diff --git a/deepspeech/exps/u2/model.py b/deepspeech/exps/u2/model.py
index 96bf8d17f71c06cb073510bcead07d1fa21db2d4..6297f1854385ca8913f3e2e73d5b66d87ba188eb 100644
--- a/deepspeech/exps/u2/model.py
+++ b/deepspeech/exps/u2/model.py
@@ -610,10 +610,11 @@ class U2Tester(U2Trainer):
             input_spec=[
                 paddle.static.InputSpec(
                     shape=[1, decoder_max_time], dtype='int32'),  # tgt
-                paddle.static.InputSpec(shape=[1], dtype='int32'),  # tgt_len
+                paddle.static.InputSpec(
+                    shape=[1, decoder_max_time], dtype='bool'),  # tgt_mask
                 paddle.static.InputSpec(
                     shape=[1, encoder_max_time, encoder_model_size],
-                    dtype='int32'),  # encoder_out
+                    dtype='float32'),  # encoder_out
             ])
         logger.info(f"Export code: {static_model}")
 
diff --git a/deepspeech/models/u2.py b/deepspeech/models/u2.py
index c3d93d8a67349b9a705f5f6073f24c64075ff973..c6a33ad589405ba59916ed661b789e8d8d1b94a4 100644
--- a/deepspeech/models/u2.py
+++ b/deepspeech/models/u2.py
@@ -927,3 +927,32 @@ class U2InferModel(U2Model):
             decoding_chunk_size=decoding_chunk_size,
             num_decoding_left_chunks=num_decoding_left_chunks,
             simulate_streaming=simulate_streaming)
+
+    def forward_attention_decoder(
+            self,
+            hyps: paddle.Tensor,
+            hyps_masks: paddle.Tensor,
+            encoder_out: paddle.Tensor, ) -> paddle.Tensor:
+        """ Export interface for c++ call, forward decoder with multiple
+            hypothesis from ctc prefix beam search and one encoder output
+        Args:
+            hyps (paddle.Tensor): hyps from ctc prefix beam search, already
+                pad sos at the begining, (B, U)
+            hyps_masks (paddle.Tensor): length of each hyp in hyps, (B, U)
+            encoder_out (paddle.Tensor): corresponding encoder output, (B=1, T, D)
+        Returns:
+            paddle.Tensor: decoder output, (B, V)
+        """
+        assert encoder_out.shape[0] == 1
+        num_hyps = hyps.shape[0]
+        assert hyps_masks.shape[0] == num_hyps
+        # encoder_out = encoder_out.repeat(num_hyps, 1, 1)
+        encoder_out = encoder_out.tile([num_hyps, 1, 1])
+        # (B, 1, T)
+        encoder_mask = paddle.ones(
+            [num_hyps, 1, encoder_out.shape[1]], dtype=paddle.bool)
+        # (num_hyps, max_hyps_len, vocab_size)
+        decoder_out, _ = self.decoder.export(encoder_out, encoder_mask, hyps,
+                                             hyps_masks)
+        decoder_out = paddle.nn.functional.log_softmax(decoder_out, dim=-1)
+        return decoder_out
diff --git a/deepspeech/modules/decoder.py b/deepspeech/modules/decoder.py
index c2bcbb48a38c4b277368f013da0b97c413663fb7..e3ab80a239f1aa576faccca665360a441498824c 100644
--- a/deepspeech/modules/decoder.py
+++ b/deepspeech/modules/decoder.py
@@ -185,3 +185,45 @@ class TransformerDecoder(nn.Module):
             y = paddle.log_softmax(self.output_layer(y), axis=-1)
 
         return y, new_cache
+
+    def export(
+            self,
+            memory: paddle.Tensor,
+            memory_mask: paddle.Tensor,
+            ys_in_pad: paddle.Tensor,
+            ys_in_mask: paddle.Tensor, ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+        """Forward decoder.
+        Args:
+            memory: encoded memory, float32  (batch, maxlen_in, feat)
+            memory_mask: encoder memory mask, (batch, 1, maxlen_in)
+            ys_in_pad: padded input token ids, int64 (batch, maxlen_out)
+            ys_in_mask: input mask of this batch (batch, maxlen_out)
+        Returns:
+            (tuple): tuple containing:
+                x: decoded token score before softmax (batch, maxlen_out, vocab_size)
+                    if use_output_layer is True,
+                olens: (batch, )
+        """
+        tgt = ys_in_pad
+        # tgt_mask: (B, 1, L)
+        tgt_mask = ys_in_mask.unsqueeze(1)
+        # m: (1, L, L)
+        m = subsequent_mask(tgt_mask.shape[-1]).unsqueeze(0)
+        # tgt_mask: (B, L, L)
+        # TODO(Hui Zhang): not support & for tensor
+        # tgt_mask = tgt_mask & m
+        tgt_mask = tgt_mask.logical_and(m)
+
+        x, _ = self.embed(tgt)
+        for layer in self.decoders:
+            x, tgt_mask, memory, memory_mask = layer(x, tgt_mask, memory,
+                                                     memory_mask)
+        if self.normalize_before:
+            x = self.after_norm(x)
+        if self.use_output_layer:
+            x = self.output_layer(x)
+
+        # TODO(Hui Zhang): reduce_sum not support bool type
+        # olens = tgt_mask.sum(1)
+        olens = tgt_mask.astype(paddle.int).sum(1)
+        return x, olens