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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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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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… reciprocal space
A long-range module with "periodic": true evaluates a frame given a cell
as a reciprocal-space lattice sum of the Fourier-transformed kernel,
- les: 4 pi exp(-k^2/4 alpha^2) / k^2,
- sog: sum_m a_m (2 pi)^{3/2} sigma_m^3 exp(-sigma_m^2 k^2 / 2),
with the self term subtracted analytically and the k=0 mode dropped
(tin-foil boundary); every latent channel must be neutral. The k grid is
enumerated eagerly per batch at the kernel's wave-number cutoff and enters
the compiled region as a "k_grid" topology of dynamic length beside
"all_pairs", which a periodic batch leaves guard-only. The module keeps
serving non-periodic batches.
The geometry reaches the graph through two leaves flattened with the grid:
the node positions, whose gradient is the force, and a zero per-frame
strain, whose gradient is the negative k-space virial. Both re-leaf inside
forward_common_lower_graph so the backward roots at graph inputs; a leaf
created inside a make_fx trace produces a NaN gradient under inductor. The
SOG transform unrolls its modes: an (nk, M) intermediate breaks inductor's
double backward of the k-space virial.
Freezing/export, compression, the C++ interface and LAMMPS reject a
periodic long-range module until they can serve the k grid. Validated
against an independent Ewald-split reference and per-mode Poisson checks
(verify_periodic_lr.py), by force/virial finite differences and by
compiled-vs-eager parity (tests in source/tests/.../test_long_range.py).
- k_grid_mode: "fixed" shares one integer k grid over the frames of a periodic batch, sized by the largest cell of the data statistics or by k_grid_bounds, and masks the points a frame does not reach; a frame outgrowing the grid is truncated to it with a warning - decouple_k_grid freezes the wave-number cutoff at the initial kernel parameters, so training the kernel no longer moves the k-point selection (the kernel values still follow the live parameters); the frozen cutoff is serialized with the model
…SPME) - k_grid_mode: "pme" spreads the charges to a static real-space mesh with a B-spline of order pme_order and evaluates the reciprocal sum by an FFT with the SPME deconvolution factor, near-linear in system size; the mesh is sized by the largest cell of the data statistics or by pme_mesh (default spacing pme_spacing = pi/k_max, respecting decouple_k_grid) and serialized with the model - the PME_MESH topology carries (box, volume, coord, strain, mask); the strain family mirrors the k-grid one (fractional coordinates are strain-invariant, the virial comes from the strained reciprocal vectors and volume) - restore the stats-sized mesh lazily after load_state_dict, and feed the graph lower torch cells in DeepEval (fixes periodic dp test for both the mesh and the k-grid paths)
- evaluate |k|^2 as the quadratic form m^T W m with the per-frame reciprocal metric W (six static monomials times six per-frame coefficients, all elementwise) instead of batched 3x3 matmuls, which were 56% of the training step on the profile; the strain folds into the metric through A = S^-T B^-1 - transform the (real) charge mesh with rfftn and sum the half spectrum with the hermitian doubling weight - fractional coordinates by elementwise multiply-sum instead of a batched GEMV f64 40^3 mesh: 34 -> 6 ms forward, 44 -> 16 ms training step
…ity into pr/6031-longrange
…MMPS Periodic long-range modules (`periodic: true`) now freeze, compress and run in DeePMD-kit C++ and LAMMPS, and the reciprocal-space evaluation is reorganized: - Topologies: every topology is a dataclass that declares its geometry fields, its autograd leaves and its dynamic axes, from which casting, re-leafing, the layout of the graph-lower inputs and the export shapes follow (neighbor_graph/topology.py); the pair topology and the reciprocal-space ones live in pair_graph.py and reciprocal_graph.py. A model gives its build parameters through topology_params() instead of overriding build_topologies, and an archive records them as metadata. - The mesh of a periodic frame is sized by its cell at a spacing, per batch, its three mode axes being inputs of dynamic (even) length, in place of a mesh sized once from the data statistics and frozen into the model; `pme_mesh` is gone. The defaults, B-splines of order 8 at three quarters of the Nyquist spacing of the wave-number cutoff, keep the mesh energy and forces within a few 1e-6 of the sum over the wave numbers; order 4 at the Nyquist spacing, the former default, was 1e-3 off in energy and 7e-3 in the forces. - dpmodel/long_range/reciprocal.py