diff --git a/.gitignore b/.gitignore index 83972fa..340b0fa 100644 --- a/.gitignore +++ b/.gitignore @@ -206,6 +206,10 @@ tempCodeRunnerFile.py # Ruff stuff: .ruff_cache/ +# step-up training artifacts and local data +outputs/ +data/processed/ + # PyPI configuration file .pypirc diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..dbb8c53 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "external/ReBIND"] + path = external/ReBIND + url = https://github.com/holymollyhao/ReBIND.git diff --git a/README.md b/README.md index ba92bc8..3d1e7e6 100644 --- a/README.md +++ b/README.md @@ -4,16 +4,23 @@ A benchmark for 2D to 3D conformer generation models. ## Getting Started -This project uses [`uv`](https://docs.astral.sh/uv/) for Python, dependency, and -environment management. +This project uses [`uv`](https://docs.astral.sh/uv/) for Python, dependency, and environment management. + +The first model (ReBind) is vendored as a git submodule under `external/ReBIND`, so clone with `--recursive`: ```bash -git clone https://github.com/LeMaterial/step-up.git +git clone --recursive https://github.com/LeMaterial/step-up.git cd step-up uv sync --dev ``` -Run the test suite: +If you already cloned without `--recursive`: + +```bash +git submodule update --init --recursive +``` + +Run the test suite (CPU-only, ~30s): ```bash uv run pytest @@ -26,15 +33,100 @@ uv run ruff format --check uv run ruff check ``` -## Usage +## CPU Smoke Runs + +Each config under `configs/*_smoke.yaml` trains a tiny model on a 100-molecule +subset for 3 epochs on CPU. The goal is to verify the pipeline end-to-end (data +loader --> model forward --> loss --> optimizer), not to produce meaningful +metrics. Loss should decrease monotonically across epochs. + +```bash +uv run step-up train -c configs/qm9_smoke.yaml # ~25s +uv run step-up train -c configs/tmqmg_smoke.yaml # ~5 min (larger complexes) +uv run step-up train -c configs/bostmc_smoke.yaml # ~6 min (larger complexes) +``` + +## Production Training + +Three full-scale configs are staged. They target GPU (`device: cuda`) and use +ReBind's published QM9 hyperparameters (8 encoder + 8 decoder layers, +d_model=512, lr=9e-5, AdamW, batch 100 for QM9 / 32 for organometallics, +20 epochs by default). + +| Config | Dataset | Rows | Notes | +|---|---|---|---| +| `configs/qm9.yaml` | QM9-full.csv | ~134K | Sanity baseline on organic systems | +| `configs/tmqmg.yaml` | tmQMg-full.csv | ~60K | Singlets, full d-block + La | +| `configs/bostmc.yaml` | BOSTMC-low-spin.csv | ~140K | Singlets + doublets, full d-block | + +To dry-run a config (validates the YAML and dataset path without training): + +```bash +uv run step-up train -c configs/qm9.yaml --dry-run +``` + +```bash +sbatch scripts/train.slurm configs/qm9.yaml +sbatch scripts/train.slurm configs/tmqmg.yaml +sbatch scripts/train.slurm configs/bostmc.yaml +``` + +The Slurm script (1) initializes the submodule, (2) runs `uv sync --dev`, and +(3) launches `uv run step-up train -c `. Each run writes its config, +TensorBoard logs, and best checkpoint (by val D-MAE) to the `output_dir` +specified in the config. default is `outputs/_full/`. + +### Adjusting training duration -The benchmark is in early development. As functionality is added, reusable code -will live under the `step_up` Python package and can be run through `uv`: +To train longer, edit the config's `epochs` field. ReBind's paper used 20 +epochs; more epochs only help if val D-MAE is still trending down at the end. +Inspect TensorBoard during the run: ```bash -uv run python +uv run tensorboard --logdir outputs/qm9_full/tb ``` +### Resuming after compute interruptions + +The current loop saves `best.pt` (model weights only) on the best val D-MAE +epoch but doesn't snapshot the optimizer / scheduler state. Resumption is a +TODO for the next iteration. For long runs, prefer over-provisioning the time +budget in the Slurm header. + +## Repository Layout + +``` +src/step_up/ +|-- data/ +| |-- csv_dataset.py # streaming CSV --> graph-dict dataset +| |-- featurize.py # XYZ path (RDKit DetermineBonds for QM9) +| |-- mol2.py # direct MOL2 parser (no RDKit, used for organometallics) +| |-- splits.py +|-- models/ +| |-- rebind.py # thin wrapper over external/ReBIND + 3 runtime patches +| eval/ +| |-- metrics.py # D-MAE, D-RMSE, coord-RMSD, per-element D-MAE +|-- train.py # config-driven training loop +|-- cli.py # `uv run step-up train -c ` + +configs/ # per-dataset YAML configs (smoke + full) +external/ReBIND/ # git submodule, vendored upstream ReBind +scripts/train.slurm +tests/ # 9 tests covering imports, dataset loading, forward pass, metrics +``` + +## Data Path Notes + +- **QM9 (smiles + xyz)**: built from the XYZ block via + `Chem.MolFromXYZBlock` + `rdDetermineBonds.DetermineBonds`. The SMILES + column is intentionally unused because its atom order doesn't match the + XYZ block in QM9-full.csv. +- **Organometallics (mol2 + xyz)**: built directly from the MOL2 block via + the in-house parser in `step_up/data/mol2.py`. No RDKit* is involved; + connectivity and bond types come straight from the MOL2 file, SYBYL atom + types provide hybridization and aromaticity (e.g. `C.ar`, `N.am`), and rings + are computed from the bond graph via Tarjan bridge-finding. + ## Contributing See [CONTRIBUTING.md](CONTRIBUTING.md) for development setup, pre-commit hooks, diff --git a/configs/bostmc.yaml b/configs/bostmc.yaml new file mode 100644 index 0000000..718acd6 --- /dev/null +++ b/configs/bostmc.yaml @@ -0,0 +1,28 @@ +# Full BOSTMC-low-spin training run (~140K complexes, singlets + doublets, full d-block). GPU-only; staged for Slurm. +# DESIGN DECISION (flagged in plan file): +# - `charge` and `spinmult` columns are currently IGNORED. +# - Recommendation for first publishable run: pre-filter to singlets-only (spinmult == 1) for the cleanest comparison to tmQMg. The CSVMoleculeDataset does not yet expose a filter knob, add one when this config is first run. +# - Follow-up: condition the model on (charge, spinmult) as a global feature. +dataset_path: /home/gridsan/jtoney/BOSTMC/datasets/BOSTMC-low-spin.csv +dataset_source: mol2 +subset_size: null +split_ratios: [0.9, 0.05, 0.05] +split_seed: 0 + +n_layers: 8 +d_model: 512 +d_ffn: 1024 +n_head: 8 +dropout: 0.0 + +epochs: 20 +batch_size: 100 +eval_batch_size: 100 +lr: 9.0e-5 +weight_decay: 0.0 +warmup_ratio: 0.1 +num_workers: 4 + +device: cuda +output_dir: outputs/bostmc_full +seed: 0 diff --git a/configs/bostmc_smoke.yaml b/configs/bostmc_smoke.yaml new file mode 100644 index 0000000..286f3e9 --- /dev/null +++ b/configs/bostmc_smoke.yaml @@ -0,0 +1,25 @@ +# BOSTMC smoke run on a 100-molecule subset. Verifies the direct MOL2 parser +# end-to-end (no RDKit) plus the LJ patch on d-block elements. +dataset_path: /home/gridsan/jtoney/BOSTMC/datasets/BOSTMC-low-spin.csv +dataset_source: mol2 +subset_size: 100 +split_ratios: [0.8, 0.1, 0.1] +split_seed: 0 + +n_layers: 2 +d_model: 64 +d_ffn: 128 +n_head: 4 +dropout: 0.0 + +epochs: 3 +batch_size: 4 +eval_batch_size: 4 +lr: 3.0e-4 +weight_decay: 0.0 +warmup_ratio: 0.1 +num_workers: 0 + +device: cpu +output_dir: outputs/bostmc_smoke +seed: 0 diff --git a/configs/qm9.yaml b/configs/qm9.yaml new file mode 100644 index 0000000..e9acd3c --- /dev/null +++ b/configs/qm9.yaml @@ -0,0 +1,26 @@ +# Full QM9-full.csv training run (~134K molecules). +# GPU-only, staged for the Slurm job once compute is available. +# Mirrors ReBind's QM9 hyperparams from external/ReBIND/experiments/conformer_prediction/rebind.sh. +dataset_path: /home/gridsan/jtoney/ElemNet/benchmarking/datasets/QM9-full.csv +dataset_source: smiles +subset_size: null +split_ratios: [0.9, 0.05, 0.05] +split_seed: 0 + +n_layers: 8 +d_model: 512 +d_ffn: 1024 +n_head: 8 +dropout: 0.0 + +epochs: 20 +batch_size: 100 +eval_batch_size: 100 +lr: 9.0e-5 +weight_decay: 0.0 +warmup_ratio: 0.1 +num_workers: 4 + +device: cuda +output_dir: outputs/qm9_full +seed: 0 diff --git a/configs/qm9_smoke.yaml b/configs/qm9_smoke.yaml new file mode 100644 index 0000000..e238b42 --- /dev/null +++ b/configs/qm9_smoke.yaml @@ -0,0 +1,26 @@ +# Smallest possible run that exercises the full pipeline on CPU. +# Goal: prove the loss strictly decreases. Numbers are not meaningful. +dataset_path: /home/gridsan/jtoney/ElemNet/benchmarking/datasets/QM9-full.csv +dataset_source: smiles +subset_size: 100 +split_ratios: [0.8, 0.1, 0.1] +split_seed: 0 + +# Tiny model — enough capacity to learn distance regression on 80 toy molecules. +n_layers: 2 +d_model: 64 +d_ffn: 128 +n_head: 4 +dropout: 0.0 + +epochs: 3 +batch_size: 8 +eval_batch_size: 8 +lr: 3.0e-4 +weight_decay: 0.0 +warmup_ratio: 0.1 +num_workers: 0 + +device: cpu +output_dir: outputs/qm9_smoke +seed: 0 diff --git a/configs/tmqmg.yaml b/configs/tmqmg.yaml new file mode 100644 index 0000000..3d33274 --- /dev/null +++ b/configs/tmqmg.yaml @@ -0,0 +1,24 @@ +# Full tmQMg-full.csv training run (~60K