def kinetic_grad(self, r):
"""
Computes the gradient of kinetic energy w.r.t. the momentum `r`.
It is equivalent to compute velocity given the momentum `r`.
:param dict r: a dictionary maps site names to a tensor momentum.
:returns: a dictionary maps site names to the corresponding gradient
"""
v = {}
for (
site_names,
mass_matrix_sqrt_inverse,
) in self._mass_matrix_sqrt_inverse.items():
r_flat = torch.cat(
[r[site_name].reshape(-1) for site_name in site_names])
# NB: using inverse_mass_matrix as in BlockMassMatrix will cost
# O(N^2 x head_size) operators and O(N^2) memory requirement;
# here, we will leverage mass_matrix_sqrt_inverse to reduce the cost to
# O(N x head_size^2) operators and O(N x head_size) memory requirement.
r_unscaled = triu_matvecmul(mass_matrix_sqrt_inverse, r_flat)
v_flat = triu_matvecmul(mass_matrix_sqrt_inverse,
r_unscaled,
transpose=True)
# unpacking
pos = 0
for site_name in site_names:
next_pos = pos + r[site_name].numel()
v[site_name] = v_flat[pos:next_pos].reshape(r[site_name].shape)
pos = next_pos
return v
def test_utilities(head_size):
size = 5
cov = torch.randn(size, size)
cov = torch.mm(cov, cov.t())
mask = torch.ones(size, size)
mask[head_size:, head_size:] = 0.0
mask.view(-1)[::size + 1][head_size:] = 1.0
arrowhead_full = mask * cov
expected = torch.flip(
torch.linalg.cholesky(torch.flip(arrowhead_full, (-2, -1))), (-2, -1))
# test if those flip ops give expected upper triangular values
assert_close(expected.triu(), expected)
assert_close(expected.matmul(expected.t()), arrowhead_full)
# test sqrt
arrowhead = SymmArrowhead(cov[:head_size], cov.diag()[head_size:])
actual = sqrt(arrowhead)
assert_close(actual.top, expected[:head_size])
assert_close(actual.bottom_diag, expected.diag()[head_size:])
# test triu_inverse
expected = expected.inverse()
actual = triu_inverse(actual)
assert_close(actual.top, expected[:head_size])
assert_close(actual.bottom_diag, expected.diag()[head_size:])
# test triu_matvecmul
v = torch.randn(size)
assert_close(triu_matvecmul(actual, v), expected.matmul(v))
assert_close(triu_matvecmul(actual, v, transpose=True),
expected.t().matmul(v))
# test triu_gram
actual = triu_gram(actual)
expected = (arrowhead_full.inverse()
if head_size > 0 else arrowhead_full.diag().reciprocal())
assert_close(actual, expected)
def unscale(self, r):
"""
Computes `inv(M^{1/2}) @ r`.
Note that `r` is generated from a gaussian with scale `mass_matrix_sqrt`.
This method will unscale it.
:param dict r: a dictionary maps site names to a tensor momentum.
:returns: a dictionary maps site names to the corresponding tensor
"""
u = {}
for site_names, mass_matrix_sqrt_inverse in self._mass_matrix_sqrt_inverse.items(
):
r_flat = torch.cat(
[r[site_name].reshape(-1) for site_name in site_names])
u[site_names] = triu_matvecmul(mass_matrix_sqrt_inverse, r_flat)
return u
def scale(self, r_unscaled, r_prototype):
"""
Computes `M^{1/2} @ r_unscaled`.
Note that `r` is generated from a gaussian with scale `mass_matrix_sqrt`.
This method will scale it.
:param dict r_unscaled: a dictionary maps site names to a tensor momentum.
:param dict r_prototype: a dictionary mapes site names to prototype momentum.
Those prototype values are used to get shapes of the scaled version.
:returns: a dictionary maps site names to the corresponding tensor
"""
s = {}
for site_names, mass_matrix_sqrt in self._mass_matrix_sqrt.items():
r_flat = triu_matvecmul(mass_matrix_sqrt, r_unscaled[site_names])
# unpacking
pos = 0
for site_name in site_names:
next_pos = pos + r_prototype[site_name].numel()
s[site_name] = r_flat[pos:next_pos].reshape(
r_prototype[site_name].shape)
pos = next_pos
return s
