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
$ pip install enformer-pytorchimport 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
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
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
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 paperDeepmind 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
$ pip install enformer-pytorch>=0.5Loading the model
from enformer_pytorch import from_pretrained
enformer = from_pretrained('EleutherAI/enformer-official-rough')Quick sanity check on a single human validation point
$ python test_pretrained.py
# 0.5963 correlation coefficient on a validation sampleThis is all made possible thanks to HuggingFace's custom model feature.
You can also load, with overriding of the target_length parameter, if you are working with shorter sequence lengths
from enformer_pytorch import from_pretrained
model = from_pretrained('EleutherAI/enformer-official-rough', target_length = 128, dropout_rate = 0.1)
# do your fine-tuningTo save on memory during fine-tuning a large Enformer model
from enformer_pytorch import from_pretrained
enformer = from_pretrained('EleutherAI/enformer-official-rough', use_checkpointing = True)
# finetune enformer on a limited budgetThis repository will also allow for easy fine-tuning of Enformer.
Fine-tuning on new tracks
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)
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
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()You can use the GenomicIntervalDataset to easily fetch sequences of any length from a .bed file, with greater context length dynamically computed if specified
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 <chromosome>, <start position>, <end position>
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
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 <chromosome>, <start position>, <end position>
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,)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
- script to load weights from trained tensorflow enformer model to pytorch model
- add loss wrapper with poisson loss
- move the metrics code over to pytorch as well
- train enformer model
- build context manager for fine-tuning with unfrozen enformer but with frozen batchnorm
- allow for plain fine-tune with fixed static context
- allow for fine tuning with only unfrozen layernorms (technique from fine tuning transformers)
- fix handling of 'N' in sequence, figure out representation of N in basenji barnyard
- take care of shift augmentation in
GenomicIntervalDataset - speed up
str_to_seq_indices - add to EleutherAI huggingface (done thanks to Niels)
- offer some basic training utils, as gradient accumulation will be needed for fine tuning
@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}
}@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}
}
