view to reshape

deepspeech/modules/attention.py

@@ -71,9 +71,9 @@ class MultiHeadedAttention(nn.Layer):
                 (#batch, n_head, time2, d_k).
         """
         n_batch = query.shape[0]
-        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)
+        q = self.linear_q(query).reshape([n_batch, -1, self.h, self.d_k])
+        k = self.linear_k(key).reshape([n_batch, -1, self.h, self.d_k])
+        v = self.linear_v(value).reshape([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)
         v = v.transpose([0, 2, 1, 3])  # (batch, head, time2, d_k)
@@ -109,8 +109,8 @@ class MultiHeadedAttention(nn.Layer):
 
         p_attn = self.dropout(attn)
         x = paddle.matmul(p_attn, value)  # (batch, head, time1, d_k)
-        x = x.transpose([0, 2, 1, 3]).contiguous().view(
-            n_batch, -1, self.h * self.d_k)  # (batch, time1, d_model)
+        x = x.transpose([0, 2, 1, 3]).contiguous().reshape([
+            n_batch, -1, self.h * self.d_k])  # (batch, time1, d_model)
 
         return self.linear_out(x)  # (batch, time1, d_model)