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from typing import Literal, Optional
import torch
import torch.nn as nn
from .blocks import Attention, RMSNorm, SwiGLUFFN, modulate
AttnAxis = Literal["spatial", "temporal", "object"]
def _reduce_replicated_register_tokens(reg_rep: torch.Tensor, group_keep: Optional[torch.Tensor]) -> torch.Tensor:
"""
Reduces replicated register tokens produced by factorized attention.
reg_rep: (B, G, N_reg, D) where G is the number of factorized groups.
group_keep: Optional (B, G) bool/float indicating which groups contain any valid tokens.
Returns: (B, N_reg, D)
"""
if reg_rep.ndim != 4:
raise ValueError(f"reg_rep must be (B,G,N_reg,D), got {tuple(reg_rep.shape)}")
if group_keep is None:
return reg_rep.mean(dim=1)
if group_keep.shape != reg_rep.shape[:2]:
raise ValueError(f"group_keep must be (B,G)={tuple(reg_rep.shape[:2])}, got {tuple(group_keep.shape)}")
w = group_keep.to(device=reg_rep.device, dtype=torch.float32).unsqueeze(-1).unsqueeze(-1)
denom = w.sum(dim=1).clamp_min(1.0)
out = (reg_rep.to(dtype=torch.float32) * w).sum(dim=1) / denom
return out.to(dtype=reg_rep.dtype)
class DiTBlockSpaceTempAltObj(nn.Module):
"""
DiT-style transformer block with AdaLN and three attention axes:
- Spatial: full attention across all vertices within each frame.
- Temporal: full attention across frames for each vertex.
- Object: attention within each object's vertices inside each frame, then scatter back.
Register tokens follow the divided-attention convention for all factorized axes:
- Replicate across groups before attention.
- Reduce back via (weighted) mean after attention.
"""
def __init__(
self,
*,
hidden_size: int,
num_heads: int,
attn_axis: AttnAxis,
mlp_ratio: float = 4.0,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
) -> None:
super().__init__()
if attn_axis not in ("spatial", "temporal", "object"):
raise ValueError(f"attn_axis must be 'spatial', 'temporal', or 'object', got {attn_axis}")
self.attn_axis: AttnAxis = attn_axis
self.norm1 = RMSNorm(hidden_size, eps=1e-6)
self.attn = Attention(
hidden_size,
num_heads=num_heads,
attn_drop=attn_drop,
proj_drop=proj_drop,
qkv_bias=True,
qk_norm=True,
)
self.norm2 = RMSNorm(hidden_size, eps=1e-6)
mlp_hidden = int(hidden_size * mlp_ratio)
self.mlp = SwiGLUFFN(hidden_size, mlp_hidden, drop=proj_drop)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(hidden_size, 6 * hidden_size, bias=True),
)
def _spatial_attn(
self,
x: torch.Tensor,
*,
num_frames: int,
num_vertices: int,
num_register_tokens: int,
rope=None,
src_key_padding_mask: Optional[torch.Tensor] = None,
group_keep: Optional[torch.Tensor] = None,
) -> torch.Tensor:
bsz, _, dim = x.shape
tokens = x[:, num_register_tokens:, :].reshape(bsz, num_frames, num_vertices, dim)
tok_sp = tokens.reshape(bsz * num_frames, num_vertices, dim)
if num_register_tokens > 0:
reg = x[:, :num_register_tokens, :]
reg_rep = reg[:, None, :, :].expand(bsz, num_frames, num_register_tokens, dim).reshape(
bsz * num_frames, num_register_tokens, dim
)
x_sp = torch.cat([reg_rep, tok_sp], dim=1)
else:
x_sp = tok_sp
y_sp = self.attn(x_sp, rope=rope, src_key_padding_mask=src_key_padding_mask)
if num_register_tokens > 0:
reg_rep_out = y_sp[:, :num_register_tokens, :].reshape(bsz, num_frames, num_register_tokens, dim)
reg_out = _reduce_replicated_register_tokens(reg_rep_out, group_keep)
