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0adab2f | 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 | """A2C2 correction head architecture for cached BEHAVIOR/OpenPI features."""
from __future__ import annotations
from dataclasses import dataclass
import math
import torch
from torch import Tensor, nn
@dataclass(frozen=True)
class A2C2CorrectionHeadConfig:
state_dim: int = 256
action_dim: int = 23
action_horizon: int = 32
base_policy_z_dim: int = 2048
use_base_policy_z: bool = True
time_dim: int = 2
dim_model: int = 512
n_heads: int = 8
n_encoder_layers: int = 6
dim_feedforward: int = 2048
dropout: float = 0.1
mlp_hidden_dim: int = 1024
def _sinusoidal_positions(length: int, dim: int) -> Tensor:
if dim % 2 != 0:
raise ValueError("dim must be even for sinusoidal positional encoding.")
position = torch.arange(length, dtype=torch.float32).unsqueeze(1)
div_term = torch.exp(torch.arange(0, dim, 2, dtype=torch.float32) * (-math.log(10000.0) / dim))
pe = torch.zeros(length, dim, dtype=torch.float32)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
return pe
class A2C2CorrectionHead(nn.Module):
"""Transformer + MLP correction head following the A2C2 residual design."""
def __init__(self, config: A2C2CorrectionHeadConfig | None = None) -> None:
super().__init__()
self.config = config or A2C2CorrectionHeadConfig()
cfg = self.config
self.cls_token = nn.Parameter(torch.zeros(1, 1, cfg.dim_model))
self.type_embedding = nn.Parameter(torch.zeros(6, cfg.dim_model))
self.state_proj = nn.Linear(cfg.state_dim, cfg.dim_model)
if cfg.use_base_policy_z:
self.z_proj = nn.Linear(cfg.base_policy_z_dim, cfg.dim_model)
self.time_proj = nn.Linear(cfg.time_dim, cfg.dim_model)
self.action_proj = nn.Linear(cfg.action_dim, cfg.dim_model)
chunk_pos = _sinusoidal_positions(cfg.action_horizon, cfg.dim_model)
self.register_buffer("chunk_pos_embedding", chunk_pos, persistent=False)
encoder_layer = nn.TransformerEncoderLayer(
d_model=cfg.dim_model,
nhead=cfg.n_heads,
dim_feedforward=cfg.dim_feedforward,
dropout=cfg.dropout,
activation="gelu",
batch_first=True,
norm_first=True,
)
self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=cfg.n_encoder_layers)
self.encoder_norm = nn.LayerNorm(cfg.dim_model)
head_input_token_count = 5 if cfg.use_base_policy_z else 4
head_input_dim = cfg.dim_model * head_input_token_count + cfg.action_dim
self.residual_head = nn.Sequential(
nn.Linear(head_input_dim, cfg.mlp_hidden_dim),
nn.GELU(),
nn.Dropout(cfg.dropout),
nn.Linear(cfg.mlp_hidden_dim, cfg.mlp_hidden_dim),
nn.GELU(),
nn.Dropout(cfg.dropout),
nn.Linear(cfg.mlp_hidden_dim, cfg.action_dim),
)
self._reset_parameters()
@staticmethod
def make_time_feature(chunk_index: Tensor, horizon: int) -> Tensor:
"""Create [sin, cos] phase features from chunk indices."""
idx = chunk_index.to(dtype=torch.float32)
denom = max(horizon - 1, 1)
phase = 2.0 * math.pi * idx / denom
return torch.stack([torch.sin(phase), torch.cos(phase)], dim=-1)
def forward(
self,
observation_state: Tensor,
selected_base_action: Tensor,
base_action_chunk: Tensor,
base_policy_z: Tensor,
time_feature: Tensor,
valid_action_mask: Tensor | None = None,
) -> Tensor:
"""Predict residual action delta.
Args:
observation_state: [B, state_dim]
selected_base_action: [B, action_dim], the current action being corrected.
base_action_chunk: [B, H, action_dim]
base_policy_z: [B, z_dim]
time_feature: [B, 2]
valid_action_mask: optional bool tensor [B, H], True for valid chunk
entries. Invalid chunk entries are ignored by transformer attention.
Returns:
Tensor [B, action_dim], the predicted residual delta.
