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体验新版 GitCode,发现更多精彩内容 >>
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c6090660
编写于
8月 22, 2020
作者:
S
ShenLiang
提交者:
GitHub
8月 22, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add Matmul op (#26411)
* add matmul_v2
上级
65ac1ef6
变更
10
隐藏空白更改
内联
并排
Showing
10 changed file
with
1290 addition
and
170 deletion
+1290
-170
paddle/fluid/operators/dot_op.h
paddle/fluid/operators/dot_op.h
+82
-76
paddle/fluid/operators/math/blas.h
paddle/fluid/operators/math/blas.h
+5
-0
paddle/fluid/operators/math/blas_impl.cu.h
paddle/fluid/operators/math/blas_impl.cu.h
+11
-0
paddle/fluid/operators/math/blas_impl.h
paddle/fluid/operators/math/blas_impl.h
+21
-0
paddle/fluid/operators/matmul_v2_op.cc
paddle/fluid/operators/matmul_v2_op.cc
+176
-0
paddle/fluid/operators/matmul_v2_op.cu
paddle/fluid/operators/matmul_v2_op.cu
+26
-0
paddle/fluid/operators/matmul_v2_op.h
paddle/fluid/operators/matmul_v2_op.h
+481
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+61
-2
python/paddle/fluid/tests/unittests/test_matmul_v2_op.py
python/paddle/fluid/tests/unittests/test_matmul_v2_op.py
+336
-0
python/paddle/tensor/linalg.py
python/paddle/tensor/linalg.py
+91
-92
未找到文件。
paddle/fluid/operators/dot_op.h
浏览文件 @
c6090660
...
@@ -26,6 +26,86 @@ template <typename T, int MajorType = Eigen::RowMajor,
...
@@ -26,6 +26,86 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename
IndexType
=
Eigen
::
DenseIndex
>
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
DeviceContext
,
typename
T
>
void
DotGradFunction
(
const
Tensor
*
tensor_x
,
const
Tensor
*
tensor_y
,
const
Tensor
*
tensor_dout
,
Tensor
*
tensor_dx
,
Tensor
*
tensor_dy
,
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
{
#ifdef __NVCC__
if
(
1
==
tensor_dout
->
dims
().
size
())
{
auto
dout
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
tensor_dout
);
if
(
tensor_dx
)
{
auto
y
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
tensor_y
);
auto
dx
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
tensor_dx
);
auto
&
dev
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
Eigen
::
DSizes
<
int
,
1
>
size
(
tensor_dx
->
numel
());
dx
.
device
(
dev
)
=
y
*
dout
.
broadcast
(
size
);
}
if
(
tensor_dy
)
{
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
tensor_x
);
auto
dy
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
tensor_dy
);
auto
&
dev
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
Eigen
::
DSizes
<
int
,
1
>
size
(
tensor_dy
->
numel
());
dy
.
device
(
dev
)
=
x
*
dout
.
broadcast
(
size
);
}
}
else
{
auto
dout
=
EigenMatrix
<
T
>::
From
(
*
tensor_dout
);
if
(
tensor_dx
)
{
tensor_dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
y
=
EigenMatrix
<
T
>::
From
(
*
tensor_y
);
auto
dx
=
EigenMatrix
<
T
>::
From
(
*
tensor_dx
);
auto
&
dev
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
Eigen
::
DSizes
<
int
,
2
>
size
(
1
,
tensor_dx
->
dims
()[
1
]);
dx
.
device
(
dev
)
=
y
*
dout
.
broadcast
(
size
);
}
if
(
tensor_dy
)
{
tensor_dy
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
x
=
EigenMatrix
<
T
>::
From
(
*
tensor_x
);
auto
dy
=
EigenMatrix
<
T
>::
From
(
*
tensor_dy
);
auto
&
dev
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
Eigen
::
DSizes
<
int
,
2
>
size
(
1
,
tensor_dy
->
dims
()[
1
]);
dy
.
device
(
dev
)
=
x
*
dout
.
broadcast
(
size
);
}
}
#else
const
auto
*
data_dout
=
tensor_dout
->
data
<
T
>
();
if
(
tensor_dx
)
{
auto
*
data_dx
=
tensor_dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
auto
*
data_y
=
tensor_y
->
data
<
T
>
();
const
framework
::
DDim
&
dim
=
tensor_x
->
dims
();
size_t
N
=
static_cast
<
size_t
>
(
framework
::
product
(
dim
));
auto
step
=
dim
[
dim
.
size
()
-
1
];
int
s
=
-
1
;
for
(
size_t
i
=
0
;
i
<
N
;
++
i
)
{
if
(
0
==
i
%
step
)
++
s
;
data_dx
[
i
]
=
data_y
[
i
]
*
data_dout
[
s
];
}
}
if
(
tensor_dy
)
{
auto
*
data_dy
=
tensor_dy
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
auto
*
data_x
=
tensor_x
->
data
<
T
>
();
const
framework
::
DDim
&
dim
=
tensor_y
->
dims
();
size_t
N
=
static_cast
<
size_t
>
(
framework
::
product
(
dim
));
auto
step
=
dim
[
dim
.
size
()
-
1
];
int
s
=
-
1
;
for
(
size_t
i
=
0
;
i
<
N
;
++
i
)
{
if
(
0
==
i
%
step
)
++
s
;
data_dy
[
i
]
=
data_x
[
i
]
*
data_dout
[
s
];
}
}
#endif
}
template
<
typename
DeviceContext
,
typename
T
>
template
<
typename
DeviceContext
,
typename
T
>
class
DotKernel
:
public
framework
::
OpKernel
<
T
>
{
class
DotKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
public:
...
@@ -84,83 +164,9 @@ class DotGradKernel : public framework::OpKernel<T> {
...
@@ -84,83 +164,9 @@ class DotGradKernel : public framework::OpKernel<T> {
if
(
tensor_dx
)
tensor_dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
if
(
tensor_dx
)
tensor_dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
if
(
tensor_dy
)
tensor_dy
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
if
(
tensor_dy
)
tensor_dy
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
#ifdef __NVCC__
if
(
1
==
tensor_dout
->
dims
().
size
())
{
auto
dout
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
tensor_dout
);
if
(
tensor_dx
)
{
auto
y
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
tensor_y
);
auto
dx
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
tensor_dx
);
auto
&
dev
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
Eigen
::
DSizes
<
int
,
1
>
size
(
tensor_dx
->
numel
());
dx
.
device
(
dev
)
=
y
*
dout
.
broadcast
(
size
);
}
if
(
tensor_dy
)
{
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
tensor_x
);
auto
dy
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
tensor_dy
);
auto
&
dev
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
Eigen
::
DSizes
<
int
,
1
>
size
(
tensor_dy
->
numel
());
dy
.
device
(
dev
)
=
x
*
dout
.
broadcast
(
size
);
}
}
else
{
auto
dout
=
EigenMatrix
<
T
>::
From
(
*
tensor_dout
);
if
(
tensor_dx
)
{
tensor_dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
y
=
EigenMatrix
<
T
>::
From
(
*
tensor_y
);
auto
dx
=
EigenMatrix
<
T
>::
From
(
*
tensor_dx
);
auto
&
dev
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
Eigen
::
DSizes
<
int
,
2
>
size
(
1
,
tensor_dx
->
dims
()[
1
]);
dx
.
device
(
dev
)
=
y
*
dout
.
broadcast
(
size
);
}
if
(
tensor_dy
)
{
tensor_dy
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
x
=
EigenMatrix
<
T
>::
From
(
*
tensor_x
);
auto
dy
=
EigenMatrix
<
T
>::
From
(
*
tensor_dy
);
auto
&
dev
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
Eigen
::
DSizes
<
int
,
2
>
size
(
1
,
tensor_dy
->
dims
()[
1
]);
dy
.
device
(
dev
)
=
x
*
dout
.
broadcast
(
size
);
}
}
#else
const
auto
*
data_dout
=
tensor_dout
->
data
<
T
>
();
if
(
tensor_dx
)
{
auto
*
data_dx
=
tensor_dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
auto
*
data_y
=
tensor_y
->
data
<
T
>
();
const
framework
::
DDim
&
dim
=
tensor_x
->
dims
();
size_t
N
=
static_cast
<
size_t
>
(
framework
::
product
(
dim
));
auto
step
=
dim
[
dim
.
size
()
-
1
];
int
s
=
-
1
;
DotGradFunction
<
DeviceContext
,
T
>
(
tensor_x
,
tensor_y
,
tensor_dout
,
for
(
size_t
i
=
0
;
i
<
N
;
++
i
)
{
tensor_dx
,
tensor_dy
,
ctx
);
if
(
0
==
i
%
step
)
++
s
;
data_dx
[
i
]
=
data_y
[
i
]
*
data_dout
[
s
];
}
}
if
(
tensor_dy
)
{
auto
*
data_dy
=
tensor_dy
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
auto
*
data_x
=
tensor_x
->
data
<
T
>
();
const
framework
::
DDim
&
dim
=
tensor_y
->
dims
();
size_t
N
=
static_cast
<
size_t
>
(
framework
::
product
(
dim
));
auto
step
=
dim
[
dim
.
size
()
-
1
];
int
s
=
-
1
;
for
(
size_t
i
=
0
;
i
<
N
;
++
i
)
{
if
(
0
==
i
%
step
)
++
s
;
data_dy
[
i
]
=
data_x
[
i
]
*
data_dout
[
s
];
}
}
#endif
}
}
};
};
...
