Merge pull request #438 from jnwei/pl_upgrades

Upgrades pl_upgrades to match main branch changes.

openfold/utils/tensor_utils.py

@@ -114,8 +114,7 @@ def tree_map(fn, tree, leaf_type):
     elif isinstance(tree, leaf_type):
         return fn(tree)
     else:
-        print(type(tree))
-        raise ValueError("Not supported")
+        raise ValueError(f"Tree of type {type(tree)} not supported")
 
 
 tensor_tree_map = partial(tree_map, leaf_type=torch.Tensor)

run_pretrained_openfold.py

@@ -20,6 +20,7 @@ import os
 import pickle
 import random
 import time
+import json
 
 logging.basicConfig()
 logger = logging.getLogger(__file__)
@@ -131,7 +132,16 @@ def generate_feature_dict(
     args,
 ):
     tmp_fasta_path = os.path.join(args.output_dir, f"tmp_{os.getpid()}.fasta")
-    if len(seqs) == 1:
+
+    if "multimer" in args.config_preset:
+        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_fasta(
+            fasta_path=tmp_fasta_path, alignment_dir=alignment_dir,
+        )
+    elif len(seqs) == 1:
         tag = tags[0]
         seq = seqs[0]
         with open(tmp_fasta_path, "w") as fp:
@@ -143,14 +153,6 @@ def generate_feature_dict(
             alignment_dir=local_alignment_dir,
             seqemb_mode=args.use_single_seq_mode,
         )
-    elif "multimer" in args.config_preset:
-        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_fasta(
-            fasta_path=tmp_fasta_path, alignment_dir=alignment_dir,
-        )
     else:
         with open(tmp_fasta_path, "w") as fp:
             fp.write(
@@ -177,7 +179,21 @@ def main(args):
     if args.config_preset.startswith("seq"):
         args.use_single_seq_mode = True
 
-    config = model_config(args.config_preset, long_sequence_inference=args.long_sequence_inference)
+    config = model_config(
+        args.config_preset, 
+        long_sequence_inference=args.long_sequence_inference,
+        use_deepspeed_evoformer_attention=args.use_deepspeed_evoformer_attention,
+        )
+
+    if args.experiment_config_json: 
+        with open(args.experiment_config_json, 'r') as f:
+            custom_config_dict = json.load(f)
+        config.update_from_flattened_dict(custom_config_dict)
+
+    if args.experiment_config_json: 
+        with open(args.experiment_config_json, 'r') as f:
+            custom_config_dict = json.load(f)
+        config.update_from_flattened_dict(custom_config_dict)
 
     if args.trace_model:
         if not config.data.predict.fixed_size:
@@ -452,6 +468,13 @@ if __name__ == "__main__":
         "--cif_output", action="store_true", default=False,
         help="Output predicted models in ModelCIF format instead of PDB format (default)"
     )
+    parser.add_argument(
+        "--experiment_config_json", default="", help="Path to a json file with custom config values to overwrite config setting",
+    )
+    parser.add_argument(
+        "--use_deepspeed_evoformer_attention", action="store_true", default=False, 
+        help="Whether to use the DeepSpeed evoformer attention layer. Must have deepspeed installed in the environment.",
+    )
     add_data_args(parser)
     args = parser.parse_args()
 

scripts/alignment_db_scripts/add_non_unique_to_alignment_db.py

+from argparse import ArgumentParser
+from pathlib import Path
+import json
+
+
+def main(args):
+    # get the super index
+    with open(args.alignment_db_super_index_path, "r") as fp:
+        super_index = json.load(fp)
+
+    # get all chains and sequences
+    chains_to_seqs = {}
+    with open(args.all_chains_fasta, "r") as fp:
+        lines = fp.readlines()
+
+        # iterate through chain-sequence pairs
+        for chain_idx in range(0, len(lines), 2):
+            chain = lines[chain_idx][1:].strip()
+            seq = lines[chain_idx + 1].strip()
+            chains_to_seqs[chain] = seq
+
+    chains_w_alignments = set(super_index.keys())
+    chains_wo_alignments = set(chains_to_seqs.keys()) - chains_w_alignments
+
+    seq_to_chain_w_alignment = {
+        chains_to_seqs[chain]: chain for chain in chains_w_alignments
+    }
+
+    print("Unique sequences with alignments:", len(seq_to_chain_w_alignment))
+
+    # map chain without alignment to alignment entry of another chain with the
+    # same sequence
+    remaining_unaligned_chains = []
+    for chain in chains_wo_alignments:
+        seq = chains_to_seqs[chain]
+
+        try:
+            corresponding_alignment = super_index[seq_to_chain_w_alignment[seq]]
+        # no corresponding chain with alignment found
+        except KeyError:
+            remaining_unaligned_chains.append(chain)
+            continue
+
+        super_index[chain] = corresponding_alignment
+
+    with open(args.output_path, "w") as fp:
+        json.dump(super_index, fp)
+
+    print(
+        f"No corresponding alignment found for the following {len(remaining_unaligned_chains)} chains:",
+        remaining_unaligned_chains,
+    )
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(
+        description="""
+        If the alignment-db index was created on unique-chain alignments only,
+        this will add the missing chain entries to the super-index file based on
+        a .fasta file that contains sequences for all chains.
+        
+        Note that this only modifies the index and not the database itself, as
+        the duplicate sequences will just point to the same alignments. 
+        """
+    )
+    parser.add_argument(
+        "alignment_db_super_index_path",
+        type=Path,
+        help="Path to alignment-db super index file.",
+    )
+    parser.add_argument(
+        "output_path", type=Path, help="Write the output super index to this path."
+    )
+    parser.add_argument(
+        "all_chains_fasta",
+        type=Path,
+        help="Path to the fasta file containing sequences for all chains.",
+    )
+
+    args = parser.parse_args()
+
+    main(args)

scripts/alignment_db_scripts/create_alignment_db_sharded.py

+"""
+This is a modified version of the create_alignment_db.py script in OpenFold
+which supports sharding into multiple files. The created index is already a
+super index, meaning that "unify_alignment_db_indices.py" does not need to be
+run on the output index. Additionally this script uses threading and
+multiprocessing and is much faster than the old version.
+"""
+import argparse
+from collections import defaultdict
+from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
+import json
+from pathlib import Path
+from typing import List
+from tqdm import tqdm
+from math import ceil
+
+
+def split_file_list(file_list, n_shards):
+    """
+    Split up the total file list into n_shards sublists.
+    """
+    split_list = []
+
+    for i in range(n_shards):
+        split_list.append(file_list[i::n_shards])
+
+    assert len([f for sublist in split_list for f in sublist]) == len(file_list)
+
+    return split_list
+
+
+def chunked_iterator(lst, chunk_size):
+    """Iterate over a list in chunks of size chunk_size."""
+    for i in range(0, len(lst), chunk_size):
+        yield lst[i : i + chunk_size]
+
+
+def read_chain_dir(chain_dir) -> dict:
+    """
+    Read all alignment files in a single chain directory and return a dict
+    mapping chain name to file names and bytes.
+    """
+    if not chain_dir.is_dir():
+        raise ValueError(f"chain_dir must be a directory, but is {chain_dir}")
+    
+    # ensure that PDB IDs are all lowercase
+    pdb_id, chain = chain_dir.name.split("_")
+    pdb_id = pdb_id.lower()
+    chain_name = f"{pdb_id}_{chain}"
+    
+    
+    file_data = []
+
+    for file_path in sorted(chain_dir.iterdir()):
+        file_name = file_path.name
+
+        with open(file_path, "rb") as file:
+            file_bytes = file.read()
+
+        file_data.append((file_name, file_bytes))
+
+    return {chain_name: file_data}
+
+
+def process_chunk(chain_files: List[Path]) -> dict:
+    """
+    Returns the file names and bytes for all chains in a chunk of files.
+    """
+    chunk_data = {}
+
+    with ThreadPoolExecutor() as executor:
+        for file_data in executor.map(read_chain_dir, chain_files):
+            chunk_data.update(file_data)
+
+    return chunk_data
+
+
+def create_index_default_dict() -> dict:
+    """
+    Returns a default dict for the index entries).
+    """
+    return {"db": None, "files": []}
+
+
+def create_shard(
+    shard_files: List[Path], output_dir: Path, output_name: str, shard_num: int
+) -> dict:
+    """
+    Creates a single shard of the alignment database, and returns the
+    corresponding indices for the super index.
+    """
+    CHUNK_SIZE = 200
+    shard_index = defaultdict(
+        create_index_default_dict
+    )  # {chain_name: {db: str, files: [(file_name, db_offset, file_length)]}, ...}
+    chunk_iter = chunked_iterator(shard_files, CHUNK_SIZE)
+
+    pbar_desc = f"Shard {shard_num}"
+    output_path = output_dir / f"{output_name}_{shard_num}.db"
+
+    db_offset = 0
+    db_file = open(output_path, "wb")
+    for files_chunk in tqdm(
+        chunk_iter, total=ceil(len(shard_files) / CHUNK_SIZE), desc=pbar_desc, position=shard_num, leave=False
+    ):
+        # get processed files for one chunk
+        chunk_data = process_chunk(files_chunk)
+
+        # write to db and store info in index
+        for chain_name, file_data in chunk_data.items():
+            shard_index[chain_name]["db"] = output_path.name
+
+            for file_name, file_bytes in file_data:
+                file_length = len(file_bytes)
+                shard_index[chain_name]["files"].append(
+                    (file_name, db_offset, file_length)
+                )
+                db_file.write(file_bytes)
+                db_offset += file_length
+    db_file.close()
+
+    return shard_index
+
+
+def main(args):
+    alignment_dir = args.alignment_dir
+    output_dir = args.output_db_path
+    output_db_name = args.output_db_name
+    n_shards = args.n_shards
+
+    # get all chain dirs in alignment_dir
+    print("Getting chain directories...")
+    all_chain_dirs = sorted([f for f in tqdm(alignment_dir.iterdir())])
+
+    # split chain dirs into n_shards sublists
+    chain_dir_shards = split_file_list(all_chain_dirs, n_shards)
+
+    # total index for all shards
+    super_index = {}
+
+    # create a shard for each sublist
+    print(f"Creating {n_shards} alignment-db files...")
+    with ProcessPoolExecutor() as executor:
+        futures = [
+            executor.submit(
+                create_shard, shard_files, output_dir, output_db_name, shard_index
+            )
+            for shard_index, shard_files in enumerate(chain_dir_shards)
+        ]
+
+        for future in as_completed(futures):
+            shard_index = future.result()
+            super_index.update(shard_index)
+    print("\nCreated all shards.")
+
+    # write super index to file
+    print("\nWriting super index...")
+    index_path = output_dir / f"{output_db_name}.index"
+    with open(index_path, "w") as fp:
+        json.dump(super_index, fp, indent=4)
+        
+    print("Done.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="""
+        This script creates an alignment database format from a directory of
+        precomputed alignments. For better file system health, the total
+        database is split into n_shards files, where each shard contains a
+        subset of the total alignments. The output is a directory containing the
+        n_shards database files, and a single index file mapping chain names to
+        the database file and byte offsets for each alignment file.
+        
+        Note: For optimal performance, your machine should have at least as many
+        cores as shards you want to create.
+        """
+    )
+    parser.add_argument(
+        "alignment_dir",
+        type=Path,
+        help="""Path to precomputed alignment directory, with one subdirectory 
+                per chain.""",
+    )
+    parser.add_argument("output_db_path", type=Path)
+    parser.add_argument("output_db_name", type=str)
+    parser.add_argument(
+        "n_shards", type=int, help="Number of shards to split the database into"
+    )
+
+    args = parser.parse_args()
+
+    main(args)

scripts/convert_v1_to_v2_weights.py

+# Copyright 2022 AlQuraishi Laboratory
+#
+# 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.
+#
+# Converts OpenFold .pt checkpoints into AlphaFold .npz ones, which can then be
+# used to run inference using DeepMind's JAX code.
+
+import logging
+import argparse
+import os
+import shutil
+import torch
+
+from openfold.utils.import_weights import convert_deprecated_v1_keys
+from deepspeed.utils.zero_to_fp32 import (
+    get_optim_files, parse_optim_states, get_model_state_file
+)
+
+
+def convert_v1_to_v2_weights(args):
+    checkpoint_path = args.input_ckpt_path
+    is_dir = os.path.isdir(checkpoint_path)
+    if is_dir:
+        # A DeepSpeed checkpoint
+        logging.info(
+            'Converting deepspeed checkpoint found at {args.input_checkpoint_path}')
+        state_dict_key = 'module'
+        latest_path = os.path.join(checkpoint_path, 'latest')
+        if os.path.isfile(latest_path):
+            with open(latest_path, 'r') as fd:
+                tag = fd.read().strip()
+        else:
+            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+        ds_checkpoint_dir = os.path.join(checkpoint_path, tag)
+        model_output_path = os.path.join(args.output_ckpt_path, tag)
+        optim_files = get_optim_files(ds_checkpoint_dir)
+        zero_stage, _, _ = parse_optim_states(optim_files, ds_checkpoint_dir)
+        model_file = get_model_state_file(ds_checkpoint_dir, zero_stage)
+    else:
+        # A Pytorch Lightning checkpoint
+        logging.info(
+            'Converting pytorch lightning checkpoint found at {args.input_checkpoint_path}')
+        state_dict_key = 'state_dict'
+        model_output_path = args.output_ckpt_path
+        model_file = checkpoint_path
+
+    model_dict = torch.load(model_file, map_location=torch.device('cpu'))
+    model_dict[state_dict_key] = convert_deprecated_v1_keys(
+        model_dict[state_dict_key])
+
+    if 'ema' in model_dict:
+        ema_state_dict = model_dict['ema']['params']
+        model_dict['ema']['params'] = convert_deprecated_v1_keys(
+            ema_state_dict)
+
+    if is_dir:
+        param_shapes = convert_deprecated_v1_keys(
+            model_dict['param_shapes'][0])
+        model_dict['param_shapes'] = [param_shapes]
+
+        shutil.copytree(checkpoint_path, args.output_ckpt_path)
+        out_fname = os.path.join(
+            model_output_path, os.path.basename(model_file))
+
+        for optim_file in optim_files:
+            optim_dict = torch.load(optim_file)
+            new_optim_dict = optim_dict.copy()
+            new_optim_dict['optimizer_state_dict']['param_slice_mappings'][0] = convert_deprecated_v1_keys(
+                optim_dict['optimizer_state_dict']['param_slice_mappings'][0])
+            out_optim_fname = os.path.join(
+                model_output_path, os.path.basename(optim_file))
+            torch.save(new_optim_dict, out_optim_fname)
+    else:
+        out_fname = model_output_path
+
+    torch.save(model_dict, out_fname)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("input_ckpt_path", type=str)
+    parser.add_argument("output_ckpt_path", type=str)
+
+    args = parser.parse_args()
+
+    convert_v1_to_v2_weights(args)

scripts/download_openfold_soloseq_params.sh

 #!/bin/bash
 #
-# Copyright 2021 DeepMind Technologies Limited
+# Copyright 2024 AlQuraishi Laboratory
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,9 +14,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-# Downloads OpenFold parameters.
+# Downloads OpenFold SoloSeq (single sequence model) parameters.
 #
-# Usage: bash download_openfold_params_huggingface.sh /path/to/download/directory
+# Usage: bash download_openfold_soloseq_params.sh /path/to/download/directory
 set -e
 
