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fix: bugs in uts for property fit #4120

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merged 7 commits into from
Sep 12, 2024
Merged

fix: bugs in uts for property fit #4120

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12feaa0
Init branch bug_test_trans
Chengqian-Zhang Sep 11, 2024
0add785
change mix_types
Chengqian-Zhang Sep 11, 2024
c7c9412
Merge branch 'devel' into bug_test_trans
Chengqian-Zhang Sep 11, 2024
ba348fd
Merge branch 'bug_test_trans' of github.com:Chengqian-Zhang/deepmd-ki…
Chengqian-Zhang Sep 11, 2024
4136afa
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 11, 2024
c2b8ae1
solve conflict
Chengqian-Zhang Sep 11, 2024
e8d9fd9
Merge branch 'devel' into bug_test_trans
Chengqian-Zhang Sep 11, 2024
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256 changes: 171 additions & 85 deletions source/tests/pt/model/test_property_fitting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,6 @@ def setUp(self):
self.rng = np.random.default_rng()
self.nf, self.nloc, _ = self.nlist.shape
self.dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE)
self.scale = self.rng.uniform(0, 1, self.nt).tolist()

def test_consistency(
self,
Expand All @@ -59,27 +58,26 @@ def test_consistency(
self.atype_ext[:, : self.nloc], dtype=int, device=env.DEVICE
)

for mixed_types, nfp, nap, fit_diag, scale, bias_atom_p in itertools.product(
[True, False],
for nfp, nap, bias_atom_p, intensive, bias_method in itertools.product(
[0, 3],
[0, 4],
[True, False],
[None, self.scale],
[
np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]),
np.array([[11, 12, 13, 4, 15], [16, 17, 18, 9, 20]]),
],
[True, False],
["normal", "no_bias"],
):
ft0 = PropertyFittingNet(
self.nt,
self.dd0.dim_out,
task_dim=5,
numb_fparam=nfp,
numb_aparam=nap,
mixed_types=mixed_types,
fit_diag=fit_diag,
scale=scale,
mixed_types=self.dd0.mixed_types(),
bias_atom_p=bias_atom_p,
intensive=intensive,
bias_method=bias_method,
).to(env.DEVICE)

ft1 = DPProperFittingNet.deserialize(ft0.serialize())
Expand Down Expand Up @@ -133,25 +131,104 @@ def test_consistency(
def test_jit(
self,
):
for mixed_types, nfp, nap, fit_diag in itertools.product(
[True, False],
for nfp, nap, intensive, bias_method in itertools.product(
[0, 3],
[0, 4],
[True, False],
["normal", "no_bias"],
):
ft0 = PropertyFittingNet(
self.nt,
self.dd0.dim_out,
task_dim=5,
numb_fparam=nfp,
numb_aparam=nap,
mixed_types=mixed_types,
fit_diag=fit_diag,
mixed_types=self.dd0.mixed_types(),
intensive=intensive,
bias_method=bias_method,
).to(env.DEVICE)
torch.jit.script(ft0)


class TestInvariance(unittest.TestCase):
class TestInvarianceOutCell(unittest.TestCase):
def setUp(self) -> None:
self.natoms = 5
self.rcut = 4
self.rcut_smth = 0.5
self.sel = [46, 92, 4]
self.nf = 1
self.nt = 3
self.rng = np.random.default_rng()
self.coord = torch.tensor(
[
[1.1042, 0.6852, 1.3582],
[1.8812, 1.6277, 0.3153],
[1.5655, 1.0383, 0.4152],
[0.9594, 1.2298, 0.8124],
[0.7905, 0.5014, 0.6654],
],
dtype=dtype,
device=env.DEVICE,
)
self.shift = torch.tensor([1000, 1000, 1000], dtype=dtype, device=env.DEVICE)
self.atype = torch.tensor([0, 0, 0, 1, 1], dtype=torch.int32, device=env.DEVICE)
self.dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE)
self.cell = torch.tensor(
[
[0.7333, 0.9166, 0.6533],
[0.1151, 0.9078, 0.2058],
[0.6907, 0.0370, 0.4863],
],
dtype=dtype,
device=env.DEVICE,
)
self.cell = (self.cell + self.cell.T) + 5.0 * torch.eye(3, device=env.DEVICE)

