run_pretrained_openfold.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
from copy import deepcopy
from datetime import date
import logging
import math
import numpy as np
import os

logging.basicConfig()
logger = logging.getLogger(__file__)
logger.setLevel(level=logging.INFO)

import pickle
from pytorch_lightning.utilities.deepspeed import (
    convert_zero_checkpoint_to_fp32_state_dict
)
import random
import sys
import time
import torch
import re

torch_versions = torch.__version__.split(".")
torch_major_version = int(torch_versions[0])
torch_minor_version = int(torch_versions[1])
if(
    torch_major_version > 1 or 
    (torch_major_version == 1 and torch_minor_version >= 12)
):
    # Gives a large speedup on Ampere-class GPUs
    torch.set_float32_matmul_precision("high")

torch.set_grad_enabled(False)

from openfold.config import model_config, NUM_RES
from openfold.data import templates, feature_pipeline, data_pipeline
from openfold.model.model import AlphaFold
from openfold.model.torchscript import script_preset_
from openfold.np import residue_constants, protein
import openfold.np.relax.relax as relax
from openfold.utils.import_weights import (
    import_jax_weights_,
)
from openfold.utils.tensor_utils import (
    tensor_tree_map,
)
from openfold.utils.trace_utils import (
    pad_feature_dict_seq,
    trace_model_,
)
from scripts.utils import add_data_args


TRACING_INTERVAL = 50


def precompute_alignments(tags, seqs, alignment_dir, args):
    for tag, seq in zip(tags, seqs):
        tmp_fasta_path = os.path.join(args.output_dir, f"tmp_{os.getpid()}.fasta")
        with open(tmp_fasta_path, "w") as fp:
            fp.write(f">{tag}\n{seq}")

        local_alignment_dir = os.path.join(alignment_dir, tag)
        if(args.use_precomputed_alignments is None and not os.path.isdir(local_alignment_dir)):
            logger.info(f"Generating alignments for {tag}...")
                
            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,
                no_cpus=args.cpus,
            )
            alignment_runner.run(
                tmp_fasta_path, local_alignment_dir
            )
        else:
            logger.info(
                f"Using precomputed alignments for {tag} at {alignment_dir}..."
            )

        # Remove temporary FASTA file
        os.remove(tmp_fasta_path)


def round_up_seqlen(seqlen):
    return int(math.ceil(seqlen / TRACING_INTERVAL)) * TRACING_INTERVAL


def run_model(model, batch, tag, args):
    with torch.no_grad(): 
        # Temporarily disable templates if there aren't any in the batch
        template_enabled = model.config.template.enabled
        model.config.template.enabled = template_enabled and any([
            "template_" in k for k in batch
        ])

        logger.info(f"Running inference for {tag}...")
        t = time.perf_counter()
        out = model(batch)
        inference_time = time.perf_counter() - t
        logger.info(f"Inference time: {inference_time}")
        update_timings({"inference": inference_time}, os.path.join(args.output_dir, "timings.json"))
   
        model.config.template.enabled = template_enabled

    return out


def prep_output(out, batch, feature_dict, feature_processor, args):
    plddt = out["plddt"]
    mean_plddt = np.mean(plddt)
    
    plddt_b_factors = np.repeat(
        plddt[..., None], residue_constants.atom_type_num, axis=-1
    )

    if(args.subtract_plddt):
        plddt_b_factors = 100 - plddt_b_factors

    # Prep protein metadata
    template_domain_names = []
    template_chain_index = None
    if(feature_processor.config.common.use_templates and "template_domain_names" in feature_dict):
        template_domain_names = [
            t.decode("utf-8") for t in feature_dict["template_domain_names"]
        ]

        # This works because templates are not shuffled during inference
        template_domain_names = template_domain_names[
            :feature_processor.config.predict.max_templates
        ]

        if("template_chain_index" in feature_dict):
            template_chain_index = feature_dict["template_chain_index"]
            template_chain_index = template_chain_index[
                :feature_processor.config.predict.max_templates
            ]

    no_recycling = feature_processor.config.common.max_recycling_iters
    remark = ', '.join([
        f"no_recycling={no_recycling}",
        f"max_templates={feature_processor.config.predict.max_templates}",
        f"config_preset={args.config_preset}",
    ])

