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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.amp.autocast_mode import autocast
from inspect import isfunction
from einops import rearrange, repeat
from typing import Optional, Any
from .util import checkpoint, zero_module
try:
import xformers # type: ignore
import xformers.ops # type: ignore
XFORMERS_IS_AVAILBLE = True
except:
print(f'[WARN] xformers is unavailable!')
XFORMERS_IS_AVAILBLE = False
# CrossAttn precision handling
import os
_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
def default(val, d):
if val is not None:
return val
return d() if isfunction(d) else d
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=0.0):
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)
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
super().__init__()
inner_dim = dim_head * heads
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):
h = self.heads
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
# force cast to fp32 to avoid overflowing
if _ATTN_PRECISION == "fp32":
with autocast(enabled=False, device_type="cuda"):
q, k = q.float(), k.float()
sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
else:
sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
del q, k
if mask is not None:
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
sim = sim.softmax(dim=-1)
out = torch.einsum("b i j, b j d -> b i d", sim, v)
out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
return self.to_out(out)
class MemoryEfficientCrossAttention(nn.Module):
# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
super().__init__()
# print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using {heads} heads.")
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
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.attention_op: Optional[Any] = None
def forward(self, x, context=None, mask=None):
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=self.attention_op
)
if mask is not None:
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 BasicTransformerBlock(nn.Module):
ATTENTION_MODES = {
"softmax": CrossAttention,
"softmax-xformers": MemoryEfficientCrossAttention,
} # vanilla attention
def __init__(
self,
dim,
n_heads,
d_head,
dropout=0.0,
context_dim=None,
gated_ff=True,
checkpoint=True,
disable_self_attn=False,
):
super().__init__()
attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
assert attn_mode in self.ATTENTION_MODES
attn_cls = self.ATTENTION_MODES[attn_mode]
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,
) # is a self-attention if not self.disable_self_attn
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,
) # is self-attn if context is none
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 = (
self.attn1(
self.norm1(x), context=context if self.disable_self_attn else None
)
+ x
)
x = self.attn2(self.norm2(x), context=context) + x
x = self.ff(self.norm3(x)) + x
return x
class SpatialTransformer(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
NEW: use_linear for more efficiency instead of the 1x1 convs
"""
def __init__(
self,
in_channels,
n_heads,
d_head,
depth=1,
dropout=0.0,
context_dim=None,
disable_self_attn=False,
use_linear=False,
use_checkpoint=True,
):
super().__init__()
assert context_dim is not None
if not isinstance(context_dim, list):
context_dim = [context_dim]
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = 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)
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock(
inner_dim,
n_heads,
d_head,
dropout=dropout,
context_dim=context_dim[d],
disable_self_attn=disable_self_attn,
checkpoint=use_checkpoint,
)
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(in_channels, inner_dim))
self.use_linear = use_linear
def forward(self, x, context=None):
# note: if no context is given, cross-attention defaults to self-attention
if not isinstance(context, list):
context = [context]
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[i])
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 BasicTransformerBlock3D(BasicTransformerBlock):
def forward(self, x, context=None, num_frames=1):
return checkpoint(
self._forward, (x, context, num_frames), self.parameters(), self.checkpoint
)
def _forward(self, x, context=None, num_frames=1):
x = rearrange(x, "(b f) l c -> b (f l) c", f=num_frames).contiguous()
x = (
self.attn1(
self.norm1(x), context=context if self.disable_self_attn else None
)
+ x
)
x = rearrange(x, "b (f l) c -> (b f) l c", f=num_frames).contiguous()
x = self.attn2(self.norm2(x), context=context) + x
x = self.ff(self.norm3(x)) + x
return x
class SpatialTransformer3D(nn.Module):
"""3D self-attention"""
def __init__(
self,
in_channels,
n_heads,
d_head,
depth=1,
dropout=0.0,
context_dim=None,
disable_self_attn=False,
use_linear=True,
use_checkpoint=True,
):
super().__init__()
assert context_dim is not None
if not isinstance(context_dim, list):
context_dim = [context_dim]
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = 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)
self.transformer_blocks = nn.ModuleList(
[
BasicTransformerBlock3D(
inner_dim,
n_heads,
d_head,
dropout=dropout,
context_dim=context_dim[d],
disable_self_attn=disable_self_attn,
checkpoint=use_checkpoint,
)
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(in_channels, inner_dim))
self.use_linear = use_linear
def forward(self, x, context=None, num_frames=1):
# note: if no context is given, cross-attention defaults to self-attention
if not isinstance(context, list):
context = [context]
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[i], num_frames=num_frames)
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
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