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import enum
import numpy as np
from scipy.sparse.linalg import LinearOperator, eigs
import jax
import jax.numpy as jnp
import jax.scipy as jsp
from jax import jit, custom_vjp, vjp, tree_util
from jax.lax import cond, while_loop
import jax.debug as jdebug
from varipeps import varipeps_config, varipeps_global_state
from varipeps.config import Grad_Fixed_Point_Method
from varipeps.peps import PEPS_Tensor, PEPS_Tensor_Split_Transfer, PEPS_Unit_Cell
from varipeps.utils.debug_print import debug_print
from .absorption import do_absorption_step, do_absorption_step_split_transfer
from .triangular_absorption import do_absorption_step_triangular
from typing import Sequence, Tuple, List, Optional
@enum.unique
class CTM_Enum(enum.IntEnum):
C1 = enum.auto()
C2 = enum.auto()
C3 = enum.auto()
C4 = enum.auto()
T1 = enum.auto()
T2 = enum.auto()
T3 = enum.auto()
T4 = enum.auto()
T1_ket = enum.auto()
T1_bra = enum.auto()
T2_ket = enum.auto()
T2_bra = enum.auto()
T3_ket = enum.auto()
T3_bra = enum.auto()
T4_ket = enum.auto()
T4_bra = enum.auto()
C5 = enum.auto()
C6 = enum.auto()
T1a = enum.auto()
T1b = enum.auto()
T2a = enum.auto()
T2b = enum.auto()
T3a = enum.auto()
T3b = enum.auto()
T4a = enum.auto()
T4b = enum.auto()
T5a = enum.auto()
T5b = enum.auto()
T6a = enum.auto()
T6b = enum.auto()
class CTMRGNotConvergedError(Exception):
"""
Exception if the CTM routine does not converge.
"""
pass
class CTMRGGradientNotConvergedError(Exception):
"""
Exception if the custom rule for the gradient of the the CTM routine does
not converge.
"""
pass
@partial(jit, static_argnums=(2,), inline=True)
def _calc_corner_svds(
peps_tensors: List[PEPS_Tensor],
old_corner_svd: jnp.ndarray,
tensor_shape: Optional[Tuple[int, int, int]],
) -> jnp.ndarray:
if tensor_shape is None:
step_corner_svd = jnp.zeros_like(old_corner_svd)
else:
step_corner_svd = jnp.zeros(tensor_shape, dtype=jnp.float64)
for ti, t in enumerate(peps_tensors):
C1_svd = jnp.linalg.svd(t.C1, full_matrices=False, compute_uv=False)
step_corner_svd = step_corner_svd.at[ti, 0, : C1_svd.shape[0]].set(
C1_svd, indices_are_sorted=True, unique_indices=True
)
C2_svd = jnp.linalg.svd(t.C2, full_matrices=False, compute_uv=False)
step_corner_svd = step_corner_svd.at[ti, 1, : C2_svd.shape[0]].set(
C2_svd, indices_are_sorted=True, unique_indices=True
)
C3_svd = jnp.linalg.svd(t.C3, full_matrices=False, compute_uv=False)
step_corner_svd = step_corner_svd.at[ti, 2, : C3_svd.shape[0]].set(
C3_svd, indices_are_sorted=True, unique_indices=True
)
C4_svd = jnp.linalg.svd(t.C4, full_matrices=False, compute_uv=False)
step_corner_svd = step_corner_svd.at[ti, 3, : C4_svd.shape[0]].set(
C4_svd, indices_are_sorted=True, unique_indices=True
)
return step_corner_svd
@partial(jit, static_argnums=(2,), inline=True)
def _calc_corner_svds_triangular(
peps_tensors: List[PEPS_Tensor],
old_corner_svd: jnp.ndarray,
tensor_shape: Optional[Tuple[int, int, int]],
) -> jnp.ndarray:
if tensor_shape is None:
step_corner_svd = jnp.zeros_like(old_corner_svd)
else:
step_corner_svd = jnp.zeros(tensor_shape, dtype=jnp.float64)
for ti, t in enumerate(peps_tensors):
for ni, name in enumerate(
(
"C1",
"C2",
"C3",
"C4",
"C5",
"C6",
"T1a",
"T1b",
"T2a",
"T2b",
"T3a",
"T3b",
"T4a",
"T4b",
"T5a",
"T5b",
"T6a",
"T6b",
)
):
# get environment tensor and reshape it into a matrix
env_tensor = getattr(peps_tensors[ti], name)
env_matrix = env_tensor.reshape(
(
env_tensor.shape[0] * env_tensor.shape[1],
env_tensor.shape[2] * env_tensor.shape[3],
)
)
# compute singular values
singular_values = jnp.linalg.svd(
env_matrix, full_matrices=False, compute_uv=False
)
step_corner_svd = step_corner_svd.at[
ti, ni, : singular_values.shape[0]
].set(singular_values, indices_are_sorted=True, unique_indices=True)
return step_corner_svd
@partial(jit, static_argnums=(3,), inline=True)
def _is_element_wise_converged(
old_peps_tensors: List[PEPS_Tensor],
new_peps_tensors: List[PEPS_Tensor],
eps: float,
split_transfer: bool = False,
) -> Tuple[bool, float, Optional[List[Tuple[int, CTM_Enum, float]]]]:
result = 0
if split_transfer:
measure = jnp.zeros((len(old_peps_tensors), 12), dtype=jnp.float64)
else:
measure = jnp.zeros((len(old_peps_tensors), 8), dtype=jnp.float64)
verbose_data = []
for ti in range(len(old_peps_tensors)):
old_shape = old_peps_tensors[ti].C1.shape
new_shape = new_peps_tensors[ti].C1.shape
diff = jnp.abs(
new_peps_tensors[ti].C1[: old_shape[0], : old_shape[1]]
- old_peps_tensors[ti].C1[: new_shape[0], : new_shape[1]]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 0].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.C1, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].C2.shape
new_shape = new_peps_tensors[ti].C2.shape
diff = jnp.abs(
new_peps_tensors[ti].C2[: old_shape[0], : old_shape[1]]
- old_peps_tensors[ti].C2[: new_shape[0], : new_shape[1]]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 1].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.C2, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].C3.shape
new_shape = new_peps_tensors[ti].C4.shape
diff = jnp.abs(
new_peps_tensors[ti].C3[: old_shape[0], : old_shape[1]]
- old_peps_tensors[ti].C3[: new_shape[0], : new_shape[1]]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 2].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.C3, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].C4.shape
new_shape = new_peps_tensors[ti].C4.shape
diff = jnp.abs(
new_peps_tensors[ti].C4[: old_shape[0], : old_shape[1]]
- old_peps_tensors[ti].C4[: new_shape[0], : new_shape[1]]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 3].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.C4, jnp.amax(diff)))
if split_transfer:
old_shape = old_peps_tensors[ti].T1_ket.shape
new_shape = new_peps_tensors[ti].T1_ket.shape
diff = jnp.abs(
new_peps_tensors[ti].T1_ket[
: old_shape[0], : old_shape[1], : old_shape[2]
]
- old_peps_tensors[ti].T1_ket[
: new_shape[0], : new_shape[1], : new_shape[2]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 4].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.T1_ket, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].T1_bra.shape
new_shape = new_peps_tensors[ti].T1_bra.shape
diff = jnp.abs(
new_peps_tensors[ti].T1_bra[
: old_shape[0], : old_shape[1], : old_shape[2]
]
- old_peps_tensors[ti].T1_bra[
: new_shape[0], : new_shape[1], : new_shape[2]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 5].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.T1_bra, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].T2_ket.shape
new_shape = new_peps_tensors[ti].T2_ket.shape
diff = jnp.abs(
new_peps_tensors[ti].T2_ket[
: old_shape[0], : old_shape[1], : old_shape[2]
]
- old_peps_tensors[ti].T2_ket[
: new_shape[0], : new_shape[1], : new_shape[2]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 6].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.T2_ket, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].T2_bra.shape
new_shape = new_peps_tensors[ti].T2_bra.shape
diff = jnp.abs(
new_peps_tensors[ti].T2_bra[
: old_shape[0], : old_shape[1], : old_shape[2]
]
- old_peps_tensors[ti].T2_bra[
: new_shape[0], : new_shape[1], : new_shape[2]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 7].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.T2_bra, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].T3_ket.shape
new_shape = new_peps_tensors[ti].T3_ket.shape
diff = jnp.abs(
new_peps_tensors[ti].T3_ket[
: old_shape[0], : old_shape[1], : old_shape[2]
]
- old_peps_tensors[ti].T3_ket[
: new_shape[0], : new_shape[1], : new_shape[2]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 8].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.T3_ket, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].T3_bra.shape
new_shape = new_peps_tensors[ti].T3_bra.shape
diff = jnp.abs(
new_peps_tensors[ti].T3_bra[
