def test_qr_mpc(hook, workers):
"""
Testing QR decomposition with an AdditiveSharedTensor
"""
bob = workers["bob"]
alice = workers["alice"]
crypto_prov = workers["james"]
torch.manual_seed(
0) # Truncation might not always work so we set the random seed
n_cols = 3
n_rows = 3
t = torch.randn([n_rows, n_cols])
t_sh = t.fix_precision(precision_fractional=6).share(
bob, alice, crypto_provider=crypto_prov)
Q, R = qr(t_sh, norm_factor=3**(1 / 2), mode="complete")
Q = Q.get().float_precision()
R = R.get().float_precision()
# Check if Q is orthogonal
I = Q @ Q.t()
assert ((torch.eye(n_rows) - I).abs() < 1e-2).all()
# Check if R is upper triangular matrix
for col in range(n_cols - 1):
assert ((R[col + 1:, col]).abs() < 1e-2).all()
# Check if QR == t
assert ((Q @ R - t).abs() < 1e-2).all()
def test_qr(hook, workers):
"""
Testing QR decomposition with remote matrix
"""
torch.manual_seed(
42) # Truncation might not always work so we set the random seed
bob = workers["bob"]
n_cols = 5
n_rows = 10
t = torch.randn([n_rows, n_cols])
Q, R = qr(t.send(bob), mode="complete")
Q = Q.get()
R = R.get()
# Check if Q is orthogonal
I = Q @ Q.t()
assert ((torch.eye(n_rows) - I).abs() < 1e-5).all()
# Check if R is upper triangular matrix
for col in range(n_cols):
assert ((R[col + 1:, col]).abs() < 1e-5).all()
# Check if QR == t
assert ((Q @ R - t).abs() < 1e-5).all()
# test modes
Q, R = qr(t.send(bob), mode="reduced")
assert Q.shape == (n_rows, n_cols)
assert R.shape == (n_cols, n_cols)
Q, R = qr(t.send(bob), mode="complete")
assert Q.shape == (n_rows, n_rows)
assert R.shape == (n_rows, n_cols)
R = qr(t.send(bob), mode="r")
assert R.shape == (n_cols, n_cols)