organometallic complexes, closed-shell singlets, full d-block + La). GPU-only; staged for Slurm. +dataset_path: /home/gridsan/jtoney/ElemNet/benchmarking/datasets/tmQMg-full.csv +dataset_source: mol2 +subset_size: null +split_ratios: [0.9, 0.05, 0.05] +split_seed: 0 + +n_layers: 8 +d_model: 512 +d_ffn: 1024 +n_head: 8 +dropout: 0.0 + +epochs: 20 +batch_size: 100 +eval_batch_size: 100 +lr: 9.0e-5 +weight_decay: 0.0 +warmup_ratio: 0.1 +num_workers: 4 + +device: cuda +output_dir: outputs/tmqmg_full +seed: 0 diff --git a/configs/tmqmg_smoke.yaml b/configs/tmqmg_smoke.yaml new file mode 100644 index 0000000..a2460b2 --- /dev/null +++ b/configs/tmqmg_smoke.yaml @@ -0,0 +1,25 @@ +# Same shape as qm9_smoke, but reading the MOL2 path on organometallic data. +# Verifies the LJ patch and the MOL2 → graph pipeline end-to-end on CPU. +dataset_path: /home/gridsan/jtoney/ElemNet/benchmarking/datasets/tmQMg-full.csv +dataset_source: mol2 +subset_size: 100 +split_ratios: [0.8, 0.1, 0.1] +split_seed: 0 + +n_layers: 2 +d_model: 64 +d_ffn: 128 +n_head: 4 +dropout: 0.0 + +epochs: 3 +batch_size: 4 # tmQMg complexes are larger (~50 atoms vs ~15 for QM9) +eval_batch_size: 4 +lr: 3.0e-4 +weight_decay: 0.0 +warmup_ratio: 0.1 +num_workers: 0 + +device: cpu +output_dir: outputs/tmqmg_smoke +seed: 0 diff --git a/external/ReBIND b/external/ReBIND new file mode 160000 index 0000000..8d240c6 --- /dev/null +++ b/external/ReBIND @@ -0,0 +1 @@ +Subproject commit 8d240c6e04a6f23867df98755100a9fbd230b24d diff --git a/pyproject.toml b/pyproject.toml index 1d30d04..a85510e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,13 +1,28 @@ [project] name = "step-up" version = "0.1.0" -description = "Add your description here" +description = "Benchmark for 2D to 3D molecular conformer generation models" readme = "README.md" authors = [ - { name = "Siddharth Betala", email = "betalas5@gmail.com" } + { name = "Siddharth Betala", email = "betalas5@gmail.com" }, + { name = "Jacob Toney", email = "jwt@mit.edu" } ] requires-python = ">=3.12" -dependencies = [] +dependencies = [ + "numpy>=1.26", + "pandas>=2.2", + "pyyaml>=6.0", + "rdkit>=2024.3.3", + "tensorboard>=2.16", + "torch>=2.1", + "torch-geometric>=2.3", + "torchmetrics>=1.0", + "tqdm>=4.66", + "transformers>=4.43", +] + +[project.scripts] +step-up = "step_up.cli:main" [build-system] requires = ["uv_build>=0.10.8,<0.11.0"] @@ -38,3 +53,19 @@ max-complexity = 12 [tool.ruff.format] quote-style = "double" indent-style = "space" + +[tool.pytest.ini_options] +testpaths = ["tests"] +filterwarnings = [ + "ignore::DeprecationWarning", + "ignore::UserWarning", +] + +[tool.uv.sources] +torch = [{ index = "pytorch-cu124" }] + +[[tool.uv.index]] +name = "pytorch-cu124" +url = "https://download.pytorch.org/whl/cu124" +explicit = true + diff --git a/scripts/train.sh b/scripts/train.sh new file mode 100755 index 0000000..d4c4df5 --- /dev/null +++ b/scripts/train.sh @@ -0,0 +1,27 @@ +#!/bin/bash +#SBATCH --job-name=step-up +#SBATCH --gres=gpu:volta:1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=64G +#SBATCH --output=train.out + +# Usage: sbatch scripts/train.slurm configs/qm9.yaml +# - Edit partition / gres / time to match your cluster. +# - The job runs from the repo root and uses uv for everything. + +set -euo pipefail + +if [[ $# -lt 1 ]]; then + echo "Usage: $0 " + exit 2 +fi +CONFIG="$1" + +cd "$(dirname "$0")/.." + +# Make sure the submodule is initialized in case the job runs on a fresh checkout. +git submodule update --init --recursive + +uv sync --dev + +uv run step-up train -c "$CONFIG" diff --git a/src/step_up/__init__.py b/src/step_up/__init__.py index a88258d..7b57afa 100644 --- a/src/step_up/__init__.py +++ b/src/step_up/__init__.py @@ -1,2 +1,3 @@ -def hello() -> str: - return "Hello from step-up!" +"""step-up: a benchmark for 2D to 3D molecular conformer generation models.""" + +__version__ = "0.1.0" diff --git a/src/step_up/cli.py b/src/step_up/cli.py new file mode 100644 index 0000000..f19a353 --- /dev/null +++ b/src/step_up/cli.py @@ -0,0 +1,45 @@ +"""Command-line entry point: ``uv run step-up ``.""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +from .train import TrainConfig, train + + +def _cmd_train(args: argparse.Namespace) -> int: + cfg = TrainConfig.from_yaml(args.config) + if args.dry_run: + print(f"[dry-run] Loaded config from {args.config}") + print(f" dataset: {cfg.dataset_path} ({cfg.dataset_source})") + if not Path(cfg.dataset_path).exists(): + print(f" ERROR: dataset path does not exist: {cfg.dataset_path}") + return 1 + print(f" subset_size={cfg.subset_size} epochs={cfg.epochs} batch={cfg.batch_size}") + print(f" model: layers={cfg.n_layers} d_model={cfg.d_model} d_ffn={cfg.d_ffn}") + print(f" device={cfg.device} output_dir={cfg.output_dir}") + return 0 + result = train(cfg) + print(f"\nBest val D-MAE: {result['best_val_dmae']:.4f}") + return 0 + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(prog="step-up") + sub = parser.add_subparsers(dest="command", required=True) + + p_train = sub.add_parser("train", help="Train a model from a YAML config") + p_train.add_argument("-c", "--config", required=True, help="Path to YAML config") + p_train.add_argument( + "--dry-run", action="store_true", help="Validate config and dataset path without training" + ) + p_train.set_defaults(func=_cmd_train) + + args = parser.parse_args(argv) + return args.func(args) + + +if __name__ == "__main__": # pragma: no cover + sys.exit(main()) diff --git a/src/step_up/data/__init__.py b/src/step_up/data/__init__.py new file mode 100644 index 0000000..084a543 --- /dev/null +++ b/src/step_up/data/__init__.py @@ -0,0 +1 @@ +"""Data loading and featurization for step-up benchmark datasets.""" diff --git a/src/step_up/data/csv_dataset.py b/src/step_up/data/csv_dataset.py new file mode 100644 index 0000000..d089082 --- /dev/null +++ b/src/step_up/data/csv_dataset.py @@ -0,0 +1,122 @@ +"""Unified CSV → graph-dict dataset. + +Backs all three current targets (QM9-full, tmQMg-full, BOSTMC) with one class. +Each row is featurized lazily into a ReBind-compatible dict that the Collator +can pad and batch. + +The dataset performs an upfront *validation pass* that attempts to featurize +every row and keeps only the indices that succeed. This is the explicit answer +to the silent-corruption failure mode where RDKit could not infer bond orders +on certain QM9 rows: rather than silently substituting a wrong feature value, +those rows are filtered out and reported. +""" + +from __future__ import annotations + +import csv +import sys +from collections.abc import Sequence +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Literal + +import pandas as pd +from torch.utils.data import Dataset +from tqdm import tqdm + +from .featurize import featurize_mol2_xyz, featurize_xyz + +# Embedded XYZ/MOL2 fields blow past the default csv.field_size_limit. Raise it +# once at import time so pandas (which uses Python's csv internally for the C +# fallback path) doesn't truncate. +csv.field_size_limit(sys.maxsize) + + +@dataclass(frozen=True) +class DatasetSpec: + """Where to find a CSV and how to read it.""" + + path: Path + source: Literal["smiles", "mol2"] + + +def _read_csv_subset(path: Path, columns: Sequence[str], nrows: int | None) -> pd.DataFrame: + df = pd.read_csv(path, usecols=list(columns), nrows=nrows, low_memory=False) + df = df.reset_index(drop=True) + return df + + +class CSVMoleculeDataset(Dataset): + """Row-streaming dataset over one of QM9-full / tmQMg-full / BOSTMC. + + The CSV is loaded into a pandas DataFrame at construction time (the + ``subset_size`` knob keeps memory bounded for smoke runs). When + ``validate=True`` (the default), the dataset then walks every row, calls + the featurizer, and keeps only indices whose featurization succeeds. The + number of dropped rows is reported once. + """ + + def __init__( + self, + path: str | Path, + source: Literal["smiles", "mol2"], + subset_size: int | None = None, + validate: bool = True, + ) -> None: + self.path = Path(path) + if not self.path.exists(): + raise FileNotFoundError(self.path) + self.source = source + + if source == "smiles": + # QM9 path: XYZ only — RDKit's DetermineBonds recovers chemistry. + # We still load the smiles column (unused) so the column-set check + # asserts the source is actually QM9-shaped. + cols = ("smiles", "xyz") + elif source == "mol2": + cols = ("mol2", "xyz") + else: + raise ValueError(f"Unknown source: {source!r}") + + self._df = _read_csv_subset(self.path, cols, nrows=subset_size) + + if validate: + self._valid_indices = self._validate_rows() + else: + self._valid_indices = list(range(len(self._df))) + + def __len__(self) -> int: + return len(self._valid_indices) + + def __getitem__(self, idx: int) -> dict[str, Any]: + real_idx = self._valid_indices[idx] + return self._featurize(real_idx) + + def _featurize(self, real_idx: int) -> dict[str, Any]: + row = self._df.iloc[real_idx] + if self.source == "smiles": + return featurize_xyz(str(row["xyz"])) + return featurize_mol2_xyz(str(row["mol2"]), str(row["xyz"])) + + def _validate_rows(self) -> list[int]: + n = len(self._df) + kept: list[int] = [] + failures: dict[str, int] = {} + pbar = tqdm(range(n), desc=f"validating {self.path.name}", unit="mol", dynamic_ncols=True) + for i in pbar: + try: + self._featurize(i) + kept.append(i) + except Exception as e: + # Bucket by error class so the summary line is interpretable. + key = type(e).