ColossalAI/colossalai/shardformer/policies/basepolicy.py

# part of code modified from https://github.com/tunib-ai/parallelformers

from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Tuple, Type

import torch
import torch.nn as nn
from transformers import AutoConfig


@dataclass
class Argument:
    r"""
    The argument class for the policy

    Args:
        attr_dict (Dict[str, Any]): The dict for the param setting
        param_funcs (:class:`List[Callable]`): The list for the param functions
    """
    attr_dict: Dict[str, Any]
    param_funcs: List[Callable]


@dataclass
class Layer:
    r"""
    The layer object for the policy

    Args:
        weight (str): The weight suffix of the layer
        bias (str): The bias suffix of the layer
        replace_layer (:class:`colosalai.nn`): The layer to replace the original layer
        ignore (bool): Whether to ignore this layer if it is not in the model
    """
    weight: str = None
    bias: str = None
    replace_layer: Any = None
    ignore: bool = False


@dataclass
class Col_Layer(Layer):
    r"""
    Class for col shard layer in MegatronLM

    Args:
        gather_output (bool): Whether to gather the output of the layer
    """
    gather_output: bool = False


@dataclass
class Row_Layer(Layer):
    r"""
    Class for col shard layer in MegatronLM
    """
    pass


class Policy():
    r"""
    The base class for all the policies
    For each different model, it should have a different policy class, like BertPolicy for Bert Model
    or OPTPolicy for OPT model.
    AutoPolicy:
        Shardformer already defined some policies for huggingface model, just set ``custom_policy`` = None
        to use the auto policy. In shardformer autopolicy, we define a base policy for one type model,
        like BertPolicy, and for each different Bert modle in huggingface like, BertForMaskedLM,
        BertForSequenceClassification, etc., for each different Bert model we difine different policy class
        and overwrite the method like ``inject_policy`` to modify the forward and backward process.

    CustomPolicy:
        If you want to define your own policy, you can set ``custom_policy`` = CustomPolicy, and overwrite
        all the methods in ``Policy`` class. You can refer to any policy we defined like the ``BertPolicy``
        class for the example.

    """

    @staticmethod
    def argument_policy(model_config, shard_config: int) -> Dict[nn.Module, Argument]:
        r"""
        Return the dict for the modify policy, the key is the original layer class and the value is the
        argument for the modify layer

        Args:
            model_config (:class:`tansformer.Config`): The config of transformer model
            shard_config (:class:`ShardConfig`): The config for sharding model

        Return:
            Dict for the modify policy,
            ::
            {
                origin layer class1 (nn.Module): Argument(
                    attr_dict = {
                        argument1: value1,
                        argument2: value2,
                        ...
                    },
                    param_funcs = [
                        staticmethod1,
                        staticmethod2,
                        ...
                    ]
                ),
                origin layer class2 (nn.Module): Argument(
                    attr_dict = {
                        argument1: value1,
                        argument2: value2,
                        ...
                    },
                    param_funcs = [
                        staticmethod1,
                        staticmethod2,
                        ...
                    ]
                ),
                ...
            }

        """
        raise NotImplementedError

    @staticmethod
    def inject_policy() -> Tuple[nn.Module, nn.Module]:
        r"""
        Return the dict for the inject model

        Return:
            The injected model, key is the original model and value is the new shardmodel
            ::
            (OrignModel, CustomModel)
            in `CustomModel`, we can overwrite the forward and backward process
        """
        return ()

    @staticmethod
    def binding_policy() -> Dict:
        r"""
        Return the dict for the binding model

        Return:
            This method should return the binding relationship for some layers share the weight or bias,
            the key and value is the suffix of the weight or bias of the model
        ::
            return {
                "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight",
            }
        """
        return NotImplementedError

    @staticmethod
    def attn_in() -> List:
        r"""
        Attention qkv layer
        In this kind of method, we should return the list of ``Layer`` object, each ``Layer`` object should be
        ``Layer`` for no slicing, ``Col_Layer`` for col slicing, ``Row_Layer`` for row slicing. And the parameters
        in ``Layer`` object can refer to the ``Layer`` class.

        Returns:
            List[Layer]: List of layer object, each layer is the new
        """
        return NotImplementedError

    @staticmethod
    def attn_out() -> List:
        r"""
        Attention output projection layer

        Returns:
            List[Layer]: List of layer object
        """
        return NotImplementedError

    @staticmethod
    def mlp_in() -> List:
        r"""
        h -> 4h mlp layer

        Returns:
            List[Layer]: List of layer object
        """
        return NotImplementedError

    @staticmethod
    def mlp_out() -> List:
        r"""
        4h -> h mlp layer

        Returns:
            List[Layer]: List of layer object
        """
        return NotImplementedError

    @staticmethod
    def embedding() -> List:
        r"""
        Partially slice the embedding layer

        Return:
            List[Layer]: List of layer object
        """
        return NotImplementedError

    @staticmethod
    def unembedding() -> List:
        r"""
        Partially slice the embedding layer

        Return:
            List[Layer]: List of layer object
        """
        return NotImplementedError