提交 1ea828c3 编写于 作者: T tianhao zhang

fix attention val bug

上级 1b9b1f54
...@@ -19,7 +19,6 @@ from typing import Tuple ...@@ -19,7 +19,6 @@ from typing import Tuple
import paddle import paddle
from paddle import nn from paddle import nn
from paddle.nn import functional as F
from paddle.nn import initializer as I from paddle.nn import initializer as I
from paddlespeech.s2t.modules.align import Linear from paddlespeech.s2t.modules.align import Linear
...@@ -56,16 +55,6 @@ class MultiHeadedAttention(nn.Layer): ...@@ -56,16 +55,6 @@ class MultiHeadedAttention(nn.Layer):
self.linear_out = Linear(n_feat, n_feat) self.linear_out = Linear(n_feat, n_feat)
self.dropout = nn.Dropout(p=dropout_rate) 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, def forward_qkv(self,
query: paddle.Tensor, query: paddle.Tensor,
key: paddle.Tensor, key: paddle.Tensor,
...@@ -87,13 +76,8 @@ class MultiHeadedAttention(nn.Layer): ...@@ -87,13 +76,8 @@ class MultiHeadedAttention(nn.Layer):
n_batch = query.shape[0] n_batch = query.shape[0]
q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) 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)
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)
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)
q = q.transpose([0, 2, 1, 3]) # (batch, head, time1, 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) k = k.transpose([0, 2, 1, 3]) # (batch, head, time2, d_k)
......
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