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3d87d8e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 | """AAM Diffusion LLM — MCTS Reasoning Engine
AlphaZero-style tree search for reasoning about narrative arrangement
from graph evidence. AAM-specific: each node = a sentence arrangement,
value = narrative coherence, policy = next arrangement step.
"""
from __future__ import annotations
import math
from dataclasses import dataclass, field
from typing import Optional, List, Dict, Any, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
class MCTSConfig:
def __init__(
self,
num_simulations: int = 64,
c_puct: float = 1.5,
temperature: float = 1.0,
max_depth: int = 10,
use_value_network: bool = True,
use_progressive_widening: bool = True,
max_children: int = 8,
) -> None:
self.num_simulations = num_simulations
self.c_puct = c_puct
self.temperature = temperature
self.max_depth = max_depth
self.use_value_network = use_value_network
self.use_progressive_widening = use_progressive_widening
self.max_children = max_children
if num_simulations <= 0:
raise ValueError(f"num_simulations must be positive, got {num_simulations}")
if c_puct <= 0:
raise ValueError(f"c_puct must be positive, got {c_puct}")
if max_depth <= 0:
raise ValueError(f"max_depth must be positive, got {max_depth}")
@dataclass
class MCTSNode:
state: Optional[torch.Tensor] = None
parent: Optional["MCTSNode"] = None
children: List["MCTSNode"] = field(default_factory=list)
visit_count: int = 0
total_value: float = 0.0
prior: float = 0.0
depth: int = 0
is_expanded: bool = False
action: Optional[int] = None
hidden_state: Optional[torch.Tensor] = None
@property
def q_value(self) -> float:
if self.visit_count == 0:
return 0.0
return self.total_value / self.visit_count
@property
def is_leaf(self) -> bool:
return not self.is_expanded
@property
def is_root(self) -> bool:
"""Whether this node is the root."""
return self.parent is None
class ValueNetwork(nn.Module):
"""Evaluate narrative coherence of a state."""
def __init__(self, d_model: int, hidden_dim: int = 256) -> None:
super().__init__()
self.network = nn.Sequential(
nn.Linear(d_model, hidden_dim, bias=False),
nn.SiLU(),
nn.Linear(hidden_dim, hidden_dim // 2, bias=False),
nn.SiLU(),
nn.Linear(hidden_dim // 2, 1, bias=False),
nn.Tanh(),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.network(x)
class PolicyNetwork(nn.Module):
"""Suggest next arrangement step."""
def __init__(self, d_model: int, num_actions: int = 8) -> None:
super().__init__()
self.network = nn.Sequential(
nn.Linear(d_model, d_model // 2, bias=False),
nn.SiLU(),
nn.Linear(d_model // 2, num_actions, bias=False),
)
self.num_actions = num_actions
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.network(x)
class MCTSReasoner(nn.Module):
"""MCTS Reasoning Engine for AAM sentence arrangement."""
