Merge pull request #407 from jnwei/pl_upgrades

Pytorch lightning upgrades

.github/workflows/undefined_names.yml

@@ -5,7 +5,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/setup-python@v5
       - run: pip install --upgrade pip
       - run: pip install flake8
       - run: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics

.gitignore

@@ -9,4 +9,4 @@ dist
 data
 openfold/resources/
 tests/test_data/
-cutlass
+cutlass/

Dockerfile

@@ -13,7 +13,7 @@ RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/
 
 RUN apt-get update && apt-get install -y wget libxml2 cuda-minimal-build-11-3 libcusparse-dev-11-3 libcublas-dev-11-3 libcusolver-dev-11-3 git
 RUN wget -P /tmp \
-    "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh" \
+    "https://github.com/conda-forge/miniforge/releases/download/23.3.1-1/Miniforge3-Linux-x86_64.sh" \
     && bash /tmp/Miniforge3-Linux-x86_64.sh -b -p /opt/conda \
     && rm /tmp/Miniforge3-Linux-x86_64.sh
 ENV PATH /opt/conda/bin:$PATH

README.md

@@ -351,7 +351,7 @@ python3 run_pretrained_openfold.py \
     --output_dir ./ \
     --model_device "cuda:0" \
     --config_preset "seq_model_esm1b_ptm" \
-    --openfold_checkpoint_path openfold/resources/openfold_params/seq_model_esm1b_ptm.pt \
+    --openfold_checkpoint_path openfold/resources/openfold_soloseq_params/seq_model_esm1b_ptm.pt \
     --uniref90_database_path data/uniref90/uniref90.fasta \
     --pdb70_database_path data/pdb70/pdb70 \
     --jackhmmer_binary_path lib/conda/envs/openfold_venv/bin/jackhmmer \
@@ -595,4 +595,4 @@ If you use OpenProteinSet, please also cite:
       primaryClass={q-bio.BM}
 }
 ```
-Any work that cites OpenFold should also cite AlphaFold.
+Any work that cites OpenFold should also cite [AlphaFold](https://www.nature.com/articles/s41586-021-03819-2) and [AlphaFold-Multimer](https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1) if applicable.

notebooks/environment.yml

@@ -3,15 +3,15 @@ channels:
   - conda-forge
   - bioconda
 dependencies:
-  - conda-forge::openmm=7.5.1
-  - conda-forge::pdbfixer
+  - openmm=7.7
+  - pdbfixer
+  - ml-collections 
+  - PyYAML==5.4.1
+  - requests
+  - typing-extensions
   - bioconda::hmmer==3.3.2
   - bioconda::hhsuite==3.3.0
   - bioconda::kalign2==2.04
   - pip:
       - biopython==1.79
       - dm-tree==0.1.6
-      - ml-collections==0.1.0
-      - PyYAML==5.4.1
-      - requests==2.26.0
-      - typing-extensions==3.10.0.2

openfold/data/data_pipeline.py

@@ -21,14 +21,11 @@ import dataclasses
 from multiprocessing import cpu_count
 import tempfile
 from typing import Mapping, Optional, Sequence, Any, MutableMapping, Union
-import subprocess
 import numpy as np
 import torch
-import pickle
 from openfold.data import templates, parsers, mmcif_parsing, msa_identifiers, msa_pairing, feature_processing_multimer
 from openfold.data.templates import get_custom_template_features, empty_template_feats
 from openfold.data.tools import jackhmmer, hhblits, hhsearch, hmmsearch
-from openfold.data.tools.utils import to_date
 from openfold.np import residue_constants, protein
 
 FeatureDict = MutableMapping[str, np.ndarray]
@@ -704,10 +701,10 @@ class DataPipeline:
     def _parse_msa_data(
         self,
         alignment_dir: str,
-        alignment_index: Optional[Any] = None,
+        alignment_index: Optional[Any] = None
     ) -> Mapping[str, Any]:
         msa_data = {}
-        if(alignment_index is not None):
+        if alignment_index is not None:
             fp = open(os.path.join(alignment_dir, alignment_index["db"]), "rb")
 
             def read_msa(start, size):
@@ -718,14 +715,14 @@ class DataPipeline:
             for (name, start, size) in alignment_index["files"]:
                 filename, ext = os.path.splitext(name)
 
-                if(ext == ".a3m"):
+                if ext == ".a3m":
                     msa = parsers.parse_a3m(
                         read_msa(start, size)
                     )
                 # The "hmm_output" exception is a crude way to exclude
                 # multimer template hits.
                 # Multimer "uniprot_hits" processed separately.
-                elif(ext == ".sto" and filename not in ["uniprot_hits", "hmm_output"]):
+                elif ext == ".sto" and filename not in ["uniprot_hits", "hmm_output"]:
                     msa = parsers.parse_stockholm(read_msa(start, size))
                 else:
                     continue
@@ -734,13 +731,22 @@ class DataPipeline:
 
