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https://github.com/lucidrains/enformer-pytorch

Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch
https://github.com/lucidrains/enformer-pytorch

artificial-intelligence attention-mechanism deep-learning dna-sequences gene-expression genomics transformer

Last synced: 22 days ago
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Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

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README 

        



## Enformer - Pytorch



Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch. This repository also contains the means to fine tune pretrained models for your downstream tasks. The original tensorflow sonnet code can be found here.



Update: finetuned for predicting pseudobulk chromatin accessibility here



## Install



```bash

$ pip install enformer-pytorch

```



## Usage



```python

import torch

from enformer_pytorch import Enformer



model = Enformer.from_hparams(

    dim = 1536,

    depth = 11,

    heads = 8,

    output_heads = dict(human = 5313, mouse = 1643),

    target_length = 896,

)

    

seq = torch.randint(0, 5, (1, 196_608)) # for ACGTN, in that order (-1 for padding)

output = model(seq)



output['human'] # (1, 896, 5313)

output['mouse'] # (1, 896, 1643)

```



You can also directly pass in the sequence as one-hot encodings, which must be float values



```python

import torch

from enformer_pytorch import Enformer, seq_indices_to_one_hot



model = Enformer.from_hparams(

    dim = 1536,

    depth = 11,

    heads = 8,

    output_heads = dict(human = 5313, mouse = 1643),

    target_length = 896,

)



seq = torch.randint(0, 5, (1, 196_608))

one_hot = seq_indices_to_one_hot(seq)



output = model(one_hot)



output['human'] # (1, 896, 5313)

output['mouse'] # (1, 896, 1643)

```



Finally, one can fetch the embeddings, for fine-tuning and otherwise, by setting the `return_embeddings` flag to be `True` on forward



```python

import torch

from enformer_pytorch import Enformer, seq_indices_to_one_hot



model = Enformer.from_hparams(

    dim = 1536,

    depth = 11,

    heads = 8,

    output_heads = dict(human = 5313, mouse = 1643),

    target_length = 896,

)



seq = torch.randint(0, 5, (1, 196_608))

one_hot = seq_indices_to_one_hot(seq)



output, embeddings = model(one_hot, return_embeddings = True)



embeddings # (1, 896, 3072)

```



For training, you can directly pass the head and target in to get the poisson loss



```python

import torch

from enformer_pytorch import Enformer, seq_indices_to_one_hot



model = Enformer.from_hparams(

    dim = 1536,

    depth = 11,

    heads = 8,

    output_heads = dict(human = 5313, mouse = 1643),

    target_length = 200,

).cuda()



seq = torch.randint(0, 5, (196_608 // 2,)).cuda()

target = torch.randn(200, 5313).cuda()



loss = model(

    seq,

    head = 'human',

    target = target

)



loss.backward()



# after much training



corr_coef = model(

    seq,

    head = 'human',

    target = target,

    return_corr_coef = True

)



corr_coef # pearson R, used as a metric in the paper

```



## Pretrained Model



Deepmind has released the weights for their tensorflow sonnet Enformer model! I have ported it over to Pytorch and uploaded it to 🤗 Huggingface (~1GB). There are still some rounding errors that seem to be accruing across the layers, resulting in an absolute error as high as `0.5`. However, correlation coefficient look good so I am releasing the 'rough'ly working version. Will keep working on figuring out where the numerical errors are happening (it may be the attention pooling module, as I noticed the attention logits are pretty high).



Update: John St. John did some work and found that the `enformer-official-rough` model hits the reported marks in the paper - human pearson R of `0.625` for validation, and `0.65` for test.



Update: As of version 0.8.0, if one were to use the `from_pretrained` function to load the pretrained model, it should automatically use precomputed gamma positions to address a difference between tensorflow and pytorch `xlogy`. This should resolve the numerical discrepancy above. If you were to further finetune and not be using the `from_pretrained` function, please make sure to set `use_tf_gamma = True` when using `.from_hparams` to instantiate the `Enformer`



```bash

$ pip install enformer-pytorch>=0.5

````



Loading the model



```python

from enformer_pytorch import from_pretrained



enformer = from_pretrained('EleutherAI/enformer-official-rough')

```



Quick sanity check on a single human validation point



```python

$ python test_pretrained.py

# 0.5963 correlation coefficient on a validation sample

```



This is all made possible thanks to HuggingFace's [custom model](https://huggingface.co/docs/transformers/master/en/custom_models) feature.



