ColossalAI/colossalai/shardformer/policies/gptj.py

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

from torch import Tensor, nn

import colossalai.shardformer.layer as col_nn

from ..modeling.gptj import (
    GPTJPipelineForwards,
    get_gptj_flash_attention_forward,
    gptj_model_forward_for_flash_attention,
)
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription

__all__ = [
    "GPTJPolicy",
    "GPTJModelPolicy",
    "GPTJForCausalLMPolicy",
    "GPTJForSequenceClassificationPolicy",
    "GPTJForQuestionAnsweringPolicy",
    "FlaxGPTJPolicy",
    "FlaxGPTJForCausalLMPolicy",
]


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

    def preprocess(self):
        self.tie_weight = self.tie_weight_check()
        self.origin_attn_implement = self.model.config._attn_implementation
        return self.model

    def module_policy(self):
        from transformers.models.gptj.modeling_gptj import GPTJ_ATTENTION_CLASSES, GPTJBlock, GPTJModel

        policy = {}

        attn_cls = GPTJ_ATTENTION_CLASSES[self.origin_attn_implement]

        embedding_cls = None
        if self.shard_config.enable_tensor_parallelism:
            embedding_cls = col_nn.VocabParallelEmbedding1D
        else:
            if self.tie_weight:
                embedding_cls = col_nn.PaddingEmbedding

        if self.shard_config.enable_sequence_parallelism:
            self.shard_config.enable_sequence_parallelism = False
            warnings.warn("GPTJ doesn't support sequence parallelism now, will ignore the sequence parallelism flag.")

        if self.shard_config.enable_tensor_parallelism:
            assert (
                self.model.config.num_attention_heads % self.shard_config.tensor_parallel_size == 0
            ), f"The number of attention heads must be divisible by tensor parallel size."
            policy[GPTJModel] = ModulePolicyDescription(
                sub_module_replacement=[
                    SubModuleReplacementDescription(
                        suffix="drop",
                        target_module=col_nn.DropoutForParallelInput,
                    ),
                ]
            )

            policy[GPTJBlock] = ModulePolicyDescription(
                attribute_replacement={
                    "attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
                    "attn.num_attention_heads": self.model.config.num_attention_heads
                    // self.shard_config.tensor_parallel_size,
                },
                sub_module_replacement=[
                    SubModuleReplacementDescription(
                        suffix="attn.k_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="attn.q_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="attn.v_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="attn.out_proj",
                        target_module=col_nn.Linear1D_Row,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="mlp.fc_in",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="mlp.fc_out",
                        target_module=col_nn.Linear1D_Row,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="attn.attn_dropout",
                        target_module=col_nn.DropoutForParallelInput,
                    ),
                    SubModuleReplacementDescription(
                        suffix="attn.resid_dropout",
                        target_module=col_nn.DropoutForParallelInput,
                    ),
                    SubModuleReplacementDescription(
                        suffix="mlp.dropout",
                        target_module=col_nn.DropoutForParallelInput,
                    ),
                ],
            )

        if embedding_cls is not None:
            self.append_or_create_submodule_replacement(
                description=SubModuleReplacementDescription(
                    suffix="wte",
                    target_module=embedding_cls,
                    kwargs=(
                        {
                            "make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by,
                            "fp8_communication": self.shard_config.fp8_communication,
                        }
                        if self.shard_config.enable_tensor_parallelism
                        else {"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by}
                    ),
                ),
                policy=policy,
                target_key=GPTJModel,
            )

        # optimization configuration
        if self.shard_config.enable_fused_normalization:
            self.append_or_create_submodule_replacement(
                description=SubModuleReplacementDescription(
                    suffix="ln_f",
                    target_module=col_nn.FusedLayerNorm,
                ),
                policy=policy,
                target_key=GPTJModel,
            )

            self.append_or_create_submodule_replacement(
                description=[
                    SubModuleReplacementDescription(
                        suffix="ln_1",
                        target_module=col_nn.FusedLayerNorm,
                    )
                ],
                policy=policy,
                target_key=GPTJBlock,
            )

        if self.shard_config.enable_flash_attention:
            self.append_or_create_method_replacement(
                description={
                    "forward": get_gptj_flash_attention_forward(),
                },
                policy=policy,
                target_key=attn_cls,
            )
            if not self.shard_config.pipeline_stage_manager:
                self.append_or_create_method_replacement(
                    description={"forward": gptj_model_forward_for_flash_attention(self.shard_config)},
                    policy=policy,
                    target_key=GPTJModel,
                )

        return policy

    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

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

        held_layers = []
        layers_per_stage = stage_manager.distribute_layers(len(module.h))
        if stage_manager.is_first_stage():
            held_layers.append(module.wte)
            held_layers.append(module.drop)
        start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
        held_layers.extend(module.h[start_idx:end_idx])
        if stage_manager.is_last_stage():
            held_layers.append(module.ln_f)
        return held_layers

