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体验新版 GitCode,发现更多精彩内容 >>
提交
c6a6d87f
编写于
5月 07, 2018
作者:
Y
Yu Yang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Rewrite Matmul, make code cleaner
上级
0285a2b9
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
258 addition
and
418 deletion
+258
-418
paddle/fluid/operators/math/blas.cc
paddle/fluid/operators/math/blas.cc
+38
-1
paddle/fluid/operators/math/blas.h
paddle/fluid/operators/math/blas.h
+33
-0
paddle/fluid/operators/math/matmul.h
paddle/fluid/operators/math/matmul.h
+0
-149
paddle/fluid/operators/matmul_op.cc
paddle/fluid/operators/matmul_op.cc
+26
-108
paddle/fluid/operators/matmul_op.h
paddle/fluid/operators/matmul_op.h
+142
-144
python/paddle/fluid/tests/unittests/test_matmul_op.py
python/paddle/fluid/tests/unittests/test_matmul_op.py
+19
-16
未找到文件。
paddle/fluid/operators/math/blas.cc
浏览文件 @
c6a6d87f
...
...
@@ -13,10 +13,47 @@
// limitations under the License.
#include "paddle/fluid/operators/math/blas.h"
#include <utility>
namespace
paddle
{
namespace
operators
{
namespace
math
{
// Do nothing. Blas is a header only library.
MatDim
GetMatDim
(
const
framework
::
DDim
&
dim
,
int
num_flatten_cols
,
bool
trans
)
{
MatDim
retv
;
if
(
num_flatten_cols
>
1
)
{
auto
flatten_dim
=
framework
::
flatten_to_2d
(
dim
,
num_flatten_cols
);
retv
.
height_
=
flatten_dim
[
0
];
retv
.
width_
=
flatten_dim
[
1
];
}
else
{
if
(
dim
.
size
()
==
1
)
{
retv
.
height_
=
1
;
retv
.
width_
=
dim
[
0
];
}
else
if
(
dim
.
size
()
==
2
)
{
retv
.
height_
=
dim
[
0
];
retv
.
width_
=
dim
[
1
];
}
else
{
if
(
dim
.
size
()
==
3
)
{
retv
.
batch_size_
=
dim
[
0
];
retv
.
height_
=
dim
[
1
];
retv
.
width_
=
dim
[
2
];
}
else
{
auto
dim_vec
=
framework
::
vectorize
(
dim
);
retv
.
batch_size_
=
1
;
for
(
size_t
i
=
0
;
i
<
dim_vec
.
size
()
-
2
;
++
i
)
{
retv
.
batch_size_
*=
dim_vec
[
i
];
retv
.
height_
=
dim_vec
[
dim_vec
.
size
()
-
2
];
retv
.
width_
=
dim_vec
[
dim_vec
.
size
()
-
1
];
}
}
retv
.
stride_
=
retv
.
height_
*
retv
.
width_
;
}
}
if
(
trans
)
{
std
::
swap
(
retv
.
width_
,
retv
.
height_
);
}
retv
.
trans_
=
trans
;
return
retv
;
}
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/blas.h
浏览文件 @
c6a6d87f
...
...
@@ -46,6 +46,17 @@ namespace paddle {
namespace
operators
{
namespace
math
{
struct
MatDim
{
int64_t
height_
;
int64_t
width_
;
int64_t
stride_
{
0
};
int64_t
batch_size_
{
0
};
bool
trans_
;
};
extern
MatDim
GetMatDim
(
const
framework
::
DDim
&
tensor
,
int
num_flatten_cols
,
bool
trans
);
template
<
typename
DeviceContext
>
class
Blas
{
public:
...
...
@@ -90,6 +101,28 @@ class Blas {
int
K
,
T
alpha
,
const
T
*
A
,
const
T
*
B
,
T
beta
,
T
*
C
,
int
batchCount
,
int64_t
strideA
,
int64_t
strideB
)
const
;
template
<
typename
T
>
void
MatMul
(
const
framework
::
Tensor
&
mat_a
,
const
MatDim
&
dim_a
,
const
framework
::
Tensor
&
mat_b
,
const
MatDim
&
dim_b
,
T
alpha
,
framework
::
Tensor
*
mat_out
,
T
beta
)
const
{
PADDLE_ENFORCE_EQ
(
dim_a
.
width_
,
dim_b
.
height_
);
CBLAS_TRANSPOSE
transA
=
!
dim_a
.
trans_
?
CblasNoTrans
:
CblasTrans
;
CBLAS_TRANSPOSE
transB
=
!
dim_b
.
trans_
?
