🤗 PEFT

State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods

Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. Fine-tuning large-scale PLMs is often prohibitively costly. In this regard, PEFT methods only fine-tune a small number of (extra) model parameters, thereby greatly decreasing the computational and storage costs. Recent State-of-the-Art PEFT techniques achieve performance comparable to that of full fine-tuning.

Seamlessly integrated with 🤗 Accelerate for large scale models leveraging DeepSpeed and Big Model Inference.

Supported methods:

LoRA: LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS
Prefix Tuning: Prefix-Tuning: Optimizing Continuous Prompts for Generation, P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
P-Tuning: GPT Understands, Too
Prompt Tuning: The Power of Scale for Parameter-Efficient Prompt Tuning

Getting started

from transformers import AutoModelForSeq2SeqLM
from peft import get_peft_config, get_peft_model, LoraConfig, TaskType
model_name_or_path = "bigscience/mt0-large"
tokenizer_name_or_path = "bigscience/mt0-large"

peft_config = LoraConfig(
    task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1
)

model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# output: trainable params: 2359296 || all params: 1231940608 || trainable%: 0.19151053100118282

Use Cases

Get comparable performance to full finetuning by adapting LLMs to downstream tasks using consumer hardware

GPU memory required for adapting LLMs on the few-shot dataset ought/raft/twitter_complaints. Here, settings considered are full finetuning, PEFT-LoRA using plain PyTorch and PEFT-LoRA using DeepSpeed with CPU Offloading.

Hardware: Single A100 80GB GPU with CPU RAM above 64GB

Model	Full Finetuning	PEFT-LoRA PyTorch	PEFT-LoRA DeepSpeed with CPU Offloading
bigscience/T0_3B (3B params)	47.14GB GPU / 2.96GB CPU	14.4GB GPU / 2.96GB CPU	9.8GB GPU / 17.8GB CPU
bigscience/mt0-xxl (12B params)	OOM GPU	56GB GPU / 3GB CPU	22GB GPU / 52GB CPU
bigscience/bloomz-7b1 (7B params)	OOM GPU	32GB GPU / 3.8GB CPU	18.1GB GPU / 35GB CPU

Performance of PEFT-LoRA tuned bigscience/T0_3B on ought/raft/twitter_complaints leaderboard. A point to note is that we didn't try to sequeeze performance by playing around with input instruction templates, LoRA hyperparams and other training related hyperparams. Also, we didn't use the larger 13B mt0-xxl model. So, we are already seeing comparable performance to SoTA with parameter efficient tuning. Also, the final checkpoint size is just 19MB in comparison to 11GB size of the backbone bigscience/T0_3B model.

Submission Name	Accuracy
Human baseline (crowdsourced)	0.897
Flan-T5	0.892
lora-t0-3b	0.863

Therefore, we can see that performance comparable to SoTA is achievable by PEFT methods with consumer hardware such as 16GB and 24GB GPUs.

Parameter Efficient Tuning of Diffusion Models

GPU memory required by different settings during training is given below. The final checkpoint size is 8.8 MB.

Hardware: Single A100 80GB GPU with CPU RAM above 64GB

Model	Full Finetuning	PEFT-LoRA	PEFT-LoRA with Gradient Checkpoitning
CompVis/stable-diffusion-v1-4	27.5GB GPU / 3.97GB CPU	15.5GB GPU / 3.84GB CPU	8.12GB GPU / 3.77GB CPU

Training An example of using LoRA for parameter efficient dreambooth training is given in ~examples/lora_dreambooth/train_dreambooth.py

export MODEL_NAME= "CompVis/stable-diffusion-v1-4" #"stabilityai/stable-diffusion-2-1"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"

accelerate launch train_dreambooth.py \
  --pretrained_model_name_or_path=$MODEL_NAME  \
  --instance_data_dir=$INSTANCE_DIR \
  --class_data_dir=$CLASS_DIR \
  --output_dir=$OUTPUT_DIR \
  --train_text_encoder \
  --with_prior_preservation --prior_loss_weight=1.0 \
  --instance_prompt="a photo of sks dog" \
  --class_prompt="a photo of dog" \
  --resolution=512 \
  --train_batch_size=1 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --num_class_images=200 \
  --use_lora \
  --lora_r 16 \
  --lora_alpha 27 \
  --lora_text_encoder_r 16 \
  --lora_text_encoder_alpha 17 \
  --learning_rate=1e-4 \
  --gradient_accumulation_steps=1 \
  --gradient_checkpointing \
  --max_train_steps=800

Try out the 🤗 Gradio Space which should run seamlessly on a T4 instance: smangrul/peft-lora-sd-dreambooth.

Parameter Efficient Tuning of LLMs for RLHF components such as Ranker and Policy

Here is an exmaple in trl library using PEFT+INT8 for tuning policy model: gpt2-sentiment_peft.py
Example using PEFT for both reward model and policy [ToDo]

INT8 training of large models in Colab using PEFT LoRA and bits_and_bytes

Here is now a demo on how to fine tune OPT-6.7b (14GB in fp16) in a Google colab:
Here is now a demo on how to fine tune whishper-large (1.5B params) (14GB in fp16) in a Google colab: and

Save compute and storage even for medium and small models

Save storage by avoiding full finetuning of models on each of the downstream tasks/datasets, With PEFT methods, users only need to store tiny checkpoints in the order of MBs all the while retaining performance comparable to full finetuning.

An example of using LoRA for the task of adapting LayoutLMForTokenClassification on FUNSD dataset is given in ~examples/token_classification/PEFT_LoRA_LayoutLMForTokenClassification_on_FUNSD.py. We can observe that with only 0.62 % of parameters being trainable, we achieve performance (F1 0.777) comparable to full finetuning (F1 0.786) (without any hyerparam tuning runs for extracting more performance), and the checkpoint of this is only 2.8MB. Now, if there are N such datasets, just have these PEFT models one for each dataset and save a lot of storage without having to worry about the problem of catastrophic forgetting or overfitting of backbone/base model.

Another example is fine-tuning roberta-large on MRPC GLUE dataset suing differenct PEFT methods. The notebooks are given in ~examples/sequence_classification.

PEFT + 🤗 Accelerate

PEFT models work with 🤗 Accelerate out of the box. Use 🤗 Accelerate for Distributed training on various hardware such as GPUs, Apple Silicon devices etc during training. Use 🤗 Accelerate for inferencing on consumer hardware with small resources.

Example of PEFT model training using 🤗 Accelerate's DeepSpeed integration

DeepSpeed version required v0.8.0. An example is provided in ~examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py. a. First, run accelerate config --config_file ds_zero3_cpu.yaml and answer the questionnaire. Below are the contents of the config file.

compute_environment: LOCAL_MACHINE
deepspeed_config:
  gradient_accumulation_steps: 1
  gradient_clipping: 1.0
  offload_optimizer_device: cpu
  offload_param_device: cpu
  zero3_init_flag: true
  zero3_save_16bit_model: true
  zero_stage: 3
distributed_type: DEEPSPEED
downcast_bf16: 'no'
dynamo_backend: 'NO'
fsdp_config: {}
machine_rank: 0
main_training_function: main
megatron_lm_config: {}
mixed_precision: 'no'
num_machines: 1
num_processes: 1
rdzv_backend: static
same_network: true
use_cpu: false

b. run the below command to launch the example script

accelerate launch --config_file ds_zero3_cpu.yaml examples/peft_lora_seq2seq_accelerate_ds_zero3_offload.py

c. output logs:

GPU Memory before entering the train : 1916
GPU Memory consumed at the end of the train (end-begin): 66
GPU Peak Memory consumed during the train (max-begin): 7488
GPU Total Peak Memory consumed during the train (max): 9404
CPU Memory before entering the train : 19411
CPU Memory consumed at the end of the train (end-begin): 0
CPU Peak Memory consumed during the train (max-begin): 0
CPU Total Peak Memory consumed during the train (max): 19411
epoch=4: train_ppl=tensor(1.0705, device='cuda:0') train_epoch_loss=tensor(0.0681, device='cuda:0')
100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:27<00:00,  3.92s/it]
GPU Memory before entering the eval : 1982
GPU Memory consumed at the end of the eval (end-begin): -66
GPU Peak Memory consumed during the eval (max-begin): 672
GPU Total Peak Memory consumed during the eval (max): 2654
CPU Memory before entering the eval : 19411
CPU Memory consumed at the end of the eval (end-begin): 0
CPU Peak Memory consumed during the eval (max-begin): 0
CPU Total Peak Memory consumed during the eval (max): 19411
accuracy=100.0
eval_preds[:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']
dataset['train'][label_column][:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']