 if [[ $# -eq 0 ]]; then

scripts/download_soloseq_embeddings.sh

+#!/bin/bash
+#
+# Copyright 2024 AlQuraishi Laboratory
+#
+# 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.
+#
+# Downloads ESM-1b embeddings used to train OpenFold SoloSeq single-seq model.
+#
+# Usage: bash download_soloseq_embeddings.sh /path/to/download/directory
+set -e
+
+if [[ $# -eq 0 ]]; then
+    echo "Error: download directory must be provided as an input argument."
+    exit 1
+fi
+
+if ! command -v aws &> /dev/null ; then
+    echo "Error: aws could not be found. Please install aws."
+    exit 1
+fi
+
+DOWNLOAD_DIR="${1}/soloseq_embeddings"
+mkdir -p "${DOWNLOAD_DIR}"
+aws s3 cp --no-sign-request --region us-east-1 s3://openfold/soloseq_embeddings/ "${DOWNLOAD_DIR}" --recursive

scripts/fasta_to_clusterfile.py

+"""
+This script takes a .fasta file as input and then clusters it on a given
+sequence identity threshold using mmseqs2. The mmseqs2 flags are identical to
+what PDB officially uses to provide their official sequence clusters
+(https://github.com/soedinglab/MMseqs2/issues/452).
+"""
+import shutil
+import subprocess
+from argparse import ArgumentParser
+from collections import defaultdict
+from pathlib import Path
+
+
+def reformat_cluster_file(cluster_file: Path, output_file: Path):
+    """
+    This function takes a mmseqs2 output cluster file and reformats it to a text
+    file where each line contains a space-separated list of {PDB_ID}_{CHAIN_ID}
+    belonging to the same cluster.
+    """
+    cluster_to_chains = defaultdict(list)
+
+    # extract all chains belonging to each cluster
+    with open(cluster_file, "r") as f:
+        for line in f:
+            line = line.strip()
+            cluster_name, chain_id = line.split()
+            cluster_to_chains[cluster_name].append(chain_id)
+
+    # write all chains belonging to the same cluster on the same line
+    with open(output_file, "w") as f:
+        for chains in cluster_to_chains.values():
+            f.write(f"{' '.join(chains)}\n")
+
+
+def main(args):
+    input_file = args.input_fasta.absolute()
+    output_file = args.output_file.absolute()
+    output_dir = args.output_file.parent
+
+    # prefix that all output files get
+    mmseqs_prefix = "_mmseqs_out"
+
+    # temporary directory that mmseqs2 uses
+    tmp_name = f"{mmseqs_prefix}_temp"
+    tmp_dir = output_dir / tmp_name
+
+    mmseqs_command = [
+        args.mmseqs_binary_path,
+        "easy-cluster",
+        input_file,
+        mmseqs_prefix,
+        tmp_name,
+        "--min-seq-id",
+        str(args.seq_id),
+        "-c",
+        "0.9",
+        "-s",
+        "8",
+        "--max-seqs",
+        "1000",
+        "--cluster-mode",
+        "1",
+    ]
+
+    # run mmseqs with PDB settings
+    print("Running mmseqs2...")
+    subprocess.run(mmseqs_command, check=True, cwd=output_dir)
+
+    cluster_file = output_dir / "_mmseqs_out_cluster.tsv"
+
+    print("Reformatting output file...")
+    reformat_cluster_file(cluster_file, output_file)
+
+    print("Cleaning up mmseqs2 output...")
+    mmseqs_outputs = [
+        output_dir / f"{mmseqs_prefix}_{suffix}"
+        for suffix in ["cluster.tsv", "rep_seq.fasta", "all_seqs.fasta"]
+    ]
+    for file in mmseqs_outputs:
+        file.unlink()
+    shutil.rmtree(tmp_dir)
+
+    print("Done!")
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(
+        description="Creates a sequence cluster file from a .fasta file using mmseqs2 with PDB settings."
+    )
+    parser.add_argument(
+        "input_fasta",
+        type=Path,
+        help="Input .fasta file. Sequence names should be in format >{PDB_ID}_{CHAIN_ID}",
+    )
+    parser.add_argument(
+        "output_file",
+        type=Path,
+        help="Output file. Each line will contain a space-separated list of {PDB_ID}_{CHAIN_ID} belonging to the same cluster.",
+    )
+    parser.add_argument("mmseqs_binary_path", type=str, help="Path to mmseqs binary")
+    parser.add_argument("--seq-id", type=float, default=0.4, help="Sequence identity threshold for clustering.")
+
+    args = parser.parse_args()
+
+    main(args)

scripts/utils.py

 import argparse
 import ctypes
 from datetime import date
+import os
 import sys
+from pathlib import Path
 
+if 'CONDA_PREFIX' in os.environ:
+    CONDA_ENV_BINARY_PATH= Path(os.environ['CONDA_PREFIX']) / 'bin'
+else:
+    CONDA_ENV_BINARY_PATH = Path('/bin')
 
 def add_data_args(parser: argparse.ArgumentParser):
     parser.add_argument(
@@ -30,22 +36,22 @@ def add_data_args(parser: argparse.ArgumentParser):
         '--bfd_database_path', type=str, default=None,
     )
     parser.add_argument(
-        '--jackhmmer_binary_path', type=str, default='/usr/bin/jackhmmer'
+        '--jackhmmer_binary_path', type=str, default=str(CONDA_ENV_BINARY_PATH / 'jackhmmer'),
     )
     parser.add_argument(
-        '--hhblits_binary_path', type=str, default='/usr/bin/hhblits'
+        '--hhblits_binary_path', type=str, default=str(CONDA_ENV_BINARY_PATH / 'hhblits'),
     )
     parser.add_argument(
-        '--hhsearch_binary_path', type=str, default='/usr/bin/hhsearch'
+        '--hhsearch_binary_path', type=str, default=str(CONDA_ENV_BINARY_PATH / 'hhsearch'),
     )
     parser.add_argument(
-        '--hmmsearch_binary_path', type=str, default='/usr/bin/hmmsearch'
+        '--hmmsearch_binary_path', type=str, default=str(CONDA_ENV_BINARY_PATH / 'hmmsearch'),
     )
     parser.add_argument(
-        '--hmmbuild_binary_path', type=str, default='/usr/bin/hmmbuild'
+        '--hmmbuild_binary_path', type=str, default=str(CONDA_ENV_BINARY_PATH / 'hmmbuild'),
     )
     parser.add_argument(
-        '--kalign_binary_path', type=str, default='/usr/bin/kalign'
+        '--kalign_binary_path', type=str, default=str(CONDA_ENV_BINARY_PATH / 'kalign'),
     )
     parser.add_argument(
         '--max_template_date', type=str,

