Implementation and replication of ProGen, Language Modeling for Protein Generation, in Jax

Related tags

Deep Learningdeep-learningproteinsartificial-intelligence
Overview

ProGen - (wip)

Implementation and replication of ProGen, Language Modeling for Protein Generation, in Pytorch and Jax (the weights will be made easily transferrable between the two)

Install

$ pip install progen-transformer

Usage

from jax import random
from haiku import PRNGSequence
from progen_transformer import ProGen

model = ProGen(
    num_tokens = 256,
    dim = 512,
    seq_len = 1024,
    window_size = 256,       # local attention window size
    depth = 12,              # depth
    heads = 8,               # attention heads
    dim_head = 64,           # dimension per head
    ff_glu = True,           # use GLU in feedforward, from Noam's paper
    global_mlp_depth = 2     # last N global gmlp layers
)

rng = PRNGSequence(42)
seq = random.randint(next(rng), (1024,), 0, 256)

params = model.init(next(rng), seq)
logits = model.apply(params, next(rng), seq) # (1024, 256)

Training from Uniref

Download Uniref50 from UniProt and place uniref50.fasta in the root directory

$ python gen_train_data.py

You should see a lot of green if everything succeeds. Then

$ python train.py

By default, the script will checkpoint and resume automatically, but if you wish to clear your progress and restart, just add a --new flag

$ python train.py --new

Model checkpoints will be saved periodically to ./ckpts

Todo

  • train tfrecords from google cloud storage path
  • generate validation tfrecords
  • add panda integration with GO annotations
  • resume from correct place in tfrecord even if batch size is changed inbetween runs, display number of sequences processed (aiming for 1 billion)
  • model parallelism with pjit
  • bfloat16 on xla
  • checkpoint and resume from a google cloud storage path
  • config to annotation to template string with jinja2 - use jinja2 for wandb html logging as well
  • manage experimental tracker state, and also allow ability to turn it off by piping to noop
  • add a confirmation before clearing a folder for --new run
  • engineer mask in cross entropy loss so that padding can be reused as end-of-string token
  • flip seq # annotation order with prob set in config
  • keep N last checkpoints

Citations

@misc{madani2020progen,
    title   = {ProGen: Language Modeling for Protein Generation}, 
    author  = {Ali Madani and Bryan McCann and Nikhil Naik and Nitish Shirish Keskar and Namrata Anand and Raphael R. Eguchi and Po-Ssu Huang and Richard Socher},
    year    = {2020},
    eprint  = {2004.03497},
    archivePrefix = {arXiv},
    primaryClass = {q-bio.BM}
}
@misc{su2021roformer,
    title   = {RoFormer: Enhanced Transformer with Rotary Position Embedding},
    author  = {Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu},
    year    = {2021},
    eprint  = {2104.09864},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}
@misc{shazeer2020glu,
    title   = {GLU Variants Improve Transformer},
    author  = {Noam Shazeer},
    year    = {2020},
    url     = {https://arxiv.org/abs/2002.05202}
}
You might also like...
Implementation of the GVP-Transformer, which was used in the paper
Implementation of the GVP-Transformer, which was used in the paper "Learning inverse folding from millions of predicted structures" for de novo protein design alongside Alphafold2

GVP Transformer (wip) Implementation of the GVP-Transformer, which was used in the paper Learning inverse folding from millions of predicted structure

Phil Wang 19 May 6, 2022
A pytorch-version implementation codes of paper:
A pytorch-version implementation codes of paper: "BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation"

BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation A pytorch-version implementation

null 11 Oct 8, 2022
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

English | 简体中文 | 繁體中文 State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained mo

Hugging Face 77.2k Jan 2, 2023
Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training

Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training Code for our paper "Predicting lncRNA–protein interactio

zhanglabNKU 1 Nov 29, 2022
Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction".

GNN_PPI Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction". Lear

Ursa Zrimsek 2 Dec 14, 2022
RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.
RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.

RITA: a Study on Scaling Up Generative Protein Sequence Models RITA is a family of autoregressive protein models, developed by a collaboration of Ligh

LightOn 69 Dec 22, 2022
Generative Models for Graph-Based Protein Design
Generative Models for Graph-Based Protein Design

Graph-Based Protein Design This repo contains code for Generative Models for Graph-Based Protein Design by John Ingraham, Vikas Garg, Regina Barzilay

John Ingraham 159 Dec 15, 2022
7th place solution of Human Protein Atlas - Single Cell Classification on Kaggle

kaggle-hpa-2021-7th-place-solution Code for 7th place solution of Human Protein Atlas - Single Cell Classification on Kaggle. A description of the met

null 8 Jul 9, 2021
Graph-based community clustering approach to extract protein domains from a predicted aligned error matrix
Graph-based community clustering approach to extract protein domains from a predicted aligned error matrix

Using a predicted aligned error matrix corresponding to an AlphaFold2 model , returns a series of lists of residue indices, where each list corresponds to a set of residues clustering together into a pseudo-rigid domain.

