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from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
# from ldm.modules.diffusionmodules.util import checkpoint, FourierEmbedder
from torch.utils import checkpoint
try:
import xformers
import xformers.ops
XFORMERS_IS_AVAILBLE = True
except:
XFORMERS_IS_AVAILBLE = False
def exists(val):
return val is not None
def uniq(arr):
return{el: True for el in arr}.keys()
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def max_neg_value(t):
return -torch.finfo(t.dtype).max
def init_(tensor):
dim = tensor.shape[-1]
std = 1 / math.sqrt(dim)
tensor.uniform_(-std, std)
return tensor
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
nn.Linear(dim, inner_dim),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
nn.Linear(inner_dim, dim_out)
)
def forward(self, x):
return self.net(x)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def Normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
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 CrossAttention(nn.Module):
def __init__(self, query_dim, key_dim, value_dim, heads=8, dim_head=64, dropout=0):
super().__init__()
inner_dim = dim_head * heads
self.scale = dim_head ** -0.5
self.heads = heads
self.dim_head = dim_head
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(key_dim, inner_dim, bias=False)
self.to_v = nn.Linear(value_dim, inner_dim, bias=False)
self.to_out = nn.Sequential( nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) )
def fill_inf_from_mask(self, sim, mask):
if mask is not None:
B,M = mask.shape
mask = mask.unsqueeze(1).repeat(1,self.heads,1).reshape(B*self.heads,1,-1)
max_neg_value = -torch.finfo(sim.dtype).max
sim.masked_fill_(~mask, max_neg_value)
return sim
def forward_plain(self, x, key, value, mask=None):
q = self.to_q(x) # B*N*(H*C)
k = self.to_k(key) # B*M*(H*C)
v = self.to_v(value) # B*M*(H*C)
B, N, HC = q.shape
_, M, _ = key.shape
H = self.heads
C = HC // H
q = q.view(B,N,H,C).permute(0,2,1,3).reshape(B*H,N,C) # (B*H)*N*C
k = k.view(B,M,H,C).permute(0,2,1,3).reshape(B*H,M,C) # (B*H)*M*C
v = v.view(B,M,H,C).permute(0,2,1,3).reshape(B*H,M,C) # (B*H)*M*C
sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale # (B*H)*N*M
self.fill_inf_from_mask(sim, mask)
attn = sim.softmax(dim=-1) # (B*H)*N*M
out = torch.einsum('b i j, b j d -> b i d', attn, v) # (B*H)*N*C
out = out.view(B,H,N,C).permute(0,2,1,3).reshape(B,N,(H*C)) # B*N*(H*C)
return self.to_out(out)
def forward(self, x, key, value, mask=None):
if not XFORMERS_IS_AVAILBLE:
return self.forward_plain(x, key, value, mask)
q = self.to_q(x) # B*N*(H*C)
k = self.to_k(key) # B*M*(H*C)
v = self.to_v(value) # B*M*(H*C)
b, _, _ = q.shape
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(b, t.shape[1], self.heads, self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b * self.heads, t.shape[1], self.dim_head)
.contiguous(),
(q, k, v),
)
# actually compute the attention, what we cannot get enough of
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None)
if exists(mask):
raise NotImplementedError
out = (
out.unsqueeze(0)
.reshape(b, self.heads, out.shape[1], self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b, out.shape[1], self.heads * self.dim_head)
)
return self.to_out(out)
class SelfAttention(nn.Module):
def __init__(self, query_dim, heads=8, dim_head=64, dropout=0.):
super().__init__()
inner_dim = dim_head * heads
self.scale = dim_head ** -0.5
self.heads = heads
self.dim_head = dim_head
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(query_dim, inner_dim, bias=False)
self.to_v = nn.Linear(query_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) )
def forward_plain(self, x):
q = self.to_q(x) # B*N*(H*C)
k = self.to_k(x) # B*N*(H*C)
v = self.to_v(x) # B*N*(H*C)
B, N, HC = q.shape
H = self.heads
C = HC // H
q = q.view(B,N,H,C).permute(0,2,1,3).reshape(B*H,N,C) # (B*H)*N*C
k = k.view(B,N,H,C).permute(0,2,1,3).reshape(B*H,N,C) # (B*H)*N*C
v = v.view(B,N,H,C).permute(0,2,1,3).reshape(B*H,N,C) # (B*H)*N*C
sim = torch.einsum('b i c, b j c -> b i j', q, k) * self.scale # (B*H)*N*N
attn = sim.softmax(dim=-1) # (B*H)*N*N
out = torch.einsum('b i j, b j c -> b i c', attn, v) # (B*H)*N*C
out = out.view(B,H,N,C).permute(0,2,1,3).reshape(B,N,(H*C)) # B*N*(H*C)
return self.to_out(out)
def forward(self, x, context=None, mask=None):
if not XFORMERS_IS_AVAILBLE:
return self.forward_plain(x)
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
v = self.to_v(context)
b, _, _ = q.shape
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(b, t.shape[1], self.heads, self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b * self.heads, t.shape[1], self.dim_head)