tok_out = y_sp[:, num_register_tokens:, :].reshape(bsz, num_frames, num_vertices, dim)
tok_out = tok_out.reshape(bsz, num_frames * num_vertices, dim)
return torch.cat([reg_out, tok_out], dim=1)
return y_sp.reshape(bsz, num_frames * num_vertices, dim)
def _temporal_attn(
self,
x: torch.Tensor,
*,
num_frames: int,
num_vertices: int,
num_register_tokens: int,
rope=None,
src_key_padding_mask: Optional[torch.Tensor] = None,
group_keep: Optional[torch.Tensor] = None,
) -> torch.Tensor:
bsz, _, dim = x.shape
tokens = x[:, num_register_tokens:, :].reshape(bsz, num_frames, num_vertices, dim)
tok_t = tokens.permute(0, 2, 1, 3).reshape(bsz * num_vertices, num_frames, dim)
if num_register_tokens > 0:
reg = x[:, :num_register_tokens, :]
reg_rep = reg[:, None, :, :].expand(bsz, num_vertices, num_register_tokens, dim).reshape(
bsz * num_vertices, num_register_tokens, dim
)
x_t = torch.cat([reg_rep, tok_t], dim=1)
else:
x_t = tok_t
y_t = self.attn(x_t, rope=rope, src_key_padding_mask=src_key_padding_mask)
if num_register_tokens > 0:
reg_rep_out = y_t[:, :num_register_tokens, :].reshape(bsz, num_vertices, num_register_tokens, dim)
reg_out = _reduce_replicated_register_tokens(reg_rep_out, group_keep)
tok_out = y_t[:, num_register_tokens:, :].reshape(bsz, num_vertices, num_frames, dim)
tok_out = tok_out.permute(0, 2, 1, 3).reshape(bsz, num_frames * num_vertices, dim)
return torch.cat([reg_out, tok_out], dim=1)
tok_out = y_t.reshape(bsz, num_vertices, num_frames, dim).permute(0, 2, 1, 3).reshape(
bsz, num_frames * num_vertices, dim
)
return tok_out
def _object_attn(
self,
x: torch.Tensor,
*,
num_frames: int,
num_vertices: int,
num_register_tokens: int,
num_objects: int,
object_nmax: int,
object_vertex_index: torch.Tensor,
object_token_keep: torch.Tensor,
rope=None,
src_key_padding_mask: Optional[torch.Tensor] = None,
group_keep: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if num_objects <= 0:
raise ValueError(f"num_objects must be > 0, got {num_objects}")
if object_nmax <= 0:
raise ValueError(f"object_nmax must be > 0, got {object_nmax}")
bsz, _, dim = x.shape
bf = bsz * num_frames
if object_vertex_index.shape != (bf, num_objects * object_nmax):
raise ValueError(
"object_vertex_index must be "
f"(B*F,O*Nmax)={(bf, num_objects * object_nmax)}, got {tuple(object_vertex_index.shape)}"
)
if object_token_keep.shape != (bf, num_objects, object_nmax):
raise ValueError(
"object_token_keep must be "
f"(B*F,O,Nmax)={(bf, num_objects, object_nmax)}, got {tuple(object_token_keep.shape)}"
)
tokens = x[:, num_register_tokens:, :].reshape(bsz, num_frames, num_vertices, dim)
tok_flat = tokens.reshape(bf, num_vertices, dim)
gather_idx = object_vertex_index.unsqueeze(-1).expand(-1, -1, dim)
tok_obj = tok_flat.gather(1, gather_idx).reshape(bf, num_objects, object_nmax, dim)
tok_obj = tok_obj.reshape(bf * num_objects, object_nmax, dim)
if num_register_tokens > 0:
reg = x[:, :num_register_tokens, :]
reg_rep = reg[:, None, None, :, :].expand(bsz, num_frames, num_objects, num_register_tokens, dim).reshape(
bf * num_objects, num_register_tokens, dim
)
x_obj = torch.cat([reg_rep, tok_obj], dim=1)
else:
x_obj = tok_obj
y_obj = self.attn(x_obj, rope=rope, src_key_padding_mask=src_key_padding_mask)
if y_obj.dtype != x.dtype:
y_obj = y_obj.to(dtype=x.dtype)
if num_register_tokens > 0:
reg_rep_out = y_obj[:, :num_register_tokens, :].reshape(bsz, num_frames * num_objects, num_register_tokens, dim)