def test_trans(self):
atype = self.atype.reshape(1, 5)
coord_s = torch.matmul(
torch.remainder(
torch.matmul(self.coord + self.shift, torch.linalg.inv(self.cell)), 1.0
),
self.cell,
)
ft0 = PropertyFittingNet(
self.nt,
self.dd0.dim_out,
task_dim=11,
numb_fparam=0,
numb_aparam=0,
mixed_types=self.dd0.mixed_types(),
).to(env.DEVICE)
res = []
for xyz in [self.coord, coord_s]:
(
extended_coord,
extended_atype,
_,
nlist,
) = extend_input_and_build_neighbor_list(
xyz,
atype,
self.rcut,
self.sel,
self.dd0.mixed_types(),
box=self.cell,
)

rd0, gr0, _, _, _ = self.dd0(
extended_coord,
extended_atype,
nlist,
)

ret0 = ft0(rd0, atype, gr0, fparam=0, aparam=0)
res.append(ret0["property"])

np.testing.assert_allclose(to_numpy_array(res[0]), to_numpy_array(res[1]))


class TestInvarianceRandomShift(unittest.TestCase):
def setUp(self) -> None:
self.natoms = 5
self.rcut = 4
Expand All @@ -166,29 +243,29 @@ def setUp(self) -> None:
self.dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE)
self.cell = torch.rand([3, 3], dtype=dtype, device=env.DEVICE)
self.cell = (self.cell + self.cell.T) + 5.0 * torch.eye(3, device=env.DEVICE)
self.scale = self.rng.uniform(0, 1, self.nt).tolist()

def test_rot(self):
atype = self.atype.reshape(1, 5)
rmat = torch.tensor(special_ortho_group.rvs(3), dtype=dtype, device=env.DEVICE)
coord_rot = torch.matmul(self.coord, rmat)
# use larger cell to rotate only coord and shift to the center of cell
cell_rot = 10.0 * torch.eye(3, dtype=dtype, device=env.DEVICE)

for mixed_types, nfp, nap, fit_diag, scale in itertools.product(
[True, False],
for nfp, nap, intensive, bias_method in itertools.product(
[0, 3],
[0, 4],
[True, False],
[None, self.scale],
["normal", "no_bias"],
):
ft0 = PropertyFittingNet(
self.nt,
self.dd0.dim_out, # dim_descrpt
task_dim=9,
numb_fparam=nfp,
numb_aparam=nap,
mixed_types=True,
fit_diag=fit_diag,
scale=scale,
mixed_types=self.dd0.mixed_types(),
intensive=intensive,
bias_method=bias_method,
).to(env.DEVICE)
if nfp > 0:
ifp = torch.tensor(
Expand All @@ -213,7 +290,12 @@ def test_rot(self):
_,
nlist,
) = extend_input_and_build_neighbor_list(
xyz + self.shift, atype, self.rcut, self.sel, mixed_types
xyz + self.shift,
atype,
self.rcut,
self.sel,
self.dd0.mixed_types(),
box=cell_rot,
)

rd0, gr0, _, _, _ = self.dd0(
Expand All @@ -222,7 +304,7 @@ def test_rot(self):
nlist,
)

ret0 = ft0(rd0, extended_atype, gr0, fparam=ifp, aparam=iap)
ret0 = ft0(rd0, atype, gr0, fparam=ifp, aparam=iap)
res.append(ret0["property"])
np.testing.assert_allclose(
to_numpy_array(res[1]),
Expand All @@ -231,42 +313,44 @@ def test_rot(self):

