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体验新版 GitCode,发现更多精彩内容 >>
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04806ffe
编写于
1月 21, 2018
作者:
C
Cao Ying
提交者:
GitHub
1月 21, 2018
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差异文件
Merge pull request #7656 from chengduoZH/feature/enhance_matmul_op
Enhance matmul_op to support 4-D inputs.
上级
8e08b0a2
782ddc5f
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
145 addition
and
38 deletion
+145
-38
paddle/operators/math/matmul.h
paddle/operators/math/matmul.h
+28
-6
paddle/operators/matmul_op.cc
paddle/operators/matmul_op.cc
+39
-8
paddle/operators/matmul_op.h
paddle/operators/matmul_op.h
+18
-3
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+14
-11
python/paddle/v2/fluid/tests/test_matmul_op.py
python/paddle/v2/fluid/tests/test_matmul_op.py
+46
-10
未找到文件。
paddle/operators/math/matmul.h
浏览文件 @
04806ffe
...
...
@@ -41,10 +41,24 @@ class MatMulFunctor {
"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."
);
PADDLE_ENFORCE_LE
(
dim_a
.
size
(),
3
,
"Input tensor a must be at most 3-dimensional."
);
PADDLE_ENFORCE_LE
(
dim_b
.
size
(),
3
,
"Input tensor b must be at most 3-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
;
...
...
@@ -67,7 +81,11 @@ class MatMulFunctor {
strideA
=
M
*
kA
;
break
;
default:
assert
(
false
);
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
())
{
...
...
@@ -88,7 +106,11 @@ class MatMulFunctor {
strideB
=
kB
*
N
;
break
;
default:
assert
(
false
);
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
(
...
...
paddle/operators/matmul_op.cc
浏览文件 @
04806ffe
...
...
@@ -41,10 +41,26 @@ class MatMulOp : public framework::OperatorWithKernel {
"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."
);
PADDLE_ENFORCE_LE
(
dim_x
.
size
(),
3
,
"Input tensor X must be at most 3-dimensional."
);
PADDLE_ENFORCE_LE
(
dim_y
.
size
(),
3
,
"Input tensor Y must be at most 3-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
;
...
...
@@ -70,7 +86,11 @@ class MatMulOp : public framework::OperatorWithKernel {
KX
=
transpose_x
?
dim_x
[
1
]
:
dim_x
[
2
];
break
;
default:
assert
(
false
);
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
;
}
switch
(
dim_y
.
size
())
{
...
...
@@ -94,7 +114,10 @@ class MatMulOp : public framework::OperatorWithKernel {
N
=
transpose_y
?
dim_y
[
1
]
:
dim_y
[
2
];
break
;
default:
assert
(
false
);
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
(
...
...
@@ -110,7 +133,11 @@ class MatMulOp : public framework::OperatorWithKernel {
std
::
vector
<
int64_t
>
dim_out
;
if
(
batchCount
)
{
dim_out
.
push_back
(
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
(
!
remove_initial_dim
)
{
dim_out
.
push_back
(
M
);
...
...
@@ -162,10 +189,14 @@ Examples without transpose:
- X: [B, M, K], Y: [K] => Out: [B, M]
- X: [M, K], Y: [B, K, N] => Out: [B, M, N]
- X: [B, M, K], Y: [B, K, N] => Out: [B, M, N]
- X: [B, ..., M, K], Y: [B, ..., K, N] => Out: [B, ..., M, N]
The behavior is designed to be similar to the `numpy.matmul` function.
The differences are:
- Currently only rank 1 to rank 3 input tensors are supported.
- When the rank of the input data is less than or equal to 3, it
is similar to the `numpy.matmul` function.
- When the rank of the input is greater than 3, the rank of X and
Y must be equal, and the first `rank - 2` dimensions must be equal.
- We add `transpose_X` and `transpose_Y` flags.
Both the input `X` and `Y` can carry the LoD (Level of Details) information,
...
...
paddle/operators/matmul_op.h
浏览文件 @
04806ffe
...
...
@@ -137,6 +137,13 @@ class MatMulGradKernel : public framework::OpKernel<T> {
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
;
...
...
@@ -149,7 +156,9 @@ class MatMulGradKernel : public framework::OpKernel<T> {
M
=
transpose_x
?
x_dims
[
2
]
:
x_dims
[
1
];
break
;
default:
assert
(
false
);
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
())
{
...
...
@@ -161,7 +170,9 @@ class MatMulGradKernel : public framework::OpKernel<T> {
N
=
transpose_y
?
y_dims
[
1
]
:
y_dims
[
2
];
break
;
default:
assert
(
false
);
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
(
...
...
@@ -172,7 +183,11 @@ class MatMulGradKernel : public framework::OpKernel<T> {
int
batchCount
=
std
::
max
(
batchCountX
,
batchCountY
);
std
::
vector
<
int64_t
>
dout_dims
=
{
M
,
N
};
if
(
batchCount
)
{
dout_dims
.
insert
(
dout_dims
.
begin
(),
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
));
...
...
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
04806ffe
...
...