Example of PEFT model inference using 🤗 Accelerate's Big Model Inferencing capabilities

An example is provided in ~examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb.

Models support matrix

Causal Language Modeling

Model	LoRA	Prefix Tuning	P-Tuning	Prompt Tuning
GPT-2	✅	✅	✅	✅
Bloom	✅	✅	✅	✅
OPT	✅	✅	✅	✅
GPT-Neo	✅	✅	✅	✅
GPT-J	✅	✅	✅	✅
GPT-NeoX-20B	✅	✅	✅	✅
LLaMA	✅	✅	✅	✅

Conditional Generation

Model	LoRA	Prefix Tuning	P-Tuning	Prompt Tuning
T5	✅	✅	✅	✅
BART	✅	✅	✅	✅

Sequence Classification

Model	LoRA	Prefix Tuning	P-Tuning	Prompt Tuning
BERT	✅	✅	✅	✅
RoBERTa	✅	✅	✅	✅
GPT-2	✅	✅	✅	✅
Bloom	✅	✅	✅	✅
OPT	✅	✅	✅	✅
GPT-Neo	✅	✅	✅	✅
GPT-J	✅	✅	✅	✅
Deberta	✅		✅	✅
Deberta-v2	✅		✅	✅

Token Classification

Model	LoRA	Prefix Tuning
BERT	✅	✅
RoBERTa	✅	✅
GPT-2	✅	✅
Bloom	✅	✅
OPT	✅	✅
GPT-Neo	✅	✅
GPT-J	✅	✅
Deberta	✅
Deberta-v2	✅

Text-to-Image Generation

Model	LoRA	Prefix Tuning	P-Tuning	Prompt Tuning
Stable Diffusion	✅

Image Classification

Model	LoRA	Prefix Tuning	P-Tuning	Prompt Tuning
ViT	✅
Swin	✅

Note that we have tested LoRA for ViT and Swin for fine-tuning on image classification. However, it should be possible to use LoRA for any compatible model provided by 🤗 Transformers. Check out the respective examples to learn more. If you run into problems, please open an issue.

The same principle applies to our segmentation models as well.

Semantic Segmentation

Model	LoRA	Prefix Tuning	P-Tuning	Prompt Tuning
SegFormer	✅

Caveats:

Below is an example of using PyTorch FSDP for training. However, it doesn't lead to any GPU memory savings. Please refer issue [FSDP] FSDP with CPU offload consumes 1.65X more GPU memory when training models with most of the params frozen.

from peft.utils.other import fsdp_auto_wrap_policy

...

if os.environ.get("ACCELERATE_USE_FSDP", None) is not None:
    accelerator.state.fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(model)

model = accelerator.prepare(model)

Example of parameter efficient tuning with mt0-xxl base model using 🤗 Accelerate is provided in ~examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py. a. First, run accelerate config --config_file fsdp_config.yaml and answer the questionnaire. Below are the contents of the config file.

command_file: null
commands: null
compute_environment: LOCAL_MACHINE
deepspeed_config: {}
distributed_type: FSDP
downcast_bf16: 'no'
dynamo_backend: 'NO'
fsdp_config:
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_backward_prefetch_policy: BACKWARD_PRE
  fsdp_offload_params: true
  fsdp_sharding_strategy: 1
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_transformer_layer_cls_to_wrap: T5Block
gpu_ids: null
machine_rank: 0
main_process_ip: null
main_process_port: null
main_training_function: main
megatron_lm_config: {}
mixed_precision: 'no'
num_machines: 1
num_processes: 2
rdzv_backend: static
same_network: true
tpu_name: null
tpu_zone: null
use_cpu: false