diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 43faac2..c968d1a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,6 +15,9 @@ # # You should have received a copy of the GNU Lesser General Public License # along with tad-multicharge. If not, see . +ci: + skip: [mypy] + repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.5.0 @@ -48,14 +51,21 @@ repos: args: [--py37-plus, --keep-runtime-typing] - repo: https://github.com/pycqa/isort - rev: 5.13.0 + rev: 5.13.2 hooks: - id: isort name: isort (python) args: ["--profile", "black", "--filter-files"] - repo: https://github.com/psf/black - rev: 23.11.0 + rev: 23.12.0 hooks: - id: black stages: [commit] + + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.7.1 + hooks: + - id: mypy + additional_dependencies: [types-all] + exclude: 'test/conftest.py' diff --git a/docs/modules/index.rst b/docs/modules/index.rst index b9794ab..6eb7454 100644 --- a/docs/modules/index.rst +++ b/docs/modules/index.rst @@ -11,3 +11,4 @@ The following modules are contained with `tad_multicharge`. defaults eeq model + typing/index diff --git a/docs/modules/typing/builtin.rst b/docs/modules/typing/builtin.rst new file mode 100644 index 0000000..65b4e97 --- /dev/null +++ b/docs/modules/typing/builtin.rst @@ -0,0 +1,2 @@ +.. automodule:: tad_multicharge.typing.builtin + :members: diff --git a/docs/modules/typing/index.rst b/docs/modules/typing/index.rst new file mode 100644 index 0000000..125237b --- /dev/null +++ b/docs/modules/typing/index.rst @@ -0,0 +1,8 @@ +.. _typing: + +.. automodule:: tad_multicharge.typing + +.. toctree:: + + builtin + pytorch diff --git a/docs/modules/typing/pytorch.rst b/docs/modules/typing/pytorch.rst new file mode 100644 index 0000000..69c4fea --- /dev/null +++ b/docs/modules/typing/pytorch.rst @@ -0,0 +1,2 @@ +.. automodule:: tad_multicharge.typing.pytorch + :members: diff --git a/pyproject.toml b/pyproject.toml index c93e8ef..e69e355 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,10 @@ disallow_untyped_defs = true warn_redundant_casts = true warn_unreachable = true warn_unused_ignores = true +exclude = ''' + (?x) + ^test?s/conftest.py$ +''' [tool.coverage.run] diff --git a/src/tad_multicharge/eeq.py b/src/tad_multicharge/eeq.py index d460098..a4e19ef 100644 --- a/src/tad_multicharge/eeq.py +++ b/src/tad_multicharge/eeq.py @@ -54,11 +54,11 @@ from tad_mctc import storch from tad_mctc.batch import real_atoms, real_pairs from tad_mctc.ncoord import cn_eeq, erf_count -from tad_mctc.typing import DD, Any, CountingFunction, Tensor from . import defaults from .model import ChargeModel from .param import eeq2019 +from .typing import DD, Any, CountingFunction, Tensor, get_default_dtype __all__ = ["EEQModel", "solve", "get_charges"] @@ -93,10 +93,10 @@ def param2019( EEQModel Instance of the EEQ charge model class. """ - - dd: dict = {"device": device} - if dtype is not None: - dd["dtype"] = dtype + dd: DD = { + "device": device, + "dtype": dtype if dtype is not None else get_default_dtype(), + } return cls( eeq2019.chi.to(**dd), diff --git a/src/tad_multicharge/model.py b/src/tad_multicharge/model.py index 9885e60..1f405bd 100644 --- a/src/tad_multicharge/model.py +++ b/src/tad_multicharge/model.py @@ -25,7 +25,8 @@ from __future__ import annotations import torch -from tad_mctc.typing import Tensor, TensorLike + +from .typing import Tensor, TensorLike __all__ = ["ChargeModel"] diff --git a/src/tad_multicharge/typing/__init__.py b/src/tad_multicharge/typing/__init__.py new file mode 100644 index 0000000..6f88af4 --- /dev/null +++ b/src/tad_multicharge/typing/__init__.py @@ -0,0 +1,25 @@ +# This file is part of tad-multicharge. +# +# SPDX-Identifier: LGPL-3.0 +# Copyright (C) 2023 Marvin Friede +# +# tad-multicharge