• Stars
    star
    183
  • Rank 203,420 (Top 5 %)
  • Language
    Python
  • License
    Apache License 2.0
  • Created about 1 year ago
  • Updated 20 days ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

A unified multi-backend utility for benchmarking Transformers, Timm, Diffusers and Sentence-Transformers with full support of Optimum's hardware optimizations & quantization schemes.

Optimum-Benchmark

Optimum-Benchmark is a unified multi-backend utility for benchmarking transformers, diffusers, peft and timm models with Optimum's optimizations & quantization, for inference & training, on different backends & hardwares (OnnxRuntime, Intel Neural Compressor, OpenVINO, Habana Gaudi Processor (HPU), etc).

The experiment management and tracking is handled using hydra which allows for simple cli with minimum configuration changes and maximum flexibility (inspired by tune).

Motivation

  • Many hardware vendors would want to know how their hardware performs compared to others on the same models.
  • Many HF users would want to know how their chosen model performs in terms of latency, throughput, memory usage, energy consumption, etc.
  • Optimum offers a lot of hardware and backend specific optimizations & quantization schemas that can be applied to models and improve their performance.
  • Benchmarks depend heavily on many factors, like input/hardware/releases/etc, but most don't report these factors (e.g. comparing an A100 to an RTX 3090 on a singleton batch).
  • [...]

Features

optimum-benchmark allows you to run benchmarks with no code and minimal user input, just specify:

  • The device to use (e.g. cuda).
  • The type of benchmark (e.g. training)
  • The backend to run on (e.g. onnxruntime).
  • The model name or path (e.g. bert-base-uncased)
  • And optionally, the model's task (e.g. text-classification).

Everything else is either optional or inferred from the model's name or path.

Supported Backends/Devices

  • Pytorch backend for CPU
  • Pytorch backend for CUDA
  • Pytorch backend for Habana Gaudi Processor (HPU)
  • OnnxRuntime backend for CPUExecutionProvider
  • OnnxRuntime backend for CUDAExecutionProvider
  • OnnxRuntime backend for TensorrtExecutionProvider
  • Intel Neural Compressor backend for CPU
  • OpenVINO backend for CPU

Benchmark features

  • Latency and throughput tracking (default).
  • Peak memory tracking (benchmark.memory=true).
  • Energy and carbon emissions (benchmark.energy=true).
  • Warm up runs before inference (benchmark.warmup_runs=20).
  • Warm up steps during training (benchmark.warmup_steps=20).
  • Inputs shapes control (e.g. benchmark.input_shapes.sequence_length=128).
  • Dataset shapes control (e.g. benchmark.dataset_shapes.dataset_size=1000).
  • Forward and Generation pass control (e.g. for an LLM benchmark.generate.max_new_tokens=100, for a diffusion model benchmark.forward.num_images_per_prompt=4).

Backend features

  • Random weights initialization (backend.no_weights=true for fast model instantiation without downloading weights).
  • Onnxruntime Quantization and AutoQuantization (backend.quantization=true or backend.auto_quantization=avx2, etc).
  • Onnxruntime Calibration for Static Quantization (backend.quantization_config.is_static=true, etc).
  • Onnxruntime Optimization and AutoOptimization (backend.optimization=true or backend.auto_optimization=O4, etc).
  • PEFT training (backend.peft_strategy=lora, backend.peft_config.task_type=CAUSAL_LM, etc).
  • DDP training (backend.use_ddp=true, backend.ddp_config.nproc_per_node=2, etc).
  • BitsAndBytes quantization scheme (backend.quantization_scheme=bnb, backend.quantization_config.load_in_4bit, etc).
  • GPTQ quantization scheme (backend.quantization_scheme=gptq, backend.quantization_config.bits=4, etc).
  • Optimum's BetterTransformer (backend.bettertransformer=true).
  • Automatic Mixed Precision (backend.amp_autocast=true).
  • Dynamo/Inductor compiling (backend.torch_compile=true).

