code/compact_v1/compact_graph_dataset.py

#!/usr/bin/env python3
"""Lazy reader for the ``gnncp_compact_v1`` graph format.

The compact format stores data that are shared by all poses of a system only
once.  A sample is reconstructed on demand with the same public PyG schema as
``build_graph_unified_enhanced.py``:

``x, edge_index, edge_attr, pos, is_protein, y_true, y_pred, y_grt``.

Nothing in this module changes the model-facing feature dimensions.  Node
features are reconstructed as float32 [N, 82] and edge features as float32
[E, 4].
"""

from __future__ import annotations

import bisect
import json
from collections import OrderedDict
from pathlib import Path
from typing import Any, Dict, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union

import torch
from torch.utils.data import Dataset
from torch_geometric.data import Data


FORMAT_NAME = "gnncp_compact_v1"
SCHEMA_VERSION = 1
STATIC_WIDTH = 44
DYNAMIC_WIDTH = 38
NODE_WIDTH = 82
EDGE_WIDTH = 4


class CompactFormatError(RuntimeError):
    """Raised when a compact dataset does not satisfy the v1 contract."""


def _as_edge_matrix(value: torch.Tensor, name: str) -> torch.Tensor:
    """Return an edge tensor as [2, E] without materialising when possible."""
    if value.ndim != 2:
        raise CompactFormatError(f"{name} must be rank 2, got shape={tuple(value.shape)}")
    if value.shape[0] == 2:
        return value
    if value.shape[1] == 2:
        return value.t()
    raise CompactFormatError(f"{name} must have shape [2,E] or [E,2], got {tuple(value.shape)}")


def _get_shard_path(entry: Mapping[str, Any]) -> str:
    for key in ("path", "file", "filename"):
        if key in entry:
            return str(entry[key])
    raise CompactFormatError("each manifest shard needs one of: path, file, filename")


def _get_shard_graph_count(entry: Mapping[str, Any]) -> int:
    for key in ("num_graphs", "n_graphs"):
        if key in entry:
            return int(entry[key])
    raise CompactFormatError("each manifest shard needs num_graphs (or n_graphs)")


class CompactGraphDataset(Dataset):
    """Map-style, mmap-backed dataset for compact GNNCP graphs.

    Parameters
    ----------
    root:
        Compact dataset directory or its ``manifest.json`` path.
    max_cached_shards:
        Per-process LRU size.  Each shard is loaded with ``mmap=True``; keeping
        a shard in this cache does not eagerly read all tensor storage.
        DataLoader workers each maintain their own cache.
    strict:
        Check inexpensive shape/range invariants while reconstructing samples.
    """

    def __init__(
        self,
        root: Union[str, Path],
        *,
        max_cached_shards: int = 2,
        strict: bool = True,
    ) -> None:
        super().__init__()
        root = Path(root).expanduser()
        if root.is_dir():
            self.root = root.resolve()
            self.manifest_path = self.root / "manifest.json"
        else:
            self.manifest_path = root.resolve()
            self.root = self.manifest_path.parent

        if max_cached_shards < 1:
            raise ValueError("max_cached_shards must be >= 1")
        self.max_cached_shards = int(max_cached_shards)
        self.strict = bool(strict)
        self.manifest = self._read_manifest(self.manifest_path)
        self.cutoff = float(self.manifest.get("cutoff", 6.0))
        if self.cutoff <= 0:
            raise CompactFormatError(f"cutoff must be positive, got {self.cutoff}")

        raw_shards = self.manifest.get("shards")
        if not isinstance(raw_shards, list) or not raw_shards:
            raise CompactFormatError("manifest.shards must be a non-empty list")
        self.shards: List[Mapping[str, Any]] = raw_shards
        self._shard_counts = [_get_shard_graph_count(s) for s in self.shards]
        self._shard_ends: List[int] = []
        running = 0
        for count in self._shard_counts:
            if count < 0:
                raise CompactFormatError(f"negative shard graph count: {count}")
            running += count
            self._shard_ends.append(running)

        graph_map = self.manifest.get("graph_map")
        if graph_map is None:
            self._graph_map: Optional[Sequence[Any]] = None
            self._length = running
        else:
            if not isinstance(graph_map, list):
                raise CompactFormatError("manifest.graph_map must be a list")
            self._graph_map = graph_map
            self._length = len(graph_map)

        declared = self.manifest.get("num_graphs", self.manifest.get("n_graphs"))
        if declared is not None and int(declared) != self._length:
            raise CompactFormatError(
                f"manifest graph count mismatch: declared={declared}, mapped={self._length}"
            )

        # This cache must never be serialised into DataLoader workers.  Each
        # process reopens mmap-backed shards independently.
        self._cache: MutableMapping[int, Mapping[str, Any]] = OrderedDict()

    @staticmethod
    def _read_manifest(path: Path) -> Dict[str, Any]:
        if not path.is_file():
            raise FileNotFoundError(f"compact manifest not found: {path}")
        with path.open("r", encoding="utf-8") as handle:
            manifest = json.load(handle)
        if not isinstance(manifest, dict):
            raise CompactFormatError("manifest root must be a JSON object")

        format_name = manifest.get("format", manifest.get("format_name"))
        if format_name != FORMAT_NAME:
            raise CompactFormatError(
                f"unsupported compact format {format_name!r}; expected {FORMAT_NAME!r}"
            )
        version = int(manifest.get("schema_version", manifest.get("version", -1)))
        if version != SCHEMA_VERSION:
            raise CompactFormatError(
                f"unsupported schema version {version}; expected {SCHEMA_VERSION}"
            )

        static_columns = manifest.get("static_columns")
        dynamic_columns = manifest.get("dynamic_columns")
        expected_static = [[0, 34], [61, 71]]
        expected_dynamic = [[34, 61], [71, 82]]
        if static_columns is not None and static_columns != expected_static:
            raise CompactFormatError(
                f"unexpected static_columns={static_columns}; expected {expected_static}"
            )
        if dynamic_columns is not None and dynamic_columns != expected_dynamic:
            raise CompactFormatError(
                f"unexpected dynamic_columns={dynamic_columns}; expected {expected_dynamic}"
            )
        return manifest

    def __len__(self) -> int:
        return self._length

    def __getstate__(self) -> Dict[str, Any]:
        state = dict(self.__dict__)
        state["_cache"] = OrderedDict()
        return state

    def _resolve_index(self, index: int) -> Tuple[int, int]:
        if not isinstance(index, int):
            try:
                index = int(index)
            except (TypeError, ValueError) as exc:
                raise TypeError(f"graph index must be an integer, got {type(index)!r}") from exc
        if index < 0:
            index += self._length
        if index < 0 or index >= self._length:
            raise IndexError(f"graph index {index} outside [0, {self._length})")

        if self._graph_map is None:
            shard_index = bisect.bisect_right(self._shard_ends, index)
            start = 0 if shard_index == 0 else self._shard_ends[shard_index - 1]
            return shard_index, index - start

        entry = self._graph_map[index]
        if isinstance(entry, Mapping):
            shard_index = entry.get("shard", entry.get("shard_index"))
            local_index = entry.get(
                "local_pose", entry.get("local_index", entry.get("graph_index"))
            )
        elif isinstance(entry, (list, tuple)) and len(entry) == 2:
            shard_index, local_index = entry
        else:
            raise CompactFormatError(
                f"graph_map[{index}] must be [shard,local_pose] or an object"
            )
        if shard_index is None or local_index is None:
            raise CompactFormatError(f"incomplete graph_map entry at index {index}: {entry}")
        shard_index = int(shard_index)
        local_index = int(local_index)
        if not 0 <= shard_index < len(self.shards):
            raise CompactFormatError(
                f"graph_map[{index}] has invalid shard index {shard_index}"
            )
        if not 0 <= local_index < self._shard_counts[shard_index]:
            raise CompactFormatError(
                f"graph_map[{index}] has invalid local pose {local_index} "
                f"for shard {shard_index}"
            )
        return shard_index, local_index

    def _load_shard(self, shard_index: int) -> Mapping[str, Any]:
        if shard_index in self._cache:
            shard = self._cache.pop(shard_index)
            self._cache[shard_index] = shard
            return shard

