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---
license: cc-by-nc-nd-4.0
---
# PTM-Mamba
A PTM-Aware Protein Language Model with Bidirectional Gated Mamba Blocks
## Install Enviroment
## Docker
Setting up env for mamba could be a pain, alternatively we suggest using docker containers.
#### Run container in interactive and detach mode, and mounte project dir to the container workspace.
```
docker run --gpus all -v $(pwd):/workspace -d -it --name plm_benji nvcr.io/nvidia/pytorch:23.12-py3 /bin/bash && docker attach plm_benji
```
#### Install pkgs in container
```
mkdir /root/.cache/torch/hub/checkpoints/ -p; wget -O /root/.cache/torch/hub/checkpoints/esm2_t33_650M_UR50D.pt https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t33_650M_UR50D.pt
cd protein_lm/modeling/models/libs/ && pip install -e causal-conv1d && pip install -e mamba && cd ../../../../
pip install transformers datasets accelerate evaluate pytest fair-esm biopython deepspeed wandb
pip install torch_geometric
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
pip install -e .
pip install hydra-core --upgrade
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source "$HOME/.cargo/env"
pip install -e protein_lm/tokenizer/rust_trie
```
## Data
We collect protein sequences and their PTM annotations from Uniprot-Swissprot. The PTM annotations are represented as tokens and used to replaced the corresponding amino acids. The data can be downloaded from [here](https://drive.google.com/file/d/151KUp79tgBxphoIky1-ohyuvzIS1gtNS/view?usp=drive_link). Please place the data on `protein_lm/dataset/`.
## Configs
The training and testing configs are `protein_lm/configs`. We provide a basic training config at `protein_lm/configs/train/base.yaml`.
## Training
##### Single-GPU Training
```
python ./protein_lm/modeling/scripts/train.py +train=base
```
The commond will use the configs in `protein_lm/configs/train/base.yaml`.
##### Multi-GPU Training
We use [Distributed training with 🤗 Accelerate (huggingface.co)](https://huggingface.co/docs/transformers/main/accelerate).
```
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 accelerate launch --num_processes=8 --multi_gpu protein_lm/modeling/scripts/train.py +train=base train.report_to='wandb' train.training_arguments.per_device_train_batch_size=256 train.training_arguments.use_esm=True train.training_arguments.save_dir='ckpt/ptm_mamba' train.model.model_type='bidirectional_mamba' train.training_arguments.max_tokens_per_batch=40000
```
- `report_to='wandb'` tracks the training using wandb.
- `training_arguments.per_device_train_batch_size=300` sets the max batch size per device when constructing a batch.
- `training_arguments.max_tokens_per_batch=80000` sets the max num of tokens within a batch. If a batch exceeds the max token limit(depending on the seq len), we will trim the batch. Tune the `per_device_train_batch_size` and ``max_tokens_per_batch`` togather to maximize the memory usage during training. The rule of thumb is setting a large batch size (e.g., 300) while searching for the max num token that fits your GPU memory.
- `training_arguments.use_esm=True` uses the ESM embedding. By default, we use ESM 650M, and set the `model.esm_embed_dim: 1280` in `base.yaml`. If disabled, the model will use its own embeddings.
- `training_arguments.save_dir='ckpt/bi_directional_mamba-esm'` where the model ckpts will be saved.
- `training_arguments.sample_len_ascending=true` is enable by default, samples sequences from short to long during the training.
##### Multi-GPU training with Deepspeed
Setup deepspeed with
```
accelerate config
```
and answer the questions asked. It will ask whether you want to use a config file for DeepSpeed to which you should answer no. Then answer the following questions to generate a basic DeepSpeed config. Use ZeRo 2 and FP32, which are sufficient for training our ~300M model without introducing overhead. This will generate a config file that will be used automatically to properly set the default options when launching training.
## Inference
The inference example is at `protein_lm/modeling/scripts/infer.py.` The model checkpoints can be downloaded from [here](https://drive.google.com/file/d/1x_rKff0xswWU7_ixKYvZvWYZPR23cd8x/view?usp=sharing). The outputs are:
```
Output = namedtuple("output", ["logits", "hidden_states"])
```
```
from protein_lm.modeling.scripts.infer import PTMMamba
ckpt_path = "ckpt/bi_mamba-esm-ptm_token_input/best.ckpt"
mamba = PTMMamba(ckpt_path,device='cuda:0')
seq = "M<N-acetylalanine>K"
output = mamba(seq)
print(output.logits.shape)
print(output.hidden_states.shape)
```
## Acknowledgement
This project is based on the following codebase. Please give them a star if you like our code.
- [OpenBioML/protein-lm-scaling (github.com)](https://github.com/OpenBioML/protein-lm-scaling)
- [state-spaces/mamba (github.com)](https://github.com/state-spaces/mamba)
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