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feat(pt_expt): latent-charge long-range module (LES/SOG) for DPA4C - #6031
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Check name Status Explanation Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly and concisely describes the main change: adding a latent-charge long-range LES/SOG module for DPA4C in the pt_expt backend.
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Inline comments:
In `@AGENTS.md`:
- Line 168: The DPA4C-LR documentation must match the supported input and kernel
contract. In AGENTS.md at lines 168-168, update the DPA4C-LR entry to state that
les is the default kernel, sog is also supported, and non-periodic input accepts
box=None or an all-zero box. In doc/model/dpa4c_lr.md at lines 36-38, document
that box=None and all-zero boxes are supported while nonzero periodic cells are
rejected.
In `@deepmd/pt_expt/fitting/dpa4c_lr.py`:
- Around line 50-52: Update DPA4CLRFitting’s parameter registration to normalize
list-valued trainable settings before assigning requires_grad: when trainable is
a list, derive the flag from its values so an all-False list freezes the
parameters; preserve scalar boolean behavior for non-list inputs.
In `@deepmd/pt_expt/infer/deep_eval.py`:
- Around line 2426-2432: Normalize box_input before constructing evaluation
graphs: convert an all-zero box to None, while preserving nonzero boxes and the
existing DPA4C-LR rejection for periodic inputs. Apply the normalized value to
both the short-range _build_eval_graph path and the long-range graph
construction so fused-cell neighbor search never receives a singular zero
lattice.
In `@deepmd/pt_expt/model/dpa4c_lr_model.py`:
- Around line 529-531: Update both long-range graph construction sites in
deepmd/pt_expt/model/dpa4c_lr_model.py at lines 529-531 and 609-611 to pass
"dense" instead of method to build_ragged_neighbor_graph, ensuring both
long-range graphs use the dense builder regardless of the resolved device
method.
In `@deepmd/pt_expt/model/get_model.py`:
- Around line 101-105: In the model-loading flow around the fitting_net type
check, assign the default "dpa4c_lr" value into data["fitting_net"]["type"]
before validating it, mirroring get_sezm_model. Preserve the existing ValueError
for explicitly different fitting types and support configurations where
fitting_net.type is omitted.
In `@deepmd/pt_expt/train/training.py`:
- Around line 1651-1657: Update both long-range graph construction calls in the
shown training flow to pass the literal method `"dense"` instead of reusing
`method`, covering both the ragged and rectangular branches. Preserve all other
arguments and behavior.
In `@deepmd/utils/argcheck.py`:
- Line 4000: Remove the *_bridging_method_args() entry from the dpa4c_lr schema
so validation no longer advertises the unsupported bridging_method option; leave
bridging support unchanged for model types accepted by expand_bridging_method.
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Inline comments:
In `@deepmd/dpmodel/model/make_model.py`:
- Around line 137-142: The traced/exported model path must preserve non-periodic
semantics for zero boxes instead of leaving a symbolic all-zero box active.
Update model_call_from_call_lower and its JAX HLO integration to specialize
exports with box=None, or explicitly reject dynamic all-zero boxes when topology
cannot change at runtime; ensure DefaultNeighborList.build uses the non-periodic
path.
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I reviewed the current head and agree that there are multiple merge-blocking correctness/stability issues already captured in the existing inline threads, so I am not duplicating those comments. In particular, the current code can (1) leave the newly promoted LR parameters trainable when a list-valued trainable setting is entirely false, (2) fail a valid config that omits fitting_net.type because the downstream factory falls back to ener, (3) pass the artificial 1e6 long-range cutoff into non-dense CUDA/spatial graph builders and trigger excessive allocation/runtime failure, and (4) mishandle all-zero non-periodic boxes in inference/traced paths before they are normalized to None. The bridging_method schema also advertises an option that the model-construction path does not support. These are concrete behavior/API issues rather than style concerns and should be fixed before merge.
The exact-head Build C++, Build C library, Test CUDA, and PyPI/package workflows are green; Test C++, Test Python, and CodeQL are still running. Please also address the active CodeQL signature findings before requesting another review.
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Re-reviewed the new head. The mixed-types-only neighbor-count cap is a sensible fix for the type-distinguished se_e2_a regression, but it does not address the DPA4C-LR blockers already identified on the preceding head.
The current code still has multiple high-confidence blocking issues: an all-false list-valued trainable setting leaves the newly promoted LR parameters trainable; omitting fitting_net.type still falls through to the generic ener factory instead of constructing dpa4c_lr; training/runtime LR graph construction still passes the 1e6 all-pairs sentinel through auto-selected neighbor builders such as NV/cell/vesin instead of forcing the dense builder; all-zero-box normalization remains too late in evaluation/export paths; and bridging_method is still advertised by the schema although model construction does not support it. These points already have precise unresolved inline threads, so I am not duplicating them.
Exact-head Test Python and Test C++ are still running; Test CUDA, CodeQL, Build C++, Build C library, and package/PyPI have passed.
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Re-reviewed the new head c59210bb440b753bcad470bdb23af4f8ddb348d0. The new delta from 9fa2464c7c711b24b8cb1f54cbe79e526415050b narrows neighbor-count capping to effectively-unbounded sentinel capacities (and applies pre-commit fixes); that change addresses the spin/type-distinguished regression without introducing a new high-confidence blocker in the touched neighbor-list paths.
The previously identified merge blockers are still present on this exact head, and their inline threads remain current/non-outdated, so I am not duplicating comments: (1) list-valued trainable is still converted with bool(self.trainable), so an all-False list leaves the promoted LR parameters trainable; (2) omitting fitting_net.type still falls through to the generic fitting factory rather than defaulting the stored type to dpa4c_lr; (3) long-range graph construction still routes the 1e6 all-pairs cutoff through the selected runtime method instead of forcing the dense builder; (4) all-zero-box normalization is still incomplete in evaluation/traced-export paths, with symbolic zero boxes explicitly left non-None; and (5) bridging_method is still advertised by the schema although construction does not support it. The AGENTS/model documentation mismatch is also still present but is not itself the reason for blocking.
Exact-head Build C++, Build C library, Test CUDA, CodeQL, and PyPI/package have passed. Test Python and Test C++ are still running; CI completion would not remove the correctness blockers above.
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Re-reviewed the new head. The only delta from the previously reviewed c59210bb440b753bcad470bdb23af4f8ddb348d0 is a test-side compatibility update in test_dpa2_graph_lower.py: the three compiled-training calls now pass the empty long-range edge placeholders required by the current compiled graph-lower signature. That change is internally consistent with the runtime _CompiledModel convention and does not introduce a new blocker.
The existing merge blockers are unchanged on this exact head, and each already has a precise current/non-outdated inline thread, so I am not duplicating comments: (1) list-valued trainable is still converted with bool(self.trainable), so an all-False list leaves the promoted LR parameters trainable; (2) omitting fitting_net.type still validates as dpa4c_lr but is not stored, allowing the downstream factory to fall back to ordinary ener; (3) long-range all-pairs construction still routes the 1e6 cutoff through auto-selected NV/cell/vesin paths instead of forcing the dense builder; (4) zero-box normalization remains incomplete in inference/traced-export paths, so symbolic/all-zero non-periodic boxes can still take periodic neighbor-list logic; and (5) the schema still advertises bridging_method although the construction path does not support it.
For the exact reviewed head, Test CUDA has passed. Test Python, Test C++, Build C++, Build C library, CodeQL, and package/PyPI are still in progress; CI completion would not remove the correctness/API blockers above.
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The graph LR correction does not preserve the atomic output mask when atom_exclude_types is combined with use_charge_constraint=True.
forward_common_atomic_graph has already zeroed latent charges on excluded atoms, but _constrain_charges_flat divides by the total node count and subtracts the correction from every node, including excluded ones. For charges [q, q, 0] and a zero target, it produces [q/3, q/3, -2q/3]: the excluded atom becomes charged again. The dense/legacy implementation correctly uses the output mask. The graph LR-energy distribution also uses n_node and writes an energy contribution onto excluded rows after their outputs have been masked.
I reproduced this on this head using a valid normalized LES configuration with atom_exclude_types=[1], use_charge_constraint=True, and rcut=3. Two active atoms were fixed at x=0 and x=1 Angstrom; a type-1 excluded atom was moved from x=10 to x=20 Angstrom. The latent-charge network was made constant by zeroing its network parameters and setting bias_atom_q=1, isolating the constraint behavior. Graph energies changed from -0.3478181411 to -0.0248750740 eV, and the excluded atom received x-forces of -0.0665229834 and -0.0156890131 eV/Angstrom. The same model on the legacy path retained energy -0.9736995243 eV and zero force on that atom in both cases. Graph output also reported a nonzero atom_energy on the row whose mask is zero.
Please carry atomic_ret['mask'] through the graph charge constraint, count only active atoms when distributing the correction, keep excluded charges zero, and distribute LR atomic energy only over active rows. Add a regression comparing graph and legacy results with exclusions enabled, including an excluded atom outside the short-range cutoff. The existing 20 non-compile model tests pass but do not cover this combination.
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Residual output-bias fitting must use the complete DPA4C-LR prediction, including the long-range energy. This model inherits change_out_bias from the shared model factory, which delegates to self.atomic_model.change_out_bias. Its prediction wrapper only calls the atomic model, so it evaluates the short-range descriptor/fitting output without the LR correction implemented in DPA4CLREnergyModel.
Consequently, the fitted residual is E_label - E_SR rather than E_label - (E_SR + E_LR). Fine-tuning's bias adjustment, standalone change-bias, and the post-training bias adjustment can absorb the existing LR contribution into the bias and then add it again at inference.
I reproduced this using the real pt_expt model_change_out_bias helper, with no presets or exclusions and no optimizer update. For two same-type atoms, I used the model's own total energy (10.315524163890055 eV) as the label, so the correct bias change is zero. The statistics predictor returned only -0.9736995242840625 eV, the bias increased by 5.644611844087059 eV per atom, and the resulting total energy became 21.604747852064172 eV.
Please provide a complete-model atomic-energy predictor for residual bias fitting and route this model through it. Add a self-label regression: using the model's own predictions as labels must leave its bias and predictions unchanged. Keep direct label statistics used for initialization separate from residual fitting.
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Non-blocking input-validation consistency note: the eager DPA4C-LR entry rejects a nonzero periodic box, but the compiled wrapper bypasses that check. It builds short-range edges using box_flat and long-range edges with box=None. I verified this through the actual _CompiledModel(..., compile_eval=True) path: eager rejected the box, whereas compiled execution returned an energy and changed it when one atom was translated by a full lattice vector.
Normal non-periodic training is unaffected, and from-scratch training on periodic data is rejected during statistics initialization. Source inspection suggests that periodic validation data with compiled validation, or checkpoint initialization/restart paths that skip statistics, can miss that early check; I have not reproduced those cases through the full CLI. Please consider sharing the non-periodic input check with the compiled entry before constructing either graph.
The new test_compiled_lower_matches_eager currently constructs _CompiledModel with the default compile_eval=False and then calls .eval(), so it delegates to eager rather than testing a compiled lower. Please enable compiled evaluation in that test and assert that the compiled cache is populated.
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An accepted linear_ener configuration silently drops the long-range contribution of a DPA4C-LR child. A standard child with a dpa4c descriptor and dpa4c_lr fitting passes argcheck.normalize() and model construction. However, the linear composition factory builds it directly as an ordinary atomic model. The fitting returns short-range energy and latent charges, while the LR correction exists only in the outer DPA4CLREnergyModel, which this composition never executes.
I compared a standalone DPA4C-LR model with a linear_ener model containing just that descriptor/fitting configuration as its sole child and weights: "sum". All 55 atomic-model state_dict entries were exactly identical. For the same two-atom non-periodic configuration, the standalone energy was 10.315524163890055 eV and the first atom's x-force was -5.6827474661292845 eV/Angstrom; the composition returned -0.9736995242840625 eV and +0.04549724560732242 eV/Angstrom, respectively. Those are the short-range-only results.
Please either preserve the complete SR+LR contribution when this fitting is used in a composition, or explicitly reject the unsupported composition at configuration/construction time. Merely adding the model name to the family-child dispatch and returning .atomic_model would still lose the outer LR calculation. Add a regression that a supported single-child, unit-weight composition reproduces the standalone energy and forces, or that an unsupported configuration fails clearly instead of evaluating a different physical model.
…e correction Add model type `dpa4c_lr` (pt_expt backend only): the compact DPA4C descriptor plus a non-periodic long-range term evaluated from per-atom latent charges produced by the `dpa4c_lr` fitting. Two kernels are available: LES (erf(alpha*r)/r, default) and a trainable sum-of-Gaussians (SOG) with multi-channel latent charges. The long-range all-pairs graph is built outside the compiled region and passed into the graph lower, so `torch.compile` sees no data-dependent neighbor construction. Only non-periodic systems (box=None) are supported. Periodic misuse is rejected with a clear error at every entry point: training statistics (fail-fast before descriptor stats), eager/ragged forward, and DeepPot evaluation. `.pt2`/`.pte` freeze is not yet implemented. Includes dp-test (DeepPot.eval) support, user documentation (doc/model/dpa4c_lr.md), and unit tests covering LES/SOG force-virial finite differences, compile parity, serialization, checkpoint inference, and periodic-input rejection.
for more information, see https://pre-commit.ci
The nsel/nnei cap introduced with DPA4C-LR used Python's min() on the frame atom count, which raises InconclusiveDimensionOperation under jax2tf export where dimensions are symbolic. Only apply the cap when nall is a concrete int, and make the all-zero-box check tracer-safe.
Type-distinguished descriptors (e.g. se_e2_a) require exactly sum(sel) nlist columns: their davg/dstd statistics have sum(sel) x 4 shape and nlist_distinguish_types depends on the column count. The unconditional nnei = min(nnei, nall) cap broke them whenever nall < sum(sel), raising "cannot reshape array of size ..." in env_mat._apply_stat (source/tests/consistent/io/test_io.py::TestDeepPot::test_deep_eval). Carry-all (mixed_types) descriptors such as DPA4C keep the cap, which still prevents absurd padding for their effectively-unbounded sentinel sel.
The nnei = min(nnei, nall) cap introduced with DPA4C-LR broke any model whose sum(sel) exceeds the frame atom count (e.g. spin models, where virtual atoms double nloc): the neighbor list was capped to nall columns while descriptor statistics and serialized lowers still require exactly sum(sel) columns, raising "shape ... is invalid for input of size ..." in env-mat application (source/tests/pt_expt/infer/test_deep_eval_spin.py .pt2/.pte inference). Only cap sentinel (effectively-unbounded) capacities such as DPA4C's; normal capacities must keep exactly sum(sel) columns.
for more information, see https://pre-commit.ci
…r in DPA2 tests The merge_dpa4C branch made lr_edge_index/lr_edge_vec/lr_edge_mask unconditional positional inputs of the _trace_and_compile_graph output. Update the three compiled-training tests to feed the empty placeholders (same convention as _CompiledModel at run time), fixing: TypeError: forward() missing 3 required positional arguments: 'arg14_1', 'arg15_1', and 'arg16_1'
- Freeze the promoted LES/SOG parameters when fitting_net.trainable is false: GeneralFitting normalizes the flag to a per-layer list, so a plain bool() on it was always True. Normalize list-valued trainable with all() before setting requires_grad, and add regression tests that compare parameter values across a real Adam step for LES and SOG. - Build every long-range all-pairs graph with the dense builder, decoupled from the short-range search policy: the hardcoded 1e6 sentinel cutoff explodes the NV capacity estimate (~1e18 neighbors, ~12.6 EB allocation) on CUDA with nvalchemiops installed. - Normalize an all-zero box to None before building eval graphs so the native periodic search never sees a singular lattice. - Default fitting_net.type to dpa4c_lr in get_dpa4c_lr_model so a config omitting it no longer builds an ener fitting. - Match DPA4CLRFitting.call / DPA4CLREnergyModel.call_common to their base-class signatures (vacuum_descriptor passthrough; explicit call_common parameters). - Drop bridging_method from the dpa4c_lr schema: expand_bridging_method only accepts standard/dpa4/sezm, so documenting it here passed validation and then failed at construction. - Document that SOG is supported and that box=None or an all-zero box is accepted, and note that exported graphs bake the periodic topology at trace time. - Remove unused local variables flagged by CodeQL.
