
);
std
::
vector
<
int64_t
>
expand_batch_portion
=
funcs
::
MatrixGetBroadcastBatchPortion
(
x_dims_vec_cut
,
y_dims_vec_cut
);
std
::
vector
<
int64_t
>
y_broadcast_dims
({
expand_batch_portion
});
y_broadcast_dims
.
insert
(
y_broadcast_dims
.
end
(),
{
y_dims_vec
[
y_dims_n
-
2
],
y_dims_vec
[
y_dims_n
-
1
]});
// dim of 'out' is the same with 'Y' after broadcast
out
->
set_dims
(
phi
::
make_ddim
(
y_broadcast_dims
));
out
->
set_dtype
(
y
.
dtype
());
out
->
set_layout
(
y
.
layout
());
out
->
share_lod
(
y
);
}
}
// namespace phi
}
// namespace phi
paddle/phi/infermeta/binary.h
浏览文件 @
f3f27d25
...
@@ -29,22 +29,43 @@ namespace phi {
...
@@ -29,22 +29,43 @@ namespace phi {
// Because functions in this file not only can infer shape, but also need
// Because functions in this file not only can infer shape, but also need
// infer lod or other useful data.
// infer lod or other useful data.
void
Atan2InferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
MetaTensor
*
out
);
void
BCELossInferMeta
(
const
MetaTensor
&
input
,
const
MetaTensor
&
label
,
MetaTensor
*
out
,
MetaConfig
config
=
MetaConfig
());
void
BincountInferMeta
(
const
MetaTensor
&
x
,
const
paddle
::
optional
<
const
MetaTensor
&>
weights
,
int
minlength
,
MetaTensor
*
out
);
void
CholeskySolveInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
bool
upper
,
MetaTensor
*
out
);
void
CompareAllInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
MetaTensor
*
out
);
void
CompareInferMeta
(
const
MetaTensor
&
x
,
void
CompareInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
const
MetaTensor
&
y
,
int
axis
,
int
axis
,
MetaTensor
*
out
);
MetaTensor
*
out
);
void
CompareAllInferMeta
(
const
MetaTensor
&
x
,
void
CrossInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
const
MetaTensor
&
y
,
MetaTensor
*
out
);
int
axis
,
MetaTensor
*
out
);
void
DotInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
MetaTensor
*
out
);
void
DistInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
float
p
,
MetaTensor
*
out
);
void
MatmulInferMeta
(
const
MetaTensor
&
x
,
void
DotInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
MetaTensor
*
out
);
const
MetaTensor
&
y
,
bool
trans_x
,
bool
trans_y
,
MetaTensor
*
out
);
void
ElementwiseInferMeta
(
const
MetaTensor
&
x
,
void
ElementwiseInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
const
MetaTensor
&
y
,
...
@@ -55,6 +76,14 @@ void ElementwiseRawInferMeta(const MetaTensor& x_meta,
...
@@ -55,6 +76,14 @@ void ElementwiseRawInferMeta(const MetaTensor& x_meta,
int
axis
,
int
axis
,
MetaTensor
*
out
);
MetaTensor
*
out
);
void
GatherNdInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
index
,
MetaTensor
*
out
);
void
GatherTreeMeta
(
const
MetaTensor
&
ids
,
const
MetaTensor
&
parents
,
MetaTensor
*
out
);
void
HuberLossInferMeta
(
const
MetaTensor
&
input_meta
,
void
HuberLossInferMeta
(
const
MetaTensor
&
input_meta
,
const
MetaTensor
&
label_meta
,
const
MetaTensor
&
label_meta
,
float
delta
,
float
delta
,
...
@@ -62,29 +91,24 @@ void HuberLossInferMeta(const MetaTensor& input_meta,
...
