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"""Transformer language model."""
from typing import Any
from typing import List
from typing import Tuple
import logging
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
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