alcm / ldm /modules /new_attention.py
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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
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 Conv1dGEGLU(nn.Module):
def __init__(self, dim_in, dim_out,kernel_size = 9):
super().__init__()
self.proj = nn.Conv1d(dim_in, dim_out * 2,kernel_size=kernel_size,padding=kernel_size//2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=1)
return x * F.gelu(gate)
class Conv1dFeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.,kernel_size = 9):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
nn.Conv1d(dim, inner_dim,kernel_size=kernel_size,padding=kernel_size//2),
nn.GELU()
) if not glu else Conv1dGEGLU(dim, inner_dim)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
nn.Conv1d(inner_dim, dim_out,kernel_size=kernel_size,padding=kernel_size//2)
)
def forward(self, x): # x shape (B,C,T)
return self.net(x)
def zero_module(module):
"""
Zero out the parameters of a module and return it.zero-initializing the final convolutional layer in each block prior to any residual connections can accelerate training.
"""
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 CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):# 如果设置了context_dim就不是自注意力了
super().__init__()
inner_dim = dim_head * heads # inner_dim == SpatialTransformer.model_channels
context_dim = default(context_dim, query_dim)
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, query_dim),
nn.Dropout(dropout)
)
def forward(self, x, context=None, mask=None):# x:(b,T,C), context:(b,seq_len,context_dim)
h = self.heads
q = self.to_q(x)# q:(b,T,inner_dim)
context = default(context, x)
k = self.to_k(context)# (b,seq_len,inner_dim)
v = self.to_v(context)# (b,seq_len,inner_dim)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))# n is seq_len for k and v
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale # (b*head,T,seq_len)
if exists(mask):# false
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
attn = sim.softmax(dim=-1)
out = einsum('b i j, b j d -> b i d', attn, v)# (b*head,T,inner_dim/head)
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)# (b,T,inner_dim)
return self.to_out(out)
class BasicTransformerBlock(nn.Module):
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True): # 1 self 1 cross or 2 self
super().__init__()
self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention,if context is none
self.ff = Conv1dFeedForward(dim, dropout=dropout, glu=gated_ff)
self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
heads=n_heads, dim_head=d_head, dropout=dropout) # use as cross attention
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.norm3 = nn.LayerNorm(dim)
self.checkpoint = checkpoint
def forward(self, x, context=None):
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
def _forward(self, x, context=None):# x shape:(B,T,C)
x = self.attn1(self.norm1(x)) + x
x = self.attn2(self.norm2(x), context=context) + x
x = self.ff(self.norm3(x).permute(0,2,1)).permute(0,2,1) + x
return x
class TemporalTransformer(nn.Module):
"""
Transformer block for image-like data.
First, project the input (aka embedding)
and reshape to b, t, d.
Then apply standard transformer action.
Finally, reshape to image
"""
def __init__(self, in_channels, n_heads, d_head,
depth=1, dropout=0., context_dim=None):
super().__init__()
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = Normalize(in_channels)
self.proj_in = nn.Conv1d(in_channels,
inner_dim,
kernel_size=1,
stride=1,
padding=0)
self.transformer_blocks = nn.ModuleList(
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
for d in range(depth)]
)
self.proj_out = zero_module(nn.Conv1d(inner_dim,
in_channels,
kernel_size=1,
stride=1,
padding=0))# initialize with zero
def forward(self, x, context=None):# x shape (b,c,t)
# note: if no context is given, cross-attention defaults to self-attention
x_in = x
x = self.norm(x)# group norm
x = self.proj_in(x)# no shape change
x = rearrange(x,'b c t -> b t c')
for block in self.transformer_blocks:
x = block(x, context=context)# context shape [b,seq_len=77,context_dim]
x = rearrange(x,'b t c -> b c t')
x = self.proj_out(x)
return x + x_in
class PositionalEncoding(nn.Module):
def __init__(self, num_hiddens, max_len=2000):
super(PositionalEncoding, self).__init__()
self.P = torch.zeros((1, max_len, num_hiddens))
X = torch.arange(max_len, dtype=torch.float32).reshape(-1, 1) / torch.pow(10000,
torch.arange(0, num_hiddens, 2, dtype=torch.float32) / num_hiddens)
self.P[:, :, 0::2] = torch.sin(X)
self.P[:, :, 1::2] = torch.cos(X)
def forward(self, x):
x = x + self.P[:, :x.shape[1], :].to(x.device)
return x
class PositionEmbedding(nn.Module):
MODE_EXPAND = 'MODE_EXPAND'
MODE_ADD = 'MODE_ADD'
MODE_CONCAT = 'MODE_CONCAT'
def __init__(self,
num_embeddings,
embedding_dim,
mode=MODE_ADD):
super(PositionEmbedding, self).__init__()
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
self.mode = mode
if self.mode == self.MODE_EXPAND:
self.weight = nn.Parameter(torch.Tensor(num_embeddings * 2 + 1, embedding_dim))
else:
self.weight = nn.Parameter(torch.Tensor(num_embeddings, embedding_dim))
self.reset_parameters()
def reset_parameters(self):
# use xavier_normal_ to initialize
torch.nn.init.xavier_normal_(self.weight)
# use sin cons to initialize
# X = torch.arange(self.num_embeddings, dtype=torch.float32).reshape(-1, 1) / torch.pow(10000,
# torch.arange(0, self.embedding_dim, 2, dtype=torch.float32) / self.embedding_dim)
# init = torch.Tensor(self.num_embeddings,self.embedding_dim)
# init[:, 0::2] = torch.sin(X)
# init[:, 1::2] = torch.cos(X)
# self.weight.data.copy_(init)
def forward(self, x):
if self.mode == self.MODE_EXPAND:
indices = torch.clamp(x, -self.num_embeddings, self.num_embeddings) + self.num_embeddings
return F.embedding(indices.type(torch.LongTensor), self.weight)
batch_size, seq_len = x.size()[:2]
embeddings = self.weight[:seq_len, :].view(1, seq_len, self.embedding_dim)
if self.mode == self.MODE_ADD:
return x + embeddings
if self.mode == self.MODE_CONCAT:
return torch.cat((x, embeddings.repeat(batch_size, 1, 1)), dim=-1)
raise NotImplementedError('Unknown mode: %s' % self.mode)
def extra_repr(self):
return 'num_embeddings={}, embedding_dim={}, mode={}'.format(
self.num_embeddings, self.embedding_dim, self.mode,
)
class TemporalTransformerSkip(TemporalTransformer):
def __init__(self, in_channels, n_heads, d_head,
depth=1, dropout=0., context_dim=None):
super().__init__(in_channels, n_heads, d_head,
depth, dropout, context_dim)
self.skip_linear = nn.Linear(2 * in_channels, in_channels)
def forward(self, x,skip, context=None):# x shape (b,c,t)
# note: if no context is given, cross-attention defaults to self-attention
x_in = x
x = self.norm(x)# group norm
x = self.proj_in(x)# no shape change
x = rearrange(x,'b c t -> b t c')
skip = rearrange(skip,'b c t -> b t c')
x = self.skip_linear(torch.cat([x,skip],dim=-1))
for block in self.transformer_blocks:
x = block(x, context=context)# context shape [b,seq_len=77,context_dim]
x = rearrange(x,'b t c -> b c t')
x = self.proj_out(x)
return x + x_in