Question

我有：

celery -A tasks worker --loglevel=info -E

这适用于第一次迭代，但是随后我遇到了下一次迭代的错误：

        context = torch.tensor(context, dtype=torch.long, device=self.device)
        context = context.unsqueeze(0)
        generated = context
        with torch.no_grad():
            past_outputs = None
            for i in trange(num_words):
                print(i, num_words)
                inputs = {"input_ids": generated}

                outputs, past_outputs = self.model(
                    **inputs,
                    past=past_outputs
                )
                next_token_logits = outputs[
                    0, -1, :] / (temperature if temperature > 0 else 1.0)

                # reptition penalty from CTRL
                # (https://arxiv.org/abs/1909.05858)
                for _ in set(generated.view(-1).tolist()):
                    next_token_logits[_] /= repetition_penalty

                filtered_logits = top_k_top_p_filtering(
                    next_token_logits, top_k=top_k, top_p=top_p)
                if temperature == 0:  # greedy sampling:
                    next_token = torch.argmax(filtered_logits).unsqueeze(0)
                else:
                    next_token = torch.multinomial(
                        F.softmax(filtered_logits, dim=-1), num_samples=1)

                generated = torch.cat(
                    (generated, next_token.unsqueeze(0)), dim=1)

我在做错什么吗？

Answer 1

我相信问题是context包含超过词汇量的整数值。我的假设是基于最后的追溯线：

return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
IndexError: index out of range in self

Answer 2

我做到了：

                outputs, past_outputs = self.models[model_name](
                    context,
                    past=past_outputs
                )
                context = next_token.unsqueeze(0)

如何与HuggingFace变形金刚GPT-2一起使用过去？

2 个答案: