class Normalizer:
"""
Normalizer class that converts text from written to spoken form.
Useful for TTS preprocessing.
Args:
input_case: expected input capitalization
lang: language specifying the TN rules, by default: English
cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache.
overwrite_cache: set to True to overwrite .far files
whitelist: path to a file with whitelist replacements
"""
def __init__(
self,
input_case: str,
lang: str = 'en',
deterministic: bool = True,
cache_dir: str = None,
overwrite_cache: bool = False,
whitelist: str = None,
):
assert input_case in ["lower_cased", "cased"]
if lang == 'en' and deterministic:
from nemo_text_processing.text_normalization.en.taggers.tokenize_and_classify import ClassifyFst
from nemo_text_processing.text_normalization.en.verbalizers.verbalize_final import VerbalizeFinalFst
elif lang == 'en' and not deterministic:
from nemo_text_processing.text_normalization.en.taggers.tokenize_and_classify_with_audio import ClassifyFst
from nemo_text_processing.text_normalization.en.verbalizers.verbalize_final import VerbalizeFinalFst
elif lang == 'ru':
# Ru TN only support non-deterministic cases and produces multiple normalization options
# use normalize_with_audio.py
from nemo_text_processing.text_normalization.ru.taggers.tokenize_and_classify import ClassifyFst
from nemo_text_processing.text_normalization.ru.verbalizers.verbalize_final import VerbalizeFinalFst
self.tagger = ClassifyFst(
input_case=input_case,
deterministic=deterministic,
cache_dir=cache_dir,
overwrite_cache=overwrite_cache,
whitelist=whitelist,
)
self.verbalizer = VerbalizeFinalFst(deterministic=deterministic)
self.parser = TokenParser()
self.lang = lang
if NLP_AVAILABLE:
self.processor = MosesProcessor(lang_id=lang)
else:
self.processor = None
print("NeMo NLP is not available. Moses de-tokenization will be skipped.")
def normalize_list(self, texts: List[str], verbose=False) -> List[str]:
"""
NeMo text normalizer
Args:
texts: list of input strings
verbose: whether to print intermediate meta information
Returns converted list input strings
"""
res = []
for input in tqdm(texts):
try:
text = self.normalize(input, verbose=verbose)
except:
print(input)
raise Exception
res.append(text)
return res
def normalize(
self, text: str, verbose: bool, punct_pre_process: bool = False, punct_post_process: bool = False
) -> str:
"""
Main function. Normalizes tokens from written to spoken form
e.g. 12 kg -> twelve kilograms
Args:
text: string that may include semiotic classes
verbose: whether to print intermediate meta information
punct_pre_process: whether to perform punctuation pre-processing, for example, [25] -> [ 25 ]
punct_post_process: whether to normalize punctuation
Returns: spoken form
"""
if punct_pre_process:
text = pre_process(text)
text = text.strip()
if not text:
if verbose:
print(text)
return text
text = pynini.escape(text)
tagged_lattice = self.find_tags(text)
tagged_text = self.select_tag(tagged_lattice)
if verbose:
print(tagged_text)
self.parser(tagged_text)
tokens = self.parser.parse()
tags_reordered = self.generate_permutations(tokens)
for tagged_text in tags_reordered:
tagged_text = pynini.escape(tagged_text)
verbalizer_lattice = self.find_verbalizer(tagged_text)
if verbalizer_lattice.num_states() == 0:
continue
output = self.select_verbalizer(verbalizer_lattice)
if punct_post_process:
output = post_process_punctuation(output)
# do post-processing based on Moses detokenizer
if self.processor:
output = self.processor.detokenize([output])
return output
raise ValueError()
def _permute(self, d: OrderedDict) -> List[str]:
"""
Creates reorderings of dictionary elements and serializes as strings
Args:
d: (nested) dictionary of key value pairs
Return permutations of different string serializations of key value pairs
"""
l = []
if PRESERVE_ORDER_KEY in d.keys():
d_permutations = [d.items()]
else:
d_permutations = itertools.permutations(d.items())
for perm in d_permutations:
subl = [""]
for k, v in perm:
if isinstance(v, str):
subl = ["".join(x) for x in itertools.product(subl, [f"{k}: \"{v}\" "])]
elif isinstance(v, OrderedDict):
rec = self._permute(v)
subl = ["".join(x) for x in itertools.product(subl, [f" {k} {{ "], rec, [f" }} "])]
elif isinstance(v, bool):
subl = ["".join(x) for x in itertools.product(subl, [f"{k}: true "])]
else:
raise ValueError()
l.extend(subl)
return l
def generate_permutations(self, tokens: List[dict]):
"""
Generates permutations of string serializations of list of dictionaries
Args:
tokens: list of dictionaries
Returns string serialization of list of dictionaries
"""
def _helper(prefix: str, tokens: List[dict], idx: int):
"""
Generates permutations of string serializations of given dictionary
Args:
tokens: list of dictionaries
prefix: prefix string
idx: index of next dictionary
Returns string serialization of dictionary
"""
if idx == len(tokens):
yield prefix
return
token_options = self._permute(tokens[idx])
for token_option in token_options:
yield from _helper(prefix + token_option, tokens, idx + 1)
return _helper("", tokens, 0)
def find_tags(self, text: str) -> 'pynini.FstLike':
"""
Given text use tagger Fst to tag text
Args:
text: sentence
Returns: tagged lattice
"""
lattice = text @ self.tagger.fst
return lattice
def select_tag(self, lattice: 'pynini.FstLike') -> str:
"""
Given tagged lattice return shortest path
Args:
tagged_text: tagged text
Returns: shortest path
"""
tagged_text = pynini.shortestpath(lattice, nshortest=1, unique=True).string()
return tagged_text
def find_verbalizer(self, tagged_text: str) -> 'pynini.FstLike':
"""
Given tagged text creates verbalization lattice
This is context-independent.
Args:
tagged_text: input text
Returns: verbalized lattice
"""
lattice = tagged_text @ self.verbalizer.fst
return lattice
def select_verbalizer(self, lattice: 'pynini.FstLike') -> str:
"""
Given verbalized lattice return shortest path
Args:
lattice: verbalization lattice
Returns: shortest path
"""
output = pynini.shortestpath(lattice, nshortest=1, unique=True).string()
return output
class MTEncDecModel(EncDecNLPModel):
"""
Encoder-decoder machine translation model.
