Megatron-lm: Training multi-billion parameter language models using model parallelism: arxiv.org/abs/1909.0805 pytorch多gpu并行训练:link-web:pytorch多gpu并行训练 北航elihe 大佬:elihe:从啥也不会到DeepSpeed———一篇大模型分布式训练的学习过程总结 北大猪猪侠大佬:猪猪侠:ZeRO: Zero Redundancy Optimizer...
1.1 数据并行(Data parallelism)不同设备执行相同模型，不同数据。数据并行 这个比较简单，贴一篇PyTorch...
deep-learningpytorchparallelismmodel-parallelismgpipepipeline-parallelismcheckpointing UpdatedJul 25, 2024 Python PaddlePaddle/PaddleFleetX Star444 飞桨大模型开发套件,提供大语言模型、跨模态大模型、生物计算大模型等领域的全流程开发工具链。 benchmarkcloudlightningelasticunsupervised-learninglarge-scaledata-parallelism...
论文实现了一个简单、有效的层内模型并行,可以训练1B以上的transformer模型 论文所提出的方法不需要一个新的编译器或者是一个新包 与之前pipeline模型是一个互补和正交的关系 在实现上,使用者用最简单的PyTorch代码,只要在里面插入一些通讯的操作即可 作者用这个方法在512张GPU上训练了一个8.3B的模型 性能15.1 PetaFL...
Model parallelism is a distributed training method in which the deep learning (DL) model is partitioned across multiple GPUs and instances. The SageMaker model parallel library v2 (SMP v2) is compatible with the native PyTorch APIs and capabilities. This makes it convenient for you to adapt your...
the SMP library offers configurable hybrid sharded data parallelism on top of PyTorch FSDP. This feature allows you to set the degree of sharding that is optimal for your training workload. Simply specify the degree of sharding in a configuration JSON object a...
🚀 Feature request This is a discussion issue for training/fine-tuning very large transformer models. Recently, model parallelism was added for gpt2 and t5. The current implementation is for PyTorch only and requires manually modifying th...
Review the following tips and pitfalls before using Amazon SageMaker's model parallelism library. This list includes tips that are applicable across frameworks. For TensorFlow and PyTorch specific tips, see and , respectively.
[0]# Set device for model parallelismifself.model_parallel:torch.cuda.set_device(self.transformer.first_device)hidden_states = hidden_states.to(self.lm_head.weight.device)# hidden_states.shape = (bs, len, hs)# lm_logits.shape = (bs, len, vocab_size)lm_logits = self.lm_head(hidden_...
When batch size is one, only tensor parallelism can take advantage of multiple GPUs at once when processing the forward pass to improve latency. In this post, we use DeepSpeed to partition the model using tensor parallelism techniques. DeepSpeed Inference supports large...