HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy

This repo contains the source code of the Python package HiFT and several examples of how to integrate it with PyTorch models, such as those in Hugging Face. We only support PyTorch for now. See our paper for a detailed description of ·HiFT. HiFT supports FPFT of 7B models for 24G GPU memory devices under mixed precision without using any memory saving techniques and various optimizers including AdamW, AdaGrad, SGD, etc.

HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy
Yongkang Liu, Yiqun Zhang, Qian Li, Tong Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schütze
Paper: https://arxiv.org/abs/2401.15207

News

26/1/2024: Publish the first version of HiFT manuscript
25/2/2024: Publish the second version of HiFT manuscript and source code
1/5/2024: Updated HiFT support for LoRA
10/5/2024: Adapt the optimizer provided by bitsandbytes
13/5/2024*: Adapt Adalora,LoRA, IA3, P_tuning, Prefix_tuning , Prompt_tuning peft method.​

Repository Overview

There are several directories in this repo:

• hift/ contains the source code for the package hift, which needs to be installed to run the examples we provide;
• examples Contains HiFT-based NER, QA, classification, text generation,instruction fine-tuning, and pre-training example implementation.
• scripts contains the script for running examples we provide.
• dsconfig contains configuration files required for mixed precision.
• data contains examples for instruction fine-tuning and pre-training.

Out-of-memory issues

Instruction fine-tuning 7B model on A6000 (48G), and the experimental results show that the maximum sequence length supported by HiFT is 2800. Beyond this limit, OOM issues may occur.

Model	Max Seq Length	Max Batch Size
llama2-7b(Alpaca)	512	8
llama2-7b(Vicuna)	2800	1

Instruction fine-tuning 7B model on RTX3090 (24G) . If you use multiple GPUs for distributed training on RTX 3090/4000, add the following commands before running: export NCCL_IB_DISABLE=1; export NCCL_P2P_DISABLE=1

Model	Max Seq Length	Max Batch Size
llama2-7b(Alpaca)	512	3
llama2-7b(Vicuna)	1400	1

Requirements

1. pytorch >= 2.1.1; transformers==4.36.2
2. pip install -r requirements.txt
3. conda install mpi4py==3.1.4
4. pip install flash-attn==2.5.8

Quickstart

1. Installing hift

pip install hift

2. Import hift package

### generation task  

from hift import HiFTSeq2SeqTrainer,GetCallBack,peft_function,Seq2SeqTrainer

### classification taks  

from hift import HiFTrainer,GetCallBack,PEFTrainer,peft_function


### QA task  

from hift import HiFTQuestionAnsweringTrainer,GetCallBack,QuestionAnsweringTrainer,peft_function

3. Add HiFT configuration

@dataclass
class HiFTArguments(ModelArguments):
    HiTaskType: str = field(
        default="SEQ_CLS",
        metadata={"help": ("HiTaskType should be consistent with PEFT TaskType" )},
    )
    peft_type: str = field(
        default=None,
        metadata={"help": ("peft_type should be in [lora,adalora,ia3,p_tuning,prefix_tuning,prompt_tuning]" )},
    )
    init_text:str = field(
        default="Predict if sentiment of this review is positive, negative or neutral",
        metadata={
            "help": (
                "the init prompt text for prompt tuning"
            )
        },
    )
    lora_rank: int = field(
        default=8,
        metadata={"help": ("rank for lora or adalora" )},
    )
    peft_path : Optional[str] = field(default=None)
    virtual_tokens:int = field(
        default=20,
        metadata={"help": ("the number of virtual tokens for p_tuning, prefix_tuning and prefix_tuning" )},
    )
    group_element: int = field(
        default=1,
        metadata={"help": ("number element for each group parameters" )},
    )
    optimizer_strategy: str = field(
        default="down2up",
        metadata={"help": ("optimizer strategy of ['down2up','down2up','random']" )},
    )
    hier_tuning: bool = field(
        default=False,
        metadata={
            "help": (
                "hierarchical optimization for LLMS"
            )
        },
    )
    freeze_layers: List[str] = field(
        default_factory=list,
        metadata={
            "help": (
                "Index of the frozen layer"
            )
        },
    )

HiTaskType should be consistent with PEFT TaskType.

sequence classification, multiple choice tasks: TaskType.SEQ_CLS

question answering task: TaskType.QUESTION_ANS

sequence labeling task: TaskType.TOKEN_CLS

generation task: TaskType.CAUSAL_LM

group_element: the number of layers included in a block. Default value is 1.

freeze_layers: Layers you want to freeze during fine-tuning. You should provide the index of the corresponding layer. The index of the embedding layer is 0, the index of the first layer is 1,...