"""
def __init__(self, cfg: MTEncDecModelConfig, trainer: Trainer = None):
cfg = model_utils.convert_model_config_to_dict_config(cfg)
# Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable
# Global_rank and local_rank is set by LightningModule in Lightning 1.2.0
self.world_size = 1
if trainer is not None:
self.world_size = trainer.num_nodes * trainer.num_gpus
cfg = model_utils.maybe_update_config_version(cfg)
self.src_language: str = cfg.get("src_language", None)
self.tgt_language: str = cfg.get("tgt_language", None)
# Instantiates tokenizers and register to be saved with NeMo Model archive
# After this call, ther will be self.encoder_tokenizer and self.decoder_tokenizer
# Which can convert between tokens and token_ids for SRC and TGT languages correspondingly.
self.setup_enc_dec_tokenizers(
encoder_tokenizer_name=cfg.encoder_tokenizer.tokenizer_name,
encoder_tokenizer_model=cfg.encoder_tokenizer.tokenizer_model,
encoder_bpe_dropout=cfg.encoder_tokenizer.get('bpe_dropout', 0.0),
decoder_tokenizer_name=cfg.decoder_tokenizer.tokenizer_name,
decoder_tokenizer_model=cfg.decoder_tokenizer.tokenizer_model,
decoder_bpe_dropout=cfg.decoder_tokenizer.get('bpe_dropout', 0.0),
)
# After this call, the model will have self.source_processor and self.target_processor objects
self.setup_pre_and_post_processing_utils(source_lang=self.src_language,
target_lang=self.tgt_language)
# TODO: Why is this base constructor call so late in the game?
super().__init__(cfg=cfg, trainer=trainer)
# TODO: use get_encoder function with support for HF and Megatron
self.encoder = TransformerEncoderNM(
vocab_size=self.encoder_vocab_size,
hidden_size=cfg.encoder.hidden_size,
num_layers=cfg.encoder.num_layers,
inner_size=cfg.encoder.inner_size,
max_sequence_length=cfg.encoder.max_sequence_length if hasattr(
cfg.encoder, 'max_sequence_length') else 512,
embedding_dropout=cfg.encoder.embedding_dropout if hasattr(
cfg.encoder, 'embedding_dropout') else 0.0,
learn_positional_encodings=cfg.encoder.learn_positional_encodings
if hasattr(cfg.encoder, 'learn_positional_encodings') else False,
num_attention_heads=cfg.encoder.num_attention_heads,
ffn_dropout=cfg.encoder.ffn_dropout,
attn_score_dropout=cfg.encoder.attn_score_dropout,
attn_layer_dropout=cfg.encoder.attn_layer_dropout,
hidden_act=cfg.encoder.hidden_act,
mask_future=cfg.encoder.mask_future,
pre_ln=cfg.encoder.pre_ln,
)
# TODO: user get_decoder function with support for HF and Megatron
self.decoder = TransformerDecoderNM(
vocab_size=self.decoder_vocab_size,
hidden_size=cfg.decoder.hidden_size,
num_layers=cfg.decoder.num_layers,
inner_size=cfg.decoder.inner_size,
max_sequence_length=cfg.decoder.max_sequence_length if hasattr(
cfg.decoder, 'max_sequence_length') else 512,
embedding_dropout=cfg.decoder.embedding_dropout if hasattr(
cfg.decoder, 'embedding_dropout') else 0.0,
learn_positional_encodings=cfg.decoder.learn_positional_encodings
if hasattr(cfg.decoder, 'learn_positional_encodings') else False,
num_attention_heads=cfg.decoder.num_attention_heads,
ffn_dropout=cfg.decoder.ffn_dropout,
attn_score_dropout=cfg.decoder.attn_score_dropout,
attn_layer_dropout=cfg.decoder.attn_layer_dropout,
hidden_act=cfg.decoder.hidden_act,
pre_ln=cfg.decoder.pre_ln,
)
self.log_softmax = TokenClassifier(
hidden_size=self.decoder.hidden_size,
num_classes=self.decoder_vocab_size,
activation=cfg.head.activation,
log_softmax=cfg.head.log_softmax,
dropout=cfg.head.dropout,
use_transformer_init=cfg.head.use_transformer_init,
)
self.beam_search = BeamSearchSequenceGenerator(
embedding=self.decoder.embedding,
decoder=self.decoder.decoder,
log_softmax=self.log_softmax,
max_sequence_length=self.decoder.max_sequence_length,
beam_size=cfg.beam_size,
bos=self.decoder_tokenizer.bos_id,
pad=self.decoder_tokenizer.pad_id,
eos=self.decoder_tokenizer.eos_id,
len_pen=cfg.len_pen,
max_delta_length=cfg.max_generation_delta,
)
# tie weights of embedding and softmax matrices
self.log_softmax.mlp.layer0.weight = self.decoder.embedding.token_embedding.weight
# TODO: encoder and decoder with different hidden size?
std_init_range = 1 / self.encoder.hidden_size**0.5
self.apply(
lambda module: transformer_weights_init(module, std_init_range))
self.loss_fn = SmoothedCrossEntropyLoss(
pad_id=self.decoder_tokenizer.pad_id,
label_smoothing=cfg.label_smoothing)
self.eval_loss = GlobalAverageLossMetric(dist_sync_on_step=False,
take_avg_loss=True)
def filter_predicted_ids(self, ids):
ids[ids >=
self.decoder_tokenizer.vocab_size] = self.decoder_tokenizer.unk_id
return ids
@typecheck()
def forward(self, src, src_mask, tgt, tgt_mask):
src_hiddens = self.encoder(src, src_mask)
tgt_hiddens = self.decoder(tgt, tgt_mask, src_hiddens, src_mask)
log_probs = self.log_softmax(hidden_states=tgt_hiddens)
return log_probs
def training_step(self, batch, batch_idx):
"""
Lightning calls this inside the training loop with the data from the training dataloader
passed in as `batch`.
"""
# forward pass
for i in range(len(batch)):
if batch[i].ndim == 3:
# Dataset returns already batched data and the first dimension of size 1 added by DataLoader
# is excess.
batch[i] = batch[i].squeeze(dim=0)
src_ids, src_mask, tgt_ids, tgt_mask, labels = batch
log_probs = self(src_ids, src_mask, tgt_ids, tgt_mask)
train_loss = self.loss_fn(log_probs=log_probs, labels=labels)
tensorboard_logs = {
'train_loss': train_loss,
'lr': self._optimizer.param_groups[0]['lr'],
}
return {'loss': train_loss, 'log': tensorboard_logs}
def eval_step(self, batch, batch_idx, mode):
for i in range(len(batch)):
if batch[i].ndim == 3:
