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import copy
from functools import partial
import json
import logging
import os
import pickle
from typing import Optional, Sequence, List, Any
import ml_collections as mlc
import numpy as np
import pytorch_lightning as pl
import torch
from torch.utils.data import RandomSampler
from openfold.data import (
data_pipeline,
feature_pipeline,
mmcif_parsing,
templates,
)
from openfold.utils.tensor_utils import tensor_tree_map, dict_multimap
class OpenFoldSingleDataset(torch.utils.data.Dataset):
def __init__(self,
data_dir: str,
alignment_dir: str,
template_mmcif_dir: str,
max_template_date: str,
config: mlc.ConfigDict,
kalign_binary_path: str = '/usr/bin/kalign',
max_template_hits: int = 4,
obsolete_pdbs_file_path: Optional[str] = None,
template_release_dates_cache_path: Optional[str] = None,
shuffle_top_k_prefiltered: Optional[int] = None,
treat_pdb_as_distillation: bool = True,
mapping_path: Optional[str] = None,
mode: str = "train",
_output_raw: bool = False,
_alignment_index: Optional[Any] = None
):
"""
Args:
data_dir:
A path to a directory containing mmCIF files (in train
mode) or FASTA files (in inference mode).
alignment_dir:
A path to a directory containing only data in the format
output by an AlignmentRunner
(defined in openfold.features.alignment_runner).
I.e. a directory of directories named {PDB_ID}_{CHAIN_ID}
or simply {PDB_ID}, each containing .a3m, .sto, and .hhr
files.
template_mmcif_dir:
Path to a directory containing template mmCIF files.
config:
A dataset config object. See openfold.config
kalign_binary_path:
Path to kalign binary.
max_template_hits:
An upper bound on how many templates are considered. During
training, the templates ultimately used are subsampled
from this total quantity.
template_release_dates_cache_path:
Path to the output of scripts/generate_mmcif_cache.
obsolete_pdbs_file_path:
Path to the file containing replacements for obsolete PDBs.
shuffle_top_k_prefiltered:
Whether to uniformly shuffle the top k template hits before
parsing max_template_hits of them. Can be used to
approximate DeepMind's training-time template subsampling
scheme much more performantly.
treat_pdb_as_distillation:
Whether to assume that .pdb files in the data_dir are from
the self-distillation set (and should be subjected to
special distillation set preprocessing steps).
mode:
"train", "val", or "predict"
"""
super(OpenFoldSingleDataset, self).__init__()
self.data_dir = data_dir
self.alignment_dir = alignment_dir
self.config = config
self.treat_pdb_as_distillation = treat_pdb_as_distillation
self.mode = mode
self._output_raw = _output_raw
self._alignment_index = _alignment_index
valid_modes = ["train", "eval", "predict"]
if(mode not in valid_modes):
raise ValueError(f'mode must be one of {valid_modes}')
if(template_release_dates_cache_path is None):
logging.warning(
"Template release dates cache does not exist. Remember to run "
"scripts/generate_mmcif_cache.py before running OpenFold"
)
if(_alignment_index is not None):
self._chain_ids = list(_alignment_index.keys())
elif(mapping_path is None):
self._chain_ids = list(os.listdir(alignment_dir))
else:
with open(mapping_path, "r") as f:
self._chain_ids = [l.strip() for l in f.readlines()]
self._chain_id_to_idx_dict = {
chain: i for i, chain in enumerate(self._chain_ids)
}
template_featurizer = templates.TemplateHitFeaturizer(
mmcif_dir=template_mmcif_dir,
max_template_date=max_template_date,
max_hits=max_template_hits,
kalign_binary_path=kalign_binary_path,
release_dates_path=template_release_dates_cache_path,
obsolete_pdbs_path=obsolete_pdbs_file_path,
_shuffle_top_k_prefiltered=shuffle_top_k_prefiltered,
)
self.data_pipeline = data_pipeline.DataPipeline(
template_featurizer=template_featurizer,
)
if(not self._output_raw):
self.feature_pipeline = feature_pipeline.FeaturePipeline(config)
def _parse_mmcif(self, path, file_id, chain_id, alignment_dir, _alignment_index):
with open(path, 'r') as f:
mmcif_string = f.read()
mmcif_object = mmcif_parsing.parse(
file_id=file_id, mmcif_string=mmcif_string
)
# Crash if an error is encountered. Any parsing errors should have
# been dealt with at the alignment stage.
if(mmcif_object.mmcif_object is None):
raise list(mmcif_object.errors.values())[0]
mmcif_object = mmcif_object.mmcif_object
data = self.data_pipeline.process_mmcif(
mmcif=mmcif_object,
alignment_dir=alignment_dir,
chain_id=chain_id,
_alignment_index=_alignment_index
)
return data
def chain_id_to_idx(self, chain_id):
return self._chain_id_to_idx_dict[chain_id]
def idx_to_chain_id(self, idx):
return self._chain_ids[idx]
def __getitem__(self, idx):
name = self.idx_to_chain_id(idx)
alignment_dir = os.path.join(self.alignment_dir, name)
_alignment_index = None
if(self._alignment_index is not None):
alignment_dir = self.alignment_dir
_alignment_index = self._alignment_index[name]
if(self.mode == 'train' or self.mode == 'eval'):
spl = name.rsplit('_', 1)
if(len(spl) == 2):
file_id, chain_id = spl
else:
file_id, = spl
chain_id = None
path = os.path.join(self.data_dir, file_id)
if(os.path.exists(path + ".cif")):
data = self._parse_mmcif(
path + ".cif", file_id, chain_id, alignment_dir, _alignment_index,
)
elif(os.path.exists(path + ".core")):
data = self.data_pipeline.process_core(
path + ".core", alignment_dir, _alignment_index,
)
elif(os.path.exists(path + ".pdb")):
data = self.data_pipeline.process_pdb(
pdb_path=path + ".pdb",
alignment_dir=alignment_dir,
is_distillation=self.treat_pdb_as_distillation,
chain_id=chain_id,
_alignment_index=_alignment_index,
)
else:
raise ValueError("Invalid file type")
else:
path = os.path.join(name, name + ".fasta")
data = self.data_pipeline.process_fasta(
fasta_path=path,
alignment_dir=alignment_dir,
_alignment_index=_alignment_index,
)
if(self._output_raw):
return data
feats = self.feature_pipeline.process_features(
data, self.mode
)
return feats
def __len__(self):
return len(self._chain_ids)
def deterministic_train_filter(
chain_data_cache_entry: Any,
max_resolution: float = 9.,
max_single_aa_prop: float = 0.8,
) -> bool:
# Hard filters
resolution = chain_data_cache_entry.get("resolution", None)
if(resolution is not None and resolution > max_resolution):
return False
seq = chain_data_cache_entry["seq"]
counts = {}
for aa in seq:
counts.setdefault(aa, 0)
counts[aa] += 1
largest_aa_count = max(counts.values())
largest_single_aa_prop = largest_aa_count / len(seq)
if(largest_single_aa_prop > max_single_aa_prop):
return False
return True
def get_stochastic_train_filter_prob(
chain_data_cache_entry: Any,
) -> List[float]:
# Stochastic filters
probabilities = []
cluster_size = chain_data_cache_entry.get("cluster_size", None)
if(cluster_size is not None and cluster_size > 0):
probabilities.append(1 / cluster_size)
chain_length = len(chain_data_cache_entry["seq"])
probabilities.append((1 / 512) * (max(min(chain_length, 512), 256)))
# Risk of underflow here?
out = 1
for p in probabilities:
out *= p
return out
class OpenFoldDataset(torch.utils.data.Dataset):
"""
Implements the stochastic filters applied during AlphaFold's training.
Because samples are selected from constituent datasets randomly, the
length of an OpenFoldFilteredDataset is arbitrary. Samples are selected
and filtered once at initialization.
