在DeepSpeedExamples也提供了bert、gan、Stable Diffusion 微调的案列,可以帮助我们更加方便的学习应用DeepSpeed。DeepSpeedExamples项目地址:GitHub - microsoft/DeepSpeedExamples: Example models using DeepSpeed。DeepSpeed发展速度非常快,一些新的大模型热点技术都实现快速支持 。目前DeepSpeed可以支持MoE模型架构训练,并且在超长...
config 基础配置 为了简化理解,配置为简单的 pp=2 dp=1 mp=0 上述配置可以在 DeepSpeedExamples/pipeline_parallelism/ds_config.json 进行配置,其中 micro batch num=train_batch_size/train_micro_batch_size_per_gpu=2. # DeepSpeedExamples/pipeline_parallelism/ds_config.json { "train_batch_size" : 256,...
Following is an example of Pipeline Parallelism with DeepSpeed# Model partitioning for pipeline parallelism model = deepspeed.init_inference(model, mp_size=4, dtype=torch.float16, pipeline_parallel=True) 4. Tensor SlicingTensor slicing helps fit the model onto hardware with limited memory by slicing...
DeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them with high performance custom inference kernels, communication optimizations and heterogeneous memory technologies to enable inference at an unprecedented scale, while achieving...
pipeline_parallelism .gitignore .gitmodules .pre-commit-config.yaml CODEOWNERS CODE_OF_CONDUCT.md LICENSE README.md SECURITY.md README.md DeepSpeed This repo contains example models that use DeepSpeed. Note on Megatron examples NOTE: We are in the process of deprecating the 3 Mega...
As a result, the effective bandwidth for data parallel communication is amplified by a combination of reduced communication volume and increased locality and parallelism. Figure 1: Example 3D parallelism with 32 workers. Layers of the neural network are divided among four pipeline stages. ...
Step 1 and Step 2 of the instruct-guided RLHF pipeline resemble regular fine-tuning of large models, and they are powered by ZeRO-based optimizations and a flexible combination of parallelism strategies in DeepSpeed training to achieve scale and speed. Step 3 of the pipeline, on the other han...
The efficiency at model sizes of 500B is comparable to state-of-the-art 3D parallelism. Unlike ZeRO-Infinity, 3D parallelism cannot scale to models with trillions of parameters due to GPU memory constraint. As a concrete example, ZeRO-Infinity achieves a sustained throughp...
DeepSpeed provides memory-efficient data parallelism and enables training models without model parallelism. For example, DeepSpeed can train models with up to 6 billion parameters on NVIDIA V100 GPUs with 32GB of device memory. In comparison, existing frameworks (e.g., PyTorch's Distributed Data Pa...
There are two general types of model parallelism: pipeline parallelism and tensor parallelism. Pipeline parallelism splits a model between layers, so that any given layer is contained within the memory of a single GPU. In contrast, tensor parallelism splits layers suc...