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feat(stat): isolated-atom energy reference through preset_out_bias and vacuum_ref - #6022
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…ts handling
`preset_out_bias` documents that the bias of an assigned type is set to
the preset value. In `change-by-statistic` mode (fine-tuning,
`dp change-bias`, `change_bias_after_training`) the preset was passed to
the least-squares fit as if it were a shift, so an assigned type ended at
`pretrained_bias + preset`, the residual of the other types subtracted
`natoms * preset` instead of `natoms * (preset - pretrained_bias)`, and
every further call accumulated the preset again. The four copies of the
mode branch (pt, pd, dpmodel atomic models and the pt model wrapper) all
had the defect.
The preset now enters the fit in the frame of the fitted statistics:
absolute in `set-by-statistic`, `preset - stored bias` in
`change-by-statistic`, so `stored + shift == preset` and repeated calls
are idempotent. The three `change_out_bias` bodies share one code path;
the pt model wrapper delegates to the atomic model with its complete
predictor instead of duplicating the fit.
All preset handling lives in the new `deepmd.utils.preset_out_bias`
module, which replaces the per-backend copies of the preset assembly and
of the config converter:
- the option accepts a dict keyed by element name
(`{"energy": {"H": -13.6, "O": -432.0}}`) besides the per-type list,
and nested lists for outputs of higher rank;
- normalization happens in the atomic-model constructor, so every
builder and deserialization accept both forms; the normalized form is
array-free nested lists, which serializes to `.dp` files and satisfies
the flax module wrapper of the jax backend;
- `change_type_map` remaps the preset together with the stored bias;
- an unknown output name is rejected at construction, a preset on a
fitting whose statistics do not distinguish types when the statistics
are computed;
- per-atom-label statistics honor the preset like frame-level ones;
- the dpmodel factory (pt_expt, jax) and the pt linear and ZBL builders
forward the option to the model that computes the bias.
The argcheck documentation describes both forms, the behavior in both
modes, and which descriptors expose `set_davg_zero`.
…istics compute_stats_do_not_distinguish_types never used its assigned_bias argument: statistics that do not resolve atom types cannot assign a per-type bias, and such a preset is now rejected before the statistics are computed. The parameter is removed together with its three call sites.
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📝 WalkthroughWalkthroughThis change adds flexible preset output-bias handling and isolated-atom vacuum references. It updates statistics, model construction, fitting, descriptors, export, serialization, backend validation, documentation, examples, and tests. ChangesModel reference features
Priority: ➖ Normal Estimated code review effort: 5 (Critical) | ~90 minutes Change: Feature Suggested reviewers: Merge Risk: 🟡 Moderate · up to Some supported vacuum-reference and partial atomic-energy configurations produce incorrect model outputs or retain deployment-time reference dependencies. Resolve these paths before merging. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 56.84% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 329 functions across 69 files. (1 skipped: 1 unsupported.) ✨ Finishing Touches🧪 Generate unit tests (beta)
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Inline comments:
In `@deepmd/dpmodel/atomic_model/base_atomic_model.py`:
- Around line 845-848: Update compute_output_stats in each of the three backend
implementations to bypass cached output statistics whenever preset_bias or
model_forward is provided; retain cache reuse only for calls without
frame-dependent inputs, while preserving the existing statistic-processing
behavior.
In `@deepmd/utils/preset_out_bias.py`:
- Line 87: Update the validation around the value check in preset bias
processing to reject every non-finite value, including positive and negative
infinity, by using the appropriate np.isfinite-based condition while preserving
rejection of NaN.
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📒 Files selected for processing (21)
deepmd/dpmodel/atomic_model/base_atomic_model.pydeepmd/dpmodel/model/model_factory.pydeepmd/dpmodel/utils/stat.pydeepmd/pd/model/atomic_model/base_atomic_model.pydeepmd/pd/model/model/__init__.pydeepmd/pd/utils/stat.pydeepmd/pt/model/atomic_model/base_atomic_model.pydeepmd/pt/model/model/__init__.pydeepmd/pt/model/model/make_model.pydeepmd/pt/utils/stat.pydeepmd/utils/argcheck.pydeepmd/utils/preset_out_bias.pysource/tests/common/dpmodel/test_atomic_model_global_stat.pysource/tests/common/dpmodel/test_model_factory.pysource/tests/common/test_argcheck_backend_docs.pysource/tests/common/test_preset_out_bias.pysource/tests/jax/test_preset_out_bias.pysource/tests/pd/model/test_atomic_model_global_stat.pysource/tests/pd/model/test_get_model.pysource/tests/pt/model/test_atomic_model_global_stat.pysource/tests/pt/model/test_get_model.py
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🟡 Changes recommended
Unresolved moderate findings cover stale-stat cache handling, generic child forwarding, dipole preset enforcement, and non-finite stored biases.
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Pull request overview
This pull request centralizes preset_out_bias handling and fixes preset enforcement across statistics updates, serialization, type-map changes, and supported backends.
Changes:
- Adds shared normalization, validation, remapping, and bias-shift utilities.
- Supports element-keyed presets and corrects repeated
set-by-statistic/change-by-statisticbehavior. - Expands backend forwarding and adds regression coverage for serialization, factories, labels, and JAX.
File summaries
| File | Summary |
|---|---|
source/tests/pt/model/test_get_model.py |
PyTorch configuration and forwarding tests. |
source/tests/pt/model/test_atomic_model_global_stat.py |
PT statistics, serialization, remapping, and validation coverage. |
source/tests/pd/model/test_get_model.py |
Paddle configuration and forwarding tests. |
source/tests/pd/model/test_atomic_model_global_stat.py |
Paddle statistics and preset regression coverage. |
source/tests/jax/test_preset_out_bias.py |
JAX end-to-end preset tests. |
source/tests/common/test_preset_out_bias.py |
Shared helper tests. |
source/tests/common/test_argcheck_backend_docs.py |
Backend documentation tests. |
source/tests/common/dpmodel/test_model_factory.py |
Factory forwarding tests. |
source/tests/common/dpmodel/test_atomic_model_global_stat.py |
Shared statistics regression tests. |
deepmd/utils/preset_out_bias.py |
Shared preset processing. Moderate (1 vote): non-finite stored bias rows can prevent enforcement during change-by-statistic. |
deepmd/utils/argcheck.py |
Configuration documentation and backend declarations. Moderate (1 vote): dipole preset enforcement is documented although dipole output bias remains unchanged. |
deepmd/pt/utils/stat.py |
PT statistics integration. Moderate (1 vote): cached statistics can bypass the supplied preset and reapply stale residuals. |
deepmd/pt/model/model/make_model.py |
PT wrapper delegates complete predictor handling. |
deepmd/pt/model/model/__init__.py |
PT model option forwarding. |
deepmd/pt/model/atomic_model/base_atomic_model.py |
PT bias lifecycle. Moderate (2 votes): cached statistics can bypass preset handling. |
deepmd/pd/utils/stat.py |
Paddle statistics integration. Moderate (1 vote): cached statistics can bypass the supplied preset and reapply stale residuals. |
deepmd/pd/model/model/__init__.py |
Paddle model option forwarding. |
deepmd/pd/model/atomic_model/base_atomic_model.py |
Paddle bias lifecycle. Moderate (2 votes): cached statistics can bypass preset handling. |
deepmd/dpmodel/utils/stat.py |
Shared statistics integration. Moderate (1 vote): cached statistics can bypass the supplied preset and reapply stale residuals. |
deepmd/dpmodel/model/model_factory.py |
Factory forwarding. Moderate (1 vote): generic expanded learned-child paths may silently ignore the preset. |
deepmd/dpmodel/atomic_model/base_atomic_model.py |
Shared bias lifecycle. Moderate (2 votes): cached statistics can bypass preset handling. |
Review details
Suppressed comments (6)
deepmd/dpmodel/model/model_factory.py:342
- This forwards only the composition-level key, but the shared
expand_bridging_methodnormalizer placespreset_out_biason the learned child. On generic expanded linear-model paths (such as the dpmodel builder and pt_expt for non-DPA4 learned children), child options are not passed into the child constructor, while the composition reads only its top-level preset. The bridged model therefore silently ignores the assignment; keep the preset on the composition or forward it when constructing the learned child, and cover that path end to end.
