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"""Sample-level validation for a GNNCP compact graph dataset.
This program never iterates the full legacy dataset. When ``--legacy`` is
provided it opens the old monolithic .pt with ``torch.load(..., mmap=True)``
and touches only the requested sample tensors.
"""
from __future__ import annotations
import argparse
import json
import random
import resource
import sys
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional, Sequence
import torch
from compact_graph_dataset import CompactGraphDataset
CORE_FIELDS = (
"x",
"edge_index",
"edge_attr",
"pos",
"is_protein",
"y_true",
"y_pred",
"y_grt",
)
FLOAT_FIELDS = {
"x",
"edge_attr",
"pos",
"is_protein",
"y_true",
"y_pred",
"y_grt",
}
def _rss_mib() -> float:
value = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
# Linux reports KiB; macOS reports bytes.
if sys.platform == "darwin":
return value / (1024.0 * 1024.0)
return value / 1024.0
def _parse_indices(text: Optional[str], length: int) -> Optional[List[int]]:
if text is None:
return None
values: List[int] = []
for token in text.split(","):
token = token.strip()
if not token:
continue
if ":" in token:
parts = token.split(":")
if len(parts) not in (2, 3):
raise ValueError(f"bad index range: {token!r}")
start = int(parts[0]) if parts[0] else 0
stop = int(parts[1]) if parts[1] else length
step = int(parts[2]) if len(parts) == 3 and parts[2] else 1
values.extend(range(start, stop, step))
else:
values.append(int(token))
normalised = []
for index in values:
if index < 0:
index += length
if not 0 <= index < length:
raise IndexError(f"sample index {index} outside [0,{length})")
normalised.append(index)
return list(dict.fromkeys(normalised))
def _choose_indices(length: int, count: int, seed: int) -> List[int]:
if length <= 0:
return []
count = min(max(int(count), 1), length)
selected = {0, length - 1}
rng = random.Random(seed)
while len(selected) < count:
selected.add(rng.randrange(length))
return sorted(selected)[:count]
def _tensor_stats(
actual: torch.Tensor,
expected: torch.Tensor,
*,
atol: float,
rtol: float,
) -> Dict[str, Any]:
result: Dict[str, Any] = {
"actual_shape": list(actual.shape),
"expected_shape": list(expected.shape),
"actual_dtype": str(actual.dtype),
"expected_dtype": str(expected.dtype),
}
if tuple(actual.shape) != tuple(expected.shape):
result.update({"passed": False, "reason": "shape_mismatch"})
return result
if actual.dtype != expected.dtype:
result["dtype_match"] = False
else:
result["dtype_match"] = True
if actual.numel() == 0:
result.update(
{
"passed": bool(result["dtype_match"]),
"exact": True,
"max_abs": 0.0,
"mean_abs": 0.0,
}
)
return result
if actual.is_floating_point() or expected.is_floating_point():
actual_f64 = actual.to(torch.float64)
expected_f64 = expected.to(torch.float64)
finite_match = torch.equal(torch.isfinite(actual_f64), torch.isfinite(expected_f64))
diff = torch.abs(actual_f64 - expected_f64)
finite_diff = diff[torch.isfinite(diff)]
max_abs = float(finite_diff.max().item()) if finite_diff.numel() else float("inf")
mean_abs = float(finite_diff.mean().item()) if finite_diff.numel() else float("inf")
close = bool(
torch.allclose(actual_f64, expected_f64, atol=atol, rtol=rtol, equal_nan=True)
)
result.update(
{
"passed": bool(close and result["dtype_match"] and finite_match),
"exact": bool(torch.equal(actual, expected)),
"finite_pattern_match": finite_match,
"max_abs": max_abs,
"mean_abs": mean_abs,
}
)
else:
