XiuchengWu
e782ef4db1
基于第一性原理 + Anthropic 2026 J-space 论文实现的具身智慧系统。 核心架构： - ODE 动力系统 + 并行专家 + J-space 工作空间广播 - 12 个异构专家（视觉/屏幕/听觉/语言/鼠标/跨模态） - workspace 256 维 + LayerNorm + RK4 积分 自主心智（最重要的能力）： - 好奇心驱动探索（内在奖励 + 世界模型） - 跨会话状态持久化（海洋不蒸发） - 自我模型（知道自己会什么不会什么） - 元学习（自适应学习率 + 策略选择） 具身 Agent（完整神经系统）： - 感知层：摄像头 + 麦克风 + 屏幕 + 键盘 + 鼠标 - 大脑皮层（workspace）+ 小脑（运动控制）+ 中枢神经（门控） - 海马体（情景记忆）+ 基底神经节（动作选择） - 执行器：鼠标控制 + 键盘输出 + 音频播放 + 屏幕绘制 多模态支持： - 原生图像/音频/视频/文本/键盘/鼠标 6 种模态 - 跨平台（macOS/Windows/Linux） 外挂模块系统： - 可热插拔的外部能力（小模型/知识库/工具） - 核心心智不依赖外挂，断开后继续工作 守护进程： - 用户主动 start/stop（不自启） - 后台静默运行，持续感知学习 - 状态自动保存，跨会话继续 J-lens 可解释性： - 观测模型内部每个 ODE 子步的想法 - Directed Modulation 验证 workspace 因果作用 - Selectivity 验证（ablate workspace） 小模型蒸馏： - 接 GPT-2/Qwen 等迁移理解能力 - 蒸馏完成后小模型可断开 验证结果： - 连续序列：JSpace 胜 Flat 39.7% - 语言进化：loss 3.95→2.40 - workspace ||w||：v1 0.05 → v2 16.0 - 实时五通道感知 + 具身闭环运行
426 lines
15 KiB
Python
426 lines
15 KiB
Python
"""
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语言版 JSpace 模型 + 自主进化机制
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核心扩展：
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1. JSpaceLanguageModel: 在 JSpaceModel 基础上加 token embedding + logit 输出
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2. 在线学习：每个 forward 累积梯度并更新参数（边推理边学习）
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3. EWC（Elastic Weight Consolidation）：保护重要参数，防灾难性遗忘
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4. ExperienceReplay：经验回放缓冲区
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5. ExpertPlasticity：专家专业化追踪，新知识优先路由到"空闲"专家
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数学形式：
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专家动力学（同 core.py）：
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dm_i/dt = -∇U_i(m_i) + J_i · w + P_i_in · embed(x)
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工作空间动力学（同 core.py）：
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τ_w · dw/dt = -w + Σ α_i · P_i_out(m_i)
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输出：
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logits = Q(w) # 从工作空间投影到词汇表
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在线学习目标：
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L = -log p(x_{t+1} | x_{0:t}) + λ_EWC · Σ_i F_i · (θ_i - θ*_i)²
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EWC 中 F_i 是 Fisher 信息矩阵对角线，衡量参数重要性。
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重要参数被"锚定"在旧值附近，新知识只能修改不重要的参数。
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"""
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from __future__ import annotations
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from dataclasses import dataclass, field
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from collections import deque
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import random
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from .core import JSpaceConfig, Expert, JSpaceWorkspace
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@dataclass
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class LanguageConfig(JSpaceConfig):
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"""语言模型配置，继承 JSpaceConfig"""
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vocab_size: int = 100 # 词汇表大小
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embed_dim: int = 16 # token embedding 维度（≠ input_dim，会投影）
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# input_dim 仍用 JSpaceConfig 的，作为工作空间接收的输入维度
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class JSpaceLanguageModel(nn.Module):
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"""
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语言版 JSpace 模型。
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流程：
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token → embedding → 投影到 input_dim → 喂入 JSpace 动力学
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工作空间 w → 投影到 vocab_size → logits → 采样 token
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每步可训练（在线学习）：
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forward 后用 cross-entropy loss 更新参数
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"""
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def __init__(self, config: LanguageConfig):
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super().__init__()
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self.config = config
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# Token embedding
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self.embedding = nn.Embedding(config.vocab_size, config.embed_dim)
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# 投影 embedding → input_dim（JSpace 期望的输入维度）
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self.input_proj = nn.Linear(config.embed_dim, config.input_dim, bias=False)
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# 专家池
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self.experts = nn.ModuleList([
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Expert(
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expert_dim=config.expert_dim,
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workspace_dim=config.workspace_dim,
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input_dim=config.input_dim,
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num_wells=config.num_wells,
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sparsity=config.jacobian_sparsity,
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)
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for _ in range(config.num_experts)
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])
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# 工作空间
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self.workspace = JSpaceWorkspace(
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workspace_dim=config.workspace_dim,
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input_dim=config.input_dim,
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num_experts=config.num_experts,
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)
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# 输出头：w → logits
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self.output_head = nn.Sequential(
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nn.Linear(config.workspace_dim, 64),
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nn.Tanh(),
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nn.Linear(64, config.vocab_size),
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)
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def init_state(self, batch_size: int, device: torch.device) -> dict:
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return {
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'w': torch.zeros(batch_size, self.config.workspace_dim, device=device),
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'm': [torch.zeros(batch_size, self.config.expert_dim, device=device)
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for _ in range(self.config.num_experts)],
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}
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def step(self, state: dict, token_ids: torch.Tensor,
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record_trajectory: bool = False) -> tuple[dict, torch.Tensor, torch.Tensor, list]:
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"""单时间步前向
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Args:
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state: {'w': ..., 'm': [...]}
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token_ids: (batch,) token indices
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record_trajectory: 是否记录 w 轨迹（J-lens 用）
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Returns:
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new_state, logits (batch, vocab_size), alpha (batch, num_experts),
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w_trajectory (list of (batch, workspace_dim)) 或空 list
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"""
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w = state['w']
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ms = state['m']
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cfg = self.config
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w_trajectory = []
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# token → embedding → input projection
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emb = self.embedding(token_ids) # (batch, embed_dim)
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x = self.input_proj(emb) # (batch, input_dim)
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# ODE 子步积分（对应 Anthropic 论文的"层"）
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for substep in range(cfg.ode_steps):
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contributions = []
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new_ms = []
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for i, expert in enumerate(self.experts):
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m_next, contrib = expert(
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ms[i], w, x,
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dt=cfg.dt, noise_std=cfg.noise_std,
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)
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new_ms.append(m_next)
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contributions.append(contrib)
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contributions = torch.stack(contributions, dim=1)
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w, alpha = self.workspace(
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w, x, contributions,
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dt=cfg.dt, tau_w=cfg.tau_w,
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)
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ms = new_ms
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if record_trajectory:
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w_trajectory.append(w.detach())
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logits = self.output_head(w) # (batch, vocab_size)
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new_state = {'w': w, 'm': ms}
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return new_state, logits, alpha, w_trajectory
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def forward(self, token_seqs: torch.Tensor, state: dict | None = None,
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record_trajectory: bool = False) -> tuple[torch.Tensor, dict]:
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"""
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Args:
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token_seqs: (batch, T) token indices
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record_trajectory: 是否记录 w 轨迹
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Returns:
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logits: (batch, T, vocab_size)
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info: {'alpha': (batch, T, num_experts), 'w_norm': (batch, T),
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'w_trajectory': list of (batch, T, workspace_dim) 或空}
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"""
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batch_size, T = token_seqs.shape
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device = token_seqs.device
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if state is None:
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state = self.init_state(batch_size, device)
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logits_list = []
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alphas = []
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w_norms = []
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w_traj_per_step = [] # list of (list of substep w)
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for t in range(T):
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state, logits, alpha, w_traj = self.step(
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state, token_seqs[:, t], record_trajectory=record_trajectory
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)
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logits_list.append(logits)
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alphas.append(alpha)
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w_norms.append(state['w'].norm(dim=-1))
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if record_trajectory and w_traj:
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w_traj_per_step.append(torch.stack(w_traj, dim=1)) # (batch, n_substeps, workspace_dim)
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logits = torch.stack(logits_list, dim=1) # (batch, T, vocab_size)
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info = {
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'alpha': torch.stack(alphas, dim=1), # (batch, T, num_experts)
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'w_norm': torch.stack(w_norms, dim=1), # (batch, T)
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}
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if record_trajectory and w_traj_per_step:
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info['w_trajectory'] = torch.stack(w_traj_per_step, dim=1) # (batch, T, n_substeps, workspace_dim)
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return logits, info
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@torch.no_grad()
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def generate(self, prompt: list[int], n_new: int = 50, temperature: float = 1.0,
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top_k: int = 5) -> list[int]:
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"""自回归生成
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Args:
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prompt: 起始 token ids
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n_new: 生成的新 token 数
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temperature: 采样温度
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top_k: top-k 采样
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"""
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self.eval()