preset_out_bias=data.get("preset_out_bias"),
deepmd/dpmodel/utils/stat.py:274
- The new
preset_biasis only consumed after the cache lookup. A model configured with a new or changed preset can therefore restore stale output statistics instead of enforcing the preset; inchange-by-statistic, a cached residual is also added again on repeated calls. Do not reuse cached output statistics unless the cache records matching preset/mode semantics, or invalidate/recompute them when a preset is supplied.
rcond : float, optional
The condition number for the regression of atomic energy.
preset_bias : dict[str, list[Optional[np.ndarray]]], optional
Assigned values of the returned bias, given by key:value pairs.
The value is a list with one element per type: None leaves the type to the
statistics, an np.ndarray of output shape assigns the type.
For example: [None, [2.]] means type 0 is not set, type 1 is set to [2.]
The values live in the frame of the returned bias: absolute biases without
deepmd/pd/utils/stat.py:366
- The new
preset_biasis only consumed after the cache lookup. A model configured with a new or changed preset can therefore restore stale output statistics instead of enforcing the preset; inchange-by-statistic, a cached residual is also added again on repeated calls. Do not reuse cached output statistics unless the cache records matching preset/mode semantics, or invalidate/recompute them when a preset is supplied.
preset_bias : dict[str, list[Optional[np.ndarray]]], optional
Assigned values of the returned bias, given by key:value pairs.
The value is a list with one element per type: None leaves the type to the
statistics, an np.ndarray of output shape assigns the type.
For example: [None, [2.]] means type 0 is not set, type 1 is set to [2.]
The values live in the frame of the returned bias: absolute biases without
`model_forward`, shifts of the model's stored bias with `model_forward`.
deepmd/pt/utils/stat.py:689
- The new
preset_biasis only consumed after the cache lookup. A model configured with a new or changed preset can therefore restore stale output statistics instead of enforcing the preset; inchange-by-statistic, a cached residual is also added again on repeated calls. Do not reuse cached output statistics unless the cache records matching preset/mode semantics, or invalidate/recompute them when a preset is supplied.
assigned_bias = {
kk: make_preset_out_bias(ntypes, preset_bias[kk])
if preset_bias is not None and kk in preset_bias
deepmd/utils/argcheck.py:3420
- This documentation promises that a dipole preset is enforced, but both
DPDipoleAtomicModel.apply_out_statimplementations still return the prediction unchanged. The option can be accepted, fitted, and serialized while never affecting the model output; either reject presets for fitting types that do not apply output bias or implement a valid dipole treatment before advertising this example.
doc_preset_out_bias = "The preset bias of the atomic output, provided as a dict keyed by the output name. Each value is either a list with one entry per type of the `type_map`, where `null` leaves the type to the data statistics, or a dict keyed by element name that assigns the listed elements only, which is the convenient form for a model with many types. For a spin model with virtual atom types, the list counts the virtual types as well, while the dict names real elements only. Taking an energy model with the `type_map` `['C', 'H', 'O']` for example, `{ 'energy': [null, 0., 1.] }` sets the energy bias of H and O to 0. and 1. and fits the bias of C from the data; the same setting reads `{ 'energy': { 'H': 0., 'O': 1. } }`. A dipole model with two atom types may set `preset_out_bias` as `{ 'dipole': [null, [0., 1., 2.]] }`; an output of higher rank takes a nested list of its shape. The preset is enforced whenever the bias is computed, from frame-level as well as from per-atom labels: both when the model is initialized from the data and when the bias of a pretrained model is changed during fine-tuning or by `dp change-bias`, the bias of an assigned type is set to the preset value and only the remaining types are fitted. A fitting whose statistics do not distinguish atom types cannot take a preset. Set `set_davg_zero` to true on descriptors that expose it (`se_e2_a`, `se_e2_r`, `se_e3`, `se_a_tpe`, `se_a_ebd_v2`, `se_atten_v2`; it already defaults to true for `se_atten`, `se_e3_tebd` and the `repinit` and `repformer` blocks of DPA-2) so that an isolated atom yields a zero descriptor input; descriptors without this option, such as DPA-3 and DPA4, need no further setting."
deepmd/utils/preset_out_bias.py:250
- If a pretrained model has
NaNfor an assigned type's stored bias (for example, a type absent from per-atom-label statistics), this subtraction producesNaN. The assigned row is then treated as unassigned by the stats solver, and_store_out_stat(add=True)keepsNaN, sochange-by-statisticfails to enforce the configured preset. Handle non-finite stored rows when applying an assigned preset, and add a regression covering a serialized model with an absent type.
shift[key] = preset.reshape(ntypes, size) - out_bias[idx, :, :size]
- Files reviewed: 22/22 changed files
- Comments generated: 3
- Review effort level: Lite
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Addressed the review findings in 687b510:
Validation:
CUDA CI is being requested for the new commit, along with another review from njzjz. |
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NEEDS HUMAN REVIEW
I reviewed the complete 27-file diff and the repository guidance, existing review threads and author replies. The current head addresses the earlier high-confidence findings: state-dependent output statistics now bypass the ordinary cache, preset values reject all non-finite inputs, assigned rows are pinned before residual prediction, dipole models reject assigned presets, and the expanded linear/ZBL paths route the preset to the composition that owns output-bias statistics. The added cross-backend tests cover repeated bias changes, absent assigned types, serialization, type-map remapping, per-atom labels, cache invalidation, JAX, and model-factory forwarding. I did not find a new high-confidence functional or compatibility blocker in the current diff.
