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fix(dpmodel): make se_e2_a type_one_side=False traceable #6073

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fix(dpmodel): make se_e2_a type_one_side=False traceable #6073

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95 changes: 31 additions & 64 deletions deepmd/dpmodel/descriptor/se_e2_a.py
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Original file line number Diff line number Diff line change
Expand Up @@ -477,14 +477,6 @@ def _tabulate_fusion_se_a(
)
return xp.sum(em[:, :, :, None] * values[:, :, None, :], axis=1)

def _add_to_slice(self, array: Array, start: int, end: int, value: Array) -> Array:
"""Return ``array`` with ``value`` added to ``array[start:end]``."""
xp = array_api_compat.array_namespace(array, value)
return xp.concat(
[array[:start], array[start:end] + value, array[end:]],
axis=0,
)

def reinit_exclude(
self,
exclude_types: list[tuple[int, int]] = [],
Expand Down Expand Up @@ -578,41 +570,28 @@ def call(
gg = self.cal_g(ss, (tt,))
gr += xp.sum(gg[:, :, :, None] * tr[:, :, None, :], axis=1)
else:
# Sort atoms by center type so each type forms a contiguous block.
# Slice indexing (arr[s:e]) is array-api compatible and lets us
# run cal_g only on atoms of the matching center type, keeping the
# same O(nf*nloc) total embedding cost as the original numpy code.
# Gather the atoms of each center type so cal_g only runs on them.
# nonzero keeps the block sizes traceable, unlike int() on a sum.
# Its indices are ascending, so the concatenated blocks are in
# (stable) sorted order.
atype_loc = xp.reshape(atype_ext[:, :nloc], (nf * nloc,))
sort_idx = xp.argsort(atype_loc)
unsort_idx = xp.argsort(sort_idx)
rr_s = xp.take(rr, sort_idx, axis=0)
mask_s = xp.take(exclude_mask, sort_idx, axis=0)
unsort_idx = xp.argsort(xp.argsort(atype_loc))
dev = array_api_compat.device(coord_ext)
gr_s = xp.zeros([nf * nloc, ng, 4], dtype=input_dtype, device=dev)
# Per-type boundaries in sorted order
type_ends = []
offset = 0
gr_blocks = []
for ti in range(self.ntypes):
offset += int(xp.sum(xp.astype(atype_loc == ti, xp.int32)))
type_ends.append(offset)
type_starts = [0, *type_ends[:-1]]
for ti in range(self.ntypes):
s, e = type_starts[ti], type_ends[ti]
if s == e:
continue
idx = xp.nonzero(atype_loc == ti)[0]
rr_t = xp.take(rr, idx, axis=0)
mask_t = xp.take(exclude_mask, idx, axis=0)
gr_t = xp.zeros([idx.shape[0], ng, 4], dtype=input_dtype, device=dev)
for tt in range(self.ntypes):
mm = mask_s[s:e, sec[tt] : sec[tt + 1]]
tr = rr_s[s:e, sec[tt] : sec[tt + 1], :]
mm = mask_t[:, sec[tt] : sec[tt + 1]]
tr = rr_t[:, sec[tt] : sec[tt + 1], :]
tr = tr * xp.astype(mm[:, :, None], tr.dtype)
ss = tr[..., 0:1]
gg = self.cal_g(ss, (ti, tt))
gr_s = self._add_to_slice(
gr_s,
s,
e,
xp.sum(gg[:, :, :, None] * tr[:, :, None, :], axis=1),
)
gr = xp.take(gr_s, unsort_idx, axis=0)
gr_t += xp.sum(gg[:, :, :, None] * tr[:, :, None, :], axis=1)
gr_blocks.append(gr_t)
gr = xp.take(xp.concat(gr_blocks, axis=0), unsort_idx, axis=0)
gr = xp.reshape(gr, (nf, nloc, ng, 4))
# nf x nloc x ng x 4
gr /= self.nnei
Expand Down Expand Up @@ -664,45 +643,33 @@ def _call_compressed(
)
else:
atype_loc = xp.reshape(atype_ext[:, :nloc], (nfnl,))
sort_idx = xp.argsort(atype_loc)
unsort_idx = xp.argsort(sort_idx)
rr_s = xp.take(rr, sort_idx, axis=0)
mask_s = xp.take(exclude_mask, sort_idx, axis=0)
unsort_idx = xp.argsort(xp.argsort(atype_loc))
dev = array_api_compat.device(rr)
gr_s = xp.zeros([nfnl, 4, ng], dtype=rr.dtype, device=dev)
type_ends = []
offset = 0
for ti in range(self.ntypes):
offset += int(xp.sum(xp.astype(atype_loc == ti, xp.int32)))
type_ends.append(offset)
type_starts = [0, *type_ends[:-1]]
gr_blocks = []
for ti in range(self.ntypes):
s, e = type_starts[ti], type_ends[ti]
if s == e:
continue
idx = xp.nonzero(atype_loc == ti)[0]
rr_t = xp.take(rr, idx, axis=0)
mask_t = xp.take(exclude_mask, idx, axis=0)
gr_t = xp.zeros([idx.shape[0], 4, ng], dtype=rr.dtype, device=dev)
for tt in range(self.ntypes):
embedding_idx = tt * self.ntypes + ti
compress_data_ii = self.compress_data[embedding_idx]
if array_api_compat.size(compress_data_ii) == 0:
continue
compress_info_ii = self.compress_info[embedding_idx]
mm = mask_s[s:e, sec[tt] : sec[tt + 1]]
rr_i = rr_s[s:e, sec[tt] : sec[tt + 1], :]
mm = mask_t[:, sec[tt] : sec[tt + 1]]
rr_i = rr_t[:, sec[tt] : sec[tt + 1], :]
rr_i = rr_i * xp.astype(mm[:, :, None], rr_i.dtype)
ss = rr_i[:, :, :1]
gr_s = self._add_to_slice(
gr_s,
s,
e,
self._tabulate_fusion_se_a(
compress_data_ii,
compress_info_ii,
ss,
rr_i,
ng,
),
gr_t += self._tabulate_fusion_se_a(
compress_data_ii,
compress_info_ii,
ss,
rr_i,
ng,
)
gr = xp.take(gr_s, unsort_idx, axis=0)
gr_blocks.append(gr_t)
gr = xp.take(xp.concat(gr_blocks, axis=0), unsort_idx, axis=0)
return xp.permute_dims(gr, (0, 2, 1))

