[doc] polish shardformer doc (#4779)

* fix example format in docstring

* polish shardformer doc

colossalai/booster/plugin/gemini_plugin.py

@@ -229,16 +229,17 @@ class GeminiPlugin(DPPluginBase):
     """
     Plugin for Gemini.
 
-    Example:
-        >>> from colossalai.booster import Booster
-        >>> from colossalai.booster.plugin import GeminiPlugin
-        >>>
-        >>> model, train_dataset, optimizer, criterion = ...
-        >>> plugin = GeminiPlugin()
-
-        >>> train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8)
-        >>> booster = Booster(plugin=plugin)
-        >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
+    ```python
+    from colossalai.booster import Booster
+    from colossalai.booster.plugin import GeminiPlugin
+
+    model, train_dataset, optimizer, criterion = ...
+    plugin = GeminiPlugin()
+
+    train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8)
+    booster = Booster(plugin=plugin)
+    model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
+    ```
 
     Args:
         chunk_config_dict (dict, optional): chunk configuration dictionary.

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -266,16 +266,17 @@ class HybridParallelPlugin(PipelinePluginBase):
     Tensor parallel, pipeline parallel and data parallel(DDP/ZeRO) can be picked and combined in this plugin.
     The size of tp and pp should be passed in by user, then the size of dp is automatically calculated from dp_size = world_size / (tp_size * pp_size).
 
-    Example:
-        >>> from colossalai.booster import Booster
-        >>> from colossalai.booster.plugin import HybridParallelPlugin
+    ```python
+    from colossalai.booster import Booster
+    from colossalai.booster.plugin import HybridParallelPlugin
 
-        >>> model, train_dataset, optimizer, criterion = ...
-        >>> plugin =  HybridParallelPlugin(tp_size=2, pp_size=2)
+    model, train_dataset, optimizer, criterion = ...
+    plugin =  HybridParallelPlugin(tp_size=2, pp_size=2)
 
-        >>> train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8)
-        >>> booster = Booster(plugin=plugin)
-        >>> model, optimizer, criterion, train_dataloader, _ = booster.boost(model, optimizer, criterion, train_dataloader)
+    train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8)
+    booster = Booster(plugin=plugin)
+    model, optimizer, criterion, train_dataloader, _ = booster.boost(model, optimizer, criterion, train_dataloader)
+    ```
 
     Args:
         tp_size (int): The size of tensor parallelism. Tensor parallelism will not be used when tp_size is set to 1.

colossalai/booster/plugin/low_level_zero_plugin.py

@@ -213,16 +213,17 @@ class LowLevelZeroPlugin(DPPluginBase):
     """
     Plugin for low level zero.
 
-    Example:
-        >>> from colossalai.booster import Booster
-        >>> from colossalai.booster.plugin import LowLevelZeroPlugin
-        >>>
-        >>> model, train_dataset, optimizer, criterion = ...
-        >>> plugin = LowLevelZeroPlugin()
-
-        >>> train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8)
-        >>> booster = Booster(plugin=plugin)
-        >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
+    ```python
+    from colossalai.booster import Booster
+    from colossalai.booster.plugin import LowLevelZeroPlugin
+
+    model, train_dataset, optimizer, criterion = ...
+    plugin = LowLevelZeroPlugin()
+
+    train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8)
+    booster = Booster(plugin=plugin)
+    model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
+    ```
 
     Args:
         strage (int, optional): ZeRO stage. Defaults to 1.

colossalai/booster/plugin/torch_ddp_plugin.py

@@ -130,16 +130,17 @@ class TorchDDPPlugin(DPPluginBase):
     """
     Plugin for PyTorch DDP.
 
-    Example:
-        >>> from colossalai.booster import Booster
-        >>> from colossalai.booster.plugin import TorchDDPPlugin
-        >>>
-        >>> model, train_dataset, optimizer, criterion = ...
-        >>> plugin = TorchDDPPlugin()
-
-        >>> train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8)
-        >>> booster = Booster(plugin=plugin)
-        >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
+    ```python
+    from colossalai.booster import Booster
+    from colossalai.booster.plugin import TorchDDPPlugin
+
+    model, train_dataset, optimizer, criterion = ...
+    plugin = TorchDDPPlugin()
+
+    train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8)
+    booster = Booster(plugin=plugin)
+    model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
+    ```
 
     Args:
         broadcast_buffers (bool, optional): Whether to broadcast buffers in the beginning of training. Defaults to True.

colossalai/booster/plugin/torch_fsdp_plugin.py

@@ -143,16 +143,17 @@ class TorchFSDPPlugin(DPPluginBase):
     """
     Plugin for PyTorch FSDP.
 
-    Example:
-        >>> from colossalai.booster import Booster
-        >>> from colossalai.booster.plugin import TorchFSDPPlugin
-        >>>
-        >>> model, train_dataset, optimizer, criterion = ...
-        >>> plugin = TorchFSDPPlugin()
-
-        >>> train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=8)
-        >>> booster = Booster(plugin=plugin)
-        >>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
+    ```python
+    from colossalai.booster import Booster
+    from colossalai.booster.plugin import TorchFSDPPlugin
+
+    model, train_dataset, optimizer, criterion = ...
+    plugin = TorchFSDPPlugin()
+
+    train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=8)
+    booster = Booster(plugin=plugin)
+    model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
+    ```
 
     Args:
         See https://pytorch.org/docs/stable/fsdp.html for details.

colossalai/cluster/dist_coordinator.py

@@ -20,14 +20,16 @@ class DistCoordinator(metaclass=SingletonMeta):
         - master: the process with rank 0
         - node master: the process with local rank 0 on the current node
 
-    Example:
-        >>> from colossalai.cluster.dist_coordinator import DistCoordinator
-        >>> coordinator = DistCoordinator()
-        >>>
-        >>> if coordinator.is_master():
-        >>>     do_something()
-        >>>
-        >>> coordinator.print_on_master('hello world')
+
+    ```python
+    from colossalai.cluster.dist_coordinator import DistCoordinator
+    coordinator = DistCoordinator()
+
+    if coordinator.is_master():
+        do_something()
+
+    coordinator.print_on_master('hello world')
+    ```
 
     Attributes:
         rank (int): the rank of the current process
@@ -131,11 +133,13 @@ class DistCoordinator(metaclass=SingletonMeta):
         other processes in the same process group. This is often useful when downloading is required
         as we only want to download in one process to prevent file corruption.
 
-        Example:
-            >>> from colossalai.cluster import DistCoordinator
-            >>> dist_coordinator = DistCoordinator()
-            >>> with dist_coordinator.priority_execution():
-            >>>     dataset = CIFAR10(root='./data', download=True)
+
+        ```python
+        from colossalai.cluster import DistCoordinator
+        dist_coordinator = DistCoordinator()
+        with dist_coordinator.priority_execution():
+            dataset = CIFAR10(root='./data', download=True)
+        ```
 
         Args:
             executor_rank (int): the process rank to execute without blocking, all other processes will be blocked
@@ -174,13 +178,14 @@ class DistCoordinator(metaclass=SingletonMeta):
         """
         A function wrapper that only executes the wrapped function on the master process (rank 0).
 
-        Example:
-            >>> from colossalai.cluster import DistCoordinator
-            >>> dist_coordinator = DistCoordinator()
-            >>>
-            >>> @dist_coordinator.on_master_only()
-            >>> def print_on_master(msg):
-            >>>     print(msg)
+        ```python
+        from colossalai.cluster import DistCoordinator
+        dist_coordinator = DistCoordinator()
+
+        @dist_coordinator.on_master_only()
+        def print_on_master(msg):
+            print(msg)
+        ```
         """
         is_master = self.is_master(process_group)
 

docs/source/en/features/shardformer.md

@@ -214,9 +214,56 @@ In addition, xFormers's `cutlass_op` can serve as a backup for flash attention.
 Enabling `Shardformer` through `Booster` initialized with `HybridParallelPlugin` is the recommended way to awaken the power of Shardformer.
 The main reason is that pipeline parallelism cannot successfully work without the calling of `execute_pipeline` method of `Booster`. Besides, `HybridParallelPlugin` provides the capacity to combine the features of `Shardformer` with other useful features, such as mixed precision training or Zero.
 
-More details about this usage can be found in chapter [Booster API](../basics/booster_api.md) and [Booster Plugins](../basics/booster_plugins.md).
+[Here](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/bert) is an example on how to trigger `Shardformer` through `HybridParallelPlugin`. Move to the root directory of this example, and execute
+```bash
+torchrun --standalone --nproc_per_node 4  finetune.py --target_f1 0.86 --plugin "hybrid_parallel" --model_type "bert"
+```
+Then you can start finetuning a bert model wrapped by `Shardformer`. The process of wrapping is operated by `HybridParallelPlugin`.
 
-[Here](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/bert) is an example on how to trigger `Shardformer` through `HybridParallelPlugin`. Please be aware that there's a difference in the way of doing forward and backward between the situation of using pipeline and not using pipeline.
+Let's delve into the code of `finetune.py`:
+
+In the `main` function, the plugin is created through the following codes:
+```python
+...
+elif args.plugin == "hybrid_parallel":
+    # modify the param accordingly for finetuning test cases
+    plugin = HybridParallelPlugin(
+        tp_size=1,
+        pp_size=2,
+        num_microbatches=None,
+        microbatch_size=1,
+        enable_all_optimization=True,
+        zero_stage=1,
+        precision="fp16",
+        initial_scale=1,
+    )
+```
+Here you can change the configuration of plugin by setting `tp_size`, `pp_size` or `zero_stage` to other values. More details about plugin configuration can be found in [Booster Plugins Doc](../basics/booster_plugins.md).
+
+If pipeline parallel is not enabled, just do the training in the same way of other booster plugins(first boost with Booster, then do forward and backward through normal way).
+However, if pipeline parallel is enabled, there are several usages different from other normal cases:
+
+1. Before doing forward or backward, the criterion function (loss function) is processed to meet the argument demand of running pipeline:
+    ```python
+    def _criterion(outputs, inputs):
+        outputs = output_transform_fn(outputs)
+        loss = criterion(outputs)
+        return loss
+    ```
+
+2. In `train_epoch` function, dataloader is converted into `Iterator` class before running pipeline:
+    ```python
+    train_dataloader_iter = iter(train_dataloader)
+    ```
+
+3. Do forward and backward passing through calling `Booster.execute_pipeline` method:
+    ```python
+    outputs = booster.execute_pipeline(
+        train_dataloader_iter, model, _criterion, optimizer, return_loss=True, return_outputs=True
+    )
+    ```
+    Backward passing has been completed by this method, so there is no need to call `loss.backward()` after executing this method.
+    More details about `Booster.execute_pipeline` can be found in [Booster API Doc](../basics/booster_api.md).
 
 
 #### 2. Enabling Shardformer Through Shardformer APIs (Not Recommended)
@@ -224,7 +271,26 @@ More details about this usage can be found in chapter [Booster API](../basics/bo
 You can also use Shardformer through manually calling Shardformer APIs. However, this usage is not recommended since pipeline parallelism can't run without `Booster`.
 
 [Here](https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/shardformer/examples/convergence_benchmark.py)
-is an example on how to trigger `Shardformer` through calling Shardformer APIs.
+is an example on how to trigger `Shardformer` through calling Shardformer APIs. In the `train` function of example code, the model is wrapped by `Shardformer` through the following few codes:
+```python
+...
+if dist.get_world_size() > 1:
+    tp_group = dist.new_group(backend="nccl")
+
+    # First create configuration for Shardformer
+    shard_config = ShardConfig(
+        tensor_parallel_process_group=tp_group,
+        enable_tensor_parallelism=True,
+        enable_all_optimization=True
+    )
+
+    # Then create ShardFormer object with created config
+    shard_former = ShardFormer(shard_config=shard_config)
+
+    # Finally shard the model using ShardFormer.optimize method
+    model, _ = shard_former.optimize(model)
+...
+```
 
 ### Precautions
 
@@ -241,6 +307,8 @@ is an example on how to trigger `Shardformer` through calling Shardformer APIs.
 
 ## How Shardformer Works
 
+### Main Idea
+
 Generally, Shardformer works through the following four kinds of *replacements*:
 
 1. Replacing original PyTorch module (e.g. `nn.Linear`, `nn.Embedding`) with a crafted distributed module.

docs/source/zh-Hans/features/shardformer.md

@@ -207,8 +207,56 @@ Shardformer的配置由类`ShardConfig`的参数控制：
 
 通过用`HybridParallelPlugin`初始化的`Booster`来启动`Shardformer`是最推荐的用法。其主要原因是如果不调用`Booster`的`execute_pipeline`方法，流水线并行就无法正常工作。此外，`HybridParallelPlugin`提供了将`Shardformer`的功能与其他功能（例如混合精度训练或Zero）相结合的能力。
 
-更多关于这一用法的细节可以参考 [Booster API 文档](../basics/booster_api.md)以及[Booster 插件文档](../basics/booster_plugins.md)。[这里](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/bert)是一个通过`HybridParallelPlugin`启动`Shardformer`的示例。
+[这里](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/bert)是一个通过`HybridParallelPlugin`启动`Shardformer`的示例。
+移动到示例的根目录下，执行命令：
+```bash
+torchrun --standalone --nproc_per_node 4  finetune.py --target_f1 0.86 --plugin "hybrid_parallel" --model_type "bert"
+```
+你便可以微调一个被`Shardformer`封装过的Bert模型，而封装的操作是由`HybridParallelPlugin`完成的。
+
+接下来一起深入挖掘一下`finetune.py`里的代码：
+
+在`main`函数中，混合并行的插件通过以下的代码创建
+```python
+...
+elif args.plugin == "hybrid_parallel":
+    # modify the param accordingly for finetuning test cases
+    plugin = HybridParallelPlugin(
+        tp_size=1,
+        pp_size=2,
+        num_microbatches=None,
+        microbatch_size=1,
+        enable_all_optimization=True,
+        zero_stage=1,
+        precision="fp16",
+        initial_scale=1,
+    )
+```
+在这里你可以通过设置不同的`tp_size`, `pp_size` 或 `zero_stage`来改变插件的配置。更多关于插件配置的信息可以在[Booster 插件文档](../basics/booster_plugins.md)中被找到。
+
+当流水并行不被启用的时候，训练的流程和其他的插件是一样的 （先用Booster封装模型和优化器，再用正常的方式做前向和后向传递）。然而，当流水线并行被启用的时候，有几处不同于寻常情况的用法：
+
+1. 在进行前向和后向之前，criterion函数（loss函数）需要被处理以满足流水线并行的传参要求:
+    ```python
+    def _criterion(outputs, inputs):
+        outputs = output_transform_fn(outputs)
+        loss = criterion(outputs)
+        return loss
+    ```
 
+2. 在 `train_epoch` 函数中, dataloader 在进行流水线的前向后向操作之前需要被转换为 `Iterator` 类:
+    ```python
+    train_dataloader_iter = iter(train_dataloader)
+    ```
+
+3. 通过调用`Booster.execute_pipeline` 方法来执行前向和后向传递:
+    ```python
+    outputs = booster.execute_pipeline(
+        train_dataloader_iter, model, _criterion, optimizer, return_loss=True, return_outputs=True
+    )
+    ```
+    该方法会自动执行后向传递，所以在执行该方法后不需要再调用 `loss.backward()`方法。
+    更多关于 `Booster.execute_pipeline` 的信息可以参考 [Booster API 文档](../basics/booster_api.md)。
 
 #### 2. 通过Shardformer API启动Shardformer (不推荐)
 
@@ -216,7 +264,26 @@ Shardformer的配置由类`ShardConfig`的参数控制：
 
 [这里](https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/shardformer/examples/convergence_benchmark.py)
 是一个通过调用Shardformer的API启动`Shardformer`的示例。
-
+在示例代码的`train`函数中，模型被以下的几行代码进行封装：
+```python
+...
+if dist.get_world_size() > 1:
+    tp_group = dist.new_group(backend="nccl")
+
+    # First create configuration for Shardformer
+    shard_config = ShardConfig(
+        tensor_parallel_process_group=tp_group,
+        enable_tensor_parallelism=True,
+        enable_all_optimization=True
+    )
+
+    # Then create ShardFormer object with created config
+    shard_former = ShardFormer(shard_config=shard_config)
+
+    # Finally shard the model using ShardFormer.optimize method
+    model, _ = shard_former.optimize(model)
+...
+```
 
 ### 注意事项
 
@@ -234,6 +301,8 @@ Shardformer的配置由类`ShardConfig`的参数控制：
 
 ## Shardformer的工作原理
 
+### 设计思想
+
 通常来说，Shardformer通过以下四种“替换”进行工作：
 
 1. 用我们设计的分布式模块替换原始的PyTorch模块（例如`nn.Linear`、`nn.Embedding`）。