"""Transformer language model.""" import logging from typing import Any, List, Tuple import torch import torch.nn as nn import torch.nn.functional as F from espnet.nets.lm_interface import LMInterface from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding from espnet.nets.pytorch_backend.transformer.encoder import Encoder from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask from espnet.nets.scorer_interface import BatchScorerInterface from espnet.utils.cli_utils import strtobool class TransformerLM(nn.Module, LMInterface, BatchScorerInterface): """Transformer language model.""" @staticmethod def add_arguments(parser): """Add arguments to command line argument parser.""" parser.add_argument( "--layer", type=int, default=4, help="Number of hidden layers" ) parser.add_argument( "--unit", type=int, default=1024, help="Number of hidden units in feedforward layer", ) parser.add_argument( "--att-unit", type=int, default=256, help="Number of hidden units in attention layer", ) parser.add_argument( "--embed-unit", type=int, default=128, help="Number of hidden units in embedding layer", ) parser.add_argument( "--head", type=int, default=2, help="Number of multi head attention" ) parser.add_argument( "--dropout-rate", type=float, default=0.5, help="dropout probability" ) parser.add_argument( "--att-dropout-rate", type=float, default=0.0, help="att dropout probability", ) parser.add_argument( "--emb-dropout-rate", type=float, default=0.0, help="emb dropout probability", ) parser.add_argument( "--tie-weights", type=strtobool, default=False, help="Tie input and output embeddings", ) parser.add_argument( "--pos-enc", default="sinusoidal", choices=["sinusoidal", "none"], help="positional encoding", ) return parser def __init__(self, n_vocab, args): """Initialize class. Args: n_vocab (int): The size of the vocabulary args (argparse.Namespace): configurations. see py:method:`add_arguments` """ nn.Module.__init__(self) # NOTE: for a compatibility with less than 0.9.7 version models emb_dropout_rate = getattr(args, "emb_dropout_rate", 0.0) # NOTE: for a compatibility with less than 0.9.7 version models tie_weights = getattr(args, "tie_weights", False) # NOTE: for a compatibility with less than 0.9.7 version models att_dropout_rate = getattr(args, "att_dropout_rate", 0.0) if args.pos_enc == "sinusoidal": pos_enc_class = PositionalEncoding elif args.pos_enc == "none": def pos_enc_class(*args, **kwargs): return nn.Sequential() # indentity else: raise ValueError(f"unknown pos-enc option: {args.pos_enc}") self.embed = nn.Embedding(n_vocab, args.embed_unit) if emb_dropout_rate == 0.0: self.embed_drop = None else: self.embed_drop = nn.Dropout(emb_dropout_rate) self.encoder = Encoder( idim=args.embed_unit, attention_dim=args.att_unit, attention_heads=args.head, linear_units=args.unit, num_blocks=args.layer, dropout_rate=args.dropout_rate, attention_dropout_rate=att_dropout_rate, input_layer="linear", pos_enc_class=pos_enc_class, ) self.decoder = nn.Linear(args.att_unit, n_vocab) logging.info("Tie weights set to {}".format(tie_weights)) logging.info("Dropout set to {}".format(args.dropout_rate)) logging.info("Emb Dropout set to {}".format(emb_dropout_rate)) logging.info("Att Dropout set to {}".format(att_dropout_rate)) if tie_weights: assert ( args.att_unit == args.embed_unit ), "Tie Weights: True need embedding and final dimensions to match" self.decoder.weight = self.embed.weight def _target_mask(self, ys_in_pad): ys_mask = ys_in_pad != 0 m = subsequent_mask(ys_mask.size(-1), device=ys_mask.device).unsqueeze(0) return ys_mask.unsqueeze(-2) & m def forward( self, x: torch.Tensor, t: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Compute LM loss value from buffer sequences. Args: x (torch.Tensor): Input ids. (batch, len) t (torch.Tensor): Target ids. (batch, len) Returns: tuple[torch.Tensor, torch.Tensor, torch.Tensor]: Tuple of loss to backward (scalar), negative log-likelihood of t: -log p(t) (scalar) and the number of elements in x (scalar) Notes: The last two return values are used in perplexity: p(t)^{-n} = exp(-log p(t) / n) """ xm = x != 0 if self.embed_drop is not None: emb = self.embed_drop(self.embed(x)) else: emb = self.embed(x) h, _ = self.encoder(emb, self._target_mask(x)) y = self.decoder(h) loss = F.cross_entropy(y.view(-1, y.shape[-1]), t.view(-1), reduction="none") mask = xm.to(dtype=loss.dtype) logp = loss * mask.view(-1) logp = logp.sum() count = mask.sum() return logp / count, logp, count def score( self, y: torch.Tensor, state: Any, x: torch.Tensor ) -> Tuple[torch.Tensor, Any]: """Score new token. Args: y (torch.Tensor): 1D torch.int64 prefix tokens. state: Scorer state for prefix tokens x (torch.Tensor): encoder feature that generates ys. Returns: tuple[torch.Tensor, Any]: Tuple of torch.float32 scores for next token (n_vocab) and next state for ys """ y = y.unsqueeze(0) if self.embed_drop is not None: emb = self.embed_drop(self.embed(y)) else: emb = self.embed(y) h, _, cache = self.encoder.forward_one_step( emb, self._target_mask(y), cache=state ) h = self.decoder(h[:, -1]) logp = h.log_softmax(dim=-1).squeeze(0) return logp, cache # batch beam search API (see BatchScorerInterface) def batch_score( self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor ) -> Tuple[torch.Tensor, List[Any]]: """Score new token batch (required). Args: ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). states (List[Any]): Scorer states for prefix tokens. xs (torch.Tensor): The encoder feature that generates ys (n_batch, xlen, n_feat). Returns: tuple[torch.Tensor, List[Any]]: Tuple of batchfied scores for next token with shape of `(n_batch, n_vocab)` and next state list for ys. """ # merge states n_batch = len(ys) n_layers = len(self.encoder.encoders) if states[0] is None: batch_state = None else: # transpose state of [batch, layer] into [layer, batch] batch_state = [ torch.stack([states[b][i] for b in range(n_batch)]) for i in range(n_layers) ] if self.embed_drop is not None: emb = self.embed_drop(self.embed(ys)) else: emb = self.embed(ys) # batch decoding h, _, states = self.encoder.forward_one_step( emb, self._target_mask(ys), cache=batch_state ) h = self.decoder(h[:, -1]) logp = h.log_softmax(dim=-1) # transpose state of [layer, batch] into [batch, layer] state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)] return logp, state_list