Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
Paddle
提交
0d96899f
P
Paddle
项目概览
PaddlePaddle
/
Paddle
大约 1 年 前同步成功
通知
2298
Star
20931
Fork
5422
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1423
列表
看板
标记
里程碑
合并请求
543
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1,423
Issue
1,423
列表
看板
标记
里程碑
合并请求
543
合并请求
543
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
0d96899f
编写于
1月 24, 2018
作者:
Y
ying
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix the documentation.
上级
d163592a
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
48 addition
and
22 deletion
+48
-22
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+1
-1
python/paddle/v2/fluid/nets.py
python/paddle/v2/fluid/nets.py
+47
-21
未找到文件。
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
0d96899f
...
...
@@ -1968,7 +1968,7 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
data = fluid.layers.data(name="data",
shape=(3, 17, 13),
dtype="float32")
fc
= fluid.layers.l2_normalize(x=data, axis=1)
normed
= fluid.layers.l2_normalize(x=data, axis=1)
"""
if
len
(
x
.
shape
)
==
1
:
axis
=
0
...
...
python/paddle/v2/fluid/nets.py
浏览文件 @
0d96899f
...
...
@@ -182,28 +182,28 @@ def scaled_dot_product_attention(queries,
Refer to `Attention Is All You Need
<https://arxiv.org/pdf/1706.03762.pdf>`_.
Note that batch data containing sequences with different lengths is not
supported by this because of the (batch) matrix multipication.
Args:
queries (Variable): The input variable which
is a Tensor or
LoD
Tensor.
keys (Variable): The input variable which is a Tensor or LoD
Tensor.
values (Variable): The input variable which is a Tensor or
LoDTensor
.
num_heads (int): Head number to compute the dot product attention
.
dropout_rate (float): The dropout rate for attention weight
.
queries (Variable): The input variable which
should be a 3-D Tensor.
keys (Variable): The input variable which should be a 3-D
Tensor.
values (Variable): The input variable which should be a 3-D
Tensor.
num_heads (int): Head number to compute the scaled dot product
attention. Default value is 1
.
dropout_rate (float): The dropout rate to drop the attention weight
.
Default value is 0
.
Returns:
Variable: The context Tensor computed by multi-head scaled dot product
Variable: A 3-D Tensor computed by multi-head scaled dot product
attention.
Examples:
.. code-block:: python
# Suppose q, k, v are tensor variables with the following
# shape: q: [3, 5, 9], k: [3, 6, 9], v: [3, 6, 10]
out, attn_scores = fluid.nets.dot_product_attention(q, k, v)
# Suppose q, k, v are Tensors with the following shape:
# q: [3, 5, 9], k: [3, 6, 9], v: [3, 6, 10]
contexts = fluid.nets.dot_product_attention(q, k, v)
out.shape # [3, 5, 10]
attn_scores.shape # [3, 5, 6]
"""
...
...
@@ -227,19 +227,30 @@ def scaled_dot_product_attention(queries,
"by the number of attention heads (%d)."
%
(
values
.
shape
[
-
1
],
num_heads
))
def
__compute_qkv
(
queries
,
keys
,
values
,
num_heads
):
if
num_heads
==
1
:
return
queries
,
keys
,
values
q
=
layers
.
fc
(
input
=
queries
,
size
=
queries
.
shape
[
-
1
],
num_flatten_dims
=
2
)
k
=
layers
.
fc
(
input
=
keys
,
size
=
keys
.
shape
[
-
1
],
num_flatten_dims
=
2
)
v
=
layers
.
fc
(
input
=
values
,
size
=
values
.
shape
[
-
1
],
num_flatten_dims
=
2
)
return
q
,
k
,
v
def
__split_heads
(
x
,
num_heads
):
"""
Reshape the last dimension of inpunt tensor x so that it becomes two
dimensions.
Args:
x(Tensor): a 3-D input Tensor.
num_heads(int): The number of heads.
x(Tensor): a 3-D input Tensor.
num_heads(int): The number of heads.
Returns:
a Tensor with shape [..., n, m/n]
Tensor: a Tensor with shape [..., n, m/num_heads], where m is size
of the last dimension of x.
"""
if
num_heads
==
1
:
return
x
if
num_heads
==
1
:
return
x
hidden_size
=
x
.
shape
[
-
1
]
# reshape the 3-D input: [batch_size, max_sequence_length, hidden_dim]
...
...
@@ -254,6 +265,19 @@ def scaled_dot_product_attention(queries,
return
layers
.
transpose
(
x
=
reshaped
,
perm
=
[
0
,
2
,
1
,
3
])
def
__combine_heads
(
x
):
"""
Reshape the last two dimensions of inpunt tensor x so that it becomes
one dimension.
Args:
x(Tensor): a 4-D input Tensor with shape
[bs, num_heads, max_sequence_length, hidden_dim].
Returns:
Tensor: a Tensor with shape
[bs, max_sequence_length, num_heads * hidden_dim].
"""
if
len
(
x
.
shape
)
==
3
:
return
if
len
(
x
.
shape
)
!=
4
:
raise
ValueError
(
"Input(x) should be a 4-D Tensor."
)
...
...
@@ -266,9 +290,11 @@ def scaled_dot_product_attention(queries,
trans_x
.
shape
[
2
]
*
trans_x
.
shape
[
3
]
]))
q
=
__split_heads
(
queries
,
num_heads
)
k
=
__split_heads
(
keys
,
num_heads
)
v
=
__split_heads
(
values
,
num_heads
)
q
,
k
,
v
=
__compute_qkv
(
queries
,
keys
,
values
,
num_heads
)
q
=
__split_heads
(
q
,
num_heads
)
k
=
__split_heads
(
k
,
num_heads
)
v
=
__split_heads
(
v
,
num_heads
)
key_dim_per_head
=
keys
.
shape
[
-
1
]
//
num_heads
scaled_q
=
layers
.
scale
(
x
=
q
,
scale
=
key_dim_per_head
**-
0.5
)
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录