Can we have a plug-in based roformer?

[HelloWorldLTY]

Hi, I design a plug-in version of rotation position embeddings:

def apply_rotary(x, sinusoidal_pos):
    sin, cos = sinusoidal_pos
    x1, x2 = x[..., 0::2], x[..., 1::2]
    # 如果是旋转query key的话，下面这个直接cat就行，因为要进行矩阵乘法，最终会在这个维度求和。（只要保持query和key的最后一个dim的每一个位置对应上就可以）
    # torch.cat([x1 * cos - x2 * sin, x2 * cos + x1 * sin], dim=-1)
    # 如果是旋转value的话，下面这个stack后再flatten才可以，因为训练好的模型最后一个dim是两两之间交替的。
    return torch.stack([x1 * cos - x2 * sin, x2 * cos + x1 * sin], dim=-1).flatten(-2, -1)

class PositionalEncoding(nn.Module):
    def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):
        super().__init__()
        self.dropout = nn.Dropout(p=dropout)

        position = torch.arange(max_len).unsqueeze(1)
        div_term = torch.exp(
            torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
        )
        pe = torch.zeros(max_len, 1, d_model)

        self.sinusoidal_pos = [torch.sin(position * div_term), torch.cos(position * div_term)]
        pe[:, 0, 0::2] = torch.sin(position * div_term)
        pe[:, 0, 1::2] = torch.cos(position * div_term)
        self.register_buffer("pe", pe)

    def forward(self, x: Tensor) -> Tensor:
        """
        Args:
            x: Tensor, shape [seq_len, batch_size, embedding_dim]
        """
        x = x + apply_rotary(x, self.sinusoidal_pos)
        return self.dropout(x)

I did not meet any bugs, is it a correct implementation? Thanks.