evaluates the k-space and mesh sums. An atom carries half of the interaction of its charges with all others, E_i = q_i phi_i / 2, as for the direct sum, in place of an equal share of the frame energy. The reciprocal energy is formed in float64: the lattice sum and the self term cancel to a small fraction of either, which cost a float32 compressed model 1e-3 eV. The cell part of the atomic virial is shared over the owned nodes only, not over padding and halo nodes. - A frozen model evaluates every periodic frame on the mesh of its cell, whatever its training mode, with the spacing fixed at freeze time. - The fixed k grid keeps its per-axis bounds, an array of fixed shape, rather than the enumerated grid, so that a checkpoint of a sized grid loads into a fresh model and DDP broadcasts it. - A pretrained model without a long-range module fine-tunes into one with it: the new module takes the input statistics of the fine-tuning data before the bias adjustment. - Rejected at construction: pair_exclude_types beside a module that serves periodic systems, whose reciprocal-space sum is global, and a linear_ener composition with a periodic module beside another long-range module. Rejected at evaluation: a cell without volume, and a model deviation of a non-periodic system in LAMMPS over models of which only some carry a long-range module. - CUDA: deepmd::latent_charge_direct_sum becomes deepmd::latent_charge_sum, which takes an optional cell per frame. Without one it is the direct sum as before; with one it evaluates the smooth particle-mesh Ewald sum (mesh_sum.cu): the B-spline stencils are evaluated once per node and axis in float64, the charges spread to the mesh in 64-bit fixed point, so that the result does not depend on the order of the atomic additions, the transforms run in float64, and the virial is that of the strain of the cell. It serves the constraint and several MPI ranks as the direct sum does. Its outputs agree with the float64 reference to about 5e-8. - C++: the compact canonical lower takes the cell as an eleventh input, and DP_DeepPotComputeCanonicalGraphGPU2 and the matching C++ entries take the host cell after the communicator; the graph lower receives the mesh of the cell. A cell requires a module that serves periodic systems; with one, the halo nodes of a single rank are images of its atoms. - LAMMPS: pair_style deepmd passes the cell of a system periodic in every dimension and none for a non-periodic one, and rejects a partially periodic box for a long-range model; deepmd/kk passes it to the compact canonical lower, with positions relative to the origin of the box, and rejects a periodic system for an uncompressed long-range model. - The author's design notes and AGENTS.md lines leave the repository. Verified: CPU and CUDA test suites of the module, the operator and fine-tuning; C++ and C API long-range tests, periodic graph and compact lowers on float and double, on CUDA and the graph lower on the CPU; LAMMPS deepmd and deepmd/kk on 1, 2 and 4 ranks, periodic and cluster, against the Python references.
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Incremental review from 47c0df3 to 56e6732: the new explicit reciprocal-space evaluation has a correctness defect when a periodic cell has no nonzero modes inside the cutoff (inline below). This is not missing supplementary performance evidence.
Reproduced with the actual AST-extracted topology builders and reciprocal evaluators using NumPy/array_api_compat: ragged and fixed grids both return zero while the PME evaluator returns the required self correction. A genuinely nonperiodic empty-grid control remains zero. This is a bounded mathematical/source reproduction, not a Torch/autograd/CUDA or LAMMPS run. Native CUDA/C++ integration review is separate; no blanket LGTM is implied.
| These are the weights of the assignment factor | ||
| :math:`D(\theta) = \sum_{l=0}^{p-2} M_p(l + 1) e^{i \theta l}` of the | ||
| smooth particle-mesh Ewald method. | ||
| """ |
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[P1] Preserve the self correction even when a periodic frame retains no k points. has_points is not a periodicity flag: a valid 3 Å cubic cell with LES alpha=0.03 and tolerance=1e-8 has k_max≈0.257516 < 2π/3, so both build_k_grid and build_fixed_k_grid produce no unmasked modes. For charges [+1,-1], this path returns [0,0] eV instead of the documented -ke*K(0)*q_i²/2 = [-0.2437239,-0.2437239] eV; the new PME path correctly returns those values. Since export switches ragged/fixed models to PME, the same learned model also changes energy on export. Carry periodic-frame ownership independently of the retained-mode count and add small-alpha/small-cell regressions for ragged/fixed versus PME, while keeping a truly nonperiodic empty topology at zero.
