简易实现里面没有考虑模型参数备份、显存优化、异步通信等等问题,只为了解如何通过torch.distributed接口来实现PipeDream的并行。 pp_group=get_pipeline_parallel_group()pp_size=pp_group.size()# Run warmup forward processoutput_chunks=[]num_warmp=min(pp_size-self.pp_rank,x.shape[0])foriinrange(num_...
(#4412,#5408,#6115,#6120). You can now run the API server with--pipeline-parallel-size. This feature is in early stage, please let us know your feedback. 2. 配置 ParallelConfig: pipeline_parallel_size: Number of pipeline parallel groups. 参数验证: EngineConfig self.model_config.verify_w...
三、Pipeline Parallel的性能优化策略 为了克服Pipeline Parallel的性能瓶颈,提高大模型训练的效率,我们可以采取以下优化策略,同时利用百度智能云文心快码(Comate)快速生成和优化相关代码: 优化Bubble Time 增加Micro-Batch Size:通过增加每个Mini-Batch中的Micro-Batch数量,可以减少Bubble Time的占比。这是因为更多的Micro-B...
3.Parallel(并行) 2017.9.25新增parallel stage支持。 Declarative Pipeline近期新增了对并行嵌套stage的支持,对耗时长,相互不存在依赖的stage可以使用此方式提升运行效率。除了parallel stage,单个parallel里的多个step也可以使用并行的方式运行。 Example 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ...
Declarative Pipeline近期新增了对并行嵌套stage的支持,对耗时长,相互不存在依赖的stage可以使用此方式提升运行效率。除了parallel stage,单个parallel里的多个step也可以使用并行的方式运行。 pipeline { agent any stages {stage('Non-Parallel Stage') { steps { ...
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...
--interleave-group-sizeThe number of microbatches in an interleaved 1F1B group. This should be ⌈d/ 2⌉ todwheredis Pipeline Parallel Size. --cpu-offloadEnable offloading. --offload-timeThe time ratios of one-way activation offload and Forward + Backward: (D2H + H2D) / 2 / (Forward...
PIPELINE PARALLEL MULTIPLIERPURPOSE: To obtain sufficiently high speed without expanding the size of a circuit by combining pipeline processing and a carry save adder type parallel multiplier.SATO JUNICHI佐藤 純一
core.pipeline_parallel.p2p_communication.send_forward_recv_forward(output_tensor:torch.Tensor,recv_prev:bool,tensor_shape:Union[List[int],torch.Size],config:megatron.core.ModelParallelConfig,overlap_p2p_comm:bool=False)→ torch.Tensor Batched recv from previous rank and send to next rank in pipeli...
{ "outputDataKeys": "mxpi_modelinfer7" }, "factory": "mxpi_dataserialize", "next": "mxpi_parallel2serial8:7" }, "mxpi_parallel2serial8":{ "factory":"mxpi_parallel2serial", "next":"appsink0" }, "appsink0": { "props": { "blocksize": "409600000" }, "factory": "appsink" }...