eliminate useless unsqueese

paddlespeech/s2t/modules/embedding.py

@@ -89,7 +89,7 @@ class PositionalEncoding(nn.Layer, PositionalEncodingInterface):
         self.max_len = max_len
         self.xscale = paddle.to_tensor(math.sqrt(self.d_model))
         self.dropout = nn.Dropout(p=dropout_rate)
-        self.pe = paddle.zeros([self.max_len, self.d_model])  #[T,D]
+        self.pe = paddle.zeros([1, self.max_len, self.d_model])  #[B=1,T,D]
 
         position = paddle.arange(
             0, self.max_len, dtype=paddle.float32).unsqueeze(1)  #[T, 1]
@@ -97,9 +97,8 @@ class PositionalEncoding(nn.Layer, PositionalEncodingInterface):
             paddle.arange(0, self.d_model, 2, dtype=paddle.float32) *
             -(math.log(10000.0) / self.d_model))
 
-        self.pe[:, 0::2] = paddle.sin(position * div_term)
-        self.pe[:, 1::2] = paddle.cos(position * div_term)
-        self.pe = self.pe.unsqueeze(0)  #[1, T, D]
+        self.pe[:, :, 0::2] = paddle.sin(position * div_term)
+        self.pe[:, :, 1::2] = paddle.cos(position * div_term)
 
     def forward(self, x: paddle.Tensor,
                 offset: int=0) -> Tuple[paddle.Tensor, paddle.Tensor]:

paddlespeech/s2t/modules/encoder.py

@@ -264,15 +264,15 @@ class BaseEncoder(nn.Layer):
             # new_att_cache = (1, head, attention_key_size, d_k*2)
             # new_cnn_cache = (B=1, hidden-dim, cache_t2)
             r_att_cache.append(new_att_cache[:, :, next_cache_start:, :])
-            r_cnn_cache.append(new_cnn_cache.unsqueeze(0))  # add elayer dim
+            r_cnn_cache.append(new_cnn_cache)  # add elayer dim
 
         if self.normalize_before:
             xs = self.after_norm(xs)
 
         # r_att_cache (elayers, head, T, d_k*2)
-        # r_cnn_cache （elayers, B=1, hidden-dim, cache_t2)
+        # r_cnn_cache (elayers, B=1, hidden-dim, cache_t2)
         r_att_cache = paddle.concat(r_att_cache, axis=0)
-        r_cnn_cache = paddle.concat(r_cnn_cache, axis=0)
+        r_cnn_cache = paddle.stack(r_cnn_cache, axis=0)
         return xs, r_att_cache, r_cnn_cache
 
     def forward_chunk_by_chunk(