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import torch
import torch.nn as nn
from ..attention import MultiHeadAttention
from ..norm import LayerNorm32
class AbsolutePositionEmbedder(nn.Module):
"""
Embeds spatial positions into vector representations.
"""
def __init__(self, channels: int, in_channels: int = 3):
super().__init__()
self.channels = channels
self.in_channels = in_channels
self.freq_dim = channels // in_channels // 2
self.freqs = torch.arange(self.freq_dim, dtype=torch.float32) / self.freq_dim
self.freqs = 1.0 / (10000 ** self.freqs)
def _sin_cos_embedding(self, x: torch.Tensor) -> torch.Tensor:
"""
Create sinusoidal position embeddings.
Args:
x: a 1-D Tensor of N indices
Returns:
an (N, D) Tensor of positional embeddings.
"""
self.freqs = self.freqs.to(x.device)
out = torch.outer(x, self.freqs)
out = torch.cat([torch.sin(out), torch.cos(out)], dim=-1)
return out
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x (torch.Tensor): (N, D) tensor of spatial positions
"""
N, D = x.shape
assert D == self.in_channels, "Input dimension must match number of input channels"
embed = self._sin_cos_embedding(x.reshape(-1))
embed = embed.reshape(N, -1)
if embed.shape[1] < self.channels:
embed = torch.cat([embed, torch.zeros(N, self.channels - embed.shape[1], device=embed.device)], dim=-1)
return embed
class FeedForwardNet(nn.Module):
def __init__(self, channels: int, mlp_ratio: float = 4.0, out_channels: Optional[int] = None):
super().__init__()
if out_channels is None:
out_channels = channels
self.mlp = nn.Sequential(
nn.Linear(channels, int(channels * mlp_ratio)),
nn.GELU(approximate="tanh"),
nn.Linear(int(channels * mlp_ratio), out_channels),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.mlp(x)
class TransformerBlock(nn.Module):
"""
Transformer block (MSA + FFN).
"""
def __init__(
self,
channels: int,
num_heads: int,
out_channels: Optional[int] = None,
mlp_ratio: float = 4.0,
attn_mode: Literal["full", "windowed"] = "full",
window_size: Optional[int] = None,
shift_window: Optional[int] = None,
use_checkpoint: bool = False,
use_rope: bool = False,
qk_rms_norm: bool = False,
qkv_bias: bool = True,
ln_affine: bool = False,
):
super().__init__()
self.use_checkpoint = use_checkpoint
self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
self.attn = MultiHeadAttention(
channels,
num_heads=num_heads,
attn_mode=attn_mode,
window_size=window_size,
shift_window=shift_window,
qkv_bias=qkv_bias,
use_rope=use_rope,
qk_rms_norm=qk_rms_norm,
)
self.channels = channels
self.out_channels = out_channels if out_channels is not None else channels
self.mlp = FeedForwardNet(
self.channels,
out_channels=self.out_channels,
mlp_ratio=mlp_ratio,
)
if self.out_channels != self.channels:
self.res_mlp = FeedForwardNet(
self.channels,
out_channels=self.out_channels,
mlp_ratio=1.0,
)
def _forward(self, x: torch.Tensor) -> torch.Tensor:
h = self.norm1(x)
h = self.attn(h)
x = x + h
h = self.norm2(x)
h = self.mlp(h)
if self.out_channels != self.channels:
x = self.res_mlp(x)
x = x + h
return x
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.use_checkpoint:
return torch.utils.checkpoint.checkpoint(self._forward, x, use_reentrant=False)
else:
return self._forward(x)
class SkinTransformerCrossBlock(nn.Module):
"""
Transformer block (MSA + FFN).
"""
def __init__(
self,
channels: int,
num_heads: int,
out_channels: Optional[int] = None,
mlp_ratio: float = 4.0,
use_checkpoint: bool = False,
qkv_bias: bool = True,
ln_affine: bool = False,
):
super().__init__()
self.use_checkpoint = use_checkpoint
self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
self.norm3 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
self.to_v = nn.Linear(channels, channels, bias=qkv_bias)
self.channels = channels
self.out_channels = out_channels if out_channels is not None else channels
self.mlp = FeedForwardNet(
self.channels,
out_channels=self.out_channels,
mlp_ratio=mlp_ratio,
)
self.joint_attn = MultiHeadAttention(
channels,
num_heads=num_heads,
type="self",
attn_mode="full",
qkv_bias=qkv_bias,
)
self.joint_mlp = FeedForwardNet(
self.channels,
out_channels=self.out_channels,
mlp_ratio=mlp_ratio,
)
if self.out_channels != self.channels:
self.res_mlp = FeedForwardNet(
self.channels,
out_channels=self.out_channels,
mlp_ratio=1.0,
)
self.res_joint_mlp = FeedForwardNet(
self.channels,
out_channels=self.out_channels,
mlp_ratio=1.0,
)
def _forward(self, x: torch.Tensor, j: torch.Tensor, skin: torch.Tensor) -> torch.Tensor:
v = self.to_v(self.norm1(j))
h = skin @ v
x = x + h
h = self.norm2(x)
h = self.mlp(h)
if self.out_channels != self.channels:
x = self.res_mlp(x)
x = x + h
h_j = self.norm3(j)
h_j = self.joint_attn(h_j)
h_j = j + h_j
h_j = self.joint_mlp(h_j)
if self.out_channels != self.channels:
j = self.res_joint_mlp(j)
j = j + h_j
return x, j
class TransformerCrossBlock(nn.Module):
"""
Transformer cross-attention block (MSA + MCA + FFN).
"""
def __init__(
self,
channels: int,
ctx_channels: int,
num_heads: int,
out_channels: Optional[int] = None,
mlp_ratio: float = 4.0,
attn_mode: Literal["full", "windowed"] = "full",
window_size: Optional[int] = None,
shift_window: Optional[Tuple[int, int, int]] = None,
use_checkpoint: bool = False,
use_rope: bool = False,
qk_rms_norm: bool = False,
qk_rms_norm_cross: bool = False,
qkv_bias: bool = True,
ln_affine: bool = False,
x_is_query: bool = False,
no_self: bool = False,
):
super().__init__()
self.use_checkpoint = use_checkpoint
self.norm1 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6) if not no_self else nn.Identity()
self.norm2 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
self.norm3 = LayerNorm32(channels, elementwise_affine=ln_affine, eps=1e-6)
if no_self:
self.self_attn = lambda x: 0
else:
self.self_attn = MultiHeadAttention(
channels,
num_heads=num_heads,
type="self",
attn_mode=attn_mode,
window_size=window_size,
shift_window=shift_window,
qkv_bias=qkv_bias,
use_rope=use_rope,
qk_rms_norm=qk_rms_norm,
)
self.cross_attn = MultiHeadAttention(
channels,
ctx_channels=ctx_channels,
num_heads=num_heads,
type="cross",
attn_mode="full",
qkv_bias=qkv_bias,
qk_rms_norm=qk_rms_norm_cross,
x_is_query=x_is_query,
)
self.channels = channels
self.out_channels = out_channels if out_channels is not None else channels
self.mlp = FeedForwardNet(
channels,
out_channels=self.out_channels,
mlp_ratio=mlp_ratio,
)
if self.out_channels != self.channels:
self.res_mlp = FeedForwardNet(
self.channels,
out_channels=self.out_channels,
mlp_ratio=1.0,
)
def _forward(self, x: torch.Tensor, context: torch.Tensor):
h = self.norm1(x)
h = self.self_attn(h)
x = x + h
h = self.norm2(x)
h = self.cross_attn(h, context)
x = x + h
h = self.norm3(x)
h = self.mlp(h)
if self.out_channels != self.channels:
x = self.res_mlp(x)
x = x + h
return x
def forward(self, x: torch.Tensor, context: torch.Tensor):
if self.use_checkpoint:
return torch.utils.checkpoint.checkpoint(self._forward, x, context, use_reentrant=False)
else:
return self._forward(x, context)
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