CblasNoTrans
:
CblasTrans
;
if
(
dim_a
.
batch_size_
==
0
&&
dim_b
.
batch_size_
==
0
)
{
this
->
template
GEMM
<
T
>(
transA
,
transB
,
dim_a
.
height_
,
dim_b
.
width_
,
dim_a
.
width_
,
alpha
,
mat_a
.
data
<
T
>
(),
mat_b
.
data
<
T
>
(),
beta
,
mat_out
->
data
<
T
>
());
}
else
{
PADDLE_ENFORCE
(
dim_a
.
batch_size_
==
dim_b
.
batch_size_
||
dim_a
.
batch_size_
==
0
||
dim_b
.
batch_size_
==
0
);
this
->
template
BatchedGEMM
<
T
>(
transA
,
transB
,
dim_a
.
height_
,
dim_b
.
width_
,
dim_a
.
width_
,
alpha
,
mat_a
.
data
<
T
>
(),
mat_b
.
data
<
T
>
(),
beta
,
mat_out
->
data
<
T
>
(),
dim_a
.
batch_size_
==
0
?
dim_b
.
batch_size_
:
dim_a
.
batch_size_
,
dim_a
.
stride_
,
dim_b
.
stride_
);
}
}
private:
const
DeviceContext
&
context_
;
};
...
...
paddle/fluid/operators/math/matmul.h
已删除
100644 → 0
浏览文件 @
0285a2b9
/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <algorithm>
#include <vector>
#include "paddle/fluid/operators/math/blas.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
// Implements the logic of numpy matmul:
// https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
//
// but allowing also for a, b to be transposed
//
// Both a & b can be 1- to 3-dimensional. Higher rank tensors are not supported
// yet.
template
<
typename
DeviceContext
,
typename
T
>
class
MatMulFunctor
{
public:
void
operator
()(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
a
,
bool
trans_a
,
const
framework
::
Tensor
&
b
,
bool
trans_b
,
T
alpha
,
framework
::
Tensor
*
out
,
T
beta
)
{
auto
dim_a
=
a
.
dims
();
auto
dim_b
=
b
.
dims
();
PADDLE_ENFORCE
(
a
.
place
()
==
b
.
place
()
&&
b
.
place
()
==
out
->
place
(),
"Tensors must all be in the same place."
);
PADDLE_ENFORCE_GE
(
dim_a
.
size
(),
1
,
"Input tensor a must be at least 1-dimensional."
);
PADDLE_ENFORCE_GE
(
dim_b
.
size
(),
1
,
"Input tensor b must be at least 1-dimensional."
);
std
::
vector
<
int64_t
>
out_dim
;
int64_t
batch_count
=
1
;
if
(
dim_a
.
size
()
>
3
)
{
PADDLE_ENFORCE
(
dim_b
.
size
()
==
dim_a
.
size
(),
"The dimensions of X and Y must be the same, and both of "
"them should be %d-dimensional."
,
dim_b
.
size
());
// The first rank-2 dimensions are accumulated on the batch_count, and the
// last two dimensions are used for matrix multiplication.
for
(
int
j
=
0
;
j
<
dim_a
.
size
()
-
2
;
++
j
)
{
PADDLE_ENFORCE_EQ
(
dim_b
[
j
],
dim_a
[
j
],
"The %d-th dimension of X and Y must be the same."
,
j
);
out_dim
.
push_back
(
dim_a
[
j
]);
batch_count
*=
dim_a
[
j
];
}
}
int
M
=
0
,
N
=
0
,
kA
=
0
,
kB
=
0
,
batchCountA
=
0
,
batchCountB
=
0
,
strideA
=
0
,
strideB
=
0
;
switch
(
dim_a
.
size
())
{
case
1
:
// similar to np.matmul:
// prepend dimension 1 (no transpose) or append dimension 1 (transpose)
M
=
trans_a
?
dim_a
[
0
]
:
1
;
kA
=
trans_a
?
1
:
dim_a
[
0
];
break
;
case
2
:
M
=
trans_a
?
dim_a
[
1
]
:
dim_a
[
0
];
kA
=
trans_a
?
dim_a
[
0
]
:
dim_a
[
1
];
break
;
case
3
:
batchCountA
=
dim_a
[
0
];
M
=
trans_a
?
dim_a
[
2
]
:
dim_a
[
1
];
kA
=
trans_a
?
dim_a
[
1
]
:
dim_a
[
2
];
strideA
=
M
*
kA
;
break
;
default:
batchCountA
=
batch_count
;
size_t
mat_s
=
dim_a
.
size
()
-
2
;
M
=
trans_a
?
dim_a
[
mat_s
+
1
]
:
dim_a
[
mat_s
];
kA
=
trans_a
?
dim_a
[
mat_s
]
:
dim_a
[
mat_s
+
1
];
strideA
=
M
*
kA
;
}
switch
(
dim_b
.
size
())
{
case
1
:
// similar to np.matmul:
// append dimension 1 (no transpose) or prepend dimension 1 (transpose)
kB
=
trans_b
?
