ColossalAI/colossalai/shardformer/policies/opt.py

from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D

from .._utils import getattr_, setattr_
from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription

__all__ = [
    'OPTPolicy', 'OPTModelPolicy', 'OPTForCausalLMPolicy', 'OPTForSequenceClassificationPolicy',
    'OPTForQuestionAnsweringPolicy'
]


class OPTPolicy(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
        """
        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.opt.modeling_opt import OPTAttention, OPTDecoder, OPTDecoderLayer

        base_policy = {
            OPTDecoder:
                ModulePolicyDescription(sub_module_replacement=[
                    SubModuleReplacementDescription(
                        suffix="embed_tokens",
                        target_module=VocabParallelEmbedding1D,
                    )
                ]),
            OPTDecoderLayer:
                ModulePolicyDescription(sub_module_replacement=[
                    SubModuleReplacementDescription(
                        suffix="fc1",
                        target_module=Linear1D_Col,
                    ),
                    SubModuleReplacementDescription(
                        suffix="fc2",
                        target_module=Linear1D_Row,
                    )
                ]),
            OPTAttention:
                ModulePolicyDescription(attribute_replacement={
                    "embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
                    "num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size
                },
                                        sub_module_replacement=[
                                            SubModuleReplacementDescription(
                                                suffix="q_proj",
                                                target_module=Linear1D_Col,
                                            ),
                                            SubModuleReplacementDescription(
                                                suffix="k_proj",
                                                target_module=Linear1D_Col,
                                            ),
                                            SubModuleReplacementDescription(
                                                suffix="v_proj",
                                                target_module=Linear1D_Col,
                                            ),
                                            SubModuleReplacementDescription(
                                                suffix="out_proj",
                                                target_module=Linear1D_Row,
                                            ),
                                        ]),
        }

        # optimization configuration
        if self.shard_config.enable_fused_normalization:
            base_policy[OPTDecoder].sub_module_replacement.append(
                SubModuleReplacementDescription(suffix="final_layer_norm",
                                                target_module=FusedLayerNorm,
                                                ignore_if_not_exist=True))
            base_policy[OPTDecoderLayer].sub_module_replacement.extend([
                SubModuleReplacementDescription(suffix="self_attn_layer_norm",
                                                target_module=FusedLayerNorm,
                                                ignore_if_not_exist=True),
                SubModuleReplacementDescription(suffix="final_layer_norm",
                                                target_module=FusedLayerNorm,
                                                ignore_if_not_exist=True)
            ])

        return base_policy

    def postprocess(self):
        return self.model


class OPTModelPolicy(OPTPolicy):

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


class OPTForCausalLMPolicy(OPTPolicy):

    def module_policy(self):
        from transformers.models.opt.modeling_opt import OPTForCausalLM

        policy = super().module_policy()
        new_item = {
            OPTForCausalLM:
                ModulePolicyDescription(sub_module_replacement=[
                    SubModuleReplacementDescription(
                        suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True))
                ])
        }

        policy.update(new_item)
        return policy

    def postprocess(self):
        binding_map = {
            'model.decoder.embed_tokens': 'lm_head',
        }

        for k, v in binding_map.items():
            src_mod = getattr_(self.model, k)
            dst_mod = getattr_(self.model, v)
            dst_mod.weight = src_mod.weight

        return self.model


class OPTForSequenceClassificationPolicy(OPTPolicy):

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


class OPTForQuestionAnsweringPolicy(OPTPolicy):

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