diff --git a/.github/workflows/documentation.yaml b/.github/workflows/documentation.yaml new file mode 100644 index 00000000..4058288d --- /dev/null +++ b/.github/workflows/documentation.yaml @@ -0,0 +1,32 @@ +name: documentation + +on: + push: + schedule: + - cron: '14 3 * * 1' # at 03:14 on Monday. + +jobs: + docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: 3.9 + + - name: Run Poetry Image + uses: abatilo/actions-poetry@v2.0.0 + with: + poetry-version: 1.2.2 + + - name: Install Sphinx Dependencies + run: | + poetry --version + poetry install + + - name: Build documentation + run: | + cd docs + poetry run sphinx-build -b html source build \ No newline at end of file diff --git a/.github/workflows/linting.yaml b/.github/workflows/linting.yaml index ab8b28c2..1b90ce0e 100644 --- a/.github/workflows/linting.yaml +++ b/.github/workflows/linting.yaml @@ -12,7 +12,7 @@ jobs: - name: Black Check uses: psf/black@stable with: - src: "./gmnn_jax" + src: "./apax" version: "22.10.0" isort: @@ -29,7 +29,7 @@ jobs: - name: run isort run: | - isort --check-only --quiet gmnn_jax + isort --check-only --quiet apax flake8: runs-on: ubuntu-latest @@ -45,4 +45,4 @@ jobs: - name: run flake8 run: | - flake8 gmnn_jax --count --show-source --statistics + flake8 apax --count --show-source --statistics diff --git a/LICENSE b/LICENSE index 4fe223e5..8b8828c2 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2022 GM-NN +Copyright (c) 2022 Moritz Schäfer and Nico Segreto Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.md b/README.md index 74876f0b..9e55120e 100644 --- a/README.md +++ b/README.md @@ -1,27 +1,10 @@ -## Roadmap - -- [x] basic loading of fixed size ASE structures into `tf.data.Dataset` -- [x] basic linear regressor atomic number -> energy -- [x] per-example model + `vmap utiliation` -- [x] loading model parameters from TF GMNN -- [x] basic training loop - - [x] basic metrics - - [x] hooks / tensorboard - - [x] model checkpoints - - [x] restart -- [ ] advanced training loop - - [ ] MLIP metrics - - [x] async checkpoints - - [x] jit compiled metrics -- [x] dataset statistics -- [x] precomputing neighborlists with `jax_md` -- [ ] tests -- [ ] documentation -- [ ] generalize to differently sized molecules -- [x] Optimizer with different lr for different parameter groups -- [x] GMNN energy model with `jax_md` -- [x] force model -- [x] running MD with GMNN +# `apax`: Atomstic learned Potentials in JAX! +[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) + +`apax` is a high-performance, extendable package for training of and inference with atomistic neural networks. +It implements the Gaussian Moment Neural Network model [2, 3]. +It is based on [JAX](https://jax.readthedocs.io/en/latest/) and uses [JaxMD](https://github.com/jax-md/jax-md) as a molecular dynamics engine. ## Installation @@ -32,16 +15,16 @@ You can install [Poetry](https://python-poetry.org/) via curl -sSL https://install.python-poetry.org | python3 - ``` -Now you can install GMNN in your project by running +Now you can install apax in your project by running ```bash -poetry add git+https://github.com/GM-NN/gmnn-jax.git +poetry add git+https://github.com/apax-hub/apax.git ``` As a developer, you can clone the repository and install it via ```bash -git clone https://github.com/GM-NN/gmnn-jax.git +git clone https://github.com/apax-hub/apax.git cd poetry install ``` @@ -57,4 +40,58 @@ pip install --upgrade pip pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html ``` -See the [Jax installation instructions](https://github.com/google/jax#installation) for more details. \ No newline at end of file +See the [Jax installation instructions](https://github.com/google/jax#installation) for more details. + + + +## Usage + +### Your first apax Model + +In order to train a model, you need to run + +```python +apax train config.yaml +``` + +We offer some input file templates to get new users started as quickly as possible. +Simply run the following commands and add the appropriate entries in the marked fields + +```python +apax template train # use --full for a template with all input options +``` + +Please refer to the documentation LINK for a detailed explanation of all parameters. +The documentation can convenienty be accessed by runnning `apax docs`. + +## Molecular Dynamics + +There are two ways in which `apax` models can be used for molecular dynamics out of the box. +High performance NVT simulations using JaxMD can be started with the CLI by running + +```python +apax md config.yaml md_config.yaml +``` + +A template command for MD input files is provided as well. + +The second way is to use the ASE calculator provided in `apax.md`. + + +## Authors +- Moritz René Schäfer +- Nico Segreto + +Under the supervion of Johannes Kästner + +## References +* [1] DOI PLACEHOLDER +* [2] V. Zaverkin and J. Kästner, [“Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials,”](https://doi.org/10.1021/acs.jctc.0c00347) J. Chem. Theory Comput. **16**, 5410–5421 (2020). +* [3] V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, [“Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,”](https://pubs.acs.org/doi/10.1021/acs.jctc.1c00527) J. Chem. Theory Comput. **17**, 6658–6670 (2021). + + +## Contributing + +We are happy to receive your issues and pull requests! + +Do not hesitate to contact any of the authors above if you have any further questions. diff --git a/gmnn_jax/__init__.py b/apax/__init__.py similarity index 67% rename from gmnn_jax/__init__.py rename to apax/__init__.py index 4a39da34..c92f42aa 100644 --- a/gmnn_jax/__init__.py +++ b/apax/__init__.py @@ -1,8 +1,11 @@ +import os import warnings import tensorflow as tf from jax.config import config as jax_config +tf.config.set_visible_devices([], "GPU") + +os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false" warnings.filterwarnings(action="ignore", category=FutureWarning, module=r"jax.*scatter") -tf.config.experimental.set_visible_devices([], "GPU") jax_config.update("jax_enable_x64", True) diff --git a/gmnn_jax/cli/__init__.py b/apax/cli/__init__.py similarity index 100% rename from gmnn_jax/cli/__init__.py rename to apax/cli/__init__.py diff --git a/gmnn_jax/cli/gmnn_app.py b/apax/cli/apax_app.py similarity index 74% rename from gmnn_jax/cli/gmnn_app.py rename to apax/cli/apax_app.py index a6a078e7..ebae3f4e 100644 --- a/gmnn_jax/cli/gmnn_app.py +++ b/apax/cli/apax_app.py @@ -7,15 +7,21 @@ from pydantic import ValidationError from rich.console import Console -from gmnn_jax.cli import templates +from apax.cli import templates console = Console(highlight=False) -app = typer.Typer(context_settings={"help_option_names": ["-h", "--help"]}) + +app = typer.Typer( + context_settings={"help_option_names": ["-h", "--help"]}, + pretty_exceptions_show_locals=False, +) validate_app = typer.Typer( + pretty_exceptions_show_locals=False, context_settings={"help_option_names": ["-h", "--help"]}, help="Validate training or MD config files.", ) template_app = typer.Typer( + pretty_exceptions_show_locals=False, context_settings={"help_option_names": ["-h", "--help"]}, help="Create configuration file templates.", ) @@ -32,9 +38,9 @@ def train( log_file: str = typer.Option("train.log", help="Specifies the name of the log file"), ): """ - Starts the training of a GMNN model with parameters provided by a configuration file. + Starts the training of a model with parameters provided by a configuration file. """ - from gmnn_jax.train.run import run + from apax.train.run import run run(train_config_path, log_file, log_level) @@ -52,7 +58,7 @@ def md( Starts performing a molecular dynamics simulation (currently only NHC thermostat) with paramters provided by a configuration file. """ - from gmnn_jax.md import run_md + from apax.md import run_md run_md(train_config_path, md_config_path, log_file, log_level) @@ -74,7 +80,7 @@ def eval( Starts performing the evaluation of the test dataset with parameters provided by a configuration file. """ - from gmnn_jax.train.eval import eval_model + from apax.train.eval import eval_model eval_model(train_config_path, n_data) @@ -84,8 +90,8 @@ def docs(): """ Opens the documentation website in your browser. """ - console.print("Opening gmnn-jax's docs at https://github.com/GM-NN/gmnn-jax") - typer.launch("https://github.com/GM-NN/gmnn-jax") + console.print("Opening apax's docs at https://github.com/apax-hub/apax") + typer.launch("https://github.com/apax-hub/apax") @validate_app.command("train") @@ -101,7 +107,7 @@ def validate_train_config( ---------- config_path: Path to the training configuration file. """ - from gmnn_jax.config import Config + from apax.config import Config with open(config_path, "r") as stream: user_config = yaml.safe_load(stream) @@ -130,7 +136,7 @@ def validate_md_config( ---------- config_path: Path to the molecular dynamics configuration file. """ - from gmnn_jax.config import MDConfig + from apax.config import MDConfig with open(config_path, "r") as stream: user_config = yaml.safe_load(stream) @@ -165,12 +171,12 @@ def visualize_model( ---------- config_path: Path to the training configuration file. """ - from jax_md.partition import space + import jax + from jax_md import space - from gmnn_jax.config import Config - from gmnn_jax.model import get_training_model - from gmnn_jax.utils.data import make_minimal_input - from gmnn_jax.visualize import model_tabular + from apax.config import Config + from apax.model.builder import ModelBuilder + from apax.utils.data import make_minimal_input with open(config_path, "r") as stream: user_config = yaml.safe_load(stream) @@ -182,16 +188,12 @@ def visualize_model( console.print("Configuration Invalid!", style="red3") raise typer.Exit(code=1) - displacement_fn, _ = space.free() - R, Z, idx = make_minimal_input() - - gmnn = get_training_model( - n_atoms=2, - n_species=10, - displacement_fn=displacement_fn, - **config.model.get_dict(), + R, Z, idx, box = make_minimal_input() + builder = ModelBuilder(config.model.get_dict(), n_species=10) + model = builder.build_energy_model( + displacement_fn=space.free()[0], ) - model_tabular(gmnn, R, Z, idx) + print(model.tabulate(jax.random.PRNGKey(0), R, Z, idx, box)) @template_app.command("train") @@ -235,23 +237,10 @@ def template_md_config(): config.write(template_content) -logo = """ -[bold white] /###### /## /## /## /## /## /##[bold turquoise2] /##### /###### /## /## -[bold white] /##__ ##| ### /###| ### | ##| ### | ##[bold turquoise2] |__ ## /##__ ##| ## / ## -[bold white]| ## \__/| #### /####| ####| ##| ####| ##[bold turquoise2] | ##| ## \ ##| ##/ ##/ -[bold white]| ## /####| ## ##/## ##| ## ## ##| ## ## ##[bold turquoise2] /###### | ##| ######## \ ####/ -[bold white]| ##|_ ##| ## ###| ##| ## ####| ## ####[bold turquoise2]|______/ /## | ##| ##__ ## >## ## -[bold white]| ## \ ##| ##\ # | ##| ##\ ###| ##\ ###[bold turquoise2] | ## | ##| ## | ## /##/\ ## -[bold white]| ######/| ## \/ | ##| ## \ ##| ## \ ##[bold turquoise2] | ######/| ## | ##| ## \ ## -[bold white] \______/ |__/ |__/|__/ \__/|__/ \__/[bold turquoise2] \______/ |__/ |__/|__/ |__/ -""" # noqa: E501, W605, W291, E261, E303 - - def version_callback(value: bool) -> None: - """Get the installed gmnn-jax version.""" + """Get the installed apax version.""" if value: - console.print(logo) - console.print(f"gmnn-jax {importlib.metadata.version('gmnn-jax')}") + console.print(f"apax {importlib.metadata.version('apax')}") raise typer.Exit() diff --git a/gmnn_jax/cli/templates/__init__.py b/apax/cli/templates/__init__.py similarity index 100% rename from gmnn_jax/cli/templates/__init__.py rename to apax/cli/templates/__init__.py diff --git a/gmnn_jax/cli/templates/md_config_minimal.yaml b/apax/cli/templates/md_config_minimal.yaml similarity index 100% rename from gmnn_jax/cli/templates/md_config_minimal.yaml rename to apax/cli/templates/md_config_minimal.yaml diff --git a/gmnn_jax/cli/templates/train_config_full.yaml b/apax/cli/templates/train_config_full.yaml similarity index 98% rename from gmnn_jax/cli/templates/train_config_full.yaml rename to apax/cli/templates/train_config_full.yaml index e1487eba..98123637 100644 --- a/gmnn_jax/cli/templates/train_config_full.yaml +++ b/apax/cli/templates/train_config_full.yaml @@ -3,7 +3,7 @@ seed: 1 data: model_path: models/ - model_name: gmnn + model_name: apax # Use either data_path for a single dataset file diff --git a/gmnn_jax/cli/templates/train_config_minimal.yaml b/apax/cli/templates/train_config_minimal.yaml similarity index 94% rename from gmnn_jax/cli/templates/train_config_minimal.yaml rename to apax/cli/templates/train_config_minimal.yaml index 81c37d46..38fbe1ea 100644 --- a/gmnn_jax/cli/templates/train_config_minimal.yaml +++ b/apax/cli/templates/train_config_minimal.yaml @@ -3,7 +3,7 @@ n_epochs: data: model_path: models - model_name: gmnn + model_name: apax data_path: n_train: 100 diff --git a/gmnn_jax/config/__init__.py b/apax/config/__init__.py similarity index 76% rename from gmnn_jax/config/__init__.py rename to apax/config/__init__.py index 729093b5..47c78f38 100644 --- a/gmnn_jax/config/__init__.py +++ b/apax/config/__init__.py @@ -1,5 +1,5 @@ -from gmnn_jax.config.md_config import MDConfig -from gmnn_jax.config.train_config import ( +from apax.config.md_config import MDConfig +from apax.config.train_config import ( CallbackConfig, Config, DataConfig, diff --git a/gmnn_jax/config/md_config.py b/apax/config/md_config.py similarity index 100% rename from gmnn_jax/config/md_config.py rename to apax/config/md_config.py diff --git a/gmnn_jax/config/train_config.py b/apax/config/train_config.py similarity index 82% rename from gmnn_jax/config/train_config.py rename to apax/config/train_config.py index da971bf8..b1450205 100644 --- a/gmnn_jax/config/train_config.py +++ b/apax/config/train_config.py @@ -2,10 +2,17 @@ from typing import List, Literal, Optional import yaml -from pydantic import BaseModel, Extra, NonNegativeFloat, PositiveFloat, PositiveInt +from pydantic import ( + BaseModel, + Extra, + NonNegativeFloat, + PositiveFloat, + PositiveInt, + root_validator, +) -class DataConfig(BaseModel): +class DataConfig(BaseModel, extra=Extra.forbid): """ Configuration for data loading, preprocessing and training. @@ -27,7 +34,7 @@ class DataConfig(BaseModel): n_valid: Number of validation datapoints from `data_path`. batch_size: Number of training examples to be evaluated at once. valid_batch_size: Number of validation examples to be evaluated at once. - shuffle_buffer_size: SIze of the `tf.data` shuffle buffer. + shuffle_buffer_size: Size of the `tf.data` shuffle buffer. energy_regularisation: Magnitude of the regularization in the per-element energy regression. """ @@ -49,6 +56,19 @@ class DataConfig(BaseModel): energy_regularisation: NonNegativeFloat = 1.0 + @root_validator(pre=False) + def set_data_or_train_val_path(cls, values): + not_data_path = values["data_path"] is None + not_train_path = values["train_data_path"] is None + + neither_set = not_data_path and not_train_path + both_set = not not_data_path and not not_train_path + + if neither_set or both_set: + raise ValueError("Please specify either data_path or train_data_path") + + return values + class ModelConfig(BaseModel, extra=Extra.forbid): """ @@ -69,6 +89,8 @@ class ModelConfig(BaseModel, extra=Extra.forbid): n_radial: PositiveInt = 5 r_min: NonNegativeFloat = 0.5 r_max: PositiveFloat = 6.0 + # n_contr: int = -1 + # emb_init: Optional[str] = "uniform" nn: List[PositiveInt] = [512, 512] b_init: Literal["normal", "zeros"] = "normal" @@ -89,9 +111,10 @@ def get_dict(self): return model_dict -class OptimizerConfig(BaseModel, frozen=True, extra=Extra.allow): +class