From cb28850371649bcb9b1477579128f0200442e662 Mon Sep 17 00:00:00 2001 From: ChangwenXu98 Date: Mon, 19 Aug 2024 22:13:56 -0400 Subject: [PATCH] Finetune CHGNet v0.3.0 on formation energy --- .../chgnet/__init__.py | 17 + .../chgnet/data/__init__.py | 0 .../chgnet/data/dataset.py | 880 +++++++++++++ .../chgnet/data/dataset_custom.py | 322 +++++ .../chgnet/graph/__init__.py | 4 + .../chgnet/graph/converter.py | 291 +++++ .../chgnet/graph/crystalgraph.py | 198 +++ .../chgnet/graph/cygraph.pyx | 170 +++ .../fast_converter_libraries/create_graph.c | 423 ++++++ .../graph/fast_converter_libraries/uthash.h | 1140 +++++++++++++++++ .../chgnet/graph/graph.py | 356 +++++ .../chgnet/model/__init__.py | 4 + .../chgnet/model/basis.py | 206 +++ .../chgnet/model/composition_model.py | 433 +++++++ .../chgnet/model/dynamics.py | 891 +++++++++++++ .../chgnet/model/encoders.py | 146 +++ .../chgnet/model/functions.py | 222 ++++ .../chgnet/model/layers.py | 453 +++++++ .../chgnet/model/model.py | 895 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benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/trainer/trainer.py create mode 100755 benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/trainer/trainer_custom_prop.py create mode 100755 benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/utils/__init__.py create mode 100755 benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/utils/common_utils.py create mode 100755 benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/utils/vasp_utils.py create mode 100644 benchmarks/matbench_v0.1_CHGNet_v0.3.0/info.json create mode 100644 benchmarks/matbench_v0.1_CHGNet_v0.3.0/results.json.gz create mode 100644 benchmarks/matbench_v0.1_CHGNet_v0.3.0/train_chgnet_mb.py diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/__init__.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/__init__.py new file mode 100755 index 00000000..f3a992ef --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/__init__.py @@ -0,0 +1,17 @@ +"""The pytorch implementation for CHGNet neural network potential.""" + +from __future__ import annotations + +import os +from importlib.metadata import PackageNotFoundError, version +from typing import Literal + +try: + __version__ = version(__name__) # read from pyproject.toml +except PackageNotFoundError: + __version__ = "unknown" + +TrainTask = Literal["ef", "efs", "efsm"] +PredTask = Literal["e", "ef", "em", "efs", "efsm"] + +ROOT = os.path.dirname(os.path.dirname(__file__)) diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/data/__init__.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/data/__init__.py new file mode 100755 index 00000000..e69de29b diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/data/dataset.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/data/dataset.py new file mode 100755 index 00000000..2bf74ee2 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/data/dataset.py @@ -0,0 +1,880 @@ +from __future__ import annotations + +import functools +import os +import random +import warnings +from typing import TYPE_CHECKING + +import numpy as np +import torch +from pymatgen.core.structure import Structure +from torch import Tensor +from torch.utils.data import DataLoader, Dataset +from torch.utils.data.sampler import SubsetRandomSampler + +from chgnet import utils +from chgnet.graph import CrystalGraph, CrystalGraphConverter + +if TYPE_CHECKING: + from collections.abc import Sequence + + from typing_extensions import Self + + from chgnet import TrainTask + +warnings.filterwarnings("ignore") +datatype = torch.float32 + + +class StructureData(Dataset): + """A simple torch Dataset of structures.""" + + def __init__( + self, + structures: list[Structure], + energies: list[float], + forces: list[Sequence[Sequence[float]]], + *, + stresses: list[Sequence[Sequence[float]]] | None = None, + magmoms: list[Sequence[Sequence[float]]] | None = None, + structure_ids: list | None = None, + graph_converter: CrystalGraphConverter | None = None, + shuffle: bool = True, + ) -> None: + """Initialize the dataset. + + Args: + structures (list[dict]): pymatgen Structure objects. + energies (list[float]): [data_size, 1] + forces (list[list[float]]): [data_size, n_atoms, 3] + stresses (list[list[float]], optional): [data_size, 3, 3] + Default = None + magmoms (list[list[float]], optional): [data_size, n_atoms, 1] + Default = None + structure_ids (list, optional): a list of ids to track the structures + Default = None + graph_converter (CrystalGraphConverter, optional): Converts the structures + to graphs. If None, it will be set to CHGNet 0.3.0 converter + with AtomGraph cutoff = 6A. + shuffle (bool): whether to shuffle the sequence of dataset + Default = True + + Raises: + RuntimeError: if the length of structures and labels (energies, forces, + stresses, magmoms) are not equal. + """ + for idx, struct in enumerate(structures): + if not isinstance(struct, Structure): + raise TypeError(f"{idx} is not a pymatgen Structure object: {struct}") + for name in "energies forces stresses magmoms structure_ids".split(): + labels = locals()[name] + if labels is not None and len(labels) != len(structures): + raise RuntimeError( + f"Inconsistent number of structures and labels: " + f"{len(structures)=}, len({name})={len(labels)}" + ) + self.structures = structures + self.energies = energies + self.forces = forces + self.stresses = stresses + self.magmoms = magmoms + self.structure_ids = structure_ids + self.keys = np.arange(len(structures)) + if shuffle: + random.shuffle(self.keys) + print(f"{type(self).__name__} imported {len(structures):,} structures") + self.graph_converter = graph_converter or CrystalGraphConverter( + atom_graph_cutoff=6, bond_graph_cutoff=3 + ) + self.failed_idx: list[int] = [] + self.failed_graph_id: dict[str, str] = {} + + @classmethod + def from_vasp( + cls, + file_root: str, + *, + check_electronic_convergence: bool = True, + save_path: str | None = None, + graph_converter: CrystalGraphConverter | None = None, + shuffle: bool = True, + ) -> Self: + """Parse VASP output files into structures and labels and feed into the dataset. + + Args: + file_root (str): the directory of the VASP calculation outputs + check_electronic_convergence (bool): if set to True, this function will + raise Exception to VASP calculation that did not achieve + electronic convergence. + Default = True + save_path (str): path to save the parsed VASP labels + Default = None + graph_converter (CrystalGraphConverter, optional): Converts the structures + to graphs. If None, it will be set to CHGNet 0.3.0 converter + with AtomGraph cutoff = 6A. + shuffle (bool): whether to shuffle the sequence of dataset + Default = True + """ + result_dict = utils.parse_vasp_dir( + base_dir=file_root, + check_electronic_convergence=check_electronic_convergence, + save_path=save_path, + ) + return cls( + structures=result_dict["structure"], + energies=result_dict["energy_per_atom"], + forces=result_dict["force"], + stresses=None + if result_dict["stress"] in [None, []] + else result_dict["stress"], + magmoms=None + if result_dict["magmom"] in [None, []] + else result_dict["magmom"], + structure_ids=np.arange(len(result_dict["structure"])), + graph_converter=graph_converter, + shuffle=shuffle, + ) + + def __len__(self) -> int: + """Get the number of structures in this dataset.""" + return len(self.keys) + + @functools.cache # Cache loaded structures + def __getitem__(self, idx: int) -> tuple[CrystalGraph, dict]: + """Get one graph for a structure in this dataset. + + Args: + idx (int): Index of the structure + + Returns: + crystal_graph (CrystalGraph): graph of the crystal structure + targets (dict): list of targets. i.e. energy, force, stress + """ + if idx not in self.failed_idx: + graph_id = self.keys[idx] + try: + struct = self.structures[graph_id] + if self.structure_ids is not None: + mp_id = self.structure_ids[graph_id] + else: + mp_id = graph_id + crystal_graph = self.graph_converter( + struct, graph_id=graph_id, mp_id=mp_id + ) + targets = { + "e": torch.tensor(self.energies[graph_id], dtype=datatype), + "f": torch.tensor(self.forces[graph_id], dtype=datatype), + } + if self.stresses is not None: + # Convert VASP stress + targets["s"] = torch.tensor( + self.stresses[graph_id], dtype=datatype + ) * (-0.1) + if self.magmoms is not None: + mag = self.magmoms[graph_id] + # use absolute value for magnetic moments + if mag is None: + targets["m"] = None + else: + targets["m"] = torch.abs(torch.tensor(mag, dtype=datatype)) + + return crystal_graph, targets + + # Omit structures with isolated atoms. Return another randomly selected + # structure + except Exception: + struct = self.structures[graph_id] + self.failed_graph_id[graph_id] = struct.composition.formula + self.failed_idx.append(idx) + idx = random.randint(0, len(self) - 1) + return self.__getitem__(idx) + else: + idx = random.randint(0, len(self) - 1) + return self.__getitem__(idx) + + +class CIFData(Dataset): + """A dataset from CIFs.""" + + def __init__( + self, + cif_path: str, + *, + labels: str | dict = "labels.json", + targets: TrainTask = "efsm", + graph_converter: CrystalGraphConverter | None = None, + energy_key: str = "energy_per_atom", + force_key: str = "force", + stress_key: str = "stress", + magmom_key: str = "magmom", + shuffle: bool = True, + ) -> None: + """Initialize the dataset from a directory containing CIFs. + + Args: + cif_path (str): path that contain all the graphs, labels.json + labels (str, dict): the path or dictionary of labels + targets ("ef" | "efs" | "efm" | "efsm"): The training targets. + Default = "efsm" + graph_converter (CrystalGraphConverter, optional): Converts the structures + to graphs. If None, it will be set to CHGNet 0.3.0 converter + with AtomGraph cutoff = 6A. + energy_key (str, optional): the key of energy in the labels. + Default = "energy_per_atom" + force_key (str, optional): the key of force in the labels. + Default = "force" + stress_key (str, optional): the key of stress in the labels. + Default = "stress" + magmom_key (str, optional): the key of magmom in the labels. + Default = "magmom" + shuffle (bool): whether to shuffle the sequence of dataset + Default = True + """ + self.data_dir = cif_path + self.data = utils.read_json(os.path.join(cif_path, labels)) + self.cif_ids = list(self.data) + if shuffle: + random.shuffle(self.cif_ids) + print(f"{cif_path}: {len(self.cif_ids):,} structures imported") + self.graph_converter = graph_converter or CrystalGraphConverter( + atom_graph_cutoff=6, bond_graph_cutoff=3 + ) + + self.energy_key = energy_key + self.force_key = force_key + self.stress_key = stress_key + self.magmom_key = magmom_key + self.targets = targets + self.failed_idx: list[int] = [] + self.failed_graph_id: dict[str, str] = {} + + def __len__(self) -> int: + """Get the number of structures in this dataset.""" + return len(self.cif_ids) + + @functools.cache # Cache loaded structures + def __getitem__(self, idx: int) -> tuple[CrystalGraph, dict[str, Tensor]]: + """Get one item in the dataset. + + Returns: + tuple[CrystalGraph, dict[str, Tensor]]: graph of the crystal structure + and dict of targets i.e. energy, force, stress + """ + if idx not in self.failed_idx: + try: + graph_id = self.cif_ids[idx] + mp_id = self.data[graph_id].get("material_id", graph_id) + structure = Structure.from_file( + os.path.join(self.data_dir, f"{graph_id}.cif") + ) + crystal_graph = self.graph_converter( + structure, graph_id=graph_id, mp_id=mp_id + ) + targets = {} + for key in self.targets: + if key == "e": + energy = self.data[graph_id][self.energy_key] + targets["e"] = torch.tensor(energy, dtype=datatype) + elif key == "f": + force = self.data[graph_id][self.force_key] + targets["f"] = torch.tensor(force, dtype=datatype) + elif key == "s": + stress = self.data[graph_id][self.stress_key] + # Convert VASP stress + targets["s"] = torch.tensor(stress, dtype=datatype) * -0.1 + elif key == "m": + mag = self.data[graph_id][self.magmom_key] + # use absolute value for magnetic moments + targets["m"] = torch.abs(torch.tensor(mag, dtype=datatype)) + return crystal_graph, targets + + # Omit structures with isolated atoms. + # Return another randomly selected structure + except Exception: + try: + graph_id = self.cif_ids[idx] + except IndexError: + print(idx, len(self.cif_ids)) + structure = Structure.from_file( + os.path.join(self.data_dir, f"{graph_id}.cif") + ) + self.failed_graph_id[graph_id] = structure.composition.formula + self.failed_idx.append(idx) + idx = random.randint(0, len(self) - 1) + return self.__getitem__(idx) + else: + idx = random.randint(0, len(self) - 1) + return self.__getitem__(idx) + + +class GraphData(Dataset): + """A dataset of graphs. This is compatible with the graph.pt documents made by + make_graphs.py. We recommend you to use the dataset to avoid graph conversion steps. + """ + + def __init__( + self, + graph_path: str, + *, + labels: str | dict = "labels.json", + targets: TrainTask = "efsm", + exclude: str | list | None = None, + energy_key: str = "energy_per_atom", + force_key: str = "force", + stress_key: str = "stress", + magmom_key: str = "magmom", + shuffle: bool = True, + ) -> None: + """Initialize the dataset from a directory containing saved crystal graphs. + + Args: + graph_path (str): path that contain all the graphs, labels.json + labels (str, dict): the path or dictionary of labels. + Default = "labels.json" + targets ("ef" | "efs" | "efm" | "efsm"): The training targets. + Default = "efsm" + exclude (str, list | None): the path or list of excluded graphs. + Default = None + energy_key (str, optional): the key of energy in the labels. + Default = "energy_per_atom" + force_key (str, optional): the key of force in the labels. + Default = "force" + stress_key (str, optional): the key of stress in the labels. + Default = "stress" + magmom_key (str, optional): the key of magmom in the labels. + Default = "magmom" + shuffle (bool): whether to shuffle the sequence of dataset + Default = True + """ + self.graph_path = graph_path + if isinstance(labels, str): + labels = os.path.join(graph_path, labels) + print(f"Importing: {labels}") + self.labels = utils.read_json(labels) + elif isinstance(labels, dict): + self.labels = labels + + if isinstance(exclude, str): + self.excluded_graph = utils.read_json(exclude) + elif isinstance(exclude, list): + self.excluded_graph = exclude + else: + self.excluded_graph = [] + + self.keys = [ + (mp_id, graph_id) for mp_id, dic in self.labels.items() for graph_id in dic + ] + if shuffle: + random.shuffle(self.keys) + print(f"{len(self.labels)} mp_ids, {len(self)} frames imported") + if self.excluded_graph is not None: + print(f"{len(self.excluded_graph)} graphs are pre-excluded") + + self.energy_key = energy_key + self.force_key = force_key + self.stress_key = stress_key + self.magmom_key = magmom_key + self.targets = targets + self.failed_idx: list[int] = [] + self.failed_graph_id: list[str] = [] + + def __len__(self) -> int: + """Get the number of graphs in this dataset.""" + return len(self.keys) + + def __getitem__(self, idx) -> tuple[CrystalGraph, dict[str, Tensor]]: + """Get one item in the dataset. + + Returns: + crystal_graph (CrystalGraph): graph of the crystal structure + targets (dict): dictionary of targets. i.e. energy, force, stress, magmom + """ + if idx not in self.failed_idx: + mp_id, graph_id = self.keys[idx] + if [mp_id, graph_id] in self.excluded_graph: + self.failed_graph_id.append(graph_id) + self.failed_idx.append(idx) + idx = random.randint(0, len(self) - 1) + return self.__getitem__(idx) + try: + graph_path = os.path.join(self.graph_path, f"{graph_id}.pt") + crystal_graph = CrystalGraph.from_file(graph_path) + targets = {} + for key in self.targets: + if key == "e": + energy = self.labels[mp_id][graph_id][self.energy_key] + targets["e"] = torch.tensor(energy, dtype=datatype) + elif key == "f": + force = self.labels[mp_id][graph_id][self.force_key] + targets["f"] = torch.tensor(force, dtype=datatype) + elif key == "s": + stress = self.labels[mp_id][graph_id][self.stress_key] + # Convert VASP stress + targets["s"] = torch.tensor(stress, dtype=datatype) * (-0.1) + elif key == "m": + mag = self.labels[mp_id][graph_id][self.magmom_key] + # use absolute value for magnetic moments + if mag is None: + targets["m"] = None + else: + targets["m"] = torch.abs(torch.tensor(mag, dtype=datatype)) + return crystal_graph, targets + + # Omit failed structures. Return another randomly selected structure + except Exception: + self.failed_graph_id.append(graph_id) + self.failed_idx.append(idx) + idx = random.randint(0, len(self) - 1) + return self.__getitem__(idx) + else: + idx = random.randint(0, len(self) - 1) + return self.__getitem__(idx) + + def get_train_val_test_loader( + self, + train_ratio: float = 0.8, + val_ratio: float = 0.1, + *, + train_key: list[str] | None = None, + val_key: list[str] | None = None, + test_key: list[str] | None = None, + batch_size=32, + num_workers=0, + pin_memory=True, + ) -> tuple[DataLoader, DataLoader, DataLoader]: + """Partition the GraphData using materials id, + randomly select the train_keys, val_keys, test_keys by train val test ratio, + or use pre-defined train_keys, val_keys, and test_keys to create train, val, + test loaders. + + Args: + train_ratio (float): The ratio of the dataset to use for training + Default = 0.8 + val_ratio (float): The ratio of the dataset to use for validation + Default: 0.1 + train_key (List(str), optional): a list of mp_ids for train set + val_key (List(str), optional): a list of mp_ids for val set + test_key (List(str), optional): a list of mp_ids for test set + batch_size (int): batch size + Default = 32 + num_workers (int): The number of worker processes for loading the data + see torch Dataloader documentation for more info + Default = 0 + pin_memory (bool): Whether to pin the memory of the data loaders + Default: True + + Returns: + train_loader, val_loader, test_loader + """ + train_labels, val_labels, test_labels = {}, {}, {} + if train_key is None: + mp_ids = list(self.labels) + random.shuffle(mp_ids) + n_train = int(train_ratio * len(mp_ids)) + n_val = int(val_ratio * len(mp_ids)) + train_key = mp_ids[:n_train] + val_key = mp_ids[n_train : n_train + n_val] + test_key = mp_ids[n_train + n_val :] + for mp_id in train_key: + if mp_id in self.labels: + train_labels[mp_id] = self.labels.pop(mp_id) + train_dataset = GraphData( + graph_path=self.graph_path, + labels=train_labels, + targets=self.targets, + exclude=self.excluded_graph, + energy_key=self.energy_key, + ) + train_loader = DataLoader( + train_dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + shuffle=True, + num_workers=num_workers, + pin_memory=pin_memory, + ) + + # Val + for mp_id in val_key: + if mp_id in self.labels: + val_labels[mp_id] = self.labels.pop(mp_id) + val_dataset = GraphData( + graph_path=self.graph_path, + labels=val_labels, + targets=self.targets, + exclude=self.excluded_graph, + energy_key=self.energy_key, + ) + val_loader = DataLoader( + val_dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + shuffle=True, + num_workers=num_workers, + pin_memory=pin_memory, + ) + + # Test + if test_key is not None: + for mp_id in test_key: + if mp_id in self.labels: + test_labels[mp_id] = self.labels.pop(mp_id) + test_dataset = GraphData( + graph_path=self.graph_path, + labels=test_labels, + targets=self.targets, + exclude=self.excluded_graph, + energy_key=self.energy_key, + ) + test_loader = DataLoader( + test_dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + num_workers=num_workers, + pin_memory=pin_memory, + ) + else: + test_loader = None + return train_loader, val_loader, test_loader + + +class StructureJsonData(Dataset): + """Read structure and targets from a JSON file. + This class is used to load the MPtrj dataset. + """ + + def __init__( + self, + data: str | dict, + graph_converter: CrystalGraphConverter, + *, + targets: TrainTask = "efsm", + energy_key: str = "energy_per_atom", + force_key: str = "force", + stress_key: str = "stress", + magmom_key: str = "magmom", + shuffle: bool = True, + ) -> None: + """Initialize the dataset by reading JSON files. + + Args: + data (str | dict): file path or dir name that contain all the JSONs + graph_converter (CrystalGraphConverter): Converts pymatgen.core.Structure + to CrystalGraph object. + targets ("ef" | "efs" | "efm" | "efsm"): The training targets. + Default = "efsm" + energy_key (str, optional): the key of energy in the labels. + Default = "energy_per_atom" + force_key (str, optional): the key of force in the labels. + Default = "force" + stress_key (str, optional): the key of stress in the labels. + Default = "stress" + magmom_key (str, optional): the key of magmom in the labels. + Default = "magmom" + shuffle (bool): whether to shuffle the sequence of dataset + Default = True + """ + if isinstance(data, str): + self.data = {} + if os.path.isdir(data): + for json_path in os.listdir(data): + if json_path.endswith(".json"): + print(f"Importing: {json_path}") + self.data.update(utils.read_json(os.path.join(data, json_path))) + else: + print(f"Importing: {data}") + self.data.update(utils.read_json(data)) + elif isinstance(data, dict): + self.data = data + else: + raise TypeError(f"data must be JSON path or dictionary, got {type(data)}") + + self.keys = [ + (mp_id, graph_id) for mp_id, dct in self.data.items() for graph_id in dct + ] + if shuffle: + random.shuffle(self.keys) + print(f"{len(self.data)} MP IDs, {len(self)} structures imported") + self.graph_converter = graph_converter + self.energy_key = energy_key + self.force_key = force_key + self.stress_key = stress_key + self.magmom_key = magmom_key + self.targets = targets + self.failed_idx: list[int] = [] + self.failed_graph_id: dict[str, str] = {} + + def __len__(self) -> int: + """Get the number of structures with targets in the dataset.""" + return len(self.keys) + + @functools.cache # Cache loaded structures + def __getitem__(self, idx: int) -> tuple[CrystalGraph, dict[str, Tensor]]: + """Get one item in the dataset. + + Returns: + crystal_graph (CrystalGraph): graph of the crystal structure + targets (dict): dictionary of targets. i.e. energy, force, stress, magmom + """ + if idx not in self.failed_idx: + mp_id, graph_id = self.keys[idx] + try: + struct = Structure.from_dict(self.data[mp_id][graph_id]["structure"]) + crystal_graph = self.graph_converter( + struct, graph_id=graph_id, mp_id=mp_id + ) + + targets = {} + for key in self.targets: + if key == "e": + energy = self.data[mp_id][graph_id][self.energy_key] + targets["e"] = torch.tensor(energy, dtype=datatype) + elif key == "f": + force = self.data[mp_id][graph_id][self.force_key] + targets["f"] = torch.tensor(force, dtype=datatype) + elif key == "s": + stress = self.data[mp_id][graph_id][self.stress_key] + # Convert VASP stress + targets["s"] = torch.tensor(stress, dtype=datatype) * (-0.1) + elif key == "m": + mag = self.data[mp_id][graph_id][self.magmom_key] + # use absolute value for magnetic moments + if mag is None: + targets["m"] = None + else: + targets["m"] = torch.abs(torch.tensor(mag, dtype=datatype)) + return crystal_graph, targets + + # Omit structures with isolated atoms. Return another randomly selected + # structure + except Exception: + structure = Structure.from_dict(self.data[mp_id][graph_id]["structure"]) + self.failed_graph_id[graph_id] = structure.composition.formula + self.failed_idx.append(idx) + idx = random.randint(0, len(self) - 1) + return self.__getitem__(idx) + else: + idx = random.randint(0, len(self) - 1) + return self.__getitem__(idx) + + def get_train_val_test_loader( + self, + train_ratio: float = 0.8, + val_ratio: float = 0.1, + *, + train_key: list[str] | None = None, + val_key: list[str] | None = None, + test_key: list[str] | None = None, + batch_size=32, + num_workers=0, + pin_memory=True, + ) -> tuple[DataLoader, DataLoader, DataLoader]: + """Partition the Dataset using materials id, + randomly select the train_keys, val_keys, test_keys by train val test ratio, + or use pre-defined train_keys, val_keys, and test_keys to create train, val, + test loaders. + + Args: + train_ratio (float): The ratio of the dataset to use for training + Default = 0.8 + val_ratio (float): The ratio of the dataset to use for validation + Default: 0.1 + train_key (List(str), optional): a list of mp_ids for train set + val_key (List(str), optional): a list of mp_ids for val set + test_key (List(str), optional): a list of mp_ids for test set + batch_size (int): batch size + Default = 32 + num_workers (int): The number of worker processes for loading the data + see torch Dataloader documentation for more info + Default = 0 + pin_memory (bool): Whether to pin the memory of the data loaders + Default: True + + Returns: + train_loader, val_loader, test_loader + """ + train_data, val_data, test_data = {}, {}, {} + if train_key is None: + mp_ids = list(self.data) + random.shuffle(mp_ids) + n_train = int(train_ratio * len(mp_ids)) + n_val = int(val_ratio * len(mp_ids)) + train_key = mp_ids[:n_train] + val_key = mp_ids[n_train : n_train + n_val] + test_key = mp_ids[n_train + n_val :] + for mp_id in train_key: + train_data[mp_id] = self.data.pop(mp_id) + train_dataset = StructureJsonData( + data=train_data, + graph_converter=self.graph_converter, + targets=self.targets, + energy_key=self.energy_key, + ) + train_loader = DataLoader( + train_dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + shuffle=True, + num_workers=num_workers, + pin_memory=pin_memory, + ) + for mp_id in val_key: + val_data[mp_id] = self.data.pop(mp_id) + val_dataset = StructureJsonData( + data=val_data, + graph_converter=self.graph_converter, + targets=self.targets, + energy_key=self.energy_key, + ) + val_loader = DataLoader( + val_dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + shuffle=True, + num_workers=num_workers, + pin_memory=pin_memory, + ) + if test_key is not None: + for mp_id in test_key: + test_data[mp_id] = self.data.pop(mp_id) + test_dataset = StructureJsonData( + data=test_data, + graph_converter=self.graph_converter, + targets=self.targets, + energy_key=self.energy_key, + ) + test_loader = DataLoader( + test_dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + num_workers=num_workers, + pin_memory=pin_memory, + ) + else: + test_loader = None + return train_loader, val_loader, test_loader + + +def collate_graphs(batch_data: list) -> tuple[list[CrystalGraph], dict[str, Tensor]]: + """Collate of list of (graph, target) into batch data. + + Args: + batch_data (list): list of (graph, target(dict)) + + Returns: + graphs (List): a list of graphs + targets (Dict): dictionary of targets, where key and values are: + e (Tensor): energies of the structures [batch_size] + f (Tensor): forces of the structures [n_batch_atoms, 3] + s (Tensor): stresses of the structures [3*batch_size, 3] + m (Tensor): magmom of the structures [n_batch_atoms] + """ + graphs = [graph for graph, _ in batch_data] + all_targets = {key: [] for key in batch_data[0][1]} + all_targets["e"] = torch.tensor( + [targets["e"] for _, targets in batch_data], dtype=datatype + ) + + for _, targets in batch_data: + for target, value in targets.items(): + if target != "e": + all_targets[target].append(value) + + return graphs, all_targets + + +def get_train_val_test_loader( + dataset: Dataset, + *, + batch_size: int = 64, + train_ratio: float = 0.8, + val_ratio: float = 0.1, + return_test: bool = True, + num_workers: int = 0, + pin_memory: bool = True, +) -> tuple[DataLoader, DataLoader, DataLoader]: + """Randomly partition a dataset into train, val, test loaders. + + Args: + dataset (Dataset): The dataset to partition. + batch_size (int): The batch size for the data loaders + Default = 64 + train_ratio (float): The ratio of the dataset to use for training + Default = 0.8 + val_ratio (float): The ratio of the dataset to use for validation + Default: 0.1 + return_test (bool): Whether to return a test data loader + Default = True + num_workers (int): The number of worker processes for loading the data + see torch Dataloader documentation for more info + Default = 0 + pin_memory (bool): Whether to pin the memory of the data loaders + Default: True + + Returns: + train_loader, val_loader and optionally test_loader + """ + total_size = len(dataset) + indices = list(range(total_size)) + random.shuffle(indices) + train_size = int(train_ratio * total_size) + val_size = int(val_ratio * total_size) + + train_loader = DataLoader( + dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + sampler=SubsetRandomSampler(indices=indices[0:train_size]), + num_workers=num_workers, + pin_memory=pin_memory, + ) + val_loader = DataLoader( + dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + sampler=SubsetRandomSampler( + indices=indices[train_size : train_size + val_size] + ), + num_workers=num_workers, + pin_memory=pin_memory, + ) + if return_test: + test_loader = DataLoader( + dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + sampler=SubsetRandomSampler(indices=indices[train_size + val_size :]), + num_workers=num_workers, + pin_memory=pin_memory, + ) + return train_loader, val_loader, test_loader + return train_loader, val_loader + + +def get_loader( + dataset, *, batch_size: int = 64, num_workers: int = 0, pin_memory: bool = True +) -> DataLoader: + """Get a dataloader from a dataset. + + Args: + dataset (Dataset): The dataset to partition. + batch_size (int): The batch size for the data loaders + Default = 64 + num_workers (int): The number of worker processes for loading the data + see torch Dataloader documentation for more info + Default = 0 + pin_memory (bool): Whether to pin the memory of the data loaders + Default: True + + Returns: + data_loader + """ + return DataLoader( + dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + shuffle=True, + num_workers=num_workers, + pin_memory=pin_memory, + ) diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/data/dataset_custom.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/data/dataset_custom.py new file mode 100755 index 00000000..781b00f6 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/data/dataset_custom.py @@ -0,0 +1,322 @@ +from __future__ import annotations + +import functools +import os +import random +import warnings +from typing import TYPE_CHECKING + +import numpy as np +import torch +from pymatgen.core.structure import Structure +from torch import Tensor +from torch.utils.data import DataLoader, Dataset +from torch.utils.data.sampler import SubsetRandomSampler + +from chgnet import utils +from chgnet.graph import CrystalGraph, CrystalGraphConverter + +from matbench.bench import MatbenchBenchmark + +if TYPE_CHECKING: + from collections.abc import Sequence + + from typing_extensions import Self + + from chgnet import TrainTask + +warnings.filterwarnings("ignore") +datatype = torch.float32 + +random.seed(0) +np.random.seed(0) +torch.cuda.manual_seed(0) +torch.cuda.manual_seed_all(0) + + +class StructureDataCustomProperty(Dataset): + """A simple torch Dataset of structures.""" + + def __init__( + self, + structures: list[Structure], + custom_property: list[float], + structure_ids: list | None = None, + graph_converter: CrystalGraphConverter | None = None, + shuffle: bool = True, + ) -> None: + """Initialize the dataset. + + Args: + structures (list[dict]): pymatgen Structure objects. + custom_property (list[float]): [data_size, 1] + structure_ids (list, optional): a list of ids to track the structures + Default = None + graph_converter (CrystalGraphConverter, optional): Converts the structures + to graphs. If None, it will be set to CHGNet 0.3.0 converter + with AtomGraph cutoff = 6A. + shuffle (bool): whether to shuffle the sequence of dataset + Default = True + + Raises: + RuntimeError: if the length of structures and labels (energies, forces, + stresses, magmoms) are not equal. + """ + for idx, struct in enumerate(structures): + if not isinstance(struct, Structure): + raise TypeError(f"{idx} is not a pymatgen Structure object: {struct}") + for name in "custom_property".split(): + labels = locals()[name] + if labels is not None and len(labels) != len(structures): + raise RuntimeError( + f"Inconsistent number of structures and labels: " + f"{len(structures)=}, len({name})={len(labels)}" + ) + self.structures = structures + self.custom_property = custom_property + self.structure_ids = structure_ids + self.keys = np.arange(len(structures)) + if shuffle: + random.shuffle(self.keys) + print(f"{type(self).