encoder = nemo_nlp.huggingface.BERT(
pretrained_model_name=args.pretrained_model, local_rank=args.local_rank)
device = encoder.bert.embeddings.word_embeddings.weight.get_device()
zeros = torch.zeros((tokens_to_add, args.d_model)).to(device=device)
encoder.bert.embeddings.word_embeddings.weight.data = torch.cat(
(encoder.bert.embeddings.word_embeddings.weight.data, zeros))
decoder = nemo_nlp.TransformerDecoderNM(
d_model=args.d_model,
d_inner=args.d_inner,
num_layers=args.num_layers,
num_attn_heads=args.num_heads,
ffn_dropout=args.ffn_dropout,
vocab_size=vocab_size,
max_seq_length=args.max_seq_length,
embedding_dropout=args.embedding_dropout,
learn_positional_encodings=True,
hidden_act="gelu",
**dec_first_sublayer_params)
decoder.restore_from(args.restore_from, local_rank=args.local_rank)
t_log_softmax = nemo_nlp.TokenClassifier(args.d_model,
num_classes=vocab_size,
num_layers=1,
log_softmax=True)
beam_translator = nemo_nlp.BeamSearchTranslatorNM(
decoder=decoder,
num_layers=args.num_layers,
embedding_dropout=args.embedding_dropout,
num_attn_heads=args.num_attn_heads,
ffn_dropout=args.ffn_dropout,
vocab_size=vocab_size,
attn_score_dropout=args.attn_score_dropout,
attn_layer_dropout=args.attn_layer_dropout,
max_seq_length=args.max_sequence_length,
share_all_layers=args.share_encoder_layers)
decoder = nemo_nlp.TransformerDecoderNM(
factory=neural_factory,
d_embedding=args.d_embedding,
d_model=args.d_model,
d_inner=args.d_inner,
num_layers=args.num_layers,
embedding_dropout=args.embedding_dropout,
num_attn_heads=args.num_attn_heads,
ffn_dropout=args.ffn_dropout,
vocab_size=vocab_size,
attn_score_dropout=args.attn_score_dropout,
attn_layer_dropout=args.attn_layer_dropout,
max_seq_length=args.max_sequence_length,
share_all_layers=args.share_decoder_layers)
log_softmax = nemo_nlp.TransformerLogSoftmaxNM(factory=neural_factory,
vocab_size=vocab_size,
d_model=args.d_model,
d_embedding=args.d_embedding)
beam_search = nemo_nlp.BeamSearchTranslatorNM(
factory=neural_factory,
decoder=decoder,
log_softmax=log_softmax,
max_seq_length=args.max_sequence_length,
d_inner=args.d_inner,
num_layers=args.num_layers,
embedding_dropout=args.embedding_dropout,
num_attn_heads=args.num_attn_heads,
ffn_dropout=args.ffn_dropout,
vocab_size=src_vocab_size,
attn_score_dropout=args.attn_score_dropout,
attn_layer_dropout=args.attn_layer_dropout,
max_seq_length=args.max_seq_length)
decoder = nemo_nlp.TransformerDecoderNM(
d_model=args.d_model,
d_inner=args.d_inner,
num_layers=args.num_layers,
embedding_dropout=args.embedding_dropout,
num_attn_heads=args.num_attn_heads,
ffn_dropout=args.ffn_dropout,
vocab_size=tgt_vocab_size,
attn_score_dropout=args.attn_score_dropout,
attn_layer_dropout=args.attn_layer_dropout,
max_seq_length=args.max_seq_length)
log_softmax = nemo_nlp.TokenClassifier(args.d_model,
num_classes=tgt_tokenizer.vocab_size,
num_layers=1,
log_softmax=True)
beam_search = nemo_nlp.BeamSearchTranslatorNM(
decoder=decoder,
log_softmax=log_softmax,
max_seq_length=args.max_seq_length,
local_rank=args.local_rank)
device = encoder.bert.embeddings.word_embeddings.weight.get_device()
zeros = torch.zeros((tokens_to_add, args.d_model)).to(device=device)
encoder.bert.embeddings.word_embeddings.weight.data = torch.cat(
(encoder.bert.embeddings.word_embeddings.weight.data, zeros))
decoder = nemo_nlp.TransformerDecoderNM(
factory=neural_factory,
d_embedding=args.d_embedding,
d_model=args.d_model,
d_inner=args.d_inner,
num_layers=args.num_layers,
num_attn_heads=args.num_heads,
ffn_dropout=args.ffn_dropout,
vocab_size=vocab_size,
attn_score_dropout=args.attn_score_dropout,
attn_layer_dropout=args.attn_layer_dropout,
max_seq_length=max_sequence_length,
embedding_dropout=args.embedding_dropout,
share_all_layers=args.share_decoder_layers,
learn_positional_encodings=True,
hidden_act="gelu")
if args.restore_decoder:
decoder.restore_from(args.decoder_restore_from,
local_rank=args.local_rank)
elif args.encoder == "nemo":
encoder = nemo_nlp.TransformerEncoderNM(
factory=neural_factory,