# Dataset returns already batched data and the first dimension of size 1 added by DataLoader
# is excess.
batch[i] = batch[i].squeeze(dim=0)
src_ids, src_mask, tgt_ids, tgt_mask, labels = batch
log_probs = self(src_ids, src_mask, tgt_ids, tgt_mask)
# this will run encoder twice -- TODO: potentially fix
_, translations = self.batch_translate(src=src_ids, src_mask=src_mask)
eval_loss = self.loss_fn(log_probs=log_probs, labels=labels)
self.eval_loss(loss=eval_loss,
num_measurements=log_probs.shape[0] *
log_probs.shape[1])
np_tgt = tgt_ids.cpu().numpy()
ground_truths = [
self.decoder_tokenizer.ids_to_text(tgt) for tgt in np_tgt
]
ground_truths = [
self.target_processor.detokenize(tgt.split(' '))
for tgt in ground_truths
]
num_non_pad_tokens = np.not_equal(
np_tgt, self.decoder_tokenizer.pad_id).sum().item()
return {
'translations': translations,
'ground_truths': ground_truths,
'num_non_pad_tokens': num_non_pad_tokens,
}
def test_step(self, batch, batch_idx):
return self.eval_step(batch, batch_idx, 'test')
@rank_zero_only
def log_param_stats(self):
for name, p in self.named_parameters():
if p.requires_grad:
self.trainer.logger.experiment.add_histogram(
name + '_hist', p, global_step=self.global_step)
self.trainer.logger.experiment.add_scalars(
name,
{
'mean': p.mean(),
'stddev': p.std(),
'max': p.max(),
'min': p.min()
},
global_step=self.global_step,
)
def validation_step(self, batch, batch_idx):
"""
Lightning calls this inside the validation loop with the data from the validation dataloader
passed in as `batch`.
"""
return self.eval_step(batch, batch_idx, 'val')
def eval_epoch_end(self, outputs, mode):
eval_loss = self.eval_loss.compute()
translations = list(
itertools.chain(*[x['translations'] for x in outputs]))
ground_truths = list(
itertools.chain(*[x['ground_truths'] for x in outputs]))
assert len(translations) == len(ground_truths)
if self.tgt_language in ['ja']:
sacre_bleu = corpus_bleu(translations, [ground_truths],
tokenize="ja-mecab")
elif self.tgt_language in ['zh']:
sacre_bleu = corpus_bleu(translations, [ground_truths],
tokenize="zh")
else:
sacre_bleu = corpus_bleu(translations, [ground_truths],
tokenize="13a")
dataset_name = "Validation" if mode == 'val' else "Test"
logging.info(f"\n\n\n\n{dataset_name} set size: {len(translations)}")
logging.info(f"{dataset_name} Sacre BLEU = {sacre_bleu.score}")
logging.info(f"{dataset_name} TRANSLATION EXAMPLES:".upper())
for i in range(0, 3):
ind = random.randint(0, len(translations) - 1)
logging.info(" " + '\u0332'.join(f"EXAMPLE {i}:"))
logging.info(f" Prediction: {translations[ind]}")
logging.info(f" Ground Truth: {ground_truths[ind]}")
ans = {
f"{mode}_loss": eval_loss,
f"{mode}_sacreBLEU": sacre_bleu.score
}
ans['log'] = dict(ans)
return ans
def validation_epoch_end(self, outputs):
"""
Called at the end of validation to aggregate outputs.
:param outputs: list of individual outputs of each validation step.
"""
self.log_dict(self.eval_epoch_end(outputs, 'val'))
def test_epoch_end(self, outputs):
return self.eval_epoch_end(outputs, 'test')
def setup_training_data(self, train_data_config: Optional[DictConfig]):
self._train_dl = self._setup_dataloader_from_config(
cfg=train_data_config)
def setup_validation_data(self, val_data_config: Optional[DictConfig]):
self._validation_dl = self._setup_dataloader_from_config(
cfg=val_data_config)
def setup_test_data(self, test_data_config: Optional[DictConfig]):
self._test_dl = self._setup_dataloader_from_config(
cfg=test_data_config)
def _setup_dataloader_from_config(self, cfg: DictConfig):
if cfg.get("load_from_cached_dataset", False):
logging.info('Loading from cached dataset %s' %
(cfg.src_file_name))
if cfg.src_file_name != cfg.tgt_file_name:
raise ValueError(
"src must be equal to target for cached dataset")
dataset = pickle.load(open(cfg.src_file_name, 'rb'))
dataset.reverse_lang_direction = cfg.get("reverse_lang_direction",
False)
elif cfg.get("use_tarred_dataset", False):
if cfg.get('tar_files') is None:
raise FileNotFoundError("Could not find tarred dataset.")
logging.info(f'Loading from tarred dataset {cfg.get("tar_files")}')
if cfg.get("metadata_file", None) is None:
raise FileNotFoundError(
"Could not find metadata path in config")
dataset = TarredTranslationDataset(
text_tar_filepaths=cfg.tar_files,
metadata_path=cfg.metadata_file,
encoder_tokenizer=self.encoder_tokenizer,
decoder_tokenizer=self.decoder_tokenizer,
shuffle_n=cfg.get("tar_shuffle_n", 100),
shard_strategy=cfg.get("shard_strategy", "scatter"),
global_rank=self.global_rank,
world_size=self.world_size,
reverse_lang_direction=cfg.get("reverse_lang_direction",
False),
)
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=1,
num_workers=cfg.get("num_workers", 2),
pin_memory=cfg.get("pin_memory", False),
drop_last=cfg.get("drop_last", False),
)
else:
dataset = TranslationDataset(
dataset_src=str(Path(cfg.src_file_name).expanduser()),
dataset_tgt=str(Path(cfg.tgt_file_name).expanduser()),
tokens_in_batch=cfg.tokens_in_batch,
clean=cfg.get("clean", False),
max_seq_length=cfg.get("max_seq_length", 512),
min_seq_length=cfg.get("min_seq_length", 1),
max_seq_length_diff=cfg.get("max_seq_length_diff", 512),
max_seq_length_ratio=cfg.get("max_seq_length_ratio", 512),
cache_ids=cfg.get("cache_ids", False),
cache_data_per_node=cfg.get("cache_data_per_node", False),
use_cache=cfg.get("use_cache", False),
reverse_lang_direction=cfg.get("reverse_lang_direction",
False),
)
dataset.batchify(self.encoder_tokenizer, self.decoder_tokenizer)
if cfg.shuffle:
sampler = pt_data.RandomSampler(dataset)
else:
sampler = pt_data.SequentialSampler(dataset)
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=1,
sampler=sampler,
num_workers=cfg.get("num_workers", 2),
pin_memory=cfg.get("pin_memory", False),
drop_last=cfg.get("drop_last", False),
)
def setup_pre_and_post_processing_utils(self, source_lang, target_lang):
"""
Creates source and target processor objects for input and output pre/post-processing.
"""
self.source_processor, self.target_processor = None, None
if (source_lang == 'en'
and target_lang == 'ja') or (source_lang == 'ja'
and target_lang == 'en'):
self.source_processor = EnJaProcessor(source_lang)
self.target_processor = EnJaProcessor(target_lang)
else:
if source_lang == 'zh':
self.source_processor = ChineseProcessor()
if target_lang == 'zh':
self.target_processor = ChineseProcessor()
if source_lang is not None and source_lang not in ['ja', 'zh']:
self.source_processor = MosesProcessor(source_lang)
if target_lang is not None and target_lang not in ['ja', 'zh']:
self.target_processor = MosesProcessor(target_lang)
@torch.no_grad()
def batch_translate(
self,
src: torch.LongTensor,
src_mask: torch.LongTensor,
):
"""
Translates a minibatch of inputs from source language to target language.
