import warnings from functools import partial from typing import Callable, Dict, List from torch import Tensor, nn import colossalai.shardformer.layer as col_nn from ..modeling.gpt2 import ( GPT2PipelineForwards, get_gpt2_flash_attention_forward, get_gpt_model_forward_for_flash_attn, get_jit_fused_gpt2_mlp_forward, get_lm_forward_with_dist_cross_entropy, gpt2_sequence_parallel_forward_fn, ) from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = [ "GPT2Policy", "GPT2ModelPolicy", "GPT2LMHeadModelPolicy", "GPT2DoubleHeadsModelPolicy", "GPT2ForTokenClassificationPolicy", "GPT2ForSequenceClassificationPolicy", ] class GPT2Policy(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 """ self.tie_weight = self.tie_weight_check() self.origin_attn_implement = self.model.config._attn_implementation self.enable_bias_gelu_fused = ( self.shard_config.enable_jit_fused and self.model.config.activation_function == "gelu" ) return self.model def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2MLP, GPT2Attention, GPT2Block, GPT2Model ATTN_IMPLEMENTATION = { "eager": GPT2Attention, } policy = {} attn_cls = ATTN_IMPLEMENTATION[self.origin_attn_implement] embedding_cls = None if self.shard_config.enable_tensor_parallelism: embedding_cls = col_nn.VocabParallelEmbedding1D else: if self.tie_weight: embedding_cls = col_nn.PaddingEmbedding if self.shard_config.enable_fused_normalization: norm_cls = col_nn.FusedLayerNorm else: norm_cls = col_nn.LayerNorm sp_mode = self.shard_config.sequence_parallelism_mode or None assert sp_mode != "all_to_all", "all_to_all sequence parallelism is not supported for GPT2" if sp_mode == "ring": warnings.warn( f"For GPT2, sequence parallelism is currently not support mode {sp_mode}, will set to be split_gather" ) sp_mode = "split_gather" overlap = self.shard_config.enable_sequence_overlap sp_partial_derived = sp_mode in ["split_gather", "ring"] use_flash_attention = self.shard_config.enable_flash_attention # todo: currently sp cannot be used with flashattention if sp_mode in ["split_gather", "ring", "all_to_all"]: if use_flash_attention: warnings.warn( f"Sequence parallelism mode {sp_mode} cannot be used with FlashAttention, will disable FlashAttention automatically." ) self.shard_config.enable_flash_attention = False use_flash_attention = False 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." policy[GPT2Model] = ModulePolicyDescription( sub_module_replacement=[ SubModuleReplacementDescription( suffix="drop", target_module=col_nn.DropoutForParallelInput, ), ] ) policy[GPT2Block] = ModulePolicyDescription( attribute_replacement={ "attn.embed_dim": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, "attn.split_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, "attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, }, sub_module_replacement=[ SubModuleReplacementDescription( suffix="attn.c_attn", target_module=col_nn.GPT2FusedLinearConv1D_Col, kwargs={ "n_fused": 3, "seq_parallel_mode": sp_mode, "overlap": overlap, }, ), SubModuleReplacementDescription( suffix="attn.c_proj", target_module=col_nn.GPT2FusedLinearConv1D_Row, kwargs={ "seq_parallel_mode": sp_mode, }, ), SubModuleReplacementDescription( suffix="mlp.c_fc", target_module=col_nn.GPT2FusedLinearConv1D_Col, kwargs={ "n_fused": 1, "seq_parallel_mode": sp_mode, "overlap": overlap, "skip_bias_add": self.enable_bias_gelu_fused, }, ), SubModuleReplacementDescription( suffix="mlp.c_proj", target_module=col_nn.GPT2FusedLinearConv1D_Row, kwargs={ "seq_parallel_mode": sp_mode, }, ), SubModuleReplacementDescription( suffix="attn.attn_dropout", target_module=col_nn.DropoutForParallelInput, ), SubModuleReplacementDescription( suffix="attn.resid_dropout", target_module=col_nn.DropoutForParallelInput, ), SubModuleReplacementDescription( suffix="mlp.dropout", target_module=col_nn.DropoutForParallelInput, ), ], ) if self.enable_bias_gelu_fused: self.append_or_create_method_replacement( description={ "forward": get_jit_fused_gpt2_mlp_forward(), }, policy=policy, target_key=GPT2MLP, ) if embedding_cls is not None: # padding vocabulary size when using pp to make it divisible by shard_config.make_vocab_size_divisible_by self.append_or_create_submodule_replacement( description=SubModuleReplacementDescription( suffix="wte", target_module=embedding_cls, kwargs={"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by}, ), policy=policy, target_key=GPT2Model, ) # optimization