diff --git a/README.md b/README.md index cc0c3b3a..27848f4b 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ machine learning interatomic potentials aiida plugin - NVE - NVT (Langevin(Eijnden/Ciccotti flavour) and Nosé-Hoover (Melchionna flavour)) - NPT (Nosé-Hoover (Melchiona flavour)) -- [ ] Training ML potentials (MACE only planned) +- [x] Training ML potentials (MACE only planned) - [ ] Fine tunning MLIPs (MACE only planned) The code relies heavily on [janus-core](https://github.com/stfc/janus-core), which handles mlip calculations using ASE. @@ -45,6 +45,7 @@ Registered entry points for aiida.calculations: * janus.opt * janus.sp * janus.md +* janus.train ``` @@ -60,6 +61,11 @@ verdi run submit_md.py "janus@localhost" --struct "path/to/structure" --model "p verdi process list -a # check record of calculation ``` +Models can be trained by using the Train calcjob. In that case the needed inputs are a config file containig the path to train, test and validation xyz file and other optional parameters. Running +```shell +verdi run submit_train.py +``` +a model will be trained using the provided example config file and xyz files (can be found in the tests folder) ## Development @@ -97,6 +103,7 @@ See the [developer guide](https://stfc.github.io/aiida-mlip/developer_guide/inde * [`sp_parser.py`](aiida_mlip/parsers/sp_parser.py): `Parser` for `Singlepoint` calculation. * [`opt_parser.py`](aiida_mlip/parsers/opt_parser.py): `Parser` for `Geomopt` calculation. * [`md_parser.py`](aiida_mlip/parsers/md_parser.py): `Parser` for `MD` calculation. + * [`train_parser.py`](aiida_mlip/parsers/train_parser.py): `Parser` for `Train` calculation. * [`helpers/`](aiida_mlip/helpers/): `Helpers` to run calculations. * [`docs/`](docs/source/): Code documentation * [`apidoc/`](docs/source/apidoc/): API documentation @@ -108,14 +115,17 @@ See the [developer guide](https://stfc.github.io/aiida-mlip/developer_guide/inde * [`submit_singlepoint.py`](examples/calculations/submit_singlepoint.py): Script for submitting a singlepoint calculation * [`submit_geomopt.py`](examples/calculations/submit_geomopt.py): Script for submitting a geometry optimisation calculation * [`submit_md.py`](examples/calculations/submit_md.py): Script for submitting a molecular dynamics calculation + * [`submit_train.py`](examples/calculations/submit_train.py): Script for submitting a train calculation. * [`tests/`](tests/): Basic regression tests using the [pytest](https://docs.pytest.org/en/latest/) framework (submitting a calculation, ...). Install `pip install -e .[testing]` and run `pytest`. * [`conftest.py`](tests/conftest.py): Configuration of fixtures for [pytest](https://docs.pytest.org/en/latest/) * [`calculations/`](tests/calculations): Calculations * [`test_singlepoint.py`](tests/calculations/test_singlepoint.py): Test `SinglePoint` calculation * [`test_geomopt.py`](tests/calculations/test_geomopt.py): Test `Geomopt` calculation * [`test_md.py`](tests/calculations/test_md.py): Test `MD` calculation + * [`test_train.py`](tests/calculations/test_train.py): Test `Train` calculation * [`data/`](tests/data): `ModelData` * [`test_model.py`](tests/data/test_model.py): Test `ModelData` type + * [`test_config.py`](tests/data/test_config.py): Test `JanusConfigfile` type * [`.gitignore`](.gitignore): Telling git which files to ignore * [`.pre-commit-config.yaml`](.pre-commit-config.yaml): Configuration of [pre-commit hooks](https://pre-commit.com/) that sanitize coding style and check for syntax errors. Enable via `pip install -e .