├── .gitmodules ├── AUTHORS ├── LICENSE ├── MANIFEST.in ├── NOTICE ├── README.md ├── evo2.jpg ├── evo2 ├── __init__.py ├── configs │ ├── evo2-1b-8k.yml │ ├── evo2-40b-1m.yml │ ├── evo2-40b-8k.yml │ ├── evo2-7b-1m.yml │ └── evo2-7b-8k.yml ├── models.py ├── scoring.py ├── utils.py └── version.py ├── notebooks ├── brca1 │ ├── 41586_2018_461_MOESM3_ESM.xlsx │ ├── GRCh37.p13_chr17.fna.gz │ └── brca1_zero_shot_vep.ipynb └── generation │ └── generation_notebook.ipynb ├── requirements.txt ├── setup.py └── test ├── test_evo2.py └── test_evo2_generation.py /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "vortex"] 2 | path = vortex 3 | url = https://github.com/Zymrael/vortex.git 4 | -------------------------------------------------------------------------------- /AUTHORS: -------------------------------------------------------------------------------- 1 | Garyk Brixi 2 | Matthew G. Durrant 3 | Jerome Ku 4 | Michael Poli 5 | Greg Brockman 6 | Daniel Chang 7 | Gabriel A. Gonzalez 8 | Samuel H. King 9 | David B. Li 10 | Aditi T. Merchant 11 | Mohsen Naghipourfar 12 | Eric Nguyen 13 | Chiara Ricci-Tam 14 | David W. Romero 15 | Gwanggyu Sun 16 | Ali Taghibakshi 17 | Anton Vorontsov 18 | Brandon Yang 19 | Myra Deng 20 | Liv Gorton 21 | Nam Nguyen 22 | Nicholas K. Wang 23 | Etowah Adams 24 | Stephen A. Baccus 25 | Steven Dillmann 26 | Stefano Ermon 27 | Daniel Guo 28 | Rajesh Ilango 29 | Ken Janik 30 | Amy X. Lu 31 | Reshma Mehta 32 | Mohammad R.K. Mofrad 33 | Madelena Y. Ng 34 | Jaspreet Pannu 35 | Christopher Ré 36 | Jonathan C. Schmok 37 | John St. John 38 | Jeremy Sullivan 39 | Kevin Zhu 40 | Greg Zynda 41 | Daniel Balsam 42 | Patrick Collison 43 | Anthony B. Costa 44 | Tina Hernandez-Boussard 45 | Eric Ho 46 | Ming-Yu Liu 47 | Thomas McGrath 48 | Kimberly Powell 49 | Dave P. Burke 50 | Hani Goodarzi 51 | Patrick D. Hsu 52 | Brian L. 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We also recommend that a 427 | file or class name and description of purpose be included on the 428 | same "printed page" as the copyright notice for easier 429 | identification within third-party archives. 430 | 431 | Copyright [yyyy] [name of copyright owner] 432 | 433 | Licensed under the Apache License, Version 2.0 (the "License"); 434 | you may not use this file except in compliance with the License. 435 | You may obtain a copy of the License at 436 | 437 | http://www.apache.org/licenses/LICENSE-2.0 438 | 439 | Unless required by applicable law or agreed to in writing, software 440 | distributed under the License is distributed on an "AS IS" BASIS, 441 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 442 | See the License for the specific language governing permissions and 443 | limitations under the License. 444 | 445 | ------------- LICENSE FOR Facebook Fairseq code -------------- 446 | 447 | MIT License 448 | 449 | Copyright (c) Facebook, Inc. and its affiliates. 450 | 451 | Permission is hereby granted, free of charge, to any person obtaining a copy 452 | of this software and associated documentation files (the "Software"), to deal 453 | in the Software without restriction, including without limitation the rights 454 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 455 | copies of the Software, and to permit persons to whom the Software is 456 | furnished to do so, subject to the following conditions: 457 | 458 | The above copyright notice and this permission notice shall be included in all 459 | copies or substantial portions of the Software. 460 | 461 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 462 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 463 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 464 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 465 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 466 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 467 | SOFTWARE. -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include LICENSE 2 | include NOTICE 3 | include README.md 4 | include requirements.txt 5 | include pyproject.toml 6 | recursive-include evo2/configs *.yml 7 | recursive-include vortex/vortex * 8 | recursive-exclude vortex/vortex *.pyc 9 | recursive-exclude vortex/vortex/__pycache__ * -------------------------------------------------------------------------------- /NOTICE: -------------------------------------------------------------------------------- 1 | Copyright 2024 Arc Institute. All rights reserved 2 | Copyright 2024 Michael Poli. All rights reserved 3 | Copyright 2024 Stanford University. All rights reserved 4 | 5 | This project incorporates and modifies the components below: 6 | 7 | 8 | - Added training and inference support for Hyena2 9 | - Scaled model training (up to 40B) and context windows (up to 1M) 10 | - Biological/genomic inference and generation evals/tasks 11 | 12 | See AUTHORS file for list of contributing authors. 13 | 14 | See LICENSE file for software license. This software is licensed under the Apache License, Version 2.0. 15 | ====================================================================== 16 | 17 | StripedHyena 18 | Copyright 2023-2024 Together 19 | Project URL: https://github.com/togethercomputer/stripedhyena 20 | 21 | This project includes software developed at Together for deep signal processing, hybrid architecture composed of rotary (grouped) attention and gated convolutions arranged in Hyena blocks, with improved scaling over decoder-only Transformers. 22 | 23 | ====================================================================== 24 | 25 | GPT-NeoX 26 | Copyright 2021-2024 EleutherAI and contributors 27 | Project URL: https://github.com/EleutherAI/gpt-neox 28 | 29 | This project includes software developed at EleutherAI for large-scale language model training and inference. 30 | 31 | ====================================================================== 32 | 33 | Megatron-LM 34 | Copyright 2019-2024 NVIDIA Corporation 35 | Project URL: https://github.com/NVIDIA/Megatron-LM 36 | 37 | This project includes software developed at NVIDIA Corporation for large-scale transformer model training. 38 | 39 | ====================================================================== 40 | 41 | DeepSpeed 42 | Copyright 2020-2024 Microsoft Corporation 43 | Project URL: https://github.com/microsoft/DeepSpeed 44 | 45 | This project includes software developed at Microsoft Corporation as part of the DeepSpeed deep learning optimization library. 46 | 47 | ====================================================================== -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Evo 2: Genome modeling and design across all domains of life 2 | 3 | ![Evo 2](evo2.jpg) 4 | 5 | Evo 2 is a state of the art DNA language model for long context modeling and design. Evo 2 models DNA sequences at single-nucleotide resolution at up to 1 million base pair context length using the [StripedHyena 2](https://github.com/Zymrael/savanna/blob/main/paper.pdf) architecture. Evo 2 was pretrained using [Savanna](https://github.com/Zymrael/savanna). Evo 2 was trained autoregressively on [OpenGenome2](https://huggingface.co/datasets/arcinstitute/opengenome2), a dataset containing 8.8 trillion tokens from all domains of life. 6 | 7 | We describe Evo 2 in the preprint: 8 | ["Genome modeling and design across all domains of life with Evo 2"](https://www.biorxiv.org/content/10.1101/2025.02.18.638918v1). 9 | 10 | ## Contents 11 | 12 | - [Setup](#setup) 13 | - [Requirements](#requirements) 14 | - [Installation](#installation) 15 | - [Checkpoints](#checkpoints) 16 | - [Usage](#usage) 17 | - [Forward](#forward) 18 | - [Embeddings](#embeddings) 19 | - [Generation](#generation) 20 | - [Notebooks](#notebooks) 21 | - [Nvidia NIM](#nvidia-nim) 22 | - [Dataset](#dataset) 23 | - [Training and Finetuning](#training-and-finetuning) 24 | - [Citation](#citation) 25 | 26 | ## Setup 27 | 28 | This repo is for running Evo 2 locally for inference or generation, using our [Vortex](https://github.com/Zymrael/vortex) inference code. For training and finetuning, see the section [here](#training-and-finetuning). 29 | You can run Evo 2 without any installation using the [Nvidia Hosted API](https://build.nvidia.com/arc/evo2-40b). 30 | You can also self-host an instance using Nvidia NIM. See the [Nvidia NIM](#nvidia-nim) section for more 31 | information. 32 | 33 | ### Requirements 34 | 35 | Evo 2 is based on [StripedHyena 2](https://github.com/Zymrael/vortex) which requires python>=3.11. Evo 2 uses [Transformer Engine](https://github.com/NVIDIA/TransformerEngine) FP8 for some layers which requires an H100 (or other GPU with compute capability ≥8.9). We are actively investigating ways to avoid this requirement. 36 | 37 | ### Installation 38 | 39 | To install Evo 2 for inference or generation, please clone and install from GitHub. We recommend using a new conda environment with python>=3.11. 40 | 41 | ```bash 42 | git clone --recurse-submodules git@github.com:ArcInstitute/evo2.git 43 | cd evo2 44 | pip install . 45 | ``` 46 | 47 | If this did not work for whatever reason, you can also install from [Vortex](https://github.com/Zymrael/vortex) and follow the instructions there. PyPi support coming soon! 48 | 49 | You can check that the installation was correct by running a test. 50 | 51 | ``` 52 | python ./test/test_evo2.py --model_name evo2_7b 53 | ``` 54 | 55 | ## Checkpoints 56 | 57 | We provide the following model checkpoints, hosted on [HuggingFace](https://huggingface.co/arcinstitute): 58 | | Checkpoint Name | Description | 59 | |----------------------------------------|-------------| 60 | | `evo2_40b` | A model pretrained with 1 million context obtained through context extension of `evo2_40b_base`.| 61 | | `evo2_7b` | A model pretrained with 1 million context obtained through context extension of `evo2_7b_base`.| 62 | | `evo2_40b_base` | A model pretrained with 8192 context length.| 63 | | `evo2_7b_base` | A model pretrained with 8192 context length.| 64 | | `evo2_1b_base` | A smaller model pretrained with 8192 context length.| 65 | 66 | To use Evo 2 40B, you will need multiple GPUs. Vortex automatically handles device placement, splitting the model across available cuda devices. 67 | 68 | ## Usage 69 | 70 | Below are simple examples of how to download Evo 2 and use it locally in Python. 71 | 72 | ### Forward 73 | 74 | Evo 2 can be used to score the likelihoods across a DNA sequence. 75 | 76 | ```python 77 | import torch 78 | from evo2 import Evo2 79 | 80 | evo2_model = Evo2('evo2_7b') 81 | 82 | sequence = 'ACGT' 83 | input_ids = torch.tensor( 84 | evo2_model.tokenizer.tokenize(sequence), 85 | dtype=torch.int, 86 | ).unsqueeze(0).to('cuda:0') 87 | 88 | outputs, _ = evo2_model(input_ids) 89 | logits = outputs[0] 90 | 91 | print('Logits: ', logits) 92 | print('Shape (batch, length, vocab): ', logits.shape) 93 | ``` 94 | 95 | ### Embeddings 96 | 97 | Evo 2 embeddings can be saved for use downstream. We find that intermediate embeddings work better than final embeddings, see our paper for details. 98 | 99 | ```python 100 | import torch 101 | from evo2 import Evo2 102 | 103 | evo2_model = Evo2('evo2_7b') 104 | 105 | sequence = 'ACGT' 106 | input_ids = torch.tensor( 107 | evo2_model.tokenizer.tokenize(sequence), 108 | dtype=torch.int, 109 | ).unsqueeze(0).to('cuda:0') 110 | 111 | layer_name = 'blocks.28.mlp.l3' 112 | 113 | outputs, embeddings = evo2_model(input_ids, return_embeddings=True, layer_names=[layer_name]) 114 | 115 | print('Embeddings shape: ', embeddings[layer_name].shape) 116 | ``` 117 | 118 | ### Generation 119 | 120 | Evo 2 can generate DNA sequences based on prompts. 121 | 122 | ```python 123 | from evo2 import Evo2 124 | 125 | evo2_model = Evo2('evo2_7b') 126 | 127 | output = evo2_model.generate(prompt_seqs=["ACGT"], n_tokens=400, temperature=1.0, top_k=4) 128 | 129 | print(output.sequences[0]) 130 | ``` 131 | 132 | ### Notebooks 133 | 134 | We provide example notebooks. 135 | 136 | The [BRCA1 notebook](https://github.com/ArcInstitute/evo2/blob/main/notebooks/brca1/brca1_zero_shot_vep.ipynb) shows zero-shot *BRCA1* variant effect prediction. This example includes a walkthrough of: 137 | - Performing zero-shot *BRCA1* variant effect predictions using Evo 2 138 | - Reference vs alternative allele normalization 139 | 140 | The [generation notebook](https://github.com/ArcInstitute/evo2/blob/main/notebooks/generation/generation_notebook.ipynb) shows DNA sequence completion with Evo 2. This example shows: 141 | - DNA prompt based generation and 'DNA autocompletion' 142 | - How to get and prompt using phylogenetic species tags for generation 143 | 144 | ### Nvidia NIM 145 | 146 | Evo 2 is available on [Nvidia NIM](https://catalog.ngc.nvidia.com/containers?filters=&orderBy=scoreDESC&query=evo2&page=&pageSize=) and [hosted API](https://build.nvidia.com/arc/evo2-40b). 147 | 148 | - [Documentation](https://docs.nvidia.com/nim/bionemo/evo2/latest/overview.html) 149 | - [Quickstart](https://docs.nvidia.com/nim/bionemo/evo2/latest/quickstart-guide.html) 150 | 151 | The quickstart guides users through running Evo 2 on the NVIDIA NIM using a python or shell client after starting NIM. An example python client script is shown below. This is the same way you would interact with the [Nvidia hosted API](https://build.nvidia.com/arc/evo2-40b?snippet_tab=Python). 