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Build the long-range all-pairs graph directly from the per-frame node counts instead of running a cutoff-based neighbor search with a 1e6 sentinel radius: - New build_all_pairs_unordered_graph emits each intra-frame pair once (i < j) via vectorized triangular indexing -- no distance search, no padding to the batch max width, and half the directed pair storage (sum_f n_f(n_f-1)/2). - Drop the 0.5 double-counting factor from the graph-path LR energy. - New _unordered_pair_force_virial scatters each pair force to both endpoints with opposite signs and splits the pair virial evenly between them; reusing the directed full-to-src attribution would make the per-atom virial depend on the arbitrary endpoint order. - One shared builder now serves the eager rectangular/ragged paths, the compiled training/evaluation wrapper, and DeepEval, independent of the short-range builder policy. Numerics are unchanged: the unordered assembly reproduces the directed edge_force_virial semantics to machine precision, and the graph path matches the legacy dense kernels (the old directed implementation) to ~1e-15 in float64, so existing checkpoints load and run identically. Benchmark (CPU): skewed batch (2000+5x100 atoms) builds 5.2x faster; a single 20k-atom frame halves edge storage (400M -> 200M edges) and builds in 34s instead of 77s. Tests: brute-force pair enumeration, directed-assembly equivalence, legacy dense reference, atom-index permutation invariance, zero/single atom frames, force-loss second-order gradients, and pair-exclusion path consistency.
for more information, see https://pre-commit.ci
Fixes the pre-commit.ci pylint failure (E8002 no-explicit-dtype).
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The unordered-pair rewrite has one new inference regression: negative-type padding slots now acquire long-range edges. This is distinct from the previously reported charge-constraint masking issue: the old DeepEval LR builder excluded these virtual endpoints, while the new branch no longer does. Details and a source-derived numerical counterexample are inline.
Review scope: the delta from 063d2cc to 69e8b79. The reproduction below evaluates the LES arithmetic from the source; it is not a full PyTorch/DeepPot runtime test.
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Re-reviewed exact head b3b3f928497e6a8d70c1ad3062b7980683ca64ca, including the full PR discussion and the current source. The head is unchanged from the numerical checks described below.
Requesting changes for the remaining correctness issues. This consolidates the still-valid earlier findings with the newly verified Hessian and default-parameter issues, so I am not opening duplicate inline threads for each existing report.
Purpose and implementation
The feature adds a non-periodic SR+LR energy model on top of DPA4C. A separate GLU fitting branch predicts latent charges, and LES or multi-channel SOG evaluates their all-pairs interaction. The shared ragged-native builder constructs unordered intra-frame pairs outside the compiled region. The force includes both explicit pair geometry and the response of the predicted charges to their local environments.
The unordered-pair rewrite is sound in the cases checked: removing the directed-pair factor of 0.5, scattering forces to both endpoints, and splitting each pair virial equally between its endpoints reproduce the directed reference. The issues below concern the surrounding model contracts and integration paths.
Required corrections
1. [P1] Residual output-bias adjustment still double-counts the LR energy
Locations: inherited change_out_bias, atomic-only statistics predictor, and outer-model LR energy correction.
The model inherits a bias-adjustment implementation that delegates to self.atomic_model.change_out_bias. Its predictor evaluates only the atomic model, whereas this PR adds LR in the outer DPA4CLREnergyModel. Residual fitting therefore uses E_label - E_SR instead of E_label - (E_SR + E_LR).
Reproduction on this head, using the real pt_expt model_change_out_bias helper:
- Start from the test file's float64 LES config, disable the charge constraint, zero the latent-charge network parameters, and set
bias_atom_q=1. - Use two type-0 atoms at x=0 and x=1 Angstrom, no box, presets, or exclusions.
- Use the model's own total energy as the energy label and perform only bias adjustment, with no optimizer update.
- Original energy: 10.315524163890055 eV.
- Statistics predictor: -0.9736995242840624 eV.
- Bias increase: 5.644611844087059 eV per participating atom.
- Resulting total energy: 21.604747852064172 eV.
Please provide a complete-model atomic-energy predictor for residual fitting and route DPA4C-LR through it. Add the self-label invariant: using the current model's predictions as labels must leave its bias and predictions unchanged. Keep direct label statistics for initialization separate from residual fitting. This affects fine-tuning/change-bias and post-training adjustment paths that use the shared helper.
2. [P1] Graph charge correction and LR energy distribution ignore atom exclusions
Locations: constraint call, constraint implementation, and atomic energy distribution.
The atomic model has already zeroed excluded outputs and supplied atomic_ret["mask"], but the graph constraint divides by the total n_node and shifts every node. For pre-constraint charges [q, q, 0] and target zero, it produces [q/3, q/3, -2q/3], charging the excluded atom again. LR atomic energy is also spread onto excluded rows, even with the constraint disabled.
Deterministic LES reproduction:
- Use the test config with
atom_exclude_types=[1], zero all model parameters, then setles_alpha=1and per-typebias_atom_q=[[1],[-1]]. - Atom types are
[0,0,1]; coordinates are x=[0,4,8]Angstrom. - With the charge constraint enabled, graph energy is -0.7442488416 eV, while legacy energy is 0. The excluded atom receives x-force -0.2325776674 eV/Angstrom.
- With the constraint disabled, total energy and forces agree, but the graph assigns 1.1163732538 eV of atomic energy to the excluded atom; legacy assigns zero.
- The same two defects reproduce with SOG.
Please use the atomic output mask for participating counts, charge sums/shifts, the final charge mask, and LR energy distribution. Add graph/legacy parity tests with atom exclusions, including excluded atoms outside the short-range cutoff. Filtering LR edges alone does not fix the incorrect charge normalization or atomic-energy allocation.
3. [P2] The new DeepPot LR builder treats virtual padding slots as real atoms
Location: deep_eval.py 2635-2641.
The new branch ignores atom_types and constructs all pairs using the rectangular slot count as n_node. Unlike the eager path, it does not compact negative-type padding. This is an additional regression from the unordered-pair integration: the previous LR builder excluded negative-type endpoints.
This time the reproduction uses a complete saved .pt checkpoint and DeepPot.eval(..., atomic=True, mixed_type=True), rather than only evaluating source-derived arithmetic:
- Use the test LES config with charge constraints enabled, zero the latent-charge network parameters, and set
bias_atom_q=1. - Types are
[0,0,-1]; the real atoms remain at x=0 and x=1. - Move only the padding coordinate from x=10 to x=20.
- DeepPot energy changes from -0.3478181411 to -0.0248750740 eV.
- The virtual atom receives x-forces -0.0665229834 and -0.0156890131 eV/Angstrom.
- Direct evaluation of the same model remains at -0.9736995243 eV at both padding positions.
Please share the compact/remap convention with eager and compiled evaluation, or preserve the full virtual-node masking contract consistently with the SR node indexing. Add padded checkpoint coverage for padding-coordinate invariance, zero virtual-atom forces, and direct/DeepPot parity. The current checkpoint tests use equal-sized real-atom frames and miss this case. This interacts with item 2, but the newly introduced virtual-endpoint edges are a separate integration defect.
4. [P2] An accepted linear_ener child still silently loses its LR contribution
Locations: linear child construction and pt_expt child dispatch.