...
paddle/fluid/operators/math/blas.h
浏览文件 @
c6090660
...
@@ -198,6 +198,11 @@ class Blas {
...
@@ -198,6 +198,11 @@ class Blas {
int
K
,
T
alpha
,
const
T
*
A
,
const
T
*
B
,
T
beta
,
T
*
C
,
int
K
,
T
alpha
,
const
T
*
A
,
const
T
*
B
,
T
beta
,
T
*
C
,
int
batchCount
,
int64_t
strideA
,
int64_t
strideB
)
const
;
int
batchCount
,
int64_t
strideA
,
int64_t
strideB
)
const
;
template
<
typename
T
>
void
BatchedGEMM
(
CBLAS_TRANSPOSE
transA
,
CBLAS_TRANSPOSE
transB
,
int
M
,
int
N
,
int
K
,
T
alpha
,
const
T
**
A
,
const
T
**
B
,
T
beta
,
T
**
C
,
int
batchCount
)
const
;
#if defined(PADDLE_WITH_MKLML) && !defined(PADDLE_WITH_CUDA)
#if defined(PADDLE_WITH_MKLML) && !defined(PADDLE_WITH_CUDA)
template
<
typename
T
>
template
<
typename
T
>
void
BatchedGEMMWithHead
(
CBLAS_TRANSPOSE
transA
,
CBLAS_TRANSPOSE
transB
,
void
BatchedGEMMWithHead
(
CBLAS_TRANSPOSE
transA
,
CBLAS_TRANSPOSE
transB
,
...
...
paddle/fluid/operators/math/blas_impl.cu.h
浏览文件 @
c6090660
...
@@ -458,6 +458,17 @@ void Blas<platform::CUDADeviceContext>::BatchedGEMM(
...
@@ -458,6 +458,17 @@ void Blas<platform::CUDADeviceContext>::BatchedGEMM(
#endif // CUDA_VERSION >= 9010
#endif // CUDA_VERSION >= 9010
}
}
template
<
>
template
<
typename
T
>
void
Blas
<
platform
::
CUDADeviceContext
>::
BatchedGEMM
(
CBLAS_TRANSPOSE
transA
,
CBLAS_TRANSPOSE
transB
,
int
M
,
int
N
,
int
K
,
T
alpha
,
const
T
**
A
,
const
T
**
B
,
T
beta
,
T
**
C
,
int
batchCount
)
const
{
for
(
int
k
=
0
;
k
<
batchCount
;
++
k
)
{
this
->
template
GEMM
<
T
>(
transA
,
transB
,
M
,
N
,
K
,
alpha
,
A
[
k
],
B
[
k
],
beta
,
C
[
k
]);
}
}
template
<
>
template
<
>
template
<
typename
T
>
template
<
typename
T
>
void
Blas
<
platform
::
CUDADeviceContext
>::
TRSM
(
CBLAS_SIDE
side
,
CBLAS_UPLO
uplo
,
void
Blas
<
platform
::
CUDADeviceContext
>::
TRSM
(
CBLAS_SIDE
side
,
CBLAS_UPLO
uplo
,
...
...
paddle/fluid/operators/math/blas_impl.h
浏览文件 @
c6090660
...
@@ -12,6 +12,7 @@
...
@@ -12,6 +12,7 @@
// See the License for the specific language governing permissions and
// See the License for the specific language governing permissions and
// limitations under the License.
// limitations under the License.
#pragma once
#pragma once
#include <algorithm>
#include <cmath>
#include <cmath>
#include <limits>
#include <limits>
#include <vector>
#include <vector>
...
@@ -655,6 +656,26 @@ void Blas<platform::CPUDeviceContext>::BatchedGEMM(
...
@@ -655,6 +656,26 @@ void Blas<platform::CPUDeviceContext>::BatchedGEMM(
#endif
#endif
}
}
template
<
>
template
<
typename
T
>
void
Blas
<
platform
::
CPUDeviceContext
>::
BatchedGEMM
(
CBLAS_TRANSPOSE
transA
,
CBLAS_TRANSPOSE
transB
,
int
M
,
int
N
,
int
K
,
T
alpha
,
const
T
**
A
,
const
T
**
B
,
T
beta
,
T
**
C
,
int
batchCount
)
const
{
#ifdef PADDLE_WITH_MKLML
const
int
lda
=
std
::
max
((
transA
==
CblasNoTrans
)
?
K
:
M
,
1
);
const
int
ldb
=
std
::
max
((
transB
==
CblasNoTrans
)
?
N
:
K
,
1
);
const
int
ldc
=
std
::
max
(
N
,
1
);
CBlas
<
T
>::
GEMM_BATCH
(
CblasRowMajor
,
&
transA
,
&
transB
,
&
M
,
&
N
,
&
K
,
&
alpha
,
A
,
&
lda
,
B
,
&
ldb
,
&
beta
,
C
,
&
ldc
,
1
/* group_count */
,
&
batchCount
);
#else
for
(
int
k
=
0
;
k
<
batchCount
;
++
k
)
{
this
->
template
GEMM
<
T
>(
transA
,
transB
,
M
,
N
,
K
,
alpha
,
A
[
k
],
B
[
k
],
beta
,
C
[
k
]);
}
#endif
}
#if defined(PADDLE_WITH_MKLML) && !defined(PADDLE_WITH_CUDA)
#if defined(PADDLE_WITH_MKLML) && !defined(PADDLE_WITH_CUDA)
template
<
>
template
<
>
template
<
typename
T
>
template
<
typename
T
>
...
...
paddle/fluid/operators/matmul_v2_op.cc
0 → 100644
浏览文件 @
c6090660
// Copyright (c) 2020 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.
#include "paddle/fluid/operators/matmul_v2_op.h"
#include <string>
#include <vector>
namespace
paddle
{
namespace
operators
{
class
MatMulV2Op
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"X"
),
"Input"
,
"X"
,
"matmul_v2"
);
OP_INOUT_CHECK
(
ctx
->
HasInput
(
"Y"
),
"Input"
,
"Y"
,
"matmul_v2"
);
OP_INOUT_CHECK
(
ctx
->
HasOutput
(
"Out"
),
"Output"
,
"Out"
,
"matmul_v2"
);
bool
trans_x
=
ctx
->
Attrs
().
Get
<
bool
>
(
"trans_x"
);
bool
trans_y
=
ctx
->
Attrs
().
Get
<
bool
>
(
"trans_y"
);
std
::
vector
<
int64_t
>
dims_x
=
paddle
::
framework
::
vectorize
(
ctx
->
GetInputDim
(
"X"
));
std
::
vector
<
int64_t
>
dims_y
=
paddle
::
framework
::
vectorize
(
ctx
->
GetInputDim
(
"Y"
));
auto
ndims_x
=
dims_x
.
size
();
auto
ndims_y
=
dims_y
.
size
();
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
{
new_dims
.
assign
(
dims_y
.
begin
(),
dims_y
.
end
()
-
2
);
}
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
out_dims
=
framework
::
make_ddim
(
new_dims
);
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
ctx
->
ShareLoD
(
"X"
,
/* --> */
"Out"
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
"X"
),
ctx
.
device_context
());
}
};
class
MatMulV2OpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"tensor of shape (d0, d1 ... M, K)"
);
AddInput
(
"Y"
,
"tensor of shape (d0, d1 ... K, N)"
);
AddOutput
(
"Out"
,
"tensor of shape (d0, d1 ... M, N)"
);
AddAttr
<
bool
>
(
"trans_x"
,
"Set true to transpose the last two dimensions of X before "
"doing multiplication"
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"trans_y"
,
"Set true to transpose the last two dimensions of Y before "
"doing multiplication"
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(Matrix multiplication Out = X * Y. A has shape (d0, d1 ... M, K),
B has shape (d0, d1 ... K, N), Out has shape ((d0, d1 ... M, N)).
