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import os |
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import torch |
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import numpy as np |
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import pandas as pd |
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import h5py |
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import re |
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from omegaconf import OmegaConf |
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import h5py |
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import lightning as L |
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from pera.nn import BidirectionalModel, sample_components_from_bidirectional_transformer, sample_perturbations, sample_embedding_perturbations |
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from esm.tokenization.sequence_tokenizer import EsmSequenceTokenizer |
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from Bio.Seq import Seq |
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device = torch.device("cuda:0") |
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sequence_tokenizer = EsmSequenceTokenizer() |
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import argparse |
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parser = parser = argparse.ArgumentParser(description="Calculating the log-likelihood of a sequence") |
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parser.add_argument('--target', type=str, required=True, help='Dataset as a string') |
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parser.add_argument('--num_samples', type=int, required=False, default=384, help='Number of samples to process (default: 100000)') |
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parser.add_argument('--version_number', type=str, required=False, default=1, help='Version number as a string') |
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parser.add_argument('--replicate', type=int, required=False, default=1, help='Replicate number as an integer') |
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args = parser.parse_args() |
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target = args.target |
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num_samples = args.num_samples |
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replicate = args.replicate |
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cfg_filename = "./config.yaml" |
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network_filename = "/scratch/groups/rotskoff/sebastian/era/protein_era/models/esm3clm/esm3_clm.pt" |
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save_folder_name = f"{target}/base_model_{num_samples}" |
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cfg = OmegaConf.load(cfg_filename) |
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sampling_temperature=1 |
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OmegaConf.update(cfg, "train.lightning_model_args.sampling_temperature", sampling_temperature) |
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esm_model = BidirectionalModel(cfg["nn"]["model"], |
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cfg["nn"]["model_args"], |
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**cfg["train"]["lightning_model_args"]).to(device) |
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esm_model.load_model_from_ckpt(network_filename) |
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esm_model.eval() |
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print("") |
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mask_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["mask"] |
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bos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["bos"] |
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eos_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["eos"] |
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pad_token_sequence = cfg["nn"]["model_args"]["residue_token_info"]["pad"] |
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os.makedirs(save_folder_name, exist_ok=True) |
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data = target |
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data_root_path = "/scratch/groups/rotskoff/sebastian/era/protein_era/data" |
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sequence_tokenizer = EsmSequenceTokenizer() |
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if data.startswith("TrpB"): |
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df = pd.read_csv(f"{data_root_path}/TrpB/scale2max/{data}.csv") |
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with open(f"{data_root_path}/TrpB/TrpB.fasta", "r") as file: |
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parent_sequence_decoded = file.readlines()[1].strip() |
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elif data == "DHFR": |
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df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv") |
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with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file: |
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nucleotide_seq = file.readlines()[1].strip() |
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nucleotide_seq = Seq(nucleotide_seq) |
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parent_sequence_decoded = str(nucleotide_seq.translate()) |
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else: |
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df = pd.read_csv(f"{data_root_path}/{data}/scale2max/{data}.csv") |
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with open(f"{data_root_path}/{data}/{data}.fasta", "r") as file: |
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parent_sequence_decoded = file.readlines()[1].strip() |
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if data != "GB1": |
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muts = df["muts"].iloc[0] |
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else: |
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muts = df["muts"].iloc[100000] |
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numbers = re.findall(r'\d+', muts) |
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mask_indices = list(map(int, numbers)) |
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num_masks_per_sequence = num_samples // 4 |
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num_to_generate_per_mask = 4 |
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parent_sequence = torch.tensor(sequence_tokenizer.encode(parent_sequence_decoded, |
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add_special_tokens=True), device=device).unsqueeze(0).long() |
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sequence_length = parent_sequence.shape[1] |
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all_masked_sequences = [] |
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all_unmasked_sequences_decoded = [] |
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all_unmasked_sequences = [] |
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all_logps = [] |
