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mace-bench/3rdparty/mace/LICENSE.md

+MIT License
+
+Copyright (c) 2022 ACEsuit/mace
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.

mace-bench/3rdparty/mace/MANIFEST.in

+include mace/py.typed

mace-bench/3rdparty/mace/mace_torch.egg-info/SOURCES.txt

+LICENSE.md
+MANIFEST.in
+README.md
+pyproject.toml
+setup.cfg
+mace/__init__.py
+mace/__version__.py
+mace/py.typed
+mace/calculators/__init__.py
+mace/calculators/foundations_models.py
+mace/calculators/lammps_mace.py
+mace/calculators/lammps_mliap_mace.py
+mace/calculators/mace.py
+mace/cli/__init__.py
+mace/cli/active_learning_md.py
+mace/cli/convert_cueq_e3nn.py
+mace/cli/convert_device.py
+mace/cli/convert_e3nn_cueq.py
+mace/cli/create_lammps_model.py
+mace/cli/eval_configs.py
+mace/cli/fine_tuning_select.py
+mace/cli/plot_train.py
+mace/cli/preprocess_data.py
+mace/cli/run_train.py
+mace/cli/select_head.py
+mace/cli/visualise_train.py
+mace/data/__init__.py
+mace/data/atomic_data.py
+mace/data/hdf5_dataset.py
+mace/data/lmdb_dataset.py
+mace/data/neighborhood.py
+mace/data/utils.py
+mace/modules/__init__.py
+mace/modules/blocks.py
+mace/modules/irreps_tools.py
+mace/modules/loss.py
+mace/modules/models.py
+mace/modules/radial.py
+mace/modules/symmetric_contraction.py
+mace/modules/utils.py
+mace/modules/wrapper_ops.py
+mace/tools/__init__.py
+mace/tools/arg_parser.py
+mace/tools/arg_parser_tools.py
+mace/tools/cg.py
+mace/tools/checkpoint.py
+mace/tools/compile.py
+mace/tools/default_keys.py
+mace/tools/finetuning_utils.py
+mace/tools/model_script_utils.py
+mace/tools/multihead_tools.py
+mace/tools/run_train_utils.py
+mace/tools/scatter.py
+mace/tools/scripts_utils.py
+mace/tools/slurm_distributed.py
+mace/tools/tables_utils.py
+mace/tools/torch_tools.py
+mace/tools/train.py
+mace/tools/utils.py
+mace/tools/fairchem_dataset/__init__.py
+mace/tools/fairchem_dataset/lmdb_dataset_tools.py
+mace/tools/torch_geometric/__init__.py
+mace/tools/torch_geometric/batch.py
+mace/tools/torch_geometric/data.py
+mace/tools/torch_geometric/dataloader.py
+mace/tools/torch_geometric/dataset.py
+mace/tools/torch_geometric/seed.py
+mace/tools/torch_geometric/utils.py
+mace_torch.egg-info/PKG-INFO
+mace_torch.egg-info/SOURCES.txt
+mace_torch.egg-info/dependency_links.txt
+mace_torch.egg-info/entry_points.txt
+mace_torch.egg-info/requires.txt
+mace_torch.egg-info/top_level.txt
+scripts/__init__.py
+scripts/eval_configs.py
+scripts/preprocess_data.py
+scripts/run_train.py
+tests/__init__.py
+tests/test_benchmark.py
+tests/test_calculator.py
+tests/test_cg.py
+tests/test_compile.py
+tests/test_cueq.py
+tests/test_data.py
+tests/test_finetuning_select.py
+tests/test_foundations.py
+tests/test_hessian.py
+tests/test_lmdb_database.py
+tests/test_models.py
+tests/test_modules.py
+tests/test_multifiles.py
+tests/test_preprocess.py
+tests/test_run_train.py
+tests/test_run_train_allkeys.py
+tests/test_schedulefree.py
+tests/test_tools.py
\ No newline at end of file

mace-bench/3rdparty/mace/mace_torch.egg-info/dependency_links.txt

+

mace-bench/3rdparty/mace/mace_torch.egg-info/entry_points.txt

+[console_scripts]
+mace_active_learning_md = mace.cli.active_learning_md:main
+mace_convert_device = mace.cli.convert_device:main
+mace_create_lammps_model = mace.cli.create_lammps_model:main
+mace_cueq_to_e3nn = mace.cli.convert_cueq_e3nn:main
+mace_e3nn_cueq = mace.cli.convert_e3nn_cueq:main
+mace_eval_configs = mace.cli.eval_configs:main
+mace_finetuning = mace.cli.fine_tuning_select:main
+mace_plot_train = mace.cli.plot_train:main
+mace_prepare_data = mace.cli.preprocess_data:main
+mace_run_train = mace.cli.run_train:main
+mace_select_head = mace.cli.select_head:main

mace-bench/3rdparty/mace/mace_torch.egg-info/requires.txt

+torch>=1.12
+e3nn==0.4.4
+numpy
+opt_einsum
+ase
+torch-ema
+prettytable
+matscipy
+h5py
+torchmetrics
+python-hostlist
+configargparse
+GitPython
+pyYAML
+tqdm
+lmdb
+orjson
+matplotlib
+pandas
+
+[cueq]
+cuequivariance-torch>=0.2.0
+
+[cueq-cuda-11]
+cuequivariance-ops-torch-cu11>=0.2.0
+
+[cueq-cuda-12]
+cuequivariance-ops-torch-cu12>=0.2.0
+
+[dev]
+black
+isort
+mypy
+pre-commit
+pytest
+pytest-benchmark
+pylint
+
+[fpsample]
+fpsample
+
+[schedulefree]
+schedulefree
+
+[wandb]
+wandb

mace-bench/3rdparty/mace/mace_torch.egg-info/top_level.txt

+mace
+scripts
+tests

mace-bench/3rdparty/mace/pyproject.toml

+[build-system]
+requires = [
+    "setuptools>=42",
+    "wheel",
+]
+build-backend = "setuptools.build_meta"
+
+# Make isort compatible with black
+[tool.isort]
+profile = "black"
+
+# Pylint
+[tool.pylint.'MESSAGES CONTROL']
+disable = [
+    "line-too-long",
+    "no-member",
+    "missing-module-docstring",
+    "missing-class-docstring",
+    "missing-function-docstring",
+    "too-many-arguments",
+    "too-many-positional-arguments",
+    "too-many-locals",
+    "too-many-return-statements",
+    "not-callable",
+    "logging-fstring-interpolation",
+    "logging-not-lazy",
+    "logging-too-many-args",
+    "invalid-name",
+    "too-few-public-methods",
+    "too-many-instance-attributes",
+    "too-many-statements",
+    "too-many-branches",
+    "import-outside-toplevel",
+    "cell-var-from-loop",
+    "duplicate-code",
+    "use-dict-literal",
+]
+
+[tool.pylint.MASTER]
+ignore-paths = [
+    "^mace/tools/torch_geometric/.*$",
+    "^mace/tools/scatter.py$",
+]
+
+[tool.pylint.FORMAT]
+max-module-lines = 1500

mace-bench/3rdparty/mace/ruff.toml

+target-version = "py38"
+
+[lint]
+select = ["FA102"]

mace-bench/3rdparty/mace/scripts/distributed_example.sbatch

+#!/bin/bash
+#SBATCH --partition=gpu
+#SBATCH --job-name=train
+#SBATCH --output=train.out
+#SBATCH --nodes=2
+#SBATCH --ntasks=20
+#SBATCH --ntasks-per-node=10
+#SBATCH --gpus-per-node=10
+#SBATCH --cpus-per-task=8
+#SBATCH --exclusive
+#SBATCH --time=1:00:00
+
+srun python mace/scripts/run_train.py \
+    --name='model' \
+    --model='MACE' \
+    --num_interactions=2 \
+    --num_channels=128 \
+    --max_L=2 \
+    --correlation=3 \
+    --E0s='average' \
+    --r_max=5.0 \
+    --train_file='./h5_data/train.h5' \
+    --valid_file='./h5_data/valid.h5' \
+    --statistics_file='./h5_data/statistics.json' \
+    --num_workers=8 \
+    --batch_size=20 \
+    --valid_batch_size=80 \
+    --max_num_epochs=100 \
+    --loss='weighted' \
+    --error_table='PerAtomRMSE' \
+    --default_dtype='float32' \
+    --device='cuda' \
+    --distributed \
+    --seed=2222 \
\ No newline at end of file