        relative = Path(_get_shard_path(self.shards[shard_index]))
        path = relative if relative.is_absolute() else self.root / relative
        if not path.is_file():
            raise FileNotFoundError(f"compact shard not found: {path}")
        try:
            shard = torch.load(
                path,
                map_location="cpu",
                mmap=True,
                weights_only=True,
            )
        except TypeError as exc:
            raise RuntimeError(
                "CompactGraphDataset requires a PyTorch version supporting "
                "torch.load(..., mmap=True, weights_only=True)"
            ) from exc
        if not isinstance(shard, Mapping):
            raise CompactFormatError(f"shard {path} is not a tensor dictionary")
        self._check_shard_header(shard, path, shard_index)

        self._cache[shard_index] = shard
        while len(self._cache) > self.max_cached_shards:
            self._cache.popitem(last=False)
        return shard

    def _check_shard_header(
        self,
        shard: Mapping[str, Any],
        path: Path,
        shard_index: int,
    ) -> None:
        required = {
            "schema_version",
            "system_graph_ptr",
            "pose_system",
            "source_graph_index",
            "system_node_ptr",
            "n_protein",
            "x_static",
            "protein_ptr",
            "protein_pos",
            "native_ligand_ptr",
            "native_ligand_pos",
            "pose_node_ptr",
            "x_dynamic",
            "pose_ligand_ptr",
            "ligand_pos",
            "pp_edge_ptr",
            "pp_edge_upper",
            "nonpp_edge_ptr",
            "nonpp_edge_upper",
        }
        missing = sorted(required.difference(shard))
        if missing:
            raise CompactFormatError(f"shard {path} is missing keys: {missing}")

        raw_version = shard["schema_version"]
        if torch.is_tensor(raw_version):
            if raw_version.numel() != 1:
                raise CompactFormatError(f"{path}: schema_version must contain one value")
            version = int(raw_version.reshape(-1)[0].item())
        else:
            version = int(raw_version)
        if version != SCHEMA_VERSION:
            raise CompactFormatError(f"{path}: schema_version={version}, expected 1")

        expected_graphs = self._shard_counts[shard_index]
        actual_graphs = int(shard["pose_system"].numel())
        if expected_graphs != actual_graphs:
            raise CompactFormatError(
                f"{path}: pose count={actual_graphs}, manifest says {expected_graphs}"
            )
        if int(shard["source_graph_index"].numel()) != actual_graphs:
            raise CompactFormatError(f"{path}: source_graph_index length mismatch")

        num_systems = int(shard["n_protein"].numel())
        pointer_lengths = {
            "system_graph_ptr": num_systems + 1,
            "system_node_ptr": num_systems + 1,
            "protein_ptr": num_systems + 1,
            "native_ligand_ptr": num_systems + 1,
            "pp_edge_ptr": num_systems + 1,
            "pose_node_ptr": actual_graphs + 1,
            "pose_ligand_ptr": actual_graphs + 1,
            "nonpp_edge_ptr": actual_graphs + 1,
        }
        for name, expected_length in pointer_lengths.items():
            if int(shard[name].numel()) != expected_length:
                raise CompactFormatError(
                    f"{path}: {name} length={shard[name].numel()}, "
                    f"expected {expected_length}"
                )
        if shard["x_static"].ndim != 2 or shard["x_static"].shape[1] != STATIC_WIDTH:
            raise CompactFormatError(
                f"{path}: x_static must be [sum_system_nodes,{STATIC_WIDTH}]"
            )
        if shard["x_dynamic"].ndim != 2 or shard["x_dynamic"].shape[1] != DYNAMIC_WIDTH:
            raise CompactFormatError(
                f"{path}: x_dynamic must be [sum_pose_nodes,{DYNAMIC_WIDTH}]"
            )

    @staticmethod
    def _bounds(pointer: torch.Tensor, index: int, name: str) -> Tuple[int, int]:
        start = int(pointer[index].item())
        end = int(pointer[index + 1].item())
        if start < 0 or end < start:
            raise CompactFormatError(f"invalid {name} interval [{start}, {end})")
        return start, end

