def test_clear_cache():
D = 100
M = 5
mpsinds = [
Index(U1Charge(np.random.randint(5, 15, D, dtype=np.int16)), False),
Index(U1Charge(np.array([0, 1, 2, 3], dtype=np.int16)), False),
Index(U1Charge(np.random.randint(5, 18, D, dtype=np.int16)), True)
]
mpoinds = [
Index(U1Charge(np.random.randint(0, 5, M)), False),
Index(U1Charge(np.random.randint(0, 10, M)), True), mpsinds[1],
mpsinds[1].flip_flow()
]
Linds = [mpoinds[0].flip_flow(), mpsinds[0].flip_flow(), mpsinds[0]]
Rinds = [mpoinds[1].flip_flow(), mpsinds[2].flip_flow(), mpsinds[2]]
mps = BlockSparseTensor.random(mpsinds)
mpo = BlockSparseTensor.random(mpoinds)
L = BlockSparseTensor.random(Linds)
R = BlockSparseTensor.random(Rinds)
enable_caching()
ncon([L, mps, mpo, R], [[3, 1, -1], [1, 2, 4], [3, 5, -2, 2], [5, 4, -3]],
backend='symmetric')
cacher = get_cacher()
assert len(cacher.cache) > 0
disable_caching()
clear_cache()
assert len(cacher.cache) == 0
def test_enable_caching():
enable_caching()
cacher = get_cacher()
assert len(_INSTANTIATED_CACHERS) == 1
assert _INSTANTIATED_CACHERS[0] is cacher
assert cacher.do_caching
disable_caching()
def test_cache():
D = 10
mpsinds = [
Index(U1Charge(np.random.randint(-5, 5, D, dtype=np.int16)), False),
Index(U1Charge(np.random.randint(-5, 5, D, dtype=np.int16)), False),
Index(U1Charge(np.random.randint(-5, 5, D, dtype=np.int16)), False),
Index(U1Charge(np.random.randint(-5, 5, D, dtype=np.int16)), True)
]
A = BlockSparseTensor.random(mpsinds)
B = A.conj()
res_charges = [
A.flat_charges[2], A.flat_charges[3], B.flat_charges[2],
B.flat_charges[3]
]
res_flows = [
A.flat_flows[2], A.flat_flows[3], B.flat_flows[2], B.flat_flows[3]
]
enable_caching()
ncon([A, B], [[1, 2, -1, -2], [1, 2, -3, -4]], backend='symmetric')
cacher = get_cacher()
sA = _to_string(A.flat_charges, A.flat_flows, 2, [2, 3, 0, 1])
sB = _to_string(B.flat_charges, B.flat_flows, 2, [0, 1, 2, 3])
sC = _to_string(res_charges, res_flows, 2, [0, 1, 2, 3])
blocksA, chargesA, dimsA = _find_transposed_diagonal_sparse_blocks(
A.flat_charges, A.flat_flows, 2, [2, 3, 0, 1])
blocksB, chargesB, dimsB = _find_transposed_diagonal_sparse_blocks(
B.flat_charges, B.flat_flows, 2, [0, 1, 2, 3])
blocksC, chargesC, dimsC = _find_transposed_diagonal_sparse_blocks(
res_charges, res_flows, 2, [0, 1, 2, 3])
assert sA in cacher.cache
assert sB in cacher.cache
assert sC in cacher.cache
for b1, b2 in zip(cacher.cache[sA][0], blocksA):
np.testing.assert_allclose(b1, b2)
for b1, b2 in zip(cacher.cache[sB][0], blocksB):
np.testing.assert_allclose(b1, b2)
for b1, b2 in zip(cacher.cache[sC][0], blocksC):
np.testing.assert_allclose(b1, b2)
assert charge_equal(cacher.cache[sA][1], chargesA)
assert charge_equal(cacher.cache[sB][1], chargesB)
assert charge_equal(cacher.cache[sC][1], chargesC)
np.testing.assert_allclose(cacher.cache[sA][2], dimsA)
np.testing.assert_allclose(cacher.cache[sB][2], dimsB)
np.testing.assert_allclose(cacher.cache[sC][2], dimsC)
disable_caching()
clear_cache()
import tensornetwork as tn
# tensornetwork and block_sparse modules
from eigh import eigh
from helper_functs import orthog, trunct_eigh, expand_dims, orthog_sym, eye_sym
from link_networks import (LiftHam, LowerDensity, RightLink, LeftLink,
CenterLink, TopLink)
from tensornetwork.block_sparse.caching import (set_caching_status,
get_caching_status,
clear_cache, enable_caching,
disable_caching,
_INSTANTIATED_CACHERS)
tn.set_default_backend('symmetric')
set_caching_status(True)
enable_caching()
clear_cache()
# set simulation parameters
chi_b = 4
chi = 4
chi_p = 4
chi_m = 16
n_levels = 3
n_iterations = 20
n_sites = 3 * (2**(n_levels + 1))
left_on = 1
right_on = 1
center_on = 1
initialize_on = 0