152 | 153 | ```python 154 | #!/usr/bin/env python3 155 | import requests 156 | import os 157 | import json 158 | from pathlib import Path 159 | 160 | key = os.getenv("NVCF_RUN_KEY") or input("Paste the Run Key: ") 161 | 162 | r = requests.post( 163 | url=os.getenv("URL", "https://health.api.nvidia.com/v1/biology/arc/evo2-40b/generate"), 164 | headers={"Authorization": f"Bearer {key}"}, 165 | json={ 166 | "sequence": "ACTGACTGACTGACTG", 167 | "num_tokens": 8, 168 | "top_k": 1, 169 | "enable_sampled_probs": True, 170 | }, 171 | ) 172 | 173 | if "application/json" in r.headers.get("Content-Type", ""): 174 | print(r, "Saving to output.json:\n", r.text[:200], "...") 175 | Path("output.json").write_text(r.text) 176 | elif "application/zip" in r.headers.get("Content-Type", ""): 177 | print(r, "Saving large response to data.zip") 178 | Path("data.zip").write_bytes(r.content) 179 | else: 180 | print(r, r.headers, r.content) 181 | ``` 182 | 183 | 184 | ### Very long sequences 185 | 186 | We are actively working on optimizing performance for long sequence processing in Vortex. Vortex can currently compute over very long sequences via teacher prompting. However please note that forward pass on long sequences may currently be slow. You can instead use [Savanna](https://github.com/Zymrael/savanna) or [Nvidia BioNemo](https://github.com/NVIDIA/bionemo-framework) for embedding long sequences. 187 | 188 | ### Dataset 189 | 190 | The OpenGenome2 dataset used for pretraining Evo2 is available on [HuggingFace ](https://huggingface.co/datasets/arcinstitute/opengenome2). Data is available either as raw fastas or as JSONL files which include preprocessing and data augmentation. 191 | 192 | ### Training and Finetuning 193 | 194 | Evo 2 was trained using [Savanna](https://github.com/Zymrael/savanna), an open source framework for training alternative architectures. 195 | 196 | To train or finetune Evo 2, you can use [Savanna](https://github.com/Zymrael/savanna) or [Nvidia BioNemo](https://github.com/NVIDIA/bionemo-framework) which provides a [Evo 2 finetuning tutorial here](https://github.com/NVIDIA/bionemo-framework/blob/ca16c2acf9bf813d020b6d1e2d4e1240cfef6a69/docs/docs/user-guide/examples/bionemo-evo2/fine-tuning-tutorial.ipynb). 197 | 198 | ## Citation 199 | 200 | If you find these models useful for your research, please cite the relevant papers 201 | 202 | ``` 203 | @article {Brixi2025.02.18.638918, 204 | author = {Brixi, Garyk and Durrant, Matthew G and Ku, Jerome and Poli, Michael and Brockman, Greg and Chang, Daniel and Gonzalez, Gabriel A and King, Samuel H and Li, David B and Merchant, Aditi T and Naghipourfar, Mohsen and Nguyen, Eric and Ricci-Tam, Chiara and Romero, David W and Sun, Gwanggyu and Taghibakshi, Ali and Vorontsov, Anton and Yang, Brandon and Deng, Myra and Gorton, Liv and Nguyen, Nam and Wang, Nicholas K and Adams, Etowah and Baccus, Stephen A and Dillmann, Steven and Ermon, Stefano and Guo, Daniel and Ilango, Rajesh and Janik, Ken and Lu, Amy X and Mehta, Reshma and Mofrad, Mohammad R.K. and Ng, Madelena Y and Pannu, Jaspreet and Re, Christopher and Schmok, Jonathan C and St. John, John and Sullivan, Jeremy and Zhu, Kevin and Zynda, Greg and Balsam, Daniel and Collison, Patrick and Costa, Anthony B. and Hernandez-Boussard, Tina and Ho, Eric and Liu, Ming-Yu and McGrath, Tom and Powell, Kimberly and Burke, Dave P. and Goodarzi, Hani and Hsu, Patrick D and Hie, Brian}, 205 | title = {Genome modeling and design across all domains of life with Evo 2}, 206 | elocation-id = {2025.02.18.638918}, 207 | year = {2025}, 208 | doi = {10.1101/2025.02.18.638918}, 209 | publisher = {Cold Spring Harbor Laboratory}, 210 | URL = {https://www.biorxiv.org/content/early/2025/02/21/2025.02.18.638918}, 211 | eprint = {https://www.biorxiv.org/content/early/2025/02/21/2025.02.18.638918.full.pdf}, 212 | journal = {bioRxiv} 213 | } 214 | ``` 215 | -------------------------------------------------------------------------------- /evo2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ArcInstitute/evo2/a796302818055b9710a6a2c4d7882a6243363fdd/evo2.jpg -------------------------------------------------------------------------------- /evo2/__init__.py: -------------------------------------------------------------------------------- 1 | from .models import Evo2 -------------------------------------------------------------------------------- /evo2/configs/evo2-1b-8k.yml: -------------------------------------------------------------------------------- 1 | model_name: shc-evo2-1b-8k-2T-v2 2 | 3 | vocab_size: 512 4 | hidden_size: 1920 5 | # Number of independent filters in Hyena-LI 6 | num_filters: 1920 7 | attn_layer_idxs: [3,10,17,24] 8 | hcl_layer_idxs: [2,6,9,13,16,20,23] 9 | hcm_layer_idxs: [1,5,8,12,15,19,22] 10 | hcs_layer_idxs: [0,4,7,11,14,18,21] 11 | 12 | hcm_filter_length: 128 13 | hcl_filter_groups: 1920 14 | hcm_filter_groups: 128 15 | hcs_filter_groups: 128 16 | hcs_filter_length: 7 17 | num_layers: 25 18 | 19 | # Length of the short, depthwise FIR applied to input projections 20 | short_filter_length: 3 21 | num_attention_heads: 15 22 | short_filter_bias: false # add bias to FIR 23 | mlp_init_method: torch.nn.init.zeros_ 24 | mlp_output_init_method: torch.nn.init.zeros_ 25 | eps: 0.000001 26 | state_size: 16 27 | rotary_emb_base: 10000 28 | make_vocab_size_divisible_by: 8 29 | inner_size_multiple_of: 16 # force GLU inner_size to be a multiple of 30 | inner_mlp_size: 5120 31 | log_intermediate_values: False 32 | # Number of groups in GQA 33 | proj_groups: 1 34 | # Number of groups in grouped 35 | hyena_filter_groups: 1 36 | # Split strategy for channels 37 | column_split_hyena: False 38 | column_split: True 39 | interleave: True 40 | # Layer > 0 nn.identity activation 41 | evo2_style_activations: True 42 | 43 | # Legacy options for MP / PP inference 44 | model_parallel_size: 1 45 | pipe_parallel_size: 1 46 | tie_embeddings: True 47 | mha_out_proj_bias: True 48 | hyena_out_proj_bias: True 49 | hyena_flip_x1x2: False 50 | qkv_proj_bias: False 51 | use_fp8_input_projections: True 52 | max_seqlen: 8192 53 | max_batch_size: 1 54 | final_norm: True 55 | use_flash_attn: True 56 | use_flash_rmsnorm: False 57 | use_flash_depthwise: False 58 | use_flashfft: False 59 | use_laughing_hyena: False 60 | inference_mode: True 61 | tokenizer_type: CharLevelTokenizer 62 | prefill_style: fft 63 | mlp_activation: gelu 64 | print_activations: False 65 | -------------------------------------------------------------------------------- /evo2/configs/evo2-40b-1m.yml: -------------------------------------------------------------------------------- 1 | model_name: shc-evo2-40b-8k-11T-v2 2 | 3 | vocab_size: 512 4 | hidden_size: 8192 5 | # Number of independent filters in Hyena-LI 6 | num_filters: 8192 7 | hcl_layer_idxs: [2,6,9,13,16,20,23,27,30,34,38,41,45,48] 8 | hcm_layer_idxs: [1,5,8,12,15,19,22,26,29,33,37,40,44,47] 9 | hcs_layer_idxs: [0,4,7,11,14,18,21,25,28,32,36,39,43,46] 10 | attn_layer_idxs: [3,10,17,24,31,35,42,49] 11 | hcm_filter_length: 128 12 | hcl_filter_groups: 8192 13 | hcm_filter_groups: 512 14 | hcs_filter_groups: 512 15 | hcs_filter_length: 7 16 | num_layers: 50 17 | 18 | # Length of the short, depthwise FIR applied to input projections 19 | short_filter_length: 3 20 | num_attention_heads: 64 21 | short_filter_bias: false # add bias to FIR 22 | mlp_init_method: torch.nn.init.zeros_ 23 | mlp_output_init_method: torch.nn.init.zeros_ 24 | eps: 0.000001 25 | state_size: 16 26 | rotary_emb_base: 100000000000 27 | rotary_emb_scaling_factor: 128 28 | use_interpolated_rotary_pos_emb: True 29 | make_vocab_size_divisible_by: 8 30 | inner_size_multiple_of: 128 # force GLU inner_size to be a multiple of 31 | inner_mlp_size: 22528 32 | log_intermediate_values: False 33 | # Number of groups in GQA 34 | proj_groups: 1 35 | # Number of groups in grouped 36 | hyena_filter_groups: 1 37 | # Split strategy for channels 38 | column_split_hyena: False 39 | column_split: True 40 | interleave: True 41 | # Layer > 0 nn.identity activation 42 | evo2_style_activations: True 43 | 44 | use_fp8_input_projections: True 45 | 46 | # Legacy options for MP / PP inference 47 | model_parallel_size: 1 48 | pipe_parallel_size: 1 49 | tie_embeddings: True 50 | mha_out_proj_bias: True 51 | hyena_out_proj_bias: True 52 | hyena_flip_x1x2: False 53 | qkv_proj_bias: False 54 | max_seqlen: 1048576 55 | max_batch_size: 1 56 | final_norm: True 57 | use_flash_attn: True 58 | use_flash_rmsnorm: False 59 | use_flash_depthwise: False 60 | use_flashfft: False 61 | use_laughing_hyena: False 62 | inference_mode: True 63 | tokenizer_type: CharLevelTokenizer 64 | prefill_style: fft 65 | mlp_activation: gelu 66 | print_activations: False 67 | -------------------------------------------------------------------------------- /evo2/configs/evo2-40b-8k.yml: -------------------------------------------------------------------------------- 1 | model_name: shc-evo2-40b-8k-11T-v2 2 | 3 | vocab_size: 512 4 | hidden_size: 8192 5 | num_filters: 8192 6 | hcl_layer_idxs: [2,6,9,13,16,20,23,27,30,34,38,41,45,48] 7 | hcm_layer_idxs: [1,5,8,12,15,19,22,26,29,33,37,40,44,47] 8 | hcs_layer_idxs: [0,4,7,11,14,18,21,25,28,32,36,39,43,46] 9 | attn_layer_idxs: [3,10,17,24,31,35,42,49] 10 | hcm_filter_length: 128 11 | hcl_filter_groups: 8192 12 | hcm_filter_groups: 512 13 | hcs_filter_groups: 512 14 | hcs_filter_length: 7 15 | num_layers: 50 16 | 17 | # Length of the short, depthwise FIR applied to input projections 18 | short_filter_length: 3 19 | num_attention_heads: 64 20 | short_filter_bias: false # add bias to FIR 21 | mlp_init_method: torch.nn.init.zeros_ 22 | mlp_output_init_method: torch.nn.init.zeros_ 23 | eps: 0.000001 24 | state_size: 16 25 | rotary_emb_base: 1000000 26 | make_vocab_size_divisible_by: 8 27 | inner_size_multiple_of: 128 # force GLU inner_size to be a multiple of 28 | inner_mlp_size: 21888 29 | log_intermediate_values: False 30 | # Number of groups in GQA 31 | proj_groups: 1 32 | # Number of groups in grouped 33 | hyena_filter_groups: 1 34 | # Split strategy for channels 35 | column_split_hyena: False 36 | column_split: True 37 | interleave: True 38 | # Layer > 0 nn.identity activation 39 | evo2_style_activations: True 40 | 41 | use_fp8_input_projections: True 42 | 43 | # Legacy options for MP / PP inference 44 | model_parallel_size: 1 45 | pipe_parallel_size: 1 46 | tie_embeddings: True 47 | mha_out_proj_bias: True 48 | hyena_out_proj_bias: True 49 | hyena_flip_x1x2: False 50 | qkv_proj_bias: False 51 | max_seqlen: 8192 52 | max_batch_size: 1 53 | final_norm: True 54 | use_flash_attn: True 55 | use_flash_rmsnorm: False 56 | use_flash_depthwise: False 57 | use_flashfft: False 58 | use_laughing_hyena: False 59 | inference_mode: True 60 | tokenizer_type: CharLevelTokenizer 61 | prefill_style: fft 62 | mlp_activation: gelu 63 | print_activations: False 64 | -------------------------------------------------------------------------------- /evo2/configs/evo2-7b-1m.yml: -------------------------------------------------------------------------------- 1 | model_name: shc-evo2-7b-8k-2T-v2 2 | 3 | vocab_size: 512 4 | hidden_size: 4096 5 | # Number of long convolution filters in each hyena block. Can be smaller than `hidden_size` 6 | num_filters: 4096 7 | hcl_layer_idxs: [2,6,9,13,16,20,23,27,30] 8 | hcm_layer_idxs: [1,5,8,12,15,19,22,26,29] 9 | hcs_layer_idxs: [0,4,7,11,14,18,21,25,28] 10 | attn_layer_idxs: [3,10,17,24,31] 11 | 12 | hcm_filter_length: 128 13 | hcl_filter_groups: 4096 14 | hcm_filter_groups: 256 15 | hcs_filter_groups: 256 16 | hcs_filter_length: 7 17 | num_layers: 32 18 | 19 | # Length of the short, depthwise FIR applied to input projections 20 | short_filter_length: 3 21 | num_attention_heads: 32 22 | short_filter_bias: false # add bias to FIR 23 | mlp_init_method: torch.nn.init.zeros_ 24 | mlp_output_init_method: torch.nn.init.zeros_ 25 | eps: 0.000001 26 | state_size: 16 27 | rotary_emb_base: 100000000000 28 | rotary_emb_scaling_factor: 128 29 | use_interpolated_rotary_pos_emb: True 30 | make_vocab_size_divisible_by: 8 31 | inner_size_multiple_of: 16 # force GLU inner_size to be a multiple of 32 | inner_mlp_size: 11264 33 | log_intermediate_values: False 34 | # Number of groups in GQA 35 | proj_groups: 1 36 | # Number of groups in grouped 37 | hyena_filter_groups: 1 38 | # Split strategy for channels 39 | column_split_hyena: False 40 | column_split: True 41 | interleave: True 42 | # Layer > 0 nn.identity activation 43 | evo2_style_activations: True 44 | # Legacy options for MP / PP inference 45 | model_parallel_size: 1 46 | pipe_parallel_size: 1 47 | tie_embeddings: True 48 | mha_out_proj_bias: True 49 | hyena_out_proj_bias: True 50 | hyena_flip_x1x2: False 51 | qkv_proj_bias: False 52 | use_fp8_input_projections: True 53 | max_seqlen: 1048576 54 | max_batch_size: 1 55 | final_norm: True 56 | use_flash_attn: True 57 | use_flash_rmsnorm: False 58 | use_flash_depthwise: False 59 | use_flashfft: False 60 | use_laughing_hyena: False 61 | inference_mode: True 62 | tokenizer_type: CharLevelTokenizer 63 | prefill_style: fft 64 | mlp_activation: gelu 65 | print_activations: False 66 | -------------------------------------------------------------------------------- /evo2/configs/evo2-7b-8k.yml: -------------------------------------------------------------------------------- 1 | model_name: shc-evo2-7b-8k-2T-v2 2 | 3 | vocab_size: 512 4 | hidden_size: 4096 5 | num_filters: 4096 6 | hcl_layer_idxs: [2,6,9,13,16,20,23,27,30] 7 | hcm_layer_idxs: [1,5,8,12,15,19,22,26,29] 8 | hcs_layer_idxs: [0,4,7,11,14,18,21,25,28] 9 | attn_layer_idxs: [3,10,17,24,31] 10 | 11 | # Number of unique convolution filters in each hyena block. Can be smaller than `hidden_size` 12 | hcm_filter_length: 128 13 | hcl_filter_groups: 4096 14 | hcm_filter_groups: 256 15 | hcs_filter_groups: 256 16 | hcs_filter_length: 7 17 | num_layers: 32 18 | 19 | # Length of the short, depthwise FIR applied to input projections 20 | short_filter_length: 3 21 | num_attention_heads: 32 22 | short_filter_bias: false # add bias to FIR 23 | mlp_init_method: torch.nn.init.zeros_ 24 | mlp_output_init_method: torch.nn.init.zeros_ 25 | eps: 0.000001 26 | state_size: 16 27 | rotary_emb_base: 10000 28 | make_vocab_size_divisible_by: 8 29 | inner_size_multiple_of: 16 # force GLU inner_size to be a multiple of 30 | inner_mlp_size: 11008 31 | log_intermediate_values: False 32 | # Number of groups in GQA 33 | proj_groups: 1 34 | # Number of groups in grouped 35 | hyena_filter_groups: 1 36 | # Split strategy for channels 37 | column_split_hyena: False 38 | column_split: True 39 | interleave: True 40 | # Layer > 0 nn.identity activation 41 | evo2_style_activations: True 42 | # Legacy options for MP / PP inference 43 | model_parallel_size: 1 44 | pipe_parallel_size: 1 45 | tie_embeddings: True 46 | mha_out_proj_bias: True 47 | hyena_out_proj_bias: True 48 | hyena_flip_x1x2: False 49 | qkv_proj_bias: False 50 | use_fp8_input_projections: False 51 | max_seqlen: 32768 52 | max_batch_size: 1 53 | final_norm: True 54 | use_flash_attn: True 55 | use_flash_rmsnorm: False 56 | use_flash_depthwise: False 57 | use_flashfft: False 58 | use_laughing_hyena: False 59 | inference_mode: True 60 | tokenizer_type: CharLevelTokenizer 61 | prefill_style: fft 62 | mlp_activation: gelu 63 | print_activations: False 64 | -------------------------------------------------------------------------------- /evo2/models.py: -------------------------------------------------------------------------------- 1 | from functools import partial 2 | import huggingface_hub 3 | from huggingface_hub import snapshot_download, constants, hf_hub_download 4 | import os 5 | import pkgutil 6 | import torch 7 | from typing import List, Tuple, Dict, Union 8 | import yaml 9 | 10 | 11 | from vortex.model.generation import generate as vortex_generate 12 | from vortex.model.model import StripedHyena 13 | from vortex.model.tokenizer import CharLevelTokenizer 14 | from vortex.model.utils import dotdict, print_rank_0, load_checkpoint 15 | 16 | from evo2.scoring import score_sequences, score_sequences_rc 17 | from evo2.utils import MODEL_NAMES, HF_MODEL_NAME_MAP, CONFIG_MAP 18 | 19 | class Evo2: 20 | def __init__(self, model_name: str = MODEL_NAMES[1], local_path: str = None): 21 | """ 22 | Load an Evo 2 checkpoint. 23 | 24 | Uses local_path if specified, otherwise checks if in local HuggingFace ~cache. 25 | Automatically downloads checkpoint from HuggingFace if it does not exist locally. 26 | 27 | Vortex automatically handles device placement on CUDA, and splits model across 28 | multiple GPUs if available. 29 | For models split across multiple GPUs, you can specify which GPUs to use with 30 | CUDA_VISIBLE_DEVICES. If using multi-gpu, do not use .to(device) manually. 31 | 32 | Notes: 33 | Evo 2 40b is too large to fit on a single H100 GPU, so needs multiple GPUs. 34 | You can change where HuggingFace downloads to by setting the HF_HOME environment 35 | variable. 36 | """ 37 | if model_name not in MODEL_NAMES: 38 | raise ValueError( 39 | f'Invalid model name {model_name}. Should be one of: ' 40 | f'{", ".join(MODEL_NAMES)}.' 41 | ) 42 | 43 | config_path = CONFIG_MAP[model_name] 44 | 45 | if local_path is not None: 46 | self.model = self.load_evo2_model(None, config_path, local_path) 47 | else: 48 | self.model = self.load_evo2_model(model_name, config_path) 49 | 50 | self.tokenizer = CharLevelTokenizer(512) 51 | 52 | def forward( 53 | self, 54 | input_ids: torch.Tensor, 55 | return_embeddings: bool = False, 56 | layer_names=None, 57 | ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: 58 | """ 59 | Forward pass with optional embedding extraction. 60 | 61 | Args: 62 | input_ids: Input token IDs 63 | return_embeddings: If True, returns embeddings from specified layers 64 | layer_names: List of layer names to extract embeddings from. Required if 65 | return_embeddings=True 66 | 67 | Returns: 68 | Tuple of (logits, embeddings_dict) if return_embeddings=True 69 | Tuple of (logits, None) otherwise 70 | """ 71 | embeddings = {} 72 | handles = [] 73 | 74 | if return_embeddings: 75 | if layer_names is None: 76 | raise ValueError( 77 | "layer_names must be specified when return_embeddings=True. Look at " 78 | "evo2_model.model.state_dict().keys() to see available layers." 79 | ) 80 | 81 | def hook_fn(layer_name): 82 | def hook(_, __, output): 83 | if isinstance(output, tuple): 84 | output = output[0] 85 | embeddings[layer_name] = output.detach() 86 | return hook 87 | 88 | # Register hooks for requested layers 89 | for name in layer_names: 90 | layer = self.model.get_submodule(name) 91 | handles.append(layer.register_forward_hook(hook_fn(name))) 92 | 93 | try: 94 | # Original forward pass 95 | with torch.no_grad(): 96 | logits = self.model.forward(input_ids) 97 | 98 | if return_embeddings: 99 | return logits, embeddings 100 | return logits, None 101 | 102 | finally: 103 | for handle in handles: 104 | handle.remove() 105 | 106 | def __call__(self, input_ids, return_embeddings=False, layer_names=None): 107 | return self.forward(input_ids, return_embeddings, layer_names) 108 | 109 | def score_sequences( 110 | self, 111 | seqs: List[str], 112 | batch_size: int = 1, 113 | prepend_bos: bool = False, 114 | reduce_method: str = 'mean', 115 | average_reverse_complement: bool = False, 116 | ) -> List[float]: 117 | scoring_func = partial( 118 | score_sequences_rc if average_reverse_complement else score_sequences, 119 | model=self.model, 120 | tokenizer=self.tokenizer, 121 | batch_size=batch_size, 122 | prepend_bos=prepend_bos, 123 | reduce_method=reduce_method, 124 | ) 125 | 126 | with torch.no_grad(): 127 | try: 128 | scores = scoring_func(seqs) 129 | except Exception as e: 130 | raise RuntimeError(f"Error during sequence scoring: {str(e)}") from e 131 | 132 | return scores 133 | 134 | def generate( 135 | self, 136 | prompt_seqs: List[str], 137 | n_tokens: int = 500, 138 | temperature: float = 1.0, 139 | top_k: int = 4, 140 | top_p: float = 1.0, 141 | batched: bool = True, 142 | cached_generation: bool = True, 143 | verbose: int = 1, 144 | force_prompt_threshold: int = None, 145 | ) -> Tuple[List[str], List[float]]: 146 | """ 147 | Generate sequences from a list of prompts. 148 | 149 | force_prompt_threshold: If specified, avoids OOM errors through teacher forcing if the prompt is longer than this threshold. 150 | 151 | If force_prompt_threshold is none, sets default assuming 1xH100 (evo2_7b) and 2xH100 (evo2_40b) to help avoid OOM errors. 152 | """ 153 | 154 | with torch.no_grad(): 155 | output = vortex_generate( 156 | prompt_seqs=prompt_seqs, 157 | model=self.model, 158 | tokenizer=self.tokenizer, 159 | n_tokens=n_tokens, 160 | temperature=temperature, 161 | top_k=top_k, 162 | top_p=top_p, 163 | batched=batched, 164 | cached_generation=cached_generation, 165 | verbose=verbose, 166 | force_prompt_threshold=force_prompt_threshold, 167 | ) 168 | return output 169 | 170 | 171 | def load_evo2_model( 172 | self, 173 | model_name: str = MODEL_NAMES[1], 174 | config_path: str = None, 175 | local_path: str = None, 176 | remove_shards: bool = True, 177 | ): 178 | """ 179 | Load HuggingFace checkpoint using StripedHyena 2. 180 | 181 | If local_path is specified, loads from local_path. 182 | Otherwise, downloads from HuggingFace. 183 | If remove_shards is True, removes HF checkpoint shards after merging to .pt file. 184 | """ 185 | if local_path is not None: 186 | print(f"Loading model from {local_path}...") 187 | print(f"Loading config from {config_path}...") 188 | config = dotdict(yaml.load(open(config_path), Loader=yaml.FullLoader)) 189 | model = StripedHyena(config) 190 | load_checkpoint(model, local_path) 191 | return model 192 | 193 | hf_model_name = HF_MODEL_NAME_MAP[model_name] 194 | filename = f"{model_name}.pt" 195 | 196 | final_weights_path = os.path.join(os.path.dirname(constants.HF_HUB_CACHE), filename) 197 | if os.path.exists(final_weights_path): 198 | print(f"Found existing merged file: {final_weights_path}") 199 | weights_path = final_weights_path 200 | 201 | hf_hub_download( 202 | repo_id=hf_model_name, 203 | filename="config.json" 204 | ) 205 | else: 206 | repo_dir = snapshot_download( 207 | repo_id=hf_model_name, 208 | ) 209 | 210 | # Check if the complete file already exists in the repo 211 | repo_weights_path = os.path.join(repo_dir, filename) 212 | if os.path.exists(repo_weights_path): 213 | print(f"Found complete file in repo: {filename}") 214 | weights_path = repo_weights_path 215 | else: 216 | print(f"Looking for checkpoint shards for {filename}") 217 | parts = [] 218 | part_num = 0 219 | 220 | while True: 221 | part_path = os.path.join(repo_dir, f"{filename}.part{part_num}") 222 | if os.path.exists(part_path): 223 | parts.append(part_path) 224 | part_num += 1 225 | else: 226 | break 227 | 228 | if parts: 229 | print(f"Found {len(parts)} shards, merging them...") 230 | with open(final_weights_path, 'wb') as outfile: 231 | for part in parts: 232 | print(f"Merging shard: {os.path.basename(part)}") 233 | with open(part, 'rb') as infile: 234 | while True: 235 | chunk = infile.read(8192*1024) 236 | if not chunk: 237 | break 238 | outfile.write(chunk) 239 | 240 | print(f"Successfully merged all shards into {final_weights_path}") 241 | weights_path = final_weights_path 242 | if remove_shards and os.path.exists(final_weights_path): 243 | for part in parts: 244 | real_path = os.path.realpath(part) 245 | if os.path.exists(real_path): 246 | os.remove(real_path) 247 | if os.path.exists(part): 248 | os.remove(part) 249 | else: 250 | raise FileNotFoundError(f"Could not find {filename} or any of its shards in {repo_dir}") 251 | 252 | config = yaml.safe_load(pkgutil.get_data(__name__, config_path)) 253 | global_config = dotdict(config, Loader=yaml.FullLoader) 254 | 255 | model = StripedHyena(global_config) 256 | load_checkpoint(model, weights_path) 257 | 258 | return model 259 | -------------------------------------------------------------------------------- /evo2/scoring.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from typing import List, Tuple, Union 3 | from Bio.Seq import Seq 4 | from tqdm import tqdm 5 | 6 | import torch 7 | from vortex.model.model import StripedHyena 8 | 9 | 10 | def prepare_batch( 11 | seqs: List[str], 12 | tokenizer: object, 13 | prepend_bos: bool = False, 14 | device: str = 'cuda:0' 15 | ) -> Tuple[torch.Tensor, List[int]]: 16 | """ 17 | Takes in a list of sequences, tokenizes them, and puts them in a tensor batch. 18 | If the sequences have differing lengths, then pad up to the maximum sequence length. 19 | """ 20 | seq_lengths = [ len(seq) for seq in seqs ] 21 | max_seq_length = max(seq_lengths) 22 | 23 | input_ids = [] 24 | for seq in seqs: 25 | padding = [tokenizer.pad_id] * (max_seq_length - len(seq)) 26 | input_ids.append( 27 | torch.tensor( 28 | ([tokenizer.eod_id] * int(prepend_bos)) + tokenizer.tokenize(seq) + padding, 29 | dtype=torch.long, 30 | ).to(device).unsqueeze(0) 31 | ) 32 | input_ids = torch.cat(input_ids, dim=0) 33 | 34 | return input_ids, seq_lengths 35 | 36 | 37 | def logits_to_logprobs( 38 | logits: torch.Tensor, 39 | input_ids: torch.Tensor, 40 | ) -> torch.Tensor: 41 | """ 42 | Takes in a tensor of logits of dimension (batch, length, vocab). 43 | Computes the log-likelihoods using a softmax along the vocab dimension. 44 | Uses the `input_ids` to index into the log-likelihoods and returns the likelihood 45 | of the provided sequence at each position with dimension (batch, length). 46 | """ 47 | softmax_logprobs = torch.log_softmax(logits, dim=-1) 48 | softmax_logprobs = softmax_logprobs[:, :-1] 49 | input_ids = input_ids[:, 1:] 50 | assert softmax_logprobs.shape[1] == input_ids.shape[1] 51 | 52 | logprobs = torch.gather( 53 | softmax_logprobs, # Gather likelihoods... 54 | 2, # along the vocab dimension... 55 | input_ids.unsqueeze(-1) # using the token ids to index. 56 | ).squeeze(-1) 57 | 58 | return logprobs 59 | 60 | 61 | def _score_sequences( 62 | seqs: List[str], 63 | model: StripedHyena, 64 | tokenizer: object, 65 | prepend_bos: bool = False, 66 | reduce_method: str = 'mean', 67 | device: str = 'cuda:0', 68 | ) -> List[float]: 69 | """Helper function to score a list of sequences based on their logprobs.""" 