Args:
src: minibatch of inputs in the src language (batch x seq_len)
src_mask: mask tensor indicating elements to be ignored (batch x seq_len)
Returns:
translations: a list strings containing detokenized translations
inputs: a list of string containing detokenized inputs
"""
mode = self.training
try:
self.eval()
src_hiddens = self.encoder(input_ids=src, encoder_mask=src_mask)
beam_results = self.beam_search(encoder_hidden_states=src_hiddens,
encoder_input_mask=src_mask)
beam_results = self.filter_predicted_ids(beam_results)
translations = [
self.decoder_tokenizer.ids_to_text(tr)
for tr in beam_results.cpu().numpy()
]
inputs = [
self.encoder_tokenizer.ids_to_text(inp)
for inp in src.cpu().numpy()
]
if self.target_processor is not None:
translations = [
self.target_processor.detokenize(translation.split(' '))
for translation in translations
]
if self.source_processor is not None:
inputs = [
self.source_processor.detokenize(item.split(' '))
for item in inputs
]
finally:
self.train(mode=mode)
return inputs, translations
# TODO: We should drop source/target_lang arguments in favor of using self.src/tgt_language
@torch.no_grad()
def translate(self,
text: List[str],
source_lang: str = None,
target_lang: str = None) -> List[str]:
"""
Translates list of sentences from source language to target language.
Should be regular text, this method performs its own tokenization/de-tokenization
Args:
text: list of strings to translate
source_lang: if not None, corresponding MosesTokenizer and MosesPunctNormalizer will be run
target_lang: if not None, corresponding MosesDecokenizer will be run
Returns:
list of translated strings
"""
# __TODO__: This will reset both source and target processors even if you want to reset just one.
if source_lang is not None or target_lang is not None:
self.setup_pre_and_post_processing_utils(source_lang, target_lang)
mode = self.training
try:
self.eval()
inputs = []
for txt in text:
if self.source_processor is not None:
txt = self.source_processor.normalize(txt)
txt = self.source_processor.tokenize(txt)
ids = self.encoder_tokenizer.text_to_ids(txt)
ids = [self.encoder_tokenizer.bos_id
] + ids + [self.encoder_tokenizer.eos_id]
inputs.append(ids)
max_len = max(len(txt) for txt in inputs)
src_ids_ = np.ones(
(len(inputs), max_len)) * self.encoder_tokenizer.pad_id
for i, txt in enumerate(inputs):
src_ids_[i][:len(txt)] = txt
src_mask = torch.FloatTensor(
(src_ids_ != self.encoder_tokenizer.pad_id)).to(self.device)
src = torch.LongTensor(src_ids_).to(self.device)
_, translations = self.batch_translate(src, src_mask)
finally:
self.train(mode=mode)
return translations
@classmethod
def list_available_models(cls) -> Optional[Dict[str, str]]:
pass
class MTEncDecModel(EncDecNLPModel):
"""
Encoder-decoder machine translation model.
"""
def __init__(self, cfg: MTEncDecModelConfig, trainer: Trainer = None):
cfg = model_utils.convert_model_config_to_dict_config(cfg)
# Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable
# Global_rank and local_rank is set by LightningModule in Lightning 1.2.0
self.world_size = 1
if trainer is not None:
self.world_size = trainer.num_nodes * trainer.num_gpus
cfg = model_utils.maybe_update_config_version(cfg)
self.src_language = cfg.get("src_language", None)
self.tgt_language = cfg.get("tgt_language", None)
self.multilingual = cfg.get("multilingual", False)
self.multilingual_ids = []
# Instantiates tokenizers and register to be saved with NeMo Model archive
# After this call, ther will be self.encoder_tokenizer and self.decoder_tokenizer
# Which can convert between tokens and token_ids for SRC and TGT languages correspondingly.
self.setup_enc_dec_tokenizers(
encoder_tokenizer_library=cfg.encoder_tokenizer.get('library', 'yttm'),
encoder_tokenizer_model=cfg.encoder_tokenizer.get('tokenizer_model'),
encoder_bpe_dropout=cfg.encoder_tokenizer.get('bpe_dropout', 0.0)
if cfg.encoder_tokenizer.get('bpe_dropout', 0.0) is not None
else 0.0,
encoder_model_name=cfg.encoder.get('model_name') if hasattr(cfg.encoder, 'model_name') else None,
decoder_tokenizer_library=cfg.decoder_tokenizer.get('library', 'yttm'),
decoder_tokenizer_model=cfg.decoder_tokenizer.tokenizer_model,
decoder_bpe_dropout=cfg.decoder_tokenizer.get('bpe_dropout', 0.0)
if cfg.decoder_tokenizer.get('bpe_dropout', 0.0) is not None
else 0.0,
decoder_model_name=cfg.decoder.get('model_name') if hasattr(cfg.decoder, 'model_name') else None,
)
if self.multilingual:
if isinstance(self.src_language, ListConfig) and isinstance(self.tgt_language, ListConfig):
raise ValueError(
"cfg.src_language and cfg.tgt_language cannot both be lists. We only support many-to-one or one-to-many multilingual models."
)
elif isinstance(self.src_language, ListConfig):
for lng in self.src_language:
self.multilingual_ids.append(self.encoder_tokenizer.token_to_id("<" + lng + ">"))
elif isinstance(self.tgt_language, ListConfig):
for lng in self.tgt_language:
self.multilingual_ids.append(self.encoder_tokenizer.token_to_id("<" + lng + ">"))
else:
raise ValueError(
"Expect either cfg.src_language or cfg.tgt_language to be a list when multilingual=True."
)
if isinstance(self.src_language, ListConfig):
self.tgt_language = [self.tgt_language] * len(self.src_language)
else:
self.src_language = [self.src_language] * len(self.tgt_language)
self.source_processor_list = []
self.target_processor_list = []
for src_lng, tgt_lng in zip(self.src_language, self.tgt_language):
src_prcsr, tgt_prscr = self.setup_pre_and_post_processing_utils(
source_lang=src_lng, target_lang=tgt_lng
)
self.source_processor_list.append(src_prcsr)
self.target_processor_list.append(tgt_prscr)
else:
# After this call, the model will have self.source_processor and self.target_processor objects
self.setup_pre_and_post_processing_utils(source_lang=self.src_language, target_lang=self.tgt_language)
self.multilingual_ids = [None]