    def _reconstruct_edges(
        self,
        shard: Mapping[str, Any],
        system_index: int,
        pose_index: int,
        pos: torch.Tensor,
        n_protein: int,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        pp_start, pp_end = self._bounds(shard["pp_edge_ptr"], system_index, "pp_edge_ptr")
        np_start, np_end = self._bounds(
            shard["nonpp_edge_ptr"], pose_index, "nonpp_edge_ptr"
        )
        pp_all = _as_edge_matrix(shard["pp_edge_upper"], "pp_edge_upper")
        nonpp_all = _as_edge_matrix(shard["nonpp_edge_upper"], "nonpp_edge_upper")
        pp = pp_all[:, pp_start:pp_end].to(torch.int64)
        nonpp = nonpp_all[:, np_start:np_end].to(torch.int64)
        upper = torch.cat((pp, nonpp), dim=1)

        num_nodes = int(pos.shape[0])
        if self.strict and upper.numel():
            if int(upper.min().item()) < 0 or int(upper.max().item()) >= num_nodes:
                raise CompactFormatError("edge endpoint outside graph node range")
            if not bool(torch.all(upper[0] < upper[1]).item()):
                raise CompactFormatError("compact edges must be upper triangular (src < dst)")
            if pp.numel() and int(pp.max().item()) >= n_protein:
                raise CompactFormatError("pp_edge_upper contains a ligand endpoint")
            if nonpp.numel() and not bool(
                torch.all(nonpp[1] >= n_protein).item()
            ):
                raise CompactFormatError(
                    "nonpp_edge_upper must contain at least one ligand endpoint"
                )

        if upper.shape[1] == 0:
            return (
                torch.empty((2, 0), dtype=torch.int64),
                torch.empty((0, EDGE_WIDTH), dtype=torch.float32),
            )

        # Compute the two geometric attributes once per undirected edge in
        # float64.  The legacy builder's scipy.cdist also computes distances
        # from float32 coordinates in float64 before casting edge_attr to f32.
        delta = pos[upper[0]].to(torch.float64) - pos[upper[1]].to(torch.float64)
        distance = torch.sqrt(torch.sum(delta * delta, dim=1))
        attr0 = (distance / self.cutoff).to(torch.float32)
        attr1 = torch.exp(-distance / 3.0).to(torch.float32)

        src = torch.cat((upper[0], upper[1]), dim=0)
        dst = torch.cat((upper[1], upper[0]), dim=0)
        attr0 = torch.cat((attr0, attr0), dim=0)
        attr1 = torch.cat((attr1, attr1), dim=0)

        # np.where in the legacy builder emits row-major (src,dst) order.
        # Restoring this order makes edge_index parity deterministic.
        order = torch.argsort(src * num_nodes + dst)
        src = src[order]
        dst = dst[order]
        edge_index = torch.stack((src, dst), dim=0)
        edge_attr = torch.stack(
            (
                attr0[order],
                attr1[order],
                (src < n_protein).to(torch.float32),
                (dst < n_protein).to(torch.float32),
            ),
            dim=1,
        )
        return edge_index, edge_attr

    def __getitem__(self, index: int) -> Data:
        shard_index, pose_index = self._resolve_index(index)
        shard = self._load_shard(shard_index)

        system_index = int(shard["pose_system"][pose_index].item())
        num_systems = int(shard["n_protein"].numel())
        if not 0 <= system_index < num_systems:
            raise CompactFormatError(
                f"pose {pose_index} references invalid system {system_index}"
            )
        n_protein = int(shard["n_protein"][system_index].item())

        static_start, static_end = self._bounds(
            shard["system_node_ptr"], system_index, "system_node_ptr"
        )
        dynamic_start, dynamic_end = self._bounds(
            shard["pose_node_ptr"], pose_index, "pose_node_ptr"
        )
        static = shard["x_static"][static_start:static_end].to(torch.float32)
        dynamic = shard["x_dynamic"][dynamic_start:dynamic_end].to(torch.float32)
        num_nodes = static_end - static_start
        if dynamic_end - dynamic_start != num_nodes:
            raise CompactFormatError(
                f"pose {pose_index}: static nodes={num_nodes}, "
                f"dynamic nodes={dynamic_end - dynamic_start}"
            )