OptimizerConfig(BaseModel, frozen=True, extra=Extra.forbid): """ Configuration of the optimizer. + Learning rates of 0 will freeze the respective parameters. Parameters ---------- @@ -106,10 +129,10 @@ class OptimizerConfig(BaseModel, frozen=True, extra=Extra.allow): """ opt_name: str = "adam" - emb_lr: PositiveFloat = 0.02 - nn_lr: PositiveFloat = 0.03 - scale_lr: PositiveFloat = 0.001 - shift_lr: PositiveFloat = 0.05 + emb_lr: NonNegativeFloat = 0.02 + nn_lr: NonNegativeFloat = 0.03 + scale_lr: NonNegativeFloat = 0.001 + shift_lr: NonNegativeFloat = 0.05 transition_begin: int = 0 opt_kwargs: dict = {} @@ -147,7 +170,7 @@ class LossConfig(BaseModel, extra=Extra.forbid): weight: NonNegativeFloat = 1.0 -class CallbackConfig(BaseModel, frozen=True, extra=Extra.allow): +class CallbackConfig(BaseModel, frozen=True, extra=Extra.forbid): """ Configuraton of the training callbacks. @@ -177,16 +200,21 @@ class CheckpointConfig(BaseModel, extra=Extra.forbid): """ Checkpoint configuration. + Parameters + ---------- ckpt_interval: Number of epochs between checkpoints. + base_model_checkpoint: Path to the folder containing a pre-trained model ckpt. + reset_layers: List of layer names for which the parameters will be reinitialized. """ ckpt_interval: PositiveInt = 1 - # TODO(Moritz): place future transfer learning start ckpt selection here + base_model_checkpoint: Optional[str] = None + reset_layers: List[str] = [] class Config(BaseModel, frozen=True, extra=Extra.forbid): """ - Main configuration of a GMNN trianing run. + Main configuration of a apax training run. Parameters ---------- @@ -204,7 +232,7 @@ class Config(BaseModel, frozen=True, extra=Extra.forbid): maximize_l2_cache: Whether or not to maximize GPU L2 cache. """ - n_epochs: PositiveInt = 100 + n_epochs: PositiveInt seed: int = 1 data: DataConfig diff --git a/apax/data/__init__.py b/apax/data/__init__.py new file mode 100644 index 00000000..de307611 --- /dev/null +++ b/apax/data/__init__.py @@ -0,0 +1,3 @@ +from apax.data import input_pipeline, preprocessing, statistics + +__all__ = ["input_pipeline", "preprocessing", "statistics"] diff --git a/gmnn_jax/data/input_pipeline.py b/apax/data/input_pipeline.py similarity index 92% rename from gmnn_jax/data/input_pipeline.py rename to apax/data/input_pipeline.py index ca970d67..6f2f8453 100644 --- a/gmnn_jax/data/input_pipeline.py +++ b/apax/data/input_pipeline.py @@ -5,28 +5,34 @@ import tensorflow as tf from jax_md import partition, space -from gmnn_jax.data.preprocessing import dataset_neighborlist, prefetch_to_single_device -from gmnn_jax.utils.data import convert_atoms_to_arrays +from apax.data.preprocessing import dataset_neighborlist, prefetch_to_single_device +from apax.utils.data import convert_atoms_to_arrays log = logging.getLogger(__name__) def initialize_nbr_displacement_fns(atoms, cutoff): + # frac coord have to be managed in the config oder + # dependent of the dataset dependent of the performence default_box = 100 - box = jnp.asarray(atoms.get_cell().lengths()) - + box = jnp.asarray(atoms.cell.lengths()) if np.all(box < 1e-6): displacement_fn, _ = space.free() box = default_box + frac_coords = False else: - displacement_fn, _ = space.periodic(box) + frac_coords = True + displacement_fn, _ = space.periodic_general( + box, fractional_coordinates=frac_coords + ) neighbor_fn = partition.neighbor_list( displacement_or_metric=displacement_fn, box=box, r_cutoff=cutoff, format=partition.Sparse, + fractional_coordinates=frac_coords, ) return displacement_fn, neighbor_fn @@ -119,8 +125,10 @@ def create_dict_dataset( neighbor_fn, inputs["ragged"]["positions"], inputs["fixed"]["n_atoms"], + box=inputs["fixed"]["box"], disable_pbar=disable_pbar, ) + inputs["ragged"]["idx"] = [np.array(i) for i in idx] return inputs, labels diff --git a/gmnn_jax/data/preprocessing.py b/apax/data/preprocessing.py similarity index 59% rename from gmnn_jax/data/preprocessing.py rename to apax/data/preprocessing.py index 83ad7880..fe268067 100644 --- a/gmnn_jax/data/preprocessing.py +++ b/apax/data/preprocessing.py @@ -12,16 +12,17 @@ @jax.jit -def extract_nl(neighbors, positions): +def extract_nl(neighbors, position): # vmapped neighborlist probably only useful for larger structures - neighbors = neighbors.update(positions) + neighbors = neighbors.update(position) return neighbors def dataset_neighborlist( neighbor_fn: NeighborFn, - positions: np.array, + positions: list[np.array], n_atoms: list[int], + box: list[np.array], disable_pbar: bool = False, ) -> list[int]: """Calculates the neighbor list of all systems within positions using @@ -46,7 +47,10 @@ def dataset_neighborlist( positions = [jnp.asarray(pos) for pos in positions] neighbors = neighbor_fn.allocate(positions[0]) idx = [] - num_atoms = n_atoms[0] + last_n_atoms = n_atoms[0] + neighbors_dict = { + "neighbor_fn_0": {"neighbors": neighbors, "box": box[0], "n_atoms": n_atoms[0]} + } pbar_update_freq = 10 with trange( @@ -57,19 +61,40 @@ def dataset_neighborlist( leave=True, ) as nl_pbar: for i, position in enumerate(positions): - if n_atoms[i] != num_atoms: - neighbors = neighbor_fn.allocate(position) - num_atoms = n_atoms[i] + if np.all(box[i] < 1e-6): + if n_atoms[i] != last_n_atoms: + neighbors = neighbor_fn.allocate(position) + last_n_atoms = n_atoms[i] + neighbors = extract_nl(neighbors, position) + + else: + reallocate = True + for neighbor_vals in neighbors_dict.values(): + if ( + np.all(box[i] == neighbor_vals["box"]) + and n_atoms[i] == neighbor_vals["n_atoms"] + ): + neighbors = extract_nl(neighbor_vals["neighbors"], position) + reallocate = False + + if reallocate: + neighbors = neighbor_fn.allocate(position, box=box[i]) + neighbors_dict[f"neighbor_fn_{i}"] = { + "neighbors": neighbors, + "box": box[i], + "n_atoms": n_atoms[i], + } - neighbors = extract_nl(neighbors, position) if neighbors.did_buffer_overflow: log.info("Neighbor list overflowed, reallocating.") - neighbors = neighbor_fn.allocate(position) + if np.all(box[i] < 1e-6): + neighbors = neighbor_fn.allocate(position) + else: + neighbors = neighbor_fn.allocate(position, box=box[i]) idx.append(neighbors.idx) if i % pbar_update_freq == 0: nl_pbar.update(pbar_update_freq) - return idx diff --git a/gmnn_jax/data/statistics.py b/apax/data/statistics.py similarity index 100% rename from gmnn_jax/data/statistics.py rename to apax/data/statistics.py diff --git a/apax/layers/__init__.py b/apax/layers/__init__.py new file mode 100644 index 00000000..4163d34e --- /dev/null +++ b/apax/layers/__init__.py @@ -0,0 +1,3 @@ +from apax.layers import activation, descriptor, ntk_linear, scaling + +__all__ = ["descriptor", "activation", "ntk_linear", "scaling"] diff --git a/gmnn_jax/layers/activation.py b/apax/layers/activation.py similarity index 100% rename from gmnn_jax/layers/activation.py rename to apax/layers/activation.py diff --git a/apax/layers/descriptor/__init__.py b/apax/layers/descriptor/__init__.py new file mode 100644 index 00000000..88d4a4a9 --- /dev/null +++ b/apax/layers/descriptor/__init__.py @@ -0,0 +1,3 @@ +from apax.layers.descriptor.gaussian_moment_descriptor import GaussianMomentDescriptor + +__all__ = ["GaussianMomentDescriptor"] diff --git a/apax/layers/descriptor/basis_functions.py b/apax/layers/descriptor/basis_functions.py new file mode 100644 index 00000000..db8d9a39 --- /dev/null +++ b/apax/layers/descriptor/basis_functions.py @@ -0,0 +1,103 @@ +from typing import Any + +import einops +import flax.linen as nn +import jax.numpy as jnp +import numpy as np + +from apax.layers.initializers import uniform_range + + +class GaussianBasis(nn.Module): + n_basis: int = 7 + r_min: float = 0.5 + r_max: float = 6.0 + dtype: Any = jnp.float32 + + def setup(self): + self.betta = self.n_basis**2 / self.r_max**2 + self.rad_norm = (2.0 * self.betta / np.pi) ** 0.25 + shifts = self.r_min + (self.r_max - self.r_min) / self.n_basis * np.arange( + self.n_basis + ) + + # shape: 1 x n_basis + shifts = einops.repeat(shifts, "n_basis -> 1 n_basis") + self.shifts = jnp.asarray(shifts, dtype=self.dtype) + + def __call__(self, dr): + dr = einops.repeat(dr, "neighbors -> neighbors 1") + # 1 x n_basis, neighbors x 1 -> neighbors x n_basis + distances = self.shifts - dr + + # shape: neighbors x n_basis + basis = jnp.exp(-self.betta * (distances**2)) + basis = self.rad_norm * basis + + return basis + + +class RadialFunction(nn.Module): + n_radial: int = 5 + basis_fn: nn.Module = GaussianBasis() + n_species: int = 119 + emb_init: str = "uniform" + dtype: Any = jnp.float32 + + def setup(self): + self.r_max = self.basis_fn.r_max + self.embed_norm = jnp.array( + 1.0 / np.sqrt(self.basis_fn.n_basis), dtype=self.dtype + ) + if self.emb_init is not None: + self._n_radial = self.n_radial + if self.emb_init == "uniform": + emb_initializer = uniform_range(-1.0, 1.0, dtype=self.dtype) + self.embeddings = self.param( + "atomic_type_embedding", + emb_initializer, + ( + self.n_species, + self.n_species, + self.n_radial, + self.basis_fn.n_basis, + ), + self.dtype, + ) + else: + raise NotImplementedError( + "Currently only uniformly initialized embeddings and no embeddings" + " are implemented." + ) + else: + self._n_radial = self.basis_fn.n_basis + + def __call__(self, dr, Z_i, Z_j): + dr = dr.astype(self.dtype) + # basis shape: neighbors x n_basis + basis = self.basis_fn(dr) + + if self.emb_init is None: + radial_function = basis + else: + # coeffs shape: n_neighbors x n_radialx n_basis + species_pair_coeffs = self.embeddings[ + Z_j, Z_i, ... + ] # reverse convention to match original + species_pair_coeffs = self.embed_norm * species_pair_coeffs + + # radial shape: neighbors x n_radial + radial_function = einops.einsum( + species_pair_coeffs, basis, "nbrs radial basis, nbrs basis -> nbrs radial" + ) + + # shape: neighbors + dr_clipped = jnp.clip(dr, a_max=self.r_max) + cos_cutoff = 0.5 * (jnp.cos(np.pi * dr_clipped / self.r_max) + 1.0) + cutoff = einops.repeat(cos_cutoff, "neighbors -> neighbors 1") + + radial_function = radial_function * cutoff + + assert radial_function.dtype == self.dtype + + return radial_function diff --git a/gmnn_jax/layers/descriptor/gaussian_moment_descriptor.py b/apax/layers/descriptor/gaussian_moment_descriptor.py similarity index 52% rename from gmnn_jax/layers/descriptor/gaussian_moment_descriptor.py rename to apax/layers/descriptor/gaussian_moment_descriptor.py index 26b8724a..cdb8724c 100644 --- a/gmnn_jax/layers/descriptor/gaussian_moment_descriptor.py +++ b/apax/layers/descriptor/gaussian_moment_descriptor.py @@ -1,91 +1,84 @@ -from typing import Optional +from typing import Any, Callable import einops -import haiku as hk +import flax.linen as nn import jax.numpy as jnp import numpy as np +from jax import vmap from jax_md import space -from gmnn_jax.layers.descriptor.basis_functions import RadialFunction -from gmnn_jax.layers.descriptor.moments import geometric_moments -from gmnn_jax.layers.descriptor.triangular_indices import ( - tril_2d_indices, - tril_3d_indices, -) -from gmnn_jax.layers.masking import mask_by_neighbor - - -class GaussianMomentDescriptor(hk.Module): - def __init__( - self, - displacement, - n_basis, - n_radial, - n_species, - n_atoms, - r_min, - r_max, - dtype=jnp.float32, - apply_mask=True, - name: Optional[str] = None, - ): - super().__init__(name) - - self.n_atoms = n_atoms - self.n_radial = n_radial - self.r_max = r_max - self.radial_fn = RadialFunction( - n_species, - n_basis, - n_radial, - r_min, - r_max, - emb_init=None, - dtype=dtype, - name="radial_fn", - ) - # TODO maybe move the radial func into call and get - # n_species and n_atoms from the first input batch - self.displacement = space.map_bond(displacement) - self.metric = space.map_bond( - space.canonicalize_displacement_or_metric(displacement) - ) +from apax.layers.descriptor.basis_functions import RadialFunction +from apax.layers.descriptor.moments import geometric_moments +from apax.layers.descriptor.triangular_indices import tril_2d_indices, tril_3d_indices +from apax.layers.masking import mask_by_neighbor + + +def get_disp_fn(displacement): + def disp_fn(ri, rj, box): + return displacement(ri, rj, box=box) + + return disp_fn + + +class GaussianMomentDescriptor(nn.Module): + displacement_fn: Callable = space.free()[0] + radial_fn: nn.Module = RadialFunction() + n_contr: int = 8 + dtype: Any = jnp.float32 + apply_mask: bool = True + init_box: np.array = np.array([0.0, 0.0, 0.0]) - self.triang_idxs_2d = tril_2d_indices(n_radial) - self.triang_idxs_3d = tril_3d_indices(n_radial) - self.apply_mask = apply_mask + def setup(self): + self.r_max = self.radial_fn.r_max + self.n_radial = self.radial_fn._n_radial - self.dtype = dtype + if not np.all(self.init_box < 1e-6): + # displacement function used for training on periodic systems + mappable_displacement_fn = get_disp_fn(self.displacement_fn) + self.displacement = vmap(mappable_displacement_fn, (0, 0, None), 0) + else: + # displacement function for gas phase training and predicting + self.displacement = space.map_bond(self.displacement_fn) - def __call__(self, R, Z, neighbor): - # if R.dtype != self.dtype: - R = R.astype(self.dtype) + self.distance = vmap(space.distance, 0, 0) + + self.triang_idxs_2d = tril_2d_indices(self.n_radial) + self.triang_idxs_3d = tril_3d_indices(self.n_radial) + + def __call__(self, R, Z, neighbor_idxs, box): + R = R.astype(jnp.float64) # R shape n_atoms x 3 # Z shape n_atoms + n_atoms = R.shape[0] + + idx_i, idx_j = neighbor_idxs[0], neighbor_idxs[1] # shape: neighbors - Z_i, Z_j = Z[neighbor.idx[0]], Z[neighbor.idx[1]] + Z_i, Z_j = Z[idx_i, ...], Z[idx_j, ...] # dr_vec shape: neighbors x 3 - dr_vec = self.displacement( - R[neighbor.idx[1]], R[neighbor.idx[0]] - ) # reverse conventnion to match TF + if not np.all(self.init_box < 1e-6): + # distance vector for training on periodic systems + # reverse conventnion to match TF + dr_vec = self.displacement(R[idx_j], R[idx_i], box).astype(self.dtype) + else: + # reverse conventnion to match TF + # distance vector for gas phase training and predicting + dr_vec = self.displacement(R[idx_j], R[idx_i]).astype(self.dtype) + # dr shape: neighbors - dr = self.metric(R[neighbor.idx[0]], R[neighbor.idx[1]]) + dr = self.distance(dr_vec).astype(self.dtype) + # TODO: maybe try jnp where dr_repeated = einops.repeat(dr + 1e-5, "neighbors -> neighbors 1") # normalized distance vectors, shape neighbors x 3 dn = dr_vec / dr_repeated - # shape: neighbors - dr_clipped = jnp.clip(dr, a_max=self.r_max) - cos_cutoff = 0.5 * (jnp.cos(np.pi * dr_clipped / self.r_max) + 1.0) - - radial_function = self.radial_fn(dr, Z_i, Z_j, cos_cutoff) + radial_function = self.radial_fn(dr, Z_i, Z_j) if self.apply_mask: - radial_function = mask_by_neighbor(radial_function, neighbor.idx) + radial_function = mask_by_neighbor(radial_function, neighbor_idxs) - moments = geometric_moments(radial_function, dn, neighbor.idx[1], self.n_atoms) + moments = geometric_moments(radial_function, dn, idx_j, n_atoms) contr_0 = moments[0] contr_1 = jnp.einsum("ari, asi -> rsa", moments[1], moments[1]) @@ -142,6 +135,6 @@ def __call__(self, R, Z, neighbor): ] # gaussian_moments shape: n_atoms x n_features - gaussian_moments = jnp.concatenate(gaussian_moments, axis=-1) + gaussian_moments = jnp.concatenate(gaussian_moments[: self.n_contr], axis=-1) assert gaussian_moments.dtype == self.dtype return gaussian_moments diff --git a/gmnn_jax/layers/descriptor/moments.py b/apax/layers/descriptor/moments.py similarity index 100% rename from gmnn_jax/layers/descriptor/moments.py rename to apax/layers/descriptor/moments.py diff --git a/gmnn_jax/layers/descriptor/triangular_indices.py