scripts/zero_to_fp32.py

-#!/usr/bin/env python
-
-# This script extracts fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints. It gets
-# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
-# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
-# application.
-#
-# example: python zero_to_fp32.py . pytorch_model.bin
-
-import argparse
-import torch
-import glob
-import math
-import os
-from collections import OrderedDict
-import re
-
-# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
-# DeepSpeed data structures it has to be available in the current python environment.
-import deepspeed
-from deepspeed.utils import logger
-
-debug = 0
-
-# load to cpu
-device = torch.device('cpu')
-
-
-def get_model_state_file(checkpoint_dir, zero_stage):
-    if not os.path.isdir(checkpoint_dir):
-        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
-
-    # there should be only one file
-    if zero_stage == 2:
-        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
-    elif zero_stage == 3:
-        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
-
-    if not os.path.exists(file):
-        raise FileNotFoundError(f"can't find model states file at '{file}'")
-
-    return file
-
-
-def get_optim_files(checkpoint_dir):
-    # XXX: need to test that this simple glob rule works for multi-node setup too
-    optim_files = sorted(glob.glob(os.path.join(checkpoint_dir, "*_optim_states.pt")))
-
-    if len(optim_files) == 0:
-        raise FileNotFoundError(
-            f"can't find '*_optim_states.pt' files in directory '{checkpoint_dir}'")
-
-    return optim_files
-
-
-def parse_model_state(file):
-    state_dict = torch.load(file, map_location=device)
-
-    if "buffer_names" not in state_dict:
-        raise ValueError(f"{file} is not a model state checkpoint")
-    buffer_names = state_dict["buffer_names"]
-    if debug:
-        print("Found buffers:", buffer_names)
-
-    # recover just the buffers while restoring them to fp32 if they were saved in fp16
-    buffers = {
-        k: v.float()
-        for k,
-        v in state_dict["module"].items() if k in buffer_names
-    }
-    return buffers
-
-
-def parse_optim_states(files, ds_checkpoint_dir):
-
-    total_files = len(files)
-    state_dicts = []
-    for f in files:
-        state_dicts.append(torch.load(f, map_location=device))
-
-    if not "zero_stage" in state_dicts[0]['optimizer_state_dict']:
-        raise ValueError(f"{files[0]} is not a zero checkpoint")
-    zero_stage = state_dicts[0]['optimizer_state_dict']["zero_stage"]
-    world_size = state_dicts[0]['optimizer_state_dict']["partition_count"]
-    param_shapes = state_dicts[0]["param_shapes"]
-    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
-    # parameters can be different from data parallelism for non-expert parameters. So we can just
-    # use the max of the partition_count to get the dp world_size.
-
-    if type(world_size) is list:
-        world_size = max(world_size)
-
-    if world_size != total_files:
-        raise ValueError(
-            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
-            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
-        )
-
-    # the groups are named differently in each stage
-    if zero_stage == 2:
-        fp32_groups_key = "single_partition_of_fp32_groups"
-    elif zero_stage == 3:
-        fp32_groups_key = "fp32_flat_groups"
-    else:
-        raise ValueError(f"unknown zero stage {zero_stage}")
-
-    if zero_stage == 2:
-        fp32_flat_groups = [
-            state_dicts[i]['optimizer_state_dict'][fp32_groups_key]
-            for i in range(len(state_dicts))
-        ]
-    elif zero_stage == 3:
-        # if there is more than one param group, there will be multiple flattened tensors - one
-        # flattened tensor per group - for simplicity merge them into a single tensor
-        #
-        # XXX: could make the script more memory efficient for when there are multiple groups - it
-        # will require matching the sub-lists of param_shapes for each param group flattened tensor
-
-        fp32_flat_groups = [
-            torch.cat(state_dicts[i]['optimizer_state_dict'][fp32_groups_key],
-                      0) for i in range(len(state_dicts))
-        ]
-
-    return zero_stage, world_size, param_shapes, fp32_flat_groups
-
-
-def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
-    """
-    Returns fp32 state_dict reconstructed from ds checkpoint
-
-    Args:
-        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
-
-    """
-    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
-
-    optim_files = get_optim_files(ds_checkpoint_dir)
-    zero_stage, world_size, param_shapes, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
-    print(
-        f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
-
-    model_file = get_model_state_file(ds_checkpoint_dir, zero_stage)
-    buffers = parse_model_state(model_file)
-
-    if zero_stage == 2:
-        return _get_fp32_state_dict_from_zero2_checkpoint(world_size,
-                                                          param_shapes,
-                                                          fp32_flat_groups,
-                                                          buffers)
-    elif zero_stage == 3:
-        return _get_fp32_state_dict_from_zero3_checkpoint(world_size,
-                                                          param_shapes,
-                                                          fp32_flat_groups,
-                                                          buffers)
-
-
-def _get_fp32_state_dict_from_zero2_checkpoint(world_size,
-                                               param_shapes,
-                                               fp32_flat_groups,
-                                               buffers):
-
-    # Reconstruction protocol:
-    #
-    # XXX: document this
-
-    if debug:
-        for i in range(world_size):
-            for j in range(len(fp32_flat_groups[0])):
-                print(f"fp32_flat_groups[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
-
-    # XXX: memory usage doubles here (zero2)
-    num_param_groups = len(fp32_flat_groups[0])
-    merged_single_partition_of_fp32_groups = []
-    for i in range(num_param_groups):
-        merged_partitions = [sd[i] for sd in fp32_flat_groups]
-        full_single_fp32_vector = torch.cat(merged_partitions, 0)
-        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
-    avail_numel = sum([
-        full_single_fp32_vector.numel()
-        for full_single_fp32_vector in merged_single_partition_of_fp32_groups
-    ])
-
-    if debug:
-        wanted_params = sum([len(shapes) for shapes in param_shapes])
-        wanted_numel = sum(
-            [sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
-        # not asserting if there is a mismatch due to possible padding
-        print(f"Have {avail_numel} numels to process.")
-        print(f"Need {wanted_numel} numels in {wanted_params} params.")
-
-    state_dict = OrderedDict()
-
-    # buffers
-    state_dict.update(buffers)
-    if debug:
-        print(f"added {len(buffers)} buffers")
-
-    # params
-    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
-    # out-of-core computing solution
-    total_numel = 0
-    total_params = 0
-    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
-        offset = 0
-        avail_numel = full_single_fp32_vector.numel()
-        for name, shape in shapes.items():
-
-            unpartitioned_numel = shape.numel()
-            total_numel += unpartitioned_numel
-            total_params += 1
-
-            if debug:
-                print(
-                    f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} "
-                )
-            state_dict[name] = full_single_fp32_vector.narrow(
-                0,
-                offset,
-                unpartitioned_numel).view(shape)
-            offset += unpartitioned_numel
-
-        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
-        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
-        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
-        # live optimizer object, so we are checking that the numbers are within the right range
-        align_to = 2 * world_size
-
-        def zero2_align(x):
-            return align_to * math.ceil(x / align_to)
-
-        if debug:
-            print(f"original offset={offset}, avail_numel={avail_numel}")
-
-        offset = zero2_align(offset)
-        avail_numel = zero2_align(avail_numel)
-
-        if debug:
-            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
-
-        # Sanity check
-        if offset != avail_numel:
-            raise ValueError(
-                f"consumed {offset} numels out of {avail_numel} - something is wrong")
-
-    print(
-        f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements"
-    )
-
-    return state_dict
-
-
-def zero3_partitioned_param_info(unpartitioned_numel, world_size):
-    remainder = unpartitioned_numel % world_size
-    padding_numel = (world_size - remainder) if remainder else 0
-    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
-    return partitioned_numel, padding_numel
-
-
-def _get_fp32_state_dict_from_zero3_checkpoint(world_size,
-                                               param_shapes,
-                                               fp32_flat_groups,
-                                               buffers):
-
-    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
-    # param, re-consolidating each param, while dealing with padding if any
-
-    avail_numel = fp32_flat_groups[0].numel() * world_size
-    # merge list of dicts, preserving order
-    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
-
-    if debug:
-        for i in range(world_size):
-            print(f"fp32_flat_groups[{i}].shape={fp32_flat_groups[i].shape}")
-
-        wanted_params = len(param_shapes)
-        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
-        # not asserting if there is a mismatch due to possible padding
-        print(f"Have {avail_numel} numels to process.")
-        print(f"Need {wanted_numel} numels in {wanted_params} params.")
-
-    state_dict = OrderedDict()
-
-    # buffers
-    state_dict.update(buffers)
-    if debug:
-        print(f"added {len(buffers)} buffers")
-
-    # params
-    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
-    # out-of-core computing solution
-    offset = 0
-    total_numel = 0
-    total_params = 0
-    for name, shape in param_shapes.items():
-
-        unpartitioned_numel = shape.numel()
-        total_numel += unpartitioned_numel
-        total_params += 1
-
-        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
-
-        if debug:
-            print(
-                f"{total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
-            )
-
-        # XXX: memory usage doubles here
-        state_dict[name] = torch.cat(
-            tuple(fp32_flat_groups[i].narrow(0,
-                                             offset,
-                                             partitioned_numel)
-                  for i in range(world_size)),
-            0).narrow(0,
-                      0,
-                      unpartitioned_numel).view(shape)
-        offset += partitioned_numel
-
-    offset *= world_size
-
-    # Sanity check
-    if offset != avail_numel:
-        raise ValueError(
-            f"consumed {offset} numels out of {avail_numel} - something is wrong")
-
-    print(
-        f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements"
-    )
-
-    return state_dict
-
-
-def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
-    """
-    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
-    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
-    via a model hub.
-
-    Args:
-        - ``checkpoint_dir``: path to the desired checkpoint folder
-        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
-
-    Returns:
-        - pytorch ``state_dict``
-
-    Note: this approach may not work if your application doesn't have sufficient free CPU memory and
-    you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
-    the checkpoint.
-
-    A typical usage might be ::
-
-        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
-        # do the training and checkpoint saving
-        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
-        model = model.cpu() # move to cpu
-        model.load_state_dict(state_dict)
-        # submit to model hub or save the model to share with others
-
-    In this example the ``model`` will no longer be usable in the deepspeed context of the same
-    application. i.e. you will need to re-initialize the deepspeed engine, since
-    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
-    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
-
-    """
-    if tag is None:
-        latest_path = os.path.join(checkpoint_dir, 'latest')
-        if os.path.isfile(latest_path):
-            with open(latest_path, 'r') as fd:
-                tag = fd.read().strip()
-        else:
-            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
-
-    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
-
-    if not os.path.isdir(ds_checkpoint_dir):
-        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
-
-    return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
-
-
-def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
-    """
-    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
-    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
-
-    Args:
-        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
-        - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
-        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
-    """
-
-    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-    print(f"Saving fp32 state dict to {output_file}")
-    torch.save(state_dict, output_file)
-
-
-def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
-    """
-    1. Put the provided model to cpu
-    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
-    3. Load it into the provided model
-
-    Args:
-        - ``model``: the model object to update
-        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
-        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
-
-    Returns:
-        - ``model`: modified model
-
-    Make sure you have plenty of CPU memory available before you call this function. If you don't
-    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
-    conveniently placed for you in the checkpoint folder.
-
-    A typical usage might be ::
-
-        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
-        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
-        # submit to model hub or save the model to share with others
-
-    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
-    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
-    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
-
-    """
-    logger.info(f"Extracting fp32 weights")
-    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-
-    logger.info(f"Overwriting model with fp32 weights")
-    model = model.cpu()
-    model.load_state_dict(state_dict, strict=False)
-
-    return model
-
-def get_global_step_from_zero_checkpoint(checkpoint_dir):
-    global_step = -1
-    latest_path = os.path.join(checkpoint_dir, 'latest')
-    if os.path.isfile(latest_path):
-        with open(latest_path, 'r') as fd:
-            tag = fd.read().strip()
-            match = re.match(r"global_step([0-9]+)", tag)
-            global_step = int(match.group(1))
-    else:
-            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
-    return global_step
-
-if __name__ == "__main__":
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "checkpoint_dir",
-        type=str,
-        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
-    parser.add_argument(
-        "output_file",
-        type=str,
-        help=
-        "path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)"
-    )
-    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
-    args = parser.parse_args()
-
-    debug = args.debug
-
-    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file)

