# ⚡️ ShardFormer

## 📚 Table of Contents

- [⚡️ ShardFormer](#️-shardformer)
  - [📚 Table of Contents](#-table-of-contents)
  - [🔗 Introduction](#-introduction)
  - [🔨 Usage](#-usage)
    - [Quick Start](#quick-start)
    - [Write your own policy](#write-your-own-policy)
  - [🗺 Roadmap](#-roadmap)
  - [💡 API Design](#-api-design)
    - [Distributed Modules](#distributed-modules)
    - [Shard Config](#shard-config)
    - [Policy](#policy)
    - [Model Sharder](#model-sharder)
    - [User-facing API](#user-facing-api)
    - [Shardformer Convergence](#shardformer-convergence)


## 🔗 Introduction

**Shardformer** is a module that automatically parallelizes the mainstream models in libraries such as HuggingFace and TIMM. This module aims to make parallelization hassle-free for users who are not from the system background.

## 🔨 Usage

### Quick Start

The sample API usage is given below:

``` python
from colossalai.shardformer import ShardConfig, Shard
from transformers import BertForMaskedLM

# launch colossalai
colossalai.launch_from_torch()

# create model
config = BertConfig.from_pretrained('bert-base-uncased')
model = BertForMaskedLM.from_pretrained('bert-base-uncased', config=config)

# create huggingface model as normal
shard_config = ShardConfig(tensor_parallel_size=2,
                           data_parallel_size=1,
                           gather_output=True)
shard_former = ShardFormer(shard_config=shard_config)
shard_former.init_distributed()
sharded_model = shard_former.shard_model(model).to('cuda')

# do everything like normal
...
```

### Write your own policy

If you have a custom model, you can also use Shardformer to parallelize it by writing your own sharding policy. More information about the sharding policy can be found in [API Design](#-api-design).

```python
from colossalai.shardformer import Policy

class MyPolicy(Policy):
    # implement your own policy
    ...

# init model and shard former
...

# use customized policy to shard model
my_policy = MyPolicy()
shard_former.shard_model(model, my_policy)

```

## 🗺 Roadmap

We will follow this roadmap to develop Shardformer:

- [x] API Design
- [x] API Implementation
- [x] Unit Testing
- [ ] Policy Implementation
  - [ ] Hugging Face
    - [ ] NLP
      - [x] BERT
      - [x] T5
      - [x] LlaMa
      - [x] GPT2
      - [x] OPT
      - [x] BLOOM
      - [ ] GLM
      - [ ] RoBERTa
      - [ ] ALBERT
      - [ ] ERNIE
      - [ ] GPT Neo
      - [ ] GPT-J
    - [ ] CV
      - [x] ViT
      - [ ] BEiT
      - [ ] SwinTransformer
      - [ ] SwinTransformer V2
    - [ ] Audio
      - [ ] Whisper
    - [ ] Multi-modal
      - [ ] To be added

## 💡 API Design

We will discuss the major components of `ShardFormer` below to help you better understand how things work.
This section serves as the design doc for Shardformer and the function signature might differ from the actual implementation.
Please refer to the code for more details.

<p align="center">
   <img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/shardformer/shardformer_flowchart.png" width="600" />
   <br/>
   <b>This diagram is deprecated, need to update it</b>
</p>



### Distributed Modules

`ShardFormer` replaces the original PyTorch module with a distributed module.
The distributed module keeps the same attributes as the original module but replaces the original parameters with distributed parameters and defines a new `forward` function to execute distributed computation.
Each distributed module implements its `from_native_module` static method to convert the PyTorch module to its corresponding distributed module.

```python
class ParallelModule(torch.nn.Module):

    @abstractmethod
    def from_native_module(module: torch.nn.Module, process_group: Union[ProcessGroup, Tuple[ProcessGroup]]) -> ParallelModule
        """
        Convert a native module to a parallelized

        Examples:

        ```python
        # replace module
        my_linear = Linear1D_Col.from_native_module(my_linear, process_group)
        ```
        """
```

### Shard Config

`ShardConfig` is a simple data class to tell `ShardFormer` how sharding will be performed.

```python
@dataclass
class ShardConfig:
    data_parallel_size: int
    tensor_parallel_size: int
    ...

    # Some possible future config fields
    pipeline_parallel_size: int # Support pipeline parallelism
    tensor_parallel_mode: Choice['1d', '2d', '2.5d', '3d'] # support different tensor parallel mode
    inference_only: bool # only inject inference-suitable sharding policy
    gather_output: bool # gather the model output
    use_flash_attention: bool # whether to use flash attention to speed up attention
```

### Policy

The `Policy` class describes how to handle the model sharding.
It is merely a description, the actual sharding will be performed by `ModelSharder`.
We abstract the policy into four stages:

1. Preprocessing: call `Policy.preprocess` to do some prior work before sharding, for example, resizing the embedding
2. Providing a new class: call `Policy.new_model_class` to get a new class for the model, this class replaces attributes and the forward function
3. Providing `ModulePolicyDescription`: call `Policy.module_policy` to get a bunch of `ModulePolicyDescription` to tell `ModelSharder` how the submodules's attributes, child parameters, and deeper submodules will be substituted.
4. Postprocessing: call `Policy.postprocess` to perform some postprocessing work, for example, binding the embedding and classifier head weights of the BERT model.

``` python
@dataclass
class ModulePolicyDescription:
    """
    Describe how the attributes and parameters will be transformed in a policy

    Args:
        attribute_replacement (Dict[str, Any]): key is the attribute name, value is the attribute value after sharding
        param_replacement (List[Callable]): a list of functions to perform in-place param replacement. The function must receive two arguments: module, process_group. One example is
                        def example_replace_weight(module: torch.nn.Module, process_group):
                            weight = module.weight
                            new_weight = shard_rowwise(weight, process_group)
                            module.weight = torch.nn.Parameter(new_weight)
        sub_module_replacement: each element in the list is a ParamReplacementDescription object which specifies the module to be replaced and the target module used to replacement
    """
    attribute_replacement: Dict[str, Any]
    param_replacement: List[Callable]
    sub_module_replacement: List[SubModuleReplacementDescription]

@dataclass
class SubModuleReplacementDescription:
    """
    Describe how a submodule will be replaced

    Args:
        suffix (str): used to get the submodule object
        target_module (ParallelModule): specifies the module class used to replace to submodule
        kwargs (Dict[str, Any]): the dictionary used to pass extra arguments to the `ParallelModule.from_native_module` method.
    """
    suffix: str
    target_module: ParallelModule
    kwargs: Dict[str, Any] = None


class Policy(ABC):

    def __init__(self)
        self.model = None

    def set_model(self, model: nn.Module) -> None:
        """
        Set model as an attribute of the Policy object so that we can access the model's attributes.
        """
        self.model = model

    @abstractmethod
    def preprocess(self) -> nn.Module:
        """
        Perform some preprocessing on the model, such as resizing the embedding size
        """
        ...

    @abstractmethod
    def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
        """
        Return the dict for the modify policy, the key is the original layer class and the value is the
        argument for the modify layer
        """
        ...

    @abstractmethod
    def new_model_class(self) -> Union[Type[nn.Module], None]:
        """
        replace the class of the model to substitute the forward and attributes
        """
        ...

    @abstractmethods
    def postprocess(self) -> nn.Module:
        """
        Perform some postprocessing on the model, such as binding the embedding with the weight of the classifier head
        """
        ...
```


### Model Sharder

`ModelSharder` is the class in charge of sharding the model based on the given policy.

```python
class ModelSharder:

    def __init__(self, model: torch.nn.Module, shard_config: ShardConfig, Policy: ShardPolicy = None)
        #TODO: input is a cls or a obj

    def shard(self) -> None:
        """
        Shard model with parallelelism with the help of pre-processing, replace_model_class, replace_module, and post-processing.
        """
        ...

    def replace_model_class(self) -> None:
        """
        Replace the model's methods and attributes with our own defined class.

        E.g. we can replace the forward function of the original BertForMaskedLM object
        with the forward function we define in BertForMaskedLM_ class.
        """
        ...

    def replace_module(self) -> None:
        """
        Replace the layer according to the policy. Call Policy.module_policy() to get the module. Call _replace_module recursively.
        """
        ...
```

### User-facing API

We only expose a limited number of APIs to the user to keep their user experience simple and clean.

```python
class ShardFormer:
    """
    Parallelize model based on the given config and policy

    Example:

    shard_former = ShardFormer(shard_config=shard_config)
    shard_former.init_distributed()
    model = shard_former.shard_model(model, policy=policy)
    dataloader = shard_former.shard_dataset(dataset)

    """

    def __init__(self, shard_config: ShardConfig):
        """
        Do two things:
        1. Create a colossalai.cluster.process_group_manager to manage process groups for dp, tp and pp
        2. serve as a store for shard config
        """
        self.shard_config = shard_config
        self.pg_manager = None

    def init_distributed(self) -> colossalai.cluster.ProcessGroupManager:
        """
        Initialize the distributed process group according to the
        """
        pg_manager = ...
        self.pg_manager = pg_manager
        return pg_manager

    def shard_model(self, model: torch.nn.Module，policy: Policy) -> torch.nn.Module:
        """
        Shard model for TP and PP
        """
        ...

    def shard_dataset(self, dataset: Dataset) -> Dataloader:
        """
        Shard dataset for DP
        """
        ...
```

### Shardformer Convergence

To validate that training the model using shardformers does not impact its convergence. We [fine-tuned the BERT model](./examples/shardformer_benchmark.py) using both shardformer and non-shardformer approaches. We compared the accuracy, loss, F1 score of the training results.

| accuracy | f1 | loss | GPU number | model shard |
| :-----: | :----: | :----: | :----: | :----: |
| 0.82594 | 0.87441 | 0.09913 | 4 | True |
| 0.81884 | 0.87299 | 0.10120 | 2 | True |
| 0.81855 | 0.87124 | 0.10357 | 1 | False |

Overall, the results demonstrate that using shardformers during model training does not affect the convergence.