4. Using HiFT Trainer

HiFT inherits the trainer of huggingface, so you can directly use the trainer provided by hift to replace the original trainer.

• Classification Task

if model_args.hier_tuning:#hier_tuning
        trainer = HiFTrainer(
            hiFThandler = GetCallBack(model_args.model_name_or_path),
            HiTaskType = model_args.HiTaskType,
            group_element = model_args.group_element,
            strategy = model_args.optimizer_strategy,
            hier_tuning= model_args.hier_tuning,
            peft_type = model_args.peft_type,
            freeze_layers = model_args.freeze_layers,
            args=training_args,
            train_dataset=train_dataset if training_args.do_train else None,
            eval_dataset=eval_dataset if training_args.do_eval else None,
            model=model,
            tokenizer=tokenizer,
            compute_metrics=compute_metrics,
            data_collator=data_collator
        )
  else:
        trainer = PEFTrainer(
            peft_type = model_args.peft_type,
            args=training_args,
            model=model,
            train_dataset=train_dataset if training_args.do_train else None,
            eval_dataset=eval_dataset if training_args.do_eval else None,
            compute_metrics=compute_metrics,
            tokenizer=tokenizer,
            data_collator=data_collator,
        )

QA Task

if model_args.hier_tuning:
        trainer = HiFTQuestionAnsweringTrainer(
            hiFThandler = GetCallBack(model_args.model_name_or_path),
            HiTaskType = model_args.HiTaskType,
            group_element = model_args.group_element,
            strategy = model_args.optimizer_strategy,
            hier_tuning= model_args.hier_tuning,
            peft_type = model_args.peft_type,
            freeze_layers = model_args.freeze_layers,
            eval_examples=eval_examples if training_args.do_eval else None,
            post_process_function=post_processing_function,
            args=training_args,
            model=model,
            train_dataset=train_dataset if training_args.do_train else None,
            eval_dataset=eval_dataset if training_args.do_eval else None,
            tokenizer=tokenizer,
            data_collator=data_collator,
            compute_metrics=compute_metrics)
 else:
        trainer = QuestionAnsweringTrainer(
            peft_type = model_args.peft_type,
            eval_examples=eval_examples if training_args.do_eval else None,
            post_process_function=post_processing_function,
            args=training_args,
            model=model,
            train_dataset=train_dataset if training_args.do_train else None,
            eval_dataset=eval_dataset if training_args.do_eval else None,
            tokenizer=tokenizer,
            data_collator=data_collator,
            compute_metrics=compute_metrics)

• Generation Task

  if model_args.hier_tuning:#hier_tuning
          trainer = HiFTSeq2SeqTrainer(
              hiFThandler = GetCallBack(model_args.model_name_or_path),
              HiTaskType = model_args.HiTaskType,
              group_element = model_args.group_element,
              strategy = model_args.optimizer_strategy,
              hier_tuning= model_args.hier_tuning,
              peft_type = model_args.peft_type,
              freeze_layers = model_args.freeze_layers,
              args=training_args,
              model=model,
              train_dataset=train_dataset if training_args.do_train else None,
              eval_dataset=eval_dataset if training_args.do_eval else None,
              compute_metrics=compute_metrics if training_args.predict_with_generate else None,
              tokenizer=tokenizer,
              data_collator=data_collator
          )
   else:
          trainer = Seq2SeqTrainer(
              peft_type = model_args.peft_type,
              args=training_args,
              model=model,
              train_dataset=train_dataset if training_args.do_train else None,
              eval_dataset=eval_dataset if training_args.do_eval else None,
              tokenizer=tokenizer,
              data_collator=data_collator,
              compute_metrics=compute_metrics if training_args.predict_with_generate else None,
          )
  

Adapt Model to HiFT

HiFT supports any model. It is very easy to adapt to HiFT.

1. Define the task types supported by your model in TaskTInterface.
2. Provides regular expressions for the embedding layer and different task header layers. The purpose of the regular expression is to uniquely identify the layer name of the corresponding layer.
3. Provide regular expressions except embedding layer and header layer in others_pattern interface.

The simplest way is to provide the layer names for all layers in others_pattern interface, and the other interfaces return an empty list []. Below is the RoBerta's example.

class RobertaCallBack(HiFTCallBack):
    def __init__(self,freeze_layers,strategy,taskType,peft_type=None):
        super().__init__(freeze_layers,strategy,taskType,peft_type)
        self.TaskTInterface = [TaskType.SEQ_CLS,TaskType.TOKEN_CLS,TaskType.QUESTION_ANS]
        self.check_task_type(taskType,"RoBERTa",self.TaskTInterface)
    @property
    def emb_pattern(self):
        if self.peft_type:
            return [rf'\.embedding\.']
        else:
            return [rf'\.embeddings\.']
    @property
    def seq_cls_head(self):
        if self.peft_type:
            return ["classifier"]
        else:
            return ["classifier"]
    @property
    def token_cls_head(self):
        if self.peft_type:
            return ["classifier"]
        else:
            return ["classifier"]
    @property
    def qa_cls_head(self):
        if self.peft_type:
            return ["qa_outputs"]
        else:
            return ["qa_outputs"]
    @property
    def others_pattern(self):
        if self.peft_type:
            return [rf'\.\d+\.']
        else:
            return [rf'\.\d+\.']
        

Instruction fine-tuning -- Vicuna

### The parameters have not been fine-tuned, this is just a demo. Please adjust the parameters based on your data.

export num_gpus=2
export output_dir="outputs/output_vicuna"
port=$(shuf -i25000-30000 -n1)
#--fsdp "full_shard auto_wrap" \
CUDA_VISIBLE_DEVICES="0,2" torchrun --master_port "$port" --nproc_per_node=$num_gpus examples/vicuna_train.py \
    --model_type llama \
    --HiTaskType "CAUSAL_LM" \
    --optim "lion_32bit" \
    --deepspeed "dsconfig/zero0_config.json" \
    --model_name_or_path /mounts/work/lyk/hierFT/llama2-7b \
    --data_path data/dummy_conversation.json \
    --eval_data_path data/sharegpt_clean.json \
    --output_dir $output_dir/model \
    --num_train_epochs 3 \
    --do_train \
    --per_device_train_batch_size 1 \
    --per_device_eval_batch_size 8 \
    --evaluation_strategy "steps" \
    --eval_steps 1500 \
    --save_strategy "steps" \
    --save_steps 1500 \
    --save_total_limit 8 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --warmup_ratio 0 \
    --lr_scheduler_type "linear" \
    --logging_steps 10 \
    --model_max_length 2800 \
    --lazy_preprocess True \
    --torch_dtype float16 \
    --ddp_find_unused_parameters False \
    --load_best_model_at_end \
    --hier_tuning \
    --group_element $1 \
    --optimizer_strategy $2

Instruction fine-tuning -- Alpaca

### The parameters have not been fine-tuned, this is just a demo. Please adjust the parameters based on your data.

export num_gpus=2
export output_dir="outputs/instruct_tuning"
port=$(shuf -i25000-30000 -n1)

CUDA_VISIBLE_DEVICES="0,2" torchrun --master_port "$port" --nproc_per_node=$num_gpus examples/instruct_tuning.py \
    --model_type opt \
    --HiTaskType "CAUSAL_LM" \
    --optim "adamw_torch" \
    --deepspeed "dsconfig/zero0_config.json" \
    --model_name_or_path opt-7b  \
    --dataset_dir alpaca_data \
    --validation_split_percentage 0.01 \
    --per_device_train_batch_size 12 \
    --per_device_eval_batch_size 8 \
    --do_train \
    --do_eval \
    --seed 12345 \
    --fp16 \
    --tf32 true \
    --num_train_epochs 1 \
    --lr_scheduler_type "cosine" \
    --learning_rate 1e-5 \
    --warmup_ratio 0.0 \
    --weight_decay 0.0 \
    --logging_strategy steps \
    --logging_steps 10 \
    --save_strategy steps \
    --save_total_limit 3 \
    --evaluation_strategy steps \
    --eval_steps 100 \
    --save_steps 200 \
    --preprocessing_num_workers 4 \
    --max_seq_length 512 \
    --output_dir $output_dir/model \
    --overwrite_output_dir \
    --logging_first_step True \
    --torch_dtype float16 \
    --ddp_find_unused_parameters False \
    --load_best_model_at_end \
    --hier_tuning \
    --group_element $1 \
    --optimizer_strategy $2