__name__ + failures[key] = failures.get(key, 0) + 1 + dropped = n - len(kept) + if dropped: + summary = ", ".join(f"{k}={v}" for k, v in sorted(failures.items())) + print( + f"[{self.path.name}] validation: kept {len(kept)}/{n} rows, " + f"dropped {dropped} ({summary})", + flush=True, + ) + return kept diff --git a/src/step_up/data/featurize.py b/src/step_up/data/featurize.py new file mode 100644 index 0000000..3e9b927 --- /dev/null +++ b/src/step_up/data/featurize.py @@ -0,0 +1,123 @@ +"""Convert raw XYZ / MOL2 blocks into ReBind-format graph dicts. + +Two paths: + +- **QM9 (XYZ + SMILES column)**: uses RDKit's ``MolFromXYZBlock`` + + ``rdDetermineBonds.DetermineBonds`` to recover the bond graph from + coordinates. SMILES is intentionally ignored because the SMILES atom order + doesn't match the XYZ order in QM9-full.csv. + +- **Organometallic (MOL2 + XYZ)**: parses MOL2 directly via + :mod:`step_up.data.mol2` — no RDKit perception. Connectivity and bond types + come straight from the file, and SYBYL atom types (``C.3``, ``C.ar``, + ``N.am``, ...) provide hybridization / aromaticity that RDKit with + ``sanitize=False`` would discard. Rings are computed from the bond graph. +""" + +from __future__ import annotations + +import importlib.util +import sys +from pathlib import Path +from typing import Any + +import numpy as np +from rdkit import Chem +from rdkit.Chem import rdDetermineBonds + +from .mol2 import mol2_to_graph_dict + +# Load ReBind's `data/utils.py` directly so we share the canonical featurization +# without putting the entire ReBIND tree on sys.path. The submodule lives at +# step-up/external/ReBIND/data/utils.py. +_REBIND_ROOT = Path(__file__).resolve().parents[3] / "external" / "ReBIND" + + +def _load_rebind_data_utils(): + path = _REBIND_ROOT / "data" / "utils.py" + if not path.exists(): + raise FileNotFoundError( + f"ReBind submodule not found at {_REBIND_ROOT}. " + "Run: git submodule update --init --recursive" + ) + spec = importlib.util.spec_from_file_location("_step_up_rebind_data_utils", str(path)) + assert spec is not None and spec.loader is not None + mod = importlib.util.module_from_spec(spec) + sys.modules["_step_up_rebind_data_utils"] = mod + spec.loader.exec_module(mod) + return mod + + +_rebind_utils = _load_rebind_data_utils() +mol_to_graph_dict = _rebind_utils.mol_to_graph_dict +ALLOWABLE_FEATURES = _rebind_utils.ALLOWABLE_FEATURES + + +def parse_xyz_block(block: str) -> tuple[list[str], np.ndarray]: + """Parse a standard XYZ block string into (symbols, coords). + + Standard XYZ format: + line 1: integer atom count + line 2: comment (ignored) + lines 3..: ``element x y z`` (whitespace-separated) + """ + lines = block.strip().splitlines() + if len(lines) < 2: + raise ValueError(f"XYZ block too short ({len(lines)} lines)") + n = int(lines[0].strip()) + atom_lines = lines[2 : 2 + n] + if len(atom_lines) != n: + raise ValueError(f"XYZ header says {n} atoms but body has {len(atom_lines)}") + symbols: list[str] = [] + coords = np.empty((n, 3), dtype=np.float64) + for i, line in enumerate(atom_lines): + parts = line.split() + symbols.append(parts[0]) + coords[i] = [float(parts[1]), float(parts[2]), float(parts[3])] + return symbols, coords + + +def mol_from_xyz_block(xyz_block: str, charge: int = 0) -> Chem.Mol: + """Build an RDKit molecule from a raw XYZ block, inferring bonds from coords. + + Atom order matches the XYZ block (no remapping). Bond *orders* are inferred + via ``rdDetermineBonds.DetermineBonds`` using the supplied formal charge. + Raises ``ValueError`` if bond inference fails — callers (typically the CSV + dataset's validation pass) are responsible for filtering bad rows out. + + Suitable for closed-shell organic systems (QM9). For organometallics we use + the MOL2 path instead, which carries explicit bonds. + + NOTE: An earlier version of this function fell back to + ``DetermineConnectivity`` (bond presence without bond orders). This was a + silent data-corruption bug: ReBind's ``safe_index`` maps unspecified bond + types to the AROMATIC bucket, so every bond on those rows was being + featurized as aromatic. We now raise instead, so the bad rows get + explicitly filtered upstream. + """ + mol = Chem.MolFromXYZBlock(xyz_block) + if mol is None: + raise ValueError("RDKit failed to parse XYZ block") + rdDetermineBonds.DetermineBonds(mol, charge=charge) + return mol + + +def featurize_xyz(xyz_block: str, charge: int = 0) -> dict[str, Any]: + """One-shot helper: XYZ-only featurization (QM9 path). + + Coordinates come from the XYZ block; bonds are inferred. The original + SMILES is unused — RDKit's ``DetermineBonds`` recovers the same chemistry + and the resulting atom order matches the XYZ. + """ + mol = mol_from_xyz_block(xyz_block, charge=charge) + return mol_to_graph_dict(mol) + + +def featurize_mol2_xyz(mol2_block: str, xyz_block: str) -> dict[str, Any]: + """One-shot helper for MOL2-sourced rows (tmQMg, BOSTMC). + + No RDKit — the MOL2 file already contains all connectivity and bond types + we need, and we keep the XYZ-block coordinates as the canonical geometry. + """ + _, coords = parse_xyz_block(xyz_block) + return mol2_to_graph_dict(mol2_block, coords=coords) diff --git a/src/step_up/data/mol2.py b/src/step_up/data/mol2.py new file mode 100644 index 0000000..552cfb3 --- /dev/null +++ b/src/step_up/data/mol2.py @@ -0,0 +1,361 @@ +"""Direct Tripos MOL2 parser — no RDKit perception. + +For organometallic data we already have the connectivity and bond types in the +MOL2 file (from molSimplify), and we have SYBYL atom types that encode +hybridization and aromaticity. Going through ``Chem.MolFromMol2Block`` with +``sanitize=False`` would give us nothing beyond what's already in the file +while throwing the SYBYL information away — so we parse MOL2 ourselves and +emit a graph dict in the same shape ReBind's collator expects. + +Atom feature semantics match ``ALLOWABLE_FEATURES`` in +``external/ReBIND/data/utils.py``: + + [atomic_num_idx, chirality_idx, degree_idx, formal_charge_idx, + numH_idx, num_radical_idx, hybridization_idx, is_aromatic_idx, + is_in_ring_idx] + +Bond features (4 per bond, undirected expanded to two directed entries): + + [bond_type_idx, bond_dir_idx, bond_stereo_idx, is_conjugated_idx] +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any + +import numpy as np + +# --------------------------------------------------------------------------- +# Element table (Z=1..103). Kept inline to avoid a periodic-table dep. +# --------------------------------------------------------------------------- +_ELEMENTS: tuple[str, ...] = ( + "H", "He", "Li", "Be", "B", "C", "N", "O", "F", "Ne", + "Na", "Mg", "Al", "Si", "P", "S", "Cl", "Ar", "K", "Ca", + "Sc", "Ti", "V", "Cr", "Mn", "Fe", "Co", "Ni", "Cu", "Zn", + "Ga", "Ge", "As", "Se", "Br", "Kr", "Rb", "Sr", "Y", "Zr", + "Nb", "Mo", "Tc", "Ru", "Rh", "Pd", "Ag", "Cd", "In", "Sn", + "Sb", "Te", "I", "Xe", "Cs", "Ba", "La", "Ce", "Pr", "Nd", + "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb", + "Lu", "Hf", "Ta", "W", "Re", "Os", "Ir", "Pt", "Au", "Hg", + "Tl", "Pb", "Bi", "Po", "At", "Rn", "Fr", "Ra", "Ac", "Th", + "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es", "Fm", + "Md", "No", "Lr", +) # fmt: skip +_SYMBOL_TO_Z: dict[str, int] = {sym: i + 1 for i, sym in enumerate(_ELEMENTS)} + +# --------------------------------------------------------------------------- +# ReBind vocab indices (mirroring ALLOWABLE_FEATURES). Keeping these inline so +# the file is independent — if upstream changes, the smoke tests will catch +# the mismatch in feature dimensions. +# --------------------------------------------------------------------------- +_POSSIBLE_ATOMIC_NUM_LEN = 118 # list(range(1, 119)) +_POSSIBLE_DEGREE = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) # plus "misc" -> idx 11 +_POSSIBLE_FORMAL_CHARGE = (-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5) # +"misc"->11 +_POSSIBLE_NUMH = (0, 1, 2, 3, 4, 5, 6, 7, 8) # +"misc"->9 + +# Chirality, hybridization, bond_type, bond_dir, bond_stereo indices line up +# with ReBind's ALLOWABLE_FEATURES tuples. We only use specific entries: +_CHI_UNSPECIFIED = 0 +_HYB = {"SP": 0, "SP2": 1, "SP3": 2, "SP3D": 3, "SP3D2": 4, "misc": 5} +_BOND_TYPE = {"SINGLE": 0, "DOUBLE": 1, "TRIPLE": 2, "AROMATIC": 3} +_BOND_DIR_NONE = 0 +_BOND_STEREO_NONE = 0 + +# SYBYL atom type -> (hybridization