2.不同stage的区别 Stage 1: 把优化器状态(optimizer states)分片到每个数据并行的工作进程(每个GPU)下 Stage 2: 把优化器状态(optimizer states) + 梯度(gradients)分片到每个数据并行的工作进程(每个GPU)下 Stage 3: 把优化器状态(optimizer states) + 梯度(gradients) + 模型参数(parameters)分片到每个数据并...
1、数据并行(Data Parallelism) 2、模型并行:包括张量并行(Tensor Parallelism)和流水线并行(Pipeline Parallelism) DeepSpeed Zero Stage 本质上是一种“节省显存”的数据并行,是一种 Fully Sharded Data Parallelism。 例如,Zero Stage 3 加载时将模型参数进行切片存储到不同的GPU上,每个GPU只保留参数的1/N。计算时...
Description & Motivation According to this Issue, seems like there is _offload version of deepspeed stage 1. But passing "deepspeed_stage_1_offload" to Trainer doesn't work. I believe it'd still work by passing a config dict, but it'd be...
deepspeed.runtime.zero.stage_1_and_2.DeepSpeedZeroOptimizer.average_tensoronly sets reduction stream waiting for default stream. This is ok in cases where the computation time is longer than the communication time, but when the communication time is longer, it may result in a rewrite of the ip...
DeepSpeed excels in four aspects (as visualized in Figure 2): • Scale: State-of-the-art large models such as OpenAI GPT-2, NVIDIA Megatron-LM, and Google T5 have sizes of 1.5 billion, 8.3 billion, and 11 billion parameters respectively. ZeRO stage one in ...
The ZeRO-1 implementation we shared in February supports the first stage, partitioning optimizer states (Pos), which saves up to 4x of memory when compared with using classic data parallelism that replicates everything. ZeRO-2 adds the support for the second stage, partitioning gradients (Po...
1、多机多卡训练基础知识 2、训练一个大模型需要多少显存? 3、DP(Data Parallelism)数据并行是单进程、多线程的并行训练方式 4、DDP (Distributed Data Parallel)分布式数据并行是多进程 5、DeepSpeed ZeRO(Zero Redundancy Optimizer)它进一步优化了显存使用和通信效率。 导航栏 1、多机多卡训练基础知识 1.1 一张1GB...
图1：DeepSpeed Chat的RLHF训练流程以及可选特性的插图。作为整个InstructGPT中3步流程中最复杂的步骤，...
MoE w. stage 1 requires contiguous gradients (CG) is enabled, which was fixed in #2250. However, this introduces a performance regression when not using MoE. This PR reverts the non-MoE case to ensure CG is disabled. /cc @siddharth9820, @tjruwase