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 __init__(self, cfg: DictConfig, trainer: Trainer = None):
# Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable
self.world_size = 1
if trainer is not None:
self.world_size = trainer.num_nodes * trainer.num_gpus
cfg = model_utils.convert_model_config_to_dict_config(cfg)
cfg = model_utils.maybe_update_config_version(cfg)
# Instantiates tokenizer and register to be saved with NeMo Model archive
# After this call, ther will be self.tokenizer which can convert between tokens and token_ids.
self.setup_tokenizer(
tokenizer_name=cfg.tokenizer.get("tokenizer_name", "yttm"),
tokenizer_model=cfg.tokenizer.get("tokenizer_model", None),
vocab_file=cfg.tokenizer.get("vocab_file", None),
bpe_dropout=cfg.tokenizer.get("bpe_dropout", 0.0),
special_tokens=cfg.tokenizer.get("special_tokens", {}))
# init superclass
super().__init__(cfg=cfg, trainer=trainer)
# make vocabulary size divisible by 8 for fast fp16 training
vocab_size = 8 * math.ceil(self.tokenizer.vocab_size / 8)
# encoder from NeMo, Megatron-LM, or HuggingFace
encoder_cfg_dict = OmegaConf.to_container(cfg.get('encoder'))
encoder_cfg_dict['vocab_size'] = 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)
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',
encoder_cfg_dict.get('pre_ln', True)),
)
self.log_softmax = TokenClassifier(
hidden_size=self.encoder.hidden_size,
num_classes=vocab_size,
activation=cfg.head.activation,
log_softmax=cfg.head.log_softmax,
dropout=cfg.head.dropout,
use_transformer_init=cfg.head.use_transformer_init,
)
# tie weights of embedding and softmax matrices
self.log_softmax.mlp.layer0.weight = self.encoder.embedding.token_embedding.weight
std_init_range = 1 / self.encoder.hidden_size**0.5
# initialize weights if not using pretrained encoder
if not self._cfg.encoder.get('pretrained', False):
self.encoder.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.tokenizer.pad_id, label_smoothing=cfg.label_smoothing)
self.eval_loss_fn = SmoothedCrossEntropyLoss(
pad_id=self.tokenizer.pad_id)
self.eval_loss = GlobalAverageLossMetric(dist_sync_on_step=False,
take_avg_loss=True)
self.eval_ppl = SequencePerplexity()
class TransformerLMModel(ModelPT):
"""
Left-to-right Transformer language model.
"""
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
# Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable
self.world_size = 1
if trainer is not None:
self.world_size = trainer.num_nodes * trainer.num_gpus
cfg = model_utils.convert_model_config_to_dict_config(cfg)
cfg = model_utils.maybe_update_config_version(cfg)
# Instantiates tokenizer and register to be saved with NeMo Model archive
# After this call, ther will be self.tokenizer which can convert between tokens and token_ids.
self.setup_tokenizer(
tokenizer_name=cfg.tokenizer.get("tokenizer_name", "yttm"),
tokenizer_model=cfg.tokenizer.get("tokenizer_model", None),
vocab_file=cfg.tokenizer.get("vocab_file", None),
bpe_dropout=cfg.tokenizer.get("bpe_dropout", 0.0),
special_tokens=cfg.tokenizer.get("special_tokens", {}))
# init superclass
super().__init__(cfg=cfg, trainer=trainer)
# make vocabulary size divisible by 8 for fast fp16 training
vocab_size = 8 * math.ceil(self.tokenizer.vocab_size / 8)
# encoder from NeMo, Megatron-LM, or HuggingFace
encoder_cfg_dict = OmegaConf.to_container(cfg.get('encoder'))
encoder_cfg_dict['vocab_size'] = 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)
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',
encoder_cfg_dict.get('pre_ln', True)),
)
self.log_softmax = TokenClassifier(
hidden_size=self.encoder.hidden_size,
num_classes=vocab_size,
activation=cfg.head.activation,
log_softmax=cfg.head.log_softmax,
dropout=cfg.head.dropout,
use_transformer_init=cfg.head.use_transformer_init,
)
# tie weights of embedding and softmax matrices
self.log_softmax.mlp.layer0.weight = self.encoder.embedding.token_embedding.weight
std_init_range = 1 / self.encoder.hidden_size**0.5
# initialize weights if not using pretrained encoder
if not self._cfg.encoder.get('pretrained', False):
self.encoder.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.tokenizer.pad_id, label_smoothing=cfg.label_smoothing)
self.eval_loss_fn = SmoothedCrossEntropyLoss(
pad_id=self.tokenizer.pad_id)
self.eval_loss = GlobalAverageLossMetric(dist_sync_on_step=False,
take_avg_loss=True)
self.eval_ppl = SequencePerplexity()
@typecheck()
def forward(self, input_ids, attention_mask):
"""
No special modification required for Lightning, define it as you normally would
in the `nn.Module` in vanilla PyTorch.