        protein_start, protein_end = self._bounds(
            shard["protein_ptr"], system_index, "protein_ptr"
        )
        native_start, native_end = self._bounds(
            shard["native_ligand_ptr"], system_index, "native_ligand_ptr"
        )
        ligand_start, ligand_end = self._bounds(
            shard["pose_ligand_ptr"], pose_index, "pose_ligand_ptr"
        )
        protein_pos = shard["protein_pos"][protein_start:protein_end].to(torch.float32)
        native_ligand_pos = shard["native_ligand_pos"][native_start:native_end].to(
            torch.float32
        )
        ligand_pos = shard["ligand_pos"][ligand_start:ligand_end].to(torch.float32)

        n_ligand = num_nodes - n_protein
        if self.strict:
            coordinate_counts = {
                "protein": int(protein_pos.shape[0]),
                "native_ligand": int(native_ligand_pos.shape[0]),
                "pose_ligand": int(ligand_pos.shape[0]),
            }
            expected_counts = {
                "protein": n_protein,
                "native_ligand": n_ligand,
                "pose_ligand": n_ligand,
            }
            if coordinate_counts != expected_counts:
                raise CompactFormatError(
                    f"pose {pose_index}: coordinate counts {coordinate_counts}, "
                    f"expected {expected_counts}"
                )
            if protein_pos.ndim != 2 or protein_pos.shape[1] != 3:
                raise CompactFormatError("protein_pos must have shape [Np,3]")
            if ligand_pos.ndim != 2 or ligand_pos.shape[1] != 3:
                raise CompactFormatError("ligand_pos must have shape [Nl,3]")
            if native_ligand_pos.ndim != 2 or native_ligand_pos.shape[1] != 3:
                raise CompactFormatError("native_ligand_pos must have shape [Nl,3]")

        x = torch.empty((num_nodes, NODE_WIDTH), dtype=torch.float32)
        x[:, :34] = static[:, :34]
        x[:, 34:61] = dynamic[:, :27]
        x[:, 61:71] = static[:, 34:44]
        x[:, 71:82] = dynamic[:, 27:38]

        pos = torch.cat((protein_pos, ligand_pos), dim=0)
        y_grt = torch.cat((protein_pos, native_ligand_pos), dim=0)
        is_protein = torch.zeros((num_nodes, 1), dtype=torch.float32)
        is_protein[:n_protein] = 1.0
        y_true = torch.zeros((num_nodes, 1), dtype=torch.float32)
        ligand_error = ligand_pos - native_ligand_pos
        y_true[n_protein:, 0] = torch.sqrt(
            torch.sum(ligand_error * ligand_error, dim=1)
        )

        edge_index, edge_attr = self._reconstruct_edges(
            shard, system_index, pose_index, pos, n_protein
        )
        return Data(
            x=x,
            edge_index=edge_index,
            edge_attr=edge_attr,
            pos=pos,
            is_protein=is_protein,
            y_true=y_true,
            # y_pred intentionally aliases pos.  It has the same value contract
            # as the legacy data and avoids an unnecessary graph-local copy.
            y_pred=pos,
            y_grt=y_grt,
            num_nodes=num_nodes,
        )

    def metadata(self, index: int) -> Dict[str, Any]:
        """Return stable source/system metadata without reconstructing a graph."""
        shard_index, pose_index = self._resolve_index(index)
        shard = self._load_shard(shard_index)
        system_index = int(shard["pose_system"][pose_index].item())
        source_index = int(shard["source_graph_index"][pose_index].item())
        result: Dict[str, Any] = {
            "dataset_index": int(index),
            "source_graph_index": source_index,
            "shard_index": shard_index,
            "local_pose_index": pose_index,
            "local_system_index": system_index,
        }
        shard_manifest = self.shards[shard_index]
        system_ids = shard_manifest.get("system_ids")
        if isinstance(system_ids, list) and 0 <= system_index < len(system_ids):
            result["system_id"] = system_ids[system_index]
        else:
            systems = shard_manifest.get("systems")
            if (
                isinstance(systems, list)
                and 0 <= system_index < len(systems)
                and isinstance(systems[system_index], Mapping)
            ):
                system_metadata = systems[system_index]
                if "system_id" in system_metadata:
                    result["system_id"] = system_metadata["system_id"]
                if "source_label" in system_metadata:
                    result["source_label"] = system_metadata["source_label"]
        return result


__all__ = [
    "CompactFormatError",
    "CompactGraphDataset",
    "FORMAT_NAME",
    "SCHEMA_VERSION",
]