create_tb_writer=True,
files_to_copy=[__file__])
# tie weight of embedding and log_softmax layers if use the same tokenizer
# for the source and the target
tie_weight = False
"""Define tokenizer
When the src and tgt languages are very different, it's better to use separate
tokenizers.
"""
if args.src_lang == 'en' and args.tgt_lang == 'de':
"""
We use YouTokenToMe tokenizer trained on joint
English & German data for both source and target languages.
"""
src_tokenizer = nemo_nlp.YouTokenToMeTokenizer(
model_path=f"{args.data_dir}/{args.src_tokenizer_model}")
src_vocab_size = src_tokenizer.vocab_size
if args.src_tokenizer_model == args.tgt_tokenizer_model:
tgt_tokenizer = src_tokenizer
# source and target use the same tokenizer, set tie_weight to True
tie_weight = True
else:
tgt_tokenizer = nemo_nlp.YouTokenToMeTokenizer(
model_path=f"{args.data_dir}/{args.tgt_tokenizer_model}")
# source and target use different tokenizers, set tie_weight to False
tie_weight = False
tgt_vocab_size = tgt_tokenizer.vocab_size
elif args.src_lang == 'en' and args.tgt_lang == 'zh':
"""
We use YouTokenToMeTokenizer for src since the src contains English words
and CharTokenizer for tgt since the tgt contains Chinese characters.
name = f"transformer-nmt_{args.src_lang}_{args.tgt_lang}-lr_{args.lr}-" \
f"optim_{args.optimizer}-warmup_{args.warmup_steps}-bs_{args.batch_size}"
tb_writer = None # SummaryWriter(name)
# instantiate Neural Factory with supported backend
device = nemo.core.DeviceType.AllGpu if args.local_rank is not None \
else nemo.core.DeviceType.GPU
neural_factory = nemo.core.NeuralModuleFactory(
backend=nemo.core.Backend.PyTorch,
local_rank=args.local_rank,
optimization_level=nemo.core.Optimization.mxprO2,
placement=device)
# define tokenizer, in this example we use YouTokenToMe tokenizer trained
# on joint English & German data for both source and target languages
tokenizer = nemo_nlp.YouTokenToMeTokenizer(
model_path=f"{args.data_root}/{args.tokenizer_model}")
vocab_size = tokenizer.vocab_size
# instantiate necessary modules for the whole translation pipeline, namely
# data layers, encoder, decoder, output log_softmax, beam_search_translator
# and loss function
train_data_layer = nemo_nlp.TranslationDataLayer(
factory=neural_factory,
tokenizer_src=tokenizer,
tokenizer_tgt=tokenizer,
dataset_src=f"{args.data_root}/{args.train_dataset}.{args.src_lang}",
dataset_tgt=f"{args.data_root}/{args.train_dataset}.{args.tgt_lang}",
tokens_in_batch=args.batch_size,
clean=True)
eval_data_layer = nemo_nlp.TranslationDataLayer(
factory=neural_factory,
