inference.py

# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import random
import sys
import time
from datetime import date

import numpy as np
import torch
from fastfold.model.hub import AlphaFold

import fastfold
import fastfold.relax.relax as relax
from fastfold.common import protein, residue_constants
from fastfold.config import model_config
from fastfold.data import data_pipeline, feature_pipeline, templates
from fastfold.utils import inject_fastnn
from fastfold.utils.import_weights import import_jax_weights_
from fastfold.utils.tensor_utils import tensor_tree_map


def add_data_args(parser: argparse.ArgumentParser):
    parser.add_argument(
        '--uniref90_database_path',
        type=str,
        default=None,
    )
    parser.add_argument(
        '--mgnify_database_path',
        type=str,
        default=None,
    )
    parser.add_argument(
        '--pdb70_database_path',
        type=str,
        default=None,
    )
    parser.add_argument(
        '--uniclust30_database_path',
        type=str,
        default=None,
    )
    parser.add_argument(
        '--bfd_database_path',
        type=str,
        default=None,
    )
    parser.add_argument('--jackhmmer_binary_path', type=str, default='/usr/bin/jackhmmer')
    parser.add_argument('--hhblits_binary_path', type=str, default='/usr/bin/hhblits')
    parser.add_argument('--hhsearch_binary_path', type=str, default='/usr/bin/hhsearch')
    parser.add_argument('--kalign_binary_path', type=str, default='/usr/bin/kalign')
    parser.add_argument(
        '--max_template_date',
        type=str,
        default=date.today().strftime("%Y-%m-%d"),
    )
    parser.add_argument('--obsolete_pdbs_path', type=str, default=None)
    parser.add_argument('--release_dates_path', type=str, default=None)


def main(args):
    # init distributed for Dynamic Axial Parallelism
    fastfold.distributed.init_dap()

    config = model_config(args.model_name)
    model = AlphaFold(config)
    import_jax_weights_(model, args.param_path, version=args.model_name)

    model = inject_fastnn(model)
    model = model.eval()
    #script_preset_(model)
    model = model.cuda()

    template_featurizer = templates.TemplateHitFeaturizer(
        mmcif_dir=args.template_mmcif_dir,
        max_template_date=args.max_template_date,
        max_hits=config.data.predict.max_templates,
        kalign_binary_path=args.kalign_binary_path,
        release_dates_path=args.release_dates_path,
        obsolete_pdbs_path=args.obsolete_pdbs_path)

    use_small_bfd = args.preset == 'reduced_dbs'  # (args.bfd_database_path is None)
    if use_small_bfd:
        assert args.bfd_database_path is not None
    else:
        assert args.bfd_database_path is not None
        assert args.uniclust30_database_path is not None

    data_processor = data_pipeline.DataPipeline(template_featurizer=template_featurizer,)

    output_dir_base = args.output_dir
    random_seed = args.data_random_seed
    if random_seed is None:
        random_seed = random.randrange(sys.maxsize)
    feature_processor = feature_pipeline.FeaturePipeline(config.data)
    if not os.path.exists(output_dir_base):
        os.makedirs(output_dir_base)
    if (args.use_precomputed_alignments is None):
        alignment_dir = os.path.join(output_dir_base, "alignments")
    else:
        alignment_dir = args.use_precomputed_alignments

    # Gather input sequences
    with open(args.fasta_path, "r") as fp:
        lines = [l.strip() for l in fp.readlines()]

    tags, seqs = lines[::2], lines[1::2]
    tags = [l[1:] for l in tags]

    for tag, seq in zip(tags, seqs):
        batch = [None]
        if torch.distributed.get_rank() == 0:
            fasta_path = os.path.join(args.output_dir, "tmp.fasta")
            with open(fasta_path, "w") as fp:
                fp.write(f">{tag}\n{seq}")

            print("Generating features...")
            local_alignment_dir = os.path.join(alignment_dir, tag)
            if (args.use_precomputed_alignments is None):
                if not os.path.exists(local_alignment_dir):
                    os.makedirs(local_alignment_dir)

