Paints-UNDO / diffusers_vdm /attention.py
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import torch
import xformers.ops
import torch.nn.functional as F
from torch import nn
from einops import rearrange, repeat
from functools import partial
from diffusers_vdm.basics import zero_module, checkpoint, default, make_temporal_window
def sdp(q, k, v, heads):
b, _, C = q.shape
dim_head = C // heads
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(b, t.shape[1], heads, dim_head)
.permute(0, 2, 1, 3)
.reshape(b * heads, t.shape[1], dim_head)
.contiguous(),
(q, k, v),
)
out = xformers.ops.memory_efficient_attention(q, k, v)
out = (
out.unsqueeze(0)
.reshape(b, heads, out.shape[1], dim_head)
.permute(0, 2, 1, 3)
.reshape(b, out.shape[1], heads * dim_head)
)
return out
class RelativePosition(nn.Module):
""" https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """
def __init__(self, num_units, max_relative_position):
super().__init__()
self.num_units = num_units
self.max_relative_position = max_relative_position
self.embeddings_table = nn.Parameter(torch.Tensor(max_relative_position * 2 + 1, num_units))
nn.init.xavier_uniform_(self.embeddings_table)
def forward(self, length_q, length_k):
device = self.embeddings_table.device
range_vec_q = torch.arange(length_q, device=device)
range_vec_k = torch.arange(length_k, device=device)
distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
distance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position)
final_mat = distance_mat_clipped + self.max_relative_position
final_mat = final_mat.long()
embeddings = self.embeddings_table[final_mat]
return embeddings
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.,
relative_position=False, temporal_length=None, video_length=None, image_cross_attention=False,
image_cross_attention_scale=1.0, image_cross_attention_scale_learnable=False,
text_context_len=77, temporal_window_for_spatial_self_attention=False):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
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(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))
self.is_temporal_attention = temporal_length is not None
self.relative_position = relative_position
if self.relative_position:
assert self.is_temporal_attention
self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
self.video_length = video_length
self.temporal_window_for_spatial_self_attention = temporal_window_for_spatial_self_attention
self.temporal_window_type = 'prv'
self.image_cross_attention = image_cross_attention
self.image_cross_attention_scale = image_cross_attention_scale
self.text_context_len = text_context_len
self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable
if self.image_cross_attention:
self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False)
self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False)
if image_cross_attention_scale_learnable:
self.register_parameter('alpha', nn.Parameter(torch.tensor(0.)) )
def forward(self, x, context=None, mask=None):
if self.is_temporal_attention:
return self.temporal_forward(x, context=context, mask=mask)
else:
return self.spatial_forward(x, context=context, mask=mask)
def temporal_forward(self, x, context=None, mask=None):
assert mask is None, 'Attention mask not implemented!'
assert context is None, 'Temporal attention only supports self attention!'
q = self.to_q(x)
k = self.to_k(x)
v = self.to_v(x)
out = sdp(q, k, v, self.heads)
return self.to_out(out)
def spatial_forward(self, x, context=None, mask=None):
assert mask is None, 'Attention mask not implemented!'
spatial_self_attn = (context is None)
k_ip, v_ip, out_ip = None, None, None
q = self.to_q(x)
context = default(context, x)
if spatial_self_attn:
k = self.to_k(context)
v = self.to_v(context)
if self.temporal_window_for_spatial_self_attention:
k = make_temporal_window(k, t=self.video_length, method=self.temporal_window_type)
v = make_temporal_window(v, t=self.video_length, method=self.temporal_window_type)
elif self.image_cross_attention:
context, context_image = context
k = self.to_k(context)
v = self.to_v(context)
k_ip = self.to_k_ip(context_image)
v_ip = self.to_v_ip(context_image)
else:
raise NotImplementedError('Traditional prompt-only attention without IP-Adapter is illegal now.')
out = sdp(q, k, v, self.heads)
if k_ip is not None:
out_ip = sdp(q, k_ip, v_ip, self.heads)
if self.image_cross_attention_scale_learnable:
out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha) + 1)
else:
out = out + self.image_cross_attention_scale * out_ip
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,
disable_self_attn=False, attention_cls=None, video_length=None, image_cross_attention=False, image_cross_attention_scale=1.0, image_cross_attention_scale_learnable=False, text_context_len=77):
super().__init__()
attn_cls = CrossAttention if attention_cls is None else attention_cls
self.disable_self_attn = disable_self_attn
self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
context_dim=context_dim if self.disable_self_attn else None, video_length=video_length)
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout, video_length=video_length, image_cross_attention=image_cross_attention, image_cross_attention_scale=image_cross_attention_scale, image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,text_context_len=text_context_len)
self.image_cross_attention = image_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, mask=None, **kwargs):
## implementation tricks: because checkpointing doesn't support non-tensor (e.g. None or scalar) arguments
input_tuple = (x,) ## should not be (x), otherwise *input_tuple will decouple x into multiple arguments
if context is not None:
input_tuple = (x, context)
if mask is not None:
forward_mask = partial(self._forward, mask=mask)
return checkpoint(forward_mask, (x,), self.parameters(), self.checkpoint)
return checkpoint(self._forward, input_tuple, self.parameters(), self.checkpoint)
def _forward(self, x, context=None, mask=None):
x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None, mask=mask) + x
x = self.attn2(self.norm2(x), context=context, mask=mask) + x
x = self.ff(self.norm3(x)) + x
return x
class SpatialTransformer(nn.Module):
"""
Transformer block for image-like data in spatial axis.
