run_singularity.py

#!/usr/bin/env python3
# Copyright 2023 David Chin
#
# This file is part of alphafold_singularity.
#
# alphafold_singularity is free software: you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# alphafold_singularity is distributed in the hope that it will be
# useful, but WITHOUT ANY WARRANTY; without even the implied warranty
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with alphafold_singularity. If not, see <https://www.gnu.org/licenses/>.

"""Singularity launch script for Alphafold Singularity image."""

import os
import sys
import pathlib
import signal
from typing import Tuple

from absl import app
from absl import flags
from absl import logging
from spython.main import Client

import tempfile
import subprocess


#### USER CONFIGURATION ####

# Path to AlphaFold Singularity image. This relies on
# the environment variable ALPHAFOLD_DIR which is the
# directory where AlphaFold is installed.
singularity_image = Client.load(os.path.join(os.environ['ALPHAFOLD_DIR'], 'alphafold.sif'))

# tmp directory
if 'TMP' in os.environ:
    tmp_dir = os.environ['TMP']
elif 'TMPDIR' in os.environ:
    tmp_dir = os.environ['TMPDIR']
else:
    tmp_dir = '/tmp'

# Default path to a directory that will store the results.
output_dir_default = tempfile.mkdtemp(dir=tmp_dir, prefix='alphafold')

logging.info(f'INFO: tmp_dir = {tmp_dir}')
logging.info(f'INFO: output_dir_default = {output_dir_default}')

#### END USER CONFIGURATION ####

### These flags correspond to the flags defined in ../run_alphafold.py
flags.DEFINE_bool(
    'use_gpu', True, 'Enable NVIDIA runtime to run with GPUs.')
flags.DEFINE_enum(
    'models_to_relax', 'best', ['best', 'all', 'none'],
    'The models to run the final relaxation step on. '
    'If `all`, all models are relaxed, which may be time '
    'consuming. If `best`, only the most confident model is '
    'relaxed. If `none`, relaxation is not run. Turning off '
    'relaxation might result in predictions with '
    'distracting stereochemical violations but might help '
    'in case you are having issues with the relaxation '
    'stage.')
flags.DEFINE_bool(
    'enable_gpu_relax', True, 'Run relax on GPU if GPU is enabled.')
flags.DEFINE_string(
    'gpu_devices', 'all',
    'Comma separated list of devices to pass to NVIDIA_VISIBLE_DEVICES.')
flags.DEFINE_list(
    'fasta_paths', None, 'Paths to FASTA files, each containing a prediction '
    'target that will be folded one after another. If a FASTA file contains '
    'multiple sequences, then it will be folded as a multimer. Paths should be '
    'separated by commas. All FASTA paths must have a unique basename as the '
    'basename is used to name the output directories for each prediction.')
flags.DEFINE_string(
    'output_dir', output_dir_default,
    'Path to a directory that will store the results.')
flags.DEFINE_string(
    'data_dir', None,
    'Path to directory with supporting data: AlphaFold parameters and genetic '
    'and template databases. Set to the target of download_all_databases.sh.')
flags.DEFINE_string(
    'docker_image_name', 'alphafold', 'Name of the AlphaFold Docker image.')
flags.DEFINE_string(
    'max_template_date', None,
    'Maximum template release date to consider (ISO-8601 format: YYYY-MM-DD). '
    'Important if folding historical test sets.')
flags.DEFINE_enum(
    'db_preset', 'full_dbs', ['full_dbs', 'reduced_dbs'],
    'Choose preset MSA database configuration - smaller genetic database '
    'config (reduced_dbs) or full genetic database config (full_dbs)')
flags.DEFINE_enum(
    'model_preset', 'monomer',
    ['monomer', 'monomer_casp14', 'monomer_ptm', 'multimer'],
    'Choose preset model configuration - the monomer model, the monomer model '
    'with extra ensembling, monomer model with pTM head, or multimer model')
flags.DEFINE_integer('num_multimer_predictions_per_model', 5, 'How many '
                     'predictions (each with a different random seed) will be '
                     'generated per model. E.g. if this is 2 and there are 5 '
                     'models then there will be 10 predictions per input. '
                     'Note: this FLAG only applies if model_preset=multimer')
flags.DEFINE_boolean(
    'benchmark', False,
    'Run multiple JAX model evaluations to obtain a timing that excludes the '
    'compilation time, which should be more indicative of the time required '
    'for inferencing many proteins.')
flags.DEFINE_boolean(
    'use_precomputed_msas', False,
    'Whether to read MSAs that have been written to disk instead of running '
    'the MSA tools. The MSA files are looked up in the output directory, so it '
    'must stay the same between multiple runs that are to reuse the MSAs. '
    'WARNING: This will not check if the sequence, database or configuration '
    'have changed.')
flags.DEFINE_string(
    'docker_user', f'{os.geteuid()}:{os.getegid()}',
    'UID:GID with which to run the Docker container. The output directories '
    'will be owned by this user:group. By default, this is the current user. '
    'Valid options are: uid or uid:gid, non-numeric values are not recognised '
    'by Docker unless that user has been created within the container.')