In addition, it also follows the broadcast rule which is similar as
numpy.matmul.
)DOC"
);
}
};
class
MatMulV2OpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
context
)
const
override
{
OP_INOUT_CHECK
(
context
->
HasInput
(
"X"
),
"Input"
,
"X"
,
"matmul_v2"
);
OP_INOUT_CHECK
(
context
->
HasInput
(
"Y"
),
"Input"
,
"Y"
,
"matmul_v2"
);
OP_INOUT_CHECK
(
context
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input"
,
"Out@GRAD"
,
"matmul_v2"
);
auto
x_dims
=
context
->
GetInputDim
(
"X"
);
auto
y_dims
=
context
->
GetInputDim
(
"Y"
);
auto
x_grad_name
=
framework
::
GradVarName
(
"X"
);
auto
y_grad_name
=
framework
::
GradVarName
(
"Y"
);
if
(
context
->
HasOutput
(
x_grad_name
))
{
context
->
SetOutputDim
(
x_grad_name
,
x_dims
);
}
if
(
context
->
HasOutput
(
y_grad_name
))
{
context
->
SetOutputDim
(
y_grad_name
,
y_dims
);
}
}
};
template
<
typename
T
>
class
MatMulV2GradOpMaker
:
public
framework
::
SingleGradOpMaker
<
T
>
{
public:
using
framework
::
SingleGradOpMaker
<
T
>::
SingleGradOpMaker
;
protected:
void
Apply
(
GradOpPtr
<
T
>
op
)
const
override
{
op
->
SetType
(
"matmul_v2_grad"
);
op
->
SetInput
(
"X"
,
this
->
Input
(
"X"
));
op
->
SetInput
(
"Y"
,
this
->
Input
(
"Y"
));
op
->
SetInput
(
framework
::
GradVarName
(
"Out"
),
this
->
OutputGrad
(
"Out"
));
op
->
SetOutput
(
framework
::
GradVarName
(
"X"
),
this
->
InputGrad
(
"X"
));
op
->
SetOutput
(
framework
::
GradVarName
(
"Y"
),
this
->
InputGrad
(
"Y"
));
op
->
SetAttrMap
(
this
->
Attrs
());
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
matmul_v2
,
ops
::
MatMulV2Op
,
ops
::
MatMulV2OpMaker
,
ops
::
MatMulV2GradOpMaker
<
paddle
::
framework
::
OpDesc
>
,
ops
::
MatMulV2GradOpMaker
<
paddle
::
imperative
::
OpBase
>
);
REGISTER_OPERATOR
(
matmul_v2_grad
,
ops
::
MatMulV2OpGrad
);
REGISTER_OP_CPU_KERNEL
(
matmul_v2
,
ops
::
MatMulV2Kernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
MatMulV2Kernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
matmul_v2_grad
,
ops
::
MatMulV2GradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
MatMulV2GradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/matmul_v2_op.cu
0 → 100644
浏览文件 @
c6090660
/* Copyright (c) 2020 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. */
#include "paddle/fluid/operators/matmul_v2_op.h"
namespace
ops
=
paddle
::
operators
;
namespace
plf
=
paddle
::
platform
;
REGISTER_OP_CUDA_KERNEL
(
matmul_v2
,
ops
::
MatMulV2Kernel
<
plf
::
CUDADeviceContext
,
float
>
,
ops
::
MatMulV2Kernel
<
plf
::
CUDADeviceContext
,
double
>
);
REGISTER_OP_CUDA_KERNEL
(
matmul_v2_grad
,
ops
::
MatMulV2GradKernel
<
plf
::
CUDADeviceContext
,
float
>
,
ops
::
MatMulV2GradKernel
<
plf
::
CUDADeviceContext
,
double
>
);
paddle/fluid/operators/matmul_v2_op.h
0 → 100644
浏览文件 @
c6090660
/* Copyright (c) 2020 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 <functional>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/dot_op.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/reduce_ops/reduce_sum_op.h"
#ifdef __NVCC__
#include "paddle/fluid/operators/reduce_ops/cub_reduce.h"
#endif
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
template
<
typename
T
>
struct
IdentityFunctor
{
HOSTDEVICE
explicit
inline
IdentityFunctor
()
{}
HOSTDEVICE
inline
T
operator
()(
const
T
&
x
)
const
{
return
x
;
}
};
template
<
typename
DeviceContext
,
typename
T
>
void
ReduceSumForMatmulGrad
(
const
Tensor
*
input
,
Tensor
*
output
,
const
std
::
vector
<
int
>&
reduce_dims
,
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
{
if
(
reduce_dims
.
empty
())
{
// FIXME maybe reduce this copy operation
framework
::
TensorCopySync
(
*
input
,
ctx
.
GetPlace
(),
output
);
return
;
}
#ifdef __NVCC__
auto
stream
=
ctx
.
cuda_device_context
().
stream
();
TensorReduce
<
T
,
T
,
cub
::
Sum
,
IdentityFunctor
<
T
>>
(
*
input
,
output
,
reduce_dims
,
static_cast
<
T
>
(
0
),
cub
::
Sum
(),
IdentityFunctor
<
T
>
(),
stream
);
#else
ReduceKernelFunctor
<
DeviceContext
,
T
,
ops
::
SumFunctor
>
(
input
,
output
,
reduce_dims
,
true
,
false
,
ctx
)
.
template
apply
<
T
>();
#endif
}
static
void
GetBroadcastFromDims
(
const
int
x_ndim
,
const
std
::
int64_t
*
x_dims
,
const
int
y_ndim
,
const
std
::
int64_t
*
y_dims
,
std
::
int64_t
*
x_bd_dims
,
std
::
int64_t
*
y_bd_dims
,
std
::
int64_t
*
out_bd_dims
)
{
const
int
ndim
=
std
::
max
(
x_ndim
,
y_ndim
);
std
::
fill
(
x_bd_dims
,
x_bd_dims
+
ndim
-
x_ndim
,
1
);
std
::
fill
(
y_bd_dims
,
y_bd_dims
+
ndim
-
y_ndim
,
1
);
std
::
copy
(
x_dims
,
x_dims
+
x_ndim
,
x_bd_dims
+
ndim
-
x_ndim
);
std
::
copy
(
y_dims
,
y_dims
+
y_ndim
,
y_bd_dims
+
ndim
-
y_ndim
);
for
(
int
i
=
0
;
i
<
ndim
;
++
i
)
{
PADDLE_ENFORCE_EQ
(
x_bd_dims
[
i
]
==
y_bd_dims
[
i
]
||
x_bd_dims
[
i
]
<=
1
||
y_bd_dims
[
i
]
<=
1
,
true
,
platform
::
errors
::
InvalidArgument
(
"Input(X) and Input(Y) has error dim."
));
if
(
x_bd_dims
[
i
]
==
0
||
y_bd_dims
[
i
]
==
0
)
{
out_bd_dims
[
i
]
=
0
;
}
else
{
out_bd_dims
[
i
]
=
std
::
max
(
x_bd_dims
[
i
],
y_bd_dims
[
i
]);
}
}
}
static
int64_t
GetIndexMessage
(
const
int
n
,
const
int64_t
*
dims
,
const
int64_t
*
index
)
{
int64_t
sum
=
0
;
for
(
int
i
=
0
;
i
<
n
;
++
i
)
{
if
(
dims
[
i
]
>
1
)
{
sum
=
sum
*
dims
[
i
]
+
index
[
i
];
}
}
return
sum
;
}
static
void
IndexIncreaseFromDims
(
const
int
ndim
,
const
int64_t
*
dims
,
int64_t
*
index
)
{
for
(
int
i
=
ndim
-
1
;
i
>=
0
;
--
i
)
{
++
index
[
i
];
if
(
index
[
i
]
>=
dims
[
i
])
{
index
[
i
]
-=
dims
[
i
];
}
else
{
break
;
}
}
}
template
<
typename
DeviceContext
,
typename
T
>
void
MatMulFunction
(
const
Tensor
*
X
,
const
Tensor
*
Y
,
const
std
::
vector
<
std
::
int64_t
>&
x_dims
,
const
std
::
vector
<
std
::
int64_t
>&
y_dims
,
Tensor
*
Out
,
bool
trans_x
,
bool
trans_y
,
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
{
const
int
x_ndim
=
x_dims
.
size
();
const
int
y_ndim
=
y_dims
.
size
();
// get data ptr
const
T
*
x_data
=
X
->
data
<
T
>
();
const
T
*
y_data
=
Y
->
data
<
T
>
();
if
(
x_ndim
==
1
&&
y_ndim
==
1
)
{
PADDLE_ENFORCE_EQ
(
X
->
numel
(),
Y
->
numel
(),
platform
::
errors
::
InvalidArgument
(
"X's numbers is not equal to Y's numbers,"
"when X/Y's dims =1"
));
VLOG
(
3
)
<<
"MatMul's case 1"
;
Out
->
Resize
({
1
});
Out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
out_eigen
=
framework
::
EigenScalar
<
T
>::
From
(
*
Out
);
auto
x_eigen
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
y_eigen
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Y
);
auto
&
dev
=
*
ctx
.
template
device_context
<
DeviceContext
>().
eigen_device
();
out_eigen
.
device
(
dev
)
=
(
x_eigen
*
y_eigen
).
sum
();
return
;
}
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
dev_ctx
);
if
(
x_ndim
==
1
)
{
const
int
N
=
X
->
numel
();
if
(
trans_y
)
{
PADDLE_ENFORCE_EQ
(
y_dims
[
y_ndim
-
1
],
N
,
platform
::
errors
::
InvalidArgument
(
"Input(Y) has error dim."
));
}
else
{
PADDLE_ENFORCE_EQ
(
y_dims
[
y_ndim
-
2
],
N
,
platform
::
errors
::
InvalidArgument
(
"Input(Y) has error dim."
));
}
std
::
vector
<
std
::
int64_t
>
out_dims
(
y_ndim
-
1
);
if
(
trans_y
)
{
std
::
copy_n
(
y_dims
.
cbegin
(),
y_ndim
-
1
,
out_dims
.
begin
());
}
else
{
std
::
copy_n
(
y_dims
.
cbegin
(),
y_ndim
-
2
,
out_dims
.
begin
());
out_dims
.
back
()
=
y_dims
.
back
();
}
Out
->
Resize
(
framework
::
make_ddim
(
out_dims
));
Out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
if
(
trans_y
)
{
const
int
M
=
Y
->
numel
()
/
N
;
VLOG
(
3
)
<<
"MatMul's case 2"
;
blas
.
GEMV
(
false
,
M
,
N
,
1.
,
y_data
,
x_data
,
0.
,
Out
->
data
<
T
>
());
}
else
{
const
int
M
=
y_dims
[
y_ndim
-
1
];
const
int
batch_size
=
Y
->
numel
()
/
(
M
*
N
);
if
(
batch_size
==
1
)
{
VLOG
(
3
)
<<
"MatMul's case 3"
;
blas
.
GEMV
(
true
,
N
,
M
,
1.
,
y_data
,
x_data
,
0.
,
Out
->
data
<
T
>
());
}
else
{
VLOG
(
3
)
<<
"MatMul's case 4"
;
blas
.
BatchedGEMM
(
CblasTrans
,
CblasNoTrans
,
M
,
1
,
N
,
1.0
f
,
y_data
,
x_data
,
0
,
Out
->
data
<
T
>
(),
batch_size
,
M
*
N
,
0
);
}
}
return
;
}
if
(
y_ndim
==
1
)
{
const
int
N
=
Y
->
numel
();
if
(
trans_x
)
{
PADDLE_ENFORCE_EQ
(
x_dims
[
x_ndim
-
2
],
N
,
platform
::
errors
::
InvalidArgument
(
"Input(X) has error dim."
));
}
else
{
PADDLE_ENFORCE_EQ
(
x_dims
[
x_ndim
-
1
],
N
,
platform
::
errors
::
InvalidArgument
(
"Input(X) has error dim."
));
}
std
::
vector
<
std
::
int64_t
>
out_dims
(
x_ndim
-
1
);
if
(
trans_x
)
{
std
::
copy_n
(
x_dims
.
cbegin
(),
x_ndim
-
2
,
out_dims
.
begin
());
out_dims
.
back
()
=
x_dims
.
back
();
}
else
{
std
::
copy_n
(
x_dims
.
cbegin
(),
x_ndim
-
1
,
out_dims
.
begin
());
}
Out
->
Resize
(
framework
::
make_ddim
(
out_dims
));
Out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
if
(
trans_x
)
{
const
int
M
=
x_dims
[
x_ndim
-
1
];
const
int
batch_size
=
X
->
numel
()
/
(
M
*
N
);
if
(
batch_size
==
1
)
{
VLOG
(
3
)
<<
"MatMul's case 5"
;
blas
.
GEMV
(
true
,
N
,
M
,
1.0
f
,
x_data
,
y_data
,
0.0
f
,
Out
->
data
<
T
>
());
}
else
{
VLOG
(
3
)
<<
"MatMul's case 6"
;
blas
.
BatchedGEMM
(
CblasTrans
,
CblasNoTrans
,
M
,
1
,
N
,
1.0
f
,
x_data
,
y_data
,
0
,
Out
->
data
<
T
>
(),
batch_size
,
M
*
N
,
0
);
}
}
else
{
const
int
M
=
X
->
numel
()
/
N
;
VLOG
(
3
)
<<
"MatMul's case 7"
;
blas
.
GEMV
(
false
,
M
,
N
,
1.0
f
,
x_data
,
y_data
,
0.0
f
,
Out
->
data
<
T
>
());
}
return
;
}
const
int
M
=
trans_x
?
x_dims
[
x_ndim
-
1
]
:
x_dims
[
x_ndim
-
2
];
const
int
K
=
trans_x
?
x_dims
[
x_ndim
-
2
]
:
x_dims
[
x_ndim
-
1
];
if
(
trans_y
)
{
PADDLE_ENFORCE_EQ
(
y_dims
[
y_ndim
-
1
],
K
,
platform
::
errors
::
InvalidArgument
(
"Input(X) has error dim."
));
}
else
{
PADDLE_ENFORCE_EQ
(
y_dims
[
y_ndim
-
2
],
K
,
platform
::
errors
::
InvalidArgument
(
"Input(X) has error dim."
));
}
const
int
N
=
trans_y
?
y_dims
[
y_ndim
-
2
]
:
y_dims
[
y_ndim
-
1
];
const
int
ndim
=
std
::
max
(
x_ndim
,
y_ndim
);
std
::
vector
<
std
::
int64_t
>
x_broadcast_dims
(
ndim
);
std
::
vector
<
std
::
int64_t
>
y_broadcast_dims
(
ndim
);
std
::
vector
<
std
::
int64_t
>
out_broadcast_dims
(
ndim
);
GetBroadcastFromDims
(
x_ndim
-
2
,
x_dims
.
data
(),
y_ndim
-
2
,
y_dims
.
data
(),
x_broadcast_dims
.
data
(),
y_broadcast_dims
.
data
(),
out_broadcast_dims
.
data
());
out_broadcast_dims
[
ndim
-
2
]
=
M
;
out_broadcast_dims
[
ndim
-
1
]
=
N
;
Out
->
Resize
(
framework
::
make_ddim
(
out_broadcast_dims
));
Out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
int
batch_dim
=
ndim
-
2
;
// broadcast message
const
bool
is_broadcast_dims
=
!
std
::
equal
(
x_broadcast_dims
.
cbegin
(),
x_broadcast_dims
.
cbegin
()
+
batch_dim
,
y_broadcast_dims
.
cbegin
());
const
std
::
int64_t
x_batch_size
=
std
::
accumulate
(
x_broadcast_dims
.
cbegin
(),
x_broadcast_dims
.
cbegin
()
+
batch_dim
,
1LL
,
std
::
multiplies
<
std
::
int64_t
>
());
const
std
::
int64_t
y_batch_size
=
std
::
accumulate
(
y_broadcast_dims
.
cbegin
(),
y_broadcast_dims
.
cbegin
()
+
batch_dim
,
1LL
,
std
::
multiplies
<
std
::
int64_t
>
());
const
std
::
int64_t
out_batch_size
=
std
::
accumulate
(
out_broadcast_dims
.
cbegin
(),
out_broadcast_dims
.
cbegin
()
+
batch_dim
,
1LL
,
std
::
multiplies
<
std
::
int64_t
>
());
if
(
out_batch_size
==
0
)
return
;
if
(
x_batch_size
==
1
&&
y_batch_size
==
1
)
{
VLOG
(
3
)
<<
"MatMul's case 8"
;
blas
.
GEMM
(
trans_x
?
CblasTrans
:
CblasNoTrans
,
trans_y
?
