inference.py

from transformers import NllbTokenizer, AutoModelForSeq2SeqLM


def create_tokenizer_with_new_lang(model_id, new_lang):
    """
    Add a new language token to the tokenizer vocabulary
    (this should be done each time after its initialization)
    """
    tokenizer = NllbTokenizer.from_pretrained(model_id)
    old_len = len(tokenizer) - int(new_lang in tokenizer.added_tokens_encoder)
    tokenizer.lang_code_to_id[new_lang] = old_len-1
    tokenizer.id_to_lang_code[old_len-1] = new_lang
    # always move "mask" to the last position
    tokenizer.fairseq_tokens_to_ids["<mask>"] = len(tokenizer.sp_model) + len(tokenizer.lang_code_to_id) + tokenizer.fairseq_offset

    tokenizer.fairseq_tokens_to_ids.update(tokenizer.lang_code_to_id)
    tokenizer.fairseq_ids_to_tokens = {v: k for k, v in tokenizer.fairseq_tokens_to_ids.items()}
    if new_lang not in tokenizer._additional_special_tokens:
        tokenizer._additional_special_tokens.append(new_lang)
    # clear the added token encoder; otherwise a new token may end up there by mistake
    tokenizer.added_tokens_encoder = {}
    tokenizer.added_tokens_decoder = {}

    return tokenizer


class Translator:
    @classmethod
    def from_pretrained(cls, path, new_lang='moo_Latn'):
        # Does the model need adaptation or not?
        # model, tokenizer = create_model_with_new_lang(
        #     model_id=path,
        #     new_lang=new_lang,
        #     similar_lang='deu_Latn'
        # )
        tokenizer = create_tokenizer_with_new_lang(path, new_lang)
        model = AutoModelForSeq2SeqLM.from_pretrained(path)
        return Translator(model, tokenizer)

    def __init__(self, model, tokenizer) -> None:
        self.model = model
        self.tokenizer = tokenizer

        # self.model.cuda()

    def translate(self, text, src_lang='moo_Latn', tgt_lang='deu_Latn', a=32, b=3, max_input_length=1024, num_beams=4, **kwargs):
        self.tokenizer.src_lang = src_lang
        self.tokenizer.tgt_lang = tgt_lang
        inputs = self.tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=max_input_length)
        result = self.model.generate(
            **inputs.to(self.model.device),
            forced_bos_token_id=self.tokenizer.convert_tokens_to_ids(tgt_lang),
            max_new_tokens=int(a + b * inputs.input_ids.shape[1]),
            num_beams=num_beams,
            **kwargs
        )
        return self.tokenizer.batch_decode(result, skip_special_tokens=True)