muppit/sample.py

import os
import hydra
import lightning as L
import numpy as np
import omegaconf
import pandas as pd
import rdkit
import rich.syntax
import rich.tree
import torch
from tqdm.auto import tqdm
import esm
import pdb

import dataloader
import diffusion
from models.classifier import muPPIt


rdkit.rdBase.DisableLog('rdApp.error')

omegaconf.OmegaConf.register_new_resolver(
  'cwd', os.getcwd)
omegaconf.OmegaConf.register_new_resolver(
  'device_count', torch.cuda.device_count)
omegaconf.OmegaConf.register_new_resolver(
  'eval', eval)
omegaconf.OmegaConf.register_new_resolver(
  'div_up', lambda x, y: (x + y - 1) // y)
omegaconf.OmegaConf.register_new_resolver(
  'if_then_else',
  lambda condition, x, y: x if condition else y
)

vhse8_values = {
    'A': [0.15, -1.11, -1.35, -0.92, 0.02, -0.91, 0.36, -0.48],
    'R': [-1.47, 1.45, 1.24, 1.27, 1.55, 1.47, 1.30, 0.83],
    'N': [-0.99, 0.00, 0.69, -0.37, -0.55, 0.85, 0.73, -0.80],
    'D': [-1.15, 0.67, -0.41, -0.01, -2.68, 1.31, 0.03, 0.56],
    'C': [0.18, -1.67, -0.21, 0.00, 1.20, -1.61, -0.19, -0.41],
    'Q': [-0.96, 0.12, 0.18, 0.16, 0.09, 0.42, -0.20, -0.41],
    'E': [-1.18, 0.40, 0.10, 0.36, -2.16, -0.17, 0.91, 0.36],
    'G': [-0.20, -1.53, -2.63, 2.28, -0.53, -1.18, -1.34, 1.10],
    'H': [-0.43, -0.25, 0.37, 0.19, 0.51, 1.28, 0.93, 0.65],
    'I': [1.27, 0.14, 0.30, -1.80, 0.30, -1.61, -0.16, -0.13],
    'L': [1.36, 0.07, 0.26, -0.80, 0.22, -1.37, 0.08, -0.62],
    'K': [-1.17, 0.70, 0.80, 1.64, 0.67, 1.63, 0.13, -0.01],
    'M': [1.01, -0.53, 0.43, 0.00, 0.23, 0.10, -0.86, -0.68],
    'F': [1.52, 0.61, 0.95, -0.16, 0.25, 0.28, -1.33, -0.65],
    'P': [0.22, -0.17, -0.50, -0.05, 0.01, -1.34, 0.19, 3.56],
    'S': [-0.67, -0.86, -1.07, -0.41, -0.32, 0.27, -0.64, 0.11],
    'T': [-0.34, -0.51, -0.55, -1.06, 0.01, -0.01, -0.79, 0.39],
    'W': [1.50, 2.06, 1.79, 0.75, 0.75, 0.13, -1.06, -0.85],
    'Y': [0.61, 1.60, 1.17, 0.73, 0.53, 0.25, -0.96, -0.52],
    'V': [0.76, -0.92, 0.17, -1.91, 0.22, -1.40, -0.24, -0.03],
}

aa_to_idx = {'A': 5, 'R': 10, 'N': 17, 'D': 13, 'C': 23, 'Q': 16, 'E': 9, 'G': 6, 'H': 21, 'I': 12, 'L': 4, 'K': 15, 'M': 20, 'F': 18, 'P': 14, 'S': 8, 'T': 11, 'W': 22, 'Y': 19, 'V': 7}

vhse8_tensor = torch.zeros(24, 8)
for aa, values in vhse8_values.items():
    aa_index = aa_to_idx[aa]
    vhse8_tensor[aa_index] = torch.tensor(values)

vhse8_tensor.requires_grad = False

esm_model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
esm_model.eval()

def precompute_embedding(sequence, tokenizer):
  tokens = tokenizer(sequence, return_tensors='pt')['input_ids']
  with torch.no_grad():
    embed = esm_model(tokens, repr_layers=[33], return_contacts=False)["representations"][33]
    vhse8_embed = vhse8_tensor[tokens]
  return torch.concat([embed, vhse8_embed], dim=-1)


@hydra.main(version_base=None, config_path='./configs',
            config_name='config')
def main(config: omegaconf.DictConfig) -> None:
  # Reproducibility
  L.seed_everything(config.seed)
  os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
  torch.use_deterministic_algorithms(True)
  torch.backends.cudnn.benchmark = False

#   _print_config(config, resolve=True)
  print(f"Checkpoint: {config.eval.checkpoint_path}")

  tokenizer = dataloader.get_tokenizer(config)
  
  pretrained = diffusion.Diffusion.load_from_checkpoint(
    config.eval.checkpoint_path,
    tokenizer=tokenizer,
    config=config, logger=False)
  pretrained.eval()

  muppit = muPPIt(d_node=1288, d_k=32, d_v=32, n_heads=4, lr=None)
  muppit.load_state_dict(torch.load(config.guidance.classifier_checkpoint_path))
  muppit.eval()

  mut_embed = precompute_embedding(config.eval.mutant, tokenizer)
  wt_embed = precompute_embedding(config.eval.wildtype, tokenizer)

  samples = []
  for _ in tqdm(
      range(config.sampling.num_sample_batches),
      desc='Gen. batches', leave=False):
    sample = pretrained.sample(
      wt_embed = wt_embed,
      mut_embed = mut_embed,
      classifier_model = muppit
    )

    samples.extend(
      pretrained.tokenizer.batch_decode(sample))

    print('\n')
    print([sample.replace(' ', '')[5:-5] for sample in samples])
  
  samples = [sample.replace(' ', '')[5:-5] for sample in samples]
  print('\n')
  print(samples)

if __name__ == '__main__':
  main()