I am not approving this head yet because relevant exact-head CI is still incomplete: Test Python, Test C++, one Test CUDA run, Build C library, CodeQL, Build/upload to PyPI, and Read the Docs are still in progress. Build C++, the other Test CUDA run, pre-commit.ci, and CodeRabbit are currently successful.
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OutisLi
changed the title
…d vacuum_ref The bias half: preset_out_bias fixes an output without statistics. A preset assigns every element observed in the data (a missing element is an error), elements outside the type map and the excluded types are ignored, assigned outputs take the preset directly in both set-by-statistic and change-by-statistic, and the output std of such an output keeps its stored value. A string entry names one of the bundled tables of isolated-atom energies (omat24, omol25, omc25, odac25, oc20, kept in deepmd/utils/preset_out_bias_tables.json) or a JSON file; it is resolved when the input is processed, in single-task and multi-task configurations alike, so the model carries the values. The network half: the fitting option vacuum_ref references the network output of every atom to the output the same network gives an isolated atom of the same type under the atom's own frame parameters, atomic parameters and case embedding, so that an atom without neighbors contributes exactly its bias. The conditioning columns are shared by the atoms and their references; without frame or atomic parameters the references are evaluated once per type. The atomic model supplies the vacuum descriptor of every type: the dpmodel graph route and the PyTorch SeZM edge route carry one reference atom per type through the same forward, conditioned as the neutral ground-state atom (zero charge, ground-state multiplicity, one Bohr magneton per unpaired electron for the native spin) from tables built once with the type map, and the dense route evaluates single-atom frames. Freezing resolves the reference on the exported model: the vacuum descriptor is evaluated once and folded into the fitting bias, or stored in the fitting as a deployment constant when frame or atomic parameters make the reference vary between atoms, so exported graphs carry no reference atoms. The fused pt_expt operators take the reference through the per-type bias. atom_ener is removed from the PyTorch and dpmodel fittings; a legacy serialized entry is dropped, and the TensorFlow and Paddle fittings reject a serialized vacuum_ref. The pt_expt DPA4 builder accepts preset_out_bias.
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Re-reviewed the new head and retrieved the complete 94-file diff plus the existing review threads/comments and repository guidance. The earlier preset/cache/non-finite-value findings remain addressed, and I did not identify a new high-confidence blocker in the portions I could evaluate, including the shared preset-row path, vacuum-reference descriptor/fitting plumbing, export folding, serialization compatibility handling, and the added cross-backend tests.
I am leaving this as NEEDS HUMAN REVIEW / COMMENT rather than approving. The latest commit expands this PR substantially (vacuum-reference behavior across multiple model/descriptor/export paths), making the overall 94-file, 6.4k-addition change too large to certify reliably in one monitoring pass, and exact-head CI is also incomplete: one CUDA run is green, while Test Python, Test C++, Build C/C++, CodeQL, packaging, and another CUDA run are still running/queued. A final approval should wait for those checks and a focused human pass over the new vacuum-reference semantics/export compatibility.
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REQUEST_CHANGES: I reviewed the full current 94-file change, the repository contribution guidance, existing review threads/replies, and exact-head checks. I found one high-confidence blocking regression in the Paddle test coverage: the new unknown-element test contradicts the shared preset_out_bias contract and the implementation on this same head. I left the concrete issue inline.
Exact-head CUDA C++/Python checks are also still in progress, and CodeQL is currently neutral because the C/C++ configuration has not reported, so this head would not yet qualify for approval even after the blocker is fixed.
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Actionable comments posted: 2
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Inline comments:
In `@deepmd/dpmodel/atomic_model/dp_atomic_model.py`:
- Around line 510-515: Update the spin preparation logic around the visible spin
concatenation so spin=None creates zero-valued (N, 3) rows for real nodes before
appending conditions["spin"] for vacuum/reference nodes. Preserve supplied
real-node spin rows and ensure the resulting tensor includes native-spin
reference rows for descriptor conditioning.
In `@deepmd/pt/model/model/sezm_model.py`:
- Line 1570: Update SeZMModel.fold_vacuum_reference() and the cache-key
construction for compiled_core_compute_cache and _SEZM_COMPILE_CACHE so the
folded vacuum state is represented, or invalidate all affected instance and
shared cache entries immediately after folding. Ensure graphs compiled before
FittingNet.fold_vacuum_reference() cannot be reused after
needs_vacuum_descriptor(), bias_atom_e, or vacuum_ref changes.
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deepmd/dpmodel/atomic_model/base_atomic_model.pydeepmd/dpmodel/atomic_model/dp_atomic_model.pydeepmd/dpmodel/atomic_model/linear_atomic_model.pydeepmd/dpmodel/atomic_model/pairtab_atomic_model.pydeepmd/dpmodel/descriptor/dpa4.pydeepmd/dpmodel/fitting/dipole_fitting.pydeepmd/dpmodel/fitting/dos_fitting.pydeepmd/dpmodel/fitting/dpa4_ener.pydeepmd/dpmodel/fitting/ener_fitting.pydeepmd/dpmodel/fitting/general_fitting.pydeepmd/dpmodel/fitting/invar_fitting.pydeepmd/dpmodel/fitting/make_base_fitting.pydeepmd/dpmodel/fitting/polarizability_fitting.pydeepmd/dpmodel/fitting/property_fitting.pydeepmd/dpmodel/model/make_model.pydeepmd/dpmodel/model/spin_model.pydeepmd/dpmodel/utils/neighbor_graph/__init__.pydeepmd/dpmodel/utils/neighbor_graph/graph.pydeepmd/pd/model/atomic_model/base_atomic_model.pydeepmd/pd/model/task/fitting.pydeepmd/pt/entrypoints/freeze_pt2.pydeepmd/pt/model/atomic_model/base_atomic_model.pydeepmd/pt/model/atomic_model/dp_atomic_model.pydeepmd/pt/model/atomic_model/linear_atomic_model.pydeepmd/pt/model/atomic_model/pairtab_atomic_model.pydeepmd/pt/model/atomic_model/sezm_atomic_model.pydeepmd/pt/model/descriptor/sezm.pydeepmd/pt/model/descriptor/sezm_nn/dens.pydeepmd/pt/model/model/make_model.pydeepmd/pt/model/model/sezm_model.pydeepmd/pt/model/model/spin_model.pydeepmd/pt/model/task/dos.pydeepmd/pt/model/task/fitting.pydeepmd/pt/model/task/invar_fitting.pydeepmd/pt/model/task/sezm_ener.pydeepmd/pt_expt/common.pydeepmd/pt_expt/fitting/ener_fitting.pydeepmd/pt_expt/kernels/graph_fitting.pydeepmd/pt_expt/kernels/triton/sezm/so2_value_path.pydeepmd/pt_expt/model/get_model.pydeepmd/pt_expt/model/make_model.pydeepmd/pt_expt/utils/serialization.pydeepmd/tf/fit/dipole.pydeepmd/tf/fit/dos.pydeepmd/tf/fit/ener.pydeepmd/tf/fit/polar.pydeepmd/utils/argcheck.pydeepmd/utils/compat.pydeepmd/utils/preset_out_bias.pydeepmd/utils/preset_out_bias_tables.jsondeepmd/utils/vacuum_reference.pydoc/model/dpa4.mddoc/model/train-energy.mdexamples/water/dpa4/e0.jsonexamples/water/dpa4/input_e0.jsonexamples/water/dpa4/input_multitask_e0.jsonsource/tests/common/dpmodel/test_atomic_model_global_stat.pysource/tests/common/dpmodel/test_fitting_call_graph.pysource/tests/common/dpmodel/test_fitting_invar_fitting.pysource/tests/common/dpmodel/test_vacuum_ref_model.pysource/tests/common/dpmodel/test_zbl_bridging.pysource/tests/common/test_examples.pysource/tests/common/test_preset_out_bias.pysource/tests/common/test_vacuum_reference.pysource/tests/consistent/fitting/test_ener.pysource/tests/consistent/io/test_io.pysource/tests/infer/gen_model_devi.pysource/tests/pd/model/test_atomic_model_global_stat.pysource/tests/pt/model/test_atomic_model_global_stat.pysource/tests/pt/model/test_descriptor_sezm.pysource/tests/pt/model/test_fitting_vacuum_ref.pysource/tests/pt/model/test_get_model.pysource/tests/pt/model/test_sezm_parallel.pysource/tests/pt/model/test_sezm_vacuum_freeze.pysource/tests/pt/model/test_sezm_vacuum_ref.pysource/tests/pt_expt/fitting/test_dpa4_ener.pysource/tests/pt_expt/model/test_dpa4_vacuum_ref.pysource/tests/pt_expt/model/test_fused_vacuum_ref.pysource/tests/pt_expt/model/test_get_model_bridging.pysource/tests/pt_expt/model/test_get_model_dpa4.py
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Re-reviewed this unchanged head because new substantive review discussion after the previous COMMENT materially changed the review context. There are now concrete correctness blockers on this head:
-
The new Paddle regression test contradicts the shared
normalize_preset_out_biascontract and this PR's own documented behavior. The PR explicitly says element keys outsidetype_mapare ignored, and the normalizer implements that withspec.get(name)overtype_map; howevertest_model_attr_energy_unknown_elementexpects{"C": 3.0}againsttype_map == ["O", "H", "B"]to raiseValueError. That test should instead verify the unknown key is ignored (matching the PT behavior), otherwise the Paddle suite fails when actually exercised. -
In the dpmodel graph vacuum-reference path,
append_vacuum_frames()appends native-spin reference rows only when the caller suppliedspin is not None. When native-spin embedding is enabled butspinis omitted, the newly appended isolated reference atoms therefore receive no neutral ground-state native-spin conditioning at all, even thoughvacuum_conditions()has the required per-typespintable and the PR specifies that reference atoms must use it. The graph path needs to materialize zero/default real-node spin rows as needed and append the reference-spin rows so the vacuum descriptor is evaluated under the intended physical condition.
The already-open inline threads identify these exact locations, so I am not duplicating them. The newly raised SeZM compiled-cache/fold thread also remains unresolved and should be addressed or convincingly ruled out before merge. Exact-head CI is not yet complete (Test Python, Test C++, and one Test CUDA run are still in progress), but the correctness issues above are independently blocking.
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The bug fix in the first commit is real and the new tests prove it: on master, test_preset_bias had the accumulated value hard-coded as its expectation and never called change-by-statistic twice; the new step-3 identity and test_preset_pinned_in_both_modes fail on the pre-fix logic (5 of 14 in the pt file, with the bias doubled to [-20, -27.2]) and pass at this head. The two features on top need more work before this can go in. The inline comments carry the blocking points; the rest is here.
CI on this head is red: 12 checks fail, 33 tests, all of which trace to the points below.
- 16 cases in
source/tests/consistent/fitting/test_ener.pyand the tf cases ofconsistent/model/test_{ener,dos,dpa1}.py(20 in total) fail onassert_equal(data1, data2)with keyatom_ener: tf and pd still serialize it, pt and dpmodel no longer do. See the inline comment oninvar_fitting.py. - 6 cases in
consistent/model/test_frozen.pyfail on keyvacuum_ref: the frozen fixtures were written without it and the live pt/pt_expt fittings now emit it. See the inline comment ongeneral_fitting.py. consistent/model/test_{dipole,polar}.pyandpt_expt/model/test_dos_graph.py[dipole, polar]fail withTypeError: DipoleFitting.call() got an unexpected keyword argument 'vacuum_descriptor'. See the inline comment ongeneral_fitting.pyat the graph call.pd/model/test_get_model.py::test_model_attr_energy_unknown_elementfails withValueError not raised. See the inline comment on that test.- Both cases of the new
source/tests/jax/test_preset_out_bias.pyfail withpreset_out_bias['energy'] does not assign the elements ['O'] that occur in the data: the PR's own new test uses a partial preset, which the PR's own new rule rejects. Either the test or the rule needs to change.
Non-blocking:
- Partial presets now raise, while the TensorFlow fitting keeps the least-squares-around-preset path (), so the backends disagree on the same input, and
deepmd-kit/deepmd/tf/fit/ener.py
Lines 330 to 342 in 8ef385e
doc/model/dprc.mdstill showsatom_ener: [null, null, 0.0, ...]under a heading that claims PyTorch support. The declared breaking change is fine; the divergence and the doc should be stated or fixed. - The only cross-backend consistency case for
atom_ener(ener_fitting_case(atom_ener=[-12345.6, None])) was deleted fromconsistent/fitting/test_ener.pyand novacuum_ref=Truecase replaces it; the consistency framework no longer covers this path at all. - The description says freezing removes the reference atoms from every exported model. The
.pt2path and the pt_expt export fold (,deepmd-kit/deepmd/pt/entrypoints/freeze_pt2.py
Lines 989 to 994 in 8ef385e
), but the TorchScriptdeepmd-kit/deepmd/pt_expt/utils/serialization.py
Lines 1831 to 1836 in 8ef385e
.pthfreeze indeepmd/pt/entrypoints/main.pynever callsfold_vacuum_reference, so a.pthkeeps the live reference computation. That is safe, but the text should say which exports fold. - Fine-tuning after a fold is safe as far as I can see: folding runs only on freshly built export objects, checkpoints are never folded, and the deployment table is not persisted. Worth one sentence in the docs, since the folded fitting has
vacuum_ref=Falseand a reader will wonder. argchecklabelsvacuum_refas pt_expt-only underfitting_enerwhile the dpmodel and jax paths implement it;doc_preset_out_biassays both thatnullleaves a type unassigned and that every element in the data must be assigned; theforward_with_edgesdocstring insezm.pyomitsspin.- The
preset_bias/assigned_biasmachinery in{pt,pd,dpmodel}/utils/stat.pyandutils/out_stat.pyhas no caller left outside TensorFlow; two implementations of preset semantics now coexist.