@@ -62,29 +91,24 @@ void HuberLossInferMeta(const MetaTensor& input_meta,
MetaTensor
*
residual
,
MetaTensor
*
residual
,
MetaConfig
config
=
MetaConfig
());
MetaConfig
config
=
MetaConfig
());
void
CholeskySolveInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
bool
upper
,
MetaTensor
*
out
);
void
TriangularSolveInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
bool
upper
,
bool
transpose
,
bool
unitriangular
,
MetaTensor
*
out
);
void
IndexSampleInferMeta
(
const
MetaTensor
&
x
,
void
IndexSampleInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
const
MetaTensor
&
y
,
MetaTensor
*
out
,
MetaTensor
*
out
,
MetaConfig
config
=
MetaConfig
());
MetaConfig
config
=
MetaConfig
());
void
CrossInferMeta
(
const
MetaTensor
&
x
,
void
LogLossInferMeta
(
const
MetaTensor
&
input
,
const
MetaTensor
&
y
,
const
MetaTensor
&
label
,
int
axis
,
float
epsilon
,
MetaTensor
*
out
);
MetaTensor
*
out
,
MetaConfig
config
=
MetaConfig
());
void
Atan2InferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
MetaTensor
*
out
);
void
MatmulInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
bool
trans_x
,
bool
trans_y
,
MetaTensor
*
out
);
void
MvInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
vec
,
MetaTensor
*
out
);
void
SegmentPoolInferMeta
(
const
MetaTensor
&
x
,
void
SegmentPoolInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
segment_ids
,
const
MetaTensor
&
segment_ids
,
...
@@ -93,37 +117,6 @@ void SegmentPoolInferMeta(const MetaTensor& x,
...
@@ -93,37 +117,6 @@ void SegmentPoolInferMeta(const MetaTensor& x,
MetaTensor
*
summed_ids
,
MetaTensor
*
summed_ids
,
MetaConfig
config
=
MetaConfig
());
MetaConfig
config
=
MetaConfig
());
void
BCELossInferMeta
(
const
MetaTensor
&
input
,
const
MetaTensor
&
label
,
MetaTensor
*
out
,
MetaConfig
config
=
MetaConfig
());
void
BincountInferMeta
(
const
MetaTensor
&
x
,
const
paddle
::
optional
<
const
MetaTensor
&>
weights
,
int
minlength
,
MetaTensor
*
out
);
void
DistInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
float
p
,
MetaTensor
*
out
);
void
GatherNdInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
index
,
MetaTensor
*
out
);
void
GatherTreeMeta
(
const
MetaTensor
&
ids
,
const
MetaTensor
&
parents
,
MetaTensor
*
out
);
void
LogLossInferMeta
(
const
MetaTensor
&
input
,
const
MetaTensor
&
label
,
float
epsilon
,
MetaTensor
*
out
,
MetaConfig
config
=
MetaConfig
());
void
MvInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
vec
,
MetaTensor
*
out
);
void
SigmoidCrossEntropyWithLogitsInferMeta
(
const
MetaTensor
&
x
,
void
SigmoidCrossEntropyWithLogitsInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
label
,
const
MetaTensor
&
label
,
bool
normalize
,
bool
normalize
,
...
@@ -131,4 +124,11 @@ void SigmoidCrossEntropyWithLogitsInferMeta(const MetaTensor& x,
...
@@ -131,4 +124,11 @@ void SigmoidCrossEntropyWithLogitsInferMeta(const MetaTensor& x,
MetaTensor
*
out
,
MetaTensor
*
out
,
MetaConfig
config
=
MetaConfig
());
MetaConfig
config
=
MetaConfig
());
void
TriangularSolveInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
bool
upper
,
bool
transpose
,
bool
unitriangular
,
MetaTensor
*
out
);
}
// namespace phi
}
// namespace phi
paddle/phi/infermeta/nullary.cc
浏览文件 @
f3f27d25
...
@@ -16,6 +16,12 @@ limitations under the License. */
...
@@ -16,6 +16,12 @@ limitations under the License. */
namespace
phi
{
namespace
phi
{
void
CreateInferMeta
(
const
ScalarArray
&
shape
,
DataType
dtype
,
MetaTensor
*
out
)
{
CreateInferMetaBase
(
shape
.
GetData
(),
dtype
,
DataLayout
::
NCHW
,
out
);
}
void
CreateInferMetaBase
(
const
std
::
vector
<
int64_t
>&
shape
,
void
CreateInferMetaBase
(
const
std
::
vector
<
int64_t
>&
shape
,
DataType
dtype
,
DataType
dtype
,
DataLayout
layout
,
DataLayout
layout
,
...
@@ -26,12 +32,6 @@ void CreateInferMetaBase(const std::vector<int64_t>& shape,
...
@@ -26,12 +32,6 @@ void CreateInferMetaBase(const std::vector<int64_t>& shape,
out
->
set_layout
(
layout
);
out
->
set_layout
(
layout
);
}
}
void
CreateInferMeta
(
const
ScalarArray
&
shape
,
DataType
dtype
,
MetaTensor
*
out
)
{
CreateInferMetaBase
(
shape
.