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Native CUDA/C++/LAMMPS incremental source review from 47c0df3 to 56e6732. One new numerical correctness finding is inline: the periodic SOG CUDA virial drops a nonzero transform derivative at a supported signed-Gaussian zero crossing. This is separate from the Python empty-reciprocal-grid self-correction finding.
Validation: compiled the exact extracted kernel_transform body as a host C++ scalar probe and compared its reported virial factor against a central finite strain derivative. Reviewed the new mesh accumulation/ownership, charge-sum dispatch, host periodic topology and C/C++/LAMMPS box plumbing, plus their added regression tests. No native CUDA, MPI or LAMMPS execution or performance qualification is claimed, and this partial-scope review is not overall approval.
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New terminal qualification evidence for this exact head, distinct from the two numerical findings already posted: periodic .pt2 export/inference currently fails before parity can be checked. Python shard 9 finishes with 5 failed, 1,026 passed and 175 skipped. In source/tests/pt_expt/model/test_long_range.py, test_periodic_frozen_model_matches_mesh_evaluation[les-ragged-.pt2], [sog-fixed-.pt2], and test_periodic_compressed_frozen_model_matches_uncompressed[sog-None] reach aoti_torch_cpu__fft_c2c with the primary error “Only supports complex dtypes, but found: Bool”.
The same primary FFT dtype error occurs during source/tests/infer/gen_dpa4c_long_range.py::_write_ref in both C++ matrix job 120547369722 and job 120547369555. Their later OutOfMemoryError at batch size 1 is a wrapper classification, not evidence that runner memory is the cause. Please diagnose the exported periodic graph/dtype path rather than treating this as missing qualification or retrying an assumed hardware OOM.
The previously reported compressed LES charge-spin tmp38 Inductor compile failure also remains; this head additionally exposes it in test_periodic_compressed_frozen_model_matches_uncompressed[les-neutral]. I am not reposting that existing finding as a new source defect. Preserve the real frozen/mesh and compressed/uncompressed parity regressions while resolving these routes. These observations come from completed hosted CI logs; I have not established the underlying graph/compiler root cause or run a local GPU/backend reproduction. No code or CI actions taken.
…perator The smooth particle-mesh Ewald sum of deepmd::latent_charge_sum is bound by float64 throughput, which is a small fraction of float32 on many devices, and by host work at small sizes: - The charge and potential meshes, the transforms and the influence function are float32; the B-spline weights are still evaluated in float64, once per node and axis in a separate pass, and rounded to float32. The fixed-point spreading keeps the result independent of the order of the atomic additions, with an integer rounding in place of the float64 conversion instruction. - Every lane of a warp takes a (k2, k3) pair of the stencil and walks the k1 axis, reading precomputed axis offsets; the interpolation forms the potential, its gradient and the force per channel in float32 and the owner sums form F (x) r from the force, so that the kernel writes four values per owner instead of fourteen in float64. The influence function takes rows of the half spectrum, one warp each. - The transforms run on cached cuFFT plans with the work area of the call; the intermediates of the mesh sum live in one workspace allocation, in which the potential mesh of a frame overwrites its charge mesh, and those of the direct sum but its chunk sums likewise. The direct sum reads the float64 coefficients directly, and the periodicity of the cells is checked on the host. On an RTX PRO 6000 (sm_120), 100,000 atoms: the LES mesh sum takes 0.67 ms instead of 3.7 ms, the SOG sum 0.50 ms instead of 3.2 ms; the host time of a call falls from about 300 us to 140 us. The forces stay within about 1e-6 of their scale of the float64 reference, as before. The energy of a frame carries the float32 rounding of the reciprocal sum, which includes the interaction of every charge with itself: for 4,000 neutral random charges it departs from the reference by 1e-7 to 5e-7 eV per atom, against 1e-9 to 5e-8 with float64 transforms, far below the error of a compressed descriptor, the models the operator evaluates. In LAMMPS with Kokkos on the compressed Air preset and the BCN diamond systems, a periodic LES step takes 1.71 ms instead of 2.08 ms at 512 atoms (1.30 ms without long range), 2.91 ms instead of 3.23 ms at 10,296 atoms (2.71 ms) and 18.9 ms instead of 24.0 ms at 100,800 atoms (18.1 ms). The virial test takes its finite difference from the float64 reference, since the float32 outputs carry no differences of 1e-5 strains; further tests cover every B-spline order and a stencil wider than the mesh.
