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# MIT License
# Copyright (c) 2022 Karl Stelzner
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# This file comes from https://github.com/stelzner/srt
import torch
from einops import rearrange
from torch import nn
class Attention(nn.Module):
def __init__(
self, dim, heads=8, dim_head=64, dropout=0.0, selfatt=True, kv_dim=None
):
super().__init__()
inner_dim = dim_head * heads
project_out = not (heads == 1 and dim_head == dim)
self.heads = heads
self.scale = dim_head**-0.5
self.attend = nn.Softmax(dim=-1)
if selfatt:
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
else:
self.to_q = nn.Linear(dim, inner_dim, bias=False)
self.to_kv = nn.Linear(kv_dim, inner_dim * 2, bias=False)
self.to_out = (
nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout))
if project_out
else nn.Identity()
)
def forward(self, x, z=None):
if z is None:
qkv = self.to_qkv(x).chunk(3, dim=-1)
else:
q = self.to_q(x)
k, v = self.to_kv(z).chunk(2, dim=-1)
qkv = (q, k, v)
q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), qkv)
dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
attn = self.attend(dots)
out = torch.matmul(attn, v)
out = rearrange(out, "b h n d -> b n (h d)")
return self.to_out(out)
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