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"""Axial self attention module."""
import attr
import torch
from torch import nn
from torch import Tensor
@attr.s(eq=False)
class SelfAttention(nn.Module):
"""Axial self attention module."""
def __attrs_pre_init__(self) -> None:
super().__init__()
dim: int = attr.ib()
dim_head: int = attr.ib()
heads: int = attr.ib()
dim_hidden: int = attr.ib(init=False)
to_q: nn.Linear = attr.ib(init=False)
to_kv: nn.Linear = attr.ib(init=False)
to_out: nn.Linear = attr.ib(init=False)
def __attrs_post_init__(self) -> None:
self.dim_hidden = self.heads * self.dim_head
self.to_q = nn.Linear(self.dim, self.dim_hidden, bias=False)
self.to_kv = nn.Linear(self.dim, 2 * self.dim_hidden, bias=False)
self.to_out = nn.Linear(self.dim_hidden, self.dim)
def forward(self, x: Tensor) -> Tensor:
"""Applies self attention."""
q, k, v = (self.to_q(x), *self.to_kv(x).chunk(2, dim=-1))
b, _, d, h, e = *q.shape, self.heads, self.dim_head
merge_heads = (
lambda x: x.reshape(b, -1, h, e).transpose(1, 2).reshape(b * h, -1, e)
)
q, k, v = map(merge_heads, (q, k, v))
energy = torch.einsum("bie,bje->bij", q, k) * (e ** -0.5)
energy = energy.softmax(dim=-1)
attn = torch.einsum("bij,bje->bie", energy, v)
out = attn.reshape(b, h, -1, e).transpose(1, 2).reshape(b, -1, d)
return self.to_out(out)
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