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
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,...
pipeline_parallelism Pipeline parallelism example (deepspeedai#50) Sep 11, 2020 .gitignore Initial commit Jan 30, 2020 .pre-commit-config.yaml DeepSpeed 0.2 support (deepspeedai#21) May 15, 2020 CODEOWNERS add codeowners and remove deepspeed.pt refs (deepspeedai#33) ...
Pipeline Parallelism Tensor Parallelism (Inference Engine) ZeRO (stage1-stage3) Activation Checkpointing ZeRO-Offload CPU Adam Fused Adam One-bit Adam MoE Zero Infinity Zero-One Adam Curriculum Learning Progressive layer dropping 请参考 Deepspeed 官方文档获取这些特性的详细说明:https://www.deepspeed.ai...
Figure 1: Example 3D parallelism with 32 workers. Layers of the neural network are divided among four pipeline stages. Layers within each pipeline stage are further partitioned among four model parallel workers. Lastly, each pipeline is replicated across two data parallel instances, and ZeRO partiti...
These innovations such as ZeRO, 3D-Parallelism, DeepSpeed-MoE, ZeRO-Infinity, etc. fall under the training pillar. Learn more: DeepSpeed-Training DeepSpeed-Inference DeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them ...
The optimized GPU resources come from using inference-adapted parallelism, which allows users to adapt the model and pipeline parallelism degree from the trained model checkpoints, and shrinking model memory footprint by half with INT8 quantization. As shown in Figure...
parallelism, model parallelism are used when models are too large (as it is in many LLMs) and can not be fully replicated across data parallel ranks. Tensor parallelism splits compute operators (i.e., attention and MLPs) within a layer and pipeline parallelism splits model in a depth-wise...
1. Microsoft Turing-NLG Microsoft has released Turing-NLG using DeepSpeed to infer an optimized version of this largest model. Techniques such as model parallelism and quantization have allowed Microsoft to reduce the inference latency of this huge model by up to 4x. ...
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...