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Merged
njzjz merged 13 commits into from
Jul 25, 2026
Merged

feat(dpmodel): add adaptive Array API neighbor search #5889

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ed5ae86
feat(dpmodel): add adaptive Array API neighbor search
njzjz-bot Jul 21, 2026
13b05a3
fix(dpmodel): stabilize backend neighbor tests
njzjz-bot Jul 22, 2026
f69b2db
test(dpmodel): restore backend test state
njzjz-bot Jul 22, 2026
8764c72
perf(dpmodel): reduce cell-list candidate sorting
njzjz-bot Jul 22, 2026
9ba1383
fix(dpmodel): gate unvalidated array devices
njzjz-bot Jul 22, 2026
0fb6a6a
test(dpmodel): cover periodic backend cell lists
njzjz-bot Jul 22, 2026
75ddbee
fix(dpmodel): guard torch compiler detection
njzjz-bot Jul 22, 2026
ae6b1f5
fix(dpmodel): ignore virtual atoms when binning
njzjz-bot Jul 22, 2026
8d55684
test(dpmodel): preserve neighbor sample dtype
njzjz-bot Jul 22, 2026
c642e8a
perf(dpmodel): reduce neighbor-list candidate traffic
njzjz-bot Jul 22, 2026
31cc119
test(dpmodel): cover periodic cell compaction
njzjz-bot Jul 23, 2026
013901b
test(dpmodel): use unittest assertion
njzjz-bot Jul 23, 2026
c8dbde1
docs(dpmodel): document neighbor selection invariants
njzjz-bot Jul 24, 2026
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8 changes: 6 additions & 2 deletions deepmd/_vendors/ndtensorflow/_namespace.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1650,7 +1650,11 @@ def take(x: Array, indices: Array, /, *, axis: int | None = None) -> Array:
axis = 0
axis = _normalize_axis(axis, tensor.shape.rank)
indices_ = tf.cast(_unwrap(indices), tf.int64)
dim = tensor.shape[axis]
# ``TensorShape.__getitem__`` returns either ``None`` or
# ``Dimension(None)`` depending on TensorFlow's global v2-shape setting.
# ``as_list`` normalizes both representations, which keeps dynamic-axis
# indexing valid when another test or caller enables legacy TensorShape.
dim = tensor.shape.as_list()[axis]
dim = (
tf.shape(tensor, out_type=tf.int64)[axis]
if dim is None
Expand All @@ -1664,7 +1668,7 @@ def take_along_axis(x: Array, indices: Array, /, *, axis: int = -1) -> Array:
tensor = _unwrap(x)
indices_ = tf.cast(_unwrap(indices), tf.int64)
axis = _normalize_axis(axis, tensor.shape.rank)
dim = tensor.shape[axis]
dim = tensor.shape.as_list()[axis]
dim = (
tf.shape(tensor, out_type=tf.int64)[axis]
if dim is None
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