# vLLM使用Ray实现分布式推理
class VLLMRayBackend:
    def __init__(self, model_name, tensor_parallel_size, pipeline_parallel_size):
        self.model_name = model_name
        self.tensor_parallel_size = tensor_parallel_size
        self.pipeline_parallel_size = pipeline_parallel_size
        
        # 使用Ray部署分布式模型
        self.deploy_distributed_model()
    
    def deploy_distributed_model(self):
        """部署分布式模型"""
        # 张量并行：将模型权重分布到多个GPU
        tensor_workers = []
        for i in range(self.tensor_parallel_size):
            worker = TensorParallelWorker.remote(
                model_name=self.model_name,
                rank=i,
                world_size=self.tensor_parallel_size
            )
            tensor_workers.append(worker)
        
        # 流水线并行：将模型层分布到多个节点
        pipeline_workers = []
        for i in range(self.pipeline_parallel_size):
            worker = PipelineParallelWorker.remote(
                model_name=self.model_name,
                stage=i,
                num_stages=self.pipeline_parallel_size
            )
            pipeline_workers.append(worker)
        
        self.tensor_workers = tensor_workers
        self.pipeline_workers = pipeline_workers
    
    async def distributed_inference(self, requests):
        """分布式推理"""
        # 1. 张量并行：每个请求在多个GPU上并行计算
        tensor_futures = []
        for request in requests:
            futures = []
            for worker in self.tensor_workers:
                future = worker.forward.remote(request)
                futures.append(future)
            tensor_futures.append(futures)
        
        # 2. 流水线并行：请求在流水线中传递
        pipeline_results = []
        for request_futures in tensor_futures:
            # 等待张量并行结果
            tensor_results = await ray.get(request_futures)
            
            # 流水线处理
            pipeline_result = await self.pipeline_inference(tensor_results)
            pipeline_results.append(pipeline_result)
        
        return pipeline_results