is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# tad-multicharge is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with tad-multicharge. If not, see . +""" +Type annotations +================ + +All type annotations for this project. +""" +from .builtin import * +from .pytorch import * diff --git a/src/tad_multicharge/typing/builtin.py b/src/tad_multicharge/typing/builtin.py new file mode 100644 index 0000000..9fa4df5 --- /dev/null +++ b/src/tad_multicharge/typing/builtin.py @@ -0,0 +1,27 @@ +# This file is part of tad-multicharge. +# +# SPDX-Identifier: LGPL-3.0 +# Copyright (C) 2023 Marvin Friede +# +# tad-multicharge is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# tad-multicharge is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with tad-multicharge. If not, see . +""" +Type annotations: Built-ins +=========================== + +Built-in type annotations are imported from the *tad-mctc* library, which +handles some version checking. +""" +from tad_mctc.typing import Any, Callable, TypedDict + +__all__ = ["Any", "Callable", "TypedDict"] diff --git a/src/tad_multicharge/typing/pytorch.py b/src/tad_multicharge/typing/pytorch.py new file mode 100644 index 0000000..80b4cfe --- /dev/null +++ b/src/tad_multicharge/typing/pytorch.py @@ -0,0 +1,42 @@ +# This file is part of tad-multicharge. +# +# SPDX-Identifier: LGPL-3.0 +# Copyright (C) 2023 Marvin Friede +# +# tad-multicharge is free software: you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# tad-multicharge is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with tad-multicharge. If not, see . +""" +Type annotations: PyTorch +========================= + +PyTorch-related type annotations for this project. +""" +from tad_mctc.typing import ( + DD, + CountingFunction, + Molecule, + Tensor, + TensorLike, + get_default_device, + get_default_dtype, +) + +__all__ = [ + "DD", + "CountingFunction", + "Molecule", + "Tensor", + "TensorLike", + "get_default_device", + "get_default_dtype", +] diff --git a/test/conftest.py b/test/conftest.py index 67400aa..693ac3f 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -147,7 +147,7 @@ def pytest_configure(config: pytest.Config) -> None: if torch.__version__ < (2, 0, 0): # type: ignore torch.set_default_tensor_type("torch.cuda.FloatTensor") # type: ignore else: - torch.set_default_device(DEVICE) # type: ignore + torch.set_default_device(DEVICE) # type: ignore[attr-defined] else: torch.use_deterministic_algorithms(True) DEVICE = None diff --git a/test/test_charge/samples.py b/test/test_charge/samples.py index 873facc..dbd41ec 100644 --- a/test/test_charge/samples.py +++ b/test/test_charge/samples.py @@ -22,7 +22,8 @@ import torch from tad_mctc.data.molecules import merge_nested_dicts, mols -from tad_mctc.typing import Molecule, Tensor, TypedDict + +from tad_multicharge.typing import Molecule, Tensor, TypedDict class Refs(TypedDict): @@ -43,133 +44,143 @@ class Record(Molecule, Refs): refs: dict[str, Refs] = { - "NH3-dimer": { - "total_charge": torch.tensor(0.0), - "q": torch.tensor( - [ - -0.8347351804, - -0.8347351804, - +0.2730523336, - +0.2886305132, - +0.2730523336, - +0.2730523336, - +0.2886305132, - +0.2730523336, - ], - ), - "energy": torch.tensor( - [ - -0.5832575193, - -0.5832575193, - +0.1621643199, - +0.1714161174, - +0.1621643199, - +0.1621643199, - +0.1714161174, - +0.1621643199, - ], - ), - }, - "NH3": { - "total_charge": torch.tensor(0.0), - "q": torch.tensor([0.0]), - "energy": torch.tensor([0.0]), - }, - "PbH4-BiH3": { - "total_charge": torch.tensor(0.0), - "q": torch.tensor( - [ - +0.1830965969, - -0.0434600885, - -0.0434600949, - -0.0434600949, - -0.0452680726, - +0.0727632554, - -0.0267371663, - -0.0267371688, - -0.0267371663, - ] - ), - "energy": torch.tensor( - [ - +0.1035379745, - -0.0258195114, - -0.0258195151, - -0.0258195151, - -0.0268938305, - +0.0422307903, - -0.0158831963, - -0.0158831978, - -0.0158831963, - ], - ), - }, - "C6H5I-CH3SH": { - "total_charge": torch.tensor(0.0), - "q": torch.tensor( - [ - -0.1029278713, - -0.1001905841, - -0.1028043772, - -0.0774975738, - -0.0007325498, - -0.0780660341, - -0.1962493355, - +0.1120891066, - +0.1205055899, - +0.1123282728, - +0.1197578368, - +0.1121635250, - -0.1711138357, - +0.1212508178, - -0.2031014175, - +0.1153482095, - +0.1143692362, - +0.1048709842, - ] - ), - "energy": torch.tensor( - [ - -0.0666956672, - -0.0649253132, - -0.0666156432, - -0.0501240988, - -0.0004746778, - -0.0504921903, - -0.1274747615, - +0.0665769222, - +0.0715759533, - +0.0667190716, - +0.0711318128, - +0.0666212167, - -0.1116992442, - +0.0720166288, - -0.1300663998, - +0.0685131245, - +0.0679318540, - +0.0622901437, - ], - ), - }, - "AmF3": { - "total_charge": torch.tensor(0.0), - "q": torch.tensor( - [ - +6.3523639599197046e-01, - -2.1174624588603730e-01, - -2.1174685327730036e-01, - -2.1174329682863302e-01, - ] - ), - "energy": torch.tensor( - [ - +1.8468940944911075e-01, - -1.5019058800412630e-01, - -1.5019103257755498e-01, - -1.5018850039426584e-01, - ], - ), - }, + "NH3-dimer": Refs( + { + "total_charge": torch.tensor(0.0), + "q": torch.tensor( + [ + -0.8347351804, + -0.8347351804, + +0.2730523336, + +0.2886305132, + +0.2730523336, + +0.2730523336, + +0.2886305132, + +0.2730523336, + ], + ), + "energy": torch.tensor( + [ + -0.5832575193, + -0.5832575193, + +0.1621643199, + +0.1714161174, + +0.1621643199, + +0.1621643199, + +0.1714161174, + +0.1621643199, + ], + ), + } + ), + "NH3": Refs( + { + "total_charge": torch.tensor(0.0), + "q": torch.tensor([0.0]), + "energy": torch.tensor([0.0]), + } + ), + "PbH4-BiH3": Refs( + { + "total_charge": torch.tensor(0.0), + "q": torch.tensor( + [ + +0.1830965969, + -0.0434600885, + -0.0434600949, + -0.0434600949, + -0.0452680726, + +0.0727632554, + -0.0267371663, + -0.0267371688, + -0.0267371663, + ] + ), + "energy": torch.tensor( + [ + +0.1035379745, + -0.0258195114, + -0.0258195151, + -0.0258195151, + -0.0268938305, + +0.0422307903, + -0.0158831963, + -0.0158831978, + -0.0158831963, + ], + ), + } + ), + "C6H5I-CH3SH": Refs( + { + "total_charge": torch.tensor(0.0), + "q": torch.tensor( + [ + -0.1029278713, + -0.1001905841, + -0.1028043772, + -0.0774975738, + -0.0007325498, + -0.0780660341, + -0.1962493355, + +0.1120891066, + +0.1205055899, + +0.1123282728, + +0.1197578368, + +0.1121635250, + -0.1711138357, + +0.1212508178, + -0.2031014175, + +0.1153482095, + +0.1143692362, + +0.1048709842, + ] + ), + "energy": torch.tensor( + [ + -0.0666956672, + -0.0649253132, + -0.0666156432, + -0.0501240988, + -0.0004746778, + -0.0504921903, + -0.1274747615, + +0.0665769222, + +0.0715759533, + +0.0667190716, + +0.0711318128, + +0.0666212167, + -0.1116992442, + +0.0720166288, + -0.1300663998, + +0.0685131245, + +0.0679318540, + +0.0622901437, + ], + ), + } + ), + "AmF3": Refs( + { + "total_charge": torch.tensor(0.0), + "q": torch.tensor( + [ + +6.3523639599197046e-01, + -2.1174624588603730e-01, + -2.1174685327730036e-01, + -2.1174329682863302e-01, + ] + ), + "energy": torch.tensor( + [ + +1.8468940944911075e-01, + -1.5019058800412630e-01, + -1.5019103257755498e-01, + -1.5018850039426584e-01, + ], + ), + } + ), } diff --git a/test/test_charge/test_charges.py b/test/test_charge/test_charges.py index fc770ec..8fde772 100644 --- a/test/test_charge/test_charges.py +++ b/test/test_charge/test_charges.py @@ -41,16 +41,16 @@ import torch from tad_mctc.batch import pack from tad_mctc.ncoord import cn_eeq -from tad_mctc.typing import DD from tad_multicharge import eeq +from tad_multicharge.typing import DD from ..conftest import