def __init__(self, args, dictionary, embed_tokens, left_pad=True):
super().__init__(dictionary)
self.dropout = args.dropout
embed_dim = embed_tokens.embedding_dim
self.padding_idx = embed_tokens.padding_idx
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim)
self.embed_positions = fairseq_transformer.PositionalEmbedding(
1024,
embed_dim,
self.padding_idx,
left_pad=left_pad,
learned=args.encoder_learned_pos,
)
self.layers = nn.ModuleList([])
self.layers.extend([
fairseq_transformer.TransformerEncoderLayer(args)
for i in range(args.encoder_layers)
])
# Variable tracker
self.tracker = VariableTracker()
# Initialize adversarial mode
self.set_gradient_tracking_mode(False)
def __init__(self, args, proj_to_decoder):
super().__init__()
self.layers = nn.ModuleList([])
self.layers.extend([
fairseq_transformer.TransformerEncoderLayer(args)
for i in range(args.encoder_layers)
])
def __init__(self, args, proj_to_decoder):
super().__init__()
self.layers = nn.ModuleList([])
self.layers.extend([
fairseq_transformer.TransformerEncoderLayer(args)
for i in range(args.encoder_layers)
])
self.output_fc = None
if args.encoder_embed_dim != args.decoder_embed_dim and proj_to_decoder:
self.output_fc = fairseq_transformer.Linear(
args.encoder_embed_dim, args.decoder_embed_dim)
