from functools import partial from typing import Callable, Dict, List, Optional, Tuple from torch import Tensor, nn from colossalai.shardformer.layer import ( DropoutForParallelInput, Embedding1D, FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D, ) from colossalai.shardformer.policies.base_policy import ModulePolicyDescription from .._utils import getattr_, setattr_ from ..modeling.jit import get_jit_fused_dropout_add_func from ..modeling.t5 import ( T5PipelineForwards, get_jit_fused_T5_layer_ff_forward, get_t5_flash_attention_forward, get_T5_layer_cross_attention_forward, get_T5_layer_self_attention_forward, ) from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ["distribute_t5_layers", "T5ModelPolicy", "T5ForConditionalGenerationPolicy", "T5EncoderPolicy"] class T5BasePolicy(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.t5.modeling_t5 import ( T5Attention, T5DenseActDense, T5DenseGatedActDense, T5LayerCrossAttention, T5LayerFF, T5LayerSelfAttention, T5Stack, ) policy = {} if self.shard_config.enable_tensor_parallelism: policy[T5Stack] = ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", target_module=DropoutForParallelInput, ), SubModuleReplacementDescription( suffix="embed_tokens", target_module=VocabParallelEmbedding1D, ) ]) policy[T5LayerSelfAttention] = ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", target_module=DropoutForParallelInput, ), ]) policy[T5LayerCrossAttention] = ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", target_module=DropoutForParallelInput, ) ]) policy[T5Attention] = ModulePolicyDescription(attribute_replacement={ "d_model": self.model.config.d_model // self.shard_config.tensor_parallel_size, "n_heads": self.model.config.num_heads // self.shard_config.tensor_parallel_size, "inner_dim": self.model.config.num_heads * self.model.config.d_kv // self.shard_config.tensor_parallel_size }, sub_module_replacement=[ SubModuleReplacementDescription( suffix="q", target_module=Linear1D_Col, ), SubModuleReplacementDescription( suffix="k", target_module=Linear1D_Col, ), SubModuleReplacementDescription( suffix="v", target_module=Linear1D_Col, ), SubModuleReplacementDescription( suffix="o", target_module=Linear1D_Row, ), SubModuleReplacementDescription( suffix="relative_attention_bias", target_module=Embedding1D, kwargs=dict(gather_output=False), ignore_if_not_exist=True) ]) policy[T5LayerFF] = ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", target_module=DropoutForParallelInput, ), ]) policy[T5DenseGatedActDense] = ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( suffix="wi_0 ", target_module=Linear1D_Col, ), SubModuleReplacementDescription( suffix="wi_1", target_module=Linear1D_Row, ), SubModuleReplacementDescription( suffix="wo", target_module=Linear1D_Col, kwargs=dict(gather_output=True)), SubModuleReplacementDescription( suffix="dropout", target_module=DropoutForParallelInput, ) ]) policy[T5DenseActDense] = ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( suffix="wi", target_module=Linear1D_Col, ), SubModuleReplacementDescription( suffix="wo", target_module=Linear1D_Row, ), SubModuleReplacementDescription( suffix="dropout", target_module=DropoutForParallelInput, ) ]) # optimization configuration if self.shard_config.enable_fused_normalization: self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="layer_norm", target_module=FusedRMSNorm, ), policy=policy, target_key=T5LayerFF) self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="layer_norm", target_module=FusedRMSNorm, ), policy=policy, target_key=T5LayerFF) self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="layer_norm", target_module=FusedRMSNorm), policy=policy, target_key=T5LayerSelfAttention) self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="layer_norm", target_module=FusedRMSNorm), policy=policy, target_key=T5LayerCrossAttention) self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="final_layer_norm", target_module=FusedRMSNorm), policy=policy, target_key=T5Stack) # use flash attention if self.shard_config.enable_flash_attention: policy[T5Attention] = ModulePolicyDescription(method_replacement={ 'forward': get_t5_flash_attention_forward(), }) # use jit operator if self.shard_config.enable_jit_fused: policy[T5LayerFF] = ModulePolicyDescription(method_replacement={ 'forward': get_jit_fused_T5_layer_ff_forward(), 'dropout_add': get_jit_fused_dropout_add_func(), }) policy[T5LayerSelfAttention] = ModulePolicyDescription(method_replacement={ 'forward': get_T5_layer_self_attention_forward(), 'dropout_add': get_jit_fused_dropout_add_func(), }) policy[T5LayerCrossAttention] = ModulePolicyDescription(method_replacement={ 'forward': get_T5_layer_cross_attention_forward(), 'dropout_add': get_jit_fused_dropout_add_func(), }) return policy def postprocess(self): return self.model @staticmethod def distribute_t5_layers(num_encoder_layers: int, num_decoder_layers: int, num_stages: int) -> Tuple[List[int], int]: """ Distribute t5 layers into stages when pipeline parallel is used. Return the layer distribution as a list and the starting stage of decoder. If decoder doesn't exist, returned decoder starting stage is set to num_encoder_layers. """ # number of encoder layers must be a positive integer if num_encoder_layers <= 0: raise ValueError("The number of encoder layers for T5 must be a positive integer.") # number of layers should be large enough to fill in every stage if num_encoder_layers + num_decoder_layers < num_stages: raise ValueError("The total number of layers can't be smaller than number of stages.") # in the case of T5EncoderModel, set decoder starting stage to num_stages since it doesn't exist if num_decoder_layers == 0: return Policy.distribute_layers(num_encoder_layers, num_stages), num_stages # the number of stages distributed between encoder and decoder is optmized in this way: # num_encoder_stages = argmin(abs(num_encoder_layers / encoder_stages - num_decoder_layers / decoder_stages)) # s.t. num_encoder_stages + num_decoder_stages = num_stages, num_encoder_stages >= 1, num_decoder_stages >= 1 def objective(num_encoder_stages): return abs(num_encoder_layers / num_encoder_stages - num_decoder_layers / (num_stages - num_encoder_stages)) num_encoder_stages = 0 optimal_diff = 2**31 - 1 for i in range(1, num_stages): attempt = objective(i) if attempt < optimal_diff: num_encoder_stages = i optimal_diff = attempt num_decoder_stages = num_stages - num_encoder_stages encoder_distribution = Policy.distribute_layers(num_encoder_layers, num_encoder_stages) decoder_distribution = Policy.distribute_layers(num_decoder_layers, num_decoder_stages) return encoder_distribution + decoder_distribution, num_encoder_stages @staticmethod def get_t5_stage_index(layers_per_stage: List[int], stage: int, decoder_starting_stage: int) -> Tuple[bool, int, int]: """ Input the distribution of layers among stages, the current stage and the first stage of decoder. Return the starting/ending idx of layers in encoder/decoder """ if stage < decoder_starting_stage: return Policy.get_stage_index(layers_per_stage[:decoder_starting_stage], stage) else: return Policy.get_stage_index(layers_per_stage[decoder_starting_stage:], stage - decoder_starting_stage) def get_held_layers(self) -> List[nn.Module]: """Get pipeline layers for current stage.""" assert self.pipeline_stage_manager is not None stage_manager = self.pipeline_stage_manager model = self.model encoder = self.model.encoder decoder = getattr(self.model, 'decoder', None) num_encoder_layers = len(encoder.block) num_decoder_layers = len(decoder.block) if decoder else 0 held_layers = [] layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers( num_encoder_layers, num_decoder_layers, stage_manager.num_stages) start_idx, end_idx = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage) if stage_manager.stage < decoder_starting_stage: # current stage is in t5's encoder if stage_manager.is_first_stage(): held_layers.append(model.shared) held_layers.append(encoder.embed_tokens) held_layers.append(encoder.dropout) if stage_manager.stage == decoder_starting_stage - 1: held_layers.append(encoder.final_layer_norm) held_layers.append(encoder.dropout) held_layers.extend(encoder.block[start_idx:end_idx]) else: # current stage is in t5's decoder if stage_manager.stage == decoder_starting_stage: held_layers.append(decoder.embed_tokens) held_layers.append(decoder.dropout) if stage_manager.is_last_stage(): held_layers.append(decoder.final_layer_norm) held_layers.append(decoder.dropout) held_layers.extend(decoder.block[start_idx:end_idx]) 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 not self.pipeline_stage_manager: raise ValueError("set_pipeline_forward method can only be called when pipeline parallel is enabled.") stage_manager = self.pipeline_stage_manager encoder = self.model.encoder decoder = getattr(self.model, 'decoder', None) num_encoder_layers = len(encoder.block) num_decoder_layers = len(decoder.block) if decoder else 0 layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers( num_encoder_layers, num_decoder_layers, stage_manager.num_stages) stage_index = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage) method_replacement = { 'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index, decoder_starting_stage=decoder_starting_stage) } self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls) class T5ModelPolicy(T5BasePolicy): def __init__(self) -> None: super().__init__() def module_policy(self): from transformers import T5Model policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="shared", target_module=VocabParallelEmbedding1D, ), policy=policy, target_key=T5Model) if self.pipeline_stage_manager is not None: self.set_pipeline_forward(model_cls=T5Model, new_forward=T5PipelineForwards.t5_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]]: module = self.model stage_manager = self.pipeline_stage_manager if stage_manager is not None and stage_manager.num_stages > 1: _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(len(module.encoder.block), len(module.decoder.block), stage_manager.num_stages) if id(module.decoder.embed_tokens.weight) == id(module.shared.weight): return [{0: module.shared.weight, decoder_starting_stage: module.decoder.embed_tokens.weight}] return [] class T5ForConditionalGenerationPolicy(T5BasePolicy): def __init__(self) -> None: super().__init__() def module_policy(self): from transformers import T5ForConditionalGeneration policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: self.append_or_create_submodule_replacement(description=[ SubModuleReplacementDescription( suffix="shared", target_module=VocabParallelEmbedding1D, ), SubModuleReplacementDescription(suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True)) ], policy=policy, target_key=T5ForConditionalGeneration) if self.pipeline_stage_manager is not None: self.set_pipeline_forward(model_cls=T5ForConditionalGeneration, new_forward=T5PipelineForwards.t5_for_conditional_generation_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]]: module = self.model stage_manager = self.pipeline_stage_manager if stage_manager is not None and stage_manager.num_stages > 1: _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(len(module.encoder.block), len(module.decoder.block), stage_manager.num_stages) shared_params = [] shared_embedding = {} if id(module.decoder.embed_tokens.weight) == id(module.shared.weight): shared_embedding[0] = module.shared.weight shared_embedding[decoder_starting_stage] = module.decoder.embed_tokens.weight if id(module.lm_head.weight) == id(module.shared.weight): shared_embedding[0] = module.shared.weight shared_embedding[stage_manager.num_stages - 1] = module.lm_head.weight if len(shared_embedding) > 0: shared_params.append(shared_embedding) return shared_params return [] class T5EncoderPolicy(T5BasePolicy): def __init__(self) -> None: super().__init__() def module_policy(self): from transformers import T5EncoderModel policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="shared", target_module=VocabParallelEmbedding1D, ), policy=policy, target_key=T5EncoderModel) if self.pipeline_stage_manager is not None: self.set_pipeline_forward(model_cls=T5EncoderModel, new_forward=T5PipelineForwards.t5_encoder_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]]: return []