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...
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
get_pipeline_model_parallel_world_size:pipeline并行的卡数 get_pipeline_model_parallel_rank:当前卡在pipeline并行中的序号 is_pipeline_last_stage(ignore_virtual=True)、is_pipeline_first_stage(ignore_virtual=True):忽略virtual stage时,判断当前stage是否为流水线并行的最后一个、第一个stage。如下图,红色框...
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...
To achieve a better throughput, we recommend setting--num-layersto a value tok * pipeline-model-parallel-size - 2where k can be any value$\ge1$. This is used to compensate for the additional embedding layer on the first/last pipeline stages which could otherwise brings bubble to all other...
{ "outputDataKeys": "mxpi_modelinfer7" }, "factory": "mxpi_dataserialize", "next": "mxpi_parallel2serial8:7" }, "mxpi_parallel2serial8":{ "factory":"mxpi_parallel2serial", "next":"appsink0" }, "appsink0": { "props": { "blocksize": "409600000" }, "factory": "appsink" }...
pipeline_parallel package core.pipeline_parallel.schedules.,,megatron.core.enums.ModelType,seq_length:int,micro_batch_size:int,decoder_seq_length:int,config,encoder_decoder_xattn:bool) Determine right tensor sizes (based on position of rank with respect to split rank) and model size. Send two ...
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 gener...
parallel:并行执行多个step。在pipeline插件1.2版本后,parallel开始支持对多个阶段进行并行执行。 parameters:与input不同,parameters是执行pipeline前传入的一些参数。 triggers:用于定义执行pipeline的触发器。 when:当满足when定义的条件时,阶段才执行。 Tips: 在使用指令时需要注意的是每个指令都有自己的“作用域”。如...
importdill@pipeline_def(py_callback_pickler=dill,...)defcreate_pipeline():src=fn.external_source(lambdasample_info:np.int32([42]),batch=False,parallel=True)... A valid value forpy_callback_pickleris either a module/object implementingdumpsandloadsmethods or a tuple where the first item is...