--- title: "Feature Overview" layout: single permalink: /features/ toc: true toc_label: "Contents" --- ## Distributed Training with Mixed Precision ### Mixed Precision Training Enable 16-bit (FP16) training by in the `deepspeed_config` JSON. ```json "fp16": { "enabled": true, "loss_scale": 0, "loss_scale_window": 1000, "hysteresis": 2, "min_loss_scale": 1 } ``` ### Single-GPU, Multi-GPU, and Multi-Node Training Easily switch between single-GPU, single-node multi-GPU, or multi-node multi-GPU execution by specifying resources with a hostfile. ```bash deepspeed --hostfile= \ \ --deepspeed --deepspeed_config ds_config.json ``` The script `` will execute on the resources specified in ``. ## Pipeline Parallelism DeepSpeed provides [pipeline parallelism](/tutorials/pipeline/) for memory- and communication- efficient training. DeepSpeed supports a hybrid combination of data, model, and pipeline parallelism and has scaled to over [one trillion parameters using 3D parallelism]({{ site.press_release_v3 }}). Pipeline parallelism can also improve communication efficiency and has accelerated training by up to 7x on low-banwdith clusters. ## Model Parallelism ### Support for Custom Model Parallelism DeepSpeed supports all forms of model parallelism including tensor slicing based approaches such as the [Megatron-LM](https://github.com/NVIDIA/Megatron-LM). It does so by only requiring the model parallelism framework to provide a *model parallelism unit* (`mpu`) that implements a few bookkeeping functionalities: ```python mpu.get_model_parallel_rank() mpu.get_model_parallel_group() mpu.get_model_parallel_world_size() mpu.get_data_parallel_rank() mpu.get_data_parallel_group() mpu.get_data_parallel_world_size() ``` ### Integration with Megatron-LM DeepSpeed is fully compatible with [Megatron](https://github.com/NVIDIA/Megatron-LM). Please see the [Megatron-LM tutorial](/tutorials/megatron/) for details. ## The Zero Redundancy Optimizer The Zero Redundancy Optimizer ([ZeRO](https://arxiv.org/abs/1910.02054)) is at the heart of DeepSpeed and enables large model training at a scale that is simply not possible with model parallelism alone. When enabled, ZeRO allows training models with over 13 billion parameters without any model parallelism, and up to 200 billion parameter models with model parallelism on current generation hardware. For more details see the [ZeRO paper](https://arxiv.org/abs/1910.02054), [GPT tutorial](/tutorials/megatron/) on integration with DeepSpeed. ### Optimizer State and Gradient Partitioning Optimizer State and Gradient Partitioning in ZeRO reduces the memory consumption of the model states (optimizer states, gradients and parmaeters) by 8x compared to standard data parallelism by partitioning these states across data parallel process instead of replicating them. ### Activation Partitioning Activation Partitioning is a memory optimization in ZeRO that can reduce the memory consumed by activations during model parallel training (MP). In MP certain activations maybe required by all MP processes, resulting in a replication of activations across MP GPUs. Activation Partitioning stores these activations in a partitioned state once they are used for computation in the forward propagation. These activations are allgathered right before they are needed again during the backward propagation. By storing activations in a partitioned state, ZeRO in DeepSpeed can reduce the activation memory footprint proportional to the MP degree. ### Constant Buffer Optimization (CBO) CBO enables high network and memory throughput while restricting memory usage to a constant size. For memory- and network-bound operations such as normalization or allreduce collectives, the performance depends on the size of the operand. Simply fusing all operands into a single large operand can enable great throughput at the expense of unnecessary memory overhead. CBO in DeepSpeed fuses smaller operands into approximately a pre-defined sized buffer large enough to achieve great performance without the unnecessary memory overhead. ### Contiguous Memory Optimization (CMO) CMO reduces memory fragmentation during training, preventing out of memory errors due to lack of contiguous memory. Memory fragmentation is a result of interleaving between short lived and long lived memory objects. During the forward propagation activation checkpoints are long lived but the activations that recomputed are short lived. Similarly, during the backward computation, the activation gradients are short lived while the parameter gradients are long lived. CMO transfers activation checkpoints and parameter gradients to contiguous buffers preventing memory fragmentation. ## ZeRO-Offload ZeRO-Offload pushes the boundary of the maximum model size that can be trained efficiently using minimal GPU resources, by exploiting computational and memory resources on both GPUs and their host CPUs. It allows training up to 13-billion-parameter models on a single NVIDIA V100 GPU, 10x larger than the state-of-the-art, while retaining high training throughput of over 30 teraflops per GPU. For more details see the [ZeRO-Offload release blog]( https://www.microsoft.com/en-us/research/?p=689370&secret=iSlooB), and [tutorial](/tutorials/zero-offload/) on integration with DeepSpeed. ## Additional Memory and Bandwidth Optimizations ### Smart Gradient Accumulation Gradient accumulation allows running larger batch size with limited memory by breaking an effective batch into several sequential micro-batches, and averaging the parameter gradients across these micro-batches. Furthermore, instead of averaging the gradients of each micro-batch across all GPUs, the gradients are averaged locally during each step of the sequence, and a single `allreduce` is done at the end of the sequence to produce the averaged gradients for the effective batch across all GPUs. This strategy significantly reduces the communication involved over the approach of averaging globally for each micro-batch, specially when the number of micro-batches per effective batch is large. ### Communication Overlapping During back propagation, DeepSpeed can overlap the communication