[pre-commit] && pre-commit install` * [`LICENSE`](LICENSE): License for the plugin diff --git a/aiida_mlip/calculations/train.py b/aiida_mlip/calculations/train.py new file mode 100644 index 00000000..f8ce249e --- /dev/null +++ b/aiida_mlip/calculations/train.py @@ -0,0 +1,190 @@ +"""Class for training machine learning models.""" + +from pathlib import Path + +from aiida.common import InputValidationError, datastructures +import aiida.common.folders +from aiida.engine import CalcJob, CalcJobProcessSpec +import aiida.engine.processes +from aiida.orm import Dict, FolderData, SinglefileData + +from aiida_mlip.data.config import JanusConfigfile +from aiida_mlip.data.model import ModelData + + +def validate_inputs( + inputs: dict, port_namespace: aiida.engine.processes.ports.PortNamespace +): + """ + Check if the inputs are valid. + + Parameters + ---------- + inputs : dict + The inputs dictionary. + + port_namespace : `aiida.engine.processes.ports.PortNamespace` + An instance of aiida's `PortNameSpace`. + + Raises + ------ + InputValidationError + Error message if validation fails, None otherwise. + """ + if "mlip_config" in port_namespace: + # Check if a config file is given + if "mlip_config" not in inputs: + raise InputValidationError("No config file given") + config_file = inputs["mlip_config"] + # Check if 'name' keyword is given + if "name" not in config_file: + raise InputValidationError("key 'name' must be defined in the config file") + # Check if the xyz files paths are given + required_keys = ("train_file", "valid_file", "test_file") + for key in required_keys: + if key not in config_file: + raise InputValidationError(f"Mandatory key {key} not in config file") + # Check if the keys actually correspond to a path + if not ((Path(config_file.as_dictionary[key])).resolve()).exists(): + raise InputValidationError(f"Path given for {key} does not exist") + + +class Train(CalcJob): # numpydoc ignore=PR01 + """ + Calcjob implementation to train mlips. + + Attributes + ---------- + DEFAULT_OUTPUT_FILE : str + Default stdout file name. + + Methods + ------- + define(spec: CalcJobProcessSpec) -> None: + Define the process specification, its inputs, outputs and exit codes. + validate_inputs(value: dict, port_namespace: PortNamespace) -> Optional[str]: + Check if the inputs are valid. + prepare_for_submission(folder: Folder) -> CalcInfo: + Create the input files for the `CalcJob`. + """ + + DEFAULT_OUTPUT_FILE = "aiida-stdout.txt" + + @classmethod + def define(cls, spec: CalcJobProcessSpec) -> None: + """ + Define the process specification, its inputs, outputs and exit codes. + + Parameters + ---------- + spec : `aiida.engine.CalcJobProcessSpec` + The calculation job process spec to define. + """ + super().define(spec) + + # Define inputs + spec.input( + "mlip_config", + valid_type=JanusConfigfile, + required=True, + help="Config file with parameters for training", + ) + spec.input( + "metadata.options.output_filename", + valid_type=str, + default=cls.DEFAULT_OUTPUT_FILE, + ) + + spec.input( + "metadata.options.scheduler_stdout", + valid_type=str, + default="_scheduler-stdout.txt", + help="Filename to which the content of stdout of the scheduler is written.", + ) + spec.inputs["metadata"]["options"]["parser_name"].default = "janus.train_parser" + spec.inputs.validator = validate_inputs + spec.output("model", valid_type=ModelData) + spec.output("compiled_model", valid_type=SinglefileData) + spec.output( + "results_dict", + valid_type=Dict, + help="The `results_dict` output node of the training.", + ) + spec.output("logs", valid_type=FolderData) + spec.output("checkpoints", valid_type=FolderData) + spec.default_output_node = "results_dict" + # Exit codes + spec.exit_code( + 305, + "ERROR_MISSING_OUTPUT_FILES", + message="Some output files missing or cannot be read", + ) + + # pylint: disable=too-many-locals + def prepare_for_submission( + self, folder: aiida.common.folders.Folder + ) -> datastructures.CalcInfo: + """ + Create the input files for the `Calcjob`. + + Parameters + ---------- + folder : aiida.common.folders.Folder + An `aiida.common.folders.Folder` to temporarily write files on disk. + + Returns + ------- + aiida.common.datastructures.CalcInfo + An instance of `aiida.common.datastructures.CalcInfo`. + """ + # The config file needs to be copied in the working folder + # Read content + mlip_dict = self.inputs.mlip_config.as_dictionary + config_parse = self.inputs.mlip_config.get_content() + + # Extract paths from the config + for file in ("train_file", "test_file", "valid_file"): + abs_path = Path(mlip_dict[file]).resolve() + + # Update the config file with absolute paths + config_parse = config_parse.replace(mlip_dict[file], str(abs_path)) + # Copy config file content inside the folder where the calculation is run + config_copy = "mlip_train.yml" + with folder.open(config_copy, "w", encoding="utf-8") as configfile: + configfile.write(config_parse) + + codeinfo = datastructures.CodeInfo() + + # Initialize cmdline_params with train command + codeinfo.cmdline_params = ["train"] + # Create the rest of the command line + cmd_line = {} + cmd_line["mlip-config"] = config_copy + # Add cmd line params to codeinfo + for flag, value in cmd_line.items(): + codeinfo.cmdline_params += [f"--{flag}", str(value)] + + # Node where the code is saved + codeinfo.code_uuid = self.inputs.code.uuid + # Save name of output as you need it for running the code + codeinfo.stdout_name = self.metadata.options.output_filename + + calcinfo = datastructures.CalcInfo() + calcinfo.codes_info = [codeinfo] + # Save the info about the node where the calc is stored + calcinfo.uuid = str(self.uuid) + # Retrieve output files + model_dir = Path(mlip_dict.get("model_dir", ".")) + model_output = model_dir / f"{mlip_dict['name']}.model" + compiled_model_output = model_dir / f"{mlip_dict['name']}_compiled.model" + calcinfo.retrieve_list = [ + self.metadata.options.output_filename, + self.uuid, + mlip_dict.get("log_dir", "logs"), + mlip_dict.get("result_dir", "results"), + mlip_dict.get("checkpoint_dir", "checkpoints"), + str(model_output), + str(compiled_model_output), + ] + + return calcinfo diff --git a/aiida_mlip/parsers/base_parser.py b/aiida_mlip/parsers/base_parser.py index 6ccbe716..8b48eab8 100644 --- a/aiida_mlip/parsers/base_parser.py +++ b/aiida_mlip/parsers/base_parser.py @@ -20,7 +20,7 @@ class BaseParser(Parser): Methods ------- __init__(node: aiida.orm.nodes.process.process.ProcessNode) - Initialize the SPParser instance. + Initialize the BaseParser instance. parse(**kwargs: Any) -> int: Parse outputs, store results in the database. @@ -33,12 +33,12 @@ class BaseParser(Parser): Raises ------ exceptions.ParsingError - If the ProcessNode being passed was not produced by a singlePointCalculation. + If the ProcessNode being passed was not produced by a `Base` Calcjob. """ def __init__(self, node: ProcessNode): """ - Check that the ProcessNode being passed was produced by a `Singlepoint`. + Check that the ProcessNode being passed was produced by a `Base` Calcjob. Parameters ---------- diff --git a/aiida_mlip/parsers/train_parser.py b/aiida_mlip/parsers/train_parser.py new file mode 100644 index 00000000..d12d2662 --- /dev/null +++ b/aiida_mlip/parsers/train_parser.py @@ -0,0 +1,211 @@ +""" +Parser for mlip train. +""" + +import json +from pathlib import Path +from typing import Any + +from aiida.engine import ExitCode +from aiida.orm import Dict, FolderData +from aiida.orm.nodes.process.process import ProcessNode +from aiida.parsers.parser import Parser + +from aiida_mlip.data.model import ModelData + + +class TrainParser(Parser): + """ + Parser class for parsing output of calculation. + + Parameters + ---------- + node : aiida.orm.nodes.process.process.ProcessNode + ProcessNode of calculation. + + Methods + ------- + __init__(node: aiida.orm.nodes.process.process.ProcessNode) + Initialize the TrainParser instance. + + parse(**kwargs: Any) -> int: + Parse outputs, store results in the database. + + _get_remote_dirs(mlip_dict: [str, Any]) -> [str, Path]: + Get the remote directories based on mlip config file. + + _validate_retrieved_files(output_filename: str, model_name: str) -> bool: + Validate that the expected files have been retrieved. + + _save_models(model_output: Path, compiled_model_output: Path) -> None: + Save model and compiled model as outputs. + + _parse_results(result_name: Path) -> None: + Parse the results file and store the results dictionary. + + _save_folders(remote_dirs: [str, Path]) -> None: + Save log and checkpoint folders as outputs. + + Returns + ------- + int + An exit code. + + Raises + ------ + exceptions.ParsingError + If the ProcessNode being passed was not produced by a `Train` Calcjob. + """ + + def __init__(self, node: ProcessNode): + """ + Initialize the TrainParser instance. + + Parameters + ---------- + node : aiida.orm.nodes.process.process.ProcessNode + ProcessNode of calculation. + """ + super().__init__(node) + + def parse(self, **kwargs: Any) -> int: + """ + Parse outputs and store results in the database. + + Parameters + ---------- + **kwargs : Any + Any keyword arguments. + + Returns + ------- + int + An exit code. + """ + mlip_dict = self.node.inputs.mlip_config.as_dictionary + output_filename = self.node.get_option("output_filename") + remote_dirs = self._get_remote_dirs(mlip_dict) + + model_output = remote_dirs["model"] / f"{mlip_dict['name']}.model" + compiled_model_output = ( + remote_dirs["model"] / f"{mlip_dict['name']}_compiled.model" + ) + result_name = remote_dirs["results"] / f"{mlip_dict['name']}_run-123_train.txt" + + if not self._validate_retrieved_files(output_filename, mlip_dict["name"]): + return self.exit_codes.ERROR_MISSING_OUTPUT_FILES + + self._save_models(model_output, compiled_model_output) + self._parse_results(result_name) + self._save_folders(remote_dirs) + + return ExitCode(0) + + def _get_remote_dirs(self, mlip_dict: dict) -> dict: + """ + Get the remote directories based on mlip config file. + + Parameters + ---------- + mlip_dict : dict + Dictionary containing mlip config file. + + Returns + ------- + dict + Dictionary of remote directories. + """ + rem_dir = Path(self.node.get_remote_workdir()) + return { + typ: rem_dir / mlip_dict.get(f"{typ}_dir", default) + for typ, default in ( + ("log", "logs"), + ("checkpoint", "checkpoints"), + ("results", "results"), + ("model", ""), + ) + } + + def _validate_retrieved_files(self, output_filename: str, model_name: str) -> bool: + """ + Validate that the expected files have been retrieved. + + Parameters + ---------- + output_filename : str + The expected output filename. + model_name : str + The name of the model as found in the config file key `name`. + + Returns + ------- + bool + True if the expected files are retrieved, False otherwise. + """ + files_retrieved = self.retrieved.list_object_names() + files_expected = {output_filename, f"{model_name}.model"} + + if not files_expected.issubset(files_retrieved): + self.logger.error( + f"Found files '{files_retrieved}', expected to find '{files_expected}'" + ) + return False + return True + + def _save_models(self, model_output: Path, compiled_model_output: Path) -> None: + """ + Save model and compiled model as outputs. + + Parameters + ---------- + model_output : Path + Path to the model output file. + compiled_model_output : Path + Path to the compiled model output file. + """ + architecture = "mace_mp" + model = ModelData.local_file(model_output, architecture=architecture) + compiled_model = ModelData.local_file( + compiled_model_output, architecture=architecture + ) + + self.out("model", model) + self.out("compiled_model", compiled_model) + + def _parse_results(self, result_name: Path) -> None: + """ + Parse the results file and store the results dictionary. + + Parameters + ---------- + result_name : Path + Path to the result file. + """ + with open(result_name, encoding="utf-8") as file: + last_dict_str = None + for line in file: + try: + last_dict_str = json.loads(line.strip()) + except json.JSONDecodeError: + continue + + if last_dict_str is not None: + results_node = Dict(last_dict_str) + self.out("results_dict", results_node) + else: + raise ValueError("No valid dictionary in the file") + + def _save_folders(self, remote_dirs: dict) -> None: + """ + Save log and checkpoint folders as outputs. + + Parameters + ---------- + remote_dirs : dict + Dictionary of remote folders. + """ + log_node = FolderData(tree=remote_dirs["log"]) + self.out("logs", log_node) + + checkpoint_node = FolderData(tree=remote_dirs["checkpoint"]) + self.out("checkpoints", checkpoint_node) diff --git a/docs/source/apidoc/aiida_mlip.calculations.rst b/docs/source/apidoc/aiida_mlip.calculations.rst index afa5e235..cda5f940 100644 --- a/docs/source/apidoc/aiida_mlip.calculations.rst +++ b/docs/source/apidoc/aiida_mlip.calculations.rst @@ -44,6 +44,16 @@ aiida\_mlip.calculations.singlepoint module :undoc-members: :show-inheritance: +aiida\_mlip.calculations.train module +------------------------------------- + +.. automodule:: aiida_mlip.calculations.train + :members: + :special-members: + :private-members: + :undoc-members: + :show-inheritance: + Module contents --------------- diff --git a/docs/source/apidoc/aiida_mlip.parsers.rst b/docs/source/apidoc/aiida_mlip.parsers.rst index 9a055908..d4b81ba9 100644 --- a/docs/source/apidoc/aiida_mlip.parsers.rst +++ b/docs/source/apidoc/aiida_mlip.parsers.rst @@ -44,6 +44,16 @@ aiida\_mlip.parsers.sp\_parser module :undoc-members: :show-inheritance: +aiida\_mlip.parsers.train\_parser module +---------------------------------------- + +.. automodule:: aiida_mlip.parsers.train_parser + :members: + :special-members: + :private-members: + :undoc-members: + :show-inheritance: + Module contents --------------- diff --git a/docs/source/user_guide/index.rst b/docs/source/user_guide/index.rst index fa3680f9..3a56c240 100644 --- a/docs/source/user_guide/index.rst +++ b/docs/source/user_guide/index.rst @@ -9,3 +9,4 @@ User guide tutorial data calculations + training diff --git a/docs/source/user_guide/training.rst b/docs/source/user_guide/training.rst new file mode 100644 index 00000000..345647a7 --- /dev/null +++ b/docs/source/user_guide/training.rst @@ -0,0 +1,87 @@ +================================ +Training machine learning models +================================ + +The `Train` class represents a `CalcJob` object within the AiiDA framework, designed for training machine learning models. + +Usage +^^^^^ + +This calculation can be executed using either the `run` or `submit` AiiDA commands. +Below is a usage example with some additional training parameters. These parameters must be AiiDA data types. + +.. code-block:: python + + TrainCalculation = CalculationFactory("janus.train") + submit(TrainCalculation, code=InstalledCode, mlip_config=JanusConfigfile, metadata=Dict({'options': {'output_filename': 'aiida-stdout.txt'}})) + + +The parameters are provided in a config file. Tha mandatory parameters are: + +.. code-block:: yaml + + name: 'test' + train_file: "./tests/calculations/structures/mlip_train.xyz" + valid_file: "./tests/calculations/structures/mlip_valid.xyz" + test_file: "./tests/calculations/structures/mlip_test.xyz" + +while the other parameters are optional. Here is an example (can be found in the tests folder) of a config file with more parameters: + +.. code-block:: yaml + + name: 'test' + train_file: "./tests/calculations/structures/mlip_train.xyz" + valid_file: "./tests/calculations/structures/mlip_valid.xyz" + test_file: "./tests/calculations/structures/mlip_test.xyz" + # Optional parameters: + model: ScaleShiftMACE + loss: 'universal' + energy_weight: 1 + forces_weight: 10 + stress_weight: 100 + compute_stress: True + energy_key: 'dft_energy' + forces_key: 'dft_forces' + stress_key: 'dft_stress' + eval_interval: 2 + error_table: PerAtomRMSE + # main model params + interaction_first: "RealAgnosticResidualInteractionBlock" + interaction: "RealAgnosticResidualInteractionBlock" + num_interactions: 2 + correlation: 3 + max_ell: 3 + r_max: 4.0 + max_L: 0 + num_channels: 16 + num_radial_basis: 6 + MLP_irreps: '16x0e' + # end model params + scaling: 'rms_forces_scaling' + lr: 0.005 + weight_decay: 1e-8 + ema: True + ema_decay: 0.995 + scheduler_patience: 5 + batch_size: 4 + valid_batch_size: 4 + max_num_epochs: 1 + patience: 50 + amsgrad: True + default_dtype: float32 + device: cpu + distributed: False + clip_grad: 100 + keep_checkpoints: False + keep_isolated_atoms: True + save_cpu: True + +Submission +^^^^^^^^^^ + +To facilitate the submission process and prepare inputs as AiiDA data types, an example script is provided. +This script can be used as is or by changing, in the file, the path to the config file, then submitted to `verdi` as shown + +.. code-block:: python + + verdi run submit_train.py diff --git a/examples/calculations/submit_train.py b/examples/calculations/submit_train.py new file mode 100644 index 00000000..28cbaf37 --- /dev/null +++ b/examples/calculations/submit_train.py @@ -0,0 +1,34 @@ +"""Example code for submitting training calculation""" + +from pathlib import Path + +from aiida.engine import run_get_node +from aiida.orm import load_code +from aiida.plugins import CalculationFactory + +from aiida_mlip.data.config import JanusConfigfile + +# Add the required inputs for aiida +metadata = {"options": {"resources": {"num_machines": 1}}} +code = load_code("janus@localhost") + +# All the other parameters we want them from the config file +# We want to pass it as a AiiDA data type for the provenance +mlip_config = JanusConfigfile( + Path("~/aiida-mlip/tests/calculations/configs/mlip_train.yml") + .expanduser() + .resolve() +) + +# Define calculation to run +trainCalculation = CalculationFactory("janus.train") + +# Run calculation +result, node = run_get_node( + trainCalculation, + code=code, + metadata=metadata, + mlip_config=mlip_config, +) +print(f"Printing results from calculation: {result}") +print(f"Printing node of calculation: {node}") diff --git a/pyproject.toml b/pyproject.toml index 4f61e3b4..f752e386 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,10 +30,11 @@ python = "^3.9" aiida-core = "^2.5" ase = "^3.22.1" voluptuous = "^0.14" +mace-torch = {git = "https://github.com/ACEsuit/mace.git", rev = "develop"} [tool.poetry.group.dev.dependencies] coverage = {extras = ["toml"], version = "^7.4.1"} -janus-core = "^0.2.0b4" +janus-core = "^v0.4.0b0" pgtest = "^1.3.2" pytest = "^8.0" pytest-cov = "^4.1.0" @@ -71,11 +72,13 @@ build-backend = "poetry.core.masonry.api" "janus.sp" = "aiida_mlip.calculations.singlepoint:Singlepoint" "janus.opt" = "aiida_mlip.calculations.geomopt:GeomOpt" "janus.md" = "aiida_mlip.calculations.md:MD" +"janus.train" = "aiida_mlip.calculations.train:Train" [tool.poetry.plugins."aiida.parsers"] "janus.sp_parser" = "aiida_mlip.parsers.sp_parser:SPParser" "janus.opt_parser" = "aiida_mlip.parsers.opt_parser:GeomOptParser" "janus.md_parser" = "aiida_mlip.parsers.md_parser:MDParser" +"janus.train_parser" = "aiida_mlip.parsers.train_parser:TrainParser" [tool.black] line-length = 88 diff --git a/tests/calculations/configs/mlip_train.yml b/tests/calculations/configs/mlip_train.yml new file mode 100644 index 00000000..f8f6a609 --- /dev/null +++ b/tests/calculations/configs/mlip_train.yml @@ -0,0 +1,46 @@ +name: 'test' +train_file: "./tests/calculations/structures/mlip_train.xyz" +valid_file: "./tests/calculations/structures/mlip_valid.xyz" +test_file: "./tests/calculations/structures/mlip_test.xyz" +# Optional parameters: +model: ScaleShiftMACE +loss: 'universal' +energy_weight: 1 +forces_weight: 10 +stress_weight: 100 +compute_stress: True +energy_key: 'dft_energy' +forces_key: 'dft_forces' +stress_key: 'dft_stress' +eval_interval: 2 +error_table: PerAtomRMSE +# main model params +interaction_first: "RealAgnosticResidualInteractionBlock" +interaction: "RealAgnosticResidualInteractionBlock" +num_interactions: 2 +correlation: 3 +max_ell: 3 +r_max: 4.0 +max_L: 0 +num_channels: 16 +num_radial_basis: 6 +MLP_irreps: '16x0e' +# end model params +scaling: 'rms_forces_scaling' +lr: 0.005 +weight_decay: 1e-8 +ema: True +ema_decay: 0.995 +scheduler_patience: 5 +batch_size: 4 +valid_batch_size: 4 +max_num_epochs: 1 +patience: 50 +amsgrad: True +default_dtype: float32 +device: cpu +distributed: False +clip_grad: 100 +keep_checkpoints: False +keep_isolated_atoms: True +save_cpu: True diff --git a/tests/calculations/configs/test.model b/tests/calculations/configs/test.model new file mode 100644 index 00000000..31e82942 Binary files /dev/null and b/tests/calculations/configs/test.model differ diff --git a/tests/calculations/configs/test_compiled.model b/tests/calculations/configs/test_compiled.model new file mode 100644 index 00000000..43452708 Binary files /dev/null and b/tests/calculations/configs/test_compiled.model differ diff --git a/tests/calculations/structures/mlip_test.xyz b/tests/calculations/structures/mlip_test.xyz new file mode 100644 index 00000000..df990098 --- /dev/null +++ b/tests/calculations/structures/mlip_test.xyz @@ -0,0 +1,116 @@ +114 +Lattice="14.656053852204064 0.0 0.0 7.152716897291043 12.83340508269681 0.0 7.3175087671203345 4.21254028622021 12.109471678044383" Properties=species:S:1:pos:R:3:initial_magmoms:R:1:dft_forces:R:3 dft_energy=-842.19986612 dft_stress="-0.002807895578799348 0.001581940291161188 0.0004783320390840146 -0.0013659013941342366 0.0012554940035235348 -0.0011820129849694034" pbc="T T T" +Zr 22.48182938 14.27491151 4.60630202 0.00000000 -0.62996178 0.95368699 0.82369577 +Zr 15.33378953 5.70638380 7.53308287 0.00000000 -0.45101320 -1.06264988 0.16373682 +Zr 17.02435992 4.55275102 4.58910306 0.00000000 0.65052108 0.84507175 -0.39204271 +Zr 20.58667988 15.41317450 7.58829048 0.00000000 1.75845798 -0.11079006 -0.85052821 +Zr 24.22636799 15.38223202 7.46185572 0.00000000 0.10864548 0.66981166 1.20893334 +Zr 13.56890565 4.59679979 4.57274878 0.00000000 -0.27590197 -0.34587007 0.71125185 +H 20.58291280 10.53438721 4.42768471 0.00000000 0.07068061 0.04305158 -0.15985085 +H 13.67565519 6.84422131 11.64659825 0.00000000 0.20732998 0.03991176 -0.31304837 +H 9.62830143 12.80497036 1.00715589 0.00000000 -0.04385990 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pbc="F F F" +Zr 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 diff --git a/tests/calculations/structures/mlip_valid.xyz b/tests/calculations/structures/mlip_valid.xyz new file mode 100644 index 00000000..c8c3337f --- /dev/null +++ b/tests/calculations/structures/mlip_valid.xyz @@ -0,0 +1,116 @@ +114 +Lattice="14.867619239232576 0.0 0.0 7.616091916881307 12.957323772206134 0.0 7.434407699978016 4.300368937881452 12.170266820017277" Properties=species:S:1:pos:R:3:initial_magmoms:R:1:dft_forces:R:3 dft_energy=-842.02405762 dft_stress="0.007312327166620456 0.005941030437235551 0.004142586144270369 -0.001150207764909111 9.194056266184159e-05 0.0013824186367755542" pbc="T T T" +Zr 