def test_split_node_rq_unitarity(dtype, num_charges):
np.random.seed(10)
a = tn.Node(get_square_matrix(50, num_charges, dtype=dtype),
backend='symmetric')
r, q = tn.split_node_rq(a, [a[0]], [a[1]])
r[1] | q[0]
qbar = tn.conj(q)
q[1] ^ qbar[1]
u1 = q @ qbar
qbar[0] ^ q[0]
u2 = qbar @ q
blocks, _, shapes = _find_diagonal_sparse_blocks(u1.tensor.flat_charges,
u1.tensor.flat_flows,
len(u1.tensor._order[0]))
for n, block in enumerate(blocks):
np.testing.assert_almost_equal(
np.reshape(u1.tensor.data[block], shapes[:, n]),
np.eye(N=shapes[0, n], M=shapes[1, n]))
blocks, _, shapes = _find_diagonal_sparse_blocks(u2.tensor.flat_charges,
u2.tensor.flat_flows,
len(u2.tensor._order[0]))
for n, block in enumerate(blocks):
np.testing.assert_almost_equal(
np.reshape(u2.tensor.data[block], shapes[:, n]),
np.eye(N=shapes[0, n], M=shapes[1, n]))
def test_split_node_rq(backend):
a = tn.Node(np.random.rand(2, 3, 4, 5, 6), backend=backend)
left_edges = []
for i in range(3):
left_edges.append(a[i])
right_edges = []
for i in range(3, 5):
right_edges.append(a[i])
left, right = tn.split_node_rq(a, left_edges, right_edges)
tn.check_correct([left, right])
np.testing.assert_allclose(a.tensor, tn.contract(left[3]).tensor)
def test_split_node_rq_unitarity_float(backend):
a = tn.Node(np.random.rand(3, 3), backend=backend)
r, q = tn.split_node_rq(a, [a[0]], [a[1]])
r[1] | q[0]
qbar = tn.conj(q)
q[1] ^ qbar[1]
u1 = q @ qbar
qbar[0] ^ q[0]
u2 = qbar @ q
np.testing.assert_almost_equal(u1.tensor, np.eye(3))
np.testing.assert_almost_equal(u2.tensor, np.eye(3))
def test_split_node_rq_unitarity_float(backend):
a = tn.Node(np.random.rand(3, 3), backend=backend)
_, q = tn.split_node_rq(a, [a[0]], [a[1]])
n1 = tn.Node(q.tensor, backend=backend)
n2 = tn.linalg.node_linalg.conj(q)
n1[1] ^ n2[1]
u1 = tn.contract_between(n1, n2)
n1 = tn.Node(q.tensor, backend=backend)
n2 = tn.Node(q.tensor, backend=backend)
n2[0] ^ n1[0]
u2 = tn.contract_between(n1, n2)
np.testing.assert_almost_equal(u1.tensor, np.eye(3))
np.testing.assert_almost_equal(u2.tensor, np.eye(3))
def test_split_node_rq_unitarity_complex(backend):
if backend == "pytorch":
pytest.skip("Complex numbers currently not supported in PyTorch")
if backend == "jax":
pytest.skip("Complex QR crashes jax")
a = tn.Node(np.random.rand(3, 3) + 1j * np.random.rand(3, 3), backend=backend)
r, q = tn.split_node_rq(a, [a[0]], [a[1]])
r[1] | q[0]
qbar = tn.conj(q)
q[1] ^ qbar[1]
u1 = q @ qbar
qbar[0] ^ q[0]
u2 = qbar @ q
np.testing.assert_almost_equal(u1.tensor, np.eye(3))
np.testing.assert_almost_equal(u2.tensor, np.eye(3))
def test_split_node_rq_names(backend):
a = tn.Node(np.zeros((2, 3, 4, 5, 6)), backend=backend)
left_edges = []
for i in range(3):
left_edges.append(a[i])
right_edges = []
for i in range(3, 5):
right_edges.append(a[i])
left, right = tn.split_node_rq(a,
left_edges,
right_edges,
left_name='left',
right_name='right',
edge_name='edge')
assert left.name == 'left'
assert right.name == 'right'
assert left.edges[-1].name == 'edge'
assert right.edges[0].name == 'edge'
def test_split_node_rq(dtype, num_charges):
np.random.seed(10)
a = tn.Node(get_random((6, 7, 8, 9, 10), num_charges, dtype=dtype),
