mirror of https://github.com/hpcaitech/ColossalAI
309 lines
12 KiB
Python
309 lines
12 KiB
Python
import warnings
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from functools import partial
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from typing import Callable, Dict, List, Union
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import torch.nn as nn
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from torch import Tensor
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from torch.nn import Module
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from transformers.models.mixtral.modeling_mixtral import MixtralDecoderLayer, MixtralForCausalLM, MixtralModel
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from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col
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from colossalai.shardformer.modeling.mixtral import EPMixtralSparseMoeBlock, MixtralPipelineForwards
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from colossalai.shardformer.policies.base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = ["MixtralPolicy", "MixtralForCausalLMPolicy"]
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class MixtralPolicy(Policy):
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def config_sanity_check(self):
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pass
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def preprocess(self):
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if self.shard_config.enable_tensor_parallelism:
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raise NotImplementedError
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# # Resize embedding
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# vocab_size = self.model.config.vocab_size
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# world_size = self.shard_config.tensor_parallel_size
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# if vocab_size % world_size != 0:
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# new_vocab_size = vocab_size + world_size - vocab_size % world_size
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# self.model.resize_token_embeddings(new_vocab_size)
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return self.model
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def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
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policy = {}
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if self.shard_config.enable_sequence_parallelism:
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self.shard_config.enable_sequence_parallelism = False
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raise NotImplementedError(
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"Mixtral dosen't support sequence parallelism now, will ignore the sequence parallelism flag."
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)
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if self.shard_config.enable_tensor_parallelism:
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raise NotImplementedError
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# assert (
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# self.model.config.num_attention_heads % self.shard_config.tensor_parallel_size == 0
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# ), f"The number of attention heads must be divisible by tensor parallel size."
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# assert (
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# self.model.config.num_key_value_heads % self.shard_config.tensor_parallel_size == 0
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# ), f"The number of key_value heads must be divisible by tensor parallel size."
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# decoder_attribute_replacement = {
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# "self_attn.hidden_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
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# "self_attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
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# "self_attn.num_key_value_heads": self.model.config.num_key_value_heads
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# // self.shard_config.tensor_parallel_size,
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# }
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# policy[MixtralDecoderLayer] = ModulePolicyDescription(
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# attribute_replacement=decoder_attribute_replacement,
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# sub_module_replacement=[
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# SubModuleReplacementDescription(
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# suffix="self_attn.q_proj",
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# target_module=Linear1D_Col,
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# kwargs={
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# 'process_group': self.shard_config.tensor_parallel_process_group,
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# }
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# ),
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# SubModuleReplacementDescription(
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# suffix="self_attn.k_proj",
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# target_module=Linear1D_Col,
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# kwargs={
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# 'process_group': self.shard_config.tensor_parallel_process_group,
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# }
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# ),
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# SubModuleReplacementDescription(
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# suffix="self_attn.v_proj",
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# target_module=Linear1D_Col,
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# kwargs={
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# 'process_group': self.shard_config.tensor_parallel_process_group,
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# }
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# ),
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# SubModuleReplacementDescription(
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# suffix="self_attn.o_proj",
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# target_module=Linear1D_Row,
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# kwargs={
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# 'process_group': self.shard_config.tensor_parallel_process_group,
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# }
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# ),
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# # SubModuleReplacementDescription(
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# # suffix="mlp.gate_proj",
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# # target_module=Linear1D_Col,
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# # ),
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# # SubModuleReplacementDescription(
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# # suffix="mlp.up_proj",
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# # target_module=Linear1D_Col,
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# # ),
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# # SubModuleReplacementDescription(
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# # suffix="mlp.down_proj",
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# # target_module=Linear1D_Row,
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# # ),
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# ],
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# )
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if getattr(self.shard_config, "ep_group", None) is None:
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# expert parallel
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self.append_or_create_submodule_replacement(
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description=[
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SubModuleReplacementDescription(
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suffix="block_sparse_moe",
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target_module=EPMixtralSparseMoeBlock,
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kwargs={"ep_group": self.shard_config.ep_group},
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)
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],
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policy=policy,
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target_key=MixtralDecoderLayer,
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)
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# optimization configuration
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if self.shard_config.enable_fused_normalization:
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self.append_or_create_submodule_replacement(
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description=[
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SubModuleReplacementDescription(
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suffix="input_layernorm",
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target_module=FusedRMSNorm,
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),
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SubModuleReplacementDescription(
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suffix="post_attention_layernorm",
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target_module=FusedRMSNorm,
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),
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],
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policy=policy,
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target_key=MixtralDecoderLayer,
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)
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self.append_or_create_submodule_replacement(
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description=SubModuleReplacementDescription(
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suffix="norm",
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target_module=FusedRMSNorm,
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),
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policy=policy,
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target_key=MixtralModel,
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)
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if self.shard_config.enable_flash_attention:
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warnings.warn("Flash attention is natively supported in transformers, will ignore the flag.")