1
:
dim_b
[
0
];
N
=
trans_b
?
dim_b
[
0
]
:
1
;
break
;
case
2
:
kB
=
trans_b
?
dim_b
[
1
]
:
dim_b
[
0
];
N
=
trans_b
?
dim_b
[
0
]
:
dim_b
[
1
];
break
;
case
3
:
batchCountB
=
dim_b
[
0
];
kB
=
trans_b
?
dim_b
[
2
]
:
dim_b
[
1
];
N
=
trans_b
?
dim_b
[
1
]
:
dim_b
[
2
];
strideB
=
kB
*
N
;
break
;
default:
batchCountB
=
batch_count
;
size_t
mat_s
=
dim_b
.
size
()
-
2
;
kB
=
trans_b
?
dim_b
[
mat_s
+
1
]
:
dim_b
[
mat_s
];
N
=
trans_b
?
dim_b
[
mat_s
]
:
dim_b
[
mat_s
+
1
];
strideB
=
kB
*
N
;
}
PADDLE_ENFORCE_EQ
(
kA
,
kB
,
"First matrix's width must be equal with second matrix's height."
);
if
(
batchCountA
&&
batchCountB
)
{
PADDLE_ENFORCE_EQ
(
batchCountA
,
batchCountB
,
"When input tensors a and b are both batched, they must have the "
"same batch dimension."
);
}
int
batchCount
=
std
::
max
(
batchCountA
,
batchCountB
);
CBLAS_TRANSPOSE
transA
=
(
trans_a
==
false
)
?
CblasNoTrans
:
CblasTrans
;
CBLAS_TRANSPOSE
transB
=
(
trans_b
==
false
)
?
CblasNoTrans
:
CblasTrans
;
auto
blas
=
GetBlas
<
DeviceContext
,
T
>
(
context
);
if
(
!
batchCount
)
{
// regular matrix multiplication
blas
.
GEMM
(
transA
,
transB
,
M
,
N
,
kA
,
alpha
,
a
.
data
<
T
>
(),
b
.
data
<
T
>
(),
beta
,
out
->
data
<
T
>
());
}
else
{
// batched matrix multiplication
blas
.
BatchedGEMM
(
transA
,
transB
,
M
,
N
,
kA
,
alpha
,
a
.
data
<
T
>
(),
b
.
data
<
T
>
(),
beta
,
out
->
data
<
T
>
(),
batchCount
,
strideA
,
strideB
);
}
}
};
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/matmul_op.cc
浏览文件 @
c6a6d87f
...
...
@@ -36,121 +36,39 @@ class MatMulOp : public framework::OperatorWithKernel {
auto
dim_x
=
context
->
GetInputDim
(
"X"
);
auto
dim_y
=
context
->
GetInputDim
(
"Y"
);
bool
transpose_x
=
context
->
Attrs
().
Get
<
bool
>
(
"transpose_X"
);
bool
transpose_y
=
context
->
Attrs
().
Get
<
bool
>
(
"transpose_Y"
);
PADDLE_ENFORCE_GE
(
dim_x
.
size
(),
1
,
"Input tensor X must be at least 1-dimensional."
);
PADDLE_ENFORCE_GE
(
dim_y
.
size
(),
1
,
"Input tensor Y must be at least 1-dimensional."
);
std
::
vector
<
int64_t
>
out_dim
;
int64_t
batch_count
=
1
;
if
(
dim_x
.
size
()
>
3
)
{
PADDLE_ENFORCE_EQ
(
dim_y
.
size
(),
dim_x
.
size
(),
"The dimensions of X and Y must be the same, and both of "
"them should be %d-dimensional."
,
dim_x
.
size
());
// The first rank-2 dimensions are accumulated on the batch_count, and the
// last two dimensions are used for matrix multiplication.
for
(
int
j
=
0
;
j
<
dim_x
.
size
()
-
2
;
++
j
)
{
PADDLE_ENFORCE_EQ
(
dim_y
[
j
],
dim_x
[
j
],
"The %d-th dimension of X and Y must be the same."
,
j
);
out_dim
.
push_back
(
dim_x
[
j
]);
batch_count
*=
dim_x
[
j
];
}
}
int
M
=
0
,
N
=
0
,
KX
=
0
,
KY
=
0
,
batchCountX
=
0
,
batchCountY
=
0
;
bool
remove_initial_dim
=
false
,
remove_final_dim
=
false
;
switch
(
dim_x
.
size
())
{
case
1
:
if
(
transpose_x
)
{
M
=
dim_x
[
0
];
KX
=
1
;
}
else
{
M
=
1
;
KX
=
dim_x
[
0
];
remove_initial_dim
=
true
;
}
break
;
case
2
:
M
=
transpose_x
?
dim_x
[
1
]
:
dim_x
[
0
];
KX
=
transpose_x
?
dim_x
[
0
]
:
dim_x
[
1
];
break
;
case
3
:
batchCountX
=
dim_x
[
0
];
M
=
transpose_x
?
dim_x
[
2
]
:
dim_x
[
1
];
KX
=
transpose_x
?
dim_x
[
1
]
:
dim_x
[
2
];
break
;
default:
batchCountX
=
batch_count
;
size_t
mat_s
=
dim_x
.
size
()
-
2
;
M
=
transpose_x
?
dim_x
[
mat_s
+
1
]
:
dim_x
[
mat_s
];
KX
=
transpose_x
?
dim_x
[
mat_s
]
:
dim_x
[
mat_s
+
1
];
break
;
}
auto
mat_dim_x
=
math
::
GetMatDim
(
GetXDim
(
dim_x
),
0
,
context
->
Attrs
().
