def __init__(self, args):
super().__init__()
self.embed_dim = args.decoder_embed_dim
self.cross_self_attention = getattr(args, "cross_self_attention",
False)
self.avg_attn = AverageAttention(self.embed_dim,
dropout=args.attention_dropout)
# differently than original paper, we use a single gate
self.aan_gating_fc = fairseq_transformer.Linear(
self.embed_dim * 2, self.embed_dim)
self.dropout = args.dropout
self.activation_fn = utils.get_activation_fn(
activation=getattr(args, "activation_fn", "relu"))
self.activation_dropout = getattr(args, "activation_dropout", 0)
if self.activation_dropout == 0:
# for backwards compatibility with models that use args.relu_dropout
self.activation_dropout = getattr(args, "relu_dropout", 0)
self.normalize_before = args.decoder_normalize_before
# use layerNorm rather than FusedLayerNorm for exporting.
# char_inputs can be used to determint this.
# TODO remove this once we update apex with the fix
export = getattr(args, "char_inputs", False)
self.avg_attn_layer_norm = LayerNorm(self.embed_dim, export=export)
self.encoder_attn = MultiheadAttention(
self.embed_dim,
args.decoder_attention_heads,
kdim=getattr(args, "encoder_embed_dim", None),
vdim=getattr(args, "encoder_embed_dim", None),
dropout=args.attention_dropout,
encoder_decoder_attention=True,
)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=export)
self.fc1 = fairseq_transformer.Linear(self.embed_dim,
args.decoder_ffn_embed_dim)
self.fc2 = fairseq_transformer.Linear(args.decoder_ffn_embed_dim,
self.embed_dim)
self.final_layer_norm = LayerNorm(self.embed_dim, export=export)
self.need_attn = True
self.onnx_trace = False
def __init__(self, args, no_encoder_attn=False):
super().__init__()
self.embed_dim = args.decoder_embed_dim
self.dropout = args.dropout
self.relu_dropout = args.relu_dropout
self.more_dropouts = args.decoder_aan_more_dropouts
if args.decoder_attn_window_size <= 0:
self.avg_attn = AverageAttention(self.embed_dim,
dropout=args.attention_dropout)
else:
self.avg_attn = AverageWindowAttention(
self.embed_dim,
dropout=args.attention_dropout,
window_size=args.decoder_attn_window_size,
)
# self.activation = getattr(args, "decoder_ffn_activation", "relu")
self.aan_layer_norm = LayerNorm(self.embed_dim)
if args.no_decoder_aan_ffn:
self.aan_ffn = None
else:
aan_ffn_hidden_dim = (args.decoder_ffn_embed_dim
if args.decoder_aan_ffn_use_embed_dim else
args.decoder_ffn_embed_dim)
self.aan_ffn = FeedForwardNetwork(
self.embed_dim,
aan_ffn_hidden_dim,
self.embed_dim,
num_layers=2,
dropout=args.relu_dropout,
)
if args.no_decoder_aan_gating:
self.aan_gating_fc = None
else:
self.aan_gating_fc = Linear(self.embed_dim * 2, self.embed_dim * 2)
self.normalize_before = args.decoder_normalize_before
if no_encoder_attn:
self.encoder_attn = None
self.encoder_attn_layer_norm = None
else:
self.encoder_attn = MultiheadAttention(
self.embed_dim,
args.decoder_attention_heads,
kdim=args.encoder_embed_dim,
vdim=args.encoder_embed_dim,
dropout=args.attention_dropout,
encoder_decoder_attention=True,
)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim)
self.ffn = FeedForwardNetwork(
self.embed_dim,
args.decoder_ffn_embed_dim,
self.embed_dim,
num_layers=2,
dropout=args.relu_dropout,
)
self.final_layer_norm = LayerNorm(self.embed_dim)
self.need_attn = True
self.onnx_trace = False