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attention2d.py
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import math
import torch
import torch.nn.functional as F
from torch import nn
# unet_grad_tts.py
class LinearAttention(torch.nn.Module):
def __init__(self, dim, heads=4, dim_head=32):
super(LinearAttention, self).__init__()
self.heads = heads
self.dim_head = dim_head
hidden_dim = dim_head * heads
self.to_qkv = torch.nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
self.to_out = torch.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)
q, k, v = (
qkv.reshape(b, 3, self.heads, self.dim_head, h, w)
.permute(1, 0, 2, 3, 4, 5)
.reshape(3, b, self.heads, self.dim_head, -1)
)
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)
out = out.reshape(b, self.heads, self.dim_head, h, w).reshape(b, self.heads * self.dim_head, h, w)
return self.to_out(out)
# unet_glide.py & unet_ldm.py
class AttentionBlock(nn.Module):
"""
An attention block that allows spatial positions to attend to each other.
Originally ported from here, but adapted to the N-d case.
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
"""
def __init__(
self,
channels,
num_heads=1,
num_head_channels=-1,
use_checkpoint=False,
encoder_channels=None,
use_new_attention_order=False, # TODO(Patrick) -> is never used, maybe delete?
overwrite_qkv=False,
):
super().__init__()
self.channels = channels
if num_head_channels == -1:
self.num_heads = num_heads
else:
assert (
channels % num_head_channels == 0
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
self.num_heads = channels // num_head_channels
self.use_checkpoint = use_checkpoint
self.norm = normalization(channels, swish=0.0)
self.qkv = conv_nd(1, channels, channels * 3, 1)
self.n_heads = self.num_heads
if encoder_channels is not None:
self.encoder_kv = conv_nd(1, encoder_channels, channels * 2, 1)
self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
self.overwrite_qkv = overwrite_qkv
if overwrite_qkv:
in_channels = 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)
self.is_overwritten = False
def set_weights(self, module):
if self.overwrite_qkv:
qkv_weight = torch.cat([module.q.weight.data, module.k.weight.data, module.v.weight.data], dim=0)[:, :, :, 0]
qkv_bias = torch.cat([module.q.bias.data, module.k.bias.data, module.v.bias.data], dim=0)
self.qkv.weight.data = qkv_weight
self.qkv.bias.data = qkv_bias
proj_out = zero_module(conv_nd(1, self.channels, self.channels, 1))
proj_out.weight.data = module.proj_out.weight.data[:, :, :, 0]
proj_out.bias.data = module.proj_out.bias.data
self.proj_out = proj_out
def forward(self, x, encoder_out=None):
if self.overwrite_qkv and not self.is_overwritten:
self.set_weights(self)
self.is_overwritten = True
b, c, *spatial = x.shape
hid_states = self.norm(x).view(b, c, -1)
qkv = self.qkv(hid_states)
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 * self.n_heads)
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
if encoder_out is not None:
encoder_kv = self.encoder_kv(encoder_out)
assert encoder_kv.shape[1] == self.n_heads * ch * 2
ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(ch, dim=1)
k = torch.cat([ek, k], dim=-1)
v = torch.cat([ev, v], dim=-1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = torch.einsum("bct,bcs->bts", q * scale, k * scale) # More stable with f16 than dividing afterwards
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
a = torch.einsum("bts,bcs->bct", weight, v)
h = a.reshape(bs, -1, length)
h = self.proj_out(h)
return x + h.reshape(b, c, *spatial)
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
class GroupNorm32(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-5, affine=True):
super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps, affine=affine)
self.swish = swish
def forward(self, x):
y = super().forward(x.float()).to(x.dtype)
if self.swish == 1.0:
y = F.silu(y)
elif self.swish:
y = y * F.sigmoid(y * float(self.swish))
return y
def normalization(channels, swish=0.0, eps=1e-5):
"""
Make a standard normalization layer, with an optional swish activation.
:param channels: number of input channels. :return: an nn.Module for normalization.
"""
return GroupNorm32(num_channels=channels, num_groups=32, swish=swish, eps=eps, affine=True)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
# unet_score_estimation.py
# class AttnBlockpp(nn.Module):
# """Channel-wise self-attention block. Modified from DDPM."""
#
# def __init__(self, channels, skip_rescale=False, init_scale=0.0):
# super().__init__()
# self.GroupNorm_0 = nn.GroupNorm(num_groups=min(channels // 4, 32), num_channels=channels, eps=1e-6)
# self.NIN_0 = NIN(channels, channels)
# self.NIN_1 = NIN(channels, channels)
# self.NIN_2 = NIN(channels, channels)
# self.NIN_3 = NIN(channels, channels, init_scale=init_scale)
# self.skip_rescale = skip_rescale
#
# def forward(self, x):
# B, C, H, W = x.shape
# h = self.GroupNorm_0(x)
# q = self.NIN_0(h)
# k = self.NIN_1(h)
# v = self.NIN_2(h)
#
# w = torch.einsum("bchw,bcij->bhwij", q, k) * (int(C) ** (-0.5))
# w = torch.reshape(w, (B, H, W, H * W))
# w = F.softmax(w, dim=-1)
# w = torch.reshape(w, (B, H, W, H, W))
# h = torch.einsum("bhwij,bcij->bchw", w, v)
# h = self.NIN_3(h)
# if not self.skip_rescale:
# return x + h
# else:
# return (x + h) / np.sqrt(2.0)