A standard child with a dpa4c descriptor and dpa4c_lr fitting passes argcheck.normalize() and construction. The composition builds the child as an atomic model, bypassing the outer LR correction.
Reproduction: use the test LES config with the charge constraint disabled, place two type-0 atoms at x=0 and x=1, and compare standalone DPA4C-LR against a single-child linear_ener with weights: "sum". Load the standalone atomic-model state_dict into the child so their atomic parameters are identical:
- Standalone energy: 11.4774559167 eV; first-atom x-force: -6.4781878936 eV/Angstrom.
- Composition energy: -0.9736995243 eV; first-atom x-force: +0.0454972456 eV/Angstrom.
Please preserve the complete SR+LR contribution in a supported composition, or explicitly reject this fitting in unsupported compositions during configuration/construction. Returning .atomic_model from an additional family dispatch would still discard LR. Add single-child/unit-weight energy and force parity, or a clear rejection regression. A standalone standard configuration does correctly dispatch to DPA4CLREnergyModel; the confirmed failure here is composition.
5. [P2] The Hessian omits the entire LR energy
Locations: new Hessian call and the invoked SR-only wrapper.
The wrapper called by _cal_hessian_ext_graph evaluates atomic_model.forward_common_atomic_graph and sums its SR energy directly. It never applies LR. Consequently, the reported Hessian is inconsistent with the returned total energy and force.
Reproduction: zero the model parameters, use two atoms of types [0,1] at x=0 and x=4 Angstrom, and call enable_hessian().
- LES: set
les_alpha=1andbias_atom_q=[[1],[-1]]. The returned Hessian is identically zero, while the central force-difference value H[3,3] is -0.4186363558 eV/Angstrom^2. - SOG: set all amplitudes to 1, all bandwidths to 2, and charge biases to
[[1,0,0],[-1,0,0]]. The returned Hessian is zero, while H[3,3] from force differences is -4.0792867035 eV/Angstrom^2. - The legacy path with
enable_hessian()instead raisesKeyError("energy_derv_r_derv_r"), because the dense override does not generate that output.
Please differentiate a complete scalar SR+LR energy wrapper whose coordinates remain connected through both graphs, or explicitly reject Hessian requests until supported. Calling the ordinary lower unchanged would retain its coordinate/edge detach behavior. Add Hessian-versus-force-finite-difference regressions for both kernels.
6. [P2] Compiled execution bypasses the non-periodic contract, and the compile-parity test still exercises eager
Locations: compiled LR branch and test_compiled_lower_matches_eager.
The compiled wrapper builds periodic SR edges from box_flat and non-periodic LR pairs without invoking the model's periodic-input rejection.
Verified through a real _CompiledModel(..., compile_eval=True) execution with a populated compiled cache: use the test LES model with charge constraints disabled, two type-0 atoms at x=0 and x=1, and a 10-Angstrom cubic cell.
- Eager rejects the cell.
- Compiled execution accepts it and returns 11.4774559167 eV.
- Moving the second atom by one full cell vector changes the compiled energy to 0.3695093956 eV.
Please share the non-periodic input check before building either graph. Normal non-periodic training is unaffected, and from-scratch periodic training already fails during statistics initialization; this check closes the compiled entry-path inconsistency.
The repository test constructs _CompiledModel with the default compile_eval=False and then calls .eval(), so it delegates to eager; its compiled cache remains empty. Enable compiled evaluation and assert cache population, then test periodic rejection. Separately, actual non-periodic compiled LES evaluation and SOG training passed the checks described below.
7. [P2] Charge constraints ignore default_fparam when the argument is omitted
Locations: flat target selection and dense target selection.
With numb_fparam=1, default_fparam=[2.0], and use_charge_constraint=True, the fitting uses its configured default. The model-level constraint still sees the original fparam=None and selects a zero target.
Using the test LES config with these settings and two type-0 atoms 4 Angstrom apart:
- Omitted fparam: energy -0.7081402796 eV.
- Explicitly passing the identical configured default
[[2.0]]: energy 2.8917710345 eV. - The discrepancy reproduces on graph and legacy paths.
Please resolve the effective frame parameter before selecting the charge target, retaining charge_spin precedence. Add omitted-versus-explicit-default parity tests for energy, forces, and the constrained charge target.
Earlier issues that are now fixed
These should not be treated as remaining blockers:
- Scalar
trainable: falsenow freezes the promoted LES/SOG parameters; the optimizer-step regressions pass, and list truthiness has been removed. - Omitted
fitting_net.typeis now stored asdpa4c_lr. - LR construction no longer sends a hardcoded 1e6 cutoff through NV/cell/vesin. The shared unordered all-pairs builder removes that allocation failure.
- Eager/DeepPot all-zero boxes are normalized before their relevant graph construction paths.
- Unordered-pair force signs and equal atomic-virial splitting are correct in the checked cases.
- Unsupported bridging sugar is no longer advertised by the LR schema.
Non-blocking performance, maintainability, and simplicity suggestions
- The new LR representation is a useful improvement: storage follows the actual pair count
sum_f n_f(n_f-1)/2, with no cross-frame pairs or padding every frame to the largest one. It halves stored pair entries relative to the directed representation. It is still quadratic in per-frame atom count, and SOG materializes pair-by-Gaussian intermediates. Benchmark construction plus forward/backward time and peak memory on realistic ragged distributions before claiming a speedup or a 2x peak-memory improvement. Blockwise evaluation can be a later optimization. - The recurring integration problem is that the complete physical energy exists only in an outer correction while bias fitting, Hessians, and composition each have atomic-only entry points. A reusable complete-energy evaluation contract would reduce these omissions. Also centralize mask handling and effective optional-input defaults so graph, legacy, compiled, and checkpoint paths cannot drift independently. Clear capability rejection is a reasonable short-term fix for genuinely unsupported paths.
- Consider moving the constructor-triggered private Inductor pattern mutation into the existing shared compile-compatibility module with an explicit version guard. This avoids every ordinary model construction silently changing process-global compiler behavior.
- The newly added
_postprocess_compiled_outputlookup has no implementation in this model and describes an eager LR correction even though LR is already in the graph lower. Removing the unused hook and stale explanation would simplify the execution contract. - Formatting and naming are generally consistent, and the comments explaining the two force paths and unordered virial split are useful. Prefer the shared energy/mask/default contracts above over copying additional path-specific implementations.
Validation and limits
Validation was performed against this exact head with a newly built native CPU library and PyTorch 2.11.0+cpu:
source/tests/pt_expt/model/test_dpa4c_lr.py: 37 passed.ruff checkandruff format --check: all 18 changed Python files passed.- Real compiled LES evaluation: energy/force matched eager and the compiled cache was populated.
- Real compiled SOG training with an energy-plus-force loss: energy error 0, maximum force error 2.22e-16; amplitude, bandwidth, and charge-bias gradients were finite and nonzero.
- Independent affine-strain virial differences: maximum errors 2.23e-11 for LES and 2.56e-10 for SOG. Atomic virial sums and net forces agreed to roundoff.
- Unordered pair indices/displacements matched brute force for empty/singleton/mixed ragged counts and 200 atoms. Singleton and mixed empty/nonempty ragged model batches worked.
- The numerical failures in items 1-7 were reproduced on this head. Passing the existing tests does not cover those missing combinations.
These are focused CPU checks, not a full backend/GPU validation or a performance benchmark. No .pt2/.pte freeze support is claimed; it is explicitly documented as unimplemented. Entirely empty batches fail in an unchanged upstream compaction path, so I have not included that as a new PR-specific defect.
At submission-time inspection, exact-head Test Python and Test C++ are still running; CodeQL, builds, and package jobs have completed successfully. The Test CUDA workflow reports success only through its summary job: both actual CUDA Python and CUDA C++ test jobs are skipped. That workflow is not evidence that this LR implementation has been exercised on a GPU.