First, project the input (aka embedding)
and reshape to b, t, d.
Then apply standard transformer action.
Finally, reshape to image
NEW: use_linear for more efficiency instead of the 1x1 convs
"""
def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None,
use_checkpoint=True, disable_self_attn=False, use_linear=False, video_length=None,
image_cross_attention=False, image_cross_attention_scale_learnable=False):
super().__init__()
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
if not use_linear:
self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
else:
self.proj_in = nn.Linear(in_channels, inner_dim)
attention_cls = None
self.transformer_blocks = nn.ModuleList([
BasicTransformerBlock(
inner_dim,
n_heads,
d_head,
dropout=dropout,
context_dim=context_dim,
disable_self_attn=disable_self_attn,
checkpoint=use_checkpoint,
attention_cls=attention_cls,
video_length=video_length,
image_cross_attention=image_cross_attention,
image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,
) for d in range(depth)
])
if not use_linear:
self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0))
else:
self.proj_out = zero_module(nn.Linear(inner_dim, in_channels))
self.use_linear = use_linear
def forward(self, x, context=None, **kwargs):
b, c, h, w = x.shape
x_in = x
x = self.norm(x)
if not self.use_linear:
x = self.proj_in(x)
x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
if self.use_linear:
x = self.proj_in(x)
for i, block in enumerate(self.transformer_blocks):
x = block(x, context=context, **kwargs)
if self.use_linear:
x = self.proj_out(x)
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
if not self.use_linear:
x = self.proj_out(x)
return x + x_in
class TemporalTransformer(nn.Module):
"""
Transformer block for image-like data in temporal axis.
First, 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,
use_checkpoint=True, use_linear=False, only_self_att=True, causal_attention=False, causal_block_size=1,
relative_position=False, temporal_length=None):
super().__init__()
self.only_self_att = only_self_att
self.relative_position = relative_position
self.causal_attention = causal_attention
self.causal_block_size = causal_block_size
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
if not use_linear:
self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
else:
self.proj_in = nn.Linear(in_channels, inner_dim)
if relative_position:
assert(temporal_length is not None)
attention_cls = partial(CrossAttention, relative_position=True, temporal_length=temporal_length)
else:
attention_cls = partial(CrossAttention, temporal_length=temporal_length)
if self.causal_attention:
assert(temporal_length is not None)
self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length]))
if self.only_self_att:
context_dim = None
self.transformer_blocks = nn.ModuleList([
BasicTransformerBlock(
inner_dim,
n_heads,
d_head,
dropout=dropout,
context_dim=context_dim,
attention_cls=attention_cls,
checkpoint=use_checkpoint) for d in range(depth)
])
if not use_linear:
self.proj_out = zero_module(nn.Conv1d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0))
else:
self.proj_out = zero_module(nn.Linear(inner_dim, in_channels))
self.use_linear = use_linear
def forward(self, x, context=None):
b, c, t, h, w = x.shape
x_in = x
x = self.norm(x)
x = rearrange(x, 'b c t h w -> (b h w) c t').contiguous()
if not self.use_linear:
x = self.proj_in(x)
x = rearrange(x, 'bhw c t -> bhw t c').contiguous()
if self.use_linear:
x = self.proj_in(x)
temp_mask = None
if self.causal_attention:
# slice the from mask map
temp_mask = self.mask[:,:t,:t].to(x.device)
if temp_mask is not None:
mask = temp_mask.to(x.device)
mask = repeat(mask, 'l i j -> (l bhw) i j', bhw=b*h*w)
else:
mask = None
if self.only_self_att:
## note: if no context is given, cross-attention defaults to self-attention
for i, block in enumerate(self.transformer_blocks):
x = block(x, mask=mask)
x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous()
else:
x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous()
context = rearrange(context, '(b t) l con -> b t l con', t=t).contiguous()
for i, block in enumerate(self.transformer_blocks):
# calculate each batch one by one (since number in shape could not greater then 65,535 for some package)
for j in range(b):
context_j = repeat(
context[j],
't l con -> (t r) l con', r=(h * w) // t, t=t).contiguous()
## note: causal mask will not applied in cross-attention case
x[j] = block(x[j], context=context_j)
if self.use_linear:
x = self.proj_out(x)
x = rearrange(x, 'b (h w) t c -> b c t h w', h=h, w=w).contiguous()
if not self.use_linear:
x = rearrange(x, 'b hw t c -> (b hw) c t').contiguous()
x = self.proj_out(x)
x = rearrange(x, '(b h w) c t -> b c t h w', b=b, h=h, w=w).contiguous()
return x + x_in
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)