DeepSpeed 是微软开发的深度学习优化库,专注于大规模模型训练的效率提升。它支持多种并行策略,包括张量并行(Tensor Parallelism)、流水线并行(Pipeline Parallelism)和数据并行(Data Parallelism)。DeepSpeed 的 ZeRO 优化器通过零冗余设计显著减少了内存占用,同时支持混合精度训练。 2.Megatron-LM Megatron-LM 是 NVIDIA 推...
(TP,Tensor Parallelism)可以将模型的张量(如权重矩阵)分割到多个 GPU 上进行并行计算,以加速模型的推理过程,特别是对于单个 GPU 上无法放下整个模型的情形。本文是作者在初读vllm源码的过程中对tp并行实现的学习记录。(基于版本0.8.1的代码) 初学TP并行可以参考Megatron-LM原文Megatron-LM: Training Multi-Billion ...
Megatron-LM NVIDIA Megatron-LM 是一个基于 PyTorch 的分布式训练框架,用来训练基于Transformer的大型语言模型。Megatron-LM 综合应用了数据并行(Data Parallelism),张量并行(Tensor Parallelism)和流水线并行(Pipeline Parallelism)。很多大模型的训练过程都采用它,例如bloom、opt、智源等。 torch.distributed(dist) 为运行...
Tensor parallelism is a type of model parallelism in which specific model weights, gradients, and optimizer states are split across devices. In contrast to pipeline parallelism, which keeps individual weights intact but partitions the set of weights, gradients, or optimizer across devices, tensor para...
Tensor parallelism takes place at the level ofnn.Modules; it partitions specific modules in the model across tensor parallel ranks. This is in addition to the existing partition of theset of modulesused in pipeline parallelism. When a module is partitioned through tensor parallelism, its for...
distributed-systemsmachine-learningdeep-learningpytorchllamapipeline-parallelismtensor-parallelism UpdatedMar 14, 2025 Python Large scale 4D parallelism pre-training for 🤗 transformers in Mixture of Experts *(still work in progress)* transformersmoedata-parallelismdistributed-optimizersmodel-parallelismmegatronmi...
I am training the LLM with DeepSpeed Pipeline Parallel (ZeRO0 or ZeRO1 used). But I have a tricky issue: Assuming global_batch_size=4, single machine with 8GPUS, and PP=8, so DP=1, and micro_batch_size=4. Further assuming the first batch contains the input sequence with shape (4,...
on 8 GPUs from 10mn with regular PyTorch weights down to 45s. This really speeds up feedbacks loops when developing on the model. For instance you don't have to have separate copies of the weights when changing the distribution strategy (for instance Pipeline Parallelism vs Tensor Parallelism)...
(e.g. GPUs). It subdivides the neural network architecture intocellsformed by one or more consecutive layers and assigns each cell to a separate accelerator. It furthermore employs pipeline parallelism by also splitting each mini-batch of training samples into severalmicro-batches that are ...
Is a medium or large size and requires larger batch sizes for training during which high parallelism is beneficial. TPU The TPU is much closer to an ASIC, providing a limited number of math functions, primarily matrix processing, expressly intended for ML tasks. A TPU is noted for high throu...