…ergy The gradient of a parameter of the kernel transform sums over every mode of the mesh, which the compiled graph reduced in a single block: two such reductions took 70% of the device time of a training step. The transform now reads its parameters through one copy per row of the modes, so that the reduction runs along the rows first. A compiled training step of DPA4C with a periodic LES module in the pme mode on the water example takes 19.6 ms instead of 32.5 ms (10.6 ms without long range).
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Incremental review of 56e6732 → c75fde9 only. The new float32/cuFFT/workspace changes do not resolve the previously reported signed-SOG zero-crossing virial issue; the new row-wise kernel-parameter reduction also leaves the periodic empty-k self-correction path unchanged. I am retaining those existing findings rather than reposting inline duplicates.
The current head's completed C++ test job still fails generating the periodic long-range fixture: at 21:51:20 UTC the primary error is Only supports complex dtypes, but found: Bool in aoti_torch_cpu__fft_c2c (buf206→buf208), followed by the misleading batch-size-1 OOM wrapper. This is continuation of review 5633197198, not a new memory-capacity finding. Other Python/C++ jobs are still running, so no overall CI conclusion yet.
Validation: 40 NumPy-backed exact-method row/flat shape-value and parameter finite-difference controls passed. Native source inspection and host probes covered the extracted fixed-point conversion (742,004 controls) and stencil lane ownership for orders 2–12, with no new confirmed finding in that bounded scope. No Torch autograd/compiled training, CUDA, MPI, LAMMPS or performance execution is claimed. This is not overall approval; the numerical and export issues above remain unresolved.
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New terminal qualification evidence at exact head c75fde9b5c681c6352c275294ccf909d3259688a: Python shard 8 has two additional integration-test failures (1,189 passed, 647 skipped). Both TestChangeTypeMap::test_missing_submodel_type_raises_validation_error and ::test_rebuilds_submodel_type_mappings fail in LinearEnergyAtomicModel.__init__, line 141, because _RecordingAtomicModel does not implement has_long_range(). The new unconditional topology-declaration query reaches this minimal short-range test double before either intended type-map assertion. Please update the fixture's atomic-model protocol to declare its non-long-range behavior and preserve both original assertions; bypassing/removing the production topology checks is not warranted by this test-double failure.
I checked the exact-head constructor and fixture against the hosted traceback. This is a concrete test integration issue, not GPU/performance evidence missing. No local backend run or code/CI changes were made.
All 46 current check runs are now terminal (35 success, 6 failure, 5 skipped). Python shard 9 still has the same five previously reported periodic FFT Bool / tmp38 export failures (997 passed, 238 skipped), and both C++ test jobs failed. Those existing findings and the two numerical findings remain; I am not duplicating their inline reports or treating this as overall approval.
The explicit wave numbers of a periodic batch were one ragged list over the frames, each paired with the nodes of its frame through gathers and segment sums whose cost and kernel count grew with the batch. The k grid now holds the wave numbers of every frame in blocks of K_BLOCK points, padded with masked guard points, beside the nodes of every frame padded with -1, so that the structure factors and the potentials are batched products of one frame's wave numbers with its own nodes, and every sum over the wave numbers runs over the points of a block first. The ragged and fixed grids share the layout; the fixed grid masks the points beyond the cutoff of a frame. A periodic module evaluates the reciprocal sum on the mesh unless `k_grid_mode` says otherwise, as frozen models already do; a periodic record that names no mode is one of the explicit wave numbers. The pair topology of a periodic batch carries four guard pairs: with two, a compiled graph identified the pair axis with the two rows of the pair index and split at the reductions over the pairs. For the same reason the trace-time samples of the k grid give its slots, blocks and points lengths of their own, of at least four. Compiled training of a small DPA4C on the water example (batch of 4, RTX PRO 6000) takes 37.5 ms per step with the ragged grid instead of 62 ms, 46.5 ms with the fixed grid instead of 127 ms, and 19.0 ms on the mesh, against 9.6 ms without long range.