Quickstart

Installation

You can install optimum-benchmark using pip:

python -m pip install git+https://github.com/huggingface/optimum-benchmark.git

or by cloning the repository and installing it in editable mode:

git clone https://github.com/huggingface/optimum-benchmark.git && cd optimum-benchmark

python -m pip install -e .

Depending on the backends you want to use, you might need to install some extra dependencies:

  • OpenVINO: pip install optimum-benchmark[openvino]
  • OnnxRuntime: pip install optimum-benchmark[onnxruntime]
  • OnnxRuntime-GPU: pip install optimum-benchmark[onnxruntime-gpu]
  • Intel Neural Compressor: pip install optimum-benchmark[neural-compressor]
  • Text Generation Inference: pip install optimum-benchmark[text-generation-inference]

You can now run a benchmark using the command line by specifying the configuration directory and the configuration name. Both arguments are mandatory for hydra. config-dir is the directory where the configuration files are stored and config-name is the name of the configuration file without its .yaml extension.

optimum-benchmark --config-dir examples/ --config-name pytorch_bert

This will run the benchmark using the configuration in examples/pytorch_bert.yaml and store the results in runs/pytorch_bert.

The result files are inference_results.csv, the program's logs experiment.log and the configuration that's been used hydra_config.yaml. Some other files might be generated depending on the configuration (e.g. forward_codecarbon.csv if benchmark.energy=true).

The directory for storing these results can be changed by setting hydra.run.dir (and/or hydra.sweep.dir in case of a multirun) in the command line or in the config file.

Command-line configuration overrides

It's easy to override the default behavior of a benchmark from the command line.

optimum-benchmark --config-dir examples/ --config-name pytorch_bert model=gpt2 device=cuda:1

Multirun configuration sweeps

You can easily run configuration sweeps using the -m or --multirun option. By default, configurations will be executed serially but other kinds of executions are supported with hydra's launcher plugins : hydra/launcher=submitit, hydra/launcher=rays, hydra/launcher=joblib, etc.

optimum-benchmark --config-dir examples --config-name pytorch_bert -m device=cpu,cuda

Also, for integer parameters like batch_size, one can specify a range of values to sweep over:

optimum-benchmark --config-dir examples --config-name pytorch_bert -m device=cpu,cuda benchmark.input_shapes.batch_size='range(1,10,step=2)'

Reporting benchmark results (WIP)

To aggregate the results of a benchmark (run(s) or sweep(s)), you can use the optimum-report command.

optimum-report --experiments {experiments_folder_1} {experiments_folder_2} --baseline {baseline_folder} --report-name {report_name}

This will create a report in the reports folder with the name {report_name}. The report will contain the results of the experiments in {experiments_folder_1} and {experiments_folder_2} compared to the results of the baseline in {baseline_folder} in the form of a .csv file, an .svg rich table and (a) .png plot(s).

You can also reuse some components of the reporting script for your use case (examples in [examples/training-llamas] and [examples/running-llamas]).

Configurations structure

You can create custom configuration files following the examples here. You can also use hydra's composition with a base configuration (examples/pytorch_bert.yaml for example) and override/define parameters.

To create a configuration that uses a wav2vec2 model and onnxruntime backend, it's as easy as:

defaults:
  - pytorch_bert
  - _self_
  - override backend: onnxruntime

experiment_name: onnxruntime_wav2vec2
model: bookbot/distil-wav2vec2-adult-child-cls-37m
device: cpu

Other than the examples, you can also check tests.

Contributing

Contributions are welcome! And we're happy to help you get started. Feel free to open an issue or a pull request. Things that we'd like to see:

  • More backends (Tensorflow, TFLite, Jax, etc).
  • More tests (right now we only have few tests per backend).
  • Task evaluators for the most common tasks (would be great for output regression).
  • More hardware support (Habana Gaudi Processor (HPU), RadeonOpenCompute (ROCm), etc).

