fix view use, because paddle support view now

paddlespeech/s2t/modules/attention.py

@@ -79,9 +79,9 @@ class MultiHeadedAttention(nn.Layer):
         """
         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)
@@ -129,8 +129,8 @@ 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]).view(n_batch, -1, self.h *
-                                           self.d_k)  # (batch, time1, d_model)
+        x = x.transpose([0, 2, 1, 3]).reshape([n_batch, -1, self.h *
+                                           self.d_k])  # (batch, time1, d_model)
 
         return self.linear_out(x)  # (batch, time1, d_model)
 
@@ -280,8 +280,8 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention):
             (x.shape[0], x.shape[1], x.shape[2], 1), dtype=x.dtype)
         x_padded = paddle.cat([zero_pad, x], dim=-1)
 
-        x_padded = x_padded.view(x.shape[0], x.shape[1], x.shape[3] + 1,
-                                 x.shape[2])
+        x_padded = x_padded.view([x.shape[0], x.shape[1], x.shape[3] + 1,
+                                 x.shape[2]])
         x = x_padded[:, :, 1:].view_as(x)  # [B, H, T1, T1]
 
         if zero_triu:
@@ -349,7 +349,7 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention):
         new_cache = paddle.concat((k, v), axis=-1)
 
         n_batch_pos = pos_emb.shape[0]
-        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
+        p = self.linear_pos(pos_emb).reshape([n_batch_pos, -1, self.h, self.d_k])
         p = p.transpose([0, 2, 1, 3])  # (batch, head, time1, d_k)
 
         # (batch, head, time1, d_k)