format

paddlespeech/s2t/modules/attention.py

@@ -19,8 +19,8 @@ from typing import Tuple
 
 import paddle
 from paddle import nn
-from paddle.nn import initializer as I
 from paddle.nn import functional as F
+from paddle.nn import initializer as I
 
 from paddlespeech.s2t.modules.align import Linear
 from paddlespeech.s2t.utils.log import Log
@@ -56,12 +56,12 @@ class MultiHeadedAttention(nn.Layer):
         self.linear_out = Linear(n_feat, n_feat)
         self.dropout = nn.Dropout(p=dropout_rate)
 
-
     def _build_once(self, *args, **kwargs):
         super()._build_once(*args, **kwargs)
         # if self.self_att:
         # self.linear_kv = Linear(self.n_feat, self.n_feat*2)
-        self.weight = paddle.concat([self.linear_k.weight, self.linear_v.weight], axis=-1)
+        self.weight = paddle.concat(
+            [self.linear_k.weight, self.linear_v.weight], axis=-1)
         self.bias = paddle.concat([self.linear_k.bias, self.linear_v.bias])
         self._built = True
 
@@ -88,7 +88,9 @@ class MultiHeadedAttention(nn.Layer):
         q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
         # k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
         # v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
-        k, v = F.linear(key, self.weight, self.bias).view(n_batch, -1, 2 * self.h, self.d_k).split(2, axis=2)
+        k, v = F.linear(key, self.weight, self.bias).view(
+            n_batch, -1, 2 * self.h, self.d_k).split(
+                2, axis=2)
 
         q = q.transpose([0, 2, 1, 3])  # (batch, head, time1, d_k)
         k = k.transpose([0, 2, 1, 3])  # (batch, head, time2, d_k)