CblasTrans
:
CblasNoTrans
,
M
,
N
,
K
,
1.0
f
,
x_data
,
y_data
,
0.0
f
,
Out
->
data
<
T
>
());
}
else
if
(
x_batch_size
==
1
)
{
if
(
M
==
1
&&
trans_y
)
{
VLOG
(
3
)
<<
"MatMul's case 9"
;
blas
.
GEMV
(
false
,
y_batch_size
*
N
,
K
,
1.0
f
,
y_data
,
x_data
,
0.0
f
,
Out
->
data
<
T
>
());
}
else
{
VLOG
(
3
)
<<
"MatMul's case 10"
;
blas
.
BatchedGEMM
(
trans_x
?
CblasTrans
:
CblasNoTrans
,
trans_y
?
CblasTrans
:
CblasNoTrans
,
M
,
N
,
K
,
1.0
f
,
x_data
,
y_data
,
0
,
Out
->
data
<
T
>
(),
out_batch_size
,
0
,
K
*
N
);
}
}
else
if
(
y_batch_size
==
1
)
{
if
(
!
trans_x
)
{
VLOG
(
3
)
<<
"MatMul's case 11"
;
blas
.
GEMM
(
CblasNoTrans
,
trans_y
?
CblasTrans
:
CblasNoTrans
,
x_batch_size
*
M
,
N
,
K
,
1.0
f
,
x_data
,
y_data
,
0.0
f
,
Out
->
data
<
T
>
());
}
else
{
VLOG
(
3
)
<<
"MatMul's case 12"
;
blas
.
BatchedGEMM
(
CblasTrans
,
trans_y
?
CblasTrans
:
CblasNoTrans
,
M
,
N
,
K
,
1.0
f
,
x_data
,
y_data
,
0
,
Out
->
data
<
T
>
(),
out_batch_size
,
M
*
K
,
0
);
}
}
else
if
(
!
is_broadcast_dims
)
{
VLOG
(
3
)
<<
"MatMul's case 13"
;
blas
.
BatchedGEMM
(
trans_x
?
CblasTrans
:
CblasNoTrans
,
trans_y
?
CblasTrans
:
CblasNoTrans
,
M
,
N
,
K
,
1.0
f
,
x_data
,
y_data
,
0
,
Out
->
data
<
T
>
(),
out_batch_size
,
M
*
K
,
K
*
N
);
}
else
{
// in the case, can't use stridedgemm
std
::
vector
<
const
T
*>
x_ptr
(
out_batch_size
);
std
::
vector
<
const
T
*>
y_ptr
(
out_batch_size
);
std
::
vector
<
T
*>
out_ptr
(
out_batch_size
);
std
::
vector
<
std
::
int64_t
>
index
(
batch_dim
,
0
);
for
(
std
::
int64_t
i
=
0
;
i
<
out_batch_size
;
++
i
)
{
// using the index to get offset
const
std
::
int64_t
x_index
=
GetIndexMessage
(
batch_dim
,
x_broadcast_dims
.
data
(),
index
.
data
());
const
std
::
int64_t
y_index
=
GetIndexMessage
(
batch_dim
,
y_broadcast_dims
.
data
(),
index
.
data
());
x_ptr
[
i
]
=
x_data
+
x_index
*
M
*
K
;
y_ptr
[
i
]
=
y_data
+
y_index
*
K
*
N
;
out_ptr
[
i
]
=
Out
->
data
<
T
>
()
+
i
*
M
*
N
;
IndexIncreaseFromDims
(
batch_dim
,
out_broadcast_dims
.
data
(),
index
.
data
());
}
VLOG
(
3
)
<<
"MatMul's case 14"
;
blas
.
BatchedGEMM
(
trans_x
?
CblasTrans
:
CblasNoTrans
,
trans_y
?
CblasTrans
:
CblasNoTrans
,
M
,
N
,
K
,
1.0
f
,
x_ptr
.
data
(),
y_ptr
.
data
(),
0.0
f
,
out_ptr
.
data
(),
out_batch_size
);
}
}
template
<
typename
DeviceContext
,
typename
T
>
void
MatMulFunction
(
const
Tensor
*
X
,
const
Tensor
*
Y
,
Tensor
*
Out
,
bool
trans_x
,
bool
trans_y
,
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
{
const
std
::
vector
<
std
::
int64_t
>
x_dims
=
vectorize
(
X
->
dims
());
const
std
::
vector
<
std
::
int64_t
>
y_dims
=
vectorize
(
Y
->
dims
());
MatMulFunction
<
DeviceContext
,
T
>
(
X
,
Y
,
x_dims
,
y_dims
,
Out
,
trans_x
,
trans_y
,
ctx
);
}
template
<
typename
DeviceContext
,
typename
T
>
class
MatMulV2Kernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
paddle
::
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
X
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
Y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
Out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
bool
trans_x
=
ctx
.
Attr
<
bool
>
(
"trans_x"
);
bool
trans_y
=
ctx
.
Attr
<
bool
>
(
"trans_y"
);
MatMulFunction
<
DeviceContext
,
T
>
(
X
,
Y
,
Out
,
trans_x
,
trans_y
,
ctx
);
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
MatMulV2GradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
X
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
Y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
dOut
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
bool
trans_x
=
ctx
.
Attr
<
bool
>
(
"trans_x"
);
bool
trans_y
=
ctx
.
Attr
<
bool
>
(
"trans_y"
);
// get dims
std
::
vector
<
std
::
int64_t
>
x_dims
=
vectorize
(
X
->
dims
());
std
::
vector
<
std
::
int64_t
>
y_dims
=
vectorize
(
Y
->
dims
());
std
::
vector
<
std
::
int64_t
>
dout_dims
=
vectorize
(
dOut
->
dims
());
int
x_ndim
=
x_dims
.
size
();
int
y_ndim
=
y_dims
.
size
();
int
ndim
=
dout_dims
.
size
();
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dy
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
// x's or y's dim = 1
if
(
x_ndim
==
1
&&
y_ndim
==
1
)
{
if
(
dx
)
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
if
(
dy
)
dy
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
if
(
dOut
->
numel
()
==
1
)
{
DotGradFunction
<
DeviceContext
,
T
>
(
X
,
Y
,
dOut
,
dx
,
dy
,
ctx
);
return
;
}
}
// It is very tricky. For this broadcast, currently using the reduce sum to
// get gradient.
if
(
x_ndim
==
1
)
{
x_dims
.
insert
(
x_dims
.
begin
()
+
0
,
1
);
x_ndim
+=
1
;
if
(
trans_x
)
dout_dims
.
push_back
(
1
);
else
dout_dims
.
insert
(
dout_dims
.
begin
()
+
ndim
-
1
,
1
);
ndim
+=
1
;
}
if
(
y_ndim
==
1
)
{
y_dims
.
push_back
(
1
);
y_ndim
+=
1
;
if
(
trans_y
)
dout_dims
.
insert
(
dout_dims
.
begin
()
+
ndim
-
1
,
1
);
else
dout_dims
.
push_back
(
1
);
ndim
+=
1
;
}
// the normal case
Tensor
dx_help
,
dy_help
;
if
(
trans_x
)
{
if
(
trans_y
)
{
// X'Y': dA = Y'G', dB = G'X'
if
(
dx
)
MatMulFunction
<
DeviceContext
,
T
>
(
Y
,
dOut
,
y_dims
,
dout_dims
,
&
dx_help
,
true
,
true
,
ctx
);
if
(
dy
)
MatMulFunction
<
DeviceContext
,
T
>
(
dOut
,
X
,
dout_dims
,
x_dims
,
&
dy_help
,
true
,
true
,
ctx
);
}
else
{
// X'Y: dX = YG', dY = XG
if
(
dx
)
MatMulFunction
<
DeviceContext
,
T
>
(
Y
,
dOut
,
y_dims
,
dout_dims
,
&
dx_help
,
false
,
true
,
ctx
);
if
(
dy
)
MatMulFunction
<
DeviceContext
,
T
>
(
X
,
dOut
,
x_dims
,
dout_dims
,
&
dy_help
,
false
,
false
,
ctx
);
}
}
else
{
if
(
trans_y
)
{
// XY': dX = GY, dY = G'X
if
(
dx
)
MatMulFunction
<
DeviceContext
,
T
>
(
dOut
,
Y
,
dout_dims
,
y_dims
,
&
dx_help
,
false
,
false
,
ctx
);
if
(
dy
)
MatMulFunction
<
DeviceContext
,
T
>
(
dOut
,
X
,
dout_dims
,
x_dims
,
&
dy_help
,
true
,
false
,
ctx
);
}
else
{
// XY: dX = GY', dY = X'G
if
(
dx
)
MatMulFunction
<
DeviceContext
,
T
>
(
dOut
,
Y
,
dout_dims
,
y_dims
,
&
dx_help
,
false
,
true
,
ctx
);
if
(
dy
)
MatMulFunction
<
DeviceContext
,
T
>
(
X
,
dOut
,
x_dims
,
dout_dims
,
&
dy_help
,
true
,
false
,
ctx
);
}
}
// get help dims
const
std
::
vector
<
std
::
int64_t
>
dx_help_dims
=
vectorize
(
dx_help
.
