62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
from functools import lru_cache
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import torch
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from torch import nn
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def apply_rotary_emb(
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x: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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) -> torch.Tensor:
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x1, x2 = torch.chunk(x.float(), 2, dim=-1)
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y1 = x1 * cos - x2 * sin
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y2 = x2 * cos + x1 * sin
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return torch.cat((y1, y2), dim=-1).to(x.dtype)
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class RotaryEmbedding(nn.Module):
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def __init__(
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self,
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head_size: int,
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rotary_dim: int,
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max_position_embeddings: int,
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base: float,
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) -> None:
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super().__init__()
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self.head_size = head_size
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assert rotary_dim == head_size
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inv_freq = 1.0 / (base**(torch.arange(0, rotary_dim, 2, dtype=torch.float) / rotary_dim))
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t = torch.arange(max_position_embeddings, dtype=torch.float)
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freqs = torch.einsum("i,j -> ij", t, inv_freq)
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cos = freqs.cos()
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sin = freqs.sin()
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cache = torch.cat((cos, sin), dim=-1).unsqueeze_(1)
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self.register_buffer("cos_sin_cache", cache, persistent=False)
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@torch.compile
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def forward(
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self,
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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cos_sin = self.cos_sin_cache[positions]
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cos, sin = cos_sin.chunk(2, dim=-1)
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query = apply_rotary_emb(query, cos, sin)
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key = apply_rotary_emb(key, cos, sin)
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return query, key
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@lru_cache(1)
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def get_rope(
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head_size: int,
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rotary_dim: int,
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max_position: int,
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base: float,
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rope_scaling: dict | None = None,
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):
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assert rope_scaling is None
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rotary_emb = RotaryEmbedding(head_size, rotary_dim, max_position, base)
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return rotary_emb
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