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1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 | import collections
from collections import deque
import datetime
from functools import partial
import importlib
import os
from os import PathLike
import pathlib
import sys
import time
from scipy.optimize import OptimizeResult
from tqdm_loggable.auto import tqdm
import h5py
import numpy as np
import jax
from jax import jit
import jax.numpy as jnp
from jax.lax import scan, cond
from jax.flatten_util import ravel_pytree
from varipeps import varipeps_config, varipeps_global_state
from varipeps.config import Optimizing_Methods, Slurm_Restart_Mode
from varipeps.peps import PEPS_Unit_Cell
from varipeps.expectation import Expectation_Model
from varipeps.config import Projector_Method
from varipeps.mapping import Map_To_PEPS_Model
from varipeps.ctmrg import CTMRGNotConvergedError, CTMRGGradientNotConvergedError
from varipeps.utils.random import PEPS_Random_Number_Generator
from varipeps.utils.slurm import SlurmUtils
from varipeps.contractions import apply_contraction_jitted
from varipeps.utils.debug_print import debug_print
from .inner_function import (
calc_ctmrg_expectation,
calc_preconverged_ctmrg_value_and_grad,
calc_ctmrg_expectation_custom_value_and_grad,
)
from .line_search import line_search, NoSuitableStepSizeError, _scalar_descent_grad
from typing import List, Union, Tuple, cast, Sequence, Callable, Optional, Dict, Any
@jit
def _cg_workhorse(new_gradient, old_gradient, old_descent_dir):
new_grad_vec, new_grad_unravel = ravel_pytree(new_gradient)
old_grad_vec, old_grad_unravel = ravel_pytree(old_gradient)
old_des_dir_vec, old_des_dir_unravel = ravel_pytree(old_descent_dir)
new_grad_len = new_grad_vec.size
iscomplex = jnp.iscomplexobj(new_grad_vec)
if iscomplex:
new_grad_vec = jnp.concatenate((jnp.real(new_grad_vec), jnp.imag(new_grad_vec)))
old_grad_vec = jnp.concatenate((jnp.real(old_grad_vec), jnp.imag(old_grad_vec)))
old_des_dir_vec = jnp.concatenate(
(jnp.real(old_des_dir_vec), jnp.imag(old_des_dir_vec))
)
grad_diff = new_grad_vec - old_grad_vec
# dx = -new_grad_vec
# dx_old = -old_grad_vec
# dx_real = jnp.concatenate((jnp.real(dx), jnp.imag(dx)))
# dx_old_real = jnp.concatenate((jnp.real(dx_old), jnp.imag(dx_old)))
# old_des_dir_real = jnp.concatenate(
# (jnp.real(old_descent_dir), jnp.imag(old_descent_dir))
# )
# PRP
# beta = jnp.sum(dx_real * (dx_real - dx_old_real)) / jnp.sum(dx_old_real * dx_old_real)
# LS parameter
# beta = jnp.sum(dx_real * (dx_old_real - dx_real)) / jnp.sum(
# old_des_dir_real * dx_old_real
# )
# Hager-Zhang
eta = 0.4
eta_k = -1 / (
jnp.linalg.norm(old_des_dir_vec) * jnp.fmin(eta, jnp.linalg.norm(old_grad_vec))
)
old_des_grad_diff = jnp.dot(old_des_dir_vec, grad_diff)
beta = (
grad_diff - 2 * jnp.linalg.norm(grad_diff) * old_des_dir_vec / old_des_grad_diff
)
beta = jnp.dot(beta, new_grad_vec) / old_des_grad_diff
beta = jnp.fmax(eta_k, beta)
beta = jnp.fmax(0, beta)
result = -new_grad_vec + beta * old_des_dir_vec
if iscomplex:
result = result[:new_grad_len] + 1j * result[new_grad_len:]
return new_grad_unravel(result), beta
@partial(jit, static_argnums=(5,))
def _bfgs_workhorse(
new_gradient, old_gradient, old_descent_dir, old_alpha, B_inv, calc_new_B_inv
):
new_grad_vec, new_grad_unravel = ravel_pytree(new_gradient)
new_grad_len = new_grad_vec.size
iscomplex = jnp.iscomplexobj(new_grad_vec)
if iscomplex:
new_grad_vec = jnp.concatenate((jnp.real(new_grad_vec), jnp.imag(new_grad_vec)))
if calc_new_B_inv:
old_grad_vec, old_grad_unravel = ravel_pytree(old_gradient)
old_descent_dir_vec, old_descent_dir_unravel = ravel_pytree(old_descent_dir)
if iscomplex:
old_grad_vec = jnp.concatenate(
(jnp.real(old_grad_vec), jnp.imag(old_grad_vec))
)
old_descent_dir_vec = jnp.concatenate(
(jnp.real(old_descent_dir_vec), jnp.imag(old_descent_dir_vec))
)
sk = old_alpha * old_descent_dir_vec
yk = new_grad_vec - old_grad_vec
skyk_scalar = jnp.dot(sk, yk)
B_inv_yk = jnp.dot(B_inv, yk)
new_B_inv = (
B_inv
+ ((skyk_scalar + jnp.dot(yk, B_inv_yk)) / (skyk_scalar**2))
* jnp.outer(sk, sk)
- (jnp.outer(B_inv_yk, sk) + jnp.outer(sk, B_inv_yk)) / skyk_scalar
)
else:
new_B_inv = B_inv
result = -jnp.dot(new_B_inv, new_grad_vec)
if iscomplex:
result = result[:new_grad_len] + 1j * result[new_grad_len:]
return new_grad_unravel(result), new_B_inv
@jit
def _l_bfgs_workhorse(value_tuple, gradient_tuple, t_objs, config):
gradient_elem_0, gradient_unravel = ravel_pytree(gradient_tuple[0])
gradient_len = gradient_elem_0.size
iscomplex = jnp.iscomplexobj(gradient_elem_0)
def _make_1d(x):
x_1d, _ = ravel_pytree(x)
if iscomplex:
return jnp.concatenate((jnp.real(x_1d), jnp.imag(x_1d)))
return x_1d
gradient_elem_0_1d = _make_1d(gradient_elem_0)
norm_grad_square = jnp.sum(gradient_elem_0_1d * gradient_elem_0_1d)
value_arr = jnp.asarray([_make_1d(e) for e in value_tuple])
gradient_arr = jnp.asarray([_make_1d(e) for e in gradient_tuple])
s_arr = -jnp.diff(value_arr, axis=0)
y_arr = -jnp.diff(gradient_arr, axis=0)
pho_arr = 1 / jnp.sum(y_arr * s_arr, axis=1)
def first_loop(q, x):
pho_s, y = x
alpha_i = jnp.sum(pho_s * q)
return q - alpha_i * y, alpha_i
q, alpha_arr = scan(
first_loop,
gradient_arr[0],
(pho_arr[:, jnp.newaxis] * s_arr, y_arr),
)
def apply_precond(x):
if hasattr(t_objs[0], "is_triangular_peps") and t_objs[0].is_triangular_peps:
contraction = "precondition_operator_triangular"
elif hasattr(t_objs[0], "is_split_transfer") and t_objs[0].is_split_transfer:
contraction = "precondition_operator_split_transfer"
else:
contraction = "precondition_operator"
if iscomplex:
x = x[:gradient_len] + 1j * x[gradient_len:]
x = gradient_unravel(x)
x = [
apply_contraction_jitted(contraction, (te.tensor,), (te,), (xe,))
+ norm_grad_square * xe
for te, xe in zip(t_objs, x[: len(t_objs)], strict=True)
] + list(x[len(t_objs) :])
return _make_1d(x)
if config.optimizer_use_preconditioning:
y_precond, _ = jax.scipy.sparse.linalg.gmres(
apply_precond,
y_arr[0],
y_arr[0],
restart=config.optimizer_precond_gmres_krylov_subspace_size,
maxiter=config.optimizer_precond_gmres_maxiter,
solve_method="incremental",
)
def calc_q_precond(y, y_precond, q):
q_precond, _ = jax.scipy.sparse.linalg.gmres(
apply_precond,
q,
q,
restart=config.optimizer_precond_gmres_krylov_subspace_size,
maxiter=config.optimizer_precond_gmres_maxiter,
solve_method="incremental",
)
return cond(
jnp.sum(q_precond * q) >= 0,
lambda y, y_precond, q, q_precond: (y_precond, q_precond),
lambda y, y_precond, q, q_precond: (y, q),
y,
y_precond,
q,
q_precond,
)
y_precond, q_precond = cond(
jnp.sum(y_precond * y_arr[0]) >= 0,
calc_q_precond,
lambda y, y_precond, q: (y, q),
y_arr[0],
y_precond,
q,
)
else:
y_precond = y_arr[0]
q_precond = q
gamma = jnp.sum(s_arr[0] * y_arr[0]) / jnp.sum(y_arr[0] * y_precond)
z_result = gamma * q_precond
def second_loop(z, x):
pho_y, s, alpha_i = x
beta_i = jnp.sum(pho_y * z)
return z + s * (alpha_i - beta_i), None
z_result, _ = scan(
second_loop,
z_result,
(pho_arr[:, jnp.newaxis] * y_arr, s_arr, alpha_arr),
reverse=True,
)
z_result = -z_result
if iscomplex:
z_result = z_result[:gradient_len] + 1j * z_result[gradient_len:]
return gradient_unravel(z_result)
def autosave_function(
filename: PathLike,
tensors: jnp.ndarray,
unitcell: PEPS_Unit_Cell,
counter: Optional[Union[int, str]] = None,
auxiliary_data: Optional[Dict[str, Any]] = None,
) -> None:
if counter is not None:
unitcell.save_to_file(
f"{str(filename)}.{counter}", auxiliary_data=auxiliary_data
)
else:
unitcell.save_to_file(filename, auxiliary_data=auxiliary_data)
def autosave_function_restartable(
filename,
tensors,
unitcell,
counter,
auxiliary_data,
expectation_func,
convert_to_unitcell_func,
old_gradient,
old_descent_dir,
best_value,
best_tensors,
best_unitcell,
random_noise_retries,
descent_method_tuple,
count,
linesearch_step,
projector_method,
signal_reset_descent_dir,
) -> None:
state_filename = os.environ.get("VARIPEPS_STATE_FILE")
if state_filename is None:
state_filename = f"{str(filename)}.restartable"
with h5py.File(state_filename, "w", libver=("earliest", "v110")) as f:
grp = f.create_group("unitcell")
unitcell.save_to_group(grp, True)
grp_aux = f.create_group("auxiliary_data")
unitcell.save_auxiliary_data(grp_aux, auxiliary_data)
grp_restart_data = f.create_group("restart_data")
grp_restart_data.attrs["autosave_filename"] = filename
grp_expectation_func = grp_restart_data.create_group("expectation_func")
try:
expectation_func.save_to_group(grp_expectation_func)
except AttributeError:
pass
if convert_to_unitcell_func is not None:
pass
if old_gradient is not None:
grp_old_grad = grp_restart_data.create_group(
"old_gradient", track_order=True
)
grp_old_grad.attrs["len"] = len(old_gradient)
for i, g in enumerate(old_gradient):
if g.ndim == 0:
grp_old_grad.create_dataset(f"old_grad_{i:d}", data=g)
else:
grp_old_grad.create_dataset(
f"old_grad_{i:d}",
data=g,
compression="gzip",
compression_opts=6,
)
if old_descent_dir is not None:
grp_old_des_dir = grp_restart_data.create_group(
"old_descent_dir", track_order=True
)
grp_old_des_dir.attrs["len"] = len(old_descent_dir)
for i, d in enumerate(old_descent_dir):
if d.ndim == 0:
grp_old_des_dir.create_dataset(
f"old_descent_dir_{i:d}",
data=d,
)
else:
grp_old_des_dir.create_dataset(
f"old_descent_dir_{i:d}",
data=d,
compression="gzip",
compression_opts=6,
)
if best_unitcell is not None:
grp_best_t = grp_restart_data.create_group("best_tensors", track_order=True)
grp_best_t.attrs["len"] = len(best_tensors)
for i, t in enumerate(best_tensors):
if t.ndim == 0:
grp_best_t.create_dataset(
f"best_tensor_{i:d}",
data=t,
)
else:
grp_best_t.create_dataset(
f"best_tensor_{i:d}",
data=t,
compression="gzip",
compression_opts=6,
)
grp_best_u = grp_restart_data.create_group("best_unitcell")
best_unitcell.save_to_group(grp_best_u, False)
grp_restart_data.attrs["best_value"] = best_value
grp_restart_data.attrs["random_noise_retries"] = random_noise_retries
grp_restart_data.attrs["count"] = count
grp_restart_data.attrs["projector_method"] = projector_method
grp_restart_data.attrs["signal_reset_descent_dir"] = signal_reset_descent_dir
if linesearch_step is not None:
grp_restart_data.attrs["linesearch_step"] = linesearch_step
if varipeps_config.optimizer_method is Optimizing_Methods.BFGS:
bfgs_prefactor, bfgs_B_inv = descent_method_tuple
grp_restart_data.attrs["bfgs_prefactor"] = bfgs_prefactor
grp_restart_data.create_dataset(
"bfgs_B_inv", data=bfgs_B_inv, compression="gzip", compression_opts=6
)
elif varipeps_config.optimizer_method is Optimizing_Methods.L_BFGS:
l_bfgs_x_cache, l_bfgs_grad_cache = descent_method_tuple
grp_l_bfgs = grp_restart_data.create_group("l_bfgs", track_order=True)
grp_l_bfgs.attrs["len"] = len(l_bfgs_x_cache)
if len(l_bfgs_x_cache) > 0:
grp_l_bfgs.attrs["len_elems"] = len(l_bfgs_x_cache[0])
for i, (x, g) in enumerate(
zip(l_bfgs_x_cache, l_bfgs_grad_cache, strict=True)
):
if len(x) != len(g) != grp_l_bfgs.attrs["len_elems"]:
raise ValueError("L-BFGS list lengths mismatch.")