__name__} imported {len(structures):,} structures") + self.graph_converter = graph_converter or CrystalGraphConverter( + atom_graph_cutoff=6, bond_graph_cutoff=3 + ) + self.failed_idx: list[int] = [] + self.failed_graph_id: dict[str, str] = {} + + @classmethod + def from_file( + cls, + file_root: str, + *, + graph_converter: CrystalGraphConverter | None = None, + shuffle: bool = True, + ) -> Self: + """Parse VASP output files into structures and labels and feed into the dataset. + + Args: + file_root (str): the directory of the VASP calculation outputs + graph_converter (CrystalGraphConverter, optional): Converts the structures + to graphs. If None, it will be set to CHGNet 0.3.0 converter + with AtomGraph cutoff = 6A. + shuffle (bool): whether to shuffle the sequence of dataset + Default = True + """ + + import pandas as pd + working_dir = os.getcwd() + os.chdir(file_root) + structures_list = [] + custom_property_list = [] + structure_ids_list = [] + # read id_prop.csv + df = pd.read_csv("id_prop.csv", header=None) + structure_ids_list = df[0].to_list() + custom_property_list = df[1].to_list() + for i in range(0, len(structure_ids_list)): + structure_now = Structure.from_file(str(structure_ids_list[i])) + structures_list.append(structure_now) + os.chdir(working_dir) + return cls( + structures=structures_list, + custom_property=custom_property_list, + structure_ids=structure_ids_list, + graph_converter=graph_converter, + shuffle=shuffle, + ) + + @classmethod + def from_matbench( + cls, + fold: float, + dataset_name: str, + is_train: bool, + include_target: bool = True, + *, + graph_converter: CrystalGraphConverter | None = None, + shuffle: bool = True, + ) -> Self: + mb = MatbenchBenchmark(autoload=False,subset=[dataset_name]) + for task in mb.tasks: + task.load() + if is_train: + df = task.get_train_and_val_data(fold, as_type="df") + else: + df = task.get_test_data(fold, as_type="df", include_target=include_target) + structures_list = df[df.keys()[0]].tolist() + custom_property_list = df[df.keys()[1]].tolist() + return cls( + structures=structures_list, + custom_property=custom_property_list, + graph_converter=graph_converter, + shuffle=shuffle, + ) + + def __len__(self) -> int: + """Get the number of structures in this dataset.""" + return len(self.keys) + + @functools.cache # Cache loaded structures + def __getitem__(self, idx: int) -> tuple[CrystalGraph, dict]: + """Get one graph for a structure in this dataset. + + Args: + idx (int): Index of the structure + + Returns: + crystal_graph (CrystalGraph): graph of the crystal structure + targets (dict): list of targets. i.e. energy, force, stress + """ + if idx not in self.failed_idx: + graph_id = self.keys[idx] + try: + struct = self.structures[graph_id] + if self.structure_ids is not None: + mp_id = self.structure_ids[graph_id] + else: + mp_id = graph_id + crystal_graph = self.graph_converter( + struct, graph_id=graph_id, mp_id=mp_id + ) + + targets = { + "c": torch.tensor(self.custom_property[graph_id], dtype=datatype), + } + + return crystal_graph, targets + + # Omit structures with isolated atoms. Return another randomly selected + # structure + except Exception: + struct = self.structures[graph_id] + self.failed_graph_id[graph_id] = struct.composition.formula + self.failed_idx.append(idx) + idx = random.randint(0, len(self) - 1) + return self.__getitem__(idx) + else: + idx = random.randint(0, len(self) - 1) + return self.__getitem__(idx) + + +def collate_graphs(batch_data: list) -> tuple[list[CrystalGraph], dict[str, Tensor]]: + """Collate of list of (graph, target) into batch data. + + Args: + batch_data (list): list of (graph, target(dict)) + + Returns: + graphs (List): a list of graphs + targets (Dict): dictionary of targets, where key and values are: + custom_property (Tensor): custom_property of the structures [batch_size] + """ + graphs = [graph for graph, _ in batch_data] + all_targets = {key: [] for key in batch_data[0][1]} + all_targets["c"] = torch.tensor( + [targets["c"] for _, targets in batch_data], dtype=datatype + ) + return graphs, all_targets + + +def get_train_val_test_loader( + dataset: Dataset, + *, + batch_size: int = 64, + train_ratio: float = 0.8, + val_ratio: float = 0.1, + return_test: bool = True, + num_workers: int = 0, + pin_memory: bool = True, + seed: int = 0, +) -> tuple[DataLoader, DataLoader, DataLoader]: + """Randomly partition a dataset into train, val, test loaders. + + Args: + dataset (Dataset): The dataset to partition. + batch_size (int): The batch size for the data loaders + Default = 64 + train_ratio (float): The ratio of the dataset to use for training + Default = 0.8 + val_ratio (float): The ratio of the dataset to use for validation + Default: 0.1 + return_test (bool): Whether to return a test data loader + Default = True + num_workers (int): The number of worker processes for loading the data + see torch Dataloader documentation for more info + Default = 0 + pin_memory (bool): Whether to pin the memory of the data loaders + Default: True + + Returns: + train_loader, val_loader and optionally test_loader + """ + + random.seed(seed) + np.random.seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + total_size = len(dataset) + indices = list(range(total_size)) + random.shuffle(indices) + train_size = int(train_ratio * total_size) + val_size = int(val_ratio * total_size) + + train_loader = DataLoader( + dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + sampler=SubsetRandomSampler(indices=indices[0:train_size]), + num_workers=num_workers, + pin_memory=pin_memory, + ) + + val_loader = DataLoader( + dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + sampler=SubsetRandomSampler( + indices=indices[train_size : train_size + val_size] + ), + num_workers=num_workers, + pin_memory=pin_memory, + ) + if return_test: + test_loader = DataLoader( + dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + sampler=SubsetRandomSampler(indices=indices[train_size + val_size :]), + num_workers=num_workers, + pin_memory=pin_memory, + ) + return train_loader, val_loader, test_loader + return train_loader, val_loader + + +def get_loader( + dataset, *, batch_size: int = 64, num_workers: int = 0, pin_memory: bool = True +) -> DataLoader: + """Get a dataloader from a dataset. + + Args: + dataset (Dataset): The dataset to partition. + batch_size (int): The batch size for the data loaders + Default = 64 + num_workers (int): The number of worker processes for loading the data + see torch Dataloader documentation for more info + Default = 0 + pin_memory (bool): Whether to pin the memory of the data loaders + Default: True + + Returns: + data_loader + """ + return DataLoader( + dataset, + batch_size=batch_size, + collate_fn=collate_graphs, + shuffle=True, + num_workers=num_workers, + pin_memory=pin_memory, + ) diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/__init__.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/__init__.py new file mode 100755 index 00000000..dc4ca4cb --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/__init__.py @@ -0,0 +1,4 @@ +from __future__ import annotations + +from chgnet.graph.converter import CrystalGraphConverter +from chgnet.graph.crystalgraph import CrystalGraph diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/converter.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/converter.py new file mode 100755 index 00000000..3fedf3c5 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/converter.py @@ -0,0 +1,291 @@ +from __future__ import annotations + +import gc +import sys +import warnings +from typing import TYPE_CHECKING, Literal + +import numpy as np +import torch +from torch import nn + +from chgnet.graph.crystalgraph import CrystalGraph +from chgnet.graph.graph import Graph, Node + +if TYPE_CHECKING: + from pymatgen.core import Structure + from typing_extensions import Self + +try: + from chgnet.graph.cygraph import make_graph +except (ImportError, AttributeError): + make_graph = None + +datatype = torch.float32 + + +class CrystalGraphConverter(nn.Module): + """Convert a pymatgen.core.Structure to a CrystalGraph + The CrystalGraph dataclass stores essential field to make sure that + gradients like force and stress can be calculated through back-propagation later. + """ + + make_graph = None + + def __init__( + self, + *, + atom_graph_cutoff: float = 6, + bond_graph_cutoff: float = 3, + algorithm: Literal["legacy", "fast"] = "fast", + on_isolated_atoms: Literal["ignore", "warn", "error"] = "error", + verbose: bool = False, + ) -> None: + """Initialize the Crystal Graph Converter. + + Args: + atom_graph_cutoff (float): cutoff radius to search for neighboring atom in + atom_graph. Default = 5. + bond_graph_cutoff (float): bond length threshold to include bond in + bond_graph. Default = 3. + algorithm ('legacy' | 'fast'): algorithm to use for converting graphs. + 'legacy': python implementation of graph creation + 'fast': C implementation of graph creation, this is faster, + but will need the cygraph.c file correctly compiled from pip install + Default = 'fast' + on_isolated_atoms ('ignore' | 'warn' | 'error'): how to handle Structures + with isolated atoms. + Default = 'error' + verbose (bool): whether to print the CrystalGraphConverter + initialization message. Default = False. + """ + super().__init__() + self.atom_graph_cutoff = atom_graph_cutoff + self.bond_graph_cutoff = ( + atom_graph_cutoff if bond_graph_cutoff is None else bond_graph_cutoff + ) + self.on_isolated_atoms = on_isolated_atoms + + # Set graph conversion algorithm + self.create_graph = self._create_graph_legacy + self.algorithm = "legacy" + if algorithm == "fast": + if make_graph is not None: + self.create_graph = self._create_graph_fast + self.algorithm = "fast" + else: + warnings.warn( + "`fast` algorithm is not available, using `legacy`", + UserWarning, + stacklevel=1, + ) + elif algorithm != "legacy": + warnings.warn( + f"Unknown {algorithm=}, using `legacy`", + UserWarning, + stacklevel=1, + ) + + if verbose: + print(self) + + def __repr__(self) -> str: + """String representation of the CrystalGraphConverter.""" + atom_graph_cutoff = self.atom_graph_cutoff + bond_graph_cutoff = self.bond_graph_cutoff + algorithm = self.algorithm + cls_name = type(self).__name__ + return f"{cls_name}({algorithm=}, {atom_graph_cutoff=}, {bond_graph_cutoff=})" + + def forward( + self, + structure: Structure, + graph_id=None, + mp_id=None, + ) -> CrystalGraph: + """Convert a structure, return a CrystalGraph. + + Args: + structure (pymatgen.core.Structure): structure to convert + graph_id (str): an id to keep track of this crystal graph + Default = None + mp_id (str): Materials Project id of this structure + Default = None + + Return: + CrystalGraph that is ready to use by CHGNet + """ + n_atoms = len(structure) + atomic_number = torch.tensor( + [site.specie.Z for site in structure], + dtype=torch.int32, + requires_grad=False, + ) + atom_frac_coord = torch.tensor( + structure.frac_coords, dtype=datatype, requires_grad=True + ) + lattice = torch.tensor( + structure.lattice.matrix, dtype=datatype, requires_grad=True + ) + center_index, neighbor_index, image, distance = structure.get_neighbor_list( + r=self.atom_graph_cutoff, sites=structure.sites, numerical_tol=1e-8 + ) + + # Make Graph + graph = self.create_graph( + n_atoms, center_index, neighbor_index, image, distance + ) + + # Atom Graph + atom_graph, directed2undirected = graph.adjacency_list() + atom_graph = torch.tensor(atom_graph, dtype=torch.int32) + directed2undirected = torch.tensor(directed2undirected, dtype=torch.int32) + + # Bond Graph + try: + bond_graph, undirected2directed = graph.line_graph_adjacency_list( + cutoff=self.bond_graph_cutoff + ) + except Exception as exc: + # Report structures that failed creating bond graph + # This happen occasionally with pymatgen version issue + structure.to(filename="bond_graph_error.cif") + raise SystemExit( + f"Failed creating bond graph for {graph_id}, check bond_graph_error.cif" + ) from exc + bond_graph = torch.tensor(bond_graph, dtype=torch.int32) + undirected2directed = torch.tensor(undirected2directed, dtype=torch.int32) + + # Check if graph has isolated atom + n_isolated_atoms = len({*range(n_atoms)} - {*center_index}) + if n_isolated_atoms: + atom_graph_cutoff = self.atom_graph_cutoff + msg = ( + f"Structure {graph_id=} has {n_isolated_atoms} isolated atom(s) with " + f"{atom_graph_cutoff=}. " + f"CHGNet calculation will likely go wrong" + ) + if self.on_isolated_atoms == "error": + # Discard this structure if it has isolated atom in the graph + raise ValueError(msg) + elif self.on_isolated_atoms == "warn": # noqa: RET506 + print(msg, file=sys.stderr) + + return CrystalGraph( + atomic_number=atomic_number, + atom_frac_coord=atom_frac_coord, + atom_graph=atom_graph, + neighbor_image=torch.tensor(image, dtype=datatype), + directed2undirected=directed2undirected, + undirected2directed=undirected2directed, + bond_graph=bond_graph, + lattice=lattice, + graph_id=graph_id, + mp_id=mp_id, + composition=structure.composition.formula, + atom_graph_cutoff=self.atom_graph_cutoff, + bond_graph_cutoff=self.bond_graph_cutoff, + ) + + @staticmethod + def _create_graph_legacy( + n_atoms: int, + center_index: np.ndarray, + neighbor_index: np.ndarray, + image: np.ndarray, + distance: np.ndarray, + ) -> Graph: + """Given structure information, create a Graph structure to be used to + create Crystal_Graph using pure python implementation. + + Args: + n_atoms (int): the number of atoms in the structure + center_index (np.ndarray): np array of indices of center atoms. + [num_undirected_bonds] + neighbor_index (np.ndarray): np array of indices of neighbor atoms. + [num_undirected_bonds] + image (np.ndarray): np array of images for each edge. + [num_undirected_bonds, 3] + distance (np.ndarray): np array of distances. + [num_undirected_bonds] + + Return: + Graph data structure used to create Crystal_Graph object + """ + graph = Graph([Node(index=idx) for idx in range(n_atoms)]) + for ii, jj, img, dist in zip(center_index, neighbor_index, image, distance): + graph.add_edge(center_index=ii, neighbor_index=jj, image=img, distance=dist) + + return graph + + @staticmethod + def _create_graph_fast( + n_atoms: int, + center_index: np.ndarray, + neighbor_index: np.ndarray, + image: np.ndarray, + distance: np.ndarray, + ) -> Graph: + """Given structure information, create a Graph structure to be used to + create Crystal_Graph using C implementation. + + NOTE: this is the fast version of _create_graph_legacy optimized + in c (~3x speedup). + + Args: + n_atoms (int): the number of atoms in the structure + center_index (np.ndarray): np array of indices of center atoms. + [num_undirected_bonds] + neighbor_index (np.ndarray): np array of indices of neighbor atoms. + [num_undirected_bonds] + image (np.ndarray): np array of images for each edge. + [num_undirected_bonds, 3] + distance (np.ndarray): np array of distances. + [num_undirected_bonds] + + Return: + Graph data structure used to create Crystal_Graph object + """ + center_index = np.ascontiguousarray(center_index) + neighbor_index = np.ascontiguousarray(neighbor_index) + image = np.ascontiguousarray(image, dtype=np.int_) + distance = np.ascontiguousarray(distance) + gc_saved = gc.get_threshold() + gc.set_threshold(0) + nodes, dir_edges_list, undir_edges_list, undirected_edges = make_graph( + center_index, len(center_index), neighbor_index, image, distance, n_atoms + ) + graph = Graph(nodes=nodes) + graph.directed_edges_list = dir_edges_list + graph.undirected_edges_list = undir_edges_list + graph.undirected_edges = undirected_edges + gc.set_threshold(gc_saved[0]) + + return graph + + def set_isolated_atom_response( + self, on_isolated_atoms: Literal["ignore", "warn", "error"] + ) -> None: + """Set the graph converter's response to isolated atom graph + Args: + on_isolated_atoms ('ignore' | 'warn' | 'error'): how to handle Structures + with isolated atoms. + Default = 'error'. + + Returns: + None + """ + self.on_isolated_atoms = on_isolated_atoms + + def as_dict(self) -> dict[str, str | float]: + """Save the args of the graph converter.""" + return { + "atom_graph_cutoff": self.atom_graph_cutoff, + "bond_graph_cutoff": self.bond_graph_cutoff, + "algorithm": self.algorithm, + } + + @classmethod + def from_dict(cls, dct: dict) -> Self: + """Create converter from dictionary.""" + return cls(**dct) diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/crystalgraph.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/crystalgraph.py new file mode 100755 index 00000000..637b359a --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/crystalgraph.py @@ -0,0 +1,198 @@ +from __future__ import annotations + +import os +from typing import TYPE_CHECKING, Any + +import torch +from torch import Tensor + +if TYPE_CHECKING: + from typing_extensions import Self + +datatype = torch.float32 + + +class CrystalGraph: + """A data class for crystal graph.""" + + def __init__( + self, + atomic_number: Tensor, + atom_frac_coord: Tensor, + atom_graph: Tensor, + atom_graph_cutoff: float, + neighbor_image: Tensor, + directed2undirected: Tensor, + undirected2directed: Tensor, + bond_graph: Tensor, + bond_graph_cutoff: float, + lattice: Tensor, + graph_id: str | None = None, + mp_id: str | None = None, + composition: str | None = None, + ) -> None: + """Initialize the crystal graph. + + Attention! This data class is not intended to be created manually. CrystalGraph + should be returned by a CrystalGraphConverter + + Args: + atomic_number (Tensor): the atomic numbers of atoms in the structure + [n_atom] + atom_frac_coord (Tensor): the fractional coordinates of the atoms + [n_atom, 3] + atom_graph (Tensor): a directed graph adjacency list, + (center atom indices, neighbor atom indices, undirected bond index) + for bonds in bond_fea + [num_directed_bonds, 2] + atom_graph_cutoff (float): the cutoff radius to draw edges in atom_graph + neighbor_image (Tensor): the periodic image specifying the location of + neighboring atom + see: https://github.com/materialsproject/pymatgen/blob/ca2175c762e37ea7 + c9f3950ef249bc540e683da1/pymatgen/core/structure.py#L1485-L1541 + [num_directed_bonds, 3] + directed2undirected (Tensor): the mapping from directed edge index to + undirected edge index for the atom graph + [num_directed_bonds] + undirected2directed (Tensor): the mapping from undirected edge index to + one of its directed edge index, this is essentially the inverse + mapping of the directed2undirected this tensor is needed for + computation efficiency. + Note that num_directed_bonds = 2 * num_undirected_bonds + [num_undirected_bonds] + bond_graph (Tensor): a directed graph adjacency list, + (atom indices, 1st undirected bond idx, 1st directed bond idx, + 2nd undirected bond idx, 2nd directed bond idx) for angles in angle_fea + [n_angle, 5] + bond_graph_cutoff (float): the cutoff bond length to include bond + as nodes in bond_graph + lattice (Tensor): lattices of the input structure + [3, 3] + graph_id (str | None): an id to keep track of this crystal graph + Default = None + mp_id (str | None): Materials Project id of this structure + Default = None + composition: Chemical composition of the compound, used just for + better tracking of the graph + Default = None. + + Raises: + ValueError: if len(directed2undirected) != 2 * len(undirected2directed) + """ + super().__init__() + self.atomic_number = atomic_number + self.atom_frac_coord = atom_frac_coord + self.atom_graph = atom_graph + self.atom_graph_cutoff = atom_graph_cutoff + self.neighbor_image = neighbor_image + self.directed2undirected = directed2undirected + self.undirected2directed = undirected2directed + self.bond_graph = bond_graph + self.bond_graph_cutoff = bond_graph_cutoff + self.lattice = lattice + self.graph_id = graph_id + self.mp_id = mp_id + self.composition = composition + if len(directed2undirected) != 2 * len(undirected2directed): + raise ValueError( + f"{graph_id} number of directed indices ({len(directed2undirected)}) !=" + f" 2 * number of undirected indices ({2 * len(undirected2directed)})!" + ) + + def to(self, device: str = "cpu") -> CrystalGraph: + """Move the graph to a device. Default = 'cpu'.""" + return CrystalGraph( + atomic_number=self.atomic_number.to(device), + atom_frac_coord=self.atom_frac_coord.to(device), + atom_graph=self.atom_graph.to(device), + atom_graph_cutoff=self.atom_graph_cutoff, + neighbor_image=self.neighbor_image.to(device), + directed2undirected=self.directed2undirected.to(device), + undirected2directed=self.undirected2directed.to(device), + bond_graph=self.bond_graph.to(device), + bond_graph_cutoff=self.bond_graph_cutoff, + lattice=self.lattice.to(device), + graph_id=self.graph_id, + mp_id=self.mp_id, + composition=self.composition, + ) + + def to_dict(self) -> dict[str, Any]: + """Convert the graph to a dictionary.""" + return { + "atomic_number": self.atomic_number, + "atom_frac_coord": self.atom_frac_coord, + "atom_graph": self.atom_graph, + "atom_graph_cutoff": self.atom_graph_cutoff, + "neighbor_image": self.neighbor_image, + "directed2undirected": self.directed2undirected, + "undirected2directed": self.undirected2directed, + "bond_graph": self.bond_graph, + "bond_graph_cutoff": self.bond_graph_cutoff, + "lattice": self.lattice, + "graph_id": self.graph_id, + "mp_id": self.mp_id, + "composition": self.composition, + } + + def save(self, fname: str | None = None, save_dir: str = ".") -> str: + """Save the graph to a file. + + Args: + fname (str, optional): File name. Defaults to None. + save_dir (str, optional): Directory to save the file. Defaults to ".". + + Returns: + str: The path to the saved file. + """ + if fname is not None: + save_name = os.path.join(save_dir, fname) + elif self.graph_id is not None: + save_name = os.path.join(save_dir, f"{self.graph_id}.pt") + else: + save_name = os.path.join(save_dir, f"{self.composition}.pt") + torch.save(self.to_dict(), f=save_name) + return save_name + + @classmethod + def from_file(cls, file_name: str) -> Self: + """Load a crystal graph from a file. + + Args: + file_name (str): The path to the file. + + Returns: + CrystalGraph: The loaded graph. + """ + return torch.load(file_name) + + @classmethod + def from_dict(cls, dic: dict[str, Any]) -> Self: + """Load a CrystalGraph from a dictionary.""" + return cls(**dic) + + def __repr__(self) -> str: + """String representation of the graph.""" + composition = self.composition + atom_graph_cutoff = self.atom_graph_cutoff + bond_graph_cutoff = self.bond_graph_cutoff + atom_graph_len = self.atom_graph + n_atoms = len(self.atomic_number) + atom_graph_len = len(self.atom_graph) + bond_graph_len = len(self.bond_graph) + return ( + f"CrystalGraph({composition=}, {atom_graph_cutoff=}, {bond_graph_cutoff=}, " + f"{n_atoms=}, {atom_graph_len=}, {bond_graph_len=})" + ) + + @property + def num_isolated_atoms(self) -> int: + """Number of isolated atoms given the atom graph cutoff + Isolated atoms are disconnected nodes in the atom graph + that will not get updated in CHGNet. + These atoms will always have calculated force equal to zero. + + With the default CHGNet atom graph cutoff radius, only ~ 0.1% of MPtrj dataset + structures has isolated atoms. + """ + return len(self.atomic_number) - torch.unique(self.atom_graph[:, 0]).numel() diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/cygraph.pyx b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/cygraph.pyx new file mode 100755 index 00000000..7b82fda7 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/cygraph.pyx @@ -0,0 +1,170 @@ +# cython: language_level=3 +# cython: initializedcheck=False +# cython: nonecheck=False +# cython: boundscheck=False +# cython: wraparound=False +# cython: cdivision=True +# cython: profile=False +# distutils: language = c + +import chgnet.graph.graph +import numpy as np +from libc.stdlib cimport free + +cdef extern from 'fast_converter_libraries/create_graph.c': + ctypedef struct Node: + long index + LongToDirectedEdgeList* neighbors + long num_neighbors + + ctypedef struct NodeIndexPair: + long center + long neighbor + + ctypedef struct UndirectedEdge: + NodeIndexPair nodes + long index + long* directed_edge_indices + long num_directed_edges + double distance + + ctypedef struct DirectedEdge: + NodeIndexPair nodes + long index + const long* image + long undirected_edge_index + double distance + + ctypedef struct LongToDirectedEdgeList: + long key + DirectedEdge** directed_edges_list + int num_directed_edges_in_group + + ctypedef struct ReturnElems2: + long num_nodes + long num_directed_edges + long num_undirected_edges + Node* nodes + UndirectedEdge** undirected_edges_list + DirectedEdge** directed_edges_list + + ReturnElems2* create_graph( + long* center_index, + long n_e, + long* neighbor_index, + long* image, + double* distance, + long num_atoms) + + void free_LongToDirectedEdgeList_in_nodes(Node* nodes, long num_nodes) + + + LongToDirectedEdgeList** get_neighbors(Node* node) + +def make_graph( + const long[::1] center_index, + const long n_e, + const long[::1] neighbor_index, + const long[:, ::1] image, + const double[::1] distance, + const long num_atoms + ): + cdef ReturnElems2* returned + returned = create_graph( ¢er_index[0], n_e, &neighbor_index[0], &image[0][0], &distance[0], num_atoms) + + chg_DirectedEdge = chgnet.graph.graph.DirectedEdge + chg_Node = chgnet.graph.graph.Node + chg_UndirectedEdge = chgnet.graph.graph.UndirectedEdge + + + image_np = np.asarray(image) + + cdef LongToDirectedEdgeList** node_neighbors + cdef Node this_node + cdef LongToDirectedEdgeList this_entry + py_nodes = [] + cdef DirectedEdge* this_DE + + + # Handling nodes + directed edges + for idx in range(returned[0].num_nodes): + this_node = returned[0].nodes[idx] + this_py_node = chg_Node(index=idx) + + node_neighbors = get_neighbors(&this_node) + + # Iterate through all neighbors and populate our py_node.neighbors dict + for j in range(this_node.num_neighbors): + this_entry = node_neighbors[j][0] + directed_edges = [] + + for k in range(this_entry.num_directed_edges_in_group): + this_DE = this_entry.directed_edges_list[k] + directed_edges.append(this_DE[0].index) + + this_py_node.neighbors[this_entry.key] = directed_edges + + py_nodes.append(this_py_node) + + free(node_neighbors) + + # Handling directed edges + py_directed_edges_list = [] + + for idx in range(returned[0].num_directed_edges): + this_DE = returned[0].directed_edges_list[idx] + py_DE = chg_DirectedEdge(nodes = [this_DE[0].nodes.center, this_DE[0].nodes.neighbor], index=this_DE[0].index, info = {"distance": this_DE[0].distance, "image": image_np[this_DE[0].index], "undirected_edge_index": this_DE[0].undirected_edge_index}) + + py_directed_edges_list.append(py_DE) + + + # Handling undirected edges + py_undirected_edges_list = [] + cdef UndirectedEdge* UDE + + for idx in range(returned[0].num_undirected_edges): + UDE = returned[0].undirected_edges_list[idx] + py_undirected_edge = chg_UndirectedEdge([UDE[0].nodes.center, UDE[0].nodes.neighbor], index = UDE[0].index, info = {"distance": UDE[0].distance, "directed_edge_index": []}) + + for j in range(UDE[0].num_directed_edges): + py_undirected_edge.info["directed_edge_index"].append(UDE[0].directed_edge_indices[j]) + + py_undirected_edges_list.append(py_undirected_edge) + + + # Create Undirected_Edges hashmap + py_undirected_edges = {} + for undirected_edge in py_undirected_edges_list: + this_set = frozenset(undirected_edge.nodes) + if this_set not in py_undirected_edges: + py_undirected_edges[this_set] = [undirected_edge] + else: + py_undirected_edges[this_set].append(undirected_edge) + + # # Update the nodes list to have pointers to DirectedEdges instead of indices + for node_index in range(returned[0].num_nodes): + this_neighbors = py_nodes[node_index].neighbors + for this_neighbor_index in this_neighbors: + replacement = [py_directed_edges_list[edge_index] for edge_index in this_neighbors[this_neighbor_index]] + this_neighbors[this_neighbor_index] = replacement + + + # Free everything unneeded + for idx in range(returned[0].num_directed_edges): + free(returned[0].directed_edges_list[idx]) + + for idx in range(returned[0].num_undirected_edges): + free(returned[0].undirected_edges_list[idx].directed_edge_indices) + free(returned[0].undirected_edges_list[idx]) + + + # Free node LongToDirectedEdgeList + free_LongToDirectedEdgeList_in_nodes(returned[0].nodes, returned[0].num_nodes) + + free(returned[0].directed_edges_list) + free(returned[0].undirected_edges_list) + free(returned[0].nodes) + + free(returned) + + return py_nodes, py_directed_edges_list, py_undirected_edges_list, py_undirected_edges diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/fast_converter_libraries/create_graph.c b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/fast_converter_libraries/create_graph.c new file mode 100755 index 00000000..7a6ee0e3 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/fast_converter_libraries/create_graph.c @@ -0,0 +1,423 @@ +#include "uthash.h" +#include + +typedef struct _UndirectedEdge UndirectedEdge; +typedef struct _DirectedEdge DirectedEdge; +typedef struct _Node Node; +typedef struct _NodeIndexPair NodeIndexPair; +typedef struct _LongToDirectedEdgeList LongToDirectedEdgeList; +typedef struct _ReturnElems2 ReturnElems2; + +// NOTE: This code was mainly written to replicate the original add_edges method +// in the graph class in chgnet.graph.graph such that anyone familiar with that code should be able to pick up this +// code pretty easily. + +long MEM_ERR = 100; + +typedef struct _Node { + long index; + LongToDirectedEdgeList* neighbors; // Assuming neighbors can only be directed edge. Key is dest_node, value is DirectedEdge struct + long num_neighbors; +} Node; + +typedef struct _NodeIndexPair { + long center; + long neighbor; +} NodeIndexPair; + +typedef struct _UndirectedEdge { + NodeIndexPair nodes; + long index; + long* directed_edge_indices; + long num_directed_edges; + double distance; +} UndirectedEdge; + +typedef struct _DirectedEdge { + NodeIndexPair nodes; + long index; + const long* image; // Only access the first 3, never edit + long undirected_edge_index; + double distance; +} DirectedEdge; + +typedef struct _StructToUndirectedEdgeList { + NodeIndexPair key; + UndirectedEdge** undirected_edges_list; + int num_undirected_edges_in_group; + UT_hash_handle hh; +} StructToUndirectedEdgeList; + +typedef struct _LongToDirectedEdgeList { + long key; + DirectedEdge** directed_edges_list; + int num_directed_edges_in_group; + UT_hash_handle hh; +} LongToDirectedEdgeList; + + +typedef struct _ReturnElems2 { + long num_nodes; + long num_directed_edges; + long num_undirected_edges; + Node* nodes; + UndirectedEdge** undirected_edges_list; + DirectedEdge** directed_edges_list; +} ReturnElems2; + +bool find_in_undirected(NodeIndexPair* tmp, StructToUndirectedEdgeList** undirected_edges, StructToUndirectedEdgeList** found_entry); +void directed_to_undirected(DirectedEdge* directed, UndirectedEdge* undirected, long undirected_index); +void create_new_undirected_edges_entry(StructToUndirectedEdgeList** undirected_edges, NodeIndexPair* tmp, UndirectedEdge* new_undirected_edge); +void append_to_undirected_edges_tmp(UndirectedEdge* undirected, StructToUndirectedEdgeList** undirected_edges, NodeIndexPair* tmp); +void append_to_undirected_edges_list(UndirectedEdge** undirected_edges_list, UndirectedEdge* to_add, long* num_undirected_edges); +void append_to_directed_edges_list(DirectedEdge** directed_edges_list, DirectedEdge* to_add, long* num_directed_edges); +void add_neighbors_to_node(Node* node, long neighbor_index, DirectedEdge* directed_edge); +void print_neighbors(Node* node); +void append_to_directed_edge_indices(UndirectedEdge* undirected_edge, long directed_edge_index); +bool is_reversed_directed_edge(DirectedEdge* directed_edge1, DirectedEdge* directed_edge2); +void free_undirected_edges(StructToUndirectedEdgeList** undirected_edges); +void free_LongToDirectedEdgeList_in_nodes(Node* nodes, long num_nodes); + + +Node* create_nodes(long num_nodes) { + Node* Nodes = (Node*) malloc(sizeof(Node) * num_nodes); + + if (Nodes == NULL) { + return NULL; + } + + for (long i = 0; i < num_nodes; i++) { + Nodes[i].index = i; + Nodes[i].num_neighbors = 0; + + // Initialize the uthash + Nodes[i].neighbors = NULL; + } + + return Nodes; +} + +ReturnElems2* create_graph( + long* center_indices, + long num_edges, + long* neighbor_indices, + long* images, // contiguous memory (row-major) of image elements (total of n_e * 3 integers) + double* distances, + long num_atoms + ) { + // Initialize pertinent data structures --------------------- + Node* nodes = create_nodes(num_atoms); + + DirectedEdge** directed_edges_list = calloc(num_edges, sizeof(DirectedEdge)); + long num_directed_edges = 0; + + // There will never be more undirected edges than directed edges + UndirectedEdge** undirected_edges_list = calloc(num_edges, sizeof(UndirectedEdge)); + long num_undirected_edges = 0; + StructToUndirectedEdgeList* undirected_edges = NULL; + + // Pointer to beginning of list of UndirectedEdges corresponding to tmp of current iteration + StructToUndirectedEdgeList* corr_undirected_edges_item = NULL; + + // Pointer to NodeIndexPair storing tmp + NodeIndexPair* tmp = malloc(sizeof(NodeIndexPair)); + + // Flag for whether or not the value was found + bool found = false; + + // Flag used to show if we've already processed the current undirected edge + bool processed_edge = false; + + // Pointer used to store the previously added directed edge between two nodes + DirectedEdge* added_DE; + DirectedEdge* this_directed_edge; + + // Add all edges to graph information + for (long i = 0; i < num_edges; i++) { + // Haven't processed the edge yet + processed_edge = false; + // Create the current directed edge ------------------- + this_directed_edge = calloc(1, sizeof(DirectedEdge)); + this_directed_edge->nodes.center = center_indices[i]; + this_directed_edge->nodes.neighbor = neighbor_indices[i]; + this_directed_edge->distance = distances[i]; + this_directed_edge->index = num_directed_edges; + this_directed_edge->image = images + (3 * i); + + // Load tmp + memset(tmp, 0, sizeof(NodeIndexPair)); + tmp->center = center_indices[i]; + tmp->neighbor = neighbor_indices[i]; + + // See if tmp is in undirected + corr_undirected_edges_item = NULL; + found = find_in_undirected(tmp, &undirected_edges, &corr_undirected_edges_item); + + if (!found) { + // Never seen this edge combination before + this_directed_edge->undirected_edge_index = num_undirected_edges; + + // Create new undirected edge + UndirectedEdge* this_undirected_edge = malloc(sizeof(UndirectedEdge)); + + directed_to_undirected(this_directed_edge, this_undirected_edge, num_undirected_edges); + + // Add this new edge information to various data structures + create_new_undirected_edges_entry(&undirected_edges, tmp, this_undirected_edge); + append_to_undirected_edges_list(undirected_edges_list, this_undirected_edge, &num_undirected_edges); + add_neighbors_to_node(&nodes[center_indices[i]], neighbor_indices[i], this_directed_edge); + append_to_directed_edges_list(directed_edges_list, this_directed_edge, &num_directed_edges); + } else { + // This pair of nodes has been added before. We have to check if it's the other directed edge (but pointed in + // the different direction) OR it's another totally different undirected edge that has different image and distance + + // if found is true, then corr_undirected_edges_item points to self.undirected_edges[tmp] + // iterate through all previously scanned undirected edges that have the same endpoints as this edge + // if there exists an undirected edge with the same inverted image as this_undirected_edge, then add this new directed edge + // and associate it with this undirected edge + for (int j = 0; j < corr_undirected_edges_item->num_undirected_edges_in_group; j++) { + // Grab the 0th directed edge associated with this undirected edge + added_DE = directed_edges_list[((corr_undirected_edges_item->undirected_edges_list)[j]->directed_edge_indices)[0]]; + + if (is_reversed_directed_edge(added_DE, this_directed_edge)) { + this_directed_edge->undirected_edge_index = added_DE->undirected_edge_index; + add_neighbors_to_node(&nodes[center_indices[i]], neighbor_indices[i], this_directed_edge); + append_to_directed_edges_list(directed_edges_list, this_directed_edge, &num_directed_edges); + append_to_directed_edge_indices((corr_undirected_edges_item->undirected_edges_list)[j], this_directed_edge->index); + processed_edge = true; + break; + } + } + // There wasn't a pre-existing undirected edge that corresponds to this directed edge + // Create a new undirected edge and process + if (!processed_edge) { + this_directed_edge->undirected_edge_index = num_undirected_edges; + // Create a new undirected edge + UndirectedEdge* this_undirected_edge = malloc(sizeof(UndirectedEdge)); + directed_to_undirected(this_directed_edge, this_undirected_edge, num_undirected_edges); + append_to_undirected_edges_tmp(this_undirected_edge, &undirected_edges, tmp); + append_to_undirected_edges_list(undirected_edges_list, this_undirected_edge, &num_undirected_edges); + add_neighbors_to_node(&nodes[center_indices[i]], neighbor_indices[i], this_directed_edge); + append_to_directed_edges_list(directed_edges_list, this_directed_edge, &num_directed_edges); + } + } + } + + ReturnElems2* returned2 = malloc(sizeof(ReturnElems2)); + returned2->num_nodes = num_atoms; + returned2->num_undirected_edges = num_undirected_edges; + returned2->num_directed_edges = num_directed_edges; + + returned2->nodes = nodes; + returned2->directed_edges_list = directed_edges_list; + returned2->undirected_edges_list = undirected_edges_list; + + free(tmp); + free_undirected_edges(&undirected_edges); + + return returned2; +} + +void print_neighbors(Node* node) { + LongToDirectedEdgeList *tmp, *neighbor; + HASH_ITER(hh, node->neighbors, neighbor, tmp) { + printf("C:neighboring atom: %lu\n", neighbor->key); + } +} + +void free_undirected_edges(StructToUndirectedEdgeList** undirected_edges) { + StructToUndirectedEdgeList* current; + StructToUndirectedEdgeList* tmp; + + HASH_ITER(hh, *undirected_edges, current, tmp) { + HASH_DEL(*undirected_edges, current); + free(current->undirected_edges_list); + free(current); + } +} + +void free_LongToDirectedEdgeList_in_nodes(Node* nodes, long num_nodes) { + LongToDirectedEdgeList* current; + LongToDirectedEdgeList* tmp; + + for (long node_i = 0; node_i < num_nodes; node_i++) { + HASH_ITER(hh, nodes[node_i].neighbors, current, tmp) { + HASH_DEL(nodes[node_i].neighbors, current); + free(current->directed_edges_list); + free(current); + } + } +} + +// Returns true if the two directed edges have images that are inverted +// NOTE: assumes that directed_edge1->center = directed_edge2->neighbor and directed_edge1->neighbor = directed_edge2->center +bool is_reversed_directed_edge(DirectedEdge* directed_edge1, DirectedEdge* directed_edge2) { + for (int i = 0; i < 3; i++) { + if (directed_edge1->image[i] != -1 * directed_edge2->image[i]) { + return false; + } + } + + // The two directed edges should have opposing center/neighbor nodes (i.e. center-neighbor for DE1 is [0, 1] and for DE2 is [1, 0]) + // We check for that condition here + if (directed_edge1->nodes.center != directed_edge2->nodes.neighbor) { + return false; + } + if (directed_edge1->nodes.neighbor != directed_edge2->nodes.center) { + return false; + } + return true; +} + +// If tmp or the reverse of tmp is found in undirected_edges, True is returned and the corresponding StructToUndirectedEdgeList pointer is placed +// into found_entry. Otherwise, False is returned. +// NOTE: does not edit the *tmp +// Assumes *tmp bits have already been 0'd at padding within a struct +bool find_in_undirected(NodeIndexPair* tmp, StructToUndirectedEdgeList** undirected_edges, StructToUndirectedEdgeList** found_entry) { + StructToUndirectedEdgeList* out_list; + // Check tmp + HASH_FIND(hh, *undirected_edges, tmp, sizeof(NodeIndexPair), out_list); + + if (out_list) { + *found_entry = out_list; + return true; + } + + // Check tmp_rev + NodeIndexPair tmp_rev; + tmp_rev.center = tmp->neighbor; + tmp_rev.neighbor = tmp->center; + + HASH_FIND(hh, *undirected_edges, &tmp_rev, sizeof(NodeIndexPair), out_list); + + if (out_list) { + *found_entry = out_list; + return true; + } + + return false; +} + + +// Creates new entry in undirected_edges and initializes necessary arrays +void create_new_undirected_edges_entry(StructToUndirectedEdgeList** undirected_edges, NodeIndexPair* tmp, UndirectedEdge* new_undirected_edge) { + StructToUndirectedEdgeList* new_entry = malloc(sizeof(StructToUndirectedEdgeList)); + memset(new_entry, 0, sizeof(StructToUndirectedEdgeList)); + + // Set up fields within the new entry in the hashmap + new_entry->key.center = tmp->center; + new_entry->key.neighbor = tmp->neighbor; + + new_entry->num_undirected_edges_in_group = 1; + new_entry->undirected_edges_list = malloc(sizeof(UndirectedEdge*)); + new_entry->undirected_edges_list[0] = new_undirected_edge; + + HASH_ADD(hh, *undirected_edges, key, sizeof(NodeIndexPair), new_entry); + +} + +// Appends undirected into the StructToUndirectedEdgeList entry that corresponds to tmp +// This function will first look up tmp +void append_to_undirected_edges_tmp(UndirectedEdge* undirected, StructToUndirectedEdgeList** undirected_edges, NodeIndexPair* tmp) { + + StructToUndirectedEdgeList* this_undirected_edges_item; + find_in_undirected(tmp, undirected_edges, &this_undirected_edges_item); + + long num_undirected_edges = this_undirected_edges_item->num_undirected_edges_in_group; + + // No need to worry about originally malloc'ing memory for this_undirected_edges_item->undirected_edges_list + // this is because, we first call create_new_undirected_edges_entry for all entries. This function already mallocs for us. + + // Realloc the space to fit a new pointer to an undirected edge + UndirectedEdge** new_list = realloc(this_undirected_edges_item->undirected_edges_list, sizeof(UndirectedEdge*) * (num_undirected_edges + 1)); + this_undirected_edges_item->undirected_edges_list = new_list; + + // Insert the undirected pointer into the newly allocated slot + this_undirected_edges_item->undirected_edges_list[num_undirected_edges] = undirected; + + // Increase the counter for # of undirected edges + this_undirected_edges_item->num_undirected_edges_in_group = num_undirected_edges + 1; +} + + +void directed_to_undirected(DirectedEdge* directed, UndirectedEdge* undirected, long undirected_index) { + // Copy over image and distance + undirected->distance = directed->distance; + undirected->nodes = directed->nodes; + undirected->index = undirected_index; + + // Add a new directed_edge_index to the directed_edge_indices pointer. This should be the first + undirected->num_directed_edges = 1; + undirected->directed_edge_indices = malloc(sizeof(long)); + undirected->directed_edge_indices[0] = directed->index; +} + + +void append_to_undirected_edges_list(UndirectedEdge** undirected_edges_list, UndirectedEdge* to_add, long* num_undirected_edges) { + // No need to realloc for space since our original alloc should cover everything + + // Assign value to next available position + undirected_edges_list[*num_undirected_edges] = to_add; + *num_undirected_edges += 1; +} + +void append_to_directed_edges_list(DirectedEdge** directed_edges_list, DirectedEdge* to_add, long* num_directed_edges) { + // No need to realloc for space since our original alloc should cover everything + + // Assign value to next available position + directed_edges_list[*num_directed_edges] = to_add; + *num_directed_edges += 1; +} + +void append_to_directed_edge_indices(UndirectedEdge* undirected_edge, long directed_edge_index) { + // TODO: don't need to realloc if we always know that there will be 2 directed edges per undirected edge. Update this later for performance boosts. + // TODO: other random performance boost: don't pass longs into function parameters, pass long* instead + undirected_edge->directed_edge_indices = realloc(undirected_edge->directed_edge_indices, sizeof(long) * (undirected_edge->num_directed_edges + 1)); + undirected_edge->directed_edge_indices[undirected_edge->num_directed_edges] = directed_edge_index; + undirected_edge->num_directed_edges += 1; +} + +// If there already exists neighbor_index within the Node node, then adds directed_edge to the list of directed edges. +// If there doesn't already exist neighbor_index within the Node node, then create a new entry into the node's neighbors hashmap and add the entry +void add_neighbors_to_node(Node* node, long neighbor_index, DirectedEdge* directed_edge) { + LongToDirectedEdgeList* entry = NULL; + + // Search for the neighbor_index in our hashmap + HASH_FIND(hh, node->neighbors, &neighbor_index, sizeof(long), entry); + + if (entry) { + // We found something, update the list within this pointer + entry->directed_edges_list = realloc(entry->directed_edges_list, sizeof(DirectedEdge*) * (entry->num_directed_edges_in_group + 1)); + entry->directed_edges_list[entry->num_directed_edges_in_group] = directed_edge; + + entry->num_directed_edges_in_group += 1; + } else { + // allocate memory for entry + entry = malloc(sizeof(LongToDirectedEdgeList)); + + // The entry doesn't exist, initialize the entry and enter it into our hashmap + entry->directed_edges_list = malloc(sizeof(DirectedEdge*)); + entry->directed_edges_list[0] = directed_edge; + entry->key = neighbor_index; + + entry->num_directed_edges_in_group = 1; + HASH_ADD(hh, node->neighbors, key, sizeof(long), entry); + + node->num_neighbors += 1; + } +} + +// Returns a list of LongToDirectedEdgeList pointers which are entries for the neighbors of the inputted node +LongToDirectedEdgeList** get_neighbors(Node* node) { + long num_neighbors = HASH_COUNT(node->neighbors); + LongToDirectedEdgeList** entries = malloc(sizeof(LongToDirectedEdgeList*) * num_neighbors); + + LongToDirectedEdgeList* entry; + long counter = 0; + for (entry = node->neighbors; entry != NULL; entry = entry->hh.next) { + entries[counter] = entry; + counter += 1; + } + + return entries; +} diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/fast_converter_libraries/uthash.h b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/fast_converter_libraries/uthash.h new file mode 100755 index 00000000..68693bf3 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/fast_converter_libraries/uthash.h @@ -0,0 +1,1140 @@ +/* +Copyright (c) 2003-2022, Troy D. Hanson https://troydhanson.github.io/uthash/ +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS +IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED +TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A +PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER +OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef UTHASH_H +#define UTHASH_H + +#define UTHASH_VERSION 2.3.0 + +#include /* memcmp, memset, strlen */ +#include /* ptrdiff_t */ +#include /* exit */ + +#if defined(HASH_DEFINE_OWN_STDINT) && HASH_DEFINE_OWN_STDINT +/* This codepath is provided for backward compatibility, but I plan to remove it. */ +#warning "HASH_DEFINE_OWN_STDINT is deprecated; please use HASH_NO_STDINT instead" +typedef unsigned int uint32_t; +typedef unsigned char uint8_t; +#elif defined(HASH_NO_STDINT) && HASH_NO_STDINT +#else +#include /* uint8_t, uint32_t */ +#endif + +/* These macros use decltype or the earlier __typeof GNU extension. + As decltype is only available in newer compilers (VS2010 or gcc 4.3+ + when compiling c++ source) this code uses whatever method is needed + or, for VS2008 where neither is available, uses casting workarounds. */ +#if !defined(DECLTYPE) && !defined(NO_DECLTYPE) +#if defined(_MSC_VER) /* MS compiler */ +#if _MSC_VER >= 1600 && defined(__cplusplus) /* VS2010 or newer in C++ mode */ +#define DECLTYPE(x) (decltype(x)) +#else /* VS2008 or older (or VS2010 in C mode) */ +#define NO_DECLTYPE +#endif +#elif defined(__MCST__) /* Elbrus C Compiler */ +#define DECLTYPE(x) (__typeof(x)) +#elif defined(__BORLANDC__) || defined(__ICCARM__) || defined(__LCC__) || defined(__WATCOMC__) +#define NO_DECLTYPE +#else /* GNU, Sun and other compilers */ +#define DECLTYPE(x) (__typeof(x)) +#endif +#endif + +#ifdef NO_DECLTYPE +#define DECLTYPE(x) +#define DECLTYPE_ASSIGN(dst,src) \ +do { \ + char **_da_dst = (char**)(&(dst)); \ + *_da_dst = (char*)(src); \ +} while (0) +#else +#define DECLTYPE_ASSIGN(dst,src) \ +do { \ + (dst) = DECLTYPE(dst)(src); \ +} while (0) +#endif + +#ifndef uthash_malloc +#define uthash_malloc(sz) malloc(sz) /* malloc fcn */ +#endif +#ifndef uthash_free +#define uthash_free(ptr,sz) free(ptr) /* free fcn */ +#endif +#ifndef uthash_bzero +#define uthash_bzero(a,n) memset(a,'\0',n) +#endif +#ifndef uthash_strlen +#define uthash_strlen(s) strlen(s) +#endif + +#ifndef HASH_FUNCTION +#define HASH_FUNCTION(keyptr,keylen,hashv) HASH_JEN(keyptr, keylen, hashv) +#endif + +#ifndef HASH_KEYCMP +#define HASH_KEYCMP(a,b,n) memcmp(a,b,n) +#endif + +#ifndef uthash_noexpand_fyi +#define uthash_noexpand_fyi(tbl) /* can be defined to log noexpand */ +#endif +#ifndef uthash_expand_fyi +#define uthash_expand_fyi(tbl) /* can be defined to log expands */ +#endif + +#ifndef HASH_NONFATAL_OOM +#define HASH_NONFATAL_OOM 0 +#endif + +#if HASH_NONFATAL_OOM +/* malloc failures can be recovered from */ + +#ifndef uthash_nonfatal_oom +#define uthash_nonfatal_oom(obj) do {} while (0) /* non-fatal OOM error */ +#endif + +#define HASH_RECORD_OOM(oomed) do { (oomed) = 1; } while (0) +#define IF_HASH_NONFATAL_OOM(x) x + +#else +/* malloc failures result in lost memory, hash tables are unusable */ + +#ifndef uthash_fatal +#define uthash_fatal(msg) exit(-1) /* fatal OOM error */ +#endif + +#define HASH_RECORD_OOM(oomed) uthash_fatal("out of memory") +#define IF_HASH_NONFATAL_OOM(x) + +#endif + +/* initial number of buckets */ +#define HASH_INITIAL_NUM_BUCKETS 32U /* initial number of buckets */ +#define HASH_INITIAL_NUM_BUCKETS_LOG2 5U /* lg2 of initial number of buckets */ +#define HASH_BKT_CAPACITY_THRESH 10U /* expand when bucket count reaches */ + +/* calculate the element whose hash handle address is hhp */ +#define ELMT_FROM_HH(tbl,hhp) ((void*)(((char*)(hhp)) - ((tbl)->hho))) +/* calculate the hash handle from element address elp */ +#define HH_FROM_ELMT(tbl,elp) ((UT_hash_handle*)(void*)(((char*)(elp)) + ((tbl)->hho))) + +#define HASH_ROLLBACK_BKT(hh, head, itemptrhh) \ +do { \ + struct UT_hash_handle *_hd_hh_item = (itemptrhh); \ + unsigned _hd_bkt; \ + HASH_TO_BKT(_hd_hh_item->hashv, (head)->hh.tbl->num_buckets, _hd_bkt); \ + (head)->hh.tbl->buckets[_hd_bkt].count++; \ + _hd_hh_item->hh_next = NULL; \ + _hd_hh_item->hh_prev = NULL; \ +} while (0) + +#define HASH_VALUE(keyptr,keylen,hashv) \ +do { \ + HASH_FUNCTION(keyptr, keylen, hashv); \ +} while (0) + +#define HASH_FIND_BYHASHVALUE(hh,head,keyptr,keylen,hashval,out) \ +do { \ + (out) = NULL; \ + if (head) { \ + unsigned _hf_bkt; \ + HASH_TO_BKT(hashval, (head)->hh.tbl->num_buckets, _hf_bkt); \ + if (HASH_BLOOM_TEST((head)->hh.tbl, hashval) != 0) { \ + HASH_FIND_IN_BKT((head)->hh.tbl, hh, (head)->hh.tbl->buckets[ _hf_bkt ], keyptr, keylen, hashval, out); \ + } \ + } \ +} while (0) + +#define HASH_FIND(hh,head,keyptr,keylen,out) \ +do { \ + (out) = NULL; \ + if (head) { \ + unsigned _hf_hashv; \ + HASH_VALUE(keyptr, keylen, _hf_hashv); \ + HASH_FIND_BYHASHVALUE(hh, head, keyptr, keylen, _hf_hashv, out); \ + } \ +} while (0) + +#ifdef HASH_BLOOM +#define HASH_BLOOM_BITLEN (1UL << HASH_BLOOM) +#define HASH_BLOOM_BYTELEN (HASH_BLOOM_BITLEN/8UL) + (((HASH_BLOOM_BITLEN%8UL)!=0UL) ? 1UL : 0UL) +#define HASH_BLOOM_MAKE(tbl,oomed) \ +do { \ + (tbl)->bloom_nbits = HASH_BLOOM; \ + (tbl)->bloom_bv = (uint8_t*)uthash_malloc(HASH_BLOOM_BYTELEN); \ + if (!(tbl)->bloom_bv) { \ + HASH_RECORD_OOM(oomed); \ + } else { \ + uthash_bzero((tbl)->bloom_bv, HASH_BLOOM_BYTELEN); \ + (tbl)->bloom_sig = HASH_BLOOM_SIGNATURE; \ + } \ +} while (0) + +#define HASH_BLOOM_FREE(tbl) \ +do { \ + uthash_free((tbl)->bloom_bv, HASH_BLOOM_BYTELEN); \ +} while (0) + +#define HASH_BLOOM_BITSET(bv,idx) (bv[(idx)/8U] |= (1U << ((idx)%8U))) +#define HASH_BLOOM_BITTEST(bv,idx) (bv[(idx)/8U] & (1U << ((idx)%8U))) + +#define HASH_BLOOM_ADD(tbl,hashv) \ + HASH_BLOOM_BITSET((tbl)->bloom_bv, ((hashv) & (uint32_t)((1UL << (tbl)->bloom_nbits) - 1U))) + +#define HASH_BLOOM_TEST(tbl,hashv) \ + HASH_BLOOM_BITTEST((tbl)->bloom_bv, ((hashv) & (uint32_t)((1UL << (tbl)->bloom_nbits) - 1U))) + +#else +#define HASH_BLOOM_MAKE(tbl,oomed) +#define HASH_BLOOM_FREE(tbl) +#define HASH_BLOOM_ADD(tbl,hashv) +#define HASH_BLOOM_TEST(tbl,hashv) (1) +#define HASH_BLOOM_BYTELEN 0U +#endif + +#define HASH_MAKE_TABLE(hh,head,oomed) \ +do { \ + (head)->hh.tbl = (UT_hash_table*)uthash_malloc(sizeof(UT_hash_table)); \ + if (!(head)->hh.tbl) { \ + HASH_RECORD_OOM(oomed); \ + } else { \ + uthash_bzero((head)->hh.tbl, sizeof(UT_hash_table)); \ + (head)->hh.tbl->tail = &((head)->hh); \ + (head)->hh.tbl->num_buckets = HASH_INITIAL_NUM_BUCKETS; \ + (head)->hh.tbl->log2_num_buckets = HASH_INITIAL_NUM_BUCKETS_LOG2; \ + (head)->hh.tbl->hho = (char*)(&(head)->hh) - (char*)(head); \ + (head)->hh.tbl->buckets = (UT_hash_bucket*)uthash_malloc( \ + HASH_INITIAL_NUM_BUCKETS * sizeof(struct UT_hash_bucket)); \ + (head)->hh.tbl->signature = HASH_SIGNATURE; \ + if (!(head)->hh.tbl->buckets) { \ + HASH_RECORD_OOM(oomed); \ + uthash_free((head)->hh.tbl, sizeof(UT_hash_table)); \ + } else { \ + uthash_bzero((head)->hh.tbl->buckets, \ + HASH_INITIAL_NUM_BUCKETS * sizeof(struct UT_hash_bucket)); \ + HASH_BLOOM_MAKE((head)->hh.tbl, oomed); \ + IF_HASH_NONFATAL_OOM( \ + if (oomed) { \ + uthash_free((head)->hh.tbl->buckets, \ + HASH_INITIAL_NUM_BUCKETS*sizeof(struct UT_hash_bucket)); \ + uthash_free((head)->hh.tbl, sizeof(UT_hash_table)); \ + } \ + ) \ + } \ + } \ +} while (0) + +#define HASH_REPLACE_BYHASHVALUE_INORDER(hh,head,fieldname,keylen_in,hashval,add,replaced,cmpfcn) \ +do { \ + (replaced) = NULL; \ + HASH_FIND_BYHASHVALUE(hh, head, &((add)->fieldname), keylen_in, hashval, replaced); \ + if (replaced) { \ + HASH_DELETE(hh, head, replaced); \ + } \ + HASH_ADD_KEYPTR_BYHASHVALUE_INORDER(hh, head, &((add)->fieldname), keylen_in, hashval, add, cmpfcn); \ +} while (0) + +#define HASH_REPLACE_BYHASHVALUE(hh,head,fieldname,keylen_in,hashval,add,replaced) \ +do { \ + (replaced) = NULL; \ + HASH_FIND_BYHASHVALUE(hh, head, &((add)->fieldname), keylen_in, hashval, replaced); \ + if (replaced) { \ + HASH_DELETE(hh, head, replaced); \ + } \ + HASH_ADD_KEYPTR_BYHASHVALUE(hh, head, &((add)->fieldname), keylen_in, hashval, add); \ +} while (0) + +#define HASH_REPLACE(hh,head,fieldname,keylen_in,add,replaced) \ +do { \ + unsigned _hr_hashv; \ + HASH_VALUE(&((add)->fieldname), keylen_in, _hr_hashv); \ + HASH_REPLACE_BYHASHVALUE(hh, head, fieldname, keylen_in, _hr_hashv, add, replaced); \ +} while (0) + +#define HASH_REPLACE_INORDER(hh,head,fieldname,keylen_in,add,replaced,cmpfcn) \ +do { \ + unsigned _hr_hashv; \ + HASH_VALUE(&((add)->fieldname), keylen_in, _hr_hashv); \ + HASH_REPLACE_BYHASHVALUE_INORDER(hh, head, fieldname, keylen_in, _hr_hashv, add, replaced, cmpfcn); \ +} while (0) + +#define HASH_APPEND_LIST(hh, head, add) \ +do { \ + (add)->hh.next = NULL; \ + (add)->hh.prev = ELMT_FROM_HH((head)->hh.tbl, (head)->hh.tbl->tail); \ + (head)->hh.tbl->tail->next = (add); \ + (head)->hh.tbl->tail = &((add)->hh); \ +} while (0) + +#define HASH_AKBI_INNER_LOOP(hh,head,add,cmpfcn) \ +do { \ + do { \ + if (cmpfcn(DECLTYPE(head)(_hs_iter), add) > 0) { \ + break; \ + } \ + } while ((_hs_iter = HH_FROM_ELMT((head)->hh.tbl, _hs_iter)->next)); \ +} while (0) + +#ifdef NO_DECLTYPE +#undef HASH_AKBI_INNER_LOOP +#define HASH_AKBI_INNER_LOOP(hh,head,add,cmpfcn) \ +do { \ + char *_hs_saved_head = (char*)(head); \ + do { \ + DECLTYPE_ASSIGN(head, _hs_iter); \ + if (cmpfcn(head, add) > 0) { \ + DECLTYPE_ASSIGN(head, _hs_saved_head); \ + break; \ + } \ + DECLTYPE_ASSIGN(head, _hs_saved_head); \ + } while ((_hs_iter = HH_FROM_ELMT((head)->hh.tbl, _hs_iter)->next)); \ +} while (0) +#endif + +#if HASH_NONFATAL_OOM + +#define HASH_ADD_TO_TABLE(hh,head,keyptr,keylen_in,hashval,add,oomed) \ +do { \ + if (!(oomed)) { \ + unsigned _ha_bkt; \ + (head)->hh.tbl->num_items++; \ + HASH_TO_BKT(hashval, (head)->hh.tbl->num_buckets, _ha_bkt); \ + HASH_ADD_TO_BKT((head)->hh.tbl->buckets[_ha_bkt], hh, &(add)->hh, oomed); \ + if (oomed) { \ + HASH_ROLLBACK_BKT(hh, head, &(add)->hh); \ + HASH_DELETE_HH(hh, head, &(add)->hh); \ + (add)->hh.tbl = NULL; \ + uthash_nonfatal_oom(add); \ + } else { \ + HASH_BLOOM_ADD((head)->hh.tbl, hashval); \ + HASH_EMIT_KEY(hh, head, keyptr, keylen_in); \ + } \ + } else { \ + (add)->hh.tbl = NULL; \ + uthash_nonfatal_oom(add); \ + } \ +} while (0) + +#else + +#define HASH_ADD_TO_TABLE(hh,head,keyptr,keylen_in,hashval,add,oomed) \ +do { \ + unsigned _ha_bkt; \ + (head)->hh.tbl->num_items++; \ + HASH_TO_BKT(hashval, (head)->hh.tbl->num_buckets, _ha_bkt); \ + HASH_ADD_TO_BKT((head)->hh.tbl->buckets[_ha_bkt], hh, &(add)->hh, oomed); \ + HASH_BLOOM_ADD((head)->hh.tbl, hashval); \ + HASH_EMIT_KEY(hh, head, keyptr, keylen_in); \ +} while (0) + +#endif + + +#define HASH_ADD_KEYPTR_BYHASHVALUE_INORDER(hh,head,keyptr,keylen_in,hashval,add,cmpfcn) \ +do { \ + IF_HASH_NONFATAL_OOM( int _ha_oomed = 0; ) \ + (add)->hh.hashv = (hashval); \ + (add)->hh.key = (char*) (keyptr); \ + (add)->hh.keylen = (unsigned) (keylen_in); \ + if (!(head)) { \ + (add)->hh.next = NULL; \ + (add)->hh.prev = NULL; \ + HASH_MAKE_TABLE(hh, add, _ha_oomed); \ + IF_HASH_NONFATAL_OOM( if (!_ha_oomed) { ) \ + (head) = (add); \ + IF_HASH_NONFATAL_OOM( } ) \ + } else { \ + void *_hs_iter = (head); \ + (add)->hh.tbl = (head)->hh.tbl; \ + HASH_AKBI_INNER_LOOP(hh, head, add, cmpfcn); \ + if (_hs_iter) { \ + (add)->hh.next = _hs_iter; \ + if (((add)->hh.prev = HH_FROM_ELMT((head)->hh.tbl, _hs_iter)->prev)) { \ + HH_FROM_ELMT((head)->hh.tbl, (add)->hh.prev)->next = (add); \ + } else { \ + (head) = (add); \ + } \ + HH_FROM_ELMT((head)->hh.tbl, _hs_iter)->prev = (add); \ + } else { \ + HASH_APPEND_LIST(hh, head, add); \ + } \ + } \ + HASH_ADD_TO_TABLE(hh, head, keyptr, keylen_in, hashval, add, _ha_oomed); \ + HASH_FSCK(hh, head, "HASH_ADD_KEYPTR_BYHASHVALUE_INORDER"); \ +} while (0) + +#define HASH_ADD_KEYPTR_INORDER(hh,head,keyptr,keylen_in,add,cmpfcn) \ +do { \ + unsigned _hs_hashv; \ + HASH_VALUE(keyptr, keylen_in, _hs_hashv); \ + HASH_ADD_KEYPTR_BYHASHVALUE_INORDER(hh, head, keyptr, keylen_in, _hs_hashv, add, cmpfcn); \ +} while (0) + +#define HASH_ADD_BYHASHVALUE_INORDER(hh,head,fieldname,keylen_in,hashval,add,cmpfcn) \ + HASH_ADD_KEYPTR_BYHASHVALUE_INORDER(hh, head, &((add)->fieldname), keylen_in, hashval, add, cmpfcn) + +#define HASH_ADD_INORDER(hh,head,fieldname,keylen_in,add,cmpfcn) \ + HASH_ADD_KEYPTR_INORDER(hh, head, &((add)->fieldname), keylen_in, add, cmpfcn) + +#define HASH_ADD_KEYPTR_BYHASHVALUE(hh,head,keyptr,keylen_in,hashval,add) \ +do { \ + IF_HASH_NONFATAL_OOM( int _ha_oomed = 0; ) \ + (add)->hh.hashv = (hashval); \ + (add)->hh.key = (const void*) (keyptr); \ + (add)->hh.keylen = (unsigned) (keylen_in); \ + if (!(head)) { \ + (add)->hh.next = NULL; \ + (add)->hh.prev = NULL; \ + HASH_MAKE_TABLE(hh, add, _ha_oomed); \ + IF_HASH_NONFATAL_OOM( if (!_ha_oomed) { ) \ + (head) = (add); \ + IF_HASH_NONFATAL_OOM( } ) \ + } else { \ + (add)->hh.tbl = (head)->hh.tbl; \ + HASH_APPEND_LIST(hh, head, add); \ + } \ + HASH_ADD_TO_TABLE(hh, head, keyptr, keylen_in, hashval, add, _ha_oomed); \ + HASH_FSCK(hh, head, "HASH_ADD_KEYPTR_BYHASHVALUE"); \ +} while (0) + +#define HASH_ADD_KEYPTR(hh,head,keyptr,keylen_in,add) \ +do { \ + unsigned _ha_hashv; \ + HASH_VALUE(keyptr, keylen_in, _ha_hashv); \ + HASH_ADD_KEYPTR_BYHASHVALUE(hh, head, keyptr, keylen_in, _ha_hashv, add); \ +} while (0) + +#define HASH_ADD_BYHASHVALUE(hh,head,fieldname,keylen_in,hashval,add) \ + HASH_ADD_KEYPTR_BYHASHVALUE(hh, head, &((add)->fieldname), keylen_in, hashval, add) + +#define HASH_ADD(hh,head,fieldname,keylen_in,add) \ + HASH_ADD_KEYPTR(hh, head, &((add)->fieldname), keylen_in, add) + +#define HASH_TO_BKT(hashv,num_bkts,bkt) \ +do { \ + bkt = ((hashv) & ((num_bkts) - 1U)); \ +} while (0) + +/* delete "delptr" from the hash table. + * "the usual" patch-up process for the app-order doubly-linked-list. + * The use of _hd_hh_del below deserves special explanation. + * These used to be expressed using (delptr) but that led to a bug + * if someone used the same symbol for the head and deletee, like + * HASH_DELETE(hh,users,users); + * We want that to work, but by changing the head (users) below + * we were forfeiting our ability to further refer to the deletee (users) + * in the patch-up process. Solution: use scratch space to + * copy the deletee pointer, then the latter references are via that + * scratch pointer rather than through the repointed (users) symbol. + */ +#define HASH_DELETE(hh,head,delptr) \ + HASH_DELETE_HH(hh, head, &(delptr)->hh) + +#define HASH_DELETE_HH(hh,head,delptrhh) \ +do { \ + const struct UT_hash_handle *_hd_hh_del = (delptrhh); \ + if ((_hd_hh_del->prev == NULL) && (_hd_hh_del->next == NULL)) { \ + HASH_BLOOM_FREE((head)->hh.tbl); \ + uthash_free((head)->hh.tbl->buckets, \ + (head)->hh.tbl->num_buckets * sizeof(struct UT_hash_bucket)); \ + uthash_free((head)->hh.tbl, sizeof(UT_hash_table)); \ + (head) = NULL; \ + } else { \ + unsigned _hd_bkt; \ + if (_hd_hh_del == (head)->hh.tbl->tail) { \ + (head)->hh.tbl->tail = HH_FROM_ELMT((head)->hh.tbl, _hd_hh_del->prev); \ + } \ + if (_hd_hh_del->prev != NULL) { \ + HH_FROM_ELMT((head)->hh.tbl, _hd_hh_del->prev)->next = _hd_hh_del->next; \ + } else { \ + DECLTYPE_ASSIGN(head, _hd_hh_del->next); \ + } \ + if (_hd_hh_del->next != NULL) { \ + HH_FROM_ELMT((head)->hh.tbl, _hd_hh_del->next)->prev = _hd_hh_del->prev; \ + } \ + HASH_TO_BKT(_hd_hh_del->hashv, (head)->hh.tbl->num_buckets, _hd_bkt); \ + HASH_DEL_IN_BKT((head)->hh.tbl->buckets[_hd_bkt], _hd_hh_del); \ + (head)->hh.tbl->num_items--; \ + } \ + HASH_FSCK(hh, head, "HASH_DELETE_HH"); \ +} while (0) + +/* convenience forms of HASH_FIND/HASH_ADD/HASH_DEL */ +#define HASH_FIND_STR(head,findstr,out) \ +do { \ + unsigned _uthash_hfstr_keylen = (unsigned)uthash_strlen(findstr); \ + HASH_FIND(hh, head, findstr, _uthash_hfstr_keylen, out); \ +} while (0) +#define HASH_ADD_STR(head,strfield,add) \ +do { \ + unsigned _uthash_hastr_keylen = (unsigned)uthash_strlen((add)->strfield); \ + HASH_ADD(hh, head, strfield[0], _uthash_hastr_keylen, add); \ +} while (0) +#define HASH_REPLACE_STR(head,strfield,add,replaced) \ +do { \ + unsigned _uthash_hrstr_keylen = (unsigned)uthash_strlen((add)->strfield); \ + HASH_REPLACE(hh, head, strfield[0], _uthash_hrstr_keylen, add, replaced); \ +} while (0) +#define HASH_FIND_INT(head,findint,out) \ + HASH_FIND(hh,head,findint,sizeof(int),out) +#define HASH_ADD_INT(head,intfield,add) \ + HASH_ADD(hh,head,intfield,sizeof(int),add) +#define HASH_REPLACE_INT(head,intfield,add,replaced) \ + HASH_REPLACE(hh,head,intfield,sizeof(int),add,replaced) +#define HASH_FIND_PTR(head,findptr,out) \ + HASH_FIND(hh,head,findptr,sizeof(void *),out) +#define HASH_ADD_PTR(head,ptrfield,add) \ + HASH_ADD(hh,head,ptrfield,sizeof(void *),add) +#define HASH_REPLACE_PTR(head,ptrfield,add,replaced) \ + HASH_REPLACE(hh,head,ptrfield,sizeof(void *),add,replaced) +#define HASH_DEL(head,delptr) \ + HASH_DELETE(hh,head,delptr) + +/* HASH_FSCK checks hash integrity on every add/delete when HASH_DEBUG is defined. + * This is for uthash developer only; it compiles away if HASH_DEBUG isn't defined. + */ +#ifdef HASH_DEBUG +#include /* fprintf, stderr */ +#define HASH_OOPS(...) do { fprintf(stderr, __VA_ARGS__); exit(-1); } while (0) +#define HASH_FSCK(hh,head,where) \ +do { \ + struct UT_hash_handle *_thh; \ + if (head) { \ + unsigned _bkt_i; \ + unsigned _count = 0; \ + char *_prev; \ + for (_bkt_i = 0; _bkt_i < (head)->hh.tbl->num_buckets; ++_bkt_i) { \ + unsigned _bkt_count = 0; \ + _thh = (head)->hh.tbl->buckets[_bkt_i].hh_head; \ + _prev = NULL; \ + while (_thh) { \ + if (_prev != (char*)(_thh->hh_prev)) { \ + HASH_OOPS("%s: invalid hh_prev %p, actual %p\n", \ + (where), (void*)_thh->hh_prev, (void*)_prev); \ + } \ + _bkt_count++; \ + _prev = (char*)(_thh); \ + _thh = _thh->hh_next; \ + } \ + _count += _bkt_count; \ + if ((head)->hh.tbl->buckets[_bkt_i].count != _bkt_count) { \ + HASH_OOPS("%s: invalid bucket count %u, actual %u\n", \ + (where), (head)->hh.tbl->buckets[_bkt_i].count, _bkt_count); \ + } \ + } \ + if (_count != (head)->hh.tbl->num_items) { \ + HASH_OOPS("%s: invalid hh item count %u, actual %u\n", \ + (where), (head)->hh.tbl->num_items, _count); \ + } \ + _count = 0; \ + _prev = NULL; \ + _thh = &(head)->hh; \ + while (_thh) { \ + _count++; \ + if (_prev != (char*)_thh->prev) { \ + HASH_OOPS("%s: invalid prev %p, actual %p\n", \ + (where), (void*)_thh->prev, (void*)_prev); \ + } \ + _prev = (char*)ELMT_FROM_HH((head)->hh.tbl, _thh); \ + _thh = (_thh->next ? HH_FROM_ELMT((head)->hh.tbl, _thh->next) : NULL); \ + } \ + if (_count != (head)->hh.tbl->num_items) { \ + HASH_OOPS("%s: invalid app item count %u, actual %u\n", \ + (where), (head)->hh.tbl->num_items, _count); \ + } \ + } \ +} while (0) +#else +#define HASH_FSCK(hh,head,where) +#endif + +/* When compiled with -DHASH_EMIT_KEYS, length-prefixed keys are emitted to + * the descriptor to which this macro is defined for tuning the hash function. + * The app can #include to get the prototype for write(2). */ +#ifdef HASH_EMIT_KEYS +#define HASH_EMIT_KEY(hh,head,keyptr,fieldlen) \ +do { \ + unsigned _klen = fieldlen; \ + write(HASH_EMIT_KEYS, &_klen, sizeof(_klen)); \ + write(HASH_EMIT_KEYS, keyptr, (unsigned long)fieldlen); \ +} while (0) +#else +#define HASH_EMIT_KEY(hh,head,keyptr,fieldlen) +#endif + +/* The Bernstein hash function, used in Perl prior to v5.6. Note (x<<5+x)=x*33. */ +#define HASH_BER(key,keylen,hashv) \ +do { \ + unsigned _hb_keylen = (unsigned)keylen; \ + const unsigned char *_hb_key = (const unsigned char*)(key); \ + (hashv) = 0; \ + while (_hb_keylen-- != 0U) { \ + (hashv) = (((hashv) << 5) + (hashv)) + *_hb_key++; \ + } \ +} while (0) + + +/* SAX/FNV/OAT/JEN hash functions are macro variants of those listed at + * http://eternallyconfuzzled.com/tuts/algorithms/jsw_tut_hashing.aspx + * (archive link: https://archive.is/Ivcan ) + */ +#define HASH_SAX(key,keylen,hashv) \ +do { \ + unsigned _sx_i; \ + const unsigned char *_hs_key = (const unsigned char*)(key); \ + hashv = 0; \ + for (_sx_i=0; _sx_i < keylen; _sx_i++) { \ + hashv ^= (hashv << 5) + (hashv >> 2) + _hs_key[_sx_i]; \ + } \ +} while (0) +/* FNV-1a variation */ +#define HASH_FNV(key,keylen,hashv) \ +do { \ + unsigned _fn_i; \ + const unsigned char *_hf_key = (const unsigned char*)(key); \ + (hashv) = 2166136261U; \ + for (_fn_i=0; _fn_i < keylen; _fn_i++) { \ + hashv = hashv ^ _hf_key[_fn_i]; \ + hashv = hashv * 16777619U; \ + } \ +} while (0) + +#define HASH_OAT(key,keylen,hashv) \ +do { \ + unsigned _ho_i; \ + const unsigned char *_ho_key=(const unsigned char*)(key); \ + hashv = 0; \ + for(_ho_i=0; _ho_i < keylen; _ho_i++) { \ + hashv += _ho_key[_ho_i]; \ + hashv += (hashv << 10); \ + hashv ^= (hashv >> 6); \ + } \ + hashv += (hashv << 3); \ + hashv ^= (hashv >> 11); \ + hashv += (hashv << 15); \ +} while (0) + +#define HASH_JEN_MIX(a,b,c) \ +do { \ + a -= b; a -= c; a ^= ( c >> 13 ); \ + b -= c; b -= a; b ^= ( a << 8 ); \ + c -= a; c -= b; c ^= ( b >> 13 ); \ + a -= b; a -= c; a ^= ( c >> 12 ); \ + b -= c; b -= a; b ^= ( a << 16 ); \ + c -= a; c -= b; c ^= ( b >> 5 ); \ + a -= b; a -= c; a ^= ( c >> 3 ); \ + b -= c; b -= a; b ^= ( a << 10 ); \ + c -= a; c -= b; c ^= ( b >> 15 ); \ +} while (0) + +#define HASH_JEN(key,keylen,hashv) \ +do { \ + unsigned _hj_i,_hj_j,_hj_k; \ + unsigned const char *_hj_key=(unsigned const char*)(key); \ + hashv = 0xfeedbeefu; \ + _hj_i = _hj_j = 0x9e3779b9u; \ + _hj_k = (unsigned)(keylen); \ + while (_hj_k >= 12U) { \ + _hj_i += (_hj_key[0] + ( (unsigned)_hj_key[1] << 8 ) \ + + ( (unsigned)_hj_key[2] << 16 ) \ + + ( (unsigned)_hj_key[3] << 24 ) ); \ + _hj_j += (_hj_key[4] + ( (unsigned)_hj_key[5] << 8 ) \ + + ( (unsigned)_hj_key[6] << 16 ) \ + + ( (unsigned)_hj_key[7] << 24 ) ); \ + hashv += (_hj_key[8] + ( (unsigned)_hj_key[9] << 8 ) \ + + ( (unsigned)_hj_key[10] << 16 ) \ + + ( (unsigned)_hj_key[11] << 24 ) ); \ + \ + HASH_JEN_MIX(_hj_i, _hj_j, hashv); \ + \ + _hj_key += 12; \ + _hj_k -= 12U; \ + } \ + hashv += (unsigned)(keylen); \ + switch ( _hj_k ) { \ + case 11: hashv += ( (unsigned)_hj_key[10] << 24 ); /* FALLTHROUGH */ \ + case 10: hashv += ( (unsigned)_hj_key[9] << 16 ); /* FALLTHROUGH */ \ + case 9: hashv += ( (unsigned)_hj_key[8] << 8 ); /* FALLTHROUGH */ \ + case 8: _hj_j += ( (unsigned)_hj_key[7] << 24 ); /* FALLTHROUGH */ \ + case 7: _hj_j += ( (unsigned)_hj_key[6] << 16 ); /* FALLTHROUGH */ \ + case 6: _hj_j += ( (unsigned)_hj_key[5] << 8 ); /* FALLTHROUGH */ \ + case 5: _hj_j += _hj_key[4]; /* FALLTHROUGH */ \ + case 4: _hj_i += ( (unsigned)_hj_key[3] << 24 ); /* FALLTHROUGH */ \ + case 3: _hj_i += ( (unsigned)_hj_key[2] << 16 ); /* FALLTHROUGH */ \ + case 2: _hj_i += ( (unsigned)_hj_key[1] << 8 ); /* FALLTHROUGH */ \ + case 1: _hj_i += _hj_key[0]; /* FALLTHROUGH */ \ + default: ; \ + } \ + HASH_JEN_MIX(_hj_i, _hj_j, hashv); \ +} while (0) + +/* The Paul Hsieh hash function */ +#undef get16bits +#if (defined(__GNUC__) && defined(__i386__)) || defined(__WATCOMC__) \ + || defined(_MSC_VER) || defined (__BORLANDC__) || defined (__TURBOC__) +#define get16bits(d) (*((const uint16_t *) (d))) +#endif + +#if !defined (get16bits) +#define get16bits(d) ((((uint32_t)(((const uint8_t *)(d))[1])) << 8) \ + +(uint32_t)(((const uint8_t *)(d))[0]) ) +#endif +#define HASH_SFH(key,keylen,hashv) \ +do { \ + unsigned const char *_sfh_key=(unsigned const char*)(key); \ + uint32_t _sfh_tmp, _sfh_len = (uint32_t)keylen; \ + \ + unsigned _sfh_rem = _sfh_len & 3U; \ + _sfh_len >>= 2; \ + hashv = 0xcafebabeu; \ + \ + /* Main loop */ \ + for (;_sfh_len > 0U; _sfh_len--) { \ + hashv += get16bits (_sfh_key); \ + _sfh_tmp = ((uint32_t)(get16bits (_sfh_key+2)) << 11) ^ hashv; \ + hashv = (hashv << 16) ^ _sfh_tmp; \ + _sfh_key += 2U*sizeof (uint16_t); \ + hashv += hashv >> 11; \ + } \ + \ + /* Handle end cases */ \ + switch (_sfh_rem) { \ + case 3: hashv += get16bits (_sfh_key); \ + hashv ^= hashv << 16; \ + hashv ^= (uint32_t)(_sfh_key[sizeof (uint16_t)]) << 18; \ + hashv += hashv >> 11; \ + break; \ + case 2: hashv += get16bits (_sfh_key); \ + hashv ^= hashv << 11; \ + hashv += hashv >> 17; \ + break; \ + case 1: hashv += *_sfh_key; \ + hashv ^= hashv << 10; \ + hashv += hashv >> 1; \ + break; \ + default: ; \ + } \ + \ + /* Force "avalanching" of final 127 bits */ \ + hashv ^= hashv << 3; \ + hashv += hashv >> 5; \ + hashv ^= hashv << 4; \ + hashv += hashv >> 17; \ + hashv ^= hashv << 25; \ + hashv += hashv >> 6; \ +} while (0) + +/* iterate over items in a known bucket to find desired item */ +#define HASH_FIND_IN_BKT(tbl,hh,head,keyptr,keylen_in,hashval,out) \ +do { \ + if ((head).hh_head != NULL) { \ + DECLTYPE_ASSIGN(out, ELMT_FROM_HH(tbl, (head).hh_head)); \ + } else { \ + (out) = NULL; \ + } \ + while ((out) != NULL) { \ + if ((out)->hh.hashv == (hashval) && (out)->hh.keylen == (keylen_in)) { \ + if (HASH_KEYCMP((out)->hh.key, keyptr, keylen_in) == 0) { \ + break; \ + } \ + } \ + if ((out)->hh.hh_next != NULL) { \ + DECLTYPE_ASSIGN(out, ELMT_FROM_HH(tbl, (out)->hh.hh_next)); \ + } else { \ + (out) = NULL; \ + } \ + } \ +} while (0) + +/* add an item to a bucket */ +#define HASH_ADD_TO_BKT(head,hh,addhh,oomed) \ +do { \ + UT_hash_bucket *_ha_head = &(head); \ + _ha_head->count++; \ + (addhh)->hh_next = _ha_head->hh_head; \ + (addhh)->hh_prev = NULL; \ + if (_ha_head->hh_head != NULL) { \ + _ha_head->hh_head->hh_prev = (addhh); \ + } \ + _ha_head->hh_head = (addhh); \ + if ((_ha_head->count >= ((_ha_head->expand_mult + 1U) * HASH_BKT_CAPACITY_THRESH)) \ + && !