70 | input_ids, seq_lengths = prepare_batch(seqs, tokenizer, device=device, prepend_bos=prepend_bos) 71 | assert len(seq_lengths) == input_ids.shape[0] 72 | 73 | with torch.inference_mode(): 74 | logits, _ = model(input_ids) # (batch, length, vocab) 75 | 76 | logprobs = logits_to_logprobs(logits, input_ids) 77 | logprobs = logprobs.float().cpu().numpy() 78 | 79 | if reduce_method == 'sum': # PLL 80 | reduce_func = np.sum 81 | elif reduce_method == 'mean': # mean PLL 82 | reduce_func = np.mean 83 | else: 84 | raise ValueError(f'Invalid reduce_method {reduce_method}') 85 | 86 | return [ 87 | reduce_func(logprobs[idx][:seq_lengths[idx]]) 88 | for idx in range(len(seq_lengths)) 89 | ] 90 | 91 | 92 | def score_sequences( 93 | seqs: List[str], 94 | model: StripedHyena, 95 | tokenizer: object, 96 | batch_size: int = None, 97 | prepend_bos: bool = False, 98 | reduce_method: str = 'mean', 99 | device: str = 'cuda:0', 100 | ) -> List[float]: 101 | """ 102 | Computes the model log-likelihood scores for sequences in `seqs`. 103 | Uses `reduce_method` to take the mean or sum across the likelihoods at each 104 | position (default: `'mean'`). 105 | 106 | Returns a list of scalar scores corresponding to the reduced log-likelihoods for 107 | each sequence. 108 | """ 109 | if batch_size is None: 110 | batch_size = len(seqs) 111 | 112 | scores = [] 113 | for i in tqdm(range(0, len(seqs), batch_size)): 114 | batch_seqs = seqs[i:i + batch_size] 115 | batch_scores = _score_sequences( 116 | batch_seqs, 117 | model, 118 | tokenizer, 119 | prepend_bos=prepend_bos, 120 | reduce_method=reduce_method, 121 | device=device, 122 | ) 123 | scores.extend(batch_scores) 124 | return scores 125 | 126 | 127 | def score_sequences_rc( 128 | seqs: List[str], 129 | model: StripedHyena, 130 | tokenizer: object, 131 | batch_size: int, 132 | prepend_bos: bool = False, 133 | reduce_method: str = 'mean', 134 | device: str = 'cuda:0', 135 | ) -> List[float]: 136 | """ 137 | Computes the model log-likelihood scores for sequences in `seqs` and for their 138 | reverse complements. 139 | Takes the mean score for the forward and reverse-complemented sequence. 140 | Uses `reduce_method` to take the mean or sum across the likelihoods at each 141 | position (default: `'mean'`). 142 | 143 | Returns a list of scalar scores corresponding to the reduced log-likelihoods for 144 | each sequence. 145 | """ 146 | scores = [] 147 | for i in tqdm(range(0, len(seqs), batch_size)): 148 | batch_seqs = seqs[i:i + batch_size] 149 | batch_seqs_rc = [ str(Seq(seq).reverse_complement()) for seq in batch_seqs ] 150 | 151 | batch_scores = _score_sequences( 152 | batch_seqs, 153 | model, 154 | tokenizer, 155 | prepend_bos=prepend_bos, 156 | reduce_method=reduce_method, 157 | device=device, 158 | ) 159 | batch_scores_rc = _score_sequences( 160 | batch_seqs_rc, 161 | model, 162 | tokenizer, 163 | prepend_bos=prepend_bos, 164 | reduce_method=reduce_method, 165 | device=device, 166 | ) 167 | batch_scores = (np.array(batch_scores) + np.array(batch_scores_rc)) * 0.5 168 | 169 | scores.extend(list(batch_scores)) 170 | return scores 171 | 172 | 173 | def positional_entropies( 174 | seqs: List[str], 175 | model: StripedHyena, 176 | tokenizer: object, 177 | prepend_bos: bool = False, 178 | device: str = 'cuda:0', 179 | ) -> List[np.array]: 180 | """ 181 | Computes the positional entropies for sequences in `seqs`. 182 | 183 | Returns a list of arrays, where each array is the same length as the 184 | corresponding sequence length. Each array contains the per-position entropy 185 | across the vocab dimension. 186 | """ 187 | input_ids, seq_lengths = prepare_batch(seqs, tokenizer, device=device, prepend_bos=prepend_bos) 188 | assert len(seq_lengths) == input_ids.shape[0] 189 | 190 | with torch.inference_mode(): 191 | logits, _ = model(input_ids) # (batch, length, vocab) 192 | 193 | softmax_logprobs = torch.log_softmax(logits, dim=-1) 194 | if prepend_bos: 195 | softmax_logprobs = softmax_logprobs[:, 1:, :] # Remove BOS entropy. 196 | 197 | entropies = -torch.sum(torch.exp(softmax_logprobs) * softmax_logprobs, dim=-1) 198 | entropies = entropies.float().cpu().numpy() 199 | 200 | sequence_entropies = [ 201 | entropies[idx][:seq_lengths[idx]] for idx in range(len(seq_lengths)) 202 | ] 203 | assert all( 204 | len(seq) == len(entropy) for seq, entropy in zip(seqs, sequence_entropies) 205 | ) 206 | 207 | return sequence_entropies 208 | 209 | 210 | def score_perplexity_along_sequence( 211 | model: StripedHyena, 212 | seq: str, 213 | reverse_complement: bool = True, 214 | entropy: bool = False 215 | ) -> np.array: 216 | ''' 217 | Get forward and reverse RC of dna sequence, pass both through model, and return average entropy or perplexity. 218 | ''' 219 | seq_rc = str(Seq(seq).reverse_complement()) 220 | 221 | entropy_forward = positional_entropies([seq], model.model, model.tokenizer)[0] 222 | 223 | if reverse_complement: 224 | entropy_reverse = positional_entropies([seq_rc], model.model, model.tokenizer)[0] 225 | entropy_reverse = entropy_reverse[::-1] 226 | 227 | average_entropy = (entropy_forward + entropy_reverse) / 2 228 | else: 229 | average_entropy = entropy_forward 230 | 231 | if entropy: 232 | return average_entropy 233 | else: 234 | return np.exp(average_entropy) -------------------------------------------------------------------------------- /evo2/utils.py: -------------------------------------------------------------------------------- 1 | MODEL_NAMES = [ 2 | 'evo2_40b', 3 | 'evo2_7b', 4 | 'evo2_40b_base', 5 | 'evo2_7b_base', 6 | 'evo2_1b_base', 7 | ] 8 | 9 | HF_MODEL_NAME_MAP = { 10 | 'evo2_40b': 'arcinstitute/evo2_40b', 11 | 'evo2_7b': 'arcinstitute/evo2_7b', 12 | 'evo2_40b_base': 'arcinstitute/evo2_40b_base', 13 | 'evo2_7b_base': 'arcinstitute/evo2_7b_base', 14 | 'evo2_1b_base': 'arcinstitute/evo2_1b_base', 15 | } 16 | 17 | CONFIG_MAP = { 18 | 'evo2_7b': 'configs/evo2-7b-1m.yml', 19 | 'evo2_40b': 'configs/evo2-40b-1m.yml', 20 | 'evo2_7b_base': 'configs/evo2-7b-8k.yml', 21 | 'evo2_40b_base': 'configs/evo2-40b-8k.yml', 22 | 'evo2_1b_base': 'configs/evo2-1b-8k.yml', 23 | } 24 | 25 | 26 | def make_phylotag_from_gbif( 27 | species_name: str, 28 | ) -> dict: 29 | """ 30 | Returns phylogenetic tags for a given species, to get new tags not in the metadata 31 | """ 32 | 33 | import requests 34 | def get_taxonomy_from_gbif(species_name): 35 | url = f"https://api.gbif.org/v1/species/match?name={species_name}" 36 | response = requests.get(url) 37 | if response.status_code == 200: 38 | data = response.json() 39 | return { 40 | "kingdom": data.get("kingdom"), 41 | "phylum": data.get("phylum"), 42 | "class": data.get("class"), 43 | "order": data.get("order"), 44 | "family": data.get("family"), 45 | "genus": data.get("genus"), 46 | "species": data.get("species") 47 | } 48 | else: 49 | print(f"Could not find taxonomy for {species_name}") 50 | 51 | taxonomy = get_taxonomy_from_gbif(species_name) 52 | if taxonomy: 53 | phylo_tag = ( 54 | f'd__{taxonomy["kingdom"]};' 55 | f'p__{taxonomy["phylum"]};' 56 | f'c__{taxonomy["class"]};' 57 | f'o__{taxonomy["order"]};' 58 | f'f__{taxonomy["family"]};' 59 | f'g__{taxonomy["genus"]};' 60 | f's__{taxonomy["species"]}' 61 | ).upper() 62 | phylo_tag = '|'+phylo_tag+'|' 63 | else: 64 | print(f"Could not find taxonomy for {species_name}") 65 | 66 | return phylo_tag.upper() 67 | 68 | -------------------------------------------------------------------------------- /evo2/version.py: -------------------------------------------------------------------------------- 1 | version = '0.1.0' 2 | -------------------------------------------------------------------------------- /notebooks/brca1/41586_2018_461_MOESM3_ESM.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ArcInstitute/evo2/a796302818055b9710a6a2c4d7882a6243363fdd/notebooks/brca1/41586_2018_461_MOESM3_ESM.xlsx -------------------------------------------------------------------------------- /notebooks/brca1/GRCh37.p13_chr17.fna.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ArcInstitute/evo2/a796302818055b9710a6a2c4d7882a6243363fdd/notebooks/brca1/GRCh37.p13_chr17.fna.gz -------------------------------------------------------------------------------- /notebooks/brca1/brca1_zero_shot_vep.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "17440de8", 6 | "metadata": {}, 7 | "source": [ 8 | "## Zero-shot prediction of *BRCA1* variant effects with Evo 2\n", 9 | "\n", 10 | "The human *BRCA1* gene encodes for a protein that repairs damaged DNA ([Moynahan et al., 1999](https://www.cell.com/molecular-cell/fulltext/S1097-2765%2800%2980202-6)). Certain variants of this gene have been associated with an increased risk of breast and ovarian cancers ([Miki et al., 1994](https://www.science.org/doi/10.1126/science.7545954?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed)). Using Evo 2, we can predict whether a particular single nucleotide variant (SNV) of the *BRCA1* gene is likely to be harmful to the protein's function, and thus potentially increase the risk of cancer for the patient with the genetic variant.\n", 11 | "\n", 12 | "We start by loading a dataset from [Findlay et al. (2018)](https://www.nature.com/articles/s41586-018-0461-z), which contains experimentally measured function scores of 3,893 *BRCA1* SNVs. These function scores reflect the extent by which the genetic variant has disrupted the protein's function, with lower scores indicating greater disruption. In this dataset, the SNVs are classified into three categories based on their function scores: `LOF` (loss-of-function), `INT` (intermediate), and `FUNC` (functional). We start by reading in this dataset." 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 1, 18 | "id": "f090aadb", 19 | "metadata": {}, 20 | "outputs": [ 21 | { 22 | "name": "stdout", 23 | "output_type": "stream", 24 | "text": [ 25 | "/bin/bash: /home/ggsun/miniconda/envs/evo2-release/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n", 26 | "Requirement already satisfied: matplotlib in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (3.10.0)\n", 27 | "Requirement already satisfied: pandas in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (2.2.3)\n", 28 | "Requirement already satisfied: seaborn in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (0.13.2)\n", 29 | "Requirement already satisfied: scikit-learn in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (1.6.1)\n", 30 | "Requirement already satisfied: openpyxl in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (3.1.5)\n", 31 | "Requirement already satisfied: contourpy>=1.0.1 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from matplotlib) (1.3.1)\n", 32 | "Requirement already satisfied: cycler>=0.10 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from matplotlib) (0.12.1)\n", 33 | "Requirement already satisfied: fonttools>=4.22.0 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from matplotlib) (4.56.0)\n", 34 | "Requirement already satisfied: kiwisolver>=1.3.1 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from matplotlib) (1.4.8)\n", 35 | "Requirement already satisfied: numpy>=1.23 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from matplotlib) (2.2.3)\n", 36 | "Requirement already satisfied: packaging>=20.0 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from matplotlib) (24.2)\n", 37 | "Requirement already satisfied: pillow>=8 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from matplotlib) (11.1.0)\n", 38 | "Requirement already satisfied: pyparsing>=2.3.1 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from matplotlib) (3.2.1)\n", 39 | "Requirement already satisfied: python-dateutil>=2.7 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from matplotlib) (2.9.0.post0)\n", 40 | "Requirement already satisfied: pytz>=2020.1 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from pandas) (2025.1)\n", 41 | "Requirement already satisfied: tzdata>=2022.7 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from pandas) (2025.1)\n", 42 | "Requirement already satisfied: scipy>=1.6.0 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from scikit-learn) (1.15.2)\n", 43 | "Requirement already satisfied: joblib>=1.2.0 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from scikit-learn) (1.4.2)\n", 44 | "Requirement already satisfied: threadpoolctl>=3.1.0 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from scikit-learn) (3.5.0)\n", 45 | "Requirement already satisfied: et-xmlfile in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from openpyxl) (2.0.0)\n", 46 | "Requirement already satisfied: six>=1.5 in /home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n" 47 | ] 48 | } 49 | ], 50 | "source": [ 51 | "# Install dependencies\n", 52 | "!pip install matplotlib pandas seaborn scikit-learn openpyxl\n", 53 | "\n", 54 | "# Required imports\n", 55 | "from Bio import SeqIO\n", 56 | "import gzip\n", 57 | "import matplotlib.pyplot as plt\n", 58 | "import numpy as np\n", 59 | "import pandas as pd\n", 60 | "import os\n", 61 | "import seaborn as sns\n", 62 | "from sklearn.metrics import roc_auc_score\n", 63 | "\n", 64 | "# Set root path\n", 65 | "os.chdir('../..')" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 2, 71 | "id": "1e26f1bf", 72 | "metadata": {}, 73 | "outputs": [ 74 | { 75 | "data": { 76 | "text/html": [ 77 | "