for j in range(grp_l_bfgs.attrs["len_elems"]):
if x[j].ndim == 0:
grp_l_bfgs.create_dataset(
f"x_{i:d}_{j:d}",
data=x[j],
)
else:
grp_l_bfgs.create_dataset(
f"x_{i:d}_{j:d}",
data=x[j],
compression="gzip",
compression_opts=6,
)
if g[j].ndim == 0:
grp_l_bfgs.create_dataset(
f"grad_{i:d}_{j:d}",
data=g[j],
)
else:
grp_l_bfgs.create_dataset(
f"grad_{i:d}_{j:d}",
data=g[j],
compression="gzip",
compression_opts=6,
)
def _autosave_wrapper(
autosave_func,
autosave_filename,
working_tensors,
working_unitcell,
working_value,
counter,
best_run,
max_trunc_error_list,
step_energies,
step_chi,
step_conv,
step_runtime,
spiral_indices,
additional_input,
):
auxiliary_data = {
"best_run": jnp.array(best_run if best_run is not None else 0),
"current_energy": working_value,
}
for k in sorted(max_trunc_error_list.keys()):
auxiliary_data[f"max_trunc_error_list_{k:d}"] = max_trunc_error_list[k]
auxiliary_data[f"step_energies_{k:d}"] = step_energies[k]
auxiliary_data[f"step_chi_{k:d}"] = step_chi[k]
auxiliary_data[f"step_conv_{k:d}"] = step_conv[k]
auxiliary_data[f"step_runtime_{k:d}"] = step_runtime[k]
spiral_vectors = None
if spiral_indices is not None:
spiral_mode = "BOTH_INDEPENDENT"
spiral_vectors = [working_tensors[spiral_i] for spiral_i in spiral_indices]
if any(i.size == 1 for i in spiral_vectors):
spiral_mode = "BOTH_SAME"
spiral_vectors_x = additional_input.get("spiral_vectors_x")
spiral_vectors_y = additional_input.get("spiral_vectors_y")
if spiral_vectors_x is not None:
spiral_mode = "FIXED_X"
if isinstance(spiral_vectors_x, jnp.ndarray):
spiral_vectors_x = (spiral_vectors_x,)
spiral_vectors = tuple(
jnp.array((sx, sy))
for sx, sy in zip(spiral_vectors_x, spiral_vectors, strict=True)
)
elif spiral_vectors_y is not None:
spiral_mode = "FIXED_Y"
if isinstance(spiral_vectors_y, jnp.ndarray):
spiral_vectors_y = (spiral_vectors_y,)
spiral_vectors = tuple(
jnp.array((sx, sy))
for sx, sy in zip(spiral_vectors, spiral_vectors_y, strict=True)
)
elif additional_input.get("spiral_vectors") is not None:
spiral_mode = "FIXED"
spiral_vectors = additional_input.get("spiral_vectors")
if isinstance(spiral_vectors, jnp.ndarray):
spiral_vectors = (spiral_vectors,)
if spiral_vectors is not None:
auxiliary_data["spiral_mode"] = spiral_mode
spiral_vectors = [
e if e.size == 2 else jnp.array((e, e)).reshape(2) for e in spiral_vectors
]
if len(spiral_vectors) == 1:
auxiliary_data["spiral_vector"] = spiral_vectors[0]
else:
for spiral_i, vec in enumerate(spiral_vectors):
spiral_i += 1
auxiliary_data[f"spiral_vector_{spiral_i:d}"] = vec
autosave_func(
autosave_filename,
working_tensors,
working_unitcell,
counter=counter,
auxiliary_data=auxiliary_data,
)
def optimize_peps_network(
input_tensors: Union[PEPS_Unit_Cell, Sequence[jnp.ndarray]],
expectation_func: Expectation_Model,
convert_to_unitcell_func: Optional[Map_To_PEPS_Model] = None,
autosave_filename: PathLike = "data/autosave.hdf5",
autosave_func: Callable[
[PathLike, Sequence[jnp.ndarray], PEPS_Unit_Cell], None
] = autosave_function,
additional_input: Dict[str, jnp.ndarray] = {},
restart_state: Dict[str, Any] = {},
) -> Tuple[Sequence[jnp.ndarray], PEPS_Unit_Cell, Union[float, jnp.ndarray]]:
"""
Optimize a PEPS unitcell using a variational method.
As convergence criterion the norm of the gradient is used.
If the very first CTMRG calculation does not converge,
a object of type :obj:`scipy.optimize.OptimizeResult`
is returned with Success=False. This case should be
handled by the scriptcalling this function.
Args:
input_tensors (:obj:`~varipeps.peps.PEPS_Unit_Cell` or :term:`sequence` of :obj:`jax.numpy.ndarray`):
The PEPS unitcell to work on or the tensors which should be mapped by
`convert_to_unitcell_func` to a PEPS unitcell.
expectation_func (:obj:`~varipeps.expectation.Expectation_Model`):
Callable to calculate one expectation value which is used as loss
loss function of the model. Likely the function to calculate the energy.
convert_to_unitcell_func (:obj:`~varipeps.mapping.Map_To_PEPS_Model`):
Function to convert the `input_tensors` to a PEPS unitcell. If ommited,
it is assumed that a PEPS unitcell is the first input parameter.
autosave_filename (:obj:`os.PathLike`):
Filename where intermediate results are automatically saved.
autosave_func (:term:`callable`):
Function which is called to autosave the intermediate results.
