从源码分析 vllm + Ray 的分布式推理流程

一、前言

随着 LLM 模型越来越大，单 GPU 已经无法加载一个模型。以 Qwen-14B-Chat 模型为例，模型权重大概 28GB，但是单个 NVIDIA A10 仅有 24GB 显存。如果想要在 A10 上部署 Qwen-14B-Chat 模型，我们需要将模型切分后部署到 2 个 A10 机器上，每个 A10 卡加载一半的模型，这种方式称之为分布式推理。

社区涌现了很多支持分布式推理的框架如 vllm、deepspeed-mii，rtp-llm 等。本文选取了 vllm 框架，从源码角度分析 vllm + Ray 如何实现 LLM 模型的分布式推理。

二、在 K8s 中部署 vllm 分布式推理应用

2.1 模型准备

下载 Qwen-14B-Chat 到 OSS 中，并在集群中创建对应的 pv，pvc。pvc 名称为 llm-model。

kubectl apply -f- << EOFapiVersion: v1kind: Secretmetadata:  name: oss-secretstringData:  akId: ${your-accesskey-id} # 用于访问oss的AK  akSecret: ${your-accesskey-secert} # 用于访问oss的SK---apiVersion: v1kind: PersistentVolumemetadata:  name: llm-model  labels:    alicloud-pvname: llm-modelspec:  capacity:    storage: 30Gi   accessModes:    - ReadOnlyMany  persistentVolumeReclaimPolicy: Retain  csi:    driver: ossplugin.csi.alibabacloud.com    volumeHandle: model-oss    nodePublishSecretRef:      name: oss-secret      namespace: default    volumeAttributes:      bucket: ${your-bucket-name}      url: ${your-bucket-endpoint} # e.g. oss-cn-hangzhou.aliyuncs.com      otherOpts: "-o umask=022 -o max_stat_cache_size=0 -o allow_other"      path: "/"---apiVersion: v1kind: PersistentVolumeClaimmetadata:  name: llm-modelspec:  accessModes:    - ReadOnlyMany  resources:    requests:      storage: 30Gi  selector:    matchLabels:      alicloud-pvname: llm-modelEOF

2.2 部署分布式 vllm 应用

1. 执行以下命令，部署 vllm 应用

kubectl apply -f- <<EOFapiVersion: apps/v1 kind: Deploymentmetadata:  name: vllm  labels:    app: vllmspec:  replicas: 2  selector:    matchLabels:      app: vllm  template:    metadata:      labels:        app: vllm    spec:      affinity:        podAntiAffinity:          requiredDuringSchedulingIgnoredDuringExecution:          - labelSelector:              matchExpressions:              - key: app                operator: In                values:                - vllm            topologyKey: kubernetes.io/hostname      volumes:      - name: model        persistentVolumeClaim:          claimName: llm-model      containers:      - name: vllm        image: kube-ai-registry.cn-shanghai.cr.aliyuncs.com/kube-ai/vllm:0.4.1        command:        - "sh"        - "-c"        - "sleep 7d"        ports:        - containerPort: 8080        readinessProbe:          tcpSocket:            port: 8080          initialDelaySeconds: 30          periodSeconds: 30        resources:          limits:            nvidia.com/gpu: "1"          requests:            cpu: 4            memory: 8Gi            nvidia.com/gpu: "1"        volumeMounts:        - mountPath: /mnt/models          name: modelEOF

2. 执行以下命令，启动 vllm 应用

• 启动 ray 在 Pod1 上运行

ray start --head# 启动后，日志中会显示ray-head-address地址

    在 Pod2 上运行

# ray-head-address 设置为pod1日志中显示的ray-head-address地址ray start --address=<ray-head-address> 

• 运行如下命令，初始化 Pod2 上的本地模型

python3 model_init.pyfrom transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfigconfig = AutoConfig.from_pretrained(    "/mnt/models/Qwen-14B-Chat",    trust_remote_code=True)tokenizer = AutoTokenizer.from_pretrained("/mnt/models/Qwen-14B-Chat", trust_remote_code=True)