Get
<
bool
>
(
"transpose_X"
));
auto
mat_dim_y
=
math
::
GetMatDim
(
GetYDim
(
dim_y
),
0
,
context
->
Attrs
().
Get
<
bool
>
(
"transpose_Y"
));
switch
(
dim_y
.
size
())
{
case
1
:
if
(
transpose_y
)
{
N
=
dim_y
[
0
];
KY
=
1
;
}
else
{
N
=
1
;
KY
=
dim_y
[
0
];
remove_final_dim
=
true
;
}
break
;
case
2
:
KY
=
transpose_y
?
dim_y
[
1
]
:
dim_y
[
0
];
N
=
transpose_y
?
dim_y
[
0
]
:
dim_y
[
1
];
break
;
case
3
:
batchCountY
=
dim_y
[
0
];
KY
=
transpose_y
?
dim_y
[
2
]
:
dim_y
[
1
];
N
=
transpose_y
?
dim_y
[
1
]
:
dim_y
[
2
];
break
;
default:
batchCountY
=
batch_count
;
size_t
mat_s
=
dim_y
.
size
()
-
2
;
KY
=
transpose_y
?
dim_y
[
mat_s
+
1
]
:
dim_y
[
mat_s
];
N
=
transpose_y
?
dim_y
[
mat_s
]
:
dim_y
[
mat_s
+
1
];
PADDLE_ENFORCE_EQ
(
mat_dim_x
.
width_
,
mat_dim_y
.
height_
);
PADDLE_ENFORCE
(
mat_dim_x
.
batch_size_
==
mat_dim_y
.
batch_size_
||
mat_dim_x
.
batch_size_
==
0
||
mat_dim_y
.
batch_size_
==
0
);
std
::
vector
<
int64_t
>
dim_out
;
if
(
mat_dim_x
.
batch_size_
!=
0
)
{
dim_out
=
framework
::
vectorize
(
dim_x
);
dim_out
[
dim_out
.
size
()
-
2
]
=
mat_dim_x
.
height_
;
dim_out
[
dim_out
.
size
()
-
1
]
=
mat_dim_y
.
width_
;
}
else
if
(
mat_dim_y
.
batch_size_
!=
0
)
{
dim_out
=
framework
::
vectorize
(
dim_y
);
dim_out
[
dim_out
.
size
()
-
2
]
=
mat_dim_x
.
height_
;
dim_out
[
dim_out
.
size
()
-
1
]
=
mat_dim_y
.
width_
;
}
else
{
dim_out
=
{
mat_dim_x
.
height_
,
mat_dim_y
.
width_
};
}
PADDLE_ENFORCE_EQ
(
KX
,
KY
,
"First matrix's width must be equal with second matrix's height."
);
if
(
batchCountX
&&
batchCountY
)
{
PADDLE_ENFORCE_EQ
(
batchCountX
,
batchCountY
,
"When Input(X) and Input(Y) are both three dimensional, they "
"must have the same batch dimension."
);
if
(
dim_x
.
size
()
==
1
&&
dim_out
[
dim_out
.
size
()
-
2
]
==
1
)
{
std
::
swap
(
dim_out
[
dim_out
.
size
()
-
2
],
dim_out
[
dim_out
.
size
()
-
1
]);
dim_out
.
resize
(
dim_out
.
size
()
-
1
);
}
int
batchCount
=
std
::
max
(
batchCountX
,
batchCountY
);
std
::
vector
<
int64_t
>
dim_out
;
if
(
batchCount
)
{
if
(
dim_x
.
size
()
>
3
)
{
dim_out
.
insert
(
dim_out
.
begin
(),
out_dim
.
begin
(),
out_dim
.
end
());
}
else
{
dim_out
.
push_back
(
batchCount
);
}
if
(
dim_y
.
size
()
==
1
&&
dim_out
[
dim_out
.
size
()
-
1
]
==
1
)
{
dim_out
.
resize
(
dim_out
.
size
()
-
1
);
}
if
(
!
remove_initial_dim
)
{
dim_out
.
push_back
(
M
);
}
if
(
!
remove_final_dim
)
{
dim_out
.
push_back
(
N
);
}
if
(
dim_out
.
size
()
==
0
)
{
// We don't support 0-dimensional Tensors (scalars), so instead
// treat the output as a Tensor of shape (1, ) in this case.
dim_out
.
push_back
(
1
);
if
(
dim_out
.
empty
())
{
dim_out
=
{
1
};
}
context
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
dim_out
));
context
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
...
...
paddle/fluid/operators/matmul_op.h
浏览文件 @
c6a6d87f
...
...
@@ -15,55 +15,56 @@ limitations under the License. */
#pragma once
#include <algorithm>
#include <functional>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/matmul.h"
namespace
paddle
{
namespace
operators
{
namespace
matmul_detail
{
inline
framework
::
DDim
GetXDim
(
const
framework
::
DDim
&
x_dim
)
{
if
(
x_dim
.
size
()
>
1
)
{
return
x_dim
;
}
return
framework
::
make_ddim
({
1
,
x_dim
[
0
]});
}
using
Tensor
=
framework
::
Tensor
;
using
DDim
=
framework
::
DDim
;
using
framework
::
make_ddim
;
using
framework
::
vectorize
;
inline
framework
::
DDim
GetYDim
(
const
framework
::
DDim
&
y_dim
)
{
if
(
y_dim
.
size
()
>
1
)
{
return
y_dim
;
}
return
framework
::
make_ddim
({
y_dim
[
0
],
1
});
}
template
<
typename
DeviceContext
,
typename
T
>
class
MatMulKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
&
x
=
*
context
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
&
y
=
*
context
.
Input
<
Tensor
>
(
"Y"
);
Tensor
*
out
=
context
.
Output
<
Tensor
>
(
"Out"
);
auto
&
x
=
detail
::
Ref
(
context
.
Input
<
framework
::
Tensor
>
(
"X"
),
"Cannot find X"
);
auto
&
y
=
detail
::
Ref
(
context
.
Input
<
framework
::
Tensor
>
(
"Y"
),
"Cannot find Y"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
bool
transpose_x
=
context
.
Attr
<
bool
>
(
"transpose_X"
);
bool
transpose_y
=
context
.
Attr
<
bool
>
(
"transpose_Y"
);
math
::
MatMulFunctor
<
DeviceContext
,
T
>
()(
context
.
template
device_context
<
DeviceContext
>(),
x
,
transpose_x
,
y
,
transpose_y
,
T
(
1
),
out
,
T
(
0
));
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
context
);
auto
mat_dim_a
=
math
::
GetMatDim
(
GetXDim
(
x
.
dims
()),
0
,
context
.
Attr
<
bool
>
(
"transpose_X"
));
auto
mat_dim_b
=
math
::
GetMatDim
(
GetYDim
(
y
.
dims
()),
0
,
context
.
Attr
<
bool
>
(
"transpose_Y"
));
blas
.
MatMul
(
x
,
mat_dim_a
,
y
,
mat_dim_b
,
T
(
1
),
out
,
T
(
0
));
}
};
template
<
typename
T
>
inline
Tensor
Reshape
(
const
Tensor
&
input
,
const
DDim
&
dims
)
{
Tensor
output
;
output
.
ShareDataWith
(
input
);
output
.
Resize
(
dims
);
return
output
;
}
// Reshape a rank-3 tensor from P x M x N to (P * M) x N.
// Identity op if the tensor is not of rank 3.
template
<
typename
T
>
Tensor
CombineBatchAndM
(
const
Tensor
&
input
)
{
Tensor
output
;
output
.
ShareDataWith
(
input
);
inline
framework
::
Tensor
CombineBatchAndM
(
const
framework
::
Tensor
&
input
)
{
auto
output
=
input
;
auto
in_dims
=
input
.
dims
();
if
(
in_dims
.
size
()
==
3
)
{
std
::
vector
<
int64_t
>
out_dims
=
{
in_dims
[
0
]
*
in_dims
[
1
],
in_dims
[
2
]};
output
.
Resize
(
make_ddim
(
out_dims
));
output
.
Resize
({
in_dims
[
0
]
*
in_dims
[
1
],
in_dims
[
2
]});
}
return
output
;
}
...
...
@@ -72,23 +73,57 @@ Tensor CombineBatchAndM(const Tensor& input) {
// (Warning: This requires transposing data and writes into new memory.)
// Identity op if the tensor is not of rank 3.
template
<
typename
DeviceContext
,
typename
T
>
Tensor
CombineBatchAndN
(
const
DeviceContext
&
context
,
const
Tensor
&
input
)
{
Tensor
output
;
inline
framework
::
Tensor
CombineBatchAndN
(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
)
{
auto
in_dims
=
input
.
dims
();
if
(
in_dims
.
size
()
==
3
)
{
output
.
Resize
({
in_dims
[
1
],
in_dims
[
0
],
in_dims
[
2
]});
output
.
mutable_data
<
T
>
(
context
.
GetPlace
());
std
::
vector
<
int
>
axis
=
{
1
,
0
,
2
};
math
::
Transpose
<
DeviceContext
,
T
,
3
>
trans
;
trans
(
context
,
input
,
&
output
,
axis
);
std
::
vector
<
int64_t
>
out_dims
=
{
in_dims
[
1
],
in_dims
[
0
]
*
in_dims
[
2
]};
output
.
Resize
({
in_dims
[
1
],
in_dims
[
0
]
*
in_dims
[
2
]});
}
else
{
output
.
ShareDataWith
(
input
);
if
(
in_dims
.
size
()
!=
3
)
{
return
input
;
}
framework
::
Tensor
output
;
output
.
Resize
({
in_dims
[
1
],
in_dims
[
0
],
in_dims
[
2
]});
output
.
mutable_data
<
T
>
(
context
.
GetPlace
());
std
::
vector
<
int
>
axis
=
{
1
,
0
,
2
};
math
::
Transpose
<
DeviceContext
,
T
,
3
>
trans
;
trans
(
context
,
input
,
&
output
,
axis
);
output
.
Resize
({
in_dims
[
1
],
in_dims
[
0
]
*
in_dims
[
2
]});
return
output
;
}
inline
void
NormalizeTensorShape
(
framework
::
Tensor
*
x
,
const
math
::
MatDim
&
mat_dim_x
)
{
int64_t
h
,
w
;
h
=
mat_dim_x
.
height_
;
w
=
mat_dim_x
.
width_
;
if
(
mat_dim_x
.
trans_
)
{
std
::
swap
(
w
,
h
);
}
if
(
mat_dim_x
.
batch_size_
)
{
x
->
Resize
({
mat_dim_x
.
batch_size_
,
h
,
w
});
}
else
{
x
->
Resize
({
h
,
w
});
}
}
inline
void
NormalizeXYOutTensorShape
(
framework
::
Tensor
*
x
,
framework
::
Tensor
*
y
,
framework
::
Tensor
*
out
,
bool
trans_a
,
bool
trans_b
)
{
auto
x_dim
=
GetXDim
(
x
->
dims
());
auto
y_dim
=
GetYDim
(
y
->
dims
());
auto
mat_dim_x
=
math
::
GetMatDim
(
x_dim
,
0
,
trans_a
);
auto
mat_dim_y
=
math
::
GetMatDim
(
y_dim
,
0
,
trans_b
);
if
(
mat_dim_x
.
batch_size_
==
0
&&
mat_dim_y
.
batch_size_
==
0
)
{
out
->
Resize
({
mat_dim_x
.
height_
,
mat_dim_y
.
width_
});
}
else
{
out
->
Resize
({
std
::
max
(
mat_dim_x
.
batch_size_
,
mat_dim_y
.
batch_size_
),
mat_dim_x
.
height_
,
mat_dim_y
.
width_
});
}
NormalizeTensorShape
(
x
,
mat_dim_x
);
NormalizeTensorShape
(
y
,
mat_dim_y
);
}
// Using dimensional constraints on matrix multiplication, it is
// straight-forward to check the following table for when X and Y
// are both matrices.
...
...
@@ -117,128 +152,91 @@ Tensor CombineBatchAndN(const DeviceContext& context, const Tensor& input) {
template
<
typename
DeviceContext
,
typename
T
>
class
MatMulGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
MatMul
(
const
framework
::
ExecutionContext
&
context
,
const
framework
::
Tensor
&
a
,
bool
trans_a
,
const
framework
::
Tensor
&
b
,
bool
trans_b
,
framework
::
Tensor
*
out
)
const
{
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
context
);
auto
mat_dim_a
=
math
::
GetMatDim
(
a
.
dims
(),
0
,
trans_a
);
auto
mat_dim_b
=
math
::
GetMatDim
(
b
.
dims
(),
0
,
trans_b
);
blas
.
MatMul
(
a
,
mat_dim_a
,
b
,
mat_dim_b
,
T
(
1
),
out
,
T
(
0
));
}
void
CalcInputGrad
(
const
framework
::
ExecutionContext
&
context
,
const
framework
::
Tensor
&
a
,
bool
trans_a
,
bool
is_combine_m_a
,
const
framework
::
Tensor
&
b
,
bool
trans_b
,
bool
is_combine_m_b
,
framework
::
Tensor
*
out
)
const
{
if
(
out
==
nullptr
)
return
;
bool
need_combine
=
(
a
.
dims
().
size
()
==
3
||
b
.
dims
().
size
()
==
3
)
&&
out
->
dims
().
size
()
==
2
;
if
(
!
need_combine
)
{
MatMul
(
context
,
a
,
trans_a
,
b
,
trans_b
,
out
);
}
else
{
auto
&
ctx
=
context
.
template
device_context
<
DeviceContext
>();
MatMul
(
context
,
is_combine_m_a
?
CombineBatchAndM
(
a
)
:
CombineBatchAndN
<
DeviceContext
,
T
>
(
ctx
,
a
),
trans_a
,
is_combine_m_b
?
CombineBatchAndM
(
b
)
:
CombineBatchAndN
<
DeviceContext
,
T
>
(
ctx
,
b
),
trans_b
,
out
);
}
}
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
&
x
=
*
context
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
&
y
=
*
context
.
Input
<
Tensor
>
(
"Y"
);
const
Tensor
&
dout
=
*
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
Tensor
*
dx
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
Tensor
*
dy
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
x
=
*
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
y
=
*
context
.
Input
<
framework
::
Tensor
>
(
"Y"
);
auto
dout
=
*
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
dx
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dy
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
bool
transpose_x
=
context
.
Attr
<
bool
>
(
"transpose_X"
);
bool
transpose_y
=
context
.