GetData
(),
dtype
,
DataLayout
::
NCHW
,
out
);
}
void
EyeInferMeta
(
int64_t
num_rows
,
void
EyeInferMeta
(
int64_t
num_rows
,
int64_t
num_columns
,
int64_t
num_columns
,
DataType
dtype
,
DataType
dtype
,
...
@@ -41,18 +41,6 @@ void EyeInferMeta(int64_t num_rows,
...
@@ -41,18 +41,6 @@ void EyeInferMeta(int64_t num_rows,
out
->
set_dtype
(
dtype
);
out
->
set_dtype
(
dtype
);
}
}
void
TruncatedGaussianRandomInferMeta
(
const
std
::
vector
<
int
>&
shape
,
float
mean
,
float
std
,
int
seed
,
DataType
dtype
,
MetaTensor
*
out
)
{
auto
out_dims
=
phi
::
make_ddim
(
shape
);
out
->
set_dims
(
out_dims
);
out
->
set_dtype
(
dtype
);
out
->
set_layout
(
DataLayout
::
NCHW
);
}
void
GaussianRandomInferMeta
(
const
ScalarArray
&
shape
,
void
GaussianRandomInferMeta
(
const
ScalarArray
&
shape
,
float
mean
,
float
mean
,
float
std
,
float
std
,
...
@@ -65,4 +53,16 @@ void GaussianRandomInferMeta(const ScalarArray& shape,
...
@@ -65,4 +53,16 @@ void GaussianRandomInferMeta(const ScalarArray& shape,
out
->
set_layout
(
DataLayout
::
NCHW
);
out
->
set_layout
(
DataLayout
::
NCHW
);
}
}
void
TruncatedGaussianRandomInferMeta
(
const
std
::
vector
<
int
>&
shape
,
float
mean
,
float
std
,
int
seed
,
DataType
dtype
,
MetaTensor
*
out
)
{
auto
out_dims
=
phi
::
make_ddim
(
shape
);
out
->
set_dims
(
out_dims
);
out
->
set_dtype
(
dtype
);
out
->
set_layout
(
DataLayout
::
NCHW
);
}
}
// namespace phi
}
// namespace phi
paddle/phi/infermeta/nullary.h
浏览文件 @
f3f27d25
...
@@ -28,25 +28,18 @@ namespace phi {
...
@@ -28,25 +28,18 @@ namespace phi {
// Because functions in this file not only can infer shape, but also need
// Because functions in this file not only can infer shape, but also need
// infer lod or other useful data.
// infer lod or other useful data.
void
CreateInferMeta
(
const
ScalarArray
&
shape
,
DataType
dtype
,
MetaTensor
*
out
);
void
CreateInferMetaBase
(
const
std
::
vector
<
int64_t
>&
shape
,
void
CreateInferMetaBase
(
const
std
::
vector
<
int64_t
>&
shape
,
DataType
dtype
,
DataType
dtype
,
DataLayout
layout
,
DataLayout
layout
,
MetaTensor
*
out
);
MetaTensor
*
out
);
void
CreateInferMeta
(
const
ScalarArray
&
shape
,
DataType
dtype
,
MetaTensor
*
out
);
void
EyeInferMeta
(
int64_t
num_rows
,
void
EyeInferMeta
(
int64_t
num_rows
,
int64_t
num_columns
,
int64_t
num_columns
,
DataType
dtype
,
DataType
dtype
,
MetaTensor
*
out
);
MetaTensor
*
out
);
void
TruncatedGaussianRandomInferMeta
(
const
std
::
vector
<
int
>&
shape
,
float
mean
,
float
std
,
int
seed
,
DataType
dtype
,
MetaTensor
*
out
);
void
GaussianRandomInferMeta
(
const
ScalarArray
&
shape
,
void
GaussianRandomInferMeta
(
const
ScalarArray
&
shape
,
float
mean
,
float
mean
,
float
std
,
float
std
,
...
@@ -54,4 +47,11 @@ void GaussianRandomInferMeta(const ScalarArray& shape,
...