                alignment_runner = data_pipeline.AlignmentRunner(
                    jackhmmer_binary_path=args.jackhmmer_binary_path,
                    hhblits_binary_path=args.hhblits_binary_path,
                    hhsearch_binary_path=args.hhsearch_binary_path,
                    uniref90_database_path=args.uniref90_database_path,
                    mgnify_database_path=args.mgnify_database_path,
                    bfd_database_path=args.bfd_database_path,
                    uniclust30_database_path=args.uniclust30_database_path,
                    pdb70_database_path=args.pdb70_database_path,
                    use_small_bfd=use_small_bfd,
                    no_cpus=args.cpus,
                )
                alignment_runner.run(fasta_path, local_alignment_dir)

            feature_dict = data_processor.process_fasta(fasta_path=fasta_path,
                                                        alignment_dir=local_alignment_dir)

            # Remove temporary FASTA file
            os.remove(fasta_path)

            processed_feature_dict = feature_processor.process_features(
                feature_dict,
                mode='predict',
            )

            batch = [processed_feature_dict]


        torch.distributed.broadcast_object_list(batch, src=0)
        batch = batch[0]

        print("Executing model...")

        with torch.no_grad():
            batch = {k: torch.as_tensor(v).cuda() for k, v in batch.items()}

            t = time.perf_counter()
            out = model(batch)
            print(f"Inference time: {time.perf_counter() - t}")

        torch.distributed.barrier()

        if torch.distributed.get_rank() == 0:
            # Toss out the recycling dimensions --- we don't need them anymore
            batch = tensor_tree_map(lambda x: np.array(x[..., -1].cpu()), batch)
            out = tensor_tree_map(lambda x: np.array(x.cpu()), out)

            plddt = out["plddt"]
            mean_plddt = np.mean(plddt)

            plddt_b_factors = np.repeat(plddt[..., None], residue_constants.atom_type_num, axis=-1)

            unrelaxed_protein = protein.from_prediction(features=batch,
                                                        result=out,
                                                        b_factors=plddt_b_factors)

            # Save the unrelaxed PDB.
            unrelaxed_output_path = os.path.join(args.output_dir,
                                                 f'{tag}_{args.model_name}_unrelaxed.pdb')
            with open(unrelaxed_output_path, 'w') as f:
                f.write(protein.to_pdb(unrelaxed_protein))

            amber_relaxer = relax.AmberRelaxation(
                use_gpu=True,
                **config.relax,
            )

            # Relax the prediction.
            t = time.perf_counter()
            relaxed_pdb_str, _, _ = amber_relaxer.process(prot=unrelaxed_protein)
            print(f"Relaxation time: {time.perf_counter() - t}")

            # Save the relaxed PDB.
            relaxed_output_path = os.path.join(args.output_dir,
                                               f'{tag}_{args.model_name}_relaxed.pdb')
            with open(relaxed_output_path, 'w') as f:
                f.write(relaxed_pdb_str)

        torch.distributed.barrier()


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "fasta_path",
        type=str,
    )
    parser.add_argument(
        "template_mmcif_dir",
        type=str,
    )
    parser.add_argument("--use_precomputed_alignments",
                        type=str,
                        default=None,
                        help="""Path to alignment directory. If provided, alignment computation 
                is skipped and database path arguments are ignored.""")
    parser.add_argument(
        "--output_dir",
        type=str,
        default=os.getcwd(),
        help="""Name of the directory in which to output the prediction""",
    )
    parser.add_argument("--model_name",
                        type=str,
                        default="model_1",
                        help="""Name of a model config. Choose one of model_{1-5} or 
             model_{1-5}_ptm, as defined on the AlphaFold GitHub.""")
    parser.add_argument("--param_path",
                        type=str,
                        default=None,
                        help="""Path to model parameters. If None, parameters are selected
             automatically according to the model name from 
             ./data/params""")
    parser.add_argument("--cpus",
                        type=int,
                        default=12,
                        help="""Number of CPUs with which to run alignment tools""")
    parser.add_argument('--preset',
                        type=str,
                        default='full_dbs',
                        choices=('reduced_dbs', 'full_dbs'))
    parser.add_argument('--data_random_seed', type=str, default=None)
    add_data_args(parser)
    args = parser.parse_args()

    if (args.param_path is None):
        args.param_path = os.path.join("data", "params", "params_" + args.model_name + ".npz")

    main(args)