reg_out = _reduce_replicated_register_tokens(reg_rep_out, group_keep)
tok_out = y_obj[:, num_register_tokens:, :].reshape(bf, num_objects, object_nmax, dim)
else:
reg_out = None
tok_out = y_obj.reshape(bf, num_objects, object_nmax, dim)
tok_out = tok_out * object_token_keep.unsqueeze(-1).to(dtype=tok_out.dtype)
tok_src = tok_out.reshape(bf, num_objects * object_nmax, dim).to(dtype=x.dtype)
tok_full = x.new_zeros((bf, num_vertices, dim), dtype=x.dtype)
tok_full.scatter_add_(
1,
object_vertex_index.unsqueeze(-1).expand(-1, -1, dim),
tok_src,
)
tok_full = tok_full.reshape(bsz, num_frames * num_vertices, dim)
if reg_out is not None:
return torch.cat([reg_out, tok_full], dim=1)
return tok_full
def forward(
self,
x: torch.Tensor,
c: torch.Tensor,
*,
num_frames: int,
num_vertices: int,
rope_spatial=None,
rope_temporal=None,
rope_object=None,
src_key_padding_mask_spatial: Optional[torch.Tensor] = None,
src_key_padding_mask_temporal: Optional[torch.Tensor] = None,
src_key_padding_mask_object: Optional[torch.Tensor] = None,
spatial_group_keep: Optional[torch.Tensor] = None,
temporal_group_keep: Optional[torch.Tensor] = None,
object_group_keep: Optional[torch.Tensor] = None,
object_vertex_index: Optional[torch.Tensor] = None,
object_token_keep: Optional[torch.Tensor] = None,
num_objects: int = 0,
object_nmax: int = 0,
) -> torch.Tensor:
if x.ndim != 3:
raise ValueError(f"x must be (B,S,D), got {tuple(x.shape)}")
if c.ndim != 2:
raise ValueError(f"c must be (B,D), got {tuple(c.shape)}")
if x.shape[0] != c.shape[0]:
raise ValueError(f"Batch mismatch: x has B={x.shape[0]}, c has B={c.shape[0]}")
bsz, seq_len, _ = x.shape
num_frames = int(num_frames)
num_vertices = int(num_vertices)
if num_frames <= 0 or num_vertices <= 0:
raise ValueError(f"num_frames and num_vertices must be >0, got {num_frames}, {num_vertices}")
num_token = num_frames * num_vertices
if seq_len < num_token:
raise ValueError(f"seq_len={seq_len} must be >= num_frames*num_vertices={num_token}")
num_register_tokens = seq_len - num_token
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=-1)
x_attn_in = modulate(self.norm1(x), shift_msa, scale_msa)
if self.attn_axis == "spatial":
attn_out = self._spatial_attn(
x_attn_in,
num_frames=num_frames,
num_vertices=num_vertices,
num_register_tokens=num_register_tokens,
rope=rope_spatial,
src_key_padding_mask=src_key_padding_mask_spatial,
group_keep=spatial_group_keep,
)
elif self.attn_axis == "temporal":
attn_out = self._temporal_attn(
x_attn_in,
num_frames=num_frames,
num_vertices=num_vertices,
num_register_tokens=num_register_tokens,
rope=rope_temporal,
src_key_padding_mask=src_key_padding_mask_temporal,
group_keep=temporal_group_keep,
)
else:
if object_vertex_index is None or object_token_keep is None:
raise ValueError("object attention requires object_vertex_index and object_token_keep")
attn_out = self._object_attn(
x_attn_in,
num_frames=num_frames,
num_vertices=num_vertices,
num_register_tokens=num_register_tokens,
num_objects=int(num_objects),
object_nmax=int(object_nmax),
object_vertex_index=object_vertex_index,
object_token_keep=object_token_keep,
rope=rope_object,
src_key_padding_mask=src_key_padding_mask_object,
group_keep=object_group_keep,
)
x = x + gate_msa.unsqueeze(1) * attn_out
x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
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