Architecture proposal: make long-range energy a first-class atomic-model componentAfter reviewing the implementation, I would like this PR to establish a clean, descriptor-independent long-range architecture rather than grow the The central requirement is: Every caller of the atomic energy model must see the complete energy definition. The outer model must not add a physical energy contribution that is invisible to the atomic model. LES/SOG are new functionality in this PR. Please do not retain This is a refactoring proposal, supplementing the existing correctness review. The class names, directories, interfaces, and configuration below are proposed, not existing APIs. Code observations are pinned to head 1. Why the atomic-model boundary mattersThe current implementation evaluates the descriptor/fitting through
Fixing each consumer with an LR-specific branch would perpetuate the same problem. Make the atomic energy complete, then make all consumers use the same input preparation and evaluator. Here, atomic means an output attribution to atoms, not a requirement that the energy depend only on a finite local neighborhood. Long-range, reciprocal-space, and many-body contributions can belong inside an atomic energy model. Their atomic attribution must be specified, but their mathematical energy need not be pairwise or local. One detail about the current scope: the Python factory already accepts both DPA4 and DPA4C, while the configuration variant only admits DPA4C. Factory acceptance alone is not complete user-facing support. The new design should remove this inconsistency and use feature/capability contracts rather than a descriptor-name whitelist. 2. Recommended ownership and hierarchyI suggest extending the existing
The neural execution is straightforward: compute descriptor features once, run the independent heads, apply the relevant field constraints, evaluate energy terms, and assemble the complete atomic output. For example, an energy head supplies the local learned contribution, while a scalar head supplies raw latent charges consumed by a latent-interaction term. Both use the same descriptor result. A GLU is a head-network implementation choice, not a reason to tie the interaction to DPA4C or to subclass the entire DPA4 energy fitting. Keep kernel parameters with the kernel: LES width and SOG amplitudes/bandwidths should not be stored in the short-range energy fitting. Give descriptor, heads, and kernels independent freezing/sharing controls. 3. Descriptor and head contractsUse an explicit feature specification:
The current DPA4 graph interface and DPA4C graph interface both expose invariant node features and no equivariant fitting tensor in that slot. Scalar LES/SOG charges can therefore share a head abstraction. DPA4's internal equivariance does not by itself provide a public dipole-head interface; dipole/multipole extensions must declare and supply the appropriate features. Separate internal fields from public atomic outputs:
This distinction matters because current output-stat initialization takes every fitting output key as a bias key. Do not automatically feed every new head output into energy-bias calibration or apply the final energy output mask to internal charge fields before they are consumed. The head container also needs a real lifecycle contract: input statistics, type-map changes, case embeddings, parameter sharing/freezing, and serialization. Vacuum reference and readout gates belong to the relevant head; they should not become one global flag applied to all heads. Reference/vacuum rows appended for descriptor evaluation must be removed before solving the real system's LR problem. 4. Upgrade the geometry/input contract, not just the class locationMoving the formula into the current atomic forward without changing its inputs is insufficient. The dense atomic interface receives extended coordinates/types, an A short-range graph is not sufficient information for global electrostatics. An extended atom array is not a list of unique physical atoms. Introduce a common, backend-friendly
For the single-rank nonperiodic direct solver, canonical nodes are the real physical atoms of each frame, without periodic images or padding counted as atoms. For future distributed solvers, node layout and communication must identify replicas and ownership explicitly. Each component declares requirements. A shared preparation layer aggregates them and builds the necessary SR graph, unordered all-pairs topology, or future periodic-solver data. All callers, including statistics and Hessian evaluation, must use this preparation contract. Keep existing ordinary-model entry points usable through adapters. An old lower interface that cannot provide required LR geometry must fail clearly for an LR-enabled model; it must not infer nonperiodicity from missing information. Normalize zero-box conventions once at the boundary. Do not emulate LR with an enormous cutoff or overload Total-charge requirements must come from the atomic component tree, not only from whether the descriptor embeds charge. Currently charge/spin input dimensions and defaults are delegated to descriptor conditioning. A descriptor that does not embed charge can still feed a charge-constrained LR term. 5. One differentiable energy, with optimized multi-geometry executionThe semantic source of truth should be the complete per-frame energy as a function of canonical physical coordinates, cell/strain, spin, and model parameters. Recommended execution:
The existing edge-native execution is valuable and can remain an optimization. Generalize it to multiple geometry inputs with a shared pullback to coordinates and strain, rather than adding an LR-specific derivative implementation in the outer model. Both dependencies must survive:
For clarity, the exact Hessians do add: For future PBC solvers, include explicit cell derivatives of reciprocal, self, and background terms. A sum of real-space edge virials alone is not a general cell derivative. Require energy/force-loss double backward and Hessian support from any custom differentiation rule that advertises those capabilities. 6. Masks, ownership, charge constraints, and atomic attributionKeep these concepts separate:
Do not use the final output mask as a universal physical participation mask. Existing atom-output exclusion does not automatically mean deleting all interactions involving that atom. If a configuration intends to remove an atom from charge physics, make that a separate explicit policy. Preserve established ordinary-model exclusion behavior. Resolve a charge target and its default once, before both descriptor conditioning and LR evaluation. The constraint should explicitly select its charge channel(s), participation set, target source, and behavior for an empty set. It must not silently interpret the first For this PR's unordered pair interactions, use a documented endpoint attribution: each endpoint gets half of each pair energy. This is more natural than uniformly spreading frame LR energy over all nodes, which makes an unrelated spectator change other atoms' assigned energy. However, do not make pair additivity a universal EnergyTerm requirement. A future reciprocal-space or many-body solver can define its own deterministic atomic attribution. Specify:
A valid atomic decomposition is not a claim of a unique physical local energy, and it does not automatically make arbitrary atom-wise gating meaningful. Avoid a second independently implemented total-energy formula that can drift from the atomic reduction. 7. Integrate existing features through the complete atomic contractBias/statistics. Linear composition. Each child produces its complete energy before child weights are applied. Summing weighted child energies is not the same operation as summing their charges and then applying a quadratic LR kernel. Constant Native spin. Differentiate complete energy with respect to spin, including LR response through spin-conditioned charges. Static inspection shows why the generic path should own this: the generic lower supplies Bridging. The existing gate fades the learned readout relative to its reference. Give this gate explicit head/term scope. Do not broadcast it to every head or multiply the complete SR+LR energy without defining the resulting potential. Gating charges and subsequently enforcing total charge can recreate nonzero charges. Keeping LR active also changes the previous analytical-only close-pair limit. Define and test the intended SR+LR+repulsion limit and near-field treatment; reject LR+bridging until that policy is implemented rather than claiming compatibility from argument forwarding alone. MPI/domain decomposition. Descriptor halo exchange does not implement global LR. A distributed solver needs global charge reductions, ownership/pair accounting, communication for the interaction, and its backward/force communication. The first nonperiodic direct solver should declare complete-system/single-rank support and reject decomposed inputs. A local Compression/fused execution. Current fused dispatch uses an atomic-model decomposition whose supported structure is a learned part plus an optional analytical pair potential. An atomic model with additional unsupported terms must be ineligible, so it falls back to the complete evaluator. No accelerated path may silently return SR-only results. Compile/export. Serialize and export the complete computation and its input requirements. Use a common runtime preparation contract for training, DeepPot, and exported-model callers. Successful 8. Training lifecycle, factory, and serialization need first-class componentsA multi-head/term design must include more than forward calls:
Existing consumers currently assume a much narrower structure: compiled sharing identifies the descriptor and first fitting child, task-buffer discovery inspects fitting and selected outer atomic buffers, and fine-tune key relocation recognizes descriptor/fitting paths. Update these through a component protocol rather than adding more LR-name checks. Use one atomic-model builder/deserializer for standalone models, The versioned atomic record should own descriptor, head collection, energy terms, constraints, kernel/solver configuration, learned state, output statistics/reference state, and the configuration needed to derive capabilities. The current record explicitly stores descriptor/fitting as its physical components, alongside inherited statistics and exclusions. Update its version and readers deliberately. Preserve existing ordinary records; do not add compatibility baggage for this PR's unpublished LR alias. Runtime graph caches and per-batch solver plans should not become checkpoint model state. 9. Suggested configuration and code organizationA possible configuration organization is:
For example, changing Suggested ownership in the tree:
Shared mathematical contracts can live in dpmodel without implying every backend already implements every solver. Backend availability remains explicit. 10. Implementation sequence
These can be reviewable commits, but the merged feature should have one complete energy definition. Moving only the formula or renaming the model would not satisfy the design. 11. Acceptance matrix
For the initial implementation, support nonperiodic complete-system LES/SOG with DPA4 and DPA4C thoroughly. Periodic, distributed, multipole, and additional descriptor support should fit the contracts without being claimed before their solvers/features and tests exist. The direct solver remains quadratic in system size. Preserve ragged-native, within-frame pair construction and avoid duplicating descriptor work. Benchmark construction, forward, force/backward, and peak memory separately; a later blocked/streaming solver should be replaceable without changing the head or atomic-model interface. The invariant to preserve throughout is that the atomic model owns the complete potential, while different execution backends and optimizations evaluate that same potential. |
…rgy model Move the latent-charge long-range energy into DPAtomicModel, so that every consumer of the atomic model evaluates the complete potential: training and compiled training, output-bias statistics and change-bias, the graph Hessian, linear compositions, fine-tuning and checkpoint inference. - A standard energy model takes a `long_range` section (type `latent_charge`, kernels `les` and `sog`, optional total-charge constraint). The charge network reads the descriptor shared with the energy fitting and takes its conditioning inputs; the energy is the direct pair sum over complete non-periodic frames, each pair energy attributed half to each atom. - Components: LatentChargeFitting (charge head), interaction kernels with their own trainable parameters (widths stored as logarithms), and the LatentChargeLongRange module in deepmd/dpmodel/long_range with pt_expt wrappers in deepmd/pt_expt/long_range. - Atomic models declare the topologies they read (`required_topologies()`); callers build the `all_pairs` topology outside the compiled region with `build_topologies`, which honours model-level pair exclusion and rejects periodic cells. The graph lower makes the pair vectors further autograd leaves and pulls them back with `pair_force_virial`, so force, virial, atomic virial, Hessian and force-loss double backward include the long-range term and the charge response. - Compiled training receives the topology as additional dynamic inputs; in a graph traced for a symbolic pair count the kernel coefficients reach the pairs through the node axis, which keeps their gradient reduction parallel for any batch. Ordinary models keep their compiled input signature and serialization record (version 2; models with a long-range module write version 3). - Only DPA4C is admitted (`check_long_range_support`). The dense lower, periodic cells, domain decomposition, compression, export, spin models, tabulated ZBL, bridging, atom exclusion and other backends are rejected explicitly. - Remove the `dpa4c_lr` model and fitting types, their routing, the long-range edge inputs of every compiled graph, and the neighbor-list and box changes the previous implementation made to ordinary models.
Export a model with a latent-charge long-range module to .pt2 and .pte through the graph lower, and evaluate the frozen model with the Python inference interface. - The graph lower of such a model takes the `all_pairs` topology after its other inputs, as `pair_index`, `pair_vec` and `pair_mask`, each with a dynamic pair axis of its own. The metadata names the topologies the lower reads (`topologies`), and the caller builds them from the coordinates of each frame, as checkpoint inference already does. - `flatten_topologies` and `unflatten_topologies` define the positional layout once; graph export, compiled training and DeepEval share it, and `build_synthetic_topologies` the trace-time samples of export and compiled training. - The C++ interface rejects such an archive when it is loaded, with a message pointing to the Python interface.
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Follow-up review of the refactor at 043c253d12efa02d839534aeb7e1792208ad3bc4, after rebuilding and running focused CPU checks.
Architecture and previously reported issues
The main architectural change is now in place. The atomic model adds the long-range contribution to its complete energy; the latent-charge head, charge constraint, interaction kernel, and all-pairs topology have separate responsibilities. The ordinary outer model handles derivatives and export. The Hessian wrapper rebuilds all geometry channels from the same coordinates, preserving charge response and mixed geometry derivatives. Pair energies are attributed half to each endpoint rather than spread uniformly over unrelated atoms. The unpublished dedicated dpa4c_lr entry has been removed.
This is a substantial improvement and does not call for another wholesale architectural rewrite. My earlier findings against b3b3f928 should not be treated as unchanged findings against this head:
- Output-bias double counting: the self-labelled bias-adjustment tests now pass.
- Atom exclusion: standalone LR construction now explicitly rejects this unsupported combination instead of returning the previously inconsistent result. A child-construction loophole remains, described inline.
- Checkpoint/DeepPot padding: LES and SOG checkpoint inference now agrees with direct forward, including atomic outputs; moving padding coordinates leaves the energy unchanged.
- Ordinary linear composition losing LR: sum/mean/explicit constant-weight composition now combines complete child energies and forces. Child-specific exclusions remain an uncovered case, described inline.
- Hessian omitting LR: LES/SOG finite-difference tests pass with geometry-dependent charges, including Hessian symmetry and charge response.
- Compiled validation and the former false-compile test: train and eval now actually exercise a populated compiled-lower cache. I also checked that both eager and
compile_eval=Truereject periodic input. - Default frame-parameter charge target: omitted and explicit default
fparamnow agree; defaultcharge_spinis also covered.
Remaining concrete findings
I have attached three P2 inline comments:
- Linear-child configuration semantics: a child's
pair_exclude_typesis silently dropped, changing the energy even in a single-child sum. A child'satom_exclude_typesis also dropped, bypassing the intended unsupported-combination rejection. Child construction and topology filtering need consistent semantics. - Incomplete-system lower input:
n_local < n_nodewithcomm_dict=Noneis accepted even though LR requires complete systems. Halo charges are then zeroed, deleting their interactions. Reject this unsupported input consistently before numerical evaluation. - Gradient test assertion: a neutral single-channel charge constraint removes the common output bias, so its exact zero gradient is valid. Requiring every parameter's gradient to be strictly nonzero makes the new LES test fail for a mathematically correct result.
The first two are silent model/interface correctness problems. The third is a test defect, not evidence that the physical gradient chain is broken. The inline comments give the exact triggering conditions and suggested fixes.
Additional numerical robustness recommendation
The LES formula uses erf(alpha * sqrt(r_sq + 1e-12)) / sqrt(r_sq + 1e-12). The softening keeps the value finite, but does not prevent cancellation in its derivatives near contact.
I reproduced this through the complete model with DP_INTERFACE_PREC=low, two atoms with fixed charges q=1, alpha=0.8, no charge constraint, and all network weights zeroed:
- At separation
1e-4 Å, the first atom's x force is+0.0156242 eV/Å; the small-distance analytic value is approximately-0.000554607 eV/Å. - At
1e-3 Å, the force differs by about 30%.
This is a narrow FP32, extremely-small-separation robustness case, not a claim that forces at ordinary interatomic distances or the default FP64 path are generally wrong. I would restore a stable small-distance series, with its switching expression also safe for autograd, and test first and second derivatives rather than only the finite contact energy. This is separate from the two ordinary semantic errors above.
Performance, maintainability, and documentation
- Building only the real unordered intra-frame pairs is a sensible representation. It avoids cross-frame pairs and padding every frame to the largest frame size. Direct evaluation is still quadratic in each frame's atom count; SOG also materializes pair-by-Gaussian intermediates. Benchmark topology construction, forward/backward throughput, and peak memory on representative ragged systems before claiming a performance improvement. Chunking can be a later optimization without changing the physical interaction.
- Keep the current decomposition. Concentrate the follow-up on shared capability checks and consistent standalone/child construction, rather than adding another LR-specific outer-model path. Unsupported combinations should fail visibly instead of silently dropping an option.
