ColossalAI/colossalai/shardformer/policies/bert.py

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

import torch.nn as nn
from torch import Tensor
from torch.nn import Module

import colossalai.shardformer.layer as col_nn

from .._utils import getattr_, setattr_
from ..modeling.bert import (
    BertPipelineForwards,
    bert_sequence_parallel_forward_fn,
    get_bert_flash_attention_forward,
    get_jit_fused_bert_output_forward,
    get_jit_fused_bert_self_output_forward,
)
from ..modeling.jit import get_jit_fused_dropout_add_func
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription

__all__ = [
    'BertPolicy', 'BertModelPolicy', 'BertForPreTrainingPolicy', 'BertLMdHeadModelPolicy', 'BertForMaskedLMPolicy',
    'BertForNextSentencePredictionPolicy', 'BertForSequenceClassificationPolicy', 'BertForTokenClassificationPolicy',
    'BertForMultipleChoicePolicy', 'BertForQuestionAnsweringPolicy'
]


class BertPolicy(Policy):

    def config_sanity_check(self):
        pass

    def preprocess(self):
        # reshape the embedding layer
        r"""
        Reshape the Embedding layer to make the embedding dimension divisible by world_size
        """
        # TODO:
        if self.shard_config.enable_tensor_parallelism:
            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):
        from transformers.models.bert.modeling_bert import (
            BertEmbeddings,
            BertLayer,
            BertModel,
            BertOutput,
            BertSelfAttention,
            BertSelfOutput,
        )

        policy = {}
        use_sequence_parallel = self.shard_config.enable_sequence_parallelism
        overlap = self.shard_config.enable_sequence_overlap
        if self.shard_config.enable_tensor_parallelism:
            policy[BertLayer] = ModulePolicyDescription(attribute_replacement={
                "attention.self.all_head_size":
                    self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
                "crossattention.self.all_head_size":
                    self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
                "attention.self.num_attention_heads":
                    self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
                "crossattention.self.num_attention_heads":
                    self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
            },
                                                        sub_module_replacement=[
                                                            SubModuleReplacementDescription(
                                                                suffix="attention.self.query",
                                                                target_module=col_nn.Linear1D_Col,
                                                                kwargs={
                                                                    "seq_parallel": use_sequence_parallel,
                                                                    "overlap": overlap
                                                                },
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="attention.self.key",
                                                                target_module=col_nn.Linear1D_Col,
                                                                kwargs={
                                                                    "seq_parallel": use_sequence_parallel,
                                                                    "overlap": overlap
                                                                },
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="attention.self.value",
                                                                target_module=col_nn.Linear1D_Col,
                                                                kwargs={
                                                                    "seq_parallel": use_sequence_parallel,
                                                                    "overlap": overlap
                                                                },
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="attention.self.dropout",
                                                                target_module=col_nn.DropoutForParallelInput,
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="attention.output.dense",
                                                                target_module=col_nn.Linear1D_Row,
                                                                kwargs={"seq_parallel": use_sequence_parallel},
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="attention.output.dropout",
                                                                target_module=col_nn.DropoutForParallelInput,
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="intermediate.dense",
                                                                target_module=col_nn.Linear1D_Col,
                                                                kwargs={
                                                                    "seq_parallel": use_sequence_parallel,
                                                                    "overlap": overlap
                                                                },
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="output.dense",
                                                                target_module=col_nn.Linear1D_Row,
                                                                kwargs={"seq_parallel": use_sequence_parallel},
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="output.dropout",
                                                                target_module=col_nn.DropoutForParallelInput,
                                                            )
                                                        ])

            policy[BertEmbeddings] = ModulePolicyDescription(sub_module_replacement=[
                SubModuleReplacementDescription(
                    suffix="word_embeddings",
                    target_module=col_nn.VocabParallelEmbedding1D,
                ),
                SubModuleReplacementDescription(
                    suffix="dropout",
                    target_module=col_nn.DropoutForReplicatedInput,
                )
            ])

        if use_sequence_parallel:
            self.append_or_create_method_replacement(
                description={'forward': bert_sequence_parallel_forward_fn(self.shard_config)},
                policy=policy,
                target_key=BertModel)

        # optimization configuration
        if self.shard_config.enable_fused_normalization:
            # Handle bert layer
            self.append_or_create_submodule_replacement(description=[
                SubModuleReplacementDescription(
                    suffix="attention.output.LayerNorm",
                    target_module=col_nn.FusedLayerNorm,
                ),
                SubModuleReplacementDescription(
                    suffix="output.LayerNorm",
                    target_module=col_nn.FusedLayerNorm,
                )
            ],
                                                        policy=policy,
                                                        target_key=BertLayer)
            # handle embedding layer
            self.append_or_create_submodule_replacement(
                description=[SubModuleReplacementDescription(
                    suffix="LayerNorm",
                    target_module=col_nn.FusedLayerNorm,
                )],
                policy=policy,
                target_key=BertEmbeddings)

