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"""Language model interface.""" |
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import argparse |
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from espnet.nets.scorer_interface import ScorerInterface |
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from espnet.utils.dynamic_import import dynamic_import |
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from espnet.utils.fill_missing_args import fill_missing_args |
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class LMInterface(ScorerInterface): |
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"""LM Interface for ESPnet model implementation.""" |
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@staticmethod |
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def add_arguments(parser): |
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"""Add arguments to command line argument parser.""" |
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return parser |
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@classmethod |
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def build(cls, n_vocab: int, **kwargs): |
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"""Initialize this class with python-level args. |
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Args: |
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idim (int): The number of vocabulary. |
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Returns: |
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LMinterface: A new instance of LMInterface. |
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""" |
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from espnet.bin.lm_train import get_parser |
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def wrap(parser): |
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return get_parser(parser, required=False) |
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args = argparse.Namespace(**kwargs) |
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args = fill_missing_args(args, wrap) |
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args = fill_missing_args(args, cls.add_arguments) |
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return cls(n_vocab, args) |
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def forward(self, x, t): |
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"""Compute LM loss value from buffer sequences. |
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Args: |
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x (torch.Tensor): Input ids. (batch, len) |
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t (torch.Tensor): Target ids. (batch, len) |
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Returns: |
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tuple[torch.Tensor, torch.Tensor, torch.Tensor]: Tuple of |
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loss to backward (scalar), |
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negative log-likelihood of t: -log p(t) (scalar) and |
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the number of elements in x (scalar) |
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Notes: |
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The last two return values are used |
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in perplexity: p(t)^{-n} = exp(-log p(t) / n) |
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""" |
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raise NotImplementedError("forward method is not implemented") |
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predefined_lms = { |
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"pytorch": { |
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"default": "espnet.nets.pytorch_backend.lm.default:DefaultRNNLM", |
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"seq_rnn": "espnet.nets.pytorch_backend.lm.seq_rnn:SequentialRNNLM", |
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"transformer": "espnet.nets.pytorch_backend.lm.transformer:TransformerLM", |
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}, |
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"chainer": {"default": "espnet.lm.chainer_backend.lm:DefaultRNNLM"}, |
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} |
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def dynamic_import_lm(module, backend): |
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"""Import LM class dynamically. |
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Args: |
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module (str): module_name:class_name or alias in `predefined_lms` |
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backend (str): NN backend. e.g., pytorch, chainer |
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Returns: |
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type: LM class |
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""" |
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model_class = dynamic_import(module, predefined_lms.get(backend, dict())) |
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assert issubclass( |
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model_class, LMInterface |
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), f"{module} does not implement LMInterface" |
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return model_class |
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