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chromosomeposition (hg19)referencealtfunction.score.meanfunc.class
01741276135TG-0.372611FUNC
11741276135TC-0.045313FUNC
21741276135TA-0.108254FUNC
31741276134TG-0.277963FUNC
41741276134TC-0.388414FUNC
51741276134TA-0.280973FUNC
61741276133CT-0.973683INT
71741276133CG-0.373489FUNC
81741276133CA0.006314FUNC
91741276132AT-0.207552FUNC
\n", 196 | "
" 197 | ], 198 | "text/plain": [ 199 | " chromosome position (hg19) reference alt function.score.mean func.class\n", 200 | "0 17 41276135 T G -0.372611 FUNC\n", 201 | "1 17 41276135 T C -0.045313 FUNC\n", 202 | "2 17 41276135 T A -0.108254 FUNC\n", 203 | "3 17 41276134 T G -0.277963 FUNC\n", 204 | "4 17 41276134 T C -0.388414 FUNC\n", 205 | "5 17 41276134 T A -0.280973 FUNC\n", 206 | "6 17 41276133 C T -0.973683 INT\n", 207 | "7 17 41276133 C G -0.373489 FUNC\n", 208 | "8 17 41276133 C A 0.006314 FUNC\n", 209 | "9 17 41276132 A T -0.207552 FUNC" 210 | ] 211 | }, 212 | "execution_count": 2, 213 | "metadata": {}, 214 | "output_type": "execute_result" 215 | } 216 | ], 217 | "source": [ 218 | "brca1_df = pd.read_excel(\n", 219 | " os.path.join('notebooks', 'brca1', '41586_2018_461_MOESM3_ESM.xlsx'),\n", 220 | " header=2,\n", 221 | ")\n", 222 | "brca1_df = brca1_df[[\n", 223 | " 'chromosome', 'position (hg19)', 'reference', 'alt', 'function.score.mean', 'func.class',\n", 224 | "]]\n", 225 | "\n", 226 | "brca1_df.head(10)" 227 | ] 228 | }, 229 | { 230 | "cell_type": "markdown", 231 | "id": "13e0c7d5", 232 | "metadata": {}, 233 | "source": [ 234 | "We then group the `FUNC` and `INT` classes of SNVs together into a single category (`FUNC/INT`)." 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": 3, 240 | "id": "ce7df7cc", 241 | "metadata": {}, 242 | "outputs": [ 243 | { 244 | "data": { 245 | "text/html": [ 246 | "
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chromposrefaltscoreclass
01741276135TG-0.372611FUNC/INT
11741276135TC-0.045313FUNC/INT
21741276135TA-0.108254FUNC/INT
31741276134TG-0.277963FUNC/INT
41741276134TC-0.388414FUNC/INT
51741276134TA-0.280973FUNC/INT
61741276133CT-0.973683FUNC/INT
71741276133CG-0.373489FUNC/INT
81741276133CA0.006314FUNC/INT
91741276132AT-0.207552FUNC/INT
\n", 365 | "
" 366 | ], 367 | "text/plain": [ 368 | " chrom pos ref alt score class\n", 369 | "0 17 41276135 T G -0.372611 FUNC/INT\n", 370 | "1 17 41276135 T C -0.045313 FUNC/INT\n", 371 | "2 17 41276135 T A -0.108254 FUNC/INT\n", 372 | "3 17 41276134 T G -0.277963 FUNC/INT\n", 373 | "4 17 41276134 T C -0.388414 FUNC/INT\n", 374 | "5 17 41276134 T A -0.280973 FUNC/INT\n", 375 | "6 17 41276133 C T -0.973683 FUNC/INT\n", 376 | "7 17 41276133 C G -0.373489 FUNC/INT\n", 377 | "8 17 41276133 C A 0.006314 FUNC/INT\n", 378 | "9 17 41276132 A T -0.207552 FUNC/INT" 379 | ] 380 | }, 381 | "execution_count": 3, 382 | "metadata": {}, 383 | "output_type": "execute_result" 384 | } 385 | ], 386 | "source": [ 387 | "# Rename columns\n", 388 | "brca1_df.rename(columns={\n", 389 | " 'chromosome': 'chrom',\n", 390 | " 'position (hg19)': 'pos',\n", 391 | " 'reference': 'ref',\n", 392 | " 'alt': 'alt',\n", 393 | " 'function.score.mean': 'score',\n", 394 | " 'func.class': 'class',\n", 395 | "}, inplace=True)\n", 396 | "\n", 397 | "# Convert to two-class system\n", 398 | "brca1_df['class'] = brca1_df['class'].replace(['FUNC', 'INT'], 'FUNC/INT')\n", 399 | "\n", 400 | "brca1_df.head(10)" 401 | ] 402 | }, 403 | { 404 | "cell_type": "markdown", 405 | "id": "8ef6f10c", 406 | "metadata": {}, 407 | "source": [ 408 | "We build a function to parse the reference and variant sequences of a 8,192-bp window around the genomic position of each SNV, using the reference sequence of human chromosome 17 where *BRCA1* is located." 409 | ] 410 | }, 411 | { 412 | "cell_type": "code", 413 | "execution_count": 4, 414 | "id": "4be1bb8e", 415 | "metadata": { 416 | "lines_to_next_cell": 2, 417 | "scrolled": true 418 | }, 419 | "outputs": [ 420 | { 421 | "name": "stdout", 422 | "output_type": "stream", 423 | "text": [ 424 | "chrom 17\n", 425 | "pos 41276135\n", 426 | "ref T\n", 427 | "alt G\n", 428 | "score -0.372611\n", 429 | "class FUNC/INT\n", 430 | "Name: 0, dtype: object\n", 431 | "--\n", 432 | "Reference, SNV 0: ...TGTTCCAATGAACTTTAACACATTAGAAAA...\n", 433 | "Variant, SNV 0: ...TGTTCCAATGAACTGTAACACATTAGAAAA...\n" 434 | ] 435 | } 436 | ], 437 | "source": [ 438 | "WINDOW_SIZE = 8192\n", 439 | "\n", 440 | "# Read the reference genome sequence of chromosome 17\n", 441 | "with gzip.open(os.path.join('notebooks', 'brca1', 'GRCh37.p13_chr17.fna.gz'), \"rt\") as handle:\n", 442 | " for record in SeqIO.parse(handle, \"fasta\"):\n", 443 | " seq_chr17 = str(record.seq)\n", 444 | " break\n", 445 | "\n", 446 | "def parse_sequences(pos, ref, alt):\n", 447 | " \"\"\"\n", 448 | " Parse reference and variant sequences from the reference genome sequence.\n", 449 | " \"\"\"\n", 450 | " p = pos - 1 # Convert to 0-indexed position\n", 451 | " full_seq = seq_chr17\n", 452 | "\n", 453 | " ref_seq_start = max(0, p - WINDOW_SIZE//2)\n", 454 | " ref_seq_end = min(len(full_seq), p + WINDOW_SIZE//2)\n", 455 | " ref_seq = seq_chr17[ref_seq_start:ref_seq_end]\n", 456 | " snv_pos_in_ref = min(WINDOW_SIZE//2, p)\n", 457 | " var_seq = ref_seq[:snv_pos_in_ref] + alt + ref_seq[snv_pos_in_ref+1:]\n", 458 | "\n", 459 | " # Sanity checks\n", 460 | " assert len(var_seq) == len(ref_seq)\n", 461 | " assert ref_seq[snv_pos_in_ref] == ref\n", 462 | " assert var_seq[snv_pos_in_ref] == alt\n", 463 | "\n", 464 | " return ref_seq, var_seq\n", 465 | "\n", 466 | "# Parse sequences for the first variant\n", 467 | "row = brca1_df.iloc[0]\n", 468 | "ref_seq, var_seq = parse_sequences(row['pos'], row['ref'], row['alt'])\n", 469 | "\n", 470 | "print(row)\n", 471 | "print('--')\n", 472 | "print(f'Reference, SNV 0: ...{ref_seq[4082:4112]}...')\n", 473 | "print(f'Variant, SNV 0: ...{var_seq[4082:4112]}...')" 474 | ] 475 | }, 476 | { 477 | "cell_type": "markdown", 478 | "id": "5acd3e9a-0b33-44c4-a95a-9be49ef61a76", 479 | "metadata": {}, 480 | "source": [ 481 | "Then, we load Evo 2 1B and score the likelihoods of the reference and variant sequences of each SNV. (Note: we use the smaller Evo 2 1B base model here as a quick demonstration, but we strongly recommend using the larger Evo 2 7B and 40B models for more accurate predictions.)" 482 | ] 483 | }, 484 | { 485 | "cell_type": "code", 486 | "execution_count": 5, 487 | "id": "362d5a24", 488 | "metadata": {}, 489 | "outputs": [ 490 | { 491 | "name": "stdout", 492 | "output_type": "stream", 493 | "text": [ 494 | "tokenizers not found, unable to use HFAutoTokenizer\n" 495 | ] 496 | }, 497 | { 498 | "name": "stderr", 499 | "output_type": "stream", 500 | "text": [ 501 | "100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:00<00:00, 220.42it/s]\n" 502 | ] 503 | }, 504 | { 505 | "name": "stdout", 506 | "output_type": "stream", 507 | "text": [ 508 | "Extra keys in state_dict: {'blocks.3.mixer.dense._extra_state', 'blocks.24.mixer.dense._extra_state', 'unembed.weight', 'blocks.10.mixer.dense._extra_state', 'blocks.17.mixer.dense._extra_state'}\n" 509 | ] 510 | }, 511 | { 512 | "name": "stderr", 513 | "output_type": "stream", 514 | "text": [ 515 | "/home/ggsun/miniconda/envs/evo2-release/lib/python3.12/site-packages/transformer_engine/pytorch/module/base.py:630: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", 516 | " state = torch.load(state, map_location=\"cuda\")\n" 517 | ] 518 | }, 519 | { 520 | "name": "stdout", 521 | "output_type": "stream", 522 | "text": [ 523 | "Loaded model evo2_1b_base from /home/ggsun/.cache/huggingface/hub/models--arcinstitute--evo2_1b_base/snapshots/6915b21845659a78b55e59a1eb603039fc81c49f/evo2_1b_base.pt!\n" 524 | ] 525 | }, 526 | { 527 | "name": "stderr", 528 | "output_type": "stream", 529 | "text": [ 530 | "/home/ggsun/evo2/vortex/vortex/model/utils.py:153: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", 531 | " return torch_load(state, map_location=device)\n" 532 | ] 533 | } 534 | ], 535 | "source": [ 536 | "from evo2.models import Evo2\n", 537 | "\n", 538 | "# Load model\n", 539 | "model = Evo2('evo2_1b_base')" 540 | ] 541 | }, 542 | { 543 | "cell_type": "code", 544 | "execution_count": 6, 545 | "id": "135bffe8", 546 | "metadata": {}, 547 | "outputs": [ 548 | { 549 | "name": "stdout", 550 | "output_type": "stream", 551 | "text": [ 552 | "Scoring likelihoods of 1326 reference sequences with Evo 2...\n" 553 | ] 554 | }, 555 | { 556 | "name": "stderr", 557 | "output_type": "stream", 558 | "text": [ 559 | "100%|███████████████████████████████████████████████████████████████████████████████████████████████| 1326/1326 [02:20<00:00, 9.45it/s]\n" 560 | ] 561 | }, 562 | { 563 | "name": "stdout", 564 | "output_type": "stream", 565 | "text": [ 566 | "Scoring likelihoods of 3893 variant sequences with Evo 2...\n" 567 | ] 568 | }, 569 | { 570 | "name": "stderr", 571 | "output_type": "stream", 572 | "text": [ 573 | "100%|███████████████████████████████████████████████████████████████████████████████████████████████| 3893/3893 [06:49<00:00, 9.51it/s]\n" 574 | ] 575 | } 576 | ], 577 | "source": [ 578 | "# Build mappings of unique reference sequences\n", 579 | "ref_seqs = []\n", 580 | "ref_seq_to_index = {}\n", 581 | "\n", 582 | "# Parse sequences and store indexes\n", 583 | "ref_seq_indexes = []\n", 584 | "var_seqs = []\n", 585 | "\n", 586 | "for _, row in brca1_df.iterrows():\n", 587 | " ref_seq, var_seq = parse_sequences(row['pos'], row['ref'], row['alt'])\n", 588 | "\n", 589 | " # Get or create index for reference sequence\n", 590 | " if ref_seq not in ref_seq_to_index:\n", 591 | " ref_seq_to_index[ref_seq] = len(ref_seqs)\n", 592 | " ref_seqs.append(ref_seq)\n", 593 | " \n", 594 | " ref_seq_indexes.append(ref_seq_to_index[ref_seq])\n", 595 | " var_seqs.append(var_seq)\n", 596 | "\n", 597 | "ref_seq_indexes = np.array(ref_seq_indexes)\n", 598 | "\n", 599 | "print(f'Scoring likelihoods of {len(ref_seqs)} reference sequences with Evo 2...')\n", 600 | "ref_scores = model.score_sequences(ref_seqs)\n", 601 | "\n", 602 | "print(f'Scoring likelihoods of {len(var_seqs)} variant sequences with Evo 2...')\n", 603 | "var_scores = model.score_sequences(var_seqs)" 604 | ] 605 | }, 606 | { 607 | "cell_type": "markdown", 608 | "id": "cbf2de1e-17b1-4f0d-9004-1eb917ed83ac", 609 | "metadata": {}, 610 | "source": [ 611 | "We calculate the change in likelihoods for each variant relative to the likelihood of their respective wild-type sequence." 612 | ] 613 | }, 614 | { 615 | "cell_type": "code", 616 | "execution_count": 7, 617 | "id": "a49d5859-87ed-49c9-8e3d-18629f073022", 618 | "metadata": { 619 | "scrolled": true 620 | }, 621 | "outputs": [ 622 | { 623 | "data": { 624 | "text/html": [ 625 | "
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chromposrefaltscoreclassevo2_delta_score
01741276135TG-0.372611FUNC/INT-0.000054
11741276135TC-0.045313FUNC/INT0.000140
21741276135TA-0.108254FUNC/INT0.000074
31741276134TG-0.277963FUNC/INT-0.000146
41741276134TC-0.388414FUNC/INT-0.000104
51741276134TA-0.280973FUNC/INT0.000084
61741276133CT-0.973683FUNC/INT0.000299
71741276133CG-0.373489FUNC/INT0.000077
81741276133CA0.006314FUNC/INT0.000352
91741276132AT-0.207552FUNC/INT-0.000300
\n", 755 | "
" 756 | ], 757 | "text/plain": [ 758 | " chrom pos ref alt score class evo2_delta_score\n", 759 | "0 17 41276135 T G -0.372611 FUNC/INT -0.000054\n", 760 | "1 17 41276135 T C -0.045313 FUNC/INT 0.000140\n", 761 | "2 17 41276135 T A -0.108254 FUNC/INT 0.000074\n", 762 | "3 17 41276134 T G -0.277963 FUNC/INT -0.000146\n", 763 | "4 17 41276134 T C -0.388414 FUNC/INT -0.000104\n", 764 | "5 17 41276134 T A -0.280973 FUNC/INT 0.000084\n", 765 | "6 17 41276133 C T -0.973683 FUNC/INT 0.000299\n", 766 | "7 17 41276133 C G -0.373489 FUNC/INT 0.000077\n", 767 | "8 17 41276133 C A 0.006314 FUNC/INT 0.000352\n", 768 | "9 17 41276132 A T -0.207552 FUNC/INT -0.000300" 769 | ] 770 | }, 771 | "execution_count": 7, 772 | "metadata": {}, 773 | "output_type": "execute_result" 774 | } 775 | ], 776 | "source": [ 777 | "# Subtract score of corresponding reference sequences from scores of variant sequences\n", 778 | "delta_scores = np.array(var_scores) - np.array(ref_scores)[ref_seq_indexes]\n", 779 | "\n", 780 | "# Add delta scores to dataframe\n", 781 | "brca1_df[f'evo2_delta_score'] = delta_scores\n", 782 | "\n", 783 | "brca1_df.head(10)" 784 | ] 785 | }, 786 | { 787 | "cell_type": "markdown", 788 | "id": "20aea762-ef7f-4687-88ca-56d9042e7a0d", 789 | "metadata": {}, 790 | "source": [ 791 | "This delta likelihood should be predictive of how disruptive the SNV is to the protein's function: the lower the delta, the more likely that the SNV is disruptive. We can show this by comparing the distributions of delta likelihoods for the two classes of SNVs (functional/intermediate vs loss-of-function)." 792 | ] 793 | }, 794 | { 795 | "cell_type": "code", 796 | "execution_count": 8, 797 | "id": "0c27729e-927e-42ec-b311-1e3d901eb29e", 798 | "metadata": {}, 799 | "outputs": [ 800 | { 801 | "data": { 802 | "image/png": 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", 803 | "text/plain": [ 804 | "
