LLM长度外推——位置插值（llama/baichuan)

位置插值(position Interpolation, PI)通过将超出训练长度的位置索引等比例缩小，映射到模型已经学习的位置范围内，实现长度外推。
好处是不用重新训练，直接在推理时加入。

llama的实现方式

论文提出 Extending Context Window of Large Language Models via Positional Interpolation
llama采用Rope位置编码，因此其实现都是针对rope编码的位置插值。

官方实现的代码：

https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L148

class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
    """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

    def forward(self, x, position_ids):
        # difference to the original RoPE: a scaling factor is aplied to the position ids
        position_ids = position_ids.float() / self.scaling_factor
        cos, sin = super().forward(x, position_ids)
        return cos, sin

Super-HOT项目的实现

位置插值原理介绍： https://kaiokendev.github.io/til#extending-context-to-8k

源代码：https://huggingface.co/kaiokendev/superhot-13b-8k-no-rlhf-test/blob/main/llama_rope_scaled_monkey_patch.py

class ScaledRotaryEmbedding(torch.nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()
        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
        self.register_buffer("inv_freq", inv_freq)
        
        max_position_embeddings = 8192

        # Build here to make `torch.jit.trace` work.
        self.max_seq_len_cached = max_position_embeddings
        t = torch.arange(
            self.max_seq_len_cached,
            device=self.inv_freq.device,
            dtype=self.inv_freq.dtype,
        )

        # These two lines:
        self.scale = 1 / 4
        t *= self.scale

参考：
1.https://zhuanlan.zhihu.com/p/679147878
2.https://blog.csdn.net/v_JULY_v/article/details/135072211
3.https://kaiokendev.github.io/til#extending-context-to-8k