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device = next(self.parameters()).device
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state = self.init_state(1, device)
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# 预热 state with prompt
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tokens = list(prompt)
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for tok in tokens:
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state, _, _, _ = self.step(state, torch.tensor([tok], device=device))
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# 生成
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generated = []
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for _ in range(n_new):
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state, logits, _, _ = self.step(state, torch.tensor([tokens[-1]], device=device))
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logits = logits[0] / max(temperature, 1e-6)
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if top_k > 0:
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top_k = min(top_k, logits.size(-1))
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vals, idxs = logits.topk(top_k)
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probs = F.softmax(vals, dim=-1)
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next_tok = idxs[torch.multinomial(probs, 1)].item()
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else:
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probs = F.softmax(logits, dim=-1)
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next_tok = torch.multinomial(probs, 1).item()
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generated.append(next_tok)
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tokens.append(next_tok)
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self.train()
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return generated
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class ExperienceReplay:
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"""
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经验回放缓冲区。
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存储见过的序列片段，训练时随机采样混入当前 batch。
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防止灾难性遗忘——旧知识被定期"复习"。
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"""
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def __init__(self, capacity: int = 1000, seq_len: int = 64):
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self.capacity = capacity
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self.seq_len = seq_len
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self.buffer: deque = deque(maxlen=capacity)
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def push(self, token_seq: torch.Tensor):
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"""push 一个序列 (T,) 或 (batch, T)"""
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if token_seq.dim() == 1:
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token_seq = token_seq.unsqueeze(0)
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for seq in token_seq:
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if len(seq) >= self.seq_len:
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self.buffer.append(seq.clone())
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def sample(self, batch_size: int) -> torch.Tensor | None:
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"""采样 (batch_size, seq_len)"""
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if len(self.buffer) < batch_size:
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return None
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samples = random.sample(list(self.buffer), batch_size)
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# 随机裁剪到 seq_len
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result = []
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for s in samples:
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if len(s) > self.seq_len:
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start = random.randint(0, len(s) - self.seq_len - 1)
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result.append(s[start:start + self.seq_len])
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else:
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result.append(s)
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return torch.stack(result)
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class EWCOptimizer:
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"""
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Elastic Weight Consolidation 优化器包装。
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核心思想：参数 θ 有"重要性" F（Fisher 信息）。
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重要参数偏离原值 θ* 会被惩罚。
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新知识只能修改不重要的参数。
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L_total = L_task + λ · Σ_i F_i · (θ_i - θ*_i)²
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工作流：
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1. 正常训练一段时间
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2. 调用 consolidate()：计算 Fisher 信息，锚定当前参数
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3. 继续训练——loss 中加入 EWC 正则
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4. 周期性 consolidate（更新锚点和重要性）
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"""
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def __init__(self, model: nn.Module, lr: float = 1e-3,
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ewc_lambda: float = 1.0, max_grad_norm: float = 1.0):
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self.model = model
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self.optimizer = torch.optim.Adam(model.parameters(), lr=lr)
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self.ewc_lambda = ewc_lambda
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self.max_grad_norm = max_grad_norm
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# Fisher 信息和锚定参数
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self.fisher: dict[str, torch.Tensor] = {}
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self.anchored_params: dict[str, torch.Tensor] = {}
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def consolidate(self, data_sample: torch.Tensor, n_samples: int = 50):
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"""计算 Fisher 信息并锚定当前参数
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Args:
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data_sample: (batch, T) 用于计算 Fisher 的数据样本
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n_samples: 采样次数（Fisher 信息的 Monte Carlo 估计）
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"""
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# 保存当前参数作为锚点
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self.anchored_params = {
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name: param.data.clone()
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for name, param in self.model.named_parameters()
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}
