def build_decoder(cls, args, task, embed_tokens):
_args = copy.deepcopy(args)
if args.adaptor_proj or args.encoder_proj: # not V0 arch
_args.encoder_embed_dim = _args.decoder_embed_dim
_args.dropout = args.decoder_dropout
_args.attention_dropout = args.decoder_attention_dropout
_args.activation_dropout = args.decoder_activation_dropout
decoder = TransformerDecoder(_args, task.target_dictionary,
embed_tokens)
decoder = cls.maybe_load_pretrained(
decoder, getattr(args, "load_pretrained_decoder_from", None))
for k, p in decoder.named_parameters():
p.requires_grad = need_finetuning(args.finetune_decoder_params, k)
return decoder
def build_decoder(cls, args, task):
_args = copy.deepcopy(args)
_args.dropout = args.mbart_dropout
_args.attention_dropout = args.mbart_attention_dropout
_args.activation_dropout = args.mbart_activation_dropout
_args.max_target_positions = 1024
dec_emb = nn.Embedding(
len(task.tgt_dict), _args.encoder_embed_dim, task.tgt_dict.pad()
)
decoder = TransformerDecoder(_args, task.tgt_dict, dec_emb)
if getattr(args, "load_pretrained_mbart_from", None):
decoder = checkpoint_utils.load_pretrained_component_from_model(
component=decoder, checkpoint=args.load_pretrained_mbart_from
)
if getattr(args, "no_final_norm_decoder", False):
decoder.layer_norm = None
for k, p in decoder.named_parameters():
# Freeze pretrained models by default
if safe_hasattr(
args, "finetune_mbart_decoder_params"
) and need_finetuning(
args.finetune_mbart_decoder_params, k
):
p.requires_grad = True
else:
p.requires_grad = False
compute_cross_attentive_loss = (
True if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else False
)
cross_attentive_loss_without_norm = getattr(
args, "attentive_cost_without_normalize", False
)
cross_attentive_loss_reverse = (
False # getattr(args, "attentive_cost_reverse", False)
)
decoder = TransformerMultiInputDecoder(
dictionary=task.target_dictionary,
spch_decoder=decoder,
text_decoder=decoder,
compute_cross_attentive_loss=compute_cross_attentive_loss,
cross_attentive_loss_with_norm=True
if not cross_attentive_loss_without_norm
else False,
cross_attentive_loss_reverse=cross_attentive_loss_reverse,
)
return decoder
def build_decoder(cls, args, task, embed_tokens):
_args = copy.deepcopy(args)
_args.dropout = args.decoder_dropout
_args.attention_dropout = args.decoder_attention_dropout
_args.activation_dropout = args.decoder_activation_dropout
_args.max_target_positions = 1024
decoder = TransformerDecoder(_args, task.target_dictionary,
embed_tokens)
if getattr(args, "load_pretrained_decoder_from", None):
decoder = checkpoint_utils.load_pretrained_component_from_model(
component=decoder,
checkpoint=args.load_pretrained_decoder_from)
for k, p in decoder.named_parameters():
# Freeze pretrained models by default
if safe_hasattr(args, 'finetune_decoder_params'
) and XMTransformerModel.finetune_params(
args.finetune_decoder_params, k):
p.requires_grad = True
else:
p.requires_grad = False
return decoder