（2）stage2和stage3的流程是不是不太对？本文在写作时，对于stage2和stage3是做了抽象的。即把整个...
本文写作时,最终选择按照论文对相关概念的定义,选择了 3\Phi ,但是实操来看是完全可以用 2\Phi 实现的。评论区有朋友提到deepspeed的某次代码更新是将stage1的通讯量从 3\Phi 降至2\Phi ,可能也是基于此做了改进。 (2)stage2和stage3的流程是不是不太对? 本文在写作时,对于stage2和stage3是做了抽象的。即...
Stage1:优化器状态(例如,对于 Adam 优化器、FP32的权重 及first, second moment estimates)在进程间(不同GPU)Split,以便每个进程仅更新其分区。 Stage 2:用于更新模型权重的梯度(gradients)也被Split,以便每个进程仅保留与其优化器状态部分相对应的梯度。 Stage3:16 位模型参数(params)在进程之间被Split。 ZeRO-3...
Hi, I am trying to benchmark a 10B parameter Huggingface RobertaForMaskedLM model with both ZERO Stage 2 and ZERO Stage 3 to compare the latency impact of parameter partitioning. I am seeing much worse performance with Stage 3 than expec...
{ "stage": 3, "overlap_comm": true, "contiguous_gradients": true, "sub_group_size": 1e9, "reduce_bucket_size": 1, "stage3_prefetch_bucket_size": 1, "stage3_param_persistence_threshold": 1, "stage3_max_live_parameters": 1e9, "stage3_max_reuse_distance": 1e9, "stage3_gather...
DeepSpeed Inference is at its early stage, and we plan to release it gradually as features become ready. As the first step, we are releasing the core DeepSpeed Inference pipeline consisting of inference-adapted parallelism, inference-optimized generic Transformer kernels...
ZeRO 具有三个主要的优化阶段(ZeRO-1,ZeRO-2,ZeRO-3),它们对应于优化器状态(optimizer states)、梯度(gradients)和参数(parameters)的分片。累积启用时: 优化器状态分区 (P_{os}) – 内存减少 4 倍,通信量与数据并行性相同 添加梯度分区 (P_{os+g}) – 内存减少 8 倍,通信量与数据并行性相同 ...
DeepSpeed Inference is at its early stage, and we plan to release it gradually as features become ready. As the first step, we are releasing the core DeepSpeed Inference pipeline consisting of inference-adapted parallelism, inference-optimized generic Transfo...
args.num_layers_per_virtual_pipeline_stage else: args.virtual_pipeline_model_parallel_size = None # Parameters dtype. args.params_dtype = torch.float if args.fp16: assert not args.bf16 args.params_dtype = torch.half if args.bf16: assert not args.fp16 args.params_dtype = ...
赞同25 3 条评论 分享收藏 大模型面试-DeepSpeed Zero Stage 3 到底是什么并行?数据并行还是模型并行? xihuichen all in llm 大模型训练通常会用到: 1、数据并行(Data Parallelism) 2、模型并行:包括张量并行(Tensor Paralleli…阅读全文 赞同23 添加评论 分享收藏浏...