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 colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col from colossalai.shardformer.modeling.deepseek import DeepseekPipelineForwards, EPDeepseekMoE from colossalai.shardformer.policies.base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ["DeepseekPolicy", "DeepseekForCausalLMPolicy"] class DeepseekPolicy(Policy): def config_sanity_check(self): pass def preprocess(self): if self.shard_config.enable_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( "Deepseek dosen't support sequence parallelism now, will ignore the sequence parallelism flag." ) if self.shard_config.enable_tensor_parallelism: raise NotImplementedError("Tensor parallelism is not supported for Deepseek model now.") if getattr(self.shard_config, "ep_group", None) is not None: # expert parallel self.append_or_create_submodule_replacement( description=[ SubModuleReplacementDescription( suffix="mlp", target_module=EPDeepseekMoE, kwargs={"ep_group": self.shard_config.ep_group}, ) ], policy=policy, target_key="DeepseekDecoderLayer", ) # 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="DeepseekDecoderLayer", ) self.append_or_create_submodule_replacement( description=SubModuleReplacementDescription( suffix="norm", target_module=FusedRMSNorm, ), policy=policy, target_key="DeepseekModel", ) if self.shard_config.enable_flash_attention: warnings.warn( "Flash attention has already been replaced in deepseek, and now set enable_flash_attention = False." ) 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__ == "DeepseekModel": 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__ == "DeepseekModel": 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 DeepseekModelPolicy(DeepseekPolicy): 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="DeepseekModel", new_forward=DeepseekPipelineForwards.deepseek_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 llama model""" return [] class DeepseekForCausalLMPolicy(DeepseekPolicy): 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 = { "DeepseekForCausalLM": 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="DeepseekForCausalLM", new_forward=DeepseekPipelineForwards.deepseek_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]]: deepseek_model = self.model.model if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1: if ( id(deepseek_model.embed_tokens.weight) == id(self.model.lm_head.weight) and self.pipeline_stage_manager.num_stages > 1 ): # tie weights return [ { 0: deepseek_model.embed_tokens.weight, self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight, } ] return []