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234013a9
编写于
1月 15, 2018
作者:
G
guosheng
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Add python wrapper for matmul_op
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e7acf32c
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python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
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python/paddle/v2/fluid/layers/nn.py
浏览文件 @
234013a9
...
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@@ -1584,3 +1584,85 @@ def split(input, num_or_sections, dim=-1):
'axis'
:
dim
})
return
outs
def
matmul
(
x
,
y
):
"""
Applies matrix multipication to two tensors.
This operator is used to perform (batched) matrix multiplication
over the last two dimensions of the input tensors `X` and `Y`.
If a transpose flag is specified, the last two dimensions of the
tensor are transposed. If the tensor is rank-1 of shape [D], then
for `X` it is treated as [1, D] in nontransposed form and as [D, 1]
in transposed form, whereas for `Y` it is the opposite: It is treated
as [D, 1] in nontransposed form and as [1, D] in transposed form.
Examples without transpose:
- X: [K], Y: [K] => Out: [1]
- X: [K], Y: [K, N] => Out: [N]
- 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]
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.
- We add `transpose_X` and `transpose_Y` flags.
Both the input `X` and `Y` can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD information with input `X`.
Args:
x (Variable): The input variable which is a Tensor or LoDTensor.
y (Variable): If :attr:`num_or_sections` is an integer,
then the integer indicates the number of equal sized sub-tensors
that the tensor will be divided into. If :attr:`num_or_sections`
is a list of integers, the length of list indicates the number of
sub-tensors and the integers indicate the sizes of sub-tensors'
:attr:`dim` dimension orderly.
dim (int): The dimension along which to split. If :math:`dim < 0`, the
dimension to split along is :math:`rank(input) + dim`.
Returns:
List: The list of segmented tensor variables.
Examples:
.. code-block:: python
# x is a Tensor variable with shape [3, 9, 5]:
x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1)
x0.shape # [3, 3, 5]
x1.shape # [3, 3, 5]
x2.shape # [3, 3, 5]
x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1)
x0.shape # [3, 2, 5]
x1.shape # [3, 3, 5]
x2.shape # [3, 4, 5]
"""
helper
=
LayerHelper
(
'split'
,
**
locals
())
input_shape
=
input
.
shape
dim
=
(
len
(
input_shape
)
+
dim
)
if
dim
<
0
else
dim
if
isinstance
(
num_or_sections
,
int
):
assert
num_or_sections
>
1
,
'num_or_sections must be more than 1.'
num
=
num_or_sections
else
:
assert
len
(
num_or_sections
)
<
input_shape
[
dim
],
'len(num_or_sections) must not be more than input.shape[dim].'
num
=
len
(
num_or_sections
)
outs
=
[
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
())
for
i
in
range
(
num
)
]
helper
.
append_op
(
type
=
'split'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
outs
},
attrs
=
{
'num'
:
num_or_sections
if
isinstance
(
num_or_sections
,
int
)
else
0
,
'sections'
:
num_or_sections
if
isinstance
(
num_or_sections
,
list
)
else
[],
'axis'
:
dim
})
return
outs
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