Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,626 Bytes
917a889 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
from typing import *
import torch
import torch.nn as nn
from ..basic import SparseTensor
class SparseRotaryPositionEmbedder(nn.Module):
def __init__(
self,
head_dim: int,
dim: int = 3,
rope_freq: Tuple[float, float] = (1.0, 10000.0)
):
super().__init__()
assert head_dim % 2 == 0, "Head dim must be divisible by 2"
self.head_dim = head_dim
self.dim = dim
self.rope_freq = rope_freq
self.freq_dim = head_dim // 2 // dim
self.freqs = torch.arange(self.freq_dim, dtype=torch.float32) / self.freq_dim
self.freqs = rope_freq[0] / (rope_freq[1] ** (self.freqs))
def _get_phases(self, indices: torch.Tensor) -> torch.Tensor:
self.freqs = self.freqs.to(indices.device)
phases = torch.outer(indices, self.freqs)
phases = torch.polar(torch.ones_like(phases), phases)
return phases
def _rotary_embedding(self, x: torch.Tensor, phases: torch.Tensor) -> torch.Tensor:
x_complex = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
x_rotated = x_complex * phases.unsqueeze(-2)
x_embed = torch.view_as_real(x_rotated).reshape(*x_rotated.shape[:-1], -1).to(x.dtype)
return x_embed
def forward(self, q: SparseTensor, k: Optional[SparseTensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
q (SparseTensor): [..., N, H, D] tensor of queries
k (SparseTensor): [..., N, H, D] tensor of keys
"""
assert q.coords.shape[-1] == self.dim + 1, "Last dimension of coords must be equal to dim+1"
phases_cache_name = f'rope_phase_{self.dim}d_freq{self.rope_freq[0]}-{self.rope_freq[1]}_hd{self.head_dim}'
phases = q.get_spatial_cache(phases_cache_name)
if phases is None:
coords = q.coords[..., 1:]
phases = self._get_phases(coords.reshape(-1)).reshape(*coords.shape[:-1], -1)
if phases.shape[-1] < self.head_dim // 2:
padn = self.head_dim // 2 - phases.shape[-1]
phases = torch.cat([phases, torch.polar(
torch.ones(*phases.shape[:-1], padn, device=phases.device),
torch.zeros(*phases.shape[:-1], padn, device=phases.device)
)], dim=-1)
q.register_spatial_cache(phases_cache_name, phases)
q_embed = q.replace(self._rotary_embedding(q.feats, phases))
if k is None:
return q_embed
k_embed = k.replace(self._rotary_embedding(k.feats, phases))
return q_embed, k_embed |