[doc] update booster tutorials

docs/sidebars.json

@@ -32,7 +32,8 @@
         "basics/engine_trainer",
         "basics/configure_parallelization",
         "basics/model_checkpoint",
-        "basics/colotensor_concept"
+        "basics/colotensor_concept",
+        "basics/booster_api"
       ]
     },
     {

docs/source/en/basics/booster_api.md

@@ -0,0 +1,89 @@
+# Booster API
+Author: [Mingyan Jiang](https://github.com/jiangmingyan)
+
+**Prerequisite:**
+- [Distributed Training](../concepts/distributed_training.md)
+- [Colossal-AI Overview](../concepts/colossalai_overview.md)
+
+**Example Code**
+- [Train with Booster](https://github.com/hpcaitech/ColossalAI/blob/main/examples/tutorial/new_api/cifar_resnet/README.md)
+
+## Introduction
+In our new design, `colossalai.booster` replaces the role of `colossalai.initialize` to inject features into your training components (e.g. model, optimizer, dataloader) seamlessly. With these new APIs, you can integrate your model with our parallelism features more friendly. Also calling `colossalai.booster` is the standard procedure before you run into your training loops. In the sections below, I will cover how `colossalai.booster` works and what we should take note of.
+
+### Plugin
+Plugin is an important component that manages parallel configuration (eg: The gemini plugin encapsulates the gemini acceleration solution). Currently supported plugins are as follows:
+
+***GeminiPlugin:*** This plugin wrapps the Gemini acceleration solution, that ZeRO with chunk-based memory management.
+
+***TorchDDPPlugin:*** This plugin wrapps the DDP acceleration solution, it implements data parallelism at the module level which can run across multiple machines.
+
+***LowLevelZeroPlugin:*** This plugin wraps the 1/2 stage of Zero Redundancy Optimizer. Stage 1 : Shards optimizer states across data parallel workers/GPUs. Stage 2 : Shards optimizer states + gradients across data parallel workers/GPUs.
+
+### API of booster
+
+
+{{ autodoc:colossalai.booster.Booster }}
+
+{{ autodoc:colossalai.booster.Booster.boost }}
+
+{{ autodoc:colossalai.booster.Booster.backward }}
+
+{{ autodoc:colossalai.booster.Booster.no_sync }}
+
+{{ autodoc:colossalai.booster.Booster.save_model }}
+
+{{ autodoc:colossalai.booster.Booster.load_model }}
+
+{{ autodoc:colossalai.booster.Booster.save_optimizer }}
+
+{{ autodoc:colossalai.booster.Booster.load_optimizer }}
+
+{{ autodoc:colossalai.booster.Booster.save_lr_scheduler }}
+
+{{ autodoc:colossalai.booster.Booster.load_lr_scheduler }}
+
+## Usage
+In a typical workflow, you should launch distributed environment at the beginning of training script and create objects needed (such as models, optimizers, loss function, data loaders etc.) firstly, then call `colossalai.booster` to inject features into these objects, After that, you can use our booster APIs and these returned objects to continue the rest of your training processes.
+
+A pseudo-code example is like below:
+
+```python
+import torch
+from torch.optim import SGD
+from torchvision.models import resnet18
+
+import colossalai
+from colossalai.booster import Booster
+from colossalai.booster.plugin import TorchDDPPlugin
+
+def train():
+    colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
+    plugin = TorchDDPPlugin()
+    booster = Booster(plugin=plugin)
+    model = resnet18()
+    criterion = lambda x: x.mean()
+    optimizer = SGD((model.parameters()), lr=0.001)
+    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1)
+    model, optimizer, criterion, _, scheduler = booster.boost(model, optimizer, criterion, lr_scheduler=scheduler)
+
+    x = torch.randn(4, 3, 224, 224)
+    x = x.to('cuda')
+    output = model(x)
+    loss = criterion(output)
+    booster.backward(loss, optimizer)
+    optimizer.clip_grad_by_norm(1.0)
+    optimizer.step()
+    scheduler.step()
+
+    save_path = "./model"
+    booster.save_model(model, save_path, True, True, "", 10, use_safetensors=use_safetensors)
+
+    new_model = resnet18()
+    booster.load_model(new_model, save_path)
+```
+
+[more design details](https://github.com/hpcaitech/ColossalAI/discussions/3046)
+
+
+<!-- doc-test-command: torchrun --standalone --nproc_per_node=1 booster_api.py  -->

docs/source/en/basics/launch_colossalai.md

@@ -87,14 +87,13 @@ import colossalai
 args = colossalai.get_default_parser().parse_args()
 