def __init__(
self,
d_model: int,
num_actions: int = 8,
config: Optional[MCTSConfig] = None,
) -> None:
super().__init__()
self.d_model = d_model
self.num_actions = num_actions
self.config = config or MCTSConfig()
if self.config.use_value_network:
self.value_network = ValueNetwork(d_model)
else:
self.value_network = None
self.policy_network = PolicyNetwork(d_model, num_actions)
self.state_encoder = nn.Sequential(
nn.Linear(d_model, d_model, bias=False),
nn.SiLU(),
nn.Linear(d_model, d_model, bias=False),
)
def forward(
self,
x: torch.Tensor,
num_simulations: Optional[int] = None,
) -> Tuple[torch.Tensor, Dict[str, Any]]:
batch_size = x.shape[0]
n_sims = num_simulations or self.config.num_simulations
encoded_state = self.state_encoder(x)
# Pool over sequence dimension if 3D input
if encoded_state.dim() == 3:
pooled_state = encoded_state.mean(dim=1) # (batch, d_model)
else:
pooled_state = encoded_state # (batch, d_model)
policy_logits = self.policy_network(pooled_state) # (batch, num_actions)
policy_probs = F.softmax(policy_logits / self.config.temperature, dim=-1) # (batch, num_actions)
if self.value_network is not None:
root_value = self.value_network(pooled_state) # (batch, 1)
else:
root_value = torch.zeros(batch_size, 1, device=x.device)
visit_counts = torch.zeros(batch_size, self.num_actions, device=x.device, dtype=torch.float32)
total_values = torch.zeros(batch_size, self.num_actions, device=x.device, dtype=torch.float32)
for sim_idx in range(n_sims):
ucb_scores = self._compute_ucb(visit_counts, total_values, policy_probs, n_sims)
selected_actions = ucb_scores.argmax(dim=-1)
if self.value_network is not None:
action_onehot = F.one_hot(selected_actions, self.num_actions).float()
action_proj = action_onehot @ self.policy_network.network[-1].weight
padding = torch.zeros(batch_size, self.d_model - action_proj.shape[-1], device=x.device)
action_embedding = torch.cat([action_proj, padding], dim=-1)
state_action = pooled_state + action_embedding
sim_values = self.value_network(state_action)
else:
sim_values = torch.rand(batch_size, 1, device=x.device) * 2 - 1
visit_counts.scatter_add_(1, selected_actions.unsqueeze(1),
torch.ones(batch_size, 1, device=x.device, dtype=visit_counts.dtype))
total_values.scatter_add_(1, selected_actions.unsqueeze(1), sim_values)
if self.config.temperature > 0:
action_probs = F.softmax(visit_counts.log() / self.config.temperature, dim=-1)
action_probs = torch.where(visit_counts > 0, action_probs, torch.zeros_like(action_probs))
row_sums = action_probs.sum(dim=-1, keepdim=True)
action_probs = torch.where(
row_sums > 1e-6,
action_probs / (row_sums + 1e-8),
torch.full_like(action_probs, 1.0 / self.num_actions),
)
else:
action_probs = F.one_hot(visit_counts.argmax(dim=-1), self.num_actions).float()
info = {
"total_simulations": n_sims,
"root_value": root_value.mean().item(),
"max_visit_count": visit_counts.max().item(),
"entropy": -(action_probs * (action_probs + 1e-8).log()).sum(-1).mean().item(),
"visit_distribution": visit_counts / (visit_counts.sum(-1, keepdim=True) + 1e-8),
}
return action_probs, info
def _compute_ucb(self, visit_counts, total_values, priors, total_simulations):
q_values = torch.where(
visit_counts > 0,
total_values / (visit_counts + 1e-8),
torch.zeros_like(total_values),
)
parent_visits = visit_counts.sum(dim=-1, keepdim=True)
exploration = self.config.c_puct * priors * torch.sqrt(parent_visits + 1) / (1 + visit_counts)
return q_values + exploration
def compute_thinking_budget(self, complexity_score: float, base_simulations: int = 16, max_simulations: int = 256) -> int:
"""Compute number of MCTS simulations based on complexity.
Adaptive compute budget: more complex inputs get more simulations.
Args:
complexity_score: Complexity score [0, 1] from ThinkingToggle.
base_simulations: Minimum number of simulations.
max_simulations: Maximum number of simulations.
Returns:
Recommended number of simulations.
"""
return int(base_simulations + (max_simulations - base_simulations) * (complexity_score ** 2))
def get_reasoning_summary(self, info: Dict[str, Any]) -> str:
"""Summary of reasoning for logging.
Args:
info: Dictionary from forward output.
Returns:
Summary string.
"""
return (
f"MCTS(sims={info['total_simulations']}, "
f"root_val={info['root_value']:.3f}, "
f"max_visits={info['max_visit_count']:.0f}, "
f"entropy={info['entropy']:.3f})"
)
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