b/apax/layers/descriptor/triangular_indices.py similarity index 100% rename from gmnn_jax/layers/descriptor/triangular_indices.py rename to apax/layers/descriptor/triangular_indices.py diff --git a/apax/layers/initializers.py b/apax/layers/initializers.py new file mode 100644 index 00000000..aabefb72 --- /dev/null +++ b/apax/layers/initializers.py @@ -0,0 +1,26 @@ +from typing import Any + +import jax.numpy as jnp +from jax import random +from jax._src import dtypes +from jax.nn.initializers import Initializer + +KeyArray = random.KeyArray +Array = Any + +DTypeLikeFloat = Any +DTypeLikeComplex = Any +DTypeLikeInexact = Any +RealNumeric = Any + + +def uniform_range(minval, maxval, dtype: DTypeLikeInexact = jnp.float_) -> Initializer: + """Builds an initializer that returns real uniformly-distributed random arrays + in a specified range. + """ + + def init(key: KeyArray, shape, dtype: DTypeLikeInexact = dtype) -> Array: + dtype = dtypes.canonicalize_dtype(dtype) + return random.uniform(key, shape, dtype, minval, maxval) + + return init diff --git a/gmnn_jax/layers/masking.py b/apax/layers/masking.py similarity index 100% rename from gmnn_jax/layers/masking.py rename to apax/layers/masking.py diff --git a/apax/layers/ntk_linear.py b/apax/layers/ntk_linear.py new file mode 100644 index 00000000..2c5649c2 --- /dev/null +++ b/apax/layers/ntk_linear.py @@ -0,0 +1,35 @@ +from typing import Any + +import flax.linen as nn +import jax.numpy as jnp + + +class NTKLinear(nn.Module): + units: int + b_init: str = "normal" + dtype: Any = jnp.float32 + + @nn.compact + def __call__(self, inputs): + inputs = inputs.astype(self.dtype) + + w_initializer = nn.initializers.normal(1.0, dtype=self.dtype) + + if self.b_init == "normal": + b_initializer = nn.initializers.normal(1.0, dtype=self.dtype) + elif self.b_init == "zeros": + b_initializer = nn.initializers.constant(0.0, dtype=self.dtype) + else: + raise NotImplementedError( + "Only random normal and zeros intialization of the bias is supported." + ) + w = self.param("w", w_initializer, (inputs.shape[0], self.units), self.dtype) + b = self.param("b", b_initializer, [self.units], self.dtype) + + bias_factor = 0.1 + weight_factor = jnp.sqrt(1.0 / inputs.shape[0]) + + wx = jnp.dot(inputs, w) + prediction = weight_factor * wx + bias_factor * b + assert prediction.dtype == self.dtype + return prediction diff --git a/apax/layers/readout.py b/apax/layers/readout.py new file mode 100644 index 00000000..41955cd0 --- /dev/null +++ b/apax/layers/readout.py @@ -0,0 +1,32 @@ +from dataclasses import field +from typing import Any, Callable, List + +import flax.linen as nn +import jax.numpy as jnp + +from apax.layers.activation import swish +from apax.layers.ntk_linear import NTKLinear + + +class AtomisticReadout(nn.Module): + units: List[int] = field(default_factory=lambda: [512, 512]) + activation_fn: Callable = swish + b_init: str = "normal" + dtype: Any = jnp.float32 + + def setup(self): + units = [u for u in self.units] + [1] + dense = [] + for ii, n_hidden in enumerate(units): + dense.append( + NTKLinear( + n_hidden, b_init=self.b_init, dtype=self.dtype, name=f"dense_{ii}" + ) + ) + if ii < len(units) - 1: + dense.append(swish) + self.sequential = nn.Sequential(dense, name="readout") + + def __call__(self, x): + h = self.sequential(x) + return h diff --git a/apax/layers/scaling.py b/apax/layers/scaling.py new file mode 100644 index 00000000..52165bd5 --- /dev/null +++ b/apax/layers/scaling.py @@ -0,0 +1,46 @@ +from typing import Any, Union + +import einops +import flax.linen as nn +import jax.numpy as jnp + + +class PerElementScaleShift(nn.Module): + n_species: int = 119 + scale: Union[jnp.array, float] = 1.0 + shift: Union[jnp.array, float] = 0.0 + dtype: Any = jnp.float32 + + def setup(self): + scale = jnp.asarray(self.scale) + shift = jnp.asarray(self.shift) + + if len(scale.shape) > 0: + n_species = scale.shape[0] + else: + n_species = self.n_species + + if len(scale.shape) == 1: + scale = einops.repeat(scale, "species -> species 1") + if len(shift.shape) == 1: + shift = einops.repeat(shift, "species -> species 1") + scale_init = nn.initializers.constant(scale) + shift_init = nn.initializers.constant(shift) + + self.scale_param = self.param( + "scale_per_element", scale_init, (n_species, 1), self.dtype + ) + self.shift_param = self.param( + "shift_per_element", shift_init, (n_species, 1), self.dtype + ) + + def __call__(self, x, Z): + # x shape: n_atoms x 1 + # Z shape: n_atoms + # scale[Z] shape: n_atoms x 1 + x = x.astype(self.dtype) + + out = self.scale_param[Z] * x + self.shift_param[Z] + + assert out.dtype == self.dtype + return out diff --git a/apax/md/__init__.py b/apax/md/__init__.py new file mode 100644 index 00000000..823f48db --- /dev/null +++ b/apax/md/__init__.py @@ -0,0 +1,4 @@ +from apax.md.ase_calc import ASECalculator +from apax.md.nvt import run_md + +__all__ = ["run_md", "ASECalculator"] diff --git a/gmnn_jax/md/ase_calc.py b/apax/md/ase_calc.py similarity index 73% rename from gmnn_jax/md/ase_calc.py rename to apax/md/ase_calc.py index e9cd2dfb..abb6c8c4 100644 --- a/gmnn_jax/md/ase_calc.py +++ b/apax/md/ase_calc.py @@ -8,11 +8,11 @@ import yaml from ase.calculators.calculator import Calculator, all_changes from flax.training import checkpoints -from jax_md import space +from jax_md import partition, space -from gmnn_jax.config.train_config import Config -from gmnn_jax.md.md_checkpoint import look_for_checkpoints -from gmnn_jax.model.gmnn import get_md_model +from apax.config.train_config import Config +from apax.md.md_checkpoint import look_for_checkpoints +from apax.model import ModelBuilder def build_energy_neighbor_fns(atoms, config, params, dr_threshold): @@ -22,17 +22,24 @@ def build_energy_neighbor_fns(atoms, config, params, dr_threshold): if np.all(box < 1e-6): displacement_fn, _ = space.free() else: - displacement_fn, _ = space.periodic(box) - - neighbor_fn, gmnn = get_md_model( - atomic_numbers=atomic_numbers, - displacement_fn=displacement_fn, - displacement=displacement_fn, - box_size=box, # if the atom box is 0,0,0, this will cause an error - dr_threshold=dr_threshold, - **config.model.get_dict(), + displacement_fn, _ = space.periodic_general(box, fractional_coordinates=False) + + Z = jnp.asarray(atomic_numbers) + n_species = int(np.max(Z) + 1) + builder = ModelBuilder(config.model.get_dict(), n_species=n_species) + model = builder.build_energy_model( + displacement_fn=displacement_fn, apply_mask=False, init_box=np.array(box) + ) + energy_fn = partial(model.apply, params, Z=Z, box=box) + neighbor_fn = partition.neighbor_list( + displacement_fn, + box, + config.model.r_max, + dr_threshold, + fractional_coordinates=False, + format=partition.Sparse, ) - energy_fn = partial(gmnn.apply, params) + return energy_fn, neighbor_fn @@ -68,13 +75,18 @@ def __init__(self, model_dir: Path, dr_threshold: float = 0.5, **kwargs): def initialize(self, atoms): energy_fn, neighbor_fn = build_energy_neighbor_fns( - atoms, self.model_config, self.params, self.dr_threshold + atoms, + self.model_config, + self.params, + self.dr_threshold, ) @jax.jit def body_fn(positions, neighbor): neighbor = neighbor.update(positions) - energy, neg_forces = jax.value_and_grad(energy_fn)(positions, neighbor) + energy, neg_forces = jax.value_and_grad(energy_fn)( + positions, neighbor=neighbor + ) forces = -neg_forces return energy, forces, neighbor @@ -86,7 +98,7 @@ def calculate(self, atoms, properties=["energy"], system_changes=all_changes): positions = jnp.asarray(atoms.positions, dtype=jnp.float32) - if self.step is None or "numbers" in system_changes or "cell" in system_changes: + if self.step is None or "numbers" in system_changes or "box" in system_changes: self.initialize(atoms) self.neighbors = self.neighbor_fn.allocate(positions) diff --git a/gmnn_jax/md/md_checkpoint.py b/apax/md/md_checkpoint.py similarity index 100% rename from gmnn_jax/md/md_checkpoint.py rename to apax/md/md_checkpoint.py diff --git a/gmnn_jax/md/nvt.py b/apax/md/nvt.py similarity index 73% rename from gmnn_jax/md/nvt.py rename to apax/md/nvt.py index 1d55e543..74d0a77f 100644 --- a/gmnn_jax/md/nvt.py +++ b/apax/md/nvt.py @@ -12,22 +12,49 @@ from ase.io import read from ase.io.trajectory import TrajectoryWriter from flax.training import checkpoints -from jax_md import quantity, simulate, space +from jax_md import partition, quantity, simulate, space from jax_md.util import Array from tqdm import trange -from gmnn_jax.config import Config, MDConfig -from gmnn_jax.md.md_checkpoint import load_md_state, look_for_checkpoints -from gmnn_jax.model.gmnn import get_md_model +from apax.config import Config, MDConfig +from apax.md.md_checkpoint import load_md_state, look_for_checkpoints +from apax.model import ModelBuilder log = logging.getLogger(__name__) +class TrajHandler: + def __init__(self, R, atomic_numbers, box, traj_path, async_manager) -> None: + self.atomic_numbers = atomic_numbers + self.box = box + self.traj = TrajectoryWriter(traj_path, mode="w") + new_atoms = Atoms(atomic_numbers, R, cell=box) + self.traj.write(new_atoms) + self.async_manager = async_manager + + def write(self, state, energy, step): + if np.any(np.isnan(state.position)): + raise ValueError( + f"Simulation failed after {step} outer steps. Unable to" + " compute positions." + ) + new_atoms = Atoms( + self.atomic_numbers, state.position, momenta=state.momentum, cell=self.box + ) + new_atoms.calc = SinglePointCalculator( + new_atoms, energy=energy, forces=state.force + ) + self.traj.write(new_atoms) + + def close(self): + self.traj.close() + + def run_nvt( R: Array, atomic_numbers: Array, masses: Array, - box: float, + box: np.array, energy_fn, neighbor_fn, shift_fn, @@ -88,7 +115,7 @@ def run_nvt( log.info("initializing simulation") neighbor = neighbor_fn.allocate(R, extra_capacity=extra_capacity) init_fn, apply_fn = simulate.nvt_nose_hoover(energy_fn, shift_fn, dt, kT) - # async_manager = checkpoints.AsyncManager() + async_manager = checkpoints.AsyncManager() restart = False # TODO needs to be implemented if restart: log.info("looking for checkpoints") @@ -113,12 +140,15 @@ def body_fn(i, state): state = apply_fn(state, neighbor=neighbor) return state, neighbor - return jax.lax.fori_loop(0, n_inner, body_fn, (state, neighbor)) + state, neighbor = jax.lax.fori_loop(0, n_inner, body_fn, (state, neighbor)) + current_temperature = quantity.temperature( + velocity=state.velocity, mass=state.mass + ) + current_energy = energy_fn(R=state.position, neighbor=neighbor) + return state, neighbor, current_temperature, current_energy traj_path = os.path.join(sim_dir, traj_name) - traj = TrajectoryWriter(traj_path, mode="w") - new_atoms = Atoms(atomic_numbers, R, cell=box) - traj.write(new_atoms) + traj_handler = TrajHandler(R, atomic_numbers, box, traj_path, async_manager) n_outer = int(np.ceil(n_steps / n_inner)) start = time.time() @@ -128,34 +158,26 @@ def body_fn(i, state): 0, n_steps, desc="Simulation", ncols=100, disable=disable_pbar, leave=True ) as sim_pbar: while step < n_outer: - new_state, neighbor = sim(state, neighbor) + new_state, neighbor, current_temperature, current_energy = sim( + state, neighbor + ) + if neighbor.did_buffer_overflow: log.info("step %d: neighbor list overflowed, reallocating.", step) neighbor = neighbor_fn.allocate(state.position) else: state = new_state step += 1 - new_atoms = Atoms( - atomic_numbers, state.position, momenta=state.momentum, cell=box - ) - new_atoms.calc = SinglePointCalculator(new_atoms, forces=state.force) - traj.write(new_atoms) + + traj_handler.write(state, current_energy, step) if step % checkpoint_interval == 0: log.info("saving checkpoint at step: %d", step) log.info("checkpoints not yet implemented") - current_temperature = quantity.temperature( - velocity=state.velocity, mass=state.mass - ) - if np.any(np.isnan(new_atoms.positions)): - raise ValueError( - f"Simulation failed after {step * n_inner} steps. Unable to" - " compute positions." - ) sim_pbar.set_postfix(T=f"{(current_temperature / units.kB):.1f} K") sim_pbar.update(n_inner) - traj.close() + traj_handler.close() end = time.time() elapsed_time = end - start @@ -194,25 +216,33 @@ def md_setup(model_config: Config, md_config: MDConfig): log.info("reading structure") atoms = read(md_config.initial_structure) - R = jnp.asarray(atoms.positions, dtype=jnp.float32) + R = jnp.asarray(atoms.positions, dtype=jnp.float64) atomic_numbers = jnp.asarray(atoms.numbers, dtype=jnp.int32) - masses = jnp.asarray(atoms.get_masses(), dtype=jnp.float32) - box = jnp.asarray(atoms.get_cell().lengths(), dtype=jnp.float32) + masses = jnp.asarray(atoms.get_masses(), dtype=jnp.float64) + box = jnp.asarray(atoms.get_cell().lengths(), dtype=jnp.float64) + log.info("initializing model") if np.all(box < 1e-6): displacement_fn, shift_fn = space.free() else: - log.info("initializing model") - displacement_fn, shift_fn = space.periodic(box) - - model_dict = model_config.model.get_dict() - neighbor_fn, gmnn = get_md_model( - atomic_numbers=atomic_numbers, - displacement_fn=displacement_fn, - displacement=displacement_fn, - box_size=box, # if the atom box is 0,0,0, this will cause an error - dr_threshold=md_config.dr_threshold, - **model_dict, + displacement_fn, shift_fn = space.periodic_general( + box, fractional_coordinates=False + ) + + Z = jnp.asarray(atomic_numbers) + n_species = int(np.max(Z) + 1) + builder = ModelBuilder(model_config.model.get_dict(), n_species=n_species) + model = builder.build_energy_model( + displacement_fn=displacement_fn, apply_mask=False, init_box=np.array(box) + ) + neighbor_fn = partition.neighbor_list( + displacement_fn, + box, + model_config.model.r_max, + md_config.dr_threshold, + fractional_coordinates=False, + format=partition.Sparse, + disable_cell_list=True, ) os.makedirs(md_config.sim_dir, exist_ok=True) @@ -223,7 +253,8 @@ def md_setup(model_config: Config, md_config: MDConfig): ) raw_restored = checkpoints.restore_checkpoint(best_dir, target=None, step=None) params = jax.tree_map(jnp.asarray, raw_restored["model"]["params"]) - energy_fn = partial(gmnn.apply, params) + + energy_fn = partial(model.apply, params, Z=Z, box=box) return R, atomic_numbers, masses, box, energy_fn, neighbor_fn, shift_fn diff --git a/apax/model/__init__.py b/apax/model/__init__.py new file mode 100644 index 00000000..d28aee47 --- /dev/null +++ b/apax/model/__init__.py @@ -0,0 +1,4 @@ +from apax.model.builder import ModelBuilder +from apax.model.gmnn import AtomisticModel, EnergyForceModel, EnergyModel + +__all__ = ["AtomisticModel", "EnergyModel", "EnergyForceModel", "ModelBuilder"] diff --git a/apax/model/builder.py b/apax/model/builder.py new file mode 100644 index 00000000..13de2e5a --- /dev/null +++ b/apax/model/builder.py @@ -0,0 +1,108 @@ +import numpy as np + +from apax.config import ModelConfig +from apax.layers.descriptor.basis_functions import GaussianBasis, RadialFunction +from apax.layers.descriptor.gaussian_moment_descriptor import GaussianMomentDescriptor +from apax.layers.readout import AtomisticReadout +from apax.layers.scaling import PerElementScaleShift +from apax.model.gmnn import AtomisticModel, EnergyForceModel, EnergyModel + + +class ModelBuilder: + def __init__(self, model_config: ModelConfig, n_species): + self.config = model_config + self.n_species = n_species + + def build_basis_function(self): + basis_fn = GaussianBasis( + n_basis=self.config["n_basis"], + r_min=self.config["r_min"], + r_max=self.config["r_max"], + dtype=self.config["descriptor_dtype"], + ) + return basis_fn + + def build_radial_function(self): + basis_fn = self.build_basis_function() + radial_fn = RadialFunction( + n_radial=self.config["n_radial"], + basis_fn=basis_fn, + n_species=self.n_species, + # n_contr=self.config["n_contr"], # TODO uncomment when hk is removed. + # emb_init=self.config["emb_init"], + dtype=self.config["descriptor_dtype"], + ) + return radial_fn + + def build_descriptor( + self, displacement_fn, apply_mask, init_box: np.array = np.array([0.0, 0.0, 0.0]) + ): + radial_fn = self.build_radial_function() + descriptor = GaussianMomentDescriptor( + displacement_fn=displacement_fn, + radial_fn=radial_fn, + dtype=self.config["descriptor_dtype"], + apply_mask=apply_mask, + init_box=init_box, + ) + return descriptor + + def build_readout(self): + readout = AtomisticReadout( + units=self.config["nn"], + b_init=self.config["b_init"], + dtype=self.config["readout_dtype"], + ) + return readout + + def build_scale_shift(self, scale, shift): + scale_shift = PerElementScaleShift( + n_species=self.n_species, + scale=scale, + shift=shift, + dtype=self.config["scale_shift_dtype"], + ) + return scale_shift + + def build_atomistic_model( + self, + displacement_fn, + scale, + shift, + apply_mask, + init_box: np.array = np.array([0.0, 0.0, 0.0]), + ): + descriptor = self.build_descriptor(displacement_fn, apply_mask, init_box=init_box) + readout = self.build_readout() + scale_shift = self.build_scale_shift(scale, shift) + + atomistic_model = AtomisticModel(descriptor, readout, scale_shift) + return atomistic_model + + def build_energy_model( + self, + displacement_fn, + scale=1.0, + shift=0.0, + apply_mask=True, + init_box: np.array = np.array([0.0, 0.0, 0.0]), + ): + atomistic_model = self.build_atomistic_model( + displacement_fn, scale, shift, apply_mask, init_box=init_box + ) + model = EnergyModel(atomistic_model) + return model + + def build_energy_force_model( + self, + displacement_fn, + scale=1.0, + shift=0.0, + apply_mask=True, + init_box: np.array = np.array([0.0, 0.0, 0.0]), + ): + atomistic_model = self.build_atomistic_model( + displacement_fn, scale, shift, apply_mask, init_box=init_box + ) + model = EnergyForceModel(atomistic_model) + return model diff --git a/apax/model/gmnn.py b/apax/model/gmnn.py new file mode 100644 index 00000000..061658ab --- /dev/null +++ b/apax/model/gmnn.py @@ -0,0 +1,75 @@ +import dataclasses +import logging +from typing import Callable, Tuple, Union + +import flax.linen as nn +import jax +import jax.numpy as jnp +from jax_md import partition +from jax_md.util import Array + +from apax.layers.descriptor.gaussian_moment_descriptor import GaussianMomentDescriptor +from apax.layers.masking import mask_by_atom +from apax.layers.readout import AtomisticReadout +from apax.layers.scaling import PerElementScaleShift +from apax.utils.math import fp64_sum + +DisplacementFn = Callable[[Array, Array], Array] +MDModel = Tuple[partition.NeighborFn, Callable, Callable] + +log = logging.getLogger(__name__) + + +@dataclasses.dataclass +class NeighborSpoof: + idx: jnp.array + + +class AtomisticModel(nn.Module): + descriptor: nn.Module = GaussianMomentDescriptor() + readout: nn.Module = AtomisticReadout() + scale_shift: nn.Module = PerElementScaleShift() + mask_atoms: bool = True + + def __call__( + self, R: Array, Z: Array, neighbor: Union[partition.NeighborList, Array], box + ) -> Array: + if type(neighbor) in [partition.NeighborList, NeighborSpoof]: + idx = neighbor.idx + else: + idx = neighbor + + gm = self.descriptor(R, Z, idx, box) + h = jax.vmap(self.readout)(gm) + output = self.scale_shift(h, Z) + + if self.mask_atoms: + output = mask_by_atom(output, Z) + + return output + + +class EnergyModel(nn.Module): + atomistic_model: AtomisticModel = AtomisticModel() + + def __call__(self, R: Array, Z: Array, neighbor: partition.NeighborList, box): + atomic_energies = self.atomistic_model(R, Z, neighbor, box=box) + total_energy = fp64_sum(atomic_energies) + return total_energy + + +class EnergyForceModel(nn.Module): + atomistic_model: AtomisticModel = AtomisticModel() + + def __call__(self, R: Array, Z: Array, idx: Array, box): + neighbor = NeighborSpoof(idx) + + def energy_fn(R, Z, neighbor, box): + atomic_energies = self.atomistic_model(R, Z, neighbor, box=box) + total_energy = fp64_sum(atomic_energies) + return total_energy + + energy, neg_forces = jax.value_and_grad(energy_fn)(R, Z, neighbor, box) + forces = -neg_forces + prediction = {"energy": energy, "forces": forces} + return prediction diff --git a/apax/optimizer/__init__.py b/apax/optimizer/__init__.py new file mode 100644 index 00000000..8d73c6f6 --- /dev/null +++ b/apax/optimizer/__init__.py @@ -0,0 +1,3 @@ +from apax.optimizer.get_optimizer import get_opt + +__all__ = ["get_opt"] diff --git a/gmnn_jax/optimizer/get_optimizer.py b/apax/optimizer/get_optimizer.py similarity index 53% rename from gmnn_jax/optimizer/get_optimizer.py rename to apax/optimizer/get_optimizer.py index 68e383b5..09af9fb4 100644 --- a/gmnn_jax/optimizer/get_optimizer.py +++ b/apax/optimizer/get_optimizer.py @@ -2,6 +2,8 @@ from typing import Any, Callable import optax +from flax import traverse_util +from flax.core.frozen_dict import freeze log = logging.getLogger(__name__) @@ -37,7 +39,17 @@ def get_schedule( return lr_schedule +def make_optimizer(opt, lr, transition_begin, transition_steps, opt_kwargs): + if lr <= 1e-7: + optimizer = optax.set_to_zero() + else: + schedule = get_schedule(lr, transition_begin, transition_steps) + optimizer = opt(schedule, **opt_kwargs) + return optimizer + + def get_opt( + params, transition_begin: int, transition_steps: int, emb_lr: float = 0.02, @@ -49,25 +61,30 @@ def get_opt( ) -> optax._src.base.GradientTransformation: """ Builds an optimizer with different learning rates for each parameter group. - Every optimizer in `optax` is supported. + Several `optax` optimizers are supported. """ log.info("Initializing Optimizer") opt = getattr(optax, opt_name) - emb_schedule = get_schedule(emb_lr, transition_begin, transition_steps) - nn_schedule = get_schedule(nn_lr, transition_begin, transition_steps) - scale_schedule = get_schedule(scale_lr, transition_begin, transition_steps) - shift_schedule = get_schedule(shift_lr, transition_begin, transition_steps) + nn_opt = make_optimizer(opt, nn_lr, transition_begin, transition_steps, opt_kwargs) + emb_opt = make_optimizer(opt, emb_lr, transition_begin, transition_steps, opt_kwargs) + scale_opt = make_optimizer( + opt, scale_lr, transition_begin, transition_steps, opt_kwargs + ) + shift_opt = make_optimizer( + opt, shift_lr, transition_begin, transition_steps, opt_kwargs + ) - label_fn = map_nested_fn(lambda k, _: k) - tx = optax.multi_transform( - { - "w": opt(emb_schedule, **opt_kwargs), - "b": opt(emb_schedule, **opt_kwargs), - "atomic_type_embedding": opt(nn_schedule, **opt_kwargs), - "scale_per_element": opt(scale_schedule, **opt_kwargs), - "shift_per_element": opt(shift_schedule, **opt_kwargs), - }, - label_fn, + partition_optimizers = { + "w": nn_opt, + "b": nn_opt, + "atomic_type_embedding": emb_opt, + "scale_per_element": scale_opt, + "shift_per_element": shift_opt, + } + param_partitions = freeze( + traverse_util.path_aware_map(lambda path, v: path[-1], params) ) + tx = optax.multi_transform(partition_optimizers, param_partitions) + return tx diff --git a/apax/train/__init__.py b/apax/train/__init__.py new file mode 100644 index 00000000..2f9e20cd --- /dev/null +++ b/apax/train/__init__.py @@ -0,0 +1,5 @@ +from apax.train import checkpoints, eval, loss, metrics +from apax.train.run import run +from apax.train.trainer import fit + +__all__ = ["checkpoints", "loss", "metrics", "fit", "run", "eval"] diff --git a/gmnn_jax/train/checkpoints.py b/apax/train/checkpoints.py similarity index 84% rename from gmnn_jax/train/checkpoints.py rename to apax/train/checkpoints.py index 9f50d6ac..15e18e68 100644 --- a/gmnn_jax/train/checkpoints.py +++ b/apax/train/checkpoints.py @@ -1,7 +1,10 @@ +import logging from pathlib import Path from flax.training import checkpoints, train_state +log = logging.getLogger(__name__) + def load_state(model, params, tx, ckpt_dir): start_epoch = 0 @@ -13,9 +16,11 @@ def load_state(model, params, tx, ckpt_dir): target = {"model": state, "epoch": 0} checkpoints_exist = Path(ckpt_dir).is_dir() if checkpoints_exist: + log.info("Loading checkpoint") raw_restored = checkpoints.restore_checkpoint(ckpt_dir, target=target, step=None) state = raw_restored["model"] start_epoch = raw_restored["epoch"] + 1 + log.info("Successfully restored checkpoint from epoch %d", raw_restored["epoch"]) return state, start_epoch diff --git a/gmnn_jax/train/eval.py b/apax/train/eval.py similarity index 78% rename from gmnn_jax/train/eval.py rename to apax/train/eval.py index ed0c6a6e..30350d41 100644 --- a/gmnn_jax/train/eval.py +++ b/apax/train/eval.py @@ -10,23 +10,19 @@ from flax.training import checkpoints from tqdm import trange -from gmnn_jax.config import Config -from gmnn_jax.data.input_pipeline import ( +from apax.config import Config +from apax.data.input_pipeline import ( TFPipeline, create_dict_dataset, initialize_nbr_displacement_fns, ) -from gmnn_jax.data.statistics import energy_per_element -from gmnn_jax.model.gmnn import get_training_model -from gmnn_jax.train.metrics import initialize_metrics -from gmnn_jax.train.run import ( - find_largest_system, - initialize_callbacks, - initialize_loss_fn, -) -from gmnn_jax.train.trainer import make_step_fns -from gmnn_jax.utils.data import load_data, split_atoms, split_label -from gmnn_jax.utils.random import seed_py_np_tf +from apax.data.statistics import energy_per_element +from apax.model import ModelBuilder +from apax.train.metrics import initialize_metrics +from apax.train.run import find_largest_system, initialize_callbacks, initialize_loss_fn +from apax.train.trainer import make_step_fns +from apax.utils.data import load_data, split_atoms, split_label +from apax.utils.random import seed_py_np_tf log = logging.getLogger(__name__) @@ -45,6 +41,7 @@ def load_test_data( config, model_version_path, eval_path, n_test=-1 ): # TODO double code run.py in progress log.info("Running Input Pipeline") + os.makedirs(eval_path, exist_ok=True) if config.data.data_path is not None: log.info(f"Read data file {config.data.data_path}") atoms_list, label_dict = load_data(config.data.data_path) @@ -56,7 +53,6 @@ def load_test_data( test_idxs = get_test_idxs(atoms_list, used_idxs, n_test) - os.makedirs(eval_path, exist_ok=True) np.savez( os.path.join(eval_path, "test_idxs"), test_idxs=test_idxs, @@ -68,6 +64,9 @@ def load_test_data( elif config.data.test_data_path is not None: log.info(f"Read test data file {config.data.test_data_path}") test_atoms_list, test_label_dict = load_data(config.data.test_data_path) + test_atoms_list = test_atoms_list[:n_test] + for key, val in test_label_dict.items(): + test_label_dict[key] = val[:n_test] else: raise ValueError("input data path/paths not defined") @@ -76,17 +75,17 @@ def load_test_data( def initialize_test_dataset(test_atoms_list, test_label_dict, config): ds_stats = energy_per_element( - test_atoms_list, lambd=config.data.energy_regularisation + atoms_list=test_atoms_list, lambd=config.data.energy_regularisation ) displacement_fn, neighbor_fn = initialize_nbr_displacement_fns( - test_atoms_list[0], config.model.r_max + atoms=test_atoms_list[0], cutoff=config.model.r_max ) ds_stats.displacement_fn = displacement_fn test_inputs, test_labels = create_dict_dataset( - test_atoms_list, - neighbor_fn, - test_label_dict, + atoms_list=test_atoms_list, + neighbor_fn=neighbor_fn, + external_labels=test_label_dict, disable_pbar=config.progress_bar.disable_nl_pbar, pos_unit=config.data.pos_unit, energy_unit=config.data.energy_unit, @@ -96,10 +95,10 @@ def initialize_test_dataset(test_atoms_list, test_label_dict, config): ds_stats.n_atoms = max_atoms test_ds = TFPipeline( - test_inputs, - test_labels, - 1, - config.data.batch_size, + inputs=test_inputs, + labels=test_labels, + n_epoch=1, + batch_size=config.data.batch_size, max_atoms=max_atoms, max_nbrs=max_nbrs, buffer_size=config.data.shuffle_buffer_size, @@ -163,7 +162,7 @@ def eval_model(config_path, n_test=-1, log_file="eval.log", log_level="error"): } logging.basicConfig(filename=log_file, level=log_levels[log_level]) - log.info("Start model evaluation") + log.info("Starting model evaluation") log.info("Loading user config") if isinstance(config_path, (str, os.PathLike)): with open(config_path, "r") as stream: @@ -185,19 +184,19 @@ def eval_model(config_path, n_test=-1, log_file="eval.log", log_level="error"): ) test_ds, ds_stats = initialize_test_dataset(test_atoms_list, test_label_dict, config) + init_input = test_ds.init_input() + init_box = np.array(init_input["box"][0]) - model_dict = config.model.get_dict() - - gmnn = get_training_model( - n_atoms=ds_stats.n_atoms, - n_species=ds_stats.n_species, + builder = ModelBuilder(config.model.get_dict(), n_species=ds_stats.n_species) + model = builder.build_energy_force_model( displacement_fn=ds_stats.displacement_fn, - elemental_energies_mean=ds_stats.elemental_shift, - elemental_energies_std=ds_stats.elemental_scale, - **model_dict, + scale=ds_stats.elemental_scale, + shift=ds_stats.elemental_shift, + apply_mask=True, + init_box=init_box, ) - model = jax.vmap(gmnn.apply, in_axes=(None, 0, 0, 0)) + model = jax.vmap(model.apply, in_axes=(None, 0, 0, 0, 0)) params = load_params(model_version_path) diff --git a/gmnn_jax/train/loss.py b/apax/train/loss.py similarity index 98% rename from gmnn_jax/train/loss.py rename to apax/train/loss.py index c3282ced..b69a6e5e 100644 --- a/gmnn_jax/train/loss.py +++ b/apax/train/loss.py @@ -4,7 +4,7 @@ import einops import jax.numpy as jnp -from gmnn_jax.utils.math import normed_dotp +from apax.utils.math import normed_dotp def weighted_squared_error( diff --git a/gmnn_jax/train/metrics.py b/apax/train/metrics.py similarity index 98% rename from gmnn_jax/train/metrics.py rename to apax/train/metrics.py index bd988ee9..151cfe68 100644 --- a/gmnn_jax/train/metrics.py +++ b/apax/train/metrics.py @@ -4,7 +4,7 @@ import jax.numpy as jnp from clu import metrics -from gmnn_jax.utils.math import normed_dotp +from apax.utils.math import normed_dotp log = logging.getLogger(__name__) diff --git a/gmnn_jax/train/run.py b/apax/train/run.py similarity index 79% rename from gmnn_jax/train/run.py rename to apax/train/run.py index 98f49a0b..afaac60d 100644 --- a/gmnn_jax/train/run.py +++ b/apax/train/run.py @@ -7,22 +7,24 @@ import numpy as np import tensorflow as tf import yaml +from flax.training import checkpoints from keras.callbacks import CSVLogger, TensorBoard -from gmnn_jax.config import Config -from gmnn_jax.data.input_pipeline import ( +from apax.config import Config +from apax.data.input_pipeline import ( TFPipeline, create_dict_dataset, initialize_nbr_displacement_fns, ) -from gmnn_jax.data.statistics import energy_per_element -from gmnn_jax.model.gmnn import get_training_model -from gmnn_jax.optimizer import get_opt -from gmnn_jax.train.loss import Loss, LossCollection -from gmnn_jax.train.metrics import initialize_metrics -from gmnn_jax.train.trainer import fit -from gmnn_jax.utils.data import load_data, split_atoms, split_idxs, split_label -from gmnn_jax.utils.random import seed_py_np_tf +from apax.data.statistics import energy_per_element +from apax.model import ModelBuilder +from apax.optimizer import get_opt +from apax.train.loss import Loss, LossCollection +from apax.train.metrics import initialize_metrics +from apax.train.trainer import fit +from apax.transfer_learning import param_transfer +from apax.utils.data import load_data, split_atoms, split_idxs, split_label +from apax.utils.random import seed_py_np_tf