def test_permu(self):
coord = torch.matmul(self.coord, self.cell)
for fit_diag, scale in itertools.product([True, False], [None, self.scale]):
ft0 = PropertyFittingNet(
self.nt,
self.dd0.dim_out,
task_dim=8,
numb_fparam=0,
numb_aparam=0,
mixed_types=True,
fit_diag=fit_diag,
scale=scale,
).to(env.DEVICE)
res = []
for idx_perm in [[0, 1, 2, 3, 4], [1, 0, 4, 3, 2]]:
atype = self.atype[idx_perm].reshape(1, 5)
(
extended_coord,
extended_atype,
_,
nlist,
) = extend_input_and_build_neighbor_list(
coord[idx_perm], atype, self.rcut, self.sel, False
)
ft0 = PropertyFittingNet(
self.nt,
self.dd0.dim_out,
task_dim=8,
numb_fparam=0,
numb_aparam=0,
mixed_types=self.dd0.mixed_types(),
).to(env.DEVICE)
res = []
for idx_perm in [[0, 1, 2, 3, 4], [1, 0, 4, 3, 2]]:
atype = self.atype[idx_perm].reshape(1, 5)
(
extended_coord,
extended_atype,
_,
nlist,
) = extend_input_and_build_neighbor_list(
coord[idx_perm],
atype,
self.rcut,
self.sel,
self.dd0.mixed_types(),
box=self.cell,
)

rd0, gr0, _, _, _ = self.dd0(
extended_coord,
extended_atype,
nlist,
)
rd0, gr0, _, _, _ = self.dd0(
extended_coord,
extended_atype,
nlist,
)

ret0 = ft0(rd0, extended_atype, gr0, fparam=None, aparam=None)
res.append(ret0["property"])
ret0 = ft0(rd0, atype, gr0, fparam=None, aparam=None)
res.append(ret0["property"])

np.testing.assert_allclose(
to_numpy_array(res[0][:, idx_perm]),
to_numpy_array(res[1]),
)
np.testing.assert_allclose(
to_numpy_array(res[0][:, idx_perm]),
to_numpy_array(res[1]),
)

def test_trans(self):
atype = self.atype.reshape(1, 5)
Expand All @@ -276,38 +360,40 @@ def test_trans(self):
),
self.cell,
)
for fit_diag, scale in itertools.product([True, False], [None, self.scale]):
ft0 = PropertyFittingNet(
self.nt,
self.dd0.dim_out,
task_dim=11,
numb_fparam=0,
numb_aparam=0,
mixed_types=True,
fit_diag=fit_diag,
scale=scale,
).to(env.DEVICE)
res = []
for xyz in [self.coord, coord_s]:
(
extended_coord,
extended_atype,
_,
nlist,
) = extend_input_and_build_neighbor_list(
xyz, atype, self.rcut, self.sel, False
)
ft0 = PropertyFittingNet(
self.nt,
self.dd0.dim_out,
task_dim=11,
numb_fparam=0,
numb_aparam=0,
mixed_types=self.dd0.mixed_types(),
).to(env.DEVICE)
res = []
for xyz in [self.coord, coord_s]:
(
extended_coord,
extended_atype,
_,
nlist,
) = extend_input_and_build_neighbor_list(
xyz,
atype,
self.rcut,
self.sel,
self.dd0.mixed_types(),
box=self.cell,
)

rd0, gr0, _, _, _ = self.dd0(
extended_coord,
extended_atype,
nlist,
)
rd0, gr0, _, _, _ = self.dd0(
extended_coord,
extended_atype,
nlist,
)

ret0 = ft0(rd0, extended_atype, gr0, fparam=0, aparam=0)
res.append(ret0["property"])
ret0 = ft0(rd0, atype, gr0, fparam=0, aparam=0)
res.append(ret0["property"])

np.testing.assert_allclose(to_numpy_array(res[0]), to_numpy_array(res[1]))
np.testing.assert_allclose(to_numpy_array(res[0]), to_numpy_array(res[1]))


class TestPropertyModel(unittest.TestCase):
Expand All @@ -329,7 +415,7 @@ def setUp(self):
task_dim=3,
numb_fparam=0,
numb_aparam=0,
mixed_types=True,
mixed_types=self.dd0.mixed_types(),
intensive=True,
).to(env.DEVICE)
self.type_mapping = ["O", "H", "B"]
Expand Down