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from torch import nn
from einops import rearrange
from .normalize import Normalize
from .conv import PaddedConv3D
from .ops import video_to_image
class LinearAttention(nn.Module):
def __init__(self, dim, heads=4, dim_head=32):
super().__init__()
self.heads = heads
hidden_dim = dim_head * heads
self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
self.to_out = nn.Conv2d(hidden_dim, dim, 1)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.to_qkv(x)
q, k, v = rearrange(
qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
)
k = k.softmax(dim=-1)
context = torch.einsum("bhdn,bhen->bhde", k, v)
out = torch.einsum("bhde,bhdn->bhen", context, q)
out = rearrange(
out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
)
return self.to_out(out)
class LinAttnBlock(LinearAttention):
"""to match AttnBlock usage"""
def __init__(self, in_channels):
super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
class AttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
self.k = torch.nn.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
self.v = torch.nn.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
self.proj_out = torch.nn.Conv2d(
in_channels, in_channels, kernel_size=1, stride=1, padding=0
)
@video_to_image
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b, c, h, w = q.shape
q = q.reshape(b, c, h * w)
q = q.permute(0, 2, 1) # b,hw,c
k = k.reshape(b, c, h * w) # b,c,hw
w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = w_ * (int(c) ** (-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = v.reshape(b, c, h * w)
w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = h_.reshape(b, c, h, w)
h_ = self.proj_out(h_)
return x + h_
def make_attn(in_channels, attn_type="vanilla"):
assert attn_type in ["vanilla", "linear", "none"], f"attn_type {attn_type} unknown"
if attn_type == "vanilla":
return AttnBlock(in_channels)
elif attn_type == "none":
return nn.Identity(in_channels)
else:
return LinAttnBlock(in_channels)
class AttnBlock3D(nn.Module):
"""
Thanks to https://github.com/PKU-YuanGroup/Open-Sora-Plan/pull/172.
"""
def __init__(self, in_channels, is_causal=True):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = PaddedConv3D(
in_channels, in_channels, kernel_size=1, stride=1, is_causal=is_causal
)
self.k = PaddedConv3D(
in_channels, in_channels, kernel_size=1, stride=1, is_causal=is_causal
)
self.v = PaddedConv3D(
in_channels, in_channels, kernel_size=1, stride=1, is_causal=is_causal
)
self.proj_out = PaddedConv3D(
in_channels, in_channels, kernel_size=1, stride=1, is_causal=is_causal
)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
# q: (b c t h w) -> (b t c h w) -> (b*t c h*w) -> (b*t h*w c)
b, c, t, h, w = q.shape
q = q.permute(0, 2, 1, 3, 4)
q = q.reshape(b * t, c, h * w)
q = q.permute(0, 2, 1)
# k: (b c t h w) -> (b t c h w) -> (b*t c h*w)
k = k.permute(0, 2, 1, 3, 4)
k = k.reshape(b * t, c, h * w)
# w: (b*t hw hw)
w_ = torch.bmm(q, k)
w_ = w_ * (int(c) ** (-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
# v: (b c t h w) -> (b t c h w) -> (bt c hw)
# w_: (bt hw hw) -> (bt hw hw)
v = v.permute(0, 2, 1, 3, 4)
v = v.reshape(b * t, c, h * w)
w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
# h_: (b*t c hw) -> (b t c h w) -> (b c t h w)
h_ = h_.reshape(b, t, c, h, w)
h_ = h_.permute(0, 2, 1, 3, 4)
h_ = self.proj_out(h_)
return x + h_
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