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self.shard_config.enable_flash_attention = False
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return policy
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def postprocess(self):
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return self.model
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def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
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"""If under pipeline parallel setting, replacing the original forward method of huggingface
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to customized forward method, and add this changing to policy."""
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if self.pipeline_stage_manager:
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stage_manager = self.pipeline_stage_manager
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if self.model.__class__.__name__ == "MixtralModel":
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module = self.model
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else:
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module = self.model.model
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layers_per_stage = stage_manager.distribute_layers(len(module.layers))
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stage_index = stage_manager.get_stage_index(layers_per_stage)
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method_replacement = {"forward": partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
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self.append_or_create_method_replacement(
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description=method_replacement, policy=policy, target_key=model_cls
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)
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return
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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assert self.pipeline_stage_manager is not None
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if self.model.__class__.__name__ == "MixtralModel":
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module = self.model
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else:
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module = self.model.model
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stage_manager = self.pipeline_stage_manager
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held_layers = []
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layers_per_stage = stage_manager.distribute_layers(len(module.layers))
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if stage_manager.is_first_stage():
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held_layers.append(module.embed_tokens)
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start_idx, end_idx = stage_manager.get_stage_index(layers_per_stage)
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held_layers.extend(module.layers[start_idx:end_idx])
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if stage_manager.is_last_stage():
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held_layers.append(module.norm)
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return held_layers
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class MixtralModelPolicy(MixtralPolicy):
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def __init__(self) -> None:
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super().__init__()
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def module_policy(self):
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policy = super().module_policy()
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if self.pipeline_stage_manager:
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# set None as default
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self.set_pipeline_forward(
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model_cls=MixtralModel,
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new_forward=MixtralPipelineForwards.mixtral_model_forward,
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policy=policy,
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)
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return policy
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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held_layers = super().get_held_layers()
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in mixtral model"""
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return []
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class MixtralForCausalLMPolicy(MixtralPolicy):
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def module_policy(self):
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policy = super().module_policy()
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# TODO: assign pg mesh from plugin to all modules
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if self.shard_config.enable_tensor_parallelism:
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# add a new item for casual lm
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new_item = {
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MixtralForCausalLM: ModulePolicyDescription(
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="lm_head",
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target_module=Linear1D_Col,
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kwargs=dict(gather_output=True),
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)
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]
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)
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}
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policy.update(new_item)
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if self.pipeline_stage_manager:
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# set None as default
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self.set_pipeline_forward(
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model_cls=MixtralForCausalLM,
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new_forward=MixtralPipelineForwards.mixtral_for_causal_lm_forward,
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policy=policy,
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)
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return policy
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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stage_manager = self.pipeline_stage_manager
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held_layers = super().get_held_layers()
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if stage_manager.is_last_stage():
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held_layers.append(self.model.lm_head)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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mixtral_model = self.model.model
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if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
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if (
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id(mixtral_model.embed_tokens.weight) == id(self.model.lm_head.weight)
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and self.pipeline_stage_manager.num_stages > 1
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):
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# tie weights
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return [
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{
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0: mixtral_model.embed_tokens.weight,
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self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight,
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}
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]
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return []
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class MixtralForSequenceClassificationPolicy(MixtralPolicy):
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def module_policy(self):
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from transformers import MixtralForSequenceClassification
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policy = super().module_policy()
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if self.shard_config.enable_tensor_parallelism:
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# add a new item for sequence classification
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new_item = {
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MixtralForSequenceClassification: ModulePolicyDescription(
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="score", target_module=Linear1D_Col, kwargs=dict(gather_output=True)
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)
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]
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)
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}
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policy.update(new_item)
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if self.pipeline_stage_manager:
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raise NotImplementedError
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return policy
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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stage_manager = self.pipeline_stage_manager
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held_layers = super().get_held_layers()
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if stage_manager.is_last_stage(ignore_chunk=True):
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held_layers.append(self.model.score)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in llama for sequence classification model"""
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return []
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