"""
hidden_states = self.encoder(input_ids=input_ids,
encoder_mask=attention_mask)
log_probs = self.log_softmax(hidden_states=hidden_states)
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)
ids, mask = batch
input_ids, labels = ids[:, :-1], ids[:, 1:]
input_mask = mask[:, :-1]
log_probs = self(input_ids=input_ids, attention_mask=input_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):
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)
ids, mask = batch
input_ids, labels = ids[:, :-1], ids[:, 1:]
input_mask, output_mask = mask[:, :-1], mask[:, 1:]
log_probs = self(input_ids=input_ids, attention_mask=input_mask)
eval_loss = self.eval_loss_fn(log_probs=log_probs, labels=labels)
self.eval_loss(loss=eval_loss,
num_measurements=log_probs.shape[0] *
log_probs.shape[1])
self.eval_ppl(log_probs=log_probs, labels=labels, mask=output_mask)
return {}
def test_step(self, batch, batch_idx):
return self.eval_step(batch, batch_idx)
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)
def eval_epoch_end(self, outputs, mode):
eval_loss = self.eval_loss.compute()
eval_ppl = self.eval_ppl.compute()
self.log(f"{mode}_loss", eval_loss, sync_dist=True)
self.log(f"{mode}_PPL", eval_ppl, sync_dist=True)
dataset_name = "Validation" if mode == 'val' else "Test"
logging.info(
f"\n\n\n\n{dataset_name} PPL: {np.round(eval_ppl.item(), 2)}")
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')
self.eval_loss.reset()
self.eval_ppl.reset()
def test_epoch_end(self, outputs):
self.eval_epoch_end(outputs, 'test')
def setup_tokenizer(self,
tokenizer_name=None,
tokenizer_model=None,
vocab_file=None,
bpe_dropout=0.0,
special_tokens=None):
supported_tokenizers = ['yttm', 'huggingface', 'sentencepiece', 'word']
if tokenizer_name not in supported_tokenizers:
raise NotImplementedError(
f"Currently we only support tokenizers in {supported_tokenizers}."
)
self.tokenizer = get_tokenizer(
tokenizer_name=tokenizer_name,
tokenizer_model=self.register_artifact(
"cfg.tokenizer.tokenizer_model", tokenizer_model),
vocab_file=vocab_file,
bpe_dropout=bpe_dropout,
special_tokens=dict(special_tokens or {}),
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_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, predict_last_k=0):
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:
metadata_file = cfg.get('metadata_file')
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 metadata.get('tar_files') is not None:
raise ValueError(
'Tar files specified in config and in metadata file. Tar files should only be specified once.'
)
dataset = TarredSentenceDataset(
text_tar_filepaths=tar_files,
metadata_path=metadata_file,
tokenizer=self.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,
)
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 = SentenceDataset(
tokenizer=self.tokenizer,
dataset=cfg.file_name,
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),
cache_ids=cfg.get("cache_ids", False),
)
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),
)
@classmethod
def list_available_models(cls) -> Optional[Dict[str, str]]:
pass
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)
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
def _loss_class_test(
rank: int,
worldsize: int,
loss_sum_or_avg: Optional[torch.Tensor],
num_measurements: Optional[torch.Tensor],
dist_sync_on_step: bool,
take_avg_loss: bool,
check_dist_sync_on_step: bool = True,
check_batch: bool = True,
atol: float = 1e-8,
):
""" Utility function doing the actual comparison between lightning class metric
and reference metric.