" 805 | ] 806 | }, 807 | "metadata": {}, 808 | "output_type": "display_data" 809 | } 810 | ], 811 | "source": [ 812 | "plt.figure(figsize=(4, 2))\n", 813 | "\n", 814 | "# Plot stripplot of distributions\n", 815 | "p = sns.stripplot(\n", 816 | " data=brca1_df,\n", 817 | " x='evo2_delta_score',\n", 818 | " y='class',\n", 819 | " hue='class',\n", 820 | " order=['FUNC/INT', 'LOF'],\n", 821 | " palette=['#777777', 'C3'],\n", 822 | " size=2,\n", 823 | " jitter=0.3,\n", 824 | ")\n", 825 | "\n", 826 | "# Mark medians from each distribution\n", 827 | "sns.boxplot(showmeans=True,\n", 828 | " meanline=True,\n", 829 | " meanprops={'visible': False},\n", 830 | " medianprops={'color': 'k', 'ls': '-', 'lw': 2},\n", 831 | " whiskerprops={'visible': False},\n", 832 | " zorder=10,\n", 833 | " x=\"evo2_delta_score\",\n", 834 | " y=\"class\",\n", 835 | " data=brca1_df,\n", 836 | " showfliers=False,\n", 837 | " showbox=False,\n", 838 | " showcaps=False,\n", 839 | " ax=p)\n", 840 | "plt.xlabel('Delta likelihood score, Evo 2')\n", 841 | "plt.ylabel('BRCA1 SNV class')\n", 842 | "plt.tight_layout()\n", 843 | "plt.show()" 844 | ] 845 | }, 846 | { 847 | "cell_type": "markdown", 848 | "id": "f3974e39-6c50-4503-9bab-829b1ac1b14a", 849 | "metadata": {}, 850 | "source": [ 851 | "We can also calculate the area under the receiver operating characteristic curve (AUROC) of this zero-shot prediction method." 852 | ] 853 | }, 854 | { 855 | "cell_type": "code", 856 | "execution_count": 9, 857 | "id": "f9e6cc5e-9c98-4010-8210-b38f570e1290", 858 | "metadata": {}, 859 | "outputs": [ 860 | { 861 | "name": "stdout", 862 | "output_type": "stream", 863 | "text": [ 864 | "Zero-shot prediction AUROC: 0.73\n" 865 | ] 866 | } 867 | ], 868 | "source": [ 869 | "# Calculate AUROC of zero-shot predictions\n", 870 | "y_true = (brca1_df['class'] == 'LOF')\n", 871 | "auroc = roc_auc_score(y_true, -brca1_df['evo2_delta_score'])\n", 872 | "\n", 873 | "print(f'Zero-shot prediction AUROC: {auroc:.2}')" 874 | ] 875 | } 876 | ], 877 | "metadata": { 878 | "jupytext": { 879 | "cell_metadata_filter": "-all", 880 | "main_language": "python", 881 | "notebook_metadata_filter": "-all" 882 | }, 883 | "kernelspec": { 884 | "display_name": "evo2-release", 885 | "language": "python", 886 | "name": "evo2-release" 887 | }, 888 | "language_info": { 889 | "codemirror_mode": { 890 | "name": "ipython", 891 | "version": 3 892 | }, 893 | "file_extension": ".py", 894 | "mimetype": "text/x-python", 895 | "name": "python", 896 | "nbconvert_exporter": "python", 897 | "pygments_lexer": "ipython3", 898 | "version": "3.12.9" 899 | } 900 | }, 901 | "nbformat": 4, 902 | "nbformat_minor": 5 903 | } 904 | -------------------------------------------------------------------------------- /notebooks/generation/generation_notebook.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Sequence Generation and Alignment Analysis with Evo2\n", 8 | "This notebook demonstrates how to generate biological sequences using the Evo2 model and analyze them using Biopython alignments.\n", 9 | "\n", 10 | "## Setup and Dependencies\n", 11 | "\n", 12 | "First, let's import our required libraries and set up our environment. Note you need Jupyter to run notebooks.\n" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 4, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "import os\n", 22 | "import argparse\n", 23 | "import csv\n", 24 | "from pathlib import Path\n", 25 | "from typing import List, Optional, Tuple\n", 26 | "import numpy as np\n", 27 | "import torch\n", 28 | "import torch.nn.functional as F\n", 29 | "from Bio import pairwise2\n", 30 | "from Bio.pairwise2 import format_alignment\n", 31 | "from Bio.Seq import Seq\n", 32 | "\n", 33 | "from evo2 import Evo2\n", 34 | "\n", 35 | "# Set random seeds for reproducibility\n", 36 | "torch.manual_seed(42)\n", 37 | "torch.cuda.manual_seed(42)\n", 38 | "\n" 39 | ] 40 | }, 41 | { 42 | "cell_type": "markdown", 43 | "metadata": {}, 44 | "source": [ 45 | "## Model Initialization\n", 46 | "Let's initialize our Evo2 model. We'll use the 7B parameter version as a default." 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 5, 52 | "metadata": {}, 53 | "outputs": [ 54 | { 55 | "name": "stderr", 56 | "output_type": "stream", 57 | "text": [ 58 | "Fetching 4 files: 100%|██████████| 4/4 [00:00<00:00, 32263.88it/s]" 59 | ] 60 | }, 61 | { 62 | "name": "stderr", 63 | "output_type": "stream", 64 | "text": [ 65 | "\n" 66 | ] 67 | }, 68 | { 69 | "name": "stdout", 70 | "output_type": "stream", 71 | "text": [ 72 | "Found complete file in repo: evo2_7b.pt\n" 73 | ] 74 | }, 75 | { 76 | "name": "stderr", 77 | "output_type": "stream", 78 | "text": [ 79 | "100%|██████████| 32/32 [00:00<00:00, 180.96it/s]\n" 80 | ] 81 | }, 82 | { 83 | "name": "stdout", 84 | "output_type": "stream", 85 | "text": [ 86 | "Extra keys in state_dict: {'blocks.2.mixer.mixer.filter.t', 'blocks.16.mixer.mixer.filter.t', 'blocks.20.mixer.mixer.filter.t', 'blocks.9.mixer.mixer.filter.t', 'blocks.27.mixer.mixer.filter.t', 'blocks.17.mixer.dense._extra_state', 'blocks.31.mixer.attn._extra_state', 'blocks.24.mixer.dense._extra_state', 'blocks.17.mixer.attn._extra_state', 'blocks.13.mixer.mixer.filter.t', 'blocks.10.mixer.attn._extra_state', 'blocks.10.mixer.dense._extra_state', 'blocks.30.mixer.mixer.filter.t', 'blocks.31.mixer.dense._extra_state', 'blocks.24.mixer.attn._extra_state', 'blocks.3.mixer.dense._extra_state', 'blocks.3.mixer.attn._extra_state', 'unembed.weight', 'blocks.6.mixer.mixer.filter.t', 'blocks.23.mixer.mixer.filter.t'}\n" 87 | ] 88 | }, 89 | { 90 | "name": "stderr", 91 | "output_type": "stream", 92 | "text": [ 93 | "/home/gbrixi/miniconda/envs/hf_tracking_test/lib/python3.11/site-packages/transformer_engine/pytorch/module/base.py:630: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", 94 | " state = torch.load(state, map_location=\"cuda\")\n", 95 | "/home/gbrixi/evo2/vortex/vortex/model/utils.py:153: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", 96 | " return torch_load(state, map_location=device)\n" 97 | ] 98 | } 99 | ], 100 | "source": [ 101 | "model_name = 'evo2_7b'\n", 102 | "\n", 103 | "model = Evo2(model_name)" 104 | ] 105 | }, 106 | { 107 | "cell_type": "markdown", 108 | "metadata": {}, 109 | "source": [ 110 | "## Data Loading\n", 111 | "Next we'll create functions to load our example sequences\n" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 23, 117 | "metadata": {}, 118 | "outputs": [ 119 | { 120 | "name": "stdout", 121 | "output_type": "stream", 122 | "text": [ 123 | "Loaded 4 sequence pairs\n" 124 | ] 125 | } 126 | ], 127 | "source": [ 128 | "def read_sequences(input_file: Path) -> Tuple[List[str], List[str]]:\n", 129 | " \"\"\"\n", 130 | " Read input and target sequences from CSV file.\n", 131 | " \n", 132 | " Expected CSV format:\n", 133 | " input_sequence,target_sequence\n", 134 | " ACGTACGT,ACGTACGTAA\n", 135 | " ...\n", 136 | " \"\"\"\n", 137 | " input_seqs: List[str] = []\n", 138 | " names: List[str] = []\n", 139 | " \n", 140 | " with open(input_file, encoding='utf-8-sig', newline='') as csvfile:\n", 141 | " reader = csv.reader(csvfile)\n", 142 | " next(reader) # Skip header\n", 143 | " for row in reader:\n", 144 | " input_seqs.append(row[0])\n", 145 | " if len(row) > 1:\n", 146 | " names.append(row[1])\n", 147 | " \n", 148 | " return input_seqs, names\n", 149 | "\n", 150 | "# Load example data\n", 151 | "\n", 152 | "sequences, names = read_sequences('../../vortex/test/data/prompts.csv')\n", 153 | "\n", 154 | "# For 'autocomplete', we split the data into input and target sequences\n", 155 | "\n", 156 | "input_seqs = [seq[:500] for seq in sequences]\n", 157 | "target_seqs = [seq[500:1000] for seq in sequences]\n", 158 | "\n", 159 | "print(f\"Loaded {len(sequences)} sequence pairs\")" 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": {}, 165 | "source": [ 166 | "### Now it's time to generate!" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": 24, 172 | "metadata": {}, 173 | "outputs": [ 174 | { 175 | "name": "stdout", 176 | "output_type": "stream", 177 | "text": [ 178 | "Initializing inference params with max_seqlen=1000\n", 179 | "Prompt: \"GAATAGGAACAGCTCCGGTCTACAGCTCCCAGCGTGAGCGACGCAGAAGACGGTGATTTCTGCATTTCCATCTGAGGTACCGGGTTCATCTCACTAGGGAGTGCCAGACAGTGGGCGCAGGCCAGTGTGTGTGCGCACCGTGCGCGAGCCGAAGCAGGGCGAGGCATTGCCTCACCTGGGAAGCGCAAGGGGTCAGGGAGTTCCCTTTCCGAGTCAAAGAAAGGGGTGATGGACGCACCTGGAAAATCGGGTCACTCCCACCCGAATATTGCGCTTTTCAGACCGGCTTAAGAAACGGCGCACCACGAGACTATATCCCACACCTGGCTCAGAGGGTCCTACGCCCACGGAATCTCGCTGATTGCTAGCACAGCAGTCTGAGATCAAACTGCAAGGCGGCAACGAGGCTGGGGGAGGGGCGCCCGCCATTGCCCAGGCTTGCTTAGGTAAACAAAGCAGCCGGGAAGCTCGAACTGGGTGGAGCCCACCACAGCTCAAGG\",\tOutput: \"AGGCCTGCCTGCCTCTGTAGGCTCCACCTCCGGGGGAAGGGCACAGCCCAACAAAAGGCGGCAGACACCTCTGCAGACTTAAATGTCCCTGTCTGACAGCTTTGAAGAGAGCAGTGGTTCTCCTAGCACGCAGCTGGAGATCTGAGAACGGGCAGACTGCCTCCTCAAGTGGGTCCCTGACCCCTGACCCCCGAGCAGCCTAACTGGGAGGCACCCCCCAGCAGGGGCACACTGACACCTCACACGGCAGGGTATTCCAACAGACCTGCAGCTGAGGATCCTGTCTGCAAGACAGCTTAGGCCCTACAACAGTCTTGCAGCCACCTCTACTGATGTAGGAAAGCCTGCCTGCCTCTGTAGGCTCCACCTCTGGGAGCAGGGCATAGACAAACAAAAAGAGGCAGCAGCAGCCTCAGCAGACAGAAACCATACCGCCTGGCAGCTTTGAAGAGAGCAGTGGATCTCCCAACACGGAGGTTGAGATCTGAGAACGGACAGAC\",\tScore: -0.26270025968551636\n", 180 | "Prompt: \"GACACCATCGAATGGCGCAAAACCTTTCGCGGTATGGCATGATAGCGCCCGGAAGAGAGTCAATTCAGGGTGGTGAATGTGAAACCAGTAACGTTATACGATGTCGCAGAGTATGCCGGTGTCTCTTATCAGACCGTTTCCCGCGTGGTGAACCAGGCCAGCCACGTTTCTGCGAAAACGCGGGAAAAAGTGGAAGCGGCGATGGCGGAGCTGAATTACATTCCCAACCGCGTGGCACAACAACTGGCGGGCAAACAGTCGTTGCTGATTGGCGTTGCCACCTCCAGTCTGGCCCTGCACGCGCCGTCGCAAATTGTCGCGGCGATTAAATCTCGCGCCGATCAACTGGGTGCCAGCGTGGTGGTGTCGATGGTAGAACGAAGCGGCGTCGAAGCCTGTAAAGCGGCGGTGCACAATCTTCTCGCGCAACGCGTCAGTGGGCTGATCATTAACTATCCGCTGGATGACCAGGATGCCATTGCTGTGGAAGCTGCCTGCAC\",\tOutput: \"TAATGTTCCGGCGTTGTTTCTTGATGTCTCTGACCAGACTTCCGTTAACAGTATTATTTTCTCCCATGAAGACGGTACGCGACTGGGCGTGGAACATCTGATCGCATTAGGTCACCAGCAAATCGCGCTGTTAGCGGGGCCATTAAGTTCTGTCTCGGCGCGTCTGAGGCTGGCGGGCTGGCATAAATATCTCACTCGCAACCATATCCAGCCGATAGCGGTACGGGAAGGCGACTGGAGTGCCATGTCCGGTTATCAACAAACGATGGAAATGCTGAATAACGGCATCGTACCGTCGGCGATGCTGGTTGCCAACGATCAGATGGCGCTGGGCGCAATGCGCGCACTGGAAGAACATAAACTTTCGGTACCGGAAGATATCTCGGTGATTGGTTATGACGATACCGAAGACAGCTCGTGTTTTATTCCGCCGTTGACCACTATCAAGCAGGATTTTCGTCTGCTGGGGCAGACAGCTGTGGACCGCCTGCTGCAACTGA\",\tScore: -0.15721526741981506\n", 181 | "Prompt: \"GTTAATGTAGCTTAAAACAAAAGCAAGGTACTGAAAATACCTAGACGAGTATATCCAACTCCATAAACAACAAAGGTTTGGTCCCGGCCTTCTTATTGGTTACTAGGAAACTTATACATGCAAGTATCCGCCCGCCAGTGAATACGCCTTCTAAATCATCACTGATCAAAGAGAGCTGGCATCAAGCACACACCCCAAGTGTAGCTCATGACGTCTCGCCTAGCCACACCCCCACGGGAAACAGCAGTAGTAAATATTTAGCAATTAACAAAAGTTAGACTAAGTTATCCTAATAAAGGACTGGTCAATTTCGTGCCAGCAACCGCGGCCATACGATTAGTCCAAATTAATAAGCATACGGCGTAAAGCGTATTAGAAGAATTAAAAAAATAAAGTTAAATCTTATACTAGCTGTTTAAAGCTCAAGATAAGACATAAATAGCCTACGAAAGTGACTTTAATAATCCTAAACATACGATAGCTAGGGTACAAACTGAGAT\",\tOutput: \"TAGATACCTCACTATGCTTAGCCATAAACCTAGGCAGAGTATAACCAATCTGCCAGCCAGAGTACTACTAGCAATAGCTTAAAACTCAAAGGACTTGGCGGTGCTTTATATCCACCTAGAGGAGCCTGTTCTATAATCGATAAACCCCGATAAACCTTACCACTTTTTGCTAATACAGTCTATATACCGCCATCTTCAGCAAACCCTTAAAAGGAATCACAGTAAGCAAAAACTTAGCACATAGGAACGTTAGGTCAAGGTGTAACCTATAAAGTGGTAAGAAATGGGCTACATTTTTTTAATTAAAAACACATTCTATACTAAACCTATGAAAATATTAAGCCTAAGGTGGATTTAGTAGTAAATTAAGAATAGAGAGCTTAATTGAATGAGAAAATTGGGCGCACACAATGCCCGTCACCCTCCTCAAATAATTATTACACAGTATAAAATACCATTAAAACAAAATCAACCAAAAAGGAGAAAAGTCGTAACAAGGT\",\tScore: -0.48031729459762573\n", 182 | "Prompt: \"GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTACGTTCAATATTACAGGCGAACATACTTACTAAAGTGTGTTAATTAATTAATGCTTGTAGGACATAATAATAACAATTGAATGTCTGCACAGCCACTTTCCACACAGACATCATAACAAAAAATTTCCACCAAACCCCCCCTCCCCCGCTTCTGGCCACAGCACTTAAACACATCTCTGCCAAACCCCAAAAACAAAGAACCCTAACACCAGCCTAACCAGATTTCAAATTTTATCTTTTGGCGGTATGCACTTTTAACAGTCACCCCCCAACTAACACATTATTTTCCCCTCCCACTCCCATACTACTAATCTCATCAATACAACCCCCGC\",\tOutput: \"GTATCTGATATGGATACGGTGAATAGTGGCTTCTTCAGTGTGCATCGTATTCATAGAAGAAACGATTTTTTTCGATACTGATTTCATTAGTGACTACGCTATCGGGCCTGCACTCTTTAAGGGATACAGATTCGTCGACTGTATTGGCCATTTGGCCACAGTGTGACCACATGACCATAATCTACATAGGTCTACATCATCGATAGGATTGCATCGGGGAGACGATACGGGGACAGGTATGTATTCATGAGCGTTGATGTCGCCACGGATGCGTTAAGCATATCCGCTCCGGATCCTCGCTGGATGGGGTATTACTGTTTATGAATGTCTTGTCTGCTCAAAGGCCGCGCTGCAGCAAAATACCCAAATGTCCAAAATGTGGGACATTTGCACATTGGGTGACAGTTGTTCGCAAAAGGCGCGCCCGAAAGGCATAATTCGGCCAACCCCAAATTCACAACTGCAAAACAGTAGTATAGTGATCGACTATATTTGCATTA\",\tScore: -1.3735215663909912\n", 183 | "['AGGCCTGCCTGCCTCTGTAGGCTCCACCTCCGGGGGAAGGGCACAGCCCAACAAAAGGCGGCAGACACCTCTGCAGACTTAAATGTCCCTGTCTGACAGCTTTGAAGAGAGCAGTGGTTCTCCTAGCACGCAGCTGGAGATCTGAGAACGGGCAGACTGCCTCCTCAAGTGGGTCCCTGACCCCTGACCCCCGAGCAGCCTAACTGGGAGGCACCCCCCAGCAGGGGCACACTGACACCTCACACGGCAGGGTATTCCAACAGACCTGCAGCTGAGGATCCTGTCTGCAAGACAGCTTAGGCCCTACAACAGTCTTGCAGCCACCTCTACTGATGTAGGAAAGCCTGCCTGCCTCTGTAGGCTCCACCTCTGGGAGCAGGGCATAGACAAACAAAAAGAGGCAGCAGCAGCCTCAGCAGACAGAAACCATACCGCCTGGCAGCTTTGAAGAGAGCAGTGGATCTCCCAACACGGAGGTTGAGATCTGAGAACGGACAGAC', 'TAATGTTCCGGCGTTGTTTCTTGATGTCTCTGACCAGACTTCCGTTAACAGTATTATTTTCTCCCATGAAGACGGTACGCGACTGGGCGTGGAACATCTGATCGCATTAGGTCACCAGCAAATCGCGCTGTTAGCGGGGCCATTAAGTTCTGTCTCGGCGCGTCTGAGGCTGGCGGGCTGGCATAAATATCTCACTCGCAACCATATCCAGCCGATAGCGGTACGGGAAGGCGACTGGAGTGCCATGTCCGGTTATCAACAAACGATGGAAATGCTGAATAACGGCATCGTACCGTCGGCGATGCTGGTTGCCAACGATCAGATGGCGCTGGGCGCAATGCGCGCACTGGAAGAACATAAACTTTCGGTACCGGAAGATATCTCGGTGATTGGTTATGACGATACCGAAGACAGCTCGTGTTTTATTCCGCCGTTGACCACTATCAAGCAGGATTTTCGTCTGCTGGGGCAGACAGCTGTGGACCGCCTGCTGCAACTGA', 'TAGATACCTCACTATGCTTAGCCATAAACCTAGGCAGAGTATAACCAATCTGCCAGCCAGAGTACTACTAGCAATAGCTTAAAACTCAAAGGACTTGGCGGTGCTTTATATCCACCTAGAGGAGCCTGTTCTATAATCGATAAACCCCGATAAACCTTACCACTTTTTGCTAATACAGTCTATATACCGCCATCTTCAGCAAACCCTTAAAAGGAATCACAGTAAGCAAAAACTTAGCACATAGGAACGTTAGGTCAAGGTGTAACCTATAAAGTGGTAAGAAATGGGCTACATTTTTTTAATTAAAAACACATTCTATACTAAACCTATGAAAATATTAAGCCTAAGGTGGATTTAGTAGTAAATTAAGAATAGAGAGCTTAATTGAATGAGAAAATTGGGCGCACACAATGCCCGTCACCCTCCTCAAATAATTATTACACAGTATAAAATACCATTAAAACAAAATCAACCAAAAAGGAGAAAAGTCGTAACAAGGT', 'GTATCTGATATGGATACGGTGAATAGTGGCTTCTTCAGTGTGCATCGTATTCATAGAAGAAACGATTTTTTTCGATACTGATTTCATTAGTGACTACGCTATCGGGCCTGCACTCTTTAAGGGATACAGATTCGTCGACTGTATTGGCCATTTGGCCACAGTGTGACCACATGACCATAATCTACATAGGTCTACATCATCGATAGGATTGCATCGGGGAGACGATACGGGGACAGGTATGTATTCATGAGCGTTGATGTCGCCACGGATGCGTTAAGCATATCCGCTCCGGATCCTCGCTGGATGGGGTATTACTGTTTATGAATGTCTTGTCTGCTCAAAGGCCGCGCTGCAGCAAAATACCCAAATGTCCAAAATGTGGGACATTTGCACATTGGGTGACAGTTGTTCGCAAAAGGCGCGCCCGAAAGGCATAATTCGGCCAACCCCAAATTCACAACTGCAAAACAGTAGTATAGTGATCGACTATATTTGCATTA']\n" 184 | ] 185 | } 186 | ], 187 | "source": [ 188 | "generations = model.generate(\n", 189 | " input_seqs,\n", 190 | " n_tokens=500,\n", 191 | " temperature=1.0,\n", 192 | ")\n", 193 | "\n", 194 | "generated_seqs = generations.sequences\n", 195 | "print(generated_seqs)" 196 | ] 197 | }, 198 | { 199 | "cell_type": "markdown", 200 | "metadata": {}, 201 | "source": [ 202 | "## Alignment Analysis\n", 203 | "### Let's analyze our generated sequences using Biopython's alignment tools." 