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while len(all_unmasked_sequences_decoded) < num_samples: |
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print(len(all_unmasked_sequences_decoded)) |
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masked_sequences = parent_sequence.clone().repeat(num_to_generate_per_mask, 1) |
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masked_sequences[:, mask_indices] = mask_token_sequence |
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sequence_id = torch.ones((num_to_generate_per_mask, sequence_length), device=device).long() * 1 |
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structure_tokens = torch.ones((num_to_generate_per_mask, sequence_length), device=device).long() * 4096 |
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structure_tokens[:, 0] = 4098 |
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structure_tokens[:, -1] = 4097 |
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coords = torch.inf * torch.ones((num_to_generate_per_mask, sequence_length, 3, 3), device=device) |
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average_plddt = torch.ones((num_to_generate_per_mask), device=device) |
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per_res_plddt = torch.zeros((num_to_generate_per_mask, sequence_length), device=device) |
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ss8_tokens = torch.zeros((num_to_generate_per_mask, sequence_length), device=device).long() |
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sasa_tokens = torch.zeros((num_to_generate_per_mask, sequence_length), device=device).long() |
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function_tokens = torch.zeros((num_to_generate_per_mask, sequence_length, 8), device=device).long() |
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residue_annotation_tokens = torch.zeros((num_to_generate_per_mask, sequence_length, 16), device=device).long() |
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with torch.no_grad(): |
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unmasked_sequences = sample_components_from_bidirectional_transformer(transformer_model=esm_model, |
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masked_sequence_tokens=masked_sequences, |
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structure_tokens=structure_tokens, |
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average_plddt=average_plddt, |
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per_res_plddt=per_res_plddt, |
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ss8_tokens=ss8_tokens, |
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sasa_tokens=sasa_tokens, |
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function_tokens=function_tokens, |
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residue_annotation_tokens=residue_annotation_tokens, |
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bb_coords=coords, |
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sequence_id=sequence_id, |
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mask_token_sequence=mask_token_sequence, |
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bos_token_sequence=bos_token_sequence, |
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eos_token_sequence=eos_token_sequence, |
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pad_token_sequence=pad_token_sequence, |
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inference_batch_size=1) |
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masked_indices = (masked_sequences == mask_token_sequence).float() |
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logits = esm_model.nn(sequence_tokens=masked_sequences, |
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structure_tokens=structure_tokens, |
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average_plddt=average_plddt, |
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per_res_plddt=per_res_plddt, |
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ss8_tokens=ss8_tokens, |
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sasa_tokens=sasa_tokens, |
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function_tokens=function_tokens, |
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residue_annotation_tokens=residue_annotation_tokens, |
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sequence_id=sequence_id, |
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bb_coords=coords)["sequence_logits"].detach() |
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logps = torch.nn.functional.log_softmax(logits/sampling_temperature, dim=-1) |
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logps = torch.gather(logps, dim=-1, index=unmasked_sequences.unsqueeze(-1)).squeeze(-1) |
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logps = (logps * masked_indices).sum(-1).detach() |
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decoded_seqs = [sequence.replace(" ", "") for sequence in sequence_tokenizer.batch_decode(unmasked_sequences[:, 1:-1])] |
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for seq, logp, masked_seq, unmasked_seq in zip(decoded_seqs, logps, masked_sequences, unmasked_sequences): |
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if seq in all_unmasked_sequences_decoded: |
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continue |
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else: |
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all_unmasked_sequences_decoded.append(seq) |
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all_logps.append(logp) |
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all_masked_sequences.append(masked_seq) |
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all_unmasked_sequences.append(unmasked_seq) |
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all_unmasked_sequences_decoded = all_unmasked_sequences_decoded[:num_samples] |
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all_masked_sequences = all_masked_sequences[:num_samples] |
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all_unmasked_sequences = all_unmasked_sequences[:num_samples] |
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all_logps = all_logps[:num_samples] |
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all_masked_sequences = torch.stack(all_masked_sequences, dim=0) |
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all_unmasked_sequences = torch.stack(all_unmasked_sequences, dim=0) |
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all_logps = torch.stack(all_logps, dim=0) |
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to_save = {"parent_sequence": parent_sequence, |
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"all_masked_sequences": all_masked_sequences, |
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"all_unmasked_sequences": all_unmasked_sequences, |
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"all_unmasked_sequences_decoded": all_unmasked_sequences_decoded, |
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"all_logps": all_logps} |
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torch.save(to_save, f"{save_folder_name}/trpb_{replicate}.pt") |
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