- Please update the PR description/title and generated summary: they still describe the removed dedicated
dpa4c_lrmodel and say.pt2/.ptefreezing is not implemented, whereas the current code and tests provide that support.
Validation and limits
Fresh native CPU build of this exact head, PyTorch 2.11.0+cpu:
source/tests/pt_expt/model/test_long_range.py: 40 passed, 1 failed. The failure is the exact-zero-gradient assertion described inline.source/tests/common/dpmodel/test_long_range.py: 29 passed.- Focused ordinary-model/integration regression checks in
test_finetune_utils.pyandtest_dpa4c_graph_lower.py: 30 passed, 8 skipped, 3 deselected. - Total executed test results: 99 passed, 1 failed, 8 skipped. Additional small probes reproduced the two semantic errors and the narrow FP32 numerical issue above.
- The seven compile/freeze/vacuum-reference cases passed, including real LES/SOG compiled training and evaluation, energy/force/virial and parameter-gradient comparisons, and
.pt2/.ptefreezing followed by DeepPot inference with changing sizes and padding. - Separate checks confirmed that frozen LES/SOG charge-network/kernel parameters do not receive gradients and that the eager/compiled public entries reject PBC.
These are focused CPU checks, not a full-repository test run, a GPU/MPI validation, or a performance benchmark. In particular, passing CI summaries or CPU compilation is not evidence of actual CUDA execution.
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Exact-head CI follow-up at 043c253. The completed hosted Python run exposes two additional integration blockers, detailed inline: the new long_range schema key is absent from the existing explicit bridging-routing contract, and the new LES .pt2 export regression fails while compiling generated C++ under the CI CPU/PyTorch 2.11.0 environment.
Evidence: Python shard 6 finished with 2 failed, 1188 passed and 156 skipped: https://github.com/deepmodeling/deepmd-kit/actions/runs/39125257637/job/117669242690 . I inspected its failure trace and the exact-head schema/routing/export-test source; a local AST check independently confirms the missing routing entry. I did not rerun PyTorch locally. The export failure is a compiler/toolchain integration failure (undeclared tmp40), not evidence of a scientific numerical failure or proof that this PR introduced the underlying compiler bug.
This follow-up does not duplicate OutisLi's three current findings or reinstate the old padding/energy-composition findings that the refactor addressed. OutisLi's focused CPU freeze passes and this hosted failure are distinct execution evidence; the supported hosted export path still needs to be reconciled. C++ CI passed. The latest CUDA summary is green, but both actual CUDA test jobs were skipped, so it adds no executed-GPU qualification.
Compress a DPA4C model that carries a latent-charge long-range module, and evaluate it on a CUDA device through the compact canonical lower, with a direct-sum operator whose memory is linear in the atom count. - `deepmd::latent_charge_direct_sum` takes the charges the charge network predicts for the nodes of complete frames, applies the total-charge constraint, and returns the atomic energy, its derivative with respect to those charges, and the force, per-atom virial and per-frame virial at fixed charges, without listing the pairs. Each pair is evaluated by both of its nodes in float32 from double-float positions; the partial sums, the constraint and the per-frame sums are added in a fixed order in float64, so the result is deterministic. The CPU kernel is the float64 reference. - The compact canonical lower of such a model takes the float64 node positions as a ninth input. The descriptor operator runs once; the charge gradient, pulled back through the charge network, joins the descriptor cotangent of the fitting, so one descriptor backward yields the edge gradient of both. - Compressing a descriptor conditioned on `charge_spin` folds the charge state into its tables; compressing the atomic model folds a constraint that reads its total charge from `charge_spin` to the same state (`fold_charge_state`), and the charge-state fold of a .pt2 archive rewrites both together. A descriptor compressed alone, whose constraint still reads the condition, is rejected. - A model the operator does not serve (pair exclusion, frame or atomic parameters of the charge network, more than 8 channels or 32 Gaussians), like any CPU export, keeps the graph lower and its pair lists.
Serve a .pt2 model with a latent-charge long-range module in the C++ interface and in `pair_style deepmd` and `deepmd/kk`, for complete non-periodic systems on a single MPI rank. - DeepPotPTExpt builds the `all_pairs` topology of the graph lower from the coordinates of each frame for an uncompressed model, and passes the node positions to the compact canonical lower of a compressed one. A periodic box, ghost atoms and multiple ranks are rejected. - `DeepPot::has_long_range()` and a `compute_canonical_graph_gpu` overload that takes the device coordinates; C API version 31 adds `DP_DeepPotHasLongRange` and `DP_DeepPotComputeCanonicalGraphGPU2`. The backend virtual signature changes, so the DeepPot backend plugin symbols become `deepmd_create_deeppot_backend_v2` and `deepmd_delete_deeppot_backend_v2`. - `PairDeepMD::init_style`, shared by `deepmd/kk`, rejects a long-range model with a periodic dimension or more than one rank; the Kokkos path hands the model node positions to the canonical lower. - Tests: gen_dpa4c_long_range.py writes an uncompressed fixture and, for the CUDA variant, a compressed one; C++, C API and LAMMPS cases compare energies and forces with the Python interface, follow `set_charge_spin`, and check the rejections.
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Reviewed the new compressed/direct-sum and C/C++/LAMMPS integration delta from 043c253 to c8ba94f. Two new correctness issues are inline: the CUDA LES approximation loses force accuracy for supported small alpha*r, and the LAMMPS boundary guard misses LR models after the first ensemble member.
Verification is source/path inspection, a host scalar reproduction of the new CUDA formula (including an independent float64 evaluation of its approximation), and eight exact-method AST/NumPy checks of the changed charge-target/folding contract. No native CUDA or LAMMPS execution is claimed. Current-head CUDA C++/Python jobs are skipped. The previously reported bridging-routing and CPU .pt2 compilation failures also recur in current-head jobs 117736863065 and 117736863003; those existing findings are not duplicated here. This is a bounded delta review, not approval of the complete rewritten feature.
The default amplitude of the sum-of-Gaussians kernel was the constant 4 pi ln b for every Gaussian. In the real-space kernel sum_k a_k exp(-r^2 / 2 sigma_k^2) that constant gives a kernel of logarithmic shape, 74 per Angstrom at contact and about 500 times 1/r at 10 Angstrom. The default amplitudes are now a_k = sqrt(2 / pi) ln b / sigma_k, the trapezoidal discretization in ln s of 1/r = sqrt(2 / pi) int_0^inf exp(-r^2 / 2 s^2) s^-2 ds at the default widths. With the default widths the kernel is 0.46 per Angstrom at contact, and its force stays within 0.4% of the Coulomb force from 6.5 to 150 Angstrom. It differs from 1/r there by a constant, which exerts no force.
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Reviewed the four-file SOG-default delta from c8ba94f to 16f40a2. The new weights sqrt(2/pi) * log(b) / sigma_k have the stated 1/r integral normalization. Exact-source AST/NumPy checks pass the changed closed-form/default test and 48 initialization/round-trip cases, including explicit amplitudes and restoration of historical stored amplitudes. An analytic radial-slope sweep gives K(0)=0.4612680757 Å^-1 and maximum fixed-charge force error 0.39136% over 6.5–150 Å.
One nonblocking documentation qualification is inline: this radial-kernel bound is at fixed charges, and does not bound the full force when the predicted charges change with geometry. No new implementation defect was found in this bounded delta. The earlier LES/ensemble and routing/export findings are outside these four changed files and remain unaddressed; this is not overall approval.
Verification here is source inspection and host NumPy probes, not a native backend/GPU test run. Current-head Python/C++ validation is still progressing; CUDA run 39183316839 has a green aggregate but both actual CUDA test jobs are skipped.