`atom_ener` stays on the PyTorch and dpmodel fittings as on master, so the serialized fitting of every backend carries the same keys: `atom_ener` as before and `vacuum_ref`, which the TensorFlow and Paddle fittings emit as `false` and reject as `true` on load. A fitting rejects the two options together. The upstream consistency tests and the JAX preset test follow the full-assignment contract of `preset_out_bias`. Review findings: the graph fitting call passes `vacuum_descriptor` only with a table, so dipole and polar fittings take the graph route again; the SeZM compile cache key includes the vacuum state and `fold_vacuum_reference` drops the compiled graphs of both heads; the Paddle unknown-element test mirrors the PyTorch contract; unused locals and an imprecise assertion in the tests are removed.
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All seven threads from the earlier rounds are addressed at this head: atom_ener is back on pt and dpmodel and mutually exclusive with vacuum_ref, @version is 5 in every backend, the graph route only forwards the vacuum descriptor to fittings that accept it, the Paddle test matches, the case-FiLM branch subtracts the zero-descriptor output again, and the DeNS case is handled by rejecting dens checkpoints at freeze time. The core preset fix is correct and consistent across the four copies, and I reconstructed the pre-fix behaviour on the merge-base with the same model and sample: the second change-by-statistic call doubles the preset ([-10, -13.6] to [-20, -27.2]), and test_preset_pinned_in_both_modes fails there and passes here. CI is green apart from the label-gated CUDA jobs.
Two things introduced in this PR still need fixing before merge; both inline, both narrow, neither touches the core fix.
Non-blocking, here since they span several files:
_convert_preset_out_bias_to_arraywas removed fromdeepmd/pt/model/model/__init__.py(merge-base) without a construction-time replacement, so a preset list of the wrong length no longer errors when the model is built, only later at stat time.deepmd-kit/deepmd/pt/model/model/__init__.py
Line 405 in 46fdc3e
conditioning_columns() now requiresdeepmd-kit/deepmd/pt/model/task/fitting.py
Line 760 in afaba87
aparam.numel() == nf * nloc * numb_aparamwithnloctaken from the descriptor, where_forward_commonused to re-derivenlocfromaparam. That is a silent contract tightening with no test on either side of it.data.pop("vacuum_ref")without a default indeepmd/pd/model/task/invar_fitting.py( ) and the four TF fittings, against the file-localdata.pop(k, None)convention; a hand-edited version-5 dict raisesKeyErrorinstead of taking the default.entries = [spec.get(name) for name in type_map]() silently maps an element missing from a bundled table todeepmd-kit/deepmd/utils/preset_out_bias.py
Line 165 in afaba87
None; if that element never occurs in the data its bias ends up zero with no message. A warning naming the element would save someone a long debugging session.- The change that makes a partial
preset_out_bias([null, 0., 1.]) a hard error is documented and deliberate, but it is a user-visible behaviour change and belongs in the release notes.
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Re-reviewed the new head after the upstream merge and independently rechecked the two new unresolved blocking threads against the current source. Both remain applicable.
-
The generic PT
InvarFittingstill accepts and serializesvacuum_ref=True, but the ordinary PTDPAtomicModelpath does not resolve/provide a vacuum descriptor.GeneralFitting.vacuum_input()therefore raises at first forward whenvacuum_refis enabled outside the supported SeZM path. This should either be rejected at construction for unsupported PT atomic models or wired through a real vacuum-reference resolver, with an end-to-end regression test. -
compute_output_stats()still nullsstat_file_path/stat_output_pathwheneverpreset_bias is not None. TensorFlow energy models passpreset_bias={"energy": atom_ener_v}wheneveratom_eneris configured, so such models now bypass the shared output-stat cache entirely, regressing the existing cross-backend stat-file behavior. The cache-suppression condition needs to be narrowed so presets do not disable the whole stats cache.
The exact-head CI is currently complete with no failures, but those checks do not cover these two semantic/runtime cases. I am not duplicating the existing exact-line inline comments because they already identify the correct locations and fix directions.
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Published 58e8c14 for the two blocking findings:
For the non-blocking observations: wrong-length preset lists already fail in Validation: the final related CPU run passed 91 tests and 11 subtests (26 skipped for unavailable backends/device conditions); the preceding GPU validation passed 31 tests and 6 subtests, including AMP and CUDA/Triton training. Normal pre-commit hooks, Ruff and |
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NEEDS HUMAN REVIEW / waiting on CI.
Re-reviewed the new head after the two blocking threads from the previous head were addressed. Both fixes are substantively correct:
- Ordinary PyTorch atomic models now reject
vacuum_ref=Trueat the atomic-model boundary before first forward, whileSeZMAtomicModelexplicitly opts into support. The new regression exercises both normal construction andBaseModel.deserializefor a realse_e2_amodel, with and without a preset, so the former late runtime failure is now converted into an actionable construction-time error without breaking the supported SeZM path. - The statistics-cache condition is narrowed to
model_forward is not Nonein dpmodel, PyTorch, and Paddle. Absolute constrained statistics usingpreset_biastherefore remain cacheable/restorable, preserving the TensorFlowatom_enershared-stat-file behavior, while model-dependent residual shifts still bypass the cache. The cross-backend regression explicitly checks both sides of that contract.
I also re-read the complete current 102-file patch, repository guidance, existing reviews/threads/replies, regular comments, and the current exact-head checks. I did not find a new high-confidence correctness or compatibility blocker in the new delta. I am not approving this head yet because exact-head CI is still incomplete: Build C++, Build C library, and one Test CUDA run are green, while Test Python, Test C++, CodeQL, package/PyPI, and the other Test CUDA run are still in progress. Given the overall 102-file cross-backend scope, a final sign-off should also wait for those checks and focused human review of the vacuum-reference/export/statistics semantics.
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Re-reviewed at 58e8c140. Both of my blocking threads from 09-18 are handled at HEAD, and both fixes carry a regression test that I reconstructed and confirmed fails on pre-fix code (3 failures for the stat fix, 1 for the vacuum_ref rejection) and passes at HEAD. CI is genuinely green: 57 of 59 checks pass, the two skipping ones are the PyPI release jobs, and both CUDA jobs actually ran.
Thread deepmd/pt/model/task/invar_fitting.py:111 — resolved. DPAtomicModel._supports_vacuum_ref is checked in __init__, which both get_standard_model and deserialize (via super().deserialize -> cls(**data)) reach, so every construction path is covered, and the new test exercises both entries with and without a preset. BaseFitting declares vacuum_ref: bool = False, so fittings outside GeneralFitting (e.g. EnergyFittingNetDirect) do not break — I checked that by constructing a direct_force_ener model at HEAD. pt_expt correctly wraps the dpmodel atomic model and is unaffected. doc/model/train-energy.md and the two argcheck supported_backends annotations agree with the guard.
Thread deepmd/dpmodel/utils/stat.py:295 — fixed literally, but it re-opens the converse hole. The narrowed guard does give TensorFlow its cross-backend stat file back, and double-application on the second change-by-statistic pass is still prevented (structurally now: change_out_bias strips preset keys into Step 1 and never passes preset_bias down). But _restore_from_file records no fingerprint of the preset, and all assigned_bias handling lives inside the cache-miss branch, so on a cache hit the preset is not consulted at all. TensorFlow is now the only caller that still passes preset_bias (
deepmd-kit/deepmd/tf/model/ener.py
Lines 250 to 261 in 58e8c14
stat_file_path with it — so changing atom_ener while a stat file exists silently keeps the old bias. This is the exact finding that coderabbit and Copilot raised earlier in this PR and that 687b5107d had closed. It also exists on master, so the reversal restores master behaviour rather than creating something new; what I would like changed is the new test assertion that locks it in (inline below).
One more point is inline on dp_atomic_model.py (the spin branch of append_vacuum_frames). Sub-threshold notes, no obligation to act:
resolve_vacuum_ref(https://github.com/deepmodeling/deepmd-kit/blob/58e8c1404b7eeba4d9e54c5056ddfa0620114c72/deepmd/utils/vacuum_reference.py#L122-L126) switchesvacuum_refoff in place whenpreset_out_biasdoes not assign that output, with no log line, andserialize()then recordsfalse— so atrueininput.jsondisappears from the checkpoint silently. Every other unsupported combination in this PR raises. Alog.warningwould be enough.- A few of the new
pt_expttest files have bare@pytest.mark.parametrizelines without the trailing comment the repository convention asks for:source/tests/pt_expt/fitting/test_dpa4_ener.py:125,source/tests/pt_expt/model/test_dpa4_vacuum_ref.py:273,source/tests/pt_expt/model/test_dpa4c_graph_lower.py:242,518,538,672,727. Worth a pre-commit rule rather than a review round.
Checked and found clean: serialization @version bumps and their deserialize branches across dpmodel/pt/pd/tf (GeneralFitting 5, polar 6, property 7, population 5, tf ener/dos/dipole 5, tf polar 6); atom_ener parity and the xx_zeros subtraction in both dpmodel and pt; remap_preset_out_bias index handling; _convert_preset_out_bias_to_array has no remaining importer anywhere in the tree; array-API rules in the new dpmodel code; numpydoc sections on the new public helpers; both branches of the new vacuum_ref boolean have tests.
Maintainability, for the record rather than as a request. Understanding cost goes up (+7k lines, two new cross-backend concepts: the preset normalisation chain and the train-time/export-time dual form of vacuum_ref), partly offset by the preset expansion logic collapsing from one copy per backend into deepmd/utils/preset_out_bias.py. Change cost: adding a backend now touches one preset call site instead of a per-backend copy, but the vacuum_ref side is net-new at five places (pt capability flag, dpmodel append_vacuum_frames, pt_expt node-axis append, tf/pd deserialize rejections). Verification cost is flat: most new behaviour has its own tests, but the TF atom_ener + existing stat file path, where the inline finding lives, has none.
My reading of the hardest piece, so you can correct me if it is wrong: BaseAtomicModel.change_out_bias now splits outputs into two groups. Outputs named in preset_out_bias skip statistics entirely and are written from preset_out_bias_rows (with keep_unassigned=True under change-by-statistic, zeros under set-by-statistic); only the remaining outputs go through compute_output_stats, and under change-by-statistic what is fitted there is the residual against the current model, not an absolute bias. That is what stops the second change-by-statistic pass from stacking the preset again, and it is also why preset_bias no longer has any caller in dpmodel/pt/pd and only TF still reaches the cache-hit hole.
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Re-reviewed this unchanged head because new substantive inline discussion materially changes the review context. Exact-head CI is now fully green, but I independently verified one high-confidence blocking cache/preset issue already identified on the existing inline threads, so I am not duplicating the same line comment.
compute_output_stats() restores bias_atom_* / std_atom_* from stat_file_path before it constructs or applies assigned_bias. Therefore, on a cache hit, the current preset_bias is never consulted. This is reachable through the TensorFlow atom_ener path, which passes both a live statistics path and preset_bias: if atom_ener changes while an old stat file exists, the stale cached bias silently wins over the current requested preset. The new regression also currently locks in the converse contamination direction by writing a preset-constrained cache and then asserting that a later call with no preset receives that constrained value.
Please make the cache identity/restore semantics account for the current preset (for example, fingerprint/validate it, or re-apply the assigned rows after restore without defeating the intended shared-stat cache), and add a regression with a populated cache plus a changed preset_bias proving the current preset wins. The separate new spin-field thread is not part of this blocking disposition because the standard training data requirements only request per-atom spin for native-spin models; the cache/preset case above is sufficient and directly reachable through the existing TensorFlow path.
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Published e25ea1e for this review round.
Output-statistics caches without preset metadata need one update-mode recomputation, since their previous constraints cannot be inferred safely. Read-only use reports missing or mismatched metadata. This affects the statistics cache, not model/checkpoint serialization. Validation: the broader statistics run passed 116 tests and 5 subtests; the cache/vacuum/selected compiled-graph run passed 91 tests; GPU vacuum, fitting, AMP and training-kernel checks passed 29 tests. The final pre-commit regression rerun passed 62 tests (25 skipped for unavailable backend conditions). Normal pre-commit hooks, Ruff and |
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NEEDS HUMAN REVIEW / waiting on exact-head CI.
Re-reviewed the new head after the cache/preset fix. The prior blocking cache semantics issue is addressed correctly in this delta: output-statistics caches now persist a canonical per-output preset record, and cache reuse requires the current normalized constraints to match it. Adding, changing, or removing a preset therefore causes recomputation in update mode, while read-only mode fails explicitly on missing/mismatched metadata instead of silently returning stale constrained biases. The bias/std/preset group is also replaced transactionally, and the added regressions cover changed/removed presets, unchanged cache reuse, read-only legacy/mismatch cases, output scoping, and interrupted writes. This closes the TensorFlow atom_ener + existing stat-file hole from the previous head.
The same commit also fixes the separate vacuum-frame spin edge case by only trying to append conditions["spin"] when native-spin embedding is actually enabled, avoiding a KeyError when sampled data happens to carry a spin field for a non-native-spin model. I did not find a new high-confidence correctness or compatibility blocker in this one-commit delta.
I am not approving this head yet because exact-head validation is still incomplete: Build C++, Build C library, CodeQL, PyPI/package, and one Test CUDA run are green, while Test Python, Test C++, and the other Test CUDA run are still in progress. Given the overall 105-file cross-backend scope, final sign-off should wait for those checks and a focused human pass over the statistics/vacuum-reference/export semantics.
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NEEDS HUMAN REVIEW / waiting on exact-head CI.
Re-reviewed the new head after the branch merged current master. The only delta from the previously reviewed e25ea1e2f4562a671c3088717f906ed268ac3e9c is upstream commit 5d5dee0fa48fb3bf3b3ec5d2c1a53331d9df0136 (fix(logging): deduplicate distributed training logs (#6032)) plus the merge commit itself; the preset-cache validation, vacuum_ref, serialization/export, and unused-spin fixes reviewed on the prior head are unchanged. I re-read the complete current PR patch and the existing review threads/comments and found no new high-confidence blocker introduced by this merge.
I am not approving this head yet because exact-head validation is still incomplete: Test Python, Build C++, Build C library, CodeQL, and PyPI/package are green, while Test C++ and one Test CUDA run are still in progress (an earlier CUDA run was cancelled and superseded by the active run). Given the overall 105-file, cross-backend scope, final sign-off should wait for those checks and focused human review of the statistics/vacuum-reference/export semantics.
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NEEDS HUMAN REVIEW.
The exact-head CI context has now materially changed since my previous review: all 55 check runs on 10a3b47f46a41ea7c6cb2ca8eb484d8154b4346e are complete, with no failing, queued, in-progress, cancelled, or action-required checks. The previously pending Test C++ and Test CUDA coverage has finished successfully; commit-status contexts (CodeRabbit, pre-commit.ci, and Read the Docs) are also green.
There is no new code delta since the prior substantive review, and I found no new high-confidence correctness or compatibility blocker in the current head. I am still not issuing APPROVE because this remains a 105-file, +7.3k/-1.6k cross-backend change spanning statistics semantics, isolated-atom physics, fitting serialization, graph lowering, compiled training, and export/freeze behavior. That scope still exceeds what this monitoring pass can responsibly certify as a complete final sign-off; a focused human review of those cross-cutting semantics remains warranted.
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Re-reviewed at 10a3b47. All three blocking points from my previous review are resolved.
-
source/tests/consistent/utils/test_stat.py— the final assertion no longer freezes the cache hole into a contract. The test now loops over removing, adding and changing a constraint (including two spellings of the same value and an explicitly unconstrained preset) and asserts, for each, that the cached call equals the freshly computed one. The "preset set + populated stat file" case is covered. -
Preset fingerprint.
deepmd/utils/stat_file.pynow writes apreset_bias_<key>record beside each output and reuses the cache only when it matches, including an explicit empty record for an unconstrained output. It is used by all three statistics implementations (deepmd/dpmodel/utils/stat.py:275,deepmd/pt/utils/stat.py:563,deepmd/pd/utils/stat.py:366), and the call site I originally flagged —deepmd/tf/model/ener.py:251-261, which passespreset_biasandstat_file_pathtogether — is now protected. Normalization to a(ntypes, -1)float64 array makes equivalent spellings compare equal. I reproduced the pre-fix failures: with the 58e8c14 sources and the head tests,source/tests/common/test_stat_file.pygives 7 failed / 21 passed,source/tests/pt/test_stat_file_mode.py4 failed / 14 passed, and the cache test insource/tests/consistent/utils/test_stat.py3 failed; all pass at head. -
append_vacuum_frames. The guard is nowself.add_spin_ebd and spin is not None, which is the same flaginit_vacuum_conditionsuses to populateconditions["spin"], so the bareKeyErroris gone. Reproduced: with the one-line revert,test_non_spin_model_ignores_unused_spinfails withKeyError: 'spin'atdp_atomic_model.py:518; it passes at head.
Two points that remain, neither blocking, left here as notes for a follow-up:
-
GeneralFitting.call_graphforwardsvacuum_descriptor=toself.__call__for every fitting, butDipoleFitting.callandPolarFitting.calldo not declare that parameter.DPDipoleAtomicModelrejects an assigned preset, so dipole is covered;DPPolarAtomicModelhas no such guard, so a polarizability model withvacuum_ref: trueand an assignedpolarizabilitypreset reachesPolarFitting.call(vacuum_descriptor=...)and raisesTypeError. Either rejectvacuum_refon the tensor fittings or guard the keyword the way the dense route does. -
vacuum_refis a plainArgumentinfitting_ener(), sovacuum_ref: truein a TensorFlow or Paddleinput.jsonpasses argcheck and is then swallowed by**kwargsindeepmd/tf/fit/ener.py. TheNotImplementedErrorguards you added fire only when deserializing an already-serialized model, so a user training on those backends silently gets a model without the reference. A construction-time rejection would close this.
The breaking changes are declared in the PR description, which is what I wanted to see. CI is green, including "Test Python on CUDA" and "Test C++ on CUDA".
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Re-reviewed the unchanged head because the new approval/discussion materially changed the review context. I independently checked the two new notes against the current source.
One is a real blocking configuration/runtime mismatch: the newly added generic fitting_ener.vacuum_ref argument is accepted by normal input validation on every backend; supported_backends(...) only changes generated documentation. TensorFlow's EnerFitting.__init__ accepts **kwargs and never consumes vacuum_ref, while Paddle's InvarFitting forwards it into a GeneralFitting constructor that likewise ignores extra kwargs. Therefore an explicit vacuum_ref: true in a TF/Paddle training input is silently dropped and trains a different model than requested, even though the new version-5 deserializers explicitly raise NotImplementedError for exactly that state. I left the concrete schema issue inline. Please reject this option at TF/Paddle construction/config processing (or make the schema backend-specific) and cover a direct input-config construction path.
I also checked the new polarizability note and am not treating it as a blocker: on this head PolarFitting.var_name == "polar", while the atomic output/preset key is polarizability; resolve_vacuum_ref() therefore switches the reference off for a valid polar preset, and an invalid polar preset is rejected earlier by check_preset_out_bias. So the reported PolarFitting.call(vacuum_descriptor=...) TypeError is not reachable through the supported configuration path as currently written.
Exact-head CI remains green (Test Python, Test C++, the superseding Test CUDA run, Build C++, Build C library, CodeQL, and package/PyPI all succeeded). The request for changes is solely for the reachable silent-ignore path above.
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Preserve the upstream frame mapping incorporated by deepmodeling#6022, retain the regression coverage, and remove the stale uniform-frame docstring restriction. Validated with 30 focused ragged cases and 142 total related CPU tests, including dynamic export, native operators, and TensorFlow inference. AI-assisted conflict resolution and verification.