    # For multi-chain FASTAs
    ri = feature_dict["residue_index"]
    chain_index = (ri - np.arange(ri.shape[0])) / args.multimer_ri_gap
    chain_index = chain_index.astype(np.int64)
    cur_chain = 0
    prev_chain_max = 0
    for i, c in enumerate(chain_index):
        if(c != cur_chain):
            cur_chain = c
            prev_chain_max = i + cur_chain * args.multimer_ri_gap

        batch["residue_index"][i] -= prev_chain_max

    unrelaxed_protein = protein.from_prediction(
        features=batch,
        result=out,
        b_factors=plddt_b_factors,
        chain_index=chain_index,
        remark=remark,
        parents=template_domain_names,
        parents_chain_index=template_chain_index,
    )

    return unrelaxed_protein


def parse_fasta(data):
    data = re.sub('>$', '', data, flags=re.M)
    lines = [
        l.replace('\n', '')
        for prot in data.split('>') for l in prot.strip().split('\n', 1)
    ][1:]
    tags, seqs = lines[::2], lines[1::2]

    tags = [t.split()[0] for t in tags]

    return tags, seqs


def generate_feature_dict(
    tags,
    seqs,
    alignment_dir,
    data_processor,
    args,
):
    tmp_fasta_path = os.path.join(args.output_dir, f"tmp_{os.getpid()}.fasta")
    if len(seqs) == 1:
        tag = tags[0]
        seq = seqs[0]
        with open(tmp_fasta_path, "w") as fp:
            fp.write(f">{tag}\n{seq}")

        local_alignment_dir = os.path.join(alignment_dir, tag)
        feature_dict = data_processor.process_fasta(
            fasta_path=tmp_fasta_path, alignment_dir=local_alignment_dir
        )
    else:
        with open(tmp_fasta_path, "w") as fp:
            fp.write(
                '\n'.join([f">{tag}\n{seq}" for tag, seq in zip(tags, seqs)])
            )
        feature_dict = data_processor.process_multiseq_fasta(
            fasta_path=tmp_fasta_path, super_alignment_dir=alignment_dir,
        )

    # Remove temporary FASTA file
    os.remove(tmp_fasta_path)

    return feature_dict


def get_model_basename(model_path):
    return os.path.splitext(
                os.path.basename(
                    os.path.normpath(model_path)
                )
            )[0]


def make_output_directory(output_dir, model_name, multiple_model_mode):
    if multiple_model_mode:
        prediction_dir = os.path.join(output_dir, "predictions", model_name)
    else:
        prediction_dir = os.path.join(output_dir, "predictions")
    os.makedirs(prediction_dir, exist_ok=True)
    return prediction_dir


def count_models_to_evaluate(openfold_checkpoint_path, jax_param_path):
    model_count = 0
    if openfold_checkpoint_path:
        model_count += len(openfold_checkpoint_path.split(","))
    if jax_param_path:
        model_count += len(jax_param_path.split(","))
    return model_count


def load_models_from_command_line(args, config):
    # Create the output directory

    multiple_model_mode = count_models_to_evaluate(args.openfold_checkpoint_path, args.jax_param_path) > 1
    if multiple_model_mode:
        logger.info(f"evaluating multiple models")

    if args.jax_param_path:
        for path in args.jax_param_path.split(","):
            model_basename = get_model_basename(path)
            model_version = "_".join(model_basename.split("_")[1:])
            model = AlphaFold(config)
            model = model.eval()
            import_jax_weights_(
                model, path, version=model_version
            )
            model = model.to(args.model_device)
            logger.info(
                f"Successfully loaded JAX parameters at {path}..."
            )
            output_directory = make_output_directory(args.output_dir, model_basename, multiple_model_mode)
            yield model, output_directory
    
    if args.openfold_checkpoint_path:
        for path in args.openfold_checkpoint_path.split(","):
            model = AlphaFold(config)
            model = model.eval()
            checkpoint_basename = get_model_basename(path)
            if os.path.isdir(path):
                # A DeepSpeed checkpoint
                ckpt_path = os.path.join(
                    args.output_dir,
                    checkpoint_basename + ".pt",
                )