"""
def __init__(self,
datasets: Sequence[OpenFoldSingleDataset],
probabilities: Sequence[int],
epoch_len: int,
chain_data_cache_paths: List[str],
generator: torch.Generator = None,
_roll_at_init: bool = True,
):
self.datasets = datasets
self.probabilities = probabilities
self.epoch_len = epoch_len
self.generator = generator
self.chain_data_caches = []
for path in chain_data_cache_paths:
with open(path, "r") as fp:
self.chain_data_caches.append(json.load(fp))
def looped_shuffled_dataset_idx(dataset_len):
while True:
# Uniformly shuffle each dataset's indices
weights = [1. for _ in range(dataset_len)]
shuf = torch.multinomial(
torch.tensor(weights),
num_samples=dataset_len,
replacement=False,
generator=self.generator,
)
for idx in shuf:
yield idx
def looped_samples(dataset_idx):
max_cache_len = int(epoch_len * probabilities[dataset_idx])
dataset = self.datasets[dataset_idx]
idx_iter = looped_shuffled_dataset_idx(len(dataset))
chain_data_cache = self.chain_data_caches[dataset_idx]
while True:
weights = []
idx = []
for _ in range(max_cache_len):
candidate_idx = next(idx_iter)
chain_id = dataset.idx_to_chain_id(candidate_idx)
chain_data_cache_entry = chain_data_cache[chain_id]
if(not deterministic_train_filter(chain_data_cache_entry)):
continue
p = get_stochastic_train_filter_prob(
chain_data_cache_entry,
)
weights.append([1. - p, p])
idx.append(candidate_idx)
samples = torch.multinomial(
torch.tensor(weights),
num_samples=1,
generator=self.generator,
)
samples = samples.squeeze()
cache = [i for i, s in zip(idx, samples) if s]
for datapoint_idx in cache:
yield datapoint_idx
self._samples = [looped_samples(i) for i in range(len(self.datasets))]
if(_roll_at_init):
self.reroll()
def __getitem__(self, idx):
dataset_idx, datapoint_idx = self.datapoints[idx]
return self.datasets[dataset_idx][datapoint_idx]
def __len__(self):
return self.epoch_len
def reroll(self):
dataset_choices = torch.multinomial(
torch.tensor(self.probabilities),
num_samples=self.epoch_len,
replacement=True,
generator=self.generator,
)
self.datapoints = []
for dataset_idx in dataset_choices:
samples = self._samples[dataset_idx]
datapoint_idx = next(samples)
self.datapoints.append((dataset_idx, datapoint_idx))
class OpenFoldBatchCollator:
def __init__(self, config, stage="train"):
self.stage = stage
self.feature_pipeline = feature_pipeline.FeaturePipeline(config)
def __call__(self, raw_prots):
processed_prots = []
for prot in raw_prots:
features = self.feature_pipeline.process_features(
prot, self.stage
)
processed_prots.append(features)
stack_fn = partial(torch.stack, dim=0)
return dict_multimap(stack_fn, processed_prots)
class OpenFoldDataLoader(torch.utils.data.DataLoader):
def __init__(self, *args, config, stage="train", generator=None, **kwargs):
super().__init__(*args, **kwargs)
self.config = config
self.stage = stage
if(generator is None):
generator = torch.Generator()
self.generator = generator
self._prep_batch_properties_probs()
def _prep_batch_properties_probs(self):
keyed_probs = []
stage_cfg = self.config[self.stage]
max_iters = self.config.common.max_recycling_iters
if(stage_cfg.supervised):
clamp_prob = self.config.supervised.clamp_prob
keyed_probs.append(
("use_clamped_fape", [1 - clamp_prob, clamp_prob])
)
if(stage_cfg.uniform_recycling):
recycling_probs = [
1. / (max_iters + 1) for _ in range(max_iters + 1)
]
else:
recycling_probs = [
0. for _ in range(max_iters + 1)
]
recycling_probs[-1] = 1.