• 在 Pod1 上运行如下命令启动 qwen 模型

python3 -m vllm.entrypoints.openai.api_server \--port 8080 \--trust-remote-code \--served-model-name qwen \--model /mnt/models/Qwen-14B-Chat \--gpu-memory-utilization 0.95 \--tensor-parallel-size 2

• 登陆 pod1，访问应用

kubectl -n <your-namespace> exec -it <pod1-name> bashcurl -H "Content-Type: application/json" \     http://localhost:8080/v1/chat/completions -X POST \     -d '{"model": "qwen", "messages": [{"role": "user", "content": "你好"}], "max_tokens": 512, "temperature": 0.7, "top_p": 0.9, "seed": 10, "stop":["<|endoftext|>", "<|im_end|>", "<|im_start|>"]}'

三、分布式推理总体流程分析

1.入口函数：vllm/entrypoints/openai/api_server.py main

if __name__ == "__main__":    # 构建engine args    engine_args = AsyncEngineArgs.from_cli_args(args)    # 构建engine    engine = AsyncLLMEngine.from_engine_args(        engine_args, usage_context=UsageContext.OPENAI_API_SERVER)    openai_serving_chat = OpenAIServingChat(engine, served_model_names,                                            args.response_role,                                            args.lora_modules,                                            args.chat_template)    openai_serving_completion = OpenAIServingCompletion(        engine, served_model_names, args.lora_modules)    app.root_path = args.root_path    uvicorn.run(app）

2.构建 LLM engine

engine = AsyncLLMEngine.from_engine_args(    engine_args, usage_context=UsageContext.OPENAI_API_SERVER)def from_engine_args():    """Creates an async LLM engine from the engine arguments."""    # Create the engine configs.    engine_config = engine_args.create_engine_config()    # ray 集群初始化    initialize_ray_cluster(engine_config.parallel_config)    from vllm.executor.ray_gpu_executor import RayGPUExecutorAsync    executor_class = RayGPUExecutorAsync    # Create the engine configs.    engine_config = engine_args.create_engine_config()    # ray 集群初始化    # 1. ray.init()    # 2. 根据集群内gpu数量 & tp并发度设置ray placement策略    initialize_ray_cluster(engine_config.parallel_config)    from vllm.executor.ray_gpu_executor import RayGPUExecutorAsync    executor_class = RayGPUExecutorAsync    #  Create the async LLM engine.    engine = cls(...) #创建一个AsyncLLMEngine实例    # AsyncLLMEngine.__init__ -> self._init_engine -> _AsyncLLMEngine.__init__ -> LLMEngine.__init__ -> executor_class() 即调用RayGPUExecutorAsync.__init__

3.初始化 Ray 集群

Ray Worker 初始化包括 Ray 集群初始化，Ray Worker 初始化。在 Ray worker 初始化时会分布式加载模型。

# RayGPUExecutorAsync 继承了RayGPUExecutor及ExecutorAsyncBase 类，初始化时会调用RayGPUExecutor的self._init_executor 方法def _init_executor(self) -> None:    # Create the parallel GPU workers. 初始化workers 核心代码    self._init_workers_ray(placement_group)def _init_workers_ray():    # 定义worker, 是vllm.worker.worker模块里的Worker类    # actor为RayWorkerWrapper类    worker = ray.remote(        num_cpus=0,        num_gpus=num_gpus,        scheduling_strategy=scheduling_strategy,        **ray_remote_kwargs,    )(RayWorkerWrapper).remote(        worker_module_name="vllm.worker.worker",        worker_class_name="Worker",        trust_remote_code=self.model_config.trust_remote_code,    )    # 在Ray Worker上依次执行如下方法    self._run_workers("get_node_and_gpu_ids",                                                use_dummy_driver=True)    self._run_workers("update_environment_variables",                      all_args=all_args_to_update_environment_variables)    self._run_workers("init_worker", all_kwargs=init_worker_all_kwargs)    self._run_workers("init_device")    self._run_workers(        "load_model",        max_concurrent_workers=self.parallel_config.        max_parallel_loading_workers,    )def _run_workers():    # Start the ray workers first.    ray_worker_outputs = [        # worker是前面定义的RayWorkerWrapper类, 继承RayWorkerWrapper类        # 实际调用了RayWorkerWrapper.execute_method 并在远程实例上执行method方法        worker.execute_method.remote(method, *worker_args,                                     **worker_kwargs)        for (worker, worker_args, worker_kwargs             ) in zip(self.workers, all_worker_args, all_worker_kwargs)    ]def init_worker():    # worker_module_name 是 vllm.worker.worker 就是_init_workers_ray方法中传入的    mod = importlib.import_module(self.worker_module_name)    # Worker    worker_class = getattr(mod, self.worker_class_name)    self.worker = worker_class(*args, **kwargs)    # Worker.__init__ -> ModelRunner.__init__def init_device():    # 初始化分布式推理的机器信息    """Initialize the distributed environment."""    init_distributed_environment(parallel_config.world_size, rank,                                 distributed_init_method, local_rank)def load_model():    self.model_runner.load_model() # ModelRunner.load_model() -> vllm.model_executor.model_loader.loader.load_model

执行完 load_model()的预期日志输出如下，可以看到两个 pod，每个加载了 13.2845 GB，即一半的模型。

INFO 04-26 09:39:46 model_runner.py:173] Loading model weights took 13.2845 GB(RayWorkerWrapper pid=3327, ip=192.168.12.132) INFO 04-26 09:39:51 model_runner.py:173] Loading model weights took 13.2845 GB

4.对外提供服务

创建 OpenAIServingChat 以及 OpenAIServingCompletion 实例，启动 uvicorn 对外提供服务。

@app.post("/v1/chat/completions")openai_serving_chat = OpenAIServingChat(engine, served_model_names,                                        args.response_role,                                        args.lora_modules,                                        args.chat_template)@app.post("/v1/completions")openai_serving_completion = OpenAIServingCompletion(    engine, served_model_names, args.lora_modules)app.root_path = args.root_pathuvicorn.run(app）

3.1 分布式推理过程

当启动参数--tensor-parallel-size > 1 时，会自动触发 ray 分布式部署。

1. 构建 LLM engine 时会对 Ray 集群进行初始化

# ray 集群初始化initialize_ray_cluster(engine_config.parallel_config)

parallel_config 的配置如下，pp=1，tp=2，world_size=2

{'pipeline_parallel_size': 1, 'tensor_parallel_size': 2, 'worker_use_ray': True, 'max_parallel_loading_workers': None, 'disable_custom_all_reduce': False, 'tokenizer_pool_config': None, 'ray_workers_use_nsight': False, 'placement_group': None, 'world_size': 2}

初始化时会为 worker 进程创建 placement_group。

1）获取 ray cluster 中所有 gpu 的数量。

2）根据 world size 申请 gpu placement_group_specs = ([{"GPU": 1}] * parallel_config.world_size)。

3）创建 placement_group，ray 会根据 placement_group 在对应 node 上启动 actor。

2. 在每个 worker 上执行 get_node_and_gpu_ids 方法

# 获取node及node上分配的gpu卡信息def get_node_and_gpu_ids(self) -> Tuple[str, List[int]]:    node_id = ray.get_runtime_context().get_node_id()    gpu_ids = ray.get_gpu_ids()    return node_id, gpu_ids

3. 在每个 worker 上执行 update_environment_variables

# 第二步获取的worker_node以及gpu信息worker_node_and_gpu_ids = self._run_workers("get_node_and_gpu_ids",                                                    use_dummy_driver=True)# Set environment variables for the driver and workers.all_args_to_update_environment_variables = [({            "CUDA_VISIBLE_DEVICES":            ",".join(map(str, node_gpus[node_id])),            "VLLM_INSTANCE_ID":            VLLM_INSTANCE_ID,            "VLLM_TRACE_FUNCTION":            os.getenv("VLLM_TRACE_FUNCTION", "0"),        }, ) for (node_id, _) in worker_node_and_gpu_ids]

