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from typing import *
import torch
import torch.nn as nn
from ..attention import MultiHeadAttention
from ..norm import LayerNorm32
from .blocks import FeedForwardNet
class ModulatedTransformerBlock(nn.Module):
"""
Transformer block (MSA + FFN) with adaptive layer norm conditioning.
"""
def __init__(
self,
channels: int,
num_heads: int,
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,
qkv_bias: bool = True,
share_mod: bool = False,
):
super().__init__()
self.use_checkpoint = use_checkpoint
self.share_mod = share_mod
self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
self.norm2 = LayerNorm32(channels, elementwise_affine=False, 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.mlp = FeedForwardNet(
channels,
mlp_ratio=mlp_ratio,
)
if not share_mod:
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), nn.Linear(channels, 6 * channels, bias=True)
)
def _forward(self, x: torch.Tensor, mod: torch.Tensor) -> torch.Tensor:
if self.share_mod:
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(
6, dim=1
)
else:
(
shift_msa,
scale_msa,
gate_msa,
shift_mlp,
scale_mlp,
gate_mlp,
) = self.adaLN_modulation(mod).chunk(6, dim=1)
h = self.norm1(x)
h = h * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1)
h = self.attn(h)
h = h * gate_msa.unsqueeze(1)
x = x + h
h = self.norm2(x)
h = h * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1)
h = self.mlp(h)
h = h * gate_mlp.unsqueeze(1)
x = x + h
return x
def forward(self, x: torch.Tensor, mod: torch.Tensor) -> torch.Tensor:
if self.use_checkpoint:
return torch.utils.checkpoint.checkpoint(
self._forward, x, mod, use_reentrant=False
)
else:
return self._forward(x, mod)
class ModulatedTransformerCrossBlock(nn.Module):
"""
Transformer cross-attention block (MSA + MCA + FFN) with adaptive layer norm conditioning.
"""
def __init__(
self,
channels: int,
ctx_channels: int,
num_heads: int,
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,
share_mod: bool = False,
):
super().__init__()
self.use_checkpoint = use_checkpoint
self.share_mod = share_mod
self.norm1 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
self.norm2 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6)
self.norm3 = LayerNorm32(channels, elementwise_affine=False, eps=1e-6)
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,
)
self.mlp = FeedForwardNet(
channels,
mlp_ratio=mlp_ratio,
)
if not share_mod:
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), nn.Linear(channels, 6 * channels, bias=True)
)
def _forward(self, x: torch.Tensor, mod: torch.Tensor, context: torch.Tensor):
if self.share_mod:
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(
6, dim=1
)
else:
(
shift_msa,
scale_msa,
gate_msa,
shift_mlp,
scale_mlp,
gate_mlp,
) = self.adaLN_modulation(mod).chunk(6, dim=1)
h = self.norm1(x)
h = h * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1)
h = self.self_attn(h)
h = h * gate_msa.unsqueeze(1)
x = x + h
h = self.norm2(x)
h = self.cross_attn(h, context)
x = x + h
h = self.norm3(x)
h = h * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1)
h = self.mlp(h)
h = h * gate_mlp.unsqueeze(1)
x = x + h
return x
def forward(self, x: torch.Tensor, mod: torch.Tensor, context: torch.Tensor):
if self.use_checkpoint:
return torch.utils.checkpoint.checkpoint(
self._forward, x, mod, context, use_reentrant=False
)
else:
return self._forward(x, mod, context)
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