: old_shape[0], : old_shape[1], : old_shape[2]
]
- old_peps_tensors[ti].T3_bra[
: new_shape[0], : new_shape[1], : new_shape[2]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 9].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.T3_bra, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].T4_ket.shape
new_shape = new_peps_tensors[ti].T4_ket.shape
diff = jnp.abs(
new_peps_tensors[ti].T4_ket[
: old_shape[0], : old_shape[1], : old_shape[2]
]
- old_peps_tensors[ti].T4_ket[
: new_shape[0], : new_shape[1], : new_shape[2]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 10].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.T4_ket, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].T4_bra.shape
new_shape = new_peps_tensors[ti].T4_bra.shape
diff = jnp.abs(
new_peps_tensors[ti].T4_bra[
: old_shape[0], : old_shape[1], : old_shape[2]
]
- old_peps_tensors[ti].T4_bra[
: new_shape[0], : new_shape[1], : new_shape[2]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 11].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.T4_bra, jnp.amax(diff)))
else:
old_shape = old_peps_tensors[ti].T1.shape
new_shape = new_peps_tensors[ti].T1.shape
diff = jnp.abs(
new_peps_tensors[ti].T1[
: old_shape[0], : old_shape[1], : old_shape[2], : old_shape[3]
]
- old_peps_tensors[ti].T1[
: new_shape[0], : new_shape[1], : new_shape[2], : new_shape[3]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 4].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.T1, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].T2.shape
new_shape = new_peps_tensors[ti].T2.shape
diff = jnp.abs(
new_peps_tensors[ti].T2[
: old_shape[0], : old_shape[1], : old_shape[2], : old_shape[3]
]
- old_peps_tensors[ti].T2[
: new_shape[0], : new_shape[1], : new_shape[2], : new_shape[3]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 5].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.T2, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].T3.shape
new_shape = new_peps_tensors[ti].T3.shape
diff = jnp.abs(
new_peps_tensors[ti].T3[
: old_shape[0], : old_shape[1], : old_shape[2], : old_shape[3]
]
- old_peps_tensors[ti].T3[
: new_shape[0], : new_shape[1], : new_shape[2], : new_shape[3]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 6].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.T3, jnp.amax(diff)))
old_shape = old_peps_tensors[ti].T4.shape
new_shape = new_peps_tensors[ti].T4.shape
diff = jnp.abs(
new_peps_tensors[ti].T4[
: old_shape[0], : old_shape[1], : old_shape[2], : old_shape[3]
]
- old_peps_tensors[ti].T4[
: new_shape[0], : new_shape[1], : new_shape[2], : new_shape[3]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, 7].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, CTM_Enum.T4, jnp.amax(diff)))
return result == 0, jnp.linalg.norm(measure), verbose_data
@partial(jit, inline=True)
def _is_element_wise_converged_triangular(
old_peps_tensors: List[PEPS_Tensor],
new_peps_tensors: List[PEPS_Tensor],
eps: float,
):
result = 0
measure = jnp.zeros((len(old_peps_tensors), 18), dtype=jnp.float64)
verbose_data = []
for ti in range(len(old_peps_tensors)):
for ni, name in enumerate(
(
"C1",
"C2",
"C3",
"C4",
"C5",
"C6",
"T1a",
"T1b",
"T2a",
"T2b",
"T3a",
"T3b",
"T4a",
"T4b",
"T5a",
"T5b",
"T6a",
"T6b",
)
):
old_shape = getattr(old_peps_tensors[ti], name).shape
new_shape = getattr(new_peps_tensors[ti], name).shape
diff = jnp.abs(
getattr(new_peps_tensors[ti], name)[
: old_shape[0], : old_shape[1], : old_shape[2], : old_shape[3]
]
- getattr(old_peps_tensors[ti], name)[
: new_shape[0], : new_shape[1], : new_shape[2], : new_shape[3]
]
)
result += jnp.sum(diff > eps)
measure = measure.at[ti, ni].set(
jnp.linalg.norm(diff), indices_are_sorted=True, unique_indices=True
)
verbose_data.append((ti, getattr(CTM_Enum, name), jnp.amax(diff)))
return result == 0, jnp.linalg.norm(measure), verbose_data
def print_verbose(verbose_data, *, ad=False):
if ad:
message = "Custom VJP: Verbose: ti {}, CTM tensor {}, Diff {}"
else:
message = "CTMRG: Verbose: ti {}, CTM tensor {}, Diff {}"
for ti, ctm_enum_i, diff in verbose_data:
debug_print(
message,
ti,
CTM_Enum(ctm_enum_i).name,
diff,
)
@jit
def _ctmrg_body_func(carry):
(
w_tensors,
w_unitcell_last_step,
converged,
last_corner_svd,
eps,
count,
elementwise_conv,
norm_smallest_S,
state,
config,
) = carry
if w_unitcell_last_step.is_triangular_peps():
w_unitcell, norm_smallest_S = do_absorption_step_triangular(
w_tensors, w_unitcell_last_step, config, state
)
elif w_unitcell_last_step.is_split_transfer():
w_unitcell, norm_smallest_S = do_absorption_step_split_transfer(
w_tensors, w_unitcell_last_step, config, state
)
else:
w_unitcell, norm_smallest_S = do_absorption_step(
w_tensors, w_unitcell_last_step, config, state
)
def elementwise_func(old, new, old_corner, conv_eps, config):
if w_unitcell_last_step.is_triangular_peps():
converged, measure, verbose_data = _is_element_wise_converged_triangular(
old,
new,
conv_eps,
)
return converged, measure, verbose_data, old_corner
converged, measure, verbose_data = _is_element_wise_converged(
old,
new,
conv_eps,
split_transfer=w_unitcell.is_split_transfer(),
)
return converged, measure, verbose_data, old_corner
def corner_svd_func(old, new, old_corner, conv_eps, config):
if w_unitcell_last_step.is_triangular_peps():
verbose_data = (
[(jnp.array(0), jnp.array(0), jnp.array(0.0))] * 18 * len(w_tensors)
)
elif w_unitcell_last_step.is_split_transfer():
verbose_data = (
[(jnp.array(0), jnp.array(0), jnp.array(0.0))] * 12 * len(w_tensors)
)
else:
verbose_data = (
[(jnp.array(0), jnp.array(0), jnp.array(0.0))] * 8 * len(w_tensors)
)
if old_corner is None:
return (
False,
jnp.nan,
verbose_data,
old_corner,
)
if w_unitcell_last_step.is_triangular_peps():
corner_svd = _calc_corner_svds_triangular(new, old_corner, None)
else:
corner_svd = _calc_corner_svds(new, old_corner, None)
measure = jnp.linalg.norm(corner_svd - old_corner)
converged = measure < conv_eps
return (
converged,
measure,
verbose_data,
corner_svd,
)
converged, measure, verbose_data, corner_svd = cond(
elementwise_conv,
elementwise_func,
corner_svd_func,
w_unitcell_last_step.get_unique_tensors(),
w_unitcell.get_unique_tensors(),
last_corner_svd,
eps,
config,
)
if config.ctmrg_print_steps:
debug_print("CTMRG: {}: {}", count, measure)
if config.ctmrg_verbose_output:
jax.debug.callback(print_verbose, verbose_data, ordered=True)
count += 1
return (
w_tensors,
w_unitcell,
converged,
corner_svd,
eps,
count,
elementwise_conv,
norm_smallest_S,
state,
config,
)
@jit
def _ctmrg_while_wrapper(start_carry):
def cond_func(carry):
_, _, converged, _, _, count, _, _, _, config = carry
return jnp.logical_not(converged) & (count < config.ctmrg_max_steps)
(
_,
working_unitcell,
converged,
_,
_,
end_count,
_,
norm_smallest_S,
_,
_,
) = while_loop(cond_func, _ctmrg_body_func, start_carry)
return working_unitcell, converged, end_count, norm_smallest_S
def calc_ctmrg_env(
peps_tensors: Sequence[jnp.ndarray],
unitcell: PEPS_Unit_Cell,
*,
eps: Optional[float] = None,
enforce_elementwise_convergence: Optional[bool] = None,
_return_truncation_eps: bool = False,
) -> PEPS_Unit_Cell:
"""
Calculate the new converged CTMRG tensors for the unit cell. The function
updates the environment all iPEPS tensors in the unit cell according to the
periodic structure.
Args:
peps_tensors (:term:`sequence` of :obj:`jax.numpy.ndarray`):
The sequence of unique PEPS tensors the unitcell consists of.
unitcell (:obj:`~varipeps.peps.PEPS_Unit_Cell`):
The unitcell to work on.
Keyword args:
eps (:obj:`float`):
The convergence criterion.
enforce_elementwise_convergence (obj:`bool`):
Enforce elementwise convergence of the CTM tensors instead of only
convergence of the singular values of the corners.
Returns:
:obj:`~varipeps.peps.PEPS_Unit_Cell`:
New instance of the unitcell with all updated converged CTMRG tensors of
all elements of the unitcell.
"""
eps = eps if eps is not None else varipeps_config.ctmrg_convergence_eps
enforce_elementwise_convergence = (
enforce_elementwise_convergence
if enforce_elementwise_convergence is not None
else varipeps_config.ctmrg_enforce_elementwise_convergence
)
init_corner_singular_vals = None
if enforce_elementwise_convergence:
last_step_tensors = unitcell.get_unique_tensors()
else:
if unitcell.is_triangular_peps():
shape_corner_svd = (
unitcell.get_len_unique_tensors(),
18,
unitcell[0, 0][0][0].chi * unitcell[0, 0][0][0].D[0],
)
init_corner_singular_vals = _calc_corner_svds_triangular(
unitcell.get_unique_tensors(), None, shape_corner_svd
)
else:
shape_corner_svd = (
unitcell.get_len_unique_tensors(),
4,
unitcell[0, 0][0][0].chi,
)
init_corner_singular_vals = _calc_corner_svds(
unitcell.get_unique_tensors(), None, shape_corner_svd
)
initial_unitcell = unitcell
working_unitcell = unitcell
varipeps_global_state.ctmrg_effective_truncation_eps = None
norm_smallest_S = jnp.nan
already_tried_chi = {working_unitcell[0, 0][0][0].chi}
best_chi = 0
best_result = None
best_norm_smallest_S = None
best_truncation_eps = None
have_been_increased = False
while True:
tmp_count = 0
corner_singular_vals = None
while tmp_count < varipeps_config.ctmrg_max_steps and (
(
not working_unitcell.is_triangular_peps()
and any(
getattr(i, j).shape[0] != i.chi or getattr(i, j).shape[1] != i.chi
for i in working_unitcell.get_unique_tensors()
for j in ("C1", "C2", "C3", "C4")
)
)
or (
working_unitcell.is_split_transfer()
and any(
getattr(i, j).shape[0] != i.interlayer_chi
for i in working_unitcell.get_unique_tensors()
for j in ("T1_bra", "T2_ket", "T3_bra", "T4_ket")
)
)
or (
working_unitcell.is_triangular_peps()
and any(
getattr(i, j).shape[0] != i.chi or getattr(i, j).shape[3] != i.chi
for i in working_unitcell.get_unique_tensors()
for j in (
"C1",
"C2",
"C3",
"C4",
"C5",
"C6",
"T1a",
"T1b",
"T2a",
"T2b",
"T3a",
"T3b",
"T4a",
"T4b",
"T5a",
"T5b",
"T6a",
"T6b",
)
)
)
):
(
_,
working_unitcell,
_,
corner_singular_vals,
_,
tmp_count,
_,
norm_smallest_S,
_,
_,
) = _ctmrg_body_func(
(
peps_tensors,
working_unitcell,
False,
init_corner_singular_vals,
eps,
tmp_count,
enforce_elementwise_convergence,
jnp.inf,
varipeps_global_state,
varipeps_config,
)
)
if tmp_count < varipeps_config.ctmrg_max_steps:
working_unitcell, converged, end_count, norm_smallest_S = (
_ctmrg_while_wrapper(
(
peps_tensors,
working_unitcell,
False,
(
corner_singular_vals
if corner_singular_vals is not None
else init_corner_singular_vals
),
eps,
tmp_count,
enforce_elementwise_convergence,
jnp.inf,
varipeps_global_state,
varipeps_config,
)
)
)
else:
converged = False
end_count = tmp_count
if converged and (
working_unitcell[0, 0][0][0].chi > best_chi or best_result is None
):
best_chi = working_unitcell[0, 0][0][0].chi
best_result = working_unitcell
best_norm_smallest_S = norm_smallest_S
best_truncation_eps = varipeps_global_state.ctmrg_effective_truncation_eps
current_truncation_eps = (
varipeps_config.ctmrg_truncation_eps
if varipeps_global_state.ctmrg_effective_truncation_eps is None
else varipeps_global_state.ctmrg_effective_truncation_eps
)
if (
varipeps_config.ctmrg_heuristic_increase_chi
and norm_smallest_S > varipeps_config.ctmrg_heuristic_increase_chi_threshold
and working_unitcell[0, 0][0][0].chi < working_unitcell[0, 0][0][0].max_chi
):
new_chi = (
working_unitcell[0, 0][0][0].chi
+ varipeps_config.ctmrg_heuristic_increase_chi_step_size
)
if new_chi > working_unitcell[0, 0][0][0].max_chi:
new_chi = working_unitcell[0, 0][0][0].max_chi
if not new_chi in already_tried_chi:
working_unitcell = working_unitcell.change_chi(new_chi)
initial_unitcell = initial_unitcell.change_chi(new_chi)
# reinitialize corner singular values
if not enforce_elementwise_convergence:
if working_unitcell.is_triangular_peps():
shape_corner_svd = (
working_unitcell.get_len_unique_tensors(),
18,
working_unitcell[0, 0][0][0].chi
* working_unitcell[0, 0][0][0].D[0],
)
init_corner_singular_vals = _calc_corner_svds_triangular(
working_unitcell.get_unique_tensors(),
None,
shape_corner_svd,
)
else:
shape_corner_svd = (
working_unitcell.get_len_unique_tensors(),
4,
working_unitcell[0, 0][0][0].chi,
)
init_corner_singular_vals = _calc_corner_svds(
working_unitcell.get_unique_tensors(),
None,
shape_corner_svd,
)
if varipeps_config.ctmrg_print_steps:
debug_print(
"CTMRG: Increasing chi to {} since smallest SVD Norm was {}.",
new_chi,
norm_smallest_S,
)
already_tried_chi.add(new_chi)
have_been_increased = True
continue
elif varipeps_config.ctmrg_heuristic_decrease_chi and (
(
norm_smallest_S < current_truncation_eps
and working_unitcell[0, 0][0][0].chi > 2
)
or (
not converged
and not have_been_increased
and norm_smallest_S
< varipeps_config.ctmrg_heuristic_increase_chi_threshold
)
):
new_chi = (
working_unitcell[0, 0][0][0].chi
- varipeps_config.ctmrg_heuristic_decrease_chi_step_size
)
if new_chi < 2:
new_chi = 2
if not new_chi in already_tried_chi:
working_unitcell = working_unitcell.change_chi(new_chi)
if varipeps_config.ctmrg_print_steps:
debug_print(
"CTMRG: Decreasing chi to {} since smallest SVD Norm was {} or routine did not converge.",
new_chi,
norm_smallest_S,
)
already_tried_chi.add(new_chi)
continue
if (
varipeps_config.ctmrg_increase_truncation_eps
and end_count == varipeps_config.ctmrg_max_steps
and not converged
):
new_truncation_eps = (
current_truncation_eps
* varipeps_config.ctmrg_increase_truncation_eps_factor
)
if (
new_truncation_eps
<= varipeps_config.ctmrg_increase_truncation_eps_max_value
):
if varipeps_config.ctmrg_print_steps:
debug_print(
"CTMRG: Increasing SVD truncation eps to {}.",
new_truncation_eps,
)
varipeps_global_state.ctmrg_effective_truncation_eps = (
new_truncation_eps
)
working_unitcell = initial_unitcell
already_tried_chi = {working_unitcell[0, 0][0][0].chi}
continue
break
if _return_truncation_eps:
last_truncation_eps = varipeps_global_state.ctmrg_effective_truncation_eps
varipeps_global_state.ctmrg_effective_truncation_eps = None
if not converged and best_result is not None:
working_unitcell = best_result
norm_smallest_S = best_norm_smallest_S
converged = True
last_truncation_eps = best_truncation_eps
if (
varipeps_config.ctmrg_fail_if_not_converged
and end_count == varipeps_config.ctmrg_max_steps
and not converged
):
raise CTMRGNotConvergedError
if _return_truncation_eps:
return working_unitcell, last_truncation_eps, norm_smallest_S
return working_unitcell, norm_smallest_S
@custom_vjp
def calc_ctmrg_env_custom_rule(
peps_tensors: Sequence[jnp.ndarray],
unitcell: PEPS_Unit_Cell,
_return_truncation_eps: bool = False,
) -> PEPS_Unit_Cell:
"""
Wrapper function of :obj:`~varipeps.ctmrg.routine.calc_ctmrg_env` which
enables the use of the custom VJP for the calculation of the gradient.
Args:
peps_tensors (:term:`sequence` of :obj:`jax.numpy.ndarray`):
The sequence of unique PEPS tensors the unitcell consists of.
unitcell (:obj:`~varipeps.peps.PEPS_Unit_Cell`):
The unitcell to work on.
Returns:
:obj:`~varipeps.peps.PEPS_Unit_Cell`:
New instance of the unitcell with all updated converged CTMRG tensors of
all elements of the unitcell.
"""
return calc_ctmrg_env(
peps_tensors,
unitcell,
enforce_elementwise_convergence=True,
_return_truncation_eps=_return_truncation_eps,
)
def calc_ctmrg_env_fwd(
peps_tensors: Sequence[jnp.ndarray],
unitcell: PEPS_Unit_Cell,
_return_truncation_eps: bool = False,
) -> Tuple[PEPS_Unit_Cell, Tuple[Sequence[jnp.ndarray], PEPS_Unit_Cell]]:
"""
Internal helper function of custom VJP to calculate the values in
the forward sweep.
"""
new_unitcell, last_truncation_eps, norm_smallest_S = calc_ctmrg_env_custom_rule(
peps_tensors, unitcell, _return_truncation_eps=True
)
return (new_unitcell, norm_smallest_S), (
peps_tensors,
new_unitcell,
unitcell,
last_truncation_eps,
)
def _ctmrg_rev_while_body(carry):
(
vjp_env,
initial_bar,
bar_fixed_point_last_step,
converged,
count,
config,
state,
) = carry
new_env_bar = vjp_env((bar_fixed_point_last_step, jnp.array(0, dtype=jnp.float64)))[
0
]
bar_fixed_point = bar_fixed_point_last_step.replace_unique_tensors(
[
t_old.__add__(t_new, checks=False)
for t_old, t_new in zip(
initial_bar.get_unique_tensors(),
new_env_bar.get_unique_tensors(),
strict=True,
)
]
)
if bar_fixed_point_last_step.is_triangular_peps():
converged, measure, verbose_data = _is_element_wise_converged_triangular(
bar_fixed_point_last_step.get_unique_tensors(),
bar_fixed_point.get_unique_tensors(),
config.ad_custom_convergence_eps,
)
else:
converged, measure, verbose_data = _is_element_wise_converged(
bar_fixed_point_last_step.get_unique_tensors(),
bar_fixed_point.get_unique_tensors(),
config.ad_custom_convergence_eps,
split_transfer=bar_fixed_point.is_split_transfer(),
)
count += 1
if config.ad_custom_print_steps:
debug_print("Custom VJP: {}: {}", count, measure)
if config.ad_custom_verbose_output:
jax.debug.callback(print_verbose, verbose_data, ordered=True, ad=True)
return vjp_env, initial_bar, bar_fixed_point, converged, count, config, state
@jit
def _ctmrg_rev_workhorse(peps_tensors, new_unitcell, new_unitcell_bar, config, state):
if new_unitcell.is_triangular_peps():
_, vjp_peps_tensors = vjp(
lambda t: do_absorption_step_triangular(t, new_unitcell, config, state),
peps_tensors,
)
vjp_env = tree_util.Partial(
vjp(
lambda u: do_absorption_step_triangular(peps_tensors, u, config, state),
new_unitcell,
)[1]
)
elif new_unitcell.is_split_transfer():
_, vjp_peps_tensors = vjp(
lambda t: do_absorption_step_split_transfer(t, new_unitcell, config, state),
peps_tensors,
)
vjp_env = tree_util.Partial(
vjp(
lambda u: do_absorption_step_split_transfer(
peps_tensors, u, config, state
),
new_unitcell,
)[1]
)
else:
_, vjp_peps_tensors = vjp(
lambda t: do_absorption_step(t, new_unitcell, config, state), peps_tensors
)
vjp_env = tree_util.Partial(
vjp(
lambda u: do_absorption_step(peps_tensors, u, config, state),
new_unitcell,
)[1]
)
if config.ad_custom_fixed_point_method is Grad_Fixed_Point_Method.ITERATIVE:
def cond_func(carry):
_, _, _, converged, count, config, state = carry
return jnp.logical_not(converged) & (count < config.ad_custom_max_steps)
_, _, env_fixed_point, converged, end_count, _, _ = while_loop(
cond_func,
_ctmrg_rev_while_body,
(vjp_env, new_unitcell_bar, new_unitcell_bar, False, 0, config, state),
)
else:
real = jax.dtypes.result_type(
*jax.tree.leaves(new_unitcell_bar)
) == jax.dtypes.canonicalize_dtype(jnp.float64)
if config.ad_custom_fixed_point_method is Grad_Fixed_Point_Method.EIGEN_SOLVER:
def f_arnoldi(x):
w = x[0]
if not real:
w = jax.tree.map(lambda x, y: x + 1j * y, w[0], w[1])
w = vjp_env((w, jnp.array(0, dtype=jnp.float64)))[0]
w = jax.tree.map(lambda v1, v2: v1 + x[1] * v2, w, new_unitcell_bar)
if not real:
w = (
jax.tree.map(lambda x: jnp.real(x), w),
jax.tree.map(lambda x: jnp.imag(x), w),
)
return (w, x[1])
if real:
eigval, eigvec = jsp.sparse.linalg.eigs(
f_arnoldi, 1, (new_unitcell_bar, 1.0)
)
else:
eigval, eigvec = jsp.sparse.linalg.eigs(
f_arnoldi,
1,
(
(
jax.tree.map(lambda x: jnp.real(x), new_unitcell_bar),
