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πŸ€— PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

πŸ€— 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 πŸ€— Accelerate for large scale models leveraging DeepSpeed and Big Model Inference.

Supported methods:

  1. LoRA: LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS
  2. 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
  3. P-Tuning: GPT Understands, Too
  4. Prompt Tuning: The Power of Scale for Parameter-Efficient Prompt Tuning
  5. AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-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 squeeze 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.

An insightful blogpost explaining the advantages of using PEFT for fine-tuning FlanT5-XXL: https://www.philschmid.de/fine-tune-flan-t5-peft

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 Checkpointing
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 πŸ€— Gradio Space which should run seamlessly on a T4 instance: smangrul/peft-lora-sd-dreambooth.

peft lora dreambooth gradio space

NEW ✨ Multi Adapter support and combining multiple LoRA adapters in a weighted combination peft lora dreambooth weighted adapter

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

  • Here is an example in trl library using PEFT+INT8 for tuning policy model: gpt2-sentiment_peft.py and corresponding Blog
  • Example using PEFT for Instrction finetuning, reward model and policy : stack_llama and corresponding Blog

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: Open In Colab

  • Here is now a demo on how to fine tune whishper-large (1.5B params) (14GB in fp16) in a Google Colab: Open In Colab and Open In Colab

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 using different PEFT methods. The notebooks are given in ~examples/sequence_classification.

PEFT + πŸ€— Accelerate

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

Example of PEFT model training using πŸ€— 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%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 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 πŸ€— 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 βœ… βœ… βœ… βœ…
Bloom βœ… βœ… βœ… βœ…
OPT βœ… βœ… βœ… βœ…
GPT-Neo βœ… βœ… βœ… βœ…
GPT-J βœ… βœ… βœ… βœ…
GPT-NeoX-20B βœ… βœ… βœ… βœ…
LLaMA βœ… βœ… βœ… βœ…
ChatGLM βœ… βœ… βœ… βœ…

Conditional Generation

Model LoRA Prefix Tuning P-Tuning Prompt Tuning
T5 βœ… βœ… βœ… βœ…
BART βœ… βœ… βœ… βœ…

Sequence Classification

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

Token Classification

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

Text-to-Image Generation

Model LoRA Prefix Tuning P-Tuning Prompt Tuning
Stable Diffusion βœ…

Image Classification

Model LoRA Prefix Tuning P-Tuning Prompt Tuning
ViT βœ…
Swin βœ…

Image to text (Multi-modal models)

Model LoRA Prefix Tuning P-Tuning Prompt Tuning
Blip-2 βœ…

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 πŸ€— 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 βœ…

Caveats:

  1. 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 πŸ€— 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

b. run the below command to launch the example script

accelerate launch --config_file fsdp_config.yaml examples/peft_lora_seq2seq_accelerate_fsdp.py
  1. When using P_TUNING or PROMPT_TUNING with SEQ_2_SEQ task, remember to remove the num_virtual_token virtual prompt predictions from the left side of the model outputs during evaluations.

  2. For encoder-decoder models, P_TUNING or PROMPT_TUNING doesn't support generate functionality of transformers because generate strictly requires decoder_input_ids but P_TUNING/PROMPT_TUNING appends soft prompt embeddings to input_embeds to create new input_embeds to be given to the model. Therefore, generate doesn't support this yet.

  3. When using ZeRO3 with zero3_init_flag=True, if you find the gpu memory increase with training steps. we might need to update deepspeed after deepspeed commit 42858a9891422abc . The related issue is [BUG] Peft Training with Zero.Init() and Zero3 will increase GPU memory every forward step

Backlog:

  • Add tests
  • Multi Adapter training and inference support
  • Add more use cases and examples
  • Explore and possibly integrate Bottleneck Adapters, (IA)^3, AdaptionPrompt ...

Citing πŸ€— PEFT

If you use πŸ€— PEFT in your publication, please cite it by using the following BibTeX entry.

@Misc{peft,
  title =        {PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods},
  author =       {Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, Sayak Paul},
  howpublished = {\url{https://github.com/huggingface/peft}},
  year =         {2022}
}

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181
star
76

olm-datasets

Pipeline for pulling and processing online language model pretraining data from the web
Python
169
star
77

instruction-tuned-sd

Code for instruction-tuning Stable Diffusion.
Python
167
star
78

optimum-neuron

Easy, fast and very cheap training and inference on AWS Trainium and Inferentia chips.
Jupyter Notebook
163
star
79

llm-swarm

Manage scalable open LLM inference endpoints in Slurm clusters
Python
156
star
80

OBELICS

Code used for the creation of OBELICS, an open, massive and curated collection of interleaved image-text web documents, containing 141M documents, 115B text tokens and 353M images.
Python
147
star
81

workshops

Materials for workshops on the Hugging Face ecosystem
Jupyter Notebook
146
star
82

cosmopedia

Python
138
star
83

api-inference-community

Python
131
star
84

diffusion-fast

Faster generation with text-to-image diffusion models.
Python
127
star
85

diarizers

Python
106
star
86

optimum-habana

Easy and lightning fast training of πŸ€— Transformers on Habana Gaudi processor (HPU)
Python
106
star
87

sharp-transformers

A Unity plugin for using Transformers models in Unity.
C#
104
star
88

competitions

Python
101
star
89

hf-hub

Rust client for the huggingface hub aiming for minimal subset of features over `huggingface-hub` python package
Rust
93
star
90

olm-training

Repo for training MLMs, CLMs, or T5-type models on the OLM pretraining data, but it should work with any hugging face text dataset.
Python
87
star
91

fuego

[WIP] A πŸ”₯ interface for running code in the cloud
Python
84
star
92

tune

Python
83
star
93

datasets-viewer

Viewer for the πŸ€— datasets library.
Python
82
star
94

optimum-graphcore

Blazing fast training of πŸ€— Transformers on Graphcore IPUs
Python
78
star
95

frp

FRP Fork
Go
73
star
96

paper-style-guide

72
star
97

block_movement_pruning

Block Sparse movement pruning
Python
70
star
98

amused

Python
68
star
99

doc-builder

The package used to build the documentation of our Hugging Face repos
Python
67
star
100

data-measurements-tool

Developing tools to automatically analyze datasets
Python
67
star