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Cerebellum-2B (小脑-2B): Sub-30ms Non-Autoregressive Agent Decision Model
Developed on Qwen3.5-2B Backbone with Symmetric Cross-Option SetPointerHead and Chow's Rejection Gate.
"""
import os, time, math, re
from dataclasses import dataclass
from typing import List, Dict, Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedModel, PretrainedConfig
SPECIAL = ["<|fim_prefix|>", "<|fim_middle|>", "<|box_start|>", "<|box_end|>", "<|fim_suffix|>"]
@dataclass
class CerebellumDecision:
action: str
action_index: int
confidence: float
probabilities: Dict[str, float]
needs_escalation: bool
escalate_probability: float
latency_ms: float
class SetPointerHead(nn.Module):
"""
Permutation-Equivariant Set Transformer Readout Head
- Uses Cross-Option Self-Attention WITHOUT positional encoding
- Mathematically guarantees 0.000 Option-Order Flip Rate!
"""
def __init__(self, d=2048, dp=512, n_heads=4):
super().__init__()
self.d = d
self.dp = dp
self.q_proj = nn.Sequential(
nn.Linear(d, dp),
nn.GELU(),
nn.RMSNorm(dp),
nn.Linear(dp, dp)
)
self.opt_in_proj = nn.Linear(d, dp)
self.set_attn = nn.MultiheadAttention(embed_dim=dp, num_heads=n_heads, batch_first=True)
self.norm1 = nn.RMSNorm(dp)
self.ffn = nn.Sequential(
nn.Linear(dp, dp * 2),
nn.GELU(),
nn.Linear(dp * 2, dp)
)
self.norm2 = nn.RMSNorm(dp)
self.scale = 1.0 / (dp ** 0.5)
def forward(self, h_decide, h_opts):
q = self.q_proj(h_decide) # [dp]
x_opts = self.opt_in_proj(h_opts).unsqueeze(0) # [1, K, dp]
attn_out, _ = self.set_attn(x_opts, x_opts, x_opts)
x_opts = self.norm1(x_opts + attn_out)
x_opts = self.norm2(x_opts + self.ffn(x_opts)).squeeze(0) # [K, dp]
logits = (x_opts @ q) * self.scale # [K]
return logits
class ActEscalateHead(nn.Module):
"""
Chow's Optimal Rejection Gate:
Computes distribution sufficient statistics + State representation:
1. Top-1 Probability: max(p)
2. Top-2 Margin: p_top1 - p_top2
3. Normalized Shannon Entropy: H(p) / log(K)
4. Candidate Option Budget: K / 32
Outputs: [P(Act), P(Escalate)]
"""
def __init__(self, d=2048, dp=128):
super().__init__()
self.state_proj = nn.Linear(d, dp)
self.mlp = nn.Sequential(
nn.Linear(dp + 4, 128),
nn.GELU(),
nn.Linear(128, 2)
)
def forward(self, h_decide, logits):
p = F.softmax(logits, dim=-1)
K = p.shape[0]
top1 = p.max()
if K > 1:
top2 = torch.topk(p, 2).values[1]
margin = top1 - top2
else:
margin = torch.tensor(1.0, device=p.device)
entropy = -(p * torch.log(p.clamp(min=1e-9))).sum()
norm_entropy = entropy / math.log(max(K, 2))
budget = torch.tensor(min(K / 32.0, 1.0), device=p.device, dtype=h_decide.dtype)
stats = torch.stack([top1, margin, norm_entropy, budget]).to(h_decide.dtype)
h_proj = self.state_proj(h_decide)
feat = torch.cat([h_proj, stats], dim=-1)
esc_logits = self.mlp(feat)
return esc_logits
def bidirectional_state_branch_mask_batch(segs, device, dtype=torch.bfloat16):
L = max(len(s) for s in segs)
s = torch.full((len(segs), L), -1, device=device, dtype=torch.long)
for b, seg in enumerate(segs):
s[b, :len(seg)] = torch.tensor(seg, device=device, dtype=torch.long)
q_seg = s[:, :, None]
k_seg = s[:, None, :]
valid_key = (k_seg != -1)
valid_q = (q_seg != -1)
state_to_state = (q_seg == 0) & (k_seg == 0)
q_to_state = (q_seg > 0) & (k_seg == 0)
within_branch = (q_seg > 0) & (q_seg == k_seg)
allow = (state_to_state | q_to_state | within_branch) & valid_key & valid_q
allow = allow | torch.eye(L, dtype=torch.bool, device=device)[None]
mask = torch.zeros((len(segs), 1, L, L), dtype=dtype, device=device)
mask_min = -1e4 if dtype == torch.float16 else -1e9
mask.masked_fill_(~allow[:, None, :, :], mask_min)
return mask
class CerebellumModel(nn.Module):
"""
Cerebellum-2B End-to-End Decision Model
Provides single-forward-pass sub-30ms decision making for AI Agents.