More Repositories

1

transformers

๐Ÿค— Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Python
125,891
star
2

pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
Python
28,073
star
3

diffusers

๐Ÿค— Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
Python
22,776
star
4

datasets

๐Ÿค— The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
Python
17,530
star
5

peft

๐Ÿค— PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Python
14,007
star
6

candle

Minimalist ML framework for Rust
Rust
12,686
star
7

tokenizers

๐Ÿ’ฅ Fast State-of-the-Art Tokenizers optimized for Research and Production
Rust
8,286
star
8

trl

Train transformer language models with reinforcement learning.
Python
8,181
star
9

text-generation-inference

Large Language Model Text Generation Inference
Python
7,240
star
10

accelerate

๐Ÿš€ A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
Python
7,008
star
11

chat-ui

Open source codebase powering the HuggingChat app
TypeScript
6,369
star
12

deep-rl-class

This repo contains the syllabus of the Hugging Face Deep Reinforcement Learning Course.
MDX
3,541
star
13

alignment-handbook

Robust recipes to align language models with human and AI preferences
Python
3,485
star
14

autotrain-advanced

๐Ÿค— AutoTrain Advanced
Python
3,283
star
15

diffusion-models-class

Materials for the Hugging Face Diffusion Models Course
Jupyter Notebook
3,126
star
16

notebooks

Notebooks using the Hugging Face libraries ๐Ÿค—
Jupyter Notebook
3,114
star
17

distil-whisper

Distilled variant of Whisper for speech recognition. 6x faster, 50% smaller, within 1% word error rate.
Python
2,964
star
18

neuralcoref

โœจFast Coreference Resolution in spaCy with Neural Networks
C
2,806
star
19

knockknock

๐ŸšชโœŠKnock Knock: Get notified when your training ends with only two additional lines of code
Python
2,682
star
20

swift-coreml-diffusers

Swift app demonstrating Core ML Stable Diffusion
Swift
2,377
star
21

safetensors

Simple, safe way to store and distribute tensors
Python
2,347
star
22

optimum

๐Ÿš€ Accelerate training and inference of ๐Ÿค— Transformers and ๐Ÿค— Diffusers with easy to use hardware optimization tools
Python
2,086
star
23

awesome-papers

Papers & presentation materials from Hugging Face's internal science day
1,996
star
24

blog

Public repo for HF blog posts
Jupyter Notebook
1,962
star
25

setfit

Efficient few-shot learning with Sentence Transformers
Jupyter Notebook
1,912
star
26

text-embeddings-inference

A blazing fast inference solution for text embeddings models
Rust
1,845
star
27

course

The Hugging Face course on Transformers
MDX
1,832
star
28

evaluate

๐Ÿค— Evaluate: A library for easily evaluating machine learning models and datasets.
Python
1,825
star
29

transfer-learning-conv-ai

๐Ÿฆ„ State-of-the-Art Conversational AI with Transfer Learning
Python
1,654
star
30

swift-coreml-transformers

Swift Core ML 3 implementations of GPT-2, DistilGPT-2, BERT, and DistilBERT for Question answering. Other Transformers coming soon!
Swift
1,543
star
31

pytorch-openai-transformer-lm

๐ŸฅA PyTorch implementation of OpenAI's finetuned transformer language model with a script to import the weights pre-trained by OpenAI
Python
1,464
star
32

cookbook

Open-source AI cookbook
Jupyter Notebook
1,357
star
33

huggingface_hub

All the open source things related to the Hugging Face Hub.
Python
1,311
star
34

Mongoku

๐Ÿ”ฅThe Web-scale GUI for MongoDB
TypeScript
1,289
star
35

huggingface.js

Utilities to use the Hugging Face Hub API
TypeScript
1,193
star
36

hmtl

๐ŸŒŠHMTL: Hierarchical Multi-Task Learning - A State-of-the-Art neural network model for several NLP tasks based on PyTorch and AllenNLP
Python
1,185
star
37