(addhh)->tbl->noexpand) { \ + HASH_EXPAND_BUCKETS(addhh,(addhh)->tbl, oomed); \ + IF_HASH_NONFATAL_OOM( \ + if (oomed) { \ + HASH_DEL_IN_BKT(head,addhh); \ + } \ + ) \ + } \ +} while (0) + +/* remove an item from a given bucket */ +#define HASH_DEL_IN_BKT(head,delhh) \ +do { \ + UT_hash_bucket *_hd_head = &(head); \ + _hd_head->count--; \ + if (_hd_head->hh_head == (delhh)) { \ + _hd_head->hh_head = (delhh)->hh_next; \ + } \ + if ((delhh)->hh_prev) { \ + (delhh)->hh_prev->hh_next = (delhh)->hh_next; \ + } \ + if ((delhh)->hh_next) { \ + (delhh)->hh_next->hh_prev = (delhh)->hh_prev; \ + } \ +} while (0) + +/* Bucket expansion has the effect of doubling the number of buckets + * and redistributing the items into the new buckets. Ideally the + * items will distribute more or less evenly into the new buckets + * (the extent to which this is true is a measure of the quality of + * the hash function as it applies to the key domain). + * + * With the items distributed into more buckets, the chain length + * (item count) in each bucket is reduced. Thus by expanding buckets + * the hash keeps a bound on the chain length. This bounded chain + * length is the essence of how a hash provides constant time lookup. + * + * The calculation of tbl->ideal_chain_maxlen below deserves some + * explanation. First, keep in mind that we're calculating the ideal + * maximum chain length based on the *new* (doubled) bucket count. + * In fractions this is just n/b (n=number of items,b=new num buckets). + * Since the ideal chain length is an integer, we want to calculate + * ceil(n/b). We don't depend on floating point arithmetic in this + * hash, so to calculate ceil(n/b) with integers we could write + * + * ceil(n/b) = (n/b) + ((n%b)?1:0) + * + * and in fact a previous version of this hash did just that. + * But now we have improved things a bit by recognizing that b is + * always a power of two. We keep its base 2 log handy (call it lb), + * so now we can write this with a bit shift and logical AND: + * + * ceil(n/b) = (n>>lb) + ( (n & (b-1)) ? 1:0) + * + */ +#define HASH_EXPAND_BUCKETS(hh,tbl,oomed) \ +do { \ + unsigned _he_bkt; \ + unsigned _he_bkt_i; \ + struct UT_hash_handle *_he_thh, *_he_hh_nxt; \ + UT_hash_bucket *_he_new_buckets, *_he_newbkt; \ + _he_new_buckets = (UT_hash_bucket*)uthash_malloc( \ + sizeof(struct UT_hash_bucket) * (tbl)->num_buckets * 2U); \ + if (!_he_new_buckets) { \ + HASH_RECORD_OOM(oomed); \ + } else { \ + uthash_bzero(_he_new_buckets, \ + sizeof(struct UT_hash_bucket) * (tbl)->num_buckets * 2U); \ + (tbl)->ideal_chain_maxlen = \ + ((tbl)->num_items >> ((tbl)->log2_num_buckets+1U)) + \ + ((((tbl)->num_items & (((tbl)->num_buckets*2U)-1U)) != 0U) ? 1U : 0U); \ + (tbl)->nonideal_items = 0; \ + for (_he_bkt_i = 0; _he_bkt_i < (tbl)->num_buckets; _he_bkt_i++) { \ + _he_thh = (tbl)->buckets[ _he_bkt_i ].hh_head; \ + while (_he_thh != NULL) { \ + _he_hh_nxt = _he_thh->hh_next; \ + HASH_TO_BKT(_he_thh->hashv, (tbl)->num_buckets * 2U, _he_bkt); \ + _he_newbkt = &(_he_new_buckets[_he_bkt]); \ + if (++(_he_newbkt->count) > (tbl)->ideal_chain_maxlen) { \ + (tbl)->nonideal_items++; \ + if (_he_newbkt->count > _he_newbkt->expand_mult * (tbl)->ideal_chain_maxlen) { \ + _he_newbkt->expand_mult++; \ + } \ + } \ + _he_thh->hh_prev = NULL; \ + _he_thh->hh_next = _he_newbkt->hh_head; \ + if (_he_newbkt->hh_head != NULL) { \ + _he_newbkt->hh_head->hh_prev = _he_thh; \ + } \ + _he_newbkt->hh_head = _he_thh; \ + _he_thh = _he_hh_nxt; \ + } \ + } \ + uthash_free((tbl)->buckets, (tbl)->num_buckets * sizeof(struct UT_hash_bucket)); \ + (tbl)->num_buckets *= 2U; \ + (tbl)->log2_num_buckets++; \ + (tbl)->buckets = _he_new_buckets; \ + (tbl)->ineff_expands = ((tbl)->nonideal_items > ((tbl)->num_items >> 1)) ? \ + ((tbl)->ineff_expands+1U) : 0U; \ + if ((tbl)->ineff_expands > 1U) { \ + (tbl)->noexpand = 1; \ + uthash_noexpand_fyi(tbl); \ + } \ + uthash_expand_fyi(tbl); \ + } \ +} while (0) + + +/* This is an adaptation of Simon Tatham's O(n log(n)) mergesort */ +/* Note that HASH_SORT assumes the hash handle name to be hh. + * HASH_SRT was added to allow the hash handle name to be passed in. */ +#define HASH_SORT(head,cmpfcn) HASH_SRT(hh,head,cmpfcn) +#define HASH_SRT(hh,head,cmpfcn) \ +do { \ + unsigned _hs_i; \ + unsigned _hs_looping,_hs_nmerges,_hs_insize,_hs_psize,_hs_qsize; \ + struct UT_hash_handle *_hs_p, *_hs_q, *_hs_e, *_hs_list, *_hs_tail; \ + if (head != NULL) { \ + _hs_insize = 1; \ + _hs_looping = 1; \ + _hs_list = &((head)->hh); \ + while (_hs_looping != 0U) { \ + _hs_p = _hs_list; \ + _hs_list = NULL; \ + _hs_tail = NULL; \ + _hs_nmerges = 0; \ + while (_hs_p != NULL) { \ + _hs_nmerges++; \ + _hs_q = _hs_p; \ + _hs_psize = 0; \ + for (_hs_i = 0; _hs_i < _hs_insize; ++_hs_i) { \ + _hs_psize++; \ + _hs_q = ((_hs_q->next != NULL) ? \ + HH_FROM_ELMT((head)->hh.tbl, _hs_q->next) : NULL); \ + if (_hs_q == NULL) { \ + break; \ + } \ + } \ + _hs_qsize = _hs_insize; \ + while ((_hs_psize != 0U) || ((_hs_qsize != 0U) && (_hs_q != NULL))) { \ + if (_hs_psize == 0U) { \ + _hs_e = _hs_q; \ + _hs_q = ((_hs_q->next != NULL) ? \ + HH_FROM_ELMT((head)->hh.tbl, _hs_q->next) : NULL); \ + _hs_qsize--; \ + } else if ((_hs_qsize == 0U) || (_hs_q == NULL)) { \ + _hs_e = _hs_p; \ + if (_hs_p != NULL) { \ + _hs_p = ((_hs_p->next != NULL) ? \ + HH_FROM_ELMT((head)->hh.tbl, _hs_p->next) : NULL); \ + } \ + _hs_psize--; \ + } else if ((cmpfcn( \ + DECLTYPE(head)(ELMT_FROM_HH((head)->hh.tbl, _hs_p)), \ + DECLTYPE(head)(ELMT_FROM_HH((head)->hh.tbl, _hs_q)) \ + )) <= 0) { \ + _hs_e = _hs_p; \ + if (_hs_p != NULL) { \ + _hs_p = ((_hs_p->next != NULL) ? \ + HH_FROM_ELMT((head)->hh.tbl, _hs_p->next) : NULL); \ + } \ + _hs_psize--; \ + } else { \ + _hs_e = _hs_q; \ + _hs_q = ((_hs_q->next != NULL) ? \ + HH_FROM_ELMT((head)->hh.tbl, _hs_q->next) : NULL); \ + _hs_qsize--; \ + } \ + if ( _hs_tail != NULL ) { \ + _hs_tail->next = ((_hs_e != NULL) ? \ + ELMT_FROM_HH((head)->hh.tbl, _hs_e) : NULL); \ + } else { \ + _hs_list = _hs_e; \ + } \ + if (_hs_e != NULL) { \ + _hs_e->prev = ((_hs_tail != NULL) ? \ + ELMT_FROM_HH((head)->hh.tbl, _hs_tail) : NULL); \ + } \ + _hs_tail = _hs_e; \ + } \ + _hs_p = _hs_q; \ + } \ + if (_hs_tail != NULL) { \ + _hs_tail->next = NULL; \ + } \ + if (_hs_nmerges <= 1U) { \ + _hs_looping = 0; \ + (head)->hh.tbl->tail = _hs_tail; \ + DECLTYPE_ASSIGN(head, ELMT_FROM_HH((head)->hh.tbl, _hs_list)); \ + } \ + _hs_insize *= 2U; \ + } \ + HASH_FSCK(hh, head, "HASH_SRT"); \ + } \ +} while (0) + +/* This function selects items from one hash into another hash. + * The end result is that the selected items have dual presence + * in both hashes. There is no copy of the items made; rather + * they are added into the new hash through a secondary hash + * hash handle that must be present in the structure. */ +#define HASH_SELECT(hh_dst, dst, hh_src, src, cond) \ +do { \ + unsigned _src_bkt, _dst_bkt; \ + void *_last_elt = NULL, *_elt; \ + UT_hash_handle *_src_hh, *_dst_hh, *_last_elt_hh=NULL; \ + ptrdiff_t _dst_hho = ((char*)(&(dst)->hh_dst) - (char*)(dst)); \ + if ((src) != NULL) { \ + for (_src_bkt=0; _src_bkt < (src)->hh_src.tbl->num_buckets; _src_bkt++) { \ + for (_src_hh = (src)->hh_src.tbl->buckets[_src_bkt].hh_head; \ + _src_hh != NULL; \ + _src_hh = _src_hh->hh_next) { \ + _elt = ELMT_FROM_HH((src)->hh_src.tbl, _src_hh); \ + if (cond(_elt)) { \ + IF_HASH_NONFATAL_OOM( int _hs_oomed = 0; ) \ + _dst_hh = (UT_hash_handle*)(void*)(((char*)_elt) + _dst_hho); \ + _dst_hh->key = _src_hh->key; \ + _dst_hh->keylen = _src_hh->keylen; \ + _dst_hh->hashv = _src_hh->hashv; \ + _dst_hh->prev = _last_elt; \ + _dst_hh->next = NULL; \ + if (_last_elt_hh != NULL) { \ + _last_elt_hh->next = _elt; \ + } \ + if ((dst) == NULL) { \ + DECLTYPE_ASSIGN(dst, _elt); \ + HASH_MAKE_TABLE(hh_dst, dst, _hs_oomed); \ + IF_HASH_NONFATAL_OOM( \ + if (_hs_oomed) { \ + uthash_nonfatal_oom(_elt); \ + (dst) = NULL; \ + continue; \ + } \ + ) \ + } else { \ + _dst_hh->tbl = (dst)->hh_dst.tbl; \ + } \ + HASH_TO_BKT(_dst_hh->hashv, _dst_hh->tbl->num_buckets, _dst_bkt); \ + HASH_ADD_TO_BKT(_dst_hh->tbl->buckets[_dst_bkt], hh_dst, _dst_hh, _hs_oomed); \ + (dst)->hh_dst.tbl->num_items++; \ + IF_HASH_NONFATAL_OOM( \ + if (_hs_oomed) { \ + HASH_ROLLBACK_BKT(hh_dst, dst, _dst_hh); \ + HASH_DELETE_HH(hh_dst, dst, _dst_hh); \ + _dst_hh->tbl = NULL; \ + uthash_nonfatal_oom(_elt); \ + continue; \ + } \ + ) \ + HASH_BLOOM_ADD(_dst_hh->tbl, _dst_hh->hashv); \ + _last_elt = _elt; \ + _last_elt_hh = _dst_hh; \ + } \ + } \ + } \ + } \ + HASH_FSCK(hh_dst, dst, "HASH_SELECT"); \ +} while (0) + +#define HASH_CLEAR(hh,head) \ +do { \ + if ((head) != NULL) { \ + HASH_BLOOM_FREE((head)->hh.tbl); \ + uthash_free((head)->hh.tbl->buckets, \ + (head)->hh.tbl->num_buckets*sizeof(struct UT_hash_bucket)); \ + uthash_free((head)->hh.tbl, sizeof(UT_hash_table)); \ + (head) = NULL; \ + } \ +} while (0) + +#define HASH_OVERHEAD(hh,head) \ + (((head) != NULL) ? ( \ + (size_t)(((head)->hh.tbl->num_items * sizeof(UT_hash_handle)) + \ + ((head)->hh.tbl->num_buckets * sizeof(UT_hash_bucket)) + \ + sizeof(UT_hash_table) + \ + (HASH_BLOOM_BYTELEN))) : 0U) + +#ifdef NO_DECLTYPE +#define HASH_ITER(hh,head,el,tmp) \ +for(((el)=(head)), ((*(char**)(&(tmp)))=(char*)((head!=NULL)?(head)->hh.next:NULL)); \ + (el) != NULL; ((el)=(tmp)), ((*(char**)(&(tmp)))=(char*)((tmp!=NULL)?(tmp)->hh.next:NULL))) +#else +#define HASH_ITER(hh,head,el,tmp) \ +for(((el)=(head)), ((tmp)=DECLTYPE(el)((head!=NULL)?(head)->hh.next:NULL)); \ + (el) != NULL; ((el)=(tmp)), ((tmp)=DECLTYPE(el)((tmp!=NULL)?(tmp)->hh.next:NULL))) +#endif + +/* obtain a count of items in the hash */ +#define HASH_COUNT(head) HASH_CNT(hh,head) +#define HASH_CNT(hh,head) ((head != NULL)?((head)->hh.tbl->num_items):0U) + +typedef struct UT_hash_bucket { + struct UT_hash_handle *hh_head; + unsigned count; + + /* expand_mult is normally set to 0. In this situation, the max chain length + * threshold is enforced at its default value, HASH_BKT_CAPACITY_THRESH. (If + * the bucket's chain exceeds this length, bucket expansion is triggered). + * However, setting expand_mult to a non-zero value delays bucket expansion + * (that would be triggered by additions to this particular bucket) + * until its chain length reaches a *multiple* of HASH_BKT_CAPACITY_THRESH. + * (The multiplier is simply expand_mult+1). The whole idea of this + * multiplier is to reduce bucket expansions, since they are expensive, in + * situations where we know that a particular bucket tends to be overused. + * It is better to let its chain length grow to a longer yet-still-bounded + * value, than to do an O(n) bucket expansion too often. + */ + unsigned expand_mult; + +} UT_hash_bucket; + +/* random signature used only to find hash tables in external analysis */ +#define HASH_SIGNATURE 0xa0111fe1u +#define HASH_BLOOM_SIGNATURE 0xb12220f2u + +typedef struct UT_hash_table { + UT_hash_bucket *buckets; + unsigned num_buckets, log2_num_buckets; + unsigned num_items; + struct UT_hash_handle *tail; /* tail hh in app order, for fast append */ + ptrdiff_t hho; /* hash handle offset (byte pos of hash handle in element */ + + /* in an ideal situation (all buckets used equally), no bucket would have + * more than ceil(#items/#buckets) items. that's the ideal chain length. */ + unsigned ideal_chain_maxlen; + + /* nonideal_items is the number of items in the hash whose chain position + * exceeds the ideal chain maxlen. these items pay the penalty for an uneven + * hash distribution; reaching them in a chain traversal takes >ideal steps */ + unsigned nonideal_items; + + /* ineffective expands occur when a bucket doubling was performed, but + * afterward, more than half the items in the hash had nonideal chain + * positions. If this happens on two consecutive expansions we inhibit any + * further expansion, as it's not helping; this happens when the hash + * function isn't a good fit for the key domain. When expansion is inhibited + * the hash will still work, albeit no longer in constant time. */ + unsigned ineff_expands, noexpand; + + uint32_t signature; /* used only to find hash tables in external analysis */ +#ifdef HASH_BLOOM + uint32_t bloom_sig; /* used only to test bloom exists in external analysis */ + uint8_t *bloom_bv; + uint8_t bloom_nbits; +#endif + +} UT_hash_table; + +typedef struct UT_hash_handle { + struct UT_hash_table *tbl; + void *prev; /* prev element in app order */ + void *next; /* next element in app order */ + struct UT_hash_handle *hh_prev; /* previous hh in bucket order */ + struct UT_hash_handle *hh_next; /* next hh in bucket order */ + const void *key; /* ptr to enclosing struct's key */ + unsigned keylen; /* enclosing struct's key len */ + unsigned hashv; /* result of hash-fcn(key) */ +} UT_hash_handle; + +#endif /* UTHASH_H */ diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/graph.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/graph.py new file mode 100755 index 00000000..ba619a79 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/graph/graph.py @@ -0,0 +1,356 @@ +from __future__ import annotations + +import sys +from abc import ABC, abstractmethod + +from chgnet.utils import write_json + + +class Node: + """A node in a graph.""" + + def __init__(self, index: int, info: dict | None = None) -> None: + """Initialize a Node. + + Args: + index (int): the index of this node + info (dict, optional): any additional information about this node. + """ + self.index = index + self.info = info + self.neighbors: dict[int, list[DirectedEdge | UndirectedEdge]] = {} + + def add_neighbor(self, index, edge) -> None: + """Draw an directed edge between self and the node specified by index. + + Args: + index (int): the index of neighboring node + edge (DirectedEdge): an DirectedEdge object pointing from self to the node. + """ + if index not in self.neighbors: + self.neighbors[index] = [edge] + else: + self.neighbors[index].append(edge) + + +class Edge(ABC): + """Abstract base class for edges in a graph.""" + + def __init__( + self, nodes: list, index: int | None = None, info: dict | None = None + ) -> None: + """Initialize an Edge.""" + self.nodes = nodes + self.index = index + self.info = info + + def __repr__(self) -> str: + """String representation of this edge.""" + nodes, index, info = self.nodes, self.index, self.info + return f"{type(self).__name__}({nodes=}, {index=}, {info=})" + + def __hash__(self) -> int: + """Hash this edge.""" + img = (self.info or {}).get("image") + img_str = "" if img is None else img.tobytes() + return hash((self.nodes[0], self.nodes[1], img_str)) + + @abstractmethod + def __eq__(self, other: object) -> bool: + """Check if two edges are equal.""" + raise NotImplementedError + + +class UndirectedEdge(Edge): + """An undirected/bi-directed edge in a graph.""" + + __hash__ = Edge.__hash__ + + def __eq__(self, other: object) -> bool: + """Check if two undirected edges are equal.""" + return set(self.nodes) == set(other.nodes) and self.info == other.info + + +class DirectedEdge(Edge): + """A directed edge in a graph.""" + + __hash__ = Edge.__hash__ + + def make_undirected(self, index: int, info: dict | None = None) -> UndirectedEdge: + """Make a directed edge undirected.""" + info = info or {} + info["distance"] = self.info["distance"] + return UndirectedEdge(self.nodes, index, info) + + def __eq__(self, other: object) -> bool: + """Check if the two directed edges are equal. + + Args: + other (DirectedEdge): another DirectedEdge to compare to + + Returns: + bool: True if other is the same directed edge, or if other is the directed + edge with reverse direction of self, else False. + """ + self_img = (self.info or {}).get("image") + other_img = (other.info or {}).get("image") + none_img = self_img is other_img is None + if self.nodes == other.nodes and (none_img or all(self_img == other_img)): + # the image key here is provided by Pymatgen, which refers to the periodic + # cell the neighbor node comes from + + # In this case the two directed edges are exactly the same, but this is not + # supposed tp happen unless there's a bug in Pymatgen. (we will never add + # the same edge twice in creating a crystal graph.) + print( + "!!!!!! the two directed edges are equal but this operation is " + "not supposed to happen", + file=sys.stderr, + ) + return True + + # In this case the first edge is from node i to j and the second edge is + # from node j to i + return self.nodes == other.nodes[::-1] and ( + none_img or all(self_img == -1 * other_img) + ) + + +class Graph: + """A graph for storing the neighbor information of atoms.""" + + def __init__(self, nodes: list[Node]) -> None: + """Initialize a Graph from a list of nodes.""" + self.nodes = nodes + self.directed_edges: dict[frozenset[int], list[DirectedEdge]] = {} + self.directed_edges_list: list[DirectedEdge] = [] + self.undirected_edges: dict[frozenset[int], list[UndirectedEdge]] = {} + self.undirected_edges_list: list[UndirectedEdge] = [] + + def add_edge( + self, center_index, neighbor_index, image, distance, dist_tol: float = 1e-6 + ) -> None: + """Add an directed edge to the graph. + + Args: + center_index (int): center node index + neighbor_index (int): neighbor node index + image (np.array): the periodic cell image the neighbor is from + distance (float): distance between center and neighbor. + dist_tol (float): tolerance for distance comparison between edges. + Default = 1e-6 + """ + # Create directed_edge (DE) index using the length of added DEs + directed_edge_index = len(self.directed_edges_list) + + # Create the DE with info + this_directed_edge = DirectedEdge( + [center_index, neighbor_index], + index=directed_edge_index, + info={"image": image, "distance": distance}, + ) + + # Record the two ends of the DE and see if they have been added + # Here the sequence of the ends doesn't matter since we want to + # search for associated undirected_edge (UDE) + tmp = frozenset([center_index, neighbor_index]) + + # This combination of two ends hasn't been added + if tmp not in self.undirected_edges: + # Create UDE index + this_directed_edge.info["undirected_edge_index"] = len( + self.undirected_edges_list + ) + # Create UDE + this_undirected_edge = this_directed_edge.make_undirected( + index=len(self.undirected_edges_list), + info={"directed_edge_index": [directed_edge_index]}, + ) + self.undirected_edges[tmp] = [this_undirected_edge] + self.undirected_edges_list.append(this_undirected_edge) + self.nodes[center_index].add_neighbor(neighbor_index, this_directed_edge) + self.directed_edges_list.append(this_directed_edge) + else: + # This pair of nodes has been added before, we need to see if this time, + # 1. it's the other directed edge of the same undirected edge or, + # 2. it's another totally different undirected edge that has + # different image and distance (this is possible consider periodicity) + for undirected_edge in self.undirected_edges[tmp]: + if ( + abs(undirected_edge.info["distance"] - distance) < dist_tol + and len(undirected_edge.info["directed_edge_index"]) == 1 + ): + # There is an undirected edge with similar length and only one of + # the directed edges associated has been added + added_dir_edge = self.directed_edges_list[ + undirected_edge.info["directed_edge_index"][0] + ] + + # See if the DE that's associated to this UDE + # is the reverse of our DE + if added_dir_edge == this_directed_edge: + # Add UDE index to this DE + this_directed_edge.info["undirected_edge_index"] = ( + added_dir_edge.info["undirected_edge_index"] + ) + + # At the center node, draw edge with this DE + self.nodes[center_index].add_neighbor( + neighbor_index, this_directed_edge + ) + + # Add this DE to DE list + self.directed_edges_list.append(this_directed_edge) + + # Add this DE to the UDE + undirected_edge.info["directed_edge_index"].append( + directed_edge_index + ) + return + + # no undirected_edge matches to this directed edge + this_directed_edge.info["undirected_edge_index"] = len( + self.undirected_edges_list + ) + this_undirected_edge = this_directed_edge.make_undirected( + index=len(self.undirected_edges_list), + info={"directed_edge_index": [directed_edge_index]}, + ) + self.undirected_edges[tmp].append(this_undirected_edge) + self.undirected_edges_list.append(this_undirected_edge) + self.nodes[center_index].add_neighbor(neighbor_index, this_directed_edge) + self.directed_edges_list.append(this_directed_edge) + + def adjacency_list(self) -> tuple[list[list[int]], list[int]]: + """Get the adjacency list + Return: + graph: the adjacency list + [[0, 1], + [0, 2], + ... + [5, 2] + ... ]] + the fist column specifies center/source node, + the second column specifies neighbor/destination node + directed2undirected: + [0, 1, ...] + a list of length = num_directed_edge that specifies + the undirected edge index corresponding to the directed edges + represented in each row in the graph adjacency list. + """ + graph = [edge.nodes for edge in self.directed_edges_list] + directed2undirected = [ + edge.info["undirected_edge_index"] for edge in self.directed_edges_list + ] + return graph, directed2undirected + + def line_graph_adjacency_list(self, cutoff) -> tuple[list[list[int]], list[int]]: + """Get the line graph adjacency list. + + Args: + cutoff (float): a float to indicate the maximum edge length to be included + in constructing the line graph, this is used to decrease computation + complexity + + Return: + line_graph: + [[0, 1, 1, 2, 2], + [0, 1, 1, 4, 23], + [1, 4, 23, 5, 66], + ... ... ] + the fist column specifies node(atom) index at this angle, + the second column specifies 1st undirected edge(left bond) index, + the third column specifies 1st directed edge(left bond) index, + the fourth column specifies 2nd undirected edge(right bond) index, + the fifth column specifies 2nd directed edge(right bond) index,. + undirected2directed: + [32, 45, ...] + a list of length = num_undirected_edge that + maps the undirected edge index to one of its directed edges indices + """ + if len(self.directed_edges_list) != 2 * len(self.undirected_edges_list): + raise ValueError( + f"Error: number of directed edges={len(self.directed_edges_list)} != 2 " + f"* number of undirected edges={len(self.directed_edges_list)}!" + f"This indicates directed edges are not complete" + ) + line_graph = [] + undirected2directed = [] + + # Loop through all the undirected edges(UDE) + for u_edge in self.undirected_edges_list: + # Create the mapping from the UDE index to one of its DE index + undirected2directed.append(u_edge.info["directed_edge_index"][0]) + + # Ignore the UDE if its length is larger than cutoff (3A for default) + if u_edge.info["distance"] > cutoff: + continue + + # Assert if the UDE is valid + # if encountered exception, + # it means after Atom_Graph creation, the UDE has only 1 DE associated + # This exception is not encountered from the develop team's experience + if len(u_edge.info["directed_edge_index"]) != 2: # noqa: PLR2004 + raise ValueError( + "Did not find 2 Directed_edges !!!" + f"undirected edge {u_edge} has:" + f"edge.info['directed_edge_index'] = " + f"{u_edge.info['directed_edge_index']}" + f"len directed_edges_list = {len(self.directed_edges_list)}" + f"len undirected_edges_list = {len(self.undirected_edges_list)}" + ) + + # This UDE is valid to be considered as a node in Bond_Graph + + # Get the two ends (centers) and the two DE associated with this UDE + # DE1 should have center=center1 and DE2 should have center=center2 + # We will need to find directed edges with center = center1 + # and create angles with DE1, then do the same for center2 and DE2 + for center, dir_edge in zip( + u_edge.nodes, u_edge.info["directed_edge_index"] + ): + for directed_edges in self.nodes[center].neighbors.values(): + for directed_edge in directed_edges: + if directed_edge.index == dir_edge: + continue + if directed_edge.info["distance"] < cutoff: + line_graph.append( + [ + center, + u_edge.index, + dir_edge, + directed_edge.info["undirected_edge_index"], + directed_edge.index, + ] + ) + return line_graph, undirected2directed + + def undirected2directed(self) -> list[int]: + """The index map from undirected_edge index to one of its directed_edge + index. + """ + return [ + undirected_edge.info["directed_edge_index"][0] + for undirected_edge in self.undirected_edges_list + ] + + def as_dict(self) -> dict: + """Return dictionary serialization of a Graph.""" + return { + "nodes": self.nodes, + "directed_edges": self.directed_edges, + "directed_edges_list": self.directed_edges_list, + "undirected_edges": self.undirected_edges, + "undirected_edges_list": self.undirected_edges_list, + } + + def to(self, filename="graph.json") -> None: + """Save graph dictionary to file.""" + write_json(self.as_dict(), filename) + + def __repr__(self) -> str: + """Return string representation of the Graph.""" + num_nodes = len(self.nodes) + num_directed_edges = len(self.directed_edges_list) + num_undirected_edges = len(self.undirected_edges_list) + return f"Graph({num_nodes=}, {num_directed_edges=}, {num_undirected_edges=})" diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/__init__.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/__init__.py new file mode 100755 index 00000000..85f535a9 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/__init__.py @@ -0,0 +1,4 @@ +from __future__ import annotations + +from chgnet.model.dynamics import CHGNetCalculator, MolecularDynamics, StructOptimizer +from chgnet.model.model import CHGNet diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/basis.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/basis.py new file mode 100755 index 00000000..1c024b89 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/basis.py @@ -0,0 +1,206 @@ +from __future__ import annotations + +import numpy as np +import torch +from torch import Tensor, nn + + +class Fourier(nn.Module): + """Fourier Expansion for angle features.""" + + def __init__(self, *, order: int = 5, learnable: bool = False) -> None: + """Initialize the Fourier expansion. + + Args: + order (int): the maximum order, refer to the N in eq 1 in CHGNet paper + Default = 5 + learnable (bool): whether to set the frequencies as learnable parameters + Default = False + """ + super().__init__() + self.order = order + # Initialize frequencies at canonical + if learnable: + self.frequencies = torch.nn.Parameter( + data=torch.arange(1, order + 1, dtype=torch.float), + requires_grad=True, + ) + else: + self.register_buffer( + "frequencies", torch.arange(1, order + 1, dtype=torch.float) + ) + + def forward(self, x: Tensor) -> Tensor: + """Apply Fourier expansion to a feature Tensor.""" + result = x.new_zeros(x.shape[0], 1 + 2 * self.order) + result[:, 0] = 1 / torch.sqrt(torch.tensor([2])) + tmp = torch.outer(x, self.frequencies) + result[:, 1 : self.order + 1] = torch.sin(tmp) + result[:, self.order + 1 :] = torch.cos(tmp) + return result / np.sqrt(np.pi) + + +class RadialBessel(torch.nn.Module): + """1D Bessel Basis + from: https://github.com/TUM-DAML/gemnet_pytorch/. + """ + + def __init__( + self, + *, + num_radial: int = 9, + cutoff: float = 5, + learnable: bool = False, + smooth_cutoff: int = 5, + ) -> None: + """Initialize the SmoothRBF function. + + Args: + num_radial (int): Controls maximum frequency + Default = 9 + cutoff (float): Cutoff distance in Angstrom. + Default = 5 + learnable (bool): whether to set the frequencies learnable + Default = False + smooth_cutoff (int): smooth cutoff strength + Default = 5 + """ + super().__init__() + self.num_radial = num_radial + self.inv_cutoff = 1 / cutoff + self.norm_const = (2 * self.inv_cutoff) ** 0.5 + + # Initialize frequencies at canonical positions + if learnable: + self.frequencies = torch.nn.Parameter( + data=torch.Tensor( + np.pi * np.arange(1, self.num_radial + 1, dtype=np.float32) + ), + requires_grad=True, + ) + else: + self.register_buffer( + "frequencies", + np.pi * torch.arange(1, self.num_radial + 1, dtype=torch.float), + ) + if smooth_cutoff is not None: + self.smooth_cutoff = CutoffPolynomial( + cutoff=cutoff, cutoff_coeff=smooth_cutoff + ) + else: + self.smooth_cutoff = None + + def forward( + self, dist: Tensor, *, return_smooth_factor: bool = False + ) -> Tensor | tuple[Tensor, Tensor]: + """Apply Bessel expansion to a feature Tensor. + + Args: + dist (Tensor): tensor of distances [n, 1] + return_smooth_factor (bool): whether to return the smooth factor + Default = False + + Returns: + out (Tensor): tensor of Bessel distances [n, dim] + where the expanded dimension will be num_radial + smooth_factor (Tensor): tensor of smooth factors [n, 1] + """ + dist = dist[:, None] # shape = (nEdges, 1) + d_scaled = dist * self.inv_cutoff + out = self.norm_const * torch.sin(self.frequencies * d_scaled) / dist + if self.smooth_cutoff is not None: + smooth_factor = self.smooth_cutoff(dist) + out = smooth_factor * out + if return_smooth_factor: + return out, smooth_factor + return out + + +class GaussianExpansion(nn.Module): + """Expands the distance by Gaussian basis. + Unit: angstrom. + """ + + def __init__( + self, + min: float = 0, # noqa: A002 + max: float = 5, # noqa: A002 + step: float = 0.5, + var: float | None = None, + ) -> None: + """Gaussian Expansion + expand a scalar feature to a soft-one-hot feature vector. + + Args: + min (float): minimum Gaussian center value + max (float): maximum Gaussian center value + step (float): Step size between the Gaussian centers + var (float): variance in gaussian filter, default to step + """ + super().__init__() + if min >= max: + raise ValueError(f"{min=} must be less than {max=}") + if max - min <= step: + raise ValueError(f"{max - min=} must be greater than {step=}") + self.register_buffer("gaussian_centers", torch.arange(min, max + step, step)) + self.var = var or step + if self.var <= 0: + raise ValueError(f"{var=} must be positive") + + def expand(self, features: Tensor) -> Tensor: + """Apply Gaussian filter to a feature Tensor. + + Args: + features (Tensor): tensor of features [n] + + Returns: + expanded features (Tensor): tensor of Gaussian distances [n, dim] + where the expanded dimension will be (dmax - dmin) / step + 1 + """ + return torch.exp( + -((features.reshape(-1, 1) - self.gaussian_centers) ** 2) / self.var**2 + ) + + +class CutoffPolynomial(nn.Module): + """Polynomial soft-cutoff function for atom graph + ref: https://github.com/TUM-DAML/gemnet_pytorch/blob/-/gemnet/model/layers/envelope.py. + """ + + def __init__(self, cutoff: float = 5, cutoff_coeff: float = 5) -> None: + """Initialize the polynomial cutoff function. + + Args: + cutoff (float): cutoff radius (A) in atom graph construction + Default = 5 + cutoff_coeff (float): the strength of soft-Cutoff + 0 will disable the cutoff, returning 1 at every r + for positive numbers > 0, the smaller cutoff_coeff is, the faster this + function decays. Default = 5. + """ + super().__init__() + self.cutoff = cutoff + self.p = cutoff_coeff + self.a = -(self.p + 1) * (self.p + 2) / 2 + self.b = self.p * (self.p + 2) + self.c = -self.p * (self.p + 1) / 2 + + def forward(self, r: Tensor) -> Tensor: + """Polynomial cutoff function. + + Args: + r (Tensor): radius distance tensor + + Returns: + polynomial cutoff functions: decaying from 1 at r=0 to 0 at r=cutoff + """ + if self.p != 0: + r_scaled = r / self.cutoff + env_val = ( + 1 + + self.a * r_scaled**self.p + + self.b * r_scaled ** (self.p + 1) + + self.c * r_scaled ** (self.p + 2) + ) + return torch.where(r_scaled < 1, env_val, torch.zeros_like(r_scaled)) + return r.new_ones(r.shape) diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/composition_model.