setup.py

@@ -52,7 +52,6 @@ def get_cuda_bare_metal_version(cuda_dir):
         return raw_output, bare_metal_major, bare_metal_minor
 
 compute_capabilities = set([
-    (3, 7), # K80, e.g.
     (5, 2), # Titan X
     (6, 1), # GeForce 1000-series
 ])
@@ -130,7 +129,7 @@ setup(
     classifiers=[
         'License :: OSI Approved :: Apache Software License',
         'Operating System :: POSIX :: Linux',
-        'Programming Language :: Python :: 3.9,'
+        'Programming Language :: Python :: 3.10,'
         'Topic :: Scientific/Engineering :: Artificial Intelligence',
     ],
 )

tests/test_model.py

@@ -202,4 +202,4 @@ class TestModel(unittest.TestCase):
         out_repro = out_repro["sm"]["positions"][-1]
         out_repro = out_repro.squeeze(0)
 
-        self.assertTrue(torch.max(torch.abs(out_gt - out_repro)) < 1e-3)
+        compare_utils.assert_mean_abs_diff_small(out_gt, out_repro, 1e-3)

tests/test_permutation.py

@@ -48,7 +48,8 @@ class TestPermutation(unittest.TestCase):
         self.chain_a_num_res = 9
         self.chain_b_num_res = 13
         # below create default fake ground truth structures for a hetero-pentamer A2B3
-        self.residue_index = list(range(self.chain_a_num_res)) * 2 + list(range(self.chain_b_num_res)) * 3
+        self.residue_index = list(
+            range(self.chain_a_num_res)) * 2 + list(range(self.chain_b_num_res)) * 3
         self.num_res = self.chain_a_num_res * 2 + self.chain_b_num_res * 3
         self.asym_id = torch.tensor([[1] * self.chain_a_num_res + [2] * self.chain_a_num_res + [
             3] * self.chain_b_num_res + [4] * self.chain_b_num_res + [5] * self.chain_b_num_res], device=device)
@@ -63,7 +64,8 @@ class TestPermutation(unittest.TestCase):
             'entity_id': self.entity_id,
             'seq_length': torch.tensor([57])
         }
-        anchor_gt_asym, anchor_pred_asym = get_least_asym_entity_or_longest_length(batch, batch['asym_id'])
+        anchor_gt_asym, anchor_pred_asym = get_least_asym_entity_or_longest_length(
+            batch, batch['asym_id'])
         anchor_gt_asym = int(anchor_gt_asym)
         anchor_pred_asym = {int(i) for i in anchor_pred_asym}
         expected_anchors = {1, 2}
@@ -76,6 +78,30 @@ class TestPermutation(unittest.TestCase):
         self.assertEqual(set(), anchor_pred_asym & expected_non_anchors)
 
     def test_2_permutation_pentamer(self):
+        """
+        Test the permutation results on a pentamer A2B3, in which protein A has 9 residues
+        and protein B has 13 residues. 
+
+        Expected outputs:
+            Only protein A should be selected as an anchor thus, in the output list, either [(0,1), (1,0)] or [(0,0), (1,1)] are allowed 
+            The 3 chains from protein B should ALWAYS be aligned in a way that predicted b1 to be aligned with ground truth b1, pred b2 to ground truth b2 
+            as shown below:
+
+            predicted structure:              a2 - a1 - b2 - b3 - b1
+            indexes in the predicted list:    0    1    2    3    4
+
+            ground truth structure:           a1 - a2 - b1 - b2 - b3
+            indexes in the ground truth list: 0    1    2    3    4
+
+            then the 2 protein A chains are free to be aligned by either order, thus either [(0,1),(1,0)] or [(0,0),(1,1)] is valid.
+
+            However, the 3 protein B chains should be strictly aligned in the following order: 
+            [(2,3), (3,4), (4,2)], regardless of how protein A chains are aligned. 
+
+            Therefore, the only 2 correct permutations are :
+            [(0, 1), (1, 0), (2, 3), (3, 4), (4, 2)] and 
+            [(0, 0), (1, 1), (2, 3), (3, 4), (4, 2)]
+        """
         batch = {
             'asym_id': self.asym_id,
             'sym_id': self.sym_id,
@@ -93,16 +119,22 @@ class TestPermutation(unittest.TestCase):
         chain_b1_pos = torch.randint(low=15, high=30, size=(self.chain_b_num_res, 3 * 37),
                                      dtype=torch.float).reshape(1, self.chain_b_num_res, 37, 3)
         chain_b2_pos = torch.matmul(chain_b1_pos, self.rotation_matrix_y) + 10
-        chain_b3_pos = torch.matmul(torch.matmul(chain_b1_pos, self.rotation_matrix_z), self.rotation_matrix_x) + 30
+        chain_b3_pos = torch.matmul(torch.matmul(
+            chain_b1_pos, self.rotation_matrix_z), self.rotation_matrix_x) + 30
         # Below permutate predicted chain positions
-        pred_atom_position = torch.cat((chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1)
+        # here the b2 chain from the ground truth is deliberately put in b1 chain's position, and predicted b3 chain to b2's position
+        # and predicted b1 chain to b3's position
+        pred_atom_position = torch.cat(
+            (chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1)
+
         pred_atom_mask = torch.ones((1, self.num_res, 37))
         out = {
             'final_atom_positions': pred_atom_position,
             'final_atom_mask': pred_atom_mask
         }
 
