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feat(pt): make se_a and dpa1 compatible with torch.export #5193

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feat(pt): make se_a and dpa1 compatible with torch.export #5193

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a4729b0
make se_a compatible with torch.export
Feb 5, 2026
26a73cf
dpa1 compatible with torch.export
Feb 5, 2026
94f0ffe
make the full model exportable
Feb 5, 2026
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41 changes: 15 additions & 26 deletions deepmd/pt/model/descriptor/se_a.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,19 +6,25 @@
from typing import (
Any,
ClassVar,
Final,
)

import numpy as np
import torch
import torch.nn as nn

from deepmd.dpmodel.utils import EnvMat as DPEnvMat
from deepmd.dpmodel.utils.seed import (
child_seed,
)
from deepmd.pt.model.descriptor import (
DescriptorBlock,
prod_env_mat,
)
from deepmd.pt.model.network.mlp import (
EmbeddingNet,
NetworkCollection,
)
from deepmd.pt.utils import (
env,
)
Expand All @@ -29,9 +35,18 @@
from deepmd.pt.utils.env_mat_stat import (
EnvMatStatSe,
)
from deepmd.pt.utils.exclude_mask import (
PairExcludeMask,
)
from deepmd.pt.utils.tabulate import (
DPTabulate,
)
from deepmd.pt.utils.update_sel import (
UpdateSel,
)
from deepmd.pt.utils.utils import (
ActivationFn,
)
from deepmd.utils.data_system import (
DeepmdDataSystem,
)
Expand All @@ -45,28 +60,6 @@
check_version_compatibility,
)

try:
from typing import (
Final,
)
except ImportError:
from torch.jit import Final

from deepmd.dpmodel.utils import EnvMat as DPEnvMat
from deepmd.pt.model.network.mlp import (
EmbeddingNet,
NetworkCollection,
)
from deepmd.pt.utils.exclude_mask import (
PairExcludeMask,
)
from deepmd.pt.utils.tabulate import (
DPTabulate,
)
from deepmd.pt.utils.utils import (
ActivationFn,
)

from .base_descriptor import (
BaseDescriptor,
)
Expand Down Expand Up @@ -776,7 +769,6 @@ def forward(

dmatrix = dmatrix.view(-1, self.nnei, 4)
nfnl = dmatrix.shape[0]
# pre-allocate a shape to pass jit
xyz_scatter = torch.zeros(
[nfnl, 4, self.filter_neuron[-1]],
dtype=self.prec,
Expand All @@ -790,9 +782,6 @@ def forward(
if self.type_one_side:
ii = embedding_idx
ti = -1
# torch.jit is not happy with slice(None)
# ti_mask = torch.ones(nfnl, dtype=torch.bool, device=dmatrix.device)
# applying a mask seems to cause performance degradation
ti_mask = None
else:
# ti: center atom type, ii: neighbor type...
Expand Down
1 change: 0 additions & 1 deletion deepmd/pt/model/descriptor/se_atten.py
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Original file line number Diff line number Diff line change
Expand Up @@ -686,7 +686,6 @@ def forward(
self.filter_neuron[-1],
self.is_sorted,
)[0]
# to make torchscript happy
gg = torch.empty(
nframes,
nloc,
Expand Down
7 changes: 2 additions & 5 deletions deepmd/pt/model/network/layernorm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,12 +87,9 @@ def forward(
yy: torch.Tensor
The output.
"""
# mean = xx.mean(dim=-1, keepdim=True)
# variance = xx.var(dim=-1, unbiased=False, keepdim=True)
# The following operation is the same as above, but will not raise error when using jit model to inference.
# See https://github.com/pytorch/pytorch/issues/85792
if xx.numel() > 0:
variance, mean = torch.var_mean(xx, dim=-1, unbiased=False, keepdim=True)
mean = xx.mean(dim=-1, keepdim=True)
variance = xx.var(dim=-1, unbiased=False, keepdim=True)
yy = (xx - mean) / torch.sqrt(variance + self.eps)
else:
yy = xx
Expand Down
19 changes: 13 additions & 6 deletions deepmd/pt/utils/nlist.py
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Expand Up @@ -308,13 +308,20 @@ def nlist_distinguish_types(
snsel = tnlist.shape[2]
for ii, ss in enumerate(sel):
# nloc x s(nsel)
# to int because bool cannot be sort on GPU
pick_mask = (tnlist == ii).to(torch.int32)
# nloc x s(nsel), stable sort, nearer neighbors first
pick_mask, imap = torch.sort(pick_mask, dim=-1, descending=True, stable=True)
# nloc x s(nsel)
mask = tnlist == ii
order = (
snsel - torch.arange(snsel, device=mask.device, dtype=torch.int64)
).view(1, 1, -1)
key = torch.where(
mask,
order.to(torch.float32),
torch.full_like(order, -1, dtype=torch.float32),
)
topk_vals, imap = torch.topk(key, ss, dim=-1, largest=True)
# nloc x nsel[ii]
inlist = torch.gather(nlist, 2, imap)
inlist = inlist.masked_fill(~(pick_mask.to(torch.bool)), -1)
valid = topk_vals > 0
inlist = inlist.masked_fill(~valid, -1)
# nloc x nsel[ii]
ret_nlist.append(inlist[..., :ss])
return torch.concat(ret_nlist, dim=-1)
Expand Down
13 changes: 7 additions & 6 deletions source/tests/pt/model/test_dpa1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,7 +108,7 @@ def test_consistency(
err_msg=err_msg,
)

def test_jit(
def test_export(
self,
) -> None:
rng = np.random.default_rng(GLOBAL_SEED)
Expand All @@ -132,8 +132,6 @@ def test_jit(
[False, True], # use_econf_tebd
):
dtype = PRECISION_DICT[prec]
rtol, atol = get_tols(prec)
err_msg = f"idt={idt} prec={prec}"
# dpa1 new impl
dd0 = DescrptDPA1(
self.rcut,
Expand All @@ -151,6 +149,9 @@ def test_jit(
)
dd0.se_atten.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE)
dd0.se_atten.dstd = torch.tensor(dstd, dtype=dtype, device=env.DEVICE)
# dd1 = DescrptDPA1.deserialize(dd0.serialize())
model = torch.jit.script(dd0)
# model = torch.jit.script(dd1)

coord_ext = torch.tensor(self.coord_ext, dtype=dtype, device=env.DEVICE)
atype_ext = torch.tensor(self.atype_ext, dtype=int, device=env.DEVICE)
nlist = torch.tensor(self.nlist, dtype=int, device=env.DEVICE)

_ = torch.export.export(dd0, (coord_ext, atype_ext, nlist))
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