Args:
rank: rank of current process
worldsize: number of processes
loss_sum_or_avg: a one dimensional float torch tensor with loss sums or means.
num_measurements: a one dimensional integer torch tensor with number of values on which sums or means from
``loss_sum_or_avg`` were computed.
dist_sync_on_step: bool, if true will synchronize metric state across processes at each call of the
method :meth:`forward()`
take_avg_loss: dict with additional arguments used for class initialization
check_dist_sync_on_step: bool, if true will check if the metric is also correctly
calculated per batch per device (and not just at the end)
check_batch: bool, if true will check if the metric is also correctly
calculated across devices for each batch (and not just at the end)
"""
# Instantiate lightning metric
loss_metric = GlobalAverageLossMetric(compute_on_step=True,
dist_sync_on_step=dist_sync_on_step,
take_avg_loss=take_avg_loss)
# verify loss works after being loaded from pickled state
pickled_metric = pickle.dumps(loss_metric)
loss_metric = pickle.loads(pickled_metric)
for i in range(rank, NUM_BATCHES, worldsize):
batch_result = loss_metric(loss_sum_or_avg[i], num_measurements[i])
if loss_metric.dist_sync_on_step:
if rank == 0:
ddp_loss_sum_or_avg = torch.stack(
[loss_sum_or_avg[i + r] for r in range(worldsize)])
ddp_num_measurements = torch.stack(
[num_measurements[i + r] for r in range(worldsize)])
sk_batch_result = reference_loss_func(ddp_loss_sum_or_avg,
ddp_num_measurements,
take_avg_loss)
# assert for dist_sync_on_step
if check_dist_sync_on_step:
if sk_batch_result.isnan():
assert batch_result.isnan()
else:
assert np.allclose(
batch_result.numpy(), sk_batch_result, atol=atol
), f"batch_result = {batch_result.numpy()}, sk_batch_result = {sk_batch_result}, i = {i}"
else:
ls = loss_sum_or_avg[i:i + 1]
nm = num_measurements[i:i + 1]
sk_batch_result = reference_loss_func(ls, nm, take_avg_loss)
# assert for batch
if check_batch:
if sk_batch_result.isnan():
assert batch_result.isnan()
else:
assert np.allclose(
batch_result.numpy(), sk_batch_result, atol=atol
), f"batch_result = {batch_result.numpy()}, sk_batch_result = {sk_batch_result}, i = {i}"
# check on all batches on all ranks
result = loss_metric.compute()
assert isinstance(result, torch.Tensor)
sk_result = reference_loss_func(loss_sum_or_avg, num_measurements,
take_avg_loss)
# assert after aggregation
if sk_result.isnan():
assert result.isnan()
else:
assert np.allclose(
result.numpy(), sk_result,
atol=atol), f"result = {result.numpy()}, sk_result = {sk_result}"
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
self.global_rank = 0
self.world_size = 1
if trainer is not None:
self.global_rank = (trainer.node_rank *
trainer.num_gpus) + trainer.local_rank
self.world_size = trainer.num_nodes * trainer.num_gpus
cfg = model_utils.maybe_update_config_version(cfg)
self.setup_enc_dec_tokenizers(cfg)
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)
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
self.global_rank = 0
self.world_size = 1
if trainer is not None:
self.global_rank = (trainer.node_rank * trainer.num_gpus) + trainer.local_rank
self.world_size = trainer.num_nodes * trainer.num_gpus
cfg = model_utils.maybe_update_config_version(cfg)
self.setup_enc_dec_tokenizers(cfg)
super().__init__(cfg=cfg, trainer=trainer)
self.src_language: str = cfg.get("src_language", None)
self.tgt_language: str = cfg.get("tgt_language", None)
# 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)
src_hiddens = self.encoder(src_ids, 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)
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])
translations = [self.decoder_tokenizer.ids_to_text(tr) for tr in beam_results.cpu().numpy()]
np_tgt = tgt_ids.cpu().numpy()
ground_truths = [self.decoder_tokenizer.ids_to_text(tgt) for tgt in np_tgt]
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]))
