data/varipeps/config.py

from dataclasses import dataclass
from enum import Enum, IntEnum, auto, unique

import numpy as np

from jax.tree_util import register_pytree_node_class

from typing import TypeVar, Tuple, Any, Type, NoReturn

T_VariPEPS_Config = TypeVar("T_VariPEPS_Config", bound="VariPEPS_Config")


@unique
class Optimizing_Methods(IntEnum):
    STEEPEST = auto()  #: Steepest gradient descent
    CG = auto()  #: Conjugate gradient method
    BFGS = auto()  #: BFGS method
    L_BFGS = auto()  #: L-BFGS method


@unique
class Line_Search_Methods(IntEnum):
    SIMPLE = auto()  #: Simple line search method
    ARMIJO = auto()  #: Armijo line search method
    WOLFE = auto()  #: Wolfe line search method
    HAGERZHANG = auto()  #: Hager-Zhang line search method


@unique
class Projector_Method(IntEnum):
    HALF = auto()  #: Use only half network for projector calculation
    FULL = auto()  #: Use full network for projector calculation
    FISHMAN = auto()  #: Use the Fishman method for projector calculation
    HALF_FISHMAN = auto()  #: Use the Fishman method but with half projectors as basis


@unique
class Wavevector_Type(IntEnum):
    TWO_PI_POSITIVE_ONLY = auto()  #: Use interval [0, 2pi) for q vectors
    TWO_PI_SYMMETRIC = auto()  #: Use interval [-2pi, 2pi) for q vectors


@unique
class Grad_Fixed_Point_Method(IntEnum):
    ITERATIVE = auto()  #: Use iterative method to calculate gradient of CTMRG routine
    LINEAR_SOLVER = (
        auto()
    )  #: Use linear solver method to calculate gradient of CTMRG routine
    EIGEN_SOLVER = (
        auto()
    )  #: Use eigen solver method to calculate gradient of CTMRG routine


@unique
class Slurm_Restart_Mode(IntEnum):
    DISABLED = (
        auto()
    )  #: Disable automatic restart of slurm job if maximal runtime limit is reached
    WRITE_NEED_RESTART_FILE = (
        auto()
    )  #: Write file to indicate that restart is needed but no slurm scripts
    WRITE_RESTART_SCRIPT = (
        auto()
    )  #: Write slurm restart script but do not submit new slurm job
    AUTOMATIC_RESTART = auto()  #: Write restart script and start new slurm job with it


@dataclass
@register_pytree_node_class
class VariPEPS_Config:
    """
    Config class for varipeps module. Normally only the blow created instance
    :obj:`config` is used.