DEVICE from .samples import samples @pytest.mark.parametrize("dtype", [torch.float, torch.double]) -def test_single(dtype: torch.dtype): +def test_single(dtype: torch.dtype) -> None: dd: DD = {"device": DEVICE, "dtype": dtype} tol = 1e-4 if dtype == torch.float32 else 1e-6 @@ -75,7 +75,7 @@ def test_single(dtype: torch.dtype): @pytest.mark.parametrize("dtype", [torch.float, torch.double]) @pytest.mark.parametrize("name", ["AmF3"]) -def test_single_with_cn(dtype: torch.dtype, name: str): +def test_single_with_cn(dtype: torch.dtype, name: str) -> None: dd: DD = {"device": DEVICE, "dtype": dtype} tol = 1e-4 if dtype == torch.float32 else 1e-6 @@ -99,7 +99,7 @@ def test_single_with_cn(dtype: torch.dtype, name: str): @pytest.mark.parametrize("dtype", [torch.float, torch.double]) -def test_ghost(dtype: torch.dtype): +def test_ghost(dtype: torch.dtype) -> None: dd: DD = {"device": DEVICE, "dtype": dtype} tol = 1e-4 if dtype == torch.float32 else 1e-6 @@ -148,7 +148,7 @@ def test_ghost(dtype: torch.dtype): @pytest.mark.parametrize("dtype", [torch.float, torch.double]) -def test_batch(dtype: torch.dtype): +def test_batch(dtype: torch.dtype) -> None: dd: DD = {"device": DEVICE, "dtype": dtype} tol = 1e-4 if dtype == torch.float32 else 1e-6 diff --git a/test/test_charge/test_general.py b/test/test_charge/test_general.py index 996fcf6..c87c34e 100644 --- a/test/test_charge/test_general.py +++ b/test/test_charge/test_general.py @@ -69,7 +69,7 @@ def test_change_device_fail() -> None: # trying to use setter with pytest.raises(AttributeError): - model.device = "cpu" + model.device = torch.device("cpu") def test_init_dtype_fail() -> None: diff --git a/test/test_grad/samples_dedr.py b/test/test_grad/samples_dedr.py index 63c653c..1c18edb 100644 --- a/test/test_grad/samples_dedr.py +++ b/test/test_grad/samples_dedr.py @@ -22,7 +22,8 @@ import torch from tad_mctc.data.molecules import merge_nested_dicts, mols -from tad_mctc.typing import Molecule, Tensor, TypedDict + +from tad_multicharge.typing import Molecule, Tensor, TypedDict class Refs(TypedDict): @@ -37,1207 +38,1225 @@ class Record(Molecule, Refs): refs: dict[str, Refs] = { - "LiH": { - "grad": torch.tensor( - [ - [ - +0.0000000000000000e00, - +0.0000000000000000e00, - -1.2302277945431074e-02, - ], - [ - +0.0000000000000000e00, - +0.0000000000000000e00, - +1.2302277945431074e-02, - ], - ], - dtype=torch.double, - ), - }, - "SiH4": { - "grad": torch.tensor( - [ - [ - -9.0537536095539402e-19, - -8.9711075080175710e-19, - -8.9711075080175710e-19, - ], - [ - +2.5699119958478493e-04, - +2.5699119958478493e-04, - -2.5699119958478488e-04, - ], - [ - -2.5699119958478412e-04, - -2.5699119958478412e-04, - -2.5699119958478412e-04, - ], - [ - +2.5699119958478493e-04, - -2.5699119958478488e-04, - +2.5699119958478493e-04, - ], - [ - -2.5699119958478488e-04, - +2.5699119958478493e-04, - +2.5699119958478493e-04, - ], - ], - dtype=torch.double, - ), - }, - "AmF3": { - "grad": torch.tensor( - [ - [ - -7.4021411463984915e-06, - +3.8177976052419793e-07, - +9.3536773201791656e-03, - ], - [ - -5.6525568841740773e-03, - -1.5287470820034010e-03, - -3.1230771573808585e-03, - ], - [ - +1.5072007235716486e-03, - +5.6622432730357572e-03, - -3.1153927236688456e-03, - ], - [ - +4.1527583017488285e-03, - -4.1338779707928803e-03, - -3.1152074391294620e-03, - ], - ], - dtype=torch.double, - ), - }, - "PbH4-BiH3": { - "grad": torch.tensor( - [ - [ - +2.7391361297140784e-09, - +3.2488573299127035e-05, - +6.2870178347078794e-20, - ], - [ - -5.5883738115394956e-05, - +1.7063470783804067e-05, - -7.7368226740032970e-21, - ], - [ - +2.7940509984088580e-05, - +1.7062534189631494e-05, - +4.8394450812532972e-05, - ], - [ - +2.7940509984088596e-05, - +1.7062534189631542e-05, - -4.8394450812533006e-05, - ], - [ - -2.1754844657862412e-11, - -9.3095225586217040e-05, - -3.5238321304723560e-21, - ], - [ - +6.0074327187720391e-10, - -6.4103751766856563e-05, - -4.3032485808259733e-21, - ], - [ - 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"Ag2Cl22-": Refs( + { + "grad": torch.tensor( + [], + dtype=torch.double, + ), + } + ), + "ZnOOH-": Refs( + { + "grad": torch.tensor( + [], + dtype=torch.double, + ), + } + ), } diff --git a/test/test_grad/samples_dqdr.py b/test/test_grad/samples_dqdr.py index 5fdeae8..9d18c7f 100644 --- a/test/test_grad/samples_dqdr.py +++ b/test/test_grad/samples_dqdr.py @@ -22,7 +22,8 @@ import torch from tad_mctc.data.molecules import merge_nested_dicts, mols -from tad_mctc.typing import Molecule, Tensor, TypedDict + +from tad_multicharge.typing import Molecule, Tensor, TypedDict class Refs(TypedDict): @@ -37,2138 +38,2156 @@ class Record(Molecule, Refs): refs: dict[str, Refs] = { - "LiH": { - "grad": torch.tensor( - [ - -0.0000000000000000e00, - -0.0000000000000000e00, - +5.2509296729341112e-02, - -0.0000000000000000e00, - -0.0000000000000000e00, - -5.2509296729341112e-02, - -0.0000000000000000e00, - -0.0000000000000000e00, - -5.2509296729341112e-02, - -0.0000000000000000e00, - 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positions.requires_grad_(True) total_charge.requires_grad_(True) - def func(pos: Tensor, tchrg: Tensor): + def func(pos: Tensor, tchrg: Tensor) -> Tensor: cn = cn_eeq(numbers, positions) return eeq.solve(numbers, pos, tchrg, eeq_model, cn)[0] diff --git a/test/test_grad/test_dedr.py b/test/test_grad/test_dedr.py index 01d0ba1..3feb79f 100644 --- a/test/test_grad/test_dedr.py +++ b/test/test_grad/test_dedr.py @@ -25,9 +25,9 @@ from tad_mctc.autograd import dgradcheck, dgradgradcheck, jac from tad_mctc.batch import pack from tad_mctc.convert import tensor_to_numpy -from tad_mctc.typing import DD, Callable, Tensor from tad_multicharge import eeq +from tad_multicharge.typing import DD, Callable, Tensor from ..conftest import DEVICE, FAST_MODE from .samples_dedr import samples @@ -270,7 +270,7 @@ def run_jacobian(dtype: torch.dtype, name: str, atol: float) -> None: positions.requires_grad_(True) fjac = jac(eeq.get_energy, argnums=1) - jacobian: Tensor = fjac(numbers, positions, charge) # type: ignore + jacobian: Tensor = fjac(numbers, positions, charge) positions.detach_() jac_np = tensor_to_numpy(jacobian) diff --git a/test/test_grad/test_dqdr.py b/test/test_grad/test_dqdr.py index bf8ec75..07a9456 100644 --- a/test/test_grad/test_dqdr.py +++ b/test/test_grad/test_dqdr.py @@ -25,9 +25,9 @@ from tad_mctc.autograd import dgradcheck, dgradgradcheck, jac from tad_mctc.batch import pack from tad_mctc.convert import reshape_fortran, tensor_to_numpy -from tad_mctc.typing import DD, Callable, Tensor from tad_multicharge import eeq +from tad_multicharge.typing import DD, Callable, Tensor from ..conftest import DEVICE, FAST_MODE from .samples_dqdr import samples @@ -179,7 +179,7 @@ def test_jacobian(dtype: torch.dtype, name: str) -> None: positions.requires_grad_(True) fjac = jac(eeq.get_charges, argnums=1) - jacobian: Tensor = fjac(numbers, positions, charge) # type: ignore + jacobian: Tensor = fjac(numbers, positions, charge) positions.detach_() jac_np = tensor_to_numpy(jacobian) diff --git a/test/test_grad/test_param.py b/test/test_grad/test_param.py index 0cb1e18..eb4fc98 100644 --- a/test/test_grad/test_param.py +++ b/test/test_grad/test_param.py @@ -26,9 +26,9 @@ from tad_mctc.batch import pack from tad_mctc.data.molecules import mols as samples from tad_mctc.ncoord import cn_eeq -from tad_mctc.typing import DD, Callable, Tensor from tad_multicharge import eeq +from tad_multicharge.typing import DD, Callable, Tensor from ..conftest import DEVICE, FAST_MODE