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The helpers in this module are deliberately free of model state. Importing the
module therefore performs no device selection, compilation, or remote access.
"""
from __future__ import annotations
from collections import defaultdict
from collections.abc import Generator, Iterable, Sequence
from contextlib import AbstractContextManager, nullcontext
from dataclasses import is_dataclass
from io import BytesIO
from typing import Any, Protocol, TypeVar, runtime_checkable
from warnings import warn
import numpy as np
import torch
import zstandard
from .esmfold2_constants_esm3 import CHAIN_BREAK_STR
from .esmfold2_utils_types import FunctionAnnotation
MAX_SUPPORTED_DISTANCE = 1e6
TSequence = TypeVar("TSequence", bound=Sequence)
@runtime_checkable
class Concatable(Protocol):
"""Protocol for sequence-like records with a class-level concatenator."""
@classmethod
def concat(cls, objs: list[Concatable]) -> Concatable: ...
def fp32_autocast_context(
device_type: str,
) -> AbstractContextManager[Any]: # type: ignore
"""Return a context that keeps numerically sensitive work in FP32."""
if device_type == "mps":
return nullcontext()
if device_type == "cpu":
return torch.amp.autocast(device_type, enabled=False) # type: ignore
if device_type == "cuda":
return torch.amp.autocast(device_type, dtype=torch.float32) # type: ignore
raise ValueError(f"Unsupported device type: {device_type}")
def maybe_tensor(value, convert_none_to_nan: bool = False) -> torch.Tensor | None:
"""Convert an optional array-like value to a tensor."""
if value is None:
return None
if isinstance(value, torch.Tensor):
return value
if isinstance(value, list) and all(isinstance(element, torch.Tensor) for element in value):
return torch.stack(value)
if convert_none_to_nan:
value = np.asarray(value, dtype=np.float32)
value = np.where(value is None, np.nan, value)
return torch.tensor(value)
def maybe_list(value, convert_nan_to_none: bool = False) -> list | None:
"""Convert an optional tensor or NumPy array to nested Python lists."""
if value is None:
return None
if not convert_nan_to_none:
return value.tolist()
if isinstance(value, torch.Tensor):
nan_mask = torch.isnan(value).cpu().numpy()
array = value.cpu().numpy().astype(object)
elif isinstance(value, np.ndarray):
nan_mask = np.isnan(value)
array = value.astype(object)
else:
raise TypeError("maybe_list can only work with torch.tensor or np.ndarray.")
array[nan_mask] = None
return array.tolist()
def replace_inf(data):
"""Replace infinite array values by the ESM API sentinel value."""
if data is None:
return None
array = np.asarray(data, dtype=np.float32)
return np.where(np.isinf(array), 1000, array).tolist()
def slice_python_object_as_numpy(
obj: TSequence,
idx: int | list[int] | slice | np.ndarray,
) -> TSequence:
"""Apply NumPy-style scalar, mask, or index-array slicing to Python data."""
normalized_idx: list[int] | slice | np.ndarray = (
[int(idx)] if np.isscalar(idx) else idx # type: ignore[arg-type]
)
if isinstance(normalized_idx, np.ndarray) and normalized_idx.dtype == bool:
selected = [obj[position] for position in np.flatnonzero(normalized_idx)]
elif isinstance(normalized_idx, slice):
selected = obj[normalized_idx]
else:
selected = [obj[position] for position in normalized_idx]
if isinstance(obj, str) and isinstance(selected, list):
return "".join(selected) # type: ignore[return-value]
return obj.__class__(selected) # type: ignore[call-arg,return-value]
def slice_any_object(
obj: TSequence,
idx: int | list[int] | slice | np.ndarray,
) -> TSequence:
"""Slice tensors, arrays, dataclasses, and ordinary Python sequences."""
if isinstance(obj, (np.ndarray, torch.Tensor)) or is_dataclass(obj):
return obj[idx] # type: ignore[index,return-value]
return slice_python_object_as_numpy(obj, idx)
def join_lists(
lists: Sequence[Sequence[Any]],
separator: Sequence[Any] | None = None,
) -> list[Any]:
"""Join lists, inserting all elements of ``separator`` between inputs."""
