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# SPDX-License-Identifier: Apache-2.0
from typing import Optional
import torch
import torch.nn as nn
CFG_TYPES = ["nocfg", "regular", "separated"]
def AutoLatentClassifierFreeGuidedModel(model: nn.Module, cfg_type: Optional[str] = "separated"):
if cfg_type == "nocfg":
return AutoLatentClassifierFreeGuidedModelNoCFG(model)
elif cfg_type == "regular":
return AutoLatentClassifierFreeGuidedModelRegular(model)
elif cfg_type == "separated":
return AutoLatentClassifierFreeGuidedModelSeparated(model)
raise ValueError(f"Unknown cfg_type {cfg_type!r}. Available: {CFG_TYPES}")
class AutoLatentClassifierFreeGuidedModelSeparated(nn.Module):
"""Wrapper around denoiser to use classifier-free guidance at sampling time."""
def __init__(self, model: nn.Module):
"""
Args:
model (nn.Module): the denoiser to wrap in CFG
"""
super().__init__()
self.model = model
def __getattr__(self, name: str):
try:
return super().__getattr__(name)
except AttributeError:
return getattr(self.model, name)
def forward(
self,
cfg_weight_text: torch.Tensor,
cfg_weight_cstr: torch.Tensor,
x: torch.Tensor,
history_len: torch.Tensor,
generation_len: torch.Tensor,
future_len: torch.Tensor,
history_mask: torch.Tensor,
generation_mask: torch.Tensor,
future_mask: torch.Tensor,
history_token_mask: torch.Tensor,
generation_token_mask: torch.Tensor,
future_token_mask: torch.Tensor,
text_feat: torch.Tensor,
text_feat_pad_mask: torch.Tensor,
timesteps: torch.Tensor,
first_heading_angle: torch.Tensor,
motion_mask: torch.Tensor,
observed_motion: torch.Tensor,
) -> torch.Tensor:
"""
Args:
cfg_weight (float): guidance weight float or tuple of floats with (text, constraint) weights if using separated cfg
x (torch.Tensor): [B, T, dim_motion] current noisy motion
x_pad_mask (torch.Tensor): [B, T] attention mask, positions with True are allowed to attend, False are not
text_feat (torch.Tensor): [B, max_text_len, llm_dim] embedded text prompts
text_feat_pad_mask (torch.Tensor): [B, max_text_len] attention mask, positions with True are allowed to attend, False are not
timesteps (torch.Tensor): [B,] current denoising step
motion_mask
observed_motion
Returns:
torch.Tensor: same size as input x
"""
# ── CFG separated batching (B=1 → B=3) ──
# Pass 0: text-only (real text, zero constraints)
# Pass 1: constraint-only (zero text, real constraints)
# Pass 2: unconditional (zero text, zero constraints)
x_3 = torch.cat([x, x, x], dim=0)
history_len_3 = torch.cat([history_len, history_len, history_len], dim=0)
generation_len_3 = torch.cat([generation_len, generation_len, generation_len], dim=0)
future_len_3 = torch.cat([future_len, future_len, future_len], dim=0)
history_mask_3 = torch.cat([history_mask, history_mask, history_mask], dim=0)
generation_mask_3 = torch.cat([generation_mask, generation_mask, generation_mask], dim=0)
future_mask_3 = torch.cat([future_mask, future_mask, future_mask], dim=0)
history_token_mask_3 = torch.cat([history_token_mask, history_token_mask, history_token_mask], dim=0)
generation_token_mask_3 = torch.cat(
[generation_token_mask, generation_token_mask, generation_token_mask],
dim=0,
)
future_token_mask_3 = torch.cat(
[
0 * future_token_mask,
future_token_mask,
0 * future_token_mask,
],
dim=0,
)
text_feat_3 = torch.cat([text_feat, 0 * text_feat, 0 * text_feat], dim=0)
text_feat_pad_mask_3 = torch.cat(
[
text_feat_pad_mask,
0 * text_feat_pad_mask,
0 * text_feat_pad_mask,
],
dim=0,
)
timesteps_3 = torch.cat([timesteps, timesteps, timesteps], dim=0)