.contiguous(),
(q, k, v),
)
# actually compute the attention, what we cannot get enough of
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None)
if exists(mask):
raise NotImplementedError
out = (
out.unsqueeze(0)
.reshape(b, self.heads, out.shape[1], self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b, out.shape[1], self.heads * self.dim_head)
)
return self.to_out(out)
class GatedCrossAttentionDense(nn.Module):
def __init__(self, query_dim, key_dim, value_dim, n_heads, d_head):
super().__init__()
self.attn = CrossAttention(query_dim=query_dim, key_dim=key_dim, value_dim=value_dim, heads=n_heads, dim_head=d_head)
self.ff = FeedForward(query_dim, glu=True)
self.norm1 = nn.LayerNorm(query_dim)
self.norm2 = nn.LayerNorm(query_dim)
self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)) )
self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)) )
# this can be useful: we can externally change magnitude of tanh(alpha)
# for example, when it is set to 0, then the entire model is same as original one
self.scale = 1
def forward(self, x, objs):
x = x + self.scale*torch.tanh(self.alpha_attn) * self.attn( self.norm1(x), objs, objs)
x = x + self.scale*torch.tanh(self.alpha_dense) * self.ff( self.norm2(x) )
return x
class GatedSelfAttentionDense(nn.Module):
def __init__(self, query_dim, context_dim, n_heads, d_head):
super().__init__()
# we need a linear projection since we need cat visual feature and obj feature
self.linear = nn.Linear(context_dim, query_dim)
self.attn = SelfAttention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
self.ff = FeedForward(query_dim, glu=True)
self.norm1 = nn.LayerNorm(query_dim)
self.norm2 = nn.LayerNorm(query_dim)
self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)) )
self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)) )
# this can be useful: we can externally change magnitude of tanh(alpha)
# for example, when it is set to 0, then the entire model is same as original one
self.scale = 1
def forward(self, x, objs):
N_visual = x.shape[1]
objs = self.linear(objs)
x = x + self.scale*torch.tanh(self.alpha_attn) * self.attn( self.norm1(torch.cat([x,objs],dim=1)) )[:,0:N_visual,:]
x = x + self.scale*torch.tanh(self.alpha_dense) * self.ff( self.norm2(x) )
return x
class BasicTransformerBlock(nn.Module):
def __init__(self, query_dim, key_dim, value_dim, n_heads, d_head, fuser_type, use_checkpoint=True):
super().__init__()
self.attn1 = SelfAttention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
self.ff = FeedForward(query_dim, glu=True)
self.attn2 = CrossAttention(query_dim=query_dim, key_dim=key_dim, value_dim=value_dim, heads=n_heads, dim_head=d_head)
self.norm1 = nn.LayerNorm(query_dim)
self.norm2 = nn.LayerNorm(query_dim)
self.norm3 = nn.LayerNorm(query_dim)
self.use_checkpoint = use_checkpoint
if fuser_type == "gatedSA":
# note key_dim here actually is context_dim
self.fuser = GatedSelfAttentionDense(query_dim, key_dim, n_heads, d_head)
elif fuser_type == "gatedCA":
self.fuser = GatedCrossAttentionDense(query_dim, key_dim, value_dim, n_heads, d_head)
else:
assert False
def forward(self, x, context, objs):
# return checkpoint(self._forward, (x, context, objs), self.parameters(), self.use_checkpoint)
if self.use_checkpoint and x.requires_grad:
return checkpoint.checkpoint(self._forward, x, context, objs)
else:
return self._forward(x, context, objs)
def _forward(self, x, context, objs):
x = self.attn1( self.norm1(x) ) + x
x = self.fuser(x, objs) # identity mapping in the beginning
x = self.attn2(self.norm2(x), context, context) + x
x = self.ff(self.norm3(x)) + x
return x
class SpatialTransformer(nn.Module):
def __init__(self, in_channels, key_dim, value_dim, n_heads, d_head, depth=1, fuser_type=None, use_checkpoint=True):
super().__init__()
self.in_channels = in_channels
query_dim = n_heads * d_head
self.norm = Normalize(in_channels)
self.proj_in = nn.Conv2d(in_channels,
query_dim,
kernel_size=1,
stride=1,
padding=0)
self.transformer_blocks = nn.ModuleList(
[BasicTransformerBlock(query_dim, key_dim, value_dim, n_heads, d_head, fuser_type, use_checkpoint=use_checkpoint)
for d in range(depth)]
)
self.proj_out = zero_module(nn.Conv2d(query_dim,
in_channels,
kernel_size=1,
stride=1,
padding=0))
def forward(self, x, context, objs):
b, c, h, w = x.shape
x_in = x
x = self.norm(x)
x = self.proj_in(x)
x = rearrange(x, 'b c h w -> b (h w) c')
for block in self.transformer_blocks:
x = block(x, context, objs)
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
x = self.proj_out(x)
return x + x_in |