diff --git a/funasr/models/decoder/sanm_decoder.py b/funasr/models/decoder/sanm_decoder.py
index 011743022..3bfcffc3f 100644
--- a/funasr/models/decoder/sanm_decoder.py
+++ b/funasr/models/decoder/sanm_decoder.py
@@ -90,6 +90,47 @@ class DecoderLayerSANM(nn.Module):
             tgt = self.norm1(tgt)
         tgt = self.feed_forward(tgt)
 
+        x = tgt
+        if self.self_attn:
+            if self.normalize_before:
+                tgt = self.norm2(tgt)
+            x, _ = self.self_attn(tgt, tgt_mask)
+            x = residual + self.dropout(x)
+
+        if self.src_attn is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm3(x)
+
+            x = residual + self.dropout(self.src_attn(x, memory, memory_mask))
+
+
+        return x, tgt_mask, memory, memory_mask, cache
+
+    def forward_chunk(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
+        """Compute decoded features.
+
+        Args:
+            tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
+            tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
+            memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
+            memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
+            cache (List[torch.Tensor]): List of cached tensors.
+                Each tensor shape should be (#batch, maxlen_out - 1, size).
+
+        Returns:
+            torch.Tensor: Output tensor(#batch, maxlen_out, size).
+            torch.Tensor: Mask for output tensor (#batch, maxlen_out).
+            torch.Tensor: Encoded memory (#batch, maxlen_in, size).
+            torch.Tensor: Encoded memory mask (#batch, maxlen_in).
+
+        """
+        # tgt = self.dropout(tgt)
+        residual = tgt
+        if self.normalize_before:
+            tgt = self.norm1(tgt)
+        tgt = self.feed_forward(tgt)
+
         x = tgt
         if self.self_attn:
             if self.normalize_before:
@@ -109,7 +150,6 @@ class DecoderLayerSANM(nn.Module):
 
         return x, tgt_mask, memory, memory_mask, cache
 
-
 class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
     """
     author: Speech Lab, Alibaba Group, China
@@ -980,7 +1020,7 @@ class ParaformerSANMDecoder(BaseTransformerDecoder):
             new_cache = cache["decode_fsmn"]
         for i in range(self.att_layer_num):
             decoder = self.decoders[i]
-            x, tgt_mask, memory, memory_mask, c_ret = decoder(
+            x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
                 x, None, memory, None, cache=new_cache[i]
             )
             new_cache[i] = c_ret
@@ -989,14 +1029,14 @@ class ParaformerSANMDecoder(BaseTransformerDecoder):
             for i in range(self.num_blocks - self.att_layer_num):
                 j = i + self.att_layer_num
                 decoder = self.decoders2[i]
-                x, tgt_mask, memory, memory_mask, c_ret = decoder(
+                x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_chunk(
                     x, None, memory, None, cache=new_cache[j]
                 )
                 new_cache[j] = c_ret
 
         for decoder in self.decoders3:
 
-            x, tgt_mask, memory, memory_mask, _ = decoder(
+            x, tgt_mask, memory, memory_mask, _ = decoder.forward_chunk(
                 x, None, memory, None, cache=None
             )
         if self.normalize_before: