class TestGenericJastrowWF(unittest.TestCase):
def setUp(self):
torch.manual_seed(101)
np.random.seed(101)
set_torch_double_precision()
# molecule
mol = Molecule(
atom='Li 0 0 0; H 0 0 1.',
unit='bohr',
calculator='pyscf',
basis='sto-3g',
redo_scf=True)
self.wf = SlaterJastrow(mol,
kinetic='auto',
configs='ground_state',
jastrow_kernel=FullyConnectedJastrowKernel)
self.random_fc_weight = torch.rand(self.wf.fc.weight.shape)
self.wf.fc.weight.data = self.random_fc_weight
self.nbatch = 10
self.pos = 1E-2 * torch.as_tensor(np.random.rand(
self.nbatch, self.wf.nelec*3))
self.pos.requires_grad = True
def test_forward(self):
wfvals = self.wf(self.pos)
def test_antisymmetry(self):
"""Test that the wf values are antisymmetric
wrt exchange of 2 electrons of same spin."""
wfvals_ref = self.wf(self.pos)
if self.wf.nelec < 4:
print(
'Warning : antisymmetry cannot be tested with \
only %d electrons' % self.wf.nelec)
return
# test spin up
pos_xup = self.pos.clone()
perm_up = list(range(self.wf.nelec))
perm_up[0] = 1
perm_up[1] = 0
pos_xup = pos_xup.reshape(self.nbatch, self.wf.nelec, 3)
pos_xup = pos_xup[:, perm_up, :].reshape(
self.nbatch, self.wf.nelec*3)
wfvals_xup = self.wf(pos_xup)
assert(torch.allclose(wfvals_ref, -1*wfvals_xup))
def test_grad_mo(self):
"""Gradients of the MOs."""
mo = self.wf.pos2mo(self.pos)
dmo = self.wf.pos2mo(self.pos, derivative=1)
dmo_grad = grad(
mo,
self.pos,
grad_outputs=torch.ones_like(mo))[0]
gradcheck(self.wf.pos2mo, self.pos)
assert(torch.allclose(dmo.sum(), dmo_grad.sum()))
assert(torch.allclose(dmo.sum(-1),
dmo_grad.view(self.nbatch, self.wf.nelec, 3).sum(-1)))
def test_hess_mo(self):
"""Hessian of the MOs."""
val = self.wf.pos2mo(self.pos)
d2val_grad = hess(val, self.pos)
d2val = self.wf.pos2mo(self.pos, derivative=2)
assert(torch.allclose(d2val.sum(), d2val_grad.sum()))
assert(torch.allclose(d2val.sum(-1).sum(-1),
d2val_grad.view(self.nbatch, self.wf.nelec, 3).sum(-1).sum(-1)))
assert(torch.allclose(d2val.sum(-1),
d2val_grad.view(self.nbatch, self.wf.nelec, 3).sum(-1)))
def test_local_energy(self):
self.wf.kinetic_energy = self.wf.kinetic_energy_autograd
eloc_auto = self.wf.local_energy(self.pos)
self.wf.kinetic_energy = self.wf.kinetic_energy_autograd
eloc_jac = self.wf.local_energy(self.pos)
assert torch.allclose(
eloc_auto.data, eloc_jac.data, rtol=1E-4, atol=1E-4)
def test_kinetic_energy(self):
eauto = self.wf.kinetic_energy_autograd(self.pos)
ejac = self.wf.kinetic_energy_jacobi(self.pos)
assert torch.allclose(
eauto.data, ejac.data, rtol=1E-4, atol=1E-4)
def test_gradients_wf(self):
grads = self.wf.gradients_jacobi(self.pos)