    Parameters:
      ad_use_custom_vjp (:obj:`bool`):
        Use custom VJP rule for the CTMRG routine during AD calculation.
      ad_custom_print_steps (:obj:`bool`):
        Print steps of fix-point iteration in custom VJP function.
      ad_custom_verbose_output (:obj:`bool`):
        Print verbose output in custom VJP function.
      ad_custom_convergence_eps (:obj:`float`):
        Convergence criterion for the custom VJP function.
      ad_custom_max_steps (:obj:`int`):
        Maximal number of steps for fix-pointer iteration of the custom VJP
        function.
      ad_custom_fixed_point_method (:obj:`~varipeps.config.Grad_Fixed_Point_Method`):
        Select method how the gradient of the CTMRG fixed point routine is
        calculated.
      checkpointing_ncon (:obj:`bool`):
        Enable AD checkpointing for the ncon calls.
      checkpointing_projectors (:obj:`bool`):
        Enable AD checkpointing for the the calculation of the proejctors.
      ctmrg_convergence_eps (:obj:`float`):
        Convergence criterion for the CTMRG routine.
      ctmrg_enforce_elementwise_convergence (:obj:`bool`):
        Enforce elementwise convergence of the CTM tensors instead of only
        convergence of the singular values of the corners.
      ctmrg_max_steps (:obj:`int`):
        Maximal number of steps for fix-pointer iteration of the CTMRG routine.
      ctmrg_print_steps (:obj:`bool`):
        Print steps of fix-point iteration in CTMRG routine.
      ctmrg_verbose_output (:obj:`bool`):
        Print verbose output in CTMRG routine.
      ctmrg_truncation_eps (:obj:`float`):
        Value for cut off of the singular values compared to the
        biggest one. Used in the calculation of the CTMRG projectors.
      ctmrg_fail_if_not_converged (:obj:`bool`):
        Flag if the CTMRG routine should fail with an error if no convergence
        can be reached within the maximal number of steps.
        If disabled, the result converged so far is returned.
      ctmrg_full_projector_method (:obj:`~varipeps.config.Projector_Method`):
        Set which projector method should be used as default (full) projector
        method during the CTMRG routine. Sensible values are
        :obj:`~varipeps.config.Projector_Method.FULL` or
        :obj:`~varipeps.config.Projector_Method.FISHMAN`.
      ctmrg_increase_truncation_eps (:obj:`bool`):
        Flag if the CTMRG routine should try higher truncation thresholds for
        the SVD based projector methods if the routine does not converge in the
        maximum number of steps.
      ctmrg_increase_truncation_eps_factor (:obj:`float`):
        Factor by which the truncation threshold should be increased.
      ctmrg_increase_truncation_eps_max_value (:obj:`float`):
        Maximal value for the truncation threshold. Do not increase higher than
        this value.
      ctmrg_heuristic_increase_chi (:obj:`bool`):
        Flag if the CTMRG routine should try higher environment bond dimension
        for if the routine found singular values above a threshold during the
        projector calculation of the last absorption step.
      ctmrg_heuristic_increase_chi_threshold (:obj:`float`):
        Threshold for the heuristic environment bond dimension increase.
      ctmrg_heuristic_increase_chi_step_size (:obj:`int`):
        Step size for the heuristic environment bond dimension increase.
      ctmrg_heuristic_decrease_chi (:obj:`bool`):
        Flag if the CTMRG routine should try lower environment bond dimension
        for if the routine found singular values below the SVD threshold during
        the projector calculation of the last absorption step.
      ctmrg_heuristic_decrease_chi_step_size (:obj:`int`):
        Step size for the heuristic environment bond dimension decrease.
      triangular_ctmrg_use_split (:obj:`bool`):
        Flag if the split projector method should be used in the
        triangular CTMRG.
      svd_sign_fix_eps (:obj:`float`):
        Value for numerical stability threshold in sign-fixed SVD.
      svd_ad_use_lorentz_broadening (:obj:`bool`):
        Enable Lorentz broadening in the AD rule for the SVD.
      svd_ad_lorentz_broadening_eps (:obj:`float`):
        Numerical stabilization constant in the Lorentz broadening in the
        AD rule for the SVD.
      optimizer_method (:obj:`Optimizing_Methods`):
        Method used for variational optimization of the PEPS network.
      optimizer_max_steps (:obj:`int`):
        Maximal number of steps for fix-pointer iteration in optimization routine.
      optimizer_convergence_eps (:obj:`float`):
        Convergence criterion for the optimization routine.
      optimizer_ctmrg_preconverged_eps (:obj:`float`):
        Convergence criterion for the optimization routine using the gradient
        calculations with the preconverged environment.
      optimizer_fail_if_no_step_size_found (:obj:`bool`):
        Flag if the optimizer routine should fail with an error if no step size
        can be found before the gradient norm is below the convergence