backend='symmetric')
left_edges = []
for i in range(3):
left_edges.append(a[i])
right_edges = []
for i in range(3, 5):
right_edges.append(a[i])
left, right = tn.split_node_rq(a, left_edges, right_edges)
tn.check_correct([left, right])
result = tn.contract(left[3])
np.testing.assert_allclose(result.tensor.data, a.tensor.data)
assert np.all([
charge_equal(result.tensor._charges[n], a.tensor._charges[n])
for n in range(len(a.tensor._charges))
])
def test_split_node_rq_unitarity_complex(backend):
if backend == "pytorch":
pytest.skip("Complex numbers currently not supported in PyTorch")
if backend == "jax":
pytest.skip("Complex QR crashes jax")
a = tn.Node(np.random.rand(3, 3) + 1j * np.random.rand(3, 3),
backend=backend)
_, q = tn.split_node_rq(a, [a[0]], [a[1]])
n1 = tn.Node(q.tensor, backend=backend)
n2 = tn.linalg.node_linalg.conj(q)
n1[1] ^ n2[1]
u1 = tn.contract_between(n1, n2)
n1 = tn.Node(q.tensor, backend=backend)
n2 = tn.linalg.node_linalg.conj(q)
n2[0] ^ n1[0]
u2 = tn.contract_between(n1, n2)
np.testing.assert_almost_equal(u1.tensor, np.eye(3))
np.testing.assert_almost_equal(u2.tensor, np.eye(3))
def test_split_node_rq_names(num_charges):
np.random.seed(10)
a = tn.Node(get_random((2, 3, 4, 5, 6), num_charges=num_charges),
backend='symmetric')
left_edges = []
for i in range(3):
left_edges.append(a[i])
right_edges = []
for i in range(3, 5):
right_edges.append(a[i])
left, right = tn.split_node_rq(a,
left_edges,
right_edges,
left_name='left',
right_name='right',
edge_name='edge')
assert left.name == 'left'
assert right.name == 'right'
assert left.edges[-1].name == 'edge'
assert right.edges[0].name == 'edge'
def test_split_node_rq_orig_shape(backend):
n1 = tn.Node(np.random.rand(3, 4, 5), backend=backend)
tn.split_node_rq(n1, [n1[0], n1[2]], [n1[1]])
np.testing.assert_allclose(n1.shape, (3, 4, 5))
def test_split_node_rq_of_node_without_backend_raises_error():
node = np.random.rand(3, 3, 3)
with pytest.raises(AttributeError):
tn.split_node_rq(node, left_edges=[], right_edges=[])
def func(self, inputs):
# C * x_nodes * y_nodes
peps_nodes = []
input_nodes = []
for i in range(self.xnodes):
peps_line = []
input_line = []
for j in range(self.ynodes):
peps_line.append(
tn.Node(self.peps_var[i][j], name=f'p_{i}_{j}'))
input_line.append(tn.Node(inputs[:, i, j], name=f'i_{i}_{j}'))
peps_nodes.append(peps_line)
input_nodes.append(input_line)
# Connect the edges
cx, cy = self.xnodes // 2, self.ynodes // 2
# Input Features
for i in range(self.xnodes):
for j in range(self.ynodes):
input_nodes[i][j][0] ^ peps_nodes[i][j][0]
# Y Bond
for i in range(self.xnodes):
for j in range(self.ynodes - 1):
index1 = self.index_result[i, j, 1]
index2 = self.index_result[i, j + 1, 3]
peps_nodes[i][j][index1] ^ peps_nodes[i][j + 1][index2]
# X Bond
for j in range(self.ynodes):
for i in range(self.xnodes - 1):
index1 = self.index_result[i, j, 0]
index2 = self.index_result[i + 1, j, 2]
peps_nodes[i][j][index1] ^ peps_nodes[i + 1][j][index2]
# Contract
# Contract the features
# contracted_nodes = []
for i in range(self.xnodes):
for j in range(self.ynodes):
input_nodes[i][j] = input_nodes[i][j] @ peps_nodes[i][j]
input_nodes[i][j].name = f'p_{i}_{j}'
input_nodes[i][j].tensor = input_nodes[i][j].tensor / \
input_nodes[i][j].tensor.norm()
# contracted_nodes.append(input_nodes[i][j])
# Contract each row
left_nodes: List[tn.Node] = input_nodes[0]
right_nodes: List[tn.Node] = input_nodes[self.xnodes - 1]
middle_nodes: List[tn.Node] = input_nodes[cx]
for i in range(1, cx):
for j in range(self.ynodes):
left_nodes[j] = left_nodes[j] @ input_nodes[i][j]
left_nodes[j].name = f'l_{j}'
# Row Normalization
row_norm = torch.mean(
torch.stack([t.tensor.norm() for t in left_nodes]))
for t in left_nodes:
t.tensor = t.tensor / row_norm
# RQ Decomposition
for j in range(self.ynodes - 1):
left_edges = []
right_edges = []
for edge in left_nodes[j].edges:
nxt_node_name = edge.node1.name if edge.node1.name != f'l_{j}' and edge.node1.name != '__unnamed_node__' else edge.node2.name
if nxt_node_name[0] == 'p':
right_edges.append(edge)
elif nxt_node_name == f'l_{j-1}':
right_edges.append(edge)
else:
left_edges.append(edge)
node1, node2 = tn.split_node_rq(left_nodes[j],
left_edges=left_edges,
right_edges=right_edges)
left_nodes[j] = node2
left_nodes[j + 1] = left_nodes[j + 1] @ node1
# left_nodes[j+1].tensor = left_nodes[j+1].tensor / \
# left_nodes[j+1].tensor.norm()
left_nodes[j].name = f'l_{j}'
left_nodes[j + 1].name = f'l_{j+1}'
# SVD
for j in range(self.ynodes - 1, 0, -1):
tmp_node = left_nodes[j] @ left_nodes[j - 1]
left_edges = []
right_edges = []
for edge in tmp_node.edges:
nxt_node_name = edge.node1.name if edge.node1.name != f'l_{j}' and edge.node1.name != '__unnamed_node__' else edge.node2.name
if nxt_node_name == f'p_{i+1}_{j}':
left_edges.append(edge)
elif nxt_node_name == f'p_{i+1}_{j-1}':
right_edges.append(edge)
elif nxt_node_name == f'l_{j+1}':
left_edges.append(edge)
else:
right_edges.append(edge)
node1, node2, _ = tn.split_node(
tmp_node,
left_edges=left_edges,
right_edges=right_edges,
max_singular_values=self.max_singular_values)
left_nodes[j] = node1
left_nodes[j - 1] = node2
left_nodes[j].name = f'l_{j}'
left_nodes[j - 1].name = f'l_{j-1}'
# QR Decomposition
left_edges = []
right_edges = []
for edge in left_nodes[j].edges:
if not edge.node2 and not edge.node1:
continue
nxt_node_name = edge.node1.name if edge.node1.name != f'l_{j}' and edge.node1.name != '__unnamed_node__' else edge.node2.name
if nxt_node_name[0] == 'p':
left_edges.append(edge)
elif nxt_node_name == f'l_{j+1}':
left_edges.append(edge)
else:
right_edges.append(edge)
node1, node2 = tn.split_node_qr(left_nodes[j],
left_edges=left_edges,
right_edges=right_edges)
left_nodes[j] = node1
left_nodes[j].name = f'l_{j}'
left_nodes[j - 1] = node2 @ left_nodes[j - 1]
# left_nodes[j-1].tensor = left_nodes[j-1].tensor / \
# left_nodes[j-1].tensor.norm()
left_nodes[j - 1].name = f'l_{j-1}'
for i in range(self.xnodes - 2, cx, -1):
for j in range(self.ynodes):
right_nodes[j] = right_nodes[j] @ input_nodes[i][j]
right_nodes[j].name = f'r_{j}'
# Row Normalization
row_norm = torch.mean(
torch.stack([t.tensor.norm() for t in right_nodes]))
for t in right_nodes:
t.tensor = t.tensor / row_norm
# RQ Decomposition
for j in range(self.ynodes - 1):
