[doc] add tutorial for booster plugins (#3758)

* [doc] add en booster plugins doc

* [doc] add booster plugins doc in sidebar

* [doc] add zh booster plugins doc

* [doc] fix zh booster plugin translation

* [doc] reoganize tutorials order of basic section

* [devops] force sync to test ci

docs/sidebars.json

@@ -26,14 +26,15 @@
       "collapsed": true,
       "items": [
         "basics/command_line_tool",
-        "basics/define_your_config",
         "basics/launch_colossalai",
+        "basics/booster_api",
+        "basics/booster_plugins",
+        "basics/define_your_config",
         "basics/initialize_features",
         "basics/engine_trainer",
         "basics/configure_parallelization",
         "basics/model_checkpoint",
-        "basics/colotensor_concept",
-        "basics/booster_api"
+        "basics/colotensor_concept"
       ]
     },
     {

docs/source/en/basics/booster_plugins.md

@@ -0,0 +1,64 @@
+# Booster Plugins
+
+Author: [Hongxin Liu](https://github.com/ver217)
+
+**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.
+
+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.
+
+More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/fsdp.html).
+
+{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }}

docs/source/zh-Hans/basics/booster_plugins.md

@@ -0,0 +1,64 @@
+# Booster 插件
+
+作者: [Hongxin Liu](https://github.com/ver217)
+
+**前置教程:**
+- [Booster API](./booster_api.md)
+
+## 引言
+
+正如 [Booster API](./booster_api.md) 中提到的，我们可以使用 booster 插件来自定义并行训练。在本教程中，我们将介绍如何使用 booster 插件。
+
+我们现在提供以下插件:
+
+- [Low Level Zero 插件](#low-level-zero-plugin): 它包装了 `colossalai.zero.low_level.LowLevelZeroOptimizer`，可用于使用 Zero-dp 训练模型。它仅支持 Zero 阶段1和阶段2。
+- [Gemini 插件](#gemini-plugin): 它包装了 [Gemini](../features/zero_with_chunk.md)，Gemini 实现了基于Chunk内存管理和异构内存管理的 Zero-3。
+- [Torch DDP 插件](#torch-ddp-plugin): 它包装了 `torch.nn.parallel.DistributedDataParallel` 并且可用于使用数据并行训练模型。
+- [Torch FSDP 插件](#torch-fsdp-plugin): 它包装了 `torch.distributed.fsdp.FullyShardedDataParallel` 并且可用于使用 Zero-dp 训练模型。
+
+更多插件即将推出。
+
+## 插件
+
+### Low Level Zero 插件
+
+该插件实现了 Zero-1 和 Zero-2（使用/不使用 CPU 卸载），使用`reduce`和`gather`来同步梯度和权重。
+
+Zero-1 可以看作是 Torch DDP 更好的替代品，内存效率更高，速度更快。它可以很容易地用于混合并行。
+
+Zero-2 不支持局部梯度累积。如果您坚持使用，虽然可以积累梯度，但不能降低通信成本。也就是说，同时使用流水线并行和 Zero-2 并不是一个好主意。
+
+{{ autodoc:colossalai.booster.plugin.LowLevelZeroPlugin }}
+
+我们已经测试了一些主流模型的兼容性，可能不支持以下模型：
+
+- `timm.models.convit_base`
+- dlrm and deepfm models in `torchrec`
+- `diffusers.VQModel`
+- `transformers.AlbertModel`
+- `transformers.AlbertForPreTraining`
+- `transformers.BertModel`
+- `transformers.BertForPreTraining`
+- `transformers.GPT2DoubleHeadsModel`
+
+兼容性问题将在未来修复。
+
+### Gemini 插件
+
+这个插件实现了基于Chunk内存管理和异构内存管理的 Zero-3。它可以训练大型模型而不会损失太多速度。它也不支持局部梯度累积。更多详细信息，请参阅 [Gemini 文档](../features/zero_with_chunk.md).
+
+{{ autodoc:colossalai.booster.plugin.GeminiPlugin }}
+
+### Torch DDP 插件
+
+更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel).
+
+{{ autodoc:colossalai.booster.plugin.TorchDDPPlugin }}
+
+### Torch FSDP 插件
+
+> ⚠ 如果 torch 版本低于 1.12.0，此插件将不可用。
+
+更多详细信息，请参阅 [Pytorch 文档](https://pytorch.org/docs/main/fsdp.html).
+
+{{ autodoc:colossalai.booster.plugin.TorchFSDPPlugin }}