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体验新版 GitCode,发现更多精彩内容 >>
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提交
705e7345
编写于
5月 10, 2018
作者:
Y
Yu Yang
提交者:
GitHub
5月 10, 2018
浏览文件
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差异文件
Merge pull request #10449 from reyoung/feature/clean_matmul
Rewrite Matmul, make code cleaner
上级
36653587
ad594b9b
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
423 addition
and
543 deletion
+423
-543
paddle/fluid/operators/math/blas.cc
paddle/fluid/operators/math/blas.cc
+31
-1
paddle/fluid/operators/math/blas.h
paddle/fluid/operators/math/blas.h
+49
-0
paddle/fluid/operators/math/blas_impl.h
paddle/fluid/operators/math/blas_impl.h
+25
-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
+299
-111
paddle/fluid/operators/matmul_op.cu.cc
paddle/fluid/operators/matmul_op.cu.cc
+0
-22
paddle/fluid/operators/matmul_op.h
paddle/fluid/operators/matmul_op.h
+0
-244
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 @
705e7345
...
...
@@ -13,10 +13,40 @@
// 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.
MatDescriptor
CreateMatrixDescriptor
(
const
framework
::
DDim
&
tensor_dim
,
int
num_flatten_cols
,
bool
trans
)
{
PADDLE_ENFORCE_GT
(
tensor_dim
.
size
(),
1
);
MatDescriptor
retv
;
if
(
num_flatten_cols
>
1
)
{
auto
flatten_dim
=
framework
::
flatten_to_2d
(
tensor_dim
,
num_flatten_cols
);
retv
.
height_
=
flatten_dim
[
0
];
retv
.
width_
=
flatten_dim
[
1
];
}
else
{
if
(
tensor_dim
.
size
()
==
2
)
{
retv
.
height_
=
tensor_dim
[
0
];
retv
.
width_
=
tensor_dim
[
1
];
}
else
{
auto
dim_vec
=
framework
::
vectorize
(
tensor_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 @
705e7345
...
...
@@ -46,6 +46,50 @@ namespace paddle {
namespace
operators
{
namespace
math
{
/**
* Matrix Descriptor of a memory buffer.
*
* It is used for Blas::MatMul. MatMul operator can be batched.
* if Mat A is [BatchSize, H, W], Mat B is [BatchSize, H, W]. It will be a
* `batch_size` times of GEMM. The batched GEMM could be faster base on the
* implementation of the blas library. The batch size could be zero. If any
* matrix of `matmul` has a batch size, the will be a batched GEMM, too. e.g.,
* Mat A is [BatchSize, H1, W2], and Mat B [H2, W2], The result matrix wil be
* [BatchSize, H1, W2]
*
* The boolean flag, `trans`, describe the memory is the transpose of matrix or
* not. If the trans is true, the last two dims of matrix are transposed. The
* memory layout of the matrix is [Width, Height] or [BatchSize, Width, Height].
*
* The MatDescriptor is not only the dimension or shape of a matrix, it also
* contains the layout, stride of matrix. It is clearer to have a structure than
* reuse `DDim`.
*/
struct
MatDescriptor
{
int64_t
height_
;
int64_t
width_
;
int64_t
stride_
{
0
};
int64_t
batch_size_
{
0
};
bool
trans_
;
};
/**
* Create Matrix Descriptor from a tensor dim, num_flatten_cols, and transpose
* flag
*
* @param tensor_dim: The dimension of the tensor. The rank of this dimension
* must larger than 1.
*
* @param num_flatten_cols: Reshape a tensor to a matrix. The matrix's first
* dimension(column length) will be the product of tensor's first `num_col_dims`
* dimensions. If num_flatten_cols is zero, the first N-2 dimension will be the
* batch_size of descriptor.
*
* @param trans: True if the matrix is transposed.
*/
extern
MatDescriptor
CreateMatrixDescriptor
(
const
framework
::
DDim
&
tensor_dim
,
int
num_flatten_cols
,
bool
trans
);
template
<
typename
DeviceContext
>
class
Blas
{
public:
...
...