        threshold. If disabled, the result converged so far is returned.
      optimizer_l_bfgs_maxlen (:obj:`int`):
        Maximal number of previous steps used for the L-BFGS method.
      optimizer_preconverge_with_half_projectors (:obj:`bool`):
        Flag if the optimizer should use only CTM half projectors for the steps
        till some converge is reached.
      optimizer_preconverge_with_half_projectors_eps (:obj:`float`):
        Convergence criterion for the preconvergence with only the half
        CTM projectors.
      optimizer_autosave_step_count (:obj:`int`):
        Step count after which the optimizer result is automatically saved.
      optimizer_random_noise_eps (:obj:`float`):
        Optimizer should try best state sofar with some random noise if
        gradient norm is below this threshold.
      optimizer_random_noise_max_retries (:obj:`int`):
        Maximal retries for optimization with random noise.
      optimizer_random_noise_relative_amplitude (:obj:`float`):
        Relative amplitude used for random noise.
      optimizer_reuse_env_eps (:obj:`float`):
        Reuse CTMRG environment of previous step if norm of gradient is below
        this threshold.
      optimizer_use_preconditioning (:obj:`bool`):
        Use (local) preconditioning method as described in
        https://arxiv.org/abs/2511.09546.
      optimizer_precond_gmres_krylov_subspace_size (:obj:`int`):
        Size of Krylov subspace built up during GMRES method for the inversion
        of the preconditioner.
      optimizer_precond_gmres_maxiter (:obj:`int`):
        Maximal number of outer iterations inside the GMRES method for the
        inversion of the preconditioner.
      line_search_method (:obj:`Line_Search_Methods`):
        Method used for the line search routine.
      line_search_initial_step_size (:obj:`float`):
        Initial step size for the line search routine.
      line_search_reduction_factor (:obj:`float`):
        Reduction factor between two line search steps.
      line_search_max_steps (:obj:`int`):
        Maximal number of steps in the line search routine.
      line_search_armijo_const (:obj:`float`):
        Constant used in Armijo line search method.
      line_search_wolfe_const (:obj:`float`):
        Constant used in Wolfe line search method.
      line_search_use_last_step_size (:obj:`bool`):
        Flag if the line search should start from the step size of the
        previous optimizer step.
      line_search_hager_zhang_quad_step (:obj:`bool`):
        Use QuadStep method in Hager-Zhang line search to find initial
        step size.
      line_search_hager_zhang_delta (:obj:`float`):
        Constant used in Hager-Zhang line search method.
      line_search_hager_zhang_sigma (:obj:`float`):
        Constant used in Hager-Zhang line search method.
      line_search_hager_zhang_psi_0 (:obj:`float`):
        Constant used in Hager-Zhang line search method.
      line_search_hager_zhang_psi_1 (:obj:`float`):
        Constant used in Hager-Zhang line search method.
      line_search_hager_zhang_psi_2 (:obj:`float`):
        Constant used in Hager-Zhang line search method.
      line_search_hager_zhang_eps (:obj:`float`):
        Constant used in Hager-Zhang line search method.
      line_search_hager_zhang_theta (:obj:`float`):
        Constant used in Hager-Zhang line search method.
      line_search_hager_zhang_gamma (:obj:`float`):
        Constant used in Hager-Zhang line search method.
      line_search_hager_zhang_rho (:obj:`float`):
        Constant used in Hager-Zhang line search method.
      line_search_hager_zhang_eps_use_grad_norm (:obj:`bool`):
        Use norm of gradient multiplied by
        :obj:`VariPEPS_Config.line_search_hager_zhang_eps_grad_norm_factor` to
        calculate eps value in Hager-Zhang line search. If disabled, the fixed
        value from config parameter
        :obj:`VariPEPS_Config.line_search_hager_zhang_eps` is used.
      line_search_hager_zhang_eps_grad_norm_factor (:obj:`float`):
        Factor used for gradient based eps calculation. See parameter
        :obj:`VariPEPS_Config.line_search_hager_zhang_eps_use_grad_norm`
        for details.
      basinhopping_niter (:obj:`int`):
        Value for parameter `niter` of :obj:`scipy.optimize.basinhopping`.
        See this function for details.
      basinhopping_T (:obj:`int`):
        Value for parameter `T` of :obj:`scipy.optimize.basinhopping`.
        See this function for details.
      basinhopping_niter_success (:obj:`int`):
        Value for parameter `niterniter_success` of
        :obj:`scipy.optimize.basinhopping`. See this function for details.
      spiral_wavevector_type (:obj:`Wavevector_Type`):
        Type of wavevector to be used (only positive/symmetric interval/...).
      slurm_restart_mode (:obj:`Slurm_Restart_Mode`):
        Mode of operation to restart slurm job if maximal runtime is reached.
      jax_compilation_cache_memory_factor (:obj:`float`):
        Limit the jax compilation cache to maximal this factor times the total
        available memory.
    """