@@ -54,4 +47,11 @@ void GaussianRandomInferMeta(const ScalarArray& shape,
DataType
dtype
,
DataType
dtype
,
MetaTensor
*
out
);
MetaTensor
*
out
);
void
TruncatedGaussianRandomInferMeta
(
const
std
::
vector
<
int
>&
shape
,
float
mean
,
float
std
,
int
seed
,
DataType
dtype
,
MetaTensor
*
out
);
}
// namespace phi
}
// namespace phi
paddle/phi/infermeta/ternary.cc
浏览文件 @
f3f27d25
...
@@ -18,6 +18,58 @@ limitations under the License. */
...
@@ -18,6 +18,58 @@ limitations under the License. */
namespace
phi
{
namespace
phi
{
void
AccuracyInferMeta
(
const
MetaTensor
&
out
,
const
MetaTensor
&
indice
,
const
MetaTensor
&
label
,
MetaTensor
*
accuracy
,
MetaTensor
*
correct
,
MetaTensor
*
total
,
MetaConfig
config
)
{
auto
inference_dim
=
out
.
dims
();
auto
label_dim
=
label
.
dims
();
// Assume indices has same shape as inference, because
// it's the output of topk.
PADDLE_ENFORCE_EQ
(
label_dim
.
size
(),
2
,
phi
::
errors
::
InvalidArgument
(
"ShapeError: label's dimensions of AccuracyOp must be 2. "
"But received label's dimensions = %d, label's shape = [%s]"
,
label_dim
.
size
(),
label_dim
));
if
(
config
.
is_runtime
)
{
PADDLE_ENFORCE_EQ
(
label_dim
[
1
],
1
,
phi
::
errors
::
InvalidArgument
(
"ShapeError: label's second dimension of "
"AccuracyOp must be 1. But received label's "
"second dimension is = %d, label's shape = [%s]"
,
label_dim
[
1
],
label_dim
));
PADDLE_ENFORCE_EQ
(
inference_dim
[
0
],
label_dim
[
0
],
phi
::
errors
::
InvalidArgument
(
"ShapeError: the output's num_rows of AccuracyOp must be"
" the same as label's num_rows. But received output's "
"shape = [%s], label's shape = [%s], output's num_rows = %d, "
"label's "
"num_rows = %d"
,
inference_dim
,
label_dim
,
inference_dim
[
0
],
label_dim
[
0
]));
}
accuracy
->
set_dims
({
1
});
accuracy
->
set_dtype
(
out
.
dtype
());
correct
->
set_dims
({
1
});
correct
->
set_dtype
(
out
.
dtype
());
total
->
set_dims
({
1
});
total
->
set_dtype
(
out
.
dtype
());
accuracy
->
share_lod
(
out
);
}
void
AddmmInferMeta
(
const
MetaTensor
&
input
,
void
AddmmInferMeta
(
const
MetaTensor
&
input
,
const
MetaTensor
&
x
,
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
const
MetaTensor
&
y
,
...
@@ -89,6 +141,107 @@ void AddmmInferMeta(const MetaTensor& input,
...
@@ -89,6 +141,107 @@ void AddmmInferMeta(const MetaTensor& input,
out
->
set_dtype
(
input
.
dtype
());
out
->
set_dtype
(
input
.
dtype
());
}
}
void
GraphSendRecvInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
src_index
,
const
MetaTensor
&
dst_index
,
const
std
::
string
&
pool_type
,
MetaTensor
*
out
,
MetaTensor
*
dst_count
)
{
auto
src_index_dims
=
src_index
.
dims
();
if
(
src_index_dims
.
size
()
==
2
)
{
PADDLE_ENFORCE_EQ
(
src_index_dims
[
1
],
1
,
phi
::
errors
::
InvalidArgument
(
"The last dim of Src_index should be 1 when it "
"is 2D, but we get %d"
,
src_index_dims
[
1
]));
}
else
{
PADDLE_ENFORCE_EQ
(
src_index_dims
.
size
(),
1
,
phi
::
errors
::
InvalidArgument
(
"The Src_index should be 1D, when it is not 2D, but we get %d"
,
src_index_dims
.
size
()));
}
auto
dst_index_dims
=
dst_index
.
dims
();
if
(
dst_index_dims
.
size
()
==
2
)
{
PADDLE_ENFORCE_EQ
(
dst_index_dims
[
1
],
1
,
phi
::
errors
::
InvalidArgument
(
"The last dim of Dst_index should be 1 when it "
"is 2D, but we get %d"
,
dst_index_dims
[
1
]));
}
else
{
PADDLE_ENFORCE_EQ
(
dst_index_dims
.
size
(),
1
,
phi
::
errors
::
InvalidArgument
(
"The Dst_index should be 1D, "
"when it is not 2D, but we get %d"
,
dst_index_dims
.
size
()));
}
PADDLE_ENFORCE_EQ
(
src_index_dims
[
0
],
dst_index_dims
[
0
],
phi
::
errors
::
InvalidArgument
(
"Src_index and Dst_index should have the same shape."