name, is_aromatic) +_SYBYL_HYB: dict[str, tuple[str, bool]] = { + "1": ("SP", False), + "2": ("SP2", False), + "3": ("SP3", False), + "4": ("SP3", False), # quaternary N -> SP3 + "ar": ("SP2", True), # aromatic + "am": ("SP2", False), # amide N + "pl3": ("SP2", False), # planar N + "cat": ("SP2", False), # guanidinium C + "co2": ("SP2", False), # carboxylate O + "spc": ("SP3", False), # SPC water O + "t3p": ("SP3", False), # TIP3P water O + "o": ("SP3", False), # generic +} + +# MOL2 bond-type string -> (canonical bond_type_name, is_conjugated) +_MOL2_BOND_TYPE: dict[str, tuple[str, bool]] = { + "1": ("SINGLE", False), + "2": ("DOUBLE", False), + "3": ("TRIPLE", False), + "ar": ("AROMATIC", True), + "am": ("SINGLE", True), # amide C-N -> single but conjugated + "du": ("SINGLE", False), # dummy + "un": ("SINGLE", False), # unknown -> treat as single + "nc": ("SINGLE", False), # not connected (shouldn't appear, but) +} + + +@dataclass +class Mol2Atom: + """One row from the ``@ATOM`` block.""" + + idx: int # 0-indexed + element: str # parsed from SYBYL type prefix + sybyl: str # full SYBYL string (e.g., "C.3", "Ni") + coords: np.ndarray # (3,) + + +@dataclass +class Mol2Bond: + """One row from the ``@BOND`` block.""" + + a: int # 0-indexed + b: int # 0-indexed + mol2_type: str # raw MOL2 type string + + +@dataclass +class Mol2: + atoms: list[Mol2Atom] + bonds: list[Mol2Bond] + + +# --------------------------------------------------------------------------- +# Parsing +# --------------------------------------------------------------------------- + + +def _element_from_sybyl(sybyl: str) -> str: + """Element symbol = SYBYL prefix before the first dot (or full string).""" + return sybyl.split(".", 1)[0] + + +def _hyb_from_sybyl(sybyl: str) -> tuple[str, bool]: + """SYBYL → (hybridization, is_aromatic). Metals/unknowns default to misc.""" + if "." not in sybyl: + # No SYBYL hybridization given (e.g. metals: "Ni", "Fe"). misc bucket. + return ("misc", False) + suffix = sybyl.split(".", 1)[1] + return _SYBYL_HYB.get(suffix, ("misc", False)) + + +def parse_mol2(text: str) -> Mol2: + """Parse a single-molecule MOL2 block string.""" + atoms: list[Mol2Atom] = [] + bonds: list[Mol2Bond] = [] + section: str | None = None + for raw_line in text.splitlines(): + line = raw_line.strip() + if not line: + continue + if line.startswith("@"): + section = line[len("@") :].strip().upper() + continue + if section == "ATOM": + atoms.append(_parse_atom_line(line, len(atoms))) + elif section == "BOND": + bonds.append(_parse_bond_line(line)) + # Other sections (MOLECULE header, SUBSTRUCTURE, etc.) are ignored. + return Mol2(atoms=atoms, bonds=bonds) + + +def _parse_atom_line(line: str, idx0: int) -> Mol2Atom: + """ATOM line: id name x y z sybyl_type [subst_id [subst_name [charge]]].""" + parts = line.split() + if len(parts) < 6: + raise ValueError(f"MOL2 ATOM line too short: {line!r}") + x, y, z = float(parts[2]), float(parts[3]), float(parts[4]) + sybyl = parts[5] + element = _element_from_sybyl(sybyl) + return Mol2Atom( + idx=idx0, + element=element, + sybyl=sybyl, + coords=np.array([x, y, z], dtype=np.float64), + ) + + +def _parse_bond_line(line: str) -> Mol2Bond: + """BOND line: id atom_a atom_b type.""" + parts = line.split() + if len(parts) < 4: + raise ValueError(f"MOL2 BOND line too short: {line!r}") + a = int(parts[1]) - 1 # MOL2 is 1-indexed + b = int(parts[2]) - 1 + return Mol2Bond(a=a, b=b, mol2_type=parts[3]) + + +# --------------------------------------------------------------------------- +# Graph-dict construction +# --------------------------------------------------------------------------- + + +def _safe_index(seq: tuple, value: Any, miss_idx: int) -> int: + try: + return seq.index(value) + except ValueError: + return miss_idx + + +def _tarjan_bridges(adj: list[list[tuple[int, int]]]) -> set[int]: + """Return the set of bridge edge-ids in an undirected graph. + + A bridge is an edge whose removal disconnects the graph. The implementation + is iterative Tarjan with explicit ``disc``/``low`` arrays. + """ + n = len(adj) + disc = [-1] * n + low = [0] * n + bridges: set[int] = set() + timer = 0 + for start in range(n): + if disc[start] != -1: + continue + # Iterative DFS rooted at ``start``. Stack entries are + # (node, parent_edge_id, neighbor_iter_index). + stack: list[list[int]] = [[start, -1, 0]] + disc[start] = low[start] = timer + timer += 1 + while stack: + node, pedge, i = stack[-1] + if i >= len(adj[node]): + stack.pop() + if not stack: + break + parent = stack[-1][0] + low[parent] = min(low[parent], low[node]) + if low[node] > disc[parent]: + bridges.add(pedge) + continue + stack[-1][2] = i + 1 + nbr, eid = adj[node][i] + if eid == pedge: + continue + if disc[nbr] == -1: + disc[nbr] = low[nbr] = timer + timer += 1 + stack.append([nbr, eid, 0]) + else: + low[node] = min(low[node], disc[nbr]) + return bridges + + +def _find_ring_atoms_and_bonds( + n_atoms: int, bond_pairs: list[tuple[int, int]] +) -> tuple[set[int], set[frozenset[int]]]: + """Return (atoms_in_any_ring, bonds_in_any_ring). + + An edge is a *bridge* iff its removal disconnects the graph, equivalently + iff it is not part of any cycle. The complement of bridges is the set of + ring bonds. + """ + adj: list[list[tuple[int, int]]] = [[] for _ in range(n_atoms)] + for eid, (a, b) in enumerate(bond_pairs): + adj[a].append((b, eid)) + adj[b].append((a, eid)) + bridges = _tarjan_bridges(adj) + ring_edge_set: set[frozenset[int]] = set() + ring_atom_set: set[int] = set() + for eid, (a, b) in enumerate(bond_pairs): + if eid not in bridges: + ring_edge_set.add(frozenset((a, b))) + ring_atom_set.add(a) + ring_atom_set.add(b) + return ring_atom_set, ring_edge_set + + +def mol2_to_graph_dict(mol2_text: str, coords: np.ndarray | None = None) -> dict[str, Any]: + """Build a ReBind-format graph dict directly from a MOL2 block. + + If ``coords`` is supplied (e.g. parsed from a separate XYZ column), + it overrides the MOL2 coordinates — useful when the canonical geometry + lives in a different column. Otherwise we use the MOL2 atom positions. + """ + parsed = parse_mol2(mol2_text) + n = len(parsed.atoms) + if n == 0: + raise ValueError("MOL2 block has zero atoms") + + # ---- Coordinates ------------------------------------------------------ + if coords is not None: + if coords.shape != (n, 3): + raise ValueError(f"coords shape {coords.shape} does not match MOL2 atom count {n}") + conformer = coords.tolist() + else: + conformer = np.stack([a.coords for a in parsed.atoms], axis=0).tolist() + + # ---- Bonds first (need degree, numH, in-ring) ------------------------- + bond_pairs: list[tuple[int, int]] = [] + bond_types: list[str] = [] + bond_conjugated: list[bool] = [] + for b in parsed.bonds: + canonical_type, is_conj = _MOL2_BOND_TYPE.get(b.mol2_type, ("SINGLE", False)) + bond_pairs.append((b.a, b.b)) + bond_types.append(canonical_type) + bond_conjugated.append(is_conj) + + ring_atoms, _ring_bonds = _find_ring_atoms_and_bonds(n, bond_pairs) + degree = [0] * n + num_h_neighbors = [0] * n + for a, b in bond_pairs: + degree[a] += 1 + degree[b] += 1 + if parsed.atoms[b].element == "H": + num_h_neighbors[a] += 1 + if parsed.atoms[a].element == "H": + num_h_neighbors[b] += 1 + + # ---- Atom features ---------------------------------------------------- + node_attr: list[list[int]] = [] + node_type: list[int] = [] + for atom in parsed.atoms: + z = _SYMBOL_TO_Z.get(atom.element) + if z is None: + raise ValueError(f"Unknown element symbol {atom.element!r} in MOL2") + atom_num_idx = z - 1 # index into list(range(1, 119)) + hyb_name, is_aromatic = _hyb_from_sybyl(atom.sybyl) + in_ring = atom.idx in ring_atoms + feat = [ + atom_num_idx, + _CHI_UNSPECIFIED, # MOL2 has no chirality field + _safe_index(_POSSIBLE_DEGREE, degree[atom.idx], len(_POSSIBLE_DEGREE)), + _safe_index(_POSSIBLE_FORMAL_CHARGE, 0, len(_POSSIBLE_FORMAL_CHARGE)), + _safe_index(_POSSIBLE_NUMH, num_h_neighbors[atom.idx], len(_POSSIBLE_NUMH)), + 0, # num radical electrons (MOL2 has no radical info) + _HYB.get(hyb_name, _HYB["misc"]), + int(is_aromatic), + int(in_ring), + ] + node_attr.append(feat) + node_type.append(atom_num_idx) + + # ---- Bond features (bidirectional) ------------------------------------ + edges_a: list[int] = [] + edges_b: list[int] = [] + edge_attr: list[list[int]] = [] + for (a, b), btype, conj in zip(bond_pairs, bond_types, bond_conjugated, strict=True): + feat = [ + _BOND_TYPE.get(btype, _BOND_TYPE["SINGLE"]), + _BOND_DIR_NONE, + _BOND_STEREO_NONE, + int(conj), + ] + edges_a.append(a) + edges_b.append(b) + edge_attr.append(feat) + edges_a.append(b) + edges_b.append(a) + edge_attr.append(feat) + + edge_index = [edges_a, edges_b] + edge_type = [feat[0] for feat in edge_attr] + edge_dir_type = [feat[1] for feat in edge_attr] + + return { + "node_attr": node_attr, + "edge_attr": edge_attr, + "edge_index": edge_index, + "node_type": node_type, + "node_chiral_type": [feat[1] for