The function has to accept the arguments `(filename, tensors, unitcell)`.
additional_input (:obj:`dict` of :obj:`str` to :obj:`jax.numpy.ndarray` mapping):
Dict with additional inputs which should be considered in the
calculation of the expectation value.
Returns:
:obj:`scipy.optimize.OptimizeResult`:
OptimizeResult object with the optimized tensors, network and the
final expectation value. See the type definition for other possible
fields.
"""
rng = PEPS_Random_Number_Generator.get_generator(backend="jax")
def random_noise(a):
return (
a
+ a
* rng.block(a.shape, dtype=a.dtype)
* varipeps_config.optimizer_random_noise_relative_amplitude
)
if isinstance(input_tensors, PEPS_Unit_Cell):
working_tensors = cast(
List[jnp.ndarray], [i.tensor for i in input_tensors.get_unique_tensors()]
)
working_unitcell = input_tensors
generate_unitcell = True
else:
if isinstance(input_tensors, collections.abc.Sequence) and isinstance(
input_tensors[0], PEPS_Unit_Cell
):
if len(input_tensors[0].get_unique_tensors()) != 1:
raise ValueError(
"You want to use spiral PEPS but you use a unit cell with more than one site. Seems wrong to me!"
)
working_tensors = cast(
List[jnp.ndarray],
[i.tensor for i in input_tensors[0].get_unique_tensors()],
) + list(input_tensors[1:])
working_unitcell = input_tensors[0]
generate_unitcell = True
else:
working_tensors = input_tensors
working_unitcell = None
generate_unitcell = False
old_gradient = restart_state.get("old_gradient")
old_descent_dir = restart_state.get("old_descent_dir")
descent_dir = None
working_value = None
max_trunc_error = jnp.nan
best_value = restart_state.get("best_value", jnp.inf)
best_tensors = restart_state.get("best_tensors")
best_unitcell = restart_state.get("best_unitcell")
best_run = restart_state.get("best_run")
random_noise_retries = restart_state.get("random_noise_retries", 0)
signal_reset_descent_dir = restart_state.get("signal_reset_descent_dir", False)
spiral_indices = None
if (
hasattr(expectation_func, "is_spiral_peps")
and expectation_func.is_spiral_peps
and additional_input.get("spiral_vectors") is None
):
if isinstance(input_tensors, collections.abc.Sequence) and isinstance(
input_tensors[0], PEPS_Unit_Cell
):
spiral_indices = list(range(1, len(input_tensors)))
else:
raise NotImplementedError("Only support spiral PEPS for unitcell input yet")
if varipeps_config.optimizer_method is Optimizing_Methods.BFGS:
bfgs_prefactor = restart_state.get(
"bfgs_prefactor",
2 if any(jnp.iscomplexobj(t) for t in working_tensors) else 1,
)
bfgs_B_inv = restart_state.get(
"bfgs_B_inv",
jnp.eye(bfgs_prefactor * sum([t.size for t in working_tensors])),
)
elif varipeps_config.optimizer_method is Optimizing_Methods.L_BFGS:
l_bfgs_x_cache = deque(
restart_state.get("l_bfgs_x_cache", []),
maxlen=varipeps_config.optimizer_l_bfgs_maxlen + 1,
)
l_bfgs_grad_cache = deque(
restart_state.get("l_bfgs_grad_cache", []),
maxlen=varipeps_config.optimizer_l_bfgs_maxlen + 1,
)
count = restart_state.get("count", 0)
linesearch_step: Optional[Union[float, jnp.ndarray]] = restart_state.get(
"linesearch_step"
)
working_value: Union[float, jnp.ndarray]
max_trunc_error_list = restart_state.get(
"max_trunc_error_list", {random_noise_retries: []}
)
step_energies = restart_state.get("step_energies", {random_noise_retries: []})
step_chi = restart_state.get("step_chi", {random_noise_retries: []})
step_conv = restart_state.get("step_conv", {random_noise_retries: []})
step_runtime = restart_state.get("step_runtime", {random_noise_retries: []})
if (
varipeps_config.optimizer_preconverge_with_half_projectors
and not varipeps_global_state.basinhopping_disable_half_projector
):
varipeps_global_state.ctmrg_projector_method = (
Projector_Method.HALF
if restart_state.get("projector_method", "HALF") == "HALF"
else None
)
else:
varipeps_global_state.ctmrg_projector_method = None
slurm_restart_written = False
slurm_new_job_id = None
with tqdm(desc="Optimizing PEPS state", initial=count) as pbar:
while count < varipeps_config.optimizer_max_steps:
runtime_start = time.perf_counter()
chi_before_ctmrg = working_unitcell[0, 0][0][0].chi
try:
if varipeps_config.ad_use_custom_vjp:
(
working_value,
(working_unitcell, _),
), working_gradient_seq = calc_ctmrg_expectation_custom_value_and_grad(
working_tensors,
working_unitcell,
expectation_func,
convert_to_unitcell_func,
additional_input,
)
else:
(
working_value,
(working_unitcell, _),
), working_gradient_seq = calc_preconverged_ctmrg_value_and_grad(
working_tensors,
working_unitcell,
expectation_func,
convert_to_unitcell_func,
additional_input,
calc_preconverged=(count == 0),
)
except (CTMRGNotConvergedError, CTMRGGradientNotConvergedError) as e:
varipeps_global_state.ctmrg_projector_method = None
if random_noise_retries == 0:
return OptimizeResult(
success=False,
message=str(type(e)),
x=working_tensors,
fun=working_value,
unitcell=working_unitcell,
nit=count,
max_trunc_error_list=max_trunc_error_list,
step_energies=step_energies,
step_chi=step_chi,
step_conv=step_conv,
step_runtime=step_runtime,
best_run=0,
)
elif (
random_noise_retries
>= varipeps_config.optimizer_random_noise_max_retries
):
working_value = jnp.inf
break
else:
if isinstance(input_tensors, PEPS_Unit_Cell) or (
isinstance(input_tensors, collections.abc.Sequence)
and isinstance(input_tensors[0], PEPS_Unit_Cell)
):
working_tensors = (
cast(
List[jnp.ndarray],
[i.tensor for i in best_unitcell.get_unique_tensors()],