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": 28, 209 | "metadata": {}, 210 | "outputs": [ 211 | { 212 | "name": "stdout", 213 | "output_type": "stream", 214 | "text": [ 215 | "\n", 216 | "Sequence Alignments:\n", 217 | "\n", 218 | "Alignment 1 (L1RE2):\n", 219 | "AGGCCTGCCTGCCTCTGTAGGCTCCACCTCC-GGGGGA-AGGGCACAGCC-CAA-CAAAA-GGCG-GCAG-ACACCTCTGCAGACTTAAA-TGTCCCTGTCTGACAGCTTTGAAGAGAGCAGTGGTTCTCCT-AGCACGCAGCTGGAGATCTGAGAACGGGCAGACTGCCTCCTCAAGTGGGTCCCTGACCCCTGACCCCCGAGCAGCCTAACTGGGAGGCACCCCCCAGCAGGGGCACACTGACACCTCACACGGCAGGGTATTCCAACAGACCTGCAGCTGAGGA-TCCTGTCTGCA--AGACAG-----CTTAGG-C--CCTAC-AACAGTCTTGCAGCCACCTCTACTGAT--GTAGGAAAGCCTGCCTGCC-TCTGTAGGC-TCCACC-TC-TGGG---AG-C----AGGGCATAG--ACAAACA-A-AAAGA-GGCAG-------CAGCAGCCTCAGCAGACA------GAAAC-C---ATACCGCCT-G-GCAGC-T-T--TG------AAGAGA--GCAGTGGATC-TC-C---CAACACGG-----AGGT-TGAGATCTGAGAACGGACA-GAC---\n", 220 | "||||||||||||||||||||||||||||| | ||||| ||||||||| ||| ||||| ||| |||| | |||||||||||||| || |||||||||||||||||||||||||||||||||||||||| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| ||||||||| ||| || | || | || | || || | | || ||||| | | | ||| | || |||||| | | |||| || | || | || ||| | |||| | ||||| || | ||| | | |||| | ||||| | | | ||| | || || | | | ||| || ||||| || || | ||||| | || || || || || || | || \n", 221 | "AGGCCTGCCTGCCTCTGTAGGCTCCACCT-CTGGGGG-CAGGGCACAG--ACAAACAAAAAGGC-AGCAGTA-ACCTCTGCAGACTT-AAGTGTCCCTGTCTGACAGCTTTGAAGAGAGCAGTGGTTCTCC-CAGCACGCAGCTGGAGATCTGAGAACGGGCAGACTGCCTCCTCAAGTGGGTCCCTGACCCCTGACCCCCGAGCAGCCTAACTGGGAGGCACCCCCCAGCAGGGGCACACTGACACCTCACACGGCAGGGTATTCCAACAGACCTGCAGCTGAGG-GTCCTGTCTG--TTAGA-AGGAAAAC-TA--ACAACC-A-GAA-AG----G-A--CA--TCTAC--A-CCG-A--AAA-C-------CCATCTGTA--CAT-CACCATCAT---CAAAGACCAAAAG----TAGATA-AAAC-CACAAAGATGG--GGAAAAAACAG-A-----A-CAGA-AAAACTGGAAACTCTAAA-A-CGC--AGAGC-GCCTCTCCT-CCTCCAAAG-GAACGCAGT---TCCTCACCAGCAACA--GAACAAAG--CTG-GA--TG-GA---GA-ATGA-TTT\n", 222 | " Score=772.4\n", 223 | "\n", 224 | "Sequence similarity: 82.80%\n", 225 | "Alignment score: 772.40\n", 226 | "\n", 227 | "Alignment 2 (ECOLAC):\n", 228 | "TAATGTTCCGGCGTTG-TTTCTTGATGTCTCTGACCAG--ACTTCCGT-T-AACAGTATTATTTTCTCCCATGAAGACGGTACGCGACTGGGCGTGGAA-CATCTGA-TCGCATTAGG-TCACCAGCAAATCGCGCTGTTAGCGGGGCC-ATTAAGTTCTGTCTCGGCGCGTCTGAG-G-CTGGCG-GGCTGGCATAAATATCTCACTCGCAAC-C--ATATCCAGCCGATAGCGGTA-CGGGAAGGCGACTGGAGTGCCATGTCCGGTTAT-CAACAAACG-ATGG-AAATGCTGAAT-AACGG-CATCGT----ACCGTCGGCGATGCTGGTTGCCAACGATCAGATGGCGCTGGGCGCAATGCGCGC----AC----T--GGAAGAACATAAA---C-TTTCGGTAC-CGGAAGATATCTCGGT-GATTGGTT-AT--GACGATACCGAAGACAGCTCG-TGTTTTAT-TCC-GCCGTTG--ACCACT-ATCAAG-CAGGATTTTCGTC-TGCTGGGGCAGA--CAGCTGTGGACCGCCT-GCTGCAA--CTGA\n", 229 | "||||||||||||||| ||||||||||||||||||||| || || | |||||||||||||||||||||||||||||||||||||||||||||| | |||||| ||||||| || |||||||||||||||||||||||| ||||| |||||||||||||||||||||||| | | ||||| |||||||||||||||||||||||||| | || | ||||||||||||| | ||||||||||||||||||||||||||||||| | |||||||| || | ||||||||||| | || |||||| || | ||||||||||||||||||||||||||||||||||||||||||||||| || | || | | | | || ||| || ||||||||||| | ||| || |||||||||||||||||||| || |||| ||| ||||| ||||| ||||| ||||||||||| | |||||||||| | |||| |||||||| || ||||||| || \n", 230 | "TAATGTTCCGGCGTT-ATTTCTTGATGTCTCTGACCAGACAC--CC--ATCAACAGTATTATTTTCTCCCATGAAGACGGTACGCGACTGGGCGTGG-AGCATCTG-GTCGCATT-GGGTCACCAGCAAATCGCGCTGTTAGC-GGGCCCATTAAGTTCTGTCTCGGCGCGTCT--GCGTCTGGC-TGGCTGGCATAAATATCTCACTCGCAA-TCAAAT-T-CAGCCGATAGCGG-AACGGGAAGGCGACTGGAGTGCCATGTCCGGTT-TTCAACAAAC-CAT-GCAAATGCTGAATGA--GGGCATCGTTCCCAC--T--GCGATGCTGGTTGCCAACGATCAGATGGCGCTGGGCGCAATGCGCGCCATTACCGAGTCCGG--G--C-T---GCGCGTT--GGT--GCG---GATATCTCGGTAG--TGG--GATACGACGATACCGAAGACAGCTC-ATG--TTATATCCCGCCGT--CAACCAC-CATCAA-ACAGGATTTTCG-CCTGCTGGGGCA-AACCAGC-GTGGACCG-CTTGCTGCAACTCT--\n", 231 | " Score=843.8\n", 232 | "\n", 233 | "Sequence similarity: 88.60%\n", 234 | "Alignment score: 843.80\n", 235 | "\n", 236 | "Alignment 3 (NC_007596.2Mammuthusprimigeniusmitochondrion):\n", 237 | "TAGATACCTCACTATGCT-TAGCCA-TAAACC--TAGGCAG-AG-TA----TAACCAA---TC-TGCCAGCCAGAGTA-CTACTAGC-AAT-AGCTTAAAACTC-AAAGGACTTGGCGGTGCTTTATATCCACCTAGAGG-AGCCTGT-TCTA-TAATC-GATA-AACCCCGATAA-ACCTTAC---CACTTTTTGCTAATA-CAGTCT-ATATACCG-CCATCTTCAGCAAACCC-TTAAA-AGG----AATCACAGTA-AGC--AA--A-AACTTAGCACAT---AGGAACGTTAGGTCA--AGGTGTAA--CCTATAAA-GT---GGT--AAGAAATGGGCTACATTTT-T-TTAATTA-AA-A-ACACATT-CT--ATACTAAAC-CTA-TGAAAATA------TTAAGCCTAAGGT-GGATTTAGTAGTAAAT-TAAGAATAGAGAGCTTAATTG---AATG---A-GAAAATTGGGCGC--ACACAAT--GCCCGTCACC-CTCCTCAA--A--T--A-ATT--ATTACA--CAGTATAAAATACCA---TTAAAACAAAATCAACCAAAA-AGGAGAA-AAGTCGTAACAAGGT\n", 238 | "||||||||||||||||| ||||| |||| | | | | || || || ||| | | || ||||||| | |||||||| | ||||||||||| |||||||||||||||||||||||||||||||| || ||||||| || ||| | ||| ||||||||| | ||||||| ||| |||||||| ||||| ||||||| ||||||||||||||||| | | ||| || | ||| ||| || | ||| | ||| | || |||||| | |||||| ||| | || ||| ||| |||||||||||||| | ||| || || | ||| | | |||| | || || |||| || | |||| ||||||||||||||| ||||||||||||||||||||| || | | ||| |||| ||||| |||||||| | |||||||| | | | | | | ||| || ||| || || || |||| |||| || || ||||| | |||||||||||||| \n", 239 | "TAGATACCTCACTATGC-CTAGCC-CTAAA-CTTT--G-A-TAGCTACCTTTA--CAAAGCT-AT-CC-GCCAGAG-AACTACTAGCCA--GAGCTTAAAACT-TAAAGGACTTGGCGGTGCTTTATATCCACCTAG-GGGAGCCTGTCTC--GTAA-CCGAT-GAACCCCGAT-ACACCTTACCGTCAC---TTGCTAAT-TCAGTC-CATATACC-ACCATCTTCAGCAAACCCCT---ATAGGGCACAA--A-AGT-GAGCTTAATCATAAC----C-CATGAAA--AA-GTTAGG-C-CGAGGTGT--CGCCT----ACGTGACGGTCAAAG--ATGGGCTACATTTTCTATTA--TAGAATAGACA-A--AC-GGATAC----CACT-CTG-AAAT-GGGTGGTT--G---AAGG-CGGATTTAGTAGTAAA-CTAAGAATAGAGAGCTTAATTGAACAA-GGCCATGAA------GCGCGTACACA--CCGCCCGTCA-CTCTCCTCAAGTACCTCCACA-TCAA--ACAATCA-TAT----TA-CAGATTT-AAAC-AAAT--AC---AAGAGGAG-ACAAGTCGTAACAAGG-\n", 240 | " Score=728.2\n", 241 | "\n", 242 | "Sequence similarity: 79.80%\n", 243 | "Alignment score: 728.20\n", 244 | "\n", 245 | "Alignment 4 (NC_012920.1_homosapiens_mitochondrion):\n", 246 | "GT--ATC-TGAT---ATGG-AT-ACGGTGA-AT-A--G-TGGCTT---CTTCAGTGTGCAT----CGTATT--CATAG--AAG-----AAACGATTTTTTTCGAT------ACTGA-TTTCAT-TAGTGACT-ACGCTATCGGGCCTGCACTCTTTAAGGG--ATACAGATTCGTCG--ACTGTATTGGCCATTT-GGCCACAGTGTGAC-CACATG-A--CCATAATCTAC--ATAGGT------CTA--CATCAT-CGATAGGATT-GCATCGGGG--AG--ACGAT-ACGGGGACAGGTATGTATTCATGAGCGTTGAT----GTCG-CCACG-GATGCGTTAAGCATATCCGCTCCGG---ATC-CTCG-CTGGAT-----GGGGTATTACTGTTTATGAATGTCTTGTCTGC-TCAAAGGC-CGC-GC--TGCAG--CAAAA----TA-C----CCA-AATGTCC--------AAA----ATGTGGGA-----CATTT-GCACATTGGGTG--AC---AGTTGTT--C---GC-A-A--AAGGCGCGCCCGAAAGG------CATAATT-CG-GCCA---ACC-C---CA-A--ATT---CACAACTG-CAA---AA-----CAGT--AGTA-TAGT----GATCGA----CT-----ATA----T-----T----TGCA--T-TA-------\n", 247 | " ||| | | | | | || | | | | | || | | | ||| || | || | || ||| || | | || | ||| || ||| || || || ||| || || | ||||| | | | | | || ||| | || | | | | ||||| | |||||| || |||||| ||| | | | | | ||| || || || | ||| || || ||| || ||| || || ||| | || ||| || || ||| ||| | | | ||| || | || || || |||| || ||| || ||||| ||||| || | ||| | || ||| | || || | ||| ||| || || ||| || | || | | || | ||| ||| | |||| || |||| ||| | || | ||| | ||| | ||| || | || || | | || |||| | || ||| | | || | | || \n", 248 | "--CCATCCT-A-CCCA--GCA-CAC----ACA-CACCGCT-GC-TAACC--C------CATACCCCG-A--ACCA-A-CCAA-ACCCCAAA-GA-------C-A-CCCCCCAC--AGTTT-ATGTAG---CTTAC-CT------CCT-CA------AA--GCAATACA----C-T-GAAA----A-TG----TTTAG---AC-G-G-G-CTCACAT-CACCCCATAA---ACAAATAGGTTTGGTCCTAGCC-T--TTC--T---ATTAGC-TC----TTAGTAA-GATTAC----AC----ATG----CA--AGC----ATCCCCGT--TCCA-GTGA---GTT---CA---CC-CTC---TAAATCAC-C-AC--GATCAAAAGG---A--AC--------AA----------GCATCAA--GCACGCAGCAATGCAGCTCAAAACGCTTAGCCTAGCCACA----CCCCCACGGGAAACAGCA-GT--GATTAACC-TTTAGCA-AT------AAACGAAAGT--TTAACTAAGCTATACTAA----C-CCC---AGGGTTGGTC--AATTTCGTGCCAGCCACCGCGGTCACACGATTAACC-CAA--GTCAATAGAAGCCGGC-GTAAAG-AGT-GTTTTAGATC-ACCCCCTCCCCAATAAAGCTAAAACTCACCTG-AGTTGTAAAAAACT\n", 249 | " Score=497.2\n", 250 | "\n", 251 | "Sequence similarity: 60.40%\n", 252 | "Alignment score: 497.20\n" 253 | ] 254 | } 255 | ], 256 | "source": [ 257 | "def analyze_alignments(generated_seqs: List[str],\n", 258 | " target_seqs: List[str],\n", 259 | " names: Optional[List[str]] = None\n", 260 | " ) -> List[dict]:\n", 261 | " \"\"\"\n", 262 | " Analyze and visualize alignments between generated and target sequences.\n", 263 | " \n", 264 | " Args:\n", 265 | " generated_seqs: List of generated sequences\n", 266 | " target_seqs: List of target sequences\n", 267 | " names: Optional list of sequence names\n", 268 | " \n", 269 | " Returns:\n", 270 | " List of alignment metrics for each sequence pair\n", 271 | " \"\"\"\n", 272 | " metrics = []\n", 273 | " print(\"\\nSequence Alignments:\")\n", 274 | " \n", 275 | " for i, (gen_seq, target_seq) in enumerate(zip(generated_seqs, target_seqs)):\n", 276 | " if names and i < len(names):\n", 277 | " print(f\"\\nAlignment {i+1} ({names[i]}):\")\n", 278 | " else:\n", 279 | " print(f\"\\nAlignment {i+1}:\")\n", 280 | " \n", 281 | " gen_bio_seq = Seq(gen_seq)\n", 282 | " target_bio_seq = Seq(target_seq)\n", 283 | " \n", 284 | " # Get alignments\n", 285 | " alignments = pairwise2.align.globalms(\n", 286 | " gen_bio_seq, target_bio_seq,\n", 287 | " match=2,\n", 288 | " mismatch=-1,\n", 289 | " open=-0.5,\n", 290 | " extend=-0.1\n", 291 | " )\n", 292 | " \n", 293 | " best_alignment = alignments[0]\n", 294 | " print(format_alignment(*best_alignment))\n", 295 | " \n", 296 | " matches = sum(a == b for a, b in zip(best_alignment[0], best_alignment[1]) \n", 297 | " if a != '-' and b != '-')\n", 298 | " alignment_length = len(best_alignment[0].replace('-', ''))\n", 299 | " similarity = (matches / len(target_seq)) * 100\n", 300 | " \n", 301 | " seq_metrics = {\n", 302 | " 'similarity': similarity,\n", 303 | " 'score': best_alignment[2],\n", 304 | " 'length': len(target_seq),\n", 305 | " 'gaps': best_alignment[0].count('-') + best_alignment[1].count('-')\n", 306 | " }\n", 307 | " \n", 308 | " if names and i < len(names):\n", 309 | " seq_metrics['name'] = names[i]\n", 310 | " \n", 311 | " metrics.append(seq_metrics)\n", 312 | " \n", 313 | " print(f\"Sequence similarity: {similarity:.2f}%\")\n", 314 | " print(f\"Alignment score: {best_alignment[2]:.2f}\")\n", 315 | " \n", 316 | " return metrics\n", 317 | "\n", 318 | "# Analyze alignments\n", 319 | "alignment_metrics = analyze_alignments(generated_seqs, target_seqs, names)" 320 | ] 321 | }, 322 | { 323 | "cell_type": "markdown", 324 | "metadata": {}, 325 | "source": [ 326 | "## Generate with species prompt" 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": 11, 332 | "metadata": {}, 333 | "outputs": [ 334 | { 335 | "name": "stdout", 336 | "output_type": "stream", 337 | "text": [ 338 | "Species tag prompt: |D__ANIMALIA;P__CHORDATA;C__MAMMALIA;O__DIPROTODONTIA;F__PHASCOLARCTIDAE;G__PHASCOLARCTOS;S__PHASCOLARCTOS CINEREUS|\n", 339 | "Initializing inference params with max_seqlen=616\n", 340 | "Prompt: \"|D__ANIMALIA;P__CHORDATA;C__MAMMALIA;O__DIPROTODONTIA;F__PHASCOLARCTIDAE;G__PHASCOLARCTOS;S__PHASCOLARCTOS CINEREUS|\",\tOutput: \"TAGTACCCCGTCCAATATTCGGAAAACGAGAACTGGACGAACTGAACTTACTTCTTTGTTGATGCACGGGAAGGATCTTCAGCTTATCACCGTCGCGTCGATCAAGTTACTGACTCACAATTCTTCTTTCTCTTCGAGGTCCTTTTCTAGATTTGTAAAGTTACGTTAGGTATTAATATCTACCGCATGTTCCGTCCAAAGTAAACGCTCCCCCTAACTGCATTATATTAAGCCGAACCGAACGAAGTTGCGCAGAAACTATGAACGTTTCCGTATTTGCGGAAGATATCTCTCAACTCTCTGCAAACTGAAATAAGCCAGTGAATATAACAATGAACGTTTCCGTATTTGCGGAAGATATCTCTCAACTCTCTGCAAACTGAAATAAGCCAGTGAATATAACAATAGAAAACCTTCGCACCTTACATTCGCGTCCATTAGGTATGCAGGCAGTTCGGCCGGGCCGAAGAATAAGAAGCCACCCCAACTCTGCAAAAAAA\",\tScore: -1.2092068195343018\n", 341 | "Koala sequence:\n", 342 | "TAGTACCCCGTCCAATATTCGGAAAACGAGAACTGGACGAACTGAACTTACTTCTTTGTTGATGCACGGGAAGGATCTTCAGCTTATCACCGTCGCGTCGATCAAGTTACTGACTCACAATTCTTCTTTCTCTTCGAGGTCCTTTTCTAGATTTGTAAAGTTACGTTAGGTATTAATATCTACCGCATGTTCCGTCCAAAGTAAACGCTCCCCCTAACTGCATTATATTAAGCCGAACCGAACGAAGTTGCGCAGAAACTATGAACGTTTCCGTATTTGCGGAAGATATCTCTCAACTCTCTGCAAACTGAAATAAGCCAGTGAATATAACAATGAACGTTTCCGTATTTGCGGAAGATATCTCTCAACTCTCTGCAAACTGAAATAAGCCAGTGAATATAACAATAGAAAACCTTCGCACCTTACATTCGCGTCCATTAGGTATGCAGGCAGTTCGGCCGGGCCGAAGAATAAGAAGCCACCCCAACTCTGCAAAAAAA\n" 343 | ] 344 | } 345 | ], 346 | "source": [ 347 | "from evo2.utils import make_phylotag_from_gbif\n", 348 | "\n", 349 | "species = 'Phascolarctos cinereus' # Koala bear\n", 350 | "\n", 351 | "species_tag_prompt = make_phylotag_from_gbif(species)\n", 352 | "\n", 353 | "print(f\"Species tag prompt: {species_tag_prompt}\") # Check if the GBIF API returned a valid species tag!