FLAGS = flags.FLAGS

_ROOT_MOUNT_DIRECTORY = '/mnt/'


def _create_bind(bind_name: str, path: str) -> Tuple[str, str]:
  """Create a bind point for each file and directory used by the model."""
  path = os.path.abspath(path)
  source_path = os.path.dirname(path) if bind_name != 'data_dir' else path
  target_path = os.path.join(_ROOT_MOUNT_DIRECTORY, bind_name)

  logging.info('Binding %s -> %s', source_path, target_path)

  # NOTE singularity binds are read-only by default
  if bind_name == 'data_dir':
    data_path = target_path
  else:
    data_path = f'{os.path.join(target_path, os.path.basename(path))}'

  return (f'{source_path}:{target_path}', f'{data_path}')


def main(argv):
  if len(argv) > 1:
    raise app.UsageError('Too many command-line arguments.')

  # You can individually override the following paths if you have placed the
  # data in locations other than the FLAGS.data_dir.

  # Path to the Uniref90 database for use by JackHMMER.
  uniref90_database_path = os.path.join(
      FLAGS.data_dir, 'uniref90', 'uniref90.fasta')

  # Path to the Uniprot database for use by JackHMMER.
  uniprot_database_path = os.path.join(
      FLAGS.data_dir, 'uniprot', 'uniprot.fasta')

  # Path to the MGnify database for use by JackHMMER.
  mgnify_database_path = os.path.join(
      FLAGS.data_dir, 'mgnify', 'mgy_clusters_2022_05.fa')

  # Path to the BFD database for use by HHblits.
  bfd_database_path = os.path.join(
      FLAGS.data_dir, 'bfd',
      'bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt')

  # Path to the Small BFD database for use by JackHMMER.
  small_bfd_database_path = os.path.join(
      FLAGS.data_dir, 'small_bfd', 'bfd-first_non_consensus_sequences.fasta')

  # Path to the Uniref30 database for use by HHblits.
  uniref30_database_path = os.path.join(
      FLAGS.data_dir, 'uniref30', 'UniRef30_2021_03')

  # Path to the PDB70 database for use by HHsearch.
  pdb70_database_path = os.path.join(FLAGS.data_dir, 'pdb70', 'pdb70')

  # Path to the PDB seqres database for use by hmmsearch.
  pdb_seqres_database_path = os.path.join(
      FLAGS.data_dir, 'pdb_seqres', 'pdb_seqres.txt')

  # Path to a directory with template mmCIF structures, each named <pdb_id>.cif.
  template_mmcif_dir = os.path.join(FLAGS.data_dir, 'pdb_mmcif', 'mmcif_files')