preset_out_biasdocuments that the bias of an assigned atom type is set to the preset value. Inchange-by-statisticmode (fine-tuning,dp change-bias,change_bias_after_training) the preset was fed to the least-squares fit as if it were a shift, so an assigned type ended atpretrained_bias + preset, the residual fitted for the other types subtractednatoms * presetinstead ofnatoms * (preset - pretrained_bias), and every further call accumulated the preset again. The four copies of the mode branch (the pt, pd and dpmodel atomic models and the pt model wrapper) shared the defect.This PR makes the preset the bias of an assigned output in every mode, adds bundled tables of isolated-atom energies so that a table is named instead of typed, and adds the fitting option
vacuum_ref, which references the network output of every atom to the output the same network gives an isolated atom of its type. With both options the energy of an atom without neighbors is exactly its preset bias, whatever the network parameters are, so the dissociation limit of an energy model is pinned to the isolated-atom energies of the reference calculation.Changes
preset_out_biasset-by-statisticandchange-by-statistic, a type absent from the data gets a zero bias at initialization and keeps its stored bias at fine-tuning, and no statistics are computed for, read from or written to the cache for such an output; its output std keeps its stored value. The types excluded byatom_exclude_types(the virtual types of a spin model, for example) need no preset, and elements outside thetype_mapare ignored. Outputs without a preset are fitted as before.type_map(nullleaves a type unassigned; the form for tensor outputs). A table is resolved once when the input is processed, in single-task configurations, in every branch of a multi-task configuration and for a preset written next tomodel_dict, and its values are stored in the model, so a trained model depends neither on the file nor on the bundled data.deepmd/utils/preset_out_bias_tables.json, one entry per name with itssourceand itsvalueskeyed by element symbol, so further tables can be added without code changes. The entries are the isolated-atom reference energies of the UMA training tasks published with fairchem (configs/uma/training_release/element_refs/iso_atom_elem_refs.yaml, MIT license), in eV:omat24(89 elements),omol25(83, neutral atoms),omc25(94),odac25(94) andoc20(97). A bundled name takes precedence over a file of the same name.deepmd.utils.preset_out_bias(normalization, table resolution, remapping, validation, per-type rows), shared by the pt, pd and dpmodel atomic models; the per-backendchange_out_biasbodies reduce to the shared helper plus the fit of the remaining outputs, and_store_out_statwrites bias rows without touching the std.change_type_mapremaps the preset together with the stored bias; an output the model does not produce, a preset on a fitting whose statistics do not distinguish atom types, and an assigned preset on a dipole model are rejected.vacuum_ref(fitting option, default off)E_i = bias(t_i) + f(x_i; c_i) - f(x_vac(t_i); c_i), wherex_vac(t)is the descriptor of an isolated atom of typetcomputed by the same descriptor with the current parameters andc_iis the atom's own conditioning (frame parameters, atomic parameters, case embedding). Without frame or atomic parameters the reference is one row per type; otherwise the reference rows follow the atoms. The reference is evaluated at every training step, so it follows the parameters; forces and virials are unchanged.deepmd.utils.vacuum_reference, from the electron-configuration table); charged, excited or differently magnetized isolated atoms keep the deviation the network learns. The condition tables are built once per type map at the atomic model (pt: buffers ofSeZMAtomicModel; dpmodel: numpy attributes ofDPAtomicModel, non-persistent buffers on pt_expt).append_isolated_frames); on the PyTorch backend the SeZM edge route appends the reference nodes inforward_with_edges. The dense route evaluates single-atom frames.vacuum_table, kept out of checkpoints and serialization) from which the fitting evaluates its references. The pt_expt export folds inside_trace_and_export_impl, sodp freeze,.pte/.pt2conversion andchange-biason frozen artifacts all resolve the reference on the object they export, and the archive keeps the live model. A SeZM checkpoint indensmode is rejected by the freeze: the DeNS head serves training alone and is not exported.graph_fitting(fit, descriptor, atype, atom_bias)); a fitting whose reference varies between atoms is served by the autograd route.vacuum_refis available for DPA4/SeZM on the PyTorch backend and for the graph-native models on pt_expt. The TensorFlow and Paddle fittings reject a serializedvacuum_ref: truemodel withNotImplementedErrorinstead of ignoring the option.atom_eneris unchanged on every backend; a fitting rejects the two options together.doc/model/train-energy.md, the argcheck entriespreset_out_biasandvacuum_ref, and the examplesexamples/water/dpa4/input_e0.json(single-task) andexamples/water/dpa4/input_multitask_e0.json(per-branch presets).Breaking changes
stat_file_mode: update. This cache validation change does not alter model or checkpoint serialization.set-by-statistic, the types of an assigned output that do not occur in the data get a zero bias instead of a fitted one, and the output std of such an output keeps its initial value; preset outputs never read or write the output-statistics cache.DescrptSeZM.forward_with_edgestakesvacuum_conditions: dict | None(the reference-atom inputs) and returns(descriptor, latent, vacuum);deepmd.pt_expt.kernels.graph_fitting.graph_fittingtakes the per-type bias explicitly.vacuum_ref, and its@versionis bumped (general fitting 4 to 5, polarizability 5 to 6, property 6 to 7, population 4 to 5); older dictionaries without the key load withvacuum_ref: false, and the TensorFlow and Paddle fittings emitfalseand rejecttruebehind the version check.compute_stats_do_not_distinguish_typesloses its unusedassigned_biasparameter.Validation
source/tests/common/test_preset_out_bias.py(forms, bundled tables, resolution in single-task and multi-task configurations, rows, excluded and unknown types), the pt, dpmodel and pdtest_atomic_model_global_stat.py(set→change→changewith an assigned output, the rejection of an unassigned observed element, the output std under a full preset, a spin model with a dict preset, presets that need no data,change_type_map), the jax end-to-end test and the argcheck example inputs.E_i = bias + f(x_i) - f(x_vac)and "an isolated atom gives exactly its bias", in float64 to 1e-10:source/tests/pt/model/test_fitting_vacuum_ref.py(the full(fparam, aparam, case_embd, aparam_as_mask) × mixed_typesproduct,torch.jit.script,default_fparam, fold and stored table, state-dict round trip,change_type_map),source/tests/common/dpmodel/test_fitting_invar_fitting.py,test_fitting_call_graph.py,test_vacuum_ref_model.py(se_e2_a dense and DPA1 graph routes),source/tests/pt_expt/fitting/test_dpa4_ener.py.source/tests/tf/test_fitting_vacuum_ref.pychecks that the TensorFlow fittings reject a serializedvacuum_refand load a dictionary of the previous version; the Paddle, dpmodel and PyTorch serialization tests do the same.source/tests/pt/model/test_sezm_vacuum_ref.py(plain, charge/spin conditioning with native spin, DeNS, forces unchanged, fold, fused training kernels under AMP),source/tests/pt/model/test_sezm_parallel.py(reference rows under the LAMMPS-style communication path),source/tests/pt_expt/model/test_dpa4_vacuum_ref.py(graph route with charge/spin and native spin, a padded node axis, dense and graph routes agree to 1e-12,.pt2and.ptefreezes with and without frame parameters, the archive keeps the live model),source/tests/pt_expt/model/test_fused_vacuum_ref.py(fused energy/force route of DPA1 and compressed DPA4C equals autograd exactly, autograd fallback with frame parameters),source/tests/pt/model/test_sezm_vacuum_freeze.py(.pt2freezes on CPU and CUDA targets, with and without frame parameters, adenscheckpoint is rejected).dp --pt freezegives an isolated O and H exactly their preset energies. Paddle is not installed here and the TensorFlow operators are not built; the pd and tf tests mirror pt and are verified by CI.Summary by CodeRabbit
New Features
vacuum_ref) energy references for supported energy models, including folding references during export.Bug Fixes