59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
"""
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RoPE rotary positional encoding, reused by the Denoiser and Transformer.
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Extracted from model/vae/autoencoder.py.
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"""
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from typing import Tuple
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import torch
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import torch.nn as nn
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class RotaryEmbedding(nn.Module):
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"""RoPE rotary positional encoding (applied to Q/K)."""
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def __init__(self, dim: int, base: int = 10000):
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super().__init__()
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if dim % 2 != 0:
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raise ValueError("RoPE dim must be even")
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inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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def get_sin_cos(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor]:
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t = torch.arange(seq_len, device=device, dtype=dtype)
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freqs = torch.einsum("i,j->ij", t, self.inv_freq.to(device=device, dtype=dtype))
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sin = freqs.sin()
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cos = freqs.cos()
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return sin, cos
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@staticmethod
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def get_sin_cos_from_positions(
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positions: torch.Tensor,
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inv_freq: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Compute sin/cos from arbitrary position indices (supports repeated indices).
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Args:
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positions: (L,) int — arbitrary position indices, duplicates allowed
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inv_freq: (D/2,) — from RotaryEmbedding.inv_freq
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Returns:
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sin, cos: each (L, D/2)
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"""
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freqs = torch.einsum("i,j->ij", positions.float(), inv_freq.to(positions))
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return freqs.sin(), freqs.cos()
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@staticmethod
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def apply_rope(x: torch.Tensor, sin: torch.Tensor, cos: torch.Tensor) -> torch.Tensor:
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"""
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x: (B, H, L, D)
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sin/cos: (L, D/2)
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"""
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x1 = x[..., 0::2]
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x2 = x[..., 1::2]
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sin = sin.unsqueeze(0).unsqueeze(0)
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cos = cos.unsqueeze(0).unsqueeze(0)
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x_rot = torch.stack([x1 * cos - x2 * sin, x1 * sin + x2 * cos], dim=-1)
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return x_rot.flatten(-2)
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