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, Linear1D_Row, PaddingEmbedding, RMSNorm, VocabParallelEmbedding1D, ) from ..modeling.qwen2 import ( Qwen2PipelineForwards, get_lm_forward_with_dist_cross_entropy, get_qwen2_flash_attention_forward, get_qwen2_model_forward_for_flash_attn, ) try: from transformers.models.qwen2.modeling_qwen2 import ( Qwen2Attention, Qwen2DecoderLayer, Qwen2FlashAttention2, Qwen2ForCausalLM, Qwen2ForSequenceClassification, Qwen2Model, Qwen2SdpaAttention, ) except ImportError: Qwen2ForCausalLM = "Qwen2ForCausalLM" Qwen2ForSequenceClassification = "Qwen2ForSequenceClassification" Qwen2Attention = "Qwen2Attention" Qwen2FlashAttention2 = "Qwen2FlashAttention2" Qwen2SdpaAttention = "Qwen2SdpaAttention" Qwen2DecoderLayer = "Qwen2DecoderLayer" Qwen2Model = "Qwen2Model" from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ["Qwen2Policy", "Qwen2ForCausalLMPolicy", "Qwen2ForSequenceClassificationPolicy"] class Qwen2Policy(Policy): def __init__(self) -> None: super().__init__() import transformers from packaging.version import Version assert Version(transformers.__version__) >= Version( "4.39.1" ), "The Qwen2 model should run on a transformers version of 4.39.1." 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 return self.model def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: ATTN_IMPLEMENTATION = { "eager": Qwen2Attention, "flash_attention_2": Qwen2FlashAttention2, "sdpa": Qwen2SdpaAttention, } policy = {} attn_cls = ATTN_IMPLEMENTATION[self.origin_attn_implement] embedding_cls = None if self.shard_config.enable_tensor_parallelism: embedding_cls = VocabParallelEmbedding1D else: if self.tie_weight: embedding_cls = PaddingEmbedding norm_cls = FusedRMSNorm if self.shard_config.enable_fused_normalization else RMSNorm 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_tensor_parallelism: 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." if hasattr(self.model.config, "num_key_value_heads"): 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, } if getattr(self.model.config, "num_key_value_heads", False): decoder_attribute_replacement["self_attn.num_key_value_heads"] = ( self.model.config.num_key_value_heads // self.shard_config.tensor_parallel_size ) policy[Qwen2DecoderLayer] = ModulePolicyDescription( attribute_replacement=decoder_attribute_replacement, sub_module_replacement=[ SubModuleReplacementDescription( suffix="self_attn.q_proj", target_module=Linear1D_Col, kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication), ), SubModuleReplacementDescription( suffix="self_attn.k_proj", target_module=Linear1D_Col, kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication), ), SubModuleReplacementDescription( suffix="self_attn.v_proj", target_module=Linear1D_Col, kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication), ), SubModuleReplacementDescription( suffix="self_attn.o_proj", target_module=Linear1D_Row, kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication), ), SubModuleReplacementDescription( suffix="mlp.gate_proj", target_module=Linear1D_Col, kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication), ), SubModuleReplacementDescription( suffix="mlp.up_proj", target_module=Linear1D_Col, kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication), ), SubModuleReplacementDescription( suffix="mlp.down_proj", target_module=Linear1D_Row, kwargs=dict(seq_parallel_mode=sp_mode, fp8_communication=self.shard_config.fp8_communication), ), ], ) 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, "fp8_communication": self.shard_config.fp8_communication, } if self.shard_config.enable_tensor_parallelism else {"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by} ), ), policy=policy, target_key=Qwen2Model, ) # optimization configuration self.append_or_create_submodule_replacement( description=[ SubModuleReplacementDescription( suffix="input_layernorm", target_module=norm_cls, kwargs={"sp_partial_derived": sp_partial_derived}, ), SubModuleReplacementDescription( suffix="post_attention_layernorm", target_module=norm_cls, kwargs={"sp_partial_derived": sp_partial_derived}, ), ], policy=policy, target_key=Qwen2DecoderLayer, ) self.append_or_create_submodule_replacement( description=SubModuleReplacementDescription( suffix="norm", target_module=norm_cls, kwargs={"sp_partial_derived": sp_partial_derived}, ), policy=policy, target_key=Qwen2Model, ) if self.shard_config.enable_flash_attention or self.shard_config.enable_sequence_parallelism: self.append_or_create_method_replacement( description={ "forward": get_qwen2_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: # replace qwen2 model forward method self.append_or_create_method_replacement( description={ "forward": get_qwen2_model_forward_for_flash_attn( self.shard_config, sp_mode, sp_size, sp_group ), }, policy=policy, target_key=Qwen2Model, ) 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 is None: return stage_manager = self.pipeline_stage_manager if self.model.__class__.__name__ == "Qwen2Model": module = self.model else: module = self.model.model if stage_manager.is_interleave: layers_per_stage = stage_manager.distribute_layers(len(module.layers)) stage_manager.stage_indices = stage_manager.get_stage_index(layers_per_stage) method_replacement = { "forward": partial(new_forward, stage_manager=stage_manager, shard_config=self.shard_config) } else: 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, shard_config=self.shard_config ) } self.append_or_create_method_replacement( description=method_replacement, policy=policy, target_key=model_cls ) self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls) 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__ == "Qwen2Model": module = self.model else: module = self.model.model stage_manager = self.pipeline_stage_manager held_layers = [] if stage_manager.is_interleave: assert stage_manager.num_model_chunks is not None layers_per_stage = stage_manager.distribute_layers(len(module.layers)) stage_indices = stage_manager.get_stage_index(layers_per_stage) if stage_manager.is_first_stage(ignore_chunk=True): held_layers.append(module.embed_tokens) for start_idx, end_idx in stage_indices: held_layers.extend(module.layers[start_idx:end_idx]) if stage_manager.is_last_stage(ignore_chunk=True): held_layers.append(module.norm) else: 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 Qwen2ModelPolicy(Qwen2Policy): def module_policy(self): policy = super().module_policy() if self.pipeline_stage_manager: # set None as default self.set_pipeline_forward( model_cls=Qwen2Model, new_forward=Qwen2PipelineForwards.qwen2_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 Qwen2 model""" return [] class Qwen2ForCausalLMPolicy(Qwen2Policy): def module_policy(self): policy = super().module_policy() setattr(self.shard_config, "causal_lm", True) if self.shard_config.enable_tensor_parallelism: # add a new item for casual lm new_item = { Qwen2ForCausalLM: ModulePolicyDescription( sub_module_replacement=[ SubModuleReplacementDescription( suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(fp8_communication=self.shard_config.fp8_communication), ) ], method_replacement={"forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)}, ) } policy.update(new_item) if self.pipeline_stage_manager: # set None as default self.set_pipeline_forward( model_cls=Qwen2ForCausalLM, new_forward=Qwen2PipelineForwards.qwen2_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(ignore_chunk=True): held_layers.append(self.model.lm_head) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: qwen2_model = self.model.model if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1: if ( id(qwen2_model.embed_tokens.weight) == id(self.model.lm_head.weight) and self.pipeline_stage_manager.num_stages > 1 ): # tie weights return [ { 0: qwen2_model.embed_tokens.weight, self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight, } ] return [] class Qwen2ForSequenceClassificationPolicy(Qwen2Policy): def module_policy(self): policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: # add a new item for sequence classification new_item = { Qwen2ForSequenceClassification: ModulePolicyDescription( sub_module_replacement=[ SubModuleReplacementDescription( suffix="score", target_module=Linear1D_Col, kwargs=dict(gather_output=True, fp8_communication=self.shard_config.fp8_communication), ) ] ) } policy.update(new_item) # to be confirmed if self.pipeline_stage_manager: # set None as default self.set_pipeline_forward( model_cls=Qwen2ForSequenceClassification, new_forward=Qwen2PipelineForwards.qwen2_for_sequence_classification_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(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 Qwen2 for sequence classification model""" return []