left_edges = []
right_edges = []
for edge in right_nodes[j].edges:
if not edge.node2 and not edge.node1:
continue
nxt_node_name = edge.node1.name if edge.node1.name != f'r_{j}' and edge.node1.name != '__unnamed_node__' else edge.node2.name
if nxt_node_name[0] == 'p':
right_edges.append(edge)
elif nxt_node_name == f'r_{j-1}':
right_edges.append(edge)
else:
left_edges.append(edge)
node1, node2 = tn.split_node_rq(right_nodes[j],
left_edges=left_edges,
right_edges=right_edges)
right_nodes[j] = node2
right_nodes[j + 1] = right_nodes[j + 1] @ node1
# right_nodes[j+1].tensor = right_nodes[j+1].tensor / \
# right_nodes[j+1].tensor.norm()
right_nodes[j].name = f'r_{j}'
right_nodes[j + 1].name = f'r_{j+1}'
# SVD
for j in range(self.ynodes - 1, 0, -1):
tmp_node = right_nodes[j] @ right_nodes[j - 1]
left_edges = []
right_edges = []
for edge in tmp_node.edges:
if not edge.node2 and not edge.node1:
continue
nxt_node_name = edge.node1.name if edge.node1.name != f'r_{j}' and edge.node1.name != '__unnamed_node__' else edge.node2.name
if nxt_node_name == f'p_{i-1}_{j}':
left_edges.append(edge)
elif nxt_node_name == f'p_{i-1}_{j-1}':
right_edges.append(edge)
elif nxt_node_name == f'r_{j+1}':
left_edges.append(edge)
else:
right_edges.append(edge)
node1, node2, _ = tn.split_node(
tmp_node,
left_edges=left_edges,
right_edges=right_edges,
max_singular_values=self.max_singular_values)
right_nodes[j] = node1
right_nodes[j - 1] = node2
right_nodes[j].name = f'r_{j}'
right_nodes[j - 1].name = f'r_{j-1}'
# QR Decomposition
left_edges = []
right_edges = []
for edge in right_nodes[j].edges:
if not edge.node2 and not edge.node1:
continue
nxt_node_name = edge.node1.name if edge.node1.name != f'r_{j}' and edge.node1.name != '__unnamed_node__' else edge.node2.name
if nxt_node_name[0] == 'p':
left_edges.append(edge)
elif nxt_node_name == f'r_{j+1}':
left_edges.append(edge)
else:
right_edges.append(edge)
node1, node2 = tn.split_node_qr(right_nodes[j],
left_edges=left_edges,
right_edges=right_edges)
right_nodes[j] = node1
right_nodes[j].name = f'r_{j}'
right_nodes[j - 1] = node2 @ right_nodes[j - 1]
# right_nodes[j-1].tensor = right_nodes[j-1].tensor / \
# right_nodes[j-1].tensor.norm()
right_nodes[j - 1].name = f'r_{j-1}'
for j in range(self.ynodes):
middle_nodes[j] = left_nodes[j] @ middle_nodes[j]
# middle_nodes[j].tensor = middle_nodes[j].tensor / \
# middle_nodes[j].tensor.norm()
for j in range(self.ynodes):
middle_nodes[j] = right_nodes[j] @ middle_nodes[j]
# middle_nodes[j].tensor = middle_nodes[j].tensor / \
# middle_nodes[j].tensor.norm()
down_node = middle_nodes[0]
up_node = middle_nodes[self.ynodes - 1]
for j in range(1, cy + 1):
down_node = down_node @ middle_nodes[j]
down_node.tensor = down_node.tensor / down_node.tensor.norm()
for j in range(self.ynodes - 2, cy, -1):
up_node = up_node @ middle_nodes[j]
up_node.tensor = up_node.tensor / up_node.tensor.norm()
result = (down_node @ up_node).tensor
# Contract the remaining peps (With Auto Mode)
# result = contractors.auto(contracted_nodes).tensor
# print(result[0].item())
result = result.view([10]) / result.norm()
return result