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# 计算 Fisher 信息（对角近似）
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fisher = {
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name: torch.zeros_like(param)
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for name, param in self.model.named_parameters()
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}
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self.model.eval()
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for _ in range(n_samples):
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self.model.zero_grad()
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logits, _ = self.model(data_sample)
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# 只用 logits[:, :-1] 对应 targets[:, 1:] 的部分
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logits_pred = logits[:, :-1] # (batch, T-1, vocab)
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probs = F.softmax(logits_pred, dim=-1) # (batch, T-1, vocab)
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# 采样 token 计算 Fisher
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sampled_tokens = torch.multinomial(
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probs.reshape(-1, probs.size(-1)), 1
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).view_as(logits_pred[:, :, 0]) # (batch, T-1)
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log_probs = F.log_softmax(logits_pred, dim=-1)
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loss = -log_probs.gather(-1, sampled_tokens.unsqueeze(-1)).mean()
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loss.backward()
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for name, param in self.model.named_parameters():
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if param.grad is not None:
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fisher[name] += param.grad.data ** 2
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# 平均
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for name in fisher:
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fisher[name] /= n_samples
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self.fisher = fisher
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self.model.zero_grad()
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self.model.train()
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def ewc_penalty(self) -> torch.Tensor:
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"""计算 EWC 正则项"""
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if not self.fisher:
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return torch.tensor(0.0, device=next(self.model.parameters()).device)
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penalty = 0.0
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for name, param in self.model.named_parameters():
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if name in self.fisher:
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penalty = penalty + (self.fisher[name] * (param - self.anchored_params[name]) ** 2).sum()
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return penalty
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def step(self, loss: torch.Tensor):
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"""一步优化：task loss + EWC 正则"""
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total_loss = loss + self.ewc_lambda * self.ewc_penalty()
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self.optimizer.zero_grad()
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total_loss.backward()
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torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=self.max_grad_norm)
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self.optimizer.step()
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return total_loss.item()
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class ExpertPlasticity:
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"""
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专家结构可塑性追踪。
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追踪每个专家的"专业化程度"：
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- 哪些专家在处理哪些模式
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- 哪些专家"负载过重"（应该分裂或新增）
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- 哪些专家"空闲"（可以接收新知识）
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这不是真正的动态增删专家（实现复杂），而是：
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- 统计专家使用率
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- 在路由时给空闲专家加权（鼓励新知识流向空闲专家）
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"""
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def __init__(self, num_experts: int, ema_alpha: float = 0.99):
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self.num_experts = num_experts
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self.ema_alpha = ema_alpha
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# 每个专家的使用率（EMA）
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self.usage = torch.ones(num_experts) / num_experts
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# 每个专家的"领地"——它擅长的 token 分布
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self.expert_specialization: list[dict[int, float]] = [{} for _ in range(num_experts)]
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def update(self, alpha: torch.Tensor, tokens: torch.Tensor):
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"""更新专家统计
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Args:
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alpha: (batch, T, num_experts) 注意力权重
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tokens: (batch, T) 对应的 token
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"""
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# 使用率（时间维度平均）
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usage_batch = alpha.mean(dim=(0, 1)).detach().cpu() # (num_experts,)
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self.usage = self.ema_alpha * self.usage + (1 - self.ema_alpha) * usage_batch
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# 专业化：每个专家最常处理哪些 token
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alpha_flat = alpha.reshape(-1, self.num_experts).detach().cpu() # (batch*T, num_experts)
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tokens_flat = tokens.reshape(-1).detach().cpu().tolist()
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for tok, weights in zip(tokens_flat, alpha_flat):
|
||
for i, w in enumerate(weights.tolist()):
|
||
if w > 0.1: # 只记录显著激活
|
||
self.expert_specialization[i][tok] = \
|
||
self.expert_specialization[i].get(tok, 0) + w
|
||
|
||
def get_diversity_bonus(self) -> torch.Tensor:
|
||
"""返回多样性奖励——给使用率低的专家加权
|
||
|
||
在路由注意力上加上这个 bonus，鼓励新知识流向空闲专家
|
||
"""
|
||
# 使用率越低，bonus 越高
|
||
bonus = (1.0 - self.usage) / self.usage.clamp(min=1e-4)
|
||
bonus = bonus / bonus.sum() # 归一化
|
||
return bonus
|
||
|
||
def get_stats(self) -> dict:
|
||
"""返回可解释性统计"""
|
||
return {
|
||
'usage': self.usage.tolist(),
|
||
'top_specialization': [
|
||
sorted(s.items(), key=lambda x: -x[1])[:5]
|
||
for s in self.expert_specialization
|
||
],
|
||
}
|