DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
10x Larger Models | 10x Faster Training | Minimal Code Change
DeepSpeed can train DL models with over a hundred billion parameters on current generation of GPU clusters, while achieving over 10x in system performance compared to the state-of-art. Early adopters of DeepSpeed have already produced a language model (LM) with over 17B parameters called Turing-NLG, establishing a new SOTA in the LM category.
Training advanced deep learning models is challenging. Beyond model design, model scientists also need to set up the state-of-the-art training techniques such as distributed training, mixed precision, gradient accumulation, and checkpointing. Yet still, scientists may not achieve the desired system performance and convergence rate. Large model sizes are even more challenging: a large model easily runs out of memory with pure data parallelism and it is difficult to use model parallelism. DeepSpeed addresses these challenges to accelerate model development and training.
Distributed, Effective, and Efficient Training with Ease
The DeepSpeed API is a lightweight wrapper on PyTorch. This means that you can use everything you love in PyTorch and without learning a new platform. In addition, DeepSpeed manages all of the boilerplate state-of-the-art training techniques, such as distributed training, mixed precision, gradient accumulation, and checkpoints so that you can focus on your model development. Most importantly, you can leverage the distinctive efficiency and effectiveness benefit of DeepSpeed to boost speed and scale with just a few lines of code changes to your PyTorch models.
DeepSpeed achieves high performance and fast convergence through a combination of efficiency optimizations on compute/communication/memory/IO and effectiveness optimizations on advanced hyperparameter tuning and optimizers. For example:
- DeepSpeed trains BERT-large to parity in 44 mins using 1024 V100 GPUs (64 DGX-2 boxes) and in 2.4 hours using 256 GPUs (16 DGX-2 boxes).
- DeepSpeed trains GPT2 (1.5 billion parameters) 3.75x faster than state-of-art, NVIDIA Megatron on Azure GPUs.
DeepSpeed provides memory-efficient data parallelism and enables training models without model parallelism. For example, DeepSpeed can train models with up to 13 billion parameters on NVIDIA V100 GPUs with 32GB of device memory. In comparison, existing frameworks (e.g., PyTorch’s Distributed Data Parallel) run out of memory with 1.4 billion parameter models.
DeepSpeed reduces the training memory footprint through a novel solution called Zero Redundancy Optimizer (ZeRO). Unlike basic data parallelism where memory states are replicated across data-parallel processes, ZeRO partitions model states and gradients to save significant memory. Furthermore, it also reduces activation memory and fragmented memory. The current implementation (ZeRO-2) reduces memory by up to 8x relative to the state-of-art. You can read more about ZeRO in our paper, and in our blog posts related to ZeRO-1.
With this impressive memory reduction, early adopters of DeepSpeed have already produced a language model (LM) with over 17B parameters called Turing-NLG, establishing a new SOTA in the LM category.
DeepSpeed supports efficient data parallelism, model parallelism, and their combination. ZeRO boosts the scaling capability and efficiency further.
- DeepSpeed provides system support to run models up to 170 billion parameters, 10x larger than the state-of-art (8 billion NVIDIA GPT, 11 billion Google T5).
- DeepSpeed can run large models more efficiently, up to 10x faster for models with various sizes spanning 1.5B to 170B. More specifically, the data parallelism powered by ZeRO is complementary and can be combined with different types of model parallelism. It allows DeepSpeed to fit models using lower degree of model parallelism and higher batch size, offering significant performance gains compared to using model parallelism alone. Read more: ZeRO paper, and GPT tutorial.
Fast convergence for effectiveness
DeepSpeed supports advanced hyperparameter tuning and large batch size optimizers such as LAMB. These improve the effectiveness of model training and reduce the number of samples required to convergence to desired accuracy.
Only a few lines of code changes are needed to enable a PyTorch model to use DeepSpeed and ZeRO. Compared to current model parallelism libraries, DeepSpeed does not require a code redesign or model refactoring. It also does not put limitations on model dimensions (such as number of attention heads, hidden sizes, and others), batch size, or any other training parameters. For models of up to 13 billion parameters, you can use ZeRO-powered data parallelism conveniently without requiring model parallelism, while in contrast, standard data parallelism will run out of memory for models with more than 1.4 billion parameters. In addition, DeepSpeed conveniently supports flexible combination of ZeRO-powered data parallelism with custom model parallelisms, such as tensor slicing of NVIDIA’s Megatron-LM.
Below we provide a brief feature list, see our detailed feature overview for descriptions and usage.
- Distributed training with mixed precision
- 16-bit mixed precision
- Model parallelism
- Support for Custom Model Parallelism
- Integration with Megatron-LM
- The Zero Redundancy Optimizer (ZeRO)
- Optimizer State and Gradient Partitioning
- Activation Partitioning
- Constant Buffer Optimization
- Contiguous Memory Optimization
- Additional memory and bandwidth optimizations
- Smart Gradient Accumulation
- Communication/Computation Overlap
- Training features
- Simplified training API
- Activation Checkpointing API
- Gradient Clipping
- Automatic loss scaling with mixed precision
- Training optimizers
- Fused Adam optimizer and arbitrary
- Memory bandwidth optimized FP16 Optimizer
- Large Batch Training with LAMB Optimizer
- Memory efficient Training with ZeRO Optimizer
- Fused Adam optimizer and arbitrary
- Training agnostic checkpointing
- Advanced parameter search
- Learning Rate Range Test
- 1Cycle Learning Rate Schedule
- Simplified data loader
- Performance analysis and debugging
Contributing to DeepSpeed
DeepSpeed welcomes your contributions! Please see our contributing guide for more details on formatting, testing, etc.
Contributor License Agreement
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Code of Conduct
Ammar Ahmad Awan
Partner Research Manager
Principal Research Engineer
Senior Research SDE
Niranjan Uma Naresh
Reza Yazdani Aminabadi
Principal Software Engineer