class TestOrbitalWF(unittest.TestCase):
def setUp(self):
torch.manual_seed(101)
np.random.seed(101)
set_torch_double_precision()
# molecule
mol = Molecule(atom='H 0 0 0; H 0 0 1.',
unit='bohr',
calculator='pyscf',
basis='sto-3g',
redo_scf=True)
self.wf = Orbital(mol,
kinetic='auto',
include_all_mo=False,
configs='single_double(2,2)')
self.random_fc_weight = torch.rand(self.wf.fc.weight.shape)
self.wf.fc.weight.data = self.random_fc_weight
self.pos = torch.tensor(np.random.rand(10, self.wf.nelec * 3))
self.pos.requires_grad = True
def test_forward(self):
wfvals = self.wf(self.pos)
ref = torch.tensor([[0.0977], [0.0618], [0.0587], [0.0861], [0.0466],
[0.0406], [0.0444], [0.0728], [0.0809], [0.1868]])
# assert torch.allclose(wfvals.data, ref, rtol=1E-4, atol=1E-4)
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)
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, kinpool=False)
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 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 = Orbital(mol,
kinetic='auto',
configs='ground_state',
jastrow_type=FullyConnectedJastrow)
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.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_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, kinpool=False)
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 TestOrbitalWF(unittest.TestCase):
def setUp(self):
torch.manual_seed(101)
np.random.seed(101)
set_torch_double_precision()
# molecule
mol = Molecule(atom='H 0 0 0; H 0 0 1.',
unit='bohr',
calculator='pyscf',
basis='sto-3g',
redo_scf=True)
self.wf = Orbital(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.pos = torch.tensor(np.random.rand(10, 6))
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_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, 2, 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, 2, 3).sum(-1).sum(-1)))
assert (torch.allclose(d2val.sum(-1),
d2val_grad.view(10, 2, 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, kinpool=False)
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)
print(grads.shape)
print(grad_auto.shape)
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)
