Spaces:
Runtime error
Runtime error
File size: 2,590 Bytes
09481f3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
"""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
class LMInterface(ScorerInterface):
"""LM Interface for ESPnet model implementation."""
@staticmethod
def add_arguments(parser):
"""Add arguments to command line argument parser."""
return parser
@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)
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"},
}
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
|