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
321 lines
12 KiB
Python
321 lines
12 KiB
Python
"""
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核心架构：专家模块 + J-space 工作空间 + ODE 动力学
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数学形式（每个 forward 时间步内做 Euler 积分若干子步）：
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专家 i 的状态 m_i:
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dm_i/dt = -∇U_i(m_i) + J_i · w + P_i_in · x
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U_i(m_i) = ½ ||m_i||² - ½ Σ_k softplus(a_ik · m_ik + b_ik)
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（多井势能：阻尼项 + softplus 形成的局部吸引子）
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工作空间 w:
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τ_w · dw/dt = -w + Σ_i α_i · P_i_out(m_i)
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α_i = softmax(<q, P_i_out(m_i)>) q = MLP(x, w)
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Jacobian 路由 J_i: 稀疏线性映射，每个专家只对 w 的少数维度敏感
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输出门控：当 ||w|| > θ 时触发输出 R(w)
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所有参数都可 backprop 训练。学习目标是预测下一时刻的输入。
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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
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@dataclass
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class JSpaceConfig:
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"""模型超参，全部可调"""
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input_dim: int = 8 # 输入 x 的维度
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workspace_dim: int = 32 # 工作空间 w 的维度（J-space）
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expert_dim: int = 16 # 每个专家内部状态 m_i 的维度
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num_experts: int = 5 # 专家数量
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num_wells: int = 4 # 每个专家势能景观的井数
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ode_steps: int = 4 # 每个时间步内 ODE 积分子步数
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dt: float = 0.1 # ODE 积分步长
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tau_w: float = 0.3 # 工作空间时间常数
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output_threshold: float = 0.5 # 输出门控阈值（船舶涌出阈值）
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jacobian_sparsity: int = 8 # 每个 J_i 只保留前 k 大的连接
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noise_std: float = 0.01 # 内部噪声 ξ(t) 的标准差
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class Expert(nn.Module):
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"""
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单个专家模块。
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状态：m_i ∈ R^{expert_dim}
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势能：U_i(m_i) = ½||m_i||² - ½ Σ_k softplus(a_k · m_i + b_k) · w_k
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- ½||m_i||² 是阻尼项（拉回原点）
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- softplus 项创造多个局部吸引子（多井势能 → 内部"思考"）
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动力学：dm_i/dt = -∇U_i(m_i) + J_i · w + P_in · x + ξ
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J_i 是稀疏 Jacobian：从工作空间 w 路由信息进来。
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"""
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def __init__(self, expert_dim: int, workspace_dim: int, input_dim: int,
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num_wells: int, sparsity: int):
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super().__init__()
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self.expert_dim = expert_dim
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self.workspace_dim = workspace_dim
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self.num_wells = num_wells
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# 势能景观参数：每个井是一个 softplus 形成的吸引子
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# U(m) = 0.5||m||^2 - 0.5 * sum_k softplus(a_k @ m + b_k)
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# ∇U(m) = m - 0.5 * sum_k sigmoid(a_k @ m + b_k) * a_k
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self.well_a = nn.Parameter(torch.randn(num_wells, expert_dim) * 0.3)
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self.well_b = nn.Parameter(torch.zeros(num_wells))
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# P_in: 输入投影 x -> m_i 的扰动
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self.P_in = nn.Linear(input_dim, expert_dim, bias=False)
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# P_out: 模块输出到工作空间的投影
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self.P_out = nn.Linear(expert_dim, workspace_dim, bias=False)
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# J_i: 稀疏 Jacobian，从 w 路由信息到 m_i
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# 用 top-k 稀疏：训练时学习一个 full matrix，但只激活 top-k
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self.J_raw = nn.Parameter(torch.randn(expert_dim, workspace_dim) * 0.1)
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self.sparsity = sparsity
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# 注意：sparsity 通过 forward 时 top-k 选择实现，可微性通过稀疏 mask 保留
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def get_sparse_J(self) -> torch.Tensor:
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"""获取稀疏化的 Jacobian：每行只保留 top-k 元素"""
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if self.sparsity >= self.workspace_dim:
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return self.J_raw
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# 对每行做 top-k（按绝对值）
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abs_J = self.J_raw.abs()
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topk_vals, topk_idx = abs_J.topk(self.sparsity, dim=-1)
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mask = torch.zeros_like(self.J_raw)
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mask.scatter_(-1, topk_idx, 1.0)
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return self.J_raw * mask
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def grad_potential(self, m: torch.Tensor) -> torch.Tensor:
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"""计算势能梯度 ∇U_i(m)
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U(m) = 0.5||m||^2 - 0.5 * sum_k softplus(a_k @ m + b_k)