# TODO: Why is this base constructor call so late in the game?
super().__init__(cfg=cfg, trainer=trainer)
# encoder from NeMo, Megatron-LM, or HuggingFace
encoder_cfg_dict = OmegaConf.to_container(cfg.get('encoder'))
encoder_cfg_dict['vocab_size'] = self.encoder_vocab_size
library = encoder_cfg_dict.pop('library', 'nemo')
model_name = encoder_cfg_dict.pop('model_name', None)
pretrained = encoder_cfg_dict.pop('pretrained', False)
checkpoint_file = encoder_cfg_dict.pop('checkpoint_file', None)
self.encoder = get_transformer(
library=library,
model_name=model_name,
pretrained=pretrained,
config_dict=encoder_cfg_dict,
encoder=True,
pre_ln_final_layer_norm=encoder_cfg_dict.get('pre_ln_final_layer_norm', False),
checkpoint_file=checkpoint_file,
)
# decoder from NeMo, Megatron-LM, or HuggingFace
decoder_cfg_dict = OmegaConf.to_container(cfg.get('decoder'))
decoder_cfg_dict['vocab_size'] = self.decoder_vocab_size
library = decoder_cfg_dict.pop('library', 'nemo')
model_name = decoder_cfg_dict.pop('model_name', None)
pretrained = decoder_cfg_dict.pop('pretrained', False)
decoder_cfg_dict['hidden_size'] = self.encoder.hidden_size
self.decoder = get_transformer(
library=library,
model_name=model_name,
pretrained=pretrained,
config_dict=decoder_cfg_dict,
encoder=False,
pre_ln_final_layer_norm=decoder_cfg_dict.get('pre_ln_final_layer_norm', False),
)
self.log_softmax = TokenClassifier(
hidden_size=self.decoder.hidden_size,
num_classes=self.decoder_vocab_size,
activation=cfg.head.activation,
log_softmax=cfg.head.log_softmax,
dropout=cfg.head.dropout,
use_transformer_init=cfg.head.use_transformer_init,
)
self.beam_search = BeamSearchSequenceGenerator(
embedding=self.decoder.embedding,
decoder=self.decoder.decoder,
log_softmax=self.log_softmax,
max_sequence_length=self.decoder.max_sequence_length,
beam_size=cfg.beam_size,
bos=self.decoder_tokenizer.bos_id,
pad=self.decoder_tokenizer.pad_id,
eos=self.decoder_tokenizer.eos_id,
len_pen=cfg.len_pen,
max_delta_length=cfg.max_generation_delta,
)
# tie weights of embedding and softmax matrices
self.log_softmax.mlp.layer0.weight = self.decoder.embedding.token_embedding.weight
# TODO: encoder and decoder with different hidden size?
std_init_range = 1 / self.encoder.hidden_size ** 0.5
# initialize weights if not using pretrained encoder/decoder
if not self._cfg.encoder.get('pretrained', False):
self.encoder.apply(lambda module: transformer_weights_init(module, std_init_range))
if not self._cfg.decoder.get('pretrained', False):
self.decoder.apply(lambda module: transformer_weights_init(module, std_init_range))
self.log_softmax.apply(lambda module: transformer_weights_init(module, std_init_range))
self.loss_fn = SmoothedCrossEntropyLoss(
pad_id=self.decoder_tokenizer.pad_id, label_smoothing=cfg.label_smoothing
)
self.eval_loss_fn = NLLLoss(ignore_index=self.decoder_tokenizer.pad_id)
def filter_predicted_ids(self, ids):
ids[ids >= self.decoder_tokenizer.vocab_size] = self.decoder_tokenizer.unk_id
return ids
@typecheck()
def forward(self, src, src_mask, tgt, tgt_mask):
src_hiddens = self.encoder(input_ids=src, encoder_mask=src_mask)
tgt_hiddens = self.decoder(
input_ids=tgt, decoder_mask=tgt_mask, encoder_embeddings=src_hiddens, encoder_mask=src_mask
)
log_probs = self.log_softmax(hidden_states=tgt_hiddens)
return log_probs
def training_step(self, batch, batch_idx):
"""
Lightning calls this inside the training loop with the data from the training dataloader
passed in as `batch`.
"""
# forward pass
for i in range(len(batch)):
if batch[i].ndim == 3:
# Dataset returns already batched data and the first dimension of size 1 added by DataLoader
# is excess.
batch[i] = batch[i].squeeze(dim=0)
src_ids, src_mask, tgt_ids, tgt_mask, labels = batch
log_probs = self(src_ids, src_mask, tgt_ids, tgt_mask)
train_loss = self.loss_fn(log_probs=log_probs, labels=labels)
tensorboard_logs = {
'train_loss': train_loss,
'lr': self._optimizer.param_groups[0]['lr'],
}
return {'loss': train_loss, 'log': tensorboard_logs}
def eval_step(self, batch, batch_idx, mode, dataloader_idx=0):
for i in range(len(batch)):
if batch[i].ndim == 3:
# Dataset returns already batched data and the first dimension of size 1 added by DataLoader
# is excess.
batch[i] = batch[i].squeeze(dim=0)
if self.multilingual:
self.source_processor = self.source_processor_list[dataloader_idx]
self.target_processor = self.target_processor_list[dataloader_idx]
src_ids, src_mask, tgt_ids, tgt_mask, labels = batch
log_probs = self(src_ids, src_mask, tgt_ids, tgt_mask)
eval_loss = self.eval_loss_fn(log_probs=log_probs, labels=labels)
# this will run encoder twice -- TODO: potentially fix
_, translations = self.batch_translate(src=src_ids, src_mask=src_mask)
if dataloader_idx == 0:
getattr(self, f'{mode}_loss')(loss=eval_loss, num_measurements=log_probs.shape[0] * log_probs.shape[1])
else:
getattr(self, f'{mode}_loss_{dataloader_idx}')(
loss=eval_loss, num_measurements=log_probs.shape[0] * log_probs.shape[1]
)
np_tgt = tgt_ids.detach().cpu().numpy()
ground_truths = [self.decoder_tokenizer.ids_to_text(tgt) for tgt in np_tgt]
ground_truths = [self.target_processor.detokenize(tgt.split(' ')) for tgt in ground_truths]
num_non_pad_tokens = np.not_equal(np_tgt, self.decoder_tokenizer.pad_id).sum().item()
return {
'translations': translations,
'ground_truths': ground_truths,
'num_non_pad_tokens': num_non_pad_tokens,
}
def test_step(self, batch, batch_idx, dataloader_idx=0):
return self.eval_step(batch, batch_idx, 'test', dataloader_idx)
@rank_zero_only
def log_param_stats(self):
for name, p in self.named_parameters():
if p.requires_grad:
self.trainer.logger.experiment.add_histogram(name + '_hist', p, global_step=self.global_step)
self.trainer.logger.experiment.add_scalars(
name,
{'mean': p.mean(), 'stddev': p.std(), 'max': p.max(), 'min': p.min()},
global_step=self.global_step,
)
def validation_step(self, batch, batch_idx, dataloader_idx=0):
"""
Lightning calls this inside the validation loop with the data from the validation dataloader
passed in as `batch`.
