[doc] Update booster user documents. (#4669)

* update booster_api.md

* update booster_checkpoint.md

* update booster_plugins.md

* move transformers importing inside function

* fix Dict typing

* fix autodoc bug

* small fix

docs/source/en/basics/booster_plugins.md

@@ -1,6 +1,6 @@
 # Booster Plugins
 
-Author: [Hongxin Liu](https://github.com/ver217)
+Author: [Hongxin Liu](https://github.com/ver217), [Baizhou Zhang](https://github.com/Fridge003)
 
 **Prerequisite:**
 - [Booster API](./booster_api.md)
@@ -15,6 +15,7 @@ We currently provide the following plugins:
 - [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.
 
@@ -43,8 +44,6 @@ We've tested compatibility on some famous models, following models may not be su
 
 Compatibility problems will be fixed in the future.
 
-> ⚠ This plugin can only load optimizer checkpoint saved by itself with the same number of processes now. This 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).
@@ -69,4 +68,24 @@ More details can be found in [Pytorch Docs](https://pytorch.org/docs/main/fsdp.h
 
 {{ 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.
+
+{{ autodoc:colossalai.booster.plugin.HybridParallelPlugin }}
+
+
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