feat in node_attr], + "edge_type": edge_type, + "edge_dire_type": edge_dir_type, + "num_nodes": n, + "num_edges": len(edge_attr), + "conformer": conformer, + } diff --git a/src/step_up/data/splits.py b/src/step_up/data/splits.py new file mode 100644 index 0000000..bb5c88b --- /dev/null +++ b/src/step_up/data/splits.py @@ -0,0 +1,25 @@ +"""Random and reproducible split helpers.""" + +from __future__ import annotations + +import torch +from torch.utils.data import Dataset, Subset + + +def random_split( + dataset: Dataset, + ratios: tuple[float, float, float] = (0.8, 0.1, 0.1), + seed: int = 0, +) -> tuple[Subset, Subset, Subset]: + """Random train/val/test split. Ratios must sum to 1.""" + if abs(sum(ratios) - 1.0) > 1e-6: + raise ValueError(f"Ratios must sum to 1, got {ratios}") + n = len(dataset) # type: ignore[arg-type] + g = torch.Generator().manual_seed(seed) + perm = torch.randperm(n, generator=g).tolist() + n_train = int(n * ratios[0]) + n_val = int(n * ratios[1]) + train_idx = perm[:n_train] + val_idx = perm[n_train : n_train + n_val] + test_idx = perm[n_train + n_val :] + return Subset(dataset, train_idx), Subset(dataset, val_idx), Subset(dataset, test_idx) diff --git a/src/step_up/eval/__init__.py b/src/step_up/eval/__init__.py new file mode 100644 index 0000000..a9113d1 --- /dev/null +++ b/src/step_up/eval/__init__.py @@ -0,0 +1 @@ +"""Evaluation utilities for step-up.""" diff --git a/src/step_up/eval/metrics.py b/src/step_up/eval/metrics.py new file mode 100644 index 0000000..7b89b6d --- /dev/null +++ b/src/step_up/eval/metrics.py @@ -0,0 +1,74 @@ +"""Conformer-quality metrics matching ReBind's evaluate.py. + +All metrics operate on padded coordinate tensors of shape ``(B, N, 3)`` with a +boolean ``node_mask`` of shape ``(B, N)`` marking valid (non-padding) atoms. +""" + +from __future__ import annotations + +import torch +import torch.nn.functional as F + + +def _pair_mask(node_mask: torch.Tensor) -> torch.Tensor: + m = node_mask.to(torch.bool).unsqueeze(-1) + return (m & m.transpose(-1, -2)).to(torch.float32) + + +def cdist_mae(pred: torch.Tensor, target: torch.Tensor, node_mask: torch.Tensor) -> torch.Tensor: + """Mean absolute error on pairwise distance matrices (ReBind ``D-MAE``).""" + mask = _pair_mask(node_mask) + d_pred = torch.cdist(pred, pred) * mask + d_true = torch.cdist(target, target) * mask + return F.l1_loss(d_pred, d_true, reduction="sum") / mask.sum().clamp_min(1) + + +def cdist_rmse(pred: torch.Tensor, target: torch.Tensor, node_mask: torch.Tensor) -> torch.Tensor: + """Root mean squared error on pairwise distance matrices (ReBind ``D-RMSE``).""" + mask = _pair_mask(node_mask) + d_pred = torch.cdist(pred, pred) * mask + d_true = torch.cdist(target, target) * mask + mse = F.mse_loss(d_pred, d_true, reduction="sum") / mask.sum().clamp_min(1) + return torch.sqrt(mse) + + +def coord_rmsd(pred: torch.Tensor, target: torch.Tensor, node_mask: torch.Tensor) -> torch.Tensor: + """Per-molecule coordinate RMSD averaged over the batch (no Kabsch alignment). + + ReBind aligns predictions to targets *inside* the model head before the loss, + so by the time tensors leave the model, this raw RMSD is already + rotation-aware. For unaligned eval (e.g., a baseline that does no + alignment), use ``kabsch_rmsd_per_mol`` below. + """ + delta = (pred - target).to(torch.float32) + sq = (delta * delta).sum(dim=-1) * node_mask.to(torch.float32) + msd = sq.sum(dim=-1) / node_mask.sum(dim=-1).clamp_min(1) + return torch.sqrt(msd).mean() + + +def per_element_dmae( + pred: torch.Tensor, + target: torch.Tensor, + node_mask: torch.Tensor, + atomic_numbers: torch.Tensor, +) -> dict[int, float]: + """Per-element D-MAE: |d_ij_pred - d_ij_true| averaged over pairs touching Z. + + ``atomic_numbers`` is ``(B, N)`` of integer Z values (use 0 for padding). + Returns a dict mapping Z -> mean |Δd| over pairs (i,j) with i or j of that + element (each pair contributes to both endpoint elements). + """ + mask = _pair_mask(node_mask) + diff = (torch.cdist(pred, pred) - torch.cdist(target, target)).abs() * mask + + zs = atomic_numbers.to(torch.long) + per_z: dict[int, float] = {} + for z in torch.unique(zs): + if int(z.item()) == 0: + continue + # Pairs where either endpoint has atomic number z. + endpoint_mask = (zs == z).unsqueeze(-1) | (zs == z).unsqueeze(-2) + pair_mask = mask * endpoint_mask.to(torch.float32) + denom = pair_mask.sum().clamp_min(1) + per_z[int(z.item())] = float((diff * endpoint_mask.to(torch.float32)).sum() / denom) + return per_z diff --git a/src/step_up/models/__init__.py b/src/step_up/models/__init__.py new file mode 100644 index 0000000..07f7cd7 --- /dev/null +++ b/src/step_up/models/__init__.py @@ -0,0 +1 @@ +"""Model wrappers for step-up benchmark.""" diff --git a/src/step_up/models/rebind.py b/src/step_up/models/rebind.py new file mode 100644 index 0000000..8e4224e --- /dev/null +++ b/src/step_up/models/rebind.py @@ -0,0 +1,341 @@ +"""Thin wrapper around the vendored ReBind implementation. + +Vendored at ``external/ReBIND``. We add that path to ``sys.path`` once on +import so the vendor's intra-package relative imports work. We also patch +``get_sigma_and_epsilon`` so that atomic numbers > 36 (i.e., second-row+ +transition metals, lanthanides, etc.) don't raise a KeyError on the +organometallic datasets. + +This is the explicit "hybrid: vendor for now, refactor later" handoff. The next +iteration will copy the model code into ``step_up.models.rebind`` natively and +delete the sys.path hack. +""" + +from __future__ import annotations + +import sys +from pathlib import Path +from typing import Any + +import torch + +_REBIND_ROOT = Path(__file__).resolve().parents[3] / "external" / "ReBIND" +if not _REBIND_ROOT.exists(): + raise FileNotFoundError( + f"ReBind submodule not found at {_REBIND_ROOT}. " + "Run: git submodule update --init --recursive" + ) + +# Put the vendor root on sys.path so its internal imports (`from .modules ...`, +# `from models import ...`) resolve. Idempotent. +if str(_REBIND_ROOT) not in sys.path: + sys.path.insert(0, str(_REBIND_ROOT)) + +from models import REBIND, Collator, REBINDConfig # noqa: E402 +from models.modules import utils as _rebind_utils # noqa: E402 +from models.rebind import collating_rebind as _rebind_collating # noqa: E402 +from models.rebind import modeling_rebind as _rebind_modeling # noqa: E402 + +__all__ = ["REBIND", "Collator", "REBINDConfig", "build_rebind", "patch_lj_parameters"] + + +# --------------------------------------------------------------------------- +# LJ-parameter extension for organometallics. +# --------------------------------------------------------------------------- +# ReBind's `get_sigma_and_epsilon` hardcodes LJ parameters for atomic-number +# indices 0..35 (i.e., Z=1..36, H through Kr). For organometallics that include +# 4d, 5d, and f-block elements, we extend with a safe fallback. The values are +# order-of-magnitude reasonable (UFF-style sigma ~3.5 A, epsilon ~0.05 kcal/mol); the +# LJ rewiring's contribution is small relative to the bond-graph signal, and +# physically realistic dispersion for metals is dominated by short-range +# Pauli repulsion which the cutoff already handles. + +_DEFAULT_LJ_SIGMA = 3.5 +_DEFAULT_LJ_EPSILON = 0.05 + + +def patch_lj_parameters() -> None: + """Replace ``get_sigma_and_epsilon`` with a Z-tolerant version. + + Idempotent: calling twice has no effect beyond the first. + """ + if getattr(_rebind_utils, "_step_up_patched", False): + return + + original_dict = { + i: {"sigma": _DEFAULT_LJ_SIGMA, "epsilon": _DEFAULT_LJ_EPSILON} for i in range(118) + } + # `lj_parameters` is defined inside `get_sigma_and_epsilon`. Re-read its + # canonical entries from a one-off call by inspecting the function's + # closure-free body: we just copy from a local clone here. + canonical = _canonical_lj_table() + original_dict.update(canonical) + + def patched_get_sigma_and_epsilon( + mol_data: Any, drugs: bool = False + ) -> tuple[torch.Tensor, torch.Tensor]: + eps_list: list[float] = [] + sig_list: list[float] = [] + for i, atom_id in enumerate(mol_data.node_type): + if drugs: + # We don't use the `drugs` branch in step-up. Fall through to + # the index-based lookup, which assumes `node_type` is already + # Z-1. + pass + idx = int(atom_id.item()) + params = original_dict.get( + idx, {"sigma": _DEFAULT_LJ_SIGMA, "epsilon": _DEFAULT_LJ_EPSILON} + ) + eps_list.append(params["epsilon"]) + sig_list.append(params["sigma"]) + del i + return torch.tensor(eps_list), torch.tensor(sig_list) + + _rebind_utils.get_sigma_and_epsilon = patched_get_sigma_and_epsilon + # `from ..modules.utils import get_sigma_and_epsilon` binds the symbol + # directly in the collator module — re-rebind it there too. + _rebind_collating.get_sigma_and_epsilon = patched_get_sigma_and_epsilon + _rebind_utils._step_up_patched = True + + +def _canonical_lj_table() -> dict[int, dict[str, float]]: + """Return ReBind's original Z=1..36 LJ parameter table (key = Z - 1).""" + return { + 0: {"sigma": 2.886, "epsilon": 0.0440}, + 1: {"sigma": 2.362, "epsilon": 0.0560}, + 2: {"sigma": 2.451, "epsilon": 0.0250}, + 3: {"sigma": 2.745, "epsilon": 0.0850}, + 4: {"sigma": 3.637, "epsilon": 0.1800}, + 5: {"sigma": 3.431, "epsilon": 0.1050}, + 6: {"sigma": 3.260, "epsilon": 0.0690}, + 7: {"sigma": 3.118, "epsilon": 0.0600}, + 8: {"sigma": 2.996, "epsilon": 0.0500}, + 9: {"sigma": 2.889, "epsilon": 0.0420}, + 10: {"sigma": 2.983, "epsilon": 0.0300}, + 11: {"sigma": 2.905, "epsilon": 0.1110}, + 12: {"sigma": 4.008, "epsilon": 0.5050}, + 13: {"sigma": 3.826, "epsilon": 0.4020}, + 14: {"sigma": 3.694, "epsilon": 0.3050}, + 15: {"sigma": 3.594, "epsilon": 0.2740}, + 16: {"sigma": 3.516, "epsilon": 0.2270}, + 17: {"sigma": 3.404, "epsilon": 0.1850}, + 18: {"sigma": 3.812, "epsilon": 0.0350}, + 19: {"sigma": 3.487, "epsilon": 0.2380}, + 20: {"sigma": 3.316, "epsilon": 0.0190}, + 21: {"sigma": 3.294, "epsilon": 0.0170}, + 22: {"sigma": 3.273, "epsilon": 0.0160}, + 23: {"sigma": 3.249, "epsilon": 0.0150}, + 24: {"sigma": 3.210, "epsilon": 0.0130}, + 25: {"sigma": 3.174, "epsilon": 0.0130}, + 26: {"sigma": 3.144, "epsilon": 0.0130}, + 27: {"sigma": 3.116, "epsilon": 0.0130}, + 28: {"sigma": 3.083, "epsilon": 0.0050}, + 29: {"sigma": 3.002, "epsilon": 0.1240}, + 30: {"sigma": 4.383, "epsilon": 0.4150}, + 31: {"sigma": 4.310, "epsilon": 0.3790}, + 32: {"sigma": 4.280, "epsilon": 0.3090}, + 33: {"sigma": 4.336, "epsilon": 0.2910}, + 34: {"sigma": 4.403, "epsilon": 0.2510}, + 35: {"sigma": 4.463, "epsilon": 0.2200}, + } + + +def _patched_encoder_forward(self, **inputs): + """Out-of-place equivalent of ``Encoder.forward`` from vendored ReBind. + + The vendored version did an in-place slice-assignment of the Laplacian + positional encoding into ``node_embedding``, which breaks autograd on + modern PyTorch (>=2.6). We replace it with a zero-padded add. + """ + node_attr = inputs.get("node_attr") + node_embedding = self.node_embedding(node_attr) + lap = inputs.get("lap_eigenvectors") + node_embedding = _add_lap_out_of_place(node_embedding, lap) + inputs["node_embedding"] = node_embedding + + attn_weight_dict: dict = {} + for i, encoder_block in enumerate(self.encoder_blocks): + block_out = encoder_block(**inputs) + node_embedding, attn_weight = block_out["out"], block_out["attn_weight"] + inputs["node_embedding"] = node_embedding + attn_weight_dict[f"encoder_block_{i}"] = attn_weight + return {"node_embedding": node_embedding, "attn_weight_dict": attn_weight_dict} + + +def _patched_decoder_forward(self, **inputs): + """Out-of-place equivalent of ``Decoder.forward`` from vendored ReBind.""" + node_embedding = inputs.get("node_embedding") + lap = inputs.get("lap_eigenvectors") + node_embedding = _add_lap_out_of_place(node_embedding, lap) + inputs["node_embedding"] = node_embedding + + attn_weight_dict: dict = {} + for i, decoder_block in enumerate(self.decoder_blocks): + block_out = decoder_block(**inputs) + node_embedding, attn_weight = block_out["out"], block_out["attn_weight"] + inputs["node_embedding"] = node_embedding + attn_weight_dict[f"decoder_block_{i}"] = attn_weight + return {"node_embedding": node_embedding, "attn_weight_dict": attn_weight_dict} + + +def _add_lap_out_of_place(node_embedding: torch.Tensor, lap: torch.Tensor) -> torch.Tensor: + """Add ``lap`` into the leading channels of ``node_embedding`` without in-place ops.""" + d = node_embedding.shape[-1] + lap_dim = lap.shape[-1] + if lap_dim < d: + pad = (0, d - lap_dim) + lap = torch.nn.functional.pad(lap, pad) + elif lap_dim > d: + lap = lap[..., :d] + return node_embedding + lap + + +def patch_inplace_lap_addition() -> None: + """Replace ``Encoder.forward`` and ``Decoder.forward`` with autograd-safe versions.""" + if getattr(_rebind_modeling, "_step_up_inplace_patched", False): + return + _rebind_modeling.Encoder.forward = _patched_encoder_forward + _rebind_modeling.Decoder.forward = _patched_decoder_forward + _rebind_modeling._step_up_inplace_patched = True + + +# Minimum predicted interatomic distance, in angstroms, used to clamp the LJ +# denominator. Real bonded distances are >0.7 A; this value is small enough to +# be a no-op for any realistic prediction and large enough that ``s**6`` (which +# scales as ``sigma**6 / D**6``) cannot overflow FP32 in the LJ block. +_LJ_D_MIN = 0.1 + + +def _patched_rebind_forward(self, **inputs): + """Out-of-place + numerically-defensive copy of ``REBIND.forward``. + + Two changes from vendored ReBind: + + 1. The predicted pairwise-distance tensor ``D_cache`` is clamped to a + small positive minimum before being used in the LJ-force expression + ``s = sigma / D_cache``. Without this, two non-bonded atoms whose + predicted coordinates collide produce ``s = inf`` and downstream + ``inf * 0`` NaNs. The diagonal mask only catches ``i == j`` collisions, + not collisions between distinct atoms — which is exactly what kills + full-scale QM9 training after ~100 steps. Clamp threshold is well + below any real interatomic distance so realistic forward passes are + unaffected. + 2. The final attraction / repulsion adjacency tensors are passed through + ``nan_to_num`` as a belt-and-suspenders guard — if anything upstream + still produces NaN, it gets neutralized to 0 before flowing into the + decoder's attention bias. + """ + conformer, node_mask = inputs.get("conformer"), inputs.get("node_mask") + + encoder_out = self.encoder(**inputs) + node_embedding = encoder_out["node_embedding"] + + cache_out = self.conformer_head( + conformer=conformer, + hidden_X=node_embedding, + padding_mask=node_mask, + compute_loss=True, + ) + loss_cache, conformer_cache = cache_out["loss"], cache_out["conformer_hat"] + + # CHANGE (1): clamp_min on the predicted distance matrix. + D_cache = torch.cdist(conformer_cache, conformer_cache).detach().clamp_min(_LJ_D_MIN) + D_M = _rebind_modeling.make_cdist_mask(node_mask) + inputs["pred_conformation"] = node_embedding + inputs["node_embedding"] = node_embedding + + sigma, epsilon = inputs.get("sigma"), inputs.get("epsilon") + s = sigma / D_cache + s6 = s**6 + LJ_force_orig = 24 * epsilon * (s6 / D_cache) * (2 * s6 - 1) + LJ_force = torch.abs(LJ_force_orig) + + adj_mask = inputs["adjacency"].bool() + self_mask = ( + torch.eye(D_cache.shape[1], device=D_cache.device) + .bool() + .unsqueeze(0) + .expand(D_cache.shape[0], -1, -1) + ) + LJ_force = LJ_force.masked_fill(adj_mask | self_mask, 0) + LJ_force = LJ_force.masked_fill(~D_M.bool(), 0) + + num_retain_edges = inputs.get("num_near_edges") + ret_val = _rebind_modeling.retain_top_k(LJ_force, num_retain_edges, descending=True) + nonzeros = ret_val.nonzero(as_tuple=False) + ret_val[nonzeros[:, 0], nonzeros[:, 1], nonzeros[:, 2]] = 1 + + LJ_nonzero_mask = LJ_force > 0 + LJ_above_zero_mask = LJ_force_orig > 0 + LJ_below_zero_mask = LJ_force_orig < 0 + + repulsive = LJ_nonzero_mask.float() * LJ_above_zero_mask.float() * ret_val * -1 + attractive = LJ_nonzero_mask.float() * LJ_below_zero_mask.float() * ret_val * 1 + # CHANGE (2): scrub any residual NaN/inf out of the attention biases. + inputs["attraction_adjacency"] = torch.nan_to_num(attractive, nan=0.0, posinf=0.0, neginf=0.0) + inputs["repulsion_adjacency"] = torch.nan_to_num(repulsive, nan=0.0, posinf=0.0, neginf=0.0) + + decoder_out = self.decoder(**inputs) + node_embedding = decoder_out["node_embedding"] + + outputs = self.residual_head( + conformer=conformer, + hidden_X=node_embedding, + padding_mask=node_mask, + compute_loss=True, + conformer_base=inputs["pred_conformation"], + ) + + return _rebind_modeling.ConformerPredictionOutput( + loss=(outputs["loss"] + loss_cache) / 2, + cdist_mae=outputs["cdist_mae"], + cdist_mse=outputs["cdist_mse"], + coord_rmsd=outputs["coord_rmsd"], + conformer=outputs["conformer"], + conformer_hat=outputs["conformer_hat"], + ) + + +def patch_rebind_forward() -> None: + """Replace ``REBIND.forward`` with the numerically-defensive version.""" + if getattr(_rebind_modeling, "_step_up_forward_patched", False): + return + _rebind_modeling.REBIND.forward = _patched_rebind_forward + _rebind_modeling._step_up_forward_patched = True + + +# Apply patches eagerly on import. +patch_lj_parameters() +patch_inplace_lap_addition() +patch_rebind_forward() + + +def build_rebind( + n_layers: int = 8, + d_model: int = 512, + d_ffn: int = 1024, + n_head: int = 8, + atom_vocab_size: int = 513, + dropout: float = 0.0, +) -> REBIND: + """Instantiate a REBIND model from a flat keyword-style config.""" + config = REBINDConfig( + n_encode_layers=n_layers, + n_decode_layers=n_layers, + embed_style="atom_type_ids", + atom_vocab_size=atom_vocab_size, + d_embed=d_model, + pre_ln=False, + d_q=d_model, + d_k=d_model, + d_v=d_model, + d_model=d_model, + n_head=n_head, + qkv_bias=True, + attn_drop=dropout, + norm_drop=dropout, + ffn_drop=dropout, + dropout=dropout, + d_ffn=d_ffn, + ) + return