You can also load, with overriding of the `target_length` parameter, if you are working with shorter sequence lengths



```python

from enformer_pytorch import from_pretrained



model = from_pretrained('EleutherAI/enformer-official-rough', target_length = 128, dropout_rate = 0.1)



# do your fine-tuning

```



To save on memory during fine-tuning a large Enformer model



```python

from enformer_pytorch import from_pretrained



enformer = from_pretrained('EleutherAI/enformer-official-rough', use_checkpointing = True)



# finetune enformer on a limited budget

```



## Fine-tuning



This repository will also allow for easy fine-tuning of Enformer.



Fine-tuning on new tracks



```python

import torch

from enformer_pytorch import from_pretrained

from enformer_pytorch.finetune import HeadAdapterWrapper



enformer = from_pretrained('EleutherAI/enformer-official-rough')



model = HeadAdapterWrapper(

    enformer = enformer,

    num_tracks = 128,

    post_transformer_embed = False   # by default, embeddings are taken from after the final pointwise block w/ conv -> gelu - but if you'd like the embeddings right after the transformer block with a learned layernorm, set this to True

).cuda()



seq = torch.randint(0, 5, (1, 196_608 // 2,)).cuda()

target = torch.randn(1, 200, 128).cuda()  # 128 tracks



loss = model(seq, target = target)

loss.backward()

```



Finetuning on contextual data (cell type, transcription factor, etc)



```python

import torch

from enformer_pytorch import from_pretrained

from enformer_pytorch.finetune import ContextAdapterWrapper



enformer = from_pretrained('EleutherAI/enformer-official-rough')

    

model = ContextAdapterWrapper(

    enformer = enformer,

    context_dim = 1024

).cuda()



seq = torch.randint(0, 5, (1, 196_608 // 2,)).cuda()



target = torch.randn(1, 200, 4).cuda()  # 4 tracks

context = torch.randn(4, 1024).cuda()   # 4 contexts for the different 'tracks'



loss = model(

    seq,

    context = context,

    target = target

)



loss.backward()

```



Finally, there is also a way to use attention aggregation from a set of context embeddings (or a single context embedding). Simply use the `ContextAttentionAdapterWrapper`



```python

import torch

from enformer_pytorch import from_pretrained

from enformer_pytorch.finetune import ContextAttentionAdapterWrapper



enformer = from_pretrained('EleutherAI/enformer-official-rough')

    

model = ContextAttentionAdapterWrapper(

    enformer = enformer,

    context_dim = 1024,

    heads = 8,              # number of heads in the cross attention

    dim_head = 64           # dimension per head

).cuda()



seq = torch.randint(0, 5, (1, 196_608 // 2,)).cuda()



target = torch.randn(1, 200, 4).cuda()      # 4 tracks

context = torch.randn(4, 16, 1024).cuda()   # 4 contexts for the different 'tracks', each with 16 tokens



context_mask = torch.ones(4, 16).bool().cuda() # optional context mask, in example, include all context tokens



loss = model(

    seq,

    context = context,

    context_mask = context_mask,

    target = target

)



loss.backward()

```



## Data



You can use the `GenomicIntervalDataset` to easily fetch sequences of any length from a `.bed` file, with greater context length dynamically computed if specified



```python

import torch

import polars as pl

from enformer_pytorch import Enformer, GenomeIntervalDataset



filter_train = lambda df: df.filter(pl.col('column_4') == 'train')



ds = GenomeIntervalDataset(

    bed_file = './sequences.bed',                       # bed file - columns 0, 1, 2 must be , , 

    fasta_file = './hg38.ml.fa',                        # path to fasta file

    filter_df_fn = filter_train,                        # filter dataframe function

    return_seq_indices = True,                          # return nucleotide indices (ACGTN) or one hot encodings

    shift_augs = (-2, 2),                               # random shift augmentations from -2 to +2 basepairs

    context_length = 196_608,

    # this can be longer than the interval designated in the .bed file,

    # in which case it will take care of lengthening the interval on either sides

    # as well as proper padding if at the end of the chromosomes

    chr_bed_to_fasta_map = {

        'chr1': 'chromosome1',  # if the chromosome name in the .bed file is different than the key name in the fasta file, you can rename them on the fly

        'chr2': 'chromosome2',

        'chr3': 'chromosome3',

        # etc etc

    }

)



model = Enformer.from_hparams(

    dim = 1536,

    depth = 11,

    heads = 8,

    output_heads = dict(human = 5313, mouse = 1643),

    target_length = 896,

)



seq = ds[0] # (196608,)

pred = model(seq, head = 'human') # (896, 5313)

```



To return the random shift value, as well as whether reverse complement was activated (in the case you need to reverse the corresponding chip-seq target data), just set `return_augs = True` when initializing the `GenomicIntervalDataset`



```python

import torch

import polars as pl

from enformer_pytorch import Enformer, GenomeIntervalDataset



filter_train = lambda df: df.filter(pl.col('column_4') == 'train')



ds = GenomeIntervalDataset(

    bed_file = './sequences.bed',                       # bed file - columns 0, 1, 2 must be , , 

    fasta_file = './hg38.ml.fa',                        # path to fasta file

    filter_df_fn = filter_train,                        # filter dataframe function

    return_seq_indices = True,                          # return nucleotide indices (ACGTN) or one hot encodings

    shift_augs = (-2, 2),                               # random shift augmentations from -2 to +2 basepairs

    rc_aug = True,                                      # use reverse complement augmentation with 50% probability

    context_length = 196_608,

    return_augs = True                                  # return the augmentation meta data

)



seq, rand_shift_val, rc_bool = ds[0] # (196608,), (1,), (1,)

```



## Appreciation



Special thanks goes out to EleutherAI for providing the resources to retrain the model, during a time when the official model from Deepmind had not been released yet.



Thanks also goes out to @johahi for finding out that there are numerical differences between the torch and tensorflow implementations of `xlogy`. He provided a fix for this difference, which is adopted in this repository in `v0.8.0`



## Todo



- [x] script to load weights from trained tensorflow enformer model to pytorch model

- [x] add loss wrapper with poisson loss

- [x] move the metrics code over to pytorch as well

- [x] train enformer model

- [x] build context manager for fine-tuning with unfrozen enformer but with frozen batchnorm

- [x] allow for plain fine-tune with fixed static context

- [x] allow for fine tuning with only unfrozen layernorms (technique from fine tuning transformers)

- [x] fix handling of 'N' in sequence, figure out representation of N in basenji barnyard

- [x] take care of shift augmentation in `GenomicIntervalDataset`

- [x] speed up `str_to_seq_indices`

- [x] add to EleutherAI huggingface (done thanks to Niels)

- [ ] offer some basic training utils, as gradient accumulation will be needed for fine tuning



## Citations



```bibtex

@article {Avsec2021.04.07.438649,

    author  = {Avsec, {\v Z}iga and Agarwal, Vikram and Visentin, Daniel and Ledsam, Joseph R. and Grabska-Barwinska, Agnieszka and Taylor, Kyle R. and Assael, Yannis and Jumper, John and Kohli, Pushmeet and Kelley, David R.},

    title   = {Effective gene expression prediction from sequence by integrating long-range interactions},

    elocation-id = {2021.04.07.438649},

    year    = {2021},

    doi     = {10.1101/2021.04.07.438649},

    publisher = {Cold Spring Harbor Laboratory},

    URL     = {https://www.biorxiv.org/content/early/2021/04/08/2021.04.07.438649},

    eprint  = {https://www.biorxiv.org/content/early/2021/04/08/2021.04.07.438649.full.pdf},

    journal = {bioRxiv}

}

```



```bibtex

@misc{liu2022convnet,

    title   = {A ConvNet for the 2020s},

    author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},

    year    = {2022},

    eprint  = {2201.03545},

    archivePrefix = {arXiv},

    primaryClass = {cs.CV}

}

```