 # launch distributed environment
-colossalai.launch(config=<CONFIG>,
+colossalai.launch(config=args.config,
                   rank=args.rank,
                   world_size=args.world_size,
                   host=args.host,
                   port=args.port,
                   backend=args.backend
 )
-
 ```
 
 
@@ -107,12 +106,21 @@ First, we need to set the launch method in our code. As this is a wrapper of the
 use `colossalai.launch_from_torch`. The arguments required for distributed environment such as rank, world size, host and port are all set by the PyTorch
 launcher and can be read from the environment variable directly.
 
+config.py
+```python
+BATCH_SIZE = 512
+LEARNING_RATE = 3e-3
+WEIGHT_DECAY = 0.3
+NUM_EPOCHS = 2
+```
+train.py
 ```python
 import colossalai
 
 colossalai.launch_from_torch(
-    config=<CONFIG>,
+    config="./config.py",
 )
+...
 ```
 
 Next, we can easily start multiple processes with `colossalai run` in your terminal. Below is an example to run the code

docs/source/en/get_started/installation.md

@@ -29,7 +29,7 @@ CUDA_EXT=1 pip install colossalai
 
 ## Download From Source
 
-> The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problem. :)
+> The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problem.
 
 ```shell
 git clone https://github.com/hpcaitech/ColossalAI.git
@@ -39,13 +39,13 @@ cd ColossalAI
 pip install -r requirements/requirements.txt
 
 # install colossalai
-pip install .
+CUDA_EXT=1 pip install .
 ```
 
-If you don't want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):
+If you don't want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer), just don't specify the `CUDA_EXT`:
 
 ```shell
-CUDA_EXT=1 pip install .
+pip install .
 ```
 
 

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

@@ -0,0 +1,89 @@
+# booster 使用
+作者: [Mingyan Jiang](https://github.com/jiangmingyan)
+
+**预备知识:**
+- [分布式训练](../concepts/distributed_training.md)
+- [Colossal-AI 总览](../concepts/colossalai_overview.md)
+
+**示例代码**
+- [使用booster训练](https://github.com/hpcaitech/ColossalAI/blob/main/examples/tutorial/new_api/cifar_resnet/README.md)
+
+## 简介
+在我们的新设计中， `colossalai.booster` 代替 `colossalai.initialize` 将特征(例如，模型、优化器、数据加载器）无缝注入您的训练组件中。 使用booster API, 您可以更友好地将我们的并行策略整合到待训练模型中. 调用 `colossalai.booster` 是您进入训练循环前的基本操作。
+在下面的章节中，我们将介绍 `colossalai.booster` 是如何工作的以及使用时我们要注意的细节。
+
+### Booster插件
+Booster插件是管理并行配置的重要组件（eg：gemini插件封装了gemini加速方案）。目前支持的插件如下：
+
+***GeminiPlugin:*** GeminiPlugin插件封装了 gemini 加速解决方案，即基于块内存管理的 ZeRO优化方案。
+
+***TorchDDPPlugin:*** TorchDDPPlugin插件封装了DDP加速方案，实现了模型级别的数据并行，可以跨多机运行。
+
+***LowLevelZeroPlugin:*** LowLevelZeroPlugin插件封装了零冗余优化器的 1/2 阶段。阶段 1：切分优化器参数，分发到各并发进程或并发GPU上。阶段 2：切分优化器参数及梯度，分发到各并发进程或并发GPU上。
+
+### Booster接口
+
+{{ autodoc:colossalai.booster.Booster }}
+
+{{ autodoc:colossalai.booster.Booster.boost }}
+
+{{ autodoc:colossalai.booster.Booster.backward }}
+
+{{ autodoc:colossalai.booster.Booster.no_sync }}
+
+{{ autodoc:colossalai.booster.Booster.save_model }}
+
+{{ autodoc:colossalai.booster.Booster.load_model }}
+
+{{ autodoc:colossalai.booster.Booster.save_optimizer }}
+
+{{ autodoc:colossalai.booster.Booster.load_optimizer }}
+
+{{ autodoc:colossalai.booster.Booster.save_lr_scheduler }}
+
+{{ autodoc:colossalai.booster.Booster.load_lr_scheduler }}
+
+## 使用方法及示例
+
+在使用colossalai训练时，首先需要在训练脚本的开头启动分布式环境，并创建需要使用的模型、优化器、损失函数、数据加载器等对象。之后，调用`colossalai.booster` 将特征注入到这些对象中，您就可以使用我们的booster API去进行您接下来的训练流程。
+
+以下是一个伪代码示例，将展示如何使用我们的booster API进行模型训练:
+
+```python
+import torch
+from torch.optim import SGD
+from torchvision.models import resnet18
+
+import colossalai
+from colossalai.booster import Booster
+from colossalai.booster.plugin import TorchDDPPlugin
+
+def train():
+    colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
+    plugin = TorchDDPPlugin()
+    booster = Booster(plugin=plugin)
+    model = resnet18()
+    criterion = lambda x: x.mean()
+    optimizer = SGD((model.parameters()), lr=0.001)
+    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1)
+    model, optimizer, criterion, _, scheduler = booster.boost(model, optimizer, criterion, lr_scheduler=scheduler)
+
+    x = torch.randn(4, 3, 224, 224)
+    x = x.to('cuda')
+    output = model(x)
+    loss = criterion(output)
+    booster.backward(loss, optimizer)
+    optimizer.clip_grad_by_norm(1.0)
+    optimizer.step()
+    scheduler.step()
+
+    save_path = "./model"
+    booster.save_model(model, save_path, True, True, "", 10, use_safetensors=use_safetensors)
+
+    new_model = resnet18()
+    booster.load_model(new_model, save_path)
+```
+
+[更多的设计细节请参考](https://github.com/hpcaitech/ColossalAI/discussions/3046)
+
+<!-- doc-test-command: torchrun --standalone --nproc_per_node=1 booster_api.py  -->

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

@@ -74,7 +74,7 @@ import colossalai
 args = colossalai.get_default_parser().parse_args()
 
 # launch distributed environment
-colossalai.launch(config=<CONFIG>,
+colossalai.launch(config=args.config,
                   rank=args.rank,
                   world_size=args.world_size,
                   host=args.host,
@@ -93,12 +93,21 @@ PyTorch自带的启动器需要在每个节点上都启动命令才能启动多
 首先，我们需要在代码里指定我们的启动方式。由于这个启动器是PyTorch启动器的封装，那么我们自然而然应该使用`colossalai.launch_from_torch`。
 分布式环境所需的参数，如 rank, world size, host 和 port 都是由 PyTorch 启动器设置的，可以直接从环境变量中读取。
 
+config.py
+```python
+BATCH_SIZE = 512
+LEARNING_RATE = 3e-3
+WEIGHT_DECAY = 0.3
+NUM_EPOCHS = 2
+```
+train.py
 ```python
 import colossalai
 
 colossalai.launch_from_torch(
-    config=<CONFIG>,
+    config="./config.py",
 )
+...
 ```
 
 接下来，我们可以轻松地在终端使用`colossalai run`来启动训练。下面的命令可以在当前机器上启动一个4卡的训练任务。

docs/source/zh-Hans/get_started/installation.md

@@ -28,7 +28,7 @@ CUDA_EXT=1 pip install colossalai
 
 ## 从源安装
 
-> 此文档将与版本库的主分支保持一致。如果您遇到任何问题，欢迎给我们提 issue :)
+> 此文档将与版本库的主分支保持一致。如果您遇到任何问题，欢迎给我们提 issue。
 
 ```shell
 git clone https://github.com/hpcaitech/ColossalAI.git
@@ -38,13 +38,13 @@ cd ColossalAI
 pip install -r requirements/requirements.txt
 
 # install colossalai
-pip install .
+CUDA_EXT=1 pip install .
 ```
 
-如果您不想安装和启用 CUDA 内核融合（使用融合优化器时强制安装）：
+如果您不想安装和启用 CUDA 内核融合（使用融合优化器时强制安装），您可以不添加`CUDA_EXT=1`：
 
 ```shell
-NO_CUDA_EXT=1 pip install .
+pip install .
 ```
 
 <!-- doc-test-command: echo "installation.md does not need test" -->