REBIND(config) diff --git a/src/step_up/train.py b/src/step_up/train.py new file mode 100644 index 0000000..552ee4e --- /dev/null +++ b/src/step_up/train.py @@ -0,0 +1,337 @@ +"""Config-driven training loop for step-up. + +The loop is deliberately minimal: it owns dataset construction, the ReBind +model, AdamW + linear-warmup, periodic validation, best-checkpoint saving, and +TensorBoard logging. No HuggingFace Trainer, no accelerate — keeping the +control flow legible while we're still iterating on the model. +""" + +from __future__ import annotations + +import json +import math +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import torch +import yaml +from torch.utils.data import DataLoader, Subset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from .data.csv_dataset import CSVMoleculeDataset +from .data.splits import random_split +from .models.rebind import Collator, build_rebind + +# --------------------------------------------------------------------------- +# Config dataclasses +# --------------------------------------------------------------------------- + + +@dataclass +class TrainConfig: + dataset_path: str + dataset_source: str # "smiles" | "mol2" + subset_size: int | None = None + split_ratios: tuple[float, float, float] = (0.8, 0.1, 0.1) + split_seed: int = 0 + + # Model + n_layers: int = 8 + d_model: int = 512 + d_ffn: int = 1024 + n_head: int = 8 + dropout: float = 0.0 + + # Optimization + epochs: int = 20 + batch_size: int = 100 + eval_batch_size: int = 100 + lr: float = 9e-5 + weight_decay: float = 0.0 + warmup_ratio: float = 0.1 + # Global gradient-norm clip. ReBind's published training inherited + # HuggingFace Trainer's default of 1.0; without it the 8-layer / d=512 + # model NaNs out in the first epoch on QM9. + grad_clip: float = 1.0 + # Skip batches whose loss comes back non-finite (the LJ block in ReBind's + # head can divide by predicted distances that are ~0 early in training). + # Abort if more than this many batches are skipped over the whole run. + nan_skip_max: int = 50 + num_workers: int = 0 + + # Runtime + device: str = "cpu" + output_dir: str = "outputs/run" + seed: int = 0 + log_interval: int = 10 + + @classmethod + def from_yaml(cls, path: str | Path) -> TrainConfig: + with open(path) as f: + raw = yaml.safe_load(f) or {} + if "split_ratios" in raw: + raw["split_ratios"] = tuple(raw["split_ratios"]) + return cls(**raw) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _set_seed(seed: int) -> None: + import random + + import numpy as np + + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + +def _move_batch_to_device(batch: dict[str, Any], device: str) -> dict[str, Any]: + return {k: v.to(device) if torch.is_tensor(v) else v for k, v in batch.items()} + + +def _nonfinite_keys(batch: dict[str, Any]) -> list[str]: + """Return floating-point tensors in ``batch`` that contain non-finite values.""" + bad: list[str] = [] + for k, v in batch.items(): + if torch.is_tensor(v) and v.dtype.is_floating_point and not torch.isfinite(v).all(): + bad.append(k) + return bad + + +def _all_params_finite(model: torch.nn.Module) -> bool: + for p in model.parameters(): + if not torch.isfinite(p).all(): + return False + return True + + +def _linear_warmup_lr(step: int, total_steps: int, warmup_steps: int, base_lr: float) -> float: + if step < warmup_steps: + return base_lr * (step + 1) / max(warmup_steps, 1) + progress = (step - warmup_steps) / max(total_steps - warmup_steps, 1) + return base_lr * 0.5 * (1.0 + math.cos(math.pi * progress)) + + +# --------------------------------------------------------------------------- +# Training loop +# --------------------------------------------------------------------------- + + +def _run_epoch( + model: torch.nn.Module, + loader: DataLoader, + optimizer: torch.optim.Optimizer | None, + device: str, + scheduler_state: dict[str, int], + base_lr: float, + total_steps: int, + warmup_steps: int, + grad_clip: float = 0.0, + nan_skip_max: int = 0, +) -> tuple[float, float]: + """Run one pass over ``loader``. Returns (mean_loss, mean_dmae). + + During training, batches whose loss comes back non-finite are skipped + (no backward, no optimizer step). The first occurrence prints whether the + badness is in the input batch or appeared inside the forward pass — useful + to distinguish data corruption from model-side numerical instability. + """ + is_train = optimizer is not None + model.train(is_train) + loss_sum, dmae_sum, n = 0.0, 0.0, 0 + pbar = tqdm(loader, leave=False, desc="train" if is_train else "val", dynamic_ncols=True) + for batch in pbar: + batch = _move_batch_to_device(batch, device) + if is_train: + lr_now = _linear_warmup_lr(scheduler_state["step"], total_steps, warmup_steps, base_lr) + for g in optimizer.param_groups: + g["lr"] = lr_now + optimizer.zero_grad(set_to_none=True) + out = model(**batch) + loss_val = out.loss + if not torch.isfinite(loss_val): + scheduler_state["nan_skipped"] = scheduler_state.get("nan_skipped", 0) + 1 + skipped = scheduler_state["nan_skipped"] + bad_inputs = _nonfinite_keys(batch) + n_atoms = batch["node_mask"].sum(dim=-1).long().tolist() + origin = ( + f"input nonfinite in {bad_inputs}" + if bad_inputs + else "input is clean -> NaN originated in the model forward" + ) + print( + f"WARN: non-finite loss at step {scheduler_state['step']} " + f"(skipped {skipped}/{nan_skip_max}); " + f"batch n_atoms={n_atoms}; {origin}", + flush=True, + ) + if skipped > nan_skip_max: + raise RuntimeError( + f"Exceeded nan_skip_max={nan_skip_max} non-finite batches. " + "Lower lr, raise grad_clip stringency, or filter the dataset." + ) + scheduler_state["step"] += 1 + continue + loss_val.backward() + # clip_grad_norm_ returns the *unclipped* total gradient norm. If + # it's non-finite, the gradients themselves contain NaN/Inf — a + # well-known failure mode of torch.svd on rank-deficient inputs, + # which the Kabsch alignment inside ReBind's conformer head can + # produce. Calling optimizer.step() with NaN gradients would + # poison every parameter with NaN, making every subsequent + # forward pass NaN. Skip the step in that case. + grad_norm = torch.nn.utils.clip_grad_norm_( + model.parameters(), max_norm=grad_clip if grad_clip > 0 else float("inf") + ) + if not torch.isfinite(grad_norm): + scheduler_state["nan_skipped"] = scheduler_state.get("nan_skipped", 0) + 1 + skipped = scheduler_state["nan_skipped"] + print( + f"WARN: non-finite gradient norm ({grad_norm.item()}) at step " + f"{scheduler_state['step']} (skipped {skipped}/{nan_skip_max}). " + f"Likely a degenerate batch through Kabsch SVD.", + flush=True, + ) + optimizer.zero_grad(set_to_none=True) + if skipped > nan_skip_max: + raise RuntimeError( + f"Exceeded nan_skip_max={nan_skip_max} non-finite batches. " + "Lower lr or filter the dataset." + ) + scheduler_state["step"] += 1 + continue + optimizer.step() + # Cheap sanity: any parameter going NaN means we have a bug + # upstream (e.g. another path that bypassed the grad-norm check). + # Fail loudly instead of producing meaningless NaN for thousands + # of steps. + if not _all_params_finite(model): + raise RuntimeError( + f"Model parameters went non-finite after optimizer step at " + f"step {scheduler_state['step']}. This indicates a code " + "bug bypassing the gradient-NaN check." + ) + scheduler_state["step"] += 1 + else: + with torch.no_grad(): + out = model(**batch) + if not torch.isfinite(out.loss): + continue + loss_sum += float(out.loss.detach()) + dmae_sum += float(out.cdist_mae.detach()) + n += 1 + pbar.set_postfix(loss=f"{loss_sum / n:.4f}", dmae=f"{dmae_sum / n:.4f}") + return loss_sum / max(n, 1), dmae_sum / max(n, 1) + + +def train(config: TrainConfig) -> dict[str, Any]: + _set_seed(config.seed) + out_dir = Path(config.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + with open(out_dir / "config.json", "w") as f: + json.dump(config.