fix the bug of the block attention

fairseq/models/speech_to_text/pys2t_transformer.py

@@ -273,8 +273,6 @@ class PyS2TTransformerEncoder(FairseqEncoder):
                     vdim=self.pyramid_embed_dims[i-1],
                     vdim=self.pyramid_embed_dims[i-1],
                     dropout=args.attention_dropout,
                     dropout=args.attention_dropout,
                     encoder_decoder_attention=True,
                     encoder_decoder_attention=True,
-                    q_noise=self.quant_noise,
-                    qn_block_size=self.quant_noise_block_size,
                 )
                 )
             else:
             else:
                 attn = None
                 attn = None
@@ -373,7 +371,7 @@ class PyS2TTransformerEncoder(FairseqEncoder):
                 x = layer(x, encoder_padding_mask, pos_emb=positions)
                 x = layer(x, encoder_padding_mask, pos_emb=positions)
                 layer_idx += 1
                 layer_idx += 1
 
 
-            if attn is not None:
+            if block_attn is not None:
                 residual = x
                 residual = x
                 x, attn = block_attn(
                 x, attn = block_attn(
                     query=x,
                     query=x,
@@ -383,8 +381,8 @@ class PyS2TTransformerEncoder(FairseqEncoder):
                 )
                 )
                 x += residual
                 x += residual
 
 
-            prev_state[i] = x
-            prev_padding[i] = encoder_padding_mask
+            prev_state.append(x)
+            prev_padding.append(encoder_padding_mask)
 
 
             if self.use_ctc and self.inter_ctc and self.ctc_layer == layer_idx:
             if self.use_ctc and self.inter_ctc and self.ctc_layer == layer_idx:
                 ctc_logit = self.ctc_layer_norm(x)
                 ctc_logit = self.ctc_layer_norm(x)