configuration self.append_or_create_submodule_replacement( description=SubModuleReplacementDescription( suffix="ln_f", target_module=norm_cls, ), policy=policy, target_key=GPT2Model, ) self.append_or_create_submodule_replacement( description=[ SubModuleReplacementDescription( suffix="ln_1", target_module=norm_cls, kwargs={"sp_partial_derived": sp_partial_derived}, ), SubModuleReplacementDescription( suffix="ln_2", target_module=norm_cls, kwargs={"sp_partial_derived": sp_partial_derived}, ), SubModuleReplacementDescription( suffix="ln_cross_attn", target_module=norm_cls, ignore_if_not_exist=True, kwargs={"sp_partial_derived": sp_partial_derived}, ), ], policy=policy, target_key=GPT2Block, ) if use_flash_attention: self.append_or_create_method_replacement( description={ "forward": get_gpt2_flash_attention_forward(), }, policy=policy, target_key=attn_cls, ) if not self.shard_config.pipeline_stage_manager: policy[GPT2Model].method_replacement = { "forward": get_gpt_model_forward_for_flash_attn(self.shard_config) } if sp_mode is not None: policy[GPT2Model].method_replacement = {"forward": gpt2_sequence_parallel_forward_fn(self.shard_config)} return policy def postprocess(self): return self.model def get_held_layers(self) -> List[nn.Module]: """Get pipeline layers for current stage.""" assert self.pipeline_stage_manager is not None if self.model.__class__.__name__ == "GPT2Model": module = self.model else: module = self.model.transformer 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.h)) stage_indices = stage_manager.get_stage_index(layers_per_stage) if stage_manager.is_first_stage(ignore_chunk=True): held_layers.append(module.wte) held_layers.append(module.wpe) held_layers.append(module.drop) for start_idx, end_idx in stage_indices: held_layers.extend(module.h[start_idx:end_idx]) if stage_manager.is_last_stage(ignore_chunk=True): held_layers.append(module.ln_f) else: layers_per_stage = stage_manager.distribute_layers(len(module.h)) if stage_manager.is_first_stage(): held_layers.append(module.wte) held_layers.append(module.wpe) held_layers.append(module.drop) start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage) held_layers.extend(module.h[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.ln_f) 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 if self.model.__class__.__name__ == "GPT2Model": module = self.model else: module = self.model.transformer if stage_manager.is_interleave: layers_per_stage = stage_manager.distribute_layers(len(module.h)) 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.h)) 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) # GPT2Model class GPT2ModelPolicy(GPT2Policy): def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2Model policy = super().module_policy() if self.pipeline_stage_manager is not None: self.set_pipeline_forward( model_cls=GPT2Model, new_forward=GPT2PipelineForwards.gpt2_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]]: """No shared params in GPT2Model.""" return [] # GPT2LMHeadModel class GPT2LMHeadModelPolicy(GPT2Policy): def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel module_policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: addon_module = { GPT2LMHeadModel: ModulePolicyDescription( sub_module_replacement=[ SubModuleReplacementDescription( suffix="lm_head", target_module=col_nn.VocabParallelLMHead1D, kwargs={ "gather_output": False, "make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by, }, ) ], ) } if self.shard_config.parallel_output: addon_module[GPT2LMHeadModel].method_replacement = { "forward": get_lm_forward_with_dist_cross_entropy(self.shard_config) } else: addon_module = { GPT2LMHeadModel: ModulePolicyDescription( sub_module_replacement=[ SubModuleReplacementDescription( suffix="lm_head", target_module=col_nn.PaddingLMHead, kwargs={"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by}, ) ] ) } module_policy.update(addon_module) if self.pipeline_stage_manager is not None: self.set_pipeline_forward( model_cls=GPT2LMHeadModel, new_forward=GPT2PipelineForwards.gpt2_lmhead_model_forward, policy=module_policy, ) return module_policy def get_held_layers(self) -> List[nn.Module]: held_layers = super().get_held_layers() if self.pipeline_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]]: """The weights of wte and lm_head are