def build_text_encoder(cls, args, src_dictionary, spch_encoder):
if args.encoder_shared_layers > 0:
mx_shared_layers = (
args.speech_encoder_layers
if args.speech_encoder_layers < args.text_encoder_layers else
args.text_encoder_layers)
args.encoder_shared_layers = (
args.encoder_shared_layers
if args.encoder_shared_layers <= mx_shared_layers else
mx_shared_layers)
cfg = {
"encoder_embed_dim": args.encoder_text_embed_dim,
"encoder_ffn_embed_dim": args.encoder_ffn_embed_dim,
"encoder_layers": args.text_encoder_layers,
"encoder_layerdrop": args.encoder_layerdrop,
"encoder_attention_heads": args.encoder_attention_heads,
"encoder_learned_pos": args.encoder_learned_pos,
"max_source_positions": args.max_source_positions,
"dropout": args.dropout,
"encoder_normalize_before": args.encoder_normalize_before,
"activation_dropout": args.activation_dropout,
"attention_dropout": args.attention_dropout,
"activation_fn": args.activation_fn,
"adaptive_input": args.adaptive_input,
"no_token_positional_embeddings":
args.no_token_positional_embeddings,
"no_scale_embedding": args.no_scale_embedding,
"quant_noise_pq": args.quant_noise_pq,
}
model_args = namedtuple("args", cfg.keys())(*cfg.values())
enc_emb = nn.Embedding(len(src_dictionary),
model_args.encoder_embed_dim,
src_dictionary.pad())
text_encoder = TransformerEncoder(model_args, src_dictionary, enc_emb)
if args.add_speech_eos:
spch_encoder = spch_encoder.encoder
if args.encoder_shared_layers > 0:
text_encoder.layer_norm = cls.set_shared_layer(
args.encoder_shared_layer_level,
text_encoder.layer_norm,
spch_encoder.layer_norm,
)
for i, ly in enumerate(
spch_encoder.
transformer_layers[-args.encoder_shared_layers:]):
ly_id = i + args.text_encoder_layers - args.encoder_shared_layers
if not isinstance(text_encoder.layers[ly_id], type(ly)):
if text_encoder.layers[ly_id]._get_name() not in (
'TransformerEncoderLayerBase',
'TransformerEncoderLayer'):
raise ValueError(
"The shared layers are expected from the same class"
)
text_encoder.layers[ly_id] = cls.set_shared_layer(
args.encoder_shared_layer_level,
text_encoder.layers[ly_id],
ly,
)
return text_encoder
def build_encoder(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_source_positions = 1024
enc_emb = nn.Embedding(
len(task.src_dict), _args.encoder_embed_dim, task.src_dict.pad()
)
text_encoder = TransformerEncoder(_args, task.src_dict, enc_emb)
spch_encoder = Wav2VecEncoderWithAdaptor(args)
if getattr(args, "load_pretrained_mbart_from", None):
text_encoder = checkpoint_utils.load_pretrained_component_from_model(
component=text_encoder, checkpoint=args.load_pretrained_mbart_from
)
if getattr(args, "stack_w2v_mbart_encoder", False):
assert getattr(args, "share_w2v_text_encoder", False) is False
spch_encoder = StackedWav2VecEncoderWithAdaptor(
spch_encoder.w2v_encoder,
text_encoder.layers,
text_encoder.layer_norm,
spch_encoder.adaptor,
args.drop_w2v_layers,
)
elif getattr(args, "stack_w2v_mbart_nonorm_encoder", False):
text_encoder.layer_norm = None
spch_encoder = StackedWav2VecEncoderWithAdaptor(
spch_encoder.w2v_encoder,
text_encoder.layers,
text_encoder.layer_norm,
spch_encoder.adaptor,
args.drop_w2v_layers,
)
elif getattr(args, "share_w2v_text_encoder", False):
spch_encoder = SharedEncoder(
spch_encoder.w2v_encoder,
text_encoder,
spch_encoder.adaptor,
args.shared_w2v_layers,
)
for k, p in spch_encoder.named_parameters():
# Freeze pretrained models by default
if safe_hasattr(
args, "finetune_w2v_params"
) and need_finetuning(args.finetune_w2v_params, k):
p.requires_grad = True
else:
p.requires_grad = False
for k, p in text_encoder.named_parameters():
# Freeze pretrained models by default
if safe_hasattr(
args, "finetune_mbart_encoder_params"
) and need_finetuning(
args.finetune_mbart_encoder_params, k
):
p.requires_grad = True
else:
p.requires_grad = False
cross_attentive_loss_before_last_layer = (
0 if getattr(args, "attentive_cost_regularization", 0.0) > 0.0 else -1
)
encoder = DualInputEncoder(
args,
spch_encoder,
text_encoder,
task.src_dict,
cross_attentive_loss_before_last_layer,
)
return encoder