grad_auto = self.wf.gradients_autograd(self.pos)
assert torch.allclose(grads, grad_auto)
grads = grads.reshape(10, self.wf.nelec, 3)
grad_auto = grad_auto.reshape(10, self.wf.nelec, 3)
assert(torch.allclose(grads, grad_auto))
def test_gradients_pdf(self):
grads_pdf = self.wf.gradients_jacobi(self.pos, pdf=True)
grads_auto = self.wf.gradients_autograd(self.pos, pdf=True)
assert torch.allclose(grads_pdf, grads_auto)
class TestOrbitalWF(unittest.TestCase):
def setUp(self):
torch.manual_seed(101)
np.random.seed(101)
set_torch_double_precision()
# molecule
mol = Molecule(
atom='Li 0 0 0; H 0 0 1.',
unit='bohr',
calculator='pyscf',
basis='sto-3g',
redo_scf=True)
self.wf = SlaterJastrow(mol,
kinetic='auto',
include_all_mo=True,
configs='cas(2,2)')
self.random_fc_weight = torch.rand(self.wf.fc.weight.shape)
self.wf.fc.weight.data = self.random_fc_weight
self.nbatch = 10
self.pos = torch.Tensor(
np.random.rand(self.nbatch, mol.nelec*3))
self.pos.requires_grad = True
def test_forward(self):
wfvals = self.wf(self.pos)
ref = torch.Tensor([[0.0522],
[0.0826],
[0.0774],
[0.1321],
[0.0459],
[0.0421],
[0.0551],
[0.0764],
[0.1164],
[0.2506]])
# assert torch.allclose(wfvals.data, ref, rtol=1E-4, atol=1E-4)
def test_antisymmetry(self):
"""Test that the wf values are antisymmetric
wrt exchange of 2 electrons of same spin."""
wfvals_ref = self.wf(self.pos)
if self.wf.nelec < 4:
print(
'Warning : antisymmetry cannot be tested with \
only %d electrons' % self.wf.nelec)
return
# test spin up
pos_xup = self.pos.clone()
perm_up = list(range(self.wf.nelec))
perm_up[0] = 1
perm_up[1] = 0
pos_xup = pos_xup.reshape(self.nbatch, self.wf.nelec, 3)
pos_xup = pos_xup[:, perm_up, :].reshape(
self.nbatch, self.wf.nelec*3)
wfvals_xup = self.wf(pos_xup)
assert(torch.allclose(wfvals_ref, -1*wfvals_xup))
# test spin down
pos_xdn = self.pos.clone()
perm_dn = list(range(self.wf.nelec))
perm_dn[self.wf.mol.nup-1] = self.wf.mol.nup
perm_dn[self.wf.mol.nup] = self.wf.mol.nup-1
pos_xdn = pos_xdn.reshape(self.nbatch, self.wf.nelec, 3)
pos_xdn = pos_xdn[:, perm_up, :].reshape(
self.nbatch, self.wf.nelec*3)
wfvals_xdn = self.wf(pos_xdn)
assert(torch.allclose(wfvals_ref, -1*wfvals_xdn))
def test_grad_mo(self):
"""Gradients of the MOs."""
mo = self.wf.pos2mo(self.pos)
dmo = self.wf.pos2mo(self.pos, derivative=1)
dmo_grad = grad(
mo,
self.pos,
grad_outputs=torch.ones_like(mo))[0]
gradcheck(self.wf.pos2mo, self.pos)
assert(torch.allclose(dmo.sum(), dmo_grad.sum()))
assert(torch.allclose(dmo.sum(-1),
dmo_grad.view(10, self.wf.nelec, 3).sum(-1)))
def test_hess_mo(self):
"""Hessian of the MOs."""
val = self.wf.pos2mo(self.pos)
d2val_grad = hess(val, self.pos)
d2val = self.wf.pos2mo(self.pos, derivative=2)
assert(torch.allclose(d2val.sum(), d2val_grad.sum()))
assert(torch.allclose(d2val.sum(-1).sum(-1),
d2val_grad.view(10, self.wf.nelec, 3).sum(-1).sum(-1)))
assert(torch.allclose(d2val.sum(-1),
d2val_grad.view(10, self.wf.nelec, 3).sum(-1)))
def test_local_energy(self):
self.wf.kinetic_energy = self.wf.kinetic_energy_autograd
eloc_auto = self.wf.local_energy(self.pos)
self.wf.kinetic_energy = self.wf.kinetic_energy_autograd
eloc_jac = self.wf.local_energy(self.pos)
ref = torch.Tensor([[-1.6567],
[-0.8790],
[-2.8136],
[-0.3644],
[-0.4477],
[-0.2709],
[-0.6964],
[-0.3993],
[-0.4777],
[-0.0579]])
# assert torch.allclose(
# eloc_auto.data, ref, rtol=1E-4, atol=1E-4)
assert torch.allclose(
eloc_auto.data, eloc_jac.data, rtol=1E-4, atol=1E-4)
def test_kinetic_energy(self):
eauto = self.wf.kinetic_energy_autograd(self.pos)
ejac = self.wf.kinetic_energy_jacobi(self.pos)
ref = torch.Tensor([[0.6099],
[0.6438],
[0.6313],
[2.0512],
[0.0838],
[0.2699],
[0.5190],
[0.3381],
[1.8489],
[5.2226]])
# assert torch.allclose(
# ejac.data, ref, rtol=1E-4, atol=1E-4)
assert torch.allclose(
eauto.data, ejac.data, rtol=1E-4, atol=1E-4)
def test_gradients_wf(self):
grads = self.wf.gradients_jacobi(self.pos)
grad_auto = self.wf.gradients_autograd(self.pos)
assert torch.allclose(grads, grad_auto)
def test_gradients_pdf(self):
grads_pdf = self.wf.gradients_jacobi(self.pos, pdf=True)
grads_auto = self.wf.gradients_autograd(self.pos, pdf=True)
assert torch.allclose(grads_pdf, grads_auto)
class TestLiH(unittest.TestCase):
def setUp(self):
torch.manual_seed(0)
np.random.seed(0)
set_torch_double_precision()
# molecule
self.mol = Molecule(atom='Li 0 0 0; H 0 0 3.015',
unit='bohr',
calculator='pyscf',
basis='sto-3g')
# wave function
self.wf = SlaterJastrow(self.mol,
kinetic='jacobi',
jastrow_kernel=FullyConnectedJastrowKernel,
configs='single(2,2)',
include_all_mo=False)
# sampler
self.sampler = Metropolis(nwalkers=500,
nstep=200,
step_size=0.05,
ndim=self.wf.ndim,
nelec=self.wf.nelec,
init=self.mol.domain('normal'),
move={
'type': 'all-elec',
'proba': 'normal'
})
# optimizer
self.opt = optim.Adam(self.wf.parameters(), lr=0.01)
# solver
self.solver = SolverSlaterJastrow(wf=self.wf,
sampler=self.sampler,
optimizer=self.opt)
# artificial pos
self.nbatch = 10
self.pos = torch.as_tensor(
np.random.rand(self.nbatch, self.wf.nelec * 3))
self.pos.requires_grad = True
def test_0_wavefunction(self):
eauto = self.wf.kinetic_energy_autograd(self.pos)
ejac = self.wf.kinetic_energy_jacobi(self.pos)
print(torch.stack([eauto, ejac], axis=1).squeeze())
assert torch.allclose(eauto.data, ejac.data, rtol=1E-4, atol=1E-4)
def test1_single_point(self):
# sample and compute observables
obs = self.solver.single_point()
e, v = obs.energy, obs.variance
# def test2_wf_opt_grad_auto(self):
# self.solver.sampler = self.sampler
# self.solver.configure(track=['local_energy'],
# loss='energy', grad='auto')
# obs = self.solver.run(5)
def test3_wf_opt_grad_manual(self):
self.solver.sampler = self.sampler
self.solver.configure(track=['local_energy'],
loss='energy',
grad='manual')
obs = self.solver.run(5)
