Source code for openspeech.models.transformer_lm.model

# MIT License
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# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
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from omegaconf import DictConfig

from openspeech.lm.transformer_lm import TransformerForLanguageModel
from openspeech.models import register_model, OpenspeechModel
from openspeech.models.transformer_lm.configurations import TransformerLanguageModelConfigs
from openspeech.tokenizers.tokenizer import Tokenizer


@register_model('transformer_lm', dataclass=TransformerLanguageModelConfigs)
class TransformerLanguageModel(OpenspeechModel):
    r"""
    Transformer language model.
    Paper: https://arxiv.org/abs/1904.09408

    Args:
        configs (DictConfig): configuration set.
        tokenizer (Tokenizer): tokenizer is in charge of preparing the inputs for a model.

    Inputs:
        - **inputs** (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
        - **input_lengths** (torch.LongTensor): The length of input tensor. ``(batch)``

    Returns:
        outputs (dict): Result of model predictions.
    """
    def __init__(self, configs: DictConfig, tokenizer: Tokenizer) -> None:
        super(TransformerLanguageModel, self).__init__(configs, tokenizer)

    def build_model(self):
        self.lm = TransformerForLanguageModel(
            num_classes=self.num_classes,
            max_length=self.configs.model.max_length,
            d_model=self.configs.model.d_model,
            d_ff=self.configs.model.d_ff,
            num_attention_heads=self.configs.model.num_attention_heads,
            pad_id=self.tokenizer.pad_id,
            sos_id=self.tokenizer.sos_id,
            eos_id=self.tokenizer.eos_id,
            dropout_p=self.configs.model.dropout_p,
            num_layers=self.configs.model.num_layers,
        )