mace-bench/3rdparty/mace/scripts/eval_configs.py

+## Wrapper for mace.cli.eval_configs.main ##
+
+from mace.cli.eval_configs import main
+
+if __name__ == "__main__":
+    main()

mace-bench/3rdparty/mace/scripts/preprocess_data.py

+## Wrapper for mace.cli.run_train.main ##
+
+from mace.cli.preprocess_data import main
+
+if __name__ == "__main__":
+    main()

mace-bench/3rdparty/mace/scripts/run_checks.sh

+# Format
+python -m black .
+python -m isort .
+
+# Check
+python -m pylint --rcfile=pyproject.toml mace tests scripts
+
+# Tests
+python -m pytest tests

mace-bench/3rdparty/mace/scripts/run_train.py

+## Wrapper for mace.cli.run_train.main ##
+
+from mace.cli.run_train import main
+
+if __name__ == "__main__":
+    main()

mace-bench/3rdparty/mace/setup.cfg

+[metadata]
+name = mace-torch
+version = attr: mace.__version__
+short_description = MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
+long_description = file: README.md
+long_description_content_type = text/markdown
+url = https://github.com/ACEsuit/mace
+classifiers =
+    Programming Language :: Python :: 3
+    Operating System :: OS Independent
+    License :: OSI Approved :: MIT License
+
+[options]
+packages = find:
+python_requires = >=3.7
+install_requires =
+    torch>=1.12
+    e3nn==0.4.4
+    numpy
+    opt_einsum
+    ase
+    torch-ema
+    prettytable
+    matscipy
+    h5py
+    torchmetrics
+    python-hostlist
+    configargparse
+    GitPython
+    pyYAML
+    tqdm
+    lmdb
+    orjson
+    # for plotting:
+    matplotlib
+    pandas
+
+[options.entry_points]
+console_scripts =
+    mace_active_learning_md  =  mace.cli.active_learning_md:main
+    mace_create_lammps_model  =  mace.cli.create_lammps_model:main
+    mace_eval_configs  =  mace.cli.eval_configs:main
+    mace_plot_train  =  mace.cli.plot_train:main
+    mace_run_train  =  mace.cli.run_train:main
+    mace_prepare_data  =  mace.cli.preprocess_data:main
+    mace_finetuning = mace.cli.fine_tuning_select:main
+    mace_convert_device  =  mace.cli.convert_device:main
+    mace_select_head =  mace.cli.select_head:main
+    mace_e3nn_cueq = mace.cli.convert_e3nn_cueq:main
+    mace_cueq_to_e3nn = mace.cli.convert_cueq_e3nn:main
+
+[options.extras_require]
+wandb = wandb
+fpsample = fpsample
+dev =
+    black
+    isort
+    mypy
+    pre-commit
+    pytest
+    pytest-benchmark
+    pylint
+schedulefree = schedulefree
+cueq = cuequivariance-torch>=0.2.0
+cueq-cuda-11 = cuequivariance-ops-torch-cu11>=0.2.0
+cueq-cuda-12 = cuequivariance-ops-torch-cu12>=0.2.0

mace-bench/3rdparty/mace/tests/__init__.py

+import os
+
+os.environ["TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD"] = "1"