));
auto
dims
=
x
.
dims
();
out
->
set_dims
(
dims
);
out
->
set_dtype
(
x
.
dtype
());
if
(
pool_type
==
"MEAN"
)
{
dst_count
->
set_dims
({
dims
[
0
]});
dst_count
->
set_dtype
(
DataType
::
INT32
);
}
}
void
LerpInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
const
MetaTensor
&
weight
,
MetaTensor
*
out
)
{
auto
x_dims
=
x
.
dims
();
auto
y_dims
=
y
.
dims
();
auto
w_dims
=
weight
.
dims
();
DDim
out_dims
;
out_dims
=
funcs
::
GetOutputDims
(
x_dims
,
y_dims
);
if
(
w_dims
.
size
()
>
1
||
w_dims
[
0
]
!=
1
)
{
out_dims
=
funcs
::
GetOutputDims
(
out_dims
,
w_dims
);
}
out
->
set_dims
(
out_dims
);
out
->
set_dtype
(
x
.
dtype
());
out
->
share_lod
(
x
);
}
void
LinspaceInferMeta
(
const
MetaTensor
&
start
,
const
MetaTensor
&
stop
,
const
MetaTensor
&
number
,
MetaTensor
*
out
)
{
auto
s_dims
=
start
.
dims
();
PADDLE_ENFORCE_EQ
(
(
s_dims
.
size
()
==
1
)
&&
(
s_dims
[
0
]
==
1
),
true
,
phi
::
errors
::
InvalidArgument
(
"The shape of Input(Start) must be [1],"
"but received input shape is [%s]."
,
s_dims
));
auto
e_dims
=
stop
.
dims
();
PADDLE_ENFORCE_EQ
(
(
e_dims
.
size
()
==
1
)
&&
(
e_dims
[
0
]
==
1
),
true
,
phi
::
errors
::
InvalidArgument
(
"The shape of Input(Stop) must be [1],"
"but received input shape is [%s]."
,
e_dims
));
auto
step_dims
=
number
.
dims
();
PADDLE_ENFORCE_EQ
(
(
step_dims
.
size
()
==
1
)
&&
(
step_dims
[
0
]
==
1
),
true
,
phi
::
errors
::
InvalidArgument
(
"The shape of Input(Num) must be [1],"
"but received input shape is [%s]."
,
step_dims
));
out
->
set_dims
(
phi
::
make_ddim
({
-
1
}));
out
->
set_dtype
(
start
.
dtype
());
}
void
NllLossRawInferMeta
(
const
MetaTensor
&
input
,
void
NllLossRawInferMeta
(
const
MetaTensor
&
input
,
const
MetaTensor
&
label
,
const
MetaTensor
&
label
,
paddle
::
optional
<
const
MetaTensor
&>
weight
,
paddle
::
optional
<
const
MetaTensor
&>
weight
,
...
@@ -319,156 +472,4 @@ void ViterbiDecodeInferMeta(const MetaTensor& input,
...
@@ -319,156 +472,4 @@ void ViterbiDecodeInferMeta(const MetaTensor& input,
scores
->
set_dtype
(
length
.
dtype
());
scores
->
set_dtype
(
length
.
dtype
());
}
}
void
LerpInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
const
MetaTensor
&
weight
,
MetaTensor
*
out
)
{
auto
x_dims
=
x
.
dims
();
auto
y_dims
=
y
.
dims
();
auto
w_dims
=
weight
.
dims
();
DDim
out_dims
;
out_dims
=
funcs
::
GetOutputDims
(
x_dims
,
y_dims
);
if
(
w_dims
.
size
()
>
1
||
w_dims
[
0
]
!=
1
)
{
out_dims
=
funcs
::
GetOutputDims
(
out_dims
,
w_dims
);
}
out
->
set_dims
(
out_dims
);
out
->
set_dtype
(
x
.
dtype
());
out
->
share_lod
(
x
);
}
void
LinspaceInferMeta
(
const
MetaTensor
&
start
,
const
MetaTensor
&
stop
,
const
MetaTensor
&
number
,
MetaTensor
*
out
)
{
auto
s_dims
=
start
.
dims
();
PADDLE_ENFORCE_EQ
(
(
s_dims
.
size
()
==
1
)
&&
(
s_dims
[
0
]
==
1
),
true
,
phi
::
errors
::
InvalidArgument
(
"The shape of Input(Start) must be [1],"
"but received input shape is [%s]."