The latent-charge direct sum of a compressed DPA4C model now spans the domains of a decomposed system. Every rank keeps the atoms it owns: the operator gathers the positions and charges of the owned atoms of all ranks over MPI, in rank order, and evaluates its own atoms against all of them, so that the ranks divide the quadratic work. Every rank shifts the constrained channel by the same sum of the gathered charges, the mean potential of the constraint adds the sums of the ranks in rank order, the forces stay with their owners, and the per-frame virial is the share of the rank. Periodic boundaries stay unsupported. - op: deepmd::latent_charge_direct_sum takes a communicator tensor, the bytes of an MPI_Comm handle as for border_op, or zero for a system held whole; the exchanges live in long_range/domain_exchange.cc and stage through pinned host memory unless MPI reads device memory. - pt_expt: the compact canonical lower of a long-range model takes the communicator as its tenth input. - api_cc, api_c: compute_canonical_graph_gpu and DP_DeepPotComputeCanonicalGraphGPU2 take the communicator (world). A rank without nodes runs the lower on one phantom halo node, so that it joins the exchanges. DP_DeepPotModelDeviHasLongRange and DeepPotModelDevi::has_long_range report a long-range module in any model of an ensemble. - lmp: pair_style deepmd and deepmd/kk accept several ranks for such a model and reject periodic boundaries for any model of a model deviation; deepmd/kk passes the communicator and evaluates the model on every rank at every step. Several ranks require the compact canonical lower, which a compressed DPA4C model frozen for a CUDA device uses when the direct-sum operator serves its long-range module; any other long-range model, like an uncompressed one, is rejected on several ranks. DP_DeepPotComputeCanonicalGraphGPU2 (API version 31) has not been released, so its argument list changes in place; a binary built against the previous commit must be rebuilt together with the library.
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Reviewed the new MPI/compact-ABI delta from 16f40a2 to 91e6b15. The ensemble-wide periodic restriction now addresses my earlier finding: DeepPotModelDevi::has_long_range examines every member, the LAMMPS guard uses it, and LR-first/LR-second rejection tests were added. The other earlier LES/routing/export/documentation findings are not changed by this commit.
One new issue is inline: the native entry copies eight bytes from a caller-owned MPI_Comm even when that handle is only four bytes. This is an out-of-bounds host read for MPICH-style handles.
Validation is scoped: source/ABI inspection; 64 cases using the extracted ensemble-query method with test-double models; 12 host NumPy partition/charge-projection/force/virial algebra checks including empty and uneven ranks; and an isolated AddressSanitizer storage-adapter reproduction of the communicator overread. These do not establish native Torch, CUDA, MPI or LAMMPS execution. Current-head CUDA run 39193972726 skipped both actual test jobs, while Python/C++ qualification is still running. No overall approval of the new multi-rank route is implied.
DPA4C formed the non-scalar block of its moment payload with two index
gathers, of the amplitude channels and of the harmonics, so that the
backward of the payload scatter-adds through the index arrays it gathers
with in the same loop. When the C++ backend of Inductor generates code for
one thread, it declares such an index array after its first use, and the
kernel does not compile ("'tmpN' was not declared in this scope"). The
CPU freeze of a DPA4C model to .pt2 therefore failed with PyTorch 2.11 and
2.13 whenever Inductor ran with one thread, as on the hosted CI, with or
without spin or a long-range module.
Degree l of the payload is the outer product of its 2l + 1 harmonics with
the leading C_l channels of the amplitude, which is now formed by
broadcasting, harmonic-major as before; its backward reduces over the
broadcast axes. The payload and the energies are unchanged bit for bit,
and forces and virials agree to the rounding. The index tables and
build_moment_indices are gone; the pt_expt descriptor drops the tables
from a checkpoint written by DeePMD-kit 3.2 when it loads one.
- LES near contact: LESKernel evaluates erf(x) / x from nine terms of its Taylor series in x^2 below x^2 = 0.0625, where the closed form loses its derivatives to cancellation: in float32 its radial derivative deviated from the 40-digit value by up to 37% at 1e-3 Angstrom, and its second derivative by more than three orders of magnitude. Together they keep the relative error of the radial derivative within 2e-14 in float64 and 1e-5 in float32. The CUDA direct sum does the same with six float32 terms below x^2 = 0.16, which brings the force of a small alpha at atomic distances to float32 precision (0.7% error in the slope at alpha r = 0.03 before) and needs no distance clamp anymore. A warp evaluates the series only for a batch with a pair below the threshold: a choice per pair cost 11-15% of the sum on a carbon cluster, this one about 3% at 100,000 atoms. - The communicator input of the compact lower holds the address of the caller's MPI_Comm, which the operator reads with the width of MPI_Comm in its MPI library, in place of eight bytes copied from the handle, which read past a four-byte MPICH handle. - A frame that one domain holds whole owns every node: the graph lower and the compact lower reject n_local < n_node without a communicator, whose halo nodes would otherwise carry no charge and drop their interactions. - linear_ener: a sub-model with a long-range module rejects its own pair_exclude_types, which the composition owns: it shapes the graph and the pair topology its sub-models share, and gives such a sub-model the semantics of the same model alone. Its atom_exclude_types is rejected as for the model alone. - bridging: long_range is routed to the learned child, beside which the composition rejects a bridging term. - The common charge bias of a constrained channel cancels in the constraint, so its gradient is exactly zero: the gradient test requires a nonzero gradient of every other parameter, and a new test checks the invariance. - The SOG documentation qualifies the force of the default kernel as that between fixed charges, whose nearly constant offset contributes through the response of the charges, and states its size and range. - The pt_expt latent-charge module names the classes of the charge network and the kernel it converts to, in place of imports kept only for their registration, which CodeQL reported as unused.
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Reply to the follow-up review of the refactor at 043c253.
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Corrective-delta review at exact head 47c0df38218ecbcb85e1a35d442eb81a38c34761, following 91e6b1576bc13e58f33c46244d34567b7b149c91.
The specific MPI-handle overread and small-alpha CUDA LES formula findings are addressed. The communicator now transports the handle's address and dereferences the actual MPI_Comm type at the receiver; exact helper bodies pass ASan/UBSan adapters with four- and eight-byte handle representations. The paired LES value/slope series removes the old small-x cancellation: 5,301 host-emulated points were checked, and the former alpha=0.03, r=1 Å example now has about 2.8e-8 relative force error. The SOG documentation now correctly limits its Coulomb-force comparison to fixed charges and includes charge-response effects.
The explicit long_range routing, child-exclusion rejection and eager whole-frame ownership checks are also present. Exact-source host checks passed 60 ownership cases, 16 factory cases, 2,432 LES value samples and 242 series-derivative comparisons. The angular broadcast rewrite preserved old payloads bitwise in 720 NumPy comparisons across all supported profiles, including empty inputs and spin tails; host VJP/layout and prefix-qualified checkpoint-key checks also passed.
No new defect was found in this corrective delta. These checks establish source/host contracts: no local native Torch, CUDA, MPI or LAMMPS execution is claimed, and host CUDA-intrinsic substitutes do not establish GPU accuracy. The author's reported one-thread PyTorch 2.11 export pass remains attributed to that report. Current-head Python and C++ workflows are still running; the actual CUDA test jobs are skipped. The CPU export regression and real CUDA/MPI qualification therefore remain pending in this review's evidence scope.
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Add a latent-charge long-range module to standard pt_expt models with a DPA4C descriptor, for complete non-periodic systems.
long_rangesection (type: latent_charge) with an LES (erf(αr)/r) or sum-of-Gaussians kernel, several charge channels and an optional total-charge constraint (neutral, fparam or charge_spin)..pt2/.ptefreezing and DeepPot all include the long-range term. Only the graph lower is supported.deepmdanddeepmd/kk), also across MPI ranks.Also in this PR: DPA4C forms its angular moments by broadcasting instead of two index gathers, for which the C++ backend of Inductor generates code that does not compile with one thread (PyTorch 2.11 and 2.13). The outputs are unchanged, and DeePMD-kit 3.2 checkpoints still load.
Documentation: doc/model/long-range.md.