def test_fuse_charges_raises():
num_charges = 5
B = 6
D = 10
np_charges = [
np.random.randint(-B // 2, B // 2 + 1, D).astype(np.int16)
for _ in range(num_charges)
]
charges = [U1Charge(c) for c in np_charges]
flows = [True, False, True, False]
with pytest.raises(ValueError):
fuse_charges(charges, flows)
def compute_sparse_lookup(
charges: List[BaseCharge], flows: Union[np.ndarray, List[bool]],
target_charges: BaseCharge) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
Compute lookup table for how dense index positions map
to sparse index positions, treating only those elements as non-zero
whose charges fuse to `target_charges`.
Args:
charges: List of `BaseCharge` objects.
flows: A list of `bool`; the flow directions.
target_charges: A `BaseCharge`; the target charges for which
the fusion of `charges` is non-zero.
Returns:
lookup: An np.ndarray of positive numbers between `0` and
`len(unique_charges)`. The position of values `n` in `lookup`
are positions with charge values `unique_charges[n]`.
unique_charges: The unique charges of fusion of `charges`
label_to_unique: The integer labels of the unique charges.
"""
fused_charges = fuse_charges(charges, flows)
unique_charges, inverse = fused_charges.unique(
return_inverse=True, sort=False)
_, label_to_unique, _ = unique_charges.intersect(
target_charges, return_indices=True)
tmp = np.full(
len(unique_charges), fill_value=-1, dtype=charges[0].label_dtype)
tmp[label_to_unique] = label_to_unique
lookup = tmp[inverse]
lookup = lookup[lookup >= 0]
return lookup, unique_charges, np.sort(label_to_unique)
def test_BlockSparseTensor_init():
np.random.seed(10)
D = 10
rank = 4
flows = np.random.choice([True, False], size=rank, replace=True)
charges = [
U1Charge.random(dimension=D, minval=-5, maxval=5) for _ in range(rank)
]
fused = fuse_charges(charges, flows)
data = np.random.uniform(0,
1,
size=len(
np.nonzero(fused == np.zeros((1, 1)))[0]))
order = [[n] for n in range(rank)]
arr = BlockSparseTensor(data, charges, flows, order=order)
np.testing.assert_allclose(data, arr.data)
for c1, c2 in zip(charges, arr.charges):
assert charge_equal(c1, c2[0])
for c1, c2 in zip(charges, arr._charges):
assert charge_equal(c1, c2)
data = np.random.uniform(
0, 1, size=len(np.nonzero(fused == np.zeros((1, 1)))[0]) + 1)
with pytest.raises(ValueError):
arr = BlockSparseTensor(data,
charges,
flows,
order=order,
check_consistency=True)
def test_diagonal(Ds, dtype, num_charges, flow):
np.random.seed(10)
backend = symmetric_backend.SymmetricBackend()
np_flow = -np.int((np.int(flow) - 0.5) * 2)
indices = [
Index(
BaseCharge(np.random.randint(-2, 3, (Ds[n], num_charges)),
charge_types=[U1Charge] * num_charges), flow)
for n in range(2)
]
arr = BlockSparseTensor.random(indices, dtype=dtype)
fused = fuse_charges(arr.flat_charges, arr.flat_flows)
inds = np.nonzero(fused == np.zeros((1, num_charges), dtype=np.int16))[0]
# pylint: disable=no-member
left, _ = np.divmod(inds, Ds[1])
unique = np.unique(np_flow * (indices[0]._charges[0].charges[left, :]),
axis=0)
diagonal = backend.diagonal(arr)
sparse_blocks, _, block_shapes = _find_diagonal_sparse_blocks(
arr.flat_charges, arr.flat_flows, 1)
data = np.concatenate([
np.diag(np.reshape(arr.data[sparse_blocks[n]], block_shapes[:, n]))
for n in range(len(sparse_blocks))
])
np.testing.assert_allclose(data, diagonal.data)
np.testing.assert_allclose(unique, diagonal.flat_charges[0].unique_charges)
with pytest.raises(NotImplementedError):
diagonal = backend.diagonal(arr, axis1=0)
with pytest.raises(NotImplementedError):
diagonal = backend.diagonal(arr, axis2=1)
with pytest.raises(NotImplementedError):
diagonal = backend.diagonal(arr, offset=1)
def test_get_diag(dtype, num_charges, Ds, flow):
np.random.seed(10)
np_flow = -np.int((np.int(flow) - 0.5) * 2)
indices = [
Index(
BaseCharge(
np.random.randint(-2, 3, (num_charges, Ds[n])),
charge_types=[U1Charge] * num_charges), flow) for n in range(2)
]
arr = BlockSparseTensor.random(indices, dtype=dtype)
fused = fuse_charges(arr.flat_charges, arr.flat_flows)
inds = np.nonzero(fused == np.zeros((num_charges, 1), dtype=np.int16))[0]
# pylint: disable=no-member
left, _ = np.divmod(inds, Ds[1])
unique = np.unique(
np_flow * (indices[0]._charges[0].charges[:, left]), axis=1)
diagonal = diag(arr)
sparse_blocks, _, block_shapes = _find_diagonal_sparse_blocks(
arr.flat_charges, arr.flat_flows, 1)
data = np.concatenate([
np.diag(np.reshape(arr.data[sparse_blocks[n]], block_shapes[:, n]))
for n in range(len(sparse_blocks))
])
np.testing.assert_allclose(data, diagonal.data)
np.testing.assert_allclose(unique, diagonal.flat_charges[0].unique_charges)
def test_index_charges():
D = 10
B = 4
dtype = np.int16
np.random.seed(10)
q1 = U1Charge(np.random.randint(-B // 2, B // 2 + 1, D).astype(dtype))
q2 = U1Charge(np.random.randint(-B // 2, B // 2 + 1, D).astype(dtype))
i = Index(charges=[q1, q2], flow=[False, True])
fused = fuse_charges([q1, q2], [False, True])
np.testing.assert_allclose(i.charges.charges, fused.charges)
def compute_fused_charge_degeneracies(
charges: List[BaseCharge],
flows: Union[np.ndarray, List[bool]]) -> Tuple[BaseCharge, np.ndarray]:
"""
For a list of charges, computes all possible fused charges resulting
from fusing `charges` and their respective degeneracies
Args:
charges: List of `BaseCharge`, one for each leg of a
tensor.
flows: A list of bool, one for each leg of a tensor.
with values `False` or `True` denoting inflowing and
outflowing charge direction, respectively.
Returns:
BaseCharge: The unique fused charges.
np.ndarray: The degeneracies of each unqiue fused charge.
"""
if len(charges) == 1:
return (charges[0] * flows[0]).unique(return_counts=True)
dims = [c.dim for c in charges]
# for small dims is faster to fuse all and use unique
# directly
if reduce(mul, dims, 1) < 20000:
fused = fuse_charges(charges, flows)
return fused.unique(return_counts=True)
partition = _find_best_partition(dims)
fused_left = fuse_charges(charges[:partition], flows[:partition])
fused_right = fuse_charges(charges[partition:], flows[partition:])
left_unique, left_degens = fused_left.unique(return_counts=True)
right_unique, right_degens = fused_right.unique(return_counts=True)
fused = left_unique + right_unique
unique_charges, charge_labels = fused.unique(return_inverse=True)
fused_degeneracies = fuse_degeneracies(left_degens, right_degens)
new_ord = np.argsort(charge_labels)
all_degens = np.cumsum(fused_degeneracies[new_ord])
cum_degens = all_degens[np.flatnonzero(np.diff(charge_labels[new_ord]))]
final_degeneracies = np.append(cum_degens, all_degens[-1]) - np.append(
0, cum_degens)
return unique_charges, final_degeneracies
def todense(self) -> np.ndarray:
"""
Map the sparse tensor to dense storage.
"""
if len(self.shape) == 0:
return self.data
out = np.asarray(np.zeros(self.shape, dtype=self.dtype).flat)
out[np.nonzero(
fuse_charges(self._charges, self._flows) ==
self._charges[0].identity_charges)[0]] = self.data
result = np.reshape(out, [c.dim for c in self._charges])
flat_order = flatten(self._order)
return result.transpose(flat_order).reshape(self.shape)
def test_fuse_charges():
num_charges = 5
B = 6
D = 10
np_charges = [
np.random.randint(-B // 2, B // 2 + 1, D).astype(np.int16)
for _ in range(num_charges)
]
charges = [U1Charge(c) for c in np_charges]
flows = [True, False, True, False, True]
np_flows = np.ones(5, dtype=np.int16)
np_flows[flows] = -1
fused = fuse_charges(charges, flows)
np_fused = fuse_ndarrays([c * f for c, f in zip(np_charges, np_flows)])
np.testing.assert_allclose(np.squeeze(fused.charges), np_fused)
def test_todense(num_charges, chargetype):
np.random.seed(10)
Ds = [8, 9, 10, 11]
rank = 4
flows = np.random.choice([True, False], size=rank, replace=True)
charges = [get_charge(chargetype, num_charges, Ds[n]) for n in range(rank)]
fused = fuse_charges(charges, flows)
mask = fused == np.zeros((num_charges, 1))
inds = np.nonzero(mask)[0]
inds2 = np.nonzero(np.logical_not(mask))[0]
indices = [Index(charges[n], flows[n]) for n in range(rank)]
arr = BlockSparseTensor.randn(indices)
dense = np.array(arr.todense().flat)
np.testing.assert_allclose(dense[inds], arr.data)
np.testing.assert_allclose(dense[inds2], 0)
def test_fromdense(num_charges, chargetype):
np.random.seed(10)
Ds = [8, 9, 10, 11]
rank = 4
flows = np.random.choice([True, False], size=rank, replace=True)
charges = [get_charge(chargetype, num_charges, Ds[n]) for n in range(rank)]
fused = fuse_charges(charges, flows)
mask = fused == np.zeros((1, num_charges))
inds = np.nonzero(mask)[0]
inds2 = np.nonzero(np.logical_not(mask))[0]
indices = [Index(charges[n], flows[n]) for n in range(rank)]
dense = np.random.random_sample(Ds)
arr = BlockSparseTensor.fromdense(indices, dense)
dense_arr = arr.todense()
np.testing.assert_allclose(np.ravel(dense)[inds], arr.data)
np.testing.assert_allclose(np.ravel(dense_arr)[inds2], 0)
def _find_transposed_diagonal_sparse_blocks(
charges: List[BaseCharge],
flows: Union[np.ndarray, List[bool]],
tr_partition: int,
order: Optional[Union[List, np.ndarray]] = None,
) -> Tuple[List, BaseCharge, np.ndarray]:
"""
Find the diagonal blocks of a transposed tensor with
meta-data `charges` and `flows`. `charges` and `flows`
are the charges and flows of the untransposed tensor,
`order` is the final transposition, and `tr_partition`
is the partition of the transposed tensor according to
which the diagonal blocks should be found.
Args:
charges: List of `BaseCharge`, one for each leg of a tensor.
flows: A list of bool, one for each leg of a tensor.
with values `False` or `True` denoting inflowing and
outflowing charge direction, respectively.
tr_partition: Location of the transposed tensor partition
(i.e. such that the tensor is viewed as a matrix between
`charges[order[:partition]]` and `charges[order[partition:]]`).
order: Order with which to permute the tensor axes.
Returns:
block_maps (List[np.ndarray]): list of integer arrays, which each
containing the location of a symmetry block in the data vector.
block_qnums (BaseCharge): The charges of the corresponding blocks.
block_dims (np.ndarray): 2-by-m array of matrix dimensions of each block.
"""
flows = np.asarray(flows)
cacher = get_cacher()
if cacher.do_caching:
hash_val = _to_string(charges, flows, tr_partition, order)
if hash_val in cacher.cache:
return cacher.cache[hash_val]
if np.array_equal(order, None) or (np.array_equal(
np.array(order), np.arange(len(charges)))):
# no transpose order
return _find_diagonal_sparse_blocks(charges, flows, tr_partition)
# general case: non-trivial transposition is required
num_inds = len(charges)
tensor_dims = np.array([charges[n].dim for n in range(num_inds)], dtype=int)
strides = np.append(np.flip(np.cumprod(np.flip(tensor_dims[1:]))), 1)
# compute qnums of row/cols in original tensor
orig_partition = _find_best_partition(tensor_dims)
orig_width = np.prod(tensor_dims[orig_partition:])
orig_unique_row_qnums = compute_unique_fused_charges(charges[:orig_partition],
flows[:orig_partition])
orig_unique_col_qnums, orig_col_degen = compute_fused_charge_degeneracies(
charges[orig_partition:], np.logical_not(flows[orig_partition:]))
orig_block_qnums, row_map, col_map = intersect(
orig_unique_row_qnums.unique_charges,
orig_unique_col_qnums.unique_charges,
axis=0,
return_indices=True)
orig_num_blocks = orig_block_qnums.shape[0]
if orig_num_blocks == 0:
# special case: trivial number of non-zero elements
obj = charges[0].__new__(type(charges[0]))
obj.__init__(
np.empty((0, charges[0].num_symmetries), dtype=charges[0].dtype),
np.arange(0, dtype=charges[0].label_dtype), charges[0].charge_types)
return [], obj, np.empty((2, 0), dtype=SIZE_T)
orig_row_ind = fuse_charges(charges[:orig_partition], flows[:orig_partition])
orig_col_ind = fuse_charges(charges[orig_partition:],
np.logical_not(flows[orig_partition:]))
inv_row_map = -np.ones(
orig_unique_row_qnums.unique_charges.shape[0],
dtype=charges[0].label_dtype)
inv_row_map[row_map] = np.arange(len(row_map), dtype=charges[0].label_dtype)
all_degens = np.append(orig_col_degen[col_map],
0)[inv_row_map[orig_row_ind.charge_labels]]
all_cumul_degens = np.cumsum(np.insert(all_degens[:-1], 0, 0)).astype(SIZE_T)
dense_to_sparse = np.empty(orig_width, dtype=SIZE_T)
for n in range(orig_num_blocks):
dense_to_sparse[orig_col_ind.charge_labels == col_map[n]] = np.arange(
orig_col_degen[col_map[n]], dtype=SIZE_T)
# define properties of new tensor resulting from transposition
new_strides = strides[order]
new_row_charges = [charges[n] for n in order[:tr_partition]]
new_col_charges = [charges[n] for n in order[tr_partition:]]
new_row_flows = flows[order[:tr_partition]]
new_col_flows = flows[order[tr_partition:]]
if tr_partition == 0:
# special case: reshape into row vector
# compute qnums of row/cols in transposed tensor
unique_col_qnums, new_col_degen = compute_fused_charge_degeneracies(
new_col_charges, np.logical_not(new_col_flows))
identity_charges = charges[0].identity_charges(dim=1)
block_qnums, new_row_map, new_col_map = intersect(
identity_charges.unique_charges,
unique_col_qnums.unique_charges,
axis=0,
return_indices=True)
block_dims = np.array([[1], new_col_degen[new_col_map]], dtype=SIZE_T)
num_blocks = 1
col_ind, col_locs = reduce_charges(
new_col_charges,
np.logical_not(new_col_flows),
block_qnums,
return_locations=True,
strides=new_strides[tr_partition:])
# find location of blocks in transposed tensor (w.r.t positions in original)
#pylint: disable=no-member
orig_row_posR, orig_col_posR = np.divmod(
col_locs[col_ind.charge_labels == 0], orig_width)
block_maps = [(all_cumul_degens[orig_row_posR] +
dense_to_sparse[orig_col_posR]).ravel()]
obj = charges[0].__new__(type(charges[0]))
obj.__init__(block_qnums,
np.arange(block_qnums.shape[0], dtype=charges[0].label_dtype),
charges[0].charge_types)
elif tr_partition == len(charges):
# special case: reshape into col vector
# compute qnums of row/cols in transposed tensor
unique_row_qnums, new_row_degen = compute_fused_charge_degeneracies(
new_row_charges, new_row_flows)
identity_charges = charges[0].identity_charges(dim=1)
block_qnums, new_row_map, new_col_map = intersect(
unique_row_qnums.unique_charges,
identity_charges.unique_charges,
axis=0,
return_indices=True)
block_dims = np.array([new_row_degen[new_row_map], [1]], dtype=SIZE_T)
num_blocks = 1
row_ind, row_locs = reduce_charges(
new_row_charges,
new_row_flows,
block_qnums,
return_locations=True,
strides=new_strides[:tr_partition])
# find location of blocks in transposed tensor (w.r.t positions in original)
#pylint: disable=no-member
orig_row_posL, orig_col_posL = np.divmod(
row_locs[row_ind.charge_labels == 0], orig_width)
block_maps = [(all_cumul_degens[orig_row_posL] +
dense_to_sparse[orig_col_posL]).ravel()]
obj = charges[0].__new__(type(charges[0]))
obj.__init__(block_qnums,
np.arange(block_qnums.shape[0], dtype=charges[0].label_dtype),
charges[0].charge_types)
else:
unique_row_qnums, new_row_degen = compute_fused_charge_degeneracies(
new_row_charges, new_row_flows)
unique_col_qnums, new_col_degen = compute_fused_charge_degeneracies(
new_col_charges, np.logical_not(new_col_flows))
block_qnums, new_row_map, new_col_map = intersect(
unique_row_qnums.unique_charges,
unique_col_qnums.unique_charges,
axis=0,
return_indices=True)
block_dims = np.array(
[new_row_degen[new_row_map], new_col_degen[new_col_map]], dtype=SIZE_T)
num_blocks = len(new_row_map)
row_ind, row_locs = reduce_charges(
new_row_charges,
new_row_flows,
block_qnums,
return_locations=True,
strides=new_strides[:tr_partition])
col_ind, col_locs = reduce_charges(
new_col_charges,
np.logical_not(new_col_flows),
block_qnums,
return_locations=True,
strides=new_strides[tr_partition:])
block_maps = [0] * num_blocks
for n in range(num_blocks):
#pylint: disable=no-member
orig_row_posL, orig_col_posL = np.divmod(
row_locs[row_ind.charge_labels == n], orig_width)
#pylint: disable=no-member
orig_row_posR, orig_col_posR = np.divmod(
col_locs[col_ind.charge_labels == n], orig_width)
block_maps[n] = (
all_cumul_degens[np.add.outer(orig_row_posL, orig_row_posR)] +
dense_to_sparse[np.add.outer(orig_col_posL, orig_col_posR)]).ravel()
obj = charges[0].__new__(type(charges[0]))
obj.__init__(block_qnums,
np.arange(block_qnums.shape[0], dtype=charges[0].label_dtype),
charges[0].charge_types)
if cacher.do_caching:
cacher.cache[hash_val] = (block_maps, obj, block_dims)
return cacher.cache[hash_val]
return block_maps, obj, block_dims
def reduce_charges(charges: List[BaseCharge],
flows: Union[np.ndarray, List[bool]],
target_charges: np.ndarray,
return_locations: Optional[bool] = False,
strides: Optional[np.ndarray] = None) -> Any:
"""
Add quantum numbers arising from combining two or more charges into a
single index, keeping only the quantum numbers that appear in
`target_charges`. Equilvalent to using "combine_charges" followed
by "reduce", but is generally much more efficient.
Args:
charges: List of `BaseCharge`, one for each leg of a
tensor.
flows: A list of bool, one for each leg of a tensor.
with values `False` or `True` denoting inflowing and
outflowing charge direction, respectively.
target_charges: n-by-D array of charges which should be kept,
with `n` the number of symmetries.
return_locations: If `True` return the location of the kept
values of the fused charges
strides: Index strides with which to compute the
retured locations of the kept elements. Defaults to trivial strides
(based on row major order).
Returns:
BaseCharge: the fused index after reduction.
np.ndarray: Locations of the fused BaseCharge charges that were kept.
"""
tensor_dims = [len(c) for c in charges]
if len(charges) == 1:
# reduce single index
if strides is None:
strides = np.array([1], dtype=SIZE_T)
return charges[0].dual(flows[0]).reduce(
target_charges, return_locations=return_locations, strides=strides[0])
# find size-balanced partition of charges
partition = _find_best_partition(tensor_dims)
# compute quantum numbers for each partition
left_ind = fuse_charges(charges[:partition], flows[:partition])
right_ind = fuse_charges(charges[partition:], flows[partition:])
# compute combined qnums
comb_qnums = fuse_ndarray_charges(left_ind.unique_charges,
right_ind.unique_charges,
charges[0].charge_types)
#special case of empty charges
#pylint: disable=unsubscriptable-object
if (comb_qnums.shape[0] == 0) or (len(left_ind.charge_labels) == 0) or (len(
right_ind.charge_labels) == 0):
obj = charges[0].__new__(type(charges[0]))
obj.__init__(
np.empty((0, charges[0].num_symmetries), dtype=charges[0].dtype),
np.empty(0, dtype=charges[0].label_dtype), charges[0].charge_types)
if return_locations:
return obj, np.empty(0, dtype=SIZE_T)
return obj
unique_comb_qnums, comb_labels = np.unique(
comb_qnums, return_inverse=True, axis=0)
num_unique = unique_comb_qnums.shape[0]
# intersect combined qnums and target_charges
reduced_qnums, label_to_unique, _ = intersect(
unique_comb_qnums, target_charges, axis=0, return_indices=True)
map_to_kept = -np.ones(num_unique, dtype=charges[0].label_dtype)
map_to_kept[label_to_unique] = np.arange(len(label_to_unique))
# new_comb_labels is a matrix of shape
# (left_ind.num_unique, right_ind.num_unique)
# each row new_comb_labels[n,:] contains integers values.
# Positions where values > 0
# denote labels of right-charges that are kept.
new_comb_labels = map_to_kept[comb_labels].reshape(
[left_ind.num_unique, right_ind.num_unique])
reduced_rows = [0] * left_ind.num_unique
for n in range(left_ind.num_unique):
temp_label = new_comb_labels[n, right_ind.charge_labels]
reduced_rows[n] = temp_label[temp_label >= 0]
reduced_labels = np.concatenate(
[reduced_rows[n] for n in left_ind.charge_labels])
obj = charges[0].__new__(type(charges[0]))
obj.__init__(reduced_qnums, reduced_labels, charges[0].charge_types)
if return_locations:
row_locs = [0] * left_ind.num_unique
if strides is not None:
# computed locations based on non-trivial strides
row_pos = fuse_stride_arrays(tensor_dims[:partition], strides[:partition])
col_pos = fuse_stride_arrays(tensor_dims[partition:], strides[partition:])
for n in range(left_ind.num_unique):
temp_label = new_comb_labels[n, right_ind.charge_labels]
temp_keep = temp_label >= 0
if strides is not None:
row_locs[n] = col_pos[temp_keep]
else:
row_locs[n] = np.where(temp_keep)[0]
if strides is not None:
reduced_locs = np.concatenate([
row_pos[n] + row_locs[left_ind.charge_labels[n]]
for n in range(left_ind.dim)
])
else:
reduced_locs = np.concatenate([
n * right_ind.dim + row_locs[left_ind.charge_labels[n]]
for n in range(left_ind.dim)
])
return obj, reduced_locs
return obj
def charges(self):
return fuse_charges(self.flat_charges, self.flat_flows)
def charges(self) -> BaseCharge:
"""
Return the fused charges of the index. Note that
flows are merged into the charges.
"""
return fuse_charges(self.flat_charges, self.flat_flows)