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/composition_model.py new file mode 100755 index 00000000..4a7e0e99 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/composition_model.py @@ -0,0 +1,433 @@ +from __future__ import annotations + +import collections +from typing import TYPE_CHECKING + +import numpy as np +import torch +from pymatgen.core import Structure +from torch import Tensor, nn + +from chgnet.model.functions import GatedMLP, find_activation + +if TYPE_CHECKING: + from collections.abc import Sequence + from pathlib import Path + + from chgnet.graph.crystalgraph import CrystalGraph + + +class CompositionModel(nn.Module): + """A simple FC model that takes in a chemical composition (no structure info) + and outputs energy. + """ + + def __init__( + self, + *, + atom_fea_dim: int = 64, + activation: str = "silu", + is_intensive: bool = True, + max_num_elements: int = 94, + ) -> None: + """Initialize a CompositionModel.""" + super().__init__() + self.is_intensive = is_intensive + self.max_num_elements = max_num_elements + self.fc1 = nn.Linear(max_num_elements, atom_fea_dim) + self.activation = find_activation(activation) + self.gated_mlp = GatedMLP( + input_dim=atom_fea_dim, + output_dim=atom_fea_dim, + hidden_dim=atom_fea_dim, + activation=activation, + ) + self.fc2 = nn.Linear(atom_fea_dim, 1) + + def _get_energy(self, composition_feas: Tensor) -> Tensor: + """Predict the energy given composition encoding. + + Args: + composition_feas: batched atom feature matrix of shape + [batch_size, total_num_elements]. + + Returns: + prediction associated with each composition [batchsize]. + """ + composition_feas = self.activation(self.fc1(composition_feas)) + composition_feas += self.gated_mlp(composition_feas) + return self.fc2(composition_feas).view(-1) + + def forward(self, graphs: list[CrystalGraph]) -> Tensor: + """Get the energy of a list of CrystalGraphs as Tensor.""" + composition_feas = self._assemble_graphs(graphs) + return self._get_energy(composition_feas) + + def _assemble_graphs(self, graphs: list[CrystalGraph]) -> Tensor: + """Assemble a list of graphs into one-hot composition encodings. + + Args: + graphs (list[CrystalGraph]): a list of CrystalGraphs + + Returns: + assembled batch_graph that contains all information for model. + """ + composition_feas = [] + for graph in graphs: + composition_fea = torch.bincount( + graph.atomic_number - 1, minlength=self.max_num_elements + ) + if self.is_intensive: + n_atom = graph.atomic_number.shape[0] + composition_fea = composition_fea / n_atom + composition_feas.append(composition_fea) + return torch.stack(composition_feas, dim=0) + + +class AtomRef(nn.Module): + """A linear regression for elemental energy. + From: https://github.com/materialsvirtuallab/m3gnet/. + """ + + def __init__( + self, *, is_intensive: bool = True, max_num_elements: int = 94 + ) -> None: + """Initialize an AtomRef model.""" + super().__init__() + self.is_intensive = is_intensive + self.max_num_elements = max_num_elements + self.fc = nn.Linear(max_num_elements, 1, bias=False) + self.fitted = False + + def forward(self, graphs: list[CrystalGraph]) -> Tensor: + """Get the energy of a list of CrystalGraphs. + + Args: + graphs (List(CrystalGraph)): a list of Crystal Graph to compute + + Returns: + energy (tensor) + """ + if not self.fitted: + raise ValueError("composition model needs to be fitted first!") + composition_feas = self._assemble_graphs(graphs) + return self._get_energy(composition_feas) + + def _get_energy(self, composition_feas: Tensor) -> Tensor: + """Predict the energy given composition encoding. + + Args: + composition_feas: batched atom feature matrix of shape + [batch_size, total_num_elements]. + + Returns: + prediction associated with each composition [batchsize]. + """ + return self.fc(composition_feas).view(-1) + + def fit( + self, + structures_or_graphs: Sequence[Structure | CrystalGraph], + energies: Sequence[float], + ) -> None: + """Fit the model to a list of crystals and energies. + + Args: + structures_or_graphs (list[Structure | CrystalGraph]): Any iterable of + pymatgen structures and/or graphs. + energies (list[float]): Target energies. + """ + num_data = len(energies) + composition_feas = torch.zeros([num_data, self.max_num_elements]) + e = torch.zeros([num_data]) + for index, (structure, energy) in enumerate( + zip(structures_or_graphs, energies) + ): + if isinstance(structure, Structure): + atomic_number = torch.tensor( + [site.specie.Z for site in structure], + dtype=int, + requires_grad=False, + ) + else: + atomic_number = structure.atomic_number + composition_fea = torch.bincount( + atomic_number - 1, minlength=self.max_num_elements + ) + if self.is_intensive: + composition_fea = composition_fea / atomic_number.shape[0] + composition_feas[index, :] = composition_fea + e[index] = energy + + # Use numpy for pinv + self.feature_matrix = composition_feas.detach().numpy() + self.energies = e.detach().numpy() + state_dict = collections.OrderedDict() + weight = ( + np.linalg.pinv(self.feature_matrix.T @ self.feature_matrix) + @ self.feature_matrix.T + @ self.energies + ) + state_dict["weight"] = torch.tensor(weight).view(1, 94) + self.fc.load_state_dict(state_dict) + self.fitted = True + + def _assemble_graphs(self, graphs: list[CrystalGraph]) -> Tensor: + """Assemble a list of graphs into one-hot composition encodings + Args: + graphs (list[Tensor]): a list of CrystalGraphs + Returns: + assembled batch_graph that contains all information for model. + """ + composition_feas = [] + for graph in graphs: + composition_fea = torch.bincount( + graph.atomic_number - 1, minlength=self.max_num_elements + ) + if self.is_intensive: + n_atom = graph.atomic_number.shape[0] + composition_fea = composition_fea / n_atom + composition_feas.append(composition_fea) + return torch.stack(composition_feas, dim=0).float() + + def get_site_energies(self, graphs: list[CrystalGraph]) -> list[Tensor]: + """Predict the site energies given a list of CrystalGraphs. + + Args: + graphs (List(CrystalGraph)): a list of Crystal Graph to compute + + Returns: + a list of tensors corresponding to site energies of each graph [batchsize]. + """ + return [ + self.fc.state_dict()["weight"][0, graph.atomic_number - 1] + for graph in graphs + ] + + def initialize_from(self, dataset: str) -> None: + """Initialize pre-fitted weights from a dataset.""" + if dataset in {"MPtrj", "MPtrj_e"}: + self.initialize_from_MPtrj() + elif dataset == "MPF": + self.initialize_from_MPF() + else: + raise NotImplementedError(f"{dataset=} not supported yet") + + def initialize_from_MPtrj(self) -> None: # noqa: N802 + """Initialize pre-fitted weights from MPtrj dataset.""" + state_dict = collections.OrderedDict() + state_dict["weight"] = torch.tensor( + [ + -3.4431, + -0.1279, + -2.8300, + -3.4737, + -7.4946, + -8.2354, + -8.1611, + -8.3861, + -5.7498, + -0.0236, + -1.7406, + -1.6788, + -4.2833, + -6.2002, + -6.1315, + -5.8405, + -3.8795, + -0.0703, + -1.5668, + -3.4451, + -7.0549, + -9.1465, + -9.2594, + -9.3514, + -8.9843, + -8.0228, + -6.4955, + -5.6057, + -3.4002, + -0.9217, + -3.2499, + -4.9164, + -4.7810, + -5.0191, + -3.3316, + 0.5130, + -1.4043, + -3.2175, + -7.4994, + -9.3816, + -10.4386, + -9.9539, + -7.9555, + -8.5440, + -7.3245, + -5.2771, + -1.9014, + -0.4034, + -2.6002, + -4.0054, + -4.1156, + -3.9928, + -2.7003, + 2.2170, + -1.9671, + -3.7180, + -6.8133, + -7.3502, + -6.0712, + -6.1699, + -5.1471, + -6.1925, + -11.5829, + -15.8841, + -5.9994, + -6.0798, + -5.9513, + -6.0400, + -5.9773, + -2.5091, + -6.0767, + -10.6666, + -11.8761, + -11.8491, + -10.7397, + -9.6100, + -8.4755, + -6.2070, + -3.0337, + 0.4726, + -1.6425, + -3.1295, + -3.3328, + -0.1221, + -0.3448, + -0.4364, + -0.1661, + -0.3680, + -4.1869, + -8.4233, + -10.0467, + -12.0953, + -12.5228, + -14.2530, + ] + ).view([1, 94]) + self.fc.load_state_dict(state_dict) + self.is_intensive = True + self.fitted = True + + def initialize_from_MPF(self) -> None: # noqa: N802 + """Initialize pre-fitted weights from MPF dataset.""" + state_dict = collections.OrderedDict() + state_dict["weight"] = torch.tensor( + [ + -3.4654e00, + -6.2617e-01, + -3.4622e00, + -4.7758e00, + -8.0362e00, + -8.4038e00, + -7.7681e00, + -7.3892e00, + -4.9472e00, + -5.4833e00, + -2.4783e00, + -2.0202e00, + -5.1548e00, + -7.9121e00, + -6.9135e00, + -4.6228e00, + -3.0155e00, + -2.1285e00, + -2.3174e00, + -4.7595e00, + -8.1742e00, + -1.1421e01, + -8.9229e00, + -8.4901e00, + -8.1664e00, + -6.5826e00, + -5.2614e00, + -4.4841e00, + -3.2737e00, + -1.3498e00, + -3.6264e00, + -4.6727e00, + -4.1316e00, + -3.6755e00, + -2.8030e00, + 6.4728e00, + -2.2469e00, + -4.2510e00, + -1.0245e01, + -1.1666e01, + -1.1802e01, + -8.6551e00, + -9.3641e00, + -7.5716e00, + -5.6990e00, + -4.9716e00, + -1.8871e00, + -6.7951e-01, + -2.7488e00, + -3.7945e00, + -3.3883e00, + -2.5588e00, + -1.9621e00, + 9.9793e00, + -2.5566e00, + -4.8803e00, + -8.8604e00, + -9.0537e00, + -7.9431e00, + -8.1259e00, + -6.3212e00, + -8.3025e00, + -1.2289e01, + -1.7310e01, + -7.5512e00, + -8.1959e00, + -8.3493e00, + -7.2591e00, + -8.4170e00, + -3.3873e00, + -7.6823e00, + -1.2630e01, + -1.3626e01, + -9.5299e00, + -1.1840e01, + -9.7990e00, + -7.5561e00, + -5.4690e00, + -2.6508e00, + 4.1746e-01, + -2.3255e00, + -3.4830e00, + -3.1808e00, + -1.6934e-02, + -3.6191e-02, + -1.0842e-02, + 1.3170e-02, + -6.5371e-02, + -5.4892e00, + -1.0335e01, + -1.1130e01, + -1.4312e01, + -1.4700e01, + -1.5473e01, + ] + ).view([1, 94]) + self.fc.load_state_dict(state_dict) + self.is_intensive = False + self.fitted = True + + def initialize_from_numpy(self, file_name: str | Path) -> None: + """Initialize pre-fitted weights from numpy file.""" + atom_ref_np = np.load(file_name) + state_dict = collections.OrderedDict() + state_dict["weight"] = torch.tensor(atom_ref_np).view([1, 94]) + self.fc.load_state_dict(state_dict) + self.is_intensive = False + self.fitted = True diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/dynamics.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/dynamics.py new file mode 100755 index 00000000..37c24316 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/dynamics.py @@ -0,0 +1,891 @@ +from __future__ import annotations + +import contextlib +import inspect +import io +import pickle +import sys +from typing import TYPE_CHECKING, Literal + +import numpy as np +from ase import Atoms, units +from ase.calculators.calculator import Calculator, all_changes, all_properties +from ase.md.npt import NPT +from ase.md.nptberendsen import Inhomogeneous_NPTBerendsen, NPTBerendsen +from ase.md.nvtberendsen import NVTBerendsen +from ase.md.velocitydistribution import MaxwellBoltzmannDistribution, Stationary +from ase.md.verlet import VelocityVerlet +from ase.optimize.bfgs import BFGS +from ase.optimize.bfgslinesearch import BFGSLineSearch +from ase.optimize.fire import FIRE +from ase.optimize.lbfgs import LBFGS, LBFGSLineSearch +from ase.optimize.mdmin import MDMin +from ase.optimize.sciopt import SciPyFminBFGS, SciPyFminCG +from pymatgen.analysis.eos import BirchMurnaghan +from pymatgen.core.structure import Molecule, Structure +from pymatgen.io.ase import AseAtomsAdaptor + +from chgnet.model.model import CHGNet +from chgnet.utils import determine_device + +if TYPE_CHECKING: + from ase.io import Trajectory + from ase.optimize.optimize import Optimizer + from typing_extensions import Self + +# We would like to thank M3GNet develop team for this module +# source: https://github.com/materialsvirtuallab/m3gnet + +OPTIMIZERS = { + "FIRE": FIRE, + "BFGS": BFGS, + "LBFGS": LBFGS, + "LBFGSLineSearch": LBFGSLineSearch, + "MDMin": MDMin, + "SciPyFminCG": SciPyFminCG, + "SciPyFminBFGS": SciPyFminBFGS, + "BFGSLineSearch": BFGSLineSearch, +} + + +class CHGNetCalculator(Calculator): + """CHGNet Calculator for ASE applications.""" + + implemented_properties = ("energy", "forces", "stress", "magmoms") + + def __init__( + self, + model: CHGNet | None = None, + *, + use_device: str | None = None, + check_cuda_mem: bool = False, + stress_weight: float | None = 1 / 160.21766208, + on_isolated_atoms: Literal["ignore", "warn", "error"] = "warn", + **kwargs, + ) -> None: + """Provide a CHGNet instance to calculate various atomic properties using ASE. + + Args: + model (CHGNet): instance of a chgnet model. If set to None, + the pretrained CHGNet is loaded. + Default = None + use_device (str, optional): The device to be used for predictions, + either "cpu", "cuda", or "mps". If not specified, the default device is + automatically selected based on the available options. + Default = None + check_cuda_mem (bool): Whether to use cuda with most available memory + Default = False + stress_weight (float): the conversion factor to convert GPa to eV/A^3. + Default = 1/160.21 + on_isolated_atoms ('ignore' | 'warn' | 'error'): how to handle Structures + with isolated atoms. + Default = 'warn' + **kwargs: Passed to the Calculator parent class. + """ + super().__init__(**kwargs) + + # Determine the device to use + device = determine_device(use_device=use_device, check_cuda_mem=check_cuda_mem) + self.device = device + + # Move the model to the specified device + if model is None: + self.model = CHGNet.load(verbose=False, use_device=self.device) + else: + self.model = model.to(self.device) + self.model.graph_converter.set_isolated_atom_response(on_isolated_atoms) + self.stress_weight = stress_weight + print(f"CHGNet will run on {self.device}") + + @classmethod + def from_file(cls, path: str, use_device: str | None = None, **kwargs) -> Self: + """Load a user's CHGNet model and initialize the Calculator.""" + return cls( + model=CHGNet.from_file(path), + use_device=use_device, + **kwargs, + ) + + @property + def version(self) -> str | None: + """The version of CHGNet.""" + return self.model.version + + @property + def n_params(self) -> int: + """The number of parameters in CHGNet.""" + return self.model.n_params + + def calculate( + self, + atoms: Atoms | None = None, + properties: list | None = None, + system_changes: list | None = None, + ) -> None: + """Calculate various properties of the atoms using CHGNet. + + Args: + atoms (Atoms | None): The atoms object to calculate properties for. + properties (list | None): The properties to calculate. + Default is all properties. + system_changes (list | None): The changes made to the system. + Default is all changes. + """ + properties = properties or all_properties + system_changes = system_changes or all_changes + super().calculate( + atoms=atoms, + properties=properties, + system_changes=system_changes, + ) + + # Run CHGNet + structure = AseAtomsAdaptor.get_structure(atoms) + graph = self.model.graph_converter(structure) + model_prediction = self.model.predict_graph( + graph.to(self.device), task="efsm", return_crystal_feas=True + ) + + # Convert Result + factor = 1 if not self.model.is_intensive else structure.composition.num_atoms + self.results.update( + energy=model_prediction["e"] * factor, + forces=model_prediction["f"], + free_energy=model_prediction["e"] * factor, + magmoms=model_prediction["m"], + stress=model_prediction["s"] * self.stress_weight, + crystal_fea=model_prediction["crystal_fea"], + ) + + +class StructOptimizer: + """Wrapper class for structural relaxation.""" + + def __init__( + self, + model: CHGNet | CHGNetCalculator | None = None, + optimizer_class: Optimizer | str | None = "FIRE", + use_device: str | None = None, + stress_weight: float = 1 / 160.21766208, + on_isolated_atoms: Literal["ignore", "warn", "error"] = "warn", + ) -> None: + """Provide a trained CHGNet model and an optimizer to relax crystal structures. + + Args: + model (CHGNet): instance of a CHGNet model or CHGNetCalculator. + If set to None, the pretrained CHGNet is loaded. + Default = None + optimizer_class (Optimizer,str): choose optimizer from ASE. + Default = "FIRE" + use_device (str, optional): The device to be used for predictions, + either "cpu", "cuda", or "mps". If not specified, the default device is + automatically selected based on the available options. + Default = None + stress_weight (float): the conversion factor to convert GPa to eV/A^3. + Default = 1/160.21 + on_isolated_atoms ('ignore' | 'warn' | 'error'): how to handle Structures + with isolated atoms. + Default = 'warn' + """ + if isinstance(optimizer_class, str): + if optimizer_class in OPTIMIZERS: + optimizer_class = OPTIMIZERS[optimizer_class] + else: + raise ValueError( + f"Optimizer instance not found. Select from {list(OPTIMIZERS)}" + ) + + self.optimizer_class: Optimizer = optimizer_class + + if isinstance(model, CHGNetCalculator): + self.calculator = model + else: + self.calculator = CHGNetCalculator( + model=model, + stress_weight=stress_weight, + use_device=use_device, + on_isolated_atoms=on_isolated_atoms, + ) + + @property + def version(self) -> str: + """The version of CHGNet.""" + return self.calculator.model.version + + @property + def n_params(self) -> int: + """The number of parameters in CHGNet.""" + return self.calculator.model.n_params + + def relax( + self, + atoms: Structure | Atoms, + *, + fmax: float | None = 0.1, + steps: int | None = 500, + relax_cell: bool | None = True, + ase_filter: str | None = "FrechetCellFilter", + save_path: str | None = None, + loginterval: int | None = 1, + crystal_feas_save_path: str | None = None, + verbose: bool = True, + assign_magmoms: bool = True, + **kwargs, + ) -> dict[str, Structure | TrajectoryObserver]: + """Relax the Structure/Atoms until maximum force is smaller than fmax. + + Args: + atoms (Structure | Atoms): A Structure or Atoms object to relax. + fmax (float | None): The maximum force tolerance for relaxation. + Default = 0.1 + steps (int | None): The maximum number of steps for relaxation. + Default = 500 + relax_cell (bool | None): Whether to relax the cell as well. + Default = True + ase_filter (str | ase.filters.Filter): The filter to apply to the atoms + object for relaxation. Default = FrechetCellFilter + Default used to be ExpCellFilter which was removed due to bug reported + in https://gitlab.com/ase/ase/-/issues/1321 and fixed in + https://gitlab.com/ase/ase/-/merge_requests/3024. + save_path (str | None): The path to save the trajectory. + Default = None + loginterval (int | None): Interval for logging trajectory and crystal + features. Default = 1 + crystal_feas_save_path (str | None): Path to save crystal feature vectors + which are logged at a loginterval rage + Default = None + verbose (bool): Whether to print the output of the ASE optimizer. + Default = True + assign_magmoms (bool): Whether to assign magnetic moments to the final + structure. Default = True + **kwargs: Additional parameters for the optimizer. + + Returns: + dict[str, Structure | TrajectoryObserver]: + A dictionary with 'final_structure' and 'trajectory'. + """ + from ase import filters + from ase.filters import Filter + + valid_filter_names = [ + name + for name, cls in inspect.getmembers(filters, inspect.isclass) + if issubclass(cls, Filter) + ] + + if isinstance(ase_filter, str): + if ase_filter in valid_filter_names: + ase_filter = getattr(filters, ase_filter) + else: + raise ValueError( + f"Invalid {ase_filter=}, must be one of {valid_filter_names}. " + ) + + if isinstance(atoms, Structure): + atoms = AseAtomsAdaptor().get_atoms(atoms) + + atoms.calc = self.calculator # assign model used to predict forces + + stream = sys.stdout if verbose else io.StringIO() + with contextlib.redirect_stdout(stream): + obs = TrajectoryObserver(atoms) + + if crystal_feas_save_path: + cry_obs = CrystalFeasObserver(atoms) + + if relax_cell: + atoms = ase_filter(atoms) + optimizer: Optimizer = self.optimizer_class(atoms, **kwargs) + optimizer.attach(obs, interval=loginterval) + + if crystal_feas_save_path: + optimizer.attach(cry_obs, interval=loginterval) + + optimizer.run(fmax=fmax, steps=steps) + obs() + + if save_path is not None: + obs.save(save_path) + + if crystal_feas_save_path: + cry_obs.save(crystal_feas_save_path) + + if isinstance(atoms, Filter): + atoms = atoms.atoms + struct = AseAtomsAdaptor.get_structure(atoms) + + if assign_magmoms: + for key in struct.site_properties: + struct.remove_site_property(property_name=key) + struct.add_site_property( + "magmom", [float(magmom) for magmom in atoms.get_magnetic_moments()] + ) + return {"final_structure": struct, "trajectory": obs} + + +class TrajectoryObserver: + """Trajectory observer is a hook in the relaxation process that saves the + intermediate structures. + """ + + def __init__(self, atoms: Atoms) -> None: + """Create a TrajectoryObserver from an Atoms object. + + Args: + atoms (Atoms): the structure to observe. + """ + self.atoms = atoms + self.energies: list[float] = [] + self.forces: list[np.ndarray] = [] + self.stresses: list[np.ndarray] = [] + self.magmoms: list[np.ndarray] = [] + self.atom_positions: list[np.ndarray] = [] + self.cells: list[np.ndarray] = [] + + def __call__(self) -> None: + """The logic for saving the properties of an Atoms during the relaxation.""" + self.energies.append(self.compute_energy()) + self.forces.append(self.atoms.get_forces()) + self.stresses.append(self.atoms.get_stress()) + self.magmoms.append(self.atoms.get_magnetic_moments()) + self.atom_positions.append(self.atoms.get_positions()) + self.cells.append(self.atoms.get_cell()[:]) + + def __len__(self) -> int: + """The number of steps in the trajectory.""" + return len(self.energies) + + def compute_energy(self) -> float: + """Calculate the potential energy. + + Returns: + energy (float): the potential energy. + """ + return self.atoms.get_potential_energy() + + def save(self, filename: str) -> None: + """Save the trajectory to file. + + Args: + filename (str): filename to save the trajectory + """ + out_pkl = { + "energy": self.energies, + "forces": self.forces, + "stresses": self.stresses, + "magmoms": self.magmoms, + "atom_positions": self.atom_positions, + "cell": self.cells, + "atomic_number": self.atoms.get_atomic_numbers(), + } + with open(filename, "wb") as file: + pickle.dump(out_pkl, file) + + +class CrystalFeasObserver: + """CrystalFeasObserver is a hook in the relaxation and MD process that saves the + intermediate crystal feature structures. + """ + + def __init__(self, atoms: Atoms) -> None: + """Create a CrystalFeasObserver from an Atoms object.""" + self.atoms = atoms + self.crystal_feature_vectors: list[np.ndarray] = [] + + def __call__(self) -> None: + """Record Atoms crystal feature vectors after an MD/relaxation step.""" + self.crystal_feature_vectors.append(self.atoms._calc.results["crystal_fea"]) # noqa: SLF001 + + def __len__(self) -> int: + """Number of recorded steps.""" + return len(self.crystal_feature_vectors) + + def save(self, filename: str) -> None: + """Save the crystal feature vectors to filename in pickle format.""" + out_pkl = {"crystal_feas": self.crystal_feature_vectors} + with open(filename, "wb") as file: + pickle.dump(out_pkl, file) + + +class MolecularDynamics: + """Molecular dynamics class.""" + + def __init__( + self, + atoms: Atoms | Structure, + *, + model: CHGNet | CHGNetCalculator | None = None, + ensemble: str = "nvt", + thermostat: str = "Berendsen_inhomogeneous", + temperature: int = 300, + starting_temperature: int | None = None, + timestep: float = 2.0, + pressure: float = 1.01325e-4, + taut: float | None = None, + taup: float | None = None, + bulk_modulus: float | None = None, + trajectory: str | Trajectory | None = None, + logfile: str | None = None, + loginterval: int = 1, + crystal_feas_logfile: str | None = None, + append_trajectory: bool = False, + on_isolated_atoms: Literal["ignore", "warn", "error"] = "warn", + use_device: str | None = None, + ) -> None: + """Initialize the MD class. + + Args: + atoms (Atoms): atoms to run the MD + model (CHGNet): instance of a CHGNet model or CHGNetCalculator. + If set to None, the pretrained CHGNet is loaded. + Default = None + ensemble (str): choose from 'nve', 'nvt', 'npt' + Default = "nvt" + thermostat (str): Thermostat to use + choose from "Nose-Hoover", "Berendsen", "Berendsen_inhomogeneous" + Default = "Berendsen_inhomogeneous" + temperature (float): temperature for MD simulation, in K + Default = 300 + starting_temperature (float): starting temperature of MD simulation, in K + if set as None, the MD starts with the momentum carried by ase.Atoms + if input is a pymatgen.core.Structure, the MD starts at 0K + Default = None + timestep (float): time step in fs + Default = 2 + pressure (float): pressure in GPa + Can be 3x3 or 6 np.array if thermostat is "Nose-Hoover" + Default = 1.01325e-4 GPa = 1 atm + taut (float): time constant for temperature coupling in fs. + The temperature will be raised to target temperature in approximate + 10 * taut time. + Default = 100 * timestep + taup (float): time constant for pressure coupling in fs + Default = 1000 * timestep + bulk_modulus (float): bulk modulus of the material in GPa. + Used in NPT ensemble for the barostat pressure coupling. + The DFT bulk modulus can be found for most materials at + https://next-gen.materialsproject.org/ + + In NPT ensemble, the effective damping time for pressure is multiplied + by compressibility. In LAMMPS, Bulk modulus is defaulted to 10 + see: https://docs.lammps.org/fix_press_berendsen.html + and: https://gitlab.com/ase/ase/-/blob/master/ase/md/nptberendsen.py + + If bulk modulus is not provided here, it will be calculated by CHGNet + through Birch Murnaghan equation of state (EOS). + Note the EOS fitting can fail because of non-parabolic potential + energy surface, which is common with soft system like liquid and gas. + In such case, user should provide an input bulk modulus for better + barostat coupling, otherwise a guessed bulk modulus = 2 GPa will be used + (water's bulk modulus) + + Default = None + trajectory (str or Trajectory): Attach trajectory object + Default = None + logfile (str): open this file for recording MD outputs + Default = None + loginterval (int): write to log file every interval steps + Default = 1 + crystal_feas_logfile (str): open this file for recording crystal features + during MD. Default = None + append_trajectory (bool): Whether to append to prev trajectory. + If false, previous trajectory gets overwritten + Default = False + on_isolated_atoms ('ignore' | 'warn' | 'error'): how to handle Structures + with isolated atoms. + Default = 'warn' + use_device (str): the device for the MD run + Default = None + """ + self.ensemble = ensemble + self.thermostat = thermostat + if isinstance(atoms, (Structure, Molecule)): + atoms = AseAtomsAdaptor().get_atoms(atoms) + # atoms = atoms.to_ase_atoms() + + if starting_temperature is not None: + MaxwellBoltzmannDistribution( + atoms, temperature_K=starting_temperature, force_temp=True + ) + Stationary(atoms) + + self.atoms = atoms + if isinstance(model, CHGNetCalculator): + self.atoms.calc = model + else: + self.atoms.calc = CHGNetCalculator( + model=model, + use_device=use_device, + on_isolated_atoms=on_isolated_atoms, + ) + + if taut is None: + taut = 100 * timestep + if taup is None: + taup = 1000 * timestep + + if ensemble.lower() == "nve": + """ + VelocityVerlet (constant N, V, E) molecular dynamics. + + Note: it's recommended to use smaller timestep for NVE compared to other + ensembles, since the VelocityVerlet algorithm assumes a strict conservative + force field. + """ + self.dyn = VelocityVerlet( + atoms=self.atoms, + timestep=timestep * units.fs, + trajectory=trajectory, + logfile=logfile, + loginterval=loginterval, + append_trajectory=append_trajectory, + ) + print("NVE-MD created") + + elif ensemble.lower() == "nvt": + """ + Constant volume/temperature molecular dynamics. + """ + if thermostat.lower() == "nose-hoover": + """ + Nose-hoover (constant N, V, T) molecular dynamics. + ASE implementation currently only supports upper triangular lattice + """ + self.upper_triangular_cell() + self.dyn = NPT( + atoms=self.atoms, + timestep=timestep * units.fs, + temperature_K=temperature, + externalstress=pressure + * units.GPa, # ase NPT does not like externalstress=None + ttime=taut * units.fs, + pfactor=None, + trajectory=trajectory, + logfile=logfile, + loginterval=loginterval, + append_trajectory=append_trajectory, + ) + print("NVT-Nose-Hoover MD created") + elif thermostat.lower().startswith("berendsen"): + """ + Berendsen (constant N, V, T) molecular dynamics. + """ + self.dyn = NVTBerendsen( + atoms=self.atoms, + timestep=timestep * units.fs, + temperature_K=temperature, + taut=taut * units.fs, + trajectory=trajectory, + logfile=logfile, + loginterval=loginterval, + append_trajectory=append_trajectory, + ) + print("NVT-Berendsen-MD created") + else: + raise ValueError( + "Thermostat not supported, choose in 'Nose-Hoover', 'Berendsen', " + "'Berendsen_inhomogeneous'" + ) + + elif ensemble.lower() == "npt": + """ + Constant pressure/temperature molecular dynamics. + """ + # Bulk modulus is needed for pressure damping time + if bulk_modulus is not None: + bulk_modulus_au = bulk_modulus / 160.2176 # GPa to eV/A^3 + compressibility_au = 1 / bulk_modulus_au + else: + try: + # Fit bulk modulus by equation of state + eos = EquationOfState(model=self.atoms.calc) + eos.fit(atoms=atoms, steps=500, fmax=0.1, verbose=False) + bulk_modulus = eos.get_bulk_modulus(unit="GPa") + bulk_modulus_au = eos.get_bulk_modulus(unit="eV/A^3") + compressibility_au = eos.get_compressibility(unit="A^3/eV") + print( + f"Completed bulk modulus calculation: " + f"k = {bulk_modulus:.3}GPa, {bulk_modulus_au:.3}eV/A^3" + ) + except Exception: + bulk_modulus_au = 2 / 160.2176 + compressibility_au = 1 / bulk_modulus_au + print( + "Warning!!! Equation of State fitting failed, setting bulk " + "modulus to 2 GPa. NPT simulation can proceed with incorrect " + "pressure relaxation time." + "User input for bulk modulus is recommended." + ) + self.bulk_modulus = bulk_modulus + + if thermostat.lower() == "nose-hoover": + """ + Combined Nose-Hoover and Parrinello-Rahman dynamics, creating an + NPT (or N,stress,T) ensemble. + see: https://gitlab.com/ase/ase/-/blob/master/ase/md/npt.py + ASE implementation currently only supports upper triangular lattice + """ + self.upper_triangular_cell() + ptime = taup * units.fs + self.dyn = NPT( + atoms=self.atoms, + timestep=timestep * units.fs, + temperature_K=temperature, + externalstress=pressure * units.GPa, + ttime=taut * units.fs, + pfactor=bulk_modulus * units.GPa * ptime * ptime, + trajectory=trajectory, + logfile=logfile, + loginterval=loginterval, + append_trajectory=append_trajectory, + ) + print("NPT-Nose-Hoover MD created") + + elif thermostat.lower() == "berendsen_inhomogeneous": + """ + Inhomogeneous_NPTBerendsen thermo/barostat + This is a more flexible scheme that fixes three angles of the unit + cell but allows three lattice parameter to change independently. + see: https://gitlab.com/ase/ase/-/blob/master/ase/md/nptberendsen.py + """ + + self.dyn = Inhomogeneous_NPTBerendsen( + atoms=self.atoms, + timestep=timestep * units.fs, + temperature_K=temperature, + pressure_au=pressure * units.GPa, + taut=taut * units.fs, + taup=taup * units.fs, + compressibility_au=compressibility_au, + trajectory=trajectory, + logfile=logfile, + loginterval=loginterval, + ) + print("NPT-Berendsen-inhomogeneous-MD created") + + elif thermostat.lower() == "npt_berendsen": + """ + This is a similar scheme to the Inhomogeneous_NPTBerendsen. + This is a less flexible scheme that fixes the shape of the + cell - three angles are fixed and the ratios between the three + lattice constants. + see: https://gitlab.com/ase/ase/-/blob/master/ase/md/nptberendsen.py + """ + + self.dyn = NPTBerendsen( + atoms=self.atoms, + timestep=timestep * units.fs, + temperature_K=temperature, + pressure_au=pressure * units.GPa, + taut=taut * units.fs, + taup=taup * units.fs, + compressibility_au=compressibility_au, + trajectory=trajectory, + logfile=logfile, + loginterval=loginterval, + append_trajectory=append_trajectory, + ) + print("NPT-Berendsen-MD created") + else: + raise ValueError( + "Thermostat not supported, choose in 'Nose-Hoover', 'Berendsen', " + "'Berendsen_inhomogeneous'" + ) + + self.trajectory = trajectory + self.logfile = logfile + self.loginterval = loginterval + self.timestep = timestep + self.crystal_feas_logfile = crystal_feas_logfile + + def run(self, steps: int) -> None: + """Thin wrapper of ase MD run. + + Args: + steps (int): number of MD steps + """ + if self.crystal_feas_logfile: + obs = CrystalFeasObserver(self.atoms) + self.dyn.attach(obs, interval=self.loginterval) + + self.dyn.run(steps) + + if self.crystal_feas_logfile: + obs.save(self.crystal_feas_logfile) + + def set_atoms(self, atoms: Atoms) -> None: + """Set new atoms to run MD. + + Args: + atoms (Atoms): new atoms for running MD + """ + calculator = self.atoms.calc + self.atoms = atoms + self.dyn.atoms = atoms + self.dyn.atoms.calc = calculator + + def upper_triangular_cell(self, *, verbose: bool | None = False) -> None: + """Transform to upper-triangular cell. + ASE Nose-Hoover implementation only supports upper-triangular cell + while ASE's canonical description is lower-triangular cell. + + Args: + verbose (bool): Whether to notify user about upper-triangular cell + transformation. Default = False + """ + if not NPT._isuppertriangular(self.atoms.get_cell()): # noqa: SLF001 + a, b, c, alpha, beta, gamma = self.atoms.cell.cellpar() + angles = np.radians((alpha, beta, gamma)) + sin_a, sin_b, _sin_g = np.sin(angles) + cos_a, cos_b, cos_g = np.cos(angles) + cos_p = (cos_g - cos_a * cos_b) / (sin_a * sin_b) + cos_p = np.clip(cos_p, -1, 1) + sin_p = (1 - cos_p**2) ** 0.5 + + new_basis = [ + (a * sin_b * sin_p, a * sin_b * cos_p, a * cos_b), + (0, b * sin_a, b * cos_a), + (0, 0, c), + ] + + self.atoms.set_cell(new_basis, scale_atoms=True) + if verbose: + print("Transformed to upper triangular unit cell.", flush=True) + + +class EquationOfState: + """Class to calculate equation of state.""" + + def __init__( + self, + model: CHGNet | CHGNetCalculator | None = None, + optimizer_class: Optimizer | str | None = "FIRE", + use_device: str | None = None, + stress_weight: float = 1 / 160.21766208, + on_isolated_atoms: Literal["ignore", "warn", "error"] = "error", + ) -> None: + """Initialize a structure optimizer object for calculation of bulk modulus. + + Args: + model (CHGNet): instance of a CHGNet model or CHGNetCalculator. + If set to None, the pretrained CHGNet is loaded. + Default = None + optimizer_class (Optimizer,str): choose optimizer from ASE. + Default = "FIRE" + use_device (str, optional): The device to be used for predictions, + either "cpu", "cuda", or "mps". If not specified, the default device is + automatically selected based on the available options. + Default = None + stress_weight (float): the conversion factor to convert GPa to eV/A^3. + Default = 1/160.21 + on_isolated_atoms ('ignore' | 'warn' | 'error'): how to handle Structures + with isolated atoms. + Default = 'error' + """ + self.relaxer = StructOptimizer( + model=model, + optimizer_class=optimizer_class, + use_device=use_device, + stress_weight=stress_weight, + on_isolated_atoms=on_isolated_atoms, + ) + self.fitted = False + + def fit( + self, + atoms: Structure | Atoms, + *, + n_points: int = 11, + fmax: float | None = 0.1, + steps: int | None = 500, + verbose: bool | None = False, + **kwargs, + ) -> None: + """Relax the Structure/Atoms and fit the Birch-Murnaghan equation of state. + + Args: + atoms (Structure | Atoms): A Structure or Atoms object to relax. + n_points (int): Number of structures used in fitting the equation of states + fmax (float | None): The maximum force tolerance for relaxation. + Default = 0.1 + steps (int | None): The maximum number of steps for relaxation. + Default = 500 + verbose (bool): Whether to print the output of the ASE optimizer. + Default = False + **kwargs: Additional parameters for the optimizer. + + Returns: + Bulk Modulus (float) + """ + if isinstance(atoms, Atoms): + atoms = AseAtomsAdaptor.get_structure(atoms) + primitive_cell = atoms.get_primitive_structure() + local_minima = self.relaxer.relax( + primitive_cell, + relax_cell=True, + fmax=fmax, + steps=steps, + verbose=verbose, + **kwargs, + ) + + volumes, energies = [], [] + for idx in np.linspace(-0.1, 0.1, n_points): + structure_strained = local_minima["final_structure"].copy() + structure_strained.apply_strain([idx, idx, idx]) + result = self.relaxer.relax( + structure_strained, + relax_cell=False, + fmax=fmax, + steps=steps, + verbose=verbose, + **kwargs, + ) + volumes.append(result["final_structure"].volume) + energies.append(result["trajectory"].energies[-1]) + self.bm = BirchMurnaghan(volumes=volumes, energies=energies) + self.bm.fit() + self.fitted = True + + def get_bulk_modulus(self, unit: str = "eV/A^3") -> float: + """Get the bulk modulus of from the fitted Birch-Murnaghan equation of state. + + Args: + unit (str): The unit of bulk modulus. Can be "eV/A^3" or "GPa" + Default = "eV/A^3" + + Returns: + Bulk Modulus (float) + """ + if self.fitted is False: + raise ValueError( + "Equation of state needs to be fitted first through self.fit()" + ) + if unit == "eV/A^3": + return self.bm.b0 + if unit == "GPa": + return self.bm.b0_GPa + raise NotImplementedError("unit has to be eV/A^3 or GPa") + + def get_compressibility(self, unit: str = "A^3/eV") -> float: + """Get the bulk modulus of from the fitted Birch-Murnaghan equation of state. + + Args: + unit (str): The unit of bulk modulus. Can be "A^3/eV", + "GPa^-1" "Pa^-1" or "m^2/N" + Default = "A^3/eV" + + Returns: + Bulk Modulus (float) + """ + if self.fitted is False: + raise ValueError( + "Equation of state needs to be fitted first through self.fit()" + ) + if unit == "A^3/eV": + return 1 / self.bm.b0 + if unit == "GPa^-1": + return 1 / self.bm.b0_GPa + if unit in {"Pa^-1", "m^2/N"}: + return 1 / (self.bm.b0_GPa * 1e9) + raise NotImplementedError("unit has to be one of A^3/eV, GPa^-1 Pa^-1 or m^2/N") diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/encoders.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/encoders.py new file mode 100755 index 00000000..c68acefb --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/encoders.py @@ -0,0 +1,146 @@ +from __future__ import annotations + +import torch +from torch import Tensor, nn + +from chgnet.model.basis import Fourier, RadialBessel + + +class AtomEmbedding(nn.Module): + """Encode an atom by its atomic number using a learnable embedding layer.""" + + def __init__(self, atom_feature_dim: int, max_num_elements: int = 94) -> None: + """Initialize the Atom featurizer. + + Args: + atom_feature_dim (int): dimension of atomic embedding. + max_num_elements (int): maximum number of elements in the dataset. + Default = 94 + """ + super().__init__() + self.embedding = nn.Embedding(max_num_elements, atom_feature_dim) + + def forward(self, atomic_numbers: Tensor) -> Tensor: + """Convert the structure to a atom embedding tensor. + + Args: + atomic_numbers (Tensor): [n_atom, 1]. + + Returns: + atom_fea (Tensor): atom embeddings [n_atom, atom_feature_dim]. + """ + return self.embedding(atomic_numbers) + + +class BondEncoder(nn.Module): + """Encode a chemical bond given the positions of two atoms using Gaussian + distance. + """ + + def __init__( + self, + *, + atom_graph_cutoff: float = 5, + bond_graph_cutoff: float = 3, + num_radial: int = 9, + cutoff_coeff: int = 5, + learnable: bool = False, + ) -> None: + """Initialize the bond encoder. + + Args: + atom_graph_cutoff (float): The cutoff for constructing AtomGraph default = 5 + bond_graph_cutoff (float): The cutoff for constructing BondGraph default = 3 + num_radial (int): The number of radial component. Default = 9 + cutoff_coeff (int): Strength for graph cutoff smoothness. Default = 5 + learnable(bool): Whether the frequency in rbf expansion is learnable. + Default = False + """ + super().__init__() + self.rbf_expansion_ag = RadialBessel( + num_radial=num_radial, + cutoff=atom_graph_cutoff, + smooth_cutoff=cutoff_coeff, + learnable=learnable, + ) + self.rbf_expansion_bg = RadialBessel( + num_radial=num_radial, + cutoff=bond_graph_cutoff, + smooth_cutoff=cutoff_coeff, + learnable=learnable, + ) + + def forward( + self, + center: Tensor, + neighbor: Tensor, + undirected2directed: Tensor, + image: Tensor, + lattice: Tensor, + ) -> tuple[Tensor, Tensor, Tensor]: + """Compute the pairwise distance between 2 3d coordinates. + + Args: + center (Tensor): 3d cartesian coordinates of center atoms [n_bond, 3] + neighbor (Tensor): 3d cartesian coordinates of neighbor atoms [n_bond, 3] + undirected2directed (Tensor): mapping from undirected bond to one of its + directed bond [n_bond] + image (Tensor): the periodic image specifying the location of neighboring + atom [n_bond, 3] + lattice (Tensor): the lattice of this structure [3, 3] + + Returns: + bond_basis_ag (Tensor): the bond basis in AtomGraph [n_bond, num_radial] + bond_basis_ag (Tensor): the bond basis in BondGraph [n_bond, num_radial] + bond_vectors (Tensor): normalized bond vectors, for tracking the bond + directions [n_bond, 3] + """ + neighbor = neighbor + image @ lattice + bond_vectors = center - neighbor + bond_lengths = torch.norm(bond_vectors, dim=1) + # Normalize the bond vectors + bond_vectors = bond_vectors / bond_lengths[:, None] + + # We create bond features only for undirected bonds + # atom1 -> atom2 and atom2 -> atom1 should share same bond_basis + undirected_bond_lengths = torch.index_select( + bond_lengths, 0, undirected2directed + ) + bond_basis_ag = self.rbf_expansion_ag(undirected_bond_lengths) + bond_basis_bg = self.rbf_expansion_bg(undirected_bond_lengths) + return bond_basis_ag, bond_basis_bg, bond_vectors + + +class AngleEncoder(nn.Module): + """Encode an angle given the two bond vectors using Fourier Expansion.""" + + def __init__(self, *, num_angular: int = 9, learnable: bool = True) -> None: + """Initialize the angle encoder. + + Args: + num_angular (int): number of angular basis to use. Must be an odd integer. + learnable (bool): whether to set the frequencies of the Fourier expansion + as learnable parameters. Default = False + """ + super().__init__() + if num_angular % 2 != 1: + raise ValueError(f"{num_angular=} must be an odd integer") + circular_harmonics_order = (num_angular - 1) // 2 + self.fourier_expansion = Fourier( + order=circular_harmonics_order, learnable=learnable + ) + + def forward(self, bond_i: Tensor, bond_j: Tensor) -> Tensor: + """Compute the angles between normalized vectors. + + Args: + bond_i (Tensor): normalized left bond vector [n_angle, 3] + bond_j (Tensor): normalized right bond vector [n_angle, 3] + + Returns: + angle_fea (Tensor): expanded cos_ij [n_angle, angle_feature_dim] + """ + # 1 - 1e-6 for torch.acos stability + cosine_ij = torch.sum(bond_i * bond_j, dim=1) * (1 - 1e-6) + angle = torch.acos(cosine_ij) + return self.fourier_expansion(angle) diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/functions.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/functions.py new file mode 100755 index 00000000..2eddd1ce --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/functions.py @@ -0,0 +1,222 @@ +from __future__ import annotations + +from collections.abc import Sequence + +import torch +from torch import Tensor, nn + + +def aggregate(data: Tensor, owners: Tensor, *, average=True, num_owner=None) -> Tensor: + """Aggregate rows in data by specifying the owners. + + Args: + data (Tensor): data tensor to aggregate [n_row, feature_dim] + owners (Tensor): specify the owner of each row [n_row, 1] + average (bool): if True, average the rows, if False, sum the rows. + Default = True + num_owner (int, optional): the number of owners, this is needed if the + max idx of owner is not presented in owners tensor + Default = None + + Returns: + output (Tensor): [num_owner, feature_dim] + """ + bin_count = torch.bincount(owners) + bin_count = bin_count.where(bin_count != 0, bin_count.new_ones(1)) + + # If there exist 5 owners, but only the first 4 appear in the owners tensor, + # we would like the output to have shape [5, fea_dim] with the last row = 0 + # So, we need to optionally add the fifth owner as a row with zero in the output + if (num_owner is not None) and (bin_count.shape[0] != num_owner): + difference = num_owner - bin_count.shape[0] + bin_count = torch.cat([bin_count, bin_count.new_ones(difference)]) + + # make sure this operation is done on the same device of data and owners + output = data.new_zeros([bin_count.shape[0], data.shape[1]]) + output = output.index_add_(0, owners, data) + if average: + output = (output.T / bin_count).T + return output + + +class MLP(nn.Module): + """Multi-Layer Perceptron used for non-linear regression.""" + + def __init__( + self, + input_dim: int, + *, + output_dim: int = 1, + hidden_dim: int | Sequence[int] | None = (64, 64), + dropout: float = 0, + activation: str = "silu", + bias: bool = True, + ) -> None: + """Initialize the MLP. + + Args: + input_dim (int): the input dimension + output_dim (int): the output dimension + hidden_dim (list[int] | int]): a list of integers or a single integer + representing the number of hidden units in each layer of the MLP. + Default = [64, 64] + dropout (float): the dropout rate before each linear layer. Default: 0 + activation (str, optional): The name of the activation function to use + in the gated MLP. Must be one of "relu", "silu", "tanh", or "gelu". + Default = "silu" + bias (bool): whether to use bias in each Linear layers. + Default = True + """ + super().__init__() + if hidden_dim is None or hidden_dim == 0: + layers = [nn.Dropout(dropout), nn.Linear(input_dim, output_dim, bias=bias)] + elif isinstance(hidden_dim, int): + layers = [ + nn.Linear(input_dim, hidden_dim, bias=bias), + find_activation(activation), + nn.Dropout(dropout), + nn.Linear(hidden_dim, output_dim, bias=bias), + ] + elif isinstance(hidden_dim, Sequence): + layers = [ + nn.Linear(input_dim, hidden_dim[0], bias=bias), + find_activation(activation), + ] + if len(hidden_dim) != 1: + for h_in, h_out in zip(hidden_dim[0:-1], hidden_dim[1:]): + layers.append(nn.Linear(h_in, h_out, bias=bias)) + layers.append(find_activation(activation)) + layers.append(nn.Dropout(dropout)) + layers.append(nn.Linear(hidden_dim[-1], output_dim, bias=bias)) + else: + raise TypeError( + f"{hidden_dim=} must be an integer, a list of integers, or None." + ) + self.layers = nn.Sequential(*layers) + + def forward(self, x: Tensor) -> Tensor: + """Performs a forward pass through the MLP. + + Args: + x (Tensor): a tensor of shape (batch_size, input_dim) + + Returns: + Tensor: a tensor of shape (batch_size, output_dim) + """ + return self.layers(x) + + +class GatedMLP(nn.Module): + """Gated MLP + similar model structure is used in CGCNN and M3GNet. + """ + + def __init__( + self, + input_dim: int, + output_dim: int, + *, + hidden_dim: int | list[int] | None = None, + dropout: float = 0, + activation: str = "silu", + norm: str = "batch", + bias: bool = True, + ) -> None: + """Initialize a gated MLP. + + Args: + input_dim (int): the input dimension + output_dim (int): the output dimension + hidden_dim (list[int] | int]): a list of integers or a single integer + representing the number of hidden units in each layer of the MLP. + Default = None + dropout (float): the dropout rate before each linear layer. + Default: 0 + activation (str, optional): The name of the activation function to use in + the gated MLP. Must be one of "relu", "silu", "tanh", or "gelu". + Default = "silu" + norm (str, optional): The name of the normalization layer to use on the + updated atom features. Must be one of "batch", "layer", or None. + Default = "batch" + bias (bool): whether to use bias in each Linear layers. + Default = True + """ + super().__init__() + self.mlp_core = MLP( + input_dim=input_dim, + output_dim=output_dim, + hidden_dim=hidden_dim, + dropout=dropout, + activation=activation, + bias=bias, + ) + self.mlp_gate = MLP( + input_dim=input_dim, + output_dim=output_dim, + hidden_dim=hidden_dim, + dropout=dropout, + activation=activation, + bias=bias, + ) + self.activation = find_activation(activation) + self.sigmoid = nn.Sigmoid() + self.norm = norm + self.bn1 = find_normalization(name=norm, dim=output_dim) + self.bn2 = find_normalization(name=norm, dim=output_dim) + + def forward(self, x: Tensor) -> Tensor: + """Performs a forward pass through the MLP. + + Args: + x (Tensor): a tensor of shape (batch_size, input_dim) + + Returns: + Tensor: a tensor of shape (batch_size, output_dim) + """ + if self.norm is None: + core = self.activation(self.mlp_core(x)) + gate = self.sigmoid(self.mlp_gate(x)) + else: + core = self.activation(self.bn1(self.mlp_core(x))) + gate = self.sigmoid(self.bn2(self.mlp_gate(x))) + return core * gate + + +class ScaledSiLU(torch.nn.Module): + """Scaled Sigmoid Linear Unit.""" + + def __init__(self) -> None: + """Initialize a scaled SiLU.""" + super().__init__() + self.scale_factor = 1 / 0.6 + self._activation = torch.nn.SiLU() + + def forward(self, x: Tensor) -> Tensor: + """Forward pass.""" + return self._activation(x) * self.scale_factor + + +def find_activation(name: str) -> nn.Module: + """Return an activation function using name.""" + try: + return { + "relu": nn.ReLU, + "silu": nn.SiLU, + "scaledsilu": ScaledSiLU, + "gelu": nn.GELU, + "softplus": nn.Softplus, + "sigmoid": nn.Sigmoid, + "tanh": nn.Tanh, + }[name.lower()]() + except KeyError as exc: + raise NotImplementedError from exc + + +def find_normalization(name: str, dim: int | None = None) -> nn.Module | None: + """Return an normalization function using name.""" + if name is None: + return None + return { + "batch": nn.BatchNorm1d(dim), + "layer": nn.LayerNorm(dim), + }.get(name.lower(), None) diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/layers.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/layers.py new file mode 100755 index 00000000..d31fa0b4 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/layers.py @@ -0,0 +1,453 @@ +from __future__ import annotations + +import torch +from torch import Tensor, nn + +from chgnet.model.functions import ( + MLP, + GatedMLP, + aggregate, + find_activation, + find_normalization, +) + + +class AtomConv(nn.Module): + """A convolution Layer to update atom features.""" + + def __init__( + self, + *, + atom_fea_dim: int, + bond_fea_dim: int, + hidden_dim: int = 64, + dropout: float = 0, + activation: str = "silu", + norm: str | None = None, + use_mlp_out: bool = True, + mlp_out_bias: bool = False, + resnet: bool = True, + gMLP_norm: str | None = None, # noqa: N803 + ) -> None: + """Initialize the AtomConv layer. + + Args: + atom_fea_dim (int): The dimensionality of the input atom features. + bond_fea_dim (int): The dimensionality of the input bond features. + hidden_dim (int, optional): The dimensionality of the hidden layers in the + gated MLP. + Default = 64 + dropout (float, optional): The dropout rate to apply to the gated MLP. + Default = 0. + activation (str, optional): The name of the activation function to use in + the gated MLP. Must be one of "relu", "silu", "tanh", or "gelu". + Default = "silu" + norm (str, optional): The name of the normalization layer to use on the + updated atom features. Must be one of "batch", "layer", or None. + Default = None + use_mlp_out (bool, optional): Whether to apply an MLP output layer to the + updated atom features. + Default = True + mlp_out_bias (bool): whether to use bias in the output MLP Linear layer. + Default = False + resnet (bool, optional): Whether to apply a residual connection to the + updated atom features. + Default = True + gMLP_norm (str, optional): The name of the normalization layer to use on the + gated MLP. Must be one of "batch", "layer", or None. + Default = None + """ + super().__init__() + self.use_mlp_out = use_mlp_out + self.resnet = resnet + self.activation = find_activation(activation) + self.twoBody_atom = GatedMLP( + input_dim=2 * atom_fea_dim + bond_fea_dim, + output_dim=atom_fea_dim, + hidden_dim=hidden_dim, + dropout=dropout, + norm=gMLP_norm, + activation=activation, + ) + if self.use_mlp_out: + self.mlp_out = MLP( + input_dim=atom_fea_dim, + output_dim=atom_fea_dim, + hidden_dim=0, + bias=mlp_out_bias, + ) + self.atom_norm = find_normalization(name=norm, dim=atom_fea_dim) + + def forward( + self, + atom_feas: Tensor, + bond_feas: Tensor, + bond_weights: Tensor, + atom_graph: Tensor, + directed2undirected: Tensor, + ) -> Tensor: + """Forward pass of AtomConv module that updates the atom features and + optionally bond features. + + Args: + atom_feas (Tensor): Input tensor with shape + [num_batch_atoms, atom_fea_dim] + bond_feas (Tensor): Input tensor with shape + [num_undirected_bonds, bond_fea_dim] + bond_weights (Tensor): AtomGraph bond weights with shape + [num_undirected_bonds, bond_fea_dim] + atom_graph (Tensor): Directed AtomGraph adjacency list with shape + [num_directed_bonds, 2] + directed2undirected (Tensor): Index tensor that maps directed bonds to + undirected bonds.with shape + [num_undirected_bonds] + + Returns: + Tensor: the updated atom features tensor with shape + [num_batch_atom, atom_fea_dim] + + Notes: + - num_batch_atoms = sum(num_atoms) in batch + """ + # Make directional messages + center_atoms = torch.index_select(atom_feas, 0, atom_graph[:, 0]) + nbr_atoms = torch.index_select(atom_feas, 0, atom_graph[:, 1]) + bonds = torch.index_select(bond_feas, 0, directed2undirected) + messages = torch.cat([center_atoms, bonds, nbr_atoms], dim=1) + messages = self.twoBody_atom(messages) + + # smooth out message by bond_weights + bond_weight = torch.index_select(bond_weights, 0, directed2undirected) + messages *= bond_weight + + # Aggregate messages + new_atom_feas = aggregate( + messages, atom_graph[:, 0], average=False, num_owner=len(atom_feas) + ) + + # New atom features + if self.use_mlp_out: + new_atom_feas = self.mlp_out(new_atom_feas) + if self.resnet: + new_atom_feas += atom_feas + + # Optionally, normalize new atom features + if self.atom_norm is not None: + new_atom_feas = self.atom_norm(new_atom_feas) + return new_atom_feas + + +class BondConv(nn.Module): + """A convolution Layer to update bond features.""" + + def __init__( + self, + atom_fea_dim: int, + bond_fea_dim: int, + angle_fea_dim: int, + *, + hidden_dim: int = 64, + dropout: float = 0, + activation: str = "silu", + norm: str | None = None, + use_mlp_out: bool = True, + mlp_out_bias: bool = False, + resnet=True, + gMLP_norm: str | None = None, # noqa: N803 + ) -> None: + """Initialize the BondConv layer. + + Args: + atom_fea_dim (int): The dimensionality of the input atom features. + bond_fea_dim (int): The dimensionality of the input bond features. + angle_fea_dim (int): The dimensionality of the input angle features. + hidden_dim (int, optional): The dimensionality of the hidden layers + in the gated MLP. + Default = 64 + dropout (float, optional): The dropout rate to apply to the gated MLP. + Default = 0. + activation (str, optional): The name of the activation function to use + in the gated MLP. Must be one of "relu", "silu", "tanh", or "gelu". + Default = "silu" + norm (str, optional): The name of the normalization layer to use on the + updated atom features. Must be one of "batch", "layer", or None. + Default = None + use_mlp_out (bool, optional): Whether to apply an MLP output layer to the + updated atom features. + Default = True + mlp_out_bias (bool): whether to use bias in the output MLP Linear layer. + Default = False + resnet (bool, optional): Whether to apply a residual connection to the + updated atom features. + Default = True + gMLP_norm (str, optional): The name of the normalization layer to use on the + gated MLP. Must be one of "batch", "layer", or None. + Default = None + """ + super().__init__() + self.use_mlp_out = use_mlp_out + self.resnet = resnet + self.activation = find_activation(activation) + self.twoBody_bond = GatedMLP( + input_dim=atom_fea_dim + 2 * bond_fea_dim + angle_fea_dim, + output_dim=bond_fea_dim, + hidden_dim=hidden_dim, + dropout=dropout, + norm=gMLP_norm, + activation=activation, + ) + if self.use_mlp_out: + self.mlp_out = MLP( + input_dim=bond_fea_dim, + output_dim=bond_fea_dim, + hidden_dim=0, + bias=mlp_out_bias, + ) + self.bond_norm = find_normalization(name=norm, dim=bond_fea_dim) + + def forward( + self, + atom_feas: Tensor, + bond_feas: Tensor, + bond_weights: Tensor, + angle_feas: Tensor, + bond_graph: Tensor, + ) -> Tensor: + """Update the bond features. + + Args: + atom_feas (Tensor): atom features tensor with shape + [num_batch_atoms, atom_fea_dim] + bond_feas (Tensor): bond features tensor with shape + [num_undirected_bonds, bond_fea_dim] + bond_weights (Tensor): BondGraph bond weights with shape + [num_undirected_bonds, bond_fea_dim] + angle_feas (Tensor): angle features tensor with shape + [num_batch_angles, angle_fea_dim] + bond_graph (Tensor): Directed BondGraph tensor with shape + [num_batched_angles, 3] + + Returns: + new_bond_feas (Tensor): bond feature tensor with shape + [num_undirected_bonds, bond_fea_dim] + + Notes: + - num_batch_atoms = sum(num_atoms) in batch + """ + # Make directional Message + center_atoms = torch.index_select(atom_feas, 0, bond_graph[:, 0]) + bond_feas_i = torch.index_select(bond_feas, 0, bond_graph[:, 1]) + bond_feas_j = torch.index_select(bond_feas, 0, bond_graph[:, 2]) + total_fea = torch.cat( + [bond_feas_i, bond_feas_j, angle_feas, center_atoms], dim=1 + ) + bond_update = self.twoBody_bond(total_fea) + + # Smooth out messages + bond_weights_i = torch.index_select(bond_weights, 0, bond_graph[:, 1]) + bond_weights_j = torch.index_select(bond_weights, 0, bond_graph[:, 2]) + bond_update = bond_update * bond_weights_i * bond_weights_j + + # Aggregate messages + new_bond_feas = aggregate( + bond_update, bond_graph[:, 1], average=False, num_owner=len(bond_feas) + ) + + # New bond features + if self.use_mlp_out: + new_bond_feas = self.mlp_out(new_bond_feas) + if self.resnet: + new_bond_feas += bond_feas + + # Optionally, normalize the bond features + if self.bond_norm is not None: + new_bond_feas = self.bond_norm(new_bond_feas) + return new_bond_feas + + +class AngleUpdate(nn.Module): + """Update angle features.""" + + def __init__( + self, + atom_fea_dim: int, + bond_fea_dim: int, + angle_fea_dim: int, + *, + hidden_dim: int = 0, + dropout: float = 0, + activation: str = "silu", + norm: str | None = None, + resnet: bool = True, + gMLP_norm: str | None = None, # noqa: N803 + ) -> None: + """Initialize the AngleUpdate layer. + + Args: + atom_fea_dim (int): The dimensionality of the input atom features. + bond_fea_dim (int): The dimensionality of the input bond features. + angle_fea_dim (int): The dimensionality of the input angle features. + hidden_dim (int, optional): The dimensionality of the hidden layers + in the gated MLP. + Default = 0 + dropout (float, optional): The dropout rate to apply to the gated MLP. + Default = 0. + activation (str, optional): The name of the activation function to use + in the gated MLP. Must be one of "relu", "silu", "tanh", or "gelu". + Default = "silu" + norm (str, optional): The name of the normalization layer to use on the + updated atom features. Must be one of "batch", "layer", or None. + Default = None + resnet (bool, optional): Whether to apply a residual connection to the + updated atom features. + Default = True + gMLP_norm (str, optional): The name of the normalization layer to use on the + gated MLP. Must be one of "batch", "layer", or None. + Default = None + """ + super().__init__() + self.resnet = resnet + self.activation = find_activation(activation) + self.twoBody_bond = GatedMLP( + input_dim=atom_fea_dim + 2 * bond_fea_dim + angle_fea_dim, + output_dim=angle_fea_dim, + hidden_dim=hidden_dim, + dropout=dropout, + norm=gMLP_norm, + activation=activation, + ) + self.angle_norm = find_normalization(norm, dim=angle_fea_dim) + + def forward( + self, + atom_feas: Tensor, + bond_feas: Tensor, + angle_feas: Tensor, + bond_graph: Tensor, + ) -> Tensor: + """Update the angle features using bond graph. + + Args: + atom_feas (Tensor): atom features tensor with shape + [num_batch_atoms, atom_fea_dim] + bond_feas (Tensor): bond features tensor with shape + [num_undirected_bonds, bond_fea_dim] + angle_feas (Tensor): angle features tensor with shape + [num_batch_angles, angle_fea_dim] + bond_graph (Tensor): Directed BondGraph tensor with shape + [num_batched_angles, 3] + + Returns: + new_angle_feas (Tensor): angle features tensor with shape + [num_batch_angles, angle_fea_dim] + + Notes: + - num_batch_atoms = sum(num_atoms) in batch + """ + # Assemble features + center_atoms = torch.index_select(atom_feas, 0, bond_graph[:, 0]) + bond_feas_i = torch.index_select(bond_feas, 0, bond_graph[:, 1]) + bond_feas_j = torch.index_select(bond_feas, 0, bond_graph[:, 2]) + total_fea = torch.cat( + [bond_feas_i, bond_feas_j, angle_feas, center_atoms], dim=1 + ) + + # Update angle features + new_angle_feas = self.twoBody_bond(total_fea) + + # Resnet and Norm + if self.resnet: + new_angle_feas += angle_feas + if self.angle_norm is not None: + new_angle_feas = self.angle_norm(new_angle_feas) + return new_angle_feas + + +class GraphPooling(nn.Module): + """Pooling the sub-graphs in the batched graph.""" + + def __init__(self, *, average: bool = False) -> None: + """Args: + average (bool): whether to average the features. + """ + super().__init__() + self.average = average + + def forward(self, atom_feas: Tensor, atom_owner: Tensor) -> Tensor: + """Merge the atom features that belong to same graph in a batched graph. + + Args: + atom_feas (Tensor): batched atom features after convolution layers. + [num_batch_atoms, atom_fea_dim or 1] + atom_owner (Tensor): graph indices for each atom. + [num_batch_atoms] + + Returns: + crystal_feas (Tensor): crystal feature matrix. + [n_crystals, atom_fea_dim or 1] + """ + return aggregate(atom_feas, atom_owner, average=self.average) + + +class GraphAttentionReadOut(nn.Module): + """Multi Head Attention Read Out Layer + merge the information from atom_feas to crystal_fea. + """ + + def __init__( + self, + atom_fea_dim: int, + num_head: int = 3, + hidden_dim: int = 32, + *, + average=False, + ) -> None: + """Initialize the layer. + + Args: + atom_fea_dim (int): atom feature dimension + num_head (int): number of attention heads used + hidden_dim (int): dimension of hidden layer + average (bool): whether to average the features + """ + super().__init__() + self.key = MLP( + input_dim=atom_fea_dim, output_dim=num_head, hidden_dim=hidden_dim + ) + self.softmax = nn.Softmax(dim=0) + self.average = average + + def forward(self, atom_feas: Tensor, atom_owner: Tensor) -> Tensor: + """Merge the atom features that belong to same graph in a batched graph. + + Args: + atom_feas (Tensor): batched atom features after convolution layers. + [num_batch_atoms, atom_fea_dim] + atom_owner (Tensor): graph indices for each atom. + [num_batch_atoms] + + Returns: + crystal_feas (Tensor): crystal feature matrix. + [n_crystals, atom_fea_dim] + """ + crystal_feas = [] + weights = self.key(atom_feas) # [n_batch_atom, n_heads] + bin_count = torch.bincount(atom_owner) + start_index = 0 + for n_atom in bin_count: + # find atoms belong to this crystal. shape = [n_atom, atom_fea_dim] + atom_fea = atom_feas[start_index : start_index + n_atom, :] + + # find weight belong to these atoms. shape = [n_atom, n_heads] + weight = self.softmax(weights[start_index : start_index + n_atom, :]) + + # Weighted summation from multiple attention heads + crystal_fea = (atom_fea.T @ weight).view(-1) # [n_heads * atom_fea_dim] + + # Normalize the crystal feature if the model output is intensive + if self.average: + crystal_fea /= n_atom + + crystal_feas.append(crystal_fea) + start_index += n_atom + return torch.stack(crystal_feas, dim=0) diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/model.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/model.py new file mode 100755 index 00000000..d2edb92a --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/model.py @@ -0,0 +1,895 @@ +from __future__ import annotations + +import math +import os +from collections.abc import Sequence +from dataclasses import dataclass +from typing import TYPE_CHECKING, Literal + +import torch +from pymatgen.core import Structure +from torch import Tensor, nn + +from chgnet.graph import CrystalGraph, CrystalGraphConverter +from chgnet.graph.crystalgraph import datatype +from chgnet.model.composition_model import AtomRef +from chgnet.model.encoders import AngleEncoder, AtomEmbedding, BondEncoder +from chgnet.model.functions import MLP, GatedMLP, find_normalization +from chgnet.model.layers import ( + AngleUpdate, + AtomConv, + BondConv, + GraphAttentionReadOut, + GraphPooling, +) +from chgnet.utils import determine_device + +if TYPE_CHECKING: + from typing_extensions import Self + + from chgnet import PredTask + +module_dir = os.path.dirname(os.path.abspath(__file__)) + + +class CHGNet(nn.Module): + """Crystal Hamiltonian Graph neural Network + A model that takes in a crystal graph and output energy, force, magmom, stress. + """ + + def __init__( + self, + *, + atom_fea_dim: int = 64, + bond_fea_dim: int = 64, + angle_fea_dim: int = 64, + composition_model: str | nn.Module = "MPtrj", + num_radial: int = 31, + num_angular: int = 31, + n_conv: int = 4, + atom_conv_hidden_dim: Sequence[int] | int = 64, + update_bond: bool = True, + bond_conv_hidden_dim: Sequence[int] | int = 64, + update_angle: bool = True, + angle_layer_hidden_dim: Sequence[int] | int = 0, + conv_dropout: float = 0, + read_out: str = "ave", + mlp_hidden_dims: Sequence[int] | int = (64, 64, 64), + mlp_dropout: float = 0, + mlp_first: bool = True, + is_intensive: bool = True, + non_linearity: Literal["silu", "relu", "tanh", "gelu"] = "silu", + atom_graph_cutoff: float = 6, + bond_graph_cutoff: float = 3, + graph_converter_algorithm: Literal["legacy", "fast"] = "fast", + cutoff_coeff: int = 8, + learnable_rbf: bool = True, + gMLP_norm: str | None = "layer", # noqa: N803 + readout_norm: str | None = "layer", + version: str | None = None, + **kwargs, + ) -> None: + """Initialize CHGNet. + + Args: + atom_fea_dim (int): atom feature vector embedding dimension. + Default = 64 + bond_fea_dim (int): bond feature vector embedding dimension. + Default = 64 + angle_fea_dim (int): angle feature vector embedding dimension. + Default = 64 + bond_fea_dim (int): angle feature vector embedding dimension. + Default = 64 + composition_model (nn.Module, optional): attach a composition model to + predict energy or initialize a pretrained linear regression (AtomRef). + The default 'MPtrj' is the atom reference energy linear regression + trained on all Materials Project relaxation trajectories + Default = 'MPtrj' + num_radial (int): number of radial basis used in bond basis expansion. + Default = 9 + num_angular (int): number of angular basis used in angle basis expansion. + Default = 9 + n_conv (int): number of interaction blocks. + Default = 4 + Note: last interaction block contain only an atom_conv layer + atom_conv_hidden_dim (List or int): hidden dimensions of + atom convolution layers. + Default = 64 + update_bond (bool): whether to use bond_conv_layer in bond graph to + update bond embeddings + Default = True. + bond_conv_hidden_dim (List or int): hidden dimensions of + bond convolution layers. + Default = 64 + update_angle (bool): whether to use angle_update_layer to + update angle embeddings. + Default = True + angle_layer_hidden_dim (List or int): hidden dimensions of angle layers. + Default = 0 + conv_dropout (float): dropout rate in all conv_layers. + Default = 0 + read_out (str): method for pooling layer, 'ave' for standard + average pooling, 'attn' for multi-head attention. + Default = "ave" + mlp_hidden_dims (int or list): readout multilayer perceptron + hidden dimensions. + Default = [64, 64] + mlp_dropout (float): dropout rate in readout MLP. + Default = 0. + is_intensive (bool): whether the energy training label is intensive + i.e. energy per atom. + Default = True + non_linearity ('silu' | 'relu' | 'tanh' | 'gelu'): The name of the + activation function to use in the gated MLP. + Default = "silu". + mlp_first (bool): whether to apply mlp first then pooling. + if set to True, then CHGNet is essentially calculating energy for each + atom, them sum them up, this is used for the pretrained model + Default = True + atom_graph_cutoff (float): cutoff radius (A) in creating atom_graph, + this need to be consistent with the value in training dataloader + Default = 5 + bond_graph_cutoff (float): cutoff radius (A) in creating bond_graph, + this need to be consistent with value in training dataloader + Default = 3 + graph_converter_algorithm ('legacy' | 'fast'): algorithm to use + for converting pymatgen.core.Structure to CrystalGraph. + 'legacy': python implementation of graph creation + 'fast': C implementation of graph creation, this is faster, + but will need the cygraph.c file correctly compiled from pip install + default = 'fast' + cutoff_coeff (float): cutoff strength used in graph smooth cutoff function. + the smaller this coeff is, the smoother the basis is + Default = 5 + learnable_rbf (bool): whether to set the frequencies in rbf and Fourier + basis functions learnable. + Default = True + gMLP_norm (str): normalization layer to use in gate-MLP + Default = 'layer' + readout_norm (str): normalization layer to use before readout layer + Default = 'layer' + version (str): Pretrained checkpoint version. + **kwargs: Additional keyword arguments + """ + # Store model args for reconstruction + self.model_args = { + key: val + for key, val in locals().items() + if key not in {"self", "__class__", "kwargs"} + } + self.model_args.update(kwargs) + if version: + self.model_args["version"] = version + + super().