1 基本原理 先回顾一下Megtron的SP(Sequence Parallelism)操作,SP完成sequence维度的并行,覆盖操作包括LayerNorm、Dropout、FC,但不能切分self-attention模块。如下图所示在SP/TP的组合中案例中,self-attention计算前聚合(all-gather)了sequence的内容。 Self-atte
1 基本原理 先回顾一下Megatron的SP(Sequence Parallelism)操作,SP完成sequence维度的并行,覆盖操作包括LayerNorm、Dropout、FC,但不能切分self-attention模块。如下图所示在SP/TP的组合中案例中,self-attention计算前聚合(all-gather)了sequence的内容。 Self-attention的计算里面为什么需要完整的序列? Attention中QKV的...
Megatron Context Parallelism:可以看成是增强版的sp,引入了类ring-attention的技术(在tp-pp-dp rank相同的位置做ring-attention),联合Megatron的各种混合并行方式进行训练。 今天,我们来讲最后一部分Megatron Context Parallelism,把它放在最后的原因是: Megatron cp可以看成是在保持megatron sp混合并行框架的基础上,引入...
Starting from SMP v2.6.0, you can use the argumentcp_comm_typeto determine which context parallelism implementation to use. The SMP library currently supports two implementations:p2pandall_gather. Thep2pimplementation uses peer-to-peer send-receive calls for key-value accumulation during the attentio...
Megatron Sequence Parallelism:本质是想通过降低单卡激活值大小的方式,尽可能多保存激活值,少做重计算,以此提升整体训练速度,一般和它家的 tp 配套使用。 DeepSpeed Ulysses:我们知道 ds 家的 zero 是模型并行的形式,数据并行的本质。在这个情况下,单张卡是完整地做一条序列的 MHA 过程的,序列长度较长时,就会对单...
Context Parallelism/Context Parallel简称'CP' 是序列(sequence)并行的一种方式,针对Self-attention模块计算在sequence维度并行的功能。在Megtron-LM框架中,CP实现主要思想有两点:用Flash-attention2方式进行分块运算, 最后对分块结果进行修正。设备之间用ring的方式传递KV值来获得分块运算的结果,原理类似ring-attention;从...
Context parallelism overview Figure 1: A transformer layer running with TP2CP2. Communications next to Attention are for CP, others are for TP. (AG/RS: all-gather in forward and reduce-scatter in backward, RS/AG: reduce-scatter in forward and all-gather in backward, /AG: no-op in forwa...
We present context parallelism for long-context large language model inference, which achieves near-linear scaling for long-context prefill latency with up to 128 H100 GPUs across 16 nodes. Particularly, our method achieves 1M context prefill with Llama3 405B model in 77s (93% parallelization effi...
sc.defaultParallelism# For my configs, this is set to 88 返回值 PySpark RDD (pyspark.rdd.RDD)。 例子 使用值列表创建 RDD 要创建 RDD,请使用parallelize(~)函数: rdd = sc.parallelize(["A","B","C","A"]) rdd.collect() ['A','B','C','A'] ...
It would be very exciting to have support forcontext parallelismwhere in each layer we split the KQV computation across GPUs. As far as an API goes, having something likeattn_implemention="ring"infrom_pretrained()would likely be the simplest way to support this feature. ...