__dict__, f, indent=2, default=str) + + # Data + dataset = CSVMoleculeDataset( + path=config.dataset_path, + source=config.dataset_source, # type: ignore[arg-type] + subset_size=config.subset_size, + ) + train_set, val_set, test_set = random_split( + dataset, ratios=config.split_ratios, seed=config.split_seed + ) + collator = Collator() + + def _make_loader(subset: Subset, batch_size: int, shuffle: bool) -> DataLoader: + return DataLoader( + subset, + batch_size=batch_size, + shuffle=shuffle, + num_workers=config.num_workers, + collate_fn=collator, + persistent_workers=config.num_workers > 0, + ) + + train_loader = _make_loader(train_set, config.batch_size, shuffle=True) + val_loader = _make_loader(val_set, config.eval_batch_size, shuffle=False) + + # Model + optimizer + model = build_rebind( + n_layers=config.n_layers, + d_model=config.d_model, + d_ffn=config.d_ffn, + n_head=config.n_head, + dropout=config.dropout, + ).to(config.device) + optimizer = torch.optim.AdamW( + model.parameters(), lr=config.lr, weight_decay=config.weight_decay + ) + + steps_per_epoch = max(len(train_loader), 1) + total_steps = steps_per_epoch * config.epochs + warmup_steps = int(total_steps * config.warmup_ratio) + + writer = SummaryWriter(out_dir / "tb") + scheduler_state: dict[str, int] = {"step": 0} + history: list[dict[str, float]] = [] + best_val = float("inf") + t0 = time.time() + for epoch in range(1, config.epochs + 1): + train_loss, train_dmae = _run_epoch( + model, + train_loader, + optimizer, + config.device, + scheduler_state, + config.lr, + total_steps, + warmup_steps, + grad_clip=config.grad_clip, + nan_skip_max=config.nan_skip_max, + ) + val_loss, val_dmae = _run_epoch( + model, + val_loader, + None, + config.device, + scheduler_state, + config.lr, + total_steps, + warmup_steps, + ) + writer.add_scalar("train/loss", train_loss, epoch) + writer.add_scalar("train/dmae", train_dmae, epoch) + writer.add_scalar("val/loss", val_loss, epoch) + writer.add_scalar("val/dmae", val_dmae, epoch) + elapsed = time.time() - t0 + print( + f"[epoch {epoch:>3}/{config.epochs}] " + f"train_loss={train_loss:.4f} train_dmae={train_dmae:.4f} " + f"val_loss={val_loss:.4f} val_dmae={val_dmae:.4f} " + f"elapsed={elapsed:.1f}s" + ) + history.append( + { + "epoch": epoch, + "train_loss": train_loss, + "train_dmae": train_dmae, + "val_loss": val_loss, + "val_dmae": val_dmae, + } + ) + if val_dmae < best_val: + best_val = val_dmae + torch.save(model.state_dict(), out_dir / "best.pt") + writer.close() + + with open(out_dir / "history.json", "w") as f: + json.dump(history, f, indent=2) + del test_set # held out; first round does not evaluate on test + return {"history": history, "best_val_dmae": best_val} diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..c45c1db --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,32 @@ +"""Shared pytest fixtures and skip-paths for tests that need the local CSVs.""" + +from __future__ import annotations + +from pathlib import Path + +import pytest + +QM9_PATH = Path("/home/gridsan/jtoney/ElemNet/benchmarking/datasets/QM9-full.csv") +TMQMG_PATH = Path("/home/gridsan/jtoney/ElemNet/benchmarking/datasets/tmQMg-full.csv") +BOSTMC_PATH = Path("/home/gridsan/jtoney/BOSTMC/datasets/BOSTMC-low-spin.csv") + + +def _skip_if_missing(p: Path) -> Path: + if not p.exists(): + pytest.skip(f"dataset not available at {p}") + return p + + +@pytest.fixture(scope="session") +def qm9_path() -> Path: + return _skip_if_missing(QM9_PATH) + + +@pytest.fixture(scope="session") +def tmqmg_path() -> Path: + return _skip_if_missing(TMQMG_PATH) + + +@pytest.fixture(scope="session") +def bostmc_path() -> Path: + return _skip_if_missing(BOSTMC_PATH) diff --git a/tests/test_csv_dataset.py b/tests/test_csv_dataset.py new file mode 100644 index 0000000..94187da --- /dev/null +++ b/tests/test_csv_dataset.py @@ -0,0 +1,52 @@ +"""Verify each CSV path produces valid ReBind graph dicts on a small subset.""" + +from __future__ import annotations + +from step_up.data.csv_dataset import CSVMoleculeDataset + + +def _check_graph_dict(g: dict) -> None: + required = {"node_attr", "edge_attr", "edge_index", "node_type", "conformer"} + missing = required - g.keys() + assert not missing, f"graph dict missing keys: {missing}" + n = g["num_nodes"] + assert n > 0 + assert len(g["node_type"]) == n + assert len(g["node_attr"]) == n + assert len(g["conformer"]) == n + # edge_index shape: 2 x num_edges (lists) + assert len(g["edge_index"]) == 2 + assert len(g["edge_index"][0]) == len(g["edge_index"][1]) + + +def test_qm9_smoke(qm9_path) -> None: + ds = CSVMoleculeDataset(qm9_path, source="smiles", subset_size=5) + assert len(ds) == 5 + for i in range(len(ds)): + _check_graph_dict(ds[i]) + + +def test_tmqmg_smoke(tmqmg_path) -> None: + ds = CSVMoleculeDataset(tmqmg_path, source="mol2", subset_size=20) + assert len(ds) == 20 + elements_seen: set[int] = set() + for i in range(len(ds)): + g = ds[i] + _check_graph_dict(g) + elements_seen.update(g["node_type"]) + # tmQMg always includes a transition metal. node_type is Z-1, so 3d/4d/5d + # metals show up at indices >= 20. Confirm at least one heavy element + # made it through the direct MOL2 parser without being silently dropped. + assert max(elements_seen) >= 20, f"no heavy elements seen: {sorted(elements_seen)}" + + +def test_bostmc_smoke(bostmc_path) -> None: + ds = CSVMoleculeDataset(bostmc_path, source="mol2", subset_size=5) + assert len(ds) == 5 + elements_seen: set[int] = set() + for i in range(len(ds)): + g = ds[i] + _check_graph_dict(g) + elements_seen.update(g["node_type"]) + # BOSTMC is d-block-only by construction. + assert max(elements_seen) >= 20, f"no transition metals seen: {sorted(elements_seen)}" diff --git a/tests/test_forward.py b/tests/test_forward.py new file mode 100644 index 0000000..1c35d8a --- /dev/null +++ b/tests/test_forward.py @@ -0,0 +1,41 @@ +"""Forward pass on a 4-molecule QM9 batch through a tiny ReBind.""" + +from __future__ import annotations + +import torch +from torch.utils.data import DataLoader, Subset + +from step_up.data.csv_dataset import CSVMoleculeDataset +from step_up.models.rebind import Collator, build_rebind + + +def test_forward_tiny_qm9(qm9_path) -> None: + ds = CSVMoleculeDataset(qm9_path, source="smiles", subset_size=8) + subset = Subset(ds, [0, 1, 2, 3]) + loader = DataLoader(subset, batch_size=4, shuffle=False, collate_fn=Collator()) + batch = next(iter(loader)) + + model = build_rebind(n_layers=2, d_model=32, d_ffn=64, n_head=4) + model.eval() + with torch.no_grad(): + out = model(**batch) + + assert out.loss.dim() == 0 + assert torch.isfinite(out.loss) + assert out.conformer_hat.shape == out.conformer.shape + b, n, _ = out.conformer_hat.shape + assert b == 4 + assert n > 0 + assert torch.isfinite(out.conformer_hat).all() + + +def test_lj_patch_supports_heavy_z(tmqmg_path) -> None: + """Verify that the LJ patch lets tmQMg (Z up to 80) pass through Collator.""" + ds = CSVMoleculeDataset(tmqmg_path, source="mol2", subset_size=2) + subset = Subset(ds, [0, 1]) + loader = DataLoader(subset, batch_size=2, shuffle=False, collate_fn=Collator()) + # This would KeyError pre-patch on transition-metal indices. + batch = next(iter(loader)) + assert "sigma" in batch and "epsilon" in batch + assert torch.isfinite(batch["sigma"]).all() + assert torch.isfinite(batch["epsilon"]).all() diff --git a/tests/test_metrics.py b/tests/test_metrics.py new file mode 100644 index 0000000..3ae92ba --- /dev/null +++ b/tests/test_metrics.py @@ -0,0 +1,46 @@ +"""Verify D-MAE / D-RMSE / coord-RMSD have expected mathematical behavior.""" + +from __future__ import annotations + +import torch + +from step_up.eval.metrics import cdist_mae, cdist_rmse, coord_rmsd, per_element_dmae + + +def _toy_batch() -> tuple[torch.Tensor, torch.Tensor]: + # Two molecules: first has 3 valid atoms, second has 4. + coords = torch.zeros(2, 4, 3, dtype=torch.float32) + coords[0, 0] = torch.tensor([0.0, 0.0, 0.0]) + coords[0, 1] = torch.tensor([1.0, 0.0, 0.0]) + coords[0, 2] = torch.tensor([0.0, 1.0, 0.0]) + coords[1, 0] = torch.tensor([0.0, 0.0, 0.0]) + coords[1, 1] = torch.tensor([2.0, 0.0, 0.0]) + coords[1, 2] = torch.tensor([0.0, 2.0, 0.0]) + coords[1, 3] = torch.tensor([0.0, 0.0, 2.0]) + mask = torch.tensor([[1, 1, 1, 0], [1, 1, 1, 1]], dtype=torch.float32) + return coords, mask + + +def test_zero_when_identical() -> None: + coords, mask = _toy_batch() + assert cdist_mae(coords, coords, mask).item() == 0.0 + assert cdist_rmse(coords, coords, mask).item() == 0.0 + assert coord_rmsd(coords, coords, mask).item() == 0.0 + + +def test_dmae_positive_on_perturbation() -> None: + coords, mask = _toy_batch() + noisy = coords + 0.1 * torch.randn_like(coords) * mask.unsqueeze(-1) + assert cdist_mae(noisy, coords, mask).item() > 0.0 + assert cdist_rmse(noisy, coords, mask).item() > 0.0 + + +def test_per_element_dmae_groups_by_z() -> None: + coords, mask = _toy_batch() + pred = coords + 0.5 # uniform shift; cdist is translation-invariant -> 0 + # Atomic numbers: first mol = C,N,O (5,6,7); second mol = H,H,H,H (0). + z = torch.tensor([[5, 6, 7, 0], [0, 0, 0, 0]], dtype=torch.long) + per = per_element_dmae(pred, coords, mask, z) + # Uniform translation leaves pairwise distances unchanged -> 0 per element. + for z_val, v in per.items(): + assert v == 0.0, f"element {z_val} should have 0 D-MAE under translation" diff --git a/tests/test_smoke.py b/tests/test_smoke.py index 7ee65a0..0cc4935 100644 --- a/tests/test_smoke.py +++ b/tests/test_smoke.py @@ -1,2 +1,7 @@ +"""Package-level import smoke test.""" + + def test_package_imports() -> None: - pass + import step_up + + assert step_up.__version__ diff --git a/uv.lock b/uv.lock index 4c6b6ee..6a823b8 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,170 @@ version = 1 revision = 3 requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < 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