gpt2.py

import transformers
from base import LM
import torch


class GPT2LM(LM):
    def __init__(self):
        self.gpt2 = transformers.GPT2LMHeadModel.from_pretrained('gpt2')
        self.tok = transformers.GPT2Tokenizer.from_pretrained('gpt2')
    
    def generate(self, context, until):
        context = torch.tensor([self.tok.encode(context.strip())], dtype=torch.long)
        res = self.gpt2.generate(context, eos_token_id=self.tok.encoder[until], do_sample=False, max_length=1024)

        # chop off the prompt and the final eos token
        return self.tok.decode(res[0][len(context[0]):-1]).strip()

    def nll_of(self, context, continuation):
        pass