if len(lists) == 0:
return []
joined = list(lists[0])
for values in lists[1:]:
if separator:
joined.extend(separator)
joined.extend(values)
return joined
def iterate_with_intermediate(
lists: Iterable,
intermediate,
) -> Generator[Any, None, None]:
"""Yield an intermediate value between consecutive input values."""
iterator = iter(lists)
yield next(iterator)
for value in iterator:
yield intermediate
yield value
def concat_objects(objs: Sequence[Any], separator: Any | None = None):
"""Concatenate one supported homogeneous collection."""
if not objs:
raise ValueError("objs must contain at least one value.")
first = objs[0]
if isinstance(first, Concatable):
return first.__class__.concat(objs)
if isinstance(first, str):
if not isinstance(separator, str):
raise TypeError("separator must be a string when joining strings.")
return separator.join(objs)
if isinstance(first, list):
return join_lists(objs, None if separator is None else [separator])
if isinstance(first, np.ndarray):
pieces = (
objs
if separator is None
else list(iterate_with_intermediate(objs, np.array([separator])))
)
return np.concatenate(pieces)
if isinstance(first, torch.Tensor):
pieces = (
objs
if separator is None
else list(iterate_with_intermediate(objs, torch.tensor([separator])))
)
return torch.cat(pieces) # type: ignore[arg-type]
raise TypeError(type(first))
def rbf(values, v_min, v_max, n_bins=16):
"""Encode values against evenly spaced radial basis centers."""
centers = torch.linspace(
v_min,
v_max,
n_bins,
dtype=values.dtype,
device=values.device,
)
centers = centers.reshape((1,) * values.ndim + (-1,))
standardized = (values.unsqueeze(-1) - centers) / ((v_max - v_min) / n_bins)
return torch.exp(-(standardized**2))
def batched_gather(data, inds, dim=0, no_batch_dims=0):
"""Gather along one data dimension while retaining leading batch axes."""
batch_indices = []
index_rank = len(inds.shape)
for axis, size in enumerate(data.shape[:no_batch_dims]):
shape = (1,) * axis + (-1,) + (1,) * (index_rank - axis - 1)
batch_indices.append(torch.arange(size).view(*shape))
tail = [slice(None)] * (len(data.shape) - no_batch_dims)
tail[dim - no_batch_dims if dim >= 0 else dim] = inds
return data[tuple(batch_indices + tail)]
def node_gather(s: torch.Tensor, edges: torch.Tensor) -> torch.Tensor:
"""Gather node features for each row of an edge-index tensor."""
return batched_gather(
s.unsqueeze(-3),
edges,
-2,
no_batch_dims=len(s.shape) - 1,
)
def knn_graph(
coords: torch.Tensor,
coord_mask: torch.Tensor,
padding_mask: torch.Tensor,
sequence_id: torch.Tensor,
*,
no_knn: int,
):
"""Build nearest-neighbor edges, using sequence distance for missing geometry."""
length = coords.shape[-2]
coords = coords.nan_to_num()
missing_pair = ~(coord_mask[..., None, :] & coord_mask[..., :, None])
excluded_pair = padding_mask[..., None, :] | padding_mask[..., :, None]
if sequence_id is not None:
excluded_pair |= sequence_id.unsqueeze(1) != sequence_id.unsqueeze(2)
distances = (coords.unsqueeze(-2) - coords.unsqueeze(-3)).norm(dim=-1)
residue_index = torch.arange(length, device=coords.device)
sequence_distance = (residue_index.unsqueeze(-1) - residue_index.unsqueeze(-2)).abs()
if not (distances[~missing_pair] < MAX_SUPPORTED_DISTANCE).all():
raise ValueError(
"Coordinate pairwise distances exceed max supported distance "
f"({MAX_SUPPORTED_DISTANCE}). "
)
rank_distance = sequence_distance.to(distances.dtype).mul(1e2).add(MAX_SUPPORTED_DISTANCE)
rank_distance = rank_distance.where(missing_pair, distances)
rank_distance = rank_distance.masked_fill(excluded_pair, torch.inf)
sorted_distance, sorted_edge = rank_distance.sort(dim=-1, descending=False)
width = min(no_knn, length)
return sorted_edge[..., :width], sorted_distance[..., :width].isfinite()
def stack_variable_length_tensors(
sequences: Sequence[torch.Tensor],
constant_value: int | float = 0,
dtype: torch.dtype | None = None,
) -> torch.Tensor:
"""Pad arbitrary tensor dimensions to their maxima, then stack."""