)
+ best_tensors[best_unitcell.get_len_unique_tensors() :]
)
working_tensors = [random_noise(i) for i in working_tensors]
working_tensors_obj = [
e.replace_tensor(working_tensors[i])
for i, e in enumerate(best_unitcell.get_unique_tensors())
]
working_unitcell = best_unitcell.replace_unique_tensors(
working_tensors_obj
)
else:
working_tensors = [random_noise(i) for i in best_tensors]
working_unitcell = None
descent_dir = None
working_gradient = None
signal_reset_descent_dir = True
count = 0
random_noise_retries += 1
old_descent_dir = descent_dir
old_gradient = working_gradient
step_energies[random_noise_retries] = []
step_chi[random_noise_retries] = []
step_conv[random_noise_retries] = []
max_trunc_error_list[random_noise_retries] = []
step_runtime[random_noise_retries] = []
pbar.reset()
pbar.refresh()
continue
if working_unitcell[0, 0][0][0].chi != chi_before_ctmrg:
jax.clear_caches()
working_gradient = [elem.conj() for elem in working_gradient_seq]
if signal_reset_descent_dir:
if varipeps_config.optimizer_method is Optimizing_Methods.BFGS:
bfgs_prefactor = (
2 if any(jnp.iscomplexobj(t) for t in working_tensors) else 1
)
bfgs_B_inv = jnp.eye(
bfgs_prefactor * sum([t.size for t in working_tensors])
)
elif varipeps_config.optimizer_method is Optimizing_Methods.L_BFGS:
l_bfgs_x_cache = deque(
maxlen=varipeps_config.optimizer_l_bfgs_maxlen + 1
)
l_bfgs_grad_cache = deque(
maxlen=varipeps_config.optimizer_l_bfgs_maxlen + 1
)
if varipeps_config.optimizer_method is Optimizing_Methods.STEEPEST:
descent_dir = [-elem for elem in working_gradient]
elif varipeps_config.optimizer_method is Optimizing_Methods.CG:
if count == 0 or signal_reset_descent_dir:
descent_dir = [-elem for elem in working_gradient]
else:
descent_dir, beta = _cg_workhorse(
working_gradient, old_gradient, old_descent_dir
)
elif varipeps_config.optimizer_method is Optimizing_Methods.BFGS:
if count == 0 or signal_reset_descent_dir:
descent_dir, _ = _bfgs_workhorse(
working_gradient, None, None, None, bfgs_B_inv, False
)
else:
descent_dir, bfgs_B_inv = _bfgs_workhorse(
working_gradient,
old_gradient,
old_descent_dir,
linesearch_step,
bfgs_B_inv,
True,
)
elif varipeps_config.optimizer_method is Optimizing_Methods.L_BFGS:
l_bfgs_x_cache.appendleft(tuple(working_tensors))
l_bfgs_grad_cache.appendleft(tuple(working_gradient))
if count == 0 or signal_reset_descent_dir:
descent_dir = [-elem for elem in working_gradient]
if varipeps_config.optimizer_use_preconditioning:
if (
hasattr(
working_unitcell.get_unique_tensors()[0],
"is_triangular_peps",
)
and working_unitcell.get_unique_tensors()[
0
].is_triangular_peps
):
contraction = "precondition_operator_triangular"
elif (
hasattr(
working_unitcell.get_unique_tensors()[0],
"is_split_transfer",
)
and working_unitcell.get_unique_tensors()[
0
].is_split_transfer
):
contraction = "precondition_operator_split_transfer"
else:
contraction = "precondition_operator"
grad_norm_squared = 1e-2 * (
jnp.linalg.norm(ravel_pytree(working_gradient)[0]) ** 2
)
tmp_descent_dir = [
jax.scipy.sparse.linalg.gmres(
lambda x: (
apply_contraction_jitted(
contraction, (te.tensor,), (te,), (x,)
)
+ grad_norm_squared * x
),
xe,
xe,
restart=varipeps_config.optimizer_precond_gmres_krylov_subspace_size,
maxiter=varipeps_config.optimizer_precond_gmres_maxiter,
solve_method="incremental",
)[0]
for te, xe in zip(
working_unitcell.get_unique_tensors(),
descent_dir[
: working_unitcell.get_len_unique_tensors()
],
strict=True,
)
] + list(
descent_dir[working_unitcell.get_len_unique_tensors() :]
)
if all(
jnp.sum(xe * x2e.conj()) >= 0
for xe, x2e in zip(
descent_dir, tmp_descent_dir, strict=True
)
):
descent_dir = tmp_descent_dir
else:
tqdm.write("Warning: Non-positive preconditioner")
del contraction
del grad_norm_squared
else:
descent_dir = _l_bfgs_workhorse(
tuple(l_bfgs_x_cache),
tuple(l_bfgs_grad_cache),
working_unitcell.get_unique_tensors(),
varipeps_config,
)
else:
raise ValueError("Unknown optimization method.")
signal_reset_descent_dir = False
if _scalar_descent_grad(descent_dir, working_gradient) > 0:
tqdm.write("Found bad descent dir. Reset to negative gradient!")
descent_dir = [-elem for elem in working_gradient]
conv = jnp.linalg.norm(ravel_pytree(working_gradient)[0])
if jnp.isinf(conv) or jnp.isnan(conv):
conv = 0
step_conv[random_noise_retries].append(conv)
try:
(
working_tensors,
working_unitcell,
working_value,
linesearch_step,
signal_reset_descent_dir,
max_trunc_error,
) = line_search(
working_tensors,
working_unitcell,
expectation_func,
working_gradient,
descent_dir,
working_value,
linesearch_step,
convert_to_unitcell_func,
generate_unitcell,
spiral_indices,
additional_input,
conv > varipeps_config.optimizer_reuse_env_eps,
)
except NoSuitableStepSizeError:
runtime = time.perf_counter() - runtime_start
step_runtime[random_noise_retries].append(runtime)
if varipeps_config.optimizer_fail_if_no_step_size_found:
raise
else:
if (
(
conv > varipeps_config.optimizer_random_noise_eps
or working_value > best_value
)
and random_noise_retries
< varipeps_config.optimizer_random_noise_max_retries
and not (
varipeps_config.optimizer_preconverge_with_half_projectors
and not varipeps_global_state.basinhopping_disable_half_projector
and varipeps_global_state.ctmrg_projector_method
is Projector_Method.HALF
)
):
tqdm.write(
"Convergence is not sufficient. Retry with some random noise on best result."
