ColossalAI/colossalai/shardformer/policies/sam.py

import colossalai.shardformer.layer as col_nn

from ..modeling.sam import forward_fn
from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription

__all__ = ["SamPolicy", "SamModelPolicy"]


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

    def preprocess(self):
        return self.model

    def module_policy(self):
        from transformers.models.sam.modeling_sam import (
            SamTwoWayAttentionBlock,
            SamTwoWayTransformer,
            SamVisionAttention,
            SamVisionLayer,
        )

        policy = {}

        if self.shard_config.enable_fused_normalization:
            norm_cls = col_nn.FusedLayerNorm
        else:
            norm_cls = col_nn.LayerNorm

        if self.shard_config.enable_tensor_parallelism:
            assert (
                self.model.config.vision_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[SamVisionLayer] = ModulePolicyDescription(
                attribute_replacement={
                    "attn.num_attention_heads": self.model.config.vision_config.num_attention_heads
                    // self.shard_config.tensor_parallel_size,
                },
                sub_module_replacement=[
                    SubModuleReplacementDescription(
                        suffix="attn.qkv",
                        target_module=col_nn.FusedLinear1D_Col,
                        kwargs={
                            "n_fused": 3,
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="attn.proj",
                        target_module=col_nn.Linear1D_Row,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="mlp.lin1",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="mlp.lin2",
                        target_module=col_nn.Linear1D_Row,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                ],
            )
            policy[SamTwoWayAttentionBlock] = ModulePolicyDescription(
                attribute_replacement={
                    "self_attn.num_attention_heads": self.model.config.mask_decoder_config.num_attention_heads
                    // self.shard_config.tensor_parallel_size,
                },
                sub_module_replacement=[
                    SubModuleReplacementDescription(
                        suffix="self_attn.q_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="self_attn.k_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="self_attn.v_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="self_attn.out_proj",
                        target_module=col_nn.Linear1D_Row,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="cross_attn_token_to_image.q_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="cross_attn_token_to_image.k_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="cross_attn_token_to_image.v_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="cross_attn_token_to_image.out_proj",
                        target_module=col_nn.Linear1D_Row,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="mlp.lin1",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="mlp.lin2",
                        target_module=col_nn.Linear1D_Row,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="cross_attn_image_to_token.q_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="cross_attn_image_to_token.k_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="cross_attn_image_to_token.v_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="cross_attn_image_to_token.out_proj",
                        target_module=col_nn.Linear1D_Row,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                ],
            )
            policy[SamTwoWayTransformer] = ModulePolicyDescription(
                attribute_replacement={
                    "final_attn_token_to_image.num_attention_heads": self.model.config.mask_decoder_config.num_attention_heads
                    // self.shard_config.tensor_parallel_size,
                },
                sub_module_replacement=[
                    SubModuleReplacementDescription(
                        suffix="final_attn_token_to_image.q_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="final_attn_token_to_image.k_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="final_attn_token_to_image.v_proj",
                        target_module=col_nn.Linear1D_Col,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                    SubModuleReplacementDescription(
                        suffix="final_attn_token_to_image.out_proj",
                        target_module=col_nn.Linear1D_Row,
                        kwargs={
                            "fp8_communication": self.shard_config.fp8_communication,
                        },
                    ),
                ],
            )

            # add `DropoutForParallelInput` layer to replace the useage of `nn.functional.dropout`
            policy[SamVisionAttention] = ModulePolicyDescription(
                attribute_replacement={
                    "dropout_layer": col_nn.DropoutForParallelInput(self.model.config.vision_config.attention_dropout)
                },
                method_replacement={"forward": forward_fn()},
                sub_module_replacement=[],
            )

        # optimization configuration
        # Handle SamVisionLayer
        self.append_or_create_submodule_replacement(
            description=[
                SubModuleReplacementDescription(
                    suffix="layer_norm1",
                    target_module=norm_cls,
                ),
                SubModuleReplacementDescription(
                    suffix="layer_norm2",
                    target_module=norm_cls,
                ),
            ],
            policy=policy,
            target_key=SamVisionLayer,
        )

        # Handle SamTwoWayAttentionBlock
        self.append_or_create_submodule_replacement(
            description=[
                SubModuleReplacementDescription(
                    suffix="layer_norm1",
                    target_module=norm_cls,
                ),
                SubModuleReplacementDescription(
                    suffix="layer_norm2",
                    target_module=norm_cls,
                ),
                SubModuleReplacementDescription(
                    suffix="layer_norm3",
                    target_module=norm_cls,
                ),
                SubModuleReplacementDescription(
                    suffix="layer_norm4",
                    target_module=norm_cls,
                ),
            ],
            policy=policy,
            target_key=SamTwoWayAttentionBlock,
        )

        # Handle SamTwoWayTransformer
        self.append_or_create_submodule_replacement(
            description=[
                SubModuleReplacementDescription(
                    suffix="layer_norm_final_attn",
                    target_module=norm_cls,
                )
            ],
            policy=policy,
            target_key=SamTwoWayTransformer,
        )

        return policy

    def postprocess(self):
        return self.model


# SamModel
class SamModelPolicy(SamPolicy):
    def __init__(self) -> None:
        super().__init__()