required for averaging parameter gradients that have already been computed with the ongoing gradient computation. This computation-communication overlap allows DeepSpeed to achieve higher throughput even at modest batch sizes. ## Training Features ### Simplified training API The DeepSpeed core API consists of just a handful of methods: * initialization: `initialize` * training: `backward` and `step` * argument parsing: `add_config_arguments` * checkpointing : `load_checkpoint` and `store_checkpoint` DeepSpeed supports most of the features described in this document, via the use of these API, along with a `deepspeed_config` JSON file for enabling and disabling the features. Please see the [core API doc](https://deepspeed.readthedocs.io/) for more details. ### Activation Checkpointing API DeepSpeed's Activation Checkpoinitng API supports activation checkpoint partitioning, cpu checkpoiniting, and contiguous memory optimizations, while also allowing layerwise profiling. Please see the [core API doc](https://deepspeed.readthedocs.io/) for more details. ### Gradient Clipping ```json { "gradient_clipping": 1.0 } ``` DeepSpeed handles gradient clipping under the hood based on the max gradient norm specified by the user. Please see the [core API doc](https://deepspeed.readthedocs.io/) for more details. ### Automatic loss scaling with mixed precision DeepSpeed internally handles loss scaling for mixed precision training. The parameters for loss scaling can be specified in the `deepspeed_config` JSON file. Please see the [core API doc](https://deepspeed.readthedocs.io/) for more details. ## Training Optimizers ### 1-bit Adam optimizer with up to 5x less communication DeepSpeed has an efficient implementation of a novel algorithm called 1-bit Adam. It offers the same convergence as Adam, incurs up to 5x less communication that enables up to 3.5x higher throughput for BERT-Large pretraining and up to 2.7x higher throughput for SQuAD fine-tuning on bandwidth-limited clusters. For more details on usage and performance, please refer to the detailed [tutorial](https://www.deepspeed.ai/tutorials/onebit-adam) and [blog post](https://www.deepspeed.ai/news/2020/09/09/onebit-adam-blog-post.md), respectively. ### Fused Adam optimizer and arbitrary torch.optim.Optimizer With DeepSpeed, the user can choose to use a high performance implementation of ADAM from NVIDIA, or any training optimizer that extends torch's `torch.optim.Optimizer` class. ### CPU-Adam: High-Performance vectorized implementation of Adam We introduce an efficient implementation of Adam optimizer on CPU that improves the parameter-update performance by nearly an order of magnitude. We use the AVX SIMD instructions on Intel-x86 architecture for the CPU-Adam implementation. We support both AVX-512 and AVX-2 instruction sets. DeepSpeed uses AVX-2 by defualt which can be switched to AVX-512 by setting the build flag, `DS_BUILD_AVX512` to 1 when installing DeepSpeed. Using AVX-512, we observe 5.1x to 6.5x speedups considering the model-size between 1 to 10 billion parameters with respect to torch-adam. ### Memory bandwidth optimized FP16 Optimizer Mixed precision training is handled by the DeepSpeed FP16 Optimizer. This optimizer not only handles FP16 training but is also highly efficient. The performance of weight update is primarily dominated by the memory bandwidth, and the achieved memory bandwidth is dependent on the size of the input operands. The FP16 Optimizer is designed to maximize the achievable memory bandwidth by merging all the parameters of the model into a single large buffer, and applying the weight updates in a single kernel, allowing it to achieve high memory bandwidth. ### Large Batch Training with LAMB Optimizer DeepSpeed makes it easy to train with large batch sizes by enabling the LAMB Optimizer. For more details on LAMB, see the [LAMB paper](https://arxiv.org/pdf/1904.00962.pdf). ### Memory-Efficient Training with ZeRO Optimizer DeepSpeed can train models with up to 13 billion parameters without model parallelism, and models with up to 200 billion parameters with 16-way model parallelism. This leap in model size is possible through the memory efficiency achieved via the ZeRO Optimizer. For more details see [ZeRO paper](https://arxiv.org/abs/1910.02054) . ## Training Agnostic Checkpointing DeepSpeed can simplify checkpointing for you regardless of whether you are using data parallel training, model parallel training, mixed-precision training, a mix of these three, or using the zero optimizer to enable larger model sizes. Please see the [Getting Started](/getting-started/) guide and the [core API doc](https://deepspeed.readthedocs.io/) for more details. ## Advanced parameter search DeepSpeed supports multiple Learning Rate Schedules to enable faster convergence for large batch scaling. ### Learning Rate Range Test Please refer to the [Learning Rate Range Test](/tutorials/lrrt/) tutorial. ### 1Cycle Learning Rate Schedule Please refer to the [1Cycle Learning Rate Schedule](/tutorials/1Cycle/) tutorial. ## Simplified Data Loader DeepSpeed abstracts away data parallelism and model parallelism from the user when it comes to data loading. Users simply provide a PyTorch dataset, and DeepSpeed data loader can automatically handle batch creation appropriately. ## Performance Analysis and Debugging For performance debugging, DeepSpeed can give you a detailed breakdown of the time spent in different parts of the training by simply enabling it in the `deepspeed_config` file. Please see the [core API doc](https://deepspeed.readthedocs.io/) for more details. ```json { "wall_clock_breakdown": true, "activation_checkpointing": { "profile": true } } ``` ## Sparse Attention DeepSpeed offers sparse attention to support long sequences. Please refer to the [Sparse Attention](/tutorials/sparse-attention/) tutorial. ```bash --deepspeed_sparse_attention ``` ```json "sparse_attention": { "mode": "fixed", "block": 16, "different_layout_per_head": true, "num_local_blocks": 4, "num_global_blocks": 1, "attention": "bidirectional", "horizontal_global_attention": false, "num_different_global_patterns": 4 } ```