4. 在每个 worker 上执行 init_device 方法

# worker的启动参数init_worker_all_kwargs = []# worker_node_and_gpu_ids 是第二步获取的worker上的gpu信息for rank, (node_id, _) in enumerate(worker_node_and_gpu_ids):    local_rank = node_workers[node_id].index(rank)    init_worker_all_kwargs.append(        collect_arg_helper_func(            model_config=self.model_config,            parallel_config=self.parallel_config,            scheduler_config=self.scheduler_config,            device_config=self.device_config,            cache_config=self.cache_config,            load_config=self.load_config,            local_rank=local_rank,            rank=rank,            distributed_init_method=distributed_init_method,            lora_config=self.lora_config,            vision_language_config=self.vision_language_config,            is_driver_worker=rank == 0,        ))def init_device(self) -> None:    if self.device_config.device.type == "cuda":        # torch.distributed.all_reduce does not free the input tensor until        # the synchronization point. This causes the memory usage to grow        # as the number of all_reduce calls increases. This env var disables        # this behavior.        # Related issue:        # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573        os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"        # This env var set by Ray causes exceptions with graph building.        os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)        self.device = torch.device(f"cuda:{self.local_rank}")        torch.cuda.set_device(self.device)        _check_if_gpu_supports_dtype(self.model_config.dtype)        torch.cuda.empty_cache()        self.init_gpu_memory = torch.cuda.mem_get_info()[0]    else:        raise RuntimeError(            f"Not support device type: {self.device_config.device}")    # Initialize the distributed environment.    init_worker_distributed_environment(self.parallel_config, self.rank,                                        self.distributed_init_method,                                        self.local_rank)    # Set random seed.    set_random_seed(self.model_config.seed)

核心方法 init_worker_distributed_environment 用于构建分布式集群的 world 信息，类似 horovod 及 deepspeed 框架中的 world info。

该方法参数如下：

work1: self.rank=0, self.local_rank=0, self.distributed_init_method="tcp://192.168.12.120:42167" (ray master)

{'pipeline_parallel_size': 1, 'tensor_parallel_size': 2, 'worker_use_ray': True, 'max_parallel_loading_workers': None, 'disable_custom_all_reduce': False, 'tokenizer_pool_config': None, 'ray_workers_use_nsight': False, 'placement_group': <ray.util.placement_group.PlacementGroup object at 0x7fdeaa896ce0>, 'world_size': 2}, {'id': PlacementGroupID(51489eb26a9335f31ed1bdb4eace04000000), 'bundle_cache': [{'GPU': 1.0}, {'GPU': 1.0}]}, self.rank=0, tcp://192.168.12.120:42167, self.local_rank=0

work2: self.rank=1,

self.local_rank=0,self.distributed_init_method="tcp://192.168.12.120:42167"

{'pipeline_parallel_size': 1, 'tensor_parallel_size': 2, 'worker_use_ray': True, 'max_parallel_loading_workers': None, 'disable_custom_all_reduce': False, 'tokenizer_pool_config': None, 'ray_workers_use_nsight': False, 'world_size': 2}, self.rank=1, tcp://192.168.12.120:42167, self.local_rank=0

