import warnings from functools import partial from typing import Callable, Dict, List, Union import torch.nn as nn from torch import Tensor from torch.nn import Module from transformers.models.mixtral.modeling_mixtral import MixtralDecoderLayer, MixtralForCausalLM, MixtralModel from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col from colossalai.shardformer.layer.linear import Linear1D_Row from colossalai.shardformer.modeling.mixtral import EPMixtralSparseMoeBlock, MixtralPipelineForwards from colossalai.shardformer.policies.base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ["MixtralPolicy", "MixtralForCausalLMPolicy"] class MixtralPolicy(Policy): def config_sanity_check(self): pass def preprocess(self): if self.shard_config.enable_tensor_parallelism: # non-moe params tensor parallelism # Resize embedding 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) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: policy = {} if self.shard_config.enable_sequence_parallelism: self.shard_config.enable_sequence_parallelism = False raise NotImplementedError( "Mixtral dosen't support sequence parallelism now, will ignore the sequence parallelism flag." ) if self.shard_config.enable_tensor_parallelism: # tensor parallelism for non-moe params 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." assert ( self.model.config.num_key_value_heads % self.shard_config.tensor_parallel_size == 0 ), f"The number of key_value heads must be divisible by tensor parallel size." decoder_attribute_replacement = { "self_attn.hidden_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, "self_attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, "self_attn.num_key_value_heads": self.model.config.num_key_value_heads // self.shard_config.tensor_parallel_size, } policy[MixtralDecoderLayer] = ModulePolicyDescription( attribute_replacement=decoder_attribute_replacement, sub_module_replacement=[ SubModuleReplacementDescription( suffix="self_attn.q_proj", target_module=Linear1D_Col, ), SubModuleReplacementDescription( suffix="self_attn.k_proj", target_module=Linear1D_Col, ), SubModuleReplacementDescription( suffix="self_attn.v_proj", target_module=Linear1D_Col, ), SubModuleReplacementDescription( suffix="self_attn.o_proj", target_module=Linear1D_Row, ), # SubModuleReplacementDescription( # TODO: enable moe tp parallel # suffix="mlp.gate_proj", # target_module=Linear1D_Col, # ), # SubModuleReplacementDescription( # suffix="mlp.up_proj", # target_module=Linear1D_Col, # ), # SubModuleReplacementDescription( # suffix="mlp.down_proj", # target_module=Linear1D_Row, # ), ], ) if self.shard_config.ep_group: # expert parallel self.append_or_create_submodule_replacement( description=[ SubModuleReplacementDescription( suffix="block_sparse_moe", target_module=EPMixtralSparseMoeBlock, kwargs={"ep_group": self.shard_config.ep_group, "tp_group": self.shard_config.tensor_parallel_process_group}, ) ], policy=policy, target_key=MixtralDecoderLayer, ) # optimization configuration if self.shard_config.enable_fused_normalization: self.append_or_create_submodule_replacement( description=[ SubModuleReplacementDescription( suffix="input_layernorm", target_module=FusedRMSNorm, ), SubModuleReplacementDescription( suffix="post_attention_layernorm", target_module=FusedRMSNorm, ), ], policy=policy, target_key=MixtralDecoderLayer, ) self.append_or_create_submodule_replacement( description=SubModuleReplacementDescription( suffix="norm", target_module=FusedRMSNorm, ), policy=policy, target_key=MixtralModel, ) if self.shard_config.enable_flash_attention: warnings.warn("Flash attention is natively supported in transformers, will ignore the flag.") self.shard_config.enable_flash_attention = False return 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__ == "MixtralModel": module = self.model else: module = self.model.model layers_per_stage = stage_manager.distribute_layers(len(module.layers)) stage_index = stage_manager.get_stage_index(layers_per_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 ) 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__ == "MixtralModel": module = self.model else: module = self.model.model stage_manager = self.pipeline_stage_manager held_layers = [] layers_per_stage = stage_manager.distribute_layers(len(module.layers)) if stage_manager.is_first_stage(): held_layers.append(module.embed_tokens) start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage) held_layers.extend(module.layers[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.norm) return held_layers class MixtralModelPolicy(MixtralPolicy): def __init__(self) -> None: super().__init__() def module_policy(self): policy = super().module_policy() if self.pipeline_stage_manager: # set None as default self.set_pipeline_forward( model_cls=MixtralModel, new_forward=MixtralPipelineForwards.mixtral_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 mixtral model""" return [] class MixtralForCausalLMPolicy(MixtralPolicy): def module_policy(self): policy = super().module_policy() # TODO: assign pg mesh from plugin to all modules if self.shard_config.enable_tensor_parallelism: # add a new item for casual lm new_item = { MixtralForCausalLM: ModulePolicyDescription( sub_module_replacement=[ SubModuleReplacementDescription( suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True), ) ] ) } policy.update(new_item) if self.pipeline_stage_manager: # set None as default self.set_pipeline_forward( model_cls=MixtralForCausalLM, new_forward=MixtralPipelineForwards.mixtral_for_causal_lm_forward, policy=policy, ) return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" stage_manager = self.pipeline_stage_manager held_layers = super().get_held_layers() if stage_manager.is_last_stage(): held_layers.append(self.model.lm_head) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: mixtral_model = self.model.model if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1: if ( id(mixtral_model.embed_tokens.weight) == id(self.model.lm_head.weight) and self.pipeline_stage_manager.num_stages > 1 ): # tie weights return [ { 0: mixtral_model.embed_tokens.weight, self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight, } ] return [] class MixtralForSequenceClassificationPolicy(MixtralPolicy): def module_policy(self): from transformers import MixtralForSequenceClassification policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: # add a new item for sequence classification new_item = { MixtralForSequenceClassification: ModulePolicyDescription( sub_module_replacement=[ SubModuleReplacementDescription( suffix="score", target_module=Linear1D_Col, kwargs=dict(gather_output=True) ) ] ) } policy.update(new_item) if self.pipeline_stage_manager: raise NotImplementedError return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" stage_manager = self.pipeline_stage_manager held_layers = super().get_held_layers() if stage_manager.is_last_stage(ignore_chunk=True): held_layers.append(self.model.score) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: """No shared params in llama for sequence classification model""" return []