jax.tree.map(lambda x: jnp.imag(x), new_unitcell_bar),
),
1.0,
),
)
converged = cond(
jnp.logical_and(
jnp.abs(jnp.real(eigval[0]))
< (1 + 1e-2 * config.ad_custom_convergence_eps),
jnp.abs(jnp.imag(eigval[0]))
< 1e-2 * config.ad_custom_convergence_eps,
),
lambda: True,
lambda: False,
)
if config.ad_custom_verbose_output:
debug_print(
"AD: Converged: {}, Eigval: {}, Eigvec[1]: {}",
converged,
eigval[0],
eigvec[1][0],
)
if real:
env_fixed_point = jax.tree.map(lambda v: jnp.real(v[..., 0]), eigvec[0])
env_fixed_point, arnoldi_worked = cond(
jnp.logical_and(
converged,
jnp.abs(eigvec[1][0])
>= 1e-2 * config.ad_custom_convergence_eps,
),
lambda x: (
jax.tree.map(lambda v: v / jnp.real(eigvec[1][0]), x),
True,
),
lambda x: (x, False),
env_fixed_point,
)
else:
env_fixed_point = jax.tree.map(
lambda v, w: v[..., 0] + 1j * w[..., 0], eigvec[0][0], eigvec[0][1]
)
env_fixed_point, arnoldi_worked = cond(
jnp.logical_and(
converged,
jnp.abs(eigvec[1][0])
>= 1e-2 * config.ad_custom_convergence_eps,
),
lambda x: (
jax.tree.map(lambda v: v / jnp.real(eigvec[1][0]), x),
True,
),
lambda x: (x, False),
env_fixed_point,
)
else:
env_fixed_point = new_unitcell_bar
arnoldi_worked = False
converged = True
end_count = 0
def run_gmres(v, e):
if config.ad_custom_verbose_output:
debug_print("AD: Computing gradient with GMRES")
def f_gmres(w):
if not real:
w = jax.tree.map(lambda x, y: x + 1j * y, w[0], w[1])
new_w = vjp_env((w, jnp.array(0, dtype=jnp.float64)))[0]
new_w = new_w.replace_unique_tensors(
[
t_old.__sub__(t_new, checks=False)
for t_old, t_new in zip(
w.get_unique_tensors(),
new_w.get_unique_tensors(),
strict=True,
)
]
)
if not real:
new_w = (
jax.tree.map(lambda x: jnp.real(x), new_w),
jax.tree.map(lambda x: jnp.imag(x), new_w),
)
return new_w
is_gpu = jax.default_backend() == "gpu"
if real:
v0 = new_unitcell_bar
else:
v0 = (
jax.tree.map(lambda x: jnp.real(x), new_unitcell_bar),
jax.tree.map(lambda x: jnp.imag(x), new_unitcell_bar),
)
v, e = jax.scipy.sparse.linalg.gmres(
f_gmres,
v0,
v0,
solve_method="batched" if is_gpu else "incremental",
atol=config.ad_custom_convergence_eps,
# maxiter=config.ad_custom_max_steps,
)
if not real:
v = jax.tree.map(lambda x, y: x + 1j * y, v[0], v[1])
return v, e
env_fixed_point, end_count, converged = jax.lax.cond(
jnp.logical_and(converged, jnp.logical_not(arnoldi_worked)),
lambda x, ec, c: (*run_gmres(x, ec), True),
lambda x, ec, c: (x, ec, c),
env_fixed_point,
end_count,
converged,
)
(t_bar,) = vjp_peps_tensors((env_fixed_point, jnp.array(0, dtype=jnp.float64)))
return t_bar, converged, end_count
def calc_ctmrg_env_rev(
res: Tuple[Sequence[jnp.ndarray], PEPS_Unit_Cell],
input_bar: Tuple[PEPS_Unit_Cell, float],
) -> Tuple[Sequence[jnp.ndarray], PEPS_Unit_Cell]:
"""
Internal helper function of custom VJP to calculate the gradient in
the backward sweep.
"""
unitcell_bar, _ = input_bar
peps_tensors, new_unitcell, input_unitcell, last_truncation_eps = res
varipeps_global_state.ctmrg_effective_truncation_eps = last_truncation_eps
t_bar, converged, end_count = _ctmrg_rev_workhorse(
peps_tensors, new_unitcell, unitcell_bar, varipeps_config, varipeps_global_state
)
varipeps_global_state.ctmrg_effective_truncation_eps = None
if not converged:
raise CTMRGGradientNotConvergedError
empty_t = [t.zeros_like_self() for t in input_unitcell.get_unique_tensors()]
return (
t_bar,
input_unitcell.replace_unique_tensors(empty_t),
jnp.zeros((), dtype=bool),
)
calc_ctmrg_env_custom_rule.defvjp(calc_ctmrg_env_fwd, calc_ctmrg_env_rev)
|