    # AD config
    ad_use_custom_vjp: bool = True
    ad_custom_print_steps: bool = False
    ad_custom_verbose_output: bool = False
    ad_custom_convergence_eps: float = 1e-7
    ad_custom_max_steps: int = 75
    ad_custom_fixed_point_method: Grad_Fixed_Point_Method = (
        Grad_Fixed_Point_Method.LINEAR_SOLVER
    )
    checkpointing_ncon: bool = False
    checkpointing_projectors: bool = False

    # CTMRG routine
    ctmrg_convergence_eps: float = 1e-8
    ctmrg_enforce_elementwise_convergence: bool = True
    ctmrg_max_steps: int = 75
    ctmrg_print_steps: bool = False
    ctmrg_verbose_output: bool = False
    ctmrg_truncation_eps: float = 1e-12
    ctmrg_fail_if_not_converged: bool = True
    ctmrg_full_projector_method: Projector_Method = Projector_Method.FISHMAN
    ctmrg_increase_truncation_eps: bool = True
    ctmrg_increase_truncation_eps_factor: float = 100.0
    ctmrg_increase_truncation_eps_max_value: float = 1e-6
    ctmrg_heuristic_increase_chi: bool = True
    ctmrg_heuristic_increase_chi_threshold: float = 1e-6
    ctmrg_heuristic_increase_chi_step_size: int = 2
    ctmrg_heuristic_decrease_chi: bool = True
    ctmrg_heuristic_decrease_chi_step_size: int = 1

    # Triangular CTMRG routine
    triangular_ctmrg_use_split: bool = False

    # SVD
    svd_sign_fix_eps: float = 1e-1
    svd_ad_use_lorentz_broadening: bool = False
    svd_ad_lorentz_broadening_eps: float = 1e-13

    # Optimizer
    optimizer_method: Optimizing_Methods = Optimizing_Methods.L_BFGS
    optimizer_max_steps: int = 300
    optimizer_convergence_eps: float = 1e-5
    optimizer_ctmrg_preconverged_eps: float = 1e-5
    optimizer_fail_if_no_step_size_found: bool = False
    optimizer_l_bfgs_maxlen: int = 15
    optimizer_preconverge_with_half_projectors: bool = False
    optimizer_preconverge_with_half_projectors_eps: float = 1e-3
    optimizer_autosave_step_count: int = 2
    optimizer_random_noise_eps: float = 1e-4
    optimizer_random_noise_max_retries: int = 5
    optimizer_random_noise_relative_amplitude: float = 1e-1
    optimizer_reuse_env_eps: float = 1e-3
    optimizer_use_preconditioning: bool = True
    optimizer_precond_gmres_krylov_subspace_size: int = 30
    optimizer_precond_gmres_maxiter: int = 3

    # Line search
    line_search_method: Line_Search_Methods = Line_Search_Methods.HAGERZHANG
    line_search_initial_step_size: float = 1.0
    line_search_reduction_factor: float = 0.5
    line_search_max_steps: int = 40
    line_search_armijo_const: float = 1e-4
    line_search_wolfe_const: float = 0.9
    line_search_use_last_step_size: bool = False

    line_search_hager_zhang_quad_step: bool = True
    line_search_hager_zhang_delta: float = 0.1
    line_search_hager_zhang_sigma: float = 0.9
    line_search_hager_zhang_psi_0: float = 0.01
    line_search_hager_zhang_psi_1: float = 0.1
    line_search_hager_zhang_psi_2: float = 2.0
    line_search_hager_zhang_eps: float = 1e-6
    line_search_hager_zhang_theta: float = 0.5
    line_search_hager_zhang_gamma: float = 0.66
    line_search_hager_zhang_rho: float = 5
    line_search_hager_zhang_eps_use_grad_norm: bool = True
    line_search_hager_zhang_eps_grad_norm_factor: float = 1e-2