,
s_dims
));
auto
e_dims
=
stop
.
dims
();
PADDLE_ENFORCE_EQ
(
(
e_dims
.
size
()
==
1
)
&&
(
e_dims
[
0
]
==
1
),
true
,
phi
::
errors
::
InvalidArgument
(
"The shape of Input(Stop) must be [1],"
"but received input shape is [%s]."
,
e_dims
));
auto
step_dims
=
number
.
dims
();
PADDLE_ENFORCE_EQ
(
(
step_dims
.
size
()
==
1
)
&&
(
step_dims
[
0
]
==
1
),
true
,
phi
::
errors
::
InvalidArgument
(
"The shape of Input(Num) must be [1],"
"but received input shape is [%s]."
,
step_dims
));
out
->
set_dims
(
phi
::
make_ddim
({
-
1
}));
out
->
set_dtype
(
start
.
dtype
());
}
void
AccuracyInferMeta
(
const
MetaTensor
&
out
,
const
MetaTensor
&
indice
,
const
MetaTensor
&
label
,
MetaTensor
*
accuracy
,
MetaTensor
*
correct
,
MetaTensor
*
total
,
MetaConfig
config
)
{
auto
inference_dim
=
out
.
dims
();
auto
label_dim
=
label
.
dims
();
// Assume indices has same shape as inference, because
// it's the output of topk.
PADDLE_ENFORCE_EQ
(
label_dim
.
size
(),
2
,
phi
::
errors
::
InvalidArgument
(
"ShapeError: label's dimensions of AccuracyOp must be 2. "
"But received label's dimensions = %d, label's shape = [%s]"
,
label_dim
.
size
(),
label_dim
));
if
(
config
.
is_runtime
)
{
PADDLE_ENFORCE_EQ
(
label_dim
[
1
],
1
,
phi
::
errors
::
InvalidArgument
(
"ShapeError: label's second dimension of "
"AccuracyOp must be 1. But received label's "
"second dimension is = %d, label's shape = [%s]"
,
label_dim
[
1
],
label_dim
));
PADDLE_ENFORCE_EQ
(
inference_dim
[
0
],
label_dim
[
0
],
phi
::
errors
::
InvalidArgument
(
"ShapeError: the output's num_rows of AccuracyOp must be"
" the same as label's num_rows. But received output's "
"shape = [%s], label's shape = [%s], output's num_rows = %d, "
"label's "
"num_rows = %d"
,
inference_dim
,
label_dim
,
inference_dim
[
0
],
label_dim
[
0
]));
}
accuracy
->
set_dims
({
1
});
accuracy
->
set_dtype
(
out
.
dtype
());
correct
->
set_dims
({
1
});
correct
->
set_dtype
(
out
.
dtype
());
total
->
set_dims
({
1
});
total
->
set_dtype
(
out
.
dtype
());
accuracy
->
share_lod
(
out
);
}
void
GraphSendRecvInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
src_index
,
const
MetaTensor
&
dst_index
,
const
std
::
string
&
pool_type
,
MetaTensor
*
out
,
MetaTensor
*
dst_count
)
{
auto
src_index_dims
=
src_index
.
dims
();
if
(
src_index_dims
.
size
()
==
2
)
{
PADDLE_ENFORCE_EQ
(
src_index_dims
[
1
],
1
,
phi
::
errors
::
InvalidArgument
(
"The last dim of Src_index should be 1 when it "
"is 2D, but we get %d"
,
src_index_dims
[
1
]));
}
else
{
PADDLE_ENFORCE_EQ
(
src_index_dims
.
size
(),
1
,
phi
::
errors
::
InvalidArgument
(
"The Src_index should be 1D, when it is not 2D, but we get %d"
,
src_index_dims
.
size
()));
}
auto
dst_index_dims
=
dst_index
.
dims
();
if
(
dst_index_dims
.