log = logging.getLogger(__name__) @@ -86,7 +88,8 @@ def initialize_datasets(config, raw_datasets): train_atoms_list, lambd=config.data.energy_regularisation ) displacement_fn, neighbor_fn = initialize_nbr_displacement_fns( - train_atoms_list[0], config.model.r_max + train_atoms_list[0], + config.model.r_max, ) ds_stats.displacement_fn = displacement_fn @@ -157,12 +160,14 @@ def initialize_callbacks(callback_configs, model_version_path): "path": "log.csv", "path_arg_name": "filename", "kwargs": {"append": True}, + "model": TFModelSpoof(), }, "tensorboard": { "class": TensorBoard, "path": "tb_logs", "path_arg_name": "log_dir", "kwargs": {}, + "model": tf.keras.Model(), }, } callbacks = [] @@ -175,9 +180,10 @@ def initialize_callbacks(callback_configs, model_version_path): kwargs = callback_info["kwargs"] callback = callback_info["class"](**path, **kwargs) + callback.set_model(callback_info["model"]) callbacks.append(callback) - return tf.keras.callbacks.CallbackList([callback], model=TFModelSpoof()) + return tf.keras.callbacks.CallbackList([callback]) def initialize_loss_fn(loss_config_list): @@ -222,35 +228,50 @@ def run(user_config, log_file="train.log", log_level="error"): raw_datasets = load_data_files(config.data, model_version_path) train_ds, val_ds, ds_stats = initialize_datasets(config, raw_datasets) - model_dict = config.model.get_dict() - - gmnn = get_training_model( - n_atoms=ds_stats.n_atoms, - # ^This is going to make problems when training on differently sized molecules. - # we may need to check batch shapes and manually initialize a new model - # when a new size is encountered... - n_species=ds_stats.n_species, - displacement_fn=ds_stats.displacement_fn, - elemental_energies_mean=ds_stats.elemental_shift, - elemental_energies_std=ds_stats.elemental_scale, - **model_dict, - ) log.info("Initializing Model") init_input = train_ds.init_input() - R, Z, idx = ( + R, Z, idx, init_box = ( jnp.asarray(init_input["positions"][0]), jnp.asarray(init_input["numbers"][0]), jnp.asarray(init_input["idx"][0]), + np.array(init_input["box"][0]), + ) + + # TODO n_species should be optional since it's already + # TODO determined by the shape of shift and scale + builder = ModelBuilder(config.model.get_dict(), n_species=ds_stats.n_species) + model = builder.build_energy_force_model( + displacement_fn=ds_stats.displacement_fn, + scale=ds_stats.elemental_scale, + shift=ds_stats.elemental_shift, + apply_mask=True, + init_box=init_box, ) rng_key, model_rng_key = jax.random.split(rng_key, num=2) - params = gmnn.init(model_rng_key, R, Z, idx) - batched_model = jax.vmap(gmnn.apply, in_axes=(None, 0, 0, 0)) + params = model.init(model_rng_key, R, Z, idx, init_box) + + do_transfer_learning = config.checkpoints.base_model_checkpoint is not None + if do_transfer_learning: + log.info( + "Transfering parameters from %s", config.checkpoints.base_model_checkpoint + ) + raw_restored = checkpoints.restore_checkpoint( + config.checkpoints.base_model_checkpoint, target=None, step=None + ) + source_params = jax.tree_map(jnp.asarray, raw_restored["model"]["params"]) + params = param_transfer(source_params, params, config.checkpoints.reset_layers) + + batched_model = jax.vmap(model.apply, in_axes=(None, 0, 0, 0, 0)) steps_per_epoch = train_ds.steps_per_epoch() n_epochs = config.n_epochs transition_steps = steps_per_epoch * n_epochs - config.optimizer.transition_begin - tx = get_opt(transition_steps=transition_steps, **config.optimizer.dict()) + tx = get_opt( + params, + transition_steps=transition_steps, + **config.optimizer.dict(), + ) fit( batched_model, diff --git a/gmnn_jax/train/trainer.py b/apax/train/trainer.py similarity index 96% rename from gmnn_jax/train/trainer.py rename to apax/train/trainer.py index 41b0e1dc..643149b9 100644 --- a/gmnn_jax/train/trainer.py +++ b/apax/train/trainer.py @@ -6,7 +6,7 @@ import numpy as np from tqdm import trange -from gmnn_jax.train.checkpoints import CheckpointManager, load_state +from apax.train.checkpoints import CheckpointManager, load_state log = logging.getLogger(__name__) @@ -119,8 +119,8 @@ def fit( def calc_loss(params, inputs, labels, loss_fn, model): - R, Z, idx = inputs["positions"], inputs["numbers"], inputs["idx"] - predictions = model(params, R, Z, idx) + R, Z, idx, box = inputs["positions"], inputs["numbers"], inputs["idx"], inputs["box"] + predictions = model(params, R, Z, idx, box) loss = loss_fn(inputs, labels, predictions) return loss, predictions @@ -132,7 +132,6 @@ def make_step_fns(loss_fn, Metrics, model): def train_step(state, inputs, labels, batch_metrics): grad_fn = jax.value_and_grad(loss_calculator, 0, has_aux=True) (loss, predictions), grads = grad_fn(state.params, inputs, labels) - state = state.apply_gradients(grads=grads) new_batch_metrics = Metrics.single_from_model_output( diff --git a/apax/transfer_learning/__init__.py b/apax/transfer_learning/__init__.py new file mode 100644 index 00000000..c31db7a2 --- /dev/null +++ b/apax/transfer_learning/__init__.py @@ -0,0 +1,3 @@ +from apax.transfer_learning.parameter_transfer import param_transfer + +__all__ = ["param_transfer"] diff --git a/apax/transfer_learning/parameter_transfer.py b/apax/transfer_learning/parameter_transfer.py new file mode 100644 index 00000000..fcee1299 --- /dev/null +++ b/apax/transfer_learning/parameter_transfer.py @@ -0,0 +1,22 @@ +import logging + +from flax.core.frozen_dict import freeze, unfreeze +from flax.traverse_util import flatten_dict, unflatten_dict + +log = logging.getLogger(__name__) + + +def param_transfer(source_params, target_params, param_black_list): + source_params = unfreeze(source_params) + target_params = unfreeze(target_params) + + flat_source = flatten_dict(source_params) + flat_target = flatten_dict(target_params) + for p, v in flat_source.items(): + if p[-2] not in param_black_list: + flat_target[p] = v + log.info("Transfering parameter: %s", p) + + transfered_target = unflatten_dict(flat_target) + transfered_target = freeze(transfered_target) + return transfered_target diff --git a/apax/utils/__init__.py b/apax/utils/__init__.py new file mode 100644 index 00000000..a517ae5a --- /dev/null +++ b/apax/utils/__init__.py @@ -0,0 +1,3 @@ +from apax.utils import convert, data, math, random + +__all__ = ["convert", "data", "math", "random"] diff --git a/gmnn_jax/utils/convert.py b/apax/utils/convert.py similarity index 100% rename from gmnn_jax/utils/convert.py rename to apax/utils/convert.py diff --git a/gmnn_jax/utils/data.py b/apax/utils/data.py similarity index 85% rename from gmnn_jax/utils/data.py rename to apax/utils/data.py index ce6508e7..4dff6aa5 100644 --- a/gmnn_jax/utils/data.py +++ b/apax/utils/data.py @@ -6,6 +6,7 @@ from ase import Atoms from ase.io import read from ase.units import Ang, Bohr, Hartree, eV, kcal, kJ, mol +from jax_md import space log = logging.getLogger(__name__) @@ -16,7 +17,8 @@ def make_minimal_input(): jnp.array([6, 8]), jnp.array([[1], [0]]), ) - return R, Z, idx + box = np.array([0.0, 0.0, 0.0]) + return R, Z, idx, box def load_data(data_path): @@ -105,7 +107,7 @@ def convert_atoms_to_arrays( }, "fixed": { "n_atoms": [], - "cell": [], + "box": [], }, } @@ -117,7 +119,7 @@ def convert_atoms_to_arrays( "energy": [], }, } - DTYPE = np.float32 + DTYPE = np.float64 unit_dict = { "Ang": Ang, @@ -127,16 +129,34 @@ def convert_atoms_to_arrays( "Hartree": Hartree, "kJ/mol": kJ / mol, } + box = np.array(atoms_list[0].cell.lengths()) + pbc = np.all(box > 1e-6) for atoms in atoms_list: - inputs["ragged"]["positions"].append( - (atoms.positions * unit_dict[pos_unit]).astype(DTYPE) - ) + box = np.diagonal(atoms.cell * unit_dict[pos_unit]).astype(DTYPE) + inputs["fixed"]["box"].append(box) + + if pbc != np.all(box > 1e-6): + raise ValueError( + "Apax does not support dataset periodic and non periodic structures" + ) + + if np.all(box < 1e-6): + inputs["ragged"]["positions"].append( + (atoms.positions * unit_dict[pos_unit]).astype(DTYPE) + ) + else: + inv_box = np.divide(1, box, where=box != 0) + inputs["ragged"]["positions"].append( + np.array( + space.transform( + inv_box, (atoms.positions * unit_dict[pos_unit]).astype(DTYPE) + ) + ) + ) + inputs["ragged"]["numbers"].append(atoms.numbers) inputs["fixed"]["n_atoms"].append(len(atoms)) - if atoms.pbc.any(): - cell = np.array(atoms.cell).diagonal().astype(DTYPE) - inputs["fixed"]["cell"].append(list(cell)) for key, val in atoms.calc.results.items(): if key == "forces": diff --git a/gmnn_jax/utils/math.py b/apax/utils/math.py similarity index 52% rename from gmnn_jax/utils/math.py rename to apax/utils/math.py index ef4d4a82..9206acec 100644 --- a/gmnn_jax/utils/math.py +++ b/apax/utils/math.py @@ -1,4 +1,15 @@ +from typing import Iterable, Optional, Union + import jax.numpy as jnp +from jax_md.util import Array + + +def fp64_sum( + X: Array, axis: Optional[Union[Iterable[int], int]] = None, keepdims: bool = False +): + dtyp = jnp.float64 + result = jnp.sum(X, axis=axis, dtype=dtyp, keepdims=keepdims) + return result def normed_dotp(F_0, F_pred): diff --git a/gmnn_jax/utils/random.py b/apax/utils/random.py similarity index 100% rename from gmnn_jax/utils/random.py rename to apax/utils/random.py diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..d0c3cbf1 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 00000000..747ffb7b --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/source/_tutorials/md_with_ase.rst b/docs/source/_tutorials/md_with_ase.rst new file mode 100644 index 00000000..4fddd42c --- /dev/null +++ b/docs/source/_tutorials/md_with_ase.rst @@ -0,0 +1,9 @@ +# The ASE calculator + +If you require some ASE features during your simulation, we provide an alternative to the JaxMD interface. + +An ASE calculator of a trained model can be instantiated as follows + +CODE + +Please refer to the ASE documentation LINK to see how to use ASE calculators. \ No newline at end of file diff --git a/docs/source/_tutorials/molecular_dynamics.rst b/docs/source/_tutorials/molecular_dynamics.rst new file mode 100644 index 00000000..b90c9997 --- /dev/null +++ b/docs/source/_tutorials/molecular_dynamics.rst @@ -0,0 +1,61 @@ +# Molecular Dynamics with JaxMD + + +JaxMD LINK is a high performance molecular dynamics engine built on top of Jax LINK. +Out of the boy, apax ships with a simple simulation loop using the Nose-Hoover-Chain thermostat implemented in JaxMD. +Note that this tutorial assumes that you have a trained model at hand. +See the previous tutorial LINK for further information. + +## Configuration +We can once again use the template command to give ourselves a quickstart. + +`apax template md --minimal` + +Open the config and specify the starting structure and simulation parameters. +If you specify the data set file itself, the first structure of the data set is going to be used as the initial structure. +Your `md_config_minimal.yaml` should look similar to this: + +```yaml +duration: 20_000 # fs +initial_structure: md17.extxyz +``` + +As with training configurations, we can use the `validate` command to ensure our input is valid before we submit the calculation. + +## Running the simulation + +The simulation can be started by running + +`apax md config.yaml md_config_minimal.yaml` + +where `config.yaml` is the configuration file that was used to train the model. + +During the simulation, a progress bar tracks the instantaneous temperature at each outer step. + +`prog bar` + +## Calculating Vibrational Spectra + +The trajectory calculated above can be used to obtain physical observables. +For this tutorial, we are going to compute an anharmonic vibrational spectrum for benzene. +Note that the code below is intended simply demonstration purposes and more sophisticated trajectory analysis tools should be used in production simulations. + +```python +# analysis.py: +... +``` + +PIC + +Congratulations, you have calculated the first observable from a trajectory generated with apax and jaxMD! + + + +## Custom Simulation Loops + +More complex simulation loops are relatively easy to build yourself in JaxMD (see their colab notebooks for examples). +Trained apax models can of course be used as `energy_fn` in such custom simulations. +If you have a suggestion for adding some MD feature or thermostat to the core of `apax`, feel free to open up an issue on Github LINK. + + + diff --git a/docs/source/_tutorials/training_a_model.rst b/docs/source/_tutorials/training_a_model.rst new file mode 100644 index 00000000..16991ea0 --- /dev/null +++ b/docs/source/_tutorials/training_a_model.rst @@ -0,0 +1,77 @@ +# Training + +## Acquiring a dataset + +In this tutorial we are going to train a model from scratch on a molecular dataset from the MD17 collection. +Start by creating a project folder and downloading the dataset. + +mkdir project +cd project + +You can obtain the benzene dataset either by running the following command or manually from this website. + +curl ... ... + +apax uses ASE to read in datasets, so make sure to convert your own data into an ASE readable format (extxyz, traj etc). + + +## Configuration files + +Next, we require a configuration file that specifies the model and training parameters. +In order to get users quickly up and running, our command line interface provides an easy way to generate input templates. +The provided templates come in in two levels of verbosity: minimal and full. +In the following we are going to use a minimal input file. To see a complete list and explanation of all parameters, consult the documentation page LINK. +For more information on the CLI, simply run `apax -h`. + +apax template train --minimal + +Open the resulting `config_minimal.yaml` file in an editor of your choice and make sure to fill in the data path field with the name of the data set you just downloaded. +For the purposes of this tutorial we will train on 1000 data points and validate the model on 200 more during the training. + +The filled in configuration file should look similar to this one. + +```yaml +data: + data_path: md17.extexyz + epochs: 1000 + n_train: 1000 + .... +``` + +In order to check whether the a configuration file is valid, we provide the `validate` command. This is especially convenient when submitting training runs on a compute cluster. + +`apax validate train config_minimal.yaml` + +Configuration files are validated using Pydantic and the errors provided by the `validate` command give precise instructions on how to fix the input file. +For example, changing `epochs` to `-1000`, validate will give the following feedback to the user: + +`PYDANTIC ERROR` + +## Training + +Model training can be started by running + +`apax train config.yaml` + +During training, apax displays a progress bar to keep track of the validation loss. +This progress bar is optional however and can be turned off in the config. LINK +The default configuration writes training metrics to a CSV file, but TensorBoard is also supported. +One can specify which to use by adding the following section to the input file: + +```yaml +callbacks: + - CSV +``` + +If training is interupted for any reason, re-running the above `train` command will resume training from the latest checkpoint. + +## Evaluation + +After the training is completed and we are satisfied with our choice of hyperparameters and vadliation loss, we can evaluate the model on the test set. +We provide a separate command for test set evaluation: + +`apax evaluate config_minimal.yaml` + +TODO pretty print results to the terminal + +Congratulations, you have successfully trained and evaluated your fitrst apax model! \ No newline at end of file diff --git a/docs/source/_tutorials/transfer_learning.rst