The strain of a topology is zero, and the energy is differentiated once with respect to it, for the virial. The value and the first derivative at zero strain are those of S^-1 = I - eps and det S = 1 + tr eps, which replace the inverse and the determinant of I + eps in the mesh and k-grid energies: their batched factorizations, error checks, and the singular value decompositions and LU solves of their derivatives ran outside the fused kernels of a compiled training step. The eager cell volumes are triple products. Compiled training of the Air preset with a periodic SOG long range (five frames of about 390 atoms per batch, RTX PRO 6000) takes 25.7 ms per step instead of 28.2 ms, against 21.1 ms without long range. Building the topologies of a batch takes 0.40 ms instead of 0.50 ms.
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Incremental source review of c75fde9b5c681c6352c275294ccf909d3259688a → ceefa8f806fa6e8607d79d10d8316b4ab325ad11, limited to the 12 changed files and necessary reciprocal/export integration context. The per-frame blocked k-grid layout, slot masking/scatter, first-order strain algebra, periodic PME default, legacy serialized-mode fallback, and distinct synthetic export-axis sizes are internally consistent in this bounded review. I found no new confirmed defect in that delta.
Validation: 220 NumPy-backed controls using the exact extracted current methods passed: 40 ragged/fixed multi-frame energy comparisons against the direct flat sum (unequal atom counts, an empty frame, multiple charge channels, and skew cells), plus 180 first-strain derivative comparisons against inverse/determinant geometry, including shear. This is not a Torch autograd/compiled/export, CUDA, or performance run.
This is not overall approval. The explicit-grid energy still gates the self term on any(k_grid.mask), so the existing periodic empty-k self-correction finding remains for ragged/fixed mode even though PME is now the periodic default. The signed-SOG native virial implementation and the previously identified short-range type-map test double are untouched. I am not duplicating their existing findings. The export-shape changes need current-head qualification before concluding that the previously observed FFT Bool / compressed tmp38 failures are resolved; previous-head failures are not being relabeled as current-head test results.
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New terminal qualification evidence at ceefa8f806fa6e8607d79d10d8316b4ab325ad11: Python shard 8 now reports 7 failed, 1,126 passed and 220 skipped. Two failures are new relative to the five previously reported export/compiler failures: test_periodic_pme_compiled_matches_eager[les] and [sog] at source/tests/pt_expt/model/test_long_range.py:1995.
The PME train-mode compiled runner completes compilation but fails comparison with eager evaluation on the same six-atom triclinic case. The failed tensor has all 9 virial components mismatched: LES maximum absolute difference 1.0873180054168168 (index (0, 8)), SOG 11.43075673507824 (index (0, 0)), versus atol=1e-8, rtol=1e-6. The source checks energy, force, then virial; this nine-component mismatch is not a missing GPU qualification or a harmless tolerance issue. Please resolve the compiled/eager PME strain/virial discrepancy while preserving this regression test. Its later backward/finite-gradient assertions are not reached, so this run does not validate them.
The existing periodic FFT Bool and compressed tmp38 export failures also remain on this head; C++ job 120808846867 again shows FFT Bool before the misleading batch-size-1 OOM wrapper. I am not duplicating those existing findings. This update is based on exact-head test source and hosted terminal logs, not a local Torch/GPU reproduction or an established compiler root cause. No code, branch, or CI changes made; not overall approval.