    # Basinhopping
    basinhopping_niter: int = 20
    basinhopping_T: float = 0.001
    basinhopping_niter_success: int = 5

    # Spiral PEPS
    spiral_wavevector_type: Wavevector_Type = Wavevector_Type.TWO_PI_POSITIVE_ONLY

    # Slurm
    slurm_restart_mode: Slurm_Restart_Mode = Slurm_Restart_Mode.WRITE_NEED_RESTART_FILE

    # JAX related settings
    jax_compilation_cache_memory_factor: float = 0.5

    def update(self, name: str, value: Any) -> NoReturn:
        self.__setattr__(name, value)

    def __setattr__(self, name: str, value: Any) -> NoReturn:
        try:
            field = self.__dataclass_fields__[name]
        except KeyError as e:
            raise KeyError(f"Unknown config option '{name}'.") from e

        if not type(value) is field.type:
            if field.type is float and type(value) is int:
                value = float(value)
            elif (
                field.type is float
                and hasattr(value, "dtype")
                and (
                    np.issubdtype(value.dtype, np.floating)
                    or np.issubdtype(value.dtype, np.integer)
                )
                and value.size == 1
            ):
                if value.ndim > 0:
                    value = value.reshape(-1)[0]
                value = float(value)
            elif (
                field.type is int
                and hasattr(value, "dtype")
                and np.issubdtype(value.dtype, np.integer)
                and value.size == 1
            ):
                if value.ndim > 0:
                    value = value.reshape(-1)[0]
                value = int(value)
            elif (
                field.type is bool
                and hasattr(value, "dtype")
                and np.isdtype(value.dtype, np.bool)
                and value.size == 1
            ):
                if value.ndim > 0:
                    value = value.reshape(-1)[0]
                value = bool(value)
            else:
                raise TypeError(
                    f"Type mismatch for option '{name}', got '{type(value)}', expected '{field.type}'."
                )

        super().__setattr__(name, value)

    def update_from_config_dict(self, new_config):
        for k in self.__dataclass_fields__:
            setattr(self, k, new_config[k])

    def update_from_config_object(self, new_config):
        for k in self.__dataclass_fields__:
            setattr(self, k, getattr(new_config, k))

    def tree_flatten(self) -> Tuple[Tuple[Any, ...], Tuple[Any, ...]]:
        aux_data = (
            {name: getattr(self, name) for name in self.__dataclass_fields__.keys()},
        )

        return ((), aux_data)

    @classmethod
    def tree_unflatten(
        cls: Type[T_VariPEPS_Config],
        aux_data: Tuple[Any, ...],
        children: Tuple[Any, ...],
    ) -> T_VariPEPS_Config:
        (data_dict,) = aux_data

        return cls(**data_dict)


config = VariPEPS_Config()


class ConfigModuleWrapper:
    __slots__ = {
        "Optimizing_Methods",
        "Line_Search_Methods",
        "Projector_Method",
        "Wavevector_Type",
        "Grad_Fixed_Point_Method",
        "Slurm_Restart_Mode",
        "VariPEPS_Config",
        "config",
    }

    def __init__(self):
        for e in self.__slots__:
            setattr(self, e, globals()[e])

    def __getattr__(self, name: str) -> Any:
        if name.startswith("__") or name in self.__slots__:
            return super().__getattr__(name)
        else:
            return getattr(self.config, name)

    def __setattr__(self, name: str, value: Any) -> NoReturn:
        if not name.startswith("__") and name not in self.__slots__:
            setattr(self.config, name, value)
        elif not hasattr(self, name):
            super().__setattr__(name, value)
        else:
            raise AttributeError(f"Attribute '{name}' is write-protected.")


wrapper = ConfigModuleWrapper()