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∇U(m) = m - 0.5 * sum_k sigmoid(a_k @ m + b_k) * a_k
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"""
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# m: (batch, expert_dim)
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# well_a: (num_wells, expert_dim)
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# a_k @ m: (batch, num_wells)
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am = F.linear(m, self.well_a, self.well_b) # (batch, num_wells)
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sig = torch.sigmoid(am) # (batch, num_wells)
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# sum_k sigmoid(...) * a_k: (batch, expert_dim)
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# well_a: (num_wells, expert_dim), sig: (batch, num_wells) -> (batch, 1, num_wells)
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# 用 matmul: sig @ well_a -> (batch, expert_dim)
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grad_wells = torch.matmul(sig, self.well_a) # (batch, expert_dim)
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return m - 0.5 * grad_wells
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def forward(self, m: torch.Tensor, w: torch.Tensor, x: torch.Tensor,
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dt: float, noise_std: float) -> tuple[torch.Tensor, torch.Tensor]:
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"""一步 ODE 积分（Euler 法）
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Args:
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m: (batch, expert_dim) 当前状态
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w: (batch, workspace_dim) 工作空间状态
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x: (batch, input_dim) 输入
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dt: 步长
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noise_std: 噪声标准差
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Returns:
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m_next: (batch, expert_dim) 下一状态
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contribution: (batch, workspace_dim) 对工作空间的贡献（pre-attention）
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"""
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# 动力学: dm/dt = -∇U(m) + J·w + P_in·x + ξ
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J = self.get_sparse_J() # (expert_dim, workspace_dim)
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w_proj = F.linear(w, J) # (batch, expert_dim)
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x_proj = self.P_in(x) # (batch, expert_dim)
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grad_U = self.grad_potential(m) # (batch, expert_dim)
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noise = torch.randn_like(m) * noise_std if noise_std > 0 else 0.0
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dm = -grad_U + w_proj + x_proj + noise
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m_next = m + dt * dm
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# 对工作空间的贡献
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contribution = self.P_out(m_next) # (batch, workspace_dim)
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return m_next, contribution
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class JSpaceWorkspace(nn.Module):
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"""
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全局工作空间 w。
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动力学: τ_w · dw/dt = -w + Σ_i α_i · P_i_out(m_i)
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α_i = softmax(<q, P_i_out(m_i)>) q = MLP(x, w)
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这个 α_i 是"注意力"——决定哪个专家的内容进入工作空间。
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"""
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def __init__(self, workspace_dim: int, input_dim: int, num_experts: int):
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super().__init__()
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self.workspace_dim = workspace_dim
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# Query 生成器：从 (x, w) 生成 query 向量
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self.query_gen = nn.Sequential(
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nn.Linear(input_dim + workspace_dim, 32),
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nn.Tanh(),
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nn.Linear(32, workspace_dim),
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)
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def forward(self, w: torch.Tensor, x: torch.Tensor,
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contributions: torch.Tensor, dt: float,
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tau_w: float) -> tuple[torch.Tensor, torch.Tensor]:
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"""一步工作空间演化
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Args:
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w: (batch, workspace_dim)
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x: (batch, input_dim)
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contributions: (batch, num_experts, workspace_dim) 各专家的贡献
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dt: 步长
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tau_w: 时间常数
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Returns:
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w_next: (batch, workspace_dim)
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alpha: (batch, num_experts) 注意力权重（可解释性用）
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"""
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# 生成 query
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q = self.query_gen(torch.cat([x, w], dim=-1)) # (batch, workspace_dim)
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# 计算每个专家的注意力分数
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# contributions: (batch, num_experts, workspace_dim)
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# q: (batch, workspace_dim) -> (batch, 1, workspace_dim)
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scores = (contributions * q.unsqueeze(1)).sum(dim=-1) # (batch, num_experts)
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alpha = F.softmax(scores, dim=-1) # (batch, num_experts)
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# 加权聚合