@@ -1794,8 +1794,9 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
def
matmul
(
x
,
y
,
transpose_x
=
False
,
transpose_y
=
False
,
name
=
None
):
"""
Applies matrix multipication to two tensors. Currently only rank 1 to rank
3 input tensors are supported.
Applies matrix multiplication to two tensors. Currently, the input
tensors' rank can be any, but when the rank of anyone 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
flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
...
...
@@ -1807,17 +1808,17 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
opposite: It is treated as :math:`[D, 1]` in nontransposed form and as
:math:`[1, D]` in transposed form.
- After transpose, the two tensors are 2-D or
3-D and matrix multip
ication
- After transpose, the two tensors are 2-D or
n-D and matrix multipl
ication
performs in the following way.
- If both are 2-D, they are multiplied like conventional matrices.
- If either is
3
-D, it is treated as a stack of matrices residing in the
last two dimensions and a batched matrix multiply supporting broadcast
- If either is
n
-D, it is treated as a stack of matrices residing in the
last two dimensions and a batched matrix multiply supporting broadcast
applies on the two tensors.
Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
nontransposed, the prepended or appended dimension :math:`1` will be
removed after matrix multipication.
Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
nontransposed, the prepended or appended dimension :math:`1` will be
removed after matrix multip
l
ication.
Args:
x (Variable): The input variable which is a Tensor or LoDTensor.
...
...
@@ -1834,6 +1835,8 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
.. code-block:: python
# Examples to clarify shapes of the inputs and output
# x: [B, ..., M, K], y: [B, ..., K, N]
fluid.layers.matmul(x, y) # out: [B, ..., M, N]
# x: [B, M, K], y: [B, K, N]
fluid.layers.matmul(x, y) # out: [B, M, N]
# x: [B, M, K], y: [K, N]
...
...
@@ -1849,9 +1852,9 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
fluid.layers.matmul(x, y, True, True) # out: [M, N]
"""
helper
=
LayerHelper
(
'matmul'
,
**
locals
())
assert
max
(
len
(
x
.
shape
),
len
(
y
.
shape
)
)
<=
3
,
'Currently only rank 1 to rank 3 input tensors are supported
.'
assert
max
(
len
(
x
.
shape
),
len
(
y
.
shape
))
<=
3
or
len
(
x
.
shape
)
==
len
(
y
.
shape
),
'Inputs
\'
rank should be equal or their rank should be less 4
.'
out
=
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
())
helper
.
append_op
(
type
=
'matmul'
,
...
...
python/paddle/v2/fluid/tests/test_matmul_op.py
浏览文件 @
04806ffe
...
...
@@ -59,19 +59,18 @@ def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
X
=
X
.
reshape
((
X
.
size
,
1
))
elif
X
.
ndim
==
2
:
X
=
X
.
T
elif
X
.
ndim
==
3
:
X
=
np
.
transpose
(
X
,
(
0
,
2
,
1
))
else
:
raise
ValueError
(
'X must have between 1 and 3 dimensions'
)
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
((
1
,
Y
.
size
))
elif
Y
.
ndim
==
2
:
Y
=
Y
.
T
elif
Y
.
ndim
==
3
:
Y
=
np
.
transpose
(
Y
,
(
0
,
2
,
1
))
else
:
raise
ValueError
(
'Y must have between 1 and 3 dimensions'
)
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
...
...
@@ -120,13 +119,50 @@ for dim_X in [1, 2, 3]:
dim_X
,
dim_Y
,
transpose_X
,
transpose_Y
))
shape_X
,
shape_Y
=
generate_compatible_shapes
(
dim_X
,
dim_Y
,
transpose_X
,
transpose_Y
)
test_class
=
type
(
test_name
,
(
Generator
,
OpTest
),
{
globals
()[
test_name
]
=
type
(
test_name
,
(
Generator
,
OpTest
),
{
'shape_X'
:
shape_X
,
'shape_Y'
:
shape_Y
,
'transpose_X'
:
transpose_X
,
'transpose_Y'
:
transpose_Y
,
})
globals
()[
test_name
]
=
test_class
# Test case n-dim
def
generate_compatible_shapes
(
dim
,
transpose_X
,
transpose_Y
):
M
=
2
N
=
4
K
=
3
shape_X
=
[
2
for
_
in
range
(
dim
-
2
)]
shape_Y
=
[
2
for
_
in
range
(
dim
-
2
)]
if
transpose_X
:
shape_X
+=
[
K
,
M
]
else
:
shape_X
+=
[
M
,
K
]
if
transpose_Y
:
shape_Y
+=
[
N
,
K
]
else
:
shape_Y
+=
[
K
,
N
]
return
shape_X
,
shape_Y
# Test case n-dim
for
dim
in
[
4
]:
for
transpose_X
in
[
False
,
True
]:
for
transpose_Y
in
[
False
,
True
]:
test_name
=
(
'TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}'
.
format
(
dim
,
dim
,
transpose_X
,
transpose_Y
))
shape_X
,
shape_Y
=
generate_compatible_shapes
(
dim
,
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
,
})
if
__name__
==
"__main__"
:
unittest
.
main
()
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