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
afeixing77
Paddle
提交
113cd6b3
P
Paddle
项目概览
afeixing77
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
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
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
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
113cd6b3
编写于
1月 23, 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
add multi-head scaled_dot_product attention.
上级
abf9395d
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
33 addition
and
17 deletion
+33
-17
python/paddle/v2/fluid/nets.py
python/paddle/v2/fluid/nets.py
+33
-17
未找到文件。
python/paddle/v2/fluid/nets.py
浏览文件 @
113cd6b3
...
...
@@ -11,14 +11,14 @@
# 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.
import
pdb
import
layers
__all__
=
[
"simple_img_conv_pool"
,
"sequence_conv_pool"
,
"glu"
,
"dot_product_attention"
,
"
scaled_
dot_product_attention"
,
]
...
...
@@ -179,7 +179,7 @@ def scaled_dot_product_attention(queries,
.. math::
Attention(Q, K, V)=
softmax(QK^\mathrm{T})V
Attention(Q, K, V)= softmax(QK^\mathrm{T})V
Refer to `Attention Is All You Need
<https://arxiv.org/pdf/1706.03762.pdf>`_.
...
...
@@ -195,8 +195,8 @@ def scaled_dot_product_attention(queries,
LoDTensor.
Returns:
tuple: The Tensor variables representing the output and attention
scores
.
Variable: The context Tensor computed by multi-head scaled dot product
attention
.
Examples:
.. code-block:: python
...
...
@@ -239,26 +239,42 @@ def scaled_dot_product_attention(queries,
Returns:
a Tensor with shape [..., n, m/n]
"""
if
num_heads
==
1
:
return
x
hidden_size
=
x
.
shape
[
-
1
]
#
# reshape the 3-D input: [batch_size, max_sequence_length, hidden_dim]
# into a 4-D output:
# [batch_size, max_sequence_length, num_heads, hidden_size_per_head].
reshaped
=
layers
.
reshape
(
x
=
x
,
shape
=
x
.
shape
[:
-
1
]
+
[
num_heads
,
hidden_size
//
num_heads
])
pass
def
__combine_heads
():
pass
q
=
__split_heads
(
quries
,
num_heads
)
x
=
x
,
shape
=
list
(
x
.
shape
[:
-
1
])
+
[
num_heads
,
hidden_size
//
num_heads
])
# permuate the original dimensions into:
# [batch_size, num_heads, max_sequence_len, hidden_size_per_head]
return
layers
.
transpose
(
x
=
reshaped
,
perm
=
[
0
,
2
,
1
,
3
])
def
__combine_heads
(
x
):
if
len
(
x
.
shape
)
==
3
:
return
if
len
(
x
.
shape
)
!=
4
:
raise
ValueError
(
"Input(x) should be a 4-D Tensor."
)
trans_x
=
layers
.
transpose
(
x
,
perm
=
[
x
.
shape
[
0
],
x
.
shape
[
2
],
x
.
shape
[
1
],
x
.
shape
[
3
]])
return
layers
.
reshape
(
x
=
layers
.
reshape
(
x
=
trans_x
,
shape
=
[
trans_x
.
shape
[
0
],
trans_x
[
1
],
trans_x
[
2
]
*
trans_x
[
3
]]))
q
=
__split_heads
(
queries
,
num_heads
)
k
=
__split_heads
(
keys
,
num_heads
)
v
=
__split_heads
(
values
,
num_heads
)
key_dim_per_head
=
keys
.
shape
[
-
1
]
//
num_heads
scale
=
key_dim_per_head
**-
0.5
scaled_q
=
layers
.
scale
(
x
=
q
,
scale
=
key_dim_per_head
**-
0.5
)
product
=
layers
.
matmul
(
x
=
k
,
y
=
scaled_q
,
transpose_y
=
True
)
product
=
layers
.
matmul
(
x
=
k
,
y
=
q
,
transpose_y
=
True
)
attn_scores
=
layers
.
reshape
(
x
=
layers
.
reshape
(
x
=
product
,
shape
=
[
-
1
,
product
.
shape
[
-
1
]],
act
=
"softmax"
),
shape
=
product
.
shape
)
context
=
layers
.
matmul
(
attn_scores
,
values
)
return
context
,
attn_scores
ctx_multiheads
=
layers
.
matmul
(
attn_scores
,
values
)
context
=
__combine_heads
(
ctx_multiheads
)
return
context
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