self.rank 全局递增，self.local_rank 是指在一个 pod 内第几个 gpu。

5. 在每个 worker 执行 load_model 方法

load_model 用于加载分布式模型，比较复杂，在下面的章节中单独介绍。

3.2 分布式模型加载流程

在每个 worker 执行 load_model 方法

def load_model():    self.model_runner.load_model() # ModelRunner.load_model() -> vllm.model_executor.model_loader.loader.load_modeldef load_model(self) -> None:    with CudaMemoryProfiler() as m:         # get_model 获取模型        self.model = get_model(            model_config=self.model_config,            device_config=self.device_config,            load_config=self.load_config,            lora_config=self.lora_config,            vision_language_config=self.vision_language_config,            parallel_config=self.parallel_config,            scheduler_config=self.scheduler_config,        )    self.model_memory_usage = m.consumed_memory    logger.info(f"Loading model weights took "                f"{self.model_memory_usage / float(2**30):.4f} GB")# get_model -> loader.load_model -> DefaultModelLoader.load_modeldef load_model(self, *, model_config: ModelConfig,               device_config: DeviceConfig,               lora_config: Optional[LoRAConfig],               vision_language_config: Optional[VisionLanguageConfig],               parallel_config: ParallelConfig,               scheduler_config: SchedulerConfig) -> nn.Module:    with set_default_torch_dtype(model_config.dtype):        with torch.device(device_config.device):            """Initialize a model with the given configurations."""            # 初始化模型            model = _initialize_model(model_config, self.load_config,                                      lora_config, vision_language_config)        # 调用对应model的load_weights方法        model.load_weights(            self._get_weights_iterator(model_config.model,                                       model_config.revision,                                       fall_back_to_pt=getattr(                                           model,                                           "fall_back_to_pt_during_load",                                           True)), )        for _, module in model.named_modules():            linear_method = getattr(module, "linear_method", None)            if linear_method is not None:                linear_method.process_weights_after_loading(module)            if hasattr(module, "process_weights_after_loading"):                module.process_weights_after_loading()    return model.eval()# 根据model config找到具体是什么模型def _initialize_model(        model_config: ModelConfig, load_config: LoadConfig,        lora_config: Optional[LoRAConfig],        vision_language_config: Optional[VisionLanguageConfig]) -> nn.Module:    """Initialize a model with the given configurations."""    # Qwen-7B-Chat/config.json中architecture字段    model_class = get_model_architecture(model_config)[0]    linear_method = _get_linear_method(model_config, load_config)    return model_class(config=model_config.hf_config,                       linear_method=linear_method,                       **_get_model_initialization_kwargs(                           model_class, lora_config, vision_language_config))# model_class 是 <class 'vllm.model_executor.models.qwen.QWenLMHeadModel'>

model.load_weights 即调用 QwenLMHeadModel 的 load_weights 方法

# QWenLMHeadModel.load_weightsdef load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):    stacked_params_mapping = [        # (param_name, shard_name, shard_id)        ("gate_up_proj", "w2", 0),        ("gate_up_proj", "w1", 1),    ]    # 模型每层权重及其名称    # self.named_parameters即model.named_parameters()    params_dict = dict(self.named_parameters())    for name, loaded_weight in weights:        # name: transformer.h.27.mlp.c_proj.weight         # loaded_weight: tensor(xxx)        if "rotary_emb.inv_freq" in name:            continue        for (param_name, weight_name, shard_id) in stacked_params_mapping:            if weight_name not in name:                continue            # 如果在stacked_params_mapping里，就需要把shard_name改为param_name            # 如 name为 transformer.h.0.mlp.w1.weight，则name需要改为 transformer.h.0.mlp.gate_up_proj.weight            name = name.replace(weight_name, param_name)            # Skip loading extra bias for GPTQ models.            if name.endswith(".bias") and name not in params_dict:                continue            param = params_dict[name]            weight_loader = param.weight_loader            weight_loader(param, loaded_weight, shard_id)            break        else:            # python的for-else语法，到达这里意味着没有执行循环中的 break 语句            # Skip loading extra bias for GPTQ models.            if name.endswith(".bias") and name not in params_dict:                continue            param = params_dict[name]            # 根据name找到对应的weight_loader方法            weight_loader = getattr(param, "weight_loader",                                    default_weight_loader)            weight_loader(param, loaded_weight)