b/docs/source/_tutorials/transfer_learning.rst new file mode 100644 index 00000000..4f0e1662 --- /dev/null +++ b/docs/source/_tutorials/transfer_learning.rst @@ -0,0 +1,22 @@ +# Transfer Learning + +apax comes with discriminative transfer learning capabilities out of the box. +In this tutorial we are going to fine tune a model trained on benzene data at the DFT level of theory to CCSDT. + +First download the appropriate dataset from the sgdml website. + + +Transfer learning can be facilitated in apax by adding the path to a pre-trained model in the config. +Furthermore, we can freeze or reduce the learning rate of various components by adjusting the `optimizer` section of the config. + +```yaml +optimizer: + nn_lr: 0.004 + embedding_lr: 0.0 +``` + +Learning rates of 0.0 will mask the respective weights during training steps. +Here, we will freeze the descriptor, reinitialize the scaling and shifting parameters and reduce the learning rate of all other components. + +We can now fine tune the model by running +`apax train config.yaml` diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 00000000..7d1f1a8c --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,39 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +import os +import sys + +sys.path.insert(0, os.path.abspath(".")) +sys.path.insert(0, os.path.abspath("../../")) + +project = "apax" +copyright = "2023, Moritz Schäfer, Nico Segreto, Johannes Kästner" +author = "Moritz Schäfer, Nico Segreto, Johannes Kästner" +release = "0.1.0" + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "sphinx.ext.autodoc", + "sphinx_autodoc_typehints", + "sphinx.ext.napoleon", + "sphinx.ext.autosummary", + "sphinx.ext.mathjax", +] + +templates_path = ["_templates"] +exclude_patterns = [] + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = "sphinx_rtd_theme" +html_static_path = ["_static"] diff --git a/docs/source/getting_started/install.rst b/docs/source/getting_started/install.rst new file mode 100644 index 00000000..1e495950 --- /dev/null +++ b/docs/source/getting_started/install.rst @@ -0,0 +1,46 @@ +============ +Installation +============ + +If you do not have Poetry_ installed already, you can obtain it by running + +.. highlight:: bash +.. code-block:: bash + + curl -sSL https://install.python-poetry.org | python3 - + + +Now you can add apax to your project by running + +.. highlight:: bash +.. code-block:: bash + + poetry add git+https://github.com/apax-hub/apax.git + +As a developer, you first need to clone the repository and install it via + +.. highlight:: bash +.. code-block:: bash + + git clone https://github.com/apax-hub/apax.git + cd + poetry install + +CUDA Support +============ +Note that the above only installs the CPU version. +If you want to enable GPU support, please overwrite the jaxlib version: + +.. highlight:: bash +.. code-block:: bash + + pip install --upgrade pip + # Installs the wheel compatible with CUDA 11 and cuDNN 8.6 or newer. + # Note: wheels only available on linux. + pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html + + +See the `Jax installation instructions `_ for more details. + + +.. _Poetry: https://python-poetry.org/ \ No newline at end of file diff --git a/docs/source/getting_started/tutorials.rst b/docs/source/getting_started/tutorials.rst new file mode 100644 index 00000000..e69de29b diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 00000000..9a6d9e06 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,47 @@ +.. apax documentation master file, created by + sphinx-quickstart on Wed Jan 25 21:12:03 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +=================================================== +apax: Gaussian Moment Neural Networks - in Jax! +=================================================== + +`apax` is an implementation of Gaussian Moment Neural Networks in Jax. +It facilitates the training and evaluation of atomistic neural networks, notably GM-NN, for molecular simulations. +Building on top of Jax allows for easy integration with the high-performance molecular dynamics engine `JaxMD `_ + +Contents +======== +.. toctree:: + :maxdepth: 1 + :caption: Getting Started + + getting_started/install + +.. .. toctree:: TODO +.. :maxdepth: 1 +.. :caption: Tutorials + +.. tutorials/... + +Package +======= +.. toctree:: + :maxdepth: 2 + :caption: Package + +Config +===================== +.. automodule:: apax.config + :members: + + .. modules/index + + +Indices and Tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/source/modules/config.rst b/docs/source/modules/config.rst new file mode 100644 index 00000000..4ac0ec5b --- /dev/null +++ b/docs/source/modules/config.rst @@ -0,0 +1,17 @@ +Configuration +============= + +:mod:`apax.config` + +Training Configuration +---------------------- + +.. autoclass:: apax.config.train_config.Config + :members: + + +Molecular Dynamics Configuration +-------------------------------- + +.. autoclass:: apax.config.md_config.MDConfig + :members: diff --git a/docs/source/modules/data.rst b/docs/source/modules/data.rst new file mode 100644 index 00000000..a7799603 --- /dev/null +++ b/docs/source/modules/data.rst @@ -0,0 +1,2 @@ +Data Pipeline +============== \ No newline at end of file diff --git a/docs/source/modules/index.rst b/docs/source/modules/index.rst new file mode 100644 index 00000000..391c5c83 --- /dev/null +++ b/docs/source/modules/index.rst @@ -0,0 +1,14 @@ +Modules +======= + +.. toctree:: + :maxdepth: 1 + + config + data + optimizer + layers + model + train + md + utils \ No newline at end of file diff --git a/docs/source/modules/layers.rst b/docs/source/modules/layers.rst new file mode 100644 index 00000000..2a9b99fd --- /dev/null +++ b/docs/source/modules/layers.rst @@ -0,0 +1,2 @@ +Layers +====== diff --git a/docs/source/modules/md.rst b/docs/source/modules/md.rst new file mode 100644 index 00000000..0eb84f19 --- /dev/null +++ b/docs/source/modules/md.rst @@ -0,0 +1,2 @@ +Molecular Dynamics +================== diff --git a/docs/source/modules/model.rst b/docs/source/modules/model.rst new file mode 100644 index 00000000..a600a8d7 --- /dev/null +++ b/docs/source/modules/model.rst @@ -0,0 +1,2 @@ +Model +===== \ No newline at end of file diff --git a/docs/source/modules/optimizer.rst b/docs/source/modules/optimizer.rst new file mode 100644 index 00000000..2dcabd65 --- /dev/null +++ b/docs/source/modules/optimizer.rst @@ -0,0 +1,2 @@ +Optimizers +========== diff --git a/docs/source/modules/train.rst b/docs/source/modules/train.rst new file mode 100644 index 00000000..80d1991b --- /dev/null +++ b/docs/source/modules/train.rst @@ -0,0 +1,2 @@ +Training +======== diff --git a/docs/source/modules/utils.rst b/docs/source/modules/utils.rst new file mode 100644 index 00000000..20858503 --- /dev/null +++ b/docs/source/modules/utils.rst @@ -0,0 +1,2 @@ +Utils +===== \ No newline at end of file diff --git a/examples/example_parabola.py b/examples/example_parabola.py index 3e8e2156..263c62aa 100644 --- a/examples/example_parabola.py +++ b/examples/example_parabola.py @@ -23,7 +23,7 @@ from flax.training import checkpoints, train_state from tensorflow.keras.callbacks import CSVLogger -from gmnn_jax.utils.random import seed_py_np_tf +from apax.utils.random import seed_py_np_tf seed_py_np_tf() diff --git a/examples/example_ragged_dataset.py b/examples/example_ragged_dataset.py index a6123726..d9a68ada 100644 --- a/examples/example_ragged_dataset.py +++ b/examples/example_ragged_dataset.py @@ -42,6 +42,7 @@ ds = tf.data.Dataset.from_tensor_slices(data) + def pad_to_largest_element(data): # pads ragged tensor to regular tensor with size of the largest element in batch data = data.to_tensor() @@ -51,18 +52,21 @@ def pad_to_largest_element(data): class PadToMaxElement: def __init__(self, n_max) -> None: self.n_max = n_max + def __call__(self, data): # pads ragged tensor to regular tensor with size of the largest element in batch for key, val in data.items(): if key == "padded_pos": shape = data[key].shape - data[key] = data[key].to_tensor(default_value=0.0, shape=[shape[0], self.n_max, shape[-1]]) + data[key] = data[key].to_tensor( + default_value=0.0, shape=[shape[0], self.n_max, shape[-1]] + ) else: data[key] = data[key].to_tensor() return data -ds = ds.shuffle(32 * 3).batch(1).map(PadToMaxElement(20))#.map(pad_to_largest_element) +ds = ds.shuffle(32 * 3).batch(1).map(PadToMaxElement(20)) # .map(pad_to_largest_element) shapes = [] diff --git a/examples/example_shuffle.py b/examples/example_shuffle.py index 3c889b0d..639dceac 100644 --- a/examples/example_shuffle.py +++ b/examples/example_shuffle.py @@ -2,7 +2,7 @@ from ase import Atoms from ase.calculators.singlepoint import SinglePointCalculator -from gmnn_jax.data.input_pipeline import TFPipeline +from apax.data.input_pipeline import TFPipeline num_data = 9 atoms_list = [] @@ -29,9 +29,7 @@ atoms.calc = SinglePointCalculator(atoms, **results) atoms_list.append(atoms) -ds = TFPipeline( - cutoff=6.0, batch_size=batch_size, atoms_list=atoms_list, n_epoch=n_epoch -) +ds = TFPipeline(cutoff=6.0, batch_size=batch_size, atoms_list=atoms_list, n_epoch=n_epoch) batch_ds = ds.shuffle_and_batch() step_per_epoch = ds.steps_per_epoch() diff --git a/gmnn_jax/data/__init__.py b/gmnn_jax/data/__init__.py deleted file mode 100644 index 200c7f5d..00000000 --- a/gmnn_jax/data/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from gmnn_jax.data import input_pipeline, preprocessing, statistics - -__all__ = ["input_pipeline", "preprocessing", "statistics"] diff --git a/gmnn_jax/layers/__init__.py b/gmnn_jax/layers/__init__.py deleted file mode 100644 index 222aa26b..00000000 --- a/gmnn_jax/layers/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from gmnn_jax.layers import activation, descriptor, ntk_linear, scaling - -__all__ = ["descriptor", "activation", "ntk_linear", "scaling"] diff --git a/gmnn_jax/layers/descriptor/__init__.py b/gmnn_jax/layers/descriptor/__init__.py deleted file mode 100644 index 1a418caf..00000000 --- a/gmnn_jax/layers/descriptor/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from gmnn_jax.layers.descriptor.gaussian_moment_descriptor import ( - GaussianMomentDescriptor, -) - -__all__ = ["GaussianMomentDescriptor"] diff --git a/gmnn_jax/layers/descriptor/basis_functions.py b/gmnn_jax/layers/descriptor/basis_functions.py deleted file mode 100644 index 963e8fdc..00000000 --- a/gmnn_jax/layers/descriptor/basis_functions.py +++ /dev/null @@ -1,80 +0,0 @@ -from typing import Optional - -import einops -import haiku as hk -import jax.numpy as jnp -import numpy as np - - -class GaussianBasis(hk.Module): - def __init__( - self, n_basis, r_min, r_max, dtype=jnp.float32, name: Optional[str] = None - ): - super().__init__(name) - - self.betta = n_basis**2 / r_max**2 - self.rad_norm = (2.0 * self.betta / np.pi) ** 0.25 - shifts = r_min + (r_max - r_min) / n_basis * np.arange(n_basis) - - # shape: 1 x n_basis - shifts = einops.repeat(shifts, "n_basis -> 1 n_basis") - self.shifts = jnp.asarray(shifts, dtype=dtype) - - def __call__(self, dr): - dr = einops.repeat(dr, "neighbors -> neighbors 1") - # 1 x n_basis, neighbors x 1 -> neighbors x n_basis - distances = self.shifts - dr - - # shape: neighbors x n_basis - basis = jnp.exp(-self.betta * (distances**2)) - basis = self.rad_norm * basis - - return basis - - -class RadialFunction(hk.Module): - def __init__( - self, - n_species, - n_basis, - n_radial, - r_min, - r_max, - emb_init=None, - dtype=jnp.float32, - name: Optional[str] = None, - ): - super().__init__(name) - - self.basis_fn = GaussianBasis(n_basis, r_min, r_max) - - self.embed_norm = jnp.array(1.0 / np.sqrt(n_basis), dtype=dtype) - self.embeddings = hk.get_parameter( - "atomic_type_embedding", - shape=(n_species, n_species, n_radial, n_basis), - init=hk.initializers.RandomUniform(-1.0, 1.0), - dtype=dtype, - ) - - self.dtype = dtype - - def __call__(self, dr, Z_i, Z_j, cutoff): - # basis shape: neighbors x n_basis - basis = self.basis_fn(dr) - - # coeffs shape: n_radialx n_basis - species_pair_coeffs = self.embeddings[ - Z_j, Z_i, ... - ] # reverse convention to match original - species_pair_coeffs = self.embed_norm * species_pair_coeffs - - # radial shape: neighbors x n_radial - radial_function = einops.einsum( - species_pair_coeffs, basis, "nbrs radial basis, nbrs basis -> nbrs radial" - ) - cutoff = einops.repeat(cutoff, "neighbors -> neighbors 1") - radial_function = radial_function * cutoff - - assert radial_function.dtype == self.dtype - - return radial_function diff --git a/gmnn_jax/layers/ntk_linear.py b/gmnn_jax/layers/ntk_linear.py deleted file mode 100644 index 22c86a28..00000000 --- a/gmnn_jax/layers/ntk_linear.py +++ /dev/null @@ -1,40 +0,0 @@ -from typing import Optional - -import haiku as hk -import jax.numpy as jnp -from haiku.initializers import Constant, RandomNormal - - -class NTKLinear(hk.Module): - def __init__( - self, units, b_init="normal", dtype=jnp.float32, name: Optional[str] = None - ): - super().__init__(name) - self.units = units - - self.w_init = RandomNormal(stddev=1.0, mean=0.0) - - if b_init == "normal": - self.b_init = RandomNormal(stddev=1.0, mean=0.0) - elif b_init == "zeros": - self.b_init = Constant(constant=0.0) - else: - raise NotImplementedError( - "Only random normal and zeros intialization of the bias is supported." - ) - self.dtype = dtype - - def __call__(self, inputs): - inputs = inputs.astype(self.dtype) - w = hk.get_parameter( - "w", shape=(inputs.shape[0], self.units), init=self.w_init, dtype=self.dtype - ) - b = hk.get_parameter("b", shape=[self.units], init=self.b_init, dtype=self.dtype) - - bias_factor = 0.1 - weight_factor = jnp.sqrt(1.0 / inputs.shape[0]) - - wx = jnp.dot(inputs, w) - prediction = weight_factor * wx + bias_factor * b - assert prediction.dtype == self.dtype - return prediction diff --git a/gmnn_jax/layers/scaling.py b/gmnn_jax/layers/scaling.py deleted file mode 100644 index 1d83b4bf..00000000 --- a/gmnn_jax/layers/scaling.py +++ /dev/null @@ -1,44 +0,0 @@ -from typing import Optional - -import einops -import haiku as hk -import jax.numpy as jnp - - -class PerElementScaleShift(hk.Module): - def __init__( - self, scale, shift, n_species, dtype=jnp.float32, name: Optional[str] = None - ): - super().__init__(name) - if scale is None: - scale = 1.0 - if shift is None: - shift = jnp.zeros(n_species) - if len(shift.shape) == 1: - shift = einops.repeat(shift, "species -> species 1") - - self.scale = hk.get_parameter( - "scale_per_element", - shape=(n_species, 1), - init=hk.initializers.Constant(scale), - dtype=dtype, - ) - - self.shift = hk.get_parameter( - "shift_per_element", - shape=(n_species, 1), - init=hk.initializers.Constant(shift), - dtype=dtype, - ) - - self.dtype = dtype - - def __call__(self, x, Z): - # x shape: n_atoms x 1 - # Z shape: n_atoms - # scale[Z] shape: n_atoms x 1 - x = x.astype(self.dtype) - out = self.scale[Z] * x + self.shift[Z] - - assert out.dtype == self.dtype - return out diff --git a/gmnn_jax/md/__init__.py b/gmnn_jax/md/__init__.py deleted file mode 100644 index d8094255..00000000 --- a/gmnn_jax/md/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from gmnn_jax.md.ase_calc import ASECalculator -from gmnn_jax.md.nvt import run_md - -__all__ = ["run_md", "ASECalculator"] diff --git a/gmnn_jax/model/__init__.py b/gmnn_jax/model/__init__.py deleted file mode 100644 index 154ff9b7..00000000 --- a/gmnn_jax/model/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from gmnn_jax.model.gmnn import GMNN, get_md_model, get_training_model - -__all__ = ["GMNN", "get_training_model", "get_md_model"] diff --git a/gmnn_jax/model/gmnn.py b/gmnn_jax/model/gmnn.py deleted file mode 100644 index d612b1a2..00000000 --- a/gmnn_jax/model/gmnn.py +++ /dev/null @@ -1,203 +0,0 @@ -import dataclasses -import logging -from typing import Callable, List, Optional, Tuple - -import haiku as hk -import jax -import jax.numpy as jnp -import numpy as np -from jax_md import partition -from jax_md.util import Array, high_precision_sum - -from gmnn_jax.layers.activation import swish -from gmnn_jax.layers.descriptor.gaussian_moment_descriptor import ( - GaussianMomentDescriptor, -) -from gmnn_jax.layers.masking import mask_by_atom -from gmnn_jax.layers.ntk_linear import NTKLinear -from gmnn_jax.layers.scaling import PerElementScaleShift - -DisplacementFn = Callable[[Array, Array], Array] -MDModel = Tuple[partition.NeighborFn, Callable, Callable] - -log = logging.getLogger(__name__) - - -class GMNN(hk.Module): - def __init__( - self, - units: List[int], - displacement: DisplacementFn, - n_atoms: int, - n_basis: int = 7, - n_radial: int = 5, - n_species: int = 119, - r_min: float = 0.5, - r_max: float = 6.0, - b_init: str = "normal", - elemental_energies_mean: Optional[Array] = None, - elemental_energies_std: Optional[Array] = None, - descriptor_dtype=jnp.float32, - readout_dtype=jnp.float32, - scale_shift_dtype=jnp.float32, - apply_mask: bool = True, - name: Optional[str] = None, - ): - super().