output_shape = [
len(sequences),
*np.max([sequence.shape for sequence in sequences], axis=0).tolist(),
]
output = torch.full(
output_shape,
constant_value,
dtype=sequences[0].dtype if dtype is None else dtype,
device=sequences[0].device,
)
for destination, source in zip(output, sequences, strict=True):
destination[tuple(slice(size) for size in source.shape)] = source
return output
def binpack(
tensor: torch.Tensor,
sequence_id: torch.Tensor | None,
pad_value: int | float,
):
"""Scatter a sequence-major tensor into the packed layout described by IDs."""
if sequence_id is None:
return tensor
sequence_counts = sequence_id.max(dim=-1).values + 1
output = torch.full(
sequence_id.shape + tensor.shape[2:],
fill_value=pad_value,
dtype=tensor.dtype,
device=tensor.device,
)
source_index = 0
for batch_index, (batch_ids, count) in enumerate(
zip(sequence_id, sequence_counts, strict=True)
):
for seqid in range(count):
selection = batch_ids == seqid
output[batch_index, selection] = tensor[source_index, : selection.sum()]
source_index += 1
return output
def unbinpack(
tensor: torch.Tensor,
sequence_id: torch.Tensor | None,
pad_value: int | float,
):
"""Restore sequence-major rows from a packed tensor and its sequence IDs."""
if sequence_id is None:
return tensor
rows = []
sequence_counts = sequence_id.max(dim=-1).values + 1
for batch_index, (batch_ids, count) in enumerate(
zip(sequence_id, sequence_counts, strict=True)
):
for seqid in range(count):
rows.append(tensor[batch_index, batch_ids == seqid])
return stack_variable_length_tensors(rows, pad_value)
def merge_ranges(
ranges: list[range],
merge_gap_max: int | None = None,
) -> list[range]:
"""Merge overlapping or sufficiently close ranges in positional order."""
maximum_gap = 0 if merge_gap_max is None else merge_gap_max
if not isinstance(maximum_gap, int) or isinstance(maximum_gap, bool):
raise TypeError("merge_gap_max must be an integer or None.")
if maximum_gap < 0:
raise ValueError(f"merge_gap_max must be non-negative, got {maximum_gap}.")
merged: list[range] = []
for current in sorted(ranges, key=lambda item: item.start):
if not merged or merged[-1].stop + maximum_gap < current.start:
merged.append(current)
continue
previous = merged[-1]
merged[-1] = range(previous.start, max(previous.stop, current.stop))
return merged
def merge_annotations(
annotations: list[FunctionAnnotation],
merge_gap_max: int | None = None,
) -> list[FunctionAnnotation]:
"""Merge overlapping annotations independently for each label."""
grouped: dict[str, list[range]] = defaultdict(list)
for annotation in annotations:
grouped[annotation.label].append(range(annotation.start, annotation.end + 1))
result = []
for label, spans in grouped.items():
result.extend(
FunctionAnnotation(label=label, start=span.start, end=span.stop - 1)
for span in merge_ranges(spans, merge_gap_max=merge_gap_max)
)
return result
def get_chainbreak_boundaries_from_sequence(
sequence: Sequence[str],
) -> np.ndarray:
"""Return half-open chain intervals split by chain-break tokens."""
boundaries = [0]
final_index = len(sequence) - 1
for index, residue in enumerate(sequence):
if residue != CHAIN_BREAK_STR:
continue
if index == final_index:
raise ValueError(
"Encountered chain break token at end of sequence, this is unexpected."
)
if index == final_index - 1:
warn(
"Encountered chain break token at penultimate position, this is unexpected.",
stacklevel=2,
)
boundaries.extend((index, index + 1))
boundaries.append(len(sequence))
assert len(boundaries) % 2 == 0
return np.asarray(boundaries).reshape(-1, 2)
def deserialize_tensors(data: bytes) -> Any:
"""Decompress a tensor-only Torch payload onto CPU."""
decompressed = zstandard.ZstdDecompressor().decompress(data)
return torch.load(
BytesIO(decompressed),
map_location="cpu",
weights_only=True,
)
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