@@ -90,6 +134,11 @@ 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
MatDescriptor
&
dim_a
,
const
framework
::
Tensor
&
mat_b
,
const
MatDescriptor
&
dim_b
,
T
alpha
,
framework
::
Tensor
*
mat_out
,
T
beta
)
const
;
private:
const
DeviceContext
&
context_
;
};
...
...
paddle/fluid/operators/math/blas_impl.h
浏览文件 @
705e7345
...
...
@@ -180,6 +180,31 @@ void Blas<platform::CPUDeviceContext>::BatchedGEMM(
#endif
}
template
<
typename
DeviceContext
>
template
<
typename
T
>
void
Blas
<
DeviceContext
>::
MatMul
(
const
framework
::
Tensor
&
mat_a
,
const
MatDescriptor
&
dim_a
,
const
framework
::
Tensor
&
mat_b
,
const
MatDescriptor
&
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_
);
}
}
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/math/matmul.h
已删除
100644 → 0
浏览文件 @
36653587
/* 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 @
705e7345
...
...
@@ -12,14 +12,257 @@ 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_op.h"
#include <algorithm>
#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"
namespace
paddle
{
namespace
operators
{
/**
* Get row matrix shape from a vector shape. If the rank of x_dim > 1, the
* original x_dim is returned.
*/
static
framework
::
DDim
RowMatrixFromVector
(
const
framework
::
DDim
&
x_dim
)
{
if
(
x_dim
.
size
()
>
1
)
{
return
x_dim
;
}
return
framework
::
make_ddim
({
1
,
x_dim
[
0
]});
}
/**
* Get column matrix shape from a vector shape. If the ran of y_dim > 1, the
* original y_dim is returned.
*/
static
framework
::
DDim
ColumnMatrixFromVector
(
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
{
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
());
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
context
);
auto
mat_dim_a
=
math
::
CreateMatrixDescriptor
(
RowMatrixFromVector
(
x
.
dims
()),
0
,
context
.
Attr
<
bool
>
(
"transpose_X"
));
auto
mat_dim_b
=
math
::
CreateMatrixDescriptor
(
ColumnMatrixFromVector
(
y
.
dims
()),
0
,
context
.
Attr
<
bool
>
(
"transpose_Y"
));
blas
.
MatMul
(
x
,
mat_dim_a
,
y
,
mat_dim_b
,
T
(
1
),
out
,
T
(
0
));
}
};
// 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.
static
framework
::
Tensor
FoldInitDims
(
const
framework
::
Tensor
&
input
)
{
auto
output
=
input
;
auto
in_dims
=
input
.
dims
();
if
(
in_dims
.
size
()
==
3
)
{
output
.
Resize
({
in_dims
[
0
]
*
in_dims
[
1
],
in_dims
[
2
]});
}
return
output
;
}
// Reshape a rank-3 tensor from P x M x N to M x (P * N).
// (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
>
static
framework
::
Tensor
FoldHeadAndLastDims
(
const
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
)
{
auto
in_dims
=
input
.
dims
();
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
;
}
/**
* Reshape a tensor to 3-D or 2-D tensor by matrix descriptor.
*
* The shape would be [BatchSize, H, W] or [H, W].
* If transposed, `H,W` will be swapped.
*/
static
void
ReshapeTensorIntoMatrixSequence
(
framework
::
Tensor
*
x
,
const
math
::
MatDescriptor
&
descriptor
)
{
int64_t
h
,
w
;
h
=
descriptor
.
height_
;
w
=
descriptor
.
width_
;
if
(
descriptor
.
trans_
)
{
std
::
swap
(
w
,
h
);
}
if
(
descriptor
.
batch_size_
)
{
x
->
Resize
({
descriptor
.
batch_size_
,
h
,
w
});
}
else
{
x
->
Resize
({
h
,
w
});
}
}
/**
* Reshape the x,y,out tensor to 3-D or 2-D tensor by matrix descriptor
* Out = matmul(x, y)
*
* This method will first calculate X,Y matrix sequence, and then calculate
* the out shape.
*
* Assume X = [BatchSize, H1, W1], Y = [BatchSize, H2, W2]
* The out = [BatchSize, H1, W2]
*
* If there is no batch size in `X` and `Y`, the out will be [H1, W2]
* If any of `X` and `Y` has batch size BatchSize, the out will have the
* BatchSize.
