Source code for espnet.nets.lm_interface

"""Language model interface."""

import argparse

from espnet.nets.scorer_interface import ScorerInterface
from espnet.utils.dynamic_import import dynamic_import
from espnet.utils.fill_missing_args import fill_missing_args

[docs]class LMInterface(ScorerInterface): """LM Interface for ESPnet model implementation."""
[docs] @staticmethod def add_arguments(parser): """Add arguments to command line argument parser.""" return parser
[docs] @classmethod def build(cls, n_vocab: int, **kwargs): """Initialize this class with python-level args. Args: idim (int): The number of vocabulary. Returns: LMinterface: A new instance of LMInterface. """ # local import to avoid cyclic import in lm_train from espnet.bin.lm_train import get_parser def wrap(parser): return get_parser(parser, required=False) args = argparse.Namespace(**kwargs) args = fill_missing_args(args, wrap) args = fill_missing_args(args, cls.add_arguments) return cls(n_vocab, args)
[docs] def forward(self, x, t): """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) """ raise NotImplementedError("forward method is not implemented")
predefined_lms = { "pytorch": { "default": "espnet.nets.pytorch_backend.lm.default:DefaultRNNLM", "seq_rnn": "espnet.nets.pytorch_backend.lm.seq_rnn:SequentialRNNLM", "transformer": "espnet.nets.pytorch_backend.lm.transformer:TransformerLM", }, "chainer": {"default": "espnet.lm.chainer_backend.lm:DefaultRNNLM"}, }
[docs]def dynamic_import_lm(module, backend): """Import LM class dynamically. Args: module (str): module_name:class_name or alias in `predefined_lms` backend (str): NN backend. e.g., pytorch, chainer Returns: type: LM class """ model_class = dynamic_import(module, predefined_lms.get(backend, dict())) assert issubclass( model_class, LMInterface ), f"{module} does not implement LMInterface" return model_class