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# alpha: (batch, num_experts, 1) * contributions: (batch, num_experts, workspace_dim)
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aggregated = (alpha.unsqueeze(-1) * contributions).sum(dim=1) # (batch, workspace_dim)
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# 动力学: τ_w · dw/dt = -w + aggregated
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dw = (-w + aggregated) / tau_w
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w_next = w + dt * dw
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return w_next, alpha
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class JSpaceModel(nn.Module):
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"""
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完整模型：N 个专家 + 工作空间 + 输出门控 + 预测头。
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forward 流程（每个时间步）：
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1. 每个专家从 (m_i, w, x) 更新 m_i，产出对工作空间的贡献
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2. 工作空间从 (w, x, contributions) 更新 w
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3. （可选）当 ||w|| > threshold 时输出 R(w)
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4. 预测头 Q(w) 预测下一时刻输入
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时间序列处理：对长度 T 的输入序列，依次跑 T 步，返回每步的预测。
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"""
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def __init__(self, config: JSpaceConfig):
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super().__init__()
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self.config = config
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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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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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# 输出门控：R(w) → action（这里 action = 预测的下一时刻输入）
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self.predictor = nn.Sequential(
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nn.Linear(config.workspace_dim, 32),
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nn.Tanh(),
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nn.Linear(32, config.input_dim),
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)
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def init_state(self, batch_size: int, device: torch.device) -> dict:
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"""初始化内部状态"""
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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, x: torch.Tensor) -> tuple[dict, torch.Tensor, torch.Tensor, torch.Tensor]:
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"""单时间步前向
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Returns:
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new_state: 更新后的状态
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pred: (batch, input_dim) 预测的下一时刻输入
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alpha: (batch, num_experts) 注意力权重（可解释性）
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w_norm: (batch,) 工作空间范数（输出门控信号）
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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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# ODE 子步积分
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for _ in range(cfg.ode_steps):
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# 1. 每个专家更新
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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) # (batch, num_experts, workspace_dim)
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# 2. 工作空间更新
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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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# 3. 输出：预测下一时刻输入（船舶涌出，但这里为了训练简化为每步都预测）
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pred = self.predictor(w)
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w_norm = w.norm(dim=-1)
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new_state = {'w': w, 'm': ms}
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return new_state, pred, alpha, w_norm
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def forward(self, xs: torch.Tensor, state: dict | None = None) -> tuple[torch.Tensor, dict]:
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"""
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Args:
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xs: (batch, T, input_dim) 输入序列
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state: 初始状态，None 则初始化
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Returns:
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preds: (batch, T, input_dim) 每步对下一时刻的预测
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info: 包含注意力、w_norm 等可解释性信息
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"""
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batch_size, T, _ = xs.shape
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device = xs.device
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if state is None:
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state = self.init_state(batch_size, device)
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preds = []
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alphas = []
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w_norms = []
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for t in range(T):
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state, pred, alpha, w_norm = self.step(state, xs[:, t])
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preds.append(pred)
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alphas.append(alpha)
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w_norms.append(w_norm)
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preds = torch.stack(preds, dim=1) # (batch, T, input_dim)
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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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'final_w': state['w'],
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'final_m': state['m'],
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}
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return preds, info
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