"""
return self.eval_step(batch, batch_idx, 'val', dataloader_idx)
def eval_epoch_end(self, outputs, mode):
# if user specifies one validation dataloader, then PTL reverts to giving a list of dictionary instead of a list of list of dictionary
if isinstance(outputs[0], dict):
outputs = [outputs]
loss_list = []
sb_score_list = []
for dataloader_idx, output in enumerate(outputs):
if dataloader_idx == 0:
eval_loss = getattr(self, f'{mode}_loss').compute()
else:
eval_loss = getattr(self, f'{mode}_loss_{dataloader_idx}').compute()
translations = list(itertools.chain(*[x['translations'] for x in output]))
ground_truths = list(itertools.chain(*[x['ground_truths'] for x in output]))
assert len(translations) == len(ground_truths)
# Gather translations and ground truths from all workers
tr_and_gt = [None for _ in range(self.world_size)]
# we also need to drop pairs where ground truth is an empty string
dist.all_gather_object(
tr_and_gt, [(t, g) for (t, g) in zip(translations, ground_truths) if g.strip() != '']
)
if self.global_rank == 0:
_translations = []
_ground_truths = []
for rank in range(0, self.world_size):
_translations += [t for (t, g) in tr_and_gt[rank]]
_ground_truths += [g for (t, g) in tr_and_gt[rank]]
if self.tgt_language in ['ja']:
sacre_bleu = corpus_bleu(_translations, [_ground_truths], tokenize="ja-mecab")
elif self.tgt_language in ['zh']:
sacre_bleu = corpus_bleu(_translations, [_ground_truths], tokenize="zh")
else:
sacre_bleu = corpus_bleu(_translations, [_ground_truths], tokenize="13a")
# because the reduction op later is average (over word_size)
sb_score = sacre_bleu.score * self.world_size
dataset_name = "Validation" if mode == 'val' else "Test"
logging.info(
f"Dataset name: {dataset_name}, Dataloader index: {dataloader_idx}, Set size: {len(translations)}"
)
logging.info(
f"Dataset name: {dataset_name}, Dataloader index: {dataloader_idx}, Val Loss = {eval_loss}"
)
logging.info(
f"Dataset name: {dataset_name}, Dataloader index: {dataloader_idx}, Sacre BLEU = {sb_score / self.world_size}"
)
logging.info(
f"Dataset name: {dataset_name}, Dataloader index: {dataloader_idx}, Translation Examples:"
)
for i in range(0, 3):
ind = random.randint(0, len(translations) - 1)
logging.info(" " + '\u0332'.join(f"Example {i}:"))
logging.info(f" Prediction: {translations[ind]}")
logging.info(f" Ground Truth: {ground_truths[ind]}")
else:
sb_score = 0.0
loss_list.append(eval_loss.cpu().numpy())
sb_score_list.append(sb_score)
if dataloader_idx == 0:
self.log(f"{mode}_loss", eval_loss, sync_dist=True)
self.log(f"{mode}_sacreBLEU", sb_score, sync_dist=True)
getattr(self, f'{mode}_loss').reset()
else:
self.log(f"{mode}_loss_dl_index_{dataloader_idx}", eval_loss, sync_dist=True)
self.log(f"{mode}_sacreBLEU_dl_index_{dataloader_idx}", sb_score, sync_dist=True)
getattr(self, f'{mode}_loss_{dataloader_idx}').reset()
if len(loss_list) > 1:
self.log(f"{mode}_loss_avg", np.mean(loss_list), sync_dist=True)
self.log(f"{mode}_sacreBLEU_avg", np.mean(sb_score_list), sync_dist=True)
def validation_epoch_end(self, outputs):
"""
Called at the end of validation to aggregate outputs.
:param outputs: list of individual outputs of each validation step.
"""
self.eval_epoch_end(outputs, 'val')
def test_epoch_end(self, outputs):
self.eval_epoch_end(outputs, 'test')
def setup_enc_dec_tokenizers(
self,
encoder_tokenizer_library=None,
encoder_tokenizer_model=None,
encoder_bpe_dropout=0.0,
encoder_model_name=None,
decoder_tokenizer_library=None,
decoder_tokenizer_model=None,
decoder_bpe_dropout=0.0,
decoder_model_name=None,
):
supported_tokenizers = ['yttm', 'huggingface', 'sentencepiece', 'megatron']
if (
encoder_tokenizer_library not in supported_tokenizers
or decoder_tokenizer_library not in supported_tokenizers
):
raise NotImplementedError(f"Currently we only support tokenizers in {supported_tokenizers}.")
self.encoder_tokenizer = get_nmt_tokenizer(
library=encoder_tokenizer_library,
tokenizer_model=self.register_artifact("encoder_tokenizer.tokenizer_model", encoder_tokenizer_model),
bpe_dropout=encoder_bpe_dropout,
model_name=encoder_model_name,
vocab_file=None,
special_tokens=None,
use_fast=False,
)
self.decoder_tokenizer = get_nmt_tokenizer(
library=decoder_tokenizer_library,
tokenizer_model=self.register_artifact("decoder_tokenizer.tokenizer_model", decoder_tokenizer_model),
bpe_dropout=decoder_bpe_dropout,
model_name=decoder_model_name,
vocab_file=None,
special_tokens=None,
use_fast=False,
)
def setup_training_data(self, train_data_config: Optional[DictConfig]):
self._train_dl = self._setup_dataloader_from_config(cfg=train_data_config)
def setup_multiple_validation_data(self, val_data_config: Union[DictConfig, Dict]):
self.setup_validation_data(self._cfg.get('validation_ds'))
def setup_multiple_test_data(self, test_data_config: Union[DictConfig, Dict]):
self.setup_test_data(self._cfg.get('test_ds'))
def setup_validation_data(self, val_data_config: Optional[DictConfig]):
self._validation_dl = self._setup_eval_dataloader_from_config(cfg=val_data_config)
# instantiate Torchmetric for each val dataloader
if self._validation_dl is not None:
for dataloader_idx in range(len(self._validation_dl)):
if dataloader_idx == 0:
setattr(
self, f'val_loss', GlobalAverageLossMetric(dist_sync_on_step=False, take_avg_loss=True),
)
else:
setattr(
self,
f'val_loss_{dataloader_idx}',
GlobalAverageLossMetric(dist_sync_on_step=False, take_avg_loss=True),
)
def setup_test_data(self, test_data_config: Optional[DictConfig]):
self._test_dl = self._setup_eval_dataloader_from_config(cfg=test_data_config)
# instantiate Torchmetric for each test dataloader
if self._test_dl is not None:
for dataloader_idx in range(len(self._test_dl)):
if dataloader_idx == 0:
setattr(
self, f'test_loss', GlobalAverageLossMetric(dist_sync_on_step=False, take_avg_loss=True),
)
else:
setattr(
self,
f'test_loss_{dataloader_idx}',
GlobalAverageLossMetric(dist_sync_on_step=False, take_avg_loss=True),
)
def _setup_dataloader_from_config(self, cfg: DictConfig):
if cfg.get("use_tarred_dataset", False):
if cfg.get("metadata_file") is None:
raise FileNotFoundError("Trying to use tarred data set but could not find metadata path in config.")
