fix attention val bug

paddlespeech/s2t/modules/attention.py

@@ -19,7 +19,6 @@ from typing import Tuple
 
 import paddle
 from paddle import nn
-from paddle.nn import functional as F
 from paddle.nn import initializer as I
 
 from paddlespeech.s2t.modules.align import Linear
@@ -56,16 +55,6 @@ 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)
-        if not self.training:
-            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
-
     def forward_qkv(self,
                     query: paddle.Tensor,
                     key: paddle.Tensor,
@@ -87,13 +76,8 @@ class MultiHeadedAttention(nn.Layer):
         n_batch = query.shape[0]
 
         q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
-        if self.training:
-            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)
-        else:
-            k, v = F.linear(key, self.weight, self.bias).view(
-                n_batch, -1, 2 * self.h, self.d_k).split(
-                    2, axis=2)
+        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)
 
         q = q.transpose([0, 2, 1, 3])  # (batch, head, time1, d_k)
         k = k.transpose([0, 2, 1, 3])  # (batch, head, time2, d_k)