feat(pt_expt): O(N) on-device NeighborGraph builders — vesin & nvalchemiops (PR-C)#5714
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feat(pt_expt): O(N) on-device NeighborGraph builders — vesin & nvalchemiops (PR-C)#5714wanghan-iapcm wants to merge 7 commits into
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July 2, 2026 14:37
Whole-branch review M1: mirror ase_builder's empty-part-list guard so the vesin builder handles nf==0 without a torch.cat([]) RuntimeError. M3: extend the dpmodel fail-fast test to cover method='nv' (not just 'vesin').
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NeighborGraph PR-C — O(N) on-device graph builders (vesin / nv)
Third PR of the NeighborGraph series (after #5581 foundation, #5583 PR-A dpa1 graph forward, #5604 PR-B
.pt2/C++). Adds two O(N) carry-all NeighborGraph builders behind the World-2neighbor_graph_methoddispatcher, replacing PR-A's O(N²)densesearch / per-frame ASE stopgap with on-device cell lists.What
build_neighbor_graph_vesin(deepmd/pt_expt/utils/vesin_graph_builder.py) —vesin.torchcell list. Device-following: runs the search on the input tensor's device (CUDA kernel on CUDA input, CPU cell list otherwise).build_neighbor_graph_nv(deepmd/pt_expt/utils/nv_graph_builder.py) — nvalchemiops GPU cell list, frame-batched (batch_idx/batch_ptr, one kernel for all frames — no Python loop). CUDA-only.build_neighbor_graph_ase: search → per-frame local(i, j, S)→neighbor_graph_from_ijs(...), which recomputesedge_vecdifferentiably from the original grad-carrying coords.neighbor_graph_method ∈ {"legacy","dense","ase","vesin","nv"}) and DeepEval.pt2graph inference (newneighbor_graph_methodkwarg, default"dense"→ existing inference byte-identical). dpmodel/jax fail-fast onvesin/nv(torch/CUDA-only).Perf-only: all builders emit the SAME neighbor set as
dense(carry-all,sel=normalization-only), proven by exact set-equality; energy/force/virial are unchanged (parity 1e-12 CPU / 1e-10 CUDA).Layering
dpmodelstays torch-free: vesin/nv builders live inpt_expt; the dpmodel dispatch only carries a fail-fast message.is_vesin_torch_available()/is_nv_available(),ImportErrorwith an install hint on absence.deepmd/pt/utils/nv_nlist.py:_matrix_to_extended_inputsStep-1 extraction.Testing
test_nv_graph_builder.py: 4 passed (set-equality vs dense periodic+non-periodic, frame-batch, differentiableedge_vec).test_graph_builder_dispatch.py: 3 passed on CUDA (vesin parity 1e-10, dpmodel reject vesin+nv, nv parity 1e-10).test_graph_deepeval.py: 6 passed on CUDA (.pt2graph dense parity + vesin, AOTI compile +torch.as_tensorextraction).Known limitations
vesin.torch.computeis single-system (no batch dim), so multi-frame vesin loops over frames (each an O(N) search;nf=1inference has zero loop cost). nv batches natively — the loop-free path for batched training.pyproject; no"auto"selector (explicit strings only).search_capacity = max(64, nloc)initial heuristic + 1.25× grow loop (.item()host-sync per grow).from_ijs(differentiable lattice translation, identity gradient;edge_vecis lattice-invariant) — consistent with the pt nv-nlist path.Implements
plan_neighbor_graph_prC_implementation; design spec: discussion wanghan-iapcm#4.