Dataloader tools for language modelling

Overview

Installation:

pip install lm_dataloader

Design Philosophy

  • A library to unify lm dataloading at large scale

  • Simple interface, any tokenizer can be integrated

  • Minimal changes needed from small -> large scale (many multiple GPU nodes)

  • follows fairseq / megatron's 'mmap' dataformat, but with improvements. Those being:

    • Easily combine multiple datasets
    • Easily split a dataset into train / val / test splits
    • Easily build a weighted dataset out of a list of existing ones along with weights.
    • unified into a single 'file' (which is actually a directory containing a .bin / .idx file)
    • index files that are built on the fly are hidden files, leaving less mess in the directory.
    • More straightforward interface, better documentation.
    • Inspectable with a command line tool
    • Can load from urls
    • Can load from S3 buckets
    • Can load from GCS buckets
    • Can tokenize on the fly instead of preprocessing

Misc. TODO: - [ ] Option to set mpu globally (for distributed dataloading)

Example usage

To tokenize a dataset contained in a .jsonl file (where the text to be tokenized can be accessed under the 'text' key):

import lm_dataloader as lmdl
from transformers import GPT2TokenizerFast 

jsonl_path = "test.jsonl"
output = "my_dataset.lmd"
tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')

lmdl.encode(
    jsonl_path,
    output_prefix=output,
    tokenize_fn=tokenizer.encode,
    tokenizer_vocab_size=len(tokenizer),
    eod_token=tokenizer.eos_token_id,
)

This will create a dataset at "my_dataset.lmd" which can be loaded as an indexed torch dataset like so:

from lm_dataloader import LMDataset
from transformers import GPT2TokenizerFast 

tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
seq_length = tokenizer.model_max_length # or whatever the sequence length of your model is

dataset = LMDataset("my_dataset.lmd", seq_length=seq_length)

# peek at 0th index
print(dataset[0])

Command line utilities

There are also command line utilities provided to inspect / merge datasets, e.g:

lm-dataloader inspect my_dataset.lmd

Launches an interactive terminal to inspect the data in my_dataset.lmd

And:

lm-dataloader merge my_dataset.lmd,my_dataset_2.lmd new_dataset.lmd

Merges the datasets at "my_dataset.lmd" and "my_dataset_2.lmd" into a new file at "new_dataset.lmd".

Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification

Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification Suncheng Xiang Shanghai Jiao Tong University Over

SunchengXiang 68 Dec 13, 2022
Causal Influence Detection for Improving Efficiency in Reinforcement Learning

Causal Influence Detection for Improving Efficiency in Reinforcement Learning This repository contains the code release for the paper "Causal Influenc

Autonomous Learning Group 21 Nov 29, 2022
PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021.

GCResNet PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021. The code will

11 May 19, 2022
Torch Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"

Photo-Realistic-Super-Resoluton Torch Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" [Paper]

Harry Yang 199 Dec 01, 2022
SimpleDepthEstimation - An unified codebase for NN-based monocular depth estimation methods

SimpleDepthEstimation Introduction This is an unified codebase for NN-based monocular depth estimation methods, the framework is based on detectron2 (

8 Dec 13, 2022
HairCLIP: Design Your Hair by Text and Reference Image

Overview This repository hosts the official PyTorch implementation of the paper: "HairCLIP: Design Your Hair by Text and Reference Image". Our single

322 Jan 06, 2023
This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

Graphormer By Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng*, Guolin Ke, Di He*, Yanming Shen and Tie-Yan Liu. This repo is the official impl

Microsoft 1.3k Dec 29, 2022
LTR_CrossEncoder: Legal Text Retrieval Zalo AI Challenge 2021

LTR_CrossEncoder: Legal Text Retrieval Zalo AI Challenge 2021 We propose a cross encoder model (LTR_CrossEncoder) for information retrieval, re-retrie

Hieu Duong 7 Jan 12, 2022
Tensorflow implementation of MIRNet for Low-light image enhancement

MIRNet Tensorflow implementation of the MIRNet architecture as proposed by Learning Enriched Features for Real Image Restoration and Enhancement. Lanu

Soumik Rakshit 91 Jan 06, 2023
Install alphafold on the local machine, get out of docker.

AlphaFold This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP

Kui Xu 73 Dec 13, 2022
The official implementation of Variable-Length Piano Infilling (VLI).

Variable-Length-Piano-Infilling The official implementation of Variable-Length Piano Infilling (VLI). (paper: Variable-Length Music Score Infilling vi

29 Sep 01, 2022
STMTrack: Template-free Visual Tracking with Space-time Memory Networks

STMTrack This is the official implementation of the paper: STMTrack: Template-free Visual Tracking with Space-time Memory Networks. Setup Prepare Anac

Zhihong Fu 62 Dec 21, 2022
Multi-Objective Reinforced Active Learning

Multi-Objective Reinforced Active Learning Dependencies wandb tqdm pytorch = 1.7.0 numpy = 1.20.0 scipy = 1.1.0 pycolab == 1.2 Weights and Biases O

Markus Peschl 6 Nov 19, 2022
A basic implementation of Layer-wise Relevance Propagation (LRP) in PyTorch.

Layer-wise Relevance Propagation (LRP) in PyTorch Basic unsupervised implementation of Layer-wise Relevance Propagation (Bach et al., Montavon et al.)

Kai Fabi 28 Dec 26, 2022
Improving Compound Activity Classification via Deep Transfer and Representation Learning

Improving Compound Activity Classification via Deep Transfer and Representation Learning This repository is the official implementation of Improving C

NingLab 2 Nov 24, 2021
ZEBRA: Zero Evidence Biometric Recognition Assessment

ZEBRA: Zero Evidence Biometric Recognition Assessment license: LGPLv3 - please reference our paper version: 2020-06-11 author: Andreas Nautsch (EURECO

Voice Privacy Challenge 2 Dec 12, 2021
Creating multimodal multitask models

Fusion Brain Challenge The English version of the document can be found here. Обновления 01.11 Мы выкладываем пример данных, аналогичных private test

Sber AI 43 Nov 28, 2022
A Jinja extension (compatible with Flask and other frameworks) to compile and/or compress your assets.

A Jinja extension (compatible with Flask and other frameworks) to compile and/or compress your assets.

Jayson Reis 94 Nov 21, 2022
YOLOv5 detection interface - PyQt5 implementation

所有代码已上传,直接clone后,运行yolo_win.py即可开启界面。 2021/9/29:加入置信度选择 界面是在ultralytics的yolov5基础上建立的,界面使用pyqt5实现,内容较简单,娱乐而已。 功能: 模型选择 本地文件选择(视频图片均可) 开关摄像头

487 Dec 27, 2022
K Closest Points and Maximum Clique Pruning for Efficient and Effective 3D Laser Scan Matching (To appear in RA-L 2022)

KCP The official implementation of KCP: k Closest Points and Maximum Clique Pruning for Efficient and Effective 3D Laser Scan Matching, accepted for p

Yu-Kai Lin 109 Dec 14, 2022