"""
cfg = self.config
batch_size = observation_state.shape[0]
device = observation_state.device
dtype = observation_state.dtype
self._validate_inputs(observation_state, selected_base_action, base_action_chunk, base_policy_z, time_feature)
cls = self.cls_token.to(device=device, dtype=dtype).expand(batch_size, -1, -1)
cls = cls + self.type_embedding[0].to(device=device, dtype=dtype)
state_token = self.state_proj(observation_state).unsqueeze(1)
state_token = state_token + self.type_embedding[1].to(device=device, dtype=dtype)
time_token = self.time_proj(time_feature).unsqueeze(1)
time_token = time_token + self.type_embedding[3].to(device=device, dtype=dtype)
selected_action_token = self.action_proj(selected_base_action).unsqueeze(1)
selected_action_token = selected_action_token + self.type_embedding[4].to(device=device, dtype=dtype)
chunk_tokens = self.action_proj(base_action_chunk)
chunk_pos = self.chunk_pos_embedding[: base_action_chunk.shape[1]].to(device=device, dtype=dtype)
chunk_tokens = chunk_tokens + chunk_pos.unsqueeze(0)
chunk_tokens = chunk_tokens + self.type_embedding[5].to(device=device, dtype=dtype)
prefix_tokens = [cls, state_token]
if cfg.use_base_policy_z:
z_token = self.z_proj(base_policy_z).unsqueeze(1)
z_token = z_token + self.type_embedding[2].to(device=device, dtype=dtype)
prefix_tokens.append(z_token)
prefix_tokens.extend([time_token, selected_action_token])
tokens = torch.cat([*prefix_tokens, chunk_tokens], dim=1)
padding_mask = None
if valid_action_mask is not None:
valid_action_mask = valid_action_mask.to(device=device, dtype=torch.bool)
prefix_mask = torch.zeros(batch_size, len(prefix_tokens), device=device, dtype=torch.bool)
padding_mask = torch.cat([prefix_mask, ~valid_action_mask], dim=1)
encoded = self.encoder(tokens, src_key_padding_mask=padding_mask)
encoded = self.encoder_norm(encoded)
cls_state = encoded[:, 0]
state_state = encoded[:, 1]
if cfg.use_base_policy_z:
z_state = encoded[:, 2]
time_state = encoded[:, 3]
selected_action_state = encoded[:, 4]
head_states = [cls_state, state_state, z_state, time_state, selected_action_state]
else:
time_state = encoded[:, 2]
selected_action_state = encoded[:, 3]
head_states = [cls_state, state_state, time_state, selected_action_state]
head_input = torch.cat(
[*head_states, selected_base_action],
dim=-1,
)
return self.residual_head(head_input)
def _validate_inputs(
self,
observation_state: Tensor,
selected_base_action: Tensor,
base_action_chunk: Tensor,
base_policy_z: Tensor,
time_feature: Tensor,
) -> None:
cfg = self.config
if observation_state.ndim != 2 or observation_state.shape[-1] != cfg.state_dim:
raise ValueError(f"observation_state must have shape [B, {cfg.state_dim}].")
if selected_base_action.ndim != 2 or selected_base_action.shape[-1] != cfg.action_dim:
raise ValueError(f"selected_base_action must have shape [B, {cfg.action_dim}].")
if base_action_chunk.ndim != 3 or base_action_chunk.shape[-1] != cfg.action_dim:
raise ValueError(f"base_action_chunk must have shape [B, H, {cfg.action_dim}].")
if base_action_chunk.shape[1] > cfg.action_horizon:
raise ValueError(f"base_action_chunk horizon cannot exceed {cfg.action_horizon}.")
if cfg.use_base_policy_z and (base_policy_z.ndim != 2 or base_policy_z.shape[-1] != cfg.base_policy_z_dim):
raise ValueError(f"base_policy_z must have shape [B, {cfg.base_policy_z_dim}].")
if time_feature.ndim != 2 or time_feature.shape[-1] != cfg.time_dim:
raise ValueError(f"time_feature must have shape [B, {cfg.time_dim}].")
def _reset_parameters(self) -> None:
nn.init.trunc_normal_(self.cls_token, std=0.02)
nn.init.trunc_normal_(self.type_embedding, std=0.02)
for module in self.modules():
if isinstance(module, nn.Linear):
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
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