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.utils import is_flash_attn_greater_or_equal_2_10 from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col from colossalai.shardformer.layer.embedding import PaddingEmbedding, VocabParallelEmbedding1D from colossalai.shardformer.layer.linear import Linear1D_Row from colossalai.shardformer.modeling.deepseek import ( DeepseekPipelineForwards, EPDeepseekMoE, get_deepseek_flash_attention_forward, get_deepseek_flash_attention_model_forward, ) 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): self.tie_weight = self.tie_weight_check() self.origin_attn_implement = self.model.config._attn_implementation """ Because transformers library's bug for AutoModel/AutoConfig, who pop “attn_implement” twice from modeling_utils.py and configuration_utils.py. This bug causes attn_cls to be set to sdpa. Here we assign it to "flash_attention_2". """ # self.origin_attn_implement = "flash_attention_2" 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]: ATTN_IMPLEMENTATION = { "eager": "DeepseekAttention", "flash_attention_2": "DeepseekFlashAttention2", "sdpa": "DeepseekSdpaAttention", } policy = {} attn_cls = ATTN_IMPLEMENTATION[self.origin_attn_implement] sp_mode = self.shard_config.sequence_parallelism_mode or None sp_size = self.shard_config.sequence_parallel_size or None sp_group = self.shard_config.sequence_parallel_process_group or None sp_partial_derived = sp_mode in ["split_gather", "ring"] if sp_mode == "all_to_all": decoder_attribute_replacement = { "num_heads": self.model.config.num_attention_heads // sp_size, } if getattr(self.model.config, "num_key_value_heads", False): decoder_attribute_replacement["num_key_value_heads"] = self.model.config.num_key_value_heads // sp_size policy[attn_cls] = ModulePolicyDescription( attribute_replacement=decoder_attribute_replacement, ) if self.shard_config.enable_sequence_parallelism: if self.pipeline_stage_manager is not None: # NOTE: we are replacing model forward for both sequence parallelism and pipeline parallelism # if both are enabled, one of them will be ignored raise NotImplementedError("Sequence parallelism is not supported with pipeline parallelism.") self.append_or_create_method_replacement( description={ "forward": get_deepseek_flash_attention_forward(self.shard_config, sp_mode, sp_size, sp_group), }, policy=policy, target_key=attn_cls, ) if self.pipeline_stage_manager is None: self.append_or_create_method_replacement( description={ "forward": get_deepseek_flash_attention_model_forward( self.shard_config, sp_mode=sp_mode, sp_size=sp_size, sp_group=sp_group, ), }, policy=policy, target_key="DeepseekModel", ) embedding_cls = None if self.shard_config.enable_tensor_parallelism: embedding_cls = VocabParallelEmbedding1D else: if self.tie_weight: embedding_cls = PaddingEmbedding 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["DeepseekDecoderLayer"] = 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, ), ], ) if embedding_cls is not None: self.append_or_create_submodule_replacement( description=SubModuleReplacementDescription( suffix="embed_tokens", target_module=embedding_cls, kwargs={"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by}, ), policy=policy, target_key="DeepseekModel", ) if self.shard_config.ep_group: # expert parallel self.append_or_create_submodule_replacement( description=[ SubModuleReplacementDescription( suffix="mlp", target_module=EPDeepseekMoE, kwargs={ "ep_group": self.shard_config.ep_group, "tp_group": self.shard_config.tensor_parallel_process_group, "moe_dp_group": self.shard_config.moe_dp_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, kwargs={"sp_partial_derived": sp_partial_derived}, ), SubModuleReplacementDescription( suffix="post_attention_layernorm", target_module=FusedRMSNorm, kwargs={"sp_partial_derived": sp_partial_derived}, ), ], policy=policy, target_key="DeepseekDecoderLayer", ) self.append_or_create_submodule_replacement( description=SubModuleReplacementDescription( suffix="norm", target_module=FusedRMSNorm, kwargs={"sp_partial_derived": sp_partial_derived}, ), policy=policy, target_key="DeepseekModel", ) if self.shard_config.enable_flash_attention: # NOTE: there is a bug for toggling flash attention in AutoModel, which has to be used for deepseek right now from transformers.dynamic_module_utils import get_class_from_dynamic_module flash_attn_cls = get_class_from_dynamic_module( "deepseek-ai/deepseek-moe-16b-base--modeling_deepseek.DeepseekFlashAttention2", "deepseek-ai/deepseek-moe-16b-base", ) class TargetFlashAttn: def __init__(self): raise RuntimeError("This class should not be instantiated") @staticmethod def from_native_module(original_attn: nn.Module, *args, **kwargs) -> nn.Module: original_attn.__class__ = flash_attn_cls original_attn._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() return original_attn self.append_or_create_submodule_replacement( description=SubModuleReplacementDescription( suffix="self_attn", target_module=TargetFlashAttn, ), policy=policy, target_key="DeepseekDecoderLayer", ) 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: if self.shard_config.enable_sequence_parallelism: # NOTE: we are replacing model forward for both sequence parallelism and pipeline parallelism # if both are enabled, one of them will be ignored raise NotImplementedError("Pipeline parallelism is not supported with sequence parallelism.") 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 []