Tristan Croll 24 Nov 23, 2022
Comments
  • protein bert uniref90 dataset

    protein bert uniref90 dataset

    (discussed in discord)

    after running the first step (create_uniref_db) of https://github.com/nadavbra/protein_bert I got a 24GB file "uniref_proteins_and_annotations.db" . It seems it could be useful for generate sequences for this project, sharing the links there

    • https://gitlab.com/rom1504/uniref data
    • colab to get the db and do a few queries https://colab.research.google.com/drive/1BGYEBDmD0yToLNou2T-t-QbJV5wCtIBz#scrollTo=21U3PpCp-pxr There are 135301051 records in the db, in a table looking like:
    CREATE TABLE "protein_annotations" (
    "index"    INTEGER,
    "tax_id"    REAL,
    "uniprot_name"    TEXT,
    "go_annotations"    TEXT,
    "flat_go_annotations"    TEXT,
    "n_go_annotations"    INTEGER,
    "complete_go_annotation_indices"    TEXT,
    "n_complete_go_annotations"    INTEGER
);

    Sample look like this:

    | | index | tax_id | uniprot_name | go_annotations | flat_go_annotations | n_go_annotations | complete_go_annotation_indices | n_complete_go_annotations | |---:|--------:|-----------------:|:-----------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------|-------------------:|:---------------------------------|----------------------------:| | 0 | 0 | 1.57204e+06 | A0A5A9P0L4_9TELE | {"GO Molecular Function": ["GO:0003755", "GO:0005524", "GO:0004672", "GO:0005509"], "GO Biological Process": [], "GO Cellular Component": []} | ["GO:0003755", "GO:0004672", "GO:0005509", "GO:0005524"] | 4 | [2761, 3561, 4193, 4205] | 4 | | 1 | 1 | 648755 | UPI0016133188 | {"GO Molecular Function": [], "GO Biological Process": [], "GO Cellular Component": []} | [] | 0 | [] | 0 | | 2 | 2 | 1.93059e+06 | A0A410P257_9BACT | {"GO Molecular Function": [], "GO Biological Process": [], "GO Cellular Component": []} | [] | 0 | [] | 0 | | 3 | 3 | 519421 | UPI0019403D63 | {"GO Molecular Function": [], "GO Biological Process": [], "GO Cellular Component": []} | [] | 0 | [] | 0 | | 4 | 4 | 72004 | A0A6B0RPA5_9CETA | {"GO Molecular Function": ["GO:0005524", "GO:0004672"], "GO Biological Process": [], "GO Cellular Component": []} | ["GO:0004672", "GO:0005524"] | 2 | [3561, 4205] | 2 | | 5 | 5 | 375764 | A0A672ZWI7_9TELE | {"GO Molecular Function": [], "GO Biological Process": [], "GO Cellular Component": []} | [] | 0 | [] | 0 | | 6 | 6 | 1.41558e+06 | A0A6P7YNV3_9AMPH | {"GO Molecular Function": ["GO:0005524", "GO:0004672"], "GO Biological Process": [], "GO Cellular Component": ["GO:0005886"]} | ["GO:0004672", "GO:0005524", "GO:0005886"] | 3 | [3561, 4205, 4526] | 3 | | 7 | 7 | 240159 | A0A4U5TZD8_COLLU | {"GO Molecular Function": ["GO:0005524", "GO:0004672"], "GO Biological Process": [], "GO Cellular Component": ["GO:0016021", "GO:0005886"]} | ["GO:0004672", "GO:0005524", "GO:0005886", "GO:0016021"] | 4 | [3561, 4205, 4526, 10019] | 4 | | 8 | 8 | 146911 | UPI00074FFD9C | {"GO Molecular Function": [], "GO Biological Process": [], "GO Cellular Component": []} | [] | 0 | [] | 0 | | 9 | 9 | 260995 | A0A6P8RG40_GEOSA | {"GO Molecular Function": ["GO:0005524", "GO:0004672"], "GO Biological Process": [], "GO Cellular Component": ["GO:0005886"]} | ["GO:0004672", "GO:0005524", "GO:0005886"] | 3 | [3561, 4205, 4526] | 3 |

    opened by rom1504 4
Releases(0.0.36)
Owner
Phil Wang
Working with Attention
Phil Wang
GitHub Repository
Implementation of Pooling by Sliced-Wasserstein Embedding (NeurIPS 2021)

PSWE: Pooling by Sliced-Wasserstein Embedding (NeurIPS 2021) PSWE is a permutation-invariant feature aggregation/pooling method based on sliced-Wasser

Navid Naderializadeh 3 May 06, 2022
Load What You Need: Smaller Multilingual Transformers for Pytorch and TensorFlow 2.0.