-        true_atom_position = torch.cat((chain_a1_pos, chain_a2_pos, chain_b1_pos, chain_b2_pos, chain_b3_pos), dim=1)
+        true_atom_position = torch.cat(
+            (chain_a1_pos, chain_a2_pos, chain_b1_pos, chain_b2_pos, chain_b3_pos), dim=1)
         true_atom_mask = torch.cat((torch.ones((1, self.chain_a_num_res, 37)),
                                     torch.ones((1, self.chain_a_num_res, 37)),
                                     torch.ones((1, self.chain_b_num_res, 37)),
@@ -111,26 +143,46 @@ class TestPermutation(unittest.TestCase):
         batch['all_atom_positions'] = true_atom_position
         batch['all_atom_mask'] = true_atom_mask
 
-        aligns, _ = compute_permutation_alignment(out, batch,
+        aligns, per_asym_residue_index = compute_permutation_alignment(out, batch,
                                                                        batch)
-        print(f"##### aligns is {aligns}")
-        possible_outcome = [[(0, 1), (1, 0), (2, 3), (3, 4), (4, 2)], [(0, 0), (1, 1), (2, 3), (3, 4), (4, 2)]]
-        wrong_outcome = [[(0, 1), (1, 0), (2, 4), (3, 2), (4, 3)], [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]]
-        self.assertIn(aligns, possible_outcome)
-        self.assertNotIn(aligns, wrong_outcome)
 
-    @unittest.skip("Test needs to be fixed post-refactor")
+        expected_asym_residue_index = {
+            1: torch.tensor(list(range(self.chain_a_num_res))),
+            2: torch.tensor(list(range(self.chain_a_num_res))),
+            3: torch.tensor(list(range(self.chain_b_num_res))),
+            4: torch.tensor(list(range(self.chain_b_num_res))),
+            5: torch.tensor(list(range(self.chain_b_num_res)))
+        }
+        chain_a_permutated_chain_b_permutated = [
+            (0, 1), (1, 0), (2, 3), (3, 4), (4, 2)]
+        chain_a_not_permutated_chain_b_permutated = [
+            (0, 0), (1, 1), (2, 3), (3, 4), (4, 2)]
+        chain_a_permutated_chain_b_not_permuated = [
+            (0, 1), (1, 0), (2, 2), (3, 3), (4, 4)]
+        chain_a_not_permutated_chain_b_not_permuated = [
+            (0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
+        
+        # test on the permutation alignments 
+        self.assertIn(aligns, [chain_a_permutated_chain_b_permutated,
+                      chain_a_not_permutated_chain_b_permutated])
+        self.assertNotIn(aligns, [chain_a_permutated_chain_b_not_permuated,
+                         chain_a_not_permutated_chain_b_not_permuated])
+
+        # test on the per_aysm_residue_index 
+        for k, v in expected_asym_residue_index.items():
+            self.assertTrue(torch.equal(v, per_asym_residue_index[k]))
+
     def test_3_merge_labels(self):
         nres_pad = 325 - 57  # suppose the cropping size is 325
         batch = {
-            'asym_id': pad_features(self.asym_id, nres_pad, pad_dim=1),
-            'sym_id': pad_features(self.sym_id, nres_pad, pad_dim=1),
-            'entity_id': pad_features(self.entity_id, nres_pad, pad_dim=1),
-            'aatype': torch.randint(21, size=(1, 325)),
+            'asym_id': self.asym_id,
+            'sym_id': self.sym_id,
+            'entity_id': self.entity_id,
+            'aatype': torch.randint(21, size=(1, 57)),
             'seq_length': torch.tensor([57])
         }
-        batch['asym_id'] = batch['asym_id'].reshape(1, 325)
-        batch["residue_index"] = pad_features(torch.tensor(self.residue_index).reshape(1, 57), nres_pad, pad_dim=1)
+        batch['asym_id'] = batch['asym_id'].reshape(1, 57)
+        batch["residue_index"] = torch.tensor([self.residue_index])
         # create fake ground truth atom positions
         chain_a1_pos = torch.randint(15, (self.chain_a_num_res, 3 * 37),
                                      dtype=torch.float).reshape(1, self.chain_a_num_res, 37, 3)
@@ -139,39 +191,64 @@ class TestPermutation(unittest.TestCase):
         chain_b1_pos = torch.randint(low=15, high=30, size=(self.chain_b_num_res, 3 * 37),
                                      dtype=torch.float).reshape(1, self.chain_b_num_res, 37, 3)
         chain_b2_pos = torch.matmul(chain_b1_pos, self.rotation_matrix_y) + 10
-        chain_b3_pos = torch.matmul(torch.matmul(chain_b1_pos, self.rotation_matrix_z), self.rotation_matrix_x) + 30
+        chain_b3_pos = torch.matmul(torch.matmul(
+            chain_b1_pos, self.rotation_matrix_z), self.rotation_matrix_x) + 30
         # Below permutate predicted chain positions
-        pred_atom_position = torch.cat((chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1)
+        pred_atom_position = torch.cat(
+            (chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos), dim=1)
         pred_atom_mask = torch.ones((1, self.num_res, 37))
-        pred_atom_position = pad_features(pred_atom_position, nres_pad, pad_dim=1)
+        pred_atom_position = pad_features(
+            pred_atom_position, nres_pad, pad_dim=1)
         pred_atom_mask = pad_features(pred_atom_mask, nres_pad, pad_dim=1)
         out = {
             'final_atom_positions': pred_atom_position,
             'final_atom_mask': pred_atom_mask
         }
-        true_atom_position = torch.cat((chain_a1_pos, chain_a2_pos, chain_b1_pos, chain_b2_pos, chain_b3_pos), dim=1)
+        true_atom_position = torch.cat(
+            (chain_a1_pos, chain_a2_pos, chain_b1_pos, chain_b2_pos, chain_b3_pos), dim=1)
         true_atom_mask = torch.cat((torch.ones((1, self.chain_a_num_res, 37)),
                                     torch.ones((1, self.chain_a_num_res, 37)),
                                     torch.ones((1, self.chain_b_num_res, 37)),
                                     torch.ones((1, self.chain_b_num_res, 37)),
                                     torch.ones((1, self.chain_b_num_res, 37))), dim=1)
-        batch['all_atom_positions'] = pad_features(true_atom_position, nres_pad, pad_dim=1)
-        batch['all_atom_mask'] = pad_features(true_atom_mask, nres_pad=nres_pad, pad_dim=1)
 
-        # tensor_to_cuda = lambda t: t.to('cuda')
-        # ground_truth = tensor_tree_map(tensor_to_cuda,ground_truth)
+        batch['all_atom_positions'] = true_atom_position
+        batch['all_atom_mask'] = true_atom_mask
+
+        # Below create a fake_input_features
+        fake_input_features = {
+            'asym_id': pad_features(self.asym_id, nres_pad, pad_dim=1),
+            'sym_id': pad_features(self.sym_id, nres_pad, pad_dim=1),
+            'entity_id': pad_features(self.entity_id, nres_pad, pad_dim=1),
+            'aatype': torch.randint(21, size=(1, 325)),
+            'seq_length': torch.tensor([57])
+        }
+        fake_input_features['asym_id'] = fake_input_features['asym_id'].reshape(
+            1, 325)
+        fake_input_features["residue_index"] = pad_features(
+            torch.tensor(self.residue_index).reshape(1, 57), nres_pad, pad_dim=1)
+        fake_input_features['all_atom_positions'] = pad_features(
+            true_atom_position, nres_pad, pad_dim=1)
+        fake_input_features['all_atom_mask'] = pad_features(
+            true_atom_mask, nres_pad=nres_pad, pad_dim=1)
+
+        # NOTE
+        # batch: simulates ground_truth features
+        # fake_input_features: simulates the data that are going be used as input for model.forward(fake_input_features)
+        # out: simulates the output of model.forward(fake_input_features)
         aligns, per_asym_residue_index = compute_permutation_alignment(out,
-                                                                       batch,
+                                                                       fake_input_features,
                                                                        batch)
-        print(f"##### aligns is {aligns}")
         labels = split_ground_truth_labels(batch)
 
         labels = merge_labels(per_asym_residue_index, labels, aligns,
                               original_nres=batch['aatype'].shape[-1])
 