exact = bool(torch.equal(actual, expected))
result.update(
{
"passed": bool(exact and result["dtype_match"]),
"exact": exact,
}
)
return result
def _invariants(graph: Any, cutoff: float, atol: float) -> Dict[str, Any]:
checks: Dict[str, bool] = {}
n = int(graph.num_nodes)
checks["x_Nx82"] = tuple(graph.x.shape) == (n, 82)
checks["edge_index_2xE"] = graph.edge_index.ndim == 2 and graph.edge_index.shape[0] == 2
edge_count = int(graph.edge_index.shape[1]) if checks["edge_index_2xE"] else -1
checks["edge_attr_Ex4"] = tuple(graph.edge_attr.shape) == (edge_count, 4)
checks["pos_Nx3"] = tuple(graph.pos.shape) == (n, 3)
checks["is_protein_Nx1"] = tuple(graph.is_protein.shape) == (n, 1)
checks["y_true_Nx1"] = tuple(graph.y_true.shape) == (n, 1)
checks["y_pred_Nx3"] = tuple(graph.y_pred.shape) == (n, 3)
checks["y_grt_Nx3"] = tuple(graph.y_grt.shape) == (n, 3)
checks["x_float32"] = graph.x.dtype == torch.float32
checks["edge_index_int64"] = graph.edge_index.dtype == torch.int64
checks["edge_attr_float32"] = graph.edge_attr.dtype == torch.float32
checks["coordinates_float32"] = (
graph.pos.dtype == graph.y_pred.dtype == graph.y_grt.dtype == torch.float32
)
checks["pos_equals_y_pred"] = bool(torch.equal(graph.pos, graph.y_pred))
if edge_count >= 0 and graph.edge_index.numel():
src, dst = graph.edge_index
checks["edge_bounds"] = bool(
(src.min() >= 0)
and (dst.min() >= 0)
and (src.max() < n)
and (dst.max() < n)
)
checks["no_self_edges"] = bool(torch.all(src != dst).item())
key = src * n + dst
checks["legacy_edge_order"] = bool(torch.all(key[1:] > key[:-1]).item())
reversed_key = dst * n + src
checks["edges_are_bidirectional"] = bool(
torch.equal(torch.sort(key).values, torch.sort(reversed_key).values)
)
distance = torch.sqrt(
torch.sum(
(
graph.pos[src].to(torch.float64)
- graph.pos[dst].to(torch.float64)
)
** 2,
dim=1,
)
)
checks["edges_within_cutoff"] = bool(
torch.all(distance <= cutoff + atol).item()
)
checks["edge_attr_distance"] = bool(
torch.allclose(
graph.edge_attr[:, 0].to(torch.float64),
distance / cutoff,
atol=atol,
rtol=0.0,
)
)
is_protein = graph.is_protein[:, 0]
checks["edge_attr_endpoint_types"] = bool(
torch.equal(graph.edge_attr[:, 2], is_protein[src])
and torch.equal(graph.edge_attr[:, 3], is_protein[dst])
)
else:
checks["edge_bounds"] = True
checks["no_self_edges"] = True
checks["legacy_edge_order"] = True
checks["edges_are_bidirectional"] = True
checks["edges_within_cutoff"] = True
checks["edge_attr_distance"] = True
checks["edge_attr_endpoint_types"] = True
protein = graph.is_protein[:, 0] > 0.5
checks["protein_first"] = bool(
not protein.numel()
or not bool((~protein).any().item())
or not bool(protein[torch.nonzero(~protein, as_tuple=False)[0, 0] :].any().item())
)
checks["protein_y_true_zero"] = bool(
torch.all(graph.y_true[protein] == 0).item()
)
ligand_error = torch.sqrt(
torch.sum((graph.y_pred[~protein] - graph.y_grt[~protein]) ** 2, dim=1)
)
checks["ligand_y_true_matches_coordinates"] = bool(
torch.allclose(
graph.y_true[~protein, 0],
ligand_error,
atol=atol,
rtol=0.0,
)
)
return {"passed": all(checks.values()), "checks": checks}
def _load_legacy(path: Path, allow_eager: bool) -> Sequence[Any]:
try:
return torch.load(
path,
map_location="cpu",
mmap=True,
weights_only=False,
)
except (TypeError, RuntimeError, ValueError) as exc:
if not allow_eager:
raise RuntimeError(
f"could not mmap legacy dataset {path}: {exc}. "
"Refusing an eager multi-GB load; pass --allow-eager-legacy "
"only inside a suitably sized Slurm job."
