mlip_arena/tasks/stability/run.py

from __future__ import annotations

from datetime import datetime, timedelta
from pathlib import Path
from typing import Literal, Sequence, Tuple

import numpy as np
from ase import Atoms, units
from ase.calculators.calculator import Calculator
from ase.calculators.mixing import SumCalculator
from ase.io import read
from ase.io.trajectory import Trajectory
from ase.md.andersen import Andersen
from ase.md.langevin import Langevin
from ase.md.md import MolecularDynamics
from ase.md.npt import NPT
from ase.md.nptberendsen import NPTBerendsen
from ase.md.nvtberendsen import NVTBerendsen
from ase.md.velocitydistribution import (
    MaxwellBoltzmannDistribution,
    Stationary,
    ZeroRotation,
)
from ase.md.verlet import VelocityVerlet
from prefect import task
from prefect.tasks import task_input_hash
from scipy.interpolate import interp1d
from scipy.linalg import schur
from torch_dftd.torch_dftd3_calculator import TorchDFTD3Calculator
from tqdm.auto import tqdm

from mlip_arena.models.utils import MLIPEnum, get_freer_device

# from mlip_arena.models.utils import EXTMLIPEnum, MLIPMap, external_ase_calculator

_valid_dynamics: dict[str, tuple[str, ...]] = {
    "nve": ("velocityverlet",),
    "nvt": ("nose-hoover", "langevin", "andersen", "berendsen"),
    "npt": ("nose-hoover", "berendsen"),
}

_preset_dynamics: dict = {
    "nve_velocityverlet": VelocityVerlet,
    "nvt_andersen": Andersen,
    "nvt_berendsen": NVTBerendsen,
    "nvt_langevin": Langevin,
    "nvt_nose-hoover": NPT,
    "npt_berendsen": NPTBerendsen,
    "npt_nose-hoover": NPT,
}

def _interpolate_quantity(values: Sequence | np.ndarray, n_pts: int) -> np.ndarray:
    """Interpolate temperature / pressure on a schedule."""
    n_vals = len(values)
    return np.interp(
        np.linspace(0, n_vals - 1, n_pts + 1),
        np.linspace(0, n_vals - 1, n_vals),
        values,
    )

def _get_ensemble_schedule(
        ensemble: Literal["nve", "nvt", "npt"] = "nvt",
        n_steps: int = 1000,
        temperature: float | Sequence | np.ndarray | None = 300.0,
        pressure: float | Sequence | np.ndarray | None = None
        ) -> Tuple[np.ndarray, np.ndarray]:
    if ensemble == "nve":
        # Disable thermostat and barostat
        temperature = np.nan
        pressure = np.nan
        t_schedule = np.full(n_steps + 1, temperature)
        p_schedule = np.full(n_steps + 1, pressure)
        return t_schedule, p_schedule

    if isinstance(temperature, Sequence) or (
        isinstance(temperature, np.ndarray) and temperature.ndim == 1
    ):
        t_schedule = _interpolate_quantity(temperature, n_steps)
    # NOTE: In ASE Langevin dynamics, the temperature are normally
    # scalars, but in principle one quantity per atom could be specified by giving
    # an array. This is not implemented yet here.
    else:
        t_schedule = np.full(n_steps + 1, temperature)

    if ensemble == "nvt":
        pressure = np.nan
        p_schedule = np.full(n_steps + 1, pressure)
        return t_schedule, p_schedule

    if isinstance(pressure, Sequence) or (
        isinstance(pressure, np.ndarray) and pressure.ndim == 1
    ):
        p_schedule = _interpolate_quantity(pressure, n_steps)
    elif isinstance(pressure, np.ndarray) and pressure.ndim == 4:
        p_schedule = interp1d(
            np.arange(n_steps + 1), pressure, kind="linear"
        )
        assert isinstance(p_schedule, np.ndarray)
    else:
        p_schedule = np.full(n_steps + 1, pressure)

    return t_schedule, p_schedule

def _get_ensemble_defaults(
        ensemble: Literal["nve", "nvt", "npt"],
        dynamics: str | MolecularDynamics,
        t_schedule: np.ndarray,
        p_schedule: np.ndarray,
        ase_md_kwargs: dict | None = None) -> dict:
    """Update ASE MD kwargs"""
    ase_md_kwargs = ase_md_kwargs or {}

    if ensemble == "nve":
        ase_md_kwargs.pop("temperature", None)
        ase_md_kwargs.pop("temperature_K", None)
        ase_md_kwargs.pop("externalstress", None)
    elif ensemble == "nvt":
        ase_md_kwargs["temperature_K"] = t_schedule[0]
        ase_md_kwargs.pop("externalstress", None)
    elif ensemble == "npt":
        ase_md_kwargs["temperature_K"] = t_schedule[0]
        ase_md_kwargs["externalstress"] = p_schedule[0] # * 1e3 * units.bar

    if isinstance(dynamics, str) and dynamics.lower() == "langevin":
        ase_md_kwargs["friction"] = ase_md_kwargs.get(
            "friction",
            10.0 * 1e-3 / units.fs,  # Same default as in VASP: 10 ps^-1
        )

