写点什么

从源码分析 vllm + Ray 的分布式推理流程

作者:阿里技术
  • 2024-06-12
    浙江
  • 本文字数:11448 字

    阅读完需:约 38 分钟

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
复制代码

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
复制代码


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
复制代码


  • 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> 
复制代码


  • 运行如下命令,初始化 Pod2 上的本地模型

python3 model_init.py
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
config = 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)
复制代码


  • 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
复制代码


  • 登陆 pod1,访问应用

kubectl -n <your-namespace> exec -it <pod1-name> bash
curl -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|>"]}'
复制代码


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)
复制代码


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__
复制代码


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
复制代码


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
复制代码


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)
复制代码

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)
复制代码

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}
复制代码

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
复制代码

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]
复制代码

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)
复制代码

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
复制代码


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
复制代码


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'>
复制代码


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)
复制代码

模型层权重及其 weight_loader 方法

# param,weight_loader
lm_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()>
复制代码


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)
复制代码


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).
复制代码


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

发布于: 刚刚阅读数: 5
用户头像

阿里技术

关注

专注分享阿里技术的丰富实践和前沿创新。 2022-05-24 加入

阿里技术的官方号,专注分享阿里技术的丰富实践、前沿洞察、技术创新、技术人成长经验。阿里技术,与技术人一起创造成长与成就。

评论

发布
暂无评论
从源码分析 vllm + Ray 的分布式推理流程_阿里技术_InfoQ写作社区