__init__() + self.atom_fea_dim = atom_fea_dim + self.bond_fea_dim = bond_fea_dim + self.is_intensive = is_intensive + self.n_conv = n_conv + + # Optionally, define composition model + if isinstance(composition_model, nn.Module): + self.composition_model = composition_model + elif isinstance(composition_model, str): + self.composition_model = AtomRef(is_intensive=is_intensive) + self.composition_model.initialize_from(composition_model) + else: + self.composition_model = None + + if self.composition_model is not None: + # fixed composition_model weights + for param in self.composition_model.parameters(): + param.requires_grad = False + + # Define Crystal Graph Converter + self.graph_converter = CrystalGraphConverter( + atom_graph_cutoff=atom_graph_cutoff, + bond_graph_cutoff=bond_graph_cutoff, + algorithm=graph_converter_algorithm, + verbose=kwargs.pop("converter_verbose", False), + ) + + # Define embedding layers + self.atom_embedding = AtomEmbedding(atom_feature_dim=atom_fea_dim) + self.bond_basis_expansion = BondEncoder( + atom_graph_cutoff=atom_graph_cutoff, + bond_graph_cutoff=bond_graph_cutoff, + num_radial=num_radial, + cutoff_coeff=cutoff_coeff, + learnable=learnable_rbf, + ) + self.bond_embedding = nn.Linear( + in_features=num_radial, out_features=bond_fea_dim, bias=False + ) + self.bond_weights_ag = nn.Linear( + in_features=num_radial, out_features=atom_fea_dim, bias=False + ) + self.bond_weights_bg = nn.Linear( + in_features=num_radial, out_features=bond_fea_dim, bias=False + ) + self.angle_basis_expansion = AngleEncoder( + num_angular=num_angular, learnable=learnable_rbf + ) + self.angle_embedding = nn.Linear( + in_features=num_angular, out_features=angle_fea_dim, bias=False + ) + + # Define convolutional layers + conv_norm = kwargs.pop("conv_norm", None) + mlp_out_bias = kwargs.pop("mlp_out_bias", False) + atom_graph_layers = [ + AtomConv( + atom_fea_dim=atom_fea_dim, + bond_fea_dim=bond_fea_dim, + hidden_dim=atom_conv_hidden_dim, + dropout=conv_dropout, + activation=non_linearity, + norm=conv_norm, + gMLP_norm=gMLP_norm, + use_mlp_out=True, + mlp_out_bias=mlp_out_bias, + resnet=True, + ) + for _ in range(n_conv) + ] + self.atom_conv_layers = nn.ModuleList(atom_graph_layers) + + if update_bond: + bond_graph_layers = [ + BondConv( + atom_fea_dim=atom_fea_dim, + bond_fea_dim=bond_fea_dim, + angle_fea_dim=angle_fea_dim, + hidden_dim=bond_conv_hidden_dim, + dropout=conv_dropout, + activation=non_linearity, + norm=conv_norm, + gMLP_norm=gMLP_norm, + use_mlp_out=True, + mlp_out_bias=mlp_out_bias, + resnet=True, + ) + for _ in range(n_conv - 1) + ] + self.bond_conv_layers = nn.ModuleList(bond_graph_layers) + else: + self.bond_conv_layers = [None for _ in range(n_conv - 1)] + + if update_angle: + angle_layers = [ + AngleUpdate( + atom_fea_dim=atom_fea_dim, + bond_fea_dim=bond_fea_dim, + angle_fea_dim=angle_fea_dim, + hidden_dim=angle_layer_hidden_dim, + dropout=conv_dropout, + activation=non_linearity, + norm=conv_norm, + gMLP_norm=gMLP_norm, + resnet=True, + ) + for _ in range(n_conv - 1) + ] + self.angle_layers = nn.ModuleList(angle_layers) + else: + self.angle_layers = [None for _ in range(n_conv - 1)] + + # Define readout layer + self.site_wise = nn.Linear(atom_fea_dim, 1) + self.readout_norm = find_normalization(readout_norm, dim=atom_fea_dim) + self.mlp_first = mlp_first + if mlp_first: + self.read_out_type = "sum" + input_dim = atom_fea_dim + self.pooling = GraphPooling(average=False) + elif read_out in {"attn", "weighted"}: + self.read_out_type = "attn" + num_heads = kwargs.pop("num_heads", 3) + self.pooling = GraphAttentionReadOut( + atom_fea_dim, num_head=num_heads, average=True + ) + input_dim = atom_fea_dim * num_heads + else: + self.read_out_type = "ave" + input_dim = atom_fea_dim + self.pooling = GraphPooling(average=True) + if kwargs.pop("final_mlp", "MLP") in {"normal", "MLP"}: + self.mlp = MLP( + input_dim=input_dim, + hidden_dim=mlp_hidden_dims, + output_dim=1, + dropout=mlp_dropout, + activation=non_linearity, + ) + else: + self.mlp = nn.Sequential( + GatedMLP( + input_dim=input_dim, + hidden_dim=mlp_hidden_dims, + output_dim=mlp_hidden_dims[-1], + dropout=mlp_dropout, + norm=gMLP_norm, + activation=non_linearity, + ), + nn.Linear(in_features=mlp_hidden_dims[-1], out_features=1), + ) + + version_str = f" v{version}" if version else "" + print(f"CHGNet{version_str} initialized with {self.n_params:,} parameters") + + @property + def version(self) -> str | None: + """Return the version of the loaded checkpoint.""" + return self.model_args.get("version") + + @property + def n_params(self) -> int: + """Return the number of parameters in the model.""" + return sum(p.numel() for p in self.parameters()) + + def forward( + self, + graphs: Sequence[CrystalGraph], + *, + task: PredTask = "e", + return_site_energies: bool = False, + return_atom_feas: bool = False, + return_crystal_feas: bool = False, + ) -> dict[str, Tensor]: + """Get prediction associated with input graphs + Args: + graphs (List): a list of CrystalGraphs + task (str): the prediction task. One of 'e', 'em', 'ef', 'efs', 'efsm'. + Default = 'e' + return_site_energies (bool): whether to return per-site energies, + only available if self.mlp_first == True + Default = False + return_atom_feas (bool): whether to return the atom features before last + conv layer. + Default = False + return_crystal_feas (bool): whether to return crystal feature. + Default = False + Returns: + model output (dict). + """ + # Optionally, make composition model prediction + comp_energy = ( + 0 if self.composition_model is None else self.composition_model(graphs) + ) + + # Make batched graph + batched_graph = BatchedGraph.from_graphs( + graphs, + bond_basis_expansion=self.bond_basis_expansion, + angle_basis_expansion=self.angle_basis_expansion, + compute_stress="s" in task, + ) + + # Pass to model + prediction = self._compute( + batched_graph, + compute_force="f" in task, + compute_stress="s" in task, + compute_magmom="m" in task, + return_site_energies=return_site_energies, + return_atom_feas=return_atom_feas, + return_crystal_feas=return_crystal_feas, + ) + prediction["e"] += comp_energy + if return_site_energies and self.composition_model is not None: + site_energy_shifts = self.composition_model.get_site_energies(graphs) + prediction["site_energies"] = [ + i + j for i, j in zip(prediction["site_energies"], site_energy_shifts) + ] + return prediction + + def _compute( + self, + g: BatchedGraph, + *, + compute_force: bool = False, + compute_stress: bool = False, + compute_magmom: bool = False, + return_site_energies: bool = False, + return_atom_feas: bool = False, + return_crystal_feas: bool = False, + ) -> dict: + """Get Energy, Force, Stress, Magmom associated with input graphs + force = - d(Energy)/d(atom_positions) + stress = 1/V * d(Energy)/d(strain). + + Args: + g (BatchedGraph): batched graph + compute_force (bool): whether to compute force. + Default = False + compute_stress (bool): whether to compute stress. + Default = False + compute_magmom (bool): whether to compute magmom. + Default = False + return_site_energies (bool): whether to return per-site energies, + only available if self.mlp_first == True + Default = False + return_atom_feas (bool): whether to return atom features. + Default = False + return_crystal_feas (bool): whether to return crystal features. + Default = False + + Returns: + prediction (dict): containing the fields: + e (Tensor) : energy of structures [batch_size, 1] + f (Tensor) : force on atoms [num_batch_atoms, 3] + s (Tensor) : stress of structure [3 * batch_size, 3] + m (Tensor) : magnetic moments of sites [num_batch_atoms, 3] + """ + prediction = {} + atoms_per_graph = torch.bincount(g.atom_owners) + prediction["atoms_per_graph"] = atoms_per_graph + + # Embed Atoms, Bonds and Angles + atom_feas = self.atom_embedding( + g.atomic_numbers - 1 + ) # let H be the first embedding column + bond_feas = self.bond_embedding(g.bond_bases_ag) + bond_weights_ag = self.bond_weights_ag(g.bond_bases_ag) + bond_weights_bg = self.bond_weights_bg(g.bond_bases_bg) + if len(g.angle_bases) != 0: + angle_feas = self.angle_embedding(g.angle_bases) + + # Message Passing + for idx, (atom_layer, bond_layer, angle_layer) in enumerate( + zip(self.atom_conv_layers[:-1], self.bond_conv_layers, self.angle_layers) + ): + # Atom Conv + atom_feas = atom_layer( + atom_feas=atom_feas, + bond_feas=bond_feas, + bond_weights=bond_weights_ag, + atom_graph=g.batched_atom_graph, + directed2undirected=g.directed2undirected, + ) + + # Bond Conv + if len(g.angle_bases) != 0 and bond_layer is not None: + bond_feas = bond_layer( + atom_feas=atom_feas, + bond_feas=bond_feas, + bond_weights=bond_weights_bg, + angle_feas=angle_feas, + bond_graph=g.batched_bond_graph, + ) + + # Angle Update + if angle_layer is not None: + angle_feas = angle_layer( + atom_feas=atom_feas, + bond_feas=bond_feas, + angle_feas=angle_feas, + bond_graph=g.batched_bond_graph, + ) + if idx == self.n_conv - 2: + if return_atom_feas: + prediction["atom_fea"] = torch.split( + atom_feas, atoms_per_graph.tolist() + ) + # Compute site-wise magnetic moments + if compute_magmom: + magmom = torch.abs(self.site_wise(atom_feas)) + prediction["m"] = list( + torch.split(magmom.view(-1), atoms_per_graph.tolist()) + ) + + # Last conv layer + atom_feas = self.atom_conv_layers[-1]( + atom_feas=atom_feas, + bond_feas=bond_feas, + bond_weights=bond_weights_ag, + atom_graph=g.batched_atom_graph, + directed2undirected=g.directed2undirected, + ) + if self.readout_norm is not None: + atom_feas = self.readout_norm(atom_feas) + + # Aggregate nodes and ReadOut + if self.mlp_first: + energies = self.mlp(atom_feas) + energy = self.pooling(energies, g.atom_owners).view(-1) + if return_site_energies: + prediction["site_energies"] = torch.split( + energies.squeeze(1), atoms_per_graph.tolist() + ) + if return_crystal_feas: + prediction["crystal_fea"] = self.pooling(atom_feas, g.atom_owners) + else: # ave or attn to create crystal_fea first + crystal_feas = self.pooling(atom_feas, g.atom_owners) + energy = self.mlp(crystal_feas).view(-1) * atoms_per_graph + if return_crystal_feas: + prediction["crystal_fea"] = crystal_feas + + # Compute force + if compute_force: + # Need to retain_graph here, because energy is used in loss function, + # so its gradient need to be calculated later + # The graphs of force and stress need to be created for same reason. + force = torch.autograd.grad( + energy.sum(), g.atom_positions, create_graph=True, retain_graph=True + ) + prediction["f"] = [-1 * force_dim for force_dim in force] + + # Compute stress + if compute_stress: + stress = torch.autograd.grad( + energy.sum(), g.strains, create_graph=True, retain_graph=True + ) + # Convert Stress unit from eV/A^3 to GPa + scale = 1 / g.volumes * 160.21766208 + stress = [i * j for i, j in zip(stress, scale)] + prediction["s"] = stress + + # Normalize energy if model is intensive + if self.is_intensive: + energy /= atoms_per_graph + prediction["e"] = energy + + return prediction + + def predict_structure( + self, + structure: Structure | Sequence[Structure], + *, + task: PredTask = "efsm", + return_site_energies: bool = False, + return_atom_feas: bool = False, + return_crystal_feas: bool = False, + batch_size: int = 16, + ) -> dict[str, Tensor] | list[dict[str, Tensor]]: + """Predict from pymatgen.core.Structure. + + Args: + structure (Structure | Sequence[Structure]): structure or a list of + structures to predict. + task (str): can be 'e' 'ef', 'em', 'efs', 'efsm' + Default = "efsm" + return_site_energies (bool): whether to return per-site energies. + Default = False + return_atom_feas (bool): whether to return atom features. + Default = False + return_crystal_feas (bool): whether to return crystal features. + Default = False + batch_size (int): batch_size for predict structures. + Default = 16 + + Returns: + prediction (dict): dict or list of dict containing the fields: + e (Tensor) : energy of structures float in eV/atom + f (Tensor) : force on atoms [num_atoms, 3] in eV/A + s (Tensor) : stress of structure [3, 3] in GPa + m (Tensor) : magnetic moments of sites [num_atoms, 3] in Bohr + magneton mu_B + """ + if self.graph_converter is None: + raise ValueError("graph_converter cannot be None!") + + structures = [structure] if isinstance(structure, Structure) else structure + + graphs = [self.graph_converter(struct) for struct in structures] + return self.predict_graph( + graphs, + task=task, + return_site_energies=return_site_energies, + return_atom_feas=return_atom_feas, + return_crystal_feas=return_crystal_feas, + batch_size=batch_size, + ) + + def predict_graph( + self, + graph: CrystalGraph | Sequence[CrystalGraph], + *, + task: PredTask = "efsm", + return_site_energies: bool = False, + return_atom_feas: bool = False, + return_crystal_feas: bool = False, + batch_size: int = 16, + ) -> dict[str, Tensor] | list[dict[str, Tensor]]: + """Predict from CrustalGraph. + + Args: + graph (CrystalGraph | Sequence[CrystalGraph]): CrystalGraph(s) to predict. + task (str): can be 'e' 'ef', 'em', 'efs', 'efsm' + Default = "efsm" + return_site_energies (bool): whether to return per-site energies. + Default = False + return_atom_feas (bool): whether to return atom features. + Default = False + return_crystal_feas (bool): whether to return crystal features. + Default = False + batch_size (int): batch_size for predict structures. + Default = 16 + + Returns: + prediction (dict): dict or list of dict containing the fields: + e (Tensor) : energy of structures float in eV/atom + f (Tensor) : force on atoms [num_atoms, 3] in eV/A + s (Tensor) : stress of structure [3, 3] in GPa + m (Tensor) : magnetic moments of sites [num_atoms, 3] in Bohr + magneton mu_B + """ + if not isinstance(graph, (CrystalGraph, Sequence)): + raise TypeError( + f"{type(graph)=} must be CrystalGraph or list of CrystalGraphs" + ) + + model_device = next(self.parameters()).device + + graphs = [graph] if isinstance(graph, CrystalGraph) else graph + self.eval() + predictions: list[dict[str, Tensor]] = [{} for _ in range(len(graphs))] + n_steps = math.ceil(len(graphs) / batch_size) + for step in range(n_steps): + prediction = self.forward( + [ + g.to(model_device) + for g in graphs[batch_size * step : batch_size * (step + 1)] + ], + task=task, + return_site_energies=return_site_energies, + return_atom_feas=return_atom_feas, + return_crystal_feas=return_crystal_feas, + ) + for key in { + "e", + "f", + "s", + "m", + "site_energies", + "atom_fea", + "crystal_fea", + } & {*prediction}: + for idx, tensor in enumerate(prediction[key]): + predictions[step * batch_size + idx][key] = ( + tensor.cpu().detach().numpy() + ) + + return predictions[0] if len(graphs) == 1 else predictions + + def as_dict(self) -> dict: + """Return the CHGNet weights and args in a dictionary.""" + return {"state_dict": self.state_dict(), "model_args": self.model_args} + + def todict(self) -> dict: + """Needed for ASE JSON serialization when saving CHGNet potential to + trajectory file (https://github.com/CederGroupHub/chgnet/issues/48). + """ + return {"model_name": type(self).__name__, "model_args": self.model_args} + + @classmethod + def from_dict(cls, dct: dict, **kwargs) -> Self: + """Build a CHGNet from a saved dictionary.""" + chgnet = cls(**dct["model_args"], **kwargs) + chgnet.load_state_dict(dct["state_dict"]) + return chgnet + + @classmethod + def from_file(cls, path: str, **kwargs) -> Self: + """Build a CHGNet from a saved file.""" + state = torch.load(path, map_location=torch.device("cpu")) + return cls.from_dict(state["model"], **kwargs) + + @classmethod + def load( + cls, + *, + model_name: str = "0.3.0", + use_device: str | None = None, + check_cuda_mem: bool = False, + verbose: bool = True, + ) -> Self: + """Load pretrained CHGNet model. + + Args: + model_name (str, optional): + Default = "0.3.0". + use_device (str, optional): The device to be used for predictions, + either "cpu", "cuda", or "mps". If not specified, the default device is + automatically selected based on the available options. + Default = None + check_cuda_mem (bool): Whether to use cuda with most available memory + Default = False + verbose (bool): whether to print model device information + Default = True + Raises: + ValueError: On unknown model_name. + """ + checkpoint_path = { + "0.3.0": "../pretrained/0.3.0/chgnet_0.3.0_e29f68s314m37.pth.tar", + "0.2.0": "../pretrained/0.2.0/chgnet_0.2.0_e30f77s348m32.pth.tar", + }.get(model_name) + + if checkpoint_path is None: + raise ValueError(f"Unknown {model_name=}") + + model = cls.from_file( + os.path.join(module_dir, checkpoint_path), + # mlp_out_bias=True is set for backward compatible behavior but in rare + # cases causes unphysical jumps in bonding energy. see + # https://github.com/CederGroupHub/chgnet/issues/79 + mlp_out_bias=model_name == "0.2.0", + version=model_name, + ) + + # Determine the device to use + device = determine_device(use_device=use_device, check_cuda_mem=check_cuda_mem) + + # Move the model to the specified device + model = model.to(device) + if verbose: + print(f"CHGNet will run on {device}") + return model + + +@dataclass +class BatchedGraph: + """Batched crystal graph for parallel computing. + + Attributes: + atomic_numbers (Tensor): atomic numbers vector + [num_batch_atoms] + bond_bases_ag (Tensor): bond bases vector for atom_graph + [num_batch_bonds_ag, num_radial] + bond_bases_bg (Tensor): bond bases vector for atom_graph + [num_batch_bonds_bg, num_radial] + angle_bases (Tensor): angle bases vector + [num_batch_angles, num_angular] + batched_atom_graph (Tensor) : batched atom graph adjacency list + [num_batch_bonds, 2] + batched_bond_graph (Tensor) : bond graph adjacency list + [num_batch_angles, 3] + atom_owners (Tensor): graph indices for each atom, used aggregate batched + graph back to single graph + [num_batch_atoms] + directed2undirected (Tensor): the utility tensor used to quickly + map directed edges to undirected edges in graph + [num_directed] + atom_positions (list[Tensor]): cartesian coordinates of the atoms + from structures + [[num_atoms_1, 3], [num_atoms_2, 3], ...] + strains (list[Tensor]): a list of strains that's initialized to be zeros + [[3, 3], [3, 3], ...] + volumes (Tensor): the volume of each structure in the batch + [batch_size] + """ + + atomic_numbers: Tensor + bond_bases_ag: Tensor + bond_bases_bg: Tensor + angle_bases: Tensor + batched_atom_graph: Tensor + batched_bond_graph: Tensor + atom_owners: Tensor + directed2undirected: Tensor + atom_positions: Sequence[Tensor] + strains: Sequence[Tensor] + volumes: Sequence[Tensor] | Tensor + + @classmethod + def from_graphs( + cls, + graphs: Sequence[CrystalGraph], + bond_basis_expansion: nn.Module, + angle_basis_expansion: nn.Module, + *, + compute_stress: bool = False, + ) -> Self: + """Featurize and assemble a list of graphs. + + Args: + graphs (list[Tensor]): a list of CrystalGraphs + bond_basis_expansion (nn.Module): bond basis expansion layer in CHGNet + angle_basis_expansion (nn.Module): angle basis expansion layer in CHGNet + compute_stress (bool): whether to compute stress. Default = False + + Returns: + BatchedGraph: assembled graphs ready for batched CHGNet forward pass + """ + atomic_numbers, atom_positions = [], [] + strains, volumes = [], [] + bond_bases_ag, bond_bases_bg, angle_bases = [], [], [] + batched_atom_graph, batched_bond_graph = [], [] + directed2undirected = [] + atom_owners = [] + atom_offset_idx = n_undirected = 0 + + for graph_idx, graph in enumerate(graphs): + # Atoms + n_atom = graph.atomic_number.shape[0] + atomic_numbers.append(graph.atomic_number) + + # Lattice + if compute_stress: + strain = graph.lattice.new_zeros([3, 3], requires_grad=True) + lattice = graph.lattice @ ( + torch.eye(3, dtype=datatype).to(strain.device) + strain + ) + else: + strain = None + lattice = graph.lattice + volumes.append( + torch.dot(lattice[0], torch.linalg.cross(lattice[1], lattice[2])) + ) + strains.append(strain) + + # Bonds + atom_cart_coords = graph.atom_frac_coord @ lattice + if graph.atom_graph.dim() == 1: + # This is to avoid structure with all atoms isolated + graph.atom_graph = graph.atom_graph.reshape(0, 2) + bond_basis_ag, bond_basis_bg, bond_vectors = bond_basis_expansion( + center=atom_cart_coords[graph.atom_graph[:, 0]], + neighbor=atom_cart_coords[graph.atom_graph[:, 1]], + undirected2directed=graph.undirected2directed, + image=graph.neighbor_image, + lattice=lattice, + ) + atom_positions.append(atom_cart_coords) + bond_bases_ag.append(bond_basis_ag) + bond_bases_bg.append(bond_basis_bg) + + # Indexes + batched_atom_graph.append(graph.atom_graph + atom_offset_idx) + directed2undirected.append(graph.directed2undirected + n_undirected) + + # Angles + # Here we use directed edges to calculate angles, and + # keep only the undirected graph index in the bond_graph, + # So the number of columns in bond_graph reduce from 5 to 3 + if len(graph.bond_graph) != 0: + bond_vecs_i = torch.index_select( + bond_vectors, 0, graph.bond_graph[:, 2] + ) + bond_vecs_j = torch.index_select( + bond_vectors, 0, graph.bond_graph[:, 4] + ) + angle_basis = angle_basis_expansion(bond_vecs_i, bond_vecs_j) + angle_bases.append(angle_basis) + + bond_graph = graph.bond_graph.new_zeros([graph.bond_graph.shape[0], 3]) + bond_graph[:, 0] = graph.bond_graph[:, 0] + atom_offset_idx + bond_graph[:, 1] = graph.bond_graph[:, 1] + n_undirected + bond_graph[:, 2] = graph.bond_graph[:, 3] + n_undirected + batched_bond_graph.append(bond_graph) + + atom_owners.append(torch.ones(n_atom, requires_grad=False) * graph_idx) + atom_offset_idx += n_atom + n_undirected += len(bond_basis_ag) + + # Make Torch Tensors + atomic_numbers = torch.cat(atomic_numbers, dim=0) + bond_bases_ag = torch.cat(bond_bases_ag, dim=0) + bond_bases_bg = torch.cat(bond_bases_bg, dim=0) + angle_bases = ( + torch.cat(angle_bases, dim=0) if len(angle_bases) != 0 else torch.tensor([]) + ) + batched_atom_graph = torch.cat(batched_atom_graph, dim=0) + if batched_bond_graph != []: + batched_bond_graph = torch.cat(batched_bond_graph, dim=0) + else: # when bond graph is empty or disabled + batched_bond_graph = torch.tensor([]) + atom_owners = ( + torch.cat(atom_owners, dim=0).type(torch.int32).to(atomic_numbers.device) + ) + directed2undirected = torch.cat(directed2undirected, dim=0) + volumes = torch.tensor(volumes, dtype=datatype, device=atomic_numbers.device) + + return cls( + atomic_numbers=atomic_numbers, + bond_bases_ag=bond_bases_ag, + bond_bases_bg=bond_bases_bg, + angle_bases=angle_bases, + batched_atom_graph=batched_atom_graph, + batched_bond_graph=batched_bond_graph, + atom_owners=atom_owners, + directed2undirected=directed2undirected, + atom_positions=atom_positions, + strains=strains, + volumes=volumes, + ) diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/model_custom.py b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/model_custom.py new file mode 100755 index 00000000..37c68fec --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/model/model_custom.py @@ -0,0 +1,882 @@ +from __future__ import annotations + +import math +import os +from collections.abc import Sequence +from dataclasses import dataclass +from typing import TYPE_CHECKING, Literal + +import torch +from pymatgen.core import Structure +from torch import Tensor, nn + +from chgnet.graph import CrystalGraph, CrystalGraphConverter +from chgnet.graph.crystalgraph import datatype +from chgnet.model.composition_model import AtomRef +from chgnet.model.encoders import AngleEncoder, AtomEmbedding, BondEncoder +from chgnet.model.functions import MLP, GatedMLP, find_normalization +from chgnet.model.layers import ( + AngleUpdate, + AtomConv, + BondConv, + GraphAttentionReadOut, + GraphPooling, +) +from chgnet.utils import determine_device + +if TYPE_CHECKING: + from typing_extensions import Self + + from chgnet import PredTask + +module_dir = os.path.dirname(os.path.abspath(__file__)) + + +class CHGNetCustomProperty(nn.Module): + """ + Fine-tuned custom property model + """ + + def __init__( + self, + *, + atom_fea_dim: int = 64, + bond_fea_dim: int = 64, + angle_fea_dim: int = 64, + composition_model: str | nn.Module = "MPtrj", + num_radial: int = 31, + num_angular: int = 31, + n_conv: int = 4, + atom_conv_hidden_dim: Sequence[int] | int = 64, + update_bond: bool = True, + bond_conv_hidden_dim: Sequence[int] | int = 64, + update_angle: bool = True, + angle_layer_hidden_dim: Sequence[int] | int = 0, + conv_dropout: float = 0, + read_out: str = "ave", + attn_readout_is_average = True, + mlp_hidden_dims: Sequence[int] | int = (64, 64, 64), + mlp_dropout: float = 0, + mlp_first: bool = False, + is_intensive: bool = True, + non_linearity: Literal["silu", "relu", "tanh", "gelu"] = "silu", + atom_graph_cutoff: float = 6, + bond_graph_cutoff: float = 3, + graph_converter_algorithm: Literal["legacy", "fast"] = "fast", + cutoff_coeff: int = 8, + learnable_rbf: bool = True, + gMLP_norm: str | None = "layer", # noqa: N803 + readout_norm: str | None = "layer", + version: str | None = None, + **kwargs, + ) -> None: + """Initialize CHGNet. + + Args: + atom_fea_dim (int): atom feature vector embedding dimension. + Default = 64 + bond_fea_dim (int): bond feature vector embedding dimension. + Default = 64 + angle_fea_dim (int): angle feature vector embedding dimension. + Default = 64 + bond_fea_dim (int): angle feature vector embedding dimension. + Default = 64 + composition_model (nn.Module, optional): attach a composition model to + predict energy or initialize a pretrained linear regression (AtomRef). + The default 'MPtrj' is the atom reference energy linear regression + trained on all Materials Project relaxation trajectories + Default = 'MPtrj' + num_radial (int): number of radial basis used in bond basis expansion. + Default = 9 + num_angular (int): number of angular basis used in angle basis expansion. + Default = 9 + n_conv (int): number of interaction blocks. + Default = 4 + Note: last interaction block contain only an atom_conv layer + atom_conv_hidden_dim (List or int): hidden dimensions of + atom convolution layers. + Default = 64 + update_bond (bool): whether to use bond_conv_layer in bond graph to + update bond embeddings + Default = True. + bond_conv_hidden_dim (List or int): hidden dimensions of + bond convolution layers. + Default = 64 + update_angle (bool): whether to use angle_update_layer to + update angle embeddings. + Default = True + angle_layer_hidden_dim (List or int): hidden dimensions of angle layers. + Default = 0 + conv_dropout (float): dropout rate in all conv_layers. + Default = 0 + read_out (str): method for pooling layer, 'ave' for standard + average pooling, 'attn' for multi-head attention. + Default = "ave" + mlp_hidden_dims (int or list): readout multilayer perceptron + hidden dimensions. + Default = [64, 64] + mlp_dropout (float): dropout rate in readout MLP. + Default = 0. + is_intensive (bool): whether the energy training label is intensive + i.e. energy per atom. + Default = True + non_linearity ('silu' | 'relu' | 'tanh' | 'gelu'): The name of the + activation function to use in the gated MLP. + Default = "silu". + mlp_first (bool): whether to apply mlp first then pooling. + if set to True, then CHGNet is essentially calculating energy for each + atom, them sum them up, this is used for the pretrained model + Default = True + atom_graph_cutoff (float): cutoff radius (A) in creating atom_graph, + this need to be consistent with the value in training dataloader + Default = 5 + bond_graph_cutoff (float): cutoff radius (A) in creating bond_graph, + this need to be consistent with value in training dataloader + Default = 3 + graph_converter_algorithm ('legacy' | 'fast'): algorithm to use + for converting pymatgen.core.Structure to CrystalGraph. + 'legacy': python implementation of graph creation + 'fast': C implementation of graph creation, this is faster, + but will need the cygraph.c file correctly compiled from pip install + default = 'fast' + cutoff_coeff (float): cutoff strength used in graph smooth cutoff function. + the smaller this coeff is, the smoother the basis is + Default = 5 + learnable_rbf (bool): whether to set the frequencies in rbf and Fourier + basis functions learnable. + Default = True + gMLP_norm (str): normalization layer to use in gate-MLP + Default = 'layer' + readout_norm (str): normalization layer to use before readout layer + Default = 'layer' + version (str): Pretrained checkpoint version. + **kwargs: Additional keyword arguments + """ + # Store model args for reconstruction + self.model_args = { + key: val + for key, val in locals().items() + if key not in {"self", "__class__", "kwargs"} + } + self.model_args.update(kwargs) + if version: + self.model_args["version"] = version + + super().