[Bart/Memory] SelfAttention only returns weights if config.outp… (#3369)

src/transformers/modeling_bart.py

@@ -217,7 +217,9 @@ class EncoderLayer(nn.Module):
             encoded output of shape `(seq_len, batch, embed_dim)`
         """
         residual = x
-        x, attn_weights = self.self_attn(query=x, key=x, key_padding_mask=encoder_padding_mask,)
+        x, attn_weights = self.self_attn(
+            query=x, key=x, key_padding_mask=encoder_padding_mask, need_weights=self.output_attentions
+        )
         x = F.dropout(x, p=self.dropout, training=self.training)
         x = residual + x
         x = self.self_attn_layer_norm(x)
@@ -316,6 +318,7 @@ class DecoderLayer(nn.Module):
     def __init__(self, config: BartConfig):
         super().__init__()
         self.embed_dim = config.d_model
+        self.output_attentions = config.output_attentions
         self.self_attn = SelfAttention(
             embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout,
         )
@@ -343,14 +346,16 @@ class DecoderLayer(nn.Module):
         if layer_state is None:
             layer_state = {}
         # next line mutates layer state
-        x, self_attn_weights = self.self_attn(query=x, key=x, layer_state=layer_state, attn_mask=attention_mask,)
+        x, self_attn_weights = self.self_attn(
+            query=x, key=x, layer_state=layer_state, attn_mask=attention_mask, need_weights=self.output_attentions
+        )
         x = F.dropout(x, p=self.dropout, training=self.training)
         x = residual + x
         x = self.self_attn_layer_norm(x)
         residual = x
         assert self.encoder_attn.cache_key != self.self_attn.cache_key
 
-        x, encoder_attn_weights = self.encoder_attn(
+        x, _ = self.encoder_attn(
             query=x,
             key=encoder_hidden_states,
             key_padding_mask=encoder_attn_mask,
@@ -527,6 +532,7 @@ class SelfAttention(nn.Module):
         key_padding_mask: Optional[Tensor] = None,
         layer_state: Optional[Dict[str, Optional[Tensor]]] = None,
         attn_mask: Optional[Tensor] = None,
+        need_weights=False,
     ) -> Tuple[Tensor, Optional[Tensor]]:
         """Input shape: Time(SeqLen) x Batch x Channel"""
         static_kv = self.encoder_decoder_attention  # type: bool
@@ -598,7 +604,10 @@ class SelfAttention(nn.Module):
         assert attn_output.size() == (bsz * self.num_heads, tgt_len, self.head_dim)
         attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
         attn_output = self.out_proj(attn_output)
+        if need_weights:
             attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+        else:
+            attn_weights = None
         return attn_output, attn_weights
 
     def _use_saved_state(self, k, v, saved_state, key_padding_mask, static_kv, bsz):