*/
static
void
ReshapeXYOutIntoMatrixSequence
(
framework
::
Tensor
*
x
,
framework
::
Tensor
*
y
,
framework
::
Tensor
*
out
,
bool
trans_x
,
bool
trans_y
)
{
auto
x_dim
=
RowMatrixFromVector
(
x
->
dims
());
auto
y_dim
=
ColumnMatrixFromVector
(
y
->
dims
());
auto
mat_dim_x
=
math
::
CreateMatrixDescriptor
(
x_dim
,
0
,
trans_x
);
auto
mat_dim_y
=
math
::
CreateMatrixDescriptor
(
y_dim
,
0
,
trans_y
);
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_
});
}
ReshapeTensorIntoMatrixSequence
(
x
,
mat_dim_x
);
ReshapeTensorIntoMatrixSequence
(
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.
//
// transpose_X | False | True | False | True
// transpose_Y | False | False | True | True
// -----------+----------+----------+----------+-----------
// dX = | dOut Y^T | Y dOut^T | dOut Y | Y^T dOut^T
// dY = | X^T dOut | X dOut | dOut^T X | dOut^T X^T
//
// When X is a vector of size K, we treat it instead as a matrix of shape
// (1, K). Similarly, when Y is a vector of size K, we treat it instead as
// a matrix of shape (K, 1).
//
// When X and Y are both 3-dimensional tensors, then the first dimension
// the batch dimension can be ignored and the exact same formulas apply
// as for two matrices.
//
// Finally, when, e.g., X is a 3-dimensional tensor but Y is a matrix, we end
// up with formulas like
//
// dY_{ij} = \sum_{p, m} X_{pmi} dOut_{pmj}
//
// To handle this sort of scenario, we reshape X : P x M x K, dOut: P x M x N
// to X: (P * M) x K, dOut: (P * M) x N.
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
::
CreateMatrixDescriptor
(
a
.
dims
(),
0
,
trans_a
);
auto
mat_dim_b
=
math
::
CreateMatrixDescriptor
(
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_fold_init_dims_a
,
const
framework
::
Tensor
&
b
,
bool
trans_b
,
bool
is_fold_init_dims_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_fold_init_dims_a
?
FoldInitDims
(
a
)
:
FoldHeadAndLastDims
<
DeviceContext
,
T
>
(
ctx
,
a
),
trans_a
,
is_fold_init_dims_b
?
FoldInitDims
(
b
)
:
FoldHeadAndLastDims
<
DeviceContext
,
T
>
(
ctx
,
b
),
trans_b
,
out
);
}
}
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
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"
);
ReshapeXYOutIntoMatrixSequence
(
&
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
());
}
}
framework
::
DDim
dy_dims
;
if
(
dy
)
{
dy_dims
=
dy
->
dims
();
if
(
dy_dims
!=
y
.
dims
())
{
dy
->
Resize
(
y
.
dims
());
}
}
using
framework
::
Tensor
;
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
)
{
CalcInputGrad
(
context
,
y
,
false
,
false
,
dout
,
true
,
false
,
dx
);
CalcInputGrad
(
context
,
x
,
false
,
false
,
dout
,
false
,
true
,
dy
);
}
else
if
(
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
);
}
if
(
dx
)
{
if
(
dx_dims
!=
x
.
dims
())
{
dx
->
Resize
(
dx_dims
);
}
}
if
(
dy
)
{
if
(
dy_dims
!=
y
.
dims
())
{
dy
->
Resize
(
dy_dims
);
}
}
}
};
class
MatMulOp
:
public
framework
::
OperatorWithKernel
{
public:
...
...
@@ -36,121 +279,41 @@ 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
::
CreateMatrixDescriptor
(
RowMatrixFromVector
(
dim_x
),
0
,
context
->
Attrs
().
Get
<
bool
>
(
"transpose_X"
));
auto
mat_dim_y
=
math
::
CreateMatrixDescriptor
(
ColumnMatrixFromVector
(
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"
);
...
...