                if not os.path.isfile(ckpt_path):
                    convert_zero_checkpoint_to_fp32_state_dict(
                        path,
                        ckpt_path,
                    )
                d = torch.load(ckpt_path)
                model.load_state_dict(d["ema"]["params"])
            else:
                ckpt_path = path
                d = torch.load(ckpt_path)

                if "ema" in d:
                    # The public weights have had this done to them already
                    d = d["ema"]["params"]
                model.load_state_dict(d)
            
            model = model.to(args.model_device)
            logger.info(
                f"Loaded OpenFold parameters at {path}..."
            )
            output_directory = make_output_directory(args.output_dir, checkpoint_basename, multiple_model_mode)
            yield model, output_directory
    
    if not args.jax_param_path and not args.openfold_checkpoint_path:
        raise ValueError(
            "At least one of jax_param_path or openfold_checkpoint_path must "
            "be specified."
        )


def list_files_with_extensions(dir, extensions):
    return [f for f in os.listdir(dir) if f.endswith(extensions)]


def main(args):
    # Create the output directory
    os.makedirs(args.output_dir, exist_ok=True)

    config = model_config(args.config_preset)
    
    if(args.trace_model):
        if(not config.data.predict.fixed_size):
            raise ValueError(
                "Tracing requires that fixed_size mode be enabled in the config"
            )
    
    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
    )

    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(2**32)
    
    np.random.seed(random_seed)
    torch.manual_seed(random_seed + 1)
    
    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

    tag_list = []
    seq_list = []
    for fasta_file in list_files_with_extensions(args.fasta_dir, (".fasta", ".fa")):
        # Gather input sequences
        with open(os.path.join(args.fasta_dir, fasta_file), "r") as fp:
            data = fp.read()
    
        tags, seqs = parse_fasta(data)
        # assert len(tags) == len(set(tags)), "All FASTA tags must be unique"
        tag = '-'.join(tags)

        tag_list.append((tag, tags))
        seq_list.append(seqs)

    seq_sort_fn = lambda target: sum([len(s) for s in target[1]])
    sorted_targets = sorted(zip(tag_list, seq_list), key=seq_sort_fn)
    feature_dicts = {}
    for model, output_directory in load_models_from_command_line(args, config): 
        cur_tracing_interval = 0
        for (tag, tags), seqs in sorted_targets:
            output_name = f'{tag}_{args.config_preset}'
            if args.output_postfix is not None:
                output_name = f'{output_name}_{args.output_postfix}'
    
            # Does nothing if the alignments have already been computed
            precompute_alignments(tags, seqs, alignment_dir, args)
        
            feature_dict = feature_dicts.get(tag, None)
            if(feature_dict is None):
                feature_dict = generate_feature_dict(
                    tags,
                    seqs,
                    alignment_dir,
                    data_processor,
                    args,
                )

                if(args.trace_model):
                    n = feature_dict["aatype"].shape[-2]
                    rounded_seqlen = round_up_seqlen(n)
                    feature_dict = pad_feature_dict_seq(
                        feature_dict, rounded_seqlen,
                    )

                feature_dicts[tag] = feature_dict

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

            processed_feature_dict = {
                k:torch.as_tensor(v, device=args.model_device) 
                for k,v in processed_feature_dict.items()
            }

            if(args.trace_model):
                if(rounded_seqlen > cur_tracing_interval):
                    logger.info(
                        f"Tracing model at {rounded_seqlen} residues..."
                    )
                    t = time.perf_counter()
                    trace_model_(model, processed_feature_dict)
                    tracing_time = time.perf_counter() - t
                    logger.info(
                        f"Tracing time: {tracing_time}"
                    )
                    cur_tracing_interval = rounded_seqlen

            out = run_model(model, processed_feature_dict, tag, args)

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

            unrelaxed_protein = prep_output(
                out, 
                processed_feature_dict, 
                feature_dict, 
                feature_processor, 
                args
            )

            unrelaxed_output_path = os.path.join(
                output_directory, f'{output_name}_unrelaxed.pdb'
            )

            with open(unrelaxed_output_path, 'w') as fp:
                fp.write(protein.to_pdb(unrelaxed_protein))

            logger.info(f"Output written to {unrelaxed_output_path}...")
            
            if not args.skip_relaxation:
                amber_relaxer = relax.AmberRelaxation(
                    use_gpu=(args.model_device != "cpu"),
                    **config.relax,
                )