__init__(name) - self.descriptor = GaussianMomentDescriptor( - displacement, - n_basis, - n_radial, - n_species, - n_atoms, - r_min, - r_max, - dtype=descriptor_dtype, - name="descriptor", - ) - - units = units + [1] - dense = [] - for ii, n_hidden in enumerate(units): - dense.append( - NTKLinear( - n_hidden, b_init=b_init, dtype=readout_dtype, name=f"dense_{ii}" - ) - ) - if ii < len(units) - 1: - dense.append(swish) - self.readout = hk.Sequential(dense, name="readout") - - self.scale_shift = PerElementScaleShift( - scale=elemental_energies_std, - shift=elemental_energies_mean, - n_species=n_species, - dtype=scale_shift_dtype, - name="scale_shift", - ) - - self.scale_shift_dtype = scale_shift_dtype - - self.apply_mask = apply_mask - - def __call__(self, R: Array, Z: Array, neighbor: partition.NeighborList) -> Array: - gm = self.descriptor(R, Z, neighbor) - h = jax.vmap(self.readout)(gm) - output = self.scale_shift(h, Z) - - assert output.dtype == self.scale_shift_dtype - if self.apply_mask: - output = mask_by_atom(output, Z) - - return output - - -def get_md_model( - atomic_numbers: Array, - displacement: DisplacementFn, - nn: List[int] = [512, 512], - box_size: float = 100.0, - r_max: float = 6.0, - n_basis: int = 7, - n_radial: int = 5, - dr_threshold: float = 0.5, - nl_format: partition.NeighborListFormat = partition.Sparse, - descriptor_dtype=jnp.float32, - readout_dtype=jnp.float32, - scale_shift_dtype=jnp.float32, - **neighbor_kwargs, -) -> MDModel: - neighbor_fn = partition.neighbor_list( - displacement, - box_size, - r_max, - dr_threshold, - fractional_coordinates=False, - format=nl_format, - **neighbor_kwargs, - ) - - n_atoms = atomic_numbers.shape[0] - Z = jnp.asarray(atomic_numbers) - # casting ot python int prevents n_species from becoming a tracer, - # which causes issues in the NVT `apply_fn` - n_species = int(np.max(Z) + 1) - - @hk.without_apply_rng - @hk.transform - def model(R, neighbor): - gmnn = GMNN( - nn, - displacement, - n_atoms=n_atoms, - n_basis=n_basis, - n_radial=n_radial, - n_species=n_species, - r_max=r_max, - descriptor_dtype=descriptor_dtype, - readout_dtype=readout_dtype, - scale_shift_dtype=scale_shift_dtype, - ) - out = gmnn(R, Z, neighbor) - return high_precision_sum(out) - - return neighbor_fn, model - - -@dataclasses.dataclass -class NeighborSpoof: - idx: jnp.array - - -def get_training_model( - n_atoms: int, - n_species: int, - displacement_fn: DisplacementFn, - nn: List[int], - n_basis: int = 7, - n_radial: int = 5, - r_min: float = 0.5, - r_max: float = 6.0, - b_init: str = "normal", - elemental_energies_mean: Optional[Array] = None, - elemental_energies_std: Optional[Array] = None, - descriptor_dtype=jnp.float32, - readout_dtype=jnp.float32, - scale_shift_dtype=jnp.float32, -) -> Tuple[Callable, Callable]: - log.info("Bulding Model") - - @hk.without_apply_rng - @hk.transform - def model(R, Z, idx): - gmnn = GMNN( - nn, - displacement_fn, - n_atoms=n_atoms, - n_basis=n_basis, - n_radial=n_radial, - n_species=n_species, - r_min=r_min, - r_max=r_max, - b_init=b_init, - elemental_energies_mean=elemental_energies_mean, - elemental_energies_std=elemental_energies_std, - descriptor_dtype=descriptor_dtype, - readout_dtype=readout_dtype, - scale_shift_dtype=scale_shift_dtype, - ) - neighbor = NeighborSpoof(idx) - - def energy_fn(R, Z, neighbor): - out = gmnn(R, Z, neighbor) - # mask = partition.neighbor_list_mask(neighbor) - # out = out * mask - energy = high_precision_sum(out) - return energy - - energy, neg_forces = jax.value_and_grad(energy_fn)(R, Z, neighbor) - forces = -neg_forces - prediction = {"energy": energy, "forces": forces} - return prediction - - return model diff --git a/gmnn_jax/optimizer/__init__.py b/gmnn_jax/optimizer/__init__.py deleted file mode 100644 index 9e1acea0..00000000 --- a/gmnn_jax/optimizer/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from gmnn_jax.optimizer.get_optimizer import get_opt - -__all__ = ["get_opt"] diff --git a/gmnn_jax/train/__init__.py b/gmnn_jax/train/__init__.py deleted file mode 100644 index 04c2409a..00000000 --- a/gmnn_jax/train/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from gmnn_jax.train import checkpoints, loss, metrics -from gmnn_jax.train.run import run -from gmnn_jax.train.trainer import fit - -__all__ = ["checkpoints", "loss", "metrics", "fit", "run", "eval"] diff --git a/gmnn_jax/utils/__init__.py b/gmnn_jax/utils/__init__.py deleted file mode 100644 index ace9366d..00000000 --- a/gmnn_jax/utils/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from gmnn_jax.utils import convert, data, math, random - -__all__ = ["convert", "data", "math", "random"] diff --git a/gmnn_jax/visualize/__init__.py b/gmnn_jax/visualize/__init__.py deleted file mode 100644 index 74ba13f2..00000000 --- a/gmnn_jax/visualize/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from gmnn_jax.visualize.vis_model import model_tabular - -__all__ = ["model_tabular"] diff --git a/gmnn_jax/visualize/vis_model.py b/gmnn_jax/visualize/vis_model.py deleted file mode 100644 index 1634c90b..00000000 --- a/gmnn_jax/visualize/vis_model.py +++ /dev/null @@ -1,16 +0,0 @@ -import haiku as hk -from rich import print - 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"eb2704ae0e253ccbe682707dd9559d3d02bfbcb0dab8bec2ef3ddf7d1fbc6b77" diff --git a/pyproject.toml b/pyproject.toml index 869c210e..4910c63e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,22 +1,22 @@ [tool.poetry] -name = "gmnn-jax" +name = "apax" version = "0.1.0" -description = "A port of GMNN to JAX" -authors = ["Moritz Schäfer "] +description = "Atomistic Learned Potentail Package in JAX" +authors = ["Moritz René Schäfer ", "Nico Segreto "] +keywords=["machine-learning", "interatomic potentials", "molecular-dynamics"] license = "MIT" readme = "README.md" -packages = [{include = "gmnn_jax"}] +packages = [{include = "apax"}] [tool.poetry.dependencies] -python = "^3.9, <4.0" -numpy = "~1.21" +python = "^3.10, <3.11" +numpy = "^1.24.2" ase = "^3.22.1" pydantic = "^1.10.2" tensorflow = "^2.11.0" -jax = "0.4.1" +jax = "^0.4.1" chex = "^0.1.5" optax = "^0.1.3" -dm-haiku = "^0.0.9" jax-md = "^0.2.3" einops = "^0.6.0" clu = "^0.0.7" @@ -24,7 +24,7 @@ jaxtyping = "^0.2.8" typer = "^0.7.0" [tool.poetry.scripts] -gmnn = 'gmnn_jax.cli.gmnn_app:app' +apax = 'apax.cli.apax_app:app' [tool.poetry.group.dev.dependencies] pytest = "^7.2.0" @@ -39,6 +39,7 @@ matplotlib = "^3.6.2" pre-commit = "^2.20.0" isort = "^5.10.1" flake8 = "^5.0.4" +sphinx-autodoc-typehints = "^1.21.8" [[tool.poetry.source]] name = "jax" @@ -63,7 +64,7 @@ multi_line_output = 3 [tool.coverage.run] branch = true -source = ["gmnn_jax"] +source = ["apax"] [tool.coverage.html] directory = "coverage_html_report" diff --git a/tests/conftest.py b/tests/conftest.py index 2b9135d7..c9d95128 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,7 +5,7 @@ from ase import Atoms from ase.calculators.singlepoint import SinglePointCalculator -from gmnn_jax.utils.random import seed_py_np_tf +from apax.utils.random import seed_py_np_tf @pytest.fixture(autouse=True) @@ -44,7 +44,10 @@ def example_atoms(num_data: int, pbc: bool, calc_results: List[str]) -> Atoms: # lattice = random.choice(["free", "sc", "fcc", "bcc"]) # at the moment we can only work with cubic cells lattice = "sc" - additional_data["cell"] = create_cell(cell_const, lattice) + if pbc: + additional_data["cell"] = create_cell(cell_const, lattice) + else: + additional_data["cell"] = [0, 0, 0] result_shapes = { "energy": (np.random.rand() - 5.0) * 10_000, @@ -69,5 +72,5 @@ def example_atoms(num_data: int, pbc: bool, calc_results: List[str]) -> Atoms: @pytest.fixture(scope="session") def get_tmp_path(tmp_path_factory): - test_path = tmp_path_factory.mktemp("gmnn-jax_tests") + test_path = tmp_path_factory.mktemp("apax_tests") return test_path diff --git a/tests/integration_tests/md/config.yaml b/tests/integration_tests/md/config.yaml index 3e49e651..cf076367 100644 --- a/tests/integration_tests/md/config.yaml +++ b/tests/integration_tests/md/config.yaml @@ -1,7 +1,7 @@ n_epochs: 2 data: - model_name: gmnn_dummy + model_name: apax_dummy data_path: dummy_ds # ds.extxyz #ethanol.traj # ds.extxyz # n_train: 4 diff --git a/tests/integration_tests/md/md_config.yaml b/tests/integration_tests/md/md_config.yaml index 5943ea8b..fdc70a4d 100644 --- a/tests/integration_tests/md/md_config.yaml +++ b/tests/integration_tests/md/md_config.yaml @@ -1,3 +1,4 @@ -temperature: 100 -duration: 2.0 -n_inner: 2 \ No newline at end of file +temperature: 5 +duration: 0.1 +n_inner: 1 +dt: 0.05 \ No newline at end of file diff --git a/tests/integration_tests/md/test_md.py b/tests/integration_tests/md/test_md.py index f4c5f94c..0aba964f 100644 --- a/tests/integration_tests/md/test_md.py +++ b/tests/integration_tests/md/test_md.py @@ -10,10 +10,10 @@ from flax.training import checkpoints from jax_md import partition, space -from gmnn_jax.config import Config, MDConfig -from gmnn_jax.md import run_md -from gmnn_jax.md.ase_calc import ASECalculator -from gmnn_jax.model.gmnn import get_training_model +from apax.config import Config, MDConfig +from apax.md import run_md +from apax.md.ase_calc import ASECalculator +from apax.model.builder import ModelBuilder TEST_PATH = pathlib.Path(__file__).parent.resolve() @@ -34,45 +34,47 @@ def test_run_md(get_tmp_path): model_config = Config.parse_obj(model_config_dict) md_config = MDConfig.parse_obj(md_config_dict) - cell_size = 10.0 - positions = np.array( + positions = jnp.array( [ + [0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], - [0.0, 0.0, 1.0], - ] + ], + dtype=jnp.float64, ) - atomic_numbers = np.array([1, 1, 8]) - cell = np.diag([cell_size] * 3) - atoms = Atoms(atomic_numbers, positions, cell=cell) + atomic_numbers = np.array([1, 2, 2]) + box = np.array([0.0, 0.0, 0.0], dtype=np.float64) + + atoms = Atoms(atomic_numbers, positions, cell=box) write(md_config.initial_structure, atoms) - n_atoms = 3 n_species = int(np.max(atomic_numbers) + 1) - displacement_fn, _ = space.periodic(cell_size) + displacement_fn, _ = space.free() neighbor_fn = partition.neighbor_list( displacement_or_metric=displacement_fn, - box=10.0, + box=box, r_cutoff=model_config.model.r_max, format=partition.Sparse, + fractional_coordinates=False, ) - neighbors = neighbor_fn.allocate(jnp.asarray(positions, dtype=jnp.float32)) + neighbors = neighbor_fn.allocate(positions) - gmnn = get_training_model( - n_atoms=n_atoms, - n_species=n_species, + builder = ModelBuilder(model_config.model.get_dict(), n_species=n_species) + model = builder.build_energy_model( displacement_fn=displacement_fn, - **model_config.model.get_dict() + apply_mask=False, ) rng_key = jax.random.PRNGKey(model_config.seed) - params = gmnn.init( + params = model.init( rng_key, - jnp.asarray(positions, dtype=jnp.float32), - jnp.asarray(atomic_numbers), + positions, + atomic_numbers, neighbors.idx, + box, ) + ckpt = {"model": {"params": params}, "epoch": 0} best_dir = os.path.join( model_config.data.model_path, model_config.data.model_name, "best" @@ -111,35 +113,34 @@ def test_ase_calc(get_tmp_path): ] ) atomic_numbers = np.array([1, 1, 8]) - cell = np.diag([cell_size] * 3) - atoms = Atoms(atomic_numbers, positions, cell=cell) + box = np.diag([cell_size] * 3) + atoms = Atoms(atomic_numbers, positions, cell=box) write(initial_structure_path.as_posix(), atoms) - n_atoms = 3 n_species = int(np.max(atomic_numbers) + 1) - displacement_fn, _ = space.periodic(cell_size) + displacement_fn, _ = space.periodic_general(cell_size, fractional_coordinates=False) neighbor_fn = partition.neighbor_list( displacement_or_metric=displacement_fn, - box=10.0, + box=box, r_cutoff=model_config.model.r_max, format=partition.Sparse, + fractional_coordinates=False, ) - neighbors = neighbor_fn.allocate(jnp.asarray(positions, dtype=jnp.float32)) + neighbors = neighbor_fn.allocate(positions) - gmnn = get_training_model( - n_atoms=n_atoms, - n_species=n_species, + builder = ModelBuilder(model_config.model.get_dict(), n_species=n_species) + model = builder.build_energy_force_model( displacement_fn=displacement_fn, - **model_config.model.get_dict() ) rng_key = jax.random.PRNGKey(model_config.seed) - params = gmnn.init( + params = model.init( rng_key, jnp.asarray(positions, dtype=jnp.float32), jnp.asarray(atomic_numbers), neighbors.idx, + box, ) ckpt = {"model": {"params": params}, "epoch": 0} best_dir = os.path.join( diff --git a/tests/unit_tests/data/test_input_pipeline.py b/tests/unit_tests/data/test_input_pipeline.py index 6ae34ba1..e87b3c95 100644 --- a/tests/unit_tests/data/test_input_pipeline.py +++ b/tests/unit_tests/data/test_input_pipeline.py @@ -2,15 +2,15 @@ import pytest import tensorflow as tf -from gmnn_jax.data.input_pipeline import ( +from apax.data.input_pipeline import ( PadToSpecificSize, TFPipeline, create_dict_dataset, initialize_nbr_displacement_fns, ) -from gmnn_jax.train.run import find_largest_system -from gmnn_jax.utils.data import convert_atoms_to_arrays, split_atoms, split_idxs -from gmnn_jax.utils.random import seed_py_np_tf +from apax.train.run import find_largest_system +from apax.utils.data import convert_atoms_to_arrays, split_atoms, split_idxs +from apax.utils.random import seed_py_np_tf @pytest.mark.parametrize( @@ -43,7 +43,6 @@ def test_input_pipeline(example_atoms, pbc, calc_results, num_data, external_lab external_labels, disable_pbar=True, ) - max_atoms, max_nbrs = find_largest_system([inputs]) ds = TFPipeline( @@ -61,12 +60,9 @@ def test_input_pipeline(example_atoms, pbc, calc_results, num_data, external_lab sample_inputs, sample_labels = next(ds) - if pbc: - assert "cell" in sample_inputs - assert len(sample_inputs["cell"]) == batch_size - assert len(sample_inputs["cell"][0]) == 3 - else: - assert "cell" not in sample_inputs + assert "box" in sample_inputs + assert len(sample_inputs["box"]) == batch_size + assert len(sample_inputs["box"][0]) == 3 assert "numbers" in sample_inputs for i in range(batch_size): @@ -175,11 +171,10 @@ def test_convert_atoms_to_arrays(example_atoms, pbc): assert "numbers" in inputs["ragged"] assert len(inputs["ragged"]["numbers"]) == len(example_atoms) - if pbc: - assert "cell" in inputs["fixed"] - assert len(inputs["fixed"]["cell"]) == len(example_atoms) - else: - assert "cell" not in inputs["fixed"] + assert "box" in inputs["fixed"] + assert len(inputs["fixed"]["box"]) == len(example_atoms) + if not pbc: + assert np.all(inputs["fixed"]["box"][0] < 1e-6) assert "n_atoms" in inputs["fixed"] assert len(inputs["fixed"]["n_atoms"]) == len(example_atoms) diff --git a/tests/unit_tests/data/test_statistics.py b/tests/unit_tests/data/test_statistics.py index 7300ec88..d1b61106 100644 --- a/tests/unit_tests/data/test_statistics.py +++ b/tests/unit_tests/data/test_statistics.py @@ -2,7 +2,7 @@ from ase import Atoms from ase.calculators.singlepoint import SinglePointCalculator -from gmnn_jax.data.statistics import energy_per_element +from apax.data.statistics import energy_per_element def test_energy_per_element(): diff --git a/tests/unit_tests/layers/descriptor.py b/tests/unit_tests/layers/descriptor.py deleted file mode 100644 index 284913cf..00000000 --- a/tests/unit_tests/layers/descriptor.py +++ /dev/null @@ -1,2 +0,0 @@ -def test_descriptor_variable_size(): - pass diff --git a/tests/unit_tests/layers/descriptor/__init__.py b/tests/unit_tests/layers/descriptor/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/unit_tests/layers/descriptor/test_basis_functions.py b/tests/unit_tests/layers/descriptor/test_basis_functions.py new file mode 100644 index 00000000..cf581caa --- /dev/null +++ b/tests/unit_tests/layers/descriptor/test_basis_functions.py @@ -0,0 +1,44 @@ +import jax +import jax.numpy as jnp + +from apax.layers.descriptor.basis_functions import GaussianBasis, RadialFunction + + +def test_gaussian_basis(): + n_basis = 5 + key = jax.random.PRNGKey(0) + basis = GaussianBasis(n_basis=n_basis) + + dr = jnp.array([0.5, 1.0, 2.0, 3.0]) # n_neighbors + + params = basis.init(key, dr) + assert len(params.unfreeze().keys()) == 0 + result = basis.apply(params, dr) + assert result.shape == (4, n_basis) # n_neighbors x n_basis + + +def test_radial_function(): + key = jax.random.PRNGKey(0) + n_species = 3 + n_basis = 5 + n_radial = 2 + + dr = jnp.array([0.5, 1.0, 2.0, 3.0], dtype=jnp.float32) # n_neighbors + Z_i = jnp.array([1, 2, 1, 2]) + Z_j = jnp.array([2, 1, 2, 1]) + # cutoff = jnp.array([1.0, 1.0, 1.0, 1.0], dtype=jnp.float32) + + radial_fn = RadialFunction( + n_species=n_species, n_radial=n_radial, basis_fn=GaussianBasis(n_basis) + ) + + params = radial_fn.init(key, dr, Z_i, Z_j) + result = radial_fn.apply(params, dr, Z_i, Z_j) + + assert params["params"]["atomic_type_embedding"].shape == ( + n_species, + n_species, + n_radial, + n_basis, + ) + assert result.shape == (4, n_radial) # n_neighbors x n_radial diff --git a/tests/unit_tests/layers/descriptor/test_gaussian_moment_descriptor.py b/tests/unit_tests/layers/descriptor/test_gaussian_moment_descriptor.py new file mode 100644 index 00000000..5c9c46d9 --- /dev/null +++ b/tests/unit_tests/layers/descriptor/test_gaussian_moment_descriptor.py @@ -0,0 +1,22 @@ +import jax +import jax.numpy as jnp +import numpy as np + +from apax.layers.descriptor.gaussian_moment_descriptor import GaussianMomentDescriptor + + +def test_gaussian_moment_descriptor(): + R = jnp.array([[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], dtype=jnp.float32) + Z = jnp.array([8, 1, 1]) + neighbor = jnp.array([[1, 2, 0, 2, 0, 1], [0, 0, 1, 1, 2, 2]]) + box = np.array([0.0, 0.0, 0.0]) + + descriptor = GaussianMomentDescriptor() + + key = jax.random.PRNGKey(0) + params = descriptor.init(key, R, Z, neighbor, box) + result = descriptor.apply(params, R, Z, neighbor, box) + result_jit = jax.jit(descriptor.apply)(params, R, Z, neighbor, box) + + assert result.shape == (3, 360) + assert result_jit.shape == (3, 360) diff --git a/tests/unit_tests/layers/masking.py b/tests/unit_tests/layers/test_masking.py similarity index 88% rename from tests/unit_tests/layers/masking.py rename to tests/unit_tests/layers/test_masking.py index 1bf180ea..5c5c6362 100644 --- a/tests/unit_tests/layers/masking.py +++ b/tests/unit_tests/layers/test_masking.py @@ -2,14 +2,14 @@ import jax.numpy as jnp import numpy as np -from gmnn_jax.layers.masking import mask_by_atom, mask_by_neighbor +from apax.layers.masking import mask_by_atom, mask_by_neighbor def make_atomic_prediction(n_atoms, n_total): n_padding = n_total - n_atoms Z = jnp.concatenate([np.ones(n_atoms), np.zeros(n_padding)]) - atomic_prediction = jnp.ones((1, n_total)) + atomic_prediction = jnp.ones((n_total, 1)) return atomic_prediction, Z @@ -42,12 +42,10 @@ def test_mask_by_atom(): preds = jnp.stack(preds, axis=0) Zs = jnp.stack(Zs, axis=0) - - batched_mask_fn = jax.vmap(mask_by_atom) - assert np.all((np.sum(preds, axis=2) - n_total) < 1e-6) - masked_preds = batched_mask_fn(preds, Zs) + batched_mask_fn = jax.vmap(mask_by_atom, 0, 0) + masked_preds = batched_mask_fn(preds[0][None, ...], Zs[0][None, ...]) assert np.all((np.sum(masked_preds, axis=(1, 2)) - n_atoms) < 1e-6) diff --git a/tests/unit_tests/layers/test_ntk_linear.py b/tests/unit_tests/layers/test_ntk_linear.py new file mode 100644 index 00000000..740ada20 --- /dev/null +++ b/tests/unit_tests/layers/test_ntk_linear.py @@ -0,0 +1,19 @@ +import jax +import jax.numpy as jnp + +from apax.layers.ntk_linear import NTKLinear + + +def test_ntk_linear(): + key = jax.random.PRNGKey(0) + + x = jnp.linspace(-1, 1, num=20) + + linear = NTKLinear(32, dtype=jnp.float32) + params = linear.init(key, x) + result = linear.apply(params, x) + + assert params["params"]["w"].shape == (20, 32) + assert params["params"]["b"].shape == (32,) + + assert result.shape == (32,) diff --git a/tests/unit_tests/layers/test_readout.py b/tests/unit_tests/layers/test_readout.py new file mode 100644 index 00000000..7ca69941 --- /dev/null +++ b/tests/unit_tests/layers/test_readout.py @@ -0,0 +1,16 @@ +import jax +import jax.numpy as jnp + +from apax.layers.readout import AtomisticReadout + + +def test_atomistic_readout(): + key = jax.random.PRNGKey(0) + + x = jnp.linspace(-1, 1, num=10) + + linear = AtomisticReadout([32, 32]) + params = linear.init(key, x) + result = linear.apply(params, x) + + assert result.shape == (1,) diff --git a/tests/unit_tests/layers/test_scaling.py b/tests/unit_tests/layers/test_scaling.py new file mode 100644 index 00000000..a773fe09 --- /dev/null +++ b/tests/unit_tests/layers/test_scaling.py @@ -0,0 +1,41 @@ +import jax +import jax.numpy as jnp + +from apax.layers.scaling import PerElementScaleShift + + +def test_per_element_scale_shift(): + key = jax.random.PRNGKey(0) + + x = jnp.array([1.0, 1.0, 1.0])[:, None] + Z = jnp.array([1, 2, 2]) + n_species = 3 + + global_shift = 2.0 + global_scale = 1.0 + + scale_shift = PerElementScaleShift( + n_species=n_species, scale=global_scale, shift=global_shift + ) + + params = scale_shift.init(key, x, Z) + result = scale_shift.apply(params, x, Z) + + assert params["params"]["scale_per_element"].shape == (n_species, 1) + assert params["params"]["shift_per_element"].shape == (n_species, 1) + + assert jnp.allclose(result, jnp.array([3.0, 3.0, 3.0])[:, None]) + + indiv_scale = jnp.array([10.0, 2.0, 3.0]) + indiv_shift = jnp.array([0.0, -2.0, 2.0]) + scale_shift = PerElementScaleShift( + n_species=n_species, scale=indiv_scale, shift=indiv_shift + ) + + params = scale_shift.init(key, x, Z) + result = scale_shift.apply(params, x, Z) + + assert params["params"]["scale_per_element"].shape == (n_species, 1) + assert params["params"]["shift_per_element"].shape == (n_species, 1) + + assert jnp.allclose(result, jnp.array([0.0, 5.0, 5.0])[:, None]) diff --git a/tests/unit_tests/model/test_apax.py b/tests/unit_tests/model/test_apax.py new file mode 100644 index 00000000..9418fe91 --- /dev/null +++ b/tests/unit_tests/model/test_apax.py @@ -0,0 +1,156 @@ +import jax +import jax.numpy as jnp +import numpy as np + +from apax.layers.descriptor import GaussianMomentDescriptor +from apax.layers.scaling import PerElementScaleShift +from apax.model.gmnn import AtomisticModel, EnergyForceModel, EnergyModel, NeighborSpoof + + +def test_apax_variable_size(): + R = np.array( + [ + [0.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + ] + ) + + Z = np.array([1, 2, 2]) + + idx = np.array( + [ + [1, 2, 0, 2, 0, 1], + [0, 0, 1, 1, 2, 2], + ] + ) + + box = np.array([0, 0, 0]) + + R_padded = np.concatenate([R, np.zeros((1, 3))], axis=0) + Z_padded = np.concatenate([Z, [0]]) + idx_padded = np.concatenate([idx, [[0], [0]]], axis=1) + + shift = jnp.array([0.0, 100.0, 200.0]) + scale = jnp.array([1.0, 1.2, 1.6])[..., None] + + model = EnergyForceModel( + AtomisticModel( + descriptor=GaussianMomentDescriptor(apply_mask=False), + scale_shift=PerElementScaleShift(scale=scale, shift=shift), + mask_atoms=False, + ) + ) + model_padded = EnergyForceModel( + AtomisticModel( + descriptor=GaussianMomentDescriptor(apply_mask=True), + scale_shift=PerElementScaleShift(scale=scale, shift=shift), + mask_atoms=True, + ) + ) + + rng_key = jax.random.PRNGKey(1) + + params = model.init(rng_key, R, Z, idx, box) + + results = model.apply(params, R, Z, idx, box) + results_padded = model_padded.apply(params, R_padded, Z_padded, idx_padded, box) + + print(results["forces"]) + print(results_padded["forces"]) + + assert (results["energy"] - results_padded["energy"]) < 1e-6 + assert np.all(results["forces"] - results_padded["forces"][:-1, :] < 1e-6) # 1e-6 + + +def test_atomistic_model(): + key = jax.random.PRNGKey(0) + + R = np.array( + [ + [0.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + ] + ) + + Z = np.array([1, 2, 2]) + + idx = np.array( + [ + [1, 2, 0, 2, 0, 1], + [0, 0, 1, 1, 2, 2], + ] + ) + neighbor = NeighborSpoof(idx=idx) + + box = np.array([0.0, 0.0, 0.0]) + + model = AtomisticModel(mask_atoms=False) + + params = model.init(key, R, Z, neighbor, box) + result = model.apply(params, R, Z, neighbor, box) + + assert result.shape == (3, 1) + + +def test_energy_model(): + key = jax.random.PRNGKey(0) + + R = np.array( + [ + [0.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + ] + ) + + Z = np.array([1, 2, 2]) + + idx = np.array( + [ + [1, 2, 0, 2, 0, 1], + [0, 0, 1, 1, 2, 2], + ] + ) + neighbor = NeighborSpoof(idx=idx) + + box = np.array([0.0, 0.0, 0.0]) + + model = EnergyModel() + + params = model.init(key, R, Z, neighbor, box) + result = model.apply(params, R, Z, neighbor, box) + + assert result.shape == () + + +def test_energy_force_model(): + key = jax.random.PRNGKey(0) + + R = np.array( + [ + [0.0, 0.0, 0.0], + [1.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + ] + ) + + Z = np.array([1, 2, 2]) + + idx = np.array( + [ + [1, 2, 0, 2, 0, 1], + [0, 0, 1, 1, 2, 2], + ] + ) + + box = np.array([0.0, 0.0, 0.0]) + + model = EnergyForceModel() + + params = model.init(key, R, Z, idx, box) + result = model.apply(params, R, Z, idx, box) + + assert result["energy"].shape == () + assert result["forces"].shape == (3, 3) diff --git a/tests/unit_tests/model/test_gmnn.py b/tests/unit_tests/model/test_gmnn.py deleted file mode 100644 index d5290df4..00000000 --- a/tests/unit_tests/model/test_gmnn.py +++ /dev/null @@ -1,62 +0,0 @@ -import jax -import jax.numpy as jnp -import numpy as np -from jax_md import space - -from gmnn_jax.model import get_training_model - - -def test_gmnn_variable_size(): - R = np.array( - [ - [0.0, 0.0, 0.0], - [1.0, 0.0, 0.0], - [0.0, 1.0, 0.0], - ] - ) - - Z = np.array([1, 2, 2]) - - idx = np.array( - [ - [1, 2, 0, 2, 0, 1], - [0, 0, 1, 1, 2, 2], - ] - ) - - R_padded = np.concatenate([R, np.zeros((1, 3))], axis=0) - Z_padded = np.concatenate([Z, [0]]) - idx_padded = np.concatenate([idx, [[0], [0]]], axis=1) - - displacement_fn, _ = space.free() - shift = jnp.array([0.0, 100.0, 200.0]) - scale = jnp.array([1.0, 1.2, 1.6])[..., None] - - gmnn = get_training_model( - 3, - 3, - displacement_fn, - [512, 512], - b_init="zeros", - elemental_energies_mean=shift, - elemental_energies_std=scale, - ) - gmnn_padded = get_training_model( - 4, - 3, - displacement_fn, - [512, 512], - b_init="zeros", - elemental_energies_mean=shift, - elemental_energies_std=scale, - ) - - rng_key = jax.random.PRNGKey(1) - - params = gmnn.init(rng_key, R, Z, idx) - - results = gmnn.apply(params, R, Z, idx) - results_padded = gmnn_padded.apply(params, R_padded, Z_padded, idx_padded) - - assert (results["energy"] - results_padded["energy"]) < 1e-6 - assert np.allclose(results["forces"], results_padded["forces"][:-1, :]) diff --git a/tests/unit_tests/optimizer/test_get_opt.py b/tests/unit_tests/optimizer/test_get_opt.py index ba09bb66..e02428d4 100644 --- a/tests/unit_tests/optimizer/test_get_opt.py +++ b/tests/unit_tests/optimizer/test_get_opt.py @@ -1,8 +1,9 @@ import jax import jax.numpy as jnp import optax +from flax.core.frozen_dict import freeze -from gmnn_jax.optimizer import get_opt +from apax.optimizer import get_opt def test_get_opt(): @@ -16,10 +17,19 @@ def test_get_opt(): "shift_per_element": jnp.ones((3,)), }, } + params = freeze(params) grads = jax.tree_util.tree_map(lambda x: x * 0.01, tree=params) - opt = get_opt(0, 500, emb_lr=0.01, nn_lr=0.05, scale_lr=0.001, shift_lr=0.1) + opt = get_opt( + params, + 0, + 500, + emb_lr=0.05, + nn_lr=0.01, + scale_lr=0.001, + shift_lr=0.1, + ) opt_state = opt.init(params=params) updates, new_opt_state = opt.update(grads, opt_state, params) diff --git a/tests/unit_tests/train/test_eval.py b/tests/unit_tests/train/test_eval.py index 2c0ae77a..e9f134ba 100644 --- a/tests/unit_tests/train/test_eval.py +++ b/tests/unit_tests/train/test_eval.py @@ -1,6 +1,6 @@ import numpy as np -from gmnn_jax.train.eval import get_test_idxs +from apax.train.eval import get_test_idxs def test_get_test_idxs(): diff --git a/tests/unit_tests/train/test_loss.py b/tests/unit_tests/train/test_loss.py index 481adf5e..ad64094e 100644 --- a/tests/unit_tests/train/test_loss.py +++ b/tests/unit_tests/train/test_loss.py @@ -1,7 +1,7 @@ import jax.numpy as jnp import numpy as np -from gmnn_jax.train.loss import ( +from apax.train.loss import ( Loss, force_angle_div_force_label, force_angle_exponential_weight, diff --git a/tests/unit_tests/train/test_metrics.py b/tests/unit_tests/train/test_metrics.py index 6ed749f4..2e2de005 100644 --- a/tests/unit_tests/train/test_metrics.py +++ b/tests/unit_tests/train/test_metrics.py @@ -1,7 +1,7 @@ import jax.numpy as jnp -from gmnn_jax.config.train_config import MetricsConfig -from gmnn_jax.train.metrics import cosine_sim, initialize_metrics +from apax.config.train_config import MetricsConfig +from apax.train.metrics import cosine_sim, initialize_metrics def test_cosine_sim(): diff --git a/tests/unit_tests/transfer_learning/__init__.py b/tests/unit_tests/transfer_learning/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/unit_tests/transfer_learning/test_parameter_transfer.py b/tests/unit_tests/transfer_learning/test_parameter_transfer.py new file mode 100644 index 00000000..ad810776 --- /dev/null +++ b/tests/unit_tests/transfer_learning/test_parameter_transfer.py @@ -0,0 +1,22 @@ +from apax.transfer_learning import param_transfer + + +def test_param_transfer(): + source = { + "params": { + "dense": {"w": 1, "b": 2}, + "basis": {"emb": 3}, + } + } + target = { + "params": { + "dense": {"w": 0, "b": 0}, + "basis": {"emb": 0}, + } + } + reinitialize_layers = ["basis"] + transfered_target = param_transfer(source, target, reinitialize_layers) + + assert transfered_target["params"]["dense"]["w"] == source["params"]["dense"]["w"] + assert transfered_target["params"]["dense"]["b"] == source["params"]["dense"]["b"] + assert transfered_target["params"]["basis"]["emb"] == target["params"]["basis"]["emb"] diff --git a/tests/unit_tests/utils/test_convert.py b/tests/unit_tests/utils/test_convert.py index 8b137891..e69de29b 100644 --- a/tests/unit_tests/utils/test_convert.py +++ b/tests/unit_tests/utils/test_convert.py @@ -1 +0,0 @@ -