def __init__(self, cfg: MTBottleneckModelConfig, trainer: Trainer = None):
super().__init__(cfg=cfg, trainer=trainer)
self.model_type: str = cfg.get("model_type", "nll")
self.min_logv: float = cfg.get("min_logv", -6)
self.latent_size: int = cfg.get("latent_size", -1)
self.non_recon_warmup_batches: int = cfg.get("non_recon_warmup_batches", 200000)
self.recon_per_token: bool = cfg.get("recon_per_token", True)
self.log_timing: bool = cfg.get("log_timing", True)
# if True, translation uses the mean of latent for VAE and MIM
self.deterministic_translate = True
# latent_size -1 will take value of encoder.hidden_size
if self.latent_size < 0:
self.latent_size = self.encoder.hidden_size
if not self.recon_per_token:
# disable reduction for train and eval loss
self.eval_loss_fn = NLLLoss(ignore_index=self.decoder_tokenizer.pad_id, reduction='none')
self.loss_fn._per_token_reduction = False
if self.model_type not in ["nll", "mim", "vae"]:
raise ValueError(f"Unknown model_type = {self.model_type}")
# project bridge dimension back to decoder hidden dimensions
self.latent2hidden = build_linear_or_identity(self.latent_size, self.decoder.hidden_size)
# project dimension of encoder hidden to latent dimension
self.hidden2latent_mean = build_linear_or_identity(self.encoder.hidden_size, self.latent_size)
# MIM or VAE
if self.model_type != "nll":
# for probabilistic latent variable models we also need variance
self.hidden2latent_logv = build_linear_or_identity(self.encoder.hidden_size, self.latent_size)
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 = []
self.encoder_tokenizer_library = cfg.encoder_tokenizer.get(
'library', 'yttm')
self.decoder_tokenizer_library = cfg.decoder_tokenizer.get(
'library', 'yttm')
# 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=self.encoder_tokenizer_library,
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,
encoder_r2l=cfg.encoder_tokenizer.get('r2l', False),
decoder_tokenizer_library=self.decoder_tokenizer_library,
encoder_tokenizer_vocab_file=cfg.encoder_tokenizer.get(
'vocab_file', None),
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,
decoder_r2l=cfg.decoder_tokenizer.get('r2l', False),
)
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(
src_lng, 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(self.src_language,
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 __init__(self, cfg: MTBottleneckModelConfig, trainer: Trainer = None):
super().__init__(cfg=cfg, trainer=trainer)
recon_per_token: bool = True
self.model_type: str = cfg.get("model_type", "seq2seq-br")
self.min_logv: float = cfg.get("min_logv", -6)
self.ortho_loss_coef: float = cfg.get("ortho_loss_coef", 0.0)
self.att_bridge_size: int = cfg.get("att_bridge_size", 512)
self.att_bridge_k: int = cfg.get("att_bridge_k", 16)
self.att_bridge_inner_size: int = cfg.get("att_bridge_inner_size",
1024)
self.non_recon_warmup_batches: int = cfg.get(
"non_recon_warmup_batches", 200000)
self.recon_per_token: bool = cfg.get("recon_per_token", True)
# TODO: add support in label smoothing for per-sample reconstruction loss
if not self.recon_per_token:
loss_fn = NLLLoss(
ignore_index=self.decoder_tokenizer.pad_id,
reduction='none',
)
self.loss_fn = self.eval_loss_fn = loss_fn
if self.model_type not in [
"seq2seq", "seq2seq-br", "seq2seq-mim", "seq2seq-vae"
]:
raise ValueError("Unknown model_type = {model_type}".format(
model_type=self.model_type, ))
if self.model_type != "seq2seq":
# project bridge dimension back to decoder hidden dimensions
if self.att_bridge_size != self.encoder.hidden_size:
self.latent2hidden = torch.nn.Linear(self.att_bridge_size,
self.encoder.hidden_size)
else:
self.latent2hidden = torch.nn.Identity()
self.att_bridge = AttentionBridge(
hidden_size=self.encoder.hidden_size,
k=self.att_bridge_k,
bridge_size=self.att_bridge_size,
)
# project dimension of encoder hidden to bridge dimension
if self.encoder.hidden_size != self.att_bridge_size:
self.hidden2latent_mean = torch.nn.Linear(
self.encoder.hidden_size, self.att_bridge_size)
else:
self.hidden2latent_mean = torch.nn.Identity()
# for probabilistic latent variable models we also need variance
if self.model_type in ["seq2seq-mim", "seq2seq-vae"]:
if self.encoder.hidden_size != self.att_bridge_size:
self.hidden2latent_logv = torch.nn.Linear(
self.encoder.hidden_size, self.att_bridge_size)
else:
self.hidden2latent_logv = torch.nn.Identity()
else:
# seq2seq
self.latent2hidden = torch.nn.Identity()