                # Relax the prediction.
                logger.info(f"Running relaxation on {unrelaxed_output_path}...")
                t = time.perf_counter()
                visible_devices = os.getenv("CUDA_VISIBLE_DEVICES", default="")
                if "cuda" in args.model_device:
                    device_no = args.model_device.split(":")[-1]
                    os.environ["CUDA_VISIBLE_DEVICES"] = device_no
                relaxed_pdb_str, _, _ = amber_relaxer.process(prot=unrelaxed_protein)
                os.environ["CUDA_VISIBLE_DEVICES"] = visible_devices
                relaxation_time = time.perf_counter() - t

                logger.info(f"Relaxation time: {relaxation_time}")
                update_timings({"relaxation": relaxation_time}, os.path.join(args.output_dir, "timings.json"))

                # Save the relaxed PDB.
                relaxed_output_path = os.path.join(
                    output_directory, f'{output_name}_relaxed.pdb'
                )
                with open(relaxed_output_path, 'w') as fp:
                    fp.write(relaxed_pdb_str)
                
                logger.info(f"Relaxed output written to {relaxed_output_path}...")

            if args.save_outputs:
                output_dict_path = os.path.join(
                    output_directory, f'{output_name}_output_dict.pkl'
                )
                with open(output_dict_path, "wb") as fp:
                    pickle.dump(out, fp, protocol=pickle.HIGHEST_PROTOCOL)

                logger.info(f"Model output written to {output_dict_path}...")

def update_timings(dict, output_file=os.path.join(os.getcwd(), "timings.json")):
    """Write dictionary of one or more run step times to a file"""
    import json
    if os.path.exists(output_file):
        with open(output_file, "r") as f:
            timings = json.loads(f)
    else:
        timings = {}
    timings.update(dict)
    with open(output_file, "w") as f:
        timings.dumps(f)
    return output_file

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "fasta_dir", type=str,
        help="Path to directory containing FASTA files, one sequence per file"
    )
    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_device", type=str, default="cpu",
        help="""Name of the device on which to run the model. Any valid torch
             device name is accepted (e.g. "cpu", "cuda:0")"""
    )
    parser.add_argument(
        "--config_preset", type=str, default="model_1",
        help="""Name of a model config preset defined in openfold/config.py"""
    )
    parser.add_argument(
        "--jax_param_path", type=str, default=None,
        help="""Path to JAX model parameters. If None, and openfold_checkpoint_path
             is also None, parameters are selected automatically according to 
             the model name from openfold/resources/params"""
    )
    parser.add_argument(
        "--openfold_checkpoint_path", type=str, default=None,
        help="""Path to OpenFold checkpoint. Can be either a DeepSpeed 
             checkpoint directory or a .pt file"""
    )
    parser.add_argument(
        "--save_outputs", action="store_true", default=False,
        help="Whether to save all model outputs, including embeddings, etc."
    )
    parser.add_argument(
        "--cpus", type=int, default=4,
        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(
        "--output_postfix", type=str, default=None,
        help="""Postfix for output prediction filenames"""
    )
    parser.add_argument(
        "--data_random_seed", type=str, default=None
    )
    parser.add_argument(
        "--skip_relaxation", action="store_true", default=False,
    )
    parser.add_argument(
        "--multimer_ri_gap", type=int, default=200,
        help="""Residue index offset between multiple sequences, if provided"""
    )
    parser.add_argument(
        "--trace_model", action="store_true", default=False,
        help="""Whether to convert parts of each model to TorchScript.
                Significantly improves runtime at the cost of lengthy
                'compilation.' Useful for large batch jobs."""
    )
    parser.add_argument(
        "--subtract_plddt", action="store_true", default=False,
        help=""""Whether to output (100 - pLDDT) in the B-factor column instead
                 of the pLDDT itself"""
    )
    add_data_args(parser)
    args = parser.parse_args()

    if(args.jax_param_path is None and args.openfold_checkpoint_path is None):
        args.jax_param_path = os.path.join(
            "openfold", "resources", "params", 
            "params_" + args.config_preset + ".npz"
        )

    if(args.model_device == "cpu" and torch.cuda.is_available()):
        logging.warning(
            """The model is being run on CPU. Consider specifying 
            --model_device for better performance"""
        )

    main(args)