else:
if not self.multilingual:
metadata_file_list = [cfg.get('metadata_file')]
else:
metadata_file_list = cfg.get('metadata_file')
datasets = []
for idx, metadata_file in enumerate(metadata_file_list):
with open(metadata_file) as metadata_reader:
metadata = json.load(metadata_reader)
if cfg.get('tar_files') is None:
tar_files = metadata.get('tar_files')
if tar_files is not None:
logging.info(f'Loading from tarred dataset {tar_files}')
else:
raise FileNotFoundError("Could not find tarred dataset in config or metadata.")
else:
tar_files = cfg.get('tar_files')
if self.multilingual:
tar_files = tar_files[idx]
if metadata.get('tar_files') is not None:
logging.info(
f'Tar file paths found in both cfg and metadata using one in cfg by default - {tar_files}'
)
dataset = TarredTranslationDataset(
text_tar_filepaths=tar_files,
metadata_path=metadata_file,
encoder_tokenizer=self.encoder_tokenizer,
decoder_tokenizer=self.decoder_tokenizer,
shuffle_n=cfg.get("tar_shuffle_n", 100),
shard_strategy=cfg.get("shard_strategy", "scatter"),
global_rank=self.global_rank,
world_size=self.world_size,
reverse_lang_direction=cfg.get("reverse_lang_direction", False),
prepend_id=self.multilingual_ids[idx],
)
datasets.append(dataset)
if self.multilingual:
dataset = ConcatDataset(
datasets=datasets,
sampling_technique=cfg.get('concat_sampling_technique'),
sampling_temperature=cfg.get('concat_sampling_temperature'),
sampling_probabilities=cfg.get('concat_sampling_probabilities'),
global_rank=self.global_rank,
world_size=self.world_size,
)
else:
dataset = datasets[0]
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=1,
num_workers=cfg.get("num_workers", 2),
pin_memory=cfg.get("pin_memory", False),
drop_last=cfg.get("drop_last", False),
)
else:
if not self.multilingual:
src_file_list = [cfg.src_file_name]
tgt_file_list = [cfg.tgt_file_name]
else:
src_file_list = cfg.src_file_name
tgt_file_list = cfg.tgt_file_name
if len(src_file_list) != len(tgt_file_list):
raise ValueError(
'The same number of filepaths must be passed in for source and target while training multilingual.'
)
datasets = []
for idx, src_file in enumerate(src_file_list):
dataset = TranslationDataset(
dataset_src=str(Path(src_file).expanduser()),
dataset_tgt=str(Path(tgt_file_list[idx]).expanduser()),
tokens_in_batch=cfg.tokens_in_batch,
clean=cfg.get("clean", False),
max_seq_length=cfg.get("max_seq_length", 512),
min_seq_length=cfg.get("min_seq_length", 1),
max_seq_length_diff=cfg.get("max_seq_length_diff", 512),
max_seq_length_ratio=cfg.get("max_seq_length_ratio", 512),
cache_ids=cfg.get("cache_ids", False),
cache_data_per_node=cfg.get("cache_data_per_node", False),
use_cache=cfg.get("use_cache", False),
reverse_lang_direction=cfg.get("reverse_lang_direction", False),
prepend_id=self.multilingual_ids[idx],
)
dataset.batchify(self.encoder_tokenizer, self.decoder_tokenizer)
datasets.append(dataset)
if self.multilingual:
dataset = ConcatDataset(
datasets=datasets,
shuffle=cfg.get('shuffle'),
sampling_technique=cfg.get('concat_sampling_technique'),
sampling_temperature=cfg.get('concat_sampling_temperature'),
sampling_probabilities=cfg.get('concat_sampling_probabilities'),
global_rank=self.global_rank,
world_size=self.world_size,
)
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=1,
num_workers=cfg.get("num_workers", 2),
pin_memory=cfg.get("pin_memory", False),
drop_last=cfg.get("drop_last", False),
)
else:
dataset = datasets[0]
if cfg.shuffle:
sampler = pt_data.RandomSampler(dataset)
else:
sampler = pt_data.SequentialSampler(dataset)
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=1,
sampler=sampler,
num_workers=cfg.get("num_workers", 2),
pin_memory=cfg.get("pin_memory", False),
drop_last=cfg.get("drop_last", False),
)
def replace_beam_with_sampling(self, topk=500):
self.beam_search = TopKSequenceGenerator(
embedding=self.decoder.embedding,
decoder=self.decoder.decoder,
log_softmax=self.log_softmax,
max_sequence_length=self.beam_search.max_seq_length,
beam_size=topk,
bos=self.decoder_tokenizer.bos_id,
pad=self.decoder_tokenizer.pad_id,
eos=self.decoder_tokenizer.eos_id,
)
def _setup_eval_dataloader_from_config(self, cfg: DictConfig):
src_file_name = cfg.get('src_file_name')
tgt_file_name = cfg.get('tgt_file_name')
if src_file_name is None or tgt_file_name is None:
raise ValueError(
'Validation dataloader needs both cfg.src_file_name and cfg.tgt_file_name to not be None.'
)
else:
# convert src_file_name and tgt_file_name to list of strings
if isinstance(src_file_name, str):
src_file_list = [src_file_name]
elif isinstance(src_file_name, ListConfig):
src_file_list = src_file_name
else:
raise ValueError("cfg.src_file_name must be string or list of strings")
if isinstance(tgt_file_name, str):
tgt_file_list = [tgt_file_name]
elif isinstance(tgt_file_name, ListConfig):
tgt_file_list = tgt_file_name
else:
raise ValueError("cfg.tgt_file_name must be string or list of strings")
if len(src_file_list) != len(tgt_file_list):
raise ValueError('The same number of filepaths must be passed in for source and target validation.')
dataloaders = []
prepend_idx = 0
for idx, src_file in enumerate(src_file_list):
if self.multilingual:
prepend_idx = idx
dataset = TranslationDataset(
dataset_src=str(Path(src_file).expanduser()),
dataset_tgt=str(Path(tgt_file_list[idx]).expanduser()),
tokens_in_batch=cfg.tokens_in_batch,
clean=cfg.get("clean", False),
max_seq_length=cfg.get("max_seq_length", 512),
min_seq_length=cfg.get("min_seq_length", 1),
max_seq_length_diff=cfg.get("max_seq_length_diff", 512),
max_seq_length_ratio=cfg.get("max_seq_length_ratio", 512),
cache_ids=cfg.get("cache_ids", False),
cache_data_per_node=cfg.get("cache_data_per_node", False),
use_cache=cfg.get("use_cache", False),
reverse_lang_direction=cfg.get("reverse_lang_direction", False),
prepend_id=self.multilingual_ids[prepend_idx],
)
dataset.batchify(self.encoder_tokenizer, self.decoder_tokenizer)
if cfg.shuffle:
sampler = pt_data.RandomSampler(dataset)
else:
sampler = pt_data.SequentialSampler(dataset)
dataloader = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=1,
sampler=sampler,
num_workers=cfg.get("num_workers", 2),
pin_memory=cfg.get("pin_memory", False),
drop_last=cfg.get("drop_last", False),
)
dataloaders.append(dataloader)
return dataloaders
def setup_pre_and_post_processing_utils(self, source_lang, target_lang):
"""
Creates source and target processor objects for input and output pre/post-processing.