dims
());
const
std
::
vector
<
std
::
int64_t
>
dy_help_dims
=
vectorize
(
dy_help
.
dims
());
std
::
vector
<
std
::
int64_t
>
dx_broadcast_dims
(
ndim
);
std
::
vector
<
std
::
int64_t
>
dy_broadcast_dims
(
ndim
);
std
::
fill
(
dx_broadcast_dims
.
data
(),
dx_broadcast_dims
.
data
()
+
ndim
-
x_ndim
,
1
);
std
::
fill
(
dy_broadcast_dims
.
data
(),
dy_broadcast_dims
.
data
()
+
ndim
-
y_ndim
,
1
);
std
::
copy
(
x_dims
.
data
(),
x_dims
.
data
()
+
x_ndim
,
dx_broadcast_dims
.
data
()
+
ndim
-
x_ndim
);
std
::
copy
(
y_dims
.
data
(),
y_dims
.
data
()
+
y_ndim
,
dy_broadcast_dims
.
data
()
+
ndim
-
y_ndim
);
std
::
vector
<
int
>
dx_reduce_dims
;
std
::
vector
<
int
>
dy_reduce_dims
;
for
(
int
idx
=
0
;
idx
<=
ndim
-
3
;
idx
++
)
{
if
(
dx_help_dims
[
idx
]
!=
1
&&
dx_broadcast_dims
[
idx
]
==
1
)
{
dx_reduce_dims
.
push_back
(
idx
);
}
if
(
dy_help_dims
[
idx
]
!=
1
&&
dy_broadcast_dims
[
idx
]
==
1
)
{
dy_reduce_dims
.
push_back
(
idx
);
}
}
// reduce sum to get grad by ReduceSum
if
(
dx
)
{
dx
->
Resize
(
dx_help
.
dims
());
ReduceSumForMatmulGrad
<
DeviceContext
,
T
>
(
&
dx_help
,
dx
,
dx_reduce_dims
,
ctx
);
dx
->
Resize
(
X
->
dims
());
}
if
(
dy
)
{
dy
->
Resize
(
dy_help
.
dims
());
ReduceSumForMatmulGrad
<
DeviceContext
,
T
>
(
&
dy_help
,
dy
,
dy_reduce_dims
,
ctx
);
dy
->
Resize
(
Y
->
dims
());
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/layers/nn.py
浏览文件 @
c6090660
...
@@ -26,7 +26,7 @@ import six
...
@@ -26,7 +26,7 @@ import six
import paddle
import paddle
from ..layer_helper import LayerHelper
from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant, NumpyArrayInitializer
from ..initializer import Normal, Constant, NumpyArrayInitializer
from ..framework import Variable, OpProtoHolder, in_dygraph_mode, dygraph_only, _dygraph_tracer, default_main_program
from ..framework import Variable, OpProtoHolder, in_dygraph_mode, dygraph_only, _dygraph_tracer, default_main_program
, _varbase_creator
from .. import dygraph_utils
from .. import dygraph_utils
from ..param_attr import ParamAttr
from ..param_attr import ParamAttr
from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
...
@@ -5033,6 +5033,7 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
...
@@ -5033,6 +5033,7 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
return out
return out
@deprecated(since="2.0.0", update_to="paddle.matmul")
def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
"""
"""
Applies matrix multiplication to two tensors.
Applies matrix multiplication to two tensors.
...
@@ -5104,7 +5105,65 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
...
@@ -5104,7 +5105,65 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
y = fluid.layers.data(name='y', shape=[3, 2], dtype='float32')
y = fluid.layers.data(name='y', shape=[3, 2], dtype='float32')
out = fluid.layers.matmul(x, y, True, True)
out = fluid.layers.matmul(x, y, True, True)
"""
"""
return paddle.matmul(x, y, transpose_x, transpose_y, alpha, name)
attrs = {
'transpose_X': transpose_x,
'transpose_Y': transpose_y,
'alpha': float(alpha),
}
if in_dygraph_mode():
out = _varbase_creator(dtype=x.dtype)
core.ops.matmul(x, y, out, 'transpose_X', transpose_x, 'transpose_Y',
transpose_y, 'alpha', float(alpha))
return out
def __check_input(x, y):
var_names = {'x': x, 'y': y}
for name, val in var_names.items():
check_variable_and_dtype(
val, name, ['float16', 'float32', 'float64'], 'matmul')
x_shape = list(x.shape)
y_shape = list(y.shape)
if len(x_shape) == 1:
x_shape = [1] + x_shape
if len(y_shape) == 1:
y_shape = y_shape + [1]
# check the inner 2 dimensions
if transpose_x:
x_shape[-2], x_shape[-1] = x_shape[-1], x_shape[-2]
if transpose_y:
y_shape[-2], y_shape[-1] = y_shape[-1], y_shape[-2]
if x_shape[-1] != y_shape[-2]:
assert (x_shape[-1] == -1) or (y_shape[-2] == -1), \
"After performing an optional transpose, Input X's width should be " \
"equal to Y's width for multiplication " \
"prerequisites. But received X's shape: %s, Y's shape: %s\n" % \
(x_shape, y_shape)
if len(y_shape) > 2 and len(x_shape) > 2:
for i, dim_x in enumerate(x_shape[:-2]):
# don't check neg shape
if dim_x < 0 or y_shape[i] < 0:
continue
if dim_x != y_shape[i]:
raise ValueError(
"When the matrix is larger than 2 dimensions, the higher "
"dimensional values of the two matrices need to be equal. "
"But received x_shape[%d] != y_shape[%d]. X's shape: %s, "
"Y's shape: %s.\n" % (i, i, x_shape, y_shape))
__check_input(x, y)
helper = LayerHelper('matmul', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='matmul',
inputs={'X': x,
'Y': y},
outputs={'Out': out},
attrs=attrs)
return out
def topk(input, k, name=None):
def topk(input, k, name=None):
...
...
python/paddle/fluid/tests/unittests/test_matmul_v2_op.py
0 → 100644
浏览文件 @
c6090660
# Copyright (c) 2020 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
import
paddle.fluid.core
as
core
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.framework
as
framework
def
reference_matmul
(
X
,
Y
,
transpose_X
=
False
,
transpose_Y
=
False
):
"""Reference forward implementation using np.matmul."""
# np.matmul does not support the transpose flags, so we manually
# transpose X and Y appropriately.
if
transpose_X
:
if
X
.
ndim
==
1
:
X
=
X
.
reshape
((
X
.
size
,
))
elif
X
.
ndim
==
2
:
X
=
X
.
T
else
:
dim
=
[
i
for
i
in
range
(
len
(
X
.
shape
))]
dim
[
-
1
],
dim
[
len
(
X
.
shape
)
-
2
]
=
dim
[
len
(
X
.
shape
)
-
2
],
dim
[
-
1
]
X
=
np
.
transpose
(
X
,
tuple
(
dim
))
if
transpose_Y
:
if
Y
.
ndim
==
1
:
Y
=
Y
.
reshape
((
Y
.
size
,
))
else
:
dim
=
[
i
for
i
in
range
(
len
(
Y
.
shape
))]
dim
[
-
1
],
dim
[
len
(
Y
.
shape
)
-
2
]
=
dim
[
len
(
Y
.
shape
)
-
2
],
dim
[
-
1
]
Y
=
np
.
transpose
(
Y
,
tuple
(
dim
))
Out
=
np
.
matmul
(
X
,
Y
)
if
not
Out
.
shape
:
# We do not support 0-dimensional Tensors (scalars). So where
# np.matmul outputs a scalar, we must convert to a Tensor of
# shape (1, ) instead.
# Everywhere else, we are compatible with np.matmul.
Out
=
np
.
array
([
Out
],
dtype
=
"float64"
)
return
Out
class
TestMatMulV2Op
(
OpTest
):
"""
case 1
"""
def
config
(
self
):
self
.
x_shape
=
(
100
,
)
self
.
y_shape
=
(
100
,
)
self
.
trans_x
=
False
self
.