    return ase_md_kwargs




@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=1))
def md(
    atoms: Atoms,
    calculator_name: str | MLIPEnum,
    calculator_kwargs: dict | None,
    dispersion: str | None = None,
    dispersion_kwargs: dict | None = None,
    device: str | None = None,
    ensemble: Literal["nve", "nvt", "npt"] = "nvt",
    dynamics: str | MolecularDynamics = "langevin",
    time_step: float | None = None,
    total_time: float = 1000,
    temperature: float | Sequence | np.ndarray | None = 300.0,
    pressure: float | Sequence | np.ndarray | None = None,
    ase_md_kwargs: dict | None = None,
    mb_velocity_seed: int | None = None,
    zero_linear_momentum: bool = True,
    zero_angular_momentum: bool = True,
    traj_file: str | Path | None = None,
    traj_interval: int = 1,
    # ttime: float = 25 * units.fs,
    # pfactor: float = (75 * units.fs) ** 1 * units.GPa,
    # mask: np.ndarray | list[int] | None = None,
    # traceless: float = 1.0,
    restart: bool = True,
    # interval: int = 500,
    # device: str | None = None,
    # dtype: str = "float64",
):
    device = device or str(get_freer_device())

    print(f"Using device: {device}")

    calculator_kwargs = calculator_kwargs or {}

    if isinstance(calculator_name, MLIPEnum) and calculator_name in MLIPEnum:
        assert issubclass(calculator_name.value, Calculator)
        calc = calculator_name.value(**calculator_kwargs)
    elif isinstance(calculator_name, str) and calculator_name in MLIPEnum._member_names_:
        calc = MLIPEnum[calculator_name].value(**calculator_kwargs)
    else:
        raise ValueError(f"Invalid calculator: {calculator_name}")

    print(f"Using calculator: {calc}")

    dispersion_kwargs = dispersion_kwargs or {}

    dispersion_kwargs.update({"device": device})

    if dispersion is not None:
        disp_calc = TorchDFTD3Calculator(
            **dispersion_kwargs,
        )
        calc = SumCalculator([calc, disp_calc])

        print(f"Using dispersion: {dispersion}")

    atoms.calc = calc

    if time_step is None:
        # If a structure contains an isotope of hydrogen, set default `time_step`
        # to 0.5 fs, and 2 fs otherwise.
        has_h_isotope = "H" in atoms.get_chemical_symbols()
        time_step = 0.5 if has_h_isotope else 2.0

    n_steps = int(total_time / time_step)
    target_steps = n_steps

    t_schedule, p_schedule = _get_ensemble_schedule(
        ensemble=ensemble,
        n_steps=n_steps,
        temperature=temperature,
        pressure=pressure,
    )

    ase_md_kwargs = _get_ensemble_defaults(
        ensemble=ensemble,
        dynamics=dynamics,
        t_schedule=t_schedule,
        p_schedule=p_schedule,
        ase_md_kwargs=ase_md_kwargs,
    )

    if isinstance(dynamics, str):
        # Use known dynamics if `self.dynamics` is a str
        dynamics = dynamics.lower()
        if dynamics not in _valid_dynamics[ensemble]:
            raise ValueError(
                f"{dynamics} thermostat not available for {ensemble}."
                f"Available {ensemble} thermostats are:"
                " ".join(_valid_dynamics[ensemble])
            )

        if ensemble == "nve" and dynamics is None:
            dynamics = "velocityverlet"
        md_class = _preset_dynamics[f"{ensemble}_{dynamics}"]
    elif issubclass(dynamics, MolecularDynamics):
        md_class = dynamics

    if md_class is NPT:
        #  Note that until md_func is instantiated, isinstance(md_func,NPT) is False
        # ASE NPT implementation requires upper triangular cell
        u, _ = schur(atoms.get_cell(complete=True), output="complex")
        atoms.set_cell(u.real, scale_atoms=True)

    last_step = 0

    if traj_file is not None:
        traj_file = Path(traj_file)

        if restart and traj_file.exists():
            traj = read(traj_file, index=":")
            last_step = traj[-1].info.get("step", len(traj) * traj_interval)
            n_steps -= last_step
            last_atoms = traj[-1]
            traj = Trajectory(traj_file, "a", atoms)
            atoms.set_positions(last_atoms.get_positions())
            atoms.set_momenta(last_atoms.get_momenta())
        else:
            traj = Trajectory(traj_file, "w", atoms)

            if not np.isnan(t_schedule).any():
                MaxwellBoltzmannDistribution(
                    atoms=atoms,
                    temperature_K=t_schedule[last_step],
                    rng=np.random.default_rng(seed=mb_velocity_seed),
                )

            if zero_linear_momentum:
                Stationary(atoms)
            if zero_angular_momentum:
                ZeroRotation(atoms)

    md_runner = md_class(
        atoms=atoms,
        timestep=time_step * units.fs,
        **ase_md_kwargs,
    )

    if traj_file is not None:
        md_runner.attach(traj.write, interval=traj_interval)

    with tqdm(total=n_steps) as pbar:

        def _callback(dyn: MolecularDynamics = md_runner) -> None:
            step = last_step + dyn.nsteps
            dyn.atoms.info["restart"] = last_step
            dyn.atoms.info["datetime"] = datetime.now()
            dyn.atoms.info["step"] = step
            dyn.atoms.info["target_steps"] = target_steps
            if ensemble == "nve":
                return
            dyn.set_temperature(temperature_K=t_schedule[step])
            if ensemble == "nvt":
                return
            dyn.set_stress(p_schedule[step] * 1e3 * units.bar)
            pbar.update()

        md_runner.attach(_callback, interval=1)

        start_time = datetime.now()
        md_runner.run(steps=n_steps)
        end_time = datetime.now()

        traj.close()

    return {"runtime": end_time - start_time, "n_steps": n_steps}