        # use flash attention
        if self.shard_config.enable_flash_attention:
            self.append_or_create_method_replacement(description={
                'forward': get_bert_flash_attention_forward(),
            },
                                                     policy=policy,
                                                     target_key=BertSelfAttention)

        # use jit operator
        if self.shard_config.enable_jit_fused:
            self.append_or_create_method_replacement(description={
                'forward': get_jit_fused_bert_self_output_forward(),
                'dropout_add': get_jit_fused_dropout_add_func(),
            },
                                                     policy=policy,
                                                     target_key=BertSelfOutput)
            self.append_or_create_method_replacement(description={
                'forward': get_jit_fused_bert_output_forward(),
                'dropout_add': get_jit_fused_dropout_add_func(),
            },
                                                     policy=policy,
                                                     target_key=BertOutput)

        return policy

    def add_lm_head_policy(self, base_policy):
        from transformers.models.bert.modeling_bert import BertLMPredictionHead

        # optimize for tensor parallelism
        if self.shard_config.enable_tensor_parallelism:
            self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
                suffix="decoder", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}),
                                                        policy=base_policy,
                                                        target_key=BertLMPredictionHead)

        # optimize with fused normalization
        if self.shard_config.enable_fused_normalization:
            # Handle bert lm prediction head
            self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
                suffix="transform.LayerNorm",
                target_module=col_nn.FusedLayerNorm,
            ),
                                                        policy=base_policy,
                                                        target_key=BertLMPredictionHead)
        return base_policy

    def add_lm_prediction_policy(self, base_policy):
        from transformers.models.bert.modeling_bert import BertLMPredictionHead
        method_replacement = {
            '_save_to_state_dict': col_nn.ParallelModule._save_to_state_dict,
            '_load_from_state_dict': col_nn.ParallelModule._load_from_state_dict,
        }
        self.append_or_create_method_replacement(description=method_replacement,
                                                 policy=base_policy,
                                                 target_key=BertLMPredictionHead)
        return base_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__ == "BertModel":
                module = self.model
            else:
                module = self.model.bert

            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':
                    partial(new_forward,
                            stage_manager=stage_manager,
                            stage_index=stage_index,
                            shard_config=self.shard_config)
            }
            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__ == 'BertModel':
            module = self.model
        else:
            module = self.model.bert
        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])
        if stage_manager.is_last_stage():
            held_layers.append(module.pooler)

        return held_layers


# BertModel
class BertModelPolicy(BertPolicy):

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

    def module_policy(self):
        policy = super().module_policy()
        from transformers.models.bert.modeling_bert import BertModel
        if self.pipeline_stage_manager:
            self.set_pipeline_forward(model_cls=BertModel,
                                      new_forward=BertPipelineForwards.bert_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 bert model"""
        return []


# BertForPreTraining
class BertForPreTrainingPolicy(BertPolicy):

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

    def module_policy(self):
        policy = super().module_policy()
        policy = self.add_lm_head_policy(policy)
        policy = self.add_lm_prediction_policy(policy)
        from transformers.models.bert.modeling_bert import BertForPreTraining
        if self.pipeline_stage_manager:
            self.set_pipeline_forward(model_cls=BertForPreTraining,
                                      new_forward=BertPipelineForwards.bert_for_pretraining_forward,
                                      policy=policy)
        return policy

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

        return held_layers

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        model = self.model
        if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
            if id(model.bert.embeddings.word_embeddings.weight) == id(model.cls.predictions.decoder.weight):
                # tie weights
                return [{
                    0: model.bert.embeddings.word_embeddings.weight,
                    self.pipeline_stage_manager.num_stages - 1: model.cls.predictions.decoder.weight
                }]
        return []


# BertLMHeadModel
class BertLMHeadModelPolicy(BertPolicy):

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

    def module_policy(self):
        policy = super().module_policy()
        policy = self.add_lm_head_policy(policy)
        policy = self.add_lm_prediction_policy(policy)
        from transformers.models.bert.modeling_bert import BertLMHeadModel
        if self.pipeline_stage_manager:
            self.set_pipeline_forward(model_cls=BertLMHeadModel,
                                      new_forward=BertPipelineForwards.bert_lm_head_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()
        stage_manager = self.pipeline_stage_manager
        if stage_manager.is_last_stage():
            held_layers.append(self.model.cls)
        return held_layers

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        bert_model = self.model.bert
        if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
            if id(bert_model.embeddings.word_embeddings.weight) == id(self.model.cls.predictions.decoder.weight):
                # tie weights
                return [{
                    0: bert_model.embeddings.word_embeddings.weight,
                    self.pipeline_stage_manager.num_stages - 1: self.model.cls.predictions.decoder.weight
                }]
        return []


