def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present
base_architecture(args)
if not hasattr(args, "max_positions"):
args.max_positions = args.tokens_per_sample
encoder_embed_tokens = TransformerModel.build_embedding(
args,
task.source_dictionary,
args.encoder_embed_dim,
args.encoder_embed_path,
)
encoder = LinformerEncoderFromTransformerEncoder(
args, task.source_dictionary, encoder_embed_tokens)
def build_model(cls, args, task):
"""Build a new model instance."""
# from fairseq.tasks.multilingual_translation import MultilingualTranslationTask
# assert isinstance(task, MultilingualTranslationTask)
# make sure all arguments are present in older models
base_architecture(args)
if args.share_encoders:
args.share_encoder_embeddings = True
### nat model
# build shared embeddings (if applicable)
src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
if args.share_all_embeddings:
if src_dict != tgt_dict:
raise ValueError("--share-all-embeddings requires a joined dictionary")
if args.encoder_embed_dim != args.decoder_embed_dim:
raise ValueError(
"--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim"
)
if args.decoder_embed_path and (
args.decoder_embed_path != args.encoder_embed_path
):
raise ValueError(
"--share-all-embeddings not compatible with --decoder-embed-path"
)
encoder_embed_tokens = TransformerModel.build_embedding(
args, src_dict, args.encoder_embed_dim, args.encoder_embed_path
)
decoder_embed_tokens = encoder_embed_tokens
args.share_decoder_input_output_embed = True
else:
encoder_embed_tokens = TransformerModel.build_embedding(
args, src_dict, args.encoder_embed_dim, args.encoder_embed_path
)
decoder_embed_tokens = TransformerModel.build_embedding(
args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path
)
student_cls = ARCH_MODEL_REGISTRY[args.student_arch]
encoder = student_cls.build_encoder(args, src_dict, encoder_embed_tokens)
decoder = student_cls.build_decoder(args, tgt_dict, decoder_embed_tokens)
student = student_cls(args,encoder,decoder)
teacher_cls = ARCH_MODEL_REGISTRY[args.teacher_arch]
if not issubclass(teacher_cls, NATransformerModel):
teacher_cls = PatchedTransformerModel
teacher_encoder = teacher_cls.build_encoder(
args, src_dict,
encoder_embed_tokens if args.share_encoder_embeddings else TransformerModel.build_embedding(
args, src_dict, args.encoder_embed_dim, args.encoder_embed_path
)
)
teacher_decoder = teacher_cls.build_decoder(
args, tgt_dict,
decoder_embed_tokens if args.share_decoder_embeddings else TransformerModel.build_embedding(
args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path
)
)
teacher = teacher_cls(args,teacher_encoder,teacher_decoder)
return cls(args, student, teacher)
