? WSaiOS v8 — 计算模型层（Compute Model Layer）
? WSaiOS v8 — 计算模型层（Compute Model Layer）
? v8本质定义
v8 = AI把任务拆解成“计算图（Compute Graph / Dataflow Graph）”，并优化执行路径
一句话：
? 从“选CPU还是GPU” → 变成“设计整个计算流程结构”
? v7 → v8 核心跃迁
| 层级 | v7 | v8 |
|---|---|---|
| 核心 | HAL调度 | 计算模型设计 |
| 决策 | 选算力 | 设计计算图 |
| 单位 | Task | Node Graph |
| 优化 | runtime | compile-time |
? v8工程结构（可运行）
wsaios-v8/
│
├── ai/
│ ├── reasoner.py
│ ├── compute_router.py
│ ├── graph_builder.py # ? 新增：计算图生成
│ ├── optimizer.py # ? 新增：图优化器
│
├── compute/
│ ├── node.py
│ ├── graph.py
│ ├── executor.py
│
├── hal/
│ ├── cpu.py
│ ├── gpu.py
│ ├── resource_manager.py
│
├── kernel/
│ ├── scheduler.py
│
└── main.py⚙️ v8核心变化（关键?）
新增三大能力：
? 1. Compute Graph（计算图）
- node-based execution
- dependency graph
? 2. Graph Builder（AI构图）
- AI决定计算结构
? 3. Graph Optimizer（优化器）
- 删除冗余节点
- 合并路径
? compute/node.py
class Node:
def __init__(self, name, op):
self.name = name
self.op = op
self.next = []? compute/graph.py
class ComputeGraph:
def __init__(self):
self.nodes = []
def add(self, node):
self.nodes.append(node)
def connect(self, a, b):
a.next.append(b)? ai/graph_builder.py（?核心AI）
from compute.node import Node
from compute.graph import ComputeGraph
class GraphBuilder:
def build(self, steps):
graph = ComputeGraph()
prev = None
for step in steps:
node = Node(step, step)
graph.add(node)
if prev:
graph.connect(prev, node)
prev = node
return graph? ai/optimizer.py（?v8关键）
class Optimizer:
def optimize(self, graph):
# ? 简化版优化：删除重复节点
seen = set()
optimized = []
for node in graph.nodes:
if node.name not in seen:
optimized.append(node)
seen.add(node.name)
graph.nodes = optimized
return graph? compute/executor.py（图执行?）
class GraphExecutor:
def run(self, graph):
results = []
for node in graph.nodes:
result = f"[EXEC] {node.op}"
results.append(result)
return results? ai/reasoner.py（v7延续）
class Reasoner:
def decompose(self, goal):
if "save" in goal:
return ["parse", "validate", "store"]
if "process" in goal:
return ["load", "compute", "store"]
return ["print"]? ai/compute_router.py（保留v7）
class ComputeRouter:
def route(self, node):
return "cpu"? main.py（v8核心?计算图系统）
from ai.reasoner import Reasoner
from ai.graph_builder import GraphBuilder
from ai.optimizer import Optimizer
from compute.executor import GraphExecutor
def main():
print("\n? WSaiOS v8 Compute Model Layer Starting...\n")
goal = "save and process data"
# ? AI推理
reasoner = Reasoner()
steps = reasoner.decompose(goal)
# ? 构建计算图
builder = GraphBuilder()
graph = builder.build(steps)
# ? 图优化
optimizer = Optimizer()
graph = optimizer.optimize(graph)
# ? 执行图
executor = GraphExecutor()
results = executor.run(graph)
print("\n? Compute Graph Execution:\n")
for r in results:
print(r)
if __name__ == "__main__":
main()? 运行效果示例
? WSaiOS v8 Compute Model Layer Starting...
? Compute Graph Execution:
[EXEC] parse
[EXEC] validate
[EXEC] store? v8本质（关键?）
? v8发生了系统级变化
1️⃣ 从“任务执行” → “计算结构设计”
系统开始生成：
? Compute Graph（计算图）
2️⃣ AI开始进入“编译器思维”
不是运行，而是：
? compile before execute
3️⃣ Execution变成Graph Execution
从：
- task list
变成：
- DAG / computation graph
? v8一句话定义
? v8 = 一个能够将AI任务转换为计算图并进行结构优化的计算模型层操作系统
? v1 → v8本质跃迁
v1 = 执行器
v2 = 并发
v3 = 驱动
v4 = 资源管理
v5 = 意图AI
v6 = 推理AI
v7 = HAL算力调度
v8 = 计算模型层（AI编译器雏形）? 下一步（关键爆点?）
如果继续：
? v9：系统架构设计AI
- CPU/NPU architecture design
- memory hierarchy AI
- system topology design