| 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 |
