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1641a08 | 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 | #embeddings.py
from __future__ import annotations
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
# ============================================================
# 1.MLP HEAD
# ============================================================
class MLPHead(nn.Module):
def __init__(self, in_dim: int, out_dim: int, hidden_dim: int = 512):
super().__init__()
self.net = nn.Sequential(
nn.Linear(in_dim, hidden_dim),
nn.GELU(),
nn.LayerNorm(hidden_dim),
nn.Linear(hidden_dim, hidden_dim // 2),
nn.GELU(),
nn.Linear(hidden_dim // 2, out_dim)
)
def forward(self, x):
return self.net(x)
# ============================================================
# 2. DECISION TRANSFORMER
# ============================================================
class GeneralistComfortDT(nn.Module):
def __init__(self, config: dict):
super().__init__()
self.config = config
d_model = config["D_MODEL"]
vocab_size = config["VOCAB_SIZE"]
max_zones = config["MAX_ZONES"]
context_dim = config.get("CONTEXT_DIM", 10)
rtg_dim = config.get("RTG_DIM", 2)
self.feat_embed = nn.Embedding(vocab_size, d_model)
self.zone_embed = nn.Embedding(max_zones, d_model)
self.val_proj = nn.Linear(1, d_model)
self.val_gamma = nn.Embedding(vocab_size, d_model)
self.val_beta = nn.Embedding(vocab_size, d_model)
self.ctx_proj = nn.Linear(context_dim, d_model)
self.rtg_embed = nn.Linear(rtg_dim, d_model)
self.pos_embed = nn.Parameter(torch.zeros(1, config["CONTEXT_LEN"], d_model))
enc_layer = nn.TransformerEncoderLayer(
d_model=d_model,
nhead=config["N_HEADS"],
dim_feedforward=4 * d_model,
dropout=config["DROPOUT"],
batch_first=True,
activation="gelu",
norm_first=True,
)
self.backbone = nn.TransformerEncoder(enc_layer, num_layers=config["N_LAYERS"])
self.ln_out = nn.LayerNorm(d_model)
self.action_head = MLPHead(d_model, config["NUM_ACTION_BINS"])
self.state_head = nn.Linear(d_model, 1)
self.state_head_4h = nn.Linear(d_model, 1)
self.return_head = MLPHead(d_model, rtg_dim, hidden_dim=256)
self._init_weights()
def _init_weights(self):
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None: nn.init.zeros_(m.bias)
elif isinstance(m, nn.Embedding):
nn.init.normal_(m.weight, mean=0.0, std=0.02)
elif isinstance(m, nn.LayerNorm):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
nn.init.normal_(self.pos_embed, std=0.02)
nn.init.ones_(self.val_gamma.weight)
nn.init.zeros_(self.val_beta.weight)
@staticmethod
def _build_time_causal_mask(T: int, K: int, device: torch.device) -> torch.Tensor:
L = T * K
ti = torch.arange(L, device=device) // K
return (ti[None, :] > ti[:, None])
def forward(
self,
feature_ids: torch.Tensor,
feature_vals: torch.Tensor,
zone_ids: torch.Tensor,
attn_mask: torch.Tensor,
rtg: Optional[torch.Tensor] = None,
context: Optional[torch.Tensor] = None,
rtg_dropout_prob: float = 0.0
) -> Dict[str, torch.Tensor]:
B, T, K = feature_ids.shape
d_model = self.config["D_MODEL"]
flat_fids = feature_ids.reshape(B, -1)
flat_vals = feature_vals.reshape(B, -1, 1)
flat_zids = zone_ids.reshape(B, -1)
val_emb = self.val_proj(flat_vals)
val_emb = self.val_gamma(flat_fids) * val_emb + self.val_beta(flat_fids)
x_base = (
self.feat_embed(flat_fids)
+ self.zone_embed(flat_zids)
+ val_emb
)
pos = self.pos_embed[:, :T, :].unsqueeze(2).expand(-1, -1, K, -1).reshape(1, -1, d_model)
x_base = x_base + pos
if context is not None:
ctx_emb = self.ctx_proj(context).unsqueeze(1)
x_base = x_base + ctx_emb
rtg_emb = torch.zeros_like(x_base)
if rtg is not None:
flat_rtg = rtg.unsqueeze(2).expand(-1, -1, K, -1).reshape(B, -1, 2)
if self.training:
flat_rtg = flat_rtg + torch.randn_like(flat_rtg) * 0.005 # Noise
rtg_emb = self.rtg_embed(flat_rtg)
if self.training:
rtg_emb = F.dropout(rtg_emb, p=0.1)
if rtg_dropout_prob > 0.0:
mask = torch.bernoulli(torch.full((B, 1, 1), 1.0 - rtg_dropout_prob, device=x_base.device))
rtg_emb = rtg_emb * mask
x = x_base + rtg_emb
flat_mask = attn_mask.reshape(B, -1)
key_padding_mask = (flat_mask == 0)
attn_mask_2d = self._build_time_causal_mask(T, K, device=x.device)
x_latent = self.backbone(x, mask=attn_mask_2d, src_key_padding_mask=key_padding_mask)
x_latent = self.ln_out(x_latent)
action_logits = self.action_head(x_latent).reshape(B, T, K, -1)
x_phys = x_latent - rtg_emb
state_preds = self.state_head(x_phys).reshape(B, T, K)
state_preds_4h = self.state_head_4h(x_phys).reshape(B, T, K)
return_preds_raw = self.return_head(x_phys).reshape(B, T, K, -1)
return_preds = return_preds_raw.mean(dim=2)
if self.training and rtg_dropout_prob > 0.0:
mask = torch.bernoulli(torch.full((B, 1, 1), 1.0 - rtg_dropout_prob, device=x_base.device))
rtg_emb = rtg_emb * mask
return {
"action_logits": action_logits,
"state_preds": state_preds,
"state_preds_4h": state_preds_4h,
"return_preds": return_preds,
"building_latent": x_latent.mean(dim=1)
} |