\n", 354 | "\n", 355 | "# Generate species tag\n", 356 | "koala_sequence = model.generate(\n", 357 | " [species_tag_prompt],\n", 358 | " n_tokens=500,\n", 359 | " temperature=1.0,\n", 360 | ")\n", 361 | "\n", 362 | "print(f\"Koala sequence:\")\n", 363 | "print(koala_sequence.sequences[0])" 364 | ] 365 | } 366 | ], 367 | "metadata": { 368 | "kernelspec": { 369 | "display_name": "hf_tracking_test", 370 | "language": "python", 371 | "name": "python3" 372 | }, 373 | "language_info": { 374 | "codemirror_mode": { 375 | "name": "ipython", 376 | "version": 3 377 | }, 378 | "file_extension": ".py", 379 | "mimetype": "text/x-python", 380 | "name": "python", 381 | "nbconvert_exporter": "python", 382 | "pygments_lexer": "ipython3", 383 | "version": "3.11.11" 384 | } 385 | }, 386 | "nbformat": 4, 387 | "nbformat_minor": 2 388 | } 389 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | biopython 2 | huggingface_hub -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import os 2 | import subprocess 3 | import sys 4 | from setuptools import setup, find_packages 5 | from setuptools.command.build import build as _build # use the top-level build command 6 | from setuptools.command.develop import develop as _develop 7 | from wheel.bdist_wheel import bdist_wheel as _bdist_wheel 8 | 9 | 10 | def update_submodules(): 11 | base_dir = os.path.dirname(__file__) 12 | # Check if the .git folder exists 13 | if os.path.exists(os.path.join(base_dir, '.git')): 14 | print("Updating git submodules...") 15 | # Run submodule init and update for 'vortex' 16 | subprocess.check_call(['git', 'submodule', 'init', 'vortex'], cwd=base_dir) 17 | subprocess.check_call(['git', 'submodule', 'update', 'vortex'], cwd=base_dir) 18 | else: 19 | print("No .git directory found; skipping submodule update.") 20 | 21 | def run_make_setup_full(): 22 | base_dir = os.path.dirname(__file__) 23 | vortex_dir = os.path.join(base_dir, 'vortex') 24 | original_dir = os.getcwd() 25 | 26 | # Ensure submodules are updated before running the Makefile 27 | update_submodules() 28 | 29 | # Ensure the Makefile uses the current Python interpreter 30 | env = os.environ.copy() 31 | env["PYTHON"] = sys.executable 32 | print(f"Running 'make setup-full' in {vortex_dir} with PYTHON={sys.executable} ...") 33 | 34 | try: 35 | os.chdir(vortex_dir) 36 | subprocess.check_call(['make', 'setup-full'], env=env) 37 | finally: 38 | os.chdir(original_dir) 39 | 40 | class CustomBuild(_build): 41 | def run(self): 42 | # Run egg_info to ensure metadata is available 43 | self.run_command('egg_info') 44 | # Update submodules and run the Makefile before building anything else 45 | run_make_setup_full() 46 | # Continue with the normal build process 47 | _build.run(self) 48 | 49 | class CustomDevelop(_develop): 50 | def run(self): 51 | update_submodules() 52 | run_make_setup_full() 53 | _develop.run(self) 54 | 55 | class CustomBDistWheel(_bdist_wheel): 56 | def run(self): 57 | self.run_command('egg_info') 58 | _bdist_wheel.run(self) 59 | 60 | def parse_requirements(filename): 61 | requirements = [] 62 | with open(filename) as f: 63 | for line in f: 64 | line = line.strip() 65 | if line and not line.startswith('#'): 66 | requirements.append(line) 67 | return requirements 68 | 69 | 70 | with open('evo2/version.py') as infile: 71 | exec(infile.read()) 72 | 73 | with open('README.md') as f: 74 | readme = f.read() 75 | 76 | requirements = parse_requirements("requirements.txt") 77 | 78 | setup( 79 | name='evo2', 80 | version=version, 81 | # Only include the evo2 package; the vortex submodule is used for build purposes. 82 | packages=find_packages(include=["evo2", "vortex/vortex"]), 83 | install_requires=requirements, 84 | cmdclass={ 85 | 'build': CustomBuild, 86 | 'develop': CustomDevelop, 87 | 'bdist_wheel': CustomBDistWheel, 88 | }, 89 | package_data={'evo2': ['evo2/configs/*.yml']}, 90 | include_package_data=True, 91 | python_requires='>=3.11', 92 | license="Apache-2.0", 93 | description='Genome modeling across all domains of life', 94 | long_description=readme, 95 | long_description_content_type='text/markdown', 96 | author='Team Evo 2', 97 | url='https://github.com/arcinstitute/evo2', 98 | ) 99 | -------------------------------------------------------------------------------- /test/test_evo2.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import csv 3 | from pathlib import Path 4 | from typing import List, Optional, Union 5 | import numpy as np 6 | import torch 7 | import torch.nn.functional as F 8 | 9 | from evo2 import Evo2 10 | 11 | def read_prompts(input_file: Path) -> Union[List[List[str]]]: 12 | """Read prompts from input file.""" 13 | promptseqs: List[str] = [] 14 | 15 | with open(input_file, encoding='utf-8-sig', newline='') as csvfile: 16 | reader = csv.reader(csvfile) 17 | next(reader) # Skip header 18 | for row in reader: 19 | promptseqs.append(row[0]) 20 | 21 | return promptseqs 22 | 23 | def test_forward_pass(*, model, sequences): 24 | """Test model forward pass accuracy on sequences.""" 25 | losses = [] 26 | accuracies = [] 27 | 28 | for seq in sequences: 29 | # Convert sequence to model input format 30 | input_ids = torch.tensor(model.tokenizer.tokenize(seq), dtype=int).to('cuda:0') 31 | 32 | with torch.inference_mode(): 33 | # Forward pass 34 | logits, _ = model.model.forward(input_ids.unsqueeze(0)) 35 | 36 | # Calculate loss and accuracy 37 | target_ids = input_ids[1:] # Shift right for next token prediction 38 | pred_logits = logits[0, :-1, :] 39 | 40 | # Cross entropy loss 41 | loss = F.cross_entropy( 42 | pred_logits, 43 | target_ids.long() 44 | ) 45 | 46 | # Get predictions 47 | pred_tokens = torch.argmax(pred_logits, dim=-1) 48 | 49 | # Calculate accuracy 50 | accuracy = (target_ids == pred_tokens).float().mean().item() 51 | 52 | losses.append(loss.item()) 53 | accuracies.append(accuracy) 54 | 55 | # Print sequence results 56 | print("\nSequence Results:") 57 | for i, (loss, acc) in enumerate(zip(losses, accuracies)): 58 | print(f"Sequence {i+1}: Loss = {loss:.3f}, Accuracy = {acc:.2%}") 59 | if acc < 0.5: 60 | print("WARNING: Forward pass accuracy is below 50% on test sequence. Model may be broken, trained models should have >80% accuracy.") 61 | 62 | return accuracies, losses 63 | 64 | def main(): 65 | """ 66 | Test sequence prediction accuracy using Evo2 models. 67 | Expected results for forward pass: 68 | - Evo 2 40B 1m: Loss ~0.216, Accuracy ~91.67% 69 | - Evo 2 7B 1m: Loss ~0.348, Accuracy ~86.35% 70 | - Evo 2 1B base: Loss ~0.502, Accuracy ~79.56% 71 | """ 72 | parser = argparse.ArgumentParser(description="Test Evo2 Model Forward Pass") 73 | parser.add_argument("--model_name", choices=['evo2_7b', 'evo2_40b', 'evo2_7b_base', 'evo2_40b_base', 'evo2_1b_base'], 74 | default='evo2_7b', 75 | help="Model to test") 76 | 77 | args = parser.parse_args() 78 | 79 | # Set random seeds 80 | torch.manual_seed(1) 81 | torch.cuda.manual_seed(1) 82 | 83 | # Initialize model 84 | model = Evo2(args.model_name) 85 | 86 | # Read sequences 87 | sequences = read_prompts('vortex/test/data/prompts.csv') 88 | 89 | # Test forward pass 90 | accuracies, losses = test_forward_pass( 91 | model=model, 92 | sequences=sequences 93 | ) 94 | 95 | # Calculate and validate results 96 | mean_loss = np.mean(losses) 97 | mean_accuracy = np.mean(accuracies) * 100 98 | print(f"\nMean Loss: {mean_loss:.3f}") 99 | print(f"Mean Accuracy: {mean_accuracy:.3f}%") 100 | 101 | # Validate against expected scores 102 | eps = 1e-3 # epsilon for float comparison 103 | expected_metrics = { 104 | 'evo2_40b': {'loss': 0.2159424, 'acc': 91.673}, 105 | 'evo2_7b': {'loss': 0.3476563, 'acc': 86.346}, 106 | 'evo2_40b_base': {'loss': 0.2149658, 'acc': 91.741}, 107 | 'evo2_7b_base': {'loss': 0.3520508, 'acc': 85.921}, 108 | 'evo2_1b_base': {'loss': 0.501953125, 'acc': 79.556} 109 | } 110 | 111 | expected = expected_metrics[args.model_name] 112 | if abs(mean_loss - expected['loss']) < eps: 113 | print(f"\nTest Passed! Loss matches expected {expected['loss']:.3f}") 114 | else: 115 | print(f"\nTest Failed: Expected loss {expected['loss']:.3f}, got {mean_loss:.3f}") 116 | 117 | if __name__ == "__main__": 118 | main() -------------------------------------------------------------------------------- /test/test_evo2_generation.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import csv 3 | from pathlib import Path 4 | from typing import List, Optional, Union 5 | import numpy as np 6 | import torch 7 | 8 | from evo2 import Evo2 9 | 10 | def read_prompts(input_file: Path) -> Union[List[List[str]]]: 11 | """Read prompts from input file.""" 12 | promptseqs: List[str] = [] 13 | 14 | with open(input_file, encoding='utf-8-sig', newline='') as csvfile: 15 | reader = csv.reader(csvfile) 16 | next(reader) # Skip header 17 | for row in reader: 18 | promptseqs.append(row[0]) 19 | 20 | return promptseqs 21 | 22 | def mid_point_split(*, seq, num_tokens): 23 | """Split sequence at midpoint for prompt and target.""" 24 | mid_point = 2*(len(seq)//4) 25 | prompt = seq[:mid_point] 26 | target = seq[mid_point:mid_point+num_tokens] 27 | return prompt, target 28 | 29 | def calculate_sequence_identity(seq1: str, seq2: str) -> Optional[float]: 30 | """Calculate sequence identity between two sequences through direct comparison.""" 31 | if not seq1 or not seq2: 32 | return None 33 | 34 | min_length = min(len(seq1), len(seq2)) 35 | matches = sum(a == b for a, b in zip(seq1[:min_length], seq2[:min_length])) 36 | return (matches / min_length) * 100 37 | 38 | def generate_and_score(*, sequences, model, generations_per_prompt=5, n_tokens=500, 39 | temperature=1.0, top_k=1, top_p=1.0): 40 | """Prompt with first half, generate and score on 2nd half.""" 41 | scores = [] 42 | prompts = [] 43 | targets = [] 44 | 45 | # Prepare all prompts and targets 46 | for seq in sequences: 47 | prompt, target = mid_point_split(seq=seq, num_tokens=n_tokens) 48 | prompts.extend([prompt] * generations_per_prompt) 49 | targets.extend([target] * generations_per_prompt) 50 | 51 | for i in range(len(prompts)): 52 | prompt = prompts[i] 53 | target = targets[i] 54 | 55 | with torch.inference_mode(): 56 | generated = model.generate( 57 | prompt_seqs=[prompt], 58 | n_tokens=n_tokens, 59 | temperature=temperature, 60 | top_k=top_k, 61 | top_p=top_p, 62 | ) 63 | 64 | decoded_seq = generated.sequences[0] # Assuming generate returns list of sequences 65 | score = calculate_sequence_identity(decoded_seq, target) 66 | scores.append(score) 67 | 68 | # Reshape scores to group by original sequence 69 | reshaped_scores = [scores[i:i + generations_per_prompt] 70 | for i in range(0, len(scores), generations_per_prompt)] 71 | 72 | return reshaped_scores 73 | 74 | def main(): 75 | """ 76 | Test sequence generation and scoring using the evo2 models 77 | Expected results (direct comparison w/o alignment): 78 | - Evo 2 40B 1m: 91.15% 79 | - Evo 2 7B 1m: 89.25% 80 | - Evo 2 1B base: 68.0% 81 | """ 82 | parser = argparse.ArgumentParser(description="Test Evo2 Model Generation") 83 | parser.add_argument("--model_name", choices=['evo2_7b', 'evo2_40b', 'evo2_1b_base'], default='evo2_7b', 84 | help="Model to test (supports evo2_7b, evo2_40b, evo2_1b_base)") 85 | 86 | args = parser.parse_args() 87 | 88 | # Set random seeds 89 | torch.manual_seed(1) 90 | torch.cuda.manual_seed(1) 91 | 92 | model = Evo2(args.model_name) 93 | 94 | # Test parameters: greedy sampling of 500 tokens 95 | test_params = { 96 | 'n_tokens': 500, 97 | 'temperature': 1.0, 98 | 'top_k': 1, 99 | 'top_p': 1.0, 100 | 'generations_per_prompt': 1, 101 | } 102 | 103 | # Read and process sequences 104 | sequences = read_prompts('vortex/test/data/prompts.csv') 105 | scores = generate_and_score( 106 | sequences=sequences, 107 | model=model, 108 | **test_params 109 | ) 110 | 111 | # Calculate and validate results 112 | mean_score = np.mean(scores) 113 | print("\nTest Results:") 114 | print("% Matching Nucleotides:", mean_score) 115 | 116 | # Validate against expected scores 117 | eps = 3 # large epsilon for direct comparison, since there are numeric differences by versions 118 | expected_scores = { 119 | 'evo2_40b': 91.15, 120 | 'evo2_7b': 89.25, 121 | 'evo2_1b_base': 68.0 122 | } 123 | 124 | expected_score = expected_scores[args.model_name] 125 | if abs(mean_score - expected_score) < eps: 126 | print(f"\nTest Passed! Score matches expected {expected_score}%") 127 | else: 128 | print(f"\nTest Failed: Expected {expected_score}%, got {mean_score}%") 129 | 130 | if __name__ == "__main__": 131 | main() --------------------------------------------------------------------------------