mace-bench/3rdparty/mace/tests/modules/test_radial.py

+import pytest
+import torch
+
+from mace.modules.radial import AgnesiTransform, ZBLBasis
+
+
+@pytest.fixture
+def zbl_basis():
+    return ZBLBasis(p=6, trainable=False)
+
+
+def test_zbl_basis_initialization(zbl_basis):
+    assert zbl_basis.p == torch.tensor(6.0)
+    assert torch.allclose(zbl_basis.c, torch.tensor([0.1818, 0.5099, 0.2802, 0.02817]))
+
+    assert zbl_basis.a_exp == torch.tensor(0.300)
+    assert zbl_basis.a_prefactor == torch.tensor(0.4543)
+    assert not zbl_basis.a_exp.requires_grad
+    assert not zbl_basis.a_prefactor.requires_grad
+
+
+def test_trainable_zbl_basis_initialization(zbl_basis):
+    zbl_basis = ZBLBasis(p=6, trainable=True)
+    assert zbl_basis.p == torch.tensor(6.0)
+    assert torch.allclose(zbl_basis.c, torch.tensor([0.1818, 0.5099, 0.2802, 0.02817]))
+
+    assert zbl_basis.a_exp == torch.tensor(0.300)
+    assert zbl_basis.a_prefactor == torch.tensor(0.4543)
+    assert zbl_basis.a_exp.requires_grad
+    assert zbl_basis.a_prefactor.requires_grad
+
+
+def test_forward(zbl_basis):
+    x = torch.tensor([1.0, 1.0, 2.0]).unsqueeze(-1)  # [n_edges]
+    node_attrs = torch.tensor(
+        [[1, 0], [0, 1]]
+    )  # [n_nodes, n_node_features] - one_hot encoding of atomic numbers
+    edge_index = torch.tensor([[0, 1, 1], [1, 0, 1]])  # [2, n_edges]
+    atomic_numbers = torch.tensor([1, 6])  # [n_nodes]
+    output = zbl_basis(x, node_attrs, edge_index, atomic_numbers)
+
+    assert output.shape == torch.Size([node_attrs.shape[0]])
+    assert torch.is_tensor(output)
+    assert torch.allclose(
+        output,
+        torch.tensor([0.0031, 0.0031], dtype=torch.get_default_dtype()),
+        rtol=1e-2,
+    )
+
+
+@pytest.fixture
+def agnesi():
+    return AgnesiTransform(trainable=False)
+
+
+def test_agnesi_transform_initialization(agnesi: AgnesiTransform):
+    assert agnesi.q.item() == pytest.approx(0.9183, rel=1e-4)
+    assert agnesi.p.item() == pytest.approx(4.5791, rel=1e-4)
+    assert agnesi.a.item() == pytest.approx(1.0805, rel=1e-4)
+    assert not agnesi.a.requires_grad
+    assert not agnesi.q.requires_grad
+    assert not agnesi.p.requires_grad
+
+
+def test_trainable_agnesi_transform_initialization():
+    agnesi = AgnesiTransform(trainable=True)
+
+    assert agnesi.q.item() == pytest.approx(0.9183, rel=1e-4)
+    assert agnesi.p.item() == pytest.approx(4.5791, rel=1e-4)
+    assert agnesi.a.item() == pytest.approx(1.0805, rel=1e-4)
+    assert agnesi.a.requires_grad
+    assert agnesi.q.requires_grad
+    assert agnesi.p.requires_grad
+
+
+def test_agnesi_transform_forward():
+    agnesi = AgnesiTransform()
+    x = torch.tensor([1.0, 2.0, 3.0], dtype=torch.get_default_dtype()).unsqueeze(-1)
+    node_attrs = torch.tensor([[0, 1], [1, 0], [0, 1]], dtype=torch.get_default_dtype())
+    edge_index = torch.tensor([[0, 1, 2], [1, 2, 0]])
+    atomic_numbers = torch.tensor([1, 6, 8])
+    output = agnesi(x, node_attrs, edge_index, atomic_numbers)
+    assert output.shape == x.shape
+    assert torch.is_tensor(output)
+    assert torch.allclose(
+        output,
+        torch.tensor(
+            [0.3646, 0.2175, 0.2089], dtype=torch.get_default_dtype()
+        ).unsqueeze(-1),
+        rtol=1e-2,
+    )
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])