ColossalAI/colossalai/shardformer/policies/mixtral.py

from functools import partial
from typing import Callable, Dict, List, Union

import torch.nn as nn
from torch import Tensor
from torch.nn import Module
from transformers.models.mixtral.modeling_mixtral import MixtralDecoderLayer, MixtralForCausalLM, MixtralModel

from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col
from colossalai.shardformer.modeling.mixtral import EPMixtralSparseMoeBlock, MixtralPipelineForwards
from colossalai.shardformer.policies.base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription

__all__ = ["MixtralPolicy", "MixtralForCausalLMPolicy"]


class MixtralPolicy(Policy):
    def config_sanity_check(self):
        pass

    def preprocess(self):
        if self.shard_config.enable_tensor_parallelism:
            # Resize embedding
            vocab_size = self.model.config.vocab_size
            world_size = self.shard_config.tensor_parallel_size

            if vocab_size % world_size != 0:
                new_vocab_size = vocab_size + world_size - vocab_size % world_size
                self.model.resize_token_embeddings(new_vocab_size)

        return self.model

    def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
        policy = {}

        if self.shard_config.enable_sequence_parallelism:
            self.shard_config.enable_sequence_parallelism = False
            raise NotImplementedError(
                "Mixtral dosen't support sequence parallelism now, will ignore the sequence parallelism flag."
            )

        if self.shard_config.enable_tensor_parallelism:
            raise NotImplementedError("Tensor parallelism is not supported for Mixtral model now.")
        if getattr(self.shard_config, "ep_group", None) is None:
            raise ValueError("You must pass in ep_group via shard_config for expert parallel!")

        # expert parallel
        self.append_or_create_submodule_replacement(
            description=[
                SubModuleReplacementDescription(
                    suffix="block_sparse_moe",
                    target_module=EPMixtralSparseMoeBlock,
                    kwargs={"ep_group": self.shard_config.ep_group},
                )
            ],
            policy=policy,
            target_key=MixtralDecoderLayer,
        )

        # optimization configuration
        if self.shard_config.enable_fused_normalization:
            self.append_or_create_submodule_replacement(
                description=[
                    SubModuleReplacementDescription(
                        suffix="input_layernorm",
                        target_module=FusedRMSNorm,
                    ),
                    SubModuleReplacementDescription(
                        suffix="post_attention_layernorm",
                        target_module=FusedRMSNorm,
                    ),
                ],
                policy=policy,
                target_key=MixtralDecoderLayer,
            )

            self.append_or_create_submodule_replacement(
                description=SubModuleReplacementDescription(
                    suffix="norm",
                    target_module=FusedRMSNorm,
                ),
                policy=policy,
                target_key=MixtralModel,
            )

        if self.shard_config.enable_flash_attention:
            raise NotImplementedError("Flash attention has already been replaced in mixtral.")

        return policy

    def postprocess(self):
        return self.model

    def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
        """If under pipeline parallel setting, replacing the original forward method of huggingface
        to customized forward method, and add this changing to policy."""
        if self.pipeline_stage_manager:
            stage_manager = self.pipeline_stage_manager
            if self.model.__class__.__name__ == "MixtralModel":
                module = self.model
            else:
                module = self.model.model

            layers_per_stage = stage_manager.distribute_layers(len(module.layers))
            stage_index = stage_manager.get_stage_index(layers_per_stage)
            method_replacement = {"forward": partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
            self.append_or_create_method_replacement(
                description=method_replacement, policy=policy, target_key=model_cls
            )

        return

    def get_held_layers(self) -> List[Module]:
        """Get pipeline layers for current stage."""
        assert self.pipeline_stage_manager is not None

        if self.model.__class__.__name__ == "MixtralModel":
            module = self.model
        else:
            module = self.model.model
        stage_manager = self.pipeline_stage_manager

        held_layers = []
        layers_per_stage = stage_manager.distribute_layers(len(module.layers))
        if stage_manager.is_first_stage():
            held_layers.append(module.embed_tokens)
        start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
        held_layers.extend(module.layers[start_idx:end_idx])
        if stage_manager.is_last_stage():
            held_layers.append(module.norm)

        return held_layers


class MixtralModelPolicy(MixtralPolicy):
    def __init__(self) -> None:
        super().__init__()

    def module_policy(self):
        policy = super().module_policy()
        if self.pipeline_stage_manager:
            # set None as default
            self.set_pipeline_forward(
                model_cls=MixtralModel,
                new_forward=MixtralPipelineForwards.mixtral_model_forward,
                policy=policy,
            )
        return policy

    def get_held_layers(self) -> List[Module]:
        """Get pipeline layers for current stage."""
        held_layers = super().get_held_layers()
        return held_layers

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        """No shared params in llama model"""
        return []


class MixtralForCausalLMPolicy(MixtralPolicy):
    def module_policy(self):
        policy = super().module_policy()
        # TODO: assign pg mesh from plugin to all modules
        if self.shard_config.enable_tensor_parallelism:
            # add a new item for casual lm
            new_item = {
                MixtralForCausalLM: ModulePolicyDescription(
                    sub_module_replacement=[
                        SubModuleReplacementDescription(
                            suffix="lm_head",
                            target_module=Linear1D_Col,
                            kwargs=dict(gather_output=True),
                        )
                    ]
                )
            }
            policy.update(new_item)

        if self.pipeline_stage_manager:
            # set None as default
            self.set_pipeline_forward(
                model_cls=MixtralForCausalLM,
                new_forward=MixtralPipelineForwards.mixtral_for_causal_lm_forward,
                policy=policy,
            )

        return policy

    def get_held_layers(self) -> List[Module]:
        """Get pipeline layers for current stage."""
        stage_manager = self.pipeline_stage_manager
        held_layers = super().get_held_layers()
        if stage_manager.is_last_stage():
            held_layers.append(self.model.lm_head)
        return held_layers

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        llama_model = self.model.model
        if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
            if (
                id(llama_model.embed_tokens.weight) == id(self.model.lm_head.weight)
                and self.pipeline_stage_manager.num_stages > 1
            ):
                # tie weights
                return [
                    {
                        0: llama_model.embed_tokens.weight,
                        self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight,
                    }
                ]
        return []