shared.""" module = self.model stage_manager = self.pipeline_stage_manager if stage_manager is not None: if stage_manager.num_stages > 1 and id(module.transformer.wte.weight) == id(module.lm_head.weight): first_stage, last_stage = 0, stage_manager.num_stages - 1 return [ { first_stage: module.transformer.wte.weight, last_stage: module.lm_head.weight, } ] return [] # GPT2DoubleHeadsModel class GPT2DoubleHeadsModelPolicy(GPT2Policy): def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2DoubleHeadsModel module_policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: addon_module = { GPT2DoubleHeadsModel: ModulePolicyDescription( sub_module_replacement=[ SubModuleReplacementDescription( suffix="lm_head", target_module=col_nn.VocabParallelLMHead1D, kwargs={ "gather_output": True, "make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by, }, ) ] ) } else: addon_module = { GPT2DoubleHeadsModel: ModulePolicyDescription( sub_module_replacement=[ SubModuleReplacementDescription( suffix="lm_head", target_module=col_nn.PaddingLMHead, kwargs={"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by}, ) ] ) } module_policy.update(addon_module) if self.pipeline_stage_manager is not None: self.set_pipeline_forward( model_cls=GPT2DoubleHeadsModel, new_forward=GPT2PipelineForwards.gpt2_double_heads_model_forward, policy=module_policy, ) return module_policy def get_held_layers(self) -> List[nn.Module]: held_layers = super().get_held_layers() if self.pipeline_stage_manager.is_last_stage(): multiple_choice_head = self.model.multiple_choice_head held_layers.append(self.model.lm_head) held_layers.append(multiple_choice_head.summary) held_layers.append(multiple_choice_head.activation) held_layers.append(multiple_choice_head.first_dropout) held_layers.append(multiple_choice_head.last_dropout) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: """The weights of wte and lm_head are shared.""" module = self.model stage_manager = self.pipeline_stage_manager if stage_manager is not None: if stage_manager.num_stages > 1 and id(module.transformer.wte.weight) == id(module.lm_head.weight): first_stage, last_stage = 0, stage_manager.num_stages - 1 return [ { first_stage: module.transformer.wte.weight, last_stage: module.lm_head.weight, } ] return [] # GPT2ForQuestionAnswering class GPT2ForQuestionAnsweringPolicy(GPT2Policy): def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2ForQuestionAnswering module_policy = super().module_policy() if self.pipeline_stage_manager is not None: self.set_pipeline_forward( model_cls=GPT2ForQuestionAnswering, new_forward=GPT2PipelineForwards.gpt2_for_question_answering_forward, policy=module_policy, ) return module_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.qa_outputs) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: """No shared_params in gpt2 for QA.""" return [] # GPT2ForTokenClassification class GPT2ForTokenClassificationPolicy(GPT2Policy): def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2ForTokenClassification module_policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: addon_module = { GPT2ForTokenClassification: ModulePolicyDescription( sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", target_module=col_nn.DropoutForParallelInput, ) ] ) } module_policy.update(addon_module) if self.pipeline_stage_manager is not None: self.set_pipeline_forward( model_cls=GPT2ForTokenClassification, new_forward=GPT2PipelineForwards.gpt2_for_token_classification_forward, policy=module_policy, ) return module_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.dropout) held_layers.append(self.model.classifier) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: """No shared params in GPT2ForTokenClassification.""" return [] # GPT2ForSequenceClassification class GPT2ForSequenceClassificationPolicy(GPT2Policy): def module_policy(self): from transformers.models.gpt2.modeling_gpt2 import GPT2ForSequenceClassification module_policy = super().module_policy() if self.pipeline_stage_manager is not None: self.set_pipeline_forward( model_cls=GPT2ForSequenceClassification, new_forward=GPT2PipelineForwards.gpt2_for_sequence_classification_forward, policy=module_policy, ) return module_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.score) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: """No shared params in GPT2ForTokenClassification.""" return []