# motion_mask / observed_motion are None for constraint-free generation
# and first_heading_angle may be None; pass None through (the inner
# denoiser fills in zeros for None constraints), matching the Regular
# variant. Only pass 1 (constraint-only) carries the real constraints.
first_heading_angle_3 = (
torch.cat(
[first_heading_angle, first_heading_angle, first_heading_angle],
dim=0,
)
if first_heading_angle is not None
else None
)
motion_mask_3 = (
torch.cat([0 * motion_mask, motion_mask, 0 * motion_mask], dim=0) if motion_mask is not None else None
)
observed_motion_3 = (
torch.cat([0 * observed_motion, observed_motion, 0 * observed_motion], dim=0)
if observed_motion is not None
else None
)
out_3 = self.model(
x=x_3,
history_len=history_len_3,
generation_len=generation_len_3,
future_len=future_len_3,
history_mask=history_mask_3 > 0.5,
generation_mask=generation_mask_3 > 0.5,
future_mask=future_mask_3 > 0.5,
history_token_mask=history_token_mask_3 > 0.5,
generation_token_mask=generation_token_mask_3 > 0.5,
future_token_mask=future_token_mask_3 > 0.5,
text_feat=text_feat_3,
text_feat_pad_mask=text_feat_pad_mask_3 > 0.5,
timesteps=timesteps_3,
first_heading_angle=first_heading_angle_3,
motion_mask=motion_mask_3,
observed_motion=observed_motion_3,
)
out_text, out_cstr, out_uncond = torch.chunk(out_3, 3, dim=0)
return out_uncond + cfg_weight_text * (out_text - out_uncond) + cfg_weight_cstr * (out_cstr - out_uncond)
class AutoLatentClassifierFreeGuidedModelRegular(nn.Module):
"""Regular (single-weight) classifier-free guidance at sampling time."""
def __init__(self, model: nn.Module):
super().__init__()
self.model = model
def __getattr__(self, name: str):
try:
return super().__getattr__(name)
except AttributeError:
return getattr(self.model, name)
def forward(
self,
cfg_weight_text: torch.Tensor,
cfg_weight_cstr: torch.Tensor,
x: torch.Tensor,
history_len: torch.Tensor,
generation_len: torch.Tensor,
future_len: torch.Tensor,
history_mask: torch.Tensor,
generation_mask: torch.Tensor,
future_mask: torch.Tensor,
history_token_mask: torch.Tensor,
generation_token_mask: torch.Tensor,
future_token_mask: torch.Tensor,
text_feat: torch.Tensor,
text_feat_pad_mask: torch.Tensor,
timesteps: torch.Tensor,
first_heading_angle: torch.Tensor = None,
motion_mask: torch.Tensor = None,
observed_motion: torch.Tensor = None,
) -> torch.Tensor:
"""Regular CFG: one conditional (real text + constraints) pass and one unconditional pass (B
-> 2B).
Uses cfg_weight_text as the single guidance weight; cfg_weight_cstr is accepted for a
uniform API but unused here.
"""
# Pass 0: conditional, Pass 1: unconditional (zero text + zero constraints)
text_feat = torch.cat([text_feat, 0 * text_feat], dim=0)
if motion_mask is not None:
motion_mask = torch.cat([motion_mask, 0 * motion_mask], dim=0)
if observed_motion is not None:
observed_motion = torch.cat([observed_motion, 0 * observed_motion], dim=0)
if first_heading_angle is not None:
first_heading_angle = torch.cat([first_heading_angle, first_heading_angle], dim=0)
out_cond_uncond = self.model(
torch.cat([x, x], dim=0),
torch.cat([history_len, history_len], dim=0),
torch.cat([generation_len, generation_len], dim=0),
torch.cat([future_len, future_len], dim=0),
torch.cat([history_mask, history_mask], dim=0),
torch.cat([generation_mask, generation_mask], dim=0),
torch.cat([future_mask, future_mask], dim=0),
torch.cat([history_token_mask, history_token_mask], dim=0),
torch.cat([generation_token_mask, generation_token_mask], dim=0),
torch.cat([future_token_mask, False * future_token_mask], dim=0) if future_token_mask is not None else None,
text_feat,
torch.cat([text_feat_pad_mask, False * text_feat_pad_mask], dim=0),
torch.cat([timesteps, timesteps], dim=0),
first_heading_angle=first_heading_angle,
motion_mask=motion_mask,
observed_motion=observed_motion,
)
out, out_uncond = torch.chunk(out_cond_uncond, 2)
return out_uncond + cfg_weight_text * (out - out_uncond)
class AutoLatentClassifierFreeGuidedModelNoCFG(nn.Module):
"""No classifier-free guidance: a single conditional denoiser pass."""
def __init__(self, model: nn.Module):
super().__init__()
self.model = model
def __getattr__(self, name: str):
try:
return super().__getattr__(name)
except AttributeError:
return getattr(self.model, name)
def forward(
self,
cfg_weight_text: torch.Tensor,
cfg_weight_cstr: torch.Tensor,
x: torch.Tensor,
history_len: torch.Tensor,
generation_len: torch.Tensor,
future_len: torch.Tensor,
history_mask: torch.Tensor,
generation_mask: torch.Tensor,
future_mask: torch.Tensor,
history_token_mask: torch.Tensor,
generation_token_mask: torch.Tensor,
future_token_mask: torch.Tensor,
text_feat: torch.Tensor,
text_feat_pad_mask: torch.Tensor,
timesteps: torch.Tensor,
first_heading_angle: torch.Tensor = None,
motion_mask: torch.Tensor = None,
observed_motion: torch.Tensor = None,
) -> torch.Tensor:
"""No guidance: run the denoiser once.
cfg_weight_text / cfg_weight_cstr are accepted for a uniform API but unused.
"""
return self.model(
x,
history_len,
generation_len,
future_len,
history_mask,
generation_mask,
future_mask,
history_token_mask,
generation_token_mask,
future_token_mask,
text_feat,
text_feat_pad_mask,
timesteps,
first_heading_angle=first_heading_angle,
motion_mask=motion_mask,
observed_motion=observed_motion,
)
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