  # Path to a file mapping obsolete PDB IDs to their replacements.
  obsolete_pdbs_path = os.path.join(FLAGS.data_dir, 'pdb_mmcif', 'obsolete.dat')

  alphafold_path = pathlib.Path(__file__).parent.parent
  data_dir_path = pathlib.Path(FLAGS.data_dir)
  if alphafold_path == data_dir_path or alphafold_path in data_dir_path.parents:
    raise app.UsageError(
        f'The download directory {FLAGS.data_dir} should not be a subdirectory '
        f'in the AlphaFold repository directory. If it is, the Singularity build is '
        f'slow since the large databases are copied during the image creation.')

  binds = []
  command_args = []

  # Mount each fasta path as a unique target directory.
  target_fasta_paths = []
  for i, fasta_path in enumerate(FLAGS.fasta_paths):
    bind, target_path = _create_bind(f'fasta_path_{i}', fasta_path)
    binds.append(bind)
    target_fasta_paths.append(target_path)
  command_args.append(f'--fasta_paths={",".join(target_fasta_paths)}')

  database_paths = [
      ('uniref90_database_path', uniref90_database_path),
      ('mgnify_database_path', mgnify_database_path),
      ('data_dir', FLAGS.data_dir),
      ('template_mmcif_dir', template_mmcif_dir),
      ('obsolete_pdbs_path', obsolete_pdbs_path),
  ]

  if FLAGS.model_preset == 'multimer':
    database_paths.append(('uniprot_database_path', uniprot_database_path))
    database_paths.append(('pdb_seqres_database_path',
                           pdb_seqres_database_path))
  else:
    database_paths.append(('pdb70_database_path', pdb70_database_path))

  if FLAGS.db_preset == 'reduced_dbs':
    database_paths.append(('small_bfd_database_path', small_bfd_database_path))
  else:
    database_paths.extend([
        ('uniref30_database_path', uniref30_database_path),
        ('bfd_database_path', bfd_database_path),
    ])

  # NB for binds:
  #    - first arg = path on host
  #    - second arg = path in container
  for name, path in database_paths:
    if path:
      bind, target_path = _create_bind(name, path)
      binds.append(bind)
      command_args.append(f'--{name}={target_path}')

  output_target_path = os.path.join(_ROOT_MOUNT_DIRECTORY, 'output')
  binds.append(f'{FLAGS.output_dir}:{output_target_path}')
  logging.info('Binding %s -> %s', FLAGS.output_dir, output_target_path)

  tmp_target_path = '/tmp'
  binds.append(f'{tmp_dir}:{tmp_target_path}')
  logging.info('Binding %s -> %s', tmp_dir, tmp_target_path)

  use_gpu_relax = FLAGS.enable_gpu_relax and FLAGS.use_gpu

  command_args.extend([
      f'--output_dir={output_target_path}',
      f'--max_template_date={FLAGS.max_template_date}',
      f'--db_preset={FLAGS.db_preset}',
      f'--model_preset={FLAGS.model_preset}',
      f'--benchmark={FLAGS.benchmark}',
      f'--use_precomputed_msas={FLAGS.use_precomputed_msas}',
      f'--num_multimer_predictions_per_model={FLAGS.num_multimer_predictions_per_model}',
      f'--models_to_relax={FLAGS.models_to_relax}',
      f'--use_gpu_relax={use_gpu_relax}',
      '--logtostderr',
  ])

  options = [
    '--bind', f'{",".join(binds)}',
    '--env', f'NVIDIA_VISIBLE_DEVICES={FLAGS.gpu_devices}',
    # The following flags allow us to make predictions on proteins that
    # would typically be too long to fit into GPU memory.
    '--env', 'TF_FORCE_UNIFIED_MEMORY=1',
    '--env', 'XLA_PYTHON_CLIENT_MEM_FRACTION=4.0',
  ]

  # Run the container.
  # Result is a dict with keys "message" (value = all output as a single string),
  # and "return_code" (value = integer return code)
  result = Client.run(
             singularity_image,
             command_args,
             nv=True if FLAGS.use_gpu else None,
             return_result=True,
             options=options
           )


if __name__ == '__main__':
  flags.mark_flags_as_required([
      'data_dir',
      'fasta_paths',
      'max_template_date',
  ])
  app.run(main)