             fp.close()
         else:
-            # Now will split the following steps into multiple processes 
-            current_directory = os.path.dirname(os.path.abspath(__file__))
-            cmd = f"{current_directory}/tools/parse_msa_files.py"
-            msa_data_path = subprocess.run(['python',cmd, f"--alignment_dir={alignment_dir}"],capture_output=True, text=True)
-            msa_data_path = msa_data_path.stdout.lstrip().rstrip()
-            msa_data = pickle.load((open(msa_data_path,'rb')))
-            os.remove(msa_data_path)
+            for f in os.listdir(alignment_dir):
+                path = os.path.join(alignment_dir, f)
+                filename, ext = os.path.splitext(f)
+
+                if ext == ".a3m":
+                    with open(path, "r") as fp:
+                        msa = parsers.parse_a3m(fp.read())
+                elif ext == ".sto" and filename not in ["uniprot_hits", "hmm_output"]:
+                    with open(path, "r") as fp:
+                        msa = parsers.parse_stockholm(
+                            fp.read()
+                        )
+                else:
+                    continue
+
+                msa_data[f] = msa
 
         return msa_data
 

openfold/data/templates.py

@@ -101,8 +101,8 @@ def empty_template_feats(n_res):
         "template_all_atom_positions": np.zeros(
             (0, n_res, residue_constants.atom_type_num, 3), np.float32
         ),
-        "template_domain_names": np.array([''.encode()], dtype=np.object),
-        "template_sequence": np.array([''.encode()], dtype=np.object),
+        "template_domain_names": np.array([''.encode()], dtype=object),
+        "template_sequence": np.array([''.encode()], dtype=object),
         "template_sum_probs": np.zeros((0, 1), dtype=np.float32),
     }
 

openfold/utils/multi_chain_permutation.py

@@ -90,15 +90,15 @@ def get_optimal_transform(
 
 def get_least_asym_entity_or_longest_length(batch, input_asym_id):
     """
-    First check how many subunit(s) one sequence has, if there is no tie, e.g. AABBB then select
-    one of the A as anchor
+    First check how many subunit(s) one sequence has. Select the subunit that is less
+    common, e.g. if the protein was AABBB then select one of the A as anchor
 
     If there is a tie, e.g. AABB, first check which sequence is the longer/longest,
     then choose one of the corresponding subunits as anchor
 
     Args:
-    batch: in this funtion batch is the full ground truth features
-    input_asym_id: A list of aym_ids that are in the cropped input features
+    batch: in this function batch is the full ground truth features
+    input_asym_id: A list of asym_ids that are in the cropped input features
 
     Return:
     anchor_gt_asym_id: Tensor(int) selected ground truth asym_id
@@ -126,7 +126,7 @@ def get_least_asym_entity_or_longest_length(batch, input_asym_id):
     min_asym_count = min(entity_asym_count.values())
     least_asym_entities = [entity for entity, count in entity_asym_count.items() if count == min_asym_count]
 
-    # If multiple entities have the least asym_id count, return those with the shortest length
+    # If multiple entities have the least asym_id count, return those with the longest length
     if len(least_asym_entities) > 1:
         max_length = max([entity_length[entity] for entity in least_asym_entities])
         least_asym_entities = [entity for entity in least_asym_entities if entity_length[entity] == max_length]

openfold/utils/script_utils.py

@@ -123,7 +123,7 @@ def parse_fasta(data):
     ][1:]
     tags, seqs = lines[::2], lines[1::2]
 
-    tags = [t.split()[0] for t in tags]
+    tags = [re.split('\W| \|', t)[0] for t in tags]
 
     return tags, seqs
 

run_pretrained_openfold.py

@@ -63,10 +63,7 @@ def precompute_alignments(tags, seqs, alignment_dir, args):
         with open(tmp_fasta_path, "w") as fp:
             fp.write(f">{tag}\n{seq}")
 
-        local_alignment_dir = os.path.join(
-            alignment_dir,
-            os.path.join(alignment_dir, tag),
-        )
+        local_alignment_dir = os.path.join(alignment_dir, tag)
 
         if args.use_precomputed_alignments is None:
             logger.info(f"Generating alignments for {tag}...")

setup.py

@@ -113,10 +113,10 @@ else:
 
 setup(
     name='openfold',
-    version='1.0.1',
+    version='2.0.0',
     description='A PyTorch reimplementation of DeepMind\'s AlphaFold 2',
-    author='Gustaf Ahdritz & DeepMind',