Attr
<
bool
>
(
"transpose_Y"
);
std
::
vector
<
int64_t
>
x_dims
=
vectorize
(
x
.
dims
());
std
::
vector
<
int64_t
>
y_dims
=
vectorize
(
y
.
dims
());
// If X is a vector, reshape it to a matrix.
if
(
x_dims
.
size
()
==
1
)
{
x_dims
.
insert
(
x_dims
.
begin
(),
1
);
}
// If Y is a vector, reshape it to a matrix.
if
(
y_dims
.
size
()
==
1
)
{
y_dims
.
push_back
(
1
);
NormalizeXYOutTensorShape
(
&
x
,
&
y
,
&
dout
,
transpose_x
,
transpose_y
);
framework
::
DDim
dx_dims
;
if
(
dx
)
{
dx_dims
=
dx
->
dims
();
if
(
dx_dims
!=
x
.
dims
())
{
dx
->
Resize
(
x
.
dims
());
}
}
int
batch_count
=
0
;
// The first rank-2 dimensions are accumulated on the batch_count, and the
// last two dimensions are used for matrix multiplication.
if
(
x_dims
.
size
()
>
3
)
{
batch_count
=
accumulate
(
x_dims
.
begin
(),
x_dims
.
end
()
-
2
,
1
,
std
::
multiplies
<
int
>
());
}
// Fix the dOut dimensions.
int
M
=
0
,
N
=
0
,
batchCountX
=
0
,
batchCountY
=
0
;
switch
(
x_dims
.
size
())
{
case
2
:
M
=
transpose_x
?
x_dims
[
1
]
:
x_dims
[
0
];
break
;
case
3
:
batchCountX
=
x_dims
[
0
];
M
=
transpose_x
?
x_dims
[
2
]
:
x_dims
[
1
];
break
;
default:
batchCountX
=
batch_count
;
size_t
mat_s
=
x_dims
.
size
()
-
2
;
M
=
transpose_x
?
x_dims
[
mat_s
+
1
]
:
x_dims
[
mat_s
];
framework
::
DDim
dy_dims
;
if
(
dy
)
{
dy_dims
=
dy
->
dims
();
if
(
dy_dims
!=
y
.
dims
())
{
dy
->
Resize
(
y
.
dims
());
}
}
switch
(
y_dims
.
size
())
{
case
2
:
N
=
transpose_y
?
y_dims
[
0
]
:
y_dims
[
1
];
break
;
case
3
:
batchCountY
=
y_dims
[
0
];
N
=
transpose_y
?
y_dims
[
1
]
:
y_dims
[
2
];
break
;
default:
batchCountY
=
batch_count
;
size_t
mat_s
=
y_dims
.
size
()
-
2
;
N
=
transpose_y
?
y_dims
[
mat_s
]
:
y_dims
[
mat_s
+
1
];
}
if
(
batchCountX
&&
batchCountY
)
{
PADDLE_ENFORCE_EQ
(
batchCountX
,
batchCountY
,
"When Input(X) and Input(Y) are both three dimensional, they "
"must have the same batch dimension."
);
}
int
batchCount
=
std
::
max
(
batchCountX
,
batchCountY
);
std
::
vector
<
int64_t
>
dout_dims
=
{
M
,
N
};
if
(
batchCount
)
{
if
(
x_dims
.
size
()
>
3
)
{
dout_dims
.
insert
(
dout_dims
.
begin
(),
x_dims
.
begin
(),
x_dims
.
end
()
-
2
);
}
else
{
dout_dims
.
insert
(
dout_dims
.
begin
(),
batchCount
);
}
if
(
transpose_x
&&
transpose_y
)
{
CalcInputGrad
(
context
,
y
,
true
,
true
,
dout
,
true
,
false
,
dx
);
CalcInputGrad
(
context
,
dout
,
true
,
true
,
x
,
true
,
false
,
dy
);
}
else
if
(
transpose_x
&&
!
transpose_y
)
{
CalcInputGrad
(
context
,
y
,
false
,
false
,
dout
,
true
,
false
,
dx
);
CalcInputGrad
(
context
,
x
,
false
,
false
,
dout
,
false
,
true
,
dy
);
}
else
if
(
!
transpose_x
&&
transpose_y
)
{
CalcInputGrad
(
context
,
dout
,
false
,
false
,
y
,
false
,
true
,
dx
);
CalcInputGrad
(
context
,
dout
,
true
,
true
,
x
,
false
,
true
,
dy
);
}
else
{
CalcInputGrad
(
context
,
dout
,
false
,
false
,
y
,
true
,
false
,
dx
);
CalcInputGrad
(
context
,
x
,
true
,
true
,
dout
,
false
,
true
,
dy
);
}
Tensor
X
=
Reshape
<
T
>
(
x
,
make_ddim
(
x_dims
));
Tensor
Y
=
Reshape
<
T
>
(
y
,
make_ddim
(
y_dims
));
Tensor
dOut
=
Reshape
<
T
>
(
dout
,
make_ddim
(
dout_dims
));
auto
&
dev_ctx
=
context
.
template
device_context
<
DeviceContext
>();
if
(
dx
)
{
dx
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
Tensor
&
dOut_for_dX
=
(
x_dims
.
size
()
==
2
&&
y_dims
.
size
()
==
3
)
?
