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1b703d5 | 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 | """Dense SDPA attention blocks used by the DINAC-AE export."""
from __future__ import annotations
from collections.abc import Callable
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from common.norms import RMSNorm
from common.rope import rotate_half, rotate_half_adjacent
from dit.position_encoding import DiTPositionEncoding
def _axial_rope_rotate_fn(
position_encoding: DiTPositionEncoding,
) -> Callable[[Tensor], Tensor]:
"""Return the head-dimension rotation matching the configured RoPE layout."""
match position_encoding:
case (
DiTPositionEncoding.ROPE_2D_AXIAL_DILATED
| DiTPositionEncoding.ROPE_2D_AXIAL_NORMALIZED
| DiTPositionEncoding.ROPE_2D_AXIAL_FREQ_AWARE
| DiTPositionEncoding.ROPE_1D
):
return rotate_half
case (
DiTPositionEncoding.ROPE_2D_AXIAL_UNNORMALIZED
| DiTPositionEncoding.ROPE_2D_AXIAL_UNNORMALIZED_DILATED
| DiTPositionEncoding.ROPE_2D_AXIAL_BETA_WARP
| DiTPositionEncoding.ROPE_2D_AXIAL_ALPHA_WARP
| DiTPositionEncoding.ROPE_3D_ZIMAGE
):
return rotate_half_adjacent
case _ as unreachable:
raise ValueError(f"Unsupported RoPE position encoding: {unreachable}")
class DitSelfAttentionCore(nn.Module):
"""Dense self-attention core with optional axial RoPE on Q/K."""
d_model: int
n_heads: int
head_dim: int
position_encoding: DiTPositionEncoding
qkv: nn.Linear
proj_out: nn.Linear
q_norm: RMSNorm
k_norm: RMSNorm
def __init__(
self,
d_model: int,
n_heads: int,
*,
position_encoding: DiTPositionEncoding,
) -> None:
super().__init__()
if d_model % n_heads != 0:
raise ValueError("d_model must be divisible by n_heads")
self.d_model = int(d_model)
self.n_heads = int(n_heads)
self.head_dim = int(self.d_model // self.n_heads)
self.position_encoding = position_encoding
self.qkv = nn.Linear(self.d_model, 3 * self.d_model, bias=False)
self.proj_out = nn.Linear(self.d_model, self.d_model, bias=False)
self.q_norm = RMSNorm(self.head_dim)
self.k_norm = RMSNorm(self.head_dim)
def reset_parameters(self) -> None:
"""Reset projections to their initialization."""
nn.init.xavier_uniform_(self.qkv.weight)
nn.init.xavier_uniform_(self.proj_out.weight)
def forward(
self, tokens: Tensor, *, rope_sincos: tuple[Tensor, Tensor] | None
) -> Tensor:
"""Apply dense self-attention to ``[B, N, D]`` tokens."""
batch, sequence_length, _width = tokens.shape
qkv = self.qkv(tokens)
q, k, v = qkv.chunk(3, dim=-1)
q = q.view(batch, sequence_length, self.n_heads, self.head_dim).transpose(1, 2)
k = k.view(batch, sequence_length, self.n_heads, self.head_dim).transpose(1, 2)
v = v.view(batch, sequence_length, self.n_heads, self.head_dim).transpose(1, 2)
q = self.q_norm(q.contiguous())
k = self.k_norm(k.contiguous())
q, k = self._apply_axial_rope_dense(q, k, rope_sincos=rope_sincos)
attn = F.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=False)
attn = (
attn.transpose(1, 2).contiguous().view(batch, sequence_length, self.d_model)
)
return self.proj_out(attn)
def _apply_axial_rope_dense(
self,
q: Tensor,
k: Tensor,
*,
rope_sincos: tuple[Tensor, Tensor] | None,
) -> tuple[Tensor, Tensor]:
"""Apply axial RoPE to dense Q/K tensors."""