"""
self.source_processor, self.target_processor = None, None
if (source_lang == 'en' and target_lang == 'ja') or (source_lang == 'ja' and target_lang == 'en'):
self.source_processor = EnJaProcessor(source_lang)
self.target_processor = EnJaProcessor(target_lang)
else:
if source_lang == 'zh':
self.source_processor = ChineseProcessor()
if target_lang == 'zh':
self.target_processor = ChineseProcessor()
if source_lang is not None and source_lang not in ['ja', 'zh']:
self.source_processor = MosesProcessor(source_lang)
if target_lang is not None and target_lang not in ['ja', 'zh']:
self.target_processor = MosesProcessor(target_lang)
return self.source_processor, self.target_processor
@torch.no_grad()
def batch_translate(
self, src: torch.LongTensor, src_mask: torch.LongTensor,
):
"""
Translates a minibatch of inputs from source language to target language.
Args:
src: minibatch of inputs in the src language (batch x seq_len)
src_mask: mask tensor indicating elements to be ignored (batch x seq_len)
Returns:
translations: a list strings containing detokenized translations
inputs: a list of string containing detokenized inputs
"""
mode = self.training
try:
self.eval()
src_hiddens = self.encoder(input_ids=src, encoder_mask=src_mask)
beam_results = self.beam_search(encoder_hidden_states=src_hiddens, encoder_input_mask=src_mask)
beam_results = self.filter_predicted_ids(beam_results)
translations = [self.decoder_tokenizer.ids_to_text(tr) for tr in beam_results.cpu().numpy()]
inputs = [self.encoder_tokenizer.ids_to_text(inp) for inp in src.cpu().numpy()]
if self.target_processor is not None:
translations = [
self.target_processor.detokenize(translation.split(' ')) for translation in translations
]
if self.source_processor is not None:
inputs = [self.source_processor.detokenize(item.split(' ')) for item in inputs]
finally:
self.train(mode=mode)
return inputs, translations
# TODO: We should drop source/target_lang arguments in favor of using self.src/tgt_language
@torch.no_grad()
def translate(self, text: List[str], source_lang: str = None, target_lang: str = None) -> List[str]:
"""
Translates list of sentences from source language to target language.
Should be regular text, this method performs its own tokenization/de-tokenization
Args:
text: list of strings to translate
source_lang: if not None, corresponding MosesTokenizer and MosesPunctNormalizer will be run
target_lang: if not None, corresponding MosesDecokenizer will be run
Returns:
list of translated strings
"""
# __TODO__: This will reset both source and target processors even if you want to reset just one.
if source_lang is not None or target_lang is not None:
self.setup_pre_and_post_processing_utils(source_lang, target_lang)
mode = self.training
prepend_ids = []
if self.multilingual:
if source_lang is None or target_lang is None:
raise ValueError("Expect source_lang and target_lang to infer for multilingual model.")
src_symbol = self.encoder_tokenizer.token_to_id('<' + source_lang + '>')
tgt_symbol = self.encoder_tokenizer.token_to_id('<' + target_lang + '>')
prepend_ids = [src_symbol if src_symbol in self.multilingual_ids else tgt_symbol]
try:
self.eval()
inputs = []
for txt in text:
if self.source_processor is not None:
txt = self.source_processor.normalize(txt)
txt = self.source_processor.tokenize(txt)
ids = self.encoder_tokenizer.text_to_ids(txt)
ids = prepend_ids + [self.encoder_tokenizer.bos_id] + ids + [self.encoder_tokenizer.eos_id]
inputs.append(ids)
max_len = max(len(txt) for txt in inputs)
src_ids_ = np.ones((len(inputs), max_len)) * self.encoder_tokenizer.pad_id
for i, txt in enumerate(inputs):
src_ids_[i][: len(txt)] = txt
src_mask = torch.FloatTensor((src_ids_ != self.encoder_tokenizer.pad_id)).to(self.device)
src = torch.LongTensor(src_ids_).to(self.device)
_, translations = self.batch_translate(src, src_mask)
finally:
self.train(mode=mode)
return translations
@classmethod
def list_available_models(cls) -> Optional[Dict[str, str]]:
"""
This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
Returns:
List of available pre-trained models.
"""
result = []
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_de_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_de_transformer12x2/versions/1.0.0rc1/files/nmt_en_de_transformer12x2.nemo",
description="En->De translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_de_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_de_en_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_de_en_transformer12x2/versions/1.0.0rc1/files/nmt_de_en_transformer12x2.nemo",
description="De->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_de_en_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_es_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_es_transformer12x2/versions/1.0.0rc1/files/nmt_en_es_transformer12x2.nemo",
description="En->Es translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_es_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_es_en_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_es_en_transformer12x2/versions/1.0.0rc1/files/nmt_es_en_transformer12x2.nemo",
description="Es->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_es_en_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_fr_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_fr_transformer12x2/versions/1.0.0rc1/files/nmt_en_fr_transformer12x2.nemo",
description="En->Fr translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_fr_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_fr_en_transformer12x2",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_fr_en_transformer12x2/versions/1.0.0rc1/files/nmt_fr_en_transformer12x2.nemo",
description="Fr->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_fr_en_transformer12x2",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_ru_transformer6x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_ru_transformer6x6/versions/1.0.0rc1/files/nmt_en_ru_transformer6x6.nemo",
description="En->Ru translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_ru_transformer6x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_ru_en_transformer6x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_ru_en_transformer6x6/versions/1.0.0rc1/files/nmt_ru_en_transformer6x6.nemo",
description="Ru->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_ru_en_transformer6x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_zh_en_transformer6x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_zh_en_transformer6x6/versions/1.0.0rc1/files/nmt_zh_en_transformer6x6.nemo",
description="Zh->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_zh_en_transformer6x6",
)
result.append(model)
model = PretrainedModelInfo(
pretrained_model_name="nmt_en_zh_transformer6x6",
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_zh_transformer6x6/versions/1.0.0rc1/files/nmt_en_zh_transformer6x6.nemo",
description="En->Zh translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_zh_transformer6x6",
)
result.append(model)
return result