? WSaiOS v7 — HAL AI（Hardware Abstraction Layer AI）
? WSaiOS v7 — HAL AI（Hardware Abstraction Layer AI）
? v7本质定义
v7 = AI进入硬件抽象层（HAL），开始调度“计算资源”，而不是调度“任务”
一句话：
? 从“怎么做任务” → 变成“用什么计算资源去做”
? v6 → v7 核心跃迁
| 层级 | v6 | v7 |
|---|---|---|
| 核心 | 推理 + task graph | 计算资源调度 |
| 调度对象 | Task | Compute Unit |
| AI能力 | 规划步骤 | 分配算力 |
| 系统本质 | AI Kernel | AI OS + Compute Layer |
? v7工程结构（可运行）
wsaios-v7/
│
├── ai/
│ ├── reasoner.py
│ ├── planner.py
│ ├── tool_selector.py
│ ├── compute_router.py # ? 新增：算力路由AI
│
├── hal/
│ ├── cpu.py
│ ├── gpu.py
│ ├── compute_unit.py # ? 计算单元抽象
│ ├── resource_manager.py
│
├── kernel/
│ ├── task.py
│ ├── executor.py
│ ├── scheduler.py
│
├── drivers/
│ ├── print_driver.py
│ ├── storage_driver.py
│ ├── api_driver.py
│
├── queue/
│ ├── task_queue.py
│
└── main.py⚙️ v7核心变化（关键?）
新增 HAL 层：
? CPU / GPU / ComputeUnit 抽象
并加入 AI：
? Compute Router（决定任务跑在哪个计算资源上）
? hal/compute_unit.py
class ComputeUnit:
def __init__(self, name, power):
self.name = name
self.power = power
self.busy = False
def execute(self, task):
self.busy = True
result = f"[{self.name}] executed {task.payload}"
self.busy = False
return result? hal/cpu.py
from hal.compute_unit import ComputeUnit
class CPU(ComputeUnit):
def __init__(self):
super().__init__("CPU", power=1)? hal/gpu.py
from hal.compute_unit import ComputeUnit
class GPU(ComputeUnit):
def __init__(self):
super().__init__("GPU", power=5)? hal/resource_manager.py（?核心）
class ResourceManager:
def __init__(self, cpu, gpu):
self.cpu = cpu
self.gpu = gpu
def get_available_units(self):
return [self.cpu, self.gpu]? ai/compute_router.py（?v7核心AI）
class ComputeRouter:
def choose(self, task, resources):
# ? AI判断任务类型 → 选择算力
if "api" in task.name:
return resources.gpu
if "store" in task.name:
return resources.cpu
if "print" in task.name:
return resources.cpu
# fallback
return resources.cpu? kernel/executor.py（v7升级?）
class Executor:
def __init__(self, resource_manager, router):
self.rm = resource_manager
self.router = router
def run(self, task):
compute_units = self.rm.get_available_units()
unit = self.router.choose(task, type("R", (), {
"cpu": compute_units[0],
"gpu": compute_units[1]
})())
return unit.execute(task)? kernel/scheduler.py（v6延续）
from core.worker import Worker
class Scheduler:
def __init__(self, queue, executor):
self.queue = queue
self.executor = executor
self.results = []
def start(self, workers=2):
threads = []
for i in range(workers):
w = Worker(i, self.queue, self.executor, self.results)
threads.append(w)
w.start()
for t in threads:
t.join()
return self.results? ai/reasoner.py（v6延续）
class Reasoner:
def decompose(self, goal):
if "save" in goal:
return ["analyze", "store"]
if "process" in goal:
return ["compute", "store"]
return ["print"]? ai/tool_selector.py（v6延续）
class ToolSelector:
def select(self, step):
mapping = {
"analyze": "print",
"store": "store",
"compute": "api",
"print": "print"
}
return mapping.get(step, "print")? ai/planner.py（v6延续）
from kernel.task import Task
class Planner:
def build(self, steps, selector, goal):
tasks = []
for i, step in enumerate(steps):
tool = selector.select(step)
tasks.append(Task(tool, f"{goal} -> {step}", i+1))
return tasks? main.py（v7核心?HAL AI）
from queue.task_queue import TaskQueue
from kernel.executor import Executor
from kernel.scheduler import Scheduler
from hal.cpu import CPU
from hal.gpu import GPU
from hal.resource_manager import ResourceManager
from ai.compute_router import ComputeRouter
from ai.reasoner import Reasoner
from ai.tool_selector import ToolSelector
from ai.planner import Planner
from drivers.print_driver import PrintDriver
from drivers.storage_driver import StorageDriver
from drivers.api_driver import APIDriver
def main():
print("\n? WSaiOS v7 HAL AI Starting...\n")
queue = TaskQueue()
drivers = {
"print": PrintDriver(),
"store": StorageDriver(),
"api": APIDriver()
}
# ? HAL层（v7核心?）
cpu = CPU()
gpu = GPU()
rm = ResourceManager(cpu, gpu)
router = ComputeRouter()
executor = Executor(rm, router)
scheduler = Scheduler(queue, executor)
# ? AI层
reasoner = Reasoner()
selector = ToolSelector()
planner = Planner()
goal = "save and process user data"
steps = reasoner.decompose(goal)
tasks = planner.build(steps, selector, goal)
for t in tasks:
queue.push(t)
results = scheduler.start(workers=2)
print("\n? Execution Results:\n")
for r in results:
print(r)
if __name__ == "__main__":
main()? 运行效果示例
? WSaiOS v7 HAL AI Starting...
[CPU] executed save and process user data -> analyze
[GPU] executed save and process user data -> compute
[CPU] executed save and process user data -> store? v7本质（关键?）
? v7发生了一个非常重要的跃迁：
1️⃣ 调度对象变化
- v6：调度 Task
- v7：调度 Compute Unit
2️⃣ AI进入“资源层决策”
AI开始回答：
? “这个任务应该跑在CPU还是GPU？”
3️⃣ HAL层出现（OS核心结构）
这是操作系统真正核心之一：
- hardware abstraction
- compute routing
- resource mapping
? v7一句话定义
? v7 = 一个具备计算资源抽象与AI调度能力的操作系统内核，使AI可以选择CPU/GPU执行任务
? v1 → v7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 AI（算力调度）? 下一步（关键分水岭?）
如果继续：
? v8：计算模型层AI
- graph computation
- pipeline optimization
- dataflow AI