# BertForMaskedLM
class BertForMaskedLMPolicy(BertPolicy):

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

    def module_policy(self):
        policy = super().module_policy()
        policy = self.add_lm_head_policy(policy)
        policy = self.add_lm_prediction_policy(policy)
        from transformers.models.bert.modeling_bert import BertForMaskedLM
        if self.pipeline_stage_manager:
            self.set_pipeline_forward(model_cls=BertForMaskedLM,
                                      new_forward=BertPipelineForwards.bert_for_masked_lm_forward,
                                      policy=policy)
        return policy

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

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        bert_model = self.model.bert
        if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
            if id(bert_model.embeddings.word_embeddings.weight) == id(self.model.cls.predictions.decoder.weight):
                # tie weights
                return [{
                    0: bert_model.embeddings.word_embeddings.weight,
                    self.pipeline_stage_manager.num_stages - 1: self.model.cls.predictions.decoder.weight
                }]
        return []


# BertForSequenceClassification
class BertForSequenceClassificationPolicy(BertPolicy):

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

    def module_policy(self):
        from transformers.models.bert.modeling_bert import BertForSequenceClassification

        policy = super().module_policy()

        if self.shard_config.enable_tensor_parallelism:
            addon_module = {
                BertForSequenceClassification:
                    ModulePolicyDescription(sub_module_replacement=[
                        SubModuleReplacementDescription(
                            suffix="dropout",
                            target_module=col_nn.DropoutForParallelInput,
                        )
                    ])
            }
            policy.update(addon_module)
        if self.pipeline_stage_manager:
            self.set_pipeline_forward(model_cls=BertForSequenceClassification,
                                      new_forward=BertPipelineForwards.bert_for_sequence_classification_forward,
                                      policy=policy)

        return policy

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

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        # no shared params for sequence classification model
        return []


# BertForTokenClassification
class BertForTokenClassificationPolicy(BertPolicy):

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

    def module_policy(self):
        from transformers.models.bert.modeling_bert import BertForTokenClassification

        policy = super().module_policy()

        if self.shard_config.enable_tensor_parallelism:
            addon_module = {
                BertForTokenClassification:
                    ModulePolicyDescription(sub_module_replacement=[
                        SubModuleReplacementDescription(
                            suffix="dropout",
                            target_module=col_nn.DropoutForParallelInput,
                        )
                    ])
            }
            policy.update(addon_module)
        if self.pipeline_stage_manager:
            self.set_pipeline_forward(model_cls=BertForTokenClassification,
                                      new_forward=BertPipelineForwards.bert_for_token_classification_forward,
                                      policy=policy)

        return policy

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

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        # no shared params for sequence classification model
        return []


# BertForNextSentencePrediction
class BertForNextSentencePredictionPolicy(BertPolicy):

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

    def module_policy(self):
        policy = super().module_policy()
        from transformers.models.bert.modeling_bert import BertForNextSentencePrediction
        if self.pipeline_stage_manager:
            self.set_pipeline_forward(model_cls=BertForNextSentencePrediction,
                                      new_forward=BertPipelineForwards.bert_for_next_sentence_prediction_forward,
                                      policy=policy)

        return policy

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

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        # no shared params for sequence classification model
        return []


# BertForMultipleChoice
class BertForMultipleChoicePolicy(BertPolicy):

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

    def module_policy(self):
        from transformers.models.bert.modeling_bert import BertForMultipleChoice

        policy = super().module_policy()

        if self.shard_config.enable_tensor_parallelism:
            addon_module = {
                BertForMultipleChoice:
                    ModulePolicyDescription(sub_module_replacement=[
                        SubModuleReplacementDescription(
                            suffix="dropout",
                            target_module=col_nn.DropoutForParallelInput,
                        )
                    ])
            }
            policy.update(addon_module)
        if self.pipeline_stage_manager:
            self.set_pipeline_forward(model_cls=BertForMultipleChoice,
                                      new_forward=BertPipelineForwards.bert_for_multiple_choice_forward,
                                      policy=policy)

        return policy

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

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        # no shared params for sequence classification model
        return []


class BertForQuestionAnsweringPolicy(BertPolicy):

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

    def module_policy(self):
        from transformers.models.bert.modeling_bert import BertForQuestionAnswering
        policy = super().module_policy()
        if self.pipeline_stage_manager:
            self.set_pipeline_forward(model_cls=BertForQuestionAnswering,
                                      new_forward=BertPipelineForwards.bert_for_question_answering_forward,
                                      policy=policy)

        return policy

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

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        # no shared params for sequence classification model
        return []