# TODO: add target language so detokenizer can be lang specific.
detokenizer = MosesDetokenizer(lang=self.tgt_language)
translations = [detokenizer.detokenize(sent.split()) for sent in translations]
ground_truths = [detokenizer.detokenize(sent.split()) for sent in ground_truths]
if self.tgt_language in ['ja']:
sp_detokenizer = SentencePieceDetokenizer()
translations = [sp_detokenizer.detokenize(sent.split()) for sent in translations]
ground_truths = [sp_detokenizer.detokenize(sent.split()) for sent in ground_truths]
assert len(translations) == len(ground_truths)
if self.tgt_language in ['ja']:
sacre_bleu = corpus_bleu(translations, [ground_truths], tokenize="ja-mecab")
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("load_from_tarred_dataset", False):
logging.info('Loading from tarred dataset %s' % (cfg.src_file_name))
if cfg.src_file_name != cfg.tgt_file_name:
raise ValueError("src must be equal to target for tarred dataset")
if cfg.get("metadata_path", None) is None:
raise FileNotFoundError("Could not find metadata path in config")
dataset = TarredTranslationDataset(
text_tar_filepaths=cfg.src_file_name,
metadata_path=cfg.metadata_path,
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),
)
@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
"""
if source_lang is None:
source_lang = self.src_language
if target_lang is None:
target_lang = self.tgt_language
mode = self.training
tokenizer = MosesTokenizer(lang=source_lang)
normalizer = MosesPunctNormalizer(lang=source_lang)
detokenizer = MosesDetokenizer(lang=target_lang)
try:
self.eval()
res = []
for txt in text:
if source_lang != "None":
txt = normalizer.normalize(txt)
txt = tokenizer.tokenize(txt, escape=False, return_str=True)
ids = self.encoder_tokenizer.text_to_ids(txt)
ids = [self.encoder_tokenizer.bos_id] + ids + [self.encoder_tokenizer.eos_id]
src = torch.Tensor(ids).long().to(self._device).unsqueeze(0)
src_mask = torch.ones_like(src)
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)
translation_ids = beam_results.cpu()[0].numpy()
translation = self.decoder_tokenizer.ids_to_text(translation_ids)
translation = detokenizer.detokenize(translation.split())
if target_lang in ["ja"]:
sp_detokenizer = SentencePieceDetokenizer()
translation = sp_detokenizer.detokenize(translation.split())
res.append(translation)
finally:
self.train(mode=mode)
return res
@classmethod
def list_available_models(cls) -> Optional[Dict[str, str]]:
pass
def configure_ddp(self, model: LightningModule, device_ids: List[int]) -> DistributedDataParallel:
logging.info(f'overriding ddp to set find_unused_parameters to {self._cfg.find_unused_parameters}')
model = LightningDistributedDataParallel(
model, device_ids=device_ids, find_unused_parameters=self._cfg.find_unused_parameters
)
return model
def setup(self, stage):
if stage == "fit":
# Update PTL trainer to use our configure_ddp
self._trainer.accelerator_backend.ddp_plugin.configure_ddp = self.configure_ddp
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_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),
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),
decoder_model_name=cfg.decoder.get('model_name') if hasattr(cfg.decoder, 'model_name') else None,
)
# 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)
# 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)
self.encoder = get_transformer(
library=library, model_name=model_name, pretrained=pretrained, config_dict=encoder_cfg_dict, encoder=True,
)
# 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,
)
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 = GlobalAverageLossMetric(dist_sync_on_step=False, take_avg_loss=True)