trans_y
=
False
self
.
dtype
=
"float64"
def
setUp
(
self
):
self
.
config
()
self
.
op_type
=
"matmul_v2"
x
=
np
.
random
.
random
(
self
.
x_shape
).
astype
(
self
.
dtype
)
y
=
np
.
random
.
random
(
self
.
y_shape
).
astype
(
self
.
dtype
)
result
=
reference_matmul
(
x
,
y
,
self
.
trans_x
,
self
.
trans_y
)
self
.
inputs
=
{
'X'
:
x
,
'Y'
:
y
,
}
self
.
attrs
=
{
'trans_x'
:
self
.
trans_x
,
'trans_y'
:
self
.
trans_y
}
self
.
outputs
=
{
'Out'
:
result
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
)
class
TestMatMuklOp2
(
TestMatMulV2Op
):
"""
case 2
"""
def
config
(
self
):
self
.
x_shape
=
(
100
,
)
self
.
y_shape
=
(
1
,
3
,
2
,
100
)
self
.
trans_x
=
False
self
.
trans_y
=
True
self
.
dtype
=
"float64"
class
TestMatMuklOp3
(
TestMatMulV2Op
):
"""
case 3
"""
def
config
(
self
):
self
.
x_shape
=
(
100
,
)
self
.
y_shape
=
(
1
,
1
,
100
,
2
)
self
.
trans_x
=
False
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp4
(
TestMatMulV2Op
):
"""
case 4
"""
def
config
(
self
):
self
.
x_shape
=
(
100
,
)
self
.
y_shape
=
(
1
,
2
,
100
,
2
)
self
.
trans_x
=
False
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp5
(
TestMatMulV2Op
):
"""
case 5
"""
def
config
(
self
):
self
.
x_shape
=
(
1
,
1
,
100
,
2
)
self
.
y_shape
=
(
100
,
)
self
.
trans_x
=
True
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp6
(
TestMatMulV2Op
):
"""
case 6
"""
def
config
(
self
):
self
.
x_shape
=
(
1
,
2
,
100
,
1
)
self
.
y_shape
=
(
100
,
)
self
.
trans_x
=
True
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp7
(
TestMatMulV2Op
):
"""
case 7
"""
def
config
(
self
):
self
.
x_shape
=
(
1
,
2
,
1
,
100
)
self
.
y_shape
=
(
100
,
)
self
.
trans_x
=
False
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp8
(
TestMatMulV2Op
):
"""
case 8
"""
def
config
(
self
):
self
.
x_shape
=
(
1
,
1
,
2
,
100
)
self
.
y_shape
=
(
1
,
1
,
100
,
2
)
self
.
trans_x
=
False
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp9
(
TestMatMulV2Op
):
"""
case 9
"""
def
config
(
self
):
self
.
x_shape
=
(
1
,
1
,
1
,
100
)
self
.
y_shape
=
(
2
,
1
,
2
,
100
)
self
.
trans_x
=
False
self
.
trans_y
=
True
self
.
dtype
=
"float64"
class
TestMatMuklOp10
(
TestMatMulV2Op
):
"""
case 10
"""
def
config
(
self
):
self
.
x_shape
=
(
1
,
1
,
2
,
100
)
self
.
y_shape
=
(
1
,
2
,
100
,
2
)
self
.
trans_x
=
False
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp11
(
TestMatMulV2Op
):
"""
case 11
"""
def
config
(
self
):
self
.
x_shape
=
(
2
,
1
,
2
,
100
)
self
.
y_shape
=
(
1
,
1
,
100
,
2
)
self
.
trans_x
=
False
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp12
(
TestMatMulV2Op
):
"""
case 12
"""
def
config
(
self
):
self
.
x_shape
=
(
2
,
1
,
100
,
2
)
self
.
y_shape
=
(
1
,
1
,
100
,
2
)
self
.
trans_x
=
True
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp13
(
TestMatMulV2Op
):
"""
case 13
"""
def
config
(
self
):
self
.
x_shape
=
(
2
,
2
,
100
,
2
)
self
.
y_shape
=
(
2
,
2
,
100
,
2
)
self
.
trans_x
=
True
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp14
(
TestMatMulV2Op
):
"""
case 14_1
"""
def
config
(
self
):
self
.
x_shape
=
(
3
,
1
,
1
,
100
,
2
)
self
.
y_shape
=
(
1
,
2
,
2
,
100
,
2
)
self
.
trans_x
=
True
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp15
(
TestMatMulV2Op
):
"""
case 14_2
"""
def
config
(
self
):
self
.
x_shape
=
(
3
,
1
,
1
,
2
,
100
)
self
.
y_shape
=
(
1
,
2
,
2
,
100
,
1
)
self
.
trans_x
=
False
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp16
(
TestMatMulV2Op
):
"""
case 16 : to check the gradient for special case
"""
def
config
(
self
):
self
.
x_shape
=
(
100
)
self
.
y_shape
=
(
1
,
2
,
2
,
100
,
1
)
self
.
trans_x
=
False
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMuklOp17
(
TestMatMulV2Op
):
"""
case 17 : to check the gradient for special case
"""
def
config
(
self
):
self
.
x_shape
=
(
2
,
1
,
100
)
self
.
y_shape
=
(
100
)
self
.
trans_x
=
False
self
.
trans_y
=
False
self
.
dtype
=
"float64"
class
TestMatMulV2API
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
places
=
[
fluid
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
self
.
places
.
append
(
fluid
.
CUDAPlace
(
0
))
def
check_static_result
(
self
,
place
):
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
input_x
=
fluid
.
data
(
name
=
"input_x"
,
shape
=
[
4
,
3
],
dtype
=
"float32"
)
input_y
=
fluid
.
data
(
name
=
"input_y"
,
shape
=
[
3
,
4
],
dtype
=
"float32"
)
result
=
paddle
.
matmul
(
input_x
,
input_y
)
x_np
=
np
.
random
.
random
([
4
,
3
]).
astype
(
"float32"
)
y_np
=
np
.
random
.
random
([
3
,
4
]).
astype
(
"float32"
)
exe
=
fluid
.
Executor
(
place
)
fetches
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"input_x"
:
x_np
,
"input_y"
:
y_np
},
fetch_list
=
[
result
])
def
test_static
(
self
):
for
place
in
self
.
places
:
self
.
check_static_result
(
place
=
place
)
def
test_dygraph
(
self
):
for
place
in
self
.
places
:
with
fluid
.
dygraph
.
guard
(
place
):
input_x
=
np
.
random
.
random
([
4
,
3
]).
astype
(
"float64"
)
input_y
=
np
.
random
.
random
([
3
,
4
]).
astype
(
"float64"
)
x
=
paddle
.
to_tensor
(
input_x
)
y
=
paddle
.
to_tensor
(
input_y
)
result
=
paddle
.
matmul
(
x
,
y
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/tensor/linalg.py
浏览文件 @
c6090660
...
@@ -35,135 +35,134 @@ __all__ = [
...
@@ -35,135 +35,134 @@ __all__ = [
]
]
def
matmul
(
x
,
y
,
transpose_x
=
False
,
transpose_y
=
False
,
alpha
=
1.0
,
name
=
None
):
def
matmul
(
x
,
y
,
transpose_x
=
False
,
transpose_y
=
False
,
name
=
None
):
"""
"""
:alias_main: paddle.matmul
Applies matrix multiplication to two tensors. `matmul` follows
:alias: paddle.matmul,paddle.tensor.matmul,paddle.tensor.linalg.matmul
the complete broadcast rules,
and its behavior is consistent with `np.matmul`.
Applies matrix multiplication to two tensors.
Currently, the input tensors' number of dimensions can be any, `matmul` can be used to
achieve the `dot`, `matmul` and `batchmatmul`.
Currently, the input tensors' rank can be any, but when the rank of any
inputs is bigger than 3, this two inputs' rank should be equal.
The actual behavior depends on the shapes of :math:`x`, :math:`y` and the
The actual behavior depends on the shapes of :math:`x`, :math:`y` and the
flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
- If a transpose flag is specified, the last two dimensions of the tensor
- If a transpose flag is specified, the last two dimensions of the tensor
are transposed. If the tensor is rank-1 of shape :math:`[D]`, then for
are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor
:math:`x` it is treated as :math:`[1, D]` in nontransposed form and as
is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas
:math:`[D, 1]` in transposed form, whereas for :math:`y` it is the
for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.
opposite: It is treated as :math:`[D, 1]` in nontransposed form and as
:math:`[1, D]` in transposed form.
