"""Length bonus module."""
from typing import Any, List, Tuple
import torch
from espnet.nets.scorer_interface import BatchScorerInterface
[docs]class LengthBonus(BatchScorerInterface):
"""Length bonus in beam search."""
def __init__(self, n_vocab: int):
"""Initialize class.
Args:
n_vocab (int): The number of tokens in vocabulary for beam search
"""
self.n = n_vocab
[docs] def score(self, y, state, x):
"""Score new token.
Args:
y (torch.Tensor): 1D torch.int64 prefix tokens.
state: Scorer state for prefix tokens
x (torch.Tensor): 2D encoder feature that generates ys.
Returns:
tuple[torch.Tensor, Any]: Tuple of
torch.float32 scores for next token (n_vocab)
and None
"""
return torch.tensor([1.0], device=x.device, dtype=x.dtype).expand(self.n), None
[docs] def batch_score(
self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
) -> Tuple[torch.Tensor, List[Any]]:
"""Score new token batch.
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.
"""
return (
torch.tensor([1.0], device=xs.device, dtype=xs.dtype).expand(
ys.shape[0], self.n
),
None,
)