CombineBatchAndN
<
DeviceContext
,
T
>
(
dev_ctx
,
dOut
)
:
dOut
;
if
(
x_dims
.
size
()
==
2
&&
y_dims
.
size
()
==
3
)
{
Y
=
transpose_y
?
CombineBatchAndM
<
T
>
(
Y
)
:
CombineBatchAndN
<
DeviceContext
,
T
>
(
dev_ctx
,
Y
);
}
if
(
transpose_x
)
{
math
::
MatMulFunctor
<
DeviceContext
,
T
>
()(
dev_ctx
,
Y
,
transpose_y
,
dOut_for_dX
,
transpose_x
,
T
(
1
),
dx
,
T
(
0
));
}
else
{
math
::
MatMulFunctor
<
DeviceContext
,
T
>
()(
dev_ctx
,
dOut_for_dX
,
transpose_x
,
Y
,
!
transpose_y
,
T
(
1
),
dx
,
T
(
0
));
if
(
dx_dims
!=
x
.
dims
())
{
dx
->
Resize
(
dx_dims
);
}
}
if
(
dy
)
{
dy
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
Tensor
&
dOut_for_dY
=
(
y_dims
.
size
()
==
2
&&
x_dims
.
size
()
==
3
)
?
CombineBatchAndM
<
T
>
(
dOut
)
:
dOut
;
if
(
y_dims
.
size
()
==
2
&&
x_dims
.
size
()
==
3
)
{
X
=
transpose_x
?
CombineBatchAndN
<
DeviceContext
,
T
>
(
dev_ctx
,
X
)
:
CombineBatchAndM
<
T
>
(
X
);
dOut
=
CombineBatchAndM
<
T
>
(
dOut
);
}
if
(
transpose_y
)
{
math
::
MatMulFunctor
<
DeviceContext
,
T
>
()(
dev_ctx
,
dOut_for_dY
,
transpose_y
,
X
,
transpose_x
,
T
(
1
),
dy
,
T
(
0
));
}
else
{
math
::
MatMulFunctor
<
DeviceContext
,
T
>
()(
dev_ctx
,
X
,
!
transpose_x
,
dOut_for_dY
,
transpose_y
,
T
(
1
),
dy
,
T
(
0
));
if
(
dy_dims
!=
y
.
dims
())
{
dy
->
Resize
(
dy_dims
);
}
}
}
};
}
// namespace matmul_detail
using
matmul_detail
::
MatMulKernel
;
using
matmul_detail
::
MatMulGradKernel
;
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/tests/unittests/test_matmul_op.py
浏览文件 @
c6a6d87f
...
...
@@ -111,21 +111,24 @@ class Generator(object):
# Generate test cases for all possibilities
for
dim_X
in
[
1
,
2
,
3
]:
for
dim_Y
in
[
1
,
2
,
3
]:
for
transpose_X
in
[
False
,
True
]:
for
transpose_Y
in
[
False
,
True
]:
test_name
=
(
'TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}'
.
format
(
dim_X
,
dim_Y
,
transpose_X
,
transpose_Y
))
shape_X
,
shape_Y
=
generate_compatible_shapes
(
dim_X
,
dim_Y
,
transpose_X
,
transpose_Y
)
globals
()[
test_name
]
=
type
(
test_name
,
(
Generator
,
OpTest
),
{
'shape_X'
:
shape_X
,
'shape_Y'
:
shape_Y
,
'transpose_X'
:
transpose_X
,
'transpose_Y'
:
transpose_Y
,
})
def
inject_test
(
dim_x
,
dim_y
,
trans_x
,
trans_y
):
test_name
=
(
'TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}'
.
format
(
dim_x
,
dim_y
,
trans_x
,
trans_y
))
shape_x
,
shape_y
=
generate_compatible_shapes
(
dim_x
,
dim_y
,
trans_x
,
trans_y
)
globals
()[
test_name
]
=
type
(
test_name
,
(
Generator
,
OpTest
),
{
'shape_X'
:
shape_x
,
'shape_Y'
:
shape_y
,
'transpose_X'
:
trans_x
,
'transpose_Y'
:
trans_y
,
})
for
dim_X
in
(
1
,
2
,
3
):
for
dim_Y
in
(
1
,
2
,
3
):
for
transose_x
in
(
False
,
True
):
for
transose_y
in
(
False
,
True
):
inject_test
(
dim_X
,
dim_Y
,
transose_x
,
transose_y
)
# Test case n-dim
...
...
@@ -149,7 +152,7 @@ def generate_compatible_shapes(dim, transpose_X, transpose_Y):
return
shape_X
,
shape_Y
# Test case n-dim
#
#
Test case n-dim
for
dim
in
[
4
]:
for
transpose_X
in
[
False
,
True
]:
for
transpose_Y
in
[
False
,
True
]:
...
...
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