模型层权重及其 weight_loader 方法

# param，weight_loaderlm_head.weight, weight_loader <bound method VocabParallelEmbedding.weight_loader of ParallelLMHead()> transformer.h.0.attn.c_attn.weight, weight_loader <bound method QKVParallelLinear.weight_loader of QKVParallelLinear()> transformer.h.0.attn.c_proj.weight, weight_loader <bound method RowParallelLinear.weight_loader of RowParallelLinear()> transformer.h.0.ln_1.weight, weight_loader <function default_weight_loader at 0x7f66201ee0e0> transformer.h.0.ln_2.weight, weight_loader <function default_weight_loader at 0x7f66201ee0e0> transformer.h.0.mlp.c_proj.weight, weight_loader <bound method RowParallelLinear.weight_loader of RowParallelLinear()> transformer.h.0.mlp.gate_up_proj.weight, weight_loader <bound method MergedColumnParallelLinear.weight_loader of MergedColumnParallelLinear()> transformer.ln_f.weight, weight_loader <function default_weight_loader at 0x7f66201ee0e0> transformer.wte.weight, weight_loader <bound method VocabParallelEmbedding.weight_loader of VocabParallelEmbedding()>

模型的每一层都有自己的分布式加载方法，如 transformer.h.0.attn.c_proj.weight 这个权重使用了 RowParallelLinear.weight_loader 方法。

class RowParallelLinear(torch.nn.Module):    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):      # 获取worker的tp_rank，根据tp_rank计算需要加载的权重范围      tp_rank = get_tensor_model_parallel_rank()      input_dim = getattr(param, "input_dim", None)      param_data = param.data      if input_dim is not None:          shard_size = param_data.shape[input_dim]          start_idx = tp_rank * shard_size          loaded_weight = loaded_weight.narrow(input_dim, start_idx,                                               shard_size)      assert param_data.shape == loaded_weight.shape      param_data.copy_(loaded_weight)

模型切分采用了 Megatron-LM 算法，详情可参考论文【文末查看】

四、分布式模型切分算法 Megatron-LM

4.1 分布式节点通信：AllReduce

https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/collectives.html#

1）Reduce：将每个 GPU 的计算结果汇总到某个特定的 GPU 上

2）Broadcast：将某个 GPU 的数据同步到所有 GPU 上

3）AllReduce = Reduce + Broadcast

4.2 Transformer 切分

Transformer 层由一个自注意力模块（self-attention block）后跟一个两层的多层感知机（MLP）实现的。

MLP

如图所示，MLP 由两个部分组成，GeLU 是非线形函数，

即所以不能采用行并行，需要采用列并行。

此时，B 需要采用行并行。如果 B 采用列并行的话，则需要进行一次 all-reduce 同步。

Dropout 是按照一定比例随机丢弃一些参数，因此 Dropout 前必须进行一次 all-reduce 同步。

Self-Attention

multi-head attention 机制中每个 attention 都是独立的 QKV 矩阵，每个 GPU 上计算部分 attention 就行。因此要求 attention head 可以被 tp_size 整除。否则会报错如下（Qwen-14b 设置 tp=3）:

ValueError: Total number of attention heads (40) must be divisible by tensor parallel size (3).

同样，Dropout 前需要进行一次 all-reduce 操作。

因此，一次 Transformer 推理需要进行 2 次 all-reduce 操作，qwen-14b 中 transformer 有 40 个，一次推理需要执行 81 一个 all-reduce 操作。跨节点部署推理服务时，网络通信将会是比较大的开销。

本文重点分析 vllm 如何实现分布式推理，具体 vllm 的推理过程可参考下方【01 推理过程解析】

参考链接

[01] 推理过程解析

https://zhuanlan.zhihu.com/p/649974825

[02] 【深度学习】【分布式训练】一文捋顺千亿模型训练技术：流水线并行、张量并行和 3D 并行

https://zhuanlan.zhihu.com/p/617087561

[03] Hugging Face 高效训练技术四：多 GPU 分布式训练（DP、PP、TP 、ZeRO）_zero-dp

https://blog.csdn.net/qq_56591814/article/details/134099476

[04] Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

https://arxiv.org/pdf/1909.08053