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Superseded by #6116. Thank you @YuzhiLiu-ai for the latent-charge long-range module (LES/SOG kernels, charge channels, the total-charge constraint, the complete energy in the atomic model, and the direct-sum operator in C++ and LAMMPS). #6116 rebuilds this work on the interfaces that #6110 reserved for it and adds periodic systems (SPME) and fine-tuning. Its first commit carries a |
…eepmodeling#6116) ## Summary This PR adds a latent-charge long-range module to pt_expt models with a DPA4C descriptor. A charge network reads the descriptor that the energy fitting reads and predicts per-atom latent charges, and every pair of atoms interacts through them: ```math E = \sum_i E_i^{\text{SR}} + k_e \sum_{i<j} \sum_{c=1}^{C} q_i^c q_j^c\, K(r_{ij}), ``` with the LES kernel erf(αr)/r (the default) or a sum of Gaussians (SOG). Non-periodic systems take the direct sum over the pairs of a frame. Periodic systems, with `periodic: true`, take the sum over the images of the cell in reciprocal space, by smooth particle-mesh Ewald (SPME) by default or over explicit wave numbers. An optional constraint shifts one channel so that it sums to the total charge of the frame (neutral, from `fparam` or from `charge_spin`). The documentation is `doc/model/long-range.md`. **Credit.** This PR supersedes deepmodeling#6031 by @YuzhiLiu-ai, which introduced the module (LES/SOG kernels, charge channels, the total-charge constraint, the complete energy in the atomic model, and the direct-sum operator in C++ and LAMMPS) for non-periodic systems. The first commit here is the rebuilt form of that work and carries a `Co-authored-by` trailer for @YuzhiLiu-ai. It is rebuilt on the interfaces that deepmodeling#6110 reserved for it, and it adds periodic systems and fine-tuning. The other two commits are the performance work that followed. ## What the module covers - **Configuration:** a `long_range` section (`type: latent_charge`) with the `les` or `sog` kernel, several charge channels, the total-charge constraint, and `periodic: true` with SPME or explicit wave numbers. Nothing existing is removed or renamed. - **One complete energy:** the atomic model owns the complete energy. Training, compiled training, DDP, `dp test`, output-bias statistics and change-bias, the Hessian and `linear_ener` compositions all see the long-range term. - **Fine-tuning:** a pretrained model without long range can be fine-tuned into one with it. The added module takes the input statistics of the fine-tuning data. - **Frozen models:** `.pt2` and `.pte`. The graph lower reads the pair list or the mesh of the cell as topologies after its inputs. Compressed DPA4C models frozen for a CUDA device evaluate the module in the compact canonical lower with `deepmd::latent_charge_sum`, which provides the direct sum, the SPME mesh, and the MPI exchange of the charges of a domain-decomposed system. - **C++ and LAMMPS:** `pair_style deepmd` and `deepmd/kk`, one or several ranks, periodic and non-periodic. A compressed model frozen for CUDA runs on several MPI ranks; every other model runs on one rank. - **Not supported with a long-range module:** multi-rank inference of a model that the dedicated operator does not evaluate (such as an uncompressed one), spin models, the tabulated ZBL potential, and short-range bridging. ## Commits 1. **feat(long-range): latent-charge long-range module for DPA4C.** The module: model, training, freezing, and the C, C++ and LAMMPS interfaces. 2. **perf(long-range): accelerate long-range DPA4C inference in LAMMPS.** - Periodic systems take part in the CUDA-graph replay of a single rank. The cell reaches the model through pinned memory that the operator reads on the device, so a replay evaluates the cell of its own call. The replay key holds the mesh size; after the size changes, the model runs eagerly for 64 calls before it captures again, so a barostat near a mesh boundary does not recapture at every crossing. - A module whose single-channel charge network the fused fitting kernels reproduce (the defaults give one) is evaluated by one fused operator, `dpa4c_canonical_compress_long_range_energy_force`. It runs one descriptor pass, the charge network, the long-range sum, the energy fitting with the charge-network backward, one descriptor backward with fixed-point force assembly, and the epilogue. - The archive records whether its compact lower reads the source CSR (`canonical_requires_source_csr`). LAMMPS then builds the source CSR for such a model only at steps that request the per-atom virial. 