@@ -233,15 +396,40 @@ class MatMulOpGrad : public framework::OperatorWithKernel {
}
};
class
MatMulOpGradMaker
:
public
framework
::
SingleGradOpDescMaker
{
public:
using
framework
::
SingleGradOpDescMaker
::
SingleGradOpDescMaker
;
protected:
std
::
unique_ptr
<
framework
::
OpDesc
>
Apply
()
const
override
{
auto
*
retv
=
new
framework
::
OpDesc
();
retv
->
SetType
(
"matmul_grad"
);
retv
->
SetInput
(
"X"
,
Input
(
"X"
));
retv
->
SetInput
(
"Y"
,
Input
(
"Y"
));
retv
->
SetInput
(
framework
::
GradVarName
(
"Out"
),
OutputGrad
(
"Out"
));
retv
->
SetOutput
(
framework
::
GradVarName
(
"X"
),
InputGrad
(
"X"
));
retv
->
SetOutput
(
framework
::
GradVarName
(
"Y"
),
InputGrad
(
"Y"
));
retv
->
SetAttrMap
(
Attrs
());
return
std
::
unique_ptr
<
framework
::
OpDesc
>
(
retv
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
matmul
,
ops
::
MatMulOp
,
ops
::
MatMulOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
ops
::
MatMulOpGradMaker
);
REGISTER_OPERATOR
(
matmul_grad
,
ops
::
MatMulOpGrad
);
REGISTER_OP_CPU_KERNEL
(
matmul
,
ops
::
MatMulKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
matmul_grad
,
ops
::
MatMulGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
);
#ifdef PADDLE_WITH_CUDA
REGISTER_OP_CUDA_KERNEL
(
matmul
,
ops
::
MatMulKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
REGISTER_OP_CUDA_KERNEL
(
matmul_grad
,
ops
::
MatMulGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
#endif
paddle/fluid/operators/matmul_op.cu.cc
已删除
100644 → 0
浏览文件 @
36653587
/* Copyright (c) 2016 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_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
matmul
,
ops
::
MatMulKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
REGISTER_OP_CUDA_KERNEL
(
matmul_grad
,
ops
::
MatMulGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
);
paddle/fluid/operators/matmul_op.h
已删除
100644 → 0
浏览文件 @
36653587
/* Copyright (c) 2016 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/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/matmul.h"
namespace
paddle
{
namespace
operators
{
namespace
matmul_detail
{
using
Tensor
=
framework
::
Tensor
;
using
DDim
=
framework
::
DDim
;
using
framework
::
make_ddim
;
using
framework
::
vectorize
;
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"
);
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
));
}
};
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
);
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
));
}
return
output
;
}
// Reshape a rank-3 tensor from P x M x N to M x (P * N).
// (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
;
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
);
}
return
output
;
}
// Using dimensional constraints on matrix multiplication, it is
// straight-forward to check the following table for when X and Y
// are both matrices.
//
// transpose_X | False | True | False | True
// transpose_Y | False | False | True | True
// -----------+----------+----------+----------+-----------
// dX = | dOut Y^T | Y dOut^T | dOut Y | Y^T dOut^T
// dY = | X^T dOut | X dOut | dOut^T X | dOut^T X^T
//
// When X is a vector of size K, we treat it instead as a matrix of shape
// (1, K). Similarly, when Y is a vector of size K, we treat it instead as
// a matrix of shape (K, 1).
//
// When X and Y are both 3-dimensional tensors, then the first dimension
// the batch dimension can be ignored and the exact same formulas apply
// as for two matrices.
//
// Finally, when, e.g., X is a 3-dimensional tensor but Y is a matrix, we end
// up with formulas like
//
// dY_{ij} = \sum_{p, m} X_{pmi} dOut_{pmj}
//
// To handle this sort of scenario, we reshape X : P x M x K, dOut: P x M x N
// to X: (P * M) x K, dOut: (P * M) x N.
template
<
typename
DeviceContext
,
typename
T
>
class
MatMulGradKernel
:
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"
);
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"
));
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
);
}
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
];
}
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
);
}
}
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
(
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
));
}
}
}
};
}
// 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 @
705e7345
...
...
@@ -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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