-    author_email='gahdritz@gmail.com',
+    author='OpenFold Team',
+    author_email='jennifer.wei@omsf.io',
     license='Apache License, Version 2.0',
     url='https://github.com/aqlaboratory/openfold',
     packages=find_packages(exclude=["tests", "scripts"]),

tests/config.py

 import ml_collections as mlc
 
-consts = mlc.ConfigDict(
+
+monomer_consts = mlc.ConfigDict(
+    {
+        "model": "model_1_ptm",  # monomer:model_1_ptm, multimer: model_1_multimer_v3
+        "is_multimer": False,  # monomer: False, multimer: True
+        "chunk_size": 4,
+        "batch_size": 2,
+        "n_res": 22,
+        "n_seq": 13,
+        "n_templ": 3,
+        "n_extra": 17,
+        "n_heads_extra_msa": 8,
+        "eps": 5e-4,
+        # For compatibility with DeepMind's pretrained weights, it's easiest for
+        # everyone if these take their real values.
+        "c_m": 256,
+        "c_z": 128,
+        "c_s": 384,
+        "c_t": 64,
+        "c_e": 64,
+        "msa_logits": 23,  # monomer: 23, multimer: 22
+        "template_mmcif_dir": None  # Set for test_multimer_datamodule
+    }
+)
+
+multimer_consts = mlc.ConfigDict(
     {
         "model": "model_1_multimer_v3",  # monomer:model_1_ptm, multimer: model_1_multimer_v3
         "is_multimer": True,  # monomer: False, multimer: True
@@ -24,6 +49,8 @@ consts = mlc.ConfigDict(
     }
 )
 
+consts = monomer_consts 
+
 config = mlc.ConfigDict(
     {
         "data": {

tests/test_deepspeed_evo_attention.py

@@ -244,9 +244,6 @@ class TestDeepSpeedKernel(unittest.TestCase):
         pair_act = np.random.rand(n_res, n_res, consts.c_z).astype(np.float32)
         pair_mask = np.random.randint(0, 2, (n_res, n_res)).astype(np.float32)
 
-        inds = np.random.randint(0, 21, (n_res,))
-        batch["target_feat"] = np.eye(22)[inds]
-
         batch = {k: torch.as_tensor(v).cuda() for k, v in batch.items()}
         template_feats = {
             k: v for k, v in batch.items() if k.startswith("template_")
@@ -309,7 +306,7 @@ class TestDeepSpeedKernel(unittest.TestCase):
         batch["residx_atom37_to_atom14"] = batch[
             "residx_atom37_to_atom14"
         ].long()
-        batch["target_feat"] = torch.nn.functional.one_hot(batch["aatype"], 21).to(torch.float32)
+        batch["target_feat"] = torch.nn.functional.one_hot(batch["aatype"], consts.msa_logits - 1).to(torch.float32)
         batch["template_all_atom_mask"] = batch["template_all_atom_masks"]
         batch.update(
             data_transforms.atom37_to_torsion_angles("template_")(batch)

tests/test_permutation.py

@@ -21,7 +21,6 @@ from openfold.utils.multi_chain_permutation import (pad_features, get_least_asym
                                                     merge_labels)
 
 
-@unittest.skip("Tests need to be fixed post-refactor")
 class TestPermutation(unittest.TestCase):
     def setUp(self):
         """
@@ -65,10 +64,16 @@ class TestPermutation(unittest.TestCase):
             'seq_length': torch.tensor([57])
         }
         anchor_gt_asym, anchor_pred_asym = get_least_asym_entity_or_longest_length(batch, batch['asym_id'])
-        self.assertIn(int(anchor_gt_asym), [1, 2])
-        self.assertNotIn(int(anchor_gt_asym), [3, 4, 5])
-        self.assertIn(int(anchor_pred_asym), [1, 2])
-        self.assertNotIn(int(anchor_pred_asym), [3, 4, 5])
+        anchor_gt_asym = int(anchor_gt_asym)
+        anchor_pred_asym = {int(i) for i in anchor_pred_asym}
+        expected_anchors = {1, 2}
+        expected_non_anchors = {3, 4, 5}
+
+        self.assertIn(anchor_gt_asym, expected_anchors) 
+        self.assertNotIn(anchor_gt_asym, expected_non_anchors)
+        # Check that predicted anchors are within expected anchor set
+        self.assertEqual(anchor_pred_asym, expected_anchors & anchor_pred_asym)
+        self.assertEqual(set(), anchor_pred_asym & expected_non_anchors) 
 
     def test_2_permutation_pentamer(self):
         batch = {
@@ -114,6 +119,7 @@ class TestPermutation(unittest.TestCase):
         self.assertIn(aligns, possible_outcome)
         self.assertNotIn(aligns, wrong_outcome)
 
+    @unittest.skip("Test needs to be fixed post-refactor")
     def test_3_merge_labels(self):
         nres_pad = 325 - 57  # suppose the cropping size is 325
         batch = {

train_openfold.py

@@ -235,6 +235,7 @@ class OpenFoldWrapper(pl.LightningModule):
 
         lr_scheduler = AlphaFoldLRScheduler(
             optimizer,
+            last_epoch=self.last_lr_step
         )
 
         return {