Pre-Training

### This is just a demo. Please adjust the parameters based on your data.

export num_gpus=8
export output_dir="outputs/pretrain_tuning"
port=$(shuf -i25000-30000 -n1)

CUDA_VISIBLE_DEVICES=0 torchrun --master_port "$port" examples/pretrain_tuning.py \
    --model_type llama \
    --HiTaskType "CAUSAL_LM" \
    --deepspeed "dsconfig/zero0_config.json" \
    --model_name_or_path llama2-7b \
    --dataset_dir "data" \
    --data_cache_dir "data_cache_dir" \
    --validation_split_percentage 0.001 \
    --per_device_train_batch_size 8 \
    --per_device_eval_batch_size 8 \
    --do_train \
    --seed 12345 \
    --fp16 \
    --max_steps 1000 \
    --lr_scheduler_type cosine \
    --learning_rate 1e-5 \
    --warmup_ratio 0.05 \
    --weight_decay 0.01 \
    --logging_strategy steps \
    --logging_steps 10 \
    --save_strategy steps \
    --save_total_limit 3 \
    --save_steps 500 \
    --preprocessing_num_workers 8 \
    --block_size 512 \
    --output_dir $output_dir/model \
    --overwrite_output_dir \
    --logging_first_step True \
    --torch_dtype float16 \
    --ddp_find_unused_parameters False \
    --hier_tuning \
    --group_element $1 \
    --optimizer_strategy $2

PEFT-Tuning

export num_gpus=8
export output_dir="outputs/e2e_opt"
port=$(shuf -i25000-30000 -n1)
# CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python -m torch.distributed.launch --nproc_per_node=$num_gpus run_glue.py \
CUDA_VISIBLE_DEVICES=7 torchrun --master_port "$port" examples/run_generation.py \
--model_name_or_path llama2-7b \
--model_type llama \
--HiTaskType "CAUSAL_LM" \
--peft_type "lora" \
--dataset_name e2e_nlg \
--do_train \
--do_eval \
--padding_side "left" \
--group_by_length \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 8 \
--save_strategy epoch \
--evaluation_strategy epoch \
--predict_with_generate \
--learning_rate 5e-5 \
--lr_scheduler_type "linear" \
--pad_to_max_length \
--max_eval_samples 2000 \
--model_max_length 512 \
--num_train_epochs 5 \
--output_dir $output_dir/model \
--overwrite_output_dir \
--logging_steps 10 \
--logging_dir $output_dir/log \
--warmup_ratio 0.0  \
--num_beams 10 \
--seed 0 \
--fp16 \
--weight_decay 0.0 \
--load_best_model_at_end \
--weight_decay 0

HIFT + PEFT

export num_gpus=8
export output_dir="outputs/e2e_opt"
port=$(shuf -i25000-30000 -n1)

CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" torchrun --master_port "$port" --nproc_per_node=$num_gpus examples/run_generation.py \
--model_name_or_path /mounts/work/lyk/hierFT/llama2-7b \
--model_type llama \
--HiTaskType "CAUSAL_LM" \
--peft_type "lora" \
--dataset_name e2e_nlg \
--do_train \
--do_eval \
--deepspeed "dsconfig/zero0_config.json" \
--padding_side "left" \
--group_by_length \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 8 \
--save_strategy epoch \
--evaluation_strategy epoch \
--predict_with_generate \
--learning_rate 5e-5 \
--lr_scheduler_type "linear" \
--pad_to_max_length \
--max_eval_samples 2000 \
--model_max_length 512 \
--num_train_epochs 5 \
--output_dir $output_dir/model \
--overwrite_output_dir \
--logging_steps 10 \
--logging_dir $output_dir/log \
--warmup_ratio 0.0  \
--num_beams 10 \
--seed 0 \
--fp16 \
--weight_decay 0.0 \
--load_best_model_at_end \
--hier_tuning \
--weight_decay 0 \
--group_element $1 \
--optimizer_strategy $2

Introduction

The detailed training process is shown in Algorithm. The first step is to determine the update strategy. Then freeze all layers. The layers to be updated, denoted by $E$, are selected from the queue $Q$ based on the parameter $m$. The selected layer $E$ is removed from head of the queue $Q$ and added to the tail of $Q$ to wait for the next update. Select the parameter $\theta_s$ that needs to be updated from $M$ based on $E$, set the parameter $\theta_s$ to a computable gradient state and set the update parameter group of optimizer $P$ to $\theta_s$. Before parameter updates, the states parameters of optimizer $P$ related to $\theta_s$ could be moved to GPU devices. After the completion of weight updates, the corresponding gradients are clean up and optimizer states parameters are moved to CPU. When all layers have been updated once, adjust the learning rate once.

HiFT iteratively updates a subset of parameters at each training step, and it will modify the full parameter after multiple steps. This vastly reduces the GPU memory requirements for fine-tuninglarge language models enables efficient task-switching during deployment all without introducing inference latency. HiFT also outperforms several other adaptation methods including adapter, prefix-tuning, and fine-tuning.

HiFT is a model-independent and optimizer-independent full-parameter fine-tuning method that can be integrated with the PEFT method.

optimizers: The latest version of HiFT is adapted to the Adam, AdamW, SGD, Adafactor and Adagrad optimizers.

Model: The latest version of HiFT supports BERT, RoBERTa, GPT-2, GPTNeo,GPT-NeoX,OPT and LLaMA-based models.

Experiments on OPT-13B (with 1000 examples). ICL: in-context learning; LP: linear probing; FPFT: full fine-tuning; Prefix: prefix-tuning. All experiments use prompts from MeZO.

GPU memory usage of fine-tuning LLaMA (7B) on the E2E dataset. Total represents the total memory used during fine-tuning. Mixed represents fine-tuning with standard mixed precision and Mixed^Hi^ represents the mixed precision adapted to HiFT. Para represents the memory occupied by the model parameters; Gra represents the memory occupied by the gradient; Sta represents the memory occupied by the optimizer state. PGS represents the sum of memory occupied by parameters , gradients and optimizer state .

Mixed Precision

Source Code

class FP16_Optimizer(DeepSpeedOptimizer):
    def __init__(self,
       init_optimizer,
       deepspeed=None,
       static_loss_scale=1.0,
       dynamic_loss_scale=False,
       initial_dynamic_scale=2**32,
       dynamic_loss_args=None,
       verbose=True,
       mpu=None,
       clip_grad=0.0,
       fused_adam_legacy=False,
       has_moe_layers=False,
       timers=None):
                 
       ....
       self.fp16_groups = []
       self.fp16_groups_flat = []
       self.fp32_groups_flat = []
                 
       ...
                 
       for i, param_group in enumerate(self.optimizer.param_groups):
           ...
           self.fp32_groups_flat.append(self.fp16_groups_flat[i].clone().float().detach())
           ...
                            

The memory required to load 1B parameters is 3.72GB (10^9 $\times$ 4 /1024/1024/1024). Standard mixed precision stores both single-precision and half-precision model parameters. Assuming you are using standard mixed precision fine-tuning of the 7B model, compared with single-precision fine-tuning, mixed precision requires an additional about 13G GPU memory overhead to store half-precision model parameters. Only when the dynamic GPU memory reduction reaches 13GB does mixed precision demonstrate its advantages. This requires using large batch size.

We reimplement the mixed-precision algorithm to adapt to HiFT's fine-tuning algorithm, which ensures that single-precision model parameters do not incur additional GPU memory overhead.

Citation

@article{liu2024hift,
  title={HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy},
  author={Liu, Yongkang and Zhang, Yiqun and Li, Qian and Feng, Shi and Wang, Daling and Zhang, Yifei and Sch{\"u}tze, Hinrich},
  journal={arXiv preprint arXiv:2401.15207},
  year={2024}
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution.