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66a2b45 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 | """HSIGene attention modules - FeedForward, CrossAttention, SpatialTransformer."""
from inspect import isfunction
from typing import Optional, Any
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from torch import einsum
from .utils import checkpoint, zero_module, exists
try:
import xformers
import xformers.ops
XFORMERS_IS_AVAILABLE = True
except ImportError:
XFORMERS_IS_AVAILABLE = False
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
import os
_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
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)
def Normalize(in_channels, num_groups=32):
return nn.GroupNorm(num_groups=num_groups, 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=0.0):
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))
if _ATTN_PRECISION == "fp32":
with torch.autocast(enabled=False, device_type="cuda"):
q, k = q.float(), k.float()
sim = einsum("b i d, b j d -> b i j", q, k) * self.scale
else:
sim = einsum("b i d, b j d -> b i j", q, k) * self.scale
del q, k
if exists(mask):
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)
sim = sim.softmax(dim=-1)
out = 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):
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.heads = heads
self.dim_head = dim_head
self.scale = dim_head ** -0.5
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),
)
if XFORMERS_IS_AVAILABLE:
out = xformers.ops.memory_efficient_attention(
q, k, v, attn_bias=None, op=self.attention_op
)
else:
sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
sim = sim.softmax(dim=-1)
out = torch.einsum("b i j, b j d -> b i d", sim, v)
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,
}
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_AVAILABLE else "softmax"
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,
)
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,
)
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):
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__()
if exists(context_dim) and not isinstance(context_dim, list):
context_dim = [context_dim]
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = Normalize(in_channels)
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] if isinstance(context_dim, list) else context_dim,
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(inner_dim, in_channels))
self.use_linear = use_linear
def forward(self, x, context=None):
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):
ctx = context[i] if i < len(context) else context[0]
x = block(x, context=ctx)
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
|