3. **perf(long-range): fuse the charge network and harden the LAMMPS path.** - The charge network has a fitting-route table of its own, which the freeze measures like that of the energy fitting. - The two fused DPA4C energy-force operators share the plan of the fitting runs and the epilogue. The outputs of models without a long-range module are unchanged. - `pair_style deepmd` rejects at init a model-deviation ensemble on a non-periodic system whose first model carries no module. `deepmd/kk` shifts positions to the box origin only for a model with a module. - New Kokkos LAMMPS regression: `source/lmp/tests/test_lammps_dpa4c_long_range_kokkos.py`, run by `test_lammps_kokkos.sh`. ## Measured performance LAMMPS with Kokkos on an RTX PRO 6000 Blackwell (compute capability 12.0), trained Air model with an LES or SOG module. Times are ms per step, before and after the second commit: | System | 512 atoms | 10,296 atoms | | --- | --- | --- | | periodic, LES | 0.665 → 0.280 | 1.469 → 1.242 | | periodic, SOG | 0.644 → 0.277 | 1.357 → 1.137 | | cluster, LES | 0.343 → 0.264 | 1.274 → 1.166 | | cluster, SOG | 0.353 → 0.263 | 1.479 → 1.302 | At 100,800 atoms (1000-step runs on two GPUs), periodic LES goes from 11.3–11.4 to 10.1–10.4 ms and periodic SOG from 9.8–10.0 to 8.9–9.0 ms. The third commit then shortens the charge-network kernels from 47 to 39 µs per step at 10,296 atoms and from 477–493 to 429 µs at 100,800 atoms. A periodic LES system whose cell is deformed at every step replays in 0.28–0.29 ms per step, as with a constant cell, against 0.35–0.38 ms evaluated eagerly. Replayed and eager trajectories agree bit for bit over 200 steps of NVE and NPT. Short-range models keep their kernel time. The commit messages give the full conditions. ## Interfaces and compatibility - **C API:** `DP_DPA4ComputeCanonicalGraphGPU` takes `const double* box`, the cell of a periodic system, right after `world`. `d_coord`, `world`, `DP_DeepPotHasLongRange` and `DP_DeepPotModelDeviHasLongRange` are the interfaces that deepmodeling#6110 reserved, now used. - API version 31 and the version-2 backend plugin interface were introduced by deepmodeling#6110 after v3.2.0 and are not in a release yet, so this PR extends them within the same version. - **Operators:** the new operators are `latent_charge_sum` and `dpa4c_canonical_compress_long_range_energy_force`. The schemas of the existing operators are unchanged. - **Frozen models:** only a model that declares long-range topologies traces the extra canonical inputs (positions, communicator, cell), as read from the export code. Archives of models without a module, including those frozen with deepmodeling#6110, keep their inputs. ## Validation on this branch The branch is based on master 4c3a598 and holds three commits cherry-picked from the development branch. - pre-commit over the whole range: all hooks passed. - Rebasing onto the merged deepmodeling#6110 needed two adjustments, both checked: - The long-range wrapper now takes the fitting-route tables from the energy model, following the structure deepmodeling#6110 adopted in review. A trace check confirmed that both measured tables reach the exported program. - The linear-composition test stub `_RecordingAtomicModel` gained `has_long_range()`. Its two `TestChangeTypeMap` tests failed before this and pass after it (7 passed). - On the development branch, which holds these commits plus unrelated later ones: `source/tests/pt_expt/model/test_long_range.py`, 126 passed on an RTX PRO 6000 Blackwell (PyTorch 2.13.0+cu130). - The other suites, the C++ tests and the Kokkos LAMMPS tests were not rerun for this branch and are left to CI, including the `Test CUDA` jobs. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> <!-- coderabbit-ai-generated:v1 --> ## Summary by CodeRabbit * **New Features** * Added latent-charge long-range energy modeling for non-periodic and periodic systems, with configurable interaction kernels and charge constraints. * Added latent-charge fitting and support for long-range evaluation in supported graph, C++, CUDA, and LAMMPS workflows. * Added fine-tuning, export, and inference support for long-range models in the PyTorch experimental backend. * **Bug Fixes** * Improved compatibility when loading older DPA4C model state dictionaries. * Added fitting-network width 64 to optimized routing support. * Improved force and virial handling for long-range graph evaluations. * **Documentation** * Added guidance on long-range configuration, supported workflows, and limitations. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
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.