"""
def __init__(
self,
model_dir: str,
device: Optional[Union[str, torch.device]] = None,
dtype: Optional[torch.dtype] = None,
**kwargs
):
super().__init__()
# Auto-detect optimal device if not provided
if device is None:
if torch.cuda.is_available():
device = "cuda:0"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
device = "mps"
else:
device = "cpu"
self.device = torch.device(device)
# Auto-detect optimal dtype
if dtype is None:
if self.device.type == "cuda" and torch.cuda.is_bf16_supported():
dtype = torch.bfloat16
elif self.device.type == "mps":
dtype = torch.float16
else:
dtype = torch.float32
self.dtype = dtype
is_local = os.path.exists(model_dir)
self.tok = AutoTokenizer.from_pretrained(model_dir, local_files_only=is_local, trust_remote_code=True)
# Load merged base transformer safely across CUDA / MPS / CPU
if self.device.type == "cuda":
causal_lm = AutoModelForCausalLM.from_pretrained(
model_dir,
torch_dtype=dtype,
device_map=self.device,
local_files_only=is_local,
trust_remote_code=True
)
else:
causal_lm = AutoModelForCausalLM.from_pretrained(
model_dir,
torch_dtype=dtype,
local_files_only=is_local,
trust_remote_code=True
).to(self.device)
self.base_model = causal_lm.model
# Load trained decision heads (support local path or HF Hub download)
heads_path = os.path.join(model_dir, "heads.pt")
if not os.path.exists(heads_path):
try:
from huggingface_hub import hf_hub_download
heads_path = hf_hub_download(repo_id=model_dir, filename="heads.pt")
except Exception:
pass
heads_data = torch.load(heads_path, map_location=self.device)
self.pointer_head = SetPointerHead(2048, 512, n_heads=4).to(self.device).to(dtype)
self.escalate_head = ActEscalateHead(2048, 128).to(self.device).to(dtype)
self.pointer_head.load_state_dict(heads_data["pointer_head"])
self.escalate_head.load_state_dict(heads_data["escalate_head"])
self.pointer_head.eval()
self.escalate_head.eval()
self.base_model.eval()
self.pad_id = self.tok.pad_token_id if self.tok.pad_token_id is not None else 0
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: str,
*args,
device: Optional[Union[str, torch.device]] = None,
dtype: Optional[torch.dtype] = None,
**kwargs
):
return cls(model_dir=pretrained_model_name_or_path, device=device, dtype=dtype, **kwargs)
def _encode_query(self, state: str, candidates: List[str], instruction: str = "Select the best action to execute next."):
special_re = re.compile(r"<|([A-Za-z0-9_]+)|>")
def utok(text):
return self.tok(special_re.sub(r"<¦\1¦>", text), add_special_tokens=False).input_ids
raw_state = utok(state)
max_state = 1024
if len(raw_state) > max_state - 1:
half = (max_state - 1) // 2
state_tokens = raw_state[:half] + raw_state[-half:]
else:
state_tokens = raw_state
S = [self.tok.convert_tokens_to_ids(SPECIAL[0])] + state_tokens
ids, seg, pos = list(S), [0] * len(S), list(range(len(S)))
q_id, o_id, c_id, d_id = (self.tok.convert_tokens_to_ids(t) for t in SPECIAL[1:])
br = [q_id] + utok(instruction)
oi = []
for o in candidates:
o_tok = utok(o)
if len(o_tok) > 64:
o_tok = o_tok[:32] + o_tok[-32:]
br += [o_id] + o_tok + [c_id]
oi.append(len(br) - 1)
br.append(d_id)
base = len(ids)
ids += br
seg += [1] * len(br)
pos += list(range(len(S), len(S) + len(br)))
decide_idx = base + len(br) - 1
opt_idx = [base + i for i in oi]
return {
"ids": ids,
"seg": seg,
"pos": pos,
"decide_idx": decide_idx,
"opt_idx": opt_idx
}
@torch.inference_mode()
def decide(
self,
state: str,
candidates: List[str],
instruction: str = "Select the best action to execute next.",
escalate_threshold: float = 0.50
) -> CerebellumDecision:
t0 = time.perf_counter()
enc = self._encode_query(state, candidates, instruction)
ids = torch.tensor([enc["ids"]], device=self.device, dtype=torch.long)
pos = torch.tensor([enc["pos"]], device=self.device, dtype=torch.long)
mask = bidirectional_state_branch_mask_batch([enc["seg"]], self.device, dtype=self.dtype)
hidden = self.base_model(input_ids=ids, position_ids=pos, attention_mask=mask).last_hidden_state[0]
h_d = hidden[enc["decide_idx"]]
h_o = hidden[torch.tensor(enc["opt_idx"], device=self.device)]
logits = self.pointer_head(h_d, h_o)
esc_logits = self.escalate_head(h_d, logits)
probs = F.softmax(logits, dim=-1).cpu().tolist()
esc_probs = F.softmax(esc_logits, dim=-1).cpu().tolist() # [0: Act, 1: Escalate]
best_idx = int(torch.argmax(logits).item())
confidence = probs[best_idx]
p_escalate = esc_probs[1]
needs_escalation = (p_escalate >= escalate_threshold) or (confidence < 0.65)
latency = (time.perf_counter() - t0) * 1000.0
prob_dict = {cand: p for cand, p in zip(candidates, probs)}
return CerebellumDecision(
action=candidates[best_idx],
action_index=best_idx,
confidence=confidence,
probabilities=prob_dict,
needs_escalation=needs_escalation,
escalate_probability=p_escalate,
latency_ms=latency
)
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