Smaller Multilingual Transformers This repository shares smaller versions of multilingual transformers that keep the same representations offered by t

Geotrend 79 Dec 28, 2022
Instance-level Image Retrieval using Reranking Transformers

Instance-level Image Retrieval using Reranking Transformers Fuwen Tan, Jiangbo Yuan, Vicente Ordonez, ICCV 2021. Abstract Instance-level image retriev

UVA Computer Vision 87 Jan 03, 2023
PushForKiCad - AISLER Push for KiCad EDA

AISLER Push for KiCad Push your layout to AISLER with just one click for instant

AISLER 31 Dec 29, 2022
Codebase for Attentive Neural Hawkes Process (A-NHP) and Attentive Neural Datalog Through Time (A-NDTT)

Introduction Codebase for the paper Transformer Embeddings of Irregularly Spaced Events and Their Participants. This codebase contains two packages: a

Alan Yang 28 Dec 12, 2022
Learning Skeletal Articulations with Neural Blend Shapes

This repository provides an end-to-end library for automatic character rigging and blend shapes generation as well as a visualization tool. It is based on our work Learning Skeletal Articulations wit

Peizhuo 504 Dec 30, 2022
9th place solution

AllDataAreExt-Galixir-Kaggle-HPA-2021-Solution Team Members Qishen Ha is Master of Engineering from the University of Tokyo. Machine Learning Engineer

daishu 5 Nov 18, 2021
On Nonlinear Latent Transformations for GAN-based Image Editing - PyTorch implementation

On Nonlinear Latent Transformations for GAN-based Image Editing - PyTorch implementation On Nonlinear Latent Transformations for GAN-based Image Editi

Valentin Khrulkov 22 Oct 24, 2022
Simulation of self-focusing of laser beams in condensed media

What is it? Program for scientific research, which allows to simulate the phenomenon of self-focusing of different laser beams (including Gaussian, ri

Evgeny Vasilyev 13 Dec 24, 2022
Code for Understanding Pooling in Graph Neural Networks

Select, Reduce, Connect This repository contains the code used for the experiments of: "Understanding Pooling in Graph Neural Networks" Setup Install

Daniele Grattarola 37 Dec 13, 2022
MultiLexNorm 2021 competition system from ÚFAL

ÚFAL at MultiLexNorm 2021: Improving Multilingual Lexical Normalization by Fine-tuning ByT5 David Samuel & Milan Straka Charles University Faculty of

ÚFAL 13 Jun 28, 2022
FastFace: Lightweight Face Detection Framework

Light Face Detection using PyTorch Lightning

Ömer BORHAN 75 Dec 05, 2022
Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based Analysis Framework"

Privacy-Aware Inverse RL (PRIL) Analysis Framework Code, environments, and scripts for the paper: "How Private Is Your RL Policy? An Inverse RL Based

1 Dec 06, 2021
Code for the paper "Controllable Video Captioning with an Exemplar Sentence"

SMCG Code for the paper "Controllable Video Captioning with an Exemplar Sentence" Introduction We investigate a novel and challenging task, namely con

10 Dec 04, 2022
[CVPR 2021] Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion

[CVPR 2021] Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion

Rex Cheng 364 Jan 03, 2023
Research shows Google collects 20x more data from Android than Apple collects from iOS. Block this non-consensual telemetry using pihole blocklists.

pihole-antitelemetry Research shows Google collects 20x more data from Android than Apple collects from iOS. Block both using these pihole lists. Proj

Adrian Edwards 290 Jan 09, 2023
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility

Tensorpack is a neural network training interface based on TensorFlow. Features: It's Yet Another TF high-level API, with speed, and flexibility built

Tensorpack 6.2k Jan 09, 2023
Global Filter Networks for Image Classification

Global Filter Networks for Image Classification Created by Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou This repository contains PyTorch

Yongming Rao 273 Dec 26, 2022
Official repository for "Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring".

RNN-MBP Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring (AAAI-2022) by Chao Zhu, Hang Dong, Jinshan Pan

SIV-LAB 22 Aug 31, 2022
MPLP: Metapath-Based Label Propagation for Heterogenous Graphs

MPLP: Metapath-Based Label Propagation for Heterogenous Graphs Results on MAG240M Here, we demonstrate the following performance on the MAG240M datase

Qiuying Peng 10 Jun 28, 2022