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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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AGENTS.mddeepmd/dpmodel/descriptor/dpa4c.pydeepmd/dpmodel/fitting/__init__.pydeepmd/dpmodel/fitting/dpa4c_lr.pydeepmd/dpmodel/model/make_model.pydeepmd/dpmodel/utils/default_neighbor_list.pydeepmd/pt_expt/fitting/__init__.pydeepmd/pt_expt/fitting/dpa4c_lr.pydeepmd/pt_expt/infer/deep_eval.pydeepmd/pt_expt/model/__init__.pydeepmd/pt_expt/model/dpa4c_lr_model.pydeepmd/pt_expt/model/get_model.pydeepmd/pt_expt/model/make_model.pydeepmd/pt_expt/train/training.pydeepmd/pt_expt/utils/graph_builder.pydeepmd/utils/argcheck.pydoc/model/dpa4c_lr.mddoc/model/index.rstsource/tests/pt_expt/model/test_dpa4c_lr.py
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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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Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## master #6031 +/- ##
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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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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.
Agent: dot
… 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.
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.