-        self.assertTrue(torch.equal(labels['residue_index'], batch['residue_index']))
+        self.assertTrue(torch.equal(
+            labels['residue_index'], batch['residue_index']))
 
         expected_permutated_gt_pos = torch.cat((chain_a2_pos, chain_a1_pos, chain_b2_pos, chain_b3_pos, chain_b1_pos),
                                                dim=1)
-        expected_permutated_gt_pos = pad_features(expected_permutated_gt_pos, nres_pad, pad_dim=1)
-        self.assertTrue(torch.equal(labels['all_atom_positions'], expected_permutated_gt_pos))
+
+        self.assertTrue(torch.equal(
+            labels['all_atom_positions'], expected_permutated_gt_pos))

train_openfold.py

@@ -2,20 +2,25 @@ import argparse
 import logging
 import os
 import sys
+import json
 
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks.lr_monitor import LearningRateMonitor
+from pytorch_lightning.callbacks import DeviceStatsMonitor
 from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
 from pytorch_lightning.loggers import WandbLogger
-from pytorch_lightning.strategies import DeepSpeedStrategy, DDPStrategy
+from pytorch_lightning.strategies import DDPStrategy, DeepSpeedStrategy
+from pytorch_lightning.plugins.environments import MPIEnvironment
+from pytorch_lightning import seed_everything
 import torch
+import wandb
+from deepspeed.utils import zero_to_fp32 
 
 from openfold.config import model_config
 from openfold.data.data_modules import OpenFoldDataModule, OpenFoldMultimerDataModule
 from openfold.model.model import AlphaFold
 from openfold.model.torchscript import script_preset_
 from openfold.np import residue_constants
-from openfold.utils.argparse_utils import remove_arguments
 from openfold.utils.callbacks import (
     EarlyStoppingVerbose,
 )
@@ -23,7 +28,6 @@ from openfold.utils.exponential_moving_average import ExponentialMovingAverage
 from openfold.utils.loss import AlphaFoldLoss, lddt_ca
 from openfold.utils.lr_schedulers import AlphaFoldLRScheduler
 from openfold.utils.multi_chain_permutation import multi_chain_permutation_align
-from openfold.utils.seed import seed_everything
 from openfold.utils.superimposition import superimpose
 from openfold.utils.tensor_utils import tensor_tree_map
 from openfold.utils.validation_metrics import (
@@ -35,11 +39,6 @@ from openfold.utils.import_weights import (
     import_jax_weights_,
     import_openfold_weights_
 )
-from scripts.zero_to_fp32 import (
-    get_fp32_state_dict_from_zero_checkpoint,
-    get_global_step_from_zero_checkpoint
-)
-
 from openfold.utils.logger import PerformanceLoggingCallback
 
 
@@ -58,6 +57,7 @@ class OpenFoldWrapper(pl.LightningModule):
 
         self.cached_weights = None
         self.last_lr_step = -1
+        self.save_hyperparameters()
 
     def forward(self, batch):
         return self.model(batch)
@@ -68,14 +68,15 @@ class OpenFoldWrapper(pl.LightningModule):
             self.log(
                 f"{phase}/{loss_name}", 
                 indiv_loss, 
-                on_step=train, on_epoch=(not train), logger=True,
+                prog_bar=(loss_name == 'loss'),
+                on_step=train, on_epoch=(not train), logger=True, sync_dist=False,
             )
 
             if (train):
                 self.log(
                     f"{phase}/{loss_name}_epoch",
                     indiv_loss,
-                    on_step=False, on_epoch=True, logger=True,
+                    on_step=False, on_epoch=True, logger=True, sync_dist=False,
                 )
 
         with torch.no_grad():
@@ -89,7 +90,8 @@ class OpenFoldWrapper(pl.LightningModule):
             self.log(
                 f"{phase}/{k}",
                 torch.mean(v),
-                on_step=False, on_epoch=True, logger=True
+                prog_bar = (k == 'loss'),
+                on_step=False, on_epoch=True, logger=True, sync_dist=False,
             )
 
     def training_step(self, batch, batch_idx):
@@ -153,7 +155,7 @@ class OpenFoldWrapper(pl.LightningModule):
 
         self._log(loss_breakdown, batch, outputs, train=False)
         
-    def on_validation_epoch_end(self, _):
+    def on_validation_epoch_end(self):
         # Restore the model weights to normal
         self.model.load_state_dict(self.cached_weights)
         self.cached_weights = None
@@ -216,11 +218,6 @@ class OpenFoldWrapper(pl.LightningModule):
         learning_rate: float = 1e-3,
         eps: float = 1e-5,
     ) -> torch.optim.Adam:
-        #        return torch.optim.Adam(
-        #            self.model.parameters(),
-        #            lr=learning_rate,
-        #            eps=eps
-        #        )
         # Ignored as long as a DeepSpeed optimizer is configured
         optimizer = torch.optim.Adam(
             self.model.parameters(),
@@ -271,37 +268,69 @@ class OpenFoldWrapper(pl.LightningModule):
             self.model, jax_path, version=model_version
         )
 
+def get_model_state_dict_from_ds_checkpoint(checkpoint_dir):
+    latest_path = os.path.join(checkpoint_dir, 'latest')
+    if os.path.isfile(latest_path):
+        with open(latest_path, 'r') as fd:
+            tag = fd.read().strip()
+    else:
+        raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
+    _DS_CHECKPOINT_VERSION = 2  # based on manual parsing of checkpoint files
+    state_file = zero_to_fp32.get_model_state_file(ds_checkpoint_dir, _DS_CHECKPOINT_VERSION)
+    return torch.load(state_file)
 
 def main(args):
-    if (args.seed is not None):
-        seed_everything(args.seed)
+    if(args.seed is not None):
+        seed_everything(args.seed, workers=True) 
+
+    is_low_precision = args.precision in [
+        "bf16-mixed", "16", "bf16", "16-true", "16-mixed", "bf16-mixed"]
 
     config = model_config(
         args.config_preset, 
         train=True, 
-        low_prec=(str(args.precision) == "16")
+        low_prec=is_low_precision,
     ) 
+    if args.experiment_config_json: 
+        with open(args.experiment_config_json, 'r') as f:
+            custom_config_dict = json.load(f)
+        config.update_from_flattened_dict(custom_config_dict)
+
     model_module = OpenFoldWrapper(config)
 
