from functools import partial from typing import Callable, Dict, List import torch.nn as nn from torch import Tensor, nn from colossalai.shardformer.layer import FusedLayerNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D from .._utils import getattr_ from ..modeling.jit import get_jit_fused_dropout_add_func from ..modeling.opt import OPTPipelineForwards, get_jit_fused_opt_decoder_layer_forward, get_opt_flash_attention_forward from .base_policy 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 """ 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.opt.modeling_opt import OPTAttention, OPTDecoder, OPTDecoderLayer policy = {} if self.shard_config.enable_tensor_parallelism: policy[OPTDecoder] = ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( suffix="embed_tokens", target_module=VocabParallelEmbedding1D, ) ]) policy[OPTDecoderLayer] = ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( suffix="fc1", target_module=Linear1D_Col, ), SubModuleReplacementDescription( suffix="fc2", target_module=Linear1D_Row, ) ]) policy[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: self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="final_layer_norm", target_module=FusedLayerNorm, ignore_if_not_exist=True), policy=policy, target_key=OPTDecoder) self.append_or_create_submodule_replacement(description=[ 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) ], policy=policy, target_key=OPTDecoderLayer) # use flash attention if self.shard_config.enable_flash_attention: policy[OPTAttention] = ModulePolicyDescription(method_replacement={ 'forward': get_opt_flash_attention_forward(), }) # use jit fused operator if self.shard_config.enable_jit_fused: policy[OPTDecoderLayer] = ModulePolicyDescription(method_replacement={ 'forward': get_jit_fused_opt_decoder_layer_forward(), 'dropout_add': get_jit_fused_dropout_add_func(), }) 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__ == 'OPTModel': module = self.model.decoder else: module = self.model.model.decoder stage_manager = self.pipeline_stage_manager held_layers = [] layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages) if stage_manager.is_first_stage(): held_layers.append(module.embed_tokens) held_layers.append(module.embed_positions) held_layers.append(module.project_in) start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) held_layers.extend(module.layers[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.final_layer_norm) held_layers.append(module.project_out) 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 self.pipeline_stage_manager: stage_manager = self.pipeline_stage_manager if self.model.__class__.__name__ == 'OPTModel': module = self.model.decoder else: module = self.model.model.decoder layers_per_stage = Policy.distribute_layers(len(module.layers), 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)} self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls) class OPTModelPolicy(OPTPolicy): def __init__(self) -> None: super().__init__() def module_policy(self): from transformers.models.opt.modeling_opt import OPTModel policy = super().module_policy() if self.pipeline_stage_manager: self.set_pipeline_forward(model_cls=OPTModel, new_forward=OPTPipelineForwards.opt_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 OPTModel.""" return [] class OPTForCausalLMPolicy(OPTPolicy): def module_policy(self): from transformers.models.opt.modeling_opt import OPTForCausalLM policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)), policy=policy, target_key=OPTForCausalLM) if self.pipeline_stage_manager: self.set_pipeline_forward(model_cls=OPTForCausalLM, new_forward=OPTPipelineForwards.opt_for_causal_lm_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]]: opt_model = self.model if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1: num_stages = self.pipeline_stage_manager.num_stages if id(opt_model.model.decoder.embed_tokens.weight) == id(opt_model.lm_head.weight): return [{0: opt_model.model.decoder.embed_tokens.weight, num_stages - 1: opt_model.lm_head.weight}] return [] def postprocess(self): if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: 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__() def module_policy(self): from transformers.models.opt.modeling_opt import OPTForSequenceClassification policy = super().module_policy() if self.pipeline_stage_manager: self.set_pipeline_forward(model_cls=OPTForSequenceClassification, new_forward=OPTPipelineForwards.opt_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 OPTForSequenceClassification" return [] class OPTForQuestionAnsweringPolicy(OPTPolicy): def __init__(self) -> None: super().__init__() def module_policy(self): from transformers.models.opt.modeling_opt import OPTForQuestionAnswering policy = super().module_policy() if self.pipeline_stage_manager: self.set_pipeline_forward(model_cls=OPTForQuestionAnswering, new_forward=OPTPipelineForwards.opt_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 OPTForSequenceClassification" return []