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 ( FusedLayerNorm, LayerNorm, Linear1D_Col, Linear1D_Row, PaddingEmbedding, PaddingLMHead, VocabParallelEmbedding1D, VocabParallelLMHead1D, ) from ..modeling.command import ( CommandPipelineForwards, get_command_flash_attention_forward, get_command_flash_attention_model_forward, get_lm_forward_with_dist_cross_entropy, ) from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription __all__ = ["CommandPolicy", "CommandForCausalLMPolicy"] class CommandPolicy(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 return self.model def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]: from transformers.models.cohere.modeling_cohere import ( CohereAttention, CohereDecoderLayer, CohereFlashAttention2, CohereModel, CohereSdpaAttention, ) ATTN_IMPLEMENTATION = { "eager": CohereAttention, "flash_attention_2": CohereFlashAttention2, "sdpa": CohereSdpaAttention, } 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 if self.shard_config.enable_fused_normalization: norm_cls = FusedLayerNorm else: norm_cls = LayerNorm 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 == "ring_attn" and not self.is_causal: raise ValueError("Ring attention is only meant for causal language modeling.") tp_size = self.shard_config.tensor_parallel_size or None num_q_heads = self.model.config.num_attention_heads num_kv_heads = getattr(self.model.config, "num_key_value_heads", None) if sp_mode == "all_to_all": num_q_heads //= sp_size decoder_attribute_replacement = {"num_heads": num_q_heads} if num_kv_heads: num_kv_heads //= sp_size decoder_attribute_replacement["num_key_value_heads"] = num_kv_heads policy[attn_cls] = ModulePolicyDescription( attribute_replacement=decoder_attribute_replacement, ) if self.shard_config.enable_flash_attention or self.shard_config.enable_sequence_parallelism: self.append_or_create_method_replacement( description={ "forward": get_command_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_command_flash_attention_model_forward( self.shard_config, sp_mode=sp_mode, sp_size=sp_size, sp_group=sp_group, ), }, policy=policy, target_key=CohereModel, ) if self.shard_config.enable_tensor_parallelism: assert ( num_q_heads % tp_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 ( num_kv_heads >= tp_size and num_kv_heads % tp_size == 0 ), f"The number of key_value heads must be divisible by, and must not be less than tensor parallel size." decoder_attribute_replacement = { "self_attn.hidden_size": self.model.config.hidden_size // tp_size, "self_attn.num_heads": num_q_heads // tp_size, } if getattr(self.model.config, "num_key_value_heads", False): decoder_attribute_replacement["self_attn.num_key_value_heads"] = num_kv_heads // tp_size policy[CohereDecoderLayer] = 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), ), SubModuleReplacementDescription( suffix="self_attn.k_proj", target_module=Linear1D_Col, kwargs=dict(seq_parallel_mode=sp_mode), ), SubModuleReplacementDescription( suffix="self_attn.v_proj", target_module=Linear1D_Col, kwargs=dict(seq_parallel_mode=sp_mode), ), SubModuleReplacementDescription( suffix="self_attn.o_proj", target_module=Linear1D_Row, kwargs=dict(seq_parallel_mode=sp_mode), ), SubModuleReplacementDescription( suffix="mlp.gate_proj", target_module=Linear1D_Col, kwargs=dict(seq_parallel_mode=sp_mode), ), SubModuleReplacementDescription( suffix="mlp.up_proj", target_module=Linear1D_Col, kwargs=dict(seq_parallel_mode=sp_mode), ), SubModuleReplacementDescription( suffix="mlp.down_proj", target_module=Linear1D_Row, kwargs=dict(seq_parallel_mode=sp_mode), ), ], ) 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=CohereModel, ) # optimization configuration self.append_or_create_submodule_replacement( description=[ SubModuleReplacementDescription( suffix="input_layernorm", target_module=norm_cls, kwargs={"sp_partial_derived": sp_partial_derived}, ), ], policy=policy, target_key=CohereDecoderLayer, ) 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=CohereModel, ) 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__ == "CohereModel": 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) 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__ == "CohereModel": 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 CommandModelPolicy(CommandPolicy): def module_policy(self): policy = super().module_policy() from transformers.models.cohere.modeling_cohere import CohereModel if self.pipeline_stage_manager: # set None as default self.set_pipeline_forward( model_cls=CohereModel, new_forward=CommandPipelineForwards.command_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 command model""" return [] class CommandForCausalLMPolicy(CommandPolicy): def module_policy(self): from transformers import CohereForCausalLM self.is_causal = True policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: # add a new item for causal lm new_item = { CohereForCausalLM: ModulePolicyDescription( sub_module_replacement=[ SubModuleReplacementDescription( suffix="lm_head", target_module=VocabParallelLMHead1D, kwargs={ "gather_output": not self.shard_config.parallel_output, "make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by, }, ) ], ) } if self.shard_config.parallel_output: new_item[CohereForCausalLM].method_replacement = { "forward": get_lm_forward_with_dist_cross_entropy(self.shard_config) } else: new_item = { CohereForCausalLM: ModulePolicyDescription( sub_module_replacement=[ SubModuleReplacementDescription( suffix="lm_head", target_module=PaddingLMHead, kwargs={"make_vocab_size_divisible_by": self.shard_config.make_vocab_size_divisible_by}, ) ], ) } policy.update(new_item) if self.pipeline_stage_manager: # set None as default self.set_pipeline_forward( model_cls=CohereForCausalLM, new_forward=CommandPipelineForwards.command_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]]: command_model = self.model.model if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1: if ( id(command_model.embed_tokens.weight) == id(self.model.lm_head.weight) and self.pipeline_stage_manager.num_stages > 1 ): # tie weights return [ { 0: command_model.embed_tokens.weight, self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight, } ] return []