The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:
- After transpose, the two tensors are 2-D or n-D and matrix multiplication
- If both tensors are 1-dimensional, the dot product result is obtained.
performs in the following way.
- If both tensors are 2-dimensional, the matrix-matrix product is obtained.
- If both are 2-D, they are multiplied like conventional matrices.
- If either is n-D, it is treated as a stack of matrices residing in the
- If the `x` is 1-dimensional and the `y` is 2-dimensional,
last two dimensions and a batched matrix multiply supporting broadcast
a `1` is prepended to its dimension in order to conduct the matrix multiply.
applies on the two tensors.
After the matrix multiply, the prepended dimension is removed.
Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
- If the `x` is 2-dimensional and `y` is 1-dimensional,
nontransposed, the prepended or appended dimension :math:`1` will be
the matrix-vector product is obtained.
removed after matrix multiplication.
- If both arguments are at least 1-dimensional and at least one argument
is N-dimensional (where N > 2), then a batched matrix multiply is obtained.
If the first argument is 1-dimensional, a 1 is prepended to its dimension
in order to conduct the batched matrix multiply and removed after.
If the second argument is 1-dimensional, a 1 is appended to its
dimension for the purpose of the batched matrix multiple and removed after.
The non-matrix (exclude the last two dimensions) dimensions are
broadcasted according the broadcast rule.
For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,
out will be a (j, k, n, p) tensor.
Args:
Args:
x (
Variable): The input variable which is a Tensor or LoD
Tensor.
x (
Tensor): The input tensor which is a
Tensor.
y (
Variable): The input variable which is a Tensor or LoD
Tensor.
y (
Tensor): The input tensor which is a
Tensor.
transpose_x (bool): Whether to transpose :math:`x` before multiplication.
transpose_x (bool): Whether to transpose :math:`x` before multiplication.
transpose_y (bool): Whether to transpose :math:`y` before multiplication.
transpose_y (bool): Whether to transpose :math:`y` before multiplication.
alpha (float): The scale of output. Default 1.0.
name(str|None): A name for this layer(optional). If set None, the layer
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
will be named automatically.
Returns:
Returns:
Variable: The product Tensor (or LoDTensor) variable
.
Tensor: The output Tensor
.
Examples:
Examples:
.. code-block:: python
# Examples to clarify shapes of the inputs and output
.. code-block:: python
# x: [B, ..., M, K], y: [B, ..., K, N]
# paddle.matmul(x, y) # out: [B, ..., M, N]
# x: [B, M, K], y: [B, K, N]
import paddle
# paddle.matmul(x, y) # out: [B, M, N]
import numpy as np
# x: [B, M, K], y: [K, N]
paddle.disable_static()
# paddle.matmul(x, y) # out: [B, M, N]
# vector * vector
x_data = np.random.random([10]).astype(np.float32)
y_data = np.random.random([10]).astype(np.float32)
x = paddle.to_tensor(x_data)
y = paddle.to_tensor(y_data)
z = paddle.matmul(x, y)
print(z.numpy().shape)
# [1]
# x: [M, K], y: [K, N]
# matrix * vector
# paddle.matmul(x, y) # out: [M, N]
x_data = np.random.random([10, 5]).astype(np.float32)
y_data = np.random.random([5]).astype(np.float32)
x = paddle.to_tensor(x_data)
y = paddle.to_tensor(y_data)
z = paddle.matmul(x, y)
print(z.numpy().shape)
# [10]
# x: [B, M, K], y: [K]
# batched matrix * broadcasted vector
# paddle.matmul(x, y) # out: [B, M]
x_data = np.random.random([10, 5, 2]).astype(np.float32)
y_data = np.random.random([2]).astype(np.float32)
x = paddle.to_tensor(x_data)
y = paddle.to_tensor(y_data)
z = paddle.matmul(x, y)
print(z.numpy().shape)
# [10, 5]
# x: [K], y: [K]
# batched matrix * batched matrix
# paddle.matmul(x, y) # out: [1]
x_data = np.random.random([10, 5, 2]).astype(np.float32)
y_data = np.random.random([10, 2, 5]).astype(np.float32)
x = paddle.to_tensor(x_data)
y = paddle.to_tensor(y_data)
z = paddle.matmul(x, y)
print(z.numpy().shape)
# [10, 5, 5]
# x: [M], y: [N]
# batched matrix * broadcasted matrix
# paddle.matmul(x, y, True, True) # out: [M, N]
x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)
y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)
x = paddle.to_tensor(x_data)
y = paddle.to_tensor(y_data)
z = paddle.matmul(x, y)
print(z.numpy().shape)
# [10, 3, 5, 5]
import paddle
import paddle.fluid as fluid
x = fluid.data(name='x', shape=[2, 3], dtype='float32')
y = fluid.data(name='y', shape=[3, 2], dtype='float32')
out = paddle.matmul(x, y, True, True)
"""
"""
op_type
=
'matmul_v2'
if
in_dygraph_mode
():
op
=
getattr
(
core
.
ops
,
op_type
)
return
op
(
x
,
y
,
'trans_x'
,
transpose_x
,
'trans_y'
,
transpose_y
)
attrs
=
{
attrs
=
{
'transpose_X'
:
transpose_x
,
'trans_x'
:
transpose_x
,
'transpose_Y'
:
transpose_y
,
'trans_y'
:
transpose_y
,
'alpha'
:
float
(
alpha
),
}
}
if
in_dygraph_mode
():
out
=
_varbase_creator
(
dtype
=
x
.
dtype
)
core
.
ops
.
matmul
(
x
,
y
,
out
,
'transpose_X'
,
transpose_x
,
'transpose_Y'
,
transpose_y
,
'alpha'
,
float
(
alpha
))
return
out
def
__check_input
(
x
,
y
):
def
__check_input
(
x
,
y
):
var_names
=
{
'x'
:
x
,
'y'
:
y
}
var_names
=
{
'x'
:
x
,
'y'
:
y
}
for
name
,
val
in
var_names
.
items
():
for
name
,
val
in
var_names
.
items
():
check_variable_and_dtype
(
check_variable_and_dtype
(
val
,
name
,
[
'float32'
,
'float64'
],
val
,
name
,
[
'float16'
,
'float32'
,
'float64'
],
'matmul'
)
'matmul'
)
x_shape
=
list
(
x
.
shape
)
y_shape
=
list
(
y
.
shape
)
if
len
(
x_shape
)
==
1
:
x_shape
=
[
1
]
+
x_shape
if
len
(
y_shape
)
==
1
:
y_shape
=
y_shape
+
[
1
]
# check the inner 2 dimensions
if
transpose_x
:
x_shape
[
-
2
],
x_shape
[
-
1
]
=
x_shape
[
-
1
],
x_shape
[
-
2
]
if
transpose_y
:
y_shape
[
-
2
],
y_shape
[
-
1
]
=
y_shape
[
-
1
],
y_shape
[
-
2
]
if
x_shape
[
-
1
]
!=
y_shape
[
-
2
]:
assert
(
x_shape
[
-
1
]
==
-
1
)
or
(
y_shape
[
-
2
]
==
-
1
),
\
"After performing an optional transpose, Input X's width should be "
\
"equal to Y's width for multiplication "
\
"prerequisites. But received X's shape: %s, Y's shape: %s
\n
"
%
\
(
x_shape
,
y_shape
)
if
len
(
y_shape
)
>
2
and
len
(
x_shape
)
>
2
:
for
i
,
dim_x
in
enumerate
(
x_shape
[:
-
2
]):
# don't check neg shape
if
dim_x
<
0
or
y_shape
[
i
]
<
0
:
continue
if
dim_x
!=
y_shape
[
i
]:
raise
ValueError
(
"When the matrix is larger than 2 dimensions, the higher "
"dimensional values of the two matrices need to be equal. "
"But received x_shape[%d] != y_shape[%d]. X's shape: %s, "
"Y's shape: %s.
\n
"
%
(
i
,
i
,
x_shape
,
y_shape
))
__check_input
(
x
,
y
)
__check_input
(
x
,
y
)
helper
=
LayerHelper
(
'matmul'
,
**
locals
())
helper
=
LayerHelper
(
'matmul
_v2
'
,
**
locals
())
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
x
.
dtype
)
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
x
.
dtype
)
helper
.
append_op
(
helper
.
append_op
(
type
=
'matmul'
,
type
=
'matmul
_v2
'
,
inputs
=
{
'X'
:
x
,
inputs
=
{
'X'
:
x
,
'Y'
:
y
},
'Y'
:
y
},
outputs
=
{
'Out'
:
out
},
outputs
=
{
'Out'
:
out
},
...
...
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