__init__() + self.atom_fea_dim = atom_fea_dim + self.bond_fea_dim = bond_fea_dim + self.is_intensive = is_intensive + self.n_conv = n_conv + + # Optionally, define composition model + if isinstance(composition_model, nn.Module): + self.composition_model = composition_model + elif isinstance(composition_model, str): + self.composition_model = AtomRef(is_intensive=is_intensive) + self.composition_model.initialize_from(composition_model) + else: + self.composition_model = None + + if self.composition_model is not None: + # fixed composition_model weights + for param in self.composition_model.parameters(): + param.requires_grad = False + + # Define Crystal Graph Converter + self.graph_converter = CrystalGraphConverter( + atom_graph_cutoff=atom_graph_cutoff, + bond_graph_cutoff=bond_graph_cutoff, + algorithm=graph_converter_algorithm, + verbose=kwargs.pop("converter_verbose", False), + ) + + # Define embedding layers + self.atom_embedding = AtomEmbedding(atom_feature_dim=atom_fea_dim) + self.bond_basis_expansion = BondEncoder( + atom_graph_cutoff=atom_graph_cutoff, + bond_graph_cutoff=bond_graph_cutoff, + num_radial=num_radial, + cutoff_coeff=cutoff_coeff, + learnable=learnable_rbf, + ) + self.bond_embedding = nn.Linear( + in_features=num_radial, out_features=bond_fea_dim, bias=False + ) + self.bond_weights_ag = nn.Linear( + in_features=num_radial, out_features=atom_fea_dim, bias=False + ) + self.bond_weights_bg = nn.Linear( + in_features=num_radial, out_features=bond_fea_dim, bias=False + ) + self.angle_basis_expansion = AngleEncoder( + num_angular=num_angular, learnable=learnable_rbf + ) + self.angle_embedding = nn.Linear( + in_features=num_angular, out_features=angle_fea_dim, bias=False + ) + + # Define convolutional layers + conv_norm = kwargs.pop("conv_norm", None) + mlp_out_bias = kwargs.pop("mlp_out_bias", False) + atom_graph_layers = [ + AtomConv( + atom_fea_dim=atom_fea_dim, + bond_fea_dim=bond_fea_dim, + hidden_dim=atom_conv_hidden_dim, + dropout=conv_dropout, + activation=non_linearity, + norm=conv_norm, + gMLP_norm=gMLP_norm, + use_mlp_out=True, + mlp_out_bias=mlp_out_bias, + resnet=True, + ) + for _ in range(n_conv) + ] + self.atom_conv_layers = nn.ModuleList(atom_graph_layers) + + if update_bond: + bond_graph_layers = [ + BondConv( + atom_fea_dim=atom_fea_dim, + bond_fea_dim=bond_fea_dim, + angle_fea_dim=angle_fea_dim, + hidden_dim=bond_conv_hidden_dim, + dropout=conv_dropout, + activation=non_linearity, + norm=conv_norm, + gMLP_norm=gMLP_norm, + use_mlp_out=True, + mlp_out_bias=mlp_out_bias, + resnet=True, + ) + for _ in range(n_conv - 1) + ] + self.bond_conv_layers = nn.ModuleList(bond_graph_layers) + else: + self.bond_conv_layers = [None for _ in range(n_conv - 1)] + + if update_angle: + angle_layers = [ + AngleUpdate( + atom_fea_dim=atom_fea_dim, + bond_fea_dim=bond_fea_dim, + angle_fea_dim=angle_fea_dim, + hidden_dim=angle_layer_hidden_dim, + dropout=conv_dropout, + activation=non_linearity, + norm=conv_norm, + gMLP_norm=gMLP_norm, + resnet=True, + ) + for _ in range(n_conv - 1) + ] + self.angle_layers = nn.ModuleList(angle_layers) + else: + self.angle_layers = [None for _ in range(n_conv - 1)] + + # Define readout layer + self.site_wise = nn.Linear(atom_fea_dim, 1) + self.readout_norm = find_normalization(readout_norm, dim=atom_fea_dim) + self.mlp_first = mlp_first + if mlp_first: # NOT for us + self.read_out_type = "sum" + input_dim = atom_fea_dim + self.pooling = GraphPooling(average=False) + elif read_out in {"attn", "weighted"}: # THIS IS US + self.read_out_type = "attn" + num_heads = kwargs.pop("num_heads", 3) + self.pooling = GraphAttentionReadOut( + atom_fea_dim, num_head=num_heads, average=attn_readout_is_average + ) # since weight is softmax processed, it is already averaged, why average again? + input_dim = atom_fea_dim * num_heads + else: # THIS IS US + self.read_out_type = "ave" + input_dim = atom_fea_dim + self.pooling = GraphPooling(average=True) + if kwargs.pop("final_mlp", "MLP") in {"normal", "MLP"}: # THIS IS US, prediction head + self.mlp = MLP( + input_dim=input_dim, + hidden_dim=mlp_hidden_dims, + output_dim=1, + dropout=mlp_dropout, + activation=non_linearity, + ) + else: + self.mlp = nn.Sequential( + GatedMLP( + input_dim=input_dim, + hidden_dim=mlp_hidden_dims, + output_dim=mlp_hidden_dims[-1], + dropout=mlp_dropout, + norm=gMLP_norm, + activation=non_linearity, + ), + nn.Linear(in_features=mlp_hidden_dims[-1], out_features=1), + ) + + version_str = f" v{version}" if version else "" + print(f"CHGNet{version_str} initialized with {self.n_params:,} parameters") + + @property + def version(self) -> str | None: + """Return the version of the loaded checkpoint.""" + return self.model_args.get("version") + + @property + def n_params(self) -> int: + """Return the number of parameters in the model.""" + return sum(p.numel() for p in self.parameters()) + + def forward( + self, + graphs: Sequence[CrystalGraph], + *, + task: PredTask = "c", + return_site_energies: bool = False, + return_atom_feas: bool = False, + return_crystal_feas: bool = False, + ) -> dict[str, Tensor]: + """Get prediction associated with input graphs + Args: + graphs (List): a list of CrystalGraphs + task (str): the prediction task. One of 'e', 'em', 'ef', 'efs', 'efsm'. + Default = 'c' + return_site_energies (bool): whether to return per-site energies, + only available if self.mlp_first == True + Default = False + return_atom_feas (bool): whether to return the atom features before last + conv layer. + Default = False + return_crystal_feas (bool): whether to return crystal feature. + Default = False + Returns: + model output (dict). + """ + # # Optionally, make composition model prediction + # comp_energy = ( + # 0 if self.composition_model is None else self.composition_model(graphs) + # ) + + # Make batched graph + batched_graph = BatchedGraph.from_graphs( + graphs, + bond_basis_expansion=self.bond_basis_expansion, + angle_basis_expansion=self.angle_basis_expansion, + compute_stress=False, + ) + + # Pass to model # AMYAO modified + custom_property = self._compute( + batched_graph, + compute_force=False, + compute_stress=False, + compute_magmom=False, + return_site_energies=return_site_energies, + return_atom_feas=return_atom_feas, + return_crystal_feas=return_crystal_feas, + ) + # custom_property["c"] += comp_energy + + return custom_property + + def _compute( + self, + g: BatchedGraph, + *, + compute_force: bool = False, + compute_stress: bool = False, + compute_magmom: bool = False, + return_site_energies: bool = False, + return_atom_feas: bool = False, + return_crystal_feas: bool = False, + ) -> dict: + """Get Energy, Force, Stress, Magmom associated with input graphs + force = - d(Energy)/d(atom_positions) + stress = 1/V * d(Energy)/d(strain). + + Args: + g (BatchedGraph): batched graph + compute_force (bool): whether to compute force. + Default = False + compute_stress (bool): whether to compute stress. + Default = False + compute_magmom (bool): whether to compute magmom. + Default = False + return_site_energies (bool): whether to return per-site energies, + only available if self.mlp_first == True + Default = False + return_atom_feas (bool): whether to return atom features. + Default = False + return_crystal_feas (bool): whether to return crystal features. + Default = False + + Returns: + custom_property + prediction (dict): containing the fields: + e (Tensor) : energy of structures [batch_size, 1] + f (Tensor) : force on atoms [num_batch_atoms, 3] + s (Tensor) : stress of structure [3 * batch_size, 3] + m (Tensor) : magnetic moments of sites [num_batch_atoms, 3] + """ + prediction = {} + atoms_per_graph = torch.bincount(g.atom_owners) + prediction["atoms_per_graph"] = atoms_per_graph + + # Embed Atoms, Bonds and Angles + atom_feas = self.atom_embedding( + g.atomic_numbers - 1 + ) # let H be the first embedding column + bond_feas = self.bond_embedding(g.bond_bases_ag) + bond_weights_ag = self.bond_weights_ag(g.bond_bases_ag) + bond_weights_bg = self.bond_weights_bg(g.bond_bases_bg) + if len(g.angle_bases) != 0: + angle_feas = self.angle_embedding(g.angle_bases) + + # Message Passing + for idx, (atom_layer, bond_layer, angle_layer) in enumerate( + zip(self.atom_conv_layers[:-1], self.bond_conv_layers, self.angle_layers) + ): + # Atom Conv + atom_feas = atom_layer( + atom_feas=atom_feas, + bond_feas=bond_feas, + bond_weights=bond_weights_ag, + atom_graph=g.batched_atom_graph, + directed2undirected=g.directed2undirected, + ) + + # Bond Conv + if len(g.angle_bases) != 0 and bond_layer is not None: + bond_feas = bond_layer( + atom_feas=atom_feas, + bond_feas=bond_feas, + bond_weights=bond_weights_bg, + angle_feas=angle_feas, + bond_graph=g.batched_bond_graph, + ) + + # Angle Update + if angle_layer is not None: + angle_feas = angle_layer( + atom_feas=atom_feas, + bond_feas=bond_feas, + angle_feas=angle_feas, + bond_graph=g.batched_bond_graph, + ) + if idx == self.n_conv - 2: + if return_atom_feas: + prediction["atom_fea"] = torch.split( + atom_feas, atoms_per_graph.tolist() + ) + # Compute site-wise magnetic moments + if compute_magmom: + magmom = torch.abs(self.site_wise(atom_feas)) + prediction["m"] = list( + torch.split(magmom.view(-1), atoms_per_graph.tolist()) + ) + + # Last conv layer + atom_feas = self.atom_conv_layers[-1]( + atom_feas=atom_feas, + bond_feas=bond_feas, + bond_weights=bond_weights_ag, + atom_graph=g.batched_atom_graph, + directed2undirected=g.directed2undirected, + ) + if self.readout_norm is not None: + atom_feas = self.readout_norm(atom_feas) + + # Aggregate nodes and ReadOut, i.e. the prediction head # AMYAO + crystal_feas = self.pooling(atom_feas, g.atom_owners) + custom_property = self.mlp(crystal_feas).view(-1) + + prediction = {} + prediction["c"] = custom_property + + return prediction + + def get_structure( + self, + structure: Structure | Sequence[Structure], + *, + batch_size: int = 16, + ) -> dict[str, Tensor] | list[dict[str, Tensor]]: + """Predict from pymatgen.core.Structure. + + Args: + structure (Structure | Sequence[Structure]): structure or a list of + structures to predict. + batch_size (int): batch_size for predict structures. + Default = 16 + + Returns: + graphs: list of graphs + """ + if self.graph_converter is None: + raise ValueError("graph_converter cannot be None!") + + structures = [structure] if isinstance(structure, Structure) else structure + + graphs = [self.graph_converter(struct) for struct in structures] + return graphs + + def as_dict(self) -> dict: + """Return the CHGNet weights and args in a dictionary.""" + return {"state_dict": self.state_dict(), "model_args": self.model_args} + + def todict(self) -> dict: + """Needed for ASE JSON serialization when saving CHGNet potential to + trajectory file (https://github.com/CederGroupHub/chgnet/issues/48). + """ + return {"model_name": type(self).__name__, "model_args": self.model_args} + + @classmethod + def from_dict(cls, dct: dict, **kwargs) -> Self: + """Build a CHGNetCustomProperty from a saved dictionary.""" + args = dct["model_args"] + args['mlp_first'] = False + model = cls(**args, **kwargs) + # model.load_state_dict(dct["state_dict"]) + """ + load pre-trained weights + """ + all_params = ['composition_model.fc.weight', 'atom_embedding.embedding.weight', 'bond_basis_expansion.rbf_expansion_ag.frequencies', + 'bond_basis_expansion.rbf_expansion_bg.frequencies', 'bond_embedding.weight', + 'bond_weights_ag.weight', 'bond_weights_bg.weight', + 'angle_basis_expansion.fourier_expansion.frequencies', + 'angle_embedding.weight', + 'atom_conv_layers.0.twoBody_atom.mlp_core.layers.0.weight', + 'atom_conv_layers.0.twoBody_atom.mlp_core.layers.0.bias', 'atom_conv_layers.0.twoBody_atom.mlp_core.layers.3.weight', 'atom_conv_layers.0.twoBody_atom.mlp_core.layers.3.bias', 'atom_conv_layers.0.twoBody_atom.mlp_gate.layers.0.weight', 'atom_conv_layers.0.twoBody_atom.mlp_gate.layers.0.bias', 'atom_conv_layers.0.twoBody_atom.mlp_gate.layers.3.weight', 'atom_conv_layers.0.twoBody_atom.mlp_gate.layers.3.bias', 'atom_conv_layers.0.twoBody_atom.bn1.weight', + 'atom_conv_layers.0.twoBody_atom.bn1.bias', + 'atom_conv_layers.0.twoBody_atom.bn2.weight', + 'atom_conv_layers.0.twoBody_atom.bn2.bias', + 'atom_conv_layers.0.mlp_out.layers.1.weight', + 'atom_conv_layers.1.twoBody_atom.mlp_core.layers.0.weight', 'atom_conv_layers.1.twoBody_atom.mlp_core.layers.0.bias', 'atom_conv_layers.1.twoBody_atom.mlp_core.layers.3.weight', 'atom_conv_layers.1.twoBody_atom.mlp_core.layers.3.bias', 'atom_conv_layers.1.twoBody_atom.mlp_gate.layers.0.weight', 'atom_conv_layers.1.twoBody_atom.mlp_gate.layers.0.bias', 'atom_conv_layers.1.twoBody_atom.mlp_gate.layers.3.weight', 'atom_conv_layers.1.twoBody_atom.mlp_gate.layers.3.bias', 'atom_conv_layers.1.twoBody_atom.bn1.weight', 'atom_conv_layers.1.twoBody_atom.bn1.bias', 'atom_conv_layers.1.twoBody_atom.bn2.weight', 'atom_conv_layers.1.twoBody_atom.bn2.bias', 'atom_conv_layers.1.mlp_out.layers.1.weight', 'atom_conv_layers.2.twoBody_atom.mlp_core.layers.0.weight', 'atom_conv_layers.2.twoBody_atom.mlp_core.layers.0.bias', 'atom_conv_layers.2.twoBody_atom.mlp_core.layers.3.weight', 'atom_conv_layers.2.twoBody_atom.mlp_core.layers.3.bias', 'atom_conv_layers.2.twoBody_atom.mlp_gate.layers.0.weight', 'atom_conv_layers.2.twoBody_atom.mlp_gate.layers.0.bias', 'atom_conv_layers.2.twoBody_atom.mlp_gate.layers.3.weight', 'atom_conv_layers.2.twoBody_atom.mlp_gate.layers.3.bias', 'atom_conv_layers.2.twoBody_atom.bn1.weight', 'atom_conv_layers.2.twoBody_atom.bn1.bias', 'atom_conv_layers.2.twoBody_atom.bn2.weight', 'atom_conv_layers.2.twoBody_atom.bn2.bias', 'atom_conv_layers.2.mlp_out.layers.1.weight', 'atom_conv_layers.3.twoBody_atom.mlp_core.layers.0.weight', 'atom_conv_layers.3.twoBody_atom.mlp_core.layers.0.bias', 'atom_conv_layers.3.twoBody_atom.mlp_core.layers.3.weight', 'atom_conv_layers.3.twoBody_atom.mlp_core.layers.3.bias', 'atom_conv_layers.3.twoBody_atom.mlp_gate.layers.0.weight', 'atom_conv_layers.3.twoBody_atom.mlp_gate.layers.0.bias', 'atom_conv_layers.3.twoBody_atom.mlp_gate.layers.3.weight', 'atom_conv_layers.3.twoBody_atom.mlp_gate.layers.3.bias', 'atom_conv_layers.3.twoBody_atom.bn1.weight', 'atom_conv_layers.3.twoBody_atom.bn1.bias', 'atom_conv_layers.3.twoBody_atom.bn2.weight', 'atom_conv_layers.3.twoBody_atom.bn2.bias', 'atom_conv_layers.3.mlp_out.layers.1.weight', 'bond_conv_layers.0.twoBody_bond.mlp_core.layers.0.weight', 'bond_conv_layers.0.twoBody_bond.mlp_core.layers.0.bias', 'bond_conv_layers.0.twoBody_bond.mlp_core.layers.3.weight', 'bond_conv_layers.0.twoBody_bond.mlp_core.layers.3.bias', 'bond_conv_layers.0.twoBody_bond.mlp_gate.layers.0.weight', 'bond_conv_layers.0.twoBody_bond.mlp_gate.layers.0.bias', 'bond_conv_layers.0.twoBody_bond.mlp_gate.layers.3.weight', 'bond_conv_layers.0.twoBody_bond.mlp_gate.layers.3.bias', 'bond_conv_layers.0.twoBody_bond.bn1.weight', 'bond_conv_layers.0.twoBody_bond.bn1.bias', 'bond_conv_layers.0.twoBody_bond.bn2.weight', 'bond_conv_layers.0.twoBody_bond.bn2.bias', 'bond_conv_layers.0.mlp_out.layers.1.weight', 'bond_conv_layers.1.twoBody_bond.mlp_core.layers.0.weight', 'bond_conv_layers.1.twoBody_bond.mlp_core.layers.0.bias', 'bond_conv_layers.1.twoBody_bond.mlp_core.layers.3.weight', 'bond_conv_layers.1.twoBody_bond.mlp_core.layers.3.bias', 'bond_conv_layers.1.twoBody_bond.mlp_gate.layers.0.weight', 'bond_conv_layers.1.twoBody_bond.mlp_gate.layers.0.bias', 'bond_conv_layers.1.twoBody_bond.mlp_gate.layers.3.weight', 'bond_conv_layers.1.twoBody_bond.mlp_gate.layers.3.bias', 'bond_conv_layers.1.twoBody_bond.bn1.weight', 'bond_conv_layers.1.twoBody_bond.bn1.bias', 'bond_conv_layers.1.twoBody_bond.bn2.weight', 'bond_conv_layers.1.twoBody_bond.bn2.bias', 'bond_conv_layers.1.mlp_out.layers.1.weight', 'bond_conv_layers.2.twoBody_bond.mlp_core.layers.0.weight', 'bond_conv_layers.2.twoBody_bond.mlp_core.layers.0.bias', 'bond_conv_layers.2.twoBody_bond.mlp_core.layers.3.weight', 'bond_conv_layers.2.twoBody_bond.mlp_core.layers.3.bias', 'bond_conv_layers.2.twoBody_bond.mlp_gate.layers.0.weight', 'bond_conv_layers.2.twoBody_bond.mlp_gate.layers.0.bias', 'bond_conv_layers.2.twoBody_bond.mlp_gate.layers.3.weight', 'bond_conv_layers.2.twoBody_bond.mlp_gate.layers.3.bias', 'bond_conv_layers.2.twoBody_bond.bn1.weight', 'bond_conv_layers.2.twoBody_bond.bn1.bias', 'bond_conv_layers.2.twoBody_bond.bn2.weight', 'bond_conv_layers.2.twoBody_bond.bn2.bias', 'bond_conv_layers.2.mlp_out.layers.1.weight', 'angle_layers.0.twoBody_bond.mlp_core.layers.1.weight', 'angle_layers.0.twoBody_bond.mlp_core.layers.1.bias', 'angle_layers.0.twoBody_bond.mlp_gate.layers.1.weight', 'angle_layers.0.twoBody_bond.mlp_gate.layers.1.bias', 'angle_layers.0.twoBody_bond.bn1.weight', 'angle_layers.0.twoBody_bond.bn1.bias', 'angle_layers.0.twoBody_bond.bn2.weight', 'angle_layers.0.twoBody_bond.bn2.bias', 'angle_layers.1.twoBody_bond.mlp_core.layers.1.weight', 'angle_layers.1.twoBody_bond.mlp_core.layers.1.bias', 'angle_layers.1.twoBody_bond.mlp_gate.layers.1.weight', 'angle_layers.1.twoBody_bond.mlp_gate.layers.1.bias', 'angle_layers.1.twoBody_bond.bn1.weight', 'angle_layers.1.twoBody_bond.bn1.bias', 'angle_layers.1.twoBody_bond.bn2.weight', 'angle_layers.1.twoBody_bond.bn2.bias', 'angle_layers.2.twoBody_bond.mlp_core.layers.1.weight', 'angle_layers.2.twoBody_bond.mlp_core.layers.1.bias', 'angle_layers.2.twoBody_bond.mlp_gate.layers.1.weight', 'angle_layers.2.twoBody_bond.mlp_gate.layers.1.bias', 'angle_layers.2.twoBody_bond.bn1.weight', 'angle_layers.2.twoBody_bond.bn1.bias', 'angle_layers.2.twoBody_bond.bn2.weight', 'angle_layers.2.twoBody_bond.bn2.bias', + 'site_wise.weight', 'site_wise.bias', 'readout_norm.weight', 'readout_norm.bias', + 'mlp.layers.0.weight', 'mlp.layers.0.bias', + 'mlp.layers.2.weight', 'mlp.layers.2.bias', + 'mlp.layers.4.weight', 'mlp.layers.4.bias', + 'mlp.layers.7.weight', 'mlp.layers.7.bias'] + # See https://discuss.pytorch.org/t/loading-a-specific-layer-from-checkpoint/52725/2 + # on how to load weights for specific layers + with torch.no_grad(): + for param_name in all_params: + if 'mlp' in param_name: + pass # this is the new prediction head that we need to train + else: + param = dict(model.named_parameters())[param_name] + param.copy_(dct['state_dict'][param_name]) + # return + return model + + @classmethod + def from_file(cls, path: str, **kwargs) -> Self: + """Build a CHGNetCustomProperty from a saved file.""" + state = torch.load(path, map_location=torch.device("cpu")) + return cls.from_dict(state["model"], **kwargs) + + @classmethod + def load( + cls, + *, + model_name: str = "0.3.0", + use_device: str | None = None, + check_cuda_mem: bool = False, + verbose: bool = True, + final_mlp = "MLP", + ) -> Self: + """Load pretrained CHGNet model. + + Args: + model_name (str, optional): + Default = "0.3.0". + use_device (str, optional): The device to be used for predictions, + either "cpu", "cuda", or "mps". If not specified, the default device is + automatically selected based on the available options. + Default = None + check_cuda_mem (bool): Whether to use cuda with most available memory + Default = False + verbose (bool): whether to print model device information + Default = True + Raises: + ValueError: On unknown model_name. + """ + checkpoint_path = { + "0.3.0": "../pretrained/0.3.0/chgnet_0.3.0_e29f68s314m37.pth.tar", + "0.2.0": "../pretrained/0.2.0/chgnet_0.2.0_e30f77s348m32.pth.tar", + }.get(model_name) + + if checkpoint_path is None: + raise ValueError(f"Unknown {model_name=}") + + mlp_out_bias = (model_name == "0.2.0") + # mlp_out_bias=True is set for backward compatible behavior but in rare + # cases causes unphysical jumps in bonding energy. see + # https://github.com/CederGroupHub/chgnet/issues/79 + + + model = cls.from_file( + os.path.join(module_dir, checkpoint_path), + mlp_out_bias=mlp_out_bias, + version=model_name, + final_mlp = final_mlp, + ) + + # Determine the device to use + device = determine_device(use_device=use_device, check_cuda_mem=check_cuda_mem) + + # Move the model to the specified device + model = model.to(device) + if verbose: + print(f"CHGNetCustomProperty will run on {device}") + return model + + @classmethod + def load_w_attn( + cls, + *, + model_name: str = "0.3.0", + use_device: str | None = None, + check_cuda_mem: bool = False, + verbose: bool = True, + attn_readout_is_average = True, + final_mlp = "MLP", + ) -> Self: + """Load pretrained CHGNet model, with attention read out layer + + Args: + model_name (str, optional): + Default = "0.3.0". + use_device (str, optional): The device to be used for predictions, + either "cpu", "cuda", or "mps". If not specified, the default device is + automatically selected based on the available options. + Default = None + check_cuda_mem (bool): Whether to use cuda with most available memory + Default = False + verbose (bool): whether to print model device information + Default = True + Raises: + ValueError: On unknown model_name. + """ + checkpoint_path = { + "0.3.0": "../pretrained/0.3.0/chgnet_0.3.0_e29f68s314m37.pth.tar", + "0.2.0": "../pretrained/0.2.0/chgnet_0.2.0_e30f77s348m32.pth.tar", + }.get(model_name) + + if checkpoint_path is None: + raise ValueError(f"Unknown {model_name=}") + + state = torch.load(os.path.join(module_dir, checkpoint_path), map_location=torch.device("cpu")) + dct = state['model'] + + + args = dct["model_args"] + args['mlp_first'] = False + args['read_out']= 'attn' + args['attn_readout_is_average'] = attn_readout_is_average + + mlp_out_bias = (model_name == "0.2.0") + # mlp_out_bias=True is set for backward compatible behavior but in rare + # cases causes unphysical jumps in bonding energy. see + # https://github.com/CederGroupHub/chgnet/issues/79 + kwargs = {"mlp_out_bias": mlp_out_bias, "version": model_name, "final_mlp": final_mlp } + model = cls(**args, **kwargs) + """ + load pre-trained weights + """ + all_params = ['composition_model.fc.weight', 'atom_embedding.embedding.weight', 'bond_basis_expansion.rbf_expansion_ag.frequencies', 'bond_basis_expansion.rbf_expansion_bg.frequencies', 'bond_embedding.weight', 'bond_weights_ag.weight', 'bond_weights_bg.weight', 'angle_basis_expansion.fourier_expansion.frequencies', 'angle_embedding.weight', 'atom_conv_layers.0.twoBody_atom.mlp_core.layers.0.weight', 'atom_conv_layers.0.twoBody_atom.mlp_core.layers.0.bias', 'atom_conv_layers.0.twoBody_atom.mlp_core.layers.3.weight', 'atom_conv_layers.0.twoBody_atom.mlp_core.layers.3.bias', 'atom_conv_layers.0.twoBody_atom.mlp_gate.layers.0.weight', 'atom_conv_layers.0.twoBody_atom.mlp_gate.layers.0.bias', 'atom_conv_layers.0.twoBody_atom.mlp_gate.layers.3.weight', 'atom_conv_layers.0.twoBody_atom.mlp_gate.layers.3.bias', 'atom_conv_layers.0.twoBody_atom.bn1.weight', 'atom_conv_layers.0.twoBody_atom.bn1.bias', 'atom_conv_layers.0.twoBody_atom.bn2.weight', 'atom_conv_layers.0.twoBody_atom.bn2.bias', 'atom_conv_layers.0.mlp_out.layers.1.weight', 'atom_conv_layers.1.twoBody_atom.mlp_core.layers.0.weight', 'atom_conv_layers.1.twoBody_atom.mlp_core.layers.0.bias', 'atom_conv_layers.1.twoBody_atom.mlp_core.layers.3.weight', 'atom_conv_layers.1.twoBody_atom.mlp_core.layers.3.bias', 'atom_conv_layers.1.twoBody_atom.mlp_gate.layers.0.weight', 'atom_conv_layers.1.twoBody_atom.mlp_gate.layers.0.bias', 'atom_conv_layers.1.twoBody_atom.mlp_gate.layers.3.weight', 'atom_conv_layers.1.twoBody_atom.mlp_gate.layers.3.bias', 'atom_conv_layers.1.twoBody_atom.bn1.weight', 'atom_conv_layers.1.twoBody_atom.bn1.bias', 'atom_conv_layers.1.twoBody_atom.bn2.weight', 'atom_conv_layers.1.twoBody_atom.bn2.bias', 'atom_conv_layers.1.mlp_out.layers.1.weight', 'atom_conv_layers.2.twoBody_atom.mlp_core.layers.0.weight', 'atom_conv_layers.2.twoBody_atom.mlp_core.layers.0.bias', 'atom_conv_layers.2.twoBody_atom.mlp_core.layers.3.weight', 'atom_conv_layers.2.twoBody_atom.mlp_core.layers.3.bias', 'atom_conv_layers.2.twoBody_atom.mlp_gate.layers.0.weight', 'atom_conv_layers.2.twoBody_atom.mlp_gate.layers.0.bias', 'atom_conv_layers.2.twoBody_atom.mlp_gate.layers.3.weight', 'atom_conv_layers.2.twoBody_atom.mlp_gate.layers.3.bias', 'atom_conv_layers.2.twoBody_atom.bn1.weight', 'atom_conv_layers.2.twoBody_atom.bn1.bias', 'atom_conv_layers.2.twoBody_atom.bn2.weight', 'atom_conv_layers.2.twoBody_atom.bn2.bias', 'atom_conv_layers.2.mlp_out.layers.1.weight', 'atom_conv_layers.3.twoBody_atom.mlp_core.layers.0.weight', 'atom_conv_layers.3.twoBody_atom.mlp_core.layers.0.bias', 'atom_conv_layers.3.twoBody_atom.mlp_core.layers.3.weight', 'atom_conv_layers.3.twoBody_atom.mlp_core.layers.3.bias', 'atom_conv_layers.3.twoBody_atom.mlp_gate.layers.0.weight', 'atom_conv_layers.3.twoBody_atom.mlp_gate.layers.0.bias', 'atom_conv_layers.3.twoBody_atom.mlp_gate.layers.3.weight', 'atom_conv_layers.3.twoBody_atom.mlp_gate.layers.3.bias', 'atom_conv_layers.3.twoBody_atom.bn1.weight', 'atom_conv_layers.3.twoBody_atom.bn1.bias', 'atom_conv_layers.3.twoBody_atom.bn2.weight', 'atom_conv_layers.3.twoBody_atom.bn2.bias', 'atom_conv_layers.3.mlp_out.layers.1.weight', 'bond_conv_layers.0.twoBody_bond.mlp_core.layers.0.weight', 'bond_conv_layers.0.twoBody_bond.mlp_core.layers.0.bias', 'bond_conv_layers.0.twoBody_bond.mlp_core.layers.3.weight', 'bond_conv_layers.0.twoBody_bond.mlp_core.layers.3.bias', 'bond_conv_layers.0.twoBody_bond.mlp_gate.layers.0.weight', 'bond_conv_layers.0.twoBody_bond.mlp_gate.layers.0.bias', 'bond_conv_layers.0.twoBody_bond.mlp_gate.layers.3.weight', 'bond_conv_layers.0.twoBody_bond.mlp_gate.layers.3.bias', 'bond_conv_layers.0.twoBody_bond.bn1.weight', 'bond_conv_layers.0.twoBody_bond.bn1.bias', 'bond_conv_layers.0.twoBody_bond.bn2.weight', 'bond_conv_layers.0.twoBody_bond.bn2.bias', 'bond_conv_layers.0.mlp_out.layers.1.weight', 'bond_conv_layers.1.twoBody_bond.mlp_core.layers.0.weight', 'bond_conv_layers.1.twoBody_bond.mlp_core.layers.0.bias', 'bond_conv_layers.1.twoBody_bond.mlp_core.layers.3.weight', 'bond_conv_layers.1.twoBody_bond.mlp_core.layers.3.bias', 'bond_conv_layers.1.twoBody_bond.mlp_gate.layers.0.weight', 'bond_conv_layers.1.twoBody_bond.mlp_gate.layers.0.bias', 'bond_conv_layers.1.twoBody_bond.mlp_gate.layers.3.weight', 'bond_conv_layers.1.twoBody_bond.mlp_gate.layers.3.bias', 'bond_conv_layers.1.twoBody_bond.bn1.weight', 'bond_conv_layers.1.twoBody_bond.bn1.bias', 'bond_conv_layers.1.twoBody_bond.bn2.weight', 'bond_conv_layers.1.twoBody_bond.bn2.bias', 'bond_conv_layers.1.mlp_out.layers.1.weight', 'bond_conv_layers.2.twoBody_bond.mlp_core.layers.0.weight', 'bond_conv_layers.2.twoBody_bond.mlp_core.layers.0.bias', 'bond_conv_layers.2.twoBody_bond.mlp_core.layers.3.weight', 'bond_conv_layers.2.twoBody_bond.mlp_core.layers.3.bias', 'bond_conv_layers.2.twoBody_bond.mlp_gate.layers.0.weight', 'bond_conv_layers.2.twoBody_bond.mlp_gate.layers.0.bias', 'bond_conv_layers.2.twoBody_bond.mlp_gate.layers.3.weight', 'bond_conv_layers.2.twoBody_bond.mlp_gate.layers.3.bias', 'bond_conv_layers.2.twoBody_bond.bn1.weight', 'bond_conv_layers.2.twoBody_bond.bn1.bias', 'bond_conv_layers.2.twoBody_bond.bn2.weight', 'bond_conv_layers.2.twoBody_bond.bn2.bias', 'bond_conv_layers.2.mlp_out.layers.1.weight', 'angle_layers.0.twoBody_bond.mlp_core.layers.1.weight', 'angle_layers.0.twoBody_bond.mlp_core.layers.1.bias', 'angle_layers.0.twoBody_bond.mlp_gate.layers.1.weight', 'angle_layers.0.twoBody_bond.mlp_gate.layers.1.bias', 'angle_layers.0.twoBody_bond.bn1.weight', 'angle_layers.0.twoBody_bond.bn1.bias', 'angle_layers.0.twoBody_bond.bn2.weight', 'angle_layers.0.twoBody_bond.bn2.bias', 'angle_layers.1.twoBody_bond.mlp_core.layers.1.weight', 'angle_layers.1.twoBody_bond.mlp_core.layers.1.bias', 'angle_layers.1.twoBody_bond.mlp_gate.layers.1.weight', 'angle_layers.1.twoBody_bond.mlp_gate.layers.1.bias', 'angle_layers.1.twoBody_bond.bn1.weight', 'angle_layers.1.twoBody_bond.bn1.bias', 'angle_layers.1.twoBody_bond.bn2.weight', 'angle_layers.1.twoBody_bond.bn2.bias', 'angle_layers.2.twoBody_bond.mlp_core.layers.1.weight', 'angle_layers.2.twoBody_bond.mlp_core.layers.1.bias', 'angle_layers.2.twoBody_bond.mlp_gate.layers.1.weight', 'angle_layers.2.twoBody_bond.mlp_gate.layers.1.bias', 'angle_layers.2.twoBody_bond.bn1.weight', 'angle_layers.2.twoBody_bond.bn1.bias', 'angle_layers.2.twoBody_bond.bn2.weight', 'angle_layers.2.twoBody_bond.bn2.bias', + 'site_wise.weight', 'site_wise.bias', 'readout_norm.weight', 'readout_norm.bias', + 'pooling.key.layers.0.weight', 'pooling.key.layers.0.bias', + 'pooling.key.layers.3.weight', 'pooling.key.layers.3.bias', + 'mlp.layers.0.weight', 'mlp.layers.0.bias', + 'mlp.layers.2.weight', 'mlp.layers.2.bias', + 'mlp.layers.4.weight', 'mlp.layers.4.bias', + 'mlp.layers.7.weight', 'mlp.layers.7.bias'] + # See https://discuss.pytorch.org/t/loading-a-specific-layer-from-checkpoint/52725/2 + # on how to load weights for specific layers + with torch.no_grad(): + for param_name in all_params: + if 'mlp' in param_name or "pooling" in param_name: + pass # this is the new prediction head that we need to train + else: + param = dict(model.named_parameters())[param_name] + param.copy_(dct['state_dict'][param_name]) + + + # Determine the device to use + device = determine_device(use_device=use_device, check_cuda_mem=check_cuda_mem) + + # Move the model to the specified device + model = model.to(device) + if verbose: + print(f"CHGNetCustomProperty will run on {device}") + return model + + +@dataclass +class BatchedGraph: + """Batched crystal graph for parallel computing. + + Attributes: + atomic_numbers (Tensor): atomic numbers vector + [num_batch_atoms] + bond_bases_ag (Tensor): bond bases vector for atom_graph + [num_batch_bonds_ag, num_radial] + bond_bases_bg (Tensor): bond bases vector for atom_graph + [num_batch_bonds_bg, num_radial] + angle_bases (Tensor): angle bases vector + [num_batch_angles, num_angular] + batched_atom_graph (Tensor) : batched atom graph adjacency list + [num_batch_bonds, 2] + batched_bond_graph (Tensor) : bond graph adjacency list + [num_batch_angles, 3] + atom_owners (Tensor): graph indices for each atom, used aggregate batched + graph back to single graph + [num_batch_atoms] + directed2undirected (Tensor): the utility tensor used to quickly + map directed edges to undirected edges in graph + [num_directed] + atom_positions (list[Tensor]): cartesian coordinates of the atoms + from structures + [[num_atoms_1, 3], [num_atoms_2, 3], ...] + strains (list[Tensor]): a list of strains that's initialized to be zeros + [[3, 3], [3, 3], ...] + volumes (Tensor): the volume of each structure in the batch + [batch_size] + """ + + atomic_numbers: Tensor + bond_bases_ag: Tensor + bond_bases_bg: Tensor + angle_bases: Tensor + batched_atom_graph: Tensor + batched_bond_graph: Tensor + atom_owners: Tensor + directed2undirected: Tensor + atom_positions: Sequence[Tensor] + strains: Sequence[Tensor] + volumes: Sequence[Tensor] | Tensor + + @classmethod + def from_graphs( + cls, + graphs: Sequence[CrystalGraph], + bond_basis_expansion: nn.Module, + angle_basis_expansion: nn.Module, + *, + compute_stress: bool = False, + ) -> Self: + """Featurize and assemble a list of graphs. + + Args: + graphs (list[Tensor]): a list of CrystalGraphs + bond_basis_expansion (nn.Module): bond basis expansion layer in CHGNet + angle_basis_expansion (nn.Module): angle basis expansion layer in CHGNet + compute_stress (bool): whether to compute stress. Default = False + + Returns: + BatchedGraph: assembled graphs ready for batched CHGNet forward pass + """ + atomic_numbers, atom_positions = [], [] + strains, volumes = [], [] + bond_bases_ag, bond_bases_bg, angle_bases = [], [], [] + batched_atom_graph, batched_bond_graph = [], [] + directed2undirected = [] + atom_owners = [] + atom_offset_idx = n_undirected = 0 + + for graph_idx, graph in enumerate(graphs): + # Atoms + n_atom = graph.atomic_number.shape[0] + atomic_numbers.append(graph.atomic_number) + + # Lattice + if compute_stress: + strain = graph.lattice.new_zeros([3, 3], requires_grad=True) + lattice = graph.lattice @ ( + torch.eye(3, dtype=datatype).to(strain.device) + strain + ) + else: + strain = None + lattice = graph.lattice + volumes.append( + torch.dot(lattice[0], torch.linalg.cross(lattice[1], lattice[2])) + ) + strains.append(strain) + + # Bonds + atom_cart_coords = graph.atom_frac_coord @ lattice + if graph.atom_graph.dim() == 1: + # This is to avoid structure with all atoms isolated + graph.atom_graph = graph.atom_graph.reshape(0, 2) + bond_basis_ag, bond_basis_bg, bond_vectors = bond_basis_expansion( + center=atom_cart_coords[graph.atom_graph[:, 0]], + neighbor=atom_cart_coords[graph.atom_graph[:, 1]], + undirected2directed=graph.undirected2directed, + image=graph.neighbor_image, + lattice=lattice, + ) + atom_positions.append(atom_cart_coords) + bond_bases_ag.append(bond_basis_ag) + bond_bases_bg.append(bond_basis_bg) + + # Indexes + batched_atom_graph.append(graph.atom_graph + atom_offset_idx) + directed2undirected.append(graph.directed2undirected + n_undirected) + + # Angles + # Here we use directed edges to calculate angles, and + # keep only the undirected graph index in the bond_graph, + # So the number of columns in bond_graph reduce from 5 to 3 + if len(graph.bond_graph) != 0: + bond_vecs_i = torch.index_select( + bond_vectors, 0, graph.bond_graph[:, 2] + ) + bond_vecs_j = torch.index_select( + bond_vectors, 0, graph.bond_graph[:, 4] + ) + angle_basis = angle_basis_expansion(bond_vecs_i, bond_vecs_j) + angle_bases.append(angle_basis) + + bond_graph = graph.bond_graph.new_zeros([graph.bond_graph.shape[0], 3]) + bond_graph[:, 0] = graph.bond_graph[:, 0] + atom_offset_idx + bond_graph[:, 1] = graph.bond_graph[:, 1] + n_undirected + bond_graph[:, 2] = graph.bond_graph[:, 3] + n_undirected + batched_bond_graph.append(bond_graph) + + atom_owners.append(torch.ones(n_atom, requires_grad=False) * graph_idx) + atom_offset_idx += n_atom + n_undirected += len(bond_basis_ag) + + # Make Torch Tensors + atomic_numbers = torch.cat(atomic_numbers, dim=0) + bond_bases_ag = torch.cat(bond_bases_ag, dim=0) + bond_bases_bg = torch.cat(bond_bases_bg, dim=0) + angle_bases = ( + torch.cat(angle_bases, dim=0) if len(angle_bases) != 0 else torch.tensor([]) + ) + batched_atom_graph = torch.cat(batched_atom_graph, dim=0) + if batched_bond_graph != []: + batched_bond_graph = torch.cat(batched_bond_graph, dim=0) + else: # when bond graph is empty or disabled + batched_bond_graph = torch.tensor([]) + atom_owners = ( + torch.cat(atom_owners, dim=0).type(torch.int32).to(atomic_numbers.device) + ) + directed2undirected = torch.cat(directed2undirected, dim=0) + volumes = torch.tensor(volumes, dtype=datatype, device=atomic_numbers.device) + + return cls( + atomic_numbers=atomic_numbers, + bond_bases_ag=bond_bases_ag, + bond_bases_bg=bond_bases_bg, + angle_bases=angle_bases, + batched_atom_graph=batched_atom_graph, + batched_bond_graph=batched_bond_graph, + atom_owners=atom_owners, + directed2undirected=directed2undirected, + atom_positions=atom_positions, + strains=strains, + volumes=volumes, + ) diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/pretrained/0.2.0/README.md b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/pretrained/0.2.0/README.md new file mode 100755 index 00000000..6dcf0356 --- /dev/null +++ b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/pretrained/0.2.0/README.md @@ -0,0 +1,74 @@ +## Model 0.2.0 + +This is the pretrained weights published with CHGNet Nature Machine Intelligence paper. +All the experiments and results shown in the paper were performed with this version of weights. + +Date: 2/24/2023 + +Author: Bowen Deng + +## Model Parameters + +```python +model = CHGNet( + atom_fea_dim=64, + bond_fea_dim=64, + angle_fea_dim=64, + composition_model="MPtrj", + num_radial=9, + num_angular=9, + n_conv=4, + atom_conv_hidden_dim=64, + update_bond=True, + bond_conv_hidden_dim=64, + update_angle=True, + angle_layer_hidden_dim=0, + conv_dropout=0, + read_out="ave", + mlp_hidden_dims=[64, 64], + mlp_first=True, + is_intensive=True, + non_linearity="silu", + atom_graph_cutoff=5, + bond_graph_cutoff=3, + graph_converter_algorithm="fast", + cutoff_coeff=5, + learnable_rbf=True, + mlp_out_bias=True, +) +``` + +## Dataset Used + +MPtrj dataset with 8-1-1 train-val-test splitting + +## Trainer + +```python +trainer = Trainer( + model=model, + targets='efsm', + energy_loss_ratio=1, + force_loss_ratio=1, + stress_loss_ratio=0.1, + mag_loss_ratio=0.1, + optimizer='Adam', + weight_decay=0, + scheduler='CosLR', + criterion='Huber', + delta=0.1, + epochs=20, + starting_epoch=0, + learning_rate=1e-3, + use_device='cuda', + print_freq=1000 +) +``` + +## Mean Absolute Error (MAE) logs + +| partition | Energy (meV/atom) | Force (meV/A) | stress (GPa) | magmom (muB) | +| ---------- | ----------------- | ------------- | ------------ | ------------ | +| Train | 22 | 59 | 0.246 | 0.030 | +| Validation | 30 | 75 | 0.350 | 0.033 | +| Test | 30 | 77 | 0.348 | 0.032 | diff --git a/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/pretrained/0.2.0/chgnet_0.2.0_e30f77s348m32.pth.tar b/benchmarks/matbench_v0.1_CHGNet_v0.3.0/chgnet/pretrained/0.2.0/chgnet_0.2.0_e30f77s348m32.pth.tar new file mode 100755 index 0000000000000000000000000000000000000000..f82e2f20a6da5b0ca05378a1dae3df2d7db07631 GIT binary patch literal 4639235 zcmbqc2Y3}l7Y!Xk@6vmRkX|5fm)@4Tv`7nq1PFwLce9VCj~;pvsZyl(E+F;o1_Tt4 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