23.16971733 14.49287408 4.68085439 0.00000000 0.72691533 1.00174361 0.94841738 +Zr 15.62831245 5.81982230 7.47628100 0.00000000 -0.08677602 -1.11688178 -0.32708030 +Zr 17.44376860 4.56901559 4.57945051 0.00000000 -1.05306721 0.96045006 0.98756058 +Zr 13.68376242 2.68567989 7.57601870 0.00000000 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5.52108660 -0.00000000 0.47967686 -0.41932085 -0.29823871 +O 15.63388581 5.42499141 5.46820789 -0.00000000 0.41366646 -0.83035980 -0.31940133 +O 16.95733780 2.76006278 5.57320545 -0.00000000 0.21495623 0.67011485 -0.01531076 diff --git a/tests/calculations/test_train.py b/tests/calculations/test_train.py new file mode 100644 index 00000000..a91e4de2 --- /dev/null +++ b/tests/calculations/test_train.py @@ -0,0 +1,99 @@ +"""Tests for model train.""" + +import pytest + +from aiida.common import InputValidationError, datastructures + +from aiida_mlip.data.config import JanusConfigfile + + +def test_prepare_train(fixture_sandbox, generate_calc_job, janus_code, config_folder): + """Test generating singlepoint calculation job.""" + + entry_point_name = "janus.train" + config_path = config_folder / "mlip_train.yml" + config = JanusConfigfile(file=config_path) + inputs = { + "metadata": {"options": {"resources": {"num_machines": 1}}}, + "code": janus_code, + "mlip_config": config, + } + + calc_info = generate_calc_job(fixture_sandbox, entry_point_name, inputs) + + retrieve_list = [ + calc_info.uuid, + "aiida-stdout.txt", + "logs", + "results", + "checkpoints", + "test.model", + "test_compiled.model", + ] + + print(sorted(calc_info.retrieve_list)) + print(retrieve_list) + # Check the attributes of the returned `CalcInfo` + assert fixture_sandbox.get_content_list() == ["mlip_train.yml"] + assert isinstance(calc_info, datastructures.CalcInfo) + assert isinstance(calc_info.codes_info[0], datastructures.CodeInfo) + assert sorted(calc_info.retrieve_list) == sorted(retrieve_list) + + +def test_file_error( + fixture_sandbox, generate_calc_job, janus_code, config_folder, tmp_path +): + """Test error if path for xyz is non existent.""" + + entry_point_name = "janus.train" + config_path = config_folder / "mlip_train.yml" + + # Temporarily modify config file to introduce an error + with open(config_path, encoding="utf-8") as file: + right_path = file.read() + + wrong_path = right_path.replace("mlip_train.xyz", "mlip_train_wrong.xyz") + with open(tmp_path / "mlip_config.yml", "w", encoding="utf-8") as file: + file.write(wrong_path) + + config = JanusConfigfile(file=tmp_path / "mlip_config.yml") + inputs = { + "metadata": {"options": {"resources": {"num_machines": 1}}}, + "code": janus_code, + "mlip_config": config, + } + + with pytest.raises(InputValidationError): + generate_calc_job(fixture_sandbox, entry_point_name, inputs) + + +def test_noname( + fixture_sandbox, generate_calc_job, janus_code, config_folder, tmp_path +): + """Test error if no 'name' keyword is given in config.""" + + entry_point_name = "janus.train" + config_path = config_folder / "mlip_train.yml" + + # Temporarily modify config file to introduce an error + with open(config_path, encoding="utf-8") as file: + original_lines = file.readlines() + + noname_lines = [line for line in original_lines if "name" not in line] + + with open(tmp_path / "mlip_config.yml", "w", encoding="utf-8") as file: + file.writelines(noname_lines) + + config = JanusConfigfile(file=tmp_path / "mlip_config.yml") + inputs = { + "metadata": {"options": {"resources": {"num_machines": 1}}}, + "code": janus_code, + "mlip_config": config, + } + + with pytest.raises(InputValidationError): + generate_calc_job(fixture_sandbox, entry_point_name, inputs) + + # Restore config file + with open(config_path, "w", encoding="utf-8") as file: + file.writelines(original_lines)