size
()
==
2
)
{
PADDLE_ENFORCE_EQ
(
dst_index_dims
[
1
],
1
,
phi
::
errors
::
InvalidArgument
(
"The last dim of Dst_index should be 1 when it "
"is 2D, but we get %d"
,
dst_index_dims
[
1
]));
}
else
{
PADDLE_ENFORCE_EQ
(
dst_index_dims
.
size
(),
1
,
phi
::
errors
::
InvalidArgument
(
"The Dst_index should be 1D, "
"when it is not 2D, but we get %d"
,
dst_index_dims
.
size
()));
}
PADDLE_ENFORCE_EQ
(
src_index_dims
[
0
],
dst_index_dims
[
0
],
phi
::
errors
::
InvalidArgument
(
"Src_index and Dst_index should have the same shape."
));
auto
dims
=
x
.
dims
();
out
->
set_dims
(
dims
);
out
->
set_dtype
(
x
.
dtype
());
if
(
pool_type
==
"MEAN"
)
{
dst_count
->
set_dims
({
dims
[
0
]});
dst_count
->
set_dtype
(
DataType
::
INT32
);
}
}
}
// namespace phi
}
// namespace phi
paddle/phi/infermeta/ternary.h
浏览文件 @
f3f27d25
...
@@ -45,16 +45,22 @@ void AddmmInferMeta(const MetaTensor& input,
...
@@ -45,16 +45,22 @@ void AddmmInferMeta(const MetaTensor& input,
float
beta
,
float
beta
,
MetaTensor
*
out
);
MetaTensor
*
out
);
void
GatherNdGradInferMeta
(
const
MetaTensor
&
x
,
void
GraphSendRecvInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
index
,
const
MetaTensor
&
src_index
,
const
MetaTensor
&
out_grad
,
const
MetaTensor
&
dst_index
,
MetaTensor
*
x_grad
);
const
std
::
string
&
pool_type
,
MetaTensor
*
out
,
MetaTensor
*
dst_count
);
void
ScatterInferMeta
(
const
MetaTensor
&
x
,
void
LerpInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
index
,
const
MetaTensor
&
y
,
const
MetaTensor
&
updates
,
const
MetaTensor
&
weight
,
bool
overwrite
,
MetaTensor
*
out
);
MetaTensor
*
out
);
void
LinspaceInferMeta
(
const
MetaTensor
&
start
,
const
MetaTensor
&
stop
,
const
MetaTensor
&
number
,
MetaTensor
*
out
);
void
NllLossRawInferMeta
(
const
MetaTensor
&
input
,
void
NllLossRawInferMeta
(
const
MetaTensor
&
input
,
const
MetaTensor
&
label
,
const
MetaTensor
&
label
,
...
@@ -65,6 +71,12 @@ void NllLossRawInferMeta(const MetaTensor& input,
...
@@ -65,6 +71,12 @@ void NllLossRawInferMeta(const MetaTensor& input,
MetaTensor
*
total_weight
,
MetaTensor
*
total_weight
,
MetaConfig
config
=
MetaConfig
());
MetaConfig
config
=
MetaConfig
());
void
ScatterInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
index
,
const
MetaTensor
&
updates
,
bool
overwrite
,
MetaTensor
*
out
);
void
ScatterNdAddInferMeta
(
const
MetaTensor
&
x
,
void
ScatterNdAddInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
index
,
const
MetaTensor
&
index
,
const
MetaTensor
&
updates
,
const
MetaTensor
&
updates
,
...
@@ -78,20 +90,4 @@ void ViterbiDecodeInferMeta(const MetaTensor& input,
...
@@ -78,20 +90,4 @@ void ViterbiDecodeInferMeta(const MetaTensor& input,
MetaTensor
*
path
,
MetaTensor
*
path
,
MetaConfig
config
=
MetaConfig
());
MetaConfig
config
=
MetaConfig
());
void
LerpInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
y
,
const
MetaTensor
&
weight
,
MetaTensor
*
out
);
void
LinspaceInferMeta
(
const
MetaTensor
&
start
,
const
MetaTensor
&
stop
,
const
MetaTensor
&
number
,
MetaTensor
*
out
);
void
GraphSendRecvInferMeta
(
const
MetaTensor
&
x
,
const
MetaTensor
&
src_index
,
const
MetaTensor
&
dst_index
,
const
std
::
string
&
pool_type
,
MetaTensor
*
out
,
MetaTensor
*
dst_count
);
}
// namespace phi
}
// namespace phi
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