Implementation of ProteinBERT in Pytorch

Related tags

Text Data & NLPdeep-learningartificial-intelligenceprotein-sequencesunsupervised-learning
Overview

ProteinBERT - Pytorch (wip)

Implementation of ProteinBERT in Pytorch.

Original Repository

Install

$ pip install protein-bert-pytorch

Usage

import torch
from protein_bert_pytorch import ProteinBERT

model = ProteinBERT(
    num_tokens = 21,
    num_annotation = 8943,
    dim = 512,
    dim_global = 256,
    depth = 6,
    narrow_conv_kernel = 9,
    wide_conv_kernel = 9,
    wide_conv_dilation = 5,
    attn_heads = 8,
    attn_dim_head = 64
)

seq = torch.randint(0, 21, (2, 2048))
mask = torch.ones(2, 2048).bool()
annotation = torch.randint(0, 1, (2, 8943)).float()

seq_logits, annotation_logits = model(seq, annotation, mask = mask) # (2, 2048, 21), (2, 8943)

Citations

@article {Brandes2021.05.24.445464,
    author      = {Brandes, Nadav and Ofer, Dan and Peleg, Yam and Rappoport, Nadav and Linial, Michal},
    title       = {ProteinBERT: A universal deep-learning model of protein sequence and function},
    year        = {2021},
    doi         = {10.1101/2021.05.24.445464},
    publisher   = {Cold Spring Harbor Laboratory},
    URL         = {https://www.biorxiv.org/content/early/2021/05/25/2021.05.24.445464},
    eprint      = {https://www.biorxiv.org/content/early/2021/05/25/2021.05.24.445464.full.pdf},
    journal     = {bioRxiv}
}
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Comments
  • bugFix: x and y not on the same device when Learner is trained on GPU

    bugFix: x and y not on the same device when Learner is trained on GPU

    When

    seq        = torch.randint(0, 21, (2, 2048)).cuda()
annotation = torch.randint(0, 1, (2, 8943)).float().cuda()
mask       = torch.ones(2, 2048).bool().cuda()

learner.cuda()

loss = learner(seq, annotation, mask = mask) # (2, 2048, 21), (2, 8943)

    OUTPUT

    ---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-2-60892e498570> in <module>
      4 learner.cuda()
      5 
----> 6 loss = learner(seq, annotation, mask = mask) # (2, 2048, 21), (2, 8943)

~/data/.conda/envs/torch/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

/mnt/5280b/wwang/proteinbert/protein_bert_pytorch.py in forward(self, seq, annotation, mask)
    365 
    366         for token_id in self.exclude_token_ids:
--> 367             random_replace_token_prob_mask = random_replace_token_prob_mask & (random_tokens != token_id)  # make sure you never substitute a token with an excluded token type (pad, start, end)
    368 
    369         # noise sequence

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
    opened by wilmerwang 0
  • How to use this bert version to use the pretrianed model?

    How to use this bert version to use the pretrianed model?

    Hi guys, thanks for great work. I'm trying to use this pytorch version protein-bert to use the pre-trained model 'ftp://ftp.cs.huji.ac.il/users/nadavb/protein_bert/epoch_92400_sample_23500000.pkl', but have no clues at all. Could you please give some suggestions? Thank you so much!

    opened by Y-H-Joe 1
Owner
Phil Wang
Working with Attention
Phil Wang
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