if rope_sincos is None:
return q, k
sin, cos = rope_sincos
rope_len = int(sin.shape[-2])
rope_dtype = sin.dtype
q_dtype = q.dtype
k_dtype = k.dtype
q_rope = q.to(dtype=rope_dtype)
k_rope = k.to(dtype=rope_dtype)
match sin.dim():
case 2:
sin_b = sin.view(1, 1, rope_len, self.head_dim)
cos_b = cos.view(1, 1, rope_len, self.head_dim)
case 3:
sin_b = sin.view(int(q.shape[0]), 1, rope_len, self.head_dim)
cos_b = cos.view(int(q.shape[0]), 1, rope_len, self.head_dim)
case _ as unreachable:
raise ValueError(f"Unsupported RoPE tensor rank: {int(unreachable)}")
rotate = _axial_rope_rotate_fn(self.position_encoding)
q_span = q_rope[:, :, :rope_len, :]
k_span = k_rope[:, :, :rope_len, :]
q_head = (q_span * cos_b) + (rotate(q_span) * sin_b)
k_head = (k_span * cos_b) + (rotate(k_span) * sin_b)
q_rope = torch.cat([q_head, q_rope[:, :, rope_len:, :]], dim=2)
k_rope = torch.cat([k_head, k_rope[:, :, rope_len:, :]], dim=2)
return q_rope.to(dtype=q_dtype), k_rope.to(dtype=k_dtype)
class CrossAttentionCore(nn.Module):
"""Dense cross-attention core used by the class-token readout."""
query_dim: int
context_dim: int
context_extra_dim: int
key_extra_dim: int
n_heads: int
head_dim: int
attn_dim: int
context_in_dim: int
attn_dropout: float
kv_proj: nn.Linear
k_extra_proj: nn.Linear | None
out_proj: nn.Linear
q_norm_heads: RMSNorm
k_norm_heads: RMSNorm
def __init__(
self,
*,
query_dim: int,
context_dim: int,
n_heads: int,
head_dim: int,
context_extra_dim: int = 0,
key_extra_dim: int = 0,
attn_dropout: float = 0.0,
) -> None:
super().__init__()
self.query_dim = int(query_dim)
self.context_dim = int(context_dim)
self.context_extra_dim = int(context_extra_dim)
self.key_extra_dim = int(key_extra_dim)
self.n_heads = int(n_heads)
self.head_dim = int(head_dim)
self.attn_dim = int(self.n_heads * self.head_dim)
self.context_in_dim = int(self.context_dim + self.context_extra_dim)
self.attn_dropout = float(attn_dropout)
self.kv_proj = nn.Linear(self.context_in_dim, 2 * self.attn_dim, bias=False)
if self.key_extra_dim == 0:
self.k_extra_proj = None
else:
self.k_extra_proj = nn.Linear(self.key_extra_dim, self.attn_dim, bias=False)
self.out_proj = nn.Linear(self.attn_dim, self.query_dim, bias=False)
self.q_norm_heads = RMSNorm(self.head_dim)
self.k_norm_heads = RMSNorm(self.head_dim)
def reset_parameters(self) -> None:
"""Reset projections to their initialization."""
nn.init.xavier_uniform_(self.kv_proj.weight)
if self.k_extra_proj is not None:
nn.init.xavier_uniform_(self.k_extra_proj.weight)
nn.init.xavier_uniform_(self.out_proj.weight)
def _split_heads(self, x: Tensor) -> Tensor:
batch, sequence_length, _width = x.shape
return x.view(batch, sequence_length, self.n_heads, self.head_dim).transpose(
1, 2
)
def _merge_heads(self, x: Tensor) -> Tensor:
batch, _heads, sequence_length, _head_dim = x.shape
return (
x.transpose(1, 2).contiguous().view(batch, sequence_length, self.attn_dim)
)
def forward(
self,
q_tokens: Tensor,
kv_tokens: Tensor,
*,
training: bool,
key_extra: Tensor | None = None,
key_padding_mask: Tensor | None = None,
) -> Tensor:
"""Apply dense cross-attention to query and context tokens."""
kv = self.kv_proj(kv_tokens)
k, v = kv.chunk(2, dim=-1)
if self.k_extra_proj is not None and key_extra is not None:
k = k + self.k_extra_proj(key_extra)
q = self.q_norm_heads(self._split_heads(q_tokens).contiguous())
k = self.k_norm_heads(self._split_heads(k).contiguous())
v = self._split_heads(v).contiguous()
if key_padding_mask is None:
attn_mask = None
else:
attn_mask = (~key_padding_mask).to(dtype=q.dtype)
attn_mask = attn_mask.view(
key_padding_mask.shape[0], 1, 1, key_padding_mask.shape[1]
)
attn_mask = attn_mask.masked_fill(attn_mask > 0, float("-inf"))
attn = F.scaled_dot_product_attention(
q,
k,
v,
attn_mask=attn_mask,
dropout_p=self.attn_dropout if training else 0.0,
is_causal=False,
)
return self.out_proj(self._merge_heads(attn))
__all__ = ["CrossAttentionCore", "DitSelfAttentionCore"]
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