gsplat.js

JavaScript Gaussian Splatting library.
TypeScript
1,114
star
38

llm-vscode

LLM powered development for VSCode
TypeScript
1,060
star
39

datatrove

Freeing data processing from scripting madness by providing a set of platform-agnostic customizable pipeline processing blocks.
Python
1,033
star
40

pytorch-pretrained-BigGAN

๐Ÿฆ‹A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.
Python
986
star
41

torchMoji

๐Ÿ˜‡A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc
Python
880
star
42

nanotron

Minimalistic large language model 3D-parallelism training
Python
810
star
43

naacl_transfer_learning_tutorial

Repository of code for the tutorial on Transfer Learning in NLP held at NAACL 2019 in Minneapolis, MN, USA
Python
718
star
44

awesome-huggingface

๐Ÿค— A list of wonderful open-source projects & applications integrated with Hugging Face libraries.
698
star
45

optimum-nvidia

Python
680
star
46

dataset-viewer

Lightweight web API for visualizing and exploring any dataset - computer vision, speech, text, and tabular - stored on the Hugging Face Hub
Python
614
star
47

transformers-bloom-inference

Fast Inference Solutions for BLOOM
Python
546
star
48

exporters

Export Hugging Face models to Core ML and TensorFlow Lite
Python
540
star
49

pytorch_block_sparse

Fast Block Sparse Matrices for Pytorch
C++
523
star
50

llm.nvim

LLM powered development for Neovim
Lua
507
star
51

swift-transformers

Swift Package to implement a transformers-like API in Swift
Swift
482
star
52

node-question-answering

Fast and production-ready question answering in Node.js
TypeScript
459
star
53

large_language_model_training_playbook

An open collection of implementation tips, tricks and resources for training large language models
Python
431
star
54

llm-ls

LSP server leveraging LLMs for code completion (and more?)
Rust
416
star
55

llm_training_handbook

An open collection of methodologies to help with successful training of large language models.
Python
385
star
56

swift-chat

Mac app to demonstrate swift-transformers
Swift
375
star
57

tflite-android-transformers

DistilBERT / GPT-2 for on-device inference thanks to TensorFlow Lite with Android demo apps
Java
368
star
58

community-events

Place where folks can contribute to ๐Ÿค— community events
Jupyter Notebook
368
star
59

nn_pruning

Prune a model while finetuning or training.
Jupyter Notebook
360
star
60

text-clustering

Easily embed, cluster and semantically label text datasets
Python
335
star
61

speechbox

Python
328
star
62

100-times-faster-nlp

๐Ÿš€100 Times Faster Natural Language Processing in Python - iPython notebook
HTML
325
star
63

education-toolkit

Educational materials for universities
Jupyter Notebook
307
star
64

controlnet_aux

Python
306
star
65

optimum-intel

๐Ÿค— Optimum Intel: Accelerate inference with Intel optimization tools
Jupyter Notebook
295
star
66

datablations

Scaling Data-Constrained Language Models
Jupyter Notebook
293
star
67

unity-api

C#
284
star
68

open-muse

Open reproduction of MUSE for fast text2image generation.
Python
284
star
69

audio-transformers-course

The Hugging Face Course on Transformers for Audio
MDX
247
star
70

hub-docs

Docs of the Hugging Face Hub
221
star
71

lighteval

LightEval is a lightweight LLM evaluation suite that Hugging Face has been using internally with the recently released LLM data processing library datatrove and LLM training library nanotron.
Python
208
star
72

quanto

A pytorch Quantization Toolkit
Python
201
star
73

simulate

๐ŸŽข Creating and sharing simulation environments for embodied and synthetic data research
Python
185
star
74

ratchet

A cross-platform browser ML framework.
Rust
184
star
75

hf_transfer

Rust
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 ๐Ÿค— 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 ๐Ÿ”ฅ interface for running code in the cloud
Python
84
star
92

tune

Python
83
star
93

datasets-viewer

Viewer for the ๐Ÿค— datasets library.
Python
82
star
94

optimum-graphcore

Blazing fast training of ๐Ÿค— 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