def serialize(self) -> dict:
Expand Down
6 changes: 5 additions & 1 deletion source/tests/pt_expt/descriptor/test_se_e2_a.py
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Original file line number Diff line number Diff line change
Expand Up @@ -164,8 +164,10 @@ def test_compressed_forward(self, tos, prec) -> None:
assert result_ref.shape == result_cmp.shape
torch.testing.assert_close(result_ref, result_cmp, atol=atol, rtol=atol)

@pytest.mark.parametrize("em", [[], [[0, 1]]]) # exclude_types
@pytest.mark.parametrize("tos", [True, False]) # type_one_side
@pytest.mark.parametrize("prec", ["float64"]) # precision
def test_make_fx(self, prec) -> None:
def test_make_fx(self, prec, tos, em) -> None:
rng = np.random.default_rng(GLOBAL_SEED)
_, _, nnei = self.nlist.shape
davg = rng.normal(size=(self.nt, nnei, 4))
Expand All @@ -179,6 +181,8 @@ def test_make_fx(self, prec) -> None:
self.rcut_smth,
self.sel,
precision=prec,
type_one_side=tos,
exclude_types=em,
seed=GLOBAL_SEED,
).to(self.device)
dd0.davg = torch.tensor(davg, dtype=dtype, device=self.device)
Expand Down
10 changes: 10 additions & 0 deletions source/tests/pt_expt/test_training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -629,6 +629,16 @@ def test_training_loop_compiled_silu(self) -> None:
config = normalize(config)
self._run_training(config)

@REQUIRES_SUPPORTED_COMPILE
def test_training_loop_compiled_two_side(self) -> None:
"""Run compiled training with type_one_side=False."""
config = _make_config(self.data_dir, numb_steps=5)
config["model"]["descriptor"]["type_one_side"] = False
config["training"]["enable_compile"] = True
config = update_deepmd_input(config, warning=False)
config = normalize(config)
self._run_training(config)


class TestCompiledModelGetattr(unittest.TestCase):
"""Unit tests for _CompiledModel attribute delegation.
Expand Down