) from exc
return torch.load(path, map_location="cpu", weights_only=False)
def validate(args: argparse.Namespace) -> Dict[str, Any]:
dataset = CompactGraphDataset(
args.compact,
max_cached_shards=args.max_cached_shards,
strict=True,
)
indices = _parse_indices(args.indices, len(dataset))
if indices is None:
indices = _choose_indices(len(dataset), args.num_samples, args.seed)
report: Dict[str, Any] = {
"compact": str(Path(args.compact).resolve()),
"num_graphs": len(dataset),
"indices": indices,
"atol": args.atol,
"rtol": args.rtol,
"rss_mib_before_samples": _rss_mib(),
"samples": [],
}
legacy: Optional[Sequence[Any]] = None
if args.legacy is not None:
legacy = _load_legacy(Path(args.legacy), args.allow_eager_legacy)
report["legacy"] = str(Path(args.legacy).resolve())
report["legacy_num_graphs"] = len(legacy)
if len(legacy) != len(dataset):
report["length_match"] = False
else:
report["length_match"] = True
all_passed = report.get("length_match", True)
for index in indices:
graph = dataset[index]
sample_report: Dict[str, Any] = {
"index": index,
"metadata": dataset.metadata(index),
"invariants": _invariants(graph, dataset.cutoff, args.atol),
}
sample_passed = bool(sample_report["invariants"]["passed"])
if legacy is not None and index < len(legacy):
reference = legacy[index]
parity: Dict[str, Any] = {}
for field in CORE_FIELDS:
if not hasattr(reference, field):
parity[field] = {
"passed": False,
"reason": "missing_in_legacy_graph",
}
continue
actual = getattr(graph, field)
expected = getattr(reference, field)
if not torch.is_tensor(actual) or not torch.is_tensor(expected):
parity[field] = {
"passed": False,
"reason": "field_is_not_tensor",
}
continue
parity[field] = _tensor_stats(
actual,
expected,
atol=args.atol if field in FLOAT_FIELDS else 0.0,
rtol=args.rtol if field in FLOAT_FIELDS else 0.0,
)
sample_report["parity"] = parity
sample_passed = sample_passed and all(
bool(result["passed"]) for result in parity.values()
)
sample_report["passed"] = sample_passed
all_passed = all_passed and sample_passed
report["samples"].append(sample_report)
report["rss_mib_after_samples"] = _rss_mib()
report["passed"] = bool(all_passed)
return report
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description="Validate compact GNNCP graphs and optionally compare with legacy tensors."
)
parser.add_argument(
"--compact",
required=True,
help="Compact dataset directory or manifest.json",
)
parser.add_argument(
"--legacy",
help="Legacy list[torch_geometric.data.Data] .pt for mmap parity checks",
)
parser.add_argument(
"--num-samples",
type=int,
default=8,
help="Number of deterministic samples when --indices is omitted (default: 8)",
)
parser.add_argument(
"--indices",
help="Comma-separated indices/ranges, e.g. '0,10,20:24,-1'",
)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument(
"--atol",
type=float,
default=1e-6,
help="Absolute tolerance for reconstructed floating tensors",
)
parser.add_argument("--rtol", type=float, default=1e-6)
parser.add_argument("--max-cached-shards", type=int, default=2)
parser.add_argument(
"--allow-eager-legacy",
action="store_true",
help="Allow fallback to an eager legacy torch.load if mmap is unavailable",
)
parser.add_argument(
"--report",
help="Optional JSON report path (written atomically by the caller/job filesystem)",
)
return parser
def main() -> int:
args = build_parser().parse_args()
report = validate(args)
rendered = json.dumps(report, ensure_ascii=False, indent=2, sort_keys=True)
print(rendered)
if args.report:
output = Path(args.report)
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(rendered + "\n", encoding="utf-8")
return 0 if report["passed"] else 1
if __name__ == "__main__":
raise SystemExit(main())
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