keyed_probs.append(
("no_recycling_iters", recycling_probs)
)
keys, probs = zip(*keyed_probs)
max_len = max([len(p) for p in probs])
padding = [[0.] * (max_len - len(p)) for p in probs]
self.prop_keys = keys
self.prop_probs_tensor = torch.tensor(
[p + pad for p, pad in zip(probs, padding)],
dtype=torch.float32,
)
def _add_batch_properties(self, batch):
samples = torch.multinomial(
self.prop_probs_tensor,
num_samples=1, # 1 per row
replacement=True,
generator=self.generator
)
aatype = batch["aatype"]
batch_dims = aatype.shape[:-2]
recycling_dim = aatype.shape[-1]
no_recycling = recycling_dim
for i, key in enumerate(self.prop_keys):
sample = int(samples[i][0])
sample_tensor = torch.tensor(
sample,
device=aatype.device,
requires_grad=False
)
orig_shape = sample_tensor.shape
sample_tensor = sample_tensor.view(
(1,) * len(batch_dims) + sample_tensor.shape + (1,)
)
sample_tensor = sample_tensor.expand(
batch_dims + orig_shape + (recycling_dim,)
)
batch[key] = sample_tensor
if(key == "no_recycling_iters"):
no_recycling = sample
resample_recycling = lambda t: t[..., :no_recycling + 1]
batch = tensor_tree_map(resample_recycling, batch)
return batch
def __iter__(self):
it = super().__iter__()
def _batch_prop_gen(iterator):
for batch in iterator:
yield self._add_batch_properties(batch)
return _batch_prop_gen(it)
class OpenFoldDataModule(pl.LightningDataModule):
def __init__(self,
config: mlc.ConfigDict,
template_mmcif_dir: str,
max_template_date: str,
train_data_dir: Optional[str] = None,
train_alignment_dir: Optional[str] = None,
train_chain_data_cache_path: Optional[str] = None,
distillation_data_dir: Optional[str] = None,
distillation_alignment_dir: Optional[str] = None,
distillation_chain_data_cache_path: Optional[str] = None,
val_data_dir: Optional[str] = None,
val_alignment_dir: Optional[str] = None,
predict_data_dir: Optional[str] = None,
predict_alignment_dir: Optional[str] = None,
kalign_binary_path: str = '/usr/bin/kalign',
train_mapping_path: Optional[str] = None,
distillation_mapping_path: Optional[str] = None,
obsolete_pdbs_file_path: Optional[str] = None,
template_release_dates_cache_path: Optional[str] = None,
batch_seed: Optional[int] = None,
train_epoch_len: int = 50000,
_alignment_index_path: Optional[str] = None,
**kwargs
):
super(OpenFoldDataModule, self).__init__()
self.config = config
self.template_mmcif_dir = template_mmcif_dir
self.max_template_date = max_template_date
self.train_data_dir = train_data_dir
self.train_alignment_dir = train_alignment_dir
self.train_chain_data_cache_path = train_chain_data_cache_path
self.distillation_data_dir = distillation_data_dir
self.distillation_alignment_dir = distillation_alignment_dir
self.distillation_chain_data_cache_path = (
distillation_chain_data_cache_path
)
self.val_data_dir = val_data_dir
self.val_alignment_dir = val_alignment_dir
self.predict_data_dir = predict_data_dir
self.predict_alignment_dir = predict_alignment_dir
self.kalign_binary_path = kalign_binary_path
self.train_mapping_path = train_mapping_path
self.distillation_mapping_path = distillation_mapping_path
self.template_release_dates_cache_path = (
template_release_dates_cache_path
)
self.obsolete_pdbs_file_path = obsolete_pdbs_file_path
self.batch_seed = batch_seed
self.train_epoch_len = train_epoch_len
if(self.train_data_dir is None and self.predict_data_dir is None):
raise ValueError(
'At least one of train_data_dir or predict_data_dir must be '
'specified'
)
self.training_mode = self.train_data_dir is not None
if(self.training_mode and train_alignment_dir is None):
raise ValueError(
'In training mode, train_alignment_dir must be specified'
)
elif(not self.training_mode and predict_alignment_dir is None):
raise ValueError(
'In inference mode, predict_alignment_dir must be specified'
)
elif(val_data_dir is not None and val_alignment_dir is None):
raise ValueError(
'If val_data_dir is specified, val_alignment_dir must '
'be specified as well'