-    if (args.resume_from_ckpt):
-        if (os.path.isdir(args.resume_from_ckpt)):
-            last_global_step = get_global_step_from_zero_checkpoint(
+    if args.resume_from_ckpt:
+        if args.resume_model_weights_only:
+            # Load the checkpoint
+            if os.path.isdir(args.resume_from_ckpt):
+                sd = zero_to_fp32.get_fp32_state_dict_from_zero_checkpoint(
                     args.resume_from_ckpt)
             else:
                 sd = torch.load(args.resume_from_ckpt)
-            last_global_step = int(sd['global_step'])
-        model_module.resume_last_lr_step(last_global_step)
-        logging.info("Successfully loaded last lr step...")
-    if (args.resume_from_ckpt and args.resume_model_weights_only):
-        if (os.path.isdir(args.resume_from_ckpt)):
-            sd = get_fp32_state_dict_from_zero_checkpoint(
-                args.resume_from_ckpt)
+            # Process the state dict
+            if 'module' in sd:
+                sd = {k[len('module.'):]: v for k, v in sd['module'].items()}
+                import_openfold_weights_(model=model_module, state_dict=sd)
+            elif 'state_dict' in sd:
+                import_openfold_weights_(
+                    model=model_module, state_dict=sd['state_dict'])
             else:
-            sd = torch.load(args.resume_from_ckpt)
-        sd = {k[len("module."):]: v for k, v in sd.items()}
+                # Loading from pre-trained model
+                sd = {'model.'+k: v for k, v in sd.items()}
                 import_openfold_weights_(model=model_module, state_dict=sd)
             logging.info("Successfully loaded model weights...")
-    if (args.resume_from_jax_params):
+
+        else:  # Loads a checkpoint to start from a specific time step
+            if os.path.isdir(args.resume_from_ckpt):
+                sd = get_model_state_dict_from_ds_checkpoint(args.resume_from_ckpt)
+            else:
+                sd = torch.load(args.resume_from_ckpt)
+            last_global_step = int(sd['global_step'])
+            model_module.resume_last_lr_step(last_global_step)
+            logging.info("Successfully loaded last lr step...")
+
+    if args.resume_from_jax_params:
         model_module.load_from_jax(args.resume_from_jax_params)
         logging.info(
             f"Successfully loaded JAX parameters at {args.resume_from_jax_params}...")
@@ -360,7 +389,20 @@ def main(args):
         callbacks.append(lr_monitor)
 
     loggers = []
-    if (args.wandb):
+    is_rank_zero = args.mpi_plugin and (int(os.environ.get("PMI_RANK")) == 0)
+    if(args.wandb):
+        if args.mpi_plugin and is_rank_zero:
+            wandb_init_dict = dict(
+                name=args.experiment_name,
+                project=args.wandb_project,
+                id=args.wandb_id,
+                dir=args.output_dir,
+                resume="allow",
+                anonymous=None,
+                entity=args.wandb_entity
+            )
+            wandb.run = wandb.init(**wandb_init_dict)
+
         wdb_logger = WandbLogger(
             name=args.experiment_name,
             save_dir=args.output_dir,
@@ -370,27 +412,28 @@ def main(args):
         )
         loggers.append(wdb_logger)
 
-    if (args.deepspeed_config_path is not None):
+    cluster_environment = MPIEnvironment() if args.mpi_plugin else None
+    if(args.deepspeed_config_path is not None):
         strategy = DeepSpeedStrategy(
             config=args.deepspeed_config_path,
+            cluster_environment=cluster_environment,
         )
-        if (args.wandb):
+        if(args.wandb and is_rank_zero):
             wdb_logger.experiment.save(args.deepspeed_config_path)
             wdb_logger.experiment.save("openfold/config.py")
     elif (args.gpus is not None and args.gpus > 1) or args.num_nodes > 1:
-        strategy = DDPStrategy(find_unused_parameters=False)
+        strategy = DDPStrategy(find_unused_parameters=False,
+                               cluster_environment=cluster_environment)
     else:
         strategy = None
  
-    if (args.wandb):
+    if(args.wandb and is_rank_zero):
         freeze_path = f"{wdb_logger.experiment.dir}/package_versions.txt"
         os.system(f"{sys.executable} -m pip freeze > {freeze_path}")
         wdb_logger.experiment.save(f"{freeze_path}")
 
-    # Raw dump of all args from pl.Trainer constructor
-    trainer_kws = set([
-        'accelerator', 'strategy', 'devices', 'num_nodes', 'precision', 'logger', 'callbacks', 'fast_dev_run', 'max_epochs', 'min_epochs', 'max_steps', 'min_steps', 'max_tim', 'limit_train_batches', 'limit_val_batches', 'limit_test_batches', 'limit_predict_batches', 'overfit_batches', 'val_check_interval', 'check_val_every_n_epoch', 'num_sanity_val_steps', 'log_every_n_steps', 'enable_checkpointing', 'enable_progress_bar', 'enable_model_summary', 'accumulate_grad_batches', 'gradient_clip_val', 'gradient_clip_algorithm', 'deterministic', 'benchmark', 'inference_mode', 'use_distributed_sampler', 'profiler', 'detect_anomaly', 'barebones', 'plugins', 'sync_batchnorm', 'reload_dataloaders_every_n_epochs', 'default_root_dir',
-    ])
+    trainer_kws = ['num_nodes', 'precision', 'max_epochs', 'log_every_n_steps',
+                   'flush_logs_ever_n_steps', 'num_sanity_val_steps', 'reload_dataloaders_every_n_epochs']
     trainer_args = {k: v for k, v in vars(args).items() if k in trainer_kws}
     trainer_args.update({
         'default_root_dir': args.output_dir,
@@ -400,6 +443,7 @@ def main(args):
     })
     trainer = pl.Trainer(**trainer_args)
 
+
     if (args.resume_model_weights_only):
         ckpt_path = None
     else:
@@ -606,44 +650,41 @@ if __name__ == "__main__":
         help="Distillation alignment index. See the README for instructions."
     )
     parser.add_argument(
-        "--num_nodes", type=int, default=1,
+        "--experiment_config_json", default="", help="Path to a json file with custom config values to overwrite config setting",
     )
     parser.add_argument(
-        "--gpus", type=int, default=1,
+        "--gpus", type=int, default=1, help='For determining optimal strategy and effective batch size.'
     )
-    parser.add_argument(
-        "--precision", type=str, default=None,
+    parser.add_argument("--mpi_plugin", action="store_true", default=False,
+                        help="Whether to use MPI for parallele processing")
+
+    trainer_group = parser.add_argument_group(
+        'Arguments to pass to PyTorch Lightning Trainer')
+    trainer_group.add_argument(
+        "--num_nodes", type=int, default=1,
     )
-    parser.add_argument(
-        "--replace_sampler_ddp", type=bool_type, default=True,
+    trainer_group.add_argument(
+        "--precision", type=str, default='bf16',
+        help='Sets precision, lower precision improves runtime performance.',
     )
-    parser.add_argument(
+    trainer_group.add_argument(
         "--max_epochs", type=int, default=1,
     )
-    parser.add_argument(
+    trainer_group.add_argument(
         "--log_every_n_steps", type=int, default=25,
     )
-    parser.add_argument(
+    trainer_group.add_argument(
+        "--flush_logs_every_n_steps", type=int, default=5,
+    )
+    trainer_group.add_argument(
         "--num_sanity_val_steps", type=int, default=0,
     )
-
-    #  parser = pl.Trainer.add_argparse_args(parser)
-    #
-    #  # Disable the initial validation pass
-    #  parser.set_defaults(
-    #      num_sanity_val_steps=0,
-    #  )
-
-    #  # Remove some buggy/redundant arguments introduced by the Trainer
-    #  remove_arguments(
-    #      parser,
-    #      [
-    #          "--accelerator",
-    #          "--resume_from_checkpoint",
-    #          "--reload_dataloaders_every_epoch",
-    #          "--reload_dataloaders_every_n_epochs",
-    #      ]
-    #  )
+    trainer_group.add_argument(
+        "--reload_dataloaders_every_n_epochs", type=int, default=1,
+    )
+    trainer_group.add_argument(
+        "--accumulate_grad_batches", type=int, default=1,
+        help="Accumulate gradients over k batches before next optimizer step.")
 
     args = parser.parse_args()
 
@@ -659,7 +700,4 @@ if __name__ == "__main__":
         raise ValueError(
             "Choose between loading pretrained Jax-weights and a checkpoint-path")
 
-    # This re-applies the training-time filters at the beginning of every epoch
-    args.reload_dataloaders_every_n_epochs = 1
-
     main(args)