pipeline_model_parallel_size(必选,默认为1):表示一个pipeline模型并行通信组中的GPU卡数,pipeline并行相当于把layer纵向切为了N个stage阶段,每个阶段对应一个卡,所以这里也就等于stage阶段数。例如 pipeline_model parallel_size 为2,tensor_model parallel_size 为4,表示一个模型会被纵向分为2个stage进行pipeline并行...
流水线并行对于模型参数的水平分割是非常简洁的操作。在 Batch Size 相同的前提下（单卡批数据量 vs 一...
表示每个device会处理几个stage,例如:对于一个有16层的transformer网络来说,训练配置tensor_model_parallel_size=1, pipeline_model_parallel_size=4, virtual_pipeline_model_parallel_size=2,表示模型会被分为4*2=8个stage,每个stage有2个layer,对于
To achieve a better throughput, we recommend setting--num-layersto a value tok * pipeline-model-parallel-size - 2where k can be any value≥1. This is used to compensate for the additional embedding layer on the first/last pipeline stages which could otherwise brings bubble to all other sta...
答: Pipeline(流水线)是 Jenkins 2.0 的精髓它基于Groovy语言实现的一种DSL(领域特定语言),简而言之就是一套运行于Jenkins上的工作流框架,用于描述整条流水线是如何进行的。它将原本独立运行于单个或者多个节点的任务连接起来,实现单个任务难以完成的复杂流程编排与可视化。 Q: 什么是DSL? 答: DSL即 (Domain Sp...
2.4) Parallel - 并行 2.5) Matrix - 模型 语法总结 script - 脚本 sh - 命令执行 agent - 代理 stages - 阶段 steps - 步骤 post - 发布 environment - 环境 options - 选项 parameters - 参数 triggers - 触发器 stage - 单阶段 Tools - 工具 ...
core.pipeline_parallel.p2p_communication.send_backward_recv_backward(input_tensor_grad:torch.Tensor,recv_next:bool,tensor_shape:Union[List[int],torch.Size],config:megatron.core.ModelParallelConfig,overlap_p2p_comm:bool=False)→ torch.Tensor
PipeDream revisits using model parallelism for performance, as opposed to the traditional motivation of working set size limitations for training large models. It uses pipelining of multiple inputs to overcome the hardware efficiency limitations of model-parallel training. A gene...
parallel { // 并行推送 10 个镜像 stage('push hospital-manage') { agent none steps { container('maven') { withCredentials([usernamePassword(credentialsId : 'aliyun-docker-registry' ,passwordVariable : 'ALIYUN_REG_PWD' ,usernameVariable : 'ALIYUN_REG_USER' ,)]) { ...
self.comm_broadcast_group = dist.new_group(ranks=[i for i in range(self.world_size)], backend=Backend.GLOO, timeout=timedelta(days=365)) ... # create DDP-enabled model when the number of data-parallel workers is changed. Note: