# Copyright (c) 2022 Microsoft # Licensed under The MIT License [see LICENSE for details] import torch import torch.nn as nn def fixed_pos_embedding(x): seq_len, dim = x.shape inv_freq = 1.0 / (10000 ** (torch.arange(0, dim) / dim)) sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(0, seq_len, dtype=torch.float), inv_freq).to(x) return torch.sin(sinusoid_inp), torch.cos(sinusoid_inp) def rotate_every_two(x): x1 = x[:, :, ::2] x2 = x[:, :, 1::2] x = torch.stack((-x2, x1), dim=-1) return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)')\ def duplicate_interleave(m): """ A simple version of `torch.repeat_interleave` for duplicating a matrix while interleaving the copy. """ dim0 = m.shape[0] m = m.view(-1, 1) # flatten the matrix m = m.repeat(1, 2) # repeat all elements into the 2nd dimension m = m.view(dim0, -1) # reshape into a matrix, interleaving the copy return m def apply_rotary_pos_emb(x, sin, cos, scale=1): sin, cos = map(lambda t: duplicate_interleave(t * scale), (sin, cos)) # einsum notation for lambda t: repeat(t[offset:x.shape[1]+offset,:], "n d -> () n () (d j)", j=2) return (x * cos) + (rotate_every_two(x) * sin) class XPOS(nn.Module): def __init__(self, head_dim, scale_base=512): super().__init__() self.head_dim = head_dim self.scale_base = scale_base self.register_buffer("scale", (torch.arange(0, head_dim, 2) + 0.4 * head_dim) / (1.4 * head_dim)) def forward(self, x, offset=0, downscale=False): length = x.shape[1] min_pos = -(length + offset) // 2 max_pos = length + offset + min_pos scale = self.scale ** torch.arange(min_pos, max_pos, 1).to(self.scale).div(self.scale_base)[:, None] sin, cos = fixed_pos_embedding(scale) if scale.shape[0] > length: scale = scale[-length:] sin = sin[-length:] cos = cos[-length:] if downscale: scale = 1 / scale x = apply_rotary_pos_emb(x, sin, cos, scale) return x