ColossalAI/colossalai/shardformer/policies/vit.py

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

import torch.nn as nn

import colossalai.shardformer.layer as col_nn

from ..modeling.vit import (
    ViTForImageClassification_pipeline_forward,
    ViTForMaskedImageModeling_pipeline_forward,
    ViTModel_pipeline_forward,
)
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription

__all__ = ['ViTPolicy', 'ViTModelPolicy', 'ViTForImageClassificationPolicy', 'ViTForMaskedImageModelingPolicy']


class ViTPolicy(Policy):

    def config_sanity_check(self):
        pass

    def preprocess(self):
        return self.model

    def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
        from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTLayer

        policy = {}

        if self.shard_config.enable_tensor_parallelism:
            policy[ViTEmbeddings] = ModulePolicyDescription(attribute_replacement={},
                                                            param_replacement=[],
                                                            sub_module_replacement=[
                                                                SubModuleReplacementDescription(
                                                                    suffix="dropout",
                                                                    target_module=col_nn.DropoutForReplicatedInput,
                                                                )
                                                            ])

            policy[ViTLayer] = ModulePolicyDescription(attribute_replacement={
                "attention.attention.num_attention_heads":
                    self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
                "attention.attention.all_head_size":
                    self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
            },
                                                       param_replacement=[],
                                                       sub_module_replacement=[
                                                           SubModuleReplacementDescription(
                                                               suffix="attention.attention.query",
                                                               target_module=col_nn.Linear1D_Col,
                                                           ),
                                                           SubModuleReplacementDescription(
                                                               suffix="attention.attention.key",
                                                               target_module=col_nn.Linear1D_Col,
                                                           ),
                                                           SubModuleReplacementDescription(
                                                               suffix="attention.attention.value",
                                                               target_module=col_nn.Linear1D_Col,
                                                           ),
                                                           SubModuleReplacementDescription(
                                                               suffix="attention.attention.dropout",
                                                               target_module=col_nn.DropoutForParallelInput,
                                                           ),
                                                           SubModuleReplacementDescription(
                                                               suffix="attention.output.dense",
                                                               target_module=col_nn.Linear1D_Row,
                                                           ),
                                                           SubModuleReplacementDescription(
                                                               suffix="attention.output.dropout",
                                                               target_module=col_nn.DropoutForReplicatedInput,
                                                           ),
                                                           SubModuleReplacementDescription(
                                                               suffix="intermediate.dense",
                                                               target_module=col_nn.Linear1D_Col,
                                                           ),
                                                           SubModuleReplacementDescription(
                                                               suffix="output.dense",
                                                               target_module=col_nn.Linear1D_Row,
                                                           ),
                                                           SubModuleReplacementDescription(
                                                               suffix="output.dropout",
                                                               target_module=col_nn.DropoutForReplicatedInput,
                                                           ),
                                                       ])

        return policy

    def new_model_class(self):
        return None

    def postprocess(self):
        return self.model

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

        if self.model.__class__.__name__ == 'ViTModel':
            module = self.model
        else:
            module = self.model.vit
        stage_manager = self.pipeline_stage_manager

        held_layers = []
        layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
        if stage_manager.is_first_stage():
            held_layers.append(module.embeddings)
        start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
        held_layers.extend(module.encoder.layer[start_idx:end_idx])
        return held_layers

    def set_pipeline_forward(self, model_cls: nn.Module, pipeline_forward: Callable, policy: Dict):
        if self.pipeline_stage_manager:
            stage_manager = self.pipeline_stage_manager
            if self.model.__class__.__name__ == 'ViTModel':
                module = self.model
            else:
                module = self.model.vit

            layers_per_stage = Policy.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
            method_replacement = {'forward': pipeline_forward(stage_manager=stage_manager, stage_index=stage_index)}
            self.append_or_create_method_replacement(description=method_replacement,
                                                     policy=policy,
                                                     target_key=model_cls)


# ViTModel
class ViTModelPolicy(ViTPolicy):

    def __init__(self) -> None:
        super().__init__()

    def module_policy(self):
        from transformers.models.vit.modeling_vit import ViTModel

        policy = super().module_policy()

        if self.shard_config.pipeline_stage_manager is not None:
            self.set_pipeline_forward(model_cls=ViTModel, pipeline_forward=ViTModel_pipeline_forward, policy=policy)
        return policy

    def get_held_layers(self) -> List[nn.Module]:
        held_layers = super().get_held_layers()
        assert self.pipeline_stage_manager is not None, "pipeline_stage_manager is None"

        module = self.model
        stage_manager = self.pipeline_stage_manager
        if stage_manager.is_last_stage():
            held_layers.append(module.layernorm)
            held_layers.append(module.pooler)

        return held_layers


# ViTForImageClassification
class ViTForImageClassificationPolicy(ViTPolicy):

    def module_policy(self):
        from transformers.models.vit.modeling_vit import ViTForImageClassification, ViTModel

        policy = super().module_policy()
        if self.shard_config.enable_tensor_parallelism:
            new_item = {
                ViTForImageClassification:
                    ModulePolicyDescription(sub_module_replacement=[
                        SubModuleReplacementDescription(
                            suffix="classifier", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True))
                    ])
            }
            policy.update(new_item)

        if self.shard_config.pipeline_stage_manager is not None:
            self.set_pipeline_forward(model_cls=ViTModel, pipeline_forward=ViTModel_pipeline_forward, policy=policy)
            self.set_pipeline_forward(model_cls=ViTForImageClassification,
                                      pipeline_forward=ViTForImageClassification_pipeline_forward,
                                      policy=policy)

        return policy

    def get_held_layers(self) -> List[nn.Module]:
        held_layers = super().get_held_layers()
        assert self.pipeline_stage_manager is not None, "pipeline_stage_manager is None"

        module = self.model.vit
        stage_manager = self.pipeline_stage_manager
        if stage_manager.is_last_stage():
            held_layers.append(module.layernorm)
            held_layers.append(self.model.classifier)

        return held_layers


# ViTForMaskedImageModeling
class ViTForMaskedImageModelingPolicy(ViTPolicy):

    def __init__(self) -> None:
        super().__init__()

    def module_policy(self):
        from transformers.models.vit.modeling_vit import ViTForMaskedImageModeling, ViTModel

        policy = super().module_policy()

        if self.shard_config.pipeline_stage_manager is not None:
            self.set_pipeline_forward(model_cls=ViTModel, pipeline_forward=ViTModel_pipeline_forward, policy=policy)
            self.set_pipeline_forward(model_cls=ViTForMaskedImageModeling,
                                      pipeline_forward=ViTForMaskedImageModeling_pipeline_forward,
                                      policy=policy)
        return policy

    def get_held_layers(self) -> List[nn.Module]:
        held_layers = super().get_held_layers()
        assert self.pipeline_stage_manager is not None, "pipeline_stage_manager is None"

        module = self.model.vit
        stage_manager = self.pipeline_stage_manager
        if stage_manager.is_last_stage():
            held_layers.append(module.layernorm)
            held_layers.append(self.model.decoder)

        return held_layers