def build_decoder(cls, args, src_dict, dst_dict):
"""Build a new (multi-)decoder instance."""
if args.multi_decoder is not None:
ngram_decoder_args = [None] * args.multi_decoder
if args.ngram_decoder is not None:
ngram_decoder_args = args.ngram_decoder
if len(ngram_decoder_args) == 1:
ngram_decoder_args = [ngram_decoder_args[0]
] * args.multi_decoder
assert len(ngram_decoder_args) == args.multi_decoder
decoders = [
RNNModel.build_single_decoder(args,
src_dict,
dst_dict,
n,
project_output=False)
for n in ngram_decoder_args
]
decoder = MultiDecoder(
src_dict,
dst_dict,
decoders=decoders,
combination_strategy=args.multi_decoder_combination_strategy,
split_encoder=args.multi_encoder is not None,
vocab_reduction_params=args.vocab_reduction_params,
training_schedule=args.multi_model_training_schedule,
)
else:
if args.multi_encoder:
args.encoder_hidden_dim *= args.multi_encoder
n = args.ngram_decoder[0] if args.ngram_decoder else None
decoder = RNNModel.build_single_decoder(args, src_dict, dst_dict,
n)
return decoder
def build_decoder(cls, args, src_dict, dst_dict):
"""Build a new (multi-)decoder instance."""
if args.multi_decoder is not None:
ngram_decoder_args = [None] * args.multi_decoder
if args.ngram_decoder is not None:
ngram_decoder_args = args.ngram_decoder
if len(ngram_decoder_args) == 1:
ngram_decoder_args = [ngram_decoder_args[0]
] * args.multi_decoder
assert len(ngram_decoder_args) == args.multi_decoder
is_lm_args = [False] * args.multi_decoder
if args.multi_decoder_is_lm is not None:
is_lm_args = list(map(bool, args.multi_decoder_is_lm))
assert len(is_lm_args) == args.multi_decoder
decoders = [
RNNModel.build_single_decoder(args,
src_dict,
dst_dict,
n,
project_output=False,
is_lm=is_lm)
for is_lm, n in zip(is_lm_args, ngram_decoder_args)
]
decoder = MultiDecoder(
src_dict,
dst_dict,
decoders=decoders,
combination_strategy=args.multi_decoder_combination_strategy,
is_lm=is_lm_args,
split_encoder=args.multi_encoder is not None,
vocab_reduction_params=args.vocab_reduction_params,
training_schedule=args.multi_model_training_schedule,
fixed_weights=args.multi_model_fixed_weights,
)
else:
if args.multi_encoder:
args.encoder_hidden_dim *= args.multi_encoder
n = args.ngram_decoder[0] if args.ngram_decoder else None
decoder = RNNModel.build_single_decoder(args, src_dict, dst_dict,
n)
return decoder