# Booster Plugins

Author: [Hongxin Liu](https://github.com/ver217), [Baizhou Zhang](https://github.com/Fridge003)

**Prerequisite:**
- [Booster API](./booster_api.md)

## Introduction

As mentioned in [Booster API](./booster_api.md), we can use booster plugins to customize the parallel training. In this tutorial, we will introduce how to use booster plugins.

We currently provide the following plugins:

- [Low Level Zero Plugin](#low-level-zero-plugin): It wraps the `colossalai.zero.low_level.LowLevelZeroOptimizer` and can be used to train models with zero-dp. It only supports zero stage-1 and stage-2.
- [Gemini Plugin](#gemini-plugin): It wraps the [Gemini](../features/zero_with_chunk.md) which implements Zero-3 with chunk-based and heterogeneous memory management.
- [Torch DDP Plugin](#torch-ddp-plugin): It is a wrapper of `torch.nn.parallel.DistributedDataParallel` and can be used to train models with data parallelism.
- [Torch FSDP Plugin](#torch-fsdp-plugin): It is a wrapper of `torch.distributed.fsdp.FullyShardedDataParallel` and can be used to train models with zero-dp.
- [Hybrid Pararllel Plugin](#hybrid-parallel-plugin): It provides a tidy interface that integrates the power of Shardformer, pipeline manager, mixied precision training, TorchDDP and Zero stage 1/2 feature.  With this plugin, transformer models can be easily trained with any combination of tensor parallel, pipeline parallel and data parallel (DDP/Zero) efficiently, along with various kinds of optimization tools for acceleration and memory saving. Detailed information about supported parallel strategies and optimization tools is explained in the section below.

More plugins are coming soon.

## Plugins

### Low Level Zero Plugin

This plugin implements Zero-1 and Zero-2 (w/wo CPU offload), using `reduce` and `gather` to synchronize gradients and weights.

Zero-1 can be regarded as a better substitute of Torch DDP, which is more memory efficient and faster. It can be easily used in hybrid parallelism.

Zero-2 does not support local gradient accumulation. Though you can accumulate gradient if you insist, it cannot reduce communication cost. That is to say, it's not a good idea to use Zero-2 with pipeline parallelism.

{{ autodoc:colossalai.booster.plugin.LowLevelZeroPlugin }}

We've tested compatibility on some famous models, following models may not be supported:

- `timm.models.convit_base`
- dlrm and deepfm models in `torchrec`
- `diffusers.VQModel`
- `transformers.AlbertModel`
- `transformers.AlbertForPreTraining`
- `transformers.BertModel`
- `transformers.BertForPreTraining`
- `transformers.GPT2DoubleHeadsModel`

Compatibility problems will be fixed in the future.

### Gemini Plugin

This plugin implements Zero-3 with chunk-based and heterogeneous memory management. It can train large models without much loss in speed. It also does not support local gradient accumulation. More details can be found in [Gemini Doc](../features/zero_with_chunk.md).

{{ autodoc:colossalai.booster.plugin.GeminiPlugin }}

### Torch DDP Plugin

More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel).

{{ autodoc:colossalai.booster.plugin.TorchDDPPlugin }}

### Torch FSDP Plugin

> ⚠ This plugin is not available when torch version is lower than 1.12.0.

> ⚠ This plugin does not support save/load sharded model checkpoint now.

> ⚠ This plugin does not support optimizer that use multi params group.

More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/fsdp.html).

{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }}


### Hybrid Parallel Plugin

This plugin implements the combination of various parallel training strategies and optimization tools. The features of HybridParallelPlugin can be generally divided into four parts:

1. Shardformer: This plugin provides an entrance to Shardformer, which controls model sharding under tensor parallel and pipeline parallel setting. Shardformer also overloads the logic of model's forward/backward process to ensure the smooth working of tp/pp. Also, optimization tools including fused normalization, flash attention (xformers), JIT and sequence parallel are injected into the overloaded forward/backward method by Shardformer.

2. Mixed Precision Training: Support for fp16/bf16 mixed precision training. More details about its arguments configuration can be found in [Mixed Precision Training Doc](../features/mixed_precision_training_with_booster.md).

3. Torch DDP: This plugin will automatically adopt Pytorch DDP as data parallel strategy when pipeline parallel and Zero is not used. More details about its arguments configuration can be found in [Pytorch DDP Docs](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel).

4. Zero: This plugin can adopt Zero 1/2 as data parallel strategy through setting the `zero_stage` argument as 1 or 2 when initializing plugin. Zero 1 is compatible with pipeline parallel strategy, while Zero 2 is not. More details about its argument configuration can be found in [Low Level Zero Plugin](#low-level-zero-plugin).

> ⚠ When using this plugin, only the subset of Huggingface transformers supported by Shardformer are compatible with tensor parallel, pipeline parallel and optimization tools. Mainstream transformers such as Llama 1, Llama 2, OPT, Bloom, Bert and GPT2 etc. are all supported by Shardformer.

> ⚠ This plugin only supports sharded checkpointing methods for model/optimizer at present. Unsharded checkpointing methods will be supported in future release.

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