)
if working_value < best_value:
best_value = working_value
best_tensors = working_tensors
best_unitcell = working_unitcell
best_run = random_noise_retries
_autosave_wrapper(
autosave_func,
autosave_filename,
working_tensors,
working_unitcell,
working_value,
"best",
best_run,
max_trunc_error_list,
step_energies,
step_chi,
step_conv,
step_runtime,
spiral_indices,
additional_input,
)
if isinstance(input_tensors, PEPS_Unit_Cell) or (
isinstance(input_tensors, collections.abc.Sequence)
and isinstance(input_tensors[0], PEPS_Unit_Cell)
):
working_tensors = (
cast(
List[jnp.ndarray],
[
i.tensor
for i in best_unitcell.get_unique_tensors()
],
)
+ best_tensors[best_unitcell.get_len_unique_tensors() :]
)
working_tensors = [random_noise(i) for i in working_tensors]
working_tensors_obj = [
e.replace_tensor(working_tensors[i])
for i, e in enumerate(
best_unitcell.get_unique_tensors()
)
]
working_unitcell = best_unitcell.replace_unique_tensors(
working_tensors_obj
)
else:
working_tensors = [random_noise(i) for i in best_tensors]
working_unitcell = None
descent_dir = None
working_gradient = None
signal_reset_descent_dir = True
count = 0
random_noise_retries += 1
old_descent_dir = descent_dir
old_gradient = working_gradient
step_energies[random_noise_retries] = []
step_chi[random_noise_retries] = []
step_conv[random_noise_retries] = []
max_trunc_error_list[random_noise_retries] = []
step_runtime[random_noise_retries] = []
if autosave_func is autosave_function:
descent_method_tuple = None
if (
varipeps_config.optimizer_method
is Optimizing_Methods.BFGS
):
descent_method_tuple = (bfgs_prefactor, bfgs_B_inv)
elif (
varipeps_config.optimizer_method
is Optimizing_Methods.L_BFGS
):
descent_method_tuple = (
l_bfgs_x_cache,
l_bfgs_grad_cache,
)
_autosave_wrapper(
partial(
autosave_function_restartable,
expectation_func=expectation_func,
convert_to_unitcell_func=convert_to_unitcell_func,
old_gradient=old_gradient,
old_descent_dir=old_descent_dir,
best_value=best_value,
best_tensors=best_tensors,
best_unitcell=best_unitcell,
random_noise_retries=random_noise_retries,
descent_method_tuple=descent_method_tuple,
count=count,
linesearch_step=linesearch_step,
projector_method=(
"HALF"
if varipeps_global_state.ctmrg_projector_method
is Projector_Method.HALF
else "FULL"
),
signal_reset_descent_dir=signal_reset_descent_dir,
),
autosave_filename,
working_tensors,
working_unitcell,
working_value,
None,
best_run,
max_trunc_error_list,
step_energies,
step_chi,
step_conv,
step_runtime,
spiral_indices,
additional_input,
)
pbar.reset()
pbar.refresh()
continue
else:
conv = 0
else:
runtime = time.perf_counter() - runtime_start
step_runtime[random_noise_retries].append(runtime)
max_trunc_error_list[random_noise_retries].append(max_trunc_error)
step_energies[random_noise_retries].append(working_value)
step_chi[random_noise_retries].append(
working_unitcell.get_unique_tensors()[0].chi
)
if (
varipeps_config.optimizer_preconverge_with_half_projectors
and not varipeps_global_state.basinhopping_disable_half_projector
and varipeps_global_state.ctmrg_projector_method
is Projector_Method.HALF
and conv
< varipeps_config.optimizer_preconverge_with_half_projectors_eps
):
varipeps_global_state.ctmrg_projector_method = (
varipeps_config.ctmrg_full_projector_method
)
working_value, (working_unitcell, max_trunc_error) = (
calc_ctmrg_expectation(
working_tensors,
working_unitcell,
expectation_func,
convert_to_unitcell_func,
additional_input,
enforce_elementwise_convergence=varipeps_config.ad_use_custom_vjp,
)
)
descent_dir = None
working_gradient = None
signal_reset_descent_dir = True
conv = jnp.inf
linesearch_step = None
if conv < varipeps_config.optimizer_convergence_eps:
working_value, (
working_unitcell,
max_trunc_error,
) = calc_ctmrg_expectation(
working_tensors,
working_unitcell,
expectation_func,
convert_to_unitcell_func,
additional_input,
enforce_elementwise_convergence=varipeps_config.ad_use_custom_vjp,
)
try:
max_trunc_error_list[random_noise_retries][-1] = max_trunc_error
except IndexError:
max_trunc_error_list[random_noise_retries].append(max_trunc_error)
try:
step_energies[random_noise_retries][-1] = working_value
except IndexError:
step_energies[random_noise_retries].append(working_value)
try:
step_chi[random_noise_retries][
-1
] = working_unitcell.get_unique_tensors()[0].chi
except IndexError:
step_chi[random_noise_retries].append(
working_unitcell.get_unique_tensors()[0].chi
)
break
old_descent_dir = descent_dir
old_gradient = working_gradient
count += 1
pbar.update()
pbar.set_postfix(
{
"Energy": f"{working_value:0.10f}",
"Retries": random_noise_retries,
"Convergence": f"{conv:0.8f}",
"Line search step": (
f"{linesearch_step:0.8f}"
if linesearch_step is not None
else "0"
),
"Max. trunc. err.": f"{max_trunc_error:0.8g}",
}
)
pbar.refresh()
if count % varipeps_config.optimizer_autosave_step_count == 0:
_autosave_wrapper(
autosave_func,
autosave_filename,
working_tensors,
working_unitcell,
working_value,
random_noise_retries,
best_run,
max_trunc_error_list,
step_energies,
step_chi,
step_conv,
step_runtime,
spiral_indices,
additional_input,
)
if working_value < best_value and not (
varipeps_config.optimizer_preconverge_with_half_projectors
and not varipeps_global_state.basinhopping_disable_half_projector
and varipeps_global_state.ctmrg_projector_method
is Projector_Method.HALF
):
_autosave_wrapper(
autosave_func,
autosave_filename,
working_tensors,
working_unitcell,
working_value,
"best",
random_noise_retries,
max_trunc_error_list,
step_energies,
step_chi,
step_conv,
step_runtime,
spiral_indices,
additional_input,
)
if autosave_func is autosave_function:
descent_method_tuple = None
if varipeps_config.optimizer_method is Optimizing_Methods.BFGS:
descent_method_tuple = (bfgs_prefactor, bfgs_B_inv)
elif varipeps_config.optimizer_method is Optimizing_Methods.L_BFGS:
descent_method_tuple = (l_bfgs_x_cache, l_bfgs_grad_cache)
_autosave_wrapper(
partial(
autosave_function_restartable,
expectation_func=expectation_func,
convert_to_unitcell_func=convert_to_unitcell_func,
old_gradient=old_gradient,
old_descent_dir=old_descent_dir,
best_value=best_value,
best_tensors=best_tensors,
best_unitcell=best_unitcell,
random_noise_retries=random_noise_retries,
descent_method_tuple=descent_method_tuple,
count=count,
linesearch_step=linesearch_step,
projector_method=(
"HALF"
if varipeps_global_state.ctmrg_projector_method
is Projector_Method.HALF
else "FULL"
),
signal_reset_descent_dir=signal_reset_descent_dir,
),
autosave_filename,
working_tensors,
working_unitcell,
working_value,
None,
best_run,
max_trunc_error_list,
step_energies,
step_chi,
step_conv,
step_runtime,
spiral_indices,
additional_input,
)
if working_value < best_value and not (
varipeps_config.optimizer_preconverge_with_half_projectors
and not varipeps_global_state.basinhopping_disable_half_projector
and varipeps_global_state.ctmrg_projector_method
is Projector_Method.HALF
):
best_value = working_value
best_tensors = working_tensors
best_unitcell = working_unitcell
best_run = random_noise_retries
if (
varipeps_config.slurm_restart_mode is not Slurm_Restart_Mode.DISABLED
and (slurm_data := SlurmUtils.get_own_job_data()) is not None
):
flatten_runtime = [j for i in step_runtime for j in step_runtime[i]]
runtime_mean = np.mean(flatten_runtime)
runtime_std = np.std(flatten_runtime)
remaining_slurm_time = slurm_data["TimeLimit"] - slurm_data["RunTime"]
if (
remaining_time_correction := os.environ.get(
"VARIPEPS_REMAINING_TIME_CORRECTION"
)
) is not None:
try:
remaining_time_correction = int(remaining_time_correction)
remaining_slurm_time -= datetime.timedelta(
seconds=remaining_time_correction
)
except (TypeError, ValueError):
pass
time_of_one_step = datetime.timedelta(
seconds=runtime_mean + 3 * runtime_std
)
if remaining_slurm_time < time_of_one_step:
print(
"Average time of optimizer step below remaining Slurm runtime",
file=sys.stderr,
)
if (
restart_needed_filename := os.environ.get(
"VARIPEPS_NEED_RESTART_FILE"
)
) is not None:
pathlib.Path(restart_needed_filename).touch()
if (
varipeps_config.slurm_restart_mode
is Slurm_Restart_Mode.WRITE_RESTART_SCRIPT
or varipeps_config.slurm_restart_mode
is Slurm_Restart_Mode.AUTOMATIC_RESTART
):
SlurmUtils.generate_restart_scripts(
f"{str(autosave_filename)}.restart.slurm",
f"{str(autosave_filename)}.restart.py",
f"{str(autosave_filename)}.restartable",
slurm_data,
)
slurm_restart_written = True
if (
varipeps_config.slurm_restart_mode
is Slurm_Restart_Mode.AUTOMATIC_RESTART
):
slurm_new_job_id = SlurmUtils.run_slurm_script(
f"{str(autosave_filename)}.restart.slurm",
slurm_data["WorkDir"],
)
if slurm_new_job_id is None:
tqdm.write(
"Failed to start new Slurm job or parse its job id."
)
break
if working_value < best_value:
best_value = working_value
best_tensors = working_tensors
best_unitcell = working_unitcell
best_run = random_noise_retries
if not (
varipeps_config.optimizer_preconverge_with_half_projectors
and not varipeps_global_state.basinhopping_disable_half_projector
and varipeps_global_state.ctmrg_projector_method is Projector_Method.HALF
):
_autosave_wrapper(
autosave_func,
autosave_filename,
working_tensors,
working_unitcell,
working_value,
"best",
best_run,
max_trunc_error_list,
step_energies,
step_chi,
step_conv,
step_runtime,
spiral_indices,
additional_input,
)
varipeps_global_state.ctmrg_projector_method = None
print(f"Best energy result found: {best_value}")
if slurm_restart_written:
print("Wrote script to restart optimizer job with Slurm.")
if slurm_new_job_id is not None:
print(f"Started new Slurm job with ID {slurm_new_job_id:d}.")
return OptimizeResult(
success=True,
x=best_tensors,
fun=best_value,
unitcell=best_unitcell,
nit=count,
max_trunc_error_list=max_trunc_error_list,
step_energies=step_energies,
step_chi=step_chi,
step_conv=step_conv,
step_runtime=step_runtime,
best_run=best_run,
slurm_restart_written=slurm_restart_written,
slurm_new_job_id=slurm_new_job_id,
)
def optimize_peps_unitcell(
unitcell,
expectation_func,
autosave_filename="data/autosave.hdf5",
restart_state={},
):
return optimize_peps_network(
unitcell,
expectation_func,
autosave_filename=autosave_filename,
restart_state=restart_state,
)
def optimize_unitcell_fixed_spiral_vector(
unitcell,
spiral_vector,
expectation_func,
autosave_filename="data/autosave.hdf5",
restart_state={},
):
return optimize_peps_network(
unitcell,
expectation_func,
additional_input={"spiral_vectors": spiral_vector},
autosave_filename=autosave_filename,
restart_state=restart_state,
)
def _map_spiral_func(input_tensors, generate_unitcell):
return input_tensors[:1], input_tensors[1:]
def optimize_unitcell_full_spiral_vector(
unitcell,
spiral_vector,
expectation_func,
autosave_filename="data/autosave.hdf5",
restart_state={},
):
return optimize_peps_network(
(unitcell, spiral_vector),
expectation_func,
_map_spiral_func,
autosave_filename=autosave_filename,
restart_state=restart_state,
)
def optimize_unitcell_spiral_vector_x_component(
unitcell,
spiral_vector_x,
spiral_vector_fixed_y,
expectation_func,
autosave_filename="data/autosave.hdf5",
restart_state={},
):
return optimize_peps_network(
(unitcell, spiral_vector_x),
expectation_func,
_map_spiral_func,
additional_input={"spiral_vectors_y": spiral_vector_fixed_y},
autosave_filename=autosave_filename,
restart_state=restart_state,
)
def optimize_unitcell_spiral_vector_y_component(
unitcell,
spiral_vector_fixed_x,
spiral_vector_y,
expectation_func,
autosave_filename="data/autosave.hdf5",
restart_state={},
):
return optimize_peps_network(
(unitcell, spiral_vector_y),
expectation_func,
_map_spiral_func,
additional_input={"spiral_vectors_x": spiral_vector_fixed_x},
autosave_filename=autosave_filename,
restart_state=restart_state,
)
def restart_from_state_file(filename: PathLike):
with h5py.File(filename, "r") as f:
unitcell, config = PEPS_Unit_Cell.load_from_group(
f["unitcell"], return_config=True
)
auxiliary_data = PEPS_Unit_Cell.load_auxiliary_data(f["auxiliary_data"])
grp_restart_data = f["restart_data"]
grp_expectation_func = grp_restart_data["expectation_func"]
exp_func_class = grp_expectation_func.attrs["class"]
if exp_func_class.split(".", maxsplit=1)[0] != "varipeps":
raise ValueError(
"Do not support restart from expectation function outside of the library."
)
exp_func_module, exp_func_class = exp_func_class.rsplit(".", maxsplit=1)
exp_func_module = importlib.import_module(exp_func_module)
exp_func_class = getattr(exp_func_module, exp_func_class)
exp_func = exp_func_class.load_from_group(grp_expectation_func)
restart_state = {}
if grp_restart_data.get("old_gradient") is not None:
restart_state["old_gradient"] = [
jnp.asarray(grp_restart_data["old_gradient"][f"old_grad_{i:d}"])
for i in range(grp_restart_data["old_gradient"].attrs["len"])
]
else:
restart_state["old_gradient"] = None
if grp_restart_data.get("old_descent_dir") is not None:
restart_state["old_descent_dir"] = [
jnp.asarray(
grp_restart_data["old_descent_dir"][f"old_descent_dir_{i:d}"]
)
for i in range(grp_restart_data["old_descent_dir"].attrs["len"])
]
else:
restart_state["old_descent_dir"] = None
restart_state["best_run"] = auxiliary_data["best_run"]
if (grp_best_u := grp_restart_data.get("best_unitcell")) is not None:
restart_state["best_unitcell"] = PEPS_Unit_Cell.load_from_group(grp_best_u)
restart_state["best_tensors"] = [
jnp.asarray(grp_restart_data["best_tensors"][f"best_tensor_{i:d}"])
for i in range(grp_restart_data["best_tensors"].attrs["len"])
]
restart_state["best_value"] = grp_restart_data.attrs["best_value"]
random_noise_retries = int(grp_restart_data.attrs["random_noise_retries"])
restart_state["random_noise_retries"] = random_noise_retries
restart_state["count"] = int(grp_restart_data.attrs["count"])
restart_state["projector_method"] = grp_restart_data.attrs["projector_method"]
restart_state["signal_reset_descent_dir"] = grp_restart_data.attrs[
"signal_reset_descent_dir"
]
restart_state["linesearch_step"] = grp_restart_data.attrs.get("linesearch_step")
restart_state["max_trunc_error_list"] = {
k: None for k in range(random_noise_retries + 1)
}
restart_state["step_energies"] = {
k: None for k in range(random_noise_retries + 1)
}
restart_state["step_chi"] = {k: None for k in range(random_noise_retries + 1)}
restart_state["step_conv"] = {k: None for k in range(random_noise_retries + 1)}
restart_state["step_runtime"] = {
k: None for k in range(random_noise_retries + 1)
}
for k in range(random_noise_retries + 1):
restart_state["max_trunc_error_list"][k] = list(
auxiliary_data[f"max_trunc_error_list_{k:d}"]
)
restart_state["step_energies"][k] = list(
auxiliary_data[f"step_energies_{k:d}"]
)
restart_state["step_chi"][k] = list(auxiliary_data[f"step_chi_{k:d}"])
restart_state["step_conv"][k] = list(auxiliary_data[f"step_conv_{k:d}"])
restart_state["step_runtime"][k] = list(
auxiliary_data[f"step_runtime_{k:d}"]
)
if config.optimizer_method is Optimizing_Methods.BFGS:
restart_state["bfgs_prefactor"] = grp_restart_data.attrs["bfgs_prefactor"]
restart_state["bfgs_B_inv"] = jnp.asarray(grp_restart_data["bfgs_B_inv"])
elif config.optimizer_method is Optimizing_Methods.L_BFGS:
restart_state["l_bfgs_x_cache"] = [
[
jnp.asarray(grp_restart_data["l_bfgs"][f"x_{i:d}_{j:d}"])
for j in range(grp_restart_data["l_bfgs"].attrs["len_elems"])
]
for i in range(grp_restart_data["l_bfgs"].attrs["len"])
]
restart_state["l_bfgs_grad_cache"] = [
[
jnp.asarray(grp_restart_data["l_bfgs"][f"grad_{i:d}_{j:d}"])
for j in range(grp_restart_data["l_bfgs"].attrs["len_elems"])
]
for i in range(grp_restart_data["l_bfgs"].attrs["len"])
]
autosave_filename = grp_restart_data.attrs["autosave_filename"]
varipeps_config.update_from_config_object(config)
if exp_func.is_spiral_peps:
spiral_mode = auxiliary_data["spiral_mode"]
spiral_vector = auxiliary_data["spiral_vector"]
if spiral_mode == "FIXED":
return optimize_unitcell_fixed_spiral_vector(
unitcell,
spiral_vector,
exp_func,
autosave_filename=autosave_filename,
restart_state=restart_state,
)
elif spiral_mode == "BOTH_SAME":
return optimize_unitcell_full_spiral_vector(
unitcell,
spiral_vector[0],
exp_func,
autosave_filename=autosave_filename,
restart_state=restart_state,
)
elif spiral_mode == "BOTH_INDEPENDENT":
return optimize_unitcell_full_spiral_vector(
unitcell,
spiral_vector,
exp_func,
autosave_filename=autosave_filename,
restart_state=restart_state,
)
elif spiral_mode == "FIXED_X":
return optimize_unitcell_spiral_vector_y_component(
unitcell,
spiral_vector[0],
spiral_vector[1],
exp_func,
autosave_filename=autosave_filename,
restart_state=restart_state,
)
elif spiral_mode == "FIXED_Y":
return optimize_unitcell_spiral_vector_x_component(
unitcell,
spiral_vector[0],
spiral_vector[1],
exp_func,
autosave_filename=autosave_filename,
restart_state=restart_state,
)
else:
raise ValueError("Unknown mode")
return optimize_peps_unitcell(
unitcell,
exp_func,
autosave_filename=autosave_filename,
restart_state=restart_state,
)
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