    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 not self.pipeline_stage_manager:
            raise ValueError("set_pipeline_forward method can only be called when pipeline parallel is enabled.")
        stage_manager = self.pipeline_stage_manager
        if self.model.__class__.__name__ == "GPTJModel":
            module = self.model
        else:
            module = self.model.transformer

        layers_per_stage = stage_manager.distribute_layers(len(module.h))
        stage_index = stage_manager.get_stage_index(layers_per_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)


# GPTJModel
class GPTJModelPolicy(GPTJPolicy):
    def __init__(self) -> None:
        super().__init__()

    def module_policy(self):
        from transformers.models.gptj.modeling_gptj import GPTJModel

        policy = super().module_policy()

        if self.pipeline_stage_manager is not None:
            self.set_pipeline_forward(
                model_cls=GPTJModel,
                new_forward=GPTJPipelineForwards.gptj_model_forward,
                policy=policy,
            )
        return policy

    def get_held_layers(self) -> List[nn.Module]:
        return super().get_held_layers()

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        """No shared params in GPT2Model."""
        return []


# GPTJForCausalLM
class GPTJForCausalLMPolicy(GPTJPolicy):
    def __init__(self) -> None:
        super().__init__()

    def module_policy(self):
        from transformers.models.gptj.modeling_gptj import GPTJForCausalLM

        policy = super().module_policy()

        if self.shard_config.enable_tensor_parallelism:
            addon_module = {
                GPTJForCausalLM: ModulePolicyDescription(
                    sub_module_replacement=[
                        SubModuleReplacementDescription(
                            suffix="lm_head",
                            target_module=col_nn.VocabParallelLMHead1D,
                            kwargs={
                                "gather_output": True,
                                "make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by,
                                "fp8_communication": self.shard_config.fp8_communication,
                            },
                        )
                    ]
                )
            }
        else:
            addon_module = {
                GPTJForCausalLM: ModulePolicyDescription(
                    sub_module_replacement=[
                        SubModuleReplacementDescription(
                            suffix="lm_head",
                            target_module=col_nn.PaddingLMHead,
                            kwargs={"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by},
                        )
                    ]
                )
            }
        policy.update(addon_module)

        if self.pipeline_stage_manager is not None:
            self.set_pipeline_forward(
                model_cls=GPTJForCausalLM,
                new_forward=GPTJPipelineForwards.gptj_causallm_model_forward,
                policy=policy,
            )
        return policy

    def get_held_layers(self) -> List[nn.Module]:
        held_layers = super().get_held_layers()
        if self.pipeline_stage_manager.is_last_stage():
            held_layers.append(self.model.lm_head)
        return held_layers

    def get_shared_params(self) -> List[Dict[int, Tensor]]:
        """The weights of wte and lm_head are shared."""
        module = self.model
        stage_manager = self.pipeline_stage_manager
        if stage_manager is not None:
            if stage_manager.num_stages > 1 and id(module.transformer.wte.weight) == id(module.lm_head.weight):
                first_stage, last_stage = 0, stage_manager.num_stages - 1
                return [
                    {
                        first_stage: module.transformer.wte.weight,
                        last_stage: module.lm_head.weight,
                    }
                ]
        return []


# GPTJForSequenceClassification
class GPTJForSequenceClassificationPolicy(GPTJPolicy):
    def __init__(self) -> None:
        super().__init__()

    def module_policy(self):
        from transformers.models.gptj.modeling_gptj import GPTJForSequenceClassification

        policy = super().module_policy()

        if self.pipeline_stage_manager is not None:
            self.set_pipeline_forward(
                model_cls=GPTJForSequenceClassification,
                new_forward=GPTJPipelineForwards.gptj_for_sequence_classification_forward,
                policy=policy,
            )
        return policy

    def get_held_layers(self) -> List[nn.Module]:
        held_layers = super().get_held_layers()
        if self.pipeline_stage_manager.is_last_stage():
            held_layers.append(self.model.score)
        return held_layers

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


# GPTJForQuestionAnswering
class GPTJForQuestionAnsweringPolicy(GPTJPolicy):
    def __init__(self) -> None:
        super().__init__()

    def module_policy(self):
        from transformers.models.gptj.modeling_gptj import GPTJForQuestionAnswering

        policy = super().module_policy()

        if self.pipeline_stage_manager is not None:
            self.set_pipeline_forward(
                model_cls=GPTJForQuestionAnswering,
                new_forward=GPTJPipelineForwards.gptj_for_question_answering_forward,
                policy=policy,
            )
        return policy

    def get_held_layers(self) -> List[nn.Module]:
        held_layers = super().get_held_layers()
        if self.pipeline_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 in GPT2ForQuestionAnswering."""
        return []