)
# An ad-hoc measure for our particular filesystem restrictions
self._alignment_index = None
if(_alignment_index_path is not None):
with open(_alignment_index_path, "r") as fp:
self._alignment_index = json.load(fp)
def setup(self):
# Most of the arguments are the same for the three datasets
dataset_gen = partial(OpenFoldSingleDataset,
template_mmcif_dir=self.template_mmcif_dir,
max_template_date=self.max_template_date,
config=self.config,
kalign_binary_path=self.kalign_binary_path,
template_release_dates_cache_path=
self.template_release_dates_cache_path,
obsolete_pdbs_file_path=
self.obsolete_pdbs_file_path,
)
if(self.training_mode):
train_dataset = dataset_gen(
data_dir=self.train_data_dir,
alignment_dir=self.train_alignment_dir,
mapping_path=self.train_mapping_path,
max_template_hits=self.config.train.max_template_hits,
shuffle_top_k_prefiltered=
self.config.train.shuffle_top_k_prefiltered,
treat_pdb_as_distillation=False,
mode="train",
_output_raw=True,
_alignment_index=self._alignment_index,
)
distillation_dataset = None
if(self.distillation_data_dir is not None):
distillation_dataset = dataset_gen(
data_dir=self.distillation_data_dir,
alignment_dir=self.distillation_alignment_dir,
mapping_path=self.distillation_mapping_path,
max_template_hits=self.train.max_template_hits,
treat_pdb_as_distillation=True,
mode="train",
_output_raw=True,
)
d_prob = self.config.train.distillation_prob
if(distillation_dataset is not None):
datasets = [train_dataset, distillation_dataset]
d_prob = self.config.train.distillation_prob
probabilities = [1 - d_prob, d_prob]
chain_data_cache_paths = [
self.train_chain_data_cache_path,
self.distillation_chain_data_cache_path,
]
else:
datasets = [train_dataset]
probabilities = [1.]
chain_data_cache_paths = [
self.train_chain_data_cache_path,
]
self.train_dataset = OpenFoldDataset(
datasets=datasets,
probabilities=probabilities,
epoch_len=self.train_epoch_len,
chain_data_cache_paths=chain_data_cache_paths,
_roll_at_init=False,
)
if(self.val_data_dir is not None):
self.eval_dataset = dataset_gen(
data_dir=self.val_data_dir,
alignment_dir=self.val_alignment_dir,
mapping_path=None,
max_template_hits=self.config.eval.max_template_hits,
mode="eval",
_output_raw=True,
)
else:
self.eval_dataset = None
else:
self.predict_dataset = dataset_gen(
data_dir=self.predict_data_dir,
alignment_dir=self.predict_alignment_dir,
mapping_path=None,
max_template_hits=self.config.predict.max_template_hits,
mode="predict",
)
def _gen_dataloader(self, stage):
generator = torch.Generator()
if(self.batch_seed is not None):
generator = generator.manual_seed(self.batch_seed)
dataset = None
if(stage == "train"):
dataset = self.train_dataset
# Filter the dataset, if necessary
dataset.reroll()
elif(stage == "eval"):
dataset = self.eval_dataset
elif(stage == "predict"):
dataset = self.predict_dataset
else:
raise ValueError("Invalid stage")
batch_collator = OpenFoldBatchCollator(self.config, stage)
dl = OpenFoldDataLoader(
dataset,
config=self.config,
stage=stage,
generator=generator,
batch_size=self.config.data_module.data_loaders.batch_size,
num_workers=self.config.data_module.data_loaders.num_workers,
collate_fn=batch_collator,
)
return dl
def train_dataloader(self):
return self._gen_dataloader("train")
def val_dataloader(self):
if(self.eval_dataset is not None):
return self._gen_dataloader("eval")
return None
def predict_dataloader(self):
return self._gen_dataloader("predict")
class DummyDataset(torch.utils.data.Dataset):
def __init__(self, batch_path):
with open(batch_path, "rb") as f:
self.batch = pickle.load(f)
def __getitem__(self, idx):
return copy.deepcopy(self.batch)
def __len__(self):
return 1000
class DummyDataLoader(pl.LightningDataModule):
def __init__(self, batch_path):
super().__init__()
self.dataset = DummyDataset(batch_path)
def train_dataloader(self):
return torch.utils.data.DataLoader(self.dataset)
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