fix(tf): reject non-prefix use_spin layouts in the spin helper#5727
fix(tf): reject non-prefix use_spin layouts in the spin helper#5727wanghan-iapcm wants to merge 1 commit into
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The legacy TensorFlow spin implementation assumes spin-enabled types form a contiguous prefix of the type map. The SE-A sel extension takes the first ntypes_spin selections (sel_a[:ntypes_spin]), and the coordinate splitting (se_a.py), force splitting (model/ener.py), and bias merging (fit/ener.py) all address the virtual block with a dense real->virtual offset (i + len(use_spin)). For a non-prefix layout such as use_spin=[False, True], these read the wrong real/virtual type ranges or raise deep inside the graph, and nothing rejected the configuration up front. Rather than refactor all four sites in this legacy backend (the maintained PyTorch backend already supports the sparse layout via Spin.spin_type), guard against it: Spin now rejects a use_spin where a non-spin type precedes a spin-enabled one, with a clear message telling the user to list spin-enabled types first. This turns a silent-wrong result or an obscure crash into an actionable error and documents the invariant. Adds source/tests/tf/test_spin_prefix_guard.py: non-prefix layouts ([False, True] and [True, False, True]) raise ValueError, while prefix layouts ([True, False], [True, True]) and an all-non-spin list ([False, False]) are accepted. The prefix requirement previously had no test. Fix deepmodeling#5680
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## master #5727 +/- ##
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- Coverage 81.26% 81.14% -0.13%
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Files 988 988
Lines 110877 110890 +13
Branches 4234 4234
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- Misses 19249 19388 +139
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Problem
Fixes #5680. The legacy TensorFlow spin implementation assumes spin-enabled types form a contiguous prefix of the type map. The SE-A
selextension takes the firstntypes_spinselections (sel_a[:ntypes_spin]), and the coordinate splitting (deepmd/tf/descriptor/se_a.py), force splitting (deepmd/tf/model/ener.py), and bias merging (deepmd/tf/fit/ener.py) all address the virtual block with a dense real-to-virtual offset (i + len(use_spin)). For a non-prefix layout such asuse_spin=[False, True], these read the wrong real/virtual type ranges or raise deep inside the graph, and nothing rejected the configuration up front. In practice all supported spin models list spin-enabled types first, so the broken layout went unnoticed.Approach
The maintained PyTorch backend already handles the sparse/non-prefix layout via
Spin.spin_type. Rather than refactor all four coupled sites in this legacy backend (which has almost no coverage and where a subtle mistake would silently corrupt training), this guards against the unsupported layout: the TFSpinhelper now rejects ause_spinwhere a non-spin type precedes a spin-enabled one, with a message telling the user to list spin-enabled types first. This turns a silent-wrong result or an obscure crash into an actionable error and documents the invariant in one place.Test
Adds
source/tests/tf/test_spin_prefix_guard.py: non-prefix layouts ([False, True]and[True, False, True]) raiseValueError, while prefix layouts ([True, False],[True, True]) and an all-non-spin list ([False, False]) are accepted. The existing TF spin model test uses a valid[True, False]prefix and continues to pass. The prefix requirement previously had no test.